# Trading and Exchanges

**Author:** Larry Harris
**Year:** 2003
**Pages:** 657

> Full text extracted from PDF for on-demand reference. Use grep/search to locate concepts.


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Trading and Exchanges
.

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FINANCIAL MANAGEMENT ASSOCIATION
Survey and Synthesis Series
The Search for Value: Measuring the Company's Cost of Capital
Michael C. Ehrhardt
Managing Pension Plans:
A
Comprehensive Guide to Improving Plan
Performance
Dennis E. Logue and Jack S. Rader
Efficient Asset Management: A Practical Guide to Stock Portfolio
Optimization and Asset Allocation
Richard 0. Michaud
Real Options: Managing Strategic Investment in an Uncertain World
Martha Amram and Nalin Kulatilaka
Beyond Greed and Fear: Understanding Behavioral Finance and the
Psychology of Investing
Hersh Shefrin
Dividend Policy: Its Impact on Firm Value
Ronald C. Lease, Kose John, Avner Kalay, Uri Loewenstein,
and Oded H. Sarig
Value Based Management: The Corporate Response to Shareholder Revolution
John
D.
Martin and J. William Petty
Debt Management: A Practitioner's Guide
John D. Finnerty and Douglas R. Emery
Real Estate Investment Trusts: Structure, Performance, and Investment
Opportunities
Su Han Chan, John Erickson, and Ko Wang
Trading and Exchanges: Market Microstructure for Practitioners
Larry Harris


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Trading and Exchanges
Market microstructure for Practitioners
LARRY HARRIS
OXFORD
UNIVERSITY PRESS
2003
NEW STAMP

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OXFORD
UNIVERSITY PRESS
Oxford New York
Auckland Bangkok Buenos Aires Cape Town Chennai
Dar es Salaam Delhi Hong Kong Istanbul Karachi Kolkata
Kuala Lumpur Madrid Melbourne Mexico City Mumbai Nairobi
Sao
Paulo Shanghai Singapore Taipei Tokyo Toronto
Copyright 2003 by Oxford University Press, Inc.
Published by Oxford University Press, Inc.
198 Madison Avenue, New York, New York, 10016
www.oup.com

Oxford is a registered trademark of Oxford University Press
All rights reserved. No part of this publication may be reproduced,
stored in a retrieval system, or transmitted, in any form or by any means,
electronic, mechanical, photocopying, recording, or otherwise,
without the prior permission of Oxford University Press.
Library of Congress Cataloging-in-Publication Data
Harris, Larry, 1956-
Trading and exchanges : market microstructure tor practitioners /
Larry Harris
p. cm.—(Financial Management Association survey and synthesis
series)
Includes bibliographical references and index.
ISBN 0-19-514470-8
1. Markets. 2. Exchange. 3. Commerce. I. Title. II. Series.
HF5470 .H37 2002
332.64—dc21
2002004622
9 8 7 6
Printed in the United States of America
on acid-free paper


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This book is dedicated to the memory of the victims
of the September 11, 2001, terrorist acts
in New York, Virginia, and Pennsylvania
and to the honor of those people
whose heroic actions on that day saved so many lives.


## Page 8

flcknowledgments
Many people contributed to the success of this project with their encour-
agement, advice, knowledge, and expertise. I would not have started this
project without their interest, and I would not have completed it without
their support. The book is much better for their contributions.
I received my first encouragement as a graduate student at the Univer-
sity of Chicago, long before I knew anything about market microstructure.
Professor Arnold Zenner advised me to publish a book based on lectures I
would give when I became a professor. Although it seemed unimaginable
to me at the time, I never forgot his advice or his confidence in me. I look
forward to the time when I will read a book written by one of my students.
Several publishers suggested that I write this book. I particularly appre-
ciate the interest shown by editors Mike Junior, Randall Adams, Maureen
Riopelle, Kenneth MacLeod, and Paul Donnelly. I have no doubt that they
all would have been very helpful in the development of this book. I regret
that I could not work with them all. Their encouragement, enthusiasm, and
vision helped kindle and sustain my interest.
This book would not have been possible without the many insights that
I learned from my academic colleagues. I especially appreciate the lessons
that they taught me, and the strong expressions of interest and valuable con-
structive criticism that they have given me. I wish to acknowledge the fol-
lowing colleagues by name:
Anat Admati

Doug Foster

Eugene Kandel
Yacob Amihud

Jennie France

Jon Karpoff
Jim Angel

Bill Freund

Don Keim
Gail Belonsky

Mendy Fygenson

Laura Kodres
Hank Bessembinder

Stuart Gabriel

Pete Kyle
Bruno Biais

Ann Gillette

Josef Lakonishok
Bernie Black

Tom Gilligan

Charles Lee
Fischer Black

Larry Glosten

Ruben Lee
Marshall Blume

Charles Goodhart

Bruce Lehmann
Michael Brennan

Sandy Grossman

Ken Lehn
Corinne Bronfman

Joe Grundfest

Marc Lipson
Mark Carhart

Eitan Gurel

Andy Lo
Henry Cheeseman

Yasushi Hamao

Francis Longstaff
Kal Cohen

Puneet Handa

Bob Lucas
Harry DeAngelo

Joel Hasbrouck

Craig MacKinlay
Linda DeAngelo

Kaj Hedvall

Ananth Madhavan
Ian Domowitz

Pierre Hillion

Steve Manaster
Rob Engle

Craig Holden

Terry Marsh
Wayne Ferson

Kose John

Tom Mclnish
Steve Figlewski

Charles Jones

Haim Mendelson
Margaret Forster

Avner Kalay

Morris Mendelson


## Page 9

viii • ACKNOWLEDGMENTS
Bob Miller
Mert Miller
David Modest
Dale Morse
Belinda Mucklow
Harold Mulherin
Rob Neal
Anthony Neuberger
Maureen O'Hara
Daniel Orr
J. Peake
Andre Perold
Mitch Petersen
Paul Pfleiderer
Avri Ravid
Mark Ready
Jay Ritter
Kevin Rock
Ailsa Roell
Tavy Ronen
Ehud Ronn
Mark Rubinstein
Tony Saunders
Myron Scholes
Bob Schwartz
Eduardo Schwartz
Bill Schwert
Duane Seppi
Kuldeep Shastri
Alan Shapiro
Eric Sirri
Sy Smidt
Vernon Smith
Chester Spatt
Sanjay Srivastava
Jim Stancill
Laura Starks
Dave Stewart
Hans Stoll
Rene Stulz
Avanidhar
Subrahmanyam
Sheridan Titman
Walt Torous
Anand Vijh
S. Viswanathan
Dan Weaver
Mark Weinstein
No Welch
Ingrid Werner
Randy Westerfield
Bob Whaley
David Whitcomb
Joe Williams
Bob Wood
This book would not
have been creditable were it not for the many
prac-
titioners and regulators who have shared their time with me to explain what
they do. I particularly appreciate the generosity and encouragement that the
following individuals have extended to me:
Stanley Abel

Gifford Fong

Marty Leibowitz
Howard Baker

Stuart Fraser

Dave Leinweber
Frank Baxter

Dean Furbush

Rich Lindsey
Brandon Becker

Jim Gallagher

Evelyn Liszka
Gil Beebower

Gary Gastineau

Bob Litterman
Jeff Benton

Brian Geary

Bill Lupien
Dale Berman

Steven Giacoma

Bernie Madoff
Charles Black

Jim Gilmore

Peter Madoff
David Booth

Phil Ginsberg

Steven Malin
Harold Bradley

Gary Ginter

David Malmquist
Kurt Bradshaw

Keith Goggin

Tim McCormick
Pearce Bunting

Wendy Gramm

Dick McDonald
Richard Cangelosi

Dick Grasso

Seth Merrin
Jim Cochrane

Bob Greber

Dick Michaud
David Colker

Leo Guzman

Mark Minister
Cromwell Coulson

Spence Hilton

Nate Most
Larry Cuneo

Dave Hirschfeld

Annette Nazareth
David Cushing

Blair Hull

Gene Noser
Harry Davidow

Billy Johnston

Bill Pratt
Pina DeSantis

Rick Ketchum

Eddie Rabin
Mike Edleson

Rick Kilcollin

Murali Ramaswami
Jim Farrell

Ray Killian

Bill Ryan
Tom Fay

Howard Kramer

Henry Sasser
Gene Finn

Ken Kramer

Evan Schulman
Ed Fleischman

Arthur Leavitt

Andy Schwarz
Russ Fogler

Charlie Lebens

Christina Sciotto


## Page 10

ACKNOWLEDGMENTS • ix
Jim Scott
Jim
Shapiro
David Shaw
Katy Sherrerd
Fred Siesel
Deborah Soesbee
George Sofianos
Eric Sorensen
Olof Stenhammar
Rob Tesar
Artie Tolendini
Jack Treynor
Wayne Wagner
Jeffery Wecker
Genie Williams
Steve Wallman
Steve Wunsch
Steve Youngren
Dorit Zeevi
Four people particularly influenced the development of this book. Wayne
Wagner thoroughly reviewed an early draft of the manuscript. His sugges-
tions greatly improved every part of the book. Craig Holden used early drafts
of the text in his trading course at Indiana University. I am especially in-
debted to him for the confidence and vision he expressed to various pub-
lishers. Ananth Madhavan commented on early drafts of the book and sup-
plied many bibliographic references. I have greatly appreciated having him
as a colleague at USC. Finally, Jack Treynor was instrumental in helping me
appreciate the importance of the zero-sum game in trading. Most principles
of market microstructure somehow involve properties of zero-sum games.
Several generous sponsors provided financial support for this project. I
received "angel financing" from the New York Stock Exchange (Dick Grasso,
Billy Johnston, Jim Cochrane, and George Sofianos), the Jefferies Group
(Frank Baxter), Mellon Capital Management Corporation (Bill Fouse and
Tom Loeb), Bernard L. Madoff Investment Securities (Bernie and Peter
Madoff), First Canada Securities International (Jim Medlock), Cantor
Fitzgerald (Stuart Fraser and Phil Ginsberg), and First Quadrant (Rob
Arnott). Their early support allowed me to take time off from my teaching
to start this project. I am also grateful to Oxford University Press, which pro-
vided a developmental grant with which I was able to pay research assistants.
I appreciate the support provided to me by the USC Marshall School of
Business through the Fred V. Keenan Chair in Finance. The generosity of
our donors has been instrumental to the remarkable development of our fac-
ulty, school, and university over the last ten years under the leadership of
Marshall School deans Jack Borsting and Randolph Westerfield, and USC
president Steven B. Sample. Our most important accomplishments appear
in the largely unseen influences that we have upon our students, and upon
the policy makers and business executives who rely upon our research when
making significant decisions. Our work rarely provides us with suitable op-
portunities to thank everyone who makes it possible. I am grateful for this
opportunity to thank our donors for their confidence, vision, and support.
I am especially pleased to recognize Fred Keenan, whose loyalty and gen-
erous support to the university have been an inspiration to us all.
Many people at USC helped me with the preparation of the manuscript.
I am grateful both for their contributions to this book and for teaching me
to write better. I gratefully thank my research assistants—Namrata Aggar-
wal, Chris Bandouveris, Claire Yu Cui, Shane Enete, Cynthia Lee, Jeff Lin,
Jennifer Ly, Anna Nguyen, Venky Panchapagesan, and Jia Ye—and my de-
partmental assistants—Deborah Jacobs, Marilyn Johnson, Terry Lichvar,
and Helen Pitts. I am particularly pleased to recognize Cynthia Lee for her
very extensive assistance with grammar, style, and proofreading.
Several people were notably helpful with proofreading early drafts. I
thank Jennifer Chang, Abby Harris, Naftali Harris, Naomi Harris, Hai Lu,


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x • ACKNOWLEDGMENTS
and especially Joey Engelberg for the careful attention that they paid to the
manuscript.
My family and friends also contributed to the successful completion of
this book in more ways that would be appropriate to list here. I particularly
appreciate the support given to me by Alex Baskin, Jack Birns, Brian Greene,
Bernard Harris, Devera Harris, Sam Jason, and Alissa Rimon. My wife,
Abby, and my children were especially supportive. Without their support, I
would not have attempted or completed this project.
My editors at Oxford University Press were extremely helpful with the
development of this book. Copyeditor Beth Wilson and Managing Editor
Lisa Stallings are skilled professionals whose contributions regrettably are
rarely well recognized. They did an excellent job and were a pleasure to work
with. I particularly appreciate the contributions of Executive Editor Paul
Donnelly. Paul's vision brought this project to Oxford; his guidance greatly
improved the book; and his promotion ensured that others would appreci-
ate it.
Finally, I am indebted to the many students at USC who studied with
me in my Trading and Exchanges course. Their interest in trading encour-
aged me to first offer the course in 1991. This book grew out of the lec-
tures that developed to present the course to them. The lessons that I learned
from my students while teaching Trading and Exchanges greatly influenced
the organization and presentation of the topics that appear in this book. I
believe that I have learned more from my students than from anyone else.
I have inevitably failed to acknowledge many people who richly deserve
it. Regrettably, the ability to remember names easily is not one of the many
gifts that I enjoy. Please forgive me for my oversight.
Thank you to everyone who has helped make this project successful.


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Contents
1 Introduction

3
2 Trading Stories

11
Part I

The Structure of Trading
3 The Trading Industry

32
4 Orders and Order Properties

68
5
Market Structures

89
6 Order-driven Markets

112
7 Brokers

139
Part II

The Benefits of Trade
8 Why People Trade

176
9 Good Markets

202
Part III

Speculators
10 Informed Traders and Market Efficiency

222
11
Order Anticipators

245
12 Bluffers and Market Manipulation

259
Part IU

Liquidity Suppliers
13 Dealers

278
14 Bid/Ask Spreads

297
15
Block Traders

322
16 Value Traders

338
17 Arbitrageurs

347
18 Buy-Side Traders

380
Part U

Origins of Liquidity and Uolatility
19 Liquidity

394
20 Volatility

410
Part UI

Eualuation and Prediction
21 Liquidity and Transaction Cost Measurement

420
22 Performance Evaluation and Prediction

442
Part Ull

Market Structures
23 Index and Portfolio Markets

484
24 Specialists

494
25 Internalization, Preferencing, and Crossing

514
26 Competition Within and Among Markets

524
NEW STAMP

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xii • CONTENTS
27 Floor Versus Automated Trading Systems

543
28 Bubbles, Crashes, and Circuit Breakers

555
29 Insider Trading

584
Bibliography

601
Index

619


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Trading and Exchanges


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1
Introduction
M
arkets are fascinating. They change constantly as prices adjust to
new information, as winning traders replace losing traders, and as
new technologies evolve.
Highly skilled professional traders employ clever strategies in their search
for trading profits. They ultimately profit from investors, gamblers, and fool-
ish traders.
The stakes in some markets are very high. Traders may arrange multi-
million-dollar trades in seconds. They sometimes make or lose fortunes
overnight.
The prices that traders negotiate ultimately determine how market-based
economies allocate their scarce resources. Free economies owe much of their
wealth to their well functioning markets.
1.1 SCOPE OF THE BOOK
This book is about trading, the people who trade securities and contracts,
the marketplaces where they trade, and the rules that govern trading. You
will learn about investors, brokers, dealers, arbitrageurs, retail traders, day
traders, rogue traders, and gamblers; exchanges, boards of trade, dealer net-
works, ECNs (electronic communications networks), crossing markets, and
pink sheets; single price auctions, open outcry auctions, and brokered mar-
kets; limit orders, market orders, and stop orders; program trades, block
trades, and short trades; price priority, time precedence, public order prece-
dence, and display precedence; insider trading, scalping, and bluffing; and in-
vesting, speculating, and gambling. This book will teach you the origins of
liquidity, transaction costs, volatility, informative prices, and trader profits.
This book is not about the securities and contracts that people trade. We
will not consider how to value them, who should trade them, how to de-
sign them, or how to issue them. Books about investments and corporate
finance examine these questions.
Market microstructure
is the branch of financial economics that investi-
gates trading and the organization of markets. This field of study has sub-
stantially grown in size and importance since the October 1987 stock mar-
ket crash.
This book presents the economics of market microstructure in simple
English prose. Although some simple mathematics and graphics appear in
a few supplementary examples, I fully explain all essential concepts in the
main text.
1.2 OBJECTIVES
This book will help you understand how markets work, and how govern-
ments and exchanges regulate them. You will learn how prices come to
NEW STAMP

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4 • TRADING AND EXCHANGES
reflect information about fundamental values, who makes markets liquid,
and why some traders consistently profit from trading while others lose. You
will be able to predict how various trading rules affect price efficiency, liq-
uidity, and trading profits. Finally, you will understand the forces that gov-
ern regulatory processes.
With this knowledge, you can improve your trading strategies, and you
can better manage the brokers who work for you. If you are—or aspire
to be—a regulator or an exchange official, this knowledge will help you de-
sign better markets.
The primary objectives of this book are to understand the origins of the
following characteristics of market quality:
•
Liquidity
Traders and regulators often talk about liquidity, but they
are rarely careful about what they mean. This book explains what liq-
uidity is, and where it comes from. If you intend to offer or take
liquidity, you must understand it well.
•
Transaction costs
Traders must effectively manage their transaction
costs to trade successfully. This book explains how to measure and man-
age transaction costs. If you trade actively, you must understand trans-
action costs.
•
Informative prices
Speculators must understand how and when prices
become informative in order to trade successfully. Informative prices
are essential to the wealth of our economy. This book explains the pro-
cesses by which prices become informative. If you intend to speculate,
you must understand price efficiency.
•
Volatility
Traders care about volatility because it can have a signifi-
cant impact on their wealth. This book explains how prices become
volatile, and how regulators try to control volatility. If risk scares you,
you must understand volatility.
•
Trading profits
Trading is a zero-sum game in which some traders
win and others lose. Traders who do not expect to win should refrain
from trading. This book explains why some traders consistently win
while other traders consistently lose. If trading profits interest you—
whether you manage your trading yourself or have someone manage it
for you—you must understand what determines trading profits.
The secondary objective of this book is to understand how market
structure—trading rules and information systems—affect each of these five
market characteristics.
1.3 INSTRUMENTS AND MARKETS
Market microstructure examines organized trading in instruments.
Instru-
ments
include common stocks, preferred stocks, bonds, convertible bonds,
warrants, options, futures contracts, forward contracts, foreign exchange
contracts, swaps, reinsurance contracts, commodities, pollution credits, water
rights, and even many betting contracts. Most ideas discussed in this book
apply equally well to trading in all these instruments.
Legislatures and judges have created numerous legal definitions of the
term "security." These definitions often distinguish between instruments that
represent ownership of assets like stocks and bonds (usually called securi-
ties) and instruments that derive their values from commodities or from


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CHAPTER 1 INTRODUCTION • 5
other security values (derivative contracts). They also universally exclude bet-
ting contracts. We will pay attention to these distinctions only when they
affect the markets through the regulatory process.
A
market
is the place where traders gather to trade instruments. That
place may be a physical trading floor, or it may be an electronic system in
which traders can easily communicate with each other. The New York Stock
Exchange, the Chicago Mercantile Exchange, and the EuroNext Amster-
dam Options Exchange are examples of markets where traders meet on trad-
ing floors. Nasdaq, the Euronext, the Hong Kong Futures Exchange, and
the interbank foreign exchange market are examples of electronically linked
markets. This book considers how trading markets are organized and how
their rules affect traders.
1.4 A BRIEF OVERVIEW OF
TRADING AND EXCHANGES
This section gives a brief overview of some main points introduced in this
book. It provides you with an outline of what you can expect to learn from
this book. Do not be alarmed if you do not understand it all now The re-
mainder of the book explains everything in detail.
Trading is a search problem. Buyers must find sellers, and sellers must
find buyers. Every trader wants to trade at a good price. Sellers seek buy-
ers willing to pay high prices. Buyers seek sellers willing to sell at low prices.
Traders also must find traders who are willing to trade the
quantities,
or
sizes,
they desire. Traders who want to trade large quantities may have to
find many willing traders to complete their trades.
Dealers and brokers help people trade.
Dealers
trade with their clients
when their clients want to trade. The prices at which a dealer will buy and
sell are the dealer's
bid
and
ask
prices. After they trade with their clients,
dealers then try to trade out at a profit by selling what they have bought or
by buying back what they have sold. In effect, clients pay dealers to take
their trading problems. The dealers then try to solve them at a profit. Deal-
ers profit by buying low and selling high. Successful dealers must be excel-
lent traders.
Brokers
are agents who arrange trades for their clients. They help their
clients find traders who are willing to trade with them. They profit by charg-
ing commissions.
Patient traders obtain better prices than impatient traders do because
they are willing to search longer and harder to arrange their trades at fa-
vorable terms. Impatient traders pay for the privilege of trading when they
want to trade.
Traders who offer to trade give other people options to trade. These op-
tions sometimes are quite valuable. Traders who expose their offers can lose
to clever traders who use various front-running trading strategies to extract
these option values. Traders therefore must expose their offers very carefully.
They should expose only to traders who are most likely to trade with them.
Traders who trade only to accommodate other traders risk trading with,
and losing to, well-informed speculators.
Speculators
are traders who trade
to profit from information they have about future prices.
Well-informed spec-
ulators
can predict futures prices better than other traders can. They then
choose to buy or sell based upon which side they expect will be profitable.


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6 • TRADING AND EXCHANGES
Dealers lose to well-informed speculators because they end up being on the
wrong side of the trade. Prices tend to move against their positions before
they can trade out of them. All traders try to avoid trading with well-
informed speculators.
Dealers recover their losses to informed speculators by widening the
spread between the bid and ask prices at which they will buy and sell. Un-
informed traders therefore pay more for their trades when dealers lose a lot
to informed traders. In effect, uninformed traders lose to well-informed
traders through the intermediation of dealers.
Traders who can estimate fundamental values cause prices to reflect their
value estimates. They buy when price is below their value estimates and sell
when price is above. Their buying pushes prices up, and their selling pulls
prices down. They do not trade if they believe that prices reflect values.
Well-informed traders make prices informative.
Bluffers
can sometimes fool uninformed traders into trading unwisely. In
general, they can profit if the price impacts of their buying and selling are
not exactly opposite to each other. Since dealers may trade when bluffers
want them to trade, dealers must be highly disciplined to avoid losing to
bluffers.
Trading is a zero-sum game when gains and losses are measured relative
to the market average. In a
zero-sum game,
someone can win only if some-
body else loses. On average, well-informed speculators and bluffers win, and
poorly informed traders and foolish traders lose. Informed traders can profit
only to the extent that less informed traders are willing to lose to them.
Poorly informed traders trade for many reasons.
Investors
use the mar-
kets to move money from the present to the future.
Borrowers
do the op-
posite.
Hedgers
trade to manage financial risks they face.
Asset exchangers
trade one asset for another they value more.
Gamblers
trade to entertain
themselves.
Exchanges and brokerages design markets to minimize the search costs
of trading. They usually organize markets so that everyone who wants to
trade gathers at the same place. A common gathering place helps traders
find those traders who will offer the best prices.
Exchanges and brokerages once organized their markets exclusively on
physical trading floors. Now they can do so within computerized commu-
nications networks that allow buyers and sellers to arrange their trades re-
motely. Electronic marketplaces have rapidly expanded as the costs of elec-
tronic communications technologies have dropped.
Most traders want to trade in well-established markets because other
traders trade there. When many traders trade in the same place, arranging
trades is very easy. The attraction of traders to other traders makes it hard
to start new markets.
Entrepreneurs create new markets when old markets do not adequately
meet the needs of a significant set of traders. Since traders face a diversity
of trading problems, no single market can best meet every trader's needs.
Many diverse markets may form when exchanges and brokerages compete
to attract traders.
Arbitrageurs
ensure that prices do not vary much across markets. When
prices diverge, they buy in cheaper markets and sell in more expensive mar-
kets. The effect of their trading is to connect sellers in cheaper markets to
buyers in more expensive markets.


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CHAPTER 1 INTRODUCTION • 7
An exchange's trading rules affect the quality of its markets. They de-
termine the balance of power between informed traders and uninformed
traders, between public traders and professional traders, and between large
traders and small traders. Trading rules are very important.
Markets work best when they trade fungible instruments. An instrument
is
fungible
if one unit (a share, a bond, a contract, etc.) of the instrument is
economically indistinguishable from all other units. If that is so, buyers do
not care which units they receive. Since all sellers offer identical units, buy-
ers can buy from any seller who offers an attractive price. Sellers likewise
can sell to any buyer. Fungible instruments therefore are easier to trade than
instruments that have idiosyncratic characteristics. In derivative markets, the
benefits of fungible instruments cause trading to concentrate in just a few
standardized contracts.
1.5 KEY RECURRENT THEMES
A number of important issues appear repeatedly throughout this book. This
section identifies these issues. Watch for them as you read this book.
Information asymmetries
Traders who know more about values and
traders who know more about what other traders intend to do have a great
advantage over those who do not. Well-informed traders profit at the ex-
pense of less-informed traders. Less-informed traders therefore try to avoid
well-informed traders. Pay attention to who is well-informed and to how
traders learn about values.
Options
The option to trade is valuable. People who write limit orders
give free trading options to other traders. Clever traders can extract the value
of these options. Pay attention to when traders create trading options and
to how they prevent other traders from extracting their values.
Externalities
People create
positive externalities
when they do something
that benefits other people without compensation. People create
negative
externalities
when they do something that harms other people without
penalty. The most important externality in market microstructure is the
order flow externality
Traders who offer to trade give other traders valuable
options to trade for which the offerers are not compensated. The order flow
externality attracts and binds traders to markets because they want to ben-
efit from free trading options. Pay close attention to when, why, and how
traders offer to trade. Also pay attention to how markets, brokerages, and
dealers benefit from the order flow externality.
Market structure Market structure
consists of the trading rules, the phys-
ical layout, the information presentation systems, and the information com-
munication systems of a market. Market structure determines what traders
can do and what they can know. It therefore affects trader strategies, the
power relationships among different types of traders, and ultimately trader
profitability. Always consider what effects market structures have on trad-
ing strategies and on the balance of power between various types of traders.
Competition with free entry and exit
Traders compete in markets to
make profits. Trading strategies that generate large profits attract traders
who want to participate in those profits. Their entry lowers the profits that
everyone makes, on average. Conversely, traders quit using trading strate-
gies that are not profitable, which allows remaining traders to make more
profits, on average. Free entry and exit ensures that alternative trading strate-


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8 • TRADING AND EXCHANGES
gies produce equal net profits, on average, after accounting for all costs.
Wherever you see people competing, consider how the costs of entry and
exit affect their ability to maintain profits or avoid losses. This principle will
help you understand the determinants of bid/ask spreads, dealer profits,
informed-trader profits, and order submission strategies.
Communications and computing technologies
Markets are essentially
information-processing mechanisms. They process information about who
wants to trade, how much, and at what prices. The resulting prices aggre-
gate information about fundamental values. The growth in information tech-
nologies has changed, and will continue to change, how people trade. Pay
attention to the role of information-processing technologies in the markets.
Price correlations
Markets for similar instruments are closely related.
They tend to have similar conditions, and they often compete fiercely with
each other for order flow. The order flow externality generally ensures that
one market among a set of closely related markets will eventually dominate
the others. Pay attention to markets that trade similar instruments and to
the differences among them that make them unique. These issues affect how
markets compete with each other.
Principal—agent problems
Principal—agent problems arise when agents
do what they want to do rather than what their principals want them to do.
The most important principal—agent problem in market microstructure in-
volves brokers and their clients. Brokers do not always do what you want
them to do, and they may not work as hard on your behalf as you would.
Pay close attention to how traders control their brokers.
Trustworthiness and creditworthiness
People are
trustworthy
if they try
to do what they say they will do. People are
creditworthy
if they can do what
they say they will do. Since people often will not or cannot do what they
promise, market institutions must be designed to effectively and inexpen-
sively enforce contracts. Pay close attention to the mechanisms which en-
sure that traders will settle their trades. Attempts to solve trustworthiness
and creditworthiness problems explain much of the structure of market
institutions.
The zero-sum game
All trades involve two or more parties. The ac-
counting gains made by one side must equal the accounting losses suffered
by the other side. Understanding the origins of trading profits therefore re-
quires that we understand both sides of a trade. We must understand why
traders on one side expect to profit, and why traders on the other side ei-
ther are willing to lose or do not understand that they should expect to lose.
1.6 OUTLINE OF THE BOOK
The book is organized into seven parts. Part I examines the structure of
trading. Several chapters describe how markets are organized and regulated,
and how traders trade in them. Although much of the information is de-
scriptive, the text also analyzes how various market structures affect trading
strategies.
Part II considers what benefits markets produce for traders and for the
wider economy. We must address these questions in order to judge whether
the markets are working well. The first of the two chapters in this part of
the book considers why people trade. The other chapter explains how mar-
kets benefit the whole economy. This chapter concludes with my opinion


## Page 22

CHAPTER 1 INTRODUCTION • 9
about what markets should do and for whom they should do it. Your opin-
ion may differ from mine.
To understand how trading rules affect traders, you must first understand
how traders behave. The book therefore next devotes many chapters to un-
derstanding what various traders do. These chapters should be especially in-
teresting to readers who want to become traders and to traders who want
to improve their trading skills. Part III includes chapters that consider var-
ious speculative trading strategies. Part IV examines the traders who offer
liquidity.
Part V contains two chapters that will help you to better understand the
origins of liquidity and volatility. Both concepts are described in relation to
the various trading strategies introduced in parts III and IV.
We consider the problems of evaluating trader performance in part VI.
You must understand these issues if you intend to manage brokers, or if you
want to know why index markets are so popular. These chapters lay the
foundation for understanding who profits, and who loses, from trading. If
you intend to trade for profit or invest your money with a money manager,
the chapter on performance evaluation and prediction will be of great in-
terest to you.
Finally, part VII concludes with several chapters that consider the eco-
nomics of various market structures. These chapters examine how markets
are organized, how they compete with each other, and how they respond to
extreme volatility. These chapters will obviously interest regulators and ex-
change officials. They should also interest farsighted traders: Being able to
predict how changes in rules, technologies, and competitive relationships af-
fect markets distinguishes winning traders from losers.
Numerous sidebars appear throughout the book. These sidebars contain
examples, stories, and historical explanations that illustrate and illuminate
points made in the text. They are useful as mnemonic devices for remem-
bering jargon and concepts. I beg your indulgence for the puns, wordplay,
lighthearted jabs, and unsolicited opinions that appear in them.
I took the examples that appear throughout the book from all types of
markets and from many different countries. A disproportionate number,
however, involve equity trading in the United States because these markets
are the best-known markets in the world. As noted above, most principles
that apply to these markets also apply to all other markets.
1.7 AN IMPORTANT DISCLAIMER
Traders often encounter significant legal and tax issues. Some types of trades
are illegal, many trades create significant tax liabilities, and many commer-
cial relationships in trading create important legal liabilities. If you trade,
you must know the legal consequences of your actions.
The purpose of this book is to examine economic issues in trading, not
legal issues. The text addresses many legal issues because legal issues often
have significant economic implications for the markets, and because eco-
nomic issues often are the basis for legal regulation. This book will help you
to better understand the economic implications of laws that regulate secu-
rities, contracts, traders, and exchanges. It will also help you understand the
economic bases for many regulations. It is not an authority for what the law
is or for which laws you should pay attention to.
Bulls and Bears
Traders call rising markets
bull markets
and falling
markets
bear markets.
According
to legend, these
terms originated from morbid
contests that promoters once
staged between bulls and
bears. Bulls fight by thrusting
upward with their horns. In
contrast, bears fight by
striking downward with their
claws. This image has
generated a small cottage
industry of artisans who
create bull-fighting-bear
sculptures that traders buy to
adorn their offices and living
rooms.


## Page 23

10 • TRADING AND EXCHANGES
Do not rely upon this book for guidance on any legal issues. I am not a
qualified legal adviser. Consult a qualified attorney when you must address
legal questions.
1.8 SUMMARY
This book will help you understand the theory and practice of trading in
exchange markets and dealer networks. When you master this subject, you
will be able to trade more effectively, you will better appreciate the organi-
zation of our markets, and you will be able to form well-reasoned opinions
about how the markets should be organized.
Markets have changed substantially during the last 100 years, and they
will continue to change in the next 100 years. The current pace of change
is fast, and is accelerating. By the time you read this, some specific de-
scriptive information in this book will undoubtedly be dated. The economic
principles governing markets and the traders in them, however, will remain
the same. These concepts will help you understand all markets—past,
present, and future.


## Page 24

2
N
Trading
Stories
T
his chapter presents stories about how traders arrange routine trades in
stocks, bonds, futures contracts, and currencies. If you are new to trad-
ing, you should read this chapter to help you appreciate the trading prob-
lems that people solve. If you are already quite familiar with trading, you
also may want to read this chapter. Although these stories describe routine
trades, they highlight difficult issues that traders confront when trading.
This chapter is full of institutional details. Do not worry if you do not
understand all of them on your first reading. After you have finished read-
ing this book, you will be able to understand these stories completely. For
now, just read them to get a feel for what trading is about. The impressions
that you form will help you appreciate the practical importance of the analy-
ses that this book presents.
2.1 A RETAIL TRADE IN
AN NYSE-LISTED STOCK
Jennifer wants to buy 200 shares of AT&T. She calls her retail broker, with
whom she has already established an account. ( Jennifer could also have used
her broker's Internet-based order entry system.) Jennifer's broker might work
for a fill-service broker/dealer, such as Merrill Lynch; a national discount
brokerage, such as Charles Schwab; or perhaps a local deep-discount bro-
kerage, such as Brown & Company.
Jennifer provides the broker with her account number and identifies her-
self. She then asks for the current quotes for AT&T common stock. The
broker looks at a screen on his desk that is similar to the Bridge Informa-
tion Systems quotation display in figure 2-1. On the screen are the best bids
and offers for AT&T that traders display in the Consolidated Quotation
System. The quotes come from dealers at the New York Stock Exchange
(NYSE), from dealers at several regional exchanges, from some indepen-
dent NASD (National Association of Securities Dealers) members, and from
some electronic communications networks (ECNs) that display limit orders
that their clients have placed with them. The broker responds by quoting
the best current prices at which traders can immediately buy or sell AT&T
(ticker symbol "T") shares. Given the information in figure 2-1, the broker
reports that the best (highest) bid for AT&T is for 19.83 dollars and the
best (lowest) offer is at 19.85. The broker also tells Jennifer that the last
trade in AT&T was at 19.84 dollars, which is down 0.10 from the previ-
ous day's close. Jennifer considers the quote. She then instructs her broker
to buy the shares.
To convey her intentions, Jennifer will use either a market order or a
limit order. A
market order
instructs the broker to buy at the best price avail-
able. A
limit order
instructs the broker to buy at the best price possible, but
in no event to pay more than a
limit price
that Jennifer specifies. If Jennifer
11


## Page 25

us;T
AT and T Corp .K
Common Stock
+

19.84
DN

0.10

(N)

14.06

H
20

L
19.51 V 8,341,400
E/MM

TIME
-HID-

SIZE(H)
E/MM
TIME
-ASK-

SIZE(H)
(N)

14.06
+

19.83

6 dq
MADF
14.06
19.85

50
CAES

14.06
19.83

1
CAES
14.06
19.85

50
(T)

14.06
+

19.83

1
(T)
14.06
19.85

50
TRIM

14.06
19.83
(C)
14.06
19.85

1
(8)

14.04
n

19.80

5
i
0
(N)
14.06
19.86

33 dq
(M)

14.06
+

19.73

1
ARCA
14.06
19.89

5
(P)

14.01
+

19.72

20
(
8
)
14.04
19.94

30
MADF

14.06
19.70

5

(M)
14.06
19.96

1
(C)

14.06
-

19.70

1
(P)
14.01
19.98

20
SHSH

14.00
19.69

1
TRIM
14.06
19.99

2
(X)

14.06
+

19.58

1
NYD
12.24
20

500
NYD

12.24
19.50

500
(X)
14.06
20.11

1
SWOT

9.42
9

1

SHSH
14.00
20.14

1
ARCA

14.06
0.01

1
SWOT
9.42
24.99

1
OI HUY

;
LEHM

FBCO

GSCO

NATW
MLCO
SBSH
MONT
OI SELL :
PRUS

UBSS

NATW

DLJP
SALB
RSSF
MONT
T/q

04-Oct-01 14:06 NYC

(c)BRIDGE
12 • TRADING AND EXCHANGES
FIGURE 2-1.
A Bridge Information Systems Quotation Montage for
NYSE-listed AT&T Common Stock
AT&T common stock (US ticker symbol T) last traded at 19.84,
down 0.10 from the previous day's closing price. The trade occurred
at the NYSE (N) at 14:06 Eastern Time when the high and low
prices of the day were respectively 20 and 19.51, and 8,341,400
shares had already traded. The quotation montage consists of two sets
of four columns each for bid and ask quotes. The first column
identifies the quote maker. Exchanges have one or three letter
symbols; dealer-brokers and electronic communications networks have
four letter symbols. The quotation sizes are expressed as hundred-
share lots. The rows are sorted so that the bids with the highest
prices and asks with the lowest prices appear at the top of their
respective columns. The first line of the montage shows that buyers at
the NYSE were willing to buy at least a total of 600 shares for 19.83
while a dealer working for Madoff Investment Securities (MADF)
was willing to sell at least 5,000 shares at 19.85. The two rows near
the bottom present order indications (01) that brokers and dealers
have asked Bridge to display for Bridge clients. Bridge subscribers
can query these firms to learn more about their orders.
Source: Reuters
uses a limit order, she will also specify when she wants the order to expire.
For example, a
day order
will expire when the trading session ends.
Jennifer decides to submit a day limit order to buy 200 shares of AT&T,
ticker symbol "T," for no more than 19.80 dollars per share. The broker en-
ters Jennifer's order into his computerized order entry system. The system
then confirms that Jennifer is authorized to make the trade. Next, the bro-
ker reads back the order to ensure that it is exactly what Jennifer intended.
(The brokerage firm records telephone calls in case a dispute arises about
what was said.) After Jennifer confirms the order, the broker releases it, and
the order entry system sends it to an exchange or to a dealer.
Although AT&T trades primarily at the New York Stock Exchange,
Jennifer's brokerage house might not send her order there. As figure 2-1
demonstrates, many other exchanges and dealers trade AT&T The broker-
age might send the order to a regional exchange or to a NASD dealer. Where
the brokerage order system sends the order may depend on the prices


## Page 26

CHAPTER 2 TRADING STORIES • 13
the various markets quote. It may also depend on cash payments and other
nonmonetary inducements that dealers offer brokerages to obtain their
order flow.
The brokerage's order entry system sends Jennifer's order to the NYSE
by transferring it to the NYSE's SuperDot order-routing system. SuperDot
then presents the order to the specialist who manages AT&T trading on
the floor of the Exchange. The specialist will act as the floor broker for
Jennifer's order. Jennifer's order appears on a workstation screen that the
specialist rents from the Exchange.
The AT&T specialist is a trader who works for a member firm of the
NYSE. He sometimes trades as a dealer for his firm's account and some-
times as a broker for his clients. The Exchange gives the specialist some
special privileges and special responsibilities. The specialist receives all of
the SuperDot order flow in AT&T. He organizes the AT&T trading to en-
sure that it is orderly, and he represents all orders entrusted to him. In re-
turn, the Exchange requires that he trade for his own account to fill cus-
tomer market orders if no one else is willing to do so.
Since Jennifer's order is a limit order, the specialist first
sees
if anyone is
interested in filling it immediately. No sellers are presently interested be-
cause other traders are bidding higher prices. The specialist then places her
order in his electronic limit order book. Jennifer's order will stand in the
book until the specialist can match it with someone who wants to sell at or
below its limit price, until the order expires, or until Jennifer tells her bro-
ker to cancel it.
(If Jennifer submitted a market order instead of a limit order, the spe-
cialist would conduct an auction to find the trader willing to sell at the low-
est price. A trader on the floor, a standing limit order in the order book, or
the specialist himself might offer that price. If no one wants to fill the mar-
ket order, the specialist will fill it himself by selling some of his own shares
in AT&T to Jennifer.)
A few minutes after Jennifer enters her order, a large seller sends a mar-
ket order through SuperDot. The specialist uses his computer to match this
order with several orders including Jennifer's. The orders all trade at 19.80
dollars. The Exchange trading systems then report the trade to the Con-
solidated Trade Reporting System, which reports trade prices and sizes to
various data vendors. These vendors immediately distribute trade reports
throughout the world.
SuperDot also reports the trade to Jennifer's broker in a process called
confirming the trade. The broker then reports the confirmation to Jennifer.
If the order had been a market order, the time between the order entry and
the final confirmation might have been less than half a minute. Jennifer
might have received her trade confirmation during the same telephone call
in which she placed her order. Since Jennifer's limit order took a while to
trade, the broker phones Jennifer with the confirmation.
Jennifer now has to pay for her purchase. That day, the brokerage house
mails her a written confirmation of the trade. The confirmation instructs
her to pay the purchase price times 200 shares purchased plus the broker-
age commission. If Jennifer trades through a deep discount brokerage, the
commission may be as low as 15 dollars. It could have been even lower had
she entered her order through the Internet.


## Page 27

14 • TRADING AND EXCHANGES
Three business days later, the trade settles. Jennifer's brokerage pays for
the stock, and the seller delivers it to her brokerage. If Jennifer has not yet
paid for the stock, her brokerage will collect the money from any cash she
holds in her account. If no cash is available and if Jennifer has executed a
margin agreement, the brokerage will lend her the money and charge her
interest on the amount due. The brokerage will use Jennifer's newly acquired
AT&T stock and other securities in her account as collateral for the loan.
If Jennifer does not pay, if she had no margin agreement with her broker-
age, or if she does not have adequate collateral to support a new loan, the
brokerage will eventually sell the stock and charge her for the commissions
and for any losses incurred.
After Jennifer pays for her stock, the brokerage places the shares in her
account. If she asks to have a stock certificate issued, her brokerage will in-
struct AT&T's transfer agent to issue the certificate and mail it to her. In
that case, Jennifer should place the certificate in a safety deposit box for
safekeeping.
2.2 A RETAIL TRADE IN A NASDAQ STOCK
Jennifer now wants to sell 100 shares that she holds in Microsoft. Unlike
AT&T, Microsoft does not trade at traditional stock exchanges like the
NYSE. Instead, about 40 independent dealers who publish their quotes in
the Nasdaq National Market System trade Microsoft. The dealers sit at their
desks throughout the country and enter their quotes into Nasdaq work-
stations that are linked through a private network.
When Jennifer asks her broker for a quote, the broker pulls up a screen
on his desk that is similar to the Bridge Information System display in fig-
ure 2-2. This display ranks the bids and offers of the 39 Nasdaq market
makers and electronic communications networks that were providing quotes
in Microsoft on October 5, 2001, at 11:26 Eastern Time. The bids are ranked
from highest to lowest, and the offers (asks) are ranked from lowest to high-
est. Looking at this screen, the broker reports that Microsoft (MSFT) last
traded at 55.97, down 0.47 from the previous close; the market is currently
55.97 bid; 55.98 offered.
With this information, Jennifer instructs her broker to sell 100 shares of
Microsoft at the market. This instruction tells the broker that she wants the
order filled quickly, at the best price available.
The broker then asks Jennifer whether she owns the shares of Microsoft
that she intends to sell. He needs to know where the shares will come from
in order to settle the trade. Jennifer tells the broker that she owns the shares.
If Jennifer did not own the shares, she would be selling the stock short. To
settle the trade, Jennifer would have to borrow 100 shares from someone.
Since borrowing shares is sometimes difficult or impossible, the broker
would have to arrange to borrow the shares before Jennifer could sell them.
The broker enters Jennifer's order into his order entry system and reads
it back to her. When Jennifer confirms the order, he releases it. If the bro-
kerage firm does not deal in Microsoft, its order entry system will send the
order to the Nasdaq Small Order Execution System (SuperSoes). If the bro-
kerage has a trader who is a Nasdaq dealer in Microsoft, the broker will
probably send the order to that trader.


## Page 28

us;MSFT
-

55.9700 DN
E/MM

TIME
Microsoft Corp •8
0.4700

(5)

11.25

H
-BID-

SIZE(H)
56.6400
E/MM

TIME
Common Stock
L

54.9400 V 15,130,100
-ASK-

SIZEIH)
(Q)
11.25

.1.
55.9700
9
(5)
11.25
55.9800
19
INCA
11.25
55.9700
9
ISLD
11.25
55.9800
19
ISLD
11.25
55.9600
6
REDI
11.25
55.9800
2
BTRD
11.25
55.9500
6
INCA
11.25
55.9900
54
FCAP
11.24
55.9400
1
GSCO
11.25
55.9900
10
ARCA
11.25
55.9100
4
BRUT
11.25
55.9900
2
SBSH
11.25
55.8900
1
ARCA
11.25
55.9900
1
NITS
11.24
55.8800
29
SCHB
11.25
56
20
SCHB
11.25
55.8800
PERT
11.24
56
11
GSCO
11.25
55.8700
10
BEST
11.24
56
10
MLCO
11.23
55.8500
10
NITS
11.24
56
6
MSCO
11.25
55.8500
10
JEFF
11.10
56
1
RED/
11.25
55.8300
10
RAJA
11.18
56
1
MART
11.25
55.8100
ABNA
11.03
56.0300
1
MONT
11.19
55.8000
1
0
FBCO
11.24
56.0300
1
OI BUY : GSCO MLCO BEST
OI SELL : FBCO GSCO CANT
MSFT/q/P82

05-Oct-01 11:26 NYC

(c)BRIDGE
CHAPTER 2 TRADING STORIES • 15
FIGURE 2-2.
Page 1 of a Nasdaq Level
II
Quotation Montage for
Microsoft Common Stock
Source: Reuters
SuperSoes routes the order to one of the Nasdaq dealers. Jennifer's
brokerage, like most other retail brokerages, specifies the dealer to which
SuperSoes sends her order. If the brokerage did not specify a dealer, Super-
Soes would have sent the sell order to one of the dealers displaying the best
(highest) bid. Her brokerage specifies a particular dealer because that dealer
has arranged to give the brokerage a cash payment or some nonmonetary
inducement to obtain her order.
The dealer who receives Jennifer's order executes it by buying the stock
for his own account. Many dealers have computer systems that automati-
cally execute small market orders when they arrive. The trade price will be
at least 55.97 dollars (assuming that the best bid has not changed). Even if
the dealer is not presently quoting a bid of 55.97, he will match that price.
After the dealer fills the order, he reports the trade to Jennifer's broker
and to Nasdaq. The broker then confirms the trade to Jennifer by phone
and by mail. If the brokerage does not already hold Jennifer's shares, the
broker will ask Jennifer to deposit her certificates in her brokerage account.
Nasdaq forwards the trade report to various data vendors, who report it to
the public.
The trade settles three business days later. At that time, the brokerage
delivers the 100 shares of Microsoft to the dealer, and the dealer pays for
them. The brokerage credits Jennifer's account with the proceeds of the
sale, less any commission charged and a small fee that the Securities and
Exchange Commission collects from sellers of securities.
2.3 AN INSTITUTIONAL TRADE
IN A NYSE STOCK
Bob is a trader who works for Rocket Science Investment Management
(RSIM), a (fictitious) investment management firm that manages money
for several corporate pension funds. These pension funds have each given
RSIM authority to trade on their behalf. RSIM manages a total of 2 bil-
lion dollars, invested primarily in U.S. equities.


## Page 29

16 • TRADING AND EXCHANGES
At 12:30
P.M.,
Eastern Time, an RSIM portfolio manager asks Bob to
buy 400,000 shares of Exxon Mobil Corporation for their clients. Since the
price of Exxon Mobil is near 40 dollars, the principal value of the trade is
about 16 million dollars. This makes it a large trade, but not especially so.
On October 4, 2001, this trade would have represented only 0.006 percent
of the total number of shares outstanding in Exxon Mobil (6.87 billion),
0.8 percent of the firm's portfolio, and 5 percent of the average daily vol-
ume (about 9 million shares) in Exxon Mobil. Bob's job is to get this order
filled at the lowest possible cost.
Bob first asks the portfolio manager why he wants to buy the stock. If
the manager wants the stock because he suspects that Exxon Mobil will very
soon announce the discovery of new reserves, Bob must trade quickly. The
price will rise as others come to the same opinion, and it will surely rise
when Exxon Mobil makes its announcement. In contrast, if the manager
wants to buy the stock because he believes that it is fundamentally under-
valued, Bob can be more patient. The prices of such stocks usually do not
rise so quickly that Bob needs to hurry to trade. The portfolio manager says
that he wants to buy Exxon Mobil because he believes it is fundamentally
undervalued.
Bob then uses an electronic information retrieval system to examine the
recent price and trade history for Exxon Mobil. He looks to see whether
other traders are trying to fill large orders. If a large seller is pushing prices
down, Bob might be able to fill his order quickly at a good price. If Bob
must compete with another large buyer, the order may be hard to execute
at a good price. Falling prices often indicate that large buys will be easier
to fill than large sells. Rising prices may indicate the opposite. Since move-
ments in the price of Exxon Mobil closely correlate with movements in the
market as a whole, Bob also compares Exxon Mobil's recent price history
against movements in the S&P 500, which is a broad market index. If Exxon
Mobil is unchanged, but the market is down, Bob's purchase may be easier
to arrange on good terms than if the market is up. Bob finds that Exxon
Mobil is down slightly while the market is largely unchanged.
Bob next tries to discover whether there is any large trader interest
in Exxon Mobil. He consults one or more of the electronic information
systems that collect information about trader interests. Bridge, Liquidnet,
Autex, and Instinet are among the more important systems. These systems
show that several traders have indicated interest in Exxon Mobil. Bob can-
not tell from the screen how serious they are, or whether they have already
filled their orders. One of the traders is a Morgan Stanley broker.
Bob calls one of the many brokers with whom RSIM does business. He
chooses a floor broker whom he trusts to keep quiet, since he does not yet
want to reveal that he is in the market. Without stating his intentions, he
asks the broker, who happens to work for Merrill Lynch, what market con-
ditions are like in Exxon Mobil. The Merrill floor broker goes to the post
where Exxon Mobil trades on the NYSE floor and tries to collect some use-
ful information. He may stand around watching the trading, or he may ask
the specialist and other nearby traders who has been interested in trading.
They may or may not tell him what he wants to know, and they may not
answer with complete honesty. Traders generally will not reveal their inter-
est without some indication that they are revealing to someone who is able
and willing to trade with them on the spot. The Merrill Lynch broker re-


## Page 30

CHAPTER 2 TRADING STORIES • 17
ports to Bob that no large trader interest is currently visible in the stock.
He also says that the Morgan Stanley broker was actively selling earlier in
the day.
At 12:50
P.M.,
Bob sends an order to buy 400,000 shares in Exxon
Mobil to POSIT. POSIT (POrtfolio System for Institutional Trading) is
an electronic trading system run by Investment Technology Group. POSIT
collects buy and sell orders from large, primarily institutional traders and
attempts to match them. The service is completely confidential. It does not
reveal any information about orders to anyone. Bob sends his order directly
to POSIT, using software it provides.
Eight times daily—at 9:40
A.M., 10 A.M.,
10:30
A.M.
and hourly from
11
A.M.
to 3
P.M.
Eastern Time, POSIT conducts crosses to match buyers
with sellers. If the total POSIT buy order volume exceeds the total POSIT
sell order volume, the sell orders are all filled completely, and the buy or-
ders are partially filled. The fraction filled depends on the extent to which
buy order volume exceeds sell order volume. If buy order volume is twice
the sell order volume, POSIT will fill half the total volume of all buy
orders. The actual fill rate for any given order depends on a complex set of
rules that POSIT uses to best serve the needs of its clients. A similar set
of matching rules applies if sell order volume exceeds buy order volume.
POSIT assigns the trade price by choosing a time at random within the
seven minutes that immediately follow the cross. At that time, POSIT com-
putes the average of the bid and ask in the primary market for the stock.
POSIT uses that average as the trade price for all crossed orders. As soon
as the cross is completed, POSIT reports the trades to its clients and to the
NASD.
At 1:10
P.M.,
Bob learns that 48,000 shares of his 400,000-share order
filled at a price of 39.84 dollars. From this information, Bob knows only
that POSIT buy order volume exceeded POSIT sell order volume. He can
roughly infer the size of the unfilled POSIT buy order volume from the to-
tal size of the cross, which he can read off the Consolidated Tape. (The
POSIT report appears on the tape with an exchange marker of "0," which
indicates third market trades.) If the cross were for 48,000 shares, Bob would
know that he was the only buyer. If the cross was for 100,000 shares, he
could roughly infer that the total POSIT buy order volume was a bit more
than 800,000 shares because the 48,000-share partial fill on his 400,000-
share order represented about half of trade volume. The estimate is rough
because POSIT does not use a strict pro rata rationing algorithm to match
the orders. After consulting trade records reported to the electronic Con-
solidated Trade Reporting System, Bob concludes that his order was prob-
ably the only large buy order in POSIT.
Bob then calls a sales trader at Morgan Stanley, with which his firm has
an account, to inquire about the posted indication he had seen earlier. This
broker sits at Morgan Stanley's equity trading desk on the trading floor at
Morgan Stanley's headquarters in midtown Manhattan. The broker says that
he has already filled his client's sell order and apologizes for failing to re-
move the indication. The broker then suggests that his client may be will-
ing to sell more. Bob expresses some interest, and the broker calls his other
client while putting Bob on hold. A negotiation takes place through the
broker's intermediation. In the end, the broker arranges a trade for 200,000
shares at 39.87.


## Page 31

18 • TRADING AND EXCHANGES
The sales trader then telephones a Morgan Stanley floor broker at the
NYSE and asks him to print the trade. The floor trader goes to the Exxon
Mobil post and tells the specialist that he would like to cross a block of
200,000 shares at 39.87. The specialist is currently quoting a market of 38.86
bid and 39.88 asked, and the last trade was at 39.86. The specialist gives
his approval. (If the proposed trade price were higher than 39.88, the spe-
cialist would have required the floor broker to allow the sell orders stand-
ing in the limit order book below that price to participate in the trade.) The
specialist reports the trade to an Exchange computer that forwards it to the
Consolidated Trade Reporting System.
At the same time, other traders standing at the post ask the Morgan
Stanley floor broker whether there is more interest on either side of the
trade. The broker says that he will call to find out. He calls his trading desk,
and the sales trader calls Bob.
Just before Bob gets this call, he receives a call from the Merrill Lynch
floor broker with whom he had earlier spoken. The broker reports that
Morgan Stanley just crossed 200,000 shares. Bob tells him that he was the
buyer. Bob then gives the Merrill Lynch broker a market-not-held buy or-
der for 80,000 shares. The market-not-held instruction tells the floor bro-
ker to buy shares at his discretion. When Bob gets the call from the
Morgan Stanley sales trader, he says that he is no longer actively interested
in the stock.
The Merrill broker now stands in the crowd and waits to see what hap-
pens. In the next hour, he buys 20,000 shares at 39.88, 32,000 shares at 39.90,
and 28,000 shares at 39.95. He calls Bob back with the confirmations.
Bob then gives the Merrill broker a market-not-held order to buy the
remaining 72,000 shares. The specialist's quote is now 39.95 bid, good for
240 round lots of 100 shares (24,000 shares) and 40.00 offered, good
for 500 round lots. Before the floor broker arranges any more trades, the
broad market starts to rise. The Merrill broker looks at the S&P 500 fu-
tures contract price and sees that it has risen faster than the S&P 500
Index, so that the spread between the future price and the index has widened.
This evidence suggests that the stock market may continue rising. It also
suggests that index arbitrageurs may soon start buying Exxon Mobil, which
is the largest stock in the S&P 500 Index. The Merrill broker turns to the
specialist and immediately asks to buy 72,000 shares for 40.00. The spe-
cialist, acting as broker for several traders whose sell limit orders are on the
book at 40.00, sells him the shares and reports the trades. The specialist
then raises his bid and offer, and the Merrill broker calls Bob to report his
trades.
Bob has now completely filled his 400,000-share order. The average cost
of his trades was 39.898 dollars per share, not counting commissions. Bob
reports the trades to his firm's back office, which will arrange settlement.
He also reports the trades to the portfolio manager. A summary of these
trades appears in table 2-1.
Bob's firm, RSIM, will not actually purchase the shares that Bob bought.
Instead, RSIM 's pension fund clients will purchase and hold the shares. The
back office must now tell the custodians of these funds that RSIM made
purchases on their behalf. RSIM divides the 400,000 shares among the var-
ious pension funds in proportion to the money that they have placed under


## Page 32

CHAPTER 2 TRADING STORIES • 19
TABLE 2-1.
Summary of Bob's Trades
SHARES
PRICE
NOTES
48,000
39.84
POSIT
200,000
39.87
Morgan Stanley block trade
20,000
39.88
Merrill Lynch floor trades
32,000
39.90
Merrill Lynch floor trades
28,000
39.95
Merrill Lynch floor trades
72,000
40.00
Merrill Lynch trades with book
400,000
management at RSIM. Three days later, the various pension fund custodi-
ans will pay for and receive their Exxon Mobil shares. Each will pay the
same price per share.
2.4 AN INSTITUTIONAL TRADE
IN A NASDAQ STOCK
Bob's portfolio manager now asks him to sell 10,000 shares in United States
Lime & Minerals (USLM), a Texas-based producer of pulverized limestone,
quicklime, and hydrated lime products. USLM trades at about 4.85 dollars
per share, so the principal value of this transaction is 48,500 dollars. Al-
though the order represents only 0.17 percent of the total shares outstand-
ing (about 6 million), it is about ten times bigger than the average daily vol-
ume in USLM. Bob expects that his order will be difficult to fill.
Bob calls up the USLM Nasdaq quote montage using Bridge Informa-
tion Systems (figure 2-3). Only three dealers are actively making a market
in USLM. The best bid is 4.85 and the best offer is 4.90, so that the bid/ask
spread is 1 percent of price, which is quite wide. Moreover, the total dis-
played size on the bid side of the market is only 500 shares. As of 11:32
Eastern Time, the stock has not yet traded. The fact that the stock has not
yet traded, the wide spread, and the small displayed size all suggest that the
order will be expensive to fill.
Bob picks up the phone and calls Spear, Leeds, Kellogg and Co. (SLKC).
SLKC is a very large dealer that is making a market in USLM. He chooses
SLKC because the firm has provided him good service in the past. SLKC
is not bidding the most aggressive price, but it is not far from the market.
He also notes that SLKC's offer is way behind the market, which suggests
that SLKC has no interest in being a seller. Bob generally assumes that the
dealers bidding the most in Nasdaq are the most eager to buy, although he
knows that this is not always the case.
When the SLKC dealer answers the phone, Bob asks him for quotes to
buy or sell 10,000 shares of USLM. He asks for both sides because he does
not yet want to reveal that he is a seller. If Bob had said that he wants to
sell, the dealer might have been tempted to lower his quote. Since the dealer
does not know whether Bob is a buyer or seller, he is more likely to quote
prices that fairly reflect his best estimate of value of the stock.


## Page 33

us;USLM

United States Lime and Minerals Inc

Common Stock
-

4.9000 UNCH

(Q) 1 DNT H

0

L

0

V 0
E/MM TIME

-BID-

SIZE(H)

E/MM TIME

-ASK-

SIZE(H)
(Q)

10.36 +

4.8500

3

(Q)

10.36

4.9000

12
HRZG

9.31

4.8500

3

LEGG 10.36

4.9000

12
SLKC

8.35

4.78001
1

HRZG

9.31

5.5000

1
LEGG 10.36

4.4000

SLKC

8.35

6.6200

1
OI BUY
OI SELL
USLM/Q

05-Oct-01 11:32 NYC

(c) BRIDGE
20 • TRADING AND EXCHANGES
FIGURE 2-3.
Nasdaq Level II Quotation Montage for United States Lime
and Minerals
Source: Reuters
Before quoting, the dealer looks at the Nasdaq market and confirms that
no recent news stories have appeared about USLM. The dealer wants to
make sure that Bob does not know something important about the stock
that he does not.
Although SLKC's Nasdaq quote is 4.78 bid, 6.62 offered for 100 shares,
he gives Bob a quote of 4.70 bid, 5.05 offered for 10,000 shares. Bob then
asks whether the dealer will pay 4.75 for 10,000. The dealer accepts the of-
fer and buys 10,000 shares from Bob. The dealer must report the trade to
Nasdaq within 90 seconds. Bob reports the trade to his back office, which
notifies the custodians of the various pension plans for which RSIM pro-
vides investment management.
2.5 A VERY LARGE BLOCK STOCK TRADE
Until the day of his death last year, John Smithson was the chairman and
principal stockholder in Smithsonian Industries, a (fictitious) firm that
trades at the NYSE. Smithson founded the firm 55 years earlier with two
friends whom he subsequently bought out. He was its CEO until five years
ago, when he decided to go into semiretirement.
Over the years, Smithsonian Industries financed its extraordinary growth
through a series of secondary stock offerings and convertible debt offerings.
The firm also made some acquisitions that it financed by swapping stock.
Although these transactions substantially reduced the fraction of shares that
Smithson owned, upon his death he still owned 4 percent of the common
stock. Various institutions owned another 54 percent of the stock. Man-
agement owned less than 2 percent. Private shareholders held the remain-
ing 40 percent. The firm currently has 40 million shares outstanding. At
the current price of 80 dollars a share, the firm has a total capitalization of
3.2 billion dollars.
Smithson was always disappointed that his children never showed any
interest in the business. He therefore left all of his 1.6 million shares in
Smithsonian to Edna Wilkerson, his only grandchild and the darling of his
golden years. He also appointed her executrix of his estate.
Edna hired a financial adviser, who told her that she would have to bor-
row money or sell shares in Smithsonian to pay inheritance taxes. He also
suggested that she should sell shares in Smithsonian to diversify her port-


## Page 34

CHAPTER 2 TRADING STORIES • 21
folio. Edna then spoke to the Smithsonian CEO and explained her prob-
lem. The CEO, who was now also the newly appointed chairman, suggested
that Edna contact Goldman Sachs. Goldman had managed the last two sec-
ondary offerings made by the firm.
Edna spoke to brokers in the private client services group at Goldman
Sachs and arranged for them to sell 900,000 shares of Smithsonian Indus-
tries. The brokers suggested that she sell all 1.6 million shares. Edna was
reluctant, however, because of a sense of loyalty to her grandfather and be-
cause she felt her grandfather had picked an excellent successor.
The private client services brokers then passed the order to brokers on
Goldman's block brokerage desk. These brokers now have to find buyers for
900,000 shares of stock. Since daily trading volume in the stock is averag-
ing only 60,000 shares, they are unlikely to find the buyers on the floor of
the exchange. They will have to find them elsewhere.
Goldman's block brokers face the following predicament. If nobody
knows that they have stock to sell, they will not be able to sell it. However,
if too many people know that a large block of stock is hanging over the
market, speculators will push the price down. The Goldman brokers thus
must be selective when approaching potential buyers.
Before contacting anyone, the brokers do some research to determine
who will most likely be interested in buying the stock. They first draw up
a list of their clients who have shown recent interest in Smithsonian In-
dustries. They then examine the CDA/Spectrum database to see what in-
stitutions currently own Smithsonian and which ones have been increasing
their positions in that stock. Various classes of investors have to make quar-
terly reports of their positions to the Securities and Exchange Commission.
CDA/Spectrum collects these publicly available 13F Holdings Reports, sorts
the positions by stock, and publishes the information in an electronic data-
base. The brokers access this information through computers on their desks.
The brokers also try to identify large funds for which an investment in
Smithsonian Industries would make strategic sense. When their research is
complete, the brokers rank potential buyers by their likely interest in the
trade and by the likelihood that they will trade on the information revealed
to them.
The Goldman block brokers then start contacting potential buyers and
try to sell the stock. The current NYSE quote for Smithsonian Industries
is 81.00 bid, 81.15 offered.
Traders who show interest in the block all ask the same questions. They
want to know who the seller is, and why he or she is selling. In particular,
they want to know that Edna is not selling the stock because she believes
that it is overvalued. The brokers explain that Edna must sell the stock to
raise money to pay inheritance taxes. They add that Edna has no other as-
sets that she could sell to raise the money. Traders also want to know the
discount at which the brokers will price the block to sell it. The brokers
must offer a discount to encourage the buyers to participate. The brokers
estimate that the discount will be 1.50 dollars. Finally, the potential buyers
want to know whether Edna is selling her entire holdings of Smithsonian
Industries. In particular, they want to know whether Edna will offer more
stock after she sells this block. If she does, the next block probably will fur-
ther depress prices. The traders who purchase the first block will see an im-
mediate loss on their investment. The brokers explain that Edna is not


## Page 35

22 • TRADING AND EXCHANGES
offering all her stock, despite their encouragement to do so. Although they
cannot guarantee that she will not offer more stock later, they do not ex-
pect her to do it soon. In an effort to put a positive spin on this informa-
tion, they argue that her reluctance to sell more of the stock suggests that
she thinks it is undervalued.
The brokers continue contacting potential buyers until they have iden-
tified sufficient commitments to complete the transaction at a reasonable
price. After contacting 10 potential buyers in 15 minutes, the Goldman
Sachs brokers obtain firm commitments from three buyers who are willing
to buy a total of 850,000 shares at 80 dollars per share. The brokers then
call one of their floor brokers and ask him to print the trade.
The Goldman Sachs floor broker approaches the post and observes that
the current quote has dropped to 80.75 bid, 81.00 offered, even though the
broad market has risen slightly. He then asks the traders there whether any-
body wants to buy Smithsonian Industries. A First Boston floor broker in-
dicates that she is interested. The Goldman broker asks how much she wants
to buy. She says 10,000 shares. He then asks whether that will fill her or-
der. She answers yes. The Goldman floor broker then tells the specialist that
he wants to print a block of 900,000 shares at 80. The specialist consults
his limit order book and says that there are buy limit orders totaling 20,000
shares on his book at prices between 80.00 and 80.75. He also indicates that
he is interested in buying 5,000 shares for his own account. The limit or-
ders and the First Boston buy order have precedence over the three primary
buyers of the block. They must be filled if the price drops to 80. The Gold-
man trader agrees to allow the limit orders, the First Boston order, and the
specialist to participate in the block trade at 80. Goldman Sachs purchases
the remaining 15,000 shares for its own account.
Everybody involved starts making reports. The specialist reports the trade
to the exchange information systems. The Goldman broker calls his block
trading desk and tells them that he had to include 35,000 shares from the
floor in the trade. The block desk calls Edna and the three primary pur-
chasers with their confirmations. The First Boston broker calls her client
with her report. The specialist has SuperDot report confirmations for the
limit orders that brokers entered into the book through SuperDot. He asks
his assistant to page two floor brokers who gave him limit orders to hold
in his book. When they call in, the assistant gives them their confirmations.
Within a few minutes, a market commentator on CNBC reports that Smith-
sonian Industries is down a dollar and 25 cents for the day following a
900,000-share block that just crossed the tape.
2.6 SOME CASH COMMODITY AND
ASSOCIATED FUTURES MARKET TRADES
Moline Meal (MM) is a (fictitious) soybean processor located on the Mis-
sissippi River in Moline, Illinois. Moline is on the Iowa-Illinois border about
150 miles west of Chicago, in the heart of soybean country at the junction
of interstate highways 75 and 80. Several railroads pass through the city.
MM buys soybeans from farmers up and down the river and from both
sides of the river. It crushes most of the beans to separate the oil from the
meal. MM sells the oil to food processors and the meal to feedlot operators.


## Page 36

CHAPTER 2 TRADING STORIES • 23
Low margins, high
volumes, and volatile prices characterize the business.
Prices are volatile because weather conditions make harvests unpredictable
and because the demand for meat (and hence for soy meal for feed) fluctu-
ates with the business cycle. To manage the risk in its business, MM hedges
extensively in the Chicago soybean futures markets.
The Chicago Board of Trade (CBOT) has three soybean contracts: a
bean contract, an oil contract, and a meal contract. The bean contract calls
for the delivery of 5,000 bushels of beans, or slightly more than 1.5 stan-
dard railroad carloads (each of which holds approximately 3,300 bushels).
The oil contract calls for the delivery of 60,000 pounds of oil, and the meal
contract calls for the delivery of 100 tons of meal. These quantities are ap-
proximately the amount of oil and meal that millers produce when they
crush 5,000 bushels of beans.
Sellers must deliver the beans in Chicago when the contracts expire.
Contracts for eight delivery months are traded. The delivery months are
November, January, March, May, July, August, and September. Trading in
each contract starts about a year before it expires.
On the morning of October 12, an MM trader speaks by phone with
the representative of a local farmers cooperative. He negotiates a purchase
of 150 carloads of beans from the cooperative. The farmers will deliver the
beans in December, following the harvest, and MM then will pay for them.
After specifying grade, quality, and delivery terms (most of which are stan-
dardized), the traders turn their attention to the price.
By custom, they express cash soybean prices relative to the price of the
Chicago futures contract nearest to expiration. The negotiators therefore
discuss only the local price differential, which traders call the basis. After a
few offers and counteroffers, the traders agree to a basis of —5 cents. When
the farmers deliver their beans, the price they will receive will be 5 cents
less than the then current January futures contract price.
MM does not yet have buyers for the beans or for the meal and oil that
they will probably press from them. If the price of beans drops after MM
pays for them, but before MM sells the beans or their pressed products,
MM will lose money. If the price rises, MM will make money. Although
on average MM will probably break even, MM management is uncom-
fortable with the price risk and does not want the firm to pay the costs of
financing large, unpredictable cash flows.
Management also is concerned that its traders may inadvertently place
the firm into speculative positions in soybeans. To reduce these risks, MM
has a risk management program. Unless specifically exempted, MM requires
that all its traders must always hedge their exposed positions.
Until MM takes delivery on the beans and pays for them, it is not ex-
posed to price risk. If the price of beans rises, it will pay more for more
valuable beans. If it falls, it will pay less for less valuable beans. After MM
takes delivery, MM will be very exposed to price risk.
To hedge this risk, the MM risk manager will hold an offsetting short
futures position in the January soybean futures contract. The risk manager
needs 99 futures contracts (150 carloads X 3,300 bushels per carload
5,000 bushels per contract) to hedge the price risk. To minimize the expo-
sure of the firm to price risk, the risk manager must construct this position
when MM takes delivery of the beans.


## Page 37

24 • TRADING AND EXCHANGES
Immediately following the trade, the MM trader calls the MM risk man-
ager and reports that he just arranged to buy 150 carloads of December soy-
beans. The trader also reports that the cooperative will exchange a January
short position of only 50 contracts with the beans in a transaction known
as an
exchange for physical
in the futures markets and as a
cash exchange
in
the grain trade. (This transaction is unusual because the cooperative nor-
mally would exchange a 99-contract short position rather than just a 50-
contract position.) The risk manager therefore will have to sell 49 additional
contracts upon delivery.
When MM takes delivery in December, the risk management officer
calls a broker who works for Iowa-Illinois Investor Services (IIIS), a (ficti-
tious) futures commission merchant. IIIS is one of many futures brokerages
with which MM maintains an account. The risk manager identifies him-
self and instructs the broker to sell 49 January soybean futures contracts at
the market. The broker enters the order into a computer, repeats it, and ob-
tains his client's confirmation. The risk manager also instructs the broker
to do an exchange of 50 contracts with the cooperative's broker.
The broker sends the market sell order to the CBOT trading floor, where
it prints on an "Electronic Clerk" printer in the soybean pit. A IIIS clerk
immediately picks up the order and gives it to the IIIS floor trader.
The pit is a structure on the floor of the exchange in which the traders
stand. The inside looks like a miniature football stadium. The bottom is flat.
The sides are terraced with steps that go all the way around the pit. The
traders stand shoulder to shoulder on the bottom and on the steps. This de-
sign makes it easier for everyone to see everyone else. Above and to the side
of the pit is a podium on which exchange price reporters stand. They watch
the trading in the pit and immediately report trade prices as traders make
them. Surrounding the pit are large screens that display the most recent
trade prices to the trading crowd. About 200 traders fill the soybean futures
pit. The CBOT has about 20 different trading pits on its main trading floor.
The traders are a diverse set of people. Each trader has a seat on the ex-
change. Some own their seat; some lease it; some use a seat provided by
their employer. Most traders are locals, and most locals are scalpers and day
traders. Locals are typically one-person operations. Scalpers are dealers who
buy and sell for their own account. They try not to hold large positions for
more than a few minutes. They are continuously acquiring and unwinding
their positions. Day traders are speculators who may be willing to hold po-
sitions through the trading session but rarely overnight. Some scalpers and
day traders also act as brokers for other traders. The remaining traders are
brokers who work either for themselves or for large national firms.
The traders all wear large identification badges that can be seen from
across the pit. Most also wear distinctive jackets, often with wild color
schemes, to make them easier to find. The jackets have large outside pock-
ets for holding papers. The traders all have a pencil in one hand and a trade
card in the other hand. Increasingly, some traders carry handheld trade re-
porting devices that transmit trades as they record them.
The IIIS clerk finds her floor trader, Jack, by looking for his yellow jacket
with two-inch pink polka dots in the place where he normally stands. She
gives Jack MM's sell order. Jack reads it with a well-practiced poker face
and then sticks it into a pocket.


## Page 38

CHAPTER 2 TRADING STORIES • 25
Traders communicate in the pit by shouting and using hand signals. They
shout out their bids and offers so that everyone can hear what they say. The
noise can be so great that few traders actually hear what they say, though
many can read lips well. Traders therefore also use hand signals to make
their bids and offers. They use fingers to indicate prices and sizes. The ori-
entation of the hand (up or sideways) shows whether they are expressing
prices or quantities. Hand orientation also shows whether the trader wants
to buy or sell: Palm out indicates an offer to sell; palm in indicates a bid
from a buyer. By using both voice and hand signals, the traders reduce the
chance that they will misunderstand each other.
Traders arrange trades by accepting another trader's bid or offer. Traders
yell "sold" to accept a trader's bid or offer. The acceptor also points at the
other trader to get his or her attention and to make eye contact. The traders
then negotiate the size of the trade.
The IIIS trader thus has two ways that he can arrange to sell soybeans
for MM. Jack can offer the beans for sale and hope that a buyer will accept
the price. Alternatively, he can wait until a buyer bids and then accept the
buyer's price.
When Jack receives the order, the last reported trade price was 678
3
/4.
Some traders are bidding 678
3
/4 cents and others are offering at 679 cents.
Jack's trader accepts the bids from two traders at 678
3
/4 cents and negoti-
ates to sell three contracts with one and four contracts with another. He
quickly writes the terms of the two trades and the two trader IDs on his
trade card. The two traders do likewise. Jack then shouts an offer for 678
1
/2
cents, but no one takes it. After about five seconds, he lowers his offer to
678
1
/4 cents, and still receives no interest. Some traders bid for 677
3
/4. He
lowers his offer to 678, and three traders take his offer. Jack negotiates to
sell the remaining 42 contracts at 678, and the four traders record their
trades.
Jack then reports the trades to his clerk. The clerk calls the Moline of-
fice with the confirmation, and the broker there then calls MM's risk man-
ager. After reporting the trade, the broker tells the risk manager that MM
must post 198,000 dollars in margin to guarantee its performance should
the price of soybeans rise. The risk manager posts the margin by transfer-
ring Treasury bills worth 200,000 dollars to this account.
Shortly after the 1:15
P.M.
(Central Time) close of trading, the final set-
tlement price for the December soybean contract is set at the closing price
of 684
1
/4 cents. Since MM is short 99 soybean contracts sold at an average
price of 678.25 cents, MM has lost 6 cents per bushel on its new position.
The total dollar loss is 6 cents per bushel times 5,000 bushels per contract
times 99 contracts, or 29,700 dollars. MM transfers this amount to IIIS
that afternoon.
None of the traders with whom Jack traded knows Jack's order came
from MM. Jack likewise does not know whether the traders with whom he
traded were trading for their own accounts or for others.
At the end of the trading session, Jack submits a list of all his trades to
the Board of Trade Clearing Corporation (BOTCC). The BOTCC takes
Jack's trade reports and those of all other traders, and attempts to match the
buys with the sells. A trade clears when two traders both report that they
traded the same quantity with each other at the same price. About 95 per-


## Page 39

26 • TRADING AND EXCHANGES
cent of all trade reports clear without problem. The remaining reports are
called out-trades. The BOTCC returns these reports to the traders, who
must resolve the discrepancies among themselves. Transcription errors gen-
erally cause most out-trades. Occasionally they result from misunderstand-
ings. On very rare occasions, out-trades are due to fraudulent reports. The
traders must resolve their out-trades before trading starts the next day.
The BOTCC guarantees all trades. Although traders negotiate their con-
tracts with each other, their contracts are actually commitments to the
BOTCC. BOTCC thus acts as the buyer for each seller and the seller for
each buyer. This arrangement ensures that traders do not need to decide
whether another trader is creditworthy before they trade. The clearing mem-
bers of the CBOT and the MidAmerica Commodity Exchange own the
BOTCC. If one of the members defaults, the others bear the resulting losses.
To reduce the potential losses, the BOTCC requires that its members post
and maintain margin (performance bonds) for each contract they clear.
2.7 AN OPTIONS MARKET TRADE
Lisa holds 2,000 shares of Microsoft stock, which is currently trading for
55 dollars per share. The stock has risen greatly in value since she bought
it in 1993 for the split-adjusted equivalent of 5 dollars per share. If she sold
it today, she would realize a capital gain of 50 dollars per share and would
have to pay a substantial capital gains tax. Lisa now thinks that Microsoft
is overvalued, but she is not certain. If it drops in value, she would like to
sell her position at today's prices and, if possible, defer the capital gain for
a few months to move it into the next tax year. If the price rises, she would
like to keep the stock and continue deferring her substantial tax liability.
To achieve her investment goals, Lisa decides to buy 20 Microsoft Jan-
uary 55 put option contracts. Each of these contracts gives her the option
to sell 100 shares of Microsoft at 55 dollars per share any time before or on
the third Friday in January. If the price of Microsoft rises before then, she
will make money on her Microsoft stocks. The options will lose their value,
however, and she will not exercise them. If the price of Microsoft falls, the
options will become quite valuable, and her loss in her Microsoft stock will
be offset, almost one for one, by her gains on the options. In that case, Lisa
will decide in January to realize the gains on the options by selling them or
by exercising them. If she sells the options, she will pay the capital gains
tax on their increase in value at her short-term combined federal and state
rate of 44 percent. If she exercises them and sells her Microsoft stock for
55 dollars per share, she will pay the tax on her capital gain at the long-
term rate of 26 percent. Although the capital gain will be larger in the stock
than in the options, she may exercise the options rather than sell them be-
cause of the higher short-term capital gains rate of 44 percent. In either
case, she will have deferred the capital gain into the next year.
Lisa calls her securities brokerage, with which she has set up an account
with options trading privileges. To obtain these privileges, Lisa had to con-
vince a senior registered options principal at the brokerage that she under-
stands how options work and the risks to which they can expose her. She
also had to demonstrate to the principal's satisfaction that she is financially
able to withstand the potential losses that can be associated with options


## Page 40

CHAPTER 2 TRADING STORIES • 27
trading. The Securities and Exchange Commission, the Options Clearing
Corporation (OCC), and the brokerage impose these suitability require-
ments upon options customers to prevent people from taking risks that they
do not understand or cannot afford.
After reaching her broker and giving him her account number, Lisa asks
for a quote for the Microsoft January 55 put option contract. To obtain the
quote for this option, the broker must first find its ticker symbol. After con-
sulting an electronic list of available trading vehicles for MSFT (the stock
ticker symbol for Microsoft), the broker confirms that the ticker symbol for
the January Microsoft 55 put contract is MSQMK. The first three letters
refer to Microsoft. The last two letters refer to the January 55 put contract.
He enters this information into his quotation system to obtain the quote
that Lisa requested. The quote for the contract is 4.20 bid, 4.50 offered, last
sale at 4.30. The broker also reports that Microsoft stock last traded at 55.44.
Upon hearing the quote, Lisa decides to issue a limit order. She instructs
her broker to buy 20 Microsoft January 55 put option contracts, limit price
4.40. The broker enters the order into his electronic order entry system, and
then reads it back to Lisa for her confirmation. After listening carefully to
the broker, Lisa confirms that the order is correct. The broker then releases
the order.
The brokerage's order-routing system sends the order electronically to
the Pacific Exchange's order-routing system. This system, called POETS
(Pacific Options Exchange Trading System), can forward the order to any
of several destinations on the Exchange options floor in San Francisco. For
this order, the brokerage's system instructs POETS to route the order to
the exchange order book, which an exchange order book official (OBO)
manages. The OBO is an employee of the Pacific Exchange who will act
as the broker for the order.
The Exchange shows its order book overhead on large computer displays
to a crowd of about 20 traders standing in front of the post where Microsoft
options trade. Some of these traders are brokers who represent their clients,
and some are dealers called market makers, who trade for their own account.
When Lisa's order arrives, all the traders notice it. Several traders try to sell
at 4.40 by immediately shouting, "Sell that book." The OBO trades a total
of 20 contracts with three traders. The OBO then enters information about
the trade into POETS, and POETS sends a report of the trade to Lisa's
broker.
The broker confirms the trade with Lisa. Although Lisa is initially quite
pleased with the trade price, it occurs to her to ask where Microsoft stock
is now trading. The broker reports that the last trade in Microsoft was 55.84,
40 cents higher than before. Since the price of Microsoft has risen, the put
contract price has dropped. Although the price she obtained was good, it
was not as good as it initially seemed. Lisa asks for the current quote in the
January 55 put option. The broker reports that the quote is now 4.00 bid,
4.30 offered.
The brokerage, the exchange, and the other traders arrange for settle-
ment of the contract through the Options Clearing Corporation. The OCC
clears all exchange-traded option contracts in the United States. It also guar-
antees performance on all contracts, using mechanisms similar to those de-
scribed above for the futures markets.


## Page 41

28 • TRADING AND EXCHANGES
2.8 A BOND MARKET TRADE
Sam works for Sheltered Life, a large (fictitious) insurance company, where
he helps manage the bond operations. The firm, like many other insurance
companies, invests a substantial fraction of its reserves in long-term corpo-
rate bonds. Periodically, the firm must buy new bonds to replace those which
have matured and to invest additional funds that its clients give it to manage.
Every day, throughout the day, Sam receives phone calls from sales traders
at various investment banks who would like to sell him bonds. The sales
traders sometimes offer newly issued bonds that their banks have under-
written. Other times, they offer seasoned bonds that their banks have in in-
ventory. Because Sam is a very big client, the traders all try to please him.
Sam talks to them to keep abreast of market conditions.
This morning, Sam has determined that he needs to buy 50 million dol-
lars of long-term corporate investment-grade bonds. After examining his
portfolio, and after studying general conditions in the credit markets, Sam
has decided that he would like to buy bonds issued by a high-tech firm.
From many years of experience, Sam believes that he will most likely ob-
tain the type of bond he wants today at the best price from Salomon Broth-
ers. He calls up the sales trader who manages his account there and asks
him to fax over a list of the long-term, investment-grade high-tech bonds
that they have in inventory.
After receiving the list, Sam sees that it contains the IBM 7
1
/2 s13 bond.
This bond pays a coupon of 7
1
/2 percent and matures in 2013. Sam is al-
ready familiar with the bond covenant (the terms of the bond) and with
IBM's general creditworthiness. He decides that if he can get a good price,
he would like to buy this bond. Sam now turns to his Bloomberg terminal
to examine prices in the credit markets to get some idea of the price the
bond should trade for.
Sam then calls the Salomon Brothers sales trader and asks him to quote
a price for the bond. The sales trader quotes him an offering price of 112,
which corresponds to 112 percent of the face value of the bond. Sam ex-
plains that this price is too high, given current market conditions and the
various options that appear in the covenants for that bond. He instead pro-
poses to buy the bond for 70 basis points over the rate of return implied by
the on-the-run 10-year Treasury bond. This bond trades in a very active
market organized by Cantor Fitzgerald, which is the world's largest U.S.
government bond brokerage. After performing some quick calculations, the
sales trader determines that the equivalent price is 111
1
/8, which is too low
for him. The two traders continue negotiating until they agree to a price
of 111
5
/s.
They both write up trade tickets and give them to their respective op-
erations clerks to settle the trade. The derks arrange for settlement through
a clearinghouse called the National Securities Clearing Corporation
(NSCC), a division of the Depository Trust and Clearing Corporation
(DTCC). NSCC is by far the largest clearinghouse in the United States. It
will act as an escrow agent in a process known as delivery versus payment.
NSCC uses a book entry settlement system in which it transfers securities
electronically from one account to another.
Sam's clerk reports the trade to Sheltered Life's custodial bank, Chase
Manhattan, which will settle the trade. A custodial bank is a firm that holds


## Page 42

CHAPTER 2 TRADING STORIES • 29
securities and money on behalf of its clients. A clerk at Chase reports the
trade to NSCC, and ensures that enough money is available in Chase's ac-
count at NSCC to cover the transaction.
Salomon Brothers acts as its own custodian. The Salomon operations
clerk reports the trade directly to NSCC and arranges to transfer the bond
to its account there.
On the day of the trade, NSCC should receive both reports of the trade.
Overnight, NSCC will match the reports. If the two reports match per-
fectly, NSCC will confirm the trade to both sides on the next day. If the
two reports do not match, or if one trader does not report a trade, NSCC
will send DK (Don't Know) notices to both sides to report the discrepan-
cies. The traders then will attempt to reconcile the reports.
On the third day after the trade, known as T+3, NSCC will simulta-
neously transfer the bond from Salomon's account to Chase's account, and
the money from Chase's account to Salomon's account. These transfers will
settle Salomon's side of the trade. The bank then will transfer the bond from
its account to Sheltered Life's account and the money from Sheltered Life's
account to the bank's account to complete the settlement for Sheltered Life.
2.9 A FOREIGN EXCHANGE TRADE
BINC is a medium-sized U.S.-domiciled manufacturer of electric parts used
in extreme pressure and chemical environments. It has decided to open a
manufacturing subsidiary in Scotland to gain a toehold in the European
Community. Until the new operation starts to generate its own revenue,
BINC will need to convert dollars to British pounds to pay its rent and
salaries.
Olive, BINC's CFO, needs to buy 5 million pounds and transfer them
to its account at a Scottish commercial bank. She first consults an Internet
page created by Yahoo! that reports current exchange-rate indications. There
she sees that the approximate exchange rate is 1.447 dollars per pound. She
then calls her banker, Bill, and asks him to quote a price for the purchase
and transfer of 5 million pounds to BINC's Scottish commercial bank. Af-
ter verifying that BINC has sufficient dollars on deposit to buy the pounds,
Bill asks Olive to hold while he calls the foreign exchange desk of his bank.
Although BINC's bank is a large regional commercial bank, it does not
make a market in British pounds. Instead, it will buy the pounds from a
dealer and then sell them to BINC.
Fred, the bank's FX (foreign exchange) trader, takes the call and imme-
diately consults an FX page on his desktop Reuters Dealing 3000 terminal
to see some representative dealer quotes. While keeping Bill on the line,
Fred picks up another line and calls Norm, a dealer at a large New York
bank, to ask him for his quote. Fred calls Norm because he believes he gen-
erally receives good service from him, and perhaps also because Norm oc-
casionally takes Fred to NBA basketball games. Norm bids 1.4473 dollars
per pound for pounds and offers pounds at 1.4475 dollars per pound. Fred
then tells Bill that the bank will sell BINC 5 million pounds at the rate of
1.4477 dollars per pound. Bill tells Olive, who agrees to the transaction. Bill
then tells Fred, who immediately arranges to buy pounds from Norm. By
convention, both Fred and Norm understand that the quotes are good only
for 1 million dollars. Fred asks to sell 7 million dollars, and Norm agrees.


## Page 43

30 • TRADING AND EXCHANGES
Fred therefore purchases 4.835 million pounds from Norm (7 million dol-
lars + 1.4475 dollars per pound). Fred and Norm then report the trade to
their clerks, who will arrange a wire transfer between their two banks. Mean-
while, Sam arranges with Olive to debit BINC's dollar account by 7,238,500
dollars (5 million pounds x 1.4477 dollars per pound) and to wire 5 mil-
lion pounds to BINC's Scottish bank. The regional bank takes the remain-
ing 154 thousand pounds from its pound accounts. Since the 5 million
pounds cost the bank 0.002 dollar per pound less than it paid for them, the
bank makes 1,000 dollars on the transaction.
NEW STAMP

## Page 44

Part I
D
The
Structure
of
Trading
T
he chapters in this part describe how traders arrange their trades. We
start in chapter 3 with a quick introduction to the trading industry.
This chapter provides some background information about who trades,
what they trade, where they trade, and how their trading is regulated.
You can safely skip reading this chapter if you are already familiar with the
industry.
Chapter 4 describes how traders communicate their orders to the bro-
kers, dealers, and exchanges that arrange their trades. We describe the or-
ders that traders use and examine the properties of those orders. We also
establish important concepts about the origins of liquidity in this chapter.
In chapter 5, we consider how market structures vary. The differences in
how markets organize their trading are important because they affect the
profitability of different types of traders. We start to consider the relative
advantages of various trading systems in this chapter.
Chapter 6 describes how exchanges use order-driven market mechanisms
to arrange trades. The discussion introduces important issues that affect how
traders formulate optimal order submission strategies.
Chapter 7 discusses how brokers serve their clients. We carefully describe
their roles as trade negotiators and as clearing and settlement agents. You
may find this chapter most interesting for its discussions about how mar-
kets prevent traders from engaging in various types of fraudulent activities.
31


## Page 45

3
The
Trading
Industry
This
chapter provides a brief survey of the trading industry. If you are al-
I
ready familiar with the industry, you can safely skip this chapter. If you
are new to trading, the discussions here will provide you with the "big pic-
ture" that will allow you to better understand the rest of this book. In par-
ticular, you will be better able to discriminate between issues of primary and
secondary importance if you know the context in which traders solve their
trading problems.
This chapter is full of financial jargon and institutional detail. Most of
it is not necessary for understanding the remainder of this book. If you are
interested only in understanding market structure, you need not master the
details.
We first consider who trades. Then we characterize trading instruments
and the markets where they trade. Finally, we examine how regulators over-
see trading.
3.1 WHO ARE THE PLAYERS?
Traders
are people who trade. They may arrange their own trades, they may
have others arrange trades for them, or they may arrange trades for others.
Proprietary traders
trade for their own accounts, and
brokers
arrange trades
as agents for their clients. Brokers are also called
agency traders, commission
traders,
or
commission merchants.
Proprietary traders engage in
proprietary
trading,
and brokers engage in
agency trading.
Traders have
long positions
when they own something. Traders with long
positions profit when prices rise. They try to buy low and sell high.
Traders have
short positions
when they have sold something that they do
not own. Traders with short positions hope that prices will fall so they can
repurchase at a lower price. When they repurchase, they
cover their positions.
Short sellers
profit when they sell high and buy low.
The trading industry has a buy side and a sell side. The
buy side
consists
of traders who buy exchange services. Liquidity is the most important of
these services.
Liquidity
is the ability to trade when you want to trade.
Traders on the
sell side
sell liquidity to the buy side. A substantial fraction
of this book considers how interactions between buy-side and sell-side
traders determine the price of liquidity.
(The buy and sell sides of the trading industry have nothing to do with
whether a trader is a buyer or a seller of an instrument. Traders on both sides
of the trading industry regularly buy and sell securities and contracts. The terms
"buy side" and "sell side" refer to buyers and sellers of exchange services.)
3.1.1
The Buy Side
The buy side of the trading industry includes individuals, funds, firms, and
governments that use the markets to help solve various problems they face.
32
NEW STAMP

## Page 46

CHAPTER 3 THE TRADING INDUSTRY • 33
TABLE 3-1.
The Buy Side of the Trading Industry
TRADER TYPE

GENERIC EXAMPLES

WHY THEY TRADE

TYPICAL INSTRUMENTS
Investors

Individuals

To move wealth from the

Stocks
Corporate pension funds

present to the future for

Bonds
Insurance funds

themselves or for their
Charitable and legal trusts

clients
Endowments
Mutual funds
Money managers
Borrowers

Homeowners

To move wealth from the

Mortgages
Students

future to the present

Bonds
Corporations

Notes
Hedgers

Farmers

To reduce business

Futures contracts
Manufacturers

operating risk

Forward contracts
Miners

Swaps
Shippers
Financial institutions
Asset

International corporations

To acquire an asset that

Currencies
exchangers

Manufacturers

they value more than the

Commodities
Travelers

asset that they tender
Gamblers

Individuals

To entertain themselves

Various
These problems typically originate outside of trading markets. For exam-
ple, investors use securities markets to solve intertemporal cash flow prob-
lems: They have income today that they would like to have available in the
future. They use the markets to buy stocks and bonds to move their income
from the present to the future. We discuss this problem and other buy-side
trading problems in chapter 8.
Many buy-side institutions are pension funds, mutual funds, trusts, en-
dowments, and foundations that invest money. These institutions are known
collectively as
investment sponsors.
Investment sponsors frequently employ
investment advisers
to manage their funds. Investment advisers are also called
investment counselors, investment managers,
or
portfolio managers.
Investment
advisers often employ traders to implement their trading decisions. These
traders are
buy-side traders.
The people and institutions who will ultimately
benefit from the funds that
investment sponsors
hold are
beneficiaries.
A sum-
mary of buy-side traders appears in table 3-1.
3.1.2 The Sell Side
The
sell side
of the trading industry includes dealers and brokers who pro-
vide exchange services to the buy side. Both types of traders help buy-side
traders trade when they want to trade.
Dealers
accommodate trades that their clients want to make by trading
with them when their clients want to trade. Dealers profit when they buy
low and sell high. We discuss dealers in chapter 13.


## Page 47

Dealers
Brokers
Market makers
Specialists
Floor traders
Locals
Day traders
Scalpers
Retail brokers
Discount brokers
Full-service brokers
Institutional brokers
Block brokers
Futures commission
merchants
Broker-dealers

Wirehouses
34 •
TRADING AND EXCHANGES
The Wire in Wirehouse
Traders often call large
broker-dealers
wirehouses.
The
word "wire" in wirehouse
once referred to the
telegraph. Following its
invention, broker-dealers used
the telegraph to collect orders
from branch offices in distant
cities. Those who quickly
adopted it were able to
expand their businesses
substantially and thereby
greatly increase their profits.
The ability to communicate
quickly was—and remains—
very important in the trading
industry.
In contrast,
brokers
trade on behalf of their clients. Brokers arrange trades
that their clients want to make by finding other traders who will trade with
their clients. Brokers profit when their clients pay them commissions for
arranging trades with other traders. We discuss brokers in chapter 7.
Many sell-side firms employ traders who both deal and broker trades.
These firms therefore are known as
broker-dealers
or
dual traders.
The sell side exists only because the buy side will pay for its services. We
therefore must understand why the buy side trades before we can under-
stand when the sell side is profitable. We consider how and why both sides
trade in subsequent chapters. Table 3-2 provides a summary of the sell side
of the trading industry.
3.2 TRADE FACILITATORS
Many
institutions help traders trade. We introduce exchanges, clearing and
settlement agents, depositories, and custodians in this section.
3.2.1 Exchanges
Exchanges
provide forums where traders meet to arrange trades. Exchange
traders may include dealers, brokers, and buy-side traders. Only members
can trade at most exchanges. Nonmembers trade by asking member-
brokers to trade for them. Historically, traders met on exchange floors.
Now, at many exchanges, traders meet only via electronic communications
networks.
Some exchanges only provide a forum where traders meet to arrange
their trades as they see fit. Other exchanges have
order-driven trading sys-
tems
that arrange trades by matching buy and sell orders according to a set
TABLE
3
-
2.
The Sell Side of the Trading Industry
TRADER TYPE

GENERIC EXAMPLES

WELL-KNOWN U.S. EXAMPLES

WHY THEY TRADE
Spear Leads & Kellogg
LaBranche & Co.
Bernard L. Madoff Investment
Securities
Knight Trading Group
TimberHill LLC
Goldman Sachs
Merrill Lynch
Salomon Smith Barney
Morgan Stanley Dean Witter
Credit Suisse First Boston
To earn trading profits by
supplying liquidity
To earn trading profits and
trading commissions
Charles Schwab & Co.

To earn commissions by
E*Trade

arranging trades for clients
Dreyfus Brokerage Services
Abel/Noser Corp.
XpressTrade
Cargill Financial Markets Group


## Page 48

CHAPTER 3 THE TRADING INDUSTRY • 35
of rules. These exchanges may use computers, clerks, or their member-
traders to process orders. Order-driven exchanges are essentially brokerages
because they arrange trades for their clients. Exchanges and brokerages
therefore often compete with each other.
Some U.S. equity trading systems are known as
electronic communications
networks
(ECNs). These are order-driven trading systems that are not reg-
ulated as exchanges. Brokerages, dealers, or other entities may own them.
The most important ECNs are Island ECN, Instinet, REDIBook, Archi-
pelago, and Bloomberg Tradebook. Many ECNs are in the process of reg-
istering to become exchanges.
Exchanges once were owned and controlled by their members. Mem-
bership organizations, however, tend not to be nimble competitors. Con-
flicts among members and cumbersome governance mechanisms often en-
sure that membership organizations cannot innovate quickly. To compete
more effectively with ECNs, brokerages, and other exchanges, many ex-
changes have converted, or are in the process of converting, to corporate
ownership. With corporate ownership, they hope to obtain highly moti-
vated, empowered, entrepreneurial management. The Nasdaq Stock Mar-
ket, the Chicago Mercantile Exchange, the Stockholm Stock Exchange, the
Toronto Stock Exchange, and the Deutsche Borse are examples of exchanges
that have recently demutualized.
Not all trading takes place at exchanges. In many markets, dealers and
brokers arrange trades
over the counter.
The corporate bond market is an ex-
ample of a large market in which almost no trading takes place at organ-
ized exchanges.
3.2.2 Clearing and Settlement Agents,
Depositories, and Custodians
Several agencies facilitate trading by helping traders settle the trades they
have arranged. They also prevent problems that can arise when some traders
are not trustworthy or creditworthy.
3.2.2.1 Clearing Agents
When traders arrange trades on exchange floors or over the telephone, the
buyers and sellers both make a record of their trades. They record the terms
of their trades and the identities of the traders with whom they traded. To
settle their trades, buyers and sellers must compare their records. In most
markets, traders submit their records to a common clearing agent to facili-
tate these comparisons. The
clearing agent
matches the buyer and seller
records and confirms that both traders agreed to the same terms. Once trades
are cleared, traders then settle their trades. The largest securities clearing
agency in the United States is the
National Securities Clearing Corporation
(NSCC).
A trade
clears
if the buyer and seller both report that they traded with
each other, and their reported terms of trade are identical. If the records do
not match exactly, the clearing agent reports the discrepancies to the traders,
who then try to resolve them. In the futures markets, such trades are called
out-trades.
In the securities markets, they are called
DKs
(for
Don't Know).
Clearing is a trivial exercise when automated order-matching systems
arrange all trades. Since these systems know everything about the trades
they arrange, they always report matched trades.


## Page 49

36 • TRADING AND EXCHANGES
►

T+5 and Counting
Down
Brokers and regulators would
like to settle security trades
as quickly as possible in
order to minimize trader
exposure to credit risks.
During the time between the
negotiation of a trade and
the time it settles, prices can
change substantially. The side
that is hurt by the price
change then may be unable
or unwilling to settle the
trade. Such failures can be
quite painful to the other side.
Traders minimize failure risk
by settling their trades quickly.
Until June 1995, the U.S.
securities industry settled
stock and bond trades on
T+5. It now settles trades on
T+3. Starting in June 2005,
the industry intends to settle
on T+1. T+1 settlement
will require most traders
to deposit money and
certificates with their brokers
before they trade, to ensure
that they can settle the
next day.
4
3.2.2.2 Settlement Agents
Settlement agents help traders settle their trades. They receive cash from
buyers and securities from sellers. When both sides have performed, the set-
tlement agent gives the cash to the seller and the securities to the buyer.
Traders use settlement agents because the agents are very efficient at set-
tling trades, and because they can help them avoid the losses that can arise
if they trade with an untrustworthy or uncreditworthy trader. In the real es-
tate markets, settlement agents are called
escrow agents.
Since clearing and
settlement are closely related, the National Securities Clearing Corporation
is also, not surprisingly, the largest U.S. securities settlement agency.
Much of the efficiency in the settlement process is due to net settlement.
Under
net settlement,
for each client, the settlement agent nets the buys and
sells in each security to a single net security position. The settlement agent
also nets all money credits and debits into a single net money position for
each client. The agent then settles only the net positions. Through netting,
the settlement agency can vastly reduce the number of transactions neces-
sary to settle trades. Net
settlement works best when all traders use the same
settlement agent.
In U.S. securities markets,
normal-way settlement
occurs three business
days after trades are arranged. Such settlement is called
T+3
settlement.
Almost all transactions settle on T+3. Traders can also arrange special set-
tlement on other days. The most common special settlement instruction is
cash settlement,
which occurs on the day of the trade.
3.2.2.3 Clearinghouses
Many futures, options, and swaps markets have clearinghouses associated
with them. The
clearinghouses
clear and settle all trades in these derivative
contracts. They also usually guarantee that both parties will perform on their
contracts. They do this by acting as buyer for every seller and as seller for
every buyer. They therefore are the issuers and guarantors of their contracts.
Clearinghouses generally are owned by
clearing members,
who are jointly
responsible for settling all trades. Traders who are not clearing members
must have a clearing member guarantee the settlement of their trades. If a
trader fails to settle a trade, his clearing member must do so. If a clearing
member fails to settle a trade—usually due to bankruptcy—the clearing-
house can tax its other members to settle the trade. The clearinghouse is
therefore like a mutual insurance company.
Since losses can be quite significant, clearinghouses pay very close at-
tention to the credit quality of their members and to the potential settle-
ment risks that they can impose upon other traders. To control these risks,
clearinghouses require that their members post collateral called
margins
to
secure their obligations, provide timely information about their financial
conditions and their trading activities, and not exceed positions limits that
the clearinghouse establishes for them. The exchanges do not allow mem-
bers to trade without approval from the clearinghouse.
In futures markets, final settlement takes place when the contracts ma-
ture. After every trading day, traders also make an intermediate settlement
of their accounts in which they transfer profits earned that day from losers
to winners. Brokers make these transfers to and from their customers' mar-
gin accounts through the intermediation of the exchange clearinghouse.
These
variation margin
adjustments ensure that the incentives to default on
a contract do not grow as prices move against a losing position.


## Page 50

The Brazilian Straddle
A trader has a
straddle
when he holds positions in two different types of
instruments. The risks in the two instruments often offset each other so that
the combined position is less risky than either position held alone. In the
options markets, a straddle consists of a position in a put and an offsetting
position in a call.
Technically bankrupt traders present a special problem to the firms that
guarantee their trades. Traders are
technically bankrupt
when they no
longer have enough wealth to settle their trades. If prices do not change in
their favor, they soon will be forced into actual bankruptcy.
When traders know that they are technically bankrupt, they have nothing
to lose by massively increasing their positions. If prices change so that their
positions make money, they may escape their financial problems. If prices
change against them so that they lose even more, those who guarantee
their trades will suffer the losses.
A trader who uses this strategy is said to hold a Brazilian straddle. A
Brazilian straddle
consists of a large market position held against a one-
way airline ticket to Brazil in the breast pocket. If the market position proves
profitable, the trader sells the ticket and comes back to trade tomorrow. If
the trader continues to lose, he runs off to Brazil and leaves his clearing
member to clean up the resulting mess.
Clearing members must carefully monitor the traders who clear through
them to ensure that their customers do not try to play the Brazilian straddle.
To avoid the problem, they require that their customers report their positions
frequently during the day. They also require that their customers make
margin payments within the day when prices move substantially against
their positions. Finally, when they determine that their customers cannot
settle their trades, they prohibit them from trading.
Clearing firms also execute contracts with their customers that allocate
any profits earned by technically bankrupt customers to the clearing firm if
the customer did not report the problem. This provision takes the profit out
of the successful Brazilian straddle. It works, however, only if the clearing
firm detects the bankruptcy.
A Typical Set of Relationships
A large state pension fund receives money from the state treasury to hold
and invest for its beneficiaries. The pension fund deposits the money in its
account at its custodian bank. It also notifies its investment adviser that it
has money available for investment.
A portfolio manager who works for the investment adviser considers
how to best invest the funds. The manager considers the portfolio that
the sponsor presently holds, the expected pension liabilities that the fund
must satisfy, and the investment opportunities that the adviser believes it
can identify. The manager decides to buy 30,000 shares of Cisco
Systems.
The portfolio manager contacts his firm's buy-side trader—a fellow
employee—and instructs her to buy 30,000 shares of Cisco Systems. She
then issues an order to the state pension fund's broker to buy the shares.
For political reasons, the state pension fund may direct its investment
adviser to use brokers domiciled in the state when trading on its behalf.
The broker calls a dealer and arranges the trade. The dealer sells the
shares to the pension fund out of its inventory. The dealer and the broker
both report the trade to the National Securities Clearing Corporation
(NSCC). The broker also reports the trade to the pension fund and to the
investment adviser. Three days later, on instructions from the dealer and the
pension fund, NSCC settles the trade. The custodian bank sends money to
the pension fund's account at the Depository Trust Company (DTC). The DTC
then provides the money to settle on behalf of the pension fund, and it
receives the 30,000 shares on behalf of the pension fund.
37


## Page 51

38 • TRADING AND EXCHANGES
Straight-Through
Processing
Trading systems that fully
automate the clearing and
settlement process provide
straight-through processing
(STP) to their clients. Traders
like STP because it is cheap
and minimizes the potential
for errors.
4
3.2.2.4 Depositories
and Custodians
Depositories and custodians hold cash and securities on behalf of their
clients. They help settle trades by quickly delivering cash and security
certificates—when properly instructed—to settlement agents. Depositories
and custodians also help ensure the security of their clients' assets.
The largest depository in the world is the
Depository Trust Company
(DTC).
DTC holds nearly 20 trillion dollars in assets for its participants
and their clients. It is a subsidiary of the Depository Trust and Clearing
Corporation (DTCC). The other major subsidiary of DTCC is the Na-
tional Securities Clearing Corporation (NSCC).
3.3 TRADING INSTRUMENTS
The securities, contracts, commodities, and currencies that traders trade are
collectively known as
trading instruments.
Trading instruments vary by type.
They include real assets, financial assets, derivative contracts, insurance con-
tracts, and gambling contracts.
Financial instruments
include financial as-
sets, derivative contracts, and insurance contracts.
This section describes various classes of trading instruments and special
aspects of the markets in which they trade. It also defines some common
trading instruments. A summary of the various classes of instruments ap-
pears in table 3-3.
TABLE 3
-
3.
Trading Instrument Summary
CLASS

INSTRUMENT

CREATORS
Real assets
Financial assets
Derivative
contracts
Insurance
contracts
Hybrid
instruments
Gambling
contracts
Spot commodities
Intellectual properties
Real estate
Pollution emission rights
Stocks and warrants
Bonds
Trust units
Currencies
Futures contracts
Forward contracts
Options
Swaps
Insurance policies
Reinsurance contracts
Warrants
Index linked bonds
Convertible bonds
Numerous types
Farmers, miners, manufacturers
Inventors and artists
Builders
Governments
Corporate issuers
Corporate issuers, governments
Trusts
Governments, banks
Sellers
Sellers
Sellers
Sellers
Corporations
Corporations
Corporate issuers
Corporate issuers
Corporate issuers
Individuals
Bookies
Casinos
Racetracks


## Page 52

CHAPTER 3 THE TRADING INDUSTRY • 39
3.3.1 Real Assets
Real assets
include physical commodities, real estate, machines, patents, and
other intellectual properties. Real assets also include
pollution credits,
which are
rights to emit a specified quantity of a given type of pollution. Real assets are
instruments that would appear only on the asset side of a balance sheet.
The real assets that trade in the most liquid markets are industrial and
precious metals, agricultural commodities, fuels, and pollution credits. These
instruments generally are quite fungible: One unit is very similar, if not iden-
tical, to all other units. Traders in these commodities generally are more
concerned about price than about quality variations. They usually can eas-
ily adjust prices for any quality variations.
3.3.2 Financial Assets
Financial assets
are instruments that represent ownership of real assets and
the cash flows that they produce. Stocks and bonds are financial assets be-
cause they represent ownership of the assets of a corporation. Stockholders
own the assets of a corporation after all creditors have been paid off. Bond-
holders own the assets of a corporation if the corporation defaults on its
creditors and becomes bankrupt. Other financial assets include currencies,
warehouse receipts that represent ownership of physical commodities, and
trust units that represent ownership of the assets of a trust.
Issuers
create all financial assets. Corporations issue stocks, bonds, and
warrants. Governments issue currencies and bonds. Warehouses issue com-
modity receipts. Trusts issue trust units. Many securities are called
issues
be-
cause issuers issue them.
Financial assets appear on both sides of a balance sheet. A financial as-
set appears as a liability on the issuer's balance sheet and as an asset on the
holders' balance sheet.
Issues trade in
primary markets
when issuers first create and sell them. Sub-
sequent trading occurs in
secondary markets. New issues
become
seasoned secu-
rities
after they are issued. Traders therefore trade new issues in primary mar-
kets and seasoned issues in secondary markets. Traders say that primary trading
in new equity issues takes place in the
initial public offering
(IPO)
market.
Issuers often use the services of
underwriters
to help them sell their se-
curities.
Underwriters
are broker-dealers at investment banks who find buy-
ers for the securities. In a
best efforts offering,
the underwriter acts strictly as
a broker. In an
underwritten offering,
the underwriter guarantees the issuer
an offering price. If the underwriter cannot find buyers for the securities at
the offering price, the underwriter buys them for its own account. In
afixed-
price open offering,
the underwriter sets a price and buyers subscribe to the
offering. If the offering is oversubscribed, the underwriter conducts a lot-
tery to allocate the shares. Underwriters generally charge issuers fees for
their services.
Commodities and currencies trade for immediate delivery in
spot markets.
They trade for future delivery in
forward markets
or
futures markets.
Farmers,
miners, and manufacturers create most physical commodities, and national
central banks create most currencies.
3.3.2.1
Definitions of
Some Common Financial
Assets
Equities
Stocks
represent ownership of corporate assets, net of corporate liabilities.
Stock values depend on corporate assets, liabilities, and income. They also


## Page 53

40 • TRADING AND EXCHANGES
Ow Stripping Bonds
When traders want more
zero-coupon bonds than are
available, zero-coupon bonds
become expensive relative to
straight bonds.
Fixed
-
income
arbitrageurs
then buy straight
bonds and
clip
the coupons.
They bundle the coupons by
their interest payment dates
and sell the bundles and the
remaining final principal
payments as zero coupon
bonds.
Traders call this process
stripping
a
bond.
The term
comes from a time when all
bonds were bearer bonds.
The owners of
bearer
bonds
are not registered with bond
issuers. Since issuers cannot
keep track of who owns their
bearer bonds, they make
interest payments only when
the bondholders present them
with interest coupons clipped
from the side of the paper
upon which the bonds are
printed. The coupons are
dated so that each one
corresponds to an interest
payment date. The final
principal repayments occur
when the bondholders present
the now fully stripped bonds
to the issuers.
depend critically on how well traders expect corporate managers will use
corporate assets in the future.
Preferred stocks
are stocks that pay dividends at contractually specified
rates. Corporations must pay all accrued dividends on preferred stocks be-
fore they can pay any dividends on common stock.
American depository receipts
(ADRs) are trust units that traders use to
trade foreign stocks in U.S. markets. Each trust holds only the stock of a
single foreign company. ADRs are popular because they allow traders to
avoid international settlement problems.
Exchange-traded funds
(ETFs) are mutual funds that trade at exchanges.
They have become extremely popular in recent years. Most ETFs are
index
funds
that try to mimic the returns of a market or industry index.
Real estate investment trusts
(REITs)
are trusts that own real estate. By
securitizing
real estate, they allow
investors and speculators to trade real
estate interests like common stock shares.
Debt Instruments
Bonds
are debt securities issued by corporations, governments, and occa-
sionally individuals. Debtors create bonds when they borrow money. Bond
values depend on interest rates, issuer creditworthiness, assets pledged as
collateral, and attached options. Traders usually quote bond prices as a per-
centage of their
par value.
For example, the price of a million-dollar Trea-
sury bond quoted at 97 is 970,000 dollars.
A
straight bond is
a bond that pays interest periodically until it matures.
At maturity, the issuer redeems the bond for its
principal
or
face value.
Straight bonds usually do not have attached options.
Credit quality,
the probability that a bond issuer will make all bond pay-
ments when they are due, greatly concerns bond investors. Investors expect
that the issuers of
investment grade bonds
will make all interest and princi-
pal payments on time. The interest and principal payments on
junk bonds
are less certain. The latter are also called
high yield bonds
because investors
require high yields to compensate for the probability that the issuers will
default on their payments. The credit quality of a bond depends on the fi-
nancial strength of its issuer and upon that collateral and bond covenants
that the issuer uses to secure the bond.
Treasury bills, Treasury notes,
and
Treasury bonds
are debt securities issued
by a country. Bills normally mature in one year or less. Notes normally ma-
ture two to five years after they are issued, and bonds normally mature ten
or more years after they are issued. Bills do not pay interest. Instead, they
sell at a discount from their
face value.
Zero coupon bonds
pay no interest. They simply return their principal value
at maturity. Since they pay no interest, buyers will buy them only at a dis-
count from their face value.
Zero coupon bonds
therefore are also known as
pure
discount bonds.
The greater the time to maturity, the greater the discount. A
straight bond is equivalent to a bundle of zero coupon bonds consisting of
one zero-coupon bond due on each interest payment date plus a zero-coupon
bond due when the straight bond matures. The principal values of the vari-
ous bonds correspond to the various payments due on the straight bond.
Commercial paper
is a short-term debt security issued by a corporation.
Commercial paper usually matures in nine months or less from the date it
is issued.


## Page 54

CHAPTER 3 THE TRADING INDUSTRY • 41
Mortgage-backed securities
are bondlike instruments which receive the
mortgage payments that borrowers make on their mortgages. The securi-
ties are backed by a specified set of mortgages called
a mortgage pool.
Since
they receive the mortgage payments as they are paid, they are examples of
pass-through securities.
Collateralized mortgage obligations
(CMOs) are mortgage-backed secu-
rities that divide rights to the cash flows from the mortgage pool into
several different
tranches.
Each tranche has different rights to the pay-
ments that the mortgage borrowers make. Issuers generally structure the
CMO tranches to look like various types of bonds. The first tranche has
the highest claim on the mortgage payments and therefore is the least
risky. When its claims are satisfied, the next tranche is paid, and so on
until the available funds are exhausted. The last tranche, which is usually
called the Z tranche, gets whatever is left over. It is obviously the most
risky tranche. CMOs are also called
real estate mortgage investment conduits
(REMICs). Companies issue CMOs to distribute mortgage prepayment
risk and interest rate risk among investors with varying degrees of risk
tolerance.
All debt instruments are collectively known as
fixed-income products.
3.3.3 Derivative Contracts
Derivative contracts
are instruments that derive their values from the values
of the
underlying instruments
upon which they are based. They are contrac-
tual agreements between buyers and sellers that specify the exchange of cer-
tain privileges and liabilities. Derivative contracts include forward contracts,
futures contracts, options, and swaps.
Sellers create derivative contracts when they first sell them. Derivative
contracts therefore are in
zero net supply.
The sum of all long positions mi-
nus the sum of all short positions is always zero.
All derivative contracts have an element
of futurity:
Their values depend
on future events. For example, the prices of futures, options, and forwards
all depend on future prices of their underlying instruments.
Almost all derivative contracts have an
expiration date.
On that date,
traders make final settlement and the contract expires. European traders re-
fer to this date as the
expiry
of the contract. Contracts that do not expire
are
infinitely lived.
Exchanges and investment banks have proposed many
infinitely lived derivative contracts, but none have been notably successful.
Derivative contracts may be physically settled or cash settled. A
physi-
cally settled contract
requires that the seller deliver the underlying instrument
to the buyer when obligated to do so. At that time, the buyer pays cash for
the instrument at the agreed price. A
cash-settled contract
requires that the
seller deliver the cash value of the underlying instrument to the buyer when
obligated to do so. At the same time, the buyer pays the agreed-upon pur-
chase price. In practice, the traders transfer only the difference between the
value and the price. If the contract is a futures contract, the difference might
be negative. In that case, the seller pays the buyer the difference. If the con-
tract is an option contract, the difference will never be negative because con-
tract holders will not exercise their options when doing so would require
that they make additional payments.
Derivative contracts always have a
notional size
or
notional value.
For
physically delivered contracts, the
notional size
is simply the amount that
Toxic Waste
The riskiest CMO tranches
are called
toxic waste
because no one wants to hold
them. They typically sell at
highly discounted prices.
Foolish people often pay too
much for them because these
tranches will realize very high
rates of return if very few
mortgage borrowers default
on their obligations.
Toxic
waste
is worthless, however,
if too many borrowers
default. The inability of
various organizations to fully
appreciate the default risks in
toxic waste has led to some
spectacular trading losses.
Pit A Tomato Forward
A tomato forward contract is
an agreement between a
buyer and a seller in which
the buyer agrees to pay a
fixed price for tomatoes that
the seller will deliver in the
future. The seller may not own
the tomatoes when they
negotiate the contract.
Tomato farmers generally
execute forward contracts
with food processors. The
farmers obtain fixed prices
for their harvests, and the
food processors obtain fixed
prices for the tomatoes they
must buy to produce their
products.
4


## Page 55

42 • TRADING AND EXCHANGES
the seller must deliver. For cash-settled contracts, a formula specifies the
notional value
that determines the final cash settlement.
Many derivative contracts require that buyers and sellers make variational
margin payments on a regular basis.
Variational margin payments
transfer
money from buyers to sellers or from sellers to buyers to adjust the prices
of their contracts to reflect current market conditions. This procedure en-
sures that contract values do not change as market conditions change. Vari-
ation margin payments therefore reduce the chance that traders will default
when their contracts expire.
3.3.3.1
Some Derivative Contract Definitions
Forward contracts are
contracts for the future sale of some commodity. The
commodity may be a physical commodity, like pork bellies, or a financial
commodity, like a currency. Since these contracts derive their values from
the values of the underlying commodities, they are derivative contracts.
Standardized futures contracts
are forward contracts that an exchange clear-
inghouse guarantees. Futures traders therefore do not care whether their
counterparts are creditworthy. They only need to consider whether the clear-
inghouse is creditworthy. Moreover, since buyers and sellers trade the same
contracts, and since the clearinghouse is a buyer to every seller and a seller
to every buyer, traders can open a position by buying a contract from one
trader and close the position by selling it to someone else. They do not need
to buy and sell with the same trader to offset their positions.
An
option
represents the right—but not the obligation—to do some-
thing.
Option contracts
give their holders the option to buy or sell an under-
lying instrument (or, in the case of a cash-settled option, the cash value of
an underlying instrument) at a fixed price. The
writer
of the option is the
trader who sold the contract. The option is
written upon
the
underlying in-
strument.
A
call option
is an option to buy at a fixed
strike price.
A
put op-
tion
is an option to sell at a fixed strike price. If the option holder can ex-
ercise the option any time before the
expiration date,
it is an
American-style
option. If
the
holder can exercise only on the expiration date, it is a
European-style option.
Since option contracts depend on underlying security
values, they are
derivative contracts.
A
futures option contract is
an option contract written on a futures con-
tract. The holder of a call option on a futures contract has the right to pur-
chase a futures contract at a specified strike price. Likewise, the holder of
a futures put option has the right to sell a futures contract at a specified
strike price. Futures option contracts trade at the exchange where the under-
lying futures contracts trade.
Swaps
are contracts for the exchange of two future cash flows. A
cash
flow
is a series of payments. An
interest rate swap
provides for the exchange
of a future series of fixed-rate interest payments for a future series of vari-
able floating-rate interest payments. When they enter the contract, the
traders negotiate the fixed-rate payments and agree upon a formula for com-
puting the future variable-rate payments. A
currency swap
provides for the
exchange of a future series of fixed payments in one currency for a future
series of payments in another currency. Since the values of these contracts
depend on the values of the cash flows that the traders swap, swaps are
derivative contracts.
The Eurex ODAX
Contract
Eurex trades a cash-settled
option contract based on the
German Deutscher
Aktienindex (DAX) equity
index. The notional value of
this ODAX contract is five
euros per index point, or
29,500 euros, given the
5,900 level of the DAX at the
end of June 2001.
If you buy an ODAX call
option with a strike price of
6,200 for 26.00 euros, you
will pay 130 euros for the
contract. If the DAX on the
expiration date closes at
6,500, you will make five
times the difference between
the closing price and the
strike price, or 1,500 euros.
If the DAX remains below
6,200, you will not exercise
the option, and it will expire
worthless.
4
Variation Margin
Example
Brad buys a 5,000-troy-ounce
silver futures contract for 4.50
an ounce from Sharon at the
COMEX division of the New
York Mercantile Exchange
(NYMEX). The NYMEX
Clearing House guarantees
that both traders will perform
on the contract. On the next
day, the price of silver rises
by 5 cents. The NYMEX
Clearing House requires that
Sharon pay it 250 dollars
(5,000 ounces times 0.05
dollar per ounce) in variation
margin. Simultaneously, the
Clearing House pays Brad
250 dollars in variation
margin. If the price of silver is
the same when the contract
expires, Brad will pay 4.55
an ounce for the silver, and
Sharon will receive 4.55
an ounce.


## Page 56

CHAPTER 3 THE TRADING INDUSTRY • 43
00
,
The Third Order Derivative of LIFFE
The London International Financial Futures and Options Exchange (LIFFE)
trades a euro interest rate swap futures contract called the Swapnote.
This is a cash-settled futures contract that prices the expiration day value of
a standard bond-pricing formula for a hypothetical fixed-rate bond. The
hypothetical bond consists of a series of notional fixed 6 percent interest
payments followed by the return of the notional principal at the maturity of
the hypothetical bond. The pricing formula uses discount rates that are
derived from the
swaps yield curve,
which is computed from ISDA
Benchmark Euribor Swap Rate fixings. The Swapnote futures contracts thus
derive their values from prices in the swaps market.
LIFFE also trades options on Swapnote futures. The Swapnote futures
option is a derivative on a derivative on a derivative. (It is an option
contract on a futures contract based on swaps contract prices.)
4
Source: www.liffe.com

Swaptions
are options on a swap contract. A trader who owns a swap-
tion call has the right to buy a swap at the specified strike price.
3.3.4 Insurance Contracts and Gambling Contracts
Insurance contracts
and
gambling contracts
are instruments that derive their
values from the outcomes of future events. For example, the value of a
fire insurance contract on a building depends on whether the building burns
down. The value of a point spread bet on the Lakers depends on whether
they win their basketball game by more than the specified point spread.
The distinction between an insurance contract and a gambling contract
depends on the reasons why people buy them. People who are concerned
about the loss that they would experience if some future event takes place
buy insurance contracts. Such traders are called
hedgers.
Gambling contracts
are arranged by people who have no other financial stake in the underlying
event. People arrange gambling contracts for entertainment, whereas they
arrange financial contracts to raise capital and reallocate risk.
Like derivative contracts, insurance contracts and gambling contracts
have an element of futurity. They are also in zero net supply.
Whether the future price of an instrument is equal to some specified
value is itself a future event. Derivative contracts therefore are contracts
whose values depend on future events. We therefore can classify derivative
contracts as insurance contracts or gambling contracts. In fact, many hedgers
use derivative contracts to insure against risks that they face, and many
traders use derivative contracts to gamble on future events in which they
have no financial interest.
3.3.5 Hybrid Contracts
Some trading instruments defy easy classification because they embody el-
ements of more than one type of instrument. For example, some oil com-
panies issue oil-linked bonds. The interest that they pay depends on the
price of oil. These bonds are financial assets because they represent owner-
ship of the assets of the firm in the event of bankruptcy. They also are
O
■
Why Discuss
Gambling Contracts?
Although we do not normally
consider gambling contracts
to be securities, the same
economics that govern
traditional securities markets
also govern gambling
markets. The close analogy
between the two markets is
both useful and harmful. It
can be a source of powerful
economic intuition, but it
also has been the source of
many important public
policy problems. We will
consider the role of gamblers
in the markets throughout this
book.


## Page 57

44 • TRADING AND EXCHANGES
Of
,
Shall We Quibble?
The distinctions between real assets, financial assets, derivative contracts,
insurance contracts, and gambling contracts are somewhat arbitrary:
•
Any instrument that defines a relation between a buyer and a seller is a
contract. For example, a bond is a contract between bondholders (the
buyers) and an issuer (the seller). We reserve the term "contract" for
agreements that define a continuing relation between generally unrelated
buyers and sellers.
•
We could consider anyone who sells a contract an issuer. We reserve
the term "issuer" for instruments that only one seller—typically a
corporation—can create.
•
All issues are in zero net supply if we count short positions of issuers.
We reserve the term "zero net supply" only for contracts that public
traders can create by selling.
•
Virtually all instruments have an element of futurity because the value of
anything that is not immediately perishable depends in large part on
future events. For example, the value of cattle sold on the spot market
depends on the future prices of meat, leather, and milk, and on the future
prices of alfalfa, energy, and veterinarian services. We apply the term
"futurity" only to contracts that settle in the future.
•
All instrument values are correlated to some extent. For example, stock
values are correlated with bond values because the discount rates that
analysts use to value stocks depend on interest rates. These observations
suggest, then, that we could classify stocks as derivative instruments. We
reserve the term, however, for instruments whose values depend directly
on other instrument values through some contractual mechanism rather
than indirectly through common valuation factors.
•
Precious metals like gold and silver are such close substitutes for money
that many people consider them financial assets as well as real assets.
Let's not quibble over this one.
derivative contracts because they derive at least part of their value from the
price of oil.
An
equity warrant
issued by a corporation is another example of a hy-
brid contract.
Warrants
are options that allow the holder to purchase stock
at a specified price from the issuing corporation at some time in the future.
Since a corporation issues them, and since they represent ownership of the
assets of the corporation under certain circumstances, they are financial as-
sets. Since their value depends on the value of the underlying stock, they
are like derivative contracts.
Convertible bonds are also hybrid contracts. The holder of a
convertible
bond
can exchange it for stock under some circumstances. A convertible
therefore is the combination of a straight bond plus an option to exchange
the bond for stock. The straight bond is a financial asset. The option gives
the convertible bond derivative properties because its value depends on the
values of the straight bond and of the stock.
3.4
WHERE ARE THE TRADING MARKETS?
We briefly survey trading markets in this section. The main points to iden-
tify are the following:


## Page 58

CHAPTER 3 THE TRADING INDUSTRY • 45
•
Stocks represent less wealth than the widespread attention given to
them by the media would suggest.
•
Trading volume depends in part on the number of available instru-
ments. Markets with a great number of different instruments are
often quite illiquid.
•
Exchanges everywhere have been consolidating.
We first characterize trading in various instrument classes. Then we discuss
where trading occurs in each instrument class.
3.4.1 The Magnitude of Trading
Organized markets appear throughout the economy. Table 3-4 characterizes
the relative importance of the various types of traded instruments in the
United States. Despite the tremendous attention given to the stock market
in the media, stocks represent only about 20 percent of the capital wealth of
the country. Most of the wealth is in real estate, which rarely trades, and in
various types of bonds. Derivative contracts represent no wealth because they
are all in zero net supply and do not represent ownership of real assets.
The major national exchanges in the United States list about 8,250
stocks, of which only a fraction trade actively. At the NYSE, the 250 most
active stocks accounted for 62 percent of the total reported trading volume,
and a larger percentage of the total dollar volume, in 2000. When trading
the most active stocks, public traders often trade with other public traders.
Otherwise, public traders often trade with sell-side dealers. Most trades are
small retail trades; large institutional traders account for most share volume.
Although listed option contracts do not trade for most stocks, the num-
ber of listed option contracts far exceeds the number of stocks. For each
option-eligible stock, options exchanges list many put and call options for
various expiration months and for various strike prices. Very few option con-
tracts trade frequently, however. The most frequently traded options are cur-
rent month calls on actively traded stocks for which the strike price is close
TABLE 3
-
4.
U.S. Markets by Instrument Class
CLASS
CAPITAL
WEALTH
TOTAL
INSTRUMENTS
ACTIVELY
TRADED
INSTRUMENTS
TRADE
FREQUENCY
NOMINAL
TRADING
VOLUME
Common stocks
20%
15,000
1,000
High
Low
Equity option contracts
0%
160,000
500
Moderate
Very low
Corporate bonds
15%
100,000
50
Low
Low
Municipal bonds
10%
1,000,000
0
Low
Low
Government bonds
10%
50
3
Moderate
Moderate
Futures contracts
0%
100
20
High
Moderate
Swap contracts
0%
20
2
Moderate
Moderate
Currencies
<1%
200
10
High
High
Spot commodities
<1%
Millions
25
High
Moderate
Real estate
50%
100,000,000
0
Very low
Low
Source: Author's estimates.
Note: The percentages add up to more than 100 percent primarily because corporations own real estate.


## Page 59

46 • TRADING AND EXCHANGES
The New York Stock Exchange's Quantitative Listing
Standards for Domestic Companies
Domestic companies that wish to list with the New York Stock Exchange
must meet all of the following quantitative listing standards:
1.
The company must have at least 2,000 U.S. shareholders that each hold
at least one round lot, or it must have at least 2,200 shareholders and
monthly average trading volume of at least 100,000 shares over the
last six months, or it must have at least 500 shareholders and average
monthly trading volume of at least 1 million shares over the last
12 months and at least 1.1 million publicly held shares.
2.
The publicly held shares of the company must have an aggregate
market value of at least 100 million dollars, or 60 million dollars if the
company is listing at the time of its initial public offering.
3.
The company must meet at least one of four alternative financial
standards. These standards are quite detailed. We therefore consider
only the first one: Pretax earnings must total at least 2.5 million dollars
in the latest fiscal year, together with 2 million dollars in each of the
preceding two years; or 6.5 million dollars in the aggregate for the last
three fiscal years, together with a minimum of 4.5 million dollars in the
most recent fiscal year, and positive amounts for each of the preceding
two years.
4
Source:
NYSE Listed Company Manual
at www.nyse.com/listed/listed.html,
quoted on
June 6, 2001.
to the stock price (at-the-money options). Public traders sometimes trade
these contracts with each other, but they typically trade with dealers when
they buy or sell options.
The large number of corporate and municipal bond issues ensures that
most issues hardly ever trade. Highly secure bonds are very good substitutes
for each other when the bonds have similar financial terms. Managers of
portfolios that hold high-quality investment grade bonds therefore are less
concerned about the specific bonds they buy than about their financial terms.
Since many fixed-income portfolios hold their bonds until maturity, some
bond issues never trade again after they are first issued. The buy side trades
bonds almost exclusively with dealers because the public buyers and sellers
rarely simultaneously want to trade the same bond issue. When they do,
they rarely know of each other's interest.
Government bond issues are far less numerous than corporate and mu-
nicipal bond issues. They are also far larger. The tremendous size of these
issues and the widespread interest in these securities make these markets
extremely liquid. Although the public often trades government bonds with
dealers, buy-side traders increasingly trade directly with other buy-side
traders in new electronic trading systems.
Some of the world's most liquid instruments trade in futures markets.
Contracts on major agricultural, industrial, and financial commodities are
extremely useful to hedgers throughout the economy. The contracts also in-
terest many speculators. Trading by hedgers and speculators, and trading
among the dealers who serve them, generate very large volumes in many
futures markets.


## Page 60

CHAPTER 3 THE TRADING INDUSTRY • 47
Tfr Some Regional Exchange Trivia
The Cincinnati Stock Exchange was founded in Cincinnati in 1885.
Following adoption of the 1975 amendments to the Securities Exchange
Act of 1934, it became the first U.S. electronic stock exchange. Its members
now trade exclusively from their offices. The Exchange's computers reside
in Chicago in the same building occupied by the Chicago Stock Exchange.
The Chicago Stock Exchange (CHX) was founded in 1882. It merged
with exchanges in St. Louis, Cleveland, and Minneapolis/St. Paul to form
the Midwest Stock Exchange in 1949. This name explains why its market
quotation symbol is M. The Midwest Stock Exchange changed its name
back to Chicago Stock Exchange in 1993. Measured by dollar trading
volume, the CHX is the third largest stock exchange in the United States
after the NYSE and Nasdaq. The CHX has aggressively used its unlisted
trading privileges to trade Nasdaq stocks.
The merger of the San Francisco Stock and Bond Exchange (founded in
1882) and the Los Angeles Stock and Oil Exchange (founded in 1899)
formed the Pacific Stock Exchange in 1957. It later changed its name to the
Pacific Exchange (PCX). Following the merger, PCX maintained separate
trading floors in Los Angeles and San Francisco where competing specialists
traded the same stocks. To save money, the PCX closed its equity floors in
2001 and 2002, and allowed its traders to trade from their offices. In
2000, PCX entered a joint venture agreement with the Archipelago ECN to
form a fully electronic exchange called Archipelago Exchange. After the
SEC
approved its application for exchange status in October
2001,
the
PCX moved its equity trading to the Archipelago Exchange in 2002.
The most important world currencies trade in extremely liquid markets.
Volumes are high because international trade and cross-border capital trans-
actions generally require currency conversions. The structure of currency
markets also ensures that dealers trade several times with each other for
every trade that they make with a client.
Real estate trades in brokered markets because every parcel is unique.
The difficulties that buyers and sellers have finding each other make the
real estate market the least liquid of the markets we have discussed. Elec-
tronic multiple listing services have lowered trader search costs, but these
costs are still very high. Clearing and settlement in real estate markets is
also quite expensive because the trades usually are large, complex, and among
traders who do not have standing credit relationships.
3.4.2 Stock Markets
Corporations apply to exchanges to
list
their stocks. Exchanges generally
list all companies that meet their listing standards and that pay their
listing
fees.
All but the smallest publicly traded stocks are listed for trading at one
or more markets.
The
listing standards
of an exchange generally require that its listed com-
panies meet specified minimum standards for capital value, numbers of
shareholders, and financial strength. Most exchanges also require listed com-
panies to report their accounts regularly according to
generally accepted
accounting practices
(GAAP). Some exchanges also regulate the control struc-
tures of their listed companies.
Control structure
refers to how the share-
holders elect the board of directors who appoint the managers and set


## Page 61

48 • TRADING AND EXCHANGES
Double- and Triple-counting Volumes
The volume figures that markets report often are not directly comparable.
Trading systems that match public buyers directly to public sellers generally
report lower volumes than do trading systems in which dealers act as
intermediaries between public traders. For example, a 100-share trade
between a public buyer and a public seller creates 100 shares of volume at
the NYSE. If the same trade took place in Nasdaq with the intermediation
of a single dealer, the total volume would be 200 shares: 100 shares when
the dealer bought from the public seller and 100 more shares when the
dealer sold to the public buyer. Even greater volume results when more than
one dealer is involved. If Dealer A buys from the public seller, Dealer B
sells to the public buyer, and Dealer B buys from Dealer A, the market will
report 300 shares.
Although both markets will accurately report their volumes, the reported
figures will have different meanings. In markets that exclusively match public
buyers directly to public sellers, volume measures only the trading activity
of public traders. In dealer markets, volume measures the total trading
activity of public traders and dealers. In such markets, volume provides only
indirect—and sometimes highly inflated—information about the activity of
public traders.
Some markets also count volume that other markets report. For example,
Nasdaq reports all volume that Nasdaq broker-dealers report. Some
Nasdaq broker-dealers, however, run trading systems that separately match
buyers to sellers. Their reported trading volume thus overstates total trading
volume because they count the same trades twice.
The
World Federation of Exchanges
(WFE) classifies markets by how
they count their volumes.
Trading System View
(TSV) markets count only
transactions that pass through their trading systems or that occur on their
trading floors.
Regulated Environment View
(REV) markets count all
transactions that are subject to their regulatory supervision. The WFE
classifies the NYSE as a TSV market and the Nasdaq Stock Market as an
REV market. it
company policy. The NYSE, for example, devotes considerable resources to
regulating corporate control structures.
3.4.2.1
The U.S. Stock Markets
The exchange where a corporate stock issue is primarily listed is its
primary
listing market.
The main primary listing markets in the United States are
the New York Stock Exchange, the American Stock Exchange, and the
Nasdaq Stock Market. Stocks listed at the New York Stock Exchange and
the American Stock Exchange are known as
listed stocks.
Nasdaq stocks
were once known as
over-the-counter stocks,
but now they are simply called
Nasdaq stocks.
The New York and American stock exchanges have floor-based trading
systems. Floor brokers arrange trades for their clients on the floor of the
exchange, often with the assistance of dealers who are known as
specialists.
The Nasdaq Stock Market is an electronic communications network that
allows brokers and dealers to meet each other in a screen-based environ-
ment managed by computers.
Most listed stocks in the United States also trade in one or more re-
gional stock markets. The
regional exchanges
presently include the Boston


## Page 62

CHAPTER 3 THE TRADING INDUSTRY • 49
Stock Exchange, the Chicago Stock Exchange, the Cincinnati Stock Ex-
change, the Archipelago Exchange, and the Philadelphia Stock Exchange.
Many more regional exchanges once existed, but after many mergers and
failures, only these five remain. In addition to listed companies, the regional
exchanges trade some Nasdaq stocks under
unlisted trading privileges
granted
to them by the U.S. Securities and Exchange Commission.
U.S. exchange-listed stocks also trade in the
third market.
The third mar-
ket includes dealers and brokers who arrange trades in exchange-listed stocks
away from an exchange. These dealers typically display their quotes on the
Nasdaq Intermarket.
Finally, U.S. stocks also trade in various electronic trading systems known
as
alternative trading systems
(ATSs). Registered broker-dealers sponsor most
of these systems.
Electronic communications networks
(ECNs) are the best-
known alternative trading systems. Many alternative trading systems are es-
sentially electronic exchanges. The term
fourth market
refers to trading in
exchange-listed stocks within these systems. A summary of the U.S. equity
markets appears in table 3-5.
TABLE 3
-
5.
Some U.S. Equity Markets with 2000 Total Dollar Volumes (billions)
MARKET TYPE
EXAMPLES
DOLLAR
VOLUME
QUOTATION
SYMBOL
Primary listing markets
New York Stock Exchange
11,060
N
American Stock Exchange
945
A
The Nasdaq National Market
20,274
Q
The Nasdaq SmallCap Market
122
S
OTC Bulletin Board Service
101
U
National Quotation Service Pink Sheets
20
Regional markets
Boston Stock Exchange
258
B
Chicago Stock Exchange
1,190
M
Cincinnati Stock Exchange
173
C
Pacific Exchange
157
P
Philadelphia Stock Exchange
80
X
Third market dealers
Madoff Investment Securities
1,000+
MADF
Knight Trading Group
1,000+
TRIM
Third market brokers
Jefferies
Group
NA
JEFF
ITG
NA
ITGI
Electronic communications
Archipelago
777
ARCH
networks (ECNs)
BRUT ECN
NA
BRUT
Instinet
3,336
INCA
Island
3,449
ISLD
REDIBook
NA
REDI
Other alternative trading
POSIT
335
systems
Global Instinet Crossing
NA
Arizona Stock Exchange
0.2
Sources: Exchange fact books and personal correspondence. Some data are not available for proprietary reasons.


## Page 63

50 • TRADING AND EXCHANGES
Communist Party
Headquarters
Poland emerged from behind
the Iron Curtain in 1989.
The government almost
immediately reorganized the
Warsaw Stock Exchange,
which had closed in 1939
following Hitler's invasion of
Poland. The new exchange
reopened for trading in
1991.
The government first
housed the Exchange in
the former Communist
Party Headquarters Building.
This site was attractive both
for its symbolic value and
for its telecommunications
infrastructure, which was the
best in Warsaw.
3.4.2.2 International Stock Markets
In
the late twentieth century, stock markets throughout the world grew
substantially as firms increasingly sought public equity financing instead of
bank loan financing and as governments privatized various enterprises. Many
exchanges consolidated to take advantage of economies of scale.
Almost all the former Communist countries have established stock ex-
changes. They often created these exchanges before they had stocks to trade,
property and bankruptcy laws to define who owns what, and securities
laws to regulate issuers and traders. Despite these deficiencies, these coun-
tries established stock exchanges because they are symbols of free market
economies. Not surprisingly, the most successful of these markets are in
countries that carefully defined property rights, privatized most of their
government-run enterprises, adopted good securities laws, and diligently
enforced those laws.
Table 3-6 presents a summary of trading activity in the larger national
stock markets. Not surprisingly, trading is most active in countries with
strong market-based economies.
3.4.3 Equity Options Markets
3.4.3.1 U.S.
Markets
Five exchanges in the United States presently trade standardized equity and
index option contracts. The Options Clearing Corporation (OCC) is the
clearinghouse for all contracts that trade at these exchanges. Buyers there-
fore can buy contracts at one exchange and sell them at other exchanges to
offset their positions. The most actively traded option contracts trade at all
of the exchanges. A list of these exchanges appears in table 3-7.
Four of the five options exchanges employ floor-based trading systems.
Each of these exchanges also employs automated systems to support their
dealers and floor brokers. The International Securities Exchange, formed in
1997, started trading in 2000 with a completely automated trading system.
Its market share has grown very quickly.
Investment banks also trade specialized option contracts
over the counter
(OTC) with their clients. These contracts usually have strike prices, matu-
rity dates, settlement terms, or other features that are different from the stan-
dardized options available at the exchanges. This business is part of the
syn-
thetic derivatives business.
Synthetic derivatives also include other
structured
products—primarily
swaps—that investment banks create for their clients.
3.4.3.2
International
Equity Derivatives
Markets
Exchange-traded equity derivatives include stock option contracts, equity
index option contracts, equity index futures contracts, options on equity in-
dex futures contracts, and futures on individual stocks. Table 3-8 provides
a characterization of organized trading in equity derivatives throughout the
world.
Outside of the United States, most organized trading in standardized
stock option contracts takes place at the same exchange at which the under-
lying stocks trade. In the United States, the SEC has not permitted equi-
ties and their associated options to trade side by side. When they trade at
the same exchange, they generally trade in different rooms.
Most organized trading in equity index futures outside of the United
States also takes place at the same exchange at which the underlying stocks
trade. In the United States, these contracts trade on futures exchanges.


## Page 64

CHAPTER 3 THE TRADING INDUSTRY • 51
TABLE 3-6.
Trading Activity in Some International Stock Markets (2001)
TIME
ZONE
EXCHANGE
VOLUME
REPORT
TYPE
DOLLAR
VOLUME
(BILLIONS)
NUMBER OF
LISTED FIRMS
YEAR-END TOTAL
CAPITALIZATION
(BILLION DOLLARS)
ANNUAL
TURNOVER
RATE
North
Amex
TSV
817
605
60
NA
America
Mexico
REV
70
172
126
34%
Nasdaq
REV
10,935
4,128
2,897
388
NYSE
TSV
10,489
2,400
11,027
88
Toronto
TSV
460
1,316
615
72
South
Buenos Aires
TSV
8
119
33
16
America
Santiago
TSV
4
250
56
9
Sao Paulo
TSV
65
429
186
36
Europe,
Athens
TSV
38
314
85
48
Africa,
Copenhagen
REV
72
217
85
80
&
Deutsche Borse
TSV
1,440
984
1,072
120
Middle
Euronext
REV
3,180
1,345
1,844
165
East
Helsinki
TSV
182
155
190
84
Irish
TSV
23
87
75
28
Istanbul
TSV
78
310
47
174
Italy
TSV
710
294
527
113
Johannesburg
TSV
70
519
147
33
Lisbon
TSV
28
99
46
63
London
REV
4,551
2,891
2,150
76
Madrid
REV
842
1,482
468
187
Oslo
REV
62
214
69
88
Stockholm
REV
387
305
237
111
Switzerland
REV
594
412
527
86
Tel-Aviv
TSV
16
649
58
30
Valencia
TSV
41
508
NA
12
Vienna
TSV
8
113
25
31
Warsaw
TSV
10
230
26
53
Asia &
Australian
TSV
244
1,410
375
61
Pacific
Hong Kong
TSV
241
867
506
47
Jakarta
TSV
10
315
23
35
Korea
TSV
381
688
194
218
Kuala Lumpur
TSV
21
807
119
19
New Zealand
TSV
10
195
18
51
Osaka
TSV
175
1,335
NA
9
Philippine
TSV
3
232
21
16
Singapore
TSV
72
386
116
56
Taiwan
TSV
545
586
293
212
Thailand
TSV
31
382
36
74
Tokyo
TSV
1,661
2,141
2,265
57
Source: World Federation of Exchanges website at www.world-exchanges.org
.
Note: Trading System View (TSV) markets count only transactions that pass through their trading systems or that occur on their
trading floors. Regulated Environment View (REV) markets count all transactions that are subject to their regulatory supervi-
sion. Dollar volumes include investment funds. The number of listed firms includes both domestic and foreign listings and excludes
investment funds. Total capitalization includes only domestic companies and excludes investment funds.


## Page 65

52 • TRADING AND EXCHANGES
Some International Stock Market Trivia
Telefonos de Mexico, S.A. de C.V. (Telmex) is the largest Mexican stock
issue. The New York Stock Exchange, however, has a greater share of its
worldwide trading volume than does the Bolsa Mexicana de Valores.
By capitalization and trading volume, the largest Israeli stock market is
the U.S. Nasdaq Stock Market. Many high-tech Israeli companies do not list
their shares at the Tel
Aviv
Stock Exchange.
The Stock Exchange of Hong Kong uses an electronic trading system to
match buyers to sellers. However, until recently, the Exchange required its
members to sit in the Trading Hall of the Exchange to trade. The Trading
Hall is a large room filled with members and their clerks, seated at desks
upon which sit computer screens and telephones. Members now also can
trade through off-floor trading devices in their offices.
Chinese law requires that all trading in securities listed at the Shanghai
Stock Exchange take place at the Exchange, and all shares held by
domestic traders (A class shares) remain on deposit at the Exchange's
depository.
Domestic
traders who wish to trade at the Exchange must
deposit funds with their brokers before trading. Since all money and
securities are on deposit before the Exchange arranges any trade, the
broker can refuse to accept orders that would produce trades which traders
cannot settle immediately. Although the
Exchange once settled its A
share
trades on the day of the trade, it now settles them on the next day (T+1).
The Shenzhen Stock Exchange uses similar procedures.
Some Options Market Trivia
The Chicago Board Options Exchange (CBOE) began trading in 1973 as
the first organized equity options exchange. Although it is a Chicago Board
of Trade subsidiary, it is independently governed, operated, and regulated.
The New York Stock Exchange, the Midwestern Stock Exchange (now
called the Chicago Stock Exchange), and Nasdaq also created organized
options markets. These markets were not notably successful. The NYSE sold
its options market to the CBOE in 1997. The Midwestern Stock Exchange
and Nasdaq simply closed their options markets.
The Securities and Exchange Commission has not approved
side-by-side
trading
of stocks and their associated options at the same exchange.
Exchanges that trade stocks and their associated options must physically
separate the stock trading from the options trading. ^
4
FLEX Options
To capture
institutional business in specialized options, the options markets
developed
FLEX Options
(FLexible EXchange) for indexes and E-FLEX
Options for equities. Using a special
request for quote
(RFQ)
procedure,
institutional traders specify the option type (call or put), strike price, maturity
date (up to three years distant), and exercise style (American or European)
for the option contract in which they are interested. Exchange market
makers then quote the option in a competitive environment. The Options
Clearing Corporation is the issuer and guarantor of all FLEX and E-FLEX
contracts, as it is for all other options traded at U.S. exchanges. 0.


## Page 66

TABLE
3
-
7.
U.S. Equity Options Exchanges and 2001 Total Contract Volumes (millions)
EXCHANGE
NICKNAME
CONTRACT VOLUME
QUOTATION SYMBOL
EQUITIES
INDEXES
Chicago Board Options Exchange
CBOE
254
52
CO
American Stock Exchange
Amex
204
1
A
Pacific Exchange
P-Coast
103
P
Philadelphia Stock Exchange
Philly
96
5
X
International Securities Exchange
ISE
65
Source: The
Options Clearing Corporation 2001 Annual Report,
p. 3, at www.optionsclearing.com/about/ann_rep/ann_rep_

pdfannual_rep_01.pdf
TABLE
3
-
8.
Contract Volumes in Some World Equity Derivatives Markets in 2000 (thousands)
TIME ZONE
EXCHANGE
STOCK
OPTIONS
INDEX
OPTIONS
INDEX
FUTURES
STOCK
FUTURES
North
AMEX (USA)
205,716
1,998
NT
NT
America
CBOE (USA)
281,182
47,387
NT
NT
CBOT (USA)
NT
200
3,572
NT
CME (USA)
NT
5,089
59,957
NT
ISE (USA)
7,716
NT
NT
NT
Montreal SE (Canada)
4,753
89
1,272
NT
PHLX (USA)
73,021
2,607
NT
NT
PSX (USA)
108,990
1
NT
NT
South
BOVESPA (Brazil)
30,295
414
NT NT
America
MMD (Mexico)
NT
NT
49
17
Europe, Africa &
Athens (Greece)
NT
NT
913
NT
Middle East
EUREX (Germany)
89,238
44,200
NT
31,595
Euronext Amsterdam
47,107
4,531
2,479
349
Euronext Brussels
589
1,693
29,519
NA
FUTOP (Denmark)
4
11
989
NT
MEFF (Spain)
16,586
766
4,183
NT
OM (Sweden)
30,692
4,167
11,477
2,145
Wiener Borse (Austria)
839
205
431
TASE (Israel)
NT
26,974
122
NT
Asia &
ASXD (Australia)
9,508
NT
NT
437
Pacific
HKFE (Hong Kong)
4,189
550
4,178
3
KLOFFE (Malaysia)
NT
349
367
NT
Korea SE (S Korea)
NT
193,829
19,667
NT
NZFOE (New Zealand)
65
NT
1
NT
Osaka (Japan)
104
5,717
8,708
NT
SFE (Australia)
NT
1,099
3,825
9
Singapore Exchange
NT
710
8,461
NT
Taiwan Futures Exchange
NT
NT
1,927
NT
Source: World Federation of Exchanges website at www.world-exchanges.org

Notes: NT = not traded; NA = not available
Since contract sizes vary substantially both within and among exchanges, the contract volume data do not permit fine compar-
isons among exchanges.
53


## Page 67

54 • TRADING AND EXCHANGES
An Irony at the NYSE
For most of its first hundred
years, traders at the New
York Stock Exchange—
originally known as the New
York Stock and Exchange
Board—primarily traded
bonds. While they still
occasionally trade bonds,
they now primarily trade
stocks.
The current NYSE bond
market uses an electronic
trading system called the
Automated Bond System. This
system was one of the first
fully automated systems used
in the trading industry. It
started automatically matching
orders in 1976.
Although the NYSE is
primarily known for its floor-
based trading system, it was
among the earliest innovators
in electronic trading.
Until December 2001, it was illegal to
trade futures on individual stocks
in the United States. The Commodity Futures Modernization Act of 2000
now permits trading in these contracts.
3.4.4 Futures Markets
Most
futures exchanges have their own clearinghouses. Exchanges therefore
do not compete to trade the same contracts. Instead, each exchange and its
associated clearinghouse try to create contracts that will attract traders. Most
exchanges have large research and marketing departments that design con-
tracts they hope will attract traders. Chapter 8 identifies some of the fac-
tors that make contracts successful.
Futures exchanges generally trade several contracts that vary by expira-
tion date for each commodity that they trade. Most commodities have at
least four
delivery months.
The contract that will expire next is called the
front contract
or front
month contract.
The other contracts are called the
back
contracts.
In financial and industrial commodities, traders mostly trade only the
front month contract. When it expires, they roll their positions into the next
contract.
The most actively traded agricultural contracts are the front month con-
tracts and the first harvest contracts. The
first harvest contract
is the first
contract on which traders can deliver the currently growing crop. Hedgers
and speculators usually have great interest in this contract.
3.4.4.1
U.S. Futures Markets
Four major and several smaller exchanges trade standardized futures con-
tracts in the United States. These exchanges are often called
boards of trade.
Futures exchanges have experienced substantial consolidation over the years,
as have other markets. Table 3-9 provides a brief summary of the currently
active U.S. futures exchanges.
3.4.4.2 International Futures Markets
Table 3-10 lists global futures exchanges with the greatest contract volumes
in 2001. Unlike U.S. futures exchanges, many of the exchanges on this list
also trade common equities.
3.4.5 Corporate and Municipal Bond Markets
Throughout the world, most corporate and municipal bonds trade
over the
counter
in investment banks or commercial banks. Some stock exchanges list
corporate bonds, but exchange bond trading volumes are generally trivial
compared to over-the-counter volumes. Less than 0.1 percent of all corpo-
rate bond trading volume occurs in the New York Stock Exchange and
American Stock Exchange bond markets. The exchange bond price tables
that appear in many daily newspapers therefore present less reliable infor-
mation than you might imagine.
3.4.6 Treasury Markets
Most national treasuries conduct public auctions at which they issue their
bills, notes, and bonds. Some smaller nations, however, use underwriters to
issue their securities. Generally, anyone may participate in Treasury auctions.
The auction rules vary by country.


## Page 68

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56


## Page 70

CHAPTER 3 THE TRADING INDUSTRY • 57
TABLE 3-10.
Top 40 Global Futures Exchanges by Contract Volume in 2001 (millions)
REGION
EXCHANGE
VOLUME
RANK
North
Chicago Mercantile Exchange, USA
316.0
2
America
Chicago Board of Trade, USA
210.0
3
New York Mercantile Exchange, USA
85.0
6
New York Board of Trade, USA
14.0
17
Bourse de Montreal, Canada
7.3
24
Winnipeg Commodity Exchange, Canada
2.9
33
Kansas City Board of Trade, USA
2.4
36
Europe
Eurex, Germany and Switzerland
435.1
1
LIFFE, UK
161.5
4
London Metal Exchange, UK
56.2
8
Paris Bourse SA, France
42.0
9
International Petroleum Exchange, UK
26.1
14
OM Stockholm, Sweden
23.4
15
MEFF Renta Variable, Spain
13.1
18
Italian Derivatives Market of the Italian Stock Exchange, Italy
6.0
26
Euronext Brussels Derivatives Market, Belgium
3.4
30
Amsterdam Exchanges, Netherlands
3.3
32
Budapest Commodity Exchange, Hungary
2.6
35
Budapest Stock Exchange, Hungary
2.3
37
Helsinki Exchanges, Finland
1.0
40
Asia
Tokyo Commodity Exchange, Japan
56.5
7
Korea Stock Exchange, Korea
31.5
11
Singapore Exchange, Singapore
30.6
12
Central Japan Commodity Exchange, Japan
27.8
13
Tokyo Grain Exchange, Japan
22.7
16
Tokyo Stock Exchange, Japan
12.5
19
Korea Futures Exchange, Korea
11.5
21
Osaka Securities Exchange, Japan
10.5
22
Tokyo International Financial Futures Exchange, Japan
7.6
23
Fukuoka Futures Exchange, Japan
6.4
25
Hong Kong Exchanges and Clearing-Derivatives Unit, China
5.8
27
Shanghai Futures Exchange, China
5.6
28
Taiwan Futures Exchange, Taiwan
4.3
29
Osaka Mercantile Exchange, Japan
3.4
31
Kansai Commodities Exchange, Japan
2.9
34
Yokohama Commodity Exchange, Japan
1.3
38
Southern
BM&F, Brazil
94.2
5
Hemisphere
Sydney Futures Exchange, Australia
34.1
10
South African Futures Exchange, South Africa
11.9
20
New Zealand Futures Exchange, New Zealand
1.0
39
Source: Futures
Industry Association,
at http://wwwfuturesindustry.org/fimagazi-1929.asp?a=756
. Reprinted by permission.
Note: Since contract sizes vary, the rankings would be different if they were based on nominal dollar volumes.


## Page 71

58 • TRADING AND EXCHANGES
r
They Trade Enormous Volume but Receive No Year-end Bonuses
The Federal Reserve conducts U.S. monetary policy primarily through its
Open
Market Operations Desk.
Traders who work at the Federal Reserve
Bank of New York implement the policy. When the Fed wants to add
monetary reserves to the banking system, it buys seasoned U.S. Treasury
bills and bonds, and simply creates the money to pay for them. When it
wants to decrease the reserves, it sells bills and bonds, and simply destroys
the money it receives in payment. Since the reserves have grown over time,
the Federal Reserve is the largest holder of U.S. Treasury instruments. On
June 20, 2001, the Fed held 533 billion dollars of government debt
instruments in its System Open Market Account (SOMA). The Desk rarely
sells securities because bills mature in the SOMA account every week.
When the Fed wants to slow the growth of the money supply, it merely
does not buy as many securities as it otherwise would.
The Federal Reserve trades only with a small set of large institutional
broker-dealers called
primary government securities dealers.
In exchange for
the privilege of dealing with the Fed, these primary dealers must quote firm
prices for large sizes whenever the Fed wants to trade. In addition, they
must participate meaningfully in Treasury auctions, and they must supply
information about market conditions to the Federal Reserve. As of October
31, 2001, there were only 24 primary dealers. Most of these were
investment and commercial banks.
In 2000, the Fed traders purchased 44 billion dollars of debt on behalf
of the government in unmatched transactions and an additional 4.4 trillion
dollars in matched Treasury bill transactions (repurchase agreements and
matched sale-and-purchases). They arrange their trades by computer from
their offices on the ninth floor at the Federal Reserve Bank of New York.
Since the Federal Reserve banks are not government agencies, their
traders are not civil servants. However, they work under a similar pay
schedule. The Fed traders make only a small fraction of what large private
buy-side traders make. They also do not receive year-end bonuses.
This salary comparison is not fair, however. The Fed traders are more
like exchange officials than like buy-side traders because they generally do
not negotiate trades and they do not trade throughout the day. Instead, they
merely request bids and offers to fill the Fed's daily requirements. They then
arrange trades with the dealers who offer the best prices. The Fed traders
occasionally also act as brokers for foreign governments, central banks, and
official international organizations.
Secondary trading
of Treasury securities occurs primarily over the counter
in investment and commercial banks. Several brokers, however, organize
markets in which government bond dealers and some larger buy-side traders
trade with each other. These
interdealer brokers
permit their clients to trade
on an anonymous basis. Dealers generally do not want other dealers to know
what trades they are doing. The largest such interdealer government bond
broker is Cantor Fitzgerald. Their government bond markets are among the
world's most liquid markets in any instrument.
3.4.7 Swaps and Spot Currency Markets
Swaps and spot currencies mostly trade over the counter in investment and
commercial banks. Some brokers and some data providers organize markets
in these instruments. Traders use their services to lower the costs of search-
ing for counterparts.


## Page 72

CHAPTER 3 THE TRADING INDUSTRY • 59
u.
Some Wholesale OTC Brokers
Reuters
is the world's leading foreign exchange broker. Its Dealing 3000
trading system is an anonymous electronic brokerage service that many
foreign exchange traders use. As of August 2001, traders using 7,500
workstations could use the system to trade 38 spot currency pairs and 22
forward currency swaps.
Garban-Intercapital is the world's leading swap broker. The firm
specializes in brokering wholesale trades between dealers and between
dealers and large customers. Its securities, derivatives, and money
brokerage businesses have daily transaction volumes in excess of 200
billion dollars.
Cantor Fitzgerald is the world's leading U.S. government bond broker.
In addition to its fixed-income businesses, it has significant presence in the
equity, foreign exchange, energy, bandwidth, and environmental markets
throughout the world. Before the September 11, 2001, terrorist attacks
destroyed its offices at the World Trade Center, its various businesses had
daily volumes in excess of 160 billion dollars.
A
Sources: about.reuters.com/transactions/d3_intro.htm
• www.garban.com
; www.cantorcorn.
So The Retail Currency Markets
If you have traveled abroad, you may be an experienced foreign exchange
trader. You probably did not negotiate the terms of your trades, however,
unless you traded in a
black market
in an alley.
Retail foreign exchange markets in airports and tourist areas are
notoriously expensive. Currency dealers usually charge a significant fee for
their transactions. They also profit from the wide spread between the prices
at which they are willing to buy and sell currencies.
The high transaction costs in the retail foreign exchange market are due
to the high rents dealers must pay for their shops at airports and near
tourist sites, to the costly security precautions that ensure they are not
robbed while tending their shops and delivering and collecting currencies,
and to the salaries they must pay clerks, who are often idle.
Transaction costs in retail currencies dropped significantly with the
introduction of international withdrawals from automatic teller machines.
When you withdraw currency from ATMs, you pay a small access fee. The
exchange rates that you receive are far better than those you can obtain
yourself. Your bank gives you a better rate because it can consolidate your
transaction with many others, and because it has much more negotiating
clout than you do. 1
3.5
MARKET REGULATION
Regulators
create and enforce rules that facilitate trading. Most traders be-
lieve that securities markets work best when they are well regulated but not
excessively regulated. Good regulations help ensure that traders communi-
cate effectively with each other, that people do not defraud others, and that
all things generally are as they appear.
Regulators sometimes create and enforce rules that promote other ob-
jectives. Such regulations may give privileges—usually protections from
competition—to favored traders, brokers, or exchanges. For example, regu-
lators often try to protect domestic markets from foreign competition. They
may also try to protect incumbent traders and incumbent markets from new


## Page 73

60 • TRADING AND EXCHANGES
domestic competition. Ideologically motivated regulators also may impose
restrictive regulations because they do not like the markets, or because they
want to redistribute wealth. For example, governments sometimes tax trad-
ing to raise money for the national treasury. Regulations that make it dif-
ficult or expensive for traders to arrange mutually beneficial trades gener-
ally harm the markets and the wider economy.
The stated purpose of a regulation often is not its true objective. Since
most people agree that regulation should be in the best interests of the mar-
kets or, at a minimum, in the national interest, regulators generally justify
their regulations with explanations about how they promote the common
good. Through ignorance, self-interest, or malice, however, regulators often
adopt regulations that do not promote the common good. The knowledge
you gain from reading this book will help you judge the true effects of reg-
ulatory policies.
Since regulators set the rules of the game, and since rules help determine
who will be successful, people devote tremendous efforts to lobbying for
rules that they favor. They naturally argue in favor of high principles, even
though they often are actually arguing for their personal gain. Regulatory
debates therefore are often very controversial.
The controversies that surround regulatory efforts make regulation an
exciting and often frustrating area in which to work. Regulators who truly
want to promote the common good take great satisfaction in the good reg-
ulations that they write and enforce. They suffer great frustration when they
lose regulatory battles to other interests.
3.5.1 Regulators
Most countries divide the responsibility for regulating markets among many
agencies. Legislatures enact laws that directly regulate markets. They gen-
erally delegate enforcement of these laws to various public and private reg-
ulatory agencies. Legislatures also enact laws that delegate their legislative
powers to these agencies. These laws authorize agencies to write regulations
that have the force of law. The agencies then enforce their regulations
through judicial proceedings.
Governments usually require that regulatory agencies regulate in the pub-
lic interest when they delegate their state powers. The definition of what is
in the public interest, however, may be vague. Regulators therefore often
have significant power to promote their personal agendas.
3.5. 1 . I Govern mental Regulatory Agencies
Most countries have created governmental regulatory agencies to oversee
traders and trading practices. These agencies generally are independent com-
missions. Table 3-11 shows that some countries delegate regulatory powers
over the markets to ministries of the executive branch of the government,
or to their national central banks.
U.S. Regulatory Agencies
The main U.S. governmental agencies that regulate trading are the
Securi-
ties and Exchange Commission
(SEC) and the
Commodity Futures Trading
Commission
(CFTC). The SEC regulates securities markets (stocks, bonds,
warrants, investment company shares, and trust units), equity options mar-
kets, and cash-settled equity index options markets. The CFTC regulates
commodity spot, forward, and futures markets. Most countries consolidate


## Page 74

CHAPTER 3 THE TRADING INDUSTRY • 61
OP
The SEC Mission
The
Securities Exchange Act of 1934
created the U.S. Securities and
Exchange Commission. Section 3f of the
Act charges the Commission as
follows:
Whenever pursuant to this title the Commission is engaged in
rulemaking, or the review of a rule of a self-regulatory organization, and
is required to consider or determine whether an action is necessary or
appropriate in the public interest, the Commission shall also consider, in
addition to the protection of investors, whether the action will promote
efficiency, competition, and capital formation.
Nearly identical text also appears in Section 2b of the Securities
Act
of
1933 and in Section 2c of the Investment Company Act of 1940. These
three acts together form the legislative foundation for securities regulation in
the United States.
Although this directive seems quite explicit, it provides no guidance to
regulators confronted with issues that require trade-offs among the named
objectives. For example, rules that prohibit insider trading protect investors
while simultaneously making markets less efficient in the sense that they
produce less informative prices. This directive also provides no guidance
about issues that require trade-offs within the named objectives. For
example, rules that protect the interests of small investors often hurt large
investors. In these cases, regulators generally freely decide what they
believe is in the public interest. *111
these regulatory functions into a single agency. Concerns over the ineffi-
ciencies of having two agencies perform similar functions have caused many
people to propose merging the two agencies.
The SEC and the CFTC write regulations to interpret and implement
the laws that fall within their jurisdictions. The law also allows them to en-
force their regulations through administrative hearings. These agencies can
also ask that the Department of Justice prosecute violators in the federal
courts. Most national regulatory agencies throughout the world have simi-
lar powers.
In addition to their regulatory functions, the SEC and CFTC collect
and disseminate information useful to traders, investors, speculators, and
legislators. The SEC collects various financial reports from issuers and po-
sition reports from large traders. Investors who are interested in estimating
security values can access these reports over the Internet via the SEC's
Edgar
information retrieval system. The CFTC likewise collects and publishes in-
formation about commodity market supply and demand conditions and large
trader positions. Traders use this information to value commodities and to
forecast what other traders might do in the future. Both organizations also
provide information to Congress through their regular annual reports, their
special reports on specific issues, their testimony at congressional hearings,
and their responses to requests for information from members of Congress
and their staffs.
Several other governmental organizations also regulate securities trading
in the United States. The Federal Reserve Board sets speculative margins.
Speculative margins
specify the minimum amount of capital that traders must
have to buy or sell securities and to hold long or short positions. Traders
call these margins
Regulation T margins, or simply Reg T margins
because
the Federal Reserve Board specifies them in its Regulation T. The various


## Page 75

62 • TRADING AND EXCHANGES
TABLE 3-1 1 .
Selected National Trading and Securities Regulators
COUNTRY

AGENCY
Korea
Malaysia
Mexico
Netherlands
Norway
Poland
Portugal
Russia
Singapore
South Africa
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States of America
Comision Nacional de Valores
Australian Securities and Investments Commission
Comissao
de Valores Mobiliarios
British Columbia Securities Commission
Ontario Securities Commission
Commission des Valeurs Mobilieres du Quebec
Superintendencia de Valores y Seguros
China Securities Regulatory Commission
Securities and Futures Commission
Finanstilsynet
Financial Supervision Authority
Commission des Operations de Bourse
Bundesaufsichtsamt fur den Wertpapierhandel (BAWE)
Capital Market Commission
Securities and Futures Commission
Securities and Exchange Board of India (Sebi)
Indonesian Capital Market Supervisory Agency
Central Bank of Ireland
Israel Securities Authority
Commissione Nazionale per le Society e la Borsa
Financial Services Agency
Ministry of Agriculture, Forestry, and Fisheries
Ministry of International Trade and Industry (MITI)
Securities and Exchange Surveillance Commission
Financial Supervisory Commission/Financial Supervisory Service
Securities Commission
Comision Nacional Bancaria y de Valores
Stichting Toezicht Effectenverkeer
Kredittilsynet
Polish Securities and Exchange Commission
Comissao
do Mercado de Valores Mobiliarios
Federal Commission for the Securities Market of the Russian Federation
Monetary Authority of Singapore
Financial Services Board
Comision Nacional del Mercado de Valores
Finansinspektionen
Commission Federale des Banques
Capital Markets Board
Financial Services Authority
United States Securities and Exchange Commission
Commodity Futures Trading Commission
Argentina
Australia
Brazil
Canada (British Columbia)
(Ontario)
(Quebec)
Chile
China (People's Republic)
Chinese Taipei
Denmark
Finland
France
Germany
Greece
Hong Kong
India
Indonesia
Ireland
Israel
Italy
Japan
Source: Agencies selected by the author from lists of ordinary and associate members of the International Organization of Securi-
ties Commissions that appear at www.iosco.org/iosco.html.



## Page 76

CHAPTER 3 THE TRADING INDUSTRY • 63
Regulatory Competition
The division of regulatory oversight between the SEC and CFTC has created
numerous conflicts over jurisdiction between these agencies. These "turf
wars" generally occur when markets or issuers create new trading products
that have characteristics of both securities and commodity contracts.
For
example, oil-linked bonds are like securities because corporations issue
them to obtain financing. They are like commodity contracts because they
derive their value primarily from the price of oil.
Before 1982, the
Commodity Exchange Act
gave the CFTC authority to
regulate any exchange contract that has an element of futurity. Although the
Securities and Exchange Commission had authorized trading in stock option
contracts, the federal courts were then considering whether the CFTC was
the proper regulator of these contracts.
At the same time, the futures markets wanted to create futures on equity
indexes. The
SEC
argued that it had regulatory authority over these
contracts because it regulated the underlying instruments.
Chairmen John Shad of the SEC and Phillip McBride Johnson of the
CFTC reached an accord in 1982 to divide regulatory responsibilities
between the two agencies. Their agreement, which Congress enacted into
law, gave the CFTC jurisdiction over futures trading in broad equity indexes
and options on those futures contracts. The SEC obtained exclusive
jurisdiction over options on individual stocks and cash-settled options on
indexes. The chairmen agreed that no futures on individual stocks or on
narrow indexes would trade anywhere. The Commodity Futures
Modernization Act of 2000 amended the law to permit trading in these
previously prohibited futures contracts.
Perhaps the most interesting aspect of the Shad-Johnson accord concerns
the regulation of equity index options. As noted above, the SEC regulates
cash-settled equity index options while the CFTC regulates futures options on
equity index futures contracts. The risks inherent in these two types of
contracts are nearly identical when they are based on the same underlying
index. Two regulatory agencies thus separately regulate instruments that
have essentially identical risk characteristics.
Some people believe that this redundancy is foolish. Others believe
that the competition between regulators has made both of them more
reasonable. Still others believe that this competition has made both
regulators too lax.
4
states also have securities commissions. These commissions primarily en-
force state antifraud statutes.
3.5.1 .2 Self-regulatory Organizations
Private regulatory agencies include exchanges, clearinghouses, and trader as-
sociations. These organizations regulate their members to lower their costs
of doing business together, to improve their business prospects, to ensure
that no member hurts another member, and to provide quality assurances
to their members' clients. Organizations that regulate their members are
called
self-regulatory organizations,
or SROs.
Exchanges primarily regulate their members' trading practices. Their
rules specify how their members arrange trades and how they should relate
to their clients. We discuss the implications of exchange rules throughout
this book.
Many securities exchanges also regulate their listed firms. For example, their
listing standards generally require a minimum level of financial reporting.


## Page 77

64 • TRADING AND EXCHANGES
The Buttonwood Tree Agreement
The New York Stock Exchange traces its beginnings to an agreement
traders made in 1792 to regulate their commissions and to trade with each
other. According to legend, the traders met under a buttonwood tree, near
what is now Wall Street in lower Manhattan. Their written agreement—
which entered the NYSE archives in 1840—indicates that they all would
charge their clients no less than
0.25
percent commission for their
brokerage services. The full text of their agreement, signed by 24 brokers
and merchants, is as follows:
We the Subscribers, Brokers for the Purchase and Sale of Public Stock,
do hereby solemnly promise and pledge ourselves to each other, that we
will not buy or sell from this day for any person whatsoever, any kind of
Public Stock, at a less rate than one quarter per cent Commission on the
Specie value and that we will give a preference to each other in our
Negotiations. In Testimony
whereof we have set our hands this 17th day
of May at New York. 1792.
The New York Stock Exchange stopped regulating brokerage commissions
183
years later, in 1975.
For much of its life, the NYSE prohibited its members from trading shares
in its listed companies away from the Exchange. In response to pressure
from its members and from the SEC, the Exchange gradually relaxed these
restrictions. It repealed Rule 390, its last restriction on off-exchange trading,
in December 1999.
Sources: New York Stock Exchange Archives; Securities and Exchange Commission Re-
lease no. 34-42758 at www.sec.gov/rules/sro/ny99480.htm.

Clearinghouses
primarily establish capital adequacy standards and trade-
reporting practices for their members. They design their regulations to en-
sure that their members and their members' clients will honor their trading
contracts. Their regulations also minimize the losses that occur when traders
fail to settle their contracts.
Trader associations regulate how traders relate to each other and to their
clients. The primary SROs that regulate brokers and dealers in the United
States are the
National Association of Securities Dealers
(NASD) and the
National Futures Association
(NFA). The law requires all U.S. futures bro-
kers and dealers to belong to the NFA and to submit themselves to its reg-
ulations. Although the law does not require that all security brokers belong
to the NASD, it is essentially impossible to do business in the United States
without being a member. The NASD and the NFA both administer a se-
ries of exams that traders take to certify their competence.
SROs enforce their regulations by threatening to expel members who do
not comply. They also enter into contracts with their members that allow
them to sue their members in civil court if they fail to comply with their
regulations. These enforcement mechanisms are most effective when the
costs of compliance are small and the benefits are large. They can be inef-
fective, however, when dishonest members can profit greatly from violating
the rules.
To prevent such problems, some governments give SROs the power to
write regulations that have the force of law. The SROs then can rely upon
the criminal justice system to help enforce their regulations. Since regula-
tors can abuse the power to write regulations, and since constitutional gov-


## Page 78

CHAPTER 3 THE TRADING INDUSTRY • 65
FP
Crying Over Onion Futures
Commodity markets can be especially volatile when the traded commodity
is perishable. When commodity supplies will soon spoil, their prices dive
if they are in excess supply, and they rocket upward if they are in short
supply.
Onions are quite perishable. They can be stored from harvest to harvest,
but nobody wants an old onion when the new harvest arrives. Prices of
onions for delivery before the new harvest therefore are quite volatile.
In the 1950s, onion futures markets suffered several extreme price
fluctuations that hurt farmers and local dealers. The farmers and dealers
attributed the volatility to speculative trading in the onion futures market.
They complained to their senators, who prevailed upon their colleagues to
solve the problem. In 1958, they complied by prohibiting all exchange
trading in onion futures. It is still illegal to trade onion futures contracts on,
or subject to, the rules of any board of trade in the United States.
Consequently, the natural beneficiaries of onion futures markets—the
farmers who produce onions and the food processors who use onions—
cannot use futures contracts to cheaply exchange onion price risk. Instead,
they now use forward contracts.
Source: U.S. Public Law 85
-
839 (7 U.S.C. 13
-
1).
ernments generally cannot delegate their legislative and judicial powers to
private agencies, SROs must have their rules approved by the government.
In the United States, the various SROs apply to the SEC or CFTC for ap-
proval of their rules. These agencies approve proposed rules if they find that
they are in the public interest.
The delegation of regulatory powers from national legislatures to regula-
tory nongovernmental agencies allows experts who are most familiar with
the markets and their problems to regulate them. This system helps avoid
the unintended consequences that often result when poorly informed legis-
lators try to micromanage regulatory policies. It also protects markets from
capricious actions that legislatures occasionally take. When the system works
well, the legislature provides a broad framework for regulatory oversight, and
the regulatory agencies implement this framework in the public interest.
3.5.1.3 Other Private Regulators
Several private agencies regulate traders, issuers, and investment managers
in the United States. The
Financial Accounting Standards Board
(FASB) sets
accounting standards by which firms must report their accounts. The SEC,
which has ultimate authority for specifying reporting standards for public
firms, has recognized the FASB standards as authoritative since 1973.
The
Association for Investment Management and Research
(AIMR) sets
performance reporting standards that many investment managers use to re-
port their results. Although the standards are voluntary, many firms choose
to comply in order to satisfy their clients.
Brokers commonly purchase insurance policies on behalf of their clients
to ensure that their clients will not lose if the brokerage goes bankrupt. The
insurance companies that write these policies regulate the brokers who pur-
chase these policies in order to minimize the probability that the brokers
will fail, and thus impose costs on their funds.


## Page 79

Chartered Financial
Analysts
The Association for Investment
Management and Research
charters financial analysts.
Financial analysts who wish
to become a
chartered
financial analyst
(CFA) must
pass rigorous examinations
that the AIMR administers
over a three-year period. The
AIMR bases its examinations
on a curriculum that covers all
aspects of financial analysis,
investment management, and
corporate finance. The AIMR
requires its CFAs to engage in
a continuing education
program to maintain their
charters. It also requires that
its CFAs uphold ethical
standards of behavior. CFAs
who violate those standards
risk losing their charters. The
AIMR charter makes CFAs
very attractive to employers
because it certifies that they
are highly knowledgeable
financial professionals.
4
For more information, see
www.aimr.org
.
66 • TRADING AND EXCHANGES
00 Unintended Consequences
In 1997, the Brazilian government imposed a 0.38 percent tax on all
financial transactions in order to raise revenue. The unintended
consequence of this tax was to cause institutional traders to trade Brazilian
stocks as American depository receipts (ADRs) in New York to avoid the
tax. The tax therefore raised less revenue than expected, and the Brazilian
equity markets lost liquidity. Daily trading volume at the
sao
Paulo Bovespa
dropped from 1.2 billion reais (1.08 billion dollars) a day in 1997 to
350 million reais (136 million dollars) a day in 2001. Although some of
the drop-off undoubtedly was due to the Brazilian financial crisis of 2001,
many of the 32 Brazilian ADRs trade more volume in New York than in
Sao Paulo. The Brazilian government announced in September 2001 that
stock transactions would be exempt from the tax in late 2001.
Source: Jennifer L. Rich, "Brazil to Exempt Stock Trades from a Tax,"
New York Times,
September 7, 2001,
p.
WI.
3.5.2 International Regulatory Organizations
Several international organizations try to coordinate market regulation
across national boundaries. Although they cannot easily impose standards
upon their members, they provide useful forums for sharing information
about market structure and for exploring solutions to common regulatory
and operational problems. The most important of these organizations are
the
International Organization of Securities Commissions
(IOSCO), the
World
Federation of Exchanges
(WFE), and the
International Councils of Securities
Associations
(ICSA). These organizations of government
regulatory agen-
cies, exchanges, and dealer associations meet regularly to discuss issues of
interest to their members.
3.6 SUMMARY
The trading industry consists of traders who trade instruments in regulated
markets. This short introduction has identified the traders, instruments,
markets, and regulators who operate in this industry.
Although our discussions describe how the trading industry is organized,
we have hardly considered why it is organized as it is. In subsequent chap-
ters, we will increasingly examine why things are as they are. This intro-
duction to the trading industry should provide you with the background for
more interesting discussions to come.
3.7 SOME POINTS TO REMEMBER
•
The buy side buys liquidity services. The sell side provides them.
•
Dealers trade for their own account. Brokers trade for others.
•
Stocks get more attention than their values would indicate.
•
Many markets compete for order flow.
•
Exchanges often compete with brokerages to arrange trades.
•
Legislatures, regulatory agencies, and SROs regulate the markets. Leg-
islatures provide the regulatory framework, the SROs provide the de-
tails, and governmental regulatory agencies provide oversight.
•
Private regulators try to create respected standards.


## Page 80

CHAPTER 3 THE TRADING INDUSTRY • 67
3.8 QUESTIONS FOR THOUGHT
1.
What do sell-side and buy-side traders have in common? How do
they differ? Do you expect much labor mobility between these two
types of traders?
2.
How do brokerages and exchanges differ? How are they alike?
3.
On what basis would you regulate brokerages and exchanges differ-
ently?
4.
Suppose that a single organization offered the functions of an ex-
change, a clearinghouse, a depository, and a brokerage under one
roof. What advantages and disadvantages would such an integrated
organization have? Should regulators create—or encourage the cre-
ation of—such organizations?
5.
Should the government require that all trading in a particular in-
strument take place in the same market?
6.
Why are markets for precious stones generally not very liquid? How
could they be made more liquid?
7.
How are the common stock, preferred stock, and bond issues of a
corporation like the tranches of a collateralized mortgage obligation?
8.
Index participations
(IPs) were instruments that several U.S. options
exchanges traded in 1989-1990. ("Index participation" is also a ge-
neric name for Canadian exchange-traded index funds.) The index
participation was an instrument cleared by the Options Clearing
Corporation that was created when a trader with no position sold it
to a buyer. At the option of the buyer or seller, traders could peri-
odically redeem the IP for a specified multiple of its specified under-
lying equity index. Otherwise, the IP had an infinite life. Traders
with short IP positions were required to pay periodic dividends to
traders with long IP positions at a rate determined by the dividend
payout rate of the underlying index. IP positions were subject to
Regulation T margins. The futures exchanges and the CFTC ar-
gued in court that the index participation was a futures contract. The
securities exchanges and the SEC claimed that it was a security. A
federal court in Chicago ruled that the IP was a futures contract.
Was it correct?
9.
What is the difference between a gambling contract and a futures
contract?
10.
What advantages and disadvantages do cash-settled futures contracts
have, compared to physically settled futures contracts?
11.
Why are markets with many instruments less liquid than markets
with few instruments?
12.
How might competition among regulatory agencies benefit the econ-
omy? What effect do you expect it would have on innovation in trad-
ing products and trading procedures?
13.
Should Congress consolidate the SEC and the CFTC into a single
regulatory agency?
14.
Why do traders, brokers, and exchanges generally welcome regula-
tion? When do they oppose it?
15.
How should regulators decide issues?
16.
Who should appoint regulators?


## Page 81

4
I>
Orders
and
Order
Properties
O
rders
are trade instructions. They specify what traders want to trade,
whether to buy or sell, how much, when and how to trade, and, most
important, on what terms. Traders issue orders when they cannot person-
ally negotiate their trades.
Orders are the fundamental building blocks of trading strategies. To trade
effectively, you must specify exactly what you want. Your order submission
strategy is the most important determinant of your success as a trader. The
proper order used at the right time can make the difference between a good
trade, a costly trade, and no trade at all.
Many markets arrange all their trades by using a set of rules to match
buy and sell orders that traders submit to them. To understand how these
markets work and to use them effectively, you must understand how traders
specify their orders.
Understanding orders will also allow you to see where liquidity comes
from.
Liquidity
is the ability to trade when you want to trade. Some orders
offer liquidity
by presenting other traders with trading opportunities. Other
orders
take liquidity
by seizing those opportunities. Trader decisions to of-
fer or take liquidity therefore affect market quality. To understand liquidity,
you must understand how traders form their order submission strategies.
This chapter will show you what orders are, how traders specify them,
and, most important, what properties they have. Traders choose orders with
properties that allow them to best solve their trading problems.
Familiarize yourself with the many trading terms introduced in this chap-
ter. We will use them throughout the book. Traders use specialized words
and phrases to communicate quickly and accurately with each other.
Whether you intend to trade or simply want to learn about trading, you
need to be familiar with market nomenclature.
Although order instructions have the same meanings in all markets, their
properties differ according to the type of market to which traders submit
them. In this chapter, we will assume that traders submit their orders to a
continuous trading market
that arranges trades as orders arrive. Identical or-
ders have slightly different properties in
call markets
that collect and process
all orders at the same time. We examine call markets and the properties of
orders submitted to them in chapter 6.
4.1 WHAT ARE ORDERS, AND WHY DO
PEOPLE USE THEM?
Orders are instructions that traders give to the brokers and exchanges which
arrange their trades. The instructions explain how they want their trades to
be arranged.
An
order
always specifies which instrument (or instruments) to trade,
how much to trade, and whether to buy or sell. An order may also include
68


## Page 82

CHAPTER 4 ORDERS AND ORDER PROPERTIES • 69
conditions that a trade must satisfy. The most common conditions limit the
prices that the trader will accept. Other conditions may specify for how long
the order is valid, when the order can be executed, whether it is okay to par-
tially fill the order, where to present the order, and how to search for the
other side. Some orders even specify the traders with whom the trader is
willing to trade.
Orders are necessary because most traders do not personally arrange their
trades. Traders who arrange their own trades—typically dealers—do not use
orders. They decide on the spot what they want to do and how to do it. All
other traders must carefully express their intentions ahead of time.
For many small traders, it is not economical to continuously monitor the
market. These traders use orders to represent their interests when they are
not paying close attention to the market.
Traders who arrange their own trades have an advantage over traders
who use orders to express their intentions. The former can respond to mar-
ket conditions as they change. The latter must anticipate such changes and
write contingencies into their orders to deal with them. Carefully written
orders will adequately represent traders' interests even when conditions
change. When orders do not do so, traders must cancel them and submit
new instructions. During the time it takes to cancel and resubmit orders,
traders can lose because their old orders trade before they can cancel them,
or because they cannot submit new orders in time to take advantage of the
changing market conditions. Traders therefore must carefully specify their
intentions when they use orders to trade.
In general, traders who can respond most quickly to changes in market
conditions have an advantage over slower traders. Traders who submit and
cancel orders manually are slower than traders who use computers to mon-
itor and adjust their orders. Where speed is of the essence, floor traders and
computerized traders are the most successful traders.
Clear and efficient communication is essential when trading in fast mar-
kets. Brokers must understand exactly what traders want. Otherwise, ex-
tremely costly errors may occur. To avoid mistakes, most traders use stan-
dard orders to decrease the probability that they will misunderstand each
other when communicating quickly. All traders recognize and understand
these orders.
This chapter introduces the standard orders and describes their proper-
ties. We must define some basic terms first.
4.2 SOME IMPORTANT TERMS
Traders indicate that they are willing to buy or sell by making
bids
and
offers.
Traders
quote
their bids and offers when they arrange their own trades.
Otherwise, they use orders to convey their bids and offers to the brokers or
automated trading systems that arrange their trades. Bids and offers usually
include information about the prices and quantities that traders will accept.
Traders call these prices
bid
and
offer prices.
They also use the terms
bidding
price, offering price, asking price,
or simply
bid
and
ask.
They refer to the quan-
tities as
sizes.
Prices are firm when traders can demand to trade at those prices. Prices
are
soft
if the traders who offer them can revise them before trading.
Orders generally have firm prices.
0 An Order Example
Harry wants
to sell 7,600
shares of Exxon Mobil (XOM)
at no less than 41.05 dollars
per share, but only if he can
trade during the current
trading session and only if he
can trade the entire quantity
at once. He would issue an
all-or-nothing, day order to
sell 7,600 shares of XOM,
limit 41.05 dollars.


## Page 83

70 • TRADING AND EXCHANGES
The highest bid price in a market is the
best bid.
The lowest offer price
is the
best offer
(or, equivalently, the
best ask).
Traders also call them the
mar-
ket bid
and the
market offer
(or
market ask)
because they are the best prices
available in the market. A
market quotation
reports the best bid and best of-
fer in a market. A market quotation is often called the
BBO,
which is the
acronym for
Best Bid and Offer.
Many markets continuously publicize their
market quotations.
The best bid and offer anywhere in the United States is
the
NBBO—National Best Bid and Offer.
The difference between the best ask and the best bid is the
bid/ask spread.
Traders sometimes call it the
inside spread
because the space between the
highest bid price and the lowest ask price is
inside the market.
The English
often refer to the spread as the
touch.
In sports betting markets, bettors and
bookies call it the
vi gorish.
An order
offers liquidity—or
equivalently
supplies liquidity—if
it gives
other traders an opportunity to trade. For example, suppose Joe issues an
order to buy 100 shares of IBM for no more than 100 dollars per share from
the first person to contact him before trading closes today. Joe's bid offers
liquidity because other traders now have the opportunity to sell IBM for
100 dollars per share. Joe's bid is a
day limit order
because it is valid only for
the day, and because Joe limits the price that he will pay.
Both buyers and sellers can offer liquidity. Buyers offer liquidity when
their bids give other traders opportunities to sell. Sellers offer liquidity when
their offers give other traders opportunities to buy.
The dual use of the word "offer" may seem confusing. It may refer to
an offer of an item for sale or to an offer of liquidity. If you think of
liquidity—the ability to trade when you want to trade—as a service that
you can buy or sell, the use of the word "offer" makes sense. This perspec-
tive leads to many useful insights. For example, dealers make money by
selling liquidity to their clients.
Standing orders
are open offers to trade. Joe's order will stand until some-
one sells to Joe at 100 dollars or less, the order expires at the end of the day,
or Joe cancels it. Standing orders are also called
open orders.
Since standing
orders allow other traders to trade when they want to trade, traders offer
liquidity when they have orders outstanding.
Traders who want to trade quickly
demand liquidity.
Traders
take liquid-
ity
when they accept offers—standing limit orders or quotes—that other
traders have made. If Sue is willing to sell 100 shares of IBM at 100 dol-
lars, she can initiate a trade by taking Joe's offer.
Traders who demand to trade immediately demand
immediacy.
We show
in chapter 19 (Liquidity) that immediacy is one of several dimensions of
liquidity.
A market is
liquid
when traders can trade without significant adverse ef-
fect on price. Markets with many standing limit orders and small bid/ask
spreads are usually quite liquid.
The prices at which orders fill are
trade prices.
Buy orders that trade at
high prices and sell orders that trade at low prices trade at
inferior prices.
Markets and traders sometimes treat orders differently, depending on
whether they are agency orders or proprietary orders.
Agency orders
are or-
ders that brokers represent as agents for their clients.
Proprietary orders
are
orders that traders represent for their own accounts. In many organized mar-
kets, agency orders have precedence over proprietary orders at the same price.


## Page 84

CHAPTER 4 ORDERS AND ORDER PROPERTIES • 71
After traders submit their orders to their brokers, but before their bro-
kers agree to accept them, the order is
pending.
Brokers often hold orders
pending confirmation that the account is authorized to trade. They also hold
short sale orders pending confirmation that securities can be borrowed to
settle the trade. After a broker accepts an order, but before it is filled or can-
celed, the order is
working.
4.3 MARKET ORDERS
A
market order
is an instruction to trade at the best price currently available
in the market. Market orders usually fill quickly, but sometimes at inferior
prices. Impatient traders and traders who want to be certain that they will
trade use market orders to demand liquidity. The execution of a market or-
der depends on its size and on the liquidity currently available in the market.
Small market orders usually fill immediately with little or no effect on
prices. A small market buy order will typically trade at the best (lowest) ask-
ing price, and a small market sell order will typically trade at the best (high-
est) bid price.
4.3.1 Market Orders Pay the Spread
Market order traders
pay the bid/ask spread.
To see why, imagine that Amy
uses a market buy order, followed by a market sell order, to complete a quick
round-trip bond trade. Her market buy order buys the bond for 102, when
the best bid is 100 and the best offer is 102. Her market sell order sells the
bond for 100, assuming that the best bid did not change. The total loss for
her two trades is the bid/ask spread. Since Amy pays the bid/ask spread for
two opportunities to trade immediately, her
transaction cost
per trade (ex-
clusive of commissions) is half of the spread. The spread—actually half
of the spread—is the price traders pay for immediacy when using market
orders.
You can also see that market order traders pay the bid/ask spread by
considering how Amy's trade prices differ from our best estimate of the
value of the bond. If we assume that Amy is an uninformed trader (her er-
ratic behavior seems to bear this out), the only information available to us
about the value of the bond is that a trader is willing to buy it for 100 and
another trader (perhaps the same trader) is willing to sell it at 102. With
no further information, our best estimate of value is the average of these
two prices, or 101. Using this estimate of value, we can see that Amy paid
102 for a bond worth 101. The difference of 1—which is half the bid/ask
spread—is what she paid for liquidity. When she sold it, she also paid
half the spread for liquidity because she received only 100 for a bond
worth 101.
4.3.2 Price Improvement
In markets where traders negotiate prices, market orders may sometimes
trade at better prices than the market bid and offer. Such orders receive
price improvement.
Price improvement takes place when a trader is willing
to step in front of the current best price to offer a better price to the in-
coming market order. This often happens when the spread is wide and
the incoming market order is small. Price improvement lowers the cost of
liquidity.
Market Order Example
AstroPower (APWR)
trades in
the Nasdaq market. Nasdaq
dealers are bidding 36.80
for APWR and offering it at
36.85. These quotes are
good for 500 shares on the
bid side and 400 shares on
the ask side. Bill submits a
market order to buy 200
shares of APWR. He buys all
200 shares for 36.85.
4


## Page 85

72 • TRADING AND EXCHANGES
r,tti Price Improvement Example
The market for AnnTaylor (ANN) at the New York Stock Exchange is
23.35 bid, offered at 23.45. At these prices, buyers are willing to
purchase 500 shares and sellers are willing to sell 1,000 shares. Torn
submits a market order to sell 300 shares of ANN. The ANN specialist (a
dealer who trades for his own account on the floor of the exchange) may
choose to fill the order at 23.38. If he does, Tom would receive a price
3 cents better than the 23.35 bid price at which his market sell order
would otherwise have traded.
The specialist, of course, would prefer to buy ANN at 23.35. Exchange
regulations, however, prevent him from buying at the same price at which
public traders are willing to buy. The specialist therefore must improve the
price to buy ANN.
4
4.3.3 Market Impact
Large market orders are more difficult to execute than small ones. Traders
willing to take the other side of a very large trade are often hard to find.
They may not be interested in the instrument, or they may be afraid to trade
with someone who may be well informed about the value of the instrument.
To attract traders, large impatient traders often must move prices. Large
buyers bid prices up to encourage sellers to sell to them. Large sellers offer
prices down to encourage buyers to buy from them. The premiums that
large buyers pay, and the discounts that large sellers offer, are
price conces-
sions.
When traders move prices to fill their orders, they have
market im-
pact.
(Market impact is also known as
price impact.)
Since market impact
increases with order size, it generally is the most significant cost of trading
large orders. Traders who submit large market orders often pay more than
half the bid/ask spread for liquidity.
The price impact of a market order depends on the liquidity available in
the market. In small, illiquid markets with few participants, small orders
may be difficult to execute without significant price impacts, and large or-
ders may be impossible to execute. Conversely, in large, liquid markets with
many active traders, traders may routinely execute very large orders involv-
ing millions of dollars without much price impact.
' Market Impact Example
Martha wants to buy 400 March orange juice concentrate futures contracts.
The contracts trade at the Citrus Associates division of the New York Cotton
Exchange, which is a subsidiary of the New York Board of Trade. Traders
there are currently bidding 84.20 cents per pound and offering 84.25
cents per pound. Yesterday 6,505 contracts traded for all delivery months.
Martha breaks her order into two equal market orders. She gives the
order to one broker who immediately buys 20 contracts for 84.25 cents,
then another 30 for 84.40 cents, and an additional 150 for 84.70 cents.
Later in the day, Martha gives a different broker a market order for the
remaining contracts. At that time, the market is 84.55 bid, offered at
84.60. The second broker buys 5 contracts for 84.60 cents, 75 contracts
for 84.80, another 45 contracts for 85.00, and the remaining 75 contracts
for 85.15. To complete her trades, Martha's brokers had to raise the price
by 0.90 cents.'14


## Page 86

csco
OET STOCK
Icsco
T
g
0
1

LAST MATCH

TODAY'S ACTIVITY
Price

19.7400

Orders

11,336
Time

11.57:26

Volume

4,416 ,987
BUY ORDERS

SELL ORDERS
SHARES

PRICE

SHARES

PRICE
92
19.7400
327
19.7600
100
19.7400
100
19.7800
100
19.7400
100
19.7900
100
19.7300
800
19.8000
100
19.7300
100
19.8000
1300
19.7200
978
19.8400
2,000
19.7200
2.5
19.8500
100
19.7200
300
19.8500
1 700
19.7100
443
19.8900
1,92.0
19.7100
557
19.8900
2.000
19.7100
1.Q.Q
19.9000
1,9129
19.7100
2
19.9200
400
19.7000
400
19.9300
15 000
19.7000
500
19.9300
2.000
19.7000
9
19.9500
(611 more)

(1,282 more)
As of 11:57:28 ET
FIGURE 4-1.
The Top of the Island ECN
Limit Order Book for Cisco
Systems at 11:57:28 ET on
January 28, 2002
Source.. www.island.corn.
Copyright 1998-2002 The Island
ECN, Inc.-Member NASD/
CSE/SPIC, The book appears in
color on the Island website.
CHAPTER 4 ORDERS AND ORDER PROPERTIES • 73
4.3.4 Execution Price Uncertainty
The prices at which market orders trade depend on current market condi-
tions. Since market conditions can change quickly, traders who use market
orders risk trading at worse prices than they expect. Economists call this
risk
execution price uncertainty.
Execution price uncertainty is due to quote
changes that may occur between the submission of an order and its execu-
tion, and to the unpredictable price concessions that may be required to fill
large orders. Traders who are concerned about this risk may prefer to sub-
mit limit orders.
4.4
LIMIT ORDERS
A
limit order
is an instruction to trade at the best price available, but only
if it is no worse than the
limit price
specified by the trader. For buy orders,
the trade price must be at or below the limit price. For sell orders, the price
must be at or above the limit price.
In continuously trading markets, a broker (or an exchange) will attempt
to trade a newly submitted limit order as soon as it arrives. If no trader is
immediately willing to take the opposite side at an acceptable price, the or-
der will not trade. Instead, it will stand as an offer to trade until someone
is willing to trade at its limit price, until it expires, or until the trader who
submitted it cancels it.
Standing limit orders are placed in a file called a
limit order book.
De-
pending on the market, a broker, an exchange, or even a dealer will main-
tain the limit order book. Figure 4-1 presents an example of a limit order
book from Island ECN.
The probability that a limit order will trade depends on its limit price.
If the limit price of a buy order is too low, the order will not trade. Like-
wise, if a sell limit price is too high, the order will not trade. Buy limit or-
ders with high prices and sell limit orders with low prices are
aggressively
priced.
Aggressively priced limit orders are the easiest limit orders to fill.
4.4.1 Limit Price Placement
Traders classify limit orders by where they place their limit prices relative
to the market.
The market
is the range of prices bounded above by the best
offer (lowest price) and below by the best bid (highest price).
A
marketable limit order
is an order that the broker (or exchange) can ex-
ecute immediately when a trader submits it. The limit price of a marketable
limit buy order is at or above the best offer. The broker therefore can arrange
to buy immediately from the seller quoting the best offer. For a sell order,
the limit price is at or below the best bid, and the broker can arrange to sell
immediately to the trader quoting the best bid.
Marketable limit orders are like market orders, except that they limit the
price concessions that brokers can make to fill them. Marketable limit or-
ders with very high limit buy prices or very low limit sell prices are essen-
tially market orders. Traders use marketable limit orders instead of market
orders to limit execution price uncertainty and to limit what they will pay
for liquidity.
Limit buy orders that stand at the best bid, and limit sell orders that
stand at the best offer, are
at the market.
The traders who submit these


## Page 87

Order prices
Bids Offers
The least aggressively priced sell orders are far
33
from the market.
32
31

These sell orders are
behind the market.
We also say
30

that they are
away from the market.
29

4---

28

The
best offer
is
at the market.
The best bid and

The space between the current best bid and offer is
best offer define

inside the market.
If a new limit order arrives here, it
the market.

makes a new market.
26

The
best bid
is
at the market.
25
24

These buy orders
are behind the market.
We also
23

say that they are
away from the market.
22
21

The least aggressively priced buy orders are far from the
market.
74 • TRADING AND EXCHANGES
TABLE 4-1.
Terms Traders Use to Describe Limit Price Placement
LIMIT PRICE PLACEMENT

BUY ORDERS

SELL ORDERS
Above the best offer

"Marketable"

"Behind the market"
At the best offer

"Marketable"

"At the market"
Between the current best bid and best offer

"In the market"

"In the market"
At the best bid

"At the market"

"Marketable"
Below the best bid

"Behind the market"

"Marketable"
orders
make the market.
Traders
make a new market
when they submit orders
that improve the current best bid or offer. Buyers make a new market when
they raise the best bid, and sellers make a new market when they lower the
best offer. Whenever traders place their limit orders
in the market
(between
the current best bid and best offer), they make a new market.
Limit orders that stand behind the best bid or offer are
behind the mar-
ket.
Such orders are
away from the market.
A buy order is behind the market
if its limit price is less than the best bid.
A sell order is behind the
market if its
limit price is above the best offer. These orders are "behind
the market" because traders who maintain manual order books place tickets
for these orders behind those of more aggressively priced orders. A summary
of this nomenclature appears in table 4-1.
To summarize, marketable limit orders are the most aggressively priced
limit orders. The next most aggressive orders are those which make a new
market, followed by orders that match the market at the best bid or offer.
The least aggressive limit orders stand behind the market. Figure 4-2 illus-
trates these relations.
FIGURE 4-2.
Terms Traders Use to Describe Standing Limit Orders
Note: This figure presents a simplified limit order book for a hypothetical market.
The market is "26 bid, offered at 28" because the best bid is 26 and the best
offer is 28.


## Page 88

CHAPTER 4 ORDERS AND ORDER PROPERTIES • 75
4.4.2 Standing Limit Orders Are Trading Options
That Offer Liquidity
Traders who submit standing limit orders offer liquidity to other traders.
Their limit orders give others the ability to trade when they want to trade.
Since traders can choose whether they want to trade with a standing
limit order, standing limit orders are options to trade. In particular, sell limit
orders are call options that give other traders opportunities to buy when
they want to buy. Buy limit orders likewise are put options that give other
traders opportunities to sell when they want to sell. The option strike prices
are the limit prices.
Knowing that standing limit orders are options is very useful. This ob-
servation will help you understand what liquidity is, why brokers are essen-
tial in many markets, why markets consolidate (why traders like to trade in
the same place), how traders choose their order submission strategies, what
the minimum price change (tick) should be, and why fast traders have an
advantage over slower traders. Since this concept is very important, we need
to understand the option characteristics of limit orders thoroughly. We will
first consider how limit orders differ from option contracts, and then what
factors determine the value of a limit order option.
Standing limit orders are options, but not option contracts. An
option
contract
is an option to trade an instrument at a specified price that a
writer
sells to another trader. The buyer of the contract pays a
premium
to the
writer regardless of whether the buyer ultimately exercises the option. In
contrast, traders who write standing limit orders do not sell them to other
traders. Instead, they freely grant their options to the market. Unlike with
option contracts, traders do not own the trading options that limit orders
represent. Any trader who wants to exercise a limit order option may do so
by submitting a market order or a marketable limit order.
The
option value
of a limit order is the value of the order to other traders.
Limit order option values depend on the limit price, how long the orders
will stand, and price volatility.
The most important variable is the limit price. If the limit price is far
behind the market, the option to trade has little value because traders pre-
fer to trade first with orders that offer better prices. Conversely, if the limit
price is at the market, traders who want to trade immediately will value the
option to trade. If the order offers an exceptionally good price, traders will
jump to take its price, and the order will fill quickly.
Limit order option values also depend upon how long traders expect the
orders will stand. Orders that will remain available for a long time allow
traders to defer their trading decisions. When presented with such options,
traders like to wait and see what happens before committing to trade. If val-
ues rise, traders will try to buy from limit sell orders, and they will ignore
limit buy orders. Conversely, if values fall, they will try to sell to limit buy
orders, and they will ignore limit sell orders. When prices change substan-
tially, these trades will be most profitable. Because values change more in
long intervals than in short intervals, limit order trading options are more
valuable when traders expect that they will remain available for a long time
rather than for a short time.
Traders cannot wait too long to trade with valuable limit orders, how-
ever. Valuable limit orders often disappear quickly because other traders take
them, or because the traders who write them cancel them. Limit orders that


## Page 89

76 • TRADING AND EXCHANGES
Option Contracts Are Sold Limit Orders
Traders who are willing to honor their limit orders until some future date
can sell their orders to other traders by writing option contracts. The traders
to whom they sell their orders pay them for the liquidity that they offer.
To sell limit orders, traders must give up their right to cancel them at any
time. No one would buy an order that the seller could cancel at any time.
Traders sell limit orders when they write option contracts. Option
contracts give the holder the right to exercise the limit order at any time
before the contract expires. Traders are willing to buy option contracts
because the exclusive right to exercise a limit order at any time before its
expiration date can be quite valuable.
Some traders who issue standing limit orders would be better off if they
sold option contracts instead. For example, suppose that you want to offer
to sell shares in Allied Arctic Authentic Apparel (AAAA) at 100 dollars
while you are away exploring the Arctic ice pack. Rather than submit a
limit order to sell at 100, you should consider selling (writing) a call option
with a strike price of 100. In both cases, you will probably sell at 100 if
the price of AAAA rises above 100 before you return. If you submit the
limit order, you will receive only 100. If you sell the option, you will receive
100 dollars plus the option premium.
The choice is not as simple as it seems, however. Suppose that the price
of AAAA rises above 100 and then falls below 100 while you are away. If
you submitted a sell limit order, you probably would have sold at 100
when the price went above 100. If you sold a call option, however, the
buyer might not have exercised the option when the price went above 100.
The buyer certainly would not have executed the option after the stock fell
below 100. If the buyer does not exercise the option, you will still own the
stock, plus, of course, the option premium. 41
are close to the market therefore are valuable to traders who want to trade
immediately, but have little value to traders who want to wait and see what
happens before deciding to trade.
Price volatility,
which refers to how fast prices change, also affects limit
order option values. Limit order options are valuable in volatile markets be-
cause volatility increases the probability of executing a profitable trade with
a limit order. In volatile markets, values may change substantially before
traders can cancel their orders. Traders who can execute limit orders quickly,
before traders cancel them, value limit orders more in volatile markets than
in stable markets.
Since traders do not like to give away option values, they often place
their limit orders far from the market when the market is volatile in order
to reduce their option values. Bid/ask spreads consequently tend to be wide
in volatile markets.
4.4.3 The Expected Compensation
for Offering Liquidity
The compensation that limit order traders hope to receive for giving away
free trading options is a better price. Buyers who submit standing limit or-
ders hope to buy at the bid. If they had submitted market orders instead,
they would pay the higher asking price. Sellers likewise hope to receive the
ask instead of the lower bid.
They do not always realize their hopes. Limit order traders receive
better prices only if their orders trade. If the market moves away from their


## Page 90

CHAPTER 4 ORDERS AND ORDER PROPERTIES • 77
orders, they may never trade. If they still want to trade, they will have to
chase the price
by raising their bid or lowering their offer. Thus, the trade
prices that they ultimately obtain may be worse than the prices they would
have obtained had they used market orders.
In chapter 14, we show that spreads adjust so that traders expect to ob-
tain better average prices by using limit order strategies than by using mar-
ket order strategies. The average difference in prices is their compensation
for offering liquidity. If there were no compensation for offering liquidity,
traders would not offer it.
4.4.4 The Risks of Using Standing Limit Orders
Traders face two risks when using standing limit orders. The first is
execu-
tion uncertainty.
When prices move away from their orders, limit order
traders fail to trade and wish that they had. Traders who are certain that
they want to trade are very concerned about this risk.
Limit order traders can minimize execution uncertainty by aggressively
pricing their limit orders to increase the probability that they will trade.
Traders who want to be very sure that they trade should submit market or-
ders. These strategies reduce execution uncertainty, but they increase the
cost of trading.
The second risk that traders face when using standing limit orders is that
they may trade and subsequently regret it. This happens when prices move
toward and through their limit prices (i.e., down for a buy order and up for
a sell order). Their orders fill, and then the market moves against their newly
established positions so that they lose money. Economists call this risk
ex post
regret.
All traders face ex post regret regardless of how and why they trade.
Ex post regret particularly concerns dealers who trade only to profit from
offering liquidity to impatient traders. Dealers do not like to trade with
traders who know more about future values than they do. When they do,
more often than not the dealers will be on the wrong side of the market. If
prices change before they can restore their original positions, they will lose
money. Economists call this risk
adverse selection risk.
Adverse selection risk
is the most important cause of ex post regret.
Limit order traders can minimize the risk of ex post regret by placing
their orders far from the market. This strategy gives them better prices,
should their orders execute, but it decreases the probability that they will
trade. Alternatively, they may place their orders close to the market to in-
crease the probability that they trade quickly. This strategy reduces the time
that they are exposed to adverse selection risk, but it worsens the price they
receive if their orders trade.
In later chapters, we will discuss ex post regret in several different con-
texts. It arises when traders employ clever strategies to extract limit order
option values (chapter 11), when bluffers try to persuade other traders to
offer liquidity foolishly (chapter 12), when dealers trade with better-
informed traders (chapter 13), and when large traders pose as small traders
(chapter 15).
4.4.5 Limit Orders Represent Absent Traders
Many traders use limit orders to tell their brokers the conditions under
which they would be willing to trade. Their brokers then monitor the mar-
ket on their behalf to determine when these conditions are met. By ex-
Chasing the Price with
a Limit Order
Jill wants to buy 500
shares
of General Motors. The
market for GM at the New
York Stock Exchange is
currently 48.05 bid, offered
at 48.11. Jill submits a limit
order to buy 500 shares of
GM for no more than 48.05
dollars per share. The price
of GM, however, rises, and
her order does not execute.
After the market moves to
48.15 bid, 48.20 asked, Jill
cancels her limit order and
replaces it with a market
order that immediately trades
for 48.20 dollars per share.
Had her original limit order
traded at 48.05, Jill would
have paid 6 cents less than
she would have if she had
initially submitted a market
order. As it happened,
she ended up paying
9 cents more.


## Page 91

78 • TRADING AND EXCHANGES
Ex Post Regret Example
Andy is a dealer who trades S&P 500 index futures contracts overnight in
the GLOBEX electronic trading system. He typically submits limit orders to
buy at the bid and to sell at the ask. He makes money when he is able to
buy at the lower bid price and sell at the higher ask price.
At 2:30
A.M.
Chicago time, Andy submits a limit buy order for 20
contracts at the best bid of 901.05. The market is 901.05 bid, offered at
901.20. Including Andy's order, the total quantity at the best bid is 30
contracts.
Five minutes later, a market sell order for 50 contracts arrives. The
GLOBEX computer immediately matches this order with the orders totaling
30 contracts at 901.05. It then fills the rest of the order with 12 contracts
bid at 901.00 and with 8 more contracts at 900.90. Following these
transactions, the market is 900.90 bid, offered at 901.20. In the next
minute, traders submit new buy and sell limit orders so that the market
spread closes to 900.95 bid, 901.00 offered.
Andy lost money. He bought 20 contracts at 901.05. If he needed to
sell them immediately, he would receive only 900.95 for them. Since the
nominal value of the S&P 500 contract is 250 times the index value, Andy
has lost 500 dollars (250 x 0.10 dollar per contract x 20 contracts). He
places a limit sell order at 901.00 and hopes the market does not fall
further.
4
Limit Order Example
Sandy wants to buy five July
sugar futures contracts if their
price drops below 10 cents
per pound. The contract
trades at the New York
Coffee, Sugar and Cocoa
Exchange. Rather than watch
the sugar market herself, she
gives her broker a limit buy
order with a 10-cent limit
price. Her broker then
watches the sugar market for
her while she attends to other
business.
pressing their orders, traders can participate in the markets while they
attend to business elsewhere.
4.5 STOP ORDERS
A
stop instruction
stops an order from executing until price reaches a
stop
price
specified by the trader. Traders attach stop instructions to their orders
when they want to buy only after price rises to the stop price or sell only
after price falls to the stop price. Orders with stop instructions are called
stop orders.
Traders can attach stop instructions to any type of order. They most
often attach them to market orders. Once they are activated, brokers and
exchanges treat stop orders like all other orders. In particular, a stop order
remains valid even if price crosses back over the stop price.
Traders most commonly use stop orders to stop their losses when prices
move against their positions. For example, suppose that Stan buys 10 cot-
ton futures contracts at 80 cents a pound. To limit the potential loss on this
position, Stan may issue a market sell order for 10 contracts with a stop
price of 70 cents. If cotton drops to or below 70 cents, Stan's broker will
immediately try to sell 10 contracts at the best price then available in the
market. Traders often call such orders
stop
loss orders.
The price at which a stop order executes may not be the stop price. In
the above example, if cotton prices fall quickly, the market order may trade
at a price substantially below the 70-cent stop price. For example, if the
price of cotton drops from 72 cents to 67 cents on news that the harvest
will be greater than expected, Stan's broker may be able to sell the 10 con-
tracts for only 66.90 cents.
To guarantee a trade at a particular price, a trader must purchase an op-
tion contract. In our example, if Stan wants to guarantee that he can sell


## Page 92

CHAPTER 4 ORDERS AND ORDER PROPERTIES • 79
OP
Stop Limit Order
Example
Suppose that you would like
to buy Ashton Technology
Group only if its price rises to
10 dollars, and then only if
you can buy it for less than
10.37. You would submit a
limit buy order with a limit
price of 10.37 and a stop
price of 10: "Buy 100 ASTN,
10 stop, 10.37 limit."
for 70 cents, he must buy a put option contract with a strike price of 70
cents. This contract transfers the risk of selling for less then 70 cents from
Stan to the writer (seller) of the put contract. The transfer makes good eco-
nomic sense if the writer is a better trader than Stan is, or if the writer has
some reason for wanting to be short a put.
4.5.1 Stop Orders and Limit Orders
Novices often confuse stop orders with limit orders because both specify
price conditions. The difference lies in the purpose of the specified price.
A stop instruction provides for the activation of an order when the market
price reaches or passes a specified stop price. In contrast, a limit order can
be executed only at a price equal to or better than a specified limit price.
To clarify the distinction, consider how a market sell order with a stop set
at 5 euros differs from a limit sell order with a limit price of 5 euros. A
trader would typically submit the stop sell order when price is above 5 euros.
It will activate only when the market price drops to the 5-euro stop price.
At that time, it becomes a standard market order, and it should trade at the
best price then available. In contrast, a trader would typically submit a limit
sell order when price is below 5 euros. It can be filled only if price rises to
5 euros or more.
When traders attach a stop instruction to a limit order, they must spec-
ify two prices. The stop price indicates when the limit order becomes ac-
tive, and the limit price indicates the terms upon which a trade may be
arranged. The combined order is a
stop
limit order.
Stop orders are like limit orders in one respect. Both order instructions
allow traders to tell their brokers conditions under which they are willing
to trade in the future. As noted earlier, traders who specify such instruc-
tions do not need to monitor the market, and thus are free to attend to other
business.
4.5.2 Stop Orders and Liquidity
Stop orders accelerate price changes. Prices often change because traders on
one side of the market demand more liquidity than is available. When these
price changes activate stop orders, the stop orders unfortunately contribute
to the one-sided demands for liquidity. Stop orders accelerate price changes
by adding buying pressure when prices are rising and selling pressure when
prices are falling. They demand liquidity when it is least available. Traders
say that stop orders
add momentum
to the market.
Traders who pursue
momentum trading strategies
buy when prices are ris-
ing and sell when prices are falling. Momentum trading therefore destabi-
lizes prices. Momentum traders often implement their strategies by giving
stop orders to their brokers, who then monitor the market on their behalf.
Alternatively, they may monitor the market themselves and submit standard
orders when they want to trade.
Contrarian traders
employ the opposite trading strategy. They buy when
prices are falling and sell when prices are rising. They therefore stabilize
prices when they trade. Contrarians can implement their trading strategies
by using standard limit orders.
The destabilizing effects that stop orders and momentum strategies have
on the market concern many regulators, traders, and exchanges. We also


## Page 93

80 • TRADING AND EXCHANGES
consider this issue when we discuss front runners in chapter 11, market ma-
nipulation in chapter 12, and extreme volatility in chapter 28.
4.6 MARKET-IF-TOUCHED ORDERS
A
market-if-touched order
(MIT) is a market order that is activated when
price reaches (touches) some preset
touch price.
In contrast to stop orders,
traders submit market-if-touched orders to buy when prices fall to their
touch prices or to sell when prices rise to their touch prices.
Market-if-touched orders are like standing limit orders because traders
can use both order types to buy when prices drop and to sell when prices
increase. They differ from limit orders because, upon activation, market-if-
touched orders become market orders that will fill at the best available price.
In contrast, limit orders must trade at their limit price or better. Otherwise,
they will not trade. Traders use market-if-touched orders instead of limit
orders when they want to be sure that they will trade if prices reach their
touch prices. Table 4-2 summarizes differences among orders with various
price contingencies.
Market-if-touched-orders are quite uncommon. Most traders use limit
orders rather than market-if-touched orders when they want to wait for
prices to move in their favor.
Market-if-touched orders demand liquidity in a narrow sense and sup-
ply liquidity in a broader sense. When they are triggered, they become stan-
dard market orders that demand immediacy. However, they supply liquid-
ity in a broader sense because they offer liquidity to traders who push prices
to their touch prices. They thus decrease the price impacts of other traders.
Traders who issue market-if-touched orders stabilize the market because
they trade against the motion of the market. They buy when the market is
falling and sell when it is rising.
TABLE 4
-
2.
Differences Among Sell Orders with Various Price Contingencies Set at £5
MARKET PRICE

WHEN THE
ORDER TYPE

USAGE

ON SUBMISSION

ORDER CAN TRADE

TRADE PRICE
After price rises to or

At or above the limit
Standing limit sell
order with a £5
limit price
Market stop sell
order with a £5
stop price
Limit-stop sell
order with a C5
limit price and a
£5 stop price
Market-if-touched
sell order with a
C5 touch price
above the limit price
After price falls to
the stop price
After price falls to
the stop price and
then when price is at
or above the limit
price
After price rises to or
above the touch
price
price
Whatever the market
will bear after the
order is activated
At or above the limit
price
Whatever the market
will bear after the
order is activated
Common

Below £5
Occasional

Above
C5
Rare

Above
£5
Very rare

Below £5


## Page 94

CHAPTER 4 ORDERS AND ORDER PROPERTIES • 81
4.7 TICK-SENSITIVE ORDERS
Traders classify prices by their relation to previous prices. Price is on an
uptick
if the current price is higher than the last price, a
downtick
if lower,
and a
zero tick
if the same. They further classify zero tick prices by the last
different price. A zero tick price is on a
zero downtick
if the last different
price was higher and a
zero uptick if
it was lower.
Traders who want to condition their orders on the last price change sub-
mit
tick-sensitive orders.
A
buy downtick order
can be filled only on a downtick
or zero downtick price. The trade price must be lower than the last differ-
ent price. Likewise, a
sell uptick order
can be filled only on an uptick or zero
uptick. The trade price must be higher than the last different price.
When brokers receive tick-sensitive orders, they immediately check to
see whether they can match them with other orders without violating their
tick conditions. If this is not possible, they hold these orders until an op-
portunity to fill them arises.
4.7.1 Tick
-
Sensitive Order Properties
The tick condition ensures that tick-sensitive orders have no market im-
pact. A broker holding a buy downtick order cannot bid up prices to en-
courage sellers. Instead, the broker must wait until someone is willing to
trade at
a
price lower than the last different price. Likewise, a broker can-
not fill a sell uptick order by offering the market down. Since tick orders
cannot have market impact, traders cannot use them to demand liquidity.
Since they allow other traders to trade when they want to trade, tick-
sensitive orders supply liquidity.
Tick-sensitive orders are essentially limit orders with dynamically ad-
justing limit prices. A buy downtick order implements the following equiv-
alent limit order strategy: Submit a buy limit order just below the last dif-
ferent price. If price rises, raise the limit price to a price just below the new
price. If price falls, leave the limit price alone. This strategy is attractive to
traders who want to keep their limit orders close to the market when prices
move away from them.
Dynamic limit order submission strategies are almost impossible to im-
plement effectively in fast markets. Traders must continuously monitor the
market and immediately cancel and resubmit their limit orders whenever
prices change. Most traders cannot cancel and resubmit quickly enough to
trade these strategies effectively. Traders therefore use tick-sensitive orders
when they want to use dynamic limit order strategies.
Tick-sensitive orders are most attractive when the minimum price in-
crement is large. The
minimum price increment—also
called the
tick
or the
minimum price variation—is
the smallest amount by which two prices
can differ. It is usually set by exchange regulations. Traders who use tick-
sensitive orders forgo taking liquidity in exchange for a price that typically
is just one tick better. The strategy therefore is more attractive when the
tick is large.
The decimalization of the U.S. stock markets in 2000 decreased the min-
imum price increment for most stocks from one-sixteenth dollar (6.25 cents)
to
1
cent. This change made tick-sensitive orders much less attractive than
they used to be.
Tick Examples
The last trade price for Coca-
Cola (KO) is 56.05 dollars.
The previous trade price is
56.08. KO is on a downtick
because 56.05 is less than
56.08. KO now trades three
times, all at 56.05. KO is
now on a zero downtick. You
now try to sell KO for 56.07.
If you succeed, you will sell
on an uptick.
Tick-Sensitive
Order Example
You want
to buy American
Skiing (SKI) at less than the
ask price, but you are not
available to cancel and
resubmit your orders if prices
rise. To achieve your
objectives, you submit a buy
downtick order to your
broker.
When your broker
receives your order, SKI is
on a zero uptick at
1.02.
Your broker cannot buy SKI
for you until price drops to or
below 1.01. Unfortunately,
price rises to
1.03.
Now the
maximum price your broker
can pay is 1.02. Your
broker continues to watch
the market. When a market
sell order arrives, you buy
for 1.02.


## Page 95

82 • TRADING AND EXCHANGES
4.8 MARKET-NOT-HELD ORDERS
Market-not-held orders
are orders that brokers do not need to fill immedi-
ately. Traders use this order instruction to tell their brokers to use their dis-
cretion when filling their orders. The brokers then can offer liquidity or take
liquidity according to current market conditions.
Traders give their brokers market-not-held orders when they want their
brokers to make order strategy decisions on their behalf. Brokers are often
better traders than their clients are because they have more experience, and
because they generally know more about current market conditions.
When brokers decide to wait to obtain better prices, they risk trading at
worse prices if prices move away from them. Since prices often move away
from even the best of brokers, traders who give their brokers discretion over
order submission strategy cannot reasonably hold their brokers accountable
when they fail to trade at prices that subsequently look attractive. Traders
submit market-not-held orders to indicate that they will not hold their bro-
kers accountable for failing to trade.
Traders who submit standard market orders can hold their brokers ac-
countable for failing to trade if suitable opportunities were available. Mar-
ket order traders often can use legal remedies to compel their brokers to
provide them with the executions that they would have received had their
brokers not been negligent. Traders who submit market-not-held orders do
not have these rights. Their only recourse is to withhold future orders from
brokers who do not provide them with consistently good service.
Traders most often use market-not-held orders when they give their bro-
kers large orders to fill. Large orders, if exposed all at once, can have very
large market impacts. To minimize these price impacts, brokers carefully
control the exposure of large orders by revealing only small portions and
only to traders they deem likely and able to trade the other side. Market-
not-held orders ensure that brokers will not be held accountable for failing
to trade if prices move away while they are waiting for better prices or for
more size.
Traders most commonly give market-not-held orders to brokers who work
on the floor of an exchange. Floor traders generally know more about mar-
ket conditions than do any other traders. Some traders also give market-not-
held orders to brokers who operate
electronic order desks
that use complex
econometric models to formulate optimal order submission strategies.
4.9 VALIDITY AND EXPIRATION
INSTRUCTIONS
Traders specify
validity and expiration instructions
to indicate when their or-
ders are valid and when their orders expire. Although traders may specify
these instructions for all order types, they are especially important for stand-
ing limit orders and stop orders. These orders generally do not trade im-
mediately upon submission. Some may never trade. Traders therefore need
to tell their brokers what to do with their unfilled orders.
Open orders
are orders that have not yet been executed or canceled. A
good order
is an order that can be executed. Traders generally do not distin-
guish between open orders and good orders because all good orders are open
orders. Although most open orders are also good orders, some are not. For


## Page 96

CHAPTER 4 ORDERS AND ORDER PROPERTIES • 83
example, an order to buy stock after some specified date is an open order,
but before that date it is not a good order.
Day orders
are valid for the trading day on which traders submit them.
They expire when the market closes if they have not been filled. The day
order instruction is the most common validity instruction. If it is not spec-
ified, most brokers assume that an order is a day order.
Good-till-cancel
(GTC)
orders
are valid until the trader expressly cancels
them. To ensure that traders do not forget about their GTC orders, many
brokers provide their clients with a list of their unfilled GTC orders at the
end of every month. They also often mail written confirmations of these
orders on the day that traders submit them. Some brokers cancel GTC or-
ders after a month or two in order to avoid the costs of keeping track of
stale orders. Traders should be careful not to forget their good-till-cancel
orders. They may trade as long as they remain open.
Good-until orders
are good until a date specified by the trader. Not all
brokers accept this order instruction because it requires that they keep track
of the expiration date.
Good-this-week
(GTW) and
good-this-month
(GTM)
are special cases of good-until orders.
Immediate-or-cancel orders
(IOC) orders are orders that are valid only
when they are presented to the market. Whatever portion of the order that
cannot be filled immediately is canceled. Traders use immediate-or-cancel
orders when they do not want to give trading options to the market. In
some markets, immediate-or-cancel orders are
called fill-or-kill
orders
(FOK)
or
good-on-sight orders.
Good-after orders
are good only after some specified date. These orders
are quite rare. Most brokers will not accept them unless you are a very im-
portant customer.
Market-on-open orders
are market orders that a broker can fill only at the
beginning of the trading session. Traders use these orders primarily in mar-
kets that open with a single price auction. In such markets, a market-
on-open order usually guarantees that the trader will receive the opening
price. Since these orders are usually easy to execute, brokers sometimes
charge lower commissions than they would for a regular market order.
Market-on-close orders
are market orders that a broker can fill only at the
close of the trading session. Traders who submit these orders often do so
because they hope to trade at the closing price. Closing prices are particu-
larly attractive to mutual funds because they value their funds for deposits
and redemptions by using net asset values computed from closing prices.
Brokers will not guarantee that market-on-close orders will fill at the clos-
ing price, however. Traders who want such guarantees can buy them from
their dealer-brokers by paying higher commissions. (See table 4-3.)
4.10 QUANTITY INSTRUCTIONS
Traders specify quantity instructions to indicate whether their brokers can
arrange multiple trades to fill their large orders. They usually do so to min-
imize the costs that they pay to clear and settle their trades.
The most common quantity instructions are all-or-none instructions and
minimum-or-none (minimum acceptable quantity) instructions. Brokers
must fill
all-or-none
(AON) orders all at once. They can arrange multiple
trades to fill
minimum-or-none orders,
but each trade must be larger than a


## Page 97

TABLE 4
-
3.
Order Validity and Expiration Instructions
84 • TRADING AND EXCHANGES
INSTRUCTION
USAGE
GOOD
EXPIRES
Immediate-or-cancel (IOC);
Good-on-sight;
Occasional
When submitted
Immediately following
submission
Fill-or-kill (FOK)
Day order
Common
When submitted
End of day
Good-this-week (GTW)
Rare
When submitted
End of week
Good-this-month (GTM)
Rare
When submitted
End of month
Good-until
Rare
When submitted
On specified date
Good-till-cancel (GTC)
Common
When submitted
Never
Good-after
Rare
After specified date
Not specified
Market-on-open
Occasional
At open
After open
Market-on-close
Occasional
At close
After close
It
■
Rolling a Futures
Position with a
Spread Order
In early June, Fulvio wants to
roll a long S&P 500 Index
futures position from the
expiring June contract into the
September contract. He issues
a spread order to sell the
June contract and to buy the
September contract. The last
trade prices for the June and
September contracts were
respectively 1276 and 1289,
which implies a spread of
13. As is usual, the back
month trades at a premium to
the current month.
Fulvio places a limit of 10,
premium to the buy side, on
the spread order. The spread,
however, never drops to his
limit, and the order does not
execute. Fulvio subsequently
replaces his limit spread
order with a market spread
order. The order then
executes at a spread
of 12.
4
minimum size that
the trader specifies. In some markets, these two in-
structions are also known as
all-or-nothing
and
minimum acceptable quantity
(MAQ) instructions.
4.11 OTHER ORDER INSTRUCTIONS
4.11.1 Spread Orders
Traders issue
spread orders
when they want to buy one instrument and si-
multaneously sell another instrument. The two instruments usually are
closely related. For example, they may be futures contracts for different de-
livery months of the same commodity. Spread orders can be market orders
or limit orders.
When the spread order is a limit order, the trader specifies a limit for
the difference between the two prices that he or she is willing to accept.
Traders always specify the limit as a premium to the buy side or to the sell
side. If you want to sell one instrument at a higher price than you want to
pay to buy the other instrument, the premium is to the sell side. The order
can be filled only if the difference between the sales and purchase prices is
greater than or equal to the limit. If the instrument you want to buy is priced
above the instrument that you want to sell, the premium is to the buy side.
In that case, the order can be filled only if the difference between the pur-
chase and sales prices is less than or equal to the limit.
4.11.2 Display Instructions
Traders give
display instructions
when they want to specify how their bro-
kers should display unfilled portions of their standing limit orders. These
instructions typically tell brokers to show no more than some maximum
quantity. Traders restrict the display of their orders when they fear that show-
ing their full sizes would cause the market to move away from them. Or-


## Page 98

CHAPTER 4 ORDERS AND ORDER PROPERTIES • 85
Undisclosed Order Example
Michael wants to sell 90,000 shares of Alcatel Cable at the Euronext Paris
Bourse. He is willing to display only 10,000 shares at a time because he is
afraid that revealing his full size might move the market down. He places
an undisclosed limit order to sell 90,000 shares at 408 francs, with
instructions to display only 10,000 shares. His order makes a new market.
Liza sees the offer to sell 10,000 shares at 408 francs. She is willing to
buy 60,000 shares, but she does not want to reveal this information. She
knows that more shares may be hidden behind the 10,000 displayed
shares. Liza submits a fill-or-kill limit order to buy 60,000 shares for 408
francs. She can be sure of trading at least 10,000 shares. If she does not
trade the full 60,000 shares, the remainder of her order will be canceled.
When the Bourse's automated trading system receives Liza's order, it
immediately fills the entire order. Liza and Michael both will receive reports
that they have traded 60,000 shares. The 30,000-share remainder of
Michael's sell order will stay in the system with 10,000 shares displayed at
408 francs and another 20,000 undisclosed shares behind it.
ders that are not fully displayed are called
undisclosed orders.
Traders also call
them
hidden orders,
or
reserve orders
(because they hold some size in reserve),
or
iceberg orders
(because other traders can see only the top of the order).
To fill an undisclosed order, a broker must fill the exposed portion first.
As the order fills, the broker can then expose more size up to the maximum
display quantity. This process continues until the broker fills the entire
order.
Some electronic exchanges, such as the Euronext, permit traders to is-
sue undisclosed limit orders. Traders who want to trade with these orders
can discover their full sizes only by submitting large marketable orders with
fill-or-kill instructions attached. If the size is present, they trade. If it is not,
their orders cancel.
4.11.3 Substitution Orders
Traders give
substitution orders
to their brokers when they want to invest or
divest a specified amount of money by trading any of several securities. The
brokers then use their discretion to choose which securities to trade, based
on which ones appear to provide the best prices.
4.11.4 Special Settlement Instructions
Traders attach
special settlement instructions
to their orders when they want
nonstandard settlement. In U.S. equity markets,
regular-way settlement
occurs three days after the trade (T +3). Traders who want to settle on a
different date attach special settlement instructions to their orders.
The most common nonstandard settlement is
cash settlement.
Cash set-
tlement trades settle that day. Traders typically specify cash settlement when
they decide at the last moment that they want to be a shareholder of record
before a vote or before some distribution.
Traders also use special settlement instructions when they pursue divi-
dend capture strategies. We describe these strategies in chapter 8.
Orders with special settlement instructions are more difficult to trade
than are regular orders. Traders who want special settlement cannot simply
Op
Substitution
Order Example
Steve wants the stock portfolio
he manages to have more
automobile industry exposure.
He does not care whether he
buys Ford, General Motors,
or Chrysler, as long as he
gets a good price for his
purchase. Steve instructs his
broker to buy 35 million
dollars of Ford, General
Motors, or Chrysler. The
broker examines the trading
in these stocks.
He
learns that
a large buyer is accumulating
GM and pushing prices up.
He therefore buys Ford and
Chrysler to fill the order.


## Page 99

86 • TRADING AND EXCHANGES
Cash Settlement Example
Today is Thursday. In two weeks, ABC will spin off its XYZ division to its
shareholders. All shareholders of record tomorrow will receive one share of
XYZ for every share of ABC that they own. You would like to own both
companies, but presently you hold no shares in ABC. If you buy ABC today
with normal three-day settlement, your trade will settle on Tuesday, and you
will not receive the XYZ shares. If you buy ABC today with cash settlement,
you will be a shareholder of record tomorrow, and you will own both
companies.
With regular-way settlement, the last day that you could have bought
ABC and still have been entitled to receive the distribution was two days
ago (Tuesday). On that day, the price reflected the values of both
companies. On Wednesday, the price of ABC fell because buyers were no
longer entitled to receive XYZ. The price you pay for your cash purchase on
Thursday will therefore be substantially higher than the normal-way price
because you will receive two companies instead of one.
1
demand it. Instead, they must negotiate for it. If no one wants to provide
special settlement, these orders will not fill. Some brokers charge higher fees
for special settlement because of the greater costs of arranging and settling
these trades.
4.12 SUMMARY
Traders use orders to communicate their intentions to the brokers and ex-
changes that arrange their trades. The most important and most common
order types are market orders and limit orders. Traders use market orders
when they want to trade immediately at the best price the market will bear.
They use limit orders when they want to place limits on the prices at which
they are willing to trade.
Orders differ by their associated uncertainties. Traders who use market
orders are uncertain about the prices at which they will trade. Conversely,
traders who use limit orders are uncertain about whether they will trade.
Orders also differ by whether traders use them to supply or demand liq-
uidity. Traders who use limit orders grant trading options to the market be-
cause they allow other traders to trade when they want to. Limit order traders
therefore supply liquidity to the market. Traders who exercise these options
take liquidity. Traders generally use market orders when they want to take
liquidity. Table 4-4 provides a summary of the properties of the various or-
ders considered in this chapter.
Since limit orders typically supply liquidity and market orders typically
demand liquidity, we must understand how traders make their order sub-
mission decisions in order to understand the origins of market liquidity. In
future chapters, we will carefully consider how traders make these decisions.
4.13 SOME POINTS TO REMEMBER
•
Traders use stylized order instructions to reduce communication
errors.
•
Market order traders demand immediacy.
•
The bid/ask spread is the cost of immediacy for small market orders.


## Page 100

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## Page 101

88 • TRADING AND EXCHANGES
•
Large market orders can have substantial and unpredictable price
impacts.
•
Limit order traders supply liquidity by granting trading options to other
traders.
•
Limit order traders who must fill their orders can lose substantially if
their orders do not fill when the market moves away from them.
•
Some traders use limit orders to participate in the market when they
are not present.
•
Stop orders are not limit orders.
•
Stop orders tend to destabilize prices.
4.14 QUESTIONS FOR THOUGHT
1.
When should a trader use a limit order as opposed to a market
order?
2.
Under what circumstances would a trader use a stop buy order?
3.
Under what circumstances should you be unwilling to expose a trad-
ing option?
4.
How would you expect order instructions to change with the inven-
tion of new electronic communications technologies?
5.
Since limit orders and quotes provide trading options to the market
as a whole, what effect would an increase in volatility have on bid/ask
spreads?
6.
Should limit order traders place their orders close to or far from the
market when they cannot adjust their limit prices quickly?
7.
Some electronic systems allow traders to issue indexed limit orders.
Indexed limit orders
have limit prices that are linked to an index so
that the limit price adjusts as the index changes. Why might traders
prefer such orders?
8.
Do contrarian traders offer or take liquidity when they trade using
market orders?
9.
Why are there no buy uptick and sell downtick orders?
NEW STAMP

## Page 102

5
I>
Market
Structures
T
he trading rules and the trading systems used by a market de
fi
ne its
market structure.
They determine who can trade; what they can trade; and
when, where, and how they can trade. They also determine what informa-
tion traders can see about orders, quotations, and trades; when they can see
it; and who can see it.
Market structure is extremely important because it determines what peo-
ple can know and do in a market. Since power comes from knowledge and
the ability to act on it, market structure helps determine power relations
among various types of traders. These relationships greatly affect who will
trade profitably.
To trade effectively, you need to know the structure of every market in
which you trade. The trading strategies that are successful in one market
often do not work well in markets with different structures. The best order
submission strategy for a given trading problem generally depends on the
structure of the market where the trader intends to solve the problem.
Traders therefore behave differently in different markets.
You must understand market structure, and how it affects trader behav-
ior, in order to understand the origins of market liquidity, price efficiency,
volatility, and trading profits. These variables all depend on trader behav-
ior. Since market structure affects trader behavior, it helps to determine
whether markets will be liquid, whether prices will be informative, and which
traders will trade profitably.
We will introduce and describe a framework for classifying market struc-
tures. This classification scheme will help you recognize how markets are
similar and dissimilar. Being able to classify market structures will be use-
ful to you because trading problems have similar solutions in similar mar-
kets. If you understand how to trade in one market, you should be able to
apply your knowledge and experience to similar markets. We will use this
classification system throughout the rest of this book.
We will start by discussing the different types of trading sessions that
exchanges, brokerages, and dealers organize. We then will discuss the vari-
ous execution systems that traders use to arrange their trades. Finally, we
will describe the information-processing systems that transmit orders into
and out of markets, present market information to traders and to the pub-
lic, and store open orders.
5.1 OVERVIEW
Trading takes place in
trading sessions.
The two types of trading sessions are
continuous trading sessions and call market sessions. In
continuous trading,
traders can attempt to arrange their trades whenever the market is open. In
call markets,
all trades take place only when the market is called.
89


## Page 103

90 • TRADING AND EXCHANGES
Physically Convened
Screen-based Markets
Although screen-based trading
systems are ideally suited for
distributed access markets,
many Asian exchanges with
screen-based trading systems
once required their traders to
be on their trading floors to
use their electronic systems.
This arrangement made it
easier for exchanges to
regulate their traders. It also
allowed them to construct
reliable communications
networks, which was once an
important issue in countries
with poor telecommunications
infrastructures.
Many traders like to
trade in physically convened
markets because they enjoy
the society of other traders.
Though exchange regulations
no longer require them to be
there, many traders have
stayed on the exchange
floor.
4
Trading forums
are the places where traders arrange their trades. In
phys-
ically convened markets,
traders must be on a
trading floor
to negotiate their
trades. Physically convened futures markets trade in
trading pits.
Physically
convened stock markets trade at
posts.
In
distributed access markets,
traders
use telephones or screen-based trading systems to arrange their trades from
their offices.
Some countries require that traders arrange all trades in a given instru-
ment at a particular exchange. For example, with few exceptions, it is ille-
gal to arrange trades in a Chicago Board of Trade corn futures contract out-
side of the corn futures trading pit on the CBOT floor. These restrictions
are common in many futures markets and in the equities markets of some
Asian and Eastern European countries.
Traders and exchanges use various
execution systems
to arrange trades. In
quote-driven systems,
dealers arrange most trades when they trade with their
customers. In
order-driven systems,
all trades are arranged by using
order prece-
dence rules
to match buyers to sellers and
trade pricing rules
to determine the
prices of the resulting trades. In
brokered trading systems,
brokers arrange
trades by helping buyers and sellers find each other.
Various systems move information in and out of the market, present it,
and store it.
Order-routing systems
send orders from customers to brokers,
from brokers to dealers, from brokers to markets, and from markets to mar-
kets. These systems also send reports of filled orders back to customers.
Order presentation systems
present orders to traders so that they can act upon
them. The systems may use
screen-based, open-outcry,
or
hand-signaling
tech-
nologies.
Order books
store open orders.
Market data systems
report trades and
quotes to the public.
In most markets, traders can use only prices that are an integer multiple
of a specified
minimum price increment.
The size of the increment, measured
as a fraction of price, varies considerably across markets. In chapter 11, we
show that the increment is an extremely important determinant of market
quality in many markets.
5.2 TRADING SESSIONS
Markets have
trading sessions
during which trades are arranged. The two types
of trading sessions are continuous market sessions and call market sessions.
5.2.1 Continuous Markets
In
continuous markets,
traders may trade anytime the market is open. Trad-
ing is continuous in the sense that traders may continuously attempt to
arrange their trades. In practice, they usually trade only when a trader de-
mands liquidity.
Continuous trading markets are very common. Almost all major stock,
bond, futures, options, and foreign exchange markets have continuous trad-
ing sessions.
5.2.2
Call Markets
In
call markets,
all traders trade at the same time when the market is called.
The market may call all securities simultaneously, or it may call the securi-
ties one at a time, in a
rotation.
Markets that call in rotation may complete
only one rotation per trading session or as many rotations as their trading


## Page 104

CHAPTER 5 MARKET STRUCTURES • 91
Price Clustering
Traders do not use all possible prices equally. Instead, their usage clusters
on round numbers. In markets with fractional prices, they use whole
numbers more often than halves, halves more often than odd quarters,
quarters more often than odd eighths, and eighths more often than odd
sixteenths. In markets with decimal prices, prices that are integer multiples
of 1.00, 0.50, 0.25, 0.20, 0.10, and 0.05 are most common. The
clustering of prices is most pronounced when the minimum price increment
is a small fraction of price, the market is highly volatile, and the instrument
is thinly traded.
Clever traders often consider the clustering of limit order prices when
they place limit orders. They frequently place their orders just above or just
below a round number to take advantage of the fact that many other
traders may place their prices at the round number.
hours permit. Markets that call in rotation were once very common. Now
only the stock markets of a few small countries call in rotation.
Many continuous order-driven exchanges open their trading sessions
with call market auctions and then switch over to continuous trading. These
markets also use calls to restart their trading after trading halts. Open-
outcry futures exchanges, however, start continuous trading immediately
when they open.
Call markets are used as the exclusive market mechanism for many in-
struments. Most governments sell their bonds, notes, and bills in call mar-
ket auctions. Some stock markets use calls to trade their least active secu-
rities. The Deutsche Borse and Euronext Paris Bourse are examples of such
markets.
Call markets usually arrange their trades using order-driven execution
systems. They most commonly use batch execution systems, but a few call
markets allow bilateral trading. Markets with
batch execution systems
arrange
all trades at the same time by matching orders with order precedence rules.
These rules usually arrange
multilateral trades
that involve more than one
buyer and more than one seller. In markets that allow
bilateral trading,
traders
arrange their trades among themselves.
5.2.3 Call Versus Continuous Trading Sessions
The main advantage of call markets is that they focus the attention of all
traders interested in a given instrument at the same time and place. When
buyers and sellers search for liquidity at the same time and place, they can
easily find each other.
The main advantage of continuous trading is that it allows traders to at-
tempt to arrange their trades whenever they want. This flexibility can be
very important to impatient traders who do not want to wait for the next
market call.
Recent developments in the equities markets suggest that traders prefer
continuous markets with opening calls to exclusive call markets. Many na-
tional equity exchanges have switched from call market rotations to con-
tinuous trading with opening calls, but none has changed from continuous
trading to exclusive call markets.
Call Market Betting at
the Horse Races
U.S. horse racetracks offer
pari
-
mutuel betting
to their
betting clients. In
pari-mutuel betting, bettors
receive a share of the total
money bet on all horses—less
a fixed percentage for the
track and the state—if their
horse wins. Bettors can place
their bets anytime until betting
closes a few moments after
the start of the race. The track
totalizator
system displays the
projected winnings for each
bet while the bettors place
their bets.
Pari-mutuel betting is a call
market auction in which the
totalizator simultaneously
prices all bets on a race. (The
price of each bet is the
amount bettors must bet to
receive a dollar if their horse
wins.) The call occurs when
betting closes. Since the
system allows only market
orders, many traders wait
until the last moment to bet so
that they can see what the
prices will be.
1


## Page 105

92 • TRADING AND EXCHANGES
5.2.4 Trading Hours
A
market's trading hours specify
when the
market accepts orders and arranges
trades. Continuously trading markets schedule their regular trading sessions
during normal business hours. They often open an hour or two after the
start of the business day and close an hour or two before the end of
the business day. The New York Stock Exchange and the Nasdaq Stock
Market, for example, open at 9:30
A.M.
and close at 4
P.M.
Eastern Time.
Traders use the hours before the open to collect and submit orders. They
use the hours after the close to settle trades and to report the results to
clients.
Some markets trade at odd hours within their time zone so that they can
be open during normal hours in another time zone. The Pacific Exchange
opens trading at 6:30
A.M.
Pacific Time and the Chicago Stock Exchange
opens trading at 8:30
A.M.
Central Time to coincide with the New York
Stock Exchange opening at 9:30
A.M.
Eastern Time. Since many currency
markets trade around the clock, foreign exchange traders often keep very
unusual hours.
Some markets permit trading after normal hours in special
after-hours
trading sessions.
Exchanges provide these sessions to appeal to clients in other
time zones or to permit traders to
clean up
(adjust) their positions after the
regular trading session. For example, the Chicago Board of Trade, the
Chicago Mercantile Exchange, and the FINEX division of the New York
Cotton Exchange all run nighttime trading sessions in many of their con-
tracts. Many electronic trading networks provide
extended trading hours
for
their clients.
5.3 EXECUTION SYSTEMS
Every market has procedures for matching buyers to sellers. These proce-
dures define the
execution system
of the market. Since the execution system
is the defining characteristic of a market, analysts frequently classify mar-
kets by their execution systems. The three main types of markets are quote-
driven markets, order-driven markets, and brokered markets.
Hybrid mar-
kets
use some combination of
these three systems.
5.3.1 Quote
-
driven Dealer Markets
In pure
quote-driven markets,
dealers participate in every trade. Anyone who
wants to trade must trade with a dealer. Either traders negotiate with the
dealers themselves, or their brokers, acting as their agents, negotiate with
the dealers. The dealers frequently trade among themselves, but public
traders cannot trade with each other. For example, if Barbara wants to buy
a security, she must find a dealer who will sell it to her from his or her
in-
ventory.
Likewise, if Saul wants to sell a security, he must find a dealer who
will buy it from him to add to his or her inventory. Although Barbara might
be willing to buy the security directly from Saul, in a pure quote-driven mar-
ket they generally cannot arrange such trades. Instead, they trade indirectly
with each other through the intermediation of one or more dealers. Such
markets are called
quote-driven markets
because the dealers quote the prices
at which they will buy and sell. They are also known as
dealer markets
be-
cause dealers supply all the liquidity.
P Market Sonar
Like the deep sea, markets
are often quite opaque. In
continuous markets, traders
can
ping
the market to obtain
information about the prices
at which other traders will
trade. Traders ping by
submitting orders to see what
happens to them.
4
I
■
Lunchtime Recess
Some continuous markets stop
trading for lunch. They divide
their trading days into a
morning
session
and an
afternoon session.
The Tokyo
Stock Exchange is the largest
exchange to stop for lunch.
At exchanges that trade
through lunch, traders either
skip lunch or eat it on the
trading floor (or at their
desks) so that they do not
miss anything.
Intraday trading activity at
the NYSE and in Nasdaq
generally is lowest at
lunchtime. Low lunchtime
volumes may simply reflect
the fact that lunchtime is
midway between the market
open and the market close,
both of which are very active
for a variety of reasons.
Alternatively, at lunchtime
people may be more
interested in lunch than in
trading.
4


## Page 106

CHAPTER 5 MARKET STRUCTURES • 93
Bookies Are Dealers
Most sport betting markets are quote-driven dealer markets in which bookies
are dealers. The bookies try to maintain a balanced book with equal
volumes bet on both sides. They profit from small differences in the payoffs
that they offer each side. The difference between these payoffs is the
vigorish
or juice. It is essentially a bid/ask spread. The vigorish for a
typical football point spread bet is 1 dollar for a 10-dollar bet. A perfectly
balanced book ensures that bookies earn 1 dollar for every 20 dollars bet.
Bookies work hard to balance their books. Interest in a game is
rarely balanced, and point spreads and odds change in response to new
information about the contest. Bookies with lopsided books will often lay off
some of their risk by placing offsetting bets with other bookies. The bets are
costly because they have to pay the vigorish.
When bookies have balanced books, their only risk is that their losing
clients will not pay up. Dealers minimize their credit risks by carefully
screening their clients. They limit the credit that they extend to clients, and
they require that their least creditworthy clients post substantial sums to
cover their potential losses. Ruthless bookies also minimize their credit losses
by threatening the kneecaps of their deadbeat clients. 1
In some dealer markets, traders can trade with each other without the
direct intervention of a dealer. Although these are not pure quote-driven
markets, they are still known as quote-driven dealer markets because deal-
ers supply most of the liquidity and arrange all the trades. The Nasdaq Stock
Market is an example of a quote-driven market in which dealers often bro-
ker trades among public traders.
In most dealer markets, dealers and their customers choose each other
when they want to trade. The customers—or their brokers acting as their
agents—choose dealers who offer good prices and good service. The deal-
ers trade only with traders they believe are trustworthy and creditworthy.
Traders who do not have credit relationships with dealers trade through bro-
kers who guarantee that they will settle their trades. Many dealers special-
ize in serving clienteles such as small retail traders or large institutional
traders. Such dealers may refuse to trade with customers who are not among
their preferred clientele. Most dealers also try to avoid trading with cus-
tomers they believe are well informed about future prices, because they
often lose to well-informed traders.
In some dealer markets,
interdealer brokers
help dealers arrange trades
among themselves. Many dealers do not like their rivals to know about their
trades. By allowing dealers to trade with each other anonymously, interdealer
brokers protect dealers and their clients from predatory actions by rivals.
Quote-driven dealer markets are very common. Almost all bond and cur-
rency markets, and many stock markets, are quote-driven markets. Most
dealer markets are informal networks of dealers who communicate with their
clients and among themselves by telephone. More structured dealer mar-
kets usually have proprietary electronic data systems to facilitate communi-
cations with and among dealers. The Nasdaq Stock Market, the London
Stock Exchange, the eSpeed government bond trading system, and the
Reuters 3000 foreign exchange trading system are examples of quote-driven
markets organized, respectively, by a dealer association, an exchange, a bro-
ker, and an electronic data vendor.


## Page 107

94 • TRADING AND EXCHANGES
You Can't Tell the Players Without a Scorecard
Brokers and dealers established the first exchanges as membership
organizations. These exchanges provided them with a common place to do
their business and with rules to regulate it. The members arranged all the
trades, either for themselves or for their clients.
Many exchanges eventually adopted rule-based order-matching systems
that arrange trades between buyers and sellers. Initially, exchange members
or clerks operated these systems. Now many exchanges use computers to
arrange trades. When exchanges adopted rule-based order-matching
systems, they became more than just a place to do business under
regulatory supervision. They essentially became brokerages.
Many brokerages have created similar rule-based order-matching systems
to provide low-cost service to their customers. These brokerages therefore
look very much like many exchanges. They grant their clients trading
privileges instead of memberships.
Recently, entrepreneurs have created many for-profit companies that offer
rule-based order-matching systems. These
electronic communications
networks
(ECNs) look like exchanges and operate like brokers. Many of
these firms also develop software and provide network communications.
Many exchanges are converting from membership organizations to for-
profit companies. They want the flexibility to compete with for-profit
organizations that have streamlined control structures.
Most government regulations distinguish between brokers and
exchanges. The practical differences between these organizations, however,
are increasingly unclear, and therefore are often a subject of regulatory
debate. Although exchanges invaded the brokerage business before brokers
invaded the exchange business, this chronology provides little guidance for
deciding how best to regulate these organizations.
5.3.2 Order
-
driven Markets
In
order-driven markets,
buyers and sellers regularly trade with each other
without the intermediation of dealers. These markets have
trading rules
that
specify how they arrange their trades. Their
order precedence rules
determine
which buyers trade with which sellers, and their
trade pricing rules
deter-
mine the trade prices.
Most order-driven markets are
auction markets.
In an auction market, the
trading rules formalize the process by which buyers seek the lowest avail-
able prices and sellers seek the highest available prices. Economists call this
the
price discovery process
because it reveals the prices that best match buy-
ers to sellers.
In order-driven markets, traders can offer or take liquidity. Traders who
offer liquidity indicate the terms at which they will trade. Traders who take
liquidity accept those terms.
Dealers can—and often do—trade in order-driven markets. In pure
order-driven markets, they trade on an equal basis with all other traders. In
some order-driven markets, dealers provide most of the liquidity. These mar-
kets are still known as order-driven markets because the dealers cannot
choose their clients. Instead, the exchange rules require that they trade with
anyone who accepts their offers.
Order-driven market structures vary considerably. Some markets conduct
single-price auctions
in which they arrange all trades at the same price fol-


## Page 108

CHAPTER 5 MARKET STRUCTURES • 95
lowing a market call. Other markets conduct
continuous two-sided auctions,
in which buyers and sellers can continuously attempt to arrange their trades
at prices that typically vary through time. Still others conduct
crossing net-
works,
in which they match orders at prices taken from other markets.
Order-driven markets vary considerably in how they implement their
trading rules. In markets that conduct
oral auctions,
traders negotiate their
trades face-to-face on an exchange floor. The trading rules in these markets
determine who can negotiate and when they can negotiate. These auctions
are also known as
open-outcry auctions
because the traders cry out their bids
and offers. In markets that have
rule-based order-matching systems,
the mar-
kets use rules to match orders that traders submit to them. Most order-
matching markets use electronic systems to match buy and sell orders au-
tomatically. Their trading rules are coded in their order-processing software.
Order-matching markets that still have manual operations use clerks to
match their orders.
The rules used by order-driven markets are extremely important because
they affect market liquidity. Some trading rules encourage traders to offer
liquidity while others discourage them. In chapter 6, we will consider how
rules affect trading strategies in order-driven markets.
Since order-driven markets use order precedence rules to arrange trades,
traders cannot choose with whom they trade. They therefore often trade
with traders with whom they have no credit relationships. To prevent set-
tlement failures, order-driven markets have elaborate mechanisms to ensure
that all their traders are trustworthy and creditworthy. We consider these
mechanisms in chapter 7.
Order-driven markets are quite common. All markets that conduct elec-
tronic auctions or open-outcry auctions are order-driven markets. These in-
clude all major futures exchanges, most stock and options exchanges, and
many trading systems created by brokerages and ECNs to organize trading
in stocks, bonds, swaps, currencies, and pollution rights. Governments com-
monly issue their new debt securities in order-driven market calls.
5.3.3 Brokered Markets
Brokers actively search to match buyers and sellers in
brokered markets.
Most
searches start when their clients ask them to fill their orders. Brokers, how-
ever, also initiate many searches when they suggest trades to their clients.
The distinguishing characteristic of a brokered market is the broker's role
in finding liquidity. In markets where traders usually do not make public
offers to trade, brokers must search for traders who will make those offers.
These markets are typically illiquid markets in which dealers will not nor-
mally trade.
Two types of traders offer liquidity in brokered markets.
Concealed traders
know that they want to trade but, for reasons that we discuss in chapters
11 and 15, they will not expose orders to the public. They offer their
liquidity when brokers present them with suitable trading opportunities.
Latent traders
do not know that they want to trade until brokers present
them with attractive trading opportunities. They thereby avoid the costs of
deciding whether they want to trade until they have the opportunity to trade.
Latent traders are common when trading decisions are costly and the op-
portunities to trade on those decisions are rare. Good brokers can find con-
cealed traders and latent traders.
The World's Most
Prolific Market
Organizer
eBay has created more
markets than any other
market organizer. At any
given moment, eBay is
conducting pure order-driven
auctions for millions of items.
In 2001, more than 30
million traders were registered
to participate in its markets.
Most of eBay's auctions
are call market auctions to
which buyers can submit only
limit orders. When the
auction closes, the highest
bidder wins. The "buy it now"
feature of some eBay markets
allows buyers to take a
seller's offer before the
auction closes.
eBay does not guarantee
settlement among its traders.
Traders therefore must be
careful when they send
money to sellers they do not
know and trust. Although
many frauds have occurred,
the settlement failure rate is
very small. To help traders
check credit, eBay provides a
rating service that allows
traders to rate each other and
to see those ratings. Traders
who are especially concerned
about settlement can hire
escrow services.


## Page 109

96 • TRADING AND EXCHANGES
Brokered markets are very common throughout the economy. They usu-
ally arise when the item traded is unique and when dealers will not hold
inventories. The most important brokered securities markets are those for
large blocks of stocks and bonds. Although these securities may trade in
very liquid markets for small sizes, brokers must find suitable counterparts
for most large blocks. Real estate markets and markets for going business
concerns are additional examples of brokered markets. In all three types of
markets, dealers generally will not take positions because the items are too
large and trade too infrequently, and order-driven markets are not viable be-
cause traders will not issue standing orders. We consider how block trading
markets operate in chapter 15.
5.3.4 Hybrid Markets
Hybrid markets
mix characteristics of quote-driven, order-driven, and bro-
kered markets. For example, although the New York Stock Exchange is
essentially an order-driven market, it requires its specialist dealers to offer
liquidity if no one else will do so. The NYSE therefore has elements of a
quote-driven market. The Nasdaq Stock Market is also a hybrid. Although
essentially a quote-driven market, it requires its dealers to display, and in
many circumstances to execute, public limit orders. Nasdaq therefore has
some elements of an order-driven market. Since brokers sometimes arrange
large block trades in both of these markets, they also have some character-
istics of brokered markets.
5.3.5 Summary
The execution system that a market uses to arrange its trades is the most
important characteristic of its market structure. Table 5-1 summarizes the
differences among the primary execution systems.
TABLE 5
-
1.
Execution System Summary
MARKET
TYPE
CLASSIFICATION
WHO OFFERS
LIQUIDITY?
WHO
ARRANGES
TRADES?
HOW ARE BUYERS
AND SELLERS
MATCHED?
COMMON
EXAMPLES
Dealer
markets
Quote-driven
Dealers
Dealers
Clients (or their
brokers) choose
dealers
OTC markets in
currencies, bonds,
and stocks
Oral
auctions
Order-driven
Dealers and
public limit
order traders
Traders
Trading rules
regulate
negotiations
Floor-based stock,
futures, and
options auctions
Order-
Order-driven
Traders who
Brokerages
Trading rules
Electronic
matching
systems
issue limit
orders
or exchanges
match orders
exchanges and
automated
brokerage
systems
Brokered
markets
Brokered
Public
traders
Brokers
Brokers match
traders
Block trading


## Page 110

CHAPTER 5 MARKET STRUCTURES • 97
5.4 MARKET INFORMATION SYSTEMS
Markets employ many systems to transmit, organize, present, and store in-
formation about orders, quotes, and trades. These systems are important be-
cause they determine what traders see and when they see it. We will briefly
examine these systems and consider how new communications technologies
are changing the markets.
5.4.1 Information Collection Systems
Markets produce information about instrument values, transactions, who
has traded, who wants to trade, and the terms on which they are willing to
trade. This information is very valuable. Traders use it to predict where prices
are going and how much trading will cost. Who has access to market in-
formation, when they can access it, and the form in which they can access
it greatly influence who will trade profitably and who will pay high trans-
action costs.
Most markets collect information from their trading systems. They pre-
sent some information to their traders, they sell some to data vendors, and
they save most for regulatory purposes.
Market data sales are often very lucrative. At many major exchanges,
data revenues account for more than 20 percent of total exchange revenues.
Markets with electronic trading systems can easily collect any market in-
formation they want because all information is already in electronic form.
In contrast, floor-based markets, telephonic markets, and other manually
operated markets must create special systems to collect information. Since
information is valuable and manual collection is quite expensive, electronic
systems have a strong cost advantage over other systems.
Many floor-based exchanges employ clerks called
market reporters
to ob-
serve and report on trading activity. The reporters enter trade prices into
electronic data systems. They may also report quotes and trade sizes. The
exchange trading rules usually require traders to ensure that the market re-
porters know what they are doing. In large futures markets, the reporters
sit on podiums above the trading pit so they can see all the traders. Some
reporters may stand in the crowd and use hand signals to pass information
to the reporters on the podium. In many markets, reporters use handheld
wireless devices to report information.
Some telephonic markets and some floor-based markets require traders
to report their trades to their electronic trade reporting systems. Traders
usually must report within a fixed time. For example, in the Nasdaq Stock
Market, dealers must report within 90 seconds of trading when they arrange
trades by telephone.
Many over-the-counter dealer markets have no formal organization.
Market data vendors often try to collect market information from the deal-
ers in these markets. Either the dealers provide the information for a fee,
or they provide their information on the condition that they can access the
information the other dealers provide. These arrangements appear in some
bond markets.
In dealer markets where interdealer brokers help dealers arrange trades
among themselves, the brokers collect much information about market con-
ditions. If the dealers object to how a broker distributes that information,
they may refuse to trade through that broker.
Archipelago's
Integrated Book
The Archipelago
ECN
distributes its
entire limit
order book, integrated with
those of several other ECNs,
via the Internet, free of
charge. The system uses a
Java applet to display the
book in real time.
Source: www.tradearca.com/
automm/arca book. asp.


## Page 111

98 • TRADING AND EXCHANGES
5.4.2 Information Distribution Systems
Markets distribute the information they collect to their members and to the
public. In most markets, members can access more information than can
unaffiliated traders. They also may obtain it faster.
Market data systems
report trades and quotes to the public. Markets sell
this information to various
data vendors,
who repackage it for distribution to
the public. Customers may buy real-time services or time-delayed services.
Customers who subscribe to
real-time services
can receive data as it is gener-
ated, but they must pay additional fees to the exchanges for these services.
Customers who subscribe to
time-delayed services
receive the data with a con-
stant 5-, 10-, 15-, or 20-minute delay. Exchanges offer both services in order
to price discriminate among those who require immediate information and
those who are less time sensitive. Table 5-2 lists the monthly fees that some
exchanges charge for providing real-time data to nonprofessional subscribers.
Data vendors offer broadcast services and query services.
Broadcast ser-
vices
provide continuous streams of information.
Price and sale feeds
and
ticker
tapes
broadcast trade prices and sizes.
Quotation feeds
broadcast quotations.
Query services
provide information on demand. Users submit requests for
information to the vendor's data server. The server then looks up the in-
formation and reports it. To fill these requests, the data servers receive and
archive broadcast information from market feeds. Some data vendors sell
software programs that allow users to run their own data servers. Traders
use these programs when their electronic proprietary trading systems re-
quire extremely fast query services.
Data vendors such as Bloomberg, Bridge Information Systems, PC
Quote, and Reuters reformat market information to make it more useful to
their customers. Traders, for example, do not have time to watch a ticker
tape when they want to see the latest prices and quotes for various instru-
ments. Instead, they subscribe to broadcast systems that present and con-
tinuously update this information on specially designed
pages.
Customers
can customize their pages to show only the information that interests them.
Securities Information
Processors (SIPS)
In the U.S. equity markets,
almost all markets, dealers,
and trading systems must
send their trades and quotes
to a
securities information
processor
(SIP), which then
distributes the consolidated
information to data vendors.
The
Securities Industry
Automation Corporation
(SIAC) is the SIP for
exchange-listed stocks, and
the NASD is the SIP for
Nasdaq and OTC stocks.
44
TABLE 5
-
2.
Monthly Exchange Fees for Real-time Data for Nonprofessional Subscribers
EXCHANGE
FEE
American Last Sale & Bid/Ask
1.00
Canadian Consolidated Equities (Alberta, Montreal, Toronto, and Vancouver)
9.50
Chicago Board of Trade and MidAmerica Commodity Exchange
30.00
Chicago Mercantile Exchange
60.00
COMEX, Inc.
60.00
Commodity Exchange Center (Coffee, Sugar, & Cocoa Exchange, New York Cotton Exchange,
and New York Futures Exchange)
94.00
MidAmerica Commodity Exchange (only)
7.50
Nasdaq Last Sale NMS & Bid/Ask (Level I)
1.00
Nasdaq Level II
10.00
New York Consolidated Last Sale & Bid/Ask
1.00
New York Mercantile Exchange
60.00
Options Price Reporting Authority (all U.S. options)
1.00
Note: Professional subscribers generally pay higher fees.


## Page 112

CHAPTER 5 MARKET STRUCTURES • 99
Most systems allow both graphic and numeric presentations. Data vendors
compete to provide easy-to-use information systems that deliver data in use-
ful formats at low cost.
In the U.S. equity markets, data vendors are subject Co the Vendor Dis-
play Rule. This rule requires vendors to construct consolidated quotes from
all trading venues. Without this rule, many data vendors would not include
quotes from small markets, which would make it very difficult for innova-
tive trading systems to compete for order flow.
Floor-based exchanges post information on electronic screens and boards
so that all floor traders can see the current state of the market. The ex-
changes place these displays in, around, and often above the trading pit or
post. The information that they publish on the floor is usually the same in-
formation that they provide to the public. Indeed, the same data vendors
often provide both services.
In screen-based trading systems, participants often can access more in-
formation than can the public. For example, the Toronto Stock Exchange
allows its members to see the complete composition of the system limit or-
der book. Public traders get to see only aggregate order sizes at the best five
prices on either side of the market.
5.4.3 Ticker Symbols
Exchanges and data vendors use
ticker symbols
to identify trading instru-
ments. The naming conventions vary by exchange, country, instrument class,
and data vendor. Generally, each exchange assigns ticker symbols to its in-
struments. The data vendors occasionally add their own suffixes or prefixes
to these symbols to make them more useful for their clients.
In the United States, listed common stocks have ticker symbols consist-
ing of one to three letters. The one-letter symbols are rare, and therefore
coveted for the special status they convey. Exchanges usually reserve them
for their largest firms. Nasdaq National Market System stocks all have four-
character ticker symbols, and Nasdaq SmallCap stocks have five-character
ticker symbols. U.S. open-end mutual funds have five-character ticker sym-
bols, the last character of which is always X.
Ticker symbols for U.S. futures contracts generally consist of a one- or
two-character code that indicates the commodity followed by a number that
indicates the expiration year and a letter that indicates the delivery month.
(Some systems place the month code before the year number.) For example,
LC2Z refers to the Chicago Mercantile Exchange December 2002 live cat-
tle futures contract. The second character of the commodity code can be a
number. For example, E7 is the contract code for the CME E-Mini Euro-
FX contract; thus E72H refers to the March 2002 E-Mini Euro contract.
A list of futures contract delivery month symbol codes appears in table 5-3.
TABLE 5
-
3.
Futures Contract Delivery Month Symbol Codes
MONTH
CODE
MONTH
CODE
MONTH
CODE
January
F
May
K
September
U
February
G
June
M
October
V
March
H
July
N
November
April
J
August
Q
December
Quotation Servers
on the Internet
Many
vendors offer price and
quotation query services on
the Internet. For example,
Yahoo.com
offers a free
query service at quotes.
yahoo.com
. It presents NYSE
quotes with a 20-minute
delay and Nasdaq quotes
with a 15-minute delay. PC
Quote at www.pcquote.com

provides real-time data to
subscribers who have paid
the appropriate exchange
fees.
Some Equity
Option Ticker
Symbol Examples
IBMAT—IBM January 100 call
IBMMT—IBM January
100 put
IBMIT—IBM
September
100
call
TJW—AT&T October 17.5
call
MSQVJ—Microsoft October
50 put
MQFDI—Microsoft April 45
call (Microsoft has multiple
options root symbols.)


## Page 113

100 • TRADING AND EXCHANGES
TABLE 5-4.
Equity Option Contract Expiration Month Designators
MONTH
CALLS
PUTS
MONTH
CALLS
PUTS
January
A
M
July
G
S
February
B
N
August
H
T
March
C
0
September
I
U
April
D
P
October
J
V
May
E
Q
November
K
W
June
F
R
December
L
X
The
Option Price Reporting Authority
(OPRA) assigns ticker symbols for
exchange-traded U.S. equity and equity index option contracts. These ticker
symbols have three to five characters. The first one to three characters form
the options root. They refer to the underlying instrument. The options root
is often the ticker symbol of the underlying instrument, or it may include
some portion of it. Securities with options having many strike prices often
have several options roots. Each root designates a different set of strike
prices. The second-to-last character indicates the option type and expira-
tion month. The final character indicates the option contract strike price.
The same character can indicate different strike prices. If an options class
includes contracts with strike prices that have the same option contract strike
price code, the security will have multiple option roots, and the option root
will distinguish between the contracts. Lists of equity option contract expi-
ration month designators and equity option contract strike price codes ap-
pear in tables 5-4 and 5-5, respectively.
Many equity index option contracts like the Nasdaq 100 Cube (QQQ
have many closely spaced strike prices. They therefore use several different
option root symbols, and they use a different set of strike price codes.
The symbol naming conventions that futures exchanges use for their op-
tions on futures contracts vary. Generally, the first two characters represent
TABLE 5
-
5.
Equity Option Contract Strike Price Codes
CODE
STRIKE PRICES
CODE
STRIKE PRICES
A
5
105
205
N
70
170
270
B
10
110
210
o
75
175
275
C
15
115
215
P
80
180
280
D
20
120
220
Q
85
185
285
E
25
125
225
R
90
190
290
F
30
130
230
s
95
195
295
G
35
135
235
T
100
200
300
H
40
140
240
U
7.5
37.5
I
45
145
245
V
12.5
42.5
J
50
150
250
W
17.5
47.5
K
55
155
255
X
22.5
52.5
L
60
160
260
Y
27.5
57.5
M
65
165
265
Z
32.5
37.5


## Page 114

CHAPTER 5 MARKET STRUCTURES • 101
TABLE 5-6.
Expiration Month Codes for Options on Futures
MONTH
CALLS
PUTS
MONTH
CALLS
PUTS
January
F
A
July
N
L
February
G
B
August
Q
O
March
H
C
September
U
P
April
J
D
October
V
R
May
K
E
November
S
June
M
I
December
T
the commodity, the third character represents the month and type of op-
tion, and the final two or three characters are numbers that represent the
strike price. The month codes for options on futures differ from the month
codes for equity options. (The call codes are the same as the futures deliv-
ery month codes for the futures contracts.) A list of these codes appears in
table 5-6.
5.4.4 Transparency
The data that markets release to the public are quite valuable. The public
uses these data to predict future price changes, to predict when their orders
will execute, and to evaluate their brokers' performance. Without this in-
formation, public traders would not have much confidence in the markets.
Markets vary in the degree to which they report information to the public.
Transparent markets
quickly report complete information to the public.
Markets that quickly report quotes and orders are
ex ante transparent.
Those
which quickly report trades are
ex post transparent.
The terms
ex ante
and
ex post
refer to the time of the trade. The terms
pre-trade transparency
and
post-trade transparency
are also used.
Opaque markets
are not transparent.
Markets that report only the best bid and offer show the
top of the book.
Markets that report bids and offers at multiple prices show the
market by
price.
These markets have
open limit order books.
The degree of transparency varies across markets. Table 5-7 shows that
U.S. equity and equity options markets are quite transparent. U.S. futures
markets are ex post transparent, but not ex ante transparent. In these oral
auctions, quotes generally do not stand long enough to be reported. Over-
the-counter corporate and municipal bond markets are generally quite
opaque. The U.S. government bond markets are more transparent. In these
markets, interdealer bond brokerages such as Cantor Fitzgerald provide in-
formation to the public.
Traders are often ambivalent about transparency. They favor transparency
when it allows them to see more of what other traders are doing, but they
oppose it when it requires that they reveal more of what they are doing.
Generally, those who know the least about market conditions most favor
transparency. Those who know the most oppose transparency because they
do not want to give up their informational advantages.
Markets produce an enormous volume of information, much of which is
redundant. Traders say that the first half of that information is much more
important than the second half. Being able to see just a little of what is
Transparency and
Dealer Profitability
Large U.S. investment banks
generally make more money
trading bonds than stocks.
The greater transparency of
the U.S. stock markets may
help explain this result. When
customers know current
market conditions well, they
negotiate better prices with
their dealers.


## Page 115

102 • TRADING AND EXCHANGES
TABLE 5
-
7.
Market Transparency in Some U.S. and International Markets
MARKET

CURRENT QUOTES

TRADE REPORTS
The best bid and offer immediately
No other prices or quantities
All dealer quotes immediately
Reported only by your broker's
representative on the floor
Generally not reported
Provided by various interdealer
bond brokers
Provided by various data vendors
Aggregate quantities of all orders at
the five best prices on both sides of
the market
All displayed order size, aggregated
by price for continuous markets, less
information for single price auctions
All orders
All dealer quotes in SEAQ(dealer
market) stocks, the best bid and
offer in SETS (electronic order
book) stocks
U.S. equity and equity
options exchanges
Nasdaq
U.S. futures markets
U.S. OTC corporate and
municipal bond markets
U.S. government bond
markets
Foreign exchange
markets
Toronto Stock
Exchange
Deutsche Borse
Euronext Paris Bourse
London Stock
Exchange
All trades immediately and in
no event later than 90 seconds
All
trades immediately and in
no event later than 90 seconds
All price changes immediately
Only through some bond
brokers
Provided by various interdealer
bond brokers
None
All trades immediately
All Xetra trades immediately;
very large privately negotiated
trades may never be reported
All trades immediately
All small trades immediately;
larger trade reports are delayed
going on is extremely important to traders who would otherwise be in
the dark.
5.4.5 Order Routing Systems
Order-routing systems
transmit orders. Customers use them to send orders
to their brokers and dealers, brokers use
them to send customer orders to
dealers and to exchanges, dealers use them to send orders to other dealers,
and exchanges use them to send orders to other exchanges. Traders also use
order-routing systems to transmit cancellation instructions, and exchanges
and dealers use them to report trades to their customers.
Good order-routing systems are fast and accurate. Traders demand fast
systems because they do not want to miss trading opportunities and because
they value their time. They need accurate systems because mistakes obvi-
ously can be very costly.
The strategies that traders can profitably undertake depend on the
order-routing systems available to
them. Short-term traders who implement


## Page 116

CHAPTER 5 MARKET STRUCTURES • 103
QuantEX
ITG is a U.S. equity brokerage and a supplier of electronic trading
software. The premier trading platform that it offers to its clients is QuantEX.
The QuantEX system runs on a dedicated Sun workstation. It receives real-
time trade and quotation feeds and has a high-speed electronic link to ITG's
order-routing system.
QuantEX includes a programming language that traders use to analyze,
present, and respond to real-time market data. QuantEX users program their
systems to process and display real-time data in any format that they want,
to manually or automatically generate and submit orders based on rules that
they specify, and to keep track of orders and the prices at which they trade.
Traders most commonly use QuantEX to manage large trading programs.
Some traders also use it to implement high-frequency proprietary trading
strategies.
4
Source: www.itginc.com/products/clientsite/index.html.

high-frequency trading strategies must submit and cancel orders quickly and
reliably in order to trade profitably. Either they must trade on the floor of
an exchange, or they must use fast order-routing systems, preferably with
good computer interfaces.
Traders use many technologies to transmit orders. They now send most
orders by electronic data transmission systems or by telephone. In some
places, however, traders still carry, shout, or hand-signal orders across rooms.
Electronic order-routing systems are usually faster, more accurate, and
cheaper to operate than other routing systems. Although they are often ex-
pensive to build, they are rapidly replacing other systems. All major mar-
kets, brokerages, and dealers now use electronic order-routing systems. The
main disadvantage of these systems is that they can handle only the stan-
dardized orders and instructions that they are programmed to recognize.
When traders want to issue special instructions, they typically use the tele-
phone. Traders also use the telephone when they want to talk to their bro-
kers about current market conditions before they submit their orders.
Which order-routing systems traders use depends on how often they
trade, on how large their trades are, and on whether they can physically ac-
cess an electronic communications network. Infrequent traders usually do
not use electronic systems because the fixed costs of building them and
learning to use them are large relative to their needs. Large traders often
use the telephone because they want more access to market information.
Their size gives them the power to demand attention from their brokers.
Floor brokers often receive their orders from
runners
who physically deliver
them or from order clerks who transmit them by hand signals. With the
invention of cellular telephones, wireless electronic data networks, and mo-
bile terminals, these floor brokers are also receiving electronic order flow.
Most retail traders send orders to their brokers by telephone. Increas-
ingly, many also use the Internet. Retail brokerages generally offer lower
commissions for orders entered through the Internet because they are less
costly to handle than telephone orders. Brokerages that take Internet orders
do not need to employ telephone order clerks, and they avoid mistakes that
occasionally occur when a telephone order clerk hears or records something
different from what a customer intends.
k
Lattice Trading
State Street offers an
electronic order management
system called Lattice.
The system allows traders to
route orders to the best
market as conditions dictate.
Among its many other
features, it enables traders to
create strategies to
dynamically adjust limit
orders as market conditions
change.
Source: www.statestreet.com
.


## Page 117

104 • TRADING AND EXCHANGES
Floor Trader Advantages
Floor traders have an advantage over off-floor traders because they can see
and react to market developments well before off-floor traders can. Off-floor
traders must obtain their information through market data systems, and they
must respond through order-routing systems. The best market data systems
report information in less than three seconds. The best order-routing systems
pass orders from the client to a broker in less than five seconds. If the
routing system requires a runner, the delay can be substantially longer.
These delays allow floor traders to take advantage of opportunities before
off-floor traders can.
Floor traders also can observe all market information revealed on an
exchange floor, and not just what the market data systems report. In
particular, they observe who is trading. Knowing who is trading can be
valuable if you can guess why they want to trade or whom they represent.
Floor traders and off-floor traders whose brokers can give them access to this
information therefore have a significant advantage over other traders.
The Telegraph and Hand Signaling at the
American Stock Exchange
The American Stock Exchange is the only U.S. stock exchange that permits
its traders to use hand signals to transmit orders on the exchange floor. The
practice originated when the Exchange literally used to trade on Broad
Street in lower Manhattan. It was then known as the New York Curb
Exchange because its members worked on the street curbs.
After the introduction of the telegraph, brokerages could quickly receive
orders from distant branch offices and send back trade reports. Brokerages
that had access to the telegraph had a competitive advantage over those
which did not.
Brokerages could not install their telegraph lines on the curb, however.
Instead, they installed them in offices that they rented above Broad Street.
Telegraph operators would give incoming orders to clerks who stood at the
windows. The clerks then used hand signals to transmit the orders down to
their brokers on the street, and brokers sent trade reports back through the
same system.
The Exchange moved indoors to its present site at
86 Trinity Place in
1921.
Its trading floor was constructed with galleries on both sides in
an attempt to replicate the street. The brokers' clerks—now telephone
operators—sat in the galleries and hand-signaled orders down to the floor.
With the introduction of telephones on the floor of the Exchange, hand
signaling became less common. A few traders, however, still use it
occasionally.
Source: Robert Sharp,
The Lore and Legends of Wall Street
(Dow Jones-Irwin, 1989),
ch. 46.
Institutional traders most commonly send orders to their brokers and
dealers by telephone. Large institutional traders often have several dedicated
telephone lines that connect them to the brokers and dealers with whom
they do the most business. These brokers and dealers often provide these
lines at no charge to attract more business from their large clients. Institu-
tional traders also increasingly use electronic systems to deliver their orders.
These systems may operate over the Internet or over private proprietary
networks provided by data vendors like Bloomberg, Reuters, and Bridge


## Page 118

CHAPTER 5 MARKET STRUCTURES • 105
It
,
Telegraphs, Ticker Tapes, Telephones,
and Electronic Data Networks
Innovations in telecommunications have substantially transformed trading.
Before the telegraph, all information coming into or going out of an
exchange moved no quicker than express riders could travel on horseback.
Consequently, every major city had its own exchange, and prices for the
same instruments would differ substantially across geographic regions.
The telegraph and subsequent inventions allowed traders to learn about
market conditions in other cities and to send orders to wherever the best
prices were. Although the first users were arbitrageurs, other traders soon
started routing their orders to the best markets. Markets that acquired a
reputation for being liquid attracted more orders and thereby became even
more liquid. Smaller markets then started to fail. They either quit trading or
combined to form larger markets.
This process continues today.
Every innovation
in telecommunications
has increased competition among traders and has led to market
consolidation.
Information Systems. In some cases, they also send electronic orders over
leased lines directly to their brokers and dealers.
Brokers, dealers, and exchanges communicate with each other by tele-
phone and by proprietary data networks. Almost all exchanges and orga-
nized dealer networks have electronic order-routing systems. These systems
bring orders in for processing and send out trade reports. At floor-based ex-
changes and in dealer networks, these systems deliver orders directly to the
traders who process them. At electronic exchanges, they deliver the orders
to the computerized order-matching system. Exchanges and dealers com-
pete with each other to provide low-cost order-routing systems that are
easy to use, fast, and reliable. Table 5-8 lists the primary electronic order-
routing systems used by U.S. equity, options, and futures exchanges.
From a regulatory viewpoint, the most interesting order-routing system
in the U.S. equity markets is the
Intermarket Trading System
(ITS). ITS is
an electronic system that allows traders in one market to send orders to an-
other market. It therefore plays an important role in keeping the U.S. eq-
uity markets integrated and competitive. We discuss ITS further when we
consider how exchanges compete with each other in chapter 26.
5.4.6 Order Presentation Systems
Order presentation systems
reveal information about orders and quotes to the
traders who arrange trades. Markets use several technologies to present this
information.
Markets with
screen-based trading systems
present the orders on computer
screens. These systems are becoming more common as electronic commu-
nications technologies become cheaper. Traders like screen-based systems
because they can use them anywhere and can update them easily.
Some screen-based trading systems broadcast all information presented
to all participants. Screen-based markets commonly use these systems. Oth-
ers systems, called
messaging systems,
allow traders to send private messages
to specific traders or classes of traders. Traders in dealer markets often use
these systems to negotiate their trades.
So Nasdaq Level I, II,
and Ill Services
Nasdaq provides three levels
of quotation services through
various data vendors:
•
Level I service consists of
real-time inside bid/ask
quotations for Nasdaq
Stock Market securities and
for OTC Bulletin Board
securities.
•
Level II service provides
real-time access to all
dealer quotations in these
securities.
•
Level III service is available
only to registered Nasdaq
market makers. It consists
of all Level II service
information plus the ability
to enter quotations, route
orders, execute orders, and
send messages.


## Page 119

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## Page 120

CHAPTER 5 MARKET STRUCTURES • 107
Screen-based systems are the technological successors to board-based
trading systems. In
board-based
trading systems, traders or exchange clerks
write orders on a big board for all to see. In some markets, orders for in-
frequently traded stocks are still presented on boards. Most boards, how-
ever, are now published electronically.
Bulletin boards
are information systems upon which people post indica-
tions of interest. An
indication of interest
(10I)—also known as an
order
indication—is
an expression of interest in trading. Indications usually show
the name of a broker-dealer, the name of the security, and whether the bro-
ker represents a buyer or a seller. Indications may also show prices, but they
are rarely firm. Traders use indications to show other traders that they are
interested in trading. They also sometimes use them to discover who is in-
terested in trading.
The U.S. equities markets have several popular bulletin boards. For listed
stocks, institutional traders primarily use AutEx or Bridge Information Sys-
tems to post electronic order indications. For thinly traded, over-the-counter
stocks, traders use the Nasdaq OTC Bulletin Board. For the smallest and
least frequently traded stocks, traders post their indications in the
Pink Sheets
published by the National Quotation Bureau.
Data vendors also offer electronic systems that present indications in pop-
up windows on a trader's workstation. These systems try to anticipate what
trades will interest traders' clients and direct only those indications to them.
Traders in oral auctions present their orders by yelling out bids and of-
fers on a trading floor. Floor traders like oral auctions because they are very
quick. Their communications are prone to error, however, because traders
often do not say what they intend to say and because traders often do not
hear what other traders have said.
Traders in many dealer markets also negotiate their trades orally. Since
they usually use the telephone, they cannot see the visual clues that help us
understand speech. They therefore must be careful to avoid misunder-
standing each other.
5.4.6.1 Accuracy in Oral Communications
All
traders who negotiate trades orally must communicate very accurately.
They must understand exactly what other traders say, and they must be sure
that other traders understand exactly what they say. Otherwise, very serious
and costly errors may result.
Effective communication can be very difficult when traders are in a noisy
environment, when significant distractions compete for their attention, and
when they hurry. On trading floors, traders may be yelling or pushing, sev-
eral phones may be ringing, one or more televisions may be on, ticker tapes
and news wires will be running, and clerks may be requesting or reporting
information. When the market moves quickly and everything happens all
at once, error-free communication can be quite a challenge.
Communications errors in some oral markets are quite common. In open-
outcry trading in large futures pits, the out-trade rate is often as high as
4 percent. An
out-trade
results when one trader reports a trade that does
not exactly match a report from another trader. In equity markets, un-
matched trade reports are called
DKs (Don't Know).
Traders use various systems to avoid misunderstandings. The most ef-
fective systems allow both traders to show each other what they intend on
The Big Board
The New York Stock
Exchange once presented
orders on boards hung on
the walls above its trading
floor. As a reflection of its
importance, the NYSE became
known as the "Big Board."
Although the Exchange long
ago replaced these boards
with electronic systems, its
nickname endures.
Early Video
Display Systems
The first video market
information systems were
closed-circuit video systems
that distributed real-time
images of exchange boards.
Digital systems replaced these
analog systems when
computing systems matured,
and when off-floor traders
wanted to use their computers
to manipulate market
information.


## Page 121

108 • TRADING AND EXCHANGES
See What You Say
In 1992, Lehman Brothers'
bond traders in New York
experimented with a
particularly innovative error
correction system. An
automatic voice recognition
system listened to their
telephone calls. As they
spoke, it wrote their words
and those of their
counterparts onto screens on
their desks so that they could
see exactly what they said.
This system allowed them to
recognize and correct errors
as they occurred. It also
forced them to enunciate
clearly.
Lehman Brothers
abandoned the system
because the speech
recognition technology then
available was too primitive to
transcribe speech accurately.
With recent advances in this
technology, attempts to use
this system should be more
successful.
electronic screens. Instinet, Nasdaq, and Liquidnet, among others, provide
messaging systems that equity traders can use for this purpose. Cantor
Fitzgerald's eSpeed government bond trading system allows traders to see
what they have said to their Cantor broker.
In face-to-face oral negotiations, traders sometimes use hand signs to
convey their intentions. These signals are especially common in futures pits.
Traders indicate that they are sellers by holding their palm away from them.
In effect, they push the item away. Buyers hold their palms inward, to pull
in the item. Traders convey prices and quantities by extending fingers, by
holding the hand upright or sideways, and by touching their faces in vari-
ous places.
Traders commonly express their bids and offers in fixed formats to avoid
confusion. When bidding, they express the price followed by the preposi-
tion "for" and then the quantity. A trader who shouts out "6 for 20" is a
buyer willing to pay 6 for 20 contracts. When offering, traders express the
quantity first followed by the preposition "at" and then the price. A trader
who shouts "40 at a quarter" is a seller willing to sell 40 contracts at the
price of a quarter.
To further reduce confusion, traders usually express only the last digit or
fractional portion of the price. Since the rest of the price is common knowl-
edge, they do not need to express it. This practice speeds up their commu-
nications, and it reduces the amount of noise that traders might mistake for
significant information.
Traders who trade over the telephone often repeat what they hear and
then ask for confirmation. Brokers commonly use this protocol to avoid er-
rors when receiving instructions from their clients. Most record their tele-
phone calls to help resolve any disputes that may arise from misunder-
standings.
When errors do occur, the involved traders attempt to determine who
was mistaken. Usually, one trader will recognize his error and correct it. If
The Pink Sheets, Nasdaq, and Technological Evolution
The National Quotation Bureau (NQB) started publishing weekly editions of
the
Pink Sheets
in 1913 and daily editions five years later. They are called
pink sheets because NOB prints them on pink paper. NOB now publishes a
weekly edition on paper and a daily electronic edition. Traders post their
indications by calling or faxing them to NQB.
Until the National Association of Securities Dealers organized the
original NASD Automated Quotation (NASDAQ) trading system, the
Pink
Sheets
were the primary source of market information about over-the-counter
stocks. Since the NASDAQ system allowed traders to present, access, and
exchange information faster, it largely replaced the
Pink Sheets.
The
NASDAQ system grew into the Nasdaq Stock Market and the Nasdaq
OTC Electronic Bulletin Board. The Nasdaq Stock Market now trades some
of the largest stocks in the world. The Nasdaq OTC Electronic Bulletin
Board reports indications for smaller stocks sponsored by NASD dealers.
The
Pink Sheets
now report order indications only for the smallest publicly
traded U.S. stocks.
NQB now offers a real-time interactive version of the
Pink Sheets.
The
new electronic bulletin board allows traders to post and revise their
indications at any time, and to send electronic messages to each other to
facilitate their negotiations.


## Page 122

CHAPTER 5 MARKET STRUCTURES • 109
Microwaving Traders at the Hong Kong Futures Exchange
When brokers first brought cellular telephones onto the floor of the Hong
Kong Futures Exchange, they inadvertently circumvented the audio tape
system that the Exchange used to record their negotiations. To obtain better
reception for their cell phones, the brokers would stand near the windows
on the sides of the trading room. Trading moved away from the center of
the pit to the windows, where the microphones could not properly pick up
their negotiations. The Exchange solved the problem by installing a low-
powered cellular phone transceiver station inside the trading room.
The Exchange later switched to an electronic trading system called
HKATS (Hong Kong Automatic Trading System) in 1995. Traders now trade
from their respective offices, and HKATS produces a complete record of
their activities.
they cannot assign responsibility, the traders often resolve the disputed trade
by splitting the difference. However, traders quickly shun traders who make
many mistakes.
5.4.7 Order Books
All
brokers, exchanges, and dealers that represent orders maintain
order books
to
hold open orders that they cannot yet fill. The order books may be elec-
tronic databases, files, boxes of paper order tickets, or simply desktop piles
of order tickets. Order books mostly contain standing limit orders. They
may also include stop orders and market-if-touched orders with price con-
tingencies that have not yet been met.
Order books hold extremely valuable information. They reveal the con-
ditions under which traders will trade. We show in chapter 11 that clever
traders who know what other traders intend to do often can make money
by trading ahead of them. Access to the order book therefore is an impor-
tant determinant of trader profitability.
Open book markets
fully
display their order books to all traders.
Closed
book markets
do not show their orders.
Traders generally want to see the order book. Many traders, however, do
not want other traders to see their orders. When trading in open book mar-
kets, these traders do not submit standing orders. They, or their brokers, in-
stead hold them until suitable trading opportunities arise.
Rule-based order-matching systems work best when traders place stand-
ing limit orders in their system order books. To protect limit order traders,
some markets either restrict access to the book or allow traders to specify
that their standing orders not be displayed in the book. Euronext, for ex-
ample, allows traders to submit undisclosed limit orders.
Brokers are responsible for representing the unfilled orders of their clients.
Since they usually cannot closely monitor all markets in which their cus-
tomers have placed orders, they may give their clients' orders to others to
manage. In markets with rule-based order-matching systems, they usually
will submit the order to the system order book. In other markets, they may
give orders to brokers or dealers who specialize in the specified securities.
Many dealers maintain order books as a service to the brokers who send
them order flow. By encouraging brokers to send them limit orders, the deal-
ers hope that brokers will also send them market orders. Moreover, some
dealers like to hold limit orders because they provide them with free trad-


## Page 123

110 • TRADING AND EXCHANGES
ing options that they often can use to trade more profitably. Dealers usu-
ally do not charge for the brokerage services that they provide to brokers.
5.5 SUMMARY
Market structures differ widely across markets. The differences determine
how the markets operate, who can supply liquidity, who knows current mar-
ket conditions best, and who can act first. Market structure therefore affects
market liquidity, transaction costs, and price efficiency. These factors help
determine who will trade profitably.
Markets differ most in how they arrange trades. In quote-driven mar-
kets, dealers arrange all trades and provide all liquidity. In order-driven
markets, traders or electronic trading systems arrange trades by matching
public orders. In brokered markets, brokers arrange trades by finding traders
who are willing to trade.
Markets differ significantly in when and where they trade. Call markets
trade only when the market is called. Continuous markets trade whenever
the market is open. Physically convened markets trade on exchange floors.
In distributed access markets, traders trade from their offices.
Markets also differ in how traders negotiate with each other. In screen-
based markets, traders communicate through electronic data systems. In oral
auctions, traders negotiate their trades by yelling to each other on an ex-
change floor. In other markets, traders negotiate over the telephone.
Finally, markets differ in their transparency. Transparent markets allow
traders to see all orders, quotes, and trades as they occur. In opaque mar-
kets, many traders never see this information.
Traders, regulators, and academics often passionately debate which struc-
ture is best for a market. The great diversity in existing market structures
suggests that good answers to this question may be complex. The best struc-
ture for a given market undoubtedly depends on the instrument, on why
people want to trade it, and on the communications and computing tech-
nologies that traders can use to trade it.
To form a reasoned opinion about the best structure for a market, you
must thoroughly understand how markets operate, why people use them,
and what traders do in them. These are the main objectives of this book.
In the last part of this book, we will return to the question of which mar-
ket structures are best.
5.6 SOME POINTS TO REMEMBER
•
Call markets convene all traders at the same time and place.
•
Continuous markets arrange trades when willing buyers arrive and find
waiting sellers, or when willing sellers arrive and find waiting buyers.
•
Dealers supply all liquidity in pure quote-driven markets. Public traders
and dealers supply liquidity in order-driven markets. Public traders
supply liquidity in brokered markets.
•
Order-driven markets arrange trades by applying a set of rules to a set
of orders.
•
Brokers arrange trades by searching for willing traders in brokered
markets.


## Page 124

CHAPTER 5 MARKET STRUCTURES • 111
•
Access to information about orders and trades is extremely valuable to
traders.
•
Public traders generally favor transparent markets.
5.7 QUESTIONS FOR THOUGHT
1.
Should markets be open around the clock?
2.
What are the arguments for and against lunch recesses?
3.
The NYSE last changed its trading hours in 1985, when the open
was moved from 10
A.M.
to 9:30
A.M.
Why did the Exchange make
this change? Should it change its trading hours again?
4.
What are the benefits of requiring that traders arrange all trades in
the same market? What are the disadvantages of such requirements?
5.
How would you trade off the advantages of focused liquidity in a
call market against the availability of a continuous market?
6.
When markets call in rotation, in what order should they call the
securities? What special problems confront arbitrageurs who trade
in markets that are called in rotation?
7.
Many regulators say, "Sunshine is the best disinfectant." What do
they mean, and what implications does this have for market trans-
parency? If you were a large buy-side trader, how would you feel
about ex ante market transparency?
8.
Which traders favor dealer quote-driven markets? Which traders fa-
vor public order-driven markets?
9.
Which instruments are best traded in quote-driven markets? In
order-driven markets? In brokered markets?
10.
Why would dealers not want other dealers to know about their
trades?
11.
Do you think that someday all markets will be fully automated elec-
tronic markets?
12.
What factors determine whether traders favor order and trade price
transparency?
13.
Brokers who are exchange members often compete with their ex-
change for business. If such brokers have power over the manage-
ment of the exchange, the exchange may not be able to compete ef-
fectively with them. What advice would you give an exchange that
faces this problem?
14.
If an exchange has regulatory power over its brokers, the brokers
may not be able to compete effectively with the exchange. What ad-
vice would you give a brokerage that faces this problem?
15.
Market data are quite valuable. Exchanges, brokerages, dealers, pub-
lic traders, and data vendors all fight over who owns information.
Who owns market data? Should the prices at which market data are
sold be regulated? If so, who should regulate these prices?
16.
Why is trade price transparency more common in exchange markets
than in labor markets?


## Page 125

6
N
Order-driuen
markets
O
rder-driven markets
use trading rules to arrange their trades. These mar-
kets include oral auctions, single price auctions, continuous electronic
auctions, and crossing networks. You will learn how these markets work and
how trading strategies depend on market structure.
Order-driven markets are quite common. Almost all of the most im-
portant exchanges in the world are order-driven markets. Most newly or-
ganized trading systems choose an electronic order-driven market structure.
Despite the great variation in how order-driven markets operate, their
trading rules are quite similar. All order-driven markets use
order precedence
rules
to match buyers to sellers and
trade pricing rules
to price the resulting
trades.
Variations in trading rules distinguish order-driven markets from each
other. The trading strategies that work best in one market may work poorly
in markets with different rules. Traders therefore need to know how trad-
ing rules affect optimal trading strategies.
If you trade in order-driven markets, the principles introduced in this
chapter will be of immediate and obvious value to you. They will also help
you understand front-running and block trading strategies that we will con-
sider in later chapters.
The topics in this chapter should interest you if you are concerned with
market structures. Most recent innovations in trading technologies involve
order-driven market structures. To evaluate new trading technologies, you
must thoroughly understand how they work.
We will first discuss how oral auctions work. In these order-driven mar-
kets, traders arrange trades by negotiating on a trading floor. Since many
readers may already be acquainted with these markets, they provide us with
a familiar context for introducing various trading rules. We then will turn
our attention to rule-based order-matching systems. These systems include
single price auctions, continuous order book auctions, and crossing networks.
6.1 ORAL AUCTIONS
Many futures, options, and stock exchanges use continuous bilateral oral
auctions to trade their contracts and securities. The largest oral auction mar-
ket is the U.S. government long treasury bond futures market. This market,
which the Chicago Board of Trade organizes, regularly attracts 500 floor
traders. It may be the most liquid market in the world. The smallest oral
auctions may include only two traders.
In an
oral auction,
traders arrange their trades face-to-face on an exchange
trading floor. Some traders cry out their bids and offers to attract other
traders. Other traders listen for bids and offers that they are willing to ac-
cept. Most traders do both. Trades occur when a buyer accepts a seller's of-
fer, or when a seller accepts a buyer's bid. In the former case, the buyer will
112


## Page 126

CHAPTER 6 ORDER-DRIVEN MARKETS • 113
call out "take it" to accept the offer. In the latter case, the seller will call out
"sold" to accept the bid. Buyers and sellers often take turns bidding and of-
fering until they agree on a price and quantity to trade. Traders
offer liq-
uidity
when they make bids or offers to trade. They
take liquidity
when they
accept bids or offers.
The traders must obey the market trading rules. These rules organize
trading to ensure fairness for all traders and to provide for the efficient ex-
change of information necessary to arrange trades. The trading rules also
help protect brokerage customers from dishonest brokers.
The first rule of an oral auction is the
open-outcry rule.
Traders must pub-
licly express all bids and offers so that all traders can act on them. This re-
quirement ensures that all traders can participate fairly in the market. In
most oral auctions, any trader can accept another trader's bid or offer, even
if he or she is not actively negotiating with that trader. The first trader to
accept a bid or offer generally gets to trade. The open-outcry rule also re-
quires traders to express their acceptances publicly, so that all traders are
aware of the trades they arrange. This information helps traders evaluate
market conditions. It also protects customers from dishonest brokers who
might try to arrange trades privately to benefit their friends instead of their
clients.
6.1.1 Order Precedence Rules
The
order precedence rules
of an oral auction determine who can bid or of-
fer, and whose bids and offers traders can accept. In oral auctions, the pri-
mary order precedence rule is always
price priority.
The secondary prece-
dence rules depend on the market. Futures markets use
time precedence.
U.S.
stock exchanges use
public order precedence
and then time precedence.
6.1.1.1 Price Priority
The
price priority rule
gives precedence to the traders who bid and offer the
best prices. Traders cannot accept bids or offers at any inferior price. Buyers
can accept only the lowest offers and sellers can accept only the highest bids.
Price priority is a
self-enforcing rule
because honest traders naturally search
for the best prices. Exchanges therefore do not have to adopt special pro-
cedures to enforce it. They keep the rule on their books so that they can
prosecute dishonest brokers.
Most oral auctions do not allow traders to bid below the best bid or of-
fer above the best offer. Since only the best bid and best offer interest traders,
bids and offers behind the market only create confusion and noise.
Traders acquire price priority by bidding or offering prices that improve
the current best bid or offer. Any trader may improve the best bid or offer
at any time.
6.1.1.2
Time
Precedence
The
time precedence rule
used in most oral auctions gives precedence to the
traders whose bid or offer first improves the current best bid or offer. While
they have time precedence, no other traders may bid or offer at the new best
bid or offer.
Traders retain their time precedence as long as they maintain their bid
or offer, or until another trader accepts it. Afterward, anyone may bid or of-
fer at the new price, and all traders at that price will have equal standing.


## Page 127

114 • TRADING AND EXCHANGES
In oral auction markets, bids and offers generally are good only for a mo-
ment. Traders say, "A quote is good only as long as the breath is warm." In
practice, traders who do not honor their quotes for a reasonable period find
that nobody wants to trade with them. Traders maintain their precedence
by repeating their quotes as often as is necessary to show that they remain
interested in trading. Traders may repeat their quotes continuously in large,
very active markets.
The time precedence rule encourages traders to improve prices aggres-
sively. Traders who want to trade ahead of a trader who has time precedence
must improve the price. Time precedence rewards aggressive traders by giv-
ing them the exclusive right to trade first at the improved price. The time
precedence rule thus encourages price competition among traders.
Time precedence is meaningful only when the minimum price increment
is not trivially small. The
minimum price increment,
or
tick,
is the smallest
amount by which a trader may improve a price. It is the incremental price
that traders must pay to acquire precedence, through price priority, when
they do not have time precedence. If it is very small, the time precedence
rule gives little privilege to the traders who improve price.
The effect of the tick on price competition varies by tick size. If the
tick is too small, it decreases price competition by weakening the time
precedence rule. If the tick is too large, traders are reluctant to improve
prices because of the expense. Since the minimum price increment signif-
icantly affects market quality, exchanges and regulators pay close attention
to it.
Unlike price priority, time precedence is not a self-enforcing rule. Most
traders do not care whose bid or offer they accept as long as they get the
same price. Traders who have time precedence must therefore defend it when
someone improperly attempts to bid or offer at the same price. When this
happens in futures markets, they usually yell out, "That's my bid," or "That's
my offer," or "It's my market."
Leapfrog
The orange juice concentrate futures market is currently
103.10
cents bid,
offered at 103.25 cents. (Traders quote prices per pound for 15,000-pound
contracts.)
Guy
is the bidder at
103.10. He
has time precedence at that
price, and he is defending it. If you want to buy at 103.10, you must wait
until
Guy
trades. If you want precedence, you must improve the bid to
103.15. You then would have price priority over his bid and time
precedence over all subsequent bids at 103.15. If Guy wants to reclaim his
precedence, he would have to improve the market again by bidding
103.20. Time precedence encourages traders to play leapfrog by jumping
over each other's prices with improved prices.
Good traders carefully consider their leapfrog strategies. For example, if
you are willing to bid 103.20 and you are confident that Guy will bid
103.20 if you bid 103.15, you may want to skip over 103.15 and bid
first at 103.20. If you bid 103.20 and Guy still wants to trade first, he will
take the offer at 103.25. In that case, he will trade immediately and you
will still have time precedence at 103.20. Of course, if you are quite
impatient to trade, your best strategy may be to take the offer at
103.25
immediately.


## Page 128

CHAPTER 6 ORDER-DRIVEN MARKETS • 115
The Common Cents Stock Pricing Act of 1997
In March 1997, Republican Representative Mike Oxley and others
introduced a bill to require that U.S. stock markets trade on dollars and
cents rather than on dollars and fractions of a dollar. The bill had wide
popular support because most people find decimal pricing simpler to
understand than fractional pricing. The bill never passed. Instead, the
exchanges decided to switch to decimals on their own.
The bill was remarkable because it represented an attempt by the U.S.
Congress to micromanage trading rules in the stock markets. The exchanges
probably decimalized at least in part to prevent the passage of this bill.
The U.S. equity markets completed their decimalization in 2001.
The minimum price increment decreased from one-sixteenth (6.25 cents) to
1 cent. Not surprisingly, the switch to a much smaller tick profoundly
changed the equity markets. Most notably, the decrease in tick size reduced
the value of time precedence and thereby greatly reduced displayed order
sizes. Many analysts believe that it would have been much better had the
markets adopted a 5
-
cent minimum price increment.
The most remarkable aspect of the bill may be that a conservative
Republican, with a reputation for fighting government intervention in the
economy, introduced it. Several representatives from both parties
recognized and commented upon this incongruity during hearings on the
bill. Perhaps Rep. Oxley's interest in the bill had something to do with the
pun in its title. Several of his legislative initiatives have titles that incorporate
the words "common sense."
6.1.1.3
Public Order Precedence
Some equity exchanges prohibit their members from trading ahead of a pub-
lic trader who is willing to trade at the same price. Exchanges adopt this
public order precedence rule
to give public traders more access to their mar-
kets and to weaken the informational advantages that floor traders have.
Without this rule, exchange members usually can acquire time precedence
at a new price before public traders can, because members see prices change
first and can quote faster than public traders can submit orders. The pub-
lic order precedence rule allows public traders to take precedence over a
member even when the member has time precedence.
The public order precedence rule also increases investor confidence in
the exchange markets by assuring them that exchange members cannot step
in front of their orders. The decrease in tick size that accompanied the dec-
imalization of the U.S. markets greatly weakened this rule. Not surprisingly,
the incomes of member dealers at the NYSE rose substantially.
6.1.2 The Trade Pricing Rule
The
trade pricing rule
used in oral auctions is quite simple. Every trade takes
place at the price proposed by the trader whose bid or offer is accepted.
Economists call this rule the
discriminatory pricing rule.
It derives its name
from a strategy that large, aggressive traders use to lower their trading costs.
Large traders often break their orders into several pieces to trade one at a
time. The first pieces trade at the best prices initially available in the mar-
ket. The remaining pieces trade at progressively inferior prices as the traders
exhaust the available liquidity and as the market discovers the true order


## Page 129

116 • TRADING AND EXCHANGES
Price Discrimination
Sally has 100 soybean
futures contracts to sell at the
market. She does not tell
anyone about her order.
Colin and Martin both
want to buy soybeans. Colin
is willing to bid as much as
6021/2 cents per bushel for
40 contracts. He is currently
bidding 602 and he is the
best bidder. Martin is willing
to bid as much as 601
1
/2 for
75 contracts, but he has not
revealed this to anyone.
Sally immediately accepts
Colin's offer and negotiates to
sell 40 at 602. Martin then
bids 601
1
/2. Sally accepts his
offer and negotiates to sell 60
at 6011/2. Her average sales
price is 601.7. Although
Colin purchased at a price he
was willing to pay, he could
have bought at 601
1
/2 if he
had known about Sally's order.
By discriminating between
Colin and Martin, Sally
obtained a better average
price for her sale. Since the
bean contract is for 50,000
bushels, her total savings are
10,000 dollars.
sizes. Large traders thus discriminate among the traders who are most will-
ing to trade and those who are willing to trade only at inferior prices. They
obtain their best prices from the former and their worst prices from the lat-
ter. This strategy lowers their trading costs because the traders most will-
ing to trade would not offer such good prices if they knew the full order
sizes.
6.1.3 Trading Floors
Futures markets that conduct oral auctions trade in trading pits. A
trading
pit
is a place on an exchange floor designated for trading a particular con-
tract or set of related contracts. For actively traded contracts, the trading
pits are depressions in the floor that have steps all around the sides. The
traders stand on the steps and on the bottom of the pit. This design allows
all traders to see all other traders clearly. The pits in smaller markets are of-
ten round tables or round rails at which the traders sit or stand.
Stock, options, and bond markets that conduct oral auctions trade at
trading posts. Like a trading pit, a
trading post
is a place on the floor of an
exchange designated for trading specific securities. The term probably first
came into usage when markets traded outdoors. The original posts may have
been light posts or hitching posts. Now, posts are simply counters around
which traders and clerks congregate.
Exchanges that run oral auctions require that traders conduct all trading
in each security or contract at its assigned post or in its assigned pit. This
rule makes it easier for buyers and sellers to find each other. It also helps
the exchange enforce its trading rules.
6.2 RULE-BASED ORDER-MATCHING
SYSTEMS
Most exchanges, some brokerages, and almost all electronic communica-
tions networks use rule-based order-matching systems to arrange their
trades.
Rule-based order-matching systems
use trading rules to arrange trades
from the orders that traders submit to them. Traders negotiate with each
other only by submitting and canceling orders. Most systems accept only
limit orders. Systems that accept market orders treat them as very aggres-
sively priced limit orders. All orders specify the maximum quantities that
traders will accept. Rule-based order-matching systems process this price
and quantity information to arrange their trades. Almost all rule-based sys-
tems now use electronic trading systems to process their orders.
If the market is a call market, the market collects the orders before the
call. Immediately following the call, its trading system makes one attempt
to arrange trades. If the market is a continuous trading market, its trading
system attempts to arrange trades whenever new orders arrive.
Every rule-based order-matching system uses the same sequence of pro-
cedures when attempting to arrange trades. They first match orders using
their order precedence rules. They then determine which matches can trade.
Trades will occur only if at least one buy order offers terms acceptable to at
least one seller. Finally, they price the resulting trades using their trade pric-
ing rules. Although the trading rules vary considerably across order-driven
markets, all markets apply them the same way.


## Page 130

CHAPTER 6 ORDER-DRIVEN MARKETS • 117
6.2.1 Order Precedence Rules
To arrange trades, markets with order-matching systems use their
order
precedence rules
to separately rank all buy and sell orders in order of increasing
precedence. They match the orders with the highest precedence first.
The order precedence rules are hierarchical. Markets first rank orders us-
ing their
primary order precedence rules.
If two or more orders have the same
primary precedence, markets then apply their
secondary precedence rules
to
rank them. They apply these rules one at a time until they rank all orders
by their precedence.
All order-matching markets use
price priority
as their
primary order prece-
dence rule.
Under price priority, buy orders that bid the highest prices and
sell orders that offer the lowest prices rank highest on their respective sides.
Market orders always rank highest because the prices at which they may
trade are not limited.
Markets use various secondary precedence rules to rank orders that have
the same price. The most commonly used rules rank orders based on their
time of submission, on their display status, and on their size. All rule-based
order-matching systems must have at least one secondary precedence rule.
Some use more than one.
Time precedence
gives orders precedence according to their time of sub-
mission.
Floor time precedence
gives the first order to arrive at a given price
precedence over all other orders at that price. The remaining orders are
at
parity
with each other and must be ranked by another secondary precedence
rule. This version of time precedence is called
floor time precedence
because
it is the same as the time precedence rule used in oral auctions.
Strict time
precedence
ranks all orders at the same price according to their submission
time. Systems that rank orders based only on price priority and strict time
precedence are
pure price-time precedence systems.
Display precedence
gives displayed orders precedence over undisclosed or-
ders at the same price. Markets give precedence to displayed orders in order
to encourage traders to expose their orders. If an order is partly displayed
and partly undisclosed, the market usually treats the two parts separately.
Size precedence
varies by market. In some markets, large orders have prece-
dence over small orders, while in other markets, the opposite holds. When
two or more orders are at parity, and they cannot all be fully filled, some
markets allocate available size on a pro rata basis. In a
pro rata allocation,
orders fill in proportion to their size. Such orders
participate
in the trade.
Most exchanges allow traders to issue orders with size restrictions.
Traders may specify that their entire order must be filled all at once, or they
may specify a minimum size for a partial execution. Orders with size re-
strictions usually have lower precedence than unrestricted orders because
they are harder to fill. Large traders use these restrictions to avoid paying
fixed costs for settling numerous small trades. These costs include exchange
fees, settlement fees, and the costs of accounting for each trade.
6.2.1.1 Order Precedence Ranking Example
Suppose that traders submit the orders in table 6-1 to an auction that uses
the pure price-time precedence hierarchy. An order book that arranges these
orders by pure price-time precedence appears in table 6-2, where the orders
with the highest precedence appear at the top on the sell side and at the
Pro Rata Allocation
Example
Two standing buy orders for
10 contracts and
20
contracts
are at parity with each other.
A sell order for 18 contracts
arrives that can trade with
both orders. In a pro rata
allocation, the two buy orders
will fill the same fraction
(18/30) of their total size.
The first buy order will fill 6
contracts, and the second
order will fill 12 contracts.


## Page 131

118 • TRADING AND EXCHANGES
TABLE 6-1.
Example Orders
TIME
TRADER
ORDER SIDE
SIZE
PRICE
10:01
Bea
Buy
3
20.0
10:05
Sam
Sell
2
20.1
10:08
Ben
Buy
2
20.0
10:09
Sol
Sell
1
19.8
10:10
Stu
Sell
5
20.2
10:15
Bif
Buy
4
market
10:18
Bob
Buy
2
20.1
10:20
Sue
Sell
6
20.0
10:29
Bud
Buy
7
19.8
bottom on the buy side. These positions would be reversed if the prices in
the center column were arranged in decreasing instead of increasing order.
Sol's sell order has highest precedence on the sell side because it offers the
lowest price. Bif's buy order has highest precedence on the buy side because
it is a market order. Bea's order and Ben's order have the same price prior-
ity, but Bea's order has time precedence over Ben's order because it arrived
first.
6.2.2 The Matching Procedure
Order matching proceeds after the market ranks its orders. In a call mar-
ket, this happens immediately following the market call. In a continuous
market, it happens whenever a new order arrives.
The market first matches the highest-ranking buy and sell orders to each
other. If the buyer will pay at least as much as the seller demands, the match
will result in a trade. The price of the trade will depend on the trade pric-
ing rules of the market, which we discuss below. If one order is smaller than
the other, the smaller order will fill completely. The market then will match
the remainder of the larger order with the next highest-ranking order on
the opposite side of the market. If the first two orders are the same size,
both will fill completely. The market then will match the next highest-
ranking buy and sell orders. This process continues until the market arranges
TABLE 6
-
2.
Example Order Book
SELLERS
ORDER PRICE
BUYERS
TRADER
SIZE
SIZE
TRADER
Sol
1
19.8
7
Bud
Sue
6
20.0
20.0
2
Ben
20.0
3
Bea
Sam
2
20.1
2
Bob
Stu
5
20.2
market buy
4
Bif


## Page 132

CHAPTER 6 ORDER-DRIVEN MARKETS • 119
all possible trades. Since the market processes orders ranked by decreasing
price priority, the last match that results in a trade often involves two or-
ders that bid and offer the same price. The next match does not result in a
trade because the buyer's bid price is below the seller's offer price.
6.2.2.1 Order
-
matching Example
Suppose that the traders in the previous example submit their orders to a
call market auction that calls at 10:30. At 10:30, the market will arrange
trades as follows:
1.
The market first matches Sol's order to sell 1 at 19.8 with Bif's or-
der to buy 4 at the market. This match fills Sol's order and leaves Bif
with a remainder of 3 to buy at the market. Sol can trade with
Bif because Bif's market order has no price restriction.
2.
The market then matches Bif's remainder of 3 with Sue's order to
sell 6 at 20.0. Sue's order goes next because it has the highest prece-
dence on the sell side now that Sol's order is filled. This match fills
the remainder of Bif's order and leaves Sue with a remainder of 3 to
sell at 20.0. Bif can trade with Sue because Bif's market order has no
price restrictions.
3.
The market then matches Sue's remainder of 3 with Bob's order to
buy 2 for 20.1. This match fills Bob's order and leaves Sue with a re-
mainder of 1 to sell at 20.0. Sue can trade with Bob because Bob is
willing to pay more than Sue demands.
4.
The market then matches Sue's remainder of 1 with Bea's order to
buy 3 for 20.0. This match fills the remainder of Sue's order and leaves
Bea with a remainder of 2 to buy for 20.0. Sue can trade with Bea
because Sue is offering 20.0 and Bea is bidding 20.0. The only price
at which they can trade is 20.0.
The next match does not result in a trade. Bea's remainder of 2 to buy
for 20.0 cannot trade with Sam's order to sell 2 at 20.1 because Bea will not
pay as much as Sam demands. No further trades are possible. Table 6-3
summarizes the trades.
The order book with the remaining unfilled orders appears in table 6-4.
Note that the buy and sell orders no longer overlap. If this market now
started continuous trading, the market quote would be 20.0 bid for 4, 2 of-
fered at 20.1. Continuous markets always have a spread between the best
bid and the best offer. If they did not, a trade would result.
TABLE 6
-
3.
Call Market Trades
MATCH
SELLER
BUYER
QUANTITY
1
Sol
Bif
1
2
Sue
Bif
3
3
Sue
Bob
2
4
Sue
Bea
1
Total:
7


## Page 133

120 • TRADING AND EXCHANGES
TABLE 6
-
4.
Order Book After the Market Call
SELLERS
ORDER PRICE
BUYERS
TRADER
SIZE
SIZE
TRADER
19.8
7
Bud
20.0
20.0
2
Ben
20.0
2
Bea
Sam
2
20.1
Stu
5
20.2
6.2.3 Trade Pricing Rules
The trade pricing rules depend on the type of market. Single price auctions
use the
uniform pricing rule.
Continuous two-sided auctions and a few call
markets use the
discriminatory pricing rule.
Crossing networks use the
derivative pricing rule.
The following three sections introduce these three
rules and the markets associated with them.
6.3 THE UNIFORM PRICING RULE AND
SINGLE PRICE AUCTIONS
Single price auctions are quite common. Most continuous order-driven stock
markets and most electronic futures markets open their trading sessions with
a single price call market auction. These markets also use single price auc-
tions to restart trading following a halt. Some call markets also trade using
single price auctions exclusively. Various national treasuries use them to sell
their bills, and the Arizona Stock Exchange offers them for trading U.S.
equities.
In a single price auction, all trades take place at the same
market-
clearing price.
The last match that leads to a feasible trade determines the
clearing price. If the buy and sell orders in this match specify the same trade
price, that price must be the market-clearing price. Any other price would
be either too high to satisfy the buy order or too low to satisfy the sell or-
der. Matching by price priority ensures that this market-clearing price is
also feasible for all previously matched orders. These matches involve buy
and sell orders with higher (or at least equal) price priority. Since all buy-
ers with higher price priority are willing to trade at higher prices than the
market-clearing price, and all sellers with higher price priority are willing
to trade at lower prices than the market-clearing price, all matches can trade
at the market-clearing price.
If the buy and sell orders in the last feasible trade specify different prices,
the buy order will bid a higher price than the sell order offers. The market
can clear at either of these two prices or at any price between them. The
market rules will specify the clearing price in this unusual event.
6.3.1 Single Price Auction Example
Suppose that the auction of the previous example is a single price auction.
The last feasible trade is between Bea and Sue. The market-clearing price


## Page 134

CHAPTER 6 ORDER-DRIVEN MARKETS • 121
The Arizona Stock Exchange and Arizona
The Arizona Stock Exchange (AZX) is an alternative trading system that
arranges single price auctions in listed and Nasdaq securities. Unlike the
single price auctions conducted at the NYSE open, the AZX auctions
provide users with continuous price and volume indications up to the time of
each auction.
The AZX has not been particularly successful, but its markets have
excited many traders. Recent changes in the timing of its auctions and
additions to the securities that it trades may substantially increase its
popularity.
R. Steven Wunsch founded the AZX as Wunsch Auction Systems. The
firm conducted its first auctions in 1991. Trading volumes did not grow
quickly, however, and the firm soon needed new financing.
In the early 1990s, the Commerce Department of the State of Arizona
was looking for ways to attract and finance high-tech industries. To improve
the state's image, Arizona provided a 2.9 million-dollar nonrecourse loan to
Wunsch Auction Systems. In exchange for this loan, the firm changed its
name to Arizona Stock Exchange, opened a small office in Phoenix, and
moved its computer there. Funding for the loan came from Arizona's
security registration fees.
Source: Author's interview with R. Steven Wunsch.
therefore must be 20.0. Bea is unwilling to buy at any higher price, and
Sue is unwilling to sell at any lower price. Sol is happy with the market-
clearing price because he is a willing seller at 19.8. Bob is happy with the
price because he is a willing buyer at 20.1. We presume that Bif is happy
with the price because he submitted a market buy order. If the price is more
than he is willing to pay, he should have submitted a limit order instead of
a market order.
6.3.2 Supply and Demand
The single price auction clears at the price where supply equals demand. The
orders in the limit book determine the supply and demand schedules. The
supply schedule
lists the total volume that sellers offer at each price. It slopes
upward because sellers will sell more at higher prices than at lower prices.
The
demand schedule
likewise lists the total volume that buyers want at each
price. It slopes downward because buyers will buy less at higher prices than
at lower prices.
These schedules allow us to determine how much the market can trade
at any given price. Since the market cannot force buyers and sellers to trade,
the total trading volume at a given price is the minimum of supply and de-
mand at that price. At prices below the clearing price, there is
excess demand:
Buyers want to buy more than sellers offer. The supply schedule then de-
termines the total quantity traded. Since the supply curve slopes upward,
the market could trade more volume at a higher price. Likewise, at prices
above the clearing price, there is
excess supply:
Sellers offer more than buy-
ers want. The demand schedule then determines the total quantity traded.
Since the demand curve slopes downward, the market could trade more vol-
ume at a lower price.
Single price auctions maximize the volume of trade by setting the clear-
ing price at the price where supply equals demand. At prices above the


## Page 135

122 • TRADING AND EXCHANGES
clearing price, volume would decrease along the demand curve. At prices
below the clearing price, volume would decrease along the supply curve.
Because prices and quantities are discrete, single price auctions often have
excess supply or demand at the market-clearing price. If there is excess sup-
ply, all buyers at that price have their orders filled, and the secondary prece-
dence rules determine which sell orders fill. If there is excess demand, all
sellers have their orders filled, and the secondary precedence rules deter-
mine which buy orders fill. Of course, ranking by price priority ensures that
all buy orders placed above the market-clearing price and all sell orders
placed below the market-clearing price also fill.
6.3.2.1 Supply and Demand Schedules
The supply and demand schedules for the orders in our example appear in
table 6-5 and figure 6-1. To construct these schedules, first sum the total
size bid or offered at each price. In our example, the only sum that must be
computed is on the buy side at 20.0 dollars, where Ben's bid and Bea's bid
total 5. Next, sum these quantities across prices in order of decreasing price
priority. Sum the supply schedule from lowest price to highest price and
sum the demand schedule in the opposite direction. To compute excess de-
mand schedule, subtract the supply schedule from the demand schedule at
every price.
Supply does not exactly equal demand at any price in this example. The
two schedules are closest at 20.0 and 20.1. The market-clearing price is 20.0
because more volume can trade at 20.0 than at 20.1. At 20.0, where there
is excess demand, the supply schedule indicates that 7 will trade. At 20.1,
where there is excess supply, the demand schedule indicates that only 6 will
trade.
The market-clearing price is easy to find in the following plot of the
supply and demand schedules. The two schedules cross at the market-
clearing price of 20.0. The schedules are not smooth because the order prices
and quantities are discrete. (See figure 6-1.)
Table 6-6 and figure 6-2 show that the supply and demand schedules no
longer cross following the auction. No further trades are possible.
6.3.3
Trader Surpluses
The single price auction also maximizes the benefits that traders derive from
participating in the auction. To explain why, we must first discuss how to
measure the benefits that traders obtain from trading.
TABLE 6
-
5.
Single Price Auction Example Supply and Demand Schedules
SELLERS

BUYERS
SUPPLY
SCHEDULE
TOTAL SIZE
AT PRICE
PRICE
TOTAL SIZE
AT PRICE
DEMAND
SCHEDULE
EXCESS DEMAND
SCHEDULE
1
1
19.8
7
18
17
7
6
20.0
5
11
4
9
2
20.1
2
6
—3
14
5
20.2
4
—10
14
Any higher price
4
4
—10


## Page 136

CHAPTER
20.3
20.2
6

ORDER-DRIVEN MARKETS • 123
—
—

J
Supply schedule
20.1
r
—
.L
)
20.0
a_
Demand schedule
— r
19
'
9
—

I
19.8
—
19.7
5

10

15

20
Quantity
FIGURE 6-1.
Single Price Auction Example Supply and Demand Schedule Plot
Economists measure trader benefits by computing their surpluses. For a
seller, the
trader surplus
is the difference between the trade price and the
seller's valuation of the item. For a buyer, it is the difference between the
buyer's valuation and the trade price. Since sellers should never offer to sell
at prices below their valuations and since buyers should never bid at prices
above their valuations, trader surpluses should always be positive. Trader
surplus measures the
gains from trading.
All traders would like to maximize
their surpluses.
When a buyer and a seller trade, the sum of their surpluses does not de-
pend on the trade price. It depends only on the difference in their valua-
tions. The buyer's surplus is the buyer's valuation minus the trade price. The
seller's surplus is the trade price minus the seller's valuation. Their combined
surplus therefore is the buyer's valuation minus the seller's valuation. Auc-
tions maximize total surplus by matching the buyers who most value the
item with the sellers who least value it.
The distribution of the surplus does depend on the trade price. The
seller naturally wants to receive a high price, and the buyer wants to pay a
low price.
Trader Surplus Example
A confectioner is willing to
pay 28 cents per pound for
two carloads of refined
domestic sugar, each of
which holds 112,000 pounds
of sugar. If he can buy sugar
for 23 cents per pound, his
total trader surplus will be
(0.28 — 0.23) x
112,000 x
2 = 11,200
dollars.
TABLE 6
-
6.
Supply and Demand Schedules Following the Single Price Auction
SELLERS

BUYERS
SUPPLY

TOTAL SIZE

TOTAL SIZE

DEMAND

EXCESS DEMAND
SCHEDULE

AT PRICE

PRICE

AT PRICE

SCHEDULE

SCHEDULE
0
19.8
7
11
11
0
20.0
4
4
4
2
2
20.1
0
—2
7
5
20.2
0
—7


## Page 137

124 • TRADING AND EXCHANGES
20.3 —
20.2 —
E
Supply schedule
20.1
— —
a)
20.0
Demand schedule
19.9 —
19.8 —
I
19.7
I
I

1
0

5
10

15
20
Quantity
FIGURE 6-2.
Supply and Demand Schedules Following the Single Price Auction
Measuring trader surpluses is difficult because we never know the val-
ues that traders place on the items they trade. Traders reveal some infor-
mation about their valuations through their orders. Rational buyers should
never set a limit price above their valuations. Buyer valuations therefore
should be greater than or equal to their limit prices. Seller valuations like-
wise should be less than or equal to their limit prices. Traders who submit
market orders presumably expect to trade at prices they would accept. Mar-
ket order buyers therefore should have valuations above the clearing price,
and market order sellers should have valuations below the clearing price.
The single price auction maximizes total trader surplus if the outcome
of the auction satisfies all traders. The outcome will satisfy all traders if no
trader regrets trading, and if no potential trader regrets not trading. No
trader will regret trading if he or she bids and offers rationally. In particu-
lar, no buyer should bid more than his valuation, and no seller should offer
less than her valuation. Traders regret not trading when they fail to trade
and wish that they had. This can happen when they do not price their or-
ders aggressively enough to participate in the auction. They then may fail
to trade at a price that would have satisfied them. If traders set their limit
prices equal to their valuations, the auction outcome will always satisfy all
traders.
The single price auction maximizes total trader surplus (if the outcome
of the auction satisfies all traders) because it uses price priority to determine
who trades. Matching by price priority matches the buyers who value the
item most with the sellers who value it least. To maximize the total surplus,
these traders must trade because their surpluses are greatest.
The clearing price is the dividing line between buyers who value the item
highly and potential buyers who value it less. It also divides sellers who do
not value the item much from potential sellers who value it dearly. The re-
sulting trades include every buyer who values the item by more than the
clearing price and every seller who values the item by less than the clearing


## Page 138

CHAPTER 6 ORDER-DRIVEN MARKETS • 125
An Inexperienced Bidder
John wants to buy a 1-million-dollar, two-year
U.S. Treasury
note that pays
5
3
/8 percent interest. The U.S. Treasury will sell the note in a single price
auction. The Treasury will offer 6 billion dollars of these notes. Buyers must
express their bids in yield percentages. The Treasury computes the actual
dollar price for the note from the market-clearing yield. A high yield implies
a low price, and vice versa.
John will accept a yield of 5.400 percent. John mistakenly believes that
his bid may affect his purchase price. He therefore bids 5.500 percent in
the hope of obtaining a lower purchase price. The auction-clearing price
turns out to be
5.495
percent, which corresponds to a price of
99.776
dollars per 100-dollar face value. John does not buy because his bid was
too low. (His quoted yield was too high.) Since he was willing to pay the
clearing price, he will regret not trading.
Had John bid his 5.400 valuation, he would have bought the note and
received a yield of 5.495 percent. His small order probably would have
had little or no effect on the clearing price. If John thinks carefully about
what happened, he probably will not make the some mistake again.
price.
They exclude every potential buyer who values the item by less than
the clearing price and every potential seller who values the item by more
than the clearing price. Because the same clearing price divides both the
successful buyers from the potential buyers and the successful sellers from
the potential sellers, no successful buyer will have a lower valuation than
will any successful seller.
Any other trading arrangement will reduce the total trader surplus. For
example, in markets that arrange trades at multiple prices, a successful buyer
in one trade might value the item less than does a successful seller in an-
other trade. In that case, the buyer would have bought the item at a lower
price than the seller would have sold it. Having this buyer sell the item back
to this seller at an intermediate price would increase the total surplus.
6.3.3.1 Trader Surpluses in the Single Price Auction Example
The trader surpluses for the single price auction example appear in table
6-7. The analysis assumes that the limit order trader valuations are equal to
their limit order prices and that the market order buyer's valuation is arbi-
trarily equal to 20.3.
TABLE 6
-
7.
Trader Surpluses in the Single Price Auction Example
TRADER
ORDER
FILLED
SALES
FILLED
BUYS
TRADE
PRICE
ASSUMED
VALUE
TRADER SURPLUSES
Sol
Sell 1 limit 19.8
1
20.0
19.8
(20.0 - 19.8) X 1 = 0.2
Sue
Sell 6 limit 20.0
6
20.0 20.0
(20.0 - 20.0) X 6 = 0.0
Bea
Buy 3 limit 20.0
1
20.0 20.0
(20.0 - 20.0) x 1 = 0.0
Bif
Buy 4 at market
4
20.0
20.3
(20.3 - 20.0) x 4 = 1.2
Bob
Buy 2 limit 20.1 2
20.0
20.1
(20.1 - 20.0) X 2 = 0.2
Totals
7 7
1.6


## Page 139

126 • TRADING AND EXCHANGES
6.4 THE DISCRIMINATORY PRICING RULE
AND CONTINUOUS TWO-SIDED AUCTIONS
Continuous rule-based order matching systems use the
discriminatory pric-
ing rule
to price their trades. The rule is the same discriminatory pricing
rule that oral auctions use. (Both are examples of two-sided auctions.) To
see how continuous order-matching auction markets apply the rule, con-
sider first how they operate.
Continuous auction markets maintain an
order book
to keep track of
standing orders that are waiting to fill. The buy and sell orders are sepa-
rately sorted by their precedence. The highest-priced bid and the lowest-
priced offer are the best bid and the best offer.
When a new order arrives, the matching system attempts to arrange a
trade between the new order and the order on the opposite side with the
highest precedence. A trade is possible only if the new order offers terms
acceptable to that order. If the new order is a buy order, the order must in-
dicate that the trader will pay at least the best offer price. If it is a sell or-
der, the order must indicate that the trader will sell at or below the best bid.
If a trade is possible, the new order is
marketable.
Market orders and ag-
gressively priced limit orders are marketable orders.
If the new order is not marketable, the market places it in the order
book—according to its precedence—to wait for orders to arrive on the op-
posite side. Traders who do not want their unfilled orders to stand in the
book must attach a fill-or-kill or an immediate-or-cancel instruction to their
orders.
If the new order is marketable, the matching system arranges a trade by
matching the new order with the highest-ranking order on the other side
of the market. If this trade does not completely fill the new order, the mar-
ket then matches the remainder of the new order with the next highest-
ranking order on the other side. This process continues until the new order
fills completely or until no further trades are feasible. The market places any
remaining size in the order book unless the trader instructs otherwise.
Under the
discriminatory pricing rule,
the limit price of the standing or-
der determines the price for each trade. If the market matches a large in-
coming order with several standing limit orders placed at different prices,
trades will take place at the various limit order prices.
6.4.1 Continuous Trading Example
Suppose that traders submit the same set of orders used in the single price
auction example to a continuous two-sided auction market. These orders
appear in table 6-1. This section explains what would have happened. We
assume that the limit order book was empty at the start of trading.
1.
At 10:01, Bea submits the first order. The market cannot match it
with any other order because no standing orders are in the book. The
market places Bea's order to buy 3 limit 20.0 in the book. The mar-
ket quote is now 20.0 bid for 3, no offer.
2.
At 10:05, Sam submits the second order, to sell 2 limit 20.1. Sam
cannot trade with Bea because Bea will not pay what Sam demands.
The market places Sam's order in the book. The market quote is now
20.0 bid for 3, 2 offered at 20.1. In some electronic screens, the quote


## Page 140

CHAPTER 6 ORDER-DRIVEN MARKETS • 127
would appear as "20.0-20.1 3 X 2." Traders read this as "20 to a dime,
3 by 2," or "20 bid for 3, 2 offered at a dime."
3. At 10:08, Ben submits the next order, to buy 2 limit 20.0. This or-
der is at the same price as Bea's buy order. The market places it in
the book behind Bea's order because Bea has time precedence. The
market quote is now 20.0 bid for 5, 2 offered at 20.1.
Order Book After First Three Orders
SELLERS

BUYERS
TRADER

SIZE

ORDER PRICE

SIZE

TRADER
20.0
2
Ben
20.0
3
Bea
Sam
2
20.1
The market is 20.0
-
20.1 5 X 2.
4. At 10:09, Sol submits the next order, to sell 1 at 19.8. Sol's order is
marketable because it can trade immediately upon submission. The
market matches Sol's order with Bea's buy order, which has highest
precedence on the buy side. Sol's order fills, and Bea's order leaves a
remainder of 2. The trade price will be 20.0, the price of Bea's stand-
ing limit order. Note that Sol sells for 20.0, although he would have
been willing to accept as little as 19.8. The market quote is now 20.0
bid for 4, 2 offered at 20.1.
Order Book After Four Orders
SELLERS

BUYERS
TRADER
SIZE
ORDER PRICE
SIZE
TRADER
20.0
2
Ben
20.0
2
Bea
Sam
2
20.1
The market is 20.0-20.1 4 X 2.
5. At 10:10, Stu submits the next order, to sell 5 limit 20.2. Stu's order
is less aggressively priced than Sam's sell order. The market places it
in the book behind Sam's order. The market quote is still 20.0 bid for
4, 2 offered at 20.1.
Order Book After Five Orders
SELLERS

BUYERS
TRADER
SIZE
ORDER PRICE
SIZE
TRADER
20.0
2
Ben
20.0
2
Bea
Sam
2
20.1
Stu
5
20.2
The market is 20.0-20.1 4 X 2.


## Page 141

128 • TRADING AND EXCHANGES
6.
At 10:15, Bif submits the next order, to buy 4 at the market. The
market first matches the order with Sam's sell order. This match fills
Sam's order and leaves Bif with a remainder of 2. The trade price will
be 20.1, the price of Sam's standing limit order. The market then
matches the remainder of Bif's order with Stu, leaving Stu with a re-
mainder of 3. The price of this second trade will be 20.2, the price of
Stu's standing limit order. The market quote is now 20.0 bid for 4, 3
offered at 20.2.
Bif benefits from discriminatory pricing. The average price of the
two trades is 20.15. Had the market used the uniform pricing rule,
Bif would have had to pay the higher price of 20.2 for both trades.
Order Book After Six Orders
SELLERS

BUYERS
TRADER
SIZE
ORDER PRICE
SIZE
TRADER
20.0
2
Ben
20.0
2
Bea
Stu
3
20.2
The market is 20.0-20.2 4 X 3.
7.
At 10:18, Bob submits the next order to buy, 2 for 20.1. The order
cannot trade, but it does improve the buy side of the market. The
market quote is now 20.1 bid for 2, 3 offered at 20.2.
Order Book After Seven Orders
SELLERS

BUYERS
TRADER
SIZE
ORDER PRICE
SIZE
TRADER
20.0
2
Ben
20.0
2
Bea
20.1
2
Bob
Stu
3
20.2
The market is 20.1
-
20.2 2 X 3.
8.
At 10:20, Sue submits the next order, to sell 6 at 20.0. The order
trades 2 with Bob at 20.1, 2 with Bea at 20.0, and 2 with Ben at 20.0.
Sue benefits from the discriminatory pricing rule because her average
sale price of 20.033 is slightly higher than the sale price of 20 im-
plied by the uniform pricing rule. The market now has no bid and
has 3 offered at 20.2.
Order Book After Eight Orders
SELLERS

BUYERS
TRADER

SIZE

ORDER PRICE

SIZE

TRADER
Stu

3

20.2
The market is 0-20.2 0 X 3.


## Page 142

CHAPTER 6 ORDER-DRIVEN MARKETS • 129
9. At 10:29, Bud submits the last order, to buy 7 for 19.8. It cannot be
filled, so the market places it in the book. The market is now 19.8
bid for 7, 3 offered at 20.2.
Order Book After Nine Orders
SELLERS

BUYERS
TRADER

SIZE

ORDER PRICE

SIZE

TRADER

19.8

7

Bud
Stu

3

20.2
The market is 19.8
-
20.2 7 X 3.
The market has now processed all the example orders from table 6-1.
Table 6-8 summarizes the trades arranged in this continuous auction.
6.4.2 Discriminatory Versus Uniform Pricing Rules
For a given set of standing orders, large impatient traders prefer the dis-
criminatory pricing rule to the uniform pricing rule. The discriminatory
pricing rule allows them to trade the first parts of their orders at better prices
than the last parts. Under the uniform pricing rule, their entire orders would
trade at the same price. That price would be the worst price they would re-
ceive under the discriminatory rule. Large impatient traders therefore trade
at more favorable terms when they can discriminate among the traders who
offer them liquidity.
Not surprisingly, for a given set of orders, standing limit order traders
prefer the uniform pricing rule. They do not want large traders to discrim-
inate among them. They would rather that all traders receive the same price
when filling a large order.
These conclusions assume that traders would issue the same orders
whether they traded under the discriminatory pricing rule or the uniform
pricing rule, hence the qualification "for a given set of orders." In practice,
traders issue different orders when trading in different market structures.
Limit order traders tend to issue more aggressively priced orders when
trading under the uniform pricing rule than under the discriminatory pric-
ing rule. When choosing a limit price, traders consider both the probabil-
ity that their orders will trade and the prices they will receive if their orders
TABLE 6
-
8.
Trades in the Continuous Auction Example
TIME
SELLER
BUYER
PRICE
QUANTITY
10:09
Sol
Bea
20.0
1
10:15
Sam
Bif
20.1
2
10:15
Stu
Bif
20.2
2
10:20
Sue
Bob
20.1
2
10:20
Sue
Bea
20.0
2
10:20
Sue
Ben
20.0
2
Total:
11


## Page 143

130 • TRADING AND EXCHANGES
trade. Under both pricing rules, the order limit price determines its prece-
dence, and therefore its probability of trading. The two rules have different
effects on the trade price, however. Under the discriminatory pricing rule,
the limit price determines the trade price. Under the uniform pricing rule,
the limit price rarely determines the trade price unless the order is very large
relative to the other orders in the auction. Limit orders often trade at bet-
ter prices, especially when they trade with large orders. Traders therefore are
more aggressive when trading under the uniform pricing rule than under
the discriminatory pricing rule. The benefits from price discrimination that
large traders actually obtain relative to uniform pricing therefore are smaller
than they would be if traders issued the same orders under either rule. The
effects of price discrimination on limit order traders likewise are overstated.
Since markets want to encourage traders to bid and offer aggressively,
continuous trading markets might consider adopting the uniform pricing
rule instead of the discriminatory pricing rule. Continuous markets cannot
enforce uniform pricing, however. Large traders who want to price-
discriminate can circumvent the uniform rule by breaking up their orders
and submitting them as a sequence of smaller orders. The first parts will re-
ceive the best prices and the last parts will receive inferior prices. They will
thus obtain discriminatory pricing for their full orders, even though the trade
pricing rule calls for uniform pricing. Under the discriminatory pricing rule,
the market splits large orders. Under the uniform pricing rule, traders would
split their orders before submitting them.
To effectively switch to a uniform pricing rule, continuous trading mar-
kets must stop trading. Some continuous markets have
trading halt rules
to
achieve this purpose. These markets halt trading if a large order imbalance
would cause the price to move too far or too quickly. (Their rules specify
the conditions that stop trading.) They resume trading after some time with
a single price auction. The trading halt therefore represents a transition from
the discriminatory pricing rule to the uniform pricing rule. Large traders
can still break up their orders, but doing so delays the execution of their
trades. If the delays are sufficiently long, they may discourage large traders
from splitting their orders.
Trading halts may also decrease volatility by alerting traders to unusual
demands for liquidity. If traders step in to supply liquidity, prices may not
change as much as they would have changed if the market immediately
processed the orders that caused the imbalance.
6.4.3 Continuous Markets Versus Call Markets
In chapter 5, we argued that the main advantage of call markets is that
they focus the attention of all traders on the same instrument at the same
time. The common focus makes it easier for buyers and sellers to find each
other. When traders can easily find each other, the total trader surplus
should be high.
We previously proved that the single price auction maximizes the gains
from trading. For a given order flow, no other method of arranging trades
can produce a higher total trader surplus than that produced in a single price
auction.
A comparison of the results from the single price auction example with
those from the continuous two-sided auction example confirms that the
continuous auction produces a smaller trader surplus when processing the


## Page 144

CHAPTER 6 ORDER-DRIVEN MARKETS • 131
TABLE 6-9.
Trader Surpluses in the Continuous Auction Example
TRADER
ORDER
FILLED
SALES
FILLED
BUYS
AVERAGE
TRADE PRICE
ASSUMED
VALUE
TRADER SURPLUS
Sol
Sell 1 limit 19.8
1
20.00
19.8
(20.00 - 19.80) X 1 = 0.2
Bea
Buy 3 limit 20.0
3
20.00
20.0
(20.00 - 20.00) X 3 = 0.0
Sam
Sell 2 limit 20.1
2
20.10
20.1
(20.10 - 20.10) X 2 = 0.0
Bif
Buy 4 at market
4
20.15
20.3
(20.30 - 20.15) X 4 = 0.6
Stu
Sell 5 limit 20.2
2
20.20
20.2
(20.20 - 20.20) x 2 = 0.0
Sue
Sell 6 limit 20.0
6
20.03
20.0
(20.03 - 20.00) X 6 = 0.2
Bob
Buy 2 limit 20.1
2
20.10
20.1
(20.10 - 20.10) X 2 = 0.0
Ben
Buy 2 limit 20.0
2
20.00
20.0
(20.00 - 20.00) x 2 = 0.0
Totals
11 11
1.0
same
order flow. The trader surpluses for the continuous auction example
appear in table 6-9. The total surplus is 1.0, which is smaller than the 1.6
total surplus of the single price auction.
The continuous auction has a lower surplus because Sam and Stu both
sold, even though they have relatively high assumed valuations of 20.1 and
20.2. Since they both sold at their assumed valuations, they did not con-
tribute to the total surplus. However, Bif, who bought from them, obtained
a lower surplus than he would have in the single price auction because of
the higher prices.
If Sam and Stu do indeed value the item at 20.1 and 20.2, they pre-
sumably would want to be buyers at 20.0. These valuations are higher than
the 20.0 valuation that Bea and Ben both have. After Sam and Stu sold to
Bif, Bea and Ben bought from Sue at 20.0 when Sue's sell order arrived.
More surplus would have been created had Sam and Stu been the buyers
instead of Bea and Ben. Had Sam and Stu repurchased their shares at 20.0,
their surpluses for these trades would have totaled (20.1 - 20.0) X 2 +
(20.2 - 20.0) X 2 = 0.6. With these trades, the total surplus for the con-
tinuous auction would have matched that of the single price auction.
Sam and Stu would essentially have been trading as dealers had they re-
purchased from Sue. In effect, their trades would have allowed Bif to trade
with Sue, even though Bif and Sue arrived at different times. Sam and Stu's
total trader surplus would have been their round-trip trading profits. These
profits are the benefits that dealers obtain from the markets in order to fa-
cilitate efficient allocations among traders.
Although the continuous auction produces less trader surplus, it does al-
low traders to trade when they want to trade. Bif paid a higher price be-
cause he wanted to buy at 10:15. Had he known that Sue would arrive at
10:20, willing to sell at 20.0, he might have been willing to wait for the bet-
ter price. Instead, he paid Sam and Stu for the ability to trade when he
wanted to trade. Being able to trade when you want to trade is valuable, but
the trader surplus does not measure this benefit.
Assuming that both auctions received the same order flow, this analysis
of trader surplus demonstrates how the concentration of order flow increases
total trader surplus. In practice, traders will not send the same orders to


## Page 145

132 • TRADING AND EXCHANGES
both market structures. Most obviously, dealers will trade differently in con-
tinuous markets than in call markets. Although they extract profits from the
markets, they help the markets efficiently allocate the item traded between
the buyers and the sellers.
For a given order flow, the single price auction will trade a lower volume
than the continuous auction. Volume therefore is a poor measure of the abil-
ity of a market to produce trader surpluses. The clearing price of a single
price auction maximizes the total volume of trade possible at a uniform
price. Continuous markets can trade more than single price auctions be-
cause they may trade at more than one price. Exchanges could maximize
their trading volumes by matching buyers with sellers in order to minimize
the difference between the buyer's valuation and the seller's valuation for
each trade. This strategy, however, minimizes the total trader surplus.
6.5 THE DERIVATIVE PRICING RULE AND
CROSSING NETWORKS
Crossing networks
are the only order-driven markets that are not auction
markets. In a crossing network, all trades take place at prices determined
elsewhere. Crossing networks obtain their
crossing prices
from other markets
that trade the same instruments. Since the prices are derived elsewhere,
crossing networks use
derivative pricing rules.
Crossing networks do not discover prices as auction markets do. In auc-
tion markets, prices adjust to match buyers to sellers. In crossing networks,
prices are completely independent of the orders that traders submit. Cross-
ing networks only discover whether traders are willing to buy or sell at the
crossing prices.
The most important crossing networks trade U.S. equities. These include
ITG's POSIT, Instinet's Global Instinet Crossing, and the New York Stock
Exchange's After-hours Trading Session I. Table 6-10 lists the major U.S.
crossing markets.
These crossing networks are all call markets. Traders submit buy and sell
orders to them before the call. After the call, the crossing networks use their
order precedence rules to match the buy orders with the sell orders. All
matches that can trade at the crossing price become trades.
Instinct's Global Instinet Crossing and the NYSE's After-hours Trad-
ing Session I both cross stocks after-hours, using 4
P.M.
closing prices for
their crossing prices. Many traders use these systems because they provide
a second chance to trade at closing prices.
POSIT crosses stock eight times daily during regular trading hours. It
assigns crossing prices for its crosses by choosing a time at random within
the seven minutes that immediately follow each call. At that time, POSIT
computes the average of the bid and ask in each stock's primary market and
uses that price as the clearing price. Traders use POSIT because it gives
them an opportunity to fill their orders at the midpoint of the spread with-
out any price impact.
Since crossing networks do not choose market-clearing prices, they in-
variably have excess demand or supply at their crossing prices. If buyers want
to buy more than the sellers offer, all sell orders fill completely. If sellers
want to sell more than the buyers offer, all buy orders fill completely. Cross-
ing networks allocate the fully filled side to the oversubscribed side accord-
ing to their order precedence rules.


## Page 146

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133


## Page 147

134 • TRADING AND EXCHANGES
Ot. Pro Rata Allocation
of Excess Demand
in POSIT
Traders submit one sell order
and two buy orders in
Stewart Information Services
Corp. to the 1
P.M.
POSIT
cross. The sell order is for
3,000 shares. The two buy
orders are for 5,000 and
10,000 shares.
POSIT uses pro rata
allocation to allocate excess
supplies and demands. Since
the total sell volume is 20
percent of the total buy order
volume, the entire sell order
will fill and 20 percent of
each buy order will fill. The
first buy order will trade
1,000 shares, and the second
order will trade 2,000
shares.
4
Crossing networks fill only a small fraction of the total order volume that
traders submit to them. Traders frequently find that no one is on the other
side of the market. Less than 10 percent of their order volume ever crosses.
Traders whose orders do not fill in crossing networks often submit their
orders elsewhere.
Traders use crossing networks because they allow buyers and sellers to
find each other without having any impact on prices. Although most order
volume does not fill, traders still attempt to cross orders because crossing
commissions are only 1 to 2 cents per share and because crossing has no
immediate market impact.
All three major crossing networks are completely confidential and
anonymous systems. They do not display trader orders, and they do not dis-
play order imbalances following their crosses. Traders want this confiden-
tiality because most will submit the unfilled remainders of their orders to
other markets. They do not want other traders to know what they intend
to do. If the crossing networks displayed their orders, traders would submit
only a portion of their orders so as to avoid displaying their full sizes. Since
these networks profit only from filled orders, they want traders to submit
their full order sizes.
Some crossing networks operate continuously. These networks attempt
to arrange trades whenever orders arrive. Orders that cannot immediately
trade either wait in the system order book or are forwarded to other mar-
kets. Many brokerages try to cross their customer orders before they for-
ward them to exchanges.
6.5.1 Price Ownership
Crossing networks work well only if traders will trade at their crossing prices.
If traders do not trust the crossing prices, they will not trade. Successful
crossing networks therefore must take their prices from markets that pro-
duce credible prices.
The primary markets from which crossing networks obtain their cross-
ing prices believe that crossing networks unfairly compete with them. The
crossing networks take many orders that otherwise might go to them. Since
the orders that lead to crosses are the easiest orders to fill, the primary mar-
kets complain that crossing networks skim the cream of their order flow
without properly compensating them for using their prices. They argue that
they should receive the crossing orders because they produce the prices that
crossing networks require to operate successfully.
Crossing network customers rebut this argument by asserting that they
should not have to pay for price discovery when they do not participate in
it. Crossing market traders who also submit orders to primary markets fur-
ther argue that primary market prices should belong to them because their
orders produce the prices.
6.5.2 Problems with Derivative Pricing
Traders who trade at derivative prices must be aware of two problems with
such prices. They must be sure that these prices are not stale, and they must
be sure that other traders do not manipulate these prices. A
stale price
is an
old price that no longer accurately reflects the value of an instrument. A


## Page 148

CHAPTER 6 ORDER-DRIVEN MARKETS • 135
Informed Trading in Crossing Networks
Stock values continue to change following the
4
P.M.
close of equity trading
in the primary U.S. listing markets (the NYSE, AMEX, and Nasdaq). After
the close, corporations may release news; governments may release reports;
many significant events may take place; and people may change their
opinions about values as they reflect on the day just ended.
We observe direct evidence of these changes in value by watching the
prices of listed stocks that continue to trade at the regional exchanges until
4:30
P.M.,
and Nasdaq stocks that continue to trade after-hours in various
ECNs. We also see changes in index futures contracts and index option
contracts that trade in Chicago until 4:15 Eastern Time.
Well-informed traders consider this information when submitting orders to
the crossing market and the NYSE After-hours Trading Session I. Both
systems cross in the early evening, using 4
P.M.
closing prices. These
crossing networks generally receive more buy order volume than sell order
volume when prices have risen in after-hours trading, when there is good
news about security values, or when the closing price occurred at the bid
instead of the offer. Similar results hold when values decrease.
To partially address the adverse selection problem, both crossing
networks do not conduct crosses for stocks that have significant after-hours
news events.
manipulated price is
a
price that a trader has deliberately changed in order
to obtain some advantage.
6.5.2.1 Stale
Prices and Well
-
informed Traders
The stale price problem arises when traders arrange trades at predetermined
prices. A price that was fair when it was determined may not still be fair
when the trade takes place. Instrument values may change in the interval.
Traders who assume that the price is still fair will find that if they can eas-
ily arrange their trades, they will often regret doing so. In addition, when
they cannot arrange their trades, they will often wish that they had. This
happens because traders who know that values have changed will eagerly
trade at stale prices if they can benefit from the change in value, and they
will refuse to trade otherwise. If values have risen, well-informed traders
will eagerly buy at the low stale price, and they will refuse to sell at that
price. If values have fallen, they will eagerly sell and refuse to buy.
The stale price problem is an
adverse selection
problem. The well-informed
traders select the side of the market on which to trade to the disadvantage
of their uninformed counterparts. Adverse selection is one of the most im-
portant forces that affect trading. As the accompanying box illustrates, it
explains some empirical regularities found in after-hours crossing markets.
6.5.2.2 Price Manipulation
The potential for price manipulation exists whenever traders agree to trade
at a price to be determined elsewhere in the future. The buyer and seller
both may be tempted to manipulate the price they will use for their trade.
The buyer would like a lower price, and the seller would like a higher price.
If their trade is large, one or both of the traders may spend considerable
Price Manipulation
and Derivative Pricing
Suppose that Bob has a
contract to buy 500,000
shares of IBM from Sally at
the last NYSE trade before
1:30
P.M.
Bob would like that
price to be low, and Sally
would like it to be high. If Bob
submits a 1,000-share market
sell order at 1:29:30
P.M.
that
depresses IBM's price by 3
cents, he will save 15,000
dollars on his 500,000 share
purchase at a cost of only
30 dollars.


## Page 149

136 • TRADING AND EXCHANGES
00 Jesse Livermore's Manipulation of Some Bucketeers
Jesse Livermore was a famous turn-of-the-century speculator. He collaborated
with Edwin Lefevre to write an autobiography titled
Reminiscences of a
Stock Operator.
The book is a classic about trading.
The book describes a number of manipulations in which Livermore
participated, as either victim or perpetrator. One such manipulation
occurred while he was trading in some illegal bucketeering shops.
A
bucketeer
is essentially a bookie who allows his customers to bet on
stocks. His business operates like a regular brokerage where traders buy
and sell stocks. The bucketeer fills the orders from his account, however,
rather than sending them to an exchange. Since the trade prices in the
bucketeer's shop are derived from the next trade prices that come over the
ticker tape, traders can manipulate them.
To profit from the bucketeers, Livermore simultaneously submitted orders
to five different bucketeers to buy 100 shares each of a somewhat illiquid
stock. At the same time, he submitted an urgent order to sell 100 shares of
the same stock to a legitimate broker. The legitimate broker wired the sell
order to the New York floor, where it filled at a low price. This low price
allowed Livermore to buy from the five bucketeers at a low price. Later, he
conducted the same operation in reverse. Although he lost money on the
100-share New York trades, he more than made it up on the 500 total
shares that he traded in the bucketeering shops.
resources to manipulate the price. If both attempt to do so, however, their
efforts probably will cancel out, and both will lose.
Price manipulation is illegal in the United States and in most other coun-
tries. However, it may be more common than is widely acknowledged be-
cause it usually is hard to detect.
Since
POSIT
traders submit their orders before the crossing prices are
determined, a potential for price manipulation exists in the primary mar-
kets. Traders who believe that they will buy in POSIT have an incentive to
place sell orders in the primary market to lower prices there, and thereby
lower the POSIT crossing price. POSIT sellers likewise would like to raise
the primary market prices. If the crossing trade is large and if traders can
move the primary market with a small order, this strategy can be profitable.
To discourage price manipulation, POSIT picks its crossing prices at
random times within seven minutes following the call. Potential manipula-
tors must therefore depress prices for a seven-minute period rather than at
a single point in time. This procedure increases the costs of manipulation
by increasing the number of orders that manipulators would have to sub-
mit. The greater number of orders also makes it easier to detect and pros-
ecute manipulators.
POSIT also frustrates potential manipulators by keeping all orders con-
fidential and by reporting crosses only after it prices them. POSIT traders
therefore cannot know before the cross whether they will trade and how
much they will trade. To protect themselves from market manipulators,
POSIT traders likewise should not allow other traders to know about their
orders.
The final settlement prices for cash-settled futures and option contracts
are derived from the cash prices of their underlying instruments. Conse-
quently, these prices are sometimes subject to manipulation when these con-
tracts expire. To prevent manipulations, some cash-settled contracts specify


## Page 150

CHAPTER 6 ORDER-DRIVEN MARKETS • 137
that the exchange may choose final settlement prices to represent fair val-
ues when market values appear to be wrong.
6.6 SUMMARY
Order-driven markets include oral auctions, single price auctions, continu-
ous rule-based auctions, and crossing networks. These markets use order
precedence rules to match buyers to sellers, and trade pricing rules to price
the resulting trades.
The trading rules are very important. They affect how traders behave,
and they determine who has power and privilege in the market. Since these
rules affect how traders form their order submission strategies, they greatly
influence whether traders decide to supply or take liquidity.
The first precedence rule at all markets is the price priority rule. This
rule encourages traders to bid high and offer low. Various secondary prece-
dence rules then follow. Time precedence rules encourage traders to submit
their orders early. In conjunction with price priority, time precedence rules
also encourage traders to bid high and offer low. Display precedence rules
encourage traders to display their orders. Public order precedence rules give
power to public traders over exchange members. Depending on the market,
size precedence rules may give precedence to large traders or to small traders.
Trade pricing rules vary by market type. Continuous trading auction mar-
kets use the discriminatory pricing rule. This rule favors large liquidity-
demanding traders over small liquidity suppliers. Single price auctions use
the uniform pricing rule. This rule gives power to small liquidity suppliers
at the expense of large traders. Crossing networks use the derivative pric-
ing rule. This rule favors well-informed traders over uninformed traders,
and market manipulators over weak and honest traders.
Many current issues in market structure involve order-driven markets.
Should oral auctions convert to automated auctions? Should crossing net-
works exist, and if so, should they be better integrated with the markets
from which they derive their prices? Should markets organize more single
price auctions and should they encourage traders to participate in them?
How large should the minimum price increment be? In general, which mar-
ket structure is best?
Each market structure has its advantages and disadvantages. This chap-
ter identifies only some of the issues. To fairly compare market structures,
you need to know more about why people trade, how they trade, and what
brokers do. We will return to discussing the pros and cons of various mar-
ket structures in the last part of the book.
6.7 SOME POINTS TO REMEMBER
•
Limit order traders favor the uniform trade pricing rule.
•
Large market order traders prefer the discriminatory trade pricing rule.
•
Price priority is self-enforcing, but secondary precedence rules are not.
•
Secondary precedence rules require a large minimum price increment
to be economically significant.
•
Single price auctions maximize trader surplus.
•
Continuous auctions generate more volume for a given order flow.
•
Markets that use the derivative trade pricing rule are subject to price
manipulation.


## Page 151

138 • TRADING AND EXCHANGES
6.8 QUESTIONS FOR THOUGHT
1.
Should exchanges make the minimum price increment very small and
get rid of secondary precedence rules?
2.
Continuous trading markets that want to enforce a uniform pricing
rule must either prevent traders from splitting their orders or some-
how reprice earlier trades when traders do split their orders. Can you
imagine mechanisms that continuous order-driven exchanges can im-
plement in electronic environments in order to effectively enforce uni-
form pricing? What considerations suggest that markets will not adopt
such mechanisms?
3.
Computerized traders in electronic trading systems have some of the
same informational advantages that floor traders have in oral auctions.
Some oral auctions have public order precedence rules to give public
traders more power in their markets. Should electronic trading sys-
tems have a similar rule to give human traders precedence over com-
puterized traders?
4.
Should crossing networks pay for the right to use prices determined
in other markets to price their crosses? Who should own the prices
produced at exchanges?
5.
How does informed trading hurt uninformed traders who use cross-
ing networks to arrange trades at closing prices?
6.
Should automated trading replace floor-based trading?
7.
Suppose that a continuous auction starts the day with an empty book.
Only one buy and one sell order arrive during the day. The buy limit
price is 20 and the sell limit price is 19. If the buy order arrives first,
the trade price will be 20. If the sell order arrives first, the trade price
will be 19. The trader who first offers liquidity thus receives the worst
price. Is this sensible? What makes this example unusual? What are
the implications of this example for trading strategies in very inactive
markets?


## Page 152

7
1>
Brokers
B
rokers
are agents who arrange trades for their clients. Unlike dealers, who
trade with their clients, brokers trade their clients' orders. Clients usu-
ally pay brokers
commissions
for their services.
Many brokers are also
financial advisers
who advise their clients about
their investments or their financial plans. They may also provide their clients
with investment information. In these capacities, they often influence the
trading decisions that their clients make.
Unless you arrange your own trades, you will use the services of a bro-
ker when you implement your trading strategies. You therefore must un-
derstand what brokers can do for you—and to you—in order to trade ef-
fectively. This chapter describes what brokers do and the problems that
traders may have with lazy or dishonest brokers.
You also need to know what brokers do if you want to be a broker your-
self. The discussions in this chapter will allow you to better understand how
brokers compete with each other for business, and how the best brokers win
these competitions.
You must understand what brokers do in order to predict when elec-
tronic order-matching systems will be successful. Automated order-driven
execution systems are essentially electronic brokers. Since traditional bro-
kers and electronic order-matching systems both match buyers to sellers,
they compete with each other. To fully understand either system, you must
understand the economics of both trading systems.
Finally, you must understand what brokers do if you are interested in the
distinctions that regulators make between automated order-driven execution
systems and traditional brokers. Some automated order-driven execution sys-
tems are regulated as exchanges, whereas other nearly identical systems are
regulated as brokers. If you are interested in these distinctions, you must ask
how the order matching done by traditional brokers differs from the order
matching done by automated systems.
We begin this chapter by considering how brokers serve their clients,
how they organize their operations, and what determines their profits. We
then discuss how the most important management problem—the principal–
agent problem—affects brokers and their clients. The chapter closes with a
discussion of problems that traders can have with dishonest brokers, and
how traders can prevent these problems.
7.1 WHAT BROKERS DO
Brokers arrange trades for their clients. They search for traders who are
willing to trade with their clients; they represent their clients at exchanges;
they arrange for dealers to fill their clients' orders; they introduce their
clients to electronic trading systems; and they match their clients' buy and
sell orders.
139


## Page 153

140 • TRADING AND EXCHANGES
TABLE 7- 1 .
Types of Brokered Transactions
MARKET TYPE

TRADES

MARKET STRUCTURE

BROKERAGE ROLE
Order flow

Small to medium sizes

Order-driven or

Brokers receive orders and match
in seasoned securities

quote-driven

them with orders and quotes made
and contracts

by other traders.
Block

Large sizes in

Brokered

Brokers receive an order on one side
seasoned securities

and must search for traders who will
and contracts

take the other side. Brokers
occasionally identify both sides.
New and

Large size offered by

Brokered

Brokers sell securities to buyers on
seasoned

an issuer or one or

behalf of issuers and large holders.
offerings

more large holders
Mergers and

Company to company

Brokered

Brokers find one or both parties.
acquisitions
Brokers conduct these activities in various types of markets. In
order flow
markets,
brokers take orders that their clients give them and match them
with orders and quotes made by other traders. Exchanges, dealers, or the
brokers themselves may operate these markets. Brokers generally search for
the best price only among traders who are willing to display their limit or-
ders and quotes in these markets. In
block markets,
brokers take large client
orders and try to find other traders to fill them. Brokers often must search
among traders who have not expressed interest in trading to discover those
traders who are willing to trade. In
offering markets,
brokers distribute new
issues and seasoned issues to traders. Brokers often must market these se-
curities to generate buyer interest. Finally, in
merger and acquisition markets,
brokers help firms buy other firms. Brokerage firms that engage in large
capital transactions are called
investment banks.
Table 7-1 summarizes the
different types of brokered transactions.
Only the largest investment banks operate in all types of markets. Most
brokerage firms specialize in only one or two of these markets.
In all markets, brokers are their clients' agents. Their clients tell them what
trades they want to make, and under what terms they will trade. The brokers
then try to arrange the best trades that they can, subject to the constraints
imposed upon them. Generally, clients expect that brokers will seek the low-
est possible prices when buying and the highest possible prices when selling.
Clients use brokers to arrange their trades because brokers usually can
arrange trades at a much lower cost than their clients can. The following
reasons explain why brokers are low-cost traders:
•
Brokers can solve clearing and settlement problems at a lower cost than
their clients can.
•
Brokers can access exchanges and dealers that their clients cannot
access.
•
Brokers generally know better than their clients who might be willing
to trade.


## Page 154

CHAPTER 7 BROKERS • 141
•
Brokers are often better negotiators than their clients are.
•
Brokers can represent orders for their clients when their clients are un-
available to represent them themselves.
We examine these points in the remainder of this section.
7.1.1 Clearing and Settlement Among Traders
The most important, but perhaps least appreciated, reason why traders use
brokers to arrange their trades involves clearing and settlement. Clearing
and settlement problems can arise whenever traders do not settle their trades
immediately after they negotiate them. During the time between arrange-
ment and final settlement, traders risk that their counterparts may not ac-
knowledge their trades, may refuse to settle their trades, or may be finan-
cially unable to settle their trades. Traders therefore are reluctant to trade
with people they do not know are trustworthy and creditworthy.
Without the assistance of brokers, traders would have to check the credit
of every trader with whom they trade. Brokers assist traders by helping them
avoid this expensive problem.
Brokers solve clearance problems by clearing their clients' trades. If a
client fails to acknowledge a trade, the broker must resolve the problem with
the client. The broker thus protects the trader on the other side of the trade.
Brokers solve the settlement problem either by guaranteeing that their
clients will settle their trades, or by staking their business reputations on
whether their clients will settle their trades. When brokers guarantee their
clients' trades, the brokers settle trades that their clients will not. When the
brokers simply vouch for their clients, they risk losing future business if they
acquire a reputation for representing clients who do not settle their trades.
In both cases, brokers must ensure that they represent only trustworthy and
creditworthy clients. Otherwise, undesirable clients will impose significant
costs upon them. The credit function that brokers provide is especially im-
portant in order-driven markets, since such markets generally arrange trades
among total strangers.
Brokers are especially good at solving settlement credit problems because
they know their clients. Brokers will not accept orders to buy more than
they believe their clients can afford, or to sell more than they believe their
clients have. To form these opinions, brokers consider what securities and
money their clients have on deposit with them, and they consider their
clients' past behavior. Brokers may also consider other information that they
obtain from their clients or from credit agencies. Most brokers use elec-
tronic systems to manage this information.
Brokers also can efficiently solve settlement credit problems because they
control assets that their clients deposit with them. When a broker settles a
trade on behalf of a client, but the client fails to settle with the broker, the
broker can liquidate assets in the client's account to cover the trade. For ex-
ample, if a client does not pay for a stock that he bought, the broker can
sell the stock and use the proceeds to settle the trade. The broker can then
charge any loss on the round-trip to the client's account and, if necessary,
liquidate assets in the account to settle the debt. Likewise, if a client does
not deliver a security that she has sold, the broker can buy or borrow the
security from another trader and use it to settle the trader's sale. The bro-
ker can then liquidate assets in the account to settle the debt.


## Page 155

142 • TRADING AND EXCHANGES
Multiplying Credit Checks
When traders arrange trades that they intend to settle in the future, they
must be confident that their counterparts can and will perform. Traders
routinely perform
credit checks
to determine whether their counterparts are
creditworthy.
In a market with no brokers, each trader must be prepared to
check the credit of every other trader. If exactly 1 million traders trade in
such a market, the total number of potential credit relationships is
999,999,000,000, or slightly less than one trillion. In such markets, traders
will check the credit only of traders with whom they intend to trade. They
will naturally prefer to arrange trades only with traders whose credit
they have already checked.
Now suppose that this market has brokers who guarantee their clients'
trades. Three types of credit relationships are present in this economy:
1.
Brokers must check the credit of their clients to protect themselves.
2.
The clients must check the credit of their brokers to ensure that they can
trust them. These credit checks may be perfunctory if everyone knows
that a broker is creditworthy.
3.
Each broker must check the credit of every other broker with whom he
or she arranges trades.
If the market has 100 brokers, each of whom serves 10,000 different
clients, the total number of potential credit relationships is only 2,009,900.
This sum is 500,000 times smaller than the total number of potential credit
relationships in the economy without brokers!
Membership Has
Its Benefits
Some large traders become
exchange members so that
they do not have to trade
through brokers. By employing
their own traders, they obtain
greater control over their
trades, and they avoid
exposing their orders to
brokers they may not trust.
4
7.1.2 Brokers Provide Access to Exchanges
Traders also use brokers because brokers can provide access to exchanges
that they cannot access themselves. Exchanges generally allow only their
members to trade. Nonmembers who want to trade must have members
arrange trades for them. The most important reasons why exchanges ex-
clude nonmember traders involve clearing and settlement issues and the
need to regulate traders on exchange floors.
7.1.2.1 Clearing and Settlement at Exchanges
Since order-driven exchanges often arrange trades among strangers, they
generally do not allow anyone to trade who does not have an approved credit
relationship with the exchange clearinghouse or with a clearing member
who will guarantee settlement of their trades. Since most traders do not
have these relationships, they must trade through brokers who do.
Some brokers neither clear nor execute their own trades. Instead, these
introducing brokers
pass their order flow to another broker who is a clearing
member. The clearing member is then responsible for execution, clearing,
and settlement. Introducing brokers are so called because they introduce
their clients to other brokers. Introducing brokers usually establish the com-
mission rates that their clients pay, even though their clients' accounts are
carried
on account
of a clearing member. Clearing members charge their in-
troducing brokers for transaction services.
Many brokers allow their clients
direct access
to the electronic order-
routing systems that exchanges, dealers, ECNs, and other brokers maintain.
When these systems connect to automated order execution systems, the


## Page 156

CHAPTER 7 BROKERS • 143
What's Happening Here?
Most first-time visitors to the floor of an exchange—especially a futures
exchange—are overwhelmed by sensory overload. Although the activity on
exchange floors is highly organized, it appears very chaotic to infrequent
visitors. Traders and clerks wearing multicolored jackets run everywhere,
carrying papers. People yell loudly, gesture wildly, and pass hand signals
among themselves. Brokers talk on their telephones and enter information
into their handheld computers. Discarded paper litters the floor. Bells
occasionally sound, personal pagers beep, and telephones ring constantly.
Overhead monitors display constantly changing information, television
screens present network talking heads pointing at lines and numbers, and
electronic ticker tapes scroll continuously.
After visitors overcome their sense of awe, their next emotion is usually
frustration. They generally cannot follow what is happening. They get
confused about who is buying, who is selling, and what they are trading.
They cannot distinguish between prices and quantities, and the prices they
hear often do not make sense.
Trading is hard for novices to understand because traders use jargon,
abbreviations, body language, and hand signals to save time and reduce
trading errors. For example, most traders yell only the last digits when
quoting a price. When they bid 10, they assume that everyone knows that
they mean 147.10. They do not say 147.10 because it takes too long and
because they do not want to confuse anyone with unnecessary digits. When
they name an instrument, they often use nicknames or ticker symbols that a
novice does not recognize. They may not even name the instrument when
other traders know what they are talking about from the context. Trader
jargon like "I'll take 30 at the figure" is hard to understand without
knowing that
take
implies that I am a buyer, and the
figure
means the
closest integer price.
No new trader walks in off the street and starts to trade in such
markets. Instead, most traders start as clerks working for other traders or
for the exchange. If they pay close attention and are reasonably sharp,
they get the hang of things in a few weeks, and they master the language
in a few months. At that point, they may not be able to trade well, but they
will
know
what is going on, and they should not be a liability to other
traders.
4
clients effectively become the traders. The brokers, however, usually remain
responsible for guaranteeing settlement.
Clients who have direct access to trading systems are often called
sub-
scribers.
Public subscribers to a trading system must have a broker sponsor
and authorize their trades. To help brokers manage credit relations with
their sponsored subscribers, many electronic trading systems allow brokers
to set real-time credit limits on their subscriber accounts.
7.1.2.2 Floor
-
based Trading Skills
Floor-based exchanges exclude nonmember traders from their floors be-
cause orderly trading on floor-based exchanges requires skilled traders who
know the trading rules, the trading protocols, the specialized jargon, and
the sign languages that traders use to negotiate their trades. Traders use
these skills to increase the speed and accuracy of their trading.
Most markets do not allow their members to trade until they pass an ex-
amination which demonstrates that they have mastered basic trading skills.


## Page 157

144 • TRADING AND EXCHANGES
The examination protects traders from unskilled traders who might slow
the markets, confuse others, and generate mistakes.
7.1.3 Brokers Provide Access to Dealers
Many traders use brokers to access dealers that they cannot access them-
selves. Brokers provide this service primarily to retail clients. Retail traders
rarely trade directly with dealers because credit, clearance, and settlement
relationships are expensive to establish. Neither retail traders nor dealers
want to pay for creating relationships they will not use often. Retail clients
also use brokers to trade with dealers because brokers usually have better
information about which dealers are offering the best prices or will likely
offer the best prices than do their clients.
Institutional clients often trade directly with dealers. Their large and fre-
quent trades make the costs of establishing direct access relationships rela-
tively small compared to the benefits of direct access. Institutional traders
usually employ
buy-side traders
to negotiate trades with dealers. These traders
typically have information systems that allow them to see all dealer quotes,
and they can rely upon their experience to determine which dealers will
most likely offer them the best prices. Buy-side traders generally do not pay
commissions to dealers when they arrange their trades. Instead, they trade
on a
net price basis.
The dealers price the trades to recover any expenses that
commissions would otherwise fund. In the U.S. equity markets, this prac-
tice is changing as bid/ask spreads have narrowed in response to the 2001
decimalization.
7.1.4 Brokers Are Expert Traders
Many traders use brokers because brokers are experts at trading. Brokers
generally know more about who wants to trade than do their clients, they
are better negotiators than are their clients, and they are better able to man-
age order exposure than their clients can. In this subsection, we consider
each of these areas of expertise.
7.1 .4.1
Block Brokers
The most successful block brokers know who wants to trade, and who would
want to trade if they were presented with suitable trading opportunities.
They can predict what securities will interest their clients and at what prices
their clients will be interested. They use this information to arrange trades.
Brokers learn about their clients by paying close attention to them. They
talk with them frequently, and they study their portfolios to determine what
interests them. If their clients manage money for others, brokers will also
consider the interests of their clients' clients.
7.1.4.2 Better Negotiators
Traders use brokers to negotiate transactions—especially very large ones—
on their behalf. Good negotiators must be careful about the information
they reveal when negotiating. Depending on their negotiating strategy, they
may want to hide information or they may want to bluff credibly. In either
event, they must represent their positions clearly and convincingly. Good
negotiators also must create relationships in which their counterparts are
willing to compromise and accommodate.
Not everyone negotiates effectively. Many of us cannot adequately con-
trol our emotions when discussing issues about which we care deeply. We


## Page 158

CHAPTER 7 BROKERS • 145
How to Break into the Major Leagues
Block brokers must know their clients well in order to serve them well. New
brokers often have trouble establishing relationships because clients
generally do not like to waste their time talking with brokers who are not
players.
A
player
can offer valuable services to his or her clients. Since new
brokers usually do not know much about who would want to take the other
side of a trade, they have little to offer their clients.
The problem that new brokers face involves a circularity. If they do not
know their clients well, they cannot serve them well. If they cannot serve
their clients well, their clients will not talk to them. If their clients will not talk
to them, they will not know them well.
Established brokers benefit from the opposite side of this circularity.
Since they know their clients well, they know who will trade what and at
what price. They therefore can provide them with good service. Clients will
talk with established brokers because they obtain good service from them.
Established brokers thereby get to know their clients well.
This circularity is an example of a
network externality.
Brokers who have
large networks of contacts can provide more service to their clients than can
brokers with few contacts. The more service that brokers can provide to
their clients, the more clients they will have, and the bigger will be their
networks of contacts. The network externality allows the strong to get
stronger, and it ensures that the weak have trouble competing.
To become players, new brokers must attract clients with something other
than information about who wants to trade. They most often offer investment
research. If their clients value their research, they will form relationships that
will allow the brokers to learn more about their clients' trading interests.
New brokers also try to meet with their clients by entertaining them.
They often take them to fancy dinners, to the Super Bowl, to the NBA finals,
or to the theater. These contracts have a twofold purpose. The brokers want
to learn more about their clients so that they can serve them better. They
also want to generate goodwill so that their clients feel obliged to use them.
Since well-established brokers compete with new brokers, they also
provide investment research and entertainment services to remain
competitive. They do not need to provide as much to stay in business as
they did to get established. Established brokers have an advantage simply
because they know more about who will trade, what they will trade, and at
what prices they will trade than do new brokers.
The economics of brokerage markets implies that the best brokers are
those who work hardest to learn about their clients. The network externality
ensures that their productivity per hour worked increases as the time they
work increases. Accordingly, brokers often work long hours without much
vacation. When they do vacation, they often do so at conferences where
they can meet their clients. Brokers therefore host many client conferences at
which they entertain them, educate them, generate goodwill, and, most
important, learn about what trades might interest them. The need to
continuously relate to their clients also explains why most brokers vacation
during the same months that their clients do. I
may be nervous about losing a valuable opportunity, desperate to avoid fur-
ther loss, mad about our circumstances, too eager to please, too proud, too
modest, or unable to control our egos. Our emotions often cloud our judg-
ment and cause us to reveal information that does not further our goals.
Many of us cannot convincingly represent our true positions or maintain a
poker face when bluffing. Finally, and unfortunately, many of us do not have
adequate social skills to develop productive relationships with people whose
interests differ from our own.


## Page 159

146 • TRADING AND EXCHANGES
Po Playing Poker
on the Floor
Floor traders scrutinize every
mannerism of the brokers
around them. They try to
identify anomalous behaviors
that might reveal information
about the orders which
brokers hold. Floor traders
especially consider whether
brokers appear nervous when
they hold large orders.
Successful brokers
therefore must be like good
poker players. They must not
reveal anything about their
orders other than what
they want to reveal. Not
surprisingly, many brokers are
very good poker players.
Traders who recognize their shortcomings as negotiators often employ
brokers to negotiate on their behalf. Since brokers usually do not have a
stake in their negotiations other than in obtaining commissions for closing
successful deals, they are often much less emotionally involved than their
clients are. In addition, since brokers are merely agents, they often do not
know their clients' final positions. Clients therefore can use their brokers to
misrepresent their positions. If they later need to back down, the brokers
can save face by claiming that they misunderstood their clients' instructions.
If negotiations break down, clients often blame their brokers, assert a mis-
understanding, and then start negotiations with new brokers. Although their
adversaries may recognize these tactics, if enough credible doubt exists about
what happened, productive negotiations may be renewed.
7.1.4.3 Brokers Provide Order Exposure Management
Traders whose orders are likely to move the market significantly do not want
to widely expose their orders. Traders either will stand out of the way un-
til the order has its impact on the market price, or they will trade ahead of
the order to profit from its expected price impact. Both strategies increase
the costs of filling large orders.
The traders who are most concerned about these issues are those who
are widely known to be well informed and are known to trade in large size.
These traders would rather trade anonymously so that no one knows with
whom they are trading. If their orders are large, they typically expose only
parts of their orders so that no one knows their full sizes.
These traders employ brokers to represent them so that they can avoid
showing who they are and how large their orders are. Their brokers display
only to traders whom the brokers expect will be willing to fill the orders.
They take special care to avoid traders who will front-run them. In markets
where traders see all exposed orders, brokers break up their orders so that
other traders cannot determine their full sizes. Large traders sometimes
distribute their orders among several brokers so that nobody knows the full
extent of their interests.
Brokers add value to the trade process by knowing how best to expose
their clients' orders. Good brokers fill their large orders without moving the
market much. Poor brokers allow information about the order to leak out
so that its execution suffers.
7.1.5 Brokers Represent Limit Orders
Many traders employ brokers to represent orders that they cannot, or do
not want, to represent themselves. Clients who have other things to do be-
sides monitoring the market often give limit orders and stop orders to their
brokers to tell them what they want to do if market conditions change. The
brokers then monitor the market for them. This brokerage function is more
important for retail clients who do not spend much time trading than for
institutional buy-side traders whose jobs require that they continuously pay
attention to the markets.
7.1.6 Summary
Brokers provide many services to their clients. They help them identify suit-
able counterparts, they help them negotiate their trades, they represent their
interests when they are unable or unwilling to represent them themselves,


## Page 160

CHAPTER 7 BROKERS •
147
and they help them clear and settle trades. Traders use brokers because bro-
kers generally can trade more effectively and at a lower cost than they can.
7.2 THE STRUCTURE OF A BROKERAGE FIRM
We now consider how brokerage firms structure their operations to provide
transaction services to their clients. Since each brokerage organizes its op-
erations differently, the discussion in this section cannot adequately repre-
sent all brokerage firms. The primary purposes of the discussion are to in-
troduce you to the complexity of brokerage operations and to expose you to
the jargon that brokers use to describe their operations.
The activities of a brokerage firm consist
of front office
operations,
back
office
operations, and
proprietary
operations. All activities that involve client
contact occur in
the front office.
These activities primarily involve soliciting
and taking orders, executing trades, and advising clients.
Back office
opera-
tions include all activities that support the front office operations. Back
office departments clear and settle trades; maintain accounts; produce in-
vestment research; and create and operate various information systems.
Pro-
prietary
operations include the cash and risk management activities of the
firm and any speculative trading that the firm conducts for its own accounts.
These classifications are somewhat arbitrary. We will use them to organize
our discussion of how brokers structure their firms.
7.2.1 Front Office Operations
Brokers solicit order flow by advertising and by contacting prospective
clients. They also often give their clients extensive investment information
and investment research to encourage them to use their brokerage services.
To further develop their business, they may entertain their clients.
Sales brokers
primarily interact with clients. They work in the
Sales and
Trading Department
of the firm.
Floor brokers
arrange trades at exchanges
and on their firm's trading floors. Their division of the firm is often called
Floor Operations.
Brokers who help distribute large stock and bond offerings generally
work in the
Corporate Finance Department
of the brokerage firm. They work
closely with sales brokers to distribute the issues.
Many brokerage firms employ
financial analysts
to produce investment
reports for their clients. The sales brokers use this information to develop
relations with their clients. These analysts usually specialize in an indus-
try or a commodity. Their primary responsibilities include forecasting fu-
ture prices and earnings. At investment banks, equity analysts sometimes
suggest mergers and acquisitions to brokers who work in the Corporate
Finance Department. Financial analysts also help clients understand how
they can use new trading instruments and techniques to achieve their ob-
jectives. The financial analysts usually work in the
Research Department
of the firm.
Most brokerages have
customer service agents
who help their clients man-
age their accounts. These agents establish, transfer, and close accounts; they
take deposits and arrange withdrawals and transfers to and from accounts;
and they help their customers interpret their account statements. They usu-
ally work in the
Customer Service Department.
Why Does the Archer
Daniels Midland
Company Broker
Agency Orders?
The Archer Daniels Midland
Company (ADM) is a large
agricultural dealer, shipper,
and food processor. The
company frequently trades in
the futures markets to hedge
risks inherent in its businesses.
It also sometimes speculates
in these markets, using
information that it obtains
about market conditions from
its extensive operations.
ADM is a member of
many futures exchanges. It
employs many floor traders at
these exchanges. The ADM
traders trade proprietary
ADM orders. They also trade
agency orders introduced to
them through a subsidiary,
ADM Investor Services, that
sells execution, clearing, and
settlement services to
introducing brokers.
ADM undoubtedly hopes
to profit from the fees it
obtains from its agency
brokerage business. The
subsidiary also provides ADM
with an important secondary
benefit: The agency order
flow makes it difficult for
other traders to determine
when ADM is trading for its
own accounts and when it is
trading for other traders.
Consequently, ADM probably
obtains better execution for its
proprietary orders.


## Page 161

148 •
TRADING AND EXCHANGES
80 The Wednesday Trading
Halts of 1968
By 1968, volumes in U.S.
equity markets had grown to
the point that brokerage firms
could not keep up with their
paperwork. Accounting,
clearing, and settlement then
still involved substantial
manual efforts. From June
through December 1968,
exchanges closed on
Wednesdays to give the back
offices a chance to catch up.
They also closed two hours
early on the other weekdays.
Firms ultimately solved the
"paperwork crisis" by
automating their accounts.
7.2.2 Back Office Operations
The back office of a brokerage is responsible for supporting the trading ac-
tivities of the firm and its clients. The primary responsibilities of the back
office include the following activities:
•
Maintaining accounts.
•
Clearing and settling trades.
•
Providing the information systems that the firm uses to transmit mar-
ket data, quotes, orders, and confirmations to employees, clients, deal-
ers, exchanges, other brokers, clearing agents, settlement agents, and
custodians.
•
Ensuring that the firm extends credit only to good credit risks.
•
Ensuring that the firm and its clients comply with all regulations to
which they are subject.
Brokerage firms often place these activities under the supervision of a
chief
information officer
(CIO) who oversees all information systems.
7.2.2.1 Accounting Systems
Brokerage firms now universally use computerized accounting systems to
keep track of their accounts and to clear and settle their trades. The bene-
fits of such systems are obvious and need no further comment.
Small brokerage firms and many large firms buy their accounting sys-
tems "off the shelf" from system vendors. Some large brokers use their own
systems, in part because they designed them to meet their special needs, but
mostly because they built them before they could buy them cheaply.
7.2.2.2 Corporate Reorganizations
Clients hold their securities in
street name
when they allow their brokers or
their depositories to hold them on their behalf. Traders often hold their se-
curities in street name to avoid losing them, to use them as collateral for
margin loans, and to ensure that they are available for settlement when they
want to sell them. When brokers hold securities on behalf of their clients,
they legally own them. Their clients hold only corresponding interests in
their accounts.
Brokers who hold securities in street name assume many responsibilities.
They must collect dividends and interest payments, and properly assign them
to the appropriate client accounts. They must keep track of and properly
handle corporate name changes, stock splits, mergers, acquisitions, and liq-
uidations. Finally, they must ensure that issuers can communicate with their
clients, the
beneficial owners.
The
Corporate Reorganizations Department
of
a brokerage firm generally handles these activities.
7.2.2.3
Market Data and Order
-
routing Systems
Brokerage firms invest very heavily in data and voice systems that allow their
brokers to communicate with their clients, with markets, with dealers, and
with each other. Third party vendors now mostly provide these systems.
The simultaneous use of market data systems written by different ven-
dors can create significant coordination problems when the systems need to
exchange information. For example, clearing and settlement systems need
to report trades to accounting systems, and accounting systems need to re-
port positions to clearing and settlement systems.


## Page 162

0
,
Squawk Boxes
In many brokerages, traders
need to talk with colleagues
who may sit on the other side
of the trading room, or in
another trading room that
may be thousands of miles
away. To facilitate such
communications, many
brokerage firms place
squawk
boxes
at each trader
workstation. Squawk boxes
are two-way intercoms that
are always open. 41
Ok How to FIX Babble
The FIX
protocol grew out of
a desire by Fidelity
Investments and Salomon
Brothers to link their
information systems and
thereby reduce their traders'
dependence on telephone
calls and handwritten records.
Following their initial
specification of the standard
in 1992, they invited other
firms to participate in its
further development.
FIX is now a public-
domain specification owned
and maintained by FIX
Protocol. The mission of the
organization is "to improve
the global trading process by
defining, managing, and
promoting an open protocol
for real-time, electronic
communication between
industry participants, while
complementing industry
standards."
I
Source: www.fixprotocol.org
.
CHAPTER 7 BROKERS • 149
Data systems are very hard to integrate when they use different proto-
cols for sharing information. The trading software industry therefore de-
veloped several communications protocols that make it easier for systems
developed by different vendors to exchange information with each other.
The most important of these protocols are the
Financial Information
eXchange
(FIX) protocol and the Open Financial Exchange (OFE) proto-
col. FIX primarily serves institutional traders, and OFE primarily serves
Internet-based retail traders. These systems allow traders to route orders
and other market information in standard formats that all FIX- or OFE-
capable systems can interpret.
7.2.2.4 Credit Management
Brokerage firms often extend credit to their clients, to other brokers, and
to dealers. They extend credit to clients when they allow clients who have
insufficient money in their account to buy securities, when clients sell se-
curities that they do not have in their accounts, when they lend money to
clients on margin, and when they guarantee that their clients will settle their
contracts. Brokerage firms extend credit to other brokerage firms and to
dealers when they settle their trades, and when they loan securities and
money to them.
To ensure that they do not extend credit to poor credit risks, brokers
must carefully evaluate all credit relationships in which they are exposed to
potential loss. The
credit manager
of the firm is responsible for checking
credit and for managing credit risks.
7.2.2.5 Compliance
The
compliance officers
of a brokerage firm ensure that the firm and its clients
comply with all applicable regulations. The regulations may concern mar-
gins, trading practices, and
client suitability.
The compliance officers of a
brokerage firm usually reside in the
Compliance Department
or the
Margin
Department
of the firm.
Many regulators do not permit brokers to arrange trades that are not
suitable for their clients. A trade creates an unsuitable position if the client
cannot afford the potential loss of the position or cannot reasonably appre-
ciate the risk in the position. Brokers who allow their clients to make such
trades risk civil lawsuits or criminal prosecution. To protect themselves from
this risk, brokers must
know their customers.
To this end, brokers often re-
quire that their clients tell them about their finances and their trading
experience. Questions about these issues usually appear on account appli-
cations. Brokers occasionally interview their clients when they become con-
cerned about the suitability of their trades or positions. Most brokers have
electronic systems that monitor their clients' accounts to ensure that their
trades are suitable for them.
7.2.3 Proprietary Operations
The
proprietary trading operations
of a brokerage firm include all trading ac-
tivities that the firm conducts for its
house account.
For pure brokers, these
activities primarily include cash management and the borrowing and lend-
ing of securities. If the firm also engages in
principal trading
as a dealer,
speculator, or arbitrageur, the proprietary trading operations of the firm in-
clude these activities.


## Page 163

150 • TRADING AND EXCHANGES
Graduated College
and Thought You'd
Never Have to Take
an Exam Again?
Some brokerage firms give
their clients a written
examination to prove that
they understand the risks
inherent in their trades.
Clients who do not pass the
examination cannot trade.
These examinations will
become more common as
brokers and their clients
increasingly relate to each
other only through the
Internet. The examinations
allow the compliance officers
to certify the competency of
their clients.
7.2.3.1 Cash Management and Stock
Lending and Borrowing
Brokerage firms that hold client assets generally invest the cash and often
lend the securities. The cash managers of the firm try to keep all cash bal-
ances fully invested. Brokers usually house their cash management opera-
tions in the
Cashier's Department
under the supervision of the firm's
cashier.
The employees responsible for security lending operations are often those
responsible for borrowing securities for short selling. They are often housed
in the
Margin Department
or in the
Stock Loan Department.
7.2.3.2 Risk Management
The
risk manager
of the firm monitors all activities of the firm to ensure
that the firm does not lose control over the risks it assumes. The risk man-
ager must ensure that large losses never surprise the firm's managers. In par-
ticular, the risk manager must make certain of the following:
•
The firm's management is aware of all significant financial and legal
risks to which the firm is exposed.
•
Adequate controls are in place to prevent rogue traders from creating
unauthorized positions.
•
The financial implications of all its proprietary positions are well rec-
ognized and understood.
•
The firm adequately understands the creditworthiness of those to
whom it extends credit.
•
The firm does not extend too much credit to poor credit risks.
The risk manager's job is not to prevent losses. Firms often lose money
because they undertake risky activities that do not work out as they hope.
Firms engage in these activities because they expect that the possible re-
wards are sufficiently large and sufficiently probable to more than compen-
sate for the possible losses. The risk manager's job is to ensure that the man-
agers of the firm adequately understand all possible losses and their
probabilities of occurrence.
The risk manager often reports directly to the CEO of the firm. The
firm usually gives the risk manager authority and power to investigate any
potential source of risk within the firm. These arrangements are necessary
to ensure that the risk manager has the independence and power to discover
any serious problems.
In smaller brokerage firms, the risk manager and the compliance officer
are often the same person. In larger firms, the compliance officer may work
for the risk manager, or the two officers may work separately.
7.3
BROKER PROFITABILITY
Like all firms, brokerages profit when their revenues exceed their expenses.
Their revenues come primarily from commissions, while their expenses are
primarily due to labor costs. Most brokerage firms have many other signif-
icant sources of revenue and cost. Their other income lines often explain
why some brokers can charge low or no commissions and still stay in busi-
ness. Their other costs help explain why large brokers often have significant
competitive advantages over smaller brokers.


## Page 164

CHAPTER 7 BROKERS • 151
In this section, we will examine the determinants of profitability. Our
discussion will help you understand how brokers compete with each other
and what factors determine the sizes of their firms.
7.3.1 Revenues
Most brokers obtain their primary revenue from commissions. Important
secondary sources of revenue include payments for orders, interest on cash
balances, margin interest on loans, underwriting fees, merger and acquisi-
tion consulting fees, and security lending fees.
7.3.1.1 Commissions
Brokerage commissions are negotiable in most countries. A few countries
have government or exchange regulations that specify
fixed commission rates
that brokers must charge. For example, the minimum stock brokerage com-
mission in Hong Kong is 0.25 percent. Such regulations are becoming quite
rare. Stock commissions were deregulated in 1975 in the United States, in
1987 in the United Kingdom, and in 1999 in Japan. As of this writing, the
Hong Kong commissions are scheduled to be deregulated on April 1, 2003.
Commissions vary substantially across brokers. Commission rates usu-
ally depend on how much service the client wants.
Deep discount brokers
offer the cheapest commissions but usually provide the least service.
Full
service brokers
charge the highest commissions but offer substantial service
and advice. Table 7-2 presents typical U.S. retail brokerage commissions for
various instruments.
Discount and deep discount brokers typically specify standard commis-
sion schedules for their clients. They may further discount their commis-
sions for their best clients. The commission schedules for full service bro-
kers are generally just list prices. Although some clients pay these prices,
most clients negotiate substantially lower commissions with their brokers.
Full service brokers increasingly charge a flat fee for accounts that they
advise. The fee covers all trading commissions, investment research fees, port-
folio management fees, and account maintenance fees that the client would
otherwise pay. Clients prefer flat fee arrangements because they greatly re-
duce the incentives that brokers have to
churn
their accounts. Brokers churn
accounts when they recommend trades primarily to produce commission rev-
enue. The typical all-inclusive fees for managed accounts range between 1
and 3 percent of the total value of the account. Like straight commissions,
they are negotiable. Fixed-fee accounts are sometimes called
wrap accounts
because the brokers wrap all commissions and expenses into a single fee.
In the United States, institutional stockbrokers typically charge a fixed
price per share traded. The average U.S. institutional commission is about
5 to 6 cents per share, but can range between 1 cent and 12 cents per share.
In most other countries, institutional stockbrokers base their commissions
on the value of the transaction. In almost all countries, commission rates
are negotiable. They may vary by the size of the trade, the difficulty of ar-
ranging it, and the soft dollars that the trade generates. (We describe soft
dollars below.) Institutional clients also sometimes get volume discounts
based on the total volume they trade during a month, quarter, or year.
Stockbrokers usually also broker options trades. U.S. deep discount bro-
kers typically charge 1.50 dollars per contract with a 20-dollar minimum.
Full-service brokers charge substantially more per trade.


## Page 165

152 • TRADING AND EXCHANGES
TABLE 7
-
2.
Typical Retail Brokerage Commissions in the U.S. (2001)
BROKER

RELATIONSHIP AND SERVICE

INSTRUMENT

COMMISSION
Deep
discount
brokers
Discount
brokers
Full-service
brokers
Clients must submit orders via the
Internet
Some electronic investment information
resources
Little or no investment research
No investment or financial planning
advice
Clients may submit orders over the phone
or at a discount via the Internet
Electronic investment information
resources and stock reports
Some investment research
Some fee-based investment and financial
planning advice
Clients and their personal brokers develop
relationships in which the brokers know
their clients well, and the clients trust
their brokers
Brokers supply investment research and
give investment and financial planning
advice
Directed account trading
Most retail full-service accounts are on an
annual "fee basis." Clients pay between 1
and 2 percent of their assets in exchange
for transaction services and research
Stocks
Stock options
Corporate
bonds
Treasury bills,
notes, and bonds
Futures contracts
Stocks
Stock options
Corporate bonds
Treasury bills,
notes, and bonds
Futures contracts
Stocks
Stock optoins
Treasury bills,
notes, and bonds
Corporate bonds
Futures contracts
$12 per trade for market orders
$15 per trade for limit orders
$1 per contract, minimum $15
per trade
$3 per bond, minimum $35
per trade
$40 per trade
$7 per contract round-turn
$30 per trade
$29 plus 1.6% of principal
$5 per bond, minimum $35
$50 per trade
$10 per side per contract, or
$20 round-turn
ft per share, minimum
$100-250 per trade, highly
negotiable
$50-100 per trade
Most full-service brokers trade
fixed-income securities as
principals rather than as
agents. They therefore charge
markups rather than
commissions. The markups
vary substantially.
$80 to $125 per contract
round-turn
Note: Commission rates vary substantially across brokers, and they change frequently.
Brokerages in the futures markets are
called futures
commission merchants
(FCMs). Discount FCMs typically charge about 30 dollars per contract
for a round-turn (two trades). They charge by the turn because they ex-
pect that their clients will close their positions before delivery. Many
FCMs, however, charge for each side so that they can advertise lower
commissions.


## Page 166

CHAPTER 7 BROKERS • 153
Soft Commissions
In the United States, before the final deregulation of stock brokerage com-
missions on May 1, 1975, brokerage commissions were much higher than
they would have been had commissions not been fixed by exchange regu-
lations. By then, automation of trading processes and growth in institutional
trading had significantly lowered broker costs. Brokers who could obtain
order flow could profit handsomely from the fixed commission rates. Bro-
kers, of course, competed intensely for the orders.
Brokers in unregulated commission markets obtain order flow by lower-
ing their commissions and by offering better service. In price-regulated mar-
kets, they can only offer better service or give their clients other things that
they value.
To obtain order flow, stockbrokers gave their institutional clients free ser-
vices. These services primarily included investment research, but brokers also
gave away accounting systems, communications systems, computing sys-
tems, and staff training. In addition, brokers provided their clients with mar-
keting incentives such as tickets to major ball games and all-expenses-paid
trips to investment conferences that they organized at expensive resorts. The
clients paid high fixed commissions and received various services besides
trade execution.
To promote fairness, brokers and clients started to keep track of the com-
missions they paid and the services they received. They ultimately created
a system of soft dollar accounting in which clients earned one
soft dollar
for
a certain number of
hard dollars
they spent on commissions. They then used
their soft dollars to buy various services from their brokers. They even asked
their brokers to buy services for them from third parties.
The soft dollar accounting system allowed brokers to compete for order
flow despite the fixed commissions. Clients benefited from more competi-
tive markets and lower net trading costs.
The soft dollar system hastened deregulation by undermining the sys-
tem of fixed commissions set by exchange rules. Commission deregulation
in the United States started in April 1971 with deregulation for trades larger
than 500,000 dollars. It continued in steps until all commissions were dereg-
ulated by May 1, 1975. (Traders refer to the deregulation as
May Day.)
Commissions dropped significantly following deregulation.
Interestingly, soft dollar usage has increased since deregulation. The U.S.
Securities and Exchange Commission estimates that the total value of re-
search paid for with soft dollars exceeded 1 billion dollars in 1998. To ob-
tain soft dollars, many institutional traders willingly pay much higher com-
missions than they would otherwise have to pay for execution services. In
1998, soft dollar brokers offered an average of 1 dollar of soft dollar ser-
vices for every 1.7 dollars of hard dollar commissions that they received.
Soft dollars persist in large part because of the way that investment funds
account for their expenses. When an investment fund pays hard dollars for
anything but a trading commission, the cost appears as an expense in its fi-
nancial accounts. Since many investors prefer investment funds that have
low expense ratios, funds try to minimize their hard dollar expenses. Al-
though investment funds pay commissions with hard dollars, they do not
treat them as direct expenses in their financial accounts. Instead, funds record
their purchases and sales on a net price basis. Commissions raise their pur-
chase prices and lower their sales prices. High commissions therefore lower


## Page 167

154 • TRADING AND EXCHANGES
their reported investment returns. Many investment funds prefer to buy
things with soft dollars so that they can avoid reporting direct expenses to
investors who are highly cost sensitive. The high volatility of portfolio re-
turns ensures that such investors cannot easily identify expenses which
reduce their investment returns.
The U.S. Securities and Exchange Commission recognizes this problem.
In 1995, it adopted regulations that require investment companies (primarily
mutual funds) to report the value of the goods and services that brokers pay
on their behalf as expenses in their accounting statements. Many invest-
ment companies avoid the intention of this requirement by hiring invest-
ment advisers to manage their funds. The brokers allocate soft dollars to the
advisers, who use them primarily to purchase research products. These soft
dollars allow investment advisers to charge lower management fees to their
investment company clients.
The SEC cannot close this loophole because Congress created a
safe
harbor
in the 1975 amendments to the Securities and Exchange Act of 1934
that specifically protects it. Section 28(e) of the amended Act allows in-
vestment advisers to cause their clients to pay more than the lowest avail-
able brokerage commissions if the advisers determine in good faith that the
amount of the commission is reasonable in relation to the value of the bro-
kerage and research services provided. What otherwise might appear to be
an improper kickback—the use of client commissions to obtain investment
research that benefits their investment advisers—is therefore legal in the
United States.
The U.S. Securities and Exchange Commission periodically considers
regulations that would require funds to provide more extensive reports of
their soft dollar expenses. Its efforts invariably encounter opposition from
funds, their soft dollar brokers, and the investment fund industry trade as-
sociation, the Investment Company Institute.
Soft commissions are common in other national markets where the same
players debate the same issues. For example, in Britain, the Financial Ser-
vices Authority (the British equivalent of the U.S. Securities and Exchange
Commission) frequently is at odds with the Fund Managers' Association
over the regulation of soft commissions.
Directed Brokerage and Commission Recapture
Many institutional investment sponsors direct their investment advisers to
use specific brokers when trading for their accounts. Sponsors create
direct
brokerage
relationships to support specific brokers. For example, political con-
siderations force many state and municipal pension funds to use in-state
brokers. Some sponsors ask their managers to direct orders to specific bro-
kers so that the sponsors can obtain services which those brokers offer in
exchange for the order flow.
Sometimes pension plan sponsors negotiate
commission recapture agree-
ments
with the brokers to whom they direct their orders. These agreements
provide that the brokers will return to the investment sponsor some of the
commissions paid to them by the sponsor. The recaptured commissions may
reflect volume discounts or they may simply be rebates. State and munici-
pal plan sponsors generally use the money to pay for investment consulting
services for which they otherwise would have no budget.


## Page 168

CHAPTER 7 BROKERS • 155
The
Employee Retirement Income Security Act
requires that the trustees of
U.S. private pension plans treat commissions as fund assets. They therefore
would have to return to their funds any recaptured commissions that bro-
kers pay them. Consequently, private pension plans do not generally nego-
tiate commission recapture agreements.
7.3.1.2 Payments for Order Flow
Payments for order flow
are payments that dealers make to brokers to obtain
orders from their clients. For many retail-based securities brokers, payments
for order are a very significant source of transaction-based revenue. For ex-
ample, in 1997, they represented 24 percent of total transaction revenue
(commissions plus payments for order flow) at E*TRADE. Their impor-
tance has since dropped, however; dealers now pay less for order flow be-
cause decimalizaton has narrowed their spreads. In the second quarter of
2001, E*TRADE's payments for order flow had dropped to 15 percent
of transaction revenue
7.3.1.3
Interest
Brokers earn interest on the margin loans that they make to their clients.
Most brokers base the rate they charge their clients on the
broker call money
rate.
It is generally about two and a half points higher than short-term Trea-
sury bill rates. Margin loan rates typically vary from two points above the
broker call money rate for small loans of less than 5,000 dollars to one point
below the rate for large loans of more than 1 million dollars. The rates are
negotiable for large loans.
Brokers also earn interest on the cash that their clients deposit with them.
The interest that they earn on these balances, however, is offset to a signif-
icant extent by the interest that they pay to their clients on these balances.
On net, the brokers profit from these balances because the rates at which
they pay interest are less than the rates at which they invest the balances.
Moreover, many brokers do not pay interest on all funds they hold on de-
posit. For example, many brokers pay interest only on balances that exceed
some minimum figure, such as 1,000 dollars.
The interest that brokers can earn on cash balances can be quite signif-
icant. If the firm can invest money in margin loans at 7.0 percent, it earns
70 dollars per year for every 1,000 dollars on which it pays no interest.
7.3.1.4 Short Interest Rebate
When a trader wants to sell a security short, his broker must have the se-
curity to deliver to the buyer. Before brokers accept sell orders, they there-
fore must make an
affirmative determination
that the securities will be avail-
able to settle the trade. Brokers often can deliver securities that they hold
in street name for their other clients. If they do not have the security, they
must borrow it from someone who does.
When the broker delivers a security that he holds in street name, the
broker keeps the cash proceeds of the sale as collateral to ensure that the
short seller will be able to repurchase the security. The broker can invest
these short proceeds and earn interest on them.
When the broker must borrow the security, the broker must deposit the
cash proceeds of the sale (plus about 2 percent more) with the lender to col-


## Page 169

156 • TRADING AND EXCHANGES
Variation Margin and Cash Management in Futures Accounts
Futures traders must maintain margin in their accounts to ensure that they
can cover any losses in their positions.
Margin
is cash or securities that
clients post as bonds to cover their potential losses. Futures brokers require
that their clients post an
initial margin
when they open their positions. The
exchange or its clearinghouse usually sets minimum initial margins. Brokers
may require higher initial margins from their clients.
When futures positions lose money, brokers deduct the losses from their
clients' accounts. When positions make money, brokers credit the gains to
the accounts. These monies pass through the clearinghouse every day from
losers to winners. The margin in futures accounts is called
variation margin
because it allows brokers to collect or credit any variation in the value of
their clients' positions.
Brokers require that their clients maintain a minimum margin deposit in
their accounts. The minimum, called the
maintenance margin,
is usually
lower than the initial margin. Like the initial margin, the exchange or its
clearinghouse sets the minimum maintenance margin, and brokers may
require higher maintenance margins from their clients.
When brokers require additional margin, they place a
margin
call
on
the account. If the trader does not supply the margin within the prescribed
time, the broker will close his or her position.
Brokers allow their clients to post their margins with Treasury bills. When
brokers need to deduct losses from an account, they first use any available
cash in the account. If no cash is available, they then sell, as necessary,
any Treasury bills posted as margin. This practice is called
breaking a T-bill.
Some brokers may allow their clients to borrow against their Treasury bills if
they have sufficient equity in their accounts. The interest rate, however,
tends to be high.
Brokers usually charge a fee when they sell Treasury bills. For small
accounts, the fee is large relative to the interest that traders could earn on
their free balances. Traders therefore tend to hold cash in their accounts
to accommodate their variation margin payments without selling their
Treasury bills.
Futures brokers generally do not pay interest on the cash balances that
their retail clients place on deposit with them. The interest that brokers earn
on these balances can be a very significant source of revenue for retail
futures brokers. For example, at 5.0 percent interest, a typical retail client
with 20,000 dollars in free cash in her account will generate 1,000 dollars
a year in income for her broker. This revenue is equivalent to 50 round-turn
commissions at 20 dollars per round-turn.
Large institutional traders usually manage the cash in their futures
accounts carefully. They buy and sell Treasury bills on a daily basis as
necessary to keep their free cash fully invested. They also may wire money
in and out of their accounts to keep their cash fully invested in interest-
bearing securities.
lateralize the loan. The lender then can invest the cash and earn interest on
it. Since the lending market is competitive, lenders must pay brokers inter-
est on the cash collateral in order to obtain their business. This interest is
called
short interest rebate.
In the United States, the short interest rebate rate
is usually the federal funds rate or the LIBOR (London InterBank Offer-
ing Rate), less a small fee for borrowing the security. The borrowing fee
depends on the availability of the security. If it is not difficult to borrow,
the fee is only about 10 basis points per year. If the security is difficult to


## Page 170

CHAPTER 7 BROKERS • 157
0. Synthetic Short Positions
Highly sophisticated retail short sellers can obtain some economic benefit
from the short proceeds that they generate by constructing synthetic short
positions instead of real ones.
A
synthetic short position
is
a position
constructed from option contracts or from futures contracts that produces the
same economic returns as an actual short position.
The
put—call
parity
theorem
proves that a long position in a put contract
coupled with a short position in a call contract with the same strike and
maturity, plus a long position in a bond with the same maturity, is
economically equivalent to a short position in the underlying security. When
options dealers receive short interest rebate on their short positions, and
when competition forces them to price their contracts to reflect those
rebates, traders who construct synthetic short positions in effect obtain short
interest rebate. In practice, the transaction costs associated with constructing
synthetic short positions can significantly reduce the economic benefits of
this strategy. In any event, it can be used to short only securities for which
option contracts trade.
Traders also can construct synthetic short positions by selling futures
contracts and buying bonds with the same maturity. By a similar argument,
this strategy also effectively produces short interest rebate.
borrow, traders say that it is
on special.
The borrowing fees for such securi-
ties depend on their scarcity.
The interest that brokers directly or indirectly earn on the proceeds of
short sales can be a very significant source of their revenue. For example,
when interest rates are 5.0 percent, brokers receive 5,000 dollars per year on
a 100,000-dollar short position. These revenues dwarf the commissions that
they charge their clients to put on and take off these short positions.
Large clients and professional traders demand that their brokers rebate
some of the interest on the proceeds of their short sales. Such interest is
also called short interest rebate.
Almost all retail brokers refuse to pay short interest rebate to their clients
as a matter of firm policy. Their nearly universal reluctance is surprising,
given the interest that brokers can earn from the short proceeds. Brokers
who pay short interest rebate, and who appropriately advertise this fact,
should be able to garner significant short proceeds. If such brokers pay out
only half the interest that they receive on these balances as short interest
rebate, they should have extremely lucrative businesses.
7.3.1.5 Underwriting Fees
Investment banks
receive
underwriting fees
when they help issuers sell secu-
rities. The fees vary by whether the broker
underwrites
the issue or merely
sells it on a
best efforts
basis. In an
underwritten offering,
the investment bank
guarantees that the issuer will receive the offering price for all shares or
bonds issued. If it cannot sell the entire issue, it will buy the remainder for
its own account. In a
best efforts offering,
the investment bank makes its best
effort to sell the security. Most offerings are underwritten offerings because
investment banks have a greater incentive to sell securities when they risk
losing if they fail.
In the United States, the fees in an underwritten initial public stock of-
fering tend to be around 7 percent of the transaction. Brokers often receive


## Page 171

158 • TRADING AND EXCHANGES
Brokerage
Compensation
Most brokerage firms are highly entrepreneurial operations in which
different units of the firm operate as substantially independent businesses.
Such firms often base the pay of their senior employees on the performance
of their unit. These compensation schemes ensure that key personnel have
strong incentives to work hard for the firm. They also reduce the probability
that key personnel will leave to start their own firms.
Most firms pay their brokers on a commission basis when they are
responsible for obtaining their order flow. The more trades the brokers
arrange, the better paid they will be. Highly productive brokers can easily
make millions of dollars per year.
Compensation for the professional staff at brokerage firms generally
consists of a low base salary plus a significant year-end bonus if the
employee has been productive and if the firm has been profitable. This
compensation scheme tends to keep employees attached to their firms (at
least until year-end), it provides them with strong incentives to work hard,
and it ensures that the firm will stay solvent when business is slow.
At firms where brokers are essentially well-trained telephone clerks, they
are paid accordingly. Most firms, however, give year-end bonuses to all
workers when the firm has done well.
additional compensation in the form of options to buy additional shares at
the offering price. These options are quite valuable if the share price rises
following the offering.
The fees in underwritten offerings compensate brokers for their efforts
and for the insurance that they provide issuers. Since brokers do not pro-
vide such insurance for best efforts offers, fees are usually lower for those
transactions.
7.3.1.6 Merger
and
Acquisition Fees
Investment banks also broker mergers and acquisitions. The brokers who
suggest and help arrange these transactions generally receive fees for their
services. The companies involved may hire them as consultants or as under-
writers of new securities created in the merger.
7.3.1.7
Security Lending Fees
Brokers who hold their clients' securities in street name often lend those se-
curities to short sellers in exchange for security lending fees. The fees de-
pend on the demand for short positions and on the availability of the shares.
Security lending fees are highest for closely held securities that greatly in-
terest short sellers.
Closely held securities
are securities for which a small num-
ber of investors hold a large majority of the shares or bonds. Such investors
often will not lend their securities because they do not want short sellers to
drive down their price. Widely held securities that do not interest short sell-
ers generally command minimal lending fees.
7.3.2 Costs
Labor costs generally are the most significant costs of running a brokerage.
Other important costs include interest payments on client cash balances and
on money borrowed to finance client margin loans, marketing costs, ac-
counting costs, clearance and settlement costs, data fees, and communica-
tions costs. Most of these costs are obvious and need no further comment.


## Page 172

CHAPTER 7 BROKERS • 159
►

Economies of Scale in Trading Technologies
The back offices of brokerage firms must be highly automated to reduce
labor costs. Brokerages therefore spend a lot on information technologies.
Most information technologies are characterized by
economies of scale.
The average cost of building and operating systems, per unit of output,
declines with system usage.
Large brokerage firms therefore have significant cost advantages over
small firms. They can spread fixed costs of creating and operating their
information systems over many accounts, and they can provide many
services at a lower cost because of their size. These scale economies
explain much of the consolidation that has occurred in the brokerage
industry since the introduction of automated information-processing systems.
To compete in the presence of such scale economies, most small brokerage
firms are
introducing brokers
that purchase trade execution and back office
services from larger firms.
7.4 THE PRINCIPAL—AGENT PROBLEM
Whenever someone works for someone else, a potential conflict of interest
arises. This problem is the well-known
principal—agent problem.
It is the prin-
cipal problem of management. Agents are supposed to do what their prin-
cipals want them to do, but the agents often do what the agents want to do.
Brokers are agents who help their clients trade. The clients expect that
their brokers will work hard and honestly. Brokers, however, may have other
agendas. They may be lazy, they may cut corners, or they may even try to
defraud their clients. Most brokers, of course, are honest and work hard on
behalf of their clients.
Brokerage clients use several standard management techniques to solve
the principal—agent problem. The techniques usually involve carrots and
sticks. Clients reward their brokers for doing good work and penalize them
for performing poorly.
Rewards may be explicit or implicit. Explicit rewards typically involve
contractual payments that clients make to their brokers when they perform
well. The formulas for these payments usually depend on explicit measures
of productivity. Implicit rewards generally entail sending more orders to bro-
kers who do better jobs.
Penalties likewise may be explicit or implicit. The most important ex-
plicit penalties that clients invoke are legal actions. Clients sue (or bring to
arbitration) brokers who are negligent or dishonest. The most common
penalty for poor performance is implicit: Clients take business away from
brokers who serve them poorly.
7.4.1
Performance Measurement
Clients must measure the performance of their brokers in order to manage
them effectively. Otherwise, they cannot reward brokers who serve them well
or penalize those who serve them poorly. In general, managers cannot solve
principal—agent problems when they cannot measure the performance of their
agents. Accurate performance measurement therefore is a prerequisite for
successful management. You cannot manage what you cannot measure.
Measuring broker productivity is quite difficult. To measure their pro-
ductivity, you must compare their product to their commissions. Measuring
commissions is easy, but measuring the quality of the transaction services


## Page 173

160 • TRADING AND EXCHANGES
that brokers produce is much more difficult. Brokers provide good service
when they buy at low prices, sell at high prices, do not fail to buy when
prices subsequently rise, and do not fail to sell when prices subsequently fall.
Measuring these attributes is difficult because clients generally cannot eas-
ily determine whether their brokers obtained the best available prices. Clients
also cannot easily determine whether their brokers failed to trade because
no one was willing to trade or because their brokers were not aggressive
enough. Consequently, measures of broker productivity are invariably im-
precise. We consider how clients evaluate brokerage services in chapter 21.
Without good measures of quality of service, brokerage clients cannot ac-
curately judge whether they obtain service commensurate with the com-
missions that they pay.
Although traders cannot easily measure the quality of service they ob-
tain from their brokers, others may do it for them. Rating agencies evalu-
ate brokerages and sell the results to interested parties. Consultants likewise
evaluate the transaction costs of their clients and compare them against those
of their other clients. Increasingly, government regulators and exchange of-
ficials require that dealers and brokers publish order-handling data and ex-
ecution price data that analysts can use to make meaningful comparisons
among them. Traders often use the reports produced by these analysts to
determine to which brokers they should direct their orders.
Clients who face principal—agency problems also benefit from the regu-
lation of brokers by governments, exchanges, and trader associations. These
agencies often supervise brokers to ensure that they do not engage in abu-
sive or dishonest business practices.
7.4.2 Best Execution
When brokers take client orders, they assume an agency responsibility to
obtain
best execution.
Unfortunately, best execution is not well defined. We
devote much of chapter 25 to understanding best execution.
"Best execution" means different things to different people. To unso-
phisticated clients, "best execution" may mean "get the best price possible"
for a market order and "trade as quickly as possible" for a limit order. This
definition suggests absolute standards for best execution. In the U.S. equity
markets, the term "best execution" generally refers to these standards.
More sophisticated traders understand that execution quality depends on
the resources (effort, skill, and systems) brokers employ to obtain it. They
know that in competitive markets, you do not get something for nothing.
When they pay their brokers well for execution services, they expect better
executions, on average, than when they do not pay much. For such traders,
"best execution" means "Get me the execution I am paying you to provide."
These traders define "best execution" in the context of their brokerage
relationships.
The most sophisticated clients understand they cannot buy something
that they cannot measure well. If brokers believe that their clients cannot
measure execution quality, they are unlikely to provide it, whether they are
paid for it or not: Any broker who spends resources to provide unrecog-
nized execution quality will be undercut by those who do not. In competi-
tive brokerage markets, such brokers cannot compete. They must either go
out of business or quit providing high-quality service. The most sophisti-
cated clients therefore pay only for the level of execution quality that they


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CHAPTER 7 BROKERS • 161
can audit. For them, "best execution" means "Get me the execution that I
expect you to provide, given what I pay you and the limitations of my abil-
ity to audit your performance." These traders define best execution relative
to the costs of auditing it.
7.4.3 The Dual Trading Problem
Dual traders
trade both as dealers and as brokers. When they trade as deal-
ers, they buy and sell for their own account. When they trade as brokers,
they buy and sell for other people's accounts. Dual traders are also known
as
broker-dealers.
Dual traders face an unavoidable conflict of interest. The conflict is most
obvious when they
internalize
orders. Dealer-brokers internalize orders when
they fill client orders themselves. When internalizing client buy orders,
broker-dealers want high sales prices and their clients want low purchase
prices. When internalizing client sell orders, broker-dealers want low pur-
chase prices and their clients want high sales prices. These price objectives
are irreconcilable. What is best for the client is never what is best for the
broker-dealer in the short run.
Dual traders also face a conflict of interest when both they and their
clients want to trade on the same side of the market. Both want to trade
first because the first traders usually get the best prices. They also want to
trade first to benefit from the market impact of the others' trades. Traders
who trade ahead of other traders to profit from the price impacts of their
orders are known as
front runners.
We discuss the front-running strategy in
section 7.5.1 below and in chapter 11.
In the long run, dealers who do not provide good service to their clients
will not keep those clients. Clients, however, must be able to evaluate the
quality of the service they receive. To the extent that they cannot do so, they
must rely upon their brokers to represent them. When their brokers are also
their dealers, the conflict of interest may become troublesome.
Many markets closely regulate dual traders because of the conflict of in-
terest problem. In the U.S. futures markets, regulations prohibit dual traders
from filling their agency orders for their own accounts. Instead, they must
offer them to other traders. At a given price, they also must fill their agency
orders before they can fill their own orders. This public precedence rule
helps ensure that they do not front-run their clients.
Some markets prohibit all dual trading. In such markets, traders must
trade either exclusively for their accounts or exclusively for their clients. The
markets that prohibit dual trading typically are large markets in which every-
one can specialize. Prohibitions against dual trading in small markets may
significantly decrease liquidity by preventing traders who otherwise would
be willing to offer liquidity from doing so.
7.4.4 Order Preferencing
Order preferencing
is the routing of order flow by a broker to a preferred
dealer. Most brokers preference orders based on the relations they have with
various dealers. The routing does not normally depend on the prices that
dealers quote or on current market conditions. The most commonly pref-
erenced orders are small retail orders to trade stocks and options.
Brokers route orders to dealers who provide them with good service, who
provide good prices to their clients, and who pay them for the order flow.


## Page 175

162 • TRADING AND EXCHANGES
Brokers who own dealer subsidiaries often route orders to their subsidiary
dealers when they internalize orders for execution.
Payments for order flow
are pecuniary and nonpecuniary inducements that dealers offer brokers in
exchange for their order flows.
Sometimes in lieu of payments for order flow, two broker-dealers will
exchange order flows. A broker whose dealer subsidiary does not deal in a
particular instrument will route orders in that instrument to a broker-dealer
who does. The other broker-dealer will reciprocate by sending orders that
the first broker-dealer trades. The broker-dealers keep track of these recip-
rocal order flow exchanges to ensure that they are balanced.
In the U.S. stock markets, dealers often pay brokers about 1 cent per
share for each market order that brokers send to them. Before the intro-
duction of new order exposure rules in 1997 and decimalization in 2000,
these payments often were substantially higher. The payments declined be-
cause the new rules and the decimalization caused bid/ask spreads to nar-
row, especially in Nasdaq stocks.
7.4.4.1 The Order-Preferencing Problem
Preferencing of order flow by brokers to dealers raises questions about
whether brokers obtain best execution for their clients. To many people,
payments for order flow seem like kickbacks. Likewise, the preferencing of
order flow to a broker's own dealer subsidiary suggests an obvious conflict
of interest, as does the exchange of order flows among brokerages and their
dealer subsidiaries.
Since preferencing relationships generally benefit brokers directly, some
clients and regulators suspect that preferencing brokers do not meet their
agency obligations to their clients. In particular, since brokers rarely nego-
tiate individual trade prices for preferenced order flows, and since the rout-
ing of preferenced orders rarely depends on the orders or on current mar-
ket conditions, these clients and regulators question whether brokers actively
search for best execution for their clients.
7.4.4.2 Best Execution Standards
Dealers and brokers involved in order-preferencing arrangements are aware
of the conflict of interest. Brokers therefore demand, and dealers generally
promise, certain levels of service that depend on order type and size. Since
brokers negotiate these promises with their dealers, these agreements im-
plicitly represent the brokers' definition of best execution. In general, bro-
kers provide best execution when they ensure that their clients' orders fill at
the best prices their clients can reasonably expect. In U.S. equity markets,
this generally means that dealers will execute market orders at the national
best bid or offer, or better.
Although clients always want their brokers to actively negotiate for the
best price when filling their orders, such negotiations are prohibitively ex-
pensive for small orders: The small commissions that traders pay for small
orders simply do not justify the individual attention that every client de-
sires. Instead, many brokers direct their orders to dealers who use complex
algorithms to provide best execution under various market conditions. Such
brokers obtain best execution for their clients when they ensure that their
clients receive good prices and high-quality service on average.
Large institutional traders who actively participate in the negotiation of
their trades are less concerned about best execution standards than are small


## Page 176

CHAPTER 7 BROKERS • 163
traders who must trust their brokers to obtain best execution. Large traders
personally negotiate with their dealers, or they insist that their brokers ne-
gotiate actively on their behalf. Regulators therefore generally presume that
such large traders can fend for themselves.
7.5 DISHONEST BROKERS
Although most brokers are honest, dishonest brokers occasionally exploit
their clients. Markets therefore have developed mechanisms to detect and
deter dishonest behavior among brokers. These mechanisms make dishon-
est broker problems uncommon in most places.
Brokers are most likely to be dishonest when their clients and their reg-
ulators cannot easily detect their frauds. The best way to deter fraud among
brokers therefore is to have good mechanisms for monitoring their behav-
ior. Markets detect dishonest brokers and deter dishonest behavior by hav-
ing officials supervise their trading, by investigating suspicious trading prac-
tices reported by honest traders, and by maintaining reliable audit trails.
An
audit trail
records the submission and disposition of every order. A
good audit trail includes detailed and unalterable information about every-
thing that happens to each order. Complete audit trails also record market
conditions at the times of submission and execution of every order. Regu-
lators use audit trails to determine whether traders have violated trading
rules. An accurate audit trail discourages dishonest behavior by brokers.
Brokers are most likely to engage in dishonest practices when the ben-
efits of their fraudulent behavior are large compared to the expected costs
of acting fraudulently. These costs include losing business from their de-
frauded clients, losing a reputation for honest dealings, acquiring a reputa-
tion for dishonest dealings, and suffering any criminal and civil sanctions
that may ultimately arise out of their behavior.
These considerations suggest that traders will encounter dishonest bro-
kers more often in unregulated markets than in well-regulated markets. They
also suggest that brokers will be more honest in markets in which they have
acquired valuable reputations for honest dealing than in markets in which
they cannot develop—or have not yet developed—such reputations. Finally,
brokers will be more honest in transparent markets with strong audit trails
Honesty Is the Best Policy
The vast majority of brokers are honest. Nonetheless, brokers who work for
large, well-established, and well-respected firms are more likely to behave
honestly than are brokers who work for small, new firms. Firms with good
reputations attract business because people trust them. The ability to attract
and retain business makes these firms valuable. Their managers must be
very vigilant to ensure that no rogue brokers exploit their reputations and
thereby depreciate the values of their firms.
Since most newly established brokerages do not start with valuable
reputations, their managers have less incentive to deploy resources to detect
and prevent fraud by their employees than do the managers of firms with
valuable reputations. Not surprisingly, most penny stock fraud takes place at
small, relatively new brokerages rather than at large, well-established ones.
The value of a reputation for honesty makes it difficult to establish new
brokerage firms. Clients naturally are wary of brokers who they do not
know well and of firms they suspect could easily disappear tomorrow.


## Page 177

164 • TRADING AND EXCHANGES
How to Check on a U.S. Broker
The regulatory arm of the National Association of Securities Dealers,
NASD
Regulation,
or NASD-R for short, maintains a registration and licensing
database called the
Central Registration Depository
(CRD). Regulators that
collect data about securities firms and individual brokers deposit these data
in the CRD. The data include information about employers; registrations;
criminal events; actions taken by federal and state regulators and by self-
regulating organizations such as exchanges; complaints; arbitrations; civil
actions; bankruptcies; and unsatisfied judgments.
Anyone can access the public records in this database via the NASD-R
Public Disclosure Program. Forms for conducting online research and for
requesting disclosure reports appear at www.nasdr.com/2000.htm.

The National Futures Association (NFA) maintains a similar database
through its
Background Affiliation Status Information Center
(BASIC). Forms
for accessing BASIC appear at www.nfa.futures.org/basic/welcome.asp.

Front-Running Example
Doug is a dishonest broker, Fran is Doug's friend, and Earl is a large client
of Doug's. Both Earl and Fran have given Doug market buy orders to
execute. Earl's order is quite large and will likely cause prices to rise. Fran's
order arrived after Earl's order but before Doug had executed Earl's order.
The time precedence rules of the market require that Doug execute Earl's
order before Fran's order because Earl submitted his order earlier.
Doug illegally trades Fran's order first so that her order front-runs Earl's
order. She then profits from the price impact of Earl's order. The average fill
price of Earl's order is worse because Fran takes some of the liquidity that
otherwise would have gone to Earl. As a rule, front running harms the
trader before whose order the front runner trades.
Front Running in Silver Futures
At a picnic hosted by a charitable organization, I met a wealthy man who
told me the following story upon learning that I was writing this book.
In the mid-1960s, Jack (not his real name) traded silver futures for his
own account on the floor of one of the two U.S. exchanges that trade silver
futures contracts. He befriended a telephone clerk who worked for a large
wirehouse that occasionally handled large orders for its industrial clients. He
and the clerk conspired to front-run these large orders and share the profits.
By arrangement, the clerk would signal Jack that he had a large buy or
sell order by how he carried the order when bringing it to his firm's floor
broker. When Jack saw that the clerk was carrying a large order, he
immediately bought or sold silver contracts according to the type of order
the clerk was signaling. Jack said that they front-ran more than 50 orders
this way before they quit. They profited on all of their trades.
I pointed out to Jack that the activity he was describing to me was
illegal. He countered that everyone was doing it. Whether it was true or
not, I imagine that Jack would not have told me the story if the statute of
limitations had not run out.
The FBI conducted sting operations in the 1990s to detect front-running
abuses in the futures markets. Agents posing as floor traders caught,
convicted, and expelled several traders from the markets.
Front running in the futures markets is now much more difficult because
audit trails have been substantially improved and because exchanges and
buy-side traders now use electronic systems to monitor the quality of their
executions. When execution quality is especially poor, or when it drops
precipitously, they initiate investigations to determine why.


## Page 178

CHAPTER 7 BROKERS • 165
11
■
' Inappropriate Order Exposure Example
Dishonest broker Doug shows Earl's large buy order to his friend Rick, who
is a small trader. Since Doug knows Rick cannot fill Earl's order, it is
inappropriate for him to expose it to Rick. Rick then buys in front of Earl's
order before Doug fills it. The inappropriate order exposure allows Rick to
make a profitable trade that hurts Earl.
Doug also exposes the full size of Earl's order to his friend Todd, who is
a small dealer. Todd then raises his offer price to avoid filling Earl's order
at a low price. Since Doug knows that Earl's order is too large for Todd to
fill by himself, Doug should have exposed only a portion of the order. The
inappropriate exposure ultimately causes Earl to pay a higher average price
to fill his order.
than in opaque markets in which traders cannot easily see what their bro-
kers are doing.
The best way to avoid losing to dishonest brokers is to avoid doing busi-
ness with them. Traders therefore should know their brokers well. In prac-
tice, many traders do not know much about their brokers. Most traders sim-
ply trust that brokers working for large, well-known firms will be honest.
If your broker proves to be dishonest, you may be able to avoid losses by
recognizing the fraud before it gets out of hand. You therefore must be aware
of how dishonest brokers can defraud their clients. The remainder of this
section considers the most common ways that dishonest brokers defraud
their clients.
7.5.1 Front Running
Front running
occurs when a broker improperly allows one order to trade
ahead of another. The order that goes first usually profits from the price im-
pact of the following order. Front runners hurt the traders whose orders they
front-run because they take liquidity that the front-running traders other-
wise would have taken. These orders then fill at worse prices than those at
which they would have filled.
Front running is most common when a broker holds a large order that
will likely move the market. The broker then trades for his own account
first, or he tips off a confederate who does the front running.
Front running also hurts the brokers who represent the orders that are
front-run. Brokerage clients who pay close attention to how well their bro-
kers perform will discover that their brokers who knowingly or unknow-
ingly allow others to trade in front of their orders do not trade effectively
on their behalf. When their poor performance becomes apparent, the clients
often direct their orders to other brokers. Firms that employ brokers there-
fore must be vigilant to ensure that their brokers do not cheat their clients
and thereby lose future business for the firm.
7.5.2 Inappropriate Order Exposure
Inappropriate order exposure
occurs when a broker shows an order to another
trader for the other trader's benefit rather than for his client's benefit. The
other trader will typically act on the information, either by front running
the order or by refusing to trade with it. Brokers must expose orders only
for their client's benefit.
lk Fraudulent Trade
Assignment Example
After dishonest broker Doug
fills Earl's buy order, the
market rises. Doug then
receives another buy order of
the same size from his friend
Alex. Doug fills Alex's order
at a higher average price
than Earl's order. To favor his
friend, Doug assigns Earl's
purchase to Alex and Alex's
purchase to Earl.
l
■
Prearranged
Trading Example
After dishonest broker Doug
receives Earl's buy order, he
arranges to trade it at a high
price with his friend
George.
Although Doug could have
obtained a lower price on
the floor, Earl does not know
this.


## Page 179

166 • TRADING AND EXCHANGES
7.5.3 Fraudulent Trade Assignment
Fraudulent trade assignment
can occur when a broker executes orders on the
same side of the market for more than one client. Each client should get
the price at which his or her order filled. A dishonest broker, however, may
assign the best prices to his favorite clients.
Fraudulent trade assignment may be especially problematic when bro-
kers also act as dealers. Without appropriate safeguards, broker-dealers may
be tempted to take the best trades for themselves and leave the worst trades
for their clients.
7.5.4 Prearranged Trading and Kickback Schemes
Prearranged trading
occurs when a broker arranges a trade without properly
exposing her client's order to other traders who might be willing to offer
better prices. Under such circumstances, the client often receives a worse
price than he might have received if his broker had properly exposed
the order.
Prearranged trading is illegal in floor-based futures markets. In such mar-
kets, traders must shout out their bids and offers so that all traders have an
opportunity to trade. It is also illegal in most electronic futures exchanges.
Many equity markets and some futures markets allow block traders to
prearrange trades that they want to print on the floor of the exchange. The
matched trades must be brought to the floor to give floor traders an op-
portunity to offer better prices if they choose to. These special procedures
allow brokers to profit when they have arranged both sides of a difficult
transaction and at the same time protect both sides of the trade from po-
tential abuse.
In a
kickback scheme,
a broker sends an order to a dealer with the under-
standing that the dealer will fill it at a poor price. The dealer gives the bro-
ker some consideration—the kickback—in exchange for the opportunity to
cheat the client. The dealer may pay the kickback in cash or with non-
monetary considerations.
Brokers often arrange to send dealers order flow in exchange for mone-
tary or nonmonetary payments. Although these
payments for order flow
arrangements appear a lot like kickback schemes, they generally are not. We
discuss the economics of payments for order flow in chapter 25.
7.5.5 Unauthorized Trading and Churning
Brokers engage in
unauthorized trading
when they make trades for their
clients that their clients have not authorized. Brokers generally make these
trades to generate commissions or to manipulate prices. Not surprisingly,
the problem is most serious among unsophisticated retail investors.
Clients must pay close attention to their accounts to ensure that their
brokers are not making trades of which they do not approve. They must pay
particular attention to the trade confirmations that they receive.
Unauthorized trading is especially difficult to detect if the broker has
changed the mailing address on the defrauded account. In that case, the vic-
tim may not quickly detect the unauthorized trading. To prevent this prob-
lem, most brokerage firms do not allow their brokers access to the systems
that maintain client mailing addresses. They also require signed instructions
to change client mailing addresses, and they compare the signatures with
those on file. Finally, they send letters to their clients to advise them when-


## Page 180

CHAPTER 7 BROKERS • 167
0
,
Churn 'em and Burn 'em
The term
churn
means to actively agitate in place. Brokers churn accounts
when they trade frequently without accomplishing anything. Since the clients
suffer, traders call the strategy
churn 'em and burn 'em.
Brokerages that use churn 'em and burn 'em sales practices must
constantly seek new clients as they exhaust the resources or patience of
their existing clients. They therefore devote much more of their resources to
client acquisition than to client retention.
Not surprisingly, firms with such aggressive sales practices often
advertise extensively and engage in endless
cold calling.
Salespeople make
cold calls
when they call upon prospects who have never indicated any
interest in the firm's products and services.
The targets of these sales efforts generally are unsophisticated people
with money. Aggressive firms especially target lonely people who are eager
to form trusting relationships, gamblers who are looking for some action,
and envious people who want to catch up with their successful peers.
P. T. Barnum's famous quote, "A sucker is born every minute," well
characterizes the prevailing attitude at churn 'em and burn 'em
brokerages.
ever someone requests a change of address for their accounts. They address
these letters to both the old and the new address to ensure that their clients
have a chance to detect a fraudulent change of address. If a fraudulent change
of address occurs, clients must respond immediately in order to protect their
assets.
Brokers
churn
accounts when they advise their clients to trade more of-
ten than is prudent. Brokers suggest these trades to take advantage of op-
portunities that, they argue, will benefit their clients. The primary purpose
of these trades, however, is to generate brokerage commissions. To help pre-
vent these problems, many brokers monitor their client accounts and care-
fully investigate instances where trading seems excessive.
If the primary beneficiaries of the trading activity are the brokers rather
than their clients, the brokers are acting unethically, regardless of whether
their clients authorize the trades. In most legal jurisdictions, brokers act-
ing as investment advisers must place the interests of their clients ahead of
their own.
Churning is most common when unsophisticated, trusting clients give
their brokers authorization to trade their accounts. To prevent churning,
regulators require that brokerages know their clients and ensure that their
trading is appropriate for them. To prevent abuses, the
compliance officers
at
many brokerage firms monitor the turnover in client accounts to identify
clients who may be trading too often.
7.5.6 Securities Theft
In extreme cases, dishonest brokers steal funds and securities that their
clients entrust to them. Although the probability of theft is very small in
most markets, the possibility of theft explains why many clearing and set-
tlement procedures exist. These procedures ensure that securities theft is a
small problem in well-developed markets.
Institutional traders prevent the theft of their assets primarily by con-
tracting with
depositories
or
custodians
to hold them for them. When bro-
kers arrange trades for clients who use depositories, the brokers report the


## Page 181

168 • TRADING AND EXCHANGES
Bearer Bonds
Bearer bonds
are bonds for which the issuer does not register its
bondholders. Like currency, whoever holds a bearer bond is its presumptive
owner. Holders of bearer bonds obtain their interest payments and final
principal repayments by presenting coupons clipped from their bonds to the
bond issuer. Most banks help facilitate these redemptions.
In the past, almost all bonds were bearer bonds because keeping track
of changes in bond ownership was quite costly. Now most new bonds are
registered bonds.
(U.S. government regulations now require that most bonds
issued by public corporations in the United States be registered.) Investors
like registered bonds because they can obtain new certificates if they lose
the original ones and because the issuers can make their interest and
principal payments by direct deposit or check.
Tax
authorities like registered
bonds because they can compel issuers to report the interest payments that
they make to investors.
Bearer bonds are popular with people who want to hide their assets or
their income. Investors who hold these bonds must be especially careful not
to lose them.
trades both to their clients and to their clients' depositories. The clients then
instruct their depositories to deliver or receive the securities, as necessary,
to settle their trades.
The depository system protects traders from fraud by making it impos-
sible for brokers to steal securities or funds, because brokers never hold them.
Brokers simply arrange trades.
The depository system is attractive to institutional traders because it al-
lows them to trade easily through any broker with whom they have an ap-
propriate credit relationship. By relying upon a single agency to ultimately
settle their trades, traders do not have to worry about whether they have
adequately funded their brokerage accounts or whether the securities they
want to sell are in their accounts with the brokerages that arranged their
sales.
Almost all large institutional traders use depositories to hold their secu-
rities. In addition, most retail brokers place the securities that they hold on
behalf of their clients in depositories. The brokers use depositories for the
same reasons that institutions do. They want to protect themselves from
rogues who might steal the securities or try to settle unauthorized trades.
They also use the depositories to facilitate clearing and settlement.
Some investors guard their securities by holding them as paper certifi-
cates that represent their ownership. Most issuers employ
security registrars
to keep track of their shareholders and bondholders. The registrars issue and
cancel certificates on behalf of their issuers. They also pay dividends and in-
terest to security holders. When a paper certificate has been issued for a se-
curity, the certificate generally must be delivered to the registrar so that the
registrar's record of who owns it can be changed. Investors therefore can
protect their ownership by guarding their certificates. Traders who take cer-
tificates must be careful not to lose them because they can be expensive or
impossible to replace.
Many issuers no longer issue certificates. Instead, their registrars keep
electronic records of who owns their issues. This registration system is called
book entry registration.
Registrars have very strong procedures to secure their
book entry records against loss and fraudulent changes.


## Page 182

CHAPTER 7 BROKERS • 169
Can Investors Vote More Than 100 Percent
of the Shares Outstanding?
When stocks are sold short, the number of shares that beneficial owners
think they own is greater than the number of shares outstanding (not
counting treasury stock). Consider an example.
BigBroker's client Cleo bought 100 shares of International Widgets, a
perennial favorite of economists. Since Cleo leaves the stock in his
brokerage account, BigBroker is the holder of record.
Shorty wants to sell short 100 shares of Widgets through his broker,
SureBroker. Since SureBroker does not have the shares, it borrows them
from BigBroker. Benny buys the shares from Shorty. Benny requests the
certificate and holds them in his own name. Benny is now a shareholder of
record for 100 shares. Cleo has no idea that BigBroker lent Widgets
shares.
Benny and Cleo both believe that they own the stock and that they have
a right to vote their shares in any matter that Widgets places before its
shareholders. However, if they both could vote, more than 100 percent of
the shares could be voted.
In fact, only shareholders of record have a right to vote their shares.
Benny has an absolute right to vote his shares. As a beneficial owner, Cleo
can only recommend to BigBroker how it should vote the shares.
Brokers usually follow the recommendations of their clients if they can do
so. If they have not lent securities, they can vote exactly as their clients
wish. Otherwise, they may have fewer shares to vote than their clients may
direct them to vote. If this happens, brokers vote their shares in proportion
to how their clients direct them to vote. In practice, since many clients do
not issue voting instructions for their shares, brokers usually have enough
shares to vote if they have not lent too many shares.
7.5.6.1 Securities in Street Name
When traders have their brokers or depositories hold their securities, they
hold their securities in
street name.
The issuers then register the brokers or
depositories as the
holders of record,
and the brokers or depositories become
responsible for keeping track of their
beneficial owners,
who no longer legally
own the securities. Instead, the beneficial owners own claims that their bro-
kers or their depositories give them for their securities.
These distinctions are important only with respect to custodial issues.
For tax purposes, the government does
not
care how you hold your securi-
ties. These distinctions explain why issuers correspond directly with you
when you
hold
securities in your own name and through your broker when
you hold securities in street name.
When you execute a
margin agreement
with your broker, you can use your
securities as collateral to borrow money from your broker. When you pledge
your securities as collateral for a loan, you
hypothecate
them. Your broker re-
quires that you hold hypothecated securities in street name so that he can
sell them if you cannot or will not repay the loan.
Margin agreements also allow brokers to lend securities to short sellers.
Clients generally do not know whether their brokers have lent their secu-
rities, and they generally do not receive any lending fees. (Some large in-
stitutions
hold
securities in their own name so that they can obtain
security-lending fees.) After the short sellers use the borrowed securities to
Cede & Co.
The Depository Trust Co.
(DTC) holds about 20 trillion
dollars in assets for its
participants and their
customers. Most of these
securities are registered in the
name of Cede & Co. as
nominee of The Depository
Trust Co.
According to folklore,
Cede was a clerk at DTC.
When DTC started to hold
securities in street name, its
filing system required a name
for the shareholder of record.
Not knowing what name to
provide, Cede used his own
name. In fact, Cede stands
for Central Depository.


## Page 183

170 • TRADING AND EXCHANGES
Sunpoint Securities
Sunpoint Securities was a full-service self-clearing broker-dealer based in
Longview, Texas. It started its business in 1989. It ceased operations on
November 18, 1999, when it became apparent that it did not have enough
assets to cover liabilities owed to its clients.
In a civil lawsuit, the U.S. Securities and Exchange Commission charged
the CEO and the CFO of Sunpoint with systematically stealing 25 million
dollars from a money market account that the firm maintained for its clients.
The SEC's complaint alleged that from December 1997 through November
18, 1999, Sunpoint illegally transferred money market funds, belonging to
its clients, to the firm's clearing account. The firm then improperly transferred
the funds to satisfy the firm's net capital requirements. The SEC further
alleged that the firm's president and CEO also used the funds for their
personal benefit. The diversion of client funds resulted in the firm having only
12 million dollars in its client money market account to cover 37 million
dollars in money market obligations to its clients. Consequently Sunpoint was
grossly below its net capital and client reserve requirements.
For more information, see SEC Litigation Release no. 16366, dated November 19, 1999
at www.sec.gov/enforce/litigrel/Ir16366.htm.

settle their short sales, the lending brokers are no longer owners of record.
The new purchasers become the holders of record on the issuer's registry.
7.5.6.2 Brokerage Bankruptcies
When a broker goes bankrupt, traders who deposited assets with the bro-
ker risk losing those assets. Bankruptcies often occur when the broker in-
curs significant trading losses on its own account, when one or more of its
clients default on their obligations to the broker, or, most commonly, when
someone steals assets from the broker. Traders therefore should carefully
consider whether their brokers are creditworthy before they entrust their as-
sets to them.
Brokers naturally want to assure their clients that they are trustworthy
and creditworthy. To increase investor confidence, brokers publish their fi-
nancial accounts for all to see. They may also take out
excess insurance
poli-
cies to protect their clients. The insurance companies help regulate brokers
to ensure that they do not get into trouble.
Many other organizations subject brokers to regulatory oversight. Ex-
changes and clearinghouses regulate their members to ensure that they are
financially viable, to minimize the costs that insolvent members can impose
upon others, and to increase public confidence in their membership. Clear-
ing members that clear for other broker-dealers regulate them to avoid losses
that they may inherit if their broker-dealer clients go bankrupt. Broker-
dealer associations and governments regulate brokers for similar reasons.
The various regulators ensure that brokers have adequate capital
reserves to meet their obligations. They also ensure that brokers have well-
functioning managerial controls in place to prevent unexpected losses due
to negligence, stupidity, poor luck, or fraud. Finally, they require that bro-
kers have accounting systems which can quickly detect such problems. Bro-
kers accept these regulatory relationships in order to increase investor con-
fidence in their financial integrity.


## Page 184

CHAPTER 7 BROKERS • 171
The SIPC
The U.S. Congress created the Securities Investor Protection Corporation
(SIPC) in
1970
to increase confidence in U.S. brokers. Almost all broker-
dealers that register with the Securities and Exchange Commission are
automatically members of the SIPC. (The only exempt brokers are those who
exclusively distribute mutual fund shares, sell variable annuities or insurance,
or conduct their business outside the United States.)
If a brokerage fails, the SIPC distributes all securities registered in clients'
names and held by the firm to those clients. The remaining securities—those
held in street name—and cash are then distributed on a pro rata basis to
the clients. The SIPC will satisfy any remaining investor claims up to a
maximum of 100,000 dollars for cash and a combined maximum of
500,000 dollars for securities and cash. The SIPC makes such distributions
from a special fund it maintains for this purpose. The money comes from
assessments that the SIPC levies on its members and from interest that the
fund earns on its investments in U.S. Treasury securities. Should the SIPC
need more money to satisfy claims, it can borrow up to a billion dollars
from the U.S. Treasury.
The largest payout the SIPC has made was in the Sunpoint Securities
liquidation. The SIPC paid 31 million dollars to restore stocks and cash that
9,738
investors apparently lost to theft at Sunpoint.
When a brokerage goes bankrupt, clients must quickly file their claims
with the bankruptcy court. Most courts accept only claims filed within 30 or
60 days of the publication date of the bankruptcy. In any event, the law
prohibits the SIPC from satisfying any claims that it receives more than six
months after the bankruptcy is published.
Clients should be notified by mail when a brokerage with which they do
business goes bankrupt. In practice, clients may not receive notice because
their broker's records are poor or because clients failed to notify their
broker of a change of address.
If you maintain an account with a brokerage whose bankruptcy might
not immediately come to your attention, you should regularly open your
mail to make sure that your brokerage has not gone bankrupt. You should
also inquire quickly into your brokerage's financial health if you fail to
receive your monthly statement.
For more information, click on www.SlPC.org
.
7.5.7
Summary
Most
brokers are honest, trustworthy, and creditworthy. They behave well
because most brokers are good and honorable people; because they know
that a good name is good for their business; and because regulators, mar-
kets, competitors, clients, and broker associations have established systems
to deter bad behavior.
Unfortunately, not all brokers behave well all the time. To weed out rogue
brokers and to help traders recover losses, regulators maintain grievance
programs.
Traders who suspect that they have been defrauded should complain to
the appropriate regulator. Although many complaints are due to misunder-
standings, some are due to dishonest or irresponsible behaviors that typi-
cally stop only when regulators take disciplinary actions. If you have been
defrauded, your complaint may eventually lead to reparations. At a mini-
mum, your complaint may prevent someone else from losing.
What Works Best When
You Never Use It?
All types of security systems—
burglar alarms, guards,
armies, criminal justice
systems, and regulatory
systems—work best when they
deter bad behavior.
Consequently, the better they
work, the less necessary they
seem. Unfortunately, many
people do not fully appreciate
the value of deterrence when
confronted with its cost. The
former is intangible, whereas
the latter is concrete.


## Page 185

172 • TRADING AND EXCHANGES
How to Complain Effectively
Regulators offer several programs that collect and attempt to resolve
complaints that the public may have about the firms and brokers who carry
their accounts. Full descriptions of these programs appear on the following
web pages:
National Association

http://www.nasdr.com/2100.asp

of Securities Dealers
Regulation
Securities and

http://www.sec.gov/complaint.shtml

Exchange Commission
National Futures

http://www.nfa.futures.org/dispute/index.html

Association
Commodity

http://www.cftc.gov/cftc/cftccomplaints.htm

Futures
Trading
Commission
In addition, most exchanges have grievance procedures.
4
7.6 SUMMARY
Brokers
help their clients arrange trades. They match orders, they find
traders willing to trade, and they clear and settle trades. Clients employ bro-
kers for these tasks because brokers can do them more cheaply than they
can themselves.
The principal—agent problem affects relations between brokers and their
clients. Brokers may not always do what their clients pay them to do. Clients
solve the problem by rewarding their brokers when they perform well and
penalizing them when they do not.
Unfortunately, most brokerage clients cannot easily measure their bro-
kers' performance. Regulators consequently concern themselves with best
execution standards. These standards define minimum service guarantees
that clients can expect from their brokers. The standards are meaningful
only if clients or market regulators can audit broker behavior.
The structures of all mechanisms which facilitate trade reflect the un-
fortunate fact that not all traders are honest and reliable. Traders have cre-
ated elaborate clearing, settlement, margin, custodial, and audit procedures
to ensure that all negotiated trades settle, all parties honor their financial
commitments, traders do not violate rules, and nobody steals assets that be-
long to others. In addition, government, exchange, and industry association
regulators oversee trading to protect the integrity of the markets.
7.7 SOME POINTS TO REMEMBER
•
Brokers help arrange and settle trades for their clients.
•
Brokers and exchanges compete with each other to arrange trades.
•
Cash management is a significant source of profits for many brokers.
•
The principal—agency problem can be a significant problem in the bro-
kerage industry because quality of service is hard to measure.
•
Soft commissions allow institutional funds to use trading commissions
to finance their expenses and thereby report lower expense ratios.


## Page 186

CHAPTER 7 BROKERS • 173
• Many aspects of brokerage operations and of clearing and settlement
mechanisms reduce the potential for fraud among traders.
7.8 QUESTIONS FOR THOUGHT
1.
Do brokers work for their clients or for themselves?
2.
Are payments for order flow kickbacks?
3.
What is best execution?
4.
Why should a client value a broker's reputation?
5.
Can you buy services that you cannot measure?
6.
How can regulators distinguish between exchanges and brokers?
7.
How do you solve the settlement credit problem when you want to
buy a used computer advertised by an individual in an eBay Inter-
net auction?
8.
What is the difference, if any, between proprietary trading and deal-
ing? Should we allow brokers or dealers to sell information about
their order flows and their limit order books to proprietary traders?
Should we allow dealers to use computers to process this informa-
tion for their own benefit?
9.
What obligations do brokers have to their clients when they send
orders to dealers who have large proprietary trading operations?
10.
Most people obtain advice about investments and about financial
planning from brokers. The brokers usually do not charge them spe-
cific fees for these services. Instead, their clients pay them large bro-
kerage commissions or annual fees based on their account balances.
Why is this the case?
11.
Brokers in most trading markets guarantee that their clients' trades
will settle. Brokers in real estate markets almost never guarantee trade
settlement. What differences between these markets account for the
difference in how brokers participate in the trade settlement process?
12.
Many exchanges limit the number of their members. Once the limit
is reached, traders who want to become members must buy a seat
from an existing member. Why would exchanges limit their mem-
berships? Why might they expand the number of their members?
What determines the value of an exchange seat?
13.
Of what value is a large entertainment budget to a new broker?
14.
How does a reputation for being a difficult negotiator affect trading
profits?
15.
Of what importance is a broker's reputation when many traders can-
not measure the quality of service they receive? What if no traders
can measure execution quality?
16.
What is the practical difference, if any, between losing a reputation
for honest dealings and acquiring a reputation for dishonest dealings?
17.
Suppose that your futures broker allows you to borrow against
T-bills you hold in your account. The T-bill yield is 5 percent; the
broker will lend to you at 10 percent; the broker will not pay you
interest on the cash in your account; and the broker charges you 40
dollars to buy or sell any number of 10,000 dollar T-bills. You have
200,000 dollars of equity in the account and you have futures posi-
tions that require only 100,000 dollars of margin. Your variation mar-


## Page 187

174 • TRADING AND EXCHANGES
gin cash flows average about 10,000 dollars per day. How much of
your equity should you invest in T-bills?
18.
Most investment advisers have many clients. What fairness prob-
lems arise when an adviser uses soft dollars funded by client com-
missions to purchase investment research?
19.
Can you explain why deep discount retail brokers typically charge a
flat commission per stock trade, U.S. institutional equity brokers typ-
ically charge a fixed rate per share, and other institutional equity bro-
kers base their commissions on the value of the transaction?
20.
Some brokers have defrauded their clients and their employers by
giving their clients false statements of their accounts. The brokers
intercepted or misdirected official account statements and sent out
fraudulent statements in their place. How can clients and brokerage
firms detect such frauds?


## Page 188

Part II
I>
The
Benefits
of
Trade
T
he two chapters in this part discuss how trading benefits individual
traders and the entire economy. Chapter 8 explains why traders trade.
We introduce 32 types of traders and identify the benefits that each
obtains from trading. Remarkably, traders often do not clearly understand
why they trade. They therefore often trade when they should not or fail to
trade when they should. Traders who understand why they trade will gen-
erally trade more effectively.
In chapter 9, we consider how well-functioning markets benefit the en-
tire economy. The primary benefits come from informative prices and from
market liquidity. We explain how well-functioning markets help market-
based economies use their resources most efficiently. We also consider a
framework for evaluating public policy.
175


## Page 189

Why
People
Trade
p
eople trade to invest, to borrow, to exchange assets, to hedge risks, to dis-
tribute risks, to gamble, to speculate, or to deal. We consider each of these
objectives in this chapter and explain how markets help traders achieve them.
You must understand why people trade in order to use markets effec-
tively. Markets provide many valuable opportunities. To take advantage of
them, you must first recognize them.
By considering why people trade, you will better understand why you
trade and whether you should trade. Many traders do not fully recognize
the reasons why they trade. Consequently, they pursue inappropriate trad-
ing strategies or they trade when trading is counterproductive to their true
interests. The optimal trading strategy for a given trading problem depends
on the problem. You cannot trade well if you do not know why you want
to trade.
Knowing why people trade may also help you determine whether other
traders understand why they are trading. This skill is very important be-
cause you can usually distinguish good money managers from poor ones
by whether they understand well why they trade. It is also important be-
cause traders who do not fully understand why they trade often trade fool-
ishly. If you can identify such traders, you may be able to profit from their
foolishness.
If you engage in any trading strategy that depends on the volume of
trade, you must understand why people trade in order to interpret volumes
properly. Many factors cause people to trade. If your trading strategy de-
pends on one of these factors, you will want to examine volumes carefully.
However, you must be careful to recognize when other factors may cause
people to trade. Otherwise, you may misinterpret volumes and trade when
you should not.
Markets are successful only when people trade in them. If you want to
design a new market, or if your business depends on trading in a success-
ful market, you must understand why—and how—people trade.
Trading is a zero-sum game in an important accounting sense. In a
zero-
sum game,
the total gains of the winners are exactly equal to the total losses
of the losers. Trading is a zero-sum game because the combined gains and
losses of buyers and sellers always sum to zero. If a buyer profits from a
trade, the seller loses the opportunity to profit by the same amount. Like-
wise, if a buyer loses from a trade, the seller avoids an identical loss.
Successful traders must understand the implications of the zero-sum
game. To trade profitably, traders must trade with people who will lose.
Profit-motivated traders therefore must understand why losers trade in or-
der to know when they should trade.
Finally, you must understand why people trade in order to form well-
reasoned opinions about market structures. Different structures favor dif-
176


## Page 190

CHAPTER 8 WHY PEOPLE TRADE • 177
ferent trader types. If you intend to influence a decision about market struc-
ture, you should first consider how the decision affects various traders. The
benefits that traders obtain from markets depend on why they trade. Reg-
ulators and other interested parties must therefore understand these reasons.
This chapter identifies the main reasons why people trade. We will re-
fer to them throughout the rest of the book. Pay close attention to the dis-
tinctions between investing, speculating, and gambling. When traders con-
fuse these important concepts, they often trade poorly. When regulators
confuse them, they often adopt policies that hurt the markets. Also con-
sider why liquid markets benefit most traders. When you understand why
people trade, you will appreciate why all market participants care about
liquidity.
For expository clarity, we will associate a stylized trader with each rea-
son for trading, and we will assume that the stylized trader trades only for
that reason. In practice, traders often trade for many reasons. The com-
plexity of their motives explains why many traders get confused and fail to
fully recognize why they trade. By considering stylized traders, we simplify
our discussions and ultimately make it easier for you to identify the differ-
ent reasons why people trade.
Our stylized traders are profit-motivated traders, utilitarian traders, or
futile traders.
Profit-motivated traders
trade only because they rationally ex-
pect to profit from their trades. Speculators and dealers are profit-motivated
traders.
Utilitarian traders
trade because they expect to obtain some benefit
from trading besides trading profits. Investors, borrowers, asset exchangers,
hedgers, and gamblers are utilitarian traders.
Futile traders
believe that they
are profit-motivated traders. Although they expect to trade profitably, their
expectations are not rational. They have no advantages that would allow
them to be profitable traders. Utilitarian traders and futile traders lose on
average to profit-motivated traders because trading is a zero-sum game.
Traders are either informed or uninformed.
Informed traders
can form re-
liable opinions about whether instruments are fundamentally undervalued
or overvalued. The
fundamental value
of an instrument is the value that all
traders would agree upon if they knew all available information about the
instrument and if they could properly analyze this information. An instru-
ment is
undervalued
when its market price is below its fundamental value.
It is
overvalued
when its price is above fundamental value. Since nobody ac-
tually knows fundamental values, traders must estimate them. Informed
traders typically form their opinions from insightful analyses of publicly
available information or from simple analyses of information that is not
widely known. Informed traders speculate on their information by buying
undervalued instruments and selling overvalued instruments. Informed
traders are therefore profit-motivated traders.
Uninformed traders
do not
know whether instruments are fundamentally undervalued or overvalued.
Either they cannot form reliable opinions about values or they choose not
to. Uninformed traders include utilitarian traders, futile traders, and some
types of profit-motivated traders.
Our discussion starts with, and primarily focuses on, utilitarian traders.
At the end of the chapter, we will introduce the profit-motivated traders
and the futile traders. Detailed discussions of how they behave appear in
subsequent chapters devoted exclusively to their various styles.
Multiple Identities
Many traders simultaneously
invest, speculate, and
gamble. They invest when
they need to move money
from the present to the future.
They speculate when they try
to use information about
future security prospects to
obtain a better return on their
investments. They gamble
when they focus more
attention on favorable
outcomes than on losing
outcomes.
Their multiplicity of
interests often compromises
their judgment. Investors
frequently speculate without
thinking about whether they
would be good speculators,
and speculators often gamble
without considering whether
their emotional needs have
influenced their judgment.


## Page 191

178 • TRADING AND EXCHANGES
8.1 UTILITARIAN TRADERS
Utilitarian traders trade to obtain some benefit besides trading profits. In-
vestors and borrowers trade to move money forward or backward through
time. Asset exchangers trade to exchange one asset for another asset of
greater value to them. Hedgers trade to exchange risks. Gamblers trade for
entertainment. Fledglings trade to learn how to trade. Cross-subsidizers
trade to transfer wealth to other people. Tax avoiders trade to minimize their
taxes by exploiting tax loopholes. We will consider each of these traders
in turn.
8.1.1 Investors and Borrowers
People often need to move money from one point in time to another. Work-
ers need to move their current earnings from the present to the future in
order to finance their retirements. Students need to move their future earn-
ings to the present to pay tuition. Young couples need to move their future
earnings to the present to buy houses.
These problems are all examples of intertemporal cash flow timing prob-
lems. People face
intertemporal cash flow timing problems
when their incomes
and expenses do not always coincide. When their incomes are more than
their expenses, they
invest
money to move money into the future or they
repay
money that they borrowed in the past. When their incomes are less
than their expenses, they
borrow
money from the future or they
liquidate
in-
vestments that they made in the past. People invest, borrow, liquidate, and
repay to move money forward or backward through time.
Corporations and governments also face intertemporal cash flow prob-
lems. The most common problem that corporations face is inadequate cur-
rent cash flow to pay for investments that will generate future revenues. To
solve this problem, they borrow money from the future by selling bonds or
stock shares. Governments most commonly borrow against their future tax
revenues to finance current spending. They may use the money to fund proj-
ects that will produce benefits in the future, to fund current services, or to
enrich poor people, disabled people, retirees, immigrants, and, in many cases,
farmers and, manufacturers.
Although people, corporations, and governments invest and borrow to
move money through time, in aggregate no money actually moves through
time. Instead, for every dollar invested, someone must borrow a dollar. The
assets that investors use to move money from the present to the future there-
fore are the same assets that borrowers use to move money from the future
to the present. Traders buy assets when they want to move money to the fu-
ture or when they repay money that they previously moved back in time from
the current present to the past. They sell assets when they want to move
money from the future to the present, or when they redeem money that they
previously moved forward in time.
Investors use various financial and real assets to move money forward
through time.
Financial assets
include stocks, bonds, mutual funds, insurance
policies, certificates of deposit, demand deposits, and currencies.
Real assets
include real estate, machinery, commodities, precious metals, and going busi-
ness concerns. Investors who cannot, or who would rather not, manage their
own funds give their money to banks, mutual funds, retirement funds, in-
surance companies, and other financial intermediaries to invest for them.


## Page 192

CHAPTER 8 WHY PEOPLE TRADE • 179
A
Ballpark Estimate
How much trading volume in the U.S. equity markets is due to trading by
pure investors? Much less than you might imagine! Here is a rough
estimate.
Saving for retirement is by far the most important investment problem
that people face. We can get a rough estimate of the annual dollar volume
of private investment transactions by estimating how much retirees spend of
their own money. In
2000,
total personal consumption in the United States
was 7 trillion dollars of which 1 trillion dollars was for health services.
Assume that 20 percent of the non-health consumption was by retirees, and
that retirees consumed 80 percent of the health services, so that retirees
consumed a total of 2 trillion dollars. Retirees received about 0.7 trillion
dollars in benefits from social security, Medicare, and Medicaid. They
therefore had to finance 1.3 trillion dollars of consumption.
In 2000, the total dollar volume of U.S. equities trading was more than
43 trillion dollars (table 3-5). If retirees financed their consumption only by
selling stocks, they would account for only 3 percent of all trading volume.
Stocks, however, represent only about 20 percent of the capital assets of
the country (table 3-4). If retirees sold all capital assets equally, their equity
trading would account for only 0.6 percent of equity volume.
Workers who invest for their retirements also trade. If they expected no
real return on their investments, if the population were not growing, if the
retirement age were constant, and if the payments from Social Security and
Medicare were expected to remain constant, workers would have to invest
1.3 trillion dollars per year to finance their retirements. Although these
assumptions are obviously wrong (a positive expected real return lowers the
estimate; a growing population raises it; a growing retirement age lowers
it; and decreasing payments from government programs increases it), they
give us a ballpark estimate for how much workers must be saving for
retirement. If workers place
20
percent of their savings in the stock market,
and if they buy and hold their securities, they will be responsible for
another 0.6 percent of equity volume.
The total of
1.2
percent from retirees and workers overestimates the
actual total investment-motivated trading volume because workers and
retirees mostly invest and disinvest through private pension funds and mutual
funds. These funds do not have to trade when the deposits made by (or on
behalf of) workers offset the redemptions made by (or on behalf of) retirees.
This happens much of the time so that the
1.2
percent investment-motivated
trading volume estimate is much too high.
It is also too high because workers will occasionally trade directly with
retirees. In which case, our estimate will double-count trading volume.
The assumptoins in this analysis are quite crude. Yet, even
if
we
increased our estimate ten-fold, we would still only account for less than
one-eighth of all equity trading volume! People clearly trade equities for
many reasons besides investment.
Borrowers create and sell various debt instruments to move money from
the future to the present. These instruments include bonds, commercial
paper, mortgage notes, home equity loans, bank loans, and credit card
obligations.
Borrowers who create and sell debt instruments to public investors are
issuers.
Only creditworthy borrowers can successfully issue debt securities.
The investors who buy their issues must be confident that the issuers ulti-
mately will redeem them. Large corporations and governments are typically
the only borrowers who can issue debt directly into the marketplace. Most


## Page 193

180 • TRADING AND EXCHANGES
individuals and
small businesses cannot issue public debt because most in-
vestors cannot easily determine whether they are creditworthy.
Borrowers who cannot issue debt instruments directly to the public bor-
row money from banks, finance companies, and other financial institutions
that will loan them money. These financial intermediaries then often issue
debt instruments to finance the loans that they make.
Banks and finance companies can lend money to individuals and small
businesses because they are organized to cheaply determine whether their
customers are creditworthy. Compared to public investors, they can more
efficiently collect on small loans, especially if they are in default.
Corporations that need to finance projects may issue equity instead of
debt securities. They may be unable to issue debt because they are not suf-
ficiently creditworthy. They may choose not to issue debt because they do
not want to assume the risks associated with highly leveraged balance sheets.
Firms that undertake very risky projects often cannot raise money
through debt offerings because investors fear the company will be unable to
pay off the debt. If that happens, the company will go bankrupt and the
debt holders will own the remaining assets of the company. When this out-
come is likely, investors prefer to start out as equity holders so that they can
exercise some control over the management of the firm from the start. In
addition, when substantial losses are likely, investors will not provide fi-
nancing unless substantial returns are likely. Since only equity can provide
such return distributions, very risky firms must raise money by issuing
equity instead of debt.
Investors and borrowers carefully choose the assets that they trade to
solve their intertemporal cash flow timing problems. Their decisions depend
on the expected returns, risks, and transaction costs of the various assets.
Investors naturally favor assets that have high expected returns, low risk,
and low transaction costs. Borrowers favor assets that they expect will cost
them the least to create, to service, and to repay. As a rule, the riskiest as-
sets and those which are most expensive to trade have the highest expected
returns. Traders must carefully consider these factors when deciding how to
move money through time.
Textbooks about investments provide detailed discussions of how in-
vestors and borrowers weigh these various factors and of how their deci-
sions affect asset prices and expected returns. For present purposes, merely
note that investors and borrowers best solve their intertemporal cash flow
timing problems when their transaction costs are low. Transaction costs are
what people pay to move money from one point in time to another. Since
transaction costs are low in liquid markets, investors and borrowers like liq-
uid markets. When transaction costs are high, trading is an expensive
method of moving money through time.
Investors expect to get a fair rate of return when using the markets to
move money into the future. Indeed, many investors will defer their con-
sumption only because of the investment returns that they expect to receive.
Since investors are uninformed traders, the rate of return that they expect
to receive does not depend on any private information that they may have.
The fair rate of return is therefore an unconditional expected return.
The
unconditional expected return
to an investment has two components.
The
real risk-free interest rate
is the return that investors expect to receive
for deferring their consumption without risk of a real loss. (Investors suffer
Economic Proof That
No One Will Ever
Invent a Time Machine
To the list of logical
contradictions associated with
time travel, consider the
following economic argument.
If a time machine could freely
move people and their money
through time, the after-tax real
rate of interest would always
be zero. If it were ever
positive, profit-motivated time
travelers would carry money
from the future to the present
to invest. They would then go
back to the future to liquidate
their investment at a profit.
The effect of their trading
would be to depress real
interest rates toward zero. If
they carried enough money or
if they repeated the cycle
often enough, they would
eventually force the real
interest rate to zero. Since
real rates of interest generally
are positive, we can
confidently conclude that
either people will never invent
time machines, or they will be
too expensive for arbitrageurs
to operate profitably.
Source: Marc Reinganum, "Is
Time Travel Impossible? A Finan-
cial Proof;"Journal
of Portfolio
Management
13, 1 (1986):
10-12.


## Page 194

CHAPTER 8 WHY PEOPLE TRADE • 181
Dfr Wealth-Moving Technologies in Less-Developed Economies
In economies where property rights are poorly defined, and where people
cannot effectively enforce contracts, financial markets do not function well.
In such economies, strongmen often steal assets and many people refuse to
settle their contracts when they have lost money.
To move wealth through time in such economies, people must resort to
other methods. Most lending is within families because family bonds often
ensure performance. Strongmen dominate public lending markets because
they can use extralegal means to enforce their contracts. Investors typically
invest in assets that they can hide or that only they can control. These
assets include gold and silver, which they can easily hide, and human
capital and housing, which thieves and corrupt officials cannot easily take.
Finally, families tend to be large in economies with poorly developed
financial markets because parents invest in children to provide for their
retirement years.
real losses
if they cannot buy as much in the future as they could have bought
had they not invested.) The real rate of interest depends on how much
money investors and borrowers want to move through time. It is usually
positive because people would want to move more money from the future
to the present than vice versa if there were no cost of doing so. Since money
cannot actually move through time, the real risk-free interest rate must be
positive to discourage some borrowers and encourage some investors.
The
risk premium
is the additional expected return that investors demand
to compensate them for the risk that their investment may not actually re-
turn the real risk-free interest rate. Risky assets have risk premiums because
they are poor vehicles for moving money through time. Investors do not like
them because they risk losing their wealth when using them. To get them to
bear these risks, they must be compensated for holding these assets.
Although all traders hope to receive extraordinary returns from their in-
vestments, investors do not expect them. In this respect, they differ from
speculators.
Speculators
trade because they expect to receive a higher return
than the unconditional return that investors require to defer their con-
sumption and to bear risks. They form these expectations based on private
information they have about future returns. The expected returns of specu-
lators are therefore
conditional expected returns.
Speculators expect higher re-
turns from their positions than do investors.
8.1.2 Asset Exchangers
Asset exchangers
use markets to exchange assets that they own for other as-
sets that are of greater immediate use to them. Spot commodity markets
and foreign exchange markets are the largest organized markets in which
asset exchangers trade. In most asset exchanges, a buyer pays money or sim-
ilar financial assets to a seller who delivers a commodity or a currency. In
bartered trades,
both traders exchange goods or services. In such trades, the
distinction between buyer and seller is not always clear.
In a sense, all voluntary trades are asset exchanges. In a voluntary trade,
traders acquire assets that are of greater value to them than the ones they
give up. They would not trade otherwise. Investing and borrowing are spe-
cial cases of asset exchanges. Investors exchange current money for assets
that allow them to move money forward through time. Borrowers create
Examples of
Asset Exchanges
•
U.S.-based Volkswagen
importers use the currency
markets to exchange U.S.
dollars for the euros they
need to buy Volkswagens
in Germany.
•
Photographic film
manufacturers use the spot
silver markets to purchase
the silver they need to
make their films.
•
Feedlot operators use the
spot soybean market to
purchase the soybeans
they need to feed their
livestock.


## Page 195

182 • TRADING AND EXCHANGES
The Carryout
Cash Market
One of the more interesting
cash markets is the market for
cash. ATM operators must
regularly put currency into
their machines so that their
customers can withdraw cash
on demand. These machines
require properly formatted
bills. If the bills are dirty,
wrinkled, or sticky, they can
jam the machines or cause
them to dispense cash
inaccurately.
A small industry has
developed to package ATM-fit
currency for use in ATM
machines. The list price for a
20,000-dollar brick of 10
bundles of 100 20-dollar bills
is 20,003.50 dollars. The
3.50 dollar premium over
face value is the price that
small ATM operators pay to
have bank cash vaults
recondition and package
currency that they can
confidently use in their ATM
machines. (Large ATM
operators negotiate better
prices.) ATM-fit currency is
thus a commodity in the same
sense that silver is a
commodity.
debt instruments that
they exchange for money they can use to buy things
now Although we could classify all trades as asset exchanges, we use the
term only for trades that traders arrange because they have a current use for
the item they acquire. Practitioners often call the markets in which such
trades take place
cash markets
or
spot markets.
Spot commodity and currency markets are some of the world's biggest
markets. The foreign exchange markets are especially large. For example,
according to the Bank for International Settlements, the average daily global
volume in the spot currency markets was 1.2 trillion dollars in April 2001.
This volume is about 10 times greater than global equities volume.
Asset exchangers like liquid cash markets. Such markets allow them to
convert their assets from one form to another at low cost. Markets also must
trade the assets that traders need. A liquid cash market for soybeans in
Chicago is of little use to a Texas dairy that needs to buy soybeans for de-
livery in Texas. The dairy would benefit more from a liquid soybean mar-
ket in Texas.
8.1.3 Hedgers and Hedging
Many economic activities expose people and businesses to serious financial
risks. Consider four examples:
•
Wheat farmers risk losing money if the price of wheat falls after they
plant their fields but before they harvest them. The price they receive
for their wheat might be too low to allow them to recover the costs of
planting and cultivating it.
•
Wholesale bakers risk losing money if the price of flour rises after they
enter fixed-price contracts to supply bread but before they have pur-
chased the flour necessary to make the bread. The cost of baking the
bread might be greater than the fixed price they will receive.
•
Traders who speculate in individual stocks risk losing money if the
stocks they buy drop because the market as a whole drops. Specula-
tors in individual stocks may be able to predict which stocks will beat
the market, but they usually cannot predict what the whole market will
do. If the market falls, they could lose money even if their stocks out-
perform the market.
•
Banks that lend money at fixed long-term rates and borrow money at
variable short-term rates risk losing money if interest rates rise. Their
revenues would be fixed, but their borrowing costs would rise.
The risks in these examples are all very substantial. They can easily cause
their holders to go bankrupt. Fortunately, markets and trading strategies
have evolved that allow traders to avoid these risks.
Hedgers
use the markets to reduce their exposure to substantial financial
risks. They hedge their risks by selling or buying instruments whose values
are correlated or inversely correlated with the risks that they face. Their po-
sitions in these instruments are their
hedges,
and the instruments are their
hedging vehicles.
When properly executed, the risks in their hedges offset
their financial risks. Their
hedged positions—the
combinations of their po-
sitions in the original risk and in the hedging vehicle—are less risky than
either position taken separately.


## Page 196

CHAPTER 8 WHY PEOPLE TRADE • 183
Traders use many instruments to hedge their financial risks. The re-
mainder of this section examines how various hedging strategies can help
solve the risk management problems in the above examples. These exam-
ples illustrate the use of forward contracts, futures contracts, option con-
tracts, and swaps as hedging vehicles.
8.1.3.1 Some Commodity Hedging Examples:
Wheat and Flour
The risk that prices will change for the worse is
price risk.
The price risk
that traders face depends on whether they lose from an increase or a de-
crease in price.
Wheat farmers and bread bakers face complementary price risks. If the
price of wheat falls, the farmers will lose money but the bakers, who have
to buy flour made from wheat, will save money. Likewise, if the price of
wheat rises, the bakers will lose money but the farmers will profit.
Wheat farmers and bread bakers are
natural hedgers
because they face
complementary risks. They can eliminate their exposure to their respective
risks by combining their operations or by entering contracts that allow them
to assume each other's risks.
Hedging by Combining Businesses
Since the two risks are complementary, farmers and bakers can arrange to
share their risks and thereby eliminate them. For example, by going into
partnership, a farmer and a baker can eliminate their collective exposure to
fluctuations in wheat prices. If the price of wheat rises, their farming prof-
its will offset their baking losses. If the price falls, their farming losses will
offset their baking profits. This partnership would be an example of a
ver-
tically integrated firm.
Many firms integrate vertically to avoid exposures to
price fluctuations in the markets for their intermediate goods.
Although a partnership would allow a farmer and a baker to manage
their price risks, it may not be the best solution to their risk management
problems. Most farmers do not know much about the wholesale baking
business, and most wholesale bakers do not know much about farming. Their
ignorance of each other's operations would complicate their management of
the partnership.
Hedging with Forward Contracts
Farmers and bakers can also manage their price risks by exchanging for-
ward contracts. A
forward contract
is an agreement to trade something in
the future at a price that is set now. Hedgers frequently use forward con-
tracts to hedge price risks.
In our example, a farmer would create and sell a forward wheat contract
to a baker. The contract would specify a price at which the baker would buy
the farmer's future harvest. The farmer then would be long wheat in the
ground and short the forward contract. The baker would be short flour and
long the forward contract. Since the value of the forward contract depends
on the price of wheat, the two traders would have hedged positions. If wheat
prices rise, the forward contract will rise in value. The farmer's greater farm-
ing profits will offset the losses on his short forward contract position,
and the baker's profits on her long forward contract position will offset her
Covering the Naked
Traders sometimes call
unhedged
positions naked
positions
because they expose
them to risks. They call
hedged positions
covered
positions
because they cover
the
risks to which
naked
positions are exposed.
4


## Page 197

184 • TRADING AND EXCHANGES
baking losses. If wheat prices fall, the forward price will drop in value. The
farmer's forward contract profits then will offset his farming losses, and the
baker's greater baking profits will offset her short forward position losses.
If the farmer and the baker can construct
perfectly hedged positions,
their to-
tal profits will no longer depend on future changes in the price of grain.
Although some farmers and bakers may manage their risks by exchang-
ing forward contracts, three significant problems make this method infea-
sible for most farmers and bakers:
•
To execute a forward contract, a farmer and a baker either must know
each other or a broker must introduce them. Without a well-organized
market, they may have trouble finding each other.
•
Each of the two contractors must trust that the other will honor his
or her commitment. If the price of wheat drops before the contract
delivery date, the baker will prefer to ignore the contract and buy
cheaper wheat. The farmer must trust that the baker will buy his wheat
at the agreed-upon price. If the price of wheat rises, the farmer
will prefer to ignore the contract and sell his wheat at a higher price.
The baker must trust that the farmer will deliver his wheat anyway.
The baker and the farmer must know each other well to confidently
trust each other.
•
The farmer and baker may find it difficult to arrange delivery terms
that are convenient to both. The delivery terms of a contract specify
where, when, how much, how, and exactly what the seller will deliver
to the buyer. The farmer and baker may be geographically distant from
each other, or the baker may be interested in a different grade of grain
than the farmer can deliver. In addition, if the baker does not mill his
own flour, he will prefer to receive flour rather than grain.
These problems ensure that forward contracts are attractive hedging
vehicles only for traders who trade with each other in the normal course of
business. Since such relationships are usually one-to-one relationships, for-
ward markets generally are not liquid.
Hedging with Futures Contracts
To address these problems, markets have developed a special type of for-
ward contract called a futures contract. A
futures contract
is a standardized
forward contract for which a
clearinghouse
guarantees the performance of
the buyer and seller by interposing itself between the buyer and seller
of every trade. It acts as the seller for every buyer and as the buyer for every
seller. The clearinghouse guarantee allows any buyer to trade with any seller
without worrying about credit risk. Contract standardization ensures that
all traders trade the same instrument. These features make futures markets
very liquid.
All futures contracts traded in a given commodity market for the same
delivery month are identical. In particular, they all have the same delivery
terms. Few traders, however, make or take delivery because most traders
prefer different delivery terms. To avoid delivery, buyers sell their contracts
and sellers repurchase their contracts before delivery. Since the contracts
have the same delivery terms, and since the clearinghouse is on the oppo-
site side of all positions, traders can close their positions by trading with


## Page 198

CHAPTER 8 WHY PEOPLE TRADE • 185
any other trader. To be released from their contractual obligations, they do
not have to trade with the person with whom they originally traded.
Suppose that the wheat farmer in our example is in North Dakota. To
hedge the price risk associated with his crop, he sells September Chicago
Board of Trade wheat futures contracts short. These contracts call for the
delivery of 5,000 bushels of wheat on the last business day of September.
The contract specifies the types and grades of wheat that may be delivered.
It also requires that the delivery occur in Chicago, Toledo, or St. Louis at
specified grain elevators. The farmer does not intend to ship his wheat to
any of these cities for delivery. Instead, he will sell it to a local grain eleva-
tor operator after he gathers his harvest. At the same time, he will repur-
chase his futures contract. Although the futures contract calls for delivery
in cities distant from North Dakota, it is still a good hedging vehicle for
the farmer because wheat prices in North Dakota are closely correlated with
Chicago wheat futures contract prices.
Wheat prices in Chicago and North Dakota are closely related because
shipping wheat between these two locations does not cost much. Under
normal conditions, the difference in the two wheat prices can be no greater
than the average cost of shipping a bushel of grain. If prices differ by more
than the shipping cost, an
arbitrageur
would buy wheat where it is cheaper
(typically in North Dakota), sell it where it is more expensive, and ship it
to make the delivery.
The difference between the Chicago wheat futures price and a local cash
wheat price is the
local basis.
Since cash prices vary slightly by location, the
basis also varies by location. The local basis typically reflects the costs of
shipping wheat from where it grows to where it is used. The basis obtained
its name because grain traders throughout the Midwest typically express
their local cash prices in terms of a premium to or discount from the Chicago
futures price. The Chicago futures price is the
base price,
and the premium
to or discount from the Chicago price is the basis.
The wholesale baker can hedge her price risk by buying wheat futures
contracts when she enters a fixed-price contract to supply bread in the
fu
ture. When she actually needs the flour, she will buy it from a local miller
and simultaneously sell her futures contracts. The futures contract is a good
hedging vehicle for her because Chicago wheat futures prices are closely
correlated with the prices she pays for flour.
Traders hedge with futures when they want to reduce price risk. The fu-
tures hedge effectively locks in a future price so that hedgers will not lose
from an adverse price change or profit from a favorable price change. A fu-
tures hedge therefore eliminates both downside risk and upside potential. Since
the gains and losses on a futures contract are almost exactly proportional to
changes in the underlying cash price, a futures hedge is a
linear hedge.
8.1.3.2 Two Stock Hedging Examples
Given careful research, Jack expects that Apple's newest computer will be
more successful than is widely thought. He therefore buys 550,000 dollars
of Apple common stock (AAPL). The price of Apple depends on the suc-
cess of the new computer and on other marketwide factors. Even if the
new computer is successful, Jack may lose money if Apple falls in a market-
wide drop.
Pork Bellies on
the Lawn
Comedians occasionally tell
stories about speculators who
buy futures contracts and
forget to close their positions
before the delivery date. In
these stories, a truck pulls up
to the procrastinator's
suburban house and dumps a
40,000-pound load of fresh
pork bellies on his lawn. (A
pork belly is an uncured side
of pork.) The comedians then
describe the resulting chaos.
In practice, brokers
attempt to contact their clients
before delivery to determine
their intentions. If a broker
cannot reach a client and if
the broker knows that the
client does not intend to
accept delivery, the broker
usually will sell the contract to
avoid delivery. In the event
that the contract actually
delivers, the delivery would
be to a warehouse rather
than to a front lawn.


## Page 199

186 • TRADING AND EXCHANGES
Hedging with Index Futures Contracts
To hedge against this risk, Jack sells December S&P 500 Index futures con-
tracts at the Chicago Mercantile Exchange. The
S&P 500 Index
is an in-
dex of stock market values prepared by Standard and Poor's Corporation.
The index is proportional to the total market value of 500 large stocks that
Standard & Poor's believes broadly represent all large U.S. stocks. The fi-
nal settlement value of the December S&P 500 Index futures contract is
250 times a special opening quotation of the S&P 500 Index computed
from the opening values of the constituent stocks on the third Friday of
December. The value of the contract therefore fluctuates with the value
of the S&P 500 Index.
Jack believes that the average relation between percentage price changes
in Apple and in the S&P 500 Index is approximately one to one. (The
beta
of the stock is 1.0.) Jack therefore wants to sell about 550,000 (550,000 X
1.0) nominal dollars of S&P 500 Index contracts to hedge his Apple posi-
tion. Since the current value of the S&P Index is 1,084, Jack sells two con-
tracts, which gives him about 542,000 (2 X 250 X 1,084) dollars of index
risk exposure.
If Jack is right about Apple, the stock will outperform the market,
whether the market moves up or down. If the market drops and pulls
Apple down with it, Jack will lose on his Apple position, but he will profit
on his short index futures position. If Apple indeed outperforms the mar-
ket, Apple will drop less than the market, and Jack will make more money
on his short position than he loses in Apple. If the market rises, Jack will
lose on his short index futures position. If Apple indeed outperforms the
market, Jack will make more money in Apple than he loses in the index
futures contract. Regardless of what the market does, Jack will profit if
Apple outperforms the market.
By hedging his bet with index futures contracts, Jack can limit his risk
exposure only to whether Apple will beat the market or not. Since his re-
search advantage is specific to Apple, the hedged position exposes him only
to those risks where he has a competitive advantage. Since he cannot pre-
dict the market any better than anyone else can, he would like to avoid ex-
posure to market risk. Speculators are generally most successful when they
expose themselves only to the risks they understand best.
Hedging with Stock Option Contracts
Jack also could hedge his position in Apple by buying Apple put options.
Put options would allow him to sell his position at a fixed
strike price
at any
time before the option
expiration date.
If the price of Apple drops below the
strike price, Jack would exercise his put options and thereby limit his losses.
The purchase price of the options—the
options premium—is
the cost of this
insurance. Jack would buy Apple put options rather than sell them because
put option prices vary inversely with common stock prices.
An options hedge is a
nonlinear hedge
because the relation between op-
tion prices and their underlying stock prices is nonlinear. Put contract prices
decrease as stock prices rise, but their rates of decrease decline as prices rise.
(The relation would be linear if the rate of decrease were constant.) This
nonlinear relation allows Jack to hedge his downside risk fully while pre-
serving the potential for upside appreciation. When the stock price is well
below the strike price, the put option is quite valuable. Changes in the put


## Page 200

CHAPTER 8 WHY PEOPLE TRADE • 187
price are approximately the same size (but of opposite sign) as changes in
the stock price, so that increases in the put option price almost completely
offset further drops in the stock price. However, when the stock price is
high relative to the strike price, the put option has little value. Changes
in the put price, then, are much smaller than changes in the stock price, so
that further losses from the put options hardly offset further increases in
stock prices.
Jack may use put options to hedge his downside risk even if he never in-
tends to exercise them. Since put option prices rise as the price of Apple
falls, Jack can achieve his hedging goals simply by selling the options when
he no longer wants to hedge.
According to the
options
put—call parity theorem,
the combination of a
long position in Apple and a long position in Apple puts is essentially the
same as a long position in Apple call options. Both strategies would allow
Jack to profit if the price of Apple rises and would limit his losses if Apple
drops for any reason. If Jack wishes to hedge a preexisting position in
Apple stock, he would probably buy puts. The strategy would be especially
attractive if Jack has a large unrealized gain in Apple stock on which he
does not want to pay taxes. If he wishes to establish a new position in
Apple with upside potential and limited downside potential, he would prob-
ably buy call options.
The difference between hedging downside loss via futures and via op-
tions lies in the trade-offs made to eliminate the downside risk. In a futures
hedge, the hedger gives up upside potential, but does not have to pay a pre-
mium for the hedge. In an options hedge, the hedger gives up a premium,
but gets to keep the upside potential.
Jack might prefer the stock options hedge to the index futures hedge if
he is uncertain about the quality of his information. The options hedge will
limit his losses if he is wrong about Apple's value.
Jack might also prefer to use the stock options hedge if he believes that
a decline in the market is more likely than an increase. The motivation for
such a decision, however, is purely speculative. Jack also could act on this
opinion by buying index futures contracts or index call option contracts.
8.1.3.3 An Interest Rate Hedging Example
Wilshire Savings and Loan borrows money from its depositors at variable
short-term rates and lends money to homeowners at long fixed rates. If in-
terest rates rise, Wilshire will have to pay more for its deposits, but it will
receive no more from its portfolio of loans. A large increase in interest rates
could cause the bank to go bankrupt.
Financial managers call this problem the
duration mismatch
problem. The
duration of the bank's assets is greater than the duration of its liabilities. To
manage this risk, Wilshire Savings and Loan needs to hedge its uncertain
cash flows.
Hedging with
Swaps
Wilshire can hedge its interest rate risk by entering an interest rate swap.
An
interest rate swap
is an agreement between two parties to swap a fixed-
rate cash flow for a variable-rate cash flow. The swap contract specifies both
cash flows. A typical swap contract involves a five-year swap of semiannual
payments. The variable-rate cash flow depends on some short-term inter-


## Page 201

188 • TRADING AND EXCHANGES
est rate index, like the London InterBank Offered Rate (LIBOR). The fixed-
rate cash flow depends on the rate that the parties negotiate when they
arrange their swap.
To reduce its interest rate exposure, Wilshire will swap a fixed-rate cash
flow for a variable-rate cash flow. If short-term interest rates rise, the bank
will have to pay more for its deposits, but an increase in the variable cash
flow from the swap will offset this cost. The net effect on the income of the
bank will be smaller than if it had not hedged. If the interest rate falls,
Wilshire will have to pay less for its deposits, but it will receive less from
the swap. The swap reduces the risk from an increase in interest rates, but
it also reduces the benefits from a decrease in rates.
The natural other side of the trade might be a pension fund that holds
a portfolio of long-term, fixed-rate bonds and wishes to hedge against pos-
sible future inflation. If inflation rises, interest rates will rise, and the value
of the portfolio will drop. The pension fund can reduce this risk by swap-
ping a fixed-rate cash flow for a variable-rate cash flow. This transaction ef-
fectively converts the fixed-rate interest payments to variable-rate payments.
If inflation rises, the net cash flow from the portfolio will also rise.
Swaps provide imperfect hedges for the interest rate risks that banks face.
The rates that banks pay for their deposits are rarely the same as the short-
term interest rate index that determines the variable cash flows in the swap.
Likewise, the cash flows that banks receive on their loan portfolios are not
fixed. They depend on the numbers of borrowers who retire their loans early
or who default on them. The hedged cash flows therefore still expose the
bank to interest rate risks. The risks should be smaller, however, than would
be expected if the cash flows were unhedged.
8.1.3.4 Hedging Markets
Hedgers like to trade in liquid markets where transaction costs are low. Low
transaction costs allow them to set up and remove their hedges cheaply.
Hedgers also like markets that trade instruments which are closely cor-
related to the risks that they face. Such instruments
replicate
the underlying
risks. A contract closely replicates a hedger's risk if the variation in his ba-
sis is small compared to the variation in the contract price.
The most successful hedging markets appeal to large numbers of natu-
ral hedgers. The hedging interest must be on both sides of the market, and
the hedgers must all face large, highly correlated (or inversely correlated)
risks. Consequently, successful hedging markets generally trade intermedi-
ate commodities that are largely undifferentiated and cheap to transport.
Intermediate commodities
are produced by one industry and used by another
industry. They usually have two-sided hedging interest because producers
and users face complementary risks. Some relatively new financial futures
markets attract many hedgers. Hedgers use these contracts to hedge finan-
cial risks associated with transactions that they expect to do in the future.
Table 8-1 presents examples of successful hedging markets.
Commodity futures markets constantly develop new contracts to offer to
potential hedgers. A few new contracts are spectacularly successful. Most at-
tract little interest and ultimately fail. The most successful recent contract
introductions have involved energy and financial products. Some interesting
failures have included contracts in sunflower seeds, wool, butter, eggs, high
fructose corn syrup, boneless beef trimmings, frozen turkeys, crop yields,


## Page 202

CHAPTER 8 WHY PEOPLE TRADE • 189
TABLE 8-1.
Examples of Successful Hedging Markets
AGRICULTURAL

INDUSTRIAL
PRODUCTS

PRODUCTS

FINANCIAL PRODUCTS
Corn
Wheat
Oats
Soybeans
Soybean meal
Soybean oil
Live cattle
Pork bellies
Lean hogs
Coffee
Cocoa
Sugar
Cotton
Orange juice
Copper
Gold
Platinum
Silver
Crude oil
Heating oil
Gasoline
Natural gas
Gas oil
Lumber
U.S. Treasury bonds
U.S. Treasury notes
U.S. Treasury bills
Eurodollars
Euroyen
German government bonds
Various foreign currencies
S&P 500 Stock Index
Dow Jones Industrial Average
Nasdaq 100 Index
Various national stock indexes
Various sector stock indexes
Fixed/variable interest rate
differentials
Note: These markets all trade futures contracts or swaps contracts.
barge freight rates, anhydrous ammonia fertilizer, diammonium phosphate
fertilizer, various Brady bonds, various yield curve spreads, the U.S. inflation
rate, various catastrophe insurance indexes, aluminum, and U.S. silver coins.
Table 8-2 lists some recent successful futures contract introductions.
8.1.4 Gamblers
Gamblers
bet on future events. Their bets are contracts whose values depend
on the uncertain outcomes of future events. Gamblers commonly bet on
sporting events, horse races, lotteries, and card games. Although financial
instruments are not gambling contracts, their values do depend on the un-
certain outcomes of future events. Given the similarity, it would be sur-
prising if some gamblers did not trade financial instruments.
Gamblers gamble because gambling excites them and makes their lives
more interesting. Gambling entertains them.
TABLE 8
-
2.
Some Recent Successful Futures Contract Introductions
CONTRACT

YEAR

EXCHANGE
Swapnote
Dow Jones Industrial Average
Natural gas
U.S. Dollar Index
Crude oil
S&P 500 Index
30-year Treasury bond
Eurodollar
2001

Euronext LIFFE
1997

Chicago Board of Trade
1990

New York Mercantile Exchange
1985

New York Cotton Exchange
1983

New York Mercantile Exchange
1982

Chicago Mercantile Exchange
1982

Chicago Board of Trade
1981

Chicago Mercantile Exchange


## Page 203

190 •
TRADING AND EXCHANGES
OP.
OTB and OTC
Some states and countries
permit off-track betting (OTB)
on horse races. OTB shops
are similar to the storefront
offices that many retail
brokers maintain for their
clients. In both types of
offices, television sets
broadcast the latest news.
Computer screens present the
latest information on prices
and results. Clerks behind
counters take orders.
Customers mill about. They
share tips with each other
and discuss their strategies.
The similarity between the
two types of offices, of
course, does not imply that all
retail brokerage customers
are gamblers. Many are
investors and some are good
speculators. Their brokers
provide them with facilities at
which they can shoot the bull
with new and old friends as a
service to obtain their
patronage.
Not all OTB patrons are
gamblers, although I imagine
that most are. Some may be
well-informed speculators who
appreciate the convenience
of being able to bet on horse
races at many tracks at
the same time.
Gamblers are different from speculators.
Speculators
are traders who use
information to predict future price changes more accurately than most other
traders can. Their superior information gives them an advantage when they
trade. Speculators trade because they expect to make money. Depending on
what they know, they may trade in betting markets or in financial markets.
In contrast, gamblers are uninformed traders. Although they hope to make
money, they have no rational reason to expect that they will do so. Gamblers
who are honest with themselves trade for entertainment. Gamblers who trade
because they believe that they will be successful speculators are foolish.
Few traders trade strictly for gambling entertainment. Instead, most
traders who gamble also trade to invest, to hedge, or to speculate. Investors,
hedgers, and speculators who gamble typically trade more intensely than
they would otherwise. They may trade more often, they may trade more
volatile instruments, and they may accept greater risks than they would given
only their other objectives. Consequently, their gambling will compromise
their performance as investors, hedgers, and speculators. People who allow
others to trade on their behalf must carefully monitor their agents to en-
sure that they do not gamble.
Many—probably most—traders who gamble in the financial markets are
unaware that they are gambling. Most believe that they are pursuing other
objectives. Traders need great discipline to discriminate between prudent risk-
taking behavior and gambling. Many traders who believe that they are spec-
ulating actually are gambling because they do not recognize that the infor-
mation upon which they trade does not give them any advantage over other
traders. Traders who gamble can sometimes be identified by their enthusiasm
for trading and by their inability to clearly articulate their reasons for trading.
The notion that some traders are gamblers is controversial. Many regu-
lators fear the damage that they can do to themselves and to the markets.
They especially worry that gamblers may make the markets more volatile.
We address these concerns in chapter 28 when we discuss the causes of ex-
cess volatility, and what regulators might do to reduce it.
Gambling is not necessarily bad for financial markets. Since gamblers
are uninformed traders, they tend to lose to well-informed traders. When
many gamblers are present, informed trading can be quite profitable. In
chapter 10, we show that gambling may lead to less volatility.
Like all other utilitarian traders, gamblers like to trade in liquid markets.
The low transaction costs in such markets allow them to acquire and divest
their positions cheaply.
Gamblers also like to trade volatile instruments because they typically
provide the greatest potential for exciting entertainment. The great popu-
larity of public lotteries suggests that some gamblers like bets which win
big with low probability and lose small with high probability. Since out-of-
the-money options provide similar return distributions, gamblers may be es-
pecially attracted to these instruments.
8.1.5 Fledglings
Fledglings
trade to learn whether they can trade profitably. They are willing
to lose money when trading to answer this question. Fledglings may try a
variety of trading styles, or they may concentrate on learning a single style.
Fledglings become profit-motivated traders if they learn to trade prof-
itably. If they do not, they eventually quit or are fired. Fledglings who can-
not trade well, and who continue to trade, are futile traders.


## Page 204

CHAPTER 8 WHY PEOPLE TRADE • 191
00 A
Common Fledgling Story
Brad traded a
paper portfolio
for several years as a hobby. Whenever he
wanted to buy or sell a security, he pretended to do so at the closing
market prices. His paper portfolio consistently outperformed the market,
even after he accounted for the commission costs that he would have paid
had he actually traded.
Brad's paper portfolio successes suggest to him that he might be able to
make a lot of money trading securities. Figuring that every profitable trader
has to start trading sometime, Brad begins to trade with real money. Since
he believes that his trading will be most successful if he gives it all his
attention, he takes a leave of absence from his work while he tries to make
a go of it as a trader.
After several months, Brad has lost enough money to convince himself
that further experimentation with his new career will not likely be
productive. He quits trading and returns to work a much wiser, but less
wealthy, man.
Trading on paper is not the same as actual trading. When real money is
at risk, traders become more emotional about their trading. They become
more risk averse. They allow their opinions about values to be influenced by
their positions and by their past gains and losses in those positions. Many
good paper portfolio traders cannot overcome these biases. Those who
can, may become successful traders.
Since measuring performance can be very difficult (see chapter 22), fledg-
lings may falsely conclude that they are skilled when they are only lucky. A
lucky, but unskilled, fledgling is still a fledgling. Many successful traders,
including professional portfolio managers, may still be fledglings. Success
does not necessarily imply skill.
Learning to trade profitably can be expensive. Most people do not suc-
ceed. Dealers, floor traders, and day traders in many different markets com-
monly say that fewer than 5 percent of fledgling traders survive to trade prof-
itably. Some of those who do, however, may profit handsomely. Rational
people therefore may be willing to lose money to learn whether they can
trade profitably. In this respect, learning trading is similar to learning disci-
plines like medicine, engineering, the arts, sports, politics, and management.
8.1.6 Cross
-
subsidizers
Cross-subsidizers
trade to produce commission revenues for their brokers in
return for various services that they otherwise might purchase themselves.
The commissions that they pay are higher than they would pay if they were
not receiving services for them.
Cross-subsidizers are usually professional money managers. They and their
brokers use
soft dollar
accounting systems to ensure that the services brokers
provide are commensurate with the commissions they receive. Many money
managers like soft commissions because they allow them to report lower ex-
pense ratios. Chapter 7 provides a detailed analysis of soft commissions.
Although few, if any, traders trade just to obtain soft dollars, soft dollar
benefits undoubtedly encourage some traders to trade more than they oth-
erwise would. Cross-subsidization therefore is a reason why people trade.
Other cross-subsidizers may trade to reward their brokers for friendship
or companionship, or for their brokers' respect. These external benefits pre-
sumably offset the commissions and other transaction costs they incur when
trading.


## Page 205

192 • TRADING AND EXCHANGES
peg
Dividend Capture
When governments tax
dividend income at a rate
lower than the rate at which
short-term capital losses
reduce taxes, traders may try
to
capture dividends
by using
the following strategy. They
buy a stock with instructions
to settle on or before its
dividend record date—the
date on which the issuer
notes which shareholders are
entitled to receive the
dividend. They simultaneously
sell the stock with instructions
to settle after the dividend
record date. They thereby
obtain the dividend but
realize an offsetting short-term
capital loss because the sale
price is lower than the
purchase price by the amount
of the dividend. After taxes,
the trade is profitable
because the after-tax value of
the dividend is greater than
the after-tax value of the short-
term loss.
In the 1980s, Japanese
insurance companies traded
extensively in the U.S.
markets to take advantage of
this strategy. Changes in
Japanese tax laws have since
made the practice less
common. Since the United
States taxes dividend income
and short-term capital gains
at the same rate for most
people and corporations,
Americans do not engage in
much dividend capture. 4.1
Tax Deferral
Many countries tax capital gains only when traders realize them. Traders
realize capital gains
when they close profitable positions. Since capital
losses generally offset capital gains, traders who have realized capital
gains can cut their taxes by realizing capital losses. Clever traders can use
this feature of the tax system to defer the taxation of their capital gains
indefinitely. Consider the following example.
Susan is a U.S. investor who has already realized substantial short-term
capital gains from her trading this year. It is now October. If she does not
plan her finances carefully, she will pay substantial taxes at the end of the
year. Susan needs short-term capital losses to offset her capital gains.
Unfortunately—actually fortunately—she does not have any positions with
losses that she can sell.
To solve her problem, Susan decides to buy the Mexico Fund (MXF) and
short sell the Mexico Equity and Income Fund (MXE). These two funds are
unrelated closed-end funds that own diversified portfolios of Mexican stocks.
Although Mexican stocks are often quite volatile, the combined position is
not very risky because the returns to these two funds are very closely
correlated.
If the Mexican stock market rises before the end of the year, Susan will
realize her loss on her short MXE position and carry her gain in MXF over
into the next year. If she holds MXF long enough, she can obtain a second
benefit of deferral: The government will tax her ultimate sale at a lower
long-term capital gains rate.
If Mexican stocks fall, she will realize her loss on her long MXF position
and carry her gain in the MXE short position into the next year, at which
time she may close the position and realize the gain. Since the tax rate on
profits from short sales does not depend on the holding period, deferral will
be her only tax benefit.
Her strategy will fail only if the Mexican stock market does not move.
Susan can protect against this possibility by holding a diversified portfolio
of these matched positions.
The government is aware of this strategy, which traders call a
tax
straddle.
Traders once routinely constructed tax straddles by trading futures
with different maturity dates. To eliminate this loophole, U.S. tax law now
requires that traders realize all gains and losses in futures contracts for tax
purposes at year-end, whether or not they have closed their positions. Other
rules prohibit traders from obtaining tax benefits from closely hedged
positions constructed from essentially identical instruments. The IRS therefore
may disallow the tax benefits from Susan's strategy. If challenged, Susan
will claim that her position is actually a risky speculation on the relative
values of different instruments. She will note that the two funds hold different
portfolios and that they have different portfolio managers. Whether she
would prevail is a question better addressed by a qualified tax attorney
than by me.
8.1.7 Tax Avoiders
Tax avoiders
trade to take advantage of tax loopholes in order to minimize
their taxes. Depending on the tax laws involved, their strategies can be very
simple or very complex. The three adjacent boxes illustrate a few of these
strategies.
8.1.8 Utilitarian Trader Summary
Utilitarian traders use the markets to solve problems that originate outside
the markets. Investors and borrowers use the markets to move money for-


## Page 206

CHAPTER 8 WHY PEOPLE TRADE • 193
,„
110 Capturing the Capital Loss Exclusion
U.S. tax law allows individual investors to reduce their taxable labor
income by up to 3,000 dollars of capital losses per year. Otherwise,
investors can use capital losses only to offset capital gains.
Ron is an uninformed long-term investor. Since he is uninformed, he
intends to buy and hold securities for the long run. To take advantage of the
capital loss exclusion, he invests in a well-diversified portfolio of individual
stocks instead of in a well-diversified mutual fund. Toward the end of each
year, he
harvests his losses
by selling stocks that have dropped in value
since he bought them. He replaces them with new stocks. Since his portfolio
is well diversified, he usually has some stocks with losses. Ron never sells
stocks with gains.
Using this strategy,
Ron
writes off
3,000
dollars of capital losses at his
ordinary income rate. Since the government ultimately will tax his long-term
capital gains at a lower rate, Ron effectively converts 3,000 dollars per
year of his labor income into long-term capital gains. Given Ron's combined
federal and state tax rate on his labor income of 40 percent, he saves
1,200 dollars a year in taxes. Over many years, the cumulative value of
these savings will be quite significant.
ward or back through time. Asset exchangers use the markets to obtain items
that are of greater value to them now than those which they tender. Hedgers
use the markets to offload risks. Gamblers use the markets to obtain enter-
tainment. Fledglings use the markets to learn whether they can be success-
ful profit-motivated traders. Cross-subsidizers use the markets to move
money from one account to another. Tax avoiders use the markets to min-
imize their taxes. Table 8-3 summarizes these traders.
All utilitarian traders want to trade in liquid markets, which allow them
to achieve their objectives at low cost. Economists sometimes call utilitarian
traders
liquidity traders
because they need liquidity to accomplish their goals.
TABLE 8-3.
Utilitarian Trader Summary
TRADER
Investors
Borrowers
Asset
exchangers
Hedgers
Gamblers
Fledglings
Cross-
subsidizers
Tax avoiders
MOTIVE
Move money from the present to the future
while obtaining a fair rate of return
Move money from the future to the present
at lowest cost
Obtain an asset of greater immediate value
than the one they tender
Reduce risk
Entertain themselves
Learn whether they can be successful profit-
motivated traders
Compensate brokers for providing services
Avoid taxes by exploiting tax loopholes
TYPICAL INSTRUMENTS
Stocks, bonds, and notes
Bonds and notes
Cash, commodities, and
currencies
Forwards, futures, and options
Volatile instruments
Various
Various
Various


## Page 207

194 • TRADING AND EXCHANGES
Utilitarian traders want to trade instruments that best solve their prob-
lems. Investors trade only instruments that expose them to risks they can
tolerate. Asset exchangers trade only for assets that they need. Hedgers
trade instruments that are closely correlated to the risks they face. Gam-
blers trade instruments that excite them.
8.2 PROFIT-MOTIVATED TRADERS
Profit-motivated traders
trade only because they expect to profit. Like all
traders, they profit if they buy low and sell high. The key to trading prof-
itably is to trade only when you have reason to believe that you will profit.
Profit-motivated traders therefore must understand why they profit in or-
der to predict when they will profit. This section introduces the various types
of profit-motivated traders. We discuss them in detail in later chapters.
The two main classes of profit-motivated traders are speculators and
dealers.
Speculators
attempt to profit by predicting how prices will change
in the future.
Dealers
attempt to profit by selling liquidity to other traders.
8.2.1
Speculators
Speculators
predict future price changes from information that they collect,
analyze, and, in some cases, produce. To profit from their insights, they buy
when they think prices will rise and sell when they think prices will fall.
Although their predictions are often wrong, successful speculators are right
more often than they are wrong. The more often they are right, the more
they profit.
Two types of traders speculate.
Informed traders
trade on information
about fundamental values. They make prices more informative. They also
sometimes make markets more liquid.
Parasitic traders
profit from the trades
that other traders do. They neither make prices more informative nor make
markets more liquid.
8.2.1
.1 Informed Traders
Informed traders
acquire and act on information about fundamental instru-
ment values. They trade when they believe that prices differ from funda-
mental values. They buy when they believe that prices are below fundamen-
tal values, and they sell when they believe that prices are above fundamental
values. They then profit if prices adjust toward their fundamental values.
Informed traders differ by how they form and act upon their opinions
about fundamental values.
Value traders
estimate fundamental values by col-
lecting and analyzing all available information.
News traders
are the first to
trade on new information.
Information-oriented technical traders
identify sys-
tematic patterns which indicate that prices differ from their fundamental
values.
Arbitrageurs
compare fundamental values across instruments. Arbi-
trageurs include
pure arbitrageurs,
who trade instruments with values that
depend on the same fundamental factors, and
statistical arbitrageurs,
who
trade instruments with values that depend on both common and instru-
ment-specific fundamental factors.
Informed traders are the only traders who cause prices to move toward
fundamental values. All other traders add noise to prices. Economists there-
fore call them
noise traders.


## Page 208

CHAPTER 8 WHY PEOPLE TRADE • 195
8.2.1.2
Parasitic Traders
Parasitic traders include
order anticipators
and
bluffers.
Order anticipators
acquire and act on information about the trades that
other traders will make. They profit when they correctly anticipate how other
traders will affect prices or when they can extract option values from the
orders other traders offer to the market.
Order anticipators differ by the information they use.
Front runners
col-
lect information about trades that other traders have decided to arrange.
Sentiment-oriented technical traders
use information to predict what unin-
formed traders will decide to do.
Squeezers
act on information about trades
that other traders must do.
Although order anticipators use information to trade profitably, their in-
formation is not about fundamental values. They therefore are uninformed
traders in the sense that we use this term.
Bluffers
create information that other traders may misinterpret. They profit
when they fool other traders into trading unwisely. Since the information they
create is not about fundamental values, bluffers are also uninformed traders.
Bluffers differ by how they fool other traders.
Rumormongers
promote or
discredit securities or commodities by disseminating misinformation.
Price
manipulators
trade to create prices and volumes that they hope other traders
will misinterpret.
8.2.1.3 Technical Traders
Our list of speculators includes two types of technical traders.
Technical
traders
try to predict price changes from
technical data
that generally include
past and current prices, volumes, short interests, money flows, block trad-
ing, and records of insider trading. Technical traders look for systematic pat-
terns in technical data that allow them to predict future price changes.
Technical traders differ by the type of information that they try to dis-
cover.
Information-oriented technical traders
are informed traders. They try to
identify when prices differ from their fundamental values. Their trades are
profitable when prices move toward fundamental values.
Sentiment-oriented
technical traders
are order anticipators. They try to identify what trades un-
informed traders will want to make. They trade profitably when they can
correctly anticipate the impacts that uninformed traders will have on prices.
8.2.2 Dealers
Dealers make themselves available so that other traders can trade when they
want to trade. They supply liquidity. They buy at their bid prices and sell
at their ask prices. The spread between these two prices is the price that
they charge impatient traders for liquidity.
To trade profitably, dealers must buy and sell equal volumes. They there-
fore must discover the prices that equate supply and demand. Dealers know
a lot about market values, but they generally do not know much about fun-
damental values. They are uninformed traders in our classification scheme.
Dealers vary by the size of the positions they take and by the time they
are willing to hold their positions.
Market makers
provide liquidity on de-
mand in small quantities. They often trade in and out of their positions
many times a day. In many markets, market makers will provide liquidity to
anyone.
Block facilitators
provide liquidity to large traders. They may take


## Page 209

Speculators
Informed traders
Value traders
News traders
Information-oriented
technical traders
Arbitrageurs
Order anticipators
Front runners
Sentiment-oriented
technical traders
Squeezers
Bluffers
Rumormongers
Price manipulators
Dealers
Market makers
Block facilitators
Predict price changes
Buy undervalued or sell
overvalued instruments
Estimate total value
Estimate changes in value
Identify patterns
inconsistent with
informative prices
Simultaneously buy
undervalued and sell
overvalued instruments
Trade ahead of other
traders
Trade ahead of submitted
orders
Trade ahead of
anticipated orders
Trade ahead of trades that
others must make
Fool other traders into
trading unwisely
Spread rumors
Have people misinterpret
their trading
Supply liquidity
Trade quickly in and out
in small sizes
Trade large blocks
196 • TRADING AND EXCHANGES
days or weeks to trade out of their positions. Block facilitators generally pro-
vide liquidity only to clients whom they choose.
8.2.3 Profit
-
motivated Trader Summary
Profit-motivated traders trade because they expect to profit from their trad-
ing. Successful profit-motivated traders must have some advantage that al-
lows them to trade profitably. Their various advantages usually involve in-
formation they have that others do not have. Table 8-4 presents a summary
of the various profit-motivated trading strategies.
TABLE 8
-
4.
Summary of Profit-motivated Trading Strategies and the Proprietary Information
upon Which They Are Based
TRADER TYPE

TRADING STRATEGY

PROPRIETARY INFORMATION

CHAPTERS
Various

10-12
Fundamental information

10
All available fundamental

10.5.1,
information

16
News about fundamental

10.5.2
values
Price patterns that indicate

10.5.3
departures from
fundamental values
Relative fundamental

10.5.4,
values

17
Information about other

11
traders
The trades that others

11.1
want to do
Predictions about trades

11.2
that others may decide
to do
Positions that other

11.3
traders have
Who can be fooled

12
The rumors they spread

12
The true purpose behind

12
their trades
The relation between

13-15
prices and the order flow
Short-term market

13-14,
conditions

24
Long-term market

15
conditions; which
securities interest their
clients


## Page 210

CHAPTER 8 WHY PEOPLE TRADE • 197
Informed traders
understand fundamental instrument values better than
other traders do. They have better access to fundamental data than do other
traders, and they can better analyze the implications of their data than other
traders can. They profit when prices track fundamental values.
Order anticipators
have information about what other traders intend to
do. They profit when they can trade before other traders do.
Bluffers
fool other traders into trading unwisely. They create information
that other traders misinterpret. They profit when other traders do not rec-
ognize the false bases of their trading decisions.
Dealers
supply liquidity. They allow other traders to trade when they want
to trade. Successful dealers must be able to identify the prices at which buy-
ers and sellers are equally willing to trade. Dealers profit when they under-
stand market conditions well, and when they know which securities will in-
terest their clients.
Profit-motivated traders do not always profit when they trade. Even the
best traders often lose because of events that they could not anticipate. Suc-
cessful traders win more often than they lose, however. Those who do not
are futile traders.
8.3 FUTILE TRADERS
Futile traders
expect to profit from trading, but they do not profit on aver-
age. They cannot recognize the difference between their expectations and
their results. They may be irrational, they may have poor information about
their results, they may rely on untrustworthy agents, or they may be of lim-
ited mental capacity.
Futile traders include inefficient traders and victimized traders. Ineffi-
cient traders are unable to produce the profits they desire. Victimized traders
rely on brokers who do not trade in their interest.
Inefficient traders
lack the skills, analytic resources, and access to infor-
mation necessary to trade profitably. They may do everything that profit-
motivated traders do, but they do not do it well enough to trade profitably.
Although they may profit from trading with some types of traders, those
profits are not sufficient to cover their losses to more skilled and better-
informed traders. Inefficient traders generally make poor decisions about
when to trade and when to refrain from trading.
The most common type of inefficient trader is the pseudo-informed
trader.
Pseudo-informed traders
believe they are well-informed traders. The
information on which they trade, however, is old news. Prices have already
reacted to the news, but they do not realize it. Consequently, their trades
are not profitable.
Victimized traders
rely on brokers, advisers, or employees who fail to meet
their fiduciary responsibilities. These agents may simply fail to provide ser-
vices for which they are paid, or they may deliberately exploit their clients
to their own advantage. Victimized traders believe that they will profit from
trading, but they do not on average.
Traders who hire conscientious but incompetent managers to profit from
trading are fledglings who have not yet learned how to manage their money.
If they refuse to learn, they become inefficient traders.
Rogue traders
victimize their employers or their clients. Rogue traders
trade in ways that serve their own purposes but are not in the best interests
A Trading Oxymoron
Investment managers
help
people manage their funds.
They may help people invest,
as their name implies, or they
may help people speculate.
Passive investment
managers
pursue
buy and
hold
strategies. Managers
who buy and hold rarely
trade.
Indexing
is the most
common buy and hold
strategy. Indexers try to
replicate the returns to an
index. Such strategies are
often appropriate for
investors.
Active investment
managers
are speculators
who try to beat the market.
Traders who "invest" with
such managers actually
speculate on whether the
manager can beat the
market.


## Page 211

198 • TRADING AND EXCHANGES
Nick Leeson and the Fall of Borings Bank
In 1994, Nick Leeson was head trader and head of settlements for Baring
Futures Singapore, a branch of Barings Bank. His supervisors thought that
he engaged in arbitrage trades that would profit from differences in the
prices of Nikkei 225 futures contracts listed on the Osaka Securities
Exchange (OSE) in Japan and on the Singapore Monetary Exchange
(SIMEX). Although such trades involve huge numbers of contracts, they are
not very risky. A short position in one contract is an excellent hedge for a
long position in the other contract.
In fact, Leeson was making substantial unhedged bets on the Nikkei
225. Following the Kobe earthquake of January 17, 1995, he even
attempted to support the Nikkei 225 through huge purchases. His apparent
motive was to protect a bonus that was to be set on February 24. The effort
was not successful. A substantial fall in the Nikkei 225 created enormous
losses for Barings. Leeson's trading losses of 1.38 billion dollars forced
Borings into bankruptcy.
Barings fell in large part because Leeson was both head trader and
head of settlements. As the head of settlements, he was able to hide his
trading losses from his supervisors. The bank collapsed because senior
management failed to properly supervise the trading of a rogue trader.
Singapore eventually convicted Leeson of forging documents and
creating fictitious accounts to hide his unauthorized trading. He was
sentenced to six and a half years in jail. In 1995, the Dutch bank ING
bought Barings for the symbolic price of one pound.
4
of their employers or their clients. They often are traders who know that
they will lose their jobs when their employers discover they have incurred
substantial trading losses. The rogues try to hide these losses while they take
large positions in an attempt to trade out of their problems. If the positions
prove to be profitable, and their subterfuges go undetected, they keep their
jobs and may even receive substantial bonuses. If the positions create sub-
stantial losses, they lose the jobs that they would have lost anyway.
8.4 SUMMARY
Traders use markets for many reasons. Whether they are successful or not
often depends on how well they understand the reasons why they trade.
Utilitarian traders trade because they expect to receive some benefit from
trading besides profits. Investors and borrowers trade to move money
through time. Asset exchangers trade one asset for another asset that has
greater immediate use for them. Hedgers trade risks. Gamblers trade to ob-
tain exciting entertainment. Fledglings trade to learn about trading. Cross-
subsidizers trade to transfer wealth to others. Tax avoiders trade to take ad-
vantage of tax loopholes.
Profit-motivated traders trade only because they expect to profit from
their trading. Profit-motivated traders include speculators and dealers. Spec-
ulators try to predict future price changes. Dealers sell liquidity to other
traders. Speculators differ by the information they use to forecast future
price changes. Informed traders use information about fundamental values,
order anticipators use information about what other traders will do, and
bluffers create information designed to convince other traders to trade
foolishly.


## Page 212

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## Page 213

200 • TRADING AND EXCHANGES
Futile traders are unskilled, irrational, or poorly advised. These traders
consistently lose, even though they expect to profit.
Utilitarian traders and futile traders lose on average to profit-motivated
traders. Without them, profit-motivated traders could not profit. Profit-
motivated traders therefore need to understand why utilitarian and futile
traders trade if they are to profit from their trading.
Many people confuse investing, speculating, and gambling. Investors are
uninformed traders who trade to move money from the present to the fu-
ture. They expect to receive a fair rate of return for the risks that they bear.
Speculators are traders who use information to predict future returns more
accurately than most traders can. They trade because they expect to profit
from trading. Gamblers are uninformed traders who trade for excitement.
Though they often think that they are speculators, they are not able to pre-
dict price changes well enough to trade profitably in the long run. Investors
typically trade only in securities markets. Speculators trade in all financial
markets and all gambling markets in which they can predict future returns.
Gamblers trade in any market that interests them.
All traders except dealers like to trade in liquid markets. Liquid markets
allow traders to achieve their objectives at low cost. Since dealers sell liq-
uidity, they prefer to trade in illiquid markets. Their services are more valu-
able in illiquid markets than in liquid markets. Their trading makes mar-
kets more liquid.
Figure 8-1 presents a taxonomy of traders. We will examine many of
these traders in greater detail throughout the remainder of this book.
8.5 SOME POINTS TO REMEMBER
•
Utilitarian traders trade because they expect to obtain some benefit
from trading besides profits.
•
Investors and borrowers move money through time.
•
Hedgers exchange risks.
•
Asset exchangers trade to obtain assets of greater value to them than
the assets that they tender.
•
Gamblers trade for entertainment.
•
Profit-motivated traders trade only because they expect to obtain
profits.
•
Speculators trade on information about future price changes.
•
Dealers profit from offering liquidity to other traders.
•
Futile traders believe that they are profit-motivated traders, but they
cannot trade successfully enough to profit in the long run.
•
Pseudo-informed traders trade on stale information.
8.6 QUESTIONS FOR THOUGHT
1.
Besides saving for retirement and borrowing for education, what other
intertemporal cash flow timing problems do people commonly face?
2.
Can the real risk-free rate of interest ever be negative?
3.
Suppose a wheat farmer sells a wholesale baker a forward contract.
What happens if hail destroys the farmer's crop or if the baker loses
a large contract to deliver bread in the future? Will their hedges pro-
tect them against these risks? How can they manage these risks?


## Page 214

CHAPTER 8 WHY PEOPLE TRADE • 201
4.
In the stock hedging example involving Apple Computer, how would
Jack's optimal hedge be different if the beta of Apple were 1.3? How
would a beta different from 1 affect the analysis in the last two para-
graphs of this example?
5.
Is gambling in financial markets bad? Should gamblers be discouraged?
6.
When traders capture dividends, they pay for the stock they buy,
they receive payment for the stock they sell, and they receive the
dividend on three different days. How do interest rates affect the
difference between the purchase price and the sales price?
7.
Sentiment-oriented technical traders anticipate the trades of unin-
formed traders. What type of trader can anticipate the trades of in-
formed traders?
8.
From whom do informed traders profit? From whom do order an-
ticipators profit? From whom do bluffers profit?
9.
From whom do dealers profit? To whom do they lose?
10.
How can utilitarian traders reduce their losses to profit-motivated
traders?
11.
How does each type of trader affect volatility?
12.
You have placed a 2 million dollar position limit on a trader who
works for you. The trader has made the firm 0.5 million dollars in
profit on a 4 million dollar position. What should you do?


## Page 215

9
Good
markets
M
arket structures have changed significantly in the last few years, and
many more changes are under consideration. Throughout the world,
people actively debate the following questions:
•
Should regulators consolidate all orders into a central limit order book?
•
Should markets use quote-driven or order-driven systems?
•
Should regulators allow internalization and preferencing?
•
Should regulators impose price limits or halts on trading?
•
Should trading use floor-based or screen-based systems?
•
Should dealers yield to their customers?
•
Should regulators require that markets be linked electronically? How
fast should those links be?
•
What trading hours should markets adopt?
•
Who should be able to see the limit order book?
•
Who owns market data?
•
What securities and contracts should regulators allow exchanges to
trade?
The markets have wrestled with these and many other issues, and they un-
doubtedly will continue to do so.
Virtually any change in market structure will have significant economic
effects on our markets. Trading rules, trading systems, and information pro-
tocols all affect liquidity, transaction costs, volatility, the quality of prices,
and the distribution of trader profits. We therefore must carefully consider
whether proposed changes in market structure are desirable. This chapter
introduces a paradigm for how we should make these decisions.
Everyone has an economic interest in how markets should be organized
because everyone—whether they trade or not—benefits from having well-
functioning markets. Not surprisingly, opinions about market structure vary
widely.
Many people try to influence market structure:
•
Legislators pass laws that dictate structures.
•
Regulators interpret those laws, propose new ones, and selectively en-
force them.
•
Government administrators propose laws, veto laws, and use their in-
fluence in myriad ways to promote their interests. In some countries,
they also write the laws.
•
Judges interpret laws and write new case law.
•
Exchanges, brokers, clearing agencies, and information providers freely
create any market structures that the legal system permits. They also
202


## Page 216

CHAPTER 9 GOOD MARKETS • 203
frequently propose—and sometimes even implement—structures that
laws and regulations do not currently permit.
•
Issuers influence market structure through the decisions they make
about where to list their securities.
•
Traders influence market structure through the decisions they make
about where to trade.
•
Investors and the general public influence market structure by voting for
politicians who favor their interests and by lobbying those politicians.
•
Finally, the leaders of trade organizations, public interest groups, and
watchdog agencies often lobby on behalf of their constituents.
These people all discuss market structure with those who have power to
promote or frustrate their interests.
Debate generally is most productive when conducted within a frame-
work for making decisions. Welfare economics provides such a framework.
Welfare economics
is the branch of economics that considers how we should
organize our economy. In this chapter, we consider principles by which we
should organize our markets.
How markets should be organized is completely subjective. Everyone is
entitled to his or her opinion. Many people think that markets should do
well whatever it is that they do. Accordingly, we will closely consider the
benefits that markets produce for our economy. At the end of this chapter,
I provide a set of weak objectives that I believe regulators should use when
evaluating alternative market structures. You may have your own opinion
about what are good markets.
If you agree that markets should be organized to maximize the benefits
they produce for the economy, then you must be familiar with these bene-
fits so you can consider them when you evaluate alternative policies. If you
believe that markets should be organized to promote other objectives, you
should at least be aware of the costs to the economy of the policies that your
objectives favor.
Even if you have no interest in influencing market structures, you should
find these discussions interesting. Well-functioning markets are largely re-
sponsible for the tremendous wealth that free market-based economies have
generated and continue to generate. This chapter helps explain why some
countries are rich while other countries are poor.
We start our discussion with a brief introduction to welfare economics.
The discussion then turns to the benefits that markets produce for individ-
uals and for the wider economy. If your only interest in this book is to be-
come a better trader, you can safely skip this chapter.
9.1 WELFARE ECONOMICS
Welfare economics involves positive and normative economic analyses. In
positive economic analyses,
analysts use theories and empirical evidence to pre-
dict the consequences of various economic policies. Positive economics is
objective in the sense that analysts who use the same assumptions and the
same data should obtain the same results. In
normative economic analyses,
an-
alysts argue for specific economic policies. Normative economics is highly
subjective. Everyone is entitled to his or her opinion about what should be.


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204 • TRADING AND EXCHANGES
Do Economists Disagree Much?
The public widely believes that economists rarely agree with each other.
Consider the popular joke, "Put two economists in a room, and you get
three opinions."
Economists actually agree more than they disagree. They appear to
disagree a lot because people remember controversies more than
agreements. Since economic controversies interest us, economists often
appear to disagree.
When economists disagree about positive analyses, they usually have
based their analyses on different assumptions or different data. Since
analysts must make many decisions about which assumptions and which
data to incorporate into their studies, policymakers must be careful when
interpreting economic analyses. They must ensure that the subjective biases
of the analysts do not influence their results.
When economists disagree about assumptions and relevant data, we
need a set of principles to evaluate the decisions that underlie their
analyses. The norms of economic science provide us with these principles:
We should evaluate assumptions by how well they represent the essential
reality of the problem at hand rather than whether we like their policy
implications. Likewise, we should evaluate data by how well they
characterize relevant past experience rather than by whether we like their
policy implications.
When economists disagree about normative analyses, they usually have
employed different social welfare functions. Economists—like everyone
else—have different opinions as to what is good and valuable. Policy
makers must consider whether they agree with the analyst's values before
accepting the conclusions of a normative analysis.
Normative analysts arrive at their conclusions by finding the policy that
maximizes a subjective measure of social welfare. The conclusions may flow
from formal mathematical models based on rigorous statistical analyses or
from simple heuristic arguments based on best guesses. Either way, a proper
normative argument has four parts:
1.
An identification of all reasonable alternative policies.
2.
A specification of subjective criteria for evaluating the alternative
policies. The criteria describe a
social welfare function
that measures
the value of each policy. If the social welfare function is based on mul-
tiple criteria, it must specify the acceptable trade-offs among the
various criteria.
3.
A positive economic analysis that evaluates the social welfare of each
alternative policy.
4.
An identification of the policy that produces the greatest social
welfare.
All normative analysts should follow this procedure. In practice, most
follow it implicitly rather than explicitly. This procedure provides a valuable
framework for debate because it clearly identifies the criteria upon which
analysts base their conclusions. Without this discipline, proponents of a pol-
icy often argue for it as though it were the objective rather than the path
to some commonly agreed-upon objective. When policy becomes the ob-
jective rather than the means to the objective, poor results often follow.


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CHAPTER 9 GOOD MARKETS • 205
Market Structure from Two Perspectives:
Why Regulation Is Challenging
Dealers like markets in which they can trade profitably. Not surprisingly,
most dealers favor quote-driven markets over order-driven markets because
they make more money when they do not have to compete with public
traders who offer liquidity.
Since trading is a zero-sum game in trading profits, dealer profits are
buy-side transaction costs. Some buy-side traders do not value the liquidity
services that dealers provide as much as these services cost them. They
therefore favor order-driven markets over quote-driven markets.
The preferences of both types of traders are perfectly understandable.
Given their different interests, no compromise on market structure can
completely please them both. These conflicts make regulating markets very
challenging.
9.1.1 Market Welfare Economics
To
decide public policy in the markets, we need a social welfare function by
which we can measure the merits of alternative policies. Many people be-
lieve that we should design markets to do well whatever it is that they do.
Whether you subscribe to this objective or not, you must understand what
markets do in order to responsibly evaluate public policy.
We identify the economic benefits that markets produce in the next two
sections. First, we describe the direct benefits that traders obtain from us-
ing the markets. These are
private benefits
because they accrue only to in-
dividual traders when they trade. We then consider how the wider economy
benefits from having well-functioning markets. These benefits are
public ben-
efits
because they accrue to everyone's benefit.
9.2 PRIVATE BENEFITS OF TRADING
Private benefits
accrue directly to traders when they trade. We know that
traders somehow benefit from trading because they trade voluntarily. They
would not trade otherwise.
To appreciate the private benefits of trading, we have to understand
why people trade. We considered this question in chapter 8. There we saw
that people trade for two main reasons.
Utilitarian traders
trade because they
hope to obtain some benefit from trading besides profits.
Profit-motivated
traders
trade only because they expect to profit from trading.
Markets work best for utilitarian traders when they are liquid. In
liquid
markets,
traders can accomplish their trading objectives at low cost.
Utilitarian traders use the markets less when transaction costs are high.
Investors and borrowers use other methods to synchronize their cash in-
flows and outflows. Asset exchangers avoid activities that require assets they
do not have. Hedgers rearrange their affairs to limit their exposure to risks
that concern them. Gamblers find their entertainment elsewhere.
Markets also work best for utilitarian traders when the assets that they
can trade are well suited to their needs. Investors and borrowers prefer to
trade instruments that produce cash flows which occur just when they need
them. Hedgers like to trade instruments that closely replicate the risks which
worry them. Gamblers like instruments that excite them.


## Page 219

206 •
TRADING AND EXCHANGES
Zero Coupon Bonds
Zero coupon bonds that
mature at various dates are
attractive to investors and
borrowers because they use
them as building blocks to
construct any cash flow that
they want. With enough
different maturity dates from
which to choose, traders can
construct a portfolio of zero
coupon bonds to represent
any cash flow.
Profit-motivated traders trade only because they expect to profit directly
from trading. The primary profit-motivated traders are dealers and specu-
lators.
Dealers
sell liquidity to impatient traders when they allow them to
trade when they want to trade.
Speculators
trade to profit from future price
changes that they can predict.
Since trading is a zero-sum game, speculators and dealers cannot profit
on average if they trade only among themselves. They can profit only if util-
itarian traders are willing to trade. Markets therefore exist only when util-
itarian traders are willing to trade. This fact suggests to me that the welfare
of utilitarian traders ultimately is more important than the welfare of profit-
motivated traders. You may disagree.
9.3 PUBLIC BENEFITS OF TRADING
The
public benefits
of trading accrue to everyone regardless of whether they
use the markets. Economists call these benefits positive externalities.
Posi-
tive externalities
result when nobody compensates people for doing things
that benefit others. Economists are especially interested in externalities be-
cause they represent situations where regulations can often make people
better off. In this section, we shall see that traders do many things which
benefit people who do not even use the markets.
The public benefits of having well-functioning markets fall into two
classes: those which come from having markets that produce informative
prices and those which come from having liquid markets. We consider each
in turn.
9.4 PUBLIC BENEFITS FROM
INFORMATIVE PRICES
Well-functioning markets produce prices that accurately reflect the funda-
mental values of the instruments they trade.
Fundamental value
is an imag-
inary concept. It is the value that everyone would agree upon if everyone
knew all relevant information about value, and if everyone knew exactly how
to process that information to estimate value. In practice, nobody knows
everything they need to know about value, and few people process infor-
mation well. Fundamental values therefore are often poorly known. Fortu-
nately, markets can aggregate information from many different sources to
produce prices that are closer to fundamental values than anyone could con-
sistently estimate by himself or herself. Markets produce
informative prices
when prices are close to fundamental values.
We discuss how prices become informative in chapter 10 when we exam-
ine how informed traders trade. In this chapter, we consider how people—
many of whom do not trade—benefit from living in economies in which mar-
kets produce informative prices.
9.4.1 Production and Allocation Decisions
All economies
must make
production and allocation decisions
about how to
organize
production and how to divide the goods and services produced.
Economies that make and implement good economic decisions become
wealthy. Those which cannot make or implement good decisions are poor.


## Page 220

CHAPTER 9 GOOD MARKETS • 207
To use their resources most efficiently, economies must allocate them to
projects and managers that can derive the most value from them. A resource
allocation is most
efficient
when the expected marginal benefit of a resource
is the same for every project that uses it in the economy. The
marginal bene-
fit
of a resource is the expected additional value that a project would produce
if another unit of the resource were devoted to it. When marginal benefits
differ across projects, moving resources from low marginal benefit projects to
high marginal benefit projects can produce more efficient allocations.
Since millions of potential projects compete for resources in large na-
tional economies, allocating resources efficiently requires an extraordinary
amount of information. Good decisions must reflect information about the
goods and services people need, how best to produce them, and how best
to distribute them. These decisions generally are highly interrelated. For ex-
ample, truckers cannot deliver clothing if road builders do not receive ce-
ment because limestone miners cannot work without adequate clothing.
9.4.1.1 Command Economies
In
command economies,
central planners make production and allocation de-
cisions. The planners may be individuals or they may be large planning bu-
reaus. The planners collect requests for the use of capital, rank them, and
then create production plans and resource allocation budgets for all proj-
ects. The planners then try to use their command authority to compel peo-
ple to implement their plans.
Command economies suffer from several serious problems:
•
Most obviously, planners must aggregate a fantastic amount of infor-
mation to produce good plans. For a large economy, the information
requirements of central planning generally overwhelm the capacity of
any planning organization to collect and process information.
•
A greater problem than the volume of information is its quality. Cen-
tral planners often receive very low-quality information about capital
requirements because the requests that managers submit are often un-
realistic, fraudulent, or based on inconsistent assumptions. Improving
the quality of the information is difficult, given the size of the prob-
lem, audit problems, and disincentives to comply.
•
Political forces and personal biases often distort the planning process.
Even if planners could solve their information problems, some would
still distort allocations to meet their political objectives, to please their
friends, or to get the job done quickly so they can go home early.
•
Even when planners make good decisions, people may not implement
their plans if the plans are unpopular. Central planners often find that
people rebel against their authority when they must make sacrifices for
which they feel inadequately compensated.
•
Centrally planned economies often have serious accountability prob-
lems. When plans fail, the planners blame the implementers and the
implementers blame the planners. Without good information about
economic conditions, assigning and accepting responsibility for short-
falls is very difficult.
These problems cause all large command economies to perform poorly.
Some Important
Production and
Allocation Decisions in
the Clothing Industry
•
How much clothing
should
be produced, and in what
styles and sizes?
•
How should the clothing be
made?
•
Who should provide the
raw materials and
machines to make the
clothing?
•
Who should operate the
machines, and who should
service them?
•
Who should receive the
clothing?
•
Who should transport and
distribute the clothing?


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208 • TRADING AND EXCHANGES
Or Overlapping Benefits
Consider a very simple economy with three investors and four projects.
Abby examines only projects 1 and 2, Barry examines only projects 2 and
3, and Charlotte examines only projects 3 and 4. Each project merits some
capital investment. For each project, the marginal benefit of additional
capital investment declines as the total capital invested increases.
Abby, Barry, and Charlotte all allocate their capital efficiently. Abby
allocates her money to projects 1 and 2 so that the marginal benefit of a
dollar invested in both projects is the same. Barry and Charlotte use the
same rule to allocate to projects 2 and 3 and 3 and 4, respectively.
Since Abby and Barry both invest in project 2, the marginal benefit of
an additional dollar invested in projects 1, 2, and 3 is the same. This result
is interesting because no one evaluates both projects
1
and 3. Likewise,
since Barry and Charlotte both invest in project 3, the marginal benefit of
an additional dollar invested in projects 2, 3, and 4 is the same.
These equalities imply that the marginal benefit of capital in projects
1 and 4 is the same. This result is remarkable because the people who
evaluate projects 1 and 4 evaluate completely disjoint sets of projects.
4
9.4.1.2 Market-based Economies
In
market-based economies, people
and companies make production and al-
location decisions as they look for the most profitable ways to use their time
and money. Companies decide what products to produce and how to pro-
duce them by considering the prices that determine their revenues and costs.
Managers who make good allocation decisions profit, and thereby retain the
authority to make more decisions. Companies that cannot efficiently pro-
duce what others want lose money, and their investors thereby lose the abil-
ity to make more decisions. Because people risk losing when they make pro-
duction and allocation decisions, they usually pay close attention to what
they do, and they usually consider only projects about which they have some
expertise.
Market-based economies work very well because decision making is dis-
tributed throughout the economy. Although no one examines more than a
small fraction of all possible projects, the collective efforts of the millions
of people who search for good investment opportunities place most proj-
ects under great scrutiny.
Market-based capital allocations are efficient because each person effi-
ciently allocates his or her capital to those projects which appear best among
the set of projects that he or she has examined. The resulting aggregate al-
locations are globally efficient because the sets of projects that investors ex-
amine overlap extensively.
In practice, most people do not compare projects directly against each
other. Instead, they evaluate projects by comparison against a common stan-
dard called the
required rate of return.
Investors undertake an investment only
if its expected rate of return is greater than its required rate of return. Since
investors can always invest in risk-free government bonds, the required rate
of return for an investment is equal to the risk-free interest rate plus an ad-
justment for the risk of the investment. Required rates of return thus de-
pend on the supply of and demand for funds for savings and investment.
Market-based economies work well when prices convey information
about values. Prices must indicate the values that people put on the goods


## Page 222

CHAPTER 9 GOOD MARKETS • 209
00 A
Toll Booth on
Every Corner
We do not pay for using most
streets and highways because
collecting tolls at every
intersection is too costly.
Governments therefore build
and maintain most roads, and
taxpayers pay for them
regardless of whether they
use them. Since no one
meters our usage and charges
us for it, we overuse many
streets in crowded cities and
thereby cause traffic jams.
In the last few years,
electronic devices for
measuring highway usage
have become much cheaper.
These devices greatly
cheapen the costs of
collecting tolls. Many new
tollways are now being
constructed, and some old
highways are being
converted to tollways. In
many places, the tolls vary by
time of day to discourage
usage when the traffic nears
the road's capacity.
that they consume, and they must indicate the values that producers put on
the factors they need to produce their goods and services. Informative prices
therefore play an extremely important role in market-based economies.
Prices in market-based economies generally reflect values because buy-
ers or sellers try to trade when prices differ from values. When prices are
too low, buyers try to buy more than sellers will offer. Buyers then bid up
the prices of undervalued items as they compete to find scarce sellers. Like-
wise, when prices are too high, sellers try to sell more than buyers will buy.
Sellers offer prices down as they compete to find scarce buyers. Market
prices adjust to the point where
supply and demand
are equal. At that point,
prices reflect a midpoint compromise between the high values that buyers
place on the item and the low values that sellers place on it.
Not all economic decisions are best made in the marketplace. Markets
work well only when the costs of negotiating are small relative to the costs
of the goods or services that trade there. Markets work poorly when trans-
action costs are large or when activities need to be highly coordinated. All
economies therefore use a mix of command and market-based allocation
mechanisms.
Companies are the most important command organizations within a
market economy. People form companies that managers control in order to
avoid the excessive market negotiation costs. The managers create and im-
plement plans for their companies. Within each company, the manager de-
termines what the firm will do, who will do it, and how it will be done. (In
large firms, managers generally delegate much of the responsibility and au-
thority for planning and implementation to subordinates whose specific
knowledge and proximity to information often make them better decision
makers on smaller issues.) By consolidating authority with managers, firms
avoid negotiating with their workers about who will do what and when.
Most firms also own or lease the machines, properties, patents, and brands
that they need to produce and market their goods and services. By putting
these physical capital resources under management control, the firm avoids
negotiating with owners of these factors of production.
Although firms are small command economies, they relate to the rest of
the world through various markets. They sell their goods in product mar-
kets, they buy raw inputs in factor markets, they hire their employees in la-
bor markets, they rent or buy their machines and property in asset markets,
and they fund their business plans in the financial capital markets.
Markets that produce informative prices help make market economies
wealthy by ensuring that resources are well allocated. Commodity and prod-
uct markets produce information that helps people allocate goods and ser-
vices to their best uses. The capital markets produce information that helps
people make better capital allocation decisions.
Investors in market-based economies make two types of capital alloca-
tion decisions that involve firms. They must decide which firms will receive
capital for new projects, and they must decide which managers will man-
age existing capital resources.
In the remainder of this section, we consider how investors use inform-
ative prices produced in primary and secondary capital markets to make cap-
ital allocation decisions.
Primary capital markets
are markets in which issuers
sell stocks and bonds to raise capital for new projects. Investors allocate
capital to new projects in primary markets.
Secondary capital markets
are


## Page 223

2W • TRADING AND EXCHANGES
Primary Market Capital Allocation Example
A firm wants to sell stock to raise 1 million dollars of capital for a new
project. The firm currently is financed entirely by 20,000 shares of equity. The
existing assets of the firm are worth 2 million dollars. Since the firm has no
debt, each share is worth 100 dollars. How many shares must the firm sell to
finance the new investment, and at what price should it sell those shares?
The answers to these questions depend on what investors think about the
new project. If they think that the project has great prospects, they will pay
a high price for the shares, and the firm will not have to sell many shares.
If they think that the project is a poor idea, the share price will be low, and
the firm will have to sell a great many shares. If the project is a very poor
idea, the firm may not be able to sell new shares at any price.
The solution involves two very simple equalities. First, the number of new
shares times their price must equal the cost of the project:
new shares x price = project cost
Second, the total market value of the firm's liabilities after the offering must
equal the total expected value of the firm's assets. Since the firm has no debt,
the total market value of the liabilities is just the new total number of shares
outstanding times their price. The total expected value of the firm's assets is
the sum of the value of the old assets plus the expected value of the project:
(old shares + new shares) x price = old assets + expected project value
The prices that appear in these two equations are the same because the
firm will not be able to sell the shares for more than traders expect they will
be worth after the sale, and because the firm will not want to sell the
shares for less than it can obtain. (For simplicity, we ignore the discounts
that often are associated with seasoned offerings.)
Manipulation of these equations yields expressions for the number of
new shares that the firm must sell and the maximum selling price:
new shares = old shares x project cost ±- new value of old shares
and
price = new value of old shares — old shares
where
new value of old shares = old assets + expected project value
— project cost
is the new aggregate value of the old shares.
If investors expect that the project will add 1.5 million dollars to the value
of the firm, the firm will sell 8,000 shares at 125 dollars per share. The
original investors will give up slightly less than 29 percent (8,000

28,000)
of the control of their firm to the new investors. The new investors receive
stock worth 1 million dollars for their 1 million dollar purchase, and the
original investors receive the entire benefit of identifying the profitable
investment. The total value of the old shares increases by 0.5 million dollars,
which is the difference between the expected value of the project and its cost.
If investors expect that the value of the project is only 0.5 million dollars,
the firm should not undertake the project because it costs more than it is
worth. If management insists on proceeding, the firm would have to sell
13,334 shares at 75 dollars per share to raise 1 million dollars. The existing
shareholders would lose 0.5 million dollars, and they would give up 40
percent of the control of their firm to the new investors. The loss that they
would experience is a tremendous disincentive to proceeding with the deal.
The price of the stock depends on the relation between the cost of the
investment project and its expected value. If the difference is positive, the
stock price will rise. Otherwise, it will fall. If the difference is negative and
greater than the value of the existing equity, the equations suggest that the
price will be negative. Investors would not finance such a poor project
unless they were paid to do so.


## Page 224

CHAPTER 9 GOOD MARKETS • 211
markets in which
seasoned securities
(previously issued securities) trade. In-
vestors use information in secondary market prices primarily to help them
choose and evaluate the managers of their firms.
9.4.2 Capital Allocation in the Primary Markets
The capital allocation problem in the primary markets is simply stated:
Millions of ideas compete for capital, but not all ideas are good. New cap-
ital should go only to the best investment ideas.
The primary markets help to solve this allocation problem by pricing in-
vestment opportunities. When prices are informative, good ideas command
high prices and poor ideas are worthless. Issuers with good ideas therefore
can easily sell securities to raise capital to implement them. Promoters with
poor ideas cannot sell their securities, or they must subsidize them with their
own money in order to proceed. Since no one wants to subsidize other peo-
ple's investments, poor ideas are rarely undertaken. New capital thus flows
to the best ideas.
9.4.3 Secondary Markets:
The Manager Allocation Problem
The allocation problem in the secondary markets is also simply stated: Mil-
lions of managers compete to manage capital, but not all managers are good.
Good management can produce a lot with a little. Poor management wastes
resources. Only the best managers should manage capital.
The secondary market helps solve this allocation problem by providing
investors with information about how well existing managers are using the
resources available to the companies they run. If prices are informative and
managers manage their companies well, their stock prices should be high
relative to their asset values. If they are poor managers, their stock prices
should be low. Investors use this information to obtain better management
in three ways.
First, shareholders compensate managers based on share price perfor-
mance. Through executive stock options, phantom stock, stock grants, and
stock price-linked bonuses, shareholders encourage their managers to work
hard. These mechanisms work well only if the link between managerial ef-
fort and managerial compensation is direct and noise free. Informative stock
prices strengthen this link.
Second, shareholders remove managers who do not perform well and re-
place them with others who, they hope, will do better. Informative stock
prices provide shareholders with useful information about how well their
managers are performing.
Finally, when shareholders are unwilling to remove poor management,
low stock prices may attract takeover attempts by speculators who believe
that they can better manage the company. They may either run the com-
pany themselves or hire better managers to run it for them. This mecha-
nism works best when stock prices are informative.
These three mechanisms all break down when stock prices are noisy.
When prices do not reflect how well managers are performing, good man-
agers may lose their jobs and shareholders may retain poor managers. In-
formative prices make that less likely.
Anyone who uses stock prices to evaluate managerial performance must
be very careful when interpreting them. Stock prices depend on many fac-
tors besides the quality of management. For example, the stock prices of oil


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212 • TRADING AND
EXCHANGES
Indexed Options
Shareholders often give their managers
executive stock options
to
encourage them to work hard. The options allow managers to buy stock in
the firm at a specified
strike price.
If managers can raise the stock price
above the strike price before their options expire, they can become quite
wealthy.
The strike prices of these options generally are constant. When stock prices
rise because the whole market rises—perhaps because interest rates fall—
stock option values rise even if managers are performing poorly. Such
compensation does little to encourage managers to work harder. Executive
stock options would be more effective if their strike prices were indexed to the
performance of comparable firms or, at a minimum, to the market as a whole.
Likewise, when stock prices fall because the market drops, executive
stock options drop in value. If prices fall far below option strike prices, the
options become worthless and thereby lose most of their incentive powers. To
reestablish incentives, corporate directors often restrike options at lower prices.
Fixed strike prices and strike prices that adjust down but never up do not
serve the interests of most shareholders. These characteristics of executive
compensation schemes suggest that the independent corporate directors
who sit on board compensation committees may not be as independent as
many shareholders would prefer. Left unexplained is why shareholders
routinely approve such compensation schemes.
Adverse tax treatment of indexed strike price options is one reason
offered for why firms do not use them more often. The tax consequences,
however, are small relative to the incentives associated with better
compensation schemes.
The Great Semantic
Irony of Communism
The words "communism" and
"communication" have the
same Latin root word,
communis,
an adjective which
indicates that a group shares
an object. Its direct translation
to English is "common."
Ironically, communism
failed in large part because
Communists could not
adequately communicate the
information necessary to run
their economies efficiently.
Free market economies are
very successful because
markets facilitate the
exchange and effective
use of information necessary
to organize production
efficiently. Traders
communicate more effectively
than do Communists.
producers rise and fall with the price of oil. A manager of an oil producer
might be exceptionally skilled at yield enhancement and cost containment,
yet still appear to perform poorly when the price of oil falls. Likewise, a
poor manager may look great when the price of oil rises. These possibilities
suggest that people who use stock prices to evaluate management must care-
fully account for other determinants of stock price performance. At a very
minimum, performance should be measured relative to industrial peers
rather than against an absolute standard.
9.4.4 Summary
Well-functioning markets produce informative prices that greatly benefit
everyone in the economy, regardless of whether they trade. People use the
information in prices to help solve production and allocation problems that
are of first-order importance to the common welfare. Economies that can-
not allocate new capital to the best projects, and the best managers to on-
going operations, waste their resources and are quite poor. Those which
make these decisions well are much wealthier.
Perhaps nothing explains the failure of national Communist systems bet-
ter than their inability to produce efficiently. Economywide planning prob-
lems are simply too complex to be solved well by any single agency, no mat-
ter how well intended and well prepared its managers are. Russia is poor
compared to the United States largely because, for 70 years, it was unable
to organize production as efficiently as the United States. Contrasts between
North and South Korea, the former East and West Germany, China and
Japan, and Albania and Greece provide clear evidence of the importance of
free markets.


## Page 226

CHAPTER 9 GOOD MARKETS • 213
Markets for the Air We Share
Clean air is a resource that we all share. We pollute that air when our
cars burn gasoline. Since we do not pay for the free clean air that we use,
we undoubtedly use more than we should. Many of our cities consequently
are quite polluted. Governments can partially solve this problem by taxing
gasoline so that its price to the consumer reflects both the value of the
energy resource and the value of the clean air that cars consume when they
burn gasoline. They can also partially solve the problem by taxing new car
sales according to how much the cars pollute.
Increasingly, governments are creating tradable permits to emit fixed
quantities of pollutants into the air and water. Industries that pollute must
have permits to do so. Since holders can freely trade their permits, permit
prices become a cost of doing business. Industries consider these costs
when choosing how to produce their goods.
The resulting decisions tend to reduce pollution at the lowest total cost to
the economy. Industries that can cheaply abate their pollution sell their
permits and abate their pollution. Industries that cannot cheaply abate their
pollution buy permits and pollute.
Since the cost of pollution becomes a cost of doing business, competition
among firms forces them to pass their pollution costs along to the consumer.
Goods that are produced with dirty technologies become expensive.
Consumers avoid them when they have cheaper alternatives and use less of
them when they do not.
Economic efficiency would rise whether the government initially sells the
permits or whether it freely distributes them to polluters or to the public. If
permits can be freely traded, industries will consider their value when they
make production decisions, either because they can sell surplus permits or
because they must buy permits to cover a deficit.
Pollution rights markets are becoming increasingly important commodity
markets. The prices that these markets discover benefit everyone by
allowing industries to mitigate pollution at low cost. They also force product
prices to reflect environmental costs.
Not surprisingly, one of the first things that countries do when they
emerge from communism is establish capital markets. Although the mar-
kets often have few securities and contracts to trade, and no modern secu-
rities laws to regulate trading, they stand as symbols of changes to come.
Many people are attracted to communism because they like its values.
Notions that everyone is economically equal, that each contributes accord-
ing to his ability, and that each receives according to his needs are quite ap-
pealing. Unfortunately, running an economy on these principles is very
costly. Proponents of communism must recognize the extremely high eco-
nomic costs associated with the implementation of their values.
The benefits of free markets are sometimes lost in market-based
economies when governments intervene with taxes, subsidies, quotas, and
restrictive regulations to promote various agendas. These policies usually
create prices that do not accurately reflect resource values. Although gov-
ernments may enact them for good reason, they can be quite costly. Policy
makers therefore need to consider whether the intended benefits of their
interventions outweigh their often substantial costs to the economy.
When transaction costs are high or when the ownership of resources is
not well defined, markets may not exist, and people may use resources in-
efficiently. In such circumstances, government interventions often improve


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214 • TRADING AND EXCHANGES
economic efficiency. Since these problems are quite common, some gov-
ernment intervention into the economy is necessary to promote economic
efficiency.
Many economists and political scientists study when and how govern-
ments should intervene in economies. They also consider the often unre-
lated issues of why governments intervene in markets. Their studies con-
tribute to the fields of public finance, welfare economics, and public choice.
9.5 PUBLIC BENEFITS OF LIQUID MARKETS
Liquid markets benefit the public through the externalities that utilitarian
traders produce when they use markets to conduct their businesses more ef-
ficiently. These externalities generally result from production efficiencies that
traders can realize by using markets to exchange assets, hedge, or share risks.
9.5.1 Public Benefits of Exchange
Asset exchangers exchange things that are of less immediate value to them
for things that are of greater immediate value to them. The people with
whom they trade do the same. The resulting exchanges make both traders
better off. Through such trades, market-based economies ensure that re-
sources go to the people who most value them. Such economies are very
productive because they allocate resources to the uses where they are most
valuable. Without such exchanges, resources are wasted.
Transaction costs make such exchanges expensive. People will not trade
if the difference in values between what they give up and what they receive
is less than the transaction cost of the trade. High transaction costs there-
fore cause people to use resources poorly.
When transaction costs are prohibitively high, nobody trades. Economies
in which nobody trades are
autarkies.
They are very poor because nobody
can specialize to produce at low cost and because people waste resources.
Liquid markets benefit us all by allowing producers to specialize and by
allowing resources to be committed to the processes for which they are most
valuable. The resulting production efficiencies lower the costs of everything
that we consume.
9.5.2 Public Benefits of Hedging
Many producers face a trade-off between the cost-saving benefits of spe-
cialization and the concentrated risk associated with specialization. They
like to specialize, but they are afraid of the risk. Liquid hedging markets
benefit such producers by allowing them to cheaply divest themselves of the
risks that scare them. When hedgers can cheaply transfer risk, they spe-
cialize in the most efficient productive processes available to them. In com-
petitive markets, we all benefit through lower prices.
9.5.3 Public Benefits of Risk Sharing
Many good projects are too large, and therefore too risky, for a single per-
son to undertake. To undertake such projects, people form public corpora-
tions to spread the ownership and associated risks over a large number of
people. Liquid markets benefit the public by allowing companies to raise
new capital at low cost.


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CHAPTER 9 GOOD MARKETS • 215
Eggs in One Basket
Sam owns a farm in North Dakota, where the soil and weather are
particularly well suited to growing wheat. Sam can also grow corn to feed
chickens, but he does not have a competitive advantage in this industry.
Chicken farming is more productive in warmer climates. After considering
all alternatives available to him, Sam believes that his farm will be most
profitable if he plants only wheat.
Sam is afraid of planting only one crop, however. If wheat prices fall or
if his crop fails, he will suffer. To protect against these possibilities, Sam
hedges his wheat by selling forward in the futures market. He also buys
crop insurance from an agricultural insurance company. He uses these
markets because they provide him with useful risk management tools at
reasonable costs.
If Sam did not use these markets, he would not specialize in a single
crop because of the risk. Instead, he would diversify his production, even
though that would lower his expected profits. His profits would be lower
because his land is best suited for wheat and because he can achieve
significant economies of scale by specializing in one crop.
Judy owns a farm in Arkansas that is best suited to growing corn for
chicken feed. She specializes in eggs and hedges her risks with corn futures
contracts. If she could not cheaply lay off risk in the futures markets, she,
too, would diversify her production and produce less value on average.
The whole economy is better off when Sam and Judy specialize in what
they do best. Specialization allows farmers to produce more wheat and
eggs in aggregate than they could produce if they all ran diversified
operations. The abundant crops that they produce lower food costs for
everyone.
Liquid hedging and insurance markets thus benefit people even if they
do not trade in them. If trading in these markets were too expensive,
hedgers would not use them and our economy would be less productive.
Investors
do not like to buy securities that trade in illiquid markets be-
cause the transaction costs lower their net investment returns. Security prices
therefore are lower in illiquid markets than in liquid markets. Companies that
have access to liquid markets thus have lower costs of capital. When product
markets are competitive, companies pass along their lower capital costs to con-
sumers so that people benefit even if they do not use the markets.
9.5.4 Other Public Benefits of Liquidity
Liquid markets also benefit investors who do not intend to trade but who
take comfort in knowing that they could trade at low cost if they wanted
to. Options to do things that you may not choose to do are valuable. Peo-
ple who have such options often arrange their affairs differently than they
otherwise would. For example, investors will more likely invest in liquid
markets than in illiquid markets when they are uncertain about when they
will next need to use their funds for other purposes. Liquid markets bene-
fit such investors even when they do not trade.
Liquid markets also provide indirect public benefits by facilitating prof-
itable informed trading. Informed traders make more money when they can
trade with little price impact than when their trades move prices. They then
can afford to collect more information about fundamental values, and they
can profitably trade on information of lesser significance than they other-


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216 • TRADING AND EXCHANGES
O
t
"
Public Lotteries and
Casinos Waste Liquidity
Gamblers who bet on games
of pure chance bet on events
that have no consequence in
our economy. Their trading
produces no public benefits.
Gamblers benefit from
the entertainment they
obtain. They also enrich the
governments that run public
lotteries, the corporations
that run casinos, and the
gangsters who run numbers
on the street. These are all
private benefits.
Gambling on games of
pure chance does not
increase liquidity in trading
markets. It does not help
utilitarian traders solve their
trading problems, and it does
not allow informed traders to
profit and thereby produce
informative prices that
improve production and
allocation decisions in our
economy.
wise could. Prices therefore should be more informative in liquid markets
than in illiquid markets.
Finally, and perhaps most controversially, liquid markets benefit the pub-
lic by attracting gamblers. Gamblers like to trade in liquid markets because
they lose less per trade in such markets. This benefit is a purely private ben-
efit. The public benefit of having gamblers trade in financial markets comes
from the wealth that they ultimately lose to profit-motivated traders. Re-
call that profit-motivated traders can profit on average only if they can trade
with utilitarian traders. Without such traders, they will not trade. If in-
formed traders cannot profit, they will not invest in their information, they
will not trade, and prices will be less informative. If dealers cannot profit,
they will not trade, and markets will be less liquid. Gamblers help make
prices informative through their willingness to lose to informed traders and
dealers. Liquid financial markets benefit the economy by attracting gam-
blers away from more traditional gambling markets in which informative
prices provide little benefit to the economy.
9.6 SOME OBJECTIVES FOR
EVALUATING MARKETS
The implications of welfare economics ultimately depend on the objectives
that people use for evaluating issues. These objectives are opinions that peo-
ple have about what is most important. In this short section, I present my
opinion about how we should evaluate public policy issues that involve mar-
ket structure. You may disagree with me.
We often evaluate opinions by whether they are reasonable. Philosophers
have tried to derive criteria for evaluating opinions from basic principles,
but these criteria, too, ultimately are subjective. You must decide for your-
self whether my objectives are reasonable.
I believe that public policy should first promote the private interests of
those traders whose needs cause markets to exist in the first place. These
are utilitarian traders who use the markets to manage cash flow timing prob-
lems, hedge risks, share risks, and exchange assets. If these traders do not
use the markets, the markets will not exist, and we will not obtain any other
benefits from having markets.
I exclude gamblers from this list of utilitarian traders because they can
entertain themselves elsewhere and because they have no natural reason to
bet on economic events rather than on sporting events or random events. I
do respect the liquidity that gamblers bring to the market. I therefore am
willing to support policies that promote their interests, but only for the sake
of obtaining their liquidity.
Second, public policy should strive to maximize the public benefits we
all obtain from liquid markets that produce informative prices. These ben-
efits are extraordinarily important to our common welfare. Perhaps this ob-
jective should appear first, given its importance. I list it second in order to
emphasize that markets will not exist if utilitarian traders do not use them.
Third, public policy should support the interests of profit-motivated
traders only when necessary to pursue the first two objectives. Since deal-
ers help make markets liquid and since informed traders help make prices
informative, these profit-motivated traders play important roles in our mar-
kets that we should support. However, we should support them only for the


## Page 230

CHAPTER 9 GOOD MARKETS • 217
00. Recent Rule Changes in U.S. Equity Markets
The U.S. Securities and Exchange Commission appears to share my
objectives. Under the leadership of Chairman Arthur Levitt, the Commission
imposed the Order Handling Rules in 1997. These rules require dealers to
expose all public orders that they hold when these orders are at the best
bid or offer. These rules make it easier for public limit order traders to
compete with dealers when offering liquidity. In effect, the Commission
decided that when dealers and the public are both willing to supply
liquidity, the public must have an opportunity to display their quotes.
The SEC adopted the Order Handling Rules shortly after the courts
imposed the Manning Rule on dealers. The Manning Rule is a public order
precedence rule. It prohibits dealers from trading before their customers at
the same price. The Order Handling Rules and the Manning Rule taken
together have substantially changed the character of the Nasdaq markets
from essentially pure quote-driven to somewhat order-driven markets. These
rule changes have benefited public traders at the expense of dealers.
In August 2000, the SEC adopted new rules concerning selective
disclosure by issuers of material nonpublic information. Before their
adoption, issuers would often disclose material information to the analysts
who follow their stocks. The analysts or their clients then would trade on
that information, often to the disadvantage of other public traders. Rule FD
now prohibits such selective disclosure. Issuers must disclose all information
to everyone at the same time. Although this rule undoubtedly hurts some
informed traders, it should have little long-run effect on the information
content of prices.
4
sake of the benefits they provide other traders and the economy as a whole.
We should not favor them when we can obtain liquidity and informative
prices more cheaply elsewhere. For example, public policy should not sup-
port dealers to the exclusion of public traders who are equally willing to
provide liquidity. Likewise, public policy should not allow informed traders
special access to information that could as easily be granted to all traders.
Finally, public policy should be hostile to the efforts of profit-motivated
traders who design trading strategies to exploit other traders. Price manip-
ulators, bluffers, and front runners hurt other traders while doing nothing
to make markets more liquid or prices more informative in the long run.
This group also includes traders who employ very high-speed trading strate-
gies to take liquidity from dealers who are slow to adjust their prices when
values change. The value of the price discipline that they provide market
makers over short intervals—typically, less than five seconds long—is small
compared to the value of the liquidity that they take from the market.
9.7 SUMMARY
Most people would like to have the best markets possible. To obtain such
markets, sometimes regulators have to intervene to impose necessary
changes, and sometimes we have to defend our markets against harmful reg-
ulatory interventions. In either event, we can best justify our policies in pub-
lic discourse by showing that they maximize social welfare.
Although the determinants of social welfare are subjective, most people
would agree that we should organize markets to maximize the benefits that
accrue to the traders who use them and to the economy at large. Accord-
ingly, this chapter has considered the private benefits that traders obtain


## Page 231

218 • TRADING AND EXCHANGES
from the markets and the public benefits that we all enjoy when markets
work well.
Traders benefit from highly liquid markets in which they can accomplish
their purposes at low cost. Most people agree that public policy should strive
to create liquid markets.
The economy benefits greatly from markets that produce informative
prices. Informative prices help us to efficiently organize economic activity.
People rely upon prices when deciding how to allocate capital to new proj-
ects and when deciding how to allocate managers to existing projects.
Economies become wealthy when these decisions are made well, and they
suffer when they are made poorly. Most people agree that public policy
should strive to create markets that produce informative prices.
Public policy becomes quite interesting when these simple objectives con-
flict with each other. For example, in chapter 29 we show that rules which
restrict insider trading usually increase liquidity while making prices less in-
formative. In such circumstances, we need a more thorough understanding
of our objectives to decide which policies promote our welfare.
Since the objectives of public policy are subjective, people will disagree
about policies. Such disagreements typically arise when a policy affects
someone's economic welfare. Although such conflicts are unavoidable, most
people more willingly accept policies that hurt them if they are founded on
deep principles which we all share. The ultimate challenge faced by honest
public policy makers is to promote the public good against powerful and
highly vocal private interests. The problem is most difficult when we poorly
understand what constitutes the public good. I hope that this chapter has
helped you to form well-reasoned opinions about what good markets are.
9.8 SOME POINTS TO REMEMBER
•
Profit-motivated traders cannot profit on average if they trade only
with each other.
•
Markets ultimately exist only because utilitarian traders benefit from
trading.
•
Markets produce information used in production decisions and allo-
cation decisions.
•
Informative primary market prices help ensure that only the most
promising projects receive new capital.
•
Informative secondary market prices help allocate the best managers
to existing capital.
•
Many schemes that investors use to motivate their managers work best
when secondary market prices are highly informative.
•
The public benefits to the economy of well-functioning markets are
largely responsible for the prosperity of market-based economies.
•
Most people believe that markets work best when transaction costs
are low and prices are informative.
9.9 QUESTIONS FOR THOUGHT
1.
Is there a trade-off between informative prices and liquidity?
2.
Should the government intervene to make the markets more liquid
or less volatile? Can such interventions be effective?


## Page 232

CHAPTER 9 GOOD MARKETS • 219
3.
Will stock prices reflect the value of the firm under poor manage-
ment if everyone expects that the managers will soon be replaced in
a takeover?
4.
Why do some firms implement internal capital markets? What should
be the relation between the costs of funds in internal and in external
capital markets?
5.
In the example that demonstrates how primary markets price invest-
ment ideas, how would the solution change if the new investors re-
quire a seasoned offering discount to fund their research into the value
of the firm?
6.
The text in the "Markets for the Air We Share" (p. 213) states that
governments can partially solve the "free" air pollution problem by
taxing gasoline or by taxing new cars according to how much they
pollute. Why would these policies only partially solve the pollution
problem? How might we arrange better solutions?
7.
Do interest rates depend on how liquid money markets are? How
would the answer to this question depend on the intertemporal cash
flow misalignment problems that borrowers, as opposed to savers,
face?
8.
Gamblers make markets more liquid. Liquidity attracts informed
traders. Since the profits that informed traders make come from other
traders, informed trading must make markets less liquid. After con-
sidering the interactions between gamblers and informed traders, does
gambling have a net positive or negative effect on liquidity?


## Page 234

W
e now turn our attention to the three main types of profit-
motivated speculators.
We consider informed traders in chapter 10. Informed traders
are well informed about fundamental values. Their trading makes prices
more informative.
We study order anticipators in chapter 11. Order anticipators are well
informed about what other traders intend do. They front-run other traders
and thereby reduce their profits. Their trading often makes prices less
informative.
In chapter 12, we examine bluffers. Bluffers try to fool other traders into
believing that they have information about future price changes. Their trad-
ing usually makes prices less informative.
Part III
I>
Speculators
221


## Page 235

10
Informed
Traders
and
market
Efficiency
I
nformed traders
are speculators who acquire and act on information about
fundamental values. They buy when prices are below their estimates of
fundamental value and sell when prices are above their estimates. Informed
traders include
value traders, news traders, information-oriented technical
traders,
and
arbitrageurs.
In this chapter, we will consider how informed traders trade and how
their trading makes prices informative. We will pay special attention to why
some informed traders make money while others do not. We also will ex-
plain why prices cannot be completely informative. This chapter will help
you understand how informed traders make money, when they make money,
and the limits to how much money they can make.
Informed trading may interest you for at least three reasons. First, you
may be an informed trader yourself. If your trading decisions depend in any
way on opinions you form about fundamental values, you are an informed
trader. Unfortunately, most traders who believe that they are informed
traders do not trade profitably because they are not truly well informed. The
principles we will discuss in this chapter should improve your trading by
helping you predict when you will trade profitably.
Second, you must understand informed trading to understand the risks
that traders face when they offer liquidity. In chapter 13, we show that deal-
ers and other traders who supply liquidity lose to well-informed traders. The
profitability of dealer operations therefore depends critically on how deal-
ers cope with informed traders. If you intend to be a dealer, if you intend
to trade with dealers, or if you intend to offer liquidity yourself, you must
understand informed trading.
Finally, you must understand informed trading to see how prices become
informative. A price is
informative
when it is near its corresponding funda-
mental value. Informative prices are extremely valuable to the economy
because they help us allocate resources efficiently. To fully appreciate how
market-oriented economies work, you must understand how informed
traders make prices informative.
10.1 FUNDAMENTAL VALUES
To discuss informed trading, we must distinguish between market values
and fundamental values. The
market value
of an instrument is the price at
which traders can buy or sell the instrument. The
fundamental value
(or
in-
trinsic value)
is the "true value" of the instrument. In financial terms, fun-
damental value is the expected present value of all present and future ben-
efits and costs associated with holding the instrument. Everyone would agree
upon this value if they all knew everything known about the instrument, if
they all used the proper analyses to predict and discount all uncertain fu-
ture cash flows, and if they all perceived the benefits and costs of holding
222


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CHAPTER 10 INFORMED TRADERS AND MARKET EFFICIENCY • 223
the instrument equally. Since
these conditions never occur, traders often dif-
fer in their opinions about fundamental values. This chapter examines how
informed traders estimate fundamental values and how they trade upon their
estimates.
Fundamental values are not perfect foresight values. Fundamental values
depend only on information that is currently available to traders.
Perfect fore-
sight values
depend on all current and future information about values. Fun-
damental values are the best estimates of perfect foresight values.
Prices are completely
informative
when they equal fundamental values.
Efficient markets
produce prices that are very informative. The difference be-
tween fundamental value and market value (price) is
noise.
Informed traders
try to identify the noise in prices by estimating fundamental values. Since
we do not observe fundamental values, we cannot easily determine whether
prices are informative or noisy.
Changes in fundamental values are completely unpredictable. Since fun-
damental values reflect all available information, they change only when
traders learn unexpected new fundamental information. If fundamental value
changes were predictable, current fundamental values would not fully reflect
the information upon which the predictions are based. Fundamental value
changes therefore must be unpredictable. Since prices are very close to fun-
damental values in efficient markets, price changes in efficient markets are
quite unpredictable.
When traders cannot predict future price changes, statisticians say that
prices follow a
random walk.
Plots of random walks through time look like
paths that wander up or down at random because random walks are com-
pletely unpredictable.
10.2 INFORMED TRADERS
Informed traders estimate fundamental values. They may base their esti-
mates on
private information
that only they have or on
public information
that any trader can obtain. Informed traders compare their value estimates
with the corresponding market prices. They consider instruments to be
undervalued
if prices are less than their estimates of fundamental value, and
overvalued if
prices are greater.
Informed traders buy instruments that they believe are significantly un-
dervalued and sell instruments that they believe are significantly overval-
ued. They hope to profit when the prices of their purchases rise and when
the prices of their sales fall. Informed traders naturally hope that these price
changes will occur quickly.
Informed traders lose money when they estimate fundamental values
poorly. When their value estimates are wrong, they pay too much for in-
struments they have overvalued, and they sell too cheaply instruments they
have undervalued. Informed traders who consistently estimate values poorly
usually quit trading when they have lost more money than they can toler-
ate or when bankruptcy forces them out of the markets.
Informed traders also can lose money even if they accurately estimate
fundamental values. This happens when prices move away from fundamen-
tal values rather than toward them. These losses, however, tend to be short-
term. In the long run, prices usually revert toward their fundamental val-
ues, so well-informed traders ultimately profit.
Fischer Black on Noise
Fischer Black was a
mathematician who made
many seminal contributions to
the development of financial
theory. Perhaps most notably,
he helped develop option-
pricing theory, for which
Myron Scholes and Robert
Merton received the 1997
Nobel Prize in economic
science. Had Fischer not
died two years before the
prize was awarded, he
undoubtedly also would have
been a Nobel laureate.
In his 1985 presidential
address to the American
Finance Association, Black
offered a now famous opinion
about noise. He believed that
we should consider stock
prices to be informative if
they are between one-half
and twice their fundamental
values! Most economists
believe that the prices of
actively traded securities are
well within these extreme
bounds, but no one can know
for sure.
Source: Fischer Black, "Noise,"
Journal of Finance
41, no. 3
(1986): 529-543.


## Page 237

224 • TRADING AND EXCHANGES
Even if prices never adjust to their fundamental values, well-informed
traders who have correctly estimated values still can profit from their trades
if they are patient. When they buy an undervalued instrument, they acquire
the rights of ownership for less than their aggregate value. By holding
the instrument, they will eventually receive the benefits of these rights—
typically interest, dividends, royalties, capital repayments, or liquidating
distributions—at a lower price than they could otherwise obtain them.
When they sell overvalued instruments, they can invest the proceeds in in-
struments with higher expected rates of return.
10.3 INFORMED TRADERS
MAKE PRICES INFORMATIVE
Informed traders, like all other traders, often significantly impact prices
when they trade. Their buying tends to push prices up, and their selling
tends to push prices down. Since they buy when price is below their esti-
mates of fundamental value and sell otherwise, the effect of their trading is
to move prices toward their estimates of fundamental value. Their trading
therefore causes prices to reflect their estimates of fundamental value. When
informed traders accurately estimate values, their trading makes prices more
informative.
Informed traders generally differ in their estimates of value. This often
happens when they base their estimates on different data. Informed traders
often trade with each other so that the price impacts of their trading tend
to cancel. The net impact of their trading is a market price that reflects an
average of their different value estimates. This price usually is more in-
formative than are any of the individual value estimates. Markets aggregate
data from many sources to produce prices that typically estimate funda-
mental values more accurately than any individual trader can.
Informed traders also may estimate different values when one or more
traders make mistakes in their analyses. If many mistaken traders are in the
market, or if one mistaken trader is quite large, their trading will make prices
less informative. In the long run, however, the losses of error-prone traders
cause them to exit the market so that prices become more informative.
Informed traders who most accurately estimate value eventually become
wealthy while informed traders who estimate value less accurately lose
wealth. Since wealthy informed traders take larger positions than do
less wealthy informed traders, wealthy traders have more influence on price
than do other traders. Prices therefore are closer to the value estimates of
wealthy traders than to those of less wealthy traders. Since wealthy traders
tend to be the best-informed traders, prices primarily reflect the value esti-
mates of the best-informed traders more than those of less informed traders.
Although informed traders usually make prices more informative, they
do not trade for this purpose. They trade to make profits. The price im-
pacts of their trading are transaction costs to them. They make less money
when their price impacts are large than when they are small. To trade prof-
itably, informed traders need to trade in liquid markets in which prices
differ significantly from fundamental values. They do not profit much in
illiquid markets, where their trading quickly eliminates potential profit op-
portunities. Informed traders want prices to adjust toward their estimates
of fundamental value only after they have established their positions.


## Page 238

CHAPTER 10 INFORMED TRADERS AND MARKET EFFICIENCY • 225
An Algebraic Illustration
This box presents an algebraic illustration of how markets aggregate
information. If you are not comfortable with algebra and symbolic notation,
skip it. The exercise only illustrates points made in the text.
Suppose that N traders each produce a different forecast of the true
value of a security. Let
f,
be the forecast of the ith trader and assume that it
is an unbiased estimate of
V,
the true fundamental value. We can represent
the forecast as
f, = V+ e,
where e, is the error in the ith trader's forecast.
The expected forecast error is 0 because the forecasts are unbiased. The
individual forecast errors might be quite large in absolute value, however.
Let
each trader's desired position in the security,
D„
be proportional to
the difference between her forecast of value and the market price, i.e.,
D
i
= a(f, — P)
where a is some constant of proportionality and
P
is the
market price. This assumption ensures that trader i will want a long position
if her forecast is greater than the market price and a short position
otherwise. It also ensures that the more different her forecast is from the
market price, the more she will want to hold.
Finally, assume that the security is in
zero net supply.
Traders create such
securities when they sell them short. Futures contracts and option contracts
are examples of zero net supply securities. This assumption simplifies the
arithmetic but does not affect our qualitative results.
We compute the market price by setting the sum of all desired positions
equal to the net supply and solving the resulting equation for
P:
N

N

N
D
i
=

a(f, — P) = a

f,
—
NaP = O.
i =1

i =1

i =1
1 N
The market price
P =

f,
is an average of the individual forecasts.
N
Substituting
f, = V +
e, into this expression gives
P = V+
e
M
, where
1

1•
4

e
M
= —
N

e, is the forecast error of the market price.
If the individual forecast errors are independent of each other, the law of
large numbers implies that the market forecast error e
M
will approach 0 as
the number of traders N gets large. Even if the number of traders is not
large, the average market forecast error will be less than the average
individual forecast error if the individual forecast errors are not identical.
The market price thus estimates the fundamental value of the security better
than any individual trader can estimate it. Prices are most informative when
many informed traders collect information independently.
r
.
10.4 INFORMED TRADING STRATEGIES
Informed traders must minimize the price impacts of their trades to maxi-
mize their trading profits. They therefore must carefully consider how they
trade. Their most important decision is whether to trade aggressively.
Informed traders should trade aggressively if they believe that their pri-
vate information—and its implications for values—will soon become com-
mon knowledge. When values are well known, traders will not trade at any
other prices. Since informed traders can profit only if they trade when prices
differ significantly from values, they must complete their trades while they
still know values better than other traders do.
Informed traders also should trade aggressively if they believe that many
other informed traders will act on the same information. Each informed


## Page 239

226 • TRADING AND EXCHANGES
The Market Is a Statistical Calculator
Statisticians teach us that we can estimate most accurately when we have
lots of data. For example, suppose you have two forecasts of value that you
believe are equally accurate. The first forecast is 30 and the second is 50.
Your best estimate of value given this information is the average of these
two forecasts, or 40. This average would more accurately estimate value
than either of the individual estimates.
If you knew that the first forecast was more accurate than the second
forecast, your best estimate of value would be closer to 30 than to 50. It
would be a weighted average of the two forecasts with a greater weight
given to the more accurate forecast. (The actual weighted-average estimate
would depend on the accuracies of the two estimates.) The combined
estimate again would be more accurate than either of the other two
estimates.
By combining information from different sources, markets generally
produce more accurate estimates of value than any one source can.
Moreover, since well-informed traders tend to take large positions, the
market gives more weight to their more accurate estimates of value than to
the less accurate estimates of less-informed traders who tend to take smaller
positions. Prices thereby approximate an optimally weighted average of
value estimates with varying degrees of accuracy. Markets are essentially
statistical calculators that aggregate value estimates from various informed
traders to obtain more accurate estimates of value.
trader will push prices closer to fundamental value. The ones who trade first
will profit the most. If the informed traders know that they are competing
with each other, they all will race to complete their trades quickly. Unfor-
tunately for them, the flood of their orders may alert other traders and sug-
gest to them that prices do not equal values. The other traders then may
become reluctant to supply liquidity, so that the informed trading has even
greater price impacts.
Informed traders who are confident that they will not soon lose their in-
formational advantages should trade slowly. By trading slowly, they make it
difficult for other traders to infer that they are well informed. Economists
call this strategy
stealth trading
because the informed traders want to com-
plete their trades without anyone knowing that they are trading.
10.5 STYLES OF INFORMED TRADING
Informed trading styles differ according to the methods that traders use to
estimate fundamental values.
Value traders
estimate the entire fundamental
value of an instrument by using all available information. They determine
whether instruments are correctly priced.
News traders
estimate only
changes in fundamental values. They predict how fundamental values will
change in response to new information.
Information-oriented technical traders
identify price patterns that are inconsistent with prices that fully reflect
fundamental values. They identify systematic errors made by the other two
trader types. Finally,
arbitrageurs
estimate relative differences in funda-
mental values. They examine the value relation between correlated instru-
ments. This section describes how these four types of traders organize their
operations.


## Page 240

CHAPTER 10 INFORMED TRADERS AND MARKET EFFICIENCY • 227
10.5.1 Value Traders
Value traders estimate fundamental values. They gather as much informa-
tion as they can about fundamental values. They then use economic mod-
els to organize this information and to estimate instrument values.
All information that can help value traders understand the value of an
instrument interests them. They collect information about sales, costs, eco-
nomic activity, interest rates, management quality, potential for competition,
growth options, labor relations, input prices, and the prospects for new tech-
nologies. They then use this information to forecast and discount future
cash flows, to value the options associated with the assets underlying the
instrument, and to value any options associated with ownership of the in-
strument itself. When they do their job well, they know more about values
than anyone else does.
Value traders may employ a variety of experts in their efforts to estimate
values. These experts include financial analysts, statisticians, actuaries, macro-
economists, industry economists, marketing professionals, accountants, en-
gineers, scientists, computer programmers, librarians, and research assistants.
To support these experts, they often build significant libraries and run large
information-processing operations.
Value traders must be very disciplined to minimize the biases that may
enter their analyses. If they are overly optimistic, they may buy an overval-
ued instrument. When prices fall, they then will lose money. Likewise, if
they are overly pessimistic, they may sell an undervalued instrument. When
prices rise, they then will lose money (if they are short) or lose the oppor-
tunity to make money (if they sold a long position).
To avoid estimation errors, large value traders usually have
pyramid-
shaped organizations
with many levels of management. Each level oversees
the operations of the levels below it. The many layers of review in a
pyramid-shaped organization help the organization make well-disciplined
decisions. Unfortunately, they also ensure that the organization will make
decisions slowly. At the bottom level are analysts who collect information
and form opinions about security values. These analysts pass their opinions
(and their supporting analyses) up to the portfolio managers, who consider
their analyses. The portfolio managers (and other senior managers within
the organization) must ensure that the analysts use comparable assumptions
when forming their opinions about their securities. Otherwise, the firm will
buy securities analyzed by optimistic analysts and sell securities analyzed by
pessimistic analysts. They also must ensure that their analysts have not ig-
nored important information. Otherwise, they will buy securities for which
they failed to identify negative information and sell securities for which they
failed to identify positive information. To avoid these biases, all successful
value traders—whether large institutional money managers or individual
investors—carefully review their research efforts to make sure that they use
consistent assumptions based on all available information. Although these
reviews make value traders slow traders, they protect them from making
costly mistakes.
Since value traders know values very well, they often supply liquidity to
large traders. In many respects, they are the liquidity suppliers of last resort.
We consider this very important aspect of value trading in chapter 16.
0
,
The Effect of Inconsistent
Assumptions on Value
Trading
Suppose that a value trader
employs two analysts who
separately specialize in the
automobile and aviation
manufacturing industries. If
the automobile analyst
believes that future interest
rates will be 10 percent and
the aviation analyst believes
that future interest rates will
be 5 percent, the automobile
analyst will discount future
Ford earnings more than the
aviation analyst will discount
Boeing's future earnings. The
firm will therefore undervalue
Ford relative to Boeing. If the
organization does not
recognize this inconsistency, it
may sell Ford and buy
Boeing.
The Effect of Incomplete
Information on Value
Trading
Suppose that an analyst
estimates the value of an oil
exploration firm without
taking into account recent
negative drilling results in one
of its more important
prospective oil fields. The
analyst will overvalue the
firm's stock.
A
value trader
who buys the stock based on
this analysis will likely pay
too much for it.


## Page 241

228 •
TRADING AND EXCHANGES
Trading on Viagra
On March 27, 1998, the U.S. Food and Drug Administration approved
Viagra for use as a prescription drug by men who suffer from penile erectile
dysfunction. Pfizer launched the drug in April. By May 22, over 1 million
U.S. men had already taken it. Each dose costs about 8 dollars. The speed
and depth of Viagra's market penetration surprised many people.
The stock of Pfizer closed at 95
3
/4 on March 27. By the end of the next
week, its price had risen in roughly equal steps to 102
7
/8. One month later,
on April 27, the stock closed at 113
7
/1o.
Although it is impossible to definitively attribute the increase in stock
price to Viagra's introduction, the conclusion seems reasonable. The first
traders to obtain, correctly analyze, and act upon the information made
money. Later traders lost the opportunity to make money because prices had
already increased to reflect the news.
Trading on
Investigative Research
into Aerospace Hiring
An aerospace firm is
competing for an important
classified contract. Analysts
widely agree that the firm
will be significantly more
valuable if it obtains the
contract. Although the firm
may not reveal the status of
its negotiations, a clever
researcher may be able to
infer how they are
progressing by considering
the numbers and types of
people the firm is trying to
hire. Such information will
be valuable to a news trader
if it is not already widely
known.
10.5.2 News Traders
News traders
collect and act upon new information about
instrument val-
ues. They try to predict how instrument values will change, given the new
information. If they think that values will change significantly, they then
buy or sell instruments, depending on whether the news is good or bad.
Material information
is information that significantly affects instrument val-
ues. News traders try very hard to discover material information before other
traders do. Successful news traders employ experts in data collection who
can quickly filter public data sources for valuable information, researchers
with strong investigative skills who can produce useful new information,
and traders who can quickly and accurately analyze implications of new in-
formation for instrument values.
Unlike value traders, news traders do not estimate the value of an in-
strument from first principles and
all
available data. Instead, they implicitly
assume that current prices accurately reflect all information except their
news. Their object is merely to estimate how values will change in response
to their new information. They estimate total instrument values by adding
to current prices their estimates of how their news changes values.
Successful news traders must collect information and act on it before
other traders do. Those who collect and respond to publicly available in-
formation must be extremely quick, because much of the news that affects
security and contract values is easy to obtain and interpret. These traders
often compete with many other traders who simultaneously try to profit
from trading on the same news. News traders who specialize in producing
information through their own investigative research need not be so quick,
but they still must be faster than their competitors. In either case, only
traders who can trade before their news has its impact will profit.
Traders who trade on inside information are news traders.
Inside infor-
mation
is material information that traders directly or indirectly obtain from
the management of a company and that is not yet publicly available. In the
United States, and in many other countries, trading on inside information
is illegal. We consider how the prohibition on insider trading affects the se-
curity markets and the managerial labor markets in chapter 29.
Large money managers who pursue information-flow trading strategies
usually have flat organizations with few management levels.
In flat organi-


## Page 242

CHAPTER 10 INFORMED TRADERS AND MARKET EFFICIENCY • 229
zations,
managers are allowed (within limits) to make whatever decisions
are necessary to pursue the firm's objectives. Flat organizations can make
quick decisions with little deliberation. This structure is well suited to firms
that trade on the flow of information because their traders generally must
act quickly in order to trade profitably on their information.
Successful firms that trade on information flows use various systems to
quickly collect and deliver information to portfolio managers, who analyze
it and possibly trade upon it. These systems may include networks of re-
porters, brokers, or analysts whom the firms reward for providing them with
valuable information. They may also include computerized systems that read
and interpret electronic news feeds produced by news services like the Dow
Jones Broad Tape. News traders may also employ clipping services to sum-
marize newspaper articles from around the nation and the world. In addi-
tion, many news traders have television monitors on their desks tuned to fi-
nancial news channels as well as computer monitors that scroll information
distributed by electronic news services.
10.5.2.1 Information, Prices, and Pseudo
-
informed traders
To trade profitably, news traders must trade before prices adjust to reflect
their information. A price
reflects
information if that information cannot be
used to forecast future price changes. If that is the case, the information is
in the price.
Information gets into the price when all traders are aware of its
significance or when informed traders push prices toward their estimates of
fundamental value. Information that is already in the price is
stale informa-
tion.
News traders cannot trade profitably on stale information.
The most common mistake that traders make is to trade on stale infor-
mation. Economists call traders who trade on stale information
pseudo-
informed traders.
Pseudo-informed traders think that they are well informed,
but in fact they are not. They lose because they tend to buy when prices are
already high and to sell when prices are already low. Pseudo-informed traders
are actually uninformed traders.
Good news traders must know whether their information is already in
the price before they trade. Unfortunately, they rarely know this. To answer
the question directly, they must estimate instrument values from first prin-
ciples. Although value traders routinely do these analyses, few news traders
are well equipped to do so. However, traders often can make an educated
guess about the quality of their information based on how they obtained it.
If they are acting on unique information that others could not have antic-
ipated, their information probably is not stale. If they have been slow to
act, if their information is widely known, if others can obtain it cheaply,
or if others could have reasonably anticipated it, their information probably
is stale.
Since news traders generally do not estimate values as well as value traders
do, they generally make more mistakes than value traders. Although news
traders usually can accurately predict the direction in which values should
change in response to their information, they often poorly estimate the sizes
of the changes. In particular, they may under- or overestimate the implica-
tions of their information for instrument values. When they underestimate
how much values should change, they lose the opportunity to make more
money. When they overestimate how much values should change, they lose
some, all, or even more than all, of what they gain as informed traders. In
Pseudo-informed
Trading on Stale
Information
The price of Greasy Earth Oil
(GEOL) is presently 90. GEOL
should be worth 100 if it
finds oil and 80 if it does
not. From careful studies of
the surrounding geology and
of the discarded tailings from
GEOL's main drilling rig, well-
informed traders believe
GEOL will find oil. They buy
GEOL and push its price up
to 100.
GEOL does indeed find
oil. When news of the find
becomes public, however,
the information is already in
the price.
If pseudo-informed traders
buy on the stale information,
they may push prices up to
110. Value traders will sell,
and prices will fall back
toward 100. The pseudo-
informed traders will lose.


## Page 243

230 • TRADING AND EXCHANGES
Pseudo-informed Trading in Occidental Petroleum
Armand Hammer was the Chairman and CEO of Occidental Petroleum
(OXY) for 34 years, until his death at age 92 on December 10, 1990.
Although many people criticized his management during the last years of
his life, his control over the firm was nearly absolute. It was difficult to
influence his decisions, and it appeared impossible to take over the firm
from him. The day after he died, OXY's stock price rose by 10 percent as
traders anticipated more favorable management. On the next day, the price
dropped back to its former level.
Armand Hammer was a sick and very old man for a long time before
his death. The fact that he died was not material news because many
expected that he would die soon. The only uncertainty was on what date.
The actual date was not material to the value of the firm. The traders who
thought that they were trading on material information should have realized
that their information was quite stale. Those who bought at high prices on
the day after his death lost the next day.
either case, value traders may recognize that prices do not accurately reflect
values when they eventually learn the news and revise their value estimates
accordingly. If prices have not changed enough, the value traders will
profit directly from acting on the new information.
If
prices have changed
too much, the value traders will profit by correcting the overreaction. Table
10-1 summarizes the mistakes that news traders make and how value-
motivated traders respond to them.
10.5.3 Information
-
oriented Technical Traders
Technical traders
attempt to predict the future course of prices by identify-
ing recurring price patterns. Such patterns can arise when informed traders
make systematic mistakes or when uninformed traders have predictable im-
pacts on price.
When technical traders recognize and trade on mistakes made by in-
formed traders, they effectively become informed traders themselves. By cor-
recting these mistakes, technical traders cause prices to reflect more accu-
rately the information that the informed traders have. This type of technical
trading is
information-oriented technical trading.
We discuss it in this section.
TABLE 1 0
-
1 .
Mistakes News Traders May Make and the Responses of Value Traders
REACTION TO
NEW MATERIAL

INITIAL PRICE

NEWS TRADER

VALUE TRADER

SUBSEQUENT
INFORMATION

CHANGE

MISTAKE

RESPONSE

PRICE CHANGE
Underreaction

Too small

Fail to exploit fully

Trade on the new

Continuation
the profit opportunity

information and
thereby become a
news trader
Overreaction

Too large

Lose some, all, or

Trade against the

Reversal
more than all of their

overreaction
trading profits


## Page 244

CHAPTER 10 INFORMED TRADERS AND MARKET EFFICIENCY • 231
Technical Trading Following an Earnings Announcement
The value of Bethlehem Steel's common stock should rise when the firm
reports better than expected earnings. Suppose that whenever this happens,
news traders or pseudo-informed traders tend to overbuy the stock and push
its price above its new higher value.
Technical traders who are aware of this systematic mistake will sell after
prices rise following positive earnings announcements. They then will profit
when prices fall to their proper (but still higher) levels. If they sell early
enough, they will attenuate the overreaction. If they wait too long, they may
lose the profit opportunity to other technical traders or to value traders who
respond faster.
Tax Timing Strategies
Practitioners and academics have observed that U.S. stocks which have
dropped significantly in one year tend to rise at the beginning of the next
year. Such patterns may result when investors sell their stocks at year-end to
realize capital losses for their taxes. Their sales tend to push prices below
their fundamental values. Prices increase when value traders recognize that
the stocks are mispriced.
Technical traders who try to profit from this information buy losers at
year-end and sell them a few weeks later. Their buying, however, reduces
the year-end price drop caused by the tax-loss sellers. As the tax-loss selling
phenomenon becomes better known, it appears to be going away.
a
When technical traders trade in response to predictable price patterns
caused by uninformed traders, they effectively act as dealers or order antic-
ipators. If they offer liquidity to the uninformed traders, they are essentially
dealers. Their trading tends to make prices more informative. If they at-
tempt to front-run the uninformed traders, they are order anticipators that
we call
sentiment-oriented technical traders.
Their trading tends to make prices
less informative. We discuss dealers and order anticipators in chapters 13
and 11, respectively.
Information-oriented technical traders
identify violations of abstract statis-
tical properties that characterize informative prices. When value traders and
news traders efficiently acquire, process, and act on information, prices will
not have predictable changes. Information-oriented technical traders profit
by identifying predictable price patterns that result when other traders make
mistakes. They are scavengers who pick up profit opportunities left by value
traders and news traders.
Information-oriented technical trading is quite difficult because it is prof-
itable only when informed traders make systematic mistakes. Since obser-
vant traders correct their mistakes, opportunities for successful technical
trading decrease as markets mature and traders become more experienced.
Technical trading strategies that exploit informed traders' mistakes there-
fore rarely are consistently profitable. Strategies that worked well in the past
fail when informed traders learn from their mistakes.
Technical traders use many methods to identify predictable price pat-
terns. They most commonly analyze price and volume charts. These tech-
niques are not very effective because our eyes often see patterns where none
truly exist. Some technical traders use computers to identify patterns in data.
Pattern Recognition
in the Food Chain
Simple principles of
evolutionary biology can
explain why we often see
patterns that do not really
exist. Our primitive ancestors
needed to recognize signs of
danger in order to survive.
Those who could not
recognize these signs
undoubtedly had fewer
children than those who
could. For example, our
ancestral aunts and uncles
who could not recognize signs
of a nearby saber-tooth tiger
probably too often found
themselves on the wrong end
of the food chain. They did
not survive to provide our
ancestors with cousins. Since
the costs of being wrong
when a danger is present are
much greater than the costs of
being wrong when there is no
real danger, survivors often
identify more dangers than
truly exist.
As
the descendants
of those survivors, we are
biologically programmed to
identify patterns where none
may exist.
Good traders must
recognize this predisposition
toward falsely identifying
patterns. Otherwise, they
often will trade foolishly. "'


## Page 245

232 •
TRADING AND EXCHANGES
The Psychology of Momentum Strategies
Financial economists have observed that stocks which have risen substantially
over the last six months of the year tend to outperform the market in the next
year. Stocks that have fallen substantially tend to underperform the market in
the next year. These results are based on averages over many stocks and
many years. The probability that any given stock beats or lags the market is
close to 50 percent regardless of its previous performance. These results
suggest that markets are not completely efficient.
Information-oriented technical traders try to profit from this information by
buying extreme winners and selling extreme losers. Although each stock is
quite risky, the risk is manageable when they buy and sell many stocks at
the same time. This strategy is a
momentum
strategy because traders hope
that prices continue moving in the same direction that they have moved.
Momentum strategies are profitable when news traders and value traders
underestimate the importance of significant new information.
The common tendency of people to resist changes in the status quo may
explain why traders make these mistakes. When great news makes a stock
much more valuable, or when terrible news makes it much less valuable,
traders have trouble believing that the current value of the stock could be so
different from its recent value. They find it difficult to buy stocks that have
risen substantially because they are afraid that they may have become
overvalued. They likewise find it difficult to sell stocks that have fallen
substantially because they are afraid that they may have become
undervalued.
The momentum strategy probably will become less profitable as the
effect becomes better known. News traders and value traders will become
more aggressive.
Technical traders who pursue momentum strategies must be extremely
careful that they are the first to trade on the strategy and not the last. The
price impacts of the first momentum traders will correct the mistakes made
by other traders. Later momentum traders will simply cause prices to
overreact.
Arbitrage in
the Gold Market
An
arbitrageur observes that
the price of gold in London is
lower than the price of gold
in New York. If the price
difference is greater than the
cost of transporting gold from
London to New York, plus the
costs of trading it, the
arbitrageur will buy gold in
London and sell gold in New
York. The arbitrageur will
profit if the price difference
grows smaller.
They may tabulate
frequency distributions, run regressions, or employ eso-
teric pattern recognition models like neural networks. Some technical traders
even consider psychological models in their attempts to predict when traders
make mistakes. Whatever the method, the defining characteristic of tech-
nical trading is its emphasis on pattern recognition rather than on economic
analyses of material fundamental information.
Technical trading is not profitable in efficient markets. To trade prof-
itably, technical traders must accurately predict price changes. In efficient
markets, price changes are unpredictable because prices are close to values
and because value changes are unpredictable.
10.5.4 Arbitrageurs
The final type of informed trader is the arbitrageur.
Arbitrageurs
simulta-
neously buy and sell similar instruments. They try to identify instruments
that are inconsistently priced relative to each other. They then buy the
cheaper instruments and sell the more expensive ones. Arbitrageurs profit
if the cheaper instruments appreciate and the expensive ones depreciate, if
the cheaper instruments appreciate faster than the expensive ones, or if the
expensive instruments depreciate faster than the cheaper ones.


## Page 246

CHAPTER 10 INFORMED TRADERS AND MARKET EFFICIENCY • 233
Instruments are similar when their values depend on common funda-
mental valuation factors. A
fundamental valuation factor is
a variable upon
which instrument values depend. Common factors may include macroeco-
nomic variables like interest rates, national income, unemployment, and ex-
pected inflation; industry variables like sales, wages, prices, product innova-
tions, and competitive conditions; physical variables like the weather,
agricultural pests, and solar activity; political variables like legislative, exec-
utive, judicial, and military interventions; and social variables like crime and
social unrest. The actual factors upon which instrument values depend, vary
substantially across instruments. Some examples appear in table 10-2.
Successful arbitrageurs must accurately estimate relative differences in
value, but they need not form an opinion about which instrument, if any, is
correctly priced. By simultaneously buying and selling similar instruments,
they protect themselves against price changes due to common factors. If
prices go up because all instruments are undervalued, they make money on
their purchases and lose money on their sales. If prices go down because all
instruments are overvalued, they lose money on their purchases and make
money on their sales. In either event, they make money on net if the in-
struments they purchase are undervalued relative to the instruments they
sell. They profit if their purchases are more undervalued then their sales, if
their purchases are not as overvalued as their sales, or if their purchases are
undervalued and their sales are overvalued.
Arbitrageurs use many methods to estimate relative differences in in-
strument values. Some arbitrageurs use statistical methods to characterize
the normal relations among instrument prices. Others use economic
TABLE 10-2.
Some Common Valuation Factors of Similar Instruments
FIRST INSTRUMENT

SECOND INSTRUMENT

SOME COMMON VALUATION FACTORS
Gold in London

Gold in New York

The value of gold
Chrysler stock

Ford stock

Auto industry conditions
General stock market conditions
Labor relations
S&P 500 Index

An S&P 500 Index portfolio

All factors that affect the S&P 500 stocks
futures contract
Soybean oil

Soybean meal

Weather
Crop pests
Livestock prices
Government bonds

Corporate bonds

Interest rates
Dollar/euro

Dollar/yen exchange rate

U.S., European, and Japanese interest rates
exchange rate

Trade relations
Monetary and fiscal policies
Corporate bonds

Stocks

Expected inflation
General economic conditions
Note: Two instruments have a common valuation factor if both instruments' values depend on that factor.


## Page 247

234 • TRADING AND EXCHANGES
The Law of One Price
The prices of live cattle and
of pork bellies both depend
on the price of corn because
feedlot operators usually
produce these commodities by
feeding corn to animals.
Although these prices also
depend on many other
common factors, the price of
corn is especially important
because corn often represents
a significant fraction of the
total value of all inputs used
to create these products. In
the long run, when the corn
prices are high, cattle and
pork prices are high.
The law of one price
holds that the prices of live
cattle and of pork bellies
should both reflect the same
information about corn
prices.
4
models to characterize how instrument prices depend on common under-
lying factors. Still others use psychological models to predict when and how
traders will misprice one instrument relative to another. Regardless of their
methods, all arbitrageurs trade when the relations between two or more
prices differ significantly from the relations that their models predict.
Although arbitrageurs trade to make profits, the effect of their trading
is to enforce the law of one price. The
law of one price
holds that identical
instruments should have identical prices. For instruments that are similar
but not identical, the law of one price holds that their prices should be con-
sistent with respect to the values of their common factors. For example, if
two instruments depend on the price of corn, the prices of both instruments
should reflect the same price of corn. In general, the law of one price im-
plies that all instrument prices reflect the same common factor values.
Arbitrageurs unwittingly enforce the law of one price when they arrange
their arbitrage trades. Their buying tends to push up the prices of cheap in-
struments, and their selling tends to lower the prices of expensive instru-
ments. When arbitrageurs correctly identify inconsistently priced instru-
ments, their trading helps rationalize instrument prices and thereby makes
prices more informative.
The price impacts of arbitrage trades are transaction costs. The less im-
pact arbitrageurs have on prices, the more money they make. Once arbitrageurs
have established their positions, they hope that prices will quickly adjust to
their proper relations. These price changes make their trades profitable.
Arbitrageurs lose money when they mistakenly conclude that instruments
are mispriced relative to each other. This often happens when the price of
one instrument changes and the price of a similar instrument does not. If
the price of the first instrument increases, an arbitrageur may sell it and buy
the second one. If the first price drops, an arbitrageur may buy the first in-
strument and sell the second one. Whether these trades are profitable de-
pends on the reason for the first price change. Three cases are possible:
•
The two instruments were priced correctly relative to each other be-
fore the initial price change, and they are priced correctly relative to
each other afterward.
Although the instruments are similar, they are not identical. A
change in some factor specific to the first instrument may have caused
the price change. Arbitrage trades in this case are not profitable be-
cause the instruments are correctly priced relative to each other. These
trades generate transaction costs, as do the trades necessary to unwind
these positions. Arbitrageurs who trade in this case therefore tend to
lose money.
•
The two instruments were priced correctly relative to each other be-
fore the price change, and no instrument-specific factor changed.
The two instruments therefore are no longer properly priced rela-
tive to each other. Either the first price should not have changed, or
the second price should have changed in the same direction. If some
common factor caused the first price to change, then the second price
should have changed, too. If no common factors changed, the first in-
strument price should not have changed. Arbitrage trades in this case
tend to be profitable because the instruments are not correctly priced
relative to each other.


## Page 248

CHAPTER 10

INFORMED TRADERS AND MARKET EFFICIENCY • 235
TABLE 10-3.
Informed Trader Types
TRADER TYPE
MOST SKILLED AT
ESTIMATING . . .
INFORMATION
SPECIALTY
TYPICAL TRADING SPEED
Value traders
Total value
All available
information
Slow
News traders
Changes in value
News
Fast on public information;
slow on private information
Information-oriented
Systematic valuation
Statistical
Fast
technical traders
mistakes
anomalies
Arbitrageurs
Relative values
Relative factor
prices
Fast
• The two instruments were not priced correctly relative to each other
before the price change, but they are afterward.
The price change corrected the price of the first instrument. Ar-
bitrage trades in this case are not profitable because the instruments
are correctly priced relative to each other. They merely generate trans-
action costs.
Successful arbitrageurs must discriminate among these three cases. Those
who can accurately identify bona fide arbitrage opportunities trade prof-
itably. Those who falsely identify too many arbitrage opportunities lose
money through excessive transaction costs.
10.5.5 Informed Trader Summary
Each of the four types of informed traders acts on a different type of infor-
mation. Value traders use the stock of all available information. News traders
profit from learning new information before other traders do. Information-
oriented technical traders profit by identifying predictable price patterns that
result when value traders and news traders make mistakes. Finally, arbi-
trageurs trade on information about relative instrument values rather than
absolute instrument values. Table 10-3 provides a summary of these informed
traders.
10.6 COMPETITION, TRADING PROFITS,
AND INFORMATIVE PRICES
The first half of this chapter describes how the various types of informed
traders trade to profit from their information. We now examine when in-
formed trading is profitable, where informed trading profits come from, and
what determines how informative prices are.
10.6.1 Informative Prices
Prices become informative in two ways. First, when fundamental values are
well known, prices reflect those values because no trader will trade at any
other prices. No buyer will pay more than the known value of an instrument,


## Page 249

Pfr. Liquidity and Predictability
It may be better to be a slightly informed trader in a very liquid market
than to be a very well-informed trader in an illiquid market. The following
example illustrates this point. (You need not follow the algebra to
understand the example.)
Futures contracts in the instructional Iowa Electronic Markets pay 1 dollar
if some event occurs and nothing otherwise. Suppose all traders believe that
an event upon which a contract depends has a 30 percent probability of
occurring. If no one has any private information, and if traders care only
about the expected value of the contract, the contract price will be 30
cents.
From very careful research, you believe the probability that the event will
occur is greater than 30 percent. Let IT represent your estimate of the
probability. If 77 is 100 percent, you are extremely well informed. If 7r is
only a little above 30 percent, your insight is not very good.
No one else knows that you are well informed. Other traders, however,
suspect that some traders may be well informed. Consequently, the more
contracts you buy, the higher their price will be. To keep this example
simple, assume that you know you are the only trader with any private
information.
For illustrative purposes, suppose that the following formula characterizes
the average price of your purchases:
Q
P = 0.3 +
4f'
where
P
is your average purchase price in dollars, Q is the total quantity
that you purchase, and
e
is a parameter that characterizes the liquidity of
the market. When
f
is large, you can buy a lot without having much price
impact.
How many contracts should you buy to maximize your expected profits?
The answer depends on the quality of your information IT and on the
liquidity of the market
f.
To derive the answer, your must compute your
expected profits. The expected value of owning the contract, given your
information, is simply
77"
because it pays a dollar with probability 7. The
expected value of your position is therefore
7r0.
The total cost of acquiring
your position is
PQ,
so your expected profits are
7r0
—
PO =
-rrQ
—
(0.3 Q +
f
)0
Q2
= (7r — 0.3)Q 4C
The quantity that maximizes this expression is
=
2f(7 — 0.3(.
You can derive this result by using calculus or by observing that the profit
formula is a quadratic formula which describes an inverted parabola with
zero profits at 0 = 0 and Q = 4C(77- — 0.3(. Since parabolas are symmetric,
the maximum is midway between these two values.
Substituting this quantity into the profit expression gives the maximum
expected profits:
f(7
— 0.3)
2
.
This expression shows that you expect to make more money if the market is
liquid and if your information is very good.
If you have poor information in a very liquid market, you may expect
to make more than if you have excellent information in an illiquid market.
For example, the expected profits for
f
= 10,000 and
IT =
40 percent are ,
100 dollars. They are only 49 dollars for
=
100 and 7r = 100 percent.
4,%
;

You can learn more about the Iowa Electronic Markets at www.biz.uiowa.edu/iem.

236


## Page 250

CHAPTER 10 INFORMED TRADERS AND MARKET EFFICIENCY • 237
and no seller will accept less than that value. Second, when values are not
well known, informed traders make prices informative. The price impacts of
their trading cause prices to reflect the information they collect. Prices there-
fore will be informative when fundamental values are well known or when
informed traders collect and act fully on all available information. Since val-
ues are not common knowledge in most markets, informed trading is the
more important process by which prices become informative.
10.6.2 Informed Trading Profits
The profits of informed traders depend on their ability to predict future
prices and on the impact their trading has on prices. They are most prof-
itable when they can accurately predict prices and when the price impacts
of their trading are small. Their trading profits must cover their costs of ac-
quiring and processing their information, their commission costs, the value
of their time, and all other normal costs of doing business. Otherwise, their
operations will not be economically viable, and they will quit trading.
10.6.2.1 Orthogonality
Since profitability of informed trading depends on liquidity, the most prof-
itable informed traders are often those who want to trade when no other
informed traders want to trade. Such traders do not have to compete with
other traders to complete their trades. Liquidity therefore is relatively cheap
for them.
Informed traders who want to trade when no other informed traders
want to trade either have unique insights that other traders do not have, or
they have estimated values incorrectly. If they have estimated values incor-
rectly, the cheap liquidity that they obtain often lowers the costs of their
mistake. If they have estimated values correctly, the cheap liquidity increases
their trading profits.
Traders who estimate values from the same information, using the same
methods, tend to estimate the same values. Their estimates are
highly corre-
lated.
They must compete with each other to profit from their insights.
Traders whose estimates are not closely correlated with the estimates of other
traders have
orthogonal
estimates.
(Orthogonal
comes from a Greek word that
means "at right angles.") Traders obtain orthogonal estimates of value when
they base their estimates on information that other traders do not use or
when they analyze data using different methods than other traders use.
The most profitable traders have very accurate estimates of value that are
uncorrelated with the value estimates made by other traders. Their value es-
timates are precise and orthogonal. They are right when nobody else is right.
Since precision and orthogonality both increase profits, a trade-off ex-
ists between them. Traders may be equally profitable with precise, highly
correlated estimates of value and with imprecise, orthogonal value estimates.
You can make a lot of money being right when nobody else is, even if it
does not happen too often. You will not make much money, however, if you
lose a lot when you are wrong and everyone else is right.
10.6.3 The Role of Uninformed Traders
Prices will not reflect information obtained by informed traders if they do
not trade. We therefore must know when informed traders profit to under-
stand the origins of informative prices.


## Page 251

238 • TRADING AND EXCHANGES
First note that informed traders cannot trade profitably if they trade only
with each other. Since trading is a zero-sum game, their aggregate profits
would be zero. The better-informed among them would profit at the ex-
pense of the less well-informed. The losers eventually would stop trading.
The remaining better-informed traders then would profit from the remain-
ing less well-informed traders. Those losers eventually would stop trading.
In the end, only the best-informed trader would want to trade, but no one
would trade with him. There would be no informed trading, and prices
would not reflect information gathered by informed traders.
Informed trading can be profitable only when informed traders trade with
uninformed traders. Although uninformed traders lose on average to in-
formed traders, they tolerate these losses because they obtain other valuable
services from the market. (Chapter 8 discusses the reasons why uninformed
traders—primarily investors, borrowers, hedgers, asset exchangers, and
gamblers—trade.)
Uninformed traders naturally do not like to trade with informed traders
because they do not want to lose to them. Uninformed traders therefore
want to know who is well informed so that they can avoid trading with
them. Informed traders, of course, do not want other traders to identify
them. They try to trade anonymously, or they pretend to be uninformed
traders. Informed trading is expensive in markets where traders can easily
identify informed traders. Such markets may have less informative prices
than other markets do.
Informed trading is most profitable in markets with many uninformed
traders. In such markets, many informed traders compete to acquire infor-
mation and act on it. These markets therefore have very informative prices.
Of course, if prices are quite informative, informed traders may have few
large profit opportunities. Uninformed traders therefore lose little individ-
ually, although they lose much in aggregate. When prices reflect funda-
mental values, uninformed traders most often trade with other uninformed
traders.
10.6.4 A Market Paradox
This analysis suggests an interesting paradox. If prices are quite informa-
tive, informed trading will not be profitable. But if informed trading is not
profitable, informed traders will not trade, and prices will not be informa-
tive! Since the conclusion of this argument is inconsistent with the as-
sumption upon which it is based, some part must be wrong.
It may be that fundamental values are well known. In that case, prices
would be informative even without informed traders. This simple solution
to the paradox is not attractive, however, because values are rarely well known.
Alternatively, prices may not always be very informative. When prices
differ significantly differ from fundamental values, informed traders trade
and make money. Their trading makes prices more informative and elimi-
nates further profit opportunities, at which point they do not trade further.
If prices or values change, prices then may significantly differ from values
so that informed traders can again profit by trading. Since prices and fun-
damental values change constantly, this resolution of the paradox seems most
reasonable. Informed traders make prices informative, but prices are not al-
ways informative.


## Page 252

CHAPTER 10 INFORMED TRADERS AND MARKET EFFICIENCY • 239
Prices move away from fundamental values when values change and prices
do not change accordingly, or when prices change without a change in val-
ues. The former often happens when news arrives. The latter happens when
trading by uninformed traders moves prices. Both situations create profit
opportunities for informed traders.
Fundamental values change constantly as the world changes, and as peo-
ple learn new information that they can use to predict what will happen in
the future. News traders who learn news first profit most from these changes
in value. If they under- or overreact to the news, value traders may recog-
nize their mistakes and trade on the resulting profit opportunities. If the
news traders or the value traders make systematic, predictable mistakes when
estimating values, information-oriented technical traders may recognize
them and trade profitably. If the changes in values are due to changes in
common valuation factors, arbitrageurs may profit if they find that similar
instruments are no longer priced correctly relative to each other.
Uninformed traders also cause prices to differ from fundamental values.
When they make large trades or when many small traders all trade on the
same side of the market, they often push prices away from fundamental val-
ues. Since uninformed traders are hard to distinguish from informed traders,
most traders cannot determine whether prices are changing because un-
informed traders are trading or because informed traders are trading in re-
sponse to changes in values. The traders most able to make these distinc-
tions are value traders. They profit when they recognize that prices differ
significantly from fundamental values. They must be very sure, however, that
no new information caused the price changes. If they miss an important de-
velopment, their value estimates will be wrong, they will trade with news
traders, and they will lose to them. Technical traders who can determine
when uninformed traders have traded also may profit. Finally, arbitrageurs
may profit if the price changes cause them to conclude that similar instru-
ments are no longer priced correctly relative to each other. Like the value
traders, however, they must be very sure that no new information about
instrument-specific factors caused the price changes. Otherwise, they will
lose to news traders.
10.6.5 Competition Among Informed Traders
Informed trading is a business in which traders compete for profits. Some
succeed and others fail. The most successful informed traders collect mate-
rial information more efficiently, and trade on that information with less
price impact, than do less successful traders. Those traders who cannot col-
lect material information at low cost or who trade poorly eventually fail.
The least successful informed traders who stay in business have trading prof-
its that just cover their total expenses.
If informed trading becomes particularly profitable, many traders enter
the market to compete for those profits. Profits drop as more informed
traders compete for the liquidity that they need to establish their positions.
Their trading drives prices closer to fundamental values and thereby de-
creases informed trading profits. The increased competition eventually
makes it impossible for additional traders to enter and profit.
The entry and exit of informed traders is a slow process because traders
cannot easily predict how profitable their operations will be. Since informed


## Page 253

240 •
TRADING AND EXCHANGES
The Dollar Price of a
Five-dollar Bill
To buy a five-dollar bill, you
must trade something for it. If
you trade one-dollar bills, the
price is usually five such bills
for a five-dollar bill. The
market in which five-dollar
bills trade for single-dollar
bills is strong-form efficient
because the value of a five-
dollar bill is common
knowledge.
On very rare occasions,
however, the price of five-
dollar bills may vary. For
example, suppose you need
to rent a baggage cart from
a machine at the airport that
accepts only one-dollar bills.
You have a five but no ones.
You may be willing to sell
your five-dollar bill for less
than five single-dollar bills if
no one is able or willing to
give you its common price.
traders do not share their information, they usually do not know how well
informed they are relative to other informed traders. They therefore must
use indirect methods to predict their profitability.
Many traders predict their profitability from their past performance. This
method is not reliable, however, because poorly informed traders often profit
by good luck, and well-informed traders sometimes lose through bad luck.
Consequently, some poorly informed traders may trade for a long time be-
fore they realize they should not be trading, and some well-informed traders
may refrain from trading because they do not recognize their advantages.
Predicting profitability is the most important problem that informed
traders face. All traders who trade only for profits must address this prob-
lem if they have any doubts as to whether they will be successful. We con-
sider the performance prediction problem in chapter 22.
10.6.6 Market Efficiency
Prices never fully reflect all information that informed traders could collect
and act on. Informed traders will not collect information that is expensive
to acquire if they cannot profit from that information. Some information
may simply be too expensive, or of such little consequence, that it does not
pay to trade on it. The information in prices therefore depends on the costs
of obtaining that information as well as the opportunities to act upon it.
Financial economists have undertaken numerous empirical studies to de-
termine how efficient various markets are. To classify their results, they have
created three traditional definitions of market efficiency.
Markets are
weak-form efficient
if prices reflect all information in past
prices so that no one can predict future price changes from knowing only
past prices. In weak-form efficient markets, price charts and statistical analy-
ses of past prices are useless. Prices simply appear to follow a random walk.
Most published empirical studies have determined that markets are weak-
form efficient. (Of course, if researchers found otherwise, they might trade
on their results rather than publish them!)
Markets are
semistrong-form efficient
if prices reflect all publicly available
information so that no one can predict future price changes using only pub-
lic information. Publicly available information includes all public news, past
prices, and volumes in all securities and contracts. If markets are semistrong-
form efficient, informed traders can make money only if they have access
to information that is not publicly available. The empirical evidence sug-
gests that markets generally are semistrong-form efficient with respect to
easily obtained and easily interpreted public information.
Markets are
strong-form efficient
if prices reflect all available public and
private information as soon as it is known. Since informed traders can never
profit in strong-form efficient markets, the only strong-form efficient mar-
kets are those which trade instruments for which values are commonly
known. Such markets are rarely interesting.
These three traditional definitions of market efficiency do not recognize
that acquiring and acting on information is costly. The following definition
is more sensitive to these market microstructure issues. In an
efficient
market,
prices reflect all information that traders can acquire and profitably
trade upon. This definition implicitly incorporates the costs of acquiring in-
formation, the costs of acting on it, and the impact that informed trading
has on prices.


## Page 254

CHAPTER 10 INFORMED TRADERS AND MARKET EFFICIENCY • 241
10.6.7 The Trade-off Between Liquidity and
Informative Prices
Although informative prices greatly benefit our economy by making pro-
duction and allocation decisions more efficient, informative prices are not
cheap. The money that uninformed traders lose to informed traders pays
for much of the information that goes into prices. These costs lower the net
benefits that uninformed traders obtain from using the markets for their
utilitarian purposes—mainly to move money through time, to exchange risks
and assets, and to gamble. Since these activities (with the possible excep-
tion of gambling) also benefit our economy, a trade-off sometimes exists be-
tween the benefits of informative prices and of liquid markets. In particu-
lar, policies that frustrate informed traders may make prices less informative
while increasing market liquidity. Restrictions on insider trading (see chap-
ter 29) are an example of such policies.
Deciding between price efficiency and market liquidity is very difficult
when evaluating alternative market structures. Most regulators would bal-
ance the benefits of these two market characteristics. Unfortunately, these
benefits are extremely hard to measure.
The total benefits to the economy of informative prices probably greatly
outweigh the money that uninformed traders lose to informed traders. In-
, formative prices are essential to efficient production and allocation decisions
in market-based economies. When people make these decisions poorly,
everyone suffers. In contrast, although the utilitarian services that unin-
formed traders obtain from our markets are also important to our economy,
traders can obtain some of these services through other means. Moreover,
if losses to informed traders merely tax the utilitarian uses of the markets
rather than curtail them, no utilitarian benefits will be lost.
These observations about total benefits are instructive, but they are not
very useful when we compare alternative market structures. To make such
comparisons properly, we must know how these benefits change when
switching from one structure to the other. A comparison of total benefits
therefore is not relevant. Only a comparison of the changes in benefits is
relevant. Such comparisons, unfortunately, usually are very hard to make.
10.6.8 The Benefits of Public Information
Some policies can increase both market liquidity and price efficiency. Poli-
cies that promote the publication of material fundamental information ad-
vance both objectives. They make informed trading less profitable while still
producing prices that are more informative.
Publishing information allows traders to know values better. If traders
can easily interpret the information, prices will immediately reflect the new
information, and informed traders will not profit. If only informed traders
can interpret the information, publication allows all such traders to act on
the information. Since only the first traders to trade will profit, they all will
try to trade quickly. Their race to profit causes prices to change quickly and
makes it easy for uninformed traders to infer their estimates of value. The
informed traders therefore do not profit as much as they would if they could
trade at a slower rate. In both events, publishing information causes prices
to become more informative while decreasing the profits that informed
traders make from uniformed traders.
The publication of fundamental information also lowers the costs of
Trading Halts for
Impending Information
Many stock markets require
that their listed firms contact
them before the release of
material information to the
public. These markets then
halt trading until after the
information release. This rule
allows all traders an
opportunity to evaluate the
new information before
trading resumes. News
traders consequently make
less money from uninformed
traders—particularly those
who offer liquidity. This is
especially true when all
traders can easily interpret
the new information.
Although trading halts for
impending information protect
uninformed traders, they also
make prices less informative.
Which effect is more
important?
The protection of
uninformed traders is
probably more important than
the lost price efficiency.
Informative prices are
extremely important to our
economy, but it is hard to
imagine that a few minutes'
delay in price formation
would significantly depreciate
the quality of resource
allocation decisions. Informed
trading, however, clearly hurts
traders who offer liquidity.
The net benefits of this trading
halt rule are probably
positive.


## Page 255

242 • TRADING AND EXCHANGES
Trading Places
and the USDA Orange Harvest Report
One of the most famous trading scenes in a motion picture appears in the
climax of the 1983 comedy Trading
Places.
(Do not read the next
paragraph if you have not seen the movie!)
Don Ameche and Ralph Bellamy are evil commodity traders who have
arranged to receive the U.S. Department of Agriculture's January
Monthly
Estimate of Orange Production
before its publication. Their abused former
employees, Dan Aykroyd and Eddie Murphy, learn of the illegal scheme.
They intercept the report, alter it to make the harvest look worse than
it is, and then pass it on to Ameche and Bellamy. In the climactic scene,
the employers and their former employees trade frozen orange juice
concentrate futures against each other on the trading floor of the New York
Cotton Exchange Citrus Associates. Ameche and Bellamy buy heavily and
push prices up. Aykroyd and Murphy sell heavily at the top. When the
USDA releases the true report, prices drop precipitously on tremendous
volume as Ameche and Bellamy try to sell out of their losing position. At the
bottom, Aykroyd and Murphy buy to cover their short positions. The villains
go bankrupt, and our heroes become multimillionaires.
Although the director, John Landis, exaggerated the scene for comic
effect, it accurately represents the importance of the USDA orange
production estimates. Since this report provides the most accurate estimate
of the coming harvest, prices often change dramatically when the estimate
is significantly different from what traders were expecting. The winter
reports are especially significant when frosts have decreased crop yields.
The USDA National Agricultural Statistics Service takes extreme care to
prevent the early release of its crop reports. Highly trusted statisticians
prepare the report overnight in shuttered rooms that have no access to
telephone or computer networks. The data they use arrives at the USDA
encrypted. The statisticians decrypt the data only after they lock down to
produce the report.
The USDA used to release the orange production report during the
futures trading session, as shown in the movie. It now releases the report at
8:30
A.M.,
before the market opens.
being an informed trader. Informed traders therefore can trade profitably
on smaller differences between prices and values then they otherwise would,
so that prices become more informative.
Many stock markets require that their listed firms publish substantial
fundamental information in a timely manner. These reports make prices
more informative and reduce the losses of uninformed traders to informed
traders.
In markets that do not have such reporting standards, many firms vol-
untarily provide this information to make their stocks more attractive to in-
vestors. The managers of these firms know that investors will pay more for
their stocks if the risk of losing to informed traders is small. Many firms
are now placing their information on the Web.
Most governments have agencies that produce statistical information
about the economy and about supply and demand conditions in various mar-
kets. Traders use this information to estimate commodity values and asset
prices. When these agencies produce high-quality information, prices be-
come more informative and farmers, manufacturers, retailers, and service
providers make better production decisions. Generally, these public agen-
cies freely distribute their reports.


## Page 256

CHAPTER 10 INFORMED TRADERS AND MARKET EFFICIENCY • 243
Numerous private companies gather information about fundamental val-
ues to sell to others. Generally, only the traders who subscribe to their ser-
vices can access their information. In the United States, these companies
include BARRA, Bloomberg, Bridge Information Services, DataStream In-
ternational, Dow Jones News Retrieval Service, Dun & Bradstreet, Edgar
Online.Com
, Ibbotson Associates, I/B/E/S, Moody's Investors Service,
Reuters, Securities Data Corporation, Standard and Poor's Compustat,
Standard & Poor's Equity Investor Services, Value Line, and Zacks Invest-
ment Research.
10.7 SUMMARY
Informed traders make prices informative. They acquire information that
they hope will allow them to estimate values accurately. They buy when
prices are lower than their value estimates and sell otherwise. Their buying
and selling push prices up and down. They move prices closer to their es-
timates of value and thereby make prices more informative.
Four types of informed traders try to profit from information about fun-
damental values. Value traders estimate fundamental values by using all avail-
able information. News traders estimate changes in fundamental values from
new information. Information-oriented technical traders identify patterns
that are inconsistent with prices which reflect fundamental values. Arbi-
trageurs estimate differences in fundamental values across instruments.
Informed traders make markets efficient. In an efficient market, prices
reflect all information that traders can acquire and profitably trade upon.
How informative prices are depends on the costs of acquiring information,
and on how much liquidity is available to informed traders. If information
is expensive, or the market is not liquid, prices will not be very informative.
Since trading is a zero-sum game, informed traders can profit only if unin-
formed traders lose to them. Prices therefore will not be informative in mar-
kets with few uninformed traders.
Informed traders compete with each other to profit from acquiring and
acting upon information. Only those traders who can collect and analyze
information at low cost, and who can trade effectively, are profitable.
No market is always completely efficient. Informed traders could not
profit in such markets. Prices become more informative when informed
traders push prices toward values. Prices become less informative when val-
ues change or when uninformed traders move prices. News traders tend to
make money when values change. Value traders tend to make money when
uninformed traders move prices.
Traders who intend to speculate should carefully consider why they ex-
pect to be successful. The most common mistake informed traders make is
to trade when they have no comparative advantage. We consider how to
predict performance in chapter 22.
10.8 SOME POINTS TO REMEMBER
•
Informed traders make prices informative.
•
Value traders estimate fundamental values.
•
News traders estimate changes in fundamental values.


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244 • TRADING AND EXCHANGES
•
Information-oriented technical traders estimate patterns that are in-
consistent with fundamental values.
•
Arbitrageurs estimate differences in fundamental values.
•
Prices are most informative when the costs of obtaining information
and the costs of trading are both low.
•
When prices fully reflect all available information, nobody can fore-
cast future price changes.
•
Prices cannot always be completely informative.
•
Trading is a zero-sum game when performance is measured relative to
the market return.
•
Informed traders profit only when other traders are willing to lose to
them. Markets therefore require utilitarian traders in order to produce
informative prices.
10.9 QUESTIONS FOR THOUGHT
1.
What are the differences between data and information? How are
the two produced?
2.
In the short run, what effect do you expect an increase in the price
of corn would have on the prices of cattle and hogs?
3.
In the "Liquidity and Predictability" example, how would the anal-
ysis differ if you competed with other informed traders? How would
it differ if you were risk averse?
4.
Should regulators exclude gamblers from financial markets? How
could they exclude them? What effect would their exclusion have
on price efficiency in the long run?
5.
Should stock markets impose financial reporting standards upon
their listed firms?
6.
Many government agencies and many private companies collect and
publish fundamental information about supply and demand condi-
tions in various markets. What is the purpose of these activities?
What effect do they have on the markets? Should the government
sector or the private sector conduct this research?
7.
In their race to profit, informed traders often duplicate their research
efforts. Since research is often very expensive, these duplicative ef-
forts suggest that the competition among informed traders creates
economic inefficiencies. Should informed traders collude to lower
their costs? Would their collusion make prices more efficient? Should
regulators address this issue? If so, how?
8.
What is the optimal level of informed trading?
9.
Do you have any reason to believe that you would be a profitable
informed trader?
10.
Futures, stock, and options exchanges constantly create markets for
new instruments. What factors determine whether a market will be
successful?
11.
Can value traders make money if fundamental values follow a ran-
dom walk?


## Page 258

11
D
Order
Anticipators
O
rder anticipators
are speculators who try to profit by trading before
other traders trade. They make money when they correctly anticipate
how other traders will affect prices or when they can extract option values
from the orders that other traders offer to the market.
Order anticipators include front runners, sentiment-oriented technical
traders, and squeezers.
Front runners
collect information about trades that
other traders have decided to arrange.
Sentiment-oriented technical traders
try
to predict trades that uninformed traders will decide to make.
Squeezers
try to exploit traders who must trade by cornering the market.
Order anticipators are
parasitic traders.
They profit only when they can
prey on other traders. They do not make prices more informative, and they
do not make markets more liquid. To trade profitably, you must avoid these
traders. You therefore must understand how they trade.
Large traders are especially vulnerable to order anticipators. You must be
familiar with parasitic traders to understand how large traders expose their
orders.
Some front runners obtain their information about trader intentions from
brokers. If you trade with brokers, if you are a broker, if you are interested
in becoming a broker, or if you regulate brokers, you must know how bro-
kers occasionally expose orders unwittingly or intentionally.
Trading by order anticipators often makes prices more volatile and mar-
kets less efficient. If volatility and price efficiency interest you, you must
consider how order anticipators affect the markets.
Uninformed traders sometimes affect prices significantly. Traders who
can predict what uninformed traders will do therefore can sometimes profit
from that knowledge. If you have these skills, how sentiment-oriented tech-
nical traders trade should interest you.
Even if you cannot predict what uninformed traders will do, you may be
able to identify what uninformed traders have done after the fact. Although
you cannot profit directly from this information, you can use it to better un-
derstand why your trading strategies worked or failed. Understanding how
sentiment-oriented technical traders collect and process information will
help you to better understand uninformed traders.
In markets that enforce time precedence, order anticipators must im-
prove price by at least the minimum price increment to trade ahead of other
traders. The size of the price increment therefore greatly affects the prof-
itability of order anticipators' strategies in such markets. You must be fa-
miliar with order anticipation trading strategies in order to form reasonable
opinions about the proper size of the minimum price increment.
11.1 FRONT RUNNERS
Front runners
collect information about trades that other traders have de-
cided to arrange. They then try to trade before those traders complete their
245


## Page 259

246 • TRADING AND EXCHANGES
An Illegal Front-running Scheme Involving a
Violation of Confidentiality
Rob is a runner who works for a large brokerage house on the floor of a
futures exchange. Rob's job is to carry orders from his firm's telephone
booth on the perimeter of the exchange floor to his firm's brokers in various
trading pits.
Nate trades commodity futures for his own account in one of those pits.
He and Rob have arranged a set of signals by which Rob can
surreptitiously tell Nate that he is carrying a large buy or sell order. They
may convey their signal by a glance at a clock, by the hand in which Rob
carries the order, by the placement of a pen in a pocket or behind an ear,
or by some other means. Rob and Nate employ a variety of signals to
make it difficult for anyone to detect what they are doing.
When Nate sees the signal for a large buy order, he immediately buys
contracts for his own account. After Rob delivers the order to his firm's
broker, the broker buys contracts to fill it. Nate profits as the broker pushes
the price up to fill the order. Nate sometimes sells his newly acquired
contracts to the broker. Afterward, Nate and Rob split the profits.
Their profits come at the expense of the broker's customers. The
customers pay higher prices when buying and obtain lower prices when
selling because Nate takes liquidity that they otherwise would have taken.
This scheme is very difficult to detect in actively traded markets. To
prevent it, brokerage firms, their customers, and exchanges must carefully
watch how prices change before and after orders arrive. They must try to
remember who traded before large orders arrived so that they can identify
systematic patterns that might suggest a front-running problem.
Brokers also must secure their communications to prevent these schemes.
At several exchanges, new wireless electronic order delivery systems
eliminate the need for floor runners and thereby remove potential for fraud
in that link of the order transmission chain.
4
trades. Front runners may obtain their information from public sources, from
the traders they front-run, or from brokers. Practitioners call them front
runners because they hurry (run) to trade before (in front of) other traders.
Front-running strategies differ according to the type of trader that they
front-run. Front runners may trade in front of aggressive traders or passive
traders.
Aggressive traders
demand liquidity, and
passive traders
offer liquidity.
11.1.1 Front Running Aggressive Traders
Aggressive traders usually issue market orders. Their demands often push
prices up when they buy and down when they sell. Front runners who trade
ahead of aggressive traders profit from the price impact of the aggressive
traders' trades.
In most markets, front running is illegal when the front runner improp-
erly obtains information about the incoming order. Front runners obtain in-
formation improperly when they violate a confidential brokerage relation-
ship or when they eavesdrop on confidential communications. These
violations may take place at any point between the receipt of the order by
the broker and its final execution.
Not all front running is illegal. Observant traders on the floor of an ex-
change can often infer an order from how a broker handles it. Brokers must
be especially careful to avoid revealing their orders inadvertently.


## Page 260

CHAPTER 11 ORDER ANTICIPATORS • 247
Legal Front Running by an Observant Trader
Rifka and Jon have traded on the same options floor for years. Although
they are not friends, their proximity to each other has allowed them to
become very well acquainted. Rifka trades for her own account. Jon is a
floor broker for a large firm.
Rifka has noticed that Jon behaves slightly differently when he receives a
large order than a small order. The differences are very subtle; Rifka cannot
even articulate what she sees. She just knows from experience when Jon
has a large order.
Jon's behavior does not reveal whether the order is a buy or sell order.
Rifka often guesses correctly because she has noticed that Jon tends to buy
after he has bought and sell after he has sold: At least one of Jon's clients
probably splits his or her large orders.
When Rifka suspects that Jon has received a large order, she will try to
front-run it. If she feels confident about the side of the order, she may try to
beat Jon to the market. Otherwise, she will wait to see which side Jon
needs to trade. She may then better his price and hope to make a profit
when Jon's client returns to the market.
Rifka's trading is legal. Her profits come from recognizing Jon's
shortcomings as a broker and from noting that Jon's clients tend to split
their orders. She is a profitable trader because she is observant and
because she acts quickly on her information.
Front runners sometimes obtain information about orders when
brokers
call them to arrange trades. Brokers who want to arrange a large trade must
call traders they think might be willing to take the other side. They often
reveal their orders in these calls. Although front runners may legally exploit
this information, those who do so risk harming their relationships with these
brokers. Brokers must be very careful to expose orders only to traders who
will most likely take the other side. They most avoid exposing their orders
to traders who would front-run their clients.
Front runners capture the benefits of price discrimination that large
traders would otherwise obtain. In continuous auctions, large traders typi-
cally split their orders so that they can discriminate among the traders who
offer them liquidity. They want to trade first with those traders offering the
best prices and then, if necessary, with traders offering inferior prices. Split-
ting their orders thus produces a better average price than they would ob-
tain if they had to fill their entire order at a single price. Front runners ap-
propriate the benefits of price discrimination by taking liquidity from the
traders offering the best prices. They then offer this liquidity back to the
large traders at inferior prices. The effect of a successful front-running strat-
egy is to force large traders to pay more uniform prices to fill their orders.
Under some very limited circumstances, front runners can be valuable to
large traders. If front runners can find liquidity more cheaply than large
traders can, the front runners may lower the costs of trading large sizes. To
be of value to large traders, front runners must consolidate the other side
and then deliver it to the large traders at a lower cost than the large traders
could obtain on their own. Large traders who believe this is true should
widely publicize their orders in some credible manner. Those who do are
sunshine traders.
Although front runners sometimes may be better traders than large
traders are, large traders will not benefit from front runners if many front
Shop the Block
Brokers
shop the block
when
they expose large orders. Not
surprisingly, prices tend to
rise when they widely shop a
large block buy order, and
fall when they widely shop a
large block sell order.


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248 • TRADING AND EXCHANGES
Front Running by Skilled Traders
Stuart wants to buy
150,000
shares of a somewhat illiquid security. If he
buys it himself without interference from front runners, he may be able to
buy 30,000 shares for 100 and 30,000 more at every 10 cents from
100.10 to 100.40. His average price will be 100.20, which is better than
the 100.40 price he would have to pay if he had to fill the entire order at
one price.
Suppose that front runners are better traders than Stuart is. If they buy
before he does, they may be able to find 50,000 shares at each price from
100 to 100.20 so that their average price is 100.10. If the front runners
then sell to Stuart for 100.20, he will be indifferent to the front running. He
may actually prefer to be front-run if they save him the costs of searching.
Moreover, if Stuart negotiates well with the front runners, he may be able to
drive them down from 100.20 toward 100.10. (Although he may be able
to drive the front runners below 100.10 on any given transaction, they will
not continue to front-run for him if he consistently forces losses on them.) Of
course, if the front runners have some market power, they may
squeeze
Stuart so that he has to pay more than
100.40
to fill his order.
runners compete to front-run them. The impact on price of many traders
competing to acquire the same positions tends to be much greater than that
of a single trader who trades strategically. Front runners generally trade less
efficiently than large traders do because they trade too quickly. Front run-
ning therefore generally hurts the traders they front-run.
When large traders recognize that they cannot trade as well as a profes-
sional trader can, they commonly hire a professional to help them trade.
They may hire a
block broker
to act as their agent, or they may ask a
block
dealer
to facilitate their trades. Block dealers who
facilitate
their customers'
trades trade their blocks at uniform prices. The dealers then try to profit by
trading the block in the market at a better average price. In a sense, block
traders are front runners whom large traders hire to help them solve their
trading problems. We discuss block trading in detail in chapter 15.
11.1.2 Front Running Passive Traders
Passive traders
offer liquidity to the market. They give other traders options
to trade when they want to trade. They usually offer limit orders or quotes
to the market. Front runners who trade in front of passive traders try to ex-
tract the option values of the passive traders' orders. Economists call such
front runners
quote matchers.
Quote matching
is a front-running strategy in which quote matchers try
to trade in front of (and on the same side as) large patient traders. Once
the quote matchers trade, the orders they front-run protect them from se-
rious losses on their positions. If prices move against them, quote matchers
limit their losses by trading with the passive traders. If prices move in their
favor, the quote matchers profit to the full extent of the price changes. Since
the decimalization of the U.S. stock markets, quote matching has also been
called
penny jumping.
The returns that quote matchers can obtain are option-like returns. They
are unbounded on one side and limited on the other side. Quote-matching
return distributions are similar to return distributions for option contracts
because quote matchers extract the option values of the standing orders that


## Page 262

CHAPTER 11 ORDER ANTICIPATORS • 249
A Case of Self-front-running?
One of several market makers who trade the Spider (SPDR) at the American
Stock Exchange told me the following story:
Over a period of several months in 1998-1999, a broker well known to
the
SPDR
market makers would come to the post to trade extremely large
orders. The largest such orders were for approximately 850,000 shares, or
about 85 million dollars at the then current price of 100 dollars per share.
The first few times this happened, the market makers would fill the orders,
only to discover that they would lose money as the market regularly moved
against their positions.
They soon paid very close attention to this broker. The market makers
discovered that whenever he traded large size, the market would almost
invariably move in the same direction within five minutes. They also noted
that the broker would usually unwind these trades the same day at a
substantial profit.
The market makers therefore started to hedge their positions by trading
S&P 500 futures immediately upon filling these SPDR orders. After
completing their hedge, they immediately continued to trade the futures on
the same side to profit from the correlation between this broker's orders and
the subsequent price change.
My correspondent reported that this happened about
70 to 90
times
over about nine months. It then stopped as abruptly as it started. The
broker's client usually correctly anticipated subsequent moves in the S&P
500 Index. The market makers made a small fortune on these trades once
they figured out what was happening. They did not know, however, how
the broker's client knew what direction the S&P 500 Index would move.
The market makers presumed that the client was front running very large
program trades in the stock market. The client may have been front running
another trader. In that case, the client, the broker, or both would have been
violating the law.
Alternatively, the client may have been front running his own orders in
an attempt to lower the net cost of his program trades. If the client was a
large equity fund with an investment policy that prohibited trading in the
futures markets, the SPDR would have been the only feasible alternative to
execute this strategy. Self-front-running may lower the cost of a program
trade when highly correlated markets do not move in perfect lockstep. By
submitting orders to both markets at the same time, a trader obtains the
liquidity in both markets without either market realizing the full size of his
trade.
they front-run. When a quote matcher buys stock in front of a large buy
limit order, the quote matcher has a long position in the stock and hopes to
use the long put option offered by the buy limit order should she need it.
The combination of a long stock position and a long put position has the
same return distribution as a call option. Likewise, when a quote matcher
sells stock in front of a large sell order, the quote matcher has a short stock
position and hopes to use the call option implicit in the sell order should she
need it. This combination has the same return distribution as a put option.
Quote matchers profit at the expense of passive traders. They take liq-
uidity that otherwise would have gone to the passive traders. If the passive
traders subsequently fail to trade because prices move away from their or-
ders, they lose the profits that the quote matchers make.
Quote Matching
Jose places a large limit
buy order at 20 pesos.
Maria sees the order and
immediately submits her own
limit buy order at 20.01. An
incoming market sell order
arrives, and Maria's order is
matched with it. If prices rise,
Maria will profit to the full
extent of the price rise. If
prices fall, she will try to sell
to Jose at 20, and her loss
will be only 1 centavo.
Maria's potential return
distribution is asymmetric.
She has the possibility of
significant gains, but her
losses may be quite limited.
If the probability of a price
increase is about the same
as a decrease, and if the
probability that Jose cancels
his order is not large, Maria's
expected profits from this
strategy would be positive.


## Page 263

250 • TRADING AND EXCHANGES
Trapping Quote
Matchers
Jose knows that quote
matchers often try to front-run
his orders. To frustrate them,
he occasionally cancels a
standing order if he suspects
that a trader has front-run
him. He also sometimes traps
quote matchers with the
following strategy.
When Jose wants to be a
seller, he baits his trap by
submitting a large standing
buy limit order. If a quote
matcher posts a buy order in
front of his order, he springs
his trap by immediately selling
to that order and
simultaneously canceling his
buy order. Otherwise, he
cancels his buy order.
If the trap works, the quote
matcher now has Jose's
trading problem. The quote
matcher needs to sell, but the
apparent buyer upon whose
order she was relying (Jose)
has disappeared.
Quote matchers
will trade profitably only if the standing orders that they
front-run are still standing should they need to close their positions quickly.
If these trading options are no longer available, quote matchers will have
trouble getting out of their positions when prices move against them. Quote
matching will not be profitable if passive traders cancel their orders, if they
adjust them frequently to reflect changes in values, or if other traders fill
the passive traders' orders.
Since quote matchers must respond to changes in market conditions
faster than the passive traders who they front-run, they must have excellent
access to the markets. Successful quote matchers will tend to be floor traders
in floor-based markets and computerized traders in screen-based markets.
Quote matching also will not be profitable if quote matchers must sub-
stantially improve prices in order to establish their positions. The price im-
pacts of their trades are transaction costs. These costs decrease their profits
if the strategy works, and they increase their losses if the strategy fails.
In markets that enforce time precedence, a large minimum price incre-
ment makes quote matching less profitable. In such markets, traders must
improve prices by at least the minimum price increment to trade ahead of
other traders. The minimum price increment therefore is the price that quote
matchers must pay to front-run passive traders. They do not pay this price
to the passive traders they front-run, however. Instead, they pay it to the
traders with whom they trade to establish their positions. These traders
would have traded with the passive traders if the quote matchers had not
front-run them. In markets that enforce time precedence, front runners must
pay traders on the other side a premium to prevent them from exercising
the trading options that the quote matchers want to exploit.
To prevent losses to quote matchers, traders who supply liquidity must
control how they expose their orders. They defend themselves from front
runners by using floor brokers to hide their orders, by breaking up their or-
ders, and by switching to market order strategies from limit order strate-
gies. Unfortunately, these responses increase their transaction costs, lower
displayed sizes, and reduce market transparency.
11.1.3 Front Runners and Market Efficiency
Whether front runners make prices more or less informative depends on
whether they front-run informed traders or uninformed traders. In both cases,
front running accelerates the price impacts of the traders they front-run.
When front runners front-run uninformed traders, their trades usually
make prices less informative. When prices equal fundamental values before
uninformed traders trade, their trading always make prices less informative.
When prices differ from fundamental values, at best uninformed traders
move prices toward fundamental values only by chance.
Front runners make prices more informative when they front-run in-
formed traders. These trades move prices closer to fundamental values sooner
than they would have otherwise.
The long-run effect of front running informed traders, however, may be
to make prices less informative. Front running decreases the profits that in-
formed traders make. Consequently, fewer informed traders will trade prof-
itably. Those who can trade profitably will invest less in their information
than they would if front runners did not front-run their trades. Front run-


## Page 264

CHAPTER 11 ORDER ANTICIPATORS • 251
ning of informed traders therefore will drive informed traders from the mar-
ket, and prices will be less informative in the long run.
11.1.4 Front Runners and Liquidity
Front runners generally make markets less liquid. Since trading is a zero-sum
game, their profits are transaction costs for other traders. If the front runners
do not provide liquidity in exchange for these transaction costs, all other traders
taken as a group would be better off if front runners did not trade.
Front runners generally provide no service to the traders they front-run.
As noted above, front runners may benefit these traders only if they are bet-
ter traders, if they do not compete with each other to quickly establish their
front-running positions, and if they do not then squeeze the traders that they
front-run. These conditions rarely are simultaneously true. If large traders
thought they could benefit from front running, they would contract directly
with block dealers or brokers to obtain these services. Rather than help
traders, front runners generally hurt them by taking liquidity away from them.
Front runners benefit the traders with whom they trade when they im-
prove prices to step in front of other traders. Front runners can offer better
prices, however, only because they can extract more value from the traders
they are front running. The increased costs that they impose on the traders
they front-run therefore more than offset the benefits that other traders ob-
tain from price improvement.
Front runners also affect liquidity through their effects on other traders.
Traders alter their trading strategies to avoid losing to front runners. Some
traders trade more aggressively. They may price their orders more aggres-
sively, or they may demand liquidity rather than supply it. Both responses
benefit traders on the other side of the market, but these benefits are ex-
actly offset by the higher transaction costs of the more aggressive traders.
Some traders trade less aggressively when confronted with the prospect of
front running and the higher transaction costs it imposes on them. Their
withdrawal from the market decreases liquidity.
Front runners are parasitic traders. They profit by extracting value from
other traders, but they do not give back anything in exchange. The simplest
proof of this conclusion lies in the observation that front runners do not of-
fer to trade when they have no one to front-run. Front runners do not bring
anything new to the table. Worse, they tend to drive away the orders from
which they profit.
11.2 SENTIMENT-ORIENTED
TECHNICAL TRADERS
Sentiment-oriented technical traders
try to predict the trades that uninformed
traders will decide to make. They then try to trade before the uninformed
traders trade. Sentiment-oriented technical traders profit when they cor-
rectly anticipate the impacts that uninformed traders will have on prices.
Sentiment-oriented technical traders differ from front runners. Senti-
ment-oriented technical traders try to predict trading decisions that other
traders have not yet made. In contrast, front runners trade on information
about orders that other traders have submitted or intend to submit.


## Page 265

252 • TRADING AND EXCHANGES
Predicting Investment Trades: The January Effect
Historically, prices in the U.S. stock markets have risen more in January
than in any other month of the year. Statistical analyses show that the
difference between the average January return and the average of the other
monthly returns appears too large to attribute just to chance. On average,
over the 75-year period from 1926 to 2000, January returns have
exceeded the other monthly returns by 0.85 percentage points.
One possible explanation involves year-end bonuses and pension fund
contributions. Traders who invest these cash flows in January may be
responsible for pushing the market up in that month. Tax selling in
December also may be responsible for the January effect.
Sentiment-oriented technical traders who can predict these investment
cash flows will buy in December or earlier to profit from the impact that
these uninformed investors will have on price. If many such technical traders
trade, they will push prices up well before January.
Data source: CRSP combined NYSE and AMEX value-weighted, dividend-adjusted
monthly return index.
Traders who can successfully predict the trades that informed traders will
make are themselves well-informed traders. Since informed traders trade on
information, only traders with their information can predict what trades
they will decide to make.
Uninformed traders trade for many reasons (see chapter 8). Sentiment-
oriented technical traders try to identify these reasons in order to predict
when uninformed traders will trade. These reasons may be rational, or they
may be the result of mistaken beliefs. Rational reasons to trade include in-
vesting, borrowing, hedging, asset exchanging, and gambling. Trading de-
cisions based on mistaken beliefs typically involve mistakes that poor spec-
ulators make.
The information resources that sentiment-oriented technical traders use
to make their predictions depend on the different types of trades that they
try to predict.
•
Traders who try to predict trades that investors and borrowers make
examine data about intertemporal cash flows. These data include in-
formation about the timing and magnitude of paychecks, year-end
bonuses, dividend payments, pension fund contributions, special sub-
scriptions, and tax payments.
•
Those who try to predict hedging trades consider the risks that con-
cern people. They may collect data about production, inventories, and
business commitments.
•
Those who try to predict asset exchanges may study how production,
sales, and international fund flows require traders to exchange assets.
•
Those who try to predict what gamblers will do consider the factors
that excite gamblers. They may collect information about what instru-
ments are volatile and what securities are in the news.
•
Those who try to predict the mistakes that speculators make consider
how speculators form their opinions. These traders may examine data
about past prices and volumes. They may also collect information from
psychometric surveys designed to measure trader confidence and outlook.


## Page 266

CHAPTER 11 ORDER ANTICIPATORS • 253
Predicting Hedging Trades: Dynamic Option Replication
Dealers who trade options often hedge their positions by buying or selling
the underlying instruments. The effect of their trading is to replicate the
returns to the options that they are hedging. The rates at which they make
these trades depend on how underlying prices change and on several other
factors that appear in mathematical formulas like the Black-Scholes option
pricing equation. These hedging strategies are
dynamic hedges
because
they require trades whenever the underlying prices change.
Some traders hedge assets by using dynamic hedges to simulate an
options return.
Portfolio insurance
is an example of one such strategy.
Observant traders can predict the trades these hedgers make from recent
price changes in the underlying instruments. These traders try to collect
information about the size of the positions that the hedgers want to protect.
They may obtain this information by examining option open interests, by
surveying traders or their advisers, or by characterizing the relation
between price changes and trading volumes.
Predicting Foreign Exchange Trades: IPOs in Asia
The mechanism for subscribing to an initial public stock offering in many
Asian countries differs from that in the United States. In the United States,
an investment bank allocates shares in an IPO before the offering. Only
people who purchase the shares tender the cash to purchase them.
In Asia, traders who want to participate in an offering deposit the full
purchase price with the investment bank before the offering. If the offering
is oversubscribed, the bank allocates the shares on a pro rata basis. If the
offering is very popular, it may be oversubscribed hundreds of times. Huge
cash flows therefore may result.
If an Asian offering is very popular among international investors, the local
currency may appreciate as investors buy it to tender to the offering. Traders
who are aware of this phenomenon will try to predict when an offering
will
be very popular so that they can buy the currency before it appreciates.
Sentiment-oriented technical trading can be quite risky because it in-
volves front running uninformed traders. The impacts that uninformed
traders have on prices often move prices away from their fundamental val-
ues. Such movements attract value traders to the other side of the market.
If the value traders trade aggressively, they may drive prices back toward
fundamental values, and sentiment-oriented technical traders then will lose.
Sentiment-oriented technical traders therefore must know when to close
their positions. If they hold their positions too long, they will lose when
prices revert to fundamental values.
Since sentiment-oriented technical traders tend to lose to value traders,
sentiment-oriented technical trading will be most profitable in instruments
that are not easily valued. Value traders trade less aggressively in hard-to-
value instruments than in instruments with values that they know well.
Perhaps the best examples of hard-to-value instruments are stocks in
developing industries like the Internet. Their values are hard to estimate
because they depend on uncertain technologies and on the development of
unknown markets. Since these stocks tend to attract many uninformed
traders, sentiment-oriented technical traders may occasionally identify prof-
itable trading opportunities in them. The stocks and bonds of companies in
emerging markets also may provide such opportunities for similar reasons.


## Page 267

254 • TRADING AND EXCHANGES
Great Companies Are Not Always Good Investments
Iomega makes high-capacity, removable media disk drives for personal
computers. When it introduced the 100-megabyte Zip drive, it represented
a very substantial improvement over existing 1.44-MB floppy disk drives. As
investors became familiar with the product, the company's stock appreciated
more than 1000 percent between June 1995 and May 1996. At its peak
in May 1996, the total market value of the company was more than 10
percent of the value of IBM. It then fell by two-thirds over the next several
months.
Many investors apparently mistook a good firm for a good investment.
Investors make this mistake when they fail to consider whether the price of
the firm already reflects the information that they believe they have. This
often happens when investors are impressed with new technologies.
Speculators who recognized the potential for this mistake early on could
have made a lot of money buying the stock even when it was overvalued.
To profit, however, they would have had to sell before the fall. The strategy
therefore would have been quite risky.
Asset Bubbles
Investors often hold assets
because they expect that they
will appreciate. If many
traders are overly optimistic
about the prospects for
capital gains, they will buy
the asset, and the impact of
their trades will make their
expectations self-fulfilling.
An
asset bubble
exists
when traders buy an asset
because they hope it will
continue to appreciate as it
has in the past. In an asset
bubble, the price of an asset
may rise well above its
fundamental value as long as
traders continue to expect that
it will keep on appreciating.
Traders who believe that
uninformed traders will
continue to buy an asset
without regard for its
fundamental value may buy
the asset first. They must be
extremely careful, however.
When sentiment changes, the
bubble will burst and prices
will fall extremely quickly.
Successful sentiment-oriented technical traders may trade successfully in
instruments whose values depend on difficult-to-measure fundamental fac-
tors. The three most important such factors are expected inflation, future
political uncertainty, and the equity risk premium. Stock, bond, and pre-
cious metal values depend crucially on these factors. Since these factors are
very hard to measure, value traders do not know well the fundamental val-
ues of instruments whose values depend on them. Uninformed traders there-
fore may significantly affect prices in these instruments. Traders who can
predict what uninformed traders will do may therefore be able to trade these
instruments profitably.
11.2.1 Sentiment-oriented Technical Traders,
Market Efficiency, and Liquidity
Sentiment-oriented technical traders are like front runners because they try
to trade before other traders. They therefore accelerate the impact that other
traders will have on price.
Since sentiment-oriented technical traders try to trade before uninformed
traders, their trading tends to make prices less informative. This is espe-
cially true when they trade into a rising asset bubble.
Sentiment-oriented technical traders decrease market liquidity. They
make markets less liquid for the traders they front-run. Although they some-
times improve prices, the additional transaction costs they impose on their
victims more than offset the price improvements that they offer to the traders
with whom they trade.
11.3 SQUEEZERS
Squeezers
try to monopolize one side of a market so that anyone who must
liquidate a position on the other side must negotiate with them. If they suc-
cessfully
corner the market,
they can demand any price they want. Squeezers
generally acquire their power by surreptitiously buying all available supply
before other buyers realize they can buy only from them.
Squeezers are order anticipators because they trade before other traders
have a chance to trade. They differ from front runners and sentiment-


## Page 268

CHAPTER 11 ORDER ANTICIPATORS • 255
Benjamin P. Hutchinson and the Great Wheat Corner of 1888
In the spring and summer of 1888, Benjamin P. Hutchinson bought
thousands of September wheat futures contracts on the floor of the Chicago
Board of Trade. At the same time, he bought much of the available
deliverable supply. At the end of September, he controlled most of the
wheat in Chicago's grain elevators, and he was long several million bushels
of September wheat.
As the contract delivery date approached, it became apparent that
Hutchinson would demand delivery on his long futures positions. Traders
who had sold the contract short needed to repurchase the contract or
prepare to deliver the wheat. Hutchinson, however, refused to sell them his
contracts or his wheat. The price of wheat therefore rose spectacularly as
traders tried to cover their short positions. Between September 22 and
September 30, the price rose from approximately 1 dollar to 2 dollars.
Traders who were caught short suffered tremendous losses. Hutchinson
ultimately specified the prices at which he released them. He made millions
of dollars from this corner.
A Short Squeeze in a Penny Stock Fraud
XYZ is a thinly traded, low-priced stock with a small float. The
float of a
stock
consists of those shares which the public can trade. A stock has a
small float
when management controls most of the shares outstanding or
when legal restrictions prevent trading of most outstanding shares.
Ian is a value trader. Given his research, Ian believes with great
confidence that XYZ is overpriced. He therefore borrows the stock and sells
it short.
The stock is in fact overpriced. It has been promoted excessively by
dishonest dealers who hope to sell it at high prices to unsuspecting traders.
Ian's sales make their plan less profitable. His sales, in effect, allow him to
participate in the profits of the fraud without paying the costs of setting it up
and without bearing any legal risk if it is detected. The promoters do not
appreciate his interference, and they do not want to share their profits with
him. Unfortunately for Ian, he unknowingly borrowed some of the stock from
the promoters.
The promoters decide to squeeze Ian. They buy stock in XYZ to raise its
price. They then demand that Ian return the shares he borrowed. Other
traders who lent Ian shares also demand that he return the shares so they
can sell them to the promoters. Ian tries to borrow the shares from someone
else, but none are available. He therefore must purchase the stock on the
open market. Ian ends up buying the stock at a high price from the
squeezers. Although Ian was right about XYZ being overpriced, he still lost
much money in this short squeeze.
1
oriented technical traders because they deliberately design situations that
force other people to trade with them.
The largest and most notorious squeezes have occurred in commodity
futures markets. Smaller squeezes occasionally take place in thinly traded
stocks.
Squeezes are now illegal in United States. In the futures markets, the
Commodity Futures Trading Commission carefully monitors trading and
open interests as contracts expire. If the Commission believes that traders
are not using the markets appropriately, they may intervene to prevent
squeezes. They generally base their opinions on whether demands to deliver
are reasonable, given market conditions.


## Page 269

256 • TRADING AND EXCHANGES
"Shooting the Moon"
in Hearts
Players lose the card game
Hearts when they pick up
the queen of spades or too
many of the various hearts.
However, if one player can
pick up the queen of spades
and all 13 of the hearts, that
player wins big, and
everyone else loses. Players
shoot the moon
when they try
to execute this strategy.
To shoot the moon, players
must conceal their intentions
lest another player foil them.
If they collect many—but not
all—of the cards they need to
shoot the moon, they will
lose badly.
Traders who try to
squeeze a market essentially
shoot the moon. If they are
successful, everyone else will
lose. But if they do not control
enough of the available
supply, prices will drop
before they can sell their
positions. Since they typically
push prices up to acquire
their positions, they lose
badly.
In the U.S. stock markets, the Securities and Exchange Commission and
the various exchanges monitor trading to detect and prevent squeezes. They
cannot identify all stock squeezes, however. Squeezers often defend their
actions by arguing that they are simply speculating on information. Since
the SEC cannot easily discriminate between intentional squeezes and
squeezes that result from price changes caused by honest speculators, short
sellers always must beware of squeezes.
Squeezers are parasitic traders because they design trading strategies that
profit only when they can exploit other traders. Their manipulations make
prices less informative, and their trades greatly increase transaction costs for
other traders.
11.4 MANIPULATION OF STOP ORDERS
Traders who use stop orders must be very careful about exposing their or-
ders. Shrewd traders who know where stop loss orders are set can employ
a manipulative trading strategy called
gunning the market
to profit from this
information.
Market manipulators gun the market when they push prices up or down
to activate stop orders. The stop orders then accelerate those price changes.
The manipulators close their positions at a profit by trading with the stop
orders.
Traders who use stop strategies can protect themselves from gunning
strategies by hiding their orders and by trading only in very liquid markets
that are expensive to manipulate. Otherwise, traders must monitor the mar-
kets to determine whether the price changes that hurt their positions are
due to fundamental changes in value or to traders attempting to exploit stop
strategies.
In some countries, including the United States, price manipulation of
this type is illegal. The law, however, is virtually impossible to enforce be-
cause it is difficult to prove that a trader was gunning the market. When
confronted, manipulators defend themselves by saying, "I bought because I
felt that price was too low, and I sold when the market realized my expec-
tations." If asked why they did not buy again when the market subsequently
fell, they say, "Since I did not expect the market to fall, the fall demon-
strated to me that I really did not understand what was happening. I was
therefore unwilling to trade again." Without reliable evidence that docu-
ments the true purpose of their trading strategies, authorities cannot con-
vict gunners of price manipulation.
11.5 SUMMARY
Order anticipators are speculators who attempt to profit from information
about the trades that other traders will make rather than from information
about fundamental values. They trade in front of other traders. They profit
when other traders move prices to complete their trades.
Order anticipators are parasitic traders because they profit only by ex-
ploiting other traders. They generally do not make prices more informative,
and they do not make the markets more liquid.
Order anticipators differ by what they know about the trades that other
traders will make.
Front runners
know exactly what
other traders have de-


## Page 270

CHAPTER 11 ORDER ANTICIPATORS • 257
cided to do.
Sentiment-oriented technical traders
try to predict what other
traders will decide to do.
Squeezers
force other traders into making trades at
very disadvantageous prices.
Traders protect themselves from order anticipators in several ways. They
protect themselves from front runners primarily by closely guarding infor-
mation about the trades that they intend to do. They protect themselves
from sentiment-oriented technical traders by trading quickly. They protect
themselves from squeezers by always making sure that they have multiple
ways to close their positions.
Markets can help protect traders from some types of order anticipators.
Markets protect liquidity suppliers from quote matchers with order prece-
dence systems that give time precedence to traders who expose their orders
first. Such systems are meaningful only if they also have a significant min-
imum price increment that forces front runners to significantly improve
prices if they want to trade first.
11.6 SOME POINTS TO REMEMBER
•
Order anticipators profit when they can exploit information about
other traders' orders.
•
Since they do not offer liquidity or make prices more informative, or-
der anticipators are parasitic traders.
•
Front runners front-run orders that other traders have submitted.
•
Quote matchers front-run traders who offer liquidity.
•
Sentiment-oriented technical traders anticipate the orders that other
traders will submit.
•
Squeezers anticipate trades that other traders must make.
•
Traders who are aware of stop orders may manipulate them.
11.7 QUESTIONS FOR THOUGHT
1.
What are the advantages and disadvantages of splitting large orders?
2.
What are the advantages and disadvantages of displaying large orders?
3.
Consider the following front-running scheme: A dishonest block
broker deliberately exposes large orders to an accomplice who front-
runs the orders. The accomplice is a large trader who plausibly might
be willing to fill these orders. Is this scheme illegal? How could any-
one catch the conspirators? How can brokerage clients protect them-
selves from this fraud? How can brokerage firms protect themselves
against employing such rogue brokers?
4.
Time precedence with a large minimum price increment helps pro-
tect liquidity suppliers from quote matchers. How large should the
minimum price increment be? What happens if it is too large?
5.
Suppose that a market decreases its minimum price increment from
10 cents to 5 cents. How might a firm circumvent this change by
splitting its stock?
6.
How can quote matchers avoid being trapped?
7.
When quote matchers front-run an order in markets that enforce
time precedence, they improve prices for traders on the other side
of the market and narrow bid/ask spreads. Should markets therefore
encourage quote matching?
Gunning the Market
Suppose that the price of
silver futures contracts is
presently 4.96 dollars a troy
ounce.
P. J.
knows that many
short sellers have placed stop
loss buy orders at 5.00.
P. J. aggressively buys
silver futures contracts to push
the price from 4.96 to 5.00
in order to trigger the stop
loss orders. She then sells her
position to the traders who set
those stop orders at 5.00
dollars per ounce. If the
average cost of her purchases
is 4.98 dollars, she would
make a nominal return of 0.4
percent on the strategy.
The actual return on her
capital will be much higher
because she does not have to
pay for the futures contracts
when she buys them. She has
to post only a small fraction of
their notional value as margin.
After this episode ends, the
price of silver will probably
drop again to 4.96.


## Page 271

258 • TRADING AND EXCHANGES
8.
What effect does quote matching have on market liquidity?
9.
Should exchanges try to discourage quote matching?
10.
What is the relation between value traders and sentiment-oriented
technical traders?
11.
What is the relation between information-oriented technical traders
and sentiment-oriented technical traders?
12.
Short squeezes occur when traders with short positions are squeezed.
Can traders with long positions also be squeezed?
13.
What type of order anticipator is a trader who guns the market to
exploit stop orders?


## Page 272

B
luffers
are profit-motivated traders who try to fool other traders into
trading unwisely. The bluffers then profit from those foolish traders. To
trade profitably, you must avoid trading with bluffers.
Bluffers use two techniques to fool their victims.
Rumormongers
spread
information that they hope will encourage people to trade as the bluffers
want them to trade. The information may be false information, or it may
be true information presented in a manner or under circumstances that
would cause traders to misinterpret it.
Price manipulators
arrange trades at
prices, volumes, and times that they hope will change people's opinions about
instrument values. The trades may be real market trades properly arranged
at arm's length, or they may be
wash trades
arranged with confederates to
create artificial market activity. Both bluffing techniques present the bluffers'
victims with information that the bluffers hope will cause them to make
false inferences about values. In both cases, bluffers try to convince other
traders that they are well-informed traders.
Market manipulation
occurs when bluffers or their victims cause prices
to change from what they would be if the bluffers did not pursue their bluff-
ing strategies. Market manipulation is illegal in the United States and many
other countries. It is very difficult to catch, however. If the bluffers do not
openly fabricate information or arrange wash trades with conspirators, they
often can easily defend themselves by claiming that they were engaged in
legitimate trading strategies.
Traders who offer liquidity to other traders must be especially careful not
to offer liquidity to bluffers. To avoid losing to bluffers, liquidity suppliers
must be very careful when they make inferences about values from prices
and volumes. If they make these inferences poorly, bluffers may manipulate
their trading and thereby profit. To fully understand how traders supply liq-
uidity, you must understand how bluffers discipline liquidity suppliers.
This chapter starts with an illustration of a bluff. We then formally char-
acterize bluffing. We discuss how bluffs work, why they sometime fail, and
why regulators cannot easily enforce laws against market manipulation. The
chapter concludes with a discussion of the implications of bluffing for traders
who offer liquidity.
12.1 A LONG-SIDE BLUFF
Bluffing is best introduced with an example. In the following invented ex-
ample, Bill undertakes a long-side bluff. In a
long-side bluff,
a bluffer tries
to profit by buying at low prices and selling later at higher prices. Our ex-
ample has two endings. In the first ending, Bill successfully completes his
bluff and makes great profits. In the second ending, value traders call Bill's
bluff, and he loses heavily.
After some careful research, Bill decides that the stock of a small firm
named Bubbles Never Burst (BNB) is a good candidate for his bluff. BNB
12
Bluffers
and
market
manipulation
Painting the Markets
Traders say that price
manipulators
paint the tape
when
they trade to influence
other traders. Traders coined
this term when automated
telegraph printers reported
trades to off-floor traders.
These printers—called
tickers
because of the sounds that
they made—produced long
paper ribbons called
ticker
tapes.
Traders who paint the
tape cause the price record to
appear differently than it
otherwise would appear.
Traders also say that price
manipulators
paint a picture
when they produce
information that does not
reflect true market conditions.
Manipulators hope that other
traders will mistake their
pictures for reality.
4
259


## Page 273

260 • TRADING AND EXCHANGES
is a young firm that has developed a new emulsifier with potentially valu-
able applications ranging from bathtub soaps to industrial foams. The stock
is followed by many investors who are excited by its growth prospects.
Most of them know little or nothing about the underlying chemistry. BNB
currently has no earnings and is trading for 5 dollars a share. The firm
has 8 million common shares outstanding, of which management holds
70 percent.
Bill starts his bluff by buying BNB shares as quietly as he can. Using
limit orders, he patiently waits for the market to come to him. Because other
traders also occasionally want to buy the stock, the price starts to rise. Over
the next 40 trading days, Bill buys 200,000 shares at prices ranging from
5 dollars to 7 dollars. His average trade price is 6 dollars.
On the thirty-first trading day, and continuing thereafter, Bill starts to
praise the stock extensively in messages he posts to various Internet mes-
sage boards. He describes BNB's technology in substantial detail, as though
he fully understood it. He also provides very optimistic cash flow projec-
tions for applications of the technology. His messages draw heavily on in-
formation presented in BNB's most recent 10-Qand 10-K reports. He posts
these messages under several different user names, so that it appears that
the stock is widely followed. He even has his various user names spar with
each other on the message boards to strengthen the impression that they
represent different people. Of course, his pessimistic user names eventually
grudgingly concede winning points to his optimistic user names.
On the morning of the forty-first trading day, BNB independently is-
sues a press release that announces it will be producing its new emulsifiers
in China. The news is not surprising to anyone who read BNB's last 10-Q
report, in which BNB provided a positive status report of its efforts to pro-
duce in China. Several electronic news services receive electronic copies of
the press release. Most news service editors run the story by publishing an
exact or slightly edited copy of BNB's press release.
Bill sees the news immediately because he subscribes to a real-time in-
formation service that he has programmed to alert him whenever stories
about BNB appear. Although the announcement has no particular funda-
mental value, Bill quickly decides that it represents the opportunity for which
he has been waiting. He immediately submits market orders to buy 50,000
shares of BNB stock. He divides the orders into several parts and submits
them to different brokers without telling them about the other parts. When
the orders converge on the market, the price rapidly rises. In 20 minutes, it
goes from 7 dollars to 10 dollars as Bill buys 50,000 shares at an average
price of 8.5 dollars. At the end of the hour, several news services are re-
porting that BNB is up substantially for the day on unusually large volume.
BNB also appears on various electronic intraday lists of the largest daily
price gainers.
Bill also starts posting notes to the Internet bulletin boards about the
importance of the China information. His notes now project price targets
of 20 and 25 dollars per share, with the possibility of more than 50 dollars
a share by the time the new plant comes on line.
12.1.1 The Successful Ending: Bill Profits
Some traders who follow BNB closely see the price change. They immedi-
ately query their electronic information retrieval services to determine why


## Page 274

CHAPTER 12 BLUFFERS AND MARKET MANIPULATION • 261
the stock is moving, and when it started to move. They find the story about
producing in China and see that the price increase immediately followed its
publication.
Although the news has no particular fundamental value, many traders
infer more from the story than they should because of the large positive
price change that followed the announcement. They mistakenly conclude
that other traders believe the story is extremely good news. They foolishly
ask themselves, "Why else would the market have gone up?" They convince
themselves that someone obviously thinks the stock is a good value. In light
of this information, they reevaluate their opinions about BNB. BNB's tech-
nology now seems more promising, and the firm's prospects look much
brighter than when they last thought about the company. They say to them-
selves, "Because I certainly am among the first to see this news, I probably
can still profit by buying BNB stock. If I wait too long, the price will con-
tinue to rise, and I will have lost my opportunity." These traders then buy
the stock. Since they are afraid that others may soon come to the same con-
clusion that they have, they submit market orders to trade quickly.
Traders who buy when the market is rising and sell when it is falling are
momentum traders.
They are particularly susceptible to bluffs.
These momentum traders primarily buy their stock from Bill! Bill lets
the stock continue to rise to close at 12 as he sells 100,000 shares at an av-
erage price of 11 dollars.
By late afternoon, the stock exchange has contacted the CFO of BNB
about the price rise. She reports that management is completely mystified
by the events. The firm considers issuing a second press release stating that
they have no idea why their stock price is rising. BNB's attorney, however,
advises against doing so, for fear of exposing the firm to lawsuits. When
news service reporters call, management declines to comment, stating that
they have a policy of not commenting on market fluctuations.
By the next day (day 42), many other traders have seen the price rise.
Some believe it indicates that the stock may do very well in the future. Oth-
ers have read Bill's notes on the various Internet bulletin boards and now
agree that the stock probably is undervalued. These traders also may have
seen news stories reporting that BNB declined to comment on the burst in
market activity. They interpret the refusal as a further indication that some-
thing is happening. These foolish traders try to buy the stock.
Other traders believe that the stock may be overvalued, but most are not
willing to act on their opinion because they are not sure whether other, more
significant, fundamental information might account for the large price rise.
Still others think the stock is overvalued, but are unwilling to sell it because
they expect that it will rise further.
Bill sells heavily throughout the day, along with a few value traders. The
stock peaks at 13 dollars and then drops to 8 dollars on very high volume.
Bill sells his remaining 150,000 shares at prices ranging from 13 to 8 dol-
lars. His average sales price for these trades is 10.5 dollars. His total profit
for the bluff, computed in table 12-1, is 1,050,000 dollars.
Over the next few days, more value traders enter and sell. Within a few
weeks, the price has dropped back to 5 dollars. Those momentum traders
who bought stock at high prices and held it, lost heavily. The value traders
who sold the stock made money.
Given the unusual pattern of trading, the exchange where BNB is listed


## Page 275

262 • TRADING AND EXCHANGES
TABLE 12-1.
Bill's Trading Profits in the Successful Ending to His Bluff
TRADES
SIDE
SHARES
AVERAGE PRICE
CASH FLOW
Days 1-40
Buy
200,000
6.0
—1,200,000
Day 41
Buy
50,000
8.5
—425,000
Day 41
Sell
—100,000
11.0
1,100,000
Day 42
Sell
—150,000

10.5
1575.000
0
Total Profit:
$1,050,000
initiates an investigation to determine who
traded and who profited. Their
investigators quickly determine that Bill was a significant player in the ep-
isode and that his trading was quite profitable. It appears to the exchange
that Bill may have been manipulating prices. The exchange then provides
the SEC with a brief of the case.
When exchange and SEC investigators confront Bill with this accusa-
tion, he acts astounded that anyone would accuse him of such a thing. Bill
claims that he was simply a well-informed trader. In his defense, Bill tells
the following story:
After reading BNB's 10-Qand 10-K reports, and after doing other
research into the emulsifier market, I became convinced that BNB
had excellent growth prospects. I therefore bought a significant po-
sition in the stock. When I saw the China announcement, I decided
to start trading aggressively. I believe that BNB's innovative emulsi-
fier will be accepted quickly in China, where they do not already have
large plants producing traditional emulsifiers. Moreover, given the
huge potential of the Chinese market, I feel that the prospects there
are extraordinary. I traded aggressively because I feared that other peo-
ple would soon see the same announcement and realize that the com-
pany was significantly undervalued. I had read several positive stories
about the China initiative on various Internet bulletin boards, and I
assumed that others had as well. I broke up my order because I do
not trust my brokers with large orders: I would rather front-run my-
self than let them front-run me. The subsequent price increase con-
firmed to me that I had been right about the stock. Although I un-
doubtedly was responsible for some of the price rise, others were
buying, too. When I saw that so many others were buying the stock,
I decided they were going to overreact and push the price above its
fundamental value. When I met my price target, I decided to sell. It
was much sooner than I expected, and I was thankful to have iden-
tified the stock while it was still undervalued.
Upon hearing his story, the investigators ask, "Why didn't you repur-
chase the stock when it fell to 5 dollars?"
Bill answered:
I was surprised, and therefore a bit scared, when the price fell back
to 5 dollars. I fully expected that the price would stay above 8 dol-
lars. Obviously, the market does not believe that this stock is as valu-


## Page 276

CHAPTER 12 BLUFFERS AND MARKET MANIPULATION • 263
able as I thought it was. Although I have been thinking of repur-
chasing it, I am reluctant to do so until I can figure out whether the
market is wrong or I am wrong.
The investigators naturally suspect that Bill was the author of the
Internet messages. Some messages contain information that Bill knew, or
should have known, was incorrect or misleading. If the investigators can de-
termine that Bill was the author, they can prosecute him for securities fraud.
They therefore subpoena the message board hosts for information about
who posted various messages about BNB. Bill is not concerned, however,
since he opened his Internet accounts using false names and he always logged
on using publicly available computers in libraries, schools, and Internet cafés.
The investigators ultimately determine that the postings originated in Bill's
hometown, but they cannot trace them directly to Bill.
The investigators conclude that they probably cannot convict Bill with
the available evidence. After giving Bill an essentially toothless warning,
they drop the case.
12.1.2 The Unsuccessful Ending:
Bill's Bluff Is Called, and He Loses
After the rapid run-up of the stock price immediately following the Chi-
nese press release, many traders start to wonder just what is going on. Among
them is a value trader named Valerie, who has long been skeptical about
BNB's prospects. Valerie is intimately familiar with the firm, with its tech-
nology, and with the markets it hopes to enter. After consulting with an ex-
pert emulsifier chemist, she believes that the firm should be worth no more
than 3 dollars per share, even though the new emulsifier has some valuable
uses. At 5 dollars, she was unwilling to sell the stock due to the uncertain-
ties inherent in her value estimate. At 10 dollars, however, the stock appears
very overpriced. She is concerned about why the price is so high.
Valerie suspects that a bluffer may be responsible. She reads the Inter-
net message boards and is aware that several authors are posting mislead-
ing messages which are overly optimistic. She wonders whether these mes-
sages might be the work of a single individual. Since she believes that the
China story was not material, she finds the price reaction to the story sur-
prising. Valerie suspects that a bluffer may have created it. To confirm her
suspicions, she asks her broker to send a floor broker to the BNB post to
determine what happened. The floor broker reports that several large buy
orders arrived at the same time. The coincidence of large orders from sev-
eral brokers arriving immediately after the announcement of seemingly in-
significant news seems strange to Valerie. She believes that a bluffer may
be responsible for the rapid increase in price.
Valerie is now quite confident that the stock is overvalued at 10 dollars.
She believes that she understands both the fundamental value of the stock
and why it is trading so high. She therefore starts selling the stock short.
Since she thinks a bluffer may also start selling soon, she sells very aggres-
sively. Her trading stops the price rise at 10 dollars.
Although Valerie may know more about BNB's value than anyone else
does, other value traders also believe that BNB is overvalued at 10 dollars.
They start selling, too.
Bill, who would like prices to advance further, tries to buy more shares.


## Page 277

264 • TRADING AND EXCHANGES
Valerie and the other value traders sell as much as he buys, however, with
no increase in price. Bill buys 100,000 additional shares at 10 dollars per
share before the end of the day. The value traders sell 100,000 shares to Bill
and another 75,000 shares to foolish momentum traders who fell for
Bill's bluff.
At the start of the next trading day, Bill is very worried. If he cannot get
the price to rise quickly, the stock will lose its momentum, and he will be
the only buyer. If he stops buying, price probably will fall. If he starts sell-
ing, price probably will dive. His situation is desperate. He gambles and de-
cides to place an opening order for 50,000 shares to move prices up.
Overnight, Valerie and other value traders have been calling around to
obtain more shares to borrow to sell short. The institutions that hold these
shares now consider what is going on. Some realize that the sudden inter-
est in their shares from well-known value traders can only mean that they
are overpriced. Rather than lend the shares, these institutions decide to sell
themselves. They place large sell orders at the open for 75,000 shares.
The market opens at 9.5 dollars, and most of the momentum traders dis-
appear. Bill now owns 50,000 more shares for a total of 400,000 shares.
Price starts to fall as more shares come onto the market.
Things now look terrible for Bill. If he does nothing, prices will drop
substantially, probably back to 5 dollars. If he starts to sell, prices will drop
even faster. If he continues to buy, he might be able to regain the momen-
tum he needs to draw other traders in, but he does not know how many
shares it will take. If it takes more shares than he can finance, further pur-
chases will only make his bad situation worse.
Bill concedes that that he has lost. Valerie has successfully called his bluff.
He now must sell 400,000 shares at the best prices he can. Unfortunately,
the only significant potential buyers are the value traders who want to close
their short positions at a profit. At current prices, however, they are still
sellers. They will not consider buying the stock until it drops significantly.
Bill manages to sell 50,000 shares at an average price of 6.5 dollars as the
stock plummets to close at 5 dollars. Over the next 18 days, he sells the re-
mainder of his position at an average price of 4.5 dollars. His total loss for
the failed bluff, computed in table 12-2, is 1.2 million dollars.
Given the unusual pattern of trading in BNB, the exchange initiates an
investigation to determine who traded and who profited. Their investiga-
tors quickly determine that Bill was a significant player in the episode, but
his trading was quite unprofitable. It appears to them that Bill manipulated
TABLE 12
-
2.
Bill's Trading Profits in the Unsuccessful Ending to His Bluff
TRADES
SIDE
SHARES
AVERAGE PRICE
CASH FLOW
Days 1-40
Buy
200,000
6.0
—1,200,000
Day 41
Buy
50,000
8.5
—425,000
Day 41
Buy
100,000
10.0
—1,000,000
Day 42 Open
Buy
50,000
9.5
—475,000
Day 42
Sell
—50,000
6.5
325,000
Days 43-60
Sell
—350,000
4.5
1.575.000
0
Total losses:
—1,200,000


## Page 278

CHAPTER 12 BLUFFERS AND MARKET MANIPULATION • 265
the market. The exchange provides the SEC with a brief of the case. Given
its limited enforcement budget and the fact that the bluff failed, the SEC
decides to take no formal action. Instead, it sends a stern letter to Bill warn-
ing him against conducting similar operations in the future.
12.1.3 Is the Example Realistic?
For expositional clarity, this invented example presents an extreme scenario in
which Bill trades quite aggressively. Although the example nicely illustrates
many issues involved in bluffing, we should consider how realistic it is.
The example is not as extreme as it may appear. The Securities and Ex-
change Commission was founded in large part in response to public con-
cerns about similar market manipulations that plagued the markets through-
out the nineteenth century and into the early 1930s. Although the SEC has
greatly decreased the number of blatant market manipulations, it still de-
votes a substantial fraction of its enforcement budget to identifying and
prosecuting market manipulators. The Commission now targets its efforts
mostly at abuses in small, low-priced growth stocks that traders often call
penny stocks.
Many people widely believe that similar bluffing operations are
still conducted frequently in less regulated markets throughout the world.
Had Bill's trading behavior not been so extreme, his bluff might have
gone undetected. It is impossible to say how often traders actually conduct
small, undetected bluffs in real markets.
Some traders probably commonly employ bluffing strategies. Bluffing
can be profitable when many traders will respond to information that bluffers
manufacture for their benefit, and when value traders are slow to react. Since
many traders are foolish, and since value traders sometimes trade slowly,
bluffing strategies often may be profitable. Cautious bluffers who are not so
greedy that their trading draws attention to themselves therefore may trade
profitably. Such traders may trade undetected in the markets.
12.2 THE FUNDAMENTALS OF BLUFFING
Bluffers profit by encouraging traders to sell when the bluffers want to buy
and to buy when the bluffers want to sell. They do this by producing or
distributing information that their victims use to form opinions about
future prices.
For example, bluffers who want to sell stock may disseminate informa-
tion about how valuable it is. Traders who see the good news may decide
to buy the stock. The bluffers then sell the stock to them at higher prices
than they otherwise would have been able to obtain. Alternatively, bluffers
may quickly buy the stock at successively higher prices. Traders who see the
price rise may conclude that informed traders are buying the stock, and then
try to buy it themselves. The bluffers will sell the stock to them, again at
higher prices than the bluffers otherwise could have obtained. Bluffers em-
ploy similar strategies when they want to buy stock.
12.2.1 Bluffers and Informed Speculators
Market manipulations are very hard to distinguish from legitimate specu-
lative trading activities. Speculators who hold large long positions naturally
want the market to value their securities and contracts highly. Like bluffers,
they may use word-of-mouth, the press, newsletters, or Internet message


## Page 279

266 • TRADING AND EXCHANGES
0
,
Nathan Rothschild and the Battle of Waterloo
While the Battle of Waterloo was waged on June 18, 1815, British
government bond traders in London awaited the outcome. If Napoleon's
Grande Armee defeated the English—Prussian allies under the command of
Lord Wellington, British consol bonds would fall. If Napoleon lost, the
consols would rise. On June 18, however, the London traders did not even
know that the battle had started. They learned the official outcome of the
battle only after the Cabinet received Wellington's dispatch at 11
P.M. on
June 21. Communications before the invention of the telegraph were much
slower than now.
The Rothschild family of investment bankers ran a private system of
couriers to move information, securities, currency, and bullion throughout
Europe. Their system was very fast, given the available technology. The
Rothschild brothers often were the first to learn news in their respective cities.
London-based Nathan Rothschild learned of Wellington's victory late on
the night of June 19. He conveyed it to the government the next day. What
happened next may be the most enduring myth in finance.
In the most common version of the story, Rothschild then went to the
Stock Exchange to trade on his information. The bond traders believed that
he would know the result of the battle before anyone else in London. They
therefore looked to him for leadership. If Rothschild started to buy consols,
they also intended to buy. If he sold, they would sell.
Nathan Rothschild knew that he could not profit from his information
under these circumstances without bluffing. Accordingly, after arriving at the
Exchange, he purportedly stood impassively as his agents started to sell
consols. The traders quickly concluded that Rothschild was a seller. As they
frantically tried to sell their consols, the price dropped dramatically.
Rothschild then bought consols at low prices. When the news of Napoleon's
defeat became public knowledge, consols rose substantially, and Rothschild
profited handsomely.
Although academic historians believe the story is a myth, it nicely
illustrates bluffing issues. Business rivals or anti—Semites probably created
the story to discredit the Rothschilds. Rothschild did profit handsomely from
buying consols following the war, but he apparently made his purchases
well after the Waterloo news was common knowledge.
Sources: Frederic Morton,
The Rothschilds: Portrait of a Dynasty, New
York: Kodansha
International, 1998; and Niall Ferguson,
The House of Rothschild,
New York: Viking,
1998.
boards to explain to all who will listen why prices should be higher. Like
bluffers, legitimate speculators also may buy instruments to show the mar-
ket that confident buyers think their positions are undervalued.
The distinguishing difference between bluffers and informed speculators
is that speculators trade on opinions about fundamental values that they
base on fundamental information. Bluffers behave as though they are in-
formed speculators, and they hope that others will believe they are well-
informed speculators, but they do not have well-founded opinions about
values. Instead, they try to fool other traders into thinking they do. Hence,
the term "bluffer."
Because informed speculators trade on fundamental information, their
trading activities make prices more informative. Since bluffers do not have
such information, their trading more often than not makes prices less in-


## Page 280

CHAPTER 12 BLUFFERS AND MARKET MANIPULATION • 267
An Internet Stock Fraud in PairGain Technologies
On April 7, 1999, at 10:24
A.M.,
the following message (number 18,280)
was posted to the Yahoo! message board for PairGain Technologies (PAIR):
BUYOUT NEWS!!! ECILF is buying PAIR . . .
by: stacylTN (32/F/Knoxville, TN)
Just found it on Bloomberg . . .
http://204.238.155.37/biz2/headlines/topfin.html

GO PAIR!!!!
SL
If you had clicked on the URL link, a seemingly properly formatted
Bloomberg page would have appeared with a very credible news story
about the acquisition agreement.
The story was false. The Web address does not point to a
Bloomberg.com
server, but rather to a free Web page service operated by
Anglefire.com
. Although the link is no longer active, you can read much of
the story in Yahoo! PAIR message number 18,477.
Following the posting, the PAIR stock price rose to a high of 11
1
/8 from
its previous close of 81/2 on very heavy volume. For the day, the stock
traded 14 million shares, which was about ten times its normal volume.
Traders detected the fraud very quickly. (See messages 18,287 and
1 8,289.) When the company issued a statement denying the story, the
stock fell to close at 9
3
/8.
PairGain provides Digital Subscriber Line (DSL) telecommunications
products. At the beginning of 1999, the growth prospects of the firm
seemed extraordinary to many people. The stock was very widely followed
by small investors: The April 7 Yahoo! posting was the 5,079th message
posted in 1999!
On April 15, the FBI arrested Gary Dale Hoke, 25, on charges of
securities fraud for allegedly fabricating the news story. The SEC later also
charged him in a civil suit.
Hoke worked for PairGain. His motive for the fraud is not obvious: He
did not trade PairGain Technologies before or after this episode. Had he
bought the stock beforehand, and sold it soon after releasing the story,
he would have profited handsomely. Had he been more careful about
how he posted the story, he might have avoided detection.
Many traders who relied upon his story lost money, as did many other
traders who simply bought because the stock price was rising. Traders who
quickly identified the fraud could have profited substantially.
Sources
.
Yahoo! PAIR messages 18,280
and
18,477.
formative. Prices are more likely to reverse following a bluff than following
a well-informed speculative trade.
Although bluffers generally are not well informed about fundamental
values, they are informed traders in a special sense. They possess highly valu-
able information that other traders do not know. In particular, they know
what they are doing as bluffers, whereas others generally do not. This knowl-
edge allows them to better interpret market conditions—that they may have
created themselves—than other traders can. Unlike fundamentally informed
traders, who work hard to obtain the information upon which they can trade
profitably, bluffers create their information!


## Page 281

268 • TRADING AND EXCHANGES
Bluffing and Poker
The game that bluffers play in the markets against value traders is similar in
many respects to the game of poker.
In poker, players try to fool other players into believing that they have
better cards than they do. The other players then consider whether this is
likely. If they suspect not, they bet against the bluffer. The players take turns
upping the ante until they drop out or until no one wants to bet further. Any
remaining bets are resolved by comparing cards.
In trading, bluffers try to fool other traders into believing that they have
material information about fundamental values that others do not have.
Value traders then consider whether the bluffers are well-informed traders. If
they bet against the bluffers, the bluffers and value traders take turns upping
the ante until one group drops out or until no one wants to bet further.
Values are resolved as time passes.
The similarities between these two games suggest that the best bluffers
may also be good poker players.
The Iwo games differ in several important respects. In most versions of
poker, players must fold if other players challenge them to ante up and they
refuse to do so. In trading, all players can stand on their positions as long
as they can finance them. In poker, all players know with whom they are
playing. In trading, most traders trade anonymously, and their trades are
rarely identified. Traders can characterize their opponents only by the
aggregate behavior of the market. In poker, the number of players is fixed.
In trading, traders can enter the market whenever they want. Finally, players
settle all poker bets when they reveal their cards. In trading, values are
never resolved with certainty. ^iii

12.2.2 Bluffers and Value Traders
Value traders foil bluffers. When bluffers cause prices to move far from their
fundamental values, value traders may recognize the resulting profitable trad-
ing opportunities. If they then trade to profit from those opportunities, the
bluffers may lose control of the market. Value traders make it difficult for
bluffers to move prices far from their fundamental values. They also make
it difficult for bluffers to profit from the price changes and order flows that
they have created. When bluffers and value traders are on opposite sides of
the market, the value traders stand in the bluffers' way. When bluffers and
value traders trade on the same side of the market, value traders take liq-
uidity that the bluffers need to trade profitably.
Bluffers can defeat value traders only if they have more capital than the
value traders do. With enough capital, bluffers can retain control over
the market and even force losses upon the value traders who trade opposite
them. If the value traders are not sufficiently capitalized, they may be un-
able to bear these losses. If that is the case, large adverse price changes may
force them to close their positions. Since such closing trades support the
bluffer's bluff, bluffers always hope to force value traders out of the market.
Even if bluffers profit from their manipulations, tenacious and well-
capitalized value traders will eventually profit when the bluff collapses and
prices eventually return to their fundamental values. The profits that value
traders make diminish the profits that bluffers make.
Value traders risk losing to better-informed fundamental traders when
they mistakenly attribute trading activity to a bluffer. Value traders must


## Page 282

CHAPTER 12 BLUFFERS AND MARKET MANIPULATION • 269
Jeffrey Vinik's Trading in Some Small Microcap Stocks
Jeffrey Vinik managed the Fidelity Magellan Fund for several years ending
in June 1996. It was then the largest stock mutual fund in the world.
Although it had underperformed the market during the last year of his
tenure, Vinik was—and still is—widely regarded as a very well-informed
trader.
After Vinik left Fidelity, he started his own investment fund in November
1996. In the next several months, Vinik bought stakes larger than 5 percent
in 13 small and microcap stocks for his new 800 million-dollar hedge fund.
The
SEC
requires that investors disclose their positions when they have
acquired more than 5 percent of outstanding shares of a company. These
reports are publicly available.
When the public learned of Vinik's purchases, the prices of 11 of the
13 stocks increased substantially, presumably because investors felt that
Vinik was well informed. However, shortly after these disclosures and their
associated price increases, Vinik sold several of his new positions.
Vinik may have sold simply because his price objectives were realized.
Alternatively, he may have been trading on his reputation as a well-
informed trader to bluff the market into believing that these securities were
more valuable than they were.
Interestingly, in 1994 and 1995, while still managing the Magellan
Fund, Vinik was selling stocks that he apparently had been promoting at the
same time in magazine interviews. The SEC considered whether his actions
constituted market manipulation, but did not charge him.
4
Sources: Susan Pulliam,
Wall Street Journal,
January 24, 1997, p. Cl; and Jeffrey
Taylor,
Wall Street Journal,
May 9, 1996, p. Cl.
therefore be very certain that they fully understand fundamental values be-
fore they attempt to call a bluffer's bluff.
Bluffs are most likely to be successful when attempted in securities that
value traders either do not follow closely or cannot trade easily. Value traders
tend not to follow illiquid securities closely because they cannot trade enough
to profit substantially from their costly fundamental research. Illiquid secu-
rities therefore may be good targets for bluffers. Value traders also tend not
to follow securities that they cannot easily value. Bluffers therefore may tar-
get securities for which little fundamental information is available to the
market. Finally, value traders generally cannot sell securities that they can-
not borrow. Bluffs therefore are more likely in securities that are hard to
borrow. These securities are often small stocks for which the bluffer con-
trols a substantial fraction of the shares outstanding. Because value traders
generally can buy securities more easily than they can short sell them, long-
side bluffs probably are more common than sell-side bluffs.
12.2.3 Prosecuting Market Manipulation
Prosecuting market manipulators is very difficult because bluffers always
claim to be well-informed speculators. The best bluffers probably are often
well informed, though not necessarily about the objects of their bluffs. Since
prosecutors, judges, and juries cannot easily determine whether an opinion
is well founded or not, they cannot distinguish between informed specula-
tion and bluffing. Moreover, a subsequent price reversal does not provide


## Page 283

270 • TRADING AND EXCHANGES
sufficient basis to conclude that the bluffer was uninformed, because even
good speculators sometimes estimate values poorly. If bluffers are not caught
disseminating false information or conducting wash trades, they can easily
pass for the speculators that they hope others will take them to be.
To convict bluffers of market manipulation, prosecutors must show that
bluffers are distributing information that they knew (or should have known)
was false or that they engaged in wash trades. Well-informed testimony
demonstrating manipulative intent also would help. Although such testi-
mony might come from confederates in the scheme or from the bluffers'
confidants, it must be quite rare.
12.3 BLUFFERS DISCIPLINE
LIQUIDITY PROVIDERS
Traders who provide liquidity to the market must be extremely careful that
bluffers do not fool them. As we shall see in chapter 13, many liquidity sup-
pliers do not know fundamental values well. Instead, they simply follow
the rule of supply and demand: They assume that prices should be higher
or lower depending on whether liquidity-demanding traders are net buyers
or net sellers. (Liquidity demanders are
net buyers
when they want to buy
more than they want to sell, and
net sellers
otherwise.) Although this rule
is generally sound, traders who use it to set the prices at which they are
willing to trade must be very careful. By manipulating supply or demand,
bluffers can cause liquidity suppliers to change their prices. Liquidity sup-
pliers therefore must ensure that bluffers do not manipulate their trading to
their disadvantage.
For example, suppose that bluffers know liquidity suppliers respond dif-
ferently to large orders than to an equal volume of small orders. In partic-
ular, suppose that liquidity suppliers will raise (or lower) their prices by 10
cents per 100 contracts bought (or sold) for 1,000 contract orders, but by
only 5 cents per 100 contracts bought (or sold) for 500 contract orders. A
clever bluffer who is aware of this relation may buy 4,000 contracts with
four 1,000-contract orders and then sell those 4,000 contracts with eight
500-contract orders. If the contract calls for the delivery of 1,000 units, the
net result will be a profit of 1.5 million dollars.
To compute the profit, suppose that the last contract price before the
bluffer started to trade was 10 dollars. (Any initial price will give the same
answer.) The four 1,000 contract buys will take place at 11, 12, 13, and 14
dollars, so that the average purchase price for the 4,000 contracts will be
12.5 dollars. The eight 500 contract sales will start at a price of 13.75 dol-
lars and take place at successively lower prices until the eighth and last sale
takes place at 12 dollars. The average sales price will be 12.875. Since the
average sales price is greater than the 12.50 average purchase price, this se-
quence of trades will be profitable. The total profit will be 4,000 times the
0.375-dollar difference in the average sale and purchase prices times the
1,000-unit contract size. The product of these three factors is 1.5 million
dollars.
You can see that something is wrong here by noting that although the
bluffer bought and sold an equal number of contracts, the last trade price
of 12 dollars is far from the initial 10-dollar market price. The bluffer was
neither a net buyer nor a net seller, but liquidity suppliers raised prices by


## Page 284

CHAPTER 12 BLUFFERS AND MARKET MANIPULATION • 271
2 dollars in response to his orders. This difference indicates that the liq-
uidity suppliers did not set their prices in a manner consistent with the prin-
ciples of supply and demand.
Had the bluffer in this example initially sold short using four 1,000-
contract sales and then covered his position by buying 500 contracts eight
times, he also would have made 1.5 million dollars. In this case, however,
the price would have moved from 10 dollars to 8 dollars.
Although this stylized example may not seem realistic, it shows clearly
that liquidity suppliers can lose to bluffers if they do not adjust their prices
carefully. To avoid these losses, liquidity suppliers must adjust prices up or
down at the same rate per quantity traded, regardless of whether the quan-
tities are large or small.
In our example, if the rate at which liquidity suppliers adjust their prices
were the same for both large and small sell orders, no bluffing profits would
be possible. Suppose that the liquidity suppliers raise or lower prices by 10
cents per 100 contracts traded, regardless of order size. A bluffer who buys
1,000 contracts four times and then sells 500 contracts eight times would
cause prices to rise from 10 to 14 and then to return to 10. The average
-
purchase price would be 12.5 and the average sell price would be 11.75, so
that the total loss would be 3 million dollars. Had the bluffer first sold 1,000
contracts four times and then bought 500 contracts eight times, the result-
ing loss would be the same. The bluffer loses because all his orders indi-
vidually have an impact on price but collectively have no net price impact.
Since the buy orders push prices up and the sell orders push prices down,
the average buy price must be greater than the average sell price if the trades
are to have no net effect on price.
(If the bluffer had made many smaller trades, his losses would have been
much smaller. For example, had he bought 100 contracts 40 times and sold
100 contracts 40 times, his losses would have been only 0.4 million dollars.
The smaller trades are less costly because they do not move prices as much.)
In general, bluffers can trade profitably when the price impact of their
purchases is different from the price impact of their sales. If selling has less
price impact than buying, bluffers will buy first and then sell. If buying has
less price impact than selling, they will sell first and then buy. In the suc-
cessful ending to the BNB example, Bill profited because the price impact
of his quick trades following the China announcement was greater than the
price impact of his sales.
Figures 12-1 and 12-2 illustrate this principle. In figure 12-1, buys have
greater price impact than sales. The bluffer first buys to raise prices and then
sells to profit from the higher prices. The bluff is profitable because the av-
erage buy price is less than the average sale price: Although the price im-
pacts of the bluffer's trades incur transaction costs, he profits because he can
raise the price during the course of his bluff. In figure 12-2, sales have greater
price impact than buys. The bluffer therefore sells first and then buys.
To avoid losing to bluffers, liquidity suppliers must be very disciplined
when they adjust their prices in response to the flow of buyers and sellers
that they serve. In particular, whenever liquidity suppliers cannot determine
whether they are trading with bluffers, they must adjust their prices so that
buy and sell orders have equal (but opposite) market impact per quantity
traded. The market impact per quantity traded must be the same for trades
of all sizes; it must be the same regardless of whether the orders arrive


## Page 285

272 • TRADING AND EXCHANGES
Price
S2
Start
Position
FIGURE 1 2- 1 .
Bluffing When Buys Have More Price Impact per Unit Traded
Than Sales
Each heavy arrow represents a trade. Arrows that lean to the right or left
represent buys or sells, respectively. Arrows that rise or fall represent
trades that increased or decreased prices, respectively. The slope of each
arrow thus represents the price impact per unit traded for the
corresponding trade. Buys B1 and B2 have greater price impact per unit
than sells S1 and S2. The bluff consists of buys B1 and B2 followed by
sales Si and S2. The bluff is profitable because the average buy price is
less than the average sale price.
Position
FIGURE 12-2.
Bluffing When Sales Have More Price Impact per Unit Traded
Than Buys
Sales S1 and S2 have greater price impact per unit than buys B1 and
B2. The bluff consists of sells S1 and S2 followed by buys B1 and B2.
The bluff is profitable because the average sale price is above than the
average buy price.


## Page 286

CHAPTER 12 BLUFFERS AND MARKET MANIPULATION • 273
Price
B2
Loss per unit

B1
S1
Average
buy price

Average
sale price
Start
and
finish
FIGURE 12-3.
Bluffing Is Not Profitable When Buys and Sales Have the Same Price
Impact per Unit Traded
Buys B1 and B2 and sells S1 and S2 all have the same price impact per unit.
A bluff consisting of buys B1 and B2 followed by sales S1 and S2 is not
profitable because the average buy price is greater than the average sale price.
quickly or slowly; and it must be the same regardless of when and how the
orders arrived. Otherwise, bluffers can arrange trades to manipulate prices
to their advantage.
In practice, bluffers may not be able to profit from small differences in
the market impacts of the buy and sell orders. Since bluffing is a very
transaction-intensive activity, the commissions and spreads that bluffers pay
when trading can significantly reduce the profits of their manipulations.
Like all traders, bluffers must control their transaction costs in order to
trade profitably.
Figure 12-3 shows that bluffing is not profitable when the price impact
per unit traded is the same for buys and sales. The bluffer loses because the
price impacts of his trades only generate transaction costs without produc-
ing a cumulative price change during the course of his trading. The loss
would be smaller if the bluffer broke his total volume into many smaller
trades, so that each trade would have less price impact.
12.4 SUMMARY
Bluffers
try
to fool traders into offering liquidity unwisely. They fool traders
by affecting the information that traders use to make their trading deci-
sions. Rumormongers disseminate information about values. It may be false
information or it may be true information that they distribute in a manner
that they believe traders will misinterpret. Price manipulators trade at prices
and in volumes that they hope will fool traders into thinking market con-
ditions are different from what they truly are. In particular, they try to fool
traders into believing that they are well-informed traders. Table 12-3 sum-
marizes the techniques that bluffers use to fool other traders.


## Page 287

274 • TRADING AND EXCHANGES
TABLE 12-3.
Bluffing Techniques
BLUFFER TYPE

BLUFFING TECHNIQUE

LEGALITY

PROSECUTION
Rumormonger

Spread false fundamental

Illegal in most

Easy if caught
information

jurisdictions
Present true information in a manner

Questionable

Difficult to prove
or under circumstances intended to

legality

manipulative intent
cause traders to misinterpret it
Price

Arrange legitimate trades designed

Questionable

Very difficult to
manipulator

to change trader opinions about

legality

prove manipulative
value

intent
Arrange wash trades with

Illegal in most

Easy if caught
confederates

jurisdictions
Bluffers generally can profit when the price impact of their purchases is
different from the price impact of their sales. When purchases have greater
impact than sales, they buy first and sell later. When sales have greater im-
pact than purchases, they sell first and buy later. Such bluffing strategies are
not profitable, however, when transaction costs are high.
The traders most vulnerable to bluffers are momentum traders and liq-
uidity suppliers. These traders trade in response to trades that they see. Since
bluffers can affect the trades that these traders see, bluffers may fool them
into making poor trading decisions. To avoid these losses, traders must be
very careful about how they interpret trade prices, sizes, and times.
Value traders can call a bluffer's bluff. If bluffers move prices away from
fundamental values, value traders may identify profitable trading opportu-
nities. Their trading makes it difficult for bluffers to control the market, and
it diminishes the profits that bluffers make. Failed bluffs can be very ex-
pensive to bluffers.
12.5 SOME POINTS TO REMEMBER
•
Bluffers profit by fooling traders into offering liquidity unwisely.
•
Bluffers hope that other traders will mistakenly identify them as well-
informed traders.
•
Momentum traders must be especially careful to avoid trading with
bluffers.
•
When the price impacts of sales and purchases differ and transaction
costs are not too large, bluffers can design profitable trading strategies.
•
Bluffing destabilizes prices.
•
Bluffers can lose when large value traders trade against their positions.
12.6 QUESTIONS FOR THOUGHT
1.
What effect does bluffing have on price efficiency? On volatility?
2.
What effect does bluffing have on price serial correlation?
3.
How much volatility is due to bluffing?


## Page 288

CHAPTER 12 BLUFFERS AND MARKET MANIPULATION • 275
4.
Why are bluffers more likely to target growth stocks than value stocks?
5.
How can traders avoid losing to bluffers?
6.
Should bluffing and market manipulation be illegal? How should
regulators enforce laws against bluffing?
7.
Are bluffers informed traders? Do bluffers have valuable information?
8.
Value traders who sell short are vulnerable to squeezes. How might
a bluffer squeeze these traders?
9.
In many jurisdictions, disseminating information about security val-
ues that you know is false is illegal. Although identifying and con-
victing violators may be expensive, the process is conceptually well
defined. Should disseminating true information in circumstances
that you believe would cause traders to misinterpret it also be ille-
gal? How would you write and enforce such a law?
10.
What effect would a shift to 24-hour trading have on bluffing?
11.
Suppose that a rumormonger profits by short selling a security, cir-
culating a false rumor that causes prices to fall, and buying to cover
at lower prices. The rumormonger has clearly defrauded the market.
Who should be entitled to damages in a civil suit? Does your an-
swer depend on whether traders would have traded regardless of
what the rumormonger did? Does your answer depend on whether
traders traded with the rumormonger or with other traders?


## Page 290

Part IU
I>
liquidity
Suppliers
T
his part of the book examines how and why traders supply liquidity to
other traders. We start in chapter 13 by discussing dealers. Dealers
make markets. They allow other traders to trade small size quickly.
Dealers tend to be high-frequency traders who do not know much about
with whom they trade or the fundamental values of the instruments that
they trade.
Chapter 14 examines bid/ask spreads in dealer markets and in order-
driven markets. The discussions in this chapter will help you to better un-
derstand the determinants of transaction costs.
Chapter 15 considers how block traders arrange large trades. Block
traders find liquidity for traders who want to trade large sizes. They gener-
ally know their clients well.
We consider value traders in chapter 16. They are the ultimate suppliers
of liquidity. These highly informed traders often supply great depth when
they believe that prices do not reflect fundamental values.
We introduce arbitrageurs in chapter 17. Arbitrageurs are informed
traders who move liquidity from one market to the other. You must under-
stand their trading strategies well to appreciate the economic effects of com-
petition among market centers for order flows.
Chapter 18 considers how buy-side traders create order submission
strategies. These decisions determine whether they supply liquidity or take
liquidity. When public traders are willing to supply liquidity, they can of-
ten displace dealers.
277


## Page 291

13
I>
Dealers
D
ealers are merchants who make money by buying low and selling high.
What you already know about merchants will help you understand how
dealers in the financial markets trade profitably.
Merchants may be dealers or distributors.
Dealers
buy from, and sell to,
their clients.
Distributors
buy from their suppliers and sell to their clients.
(In practice, many distributors are also commonly known as dealers. Con-
sider, for example, new car dealers.) Traders act as dealers when they make
a market in seasoned securities or in contracts. They act as distributors when
they help firms sell new securities or when they help a client sell a large
block of securities.
All dealers face the same problems regardless of what they trade. They
must set prices, they must market their services to acquire clients, they must
manage their inventories, and they must be careful that they do not trade
with better-informed traders. The relative importance of these problems
varies by what the dealers trade.
Dealers in the financial markets supply liquidity to their clients who want
to buy and sell trading instruments. They allow people to trade when they
want to trade. They buy when their clients want to sell, and they sell when
their clients want to buy.
Dealers make money by buying at low prices and selling at high prices.
They lose money when market conditions force them to sell at low prices
or buy at high prices. These losses often occur after they trade with in-
formed traders.
When dealers purchase something, they usually do not know to whom
they will sell it or at what price they will sell it. If the price drops before they
can sell the item, they lose money. Likewise, when they sell something,
they usually do not know the price that they will pay to repurchase it. These
unknowns make being a dealer challenging, exciting, and very risky. Dealers
assume significant risks when they trade.
Dealers are passive traders.
Passive traders
trade when other traders want
to trade. Since passive traders do not control the timing of their trades, they
must be very careful about how they offer to trade and to whom they offer
to trade. They must ensure that when they do trade, their trades benefit
them and not just their clients. Dealers must be especially vigilant to avoid
losing to informed traders and bluffers.
In this chapter, we will examine the principles by which dealers conduct
their businesses. You will learn how dealers set their quotes, how they man-
age their inventories, how they respond to informed traders, and how they
learn about the values of the instruments that they trade. The principles that
we will discuss apply to all dealers, whether they trade securities, com-
modities, or retail goods. If you are—or intend to be—a dealer, under-
standing these principles will help you maximize your trading profits.
Even if you have no interest in being a dealer, you must understand how
278


## Page 292

CHAPTER 13 DEALERS • 279
dealers behave in order to trade successfully in financial markets. Whether
you trade with dealers or compete with them to offer liquidity, their trad-
ing decisions affect you. In particular, you must consider how dealers trade
when you decide whether to take or offer liquidity.
In markets where dealers are the primary suppliers of liquidity, the cost
of liquidity depends on the factors that determine dealer profits. If you are
interested in market liquidity, you must understand how dealers trade and
when they are profitable.
We start this chapter with introductory discussions about who dealers
are, how traders negotiate with dealers, and how dealers attract order flow.
We then consider how dealers control their inventories and how they set
their prices. The chapter closes by examining how dealers relate to value
traders and to bluffers.
13.1 WHO ARE DEALERS?
Dealers are profit-motivated traders who allow other traders to trade when
they want to trade. The liquidity service they sell—immediacy—is valuable
to impatient traders. Dealers profit when they buy from impatient sellers at
low prices and sell to impatient buyers at high prices. The difference in
prices compensates them for providing immediacy.
Many dealers are professional traders who work on the floors of ex-
changes or in the offices of trading firms. These professionals sometimes
use computer systems to support their dealing or to implement their trad-
ing strategies.
Other dealers are individuals who access the markets through their bro-
kers, often via Internet order entry systems. Such traders generally supply
immediacy by issuing limit orders. These individuals often do not recognize
that they are acting as dealers. They consequently do not always fully ap-
preciate the risks that they face and the circumstances under which they will
lose or profit.
Many markets officially register some traders as dealers. In exchange for
special privileges, these markets may require that their registered dealers
supply liquidity. We discuss these arrangements in chapter 24.
Dealers often are known by other names. At futures exchanges, dealers
are often called
scalpers, day traders, locals,
or
market makers.
At many stock
exchanges and options exchanges, they are known as
specialists
or
market
makers.
Many dealers are also brokers. We discuss brokers and the dual trading
problem that broker-dealers present in chapter 7.
In addition to offering liquidity to other traders, many dealers speculate.
Dealers sometimes can predict future price changes by inferring why traders
demand to trade. They also can use quote-matching strategies to capture
the option values of limit orders that they see. In many actively traded mar-
kets, competition among dealers may be so intense that they cannot profit
only by providing liquidity to customers. In such markets, dealers must spec-
ulate successfully to stay in business. Such dealers are sometimes called
position traders
as opposed to spread traders.
Spread traders
profit exclusively
from buying at the bid and selling at the ask.
In this chapter, we consider only how dealers supply liquidity. Although
we discuss how dealers infer information from the order flow, and how they


## Page 293

280 •
TRADING AND EXCHANGES
Example of a Small
Realized Spread
Dell is a dealer who is
bidding 35.0 and offering
35.3 for a security. A client
arrives and sells at Dell's bid
of 35.0. Dell now needs to
sell the security to restore her
former position.
Bad news about the
fundamental value of the
security subsequently arrives.
To avoid buying from well-
informed traders, Dell must
lower her bid to 34.6. To
encourage traders to buy
from her so that she can sell
the security, she must lower
her ask to 34.9.
A buyer arrives and buys
from Dell at 34.9. Although
Dell's quoted bid/ask spread
before both trades was 0.3,
the realized spread for her
round-trip buy and sell was
—0.1 = 34.9 — 35.0. Dell
lost money because she was
holding the stock when its
value dropped.
react to it, we do not consider how they may speculate on it. Chapters 10
and 11 examine the speculative trading strategies that dealers most often
employ.
Because dealing can be quite risky, successful dealers tend to be traders
who tolerate risks well. They generally do not enjoy bearing them, however.
The risks of dealing are serious and scary. Many dealers have gone bank-
rupt because they assumed risks that did not work out. Dealers constantly
think about the risks that they bear and how to avoid them. Since bearing
risk is unpleasant, dealers demand appropriate compensation when forced
to bear large risks.
13.2
DEALER QUOTATIONS
The prices at which dealers are willing to buy and sell are their
bid
and
ask
prices. Dealers usually quote these prices to their clients before they trade.
Dealers bid to buy at their bid prices and offer to sell at their ask prices.
Sellers receive bid prices when they sell to dealers, and buyers pay ask prices
when they buy from dealers. Ask prices are also known as
offering prices.
Traders who want to buy from a trader who is offering to sell
take the
offer.
Traders who want to sell to a trader who is offering to buy
hit the bid.
Dealers always set their ask prices above their bid prices. The difference
between the ask and the bid is the
bid/ask spread.
When the ask is close to
the bid, the spread is
narrow
or
tight.
When the ask is much higher than
the bid, the spread is
wide.
Dealers make money by buying low at their bid prices and selling high
at their ask prices. This strategy is profitable if dealers can fill orders on
both sides of the market without changing their prices. In practice, this
strategy is quite difficult to implement profitably because dealers rarely re-
ceive buy and sell orders in equal volumes, and because unforeseen price
changes are very common.
The realized spreads that dealers earn are often smaller than their quoted
spreads. The
realized spread is
the difference between the prices at which
dealers actually buy and sell. Realized spreads are usually smaller than quoted
spreads because dealers occasionally trade at better prices than they quote
and because they often adjust their bid and ask prices between trades.
Dealers who quote both bid and ask prices quote a
two-sided market.
Their quotes
make a market.
Those who quote only one side quote a
one-
sided market.
Although most dealers will quote a two-sided market, they
usually aggressively price only the side on which they would prefer to trade.
For example, dealers who want to buy usually quote high (aggressive) bid
prices to encourage sellers to sell to them. They also quote high uncom-
petitive ask prices to discourage buyers from buying from them. Dealers
who want to sell quote low bid and ask prices.
The
inside spread
is the difference between the highest bid and the low-
est ask. The inside spread usually is much narrower than the average dealer
spread. By definition, it can be no wider than the narrowest individual dealer
spread.
The quotes that dealers offer are either
firm
or
soft.
Dealers who offer
firm quotes
must trade at their quoted prices, which are known as
firm prices.
Firm quotes are good only up to some maximum quantity that the dealer
specifies. Dealers who offer
soft quotes
can revise their prices when asked to


## Page 294

TABLE 1 3- 1 .
Dealer Quotes in
RoberstonBooks.com

DEALER
BID
ASK
Charles
99.9
100.4
Cheryl
100.0
100.5
Goldie
100.1
100.6
Larry
100.2
100.7
Teri
100.3
100.8
Warm Quotes in
Oral Markets
Futures traders say that the
bids and offers they shout out
in oral markets are good "only
for as long as the breath is
warm." Traders who want to
hit a bid or take an offer must
do so immediately after the
bid or offer is made.
CHAPTER 13 DEALERS • 281
Charles
FIGURE 1 3
-
1 .
The Inside Spread in RobertsonBooks.com

100.8
100.7
100.6
100.5
100.4 —

100.3

100.2
100.1
100.0
99.9
T
The inside spread
11
Larry
Goldie
Cheryl
The Inside Spread
Charles, Cheryl, Goldie,
Larry, and Teri are dealers in
RobertsonBooks.com
. Each
quotes a two-sided market.
Their quotes appear in table
13-1. Although each dealer's
bid/ask spread is 50 cents,
the inside bid/ask spread is
only 10 cents. Teri is the best
bidder at 100.3 and Charles
has the best offer at 100.4.
Figure 13-1 illustrates how the
inside spread is composed
from dealer quotations.
4
-• Best offer (Ted)
- -• Best bid (Charles)
Teri
•••
trade, or they can even refuse to trade. A soft quote is simply an indication
of interest. Dealers who do not honor their indications risk alienating their
customers.
Depending on the market, dealers may provide their quotes only on re-
quest, or they may quote continuous firm two-sided markets. Dealers in
most corporate bond markets, and in some foreign exchange markets, quote
only on request. Most organized quote-driven stock markets require that
their registered dealers quote firm two-sided markets. For example, dealers
in the Nasdaq Stock Market must continuously post firm prices at which
they will trade.
When dealers quote only on request, their quotes are good only for some
limited time. Customers must either trade while the quote is good or ask
later for a new quote. They cannot assume that the dealer will continue to
offer the same prices. The length of time that a quote is good depends on
the rules or conventions of the market. In general, quotes expire quickly in
actively traded markets with volatile prices or narrow spreads.
Dealers also quote sizes when they make firm quotes. Their
bid sizes
and
ask sizes
are the maximum quantities they must buy or sell when they make
firm bids and offers. Upon request, dealers often agree to trade larger sizes
at their quoted prices.
13.3 TRADING WITH DEALERS
Dealers frequently will trade at better prices than they quote to the pub-
lic. Traders therefore often negotiate for the best possible price. Dealers
may offer better prices to their smaller customers, to their more active cus-
tomers, and to customers they believe are not well informed about funda-
mental values.
Many institutional traders negotiate directly with dealers without the in-
tervention of a broker. Dealers usually do not charge these clients commis-
sions to trade. Instead, they incorporate any fees for trading into their bid
and ask prices. The resulting trades are on a
net price basis.
Most retail traders and many institutional traders use brokers as inter-
mediaries when trading in dealer markets. The brokers' job is to obtain the
best possible price. For small orders, however, the benefits of actively ne-
gotiating prices are usually smaller than the costs of doing so. In such in-


## Page 295

282 • TRADING AND EXCHANGES
stances, brokers will route their orders to dealers they believe most often of-
fer the best combination of price and service. If their preferred dealers are
not currently quoting the best price in the market, the brokers will insist
that the dealers fill their clients' orders at the best quoted price. In some
markets, brokers may occasionally expect to receive better prices. Orders
that receive better than quoted prices receive
price improvement.
When a broker sends an order to a specific dealer, the broker
preferences
' the order to that dealer. In many markets,
preferencing
arrangements among
brokers and dealers are quite common. Nasdaq is an example of such a mar-
ket. By forming stable relationships, brokers and dealers often can lower the
total costs of trading.
Wholesalers
are dealers who trade primarily with traders
introduced by retail brokers. Wholesalers usually have preferencing rela-
tionships with many brokers. Preferencing raises important regulatory prob-
lems that we consider in chapter 25.
When experienced traders negotiate prices with dealers, they usually ask
for a two-sided quote before they say whether they want to buy or sell. If
the quoted spread is too wide, they may seek another dealer or refuse to
trade. This strategy discourages dealers from quoting excessively high prices
to buyers and excessively low prices to sellers. It is especially important in
markets with few dealers because their limited competition may not pro-
vide adequate discipline against the exploitation of traders who are known
to be buyers or sellers. In markets with many dealers, those who try to ex-
ploit known buyers and sellers lose their customers to dealers who quote
better prices.
13.4 ATTRACTING ORDER FLOW
Dealers must attract order flow in order to trade profitably. A dealer's
order
flow
is the stream of requests to trade that other traders make of the dealer.
Dealers attract order flow by quoting aggressive prices and large sizes,
providing high-quality service at low prices, advertising, creating marketing
relationships, and purchasing it.
In many markets, dealers primarily attract order flow by quoting ag-
gressive prices. Impatient buyers naturally look for the lowest ask prices, and
impatient sellers look for the highest bid prices. Dealers who quote the best
prices often get the orders.
Dealers also may obtain order flow by showing that they are willing
to trade large sizes. Traders who want to trade large size generally prefer
to trade with dealers who will commit to trading large size.
In markets where quotes are not publicly exposed, or where price im-
provement is common, dealers attract order flow by cultivating a reputation
for providing good prices, good service, and large sizes. They acquire such
reputations by consistently satisfying their clients. Dealers also may market
their businesses by collecting and disseminating statistical evidence that doc-
uments the quality of their services.
In some markets, dealers actively advertise to acquire order flow. They
design their marketing to promote their image, to provide information about
their services, and to document the quality of their services. When clients
choose the dealers with whom they trade, advertising is particularly impor-
tant. When brokers choose the dealers with whom their clients trade, ad-
vertising may be less important.


## Page 296

CHAPTER 13 DEALERS • 283
Dealers also acquire order flow by cultivating relationships with clients
who can send them orders. These clients are typically brokers and large in-
stitutional traders. Many dealers commonly provide their clients with mar-
ket information in an attempt to attract their orders. They also may provide
market research, training, electronic order-routing systems, accounting sys-
tems, and electronic information systems to develop their relationships. Many
dealers also entertain their clients extensively. They take their clients to din-
ner, to the theater, and often to major sports events like the Super Bowl, the
NBA finals, the NCAA basketball Final Four, and the World Series.
Finally, some dealers acquire order flow by buying orders from brokers
who collect them.
Payment for order flow
arrangements are common in some
stock markets. The dealers who pay for order flow typically only buy mar-
ket orders. In the U.S. markets, the payments currently average less than
1 cent per share. In the past, they have been as high as 3 cents per share
for certain stocks. Sometimes arrangements involve nonpecuniary payments
of services, or reciprocal exchanges of order flows among broker-dealers.
Payment for order flow raises difficult regulatory issues that we address in
chapter 25, where we discuss internalization and preferencing.
13.5 DEALER QUOTATION DECISIONS
The most important decisions that dealers make concern their quotations.
They must decide where to place their bid and offer prices, what the spread
between them should be, and what sizes they will trade at their bid and of-
fer. The remainder of this chapter considers how dealers decide where to
place their quotes. Chapter 14 considers how dealers set their spreads.
Where dealers set their bid and ask prices is the most important and
most difficult decision they make. When dealers set their prices poorly, they
tend to buy and later wish that they had sold, or sell and later wish that
they had bought. Dealers therefore pay very close attention to these deci-
sions. We shall see that dealers set their quotes to control their inventories
and to avoid losses to informed traders.
13.6 DEALER INVENTORIES
The positions that dealers have in the instruments they trade are their
inventories.
These positions may be long or short. Dealer inventories rise
when they buy more than they sell, and they fall when they sell more than
they buy. Since dealers allow their customers to determine the side on which
they trade, dealer inventories fluctuate in response to the demands of their
customers. Dealer inventories drop when traders buy from dealers, and they
rise when traders sell to dealers.
Target inventories
are the positions that dealers want to hold. Dealer in-
ventories are
in balance
when they are near their target levels and
out of bal-
ance
otherwise. A dealer's
inventory imbalance
is the difference between his
actual inventory position and his target inventory position.
If short and long positions are equally costly to create and hold, the tar-
get inventories of dealers who do not also speculate, hedge, or invest are
zero. Dealers who hold no inventory avoid the costs of financing their po-
sitions, and they do not lose when prices move against their positions. In
some markets, selling from a short position often costs more than selling
Imagine Being Fired
for
Not
Spending Your
Expense Account!
Some firms partly measure the
marketing efforts of their
dealers and brokers by how
much they spend in their
expense accounts. These firms
assume that employees who
do not spend "enough" are
not doing enough to cultivate
their businesses.
Though it seems
remarkable that people would
need to be encouraged to
spend their expense accounts,
many traders tire of the
constant entertaining they
must do. When entertaining is
essential to business
development, young
unmarried traders often have
an advantage over married
traders who want to be with
their families in the evenings
and on weekends.
4


## Page 297

284 • TRADING AND EXCHANGES
from a long position, and holding a short position often costs more than
holding a long position. In such markets, dealers try to hold positive in-
ventories in order to avoid these higher costs.
Dealers who speculate, hedge, or invest have target inventories that re-
flect these objectives. For example, the target inventories of dealers who also
speculate are long when they think their instruments are undervalued or
when they anticipate excess demand.
If dealers allow their inventories to get too far out of balance, they will
not have enough capital to finance their purchases or secure their short sales.
At that point, whoever clears their trades will force them to liquidate. If
they have lost money, they may go bankrupt.
13.6.1 How Dealers Control Their Inventories
Dealers must
control their inventories
to keep them in balance. They must
buy when their inventories are below their targets and sell when their in-
ventories are above their targets.
Dealers control their inventories primarily by influencing the buying and
selling decisions of their clients. When dealers want to decrease their in-
ventories, they lower their bid and ask prices. Lower ask prices encourage
traders to buy from them, which would decrease their inventories. Lower
bid prices discourage traders from selling more to them, which would in-
crease their inventories. Dealers also may decrease their bid sizes, and raise
their ask sizes, to decrease their inventories. Smaller bid sizes discourage
large traders from selling to them and larger ask sizes encourage large traders
to buy from them. When dealers want to increase their inventories, they
raise their bid and ask prices, increase their bid sizes, and decrease their ask
sizes. Higher bid prices and larger bid sizes encourage traders to sell to them.
Higher ask prices and smaller ask sizes discourage traders from buying from
them.
Dealers who want to adjust their inventories quickly may not be willing
to wait for another trader to come to them. Instead, they may initiate a trade
with another trader who is offering to trade. This tactic quickly solves the
inventory problem, but it is expensive. Dealers who demand liquidity from
other traders typically buy at the ask price and sell at the bid price; thus
their realized spreads will be negative.
Dealers must control their inventories in order to trade profitably. Large
positions are expensive to finance. They also expose dealers to serious losses
if prices move against them. Economists call this risk
inventory risk.
Traders
must control their inventories to avoid inventory risk.
When dealer inventories are in balance, dealers want to buy and sell in
equal quantities so that their inventories remain near their target levels. A
two-sided order flow
includes a mix of buyers and sellers who want to trade
equal quantities. Dealers try to set their prices to obtain two-sided order flows.
The search for prices that produce a two-sided order flow is called the
price discovery process.
Dealers try to discover the prices which ensure that
buying and selling quantities are just in balance. At these prices, supply
equals demand. Prices that balance supply and demand determine
market
values.
Dealers try to discover market values.
Dealing is most profitable when dealers can sell immediately after buy-
ing and buy immediately after selling. Dealers profit from these round-trip
transactions if they can buy at lower prices than those at which they can


## Page 298

CHAPTER 13 DEALERS • 285
TABLE 13-2.
Tactics Dealers Use to Manage Their Inventories and Order Flows
CONDITION

TACTIC

PURPOSE
Inventories are too low
or clients are net buyers
Inventories are too high
or clients are net sellers
Raise bid price
Increase bid size
Raise ask price
Decrease ask size
Take another trader's offer (buy
at another trader's ask price)
Buy a correlated instrument
Lower ask price
Increase ask size
Encourage clients to sell
Discourage clients from buying
Immediately raise inventories
Hedge the inventory risk
Encourage clients to buy
Lower bid price
Decrease bid size
Hit another trader's bid (sell at
another trader's bid price)
Sell a correlated instrument
Discourage clients from selling
Immediately lower inventories
Hedge the inventory risk
sell. Dealers who can quickly rebalance their inventories minimize the prob-
ability that prices will move against their positions while their inventories
are out of balance. Table 13-2 summarizes the strategies that dealers use to
manage their inventories and order flows.
13.7 INVENTORY RISK
Dealers face two types of inventory risk. The risks differ according to
whether future price changes are correlated with their inventory imbalances.
If future price changes are independent of their inventory imbalances, the
risk is a
diversifiable inventory risk.
If they are inversely correlated, the risk
is an
adverse selection risk.
13.7.1 Diversifiable Inventory Risk
Diversifiable inventory risks
are due to events that cause price changes no
one can predict. Such price changes are sometimes positive and sometimes
negative. On
average, they are zero. Otherwise, they would be predictable.
Diversifiable inventory risks are benign compared to adverse selection
risk. Although dealers lose when prices unexpectedly move against their po-
sitions, they gain when prices unexpectedly move in their favor. Since the
price changes are uncorrelated with their inventory imbalances, dealers gain
and lose with equal probabilities. Diversifiable risks make dealing a scary
business, but they do not cause dealers to lose in the long run.
Diversifiable risks are diversifiable because dealers can minimize their to-
tal inventory risk by dealing in many instruments. Unexpected gains in some
instruments often offset unexpected losses in other instruments. The varia-


## Page 299

286 • TRADING AND EXCHANGES
tion in total dealer profitability due to diversifiable inventory risk therefore
is a lower fraction of their expected dealing profits than it would be if they
traded only one security. Firms often deal in hundreds or thousands of in-
struments in order to diversify their exposure to diversifiable inventory risks.
13.7.2 Adverse Selection Risk
Dealers face
adverse selection risk
when they trade with informed traders.
This risk is not benign. Dealers—like all traders—lose money when they
trade with better-informed traders.
Informed traders buy when they think that prices will rise and sell other-
wise. If they are correct, they profit, and whoever is on the other side of
their trades loses. When dealers trade with informed traders, prices tend to
fall after the dealers buy and rise after the dealers sell. These price changes
make it difficult for dealers to complete profitable round-trip trades. When
dealers trade with informed traders, their realized spreads are often small or
negative. Dealers therefore must be very careful when trading with traders
they suspect are well informed.
Since informed traders trade only on the side of the market that their
information favors, they make order flows one-sided when they trade. In-
formed trading therefore causes dealer inventories to diverge from their tar-
get values. If prices change to reflect the informed traders' information be-
fore the dealers can restore their target inventories, the dealers will lose.
Economists call these losses
adverse selection losses
because informed traders
select the side of the market that is adverse to the dealers' profits.
Adverse selection from informed traders causes dealer inventory imbal-
ances to be inversely correlated with future price changes. When informed
traders buy, dealer inventories fall short of their targets, and prices subse-
quently rise. When informed traders sell, dealer inventories exceed their tar-
gets, and prices fall.
Dealers avoid adverse selection risk only by avoiding informed traders.
The best way they can avoid informed traders is to set their quotes near
fundamental values so that informed traders will not want to trade.
13.7.3 Market Values Versus Fundamental Values
Dealers avoid both types of inventory risk—diversifiable and adverse
selection—when they keep their inventories under control. They usually do
not care whether they trade with informed traders or uninformed traders as
long as they can quickly restore their target inventories. Most dealers there-
fore devote much more attention to discovering the market values that pro-
duce two-sided order flows than to discovering fundamental values.
Many dealers are uncomfortable with academic arguments that explain
their behavior in terms of asymmetrically informed traders. These dealers
generally do not know much about fundamental values, they care even less
about them, and they may not even believe that they exist. They are sim-
ply interested in discovering market values. These same dealers, however,
will readily acknowledge that some traders are right more often than not,
and that they trade more effectively when they are aware of such traders.
However expressed, adverse selection is the most important determinant of
dealer profitability.
The remainder of this chapter discusses how dealers respond to adverse
selection from informed traders. We focus closely on adverse selection be-


## Page 300

CHAPTER 13 DEALERS • 287
cause informed trading is the most important—and most dangerous—cause
of one-sided order flows. Dealers must have a thorough understanding of
informed trading to best discover market values and avoid substantial losses.
Dealers generally discover fundamental values as a by-product of their search
for market values.
13.8 DEALER RESPONSES
TO ADVERSE SELECTION
Successful dealers must confront the informed trader problem continuously.
They must respond appropriately when they suspect that they have traded
with an informed trader. They must quote properly when they expect that
they may trade with an informed trader. Perhaps most obviously, they must
try to avoid trading with informed traders. This section describes how deal-
ers address these three objectives.
13.8.1 What Dealers Do When They
Trade with Informed Traders
Dealers who suspect that they have traded with an informed trader must
adjust their quotes to avoid further losses. If they do not adjust their quotes,
informed traders will continue to trade with them, their order flow will re-
main unbalanced, and their inventory situation will worsen.
Dealers also must adjust their quotes to cover their positions quickly,
before prices move against them. If they can rebalance their inventories be-
fore prices change, they will avoid losing when prices change. The traders
to whom they unload their positions will lose instead.
When dealers suspect that they have bought from a well-informed seller,
they must lower their bid and ask prices. They lower their bid prices to dis-
courage informed traders from selling more to them. They lower their ask
prices to encourage other traders to buy their inventory so that they can get
back into balance before prices drop.
Likewise, when dealers suspect that they have sold to a well-informed
buyer, they must raise their bid and ask prices. They raise their ask prices
to discourage informed traders from buying more from them. They raise
their bid prices to encourage other traders to sell to them so that they can
replenish their inventories before prices rise.
Dealers who are especially concerned about holding positions that they
do not like will quickly try to pass them to someone else. Instead of pas-
sively waiting until someone wants to take their positions, they actively put
them to other traders by buying at the ask or selling at the bid. Although
these trades are expensive, they allow dealers to quickly divest themselves
of risks that they are unwilling to bear.
Table 13-3 presents the tactics that dealers can use when they suspect
they have traded with a well-informed trader. The art of being a successful
dealer lies in knowing when to use these tactics.
13.8.2 How to Quote to
Avoid Informed Traders
Dealers avoid trading with informed traders by setting their prices close to
fundamental values. Informed traders will not buy if they have to pay more
than fundamental value, and they will not sell if they receive less than fun-
damental value. To avoid informed traders, dealers therefore try to set their
Two Brothers and a
Piece of Cake
How should two selfish
brothers fairly
divide a piece
of cake between them? A
clever solution has one
brother divide the cake, and
the other brother choose
which half he wants. Since
the chooser will take the
larger piece, the divider must
try to divide the cake exactly
in half to maximize his share.
This solution is not entirely
fair. The chooser has a slight
advantage over the divider. If
the divider cannot divide the
cake exactly in half, a careful
chooser will always take the
bigger piece. The divider
faces the adverse selection
problem.
When dealers quote two-
sided markets, they divide the
number line of possible prices
into two parts. If they set their
prices too low or too high,
informed traders will take the
more attractive side. To avoid
adverse selection, dealers
must set their bids below
fundamental values and their
offers above them.
You can be confident that
dealers offer fair prices when
they quote tight bid/ask
spreads before they know
whether you want to buy or
sell. Such quotes indicate that
your transaction costs will be
small and that your trade price
will be close to the dealer's
best estimate of the market
value of the security.


## Page 301

288 • TRADING AND EXCHANGES
TABLE
13
-
3.
Tactics Dealers Use When They Suspect They Have Traded with a Well-informed Trader
SUSPECTED CONDITION

TACTIC

PURPOSE
Sold to a well-
informed trader
Bought from a well-
informed trader
Raise ask price
Lower ask size
Raise bid price
Raise bid size
Buy from another
traders at his/her
ask price
Buy a correlated
instrument
Lower bid price
Lower bid size
Lower ask price
Raise ask size
Sell to another
trader at his/her
bid price
Sell a correlated
instrument
Discourage further sales to informed traders
Encourage clients to sell quickly and thereby restore
inventory position before prices rise
Quickly restore target inventory; this strategy pays
for liquidity, but the cost may be less than the loss
that will result if prices rise while the dealer is short
Hedge inventory risk and speculate on information
Discourage further purchases from informed traders
Encourage clients to buy quickly and thereby restore
inventory position before prices
fall
Quickly restore target inventory; this strategy pays
for liquidity, but the cost may be less than the loss
that will result if prices drop while the dealer is long
Hedge inventory risk and speculate on information
bid prices just below fundamental values and their ask prices just above fun-
damental values.
Although dealers rarely know fundamental values as well as the better-
informed traders with whom they trade, clever dealers can infer values from
the orders, prices, and quotes that they see. Dealers therefore always pay
very close attention to market data when they set their prices.
Dealers make these inferences by using the simple principle that values
probably are greater than current prices if informed traders are buying, and
lower if informed traders are selling. Dealers therefore try hard to deter-
mine what informed traders are doing. If they suspect that informed traders
are buying, dealers will raise their quotes. If they suspect that informed
traders are selling, they will lower their quotes. These quotation price ad-
justments cause prices to reflect the informed traders' information about
fundamental values.
To make these inferences accurately, dealers must form opinions about
which traders are well informed, and how important their information is.
Dealers need to adjust prices substantially when they are confident that in-
formed traders are trading, and when they believe that their information is
highly significant.
Unfortunately, dealers generally do not know which traders are well in-
formed. Well-informed traders rarely reveal themselves because they do not
want dealers to know that they have mispriced their instruments. They gen-


## Page 302

CHAPTER 13 DEALERS • 289
erally use brokers to represent their orders so that they can trade anony-
mously. Even when dealers know with whom they are trading, they still may
be uncertain about whether their clients are well informed. Well-informed
traders usually claim to be uninformed in order to fool dealers into offer-
ing liquidity cheaply. Dealers therefore cannot easily identify informed
traders.
Dealers form opinions about how well informed their clients are by
knowing them well, by paying close attention to their trading, by watching
the order flow, and by observing market conditions. Dealers believe that
traders who trade for large, actively managed portfolios are often well in-
formed; that large traders are often better informed than small traders; that
impatient traders are often better informed than patient traders; and that
small retail traders tend to be uninformed. These rules are useful but not
always reliable.
Since dealers usually cannot form reliable opinions about which traders
are well informed, they must assume that all traders may be well informed.
Dealers accordingly draw inferences about fundamental values from all or-
ders. The significance that they attach to each order depends on how strongly
they suspect that a well-informed trader submitted it. They adjust their
prices more if they suspect that the order came from a well-informed trader
than from an uninformed trader.
The amount by which dealers adjust their prices also depends on the
significance of the information that they believe informed traders have.
Material information
is information that will significantly affect prices when
it becomes well known. Dealers adjust prices more when they believe that
informed traders have highly material information than when they believe
that informed traders are unlikely to have any deep insights into funda-
mental values.
Dealers form opinions about materiality by paying close attention to fun-
damentals. They generally believe that well-informed traders are more likely
to have highly material information about instruments for which publicly
available fundamental information is scarce or highly ambiguous. Such in-
struments are hard to value and often are highly volatile.
13.8.3 The Adverse Selection Spread Component
Dealers do not simply adjust their quotes after they believe that they have
traded with an informed trader. Before they set their quotation prices, they
also take into account the possibility that the next trader will be an informed
trader.
Successful dealers consider what they will learn about fundamental
values when traders choose to trade with them. If the next trader is a well-
informed buyer, prices should be higher. If the next trader is a well-informed
seller, prices should be lower. Good dealers incorporate this information into
their quotes beforehand rather than waiting until the next trader arrives.
They base their ask prices on their best estimates of fundamental values,
conditional
on the next trader being a buyer. They base their bid prices on
their best estimates of fundamental values,
conditional
on the next trader be-
ing a seller. Since these conditional estimates are different, ask prices are
greater than bid prices. The portion of the bid/ask spread that is due to the
different value inferences that dealers make—conditional on which side the
next trader chooses to take—is the
adverse selection component
of the bid/ask


## Page 303

290 • TRADING AND EXCHANGES
• How Madoff Controls Adverse Selection
Bernard L. Madoff Investment Securities is the largest dealer in NYSE-listed
stocks in the United States. The firm is not a member of the New York Stock
Exchange, however. The company trades approximately 15 percent of the
transaction volume in NYSE-listed stocks. Its share of total volume is smaller
because Madoff's average trade size is smaller than the average trade size
at the NYSE.
Madoff obtains most of its order flow through order-preferencing
arrangements that it negotiates with retail brokers. Since the firm is not a
member of the New York Stock Exchange, it can choose with whom it is
willing to trade. Bernie Madoff and his brother Peter have chosen to
provide liquidity primarily to retail clients, and primarily in the common
stocks of large firms. The Madoffs, along with most investment professionals,
believe that retail traders generally are not well-informed traders when they
trade large firm stocks.
Madoff's dealers are less exposed to adverse selection than are dealers
who trade on the floor of the NYSE, who cannot choose their clients. The
firm therefore often offers more liquidity to its clients than they can find on
the NYSE floor.
Many institutional traders would like to trade with Madoff in order to
access the liquidity that its dealers offer. The firm, however, will not accept
them as clients unless it is convinced that they are generally uninformed
traders.
Madoff offers its interested institutional clients a service it calls Time
Slicing. Institutional clients who use Time Slicing send Madoff large orders
that Madoff's computers break into small pieces to trade at periodic
intervals. Time Slicing is attractive to institutions that do not want their
orders to have immediate market impact. It is also attractive to institutions
that want to have a time-weighted average price for their trades. Time
Slicing is attractive to Madoff because it allows its dealers time to adjust
their inventories while filling large orders.
Through its Time Slicing service, Madoff ensures that its large institutional
clients do not include traders who demand immediate execution of their
orders. The service thus is not attractive to well-informed traders who trade
on material information that will soon become public.
Time Slicing allows Madoff to control the adverse selection problem that
all dealers face. By refusing to offer immediate liquidity to well-informed
traders, Madoff can offer more liquidity to uninformed traders.
Source: www.madoff.com

spread. Dealers build these inferences into their quotes ahead of time in or-
der to avoid regretting that they traded.
Since dealers generally do not know whether the next trader is well in-
formed, they set their quotes based on their estimates of the probability that
the next trader will be well informed. If they believe that the next trader is
likely to be trading on material information, their ask prices will be sub-
stantially greater than their bid prices.
The dealers' response to adverse selection makes trading large orders very
expensive. Dealers generally believe that traders with large orders tend to
be well informed. They believe this because well-informed traders like to
acquire large positions in order to maximize their profits and because large
institutions can afford to be well informed because they can spread their re-
search costs over a large portfolio. Dealers therefore quote wide spreads to
fill large orders. They also adjust their prices substantially when they be-


## Page 304

CHAPTER 13 DEALERS • 291
lieve that traders have split large orders into small pieces to obtain better
prices. The price adjustments that dealers make to avoid adverse selection
cause large orders to have substantial market impact.
The substantial impacts that large anonymous orders have on price make
it very difficult to trade large orders. We discuss how large traders solve this
problem in chapter 15.
13.8.4 Dealers Sometimes Refuse to Trade with
Informed Traders
Some dealers avoid adverse selection risks by refusing to trade with well-in-
formed traders. Many dealers will not trade with informed traders if they
can identify them and if regulations do not require that they trade with them.
Informed traders therefore prefer to trade anonymously, so that dealers
cannot identify their trading. To hide their identities, informed traders use
brokers to arrange their trades. Brokers often refer to this as
bearding the trade.
Some dealers will trade only with clients they believe are relatively un-
informed. For example, some dealers trade only with retail customers be-
cause retail customers are rarely well-informed traders. Other dealers refuse
to trade with large institutions that actively manage their portfolios because
such traders are often well informed. Still other dealers trade only with cus-
tomers that they know. They do not offer liquidity to anonymous traders
because informed traders tend to trade anonymously.
Most dealers prefer not to display large size. Instead, they hope that traders
will come to them and ask for more size when they want it. By forcing traders
to ask for size, dealers can better determine with whom they will be trading.
If they believe that an informed trader wants the size, they will not offer it,
or they will offer it at a substantial price concession. If they believe that an
uninformed trader wants the size, they may be far more generous.
13.9 PRICING MISTAKES DEALERS MAKE,
AND HOW THEY AVOID THEM
Dealers make two kinds of mistakes when adjusting their quotes. They may
fail to adjust their quotes adequately when they have traded with informed
traders. They then will lose when prices move against their inventories. Al-
ternatively, they may adjust their quotes too much, thinking that they have
traded with informed traders when they in fact have not. In that case, they
may move prices away from fundamental values and thereby create prof-
itable trading opportunities for well-informed value traders. Value traders
quickly restore dealers to their target inventories following such mistakes,
but the dealers will make smaller realized spreads on their round-trip trades
than they otherwise would have.
Table 13-4 provides a summary of the responses dealers make, and the
consequences they face, when trading with well-informed and uninformed
buyers. Dealers make similar responses, and face similar consequences, when
trading with sellers. Unfortunately, dealers rarely know whether they are
trading with informed or uninformed traders.
Dealers use all information available to them to determine where to place
their quotes. In addition to the information in their order flow, they extract
information from orders that other dealers receive. Although they usually
do not see these orders, they may see the trades that result from them and
the changes in quotes that other dealers make as they fill them. This infor-
Dealers as Card Players
Good
dealers are often
excellent card players. The
ability to remember which
cards have been played in a
game, and who played them,
requires the same short-term
memory skills as dealing in
financial markets. Like card
players, dealers must be able
to estimate conditional
probabilities quickly and
accurately, they must be able
to make quick decisions
based upon all information
available to them, and they
must be able to conceal their
intentions completely. If
professional traders invite you
to play poker with them, be
prepared to learn more than
you earn.
1


## Page 305

292 • TRADING AND EXCHANGES
TABLE 13
-
4.
Dealer Responses and Consequences When Trading with Well-informed and Uninformed Buyers
DEALER RESPONSE
BUYER IS

RAISE QUOTES

DO NOTHING

INITIATE BUY AT ASK
Well informed

The correct tactic
Prevents further
informed trading
Increases probability
that dealer will cover
before prices change
Decreases realized
spreads and dealer
profits
A mistake
More informed buyers
will follow
Dealer will lose when
prices change
Greatly decreases
realized spreads
May be correct tactic
Correct tactic if prices will
soon rise significantly
Dealer will limit
subsequent losses
Decreases realized spreads,
but not by as much as they
might decrease otherwise
Uninformed
A mistake
Value traders will sell
Decreases realized
spreads and dealer
profits
The correct tactic
Price presumably is still
correct
If the next client is a
seller, the dealer will earn
the full quoted spread
Big mistake
Dealer unnecessarily pays
for liquidity
Greatly decreases realized
spreads
Bk' Bagging a Front-
running Dealer
Dealers who try to speculate
on information that they infer
from their trades with
informed traders must be very
careful that informed traders
do not try to manipulate their
trading by bluffing.
For example, suppose that
a clever informed trader
knows a particular dealer will
speculate on information that
he infers from his trades. If
the informed trader wants to
sell substantial size, he might
give a small buy order to
the dealer. When the dealer
then tries to buy substantial
size to speculate on his
"information," the informed
trader can sell it to him
anonymously through a
broker. When prices fall,
the dealer will lose.
mation allows attentive dealers to infer information about the orders that
other traders receive. Dealers also subscribe to electronic news services that
publish information about their instruments. They pay close attention to
these news stories to determine what effect they will likely have on funda-
mental values and to help them interpret the order flow.
13.9.1 Dealers Sometimes Choose
to Trade with Informed Traders
Dealers occasionally choose to trade with well-informed traders. Although
they take the wrong side of these transactions, the value of the information
that they obtain by trading with a trader they know is well informed may
more than offset the costs of being on the wrong side. When dealers trade
with known informed traders, they learn whether their prices are too low
or too high. Dealers can then adjust their prices to eliminate future adverse
selection. They also may speculate on their secondhand information.
Dealers who trade with well-informed traders cannot offer them too
much liquidity. To avoid losing, they must be able to trade out of their po-
sitions quickly. Otherwise, they may be holding the wrong position when
prices move against them.
13.10 WELL-INFORMED AND POORLY
INFORMED DEALERS
Dealers who are poorly informed about fundamental values are less able to
judge whether their clients are well informed than are well-informed deal-


## Page 306

CHAPTER 13 DEALERS • 293
t.

Do Foreign Exchange Markets Trade Incredible Volumes?
The trading volumes in foreign exchange markets are remarkably large
compared to the economic activities that motivate foreign exchange
transactions. In April 2001, the total daily dollar volume in all major
foreign exchange markets was approximately 1.21 trillion dollars! Placed
on an annual basis, this figure is approximately 15 times the gross domestic
product of the world economy and 40 times the dollar value of all
international trade.
Most foreign exchange trades are among dealers. Why do dealers trade
so much with each other in these markets?
Foreign exchange markets historically have been quite opaque. When
dealers cannot see the trades and quotes made by other dealers, they must
trade actively with each other to learn about market conditions. Any dealer
who does not trade when called upon may not be called the next time.
Dealers who are not called do not know what is happening, and will not
stay in business long. In opaque markets, dealers often buy and sell
positions that they do not want to have so that they can remain in the flow
of information. After they take these positions, they very often immediately
dispose of them by trading with another dealer.
Trades that could be easily arranged if a mechanism existed to match
natural buyers to natural sellers often pass through the hands of many
dealers. Each dealer takes the position and then passes it along until some
dealer receives a position that restores his target inventory. This intense
trading is possible because foreign exchange markets trade highly
standardized instruments (currencies) for which trades can be settled very
cheaply.
As foreign exchange markets become more transparent and as market
mechanisms develop that allow natural buyers and natural sellers to be
easily matched, volumes in these markets will probably decrease. Foreign
exchange trading volumes decreased 19 percent between April 1998 and
April 2001, most probably for these reasons.
Data source: Bank for International Settlements,
Triennial Central Bank Survey of Foreign
Exchange and Derivatives Markets Activity
at http://www.bis.org/press/p011009.pdf.

ers.
Poorly informed dealers therefore more often mistakenly assume that
an uninformed client is an informed trader than do well-informed dealers.
Consequently, poorly informed dealers tend to trade quickly to keep their
inventories near their target levels. Their efforts to stay in balance cause
them to earn smaller realized spreads than well-informed dealers. Although
their realized spreads are small, they may still be very profitable if they can
turn their inventory quickly without holding large positions for long peri-
ods of time. These dealers trade most often in active markets. They are
often known as
day traders
or
scalpers.
Dealers who are well informed about fundamental values are better able
to bear the risks of holding large positions than are poorly informed deal-
ers. When dealer prices are close to fundamental values, most orders that
dealers receive must come from uninformed traders. Since well-informed
dealers can keep their prices near fundamental values, they are less exposed
to adverse selection than are poorly informed dealers. They also depend less
on the order flow when setting their quotes than do poorly informed deal-
ers. With less concern about inventory risk, well-informed dealers do not
have to balance their inventories as quickly as do poorly informed dealers.
Dealing and
Steamrollers
High-frequency dealing is a bit
like picking up pennies in front
of a steamroller. Sometimes
you get in and out quickly, and
profit a little. Sometimes you
miss an opportunity or you
pass because you have no
safe opportunity. However, if
you are not very careful, you
get caught and lose
everything!
1


## Page 307

294 • TRADING AND EXCHANGES
IP
Dealer Layoffs
A large uninformed trader
buys stock in multiple
transactions from many poorly
informed dealers. (Equivalently,
many small uninformed traders
buy the stock.) Dealer
inventories drop. The dealers
raise their ask prices to avoid
losing more inventory to
traders they suspect are well
informed. They raise their bid
prices to try to restore their
target inventories.
A value trader sees that
prices have risen above their
fundamental values. He sells to
the dealers. These sales allow
the dealers to restore their
target inventories. The dealers
effectively lay off their short
positions on the value trader.
The value trader then patiently
waits until prices fall. 4
They therefore are more willing to take larger positions and hold them longer I
than are poorly informed traders. Since well-informed dealers can patiently
wait for traders to come to them, they earn larger realized spreads than do
poorly informed dealers.
Traders who are willing to take large positions are often called
block
traders.
We discuss them further in chapter 15.
13.11 DEALERS AND VALUE TRADERS
I Dealers who are extremely well informed about fundamental values are es-
sentially
Isentially value traders. Value traders trade when prices diverge significantly
I
from fundamental values. This often happens when poorly informed deal-
ers mistakenly identify uninformed traders as informed traders. It also hap-
pens when risk-averse dealers demand and receive substantial price conces-
.
[
mons to take large inventory positions. These events cause prices to diverge
from fundamental values. Value traders then step in and typically trade with
the dealers.
Value traders supply liquidity to the market when they trade in response
(
to the demands for liquidity made by other traders. Unlike dealers, who pri-
marily supply immediacy, value traders primarily supply depth. A market is
deep
when traders can buy or sell substantial size without significantly im-
pacting prices. We discuss in detail how value traders supply liquidity in
chapter 16.
13.12 DEALERS AND BLUFFERS
As noted in chapter 12, dealers must be especially careful when adjusting
their prices to ensure that bluffers do not fool them into offering liquidity
unwisely. Dealers adjust their prices in response to the order flow. Since
bluffers can control the composition of the order flow, they can manipulate
dealer prices. To avoid losing to bluffers, dealers must be sure that they ad-
just their prices in a manner that will not allow bluffers to trade profitably.
In particular, when dealers do not know well those with whom they trade,
they must assume that a bluffer may be present. To avoid losing to bluffers,
dealers must always adjust their prices at the same rate per quantity traded,
whether they are buying or selling, with traders they cannot identify. The
rate must be the same whether the orders arrived quickly or slowly, whether
the orders are large or small, and without regard to whether the orders fol-
lowed other orders of the same type.
13.13 SUMMARY
Dealers sell immediacy—the ability to buy or sell quickly when you want
to—to their clients. Dealers acquire their clients by offering attractive prices
and good service, by advertising, and by paying brokers to direct their client
orders to them. The bid/ask spread is the price of liquidity that they sell.
Dealers try to buy and then quickly sell, or to sell and then quickly buy.
They do not like to accumulate large inventory positions. When they hold
large inventories, they risk large losses should prices change against them.
Dealers set their bid and offer prices to obtain and maintain two-sided
order flows. Two-sided order flows allow them to keep their inventories at


## Page 308

CHAPTER 13 DEALERS • 295
their target levels. When inventories deviate from their target levels, deal-
ers must adjust their bid and offer prices to encourage their clients to ini-
tiate trades that will restore their inventories, and to discourage their clients
from initiating trades that would cause them to deviate further. Dealers
sometimes demand liquidity from other traders when they are especially im-
patient to adjust their inventories.
Dealers lose to well-informed traders who can predict future price
changes. When informed traders are trading, the order flows that dealers
receive are not balanced, dealer inventory imbalances become inversely cor-
related with future price changes, and dealers thereby lose money. Dealers
avoid these adverse selection losses by setting their bid and offer prices so
that they surround their best estimates of fundamental values. They esti-
mate values by using all information available to them. Dealers pay partic-
ularly close attention to their order flows because they partially reveal what
informed traders believe about values. Successful dealers also try to avoid
trading with informed traders if they can.
When setting their bid and ask prices, dealers anticipate what they will
learn about values when they discover whether the next trader is a buyer or
a seller. If a buyer arrives, values may be higher than dealers otherwise esti-
mated. Dealers accordingly set their ask prices slightly higher than they oth-
erwise would. Likewise, they set their bid prices slightly lower than other-
wise to reflect what they will learn about values should a seller next arrive.
These price adjustments constitute the adverse selection spread component.
Dealing is a complex activity in which dealers try to discover who is in-
formed, who is bluffing, and who wants to trade for other reasons. Dealers
must constantly make these judgments as they try to discover the market
prices that will generate the balanced order flows that they need to easily
control their inventories.
13.14 SOME POINTS TO REMEMBER
•
Dealers quote prices to control their inventories and to obtain two-
sided order flows.
•
Dealers attract order flow by quoting aggressively and by offering or-
der flow inducements.
•
Informed trading hurts dealers.
•
Dealers learn about values from their order flow and adjust their quotes
accordingly.
•
Dealers often discover fundamental values in their search for market
values.
•
The inferences dealers make about future order flows create a spread
between their bid and ask prices. This spread is called the adverse se-
lection spread component.
•
The adverse selection spread component increases with trade size.
13.15 QUESTIONS FOR THOUGHT
1.
Which traders do you expect are more risk averse, dealers or brokers?
2.
Which traders do you expect make better dealers, risk tolerant
(mildly risk-averse) individuals or very risk-averse individuals?
3.
Are preferencing arrangements good for brokerage customers?


## Page 309

296 • TRADING AND EXCHANGES
4.
How can dealers control the risk of trading with informed traders?
5.
Could a proprietary trading firm program a computer to trade prof-
itably as a dealer? What risks would such a trading operation en-
counter? In what markets would such systems be most successful?
Would you be willing to trust a computer to trade on your behalf?
6.
Would you expect that a dealer's inventory imbalance would be pos-
itively correlated with future price changes? Why or why not?
7.
Why will dealers always quote the wrong prices if they do not know
whether they will trade next with a well-informed trader or an un-
informed trader?
8.
When and why would uninformed traders cause one-sided order
flows? How should dealers respond to one-sided order flows from
uninformed traders?
9.
Under what circumstances might market values differ from funda-
mental values for prolonged periods?
10.
For what instruments would dealers have the most difficulty setting
their quotes?
11.
In what market structures would dealers have the most difficulty set-
ting their quotes?
12.
For what instruments would you expect informed traders to have
highly material information?
13.
Since all order flow is informative, dealers might want to change
their quotes after every trade. In practice, most dealers do not change
their quotes so often. Why might that be the case?
14.
The minimum price increment employed in many markets limits the
set of prices that dealers can use to quote their markets. If the in-
crement is large, how might it affect dealer quotation behavior?
15.
Traders who expose large sizes risk attracting quote matchers. Are
dealers more or less vulnerable to quote matchers than are public
limit order traders?


## Page 310

14
Bidllisk
Spreads
The
bid/ask spread is the price impatient traders pay for immediacy. Im-
patient traders buy at the ask price and sell at the bid price. The spread
is the compensation dealers and limit order traders receive for offering
immediacy.
The spread is the most important factor that traders consider when they
decide whether to submit limit orders or market orders. When the spread
is wide, immediacy is expensive, market order executions are costly, and limit
order submission strategies are attractive. When the spread is narrow, im-
mediacy is cheap, and market order strategies are attractive. If you are in-
terested in optimizing your order submission strategies, you must under-
stand what determines bid/ask spreads so that you can judge whether they
are wide or narrow, given current market conditions.
The spread is also the most important factor that dealers consider when
they decide whether to offer liquidity in a market. If the spread is too nar-
row, dealing may not be profitable and dealers may quit trading. If it is wide,
dealing will be profitable and other dealers may enter the market. If you are
interested in dealer profitability, you must understand the factors that de-
termine bid/ask spreads.
In this chapter, we will consider what determines bid/ask spreads in dealer
markets and in order-driven markets. We will discuss when immediacy is ex-
pensive, when it is cheap, and why. The most important factors that deter-
mine spreads are adverse selection due to well-informed traders, volatility,
and market activity. We will closely examine these factors and many others.
The most important lesson you may learn from this book appears in this
chapter. You will learn why uninformed traders lose to well-informed traders
whether they submit limit orders or market orders. Uninformed traders lose
simply because they trade. If you are an uninformed trader and do not want
to lose, you should minimize your trading.
14.1 DEALER BID/ASK SPREADS
Dealers set their spreads to maximize their profits. Their spreads must be
wide enough to allow them to recover their costs of doing business. Other-
wise, they will not be profitable, and they will quit dealing. Their spreads
cannot be so wide, however, that no one will trade with them. Their rev-
enues then would not cover their expenses.
Dealers profit when their revenues exceed their expenses. Dealer rev-
enues depend on the effective spreads they earn on their round-trip trades,
on how often they can turn their inventory, and on how much they lose to
informed traders. Dealer business expenses reduce their profits. These ex-
penses include financing costs for their inventories, wages for their staff,
exchange membership dues, and expenditures for telecommunications, re-
search, trading system development, clearing and settlement, accounting,
office space, utilities, and other such items.
297


## Page 311

298 •
TRADING AND EXCHANGES
One Dealer Does
Not Necessarily a
Monopolist Make
The specialists at the New
York Stock Exchange are the
unique dealers in their
specialty stocks. Although
their unique positions may
give them some market power
on the floor of the Exchange,
they are hardly monopolists.
They face competition from
public limit order traders and
from dealers at other
exchanges that trade the
same securities.
14.1.1 Monopoly Dealers
When dealers face little competition, they may quote wide spreads in order
to maximize their profits. The optimal monopoly spread depends on the de-
mand for their services. If clients are willing to trade regardless of the spread,
spreads will be wide. If clients are sensitive to their transaction costs, spreads
will be low.
Monopoly dealers set their spreads so that the additional revenue from
a slight decrease in spread is just equal to the additional cost of providing
the additional liquidity that traders will demand at the slightly lower spread.
A similar result appears in all introductory economics textbooks. We will
not explain it here because dealers can rarely behave as monopolists in fi-
nancial or commodity markets.
Monopolies are successful only when monopolists can prevent competi-
tors from entering their markets. In most security and contract markets, the
barriers to entry that dealers face are low. Dealers always look for markets in
which they can make money. If dealing profits are excessively high in some
market, they will enter that market and try to participate in the excess prof-
its. Their entry tends to lower spreads, and thereby the profits of all dealers
I in the market. The threat of entry therefore may prevent a dealer from be-
' having as a monopolist even when no other dealers are in the market.
In many markets, dealers also face competition from public limit order
traders. Limit orders are essentially the same as dealer quotes. Both are of-
fers to trade that other traders may take when they want to trade. Dealers
who compete with aggressive public limit order traders cannot earn large
effective spreads because the limit order traders will undercut their quotes.
14.1.2 Spreads in Competitive Dealer Markets
In competitive dealer markets, dealer spreads ultimately depend on the costs
that dealers incur in running their business. The free entry and exit of deal-
ers ensures that spreads will adjust so that dealers just earn normal profits
for providing their liquidity services. When spreads are too high, so that in-
cumbent dealers earn excessive profits, new dealers will enter the market.
Their competition for order flow will cause spreads to fall. As the spreads
fall, so will the excess profits. If spreads are too low, so that dealers are los-
ing money, some will eventually quit because nobody can lose money for-
ever. With less competition, the remaining dealers will be able to raise their
spreads and thereby decrease their losses. Only when spreads are'set so that
dealers earn normal profits will dealers neither enter nor leave the market.
Dealers earn normal profits when their revenues just cover their total
economic costs of doing business. These costs include all costs described
above, a fair rate of return on their invested capital, and fair compensation
\ for their entrepreneurial efforts. Economists call the difference between rev-
enues and the total economic costs of doing business
economic profit.
When
dealers earn normal profits, economic profits are zero. Firms that make nor-
mal profits have accounting profits that just cover the value of the entre-
preneurs' time and the rental of their capital.
14.2 SPREAD COMPONENTS
For analytic purposes, economists break the bid/ask spread into two com-
ponents. The decomposition makes it easier to understand what factors de-
termine bid/ask spreads.


## Page 312

CHAPTER 14 BID/ASK SPREADS • 299
The
transaction cost spread component
is the part of the bid/ask spread that
compensates dealers for their normal costs of doing business. We enumer-
ated these costs above. This component also funds any monopoly profits
that the dealer may make and any risk premium that dealers may require
for bearing inventory risk.
The
adverse selection spread component
is the part of the bid/ask spread
that compensates dealers for the losses they suffer when trading with well-
informed traders. This component allows dealers to earn from uninformed
traders what they lose to informed traders. We also discuss this component
in chapter 13 when we consider how dealers learn about values from the or-
der flow. There we examine the component from an information perspec-
tive. Here we examine it from an accounting perspective. Remarkably, al-
though the two perspectives are quite different, they both imply the same
size adverse selection spread component.
The two components taken together constitute the total spread. Dealers
never quote both components separately. They simply quote their bid and
ask prices. To actually estimate the two spread components, analysts must
use econometric methods.
14.2.1 The Transaction Cost Component
If all traders knew instrument values with complete certainty, the transac-
tion cost component would constitute the entire spread. Prices would sim-
ply bounce back and forth between bid prices, which would be set slightly
below instrument values, and ask prices, which would be set slightly above
instrument values. Competition among dealers would cause the spread to
equal the normal costs of doing business. If dealers had monopoly power,
they would set wider spreads.
Economists also call the transaction cost spread component the
transi-
tory spread component
because price changes associated with this component
are transitory.
Transitory
price changes regularly reverse. Price changes
caused by a jump from the bid to the ask most frequently follow price
changes caused by a jump from the ask to the bid. Such price changes oc-
cur when the order flow includes a mix of buyers and sellers.
Traders call the bouncing back and forth between bid and ask prices
bid/ask bounce.
Bid/ask bounce is a minor form of price volatility caused by
impatient traders who demand immediacy. The transitory spread compo-
nent is responsible for bid/ask bounce.
14.2.2 The Adverse Selection Spread Component
Since dealers do not know fundamental values well, they expose themselves
to adverse selection from better-informed traders when they offer liquidity.
The better-informed traders choose the side of the market on which they
trade, and the dealers end up losing money to them. When some traders
are better informed than other traders, traders are
asymmetrically informed.
If dealers set their spreads to reflect only their normal costs of doing
business, their losses to well-informed traders would eventually force them
out of business. Dealers must widen their spreads further to cover their losses
to informed traders. This additional widening of the spread is the
adverse
selection spread component.
It allows dealers to recoup from uninformed
traders what they lose to informed traders. By widening the spread, it also
decreases dealer losses to informed traders by ensuring that informed traders
trade at less attractive prices.


## Page 313

300 • TRADING AND EXCHANGES
Economists also call the adverse selection spread component the
perma-
nent spread component.
Price changes due to the adverse selection spread com-
ponent are permanent in the sense that they do not systematically reverse.
Subsequent price increases and decreases are equally likely. Price changes
due to the adverse selection spread component reflect changes in dealers'
estimates of instrument values. When dealers efficiently use all information
I
f available to them to estimate values, the resulting sequence of estimate re-
, visions should be unpredictable. (If their future revisions were predictable,
the dealers would not be estimating values efficiently: They should have in-
corporated information upon which any predictable revision could be based
in their earlier estimates.) A process with unpredictable changes is essen-
tially a random walk. Every change in a random walk is permanent in the
sense that it affects the levels of all subsequent values of the random walk.
14.2.3 Two Explanations for the
Adverse Selection Component
The adverse selection spread component has two aspects. From an infor-
mation perspective (see chapter 13), it is the difference in the value esti-
' mates that dealers make conditional on the next trader being a buyer or a
seller. From an accounting perspective, it is the portion of the bid/ask spread
that dealers must quote to recover from uninformed traders what they ex-
pect to lose to informed traders.
Remarkably, these two perspectives imply the same size for the adverse
selection spread component. A simple proof of this result, known as the
Glosten-Milgrom theorem, appears in the appendix to this chapter. You can
easily understand the result by considering what determines the adverse se-
lection component from both perspectives. To simplify our discussion, as-
sume that dealers know exactly what values are if informed traders are trad-
ing. (Our result does not depend on this assumption.) The dealers, however,
do not know when informed traders are trading.
From the information perspective, the adverse selection spread compo-
nent is the amount that dealers should update their estimates of value when
they learn whether the next trader is a buyer or a seller. If a dealer trades
with a known uninformed trader, the dealer learns nothing and his estimate
of value should remain the same. If the dealer trades with a known informed
trader, the dealer should adjust his bid and offer to reflect the proper value
of the instrument. This adjustment is the dealer's pricing error, the differ-
ence between the proper value of the instrument and the dealer's original
estimate of its value. Since the dealer does not know which clients are well
informed and which are uninformed, the dealer must adjust his estimate of
value partially following every trade. In particular, he will discount the pric-
ing error by the probability that his next client is well informed. From the
information perspective, the adverse selection spread component thus is the
product of the pricing error (assuming that the trader is informed) times
the probability of trading with an informed trader.
From the accounting perspective, the adverse selection spread compo-
nent is the amount that dealers should charge all their clients to recover
their losses to informed traders. In our simple analysis, assume the dealer
loses the difference between his original estimate of value, and the proper
value, when he trades with an informed trader. Since the dealer incurs this
loss only when he trades with a well-informed trader, the average loss per


## Page 314

If the next trader is a
seller.
The transaction cost
Vs

component
Bid,
CHAPTER 14 BID/ASK SPREADS • 301
, The Total Spread
Figures 14-1 and 14-2 illustrate the conceptual process by which dealers set
and adjust their spreads. If charts intimidate you, skip them. The figures
represent the same information presented in the text.
Figure 14-1 shows that the total spread is the sum of the two spread
components. In principle, a dealer derives her bid and ask prices as
follows:
•
She first estimates the instrument value, using all information currently
available to her. This estimate, V
o
in the figure, is the basis for her
quoted bid and ask prices. It determines the level of her price quotes.
•
Using this basis, she then estimates values, assuming that the next trader
is a buyer (Vgj or a seller (Vg). The difference between these two
estimates is the adverse selection spread component. If the probabilitigs
of trading with informed buyers and sellers are equal, and if the
expected
,
pricing errors in both instances are equal, the two value
estimates will be equally distant from her initial estimate, V
o
. She then
simply adds and subtracts half of the adverse selection spread
component to V
o
to obtain them.
•
She obtains her offer price by adding half of the transaction cost spread
component to her value estimate for a buyer. She likewise obtains her
bid price by subtracting half of the transaction cost spread component
from her value estimate for a seller.
In practice, dealers set their bid and ask prices by using their experience
to interpret current market conditions. Although they rarely form their
estimates as described here, they regularly consider the issues discussed
here.
When the next trader arrives, the dealer learns whether she wants to
buy or sell. If the dealer learns nothing more about the trader or about
values, the dealer's new unconditional value estimate will be the
appropriate previous conditional value estimate.
Figure 14-2 illustrates the quotation adjustments following the arrival of a
buyer
(P
0
=
Ask
o
).
The bid and ask both rise by half of the adverse
selection spread component.
4
FIGURE 14-1.
The Components of the Total Spread


## Page 315

Ask,

Last trader was a

If the next trader is a
buyer.
uyer.

Vo
Bid,

s
If the next trader is
Ti
o
a seller.
Bid,

302 • TRADING AND EXCHANGES
Value
FIGURE 14
-
2.
Quotation Adjustments After a Buyer Arrives
I trade to informed traders is the loss from trading with an informed trader
times the probability of trading with an informed trader. This average loss
is the adverse selection spread component.
These two expressions for the adverse selection spread component are
the same if the loss from trading with an informed trader is the same as the
pricing error when trading with an informed trader. This is true if dealers
cannot restore their target inventories before prices change to reflect the in-
formed traders' information.
If dealers can restore their target inventories, the result still holds with
a caveat. If dealers do not recognize that they have traded with an informed
trader, they will not fully adjust prices. Informed traders therefore will trade
with them again. Dealers will continue losing until the price reflects the in-
formed traders' information. Their cumulative losses will equal their origi-
nal pricing error, so the Glosten-Milgrom theorem still holds.
If dealers do recognize that they have traded with an informed trader,
but others in the market have not, they may be able to trade on the side
that the information favors. They then will act as speculators rather than as
dealers. They will be eager to trade on the informed side but unwilling to
offer liquidity on the other side. We can restate the Glosten-Milgrom the-
orem to include this situation, but the restatement is beyond the scope of
this book.
14.2.4 Discriminating Between the
Two Spread Components
Econometricians have estimated the two components of the bid/ask spread
so that we can determine their relative importance. The decomposition is
possible because the two components give prices different statistical prop-
,
erties. The transaction cost spread component causes prices to bounce back
and forth between bid and offer prices. The adverse selection spread com-
ponent causes unpredictable price changes that have the properties of a ran-
dom walk. In both cases, the price changes depend on whether a buyer or
a seller initiated the transaction.


## Page 316

CHAPTER 14 BID/ASK SPREADS • 303
The most common decomposition method involves the estimation of an
equation to explain current and future price changes using information about
whether each trade was buyer- or seller-initiated. These analyses indicate
that in most markets the adverse selection spread component accounts for
more of the total spread than does the transaction cost spread component.
14.3 ADVERSE SELECTION AND UNINFORMED
TRADERS: THE MOST IMPORTANT LESSON
IN THIS BOOK FOR MOST READERS
Adverse selection explains why uninformed traders lose to informed traders,
regardless of whether they trade with limit or market orders. In both cases,
they suffer the effects of adverse selection.
When uninformed traders use limit orders, their orders fill quickly if they
overprice their bids or underprice their offers. Informed traders then eagerly
trade with them, and the uninformed traders ultimately will regret trading.
In this case, uninformed traders directly suffer the effects of adverse selec-
tion, just as dealers do.
When limit order traders and informed traders compete to trade on the
same side of the market, their limit orders often do not fill. Since informed
traders tend to forecast future price changes correctly, prices often will move
away from the limit orders, and the uninformed traders will regret not trad-
ing. In this case, adverse selection causes uninformed traders to lose prof-
itable trading opportunities.
Uninformed traders thus often regret using limit orders. When they trade
with informed traders, they regret trading. When they compete with in-
formed traders to fill their orders, they often do not fill, and they regret not
trading.
Uninformed traders who use market orders ensure that they trade, but 1
they still suffer the effects of adverse selection. Since dealers widen their \
spreads to recover from uninformed traders what they lose to informed
traders, uninformed market order traders trade at wider bid/ask spreads than
they would if there were no informed trading. In effect, the adverse selec-
tion spread is a fee that dealers charge market order traders for bearing ad-
verse selection risk.
Uninformed traders do not lose because they systematically want to trade
on the wrong side. Even if they flip a coin to decide on which side to trade,
uninformed traders tend to lose. A fair coin ensures that they will be right
about future price changes half the time, but the costs of filling their orders
will cause them to lose on average.
Uninformed traders thus ultimately lose to informed traders regardless
of how they trade. They lose simply because they trade. They can avoid the
problem only by not trading. Table
14-1
summarizes why uninformed
traders lose when trading.
14.4 EQUILIBRIUM SPREADS IN CONTINUOUS
ORDER-DRIVEN AUCTION MARKETS
Continuous order-driven auction markets arrange trades when they match I
an arriving market order (or marketable limit order) with a standing limit
order. Traders who use these markets must decide whether to offer liquid- '
ity by submitting limit orders or to take liquidity with market orders.


## Page 317

304 • TRADING AND EXCHANGES
TABLE 14-1.
Uninformed Traders Tend to Lose to Informed Traders Regardless of How They Trade
UNINFORMED

INFORMED

CONSEQUENCES TO
TRADER'S ORDER

TRADERS

ORDER DISPOSITION

SUBSEQUENT PRICES

UNINFORMED TRADER
Limit order
Market order
Want to trade
on the other
side
Want to trade
on the same
side
Does not
matter
Informed traders
quickly fill the
order
The order competes
with informed
traders and does not
fill
Dealer quickly fills
order at spreads
made wide by
informed trading
Price moves against
the newly acquired
position
Price moves away
from the limit order
Regrets trading
Regrets not trading
Bid/ask bounce

Loses half of the
causes price to move

spread
against the newly
acquired position
Chapter 4 discusses limit and market order properties in detail. Briefly,
market order traders get immediate executions, but they pay the bid/ask
spread to trade. Limit order traders get good prices if their orders execute,
but they risk failing to trade if the market moves away from their orders. If
they fail to trade and still wish to trade, they must replace their limit orders
with more aggressively priced orders. In the end, they may trade at much
worse prices than they would have received had they initially used market
orders.
The bid/ask spread determines how attractive limit and market order
i
trading strategies are. If the spread is wide, market orders will be expensive,
and no one will use them. If the spread is narrow, market orders may be
more attractive than limit orders.
This section describes how traders decide which type of order to use. We
must understand their decisions in order to identify the determinants of
bid/ask spreads in auction markets.
We will consider the problem by first analyzing a simple, but very un-
realistic, situation in which we can easily predict how traders will behave.
Once we understand that situation, we will be better able to analyze more
interesting and realistic situations. The foundation for our analysis appears
in a paper about equilibrium spreads by Kalman Cohen, Steven Maier,
Robert Schwartz, and David Whitcomb.
14.4.1 A Simple Equilibrium Spread Analysis
Suppose that all traders in a continuous order-driven public auction are es-
sentially the same. They all want to trade the same sizes, and no one is bet-
ter informed than anyone else. No one is risk averse, no one values his or
her time at all, and no one is in any hurry to trade. Everyone knows values
instantaneously as they change, but no one can forecast changes in those
values. In addition, everyone can submit and cancel orders instantaneously
without any cost, and their trading commissions do not depend on order
type. Our traders differ from each other only in that some want to buy and
some want to sell. These traders are clearly like none that we will ever meet!


## Page 318

CHAPTER 14 BID/ASK SPREADS • 305
These extraordinarily unrealistic assumptions have two equally unrealis-
tic implications. First, when deciding which type of order submission strat-
egy to pursue, the traders care only about their expected trading costs. We
i

explicitly assumed that they do not care about the value of their time, when
they trade, or the risk of their strategies.
Second, when each trade takes place, the price will equal the current in-
strument value. Limit order traders will constantly adjust their limit prices
to reflect any changes in value. They can make these adjustments because
they instantaneously know values as they change. They will make these ad-
justments because they can cancel and resubmit their orders without any
cost. They must make these adjustments to ensure that they trade if prices
move away from them and to avoid trading at a loss if prices move through
their orders. Although the market order traders would like to trade at prices
different from values, limit order traders will not allow them to do so.
The expected cost of trading a market order is half the bid/ask spread
less any profits that they expect to make by carefully timing their trades.
Since market order traders cannot forecast price changes, and since limit
order traders will not allow them to trade at any price different from value,
they cannot expect to profit from timing their orders. The expected costs
of trading a market order therefore will be exactly half the bid/ask spread.
We now introduce a very simple principle to obtain our results about
bid/ask spreads. Since our imaginary traders can choose whether to submit
limit orders or market orders and since they are all essentially identical, the
spread must make both strategies equally attractive. Otherwise, every trader
would want to use the more attractive strategy. If the spread is too narrow,
everyone will want to use market orders, and no one will trade. If the spread
is too wide, everyone will want to use limit orders, and no one will trade.
The spread which ensures that traders are indifferent between using a limit
order and a market order is the
equilibrium spread.
The traders discover the
equilibrium spread through the following mechanism.
If most traders try to use market orders so that few traders use limit or-
ders, the few limit order traders will set their limit prices far from the mar-
ket. They do not need to be aggressive because the surplus of market or-
ders ensures that they will trade. The resulting wide spreads, however, will
discourage market order traders. Some will choose to submit limit orders
instead. With more competition to supply liquidity, limit order traders will
have to set their limit prices closer to the market, and spreads will narrow.
Conversely, if most traders use limit orders and few traders use market
orders, bid/ask spreads will be small as limit order traders compete to trade
with the few market order traders. The narrow spread will encourage some
limit order traders to submit market orders instead. The reduced competi-
tion among the limit order traders will cause bid/ask spreads to widen.
In summary, spreads that are too wide cause traders to switch from mar-
ket orders to limit orders and thereby narrow the spread. Spreads that are
too narrow cause traders to switch from limit orders to market orders and
thereby widen the spread. At some intermediate spread, traders are indif-
ferent between submitting limit orders and market orders. This spread is
the equilibrium spread.
With this principle, we can now determine that bid/ask spreads in this
rather unusual market must be zero! Since the traders care only about their
expected transaction costs, the equilibrium expected costs of using market
and limit order strategies must be the same. Because trading is a zero-sum
Economists
and
Their Can Openers
Economists often make highly
unrealistic assumptions to
simplify their analyses. Many
people poke fun at economists
for their propensity to create
impossibly abstract models.
You may know the joke
about the three hungry
castaways on a deserted
island who are trying to open
a can of food. The chemist
suggests heating the can until
it bursts. The engineer
proposes breaking it open
with a sharp rock. The
economist suggests,
"Assume
we have a can opener. . . ."
The joke is unfair, but it
reflects the discomfort people
feel about many abstract
economic models.
Economists
make
unrealistic assumptions when
analyzing issues to ensure
that they can create a simple
situation (model) that they
fully understand. They then
consider what happens to
their results when they replace
the unrealistic assumptions
with more realistic ones. This
method allows economists
to thoroughly understand
complex situations that might
otherwise elude them. It is
especially useful for identifying
the importance of the issues
that affect the results.
By
starting with simple
assumptions, economists can
open many complex
problems.


## Page 319

306 • TRADING AND EXCHANGES
'-
ru
n
t
foo
0. Market Manipulation of Quoted Spreads
Markets that wish to lower their quoted spreads can charge commissions
only on market and marketable limit orders. If the commissions are
proportional to the quantity traded, spreads will drop by exactly the
difference between the limit and market order commissions. In equilibrium,
this change would have no effect on order submission strategies or trader
profitability.
In practice, markets can undertake this strategy only if they can control
the commissions that all buyers and sellers pay. If a single broker within a
multibroker market tried to raise the commissions on market orders and
lower them on limit orders, traders would send only limit orders to that
broker, and he would lose revenue. Only brokers who run their own
markets and only exchanges that can regulate commissions can manipulate
quoted spreads.
Cantor Fitzgerald organizes government bond markets for its customers
through its eSpeed subsidiary. Since Cantor Fitzgerald charges commissions
only on market orders, spreads in its markets are smaller than they would
be if it evenly distributed the commissions between the limit order traders
and the market order traders.
Some ECNs like Archipelago also charge different fees for market orders
and limit orders. Although the fees are paid by the brokers who route
orders to them, in perfectly competitive brokerage markets, the differential
fees will be reflected in the commissions that traders pay for different types
of executed orders.
game, expected transaction costs to limit order traders are exactly equal to
expected trading profits to market order traders and vice versa. To be equal,
the expected costs of both order types therefore must be zero. Since the ex-
pected cost of the market order strategy is half the bid/ask spread, the bid/ask
spread in this highly unrealistic model must be zero.
14.4.2 More Realistic Equilibrium Spread Results
We now obtain more realistic results by relaxing some of the remarkable as-
sumptions made above.
14.4.2.1 Differential Commissions
If
limit order traders pay greater trading commissions than market order
traders, limit orders will be relatively less attractive. Without some com-
pensating differential in the spreads, no trader will submit a limit order.
Since traders must be indifferent between using both order strategies in
equilibrium, spreads must widen so that limit and market order traders
equally share the difference in commissions. In this simple model, the cost
of trading a market order is half the bid/ask spread. It must equal half the
difference in the commissions to make traders indifferent between the two
trading strategies. The equilibrium spread therefore must equal the differ-
ence between the two commissions.
14.4.2.2 Costly
Limit Order Management
In practice, canceling and resubmitting limit orders is costly. Brokers and
exchanges may charge fees for canceling orders, and traders lose the op-
portunity to do other things while they manage their orders. Limit order
traders therefore will not continuously adjust their orders. Instead, they will
adjust their orders only when values diverge significantly from their limit


## Page 320

CHAPTER 14 BID/ASK SPREADS • 307
prices. The divergence that triggers a limit price adjustment depends on the
cost of canceling and resubmitting. If these costs are large, limit order traders
will adjust their limit prices infrequently.
Costly limit order management makes limit orders less attractive than
market orders for two reasons. First and most obviously, limit order traders
incur costs to adjust their orders that market order traders do not incur. Sec-
ond and more important, limit order traders give a valuable
timing option
to market order traders when they do not continuously update their limit
order prices. Market order traders will wait to see what happens to values
before they trade. If a market order trader wants to buy, he will wait to see
if value rises. If value rises past the limit price of a standing limit sell order,
he will then buy at the limit price and profit by the difference. If value falls,
he will wait until limit order sellers drop their prices. He then can buy at a
lower price. This timing option makes market orders more attractive than
limit orders.
Market order traders who exercise the timing option subject limit order
traders to a form of adverse selection. When the limit order traders trade, they
wish they had been able to adjust their prices. When the limit order traders
do not trade, they wish that they had. In a sense, the market order traders are
better informed traders than the limit order traders. When they trade, the
market order traders have more current information than the information that
limit order traders had at the time they set their limit prices.
The timing option is most valuable when market order traders can re-
spond to changing conditions faster than limit order traders can. If the limit
order traders can adjust their limit prices before market order traders can
take advantage of changes in value, the timing option will not be valuable.
The timing option is also most valuable when few market order traders
compete to take advantage of it. When many market order traders try to
exercise the same timing option, they must act quickly as soon as it becomes
valuable. Market order traders who wait too long will lose the option to a
quicker trader.
To ensure that traders are indifferent between using limit orders and mar-
ket orders, the equilibrium spread must ensure that the limit order traders
are compensated for the timing options that they give to market order
traders. The equilibrium spread also must ensure that the two types of traders
equally share the limit order management costs. Since the spread is the to-
tal cost of using two market orders to complete a round-trip trade, the equi-
librium spread must equal the expected cost of managing the limit orders
plus twice the value of the timing option.
In practice, limit order traders cannot instantaneously reprice their or-
ders exactly when repricing would be optimal. The costs of paying atten-
tion ensure that values may change substantially before they notice the
change. Order entry, order routing, and order handling delays also ensure
that values may change before instructions to cancel limit orders become ef-
fective. These delays make the timing option more valuable. The equilib-
rium spread therefore also depends on the average time it takes limit order
traders to successfully cancel their orders. If the order cancellation process
is slow, equilibrium spreads will be wide.
When the order cancellation process is slow, equilibrium spreads also will
depend on the volatility of the instrument. The timing option will be quite
valuable for volatile instruments because prices may change substantially


## Page 321

308 • TRADING AND EXCHANGES
A Simple Timing Option Example
Suppose the value of a contract is 20 dollars. In the next five minutes, the
value may stay the same, rise by 12 cents, or fall by 12 cents. These
alternatives are equally probable.
Lisbet, a limit order trader, submits a sell order with a limit price of
20 dollars.
In the next five minutes, Mark, a price-insensitive market order buyer,
may arrive and trade with Lisbet. The probability that Mark arrives is one-
half. This probability does not depend on the value of the contract.
Tim is an opportunistic market order buyer who monitors the market to
exploit timing options.
He
will buy from Lisbet if the
opportunity
looks
attractive and if the opportunity is still then available. In particular, Tim will
buy if values rise and if Mark does not arrive first. The probability that
value rises is one-third. The probability that Mark arrives is one-half. Since
the two events are independent, the probability that Tim buys is one-sixth. If
Tim buys, he will earn 12 cents because he will buy at 20 dollars a
contract worth
20.12
dollars. The expected value of his trading strategy
therefore is one-sixth of
12 cents, or 2 cents.
Dieter is
a dealer who is always willing to buy at 6 cents below the
current value of the contract. Lisbet will trade with Dieter if her order does
not fill after five minutes.
Consider Lisbet's expected trade price. We will first compute it assuming
that Tim is not in the market and then assuming that he is present.
Assume that the probability that Lisbet trades with Mark at 20 dollars is
one-half. If Mark does not arrive (and Tim is not in the market), Lisbet then
would trade with Dieter. That trade price would be 19.82 if value falls,
19.94 if value does not change, and 20.06 if value rises. Since these
events are equally likely, her expected trade price if Mark does not arrive
(and Tim is not in the market) is 19.94. Accordingly, Lisbet's expected trade
price when Tim is not in the market is the average of 20 (if she trades with
Mark) and 19.94 (if she trades with Dieter), which is 19.97 dollars.
Now suppose that Tim is in the market. If Mark does not arrive, Lisbet
will trade with Dieter at 19.82 if value falls, with Dieter at 19.94 if value
does not change, and with Tim at 20.00 if value rises. Since these events
are equally likely, her expected trade price if Mark does not arrive, and Tim
is in the market, is 19.92. If Mark arrives, Lisbet will trade with him at 20
dollars. Since the probability that Mark will arrive is one-half, Lisbet's
expected trade price is 19.96 if Tim is in the market.
When Tim is present, Lisbet's expected sales price is a penny lower than
it would be if Tim were not in the market. If Tim is in the market, Lisbet
loses a penny because she will never trade at 20.06. Otherwise, she will
trade at 20 instead of 20.06 one-sixth of the time (when value rises, and
Mark arrives).
Now
consider how Tim affects Dieter's profitability. If Tim is not in the
market,
Dieter
trades if Mark does not arrive. Since
Dieter
always buys at
6
cents below value, Dieter expects to make 3 cents profit. If Tim is in the
market, Dieter trades only if Mark does not arrive, and prices fall or stay
the same. The probabilities of these two events are both one-sixth, so that
Dieter expects
to make
2
cents profit if
Tim is
in the market. When
Tim is
present, Dieter expects to
make 1 cent less than he would make if Tim were
not in the market.
Tim's two-cent expected profit therefore comes partly from the timing
option that he exercises against Lisbet (1 cent) and partly from quote
matching in front of Dieter's order (1 cent).
6'


## Page 322

CHAPTER 14 BID/ASK SPREADS • 309
before traders can reprice their orders. Equilibrium spreads therefore must
be wider for volatile instruments than for relatively stable instruments.
14.4.2.3 Valuable Time and Risk Aversion
When traders value their time, limit order strategies are more expensive than
market order strategies because the former take longer to implement than
the latter. Equilibrium spreads therefore must widen to make limit orders
more attractive and market orders less attractive. When traders value their
time highly, spreads must be wide.
Since the spread reflects the value of time, the spread is the price of im-
mediacy. Traders say that market order traders
buy time
and limit order
traders
sell time.
Limit and market order strategies expose traders to different risks. Limit
order traders risk having to chase the market if prices move away from their
orders. Market order traders risk trading at unexpected prices. Quotes
may change after they submit their orders but before they are filled. Their
large orders also may have unpredictable price impacts. For small traders,
limit order strategies are generally more risky than market order strategies.
For large traders, market order strategies are probably more risky.
If risk-averse traders fear using one strategy more than the other, spreads
will have to adjust to ensure that traders are indifferent between using the
two strategies. In our simple model, all traders have equal-sized orders, and
they all know the prices at which they can trade when using market orders.
Using limit order strategies therefore is more risky to them than using mar-
ket order strategies. If they are to offer liquidity, equilibrium spreads must
widen to compensate limit order traders for the risk that they will not trade.
The more risk averse traders are, the wider the equilibrium spreads will be.
In practice, since the sizes associated with inside spreads tend to be small,
we can reasonably infer that small traders predominantly set inside spreads.
Accordingly, limit order execution risk probably has a stronger effect on
spreads than market order price risk.
In the real world, not all traders are equally risk averse, and not all traders
value their time equally. In equilibrium, few traders will be indifferent be-
tween using a market order strategy and a limit order strategy. Traders who
are the most risk averse or who value their time the most will use market
orders. Traders who are most risk tolerant and for whom monitoring orders
is least costly will use limit orders. Somewhere between these extremes will
be traders for whom the equilibrium spread ensures that they are indiffer-
ent between using market order and limit order trading strategies.
14.4.2.4 Traders Do Not Know Values Instantaneously
To simplify our analysis, we assumed that all traders always know values as
they change. This assumption is unnecessarily strict. With one caveat, we
can obtain our equilibrium spread results merely if all traders are always
equally well informed about values. In that case, they all always estimate the
same values. Since nobody knows true values, knowing common value es-
timates is essentially the same as knowing true values.
The one caveat concerns results involving volatility. A theorem from sta-
tistics proves that the volatility of an estimate of a variable is less than the

OA./
volatility of the variable being estimated. Value estimate volatility therefore
Indexed (or Floating)
Limit Orders
Some alternative trading
systems like the Primex
Au
_
ction System allow traders
to submit
indexed limit orders.
These systems automatically
change indexed order limit
prices when the value of an
index changes. The trader
specifies the index and the
linking formula. Traders also
know these orders as
floating
limit orders.
By automatically adjusting
limit orders, these systems
reduce the cost of using limit
order strategies. Equilibrium
spreads in these trading
systems therefore should be
smaller than they otherwise
would be.
Automated Limit Order
Management Systems
Several data vendors,
brokerage firms, and trading
technology firms have
products that allow traders to
manage their limit orders
automatically. Traders can
program systems like ITG's
Quantex to follow any set of
instructions.
For
example, they
can automate instructions to
replace unfilled limit orders
with market orders after a
specified time.
These systems lower
equilibrium spreads by
decreasing the costs of
implementing limit order
strategies. Traders use these
systems to submit aggressively
priced limit orders that they
otherwise probably would
have submitted as market
orders.


## Page 323

310 • TRADING AND EXCHANGES
is lower than value volatility. Consequently, spreads should be smaller when
traders do not know values well.
This result is correct, but it does not seem right. Spreads generally are
larger when traders are uncertain about values. The discrepancy has to do
with the distribution of information. This unusual result comes from our
assumption that all traders are equally ignorant. In practice, traders are asym-
metrically informed.
14.4.2.5 Asymmetrically Informed Traders
In real markets, some traders are better informed than other traders. If the
well-informed traders compete with each other to profit from their infor-
mation, they all must trade quickly. Slow traders will find that faster traders
have already caused prices to change. Well-informed traders therefore tend
to submit market orders rather than limit orders. The well-informed traders
subject the limit order traders to adverse selection. Their limit orders exe-
cute quickly if they are on the wrong side of the market, but they do not
execute if they are on the informed side of the market. This adverse selec-
tion makes using limit order strategies relatively more expensive than mar-
ket order strategies for uninformed traders. Equilibrium spreads therefore
must widen to compensate. The additional widening of the bid/ask spread
is the adverse selection spread we discussed earlier.
For most securities and contracts, the degree of information asymmetry
varies inversely with how well traders estimate values. When most traders
estimate values poorly, those traders who can estimate values well have a
great advantage. Such traders typically have access to information that other
traders do not have. Spreads therefore should be wider for instruments that
most traders cannot easily value.
14.4.2.6 Summary
Equilibrium spreads in continuous order-driven auction markets depend on
many factors. The most important factors are the degree of information
asymmetry among the traders, how quickly traders can cancel their limit or-
ders, and the volatility of the instrument. Table 14-2 provides a summary
of the factors that determine equilibrium spreads in continuous order-driven
markets.
When all traders are alike, the equilibrium spread will ensure that traders
are indifferent between using market order and limit order trading strate-
gies. This remarkable result is due to trader efforts to minimize their total
costs oftrading. These costs primarily include the spread (paid or received),
commissions, order management costs, and the loss or exploitation of the
trade timing option.
When traders differ, the equilibrium spread sets the supply of liquidity equal
to the demand for liquidity. Traders who value their time highly, who trade on
material information, or who are risk averse generally use market orders. Traders
who can quickly adjust their limit orders with little cost, who are risk tolerant,
or who do not value their time highly generally use limit orders.
14.5 SPREADS WHEN PUBLIC TRADERS
COMPETE WITH DEALERS
In many markets, dealers and public limit order traders compete to offer
liquidity. They compete unequally in two respects.


## Page 324

CHAPTER 14 BID/ASK SPREADS • 311
TABLE 14-2.
Equilibrium Spread Determinants in Continuous Order-driven Markets
EFFECT
FACTOR

ON SPREADS

IMPORTANCE
Degree of information asymmetry among traders

Increases

Great
Time to cancel limit orders

Increases

High
Volatility

Increases

High
Limit order management costs

Increases

Moderate
Value of trader time

Increases

Moderate
Difference between limit and market order

One for one

Depends on the difference
trade commissions
Degree of trader risk aversion

Increases

Low
Public limit order traders do not have the same business costs that deal-
ers have. They therefore can quote more aggressive prices than can dealers
who must fund their costs of doing business. In particular, public traders
who are intent on filling their orders may use limit orders instead of mar-
ket orders in an effort to lower their trading costs. Such traders price their
orders aggressively in order to minimize their risk of not trading. The re-
sulting spreads can be too small to allow dealers to recover their normal
costs of doing business. Public limit order traders therefore may drive deal-
ers out of the market.
Dealers, however, see more of the order flow than do most public traders.
Most dealers also can change their quotes faster than public limit traders
can change their limit prices. Dealers therefore may survive in markets with
very narrow spreads by profiting from speculative trading opportunities that
they can identify from analyzing their order flows. In particular, dealers may
profitably employ quote matching, order anticipation, and trade timing
strategies that public traders cannot identify or implement quickly enough.
We discuss these strategies in chapters 11 and 24.
When dealers regularly exercise these strategies, limit order strategies be-
come less attractive to public traders. Public traders will use market order
strategies more often, and bid/ask spreads will be wider than they would be
if dealers did not exploit their order flows.
14.6 CROSS-SECTIONAL
SPREAD PREDICTIONS
The above analyses have many implications for bid/ask spreads. The impli-
cations all relate to three primary factors that determine whether spreads
will be wide or narrow. These
primary spread determinants
are information
asymmetries among traders, volatility, and utilitarian trading interest. (Chap-
ter 8 shows that utilitarian traders trade because they obtain some value
from trading besides profits.) If you know these factors, you can predict
bid/ask spreads.
Unfortunately, two of the primary spread determinants are not easily
measured, and none are easily predicted. We can easily measure volatility,


## Page 325

312 • TRADING AND EXCHANGES
0'. Proxy Contests
When an observable variable
varies with an unobservable
variable, economists say that
the observable variable is a
proxy
for the unobservable
variable. Economists infer the
values of unobservable
variables from
proxy
variables.
Empirical
economics is a
contest in which economists
try to convince us (and each
other) that they have
identified good proxies for
variables that we wish we
could measure well.
but we cannot directly measure information asymmetries or utilitarian trad-
ing interest. To assess the importance of these factors, we must infer their
values from observable instrument and market characteristics. We will call
these characteristics
secondary spread determinants.
Some secondary spread determinants are very highly correlated with
spreads. For example, trading activity is an excellent proxy for utilitarian
trading interest. In many markets, trading activity is the best observable pre-
dictor of spreads. Accordingly, many authors would classify it as a primary
spread determinant. We do not because we want to preserve the distinction
between theoretical and observable spread determinants. The distinction re-
minds us of the factors that ultimately determine spreads.
This section first summarizes the reasons why the three primary spread
determinants affect spreads. We then consider predictions about how ob-
servable instrument and market characteristics (secondary spread determi-
nants) affect bid/ask spreads.
Although the predictions specifically concern bid/ask spreads, most also
apply to the provision of liquidity in general. The spread is only one of sev-
eral aspects of liquidity. It measures the cost of immediacy for small orders.
Other aspects of liquidity (discussed in chapter 19) include the cost of trad-
ing large orders (depth) and the ability of the market to recognize when un-
informed traders have moved prices (resiliency). Since the factors that af-
fect spreads typically also affect other aspects of liquidity, the predictions in
this section are of more general interest than they might otherwise seem.
Since instrument prices vary considerably, spread comparisons are inter-
esting only when we express the spreads as a fraction of price. For example,
although a 10-cent spread on a 1-dollar stock is smaller than a 1-dollar
spread on a 100-dollar stock, the spread on the 1-dollar stock is 10 percent
of its price while the spread on the 100-dollar stock is only 1 percent of its
price. The 1-dollar stock therefore is much more expensive to trade. The
ratio of spread to price is the
relative spread.
When we compare spreads, we
will implicitly refer to relative spreads.
14.6.1 The Three Primary Spread Determinants
The three primary spread determinants are asymmetric information, volatil-
ity, and utilitarian trading interest. Their effects on spreads are not inde-
pendent of each other. For example, if information asymmetries are high,
spreads will be wide. Wide spreads, however, discourage uninformed in-
vestors, decrease trading volumes (a secondary spread determinant), and
thereby make spreads even wider.
Asymmetric Information
The adverse selection spread model suggests
that markets with asymmetrically informed traders will have wide spreads.
Spreads will be widest when well-informed traders know material informa-
tion about instrument values that would have an immediate and significant
effect on values if it were common knowledge. When traders are asymmet-
rically informed, liquidity suppliers set their prices far from the market to
recover from uninformed traders what they lose to well-informed traders.
Volatility
The equilibrium spread model suggests that volatile instru-
ments should have wide spreads. The spreads should be widest when limit
order traders and dealers cannot easily adjust their orders. Since volatility
increases limit order option values, traders widen their spreads when trad-
ing volatile instruments to
minimize
the value of the timing option.


## Page 326

CHAPTER 14 BID/ASK SPREADS • 313
Volatility also makes diversifiable inventory risks more frightening to
risk-averse dealers. The transaction cost spread component therefore will be
wider for volatile instruments than for stable instruments because dealers
require a premium for bearing unpleasant risks. This effect probably is im-
portant only for highly volatile instruments.
Volatility and uncertainty about values undoubtedly are closely correlated.
Instruments whose fundamental values change quickly are difficult to value
because traders must be certain that they have all available information when
they form their value estimates. Since it is harder for traders to be fully in-
formed about volatile instruments than about stable instruments, asymmet-
ric information problems are probably greater for volatile instruments than
for stable instruments. Volatility therefore has a strong secondary effect on
spreads because it is a good proxy for asymmetric information. The adverse
selection spread component generally will be large for volatile instruments.
Utilitarian Trading Interest
Utilitarian traders—primarily investors,
borrowers, hedgers, asset exchangers, and gamblers—trade because they ex-
pect to obtain some benefit from trading besides profits. Markets would not
exist without utilitarian traders because purely profit-motivated traders can-
not all profit when trading only among themselves. Actively traded instru-
ments ultimately are those which interest utilitarian traders.
Utilitarian trading interest affects bid/ask spreads two ways. First, when
utilitarian interest is strong, markets are very active. For reasons explained
below, active markets tend to have narrow bid/ask spreads. Second, since
utilitarian traders are uninformed, they dilute information in the order flow
when they trade. The adverse selection spread component therefore will be
small when utilitarian trading interest is strong. Since we discussed above
how asymmetric information affects bid/ask spreads, we now focus on how
market activity affects bid/ask spreads.
Dealers who trade frequently can spread their fixed costs of doing busi-
ness over mmyolume than can dealers who trade infrequently. The trans-
action cost spread component therefore should be smaller for actively traded
instruments than for infrequently traded instruments.
Dealers also face smaller inve
ntory
isks_ when trading in active markets
than in inactive markets. In active markets, they can quickly lay off inven-
tory imbalances. Since they can more easily control their inventories in ac-
tive markets, they face less inventory risk--diversifiable inventory risk and
adverse selection risk. They therefore can quote smaller spreads for actively
traded instruments than they can for infrequently traded instruments.
Public traders who are committed to trading are also more willing to of-
fer limit orders in active markets because the probability that their orders
will execute quickly is larger in active markets than in inactive markets. They
also are more willing to offer limit orders in active markets because the tim-
ing options that limit orders_ give
__
_ up are less valuable when many market
or er traders compete for them. Public limit order traders therefore will
make spreads narrower in active markets than in inactive markets.
Inactive markets cannot support many dealers. Dealers in such markets
therefore may exercise some market power when setting their spreads. They
will have more market power if the small size of the market makes other
dealers reluctant to enter and if public limit order traders are unable or un-
willing to offer liquidity. Spreads in such small markets therefore may be
-Wider than we would otherwise expect.


## Page 327

314
•
TRADING AND EXCHANGES
A Gap in Spreads Due
to the Gap Between
German and U.S. GAAP
The generally accepted
accounting principles
(GAAP) by which German
corporations historically
reported their financial results
allowed them considerable
latitude in how they presented
their accounts. Using various
hidden reserve accounts, they
were able to smooth their
earnings considerably, so that
it was difficult or impossible
for analysts to estimate firm
values accurately. In contrast,
U.S. GAAP do not allow
U.S. corporations as much
freedom in how they report
their results. The difference in
accounting standards suggests
that firms which report using
German GAAP will have
wider spreads than will firms
that report using U.S. GAAP
(all other things being equal).
The New York Stock
Exchange requires its listed
firms to report their accounts
using U.S. GAAP. They may
also report using International
GAAP, which are essentially
similar. Although the NYSE
would like to list many large
German firms, it will not list
them until they report their
results using more transparent
accounting standards.
German firms are
increasingly reporting their
results using International
GAAP. The trend is due to the
desire of German firms to
trade in more liquid markets,
and to efforts by the
European Community to
harmonize its financial
markets.
14.6.2 Spread Predictions Based on Secondary Factors
Various secondary factors allow us to draw inferences about the primary
factors. This subsection examines these factors and considers how they are
related to spreads.
Our presentation classifies the secondary factors by the primary spread
determinant that they most closely represent. The classification, however, is
somewhat arbitrary because many secondary factors are correlated with more
than one primary factor. For example, firm size (a secondary factor) gener-
ally is both correlated with investor interest and inversely correlated with
volatility.
The various secondary factors are often correlated with each other. The
observed effect of a secondary factor in a statistical analysis therefore some-
times differs from its predicted effect. For example, we argue below that
firms in emerging industries should have wider spreads than firms in es-
tablished industries because of asymmetric information problems. However,
firms in emerging industries tend to have substantial investment interest,
which creates substantial volumes and therefore narrower spreads. Firms in
emerging industries therefore may have narrower spreads than firms in es-
tablished industries, although we predict otherwise. When factors are cor-
related, it is often necessary to use statistical methods to disentangle their
conflicting effects.
14.6.2.1 Asymmetric Information Proxies
Information Disclosure Rules
Rules that require information disclosure de-
crease information asymmetries. Stock markets that require their listed firms
to disclose reliable, comprehensive financial information on a regular and
timely basis will have narrower spreads than those which do not require ex-
tensive disclosure. To decrease information asymmetries, many markets com-
monly require that their listed firms conform to audit, accounting, and re-
porting standards that are more demanding than the local legal standards.
Market Condition Reports
Commodity markets that compile and pub-
lish extensive reports of market supply and demand conditions will have
smaller spreads than they would have otherwise. To reduce information
asymmetries, many governments have agencies that collect and publish in-
formation on market conditions.
Analysts
Securities and commodities that many public analysts follow
will have smaller spreads than will those which few analysts follow. Ana-
lysts evaluate the financial conditions and the economic prospects of vari-
ous instruments. The information that they produce and publish reduces in-
formation asymmetries among traders.
Information Vendors
Stocks of corporations that the financial press cov-
ers closely will have smaller spreads than will those the press ignores. Like
analysts, the press produces information that reduces information asym-
metries.
Major Commodity Contracts
Spreads in contracts that represent econo-
mywide risks will be quite small. Traders rarely have significant material in-
formation about supply and demand conditions when information about
those conditions is distributed throughout the economy. Oil, gold, silver,
wheat, soybeans, currencies, and financial futures are examples of such com-
modities. Although traders occasionally have significant information about


## Page 328

CHAPTER 14 BID/ASK SPREADS • 315
local supply and demand conditions, such information generally does not
have a material effect on the whole market.
Diversified Portfolios
Relative spreads will be smaller for contracts on
well-diversified portfolios than for the individual assets in the portfolio. Al-
though traders may have significant material information about individual
assets, such information rarely is material to all assets in the portfolio. A
large portfolio dilutes the importance of information about any single con-
stituent asset. In addition, portfolios generally are easier to value than indi-
vidual assets: Mistakes made valuing one asset may offset mistakes made in
valuing other assets. For both reasons, stock index futures contracts have
smaller spreads than individual stocks.
Diversified Stocks
The stocks of well-diversified corporations will have
smaller spreads than those of similar-sized corporations that focus on a sin-
gle line of business. A well-diversified corporation is a portfolio of assets.
Although traders may have significant material information about some of
the asset values, such information often is not material to valuing all the as-
sets. Conglomerates therefore have smaller spreads than undiversified firms.
Established Versus Emerging Industries (Value Versus Growth Stocks)
Firms
in established industries are easier to value than firms in emerging indus-
tries. Firm values in emerging industries depend primarily on uncertain fu-
ture growth prospects. In contrast, firm values in established industries de-
pend primarily on cash flows that are relatively easy to predict. Since valuing
firms in emerging industries is more difficult than valuing firms in estab-
lished industries, information asymmetries among traders are likely to be
bigger in the former than in the latter. Firms in established industries there-
fore will have smaller spreads than firms in emerging industries (all other
things held constant).
Age of the Firm
Young firms are often harder to value than older firms
doing business in the same markets. Young firms often use newer technolo-
gies. Although such technologies may be very promising, their values are of-
ten uncertain when they have not been fully tested. Even when the
fi
rms use

the same technologies, younger firms are harder to value than older firms
because new management systems are often less mature than old manage-
ment systems. Bid/ask spreads will be wider for new firms than for old firms.
Insider-trading Rules
Markets that effectively enforce insider-trading
rules protect their liquidity suppliers from adverse selection. Insider-trading
rules prevent traders from trading on certain types of material information.
Bid/ask spreads will be smaller when such insiders cannot trade. We dis-
cuss this prediction further in chapter 29.
When Material Information Is Expected
Spreads will be wider when liq-
uidity suppliers expect that some traders may have material information. For
example, stock spreads tend to widen before and after earnings announce-
ments. They widen before because insiders sometime trade on the infor-
mation before it becomes public. (Such trading is illegal in many jurisdic-
tions.) They widen after because some traders are better able to evaluate the
significance of the news than others.
14.6.2.2 Volatility Proxies
Since volatility is easily measured, we generally do not need to identify prox-
ies for it when analyzing bid/ask spreads. However, if you want to predict
Why Speeches by
Fed Chairmen Are
Rarely Interesting
Contracts
on macroeconomic
variables like stock index
futures, Treasury bond futures,
and T-bill futures typically
trade with very narrow
spreads. The values of these
contracts depend primarily on
expectations about interest
rates and macroeconomic
activity. Spreads are narrow
because few people have
reliable information about
how these factors will
change.
In the United States, the
only people who may be able
to predict future interest rates
accurately are the Federal
Reserve Board chairman and
the other members of the
Federal. Reserve Open Market
Committee. They, of course,
may not trade on their
information, and they may not
offer it to other traders.
If you have ever listened
to speeches by a Fed
chairman or a Fed governor,
you know that they rarely say
anything interesting about
interest rates. They do not
want to reveal any
information that would allow
informed traders to predict
the future course of interest
rates.


## Page 329

316 • TRADING AND EXCHANGES
Orange Juice Futures
Orange juice futures prices
often become quite volatile
during the winter when
freezing weather in Florida
threatens the orange crop. At
those times, meteorologists
and local farmers have an
informational advantage over
other futures traders.
Accordingly, spreads on
futures widen when cold air
descends into Florida.
future bid/ask spreads, you must predict future volatility. The problem is es-
pecially difficult when the instruments in which you are interested have not
yet traded.
Practitioners use various methods to predict volatility. Since we describe
these methods in chapter 20, we consider the issue here only briefly.
Many factors make instruments volatile. For stocks, the most important
factors are financial leverage, operating leverage, and uncertain growth op-
portunities. Commodities are volatile when supply and demand conditions
are uncertain. They are especially volatile when they are perishable, when
the costs of storage are high, when inventories are low, or when the supply
depends on weather conditions. Currencies are volatile when traders are un-
certain about political stability, inflation, and interest rates. Finally, bonds
are volatile when traders are uncertain about inflation, interest rates, and
credit quality. These factors all cause spreads to be wide when they are
significant.
14.6.2.3 Proxies for Utilitarian Trading Interest
Trading Activity
Measures of trading activity such as traded volumes and
numbers of transactions are good proxies for utilitarian trading interest. Mar-
kets cannot sustain high volumes without traders who are willing to trade
even when they do not expect to profit from trading. Markets that have
high volumes therefore have many such traders. Actively traded markets
usually have narrow spreads.
Firm Size
Large firms' stocks tend to have smaller relative spreads than
do small firms' stocks. The substantial investor interest in large firms ensures
that their stocks are actively traded. This interest also ensures that the press
and many analysts follow them. Large firms also tend to be less volatile be-
cause they often are mature, well-diversified companies that use established
technologies. Higher average trading activity and lower average volatility both
suggest that large firms have smaller spreads than do small firms.
Debt Issue Size
Large debt issues often attract the interest of many in-
vestors. Investors are especially interested in government debt. Widely dis-
tributed debt issues tend to trade in active markets when they are first is-
sued. Market activity quickly diminishes, however, as the issues age because
many investors buy and hold debt until maturity. Spreads in large debt is-
sues therefore start small and rise through time. Government debt issues
are most liquid when they are
on-the-run.
On-the-run issues are debt issues
that the government has most recently issued. They become
seasoned issues
when the government sells a new issue with the same initial maturity.
Risk Replication
Commodity contracts that closely replicate risks which
bother many potential traders attract substantial hedging interest. Such con-
tracts are most successful when the natural hedging interests on the long
and short sides are approximately equal. Contracts that meet these criteria
usually trade actively and therefore have small spreads. Accordingly, com-
modity markets try very hard to write standardized contracts that will ap-
peal to many hedgers.
Volatility
In additional to its primary effect on spreads, described above,
volatility may have a strong secondary effect on spreads through in effect
on gambling interest. Gamblers like to trade volatile instruments. Volatility
also may have a secondary effect on spreads through its effect on hedging


## Page 330

CHAPTER 14 BID/ASK SPREADS • 317
interest. Hedgers often have to adjust their positions more in volatile mar-
kets than in stable markets. These effects both suggest that volatile instru-
ments will be actively traded and therefore will have small spreads. This sec-
ondary effect of volatility on spreads works in the opposite direction to the
primary effect.
14.6.3 Liquidity and Capital Structure
When the asymmetric information problem is particularly severe, or when
utilitarian interest is very small, spreads may be so wide that no trading oc-
curs. Dealers will not make markets in such securities because the losses
they expect from trading with well-informed traders—and the costs of de-
termining security values to avoid those losses—are greater than their po-
tential trading revenues. When this happens, economists say that the mar-
ket has failed.
Market failure
does not mean that something is wrong, but
rather that the market fails to exist.
Market failure explains why small businesses rarely finance their opera-
tions by issuing publicly traded equity. Investors generally will not buy stock
in firms that they cannot trade.
Small firms must therefore obtain their financing from banks and ven-
ture capitalists. Banks buy private debt securities (loans) and venture capi-
talists buy private equity issues. These agencies spend substantial time and
money doing
due diligence
to determine whether their investments will be
profitable. They also impose many restrictions on the management of the
firm, foremost of which is the right to supervise the firm. These restrictions
allow them to protect their interests. The due diligence, the managerial re-
strictions, and the special surveillance rights are possible only in the con-
text of a confidential and trusting relationship between the investor and the
firm. Such relationships are feasible in private financing markets, but not in
public financing markets.
14.7 SUMMARY
Bid/ask spreads depend on numerous factors. The most important are asym-
metric information, volatility, and utilitarian trader interest.
Information is asymmetrically distributed among traders when some
traders are better informed than others. Asymmetric information makes the
order flow informative and causes dealers to lose money to better-informed
traders. Dealers widen their spreads to recover from uninformed traders what
they lose to informed traders. Dealers also widen their spreads to anticipate
what they learn from the order flow when they discover whether the next
trader is a buyer or a seller. These two explanations imply the same adverse
sele
-
c
-
fihn spread component. It will be large when the order flow includes
many informed traders and when the informed traders have highly mate-
rial information.
Volatility affects bid/ask spreads by increasing the option values of stand-
ing limit orders and dealer quotes. When liquidity suppliers cannot adjust
their prices quickly, they give timing options to market order traders. The
value of this timing option increases with volatility. Dealers widen their
spreads, and limit order traders back away from the market, to decrease
00 Why Are Shares in
Tommy's Burgers Not
Publicly Traded?
Tommy's Burgers is a small,
single-outlet burger stand.
Tommy is doing well and
would like to expand his
business. To finance his
expansion, Tommy considers
issuing equity shares and
selling them to the public.
One of Tommy's regular
customers is an investment
banker. Tommy asks him for
advice. The banker explains
that almost nobody will buy
his stock because the
secondary market will be
highly illiquid. Dealers will
not make a market in the
security because they would
be afraid of trading with
much better informed traders.
Tommy
ultimately obtains
his financing from the bank
that provides him with
transaction services. Since the
bank can continuously
monitor his checking account,
it can easily supervise its
investment.


## Page 331

318 • TRADING AND EXCHANGES
the
value of the timing option. Volatility also indirectly determines bid/ask
spreads because it is a good proxy for asymmetric information.
Utilitarian trader interest ultimately determines market trading activity.
Actively traded instruments have narrow spreads because dealers can spread
their costs of doing business over more trades, because dealers can more ef-
fectively manage their inventory risks, and because the competition to sup-
ply liquidity is intense. Measures of market activity, such as trading volumes
and trading, frequency therefore vary inversely with spreads. Factors that
determine market activity, such as firm size, hedging suitability', and volatil-
ity, therefore also are inversely correlated with bid/ask spreads.
The equilibrium model shows that spreads in order-driven markets
adjust to ensure that some traders will be indifferent between taking liq-
uidity with market orders (and marketable limit orders) and offering
liquidity with limit orders. Those who value their time highly and those
who are most risk averse will take liquidity. Those who can adjust their or-
der prices quickly may offer liquidity. On average, limit order strategies will
execute at slightly better prices than market orders strategies because mar-
ket order traders must compensate limit order traders for the additional
management time, price risk, and timing options associated with limit
order strategies.
Adverse selection helps us understand why uninformed traders lose
whether they submit limit or market orders. If they use limit orders, they
suffer adverse selection. When they compete with informed traders, their
limit orders do not fill, and they subsequently wish they had traded. When
they offer liquidity to informed traders, their limit orders quickly fill,
and they subsequently wish that they had not traded. If they use marl
they avoid direct adverse selection, but they still suffer its indirect ef-
fects because they must pay dealers the adverse selection spread. The ad-
verse selection spread is effectively a fee dealers charge uninformed traders
for bearing their adverse selection risk. Uninformed traders thus lose how-
ever they trade. If they want to avoid losing, they must avoid trading.
Asymmetric information is extremely important in trading. In this chap-
ter, we used it to explain bid/ask spreads, why uninformed traders lose no
matter how they trade, and why small firms cannot obtain public financing.
In subsequent chapters, we will use the model to help us understand block
trading, why governments regulate insider trading, and why stock index con-
tracts are so liquid.
14.8 SOME POINTS TO REMEMBER
•
Competition among dealers and from limit order traders keeps spreads
small.
•
Dealer spreads depend on their costs.
•
Informed trading makes spreads wide.
•
Uninformed traders indirectly lose to informed traders when they pay
spreads made wide by adverse selection.
•
Spreads are small in active markets for well-known assets.
•
Large anonymous traders are widely thought to be well informed.
•
Limit order traders give away timing options to traders who can re-
spond to changing market conditions more quickly.
•
When all traders are identical, limit order strategies produce better


## Page 332

CHAPTER 14 BID/ASK SPREADS • 319
prices on average than market order strategies because traders value
their time and do not like to risk failing to trade.
14.9 QUESTIONS FOR THOUGHT
1.
Why do many securities markets, but not most futures markets, have
insider-trading rules?
2.
Does the risk of trading with informed traders vary by time of day?
By time of year? By proximity to earnings reports?
3.
Would the adverse selection spread be smaller if dealers sometimes
could trade out of their positions before values are realized? What
effect would this have on uninformed traders?
4.
Would the adverse selection spread be smaller if dealers knew which
of their customers were well informed?
5.
Would a dealer ever knowingly want to trade with an informed trader?
6.
What determines how many dealers will be in a market?
7.
In some markets, a large minimum price increment puts a lower
bound on the spreads that dealers can quote. When this constraint
is binding, dealers cannot quote smaller spreads. Suppose that mar-
ket making generates excess profits so that new dealers enter the
market. If the spread is already equal to the minimum price incre-
ment, how can new entrants attract order flow?
8.
Suppose dealers can obtain valuable information about future price
changes through their dealing activities. In competitive markets, can
dealers survive simply by offering liquidity, or must they speculate
as well? What happens to bid/ask spreads when dealers speculate
successfully on information obtained from their dealing operations?
Which component of the bid/ask spread is affected?
9.
Are there economies of scale in dealing? What advantages do deal-
ers have when they see a large fraction of the order flow?
10.
When limit order traders and market order traders trade at the same
time and on the same side of the market, the limit order traders re-
ceive better prices. Why is this difference not sufficient to compen-
sate limit order traders for offering liquidity?
11.
When should limit order traders adjust their limit prices? How does
your answer depend on the costs of adjustment, the number of mar-
ket order traders who monitor the market, and the expected delay be-
tween the decision to adjust the order and its effective implementation?
12.
Can you put an upper bound on the value of the timing option that
limit orders traders give to faster market order traders? How does it
depend on the cost of adjusting limit orders? Does it also depend
on the number of market order traders who monitor the market?
13.
If limit order traders could continuously monitor the market and if
they could adjust their limit prices instantaneously, would the value
of the timing option depend on volatility?
14.
If spreads are set so that dealers expect zero economic profit, can
public limit order traders expect to profit by randomly submitting
limit orders at the best bid or best offer?
15.
In the equilibrium spread model introduced in the text, what effect
would diverse order sizes have on equilibrium spreads? Why is this
question hard to answer?


## Page 333

320 • TRADING AND EXCHANGES
16.
Why might a corporation that is domiciled in a jurisdiction with lax
financial reporting standards voluntarily report more information
than is required?
17.
Many corporations issue both debt and equity to finance their op-
erations. Which issues would you expect to have smaller relative
spreads? In practice, equity issues often have smaller relative spreads
than the debt issues of the same companies. Why might this be so?
18.
Suppose the values of two assets in two different markets are closely
correlated. How would the liquidity in one market affect the liq-
uidity in the other market?
19.
Underwriters generally will refuse to underwrite equity issues in
firms that are smaller than some minimum size that they each de-
termine. In which industries do you expect that that minimum size
would be smallest?
20.
Legal systems vary considerably across countries. Some are quick, just,
and thorough. Others are slow, corrupt, and incomplete. How does the
legal environment affect cross-country bid/ask spread comparisons?
14.10 APPENDIX: A SIMPLE GLOSTEN-
MILGROM MODEL OF THE ADVERSE
SELECTION SPREAD COMPONENT
The information and accounting perspectives of the adverse selection spread
component both imply the same size for the component. We can explore
this result easily in a simple example based on the Glosten-Milgrom model.
Suppose that a dealer believes the following:
•
The unconditional value of a security is
V.
•
The next trader is equally likely to be a buyer or a seller.
•
The probability that the next trader is well informed is
P,
so that the
probability that the next trader is uninformed is 1 —
P.
•
The security is worth
V + E
if an informed trader wants to buy it and
V — E
if an informed trader wants to sell it.
The dealer knows neither whether the next trader will be well informed or
not, nor whether the next trader will be a buyer or seller.
Consider first the derivation of the adverse selection spread component
from the information perspective. What is the dealer's best estimate of value,
given that the next trader is a buyer? If the next trader is an uninformed
trader, the dealer will not learn anything, so his best estimate of the secu-
rity value will remain
V.
If the next trader is an informed trader, he expects
that the security value will be
V + E.
Since the dealer does not know whether
the buyer will be well informed or not, his best estimate of the security value,
given that the next trader is a buyer, is the probability-weighted average of
these two values: (1 —
P) • V + P • (V + E) = V + P • E.
Likewise, given
that the next trader is a seller, his best estimate of the security value is
V — P • E.
The difference between these two values, 2 •
P • E,
is the ad-
verse selection spread component.
The derivation of the adverse selection spread from the accounting per-
spective estimates the spread the dealer must quote to ensure that he ex-
pects to just break even, ignoring his normal costs of doing business. We
derive this spread by computing the dealer's expected trading profits. Sup-


## Page 334

CHAPTER 14 BID/ASK SPREADS • 321
pose first that the next trader is a seller. The dealer will buy the security at
his bid price
B.
If the seller is uninformed, the dealer pays
B
for something
worth
V,
so he expects his trading profits will be
V — B.
If the seller is well
informed, the dealer likewise expects trading profits of
(V — E) — B.
Since
the dealer does not know whether the seller is informed or uninformed, his
best estimate of his trading profits, given that the next trader is a seller, is
the probability-weighted average of these two values: (1 —
P) • (V — B) +
P • ((V — E) — B) = V — B — P • E.
If the next trader is a buyer, the dealer
will sell at the ask price
A.
He expects trading profits of
A — V
if he trades
with an uninformed trader and
A — (V — E)
if he traders with an informed
trader. His best estimate of his trading profits, given that the next trader is
a buyer, is therefore
A — V — P • E.
Since the next trader is equally likely
to be a buyer or a seller, the dealer expects that his profits on the next trade
will be the equal-weighted average of the profits he expects given that the
next trader is a buyer or a seller:
1
/2(
V
—
B — P • E) +
1
/2(A — V — P -
E) =
1
/2(A — B) — P • E.
The dealer expects to just break even when his
expected profits are zero. Setting this expression equal to zero implies that the
break-even bid/ask spread
A — B
is 2 •
P • E.
This expression is equal to the
adverse selection component derived from the information perspective.


## Page 335

15
Block
Traders
B
lock trades
result from orders that are too large to fill easily using stan-
dard trading procedures. Such orders generally demand more liquidity
than is normally available at exchanges or in dealer networks. Traders who
wish to trade large blocks therefore must look elsewhere for liquidity. They
usually turn to block traders to arrange their trades.
Block traders
include block dealers and block brokers.
Block dealers
arrange
block trades when they fill their clients' large orders.
Block brokers
ar-
range block trades when they find other traders who are willing to fill their
clients' orders. Both types of block traders usually arrange their trades by
telephone in the
upstairs block market.
The traders who initiate large trades
are
block initiators.
We will call the traders who fill their orders
block liquid-
ity suppliers.
Block liquidity suppliers include dealers and large buy-side
traders.
Although block trades represent a small fraction of all trades in most
markets, they often account for much of the total trading volume due to
their large sizes. Block traders arrange most block trades on behalf of large
institutions and very wealthy individuals.
Large traders often have a significant impact on prices. They therefore
must arrange their trades very carefully in order to control their transaction
costs. Block traders must especially consider how they expose their orders
so as to avoid losing to front runners and quote matchers.
Since block trades significantly affect volumes and prices, traders must
understand block trading in order to interpret volumes and prices. If you
intend to extract information from volumes and prices, you must under-
stand block trading.
In this chapter, you will learn how large traders expose their orders and
how block traders arrange their trades. You will learn that block dealers and
block brokers want to serve only uninformed clients who honestly tell them
the true sizes of their orders. Block initiators—whether they are uninformed
or informed, honest or deceitful—therefore must convince block liquidity
suppliers that they are uninformed and honest. We therefore will consider
how traders convince others that they are uninformed and honest.
15.1 STATISTICAL DEFINITIONS
OF BLOCK TRADES
For our purposes, a
block trade
is any trade that results from an order that
is too large to fill easily using normal trading procedures. Such orders typ-
ically represent more than a day's normal trading volume. In thinly traded
instruments, these orders may represent only a few thousand shares or tens
of contracts. In actively traded instruments, such orders are many times
larger. Most block traders think of a block as exceeding a quarter of a day's
average trading volume in an actively traded stock.
322


## Page 336

CHAPTER 15 BLOCK TRADERS • 323
For statistical purposes, exchanges often arbitrarily designate trades as
block trades if they exceed some fixed size. These classification schemes vary
by exchange. The New York Stock Exchange defines a block trade as 10,000
shares or more, regardless of trading activity or price level. Traders, how-
ever, routinely arrange such trades on the Exchange floor in actively traded
stocks and in low-priced stocks. Although officially classified as block trades,
these trades are normal trades in all other respects. In thinly traded stocks,
or in very high priced stocks like Berkshire Hathaway (priced as of this writ-
ing above 70,000 dollars per share), trades smaller than 10,000 shares often
cannot easily be arranged on the floor of the Exchange.
15.2 BLOCK TRADING PROBLEMS
Block initiators face four problems when they attempt to arrange their
traders. The
latent demand problem
makes it hard to find block liquidity sup-
pliers who are not in the market. The
order exposure problem
makes block
initiators reluctant to advertise for liquidity for fear of driving the market
away from them. The
price discrimination problem
makes liquidity suppliers
reluctant to trade with large traders because they fear that more size will
follow. The
asymmetric information problem
makes liquidity suppliers reluc-
tant to trade with block initiators because they fear that the block initiators
are well informed.
15.2.1 The Latent Demand Problem
The most obvious problem that large traders face is finding traders to with
whom to trade. Many block liquidity suppliers are unwilling to expose their
interest. Many more might trade if asked, but they have not yet issued
orders to trade. Block traders must find these traders in order to complete
their trades.
Traders who would be willing to trade if asked, but who have not yet is-
sued trading orders, have
latent trading demands.
They may not issue orders
because writing orders is costly, or because they do not realize that they are
willing to trade.
When the probability of trading is small, traders often do not issue or-
ders because they are costly to manage. For example, a trader may be a will-
ing buyer of hundreds of different stocks at prices 5 percent below their cur-
rent market prices. If he creates and submits orders for each stock, he risks
buying all the stocks if the market as a whole drops significantly. Since he
cannot afford to buy all the stocks, he cannot allow so many orders to stand
at once. Moreover, if all stocks drop together, he may not be a willing buyer
in any stock. He therefore waits to see which stocks drop. He has latent
trading demands for many stocks, but block traders must discover them be-
fore he will trade.
Other traders simply do not know that they are willing to trade. Form-
ing opinions about thousands of securities is costly. Instead, they often wait
until events force them to think about trading opportunities. When pre-
sented with an attractive opportunity, they may decide to trade.
Traders who are willing to trade but who do not initiate their trades are
responsive traders.
They respond to demands for liquidity. Most traders who
supply liquidity are responsive traders.
Block traders must discover the latent demands of responsive traders
A
Humble Suggestion
Block trading statistics would
be more useful if block trades
were classified by whether
they exceed some fraction of
average daily volume rather
than by whether they exceed
some fixed size.
4


## Page 337

324 • TRADING AND EXCHANGES
when they cannot find adequate liquidity in the market. They find liquid-
ity primarily by calling traders they think would be willing trade.
\
Block traders often move prices significantly to discover the latent de-
' mands of responsive traders. Buyers bid prices up, and sellers offer prices
down to encourage responsive traders to pay attention and respond. Block
initiators give
price concessions
to block liquidity suppliers so as to encourage
them to trade.
Good block traders know where to look for traders willing to provide
liquidity at the lowest cost. They keep track of who is interested in various
securities, and who has traded those securities in the past. They also try to
know what instruments will appeal to different traders so that they can pre-
dict who will be most willing to trade when presented with attractive trad-
ing opportunities.
Most large traders do not know as much about latent demands as do
professional block traders who specialize in collecting this information.
Large traders therefore often contract with block traders to arrange their
trades.
Large traders do not initiate all block trades. Sometimes
sales traders
in
large wirehouses broker block trades by identifying latent trading demands
on both sides of the trade.
15.2.2 The Order Exposure Problem
When looking for liquidity, block traders must be very careful about to
whom they expose their orders. Traders who know about impending blocks
often use that information when trading, to the disadvantage of the block
traders. Some traders create orders expressly to front-run pending blocks.
Other traders who intend to trade on the same side as the block accelerate
their trading to avoid the price impact of the block. Traders who intend to
trade on the opposite side retard their trading to capitalize on the price im-
pact of the block. These strategies accelerate the price impact of the block
by demanding liquidity in front of the block or by withholding liquidity
from the block. Block traders then ultimately obtain less favorable prices for
their blocks. To avoid these problems, block traders try to display their or-
ders first to traders who will most likely fill them.
Block traders
shop the block
when they expose their orders while search-
ing for liquidity. Widely shopped blocks
hang over the market
as information
about them
leaks out.
Block traders
spoil their market
when prices run away
from their orders because they have foolishly exposed them. Table 15-1 de-
scribes strategies that clever traders use to exploit information about a block
hanging over the market.
Successful block traders carefully consider whether the traders to whom
they have exposed orders are front running them. They pay attention to
prices, volumes, and any available information about who is trading. Block
traders who suspect that traders are front running their orders must avoid
exposing their orders to those traders in the future.
Traders who front-run block orders, or who allow information about
block orders to leak, risk acquiring a reputation for being untrustworthy.
Block traders do not make their first calls to such traders. Untrustworthy
traders may thus lose the opportunity to participate in future blocks. They
also lose early access to information that might allow them to better inter-
pret market conditions. Since blocks initiators often give block liquidity sup-
Hard Work Pays
Suppose that brokers can
develop and maintain one
buy-side trader client for each
hour per week that they work.
A broker who works only 20
hours per week has 190 ways
to arrange trades among
pairs of his 20 clients. If the
broker works an additional
20 hours a week, he has
780 ways to arrange trades.
If he works 60 hours a week,
he can arrange trades
1,770 ways.
This illustration shows
that well organized brokers
become more productive
the harder they work.
Hardworking brokers
benefit from the network
externality.
41
Block Traders Play
Concentration Well
Block traders play a game
similar to the card game
Concentration, in which
players take turns uncovering
cards two at a time and
attempt to match them. To
match buyers to sellers, block
traders must remember who
was, who is, and—most
important—who would be
interested in trading hundreds
of securities.
Unlike card players, block
traders can take notes, and
they obviously do not have to
take turns when playing. Not
surprisingly, good block
traders spend most of their
time on the telephone. Many
enter copious notes into
electronic contract
management systems.


## Page 338

CHAPTER 15 BLOCK TRADERS • 325
TABLE 1 5- 1 .
Strategies Clever Traders Pursue When a Block Is Hanging over the Market
TRADERS

STRATEGY

PURPOSE

EFFECT
Front

Trade on the same

Profit from the price
runners

side before the block

impact of the block
trades
Same-side

Accelerate their

Avoid the price
traders

intended trades

impact of the block
Opposite-

Delay their intended

Take advantage of
side traders

trades

the price impact of
the block
Accelerate the price impact of the
block
Accelerate the price impact of the
block
Reduce liquidity available to same-
side traders so that their trades
have greater price impact
pliers substantial price concessions, supplying liquidity to block initiators
often is quite profitable. When block traders can identify front running,
traders have substantial incentives to cultivate trustworthy reputations.
To avoid order exposure problems, block traders favor trading systems
that do not expose their orders. Crossing markets like POSIT serve these
traders by allowing them to arrange trades on a completely confidential
basis. At exchanges that permit hidden limit orders (such as Euronext,
GLOBEX, and Island), large traders frequently hide their orders to limit
their impact upon the market. At exchanges that do not have such facili-
ties, large traders give their orders to honest brokers who expose them se-
lectively, or they break their orders into small pieces so that nobody can de-
termine their full size.
15.2.3 The Price Discrimination Problem
Block initiators have trouble finding liquidity because block liquidity sup-
pliers are afraid that they will price discriminate among them. Block liq-
uidity suppliers do not want to be the first to offer liquidity to a large trader,
only to see prices move against them when the large trader continues to
trade. They therefore want to know how much the large trader truly
wants to trade before they offer liquidity. Block initiators—especially those
whose orders are not so large that they will greatly benefit from price
discriminating—may obtain better prices from block liquidity suppliers if
they can credibly convince them of the true sizes of their orders.
Traders cannot credibly reveal the true sizes of their orders in markets
where they trade anonymously. Traders will lie in anonymous markets be-
cause lying has no negative consequences in such markets. The primary
penalty for lying is losing an honest reputation. Since traders cannot culti-
vate reputations in anonymous markets, they will lie with impunity. Block
initiators who want to solve the price discrimination problem must there-
fore trade in markets where they know with whom they are trading.
Since most large traders do not trade often enough to acquire strong rep-
utations for being honest, they often use block traders who have such
reputations to arrange their trades. Block traders acquire their reputations
by consistently telling the truth. To protect their reputations, they must en-
sure that their clients do not lie to them. Dishonest clients improperly try
to exploit the honest reputations of their agents.
A
Quick Ticket to
the Doghouse
Blair tells Sawyer that he
wants to buy 200,000 shares
of IBM. Sawyer asks whether
this is the full size of his
order. Blair assures him that it
is indeed. They arrange to
trade IBM at 50 cents above
its prevailing price.
Blair then contracts with
another trader to buy
200,000 more shares of IBM
at a price 50 cents higher
than he paid Sawyer.
Sawyer immediately loses
100,000 dollars on his sale.
Had he known Blair wanted
to buy 400,000 shares, he
would have demanded a
higher price. Sawyer will now
put Blair
in the doghouse.
He
will not knowingly offer
liquidity to Blair for a long
time.


## Page 339

326 • TRADING AND EXCHANGES
Ok
Untrustworthy Traders
Do Not Get Shares
in Hot IPOs
Buying a hot issue during its
initial public offering can be
extremely profitable. The
prices of these issues often
jump substantially on the first
day of trading.
The investment banks that
control the distribution of
these shares can allocate the
shares to whomever they
please. They naturally will not
allocate shares to customers
who exploit them. Such
customers include block
initiators who are not honest
about the full sizes of their
trades and traders who
front-run blocks displayed
to them.
Block
traders keep their clients honest by knowing them well and by pe-
nalizing them when they are dishonest. They are most effective when they
have well-established relationships with their clients. Block traders there-
fore often work for large investment banks that provide services besides
transaction services to their clients. These other services may include in-
vestment advice, research, banking, and clearance and settlement. Through
these relationships, block traders get to know their clients well, and they
often can penalize clients who lie to them.
Since large traders can send portions of their orders to multiple block
traders, block traders may not easily determine the full extent of their clients'
orders. In the U.S. equities markets, many large institutions must report
their portfolio holdings on a quarterly basis to the Securities and Exchange
Commission. Data vendors such as CDA/Spectrum collect these
13F Hold-
ings Reports
and disseminate the information in them to their clients. Block
traders use this information to determine after the fact whether their clients
were truthful in their dealings with them. Block traders also use informa-
tion about portfolio positions to estimate the maximum amount that a large
trader might trade. The information is particularly useful when the large
trader is a seller whose investment policy prohibits short sales. Such traders
can sell no more than they own. If they want to sell their entire positions,
block traders know that no further size will follow.
15.2.4 The Asymmetric Information Problem
Block initiators have trouble finding liquidity because block liquidity sup-
pliers suspect that they are well informed. They base their suspicions on two
arguments. First, large traders can afford to invest more in information than
can small traders because they can spread the fixed costs of research over
larger portfolios. Second, well-informed traders want to trade large sizes in
order to obtain the maximum profit from their information. Taken together,
these arguments suggest that large traders are often well informed. Traders
therefore do not like to trade with them. When they do, they demand very
large price concessions.
Block liquidity suppliers demand these price concessions for the same
reasons that dealer bid/ask spreads include an adverse selection component.
These price concessions allow liquidity suppliers to recover from uninformed
traders what they lose to informed traders. They also reflect the inferences
about fundamental values that liquidity suppliers make when they suspect
that they may be trading with well-informed traders.
Block liquidity suppliers especially suspect that large anonymous traders
are well informed. Informed traders like to trade anonymously because they
do not want to acquire reputations for being well informed. Such reputa-
tions would allow liquidity suppliers to avoid them. They also like to trade
anonymously because they do not want front runners to profit from the fun-
damental information that their orders reveal. Since uninformed traders do
not share these concerns, block liquidity suppliers suspect that anonymous
traders tend to be well informed. Accordingly, block liquidity suppliers avoid
anonymous traders.
Large block initiators solve the asymmetric information problem by con-
vincing block liquidity suppliers that they are uninformed. To do this, they
must reveal their identities. If they have a reputation for being uninformed,
traders may then offer them liquidity that they otherwise would not have


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CHAPTER 15 BLOCK TRADERS • 327
offered. If they do not have a reputation for being uninformed, they must
submit to an audit of their trading intentions. If block traders conclude that
the large traders are indeed uninformed, they may arrange trades for them.
15.2.5 Summary
Block initiators have trouble finding liquidity because most block liquidity
suppliers do not express their trading interests, because block initiators can-
not widely expose their orders without spoiling their markets, because block
liquidity suppliers fear that block initiators will price discriminate, and be-
cause block liquidity suppliers fear that block initiators are well informed.
Block initiators must address these issues in order to obtain liquidity.
To address each issue, block initiators must reveal credible information
about themselves to block liquidity suppliers. They must tell them they want
to trade to solve the latent demand problem. They must expose only to the
most trustworthy traders to avoid order exposure problems. They must cred-
ibly reveal the full size of their orders to solve the price discrimination prob-
lem. Finally, block initiators must convince block liquidity suppliers that they
are uninformed traders to address the asymmetric information problem.
Successful block trading therefore requires significant exchanges of in-
formation among traders besides the usual price and size information that
all traders must exchange. Since most exchanges and dealer networks are
equipped only to exchange price and order size information, block traders
arrange most block trades by telephone in the upstairs market.
Block initiators choose between two strategies to convey information
about themselves to the market.
Sunshine traders
try to communicate di-
rectly to the market. The sunshine trading strategy is rarely effective, how-
ever. We consider it because it allows us to better understand the alterna-
tive strategy in which large traders use the services of block traders in the
upstairs market.
15.3 SUNSHINE TRADING
Traders who announce to the market who they are, what they intend to do,
the full extent of their orders, and why they intend to trade are
sunshine
traders.
Sunshine trading works well when sunshine traders are well known
and are known to be uninformed and honest. In large markets, sunshine
trading at best works only for the largest traders, since only those traders
will be able to acquire credible reputations.
Sunshine trading does not work if traders suspect that the sunshine trader
may be well informed or dishonest. If traders could always obtain more
liquidity merely by revealing their identities, all traders would do so. Un-
known informed traders would pretend to be uninformed traders, and
well-known informed traders would create new identities to mask their trad-
ing. Well-informed traders who try to pass for uninformed traders are
wolves
in sheep's clothing.
Sunshine trading generally does not work well because it
is hard to determine whether sunshine traders are indeed uninformed traders
and whether they have indeed revealed their entire trading interests. Good
answers to such questions generally require thorough investigations of their
motives for trading. Traders cannot conduct such investigations on exchange
floors or in screen-based trading systems.
Although sunshine trading may solve the asymmetric information
Ignorance Is Bliss
In almost everything we do,
a reputation for being well
informed serves us well.
Trading is the notable
exception. Although the most
profitable traders are well
informed, they must appear
to be uninformed when they
trade. Otherwise, they will
not be able to trade at
low cost.
4


## Page 341

328 • TRADING AND EXCHANGES
LOR's Sunshine Trading in S&P 500 Futures
Leland O'Brien Rubinstein Associates (LOR) was an institutional money
manager that popularized the portfolio insurance trading strategy in the
early 1980s. The object of the strategy is to replicate the returns of a
covered put position. Traders implement the strategy by buying securities
when the market rises and selling them as it falls. A formula from the well-
known Black-Scholes option pricing theory specifies the trade size.
LOR used S&P 500 futures contracts to provide portfolio insurance for
clients whose portfolio returns were closely correlated with the S&P 500
Index. Since the strategy was extremely popular, LOR became a very large
and very well known trader in the S&P 500 futures pit.
In an attempt to lower the cost of their trades, LOR would make public
announcements of its orders and of when it would fill them. It thus hoped to
notify other traders of the trading opportunities that it offered. Since traders
knew LOR was an uninformed trader following a well-known and well-
understood trading strategy, LOR hoped that its announcements would solve
the price discrimination and asymmetric information problems that large
traders normally face.
PP.
Wolves and Sheep
Traders sometimes call well-
informed traders wolves and
uninformed traders sheep.
This biological analogy
represents their relationship
quite well. Just as wolves must
eat sheep to survive, well-
informed traders must trade
with uninformed traders to
profit.
Proprietary traders who
use computers to implement
trading strategies that offer
liquidity_write program codes'
they call
wolf detectors
to
identify and avoid trading
with well-informed traders.
problem for
some very well-known traders, it introduces another serious
problem. By revealing their intended trades, sunshine traders give free trad-
ing options to the market. They therefore attract front runners, quote match-
ers, and, under some circumstances, squeezers. (Chapter 11 describes how
these order anticipation strategies Work.) Sunshine traders may therefore
have higher transaction costs than they would have if they controlled their
order exposure more carefully.
15.4 THE UPSTAIRS MARKET
The
upstairs market
serves large traders who cannot convey credible infor-
mation about their trading motives and intentions to traders in the regular
market. These block initiators use block dealers and block brokers to help
them fill their orders. Both types of block traders investigate their clients
to determine whether they are well informed and whether they have re-
vealed the full sizes of their orders.
15.4.1 Block Dealers
Block dealers fill large client orders when they trade for their own accounts.
Because they take their clients' positions, block dealers are also known as
block positioners.
They are also called
block facilitators
because they facilitate
their clients' demands for liquidity.
After block dealers take their clients' positions, they then try to trade out
of them. They may try to identify large traders who are interested in their
new positions, or they may break their positions into small parts to distrib-
ute into the market over time.
Good block traders must carefully set the prices at which they facilitate
trades with their clients to ensure that they will be able to trade out of their
positions at a profit. To do this, they must be able to predict what their
transaction costs will be when they liquidate their positions. Some block
dealers therefore make substantial investments in transaction cost analyses
so that they can predict future transaction costs.


## Page 342

CHAPTER 15 BLOCK TRADERS • 329
II.
The Upstairs Market
The upstairs market acquired
its name because its traders
arrange block trades at
trading desks in the offices of
the wirehouses for which they
work. When traders used to
have offices that were in the
New York Stock Exchange
building or across the street,
these offices were generally
above the street level on
which the trading floor was
(and still is) located.
4
Since block dealers take positions that they generally intend to liquidate,
they must be very careful that they do not trade with well-informed traders
or with traders who have more size to trade. If they trade with informed
traders, they risk losing if prices change against their positions before they
have liquidated them. If they trade with traders who have more size to trade,
they must compete with their clients to liquidate their positions. Block deal-
ers therefore have a very strong and direct interest in why their clients want
to trade.
Many large traders choose to trade with block dealers because block deal-
ers are better traders than they are. Although the block initiators might be
able to handle the trade themselves by breaking it up, they presumably would
incur greater transaction costs, especially if they want to trade quickly. To
avoid these costs, block initiators pay block dealers to take their trading
problems.
15.4.2 Block Brokers
Block brokers help block initiators identify traders who will fill their orders.
Since they often must assemble many traders to fill a large order, block bro-
kers are also known as
block assemblers.
The traders whom block brokers organize to fill their clients' block or-
ders care as intensely about whether they will lose when trading with block
initiators as block dealers do. Block liquidity suppliers do not want to see
prices rise after they sell or fall after they buy.
Block liquidity suppliers rely upon the block brokers who arrange their
trades to determine whether the block initiators are well informed and
whether they are honest about the full size of their intended trades. If sub-
sequent events suggest that a broker failed to adequately screen his clients,
block liquidity suppliers will be reluctant to do more business with that bro-
ker. To protect their reputations, block brokers therefore must know their
clients well. To cultivate their reputations, block brokers often estimate
transaction costs for their clients to demonstrate that they have served them
well on average.
Block brokers charge block initiators commissions for their services.
In many markets, they also collect commissions from the block liquidity
suppliers.
15.4.3 Brokers Versus Dealers
Block brokers and block dealers tend to specialize in different segments
based on their comparative advantages. Dealers have a comparative advan-
tage when trading with impatient traders because dealers can take positions
before they know where they will place them. They also have a compara-
tive advantage filling small blocks because they often can trade out of their
positions by patiently trading in the market. Brokers cannot compete well
in these segments because they do not trade for their own accounts. Bro-
kers have a comparative advantage assembling blocks for the largest traders
because dealers are unwilling to hold very large positions.
In practice, most block traders act both as brokers and as dealers. Dual
traders can better serve their clients than can pure brokers or pure dealers
because they can do whatever is best for their clients at the moment. In ad-
dition, brokers who are willing to participate in a trade have an important
advantage over brokers who will not. They can more credibly assure poten-


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330 • TRADING AND EXCHANGES
0' A Finger in
the Guillotine
Some magicians use trick guillotines to entertain their audiences. After
demonstrating that the guillotine will easily cut a potato, the magician calls
upon a volunteer to place her hand in the guillotine. The magician then
drops the guillotine again. This time, of course, the guillotine does not cut
off the hand, although it appears to the audience that it will.
Since a magician's guillotine will cut off a hand if not used properly,
magicians must be very careful that they operate the guillotine correctly. To
focus their attention, they commonly put one of their fingers in the guillotine
along with the volunteer's hand.
When brokers propose trades to block liquidity suppliers, the suppliers
must trust that the brokers are suggesting trades that benefit them and not
just the brokers or the trade initiators. Brokers who put their finger in the
guillotine by trading along side other block liquidity suppliers assure the
block liquidity suppliers that they are acting in their interests.
Hot Potatoes
Dealing with informed traders
is like passing a hot potato.
A dealer who takes a position
from a well-informed trader
hopes to pass it along before
its true value becomes well
known. When traders can
infer values from trade
reports, trading with well-
informed traders is more
risky.
tial traders that a trade will not hurt them when they also participate in it.
Rather than staking just their reputations on their audits, as brokers do,
broker-dealers also stake their wealth.
15.4.3.1 Trade Reporting Issues
Rules that require full and timely trade reporting favor block brokers over
block dealers. Such rules have little effect on block brokers because their
work is finished when they arrange trades for their clients. They affect block
dealers because dealers must liquidate their positions after they facilitate
their clients' orders.
When dealers must quickly report their block trades, clever traders may
use this information to predict trades that dealers must arrange to liquidate
their newly acquired positions. These traders may then front-run the deal-
ers and thereby increase their costs of liquidating their positions.
Clever traders may also use trade reports to infer whether block initia-
tors are well informed. Such inferences increase the probability that dealers
will lose when they offer liquidity to well-informed block initiators. In par-
ticular, dealers may be unable to liquidate their positions before informa-
tion about their trades causes prices to move against them. Timely trade re-
porting therefore increases the importance of the audit that dealers must
make of their clients' reasons for trading.
Not surprisingly, the markets with the weakest trade reporting practices
tend to be dealer markets. For example, most corporate bond markets are
pure dealer markets. These markets have no trade reporting requirements.
In the United States, the SEC has been pressing for the adoption of a bond
trade reporting system, but the dealers are quite resistant.
15.4.4 The Trading Motive Audit
To avoid helping a well-informed trader, block traders research their clients'
motives for trading to determine whether they are uninformed._Uninformed
traders have utilitarian reasons for trading that are unrelated to fundamen-
far
-
Values (see chapter 8). When these reasons are apparent, block traders
may conclude that their clients are uninformed.


## Page 344

CHAPTER 15 BLOCK TRADERS • 331
TABLE 15-2.
Examples of Trader Motives and Likely Interpretations
GIVEN REASON

INTERPRETATION

QUESTIONS
A firm needs to liquidate a diversified
portfolio to fund a large project.
A speculator needs to purchase stock to
cover a short position that is losing money.
An heir needs to diversify a recently
inherited stock portfolio.
An heiress needs to diversify a stock
portfolio inherited two years ago.
A corporate insider needs to fund a college
fund by selling founder's stock.
A legatee receives a large bequest and wants
to buy a single stock.
A baker needs to buy grain futures to hedge a
newly won contract to feed the army.
Probably
uninformed
Probably
uninformed
Probably
uninformed
Possibly
informed
Possibly
informed
Probably
informed
Probably
uninformed
A large grain operator wants to sell grain

Possibly well
futures to hedge millions of bushels in

informed
storage.
An electronics distributor needs to buy 50

Probably
million dollars of Bolivian bolivianos to

uninformed
fund a new joint venture.
Can the speculator liquidate
other assets?
Why trade now?
Why trade now?
Why buy only one stock when
portfolio theory suggests a basket?
Does the firm speculate in its
hedging accounts?
Does the distributor need the
money now?
lk Block Trade Reporting
at the London Stock
Exchange
The London Stock Exchange
historically has been a dealer
market. It once allowed its
dealers to report their largest
blocks as long as seven days
after the trade. The Exchange
now requires that all traders
report their trades within
90
minutes. By
contrast, traders
in the United States must
report all equity trades within
90 seconds while the markets
are open.
Block traders are most confident that their clients are uninformed when
their clients have easily verified utilitarian reasons to trade the instrument
that they propose to
trade when they want to trade it. When the block ini-
tiator has some discretion in choosing which instrument to trade, or when
to trade it, the block trader may suspect that the trader chose the security
or time to trade strategically. (See table 15-2 for trader motives.)
Some traders scoff at the notion that block brokers audit trader motives
and total trading interest. Although they acknowledge that information
about motives and total trade sizes is very important, they claim that block
brokers are completely unreliable when it comes to providing this informa-
tion. In particular, they assert that most block brokers are not trustworthy.
There may be some merit in their opinion. Since prices in many mar-
kets are quite volatile, block liquidity suppliers may be unable to determine
whether adverse price changes are related to their trades or simply due to
normal security price fluctuations. In such environments, block traders may
not be able to cultivate reputations for effectively auditing trader motives
and total order sizes. If that is so, block traders will shirk on their audits,
and each block liquidity supplier will have to rely upon his or her own re-


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332 • TRADING AND EXCHANGES
Block Traders and Brokerage Recommendations
Broker-dealers often recommend to their clients stocks the broker-dealers
recently acquired by positioning blocks for their larger clients. If their clients
listen to them, the brokers can then liquidate their positions by selling to
their clients. Such dealers are called
distributors
because they distribute the
blocks to their brokerage clients.
These recommendations often perform poorly. Stocks recommended by
broker-dealers historically have underperformed the market.
Brokers who exploit their clients too often eventually lose them.
Accordingly, the brokers most likely to exploit their clients are brokers who
have the least to lose. These brokers usually employ hard sales tactics to
acquire new accounts from unsophisticated retail investors.
Block distributions to retail brokerage clients are not necessarily
nefarious. Honest broker-dealers do their clients a service by identifying and
selling undervalued securities to their clients.
4
An Uninformative
Divestiture
The Employee Retirement
Income Security Act (ERISA)
limits how much employer-
sponsored pension plans may
invest in their sponsors'
stocks.
Suppose that a pension
plan holds the maximum legal
investment in its sponsor's
stock. The sponsor then buys
a firm in which the plan has
also invested, and pays for
the purchase by exchanging
stock. Following the
transaction, the pension plan
will hold more of its sponsor's
stock than the legal
maximum. Since the law
requires that it divest the
excess stock, a block trader
may reasonably conclude that
the sale is not information-
motivated.
4
search to determine whether to offer liquidity. Traders who do not do their
research risk offering liquidity foolishly.
15.5 A TELLING STATISTIC
We often can identify whether a seller or a buyer initiated a block trade by
comparing the block trade price against the quotation prices that prevailed
when the block traded. If the block trade price is closer to the bid than to
the ask, it probably was seller-initiated. Otherwise, it was probably buyer-
initiated. This classification scheme presumes that the block initiator paid
a concession to fill the order. Since this presumption is reasonable, the clas-
sification scheme is quite accurate.
Using this classification scheme, analysts have determined that sellers
initiate approximately 80 percent of all large block trades in the U.S. stock
markets. The remarkable asymmetry between block buyer- and seller-
initiated trades is consistent with three of the four block trading problems
discussed in this chapter:
Consider first the latent demand problem. Block sellers can sell their
blocks to any interested traders. In contrast, when short selling is difficult,
block buyers generally can buy large blocks only from traders who own the
securities in which they are interested. Buying large blocks therefore may
often be harder than selling large blocks.
Now consider the price discrimination problem. Block sellers often can
credibly reveal the full size of their orders if they cannot sell short. Few buy-
ers, however, operate under such constraints. Although restrictions on the
maximum quantities that institutional investors may hold in any one secu-
rity may limit the purchases of some buyers, these restrictions rarely are
binding. These observations suggest that block liquidity suppliers will more
often trade with sellers than with buyers.
Finally, information asymmetries may also explain why block sellers ini-
tiate more trades than block buyers. Sellers generally can offer more con-
vincing stories about why they are uninformed than can buyers. Sellers oc-
casionally must sell specific securities to raise cash or to manage the risk of
undiversified portfolios. Buyers, however, can buy any security. Since unin-


## Page 346

CHAPTER 15 BLOCK TRADERS • 333
formed buyers should not construct undiversified portfolios, traders assume
that most large buyers are well informed. Block liquidity suppliers therefore
will more often offer liquidity to sellers than to buyers.
15.6 THE RELATION BETWEEN BLOCK
MARKETS AND REGULAR MARKETS
Since block markets trade the same instruments that regular markets trade,
regulators must consider how the two types of markets relate to each other.
In particular, regulators must ensure that neither market has significant neg-
ative impact upon the other. Regulators are especially concerned about the
potential for large traders to use block markets to subvert rules in the reg-
ular markets. Regulators generally want to protect the rights of traders in
the regular markets and to protect block clients from potential trading
abuses. The regulation of block trading markets is difficult because regula-
tors must respect the needs of large traders who use the block markets to
organize liquidity that they otherwise could not find in the regular markets.
This short section considers some regulatory issues that arise when block
markets trade alongside regular markets.
15.6.1 Equity Markets
In the U.S. listed equity markets, brokers and dealers who are members of
an exchange must
print
their block trades in exchange-listed securities at an
exchange if the trade takes place while the market is open. To print a trade,
the block trader presents the matched buy and sell orders to the market.
The purpose of these exchange rules is to expose all orders to the market
so that the exchange order precedence rules protect the interests of all traders
in the market.
Sometimes standing orders already in the market may have higher order
precedence than some of the matched orders that block traders present. This
happens when traders in the market have price priority because they are of-
fering better prices or when traders have time precedence because they of-
fered to trade at the block price before the matched block orders arrived.
In such cases, the exchange must mediate between the interests of the block
traders and the traders who submitted standing orders with higher prece-
dence. The block traders want to cross their blocks without interference
from other traders. Traders with higher precedence want to participate in
the block trade.
The U.S. equity exchanges address this problem with a
size _precedence
rule.
For crosses that exceed 25,000 shares, the matched block orders are al-
lowed to
outsize the book
at the trade price. In particular, large matched or-
ders jump ahead of orders with greater time precedence. All orders stand-
ing on the book at better prices, however, are incorporated into the block
trade at the block price. These orders displace some of the matched orders
that the block trader presents. The block trader
cleans up the book
by filling
these standing orders.
The size precedence rule represents a compromise between the interests
of the block traders and those of the standing limit order traders. The block
traders want to protect the efforts that they have made in arranging their
blocks. When their block crosses are broken, they make less in commis-
sions, they make less dealer profit, and they may have to tell some of their


## Page 347

334 • TRADING AND EXCHANGES
clients that they did not trade when they expected to. Since the block traders
provide services that are not generally available on exchange floors, ex-
changes are interested in protecting them. (They are also interested in col-
lecting the print revenues that data vendors pay to exchanges in proportion
to their trading volumes.) The standing limit order traders want to earn a
return on the liquidity that they offer to the market. Since exchanges value
the liquidity that they offer, exchanges are also interested in protecting these
traders. By universally enforcing the price priority rule, the exchanges main-
tain strong incentives for traders to improve prices. The price priority rule
also encourages block brokers to access liquidity on the exchange before they
access other sources of liquidity. Block initiators like this incentive because
identifying liquidity offered at the exchange is cheaper than identifying liq-
uidity from traders with latent trading interest. The 25,000-share threshold
for sizing the book ensures that only very large crosses may violate time
precedence.
To avoid limit orders that might break up their blocks, block traders of-
ten print their crosses at regional exchanges. These exchanges usually do
not have as many orders standing in their books as the primary stock ex-
changes do.
15.6.2 Futures Markets
Most futures markets require that traders arrange all trades in their con-
tracts within their trading systems, whether they use floor-based or screen-
based systems. These rules prevent abuses that can arise when brokers di-
rect orders to confederates who offer prices inferior to those available at the
exchange. Since block trading generally involves trades arranged away from
the exchange, futures markets historically have had either no block trading
procedures or cumbersome block trading procedures.
Various futures markets have experimented with block trading rules.
These rules generally allow brokers to arrange block trades that exceed some
specified quantity, but only among eligible participants. Exchange rules de-
fine
eligible participants
as sophisticated traders who presumably are aware
of the problems associated with negotiating trades away from the market.
Such traders typically include sell-side traders and buy-side professionals
working for large institutional managers. Notwithstanding these qualifica-
tions, the exchanges still require the block prices to be fair and reasonable,
given current market conditions and the circumstances of the trades.
Futures traders also use
exchangefbr physical
(EFP)
trading procedures to
arrange block trades. In an EFP, traders agree to exchange a long physical
position for a long futures position plus cash. Exchange clearinghouses orig-
inally developed EFP trading procedures to allow commercial operators in
the agricultural markets to transfer hedged cash positions.
The EFP trading procedure is attractive to block traders because it can
be employed away from the exchange. The procedure therefore is also used
to arrange after-hours trades. For example, traders in currency futures at the
Chicago Mercantile Exchange (CME) often use the procedure immediately
after trading closes to offset a position that they do not want to hold
overnight. The CME floor traders generally execute these EFPs with cur-
rency traders at large banks. Exchanges call these EFPs
transitory EFPs
be-
cause the floor traders buy or sell the cash currency at the same time they
execute the EFP.
10
,
Block Trading at LIFFE
The London International
Financial Futures and Options
Exchange (LIFFE) introduced
a block trading facility in
April 1999. LIFFE's block
trading procedures permit
its members and their
qualified clients—known as
wholesale clients—to quickly
trade large blocks at
bilaterally negotiated prices.
Traders may use the facility
only for trades larger than a
predetermined size.
Source: www.liffe.com



## Page 348

CHAPTER 15 BLOCK TRADERS • 335
An EFP in Wheat
Frank is a North Dakota wheat farmer who has hedged his crop by selling
wheat futures contracts at the Chicago Board of Trade. He intends to sell
the crop to Oscar, his local grain operator.
Oscar owns wheat silos and facilities for loading wheat onto railroad
cars. He knows much about wheat storage and shipping, but little about
global wheat supply and demand. He therefore hedges his physical wheat
positions to avoid exposure to price risks that he does not understand.
Whenever he buys cash wheat, he sells futures contracts, and vice versa.
When Frank sells his crop to Oscar, Frank must close his short hedge
and Oscar must create the same short hedge. Rather than separately
trading futures in Chicago, they arrange an EFP. Frank gives his physical
wheat to Oscar in exchange for cash and a long position in the futures
contract. The long futures position that Frank receives offsets his short
hedge. The long futures position that Oscar gives, leaves him short futures,
so that his newly acquired cash wheat position is hedged. Through this
mechanism, Frank transfers his hedged cash wheat position to Oscar.
4
15.7 SUMMARY
Block
trades are trades that are too large to arrange easily using normal trad-
ing methods. They usually involve more size than is typically available at an
exchange or in a dealer network.
Four problems make block trades costly to arrange:
•
Block liquidity suppliers may be hard to find because most traders do
not express their trading interests.
•
Block initiators are reluctant to advertise their interests for fear of spoil-
ing their markets.
•
Block liquidity suppliers fear that block initiators will try to price dis-
criminate among them by breaking up their orders.
•
Block liquidity suppliers fear that block initiators may be well informed.
Block traders solve these problems by keeping track of who might be in-
terested in trading, by selectively exposing block orders, by determining the
full size of their clients' orders, and by determining whether their clients are
well informed. Since traders cannot easily undertake these activities on the
floor of an exchange, traders arrange most large block trades off the ex-
change floors. Table 15-3 provides a summary of the skills that good block
traders must have.
Block trading markets work well only when traders know each other well.
Anonymous traders generally cannot credibly exchange the information that
block traders require of each other. Since anonymous traders cannot estab-
lish reputations, they have no incentive to reveal information honestly. Trad-
ing systems that match anonymous buyers to anonymous sellers therefore
cannot easily arrange block trades.
Block traders must be very careful when they agree to help a block ini-
tiator find liquidity. If they act as dealer and offer the liquidity themselves,
they must be confident that prices will not move against them before they
can divest the blocks that they facilitate. Otherwise, they will lose. If they
act as broker and arrange to have other traders fill the order, they also must
A Transitory EFP in
German Marks
Geraldine trades German
mark futures at the Chicago
Mercantile Exchange. At the
end of the trading day, she
discovers that she is long
more contracts than she is
willing to hold overnight. To
divest her position, Geraldine
executes an EFP with a
currency trader at a large
money bank. She gives U.S.
dollars and the futures
contracts to the bank and in
exchange receives German
marks. She simultaneously
sells German marks for U.S.
dollars to the same currency
trader. In effect, she has sold
her futures contracts to the
bank after hours.


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336 • TRADING AND EXCHANGES
TABLE 15-3.
Block Trader Skills
REQUIRED SKILL

BLOCK DEALERS

BLOCK BROKERS
Ability to identify
latent demands
Ability to conduct
effective client audits
Ability to trade at low
cost in the market
Ability to analyze
transaction costs
Ability to predict
transaction costs
Needed to trade out of their positions
and to predict the costs of doing so
Needed to avoid trading losses
Needed when liquidating positions
in the market over time
Needed to predict transaction costs
Needed to price block trades
Needed to arrange block trades
Needed to preserve their
reputations
Not necessary
Needed to cultivate reputation
for delivering profitable trades
Useful but not essential
be confident that prices will not move against their clients whom they have
encouraged to offer liquidity. Otherwise, these clients will be reluctant to
participate in future trades that the block brokers may propose.
15.8 SOME POINTS TO REMEMBER
•
Block trading markets primarily serve large uninformed traders.
•
Order, exposure is very important to large traders.
•
Informed traders may pretend that they are uninformed to obtain
liquidity more cheaply.
•
Large traders may split their orders to price discriminate among liq-
uidity suppliers.
•
To trade successfully, block dealers and brokers must determine
whether their clients are well informed and whether their clients want
to price discriminate.
•
Block dealers lose their capital when they do not know their clients
well.
•
Block brokers lose their reputations when they do not know their
clients well.
•
Delayed trade reporting favors informed over uninformed traders, and
block dealers over block brokers.
15.9 QUESTIONS FOR THOUGHT
1.
Exchanges classify block trades as all trades larger than some thresh-
old. The threshold can be a given trade size, a given trade value, a
given fraction of daily trading volume, or a given fraction of
shares/contracts outstanding. What are the advantages and disadvan-
tages of these various alternatives?
2.
Floor brokers on the floor of the NYSE often arrange institutional-
sized trades. Do they offer their clients services that they could not
obtain in an electronic trading system? Of what value is an honest
reputation to a floor broker? How do they protect their reputations?


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CHAPTER 15 BLOCK TRADERS • 337
3.
When a large trader splits his order among many block trades, should
the trader submit the parts sequentially or simultaneously?
4.
How might a block trader recognize when a large trader has split his
order among many brokers?
5.
When should exchanges allow size to take precedence over time at a
given price?
6.
Should regulators allow delayed reporting for large trades?
7.
Which customers—informed or uninformed—favor delayed reporting?
8.
How should an informed trader arrange his trades to obtain liquid-
ity cheaply?


## Page 351

16
Ualue
Traders
V
alue traders
are speculators who form opinions about instrument values
by using all information available to them. They buy instruments that
they believe are undervalued and sell instruments that they believe are over-
valued. We describe their speculative trading strategies in chapter 10.
Value traders are also liquidity providers, though they often do not see
themselves this way. This chapter explains how and when value traders of-
fer liquidity. We shall see that they are the ultimate suppliers of market liq-
uidity. They trade when no one else will. We therefore must understand this
aspect of value trading to fully understand who makes markets liquid.
Value traders who understand that they supply liquidity will trade more
successfully than will those who do not realize this. By considering the im-
plications of their roles as liquidity suppliers, value traders will make better
decisions about when to trade and at what price to trade. If you are inter-
ested in being a value trader, the principles discussed in this chapter will be
of particular concern to you.
Dealers often trade with value traders when they want to restore their tar-
get inventories. Dealers therefore have mixed feelings about value traders. On
the one hand, they compete with them to provide liquidity. On the other
hand, they depend upon them for liquidity when they are unwilling to carry
large inventory positions. If you are a dealer or if you are interested in being
a dealer, you need to thoroughly understand how dealers relate to value traders.
Value traders must confront an economic problem called the winner's
' curse to trade successfully. Traders suffer the
winner's curse
when they win
an auction and subsequently regret that they traded because they paid too
much or sold for too little. Everyone who competes with others to buy or
sell items faces the winner's curse. You need to know about the winner's
curse when you buy a house, when you trade on eBay, and when you bid on
a job. Even if you do not intend to trade securities or contracts, you should
find this chapter useful.
16.1 VALUE TRADERS SUPPLY LIQUIDITY
Although value traders trade to make speculative profits, the effect of their
trading is to provide liquidity to the market. This characterization of their
trading is apparent when you consider when they trade profitably.
Value trading is profitable only when price differs from fundamental
value. Price can differ from fundamental value two ways:
•
When new information causes fundamental value to change, and
thereby deviate from price, or
•
When uninformed traders push price away from fundamental value.
In the first case, news traders profit because—by definition—they are
the first to receive new information. Their trading tends to push price to
338


## Page 352

CHAPTER 16 VALUE TRADERS • 339
the new fundamental value. (Chapter 10 discusses information flow trad-
ing.) In the second case, value traders profit. Through their research, they
are able to determine that price no longer reflects fundamental value. Their
trading tends to push price back to its fundamental value. Value trading
/

therefore is profitable only when value traders trade in response to demands'
for liquidity made by uninformed traders.
In the following discussion, we will need to refer frequently to "the un-
informed traders whose demands for liquidity cause prices to change." To
simplify our discussion, we will simply call them the
uninformed liquidity
demanders.
They may be one or more large traders, or they may be numer-
ous small traders who all want to trade on the same side of the market. They
cause prices to change as they try to fill their orders.
16.1.1 Uninformed Traders Cause Prices to
Deviate from Fundamental Values
Price deviates from fundamental value when uninformed traders demand liq-
uidity, and when the traders who offer them liquidity do not realize that they
are uninformed. This situation often happens when dealers do not know their
clients well. To protect themselves from adverse selection losses to informed
traders, dealers must make inferences from their order flow. If uninformed
traders dominate the order flow on the same side of the market, dealers will
mistake those traders for informed traders and adjust prices accordingly.
These price adjustments cause price to deviate from fundamental value.
Dealers also may adjust prices even when they know that their clients
are uninformed. When dealers supply liquidity only on one side of the mar-
ket, their inventories diverge from their target levels. The resulting off-
target inventories expose them to substantial inventory risks. If their unin-
formed clients demand more liquidity than dealers are willing to supply,
dealers will demand substantial price concessions to bear the resulting in-
ventory risk. These price adjustments will be especially large when dealers
fear that they will not easily find traders on the other side of the market.
Uninformed traders may also cause prices to deviate from fundamental
values in order-driven markets that do not have dealers. In such markets,
prices change when traders demand liquidity on one side of the market and
exhaust the liquidity supplied there.
16.1.2 How Value Traders Respond
Value traders may trade directly with the uninformed liquidity demanders,
or they may trade indirectly with them through the intermediation of deal-
ers and other traders who employ dealing strategies. Value traders trade di-
rectly with them when value traders offer limit orders that the uninformed
liquidity demanders take, or when block brokers ask value traders to fill or-
ders for their uninformed liquidity-demanding clients. In these situations,
value traders supply immediacy to the uninformed liquidity demanders be-
cause they allow them to trade when they want to trade.
Value traders also indirectly supply liquidity to the uninformed liquidity
demanders. We can best introduce this situation with an example.
Suppose that uninformed liquidity demanders want to sell stock in a
hurry. They sell to dealers who offer them immediacy. The dealers accu-
mulate large long positions as they buy the inventory. Since they do not
know their clients very well, they suspect that the uninformed liquidity


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340 • TRADING AND EXCHANGES
demanders may be informed traders. The dealers therefore adjust their prices
accordingly. They also adjust their prices because they fear that they will not
easily find traders on the other side of the market, in which case they will
be exposed to more inventory risk than they would like to bear. These ad-
justments cause price to fall below fundamental value so that value trading
becomes profitable. To restore their target inventories, the dealers lower their
quotes. Value traders buy from the dealers at their ask prices when they see
that they can buy substantial size at discounted prices.
The dealers solicit liquidity from the value traders by lowering their ask-
ing prices. When the value traders respond, they take liquidity from the
dealers in the form of immediacy, but simultaneously supply liquidity to the
dealers in the form of size or
depth.
In effect, the value traders indirectly
supply liquidity to the uninformed liquidity suppliers through the interme-
diation of the dealers.
Even though the dealers lay off their inventory on the value traders, both
sets of traders may profit. The dealers will profit if they sell their invento-
ries at prices above what they paid to the uninformed liquidity demanders.
The value traders will profit when prices return to fundamental values.
16.1.3 Market Resiliency
When uninformed traders cannot change prices substantially, the market is
resilient
to their trading. Value traders make markets resilient by standing
ready to trade when prices move away from fundamental values.
Dealers will take larger positions when trading with their uninformed
clients in resilient markets than in markets that lack resiliency. In resilient
markets, dealers know that they can rely upon value traders to restore their
target inventories if their order flows remain unbalanced.
16.2 THE OUTSIDE SPREAD
AND ITS DETERMINANTS
The prices at which a value trader is willing to trade define his or her
out-
side spread.
Since value traders are well-informed traders, they rarely quote
these prices. They do not want to reveal their value estimates, and they do
not want to give free trading options to the market.
The spreads of value traders depend on the risks and costs of their busi-
ness. When they are large, their spreads will be large. This section consid-
ers what determines the outside spread.
16.2.1 The Risks of Value Trading
Value traders face two serious risks when they trade: averse selection and
the winner's curse. Like dealers, value traders face adverse selection when
they supply liquidity to better-informed traders. They face the winner's curse
when they have misestimated instrument values.
The two risks are closely related. Both arise when value traders are not
fully informed. When they suffer adverse selection, they lack information
that better-informed traders have. When they suffer the winner's curse, they
have mistakenly valued their instruments.
16.2.1.1
The Adverse Selection Risk
Value traders are subject to adverse selection risk because they offer liquid-
ity in response to other traders who demand it. They must be particularly


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CHAPTER 16 VALUE TRADERS • 341
careful that they do not trade with news traders who have new information
that the value traders do not have.
When value traders trade with better-informed traders, they buy or sell
instruments that they think are undervalued or overvalued but in fact are
overvalued or undervalued. They eventually lose when prices move against
their positions as traders learn the new information.
Value traders respond to adverse selection risk just as dealers do. They
widen their spreads to recover from uninformed traders what they lose to
better-informed traders. Equivalently, they widen their spreads to reflect the
inferences they will make about values when they learn that other traders
want to trade with them.
Value traders avoid adverse selection risk by trying to know everything
that they can about instrument values. Because research takes time, value
traders are often slow to respond to apparent profit opportunities.
16.2.1.2 The Winner's Curse and Value Trading
To understand how the winner's curse affects value traders, we must first
describe the winner's curse. This subsection starts with a general discussion
of the problem. Its implications for value trading follow.
The Winner's Curse
The winner's curse can affect buyers or sellers. To keep our discussion sim-
ple, we initially consider only how it affects buyers. Once we understand
the problem from the buyer's perspective, the problem from the seller's per-
spective is obvious.
Buyers can suffer the winner's curse when they compete to buy some-
thing that has a common, but unknown, value. An item has a
common value
when its value is the same for everyone. Most trading instruments have
common values because people value them only for the cash flows that they
ultimately will produce. Artworks do not have common values because what
is beautiful to one is often unattractive to another.
When the value of a common value item is uncertain, everyone inter-
ested in owning that item must estimate its value. Since people use differ-
ent models and different information to estimate values, they typically ob-
tain different value estimates. Some estimates will be closer to the true value
than others. Unfortunately, since people do not know true values, they do
not know the errors in their estimates.
Buyers suffer the
winner's curse
when they pay more for an item than it is
worth. Although they win the auction, they are cursed by the price they pay.
The winner's curse arises because the highest bidders in an auction tend
to be buyers who overestimate values. If they bid at prices near their value
estimates, and if they pay those prices, they will regret trading if their esti-
mates prove to be too high. On average, those estimates do prove to be too
high because extreme estimates rarely are as accurate as estimates closer to
the mean estimate. Bidders who pay prices near their estimates of value tend'
to pay too much if they win the auction.
Buyers avoid the winner's curse by considering the implications of being
the highest bidder in an auction. The highest bidder learns that his value
estimate may be the highest estimate among all buyers. If he knew this in-
formation beforehand, he could have improved his estimate by lowering it
toward the common mean. Buyers avoid the winner's curse by lowering their


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342 • TRADING AND EXCHANGES
The Winner's Curse
in Oil and Gas
Lease Auctions
The government often auctions
oil and gas leases. The
winners of these auctions
have the right to explore for
and develop oil within a
particular tract.
Before an auction, each
company interested in the
tract has its geologists survey
the site and produce an
estimate of its prospects. The
companies use this information
to make their bids.
Until they learned better,
oil companies systematically
overpaid for these leases.
The leases frequently proved
to be less productive than the
geologists expected. The
winners confronted their
geologists and asked why
they were so optimistic. The
geologists responded that
they were correct, on
average, about the various
tracts that they had valued.
Unfortunately, they
overestimated values for the
tracts that they won and
slightly underestimated values
for the tracts that they lost.
The geologists also pointed
out that they usually were
glad that they had not
purchased the tracts they had
lost at the prices the winning
companies paid for them.
With this evidence, the
companies learned that they
had failed to consider the
winner's curse when setting
their bids.
bids
to reflect the additional information that they will learn about their
value estimates if they win the auction.
The adjustments that buyers must make depend on the expected sizes
of their valuation errors, assuming that they win the auction. The expected
winner's valuation error depends on the number of other buyers in the auc-
tion and upon their uncertainty about values.
The winning buyer learns more about his estimate when he wins an
auction competing against many other buyers than against only a few buy-
ers. When the winner outbids many people, he probably grossly overesti-
mated value because all other bidders estimated lower values. To avoid the
winner's curse, bidders therefore should lower their bids more when bidding
against many buyers than against just a few.
This result is counterintuitive. Most people believe that they must bid
more aggressively to win an auction when competing against many other
,
traaers. They forget that the object is not to win. The object is to win at a
satisfactory price. It is better to lose than to win and pay too much.
Another reason why the result is counterintuitive involves market power.
If you are the only bidder, or if you compete with very few bidders, you may
underbid in the hope of winning the auction at a significant discount to
value. If you compete with many other traders, however, this bidding strat-
egy has little chance of success. Accordingly, you should bid more aggres-
sively when competing against several traders than against just one or two.
This result is important only if you compete with very few traders.
Buyers make larger estimation errors when they are uncertain about val-
ues than when they know them well. The most extreme estimation error
therefore will likely be large when buyers do not know values well. To avoid
the winner's curse, bidders should lower their bids more when values are
hard to estimate than when they can easily estimate them accurately.
To summarize, buyers face the winner's curse when they bid in auctions
for items with common value. They avoid the winner's curse by lowering
their bids to reflect what they learn about their value estimates should they
win the auction. They must lower their bids substantially when they com-
pete with many other buyers and when they cannot estimate values accu-
rately. Similar arguments suggest that sellers who will receive the prices they
offer should raise their offers to avoid the winner's curse.
An important implication of the winner's curse is that you do not want
to compete against foolish traders. When people bid foolishly, you have no
choice but to accept that you should lose the auction. You cannot make
money bidding against people whose bidding strategies ensure that they will
lose money. Many Internet entrepreneurs in the late 1990s now wish that
they had understood this principle better.
The Winner's Curse in Two
-
sided Auctions
The winner's curse does not pose a significant problem in a two-sided sin-
gle price auction in which buyers and sellers both attempt to value the item
sold. In such auctions, traders who estimate high values are buyers, and
traders who estimate low values are sellers. The traders with the highest bids
and the lowest offers trade with each other at the price that clears the mar-
' ket. Since the market-clearing price typically is near the median estimate
among all traders, no one faces a serious winner's curse problem. The
various traders are not trading at the prices they bid and offer. Buyers who


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CHAPTER 16 VALUE TRADERS • 343
If You Are Not a Buyer, Should You Be a Seller?
Some traders believe that they must act on everything about which they
have an opinion. For example, if they own securities they think are
overvalued, it is not sufficient for them to sell their long positions. They
continue selling until they have established short positions.
The notion that you are either a buyer or a seller is inconsistent with the
optimal response to the winner's curse. The uncertainties in your estimates of
value imply that you should not speculate unless you believe instruments are
very mispriced. Of course, the greater the mispricing you believe you have
identified, the more likely it is that you have made a mistake.
estimate the highest values are saved from serious losses by sellers who es-
timate the lowest values, and vice versa.
In continuous markets, traders do trade at the prices that they bid and
offer, and their orders may have significant market impact. They therefore
must be careful about the winner's curse. Although the market price aggre-
gates the information held by all traders, the process of aggregation is se-
quential, not simultaneous. Traders who significantly under- or overestimate
values may therefore lose when they push prices away from fundamental
values. These losses are due to the winner's curse.
Value Traders and the Winner's Curse
Value traders are exposed to the winner's curse because they trade only when
they believe that price differs significantly from their estimates of funda-
mental value. If their estimates are wrong, they may regret trading.
Value traders misestimate values when they use the wrong economic
models to value their instruments, when they fail to consider significant in-
formation, and when they misinterpret the information that they have.
When they make these mistakes, they trade when they should not. Although
these mistakes do not necessarily cause them to buy overvalued instruments
or sell undervalued instruments, the market impact of their trades may cause
instruments to become undervalued or overvalued. When value traders value
instruments poorly, they lose because their trades have market impact. They
lose because they trade when they should not trade.
Value traders avoid these losses by widening the spread between the prices
at which they are willing to buy and sell. When the winner's curse problem
is very serious, value traders will have wide outside spreads.
The outside spreads of value traders overlap when their value estimates
vary. The most aggressive value buyers and sellers are, respectively, the ones
who estimate the highest and lowest values. In extreme cases, value traders
will trade with each other when a very optimistic buyer is willing to buy at
a price above the offer price of a very pessimistic seller.
16.2.2 The Costs of Value Trading
The outside spread must be wide enough to allow value traders to recover
their direct costs of doing business. The most important of these costs are
their expenditures for research.
Successful value traders must ensure that they are very well informed.
They accordingly invest substantial sums to acquire and analyze data. Their
outside spreads must be wide enough to recover these costs. Most instru-
Why Were Internet
Stocks So Volatile?
The winner's curse helps
explain why Internet stocks
were so volatile in the late
1990s. Since Internet stocks
were hard to value and since
so many traders tried to value
them, the winner's curse
problem was especially
dangerous then to value
traders. To protect themselves,
they would trade only at very
wide outside spreads. Some
of the volatility was due to
the bouncing of prices within
the wide outside spreads of
value traders.


## Page 357

344 • TRADING AND EXCHANGES
ments that value traders analyze are properly priced. Their outside spreads
therefore must recover the costs that they incur analyzing the instruments
that they ultimately trade and also the instruments that do not currently
present profitable value trading opportunities.
16.3 OUTSIDE VERSUS DEALER SPREADS
The outside spreads of value traders are much wider than the spreads that
dealers quote. The differences are due to differences in the speeds at which
they trade, the sizes of their positions, their research costs, their exposures
to adverse selection and the winner's curse, and their total volumes of trade.
Dealers can quote narrow spreads because they trade in and out quickly.
When they can quickly and profitably restore their target inventories, they
do not care whether the market price is close to or far from fundamental
r value. They only care about discovering current market values. When they
make a mistake, they usually can correct it quickly, before prices change too
much. Dealers learn about their mistakes when they see that the order flow
I is one-sided.
In contrast, value traders usually trade when the order flow is one-sided.
Accordingly, they cannot easily use the order flow to determine when they
are mistaken. When value traders trade, they set market values rather than
simply try to discover them. They therefore have greater exposure to the
f winner's curse than dealers do. Value traders typically learn about their mis-
takes only after they have acquired significant positions. If prices subse-
quently change against them, they incur substantial losses.
Value traders generally take larger positions than dealers do. They there-
fore expose themselves to greater inventory risks than dealers do. Value
traders control their exposure to adverse selection by trying to be the best-
informed traders in the market and by trying to determine whether the
traders to whom they ultimately offer liquidity are uninformed. Since these
research activities are expensive, outside spreads must be larger than dealer
spreads. In contrast, dealers control their adverse selection exposure by ad-
justing prices to find market values, by avoiding well-informed traders, and
by refusing to take large inventory positions.
Since value traders speculate on fundamental values, they must hold their
positions until market prices adjust to reflect the fundamental values. Such
adjustments may take a long time when values are quite uncertain. During
that time, they must finance their positions, which typically are larger than
dealer inventories. Their financing costs therefore cause outside spreads to
be wider than dealer spreads. In addition, while value traders wait for prices
to revert to fundamental values, new information may arrive that changes
values. Such changes make their positions risky. If the new information is
inversely correlated with their positions, they have suffered adverse selec-
tion from news traders. If the new information is uncorrelated with their
positions, it will be diversifiable. In either event, value traders must trade at
wider spreads than dealers must because they face greater inventory risks.
Dealer spreads also are narrower than outside spreads because dealers
typically trade much greater volumes than value traders do. They can there-
fore spread their fixed costs of doing business over greater volumes than
value traders can.
Finally, dealer spreads are narrower than outside spreads because dealers


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CHAPTER 16 VALUE TRADERS • 345
often earn the entire spread when they complete a round-trip trade. The
best that a value trader can hope for is to make half the outside spread when
price returns to its fundamental value.
16.4 VALUE TRADERS AND NEWS TRADERS
Value traders and news traders are both well-informed traders. The bases
for their trading profits differ, however. Value traders profit when prices de-
viate from fundamental values because uninformed liquidity demanders have
pushed prices way. They offer liquidity to other traders. News traders profit
when they know before others that values have changed. They generally take
liquidity when they trade.
Value traders and news traders often profit at each other's expense. Value
traders lose to news traders when they mistake news traders for uninformed
traders. News traders lose to value traders when they do not realize that the
information upon which they are trading is already in the price.
To trade successfully as a well-informed trader, you must know when you
are trading as a value trader and when you are trading as an news trader.
The two styles require different trading disciplines.
When you are a value trader, you must be sure that you offer liquidity
only to uninformed liquidity demanders and not also to news traders. You
can trade most confidently when you are certain that you have all available
fundamental information, and when you clearly know that the traders to
whom you are offering liquidity are uninformed. You will rarely have such
information. At best, you should be sure that you have all publicly available
fundamental information. You also should have some idea why uninformed
traders are demanding liquidity. If you do not consider whether you are in-
deed best informed, your risk of losing to an news trader might be greater
than you assume.
When you are a news trader, you must be sure that you are truly trad-
ing on fundamental information that is not yet in the price. You can trade
most confidently when you know that nobody else has your information.
You will rarely know this, however. At best, you should know how your in-
formation came to your attention, when it first became available, and who
else might know it. You also should be familiar with the recent price his-
tory of the instrument in which you are interested. The price history will
allow you to form a crude opinion as to whether your information is already
in the price if you know when the information first became available. If you
do not consider whether you are indeed best informed, your risk of losing
to a value trader might be greater than you assume.
16.5 SUMMARY
Value traders supply liquidity to uninformed traders whose trading pushes
prices away from fundamental values. Value traders are liquidity suppliers
of last resort. When no one else will trade—when dealers have large posi-
tions or when liquidity suppliers fear informed traders—value traders may
trade. Since they allow uninformed traders to trade large positions, they sup-
ply depth to the market. Since they often recognize when uninformed traders
have caused prices to move from fundamental values, they also make mar-
kets resilient.


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346 • TRADING AND EXCHANGES
Value traders can afford to take large positions only by being the best-
informed traders in the market. They risk being wrong, however. They suf-
fer adverse selection when they lose to news traders who know news that
they do not yet know, and they face the winner's curse when they under-
' or overestimate values. They widen their spreads to avoid these risks and to
ensure that they recover from uninformed traders what they occasionally
lose to news traders and to other value traders.
By making markets resilient, value traders allow dealers to offer more
immediacy to uninformed traders than they otherwise would be willing to
offer. Dealers know that they often will be able to lay off their inventories
when value traders closely follow the market. Accordingly, they are more
willing to take large positions because they know that they may not have to
hold them long.
16.6 SOME POINTS TO REMEMBER
•
Value traders are the ultimate suppliers of liquidity to uninformed
traders.
•
The winner's curse hurts traders who make the highest bids or the low-
est offers if they do not anticipate what they will learn about the val-
ues that other traders estimate.
•
The outside spread represents the prices at which value traders will buy
and sell.
•
The outside spread is wider than the inside spread.
16.7 QUESTIONS FOR THOUGHT
1.
How does the winner's curse affect bidding strategies for single-
family houses?
2.
How does the liquidity provided by well-informed value traders af-
fect the losses that other value traders incur when they are mistaken
about instrument values?
3.
Dealers suffer adverse selection when they trade with news traders.
Do they also suffer adverse selection when they trade with value
traders?
4.
What arguments suggest that value traders should specialize in a sin-
gle industry or commodity, and what arguments suggest that they
should diversify across industries or commodities?
5.
Can you compete in markets in which your competitors do not un-
derstand the winner's curse? Should you?


## Page 360

1
flrbitrageurs
A
rbitrageurs
are speculators who trade on information about relative val-
ues. They buy instruments that seem relatively cheap and sell those
which seem relatively expensive. Arbitrageurs profit when prices converge
so that their purchases appreciate relative to their sales.
We introduced arbitrage trading strategies when we examined informed
traders in chapter 10. There we described how arbitrageurs acquire infor-
mation about relative values, how the price impacts of their trades cause
prices to converge, and how they thereby unwittingly enforce the law of one
price. This price characterization of arbitrage helps us understand how ar-
bitrageurs trade as informed traders.
This chapter continues our study of arbitrageurs. Besides being informed
traders, we shall see that arbitrageurs supply liquidity, move liquidity, and
produce financial products. This quantity characterization of arbitrage helps
explain why arbitrage opportunities arise.
Successful arbitrageurs must understand both the price and the quantity
characterizations of arbitrage. Although many arbitrageurs can trade suc-
cessfully merely by responding to arbitrage opportunities as they arise, ar-
bitrage is more profitable when arbitrageurs also can predict when and where
those opportunities will arise. Arbitrageurs who consider the quantity char-
acteristics of their arbitrages will make better decisions about when and at
what prices to trade.
This chapter characterizes different types of arbitrages and discusses the
risks that arbitrageurs face. If you intend to be an arbitrageur or if you trade
with arbitrageurs, this discussion should greatly interest you.
We shall see that arbitrageurs sometimes compete with dealers to offer
liquidity. Dealers often lose to arbitrageurs because arbitrageurs usually
are better-informed traders. If you intend to be a dealer, you must under-
stand how arbitrageurs can hurt your business. If you merely wish to un-
derstand the origins of market liquidity, you also must understand what ar-
bitrageurs do.
In chapter 26, we show that arbitrage is one of three processes that keep
fragmented markets together. If you are interested in how markets compete
with each other to trade similar or identical instruments, you must thor-
oughly understand why arbitrageurs trade. You must particularly understand
arbitrage to estimate the costs of competition among marketplaces.
Commentators sometimes blame arbitrageurs when markets crash. For
example, some people believe that index arbitrageurs were at least partly re-
sponsible for the 1987 stock market crash. To understand how traders trans-
mit volatility among markets, you must consider what arbitrageurs do.
Complaints about arbitrage have led to restrictions upon arbitrage trad-
ing strategies in some markets. These restrictions can be costly because ar-
bitrage trading benefits more than just arbitrageurs. It also benefits other
traders and the economy as a whole. To estimate the sometimes significant
347


## Page 361

348 •
TRADING AND EXCHANGES
Barter, Arbitrage,
and Relative Values
Modern economies use
money as a
medium of
exchange.
Almost all trades
involve money. The buyer
pays it, and the seller
receives it. When a trader
wants to dispose of one item
and acquire another, the
trader usually sells the first
and buys the second. The
nice thing about money is
that it allows us to buy and
sell from different people.
Barter
involves the
exchange of two (or more)
items without the use of
money. Barter is not common
because both traders must be
interested in both items.
Traders who want to
exchange apples for oranges
often cannot easily find
traders willing to exchange
oranges for
apples.
Trades involving money
can be considered special
cases of barter in which one
of the traded items is money.
This characterization of
trading reminds us that all
trades are relative value
trades. People buy things
when they value the item
more than the money they
exchange for it. They sell
things when they value the
money more than the item.
In a sense, all trades are
arbitrages because all trades
are relative value trades.
costs
of arbitrage restrictions, you must appreciate the quantity characteris-
tics of arbitrage.
Our presentation starts with some definitions. We then characterize ar-
bitrage so that we can understand it in light of trading strategies that we
have already discussed. Next, we introduce and discuss various types of ar-
bitrages. We follow this with discussions about the risks of arbitrage and
about how arbitrageurs control these risks. We conclude the chapter with
discussions about the quantity characteristics of arbitrage and about how
dealers and arbitrageurs relate to each other.
17.1 DEFINITIONS
Arbitrageurs
trade instruments whose prices are correlated.
Correlated prices
tend to rise or fall together. Instruments typically have correlated prices
when their values depend on common fundamental factors. They also may
have correlated prices when the demands of uninformed traders to buy or
sell the instruments are correlated.
Arbitrageurs form opinions about the normal relations among corre-
lated instruments. An
arbitrage opportunity
arises when the prices of cor-
related instruments diverge from their normal relations. Arbitrageurs then
buy those instruments which have become relatively cheap and sell those
which have become relatively expensive. The strategy is profitable if the
prices of the instruments return to their normal relations. When that hap-
pens, the prices have
converged.
Arbitrageurs profit from
price convergence.
When arbitrageurs take arbitrage positions, they
put on
the arbitrage.
When they close their positions, they
unwind
their positions or
take off
the
arbitrage. Arbitrageurs usually unwind their positions when the arbitrage
converges. Since prices may again diverge after they converge, arbitrageurs
need to watch their positions closely so that they can close them at favor-
able prices.
The portfolios that arbitrageurs construct are their
hedge portfolios.
The
various positions in the hedge portfolio are the
legs
of
the arbitrage.
Hedge portfolios usually consist of one or more long positions and one
or more short positions in various correlated instruments. Arbitrageurs gen-
erally construct hedge portfolios to minimize the total risk of the portfolio,
given some measure of its size.
(In some instances, hedge portfolios may consist only of short positions
or only of long positions. Traders construct such hedge portfolios of in-
struments whose returns are inversely correlated. Such portfolios typically
include put contracts. For example, a long position in a put contract is a
hedge for a long position in the underlying instrument.)
i Traders usually identify one leg of the hedge portfolio as the
arbitrage
numerator
or
reference instrument.
They use the numerator to measure the
size of the portfolio. The arbitrage numerator is usually the security, con-
tract, or commodity that traders most closely identify with the common risk
factor which causes the correlations among the various instruments in the
hedge portfolio. The arbitrage numerator for hedge portfolios involving a
derivative contract and its underlying cash instrument is usually the cash
instrument.
The ratios of holdings in other legs to holdings in the numerator are the
portfolio
hedge ratios.
Traders choose their hedge ratios to minimize the to-
tal risk of the portfolio.


## Page 362

CHAPTER 17 ARBITRAGEURS • 349
Arbitrageurs may have long or short positions in the hedge portfolio.
They are long the hedge portfolio when they have a long position in the
instrument that serves as the arbitrage numerator. Since hedge portfolios
usually have both long and short positions, a long hedge portfolio will have
one or more short positions. A short hedge portfolio will likewise have one
or more long positions.
Hedge portfolios have carrying costs.
Carrying costs
are the costs of hold-
ing a hedge portfolio. Depending on the
,
arbitrage, these costs may include
interest paid or forgone to finance positions in the hedge portfolio, divi-
dends paid on short positions, fees paid to physically store commodities,
and depreciation incurred as commodities age and spoil. Carrying costs are
sometimes offset by dividends, interest income, and lending fees earned on
long positions, or by interest earned on the proceeds from short sales. Car-
rying costs can make some arbitrages very expensive.
The difference in prices between instruments in the hedge portfolio is
the
basis.
The
fair value
of the basis is the basis that would result if all in-
struments were correctly priced relative to each other. Fair values depend
on carrying costs. Arbitrageurs must estimate fair values because they usu-
ally are not common knowledge.
The
arbitrage spread
is the difference between the basis and the fair value
of the basis. Arbitrageurs trade when the arbitrage spread is sufficiently large.
The values of the basis at which arbitrageurs are just willing to trade are
called
arbitrage bounds.
They are on either side of fair value. Arbitrageurs
generally put on their arbitrages only when the basis is outside of the arbi-
trage bounds.
Hedge portfolios generally are less risky than the positions in the indi-
vidual instruments from which arbitrageurs construct them. The reduction
in risk is due to the offsetting effects of having long and short positions in
instruments whose values depend on the same factors. When changes in
these factors cause instrument values to fall, gains in the short positions off-
set losses in the long positions. Likewise, when changes in these factors
cause instrument values to rise, gains in the long positions offset losses in
the short positions.
The risk that an arbitrage hedge portfolio will lose value is called
basis
risk.
Analysts also call this
residual risk
because it remains after the common
factor risks in the various portfolio instruments cancel each other. Basis risk
arises because prices depend on instrument-specific factors as well as com-
mon factors. The specific factors may be fundamental valuation factors, or
they may be due to the price impacts that uninformed traders have on prices.
Since specific factors are unique to each instrument, no combination of long
and short positions can create a hedge portfolio that has no exposure to
these risks. The contribution of instrument-specific factors to basis risk may
be small, however, if the hedge portfolio is a well-diversified portfolio of
many instruments. Most stock index arbitrage portfolios have very little re-
sidual risk.
17.2 A SIMPLE CHARACTERIZATION
OF ARBITRAGE
Arbitrage is particularly easy to understand if you imagine that the arbi-
trage hedge portfolio is an instrument that traders buy or sell like any other
instrument. When traders "buy" the hedge portfolio, they buy its long po-
Some Confusing
Nomenclature
Hedge funds
are managed by
speculators who sometimes
hedge their positions to
minimize exposure to risks
that they cannot forecast. They
may engage in arbitrage, but
they often do not.
Long-short portfolios
are
actively managed portfolios
that have no net exposure to
marketwide risk. They
typically consist of long and
short positions of
approximately equal
aggregate sizes. Portfolio
managers call the portion of
an equity portfolio return that
is correlated with the market
index
deadweight.
Well-
designed long-short portfolios
have no deadweight. Since
long-short portfolio managers
construct hedged positions,
they may be characterized as
arbitrageurs. They rarely see
themselves this way, however.
Most simply see themselves as
active managers who want to
avoid exposure to market risk.
In the futures markets, a
long hedger
is a trader who
holds the cash position and
hedges it with a short futures
position. His short futures
position is his
short hedge.
Likewise, a short hedger
is a
trader who hedges a short
cash position with a long
futures position. Hedgers
usually trade to reduce risks
in their cash positions rather
than to obtain arbitrage
profits.


## Page 363

350 • TRADING AND EXCHANGES
sitions and sell its short positions. When they "sell" the hedge portfolio, they
sell its long positions and buy its short positions. Viewed this way, arbi-
trageurs are simply traders who use various trading strategies to trade the
arbitrage portfolio.
Arbitrageurs who employ high-frequency trading strategies are essen-
tially dealers in the hedge portfolio. They buy and sell the hedge portfolio
to profit from short-term reversals in its value. Such reversals typically oc-
cur when uninformed traders in one or more of the instruments cause prices
in that instrument to change when they demand liquidity. Dealer-type ar-
bitrageurs generally are less concerned about the fair value of basis than in
its short-term behavior. They offer immediacy when they trade the hedge
portfolio, but they usually are unwilling to trade large size when they do not
know fair values well. Dealer-type arbitrageurs are usually professional
traders who have very quick access to the markets.
Arbitrageurs who take large arbitrage positions must be very certain about
the fair value of the basis. Such arbitrageurs are essentially value traders in
the hedge portfolio. They supply substantial depth, but they may not be the
fastest traders. When dealer-type arbitrageurs want to lay off positions in
the hedge portfolio, they often trade with value-type arbitrageurs. (Chap-
ter 16 describes the relation between value traders and dealers.)
17.3 TYPES OF ARBITRAGE
Arbitrages differ by the nature of the basis risk that arbitrageurs assume.
Pure arbitrages
involve instruments for which the value of the hedge port-
folio is strictly
mean reverting. Speculative arbitrages,
also known as
risk ar-
bitrages,
involve instruments for which the value of the hedge portfolio
is
nonstationary.
This section defines these concepts and discusses their im-
plications for arbitrageurs.
17.3.1 Pure Arbitrages
A variable is
mean reverting
if its values tend to return to some average value.
Mean-reverting variables tend to drop when their values are above their
long-run average values, and they tend to rise when their values are below
their long-run averages. The best long-range estimate of the value of a mean-
reverting variable is always the mean of the variable. Long-run value esti-
mates never depend much on the current value of the variable.
Traders like pure arbitrages because a mean-reverting base eventually
converges to its mean value. The mean typically is the fair value of the ba-
sis. Pure arbitrages therefore are not very risky in the long run. They may
be quite risky in the short run, however, if the factors that have caused the
wide arbitrage spread continue to widen the basis after arbitrageurs have es-
tablished their positions.
Pure arbitrages typically involve instruments whose fundamental values
depend on nearly identical factors and for which some mechanism ensures
that the basis will eventually close. The mechanism generally places strong
bounds on how wide the basis will be.
We can classify pure arbitrages by the type of mechanism that causes
prices to converge. The basis in a
shipping arbitrage
depends on the cost of
shipping an instrument from one market to another. In a
delivery arbitrage,
EFPs Are Hedge
Portfolio Trades
Arbitrageurs buy one item
and sell another when they
believe that the first is cheap
relative to the second. They
sometimes do these
transactions as a single barter
trade. The most important
example of an arbitrage
barter trade is an
exchange
for physical
(EFP) in the
futures markets.
In an EFP, one trader
offers a futures contract in
exchange for the underlying
physical commodity while the
other trader offers the
commodity in exchange for
the contract. The trader
offering the futures contract
also must offer cash because
the futures contract is only a
commitment to buy the
commodity in the future. The
resulting trade is essentially a
trade in the arbitrage hedge
portfolio. Arbitragers who
want to buy the commodity
and sell futures contracts (or
vice versa) often engage
in EFPs.
D Dice Are
Mean-reverting
The completely random
sequence of sums generated
by rolling a pair of dice is
mean reverting. The mean
value of this sequence is
seven. When a roll produces
a sum above seven, the next
sum will more likely be lower
than higher. Likewise, when a
roll produces a sum below
seven, the next sum more
probably will be higher than
lower.


## Page 364

CHAPTER 17 ARBITRAGEURS • 351
OP
What Would You Do?
Cash corn is cheap relative to the near corn futures contract. You sell the
futures contract and buy corn with the intention of delivering it. The price of
corn then rises, and the basis continues to widen. The increase in price
causes you to lose money on your futures position and to make money on
your cash position. Unfortunately, since the basis widened, you lost more
than you made.
You receive a margin call from your futures commission merchant
(broker), who requires that you cover your loss in the futures contract.
Should you close your position and limit your losses, hold your position and
wait until the basis closes, or increase your position now that the basis is
wider?
Suppose you increase your position. The next day, prices continue to
rise, the basis continues to widen, and you continue to lose money. What
should you do now?
The next day the same thing happens again. You are becoming financially
distressed. If you are right about the basis, you now face an incredibly
profitable arbitrage opportunity. If you are wrong, or if prices continue to
move against you, you may be ruined. What should you do now?
Your answers should depend on how well you understand the arbitrage
relation, on your ability to finance your positions, and on your degree of
risk aversion.
the basis depends on the costs
of delivering on a contract. In a
conversion
arbitrage,
it depends on the costs of converting a risk from one form to an-
other. The next subsections discuss these types of pure arbitrages.
1 7.3.1 .1
Shipping Arbitrages
Shipping arbitrages
involve two essentially identical instruments that trade
in different markets. Arbitrageurs buy the cheaper instrument and sell the
more expensive one. If necessary, they then ship it between the two mar-
kets to settle their trades. The costs of shipping the instrument (and of
financing it while in transit) generally determine arbitrage bounds for the
arbitrage spread. When the spread is wider than these costs, arbitrage is es-
sentially risk free.
In practice, arbitrageurs often do not have to ship the instrument. In-
stead, they hope to unwind their positions if the prices converge on their
own accord. When this happens, they avoid the shipping costs. The com-
petition among arbitrageurs for profits therefore often forces them to put
on these shipping arbitrages at much narrower spreads than their shipping
costs would indicate. Shipping arbitrage thus can be risky. If arbitrageurs
can unwind without shipping, they profit. If they must ship, they may lose.
Shipping arbitrage opportunities arise when one market has an excess
of buyers or another market has an excess of sellers. The effect of their trad-
ing causes the basis to widen. By trading on opposite sides of these mar-
kets, shipping arbitrageurs essentially connect excess demand in one market
to excess supply in the other market. Since they supply liquidity in one or
both of the markets, they compete with the dealers in those markets.
Two types of traders typically engage in shipping arbitrages. They differ
by whether they will actually ship the instrument.
Virtual shippers
are arbi-
trageurs who do not ship. They try to hold their hedge portfolios until they


## Page 365

352 • TRADING AND EXCHANGES
Persian—or Mexican—Gulf Oil
Crude oil futures trade in New York and in London. When the price of oil is
significantly higher in New York than in London, traders sell oil in New
York and buy it in London.
Although floor traders at the New York Mercantile Exchange and at
London's International Petroleum Exchange are the first to engage in this
arbitrage, the ultimate arbitrageurs are shippers who have oil in transit from
the Persian Gulf or the Gulf of Mexico. These shippers typically hedge their
cargoes by selling oil futures in the market near where they expect to
deliver their oil. When oil becomes more expensive in New York than in
London, these shippers reroute their ships from
Europe to New
York. They
simultaneously buy crude oil futures in London and sell futures crude oil in
New York to transfer their hedge from London to New York.
The normal basis between the two crude oil futures contracts reflects
differences in delivery specifications between the two contracts. The contract
that requires delivery of a higher grade will be more expensive. It also
depends on local storage and delivery conditions. Oil will be more
expensive in the port where it is more expensive to deliver.
After adjusting for quality, storage, and delivery differentials, the basis
cannot be any larger than the costs of shipping oil across the Atlantic
Ocean. In practice, it is smaller because oil companies never ship crude oil
from one market to the other. Instead, they ship it from its source to one or
both of the markets. The normal basis therefore depends on the difference
in the costs of shipping oil to the two markets. When oil companies ship oil
from the Persian Gulf to both Europe and New York, the New York crude
oil price rarely exceeds the London price by more than the difference in the
shipping costs. Likewise, when they ship oil from the Gulf of Mexico to both
Europe and New York, the London price rarely exceeds the New York price
by more than the difference in shipping costs from the Gulf of Mexico.
A Wheat Delivery Arbitrage
If the wheat futures contract price is sufficiently greater than the cost of
delivering cash wheat, arbitrageurs will buy cash wheat and sell wheat
futures. If the basis closes before the delivery date, arbitrageurs then may
offset these trades. Otherwise, arbitrageurs will deliver the wheat. This
arbitrage is profitable only when the futures price is greater than the
combined costs of buying the cash wheat, financing and storing it until
delivery, and shipping it to the contract delivery point.
If the wheat futures contract price is substantially lower than the cash
price, arbitrageurs will buy the futures contract and sell cash wheat short.
To
sell cash wheat short, they must borrow wheat from a grain operator.
They return the wheat when they take delivery on the futures contract or
when they reverse the arbitrage by selling the futures contract and buying
cash wheat.
Since the options to demand or take delivery are often valuable to
local grain operators, as contracts approach expiration, grain operators
who can handle the cash wheat at the delivery points are the primary
arbitrageurs.
can unwind them at a
profit. If they are unable or unwilling to bear the risks
and costs associated with carrying their hedge portfolios, they
lay off
their
positions (typically at a loss) to actual shippers.
Actual shippers
physically
ship the instrument from one market to the other.


## Page 366

CHAPTER 17 ARBITRAGEURS • 353
0
,
Stock Index Futures Arbitrage
Traders who want exposure to S&P 500 Index risk can hold a portfolio of
index stocks, or they can hold index futures contracts. (Other alternatives
also exist.) When traders want to hold more index risk in the form of futures
contracts than contracts are available to them, futures prices rise relative to
the underlying index. The wide basis encourages index arbitrageurs to sell
futures contracts and buy a representative portfolio of the 500 index stocks,
usually with a program trade. They reverse the transaction when the basis
closes or when the futures contract expires. To avoid transaction costs in the
individual stocks, arbitrageurs also may
roll
their futures positions from the
current delivery month to a later expiration month. (Traders
roll a position
when they exchange a position in a near contract for a similar position in a
more distant contract.)
The hedge portfolio that index arbitrageurs construct may not include all
500 stocks because trading 500 stocks is quite expensive. Instead, index
arbitrageurs may trade a representative portfolio that they design to closely
replicate the returns to the S&P 500 Index. The representative portfolio
typically includes the largest firms in the index and a carefully selected set
of smaller firms designed to provide broad industrial representation of the
smaller firms in the Index. The included smaller firms are overweighted to
compensate for the omitted ones. When two arbitrage trades follow one
another on the same side of the market, the second program trade includes
small stocks that the first program trade omitted so that the combined hedge
portfolio will more closely replicate the Index.
Index futures contracts normally trade at slight premiums to the cash
value of their underlying indexes. The premiums are necessary to
compensate traders for the costs of financing long cash index portfolios. The
dividends that traders expect to receive from holding the stocks in the cash
portfolio slightly reduce these required premiums because traders who hold
the futures contracts do not receive these dividends. Since traders cannot
perfectly predict financing costs and future dividends, stock index arbitrage
is not risk free.
Virtual shippers are most successful when they can predict whether (and
when) the arbitrage basis will close so that they can unwind their positions
at a profit. Since they often compete with many other traders, they must be
very quick to capitalize on arbitrage opportunities as they arise.
Actual shippers are most successful when they can ship the instrument
cheaply between markets. Since shippers compete with each other, arbitrage
spreads generally are no wider than the shipping costs of the most efficient
(lowest-cost) shippers.
17.3.1.2
Delivery Arbitrages
Delivery arbitrages
involve contracts for the future delivery of a commodity
or, in some cases, its cash equivalent. The delivery mechanism causes the
basis between the contract price and the underlying cash price to converge
to zero—plus or minus the costs and benefits of delivery—on the maturity
date. Before the delivery date, the basis depends on the carrying costs of
the underlying commodity. When the cash commodity is cheap relative to
the contract price, arbitrageurs buy the cash and sell the contract. When the
cash commodity is expensive relative to the contract price, arbitrageurs sell
the cash and buy the contract.
00
,
Financial Engineering
The term
financial engineering
has emerged to describe the
highly technical body of
knowledge that traders use
to manage risk and to convert
it from one form to another.
The word
engineering
very
accurately suggests that
financial engineers engage in
a productive process that
consumes feedstock (various
instruments) and produces
products (other instruments).
Unlike other productive
processes, most financial
engineering processes are
completely reversible.


## Page 367

354 • TRADING AND EXCHANGES
Index Enhancement
Investors can very closely
replicate the returns to an
index by holding a properly
weighted portfolio of the
index stocks or by holding the
corresponding stock index
futures contract plus cash
invested in secure short-term
debt instruments like Treasury
bills. Some index funds are
indifferent between these two
alternatives. When investing
new cash, they will buy
whichever is cheaper.
Some
index enhancement
funds
try to produce an
enhanced index return by
switching between the cash
index stock portfolio and the
equivalent futures contracts
when their prices diverge.
They buy futures and sell
stock when futures are
relatively cheap, and they sell
futures and buy stock when
stock is relative cheap. The
profits that they make from
these arbitrage trades,
conducted from a long
position, allow them to ratchet
up their returns.
Manufacturing Stock Option Contracts
Public investors generally want to buy more option contracts than they are
willing to sell. This is true for both calls and for puts. Since option contracts
are in zero net supply, arbitrageurs generally fill the excess demands.
Arbitrageurs who sell option contracts manufacture them by constructing
hedge portfolios. To do so, they must buy or sell enough of the underlying
stocks so that the values of their hedge portfolios do not change when
prices change. In effect, arbitrageurs convert equity risk from stock shares to
option contracts.
The hedge is difficult to maintain because the value of an option contract
is a nonlinear function of the value of the underlying stock.' As prices
change, hedge ratios also change so that arbitragers must buy or sell
stocks. Arbitrageurs therefore must constantly monitor their hedge portfolios
to ensure that they remain well hedged when prices change.
Arbitrageurs do not actually adjust their hedge portfolios whenever
prices change, because trading is costly. Instead, they adjust them only
when their hedges become intolerably risky. Options arbitrage therefore is
risky because prices often change when arbitrageurs are imperfectly
hedged.
Options arbitrage is also risky because arbitrageurs cannot always trade
when they want to rebalance their hedge portfolios. For example, an
arbitrageur who is hedging a call option must sell stock when the
underlying stock price falls. If the price drops too quickly, however, the
arbitrageur may be unable to sell stock while the price is falling. The hedge
portfolio therefore will have too much stock when stock prices are falling.
The imbalance causes arbitrageurs to lose more money on their stock
positions than they make on their short call positions. Arbitrageurs also face
this problem when prices rise quickly. Their hedge portfolios then have too
little stock to adequately hedge their short call positions. The imbalance
causes them to lose more money on their short call positions than they make
on their long—but too small—stock positions. Price changes in either
direction therefore cause arbitrageurs to lose money.
Arbitrageurs are aware of the potential costs of their hedging programs.
When they expect high volatility, they require substantial option premiums to
cover their expected hedging costs. (This is another way of explaining why
option values depend on volatility.) Once arbitrageurs have established their
continues next
page
17.3.1.3 Conversion Arbitrages
A
conversion arbitrage
involves the purchase and sale of two or more in-
struments that embody essentially the same risks expressed in different
forms. When buying one instrument and selling another, the arbitrageur in
effect converts risk from one form to another. Viewed this way, the
conver-
sion arbitrageur
is a manufacturer who produces financial instruments that
interest traders.
Conversion arbitrage opportunities generally arise when traders are more
interested in holding risk in one form than in another. The basis widens
as traders attempt to buy the popular instrument or sell the less popular
instrument. The wide basis is the signal that attracts arbitrageurs to their
work.
The arbitrage bounds for a conversion arbitrage depend on the costs of
doing the conversion and of carrying the hedge portfolio. When trading
costs and carrying costs are high, traders must pay large arbitrage spreads
to encourage arbitrageurs to convert risk from one form to another.


## Page 368

CHAPTER 17 ARBITRAGE • 355
continued from previous
page
positions, they hope that volatility will be small. Since volatility varies,
options arbitrage can be quite risky. If prices prove to be more volatile than
arbitrageurs expect, they lose money.
Options dealers do most stock options arbitrage. When making their
markets, they limit their risk exposure by hedging with the underlying stock
or with related option contracts. Although these dealers may engage in
numerous arbitrages among many different instruments, they generally do
not consider each one separately. Instead, when making trading decisions,
they consider only how their decisions will affect the risk profiles of their
combined positions.
1. The relation between option values and underlying stock values depends criti-
cally on the difference between the option strike price and the stock price. It varies by
whether the option is in, at, or out of the money.
An option is
in the money
when it would be valuable if it expired immediately. It
is
out of the money
when it would be worthless if it expired immediately.
Standard call option contracts are in the money when the underlying stock price is
above the strike price. Upon expiration, the option holder would exercise the option
to buy stock at the strike price because the stock is worth more than the strike price. If
the strike price were higher than the stock price, the option would be worthless: Op-
tion holders will not pay the strike price when they can buy the same stock at a lower
price in the stock market. Put options are likewise in the money when the underlying
stock price is below the strike price, and out of the money otherwise. Both
types
of
options are
at the money
when the underlying stock price is equal to their strike
prices.
When an option is far out of the money, a small change in the underlying price
has little effect on the value of the option because the option will likely expire worth-
less. When an option is deep in the money, a small change in the underlying price
will cause a nearly equal change in the option value. Option values therefore have a
nonlinear relation to underlying instrument values.
The rate at which options values change in response to a change in the under-
lying instrument price is called the
delta
of the option. Deltas are positive for call op-
tions and negative for put options. They are near zero for far out of the money op-
tions and near 1 (in absolute value) for deep in the money options. The number of
stock shares that an arbitrageur must hold in the hedge portfolio is equal to delta
times the number of shares covered by the options contract. Arbitrageurs typically
compute deltas by using complicated mathematical models. The Black-Scholes option-
pricing model is one of the simplest of these models.
The traders who profit most from conversion arbitrages are those who
can do the conversions at lowest cost and those whose costs of holding the
arbitrage portfolio are lowest. They are often traders who have a natural rea-
son to engage in the arbitrage transactions.
Many conversion arbitrages require that arbitrageurs continuously mon-
itor and periodically adjust their hedge portfolios. Such hedges are
dynamic
hedges.
They arise whenever the value of one instrument is not an exact lin-
ear function of the value of another instrument. (A variable is an exact
linear function
of another variable when all possible pairs of their theoreti-
cal values plot along a straight line.) The most common dynamic hedges
involve option contracts. Dynamic hedges can be risky because price volatil-
ity makes them perform poorly.
Some conversion arbitrages are the financial equivalent of an underlying
physical production process. The soybean crush may be the most interest-
ing of these arbitrages. It is a three-legged arbitrage involving futures con-
tracts for soybeans, soybean oil, and soybean meal.


## Page 369

356 • TRADING AND EXCHANGES
Crushing Soybeans on the Floor of the Chicago Board of Trade
Millers make soy meal and soy oil by crushing soybeans. They sell the
meal primarily to feedlot operators for use as animal food. They sell the oil
to food processors and chemical firms, which use it in a variety of products.
(The process also releases a significant, but economically trivial, quantity
of water.)
The Chicago Board of Trade trades futures contracts in all three
commodities. The prices of beans, meal, and oil obviously are closely
related. The notional value of the bean contract is approximately equal to
the sum of the values of the meal and oil contracts less the cost of milling
the beans and net difference in the costs of storing the three commodities.
The three soy contracts trade in adjacent trading pits. At the tops of
these pits stand arbitrageurs who monitor prices in the three markets. When
they determine that beans are cheap compared to oil and meal, they buy
beans and sell oil and meal. Arbitrageurs thus crush soybeans on the floor
of the Board of Trade!
Unlike the physical millers, arbitrageurs often reconstitute beans. They do
this when they reverse the crush by buying oil and meal and selling beans.
Exchange locals do most of the arbitrage on the floor of the exchange
because they are the fastest traders. When they are no longer willing to do
arbitrage, either because they are unwilling to bear the risk or because they
cannot finance additional positions, they often lay off their positions to
millers for whom the soybean crush is a portfolio of three delivery
arbitrages.
The costs of physically crushing beans place an economic bound on one
side of the arbitrage spread. If beans are too cheap relative to meal and
oil, millers will start to crush beans and thereby raise the price of beans
and lower the prices of meal and oil. No such bound exists on the other
side of the arbitrage, however, since millers cannot recombine oil and meal
to sell as beans. Instead, when beans are expensive relative to meal and
oil, millers stop crushing them until oil and meal consumption depletes
supplies and thereby raises their relative prices. 4
Perhaps the
most intriguing conversion arbitrages are
cyclic arbitrages,
in which arbitrageurs exchange A for
B, B
for C, and C for A. When
successful, these arbitrages seemingly create arbitrage profits from thin air.
Currency cross-rate arbitrages are examples of cyclic arbitrages.
17.3.2 Speculative Arbitrages
Speculative arbitrages
involve arbitrage hedge portfolios whose values are
nonstationary. A variable is
nonstationary
if an increase or decrease in its
current value causes us to expect that all future values of the variable will
be higher or lower. Nonstationary variables tend to wander around without
regularly returning to any particular value. The best estimate of a distant
value of a nonstationary process always depends critically on the current
value of the process. These estimates therefore change through time. Non-
stationary variables do not have mean values. Long-run averages of their
values do not converge. Instead, averages of their values also wander about.
Variables are either mean reverting or nonstationary; they cannot be both.
The sum of a mean-reverting process and a nonstationary process is a non-
stationary process because the properties of the nonstationary process even-
tually dominate the properties of the mean-reverting process. In the short-
run, however, if the variation in a mean-reverting process is large compared


## Page 370

CHAPTER 17 ARBITRAGEURS • 357
A
Three-legged Currency Money Machine
Various dealers make markets in the yen-euro, the euro-dollar, and the
dollar-yen foreign exchange markets. The prices in these markets are
codependent. Any two of the exchange rates imply the third exchange rate.
For example, if the dollar-yen rate is 110 yen per dollar, and the dollar-
euro rate is 1.10 dollars per euro, the implied yen-euro rate is 100 yen
per euro.
On very rare occasions, an implied rate obtained from two of the
markets is substantially different from the actual rate in the third market.
When this happens, traders can do cyclic arbitrage. For example, suppose
that the implied yen-euro rate obtained from the euro-dollar and dollar-yen
markets is 100 yen per euro, as quoted above, but the actual yen-euro rate
is 99.9 yen per euro. Arbitrageurs could buy 99.9 million yen for 1 million
euro, buy 1 million euro for 1.10 million dollars, and buy 1.10 million
dollars for 100 million yen. Following the three trades, the traders would
have 0.1 million more yen—about 1,000 dollars—than when they started.
If these relations persisted forever, traders could become infinitely
wealthy. In practice, price pressure from the arbitrage trades quickly causes
the exchange rates to adjust so that further arbitrage will be unprofitable. In
our example, the euro would appreciate relative to the yen, the dollar
would appreciate relative to the euro, and the yen would appreciate
relative to the dollar. The implied yen-euro rate thus would fall while the
actual yen-euro rate would rise.
Dealers are the natural arbitrageurs in the currency markets because they
are the fastest traders. These arbitrages typically arise when traders are
unaware of price changes that take place in related currency markets.
to
the variation in a nonstationary process, the process may appear to be a
mean-reverting process.
Speculative arbitrages
involve nonstationary hedge portfolios that arbi-
trageurs believe have a strong tendency toward short-term mean reversion.
The nonstationariness is due to instrument-specific valuation factors that cause
prices to follow a random walk in the long run. The mean reversion may come
from inconsistent pricing of the common factors among the instruments in
the hedge portfolio or from mispricing of one or more specific factors. Both
types of errors cause mean-reverting price changes. Table 17-1 provides some
examples of common and specific factors for several pairs of correlated assets.
Speculative arbitrages can be quite risky because the value of the hedge
portfolio will not ultimately converge to some mean. Arbitrageurs should
engage in speculative arbitrages only when they believe that mean reversion
in the value of the hedge portfolio will dominate nonstationariness in the
short run. Since speculative arbitrages are often quite risky, traders often call
them
risk arbitrages.
We
can characterize the risk in an arbitrage by the volatility expected in
the basis over a given period. Since the basis is nonstationary, its expected
volatility over a given period increases with the length of the period. Spec-
ulative arbitrages that may not converge quickly therefore are more risky
than are those for which traders expect quick convergence.
17.3.2.1 Spreads
Many common speculative arbitrages are spreads.
Spreads
involve the pur-
chase and sale of instruments that are similar to each other in all respects
Random Walks
A random walk is the best-
known example of a
nonstationary process. To
generate a simple random
walk, flip a coin repeatedly
and keep track of the
difference between the
number of times it comes up
heads and the number of
times it comes up tails. This
difference is a nonstationary
process. After each flip, it is
equal to its previous value
plus 1 if the result is heads
and minus 1 if the result is
tails. Statisticians call this
process a
random walk
because it describes the path
a walker would take if after
every step he flipped a coin
to decide whether to next step
forward or backward.
Fully informative prices
seem to follow random walks
because no one can predict
future price changes from
past information when prices
fully reflect that information.
When price changes are
unpredictable, they appear
random.


## Page 371

358 • TRADING AND EXCHANGES
TABLE 1 7
-
1 .
Common and Specific Valuation Factors of Some Correlated Instruments
SECURITY PAIR

COMMON VALUATION FACTORS

SPECIFIC FACTORS
Ford common stock
GM common stock
IBM
common stock
S&P 500 futures contracts
June gold futures contracts
July gold futures contracts
Automotive industry
fundamentals, interest rates,
labor conditions
Gold spot market conditions
Consumer acceptance of their
products, quality of management
Anticipated differences in gold
market conditions between June
and July, future interest rates
Marketwide risk

IBM-specific risk
but one. In a
maturity spread,
the instruments differ by when they mature
or expire. In a
credit spread,
the instruments differ by issuer credit quality.
Maturity spreads are some of the least risky speculative arbitrages. In a
maturity spread, the arbitrageur buys and sells contracts that mature on dif-
ferent dates but are otherwise identical. The prices of such contracts are
usually very highly correlated, especially if the contracts mature in the dis-
tant future and their maturity dates are near each other, so that most fac-
tors which determine their values are common to both contracts.
Fundamental contract values in a maturity spread differ only with respect
to events forecast to occur between the different contract maturity dates.
Since such valuation factors make the basis nonstationary, maturity spreads
are speculative arbitrages. In practice, the mean-reverting component of the
basis usually dominates.
The most common maturity spreads are calendar spreads and yield curve
spreads.
Calendar spreads
involve futures contracts or option contracts that
mature on different dates.
Yield curve spreads
involve bonds that mature on
different dates.
17.3.2.2 Pairs Trading
Pairs traders
try to identify pairs of instruments that they believe are mis-
priced relative to each other. They then buy the one that appears cheap and
sell the one that appears expensive.
Pairs trading is profitable when the prices of the two instruments have
diverged because their common fundamental valuation factors are not con-
sistently priced or because traders have mispriced some instrument-specific
factors. This often happens when informed buyers bid up the price of one
instrument or uninformed sellers push down the price of the other instru-
ment. It also happens when the price of one instrument has changed in re-
sponse to a change in some common valuation factor, but the price of the
other instrument has not yet changed. In these events, prices tend to con-
verge. If the convergence occurs before specific factors cause the spread to
widen, a pairs trade arbitrage will be profitable.
Pairs trading is not profitable when the change in relative prices is due to
a change in instrument-specific valuation factors. When specific valuation
factors change, the arbitrageurs cannot expect that the basis will converge.


## Page 372

CHAPTER 17 ARBITRAGEURS • 359
Calendar Spreads in Oat Futures Contracts
In North America, the oat harvest starts in late May in the northern
Mexican states and moves north into Canada throughout the summer. Oat
contracts trade at the Chicago Board of Trade for delivery in July,
September, December, March, and May.
The December contract is normally cheaper than the March contract
because sellers in March must carry their inventory for a longer period than
sellers in December. This normal relation depends on interest rates, the costs
of storage, and expected spoilage.
In September, after most of the crop has been harvested, Arnie sees that
December contracts are trading at an unusually large discount to March
contracts. He therefore sells March contracts, buys December contracts, and
hopes that the normal relation will be restored. This calendar spread
involves little risk because most factors that determine the value of oats for
delivery in December also determine the value of oats for delivery in
March.
The May contract normally trades at a very substantial premium to the
July contract because traders generally deliver oats stored from the previous
harvest for the May contract, whereas they deliver newly harvested oats on
the July contract. The spread between the two prices can vary substantially,
however, when traders are uncertain about the size of the new harvest or
about whether supplies will be adequate for May delivery. If traders expect
a small new harvest, July prices will be relatively high. If they expect
unusually large May inventories, May prices will be relatively low.
In March, Arnie sees that the May contract is trading at a substantially
higher premium to the July contract than he expects. He buys the July
contract, sells the May contract, and hopes that their prices will converge.
This calendar spread is highly risky because traders in March cannot
accurately estimate the size of the new harvest and the oat inventory that
will remain in May.
A Successful Pairs Trade in Washington Mutual and FirstFed
Bernie sees that Washington Mutual, a very actively traded large banking
stock, rises following good macroeconomic news about interest rates.
FirstFed Financial Corporation, a small and less frequently traded savings
and loan, does not rise, however. Bernie knows that after taking into
consideration their great difference in size, both firms have approximately
the same exposure to interest rate risk.
Bernie concludes that Washington Mutual has moved in response to
the news but that FirstFed has not.
He
therefore buys FirstFed and sells
Washington Mutual short. The next day, traders interested in buying FirstFed
arrive.
Bernie
sells them his position for a
3
percent profit and then
immediately closes his Washington Mutual hedge position with a
0.2 percent
loss. His
net profit is
2.8
percent of the size of his long position. Since he
borrowed half the money he needed to finance his position, his profit on the
money he actually committed to the arbitrage was 5.2 percent.
Pairs trading is risky regardless of whether the pairs trader has correctly
identified a true arbitrage opportunity. While pairs traders wait for conver-
gence, they are exposed to the possibility that some instrument-specific event
will cause one or both of the prices to permanently move against their
positions.
Fortunately, the opposite may also happen. Since good and bad events
tend to offset each other, pairs traders do not expect to lose money, on av-


## Page 373

360 •
TRADING AND EXCHANGES
A Failed Pairs Trade
in Ford and GM
Gerry sees that Ford has risen
in value relative to GM, but
he can see no fundamental
reason to explain the change
in relative prices. Gerry
therefore buys GM and sells
Ford short.
Unfortunately, the spread
never closes. Ford soon
reports that sales of its huge
SUV, the Excursion, are
soaring. The sales apparently
are coming at the expense of
GM's Suburban. Gerry
eventually closes his arbitrage
at a slight loss due to his
transaction costs and to the
costs of financing his
positions.
erage, when they fail to identify a profitable arbitrage opportunity. How-
ever, pairs traders may lose due to the costs of establishing and financing
their positions.
Pairs traders are most successful when they can accurately predict whether
changes in a spread will be mean reverting or permanent. They therefore
pay close attention to the order flow to determine whether prices are chang-
ing in response to trades made by uninformed traders or informed traders.
Since pairs traders generally do not know whether other traders are well in-
formed, they often make mistakes when trading.
Pairs traders also pay close attention to how quickly and how efficiently
markets respond, on average, to new information about common funda-
mental factors. Arbitrageurs generally should be reluctant to trade against
markets that quickly and efficiently aggregate new information because the
prices in such markets tend to accurately reflect fundamental values.
1 7.3.2.3 Statistical Arbitrage
Statistical arbitrageurs
use factor models to generalize the pairs trading strat-
egy to many instruments.
Factor models
are statistical models that represent
instrument returns by a weighted sum of common factors plus an instru-
ment-specific factor. The weights, called
factor loadings,
are unique for each
instrument. The arbitrageur must estimate them.
Either statistical arbitrageurs specify the factors, or they use statistical
methods to identify the factors from returns data for many instruments.
Specified factors typically include macroeconomic variables such as interest
rtes, inflation rates, industrial production, credit spreads, stock index lev-
els, and market volatility. The statistical methods that some arbitrageurs use
to identify factors include factor analysis, principal components, and canon-
ical correlations. These methods appear in books on multivariate statistics.
Statistical arbitrageurs estimate current factor values in order to deter-
mine which instrument prices appear to be inconsistent with their common
factor representations. The arbitrageurs then sell those instruments which
appear overpriced and buy those which appear underpriced.
Some statistical arbitrageurs also try to forecast future factor values.
When they can successfully predict these values, they will speculate on this
information by incorporating it into their trading models.
Statistical arbitrageurs carefully select the instruments and the quanti-
ties that they trade in order to control risk in their hedge portfolios while
maximizing their expected returns. They also carefully consider how their
transaction costs will affect their profitability. They employ sophisticated
risk and transaction cost models, and they use numeric optimization meth-
ods to fine-tune their decisions.
1 7.3.2.4 Risk Arbitrage
Although
risk arbitrage
may refer to many different kinds of speculative ar-
bitrages, traders most often use the term to refer to speculations and arbi-
trages that involve firms which may or will soon merge. In most mergers,
a
bidder
(or
acquiring firm)
acquires a
target firm.
Target firm share prices frequently rise because most bidders must pay
a premium over current market value to obtain control. Since investors of-
ten suspect that bidders pay too much for their acquisitions, the stock prices
of bidders sometimes fall on the announcement of an acquisition.


## Page 374

CHAPTER 17 ARBITRAGEURS • 361
Identifying Likely Arbitrage Opportunities in
the GE-S&P 500 Futures Spread
General Electric common stock price changes are correlated with changes
in the S&P 500 Index because the values of the two instruments depend on
many common macroeconomic factors. These factors include interest rates,
the general health of the economy, and trader sentiment about the future.
When the spread between GE and the S&P 500 Stock Index futures
contract widens (when GE rises relative to the Index), pairs traders may sell
GE and buy S&P 500 futures contracts.
Clever traders discriminate between different causes for the change in
the spread before they decide to trade. The spread may widen because
(1) the price of GE has risen while the Index futures price has remained
largely unchanged, or because (2) the price of GE has remained largely
unchanged while the Index futures price has fallen. Successful traders
consider which situation is more likely to offer a profitable arbitrage
opportunity.
Situation 1 may occur when uninformed traders buy GE and push up its
price. If that is the case, the arbitrage will probably be profitable. This
scenario is probable because uninformed traders often trade GE and affect
its price.
Situation 1 may also occur when the value of GE rises in response to a
change in some factor specific only to GE. In this event, the arbitrage will
not be profitable. This scenario is also probable because information
specific to GE frequently changes the price of GE common stock.
Finally, situation 1 may occur when marketwide factors cause GE to rise
while the futures market remains unchanged. If that is the case, the
arbitrage will probably be profitable. This scenario, however, is unlikely,
given the informational efficiency of the S&P 500 Index futures market.
Situation 2 may occur when a change in some common valuation factor
causes the market to drop. GE may not drop, however, because the markets
for individual stocks—even those as large as GE—generally respond to
marketwide factors more slowly than does the S&P 500 Index futures
market. The futures market responds faster to changes in marketwide factors
because it focuses exclusively on those factors and because it is the more
actively traded market. Arbitrage in this likely scenario probably would be
profitable.
Situation 2 also may occur when changes in marketwide factors cause
the S&P 500 Index to fall while offsetting changes in firm-specific factors
cause GE to remain unchanged. Arbitrage in this scenario would be
unprofitable. The coincidence, however, is unlikely.
Finally, situation 2 may occur when uninformed sellers in the futures
market cause futures prices to drop. In this scenario, the arbitrage probably
would be profitable. Although this scenario is possible, it is not likely
because the S&P 500 Index futures market is highly liquid.
This discussion of the various alternatives suggests that arbitrage will
more likely be profitable in situation 2 than in situation 1. In the first
situation, the arbitrageur speculates that either GE rose improperly or the
Index failed to rise. The former alternative is possible, but the latter is
unlikely. In the second situation, the arbitrageur speculates that either GE
failed to drop or that the Index dropped improperly. The former alternative
is likely but the alternative again is unlikely. Table 17-2 presents a summary
of the various contingencies discussed in this box.


## Page 375

362 • TRADING AND EXCHANGES
TABLE
17
-
2.
Event Contingencies When the Price of General Electric Common Stock Rises Relative to the Price of
the S&P 500 Index Futures Contract
ARBITRAGE
OBSERVED EVENT

POSSIBLE CAUSE

OPPORTUNITY

LIKELIHOOD
GE rises while

Positive changes in GE-specific fundamental

No

Possible
the Index is

factors raise GE's price.
unchanged.
Uninformed buyers push up the GE price.

Yes

Possible
Positive changes in marketwide fundamental

Yes

Highly
factors raise GE's price, but the Index market

unlikely
is slow to respond.
GE does not

Negative changes in marketwide

Yes

Likely
change while the

fundamental factors lower the Index, but GE
Index falls.

is slow to respond.
Negative changes in marketwide

No

Improbable
fundamental factors lower the Index and GE,
but positive changes in GE-specific
fundamental factors coincidentally offset the
negative market news.
Uninformed selling in the Index futures

Yes

Possible but
market drives down its price.

unlikely
Note: This table shows some possible events that could cause the spread between General Electric stock and the S&P 500 Index
to widen. The likelihood assigned to each possibility suggests that arbitrages will be more profitable when the spread widens
because the Index has fallen rather than because GE has risen. The table omits some very unlikely event contingencies.
Traders who can predict merger transactions therefore can profit from
their insights by buying the target firms and selling the acquiring firms.
Since the targets and the bidders usually are in the same (or closely related)
industries, their equity values depend on many common fundamental
valuation factors. Their prices therefore are closely correlated. The "hedge
portfolio" thus has less risk than its component positions. Although called
risk arbitrage,
this strategy is more like a risky speculation than a mean-
reverting arbitrage. It is highly speculative because the hoped-for merger
may never take place.
When firms announce their intention to merge, they usually publish the
terms of the merger negotiated by their respective managements. These
terms fix the price of the target firm. In a
cash offer,
the price is either a
fixed sum or a sum that depends on some observable variables. In a
stock
offer,
the terms specify the number of shares the acquiring firm will exchange
for each share of the target firm. In
a fixed rate stock offer,
the exchange rate
is constant. In a
constant value stock offer,
the exchange rate varies in inverse
proportion to the acquiring firm's stock price so that the value of the offer
is constant. In
mixed offers,
the terms include both cash and stock.
The terms of the merger thus define the value of the target firm and,
hence, a potential arbitrage relation. In a cash offer or in a constant value


## Page 376

CHAPTER 17 ARBITRAGEURS • 363
stock offer, the stock of the target firm essentially becomes a debt security
that matures upon the successful completion of the merger. In such merg-
ers, the arbitrage portfolio consists of a long (or short) position in the tar-
get stock offset by a short (or long) position in a short-term debt security.
(The portfolio may also include other instruments if the cash payment de-
pends on other variables.) In a fixed rate stock offer, the stock of the target
firm essentially becomes the same as the stock of the acquiring firm. In such
mergers, the arbitrage portfolio consists of a long (or short) position in the
target stock offset by a short (or long) position in the acquiring firm. Risk
arbitrageurs trade when the prices of the various instruments are inconsis-
tent with the terms of the merger.
The risk in these arbitrages depends on whether the deal will fail, whether
the terms will change, and whether the deal will close when scheduled.
When the merger closes at the announced terms on schedule, the basis will
revert to its theoretical fair value. The arbitrage then would be a pure arbi-
trage with no long-term basis risk. In the short run, the basis might differ
from its fair value because traders may be unaware of prices in other mar-
kets, or because an excess of buyers in one market or of sellers in another
market causes prices to diverge. The arbitrageurs connect the two markets
and ensure that common risks expressed in essentially similar instruments
are priced the same.
The merger may not close, however, if the shareholders do not support
it, or if the merger requires government approval and the government does
not approve. The basis under such circumstances will be nonstationary. If
the deal fails, the price of the target firm will freely change in response to
market conditions.
Target prices usually rise upon merger announcements, and they gener-
ally drop when deals fail to close. Accordingly, when traders doubt that
mergers will close, target firms will often trade at discounts to their theo-
retical values implied by the terms of the merger. When the probability of
failure is large, risk arbitrage is more like risky speculation than mean-
reverting arbitrage. Well-informed traders who believe a deal will close, buy
the arbitrage portfolio. Traders who believe that a deal will fail, sell the ar-
bitrage portfolio.
Many merger agreements have collars.
Collars
are clauses in merger
agreements that modify the terms of the agreement based on the price of
the acquiring firm. Collars introduce optionlike characteristics into the ar-
bitrage relation between the acquiring firm and the target firm share prices.
Traders who attempt risk arbitrage in these situations usually implement
dynamic hedges.
17.4 ARBITRAGE RISKS
Several risks make arbitrage strategies difficult to implement profitably.
Im-
plementation risk
is the risk that arbitrageurs will trade at worse than ex-
pected prices.
Basis risk
is the risk that the arbitrage basis will move against
the arbitrageur's position.
Model risk
is the risk that arbitrageurs will fail to
understand relative instrument values properly.
Carrying cost risk
is the risk
that hedge portfolios prove to be more costly to carry than expected. Arbi-
trageurs must manage each of these risks in order to trade successfully.
A Rumored Merger
On June 8, 2000, CNBC
reported that Broadwing was
in talks to acquire Intermedia
Communications. That day,
Intermedia rose 16 percent,
and Broadwing fell 13
percent, from their previous
day's closing prices on very
high volumes.
The next day, CNBC
reported that the talks seemed
to have failed over the
treatment of a well-known
accounting issue. Intermedia
fell 14 percent, and
Broadwing rose 7 percent.
Had Broadwing and
Intermedia reached a deal,
Intermedia might have risen
even more than it did. As it
happened, traders who
bought Intermedia after the
reported talks lost heavily the
next day.
It is possible that traders
who wanted to sell Intermedia
stock fed the story to CNBC.
As a matter of policy, neither
firm would confirm that
negotiations had taken
place.


## Page 377

364 • TRADING AND EXCHANGES
How Definitive Is "Definitive"?
In a February 7, 2000, press release, Lucent announced that it had
reached a "definitive merger agreement" with Ortel. Under the agreement,
Lucent would exchange 3.135 shares of Lucent for each share of Ortel. The
press release stated that Lucent expected to complete the acquisition during
the quarter ending June 30, 2000. The deal actually closed on April 28.
By the close of trading on February 8, the news was well known to all
interested professional traders. Ortel closed at 1611/2 and Lucent closed at
54
3
A
o.
Assuming that the deal would close, the implied value of a share of
Ortel then was 169.88 = 3.135 x 54
3
/16. Ortel therefore traded at a 5
percent discount to its theoretical value. This discount steadily narrowed to
zero on April 28 when the deal closed.
If you bought Ortel and sold 3.135 shares of Lucent on February 8, you
would have made 5 percent of the Ortel price. In addition, if you received
interest on the proceeds of your Lucent short sale, you would have made
another 1 percent in short interest. A 6 percent return over the 80-day
period corresponds to a 32 percent annualized return.
Had the deal closed on June 30, you would have made 5 percent
plus about 1.5 percent in short interest. You also would have had to pay
the 2 cents per share dividend (0.04 percent) that Lucent paid on June 1 to
its stockholders of record on April 30. Over this almost five-month period,
your return would have been approximately 17 percent on an annualized
basis.
Since nobody likes exposure to risk, arbitrageurs must be compensated
for taking risks. Arbitrageurs obtain their compensation when they trade at
favorable prices. In particular, they will not enter a risky arbitrage if their
expected profits seem small. Arbitrage price bounds therefore tend to be
wide when arbitrage is risky.
17.4.1 Implementation Risk
Arbitrageurs face implementation risk when they establish and close their
positions.
Implementation risk
is the risk that their transaction costs will be
greater than they expect. Since transaction costs reduce trading profits, ar-
bitrageurs must estimate their transaction costs before they trade. They must
trade only when they expect that their transaction costs will be less than the
profit they hope to obtain from reversion in the basis. If their actual trans-
action costs prove to be greater than they expect, arbitrageurs will make less
arbitrage profit than they expect. If their transaction costs are larger than
the expected basis reversion, arbitrageurs may lose money.
Arbitrageurs face implementation risk whether they use market orders
or limit orders. When using market orders, their implementation risk is due
to
execution price uncertainty:
Their market orders may trade at less favor-
able prices than they expect. Their orders may have greater than expected
price impact, or prices may move against them during the interval between
when they submit their orders and when their orders actually fill. Of course,
arbitrageurs sometimes obtain better than expected execution prices. Un-
fortunately, the probabilities of trading at better than expected prices gen-
erally are less than the probabilities of trading at worse than expected prices.
The asymmetry is due to competition that arbitrageurs face from other ar-
bitrageurs and from other informed traders. Their trading tends to cause
the basis to narrow.


## Page 378

CHAPTER 17 ARBITRAGEURS • 365
The Seagram-Vivendi Merger
On June 20, 2000, The Seagram Co. Ltd. and Vivendi S.A. entered into a
merger agreement. Under the terms of the agreement, each share of
Seagram would be exchanged for shares in Vivendi. The exchange rate
would depend on an average of Vivendi's ordinary common stock daily
closing prices at the Paris Bourse, expressed in dollars. The average would
be computed over a 20-day period ending three days before the effective
date of the merger. If the average price was less than 96.6875 dollars, the
exchange rate would be 0.8 share of Vivendi for each share of Seagram. If
the average was greater than 124.3369 dollars, the exchange rate would
be 0.6221. If the average was between these two prices, the exchange
rate would be equal to 77.35 dollars divided by the average Vivendi
share price.
The value of the deal on the effective date of the merger depended on
the exchange rate and the then current Vivendi share price. If the current
share price was equal to the average share price and the average was
between the two cutoff prices, the deal would be worth exactly 77.35
dollars per Seagram share. The deal would be worth more if the Vivendi
average price was above 124.3369 dollars and less if the average was
below 96.6875 dollars. Figure 17-1 plots the value of the deal as a
function of the Vivendi average stock price.
If the Vivendi share price on the effective date of the merger was equal
to its average closing price, a portfolio of the following three positions
would produce an identical return to holding a share of Seagram:
•
A long position in a pure discount bond worth 77.35 dollars on the
effective merger date
•
A long position of 0.6221 Vivendi calls struck at 124.3369 dollars
expiring on the effective merger date
•
A short position of 0.8 Vivendi puts struck at 96.6875 dollars expiring
on the effective merger date
Given our price assumption, and assuming further that the merger will
close, the value of a Seagram share therefore must equal the value of this
portfolio.
The assumption that Vivendi's share price on the effective date will equal
the average closing price is too unrealistic to produce useful valuation
formulas and hedge portfolios for the risk arbitrage. This analysis, however,
shows that the merger agreement introduces option characteristics into the
valuation of Seagram shares. Financial engineers who undertake this
arbitrage must derive appropriate formulas that do not depend on our
simplifying assumption. They also must adjust for options to buy 19.9
percent of Seagram's stock at 77.35 dollars that Vivendi received in the
merger agreement.
The July 26, 2000, NYSE closing price for Seagram was 57
3
A o dollars.
Vivendi closed at 88.90 euros at the Paris Bourse. Given the then current
exchange rate of 0.9429 euros per dollar, the dollar equivalent Vivendi
closing price was 83.83 dollars. If the Vivendi average price were equal to
this price, and if Vivendi was worth this amount on the effective date, each
share of Seagram would be worth 67.06 dollars. Seagram traded at a
discount to this value because subsequent increases in Vivendi's share price
would have little effect on Seagram's value until Vivendi rose above
124 dollars. Any decrease in Vivendi's share price, however, would
proportionally decrease Seagram's value. Seagram may also have been
trading at a discount because traders doubted that the deal would close.


## Page 379

366 • TRADING AND EXCHANGES

100
90

Va
lue
o
f Seag
ra
m

Share

80 -

70 -

60 -

50
70

90

110

130

150
Vivendi Average Closing Stock Price in Dollars
FIGURE 1 7- 1 .
Value of a Seagram Share at the Effective Date of the Merger
This analysis assumes that the Vivendi share price on the effective date of the
merger is equal to its average closing price over the 20 trading days ending
three days before the effective date.
Itt• Arbitrage and Ice Floes
Arbitrageurs are like artic
explorers who jump from one
ice floe to another. When
both legs are on the same
side, they go with the floe!
But if they straddle two floes,
a foot on each, they face a
substantial risk of being
soaked.
Arbitrageurs likewise jump
between relatively secure
positions when they put on or
take off their arbitrages.
When they are fully hedged,
they do not care where prices
take them. They get into
trouble when they are stuck in
the middle with one leg on
and one leg off.
When using limit orders, arbitrageurs face implementation risk due to
execution uncertainty:
Their orders will not execute when prices move away
from them. Arbitrageurs then must either cancel and resubmit their orders
at less favorable prices or abandon their arbitrages. If they abandon their
arbitrages before they have traded any instruments, they merely lose the op-
portunity to make arbitrage profits. If they abandon their arbitrage after
trading one or more arbitrage legs, they must close those positions. These
reversals usually generate additional transaction costs.
Failure to execute is an especially serious problem when arbitrageurs have
already executed one or more arbitrage legs. Until they complete all the
trades necessary to acquire (or liquidate) their hedge portfolios, their posi-
tions will be poorly hedged. Any price changes that occur during this time
have a very significant effect on their trading profits. Arbitrageurs therefore
try very hard to trade all legs of their hedge portfolio at the same time.
Some arbitrageurs try to keep their trades synchronized by using only
market orders. This strategy is sensible when bid/ask spreads are small, and
when the execution price uncertainty associated with market orders is small
relative to the execution uncertainty associated with limit orders. These con-
ditions generally hold in liquid markets.
When one instrument in an arbitrage trades in a liquid market and the
other trades in an illiquid market, arbitrageurs often try to trade the illiq-
uid instrument first. After that leg trades, they then trade the other leg. This
strategy allows them to cancel their arbitrage cheaply if the basis closes be-
fore the illiquid leg trades.
When both instruments of an arbitrage trade in illiquid markets, arbi-
trageurs often split their orders into parts that they interleave to minimize their
exposure to unhedged risks. In such markets, arbitragers often must use limit
orders because the execution price risk of market orders can be quite large.
Implementation risk is greatest when the market is moving quickly and


## Page 380

CHAPTER 17 ARBITRAGEURS • 367
Implementation Risk in a Takeover Spread
When two firms agree to a merger in which one firm buys the other with
an exchange of stock, the stock prices of the two firms become highly
correlated. The price of the target firm times the agreed-upon stock
conversion rate becomes approximately equal to the price of the acquiring
firm. In practice, the target firm usually trades at a slight discount (or
occasionally at a slight premium) to the acquiring firm. The difference may
be due to relative mispricing of the two stocks or to speculation that the
transaction will not close.
Risk arbitrageurs who wish to speculate on price discrepancies between
the two stocks buy the relatively cheap stock and sell the comparatively
expensive one. The spread that they earn for doing this transaction must
compensate them for the loss they will experience if the transaction falls
through, and it must cover the costs of financing the hedge portfolio.
When traders are quite certain that the transaction will close, the basis is
usually small in comparison to the volatility in the stocks. Traders who try to
buy one stock and sell the other risk failing to capture the arbitrage spread if
prices change between the completions of their two trades. This can happen
if they buy the long position first, and prices fall before they can sell the
short position. They likewise may lose if they sell the short position first, and
prices rise before they can buy the long position. Of course, if the opposite
happens, they will profit by more than they expect.
Building a Balanced Position Is like Loading a Small Boat
Darlene wants to buy 75 contracts of ABC and sell 60 contracts of XYZ to
implement an arbitrage in some illiquid markets. She enters the arbitrage by
simultaneously submitting limit orders to buy 25 ABC and sell 20 XYZ.
Darlene's next trading decision depends on which order fills first.
Suppose the execution report for ABC arrives first. If after a while the XYZ
order still does not execute, she then may submit a second limit order to sell
20 XYZ, using a more aggressively priced order than the first XYZ order.
This new order therefore should execute before the first XYZ order. When it
executes, she will have a properly hedged portfolio. If both XYZ orders
execute, she then will submit another order to buy 25 more ABC. If neither
XYZ order executes, she will cancel and replace the first order with an even
more aggressively priced XYZ order. Darlene will not submit another ABC
order until she fills an XYZ order because she needs to hedge her 25-
contract ABC position.
In this way, Darlene alternates fills between the two instruments to ensure
that her positions are never too far froin being properly hedged. If the basis
closes while she is not fully hedged, she will cancel any unfilled orders and
reverse one of her completed trades to establish a hedged position.
Building a balanced arbitrage position in illiquid markets is like
loading a small boat. If you put everything in on one side first, you risk
capsizing.
when arbitrageurs cannot easily estimate the price impacts of their trades.
It is a particularly significant risk when the arbitrage spread is small com-
pared to short-term price volatility, and when the interval between when
arbitrageurs submit their orders, and when they execute, is long.
17.4.2 Basis Risk and Arbitrage Scale
Basis risk
is the risk that the arbitrage basis will move in the wrong direc-
tion. The hedge portfolio decreases in value when this happens. Adverse


## Page 381

368 • TRADING AND EXCHANGES
Index Arbitrage
Program Trading
Index arbitrageurs use
program trades to buy or sell
a basket of index stocks while
simultaneously selling or
buying the corresponding
index futures contracts.
Because of differences in
market structures, their futures
orders usually fill faster than
their stock orders.
Index arbitrageurs often
split their futures orders to
synchronize the execution of
their program trades with the
execution of their futures
trades. They first submit their
program trade. They then
submit half of their futures
order when the stock orders
start to fill. They submit the
remainder of their futures
order when most of the stock
orders have filled.
price changes may occur when instrument-specific factors diverge or when
common factors are not priced correctly. For many arbitrages, basis risk is
the most important risk.
As noted above, basis risk differs by whether the basis is mean reverting
or nonstationary. Mean-reverting arbitrages (pure arbitrages) generally are
less risky in the long run than are nonstationary arbitrages (speculative ar-
bitrages). In the short run, both types of arbitrage may be very risky.
Arbitrageurs can do little to control basis risk besides ensuring that they
correctly understand the arbitrage relation. However, they can control their
total exposure to basis risk by carefully choosing the scale of their arbitrage
operations.
The total basis risk in an arbitrage is proportional to the size of the ar-
bitrage portfolio. The larger the portfolio, the more risky it will be. When
the basis is quite risky, arbitrageurs generally choose to hold smaller port-
folios. When the basis risk is quite small, arbitrageurs often leverage their
returns by creating large portfolios.
Arbitrageurs must be careful when they choose the size of their portfo-
lios. Arbitrageurs lose money when the basis widens.
If
they cannot finance
their losses, they may have to liquidate their positions earlier than they want
to. Forced liquidations are especially costly when arbitrageurs engage in pure
arbitrages because the liquidations take place when the arbitrage is most at-
tractive. Arbitrageurs therefore should never leverage their positions to the
maximum extent that their capital permits. Those who do, have no
staying
power
if the basis moves against them.
17.4.3 Model Risk
Model risk,
also known as
analytic risk,
is the risk that arbitrageurs will fail
to properly understand the fundamental value relations among the instru-
ments they trade. They then may mistakenly enter an arbitrage on the wrong
side, they may construct a hedge portfolio that exposes them to risks that
they do not recognize, or they may construct a hedge portfolio with the
wrong hedge ratios.
The most common mistake that arbitrageurs make is to incorrectly iden-
tify a change in the basis as an arbitrage opportunity. Not all basis changes
indicate that prices are inconsistent. Some changes move the basis toward
its fair value. To determine whether changes in the basis move it closer to
or further from its fair value, arbitrageurs must accurately estimate the fair
value of the basis. Their estimation problem is especially difficult when the
fair value of the basis changes frequently.
The fair value of the basis changes when any of the factors that deter-
mine it change. In all arbitrages, the fair value depends on the cost of carry
factors such as interest rates, storage costs, and spoilage conditions. If the
relation between the instruments is nonlinear, the fair value also depends
on the common fundamental factors that cause the instrument values to be
correlated. As these factors change, the theoretical values of the instruments
either diverge or converge. Finally, if the arbitrage is a speculative arbitrage,
the fair value of the basis also depends on any instrument-specific valuation
factors. Changes in instrument-specific valuation factors by definition af-
fect only the value of the specific instrument, and therefore must change
the fair value of the basis.


## Page 382

CHAPTER 17 ARBITRAGEURS • 369
Wrong Way to Be Right
Long-Term Capital Management (LTCM) was a hedge fund that engaged in
many pure arbitrages and highly mean-reverting speculative arbitrages.
Most of their positions were in the bond and swaps markets, where it is
possible to create very highly leveraged positions. LTCM employed
substantial leverage to create very large positions in arbitrages whose
bases it thought were not particularly risky.
On July 31, 1998, LTCM controlled approximately 125 billion dollars in
assets from an equity capital base of only 4.1 billion dollars. The gross
notional amounts of its contracts on futures exchanges exceeded 500 billion
dollars, its swaps contracts exceeded 750 billion dollars, and its options
and other OTC derivatives exceeded 150 billion dollars. Of course, the
risks inherent in most of these positions substantially offset each other.
Most of LTCM's portfolio consisted of long positions in illiquid securities
offset by short positions in highly correlated liquid securities. LTCM therefore
was essentially selling liquidity to the market.
In August 1998, when Russia defaulted on its government bonds, credit
and liquidity spreads widened considerably as investors sought more secure
investments and shunned illiquid investments. Although LTCM lost
substantially on its positions, most of them then appeared more attractive
than ever before.
By September 21, LTCM's capital had deteriorated to the point that the
firm would likely default within a few days. On September 23, a consortium
of LTCM's primary creditors arranged to inject 3.6 billion dollars of new
equity into the firm in exchange for 90 percent of LTCM's equity.
LTCM's partners lost nearly everything they had invested in the firm,
yet their positions were still quite attractive. Although the subsequent
performance of many LTCM's positions was not as good as the partners
expected, many positions were still quite profitable by any measure. Had
the firm been able to weather the crisis,
its partners would now be much
wealthier.
Arbitrageurs can avoid model risk only by being very careful when they
construct their valuation models. They must ensure that they estimate the
fair value of the basis using as much information as they can practically ac-
quire. Traders who do not have the appropriate tools and data to model fi-
nancial relations should avoid trading arbitrages whose values are not easy
to derive. In addition, traders who find that their positions are losing money
should carefully examine their models to determine whether they are in er-
ror or whether market conditions have simply changed against them.
17.4.4 Carrying Cost Risk
Carrying cost risk
is the risk that hedge portfolios will prove to be more costly
to carry than arbitrageurs expect. The unexpected costs might be due to un-
expected increases in the nominal size of the hedge portfolio, longer-than-
expected holding periods, unexpected increases in interest rates, unexpected
physical costs of carrying the hedge portfolio, or unexpected security bor-
rowing costs.
1 7.4.4.1
Unexpected Costs of Carry
Arbitrage portfolios are costly to carry. Traders must finance their long po-
sitions. If the long positions consist of physical commodities, arbitrageurs


## Page 383

370 • TRADING AND EXCHANGES
Valuing Stock Stubs
Some publicly traded corporations own very substantial positions in other
publicly traded corporations. In rare cases, the total market value of the
parent's positions in its subsidiaries may be greater than the parent's total
market value. The implied market value of the remainder of the firm's assets
thus might appear to be less than zero.
Arbitrageurs who believe that those assets have positive values may
attempt to profit in these circumstances by buying the parent firm and
selling short their pro rata (undistributed) shares in the subsidiaries. This
strategy, when properly executed, buys the parent's assets and liabilities
exclusive of its marketable holdings. Traders call this strategy
buying the
stub
of the parent.
When evaluating this strategy, it is extremely important to consider the
parent's liabilities. If the parent is heavily encumbered by debt, the stub
may have a negative implied price simply because of the debt. In that case,
the apparent arbitrage opportunity may not be real.
The parent's liabilities may also include obligations to pay taxes in the
future. If the parent sells its substantially appreciated assets, the parent will
realize substantial capital gains. (It may be able to
spin off
its holdings in a
tax-free pro rata distribution to its shareholders if it owns a sufficiently large
fraction of the subsidiaries' shares.) The unrealized capital gains imply
unrealized tax liabilities on the firm. In effect, the government shares
ownership of the subsidiaries. Since these unrealized tax liabilities increase
with the subsidiaries' values, arbitrageurs must account for them when
computing their hedge ratios. Otherwise, their short positions will be too
large. If the subsidiaries' values rise, the value of their hedge portfolios will
fall, and vice versa.
Arbitrageurs who fail to properly evaluate stub values risk overpaying for
their stubs. Those who fail to properly adjust their hedge ratios for the
unrealized tax liabilities inadvertently take larger short positions in the
subsidiaries than they should. '4
must
store them and insure them. If the commodities are perishable, they
will spoil and depreciate. If the arbitrageurs must borrow securities to es-
tablish their short positions, they must pay borrowing fees to the lender.
They also must pay lenders any dividends that they would have received if
they had not lent their securities. These expenses may be offset by dividends
paid on long positions, by lending fees that arbitrageurs receive by lending
their long positions, and by interest that they may earn on the proceeds of
their short sales. Table 17-3 presents examples of factors that determine car-
rying costs.
The fair value of the basis depends on the carrying costs of the arbitrage.
When these costs increase, arbitrage profits will be less than arbitrageurs
expect.
17.4.4.2 Unexpected Price Increases
For arbitrages with finite horizons, and for many other arbitrages as well,
the most important carrying cost risk is a significant, unexpected increase
in the prices of the instruments in the hedge portfolios. When prices rise,
hedge portfolio positions increase in value. Arbitrageurs profit from the in-
creases in their long positions, but they lose from the increases in their short
positions. In many markets, arbitrageurs must immediately fund the losses


## Page 384

CHAPTER 17 ARBITRAGEURS • 371
How Fischer Black Destroyed the ValueLine
Index Futures Contract
Fischer Black was an MIT finance professor when he decided to take a
year's leave to work for Goldman Sachs in New York in 1984.
At
that time, the
Kansas
City Board of Trade's ValueLine Index futures
contract was still very actively traded. After its introduction in 1982 as the
world's first stock index futures contract, it quickly became extremely
popular.
The ValueLine Index is an unusual stock index. Most indexes are
weighted arithmetic sums of stock prices. The ValueLine Index is based on a
weighted sum of the logarithms of stock price relatives. A
stock price
relative
is the ratio of the current stock price to a previous stock price.
Geometric indexes are easily approximated by arithmetic price indexes.
Most ValueLine arbitrageurs used these approximations to value the futures
contract and to compute their hedge ratios.
Fischer Black, however, knew that the mathematical properties of the
geometric index implied that the exact theoretical fair value for the futures
contract should depend on the volatilities of the individual stocks in addition
to their prices. The formula he derived implied different theoretical values
for the contract than those derived by traders who approximated the Index
by an arithmetic average.
Goldman Sachs aggressively traded the arbitrage. Other arbitrageurs
also aggressively traded the arbitrage, but on the other side. Many of them
traded with Goldman. At one point, Goldman held 30 percent of the open
interest in the contract.
Goldman Sachs prevailed in the end. The firm earned more than 125
million dollars on their trades!
The other traders reportedly said that they knew the theoretical value of
the futures contract depended on the stock volatilities, but they assumed that
the dependence was not economically significant. Had they attended
Fischer's finance classes at MIT in 1982 and 1983, they would have
known better. Fischer repeatedly told his MBA students about the pricing
anomaly, and how he expected that it would soon go away. He probably
had no idea then that he would make it happen.
Fischer Black remained at Goldman Sachs, where he ultimately was
made partner.
After losing substantially in the ValueLine contract, other traders stopped
trading it. The Kansas City Board of Trade quickly lost its market share in
stock index futures trading to the Chicago Mercantile Exchange. The CME
bases its most successful stock index futures contract on the S&P 500 Index,
which is an arithmetic index. In an effort to regain market share, Kansas
City changed the specifications of its contract to base it on an arithmetic
form of the ValueLine Index. The reformulated contract never obtained any
significant market share.
This is an unusual story about the analytic risks associated with
proprietary trading. In this story, the market was wrong but Goldman Sachs
was right. More commonly, a few traders are wrong and the market is
right.
Goldman was able to profit from its minority position because the
contract's cash settlement mechanism ensured that the basis would close by
the contract expiration date. Had the arbitrage been a speculative arbitrage
instead of a pure arbitrage, the valuation mistakes made by the majority of
the traders probably would have influenced prices well into the distant
future. In that case, Goldman would not have made its profits nearly so
quickly.
4)


## Page 385

372 • TRADING AND EXCHANGES
TABLE 17-3.
Carrying Cost Factors
FACTORS THAT INCREASE CARRYING COSTS

FACTORS THAT DECREASE CARRYING COSTS
Interest paid or forgone to finance long
positions
Fees paid to borrow securities sold short
Dividends that must be paid on short positions
Storage costs of physical commodities
Spoilage and depreciation of perishable
commodities
Costs of insuring physical positions
Interest earned on proceeds from short
positions
Fees earned from lending securities
Dividends received on long positions
in their short positions. These payments impose greater financing costs on
the arbitrageur.
When arbitrageurs cannot fund the losses in their short positions when
required, their brokers or their counterparts will liquidate the arbitrageurs'
positions to satisfy their obligations. Unlike liquidations caused by adverse
changes in the basis, these liquidations do not necessarily happen when the
basis widens and arbitrageurs are least willing to give up their positions.
However, the increase in prices is often correlated with increased volatility
in the basis. Losing the option to continue the arbitrage therefore may be
costly. The lesson is worth repeating: Successful arbitrageurs do not lever-
age their positions to the maximum extent that their capital permits. In-
stead, they leave themselves some margin to ensure that they have staying
power if their hedge portfolios increase in value.
17.4.4.3 Slow
Convergence
For arbitrages that have no finite horizons, the most important carrying cost
risk is often an unexpected increase in the holding period. If the arbitrage
basis takes longer to revert than expected, the costs of carrying the hedge
portfolio will be greater than expected. Successful arbitrageurs not only must
value the basis correctly, they also must be able to predict when it will close.
17.4.4.4 Unexpected Buy
-
ins
When arbitrageurs borrow securities to short sell them, they generally
must return those securities when the lender demands them. Upon such
demands, if the arbitrageurs or their brokers cannot arrange another loan,
the arbitrageurs must repurchase their short positions in a
forced buy-in.
Such repurchases force them to terminate their arbitrages earlier than they
would prefer.
Forced buy-ins often occur when the lender wants to sell because the se-
curity has appreciated significantly. Under such circumstances, the basis
probably has moved against arbitrageurs. Such buy-ins therefore are espe-
cially costly.
On rare occasions, short squeezers may manipulate arbitrageurs. Short
squeezes (described in chapter 11) are particularly costly to arbitrageurs.


## Page 386

CHAPTER 17 ARBITRAGEURS • 373
Not All Stubs Are Mean-reverting
Intermedia Communications owns about 62 percent of Digex. Between
December 1999 and early June 2000, Intermedia's Digex position
accounted for approximately 50 percent of its total market value.
During this period, Marcia observed that the relation between Intermedia
and Digex appeared to be nearly linear, as theory would suggest. She also
observed that the basis between the two—the Intermedia stub—appeared to
be highly mean reverting.
Given these facts, Marcia decided to buy Intermedia and sell Digex
when the stub fell from its average value of 16 to its historic low of 10 on
June 30, 2000. Unfortunately, the stub did not revert to its former average
value. Instead, Intermedia announced a few days later that earnings in its
main line of business would fall substantially short of market expectations.
The announcement specifically mentioned that its problems did not affect
Digex. Intermedia shares fell very hard while Digex shares rose slightly. The
Intermedia stub dropped below zero and reached a low of —3 on July 21.
Marcia lost heavily because she failed to recognize that firm-specific
factors had caused the fair value of the stub to drop to 10. Given this
mistake, she did not consider whether traders had already impounded the
bad news in Intermedia's stock price.
They typically occur when the basis has widened greatly. Arbitrageurs who
are caught in a squeeze are forced to sell their positions after they have lost
heavily, and when their positions look most attractive going forward.
17.5 THE CAUSES OF ARBITRAGE
OPPORTUNITIES
Two sets of circumstances cause arbitrage opportunities. In the
slow price
adjustment scenario,
arbitrage opportunities arise when common factor val-
ues change, but not all prices that depend on those factors change appro-
priately. Usually, one or more prices are slow to change. Occasionally, some
prices may change too much in response to the news. Arbitrage trading in
this scenario causes prices to adjust to their new fundamental values.
In the
uninformed liquidity demand scenario,
arbitrage opportunities arise
when fundamental values are constant, but uninformed traders cause prices
to change as they buy in some markets or sell in other markets. Arbitrage
trading in this scenario connects demands for liquidity that traders make in
different markets. The arbitrageurs sell to the buyers in markets where buy-
ers have pushed prices up, and they buy from sellers in markets where
sellers have pushed prices down. Their trading tends to restore prices to
their fundamental values.
Although arbitrage generally will be profitable in both scenarios, arbi-
trageurs who recognize the causes of their arbitrage opportunities often will
trade more profitably than will those who cannot recognize them. The for-
mer can formulate better trading strategies. In addition, since they know
more about why they are trading, they are less likely to identify changes in
the basis as arbitrage opportunities when those changes are due to changes
in the fair value of the basis.
How arbitrageurs implement their trading strategies should depend on
the cause of the arbitrage opportunity. When the opportunity arises because
Carrying Cost Risk in
Index Futures Arbitrage
When an index futures
contract trades at a
substantial discount to its fair
value, index arbitrageurs sell
the futures contract and buy
the underlying index stocks.
Carrying the hedge portfolio
is expensive because the
arbitrageur must finance the
long stock purchases. The
long positions, however,
sometimes pay dividends,
which reduce the carrying
cost. Arbitrageurs therefore
consider the expected costs of
carrying their positions when
they decide whether to trade.
The fair value of the futures
contract is the current value of
the cash index, reduced by
the expected interest costs of
carrying the cash positions
plus the dividends that the
cash positions are expected
to pay. Arbitrageurs buy the
index contract when its price
is below its fair value.
If interest rates
unexpectedly rise while
arbitrageurs hold their hedge
portfolios, or if dividend
payouts are smaller than
expected, the arbitrageur's
profits will be less than
expected.


## Page 387

374 • TRADING AND EXCHANGES
Economists Must Be
Patient Traders
Economists
often correctly
predict future events, but they
frequently cannot accurately
predict when they will occur.
The laws of economic science
are often suspended, but they
are never rescinded. 6,
Financing a Losing Short Position in Plug Power
Mechanical Technology (MKTY) owns approximately 32 percent of the
common stock shares of Plug Power (PLUG). On January 6, 2000, the
market value of that position was substantially greater than the market value
of all shares outstanding in MKTY. After accounting for MKTY's unrealized
capital gains on its PLUG holdings, the MKTY stub—Mechanical Technology
exclusive of its holdings in Plug Power—was worth approximately 30 cents
per share.
Arbitrageurs who thought that the stub should be worth more would
have bought shares in Mechanical Technology and sold shares in Plug
Power. The proper hedge ratio depends on MKTY's corporate tax rate, its
shares outstanding, its executive stock options outstanding, and its PLUG
holdings. At that time, the hedge ratio was probably around 0.64 share of
PLUG per share of MKTY.
The closing prices of MKTY and PLUG on January 6 were
23
1
1/2
and
36
1
/4,
respectively. If an arbitrageur bought 1,000 shares of MKTY and sold
640 shares of PLUG at their closing prices, he would have spent 23,500
dollars on MKTY and received 23,200 dollars from PLUG. The difference of
300 dollars for 1000 shares is the value of 1000 MKTY stubs at 30 cents
per stub.
If this arbitrageur does not receive interest on the short proceeds of his
sale, and if his cost of funds is 8 percent per year, the carrying cost of his
position is 5.15 dollars per day, or 0.515 cents per stub per day. On
average, the stub must appreciate every day by more than 0.5 cent for the
arbitrage to be profitable.
By March 10, the prices of MKTY and PLUG had quadrupled! That
day, MKTY closed at 90, and PLUG closed at 149
3
/4. The arbitrageur now
had a 90,000-dollar long position in MKTY and a 95,840-dollar short
position in PLUG. The difference implies a stub value of —5.84 dollars.
The arbitrageur lost 6,140 dollars on his positions. He also lost more than
325 dollars in financing costs for the 63 days between January 6 and
March 10 that he held his position.
By March 10, the arbitrageur needed to give his broker a cumulative
sum of 72,640 dollars to cover his losses in PLUG. If the arbitrageur could
not have produced these funds, his broker would have closed his position.
The increase in PLUG's price increased the arbitrageur's cost of carry to
21.01 dollars per day, or 2.1 cents per stub per day. The stub must now
appreciate by more than a dollar every 50 days for the arbitrageur to
avoid losing more money.
Fortunately, the arbitrageur's payday arrived a few days later. Over the
next few days, MKTY and PLUG both fell substantially, with PLUG falling
faster. At the close on March 14, the stub was worth 12.6 dollars per
share. Had the arbitrageur sold then, he would have made a net profit after
all costs of about 12,000 dollars.
not all prices have appropriately adjusted to news about common funda-
mental factors, arbitrageurs should trade quickly. Otherwise, they may lose
the opportunity to profit as news traders or value traders compete to trade
first, and as dealers and limit order traders adjust their prices in response to
the news. Arbitrageurs therefore should demand liquidity when trading in
the slow price adjustment scenario.
When the arbitrage opportunity arises because uninformed traders cause
prices to move from their fundamental values, arbitrageurs often have more
time to trade. Indeed, they may sometimes wait to see whether uninformed


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CHAPTER 17 ARBITRAGEURS • 375
traders push prices farther from their fundamental values. In this scenario,
arbitrageurs compete primarily with value traders to supply liquidity to the
uninformed traders. They also supply liquidity to dealers whose inventories
are out of balance because they have already supplied liquidity to the unin-
formed traders. In the uninformed liquidity demand scenario, arbitrageurs
offer liquidity when trading. They therefore should not trade too aggressively.
Arbitrageurs of course often compete with each other to offer liquidity.
When their competition is intense, they must trade more aggressively and
at smaller margins, regardless of the cause of the arbitrage opportunity.
17.6 QUANTITY CHARACTERIZATIONS
OF ARBITRAGE
Arbitrage is best understood from two perspectives. The
price characteriza-
tion of arbitrage
focuses on fundamental information. In this view, arbi-
trageurs are informed traders who trade on information about relative val-
ues. They trade when they conclude that instruments are inconsistently
priced. The effect of their trading is to enforce the law of one price. This
characterization holds regardless of the cause of the arbitrage opportunity.
The
quantity characterization of arbitrage
considers the economic roles ar-
bitrageurs play when they trade instruments that are inconsistently priced.
As noted above, inconsistent prices arise when prices are slow to adjust to
new information about common factors, or when uninformed traders cause
prices to move from their fundamental values.
When trading in the slow price adjustment scenario, arbitrageurs act as
disciplinarians. Their trading tends to correct valuation mistakes that other
traders make when common factor values change. This perspective explains
why other traders often resent arbitrageurs. Nobody likes to take discipline
from others.
When trading in the uninformed liquidity demand scenario, arbi-
trageurs act as shippers or repackagers of common factor risk. They act as
shippers when they move the underlying risk from buyers in some mar-
kets to sellers in other markets. If the underlying risk is traded in differ-
ent forms in the various markets, arbitrageurs also act as repackagers. When
they buy an instrument in one market and sell a similar but different in-
strument in another market, they essentially repackage the underlying com-
mon factor risk.
In the derivative markets, risk repackaging is a productive process gen-
erally run by financial engineers. Arbitrageurs who buy cash instruments
and then sell derivative contracts are essentially derivative contract manu-
facturers. They allow other traders to buy long contract positions. If the ar-
bitrageur sells short to someone who is not covering a short position, the
transaction increases the open interest in the contract. Likewise, arbitrageurs
who buy derivative contracts and sell cash instruments essentially manufac-
ture short derivative positions for other traders. If the arbitrageur is not short
and if the seller is not covering a long position, the transaction increases the
open interest. These processes also work in reverse. When arbitrageurs al-
low other traders to cover their positions, open interest usually drops.
The price and quantity characterizations of arbitrage are closely related.
The price discrepancies that motivate arbitrage trading are signals to arbi-
trageurs to offer their productive services. The arbitrage spread—the dif-
Crossed Markets
The simplest form of arbitrage
involves crossed markets. In a
crossed market,
a buyer bids
a higher price than a seller
simultaneously offers for
exactly the some instrument.
An arbitrageur who can buy
from the seller at the lower
price and sell to the buyer at
the higher price will make an
immediate arbitrage profit.
This arbitrage is especially
attractive because it generally
does not require a hedge
portfolio.
Crossed markets typically
arise when trading is active
and prices move very quickly.
Markets cross when sellers do
not quickly withdraw their
offers when prices are rising
or when buyers do not
quickly withdraw their bids
when prices are falling.
When these slow traders
trade with an arbitrageur,
they naturally regret it.


## Page 389

376 •
TRADING AND EXCHANGES
Time and Place
Traders say that liquidity is all
about
time and place.
When
buyers and sellers arrive at
the same time and place,
trades are easy to arrange.
When they arrive at different
times or at different places,
they need the assistance of
an intermediary to complete
their trades.
Dealers and arbitrageurs
are such intermediaries.
Dealers use their inventories to
match buyers and sellers who
arrive at different times in the
same place. Arbitrageurs use
their hedge portfolios to match
buyers and sellers who arrive
at the same time in different
places.
ference between the basis and its fair value—is the compensation that ar-
bitrageurs expect to receive for their services.
17.7
ARBITRAGEURS, DEALERS,
AND BROKERS
Arbitrageurs often trade in response to demands for liquidity that unin-
formed traders make. When the price impacts of uninformed trades cause
price discrepancies across markets, arbitrageurs enter to provide liquidity.
Since dealers also provide liquidity, arbitrageurs and dealers often com-
pete with each other. The two types of traders, however, operate in differ-
ent dimensions. Dealers offer liquidity to buyers and sellers who arrive at
different times in the same market. In contrast, arbitrageurs offer liquidity
to buyers and sellers who arrive at the same time but in different markets.
The two trader types also use different technologies to connect buyers
to sellers. Dealers use their inventories to span time gaps between buyers
and sellers. Arbitrageurs use their hedge portfolios to span spatial gaps be-
tween buyers and sellers.
Both traders need mean reversion in order to trade profitably. Dealers
profit when prices have a strong mean-reverting component. Arbitrageurs
profit when the basis has a strong mean-reverting component.
The competition among dealers and arbitrageurs explains why dealers
often do not appreciate having arbitrageurs trade in their markets. Dealing
would be more profitable if dealers did not have to compete with arbitrageurs
to supply liquidity. Dealing also would be more profitable if arbitrageurs did
not trade with dealers when dealers are slow to change their prices in re-
sponse to new information.
Dealers do appreciate trading with arbitrageurs when the arbitrageurs
help them solve inventory problems that trouble them. Such trades usually
occur after a dealer fills a large order or a sequence of small orders on one
side of the market. To reduce the resulting inventory imbalance, dealers of-
ten adjust their prices to encourage other traders to trade with them on the
other side. When these price changes cause the basis to widen, arbitrageurs
will trade and take the dealers' unwanted inventory positions.
Arbitrage also would be more profitable if arbitrageurs did not have to
compete with dealers to supply liquidity. When dealers offer liquidity, they
decrease the impact that uninformed traders have on prices. Arbitrageurs
therefore see fewer profitable arbitrage opportunities than they otherwise
would see. Stated differently, dealers make prices less mean reverting when
they offer liquidity to uninformed traders. Less mean reversion in prices
generally implies less mean reversion in the basis, and therefore less arbi-
trage profit.
Traders who can simultaneously act as dealers and arbitrageurs often have
an advantage over traders who specialize in only one strategy. When con-
fronted with an order flow imbalance, such traders can choose whether to
supply liquidity by dealing from their inventories or by constructing hedge
portfolios. This flexibility allows them always to choose the strategy with
the greatest profit potential.
Arbitrageurs also compete with brokers. Arbitrageurs buy from traders
in one market and sell to traders in another market. They therefore match


## Page 390

CHAPTER 17 ARBITRAGEURS • 377
I,
Options Market Makers Like Options
Traders who make markets in stock option contracts typically quote every
series within an options class. An
options class
includes all options that
trade on the same underlying instrument. An
option series
is a specific
option contract. Options series vary by their strike prices, by their expiration
dates, and by their type—whether they are puts or calls.
When options dealers take a position in a given series, they may offset
the risk in that position by a transaction in the same series or by a position
in any other series in the class. They also may offset the risk by taking a
position in the underlying instrument. The option to offset their risks in a
multitude of different ways gives options market makers a great deal of
flexibility. This flexibility allows them to take much greater positions in an
individual series than they otherwise would take.
Options traders keep track of their total risk exposure by expressing the
risk in each of their positions in terms of the underlying instrument. They do
this by multiplying their options positions by their deltas.' The sum of their
delta-adjusted positions is their net exposure to the risk in the underlying
instrument. The delta of a perfectly hedged portfolio is zero. Options traders
who do not want to speculate on price changes in the underlying instrument
try to keep their net delta near zero.
Deltas change as the underlying instrument prices change. Options
traders keep track of the net gamma of their positions so that they know
how much of the underlying instrument they must trade to maintain their
hedge when the underlying instrument price drops.
Option prices also depend on the volatility of the underlying instrument.
Options traders keep track of the net vega of their positions so that they
know how much they will gain or lose when volatility changes. Since most
public traders buy puts or buy calls, options dealers typically have positions
with negative net vega. They therefore make money when volatility falls or
when prices do not change much before expiration.
1. The
delta
of an option is the rate at which the option value changes when the
price of the underlying security changes. (It is approximately equal to the change in
option value that would result from a one-dollar increase in the underlying security
price.) Calls have positive deltas, and puts have negative deltas. The
gamma
of an
option is the rate at which its delta changes when the price of the underlying security
changes. The
vega
of an option is the rate at which the option value changes when
the volatility of the underlying security changes. Puts and calls have positive vegas.
buyers with sellers, which is essentially what brokers do. Brokers arrange
matches on an agency basis: Their clients pay them commissions for ar-
ranging their trades. Arbitrageurs arrange these matches on a proprietary
basis: The arbitrage spread rewards them for matching willing buyers with
willing sellers. Since arbitrageurs are in some respects like brokers, the skills
that make good brokers—being able to identify buyers and sellers who are
willing to trade—also make good arbitrageurs.
17.8 COMPETITION AMONG ARBITRAGEURS
AND EQUILIBRIUM ARBITRAGE SPREADS
Arbitrageurs compete with each other to make arbitrage profits. Although
arbitrage is most profitable when the arbitrage spread is wide, arbitrageurs
who wait for wide spreads often lose the opportunity to trade when more


## Page 391

378 • TRADING AND EXCHANGES
aggressive arbitrageurs trade first. Competition among arbitrageurs there-
fore tends to narrow arbitrage spreads so that only the most efficient arbi-
trageurs can survive.
When arbitrageurs can freely enter and exit, the resulting arbitrage spreads
are just wide enough to ensure that most arbitrageurs make only normal eco-
nomic profits on average. When arbitrage is more profitable, new traders en-
ter and spreads narrow. When arbitrage is less profitable, some arbitrageurs
give up, and arbitrage spreads widen.
Arbitrage spreads therefore reflect the costs of doing arbitrage. When
arbitrage is expensive, arbitrage bounds will be wide.
Since only the most efficient arbitrageurs survive, arbitrageurs tend to be
excellent traders and excellent estimators of relative values. In markets where
the arbitrage relation is easy to understand, the most successful arbitrageurs
are excellent traders who have very high-speed access to their markets. In
markets where the arbitrage relation is difficult to understand, the most suc-
cessful arbitrageurs are excellent analysts.
Arbitrageurs are often traders who have a natural interest in one or more
legs of the hedge portfolio. Such traders are often the lowest cost arbi-
trageurs because they already are committed to doing one or more of the
trades necessary to construct the hedge portfolio. For example, for agricul-
tural delivery arbitrages, local operators near the contract delivery point are
often the lowest cost arbitrageurs because they often need to buy or sell the
commodity there anyway.
17.9 SUMMARY
Arbitrageurs play many roles in the markets. They enforce the law of one
price, they discipline slow traders, they connect buyers to sellers, and they
repackage risks into forms that other traders find most useful. These activ-
ities provide benefits to the economy and to individual traders.
Arbitrage trading strategies are special cases of more basic trading strate-
gies. Arbitrageurs are simply traders who deal or speculate in their hedge
portfolios. Trading two or more instruments simultaneously can be difficult,
however, especially when price volatility is large relative to the basis. Good
arbitrageurs therefore tend to be good traders.
Arbitrage is not easy. To trade successfully, arbitrageurs must properly
understand the arbitrage relation, they must successfully implement their
positions, and they must control their carrying costs. Arbitrageurs risk los-
ing when the basis moves against them, when carrying costs are unexpect-
edly large, when they falsely identify arbitrage opportunities, and when they
can no longer carry their positions because they have grown too large. Suc-
cessful arbitrageurs must anticipate these problems and deal with them as
they arise.
Arbitrageurs profit from mean reversion in the basis. In pure arbitrages,
the basis must revert. Arbitrageurs can still lose, however, if the reversion
takes too long. In speculative arbitrages, the nonstationary component of
the basis may dominate the mean-reverting component, and the basis may
never revert.
The most successful arbitrageurs understand why their arbitrage oppor-
tunities arise. They then know how best to trade, and they are less likely to
trade when they should not trade.


## Page 392

CHAPTER 17 ARBITRAGEURS • 379
Since arbitrageurs trade in more than one market, we need to consider
their behavior when we consider how markets relate to each other. We dis-
cuss how arbitrageurs transmit volatility between markets in chapter 20, and
in chapter 26 we show that arbitrageurs are instrumental in keeping frag-
mented markets together.
17.10 SOME POINTS TO REMEMBER
•
Arbitrageurs trade similar risks in related markets.
•
Arbitrageurs generally are dealers or value traders in their arbitrage
hedge portfolios.
•
Arbitrageurs convert assets and risks to different types, forms, or
locations.
•
Arbitrageurs are cross-sectional dealers.
•
A large basis tells arbitrageurs to move liquidity from one market to
another or to convert risk from one form to the other.
•
The basis pays for the arbitrageurs' services.
•
Arbitrage enforces the law of one price across fragmented markets.
•
Successful arbitrageurs are low cost traders.
17.11 QUESTIONS FOR THOUGHT
1.
What limits arbitrage profits?
2.
Can you repeat the intermarket currency cross-rate arbitrage until
you are rich beyond your wildest dreams? Why not?
3.
What would happen to arbitrage spreads if the government taxed
trading?
4.
Would index futures contracts have more appeal to hedgers and ar-
bitrageurs if they required short holders to pay long holders the
amount of dividends that they would have received had they held
the cash portfolio instead of the futures contract?
5.
Can arbitrage trading cause prices in one or more markets to move
away from their fundamental values? If this happens, which traders
would restore prices to their proper values?
6.
How are bid/ask spreads in option contracts related to bid/ask
spreads in their underlying instruments?
7.
When one market becomes volatile, arbitrageurs often cause prices
in related markets to change. Does arbitrage therefore increase to-
tal volatility?
8.
What effect does arbitrage have on total market liquidity?
9.
What advantages do commodity operators have as arbitrageurs?
10.
What can traders learn about future price changes from where prices
lie within arbitrage bounds?
11.
Is a spread between two option contracts with the same expiration date
but different strike prices a speculative arbitrage or a pure arbitrage?
12.
What are the missing event contingencies referred to in the foot-
note to table 17-2 about identifying likely arbitrage opportunities in
the GE—S&P 500 futures spread?


## Page 393

18
I>
Buy-Side
Traders
T
raders must pay close attention to their order submission strategies in or-
der to trade effectively. Traders who optimize their trading strategies will
have lower transaction costs and higher portfolio returns than those who do
not carefully consider their trading problems.
Order submission strategy is the most important determinant of execu-
tion quality that traders control. Traders must decide when to submit mar-
ket orders and when to submit limit orders. When they submit limit or-
ders, they must know where to place their limit prices. If their limit orders
do not execute, they must know when, and how, to resubmit their orders.
Large traders also must pay close attention to how they display their or-
ders. Traders who display large orders often attract front runners and scare
away liquidity suppliers. Large traders therefore must consider the follow-
ing questions:
•
Whether to actively look for the other side or to wait for it to come
to them
•
Whether to show their full interest or to hide it
•
Whether to break up their orders and spread them over time or to
bring their whole orders to market at once
•
Whether to employ a single broker or to use multiple brokers to hide
their total interest
•
Whether to trade in one market or in many markets
Display decisions are the most important trading decisions that large buy-
side traders make.
The decision to use limit orders versus market orders is related to the
order display decision. Traders who want to display their trading interest of-
ten use limit orders to show that they are willing to trade. Those who do
not want to show their interest often use market orders. Traders do not have
to display their limit orders, however. They often can use the services of a
confidential broker, or they can submit their orders to electronic trading sys-
tems that permit undisclosed limit orders.
The strategies that traders should use, depend on their trading problems.
Informed traders who have material information that will soon become pub-
lic will trade very differently from value traders who can identify mispriced
instruments. Both types of traders will trade differently from traders who
need to raise cash before a deadline or index traders who need to rebalance
their index portfolios in response to changes in the composition of their tar-
get indexes.
In this chapter, we examine the issues that buy-side traders weigh when
deciding how to trade. We start by considering the decision to use market
orders versus limit orders. We then analyze the benefits and costs of expo-
380


## Page 394

CHAPTER 18 BUY-SIDE TRADERS • 381
sure, and consider how traders can defend against parasitic trading strate-
gies that order anticipators may exercise against them. Finally, we discuss
how exchanges, brokers, and regulators can structure markets to promote
the interests of buy-side traders.
Although these issues obviously interest buy-side traders, they also should
interest anyone who wants to understand the origins of liquidity. Order sub-
mission strategies affect the supply and demand of liquidity. Traders demand
liquidity when they submit market orders, and they supply liquidity when
they submit limit orders. We therefore must consider how traders choose
their order submission strategies in order to fully understand liquidity.
Chapters 4, 7, 11, 12, 14, and 15 introduce many of the issues discussed
in this chapter. The value-added in this chapter comes from the discussion
of these issues from the point of view of the buy-side trader. To avoid un-
necessary duplication, the text in this chapter assumes some familiarity with
some of these issues. Readers who are not familiar with the markets may
want to read these other chapters first.
18.1 MARKET VERSUS LIMIT ORDERS
The equilibrium spread model presented in chapter 14 shows that order
submission strategy does not matter when all traders have identical needs.
Bid/ask spreads simply adjust to ensure that traders are indifferent between
using market orders and limit orders. In practice, however, traders are not
identical. Some traders need to trade faster than other traders. Impatient
traders generally should use market orders and patient traders should use
limit orders. Some traders are also more sensitive to order exposure issues
than other traders. Those who do not want to display often use market or-
ders to avoid exposing limit orders.
In all cases, the decision to use limit orders or market orders depends
critically on the bid/ask spread. When the spread is wide, taking liquidity
is expensive and offering liquidity is attractive. When the spread is narrow,
market orders are attractive relative to limit orders. Traders judge whether
spreads are wide or narrow from their experience in the market. They can
much better organize that experience by being familiar with the bid/ask
spread determinants discussed in chapter 14.
The prices at which traders place their limit orders depend on how they
value the trade-off between execution price and execution probability. Ag-
gressively priced orders will more likely execute than will less aggressively
priced orders, but the execution prices will be inferior. Traders who are more
concerned about price than about trading will more likely use limit orders.
Traders who are more concerned about trading than price will more likely
use market orders.
The decision to use market orders versus limit orders also depends on
what will happen when limit orders do not execute. Traders who must fill
their orders will trade at inferior prices when the market moves away from
their limit orders. They can reduce their exposure to this risk by using mar-
ket orders or by placing their limit orders close to the market to increase
the probability that they will execute quickly. Traders who are not commit-
ted to trading, trade only if they can obtain a good price. These traders—
primarily traders who employ dealing strategies—often use limit orders or
firm quotes to profit from the bid/ask spread. When their limit orders do


## Page 395

382 • TRADING AND EXCHANGES
A Problem with Rules
The rule to take liquidity when the spread is small and offer it when the
spread is large is valid only when you do not know anything about value.
For
example, suppose that the market is
48
bid, offered at
50.
If the
spread normally is 10, market orders would appear to be extremely
attractive relative to limit orders.
Now
suppose that you knew the true value of the instrument is
45. A
market sell order would execute at an excellent price relative to value, but a
market buy order would execute at a poor price.
This situation often arises when an impatient limit order trader places an
aggressively priced order in the market.
For example,
suppose that the
market was 40 bid, offered at 50, before an aggressive buyer placed a
limit buy order at 48. The limit order buyer improves the market
substantially for market order sellers, but provides no benefit to market
order buyers. Sellers who can recognize this situation should take liquidity.
The new bid also affects the decisions that buyers make to offer or take
liquidity. Since the new bid decreases the probability that limit orders
placed below 48 will ultimately execute, limit order strategies which would
otherwise place buy orders below 48 are less attractive. The new bid
makes market orders more attractive to buyers, though not nearly as much
as the abnormally narrow spread would suggest.
The rule is correct when traders know nothing about values. In our
example, sellers benefit greatly if they use market orders rather than limit
orders, and buyers may be slightly better off with market orders than with
limit orders. The narrow spread makes market orders more attractive than
limit orders—on average—to uninformed traders.
not execute, they simply cancel them or replace them with orders placed at
new prices.
To derive optimal trading strategies, traders must know how execution
probabilities depend on limit order prices. The relation between these vari-
ables depends on market conditions. The most important factors include
total limit order size at better prices, price volatility, and trader interest in
the instrument.
Traders can acquire information about the relation between execution
probabilities and limit order prices by using formal econometric models.
Numerous vendors sell access to optimized order generators
that suggest
or-
der strategies based on current market conditions.
Traders also acquire information about the relation between execution
probabilities and limit order prices through experience. Experienced traders
who pay close attention to the market get a feel for what may happen. Buy-
side traders who give their brokers market-not-held orders give them tim-
ing discretion over their orders. They expect that their brokers will use their
experience and knowledge of current market conditions to determine the
best strategies for filling their orders and to continuously revaluate those
strategies as conditions change.
18.2 THE ORDER EXPOSURE DECISION
Traders expose their intentions many different ways. On one extreme, traders
can publicize their trading interests by submitting limit orders to systems
that widely display their orders. They then hope that other traders will trade
with them. On the other extreme, traders can hide their interests until an


## Page 396

CHAPTER 18 BUY-SIDE TRADERS • 383
exchange or a broker presents an acceptable opportunity to trade. Traders
may also trade only through brokers and exchanges that settle their trades
on an anonymous basis so that neither side knows with whom it traded. A
multitude of order exposure strategies lies between these two extremes.
Traders decide to expose by weighing the benefits of display against the
costs of display. The benefits are obvious. Buyers and sellers can find each
other most easily when they both show that they want to trade. This sim-
ple observation helps explain why trading tends to consolidate into a single
central market. Traders who display their interests make it easy for other
traders to find them.
The display decision would be simple if exposing trading interests did
not often have significant negative consequences. For large traders, how-
ever, exposure can be quite costly. These costs are due to the actions that
other traders may take in response to the exposed information.
We will call these other traders
parasitic traders
and
defensive liquidity
suppliers,
depending on their response to the exposed information.
Parasitic
traders
are order anticipators who profit only by exploiting other traders. We
discuss their trading strategies in chapter 11. Parasitic traders use exposed
information to create trading strategies that profit at the expense of the ex-
posed traders.
Defensive liquidity suppliers
use exposed information about trading in-
tentions to step out of the way of large traders who would otherwise price-
discriminate against them. Their response to exposed information is to re-
frain from trading so that the large traders ultimately pay more for liquidity.
Exposure issues generally do not concern small traders much. Their or-
ders usually are too small to interest parasitic traders. Sometimes many small
orders on the same side of the market may be the equivalent of a large or-
der. The aggregate order size then may attract a costly response from other
traders. Although small traders then would have a collective interest in man-
aging their order exposure, no small trader has much incentive to do so be-
cause the benefits would accrue to all traders.
18.3 THE BENEFITS OF EXPOSURE
Trades are easier to arrange when traders publicize their interests. Traders
who widely expose their orders make it easy for other traders to find them.
They also attract traders with
latent trading interest
who are not able or will-
ing to articulate their trading interests. These traders are
reactive traders
be-
cause they trade in response to trading opportunities that other traders pre-
sent them. The opposite of a reactive trader is a
proactive trader.
Proactive
traders articulate their interests.
Cost is probably the most important reason why traders choose to be re-
active rather than proactive. Proactive traders express their orders and give
them to their brokers before they know whether they will have an oppor-
tunity to trade. They may make many decisions and issue many instructions
that produce no value if suitable trading opportunities do not arise. In con-
trast, reactive traders decide whether they want to trade only after they are
presented with trading opportunities.
In trading situations where suitable trading opportunities are rare, many
traders find it cheaper to be reactive than proactive. Reactive traders, how-
ever, risk that proactive traders may identify and act on valuable trading op-
portunities first.


## Page 397

384 •
TRADING AND EXCHANGES
Dimensional
Fund Advisors
Dimensional fund advisors
often purchases large blocks
of stock in small firms. Its
traders find it cheaper to wait
for opportunities to arise than
to continuously search all
small-firm markets for suitable
trading opportunities. They
obtain better prices by
waiting until traders come to
them. The firm has
successfully provided
substantially enhanced index
returns to its investors by
selling liquidity.
Proactive traders expose their orders to attract liquidity from reactive
traders. Without such exposure, the reactive traders cannot react. Two re-
active traders will never trade with each other.
The reactive—proactive continuum is similar to the aggressive—passive
continuum. The former represents the willingness of traders to bear the costs
of searching, and the latter represents the eagerness of traders to arrange
trades. Aggressive traders tend to be proactive and passive traders tend to
be reactive. Traders who can search at low cost tend to be proactive, while
traders for whom search is expensive tend to be reactive.
18.4 THE COSTS OF EXPOSURE
Large traders who display their interests may reveal three types of infor-
mation useful to other traders: why they want to trade, the potential price
impacts of their future trades, and valuable trading options. Traders may act
on this information to the disadvantage of the exposing traders. This sec-
tion examines these three situations.
18.4.1 Exposure May Reveal Trader Motives
Traders who expose their intentions risk that others will learn why they want
to trade. Traders may use this information to compete with them, to with-
hold liquidity from them, or in some cases, to take other damaging actions
against them.
Several types of traders do not want others to know why they want to
trade:
•
Well-informed traders do not want to reveal their unique information
and proprietary analyses. They do not want other traders to compete
with them or to refuse to trade with them.
•
Traders engaged in corporate control battles avoid revealing their in-
tentions because they want to minimize the time available for corpo-
rate managers to organize their defenses. They also do not want to re-
veal information that would allow others to infer the proprietary
analyses of value upon which they base their choice of targets.
•
Traders who must trade to satisfy various obligations do not reveal their
obligations in order to avoid front runners and squeezers.
•
Squeezers acquire power by surreptitiously cornering the market be-
fore traders realize that they have lost the option to negotiate with oth-
ers. Squeezers therefore cannot reveal their intentions before they have
established their corners.
•
Finally, bluffers do not want to reveal their trading intentions because
they do not want value traders to call their bluffs.
These traders must all be very careful about how they reveal their trad-
ing interests. If other traders can infer their motives from how they trade,
their trading strategies will be much less effective.
Well-known traders who do not want to reveal that they are trading of-
ten use brokers as intermediaries. The brokers expose their orders without
revealing their identities.
Even when traders trade anonymously, other traders may be able to in-
fer their intentions in some instances. For example, order flows often con-


## Page 398

CHAPTER 18 BUY-SIDE TRADERS • 385
The Better You Are, the Harder It Gets
Traders widely believe that Tom is well informed about security values. He
has a strong reputation for the quality of his research and for his unique
insights. His portfolios have consistently outperformed the market. Everyone
understands that Tom buys when he thinks securities are undervalued and
sells when he thinks they are overvalued.
Tom's competitors would like to know his estimates of security values.
They would use this information to compete with him to establish profitable
positions. Their competition would raise Tom's transaction costs and might
prevent him from successfully completing his trading objectives.
The people with whom Tom would like to trade would also like to know
Tom's intentions. Anyone who trades with Tom will likely be on the wrong
side of the trade. Traders therefore try to avoid offering him liquidity. If
exchange regulations, or their business plans, require that they trade with
Tom, they will offer him inferior prices and little size. Even traders who are
eager to trade with Tom will refrain from trading until Tom's trades impact
the market. These responses to Tom's trading raise his transaction costs and
may prevent him from achieving his objectives.
Well-informed traders like Tom have very difficult trading problems. If
they expose their intentions, their transaction costs may increase. If they do
not expose their intentions, they may find it hard to trade. Traders who
have a reputation for success therefore must be very careful about how they
expose their interest. The best-known traders often have the hardest time
finding liquidity.
taro information about
values because well-informed traders buy when they
believe prices are low and sell otherwise. Astute traders can make inferences
about values just from observing the order flow.
Although well-informed traders generally do not want to expose their
orders, they sometimes do want people to pay close attention to them. Af-
ter they acquire their positions, informed traders want prices to move quickly
in their favor. They then want prices to stabilize at their new level so that
they can close their positions and realize their profits. Well-informed traders
can achieve these objectives by sharing their information with other traders.
They are most credible when they allow other traders to see that they are
trading.
Bluffers also sometimes want to expose their trading. To profit from their
bluffs, they must encourage momentum traders to follow their trades.
Bluffers do this by trading aggressively, as though they were well-informed
traders trading on material information that will soon be revealed to the
public. At this point in their strategy, bluffers will expose their orders be-
cause they want other traders to notice their trading. When they later try
to trade out of their positions, they will not want to be observed.
In summary, traders who expose their intentions make it easier for oth-
ers to infer their motives. Exposed traders therefore risk having other traders
compete with them, withhold liquidity from them, or otherwise act against
their interests.
18.4.2 Exposure May Reveal Future Price Impacts
Large impatient traders often significantly affect prices when they trade.
Other traders who know their intentions may therefore front-run them to
profit from the market impacts of their large orders. Front runners increase


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386 • TRADING AND EXCHANGES
large trader transaction costs by taking liquidity that might otherwise have
gone to the large trader.
To trade profitably, front runners do not need to know why traders want
to trade. They merely need to know that a large trader intends to complete
a trade. Large impatient traders therefore carefully manage the exposure of
their orders in order to control their transaction costs.
18.4.3 Exposure May Reveal Valuable Trading Options
Even if large traders patiently wait for the market to come to them, expos-
ing their orders can still adversely affect their trading costs. Standing limit
orders provide free trading options to other traders. Quote matchers can ex-
tract the values of these options.
Quote matching is a front-running strategy in which quote matchers try
to trade before large patient traders. When the quote matchers trade first,
the options offered by the large traders protect the quote matchers from se-
rious losses on their new positions. If prices move in their favor, they can
profit to the full extent of the movements. If prices move against them, they
may limit their losses by trading with the large traders.
Quote matchers profit at the expense of the large patient traders by tak-
ing liquidity that otherwise would have gone to the large traders. If the large
traders subsequently fail to trade because prices move away from their or-
ders, they lose the profits that the quote matchers make. To prevent losses
to quote matchers, large patient traders must control their order exposure
so that quote matchers cannot exploit the trading options associated with
their orders.
18.5 DEFENSIVE STRATEGIES
The preceding section shows that large traders must carefully control the
exposure of their trading intentions in order to avoid losses that other traders
can impose upon them. This section describes three strategies—evasive, de-
ceptive, and offensive—that large traders use to deal with these problems.
Large traders use evasive strategies to keep other traders from learning in-
formation about their trading intentions. They use deceptive strategies to
fool other traders into making wrong inferences about their intentions. They
use offensive strategies to attack parasitic traders.
18.5.1 Evasive Strategies
Traders may use several strategies to avoid exposing information to traders
who might act on it to their disadvantage.
•
Where traders negotiate face-to-face, large traders typically hire bro-
kers to negotiate trades on their behalf in order to preserve their
anonymity. Very large traders often will use multiple brokers to ensure
that no broker knows the full extent of their interest, and to prevent
other traders from inferring their interest by watching a single broker.
•
To avoid front running that might be due to dishonest or incompe-
tent brokers, large traders often prefer to use electronic trading sys-
tems that do not display their identities.
•
When traders must show their interest in order to arrange a large trade,
they, or their brokers, carefully select the traders to whom they first
display their interest. They try to display first to those traders who will


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CHAPTER 18 BUY-SIDE TRADERS • 387
most likely trade with them, and who are least likely to act on the in-
formation otherwise.
Being able to identify the best sequence of traders to whom to dis-
play interest is the most essential skill that institutional brokers and
buy-side traders must master. This is the art of block trading. Traders
develop this skill by cultivating reliable relationships, by observing mar-
ket activity, by understanding what portfolio managers want, by keep-
ing track of what traders hold, and by noting recent changes in their
holdings. Among the most important brokerage services, selective or-
der exposure is the service that computerized systems are least likely
to provide efficiently.
•
Large traders often limit their exposure by not showing the full extent
of their interest in their orders. They, or their brokers, break their or-
ders into small parts so that they display only a small part at a time.
•
Large traders often use brokers and exchange systems that do not dis-
play their orders to other traders. eSpeed, POSIT, Global Instinet
Crossing, GLOBEX, Euronext, and Island, among others, provide sys-
tems that accept and manage undisplayed orders. Traders can learn
about these orders by committing to trade with them.
•
Large traders often solicit interest without committing to trade by pub-
licizing order indications on systems like Autex, Bridge, and Instinet.
Order indications
merely show that someone would like to talk about
trading. Since they do not represent commitments to trade, any trader
may indicate without intending to trade. (If contacted, traders who do
not intend to trade say that they canceled or already filled their orders.
They then apologize for failing to remove their indications. Alterna-
tively, they quote prices so poor that no trades are likely.) Since the
only cost of false indications is some loss in reputation if asked to trade,
many traders do not take order indications seriously. Order indications
therefore do not generally reveal much information.
•
Finally, traders can avoid exposing their intentions by waiting until
someone else exposes a trading opportunity that interests them. If an-
other trader exposes a firm commitment, the trader can use a market
order to trade with it. In this way, traders do not expose their interest
until they actually trade.
If the other trader merely exposes an order indication, traders con-
cerned about controlling the exposure of their interests should be wary.
Inquiries about the indication may reveal their interests to the trader
who posted the order indication.
18.5.2 Deceptive Strategies
Traders sometimes use deceptive strategies to confound traders who would
try to infer their intentions. The following strategies are deceptive because
traders actively disseminate false information or because they create situa-
tions in which other they may make false inferences:
•
Traders may make a small trade on the side opposite the one in which
they have substantial interest. If the small trade is widely publicized,
the resulting confusion may make it harder for traders to identify the
true interest. Traders may likewise post order indications on the op-
posite side to confuse other traders.
Fishing for Business
Brokers sometimes post
indications to solicit business.
When a trader contacts such
a broker, the broker either
apologizes for failing to
cancel the indication or says
that he will check to see
whether his client is still
interested. The broker then
tries to find someone who
would be interested in taking
the other side. Brokers who
use this strategy can
sometimes arrange trades
without receiving formal
orders from either side.
Traders who respond to
indications only to discover
that they cannot immediately
trade become very annoyed.
They object to the waste of
their time and to being
tricked into exposing their
interest. When this happens,
they often penalize the
offending broker by refusing
to do business with him for
some period.


## Page 401

388 • TRADING AND EXCHANGES
00 The Cost of Lying
Max sells 100,000 shares
of a small stock through a
block broker. To trade without
substantially depressing
prices, Max tells his broker
that he has no further interest
in selling the stock. The
broker uses this information
to encourage buyers to take
the stock.
Max then uses another
broker to sell an additional
100,000 shares at a
substantially depressed price.
The buyers of the first
100,000 shares lose money
and are very upset at the first
broker who arranged their
trade. That broker's reputation
will suffer accordingly.
Max successfully
discriminated among the
buyers, but he probably
will not be able to use the
first broker again in the
near—and possibly distant—
future. Brokers naturally do
not want clients who damage
their reputations.
4
•
Traders may say that they have finished trading when they have not,
and they may say that they want a small position when they want a
large one. These strategies may fool other traders into offering liquid-
ity on better terms than they might otherwise.
•
Traders may express interest away from the markets that truly interest
them in order to divert attention from their true intentions.
•
Traders may cancel orders that they want to fill so as to create uncer-
tainty about their commitments to trading.
To increase the credibility of these strategies, some traders cultivate brokers
they know cannot keep a secret, using them to unwittingly reveal false
information.
Traders who actively deceive others risk damaging valuable relation-
ships. Deceptions therefore must be weighed against the value of their
relationships.
Because talk is very cheap, many traders discount information they re-
ceive if the source has little stake in its accuracy. For example, information
obtained from brokers with whom you have no relationship is likely to be
much less reliable than information obtained from brokers who depend upon
you for their livelihoods.
18.5.3 Offensive Strategies
Traders may use offensive strategies to attack parasitic traders. Offensive
strategies can increase the costs and risks of parasitic trading and thereby
drive it away.
The most important offensive strategy involves a sting. If a trader real-
izes that someone is regularly front running his orders, he may want to sting
the front runner to shake him off. To set up the sting, the trader displays
an order on the opposite side of his true interest. If fooled, the front run-
ner then tries to trade on that side ahead of the exposed order. The stinger
then trades with the front runner—perhaps through a broker—and then
cancels his false order.
If the sting is successful, the stinger will complete his desired trade with
the front runner and leave the front runner on the wrong side of the mar-
ket. To trade out of his position, the front runner must now solve the same
trading problem that the stinger faced. In effect, the stinger transfers his
trading problem to the front runner.
Not all stings are successful. Another trader may fill the false order, or
the front runner may fill the order if he somehow recognizes the trap. In
either event, the stinger will end up with a position just opposite to the one
he wants to establish. To establish his desired position, the stinger will now
have to trade twice as much as he originally intended, or three times as
much if we count the trade made in the failed sting.
18.5.4 Summary
Traders use a variety of strategies to control the exposure of their trading
intentions and to prevent losses to other traders who would act upon that
information if they could infer it. Evasive strategies avoid revealing infor-
mation to those who would use it against you. Deceptive strategies attempt
to confuse these traders. Offensive strategies try to attack these traders. Un-
fortunately, all of these strategies are costly to implement.


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CHAPTER 18 BUY-SIDE TRADERS • 389
Some of the deceptive and offensive strategies may not be legal in some
jurisdictions. Traders who are concerned about such issues should consult a
competent legal authority.
18.6 HOW MARKETS HELP TRADERS
CONTROL EXPOSURE COSTS
Markets can reduce the costs of order exposure by adopting rules and trad-
ing systems that protect traders who expose their orders.
Markets can reduce front running by adopting a time precedence rule to
make it impossible for traders to trade before a standing order at the same
price. A large minimum price increment then would make front running
expensive by forcing front runners to offer a significantly better price to
trade first.
Markets can also protect large traders by allowing them to submit undis-
closed orders. These facilities allow large traders to make firm offers to trade
that other traders can discover only by committing to trade with them.
Traders who look for these undisplayed orders can avoid exposing their own
interest by attaching fill-or-kill instructions to their orders. If their orders
discover undisclosed liquidity, they trade. If they do not, only the trading
system knows that they inquired. Completely confidential trading systems
work well when traders know what trades they want to do. They therefore
primarily serve proactive rather than reactive traders.
Most markets maintain rules and procedures to protect traders against front
running by dishonest brokers. A detailed and accurate audit trail is especially
important for identifying and prosecuting front-running frauds. To further re-
duce the potential for encountering these problems, some markets prohibit
dual trading (acting as both a broker and a dealer) by their members.
Finally, markets can protect some types of traders by allowing them to
report their trades late. Delayed trade reporting helps traders who are in the
middle of acquiring or divesting large positions by making it difficult for
other traders to infer the full size of their interests.
Exchanges and regulators must weigh the benefits of delayed reporting
against its costs. By obscuring the market, delayed reporting can greatly in-
crease transaction costs for many types of traders. This result should not be
surprising because traders can benefit from delayed reporting only if they
hurt other traders. Since delayed trade reporting helps dealers more than
brokers, it is more common in markets organized by dealers than in those
organized by brokers and exchanges.
18.7 SUMMARY
Buy-side traders must pay close attention to their order submission strate-
gies in order to trade effectively. The strategies that they choose depend on
the problems that they solve. Traders who are impatient to trade generally
use market orders, while those who are patient or who do not need to com-
plete their trades use limit orders.
All traders pay close attention to the price of liquidity. When spreads
are wider than normal, limit orders may be more attractive than market or-
ders. When spreads are narrower than normal, market orders may be more
attractive.
Order Exposure
and Minimum
Price Increments
Large traders should expose
more when time precedence
and a large minimum price
increment protect their
interests. I examined this
proposition by studying the
propensity of large traders to
display their orders at the
Paris Bourse and the Toronto
Stock Exchange in 1994 and
1995. Both exchanges then
had electronic trading systems
that allowed traders to submit
undisclosed orders. (The
Toronto Stock Exchange
recently dropped this feature
of its market.)
As expected, traders
submitted more undisclosed
orders when the minimum
price increment was a small
fraction of price than when
it was a large fraction of
price.


## Page 403

390 • TRADING AND EXCHANGES
The Liquidity in Liquidnet
Liquidnet is an institutional equity broker that runs an innovative electronic
alternative trading system. Clients plug the Liquidnet trading system into
their electronic order management systems. Liquidnet then looks at the
orders that their clients have placed in their order blotters. If Liquidnet sees
that a client wants to buy a security that another client wants to sell, it
suggests that they negotiate by placing an indication of this fact on their
two workstations. Either of the two traders then can contact the other by
sending an anonymous message through the Liquidnet system. If the
receiving trader responds to the message, the two traders then try to
negotiate a trade, using a Liquidnet messaging window designed for this
purpose. If they agree to a price and quantity, they report the trade to
Liquidnet. Liquidnet then crosses the trade so that neither trader ever knows
with whom he or she traded. Liquidnet charges each trader 2 cents per
share commission for its services.
To limit the number of potential negotiations, traders can specify a
minimum fraction of their order size that other traders must have in their
order books to negotiate with them. Liquidnet then suggests negotiations
only among traders whose orders are adequately large.
To limit the number of unproductive negotiations, when Liquidnet suggests
a negotiation, it presents traders with information about how often the other
trader has responded and how often negotiations with that trader have
produced a trade. Traders then can avoid negotiating with traders with
whom they have not been able to trade in the past.
When traders can negotiate with more than one trader, Liquidnet ranks
the traders by their order sizes. Traders use this information and the
information described in the previous paragraph to choose with whom to
initiate negotiations.
Otherwise, the Liquidnet trading system is completely confidential. The
system does not present information about orders, prices, quantities, or
trade negotiations to anyone.
Liquidnet became very successful very quickly because traders do not
have to submit orders to it. Instead, Liquidnet observes the potential liquidity
that traders have in their order blotters.
Source: www.liquidnet.corn.
Trades are easiest to arrange when traders broadly expose their orders.
Unfortunately, exposure can reveal trader motives, the potential price im-
pacts of future trades, and valuable trading options. Traders can exploit this
information to their advantage and to the detriment of exposing traders.
Exposure decisions are the most important decisions that large traders
make. Good traders know when and to whom to expose their interests. Poor
traders expose to the wrong traders, they expose at the wrong times, or they
fail to expose when they should.
Traders concerned about exposing their interests employ a variety of tech-
niques to control their exposure. They use brokers to represent them anony-
mously, they split their orders, they use market orders instead of limit or-
ders, and they selectively expose to those traders who are most likely to trade
with them and least likely to front-run them.
Markets can help traders who are concerned about order exposure by
adopting rules that protect them. Time precedence, in conjunction with an


## Page 404

CHAPTER 18 BUY-SIDE TRADERS • 391
SOES Banditry
Some markets have rules designed to exclude parasitic traders. The response
of the Nasdaq Stock Market to SOES banditry illustrates this approach.
SOES bandits
were traders who used the Nasdaq Small Order Execution
System (SOES) to submit orders designed to profit from very short-term price
changes. The losers generally were Nasdaq dealers who adjusted their
quotes a few seconds too slowly. These dealers naturally complained
vociferously.
These high-speed trading strategies ultimately caused spreads to widen
because dealers and other traders had to recover from other traders what
they lost to the SOES bandits. Since Nasdaq wanted its markets to have
narrow spreads, it tried to protect its dealers from SOES bandits. To this
end, Nasdaq tried to classify SOES bandits as professional short-term
traders and thereby restrict their access to the SOES system.
SOES banditry is no longer as controversial as it once was. Dealers are
now more responsive to price changes. In addition, much more liquidity in
Nasdaq now comes from public lihit order traders than before. High-
frequency proprietary traders undoubtedly exploit these traders when prices
change quickly, but unlike Nasdaq dealers, public limit order traders are
not organized to complain about it.
economically significant minimum price increment, helps protect exposed
orders by making front-running strategies less profitable.
Since regulators and exchanges can specify the size of the minimum price
increment, they can control the degree to which traders expose their orders.
Moreover, since traders clearly appear to be reluctant to display their large
orders, exchanges that do not presently offer facilities to represent undis-
closed orders may be able to obtain more order flow by offering these fa-
cilities. Without these facilities, traders tend to split their large orders into
pieces.
18.8 SOME POINTS TO REMEMBER
•
Buy-side traders must choose the best order submission strategies for
the trading problems that they solve.,
•
The choice between limit order strategies and market order strategies
depends on the price of liquidity, the size and price placement of stand-
ing limit orders in the trading system, and the consequences of failing
to trade.
•
In general, traders should offer liquidity when it is expensive and buy
it when it is cheap. Experienced traders know when liquidity is ex-
pensive or cheap.
•
Order exposure decisions are the most important decisions large traders
make.
•
Traders expose to encourage other traders to trade with them.
•
Traders avoid exposing when they fear that other traders will front-
run their orders or avoid trading with them.
•
The art of trading large orders lies in knowing when and how to ex-
pose trading interest.


## Page 405

392 • TRADING AND EXCHANGES
18.9 QUESTIONS FOR THOUGHT
1.
Do defensive liquidity suppliers make prices more or less informative
when they refrain from trading with well-informed traders?
2.
Are parasitic traders truly parasitic? In the long run, do they make
prices more informative, or do they make markets more liquid?
3.
What is the difference between passive traders who wait for other
traders to trade with them and reactive traders who trade in response
to trading opportunities that other traders present them?
4.
Why might exchanges want to protect the interests of large traders
over smaller traders?
5.
How would you construct a theoretical model to characterize the
trade-off between limit prices and execution probabilities? How would
you construct an econometric model to provide a statistical charac-
terization of this trade-off? What would be the relative advantages
and disadvantages of these two approaches to modeling limit order
execution probabilities?


## Page 406

Part U
Origins
of
liquidity
and
Uolatility
n the next two chapters, we provide broad characterizations of liquidity
and of volatility. These concepts mean different things to different peo-
ple. Consequently, people often are confused when they discuss them. The
discussions in these chapters should give you a much more complete un-
derstanding of the origins of liquidity and volatility, and of their many
dimensions.
Chapter 19 explains that liquidity is the successful outcome of a bilat-
eral search in which buyers look for sellers and sellers look for buyers. This
characterization of liquidity explains why liquidity has multiple attributes.
The chapter concludes by showing how various types of traders cooperate
and compete with each other to supply liquidity.
Chapter 20 breaks total volatility into fundamental and transitory com-
ponents. Transitory volatility is closely related to the transaction costs that
uninformed traders bear. Regulators therefore are quite interested in it. The
chapter concludes with a discussion about how statisticians can discrimi-
nate between the two volatility components.
393


## Page 407

19
Liquidity
Market Frictions
Economists like liquid
markets—securities markets,
contract markets, product
markets, and labor markets—
because their models work
better when they do not have
to consider how transaction
costs affect economic
decisions. When confronted
with transaction costs, people
trade less often. If the costs
are high enough, they do not
trade at all.
Transaction costs in an
economic system therefore are
like frictions in a mechanical
system. They both slow things
down and can ultimately stop
all activity. Economists
therefore call transaction costs
market frictions.
The Most Important
Bilateral Search
For many people, finding a
life partner is the most
important bilateral search
problem that they encounter.
For others, it is finding a job.
Although this book is not
about how people form life
relationships or obtain jobs,
all bilateral search problems
have similar structures. What
you learn about how people
trade securities and contracts
may help you understand
how people find partners
and jobs.
L
iquidity
is the ability to trade large size quickly, at low cost, when you
want to trade. It is the most important characteristic of well-functioning
markets.
Everyone likes liquidity. Traders like liquidity because it allows them to
implement their trading strategies cheaply. Exchanges like liquidity because
it attracts traders to their markets. Regulators like liquidity because liquid
markets are often less volatile than illiquid ones.
Everyone in the markets has some affect on liquidity. Impatient traders
take liquidity. Dealers, limit order traders, and some speculators offer liq-
uidity. Brokers and exchanges organize liquidity.
Given its importance, you would expect that the term
liquidity
would be
well defined and universally understood. In fact, liquidity means different
things to different people. Traders and regulators talk about it all the time,
but rarely are they clear about what they mean. Consequently, they often
fail to communicate effectively about liquidity.
The confusion is due to the many dimensions of liquidity. When peo-
ple think about liquidity, they may think about trading quickly, about trad-
ing large size, or about trading at low cost. Some dimensions of liquidity
are more important to some people than to others. Unfortunately, people
rarely distinguish among these dimensions when discussing liquidity.
In this chapter, you will see that liquidity—the ability to trade—is the
object of a
bilateral search
in which buyers look for sellers and sellers look
for buyers. The various liquidity dimensions are related to each other through
the mechanics of this bilateral search. Traders must understand these rela-
tions in order to trade effectively.
Understanding liquidity is one of the primary objectives of this book. In
this chapter, we will carefully define liquidity and its various dimensions.
We then will identify the types of traders who supply liquidity and discuss
how they compete with each other.
These discussions will be especially useful to you if you are a trader who
needs to know where to look for liquidity. They also will be useful to you
if you intend to offer liquidity. In that case, you must understand with
whom you will compete so that you can predict when you can expect to be
successful.
You also need to understand liquidity to measure it effectively. Many
traders and regulators regularly measure liquidity. Traders measure liquidity
to determine whether their trading strategies are sensible, given the avail-
able liquidity. They also measure liquidity to evaluate the service they ob-
tain from their brokers. Brokers likewise measure liquidity to evaluate the
service they obtain from their dealers. Regulators measure liquidity to de-
termine which market structures are best. No one can answer these ques-
tions, however, without clearly understanding what they are measuring. As
a rule, you cannot measure something that you cannot define. The concepts
394


## Page 408

CHAPTER 19 LIQUIDITY • 395
A
Unilateral Search for a 35mm Camera
Fred wants to buy a specific camera model at a low price. His time is
worth 30 dollars an hour.
Fred goes onto the Internet to search for the best price among mail-order
photography stores that offer the camera. After five minutes of searching at
Al ePhoto's site, he discovers that they will sell the camera for 112 dollars.
After another five minutes, he discovers that BNDPicture will sell it for 109
dollars. He decides to search again. Six minutes later, he finds that CDBirD
will sell it for 119 dollars. No bargain there.
Should Fred search more? He estimates that if he finds a lower price, it
would be no lower than 99 dollars (i.e., no more than 10 dollars less than
his current best price). He also estimates that the probability that he will find
a price that low at less than 0.25. Fred's expected gain from searching
again therefore is less than 2.50 dollars (10 x 0.25(. Since it appears that
each search takes at least five minutes, his expected cost of searching
again is more than 2.50 dollars, given the 30-dollar value he places on an
hour of his time. Since the expected cost is greater than the expected gain,
Fred decides to stop searching. He buys the camera from BNDPicture for
109 dollars.
presented in this chapter provide a basis for the measurement methods pre-
sented in chapter 21.
19.1 THE SEARCH FOR LIQUIDITY
Liquidity is the object of bilateral search. In a
bilateral search,
buyers search
for sellers, and sellers search for buyers. When a buyer finds a seller who
will trade at mutually acceptable terms, the buyer has found liquidity. Like-
wise, when a seller finds a buyer who will trade at mutually acceptable terms,
the seller has found liquidity. To understand trading, you must understand
the strategies that traders use to conduct these bilateral searches.
Bilateral searches are similar to—but more complicated than—unilateral
searches. You will find bilateral search strategies easier to understand if you
first understand unilateral search strategies.
19.1.1
Unilateral Searches
In a unilateral search, you actively search for a good match—a good price,
for example. The main decision that you must make is when to stop the
search. The general rule is to continue searching as long as the expected
benefit from an additional inquiry is greater than the expected cost of the
inquiry. The expected benefit depends on the probability that you will find
a better match than you have already found. As the search proceeds, this
probability declines as you find progressively better matches. The expected
benefit also depends on how much better unfound matches might be than
the best match you have already found. As the search proceeds, the possi-
ble improvement declines as you find progressively better matches. At some
point, your expected benefit from an additional inquiry becomes less than
the cost of the inquiry. You then stop the search and pick the best match
that you have found.
If searching is expensive, you will often stop before you have found the
best possible match. You may later discover that you could have arranged a
The Economics
of Divorce
Some people divorce because
they learn that they have
arranged a poor match. They
either learn that their
relationship did not develop
as they expected, or that their
opportunities to form other
relationships were better than
they expected.
Other people divorce
because they are not mature
enough to accept that even
when they search optimally,
they generally will not
arrange a perfect match.
When they later see other
matches that they believe
would have been better for
them, they forget that they
stopped searching because it
was too costly. They also
forget that the search for a
spouse is a bilateral search.
In particular, they forget that
they cannot arrange every
match that they might want to
arrange.
In either event, people
who initiate divorce
presumably believe that the
benefits from searching
again, or from being single,
are greater than the
substantial emotional, social,
and financial costs of not
breaking their matches.
Unfortunately, many
subsequently learn that their
expectations were poorly
founded.


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396 • TRADING AND EXCHANGES
Exchanges Are Search Engines
A
search engine
is a system that collects information in which people may be
interested. It allows people to search through that information at a low cost.
In the previous example, had Fred been able to use a search engine to
locate the best price for his camera, he might have discovered that GR8
Film and Photo is offering the camera for 87 dollars. Search engines make
markets more competitive by lowering the costs of searching.
Exchanges and electronic quotation services collect information about
who wants to trade. They then organize it so that traders can easily find the
best trading opportunities. They are search engines that allow buyers to
cheaply find sellers who are offering the lowest prices and sellers to
cheaply find buyers who are bidding the highest prices.
1
Librarians Are
Information Brokers
Librarians help researchers
solve unilateral search
problems. Their training and
experience help them make
good decisions about where
to look first for the information
that their clients seek.
better match had you known more about the alternatives. An optimal search
produces the best possible result only if you are lucky enough to find it.
If you knew ahead of time where to find the best possible match, you
would of course go there first. In that case, the costs of searching would be
very low because you already know the outcome. In general, you will get a
better outcome when your costs of searching are low.
Whether your search produces a good outcome or not depends to some
extent on luck and to some extent on your skill as a searcher. Good searchers
know the best places to look for what they want, and they look there first.
A good trader knows where to look first for liquidity.
19.1.2 Bilateral Searches
Bilateral searches differ in two respects from unilateral searches. First, you
may search either actively or passively. Active searchers try to find matches.
Passive searchers wait for others to find them. Second, whatever your search
strategy, you may not always be able to return to the best match that you
identified during the course of your search. While you continue your search,
so does the other side. When you decide that you want to return to your
best match, you may discover that it is no longer available. These differ-
ences make bilateral search strategies significantly more complicated than
unilateral search strategies.
The stopping rule for an active bilateral search is the same as for a uni-
lateral search: You should stop when the expected benefit from an additional
search is less than the expected cost of an additional search. Now, however,
the cost of continuing to search must include the potential loss of your pre-
vious best match. This additional cost suggests that you will try to stop
searching sooner when engaged in an active bilateral search than in an oth-
erwise identical unilateral search. Your search may not end sooner, however:
Your best match may no longer be available when you want to stop search-
ing. In that case, you will have to continue your search.
The search strategies that traders employ to find liquidity vary by their
trading objective. Impatient traders generally search actively. Patient traders
usually are passive searchers. They wait for impatient traders to find them.
Since impatient traders initiate trades, we say that they demand liquidity.
Patient traders supply liquidity.
Passive traders often display their interest in trading to make it easier for
active traders to find them. They offer limit orders, quote markets, post indi-


## Page 410

CHAPTER 19 LIQUIDITY • 397
Sometimes You Can't Go Back
Although John probably will not admit it, he has been looking for a wife
for the last 10 years. As he sees his friends getting married, he realizes that
he must get serious about his search. John fondly remembers that Ruth, a
woman he dated seven years ago, would make an especially good match.
Not surprisingly, she is no longer available. Although she liked John, she
later met and married Boaz.
Even if Ruth were still available, she might not have welcomed John's
renewed interest. How people value a match often depends upon how they
conduct their searches. Ruth may never be able to trust John again because
he once lost interest in her.
cations on bulletin boards, or simply advise their brokers. Passive traders who
most widely display their interests typically trade before those who do not.
In many markets, passive traders commit to trade at prices that they post.
These commitments increase the probability that an active trader will call
upon them. Active traders prefer to call upon such traders because they are
confident that they can arrange trades with them and thereby reduce their
overall search costs.
Problems that large traders encounter when they expose their orders of-
ten complicate their liquidity search strategies. Large traders generally do
not like to show that they want to trade because they fear that front run-
ners will trade ahead of them or that liquidity suppliers will retreat from
before them. Large traders therefore either actively search for liquidity
among traders who post firm quotes or become passive searchers who do
not display their trading interests. Such passive traders have
latent
trading
demands that active searchers must discover. Table 19-1 provides a sum-
mary of the different types of displayed and undisclosed liquidity.
Brokers often help traders solve their search problems. Brokers search
more efficiently than their clients do because they know more about who
0' Order Exposure in the
Marriage Market
Many people are unwilling to
expose that they are looking
for a spouse for fear of
appearing needy. Rightly or
wrongly, people may make
inferences about your position
from how you search.
Matchmakers are
marriage brokers who help
solve this problem by
confidentially proposing
matches among people who
indicate that they are
interested in getting married.
Matchmakers must know their
clients well to propose
successful matches.
TABLE 19
-
1.
Types of Displayed and Undisclosed Liquidity
TYPE

EXAMPLES

SEARCH STRATEGY
Displayed quotes

Dealer quotes
and orders

Exposed orders in open limit order books
Not-displayed at

Undisclosed order size in limit order books
market venue

Undisclosed orders held by floor brokers
Displayed indications

Order indications
Not-displayed at

Agency orders held by brokers in the upstairs market
broker-dealers

Liquidity dealers will offer but not display
Not displayed

Orders in buy-side desk order blotters that have not
at buy side

yet been sent to a broker, dealer, or exchange
Latent

Liquidity that buy-side traders will offer when asked
Take it
Offer to trade
Ask
Ask
Ask
Use Liquidnet
Ask
Source: This table expands upon a classification first created by George Sofianos of Goldman Sachs.


## Page 411

398 • TRADING AND EXCHANGES
Dance Strategies
In many respects, the search
for liquidity is similar to how
teenagers find partners at a
dance. Dancers employ three
strategies to arrange their
dances. Those most interested
in dancing ask potential
partners whether they would
like to dance.
Other dancers stand on
the side of the dance floor,
tapping their feet and
bouncing up and down to
indicate that they are
interested in dancing. These
dancers display their interest,
but they wait passively until
others ask them to dance.
They would have a greater
chance of dancing if they
were willing to commit to
dancing if asked. Most,
however, want to judge the
partner before accepting the
invitation.
Wallflowers use the last
strategy. They lean against
the wall, seemingly ignoring
the dance floor. They may
want to dance, but they will
not ask to dance and/or
display their interest. They
may dance when asked, but
only if they like the match.
4
TABLE 19
-
2.
Liquidity Trade-offs
would want
to trade. Large traders use brokers to arrange their trades to
avoid displaying their orders to other traders.
The costs of finding liquidity vary substantially across instruments and
often through time for a given instrument. Liquidity is easiest to find when
many people on both sides of the market are looking for it at the same time.
Widely held instruments therefore usually trade in very liquid markets. In-
struments that are in the news and therefore are the subject of widespread
interest also tend to trade in very liquid markets. Liquidity tends to dry up
when people are paying attention to other things. For example, trading large
size can be very difficult near holidays, when many traders are not working.
19.2 LIQUIDITY DIMENSIONS
Searches are productive processes in which searchers use inputs to produce
outputs. In the search for liquidity, the primary input is the time spent
searching. The main outputs are good prices and adequate sizes.
We can characterize the expected outcome of a search problem as a pro-
duction function that explains how the inputs to the search are related to
the expected products of the search. This characterization of the search prob-
lem allows us to easily recognize trade-offs among various dimensions of
liquidity. In particular, when traders are willing to search longer, they can
generally expect to find more size at a given price, or a better price for a
given size. Likewise, when traders want to trade more size, they can expect
to obtain a worse price or spend more time searching. Finally, when traders
offer better prices to other traders, they can expect to find greater size or
spend less time searching. Table 19-2 summarizes these trade-offs.
These inputs and outputs of the bilateral search process correspond to the
following three dimensions of liquidity to which traders commonly refer:
•
Immediacy
refers to how quickly trades of a given size can be arranged
at a given cost. Traders generally use market orders to demand imme-
diate trades.
•
Width
refers to the cost of doing a trade of a given size. For small
trades, traders usually identify width with the bid/ask spread. It also
includes brokerage commissions. Width is the cost per unit of liquid-
ity. Traders often refer to market width by the term
market breadth.
•
Depth
refers to the size of a trade that can be arranged at a given cost.
Depth is measured in units available at a given price of liquidity.
CHANGE
Spend more
time searching
Increase size of
desired trade
Offer a better
price
HOLD CONSTANT

IMPLICATION
Size of trade

Expect to find a better average price
Price you are willing to pay or receive

Expect to find more size
Time spent searching

Expect to find a worse average price
Price

Expect to spend more time searching
Size of trade

Expect to spend less time searching
Time spent searching

Expect to find more size


## Page 412

CHAPTER 19 LIQUIDITY • 399
Breadth and depth are closely related. Mathematicians say that they are
duals
to each other. Traders who want to minimize the cost of trading a
given size solve a problem that is essentially identical to that of traders who
want to maximize the size they trade at a given cost. The strategies that
best solve both problems are the same. In both cases, traders must search
efficiently. Depth—the size that you can trade at a given price—and
breadth—the price at which you can trade a given size—therefore summa-
rize essentially the same information about liquidity conditions.
These three dimensions of liquidity help us understand why traders are
often confused about the nature of liquidity. Impatient traders focus pri-
marily on immediacy and its cost, which for small trades is represented by
width. Large traders focus on depth. Different traders focus on different as-
pects of the search problem.
To summarize, liquidity is the ability to quickly trade large size at low
cost. "Quickly" refers to immediacy; "size," to depth; and "cost," to width.
Since liquidity is the ability to trade, we can characterize liquidity as a
function that tells us the probability of trading a given size at a given price,
given the time we are willing to put into our search. This characterization
allows us to consider many other factors besides size, price, and time that
affect the probability of trading. Some of the more important factors in-
volve the following issues:
•
Why do you want to trade? Traders are far more willing to trade with
uninformed traders than with well-informed traders. Liquidity thus is
different for traders who are known to be uninformed than for those
who are known as informed traders. Markets may be liquid for the for-
mer but not for the latter. In practice, traders often do not know who
is well informed. Traders who can convince others that they are not
well informed generally obtain better prices or more size.
•
What is being traded? Instruments that interest large numbers of
traders trade in much more liquid markets than do instruments that
interest only a few traders.
•
How well do traders know fundamental values? Instruments for which
fundamental values are not well known tend to trade in illiquid mar-
kets because liquidity suppliers are afraid that they might trade with
better-informed traders.
•
When is the trade to be arranged? Trades are harder to arrange when
markets are closed than when they are open. Markets are also less liq-
uid when traders suspect that some traders have information that is
not yet in the prices.
•
What are other traders doing? It is easier to buy while prices are falling
than when prices are rising.
•
Is there an imbalance between displayed buying and selling orders?
Imbalances often indicate that liquidity will be cheap for one side and
expensive for the other side.
•
Who is trading? All other things held constant, a good trader is more
likely to complete a trade than is a poor trader. Good traders know
how to display their interest, who wants to trade, and how to approach
traders.
Traders, regulators, and academics often refer to a fourth dimension
of liquidity called market
resiliency.
This dimension also is related to the


## Page 413

400 • TRADING AND EXCHANGES
bilateral search, but much less directly than are immediacy, width, and depth.
Resiliency
refers to how quickly prices revert to former levels after they
change in response to large order flow imbalances initiated by uninformed
traders. We will discuss it further below when we discuss the role that value
traders play in offering liquidity
19.3 THE WHO, HOW, AND
WHY OF LIQUIDITY
We now will review who offers liquidity, why they offer liquidity, and the
relation between their trading strategies and the various dimensions of
liquidity. This review summarizes discussions about the various liquidity-
supplying traders who appear in chapters 13-18. It provides an integrated
view of all aspects of market liquidity
19.3.1
Overview
Traders offer liquidity whenever they trade in response to orders that oth-
ers initiate. Liquidity-offering traders may be market makers, block dealers,
buy-side institutions, or individual investors. Market makers and block deal-
ers offer liquidity when they fill marketable orders or do customer facilita-
tions. Buy-side institutions and individual investors typically offer liquidity
when they submit standing limit orders. They also offer liquidity when they
use market orders to trade in response to requests for liquidity that others
make. Order type therefore is not always the best indicator of whether a
trader is offering liquidity or taking it. In general, traders offer liquidity
when they want to exploit opportunities that liquidity-demanding traders
create when they demand to trade.
Traders do not need to display their orders to offer liquidity. For exam-
ple, a trader who submits an undisplayed order to an electronic trading sys-
tem offers liquidity that traders can discover by submitting suitably priced
orders. Likewise, a trader who will trade only if asked by a broker also of-
fers liquidity. In both cases, these traders allow other people to trade, but
they show their offers under very limited circumstances.
Traders offer liquidity because they hope to profit from selling at high
prices and buying at low prices. Whether their trading is profitable or not
depends on whether their orders execute, and on how prices change after
their orders execute.
We can classify liquidity suppliers into two groups according to their pri-
mary motive for trading. Dealers and value traders trade primarily because
they hope to profit. Dealers hope to profit from offering liquidity. Value
traders hope to profit from speculating successfully on their information about
fundamental values. These traders are
passive liquidity suppliers
because they
generally will not trade unless impatient traders demand liquidity. Other
traders offer liquidity only to lower the cost of trades that they already in-
tend to make, but which they are in no hurry to complete. Such traders may
want to trade to speculate, invest, hedge, exchange assets, or gamble. We will
call these traders
precommitted liquidity suppliers
because they would demand
liquidity if they did not offer it. Precommitted liquidity suppliers may even-
tually demand liquidity if their limit orders do not fill.
In the remainder of this section, we consider how market makers, block
dealers, value traders, precommitted traders, and arbitrageurs contribute to


## Page 414

CHAPTER 19 LIQUIDITY • 401
market liquidity. Here, as before, we treat these traders as though they use
only their single characteristic trading strategy. In practice, most traders use
multiple trading strategies.
By focusing on characteristic strategies, we can analyze complex behav-
iors by breaking them down into simple components. If you are a trader,
these analytic skills will help you to attribute your profits more accurately
to the various strategies that you pursue. You therefore should be able to
better identify when you have a comparative advantage.
19.3.2 Market Makers
Market makers
are dealers who allow their impatient customers to trade at
bid and ask prices that the market makers quote. Market makers often trade
very frequently. They try to buy after they sell, and vice versa. They avoid
large inventory positions because they generally do not know the funda-
mental values of the instruments that they trade very well. Large inventory
positions expose them to losses if the market moves against them. Market
makers simply try to discover the prices that produce balanced two-sided
order flows.
Market makers primarily supply liquidity in the form of immediacy. They
usually quote narrow markets, but only for small size. If asked to trade large
size, they will quote wide markets to protect themselves from losses to well-
informed traders.
Market makers are passive traders. They generally wait until their cus-
tomers want to trade with them. They use their quotes to solicit trades that
will help them reduce large inventory positions. If they are especially un-
comfortable with their inventory positions, they may demand liquidity from
other traders.
Market makers supply liquidity only when they are confident that they
can recover from uninformed traders what they expect to lose to informed
traders. They naturally try to avoid informed traders. Since they generally do
not know their customers well, they occasionally trade with informed traders.
Market makers trade most effectively when they can identify whether
they are trading with well-informed traders. They also have an advantage
as order-anticipating speculators (see chapter 11) because they generally see
more order flow than other traders.
Market makers need capital to finance their inventories. The capital avail-
able to them thus limits their ability to offer liquidity. Because market mak-
ing is very risky, investors generally do not like to invest in market-making
operations. Investors are less concerned about inventory risk—most of which
is diversifiable—than about trader incentives. Financiers know that most
people do not work as hard when working for others as when working for
themselves. Since market making requires continuous attention in order to
avoid significant losses, incentives are especially important. Market makers
therefore often cannot easily raise capital by borrowing or by issuing equity.
Market-making firms that have significant external financing typically
have excellent risk-management systems that prevent their dealers from gen-
erating large losses. These systems tightly limit the capital that each dealer
can commit. Market-making firms also ensure that their traders' compen-
sation contracts reward them for making profits and penalize them for gen-
erating losses. These contracts give traders equity-like positions and thereby
align their interests with those of the firm's shareholders and bondholders.


## Page 415

402 • TRADING AND EXCHANGES
Many exchanges and dealer networks require that their dealers meet min-
imum capital standards in an effort to make their markets more liquid. The
strategy is not necessarily successful, however. Dealers who have more cap-
ital can offer more liquidity, but they may not choose to do so. Unless com-
pelled to provide liquidity, dealers provide liquidity only to the extent that
they feel comfortable. If they face too much adverse selection from informed
traders, no amount of capital will make them willing to supply liquidity.
Capital adequacy regulations therefore only ensure that dealers can offer liq-
uidity if they desire to do so.
19.3.3 Block Dealers
Block dealers
are traders who offer liquidity to clients who want to trade large
positions. The positions may be large blocks of a single security or contract,
or they may be portfolios of many instruments. Block dealers offer liquid-
ity when they buy or sell the positions that their customers offer to them.
Traders sometimes call these trades
facilitations
because the block dealers
facilitate their customers' trading.
Since block dealers take large positions, they are especially concerned
about trading with well-informed traders. To avoid this risk, they carefully
consider with whom they trade. Block dealers usually know their clients well
and facilitate trades only with clients they believe will not hurt them. Since
block dealers need to study why their clients want to trade, they often do
not trade quickly. They generally do not want to supply immediacy because
impatient traders are often well informed.
Block dealers offer liquidity to their customers in the form of depth. By
knowing their clients well, they can offer to trade much larger size than
market makers will offer. They offer to trade only with clients they think
are not well informed, however. Block dealers have no desire to facilitate
trades for well-informed traders.
Block dealers need much capital to run their businesses. They must there-
fore have strong risk-management systems to ensure that traders do not take
foolish positions. These systems tend to slow their trading. Since holding
large blocks is quite risky, block dealers tend to work for large firms that
can spread their inventory risks over many positions.
Block dealers have an advantage over market makers when offering liq-
uidity to large traders because they know more about who wants to trade
than do market makers. Market makers have an advantage over block deal-
ers when offering liquidity to small traders because they can trade much
quicker and because they know more about what prices will produce two-
sided order flows over short intervals.
The best block dealers know how to trade out of their positions. Either
they break them up and distribute them into the market, or they place them
with other clients. Those who distribute their blocks must be excellent
traders. Those who place their blocks must be excellent salesmen.
19.3.4 Value Traders
Value traders
are informed traders who collect as much information about
fundamental values as is economically sensible. They then use their infor-
mation to form opinions about security and contract values. Value traders
trade when prices differ substantially from their estimates of value.
Value traders typically trade only when they are supplying liquidity. To
understand why they supply liquidity, consider the two scenarios under


## Page 416

CHAPTER 19 LIQUIDITY • 403
which prices may differ from fundamental values. In the first scenario, fun-
damental values differ from prices when fundamental values have changed
but prices have not yet adjusted. Fundamental values change when events
occur that change valuations. For example, the value of a factory will drop
substantially if it burns down. Prices change to reflect changes in funda-
mental value when traders receive and digest news about events that change
valuations. The traders who act on this information are news traders (see
chapter 10) because, by definition, they trade on news that represents
changes in the stock of information.
In the second scenario, fundamental values differ from prices when prices
change but fundamental values have not. Prices often change when unin-
formed traders demand liquidity when trading. Such traders generally have
an impact upon prices because dealers are unable to distinguish between in-
formed and uninformed traders, because dealers require substantial rewards
for taking large inventory positions, and because traders often require sub-
stantial price incentives to give up positions that they like or for which they
have unrealized capital gains. Value traders exploit these opportunities when
they buy underpriced instruments or sell overpriced instruments. When mak-
ing these trades, value traders trade in response to the demands that other
traders make for liquidity. Value traders therefore are liquidity suppliers.
Value traders are the ultimate suppliers of liquidity. When nobody else
will trade, value traders will trade if the price is right. They supply liquid-
ity in the form of depth. They are best able to supply depth because they
are best able to solve the adverse selection problem. They solve it by know-
ing values better than anybody else does. They generally do not care with
whom they trade as long as they are confident that they have all available
fundamental information.
Value traders are usually slow traders. They must be very confident that
they know everything relevant to estimating values. They therefore tend to
trade after uninformed demands for liquidity have caused prices to change.
Value traders often compete with each other to supply liquidity. Fre-
quently some event may cause many value traders to try to trade at the same
time. In that case, the quickest value traders will be the most profitable be-
cause they will incur the lowest transaction costs.
Value traders make markets
resilient.
Markets are resilient when trading
by uninformed traders has little effect on prices, and when the effects of
their trading on prices are very short-lived. Markets are resilient when value
traders are well capitalized, well informed, and willing to trade. Value traders
cause prices to return to fundamental values after liquidity demanders cause
them to diverge.
The prices at which value traders will buy and sell constitute the
outside
spread.
Outside spreads tend to be much wider than market maker spreads
because value traders generally trade much larger sizes and because they
must fund their research costs. Although value traders trade with better-
informed traders less often than do market makers, value traders expose
themselves to greater adverse selection risk because they trade much larger
sizes that they often must hold for much longer periods. Value traders lose
only to news traders who are better informed than they are about the lat-
est news concerning fundamental values. They also lose to other value traders
who estimate values more accurately than they do.
Market makers who acquire inventory positions with which they are es-
pecially uncomfortable often lay off those positions onto value traders. Such


## Page 417

404 • TRADING AND EXCHANGES
layoffs occur after market makers have bought inventory from uninformed
traders while lowering prices. The low prices may attract value traders who
buy the market makers' inventories. Layoffs also occur after market makers
have sold inventory to uninformed traders while raising prices. In that case,
the high prices attract value traders who sell inventory to the market makers.
19.3.5 Precommitted Traders
Precommitted traders
offer liquidity to obtain better prices for trades that they
would like to complete. These traders typically offer limit orders in an at-
tempt to buy at the bid or sell at the ask. They are successful only if their
orders trade. If the market moves away from their orders, they may lose the
opportunity to make a favorable trade, or they may ultimately make the
trade at much worse prices. To avoid this risk, precommitted traders place
their orders close to the market. (If they were especially afraid of not trad-
ing, these traders would demand liquidity with market orders.) Precom-
mitted traders therefore are often the most aggressive suppliers of liquidity.
Bid/ask spreads are small in public auction markets with many precommit-
ted traders.
Precommitted traders can drive dealers out of a market because they can,
and often do, price their orders more aggressively than dealers place their
quotes. Dealers must quote spreads that allow them to recover their costs
of doing business. Since precommitted traders do not face these costs, they
can drive dealers out of business.
Dealers have an advantage over precommitted traders, however. Dealers
generally can adjust their quotes faster than precommitted traders can ad-
just their limit orders. If they have limit order books, they also can decide
whether they want to fill an incoming marketable order or allow it to fill
with orders on their limit order books. Dealers naturally will make these
decisions to their advantage. Limit order traders therefore often will find
that they trade when they wish they had not, or that they did not trade
when they wish they had.
Precommitted limit order traders supply liquidity in the form of imme-
diacy. When they are especially aggressive, they may offer very narrow
spreads. They typically do not offer significant depth, however, because large
traders are reluctant to display their standing limit orders. Those who do
display their large orders invite traders to employ quote-matching strategies
against them.
19.3.6 Arbitrageurs
Arbitrageurs
are traders who trade on price discrepancies between two or
more markets. The effect of their trading is to connect demands for liquid-
ity made in one market with offers of liquidity made in another market. Ar-
bitrageurs therefore are not suppliers of liquidity but porters of liquidity.
They demand liquidity in the market where it is most available and supply
that liquidity in the market where traders demand it.
Since arbitrage is generally a low-risk strategy, arbitrageurs can move
substantial liquidity from one market to another. Arbitrageurs increase the
depth in a market by bringing in more liquidity from other markets when
traders demand it.
Arbitrageurs are essentially market makers who connect buyers in one
market with sellers in another market at same time. In contrast, dealers are


## Page 418

CHAPTER 19 LIQUIDITY • 405
market makers who connect buyers in one market to sellers in the same
market who arrive at different times. Thus, arbitrageurs and dealers com-
pete with each other.
19.3.7 Summary
To some extent, all liquidity suppliers compete with each other. Each type,
however, specializes in a niche to which it is best suited to offer liquidity.
Their advantages generally depend on their information about fundamen-
tal values or other traders.
•
Market makers have little information about fundamental values or
their clients. They specialize in offering immediacy to small traders.
•
Block dealers know a lot about their clients. They offer depth to un-
informed clients.
•
Value traders know more than everyone else does about fundamental
values. They generally do not care much about with whom they trade.
Their confidence in fundamental values allows them to be the ultimate
suppliers of depth.
•
Precommitted traders trade for reasons other than to supply liquidity.
Since they already intend to trade, they do not care much about with
whom they trade. They typically supply immediacy.
•
Arbitrageurs are well informed about relative instrument values. When
their arbitrages involve very low risk, they ensure that traders can ac-
cess the depth in any market that trades the instruments that interest
them.
Table 19-3 presents a summary of the traders who offer liquidity.
19.4 AN ILLUSTRATIVE EXAMPLE
This section presents an example that illustrates many of the principles dis-
cussed in the previous section. Although the example is fictitious, the sce-
nario described regularly occurs in many markets.
Suppose that an impatient uninformed trader wants to sell a large block
of XYZ stock on the floor of the New York Stock Exchange. The trader is
demanding more liquidity than traders can easily find there. He therefore
will have a substantial effect on XYZ's stock price.
Although we assume that the trader is uninformed, the trader probably
thinks that he is a well-informed trader who is trying to profit from mate-
rial information that will soon become public. Otherwise, he would have
been foolish to demand so much liquidity on the floor. If he knew that he
was not well informed, and if he still wanted to do the trade, he should have
traded using a less aggressive order submission strategy.
The trader sends his order to Florence, his floor broker on the floor of
Exchange. When the order arrives, the XYZ NYSE specialist is quoting a
tight market of 40 bid, offered at 40.05 for small size. The specialist's quote
offers immediacy in a tight market for small size.
When Florence presents the order at the specialist's post, the specialist
is the only trader standing there. Florence tells him that she has a large or-
der to sell XYZ and asks for his assistance to fill it. The specialist asks about
the order, and Florence tells him that she knows only that her client wants


## Page 419

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## Page 420

CHAPTER 19 LIQUIDITY • 407
it filled quickly. Under the circumstances, both the specialist and Florence
assume that the client is well informed.
The specialist shows Florence that there is insufficient size in the book
to fill the order. The specialist then tells her about some other floor brokers
he suspects have clients who are interested in XYZ. They then page those
traders. When they arrive at the post, Florence determines the extent of
their interest. Some of the floor traders hold working orders issued by their
clients. Others contact their clients to obtain instructions. Some of these
clients express interest when presented with the trading opportunity. Given
the information that the floor brokers collect and reveal, it becomes appar-
ent that the price will have to drop to 37 dollars to fill the order. Florence
agrees, and the specialist arranges the following trades.
The specialist first matches the large order with orders from traders on
the book at prices between 37 and 40. These traders include some pre-
committed traders and some value traders. They supply immediacy directly
to the large trader, but they do not offer much size. The specialist matches
them all at 37 dollars.
The floor brokers then arrange to fill some of the order for their clients
at 37 dollars. Since the specialist brought these traders together, he acts as
a broker for these trades. He will not collect a commission for his efforts,
however. Brokering such trades is a responsibility of his position and a fa-
vor he does to please floor brokers. In any event, all he did was tell Flo-
rence about brokers who had previously inquired about the stock. The floor
brokers' clients supply substantial depth to the large trader, but it comes at
substantial cost.
Finally, the specialist buys the remainder of the order for his own ac-
count at 37 dollars. The specialist offers depth, but only at substantial cost.
The specialist now holds significantly more of XYZ stock than he wants
to hold. To sell inventory and to avoid buying more, he lowers the market
quote to 37 bid for moderate size, 37.30 offered for moderate size. The spe-
cialist bids for moderate rather than small size because he does not want
other traders to suspect that the buy side of the book is empty. If traders
suspect that they can drive the price down, they may try to do so, which
could seriously hurt the specialist with his large long inventory position.
The specialist hopes that the substantially lower prices will attract more buy-
ers than sellers.
The specialist is using his quotes to solicit liquidity for his own account.
There are no orders on the book on the buy side and none on the book on
the sell side below 40 dollars.
Very soon afterward, an arbitrageur in the options markets sends a mar-
ket order to buy moderate size through the SuperDot order-routing system.
The specialist fills the arbitrageur's order from his own inventory at 37.30.
At the same time in the options market, the arbitrageur sells XYZ calls and
buys an equal number of XYZ puts struck at the same price and expiring
on the same date. (This combination of long puts and short calls produces
a short
synthetic stock
position that has essentially the same risk character-
istics as a short stock position. The arbitrageur thus buys and sells positions
that are essentially the same.)
Although the arbitrageur demands immediacy from the specialist, she
also supplies depth to the specialist, and therefore indirectly to the large
trader. The arbitrageur uses a market order because she wants to trade quickly


## Page 421

408 • TRADING AND EXCHANGES
so that she can synchronize her stock and options trades. She synchronizes
her trades to reduce the risk in her arbitrage.
The buyers of the calls and the sellers of the puts also indirectly supply
liquidity to the large uninformed trader through the intermediation of the
arbitrageur and the specialist.
Hours, or perhaps days, later a value trader uses a market order to buy
moderate size from the specialist at 38.25 when the stock is quoted for 38.10
bid, 38.25 asked. The value trader supplies liquidity (depth) directly to the
specialist and indirectly to the large uninformed trader. The value trader
uses a market order to demand immediacy, perhaps because other tasks com-
pete for his time. The value traders help restore prices to their former lev-
els. They make markets resilient.
Days, or perhaps weeks, later, the specialist is again quoting prices near
40 and his inventory is near its target level.
In this example, the large trader lost money because he traded too ag-
gressively. His aggressive trading fooled the market into thinking that he
was well informed. When traders concluded otherwise, prices rose. De-
pending on what he did with the money, he probably would have been bet-
ter off had he not traded. He certainly would have been better off had he
been able to convince other traders that he was uninformed.
19.5 SUMMARY
Liquidity is often discussed but rarely well understood. The confusion has
its origins in the complexity of the bilateral search problem, in which buy-
ers search for sellers and sellers search for buyers.
Liquidity is the object of this bilateral search. It is the ability to trade
large size quickly at low transaction cost. This simple definition reflects the
complexity of the concept. Liquidity has size, time, and cost dimensions.
Traders generally refer to these respective dimensions as depth, immediacy,
and width.
Impatient small traders easily solve the bilateral search problem because
they typically trade at exchanges and in dealer networks that assemble in-
formation about who wants to trade and the prices at which they will trade.
These trading systems act as search engines. To increase the probability that
liquidity-demanding traders will trade with them, most liquidity suppliers in
these systems provide firm quotes and orders. Small liquidity-demanding
traders therefore need to search only for the best price. Order-driven trad-
ing systems that use price priority rules in their matching systems provide
this service automatically.
Patient small traders offer limit orders, hoping that someone will find
them. This trading strategy is more complex than simple market order strate-
gies because their orders may not fill. When they do fill, however, they typ-
ically get better prices than do market order traders.
For large traders, the bilateral search problem is more complex. Many
large traders are reluctant to expose their orders. Markets for large size there-
fore are not very transparent. Search costs can be high, and search strate-
gies may greatly affect the ultimate execution prices.
Five types of traders offer liquidity. Market makers offer immediacy at
narrow spreads to small anonymous traders. Block dealers offer depth to
large uninformed traders. Value traders offer depth to all traders. Precom-


## Page 422

CHAPTER 19 LIQUIDITY • 409
mitted traders offer immediacy at very narrow spreads in an effort to lower
the costs of trades that they already intend to do. Arbitrageurs move liq-
uidity from one market to another market and thereby ensure that traders
can find depth wherever they trade.
19.6 SOME POINTS TO REMEMBER
•
Liquidity is the object of a bilateral search problem.
•
Liquidity is the ability to trade when you want to trade, at low cost.
•
Brokers and exchanges organize liquidity that traders offer.
•
Liquidity has several related dimensions.
•
Market makers primarily supply immediacy.
•
Upstairs traders primarily supply depth.
•
Value traders make markets resilient.
19.7 QUESTIONS FOR THOUGHT
1.
How do the classified ads in a newspaper help traders search for
each other?
2.
How does eBay help traders find each other?
3.
What are the advantages and disadvantages of open limit order
books? Should exchanges have open limit order books?
4.
Can traders spend too much time looking for liquidity?
5.
Can a buyer and a seller find each other if neither is willing to dis-
play his interest?
6.
If the cost of searching drops, will merchants charge lower prices on
average? Does your answer depend on whether merchants can cul-
tivate reputations for low prices?
7.
Do market orders always demand liquidity?
8.
In the example of section 19.4, why did the large trader not trade
directly with the value trader?
9.
Does the fact that securities are fungible make the bilateral search
for liquidity qualitatively different from bilateral searches for life
partners or for dance partners?
10.
If you were a market regulator, how could you make markets more
liquid?
11.
Which dimension of liquidity do you believe is the most important?
Which dimension is most important to large traders? Which di-
mension is most important to small traders? Which dimension
should regulators most care about?


## Page 423

20
Uolati
lity

V
olatility
is the tendency for prices to change unexpectedly. Prices change
in response to new information about values and in response to the de-
mands of impatient traders for liquidity.
Volatility itself changes through time. Sometimes prices are very volatile.
Other times, prices are very stable and hardly change at all. Large price
changes sometimes occur in short time intervals. Regulators and traders re-
fer to episodes of such price changes as
episodic volatility.
Episodic volatil-
ity concerns many people because it can be quite scary.
Volatility, risk, and profit are closely related. Every drop in prices creates
losses for traders who have long positions and profits for traders with short
positions. Likewise, every price rise causes losses for traders with short po-
sitions and profits for traders with long positions. Traders therefore are very
interested in volatility because it can have a significant impact on their
wealth. If risk scares you or profits interest you, you need to know about
volatility.
Volatility especially concerns options traders. Option contract values de-
pend critically on the volatility of the underlying instrument. Options traders
must be able to measure and predict volatilities in order to trade profitably.
Both skills require that they understand well the origins of volatility.
Technical traders who try to interpret trading volumes also pay close
attention to volatility because volumes and volatility are often correlated.
The relation between the two variables is not simple, however. It depends
on the origins of the volatility.
Volatility greatly concerns regulators. Excessive volatility may indicate that
markets are not functioning well. Since accurate prices are extremely impor-
tant in the economy, regulators pay close attention to the markets when prices
are highly volatile. They are especially attentive when markets crash.
In this chapter, we identify the origins of volatility and distinguish be-
tween its two types.
Fundamental volatility
is due to unanticipated changes
in instrument values, and
transitory volatility
is due to trading activity by
uninformed traders.
The distinction is important both for traders and for regulators. Traders
must distinguish between the two volatility types in order to accurately pre-
dict future volatility, the profitability of dealing strategies, and transaction
costs. Regulators must distinguish between them because they cannot have
any lasting effect on fundamental volatility, but they often can substantially
affect transitory volatility. Depending on the policies that regulators adopt,
they may decrease or increase transitory volatility.
We start this short chapter with discussions about the origins of the two
types of volatility. We then finish by considering how to distinguish between
them. Chapter 28 considers what regulators can do about volatility when it
appears excessive to them.
410


## Page 424

CHAPTER 20 VOLATILITY • 411
20.1 FUNDAMENTAL VOLATILITY
Since economies use prices to allocate resources, it is very important that
prices reflect fundamental values. Values change when the fundamental fac-
tors that determine them change. Prices therefore should change when peo-
ple learn that fundamental factors have unexpectedly changed. Such price
changes contribute to fundamental volatility.
When new information about changes in fundamental values is common
knowledge, prices may change without any trading. For example, suppose
that an unexpected killer frost descends upon Florida overnight. The morn-
ing news will undoubtedly report the event. The next day, orange juice fu-
tures contracts will open at a much higher price than the last price of the
previous trading day.
When only a few people know new information about changes in fun-
damental values, prices generally will change on high trading volumes. The
well-informed traders will trade on their information. The pressures their
trades put on prices will cause prices to change to reflect the new funda-
mental values.
Since informed traders generally hurt dealers, and since dealers generally
do not know when they trade with informed traders, dealers try to infer in-
formation about fundamental values from their order flows. The inferences
that they make contribute to the adverse selection spread component intro-
duced in chapter 13. Price changes due to the adverse selection spread com-
ponent thus contribute to fundamental volatility.
2.1.1 Fundamental Volatility Factors
Any factor that determines the value of a trading instrument can cause the
price of that instrument to change. For a commodity, the most important
factors are cash market supply and demand conditions. Other important fac-
tors are interest rates and storage costs. For a bond, the most important
factors are interest rates and the credit quality of the issuer. For a stock, the
most important factors are quality of management, the values of the com-
pany's resources and technologies, the supply and demand conditions in its
product markets and in its input markets, and interest rates. For currencies,
the important valuation factors include national inflation rates, macroeco-
nomic policies, and trade and capital flows. Unexpected changes in any of
these factors generate fundamental volatility in the instrument.
2.1.2 Predictability
Expected changes in fundamental factors generally do not change prices.
Informative prices usually fully incorporate all available information about
future values. Since people base their expectations on existing information,
fully informative prices will already incorporate expected changes in funda-
mental factors. When the expected event occurs, it is not surprising, and it
therefore should not cause prices to change. Only unexpected events cause
fundamental price volatility. Consequently, the identifying characteristic of
fundamental volatility in fully informative prices is unpredictable price
changes. An unpredictable price process is called a
random walk.
Chapter
10 provides a more complete explanation of the properties of fully inform-
ative prices.


## Page 425

412 • TRADING AND EXCHANGES
The one exception to this rule involves price changes that are necessary
to compensate instrument holders for their carrying costs and for bearing
risk. For example, the prices of zero-coupon bonds creep upward over time
as they approach maturity. Since they pay no interest, investors buy them
at substantial discounts to their face values. The creep in prices compen-
sates them for the interest payments that they would have received if they
had invested in a straight bond. These creeping price changes are fully pre-
dictable, and therefore do not contribute to fundamental volatility. Note,
however, that if interest rates unexpectedly fall, the prices of zero-coupon
bonds will immediately rise to reflect the new interest rates. This unexpected
price change would contribute to fundamental volatility.
2.1.3 Storage Costs
Commodities that are expensive to store are often quite volatile. The high
storage costs ensure that producers and distributors generally will not hold
large inventories. When demand exceeds supply, buyers can quickly deplete
inventories. Prices then spike up until new production can relieve the short-
age. Conversely, when inventories are large and new products will soon ar-
rive, distributors may greatly discount the inventory to make room for the
new arrivals.
Price volatility in high-storage-cost commodities depends on the time it
takes to adjust the flow of product from producers to consumers. If the pro-
duction pipeline is quite long, so that adjustments take a long time, prices
may be quite volatile.
Price volatility in high-storage-cost commodities also depends on de-
mand variation. When demand is highly variable, inventory imbalances may
often occur. Production and distribution may be unable to adjust as quickly
as demand changes. For low-storage-cost commodities, inventories gener-
ally buffer mismatches in the rates of production and consumption, so that
prices are more stable.
Finally, price volatility in high-storage-cost commodities also depends
on whether people can easily do without those commodities. If the demand
is highly
inelastic,
people will demand approximately the same quantities at
any price. Such goods often experience sharp price spikes when shortages
develop.
Perishable goods
are goods that become worthless if they are not used be-
fore they spoil or expire. The prices of perishable goods are often especially
volatile because they cannot be stored indefinitely. Where a surplus of soon-
to-perish goods exists, prices fall very quickly as their owners try to avoid
a complete loss. If a shortage of perishable goods develops, prices may rise
very quickly.
2.1.4 Fundamental Uncertainties
Uncertain knowledge about fundamental factors often causes substantial
fundamental volatility. The stocks of companies involved in technological
research tend to be highly volatile because their values depend critically upon
the outcomes of their research and upon the markets for products that
presently do not exist. Since even the best-informed traders have little in-
formation about these issues, the prices of technology stocks tend to vary
substantially when new information arrives or when analysts develop new
valuation models.
Gasoline, Diesel Fuel,
and Heating Oil
Volatility
Gasoline, diesel fuel, and
heating oil are expensive to
store because they require
very large tanks. The
available producer storage in
the United States amounts to
only 9 days of consumption
of gasoline and 18 days of
distillate fuels (heating oil
and diesel fuel). Since the
demands for these fuels are
highly inelastic, unexpected
fluctuations in demand
caused by weather, refinery
accidents, or changes in
the economy often cause
substantial variation in
the prices of these
commodities.
Source: Year 2000 consumption
and refinery working storage
capacity data obtained from the
Energy Information Administration,
U.S. Department of Energy, at
www.eia.doe.gov
.


## Page 426

CHAPTER 20 VOLATILITY • 413
Generally, companies with high price to earnings (P/E) ratios tend to
have volatile stocks. Most of these companies have high prices because peo-
ple expect that their earnings will grow substantially through time. These
growth expectations, however, generally depend on many future fundamen-
tal uncertainties. These uncertainties make high P/E stocks more volatile
than low P/E stocks.
Instruments that are subject to substantial political risks likewise are quite
volatile.
Political risks
are risks associated with government actions. For ex-
ample, the prices of sovereign debt bonds in emerging markets depend on
whether issuing governments will default on their debts and on whether
they will inflate their money supplies to depreciate the real values of their
bonds. The prices of firms in industries that may be subject to nationaliza-
tion or to substantial government regulation likewise are quite volatile. The
extreme example of political risk is war. Wars have completely destroyed the
capital assets of many countries throughout the ages. Although governments
generally can control the political risks upon which many security values de-
pend, they often choose not to, in favor of other objectives.
Highly leveraged firms tend to have very volatile stocks because the own-
ership of their assets is divided between bondholders and equity holders.
Since the equity holders must pay off the bondholders before they can ben-
efit from the assets, equity holders bear most of the volatility in the asset
values. Where there is little equity relative to debt, small changes in asset
values will cause large changes in equity values.
20.2 TRANSITORY VOLATILITY
Transitory volatility
results when the demands of impatient uninformed
traders cause prices to diverge from fundamental values. These price changes
are transitory because prices eventually revert to fundamental values.
The simplest form of transitory volatility is bid/ask bounce.
Bid/ask bounce
occurs when market order traders buy at the ask and sell at the bid. Their
Fundamental Volatility in Perishable Commodity Prices
Price changes for perishable commodities often display extreme negative
serial correlation because prices often spike up when shortages occur or
collapse when surpluses occur. A casual observer may attribute this
negative serial correlation to transitory volatility.
Such attributions, however, can be wildly mistaken. A sequence of spot
prices is not a sequence of prices for the same item. It is a sequence of
prices for a sequence of items that differ by their date of delivery. For
example, the spot price of fish on Monday is the price of fish for Monday
delivery. The Tuesday spot price of fish is for Tuesday delivery, and so on.
The sequence of spot prices can be highly negatively correlated when
storage costs are high. The negative correlation reflects variations in
fundamental factors over time as they affect delivery on different dates.
Many commodities have futures contracts written on them. These contracts
price the delivery of the commodity on a specific day. When the underlying
commodity is highly perishable, price changes in the futures contracts will
not have nearly as much negative serial correlation as will price changes in
the spot contract. Negative serial correlation in these contracts generally
will be due primarily to transitory volatility. Negative serial correlation in the
spot prices may be due either to changes in fundamentals across delivery
dates or to transitory volatility.
Electrifying Moments
in California
Electricity is the ultimate
perishable commodity
because it is extremely
expensive to store. Most
electricity is either used as it
is produced or lost forever.
The spot market for electricity
therefore is extremely
volatile. California
experienced an extreme
example of this volatility in
2000-2001 when the price
of electricity occasionally
spiked dramatically upward
for short periods when
people demanded more
electricity than generators
could supply.


## Page 427

414 • TRADING AND EXCHANGES
trades cause prices to bounce from bid to ask. These price changes reverse
when traders arrive on the other side of the market. The transaction cost
component of the bid/ask spread is responsible for bid/ask bounce. This
spread component—which is also called the transitory spread component—
therefore contributes to transitory volatility.
Large orders and cumulative order imbalances created by uninformed
traders also cause prices to move from their fundamental values. The price
changes reverse when value traders or arbitrageurs recognize that prices dif-
fer from fundamental values. Their trades then push prices back.
Transitory volatility includes both the price changes that impatient un-
informed traders cause and the subsequent reversals of those price changes.
Value traders, arbitrageurs, and dealers do not cause transitory volatility, but
they do contribute to its ultimate resolution.
Transitory volatility and the transaction costs of uninformed traders are
very closely correlated. The impacts that uninformed traders have on prices
are transaction costs that they bear. These price changes contribute to tran-
sitory volatility. Transitory volatility therefore is small in liquid markets.
Regulators are very concerned about transitory volatility because high
transitory volatility indicates that markets are illiquid. When volatility is
high, people often pressure regulators to intervene to decrease it. Before
doing so, regulators must be confident that the high volatility is due to the
transitory component of volatility and not to its fundamental component.
20.3 MEASURING VOLATILITY
AND ITS COMPONENTS
Total volatility
is the sum of fundamental volatility and transitory volatility
People generally measure total volatility by using variances, standard devi-
ations, or mean absolute deviations of price changes. The
variance
of a set
of price changes is the average squared difference between the price change
and the average price change. The
standard deviation
is the square root of
the variance. The
mean absolute deviation
is the average absolute difference
between the price change and the average price change.
Statistical models are necessary to identify and estimate the two com-
ponents of total volatility. These models exploit the primary distinguishing
characteristics of the two types of volatility: Fundamental volatility consists
of seemingly random price changes that do not revert, whereas transitory
volatility consists of price changes that ultimately revert. The transitory price
changes are generally correlated with order flows of uninformed liquidity-
demanding traders. Fundamental price changes may be correlated with or-
der flows of informed traders, but need not be.
The reversion of transitory price changes causes price changes to be neg-
atively correlated. In particular, increases tend to follow decreases and vice
versa, so that price reversals are more common than price continuations.
The presence of negative serial correlation in price series is therefore a strong
indicator of transitory volatility.
Transaction-induced negative serial correlation in price changes may ap-
pear over various horizons. Bid/ask bounce causes negative serial correla-
tion in transaction-to-transaction price changes. The price impacts of large
orders and of order imbalances generated by uninformed traders may cause
negative price change serial correlation measured over minutes, hours, days,
or even months.


## Page 428

CHAPTER 20 VOLATILITY • 415
Roll's Serial Covariance Spread Estimator Model
For readers who would feel unfulfilled learning economics without the
use of some abstract notation, I offer the following analysis of Roll's serial
covariance spread estimator model. (All other readers can safely skip this.)
Let
Pf
=
Price of trade
t
S =
Spread
V
1
=
Fundamental value at trade
t
Et =
Change in fundamental value
1 if trader at
t
is a buyer
(D
r
=
—1 if trader at
t
is a seller
Assume
that
•
Fundamental value follows a random walk so that the value innovation
Et
is independently distributed through time.
•
The value innovation r, has zero mean and variance u
2
.
•
The probability that the trader at t is a buyer is one-half.
•
The probability that the trader at
t
is a buyer is independent of whether
any previous trader was a buyer.
•
The probability that trader
t
is a buyer is independent of
et
and e
t

Let price at time
t
equal fundamental value plus or minus one-half of the
spread depending on whether the tth trader is a buyer or a seller:
P, = 1/
1
+
a
2
S,
so that the price change is
AP, =
V
t
+
AQ,
S
=
e,
+ AQ,
S. S.
These assumptions imply that the price change variance is
Var(AP,) =
E(e,
+ AQ,
2
5)
2

= EE? +
2EE
t
4Q,
2
S + E(Oa)
2
,1 S
2

=
0
.2
+0

52
E
(Q
t
_ Q
t 1
)2
= a-
2
+
n
5
2
(EW —
2Eaa
-
1 + EC4
= a-
2
+ S
2
(1 — 0 + 1)
=
0
.2

52
.
The two terms are the fundamental and transitory volatility components.
Roll showed that we can estimate the latter term from the expected serial
covariance. It is
SCov(AP,,
AP,
-1)
=
E(e,
+

S)(E,
1
+
AQ,
1
z
5)
= Ee,
Et-1 +
EE,AQ

S+

S+

,+
1
5
2

= 0 + 0 + 0 +
4
S
2
fla Qt - 1)(Qt- —

-2)
= 74
-
S
2
(Ea a
-
1
— Eaa -2 — Ea-1a-1 + Ea-1a-2)
=
S
2
(0 — 0 — 1 + 0)

=
-4
5
2
.

Inverting this expression gives
S =
2V
—
SCov(AP,,AP
t
_
1
).
Roll's serial covariance spread estimator substitutes the sample serial
covariance for the expected serial covariance in this last expression. 4


## Page 429

416 • TRADING AND EXCHANGES
The simplest statistical model that can estimate these variance compo-
nents is Roll's serial covariance spread estimator model. Roll analyzed this
simple model to create a simple serial covariance estimator of bid/ask
spreads. The model assumes that fundamental values follow a random walk,
and that observed prices are equal to fundamental value plus or minus half
of the bid/ask spread. Total variance in this model is therefore the sum of
variance due to changes in fundamental values and of variance due to bid/ask
bounce. In the model, the latter variance is proportional to the square of
the spread. The two components can be estimated from estimates of the to-
tal price change variance and of the serial covariance of price changes.
The main limitation of Roll's model is that it predicts that only adjacent
price changes will be negatively correlated. If the reversion of prices takes
longer than one transaction, price changes beyond the next one also will be
negatively correlated with the current price change. Variance component es-
timates based on Roll's model therefore underestimate transitory volatility.
More complex variance component models identify transitory volatility
by using various statistical methods that can decompose a series into a ran-
dom walk component and a mean-reverting component that may have neg-
ative serial correlation over many intervals. These methods are quite com-
plex, and well beyond the scope of this book.
20.4 SUMMARY
Traders pay close attention to volatility because price changes affect their
profits and losses. Periods of high volatility are highly risky to traders. Such
periods, however, also can present them with opportunities for great profits.
Regulators pay close attention to volatility because one form of volatility—
transitory volatility—is correlated with transaction costs. Regulators generally
try to create liquid markets that produce highly informative prices. High
volatility suggests to them—and to many others—that markets need to be
fixed. We discuss the regulatory responses to extreme volatility in chapter 28.
20.5 SOME POINTS TO REMEMBER
•
Fundamental volatility is due to unexpected changes in fundamental
valuation factors.
•
Fundamental price changes are correlated with volume when only a
few traders know new information about fundamental values. When
such information is common knowledge, prices can change on little or
no volume.
•
Fundamental volatility may be scary, but it is necessary for the effi-
cient allocation of resources.
•
Prices must change as the world changes if they are to reflect all cur-
rent information about instrument values.
•
Transitory volatility consists of price changes caused when impatient
uninformed traders seek liquidity.
•
Transitory volatility and transaction costs are closely related. Both are
high in illiquid markets.
•
The price changes associated with transitory volatility tend to revert.
Price reversion causes negative correlation in a price change series.
•
Transitory volatility is identified by the negative serial correlation due
to price reversals.


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CHAPTER 20 VOLATILITY • 417
20.6 QUESTIONS FOR THOUGHT
1.
What are the relations among liquidity, transaction costs, and transi-
tory volatility?
2.
To which volatility components do the adverse selection and trans-
action cost spread components contribute?
3.
To which volatility component do the price impacts of news traders
contribute? To which volatility component do the price impacts of
value traders contribute?
4.
What causes volatility to vary over time? How would you measure
time-varying volatilities?
5.
Why are absolute price changes correlated with volumes? Under what
circumstances would you expect the correlation to be strongest?
6.
Suppose that people trade—and prices change—whenever news ar-
rives in the market. If the number of news events occurring each day
were constant, would absolute price changes and volume be corre-
lated? How would your answer be different if the number of news
events varied each day?
7.
One interpretation of episodic volatility is that it occurs when the flow
of information quickly increases. If this were a complete explanation
of episodic volatility, would we still care about episodic volatility?
8.
How would Roll's serial covariance spread estimator be different if
the spread were a random variable with a constant mean and vari-
ance, distributed independently through time and independently of
all other variables?


## Page 432

Part UI
1>
Evaluation
and
Prediction
n the next two chapters, we consider how traders measure and predict
portfolio performance. Traders need to monitor their performance so that
they can determine what they are doing well and what they are doing
poorly. They then can better manage their trading. As a rule, you cannot
manage what you cannot measure.
How well a portfolio performs depends on the instruments that are in
the portfolio and upon the costs of constructing and maintaining the port-
folio. The problem of choosing the best instruments to maximize portfolio
performance is the
portfolio selection/composition problem.
The problem of im-
plementing portfolio composition decisions is the
portfolio implementation
problem.
Traders must obtain good solutions to both problems in order to
perform well.
In practice, few profit-motivated traders consistently outperform the
market. Most active traders lose because they trade too much and because
they pay too much to trade. The costs of trading eventually overwhelm any
informational advantages they may have. Traders therefore must understand
their trading costs.
In chapter 21, we focus first on measuring and predicting implementa-
tion performance. For most traders, the portfolio implementation problem
is easier to solve than the selection/composition problem. Because most
traders cannot consistently outperform the market, the implementation
problem is their more important problem. In chapter 22, we consider why
superior selection/composition performance is difficult to achieve and even
more difficult to predict.
419


## Page 433

21
I>
Liquidity
and
Transaction
Cost
measurement
T
raders pay attention to their transaction costs because transaction costs
make implementation of their trading strategies expensive. Transaction
costs are most important to traders who trade frequently or who trade large
sizes. For most active traders, transaction costs are the most significant de-
terminants of their total returns. Speculators who perform poorly usually
do so because their transaction costs exceed the values of their trading
strategies.
Traders measure their transaction costs to evaluate how well they and
their brokers have implemented their trading strategies. Traders must eval-
uate implementation in order to manage it effectively. They must know
whether they have been trading too aggressively—or not aggressively
enough—to optimize their order submission strategies. They also must know
how well their brokers work on their behalf to decide which brokers should
receive their orders in the future.
Traders also estimate future transaction costs to predict the costs of im-
plementing various trading strategies. Clever strategies may not be prof-
itable if the costs of implementing them are too great. Transaction cost pre-
diction especially concerns large traders in illiquid markets. Their strategies
may be profitable if implemented in small size, but the price impacts of im-
plementing them in large size may cause them to lose on net.
Transaction cost measurement also interests exchanges, brokers, regula-
tors, and investment sponsors for the following reasons:
•
Exchanges conduct transaction cost measurement studies to document
the quality of their markets. They use the results in their marketing
efforts. They may also use them to evaluate their brokers, dealers, and
specialists.
•
Brokers conduct transaction cost measurement studies to document
their performance. They use the results to identify their shortcomings,
to market their firm's services, and to confirm that they obtain best ex-
ecution for their clients. The last purpose is especially important when
dealers pay brokers to route orders to them. Government regulations,
exchange regulations, and common law require that brokers ensure that
payments for order flow arrangements do not hurt their clients. Bro-
kers therefore must regularly and rigorously examine execution qual-
ity to ensure the most beneficial terms for their customers' orders.
•
Investment sponsors must ensure that they obtain value for the com-
missions that their investment managers spend on their behalf. The U.S.
Department of Labor requires that pension funds covered by the Em-
ployee Retirement Income Security Act (ERISA) recognize that trad-
ing commissions are fund assets that they must conserve. Fund trustees
therefore conduct transaction cost measurement studies to determine
whether their funds obtain appropriate value for their commissions.
420


## Page 434

CHAPTER 21 LIQUIDITY AND TRANSACTION COST MEASUREMENT • 421
• Regulators often try to promote policies that lower transaction costs.
Regulators therefore conduct transaction cost measurement studies to
characterize the performance of various market structures. The U.S.
Securities and Exchange Commission now requires that all market
centers—exchanges, ECNs, and dealers—collect and publish highly
disaggregated data that traders can use to evaluate average execution
quality for various order types and sizes.
We consider how to measure liquidity in this chapter. We will examine
both retrospective and prospective measures of transaction costs. We con-
sider first retrospective measures of transaction costs. We then consider how
traders use information about past transaction costs to predict future trans-
action costs.
21.1 TRANSACTION COST COMPONENTS
Defining and measuring exactly what we mean by the term "transaction
costs" is difficult. This entire book is about understanding what transaction
costs are, where they come from, and how to measure them. We explore
these questions in detail throughout this book.
For our present purpose,
transaction costs
include all costs associated with
trading. These costs include
explicit costs, implicit costs,
and
missed trade
op-
portunity
costs.
Explicit transaction costs
are all costs that a cost accountant would easily
identify. These costs include commissions paid to brokers, fees paid to ex-
changes, and taxes paid to government. Explicit transaction costs also in-
clude any resources that traders devote to the trading process. For example,
the costs of setting up, staffing, and running a buy-side trading desk are ex-
plicit costs of trading.
Implicit transaction costs
are the costs of trading that arise because traders
generally have an impact upon prices. For example, traders who buy at ask-
ing prices and sell at bid prices pay the bid/ask spread when trading. The
spread is an obvious and important cost of trading. Likewise, when large
buyers push prices up and large sellers push prices down, the price impacts
of their trading are transaction costs.
Missed trade opportunity costs
arise when traders fail to fill their orders or
fail to fill their orders in a timely manner. Suppose that a speculator wants
to buy 100 cotton futures contracts at the New York Board of Trade when
the price is 65 cents per pound. In an effort to obtain a good price, the
trader submits a buy limit order with a limit price of 64.95 cents. The price
of cotton subsequently rises to 68 cents, and the order does not execute.
Had the trader traded more aggressively and filled the order at an average
price of 65.25 cents, he would have made 2.75 cents per pound, or 1,375
dollars for each 50,000-pound contract. Because the trader failed to trade
aggressively, he lost the opportunity to make 137,500 dollars. Traders need
to keep track of their opportunity costs so that they can determine whether
they are trading aggressively enough.
Explicit transaction costs are the most easily measured of the three types
of transaction costs. Measuring them is a simple cost accounting exercise in
which the analyst identifies and sums all commissions, fees, and explicit
expenses associated with trade process.


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422 •
TRADING AND EXCHANGES
The Flip Side
Transaction costs concern
everyone in the trading
industry. Sell-side
institutions—brokers, dealers,
and exchanges—try to sell
low-cost transaction services.
Buy-side institutions try to
obtain transaction services at
low cost. To a casual
observer, it would appear
that everyone wants low
transaction costs.
Not so. Transaction costs
to the buy side are revenues
to the sell side. Sell-side
institutions would like their
revenues to be as high as
possible. They market low-cost
transaction services only
because they compete with
each other for buy-side
business.
These comments suggest
that sell-side institutions benefit
from high transaction costs.
While this might be true in the
short run, it has not been true
in the long run. Decreases in
transaction costs have caused
buy-side traders to greatly
increase the volume of their
trading. The increased volume,
coupled with substantial
decreases in the costs of
providing transaction services,
have increased sell-side profits
even as buy-side transaction
costs have fallen.
Implicit transaction costs and missed trade opportunity costs are harder
to measure because they require some benchmark against which to compare
trade and no-trade prices. To measure the price impact of a completed trade,
analysts must estimate what prices would have been if the trade had not
taken place. To measure the opportunity cost of an uncompleted trade, an-
alysts must estimate the average prices at which the trade would have taken
place if it had been completed. These estimation problems make transac-
tion cost measurement a difficult and imprecise science.
21.2 IMPLICIT TRANSACTION COST
ESTIMATION METHODS
Traders estimate implicit transaction costs by using
specified price benchmark
methods
and
econometric transaction cost estimation methods.
The price bench-
mark methods are the most commonly used. They are easier to implement
than the econometric methods and generally more useful when traders need
to evaluate transaction costs for specific trades. The econometric methods
are most useful for estimating average transaction costs for a whole market.
Most traders measure transaction costs relative to specific price bench-
marks. The price benchmark provides a basis for determining whether buy-
ers paid, and sellers received, good or bad prices.
When traders use a specified price benchmark, they estimate the per unit
transaction cost as the difference between the trade price and the bench-
mark price. For a purchase, the estimated cost is the excess of the trade price
over the benchmark price. For a sale, it is the opposite. They then multiply
this difference by the trade size to obtain the estimated transaction cost:
Estimated Cost =
Trade Size X Trade Price — Benchmark Price for a purchase
Benchmark Price — Trade Price for a sale
Estimated transaction costs thus are high when buyers pay high prices and
when sellers receive low prices.
Note that the estimated transaction costs for all buyers and sellers in a
trade sum exactly to zero. Transaction cost to one side is trading profit to
the other side. Traders who demand liquidity tend to pay transaction costs
and those who offer liquidity have negative transaction costs.
For convenience, the difference between the trade price and the bench-
mark price is often called the
signed difference,
where the sign of the differ-
ence is understood to be 1 if the trade is a purchase and —1 if the trade is
a sale:
Estimated Cost =
1
where Trade Sign = _
Trade Size X Trade Sign
X (Trade Price — Benchmark Price)
for a purchase
for a sale
An ideal price benchmark would be the price that would have prevailed
if the trader had not tried to trade. The difference between this price and
the trade price would be entirely due to the trade, and therefore a good es-
timate of the implicit cost of trading. Unfortunately, no one can confidently
specify such a price. Instead, traders commonly use a volume-weighted av-
erage price; the opening price; the closing price; an average of the open,


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CHAPTER 21 LIQUIDITY AND TRANSACTION COST MEASUREMENT • 423
high, low, and closing prices; or an average of bid and ask prices near the
time of the trade. We discuss the virtues and drawbacks of each of these
benchmarks in the next section.
Econometric transaction cost estimation methods
use statistical methods to
estimate transaction costs. The simplest econometric methods extract in-
formation about transaction costs from price reversals that traders cause
when they have an impact on price. More complex econometric models ex-
tract information about transaction costs from the relation between executed
orders and price changes. We can interpret both types of methods as bench-
mark methods in which the analyst estimates the benchmark instead of spec-
ifying it.
21.2.1 Trade Side Identification
Traders who conduct transaction cost measurement studies usually have a
set of trades in which they participated for which they want to measure their
transaction costs. They therefore know whether they were the buyer or the
seller for each trade.
Analysts sometimes want to estimate transaction costs for trades in which
they did not participate. Since all trades have at least one buyer and one
seller, such analysts must identify the side of the trade in which they are in-
terested. (Ignoring commissions, the sum of the costs on both sides is al-
ways zero.) They typically direct their interest exclusively to the side that
appears to be taking liquidity.
Analysts typically identify that side by the relation between the trade
price and the bid and asking prices. If the trade price is closer to the bid,
they assume that the buyer was the aggressive trader. If it is closer to the
asking price, they assume the seller was the aggressive trader. If the trade
price was exactly in the middle between the bid and the ask, they look to
the last price change. If the trade took place on an uptick or a zero uptick,
they identify the trade as initiated by an aggressive buyer. Otherwise, they
identify it as seller-initiated. This procedure is commonly known at the
Lee
and Ready algorithm
after the two academic researchers who popularized it.
Analysts who use the Lee and Ready algorithm to measure transaction
costs must recognize its two major shortcomings. First, it causes analysts to
estimate higher transaction costs than most traders incur because most
traders do not exclusively demand liquidity. Second, it cannot identify when
an order has been filled with multiple trades. If the trades are at different
prices because the order had market impact, the cost of filling the order will
be underestimated. We discuss this problem further below.
21.3 MEASURING TRANSACTION COSTS
WITH SPECIFIED PRICE BENCHMARKS
In this section, we introduce and discuss methods for measuring implicit
transaction costs by using various specified benchmarks. Our presentation
starts with a description of the various benchmarks. Then we consider the
properties of the resulting estimators.
21.3.1 Benchmark Prices
Many traders estimate the cost of trading by the signed difference between
the trade price and a
quotation midpoint.
The
quotation midpoint
is the av-
erage of the bid and ask prices in a quotation.
Money Flow
Technical traders use a
version of this trade
identification procedure when
computing
money flow
indicators.
Money flow
is
volumes
on upticks minus
volumes on downticks.
Technical traders believe that
money flow indicates whether
aggressive traders are net
buyers or sellers.
Passive-Aggressive
Behavior in the
Markets
Analysts generally classify
traders who offer standing
limit orders as passive
traders. They wait for the
market to come to them.
However, they also may be
very aggressive traders.
For
example, a buyer can
peg
the market
at his bid as long
as he can afford to buy at
that price. Although such
traders may very aggressively
accumulate or divest
positions, the Lee and Ready
algorithm will classify them as
passive traders.


## Page 437

424 • TRADING AND EXCHANGES
Traders obtain different transaction cost estimates according to which
quotation midpoint they use. The quotation midpoint that prevailed at the
time of the trade produces a transaction cost estimate that analysts call the
effective spread
(or sometimes the
liquidity premium).
Post-trade quotation midpoints produce
realized spreads.
Analysts most
commonly compute realized spreads using quotation midpoints obtained 5,
10, 15, or 60 minutes after the trade.
Analysts also use pre-trade quotation midpoints. The most common
transaction cost estimator based on a pre-trade quotation midpoint uses the
quotation midpoint at the time the portfolio manager decided to trade. An-
alysts usually call this method
Perold's implementation shortfall
(after Andre
Perold, who popularized it in an influential 1988
Journal of Portfolio Man-
agement
article). Jack Treynor, writing seven years earlier, called it the
method
of paper porolios.
Traders also estimate their transaction costs by using various dailyprices.
The most common daily benchmark is the
volume-weighted average price
(VWAP). The VWAP is the average trade price of the day where each trade
price is weighted by the size of the associated trade. Traders like the VWAP
benchmark because they would like to trade at least as well as the average
Consultant Benchmarks
Several investment consultants compute transaction cost estimates for their
clients. The consultants generally use different price benchmarks.
Abel/Noser first popularized VWAP transaction cost estimates.
Able/Noser is a U.S. discount institutional stockbroker. The firm started to
measure transaction costs to show its clients that it could obtain good
execution prices for discounted commissions.
SEI popularized transaction cost estimates based on closing price
benchmarks. SEI provides investment consulting, investment software, and
mutual fund management.
The
Plexus Group computes implementation shortfall transaction
cost
analyses. Its clientele consists primarily of investment sponsors and
investment managers who want to optimize their trade implementation or
who need to demonstrate that they are not wasting their commissions. The
Plexus Group primarily provides transaction cost analyses and trade process
consulting.
The Transaction Auditing Group (TAG) computes effective spread
(liquidity premium) analyses, among many other transaction audit functions.
Their clientele consists primarily of broker-dealers who need to demonstrate
to regulators and clients that they are obtaining best execution for their
clients.
The Elkins/McSherry division of State Street computes transaction cost
estimates primarily by using an average of the daily opening, high, low, and
closing prices as the benchmark price. Their clients consist mostly of pension
funds and investment managers who are interested in transaction cost
comparisons across the
42 countries
in the Elkins/McSherry universe.
For more information, browse:
www.AbelNoser.com

www.SEIC.com

www.PlexusGroup.com

www.TAGaudit.com

www.Elkins-McSherry.com



## Page 438

CHAPTER 21 LIQUIDITY AND TRANSACTION COST MEASUREMENT • 425
trader on that day. The VWAP is computed most easily by dividing the to-
tal dollar value of all trades by the total trading volume:
Dollar Volume

Trade Size,Price,
VVVAP —

— w,Price,
Trade Volume

Trade Sizet
Trade Size,
where weight w, =
Trade Volume

. Traders also use the daily opening price,
the daily closing price, or the average of the daily open, high, low, and clos-
ing prices as benchmark prices.
21.3.1.1
Effective Spreads
The signed difference between the trade price and the time-of-trade quo-
tation midpoint is an intuitively simple transaction cost estimate. This
method exactly measures the implicit cost of trading a round-trip when the
quotation midpoint does not change. For example, if a trader buys at the
ask and then sells the same quantity at the bid, the trader will have done
nothing but trade. His per unit loss for the two-trade round-trip is the
bid/ask spread. The cost of trading per trade therefore is half of the bid/ask
spread. The quotation midpoint benchmark gives us this result: The cost of
the purchase is the ask minus the quotation midpoint, or half of the spread.
Likewise, the cost of the sale is the quotation midpoint minus the bid, which
is also half of the spread.
The
liquidity premium
is the signed difference between trade price and
the time-of-trade quotation midpoint. The
effective spread
is twice the liq-
uidity premium. The effective spread equals the quoted bid/ask spread when
all purchases take place at the bid and all sales take place at the offer. When
trades take place within the spread because dealers or brokers arrange
price
improvement,
the effective spread that traders pay is smaller than the quoted
spread. Likewise, when large orders fill at prices outside the bid/ask spread,
the effective spread is greater than the quoted spread.
The effective spread is the transaction cost estimation method that re-
tail market order traders most commonly use. Retail traders primarily com-
pare their trade prices against the bid and offer prices that prevailed when
they submitted their orders. Most such traders are unaware that they en-
gage in transaction cost analyses. They simply want to check whether they
are receiving good prices.
21.3.1.2
Realized Spreads
The
realized spread
is twice the signed difference between the trade price
and the quotation midpoint observed at some specified time following the
trade. Realized and effective spreads are equal when the quotation midpoint
does not change over the measurement interval. Prices often change, how-
ever, when traders raise prices in response to aggressive buyers or lower prices
in response to aggressive sellers. Realized spreads therefore tend to be smaller
than effective spreads.
Realized spreads interest dealers because their profits depend on the
prices at which they establish their positions and the prices at which they
subsequently liquidate their positions. The spreads that dealers actually re-
alize are less than their quoted spreads because they often provide price im-
provement and because they sometimes trade with informed traders. The


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426 • TRADING AND EXCHANGES
difference between quoted spreads and effective spreads measures the price
improvement that dealers provide. The difference between effective spreads
and realized spreads measures dealers' losses to well-informed traders.
21.3.1.3 Implementation Shortfalls
We can interpret implementation shortfalls as the difference in values be-
tween an actual portfolio and a corresponding paper portfolio. A
paper port-
folio
is an imaginary portfolio that people construct on paper to see what
would have happened if they had actually traded. People analyze paper port-
folios for fun, to test new trading ideas, and to measure transaction costs.
To measure transaction costs, traders must specify a benchmark price at
which they buy or sell instruments for their paper portfolios. The quotation
midpoint at the time they decide to trade produces an easy-to-interpret mea-
sure of transaction cost. The midpoint quotation price represents a naive
best estimate of instrument value at the time they decide to trade. The dif-
ference in value between their actual portfolio and the corresponding paper
portfolio measures the costs of implementing their trading decisions rela-
tive to this benchmark. Since implementation generally is costly, paper port-
folios typically are more valuable than the corresponding actual portfolios.
Analysts break the total implementation shortfall into components. The
breakdown depends on whether the order was filled. If a trade occurred, the
shortfall is the total trade size times the signed difference between the av-
erage trade price and the quotation midpoint at the decision time. If the
trade did not take place, or if the order was not completely filled, the short-
fall is the unfilled size multiplied by the difference between the current price
and the benchmark price. The first component estimates the transaction
cost of completed trades. The second component estimates the missed trade
opportunity cost.
When they are constructing paper portfolios to evaluate new trading
ideas, traders make assumptions about transaction costs that are often crit-
ical. If they assume costs that are too low, they may adopt unprofitable trad-
ing strategies. If they assume costs that are too high, they may reject oth-
erwise profitable trading strategies. Traders typically assume that they pay
the quotation midpoint plus some premium when buying. They likewise as-
sume that they receive the quotation midpoint less some discount when sell-
ing. They obtain the premiums and discounts that they use for these analy-
ses from transaction cost studies.
21.3.1.4 VWAP, Opening Prices, and Closing Prices
Many investment sponsors do not know when during the day that the trades
made on their behalf by their investment managers took place. Unless they
make special arrangements with their brokers and with their investment man-
agers, they typically receive only daily reports of the share-weighted average
prices of the trades they made that day. These investment sponsors therefore
can conduct transaction cost analyses only with daily price benchmarks.
The daily market volume-weighted average price is an attractive trans-
action cost benchmark to such investment sponsors because it allows them
to determine whether they received a higher or lower price than the aver-
age trader that day. Transaction costs measured relative to opening and clos-
ing prices likewise allow traders to compare their trade prices against the
prices that prevailed before and after their trades. We shall see below that


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CHAPTER 21 LIQUIDITY AND TRANSACTION COST MEASUREMENT • 427
all three measures have serious problems which complicate their interpre-
tations as transaction costs estimators.
21.4 PROPERTIES OF TRANSACTION COST
PRICE BENCHMARK ESTIMATORS
In this section, we enumerate desirable properties for transaction cost estima-
tors and consider which of the benchmark estimators have these properties.
21.4.1 Data Requirements
Transaction cost estimates should be easy to compute. Estimates that com-
pare daily trade summaries against daily price benchmarks are easy to com-
pute. Estimates that are based on quotation midpoints are more expensive
because they require information about intraday trades and quotations.
21.4.2 Accuracy
Random events that are completely unrelated to the implementation of a
trade should not affect the transaction cost estimate for that trade. For ex-
ample, suppose that a buyer negotiates an excellent purchase price for a stock
early in the morning. Later in the day, the market learns terrible news about
the firm and the price drops significantly. If an analyst measures the trans-
action cost relative to the low closing price, the trader will appear to have
negotiated a very poor price. Statisticians call such estimates noisy. In this
example, if the transaction cost was measured relative to the opening price,
it would have been much more accurate.
In general, the greater the time between the trade and the determina-
tion of the benchmark price, the noisier the transaction cost estimator will
be. Transaction costs based on opening or closing prices therefore are nois-
ier than transaction costs based on average prices. All daily benchmarks,
however, are noisy because they use the same benchmark prices for all trades
that take place within the day. The least noisy transaction cost estimator is
the effective spread because it uses a contemporaneous price benchmark.
The noise in realized spreads and in implementation shortfalls increases the
further in time the benchmark price is from the trade price.
21.4.3
Trade Timing
Issues
Many traders use transaction cost estimates to evaluate how well their bro-
kers fill their orders. When managers give their brokers discretion over the
timing of their trades, they expect that their brokers will try to trade when it
is most advantageous. In particular, they hope that their brokers will use their
experience, and the information available to them, to recognize and exploit
predictable short-term price moves. For example, they hope their brokers will
recognize that prices tend to be high after uninformed traders have bought.
Under such conditions, brokers with timing discretion should immediately
execute sell orders. Those with buy orders may wait until prices fall.
Traders who give their brokers timing discretion must pay close atten-
tion to their transaction costs to determine whether their brokers use their
discretion appropriately. Since brokers generally are paid commissions only
for completed trades, brokers may prefer to complete trades rather than wait
for the best time to trade.
Good transaction cost estimators should produce information that al-


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428 • TRADING AND EXCHANGES
lows traders to identify whether their brokers are skilled trade timers. The
effective spread estimator is nearly useless for this purpose because it mea-
sures transaction cost only relative to the current quotation midpoint. If bro-
kers can recognize any short-term predictability in the quotation midpoint,
this estimator will not reveal it.
In general, a transaction cost estimator will best measure trade-timing
effects when the benchmark price does not depend on the time of the trade.
When it depends on the time of trade—as it does for the realized spread—
the estimator will best measure trade-timing effects when the interval be-
tween the trade and the benchmark price is long.
Unfortunately, trade-timing considerations run exactly counter to esti-
mator accuracy considerations. Accurate estimates require close benchmark
prices, while estimates with the power to discover trade timing require dis-
tant price benchmarks. Analysts therefore must analyze many trades to ac-
curately measure trade-timing effects.
21.4.4 Estimator Biases
Transaction cost estimates should be unbiased. They should measure only
costs of implementing a trading strategy given current market conditions.
Biased transaction cost estimators often produce cost estimates that depend
on how or why the trade is made. Biases may arise when traders split their
orders, when their decisions to trade depend on past price changes, when
they are well informed about future price changes, and when brokers know
that their clients will use transaction cost estimates to evaluate their trading.
21.4.4.1
Split Orders
Transaction cost estimates should measure the total transaction cost of an
order that traders fill in multiple parts. Traders often split their large orders
to avoid showing the market the full size of their interest. They also split
large orders to price-discriminate as they push prices up or down. Accord-
ingly, the last part of the order usually is the most expensive to execute. The
transaction cost estimation method should estimate the total cost of exe-
cuting the entire order and not just the sum of the apparent costs of
executing each piece considered separately.
The liquidity premium (effective spread) is a poor transaction cost esti-
mator for large trades that traders split into small parts. For example, sup-
pose that a trader splits a 4,000-share buy order into two equal parts. The
first 2,000 shares trade at 30.10 when the market quotation is 30 bid, 30.10
offered. The next 2,000 shares trade at 30.20 when the market quotation is
30.10 bid, 30.20 offered. The liquidity premium transaction cost per share
for both trades is 0.05, or 200 dollars. The second trade, however, was more
expensive than the first because the first had market impact. The impact of
the first trade raised the bid and the offer associated with the second trade.
The VWAP transaction cost estimator also may poorly estimate the cost
of executing a large order.
In
the previous example, suppose that the trader
was the only buyer of the instrument that day. The VWAP transaction cost
estimate then would be zero (ignoring commission costs) because the aver-
age price of the trader's purchases is exactly equal to the average price of all
market trades that day. Although the buyer paid the bid/ask spread and had
market impact, the VWAP estimator will not identify any costs.


## Page 442

CHAPTER 21 LIQUIDITY AND TRANSACTION COST MEASUREMENT • 429
In general, the split order problem arises when the price benchmark de-
pends on the trades that fill the order. Since split orders tend to change sub-
sequent quotation midpoints, the effective spread estimator underestimates
total transaction costs. Likewise, since the price impacts of split orders af-
fect the volume-weighted average price, the VWAP transaction cost esti-
mator also underestimates total transaction costs.
The split order problem does not affect estimators—such as the imple-
mentation shortfall estimator—that use benchmark prices which are deter-
mined before the order has an impact on market prices. In our example,
suppose that the decision to trade was made when the quotation midpoint
was 30.05 (the first quotation midpoint). The implementation shortfall es-
timate of the total cost of filling the order would be 5 cents per share for
the first trade and 15 cents per share for the second trade. The total cost of
filling the order with the two equal sized trades therefore would be 10 cents
per share, or 400 dollars.
The split order problem most seriously affects estimators based on bench-
marks that are significantly affected by the market impact of the large or-
der. In our example, suppose that the market closes at 30.20, the price of
the second trade. Using the closing price as the benchmark price produces
estimated transaction costs of —10 cents per share for the first trade and
zero cents for the second trade, for a total of —200 dollars. Although the
trader paid the bid/ask spread and had an impact on price, the closing price
estimator makes it appear that he had negative trading costs.
When properly estimated, negative transaction costs are trading profits.
The trader in our example made a 200 dollar unrealized profit. If the trader
tried to realize the profit, however, he probably would have had a net loss.
For example, suppose that the trader closed his position by selling 2,000
shares at 30.10 when the market quotation was 30.10 bid, 30.20 offered,
and then sold another 2,000 shares at 30.00 when the market quotation had
dropped to 30.00 bid, 30.10 offered. Since the trader sold all shares for 10
cents less than he bought them, his total loss for the round-trip would have
been 10 cents per share or 400 dollars.
21.4.4.2 Momentum and Contrarian Traders
Transaction costs measured relative to some benchmarks may be systemat-
ically high or low, depending on whether the trader pursues
momentum
or
contrarian trading strategies.
Traders who use
momentum trading strategies
buy
after prices rise and sell after prices fall. Traders who use
contrarian trading
strategies
do the opposite.
Transaction cost estimates based on opening prices are particularly bi-
ased when traders use momentum or contrarian trading strategies. Mo-
mentum traders overestimate their transaction costs because they buy when
prices have risen, so that the opening price benchmark is low. They like-
wise sell when the opening price benchmark is high. Conversely, contrari-
ans underestimate their transaction costs because they buy when the open-
ing price benchmark is high, and they sell otherwise.
Transaction cost estimates generally are biased when traders base their
trading decisions on price changes that take place after the price bench-
marks against which their trades will be measured have been determined.
Transaction costs based on opening prices and—to a lesser extent—those


## Page 443

430 • TRADING AND EXCHANGES
based on the VWAP therefore produce biased results when used by mo-
mentum and contrarian traders.
21.4.4.3 Informed
Traders
Prices tend to rise after well-informed traders buy and fall after they sell.
Transaction costs that are based on price benchmarks which follow their
trades therefore will underestimate their trading costs. For example, in-
formed traders tend to measure smaller realized spreads than effective
spreads. The difference is due to their successful speculations and not to the
quality of their trade implementation.
The informed trader bias generally is greatest when the benchmark price
is determined long after the trade takes place. The closing price and—to a
lesser extent—the VVVAP benchmarks therefore underestimate transaction
costs for well-informed traders. The underestimation is most acute when
the informed traders trade on short-term information that quickly becomes
public.
21.4.4.4 Gaming
When traders use transaction cost estimates to evaluate their brokers, the
brokers should not be able to manipulate the results by trading in ways that
are not in the traders' best interests. Brokers who can affect their evalua-
tions without delivering better prices to their clients are able to
game the
measure.
Brokers who
game
their evaluations arrange trades to optimize their
evaluations rather than to provide best execution services to their clients.
Brokers who have discretion over how aggressively they fill their orders
can easily game the effective spread transaction cost estimator. To game this
measure, brokers always offer liquidity and never take it. They then always
buy at the bid or sell at the offer. If the market moves away from their or-
ders, they adjust their order prices and try again to buy at the bid or sell at
the offer. By only offering liquidity, these brokers ensure that their estimated
transaction costs will always be negative. Unfortunately, they also fail to
trade at prices that their clients would have preferred. Brokers who con-
stantly chase after the market provide very poor service to their clients, es-
pecially if their clients are impatient to trade.
Brokers can game the effective spread estimator because the benchmark
price depends on when the order executes. Every time the market moves
away from the order, the broker fails to obtain a good price for the client.
The broker, however, is not penalized for waiting because the benchmark
price moves with the market.
Brokers who have discretion over the timing of their trades can game
any measure of transaction costs for which the benchmark price depends on
the timing of their trades. For example, suppose that a broker knows her
client will evaluate her trades by using an opening price benchmark. Near
the end of the day, the broker receives an order to buy. She will immedi-
ately fill the order at any reasonable price if prices have fallen during the
day. If prices have risen, she will try to hold the order until the next day, so
that she does not record a high-priced buy on a day when the market opened
much lower.
This gaming problem is most serious when sponsors measure transac-
tion costs relative to opening prices. To a lesser extent, it also arises for the


## Page 444

CHAPTER 21 LJQUIDITY AND TRANSACTION COST MEASUREMENT • 431
VWAP benchmark and other average price benchmarks that are partially
determined by when the broker decides when to trade.
Measuring average price benchmarks over long intervals can mitigate
gaming problems. For example, clients can compare their trade prices against
the VWAP for the week surrounding their trades rather than only for the
day of their trades. This procedure reduces the benefit to the broker of de-
laying a trade to obtain a new benchmark. Unfortunately, it also increases
the noise in the transaction cost estimates.
Brokers with order timing discretion also can game transaction cost mea-
sures by timing their trades to coincide with the determination of their bench-
marks. For example, brokers whose clients evaluate their trades by using a
VWAP benchmark can ensure that their estimated transaction costs will be
very near zero by spreading the execution of their orders over the course of
the day so that their volume-weighted average trade price is approximately
equal to the market VWAP. Likewise, brokers whose clients evaluate their
trades by using a closing price benchmark can execute their orders only at
the close so that they will have zero transaction cost estimates.
The gaming problem cannot arise when the benchmark price is firmly
determined before the broker receives the order. The implementation short-
fall therefore cannot be gamed.
When brokers blatantly game their evaluations, clients will easily recog-
nize it. They then can appropriately discipline their brokers. Clever brokers
can avoid this discipline by gaming judiciously. The gaming problem there-
fore may affect all measures that can be gamed. The size of the problem
will depend on the difficulty of detection and on the propensity of the bro-
ker to exploit the game.
Few brokers game their measures even when they can do so without de-
tection. Most brokers work honestly on behalf of their clients to provide the
best service possible, regardless of how they are measured. They may advise
their clients, however, when they make trades that are in their clients' in-
terest but will generate unusually large estimated transaction costs.
21.4.5 Summary
Which price benchmark analysts use when evaluating implementation per-
formance determines what they measure. Analysts who use the quotation
midpoint at the time of the trade measure whether the quality of the trade
execution was good relative to contemporaneous quoted prices. They learn
nothing, however, about the cumulative market impact of split orders. They
also learn little about whether traders can exercise valuable timing discre-
tion. Analysts who use price benchmarks obtained long after the trade may
learn about these issues, but they also measure whether portfolio managers
made good portfolio composition decisions. Analysts who use price bench-
marks obtained long before managers decided to trade are immune to the
split order problem, but they also measure the degree to which their port-
folio composition decisions depend on past prices.
No price benchmark is perfect for estimating implicit transaction costs.
Analysts must make trade-offs between estimation cost and various esti-
mator properties. When data acquisition costs are no consideration, the im-
plementation shortfall is the best transaction cost estimator. It is not sub-
ject to any of the biases discussed above. When the costs of collecting data


## Page 445

432 • TRADING AND EXCHANGES
are significant, traders may prefer other estimators. Retail traders typically
use the effective spread transaction cost estimator because it is generally un-
biased for small orders. Institutional investment sponsors often use VWAP
when they do not know when their managers ordered their trades. The
VWAP transaction cost estimator has some bias, gaming, and estimation
noise problems, but for most traders these problems are less significant than
those found for the opening and closing price benchmarks. Table 21-1 pro-
vides a summary of the properties of price benchmark transaction cost
estimators.
All transaction cost estimation methods produce noisy results when ap-
plied to single trades. Average transaction costs, measured over many trans-
actions, therefore provide more reliable information about transaction costs
than does the estimate from any one transaction. Averaging, however, does
not solve any bias or gaming problems.
Traders who use transaction cost estimates to evaluate their brokers must
carefully analyze their estimates. They cannot meaningfully evaluate their
brokers without having some benchmarks against which they can judge their
performance. To compare brokers, either traders must measure performance
across many brokers, or consultants must provide them with meaningful
comparison norms. In either event, comparisons across brokers are mean-
ingful only when traders give brokers similar trading problems. Otherwise,
brokers who receive difficult trading problems will appear to be less pro-
ductive than are those who receive simple trading problems.
These problems make it imperative that clients evaluate their brokers by
using quantitative methods. Traders use these methods because the infor-
mation that they produce is very valuable when they can interpret it well.
21.5 MEASURING IMPLICIT TRANSACTION
COSTS WITH ECONOMETRIC METHODS
Econometric transaction cost measurement models use statistical methods
to measure the impacts that traders have upon prices. These models gener-
ally examine either price reversals or the relation between order flow and
price changes.
Analysts usually use these models when they do not have quotation data
or order flow data. For example, open outcry futures markets rarely record
the bids and offers shouted in their pits. Their market reporters primarily
record trade prices. Traders who want to measure transaction costs in these
markets therefore must use methods that do not depend on bid and ask
price quotations.
Econometric transaction cost measurement models generally estimate av-
erage transaction costs for the market as a whole. When traders are inter-
ested in the costs of specific trades, they typically measure them relative to
a specified benchmark.
21.5.1 Price Reversal Models
The simplest econometric transaction cost models measure the price rever-
sals that traders cause when they buy and sell. The most common price re-
versals are due to
bid/ask bounce.
Prices tend to bounce between bid and ask-
ing prices as impatient sellers and buyers demand liquidity. Traders also
cause longer-term price reversals when an imbalance of uninformed buyers


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## Page 447

434 • TRADING AND EXCHANGES
or sellers causes prices to rise or fall. Prices ultimately reverse when value
traders recognize the resulting profit opportunities. These price reversals
may occur over short intraday intervals or intervals as long as months.
The average absolute transaction price change is a very simple estimator
of transaction costs. It exactly estimates the bid/ask spread when bid and
ask prices are constant, and when all trades take place only at bid and ask
prices. Bid and ask prices vary, of course, and not all trades take place at bid
and ask prices. In practice, the average absolute transaction price change
tends to underestimate bid/ask spreads when bid and offer prices change in
increments that are smaller than the bid/ask spread, and it overestimates
bid/ask spreads otherwise. Ignoring very small and very large price changes
can improve the estimates.
Better transaction cost estimators are based on price change serial co-
variances. The
serial covariance
of a sequence of price changes is the aver-
age of the product of adjacent price changes. When prices tend to reverse,
the serial covariance is negative because the product of adjacent positive and
negative (or negative and positive) price changes is negative. Since the ef-
ficient markets hypothesis suggests that price changes should have no se-
rial covariance if there are no transaction costs, negative price change serial
covariance indicates transaction costs. Several econometric models exploit
this insight. The best known of them is
Roll's serial covariance spread esti-
mator,
which we describe in chapter 20. It is
Effective Spread =
2\/—SCov.
Roll's estimator is unbiased when the sample size is large, when aggres-
sive buyers and sellers arrive at random with equal probability, and when
changes in the effective bid and offer prices are uncorrelated with the se-
quence of arriving traders. Violations of these assumptions typically cause
the estimator to underestimate the bid/ask spread. Analysts therefore use
Roll's method only when no quotation data are available.
21.5.2 Order Flow Models
More complex econometric models directly estimate the price effects that
aggressive traders cause. These methods typically use regression models to
characterize how prices change in response to order flow. These models ex-
ploit the principle that prices rise in response to aggressive buyers and fall
in response to aggressive sellers. Analysts usually identify the more aggres-
sive side in each trade by using the Lee and Ready algorithm described
above. These regression models typically used signed trades to explain price
changes.
21.6 MISSED TRADE OPPORTUNITY COSTS
Failing to trade is often more costly than trading. Informed traders lose
profits when their orders go unfilled. Unfilled orders also may hurt unin-
formed traders, especially if they are hedging. Traders must pay attention to
their missed trade opportunity costs in addition to their transaction costs in
order to best manage their trading.
Analysts generally measure missed trade opportunity costs as the unfilled
size times the signed difference between a subsequent price and a bench-
mark price. For example, suppose that a trader decided to sell 100,000 shares


## Page 448

CHAPTER 21 LIQUIDITY AND TRANSACTION COST MEASUREMENT • 435
Glosten-Harris, Meet Glosten-Milgrom
Here is one more treat for the mathematically inclined.
The
Glosten-Harris spread components estimation model
is a simple
example of an order flow transaction costs estimation model. This model is
based on the Glosten-Milgrom spread component model introduced in
chapter 14.
These models assume that aggressive traders have two effects on prices.
The
permanent price effect
is due to inferences that dealers (and other
liquidity suppliers) make about informed traders from the order flow. It
corresponds to the adverse selection spread component introduced in
chapter
13.
Information theoretic considerations suggest that the permanent
effect should be proportional to trade size. (See chapters 12 and 14.) The
transitory price effect
is due to costs of supplying liquidity to impatient
traders. It corresponds to the transaction cost spread component introduced
in chapter 14. The transitory effect probably has fixed and proportional
effects on price.
The Glosten-Harris model represents these relations by specifying an
equation that represents observed prices, P,, as the sum of underlying
fundamental value, V
,
plus the transaction cost spread component,
O
f
:
Pt = 14+ o
f
.
Another equation expresses the transaction cost spread component as a
linear function of the sign of the trade, 0, (1 for a purchase and —1 for a
sale), and the signed size, Q,Size
t
:
Of =
aQ
t
+
bQ
t
Size
t
.
Finally, the permanent effect appears in an equation that describes how
fundamental value changes in response to information inferred from the sign
and size of the trade, and in response to other information that affect
values,
Et:
V,
=
A
Q
t
Size
t
+
e
t
.
Combining these equations produces the following equation for the
observed change in price:
=
AV,
+ 0B, =
A
Q
t
Size, +
e
t
+
aa,Q, + bA(Q,Size
i
).
Econometricians estimate the coefficients
A,
a,
and
b
in this equation by
interpreting the equation as a regression model in which the transaction
price change,
AP,
is explained by three independent regressor variables
and an error term. The regressors are Q
t
Size
t
, the signed size; AQ,, the
change in the trade sign; and A(Q,Size,), the change in the signed size.
The error term is
Et,
the change in values that is unrelated to the order flow.
The statistical properties of standard regression methods ensure that the
regression error term is uncorrelated with the independent regressor
variables. This is consistent with its interpretation as information that affects
values independently of the trade process. Analysts can use the estimated
coefficients to compute transaction cost estimates for any trade by
evaluating the expressions for the permanent and transitory price
components.
We can interpret the Glosten-Harris model as a benchmark price
transaction cost estimation model. The price benchmark is the unobserved
fundamental value, V. Although the model produces transaction cost
estimates without estimating the benchmark, econometricians can easily
estimate benchmark values if necessary.


## Page 449

436 • TRADING AND EXCHANGES
of DINE (Advantica Restaurant Group, owners of Denny's) when the quo-
tation midpoint was 1.00 dollar. The trader submitted a limit sell order that
filled 30,000 shares. The remaining 70,000 shares never filled. The price of
DINE is now 82 cents. If we use the 1.00-dollar quotation midpoint at the
time the trader decided to do the trade as the benchmark price, we obtain
the implementation shortfall estimate of the missed trade opportunity cost.
It is 12,600 dollars (70,000 shares X 18 cents).
Analysts who measure missed trade opportunity costs face two problems
that they do not face when measuring transaction costs. They must decide
when to measure their opportunity costs, and they must be sure that their
orders are real and not just wishful.
Analysts will measure different opportunity costs depending on when
they measure them. In the example above, suppose that one week after
DINE dropped to 82 cents, it rose back 1.00 dollar. The estimated oppor-
tunity cost then would be zero. The measurement of opportunity costs de-
pends on the date on which it is measured.
Even this result is not entirely clear. Had our trader originally sold the
full 100,000 shares at 1.00, he might have bought 100,000 shares when the
price dropped to 82 cents. If so, the trader lost the opportunity to make
18,000 dollars when the price went back up to 1.00.
In practice, traders measure opportunity costs at some interval after they
first decide to trade, or after they finally give up trying to further fill the
order. The interval may be one day, one week, or one month.
Opportunity cost analysts also must decide whether the orders that go
unfilled are serious orders or merely wishful ones. Consider our example
again. Suppose instead of wanting to sell 100,000 shares, our trader wanted
to sell 4 million shares. Four million shares represents 10 percent of all shares
outstanding and is 50 times the average daily volume of 80,000 shares. The
implementation shortfall estimate of the opportunity cost of failing to sell
the unfilled remainder of 3,970,000 shares is 714,600 dollars. This greatly
overestimates the true opportunity cost because the trader probably could
not have sold so many shares under any circumstances without depressing
the price below 82 cents. The trader may have wished that he could sell
those shares, but the wish is unrealistic. Even if he was certain that the price
would drop to 82 cents, he did not lose the opportunity to make 714,600
dollars in profits because he never had that opportunity. Opportunity costs
also must depend on the reasonableness of the order size.
The theoretically proper way to deal with this second problem is to use
a benchmark price that reflects the average price at which the trade would
have taken place if it had been completed under the most favorable condi-
tions. Since the estimation of such a price is difficult, traders do not solve
the problem this way. Instead, they simply monitor their orders to ensure
that their sizes are reasonable.
Analysts can measure missed trade opportunity costs relative to any
benchmark price. It is most sensible to measure them against the same
benchmark price that they use to estimate their transaction costs. By using
the same benchmark prices, they state their transaction costs and their
missed trade opportunity costs on a comparable basis.
Traders should compare their transaction costs against their missed trade
opportunity costs. If the additional transaction costs that they would incur
if they traded more aggressively are less than the opportunity costs they


## Page 450

CHAPTER 21 LIQUIDITY AND TRANSACTION COST MEASUREMENT • 437
The Plexus Iceberg of Transaction Costs
The Plexus Group provides its clients with a detailed breakdown of their
implementation shortfalls into timing, market impact, commission, and
missed trade opportunity costs. The firm measures two timing cost
components for executed trades. The first is
manager timing.
Plexus also
calls this component
system timing
or
model timing. It
is the signed
difference between the decision price and the price at the time of
submission of the order to the buy-side trading desk. The
decision price
is
the market price at the time the manager determined that he or she wanted
to trade. Most managers use the previous day's closing price as the
decision price. Manager timing measures implementation shortfall while the
order is still under the primary control of the portfolio manager. Portfolio
managers who have high manager timing costs generally should submit
their orders to their buy-side desks faster.
Trader timing
is the second timing cost component. It is the signed
difference between the price at the time of submission of the order to the
buy-side trading desk and the release of the order to a broker. If the buy-
side desk breaks up the order, the Plexus Group measures separate trader
timing costs for each part. Trader timing measures implementation shortfall
while the order is still under the primary control of the buy-side desk. Buy-
side traders often have high trader timing costs when they shop a large
block so that the market becomes aware that significant size will soon move
prices. (See chapter 15.) They also may have high trader timing costs when
they split orders into parts and stagger the release of the parts to brokers
through time. The market impacts of the initial parts cause what the Plexus
Group
identifies as trading timing for the remaining parts.
The Plexus Group measures market
impact
as the signed difference
between the price at the time of the release of an order to a broker and the
execution price. If the order results in several trades, Plexus estimates
separate market impacts for each trade. Market impact measures the
implementation shortfall while the broker is managing the order. Since most
traders will not let a broker sit on an open order overnight, market impact
is an intraday effect. Interday timing effects contribute to the trader timing
component.
Finally, the Plexus Group measures missed trade opportunity costs as the
signed difference between the decision price and the price of the instrument
30 trading days after the decision to trade was made, for the part of the
order that was not completed.
To illustrate the relative magnitudes of these various transaction cost
components, the Plexus Group created and popularized their
Iceberg of
Transaction Costs.
The visible costs of trading—commissions and market
impact—correspond to the part of an iceberg that is above the water. The
invisible costs—timing and missed trades—correspond to the bulk of the
iceberg that is underwater. The Plexus Group estimates that their clients pay
12 basis points in commissions and 20 basis points in market impact. They
lose
53
basis points in timing costs as prices move away from them, and
they lose an additional 16 basis points due to orders that do not fill. The
hidden costs of trade implementation (timing and opportunity costs) thus are
much larger than the visible costs (commission and market impact).
The four Plexus components sum to the total implementation shortfall.
Plexus believes that it is necessary to measure all these components because
costs are readily shifted from one category to another. Traders need to
avoid the mental trap where visible costs appear to have been reduced
when in fact they have only been moved to the more subtle timing
categories.
Source: www.PlexusGroup.com



## Page 451

438 • TRADING AND EXCHANGES
would save from trading more aggressively, they would earn more profit if
they traded more aggressively. Likewise, if the transaction costs that they
would save if they traded less aggressively are more than the additional op-
portunity costs they would incur from trading less aggressively, they would
earn more profit if they traded less aggressively.
In practice, most traders do not estimate these marginal costs. Instead,
they assume that their marginal costs equal their average costs. Accordingly,
they try to trade more or less aggressively if, on a per unit basis, their missed
trade opportunity costs are respectively greater or less than their transaction
costs. Traders who are concerned about this issue should estimate their mar-
ginal transaction costs from the costs of executing the last trades that fill
their orders.
21.7 TRANSACTION COST PREDICTION
Traders need to predict transaction costs in order to evaluate active trading
strategies. To this end, traders develop, estimate, and use transaction cost
prediction models. Most transaction cost prediction analyses use explicit and
implicit information to predict transaction costs.
21.7.1 Explicit Information About
Future Transaction Costs
Explicit information about future transaction costs consists of the contrac-
tual information about commissions and trading fees enumerated above. It
also includes current market information about liquidity conditions that
market quotations and exposed limit order books typically reveal. When
traders seek to trade less size than markets display, they can confidently ob-
tain an upper bound on the market impact of their orders. (These bounds,
of course, assume that they can access the liquidity before other traders do.)
Quotation and limit order books rarely reveal the full liquidity available
in a market, however. Traders often will not display the full size of their in-
terest for the following reasons:
•
They fear front runners.
•
They do not want to give away option values.
•
They do not want to reveal proprietary information about fundamen-
tal values or about their positions.
•
They want to avoid offering liquidity to large price-discriminating
traders or informed traders.
These issues ensure that traders often can trade at more favorable prices
or for greater sizes than is apparent from displayed market information.
Traders who want to accurately estimate their transaction costs therefore
must estimate how much liquidity might be behind any displayed liquidity
that they see.
21.7.2 Implicit Information About
Future Transaction Costs
The implicit information that traders use to predict transaction costs con-
sists
of information about previous implicit transaction costs. Traders try to
characterize this information by assuming that they can predict the market
impacts of future orders from the market impacts of previous orders. The


## Page 452

CHAPTER 21 LIQUIDITY AND TRANSACTION COST MEASUREMENT • 439
assumption is valid when liquidity conditions do not change. Substantial
evidence, however, suggests that liquidity varies through time. To the ex-
tent that they can, some traders try to model this variation so that they can
better predict their transaction costs.
The primary quantitative approach to implicit transaction cost predic-
tion uses econometric regression models to explain past transaction costs by
using observable variables. These variables characterize the orders, contem-
poraneous market conditions, and general market conditions.
The most important variables that characterize orders are order size and
price placement. Large orders naturally generate higher transaction costs
than do small orders. Likewise, aggressively priced orders typically gener-
ate higher transaction costs than do less aggressively priced orders.
The most important variables that characterize contemporaneous mar-
ket conditions are bid/ask spread and current displayed size. These charac-
terize width and depth. Other variables that traders use include recent vol-
ume, recent price changes (price momentum), and sums of volume on
upticks versus volume on downticks (money flow). These variables measure
transitory liquidity conditions.
The most important variables that characterize general market condi-
tions are average volume and volatility. Transaction costs tend to be low in
actively traded markets and in markets with stable prices. In equity mar-
kets, analysts often use market capitalization as a proxy for the size of the
market. Large firm stocks tend to trade in more liquid markets than do
small firm stocks.
21.8 CLOSING COMMENTS ON INTELLIGENT
TRANSACTION COST MANAGEMENT
Institutional investment managers and investment sponsors commonly sepa-
rately evaluate their trade implementation and portfolio selection/composition
processes. The two processes are not dichotomous, however. Total performance
often can be enhanced when the strategists who generate trading strategies
regularly interact with the traders who implement those strategies.
As noted at the beginning of the chapter, portfolio strategists need to
know what transaction costs will be before they adopt a trading strategy.
Traders therefore need to advise strategists of what is feasible. Strategists
must know how much they can trade, and at what cost, in order to form
orders that will optimize portfolio values.
Strategists likewise need to tell their traders why they are trading, so that
their traders can make good decisions about how aggressively they should
trade. For example, when strategists base their orders on short-term infor-
mation, they must tell their traders to trade aggressively. They also must
provide their traders with price limits that reflect the value of their infor-
mation. Well-informed traders should willingly incur substantial transaction
costs, especially when they trade on very short-term information. Other-
wise, they may fail to trade and thereby lose the opportunity to make sub-
stantial profits. Traders must know why they are trading in order to make
the best decisions about how they should trade.
Portfolio strategists and the traders who implement their decisions must
cooperate to maximize the value of their combined efforts. The schemes
that investment sponsors use to evaluate their operations should not make


## Page 453

440 • TRADING AND EXCHANGES
their strategists and traders adversaries. Portfolio strategists should not be
rewarded for blaming traders when they cannot profitably implement their
trading strategies, and traders should not be rewarded for blaming strate-
gists when they cannot fill unrealistically large orders. Instead, they should
cooperate to obtain the most value from their joint operations.
Joint incentive contracts that reward everyone when the total perfor-
mance is good provide strong incentives for cooperative behavior. Incentive
contracts that attempt to separately identify and reward the contributions
of the different participants in the trading process are counterproductive
when managers cannot separately identify their contributions. Disentangling
contributions is very difficult when personnel must cooperate to produce
the best product.
21.9 SUMMARY
Traders estimate their transaction costs so that they can better manage their
trading. Good information about their transaction costs allow them to do
the following:
•
Determine whether they obtain good value for the commissions they
pay their brokers
•
Balance their transaction costs and their missed trade opportunity costs
to optimize their order submission strategies
•
Ensure that their trading strategies are—and will be—profitable when
implemented
Although transaction cost measurement is noisy, it can produce valuable in-
formation that traders can use to help solve these problems.
For many investment managers, reducing transaction costs through more
effective trade implementation management improves performance more
than would devoting the same resources to improving their portfolio selec-
tion decisions. Decreasing transaction costs is often easier and more reliable
than improving portfolio section decisions.
21.10 SOME POINTS TO REMEMBER
•
Total performance depends on portfolio selection and trade imple-
mentation.
•
Transaction costs lower portfolio performance.
•
Traders estimate transaction costs so that they can better manage them.
•
Missed trade opportunity costs can be more significant than transac-
tion costs. Good traders find a balance between the two types of costs
when they decide how aggressively to trade.
•
Transaction cost measurement depends on price benchmarks.
•
Reliable inferences about transaction costs require reasonable bench-
marks and many trades.
•
Informed trading, contrarian trading, order splitting, and gaming may
bias some transaction cost estimates.
•
Brokers often can game a transaction cost measure by deferring trades
or by mimicking their benchmarks.
•
Transaction costs to the buy side are revenues to the sell side.


## Page 454

CHAPTER 21 LIQUIDITY AND TRANSACTION COST MEASUREMENT • 441
• Good managers consider transaction costs when making portfolio com-
position decisions.
21.11 QUESTIONS FOR THOUGHT
1.
What services besides low transaction costs should brokers provide?
2.
Analysts can conduct implementation shortfall transaction cost stud-
ies by using ask prices as price benchmarks when buying and bid prices
as price benchmarks when selling. What advantages and disadvan-
tages would this method have relative to the standard quotation mid-
point benchmark method?
3.
What is the relation between missed trade opportunity costs and
transaction costs when traders optimize their trading?
4.
Which types of profit-motivated traders would you expect should have
high measured transaction costs? Which types should have low mea-
sured transaction costs?
5.
In the extended example of section 21.4.4, the implementation short-
fall transaction cost estimate for the four-trade round-trip was 400
dollars. If the market closed at 30.05 dollars, the closing price trans-
action cost estimate also would be 400 dollars. These two estimates
are not normally equal. Why are they equal for the four-trade round-
trip in this example?


## Page 455

22
Performance
Eualuation
and
Prediction
M
any people trade because they want to speculate successfully. They
hope to profit by buying securities and contracts that will rise in value
and by selling those that will fall.
Unfortunately for those of us who would like to get rich quickly, pre-
dicting future prices is quite difficult. Some people can do it, but most can-
not. Successful speculators must predict future prices well enough to beat
the market on average. Unsuccessful speculators eventually lose money when
trading. At best, they make less money than they would have made if they
had simply bought and held index funds. If they trade only because they
want to earn speculative trading profits, they should stop trading.
In this chapter, we consider how to measure past performance and how
to predict future performance. The two questions are closely related. Most
people measure past performance primarily because they want to predict fu-
ture performance. We shall see why predictions based only on past perfor-
mance generally are quite unreliable. We can predict performance better by
using other information.
You must be able to predict performance if you intend to speculate. Spec-
ulators trade only because they expect to profit. Successful speculators there-
fore must constantly consider whether their trades will be profitable. If you
cannot predict whether you will trade profitably, you should not speculate.
The most important decision speculators make is whether they should trade.
You also must be able to predict performance if you employ active in-
vestment managers to speculate on your behalf.
Active investment managers
speculate with their clients' money. They are
active,
as opposed to
passive,
because they actively try to identify and exploit speculative opportunities.
Accordingly, they often trade frequently. You can hire their services by em-
ploying them as investment advisers or you can obtain their services indi-
rectly by buying the mutual funds and commodity pools that they manage.
In either event, when managers speculate on your behalf, you speculate on
their success. To select good active investment managers, you must predict
which ones will speculate successfully. If you cannot predict which man-
agers will be successful, you should not employ active investment managers.
The most important decision investment sponsors make is whether to em-
ploy active managers.
Investors who believe that they cannot speculate successfully often in-
vest their money with passive investment managers.
Passive investment man-
agers
use
buy and hold strategies.
They simply buy and hold securities. Pas-
sive managers therefore rarely trade. The most common buy and hold
strategy is the index replication strategy.
Index replicators
buy and hold port-
folios that they design to replicate the returns to a broad market index. We
discuss how they do this in chapter 23.
Indexing is very popular because many investors have decided that they
do not want to speculate. They do not believe that they would be success-
442


## Page 456

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 443
ful traders, and they do not believe that they can pick successful managers.
The limitations of performance evaluation and prediction help explain why
index markets are so popular.
People often design investment management contracts so that the pay-
ments investment managers receive depend on their performance. Such con-
tracts encourage investment managers to better serve their clients. You must
appreciate the limitations of performance evaluation in order to understand
how to best compensate investment managers and to understand the prob-
lems that arise in typical investment management contracts.
We begin with a discussion of the principal problem of discriminating
between skill and luck. We then briefly consider the mechanics of perfor-
mance evaluation. If you already know how analysts compute returns, and
how and why they compare them against benchmark returns, you can skip
this section. The discussion then turns to the problem of predicting perfor-
mance. We first consider how statisticians approach the problem and ex-
plain why their approach is not very powerful. We then consider alternative
approaches to performance prediction based on economic theory.
By the end of this chapter, you will understand why past performance
does not necessarily predict future returns. You will also understand how
sample selection biases affect the inferences you may make about invest-
ment decisions. Failures to understand these issues probably account for
more trading losses than any other mistakes traders make.
22.1 THE PERFORMANCE
EVALUATION PROBLEM
Portfolio performance depends partly on the quality of its management.
Managers who perform well add value to their portfolios. Managers who
perform poorly waste value.
Portfolio performance also depends on every factor that determines the
values of the instruments in the portfolio. The factors may include macro-
economic, microeconomic, and firm-specific factors.
Macroeconomic factors
include changes in interest rates, general economic activity, productivity, and
exchange rates.
Microeconomic factors
include industry supply and demand
conditions, technological innovations, and government interventions.
Firm-
specific factors
include a host of issues ranging from whether the firm is well
managed to whether factories accidentally burn down to whether researchers
make fortuitous discoveries.
Active managers try to foresee every factor that will affect values. They
then buy instruments that they expect will appreciate and sell those they ex-
pect will depreciate. If they are very skilled, they will be able to add sub-
stantial value to their portfolios by selecting which instruments to buy and
which to sell.
No one can anticipate most factors that affect portfolio returns. Unfore-
seeable factors therefore have a seemingly random effect on performance.
Portfolios that perform well may be managed by skilled managers or by
lucky managers. Likewise, poorly performing portfolios may be managed by
unskilled managers or by unlucky managers.
The investment policies that govern many portfolios often have a sub-
stantial effect on portfolio performance. These policies may prohibit skilled
managers from exploiting positive factors or from avoiding negative factors.


## Page 457

444 • TRADING AND EXCHANGES
Sizzler Sick with
E. Co
li

Sizzler International is an operator/franchiser of family restaurants that
specialize in grilled meats. Sizzler operates 65 company restaurants in the
United States, and it franchises another 200. In 1998, the company
reorganized in a voluntary Chapter 11 bankruptcy. By the middle of 2000,
its earnings were accelerating and many analysts, who had carefully
studied the company and its prospects, believed that it had a winning
strategy for revitalizing the chain. Based on their research, many investment
managers bought Sizzler, expecting that it would outperform the market.
They did not immediately realize their expectations. In July 2000, the
bacterium
E. coli
infected 42 patrons of a Milwaukee Sizzler franchise.
Seventeen were hospitalized, and a three-year-old girl died from
complications resulting from the infection.
Undercooked meats or food contaminated by raw meat spread the
E. coli
infection. The Milwaukee health commissioner believed that the
infected patrons ate contaminated watermelon.
News about the outbreak was well publicized throughout the country.
Following the announcement, Sizzler's stock fell 35 percent as investors
feared the lawsuits and the lost reputation that would surely follow this
tragic event.
The speculators who bought Sizzler may have been very well informed
about its prospects. They undoubtedly knew that food poisoning episodes
occasionally plague restaurant chains, and they probably discounted Sizzler's
stock—and those of other restaurant chains—accordingly. Some may even
have considered whether Sizzler designed and implemented adequate
sanitation procedures to appropriately control the risks that they faced.
Despite all these considerations, the Sizzler
E. coli
food poisoning episode
probably was not predictable. These investors were simply unlucky.
They also may force unskilled managers to unknowingly exploit positive fac-
tors or avoid negative factors. Portfolios that perform well therefore may be
managed by skilled managers or simply by lucky managers subject to in-
vestment policies that the market currently favors. Likewise, poorly per-
forming portfolios may be managed by unskilled managers or by skilled
managers subject to investment policies that currently are out of favor.
For example, consider the performance of portfolios that must be fully
invested in equity. These portfolios fluctuate in value with the market re-
gardless of how management operates. When the market rises, even the
most poorly managed portfolios may have high positive returns. A market
rise can offset losses due to inept management. Likewise, when the market
falls, the best-managed portfolios may have negative returns. A market fall
can overwhelm gains due to superior management. Analysts often charac-
terize this phenomenon by saying, "A rising tide lifts all boats."
Unforeseeable factors and unavoidable factors greatly complicate the per-
formance evaluation problem. These factors make it difficult to estimate the
manager's contribution to portfolio performance. Good performance eval-
uations must discriminate between skill and luck. Analysts must break to-
tal performance into separate components representing the contribution due
to management and the contribution due to factors that no one could an-
ticipate or control.
The task of estimating the contribution of factors that no one could an-
ticipate or control is very difficult because so many factors affect portfolio


## Page 458

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 445
The Combinations of Luck and Skill
Since luck is random, no relation exists between luck and skill. Skilled
people are sometimes lucky and sometimes unlikely. Likewise, unskilled
people are sometimes lucky and sometimes unlucky. For your amusement,
consider the following characterizations of the four possible combinations of
luck and skill:
Skilled

Unskilled
Lucky
Unlucky
Blessed

Insufferable
Cursed

Doomed
returns. Even the best estimates of manager performance are quite noisy.
Discriminating between skill and luck as explanations for portfolio perfor-
mance is very difficult.
22.2 PERFORMANCE EVALUATION METHODS
These considerations suggest that relative performance evaluations are more
valuable than absolute performance evaluations.
Absolute performance evalua-
tions
simply compute total portfolio returns.
Relative performance evaluations
estimate how much portfolios have outperformed or underperformed the
benchmarks or peers against which they are measured. By choosing bench-
marks to represent how analysts expect portfolios would have performed
without active management, they are better able to identify the contribution
of management.
To measure the relative performance of a portfolio, analysts examine the
difference between the total return to the portfolio and the concurrent to-
tal return to the benchmark. We therefore must consider how analysts mea-
sure absolute portfolio performance before we consider how to measure rel-
ative portfolio performance.
22.2.1
Absolute Performance Measurement
Analysts measure the
absolute performance
of a portfolio when they estimate
how much the nominal value of the portfolio has changed over some mea-
surement interval. The measurement interval may be a year, a quarter, a
month, a week, or a day; or it may be a current period to date, such as
year-
to-date
(YTD).
Measuring absolute performance is a relatively simple problem when in-
vestors have not transferred money or securities into or out of the portfolio.
The
total return
to the portfolio is the percentage change in its value over
the measurement interval. For example, if the value of a portfolio increases
from 100 to 120 over a year, its total return for the year is 20 percent.
The
total return
includes the value of any cash and securities that the
portfolio receives or pays in conjunction with its holdings. Cash inflows typ-
ically include dividends, interest, and security lending fees received. Out-
flows include brokerage commissions, management fees, interest paid to pur-
chase securities on credit, security borrowing fees, and short dividends.
Portfolio cash flows also include all
variational margin payments
that the
portfolio receives, or must make, when holding long or short positions in
futures, options, and swaps, and when holding short positions in securities.


## Page 459

446 • TRADING AND EXCHANGES
The Beardstown Ladies
Founded in 1983, the Beardstown Business and Professional Women's Club
is a famous investment club composed of about 15 women. By 1993, most
of its members were in or near retirement, but still actively managing their
portfolio.
The club wrote several books and produced videos about their approach
to investing.
The Beardstown Ladies' Common-Sense Investment Guide: How
We Beat the Stock Market—And How You Can, Too
(1994) became an
international best seller.
On the jacket of that book, the Beardstown ladies claimed that they
earned an average annual return of 23.4 percent in the 10 years ending in
1993. When challenged, they recomputed their returns with the assistance
of the Price Waterhouse accounting firm. Their actual annual average return
was 9.1 percent, significantly less than the 14.9 percent average annual
total return to the S&P 500 Index over the same period. Many people
wondered how they could have thought that they were beating the market
when they were actually underperforming it by an annual average of almost
6 percent.
The ladies reported that they failed to properly use investment
accounting software with which they were unfamiliar. They consequently
mistook a 2-year return for a 10-year return. Additional information in their
explanation supports the conclusion that they did not have strong computer
skills—which would not be surprising for professional women (or men) in or
nearing retirement in 1993. Their apparent lack of computational
sophistication suggests that their reported explanation may be accurate.
Many people, however, think that the women simply failed to account
for the regular contributions that they made to their fund. Over 10 years,
such a failure would lead to a gross overstatement of their total returns.
Such a mistake would have been far more embarrassing to admit than the
computational mistake that they reported. 4i
Sources: www.time.com/time/magazine/1998/dom/980330/business.jail
the_
beards12.html (Overview); www.better-investing.org/clubs/from-the-ladies.html

(explanation by Betty Sinnock, Beardstown BPW Club senior partner).
Inflows and outflows of securities typically involve corporate reorganizations
such as mergers, acquisitions, and spin-offs.
When investors transfer money (or securities) into or out of a portfolio,
or when the portfolio distributes dividends to its investors, the total value
of the portfolio changes. Without an adjustment, the change in portfolio
value would misrepresent the actual performance of the portfolio. Capital
additions would inflate the performance and capital distributions would de-
flate it.
Analysts use two approaches to address the problem of capital additions
and distributions. The most common approach is to compute the internal
rate of return for the portfolio. The
internal rate of return
(IRR) is the com-
pounded rate of return that a savings account would have to earn to exactly
replicate the capital flows into and out of the portfolio. The IRR calcula-
tion assumes that beginning and ending savings account balances are equal
to the beginning and ending portfolio values. The IRR is approximately a
time- and value-weighted geometric average of the total returns measured
between each capital addition and distribution.
For many purposes, people would prefer to know the
holding period re-
turn
for a share of a portfolio rather than the average return earned by all


## Page 460

l
■
When Is 100 — 50 = 0?
Investors must
be
very careful
when summing percentage
returns. Suppose that a
portfolio has a 100 percent
increase in one year,
followed by a 50 percent
decrease in the next year. The
summed arithmetic return over
the two years is 50 percent.
The actual holding period
return over the two years,
however, is exactly zero.
(Prices double the first year
and halve the second year.)
To properly compute a
holding period return from a
series of returns, you must
compute their geometric sum
rather than their arithmetic
sum. You add 1 to each
return, then multiply the
resulting sums, and finally
subtract 1 from the product.
—94.7% + 95.6% =
Very Bad News
ProFunds UltraOTC Fund lost
94.7 percent between March
10, 2000, and April 4,
2001, and gained 95.6
percent from April 4 to May
2, 2001. Had you invested
10,000 dollars in the fund on
March 10, 2000, your
position would have been
worth just 1,037 dollars on
May 2, 2001.
4
Source: Karen Damato, "Doing
the Math: Tech Investors' Road to
Recovery Is Long,"
Wall Street
Journal
May 18, 2001, p. C1
(Western edition).
CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 447
investors in the portfolio. The holding period return estimates how much
an investor would have made if he or she had invested a dollar in the port-
folio at the beginning of the measurement period. Analysts typically com-
pute holding period returns by assuming that all distributions are reinvested
in the portfolio when they are paid.
The holding period return and the internal rate of return differ when
capital additions and distributions do not occur on a pro rata basis within
the measurement period. Capital additions and distributions are on a
pro
rata
basis occur when all investors participate in the capital transactions in
exact proportion to their ownership shares. Since pro rata additions and dis-
tributions do not affect the proportion of the portfolio that each owner
owns, all investors have the same internal rate of return over the holding
period.
Analysts often separate the total return into its
current yield
component
and its
capital gains
component. The
current yield
of a portfolio (or of a se-
curity) is the total income—typically interest and dividends—that the port-
folio receives from its assets divided by the value of the portfolio. For ex-
ample, if the securities in a 100-million-dollar portfolio pay 10 million
dollars in dividends to the portfolio in a year, the current yield for that year
is 10 percent. The
capital gains return
of a portfolio is the difference between
the total return and the yield. It is the percentage change in the value of
the assets of the portfolio, exclusive of the income that the assets pay. The
distribution of total return into the current yield and the capital gains re-
turn is of particular interest to investors for whom ordinary income and cap-
ital gains are taxed at different rates.
The current yield of the assets of a portfolio can be different from the
current yield that owners of a portfolio receive. This happens when the port-
folio pays its investors more or less income than it receives from its assets.
Analysts who measure performance must estimate values for all portfolio
positions at the beginning and end of the measurement period. Estimating
values for instruments that trade in active markets is quite simple. Analysts
usually estimate such values by the last trade price or by the midpoint be-
tween the last bid and offer prices. The estimation problem is much more
difficult for instruments that do not trade often. In that case, analysts must
estimate values from the prices of related instruments. For example, esti-
mating the value of a real estate portfolio can be extremely difficult. Man-
agers must generally estimate values by means of appraisals that depend crit-
ically on the sales prices of comparable properties. Estimated valuations of
infrequently traded bonds likewise depend on the sales prices of similar debt
instruments.
22.2.2 Relative Performance Measurement
Raw returns are not useful for evaluating performance without some basis
for comparison. For example, an equity portfolio that drops 10 percent when
the market drops 20 percent has performed well relative to the market. In
contrast, an equity portfolio that rises 15 percent when the market is up 30
percent has performed relatively poorly.
Analysts compute
relative returns
to facilitate performance comparisons.
A
relative return
is the difference between a portfolio return and a corre-
sponding
benchmark return.
Analysts choose benchmarks to represent how
they expect the portfolio would have performed without active management.


## Page 461

448 • TRADING AND EXCHANGES
Smooth Sailing, Rapids Ahead
Investors generally like stable portfolios because they are risk averse. Their
investment managers therefore hope to report stable values.
Managers of portfolios that include highly illiquid assets must estimate
values for their assets when there are no sales prices. To create stable
values, unethical managers may be slow to change their estimates when
values change. They may be especially slow to adjust values when they fall.
Investors in such funds therefore may believe that their funds are less risky
than they actually are.
When true asset values differ significantly from the manager's estimated
values, various processes may force the manager to revalue them quickly.
For example, if investors try to withdraw funds from the portfolio, the
manager may have to sell assets. The resulting sales may be at substantially
lower prices than the manager last estimated. These sales will cause the
reported value of the portfolio to drop significantly. Seemingly secure funds
therefore may be quite risky.
Investors who are concerned about the valuation of illiquid assets should
look at the sequence of reported portfolio returns. Returns display positive
serial correlation when managers are smoothing values. '04
The purpose of the benchmark comparison is to remove noise in the per-
formance evaluation. A good benchmark adjusts raw returns to account for
performance that should not be attributed to the manager. The remaining
measure of performance therefore better represents the performance of the
manager.
22.2.2.1 Market
-
adjusted Returns
The most common benchmarks that analysts use to evaluate performance
for equity portfolios are market indexes. For example, analysts generally com-
pare portfolios that primarily hold a diversified set of large capitalization
U.S. stocks against the S&P 500 Index. They use specialized indexes to
evaluate portfolios that invest in other asset classes. Table 22-1 provides a
list of commonly used benchmark indexes.
Market-adjusted returns
are portfolio returns minus corresponding mar-
ket index returns. The market-adjusted returns in the above example are 10
percent and —15 percent. For most purposes, market-adjusted returns dem-
onstrate how well the portfolio has performed better than raw returns do.
22.2.2.2 Risk-adjusted Returns
Analysts sometimes further adjust raw returns to account for the exposure
of portfolios to known risks. For example, consider the exposure of a port-
folio to
market risk.
Market risk is the risk that values will rise or fall with
marketwide changes in value. It varies by security, and therefore also by port-
folio. Analysts characterize market risk by the
market beta
of a security. Beta
measures the extent to which the security fluctuates in value with the mar-
ket. A stock with a beta of 0.5 tends to rise or fall only 0.5 percent for every
1 percent rise or fall in the market. It is only half as risky as the market.
The beta of a portfolio is the value-weighted average of the betas of the
various securities in the portfolio. Since the market has a beta of 1, and since
the market is just a portfolio of all available securities, the value-weighted
average beta of all securities is 1.
AIMR-PPS
Although performance
measurement seems very
straightforward, difficulties
involving exotic contracts,
infrequently traded instruments,
foreign exchange rates, and
complex portfolio ownership
structures complicate many
performance measurement
problems. Reasonable people
often can arrive at different
results using the same data.
To ensure that performance
measurements are presented
on a comparable basis, the
Association for Investment
Management and Research
(AIMR) developed
performance presentation
standards that they encourage
analysts to use. The AIMR
performance presentation
standards (AIMR-PPS) allow
investors to directly compare
the performance of different
investment managers.
-k
Source: The AIMR presentation
standards appear at www.aimr.
com/standards/pps/ppsstand
.
html.


## Page 462

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 449
TABLE 22-1.
Some Common Benchmark Indexes
ASSET CLASS

BENCHMARK
Large capitalization U.S. equities

S&P 500 Index and S&P 100 Index
Russell 1000
NYSE Composite Index
Small capitalization U.S. equities

Russell 2000
Wilshire Small Cap 1750 Index
Total U.S. stock market

Russell 3000
Wilshire 5000 Total Market Index
Utilities

Dow Jones Utility Index
S&P Utility Index
NYSE Utility Index
Oil industry equities

CBOE OIX Index
AMEX XIO
U.S. large capitalization

Nasdaq 100
technology equities

Merrill Lynch 100 Technology Index
Goldman Sachs Technology Industry Composite Index
German equities

DAX
London equities

FTSE 100
Tokyo equities

TOPIX
Nikkei 225 Stock Average
French Equities

CAC 40
European equities

Morgan Stanley Capital International (MSCI) Europe Index
World equities

MSCI World Index
Southeast Asian equities

MSCI Pacific Free Index
Commodity futures/Some hedge funds

LIBOR
Goldman Sachs Commodity Index
3-month Treasury bill yields
Investment grade U.S. corporate bonds

Lehman Brothers Corporate Intermediate Bond Index
Merrill Lynch Long-term Corporate Index
High yield U.S. corporate bonds

Lehman Brothers High Yield Bond Index
(junk bonds)

Credit Suisse First Boston High Yield Index
Merrill Lynch High Yield Master Index
U.S. municipal bonds

Lehman Brothers Municipal Index
U.S. money market funds

6-month Treasury bill yields
Merrill Lynch 6-month Treasury Bill Index


## Page 463

450 • TRADING AND EXCHANGES
Foolish John
John took money out of a
savings account where it was
earning 5 percent to invest in
the stock market. He made
23 percent trading actively
and was delighted with his
performance. He thought that
he could pick stocks really
well!
That same year, the S&P
500 rose 33 percent. Had
John invested in an S&P 500
Index fund, he would have
made 33 percent.
John was pleased with his
performance because he used
the wrong frame of reference.
He beat his measly savings
account return by 18 percent.
His performance was terrible,
however, because he did not
get the average return that
other people got for bearing
similar equity risks. He
underperformed the market by
10 percent.
Satisfied with his expertise,
John decided to continue to
trade actively. The next year,
he again underperformed the
market by
10
percent.
Unfortunately, that year the
S&P 500 lost 10 percent.
John lost 20 percent, which is
25 percent worse than he
would have done had he kept
his money in his savings
account.
His total return for the two
years was —1.6 percent.
(The holding period is
computed as (1 + 0.23) x
(1 — 0.20) — 1.) The
corresponding two-year S&P
500 return was 19.7 percent.
Had John kept his money in
the savings account, he would
have earned 10.3 percent.
Managers who construct low beta portfolios tend to underperform the
market when it is rising and outperform it when it falling. Likewise, those
who construct high beta portfolios tend to outperform the market when it
is rising and underperform it when it is falling.
To account for these effects, analysts compute
risk-adjusted excess returns
by subtracting
the average portfolio beta times the market return from the
raw portfolio return. The resulting measure is also called the portfolio's
realized alpha.
The realized alpha helps analysts determine whether a man-
ager successfully selects winners and losers after accounting for market risk.
22.2.2.3 Market Timing
An
equity manager
times the market
when she changes the portfolio beta to
exploit her predictions about the future direction of the market. Managers
change their portfolio betas by exchanging high beta assets for low beta as-
sets or vice versa. For example, managers lower their portfolio betas by sell-
ing securities and leaving the proceeds in cash, by selling high beta securi-
ties and buying low beta securities, by selling index futures, by buying puts,
by selling calls, or by selling securities short. They raise their portfolio
betas by doing the opposite.
Risk-adjusted excess returns are best computed frequently because the
portfolio beta changes whenever the manager exchanges assets that have
different betas. To accurately estimate risk-adjusted returns, analysts must
multiply market returns by concurrent portfolio betas.
Analysts who compute risk-adjusted returns often also compute
market-
timing returns.
The market-timing return is the difference between the port-
folio beta times the market return and the market return. It indicates whether
the portfolio manager is a skilled market timer.
To summarize, raw portfolio returns can be broken into the sum of three
parts: the market return, the market timing return, and the risk-adjusted ex-
cess return:
Raw Return = (Raw Return — Beta X Market Return)
+ (Beta X Market Return — Market Return)
+ Market Return
= Excess Return + Market Timing Return + Market Return.
Analysts use these decompositions to attribute performance to the market,
to the manager's timing ability, and to all other factors—including, of course,
the manager's risk-adjusted selection skills.
Analysts can easily generalize this decomposition to include other risk
factors besides market risk. Finance courses and textbooks about invest-
ments describe these
multifactor risk models.
The most common additional
risk factors that analysts use to analyze equity portfolios are factors that
measure risks associated with firm size and with expected growth.
22.3 THE PERFORMANCE
PREDICTION PROBLEM
People often evaluate past performance because they want to predict the
future performance of a manager. Such analyses are valuable only if the fac-
tors that determined past performance will continue to determine future
performance. Most people rarely think carefully about whether this will
be true.


## Page 464

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 451
Buy and Hold Benchmarks
The difference between the portfolio return and the return that the portfolio
would have made had the manager not traded is a direct measure of an
active manager's contribution to portfolio performance. This measure is
intuitively attractive because it focuses only on the manager's decisions. The
benchmark index for this approach to performance measurement is the
beginning-of-period portfolio. Unfortunately, a serious problem complicates
the use of this approach.
This approach identifies only superior performance that takes place
during the measurement period. Any superior performance that accrues in
subsequent periods contributes to the performance of subsequent
benchmarks. For example, suppose a manager buys a stock at the
beginning of the year. The manager believes the stock will appreciate
significantly over the next two years. The appreciation in the first year will
accrue to his benefit, but the appreciation in the second year will not. A
similar problem applies to sales. This problem causes managers to focus on
short-term ideas. It also causes them to delay the implementation of ideas
generated near the end of the year.
Changing the definition of the benchmark index can reduce this
problem. Instead of the beginning-of-period portfolio, a better benchmark
would be the portfolio that would have been created if all trades made by
the manager occurred one year later. With this benchmark, all trades get
the benefit of at least one full year of evaluation.
Whether the factors that determined a manager's past performance will
continue to determine his or her future performance depends on three fun-
damental conditions. Each of the following conditions must be true to suc-
cessfully predict future returns from past performance:
•
Past performance must reflect the manager's skills.
If past performance
was due only to luck, it will have no bearing on future returns.
•
The manager's skills will continue to generate goodfuture returns.
The skills
necessary to perform well may vary by market conditions. For example,
traders who succeed in bull markets may not succeed in bear markets.
Past performance will have no bearing on future returns if the skills that
generated it are no longer effective.
•
The manager still has the skills necessary for success.
Investment man-
agement firms often lose essential skills when they lose employees or
access to valuable resources. Investment managers also often lose es-
sential skills as they age or lose their drive. Past performance will have
no bearing on future returns if the skills that generated it are no longer
available.
If any of these conditions is not true, attempts to predict future performance
from past performance will not be productive. People therefore should ver-
ify these conditions before attempting to predict future returns from past
performance.
Analysts may use analytic or statistical methods to determine whether
performance is due to luck or to skill (the first condition). In the analytic
approach, analysts try to identify the skills that determined past perfor-
mance. This task is quite difficult because analysts must understand the
management process well enough to recognize what determined past per-
formance. In the statistical approach, analysts use statistical methods to show
that luck alone cannot reasonably explain past performance. Analysts often
Regulation FD
Regulation
FD
requires that
publicly traded corporations
immediately disclose to the
entire public any material
information that they disclose
to any unrelated person.
Before the U.S. Securities and
Exchange Commission
adopted this regulation in
2000, investment managers
who were skilled at arranging
and conducting interviews
with corporate insiders often
could obtain valuable
material information from
them. These skills no longer
are as valuable as they once
were now that
Regulation FD
prohibits this practice. The
past performance of
managers who had these
skills therefore is not a good
predictor of their future
performance.


## Page 465

452 • TRADING AND EXCHANGES
Our Faith in Past
Performance
Why do so many people
assume that past performance
can predict future returns?
This assumption is so
pervasive and so wrong, it
begs for an explanation.
Perhaps behavioral biology
can help us better understand
ourselves.
During our evolutionary
history as creatures that could
learn from our environment,
our ancestors survived by
learning that things which
happened in the past often
would happen again. Our
ancestral aunts and uncles
who did not use the past to
predict the future often did
not survive to reproduce.
Other creatures ate them,
they fell off cliffs, they
starved, or they froze to
death. We are here today
because our ancestral parents
successfully used the past to
predict the future. Natural
selection has hardwired us to
believe that performance is
persistent. 1
use the statistical approach to avoid the difficulties associated with the an-
alytic approach. The statistical approach, however, has its own difficulties.
We discuss them in the next section.
To determine whether the manager's skills will continue to generate good
future returns (the second condition), analysts must first identify the skills
that determined past performance. They then must decide whether those
skills will continue to be useful. This second task is more difficult than
the first task because explaining the past generally is easier than predicting
the future. Predictions about the future necessarily depend on uncertain
assumptions.
Analysts generally can determine whether the manager still has the nec-
essary skills to perform well (the third condition) by direct inquiry. The de-
termination, of course, is reliable only if the analysts know what skills to
look for.
Since the verification of all three conditions is difficult, most people sim-
ply do not do it. Instead, they just assume that past performance can pre-
dict future returns. The prediction of future returns from past performance,
however, is notoriously imprecise. We know this both from experience and
from statistical theory.
22.3.1 Some Empirical Evidence
In the next section, we examine the problems that make statistical perfor-
mance evaluation unreliable. Before we do so, let us consider some com-
pelling empirical evidence.
Financial researchers have observed that essentially no correlation exists
between the best-performing funds in one year and the best-performing
funds in the next year. Good past performers are about equally likely to be
good future performers as are poor past performers. Good past performance
simply does not regularly predict good future performance.
(The very worst-performing funds, however, tend to remain at the bot-
tom from year to year. These funds typically lose because they trade too
much and because they have high management fees. As long as these con-
ditions do not change, they stay at the bottom.)
These results are very robust. They are true for equity funds, bond funds,
and commodity pools. The results are uniform across years and across coun-
tries. The results are similar when performance is measured by quarter or
by month. The results do not depend on the criteria for identifying the best
funds. These empirical results strongly suggest that statistical methods can-
not reliably predict future performance from past returns.
22.4 STATISTICAL PERFORMANCE
EVALUATION
Past performance poorly predicts future returns primarily because past per-
formance generally is due more to luck than to skill. Unpredictable return
factors very often obscure the value that skilled management can add to a
portfolio.
This result is not surprising. Future price changes are largely unpre-
dictable—even for well-informed traders—because informed traders make
most instrument prices quite informative. Moreover, the competition among
informed traders to profit from information ensures that few traders will


## Page 466

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 453
have great insights into the future. We therefore do not expect that even
highly skilled managers will have remarkably large returns on average.
We can see why past performance is so uninformative by considering
how statisticians judge whether a manager can systematically beat the mar-
ket. Their methods measure whether actual performance is greater than luck
alone can reasonably explain. To determine what is reasonable, statisticians
examine return distributions. The
distribution
of a variable tells us the prob-
ability of every possible value of the variable.
In a typical statistical analysis, statisticians use probability theory to char-
acterize the distribution of possible returns that we would expect if man-
agers were not skilled. This distribution therefore assumes that only luck
determines performance. If the actual market-adjusted return is significantly
greater than we would typically expect, based on luck alone, statisticians
conclude that some factor other than luck probably contributed to the re-
turn. The likely factor is the skill of the investment manager.
This section presents the test that analysts most commonly use to de-
termine whether managers are skilled. We describe the reliability of the test,
and how analysts should use it when deciding whether to invest with an ac-
tive manager. Although our presentation assumes that analysts will apply
the test to equity managers, the methods apply equally well to all other types
of managers.
I wrote this section so that any reader should be able to understand it.
You do not need to know statistics to understand it. Without using math-
ematical notation, the text explains every statistical concept that you need
to know to understand statistical performance evaluation.
If you do not want to have a deeper understanding of the statistical prob-
lems associated with performance evaluation, you may wish to skip this sec-
tion. (You would be poorly advised to do so, however, if you work in, or in-
tend to work in, investment management.) The most important principle
that you need to know is that statistical performance evaluation is generally
unreliable because unpredictable return factors make it very difficult to iden-
tify managerial skill. In practice, more than 20 years of returns data are typ-
ically required to obtain useful results for a given investment manager. Even
more data are required to determine whether the most successful investment
manager, selected from of a large group of managers, was skilled or just lucky.
22.4.1 The t
-
Test
To determine whether a manager can systematically beat the market, stat-
isticians typically examine the ratio between the manager's average market-
adjusted return and a measure of average size they call the
standard error of
the mean.
The
standard error
is a number that statisticians compute based
on results from probability theory. It is proportional to the average size of
the market-adjusted return that we would expect to observe if only luck
were responsible for the manager's returns. If this ratio—called
Student's
t-statistic
or simply the
t-ratio—is
large, statisticians conclude that luck alone
probably cannot explain the returns.
For example, if the manager has no skill, the probability that the
t-statistic will be greater than 1.64 is only 5 percent. (The calculation of this
probability assumes a one-sided t-test.) If statisticians conclude that the
manager is skilled whenever the t-statistic for a manager's portfolio exceeds
this
critical value,
they will be mistaken 5 percent of the time.


## Page 467

454 • TRADING AND EXCHANGES
Student's Brew Samples
W. S. Gosset first identified
the t-test and the associated
t-distribution. Gosset was a
statistician who worked for
Guinness Brewery in Great
Britain. He invented the t-test
in 1908 to analyze small
brew samples for quality
control.
The brewery did not
want Gosset to publish under
his own name. Gosset
therefore published under the
pseudonym Student. This
restriction explains why the
t-distribution is also known as
Student's distribution.
4
The
critical value
depends primarily on the
confidence level of the test—in
this example, 95 percent—and to a lesser extent on the sample size. The
critical value for a less discriminating test is smaller. For example, the crit-
ical value corresponding to a 90 percent confidence level is only 1.31. The
critical value corresponding to a 50 percent confidence level is zero. If luck
alone determines market-adjusted returns, they will be positive 50 percent
of the time.
(Statisticians generally express the confidence level of a test in terms of
its
significance level.
The significance level of a test is 1 minus the confi-
dence level. This exposition uses confidence levels instead of significance
levels because they are less confusing.)
The test works because luck and skill have different long-term effects on
average portfolio returns. Luck, by definition, is random and completely un-
predictable. Good luck is as common as bad luck. Good luck increases re-
turns and bad luck lowers them. Given enough time, however, the net ef-
fect of luck on average returns is small. In contrast, skill has a systematic
positive effect on returns. Over time, a skillful manager should outperform
the market.
22.4.2 A Power Calculation
This t-test is
powerful
when statisticians are likely to conclude that the man-
ager is skilled, given that the manager is indeed skilled. The probability that
the t-ratio will exceed the critical value, given that the manager is skilled, is
the
power
of the test. Statisticians calculate power using probability theory.
The power of the test depends on several factors:
•
Power increases when the confidence level of the test decreases. Low
confidence level tests have low critical values for the t-ratio. If the man-
ager is skilled, the probability that his t-ratio will exceed the critical
value (the power of the test) is greater when the critical value is small.
•
Power also increases with the skill of the manager. A highly skilled
manager should have higher returns, and therefore a higher t-ratio.
•
The power of the test decreases with the importance of luck as a fac-
tor that determines returns. When luck primarily determines returns,
bad luck can cause a skilled manager to perform poorly, and therefore
have a t-statistic that is smaller than the critical value.
•
Finally, power increases with the years of data analyzed. Over a long
period, good luck and bad luck tend to offset each other so that the
manager's t-ratio depends more on skill than on luck. The probability
that a skilled manager's t-ratio will exceed the critical value (the power
of the test) therefore is greater when many years of returns data are
included in the test.
To compute the power of the t-test, we must specify quantities for these
four factors. We specify the confidence level when we create the test. The
number of years of returns generally depends on what data are available. We
specify the last two factors—the true skill of the manager and the impor-
tance of luck as a determinant of returns—by considering the economic con-
text of the performance evaluation problem.
We quantify the true skill of a manager by how much we expect him to
beat the market each year on average. A large number will imply a power-


## Page 468

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 455
flu test, but we cannot reasonably assume a large expected return. The com-
petition among informed traders to profit from information, the costs of
obtaining information, and the informational efficiency of most financial
markets make large expected returns unrealistic. Given the competitive en-
vironment, most professional managers would be delighted beyond de-
scription if they were certain that they could beat the market by 2 percent
per year, on average.
To put this number into perspective, consider the performance of two
of the most successful equity managers ever. Over 36 years through 2000,
Warren Buffet, chairman of Berkshire Hathaway, beat the market by an av-
erage of 11.8 percent per year. During much of this period, however, he may
have been quite lucky as well as quite skilled. Between 1991 and 2000, his
performance dropped to 6.8 percent per year over the market. I will com-
ment more on his performance below.
Consider also the performance of Peter Lynch. During his 13 years as
manager of the Fidelity Magellan Fund, the fund outperformed the mar-
ket by an average of 12.7 percent per year. In his last five years, however,
the Fund outperformed the market by an average of only 5.1 percent per
year. Like Warren Buffet, Peter Lynch may have been quite lucky as well
as quite skilled.
The performance of these superstars was quite remarkable. The fact that
they have not been able to sustain their initial performance rates suggests
that they may have been quite lucky in the beginning of their tenures. (The
decline may also be due to the increased difficulties of managing ever-larger
funds.) If we assume that their real skill is somewhere between 5 and
7 percent—this still seems high to me—it is reasonable to assume that the
skill of a typical skilled manager is only 2 percent.
We quantify the importance of luck as a determinant of returns by the
standard deviation
that we would expect market-adjusted returns would have
if only pure luck generated them. The
standard deviation
is a probability
concept that measures the variation of a variable about its mean. The stan-
dard deviation of market-adjusted returns depends on the standard devia-
tion of the portfolio returns, the standard deviation of the market returns,
and the correlation between the two returns. An increase in either standard
deviation increases the market-adjusted return standard deviation. A high
correlation lowers the market-adjusted return standard deviation. Probabil-
ity theory gives us a formula
for the market-adjusted
return standard devi-
ation,
o
-
Adj
.
It is =
Void
+
uhk
—
2p0p
kr
, cr
mk
where
p
is the correla-
tion. If the portfolio is well diversified, empirical experience suggests that
reasonable annual values for these parameters are
Crp
or
t =
16%,
a
m
=
14.5%,
and
p =
0.9. These values imply a market-adjusted return standard devia-
tion of 7.0 percent.
Table 22-2 presents power calculations for various combinations of these
parameters. The results show that if we want to be 95 percent confident
that we do not identify an unskilled manager as skilled, the probability that
the t-test will identify a skilled (2 percent) manager using five years of
monthly data is only 15 percent! An additional five years of data only raises
this probability only to 23 percent. The test requires more data than are
generally available to confidently separate skill from luck.
If we use a test with a lower confidence level of 75 percent, we will iden-
tify an unskilled manager as a skilled manager one time out of four. Even


## Page 469

456 • TRADING AND EXCHANGES
TABLE 22-2.
The Power of a Standard One-sided t-Test of Manager Performance
TRUE

TEST
MANAGER

CONFIDENCE

YEARS OF

TEST
SKILL

LEVEL

RETURNS

POWER
0%
50%
75
95
Any number
Any number
Any number
50%
75
95
2
50
1
5
10
61
74
82
75
1
5
10
34
48
59
95
1
5
10
8
15
23
4
50
1
5
10
71
90
96
75
1
5
10
45
72
87
95
1
5
10
12
35
56
Notes: The t-test attempts to determine whether a manager is skilled in the sense that he
can beat the market on average. The confidence level of the test is the probability that the
test does not identifii an unskilled manager as a skilled manager. The power of the test is
the probability that the test will identift a truly skilled manager as a skilled manager.
We quantift the true skill of the manager by how much we expect him to beat the market
each year on average. If the manager is unskilled, we assume that his expected market-
adjusted return is zero.
The calculations assume that monthly market-adjusted returns are normally distrib-
uted with an annual standard deviation of 7.0 percent. The power calculator used to
construct this table appears at home.stat.ucla.edu/calculators/powercalc/normaN-1/

n-l-power.html.
with this low standard, we will still only identify truly skilled managers less
than one half of the time (48 percent) given five years of data and a bit more
than half the time (59 percent) given 10 years of data.
If we use a 50 percent confidence level test, we will identify an unskilled
manager as skilled half of the time. With this extremely low confidence
level, the probability that we identify skilled managers as skilled is only 74
percent using five years of data and 82 percent with 10 years of data. For


## Page 470

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 457
comparison, note that the probability of identifying a skilled manager as
skilled is 50 percent if we merely flip a coin to decide the issue! Clearly, we
require more data than are generally available to confidently separate skill
from luck when using statistical methods.
22.4.3 How Much Data Are Required?
Power calculations can also tell us how many years of returns we need in or-
der to conduct a test with a given confidence level and power. Table 22-3
presents the results of these calculations for several confidence levels and
powers.
These results show that even tests with low confidence and power lev-
els require more years of returns than are commonly available. For example,
suppose that skilled managers can beat the market by 2 percent on average.
A test that does not identify unskilled managers as skilled managers 75 per-
cent of the time, and that identifies skilled managers as skilled managers 75
percent of the time, requires 22 years of monthly returns! If skilled man-
agers were extraordinarily skilled, so that they could beat the market by 4
percent on average, we would still require six years of data to run a test with
these low confidence and power levels.
22.4.4 The Statistical Argument for Indexing
Before we abandon the statistical approach to determining whether a man-
ager is skilled, consider how we might sensibly choose the confidence and
power levels of our test. These test characteristics depend on how the test
results will be used.
For example, suppose that we will invest with an active manager if the
test indicates he is skilled and invest in an index fund otherwise. If the man-
ager is not skilled, but the test indicates that he is, we will expect to lose.
If we expect large losses, the test confidence level should be high in order
to avoid identifying unskilled managers as skilled managers. Likewise, if the
One Year of Data Just
Doesn't Cut It
Many people make decisions
about managers from just one
year of data. If this were not
so, the business sections of
newspapers would not be full
of ads that publicize last
year's returns for various
mutual funds. Of course,
the only funds that advertise
are those which performed
well last year.
The probability of correctly
identifying a truly skilled
manager with a year of data
is less than 10 percent in any
test that will not regularly
identify an unskilled manager
as skilled. Last year's return
therefore is essentially
worthless for judging
managerial skill.
TABLE
22-3.
Years of Data Required to Obtain Specified Confidence and Power Levels for the Standard
One-sided t-Test of Manager Performance
POWER
TRUE MANAGERIAL SKILL
CONFIDENCE LEVEL
50%
75%
95%
Skilled manager adds 2% per year
50%
0
6
33
75%
0
22
66
95%
0
66
132
Skilled manager adds 4% per year
50%
0
2
8
75%
0
6
17
95%
0
17
33
Notes.• The confidence level of the test is the probability that the test does not identift an unskilled manager as a skilled manager.
The power of the test is the probability that the test will identift a truly skilled manager as a skilled manager. We quantify the
true skill of the manager by how much we expect him to beat the market each year on average.
The calculations assume that monthly market-adjusted returns are normally distributed with an annual standard deviation
of 7.0 percent. The power calculator used to construct this table appears at ebook.stat.ucla.edu/cakulators/powercalc/normal/

n-1/n-1-samp.html.


## Page 471

458 • TRADING AND EXCHANGES
manager is indeed skilled, and the test indicates that he is, we will expect
to profit. If we expect high profits, the power of the test should be high to
avoid failing to identify skilled managers. Finally, if the expected returns to
investing in an index fund are large, the confidence level should be high.
The confidence and power levels that we choose therefore should de-
pend on the costs and benefits associated with our decision. Depending on
these consequences, good decisions may not require high confidence and
power levels. In this subsection, we consider what should be the confidence
and power levels for a given sample size. We will assume that we will use
the test to choose between investing with an active investment manager and
investing in an index fund.
Our analysis will choose the confidence and power levels to maximize
our expected market-adjusted return. The results therefore will show how
much we expect to profit from choosing between an active manager and an
index function, assuming that we design the best possible test. If these ex-
pected profits are small, we should not consider choosing a manager based
only on past performance.
To estimate the expected return, we must make some assumptions about
the consequences of our decision and about how common skilled managers
are. We start by assuming that a previously skilled manager can produce 2
percent market-adjusted returns per year, on average, before accounting for
management fees and trading commissions. (We implicitly assume as-
sumptions 2 and 3 of section 22.3, so that a skilled manager will continue
to generate good future returns, on average.) Although 2 percent may seem
low, it is a reasonable assumption given the competition among managers
for trading profits. As noted above, most managers would be delighted be-
yond expression if they and their clients were certain that they could beat
the market by 2 percent per year on average.
Since trading is a zero-sum game, unskilled managers must lose on av-
erage to skilled managers. Their losses will depend on the fraction of man-
agers who are skilled. Assume that one-third of managers are skilled, so that
the average unskilled manager underperforms the market by about 1 per-
cent per year, before accounting for management fees and trading commis-
sions:
-
3
-
X
2% +
3
X
-
1% = 0%. This assumption seems generous to me.
All active managers—skilled and unskilled taken together—underperform
the market by an average of more than 1 percent per year, after accounting
for expenses. (The average U.S. equity mutual fund underperformed the S&P
500 Index by 1.4 percent between 1962 and 1997.) This average implies that
commissions plus management fees for all active managers average at least
1 percent. Assume that they total just 1 percent. Accordingly, we expect that
skilled managers will beat the market by 1 percent per year, on average, af-
ter expenses, and unskilled managers will underperform the market by 2 per-
cent per year, on average, after expenses.
Finally, we assume that index funds underperform the market by —0.15
percent. This is typical for index funds that have very low management fees
and tend to track the market extremely closely.
The consequences will vary according to whether the manager is truly
skilled and according to whether the test indicates that the manager is
skilled. We therefore must assign costs and benefits to four different states.
Since we need to compute the overall expected return, we also need to spec-


## Page 472

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 459
ify the probabilities of these four states. Our assumptions allow us to
specify these costs, benefits, and probabilities:
•
If the manager is not skilled, and the test indicates that the manager
is not skilled, we will invest in an index fund and our market-adjusted
returns will be —0.15 percent. The probability of this situation is the
confidence level times the probability that the manager is not skilled.
•
If the manager is not skilled, and the test indicates that the manager
is skilled, we expect to underperform the market by 2 percent per year,
on average, by investing with the manager. The probability of this sit-
uation is 1 minus the confidence level times the probability that the
manager is not skilled. Statisticians call this type of mistake the
Type
I error
of the test.
•
If the manager is skilled, and the test indicates that the manager is
skilled, we expect the manager to beat the market by 1 percent per
year, on average. The probability of this situation is the power of the
test times the probability that the manager is indeed skilled.
•
If the manager is skilled, and the test indicates that the manager is not
skilled, we will invest in an index fund and our market-adjusted re-
turns will be —0.15 percent. The probability of this situation is 1 mi-
nus the power of the test times the probability that the manager is in-
deed skilled. Statisticians call this type of mistake the
Type II error
of
the test.
Table 22-4 summarizes this information.
The overall expected market-adjusted return associated with the deci-
sion is the average of the expected consequences weighted by their respec-
tive probabilities. For a given sample size, the resulting expected return is
maximized by choosing a confidence level, which implies the test power.
Table 22-5 presents results for several sample sizes.
The results show that the optimal tests require high confidence levels for
all sample sizes. For small sample sizes, the optimal confidence level is very
high because there is not enough information to reliably discriminate be-
tween skilled and unskilled managers. Since investing with an unskilled
manager is costly relative to investing with an index fund, and since skilled
managers are relatively rare, a trader who follows the optimal strategy will
TABLE 22
-
4.
Assumed Consequences and Probabilities of All Possible States
TRUE MANAGER STATUS
ASSUMED PROPERTY
TEST RESULT
SKILLED
NOT SKILLED
Annual expected
Skilled: Invest with active manager
1.00%
—2.00%
net excess return
Not skilled: Invest in index fund
—0.15% —0.15%
Probability
Skilled: Invest with active manager
X Power
2
X (1
—
Confidence)
Not skilled: Invest in index fund
3
X
(
1
-
Power)
-} X Confidence
Note: This table presents the annual expected return consequences and occurrence probabilities of the four possible states associated
with the decision to invest with an active manager i f a t-test identifies the manager as being skilled.


## Page 473

460 • TRADING AND EXCHANGES
TABLE 22-5.
Optimal t-Test Confidence and Power Levels and Maximized Expected Returns
YEARS OF
MONTHLY
RETURNS
OPTIMAL
CONFIDENCE
LEVEL
OPTIMAL
POWER
LEVEL
MAXIMIZED ANNUAL EXPECTED
RETURN IN EXCESS OF INDEX
FUND RETURN
PROBABILITY OF
INVESTING WITH AN
ACTIVE MANAGER
1
100%
0%
0.000%
0%
2
99
4
0.001
2
5
95
24
0.030
11
10
94
42
0.086
18
20
94
64
0.171
25
50
97
87
0.298
31
Notes: This table presents optimal test confidence and power levels for the t-test of whether a manager is skilled. The optimal
levels maximize the expected returns that result from investing with an active manager if the test identifies the manager as
skilled, and from investing in an index fund otherwise.
The costs and benefits of the four possible outcomes appear in table 22-4. The calculations assume that monthly market-
adjusted returns are normally distributed with an annual standard deviation of 7.0 percent. The author derived these results
using the power calculator at ebook.stat.ucla.edu/calculators/powercalc/normal/n-1/n-1-power.html.

almost always invest in index funds. Accordingly, the maximized expected
return is essentially the same as the index fund return. For very large sam-
ple sizes, enough information is available to reliably discriminate between
skilled and unskilled managers. The optimal confidence level remains high
to avoid the mistake of investing with unskilled managers. The high power
ensures that we invest with an active manager if he is skilled.
We assumed that a skilled manager will outperform an index fund by
1.15 percent on average. If the test could perfectly discriminate between
skilled and unskilled traders, the expected value of conducting the test would
be one-third of this value because—by assumption—only one-third of ac-
tive managers are skilled. Note that even with 50 years of data, the expected
return of using the test (0.298 percent) is still only 78 percent of its theo-
retical maximum (0.383 = s X 1.15 percent).
The maximized expected return from using the t-test—expressed rela-
tive to the expected index fund return—is the value of the option to decide
whether to invest with an active manager when the alternative is to invest
in an index fund. It is always positive because you can always choose to in-
vest in an index fund. For sample sizes of ten or fewer years, the value of
this option is extremely low. With ten years of data, it is only 8.6 basis
points. Not surprisingly, many investors choose to ignore these options. They
invest in index funds because they do not believe that they can add signif-
icant value to their wealth by choosing managers.
22.4.5 Choosing Among Many Managers
In practice, investors rarely decide only between just one active manager and
an index fund when choosing whether to invest with an active manager. In-
stead, they usually consider many managers, and generally, only those they
know have done well in the past.
The best-performing managers of a large group of managers always will
have performed very well. In a large group of people, extreme luck can pro-
duce very impressive results. Standard statistical tests of whether such man-


## Page 474

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 461
agers are skilled or just lucky will invariably indicate that the managers are
skilled.
Standard tests in this application, however, produce highly unreliable re-
sults. Standard tests consider whether a manager chosen at random is skilled.
In this application, we already know that a manager has performed well,
and we examine him or her only because we know that the manager has
performed well. The proper test must consider whether the manager is
skilled, given that we already know that the manager was among the best-
performing managers of a large group of managers. Whether a given man-
ager is skilled, and whether the best-performing manager among a group
of managers is skilled, are different questions. To answer different questions,
we require different tests.
Managers who have performed well may have been skilled, or they may
have been exceptionally lucky. Unfortunately, the larger the group of man-
agers from which we select the best performing managers, the more im-
pressive are the performances of the luckiest unskilled managers. In a large
sample of managers, some unskilled managers will be extremely lucky sim-
ply by chance.
Table 22-6 shows just how lucky the best-performing manager from a
large group of unskilled managers can be. The calculations assume that all
the managers have constructed well diversified portfolios which are closely
correlated with the market. We therefore assume that the annual standard
deviation of their market-adjusted returns is only 7 percent per year.
The results in the third column (labeled Median) of the first panel show
that in half of all years, on average, the best-performing manager from a
group of 10,000 unskilled managers will beat the market by almost 27 per-
cent! The winner will beat the market by more than 28 percent in one year
out of four (next column) and by almost 31 percent in one year out of 20
(column labeled 95th percentile). The results are even more impressive when
the best manager comes from a larger group.
The second and third panels present results for five- and ten-year peri-
ods. On average, the luckiest of 10,000 unskilled managers will beat the
market by an annual average of more than 8 percent in half of all ten-year
periods, by almost 9 percent in one ten-year period out of four, and by al-
most 10 percent in one ten-year period out of twenty.
The last three columns show similar results for the 99th percentile man-
ager in each group size. This manager's performance is also very impressive,
although not nearly as extreme as the best-performing manager.
Exceptionally lucky managers perform very well in comparison to skilled
managers of average luck. These returns are all greater than the 2 percent
per year that we assumed a good skilled manager could produce on aver-
age. Even in ten-year periods, it is much better to be very lucky than skilled
with average luck!
The results in this table indicate that if you want to be more than 95
percent confident that you do not identify an unskilled manager as a skilled
manager when examining ten years of returns for the best-performing man-
ager out of 10,000, you must classify as unskilled any manager whose aver-
age market-adjusted performance is less than 10 percent per year. Tests with
such confidence have essentially no power to identify any but the luckiest—
or most skilled—managers.
The results in this table greatly underestimate the actual performance of


## Page 475

BEST MANAGER
MARKET-ADJUSTED AVERAGE
RETURN DISTRIBUTION
99TH
PERCENTILE MANAGER
MARKET-ADJUSTED AVERAGE
RETURN DISTRIBUTION
MANAGERS

75TH

95TH

75TH

95TH
PERIOD

IN GROUP

MEDIAN PERCENTILE PERCENTILE MEDIAN PERCENTILE PERCENTILE
1 year
5 years
10 years

10

10.5%

13.3

18.0

N/A

N/A

N/A

100

17.2

19.3

23.0

16.1

17.8

20.4

1,000

22.4

24.1

27.2

16.2

16.8

17.7

10,000

26.7

28.2

30.9

16.3

16.5

16.7

100,000

30.4

31.7

34.2

16.3

16.3

16.4

1,000,000

33.8

35.0

37.3

16.3

16.3

16.3

10

4.7

6.0

8.0

N/A

N/A

N/A

100

7.7

8.6

10.3

7.2

8.0

9.1

1,000

10.0

10.8

12.2

7.3

7.5

7.9

10,000

11.9

12.6

13.8

7.3

7.4

7.5

100,000

13.6

14.2

15.3

7.3

7.3

7.3

1,000,000

15.1

15.7

16.7

7.3

7.3

7.3

10

3.3

4.2

5.7

N/A

N/A

N/A

100

5.4

6.1

7.3

5.1

5.6

6.5

1,000

7.1

7.6

8.6

5.1

5.3

5.6

10,000

8.4

8.9

9.8

5.1

5.2

5.3

100,000

9.6

10.0

10.8

5.1

5.2

5.2

1,000,000

10.7

11.1

11.8

5.1

5.2

5.2
462 • TRADING AND EXCHANGES
TABLE 22-6.
Distributions of the Average Market-adjusted Performance of the Luckiest Managers
of Various Group Sizes
Notes: This table presents three percentiles of distributions of average annual market-adjusted returns that we expect for the
luckiest managers of a group of unskilled managers. Distributions are reported for the manager with the highest return and for
the manager whose return was higher than 99 percent of the other managers' returns.
The calculations assume that annual market-adjusted returns are independently normally distributed with mean zero and
standard deviation of 7 percent. The 99th percentile manager is not meaningful in a group of only 10 managers. The distribu-
tion of returns for the 90th percentile manager in a group of 10 unskilled managers is as follows:
75TH

95TH
PERIOD
MEDIAN
PERCENTILE
PERCENTILE
1 year
8.8%
11.1
14.5
5
years
3.9
5.0
6.5
10 years
2.8
3.5
4.6
Source: Author's tabulations of simulated returns.
the luckiest managers because the best (and worst)-performing managers
typically construct undiversified portfolios. The annual standard deviation
of their market-adjusted returns therefore is much greater than the 7 per-
cent we assumed.
22.4.6 Summary and Discussion
The results in this section strongly demonstrate that past returns data are
not very useful for determining whether a manager is skilled. For them to


## Page 476

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 463
Is Warren Buffet Skilled or Lucky?
Berkshire Hathaway is a firm that Warren Buffet has managed since 1965.
Although it has many operating divisions, in many respects it is essentially a
closed-end investment fund. Because Berkshire Hathaway is primarily an
insurance company, its reported book value is based on the market values
of its holdings rather than on historic costs of its holdings. The book value
therefore is essentially the net asset value of the firm.
Many people regard Warren Buffet as the most skilled investment
manager of the late twentieth century. Since he took control of Berkshire
Hathaway, its book value had appreciated 2,078-fold through December
2000. This corresponds to a compounded average growth rate of 23.6
percent per year. By comparison, the average annual total return (capital
gains plus dividends) of the S&P 500 Index during this period was 11.8
percent. Berkshire Hathaway outperformed the market by an average of
11.8 percent per year. The firm exactly doubled the performance of the
S&P 500 Index over this period.
Is Warren Buffet indeed a skilled investment manager, or has he simply
been a very lucky manager?
A standard t-test indicates that he is exceptionally skilled. Over the
36 years, Berkshire Hathaway's annual market-adjusted return standard
deviation was 14.3 percent per year, so that the standard t-statistic is 4.9.
The probability that an unskilled manager would have a t-statistic larger
than 4.9 is only 0.0011 percent.
This test, however, is not the proper test. Warren Buffet came to our
attention only because he had exceptionally high returns. If his performance
had not been exceptionally good, you probably would have never heard of
him, and I certainly would not be writing about him. To properly address
the question, we must consider whether Warren Buffet's investment
performance is significantly better than we would expect from the best
managers in a large group of unskilled managers.
Assume that Warren Buffet competed with at least 10,000 investment
managers in 1965. Many of these managers underperformed the market
and subsequently quit or were dismissed from their jobs. The total number
of managers in 1965 would be much greater if we considered every
amateur investor who would have become a professional investor if his or
her investment performance had been better than it was.
If 10,000 unskilled managers constructed portfolios with normally
distributed market-adjusted returns having mean zero and standard
deviation 14.3 percent, the probability that the best-performing manager
would beat the market by an annual rate of 11.8 percent or greater is only
0.5 percent. If there were 100,000 managers, the probability would have
been 5 percent. These results suggest that Warren Buffet very likely is a
skilled manager.
Source: Author's calculations based on data at www.berkshirehathaway.com/2000ar/

20001etterhtml.
be of much value, we must base statistical analyses of past returns on more
years of data than are normally available to us.
The problem is that skill is a far less significant determinant of portfo-
lio returns than is luck. In particular, the additional return that we expect a
manager can add to a portfolio is small relative to the variation in portfo-
lio returns due to factors which managers cannot anticipate or act upon.
Statisticians and engineers say that this problem has a low
signal to noise
ratio.
The signal—whether the manager is skilled—is hard to find because
it is lost in noise (variation due to other factors).


## Page 477

CORRELATION
AMONG MANAGER
MARKET-ADJUSTED
RETURNS
BEST MANAGER
MARKET-ADJUSTED AVERAGE
RETURN DISTRIBUTION
75TH

95TH
MEDIAN

PERCENTILE

PERCENTILE
99TH PERCENTILE MANAGER
MARKET-ADJUSTED AVERAGE
RETURN DISTRIBUTION
75TH

95TH
MEDIAN

PERCENTILE

PERCENTILE
464 • TRADING AND EXCHANGES
07
,
A Last Word for the Statisticians
In practice, many managers pursue similar trading strategies so that they
obtain similar results. The market-adjusted returns of these traders are
correlated. I derived the results that appear in table 22-6 by assuming that
the market-adjusted returns of all managers are uncorrelated. They therefore
overstate the performance of the best-performing managers.
You can easily understand the problem by assuming that all managers
pursue the same investment strategy so that they all obtain the same results.
The distribution of the returns of the best-performing manager within a
group of these identical managers does not depend on the size of the
group because they all produce the same returns. Although many managers
may be in the group, the effective size of the group is only one because
they all pursue only one strategy. The correlation of trading strategies
among managers therefore reduces effective group size.
Table 22-7 tabulates extreme average market-adjusted return distributions
for varying degrees of correlation among the managers' market-adjusted
returns. The results summarize 10-year average market-adjusted returns for
the best manager and the 99th percentile manager within a group of
10,000 managers.
Comparing these results against the results in table 22
-
6 shows that
when the correlation is 0.25, the return distribution for the best manager
within a group of 10,000 managers is similar to the distribution for the best
manager within a group of approximately 2000 managers with
uncorrelated returns. (The approximation is based on a logarithmic
interpolation.) For a given group size, if we account for the correlation
among manager market-adjusted returns, the performance of managers
like
Warren Buffet appears more remarkable. /
The problem would be much easier to solve if we believed that skilled
managers could add more, on average, than the 2 percent per year to a port-
folio that we assumed in our analyses. Unfortunately, it is unreasonable to
assume much greater skill than 2 percent because trading is a highly com-
petitive zero-sum game. Even if we assume greater skills, we would have to
assume that fewer managers have them. Although the test then would dis-
criminate better, the greater scarcity of skilled managers would offset the
improvement to some extent.
TABLE 22
-
7.
Distributions of the 10-year Average Market-adjusted Performance of the Luckiest Managers in a Group
of 10,000 Managers for Varying Degrees of Correlation Among the Manager Market-adjusted Returns
0.00
8.4%
8.9
9.8
5.1
5.2
5.3
0.25
7.4
8.2
9.5
4.4
5.2
6.3
0.50
6.0
7.1
8.7
3.6
4.7
6.2
0.75
4.3
5.6
7.5
2.6
3.9
5.7
1.00
0.0
1.5
3.7
0.0
1.5
3.7
Source: Author's tabulations of simulated returns using the assumptions in table 22-6.


## Page 478

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 465
Pt Closet Indexers
Closet indexers
are active equity investment managers who create portfolios
that very closely replicate the benchmark indexes their clients use to
measure their performance. Their clients presumably expect that their
managers will choose portfolios to beat their benchmarks. It is very difficult,
however, to significantly outperform an index with a portfolio that is very
closely correlated to it.
Closet indexers choose closely correlated portfolios because such
portfolios rarely significantly underperform their benchmark indexes. Closet
indexers minimize the risk of significant failure.
Clients generally do not like closet indexers because they pay them high
fees for their "active management" but they essentially receive only index
portfolios in exchange. The management fees for index funds are much
lower than those for actively managed portfolios.
Since the returns of closet indexers are very closely correlated to their
benchmark indexes, analysts can determine whether closet managers are
skilled managers with much less data than they require to determine
whether true active managers are skilled.
4
The problem would be much easier to solve if the variation due to other
factors were smaller. In our analysis, we assumed that the variation of mar-
ket-adjusted portfolio returns is 7 percent per year. We derived this num-
ber by assuming that the portfolio return standard deviation is 16 percent,
the market return standard deviation is 14.5 percent, and the correlation be-
tween the two returns is 0.9. If the problem were characterized by lower
standard deviations or by a higher correlation, stronger tests would be pos-
sible. For example, if the correlation were 0.95 instead of 0.9, the market-
adjusted portfolio return standard deviation would be 5 percent per year.
The power of a 95 percent confidence level test using five years of returns,
assuming manager skill of 2 percent, would rise from 15 percent (assuming
a 7 percent market-adjusted return standard deviation) to 22 percent. If the
correlation were 0.99, the market-adjusted return standard deviation would
drop to 2.6 percent, and the test power would rise to 52 percent. Unfortu-
nately, the portfolio returns of most equity managers are not so highly cor-
related with the market.
To solve a low signal to noise ratio problem, we must either know more
about the signal or somehow reduce the noise. In practice, analysts most
frequently attempt to lower the noise by using factor models to explain unan-
ticipated portfolio returns. The analysis of market-adjusted returns that we
studied in this section is based on a simple one-factor market-adjusted model
of portfolio returns. More complex models attempt to attribute returns to
other factors, such as interest rates, firm size, and expected growth rates.
Since the variation in the resulting factor-adjusted excess returns is lower
than the variation in the market-adjusted returns, tests of manager perfor-
mance will be more powerful for a given sample size. These tests, however,
identify manager skill only after accounting for factor returns. They will not
measure the value added by managers who can successfully predict factor
returns, and who can adjust their portfolios to benefit from their skills.
With five years of monthly returns data, a test with 95 percent confi-
dence and 75 percent power to identify 2 percent skilled managers requires
an annual factor-adjusted excess return standard deviation of only 1.9 per-
cent. Unfortunately, factor models generally cannot explain returns so pre-


## Page 479

466 • TRADING AND EXCHANGES
A Good
Dissertation Topic
We know that skilled
managers should perform
better in liquid markets than
in illiquid markets because the
costs of establishing positions
are lower in liquid markets.
This observation is especially
important for large fund
managers and for skilled
managers who pursue
strategies that many other
skilled managers pursue.
Since liquidity varies
through time, so will the
performance of such skilled
managers. Tests of manager
skill therefore should
incorporate information about
time-varying liquidity. To the
best of my knowledge, no
such tests have ever been
done.
cisely. Such models would have to explain 98.6 percent of the total varia-
tion in returns to achieve such precision.
The fraction of total variation that a statistical model explains is called
the R
2
of the model. Factor models typically have R
2
of less than 90 per-
cent in annual data. For comparison, the R
2
of the simple market-adjusted
return model is 81 percent when the portfolio standard deviation is 16 per-
cent, and the market-adjusted return standard deviation is 7.0 percent
[0.81 = (0.9)
2
= (16
2
— 7
2
).16
2
). (In the simple market-adjusted return
model, the R
2
is equal to the square of the correlation coefficient of the port-
folio returns with the market returns.)
In principle, analysts could construct stronger tests if they knew more
about a manager's presumed skill. For example, suppose an analyst believes
that a manager may be skilled only in rising markets but not in falling mar-
kets. This information would allow the analyst to construct a stronger test
of whether the manager is skilled. In particular, the analyst would examine
returns only in rising markets. In general, analysts can better identify skill
if they can identify some variables that are correlated with it.
22.4.6.1 Length of Sample Versus Number of Observations
In
most statistical analyses, the greater the number of observations, the more
powerful the results will be. The results presented throughout this section il-
lustrate this principle. The more years of data upon which statistical perfor-
mance evaluations are based, the more powerful those evaluations will be.
For a given sample period, the number of observations can be increased
by sampling more frequently. For example, in a 10-year period, analysts can
examine 10 annual, 120 monthly, and approximately 522 weekly nonover-
lapping returns.
In performance evaluations, however, statistical power depends prima-
rily on the length of the sample period and not on the frequency of sam-
pling within that period. This is because the total performance within a pe-
riod is the same regardless of how often we observe it within the period.
Analyses that attempt to discriminate between luck and skill need long sam-
ple periods to separate the systematic contribution of skilled managers from
the noise of unanticipated factors. Sampling more often within a period
does not address this need.
More frequent sampling, however, often produces slightly more power-
ful tests, especially when the total number of observations otherwise would
be less than 20. The standard error of the mean—which appears in the de-
nominator of the t-statistic--often can be estimated more accurately from
more frequent observations for a given sample period. Whether it can or
cannot depends on whether prices follow a random walk at various obser-
vation intervals. The issue is technical and need not concern us further. It
explains, however, why I presented analyses for monthly returns instead of
annual returns in this section.
22.5 MORE IMPORTANT PROBLEMS WITH
STATISTICAL PERFORMANCE EVALUATION
Statistical performance evaluation often is even more difficult than the re-
sults in the previous section indicate. If the returns being analyzed are not
well characterized by the assumptions upon which statisticians base their
analyses, the conclusions that statisticians reach will not be reliable.


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CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 467
Two main problems may cause returns data to have substantially differ-
ent properties than statisticians expect. These problems involve distribu-
tional shape and the accuracy of the returns data. These issues are very im-
portant because some investment strategies produce highly unusual return
distributions and because—through malice, negligence, or simple over-
sight—returns data sometimes do not represent true performance. Investors
who fail to recognize these problems risk making decisions that often cause
them to lose their entire investments.
22.5.1 Distributional Shape
The
shape
of the distribution of a variable refers to how the probabilities of
different values of the variable are distributed. If the distribution is
flat,
all
values have equal probabilities. If the distribution is
fat-tailed,
extreme val-
ues are common.
Distributional shape is important because statisticians base their tests on
assumptions about the return probabilities based on luck alone. If the shape
they assume is different from the true shape of random returns, their infer-
ences will be wrong.
The quantitative properties of the t-tests presented above were derived
by assuming that portfolio returns are
normally distributed.
The
normal dis-
tribution is
a specific bell-shaped distribution. (The probabilities of each
outcome plotted against their corresponding outcomes trace the shape of a
bell.) For reasons that need not concern us now, the normal distribution
very often provides excellent characterizations of the distributions of a wide
variety of random variables. Statisticians therefore most commonly base their
analyses on this distribution.
22.5.1.1 Normality
The actual distribution of portfolio returns, however, is not normal. Many
studies demonstrate that extreme values are more common for portfolio re-
turns than we would expect if they were normally distributed. The actual
distribution of returns therefore is fat-tailed in comparison to the normal
distribution. Although the difference is not great, the unfortunate conse-
quence of the more common extreme values is that t-tests are less power-
ful than they would be if the returns were normally distributed. The results
we discussed above, discouraging as they are, actually overstate the reliabil-
ity of t-tests.
22.5.1.2 The Peso Problem
One particular property of the normal distribution is of critical interest to
us. The normal distribution is a
symmetric distribution.
In a
symmetric dis-
tribution,
outcome probabilities depend only on their distance from the me-
dian value of the distribution. The probabilities of outcomes at equal dis-
tances above and below the median therefore are the same. The returns of
well-diversified portfolios are approximately symmetrically distributed. Al-
though there are some systematic departures from symmetry (large nega-
tive values are slightly more common than large positive values), these de-
partures usually affect index returns as well. Accordingly, market-adjusted
returns tend to be quite symmetric for well-diversified portfolios because
the asymmetries in the portfolio and index returns offset each other.
Certain portfolio strategies, however, can produce highly asymmetric


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468 • TRADING AND EXCHANGES
How to Fool Most
People Most of the
Time: Sell Volatility
An equities manager can
produce an enhanced index
return in most years by
holding an index portfolio
(or a closet index portfolio)
and writing out-of-the-money
options. The index portfolio
produces the index return.
The out-of-the-money options
produce a small additional
return if the options are not
exercised. If the options are
exercised, the portfolio
generally will suffer a
significant loss. The options
will be exercised when the
market is highly volatile.
Since it takes essentially no
skill to set up this strategy, it
is an example of an
informationless trading
strategy.
The market-adjusted returns
of this portfolio will be small
and positive most of the time.
Only very rarely will the fund
underperform the market.
When it does, however, the
underperformance will be
very large.
If the manager has not
been unlucky, t-tests will
indicate that the manager is
highly skilled, even in small
samples. Clients therefore
must be very vigilant to
ensure that their managers
have not given them a peso
problem. 4,
return distributions. Unfortunately, t-tests applied to returns generated by
these portfolios can produce highly unreliable results. The true confidence
levels and power of t-tests based on highly asymmetric distributions are
vastly different from those based on the normal distribution.
The
peso problem
is an extreme example of this problem. A
peso problem
arises when a trading strategy almost always produces a small positive re-
turn. Very rarely, however, the strategy produces a very large negative return
that may more than offset the many small gains the portfolio normally pro-
duces. This distribution of returns is highly asymmetric.
The peso problem is of special concern to performance evaluation. Un-
til a calamity occurs, a manager who holds a portfolio with a peso problem
appears to be quite skilled. The manager, however, is lucky rather than
skilled.
It is almost impossible to use statistical methods to evaluate managers
who have peso problems. A reliable evaluation requires enough data to en-
sure that calamities are adequately represented in the sample. Since calami-
ties are quite rare, the sample must be extraordinarily long. The only reli-
able way that clients can determine whether their managers are creating
peso problems is to directly examine the strategies that their portfolio man-
agers use.
The Peso Problem
The following story is part of the folklore of the
Economics Department
at
the University of Chicago. I have no idea of its veracity.
In the 1960s and 1970s, inflation in Mexico was significantly higher
than in the United States. Interest rates therefore were higher in Mexico than
in the United States. Had there been a floating exchange rate regime then,
the Mexican peso would have depreciated relative to the dollar at a rate
that would have made investors roughly indifferent between investing in the
United States and in Mexico. For example, a U.S. investor would have
earned higher interest in Mexico than in the United States, but the premium
would have been offset by a decrease in the dollar value of the peso over
the period of the investment.
In fact, the Mexican government fixed the exchange rate so that it could
not change. The continuing inflation, however, forced the government to
devalue the peso on an irregular basis. Anyone who had assets
denominated in Mexican pesos suffered a large loss every time the peso
was devalued.
Investment in Mexican debt securities therefore created a
peso problem.
As long as there was no devaluation, the investment would systematically
outperform similar dollar-denominated investments. Whenever a devaluation
occurred, however, the gains would be lost overnight.
A
certain Chicago professor is said to have invested in Mexican debt
instruments to take advantage of the interest rate differential. When
confronted by his colleagues about the peso problem, the professor was
reported to have said that he was not worried: As an authority on
international economics who had trained a significant number of Mexican
economists, he was certain that his former students would call him for his
advice before they devalued the peso. He therefore expected that he would
be able to avoid the peso problem by selling immediately after he took
their call.
He was right. His students did call, but they could not reach him
because he was traveling!


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CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 469
1/0 The St. Petersburg Paradox: Why Not Double-down?
Bet a dollar on the outcome of a coin flip. If you win, quit with a 1-dollar
profit. If you lose, bet 2 dollars on the outcome of another coin flip. If you
win, quit. Your total profit from both coin flips will be 1 dollar. If you lose,
double your bet again to 4 dollars. If you win this third flip, quit with a
1-dollar profit (4 — 2 — 1), otherwise continue doubling your bet until
you win.
This
doubling-down
betting strategy is called the
St. Petersburg paradox.
If you have infinite wealth, you will always win 1 dollar by playing it until
you finally win. If your wealth is finite, however, you will eventually go
bankrupt if you play the game often enough. It is a paradox because these
results do not depend on whether the coin is fair.
An equities manager can produce an enhanced index return in most
years by holding an index portfolio and making a short-term bet on some
investment idea. The index portfolio produces the index return. If the bet
does not work out, bet again with twice the money. Continue doubling
down until you win. This simple trading strategy ensures that the fund
manager outperforms the market as long as the fund does not go bankrupt.
The doubling-down strategy is very attractive to undisciplined investment
managers because it usually allows them to avoid the psychological
consequences of their poor decisions. They rationalize by thinking that they
initially may have been wrong about an idea, but in the end they got it
right.
The strategy is also attractive to unethical investment managers who fear
that their clients will dismiss them if they perform poorly. They will play the
strategy if they have performed poorly. Such managers are unethical
because they do not care about the risks that they impose upon their clients,
and because they manage their portfolios to benefit themselves rather than
their clients.
4
4
22.5.2 Fraudulent Returns
An implicit assumption of statistical performance evaluation is that the re-
turns under analysis are true returns. Statistical tests applied to returns that
are not accurate obviously will not produce accurate results. Computer sci-
entists and statisticians are both fond of saying, "Garbage in, garbage out."
You need to be aware of two processes that can cause returns to be fraud-
ulent. They involve return smoothing and pyramid schemes.
22.5.2.1 Return Smoothing
As noted in "Smooth Sailing, Rapids Ahead" (p. 448), managers who value
portfolios of infrequently traded assets may adjust the values of those assets
less quickly than they should. The portfolio returns that they compute from
these values therefore will be inaccurate. In particular, they will change too
slowly. Their variation from period to period will be less than it should be,
and the ratio of return continuations to return reversals (return serial cor-
relation) will be higher than it should be. Consequently, the returns will ap-
pear to be smoother than they should be.
Artificial smoothness affects statistical performance evaluation because
smoothing decreases return volatility. The artificially low return volatility
causes statisticians to conclude that large mean returns are more significant
than they are. In particular, recall that the t-test is based on the ratio of
the mean return to a measure of its expected dispersion called the standard
error. Since statisticians usually estimate the standard error from the return


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470 • TRADING AND EXCHANGES
volatility, artificially low return volatility causes artificially low standard
error estimates, which cause artificially high t-ratios, and therefore over-
confident conclusions.
When managers smoothe too much, their reported valuations may sig-
nificantly differ from true valuations. At some point, their regulators, cus-
tomers, brokers, auditors, or custodians may recognize the discrepancy and
demand immediate adjustment. Since smoothing invariably delays the
recognition of capital losses rather than of capital gains, the resulting reval-
uations produce very significant negative returns. Smoothing thus can pro-
duce an artificial peso problem. From the investors' point of view, however,
there is nothing artificial about the problem.
22.5.2.2 Pyramid Schemes
A
pyramid scheme
is a fraud that dishonest investment managers commit
against their clients. In these schemes, the manager explicitly or implicitly
promises a high rate of return on an investment. Investors then place their
money with the manager, who usually very actively promotes the scheme.
The manager then uses their capital to pay high returns to initial investors.
The apparently high returns realized by the initial investors attract new in-
vestors. As long as the manager can attract an ever-growing base of new
investors, he can pay off earlier investors, and the scheme can survive. At
some point, however, the fraud gets so large that the manager can no longer
pay earlier investors. At that point, it collapses and anyone who has not yet
been paid usually loses everything. The promoter profits either by stealing
funds or by charging management fees. Pyramid schemes, of course, are il-
legal almost everywhere.
These schemes are called pyramid schemes because they are built upon
an ever-enlarging base of investors. The investors at the top profit from the
"support" provided by the investors at the base. They are similar to chain
letters. Pyramid schemes are extreme examples of "robbing Peter to pay
Paul." Pyramid schemes are also known as Ponzi schemes after Charles K.
Ponzi, who ran an extremely large pyramid scheme in 1920.
Until pyramid schemes collapse, the returns that they generate are re-
markably good. If you analyze these returns to determine whether a man-
ager is skilled or just lucky, you will conclude that the manager is skilled.
The only way to protect against losing to a pyramid scheme is to deter-
mine whether the manager's accounting systems accurately report invest-
ment assets, account liabilities, investment income, and capital gains distri-
butions. In a pyramid scheme, actual assets and actual investment income
generally will be substantially less than reported.
If pyramid scheme promoters are not too greedy, if their excess returns
are not too large, if they can convince their clients to not withdraw their
funds, and if they can somehow control the audits of their portfolios, pyra-
mid schemes can go undetected for a very long time. An investment man-
ager's performance record therefore is no substitute for doing the
due dili-
gence
that all prudent investors must undertake to ensure that they are not
contributing to pyramid schemes.
22.6 THE SAMPLE SELECTION BIAS
The two preceding sections demonstrate that statistical tests for managers
generally do not produce useful information about their skill. The proper-


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CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 471
Ponzi's Ponzi Scheme
Charles K. Ponzi established the Securities Exchange Company in
December 1919 in Boston. His company purportedly traded postal reply
coupons.
Postal reply coupons are coupons redeemable for postage. People who
wanted to enclose return postage used them when corresponding with people
in other countries. The postal reply coupon system was set up in 1906 by a
postal convention of over 60 countries. Participants in the system agreed to
honor the coupons at fixed exchange rates. When the system was initially
established, coupons everywhere sold for slightly more than they were worth
in postage. Following World War I, however, changes in exchange rates
made it possible to buy coupons in some countries and redeem them at a
substantial percentage profit in other countries. Although the percentage
profits could be quite large, the total amounts that anyone could earn this
way were quite small because the coupons were not worth much.
To fund his operation, Ponzi sold bonds to investors that promised them
a 50 percent return over 90 days. He claimed that he could produce this
income through the 400 percent returns he was making by redeeming
postal reply coupons. No evidence suggests that Ponzi ever redeemed more
than a trivial quantity of coupons.
Ponzi repaid his bonds at face value in 45 days. The resulting
excitement caused his business to grow exponentially. By July, the business
was taking in 1 million dollars per week from offices in several cities in the
Northeast.
The government forced Ponzi to halt sales of his notes on July 26.
By that time, he had collected about 9.5 million dollars from more than
10,000 investors. On July 28, investors started to demand payment on their
notes. Remarkably, instead of running away with the money, Ponzi paid the
notes until he ran out of cash on August 7.
Ponzi was arrested on August 12 and charged with many counts of
federal mail fraud. He negotiated a guilty to plea to one count, and the
court sentenced him to five years in prison. Following his release, he
engaged in other financial frauds for which he was caught and convicted.
Source: Charles K. Ponzi Website:
The Remarkable Criminal Financial Career of
Charles K. Ponzi
by Mark
C.
Knutson at www.mark-knutson.com
, May 31, 2001.
ties of the tests described there were derived by assuming that analysts would
use the tests under ideal circumstances. In practice, statistical performance
evaluations rarely are applied under ideal circumstances. Consequently, the
tests are still less useful than they seem.
The most important problem that plagues these tests is the sample se-
lection problem. This problem can affect inferences whenever people learn
from the past. Their learning may involve formal analyses like the t-test de-
scribed above, or it may simply involve judgments that people make based
on their experience. In either event, you must be very careful that the
sample selection bias
does not affect your conclusions.
The
sample selection bias
arises when some process selects the informa-
tion that you see about some object. If the process does not randomly se-
lect the information that you see, you will see only selected aspects of the
object and your impression of it will not be accurate. Decisions that you
make based upon your information therefore very likely will be faulty.


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472 • TRADING AND EXCHANGES
Et Blind Men Describe
an Elephant
The story of the blind men
who examine different parts
of an elephant illustrates the
sample selection bias: Each
man feels only the trunk, leg,
side, or tail, and each
respectively concludes that the
elephant is like a snake, a
tree trunk, a wall, or a rope.
Since each man examines
only one aspect of the
elephant, none of them
appreciates the totality of the
elephant. Each man would
make a poor decision about
elephants based only on the
information produced by
his examination of the
elephant.
22.6.1 Sample Selection in the Mutual Fund Industry
Suppose you examine the marketing literature of a company that manages
and distributes a large family of mutual funds. Each fund accurately reports
its performance relative to appropriate benchmarks. By reviewing the data,
or by doing some formal statistical calculations, you conclude that their per-
formance has been very impressive. On average, the funds in this family
have substantially beaten their benchmarks. The performance obviously is
both statistically significant and economically significant. The funds have
performed much better than you would expect if their managers were not
skilled. You therefore conclude that their managers are skilled and decide
to invest your money with this company.
You may be very disappointed with your results. Many mutual fund dis-
tributors feature only their best-performing funds in their marketing literature.
Although they may offer other reasons for why they select which funds to fea-
ture, they undoubtedly know that they will benefit from this presentation of
their products. The average performance of all funds in the family actually may
be negative. If you knew this, you might have concluded that it is unlikely the
company's funds will beat the market on average, and that the managers of
their best-performing funds probably were only lucky. If the managers indeed
are not skilled, you probably will be disappointed with your decision. You would
have made a better decision had you known about the other funds.
A sample selection bias affected your inference. You only saw the funds
that the marketing literature presented and therefore only saw a selected
view of the company.
Even if the company presents the results for all its funds, you still may
not see the entire picture. Mutual fund distributors often kill their poorly
performing funds, usually by merging them with better-performing funds.
The reported performance of the survivor is based only on its performance
and does not include the performance of the failed fund. Mutual fund com-
panies may kill their losers because they become expensive to operate when
they get small. They may also kill them because they do not want to report
their performance. In either event, by killing poorly performing funds, they
raise the computed average performance of the surviving funds. The aver-
age performance of all funds may be negative, but you could not know this
without knowing about the other funds. This type of sample selection bias
is called the
survivorship bias.
Some large mutual fund companies start many new mutual funds every
year. They keep the ones that perform well and kill the ones that fail. In
this way, they are able to create the winners that they need to market their
funds. If you are unaware of this process, you may give too much signifi-
cance to past returns. You may not realize that the fund which generated
superior past performance came to your attention only because it was among
the best-performing funds of a large group of funds.
22.6.2 Avoiding the Sample Selection Bias
Sample selection biases may be responsible for more trading losses than
any other cause. They usually arise because people do not see the whole
picture or because they fail to ask the right questions when they interpret
what they see.
Many people invest with active investment managers who have per-
formed well. As noted in section 22.4.5, whether their performance is sta-


## Page 486

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 473
Ot
,
My Favorite Fraud
The following scheme is illegal. Do not use it. I present it only so that you
can better appreciate the sample selection bias, and so that you can avoid
falling for this scheme should a con man ever try to target you.
Buy a mailing list of 20,480 wealthy and gullible people. Send them a
letter in which you explain that you are a very successful trader who has
more money than you can spend.
Boast
about how you correctly predicted
what direction prices would move during nine of the last ten months. Tell
some story about how you did it. Then tell them that you now crave their
recognition for being a market genius, since money is no longer very
meaningful to you. Give them your prediction for the next month.
Divide your names into two equal groups. To the first group, predict that
the market will rise. To the second group, predict that it will fall.
One month later, write a letter to the 10,240 people for whom you
correctly predicted the market. Continue boasting and remind them that you
correctly picked the market for them. Tell them how much money they could
have made if they had traded on your recommendation. Then give them
another pick. Of course, you predict an increase to one half of the group
and a decrease to the other half.
Repeat this for ten months until 20 people have seen you correctly
predict the market 10 times in a row (20 = 20480/2
10
). If they are not
aware of your scheme, they will be convinced that you have correctly
picked the market 10 times for them, and they will be prepared to believe
that you did indeed do it correctly in nine of the ten months before you
started to correspond with them. Such results are exceedingly unlikely to
occur at random. They will swear that you are a genius.
You now have these people wrapped around your finger. If they are
gullible, you may be able to abuse their trust.
If you also send letters to people for whom you incorrectly predicted the
market, you can increase your yield. Simply tell them that you are only
human and cannot get it right every time. You will have 200 people for
whom you have correctly called the market exactly nine times and another
900
for whom you have correctly called the market exactly eight times.
You
probably will not want to continue corresponding with those for whom you
failed to call the market three or more times.
The "lucky" ones for whom you correctly predicted the market cannot
possibly make the proper inference about your skill without knowing your
scheme. If they do know the scheme, the only rational inference is that you
are not skilled.
4
tistically
significant
must be judged relative to the entire sample of man-
agers. What appears to be significant out of context may not be notewor-
thy in context. The important point to remember is that bad managers do
not come to your attention. Nobody talks about them. Nobody writes about
them. They do not write books. You learn about the best managers only be-
cause they had great performance. Everyone talks about them. Everyone
writes about them. They write books that everyone reads. To make reason-
able statistical inferences about whether great performing managers are
skilled, you must know the total number of managers who might have been
lucky but were not.
Similar comments apply to investment newsletters. Only the newsletters
that have great records for recommending securities and contracts survive
to come to your attention. The ones with poor records fail. The surviving
newsletters may be written by insightful authors or by lucky authors. Un-


## Page 487

474 • TRADING AND EXCHANGES
fortunately, you generally cannot assess the authors' skills from their per-
formance record.
22.6.3 Regression to the Mean
Although sample selection can produce an upward bias in an average of past
returns, it cannot affect subsequent returns. Perhaps the clearest evidence
that sample selection has affected an average of past returns is the differ-
ence between that average and an average of subsequent returns. When se-
lection has caused the past return average to be high, the subsequent return
average is invariably lower. This phenomenon is called
regression to the mean.
Researchers have identified it in many different contexts.
22.6.4 Summary
Whenever some process filters the data that you see, you get only a selected
aspect of reality. When you make decisions based on that view, you often
will make poor decisions. The only way to avoid the sample selection bias
is to understand how the data came to your attention. You must always ask
whether an event is significant relative to the process that generated it, and
not simply relative to what you know about it.
The Regression of Public Commodity Pools
In the early and middle 1980s, approximately 2,000 commodity trading
advisers were registered with the National
Futures Association.
The vast
majority of these advisers managed private accounts.
Between July 1979 and June 1985, these advisers offered approximately
15 new commodity funds—commonly called
public commodity
pools—per
year to public investors. The offerings were accompanied by prospectuses
that reported the average returns for the adviser's accounts for at least the
previous 36 months (if the adviser had been advising that long).
Their average monthly return in the 36 months before the offering was
4.1 percent. These funds had performed remarkably well!
In the first year after going public, their average monthly return was
0.23 percent. The results did not improve much in subsequent years. These
returns were stunningly disappointing in comparison to the prior returns.
These results suggest that most of the advisers who offered public funds
probably had simply been lucky in the previous 36 months. Once the funds
went public, their average returns regressed to the mean.
To support this conclusion, consider how many of the 2,000 advisers we
would expect would have average returns greater than 4.1 percent per
month over the previous 36 months, purely by chance. During this period,
all public commodity funds had a mean monthly return of approximately
0.7 percent per month with a standard deviation of 11.3 percent per
month. If we assume that returns are normally distributed with this mean
and standard deviation, by pure chance, a probability calculation (not
reported) predicts that we would expect 70 of 2,000 advisers would have
an average monthly return of greater than
4.1
percent over the last three
years. Chance alone therefore provides more than enough advisers to
explain the
4.1
percent monthly return average of the advisers who offered
public commodity funds. (Note that the average includes managers with
prior average returns both greater and less than 4.1 percent.) 4
Source: Edwin J. Elton, Martin J. Gruber and Joel Rentzler, "New Public Offerings,
Information, and Investor Rationality: The Case of Publicly Offered Commodity Funds,"
Journal of Business
62, no. 1 (1989): 1-15.


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CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 475
oe Warren Buffet Reconsidered
In the 26 years between 1965 and 1990, the book value of Berkshire
Hathaway increased by an average of 13.2 percent more than the total
return on the S&P 500 Index. By 1990, Warren Buffet was widely
acclaimed to be a highly skilled investment manager.
During the next 10 years, the book value of Berkshire Hathaway
increased only by an average of only 6.84 percent more than the total
return on the S&P 500 Index. Although this performance is still quite
impressive, it is not as impressive as the earlier performance. The
performance clearly regressed toward the mean. In his first 26 years,
Warren Buffet probably was lucky as well as skilled. In the subsequent
10 years, he was either less lucky or it was much more difficult to produce
the same extreme returns with a much larger firm.
Suppose that in 1990, you decided to collect returns data for the next
ten years to determine whether Warren Buffet was truly a skilled trader. The
standard t-test then would be appropriate because the subsequent returns
would not be subject to the sample selection bias. Using only annual data
from 1991 through 2000, the t-statistic is 1.95. The probability that an
unskilled manager would have a t-statistic greater than this value is slightly
greater than 4 percent. These results suggest that only one unskilled
manager in 25 would have performed better than Warren Buffet over this
period. Warren Buffet probably is a skilled manager.
In trading, the sample selection bias is especially important because win-
ners come to our attention much more often than losers do. Unless we are
very disciplined, we may easily overvalue winners.
The sample selection problem is especially difficult to recognize because
most of us want to believe that winners are skilled and not just lucky. Our
natural inclination is to attribute performance to skill or the lack of it. Most
of us believe that there is a reason for everything. We would rather that the
reason be skill than luck. When making inferences, always beware of rose-
colored glasses.
Our desire to attribute performance to skill or the lack of it applies pri-
marily to others. When we consider our own performance, our natural in-
clination is to attribute good performance to skill and bad performance to
luck. We do this because we want to feel good about ourselves. We there-
fore tend to remember good results and try to forget bad results. This may
be the most dangerous selection bias that we face.
22.7 ECONOMIC APPROACHES TO
PERFORMANCE PREDICTION
The difficulties associated with statistical performance prediction suggest that
inferences based only on past performance will not be useful for predicting
future performance. Past performance simply does not predict future returns
with enough confidence to be of much use for most applications.
Fortunately, economic theory—simple game theory, actually—suggests
another approach. In the long run, players win games when they have a
com-
parative advantage
over their opponents. Players have a
comparative advan-
tage
when they have greater skills or greater resources than their opponents.
The skills and resources, of course, must be those which will help the play-
ers excel. They therefore will vary by the strategies that the players pursue.
Ot More Caveman
Psychology
Our evolutionary history has
hardwired our brains to
believe that everything has a
cause. Our ancestral aunts
and uncles who could not
connect events to causes often
failed to exploit opportunities
that enhanced their survival,
or to avoid risks that
threatened their survival. They
therefore did not survive to
reproduce as often as did our
ancestral parents. We know
that our ancestral parents
survived because we are here
to speculate on how they did
it. They must have been able
to connect events to their
causes better than our
ancestral aunts and uncles.
Our minds therefore are
probably hardwired to seek
an explanation for every
event.
Many people think that the
desire for an explanation for
everything is the reason why
many people are religious.
Although I do not believe this,
it seems reasonable to me
that this hardwired desire to
link causes to events explains
why people want to believe
that successful people are
skilled and not just lucky.
It Seems Like Everyone
Is a Winner
People talk about their
successes far more often than
their failures. They enjoy
describing their winnings and
they are embarrassed to
admit their mistakes.
If you listen only to what
people say about their trades,
you will think that most
traders are winners. If you
draw inferences from what
you hear, you might think that
you, too, would be a winner,
if only you traded.
1


## Page 489

476 • TRADING AND EXCHANGES
Winning the Olympic Marathon
On average,
better
players win games. Good players and even great
players do not generally win when they play against even better players.
A player has an
absolute advantage
when he or she can do something
well. For example, a marathoner who can complete the marathon in less
than 2:20 is incredibly fast. Unless you are involved in track and field, you
probably have never met anyone so fast. A 2:20 marathoner will win the
vast majority of marathons that are run every year.
Such a time, however, would have been good for only 36th place in the
men's marathon at the 2000 Olympics. Most marathons do not attract
world-class runners. Those which do, however, are highly competitive. You
do not win such races by running extremely fast. You win them by running
faster than every other runner.
To win a game, you must not just play it well. You must play it better
than your opponents.
Since trading is essentially a game, we should be able to predict long-
run performance by identifying the factors that produce it. Those traders
who have comparative advantages in those factors should be the long-run
winners.
Tables 22-8 and 22-9 provide a partial list of the factors that may pre-
dict the performance of managers, traders and the organizations for which
they work. These factors vary in importance depending on the investment
styles and the trading strategies that the manager, trader, or firm pursue.
The most important comparative advantage that a manager or trader
should have is a thorough appreciation of the need to have a comparative
advantage. Traders who do not understand why comparative advantage is
important will not consider whether they have a comparative advantage be-
fore they trade. If they do not consider this question, they can have no rea-
son to expect that they will trade profitably.
Traders who appreciate the importance of comparative advantage con-
sider both why their trading strategy should work, and why they expect other
traders will lose to them. Since trading is a zero-sum game, the two issues
are inseparable. Most traders, however, focus only on why they think they
will profit and not also on why they think other traders will lose to them.
Traders who understand both sides of their trades will undoubtedly be more
successful than those who consider only the logic of their side.
Most people have trouble identifying comparative advantage. We often
mistake absolute advantage for comparative advantage. We may err because
absolute advantage is correlated with comparative advantage. The more
skilled a manager is, the more skilled the manager will be in comparison
with other managers. To identify comparative advantage, however, you must
compare managers. The point bears repeating: To win a zero-sum game, you
must not just be good, you must be better.
22.8 SUMMARY
People primarily examine past performance because they want to predict
good future performance. Unfortunately, good past performance does not
necessarily predict good future returns. In fact, it rarely does. Over human
time frames, luck is generally a more important determinant of good per-


## Page 490

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 477
TABLE 22-8.
Factors Correlated with the Performance of Investment Managers and Traders
FACTOR
Intelligence
Experience
Education and
training
Creativity
Memory
Discipline
Compensation and
other performance
incentives
Interpersonal skills
Presentation skills
RATIONALE
Intelligent managers and traders recognize
theoretical opportunities and avoid
theoretical mistakes
Experienced managers and traders recognize
recurring opportunities and avoid past mistakes
Well-educated managers and traders and well-
trained managers and traders understand the
theoretical basis for the problems they solve
Creative managers and traders solve
problems well
Managers and traders who can recall what they
have seen and known often can find seemingly
hidden opportunities
Disciplined managers and traders make few
mistakes; they focus on what they know they
can do well and avoid what they cannot
do well
Organized managers and traders do not miss
opportunities
Managers and traders with strong drive work
hard and efficiently
Managers and traders generally work harder
when their compensation depends on their
performance
People cooperate with, and try to please,
managers and traders who have good
interpersonal skills
Traders occasionally have to bluff or withhqld
information
INDICATORS
Intelligence test scores
Insights expressed in written
works
Years of job experience
Formal education
Participation in training
programs
Attendance at conferences
Insights expressed in written
works
Remembers facts, people, and
history well
Plays
Concentration well
Consistency
The ability to articulate the
importance of comparative
advantage
Presentation skills
Filing systems
Personality
Attendance at conferences
Working hours
Compensation contracts
Personality
Club memberships
Acting experience
Skilled poker player
Organizational
skills
Drive
formance than is
skill. In addition, the skills that may have been responsi-
ble for good past performance may not produce good future performance.
Moreover, a formerly skilled manager may not still be skilled. These issues
make the prediction of good future performance from good past perfor-
mance an essentially worthless activity.
Even the most sophisticated statistical tests rarely can separate skill from
luck. The typical contribution of skill to performance is simply too small.


## Page 491

478 • TRADING AND EXCHANGES
TABLE 22-9.
Factors Correlated with the Performance of Investment Management Firms and Trading Firms
FACTOR
Qualified
personnel
Information
resources
Research
RATIONALE
Investment management and
trading depend critically on
human-capital resources
Valuation and trading are
information industries
Research creates information
from data
Resources are most valuable
when their usage is well
organized
Disciplined organizations do only
what they intend to do
All
ideas require
implementation
Management is responsible for
the morale of the firm and for
all organizational issues
INDICATORS
The factors presented in table 22-8
Staff training programs
Access to real-time and historic databases
Data collection facilities
Correspondent networks
Good models
Qualified analysts
Research reports and conferences
Structure reflects functional needs
Internal communications systems
Good risk management systems
Accounting and audit systems
Physical trading desk
Communication systems
The factors presented in table 22-8
Organizational
structure
Internal controls
Trading facilities
Leadership
The contribution is small because many traders compete with each other to
profit. Their trading makes prices quite informative, so that most price
changes are not predictable. Statistical performance evaluation therefore is
unreliable without more data than are generally available to us.
The importance of luck cannot be overemphasized. It is much better to
be lucky than skilled. The luckiest managers in a large group of managers
will certainly perform better than almost all skilled managers with average
luck. Superior past performance—even that of the most acclaimed man-
agers—by itself does not necessarily indicate skill.
These results do not imply that there are no skilled managers. The dis-
cussions in chapters 10-12 about speculative trading strategies suggest that
skilled managers exist and are profitable. Some skilled managers may even
be able to beat the market by more than the average 2 percent that we have
assumed throughout this chapter. Unfortunately, we probably cannot iden-
tify these managers only from past returns.
To identify skilled managers, it is best to consider the characteristic fac-
tors that generate superior performance. These factors include intelligence,
experience, education, training, creativity, memory, discipline, drive, and ac-
cess to data. Managers who have these characteristics tend to perform bet-
ter than those who do not.
Most professional managers have these characteristics and therefore
appear to be good managers. They probably can manage better than most
people. However, they mostly compete with other managers, not with the


## Page 492

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 479
Why Do We Mistake Absolute Advantage for
Comparative Advantage?
A very important difference between physical survival and trading explains
why our successful evolutionary history has not prepared us to be successful
traders.
Survival is primarily a game played against nature in which nature does
not actively create adaptive strategies to defeat us. To survive, our ancestors
merely had to be good at survival.
In contrast, trading is a zero-sum game in which our competitors
constantly try to adapt to defeat us. Good competitors win only if they are
the best competitors.
Our evolutionary history has trained us to appreciate absolute
advantage but not to seek comparative advantage.
Handicapping a Horse Race
Handicapping a horse race involves essentially the same processes as
choosing an investment manager. Handicappers and investment sponsors
both try to predict the outcomes of zero-sum games. They both consider
three classes of factors when they estimate the odds that a horse will win
the race, or a manager will outperform other managers: past performance,
absolute advantage, and comparative advantage. The similarity between
the two problems explains why investment sponsors often call their
searches for investment managers
horse races.
Table 22-10 illustrates these
parallels.
average person. Winning managers are those who have a comparative advan-
tage. They are not just good managers, they are better managers. To identify
a successful manager, you must therefore be familiar with many managers so
that you can compare them. If you do not have the characteristics of a suc-
cessful manager, you probably will not trade successfully in the long run.
TABLE 22
-
1 0.
Parallels Between Handicapping Horses and Choosing Investment Managers
FACTORS

HORSES

INVESTMENT MANAGERS
Past
performance
Absolute
advantage
Comparative
advantage
How has the horse run in the past?
How did it place against its
competition?
Does the horse look like it will run
fast? Does the horse possess
characteristics that determine speed,
such as stride, lung capacity, muscle
fiber type, weight of horse, weight to
be carried, time since last race, and
race experience?
How do the horse's past performance
and absolute advantage compare with
those of the other horses in the race?
What returns did the manager generate
in the past?
How did those returns compare with the
market and with other similar managers?
Does the manager look like he will
generate high returns?
Does the manager possess
characteristics that determine
performance, such as intelligence,
education, experience, discipline,
creativity, and access to data?
How do the manager's past performance
and absolute advantages compare with
those of the other managers in the market?


## Page 493

480 • TRADING AND EXCHANGES
Perhaps the most important indicator of a skilled manager is whether
the manager clearly understands that success comes from having a compar-
ative advantage. Managers who are not constantly thinking about their com-
parative advantages cannot know when they should trade. I would be very
reluctant to invest with managers who confuse absolute advantage with com-
parative advantage. Successful managers should be able to clearly articulate
the comparative advantages that they believe will allow them to profit in
the zero-sum game.
22.9 SOME POINTS TO REMEMBER
•
Distinguishing skill from luck is very difficult.
•
The skills that produced superior performance in the past may not pro-
duce such performance in the future.
•
Past returns do not necessarily indicate future returns.
•
Even when they have no skill, many traders will perform very well just
by chance.
•
Sample selection biases seriously affect common inferences.
•
To avoid the sample selection problem, we must always consider how
information came to our attention.
•
An analysis of comparative advantage is probably the only reliable way
to determine who can trade well.
22.10 QUESTIONS FOR THOUGHT
•
Do equal-dollar investors always have greater returns than buy and
hold investors? How does the answer depend on the serial correlation
of prices? Which strategy is more attractive to contrarians? To mo-
mentum traders?
•
Peter Lynch, former portfolio manager of the Fidelity Magellan Fund,
was an extremely successful investment manager. During his 13-year
tenure (May 1977 to May 1990), the fund outperformed the market
by 1.03 percent per month. The standard deviation of the market-
adjusted return was 2.21 percent per month. Was Peter Lynch skilled
or just lucky? How did you first learn about him and about the Fi-
delity Magellan Fund?
•
How is information in the newspaper subject to sample selection
biases?
•
How is selecting a manager like selecting stocks for a portfolio?
•
Where do you get your investment ideas? Is your idea generation pro-
cess subject to selection biases?
•
Do you have any reason to believe that you would be a successful ac-
tive manager?
•
Do you have any reason to believe that you could choose a successful
active manager?
•
A highly skilled manager will probably demand higher compensation.
How will the compensation affect his or her subsequent returns? If it
were easier to determine whether managers are skilled, how would the
labor market for investment managers be different? How would in-
vestment returns, net of managerial fees, be different?


## Page 494

CHAPTER 22 PERFORMANCE EVALUATION AND PREDICTION • 481
•
Corporate managers typically manage portfolios of real assets, whereas
investment managers manage portfolios of financial assets. Are they
otherwise similar? Do the principles discussed in this chapter about
evaluating and predicting performance for investment managers also
apply to corporate managers?
•
Closed-end mutual funds are corporations that hold portfolios of fi-
nancial assets. Unlike open-end funds, investors cannot buy or sell
shares directly. Instead, they buy and sell shares in the secondary mar-
ket. Accordingly, the market price and the net asset value of closed-
end funds can differ significantly. What can the market discount or
premium over net asset value tell us about the manager's skill?
•
Why were so many investors willing to extend so much credit to
Charles Ponzi?
•
How might a statistician recognize when an investment manager is ad-
justing the valuations of illiquid portfolio assets too slowly?


## Page 496

Part Ull
I>
market
Structures
T
he final chapters of this book examine the economics of market
structures. The topics we consider encompass many active regulatory
debates.
In chapter 23, we consider why index markets are organized as they are.
Our discussion shows how uninformed traders benefit from trading indexes.
Chapter 24 examines the specialist trading system. Specialists are broker-
dealers who supply liquidity and arrange trades at exchanges and at some
proprietary trading firms. Exchanges, regulators, and their business models
sometimes compel specialists to supply liquidity when they otherwise would
not want to do so. To encourage them to offer such liquidity, they must re-
ceive some benefit from their unique positions.
The next three chapters examine how markets and dealers compete
against each other for order flow. We examine internalization and order
preferencing by dealers in chapter 25, why markets consolidate and frag-
ment in chapter 26, and screen-versus floor-based trading in chapter 27. We
pay special attention to the problems that result when traders can trade the
same instruments in different places.
Chapter 28 discusses the origins of extreme volatility. There we con-
sider how market structures contribute to—and mitigate—volatility. We
also consider whether markets should have circuit breakers to control ex-
cess volatility.
Chapter 29 considers the benefits and consequences of prohibiting in-
sider trading. Interestingly, the most important issues involve labor eco-
nomics rather than market microstructure.
483


## Page 497

2 3
Index
and
Portfolio
Markets
I
ndex trading is one of the most important financial innovations of the
twentieth century. The nominal dollar value of trading in equity index prod-
ucts now is greater than the total dollar value of trading in the underlying
securities. The growth of index trading has had a profound effect on equity
markets. It is also increasingly affecting debt markets.
Index markets
trade index products.
Index products
include index futures
contracts, index option contracts, and securities that represent ownership in
index funds.
Index funds
are portfolios that their managers design to repli-
cate the performance of various price indexes. Most index funds track mar-
ket equity indexes. Some funds track debt indexes and sector equity indexes.
Index products and index markets are extremely popular. Many people
have decided that they would rather invest in an index product than risk
losing money investing with an active investment manager. Index products
also are attractive to speculators who want to speculate only on index risks
or only on firm-specific risks. The former buy or sell index products to es-
tablish their speculative positions. The latter sell or buy index products to
hedge the index risk in their long or short positions in individual securities.
The widespread use of index strategies has changed the character of mar-
kets. Index markets are far more liquid than the underlying cash markets
upon which their products are based. Price changes in index products gen-
erally lead changes in the cash index. Consequently, many people believe
that index markets are the "tail that wags the dog." You must understand
index strategies in order to understand the relation between index markets
and their underlying cash markets.
In this chapter, we will briefly consider how indexes are computed and
how index funds are managed. We will then turn to why index products and
index markets are so popular. You may find this section particularly useful
if you are unsure whether you should invest or speculate in equities. The
chapter closes with a discussion of the various ways that traders exchange
index risks.
23.1 PRICE INDEXES
People use
price indexes
to characterize the values of lists of instruments.
The instruments upon which a price index is based are the
index compo-
nents.
The index components determine the character of the index. Indexes
exist for entire markets, for subsets of a market, and for sets of markets. The
instruments may be equities, debt securities, commodities, or currencies. In-
dexes that include only a small subset of market securities are
narrow in-
dexes.
Narrow indexes have been defined for small and large securities, value
and growth securities, industry sector securities, and securities of firms that
do business in narrow geographic regions.
Most price indexes are proprietary products that exchanges, brokers, or
484
NEW STAMP

## Page 498

CHAPTER 23 INDEX AND PORTFOLIO MARKETS • 485
The Major Market Index and
The
Major Market Index
Dow Jones and Co. owns the Dow Jones Industrial Average (DJIA), which is
a price-weighted index of 30 large U.S. stocks. The list originally included
only industrial stocks. It now includes some stocks in the finance and
services sectors of the economy. The Dow 30 is the best-known U.S. market
index. It is
the
major market index.
For many years, Dow Jones refused to license the DJIA to options and
futures exchanges that wanted to create contracts based upon it. The
American Stock Exchange therefore created an index called the Major
Market Index (MMI). The MMI is a price-weighted index of 20 blue chip
stocks. Not coincidentally, most of the stock MMI stocks are also Dow 30
stocks. Changes in the MMI therefore are very closely correlated with
changes in the DJIA. The American Stock Exchange trades options on the
MMI using the ticker symbol XMI. It also licensed the index to the Chicago
Mercantile Exchange, which traded futures on it.
In 1997, Dow Jones finally licensed the DJIA to the Chicago Board of
Trade (CBOT) and to the Chicago Board Options Exchange (CBOE). The
CBOT Dow Jones Industrials futures and the CBOE Dow Jones Industrials
option contracts have been very successful. Both have killed their respective
MMI competitors. The CME stopped trading its MMI contract in 1999.
Although the AMEX MMI option contract continues to trade (as of December
2001), it no longer has significant open interest.
data vendors compute. Although
index creators
sometimes sell their indexes,
they often offer them to their clients to promote their businesses. Many in-
dex creators license their indexes to firms that base index products upon
them.
All price indexes are essentially just averages of the prices of their index
components. Indexes differ by the methods used to compute those averages,
however. The two most common index types are price-weighted and value-
weighted indexes.
A
price-weighted index
is proportional to the sum of the prices of the in-
dex components. The highest priced instruments therefore have the great-
est influence over the values of price-weighted indexes. The Dow Jones
Industrial Average (DJIA) and the Nikkei 225 Stock Average are the best-
known price-weighted indexes.
A
value-weighted index
is proportional to the total capital value of
all index components. Traders therefore also call value-weighted indexes
capitalization-weighted indexes.
Securities with the highest capital value have
the greatest influence over the values of value-weighted indexes. Most price
indexes are value-weighted. The S&P 500 Index is the best-known value-
weighted index.
The value of an index is obtained by dividing the price or value sum by
a constant
index divisor.
The divisor originally was a number that the index
creator chose to ensure that the index started at an arbitrary initial value.
Divisors now change only when necessary to ensure that the value of an in-
dex does not change when the creator adds or deletes index components or,
in the case of a price-weighted index, when a stock splits. For example, the
divisor of a price-weighted index must increase when a high priced stock
replaces a low priced stock. Otherwise, the change would unnaturally in-
crease the value of the index. Likewise, the divisor of a value-weighted in-
dex must increase when a high capitalization stock replaces a low capital-


## Page 499

486 • TRADING AND EXCHANGES
ization stock. The divisors of value-weighted indexes do not have to change
when stocks split, because splits do not change total capital values.
You may occasionally encounter
equal-weighted
and
geometrically weighted
indexes. Equal-weighted indexes
measure the returns from investing an equal
dollar amount in each index component. The index values represent the cu-
mulative returns to this hypothetical investment strategy. The best-known
equal-weighted index is the CRSP (Center for Research in Security Prices)
equal-weighted market index. It is used primarily in academic research.
Geometrically weighted indexes
average logarithmic returns rather than prices.
The Value Line Geometric Index is a value-weighted index of logarithmic
returns.
A price index is
dividend-adjusted
if it
is adjusted upward when securi-
ties pay dividends. Traders also call dividend-adjusted indexes
total return
indexes
because they measure the total return—capital gains plus income
yield—that investors would receive if they could invest in the index with-
out any transaction costs. People generally use total return indexes as bench-
marks against which they measure the performance of their portfolios. The
DJIA and the S&P 500 Index are not dividend-adjusted indexes. Corre-
sponding total return indexes for these two indexes, however, are widely
available.
23.2 INDEX FUNDS
An
index fund
is a portfolio that
index managers
design to replicate the per-
formance of an index.
Tracking error
is the difference between the portfolio
return and the corresponding dividend-adjusted index return. Index fund
managers try to minimize their tracking errors. Most U.S. index funds try
to replicate the S&P 500 Index, although other indexes are becoming in-
creasingly popular.
Replicating a value-weighted equity index is quite simple. If the value of
the index fund is 0.01 percent of the total capitalization of all the index
components, the index fund manager simply buys 0.01 percent of the out-
standing shares of each index component. The value of the fund therefore
is exactly proportional to the total value of all index components, which is
proportional to the value of the value-weighted index. Consequently, per-
centage changes in these three quantities will be identical. Index managers
must rebalance a value-weighted portfolio only when the list of index com-
ponents changes. Otherwise, the fund simply holds its securities.
Replicating a price-weighted equity index is equally simple. The index
fund simply holds an equal number of shares in each index component. The
value of the portfolio therefore is proportional to the sum of the prices of
the index components, which is proportional to the price-weighted index.
Percentage changes in these three quantities therefore will be identical. In-
dex managers must rebalance price-weighted index portfolios whenever the
index list is changed and whenever stocks split.
To replicate the returns to a dividend-adjusted index, index funds must
reinvest their dividends as they are paid.
Index funds generally slightly underperform their target indexes because
various frictions
drag down their performance. These frictions include trans-
action costs resulting from dividend reinvestment, accommodating deposits
They Each Manage
About 3 Percent of
All World Equity
Barclays Global Investors is
the world's largest index fund
manager. As of December
2000, the firm had 571
billion dollars under
management in various U.S.
and international equity index
funds. (The firm had 802
billion dollars of assets under
management, counting all
asset classes.) By comparison,
total world traded equity
market capitalization was
then approximately
31
trillion
dollars. Counting only index
funds, Barclays Global
Investors manages a bit less
than 2 percent of all traded
equity in the world.
The world's largest equity
manager is Deutsche Asset
Management, which manages
about 1 trillion dollars
worldwide in many
subsidiaries.
Sources: http://www.barclays

global.com/about/who_we_are/

assets_ronkings.ihtmb
http://www.fibv.com/
publications/Ta 1300. pdf.


## Page 500

CHAPTER 23 INDEX AND PORTFOLIO MARKETS • 487
The Price Impacts of the Annual Russell Reconstitution
Many U.S. stock index funds try to replicate the value-weighted Russell
1000, 2000, or 3000 Index. The Russell 1000 and 3000 Indexes
respectively consist of the 1,000 and 3,000 largest publicly traded U.S.
firms, ranked by their common stock market capitalization. The Russell 2000
Index consists of firms ranked between 1,001st and 3,000th in market
capitalization.
The Frank Russell Company, an investment management consultant,
annually reconstitutes its indexes at the close of trading on the last trading
day in June, based on market capitalizations as of the close of trading on
the last trading day in May. Stocks that have grown in size are added to
the Russell 3000 or are moved up from the Russell 2000 to the Russell
1000. Stocks that have lost value are moved down from the Russell 1000
to the Russell 2000 or are dropped from the Russell 2000 and 3000.
Stocks that stop trading due to bankruptcies or mergers are dropped when
they stop trading.
Index funds that replicate the Russell Indexes rebalance their portfolios
when the Indexes are reconstituted each June. Since these funds must buy
the additions and sell the deletions near the same date, they often have
substantial price impacts on these stocks during the months of June and July.
In the six years from 1996 to 2001, the Russell 3000 additions
outperformed the deletions by an average of 15 percent in June and
underperformed the deletions by 5 percent in July.
The Russell
3000
reconstitution price impacts are quite large because
these stocks are quite small. The reversal in prices in July indicates that
some of the price impact is transitory. The remaining difference in June
returns may be due to the increased value investors place on stocks in the
Russell Indexes—perhaps because they trade in more liquid markets—or to
a well-known momentum anomaly that affects the returns of small stocks.
Some of the difference may also reverse in August and later months.
Portfolio rebalancing has similar effects when Standard & Poor's changes
its stock index components. 4it
Source: Ananth Madhavan, "The Russell Reconstitution Effect," manuscript, 2002. To
be published in
Financial Analysts Journal.
and
redemptions, and rebalancing transactions when the index list changes.
Management fees also reduce fund performance.
Index funds can slightly improve their returns by careful management of
their trading. In particular, they can supply rather than demand liquidity
when trading, they can substitute nonindex components when index com-
ponents are expensive to trade, they can rebalance only when accommodat-
ing deposits and redemptions and when reinvesting dividends, and they can
hold only a subset of the index components to minimize the number of se-
curities that they have to trade. Although these policies tend to increase re-
turns, they also generally increase tracking error. Many funds therefore do
not aggressively employ them.
23.3
THE ARGUMENT FOR INDEXATION
Active portfolio managers
are speculators who try to beat the market by clever
trading. Active managers may be informed traders, value traders, or techni-
cal traders. The
turnover
of a portfolio is the ratio between the total dollar


## Page 501

488 • TRADING AND EXCHANGES
A Perspective on Bad
Active Management
Active managers do not lose
because they consistently buy
instruments that then fall and
sell instruments that then rise.
Funds that consistently make
such mistakes could greatly
increase their profits simply
by selling whenever their
research suggests that they
should buy, and buying
whenever their research
suggests that they should sell.
Bad research does not create
systematically wrong
signals—it merely creates
random noise.
Active managers do not
lose because they consistently
buy losers and sell winners.
They lose because they
consistently buy and sell.
value of all portfolio purchases (or sales, or average of purchases and sales)
in a given period and the total value of the portfolio. Active managers of-
ten have turnover rates of more than 100 percent per year. They typically
charge between 1 and 3 percent for their services.
In contrast,
passive managers
construct portfolios and then leave them
alone. Since they rarely trade, passive managers usually have turnover rates
between 0 and 10 percent per year. Passive managers typically charge less
than 15 basis points (0.15 percent) for their services, whereas active man-
agers charge 50 to 100 or more basis points.
Most active managers cannot beat the market because transaction costs—
brokerage commissions and management fees—reduce performance in what
is otherwise a zero-sum game. Without transaction costs, the value-weighted
average return of all portfolios would be equal to the value-weighted mar-
ket index return. Transaction costs ensure that the average portfolio return
is always less than the market index return. Since active managers trade fre-
quently, they tend to underperform the market.
These implications of the zero-sum game are logical conclusions based
on simple accounting principals. They are always true.
Since these implications must be true, empirical results on the perfor-
mance of active fund managers cannot be surprising: As a group, active fund
managers underperform the market. In any given quarter, only one-fourth
of all mutual funds beat the market. If there were no transaction costs, if
mutual funds represented a random sample of all funds, and if small funds
on average performed no better than large funds, we would expect that half
of all mutual funds would beat the market. Indeed, if you add back trans-
action costs, about half of all mutual funds do beat the market. Funds un-
derperform because of their transaction costs.
Interestingly, the set of winners varies from quarter to quarter. Funds
generally do not persistently outperform the market. This result is not sur-
prising: Our discussions in chapter 22 suggest that luck is a more impor-
tant determinant of performance than skill.
The set of extreme losers does not vary as much as the set of winners.
Extreme losers lose because they trade too much. It is much easier to con-
sistently lose than to consistently win.
Some managers undoubtedly can beat the market on average, even after
accounting for their transaction costs and management fees. Unfortunately,
as we saw in chapter 22, identifying such managers is very difficult.
Many uninformed investors employ
buy and hold strategies
to avoid the
difficulties of selecting skilled active managers and the costs of investing
with unskilled active managers. Buy and hold investors avoid trading losses
by not trading. They also avoid high management fees.
Since index funds implement buy and hold strategies, they are very at-
tractive to investors who want exposure to index risk without the risk of
substantially underperforming the market. The minor frictions associated
with index fund management ensure that index funds will slightly under-
perform their indexes. Although index funds slightly underperform their in-
dexes, they regularly beat three-quarters of all active managers.
Once again, note that this regularity is not simply an empirical fact. It
is an implication of the zero-sum game.
Although investors can save transaction costs by pursuing any buy and
hold strategy, they generally choose to invest in broad-based market index


## Page 502

CHAPTER 23 INDEX AND PORTFOLIO MARKETS • 489
funds because they offer well-diversified portfolios that replicate the mar-
ket. Since market index returns are widely published, index investors can
easily audit whether their managers are doing what they expect them to do.
23.4 LIQUIDITY AND PRICE FORMATION IN
INDEX MARKETS
Index markets
and
index trading mechanisms
allow traders to trade index risk
more cheaply than they could by trading each component instrument sep-
arately. Several factors ensure that index products have low transaction costs.
First, index dealers face little risk of trading with well-informed traders.
Most index traders are uninformed investors. Few traders have valuable in-
sights into the future direction of the entire market. Accordingly, index deal-
ers do not have to quote wide spreads to recover from uninformed traders
what they lose to informed traders.
Second, index markets tend to be very active because most people trade
the same index products. Buyers therefore can easily find sellers. Moreover,
since dealers can turn over their inventories quickly in active markets, they
face little inventory risk, which allows them to quote tight markets.
Finally, traders of index products generally need to arrange, clear, and
settle only a single transaction. Reducing trade to a single transaction saves
time and effort. Index traders who trade the underlying component instru-
ments have to arrange many trades, which is substantially more expensive.
Traders trade the index components when they need to assemble or dis-
assemble index portfolios. These trades generally are arranged as
program
trades.
A
program trade
involves the simultaneous submission of many or-
ders at the same time. For statistical purposes, the New York Stock Ex-
change and the Securities and Exchange Commission classify program
trades as any trades that involve 15 or more coordinated transactions hav-
ing a total value of 1 million dollars or more. These trades represent about
27 percent of trading volume at the NYSE. Index arbitrageurs who need to
construct or liquidate index portfolios do about 9 percent of the reported
program trade volume at the NYSE and about 2.4 percent of total NYSE
volume. The remaining 91 percent of program trading volume is due to
other portfolio trading strategies, many of which are also index-based.
Traders who do program trades generally use specialized
order list processing
software
to manage their orders.
Price changes in index markets generally lead changes in the cash index.
Index traders are concerned only about discovering the price of index risk.
In contrast, traders in the component securities must concern themselves
with pricing all of the risks inherent in their securities. Index risk is usually
much less important to them than security-specific risks. Most index mar-
kets therefore discover the prices of index risk much faster than do the many
individual markets in which their index components trade. Traders in indi-
vidual security markets therefore look to index markets to get a sense of
where the market is going.
23.4.1 Package Trading, Basket Trading,
and Portfolio Trading
Package dealers
make firm bids or offers for entire portfolios. When these
portfolios are index portfolios, the costs of trading them are often quite low.
The Program
Trade Buzzer
The New York Stock
Exchange used to print
electronically routed
(SuperDot) orders on the
exchange floor. Each trading
post had several dot matrix
printers that printed the
orders. Floor traders could
easily recognize when
program traders submitted
their orders by the
simultaneous buzz that these
printers made when printing
order tickets for the various
stocks in the program trades.
The Exchange no longer
prints Super Dot orders.
Instead, it routes them directly
to the specialists' electronic
order books.


## Page 503

490 • TRADING AND EXCHANGES
ESP at the NYSE
Following the stock market crash of 1987, the New York Stock Exchange
decided that it would create an organized market for institutional-sized
package trading in the S&P 500 portfolio. The Exchange created the
Exchange Stock Portfolio
(ESP), a portfolio of all S&P 500 stocks. Due to
problems with fractional shares for smaller index stocks, the NYSE sized the
ESP so that it was worth about 6 million dollars.
The NYSE enlisted five major investment banks to act as competitive
market makers in the ESP. Each was required to make a firm bid and offer
for the ESP.
The ESP started trading in October 1989. The product was not
successful. Trades only occurred only when one dealer picked off another
dealer who was slow to adjust his quotes in response to changing market
conditions. During the 25 months that the ESP traded, fewer than of the
269 total trades were agency trades arranged on behalf of a client.
The result was not surprising, considering that the price of S&P 500
Index risk then was (and still is) primarily discovered in the S&P 500 futures
pit on the floor of the Chicago Mercantile Exchange (CME). The ESP
dealers could never quote a market as tight as the futures market because
they were away from the pit, and therefore away from the most recent and
most reliable information about S&P 500 Index values. The ESP dealers
were also at a disadvantage compared to the CME floor traders because
the ESP dealers had to quote continuous firm markets for a 6 million dollar
transaction. In contrast, CME floor traders were not—and still are not—
obligated to quote firm markets for any size.
To facilitate trading of the ESP, the NYSE modified its trading systems to
permit decimal pricing. Few people know that the NYSE was able to trade
on decimals for 11 years before it switched its common stocks to decimal
pricing.
This market is variously known as the
firm bid/offer market,
the
package trad-
ing market,
the
basket market,
or the
portfolio trading market.
The firm bid/offer market works as follows. A trader submits a charac-
terization of the portfolio to one or more package dealers. The characteri-
zation indicates whether the portfolio is an index portfolio. If it is not an
index portfolio, the characterization includes information about the securi-
ties in the portfolio. The information includes summary statistics about
quantities, firm sizes, betas, price levels, average trading volumes, volatili-
ties, primarily exchange listings, and index components. The package deal-
ers use this information to quote firm prices for the portfolio. They typi-
cally express prices relative to the end-of-day value of the portfolio. For
example, a package dealer may bid the closing value minus 15 cents per
share to buy the portfolio, or ask the closing value plus 20 cents per share
to sell the portfolio. The trader usually arranges the trade with the package
dealer who offers the best price. To prevent market manipulations, the trader
reveals the list of securities only after the market closes.
Traders often solicit firm bids and offers for portfolios because they can
often obtain better prices and faster trades than if they traded each security
separately. Package dealers generally offer better prices for portfolios than
for individual securities because informed traders are less likely to trade port-
folios than individual securities. Dealers therefore are less likely to be hurt
by informed traders when trading portfolios than when trading individual
securities.


## Page 504

CHAPTER 23 INDEX AND PORTFOLIO MARKETS • 491
Traders who do not have trading systems that allow them to easily do
program trades especially benefit from the firm bid/offer market. They pay
the package dealers to assume their trading problems. Package dealers can
offer better prices than their clients can obtain because they generally are
better traders than their clients are. Package dealers also may be able to place
the portfolio, or significant parts of it, with their other clients. If so, they
can facilitate the trade quite cheaply.
23.5 INDEX PRODUCTS
Index risks trade in many forms. Index products include several types of
index securities and derivative contracts. Table 23-1 presents a summary
of the major U.S. broad-based index products.
The most common index securities are open-end mutual funds that
hold index portfolios. Traders buy these securities directly from the fund at
TABLE 23-1.
Major U.S. Broad-based Index Products
TYPE

PRODUCT

EXAMPLES

PRIMARY MARKET

OPEN INTEREST
Securities

Open-end
mutual
funds
Cash-settled
index
options
Vanguard 500 Index
Fund Investor Shares
Fidelity Spartan 500
Index Fund
S&P 500 Futures
Contract
Nasdaq 100 Futures
Contract
Dow 30 Futures
Contract
S&P 500 Index
Options (SPX)
S&P 100 Index
Options (OEX)
Dow 30 Options (DJX)
By deposit to and
redemption from the fund
By deposit to and
redemption from the fund
AMEX
AMEX
AMEX
CME
CME
CB OT
CBOE
CBOE
CBOE
CME
$96 billion
$10 billion
$27 billion
$28 billion
$2 billion
$290 billion
$11 billion
$3 billion
2.0 million contracts
0.3 million contracts
1.0 million
contracts
0.17 million
contracts
Derivative

Cash-settled
contracts

futures
contracts
Exchange-

S&P Depository Receipts,
traded funds

"Spiders"
Nasdaq 100 Trust,
"Cubes"
Diamonds Trust Series
1 (DJIA)
Options on

S&P 500 Index
futures

Options
contracts
Source: Various Web pages.
The reported open interest is as of Apri120, 2001.


## Page 505

492 • TRADING AND EXCHANGES
closing net asset value prices. They likewise redeem their shares for cash by
trading directly with the fund. The main disadvantages of these funds are
that traders cannot trade them within the day, the funds must maintain cash
balances to accommodate deposits and redemptions, and the funds must
trade their underlying securities when deposits and redemptions do not net
to zero.
Exchange-traded index funds
(ETFs) are becoming increasingly popular.
ETFs are trusts that typically hold index portfolios. Units of the trust trade
like stocks. Most people buy and sell these trust units at exchanges and
ECNs. Large traders, however, can create new units by depositing an index
portfolio with the trust. They likewise can redeem units by giving them
to the trust in exchange for their pro rata share of the index portfolio.
Exchange-traded funds are growing in popularity because traders can trade
them at any time and because they do not have to accommodate small in-
vestor deposits and redemptions. ETFs also generate fewer tax liabilities for
investors than do open-end funds because they do not buy and sell securi-
ties when investors deposit and redeem shares. Finally, ETFs do not have
to manage shareholder accounts as open-end mutual funds do.
The most important derivative index products are index futures contracts.
Futures contracts are especially popular among hedgers and speculators be-
cause they can buy and sell them without posting large margins. Their main
disadvantage as a vehicle for holding long-term index risk is that traders
must
roll over
their positions into new contracts when their current con-
tracts expire. These rollover transactions generate transaction costs.
Cash-settled index option contracts constitute the last major class of
index products. Speculators, hedgers, and gamblers primarily use them.
23.6 SUMMARY
Interest in index markets has increased substantially since the early 1970s
as investors have better understood the implications of the zero-sum game.
On average, active managers cannot outperform the market. Transaction
costs and high management fees ensure that they underperform the market
on average. Investors who do not want to actively speculate—either by them-
selves or by choosing investment managers—find that index funds are quite
attractive.
The removal of index order flow from underlying security markets to in-
dex product markets has greatly decreased the costs of pursuing index strate-
gies. Index markets are quite liquid because they concentrate order flow and
because few traders are well informed about broad-based index values. In-
dex products are much cheaper to trade than the component instruments.
Low transaction costs in index markets have made these markets very
attractive to speculators. They use them to speculate in index risk or to hedge
out the index risk associated with their speculative positions in individual
securities.
23.7 SOME POINTS TO REMEMBER
•
Indexes characterize the average price performance of a set of index
stocks.
•
Index funds hold portfolios designed to replicate the returns of a price
index.


## Page 506

CHAPTER 23 INDEX AND PORTFOLIO MARKETS • 493
•
Index funds have very low turnover rates because managers rarely need
to rebalance index portfolios.
•
Investors hold index products to avoid transaction costs and to elimi-
nate losses often associated with active management.
•
Index markets provide low-cost ways to trade index risk.
•
Index dealers are generally unconcerned about security-specific risks.
23.8 QUESTIONS FOR THOUGHT
1.
What is the relation between a price index and a total return index?
2.
What would happen to liquidity if buy and hold managers held 90
percent of all equity? Would prices become less informative? What
active traders, if any, would benefit?
3.
What effect do you think the switch of index trading from program
trading to index products has had on liquidity in underlying markets?
4.
Has the decrease in index transaction costs made new trading strate-
gies possible? How do we all benefit from traders who pursue these
strategies?
5.
Index funds generally just slightly underperform the market. Is the
quest for the average market index return an immoral search for
mediocrity?
6.
What strategies can index funds employ to improve their returns?
7.
How can order anticipators and price manipulators profit from the
Russell reconstitution?


## Page 507

2 4
Specialists
S
ome exchanges assign special responsibilities to members they designate
as specialists. The
specialists
must continuously quote two-sided markets
so that markets always exist in their
specialties.
They must also ensure that
their markets are orderly and that prices do not jump too quickly.
Exchanges that designate specialists have
designated primary market maker
trading systems
or, more simply,
specialist trading systems.
The largest equity
exchange that designates specialists is the New York Stock Exchange.
Exchanges with specialist trading systems believe that their specialists
enhance market quality and thereby attract traders to their exchanges. They
believe that continuous and orderly markets increase investor confidence so
that investors are more willing to invest in the exchanges' listed companies.
By attracting investor interest, these exchanges encourage issuers to list their
securities with them. The exchanges thereby obtain greater revenues from
listing fees
and
transaction fees,
and their members make greater profits as
brokers and dealers.
In this chapter, we describe the various obligations that exchanges im-
pose upon their specialists. We show how these obligations often require that
specialists trade when they do not want to trade and refrain from trading
when they do want to trade. Such obligations therefore can be quite costly
to specialists. To encourage traders to accept these obligations, exchanges give
specialists various trading privileges. We describe these privileges and explain
how specialists profit from them. To prevent abuses of these privileges, ex-
changes also impose restrictions on when specialists may trade.
Although all traders appreciate the liquidity that exchanges obligate their
specialists to provide, many traders resent that they have special privileges.
The special privileges can be quite valuable to specialists, and hence costly
to other traders. The specialist trading system therefore is the subject of reg-
ulatory controversy. Regulators must consider whether the value that spe-
cialists obtain from their privileges is commensurate with the value of the
services they provide. The traders who benefit when specialists fulfill their
obligations usually are not the same traders who are hurt when specialists
exercise their privileges. Regulators therefore also must consider whether the
resulting transfers of wealth among traders are appropriate.
You must understand the specialist trading system if you trade at ex-
changes that employ such systems. At such exchanges, the execution of your
orders will somehow involve specialists. They may act as your broker, they
may act as dealers and fill your orders for their accounts, or they may con-
duct the auctions in which brokers match your orders to other traders' or-
ders. You will make better trading decisions when you understand how spe-
cialists handle your orders.
You also must understand the specialist trading system in order to un-
derstand how markets compete with each other. The liquidity services that
specialists offer are
public goods
in the sense that everyone benefits from
494


## Page 508

CHAPTER 24 SPECIALISTS • 495
them. Unfortunately, public goods are hard to obtain in competitive mar-
kets. Few people will pay for them when they can freely obtain them. Reg-
ulators who value the liquidity that specialists offer must therefore carefully
consider how markets compete with each other. We introduce these issues
at the end of this chapter and expand upon them when we consider how
markets compete with each other in chapter 26.
Finally, you must understand the specialist trading system in order to re-
sponsibly consider whether floor-based exchanges that use specialist trad-
ing systems should convert to screen-based trading systems. Although ex-
changes can build a screen-based specialist trading system, many issues make
such a structure unlikely. Analyses of conversions to screen-based trading
therefore should consider the benefits lost and the costs saved if the spe-
cialist trading system were scrapped. This chapter will help you identify
these benefits and costs.
Specialist trading systems differ across exchanges. The distinguishing
characteristic of these systems is that they impose obligations on dealers to
supply liquidity The obligations vary, however. Most exchanges restrict the
trades that specialists can do, but some do not. This chapter provides a gen-
eral discussion of the principal economic and regulatory issues that arise in
connection with all designated primary market maker trading systems. These
issues are common to all variants of these systems. The examples that we
will consider to illustrate these principles, however, are specific to the spe-
cialist trading systems that the New York and the American Stock Ex-
changes use.
24.1 OVERVIEW
Specialist trading systems are found primarily at U.S. stock and options ex-
changes. Some markets in other countries also use them. The specialist trad-
ing system is most important at the New York and American Stock Ex-
changes. The U.S. regional stock exchanges also have specialist trading
systems, but most regional specialists are more like third market dealers than
primary stock exchange specialists. The equity options markets organized
by the Chicago Board Options Exchange (CBOE), the American Stock
Exchange, the Pacific Exchange, and the Philadelphia Stock Exchange also
use specialist trading systems.
Specialists are known by different names in various markets. The CBOE
calls its specialists
designated primary market makers.
The Deutsche Borse
calls its specialists
designated sponsors
in English and
Betreuers
in German.
At the Paris Bourse, they are known as
animateurs.
Third market dealers also often call their traders "specialists." The busi-
ness models of these firms often obligate their traders to offer liquidity when
they otherwise might not want to do so. These obligations are voluntary,
however. The dealers propose and accept them as conditions for obtaining
order flow from brokers. The NASD, the SEC, and some court decisions
restrict the trades that these dual traders can do. These restrictions, how-
ever, usually are not as severe as those which exchanges impose upon their
specialists.
Most specialists are dual traders who sometimes broker orders for their
clients and at other times fill orders for their clients from their own inven-
tories. Exchanges that permit dual trading generally employ many regula-


## Page 509

496 • TRADING AND EXCHANGES
tory safeguards to solve the resulting conflict of interest problems intro-
duced in chapter 7. Specialists therefore are subject to many regulations.
Exchanges with specialist trading systems usually assign only one spe-
cialist to each stock or options class. We will discuss below how they make
these assignments.
Some exchanges use a
designated multiple market maker trading system
for
trading their securities. These systems are similar to specialist systems ex-
cept that the exchanges obligate multiple traders to offer liquidity that they
otherwise might not want to offer. These obligations are best enforced in
electronic markets because traders in open outcry markets will hide when
they do not want to trade. When nobody wants to trade, a computer usu-
ally assigns the obligation to trade in rotation to each of the designated mar-
ket makers.
The CBOE uses a designated multiple market maker trading system for
its most actively traded index options series. Unlike most specialists, their
designated market makers are not dual traders. They deal only for their ac-
count, and they do not broker agency orders. Like specialists, the market
makers have some obligations to provide liquidity when markets are not
trading normally. Similar structures appear at some European options ex-
changes and at some futures exchanges.
The number of stocks or option classes that each specialist trades de-
pends on how actively traded the instruments are. Specialists who special-
ize in very actively traded securities usually trade only one security or op-
tion class. Those who specialize in less frequently traded securities trade
larger lists. Most specialists trade only a few securities. For example, most
of the 482 individual specialists at the New York Stock Exchange trade be-
tween three and six stocks each. They usually have one actively traded se-
curity and a few less actively traded ones.
Most specialists work for firms that employ many specialists. In Decem-
ber 2001, only eight firms employed all specialists at the NYSE. Five of these
firms handled stocks representing 95 percent of all the NYSE dollar volume.
Specialists play three roles in most markets. They are dealers when they
trade for their own account. They are brokers when they broker orders and
trades for other brokers. Finally, they are exchange officials who are re-
sponsible for conducting orderly markets. We consider these three main roles
in the next three sections.
24.2 SPECIALISTS AS DEALERS
Specialists act as dealers when they trade for their own accounts. Exchanges
greatly regulate the trading that specialists can and must do for their own
accounts.
Two sets of regulations govern specialist trading.
Affirmative obligations
obligate specialists to offer liquidity in various circumstances.
Negative ob-
ligations
prevent them from trading in other circumstances. Specialists ac-
cept these obligations because they enjoy the various privileges that come
with them.
24.2.1 Affirmative Obligations
The specialists' primary affirmative obligation is to ensure that a reasonable
market always exists in their specialties. When no one else is willing to trade,


## Page 510

CHAPTER 24 SPECIALISTS • 497
specialists must be willing to trade. They must quote two-sided markets
when no one else will, and their quotes must be meaningful in the sense
that the spread between the best bid and the best offer cannot be too wide.
Since specialists often must trade when no one else is willing to trade, they
are the
traders of last resort.
Their obligation to make markets is limited, however. Specialists do not
have to make firm quotes for large block sizes, and they are not required to
support prices when values are falling or restrain prices when values are ris-
ing. They simply have to ensure that public traders can always trade some
meaningful quantity.
Exchanges expect that their specialists will smooth prices by intervening
to prevent large price reversals. A
price reversal
occurs when price rises and
then falls or falls and then rises. Large reversals often result when unin-
formed traders demand liquidity that is not present in the market. If such
traders insist upon trading, they often must move prices substantially to find
someone with whom to trade. Prices then jump back to their former levels
when someone else demands liquidity on the other side of the market.
A market has
price continuity
if prices move smoothly, without jumping
too much. Specialists are responsible for creating price continuity. Exchanges
like price continuity because it helps assure public traders that brokers fill
their agency orders fairly.
Exchanges evaluate how well specialists meet their affirmative obligations
by measuring the average width of the quoted bid/ask spread, the average
depth of the quotes, the number of large price reversals, and the average size
of price reversals. Specialists do their jobs well when the spreads in their
So Smoothing Jumps
An electronic order-driven market has the following limit book:
Aggregate

Aggregate
buy size

Price

sell size

40

22.1

5

22.2

22.3

2
22.4
22.5
22.6

22.7

8

22.8

10
The market initially is 22.2 bid for 5; 2 offered at 22.3.
Suppose that a four-contract market buy order arrives, followed by a
two-contract market sell order. The buy order will completely fill the sell
order at 22.3. The remainder of the buy order will then fill two of the eight
offered at 22.7. The market sell order will then fill two of the five bid at
22.2. The price will jump up from 22.3 to 22.7 and then down to 22.2.
The large reversal is due to the gap in sell orders between 22.3 and
22.7. If a specialist were in this market, she might have filled the remainder
of the market buy order at 22.4. Had she done so, she could have filled
the market sell order at 22.3 for a profit of 0.1 per contract.
4


## Page 511

498 • TRADING AND EXCHANGES
Price Continuity and Telegraph Ticker Tapes
When telecommunication technologies were much less capable and much
more expensive then they presently are, exchanges could not cheaply
disseminate as much market information as they do now. For many years,
most public traders did not know prevailing bid and offer prices at the time
they submitted their orders. Instead, they knew only the last trade price
transmitted by automated telegraph systems. Traders called these systems
ticker tapes
because they made ticking sounds as they printed on paper
tapes.
With such limited information resources, traders could not easily
determine whether they received fair trade prices. The best that they could
do was check whether their trade prices were much different from the prices
that immediately preceded and followed their trades. Exchanges therefore
wanted to regulate price continuity to assure public traders that their orders
were treated fairly.
Traders now can access much more information to determine whether
their trade prices are fair. Traders are particularly interested in the quoted
bid and ask prices. When dealers quote prices without knowing whether
they will next trade with a buyer or a seller, they must quote fair prices. If
they quote prices that are too high or too low, they risk trading with
informed traders. Public traders therefore can be confident that their trade
prices are fair when quotations are widely disseminated so that informed
traders could take them if they wanted to trade. The width of the bid/ask
spread therefore is a more important indicator of price fairness than price
continuity is.
markets are narrow, the quoted sizes are large, large price reversals are un-
common, and price reversals are small on average.
Creating price continuity is sometimes quite expensive. Specialists must
trade when no one else is willing to trade. Whether these trades prove to
be expensive depends on why no one else wants to trade. If no one wants
to trade because informed traders believe that values are changing, special-
ists will trade on the losing side of the market. They will buy when prices
are falling or sell when prices are rising. If no one wants to trade simply be-
cause no one is paying attention, specialist trades can be quite profitable.
Uncertainty about what prices will do in the future makes being a spe-
cialist very difficult. Consider what happens when specialists take prices
down quickly because no one wants to buy in the face of strong selling pres-
sures. If prices do not subsequently rebound, the specialists will have done
a good job of finding market-clearing prices. If prices subsequently rebound,
however, people may accuse them of failing to offer enough liquidity to en-
sure adequate continuity. This tension makes being a specialist difficult.
24.2.2 Negative Obligations
The specialists'
negative obligations
restrict their trading. At the New York
and American Stock Exchanges, specialists are bound by exchange order
precedence rules and by the principle that they should not take liquidity
which would otherwise be available to public traders.
The exchange order precedence rules require that specialists yield to pub-
lic orders at the same price or better. Specialists cannot trade at a given price
unless no public traders are willing to trade at that price (the public order


## Page 512

CHAPTER 24 SPECIALISTS • 499
precedence rule), and no other traders are willing to trade at a better price
(the price priority rule). These rules give precedence to public traders who
offer liquidity over specialists.
Exchanges also discourage their specialists from trading with limit or-
ders on their books. When specialists fill standing limit orders, they take
liquidity that public traders could otherwise take. Since exchanges want to
preserve this liquidity for public traders, we shall call this principle the
public liquidity preservation principle.
The public liquidity preservation principle also protects traders who offer
limit orders. For example, suppose that a limit order trader places a limit or-
der to sell at 50 when the market is 49.80 bid, 50 offered. If good news ar-
rives, if the market as a whole rises, or if the prices of similar stocks rise, the
limit order trader may want to cancel the order and resubmit another at a
higher price. In these circumstances, however, the specialist may want to fill
the order. If the specialist can fill standing orders for his own account, the
limit order trader probably will not have a chance to cancel his order. The
specialist will generally fill the order before the limit order trader can cancel
it because specialists can see and react to changing market conditions faster
than most other traders can. Moreover, if the audit trail is not perfect, a dis-
honest specialist may fill the order immediately after receiving the cancel in-
struction. In that case, the specialist will claim that the order was filled first,
and he will return the cancel instruction with a trade confirmation. The pub-
lic liquidity preservation principle protects limit order traders by giving them
more time to change their orders in response to changing market conditions.
The negative obligations ensure that specialists can only offer liquidity,
and then only if no public traders are willing to offer liquidity at the same
or better prices. Specialists subject to the public liquidity preservation prin-
ciple therefore can trade only with incoming marketable orders—market or-
ders and marketable limit orders. If they want to trade ahead of public limit
orders, they must offer better prices. If they want to trade at the best quoted
price, they can trade only after all public orders are filled at that price.
Since the negative obligations prohibit trades that specialists might oth-
erwise want to do, these obligations must be costly to them. Specialists sub-
ject themselves to them because the value of their special privileges more
than compensates for their inability to do certain desirable trades.
Third market dealers and regional specialists generally are not subject to
the public liquidity preference principle. They can fill limit orders on their
books for their own accounts whenever they want. The opportunity to trade
with the limit order book is especially advantageous when the market is mov-
ing quickly and limit order traders cannot quickly cancel their orders. At
such times, quick traders can buy at low prices when the market is rising and
sell at high prices when the market is falling. Traders say that limit orders
are
stale
when their prices no longer reflect current market conditions.
The
limit order price protection guarantees
that third market dealers and
regional specialists give brokers to obtain their order flows require that these
dealers fill limit orders on their books under certain circumstances. Chap-
ter 25 describes these guarantees. When markets are stable, limit order price
protection can be costly to dealers because they often must buy at the ask
and sell at the bid to fill agency limit orders. These obligations therefore
offset to some extent the benefits of trading with stale limit orders.
Confirmation versus
Cancellation
In floor-based markets with
poor audit trails, traders
sometimes say that a request
to cancel a limit order is
actually a request for a trade
confirmation. Their experience
has led them to believe that
floor traders often fill their
orders when presented with
requests to cancel.
Limit order traders
undoubtedly think that this
abuse happens more often
than it actually does. Long
delays between the execution
of an order and the receipt
of the associated trade
confirmation, and long delays
between the submission of a
request to cancel and its
receipt by the specialist,
ensure that specialists often
may receive requests to
cancel
after
the orders have
filled. Traders then receive
trade confirmations instead
of their desired order
cancellations. Markets can
avoid these misunderstandings
by ensuring that their order-
routing systems, trade
reporting systems, and trade
confirmation systems all
operate at very high
speeds.
.4


## Page 513

500 • TRADING AND EXCHANGES
Tick Size, Negative Obligations, and
Specialist Participation Rates
Specialist trading is especially difficult when only one tick separates the
best-priced public buy and sell limit orders. The public precedence rule
prohibits specialists from buying at the best bid until all public orders at that
price are filled, and the public liquidity preservation principle prohibits them
from buying at the ask by trading with sell orders in their limit order books.
Specialists who want to buy ahead of their booked orders therefore must
wait until an incoming marketable sell order arrives and then fill it at the
asking price. Likewise, specialists who want to sell ahead of their booked
orders must wait until they can fill an incoming marketable buy order at the
bid price.
As a rule, traders do not profit by buying at ask prices and selling at
bid prices. Specialists will make these trades only when they want to
speculate on future price changes or when they are very uncomfortable with
their inventory positions.
A decrease in tick size relaxes the public order precedence rule by
decreasing the cost of stepping in front of booked orders. When the tick
size is small, so that more than one tick separates the best public bid from
the best public offer, specialists who want to trade can step ahead of their
books while still trading within the best bid and offer. Since these trades are
less costly, they will tend to be more profitable.
The
specialist participation rate
is the fraction of orders that specialists
fill (or partially fill) while trading as dealers. When tick sizes decreased in
the United States from one-eighth dollar to one-sixteenth dollar in 1997, and
to 1 penny in 2000, specialist participation rates and specialist profitability
both increased substantially.
24.3 SPECIALISTS AS BROKERS
Exchange specialists
often broker orders for other brokers. Specialists re-
ceive orders from brokers via exchange order-routing systems and through
direct contacts.
Most exchanges maintain electronic order-routing systems that allow
brokers to cheaply deliver customer orders to them. Electronic order-driven
exchanges deliver these orders directly to their order-matching systems.
Floor-based exchanges deliver these orders either to their specialists or to
exchange officials called
order book officials.
In either event, the recipient then
acts as the broker for these
system orders.
Brokers route orders through ex-
change order-routing systems when they do not have a presence on the
exchange floor or when they do not want to tie up their floor brokers with
small orders.
Exchanges regulate the commissions that specialists can charge for rep-
resenting the system order flow. At the NYSE, specialists can charge com-
missions only on limit orders that they hold for more than five minutes.
They receive no commissions for representing market orders and limit or-
ders that fill within five minutes.
Floor brokers also often give their agency orders to specialists to man-
age so that they do not have to stand around waiting for traders to arrive
with whom they might arrange their trades. The specialists then
work
these
orders. This procedure allows floor brokers to handle more orders and to
direct their attention to the orders that most require their skills and imme-


## Page 514

CHAPTER 24 SPECIALISTS • 501
diate action. Specialists generally charge brokers commissions for these ser-
vices. The brokers' clients never see these commissions.
Specialists also broker trades by acting as oral bulletin boards for other
brokers. Brokers sometimes tell specialists that their clients are large buyers
or sellers. The specialists then tell the brokers whether somebody else has
expressed substantial interest on the other side. If somebody has, the spe-
cialist will page the other side and put the parties together. Otherwise, the
specialist will promise to page the brokers if someone with interest appears.
Brokers usually do not pay specialists for the trades that specialists arrange
this way, but all traders value the goodwill that the specialists create.
24.4 SPECIALISTS AS AUCTIONEERS
AND EXCHANGE OFFICIALS
Markets that designate specialists usually make their specialists responsible
for conducting orderly markets in their specialties. The specialists ensure
that all traders follow the exchange rules so that all orders are fairly repre-
sented. At markets that open with a single price auction—such as those or-
ganized at the New York and American Stock Exchanges—the specialists
are responsible for conducting those auctions. To assist them, the electronic
systems that maintain their limit order books also summarize the excess de-
mand or supply at each possible auction price.
The obligation to act as an auctioneer can be costly when specialists want
to direct their attention to other issues. Specialists, however, receive no di-
rect compensation for assuming these responsibilities. Instead, they accept
them in exchange for the privileges that come with being a specialist.
24.5 SPECIALIST PRIVILEGES
Specialists have several privileges that allow them to profit from their unique
positions. These include access to information about order flows, the right
to make decisions after others make their decisions, the ability to create the
market quote, the ability to create and exercise certain look-back timing op-
tions, and the right to collect brokerage commissions from executing sys-
tem order flow.
Perhaps the greatest advantage specialists have comes from their access
to information about orders. Specialists see the entire system order flow as
it arrives. They also see much of the order flow that floor brokers handle,
either because they observe the floor brokers or because the floor brokers
give them their clients' orders to work. Finally, they also see their limit or-
der books, in which they have placed the orders that they cannot immedi-
ately execute.
Although specialists must sometimes share this information with other
traders, they always have an advantage because they see it first. In markets
where specialists do not have to share the information, they obviously have
a greater advantage. The next several subsections provide examples of how
specialists may act upon information about orders.
24.5.1 Speculative Strategies
Information about order flows may allow clever specialists to forecast short-
term price changes better than other traders can. Specialists may use this


## Page 515

502 • TRADING AND EXCHANGES
TABLE 24-1.
How Specialists Affect Their Participation Rates
DESIRE

ACTION
Increase their probability of buying
Increase their probability of selling
Decrease their probability of buying
Decrease their probability of selling
Quote the most aggressive bid price
Improve prices for incoming marketable sell orders
Augment the quotation bid size
Quote the most aggressive offer price
Improve prices for incoming marketable buy orders
Augment the quotation offer size
Quote only the best bid on the book and no more than the
aggregate order size on the book at that price
Quote only the best offer on the book and no more than
the aggregate order size on the book at that price
information to speculate on the side that their information favors or to re-
frain from offering liquidity on the other side. If they can indeed predict
short-term price changes, they will be able to make more profitable trades
and avoid costly trades.
To exploit their information, specialists must alter the probabilities that
they will next be a buyer or a seller. The specialists' affirmative and nega-
tive obligations complicate their efforts to trade and avoid trading:
•
To increase the probability that they trade, specialists must get in front
of the orders on their books. They may quote more aggressive prices
than their books offer and hope that their quotes attract other traders,
or they may step in front of the book when an incoming market or-
der arrives with which they want to trade.
•
To decrease the probability that they trade, specialists hide behind their
books. When marketable orders arrive that they do not want to fill,
they match these orders with booked limit orders. They can avoid trad-
ing as long as the limit order book holds orders that are not too far
from the market.
Table 24-1 summarizes the strategies that specialists use to trade and avoid
trading.
24.5.2 Quote
-
matching Strategies
Specialists do not need to predict future price changes to profit from in-
formation in their limit order books. Their information allows them to pre-
dict what trades they may—and may not—have to make to satisfy their af-
firmative obligations. For example, when their books are heavy on the buy
side but not on the sell side, they know that that they are somewhat pro-
tected on the buy side. Unless their market drops quickly on high volume,
they will not need to provide liquidity on the buy side in the near future.
Specialists who see such asymmetries may engage in the quote-matching
strategies described in chapter 11. For example, they may try to buy in front
of a limit order book that is heavy on the buy side. If values subsequently
rise, they will profit with the price rise. If values fall, they will not need to


## Page 516

CHAPTER 24 SPECIALISTS • 503
buy until market order sellers exhaust the liquidity on the buy side of their
books. During that time, the specialists may sell their positions to any mar-
ket order buyers who arrive.
The quote-matching strategy does not need to be profitable on its own
to increase specialist profitability. 'Whenever incoming marketable orders ar-
rive, specialists must decide whether to improve prices or to let the orders
go to their books. The information they have about orders in their books
allows them to make better decisions than they otherwise would make. Bet-
ter decisions lead to greater profits.
The quote-matching strategy is more profitable in markets where deal-
ers can freely trade with limit orders that they hold. In such markets, the
dealers can immediately trade with their books when they want to close los-
ing positions.
24.5.3 Cream
-
skimming Strategies
Specialists also can profit from knowing who wants to trade. Specialists—
like all other dealers—generally will supply more liquidity to uninformed
traders than to well-informed traders. Traders who offer firm orders and
quotes must fill marketable orders as they arrive without regard to whose
they are. In contrast, when specialist quotes simply reflect the best bids and
offers in their books, specialists can decide whether they want to trade with
an incoming market order
after
they see where it comes from. If they want
to trade, they step in front of their books by improving the price.
Since small retail traders tend not to be well informed, specialists im-
prove prices for retail traders more often than for large institutional traders.
Because specialists can see the order flow before they commit to trading,
they can discriminate among the different types of liquidity-demanding
traders. This trading option can be very valuable.
Specialists also see the size of the incoming marketable orders before
they decide to trade. They therefore can avoid the price discrimination—
described in chapters 6 and 15—that large order traders try to exercise
against liquidity suppliers. Accordingly, specialists are more likely to let large
orders go to the book than small orders.
Traders call selective filling of orders
cream skimming
because the spe-
cialists take the richest orders and leave the less desirable orders for their
books. Cream skimming makes limit order strategies less attractive than
they otherwise would be. Since cream skimming benefits small market or-
der traders who obtain improved prices, the practice makes market order
strategies relatively more attractive than limit orders strategies for small
traders.
The cost of exercising the option to step in front of the book is the min-
imum price increment. Specialists must improve prices by the increment to
free themselves from the constraints of the public order precedence rule.
When the minimum price increment is very small, specialists fill market or-
ders that they believe will be profitable to fill, and they let their limit order
books fill the market orders that they do not want.
24.5.4 Another Order Anticipation Strategy
Dealers may manipulate their trading to profit from stop orders in their
books. Suppose that a dealer has a large sell order with a stop price of 20
in his book. Suppose further that no buy orders are on the book at 20 or


## Page 517

504 • TRADING AND EXCHANGES
r An Accidental Shooting When Gunning Against a Stop Order
The SPDR (pronounced "Spider") is an exchange-traded fund that holds an
S&P 500 index portfolio. (SPDR is an acronym for Standard & Poor's
Depository Receipt.) It trades primarily at the American Stock Exchange
under the ticker symbol SPY. A specialist manages trading in the SPDR. The
specialist competes with several market makers who stand before his post.
The specialist does not display his order book to the market makers.
A trustworthy source told me the following story. (The numbers in the
story are all approximate, but they represent the essence of the event.)
One day in 1998 or 1999, the SPDR specialist received a very large
market order to sell 500,000 shares with a stop price of 100. The SPDR
was then trading at about 1001/2. Soon after receiving the order, the
specialist aggressively sold the S&P 500 futures contract in Chicago to gun
the market. The futures price fell. This fall caused other traders to sell the
SPDR. These sales cleared buy limit orders priced above 100 from the SPDR
order book. Soon the best bid in the SPDR was 100.
The specialist then waited for an incoming market sell order that he
could fill at 100. A trade at that price would activate the 500,000-share
stop order. The specialist then would buy the 500,000 shares at 100 to
hedge his short futures position. Since the specialist sold the futures at
higher prices (even after accounting for the normal fair-value premium), this
purchase would lock in a substantial profit.
A moment before a market sell order arrived, however, one of the
market makers in the crowd bid 100V o for 100,000 shares. Her firm
probably issued the order in an attempt to exploit the price discrepancy
between the SPDR and the underlying index stocks.
Price subsequently rose, and the specialist was unable to execute the
stop order. He therefore lost substantially on his short futures position.
The ethics of his behavior are questionable. It appears that the specialist
gunned the futures market to exploit the stop order. Such behavior would
appear to violate his agency obligations to the trader who gave him the
stop order. If a regulator confronted him, however, his response would have
been that he believed the market was dropping, and he sold the futures so
that he could give a better price to the stop order if it were activated.
above, and
that some sell orders are in the book at 20.10. When an in-
coming market buy order arrives, the dealer may sell in front of the limit
sell orders because he knows that he may be able to repurchase his inven-
tory from the stop order at 20. The dealer can make this happen when a
market sell order arrives by filling the order at 20, which will activate the
stop sell order. If the dealer does not want to activate the stop order, he will
fill the market sell order at a price above 20. The stop order allows the spe-
cialist to be a more aggressive seller than he otherwise might be because it
gives him an option to easily offset his position.
24.5.5 Specialists Control the Market Quotes
At many exchanges, specialists establish the market quotes that the exchanges
disseminate to the public. This privilege is most valuable at markets that pub-
lish only the best bid and offer—the
top of the book—as
opposed to markets
that publish the entire book or a summary of the
book—market by price.
At markets that publish only the best bid and offer, specialists can set
these quotes however they want, within some constraints. Of course, they
must honor the quotes that they set.


## Page 518

CHAPTER 24 SPECIALISTS • 505
OP
A
Defensive Quotation Strategy
Jack is the specialist for a small stock that has the following thin limit order
book:
Aggregate

Aggregate
buy size

Price

sell size
4

11.00
10

12.00

12.10

18
Jack quotes the book. His market is 12.00 bid for 10 100-share lots, 18
lots offered at 12.10.
A market order to sell 2,000 shares arrives. Jack fills the first 1,000
shares of this order by matching it with the buy limit orders on his book at
12. He then fills the remainder of the order for his own account at the same
price.
Jack now holds 1,000 shares that he hopes to sell at a profit. He will
not profit, however, if traders decide that the stock is worth less than 12
dollars.
Jack now considers what market he should quote. His affirmative
obligations prohibit him from quoting the best prices on the limit order book
(11.00 to 12.10) because the spread would be too wide. At a minimum,
Jack will have to supply a firm bid for his own account. He considers
bidding 11.95 for 100 shares because he does not want to buy more
stock. He also considers offering 1,000 shares at 12.05 because he wants
to sell his inventory. He is reluctant to offer these quotes, however, because
he does not want to show the weakness in the stock. He is afraid that if he
lowers the price, he may panic sellers and cause buyers to wait to see what
happens. Instead, he decides to leave the quote unchanged to hide the
weakness. Jack hopes that a market buy order will next arrive to which he
can offer an improved price of 12.09.
The main constraints on specialist quotations come from
order exposure
rules.
These rules require that specialists expose the most aggressively priced
offers to trade. They may come from orders on their books, traders on the
floor, or the specialists themselves.
The order exposure rules in the U.S. equity markets require that spe-
cialists quote prices which are at least as good as the best bid and offer prices
in their limit order books. The quotation sizes must be at least as large as
the aggregate order sizes at the quoted prices. Floor traders also can request
that specialists include their orders in the market quote.
Specialists can quote better prices or greater sizes to represent their own
market-making commitments. Exchanges, however, discourage them from
quoting a market for small size that hides large size offered by the public.
Within these constraints, specialists can set their quotes to influence the
inferences some traders make about values. The specialists may thereby in-
fluence their order flows.
24.5.6 Stopping Stock and the
Look-back Timing Option
Specialists at some exchanges can
stop
the execution of an incoming mar-
ketable order. When they stop an order, they guarantee that the order will
eventually execute at a price that is at least as good as the best price at which


## Page 519

506 • TRADING AND EXCHANGES
Why Stopped Stock Look-back Timing Options Are Valuable
Johanna is an
NYSE
specialist who is quoting a market that reflects the
orders on her limit order book. The market is 50 bid for 20, 27 offered at
50.10.
When an incoming limit market order to buy 10 arrives, Johanna
decides to stop the order at 50.05. The NYSE requires that she then adjust
her quote to reflect the stopped stock. She therefore quotes the market as
50.05 bid for 10, 27 offered at 50.10. Johanna now waits to see what
happens. If no marketable sell orders arrive, she will have to fill the stopped
buy order by selling from her inventory.
Suppose that while she is waiting, the prices of stocks that are similar to
her stock start to rise. Johanna expects that her stock also will rise in value.
She therefore does not want to sell her inventory. Instead, she waits and
hopes that some other seller will arrive who will fill the stopped order and
thereby relieve her of the obligation to do so. If this happens, she will not
lose the opportunity to profit as prices rise.
Suppose instead that the prices of similar stocks start to fall. Johanna
now fears that the value of her inventory will fall. She therefore executes the
stopped order for her account. She must act quickly, before a public trader
takes the option from her.
The stopped stock timing option is valuable to Johanna because she can
look back to see whether prices are falling or rising when she decides
whether to exercise the option. Although she ultimately may have to trade
the stock when she would rather not, some other trader may arrive who will
relieve her of this responsibility. The option to wait for such traders when
she would rather not trade, and the option to exercise quickly when she
does want to trade, make the stopped stock look-back timing option
valuable.
the order would
execute if it filled immediately. Specialists provide this guar-
antee by committing to execute the order for their own accounts if no one
else is willing to take the other side.
While an order is stopped, if an incoming market order on the other side
arrives, the specialist must match it with the stopped order and execute the
trade. If no such orders arrive, specialists must execute stopped orders for
their own accounts before the end of the trading day. Otherwise, specialists
may execute stopped orders for their own account at any time they wish.
A stopped order is a trading option that specialists may exploit to their
advantage. For example, when specialists stop market sell orders, they cre-
ate call options. They can exercise these options as long as no market order
buyers arrive first. Unlike most options, however, specialists cannot aban-
don them when they expire. Instead, they must then exercise them.
The stopped stock trading options is a timing option.
Timing options
al-
low people to choose when they want to do something. Stopped stock tim-
ing options are valuable because specialists can continuously decide whether
they want to exercise them now or risk—or hope—that someone else will
do so. Since they can decide after they have seen what has happened in the
market, these options are
look-back options.
The specialists can look back at
what has happened before they decide whether to execute them.
Specialists often stop stock to improve prices without trading when
spreads are wide. They then hope that another trader will arrive on the other
side who will fill the order. Such traders will also receive an improved price.


## Page 520

CHAPTER 24 SPECIALISTS • 507
We will call such traders
subsequent traders.
Although specialists could make
more money by filling the stopped and subsequent orders themselves at their
bid and ask prices, they cannot do so when public limit orders are on their
books at these prices. Since exchanges and brokers rate specialists by how
often they provide price improvement, the stopped stock strategy often al-
lows them to improve prices without trading when they could not trade
profitably otherwise.
24.5.6.1 Whom Does
the
Stop Harm?
Since trading is a zero-sum game, someone must lose if specialists profit
from creating and exercising stopped stock options. Who loses is not im-
mediately obvious, however. The only possibilities are the traders whose or-
ders are stopped, the subsequent traders who fill stopped orders before the
specialists must fill them, and the limit order traders whose orders would
have filled the stopped orders if the specialist had not stopped the stock or
immediately filled the orders at improved prices. We will now consider the
interests of each of these traders.
The stops do not harm the traders whose orders the specialists stop.
These traders receive the same or better prices than they otherwise would
have received. Since specialists guarantee their executions, they usually do
not even know that their orders have been stopped. As soon as specialists
stop their orders, they receive reports of their guaranteed trade prices. Re-
tail customers then receive normal execution reports.
The subsequent traders who fill stopped orders before the specialists fill
them will regret trading on average. As a rule, specialists will quickly fill
their stopped orders when they believe that filling them will be profitable,
and wait otherwise. Therefore, if specialists have any skill at forecasting fu-
ture price changes, filling stopped orders that specialists do not want to fill
should not be profitable on average.
Specialists also may be reluctant to fill stopped orders when filling them
would cause already out-of-balance inventories to move further from their
target values. Under such circumstances, filling their stopped orders will be
harmful if their inventories are inversely correlated with future price changes,
as we would expect if the specialists have been trading with informed traders.
Whether the public traders who fill stopped orders are harmed depends
on whether they would have traded anyway or whether their decisions
to trade were influenced by the improved prices that the specialists quote
to reflect the stopped orders. If they would have traded anyway, they are no
worse off than they would have been. They may even obtain better prices
than they otherwise would have obtained. However, if the improved prices
influenced their decisions to trade, they will regret that they traded. These
traders take the specialists out of positions that the specialists do not want.
The expected costs of filling stopped orders that specialists do not want
to fill are greater when the order has stood unfilled for a long time than when
the order has stood unfilled for a short time. The more time that passes with-
out the specialists filling their orders, the more likely is it that the special-
ists have learned information that makes them reluctant to fill such orders.
Order stopping also harms the limit order traders who would have filled
the stopped orders if the specialists did not choose to stop them. Consider
the position of a limit order buyer on the book at the best bid who fails to
trade when a specialist stops an incoming market sell order. If the market


## Page 521

508 • TRADING AND EXCHANGES
then moves up, this limit order trader will regret not trading. Stopping stock
thus harms limit order traders by decreasing their opportunities to trade
profitably. In the long run, when specialists frequently stop stock, limit or-
der trading strategies become less attractive, so that public traders submit
more market orders.
24.5.7 The Market Open
Specialists conduct the opening single price auction at the NYSE. When
the order flow at the open is imbalanced, as it often is, specialists step in to
supply liquidity on the weak side of the market. Since this side is weak, the
market-clearing price generally favors it. For example, if traders want to
buy more than they want to sell at the previous closing price, the market-
clearing price will be lower than the previous close, and the specialist will
typically be a buyer. Prices often reverse in these circumstances, so that spe-
cialists often profit from their opening trades.
Specialists have a unique advantage at the open relative to other traders
because they can see all the orders. They also have an advantage because
they can decide how much they want to trade after everyone else has sub-
mitted their orders. Since their participation in the auction can determine
the trade price, they have a significant advantage over other traders.
24.5.8 Brokerage Commissions
Receiving commissions for brokering orders is a valuable specialist privilege.
Specialists receive and represent orders that brokers route though exchange
order-routing systems. As noted above, specialists can charge brokerage
commissions on some of these orders. Specialists also charge floor brokers
commissions on the orders that they leave with them to work. The com-
bined brokerage revenues can be significant.
24.5.9 Dealer Profits
The final privilege that specialists have is not uniquely theirs, but one at
which they have a distinct advantage due to their unique positions. Spe-
cialists can supply liquidity to marketable orders when no one else is pre-
sent. In thinly traded markets and occasionally in actively traded markets,
market orders may arrive when no aggressively priced limit orders are pres-
ent to supply liquidity. Specialists supply liquidity to these marketable or-
ders by buying at the bid and selling at the offer. If their spreads are suffi-
ciently wide, if they receive enough marketable order flow, and if the order
flow is not too well informed, they will profit from filling marketable
orders.
Specialists compete with other traders to supply liquidity to marketable
orders. In thinly traded markets, they have an advantage over other traders
because they are always at their posts. Traders who might compete with
them can fill marketable orders only when they are present, either by being
in the crowd or by being in the limit order book. Most floor traders, how-
ever, will not wait at a post for orders in thinly traded securities to arrive
because they cannot make enough money that way. Many limit order traders
likewise are reluctant to offer liquidity in thinly traded stocks because their
costs of managing these orders are too high. Compared with specialists, off-
floor traders do not have as much information, they cannot adjust their
prices as quickly, and they cannot cream skim.


## Page 522

CHAPTER 24 SPECIALISTS • 509
The Opening Option
The previous close was 35.2. To manage his inventory, the specialist would
like to sell
16
lots at the open.
Just
before the specialist opens the market,
the following supply and demand schedules summarize the order book:
Demand

Supply

Excess
Price

schedule

schedule

demand
35.1
31
14
17
35.2
30
15 15
35.3
18
34
—16
If the specialist does not participate in the auction, the auction clearing
price will be 35.3; 18 lots will trade; and there will be excess supply of
16 lots at that price.
If the specialist opens the market at 35.3, he may feel obligated to buy
the
16 lots
to fill the excess supply. Since the specialist does not want to
buy, this option is not attractive.
If the specialist sells 16 lots, the opening price will be 35.1 and the
specialist will probably sell 17 lots to ensure that all orders at 35.1 fill. If
the specialist sells just 15 lots, the opening price will be 35.2. Although the
specialist wants to sell 16 lots, he sells only 15 because he does not want
to depress the price on his sales. The option to choose the clearing price
after all other traders have submitted their orders is quite valuable.
Electronic proprietary traders
use computers to create, submit, cancel, and
adjust limit orders in response to changing market conditions. These traders
are becoming increasingly important in markets that have fast electronic in-
terfaces to their trading systems. As these systems grow and as more elec-
tronic information about market conditions becomes immediately available,
the advantages that floor-based specialists have as dealers will diminish.
24.6 WHO ASSIGNS SPECIALISTS?
The specialist system originated when some traders found that they could
trade more successfully if they focused their attention on a small number of
stocks. Those early specialists better understood who was trading, why they
were trading, and the value of what they were trading than did other traders.
Their specific knowledge helped them trade more successfully as dealers. It
also made them attractive to traders who needed knowledgeable brokers.
As the specialists became well known, they were able to obtain some-
thing of a natural monopoly in their specialties. The more that specialists
knew about their specialties, the more effectively they could compete with
others who might want to replace them. The most effective specialists were
the ones who obtained the most order flow. Those who obtained the most
order flow learned the most about their specialties, and thereby became more
effective specialists. This circularity is an example of the
order flow exter-
nality
that influences market structures.
When exchanges started installing electronic order-routing systems, their
specialist systems were well entrenched. Specialists naturally become the re-
cipients of the system order flows.
In principle, specialties at most exchanges are still contestable by any ex-
change member who wants to specialize in a stock. In practice, it is now
0
1
" Boyd 's Broken Leg
Folklore at the New York
Stock Exchange holds that the
first specialty started there
when a man named Boyd
broke his leg in 1875. Not
being able to walk around
the floor, he sat down on a
chair on the exchange floor
and announced that he would
trade only Western Union, a
popular stock of the time.
Brokers who felt sorry for
him, and brokers who valued
the fact that he would not
miss any trading opportunities
in Western Union, left their
orders with him. He thus
became the first specialist by
providing good service to his
fellow brokers.
Source: Robert Sharp,
The Lore
and Legends of Wall Street
(Dow
Jones-Irwin, 1989), p. 139.


## Page 523

510 • TRADING AND EXCHANGES
Consolidation among
NYSE Specialist
Trading Firms
The number of firms
employing NYSE specialists
has decreased very
substantially through mergers
and acquisitions. In 1933,
230 specialist trading firms
traded at the NYSE. This
number shrank to 59 by
1983, and to 8 at the end
of 2001.
Many factors probably
explain the consolidation of
specialist firms. Large, well-
capitalized firms are better
able to carry large inventory
positions, to diversify their
inventory risks, and to finance
expensive information systems
than are smaller firms.
Allowing newly listed
issuers to pick their specialists
undoubtedly has caused
many small specialist firms to
merge. Since issuers generally
do not know much about
specialists, large firms with
large capital bases are more
attractive to them than are
small firms.
virtually impossible to
obtain a specialty through direct head-to-head com-
petition. The main impediment to competition is the assignment of the elec-
tronic order flow. Without that order flow, new specialists cannot success-
fully challenge incumbents.
When an exchange lists a new stock or options series, the exchange asks
its members to apply to be the specialist. A committee evaluates the ap-
plicants based on their prior performance and on their ability to make liq-
uid markets in the security. The committee then chooses either the win-
ning applicant or a set of finalists from whom the issuer chooses the winning
applicant.
Exchanges periodically evaluate the performance of their specialists. They
base their evaluations on objective criteria and upon subjective ratings pro-
vided by floor brokers. The objective criteria measure the width and size of
quoted spreads, the frequency and size of price reversals, and price im-
provement rates for market orders. The floor brokers rate the specialists on
various dimensions which characterize the quality of the service that the
specialists provide to them and to their clients. In very rare cases, exchanges
take specialties away from specialists who provide very poor service or who
break exchange rules.
24.7 REGULATORY ISSUES
AND PERSPECTIVES
Regulators must consider whether the value specialists obtain from their
privileges is commensurate with the value of the additional liquidity they
provide. If the privileges are more valuable than the additional liquidity,
many traders will resent the money and profitable trading opportunities that
they lose to specialists.
Given the difficulties of measuring and valuing liquidity services, regu-
lators cannot easily find the proper balance between privileges and obliga-
tions. In practice, exchanges squeeze their specialists when specialists are
making lots of money and public traders are complaining about their trad-
ing costs. Exchanges then demand more service, they further restrict spe-
cialist trades, and they limit the commissions that specialists can collect for
executing system orders. Of course, the specialists scream loudly and threaten
to quit if things get too bad. Since they rarely quit and since many traders
want to be specialists, exchanges probably do not squeeze them too hard.
Regulators also must consider whether the distribution of the benefits
and costs of the specialist system are equitable. The public traders who ben-
efit most from the liquidity that specialists offer generally are not the traders
whose trading costs increase when the specialists exercise their privileges.
The beneficiaries of the specialist trading system are primarily small, unin-
formed market order traders who receive price improvement from special-
ists. Momentum traders who can quickly submit orders into rapidly rising
or falling markets also benefit from the liquidity that specialists offer in or-
der to maintain price continuity when the markets are volatile. The bene-
factors of the specialist system are the limit order traders in front of whose
orders the specialists often step. These traders often generally do not un-
derstand the system well enough to appreciate how it increases their trans-
action costs. Since the beneficiaries and the benefactors generally are not
the same traders, the specialist system transfers wealth from one group to


## Page 524

CHAPTER 24 SPECIALISTS • 511
another. How regulators regard these transfers often depends on which
group is more able to lobby for its interests.
The beneficiaries and benefactors of the specialist system also differ by
where they trade. The liquidity that specialists provide benefits all traders,
regardless of whether they normally trade at the specialists' exchange or else-
where. The benefactors of the specialist system, however, are only those
traders who trade where the specialists trade.
This mismatch is problematic for specialists and for their exchanges. The
profits that specialists normally make must fund the losses that they typi-
cally incur when no one else will supply liquidity. However, when trading
is normal, competition from dealers in other markets reduces specialist prof-
its. When increases in uncertainty and volatility cause liquidity suppliers to
withdraw from the markets, the specialists are the only traders who remain
to supply liquidity. At such times, the dealers with whom the specialists nor-
mally compete to offer liquidity may demand liquidity from them.
Exchanges can afford such generosity when their market shares are sub-
stantially greater than 50 percent. In that case, the beneficiaries and bene-
factors of the specialist system mostly trade at the same exchange. When
their market shares are smaller, however, specialists may be unable to earn
enough from their customers during normal times to fund the liquidity they
provide to the entire market when no one else will. Exchanges with small
market shares therefore cannot expect that their traders will provide much
price continuity.
If regulators believe that markets should provide price continuity, they
must somehow require that all dealers who supply liquidity in normal con-
ditions also supply liquidity when markets are volatile. Since regulators can-
not effectively prevent traders from free riding, markets with many com-
peting dealers will not have much price continuity.
Exchanges impose
capital adequacy standards
on their specialists to en-
sure that their specialists have enough capital to conduct their businesses.
The exchanges monitor these standards closely in order to identify and
quickly correct problems before they affect their customers.
The capital adequacy standards also ensure that specialists will have ad-
equate capital to provide liquidity if no one else will. Without such stan-
dards, specialists may distribute the profits that their firms make in normal
markets so that they are unavailable for providing liquidity in extraordinary
markets.
24.8 SUMMARY
Exchanges and third market dealers designate specialists who must supply
liquidity when no liquidity would otherwise be available. Traders like con-
tinuous, liquid markets. Exchanges and dealers hope to attract customers by
offering such markets.
Specialists do not willingly take these obligations upon themselves with-
out compensation. The compensation that specialists obtain is access to
order flows which allow them to earn dealing profits and brokerage com-
missions. Information in the order flows also allows them to speculate suc-
cessfully on short-term price changes.
The profits that specialists make are transaction costs for other traders.
Specialist trading is most costly to limit order traders with whom they
Nasdaq's Partial
Solution to the Free
Rider Problem
To discourage its dealers from
hiding when volatility rises,
the Nasdaq Stock Market
requires that they wait 20
trading days before they
resume trading a stock in
which they stopped making a
market. Although the rule
does not compel dealers to
offer aggressively priced
quotes, it does keep them
attentive when markets are
volatile.


## Page 525

512 • TRADING AND EXCHANGES
compete to offer liquidity. Specialists harm them by selectively stepping in
front of their limit orders when specialists believe the resulting trades will
be profitable.
Specialists consider many factors when deciding whether to trade. They
prefer to trade with small uninformed traders. They like to trade in front
of the heavy side of the limit order book in order to extract order option
values. If exchanges allow specialists to stop market orders, they do so to
create valuable look-back timing options.
In markets that enforce public order and time precedence rules, the prof-
itability of specialist trading strategies depends on the minimum price in-
crement. When the increment is small, the cost to specialists of stepping in
front of other traders is small.
Although traders value the liquidity that specialists offer, they do not like
to pay for it if they can avoid it. Since the specialist system transfers wealth
from limit order traders to market order traders, not all traders appreciate
it. Regulators must therefore decide whether the liquidity supplied by spe-
cialists to some traders is worth the costs of the system to other traders.
Many traders and commentators believe that the regulatory problems as-
sociated with the specialist trading system are intractable. They believe that
the special privileges which specialists enjoy allow them to take too much
value from the markets, and that regulators can never adequately compel spe-
cialists to return commensurate value to the markets. These people lobby to
replace the specialist trading system with a multiple designated market maker
system or with a pure price-time priority screen-based trading system.
24.9 SOME POINTS TO REMEMBER
•
Specialists are dual traders.
•
The specialists' affirmative obligations require that they provide liq-
uidity when no one else will.
•
The specialists' negative obligations require that they refrain from pro-
viding liquidity in competition with the public.
•
Specialists accept their obligations in exchange for some special
privileges.
•
Regulators and exchanges must balance the value of the special privi-
leges with the value of the services that specialists provide.
•
Price continuity can be expensive to provide.
•
Price continuity is a public good that competitive markets generally
will not provide.
24.10 QUESTIONS FOR THOUGHT
1.
Are specialists monopolists in their specialties? Do their unique po-
sitions give them economic power? What limits their power?
2.
How can exchanges enforce affirmative obligations by using a mul-
tiple designated market maker system?
3.
Can specialists compete with third market dealers who do not face
similar obligations?
4.
How valuable is price continuity? How could you estimate its value?
5.
Who should pay for the liquidity services that specialists provide?
6.
Can specialists survive and thrive in electronic exchanges?


## Page 526

CHAPTER 24 SPECIALISTS • 513
7.
Of what importance are quick trade confirmations when the only
prices you can see are last trade prices?
8.
When evaluating specialists, should exchanges give more weight to
price continuity or to narrow spreads?
9.
What effect does order stopping have on the order placement deci-
sions that public traders make?
10.
What effect would an increase in the minimum price increment have
on specialist profitability? How large should the minimum price in-
crement be?
11.
Perhaps specialists should be required to improve prices by at least
5 cents to trade in front of their books when trading in stocks that
trade on pennies. What long-run effect would such a
step-ahead in-
crement rule
have on the spread between the best bid and offer in the
limit order book? Should exchanges apply such a rule to all limit or-
der traders? Should the step-ahead increment ever be different from
the minimum price increment?
12.
How do the specialists' negative obligations help solve conflict of in-
terest problems that arise because specialists are dual traders?
13.
Exchange-based U.S. equity traders use the Intermarket Trading
System (ITS) to route orders from one market to another. Should
exchange specialists be allowed to use the ITS to route orders for
their own accounts to other exchanges? Would a prohibition on such
usage prohibit specialists from trading with each other?
14.
Dealers who provide limit order price protection must often buy at
the ask and sell at the bid. Such trades are unprofitable when the
market is stable, but they can be quite profitable when the market
is moving. Under what conditions do you expect that the opportu-
nity to fill agency limit orders, coupled with the responsibility to
provide limit order price protection, is profitable on net?
15.
Are the regulatory problems associated with specialist trading sys-
tems intractable? Should exchanges with specialist trading systems
convert to other market structures?


## Page 527

25
Internalization,
Preferencing,
and
Crossing
ealers
internalize
orders when they fill their clients' orders. Brokers
pref-
erence
orders when they route their clients' marketable orders to dealers
'n exchange for various monetary or nonpecuniary
payments for order flow.
Brokers also preference when they route their clients' limit orders to elec-
tronic communications networks (ECNs) that pay them
liquidity fees
when
standing limit orders execute. Brokers
cross
orders internally when they
arrange trades among their clients.
Internalization, order preferencing, and internal order crossing all arrange
trades away from organized markets. Traders say that these practices
frag-
ment
the markets.
Internalization, preferencing, and crossing practices raise important
regulatory questions. Most notably, clients and regulators wonder whether
brokers who engage in these practices meet their obligations to obtain best
execution when filling their clients' orders. Less obviously, traders and reg-
ulators wonder whether these practices hurt markets by making it more dif-
ficult for traders to find each other. All three practices decrease order trans-
parency. Many, people wonder whether the markets would be better off
with a single, consolidated limit order book to which brokers would send
all orders.
In this short chapter, we discuss the issues that underlie these processes.
We shall see that internalization and preferencing may benefit small mar-
ket order traders. The practices generally harm limit order traders, however.
Internal order crossing tends to benefit crossing brokers and their clients at
the expense of traders with whom the clients might otherwise have traded.
Internalization, preferencing, and crossing affect all markets in which
traders can trade away from a central exchange. These practices have at-
tracted the substantial attention in U.S. equity markets. The attention un-
doubtedly comes from the high volumes traded in these markets, the high
degree of transparency in these markets, and the ease with which traders
and regulators can compare dealer and public auction markets that simul-
taneously trade similar securities. Most of the discussion that appears in this
chapter therefore draws on examples from U.S. markets. The forces that
cause internalization, order preferencing, and internal order crossing, how-
ever, are present in all markets. If you understand how these forces work in
U.S. equity markets, you will be able to identify them in all other markets.
We start by discussing best execution and the effects of internalization
and preferencing on commissions. We then turn to the anticompetitive as-
pects of internalization and preferencing. The chapter ends with a short dis-
cussion of issues associated with internal order crossing.
25.1 BEST EXECUTION PRACTICES
Brokers who internalize and accept payments for order flow have a signif-
icant conflict of interest that concerns clients and regulators. Brokers ad-
514


## Page 528

CHAPTER 25 INTERNALIZATION, PREFERENCING, AND CROSSING • 515
dress these concerns by trying to provide
best execution
to their clients. They
provide best execution when they ensure that their clients' orders fill quickly
at the best available prices.
Definitions of best execution are controversial because determining
whether dealers fill orders at the best available prices is difficult. It is espe-
cially difficult when willing traders do not display their orders and quotes.
Definitions of best execution are also controversial because price is only
one dimension of execution quality. Traders value speed of execution as well
as price. The relative importance of speed and price depends on order type.
Market order traders are primarily concerned with speed, whereas limit or-
der traders are most concerned about price. All traders, however, value both
dimensions and accept reasonable trade-offs between speed and price.
Brokers and the dealers to whom they preference orders have created a
standard set of order handling practices that they claim assures best execu-
tion if followed. You may disagree. The procedures vary by order type.
In U.S. equity markets, dealers claim that they provide best execution
when they fill marketable orders at the national best bid or offer (NBBO)-
the best bid or offer quoted by any other dealer or limit order trader. This
standard applies only to marketable orders that are smaller than the total
displayed quotation size at the best price. Best execution for larger mar-
ketable orders is harder to define because undisclosed size might also be
available at the best price. To attract order flow, some dealers guarantee to
brokers that they will always fill orders at the NBBO up to a specified max-
imum size, regardless of the size displayed in the market.
In some markets, small market orders often trade at better prices than
the best quoted price. In such markets, brokers must obtain average rates of
price improvement
for small market orders in order to ensure best execution.
Dealers who fill their orders generally use complex algorithms to provide
price improvement given various market conditions. These algorithms of-
ten expose an order to the market at an improved price. If anyone trades
anywhere at that price, dealers then fill the order at that price.
Best execution standards for standing limit orders also are difficult to de-
fine. Since standing limit orders typically execute at their limit prices, bro-
kerage clients are most concerned about whether and when their orders ex-
ecute. Best execution for orders that are not marketable upon submission
therefore depends on whether and when they execute.
In markets that match public orders to public orders, best execution stan-
dards for standing limit orders are especially difficult to define. Clients gen-
erally expect that their brokers will represent their orders wherever they have
the highest probability of executing. Brokers who internalize their agency
orders or who preference them to dealers must ensure that they execute at
least as soon as they would otherwise execute in the primary markets. Many
dealers who accept limit orders have
limit order price protection
procedures
to provide this standard of best execution.
The fact that dealers pay for marketable orders suggests—but does not
necessarily imply—that they could provide better execution services than
they do. In particular, dealers may not be providing as much price im-
provement for marketable orders as they might. They also may be extract-
ing too much option value from standing limit orders that brokers often
force them to accept as a condition of receiving marketable orders. If bro-
kers demanded more price improvement and better limit order executions,
dealers would pay less for their preferenced order flows.
The Primex
Auction System
The Primex Auction System
allows participants to expose
their marketable orders to an
electronic crowd of traders
who may offer improved
prices. The Nasdaq Stock
Market operates the system as
a facility under an agreement
between Nasdaq and Primex
Trading.
Several large financial
services firms, which have
very large dealing operations,
own Primex Trading. They
include Bernard L. Madoff
Investment Securities,
Goldman Sachs, Merrill
Lynch, Morgan Stanley Dean
Witter and Co., and Salomon
Smith Barney.
Source: www.primextrading.corn.


## Page 529

516 • TRADING AND EXCHANGES
Best Execution of Limit Orders by Third Market Dealers
in NYSE-listed Stocks
Many third market dealers who accept agency limit orders for NYSE-listed
stocks use some version of the following algorithm to provide best execution
for limit buy orders. They use similar procedures for limit sell orders.
If the limit order bids a better price than the dealer is bidding, the
dealer will adjust his bid to reflect the limit order price and size. If the Iwo
prices are the same, but the limit order bids for more size than does the
dealer, the dealer will increase his bid size to the order size.
While the limit price matches the best bid in the market, the dealer will
match any marketable sell orders that he receives with the limit buy order. If
a trade occurs anywhere at a price below the limit order price, the dealer
will fill the limit order for his own account at the limit price. The dealer may
fill all of the order, or just the size of the trade that occurred below the limit
order price.
When the limit order price is first equal to the best bid in the market,
and the NYSE quote is also at the best bid, the dealer will record the size
of the NYSE bid. If the NYSE bid is behind the best bid, the dealer will
record zero NYSE size. He then will count volume traded everywhere at the
limit price. When that total volume exceeds the recorded NYSE size, the
dealer will execute the limit buy order for his own account.
The dealer also may place a limit order into the NYSE specialist's limit
order book at the same price. If the NYSE order fills, he will fill his agency
limit order. If he fills the agency order first, he will immediately cancel the
NYSE order. By placing the NYSE order, he ensures that he will obtain no
worse execution for the agency order than it would have received if it had
gone to the NYSE. He also can query the SuperDot order-routing system for
the size ahead of the order. 44
To fully understand how payments for order flow affect retail traders, we
must consider more than just that the payments exist. We also must con-
sider how those payments are determined, and what effect they have on re-
tail brokerage commissions. The next subsection describes the economic fac-
tors that determine payments for order flow, and how they affect brokerage
commissions.
25.2 THE ECONOMICS OF BEST EXECUTION
We start our discussion with a well-known property of all competitive mar-
kets. In
perfectly competitive markets,
nobody earns profits in excess of a fair
rate of return on their dedicated resources. Perfectly competitive markets
arise when suppliers with identical cost functions can freely enter or exit the
market at low cost. When suppliers in such markets make excess profits,
they tend to lower price or provide better service to attract more business.
New suppliers also enter to share in the excess profits. These responses drive
excess profits to zero. When suppliers earn less than their required profits,
they raise prices, cut service, or leave the market. These forces tend to raise
profits. As a result, excess profits are zero in equilibrium.
Consider now the application of this principle to wholesale and retail or-
der flow markets in which dealers and brokers, respectively, compete. Both
markets are highly competitive. Dealers compete with many other dealers
to fill orders; brokers likewise compete with many other brokers to arrange
trades. In both markets, entry and exit are not too costly.


## Page 530

CHAPTER 25 INTERNALIZATION, PREFERENCING, AND CROSSING • 517
Dealers compete to obtain wholesale order flow from brokers. Orders are
valuable to dealers when brokers are unable or unwilling to enforce high ex-
ecution standards. Dealing is especially profitable when dealers can execute
market orders at wide bid/ask spreads. To earn these profits, however, deal-
ers first must obtain wholesale order flows from brokers. In perfect compe-
tition, dealers will compete away any excess profits by offering various in-
ducements to brokers to obtain their client order flows. If executing the
order flow is profitable, the payments to brokers for obtaining that order
flow will be high.
Brokers compete to obtain order flow from their retail clients. Retail or-
der flows are valuable to brokers because they can preference these orders
to dealers in exchange for payments. They also can internalize them and
thereby directly profit from them. To earn these profits, however, brokers
must first obtain retail orders. In perfect competition, brokers will compete
away any excess profits by offering their clients various inducements to ob-
tain their orders. These inducements include low commissions and a wide
variety of ancillary services, such as free or discounted investment informa-
tion and investment advice. If brokering retail order flow is profitable, the
inducements that brokers offer their clients to obtain their orders will be
valuable.
In perfectly competitive markets, if brokers or regulators demand that
dealers provide higher execution quality, wholesale payments for order flow
and retail inducements for order flow will fall. A trade-off thus exists be-
tween execution quality and the price and level of brokerage services of-
fered. Holding everything else constant, if traders demand greater price im-
provement for market orders, commissions will rise by a corresponding
amount. In perfectly competitive markets, net transaction costs for small
market orders (bid/ask spread plus commissions) will not depend on how
brokers and dealers define best execution.
Consider now the forces that determine the allocation of net transaction
costs between bid/ask spreads and commissions for market orders. We start
by explaining why retail traders pay more attention to low commissions than
to good prices. We then explain why bid/ask spreads tend to be wide.
25.2.1 Low Commissions
Retail traders pay more attention to low commissions than to good prices
because you cannot buy anything that you cannot measure. Suppliers who
offer expensive quality that buyers cannot recognize can be undercut by those
who claim to do so but do not. If buyers cannot tell the difference between
high quality and low quality suppliers, suppliers can give them low quality
products regardless of what they promise. As a result, buyers in such
markets tend to purchase from low cost suppliers. Price reflects the costs of
low quality suppliers, and high quality suppliers will not be able stay in
business.
Consider the implications of this principle for retail market order traders:
Good execution for marketable orders means good prices and quick service.
Retail market order traders can easily audit response times, but not the qual-
ity of the prices that they receive. Reliable transaction cost measurement is
very difficult for most traders, and especially difficult for retail traders who
trade in very active markets (see chapter 21). To effectively measure execu-
tion price quality, traders must carefully assess their execution prices in


## Page 531

518 • TRADING AND EXCHANGES
SEC Rules 1 litcl-5 and 11Acl -6
To help retail investors deal with these measurement problems, the SEC
adopted two rules to improve public disclosure of order execution and
routing practices. Rule 11Ac1-5 requires market centers (dealers,
exchanges, and ECNs) to make monthly electronic reports that include
uniform statistical measures of execution quality. Rule 1 1Ac1-6 requires
broker-dealers who route customer orders in equity and option securities to
make publicly available quarterly reports that, among other things, identify
the venues to which they route their customer orders for execution.
These rules allow rating agencies to compile reports that rate dealers
and brokers. The resulting reports give dealers and brokers substantial
incentive to provide high quality service, at least as it is defined by the
uniform execution quality measures that they must report.
Source: SEC Release no. 34-43590 (17 CFR Part 240) at www.sec.gov/rules/

fina1/34-43590.htm.
relation to nearby trade and quotation prices. Most retail traders cannot eas-
ily access the relevant data or are unwilling to spend the time necessary to
analyze the data. Unless their trade prices are unusually good or bad, most
retail traders have no idea how well their market orders execute.
The execution audit problem for limit order traders is even more difficult
because it can be hard to determine whether a limit order that matches or
improves the NBBO should have executed. The uncertainty associated with
limit order executions greatly complicates the execution audit problem.
Even if traders accurately estimate execution price quality, without norms
against which to compare their estimates, they cannot judge whether their
trades are well executed. To benchmark their trades, they must trade with
several brokerages, or they must compare their results with other traders. In
either event, they must be careful to compare apples with apples and or-
anges with oranges because some securities are harder to trade than are oth-
ers. Retail traders rarely make such comparisons because they are too costly.
Since retail clients generally do not know whether they receive good ex-
ecutions on average, brokerage firms have little direct incentive to demand
that dealers provide better execution than whatever is generally accepted as
best execution. If they did, they would obtain fewer order flow inducements
from dealers. Instead, brokers accept the prevailing best execution standards
and use the resulting order flow inducements to lower their brokerage com-
missions, which their clients can readily audit.
The brokerage industry is not particularly concerned about this result.
When deciding to trade, most clients give more weight to visible commis-
sion costs than to largely hidden bid/ask spreads that they pay. The bro-
kerage industry therefore prefers low commissions to good executions be-
cause low commissions encourage clients to trade.
Although retail brokers have little incentive to demand higher best exe-
cution standards, they have a very strong incentive to ensure that dealers
provide them with best execution. Brokers who do not ensure that their cus-
tomers obtain best execution expose themselves to regulatory discipline and
civil lawsuits. Brokers therefore pay close attention to the quality of execu-
tion that they obtain for their clients. A small consulting industry has grown
The SEC Mandate
"The Commission believes
that broker-dealers deciding
where to route or execute
small customer orders in listed
or OTC securities must
carefully evaluate the extent
to which this order flow
would be afforded better
terms if executed in a market
or with a market maker
offering price improvement
opportunities. In conducting
the requisite evaluation of its
internal order handling
procedures, a broker-dealer
must regularly and rigorously
examine execution quality
likely to be obtained from the
different markets or market
makers trading a security. If
different markets may be
more suitable for different
types of orders or particular
securities, the broker-dealer
will also need to consider
such factors."
Source: SEC Release no. 34-
37619A (17 CFR Part 240) at
www.sec.gov/rules/final/

37619a.bct.


## Page 532

CHAPTER 25 INTERNALIZATION, PREFERENCING, AND CROSSING • 519
0 The Industry Response
Several consultants provide transaction audit services to retail brokers.
Brokers use these services to ensure that the dealers to whom they route
their orders provide them with best execution. The leading consultants in this
area are the Transaction Auditing Group (TAG) and Market Systems Inc.
(MSI).
Both companies nightly collect all transactions from their subscribing
brokers. They then compare them with trade price and quote data to
determine whether dealers properly handled the orders sent to them. These
analyses produce
F+1
exception reports
that list trades which do not
conform to execution standards that the brokers specify. The brokers use
these reports to demand explanations or adjustments from their dealers.
Both companies also produce monthly and quarterly reports that
characterize various aspects of execution quality. 1
Source: Full descriptions of the TAG and MSI products and samples of their reports
appear
at www.tagaudit.com
and www.marketsystems.com
.
to help brokers measure best execution and manage their order preferenc-
ing relationships.
25.2.2 Wide Bid/Ask Spreads
In chapter 14,
we show that the competition among dealers and limit or-
der traders to attract orders which traders route to the best displayed prices
determines bid/ask spreads. Spreads therefore critically depend on the or-
ders that brokers do not preference or internalize. Most brokers internalize
and preference all orders that interest dealers. They send the remaining
orders to whoever offers the best prices. These orders therefore are the ones
that determine bid/ask spreads.
Dealers who offer firm quotes do not want to receive orders from well-
informed traders, or from large traders, many of whom are well informed.
Well-informed traders force them to acquire positions on the wrong side of
the market. Large traders often price discriminate against dealers and force
them to take large positions that they cannot easily divest.
Dealers expose themselves to well-informed traders and to large traders
when they offer firm quotes that any trader can take. They therefore quote
wider spreads than they would quote if they traded only with small unin-
formed traders. The wider spreads compensate dealers for their losses to in-
formed traders and to price discriminating traders. These spreads also com-
pensate dealers for the costs incurred when searching for traders to take
large unwanted positions.
Small uninformed traders who trade at quoted bid and offer prices there-
fore are indirectly hurt by the presence of well-informed traders and large
traders because of the wider spreads that they must pay. They pay higher
prices when buying and receive lower prices when selling than they would
otherwise pay and receive.
Payments for order flow benefit small uninformed traders when dealers
can determine who they are, and when the wholesale and retail markets for
order flow are highly competitive. When dealers can
skim the cream
of the
order flow by offering liquidity only to small uniformed traders, payments
0
,
Hot Order Flows
Dealers who pay for order
flow pay close attention to the
orders they receive. They
regularly measure the profits
that they make handling these
orders by considering, among
other things, what happened
to prices after they received
the orders. Order flows are
hot when prices tend to rise
following the submissions of
market buy orders and fall
following the submissions of
market sell orders. When
dealers find that they are
receiving hot order flows,
they will not pay for them
unless brokers force them to
take the hot orders in
exchange for other, more
profitable orders. Obviously,
brokers who force hot order
flows on dealers will not
obtain as generous payments
for order flow as they would
have if they sold only benign
order flow.


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520 • TRADING AND EXCHANGES
We Will Meet or Beat
Any Advertised Price!
Retailers often offer to meet or
beat any advertised price in
town for the products they
sell. In practice, they simply
match the quoted price, less
some small amount.
Although this practice
suggests that these retailers
will aggressively compete on
price in order to obtain
orders, it is actually quite
anticompetitive because it
weakens the incentives to
advertise low prices. The
incentives to offer low prices
are strongest when the
retailers who offer them attract
orders. When other retailers
are willing to match prices,
retailers who offer the lowest
prices do not get as much
order flow. Consumers would
be better off if retailers could
attract orders only by offering
the lowest prices, rather than
by offering to match the best
prices.
for order
flow ensure that these small traders ultimately obtain lower com-
missions in compensation for the high spreads that they pay.
25.3 ANTICOMPETITIVE ASPECTS OF
INTERNALIZATION AND PREFERENCING
Dealers and limit order traders will more likely offer aggressive prices when
their quotes and limit orders attract order flow than when they have no ef-
fect on the orders that come to them. The internalization and preferencing
of order flows take orders away from traders who price aggressively. Inter-
nalization and preferencing therefore place aggressive dealers and limit or-
der traders at a disadvantage, and thereby weaken incentives to quote ag-
gressively. Internalization and preferencing thus increase bid/ask spreads
relative to what they would be if brokers routed all orders to the traders who
offer the best prices. This result would occur even if all traders were equally
well informed and traded the same size.
In Nasdaq stocks, brokers increasingly preference their market orders to
dealers and their limit orders to ECNs. The dealers pay brokers for market
orders and the ECNs pay brokers
liquidity fees
for standing limit orders that
execute. The liquidity fees are funded by the fees that traders pay to the
ECN when they use marketable orders to take liquidity. Since ECNs rep-
.
resent their orders in Nasdaq, and since all Nasdaq participants can trade
with those orders, brokers can claim that they are properly representing limit
orders that they send to ECNs.
Limit orders in ECNs, however, are undoubtedly subject to serious ad-
verse selection. When they are at the
NBBO,
preferencing and internaliza-
tion ensure that they will not often be matched with marketable orders. If
prices move away from them, they also will not execute. However, if prices
move toward them, they will quickly execute, and the traders who submit-
ted these limit orders will regret trading. Adverse selection hurts these traders
by reducing their opportunities to profit from trades that are followed by
price changes which benefit their positions.
Dealers and public limit order traders compete with each other to offer
liquidity. By decreasing the probability that limit orders will execute, inter-
nalization and preferencing shift the balance of power in this competition
toward dealers.
25.4 INTERNAL ORDER CROSSING
Brokers cross orders internally when they arrange trades among their clients.
They may use formal order-driven trading systems or more traditional meth-
ods to match orders that their clients give them.
Brokers arrange internal order crosses when they run crossing networks,
alternative trading systems, and electronic communications networks (ECNs).
Block brokers also often arrange internal order crosses.
Brokers who arrange internal crosses for their customers generally pro-
vide them with services or service prices that they cannot otherwise obtain.
If their clients were not pleased with the combination of service and price
that they obtain, they generally would not use these brokers.
Regulatory concerns about internal order crossing involve order exposure
and agency problems. The order exposure problem arises because brokers


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CHAPTER 25 INTERNALIZATION, PREFERENCING, AND CROSSING • 521
who cross internally may expose their client orders to their clients only, and
not also to other traders who might be willing to fill the orders if given the
chance. Because brokers want to receive commissions from both sides of the
trade, they are reluctant to expose their orders to traders represented by other
brokers. The order exposure problem concerns regulators because it makes
the trade search problem more difficult for buyers and sellers.
Brokers defend the practice by noting that they work harder to fill or-
ders when they get two commissions rather than one. Clients, however, note
that they need not work harder if they can easily find the liquidity neces-
sary to fill the order elsewhere.
The agency problem arises when brokers favor some clients over other
clients. The brokers may then arrange trades to the advantage of their fa-
vored clients. If the harmed clients cannot easily estimate the costs of their
transaction, they may not easily recognize that they received poor prices.
Exposure of orders to markets helps protect traders from these abuses.
You can view order preferencing as internal crosses. When a broker pref-
erences an order to a dealer, the broker essentially arranges a trade between
the client and the dealer. Although we do not normally consider dealers to
be clients of order preferencing brokers, this characterization of their rela-
tionship is completely consistent with our understanding of what brokers
do. Payments for order flow are essentially commissions that dealers pay
brokers to arrange trades for them. From this perspective, regulatory con-
cerns about preferencing are easy to understand. Regulators fear that bro-
kers will favor dealers—with whom they do much repeated business—over
their clients, with whom they do much less business on an individual basis.
Regulators can prohibit internal crossing, or they can require that bro-
kers represent their clients' orders to the market. Although these restrictions
would consolidate order flows, they would not necessarily be desirable. Bro-
kers who cross orders internally often provide services to their clients that
they could not—or would not—provide if they were required to expose their
orders.
25.5 SUMMARY
Brokers internalize and preference order flows in order to extract value from
largely uninformed orders that execute at wide spreads. Payments for order
flow ensure that dealers in perfectly competitive wholesale dealing markets
do not obtain excess profits from trading these orders at wide spreads. The
commissions and other order flow inducements which brokers must offer
their clients to obtain their orders ensure that brokers in perfectly compet-
itive retail brokering markets do not profit excessively from internalization
or payments for order flow. When competition is perfect in retail and whole-
sale order flow markets, low commissions offset poor execution so that net
prices do not ultimately depend on best execution standards.
In no market is competition perfect, however. Dealers and brokers with
market power will exploit that power and ultimately obtain excess profits
from public traders. How much excess profit they obtain depends on how
competitive these markets are.
Wholesale dealers have some market power by virtue of the economies
of scale associated with their operations. These economies have led to
substantial consolidations through mergers and acquisitions in the dealing
Ito Exclusive Listings
Real estate agents usually try
to arrange trades exclusively
among their clients when they
first obtain a listing. To
minimize the probability that
they may have to allow
another broker to participate
in the trade, brokers almost
always show newly listed
properties to their clients
before they post them on
multiple listing services,
before they post a for-sale
sign, and before they
advertise the property.
Real estate trade
organizations are well aware
of the problem. They often set
standards to which their
members must adhere to
prevent serious abuses.
11 The Open Outcry
Principle
Floor-based futures markets
require that traders arrange
all their trades in the pit, in
front of all other traders. By
allowing anyone to offer a
better price, this procedure
ensures that a broker cannot
arrange an unfair internal
order cross.
Some futures markets now
permit their traders to
negotiate block trades off-
floor for qualified clients. The
qualification standards ensure
that the clients are
sophisticated traders who can
look out for their own
interests.
4


## Page 535

522 • TRADING AND EXCHANGES
ACATS
Procedures that allow retail traders to transfer their accounts cheaply from
one broker to another are essential to prevent brokers from accumulating
market power over their retail clients. In the United States, brokers and their
clients use the
Automated Customer Account Transfer Service
(ACATS),
operated by the National Securities Clearing Corporation to facilitate the
transfer of customer accounts from broker to broker. This automated system
generally can move an account in less than six business days. Verification
of the trader's authorization to request the change takes most of the time.
Brokers try to accumulate market power through various programs that
make switching brokers expensive for their clients. For example, some
brokers encourage their clients to store personal financial data with them.
Clients who have done so are reluctant to switch brokers because they
cannot transfer their data.
Source: www.nasdr.com/2500
trans_proc.htm.
segment of the trading industry. Although the remaining firms undoubt-
edly increased their market power, the economies of scale have also made
them more efficient. These economies of scale make it difficult for new en-
trants to compete aggressively in the wholesale order flow market.
Generally, the more convoluted a competitive system is, the less efficient
it will be. The wholesale and retail order flow market system is a more com-
plex competitive system than a centralized market. We therefore can pre-
sume that it will be less efficient.
By taking orders away from common market mechanisms, internaliza-
tion, preferencing, and internal order crossing practices make it harder for
natural buyers and sellers to find each other. Internalization and preferenc-
ing also weaken central markets by reducing incentives to quote aggressively.
These practices therefore must ultimately increase the total transaction
costs of all buy-side traders. Internalization and preferencing, however, prob-
ably provide small uninformed traders with better net prices—spread plus
commission—than they would otherwise obtain. Internal order crossing
likewise provides many traders with services that exchanges and brokers
would not otherwise provide. Regulators who would restrict internalization,
order preferencing, or internal order crossing must consider the trade-offs
between the benefits and costs of these practices.
Many people believe that the benefits some traders obtain from inter-
nalization, preferencing, and order crossing practices do not justify the dam-
age these practices do to central markets. They would instead prefer a
con-
solidated limit order book
market structure that would bring all traders
together in the same place. In chapter 26, we further consider the economic
forces that consolidate and fragment markets.
25.6 SOME POINTS TO REMEMBER
•
Competition among dealers and brokers ensures that payments for or-
der flow reduce commissions.
•
Retail customers can easily audit commissions but cannot easily audit
trade executions.
•
Internalization and preferencing decrease incentives to quote aggres-
sively.


## Page 536

CHAPTER 25 INTERNALIZATION, PREFERENCING, AND CROSSING • 523
•
Internalization and preferencing probably provide better net prices
(spread plus commission) to small uninformed traders.
•
Internalization and preferencing shift power from public limit order
traders to dealers.
•
Brokers like internal order crosses because they often can collect com-
missions from both sides of the trade.
25.7 QUESTIONS FOR THOUGHT
1.
Should the payments that dealers make for order flow depend on the
characteristics of the stocks that they trade? How so?
2.
The economic analysis of payments for order flow presented in the
text assumes that the wholesale and retail order flow markets are per-
fectly competitive. How would the analysis differ if competition in
these markets is less than perfect?
3.
Why do you suppose that dealers who receive preferenced order flow
and broker-dealers who internalize order flow founded Primex?
4.
Why do dealers generally not pay to obtain limit orders?
5.
What effect does internalization have on price discovery in the pri-
mary markets? How would prices be determined if all orders were
internalized?
6.
Why do corporate bond markets and equity markets have such dif-
ferent market structures? How do the differences in their structures
affect their qualities?


## Page 537

26
I>
Competition
Within
and
Among
markets
I
n the last few years, many exchanges, brokers, electronic communications
networks (ECNs), and dealers have created innovative trading systems to
provide traders with better services at lower costs. The competition among
these
market centers
is significantly changing how all markets operate, and
the pace of change is accelerating.
The competition among market centers has some worrisome conse-
quences, however. The proliferation of market centers is fragmenting the
markets. Buyers and sellers often are in different places, so that they may
have trouble finding each other. Their transaction costs therefore may be
higher than they would be if all traders traded in the same place. The ben-
efits of competition among market centers may be offset by the increased
costs it creates for traders who are searching for the best price.
A market in which people can trade essentially the same thing in dif-
ferent market centers is a
fragmented market.
A market in which all traders
trade in the same market center is
a consolidated market.
Regulators and practitioners wonder whether markets should be consol-
idated or fragmented. Regulations can produce either alternative. Most fu-
tures markets are fully consolidated, as are some national stock markets.
The issue is quite complicated. The competition among traders to ob-
tain the best price works best in consolidated markets. The competition
among market centers to provide low-cost services to traders, however, im-
plies fragmented markets. The two competitions therefore are inconsistent
with each other. Any reasonable attempts to address competitive issues must
consider why market fragmentation occurs, and the benefits and costs of
market diversity.
In this chapter, we consider the economic forces that cause markets to
consolidate and to fragment. Our discussion starts with a short description
of how technology has changed trading markets. This section presents the
technological context of the main issues. The economic analysis starts with
a discussion of why markets consolidate. We then consider why markets
fragment, and how fragmented markets coalesce into segmented markets.
Finally, we address the public policy problems related to externalities among
market segments.
26.1 TRADING SYSTEMS AND TECHNOLOGY
New trading systems have proliferated largely due to advances in commu-
nications and computing technologies. New communications technologies
have given traders instantaneous presence in markets that they formerly
could not attend. Traders no longer need to be on an exchange floor to know
what is happening there or to trade effectively. Instantaneous market data
reporting systems and order-routing systems now allow traders anywhere in
the world to see and act upon opportunities wherever they occur.
524


## Page 538

CHAPTER 26 COMPETITION WITHIN AND AMONG MARKETS • 525
Where Is the Market for AOL?
AOL Time Warner common stock trades in each of the following market
centers:
•
The New York Stock Exchange, its primary listing market
•
All U.S. regional exchanges: Boston, Chicago, Cincinnati, Pacific, and
Philadelphia
•
Most
ECNs
and alternative trading systems. The most important of these
are Island, Instinet, REDIBook, Archipelago, Bloomberg Tradebook, BRUT,
and POSIT.
•
The third market and Nasdaq—Bernard Madoff Investment Securities and
Knight Capital Markets are the largest dealers in these markets
•
The upstairs block trading market
•
Some large foreign stock exchanges
The risk in AOL common stock also trades in the following derivative
contract markets:
•
U.S. options exchanges all trade AOL stock option contracts cleared by
the Options Clearing Corporation. These include the Chicago Board
Options Exchange, the American Stock Exchange, the Pacific Exchange,
the Philadelphia Stock Exchange, and the International Securities
Exchange.
•
Futures contracts on AOL
common stock shares will trade at several
exchanges starting in late 2002.
•
Many large investment banks will write individually tailored synthetic
derivative contracts in AOL for their clients.
,
4
New computing technologies have allowed market centers to organize
sophisticated algorithm-based order-matching systems that would be im-
possible to implement by hand. These systems provide traders with com-
plex order management tools that permit traders to more effectively solve
their trading problems. Examples of such features include systems that
•
Display orders only to traders who commit to filling them
•
Ensure that a trader buys and sells equal dollar values
•
Ensure an order that is part of a larger strategy will fill only if all
orders in the strategy fill
•
Allow traders to submit orders with limit prices indexed to market
conditions
•
Substitute orders in one instrument for orders in another instrument
based on market conditions.
Trading systems that incorporate these features use complex rules to treat
all traders fairly, subject to various constraints. They could not be imple-
mented without the assistance of a computer. New computing technologies
therefore have allowed markets to develop new applications that formerly
would have been economically infeasible.
Even when clerks can effectively operate a trading system by hand, they
are not as cost effective as computers. New computing technologies there-
fore have allowed market centers to lower the costs of existing services in
addition to providing new services.
Slogans Don't Help
All languages promote
wisdom with slogans.
Slogans, however, will not
resolve debates on market
structure.
For
example,
"United We Stand, Divided
We Fall" suggests that
consolidated markets are
good, but "Strength Through
Diversity"
suggests that
fragmented markets are good.
When applied to market
structure, these two slogans
promote the two different
competitions that take place in
the trading industry. The first
slogan promotes the
competition of traders to find
the best price, and the second
promotes the competition
among market centers to
provide the best services.
From Dispatch
Messenger to ECN
In the beginning, markets
reported some trade prices—
and almost no quotations—by
dispatch messengers. They
usually traveled by horseback
and ship.
Later
they reported
prices by carrier pigeon,
semaphore, telegraph, telex,
and telephone. Now most
organized markets
continuously report all trade
prices and all quotations as
they occur via dedicated
communications systems run
by computers. Information that
once moved at equine speed
now moves at the speed of
light.
Likewise, traders once
made all trading decisions
themselves and brokers once
arranged all trades manually.
Now computers commonly
make and implement trading
decisions while dedicated
exchange, ECN, broker, and
dealer trading systems arrange
trades automatically.


## Page 539

526 • TRADING AND EXCHANGES
The OM SAXESS
Trading System
OM Gruppen of Stockholm
sells and operates exchange
trading systems. Their SAXESS
system allows traders to
submit various types of
contingency orders:
combination orders, linked
orders, stop loss orders, block
orders, and balance orders.
The algorithms necessary to
provide these services are
quite complex because the
execution of some orders is
contingent on the execution of
other orders.
4
Source: http://www.omgroup.
com/.

The Cheese Cart Crowd
Markets consolidate for the
same reason a crowd forms
when a supermarket gives
free appetizer samples.
Everyone likes to obtain
something for nothing.
Traders give away free
options when they offer limit
orders, offer quotes, or
accept offers to trade. Since
traders value these options,
these offers attract a crowd.
In the supermarket, only
the manufacturer's
representative offers free
cheese. In trading markets,
traders who are attracted by
free options often also offer
options.
26.1.1 A Very Short History of
Fragmentation and Consolidation
In
the beginning, most trading occurred on the trading floors of regional
exchanges. Professional traders wanted to belong to these exchanges because
only by being on these floors could they learn about market conditions and
access trading opportunities. Nonmembers traded through member-brokers
because that was the only way they could trade in these markets. Although
no single market structure can simultaneously best serve the needs of all
traders, most traders traded at exchanges because everyone traded there.
Trading in many instruments fragmented across regional exchanges be-
cause impatient traders would not send their orders to distant exchanges.
These traders incurred high transaction costs to compensate dealers who
moved liquidity through time and arbitrageurs who moved liquidity from
market to market. Wide arbitrage spreads reflected the high costs of ob-
taining information and acting upon it across large distances.
When new communications technologies reduced the costs of transmit-
ting market information and orders, regional exchanges consolidated to form
large international markets. Where permitted, many alternative trading sys-
tems operate on the periphery of these markets. These systems provide spe-
cial services to traders whose needs vary substantially. Arbitrageurs ensure
that prices in all systems reflect market conditions throughout the world.
Traders now trade in whatever trading system best serves their particu-
lar needs, confident that prices in that market segment will reflect liquidity
conditions in all other segments. New trading systems have proliferated as
entrepreneurial exchanges, brokers, dealers, data vendors, and software
providers compete to help satisfy the liquidity demands of diverse traders.
26.2 MARKET CONSOLIDATION
Markets are
consolidated
when all traders trade in the same place. Markets
naturally consolidate. Since trades are easiest to arrange on good terms in
liquid markets, traders gravitate to the most liquid market. Each trader who
joins a market adds liquidity to that market. The additional liquidity then
attracts more traders, who add more liquidity. Economists call this phe-
nomenon the
order flow externality.
It causes markets to consolidate with-
out any regulatory intervention.
We can best understand the implications of the order flow externality by
momentarily adopting a simple but highly unrealistic assumption. Assume
that all traders are essentially identical. In particular, assume that all traders
trade for similar reasons; they trade the same sizes, they are equally patient—
or impatient—to trade; they are equally creditworthy; and they pursue
roughly the same investment strategies.
If this extreme assumption were true, the same exchange services would
interest all traders. Whatever market pleased one trader would please all
other traders. No trader would want to trade anywhere but where all other
traders trade. Traders would find the best terms for their trades there be-
cause all interested traders would be there. With all interested traders in the
same market, the search for best price would be least costly.
The market in which identical traders would trade need not convene in
any one physical location. It could reside anywhere that traders can expose
their orders to everyone and trade with any order. Electronic trading sys-


## Page 540

CHAPTER 26 COMPETITION WITHIN AND AMONG MARKETS • 527
tems are becoming increasingly common because electronic networks often
provide cheaper and more efficient communications than face-to-face net-
works do.
This consolidated market would treat each trader equally. No one would
receive any special preferences based on size, creditworthiness, or experience
because we assumed that no such differences exist.
26.2.1 Innovative Markets
Occasionally, someone may want to create a new market with different trad-
ing rules or with a new technology. If the innovation lowers transaction costs
or provides more service, all traders would join the new market, and the
market would remain consolidated. All traders would want to join the new
market because all traders are identical. If one trader decides that it is op-
timal to join, all other traders will reach the same conclusion.
It may be difficult to convince all traders to switch to the new market at
the same time, even if everyone would be better off trading there. The or-
der flow externality gives the incumbent market a tremendous advantage
over new rivals. No trader wants to be the first trader in a new market, no
matter how good it might be. If the new market structure is not substan-
tially better than the incumbent one, or if it is too costly for traders to redi-
rect their orders, it may not be possible to convince enough traders to switch
to make the new market viable. An innovative market may fail simply be-
cause it cannot take the order flow externality away from the incumbent
market. The order flow externality may allow an incumbent market to sur-
vive even if another market structure could provide better service.
The Optimark Experience: 406 Million Dollars Lost!
Optimark was a highly innovative trading system that permitted traders to
create "profiles" for their orders. Using a graphical interface, traders could
express degrees of preference (trader satisfaction) for various combinations
of price and quantity. A Cray supercomputer processed these profiles to
match buyers to sellers according to a complex set of preference rules.
Many people—especially institutional traders—were very excited by the
system when it was under development in the mid-1990s. The novel means
by which the system could allow them to express their preferences, and the
novel ways in which these expressions could facilitate negotiation of cheap,
mutually satisfactory trades, particularly enchanted them.
Unfortunately, their excitement did not generate much order flow. After a
couple of years of very poor performance, Optimark closed the U.S.
equities segment of its business in September 2000. Accumulated deficits
for the entire firm through September 2001 totaled 406 million dollars.
Much of the loss was due to technology development and marketing.
How you interpret this story depends on what you think about the
technology that Optimark introduced. If you believe that the technology
represents a significant improvement over existing exchange technologies,
then you learned that the order flow externality is extremely hard to
overcome, especially for systems that traders cannot easily understand. If you
believe—as I do—that the technology was inferior to existing exchange
technologies, then you learned that traders enthusiastically support interesting
development efforts as long as they do not have to pay for them.
4
Source: www.sec.gov/Archives/edgar/data/1062023/000095012301500801/

y47391a2e10-ka.txt.


## Page 541

528 • TRADING AND EXCHANGES
0 Why ECNs Compete Well with Nasdaq but Not with the NYSE
Several ECNs provide electronic order-driven markets for U.S. equity
traders. ECNs get around the order flow externality problem through clever
manipulation of their linkages with the Nasdaq trading system. ECNs can
route orders to Nasdaq, and they can post and quickly revise quotes on
Nasdaq. They use these facilities to expose their clients to the liquidity
available in Nasdaq.
When an ECN receives a market order, it determines whether the order
would be best executed by crossing it with standing orders in the ECN
order book or by sending it to Nasdaq. If the order would obtain a better
execution through Nasdaq, the ECN sends the order there. Otherwise, it
crosses the order internally. This procedure ensures that market order traders
who send their orders to ECNs obtain execution that are at least as good
as they would obtain on Nasdaq.
When an ECN receives a limit order, it first determines whether it is
marketable in Nasdaq or in its own system. If it is marketable, the ECN
treats it like a market order and sends it to the best market. If the order is
not marketable, the ECN places it in its order book. If the order matches or
improves the best price on the ECN book, the ECN revises its Nasdaq
quote to reflect the improved price or size of the new order. If a market
order then arrives at the ECN with which the ECN can match the standing
limit order, the ECN crosses the order and adjusts its Nasdaq quote. If
Nasdaq routes a marketable order to the ECN, the ECN fills the order if it
has not already been filled. This procedure ensures that limit orders sent to
ECNs are exposed to the entire market.
The exposure of a limit order in two markets at once puts the order in
double jeopardy
of executing twice. The problem is especially serious if
either market has slow execution, quotation, order-routing, or trade
reporting systems. In that case, both markets may try to execute the order
before either market can cancel an order or adjust a quote.
Since most traders will not bear the risk of double execution, one market
must take precedence over another. Existing order-routing systems ensure
that ECNs have precedence over Nasdaq but not over the NYSE. This
difference explains why ECNs have taken significant market share from
Nasdaq but not from the NYSE. An ECN cannot cross a market order with
a limit order that it has routed to the NYSE until it cancels the NYSE limit
order and receives a report confirming that the order is canceled. This
process generally takes much longer than most market order traders are
willing to wait.
To initially compete with an incumbent market, a new market must find
a way around the order flow externality problem. The new market must be
closely integrated with the incumbent market so that traders can easily ob-
tain liquidity in either market, or the new market must have an extremely
effective advertising campaign that can convince many traders to switch at
the same time. The ECNs that started to complete with Nasdaq in the late
1990s took the first approach.
26.2.2 The Order Flow Externality, Order Exposure,
and Preferencing
When a market fully displays its orders and quotes, dealers can compete
against the order-flow externality held by that market by filling market or-
ders on the same terms available in that market. Since their clients get the


## Page 542

CHAPTER 26 COMPETITION WITHIN AND AMONG MARKETS • 529
same execution that they otherwise would have received, they do not care
where their market orders fill.
If permitted to do so, dealers frequently establish preferencing arrange-
ments to compete against such markets. If they arrange to fill orders that
primarily come from uninformed traders, dealers may be able to offer bet-
ter terms than are available in the primary market. Dealers then will take
much of that order flow away from the primary market.
When dealers take market orders away from the primary market, the pri-
mary market becomes less attractive to limit order traders. Traders then ei-
ther send their limit orders elsewhere or use market orders instead.
The order flow externality is strongest when traders are uncertain about
what orders and quotes are available in a market. If so, traders need to be
in that market to take advantage of whatever opportunities are present there.
These issues are of greatest concern to traders who want to fill large orders
because other traders generally are unwilling to display large sizes. To fill
their orders, they therefore must participate in the market where other large
traders trade.
Facilities that allow traders to control the exposure of their orders
strengthen the order flow externality where it is strongest. Strong markets
do not want to display their quotes and orders because doing so only allows
dealers to compete along side of it. Electronic markets that permit traders
to place undisplayed orders, and floor-based markets in which brokers hold
undisplayed orders, force traders to come to them to trade. Strong markets
do not benefit from exposing their orders.
The order flow externality is very strong at the New York Stock Ex-
change because much of the liquidity there is in the hands of floor brokers
who do not fully disclose their orders. Dealers successfully compete against
the NYSE market only when filling small market orders that come prima-
rily from uninformed traders, and very large orders that require more liq-
uidity than is available on the floor of the exchange.
In contrast, the order flow externality is weak at the Nasdaq Stock
Market because traders who route their orders cannot learn any more about
market conditions than do traders who preference their orders to specific
dealers of ECNs. Dealers and ECNs therefore have competed very suc-
cessfully against the Nasdaq Stock Market.
26.2.3 Public Policy Implications
The role for public policy would be quite limited if all traders were identi-
cal. Good public policy would simply allow traders to choose for themselves
the trading system that they prefer. Although the best market system would
be a consolidated system, regulators would not need to impose one on iden-
tical traders: They would choose it for themselves.
Regulatory efforts to impose a consolidated system risk choosing the
wrong market structure or stifling innovation. When regulators consolidate
by fiat, they have to determine what structure to use and when to change
it as new technologies and demands for service emerge. Should the consol-
idated system enforce strict price-time precedence, as the Tokyo Stock Ex-
change does? Open-outcry futures markets generally do not. Should the
consolidated system display all orders, as the CATS system in Toronto does?
Most trading systems do not. Should displayed orders have precedence over
undisclosed orders that were submitted first, as is the case in the GLOBEX
Order Exposure on
the NYSE Floor
Floor brokers generally
handle large market-not-held
orders on the floor of the
NYSE. The floor brokers often
reveal their orders only after
identifying traders likely to be
interested in trading, and then
only to the extent that they
believe the interested traders
are willing to trade.
Try as they might,
exchange floor brokers do
not always manage order
exposure perfectly. They may
misjudge who might be
interested in trading or the
size that a trader will trade.
They may inadvertently expose
their orders by the way they
walk, talk, or otherwise
present themselves. They also
may deliberately reveal their
orders to reward friends or to
exchange favors with traders
with whom they must deal,
shoulder-to-shoulder, every day
of the year.
Large traders protect
themselves against these risks
by breaking their orders into
parts. They then sequentially
submit the parts to their
broker as each part fills. They
may also submit the parts to
different brokers.


## Page 543

530 • TRADING AND EXCHANGES
and Paris CAC trading systems? Most systems do not even allow undis-
closed orders. These differences in market structure are very significant. The
public welfare depends on the market structures that regulators chose.
When traders choose where they trade, the competition for their orders
helps reveal the market structures that best serve them. This competition
occasionally fragments the market as innovative systems take order flow
from incumbent systems. If traders are identical, however, the fragmenta-
tion will be transitory. The best market structure will eventually garner all
the order flow, if new markets can overcome the advantages of the order
flow externality.
Regulators can help ensure that competition reveals the best market
structure by helping disseminate reliable and unbiased information about
competing market structures. Such information makes it easier for traders
to switch to better trading systems as they become available.
It also might appear desirable for regulators to require that incumbent
trading systems permit fast linkages between their trading systems and those
of their rivals. Although such policies make it easier for rivals to overcome
the advantage of the order flow externality, they can seriously disrupt in-
cumbent trading systems that use slow trading technologies. If the slow
technology is slow because the market has failed to innovate, requiring fast
linkages will promote efficiency-enhancing competition. But if the tech-
nology is slow because traders need time to arrange trades that they could
not otherwise arrange, requiring fast linkages will disrupt the incumbent
market and possibly destroy its valuable trading system. The decision to re-
quire fast linkages therefore is not merely a decision to promote competi-
tion; it can unintentionally impose an inferior market structure upon cer-
tain markets.
26.3 MARKET FRAGMENTATION
Markets fragment because traders are not all identical and because their
trading problems differ considerably. Some market structures therefore bet-
ter serve the needs of some traders than other market structures do. Con-
sequently, identical instruments—and very similar instruments—may si-
multaneously trade in multiple market centers.
Although different traders prefer different market structures, they all
greatly appreciate the order flow externality. Every trader wishes that all other
traders would trade exclusively in his or her preferred market structure.
Traders naturally want their preferred market to be as liquid as possible.
In the remainder of this section, we identify how traders differ, and how
these differences cause them to prefer different market structures.
26.3.1 Unequal Sizes
Traders differ in the quantities that they want to trade. Some traders are so
large that their orders can significantly move the market. Others are so small
that their individual orders rarely have any price impact.
Large traders are reluctant to reveal their trading plans. They fear that
if their orders were widely revealed before they arranged their trades, other
traders would front-run them and thereby increase their trading costs. Large
traders manage this risk by controlling the exposure of their orders. They
prefer to expose their orders only to traders who will commit to trading with
them.


## Page 544

CHAPTER 26 COMPETITION WITHIN AND AMONG MARKETS • 531
Order Exposure in Cantor Fitzgerald's Government
Bond Trading System
eSpeed, the government bond trading system used by Cantor Fitzgerald, is
an electronic trading system designed to serve the needs of large traders.
Traders confidentially indicate to eSpeed that they are willing to trade at a
given price. The system continuously publishes the best bid and ask prices.
When a trader indicates that he will take a standing bid or offer, the taking
trader and the standing trader take turns revealing how much they want to
trade. They reveal increasing sizes until one of the two traders no longer
wants to increase the size of the trade. At that point, the eSpeed system
executes the trade at the last agreed-upon size.
All traders are able to see the agreed-upon size of the trade as it is
growing, but no one except the two parties to the trade and the broker can
see the negotiations. The system thus allows the two traders to see each
other's orders only to the extent that they are willing to trade while ensuring
that neither trader knows with whom he or she is trading.
Some large traders are so sensitive about revealing size that they split
their orders so that no one can confidently infer the full size of their orders.
Occasionally, two such traders will unknowingly trade with each other two
or more times in a row simply because neither trader is willing to let any
other trader know the full size of the order.
Executing large orders can be difficult and expensive in markets that
widely display orders. If traders do not expose their orders, finding the other
side is difficult. If they expose their orders, they may scare away traders who
might otherwise supply liquidity to them, and other traders may front-run
them. Large traders therefore prefer market structures that allow them to
find traders willing to trade while minimizing the information they must
expose to find these traders.
Large traders also prefer markets that enforce strict time precedence rules
in conjunction with an economically significant minimum price increment.
These rules protect them from quote-matching front runners when they ex-
pose their orders. Chapter 11 discusses front-running strategies.
In contrast, small traders like to expose their orders. They do not fear
front runners because front running a small order is not generally profitable.
Wide exposure allows small traders to fill their orders quickly, at the best
prices available. They prefer market structures in which they can expose their
orders.
Small market order traders like markets that have small minimum price
increments because they pay the bid/ask spread when they trade. Since
spreads cannot be smaller than the minimum price increment, a large price
increment can force them to pay artificially high spreads.
26.3.2 Asymmetric Information
Well-informed and uninformed traders generally prefer different market
structures. Most traders want to avoid trading with well-informed traders.
Well-informed traders therefore prefer to trade in fully consolidated mar-
kets, in which all traders trade anonymously, so that they cannot easily be
identified as informed traders.
In contrast, uninformed traders prefer to trade in markets that expose
trader identities so that they can avoid informed traders. They also prefer
to trade in markets where they can try to convince other traders that they
are uninformed. Retail traders benefit from the preferencing of their orders
e0 Order Exposure in
Crossing Networks
Electronic crossing networks,
such as POSIT, allow large
traders to avoid exposing
their orders. These
computerized trading systems
take electronically transmitted
orders and match them at
prices determined elsewhere.
The systems are completely
confidential. They reveal
only the aggregate sizes of
the matches they have
arranged.


## Page 545

532 • TRADING AND EXCHANGES
Hybrid Trading Systems
The New York Stock
Exchange and the Nasdaq
Stock Market both have
hybrid trading systems. The
NYSE is essentially an order-
driven public auction in
which specialists ensure that
impatient traders can always
trade. Nasdaq is essentially
a quote-driven dealer market
in which public traders can
offer liquidity by exposing
their orders. Both systems
are hybrids designed to meet
the needs of all types of
traders.
to dealers who discriminate between their orders and more informed or-
ders. Chapters 15, 23, and 25 discuss how fragmented market structures
have evolved to benefit uninformed traders.
26.3.3 Unequal Patience
Some traders are more patient than are others. Impatient traders want to
trade quickly. They generally will pay bid/ask spreads and high commissions
to increase the probability that they trade. Impatient traders like market struc-
tures in which dealers are always available to provide them with liquidity
when they want to trade. In contrast, patient traders are cost-sensitive and
willing to wait for the market to come to them. They tend to supply liquid-
ity through their limit orders and through the floor brokers who represent
them.
Some market structures serve the needs of impatient traders better than
those of patient traders. For example, Nasdaq's Small Order Execution Sys-
tem (SOES) is a quote-based system that allows small impatient traders to
trade immediately whenever they want to. Users of this system, however,
generally must buy at the ask and sell at the bid. Since Nasdaq allows traders
to preference their orders to specific dealers, and thus does not enforce time
precedence, Nasdaq does not best serve the needs of patient traders. Patient
traders instead prefer consolidated order-driven trading systems that enforce
universal time precedence. Such systems increase their probability of trad-
ing when they expose their orders.
All traders must decide whether they value execution certainty more than
they value transaction cost savings. Those who value the former opt for
quote-driven market systems that provide execution certainty at the expense
of transaction costs. Those who value transaction cost savings opt for
order-driven market systems that provide lower transaction costs for exe-
cuted trades, but lower certainty that orders will trade. Diverse market struc-
tures exist because no single trading system best serves the needs of both
patient and impatient traders.
26.3.4 Unequal Access
By design or historic accident, markets often deny some traders access to in-
formation or facilities that other traders have. For example, exchange mem-
bers may have direct access to floor information or trading opportunities that
are unavailable to off-floor traders. The latter group of traders generally can
trade only by purchasing brokerage services from exchange members.
Many large institutions and sophisticated individual investors believe that
they could execute their trades at a lower cost if they had the same access
to information and trading facilities that exchange members have. Disad-
vantaged traders naturally favor market structures in which they have
stronger and more equal roles. Diversity in market structures is partly due
to competitive responses to their disenfranchisement. Trading systems like
Instinet and POSIT attract order flow, in part, because users can arrange
their trades without the intermediation of traditional brokers.
26.3.5 Unequal Creditworthiness and Trustworthiness
Traders differ in their creditworthiness and trustworthiness. Trades settle
only if traders acknowledge and fulfill the terms of their agreements. Traders
who do not settle, impose costs upon other traders.


## Page 546

CHAPTER 26 COMPETITION WITHIN AND AMONG MARKETS • 533
Trustworthy and creditworthy traders therefore prefer to trade with each
other and exclude less worthy traders. When they cannot do so, they bear
the costs that less worthy traders impose upon them. Depending on the
market, they may directly bear these costs when a deadbeat fails to settle
properly with them, or they may indirectly bear these costs by settling their
trades through a clearinghouse that guarantees their performance.
Markets exclude individuals who would impose costs upon others if al-
lowed to trade. The excluded individuals may still trade, but usually they
must trade through intermediaries who guarantee their trades. Exchanges,
dealer networks, and clearinghouses impose financial and ethical standards
upon their members in order to exclude traders who might impose unnec-
essary settlement costs upon others. Any consolidated trading system that
imposes the same standards on all traders requires the more creditworthy
and more trustworthy traders to subsidize the less worthy ones.
26.4 MARKET SEGMENTATION: HOW
FRAGMENTED MARKETS CONSOLIDATE
The two preceding sections suggest that a trade-off may exist between the
cost-reducing benefits of market consolidation and the service-enhancing
benefits of market diversity. Within any given market structure, liquidity is
greatest and transaction costs are lowest when all traders trade in that struc-
ture. All traders therefore want all other traders to trade in the market
structure that they prefer. Differences among traders, however, cause them
to prefer diverse market structures. Unfortunately, no single market best
meets the service needs of all traders; thus, in many markets, a diversity of
market structures has evolved to serve the various needs of different traders.
The resulting fragmentation suggests that some of the cost-reducing bene-
fits of market consolidation may be lost. In particular, regulators and prac-
titioners fear that fragmented markets substantially increase transaction
costs.
These concerns would be well founded if traders in various market frag-
ments did not know about—and respond to—market conditions in other
fragments. Each fragment then would constitute an isolated market in which
price formation would take place independently of all other fragments. The
resulting prices would depend only on market conditions within each frag-
ment. Prices would not efficiently incorporate all available information about
fundamental asset values because information in one fragment would not
affect trading in other fragments. Transaction costs would be high because
liquidity demands in one fragment could not meet liquidity supplies in other
fragments. Traders thus would have to satisfy all liquidity demands sepa-
rately within each fragment.
Market diversity, however, does not necessarily imply inferior price for-
mation and high transaction costs. Traders can obtain the benefits of con-
solidation in fragmented markets when information flows freely between
market fragments, and when some traders can choose which fragment in
which to trade. These two conditions are sufficient to coalesce a fragmented
market into a unified complex of diverse segments. The first condition en-
sures that traders know what is happening in each market segment. The
second condition ensures that some traders can act on that information when
prices or liquidity conditions diverge.


## Page 547

534 • TRADING AND EXCHANGES
The Route to
Best Execution
Many trade facilitators sell
sophisticated order-routing
systems to their clients. These
systems take client orders and
route them to the best
available market. They are
particularly useful to large
traders who want to
sweep
the market
by taking liquidity
from all sources at once. Most
of these vendors bundle their
order-routing systems with
buy-side order management
systems that they offer to
their clients. Table 26-1
provides a partial list of
these vendors.
Three mechanisms consolidate a fragmented market. First, within each
market segment, traders adjust their orders to reflect information that traders
reveal in other segments. These adjustments cause prices to reflect infor-
mation from all segments.
Second, some traders route their orders to market segments where they
expect to obtain the best prices. Traders who demand liquidity route their
orders to segments that are currently most liquid. Traders who supply liq-
uidity route their orders to segments with the greatest current demands for
liquidity. These order-routing decisions help balance the supply and demand
for liquidity in all market segments.
Finally, arbitrageurs specialize in moving liquidity among market seg-
ments. They trade whenever prices in one segment are inconsistent with
prices in another segment. Their trading enforces the law of one price across
market segments as they connect buyers in one segment to sellers in an-
other segment. Chapter 17 discusses how and why arbitrageurs move liq-
uidity among market segments.
The forces that consolidate market segments are quite robust. Even if
some traders can trade only in one market segment, the market will remain
consolidated if other traders can freely route their orders to other market
segments. In the worst case, if an order cannot move to its best market, ar-
bitrageurs will move the best market to the order.
These three mechanisms will consolidate a fragmented market only if
information about trades and orders in each market segment is publicly
available at low cost. Without this information, traders cannot easily search
for the best price across market segments.
Traders will consolidate fragmented markets only if they always seek the
best prices for their orders. Problems may arise, however, when traders use
brokers to arrange their trades. Although traders expect that their brokers will
seek the best prices for their orders, brokers may not always do so. If they do
not, market fragmentation may reduce liquidity and make the price forma-
tion process less efficient. Chapter 7 discusses this agency problem in detail.
TABLE 26
-
1.
Buy-Side Equity Order-Routing Systems
PRODUCT

VENDOR

LINK
Blackwood PRO
Bloomberg Tradebook
IOE
RouteNet
Lava ColorBook
REDIPlus
Smart BRASS;
PowerNet
RealTick
Blackwood
Bloomberg
Bridge Trading, a unit of Reuters
Investment Technology Group
Lava Trading
Spear, Leeds & Kellogg, a unit of Goldman
Sachs
SunGard Data Systems
Townsend Analytics
www.blackwoodtrading.com

www.bloomberg.com

www.bridge.com

www.itginc.com

www.lavatrading.com

www.redi.com

www.trading.sungard.com

www.realtick.com



## Page 548

CHAPTER 26 COMPETITION WITHIN AND AMONG MARKETS • 535
Agency problems at their worst involve frauds that brokers perpetrate
upon their clients. For example, brokers may arrange to have confederates
fill orders at inferior prices. Such frauds are much easier to commit in frag-
mented markets than in consolidated markets because fewer people moni-
tor trading in small market segments than in fully consolidated markets.
Any consideration of the trade-offs between market consolidation and mar-
ket diversity therefore must consider the potential for fraud in fragmented
markets.
Public policy makers who consider whether to consolidate fragmented
markets by regulation should compare the benefits and costs of diversity.
Unfortunately, both are hard to measure.
We can identify a lower bound for the costs of diversity. The total costs
of trading in a segmented market must exceed the trading costs in a fully
consolidated market by at least the cost of the information systems neces-
sary to consolidate the market plus the resources that arbitrageurs use to
move liquidity among market segments. If this lower bound is high, mar-
ket diversity is very expensive.
In very active markets, information usually is quite cheap relative to the
volume of trade, and the competition among arbitrageurs to profit from
trading opportunities ensures that they provide cheap and efficient service.
The benefits of complete consolidation in such markets therefore are small
relative to the benefits of market diversity. Active markets therefore can sup-
port more diverse market structures than less active markets can.
26.5 EXTERNALITIES IN THE COMPETITION
AMONG MARKET CENTERS
The discussion in the preceding section suggests that competition among
market centers to satisfy different service needs of diverse clienteles is gen-
erally beneficial. The resulting segmentation helps traders solve their vari-
ous trading problems at minimum cost. If information flows freely between
market segments and if no serious agency problems are present, segmenta-
tion is unlikely to have any overwhelmingly negative effects on price for-
mation and transaction costs.
The conclusion that competition among market centers is beneficial, how-
ever, depends on the assumption that no significant externalities affect the
competition. An
externality
arises whenever someone does something that has
an impact upon others for which he or she is neither adequately compensated
nor properly penalized. When no one compensates people for the benefits
they provide others, they tend to do less than would be socially desirable.
Likewise, when no one penalizes people for the costs they impose upon oth-
ers, they tend to do more than would be socially desirable. Unfortunately,
competition among market centers involves several such externalities.
26.5.1 The Order Flow Externality
We have already discussed the most important externality that affects com-
petition among markets. The order flow externality makes it very difficult
for new markets to compete effectively against incumbent markets.
The order flow externality is an example of a
network externality.
Net-
work externalities arise whenever the value of a system to a user increases
as more people use the system. For example, a phone network is more valu-
Anyone Care to Swim?
Traders say that they access
liquidity pools
in fragmented
markets. The order flow
externality causes a pool of
liquidity to form in each
market fragment.


## Page 549

O
P
How Many Disks
Have You Received
from AOL?
When a new market with a
network externality opens,
competitors must rush to
quickly build their networks.
AOL is the best-known
example of a recent winner in
a new market with a network
externality. People once joked
about how many computer
disks AOL mailed to potential
subscribers. Those disks,
however, helped to create
AOL's unassailable market
position.
I
536 • TRADING AND EXCHANGES
able to each subscriber when the network connects many subscribers as op-
posed to few subscribers.
Trading systems are networks that link many potential buyers to many
potential sellers. The more buyers and sellers who participate in the system,
the more valuable it is to everyone who uses it.
Network externalities can create tremendous barriers to entry. Usually,
one trading system grows large, and no other system can become large
enough, quickly enough, to be a viable economic competitor. Markets with
network externalities are
winner-take-all markets.
Without government reg-
ulation, new entrants often cannot get a toehold.
The U.S. government requires that all phone companies allow all other
phone companies to access their networks. Without such linkages, new
phone companies could not compete with existing companies. The cellular
telephone, telephone-over-cable, and telephone-over-Internet industries
would not exist today were it not for these open access regulations. The gov-
ernment, of course, specifies the
interconnect access fees
that companies can
charge each other for access to their networks.
Governments can acquire, and have required, linkages among trading
networks. In 1975, the U.S. government required that U.S. equity exchanges
establish the Intermarket Trading System (ITS) to link their trading floors.
The exchanges created a rather inefficient system, so ITS has had little ef-
fect on the markets. A redesign of the system is presently very high on the
political agendas of many market centers. For example, the ECNs would
like to have access to a revised ITS system through which they can route
firm commitments at high speed. Most incumbent exchanges, of course, are
not interested in improving the system.
Order routing systems created by various data vendors also link traders
with various trading systems. These links connect to the
application pro-
gramming interface
(API) of each trading system. (APIs are portals through
which computer systems talk with each other.) This approach to market
center linkage may ultimately accomplish all that coordinated regulatory
linkages attempt to accomplish.
26.5.2 Secondary Precedence Rules
The second most important externality
in exchange competition is also due
to the option values implicit in orders. Traders who offer standing limit or-
ders benefit other traders, but no one compensates them for the benefits
that they provide. They therefore provide less liquidity than would be so-
cially optimal.
The liquidity offered by limit order traders benefits markets because it
attracts traders. Exchanges and ECNs therefore try to encourage traders to
offer limit orders. Traders will offer liquidity when they are rewarded for—
and not hurt by—offering liquidity.
Some ECNs reward limit order traders by charging lower fees for limit
orders than for market orders. In effect, market order traders pay limit or-
der traders a fee for their liquidity in these systems. In chapter 14, we show
that in competitive order-driven markets, such differential fees simply nar-
row equilibrium bid/ask spreads so that the long-run incentives to supply
or demand liquidity are unchanged.
In general, any preference given to limit order traders will have no net
effect on the supply of liquidity in a competitive equilibrium in which all


## Page 550

CHAPTER 26 COMPETITION WITHIN AND AMONG MARKETS • 537
Lost Time Precedence
Time precedence rules govern trading in U.S. exchange-listed stocks at
exchanges, but not among exchanges and over-the-counter dealers.
Regional exchange specialists and third market dealers like Bernard Madoff
regularly fill market orders at the same bid or ask prices that traders at the
New York and American Stock Exchanges first quoted. Likewise, exchange
traders occasionally fill market orders at prices that regional specialists and
third market dealers quoted first. In either event, the trader who quoted first
does not receive a trade that he or she would have received if time
precedence were universally enforced across markets.
Order crossing by brokers also often violates time precedence. Many
brokers like to match orders internally for execution because internal
matching ensures that they obtain two commissions for the trade instead of
one. These brokers then print the trade at a market with a thin limit order
book to ensure that no standing limit order interferes with the trade. Traders
who place their orders in exchange order books would be better off if the
brokers had to print their crosses in a consolidated market.
In both examples, limit order traders would be better off if time
precedence were universally enforced.
traders are precommitted to trading. In such models, spreads adjust so that
traders are indifferent between offering and taking liquidity.
In most markets, however, not all potential traders are committed to trad-
ing. In particular, quote-matching order anticipators trade only if they can
extract option values from limit orders. (Chapter 11 discusses the quote-
matching strategy.) Their trading therefore taxes liquidity. In equilibrium,
quote matchers' profits imply higher transaction costs for precommitted
traders whether they use limit orders or market orders.
Since quote matchers directly hurt limit order traders, and thereby indi-
rectly hurt market order traders, markets have an incentive to exclude quote
matchers. The only effective way that they can do so is by maintaining sec-
ondary precedence rules—time precedence and public order precedence—
that give precedence to limit order traders who display their orders. An eco-
nomically significant minimum price increment, of course, must be set to
make these precedence rules meaningful.
An externality problem arises when market segments compete with each
other because a market segment cannot meaningfully enforce secondary
precedence rules when other segments trading the same—or essentially the
same—instruments do not. Quote matchers simply place their orders and
quotes in other markets when they do not have precedence in a given mar-
ket. If they can get their orders filled in these other markets, they get around
the secondary precedence rules.
Markets that attract few limit orders have little incentive to maintain an
economically significant minimum price increment to protect limit order
traders. On the contrary, with a small increment, they can attract orders of
quote matchers who will improve the consolidated quote in order to obtain
market orders. Since brokers generally must execute orders at the best avail-
able prices, they often route to markets that display the best prices. A small
market with little volume may therefore obtain order flow by offering a small
minimum price increment. Although larger markets may want to protect
limit order traders, they cannot do so: To remain competitive, they must


## Page 551

538 •
TRADING AND EXCHANGES
The Race to the Bottom
Reduction of the minimum
price increment in U.S.
equities markets started in the
1990s and culminated in the
full decimalization of the
markets in 2001. As the
above analysis suggests, the
first markets to offer smaller
minimum price increments
were weaker markets like the
American Stock Exchange
and the ECNs. They moved
first because they had little
incentive to protect limit order
traders. (They then had few
such traders.) The larger
markets had to decrease their
price increments to remain
competitive in the face of best
execution standards that
require brokers to obtain the
best available prices for their
clients' orders.
lower their minimum price increments to equal the smallest minimum in-
crement offered by any market.
Unregulated competition among markets therefore does not permit mar-
kets to enforce trading rules that would solve the order exposure external-
ity problem. Consequently, public limit order traders offer less liquidity than
they would have if they traded in a fully consolidated trading system that
used an economically meaningful minimum price increment to enforce sec-
ondary order precedence rules.
26.5.3 Regulatory Services
Markets compete for order flow by offering services that they believe will
be attractive to their clienteles. We can divide the services that markets of-
fer into two groups, according to whether the benefits they provide are pri-
vate or public services.
Private services benefit only the traders who use the market. Order-
routing systems and accounting systems are examples of such services. Since
usage of these services is easy to measure, markets can charge their traders
to cover the costs of providing these services. Markets therefore will pro-
vide whatever private services their users demand.
Public services benefit everyone, regardless of where—or sometimes
whether—they trade. The promotion of price continuity, and the regulation
of insider trading, manipulative trading practices, and capital structures, are
examples of services that produce public benefits. These services improve
market quality for everyone. Exchanges fund them through fees that they
charge traders who use their markets. Traders can avoid these fees by pa-
tronizing markets that do not provide these regulatory services. Since mar-
kets can charge only their traders for these services that benefit everyone,
unregulated competition among market centers produces fewer public ser-
vices than would be socially desirable.
26.6 CONSOLIDATION OF MARKETPLACES
The preceding sections discuss consolidation and fragmentation of trading
in a single instrument. In this section, we identify additional forces that cause
market centers—exchanges, ECNs, brokers, and dealers—to consolidate
through mergers, acquisitions, joint ventures, and joint operating agreements.
Two major waves of such consolidations have occurred. The first fol-
lowed the invention and widespread adoption of the telegraph and tele-
phone. These communications technologies greatly decreased the costs of
knowing what was going on in distant markets and of routing orders to
those markets. Once, every major city had exchanges that traded many of
the same securities, and many of these cities also had futures markets that
traded similar contracts. With improvements in telecommunications, traders
seeking better prices eventually caused markets to consolidate. The markets
that lost order flow failed or merged with other exchanges.
The second wave of consolidations started in the early 1990s and is con-
tinuing to this day. This wave is occurring largely in response to three fac-
tors. The first factor is the order flow externality mentioned above. It is re-
sponsible for much of the consolidation among dealers and among some
ECNs.
The second factor is related to changes in the costs of operating trading
systems. Advances in computing technologies have caused the ratio of vari-


## Page 552

CHAPTER 26 COMPETITION WITHIN AND AMONG MARKETS • 539
An Academic Proposal for Exchange Competition
Consider the following proposal for competition among equity markets:
1.
Regulations would completely consolidate all trading in an equity issue
into a single market. Different equities might trade in different markets,
but all trading in a given equity would be in the same market.
2.
Shareholders would decide each year at which market their equity
issues will trade.
This proposal would produce many benefits of regulatory consolidation
while preserving the benefits of competition among markets. Instead of
competing for order flow, markets would compete for listings. Although the
proposal provides a simple and attractive solution to a complex problem,
only academics have shown any interest in it.
able costs to fixed costs to decline. Consequently, the economies of scale in
operating trading systems have increased. Operating small trading systems
has become more costly relative to operating large trading systems. To re-
duce costs, many exchanges and brokerages have consolidated.
Finally, much consolidation is taking place because regulatory restric-
tions on cross-border cooperation and competition have loosened. These
changes are most obvious in the European Community. Markets there are
quickly consolidating to take advantage of the order flow externality and
economies of scale in operating large trading systems. Likewise, the relax-
ation of regulatory restrictions has led to several international joint operat-
ing agreements among futures exchanges.
26.7 SUMMARY
Markets consolidate because traders attract traders. Trading is easiest and
cheapest where most traders of an instrument or similar instruments trade.
Liquidity attracts liquidity.
Markets fragment because the trading problems that traders solve, dif-
fer. Different market structures serve some traders better than others.
Markets fragment when, for enough traders, benefits from differentiation
exceed benefits from consolidation.
Some traders are small and unconcerned about the price impacts of their
trades, while other traders are large and very concerned about front run-
ning. Small traders prefer market structures that widely expose their orders
so that everyone can see and react to them. Large traders prefer market
structures that allow them to control how and to whom their orders are
exposed.
Some traders are well informed about fundamental values and therefore
very concerned about revealing their information, while others are relatively
uninformed and very concerned about minimizing transaction costs. Unin-
formed traders prefer markets where they can be identified and given bet-
ter prices. Informed traders prefer consolidated markets with anonymous
trading so that they can hide in the order flow.
Some traders are impatient to trade and therefore willing to pay for
liquidity, while others are patient and willing to wait for their price. The
former prefer quote-driven markets, while the latter prefer order-driven
markets.
,to Should Apples and
Oranges Trade
Together?
The order flow externality
most obviously applies to a
single instrument. Traders
interested in trading a given
instrument are attracted to the
market with the most order
flow in that instrument.
This principle also applies
to instruments that are similar
to each other. Instruments are
similar when their values
largely depend on the same
common valuation factors.
Many traders therefore regard
them as good substitutes for
each other. Such traders are
attracted to markets that
actively trade any of these
instruments. Markets that
actively trade many similar
instruments are especially
attractive to traders who are
interested in exposure to the
common valuation factors.
The order flow externality thus
applies to common factors as
well as to individual
instruments.
Markets that trade similar
instruments often merge to
take advantage of the order
flow externality. By
concentrating order flow in
similar instruments, they
increase the liquidity of
underlying common factors.
This effect explains why
mergers of markets within a
country generally have been
quite successful. It also helps
explain why stocks generally
do not trade well outside of
their national markets. 1


## Page 553

540 • TRADING AND EXCHANGES
RISC versus CISC
Issues involving market structure are similar to issues involving computer
microprocessor architecture.
The reduced instruction set computing (RISC) approach to microprocessor
design uses a simple processor to process a limited set of instructions very
quickly. Software parses complex instructions into simpler instructions for
execution. This architecture can be very efficient because RISC processors
are very fast.
The complex instruction set computing (CISC) approach uses a complex
processor to process complex instructions. This architecture can be very
efficient when complex instructions are quite common.
The RISC approach corresponds to fully consolidated markets. A simple
market structure that receives all order flow can work very quickly and
efficiently, but it can solve only simple trading problems. Traders with
complex trading problems typically break up their orders into smaller pieces
when trading in these markets.
The CISC approach corresponds to fragmented markets. Fragmented
markets can provide more service to diverse clienteles than can fully
consolidated markets.
4
OP
The Libertarian View
Although enlightened regulation
of the markets might benefit
everyone, many people are
reluctant to give regulators
much power to regulate. They
fear that regulators—through
ignorance or malice—may
abuse their power. The history
of regulation is replete with
examples of regulations that
have been more costly than
beneficial.
4
Not withstanding
these differences, all traders appreciate the benefits of
consolidation. Traders often trade in markets that they do not like simply
because those markets are most liquid. Conversely, no market will attract
and keep liquidity if it does not provide good service to many traders. Com-
petition among market structures generally reveals the market structures that
best serve various types of traders.
Fragmented markets consolidate when traders can access information
about market conditions within each segment. Traders use this information
to adjust their orders, reroute their orders, or issue new orders. Prices and
liquidity in each segment thereby reflect information from all other segments.
Traders naturally enforce price priority in segmented markets when they
seek the best prices for their orders. Traders do not enforce secondary or-
der precedence rules, such as time precedence, across market segments. Only
coordinated regulation can implement such rules.
Fragmented markets generally will provide less regulatory oversight than
is socially optimal. Good regulatory activities benefit everyone, but ex-
changes can charge only those traders who trade in their segments. Only
coordinated regulation can ensure that markets provide adequate regulatory
oversight.
Two types of competition characterize segmented markets. Traders com-
pete for the best price, and market centers compete to serve diverse traders.
Unfortunately, policies that promote the benefits from one competition can
decrease the benefits from the other. Regulators therefore must balance the
benefits obtained from these two types of competition.
26.8 SOME POINTS TO REMEMBER
•
Markets consolidate because traders
attract traders. Liquidity attracts
liquidity.
•
Consolidation maximizes competition among traders and thereby most
efficiently reveals the best price.


## Page 554

CHAPTER 26 COMPETITION WITHIN AND AMONG MARKETS • 541
•
A better market structure may never emerge if it cannot attract enough
traders to move away from an incumbent market to make it liquid.
•
The order flow externality is strongest when search costs are highest.
•
When a market displays enough information about orders and quotes
to accurately predict the average execution price of a market order,
preferencing to dealers of such market orders can weaken the order
flow externality held by that market.
•
Markets fragment as exchanges, brokers, ECNs, and dealers compete
to meet the diverse service requirements of different traders.
•
Fragmented markets consolidate when traders can observe and act
upon information in all market segments.
•
Arbitrageurs help consolidate fragmented markets.
•
Externality problems affect the competition among market centers to
provide exchange services. Unregulated competition therefore may not
create the best market structures.
26.9 QUESTIONS FOR THOUGHT
1.
Should regulators consolidate all trading to maximize price compe-
tition among traders and to lower liquidity search costs? If so, to
what market structure should they consolidate?
2.
How important are the externalities that affect the competition for
order flow? Is time precedence valuable? Is order exposure valuable?
Is market surveillance valuable? Is price continuity desirable?
3.
Are regulatory services valuable? Should laws compel exchanges to
provide regulatory services, or should governmental agencies directly
supply these services?
4.
Who should pay for market regulation?
5.
How should regulators trade off the interests of diverse traders?
Should we favor small individual traders over large institutional
traders? Should we favor impatient traders over
patient traders?
Should we favor informed traders over uninformed traders? Should
we favor public traders over exchange members?
6.
Can domestic regulators regulate market structure when market cen-
ters compete globally to provide exchange services?
7.
How does market fragmentation affect the information in prices?
8.
Why is the order flow externality called an externality?
9.
Before decimalization, the Nasdaq Stock Market had a smaller min-
imum price increment than the New York Stock Exchange. Can you
explain this fact in light of their different market structures?
10.
How does the order flow externality make the provision of price con-
tinuity possible?
11.
What problems do you see with "An Academic Proposal for Ex-
change Competition?" How might clienteles specialize in various
stocks? Would market structure affect portfolio allocation decisions
or corporate control decisions?
12.
Can markets consolidate even if no coordinated mechanism, like the
Intermarket Trading System (ITS), routes orders from one market seg-
ment to another? Do proprietary electronic routing systems that allow
traders and brokers to quickly select and route to the best markets for
their orders make coordinated intermarket routing systems unnecessary?


## Page 555

542 • TRADING AND EXCHANGES
13.
Innovative markets fail if the cost of convincing traders that they are
beneficial is too high relative to the additional benefits they provide.
What is the exact condition for failure? How does it depend on who
bears the cost of educating traders? How does it depend on the abil-
ity of the new market to charge traders for the additional benefits
that they receive?
14.
The proponents of CISC and RISC microprocessors compete with
each other in the marketplace. How does their competition differ
from the competition among markets for order flow?


## Page 556

2 7
Floor
Uersus
automated
Trading
Systems
A
dvances in communications and computing technologies now allow ex-
changes to completely automate their trading systems. Many exchanges
have done so, and many brokers, ECNs, and dealers have created automated
trading systems.
Despite these developments, many of the most liquid exchanges in the
world still employ floor-based trading systems. The New York Stock Ex-
change, the Chicago Board of Trade, the Chicago Mercantile Exchange, the
New York Mercantile Exchange, and almost all U.S. options exchanges pri-
marily use floor-based trading systems. Although traders on their floors now
rely extensively on electronic systems to route orders and report confirma-
tions, they still arrange trades essentially as they did when these markets
first started.
If the floor-based market structures at these exchanges encourage traders
to offer liquidity, eliminating their floors would be foolish. However, if other
reasons account for the liquidity in these markets, switching to electronic
trading may be desirable.
When floor-based trading systems and electronic trading systems have
competed head-to-head, the results have been mixed. During the 1980s, the
London Stock Exchange was the most important market for large French
stocks. In 1989, the Paris Bourse introduced an automated electronic trad-
ing system. Since then, much of the trading in French stocks has migrated
from London to Paris. More recently, the electronic German DTB futures
exchange wrested trading in German T-bond futures from the floor-based
London International Financial Futures Exchange (LIFFE). Neither ex-
ample, however, provides definitive evidence for or against floor-based trad-
ing because other events have influenced the outcomes. The market share
of the Paris Bourse grew following the 1994 repeal of a French transaction
stamp tax that traders formerly avoided by trading in London. Likewise,
the German T-bond futures contract moved from LIFFE to DTB in re-
sponse to an effort by German banks to repatriate their market.
In contrast, brokers, dealers, exchanges, and ECNs have created many
automated systems for trading NYSE stocks and U.S. equity options. Some
of these systems have been notably successful. Optimark—discussed in chap-
ter 26—failed spectacularly. The Arizona Stock Exchange proved to be a
disappointment to people who believe that markets would benefit from elec-
tronic call markets. Instinet, Archipelago, and Island ECN have not taken
substantial market share from the New York Stock Exchange despite their
tremendous success competing against the electronic Nasdaq Stock Mar-
ket. The electronic International Securities Exchange obtained a 16 percent
share of U.S. equity option trading in the issues it trades, within 18 months
of its 2000 launch. However, it has not yet displaced the traditional floor-
based options markets. The most successful electronic competitors of the
NYSE have been third market dealers, like Bernard L. Madoff Investment
543


## Page 557

544 • TRADING AND EXCHANGES
PiP
The Bangladeshi Stock
Exchange and the New
York Stock Exchange
In 1999, the Bangladeshi
Stock Exchange replaced its
trading floor with an
automated trading system. At
the same time, the New York
Stock Exchange considered
where to build a new trading
floor.
The continued commitment
of the New York Stock
Exchange to its trading floor
may be its most important
decision at the turn of the
millennium. The members and
officers of the xchange are
fully aware of its significance.
They believe that the
tremendous success of the
NYSE is due in large part to
its floor-based market
structure. They also know that
they may lose much, if not
all, of their franchise if they
are wrong.
Securities and Knight Capital Markets. Their automated trading systems
provide very quick service primarily to retail traders represented by discount
brokers.
Floor-based oral auctions and automated rule-based auctions are very
similar. Both are order-driven markets that match buy orders to sell orders
using very similar rules. Their primary difference lies in the technologies
they use to arrange these matches. Traders in oral auctions arrange trades
by personally exchanging information among themselves, whereas in auto-
mated markets, computers arrange the trades.
Since the two market structures are so similar, exchange officials, regu-
lators, and traders naturally consider which is best. There is no simple an-
swer. Both structures have strengths and weaknesses.
In this chapter, we consider the arguments for and against these two
trading structures. Our discussion examines how they differ in fairness, con-
venience, capacity, speed, efficiency, and cost.
27.1 FAIRNESS
Two concepts of market fairness concern traders. Traders want their mar-
kets to operate fairly, and they want fair access to those markets. In
opera-
tionally fair markets,
trading rules are uniformly applied, and no cheating
occurs. In
fair access markets,
all traders have an equal chance to take ad-
vantage of any opportunities that arise.
27.1.1
Operational Fairness
Many traders believe that automated trading systems are the fairest of all
market structures. Automated systems do only what they are programmed
to do. They implement their trading rules exactly and without exception.
They expose orders only as instructed, and only to those traders to whom
the system permits orders to be exposed.
In contrast, fairness in oral auctions depends on the skill and honesty of
the traders who arrange the trades. Traders must be highly skilled to follow
the trading rules faultlessly when the market is active, and when prices are
moving quickly. They must follow those rules honestly even when doing so
may cause them to lose an advantage.
Although most oral auctions are quite fair, all oral auction markets have
suffered from well-documented trading scandals. These scandals usually in-
volve front running, inappropriate order exposure, fraudulent trade assign-
ment, or prearranged trading by dishonest brokers.
Although these problems can also arise in automated markets, they can-
not take place within their automated trading systems. Instead, dishonest
brokers must conduct their frauds on the side.
Markets prevent these frauds by having officials supervise trading, by in-
vestigating suspicious trading practices reported by honest traders, and by
maintaining—and reviewing—reliable audit trails. An
audit trail
records the
submission and disposition of every order. Good audit trails include detailed
information about everything that happens to each order. Regulators use
audit trails to determine whether traders have violated trading rules. An ac-
curate audit trail helps keep brokers honest.
Floor-based markets have extensive rules that govern how traders pro-
cess orders and record trades. Markets design these rules to make the audit
trail complete, reliable, and accurate. These rules require traders to time-


## Page 558

CHAPTER 27 FLOOR VERSUS AUTOMATED TRADING SYSTEMS • 545
00 What Would You Think?
Eli needed to roll a 10-contract short futures position in the
Dow Jones
Industrial
Average
Index futures from
June
to September contracts. Via the
Internet, he submitted a spread order to buy 10 June contracts and sell 10
September contracts with a limit of 75, premium to the sell side. The DJIA
index futures contracts trade in a pit on the floor of the Chicago Board of
Trade.
About a half hour later, Eli queried his broker's Internet site and
discovered that he bought the
June
contracts for 11,060 and sold the
September contracts at 11,130. The 70-point difference was less than the
75
points that Eli specified.
Since the nominal size of the DJIA contract is 10 times the Dow index,
each point is worth 10 dollars per contract. For ten contracts, the five-point
difference between the reported spread and the limit represents 500
dollars.
Eli naturally called his broker and inquired about the discrepancy. Since
the broker did not follow his instructions, Eli could have refused to accept
the trade, or he could have demanded that his broker make up the
difference. The sales broker who answered the phone asked him to hold the
line while she called the floor to inquire about the problem. One minute
later, she reported that the floor incorrectly reported the trade price of the
sale. She said that the September contract actually sold for 11,137 so that
the spread trade occurred at 77 rather than
70.
Eli was pleased with the
result.
What really happened? Consider the following four alternatives:
A.
Somebody incorrectly reported the trade, most probably due to a typo
or a transcription error. Had Eli not reported the discrepancy, someone
would have noted it later, and the broker would have properly adjusted
Eli's account.
B.
Somebody incorrectly reported the trade. Had Eli not reported the
discrepancy, the broker might have pocketed the difference.
C.
The floor trader executed the trade incorrectly by mistake. The trader or
Eli's broker made up the difference and added two points to keep Eli
happy.
D.
The floor trader intentionally executed the trade incorrectly and hoped
that Eli would not notice the mistake. The trader or broker made up the
difference and added two points to keep Eli happy.
Most probably, A characterized what happened. However, if you believe
that brokers may conspire to steal from their clients through fraudulent trade
assignment or prearranged trading, and that the audit trail cannot detect all
such frauds, the other alternatives are possible. Stories like this one tarnish
the image of floor-based trading systems regardless of whether their origins
are innocent or nefarious. If an automated trading system had arranged the
trades, this story simply could not have occurred.
stamp their orders when they receive them and when they fill them, to record
trades sequentially, and to report trades immediately.
Automated trading systems easily produce complete and flawless audit
trails of all activity that takes place within them. Many traders and regula-
tors especially like these systems for this reason.
27.1.2 Fair Access
Markets provide fair access when all traders have equal access to the mar-
ket. In such markets, no traders have special advantages over other traders.
Few trading systems provide pure fair access.


## Page 559

546 • TRADING AND EXCHANGES
In floor-based trading systems, floor traders have an advantage over off-
floor traders. Floor traders can see and react to market developments well
before off-floor traders can. Off-floor traders must obtain their information
through market data systems, and they must respond through an order-
routing system. The best market data systems report information in less than
two seconds. The best order-routing systems pass orders from the client to
a broker in less than five seconds. If the routing system requires a
runner
to
physically deliver the order to a floor broker, the delay can be substantially
longer. These delays allow floor traders to take advantage of opportunities
before off-floor traders can.
Floor traders also can observe all market information revealed on an ex-
change floor, and not just what market data systems report. They are par-
ticularly interested in who is trading. This information can be valuable if
you can guess why they want to trade or whom they represent. Floor traders
also often ask whether an order represents the full size that a client wants
to trade. Brokers who have a reputation for reliably reporting this informa-
tion often can obtain better prices than other traders can. Traders who have
access to floor information that market data systems do not report have a
significant advantage over other traders.
In large, actively trading futures pits, being able to see and be seen, and
being able to hear and be heard are very important. Thus, in some floor-
based markets, human anatomy also affects the fairness of the trading. Phys-
ically large traders have some advantage over smaller traders because they
can control the "real estate" within the pit that offers the best sight lines.
They simply move to where they want to be and plant themselves. Although
futures markets have rules against physical contact, bumping is common in
futures pits. Big traders tend to get the best spots. Tall traders have some
advantage over short traders because they can see and be seen more easily.
Traders with loud voices have some advantage over less audible traders be-
cause they can more easily attract attention when yelling. Traders with shrill
voices—typically women—have an advantage over traders with bass voices
because high-frequency sounds are highly directional. Their voices pene-
trate through the noise and easily attract attention. These anatomical char-
acteristics give some traders an advantage in oral markets. They are mean-
ingless in automated markets.
Automated markets favor traders with good keyboard skills and abstract
visualization skills. These issues worry some traders when markets convert
from floor-based systems to electronic systems.
Automated markets also favor traders who use computer systems to gen-
erate their orders. Such systems can monitor electronic data feeds and re-
spond instantly to new information. Although this advantage is a natural
consequence of faster trading technologies, many manual traders resent com-
peting with such automated traders.
27.2 THE CONVENIENCE OF
DISTRIBUTED ACCESS
A primary advantage of automated trading systems is that they allow traders
to trade from their office desks rather than on an exchange floor. Aside from
the obvious physical convenience, distributed access allows traders to sit next
to their telephones, talk with their colleagues, and consult any data systems
they want in support of their trading.


## Page 560

CHAPTER 27 FLOOR VERSUS AUTOMATED TRADING SYSTEMS • 547
Such facilities are often difficult or impossible to arrange on an exchange
floor. Floor traders who want instant access to telephone and data services
must carry cell phones and portable terminals onto the trading floor. These
instruments are often cumbersome, and a few markets still do not permit
them.
27.3 SYSTEM CAPACITY
Electronic order matching technologies are much more scalable than oral
order matching technologies. A technology is
scalable
when it can operate
with equal or greater efficiency as it grows.
More traders can directly participate in an automated auction than in an
oral auction. The number of traders who can effectively communicate with
each other simultaneously limits the size of an oral auction. When too many
traders try to participate in the same oral auction, they exceed its capacity
to process information in an orderly fashion. When the number of traders
bidding and offering is large, traders in an oral auction cannot easily keep
track of who is quoting the best prices. They then may arrange trades that
violate time precedence or even price priority.
Futures and options markets designate such disorderly markets as
fast
trading markets.
The designation tells brokerage customers that they cannot
expect their brokers to fill their orders at the best available prices when the
market is trading fast. In the confusion of a fast market, the brokers may
be unaware of the best trading opportunities.
Great numbers of traders can simultaneously interact in automated trad-
ing systems because these systems process order messages much faster than
people can. Traders who use automated systems do not have to keep track
of the best bid and offer. The system does it for them. They also do not
have to arrange their trades. The system does it for them according to the
market's trading rules. By supporting these functions, automated trading
systems allow traders to focus their attention exclusively on creating and
submitting their orders.
27.4 NEGOTIATION SPEED
Some floor dealers believe that they can trade more quickly on a floor than
in an electronic market. They claim that they can shout a bid or offer, or
accept a bid or offer, faster than they can enter this information into a com-
puter. Although this probably is true, modern trading systems with graph-
ical interfaces allow traders to enter information almost as fast as they could
shout it out.
In any event, automated trading systems can complete trades much more
quickly than individual traders can. In an oral auction, traders must manu-
ally record the price, size, counterpart, and instrument traded for each trade.
In many markets, they also must record the time of the trade for the audit
trail. Since trading requires both order entry and trade record keeping,
traders can complete trades more quickly in electronic markets than in oral
markets.
Oral trading, however, is undoubtedly faster than screen-based trading
when traders want to negotiate their trade sizes. In such negotiations, traders
often will not reveal the full size of their order unless they are sure that the
other trader will trade the same size. After agreeing on a price, they often
Ok
Why Doesn't Microsoft
Trade at the New York
Stock Exchange?
Before 1990, most large U.S.
corporations listed their stocks
at the New York Stock
Exchange when they grew
large enough to meet its
listing standards. Since then,
Microsoft and some other
large technology companies
that list on the Nasdaq Stock
Market have not switched to
the NYSE.
Although there are many
reasons why Microsoft has
remained a Nasdaq stock,
one is of particular note to
this discussion. Microsoft
creates and sells distributed
access computing systems.
Although Nasdaq does not
organize automated auctions,
it is a distributed access
communications facility.
Microsoft may have chosen
to stay at Nasdaq for
ideological reasons, among
others.
The New York Stock
Exchange hopes that
Microsoft soon will choose to
list its stock there. Many
people believe that the NYSE
is reserving the single
character symbol "M" for
Microsoft's ticker symbol.


## Page 561

548 • TRADING AND EXCHANGES
take turns proposing successively higher sizes. Negotiations stop when one
trader proposes a greater size than the other trader will accept. The trade
size is then the last agreed-upon size. This back-and-forth negotiation is
very fast in oral auctions. When conducted through a computer that accepts
only firm orders, the negotiation is much slower because the traders must
split their orders into pieces to avoid displaying their full sizes.
Some electronic trading systems solve this order display problem by pro-
viding messaging systems that allow buyers and sellers to negotiate their
trade sizes by exchanging messages on their screens. Liquidnet and Instinet
are two such systems.
A second way some automated exchanges solve this order display prob-
lem is by allowing large traders to place limit orders with undisclosed size.
The automated trading system is aware of the full order size, but it does
not display the size to other traders. When the system arranges a match be-
tween an undisclosed hidden order and another order, the trade size is set
to the minimum of the buy and sell order sizes. This procedure automati-
cally accomplishes what oral traders must accomplish through their back-
and-forth negotiations. Euronext, GLOBEX, and Island are markets in
which traders often use undisclosed orders.
27.5
EXCHANGE OF INFORMATION ON
TRADING FLOORS
Floor-based trading systems dominate electronic trading systems when bro-
kers need to exchange information about buyers and sellers to arrange their
trades. This is especially important when traders want to know information
about their counterparts before agreeing to trade. Most electronic trading
systems cannot provide this information. These issues are most important
for traders who want to avoid trading with well-informed traders and with
large price-discriminating traders.
All traders want to avoid trading with traders who are well informed
about instrument values. When a well-informed trader wants to sell or buy,
prices respectively are either too high or too low. Whoever trades with a
well-informed trader therefore will probably regret trading. Since it usually
is better not to trade than to trade at a poor price, traders prefer to trade
only with uninformed traders.
Large traders often direct their brokers to trade only with institutions
that they deem to be uninformed. They prefer to trade with institutions that
have no research staffs, and that trade only to invest and disinvest rather
than to speculate. They often refuse to trade with the proprietary trading
groups of investment banks for fear of losing to them. Large traders can is-
sue these instructions to their brokers because they can afford the signifi-
cant commissions necessary to obtain these services, which require a lot of
personal attention. When arranging large trades, traders carefully audit the
motives of their trade counterparts.
Traders also do not want to trade with a large trader if that trader in-
tends to continue trading on the same side. The market impact of the large
trader's subsequent trades will generate immediate losses for the first traders
who trade with the large trader. Traders therefore instruct their brokers to
ask how much more size the other side wants to trade. If traders expect that
more size will follow, they will offer less favorable terms to the large trader.
Chapter 15 discusses these price discrimination issues.


## Page 562

CHAPTER 27 FLOOR VERSUS AUTOMATED TRADING SYSTEMS • 549
On an exchange floor, a broker who has no additional size to fill will
freely offer this information to obtain a better price. Brokers who have sub-
stantial additional size usually indicate that they either do not know, or can-
not tell, how much additional size remains. If they say that they have no
additional size, but soon return to do more size, they depreciate their rep-
utations, and traders will no longer trust them. Brokers therefore do not like
clients who lie to them about the full size of their orders. In most electronic
trading systems, no similar mechanism allows traders to credibly indicate to
each other how much additional size remains in their orders.
Finally, brokers often know of traders who might be interested in trad-
ing but have not yet submitted orders. Such traders may be unwilling to
display their orders, or may not realize that they want to trade, until a bro-
ker approaches them with a suitable trading opportunity. Brokers in pos-
session of this information often can arrange trades that otherwise might
not be possible in an electronic environment.
27.6 MARKET DATA REPORTING
Automated systems report market data to the public much faster and much
more accurately than floor-based trading systems. In floor-based systems,
traders or market reporters must manually enter quotes and trades into the
market information system to report them to the public. In automated sys-
tems, these data are already in electronic form. Automated systems there-
fore report faster and more accurately.
27.7 COST
Screen- and floor-based trading systems have different cost structures. Both
systems have high initial creation costs. Screen-based systems generally have
low operating costs, while floor-based systems are quite costly to operate.
27.7.1 Electronic Trading Systems
Fully automated trading systems require the construction of extensive data
networks and data processing systems. These systems must validate users,
accept orders, process orders, report trades, and report order status. These
functions must be reliable, secure, and fast.
Reliability
Automated trading systems must be reliable because trading stops when the
computer or network is down. If only part of the network is down, the af-
fected traders will be extremely upset. Traders generally prefer that all trad-
ing halts if they cannot trade. Traders will not use unreliable systems.
To build reliable trading systems, markets must make substantial invest-
ments in redundant hardware and software systems. They must eliminate all
single points of failure. Since failures are inevitable, given current technolo-
gies, markets also must invest in systems that allow them to recover from
service interruptions. Markets—as well as brokers and dealers—employ many
of the following processes to create reliable trading systems:
•
They use fault-tolerant computer hardware.
•
They build redundant computer systems.
•
They build redundant network connections.


## Page 563

550 • TRADING AND EXCHANGES
Some Examples of the Risks of Trading Through
Unreliable Data Networks
•
A trader submits a limit order to an electronic market. After the order is
accepted, but before it trades, the trader's network connection fails. The
trader does not know whether she has traded. If she knew that she had
not traded, she would do the trade in another market. If she goes
elsewhere without knowing whether her trade has executed, she risks
trading twice.
•
An arbitrageur uses an electronic order-routing system to buy Nasdaq
100 futures contracts and sell
S&P 500
futures contracts. The arbitrageur
initially buys the Nasdaq 100 contracts. His network connection then
fails before he can sell the S&P 500 contracts to establish his hedge. If
the Nasdaq 100 falls, he may suffer a great loss. If he had hedged his
position, a correlated decrease in the S&P 500 probably would offset
his loss.
•
A short-term speculator uses an electronic market to buy a stock whose
price he believes is momentarily depressed by a large uninformed seller.
If he is right, he will profit when the price rebounds. If the large seller is
well informed, however, the price probably will continue to fall. The
speculator therefore intends to sell his position if it does not show a profit
in the next 15 minutes. His network connection fails immediately after he
buys the stock. If the market drops significantly before he can sell his
position, he will lose substantially. (To protect against this possibility, the
speculator could issue a stop loss order immediately after he purchases
the stock. The stop order would represent his interests even if he cannot
communicate with the market.)
4
•
They route network connections through different vendors and
through different physical paths.
•
They provide backup power supplies.
•
They replicate all systems at a remote "hot disaster recovery site," to
which all activity can switch instantly if a disaster affects the primary
processing site.
•
They build significant excess capacity to protect against unexpected
surges in demand.
•
They maintain high-quality controls in software development.
•
They maintain redundant data backup procedures.
•
They test software thoroughly, using independent quality assurance
groups.
Security
Automated trading systems must be secure because traders enter contracts
involving large sums of money. Traders must be confident that computer
hackers cannot tamper with their systems and that impersonators cannot
fraudulently trade in their name. Markets build secure systems by using var-
ious data encryption systems and user authentication systems.
Speed
Automated trading systems must be fast because traders want instant ac-
cess to markets. They want to see everything as it happens, and they want
everything they do to happen instantly. These are reasonable requests. In


## Page 564

CHAPTER 27 FLOOR VERSUS AUTOMATED TRADING SYSTEMS • 551
active markets, prices may change every second. Traders who do not have
the latest information will make poor decisions.
Costs
Several vendors sell fully automated exchange trading systems off the shelf.
These have been particularly popular in emerging markets. Such systems
typically are pure price-time precedence systems. They cost about 5 million
dollars to purchase and set up.
Most electronic trading systems trade over private data networks. These
networks are generally quite secure, and they provide high-quality service.
They are quite expensive to operate, however, because they usually require
dedicated leased lines that traders rarely use to their full capacity. In the
future, many electronic trading systems probably will send encrypted data
over the Internet. Which traders use Internet connections will depend on
the speed they require, and the quality of service they can obtain from the
Internet.
Once set up, electronic trading systems have small operating costs be-
cause everything is automated. The main operational costs are telecommu-
nications costs and costs of adequately backing up data.
27.7.2 Floor-based Trading Systems
Exchanges that use floor-based trading systems must acquire or build suit-
able trading floors. Trading floors can be quite expensive, especially where
real estate is expensive. A floor-based market also must have adequate
telecommunications systems to route orders and to report market data and
trade confirmations. These systems are comparable to those that electronic
trading systems use. Floor-based markets also must invest in information
display systems to assist their floor-based traders. Data vendors often pro-
vide these systems.
Actively traded floor markets must employ automated systems to sup-
port their trading. These systems may include electronic limit order books,
broker paging systems, wireless order-routing systems for floor brokers, elec-
tronic reporting systems, and automatic execution systems for small orders.
Costs of designing and implementing these systems can be quite high. When
faced with these costs, many exchanges have chosen to switch to completely
automated trading systems.
Floor-based trading systems are often quite expensive to operate. They
require substantial labor to run. Brokers must arrange trades, reporters or
traders must report the trades, officials must watch for trading abuses, and
runners often must carry messages to and from brokers. Although new elec-
tronic trading systems are increasing broker productivity in some markets,
floor-based trading systems will always be labor-intensive.
To operate well, floor-based trading systems require well-trained floor
brokers. The brokers must know and follow the trading rules and proce-
dures. They also must know how to best expose their orders and negotiate
their trades. Although floor brokers provide special services that distinguish
floor-based from automated trading systems, their substantial training makes
employing them quite expensive.
Since floor brokers are often quite busy on the floor, they often cannot
communicate directly with their clients. In that case, their clients give their
orders to sales brokers who forward them to the floor brokers. The depen-
Communications
Delays in GLOBEX
GLOBEX
is an electronic
trading system in which the
futures contracts of the
Chicago Mercantile Exchange
and the MATIF (Marche a
Terme International de France)
trade after hours. It is located
in Chicago. During
development, traders in
Australia were concerned that
they would have slower
access than traders who were
close to Chicago. The system
designers therefore designed
the system to ensure that all
traders would receive a
minimum quality of service
regardless of where they are
in the world.
The issue is particularly
important in computerized
trading systems that allow
their users to submit computer-
generated orders.
Computerized traders who
can act most quickly on new
information take advantage of
market opportunities first.
When
two
computerized
trading systems employ the
same trading strategies, the
first to submit its orders will
be the more profitable system.
Since the difference in their
submission times may be less
than a millisecond, speedy
connections are essential to
trading successfully.
Although the GLOBEX
communications network is
extremely fast, traders for
whom reaction time is of the
essence undoubtedly place
their computers in Chicago.


## Page 565

552 •
TRADING AND EXCHANGES
dence on sales brokers increases the costs of floor-based trading systems. It
also slows them down.
In contrast, electronic trading systems do not require well-trained bro-
kers to operate. In many such systems, brokerage clients can access the mar-
ket themselves, without the intermediation of their brokers. In such
direct
access
systems, brokers merely guarantee and settle their clients' trades.
Skilled brokers can provide valuable services to their clients in electronic
environments by forming and implementing good order submission strate-
gies. These brokers, however, are not necessary to run the system. Electronic
trading systems are cheaper to operate because they do not require floor
brokers and sales brokers.
27.8 SUMMARY
Floor-based trading systems and automated trading systems have different
strengths and weaknesses. Consequently, they appeal to different clienteles.
It is unlikely that one market structure will dominate all trading. Table
27-1 summarizes strengths and weaknesses of floor-based and automated
trading systems.
Fully automated systems are very fast and generally cheap to use and
TABLE 27
-
1.
Floor Versus Automated Trading Systems
ISSUE
Speed
Information
exchange
Potential for order
exposure strategies
Trader convenience
Buy-side access
System scalability
Initial costs
Communications
costs
Labor costs
Fairness
Audit trails
FLOOR-BASED SYSTEMS
Relatively slow
Floor-based traders can exchange any
information they want
Limited by floor brokers' skills
Traders must be on floor
Through floor brokers and possibly sales
brokers
Limited by difficulties of physically
exchanging messages
High costs to acquire and construct floor
High costs to train floor brokers and
exchange officials
Low if based on telephones
Moderate if based on electronic data
systems
High labor costs
Requires substantial surveillance of floor
trading
Require substantial efforts
AUTOMATED SYSTEMS
Very fast
Traders can communicate
only through a limited set
of order instructions
Limited by the set of order
instructions
Distributed access
Direct access is possible
Highly scalable
Relatively lower costs for the
exchange software
Moderate costs for electronic
data systems
Low labor costs
Very fair
Perfect


## Page 566

CHAPTER 27 FLOOR VERSUS AUTOMATED TRADING SYSTEMS • 553
operate. These characteristics ensure that active markets and markets that
serve small traders will use automated trading systems extensively. In the
U.S. equities markets, Bernard L. Madoff Investment Securities, Knight
Capital Markets, and other dealers who offer automated execution systems
provide excellent service to high volumes of small traders. Options markets
tend to have high order volumes and small transaction sizes. We can expect
that these markets will increasingly automate their trading.
Fully automated systems also allow traders to exercise direct control over
their orders. They therefore appeal to traders who do not trust their bro-
kers or who do not want to pay for brokerage services. Large institutions
that are concerned about how brokers expose their orders often favor auto-
mated systems if they are willing to employ their own buy-side traders.
Floor systems work best when traders need to exchange information
about each other before they trade. They also work best when brokers need
to actively' search for traders to fill their orders. Since these advantages are
most important to large traders, floor-based markets will serve primarily
large institutional traders. The NYSE increasingly is an institutional mar-
ket. Although people have been predicting the demise of the NYSE floor
since the mid-1960s, it will not disappear as long as the NYSE floor traders
continue to provide valuable services to traders that other systems cannot
provide.
The communications and computational technologies that have enabled
automated electronic markets allow these markets to exploit huge economies
of scale. Consequently, many exchanges have merged to take advantage of
scale economies. Many more will probably merge in the future.
In chapter 26, we noted that the order flow externality can make an in-
cumbent market highly liquid even if it employs an inefficient trading tech-
nology. The continued existence of large floor-based markets therefore does
not necessarily imply that their trading systems are better than fully auto-
mated trading systems.
27.9 SOME POINTS TO REMEMBER
•
Electronic trading systems provide much better audit trails than floor-
based trading systems.
•
Electronic trading systems provide faster access to markets than floor-
based trading systems.
•
Floor-based trading systems are not as scalable as electronic trading
systems.
•
Floor-based trading systems allow brokers to exchange information
that they cannot easily exchange in electronic trading systems. This in-
formation often is essential for arranging good trades for large traders.
•
The order flow externality makes it impossible to conclude that large
floor-based trading systems survive because their floor-based trading
technologies are superior to electronic trading technologies.
27.10 QUESTIONS FOR THOUGHT
1. How would you start an automated exchange that will compete with
an established floor-based exchange? In particular, how can a new ex-
change overcome the order flow externality enjoyed by an incumbent
exchange?


## Page 567

554 •
TRADING AND EXCHANGES
2.
What are the labor savings when a floor-based exchange converts to
an automated exchange? What jobs no longer need to be done? What
is the impact of the change upon dealers, floor brokers, sales brokers,
exchange clerks, and surveillance officers? Where do the people who
still have jobs work? Who pays for their office space and their com-
munications infrastructure?
3.
Does the decision to use screen-based versus floor-based trading sys-
tems depend on the assets to be traded? Which type of trading
system would be best suited to trading instruments in the following
asset classes: futures, stocks, bonds, currencies, spot commodities, pol-
lution credits, and options?


## Page 568

28
Bubbles,
Crashes,
and
Circuit
Breakers
B
ubbles and crashes occur when prices di
ff
er greatly from fundamental
values. The wealth that these events create, destroy, and redistribute is
often enormous. Bubbles and crashes thus are quite scary when prices change
quickly.
Extreme volatility concerns many people:
•
Traders pay close attention to it because large, unexpected price changes
expose them to tremendous risks and opportunities.
•
Clearinghouses worry about extreme volatility because traders who ex-
perience large losses may be unable to settle their trades or contracts.
Clearinghouses and their members must bear the costs of resulting set-
tlement failures.
•
Exchanges and brokers plan for extreme volatility because extreme price
changes usually generate—or are generated by—huge volumes that can
overwhelm their trading systems and cause them to fail. Large sus-
tained price drops especially concern them because trading volumes
usually shrink substantially and remain low for a long time afterward.
•
Microeconomists fret over extreme volatility because very large price
changes often appear to be inconsistent with rational pricing and in-
formative prices. They wonder whether excess price volatility causes
people to make poor decisions about the use of economic resources.
•
Macroeconomists fear that the wealth effects associated with large,
broad-based changes in market values may adversely affect the invest-
ment and consumption spending decisions that companies and indi-
viduals make. Poor spending decisions can cause unsustainable booms
and protracted contractions in economic activity.
These concerns explain why market regulators regularly examine trading
practices and trading rules that might induce or attenuate extreme volatil-
ity. Some policies that they consider can create markets which are more re-
silient. Other policies have little value, and many policies can harm the mar-
kets. Regulators therefore must carefully analyze how market structure
affects volatility before adopting new policies.
In this chapter, we consider what causes extreme volatility, and how reg-
ulations might make it less likely or less dangerous. Not surprisingly, ana-
lysts generally understand the causes of extreme volatility better after the
fact than beforehand. Volatility episodes rarely have common causes. They
do, however, tend to follow a common pattern. Traders who can recognize
conditions that may lead to extreme volatility can take positions that are
highly profitable. Regulators who can recognize these conditions can occa-
sionally adopt policies to reduce the harmful aspects of extreme volatility.
We start our discussion by distinguishing among the types and causes
of extreme volatility, and then illustrate these points by considering several
555


## Page 569

11
,
The Price Accelerator
Increases in prices transfer
wealth from pessimistic
traders who have short
positions to optimistic traders
who have long positions.
These transfers can cause
accelerated price changes.
When prices rise, optimistic
traders get wealthier. The most
optimistic traders may buy
more. If they do, they may
cause prices to rise further.
When prices rise,
pessimistic traders lose. The
losses of the most pessimistic
traders may force them to buy
back short positions to cover
margin calls. Their buying
may cause prices to rise
further.
In both cases, the sellers
will be traders who do not
have such strong opinions.
Mild pessimists will sell
because the increase in
market price makes their short
positions more attractive. Mild
optimists will sell because the
increase in market price
makes their long positions less
attractive.
4
Source: "The Canonical Bubble,"
manuscript by Jack Treynor.
556 • TRADING AND EXCHANGES
examples of bubbles
and crashes. We next examine how changes in market
structure can affect extreme volatility. Finally, we briefly consider how pol-
itics affects regulatory policies taken in response to extreme volatility
28.1 BUBBLES AND CRASHES
Bubbles
occur when prices rise to levels that are substantially above funda-
mental values. (Fundamental values, of course, are not common knowledge.
If they were, crashes and bubbles would not occur.) Some bubbles occur
very quickly. Others occur over long periods. Many bubbles end with a crash.
Traders say that such bubbles pop.
Crashes
occur when prices fall very quickly. Crashes often follow bubbles,
but they also occur in other circumstances. Crashes sometimes are called
market breaks
because the price path breaks when prices fall very quickly.
They also are called
market meltdowns
when they overload the order han-
dling capacity of a market.
Bubbles and crashes may affect an individual trading instrument or many
instruments at once. Those which simultaneously affect many instruments
are
broad-based events
or
marketwide events.
Very large price changes most
commonly affect only an individual instrument. Broad-based bubbles and
crashes are quite rare.
28.1.1 Typical Bubble and Crash Dynamics
Bubbles start when buyers become overly optimistic about fundamental val-
ues. The potential of new technologies and the potential growth of new
markets can greatly excite some traders. Unfortunately, many of these traders
cannot recognize when prices already reflect information about these po-
tentials. They also may not adequately appreciate the risks associated with
holding the securities that interest them. If enough of these enthusiastic
traders try to buy at the same time, they may push prices up substantially.
The resulting price increases may encourage momentum traders to buy,
in the hope that past gains will continue. Some momentum traders may buy
because they hope to obtain the profits that their neighbors and friends have
already earned. If enough traders follow them, they will realize their hopes.
The last buyers, however, will lose badly.
Order anticipators may buy in anticipation of new uninformed buyers.
They will profit if they can get out before prices fall.
The combined trading of these traders can cause a bubble in which prices
exceed fundamental values. Momentum traders and order anticipators, in
particular, tend to accelerate price changes. Prices also accelerate when early
buyers grow more confident as their wealth increases, and when early sell-
ers repurchase their positions to stop their losses.
Value traders and arbitrageurs may recognize that prices exceed values,
but they may be unable or unwilling to sell in sufficient volume to prevent
the bubble from forming. These traders may be unable to sell as much as
they want to sell if they do not have large positions to sell, if they do not
have enough capital to carry large short positions, or if they cannot easily
sell short. They may be unwilling to sell if they suspect that uninformed
traders will continue to push prices up, or if they lack confidence in their
abilities to estimate values well.
Eventually prices rise to a level that causes sellers to start trading ag-


## Page 570

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 557
Bo
,
You Believe You Are Right, but ... (Confidence Is Everything)
Even when value traders believe that prices greatly differ from fundamental
values, they may lack the confidence to trade on their opinions. Trading
against the majority opinion requires great courage. Since markets
generally aggregate information from diverse sources extremely well, value
traders must always wonder why they believe they understand values better
than everyone else does. Value traders will not trade unless they are
confident they are right, even after considering that the majority of traders
think otherwise.
Value trading is especially difficult when unresolved uncertainties make it
impossible for anyone to estimate values well. In that case, value traders
will not trade until price is far from their estimates of value. This observation
explains why bubbles often form in the stock prices of companies that hope
to profit from highly promising, but unproven, technologies.
gressively. The sellers may be long-term holders, early buyers who want to
realize their gains, contrarians, value traders, or arbitrageurs. Once their sell-
ing causes prices to fall, momentum buyers lose their interest. Overly opti-
mistic buyers lose their confidence, and sellers become more confident. Late
buyers especially worry about their positions, and often start selling to stop
their losses. Traders who financed their positions on margin may have to
sell their positions to satisfy margin calls from their brokers. Other long
holders who have placed stop loss orders also will start to sell. Order antic-
ipators may anticipate these margin calls and stop orders, and sell before
them. A crash occurs when the combined effect of all their selling causes
prices to fall quickly.
The uncertainty associated with crashes, and the great demands for liq-
uidity that panicked sellers make during crashes, can cause prices to drop
below fundamental values. A bounce back in price therefore follows many
crashes when traders recognize that the market overreacted. Traders call this
bounce a
dead cat bounce,
for reasons that you can speculate on privately.
28.1.2 Fundamental and Transitory Volatility
Bubbles and crashes—like all price changes—may be due to fundamental or
transitory factors. Unexpected information about fundamental values causes
fundamental volatility. The demands for liquidity by uninformed traders cause
transitory volatility. Most bubbles and crashes involve both types.
The two volatility types have different effects on prices. Fundamental
price changes have a permanent effect in the sense that subsequent price
changes are unrelated to previous price changes. Transitory price changes
tend to reverse when value traders and arbitrageurs act on the differences
between prices and fundamental values.
Bubbles generally start when prices rise on good news about fundamen-
tal values. These initial price changes contribute to fundamental volatility.
Pseudo-informed traders then overreact to the news and to the resulting
price increases. They eventually cause prices to exceed values. This portion
of the bubble contributes to transitory volatility.
Bubbles often burst when traders react to bad fundamental news that
causes prices to fall. The combination of bad news and falling prices shakes
trader confidence and becomes a focal point for rational thought. As traders
start thinking more carefully about values, many conclude that prices are
You Know You Are
Right, but ... (Timing
Is Everything)
Consider
the trade timing
decisions that value traders
must make when they believe
that prices are too high.
If they initially have long
positions, and they sell them
too soon, they will lose the
opportunity to sell at higher
prices as uninformed traders
cause prices to continue to
rise.
If they initially have no
positions, and they sell short
too soon, they initially will
lose on their short positions. If
they cannot finance their
losses, their brokers will force
them to buy to cover their
losses, and they will lose the
opportunity to profit when
prices fall.
If value traders wait too
long to sell, they will lose the
opportunity to profit. Other
value traders will enter and
push prices down, early
buyers will try to realize their
gains, or the flow of
uninformed traders necessary
to maintain the bubble may
end.


## Page 571

558 • TRADING AND EXCHANGES
The News Just Wasn't
Good Enough
In 1999 and 2000, the prices
of many Internet companies
fell after they reported record
earnings. These earnings
announcements caused traders
to think carefully about values.
Prices crashed because these
companies could not produce
the extremely high earnings
growth necessary to sustain
overly optimistic price
expectations.
too
high, and sell. The resulting volumes and decreases in price are vastly
disproportionate to the volumes and price changes that we would normally
have expected in response to the news. The extreme response is due to the
initial overpricing rather than to overreaction to the news. The bad funda-
mental news merely triggers the crash. The true causes of the crash lie in
the bubble that preceded it. Although these events move prices toward their
fundamental values, this reversion in prices contributes to transitory volatil-
ity. It occurs only because uninformed traders caused prices to rise during
the bubble.
Panicked sellers sometimes cause prices to drop below fundamental value
at the end of a crash. These price overreactions, and the price reversals that
follow them, create additional transitory volatility.
28.2 SOME BUBBLE AND CRASH EXAMPLES
This section describes several bubbles and crashes to illustrate how bubbles
and crashes occur. I selected these events because they involve important
market microstructure issues.
Traders and regulators are very familiar with the most important of these
examples. If you intend to work in the markets, you should be as well. The
seven events that we discuss here do not include all important examples of
bubbles and crashes. You may wish to consult other sources to learn about
the Dutch tulip bulb mania and crash of 1637, the British South Sea Com-
pany bubble and crash of 1720, and many similar events of great interest to
economic historians.
As you read through these stories, consider the role that investor senti-
ment played in these bubbles and crashes. In most cases, you will find that
sentiment was a more important cause of these events than any issue in-
volving market structure. The public, however, often demands changes in
market structure following crashes. We discuss regulatory proposals to deal
with crashes in the following section.
28.2.1 The 1929 Stock Market Crash
On October 28-29, 1929, the U.S. stock markets crashed in what may be
the most famous stock market crash of all times. The Dow Jones Industrial
Average (DJIA) dropped 13 percent on October 28 and another 12 percent
on October 29. Although it rose 12 percent on October 30 and 3 percent
more on October 31, the market continued to drop substantially over the
next several weeks, months, and years. Figure 28-1 shows that by July 8,
1932, the DJIA had dropped to only 11 percent of its previous maximum
closing value, reached on September 7, 1929.
This crash followed tremendous growth in prices in the late 1920s. The
DJIA steadily rose by almost 300 percent from the beginning of 1924 to
the September 1929 peak. Following the crash, the DJIA did not make a
new high until more than 25 years later, in November 1954.
Many commentators attribute the bubble that preceded the 1929 crash
to uninformed speculators who borrowed excessively to buy stocks. Traders
were very excited by the prospects of new technology and media companies
that were developing radio. In hindsight, traders clearly overvalued these
stocks. For example, RCA—which proved to be one of the strongest radio
stocks—did not rise to a new high until 34 years after the crash.


## Page 572

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 559
400 -
300 -
200 -
100 -
0

1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950
FIGURE 28-1.
Dow Jones Industrial Average, 1900
-
1950
Source: Dow Jones and Co., at www.djindexes.com
.
•
The crash partly occurred because traders needed to sell stock to satisfy
margin calls following the decline in stock plikces over the previous month.
Much selling undoubtedly also was due to value traders who sold the mar-
ket short, and to speculators who anticipated the sell orders that the
margin calls would create.
Panicked sellers also caused prices to drop. Although panicked traders
generally do not make good decisions, in hindsight, those who sold in the
crash were quite lucky. Had they held their positions, they would have lost
even more money in the next months and years. We shall shortly see that
the same was not true in the 1987 stock market crash.
In response to concerns about excessive speculation on margin, the U.S.
Congress gave the Federal Reserve Board authority to regulate the margins
upon which speculators could buy stock. (The authority appears in the Se-
curities Exchange Act of 1934.) The Fed immediately set stock loan mar-
gins at 45 percent, so that speculators had to have at least 45 cents of eq-
uity for every dollar of stock they purchased.
The 1929 stock market crash preceded the Great Depression of the
1930s. Many people associate the two events and believe that the crash
caused the Great Depression. Few economists believe this, however. The
Great Depression was more likely caused by excessively tight monetary pol-
icy that led to widespread banking failures from late 1930 through early
1931, and again in 1933. Most economists also believe that spending cuts
the government made in 1937 further extended the Great Depression.
28.2.2 The October 1987 Stock Market Crash
The U.S. stock markets crashed most dramatically in October 1987. (See
figure 28-2.) The
DJIA
lost 23 percent on Monday, October 19. This loss
was—and remains—its largest daily percentage loss. The loss followed a 5
percent loss on the previous Friday, a cumulative loss of 9 percent in the
previous week, and a cumulative 17 percent loss from its previous all-time
high, achieved on August 25.
Market Failures or
Market Corrections?
Analysts generally cannot
attribute the large price
changes that occur in broad-
based market crashes to
unexpected bad fundamental
news of commensurate
importance. Many people
therefore believe that crashes
demonstrate that markets do
not produce informative
prices. When they see prices
change substantially on
seemingly trivial information,
they conclude that crashes
represent market failures.
Most crashes, however,
more likely represent
corrections to previous pricing
errors rather than market
failures. They generally bring
prices closer to fundamental
values rather than move
prices further from them.
Evidence for this
conclusion lies in the behavior
of prices following crashes. If
crashes represented market
failures, prices would
generally rebound completely
when traders discovered that
prices were below values. In
fact, prices generally rebound
little following most crashes.
Most crashes therefore more
likely correct previous
problems than create new
ones.


## Page 573

560 • TRADING AND EXCHANGES
Portfolio Insurance or
Portfolio Manager
Insurance?
In principle, portfolio
managers use portfolio
insurance to protect their
investment sponsors from
losses that would result if the
values of their portfolios drop
below the values of the
liabilities that the sponsors
must fund. These liabilities
typically are future pension
benefits. In this application,
the target guaranteed
minimum portfolio value—the
strike price of the put—should
be the value of the liabilities
that the sponsor must fund.
Since these liabilities do not
change much over time,
neither should the target
guaranteed minimum portfolio
value.
In practice, portfolio
insurers often increased the
put strike price as the market
value of their portfolios
increased. This practice
caused many people to refer
to portfolio insurance as
"portfolio manager insurance"
because managers—and their
sponsors—appeared to be
locking in past successes
rather than insuring against
future shortfalls. The
unfortunate consequence of
these strike price increases
was that portfolio managers
would have to sell more when
the market dropped than they
would have needed to sell
had they not ratcheted up the
strike prices.
1980

1982

1984

1986

1988

1990

1992
FIGURE 28-2.
Dow Jones
Industrial Average, 1980-1992
Source: Dow Jones and Co., at www.djindexes.com
.
These losses followed a sObstantial price increase. From the beginning
of 1987 through August 25, the Dow rose 44 percent.
The market recovered quite quickly. The Dow rose 2 percent and 10 per-
cent, respectively, on October 20 and 21. Although the market experienced
many violent swings up and down over the next couple of months, it quickly
resumed its previous growth. The Dow achieved a new all-time closing high
less than two years later, on August 24, 1989. Remarkably, the Dow rose
2 percent in 1987 despite the crash.
The 1987 stock market crash was a very complex event with many causes.
The most notable cause of the crash was the use of portfolio insurance by
institutional investors.
Portfolio insurance
is a dynamic trading strategy that
portfolio managers use to replicate the combined returns of a portfolio plus
a put option. When correctly and successfully implemented, the strategy en-
sures that the total value of the portfolio will not fall below a value that the
manager specifies. The specified value corresponds to the strike price of the
dynamically replicated put option.
The strategy depends upon a formula that tells managers how much they
should buy or sell given changes in the value of the portfolio. The formula,
which comes from the Black-Scholes option-pricing model, depends on the
volatility of the portfolio, the period over which the managers wish to pro-
tect the portfolio, and the minimum value that they wish to guarantee. Since
managers must trade whenever prices change, the strategy is a
dynamic trad-
ing strategy.
Portfolio insurance is highly destabilizing to market prices. When prices
rise, portfolio insurers must buy stock. When prices fall, they must sell stock.
Portfolio insurance therefore has the same effect on the market as stop or-
ders. Indeed, many portfolio managers implemented their strategies using
stop orders.
Portfolio insurance can work well when the total money covered by the
strategy is small. Portfolio insurers then can execute their orders without


## Page 574

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 561
much price impact. Great problems arise, however, when managers try to
insure a significant fraction of the market. When sellers want to sell large
quantities at the same time, prices can fall very substantially. Moreover, when
everyone knows that large sellers will come onto the market when prices
fall, order anticipators will quickly sell at the first sign of a price drop. Like-
wise, buyers will withdraw from the market until prices have dropped sub-
stantially. Expectations about portfolio insurance selling therefore can drive
the market down, even if no portfolio insurance selling occurs.
Many commentators believe that unrealistic expectations about how
well the portfolio insurance strategy would perform caused the crash. By
October 1987, portfolio insurance had become very popular. A rough
calculation—presented in the box below—suggests that portfolio insurers
would need to sell 10 million shares for every 1 percent drop in the mar-
ket. This potential sell order volume is very large compared to the then
current daily average volume of only 160 million shares. It is especially large
because it represents money that managers would completely take out of
the market rather than simply reallocate among other stocks. Many peo-
ple wondered who would buy that equity risk.
Many traders were aware of the portfolio insurance problem. Several aca-
demics had written widely circulated papers about the problem, and it was
a major topic of discussion at several practitioner conferences. Unfortunately,
everyone was uncertain about how much money managers devoted to the
strategy. This uncertainty undoubtedly contributed to the crash because
nobody would want to buy before he or she believed that all'sellers were
either satisfied or discouraged.
The uncertainty would not have been so severe had portfolio insurers
bought exchange-listed put option contracts instead of trying to replicate
put options with dynamic trading strategies. Had they used contracts, every-
one would have known the total open interest in the contracts. Traders there-
fore could have better estimated how much risk would be transferred when
prices fell.
The 9 percent decline in prices in the week leading up to the crash, and
especially the 5 percent decline on Friday before the crash, undoubtedly
suggested to many traders that the markets faced great downside risk. Not
ti
surprisingly, the market fell hard when it opened on Monday, October 19.
(See figure 28-3.)
The price declines before the crash may have been due to bad news about
economic fundamentals. Traders were concerned about the trade deficit,
trade tensions with Germany, international interest rates, the value of the
dollar, and anti-takeover legislation pending in Congress. Moreover, just be-
fore the market opened on October 19, the United States announced that
it had attacked Iranian oil platforms in the Persian Gulf. Although each of
these stories should have decreased fundamental values, even when taken
together, they were not so significant or so surprising that they could have
reasonably accounted for the 36 percent drop in the Dow from its August
25 high to its October 19 close.
Several factors besides portfolio insurance selling probably contributed
to the crash. First, prices may have been above fundamental values before
the crash. Managers who believed that portfolio insurance protected them
from significant loss may have switched their asset allocations from bonds
to stocks in the months before the crash. The pressure of their purchases
The Overhang of
Portfolio Insurance
on the Market
The total U.S. stock market
capitalization before the
1987 stock market crash was
about 2 trillion dollars.
Institutional management
controlled about 50 percent
of this total. About 10 percent
of these institutional funds
were subject to portfolio
insurance strategies. These
figures imply that about
100 billion dollars was
subject to portfolio insurance.
The delta of an option
indicates how much the value
of the option will change
when the value of the
underlying instrument changes.
The typical option delta for
portfolio insurance was
probably about 0.4, so that a
1 percent drop in the market
would require that managers
sell 0.4 percent of their
portfolios.
At
an approximate
average price of 40 dollars
per share, 0.4 percent of
100 billion dollars represents
10 million shares.


## Page 575

562 • TRADING AND EXCHANGES
2800
2400
2000
1600
Sep 1

Sep 15 Sep 29 Oct 13 Oct 27 Nov 10 Nov 24
FIGURE 28-3.
Dow Jones Industrial Average, September
—
November 1987
Source: Bridge Information Systems.
may have created a small bubble as prices quickly rose in the first three quar-
ters of 1987.
Second, the huge volumes that traders wanted to trade during the crash
exceeded the trade processing capacity of the New York Stock Exchange
and its floor traders. The most significant capacity problem involved dot
matrix printers on the floor that printed orders which traders sent to the
Exchange through the SuperDot order-routing system. During the crash,
these printers could not print orders as fast as they arrived, which created
an order backlog in the Exchange computer print queues. Breakdowns of
several printers further compounded the problem. Consequently, many
SuperDot orders had to wait more than an hour until they could be printed
and executed. The traders who submitted them had no idea whether their
orders were filled. Many undoubtedly telephoned sell orders to their floor
brokers when they could not confirm that they had sold via SuperDot. The
uncertainty associated with the capacity problem surely contributed to feel-
ings of panic that many traders experienced.
Third, traders may have panicked as they watched the index futures mar-
ket lead the stock market down. In normal trading, the index futures
market generally leads the stock market because the futures market employs
a faster trading system than the stock market. It also leads because index
futures traders are interested only in finding the price of index risk. In con-
trast, the cash stock market consists of thousands of markets for individual
stocks in which most traders are more concerned about firm-specific risk
than index risk. When prices started to drop, they dropped first in the in-
dex futures market. Traders who saw those decreases in price knew that
prices in the stock market would soon drop.
When capacity problems slowed the stock market, the spread between
the cash index and the futures contract widened to unprecedented levels as
the futures dropped faster than the stocks. The two markets, which gener-
ally follow each other very closely, became
disconnected.
Before the October


## Page 576

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 563
Po Where's the Price?
The markets almost never give away anything for free. When traders want
to buy an option contract, they must pay a premium to the seller. When
traders want to replicate an otherwise identical option with a dynamic
trading strategy, they likewise must incur some cost. Otherwise, no well-
schooled trader would ever buy option contracts.
Unfortunately, traders can neither observe nor easily estimate the price of
a dynamically created option. Dynamic option replication is expensive
because traders always buy or sell after the price changes that require them
to trade. They therefore trade later than they wish they could.
Understanding exactly why dynamic options replication is expensive
requires high-level mathematics that most portfolio managers and investment
sponsors do not know.
Option replication is also expensive due to the impact that traders have
on price when they trade to replicate options. Traders frequently
underestimate these costs. When transaction costs are high, options
replication is very expensive.
Many traders who adopted portfolio insurance undoubtedly ignored or
underestimated these costs of option replication. For such traders, portfolio
insurance must have appeared to be a tremendous bargain that gave them
unlimited upside potential with limited downside potential for little cost.
Few traders now use the portfolio insurance strategy. Now that they
know the full cost of portfolio insurance, most traders choose not to
buy it.
19 close, the S&P 500 Index futures contract was trading at a discount of
more than 10 percent to concurrent value of the cash index!
Index arbitrage normally would ensure that such a huge discount would
never exist. Arbitrageurs, however, largely stopped trading because they could
not obtain quick executions of their sell orders in the stock market. They
would not trade because they had no idea whether their stock sales would
trade at the high prices the market was currently indicating or at lower prices
that they feared would prevail when their orders actually executed.
The huge discount of the futures to the cash market undoubtedly caused
some traders to submit more sell orders to the stock market. The unprece-
dented discounts also confused many traders and thereby contributed to
their panic.
Finally, many buy-side traders could not contact their Nasdaq dealers
during the crash. Before the 1987 crash, most Nasdaq dealers took their or-
ders over the phone. During the crash, many traders could not reach their
dealers because the dealers were overwhelmed with client orders. Moreover,
some dealers simply took their phones off the hook because they did not
want to trade. The inability to contact the Nasdaq dealers while the mar-
ket was falling also must have contributed to trader panic.
In summary, the 1987 stock market crash had many causes. Chief among
them was the confusion that traders felt when confronted with uncertainty
about risk. Traders were uncertain about portfolio insurance selling, their
ability to execute their orders, and the meaning of the extremely large spread
between the cash index and the index futures contract price. These uncer-
tainties undoubtedly caused many panicked traders to order sales that they
later wished they had not.


## Page 577

564 • TRADING AND EXCHANGES
Global Confusion
The 1987 stock market crash was a global event. Prices fell in almost every
world market. In many cases, the percentage falls were greater than in the
U.S. market.
This fact suggests that we cannot base any complete explanation for the
1987 crash only on U.S. factors. Either the crash had causes that affected
all international markets, or some mechanism must have transmitted the
volatility from the U.S. markets to other markets. Analysts have carefully
examined this issue, but no clear picture has emerged. We do not all agree
upon the causes of the 1987 global stock market crash.
Some analysts believe that common factors must have caused the crash
throughout the world. Since the Asian and European markets crashed on
Monday, October 19, before the U.S. markets opened, these analysts
believe that the U.S. markets did not
transmit
their volatility to the foreign
markets.
Analysts who believe that the causes of the crash lie in the U.S. markets
suggest that the Asian and European markets may have crashed in
anticipation of the U.S. market crash. As we noted above, over the
weekend, many traders expected the Monday crash. Indeed, their
expectations may have helped cause the crash.
All analysts agree on some relevant facts. First, the U.S. stock and
product markets are extremely important because they are so large and
because the United States engages in so much world trade. Fears that
recession in the United States would cause recession in other countries were
reasonable, and could account for the crashes in the other markets. Second,
cross-border ownership closely links international stock markets. International
investors often use the same trading strategies in many markets. They also
may have satisfied margin calls that they received in one market by selling
in another market. Third, prices in all stock markets are correlated because
the capital market is an international market. Changes in the real rate of
interest in one country ultimately will affect the real rate of interest in all
other countries that have open economies. Although these facts cannot
resolve the debate, they do suggest that U.S. factors could have been
responsible for the global crash.
28.2.3 The October 1989 Mini
-
Crash
On
the afternoon of October 13, 1989, the U.S. stock markets dropped
7 percent. The abrupt drop occurred immediately after a consortium of banks
announced at 2:54
P.M.
that they would
not finance a levered buy-out of
UAL Corporation, the parent of United Airlines. UAL quickly fell, as did
several other stocks that traders had identified as potential takeover targets.
The index futures market also fell abruptly. As usual, the index futures mar-
ket led the cash market down. This event became known as the
October 1989
Mini
-
Crash.
A very quick recovery followed the Mini-Crash. Figure 28-4 shows that
the market returned to normal within just a few weeks of the event.
The cause of this crash is hard to understand because it clearly seems
linked to the bad news about UAL. The bad UAL news easily explains why
that stock dropped. If traders assumed that the news indicated that takeover
financing would become more difficult for other firms to obtain as well, the
news may also explain why the stocks of other potential takeover targets
fell. (Although with hindsight, we now know that the UAL failure did rep-
resent the end of the merger and levered buy-out wave that occurred in the


## Page 578

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 565
2900 —
Fil
l

111
1

"
2700 —
III

11
1
" 1
1
1
11 1

2600 —
2500 —
!
lit
i
t'll
!
ll lir

1,
`
2800 —
2400

Sep 1 Sep 15 Sep 29 Oct 13 Oct 27 Nov 10 Nov 24
FIGURE 28-4.
Dow Jones Industrial Average, September
—
November 1989
Source: Bridge Information Systems.
1980s, it is not obvious that traders then knew this.) It is hard to under-
stand, however, why the UAL news caused the broad market to fall.
Although no adequate explanation exists for this event, some intriguing
information may help us understand what happened. Until the UAL infor-
mation arrived, trading had been quite uneventful. By 2:30, many traders
in the New York stock and index futures markets, and in the Chicago in-
dex futures markets, had left early to go home for the weekend. The warm
Indian summer weather that both cities enjoyed that day undoubtedly in-
fluenced their decisions to leave early: Many traders probably were either
consciously or subconsciously aware that they might not enjoy such good
weather again until after the coming winter. The departure of many traders
from the markets thus removed much liquidity. If no unusually large de-
mands for liquidity had occurred, the day surely would have ended normally.
The market dropped because the UAL news arrived when the market was
not well prepared to handle the large demands for liquidity that sellers sub-
sequently placed upon it.
The early departure of many traders, however, does not adequately ex-
plain why the whole market fell. One highly speculative explanation was ru-
mored but never confirmed. Traders who recognized that the market would
be vulnerable to a downside bluff may have deliberately caused the Mini-
Crash. (Chapter 12 describes how bluffers can profit from manipulative
trading strategies.) Under this scenario, these traders recognized that the
market was weak, or they were waiting for some bad information that they
thought other traders might misinterpret if it were associated with a large
price fall. In either event, the conditions for a marketwide bluff were ideal:
The market was unusually illiquid; the negative news about UAL grabbed
everyone's attention; and traders undoubtedly were sensitive to volatility near
the second anniversary of the October 1987 stock market crash. If a bluff
did take place, the bluffers would most likely have executed the bluff by sell-
ing aggressively in the index futures pits.
No Quorum
Following
the 1987 stock
market crash, the Chicago
Mercantile Exchange adopted
a rule to halt trading for an
hour in its S&P 500 Index
futures contract in the event of
a large price change. The rule
required that a quorum of the
members of the S&P 500
Index Futures Price Limit
Committee determine that the
market was limit down. The
members of the Committee are
Exchange members who trade
in the pit. Their primary
responsibility is to report the
time of the halt so that the
Exchange can determine when
trading should resume. The
quorum was not necessary to
stop trading because the rules
prohibited trading at prices
below the price limit.
When the market dropped
on the afternoon of October
13, 1989, the CME could not
quickly obtain a quorum of
the Price Limit Committee. It
was rumored that too many
members had left the pit early
to enjoy the late Indian
summer afternoon.


## Page 579

566 • TRADING AND EXCHANGES
An Appeal for the Truth
Since the statute of limitations
on market manipulation
expired a long time ago,
someone may someday write
a credible memoir in which he
or she explains how he or she
participated in a marketwide
bluff on October 13, 1989.
Of course, if no such bluff
occurred, any such memoir
would be fictional rather than
autobiographical.
4
The SEC and the CFTC did not cite any evidence of manipulation in
the public reports of their investigations into the Mini-Crash. Any index
futures sellers that they may have contacted undoubtedly explained that they
sold because they recognized the implications of the UAL news. Unless
these agencies could prove beyond a doubt that manipulation took place—
an essentially impossible objective, given the UAL news and its potential
implications—they could not have publicly acknowledged that they strongly
suspected bluffing without severely shaking confidence in the markets. To
date, no publicly available evidence has surfaced to suggest that there is any
truth to this rumor.
28.2.4 The Palladium Cold Fusion Bubble
On March 23,1989, electrochemists Martin Fleischmann and Stanley Pons
announced that they had achieved cold fusion by supersaturating a palla-
dium cathode with deuterium in an electrolytic cell. They claimed that the
process produced excess energy at room temperatures. They also provided
an intriguing conjecture as to why the process worked.
The announcement was extremely exciting. If the results were correct,
the new process might very likely provide a clean, cheap, and inexhaustible
source of energy.
Unfortunately, other researchers have not reliably replicated their results.
Although some electrolytic cold fusion research continues to this day, most
physicists do not believe that the research will ever lead to a reliable source
of power. Intriguingly, some responsible physicists believe that electrolytic
cold fusion experiments have revealed new physics.
The press widely reported the announcement by Fleischmann and Pons,
as well as the skepticism of some high-energy physicists. A few days after-
ward, some enthusiastic traders started buying palladium futures contracts.
They presumably concluded that the demand for palladium would increase
0.9 -
0.8 -
z
0.7
-
a)
a_
0.6 -
0.5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
FIGURE 28-5.
Mean Absolute Daily Returns to the Dow Jones Industrial Average, by
Calendar Month, 1901-2000
Source: Author's calculations, using index data from ,Dow Jones and Co. at
www.djindexes.com
.


## Page 580

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 567
Why October?
October has been one of the most volatile months of the year in the U.S.
stock markets. The 1929 and 1987 stock market crashes, as well as the
1989 Mini-Crash, took place in October. More recently, October 1997 was
quite volatile as well. Four Octobers (1929, 1931, 1932, and 1987)
appear in the list of the 10 most volatile months of the twentieth century.
Why are Octobers so volatile?
If you are a probabilist, you might want to consider whether volatile
Octobers are simply coincidental. If we assume that three extreme events
occur at random among the 1,200 months in a century, the probability that
they all would occur in the same calendar month (not necessarily October)
is only
0.68
percent.' The probability that any month would be
represented—at random—four or more times in the top 10 list of volatile
months is less than 8 percent. It is not likely that these events happened
only by chance.
If you are a social historian, you might explain that history repeats itself.
Perhaps traders are more likely to reflect on valuations in months when
history reminds them that they may be at risk. The first extreme event could
have taken place in any calendar month. Subsequent extreme events may
have occurred in the same calendar month because the anniversary of the
first event served as a psychological focal point for traders. The high
volatility that markets experienced in October 1997—the ten-year
anniversary of the 1987 crash—is consistent with this theory.
If you are a political scientist, you might note that October is the month
when the government gets down to business after its summer recess. The
government often intervenes in the economy, and these interventions often
have strong implications for stock values.
If you are a psychiatrist or an evolutionary biologist, you might note that
people most notice the end of summer and the approach of winter in
October, when changes in the weather and in the total hours of daylight
are most obvious. The darkness and thoughts of the coming winter depress
many people and make them more risk averse. These influences may cause
prices to fall in October. (The change from daylight savings time to
standard time may also be important.) Although this theory predicts that
prices in southern hemisphere markets should be most volatile in April, the
strong correlation between price changes in southern and northern
hemisphere markets—due in part to share ownership of southern
hemisphere companies by northern hemisphere investors—probably would
make tests of this prediction inconclusive.
The last two theories suggest that other fall months should also be
volatile. Figure 28-5 shows that they indeed have been.
Source: Author's calculations using index data from Dow Jones and Co. at www.djin-
dexes.com
.
1. If you
are
indeed a probabilist, you may appreciate the derivation of this prob-
ability: The total number of different ways to choose three of 1,200 months is

(1200) _ 1200!

1200
x
1199
x
1198
3 I

1197! 3!

6
Likewise, the total number of ways to choose three years from 100 years of a given
calendar month is
(
1
00
).
0
). Finally,
any
one of the 12 calendar months could be the
given calendar month. The probability that three events occur in the same calendar
month at random in a century is therefore
12 x
(100) /(1200)
0.0068.
\ 3

3


## Page 581

568 • TRADING AND EXCHANGES
200
160
8
C
Cold fusion
announcement
0
40
0

Jan 89 Mar 89 May 89 Jul 89

Sep 89 Nov 89 Jan 90
FIGURE 28-6.
London Daily Palladium Price Fixings, 1989
Source: Data Stream.
Great Companies and
Great Investments
The traders who bought
!omega at prices substantially
above values made one of
the most common investing
mistakes. They mistook a
great company for a great
investment. In 1996, !omega
produced excellent products
that served extremely large
markets. The firm, however,
was a poor investment then
because it was overpriced.
A good company is a
good investment only if the
price is not too high. A poor
company can be a great
investment if the price is low
enough—assuming, of course,
that the prospects of the
company are not so bad that
the company has no value
at all.
substantially if other researchers confirmed the results. The price of palla-
dium quickly spiked upward by 24 percent over the next three weeks. On
several days, the futures contract closed up the daily price limit of 6 dollars.
Figure 28-6 shows that the price fell back as laboratories throughout the
world announced that they were unable to replicate the results.
Although the palladium bubble appears to be an example of transitory
volatility, it is better classified as an unusual example of fundamental volatil-
ity. The traders who bought palladium probably formed rational expecta-
tions about the future value of the metal, given all available information.
Although they were not physicists, they knew that many physicists were
skeptical about the results. They also knew, however, that if the results proved
true, palladium might become many times more valuable. With hindsight,
we know that these speculators were wrong. However, given the informa-
tion people had at the time, their trading decisions seemed quite sensible.
The palladium bubble therefore was an example of unexpected extreme good
fundamental news followed by a stream of bad fundamental news that ul-
timately resolved uncertainty about the discovery.
28.2.5 The Iomega Bubble and Crash
Iomega is a removable media computer disk drive manufacturer. Its best-
known product—the Zip drive—uses disks just slightly larger than standard
3
1
/2-inch floppy disks to store almost 70 times as much data. Soon after
Iomega introduced the Zip drive, many people became wildly excited about
prospects for its stock. They expected that the Zip drive would replace floppy
disk drives in all new computers.
In early 1996, eager buyers pushed 'omega's stock price up to an intra-
day high of 55
1
/8 dollars per share. (See figure 28-7.) At that price, the price
to earnings ratio for the stock, based on 'omega's
1995
earnings, was almost
1,000. Buyers clearly expected phenomenal earnings growth. Such extreme
growth, however, never materialized. In its best year, the company earned
only 1.26 dollars per split-adjusted share. (The company's earnings and share


## Page 582

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 569
60 —
40 -
20 -
0

1994

1995

1996

1997

1998

1999

2000

2001
FIGURE 28-7.
Iomega Split-adjusted Closing Share Prices, 1994-2001
Iomega's all-time high closing price occurred on May 23, 1996, three days
after the stock split 2 for 1. The stock also split 2 for 1 on December 23,
1997, 3 for 1 on February 1, 1996, and 5 for 4 on November 25, 1994. I
adjusted earlier and later prices in this plot for these splits so that you can
compare them to the May 23 high.
Source: Center for Research in Security Prices.
price crashed in 2001 when its removable media drives experienced with-
ering competition from cheap CD and DVD optical drives that can record
and rewrite on much less expensive media.) Perhaps the most convincing
measure of the bubble appears in the following comparison: At its peak, the
entire value of Iomega stock was worth more than 10 percent of the entire
value of IBM stock! Not surprisingly, Iomega's stock price crashed.
28.2.6 The Nasdaq Bubble
The Nasdaq bubble (figure 28-8) was in many ways similar to the bubble
that preceded the 1929 stock market crash. Traders who were excessively
optimistic about prospects for new technology companies caused both bub-
bles. In the Nasdaq case, these were companies primarily in the Internet,
telecommunications, computer, and biotechnology sectors. Momentum
traders who wanted to participate in the gains made by their friends and
acquaintances also fueled both bubbles.
The Nasdaq bubble was fueled in part by many momentum investors
who placed money in largely undiversified mutual funds that had performed
well in the recent past. These funds often invested the avalanche of new
money that they received in the same stocks that they held. The new money
pushed up the prices of their holdings, which ensured that the funds would
continue to produce high returns. The high returns led to more inflows of
new money. The small fraction of shares available to public investors in many
of the technology companies exacerbated the phenomenon. In the end, of
course, many investors learned the hard way that past performance is a poor
predictor of future returns.
To some extent, a new faith taught to investors by investment advisers
(and by some academics) also contributed to the Nasdaq bubble. These


## Page 583

570 • TRADING AND EXCHANGES
6000
5000
4000
3000
2000
1000
0
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
FIGURE 28-8.
Nasdaq Composite Index, 1991
-
2002
Source: Bridge Information Systems.
You Should Live
So Long!
It is impossible to say now
whether Nasdaq stock returns
will eventually catch up with
bond returns. Investors who
bought the Nasdaq market
near its 5132.52 peak on
March 10, 2001,
undoubtedly will have to wait
a long time until their
investments catch up with the
bond investments they
otherwise might have made
then. Let us hope that they
will all live long enough to
see it happen. Of course,
they will not be waiting
alone. Among others,
investors who bought
Japanese equities around
December 1989 when the
Nikkei 225 Stock Average
was near its peak of 38,916
(see figure 28-9) will keep
them company.
Once again, many
investors learned the hard
way that past performance is
a poor predictor of future
returns.
advisers told long-term investors not to worry about equity market risk be-
cause, over the long run, stocks have always beat investments in all other
broad asset classes. Many investors were emboldened by the idea that they
could not lose if they waited long enough.
Interestingly, the empirical results upon which the equity faith is based
come primarily from the U.S. markets. Stocks in many other markets have
not performed as well as comparable alternative investments.
A final factor worth noting involves new traders who traded over the In-
ternet. Giving orders to brokers by phone intimidates many people. Many
people are afraid that their brokers will judge them for the trading decisions
they make, for their inability to properly articulate their desires, or for tak-
ing too long on the telephone. Even though their brokers may forever be
strangers to them, the fear of trading with a judgmental broker undoubt-
edly inhibited many uninformed traders. The advent of Internet trading
allowed many of these timid traders to access the market at their leisure,
without worrying about what others would think of them. Widespread
Internet-based trading therefore probably brought many new uninformed
traders into the market. The money these traders put into the market prob-
ably contributed to the Nasdaq bubble.
28.2.7 The Japanese Asset Bubble
Japanese equity and Japanese real estate markets experienced a large bubble
that peaked in the end of 1989. Figure 28-9 shows that the equity markets
fell to a fraction of their peak levels and have not recovered. The real estate
markets likewise have not recovered.
Like all bubbles, the Japanese asset bubble had many causes. Investors
both in Japan and in the rest of the world were extremely confident in the
Japanese economy. In the late 1980s, Japanese companies were widely cited
as examples of efficiency and productivity. Many people undoubtedly in-
vested in Japan to participate in what was then known as the Japanese eco-
nomic miracle.


## Page 584

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 571
40,000
30,000
20,000
10,000
0
1960 1965 1970 1975 1980 1985 1990 1995 2000
FIGURE 28-9.
Nikkei 225 Stock Average, 1960
-
2000
Source: Bridge Information Systems.
Japanese monetary policy probably contributed to the bubble. Interest
rates in the mid- and late 1980s were extremely low, and the money
supply grew very quickly. Many commentators said that there was simply
too much money in Japan. Since Japanese investors—both individual and
institutional—historically have not placed much of their money abroad,
they invested the excess money locally. This money pushed up equity and
real estate prices.
Although it is interesting to speculate on why Japanese monetary policy
was so loose, doing so is beyond the scope of this book. The answer un-
doubtedly lies in the complex relationships among Japanese firms, banks,
monetary authorities, and political parties.
28.2.8 Summary
Market crashes are like automobile and airplane crashes. In both cases,
crashes usually do not occur for a single simple reason. Instead, a number
of factors cause people to become confused about what is happening. Their
confusion becomes most dangerous when they are uncertain about risk. They
then pay attention to the wrong issues, they ignore important risks, and, if
things happen too quickly, they panic and lose their ability to make good
decisions. In all types of crashes, the survivors rarely make the same mis-
takes again. When crashes reoccur, it is often because new participants have
failed to learn lessons learned by others.
The examples in this section show that most major market crashes are
not short-term transitory trading problems. More often, the conditions that
led to the crash started creating a bubble long before the crash occurred.
Crashes more often represent a final restoration of rational pricing rather
than a transitory problem in need of correction.
Regardless of these observations, political passion for change is often
quite high following some crashes. Many people demand that regulators do
something to prevent future occurrences. We next consider public policy re-
sponses to extreme volatility.


## Page 585

572 • TRADING AND EXCHANGES
28.3 REGULATORY RESPONSES
TO EXTREME VOLATILITY
Policy makers have responded to extreme volatility primarily by proposing
circuit breakers to restrain traders.
Circuit breakers
are trading rules that limit
trading activity:
•
Trading halts
stop trading when prices have moved, or will imminently
move, by some specified amount. Trading may remain halted until the
order imbalance is resolved or until some specified period passes.
•
Price limits
require all trade prices to be within a certain range on a
given day. If traders are unwilling to negotiate prices within that range,
trade stops until traders are willing to trade within the limits.
•
Transaction taxes
restrict trading by taxing it.
•
Margin requirements
and
position limits
restrict the size of positions that
traders can accumulate.
•
Collars
restrict access to trading systems.
In this section, we consider how these rules may affect volatility, liquid-
ity, and price efficiency. We shall see that some of these proposals are eco-
nomically sensible while others are quite controversial.
28.3.1 Trading Halts and Price Limits
Arguments in Favor
Proponents of trading halts and of price limits believe that these circuit
breakers reduce short-term volatility by slowing price changes. Whether this
is desirable depends on the cause of the volatility.
If new fundamental information causes the volatility, the halt will merely
postpone the inevitable. While the markets are closed, prices will be less in-
formative, and no one will be certain of the new price levels. Worse, the un-
certainty associated with the halt may cause uninformed traders to panic,
which may generate unnecessary transitory volatility when the market re-
opens. On the other hand, if uninformed traders panic when price is mov-
ing quickly, the trading halt may cut them off before they can act.
If an order imbalance originating among uninformed traders causes the
volatility, a trading halt that stops their trading may be beneficial. The ben-
efits come from protecting the market from their volatility-inducing trades
and from protecting uninformed traders from trading losses that they can
incur in poorly functioning markets.
Trading halts also can give informed traders an opportunity to enter and
provide liquidity. Traders who are willing to supply liquidity may not be able
to do so if the market is moving quickly and they are not paying attention.
Markets therefore may attenuate volatility by halting trading to publicize
order imbalances.
A trading halt rule may make order-driven markets more liquid by
switching the trade pricing rule from the discriminatory pricing rule used
in continuous trading to the uniform pricing rule used in the single price
auctions that resume trading. In a continuous auction, standing limit orders
typically trade at their limit prices. When price is dropping quickly, buyers
with standing limit orders suffer immediate losses when their orders exe-
cute and prices continue to fall. A trade halt can prevent these losses by


## Page 586

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 573
Coordinated Trading Halt Rules
Following the 1987 stock market crash, U.S. stock and index futures
exchanges adopted a set of coordinated trading halt rules. These rules halt
trading for various periods, based on how much the market has dropped
and when it has dropped. The New York Stock Exchange version of this
rule is known as Rule 80B.
The current version of these rules calls for a Level 1 halt if the Dow Jones
Industrial Average drops by more than approximately 10 percent from its
closing value on the previous day. In that case, the markets will halt for one
hour if the event occurs before 2:30
P.M.,
and for half an hour if the event
occurs after 2:30
P.M.
but before 3:30
P.M.
If the event occurs after 3:30,
and there is no Level 2 halt, the market will not halt.
A Level 2 halt will occur if the DJIA drops by more than 20 percent from
its previous closing value. In that case, the markets will halt for two hours if
the drop occurs before 1
P.M.
and for one hour if it occurs between 1
P.M.
and 2
P.M.
If the 20 percent drop occurs after 2
P.M.
and there is no Level 3
halt, the market will not halt.
A Level 3 halt will close trading for the rest of the day at any time the
Dow drops by more than 30 percent from its previous closing value.
These coordinated trading halt rules have halted trading only once. On
October 27, 1997, the DJIA had dropped 350 points, or 4.5 percent, by
2:35
P.M.
The then current version of these rules halted trading for 30
minutes. When trading resumed, the DjiA dropped 200 more points by
3:30
P.M.,
which halted trading for the day. Many people felt that these
halts occurred too quickly. The various exchanges therefore amended their
coordinated halt rules in early 1998 to provide for halts at 10, 20, and 30
percent thresholds, as described above.
0,
Source: NYSE Rule 80B at www.nyse.com/pdfs/Im9815.pdf.

forcing the market to clear at a single price when it resumes trading. All
limit order buyers who participate in the single price auction receive the
same price, regardless of their limit order prices. When protected by an ef-
fective trading halt rule, limit order traders therefore may be more willing
to offer liquidity under normal circumstances. A trading halt rule therefore
can decrease transitory volatility by encouraging traders to offer more
liquidity.
Finally, a trading halt rule may decrease transitory volatility by allowing
traders greater time to respond to intraday margin calls and to remove stop
loss orders. When traders are unable to satisfy their margin calls, brokers
will trade to stop their losses. Since stop loss orders further imbalance an
uninformed order flow, a halt that reduces their numbers may reduce tran-
sitory volatility.
Arguments Against
Opponents of trading halts and price limits offer two arguments that sug-
gest these circuit breakers may actually increase transitory volatility. First, if
traders fear that a halt will occur before they can submit their orders, they
may submit their orders earlier to increase the probability that they execute.
Greater volatility therefore will result as the price limit attracts orders from
rationally fearful traders. Economists and traders call this effect the
gravi-
tational effect.


## Page 587

574 • TRADING AND EXCHANGES
Am I Bankrupt?
Suppose that Spencer has a 1 million-dollar long futures position for which
he posted a 100,000-dollar margin. For convenience, assume that the
contract is denominated so that each point in price is worth 10,000 dollars
to Spencer. Spencer has 100,000 dollars in other assets in addition to his
margin. He therefore knows that he will be bankrupt if the price drops by
20 points.
If the futures price were to drop immediately by 25 points, he should
lose everything. In practice, he would immediately lose only his 100,000-
dollar margin. His broker would then try to collect the additional 150,000
dollars. Although Spencer would be able to pay 100,000 dollars, he
probably would be reluctant to pay his broker anything. The broker might
be able to collect Spencer's debt only at great expense. If Spencer can hide
his remaining wealth, the broker might not even collect Spencer's remaining
100,000 dollars. In that case, the broker would lose 150,000 dollars plus
collection expenses.
Now suppose instead that the futures exchange limits the price drop to
five points per day. On the first day, the contract would close limit down
five points. Spencer's broker would collect 50,000 dollars from Spencer's
100,000-dollar margin. He also would ask Spencer to post an additional
50,000 dollars to cover his losses. If Spencer does not realize that the total
drop will ultimately be 25 points, he might voluntarily post the margin.
The next day, the contract would again close limit down five points.
Spencer's broker would again ask him to post additional margin to cover
his losses. If Spencer still does not realize that the price will ultimately drop
15 more points, he might make the margin call with his last 50,000 dollars
and hope that price rebounds when trading resumes.
On the third, fourth, and fifth days, the contract would again drop by
the limit. Spencer's broker would ask for more margin, but Spencer would
not be able to cover his losses. The broker would then deduct the third- and
fourth-day losses from the 100,000-dollar margin. Since Spencer could not
meet the margin calls, the broker would close the position when the market
restarted, trading 25 points down. Spencer then would owe the broker
50,000 dollars that he does not have
,
t
If
the gravitational effect is strong, a discretionary halt procedure may be
better than a price triggered trading halt rule because the former is less pre-
dictable. For example, the New York Stock Exchange allows floor officials
to halt trading if, in their opinion, trading would otherwise become disor-
derly. They usually do this when an order imbalance exists or when they
know that a company will soon release material information. Since off-floor
traders generally cannot predict when floor officials will halt trading, this
discretionary trading halt rule has little gravitational effect.
Second, value traders may reduce their surveillance of the market if they
know that the media will notify them when a trading halt occurs. If so, a
trading halt rule would make the market less liquid between trading halts.
Transitory volatility would increase and more trading halts would occur.
Other Issues
When several markets trade essentially the same risk, the imposition of a
trading halt or a price limit on one market will have consequences for the
other markets. In particular, when only one market halts trading, traders
will divert order flow to other markets. If circuit breakers are desirable, ex-
changes should coordinate their regulations. Otherwise, uncoordinated cir-


## Page 588

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 575
cuit breakers may increase volatility by forcing traders to resolve all excess
demands for liquidity in a single market.
Trading halts and price limit rules also benefit the markets by making it
easier for brokers and clearinghouses to collect margins. Traders who know
that they are bankrupt generally do not make margin payments. Traders
who are uncertain about being bankrupt may make margin payments to
avoid losing their positions. Trading halts make it difficult for traders who
are near bankruptcy to determine whether they are bankrupt because the
halts prevent traders from knowing at what prices trading will ultimately
resume. Markets that have trading halt rules therefore can have lower mar-
gin requirements.
28.3.2 Increased Margin Requirements
and Transaction Taxes
Throughout history, many people have believed that excessive trading in-
creases volatility. Some commentators therefore have proposed that regula-
tors increase margins or impose transaction taxes to reduce trading in stocks,
futures, and options.
Margins and transaction taxes have related but slightly different effects
on trading. Large margins decrease position sizes by increasing carrying costs
and by preventing capital-constrained traders from acquiring large positions.
Transaction taxes reduce trading by making it more expensive. Taxes pe-
nalize high frequency trading strategies more than buy and hold strategies.
To evaluate the merits of these proposals, we must review why people
trade. Since people trade for many reasons, we must consider several types
of traders.
Restrictions on informed traders would make prices less informative.
News traders cause prices to reflect the latest fundamental news. Restric-
tions on their trading slow fundamental price changes. Value traders cause
prices to move back toward fundamental values when uninformed traders
push prices away. Since value traders ultimately offer liquidity to uninformed
traders, and since they make markets resilient to the price impacts of unin-
formed traders, restrictions on their trading would increase transitory price
volatility.
Dealers offer liquidity to other traders. Any measures that increase their
transaction costs will decrease market liquidity and increase transitory
volatility.
Order anticipators trade in front of other traders. When they front-run
uninformed traders, they increase transitory volatility. When they front-
run informed traders, they may make prices more informative in the short
run. In the long run, however, the additional transaction costs that they
impose upon informed traders are the same as a tax on informed trading.
Measures that would decrease trading by order anticipators therefore would
decrease transitory volatility and make prices more informative.
Bluffers manipulate prices to trick uninformed traders into offering liq-
uidity foolishly. Since their price manipulations are not related to funda-
mental information, they contribute to transitory volatility. Regulations that
increase their trading costs would decrease transitory volatility.
Utilitarian traders use markets to help them solve problems that they
face. In particular, investors and borrowers use markets to move money
through time, hedgers use them to exchange risks, and asset exchangers use


## Page 589

576 • TRADING AND EXCHANGES
them to obtain instruments that are of greater value to them than the in-
struments that they tender. Since utilitarian traders are uninformed traders,
their trading generally increases transitory volatility. Restrictions on their
trading therefore would decrease transitory volatility in the short run. Such
restrictions would be highly undesirable, however, because they would ob-
struct utilitarian traders from solving important problems that they face.
Since good solutions to their problems benefit the economy as a whole, re-
stricting their trading would be foolish.
Gamblers trade for entertainment. Many gamblers, however, do not re-
alize that they are gambling. Instead, they generally believe that they
are well-informed traders. (If they traded successfully, they would indeed be
well-informed traders.) In practice, gamblers usually trade on stale infor-
mation that prices already reflect. Gamblers therefore increase transitory
volatility. Measures that would discourage them from trading would de-
crease transitory volatility.
These discussions suggest that the markets would be best served if reg-
ulators could restrict trading by order anticipators, bluffers, and gamblers
without unduly affecting trading by dealers, informed traders, and the util-
itarian traders, for whom the markets ultimately exist. It is difficult, how-
ever, to imagine how regulators could design regulations that would effec-
tively discriminate among these types of traders.
Taxes on trading fall more heavily on high frequency traders than on low
frequency traders. Since most utilitarian trading problems do not require
many trades to solve, a transaction tax would have less effect on utilitarian
traders than on gamblers, bluffers, and order anticipators, who may trade fre-
quently. A transaction tax, however, would fall disproportionately on dealers,
who trade quite frequently. Although it might be possible to exempt dealers
from a transaction tax, fair and meaningful regulatory distinctions between
dealers and other high frequency traders can be difficult to establish.
Removing uninformed traders from markets may actually increase tran-
sitory volatility in the long run. Informed traders can profit only when un-
informed traders trade and lose to them on average. Without these profits,
informed traders would not invest in the research necessary to estimate val-
ues, and prices therefore would be less informative. In the long run, a de-
crease in the number of uninformed traders may actually increase transitory
volatility by discouraging traders from doing the research necessary to iden-
tify fundamental values, and thereby discriminate between transitory volatil-
ity and fundamental volatility.
28.3.3 Trading Collars
Trading collars are rules that explicitly prevent traders from trading under
certain conditions. The only significant trading collar of which I am aware
is NYSE Rule 80A.
28.3.4 Circuit Breaker Summary
The theoretical effects of circuit breakers on volatility are mixed. We can
confidently say that circuit breakers slow price changes associated with
changes in fundamental information. Their effects on transitory volatility are
less certain. They decrease transitory volatility when they restrain the trad-
ing of the traders who most contribute to it. These traders are typically un-
informed traders, order anticipators, and bluffers. They increase transitory


## Page 590

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 577
0
,
NYSE Rule 80A
NYSE Rule 80A prevents index arbitrageurs from using market orders to
trade their index arbitrage program trades in S&P 500 Index stocks after
the Dow Jones Industrial Average has moved up or down by approximately
2 percent. Instead, when the collar is in effect, arbitrageurs must use tick
sensitive orders to trade S&P 500 stocks.
The primary effect of Rule 80A is to make index arbitrage more difficult
and expensive when the collar is active. It probably increases transitory
volatility because it forces the cash and futures markets to operate more
independently. When arbitrage is unrestricted, arbitrageurs move liquidity
from the market where it is in greatest demand to the market where it is in
greatest supply. Their efforts reduce transitory volatility because order flow
imbalances in the two markets sometimes cancel. When the collar restricts
arbitrage, each market must separately satisfy demands for liquidity. The
collar therefore discourages arbitrageurs who try to cross a buy order
placed in one market with a sell order placed in another market.
In practice, index arbitrageurs can subvert the collar in several ways.
They can submit their program trades through floor brokers, they can submit
fewer than 15 orders through SuperDot to avoid classification as a program
trade, and they can construct their hedge portfolios using stocks that are not
in the S&P 500 Index. These methods all increase arbitrage costs and risks,
but not appreciably so. The collar therefore probably does not have much
effect on the markets.
Proponents of the collar argue that it decreases volatility at the NYSE by
preventing the more volatile futures market from transmitting volatility to the
cash market. Although this is undoubtedly true, the argument fails to
distinguish between fundamental and transitory volatility. The futures market
is more volatile than the cash market over short time intervals because it is
better organized to discover fundamental index value quickly. Unlike cash
market traders, futures traders are not concerned about price continuity or
firm-specific risks in the markets for 500 securities.
volatility when they restrain the trading of the traders whose trading nor-
mally limits transitory volatility. These traders include informed traders, deal-
ers, and arbitrageurs. Since we do not know which effect is larger, the the-
oretical net effect of circuit breakers on transitory volatility is indeterminate.
Some circuit breakers may increase transitory volatility while others de-
crease it. For example, the NYSE Rule 80A collars probably slightly in-
crease transitory volatility because they primarily restrict the trading of ar-
bitrageurs while having almost no effect on uninformed traders. Conversely,
trading halts may decrease transitory volatility because they primarily pro-
tect liquidity suppliers while frustrating uninformed traders.
Economists have conducted numerous empirical studies to determine
what effect circuit breakers have on volatility. These studies generally have
been inconclusive. Extreme volatility events have not occurred often enough
for investigators to confidently evaluate how well circuit breakers perform.
28.3.5 Other Responses to Extreme Volatility
Regulators and market organizers have been quick to respond when excess
volatility appears to have been related, at least in part, to deficiencies in trad-
ing systems. For example, following the 1987 stock market crash, the NYSE
invested billions of dollars in its information infrastructure to ensure that it
would not experience capacity problems again.


## Page 591

578 • TRADING AND EXCHANGES
The Nasdaq market likewise improved the Small Order Execution Sys-
tem (SOES) to ensure that its dealers would always be available to handle
orders. Before the 1987 crash, dealer participation in the SOES system was
voluntary. The system therefore was not well used because dealers did not
want to accept the trade liabilities it imposed upon them. Following the
crash, Nasdaq made participation in SOES—now SuperSoes—mandatory.
Nasdaq market makers who withdraw from SuperSoes in the middle of a
trading day must wait 20 trading days until they can resume dealing in that
stock.
Markets also have introduced new products in response to extreme
volatility. The most notable of these have been various long-dated options
that entrepreneurs created to meet the needs of portfolio insurers. These se-
curities have not been very successful because portfolio managers apparently
will not buy insurance when they can easily observe its high cost.
28.4 THE POLITICS OF REGULATORY
INTERVENTION
The clamor for regulatory responses to crashes undoubtedly influences the
policies that regulators ultimately adopt. When regulators and traders un-
derstand the political aspects of regulation well, better regulations probably
result. This section therefore briefly discusses the political economy of
regulation.
We consider two models of regulation. The first involves regulatory risk,
and the second involves rent seeking. The first model helps us understand
why regulators may regulate even when their regulations will produce little
or no economic value. The second model helps us understand how regulated
people and institutions can use the regulatory process for their private ben-
efit. We illustrate these discussions with two regulations that came out of the
1987 stock market crash: New York Stock Exchange Rules 80A and 80B.
28.4.1 Regulatory Risk
Following the October 1987 stock market crash, the markets adopted a co-
ordinated trading halt rule that was almost meaningless. At the time of its
adoption, NYSE Rule 80B would halt trading only if the Dow dropped by
more than 12 percent. Such a large drop had occurred only once in the life
of the U.S. markets (during the October 1987 crash). Why did the markets
adopt this mild trading halt rule?
In the days and weeks after the 1987 crash, many people demanded that
regulators act to prevent future crashes. Fewer people considered whether
regulators could prevent crashes or whether their efforts to do so would im-
pose serious costs upon the market. In this environment, we can easily imag-
ine that government and exchange regulators reasoned as follows:
•
If we fail to adopt any circuit breakers and another crash occurs, the
public will hold us responsible for failing to protect them, regardless
of whether the circuit breakers would—or even could—have made a
difference.
•
If we fail to adopt any circuit breakers, and no crash occurs, we will
have saved whatever costs the circuit breakers might impose upon the
markets, but nobody will credit us with our wisdom.


## Page 592

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 579
TABLE 28-1.
Perceived Costs and Benefits to Regulators of Their Decision to Adopt Circuit Breakers
REGULATORY ACTION
SUBSEQUENT EVENTS

ADOPT CIRCUIT BREAKERS

DON'T ADOPT CIRCUIT BREAKERS
Another crash follows

No cost

High cost
No crash follows

Some benefit

No cost
•
If we adopt circuit breakers and another crash occurs, people will
learn that we cannot prevent crashes, but they will not blame us for
not trying.
•
If we adopt circuit breakers, and no crash occurs, people may credit us
with preventing another crash, even if the circuit breakers are not ef-
fective. Although we will be responsible for imposing potentially costly
and unnecessary restrictions on the market, people may not recognize
these costs, and they probably will not attribute them to us.
Table 28-1 summarizes these costs and benefits. If regulators act to max-
imize their personal welfare, these costs and benefits imply that they would
be better off adopting circuit breakers, whether or not another crash occurs.
Consider now the decision to adopt mild or severe circuit breakers. Mild
circuit breakers are unlikely to change trading practices significantly, whereas
severe circuit breakers will alter the character of trading. Regulators may
have reasoned as follows:
•
If we adopt mild circuit breakers and another crash follows, people may
blame us for not acting more strongly. They may reserve their judg-
ment, however, because many may conclude from the second crash that
regulators cannot prevent crashes.
•
If we adopt severe circuit breakers and another crash follows, most peo-
ple will conclude that regulators cannot prevent crashes. Some, how-
ever, will claim that had the severe circuit breakers not been in place,
the unconstrained market would have been able to handle the crisis.
People may blame us for causing the crash.
•
If we adopt mild circuit breakers, and no crash follows, we will have
had little effect on the markets.
•
If we adopt severe circuit breakers, and no crash follows, people will
blame us for overreacting.
Table 28-2 summarizes these costs and benefits of the decision to adopt
mild or severe circuit breakers. These assumptions imply that regulators
would be better off if they adopt mild circuit breakers, whether or not an-
other crash occurs.
The conclusions in these simple analyses obviously depend on the costs
and benefits that I assumed. If you make other assumptions, you may obtain
different conclusions, or you may be unable to obtain any conclusions with-
out making additional assumptions about the probabilities of future crashes.
These assumptions seem reasonable to me, but you may think otherwise.
Et Odd/Even Gas
Rationing
The imposition of odd/even
gas rationing during the
1974 oil crisis is an extreme
example of an ineffective
regulatory action. Following
the October 1973 war
between Israel and most of its
Arab neighbors, many Arab
states embargoed exports of
oil to the United States. The
embargo would have raised
gasoline prices in the United
States if they had not been
regulated. Instead, long lines
formed at gas stations as
consumers tried to purchase
scarce gasoline.
Rather than raise the price
of gas to ration it, the
government imposed
odd/even rationing. Motorists
could buy gas only on odd-
numbered days for cars with
odd-numbered license plates
and only on even-numbered
days for cars with even-
numbered license plates. This
rationing scheme had little
effect on gas lines because
almost no drivers buy gas
everyday. It did prevent
panicked drivers from filling
up every day, however.


## Page 593

580 • TRADING AND EXCHANGES
TABLE 28-2.
Perceived Costs and Benefits to Regulators of Their Decision to Adopt Mild or Severe Circuit Breakers
REGULATORY ACTION
SUBSEQUENT EVENTS
ADOPT MILD CIRCUIT BREAKERS

ADOPT SEVERE CIRCUIT BREAKERS
Another crash follows

Some cost

Possibly high cost
No crash follows

No cost

High cost
The conclusions are consistent with well-recognized regulatory behav-
ior. Regulators often respond to crises with policies that have little or no
actual impact. When the public demands action, regulators tend to act.
However, when faced with uncertainty about the consequences of regula-
tory actions, regulators frequently do not act decisively. They do not want
the public to blame them for exacerbating a problem or for creating new
problems. When regulators do act decisively, they usually have a strong po-
litical mandate based on a clearly defined ideology or unambiguous empir-
ical results. (Consider, for example, deregulation in the Reagan years or the
ban on the birth defect-causing sedative Thalidomide.) No such mandate
for extreme change followed the 1987 crash.
28.4.2 Regulatory Capture
The other circuit breaker adopted by the New York Stock Exchange fol-
lowing the 1987 stock market crash was the Rule 80A collar on index ar-
bitrage program trading. Although a similar analysis of regulatory risks may
explain why the Exchange adopted this rule, an analysis of rent-seeking be-
havior (self-interest) is more insightful.
Arbitrageurs compete with specialists to supply liquidity. Both traders
move liquidity between buyers and sellers who are unable or unwilling to
trade with each other. Arbitrageurs construct hedge portfolios to move liq-
uidity from one market to another at one point in time. Specialists use their
inventories to move liquidity from one point in time to another within their
market. The two types of traders compete in the following sense: When ei-
ther trader cannot act, the profit opportunities for the other are greater.
Since Rule 80A restricts index arbitrage, it benefits specialists at the expense
of arbitrageurs.
Specialists also benefit from Rule 80A because it helps them avoid trad-
ing with arbitrageurs. When a specialist provides liquidity to an arbitrageur,
the specialist is often on the wrong side of the market. For example, if the
futures market is falling, and the cash market is lagging behind, arbitrageurs
will be selling to specialists. The specialists will be buying stock at prices
that very likely are too high, and will thereby lose money. These arguments
suggest that specialists had a strong interest in adopting Rule 80A.
The Exchange adopted the rule, at least in part, because specialists have
more power at the NYSE than do arbitrageurs. If power were based only
on relative numbers, specialists certainly would dominate over arbitrageurs.
Far more members of the NYSE conduct specialist operations than arbi-
trage operations. In November 1997, 470 of the 1366 members of the Ex-
change were full-time specialists. In contrast, only 20 members then con-
ducted 95 percent of the program trades. These numbers were similar in


## Page 594

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 581
1988. Although this comparison is overwhelming, it is not necessarily con-
clusive. Many arbitrageurs are very large wirehouses whose influence at the
Exchange may be disproportionate to their numbers.
Two other arguments also support the conclusion. First, the loyalties of
independent floor broker members lie primarily with specialists, with whom
they regularly deal, rather than with wirehouses, with which they compete.
Second, the interests of the senior management of the exchange are more
likely aligned with specialists than with arbitrageurs because specialist trad-
ing exclusively benefits the NYSE, whereas arbitrage trading also benefits
competing futures markets. These arguments suggest that specialists have
more political power than do arbitrageurs at the NYSE. We therefore should
not be surprised that the NYSE adopted Rule 80A.
This analysis helps explain two significant differences between the Rule
80A collars and the Rule 80B trading halts adopted by the NYSE. First,
Rule 80A is triggered by much smaller price changes than those triggering
trading halts. Until the Exchange amended the rule in 1999, the collars were
imposed almost every trading day in the years prior to the amendment. Sec-
ond, Rule 80A applies both to price decreases and to price increases, as op-
posed to just price decreases. These differences ensure that Rule 80A is trig-
gered much more often than Rule 80B. The difference should not surprise
us, given the specialists' interest in restricting arbitrage trading.
28.5 SUMMARY
Extreme volatility is quite scary. Large price changes can quickly create, de-
stroy, or transfer enormous wealth. Although people do not like extreme
price changes, they can better accept them when they are due to funda-
mental valuation factors than when they are due to human folly. Rarely,
however, is news about fundamental values so important and so surprising
that it can explain dramatic price changes. Large price changes therefore
often are the result of mistakes that people make. People pay close atten-
tion to these events because they do not want to repeat their mistakes.
Perhaps the most common mistake that traders make is to overvalue as-
sets. They make this mistake when they are overly optimistic about future
prospects, or when they do not fully appreciate risks. If enough traders share
their enthusiasm, they push asset prices beyond fundamental values. Al-
though well-informed value traders may recognize the problem, they may
be unwilling or unable to trade in sufficient quantities to prevent a bubble
from forming. The bubble pops when traders lose confidence.
Most regulatory initiatives associated with extreme volatility come too
late. They also tend to address problems that have more to do with crashes
than with the formation of the bubbles that ultimately lead to crashes. Trad-
ing halts and price limits, for example, at best only ensure that extreme price
changes occur in an orderly fashion. They mainly attenuate volatility only
to the extent that they prevent traders from overreacting.
The best way to prevent the formation of bubbles is to empower value
traders who can recognize and trade against bubbles. Value traders are most
willing to trade when they are confident that they understand values well.
Regulators therefore should direct their policies toward lowering the costs
of obtaining high quality information that value traders need to form reli-
able opinions about fundamental values.


## Page 595

582 • TRADING AND EXCHANGES
Regulators also should try to remove any impediments to short selling
that value traders may face. In particular, U.S. regulators should consider
eliminating the short-sale rule that requires short sellers to sell stock on an
uptick. They also should consider policies that would make it easier for short
sellers to obtain short interest on the proceeds of their sales.
28.6 SOME POINTS TO REMEMBER
•
The most common mistake traders make is to identify a good com-
pany as a good investment.
•
Momentum traders are especially susceptible to losing in bubbles and
crashes.
•
Traders should not speculate if they cannot value the instruments that
they trade.
•
The portfolio insurance trading strategy is destabilizing when many
traders use it, and when traders are uncertain about the total funds
under portfolio insurance management.
•
Portfolio insurance does not reduce fundamental equity risk. It merely
attempts to transfer risk among traders.
•
Trade halts and price limits protect liquidity suppliers when prices
change quickly. These rules change the trade pricing rule from dis-
criminatory pricing to uniform pricing.
•
Traders can panic when they are uncertain about risk, and when un-
usual situations confront them with new trading problems that require
quick solutions.
•
Trade halts and price limits can stop a panic by giving traders time to
obtain and analyze more information.
•
Price limits can protect the clearing system by increasing the margin
payments that bankrupt traders pay before they default.
•
The gravitational effect associated with trade halts and price limits can
increase volatility if traders fear that they will not be able to complete
their trades before trading stops.
•
After a crisis, regulators often adopt regulations that have little eco-
nomic value simply to respond to public demands for action.
28.7 QUESTIONS FOR THOUGHT
1.
What role do pseudo-informed traders have in the formation of
bubbles?
2.
What effect do circuit breakers have on public welfare?
3.
Was the 1987 stock market crash a problem or a correction to a
problem?
4.
How can a stock market crash cause a recession?
5.
How might the normal correlation among security prices affect prices
when the price of one security falls dramatically?
6.
Do price correlations rise or fall during extreme volatility events?
7.
The 1987 stock market crash showed that portfolio insurers grossly
underestimated their trading costs. Why did they make this mistake?
8.
Should the government try to prevent traders from trading foolishly?
How can it do so?


## Page 596

CHAPTER 28 BUBBLES, CRASHES, AND CIRCUIT BREAKERS • 583
9.
How can the government help investors better understand their trad-
ing problems?
10.
To what extent, if any, would the susceptibility of a market to crashes
and bubbles depend on whether it uses a floor-based trading system
versus a screen-based trading system? Would the use of a quote-
driven trading system versus an order-driven trading system make
any difference?
11.
The SEC, the CFTC, and the various exchanges adopted some cir-
cuit breakers following the 1987 stock market crash. Despite many
calls for higher margins, the Federal Reserve Board, which is re-
sponsible for setting margins for securities borrowing and lending,
left margins unchanged. Why did the Fed not act when the other
agencies did?
12.
In the United States, the margins required to obtain equivalent-sized
risk exposure in equities, equity options, and equity futures are quite
unequal. Should margin rates be standardized across similar instru-
ments? What accounts for the differences in margin rates in these
markets?
13.
During crashes, volume often rises to very extremely high levels. How
much excess capacity should markets design into their systems to en-
sure that they are not overwhelmed in a crash? How would you bal-
ance the very high costs of building system capacity with the very high
costs of failing to meet demands for that capacity which may never
occur?
14.
Can circuit breakers lower the optimal design capacity of a trading
system?
15.
Immediately following the terrorist atrocities on Tuesday morning,
September 11,2001, all trading in U.S. financial markets was halted.
Trading in bonds and in all futures but equity index futures resumed
on Thursday. Trading in equities and in equity options and futures
resumed on Monday, September 17. Was it wise to halt trading
everywhere in the United States? Should trading have resumed ear-
lier? Why did the over-the-counter bond markets resume trading
before the exchange stock markets?


## Page 597

29
I>
Insider
Trading
T
raders engage in
insider trading
when they base their trades on material
information about the value of an instrument that is not publicly avail-
able. Most insider trading involves private information that corporate man-
agers know about the prospects of their companies. Insider trading also may
involve information that traders improperly obtain from other sources.
In most countries, insider trading is illegal and punishable by fines or
imprisonment. Insider-trading laws are very difficult to enforce, however.
Only a few countries—primarily the United States, Canada, and Great
Britain—regularly and seriously attempt to enforce their insider-trading
laws.
Insider trading has many economic effects. In the financial markets, it
affects investor confidence, price efficiency, and liquidity. In the overall econ-
omy, insider trading affects the labor market for senior corporate managers,
and the quality of management decisions that these executives make.
In this chapter, we define insider trading and explain how regulators en-
force insider-trading laws. We then consider the debate over whether to re-
strict insider trading. As we debate the two sides of the issue, we will iden-
tify the effects that insider trading has on the markets and on the overall
economy.
If you trade, you must recognize insider information in order to avoid
making illegal trades. More generally, you must understand insider trading
to fully understand market liquidity. Finally, and perhaps most unexpect-
edly, you must understand the effects of insider trading on managerial la-
bor markets so as to fairly interpret comparisons of senior executive com-
pensation across countries.
29.1 INSIDE INFORMATION
AND INSIDER TRADING
Insider trading and inside information are hard to define. Both are complex
legal concepts that are subject to substantial interpretation. If you are con-
fronted with an issue that may involve insider trading, you should consult
a competent attorney.
The primary purpose of this chapter is to help you understand the eco-
nomic issues that surround insider trading. For this purpose, we define
in-
side information
as material information about the value of a security that is
not available to public traders.
Material information
is information that would
cause prices to change if it were widely known. In the equity markets, cor-
porate managers control most inside information.
In jurisdictions that prohibit insider trading, nobody can trade on inside
information until after the information is publicly available. In particular,
corporate managers cannot trade on the information nor can their friends
nor can the friends of their friends. Inside information generally retains its
584


## Page 598

CHAPTER 29 INSIDER TRADING • 585
An SEC Definition of Insider Trading
Statutory laws, the government regulations that implement them, and the
case law created by successful and unsuccessful attempts to prosecute inside
traders define insider trading. As a public service, the U.S. Securities and
Exchange Commission provides a one•paragraph summary definition of
insider trading on its Web page:
"Insider trading" refers generally to buying or selling a security, in
breach of a fiduciary duty or other relationship of trust and confidence,
while in possession of material, nonpublic information about the security.
Insider trading violations may also include "tipping" such information,
securities trading by the person "tipped" and securities trading by those
who misappropriate such information. Examples of insider trading cases
that have been brought by the Commission are cases against: corporate
officers, directors, and employees who traded the corporation's securities
after learning of significant, confidential corporate developments; friends,
business associates, family members, and other "tippees" of such
officers, directors, and employees, who traded the securities after
receiving such information; employees of law, banking, brokerage and
printing firms who were given such information in order to provide
services to the corporation whose securities they traded; government
employees who learned of such information because of their employment
by the government; and other persons who misappropriated, and took
advantage of, confidential information from their employers.
This summary nicely illustrates the complexity of the law on insider
trading.
Source: www.sec.gov/divisions/enforce/insider.htm,
January 4, 2002.
status regardless of how many people have passed it. You may not trade on
a stock tip that you receive from your barber, who received it from another
client, who received it from a corporate insider, if the tip is based on inside
information. Inside information loses its special status only when it becomes
available to the public. After a firm releases information to the public
through a broadly distributed press release or a public filing, the informa-
tion is publicly available.
Sometimes, information that traders do not obtain directly from man-
agement is inside information. For example, suppose that a financial printer
prints a prospectus for a takeover offer. While operating the presses, a press-
man reads the copy and calls a friend to tell him to buy the target. The
friend will be trading on insider information.
Managers must control the dissemination of material information. In
particular, they must either keep it secret or distribute it widely. When they
distribute confidential information to business associates, they must execute
confidentiality agreements. If managers distribute inside information to their
friends, who then trade upon it, the managers risk prosecution.
Many analysts produce valuable private information by carefully analyz-
ing publicly available information. If they base their analyses only on in-
formation that is available to the public, the private information that they
produce is not insider information.
In general, traders may not trade on information that they obtain un-
fairly. In particular, traders may not profit from misappropriating (stealing)
information. For example, traders who know the contents of an economic
Texas Gulf Sulfur
In late 1963, Texas Gulf
Sulfur discovered very
valuable deposits of copper,
zinc, and silver in Ontario.
Between November 12,
1962, and April 16, 1964,
officers, directors, employees,
and their friends bought Texas
Gulf Sulfur stock and call
options.
During
this period,
the stock price rose from
17
3
/8 to 29
3
/8 dollars.
The company, however,
did not disclose information
about the find until April 12,
1964. On that date, it merely
revealed that its drilling had
"not been conclusive" and
that "the rumors about the
discovery were unreliable . .
premature and possibly
misleading." Four days later,
on April 16, the company
announced a major ore
discovery. Following the
announcement, the stock price
rose to 71 dollars.
The Securities and
Exchange Commission sued
various directors, managers,
and employees of Texas Gulf
Sulfur, alleging insider trading
and deliberate efforts to
mislead the public. The suit
was successful.
Source: Facts paraphrased from
Jie Hu and Thomas H. Noe, "The
Insider Trading Debate,"
Federal
Reserve Bank of Atlanta Economic
Review,
Fourth Quarter 1997,
p. 36.


## Page 599

586 • TRADING AND EXCHANGES
Vincent Chiarella, the Printer
In 1975 and 1976, Vincent Chiarella worked as a "markup man" in the
New York composing room of Pandick Press, a financial printer. Among the
documents that he handled were five announcements of corporate takeover
bids. When these documents were delivered to the printer, the identities of
the acquiring and target corporations were concealed by blank spaces or
false names. The true names were sent to the printer on the night of the
final printing.
Chiarella, however, was able to deduce the names of the target
companies before the final printing from other information contained in the
documents. Without disclosing his knowledge, he purchased stock in the
target companies and sold the shares immediately after the takeover
attempts were made public. By this method, Chiarella realized a gain of
slightly more than 30,000 dollars in the course of 14 months. Subsequently,
the Securities and Exchange Commission began an investigation of his
trading activities. In May 1977, Chiarella entered into a consent decree
with the SEC in which he agreed to return his profits to the sellers of the
shares. On the same day, Pandick Press discharged him.
Chiarella was later convicted of 17 counts of violating section 10(b) of
the Securities Exchange Act of 1934 and SEC rule 10b-5, under the
principle that Chiarella owed a responsibility to the sellers to disclose his
information. In 1980, the U.S. Supreme Court reversed the conviction
because he had no fiduciary duty to the acquiring or target firms.
The law has subsequently changed. If Chiarella were brought to trial
now, he would be convicted of insider trading because he misappropriated
information.
Source:
The
first two paragraphs are taken almost verbatim from section I of the
Supreme Court decision in
Chiarella v. United States, 445
U.S. 222 (1980). The
decision appears at caselaw.lp.findlaw.com/scripts/getcase.pl?court=us&vol=

445&invol
=222.
A Noble Cause and a Base Explanation
In the fall of 2000, the U.S. Securities and Exchange Commission adopted
Regulation FD. This regulation requires that "whenever an issuer . . .
discloses any material nonpublic information regarding that issuer or its
securities to any person . . , the issuer shall make public disclosure of that
information . . . simultaneously, in the case of an intentional disclosure; and
promptly, in the case of a non-intentional disclosure."
Before the adoption of Regulation FD, corporations would frequently tell
their analysts material information before they reported it to the public. The
analysts, their clients, or both would then trade on this information.
Not surprisingly, analysts adamantly opposed the new regulation. They
claimed that it would make security prices less informative. In particular,
they argued that their reports would be much less informative if they could
not privately interview management and ask them probing questions.
Without this privilege, they claimed, they would not be able to discover
incompetent or dishonest management.
They undoubtedly also opposed the new regulation because it eliminated
a valuable privilege that they formerly enjoyed. /
Source: The complete text of Regulation FD appears
at
www.lawuc.edu/CCL/regFD/

index.html.


## Page 600

CHAPTER 29 INSIDER TRADING • 587
So Heard on the Street
In 1982 to 1984, R. Foster Winans was one of two regular authors of the
Wall Street Journal
column "Heard on the Street." This popular column
regularly reported news, analyses, perspectives, and rumors about
companies. Although much of the material was public information, the
prices of stocks featured in the column often moved following the
publication of the column.
From October 1983 through March 1984, Winans conspired with
Kidder, Peabody and Co. stockbrokers Peter N. Brant and Kenneth P. Felis
to provide them with information about the content of his upcoming "Heard
on the Street" columns. They made 600,000 dollars on the information.
Winans was paid 31,000 dollars for his part in the scheme. An observant
investigator at the SEC identified the pattern of their trades. Two others
were involved in the conspiracy.
In 1985 Winans was convicted of 59 counts of securities fraud and
conspiracy, even though he claimed that his columns did not contain inside
information about the corporations discussed in his column. However, he
did violate the
Wall
Street Journal's
confidentiality policy. The legal doctrine
under which he was convicted is called the
misappropriation doctrine.
Brant
and Felis also were convicted of fraud and conspiracy.
Winans was sentenced to 18 months in federal prison and fined 10,000
dollars. Felis was sentenced to six months and fined 10,000 dollars. Brant,
who cooperated with the prosecution, was sentenced to eight months and
fined 10,000 dollars. Both Felis and Winans were permitted to serve their
time over consecutive weekends. All three disgorged their profits to the
government.
report that the government will soon release may not trade on such infor-
mation until the government releases the report. Likewise, traders who im-
properly obtain a report written by a widely read analyst may not trade on
the information in that report until the analyst publishes it.
29.1.1 Trading by Insiders
Corporate insiders
are senior managers, corporate directors, large sharehold-
ers, and key employees. Insiders generally know material information about
a firm that is not publicly available. Insiders also include anyone involved
with corporate activities that will materially affect the value of the firm.
Insiders may trade stock in their companies, but insider-trading laws pre-
vent them from trading on nonpublic material information. How managers
could make trading decisions that are not influenced by confidential infor-
mation in their possession is hard to imagine.
In the United States, insiders must report their insider trades to the Se-
curities and Exchange Commission. They must report their initial purchases
within ten days of making the trade. They must report any subsequent pur-
chases or sales within the first ten days of the month following these trades.
To prevent insiders from resigning their positions in order to trade on in-
side information, traders must report their trades for six months after they
lose their insider status. Many countries have similar requirements.
Studies of insider trades generally indicate that insiders are well informed
about the long-term prospects for their firms. Accordingly, many traders
closely follow insider-trading reports.


## Page 601

588 • TRADING AND EXCHANGES
Because managers often know nonpublic material information, all their
trading could be construed to be insider trading. To permit insiders to trade,
the U.S. Securities and Exchange Commission allows insiders to avoid pros-
ecution by establishing prearranged trading programs. Although insiders
cannot establish these programs because they want to profit from nonpub-
lic material information, these programs allow them to trade when they
know such information. Interestingly, insiders do not have to disclose these
plans to the public.
Some insider-trading laws restrict insiders from profiting from short-term
price swings in their stocks. In the United States, the law also requires in-
siders to return to issuers any profits they make on positions they open and
close within six months. These profits are known as
short-swing
profits.
Most insider-trading laws also restrict insiders from taking short posi-
tions in the stocks of their employers. These restrictions ensure that insid-
ers do not benefit when the firms they manage and oversee perform poorly.
Some corporations execute agreements with their employees that re-
strict their trading. These agreements often require that employees obtain
corporate approval before they trade. If the proposed trade would occur be-
fore the corporation releases information, the corporation may not allow
the trade.
Some firms allow their employees to trade only during specific intervals.
The
trading window
is usually for one or more weeks following the week
of their quarterly earnings announcement. Firms design these rules to re-
duce the probability or perception that their managers trade on material in-
formation that is not available to the public.
29.2 PRACTICAL JUDICIAL ISSUES
Enforcing insider-trading laws is quite difficult. Insiders rarely trade on their
information themselves because it is illegal. Instead, they give their infor-
mation to confederates who trade on their behalf. Identifying who is trad-
ing on insider information therefore is often impossible.
Exchange officials identify most insider-trading cases. In the United
States, each stock exchange has a market surveillance department that mon-
itors its markets for trading irregularities. The surveillance officers look for
suspicious events—typically, large price changes on large volumes. When
they identify unusual trading, they next try to determine whether a recent
release of public information would explain it. If they cannot find an obvi-
ous explanation, they call the listed firm and ask whether it is aware of in-
formation that could have caused the price change. If the surveillance offi-
cers learn that the firm will soon release information, or if they later learn
that the firm released information soon after the unusual trading activity,
they may suspect insider trading. In general, large price changes associated
with high volumes that occur before releases of significant unexpected news
often indicate that insiders or their confederates were trading.
When surveillance officers suspect insider trading, they pass the case to
investigators for further study. The investigators may work for the exchange
or for the government. Exchange investigators generally can subpoena and
question only exchange members. Government investigators can obtain
greater subpoena powers from the courts. In the United States, the gov-
ernment agencies most responsible for enforcing the insider-trading laws


## Page 602

CHAPTER 29 INSIDER TRADING • 589
Degrees of Separation
If you do not know the president, but you know someone who does, the
president is one degree of separation from you. A popular play,
Six
Degrees of Separation,
addresses the notion that no more than six people
connect all people to each other.
Computer programs that access large databases of related names can
determine the degree of separation between you and another person.
Investigators searching for insider trading use these programs to identify
potential relationships between people who knew inside information and
people who may have benefited from such knowledge.
Many databases link individuals together. Here are a few examples of
publicly available databases:
•
Address databases identify current and former neighbors.
•
E-mail address databases identify coworkers.
•
College yearbooks identify classmates, fraternity brothers, sorority sisters,
teammates, and members of the band.
•
Databases of vital records permit the construction of family trees and the
identification of in-law relationships.
•
Databases collected by direct-order marketing firms and their consultants
classify people by their hobbies and their religions.
In
addition, governments can access confidential databases that include
armed forces service records and employment records.
are
the Securities and Exchange Commission and the Department of
Justice.
The investigators form a list of everyone who knew the insider infor-
mation and a list of everyone whose trades—in stocks, options, or bonds—
benefited from the ultimate revelation of that information. Anyone who ap-
pears on both lists may be guilty of insider trading. For obvious reasons, the
same people rarely are on both lists. To identify insider trading, the inves-
tigators must try to determine how people on the first list might know peo-
ple on the second list. Such relationships do not prove insider trading, but
they may provide clues about how information passed from an insider to a
trader.
Occasionally, investigators will find that someone engaged in highly un-
usual trading. For example, a gardener may have bought a substantial posi-
tion in a security that subsequently was taken over at a large premium. Such
a trade would be very unusual if the gardener had never traded before or if
the gardener's position represented a very large fraction of his wealth. In
that case, investigators may interview the gardener to ask him why he traded.
Such interviews often allow investigators to work backward to the source
of the information.
When the investigators are convinced that they have identified insider
trading, they turn their cases over to the Securities and Exchange Com-
mission enforcement officers for civil action and to the Department of
Justice prosecutors for criminal prosecution. These prosecutors then must
try to resolve these cases by negotiated settlement, plea bargain, or trial.
Prosecutors often will grant immunity to the last person in a chain of
informed traders in exchange for his or her testimony. These traders may
not even know that they were trading on insider information. Prosecutors
Colt Industries
On July 20, 1986, Colt
Industries announced that it
would recapitalize by
purchasing most of its
outstanding stock shares. On
the next day, the price rose
by almost 30 dollars.
Israel Grossman
apparently learned about the
recapitalization from a
colleague at his law firm with
whom he shared a secretary.
Colt's pension plan trustees
retained the law firm on July
9 to represent their interests
in the recapitalization.
Grossman, however, did not
work on the case.
Between July 11 and 18,
Grossman called 40 friends
and relatives, who together
bought 1,400 "deep out of
the money" options. Following
the announcement of the
plan, these options, which
were purchased for 38,000
dollars, became worth 1.5
million dollars.
The Philadelphia Stock
Exchange market surveillance
unit discovered their trades.
Newspaper articles credit the
Exchange surveillance
computers with discovering
the fraud. Undoubtedly,
traders who sold the options
also brought the matter to
Exchange investigators.
The Exchange investigators
solved the case by placing
pins in a map to mark the
homes of each person who
bought the options. All the
buyers lived within a few
blocks of each other in
Brooklyn.
Grossman
was convicted
of 38 counts of mail fraud
and securities fraud. He was
sentenced to two years in
prison and fined 25,000
dollars.
Source: "Insider Trading: There's
Just No Stopping It,"
Los Angeles
Times,
September 3, 1989 p. 4-1.


## Page 603

590 • TRADING AND EXCHANGES
Inside Information, Skilled Speculation, or Good Advice?
Spencer Speculator buys shares in ABC just before XYZ announces that it
will buy ABC at a substantial premium. Spencer and Charles E. Officer, the
CEO of ABC, are close confidants. What happened?
A.
Charles told Spencer about the pending transaction, so Spencer traded
on inside information.
B.
Through careful and completely independent analysis of publicly
available information, Spencer concluded that ABC would be a good
takeover target for XYZ. He bought it, and events soon proved that he
was correct.
C.
Through careful and completely independent analysis of publicly
available information, Spencer concluded that ABC would be a good
takeover target for XYZ. He bought it. He then told Charles why the
deal made sense. Charles agreed, and soon arranged the deal.
D.
Alternative A is true, but when accused of insider trading, Spencer
claims that alternative B is true.
4
Suppose that Alternative D is true. How would the government prove its case?
therefore cannot easily convict them of insider trading. By obtaining their
cooperation, prosecutors often can build stronger cases against traders who
were closer to the inside information.
Proving that a trader traded on inside information is very difficult with-
out clear evidence about how the trader obtained inside information. When
the government accuses a sophisticated speculator of insider trading, the
speculator will claim to have acquired his or her information strictly through
insightful analysis of publicly available data. If the inside information in-
volved a decision that management made as a result of some calculation,
the speculator often can credibly claim to have independently made the same
calculation. When confronted with the fact that the trades were very well
timed, the insider trader will attribute the coincidence to chance.
Detecting and prosecuting insider trading is almost impossible if the in-
formation is not revealed in a discrete event. For example, suppose that man-
agement recognizes that its firm's prospects are exceptionally good, perhaps
because some basic research has been much more successful than expected.
The firm may not report this information for months or years, however. It
does not want to give its competitors information that would allow them
to catch up or that would discourage them from pursuing less successful
lines of research. Insiders or their confederates who trade on this informa-
tion are essentially impossible to detect only from prices and volumes. The
only exception would be if many insiders all traded at the same time. If their
trading causes prices to change substantially, a subsequent investigation
might identify the inside information.
Since catching insider traders is quite difficult, the penalties for insider
trading must be severe. Rational criminals engage in crime when the ex-
pected benefit is greater than the expected cost. The expected cost of in-
sider trading equals the product of the probability of being caught times the
penalty for being caught. If the probability of being caught is low, the penalty
must be large. Otherwise, the expected cost of insider trading will be small.
The typical penalty for insider trading includes the disgorgement (re-
payment) of any profit that the trader made. In addition, the insider trader
Bounty Hunting in the
Financial Markets
To encourage people to
report insider trading, U.S.
law permits the Securities and
Exchange Commission to offer
up to 10 percent of the civil
penalty eventually collected
for insider trading as a
bounty to anyone who
provides information that
leads to the imposition of
such penalties.
Source: www.sec.gov/divisions/

en force/insiderhtm.


## Page 604

CHAPTER 29 INSIDER TRADING • 591
often must pay a fine. In rare cases, traders go to jail. The penalties usually
are greater for traders who are closest to the source of the inside informa-
tion. Traders who benefited from acting on a tip without knowing its source
rarely are penalized severely. In the United States, the maximum criminal
penalties for insider trading now are ten years in prison and a
1
million-
dollar fine. The maximum civil penalty is three times the profit gained (or
loss avoided) due to the insider trading.
29.3 THE DEBATE OVER INSIDER TRADING
Efforts to restrict insider trading have been very controversial. Regulators,
attorneys, and economists have created a substantial legal and economic lit-
erature on the issue. In this section, we survey many of the arguments that
they offer for and against restricting insider trading.
29.3.1 Why Restrict Insider Trading?
People opposed to insider trading offer three main arguments in defense of
their position. They believe that effective restrictions on insider trading in-
crease investor confidence, lower transaction costs, and solve corporate con-
trol problems which arise when insiders can trade on inside information.
29.3.1.1
Fairness
The most commonly offered reason for restricting insider trading is to sat-
isfy the public's sense of fairness. That insiders can profit from access to in-
formation others do not have bothers many people. Many people favor
insider-trading rules because they believe that these rules help create fair
markets. For them, fair markets reward traders for insightful research that
anyone could do rather than for personal connections that only some peo-
ple have.
Traders avoid markets that they do not believe are fair. Restrictions
against insider trading therefore benefit the markets by increasing investor
confidence. Greater investor confidence increases the funds that investors
will invest in stocks, raises prices, and thereby lowers corporate costs of
capital.
Rebuttal
If restrictions on insider trading benefit listed firms, firms will place these
restrictions in their employee labor contracts. Since many do, there may be
no role for government regulation.
Rejoinder
Restrictions on insider trading primarily benefit firms that need to raise new
capital. To a lesser extent, they also may benefit existing shareholders by in-
creasing the value of their shares. Corporate directors, however, are unlikely
to enact and enforce restrictions that limit their ability to engage in insider
trading.
In
addition, since insider trading is difficult to detect, effective re-
strictions require the backing of the government to enforce.
29.3.1.2
Liquidity
Insider trading—like all informed trading—hurts traders who supply liq-
uidity. Insider trading consequently increases dealer bid/ask spreads and
thereby increases transaction costs for uninformed market order traders.


## Page 605

592 • TRADING AND EXCHANGES
(Chapters 13 and 14 explain how dealers and limit order traders respond to
the adverse selection due to informed traders.)
Insider trading also increases transaction costs for uninformed limit or-
der traders. Like dealers, limit order traders lose when they trade with in-
siders. When they try to trade on the same side the insiders trade on, their
orders often do not execute because they compete with insiders to fill their
orders. They thus often fail to profit when they want to trade on the side
of the market that will prove to be profitable. When insiders are trading,
uninformed limit order traders regret trading when their orders execute, and
wish that they had traded when their orders do not execute.
Effective restrictions against insider trading make markets more liquid
for uninformed traders by removing an important class of informed traders
from trading in the markets. This conclusion follows from our understand-
ing of how adverse selection affects markets. It is also a trivial implication
of accounting in a zero-sum game. When informed traders make more prof-
its, uninformed traders must lose more. Since uninformed traders are on the
wrong side of the market about as often as they are on the right side, their
losses on average must be due to transaction costs. Effective restrictions on
insider trading therefore reduce uninformed trader transaction costs.
Rebuttal: The Competition to Exploit Insider Information
Whether insider trading makes markets less liquid ultimately depends on
how the insiders trade. The argument against insider trading is most com-
pelling when only one trader knows the inside information, and the firm
will not release the information soon. This insider will strategically manage
his or her trading to obtain maximum value from the information. In prac-
tice, the trader probably will trade slowly and unobtrusively to avoid detec-
tion. Such a trader—if well capitalized—would impose the greatest costs
upon all other traders.
In contrast, suppose that many insiders learn the inside information at
the same time. Each insider must trade quickly to profit from the informa-
tion before another insider does. Those who wait will lose the opportunity
to profit as other insiders exhaust the available liquidity and push prices
closer to fundamental values. With all insiders trading at once, prices will
quickly change as dealers and other liquidity suppliers suspect that news is
in the market. In some markets, the resulting order imbalance may cause
trading to halt so that prices change on little volume. Competition among
many insiders to profit from their informational advantage therefore quickly
reveals their inside information. The total profits that insiders consequently
obtain will be less than the profits they could obtain if they colluded to trade
more slowly.
By making insider trading illegal, we force insiders to trade slowly and
unobtrusively to avoid prosecution. The threat of prosecution thus gives in-
siders strong incentives to collude to avoid detection, and thereby maximize
their trading profits. Without restrictions on insider trading, collusive agree-
ments would be extremely hard to enforce because each insider could cheat
without threat of prosecution. With restrictions on insider trading, insiders
who do not collude increase their probability of being caught.
Rejoinder
In chapter 12, we show that if the rate at which prices adjust to an imbal-
anced anonymous order flow depends on its composition, bluffers can make


## Page 606

CHAPTER 29 INSIDER TRADING • 593
money. If, as asserted above, traders can obtain more liquidity when they
trade slowly than when they trade quickly, bluffers will trade quickly, as
though they were informed traders, and then try to leave the market slowly.
Another Rebuttal: The Effects of Insider Trading on
Managerial Compensation
Managers receive direct and indirect compensation for doing their jobs.
Their
direct compensation
includes all income and services that their em-
ployers give them. This compensation generally includes salaries, bonuses,
option grants, retirement benefits, and health benefits.
Indirect compensation
includes all benefits that managers obtain from their jobs and are not ex-
plicitly given to them by their employers. These indirect benefits may in-
clude the prestige that society grants managers, the satisfaction associated
with managing a firm, the satisfaction associated with making decisions that
affect other people, and—in some places—the opportunity to trade on in-
sider information. When insiders can trade without restriction, the oppor-
tunity to trade on insider information may be their most important indirect
employment benefit.
Since the market for managerial talent is highly competitive, indirect
compensation can act as a substitute for direct compensation. In particular,
the greater the indirect benefits that managers receive from their jobs, the
less money their firms must pay them to do their jobs. If all other things
are held constant, direct compensation therefore must be higher when in-
siders cannot trade on inside information.
When managers can trade on inside information, their firms need not
pay them as much in direct compensation. With lower managerial com-
pensation, reported corporate earnings will be higher than they would be if
insider trading were effectively restricted. Although greater earnings gener-
ally increase valuations, this increase would be offset by the illiquidity ef-
fects of insider trading. On net, holding all other things constant, permit-
ting insider trading should not affect corporate values.
As a result, if no other problems were associated with insider trading, al-
lowing insider trading merely shifts compensation expense from the firm to
the shareholders. Since the shareholders own the firm anyway, they collec-
tively should not care whether the firm pays their managers or whether they
pay them through their trading losses. If all shareholders bore these costs
equally, and if insiders were able to exploit their positions predictably, in-
sider trading would have no net effect on values.
This conclusion ignores several secondary issues. For example, different
tax rates on corporate and personal income, and on ordinary and capital
gains income, ensure that shareholders are not indifferent between restricted
and unrestricted insider trading. Managerial risk aversion can also affect cor-
porate values because managers tend to prefer compensation schemes that
do not force them to bear unpredictable risks.
A switch from direct compensation to compensation through insider
trading would benefit long-term investors at the expense of high frequency
traders. Traders who trade most frequently generally lose the most to in-
formed traders.
Rejoinder
The result that shareholders as a group should be indifferent between
restricted and unrestricted insider-trading regimes assumes that insider
International
Comparisons of
CEO Compensation
It is impossible to reasonably
compare managerial
compensation among
countries without considering
whether insiders can trade on
insider information. In
countries where insiders can
freely trade on their
information, direct
compensation will be lower
than in countries where the
law effectively restricts insider
trading.
This observation helps
explain why U.S. corporations
pay their CEOs higher
salaries than do Japanese or
German
corporations. Insider
trading is far more effectively
restricted in the United States
than in Japan or Germany.


## Page 607

594 • TRADING AND EXCHANGES
trading does not cause other problems. Below, we discuss many other
insider-trading problems.
29.3.1.3 Corporate Control Issues
The least recognized reason why we have insider-trading rules may be the
most important reason. Insider-trading rules help ensure that managerial
labor markets operate efficiently and that managers do not abuse their po-
sitions within their corporations. Insider-trading rules help keep publicly
traded firms productive.
Perhaps the most damaging effect of unrestricted insider trading is that
it makes insiders reluctant to share information. Managers who freely share
information lose their comparative advantage as informed traders. Corpo-
rate directors and shareholders therefore know less about the firm than they
otherwise would. Consequently, directors find it more difficult to evaluate,
and ultimately to control, corporate managers. Shareholders likewise find it
more difficult to value their investments.
When insiders can trade on inside information, they may also make man-
agerial decisions that maximize their unique advantage as informed traders.
In extreme cases, managers may choose investment projects based on the
degree to which projects increase their informational advantage rather than
strictly on the net benefits of the projects to the shareholders. Such deci-
sions obviously decrease firm values.
Finally, when managers can trade on inside information, they may front-
run trades that their firms make. Firms trade when they repurchase their
shares, issue new shares, purchase or sell shares of other firms, take over or
merge with other firms, and purchase or sell commodities. Front running
such trades spoils the prices that their firms ultimately obtain. Such abuses
therefore directly dilute shareholder values.
29.3.2 Why Permit Insider Trading?
The main arguments for unrestricted insider trading involve price efficiency,
the costs of enforcement, and incentives for entrepreneurial behavior by
managers.
29.3.2.1 Informative Prices
Since insiders are well-informed traders, the price impacts of their orders
push prices toward fundamental values. Their trading therefore makes prices
more informative.
Effective restrictions on insider trading remove insiders and their infor-
mation from the market. Insider-trading restrictions therefore make prices
less informative. Since informative prices are essential to efficient allocation
decisions in market-based economies, insider trading makes production
more efficient.
Rebuttal
The value of having informative prices sooner rather than later depends on
how much longer it would take prices to adjust to new information if insid-
ers were not allowed to trade. If insiders trade on information that will be
common knowledge tomorrow, the incremental value of more informative
prices probably is not great. If insiders accelerate the flow of information by
months or years, restrictions on insider trading may be quite harmful.


## Page 608

CHAPTER 29 INSIDER TRADING • 595
In practice, as noted above, effectively enforcing insider-trading laws is
almost impossible when firms do not soon reveal the information on which
insiders trade. Therefore, as a practical matter, restrictions on insider trad-
ing are effective only against insider trading on information that will soon
be made public. Consequently, the increased economic efficiency associated
with more informative prices cannot be that great. Moreover, since people
usually can reverse or revise recent decisions in response to new informa-
tion, restrictions on insider trading cannot be very costly if they affect only
short-term trading decisions.
Insider trading can make prices more informative only if all information
that would be made public with restricted insider trading also would be
made public with unrestricted insider trading. As noted above, however, un-
restricted insider trading diminishes managerial incentives to share infor-
mation. Unrestricted insider trading therefore may actually decrease the in-
formation in prices.
29.3.2.2 Costs of Enforcement
Insider-trading laws are extremely difficult to enforce because detecting in-
sider trading by confederates is very difficult. Unfortunately, the generally
low probability of detection means that no reasonable punishment will de-
ter unethical people from illegal insider trading. Moreover, the costs of ef-
fectively enforcing insider-trading restrictions could be a substantial frac-
tion of—if not greater than—the economic value of enforcement.
Many people believe that it is unproductive to have laws we cannot
effectively enforce. When laws are routinely broken without consequence,
respect for authority often suffers.
Such circumstances often lead to selective enforcement problems. When
many people violate the law, but only a few are prosecuted, the authorities
who choose whom to prosecute often have tremendous power to selectively
prosecute individuals or groups they do not like. Selective prosecution can
be highly unfair. If applied to groups based on racial, ethnic, sexual, na-
tional, or religious grounds, it can destabilize society.
Rebuttal
A law that is not effectively enforced is very different from no law at all. In
societies where people respect the law, most people respect an ineffectively
enforced law simply because they are ethical. For most people, the ethical costs
of breaking the law are much greater than the expected costs of prosecution.
They therefore obey the law much more than we would otherwise expect.
Rejoinder
People respect laws in large part because they are enforced. When enforce-
ment drops and violation rises, respect ultimately falls, as does this argument.
29.3.2.3 Entrepreneurial Incentives
In a famous book and related article, Henry Manne argues that insider trad-
ing gives managers incentives to engage in entrepreneurial behavior. In par-
ticular, managers who have good ideas can create their own rewards for im-
plementing those ideas by buying stock in their firm before the idea is
common knowledge and by selling the stock when its price reflects the value
of the new idea. Insider trading allows employees with ideas to buy a piece


## Page 609

596 • TRADING AND EXCHANGES
The War on Drugs
Many people believe that laws against the illegal use of drugs are
impossible to enforce effectively. Use remains high despite the large number
of people who are in jail for producing, transporting, dealing, possessing,
or using illegal drugs.
The illegal drug trade is highly lucrative in large part because it is
illegal. The legal restrictions placed on the trade have decreased supply
relative to demand, which has caused prices to be much higher than they
otherwise would be. The legal restrictions also have allowed dealers to form
territorial monopolies because competitors can hardly petition the
government for relief under antitrust statutes. The high penalties for drug
trafficking also make the penalties for murder committed to protect territorial
monopolies relatively less significant.
The high prices of illegal drugs are responsible for much of the burglary,
robbery, and prostitution in Western societies. Addicts who need their next
fix must get it. The more expensive is the fix, the more crime they must
commit. Since criminals often use force to commit these crimes, they expose
those who do not use drugs to substantial personal risks.
The high prices of illegal drugs also cause users to become dealers in
order to support their habits. These dealers have tremendous incentives to
hook new clients. Ironically, because of this factor alone, usage is probably
higher than it would be if drug usage and distribution were legal and
regulated.
The huge wealth accumulated by drug dealers corrupts society. Criminals
use their wealth to corrupt the legal system. Their wealth also corrupts the
values of people who see how successful they are. The war on drugs is
largely responsible for the extreme social instabilities found in many drug-
producing countries.
Although the restriction of insider trading fortunately has not had the
same negative effects as the war on drugs, many people believe that the
two problems share many essential aspects.
4
of their firm so that they can realize the benefit of their ideas. It allows
them to be entrepreneurs.
Manne argues that this compensation scheme is much more effective
than any other compensation scheme that shareholders can construct. Un-
like formal compensation schemes that employees must negotiate with their
managers or with the shareholders, employees can enter this "entrepreneur-
ial" scheme whenever they want. Although the rewards—insider-trading
profits—for implementing a good idea can be quite great, the insider risks
losing if the share prices drops. This compensation scheme thus is quite un-
usual. Shareholders generally cannot create compensation contracts that put
their employees at risk of losing substantial wealth.
The risk of loss is the distinguishing characteristic of compensation
through insider trading. The only employees who opt for this form of com-
pensation are those who firmly believe that the firm will outperform the
market. Shareholders want to retain these employees more than other em-
ployees. If employees with good ideas cannot profit from them, they may
quit and take them elsewhere. Shareholders cannot compensate everyone
who claims to have a good idea because everyone will make such claims.
Instead, they must evaluate each claim. Shareholders, however, may be
poorly suited for making such evaluations. By allowing compensation that
places the employee at substantial risk of loss, shareholders do not have to


## Page 610

CHAPTER 29 INSIDER TRADING • 597
make these evaluations. Instead, employees self-select their compensation
schemes. Only those who are willing to take a position in the firm get the
rewards. Insider trading thus encourages entrepreneurial initiative.
Shareholders can give employees stock options to encourage entrepre-
neurial initiative. They must be careful, however, about to whom they give
the options. If they give options to all employees, they give away value with-
out any assurance that the firm will outperform the market. Stock option-
based compensation is most effective when employees can choose between
taking salary and taking options. In that case, employees are essentially in-
sider traders who use their salaries to buy options.
Rebuttal
Most entrepreneurial ideas take a long time to implement. To provide ef-
fective incentives to entrepreneurs within the firm, compensation schemes
must benefit insiders when they are successful and penalize them when they
are unsuccessful. Insider trading can provide this type of compensation, but
it does so only when the insiders establish their positions before they know
the outcomes of their initiatives. If insiders can trade after they learn that
their initiatives are successful, they will not be penalized if they fail. Because
they have little risk of losing, insiders would then have too much incentive
to undertake risky projects.
These considerations suggest that regulators should prevent insiders from
trading on short-term information which firms will soon release to the pub-
lic. Such information often is due to surprising financial reports and to sig-
nificant research advances or failures. The entrepreneurial decisions that led
up to these results typically occurred long before the information about the
results is available. Regulations should permit insiders to establish their po-
sitions before they make these decisions, but not afterward. These consid-
erations suggest that the U.S. restriction on short-term insider-trading prof-
its is sensible.
As discussed above, regulators generally cannot identify insider trading
on material information that firms will not soon reveal. Restrictions on in-
sider trading therefore do not effectively preclude the insider trading that
can produce proper entrepreneurial incentives. This conclusion, of course,
assumes that the law does not prohibit insiders from buying and holding
shares for long periods.
Any argument for insider trading based on incentives it creates for cor-
porate insiders to engage in constructive entrepreneurial activity must dis-
criminate between insider trading that produces long positions and that
which produces short positions. If insiders can construct net short positions
in their firms, they will want to destroy rather than create value. Since de-
stroying value is always much easier than creating value, almost all regula-
tory authorities prohibit managers from creating short positions in their
firms.
29.4 SUMMARY
Regulators try to restrict insider trading in order to remove a class of in-
formed traders from the market place. The assumed purpose of these laws
is to make trading more fair, to protect liquidity suppliers from informed
traders so that they can supply more liquidity to uninformed traders, and


## Page 611

598 • TRADING AND EXCHANGES
to control corporate managers. These laws narrow bid/ask spreads and make
prices less informative in the short run.
The debate on insider trading is quite complex. Although some good ar-
guments suggest that insider-trading laws are not necessary or desirable,
these laws are quite popular. Little chance of an early repeal exists. Of greater
concern is how much money regulators should spend to enforce insider-
trading laws.
29.5 SOME POINTS TO REMEMBER
•
Profitable insider trading hurts dealers and uninformed traders.
•
Insider trading is hard to detect. Trading on inside information that
firms will not soon reveal is especially difficult to detect.
•
Insider trading is most profitable when insiders do not compete with
each other to profit from their information.
•
Profitable insider trading is associated with lower salaries for corpo-
rate insiders.
•
Insider trading may encourage entrepreneurship among corporate
managers.
29.6 QUESTIONS FOR THOUGHT
1.
After weighing the arguments for and against insider trading, do you
think that insider trading should be restricted? Which arguments do
you find most compelling?
2.
When can a manager safely trade without worrying about prosecu-
tion for insider trading?
3.
When can we reasonably assume that insiders are not trading on in-
side information?
4.
Why do people follow reports of insider trades?
5.
Suppose a corporation will sell its forecast of its future earnings to
anyone for 50,000 dollars. If you buy this information and base your
trading decisions upon it, would you be trading on insider informa-
tion? Would your answer be different if the corporation sells the
forecast for the cost of "postage and handling"?
6.
Suppose you tell your friend who is a CEO that you intend to sell
stock in his firm. He tells you that now would be a very poor time
to sell. You therefore decide not to sell. You do not buy, however.
Price subsequently rises when another firm offers to buy the com-
pany for a substantial premium. You clearly benefited from insider
information. How should the law treat this situation?
7.
Suppose that a corporation posts material information to its Web
site at an unannounced and unscheduled time. By chance, you see
the information and trade on it. Are you trading on insider infor-
mation? Suppose the CEO tells you to look at the Web site. Are
you then trading on insider information?
8.
A corporate CEO gives a keynote speech at a large trade show in
which she describes her expectations for the future prospects of her
firm. Her vision surprises many analysts, who revise their valuations
of the corporation. If they then trade on this information, will they
be trading on insider information? Would the answer be different if


## Page 612

CHAPTER 29 INSIDER TRADING • 599
only mutual fund analysts could attend the trade show or if the CEO
spoke only in a small session of a large conference?
9.
How might a clever insider arrange her trades to minimize the prob-
ability of prosecution for insider trading while maximizing her gains
from trading on inside information?
10.
Should professional securities analysts have better access to infor-
mation produced by management than do other people? If so, which
analysts would qualify for this privilege? What limits, if any, would
you place on their trading?


## Page 614

Bibliography
This bibliography provides selected references for further reading.
I
classi-
fied the references using the chapter outline of this book. Since some arti-
cles and books cover topics that appear in many of the chapters, the classi-
fication is somewhat arbitrary.
The market microstructure literature has grown large very quickly. This
bibliography therefore is not comprehensive.
I
included many works because
they provide the first clear presentation of a principle or because they in-
clude extensive bibliographies of their topics. I included other works be-
cause
I
especially appreciated what I learned from them or because
I
found
them to be particularly well written.
I
undoubtedly failed to include many excellent works simply because I
did not remember them. Although
I
am blessed with an excellent memory
for ideas, I regrettably have a poor memory for names. The omission of
many excellent works from this bibliography therefore reflects more on me
than on them.
General Works
Belonsky, Gail M., and David M. Modest. 1993. Market microstructure: An
empirical retrospective. Working paper, Haas School of Business, University
of California, Berkeley.
Cohen, Kalman J., Steven F. Maier, Robert A. Schwartz, and David K. Whit-
comb. 1986.
The Microstructure of Securities Markets
(Prentice
-
Hall, Engle-
wood Cliffs, NJ).
Coughenour, Jay, and Kuldeep Shastri. 1999. Symposium on market micro-
structure: A review of empirical research.
Financial Review
34(4), 1-28.
Dalton, John M., ed. 1993.
How the Stock Market Works
(New York Institute of
Finance, New York).
Downes, John, and Jordan E. Goodman, eds. 1991.
Dictionary of Finance and
Investment Terms
(Barron's Educational Series, New York).
Fan, Ming, Sayee Srinivasan, Jan Stallaert, and Andrew B. Whinston. 2002.
Electronic Commerce and the Revolution in Financial Markets
(South
-
Western,
Mason, OH).
Lee, Rubin. 1998.
What Is an Exchange: The Automation, Management and Reg-
ulation of Financial Markets
(Oxford University Press, New York).
Lyons, Richard K. 2002.
The Microstructure Approach to Exchange Rates
(MIT
Press, Cambridge, MA).
Madhavan, Ananth. 2000. Market microstructure: A survey.
Journal of Finan-
cial Markets
3(3), 205-258.
O'Hara, Maureen. 1995.
Market Microstructure Theory
(Basil Blackwell, Cam-
bridge, MA).
Schwartz, Robert A. 1991.
Reshaping the Equity Markets: A Guide for the 1990s
(Harper Business, New York).
Sharp, Robert. 1989.
The Lore and Legends of Wall Street
(Dow Jones-Irwin, New
York).
601


## Page 615

602 • BIBLIOGRAPHY
Stoll, Hans R. 1992. Principles of trading market structure.
Journal of Financial
Services Research
6(1), 75
-
107.
Teweles, Richard J., Edward S. Bradley, and Ted M. Teweles. 1992.
The Stock
Market
(John Wiley & Sons, New York).
Teweles, Richard J., and Frank J. Jones. 1999.
The Futures Game: Who Wins? Who
Loses? And Why?
Ben Warwick, ed., 3rd ed. (McGraw-Hill, New York).
Wagner, Wayne, ed. 1989.
The Complete Guide to Securities Transactions: En-
hancing Performance and Controlling Costs
(John Wiley & Sons, New York).
Chapter 3: The Trading Industry
Bank for International Settlements. 2001.
Triennial Central Bank Survey of
Foreign Exchange and Derivatives Markets Activity
(Bank for International
Settlements, Basel, Switzerland).
Garbade, Kenneth D., and William L. Silber. 1978. Technology, communica-
tion and the performance of financial markets: 1840-1975.
Journal of Finance
33(3), 819-832.
Keim, Donald B., and Ananth Madhavan. 2000. The relationship between stock
market movements and NYSE seat prices.
Journal of Finance
55(6), 2817-
2840.
Schwert, G. William. 1977. Stock exchange seats as capital assets.
Journal of
Financial Economics
4(1), 51
-
78.
Securities Industry Association. 2001.
Securities Industry Fact Book
(Securities
Industry Association, New York).
U.S. Congress, General Accounting Office. 1986.
Stocks and Futures: How the
Markets Developed and How They Are Regulated.
GAO/GGD
-
86
-
26 (Gen-
eral Accounting Office, Washington, DC).
U.S. Congress, General Accounting Office. 1991.
Global Financial Markets:
International Coordination Can Help Address Automation Risks.
GAO/
IMTEC-01-62 (General Accounting Office, Washington, DC).
U.S. Congress, Office ofTechnology Assessment. 1990a.
Electronic Bulls & Bears:
US Security Markets & Information Technology.
OTA
-
CIT
-
459 (U.S. Gov-
ernment Printing Office, Washington, DC).
U.S. Congress, Office of Technology Assessment. 1990b.
Trading Around the
Clock: Global Securities Markets and Information Technology
—
Background Paper.
OTA-BP-CIT-66 (U.S. Government Printing Office, Washington, DC).
Chapter 4: Orders and Order Properties
Angel, James J. 1998. Nonstandard-settlement transactions.
Financial Manage-
ment
27(1), 31-46.
Copeland, Thomas E., and Dan Galai. 1983. Information effects on the bid-
ask spread.
Journal of Finance
38(5), 1457-1469.
Chapter 5: Market Structures
Amihud, Yakov, and Haim Mendelson. 1987. Trading mechanisms and stock
returns: An empirical investigation.
Journal of Finance
42(3), 533-553.
Amihud, Yakov, Haim Mendelson, and Beni Lauterbach. 1997. Market micro-
structure and securities values: Evidence from the Tel Aviv Stock Exchange.
Journal of Financial Economics
45(3), 365
-
390.
Ball, Clifford A., Walter A. Torous, and Adrian E. Tschoegl. 1985. The degree
of price resolution: The case of the gold market.
Journal of Futures Markets
5(1), 29-43.
Cohen, Kalman J., and Robert A. Schwartz. 1989. An electronic call market:
Its design and desirability. In Henry C. Lucas, Jr., and Robert A. Schwartz,


## Page 616

BIBLIOGRAPHY • 603
eds.,
The Challenge of Information Technology for the Securities Markets
(Dow
Jones-Irwin, Homewood, IL).
Coval, Joshua D., and Tyler Shumway. 2001. Is sound just noise?
Journal of
Finance
56(5), 1887-1910.
Domowitz, Ian. 1993. A taxonomy of automated trade execution systems.
Jour-
nal of International Money and Finance
12(6), 607
-
631.
Fishman, Michael J., and Kathleen M. Hagerty. 1995. The mandatory disclo-
sure of trades and market liquidity.
Review of Financial Studies
8(3), 637-676.
Franks, Julian, and Stephen Schaefer. 1995. Equity market transparency on the
London Stock Exchange.
Journal of Applied Corporate Finance
8(1), 70
-
77.
Goodhart, Charles, and Ricardo Curcio. 1992. Asset price discovery and price
clustering in the foreign exchange market. Working paper, London School
of Business.
Grossman, Sanford, and Merton Miller. 1988. Liquidity and market structure.
Journal of Finance
43(3), 617
-
633.
Harris, Lawrence. 1991. Stock price clustering and discreteness.
Review of Fi-
nancial Studies
4(3), 389
-
415.
Ho, Thomas S., Robert A. Schwartz, and David K. Whitcomb. 1985. The trad-
ing decision and market clearing under transaction price uncertainty.
Jour-
nal of Finance
40(1), 21
-
42.
Huang, Roger D., and Hans R. Stoll. 1992. The design of trading systems:
Lessons from abroad.
Financial Analysts Journal
48(5), 49-54.
Huang, Roger D., and Hans R. Stoll. 1996. Dealer versus auction markets: A
paired comparison of execution costs on NASDAQand the NYSE.
Journal
of Financial Economics
41(3), 313
-
357.
Katz, Michael L., and Carl Shapiro. 1985. Network externalities, competition,
and compatibility.
American Economic Review
75(3), 424-440.
Lucas, Henry C., Jr., and Robert A. Schwartz, eds. 1989.
The Challenge of
Information Technology for the Securities Markets
(Dow Jones
-
Irwin, Home-
wood, IL).
Madhavan, Ananth, David Porter, and Daniel Weaver. 2000. Should securities
markets be transparent? Working paper, Marshall School of Business, Uni-
versity of Southern California.
Naik, Narayan Y., Anthony Neuberger, and S. Viswanathan. 1999. Trade dis-
closure regulation in markets with negotiated trades.
Review of Financial
Studies
12(4), 873-900.
Niederhoffer, Victor. 1965. Clustering of stock prices.
Operations Research
13(2),
258-265.
Pagano, Marco, and Ailsa Rod.. 1992. Auction and dealership markets: What
is the difference?
European Economic Review
36(2/3), 613-623.
Porter, David, and Daniel Weaver. 1998. Post-trade transparency on Nasdaq's
national market system.
Journal of Financial Economics
50(2), 231-252.
Schnitzlein, Charles R. 1996. Call and continuous trading mechanisms under
asymmetric information: An experimental investigation.
Journal of Finance
51(2), 613-636.
Smidt, Seymour. 1979. Continuous vs. intermittent trading on auction markets.
Journal of Financial and Quantitative Analysis
14(4), 837
-
886.
Tinic, Seha M., and Richard R. West. 1974. Marketability of common stocks
in Canada and the U.S.A.: A comparison of agent versus dealer dominated
markets.
Journal of Finance
29(3), 729-746.
Wagner, Wayne. 1989. Buttonwood II: Considering alternative market struc-
tures. In Wayne Wagner, ed.,
The Complete Guide to Securities Transactions
(John Wiley & Sons, New York).


## Page 617

604 • BIBLIOGRAPHY
Wunsch, Steven. 1989. The single-price auction. In Wayne Wagner, ed.,
The
Complete Guide to Securities Transactions
( John Wiley & Sons, New York).
Chapter 6: Order-driven Markets
Domowitz, Ian. 1990. The mechanics of automated trade execution systems.
Journal of Financial Intermediation
1(2), 167
-
194.
Mendelson, Haim. 1982. Market behavior in a clearing house.
Econometrica
50(6), 1505-1524.
Chapter 7: Brokers
Brennan, Michael J., and Tarun Chordia. 1993. Brokerage commission sched-
ules,
Journal of Finance
48(4), 1379-1402.
Chan, Yuk-Shee, and Mark Weinstein. 1993. Reputation, bid-ask spread and
market structure.
Financial Analysts Journal
49(4), 57-62.
Fishman, Michael J., and Francis A. Longstaff. 1992. Dual trading in futures
markets.
Journal of Finance
47(2), 643-672.
Roell, Ailsa. 1990. Dual capacity trading and the quality of the market.
Journal
of Financial Intermediation
1(2), 105
-
124.
Sofianos, George, and Ingrid Werner. 2000. The trades of NYSE floor brokers.
Journal of Financial Markets
3(2), 139
-
176.
Weiss, David M. 1993.
After the Trade Is Made: Processing Security Transactions
(NYFI, New York).
Chapter 8: Why People Trade
Barber, Bard M., and Terrance Odean. 2000. Trading is hazardous to your
wealth: The common stock investment performance of individual investors.
Journal of Finance
55(2), 773
-
806.
Bernstein, Peter L. 1996. Against the Gods: The Remarkable Story of Risk.
(John Wiley & Sons, New York).
DeLong, Bradford J., Andrei Shleifer, Lawrence H. Summers, and Robert J.
Waldmann. 1989. The size and incidence of the losses from noise trading.
Journal of Finance
44(3), 681
-
696.
DeLong, Bradford J., Andrei Shleifer, Lawrence H. Summers, and Robert J.
Waldmann. 1991. The survival of noise traders in financial markets.
Journal
of Business
64(1), 1
-
20.
Gervais, S., and Terrance Odean. 2001. Learning to be overconfident.
Review
of Financial Studies
14(1), 1
-
27.
Grinblatt, Mark, and Matti Keloharju. 2001. How distance, language and cul-
ture influence stockholdings and
trades. ournal of Finance
56(3), 1053-1073.
Huberman, Gut 2001. Familiarity breeds investment.
Review of Financial Stud-
ies
14(3), 659-680.
Karpoff, Jonathan M., and Ralph A. Wallding. 1988. Short-term trading around ex-
dividend days: Additional
evidence.Journa/ofFinancialEconomics
21(2), 291-298.
Karpoff, Jonathan M., and Ralph A. Walkling. 1990. Dividend capture in
NASDAQstocks.
Journal of Financial Economics
28(1/2), 39
-
66.
Rashes, Michael S. 2001. Massively confused investors making conspicuously
ignorant choices (MCI-MCIC).
Journal of Finance
56(5), 1911-1927.
Reinganum, Marc R. 1986. Is time travel impossible? A financial proof.
Jour-
nal of Portfolio Management
13(1), 10
-
12.
Sauer, Raymond D. 1998. The economics of wagering markets.
Journal of Eco-
nomic Literature
36(4), 2021
-
2064.
Chapter 9: Good Markets
Amihud, Yakov, and Haim Mendelson. 1986. Asset pricing and the bid-ask
spread.
Journal of Financial Economics
17(2), 223
-
249.


## Page 618

BIBLIOGRAPHY • 605
Amihud, Yakov, and Haim Mendelson. 1991. Liquidity, maturity and the yields
on U.S. Treasury securities.
Journal of Finance
46(4), 1411-1426.
Black, Fischer. 1986. Noise.
Journal of Finance
41(3), 529-543.
Brennan, Michael J., and Avanidhar Subrahmanyam. 1996. Market microstruc-
ture and asset pricing: On the compensation for illiquidity in stock returns.
Journal of Financial Economics
41(3), 441
-
464.
Chapter 10: Informed Traders and Market Efficiency
Black, Fischer. 1971. Random walk and portfolio management.
Financial Ana-
lysts Journal
27(2), 16
-
22.
Chakravarty, Sugato. 2001. Stealth
-
trading: Which traders' trades move stock
prices?
Journal of Financial Economics
61(2), 289
-
307.
Fama, Eugene F. 1965. Random walks in stock market prices.
Financial Analysts
Journal21(5),
55
-
59; reprinted [1995] in
Financial AnalystsJournal51(1),
75
-
80.
Figlewski, Stephen. 1982. Information diversity and market behavior.
Journal of
Finance
37(1), 87-102.
Grossman, Sanford J., and Joseph E. Stiglitz. 1980. On the impossibility of in-
formationally efficient markets.
American Economic Review
70(3), 393-408.
Hasbrouck, Joel. 1991. Measuring the information content of stock trades.
Jour-
nal of Finance
46(1), 179
-
208.
Madhavan, Ananth. 1996. Security prices and market transparency.
Journal of
Financial Intermediation
5(3), 255
-
283.
Malkiel, Burton G. 1973.
A Random Walk Down Wall Street
(Norton, New York).
Pinches, George E. 1970. The random walk and technical analysis.
Financial
Analysts Journal
26(2), 104
-
109.
Silber, William L. 1994. Technical trading: When it works and when it doesn't.
Journal of Derivatives
1(3), 39
-
44.
Treynor, Jack L. 1981. What does it take to win the trading game?
Financial
Analysts Journal
37(1), 55
-
60.
Treynor, Jack L. 1999. Zero sum.
Financial Analysts Journal
55(1), 8-12.
Van Home, James C., and G. G. C. Parker. 1968. Technical trading rules.
Financial Analysts Journal
24(4), 128
-
132.
Chapter 11: Order Anticipators
Amihud, Yakov, and Haim Mendelson. 1990. Option market integration: An eval-
uation. Paper submitted to the U.S. Securities and Exchange Commission.
Angel, James J. 1997. Tick size, share prices, and stock splits.
Journal of Finance
52(2), 655-681.
Daniel, Kent, David Hirshleifer, and Avanidhar Subrahmanyam. 1998. Investor
psychology and security market under- and overreactions.
Journal of Finance
53(6), 1839-1885.
Harris, Lawrence. 1997. Order exposure and parasitic traders. Working paper,
Marshall School of Business, University of Southern California.
Jennings, Robert. 2001. Getting pennied: The effect of decimalization on traders'
willingness to lean on the limit order book at the New York Stock Exchange.
NYSE Working Paper 2001-01.
Jones, Charles M., and Marc L. Lipson. 2001. Sixteenths: Direct evidence on
institutional execution costs.
Journal of Financial Economics
59(2), 253-278.
Kavajecz, Kenneth A., and Elizabeth R. Odders-White. 2002. Technical anal-
ysis and liquidity provision, Working paper, The Wharton School, Univer-
sity of Pennsylvania.
Lo, Andrew W., Harry Mamaysky, and Jiang Wang. 2000. Foundations of tech-
nical analysis: Computational algorithms, statistical inference, and empirical
implementation.
Journal of Finance
55(4), 1705-1765.


## Page 619

606 • BIBLIOGRAPHY
Schultz, Paul. 2000. Stock splits, tick size, and sponsorship.
Journal of Finance
55(1), 429-450.
Chapter 12: Bluffers and Market Manipulation
Biais, Bruno, Pierre Hillion, and Chester Spatt. 1999. Price discovery and learn-
ing during the preopening period of the Paris Bourse.
Journal of Political
Economy
107(6), 1218-1248.
Jarrow, Robert A. 1992. Market manipulation, bubbles, corners, and short
squeezes.
Journal of Financial and Quantitative Analysis
27(3), 311
-
336.
Jarrow, Robert A. 1994. Derivative security markets, market manipulation, and
option pricing theory.
Journal of Financial and Quantitative Analysis
29(2),
241-261.
Kumar, Praveen, and Duane J. Seppi. 1992. Futures manipulation with "cash
settlement."
Journal of Finance
47(4), 1485-1502.
Kyle, Albert. 1985. Continuous auctions and insider trading.
Econometrica
53(6),
1315-1335.
Chapter 13: Dealers
Amihud, Yakov, and Haim Mendelson. 1980. Dealership market: Market mak-
ing with inventory.
Journal of Financial Economics
8(1), 31-53.
Amihud, Yakov, and Haim Mendelson. 1989. Liquidity and the cost of capital:
Implications for corporate management.
Journal of Applied Corporate Finance
2(3), 65-73.
Chowdhry, Bhagwan, and Vikram Nanda. 1991. Multimarket trading and mar-
ket liquidity.
Review of Financial Studies
4(3), 483-511.
Forster, Margaret M., and Thomas J. George. 1992. Anonymity in securities
markets.
Journal of Financial Intermediation
2(2), 168
-
206.
Garman, Mark B. 1976. Market microstructure.
Journal of Financial Economics
3(3), 257-275.
Hansch, Oliver, Narayan Y. Naik, and S. Viswanathan. 1998. Do inventories
matter in dealership markets? Evidence from the London Stock Exchange.
Journal of Finance
53(5), 1623
-
1656.
Lyons, Richard K. 1997. A simultaneous trade model of the foreign exchange
hot potato.
Journal of International Economics
42(3/4), 275
-
298.
Madhavan, Ananth, and Seymour Smidt. 1991. A Bayesian model of intraday
specialist pricing.
Journal of Financial Economics
30(1), 99-134.
Madhavan, Ananth, and Seymour Smidt. 1993. An analysis of changes in spe-
cialist inventories and quotations.
Journal of Finance
48(5), 1595-1628.
Manaster, Steven, and Steven C. Mann. 1996. Life in the pits: Competitive
market making and inventory control.
Review of Financial Studies
9(3),
953-975.
Niederhoffer, Victor, and M. F. M. Osborne. 1966. Market making and rever-
sal on the stock exchange.
Journal of the American Statistical Association
61(316), 897-916.
Sanas, Patrik. 2001. Adverse selection and competitive market making: Em-
pirical evidence from a limit order market.
Review of Financial Studies
14(3),
705-734.
Silber, William L. 1984. Marketmaker behavior in an auction market: An anal-
ysis of scalpers in futures markets.
Journal of Finance
39(4), 937-953.
Stoll, Hans R. 1976. Dealer inventory behavior: an empirical investigation of
NASDAQ stocks.
Journal of Financial and Quantitative Analysis
11(3),
359-380.
Stoll, Hans R. 1978. The supply of dealer services in securities markets.
Jour-
nal of Finance
33(4), 1133-1151.


## Page 620

BIBLIOGRAPHY • 607
Chapter 14: Bid/Ask Spreads
Bagehot, Walter (pseudonym for Jack Treynor). 1971. The only game in town.
Financial Analysts Journal
27(2), 12
-
14, 22; reprinted [1995] in
Financial
Analysts Journal
51(1), 81
-
83.
Benston, George J., and Robert Hagerman. 1974. Determinants of bid-asked
spreads in the over-the-counter market.
Journal of Financial Economics
1(4),
353-364.
Branch, Ben, and Walter Freed. 1977. Bid-asked spreads on the AMEX and
the Big Board.
Journal of Finance
32(1), 159-163.
Cohen, Kalman J., Steven F. Maier, Robert A. Schwartz, and David K. Whit-
comb. 1981. Transaction costs, order placement strategy, and existence of
bid-ask spread.
Journal of Political Economy
89(2), 287-305.
Demsetz, Harold. 1968. The cost of transacting.
Quarterly Journal of Economics
82(1), 33-53.
Dutta, Prajit K., and Ananth Madhavan. 1997. Competition and collusion in
dealer markets.
Journal of Finance
52(1), 245-276.
Glosten, Lawrence R., and Lawrence Harris. 1988. Estimating the components
of the bid-ask spread.
Journal of Financial Economics
14,123-142.
Glosten, Lawrence R., and Paul Milgrom. 1985. Bid, ask, and transaction prices
in a specialist market with heterogeneously informed traders.
Journal of
Financial Economics
14(1), 71
-
100.
Harris, Lawrence. 1994. Minimum price variations, discrete bid/ask spreads and
quotation sizes.
Review of Financial Studies
7(1), 149-178.
Ho, Thomas S. Y., and Richard G. Macris. 1984. Dealer bid-ask quotes and
transaction prices: An empirical study of some AMEX options.
Journal of
Finance
39(1), 23-45.
Ho, Thomas S. Y., and Hans R. Stoll. 1983. The dynamics of dealer markets
under competition,
Journal of Finance
38(4), 1053-1074.
Huang, Roger D., and Hans R. Stoll. 1997. The components of the bid-ask
spread: A general approach.
Review of Financial Studies
10(4), 995-1034.
Neal, Robert, and Simon M. Wheatley. 1998. Adverse selection and bid-ask
spreads: Evidence from closed-end funds.
Journal of Financial Markets
1(1),
121-149.
Stoll, Hans R. 1993. Equity trading costs in-the-large.
Journal of Portfolio Man-
agement
19(4), 41-50.
Subrahmanyam, Avanidhar, and Sheridan Titman. 1999. The going public de-
cision and the development of financial markets.
Journal of Finance
54(3),
1045-1082.
Tinic, Seha M., and Richard R. West. 1972. Competition and the pricing of
dealer service in the over-the-counter stock market.
Journal of Financial and
Quantitative Analysis
7(3), 1707
-
1727.
Chapter 15: Block Traders
Admati, Anat R., and Paul Pfleiderer. 1991. Sunshine trading and financial mar-
ket equilibrium.
Review of Financial Studies
4(3), 443-481.
Angel, James J., Gary L. Gastineau, and Clifford J. Weber. 1997. Reducing the
market impact of large stock trades.
Journal of Portfolio Management
24(1),
69-76.
Barclay, Michael J., and Clifford G. Holderness. 1992. The law and large-block
trades.
Journal of Law and Economics
35(2), 265
-
294.
Burdett, Ken, and Maureen O'Hara. 1987. Building blocks: An introduction to
block trading.
Journal of Banking and Finance 11(2),
193
-
212.
Chan, Louis K. C., and Josef Lakonishok. 1993. Institutional trades and intra-
day stock price behavior.
Journal of Financial Economics
33(2), 173-200.


## Page 621

608 • BIBLIOGRAPHY
Chan, Louis K. C., and Josef Lakonishok. 1995. The behavior of stock prices
around institutional trades.
Journal of Finance
50(4), 1147-1174.
Easley, David, and Maureen O'Hara. 1987. Price, trade size, and information
in securities markets.
Journal of Financial Economics
19(1), 69-90.
Gemmill, Gordon. 1996. Transparency and liquidity: A study of block trades on
the London Stock Exchange under different publication rules.
Journal of Fi-
nance
51(5), 1765-1790.
Grossman, Sanford J. 1992. The informational role of upstairs and downstairs
trading.
Journal of Business
65(4), 509-528.
Holthausen, Robert W., Richard W. Leftwich, and David Mayers. 1987. The
effect of large block transactions on security prices: A cross-sectional analy-
sis.
Journal of Financial Economics
19(2), 237
-
268.
Holthausen, Robert W., Richard W. Leftwich, and David Mayers. 1990. Large-
block transactions, the speed of response, and temporary and permanent
stock-price effects.
Journal of Financial Economics
26(1), 71-96.
Kraus, Alan, and Hans R. Stoll. 1972. Price impacts of block trading on the
New York Stock Exchange.
Journal of Finance
27(3), 569-588.
Madhavan, Ananth, and Mender Cheng. 1997. In search of liquidity: Block
trades in the upstairs and downstairs markets.
Review of Financial Studies
10(1), 175-203.
Saar, Gideon. 2001. Price impact asymmetry of block trades: An institutional
trading explanation.
Review of Financial Studies
14(4), 1153-1182.
Seppi, Duane J. 1990. Equilibrium block trading and asymmetric information.
Journal of Finance
45(1), 73
-
94.
Smith, Brian F., D. Alasdair S. Turnbull, and Robert W. White. 2001. Upstairs
market for principal and agency trades: Analysis of adverse information and
price effects.
Journal of Finance
56(5), 1723-1746.
Chapter 16: Value Traders
Kagel, John H., and Dan Levin. 1986. The winner's curse and public informa-
tion in common value auctions.
American Economic Review
76(5), 894-920.
Thaler, Richard H. 1988. Anomalies: The winner's curse.
Journal of Economic
Perspectives
2(1), 191-202.
Treynor, Jack. 1987. The economics of the dealer function.
Financial Analysts
Journal
43(6), 27-34.
Chapter 17: Arbitrageurs
Brennan, Michael J., and Eduardo S. Schwartz. 1990. Arbitrage in stock index
futures.
Journal of Business
63(1), S7—S32.
Holden, Craig W. 1995. Index arbitrage as cross-sectional market making.
Jour-
nal of Futures Markets
15(4), 423
-
455.
Shleifer, Andrei, and Robert W. Vishny. 1997. The limits of arbitrage.
Journal
of Finance
52(1), 25-55.
Chapter 18: Buy
-
side Traders
Bertsimas, Dimitris, and Andrew W. Lo. 1998. Optimal control of execution
costs.
Journal of Financial Markets 1(1),
1
-
50.
Biais, Bruno, Pierre Hillion, and Chester Spatt. 1995. An empirical analysis
of the limit order book and the order flow in the Paris Bourse.
Journal of
Finance
50(5), 1655-1689.
Chakravarty, Sugato, and Craig W. Holden. 1995. An integrated model of mar-
ket and limit orders.
Journal of Financial Intermediation
4(3), 213-241.
Handa, Puneet, and Robert A. Schwartz. 1996. Limit order trading.
Journal of
Finance
51(5), 1835-1861.


## Page 622

BIBLIOGRAPHY • 609
Harris, Jeffrey H., and Paul H. Schultz. 1998. The trading profits of SOES ban-
dits.
Journal of Financial Economics
50(1), 39
-
62.
Harris, Lawrence. 1996. Does a large minimum price variation encourage or-
der display? Working paper, Marshall School of Business, University of
Southern California.
Harris, Lawrence. 1998. Optimal dynamic order submission strategies in some
stylized trading problems.
Financial Markets, Institutions & Instruments
7(2),
1-76.
Harris, Lawrence, and Joel Hasbrouck. 1996. Market vs. limit orders: The
Superdot evidence on order submission strategy.
Journal of Financial and
Quantitative Analysis
31(2), 213
-
231.
Keim, Donald B. 1999. An analysis of mutual fund design: The case of invest-
ing in small-cap stocks.
Journal of Financial Economics
51(2), 173-194.
Keim, Donald B., and Ananth Madhavan. 1995. Anatomy of the trading pro-
cess: Empirical evidence on the motivation for and execution of institutional
equity trades.
Journal of Financial Economics
37(3), 371-398.
Keim, Donald B., and Ananth Madhavan. 1996. The upstairs market for large-
block transactions: Analysis and measurement of price effects.
Review of
Financial Studies
9(1), 1
-
36.
Lo, Andrew W., Craig A. MacKinlay, and June Zhang. 2002. Econometric mod-
els of limit-order executions.
Journal of Financial Economics
65(1).
Chapter 19: Liquidity
Admati, Anat R., and Paul Pfleiderer. 1988. A theory of intraday trading pat-
terns.
Review of Financial Studies
1(1), 3
-
40.
Black, Fischer. 1995. Equilibrium exchanges.
Financial Analysts Journal
51(3),
23-29.
Bloomfield, Robert, and Maureen O'Hara. 1999. Market transparency: Who
wins and who loses?
Review of Financial Studies
2(1), 5-35.
Bloomfield, Robert, and Maureen O'Hara. 2000. Can transparent markets sur-
vive?
Journal of Financial Economics
55(3), 425
-
459.
Board, John, and Charles Sutcliffe. 2000. The proof of the pudding: The effects
of increased trade transparency in the London Stock Exchange.
Journal of
Business Finance and Accounting
27(7/8), 887
-
909.
Chordia, Tarun, Richard Roll, and Avanidhar Subrahmanyam. 2000. Com-
monality in liquidity.
Journal of Financial Economics
56(1), 3-28.
Chordia, Tarun, Richard Roll, and Avandihar Subrahmanyam. 2001. Market
liquidity and trading activity.
Journal of Finance
56(2), 501-503.
Harris, Lawrence. 1990. Liquidity, trading rules and electronic trading systems.
New York University Salomon Center Monograph Series in Finance and Eco-
nomics
1990
-
4.
Hasbrouck, Joel, and Duane J. Seppi. 2001. Common factors in prices, order
flow and liquidity.
Journal of Financial Economics
59(3), 383-411.
Lamoureux, Christopher G., and Charles R. Schnitzlein. 1997. When it's not
the only game in town: The effect of bilateral search on the quality of a dealer
market.
Journal of Finance
52(2), 683-712.
Pagano, Marco, and Ailsa Wien. 1996. Transparency and liquidity: A compari-
son of auction and dealer markets with informed trading.
Journal of Finance
51(2), 579-611.
Chapter 20: Volatility
Anderson, Torben G., Tim Bollerslev, Francis X. Diebold, and Heiko Ebens.
2001. The distribution of stock return volatility. Journal
of Financial Economics
61(1), 43-76.


## Page 623

610 • BIBLIOGRAPHY
Barclay, Michael J., Robert H. Litzenberger, and Jerold B. Warner. 1990. Pri-
vate information, trading volume, and stock-return variances.
Review of Fi-
nancial Studies
3(2), 233
-
254.
Barclay, Michael J., and Jerold B. Warner. 1993. Stealth trading and volatility:
Which trades move prices?
Journal of Financial Economics
34(3), 281-306.
Brown, Gregory W., and Jay C. Hartzell. 2001. Market reaction to public in-
formation: The atypical case of the Boston Celtics.
Journal of Financial
Economics
60(2-3), 333-370.
French, Kenneth R., and Richard Roll. 1986. Stock return variances: The ar-
rival of information and the reaction of traders.
Journal of Financial Economics
17(1), 5-26.
Kawai, Masahiro. 1983. Spot and futures prices of nonstorable commodities un-
der rational expectations.
Quarterly Journal of Economics
98(2), 235-254.
Roll, Richard. 1984. Orange juice and weather.
American Economic Review
74(5),
861-880.
Schwert, William G. 1989. Why does stock market volatility change over time?
Journal of Finance
44(5), 1115
-
1154.
Schwert, William G., and Paul J. Seguin. 1990. Heteroskedasticity in stock re-
turns.
Journal of Finance
45(4), 1129-1155.
Chapter 21: Liquidity and Transaction
Cost Measurement
Beebower, Gilbert. 1989. Evaluating transaction cost. In Wayne Wagner, ed.,
The Complete Guide to Securities Transactions
( John Wiley & Sons, New York).
Berkowitz, Stephen A., Dennis E. Logue, and Eugene A. Noser, Jr. 1988. The
total cost of transactions on the NYSE.
Journal of Finance
41(1), 97-112.
Choi, J. Y., Dan Salandro, and Kuldeep Shastri. 1988. On the estimation of bid-
ask spreads: Theory and evidence. Journal
of Financial and Quantitative Anal-
y
sis
23(2), 219-230.
George, Thomas J., Gautam Kaul, and M. Nimalendran. 1991. Estimation of
the bid-ask spread and its components: A new approach.
Review of Finan-
cial Studies
4(4), 623-656.
George, Thomas J., Gautam Kaul, and M. Nimalendran. 1994. Trading volume
and transaction costs in specialist markets.
Journal of Finance
49(4), 1489-
1505.
Glosten, Lawrence R. 1987. Components of the bid/ask spread and the statis-
tical properties of transaction prices.
Journal of Finance
42(5), 1293-1308.
Harris, Lawrence. 1990. Statistical properties of the roll serial covariance bid/ask
spread estimator.
Journal of Finance
45(2), 579-590.
Huang, Roger D., and Hans R. Stoll. 1997. The components of the bid-ask
spread: A general approach.
Review of Financial Studies
10(4), 995-1034.
Keim, Donald B., and Ananth Madhavan. 1997. Transactions costs and invest-
ment style: An interexchange analysis of institutional equity trades.
Journal
of Financial Economics
46(3), 265
-
292.
Keim, Donald B., and Ananth Madhavan. 1998. The cost of institutional eq-
uity trades.
Financial Analysts Journal
54(4), 50-69.
Lee, Charles M. C., and Mark A. Ready. 1991. Inferring trade direction from
intraday data.
Journal of Finance
46(2), 733-746.
Loeb, Thomas F. 1983. Trading cost: The critical link between investment in-
formation and results.
Financial Analysts Journal
39(3), 39-44.
Perold, Andre. 1988. The implementation shortfall: Paper versus reality.
Journal
of Portfolio Management
14(3), 4
-
9.


## Page 624

BIBLIOGRAPHY • 611
Roll, Richard. 1984. A simple implicit measure of the effective bid-ask spread.
Journal of Finance
39(4), 1127
-
1139.
Schultz, Paul. 2001. Corporate bond trading costs: A peek behind the curtain.
Journal of Finance
56(2), 677
-
698.
Stoll, Hans R. 1989. Inferring the components of the bid-ask spread: Theory
and empirical tests.
Journal of Finance
44(1), 115-134.
Wagner, Wayne H., and Mark Edwards. 1993. Best execution.
Financial Ana-
lysts Journal
49(1), 65
-
71.
Chapter 22: Performance Evaluation and Prediction
Ashton, D. J. 1996. The power of tests of fund manager performance.
Journal
of Business Finance and Accounting
23(1), 1
-
11.
Barber, Brad M., and John D. Lyon. 1997. Detecting long-run abnormal stock
returns: The empirical power and specification of test statistics.
Journal of Fi-
nancial Economics
43(3), 341
-
372.
Barber, Brad M., and Terrance Odean. 2000. Too many cooks spoil the profits:
Investment club performance.
Financial Analysts Journal
56(1), 17-25.
Bauman, W. Scott, and Robert E. Miller. 1994. Can managed portfolio perfor-
mance be predicted?
Journal of Portfolio Management
20(4), 31-40.
Bierman, Harold, Jr., Lawrence E. Fouraker, and Robert K. Jaedicke. 1961. The
use of probability and statistics in performance evaluation.
Accounting Re-
view
36(3), 409-417.
Brinson, Gary P., L. Randolph Hood, and Gilbert L. Beebower. 1986. Deter-
minants of portfolio performance.
Financial Analysts Journal
42(4), 39-44.
Brinson, Gary P., Brian D. Singer, and Gilbert L. Beebower. 1991. Determi-
nants of portfolio performance II: An update.
Financial Analysts Journal
47(3), 40-48.
Brown, Stephen J., William Goetzmann, Roger G. Ibbotson, and Stephen A.
Ross. 1992. Survivorship bias in performance studies.
Review of Financial
Studies
5(4), 553-580.
Chen, Zhiwu, and Peter J. Knez. 1996. Portfolio performance measurement:
Theory and applications.
Review of Financial Studies
9(2), 511-555.
Ellis, Charles D. 1975. The loser's game.
Financial Analysts Journal
31(4), 19-20,
22, 24, 26; reprinted in
Financial Analysts Journal
51(1), 95-100.
Elton, Edwin J., Martin J. Gruber, and Christopher R. Blake. 1996. Survivor-
ship bias and mutual fund performance.
Review of Financial Studies
9(4),
1097-1120.
Elton, Edwin J., Martin J. Gruber, and Joel C. Rentzler. 1987. Professionally man-
aged, publicly traded commodity funds.
Journal of Business
60(2), 175-200.
Elton, Edwin J., Martin J. Gruber, and Joel Rentzler. 1989. New public offer-
ings, information, and investor rationality: The case of publicly offered com-
modity funds.
Journal of Business
62(1), 1-16.
French, Dan W., and Glenn V. Henderson, Jr. 1985. How well does perfor-
mance evaluation perform?
Journal of Portfolio Management
11(2), 15-18.
Fung, William, and David A. Hsieh. 1997. Survivorship bias investment style
in the returns of CTAs.
Journal of Portfolio Management
24(1), 30-41.
Garcia, C. B., and F. J. Gould. 1993. Survivorship bias.
Journal of Portfolio Man-
agement
19(3), 52-56.
Grinblatt, Mark, and Sheridan Titman. 1989. Portfolio performance evaluation:
Old issues and new insights.
Review of Financial Studies
2(3), 393-422.
Hau, Harald. 2001. Location matters: An examination of trading profits.
Jouranl
of Finance
56(5), 1959-1983.


## Page 625

612 • BIBLIOGRAPHY
Murphy, J. Michael. 1980. Why no one can tell who's winning.
Financial Ana-
lysts Journal
36(3), 49
-
57.
Chapter 23: Index and Portfolio Markets
Atchison, Michael D., Kirt C. Butler, and Richard R. Simonds. 1987. Nonsyn-
chronous security trading and market index autocorrelation.
Journal of Fi-
nance
42(1), 111-118.
Cohen, Kalman
J.,
Steven F. Maier, Robert A. Schwartz, and David K. Whit-
comb. 1979. On the existence of serial correlation in an efficient securities
market.
TIMS Studies in the Management Sciences
11(1), 151
-
168.
Gastineau, Gary L. 2002.
The Exchange
-
Traded Funds Manual
(John Wiley &
Sons, New York).
Harris, Lawrence. 1989. The October 1987 S&P 500 stock-futures basis.
Journal of Finance
44(1), 77
-
100.
Harris, Lawrence. 1990. The economics of cash index alternatives.
Journal of
Futures Markets
10(2), 179
-
194.
Harris, Lawrence, and Eitan Gurel. 1986. Price and volume effects associated
with changes in the S&P 500 List: New evidence for the existence of price
pressures.
Journal of Finance
41(4), 815-829.
Harris, Lawrence, George Sofianos, and Jim Shapiro. 1994. Program trading
and intraday volatility.
Review of Financial Studies
7(4), 654-686.
Kawaller, Ira G., Paul
D.
Koch, and Timothy W. Koch. 1987. The temporal
price relationship between S&P 500 futures and the S&P 500 Index.
Jour-
nal of Finance
42(5), 1309-1329.
MacKinlay, Craig A., and Krishna Ramaswamy. 1988. Index-futures arbitrage
and the behavior of stock index futures prices.
Review of Financial Studies
1(2), 137-158.
Renshaw, Edward F., and Paul J. Feldstein. 1960. The case for an unmanaged
investment company.
Financial Analysts Journal
16(1), 43-46; reprinted
[1995] in
Financial Analysts Journal
51(1), 59
-
62.
Stoll, Hans R. 1988. Portfolio trading.
Journal of Portfolio Management
14(4),
20-24.
Stoll, Hans R., and Robert E. Whaley. 1988a. Futures and options on stock in-
dexes: Economic purpose, arbitrage, and market structure.
Review of Futures
Markets
7(2), 224-249.
Stoll, Hans
R.,
and Robert E. Whaley. 1988b. Volatility and futures: Message
versus messenger.
Journal of Portfolio Management
14(2), 20-22.
Subrahmanyam, Avanidhar. 1991. A theory of trading in stock index futures.
Review of Financial Studies
4(1), 17
-
51.
Treynor, Jack L. 1994. The invisible costs of trading.
Journal of Portfolio Man-
agement
21(1), 71-78.
Wagner, Wayne, and Al Winnikoff. 2001.
Millionaire: The Best Explanation of
How an Index Fund Can Turn Your Lunch Money into a Fortune
(Renaissance
Books, Los Angeles).
Chapter 24: Specialists
Bacidore, Jeffrey M., and George Sofianos. 2002. Liquidity provision and spe-
cialist trading in NYSE-listed non-U.S. stocks.
Journal of Financial Econom-
ics
63(1), 133-158.
Conroy, Robert M., and Robert L. Winkler. 1986. Market structure: The spe-
cialist as dealer and broker.
Journal of Banking and Finance
10(1), 21-36.


## Page 626

BIBLIOGRAPHY • 613
Edwards, Amy K. 1999. NYSE specialists competing with limit orders: A source
of price improvement. Working paper, U.S. Securities and Exchange Com-
mission.
Glosten, Lawrence R. 1989. Insider trading, liquidity, and the role of the mo-
nopolist specialist.
Journal of Business
62(2), 211-236.
Goldman, M. Barry, and Avraham Beja. 1979. Market prices vs. equilibrium
prices: Returns' variance, serial correlation, and the role of the specialist.
Jour-
nal of Finance
34(3), 595
-
607.
Goldstein, Michael A., and Kenneth A. Kavajecz. 2000. Eighths, sixteenths, and
market depth: Changes in tick size and liquidity provision on the NYSE.
Journal of Financial Economics
56(1), 125
-
149.
Hakansson, Nils H., Avraham Beja, and Jivendra Kale. 1985. On the feasibil-
ity of automated market making by a programmed specialist.
Journal of Fi-
nance
40(1), 1-20.
Harris, Lawrence, and Venkatesh Panchapagesan. 2002. The information con-
tent of the limit order book: Evidence from NYSE specialist trading deci-
sions, Working paper, Washington University, St. Louis, MO.
Hasbrouck, Joel, and George Sofianos. 1993. The trades of market makers: An
empirical analysis of NYSE specialists.
Journal of Finance
48(5), 1565-1594.
Kavajecz, Kenneth A. 1999. A specialist's quoted depth and the limit order book.
Journal of Finance
54(2), 747
-
771.
Kavajecz, Kenneth A., and Elizabeth Odders-White. 2001. An examination of
changes in specialists' posted price schedules.
Review of Financial Studies
14,
681-704.
Madhavan, Ananth, and George Sofianos. 1998. An empirical analysis of NYSE
specialist trading.
Journal of Financial Economics
48(2), 189-210.
Mclnish, Thomas H., and Robert A. Wood. 1995. Hidden limit orders on the
NYSE.
Journal of Portfolio Management
21(3), 19
-
26.
Ready, Mark 1999. The specialist's discretion: Stopped orders and price im-
provement.
Review of Financial Studies
12(5), 1075-1112.
Seppi, Duane J. 1997. Liquidity provision with limit orders and a strategic spe-
cialist.
Review of Financial Studies
10(1), 103
-
150.
Sofianos, George. 1995. Specialist gross trading revenues at the New York Stock
Exchange. Working paper, New York Stock Exchange.
Stoll, Hans R. 1985. The stock exchange specialist system: An economic anal-
ysis.
New York University Salomon Center Monograph Series in Finance and
Economics
2.
Chapter 25: Internalization, Preferencing, and Crossing
Battalio, Robert H. 1997. Third market broker-dealers: Cost competitors or
cream skimmers?
Journal of Finance
52(1), 241-252.
Battalio, Robert, and Craig W. Holden. 2001. A simple model of payment for
order flow, internalization, and total trading cost.
Journal of Financial Mar-
kets
4(1), 33-71.
Bloomfield, Robert, and Maureen O'Hara. 1998. Does order preferencing mat-
ter?
Journal of Financial Economics
50(1), 3
-
37.
Chordia, Tarun, and Avanidhar Subrahmanyam. 1995. Market making, the tick
size, and payment-for-order flow: Theory and evidence.
Journal of Business
68(4), 543-575.
Easley, David, Nicholas M. Kiefer, and Maureen O'Hara. 1996. Cream-
skimming or profit sharing? The curious role of purchased order flow.
Journal of Finance
51(3), 811
-
833.


## Page 627

614 • BIBLIOGRAPHY
Hansch, Oliver, Narayan Y. Naik, and S. Viswanathan. 1999. Preferencing, in-
ternalization, best execution, and dealer profits.
Journal of Finance
54(5),
1799-1828.
Harris, Lawrence. 1996. The economics of best execution. Working Paper, Mar-
shall School of Business, University of Southern California.
Kandel, Eugene, and Leslie M. Marx. 1999. Odd-eighth avoidance as a defense
against SOES bandits.
Journal of Financial Economics
51(1), 85-102.
Macy, Jonathan
R.,
and Maureen O'Hara. 1997. The law and economics of best
execution.
Journal of Financial Intermediation
6(3), 188
-
223.
Securities and Exchange Commission, Division of Market Regulation. 1994.
Market 2000• An Examination of Current Equity Market Developments (U.S.
Government Printing Office, Washington D.C.).
Chapter 26: Competition Within and Among Markets
Biais, Bruno. 1993. Price formation and equilibrium liquidity in fragmented and
centralized markets.
Journal of Finance
48(1), 157-184.
Black, Fischer. 1971. Toward a fully automated exchange.
Financial Analysts
Journal
27(4), 28-35,44.
Cohen, Kalman J., Steven F. Maier, Robert A. Schwartz, and David K. Whit-
comb. 1982. An analysis of the economic justification for consolidation in a
secondary security market.
Journal of Banking and Finance
6(1), 117-136.
Domowitz, Ian. 1995. Electronic derivatives exchanges: Implicit mergers, net-
work externalities, and standardization.
Quarterly Review of Economics and
Finance
35(2), 163-175.
Fabozzi, Frank J., and Christopher K. Ma. 1988. The over-the-counter market
and New York Stock Exchange trading halts.
Financial Review
23(4), 427-438.
Foerster, Stephen, and Andrew G. Karolyi. 1998. Multimarket trading and liq-
uidity: A transaction data analysis of Canada-US interlistings.
Journal of
International Financial Markets, Institutions and Money
8(3
-
4), 393
-
412.
Garbade, Kenneth D., and William Silber. 1979. Dominant and satellite mar-
kets: A study of dually-traded securities.
Review of Economics and Statistics
61(3), 455-460.
Hamilton, James L. 1987. Off-board trading of NYSE-listed stocks: The ef-
fects of deregulation and the national market
system. Journal of Finance
42(5),
1331-1345.
Harris, Lawrence. 1993. Consolidation, fragmentation, segmentation and reg-
ulation.
Financial Markets, Institutions & Instruments
2(5), 1
-
28; reprinted in
Robert A. Schwartz, ed.,
Global Equity Markets: Technological, Competitive
and Regulatory Challenges
(Irwin, Chicago).
Hasbrouck, Joel. 1995. One security, many markets: Determining the contribu-
tions to price discovery.
Journal of Finance
50(4), 1175-1199.
Hendershott, Terrence, and Haim Mendelson. 2000. Crossing networks and
dealer markets: Competition and performance.
Journal of Finance
55(5),
2071-2115.
Lee, Charles M. C. 1993. Market integration and price execution for NYSE-
listed securities.
Journal of Finance
48(3), 1009-1038.
Madhavan, Ananth. 1995. Consolidation, fragmentation, and the disclosure of
trading information.
Review of Financial Studies
8(3), 579-603.
Mendelson, Haim. 1987. Consolidation, fragmentation, and market perfor-
mance.
Journal of Financial and Quantitative Analysis
22(2), 189
-
208.
Miller, Merton
H.,
and Charles W. Upton. 1991. Strategies for capital market
structure and regulation. In Alexander Miller, ed.,
Financial Innovations and
Market Volatility
(Blackwell, Malden, MA).


## Page 628

BIBLIOGRAPHY • 615
Chapter 27: Floor Versus Automated Trading Systems
Benveniste, Lawrence M., Alan J. Marcus, and William J. Wilhelm. 1992. What's
special about the specialist?
Journal of Financial Economics
32(1), 61-86.
Glosten, Lawrence R. 1994. Is the electronic open limit order book inevitable?
Journal of Finance
49(4), 1127
-
1161.
Mendelson, Morris, and Junius W. Peake. 1979. Which way to a national mar-
ket system?
Financial Analysts Journal
35(5), 31-34,37-42.
Mendelson, Morris, Junius W. Peake, and R. T. Williams. 1979. Toward a mod-
ern exchange: The Peake-Mendelson-Williams proposal for an electronically
assisted auction market. In Ernest Block and Robert A. Schwartz, eds.,
Impending Changes for Securities Markets: What Role for the Exchanges?
( JAI
Press, Greenwich, CT).
Peake, Junius W. 1978. The national market system.
Financial Analysts Journal
34(4), 25-33,81-84.
Venkataraman, Kumar. 2001. Automated versus floor trading: An analysis of ex-
ecution costs on the Paris and New York exchanges.
Journal of Finance
56(4),
1445-1485.
Chapter 28: Bubbles, Crashes, and Circuit Breakers
Brennan, Michael J. 1986. A theory of price limits in futures markets.
Journal
of Financial Economics
16(2), 213
-
233.
Chance, Don M. 1993. Is federal regulation of stock index futures margins nec-
essary?
Journal of Financial Engineering
2(1), 1
-
14.
Coursey, Don L., and Edward A. Dyl. 1990. Price limits, trading suspensions,
and the adjustment of prices to new information.
Review of Futures Markets
9(2), 342-360.
Figlewski, Stephen. 1984. Margins and market integrity: Margin setting for
stock index futures and options.
Journal of Futures Markets
4(3), 385-416.
Friedman, Milton, and Anna Jacobson Schwartz. 1963.
A Monetary History of
the United States, 1867-1960
(Princeton University Press, Princeton, NJ).
Greenwald, Bruce C., and Jeremy C. Stein. 1991. Transactional risk, market
crashes, and the role of circuit breakers.
Journal of Business
64(4), 443-462.
Grossman, Sanford. 1988. An analysis of the implications for stock and futures
price volatility of program trading and dynamic hedging strategies.
Journal
of Business
51(3), 275-298.
Grundfest, Joseph A., and John B. Shoven. 1991. Adverse implications of a se-
curities transactions excise tax.
Journal of Accounting, Auditing and Finance
6(4), 409-442.
Hakansson, Nils H. 1978. Welfare aspects of options and supershares.
Journal
of Finance
33(3), 759-776.
Harris, Lawrence. 1989. The dangers of regulatory overreaction to the October
1987 stock market crash.
Cornell Law Review
74(5), 927-942.
Harris, Lawrence. 1998. Circuit breakers and program trading limits: What have
we learned? In R. E. Litan and A. M. Santomero, eds.,
Brooking-Wharton
Papers on Financial Services
(Brookings Institution, Washington DC).
Hsieh, David A., and Merton H. Miller. 1990. Margin regulation and stock
market volatility.
Journal of Finance
45(1), 3-30.
Katzenbach, Nicholas. 1987. An overview of program trading and its impact on
current market practices. NYSE Report, December 21.
Kindleberger, Charles P. 2001. Manias, Panics, and Crashes: A History of Fi-
nancial Crises, 4th ed. (Wiley Investment Classics, New York.
Kleidon, Allan W. 1992. Arbitrage, nontrading, and stale prices: October 1987.
Journal of Business
65(4), 483
-
508.


## Page 629

616 • BIBLIOGRAPHY
Kodres, Laura E., and Daniel P. O'Brien. 1994. The existence of Pareto-
superior price limits.
American Economic Review
84(4), 919-932.
Kyle, Albert S. 1988. Trading halts and price limits.
Review of Futures Markets
7(3), 426-434.
Lee, Charles M. C., Mark J. Ready, and Paul J. Seguin. 1994. Volume, volatil-
ity, and New York Stock Exchange trading halts.
Journal of Finance
49(1),
183-214.
Leland, Hayne E. 1980. Who should buy portfolio insurance.
Journal of Finance
35(2), 581-594.
New York Stock Exchange, Office of Research and Planning. 1991. The Rule
80A index arbitrage tick test. Report to the U.S. Securities and Exchange
Commission, May 31.
Roll, Richard. 1988. The international crash of October 1987.
Financial Ana-
lysts Journal
44(5), 19
-
35.
Rubinstein, Mark. 1985. Alternative paths to portfolio insurance.
Financial
Analysts Journal
41(4), 42
-
52.
Rubinstein, Mark, and Hayne Leland. 1981. Replicating options with positions
in stock and cash.
Financial Analysts Journal
37(4), 63-72.
Santoni, Gary J., and Tung Liu. 1993. Circuit breakers and stock market volatil-
ity.
Journal of Futures Markets
13(3), 261
-
278.
Schwert, William G. 1989. Margin requirements and stock volatility.
Journal of
Financial Services Research
3(2/3), 153
-
164.
Schwert, William G., and Paul Seguin. 1993. Securities transactions taxes: An
overview of costs, benefits and unresolved questions.
Financial Analysts Jour-
nal
49(5), 27-35.
Stiglitz, Joseph E. 1989. Using tax policy to curb speculative short-term trad-
ing.
Journal of Financial Services Research
3(2/3), 101
-
115.
Subrahmanyam, Avanidhar. 1994. Circuit breakers and market volatility: A the-
oretical perspective.
Journal of Finance
49(1), 237-254.
Subrahmanyam, Avanidhar. 1995. On rules versus discretion in procedures to
halt trade.
Journal of Economics and Business
47(1), 1
-
16.
Subrahmanyam, Avanidhar. 1997. The ex ante effects of trading halting rules
on informed trading strategies and market liquidity.
Review of Financial Eco-
nomics
6(1), 1-14.
Subrahmanyam, Avanidhar. 1998. Transaction taxes and financial market equi-
librium.
Journal of Business
71(1), 81-118.
Summers, Lawrence H., and Victoria P. Summers. 1989. When financial mar-
kets work too well: A cautious case for a securities transactions tax.
Journal
of Financial Services Research
3(2/3), 261
-
286.
Treynor, Jack L. 1988. Portfolio insurance and market volatility.
Financial An-
alysts Journal
44(6), 71
-
73.
Treynor, Jack. 1998. Bulls, bears, and market bubbles.
Financial Analysts Journal
54(2), 69-74.
U.S. Securities and Exchange Commission. 1988.
The October 1987 Market
Break: A Report by the Division of Market Regulation, U.S. Securities and Ex-
change Commission
(U.S. Government Printing Office, Washington DC).
Warshawsky, Mark. 1989. The adequacy and consistency of margin require-
ments: The cash, futures, and options segments of the equity market.
Re-
view of Futures Markets
8(3), 420
-
437.
Chapter 29: Insider Trading
Arshadi, Nasser, and Thomas H. Eyssell. 1993.
The Law and Finance of Corpo-
rate Insider Trading: Theory and Evidence
(Kluwer Academic Publishers,
Boston).


## Page 630

BIBLIOGRAPHY • 617
Cornell, Bradford, and Erik R. Sirri. 1992. The reaction of investors and stock
prices to insider trading.
Journal of Finance
47(3), 1031-1060.
DeMarzo, Peter M., Michael J. Fishman, and Kathleen M. Hagerty. 1998. The
optimal enforcement of insider trading regulations.
Journal of Political Econ-
omy
106(3), 602-632.
Hagerman, Robert L., and Joanne P. Healy. 1992. The impact of SEC-required
disclosure and insider-trading regulations on the bid/ask spreads in the over-
the
-
counter market.
Journal of Accounting and Public Policy 11(3),
233
-
243.
Holden, Craig, and Avanidhar Subrahmanyam. 1992. Long-lived private infor-
mation and imperfect competition.
Journal of Finance
47(1), 247-270.
Hu, Jie, and Thomas H. Noel. 1997. The insider trading debate.
Federal Reserve
Bank of Atlanta Economic Review
1997(4),
34
-
45.
Manne, Henry G. 1966a. In defense of insider trading.
Harvard Business Re-
view
44(6),
113
-
122.
Manne, Henry G. 1966b.
Insider Trading and the Stock Market
(The Free Press,
New York).
Meulbroek, Lisa. 1992. An empirical analysis of illegal insider trading.
Journal
of Finance
47(5), 1661-1700.
Spiegel, Matthew, and Avanidhar Subrahmanyam. 1992. Informed speculation
and hedging in a noncompetitive securities market.
Review of Financial Stud-
ies
5(2), 307-330.
NEW STAMP

## Page 632

Index
Italic page numbers indicate defined terms
Abel/Noser (co.), 424
absolute advantage,
476,
479
absolute performance evaluation,
445
-
47
ACATS.
See
Automated Customer
Account Transfer Service
acquiring firm,
360
active manager,
197, 442,
443, 458,
487
-
88
actual shipper,
352,
353
ADRs.
See
American depository
receipts
adverse selection, 287-91, 303
adverse selection loss,
286
adverse selection risk, 77, 135, 286,
340-41
adverse selection spread component,
289
-
90, 299,
300-302, 320-21
affirmative determination, 155
after-hours trading session,
92
afternoon session,
92
agency order,
70
agency trader.
See
brokers
agency trading, 32
aggressively priced limit order,
73
aggressive trader, 246, 423
AIMR.
See
Association for
Investment Management and
Research
air pollution, 213
all-or-none order,
83
alternative trading systems (ATSs),
49
American depository receipts
(ADRs),
40,
66
American Stock Exchange
hand signals, 104
listed stocks,
48
Major Market Index, 485
specialists, 495
American-style option,
42
analytic risk.
See
model risk
animateurs, 495
AOL Time Warner, 525, 536
application programming interface,
536
arbitrage, 347-79
conversion,
351,
354-56
crossed markets,
375
delivery,
350,
353
equilibrium spreads, 377-78
index, 563, 577, 580
opportunities, 373-75
pairs trading, 358-60
pure, 350-56
quantity characterization, 375-76
risks,
357, 360,
362-73
shipping,
350,
351-53
simple characterization, 349-50
speculative, 356-63
statistical, 360
terminology, 348-49
types of, 350-63
See also
arbitrageurs
arbitrage numerator,
348
arbitrage opportunity,
348
arbitrage spread,
349,
377-78
arbitrageurs, 185, 243
competition among, 377-78
and dealers and brokers, 376-77
definition of, 6, 194, 232, 347,
348, 404
fixed-income, 40
as liquidity porters, 404, 406, 534
and October 1987 crash, 563
statistical,
194, 360
trading style and strategies, 196,
226, 232-35
types of, 194
See also
arbitrage
Archer Daniel Midlands Company,
147
Archipelago Exchange, 35, 49, 97,
306, 543
Arizona Stock Exchange, 121, 543
Asian markets, 53, 57, 90, 253
619


## Page 633

620 •
INDEX
ask price,
5, 69,
280, 295
ask size,
281
asset, 178, 180, 254
asset bubble,
254
asset exchanger,
6,
33,
181
-
82,
193,
214, 252
Association for Investment
Management and Research
(AIMR), 65, 66, 448
asymmetrically informed trader,
299,
310
asymmetric information, 7, 312,
314-15, 323, 326-27, 332,
531-32
AT&T, 11-14
ATM-fit currency, 182
at parity order,
117
ATSs.
See
alternative trading systems
auction market,
94, 95
-
96,
303-10,
342-43, 501
audit trail,
163, 544
autarkies,
214
Automated Customer Account
Transfer Service (ACATS), 522
automated trading.
See
electronic
trading systems
away from the market,
74
back contract, 54
Bangladeshi Stock Exchange, 544
bankruptcy, 37, 170-71
Barclays Global Investors, 486
Barings Bank, 198
barter,
348,
350
bartered trade,
181
base price,
185
basis,
349,
352, 353
basis risk,
349, 363, 367
-
68
basket market,
490
batch execution systems,
91
Battle of Waterloo, 266
BBO (best bid and offer).
See
market
quotation
bearding the trade,
291
Beardstown Business and
Professional Women's Club, 446
bearer bonds, 40,
168
bear market,
9
behind the market,
74
benchmark indexes, 449, 451, 465
benchmark returns,
447
-
48
beneficial owners,
148, 169
beneficiaries,
33
Berkshire Hathaway, 323, 455, 463,
475
Bernard Madoff.
See
Madoff
Investment Securities
best ask.
See
best offer
best bid,
70,
217
best efforts offering,
39, 157
best execution,
160
-
61,
162-63,
514-20
best offer,
70,
217
Betreuers, 495
betting, 91
bid,
69,
125
bid/ask bounce,
299, 413,
432
bid/ask spread, 71, 93, 297-321
adverse selection and uninformed
traders, 303
adverse selection component,
289
-
90,
299-302
cross-sectional predictions, 311-17
dealer, 297-98, 313, 519
definition of, 70, 280
equilibrium spreads in continuous
order-driven auction markets,
303-10
market versus limit orders, 381
primary determinants,
311,
312-13
and public traders' competiton
with dealers, 310-11
secondary determinants,
312,
314-17
transaction cost component, 299
bidder,
360
bidding price.
See
bid price
bid price,
5, 69,
70, 280, 295
bid size,
281
bilateral search,
394, 395,
396-98
bilateral trading,
91
BINC (co.), 29-30
Black, Fischer, 223, 371
black market, 59
Black-Scholes option pricing theory,
253, 328
block assemblers.
See
block brokers
block brokers, 144, 145, 248,
322,
329-30
block dealers,
248, 322,
328-30,
402,
406
block facilitators,
195,
196, 328
block initiators,
322
block liquidity suppliers,
322,
330
block markets,
140
block positioners.
See
block dealers
block traders, 322-37, 521
asymmetric information problem,
323, 326-27, 332


## Page 634

INDEX • 621
definition of, 294,322
hardworking, 324
latent demand problem, 323-24,
332
motives, 330-31
order exposure problem, 323,
324-25
price discrimination problem, 323,
325-26,332
and regular markets, 333-34
seller- versus buyer-initiated
trades, 332-33
skills, 336
statistical definitions of trades,
322-23
sunshine, 327-28
trade reporting issues, 330
trading problems, 323-27
upstairs market, 322,328-32
block trades,
322
Bloomberg Tradebook, 35
bluffers
and dealers, 292,294
definition of, 195,197,259,273
exposure of trading, 385
fundamentals of bluffing, 265-70
and informed speculators, 265-67
and liquidity providers, 270-73,
575
long-side bluff, 259-65
and market manipulation, 259-75
and poker, 268
techniques, 274
trading strategies, 196
and uninformed traders, 6
and value traders, 268-69
board-based trading systems,
107
board of trade,
54
Board of Trade Clearing Corporation
(BOTCC), 25-26
bond(s)
bearer,
40,168
convertible, 44
corporate, 54
definition of, 40
government, 46
lack of trading in, 46
municipal, 54
NYSE, 54
stripping,
40
trade, 28-29
Treasury,
40,
54,58
types of, 40
wealth in, 45
zero-coupon,
40,
206
bonuses, 158,252
book entry registrations,
168
bookies, 93,136
borrowers,
6,
33,178-81,193,205,
252
Boston Stock Exchange, 48-49
BOTCC.
See
Board of Trade
Clearing Corporation
Brazil, 66
Brazilian straddle,
37
breaking a T-bill,
156
Bridge Information Systems, 11,12,
19,20
broad-based events,
556,
559
broadcast services,
98
Broadwing (co.), 363
brokerages,
6,
147-50
broker call money rate,
155
broker-dealers,
34, 161,
332
brokered markets,
90, 95
-
96
brokers, 139-74
access to dealers, 144
access to exchanges, 142-44
accounting systems, 148
and arbitrageurs, 376-77
back office operations, 148-49
bankruptcies, 170-71
best execution, 160-61,162-63,
514-15,518,519
broker-to-broker transfers, 522
cash management and stock
lending and borrowing, 150
clearing and settlement among
traders, 141
commissions, 139,151-55,158,
508
compensation, 158
compliance, 149
costs, 158
credit management, 141,149
data and voice systems, 148-49
and dealers, 144,281-82,283
definition of, 5,32,34,139
dishonest, 163-71
display instructions, 84-85
distinguishing from exchanges, 94
dual trading problem, 161
exam for competency of clients,
150
as expert traders, 144-46
floor-based trading skills, 143-44,
146
front office operations, 147
functions of, 139-47
indication of interest, 387


## Page 635

622 • INDEX
brokers
(Continued)
information technologies, 159
limit orders, 146
market-not-held orders, 82
merger and acquisition fees, 158
as negotiators, 144-46
new versus established, 145
and order books, 109
order crossing by, 537
order exposure management, 146
and order flow, 155,517,518,521
order preferencing, 161-63
performance measurement, 159-60
principal-agent problem, 159-63
profitability, 150-58
proprietary operations, 149
revenues, 151-58
risk management, 150
and search problems, 397-98
security lending fees, 158
and sell side, 34
short interest rebate, 155-57
specialists as, 500-501,508
structure of firm, 147-50
transaction audit services, 519
and transaction costs, 430
transaction types, 140
underwriting fees, 157-58
Wednesday trading halts of 1968,
148
wholesale over-the-counter, 59
bubbles, 555-83
definition of, 556
examples of, 558-71
fundamental and transitory
volatility, 557-58
typical dynamics, 556-57
bucketeers,
136
Buffet, Warren, 455,463,475
bulletin board,
107
bull market,
9
buy and hold strategies,
197,
442,488
buy downtick orders,
81
buying the stub,
370,
373,374
buying time,
309
buy-ins, 372-73
buy side,
32,
33,534
buy-side traders, 380-92
benefits of exposure, 383-84
costs of exposure, 384-86
defensive strategies, 386-89
definition of, 33,144
exposure decisions, 382-83,390
market control of exposure costs,
389
market versus limit orders, 381-82
and October 1987 crash, 563
trader timing, 437
calendar spread,
358
California, 413
call markets,
89, 90
-
91,
110,116,
119-20,130-32
call options,
42,
355
cancellations, 499
Cantor Financial Futures Exchange
(CX), 56
Cantor Fitzgerald, 28,58,59,108,
306,531
capital adequacy standards,
511
capital allocation, 210,211
capital gains, 192
capital gains return,
447
capitalization-weighted index,
485
capital loss exclusion, 193
card playing, 256,268,291,324
carrying cost,
349
carrying cost risk,
363, 369,
370-73
cash commodity trade, 22-26
cash exchange,
24
cash flow,
42,
178,180
cashier,
150
cash market,
182
cash offer,
362
cash-settled contract,
41
cash settlement, 36,85, 86
casinos, 216
CBOE.
See
Chicago Board Options
Exchange
CBOT.
See
Chicago Board of Trade
Cede & Co., 169
cellular phones, 109
Center for Research in Security
Prices.
See
CRSP
Central Registration Depository, 164
CEO.
See
chief executive officer
CFA.
See
chartered financial analyst
CFTC.
See
Commodity Futures
Trading Commission
chartered financial analyst (CFA),
66
Chiarella, Vincent, 586
Chicago Board of Trade (CBOT), 23
contract volumes, 55
Dow Jones Industrial Average, 485
floor-based trading, 543
nighttime trading sessions, 92
oats trading, 359
soybean trading, 356
trading floor, 24-25,90
wheat futures, 335


## Page 636

INDEX • 623
Chicago Board Options Exchange
(CBOE), 52, 485, 495, 496
Chicago Mercantile Exchange
contract volumes, 55
demutualization, 35
exchange for physical procedure,
334
and Exchange Stock Portfolio, 490
floor-based trading, 543
Major Market Index, 485
nighttime trading sessions, 92
rule covering large price changes,
565
Chicago Stock Exchange, 47, 49, 52,
92
chief executive officer (CEO), 593
chief information officer,
148
churning,
151, 167
Cincinnati Stock Exchange, 47, 49
circuit breakers,
572,
573-80
Cisco Systems, 37, 73
cleaning up the book,
333
clearance, 141, 142-43
clearing agents,
35
clearinghouses
definition of, 36
of futures exchanges, 54, 184
as self-regulatory, 64
clearing members,
36,
37
closed book markets,
109
closely held securities,
158
closet indexer,
465
closet index portfolio, 468
clothing industry, 207
CMOs.
See
collateralized mortgage
obligations
CNBC (network), 363
cold fusion, 566, 568
collars,
572,
576, 580
collateralized mortgage obligations
(CMOs),
41
command economies,
207
commercial paper,
40
commission merchants.
See
brokers
commission recapture agreements,
154
commissions,
139,
151-55, 158, 306,
437, 500, 508, 517-19
commission traders.
See
brokers
Commodity Exchange Act, 63
Commodity Futures Modernization
Act, 63
Commodity Futures Trading
Commission (CFTC), 60-61, 63
commodity markets, 65, 314-15, 474
common value,
341
communications, 8
accuracy in oral, 107-9
technology, 524-26, 553
telecommunications, 105
communism, 212-13
Communist stock exchanges, 50
comparative advantage,
475
-
76
compensation, 158, 593, 596
competition
among equity markets, 539
among informed traders, 239-40
among market centers, 535-38
among regulators, 63
between dealers and public traders,
310-11
to exploit insider information, 592
with free entry and exit, 7-8
secondary precedence rules, 536-38
spreads in dealer markets, 298,
310-11
within and among markets,
524-42
complex instruction set computing
(CISC) approach, 540
compliance officers,
149,
167
computation, 446, 447, 553
computers, 524-25, 551, 589
See also
electronic trading systems
concealed traders,
95
conditional estimates,
289
conditional expected returns,
181
confidence, 557
confidence level of test,
454
confidentiality, 246
confirmations, 499
conflict of interest, 161, 162
consolidated limit order book,
522
consolidated market,
524,
526-30,
533-35, 538-40
constant value stock offer,
362
continuous order-driven auction
markets, 303-10
continuous trading sessions,
89, 90,
91, 92, 110
contract(s)
commodity, 314-15
definition of, 44
derivative, 41-43, 491
futures,
42,
46,
184
-
85,
186, 189,
215, 255, 335
gambling,
43
hybrid, 43-44
insurance,
43
options,
42,
45,
75,
76, 100,
186-87, 354, 563


## Page 637

624 • INDEX
contrarian traders,
79,
429
control of inventory,
284
control structure,
47
conversion arbitrage,
351,
354-56
conversion arbitrageur,
354
convertible bonds,
44
corn, 215, 233, 351
cornering the market,
254,
255
corporate bonds, 54
corporate insiders,
587
corporations, 178, 180, 315, 370
correlated prices,
348
covered positions,
32, 183
crashes, 555-83
definition of, 556
examples of, 558-71
fundamental and transitory
volatility, 557-58
typical dynamics, 556-57
cream skimming,
503,
519
credit, 141, 149
credit check, 142
credit manager,
149
credit quality,
40
credit spread,
358
creditworthiness,
8,
532-33
critical value, 453,
454
crossed market,
375
crossing.
See
internal order crossing
crossing networks
definition of, 132
and derivative pricing rule, 132-37
informed trading in, 135
order exposure in, 531
price ownership, 134
problems with derivative pricing,
134-37
crossing prices,
132,
134
cross-subsidizers,
191,
193
CRSP (Center for Research in
Security Prices), 486
currency, 47, 240
ATM-fit, 182
exchange for physical, 335
markets, 357
peso problem,
468
retail markets, 59
See also
foreign exchange
currency swap,
42
current yield,
447
custodial bank,
28
-
29
custodian,
38,
167
customer service agent,
147
CX. See
Cantor Financial Futures
Exchange
cyclic arbitrage,
356,
357
dance strategies, 398
data vendors,
98
-
99,
107
DAX.
See
German Deutscher
Aktienindex
day limit orders,
70
day orders,
12, 83,
84, 293
day traders,
24,
279
dead cat bounce,
557
deadweight,
349
dealer markets.
See
quote-driven
markets
dealers
adverse selection risk, 286
and arbitrageurs, 376
attracting order flow, 282-83
best execution, 515, 516-17, 518
and bid/ask spreads, 297-98,
310-11, 313, 519
and bluffers, 292, 294
bookies as, 93
and brokers, 144, 281-82, 283
competition with public traders,
310-11
definition of, 5, 33, 197, 278
diversifiable inventory risk, 285-86
and informed traders, 287-92,
295, 519
inventories, 283-87, 294-95
layoffs, 294
as liquidity suppliers, 278-96, 575
market values versus fundamental
values, 286-87
monopoly, 298
and order books, 109-10
Order Handling Rules, 217
order
preferencing,
161-63
pricing mistakes, 291-92
and profits, 195-96, 197, 205, 206,
279, 297, 508-9
quotations, 280-81, 283, 287-92
quote-driven markets, 90, 92-93
responses to adverse selection,
287-91
and sell side, 34
and specialists, 279, 496-99,
508-9
and speculators, 6
spreads, 297-98, 310-11, 313,
344-45
trading with, 281-82
and value traders, 294
well-informed and poorly
informed, 292-94
Dealing 300 trading system, 59
debt instruments, 40-41
debt issues, 316


## Page 638

INDEX • 625
deceptive strategies, 387-88
decimal pricing, 115
decision price,
437
deep discount broker,
151,
152
deep market,
294
defensive liquidity supplier,
383
defensive strategies, 386-89
delivery arbitrage,
350,
353
delivery months, 54
delta of the option,
355,
377, 561
demand of liquidity,
70
demand schedule,
121,
122-24
Department of Agriculture (U.S.),
242
depositories,
38,
167-68
Depository Trust and Clearing
Corporation (DTCC), 28
Depository Trust Company (DTC),
37, 38, 169
depth, 340,
398,
399
derivative pricing rules,
132,
134-37
derivatives,
41
-
43,
50, 53, 491
designated multiple market maker
trading system,
496
designated primary market maker,
495
designated primary market maker
trading systems.
See
specialist
trading systems
designated sponsors,
495
Deutsche Borse, 35, 91, 495
dice, 350
Digex (co.), 373
Digital Subscriber Line (DSL)
telecommunications, 267
Dimensional Fund Advisors, 384
direct access,
142
-
43,
552
direct brokerage relationships,
154
direct compensation,
593
discount brokers, 151, 152
discriminatory pricing rule,
115
-
16,
126-32
display instructions,
84
-
85
display precedence,
117
distributed access markets,
90
distribution,
453,
467
distributors,
278, 332
diversifiable inventory risks,
285
-
86
diversified portfolios, 315
dividend capture,
192
dividend record date,
192
divorce, 395
DJIA.
See
Dow Jones Industrial
Average
DKs (Don't Knows),
35, 107
dollar-yen, 357
dot matrix printers, 489, 562
doubling-down betting strategy,
469
Dow Jones and Co., 485
Dow Jones Industrial Average
(DJIA), 485, 545, 558, 559-60,
573, 578
downtick,
81,
423
drugs, 596
DTB futures exchange, 543
DTC.
See
Depository Trust
Company
DTCC.
See
Depository Trust and
Clearing Corporation
duals,
399
dual traders.
See
broker-dealers
due diligence,
317,
471
duration mismatch,
187
dynamic hedges,
253, 355
dynamic trading strategy,
560
eBay, 95
ECNs.
See
electronic
communications networks
E. coli
infection, 444
econometric transaction cost
estimation methods, 422,
423,
432, 434
economies
command,
207
disagreements among economists,
204
less-developed, 181
market-based,
208
-
9,
210, 212-13
welfare, 203-5
economies of scale,
159
economists, 204, 305, 374, 394
effective spreads,
424, 425,
428
efficient markets,
223, 240,
243,
250-51, 254
efficient resource allocation,
207
EFP.
See
exchange for physical
EFTs.
See
exchange-traded funds
electricity, 413
electronic communications networks
(ECNs), 11,
35,
49, 94, 514,
520, 528, 536
electronic marketplaces.
See
electronic
trading systems
electronic order-routing systems,
103,105,139,500
electronic proprietary traders,
509
electronic trading systems, 6, 526-27,
531
convenience of distributed access,
546-47
costs, 549-51


## Page 639

626 • INDEX
electronic trading systems
(Continued)
and fairness, 544-46
market data reporting, 107, 549
negotiation speed, 547-48
system capacity, 547
unreliable data networks, 550
versus floor-based systems, 97,
543-54
eligible participants,
334
Elkins/McSherry (co.), 424
Employee Retirement Income
Security Act (ERISA), 155,
332, 420
entrepreneurship, 6, 595-97
episodic volatility,
410
equal-weighted index,
486
equilibrium spreads, 381
analysis of, 304-6
in continuous order-driven
markets, 303-11
costly limit order management,
306-7, 309
definition of, 305
differential commissions, 306
equities, 39-40, 49
equity index options, 63
equity options markets, 50, 53-54
equity warrants, 44
ERISA.
See
Employee Retirement
Income Security Act
escrow agents,
36
eSpeed, 108, 531
euro-dollar, 357
Euronext, 85, 91, 109
European-style options,
42
evasive strategies, 386-87
evolution, 452, 475, 479
ex ante transparent,
101
excess demand,
121,
134
excess supply,
121
exchange for physical (EFP),
24,
334,
335, 350
exchange order preference rules,
498
-
99
exchanges
block trades, 323, 334
broker access to, 142-44
clearing and settlement at, 142-43
Communist, 50
corporate ownership, 35
definition of, 6
equity options, 53
futures, 55-57
historic anecdotes, 47
lunchtime recess, 92
and release of information, 241, 242
rule-based order-matching
systems, 94
as search engines, 396
as self-regulatory, 63
and specialists, 501, 509-10
as trade facilitators,
34
-
35
trading rules, 7
See also specific exchanges
Exchange Stock Portfolio,
490
exchange-traded funds (EFTs),
40,
492
execution price uncertainty,
72, 364,
366
execution systems, 92-96
batch,
91
brokered markets,
90, 95
-
96
definition of, 92
hybrid markets,
96
order-driven,
90, 94
-
95,
139
quote-driven dealer markets,
90,
92
-
93
types of, 90, 96
execution uncertainty,
77
executive stock options,
212
expense accounts, 283
expiration dates,
41,
42, 186
expiry of contract,
41
explanations, 475
explicit transaction costs,
421
ex post regret, 77, 78
ex post transparent,
101
extended trading hours,
92
externalities, 7, 145,
535,
536
Exxon Mobil Corporation, 16-19
face value, 40
facilitations,
402
factor loadings,
360
factor models,
360
fair access markets,
544,
545-46
fair value,
349
FASB.
See
Financial Accounting
Standards Board
fast trading markets,
547
fat-tailed distribution,
467
FCMs.
See
futures commission
merchants
Federal Reserve, 58, 61, 315
Fidelity Investments, 149
Fidelity Magellan Fund, 269, 455
fill-or-kill orders.
See
immediate-or-
cancel orders
Financial Accounting Standards
Board (FASB), 65


## Page 640

INDEX • 627
financial advisers,
139
financial analysts, 66,
147
financial assets,
39
-
41, 178
financial engineering,
353
financial instruments,
38
FINEX (New York Cotton
Exchange), 92
firm bid/offer market,
490
firm prices,
69, 280
firm quotes,
280
firm-specific factors,
443
First Boston, 22
first harvest contract,
54
fixed commission rates,
151
fixed-income arbitrageurs, 40
fixed-income products,
41
fixed-price open offering,
39
fixed-rate stock offer,
362
FIX Protocol, 149
flat distribution,
467
flat organizations,
228
-
29
fledglings,
190
-
91,
193
Fleischmann, Martin, 566
FLEX Options, 52
float,
255
floating limit orders,
309
floor-based trading, 48
advantages of, 104
costs, 551-52
exchange of information in,
548-49
explanation of, 99
and fairness, 544-46
futures markets, 521
market data reporting, 549
negotiation speed, 547-48
in options exchanges, 50
skills, 143-44, 146
versus automated trading, 543-54
floor brokers, 146,
147,
500, 530
floor time precedence,
117
flour, 183-85
forced buy-in,
372
foreign exchange
high transaction costs of, 59
postal reply coupons, 470
predicting trades, 253
rates, 357
trade, 29-30, 293
See also
currency
forward contracts
definition of, 42, 183
hedging with, 183-84, 215
tomato, 41
forward market,
39
fourth market,
49
fractional pricing, 115
fragmented market,
524,
526,
530-35, 539-40
fraud, 255, 469-71, 473, 535
fraudulent trade assignment, 165,
166
free-market economies.
See
market-
based economies
frictions, 394,
486
front contracts,
54
front month contracts.
See
front
contracts
front runners, 245-51, 256
aggressive, 246
and block trading, 324-25
definition of, 161, 165, 195, 245
examples of, 164
illegal scheme, 246
legal operation by observant trader,
247
and liquidity, 251
and market efficiency, 250-51
passive, 248-50
quote matching, 386
self-front-running, 249
skilled, 248
trading strategy, 196
fuels, 412
full-service brokers,
151,
152
fundamental valuation factor,
233
fundamental values
changes in, 239
definition of, 177, 206, 222-23
information about, 243
and informed traders, 222-24,
226-27
of instrument, 177, 222
and value traders, 403
versus market values, 286-87
fundamental volatility,
410,
411-13,
416
fungible instruments,
7
futile traders,
177, 197
-
98,
200
futures commission merchants
(FCMs),
152
futures markets, 54
and arbitrage, 350-53, 359,
361-62, 368
bids and offers, 281
block trading, 334-35, 521
contracts,
42,
46,
184
-
85,
186,
189, 215, 255, 335
exchange for physical, 350
global exchanges by contract
volume, 57


## Page 641

628 • INDEX
futures markets
(Continued)
hand signs, 108
hedging, 184-85, 188, 215, 335
index, 562-63
margins, 156
onion, 65
option contracts,
42
regulators, 63
standardized contracts,
42
ticker symbols, 99, 101
trade, 22-26, 39, 46
trading floor, 143
U.S. exchanges by contract
volume, 55-56
ValueLine Index, 371
futurity,
41,
44
GAAP.
See
generally accepted
accounting practices
gains from trading,
123
gamblers, 6, 33, 177,
189
-
90,
193,
200, 205, 216, 252, 576
gambling contracts,
43
gaming the measure,
430
gamma of the option,
377
Garban-Intercapital, 59
gas, 342
General Electric, 361-62
generally accepted accounting
practices (GAAP), 47, 314
geometrically weighted index,
486
German Deutscher Aktienindex
(DAX), 42
Germany, 314
Global Instinet Crossing, 132, 133
GLOBEX trading system, 78, 325,
551
Glosten-Harris spread components
estimation model,
435
Glosten-Milgrom theorem, 300, 302,
320-21, 435
gold, 232, 233
Goldman Sachs, 21-22, 371
good-after orders,
83,
84
good-on-sight orders.
See
immediate-
or-cancel orders
good orders,
82
-
83
good-this-month orders,
83,
84
good-this-week orders,
83,
84
good-till-cancel orders,
83,
84
good-until orders,
83,
84
Gosset, W.S., 454
government, 178, 213, 242, 413
government bonds, 46, 59
gravitational effect,
573,
574
Grossman, Israel, 589
Gulf of Mexico, 352
gunning the market,
256,
257
Hammer, Armand, 230
handicapping, 479
hand signaling, 90, 104, 108
hanging over the market,
324,
325
hard dollars,
153
harvesting losses,
193
"Heard on the Street" (column), 587
hedged positions,
182
hedge funds,
349
hedge portfolios,
348,
349-50, 353,
354, 356-57, 362, 373
hedge ratios,
348
hedgers and hedging, 33, 46, 182-89,
193, 205
definition of, 6, 43, 182
with forward contracts, 183-84,
215
with futures contracts, 184-85,
215, 335
hedging by combining businesses,
183
with index futures contracts, 186
markets, 188-89
predicting trades, 253
public benefits of, 214
with stock options contracts,
186-87
with swaps, 187-88
wheat and flour example, 183-85
hedges,
182
hedging vehicles,
182
hidden orders.
See
undisclosed orders
highly correlated estimates,
237
highways, 209
high-yield bonds, 40
hitting the bid,
280
Hoke, Gary Dale, 267
holders of record,
169
holding period return,
446
-
47
Hong Kong Futures Exchange, 109
Hong Kong Stock Exchange, 52
horse racing, 91, 190, 479
hot order flows,
519
house account, 149
Hutchinson, Benjamin P., 255
hybrid contracts,
43
-
44
hybrid markets,
96,
532
hypothecated securities,
169
IBM,
28, 325
Iceberg of Transaction Costs,
437


## Page 642

INDEX • 629
iceberg orders.
See
undisclosed orders
ICSA.
See
International Councils of
Securities Associations
immediacy,
70,
279, 294, 297,
398,
399
immediate-or-cancel orders,
83,
84
implementation risk,
363, 364,
366-67
implementation shortfalls, 424, 426
implicit transaction costs,
421,
422-23
inappropriate order exposure,
165
in-balance inventories,
283,
284
index arbitrage, 563, 577, 580
index components,
484
index creators,
485
index divisors,
485
indexed limit orders,
309
index enhancement funds,
354
index funds, 40,
484, 486
-
87
index futures market, 562-63
indexing,
197,
457-60
index managers,
486
index markets,
484,
487-93
index participations,
67
index portfolios, 468, 469, 489-91
index products,
484,
491-92
index replicators,
442
index trading mechanisms,
489
indication of interest,
107, 387
indirect compensation,
593
inefficient traders,
197
inelastic demand,
412
inferior prices,
70
infinitely lived contracts,
41
information
asymmetries, 7, 312, 314-15, 323,
326-27, 332, 531-32
collection systems, 97
disclosure rules, 314
distribution systems, 98-99
and floor-based trading, 548-49
inside,
228,
584-88
public,
223,
241-43
of specialists, 501-2
technologies, 8, 524-26
unequal access to, 532
See also
informed traders
informationless trading strategy,
468
information-oriented technical
traders,
194, 195,
196,
226,
230
-
32,
235, 243
informative prices, 4, 206-14, 218,
222,
224, 235, 237, 238-39,
241, 243
informed traders, 223-24, 252,
384-85
asymmetric information,
299,
310,
531
and block trading, 330
competition among, 239-40,
243
in crossing networks, 135
and dealers, 287-92, 295, 519
definition of, 177, 194, 197, 222
and informative prices, 224,
238-39, 243
liquidity and predictability, 236,
237, 239-40
market paradox, 238-39
profits, 237, 238
trading strategies, 196, 225-26
trading styles, 226-35
and transaction costs, 430
initial margin,
156
initial public offering (IPO), 39, 253
innovative market, 527-28
inside information,
228,
584-88
insider trading, 315, 584-99
bounty hunting for, 590
corporate control issue, 594
debate over, 591-97
definition of, 584
and inside information, 584-88
practical judicial issues, 588-91
proving, 590
inside spread,
280,
281
See also
bid/ask spread
inside the market,
70
Instinet, 35, 132, 543
institutional traders, 281, 283, 290
instrument(s)
correlation of, 44
debt, 40-41
definition of, 4
and informed traders, 223
similar, 539
spread orders,
84
straddle,
37
U.S. markets by class, 45
See also
trading instruments
insurance companies, 65
insurance contracts,
43
interconnect access fees,
536
interdealer brokers,
58, 93
interest, 155-56, 315
interest rate swap,
42, 187
Intermarket Trading System,
105
Intermedia Communications, 363,
373


## Page 643

630 • INDEX
intermediate commodities,
188
internalization of orders,
161, 514,
520, 522
internal order crossing, 514, 520-21,
522, 537
internal rate of return,
446,
447
International Councils of Securities
Associations (ICSA), 66
international equity derivatives
markets, 50, 53
international futures markets, 54
International Organization of
Securities Commissions
(IOSCO), 66
International Securities Exchange,
50, 543
international stock markets, 50-52
Internet, 99, 267, 343, 545, 558
intertemporal cash flow timing
problems,
178,
180
in the doghouse,
325
in the market,
74
in the price,
229
intrinsic values.
See
fundamental
values
introducing brokers,
142
inventories, 92,
283,
284-87, 294-95
inventory risk,
284
investment advisers,
33, 197,
560
investment banks,
140, 157,
326
investment counselors.
See
investment advisers
investment grade bonds, 40
investment managers.
See
investment
advisers; managers
investment sponsors,
33
Investment Technology Group, 17
investors,
6,
33, 178-81, 193, 200,
205, 252
Iomega (co.), 254, 568-69
IOSCO.
See
International
Organization of Securities
Commissions
IPO.
See
initial public offering
Island ECN, 35, 73, 325, 543
Israel, 52
issuers,
39,
44,
179
issues,
39
Japan, 192
Japanese asset bubble, 570-71
Johnson, Phillip McBride, 63
juice.
See
vigorish
junk bonds, 40
Kansas City Board of Trade
(KCBT), 55, 371
KCBT.
See
Kansas City Board ofTrade
kickback schemes,
166
Knight Capital Markets, 544, 553
latent traders,
95
latent trading demands,
323
-
24,
332,
397
latent trading interest,
383
Lattice system, 103
law of one price,
233, 234
laying off, 294, 352
leaking of information, 324
leapfrog strategies,
114
Lee and Ready algorithm,
423
Leeson, Nick, 198
Lefevre, Edwin, 136
legal issues, 9-10
legs of arbitrage,
348
Lehman Brothers, 108
Leland O'Brien Rubinstein
Associates, 328
Levitt, Arthur, 217
librarians, 396
LIFFE.
See
London International
Financial Futures and Options
Exchange
limit order book,
73,
497, 522
limit order price protection,
499,
515, 516
limit orders, 12, 13, 72-78, 86
aggressively priced, 73, 74
and brokers, 146
costly management, 306-7, 309
definition of, 11, 72
example, 78
execution audit problem, 518
and liquidity, 536
at the market, 73
marketable,
73
and prices, 76-77
as representing absent traders,
77-79
and specialists, 499
spread,
84
standing, 74-76, 77
and stop orders, 79
versus market orders, 381-82
limit prices, 72
definition of, 11
market, 73
placement, 73-74
and standing orders, 75, 77
terminology, 74


## Page 644

INDEX • 631
linear function,
355
linear hedge,
185
liquidation,
178
liquidity, 4, 68, 394-409
and block trading, 324-27, 335
bluffers and providers of, 270-73
in brokered markets, 95
and capital structure, 317
definition of, 32, 394
demand of,
70
dimensions, 398-400
expected compensation for
offering, 76-77
and front runners, 251
in index markets, 489-91
and informed traders, 236, 237
and insider trading, 591-92
in limit orders, 536
Liquidnet trading system, 390
offers of,
68, 70,
113
in order-driven markets, 94, 95
overview, 400-401
search for, 395-98
and sentiment-oriented technical
traders, 254
and standing orders, 75-76
and stop orders, 79-80
suppliers, 277-392, 400-405, 406,
534
taking of,
68, 70,
113
and tick-sensitive orders, 81
time and place, 376
trade-offs, 241, 398
and transaction cost measurement,
420-41
uninformed demanders,
339
-
40,
373, 376
and value traders, 338-40
liquidity fees,
520
liquidity pools,
535
liquidity premiums,
424, 425,
428
liquidity traders.
See
utilitarian
traders
liquid markets,
70,
205, 214-16
Liquidnet trading system, 390
listed stocks,
48
listing fees, 47,
494
listing standards,
47,
63
Livermore, Jesse, 136
local basis,
185
locals,
279,
356
London International Financial
Futures and Options Exchange
(LIFFE), 43, 57, 334, 543
London Stock Exchange, 331, 543
long hedger,
349
long position,
32,
373
long-short portfolio,
349
long-side bluff,
259,
260-65
Long-Term Capital Management,
369
look-back options,
506
Los Angeles Stock and Oil
Exchange, 47
lotteries, 216
Lucent (co.), 364
luck, 445, 454, 455, 461-64, 478
lunchtime recess, 92
Lynch, Peter, 455
macroeconomic factors,
443
Madoff Investment Securities, 290,
537, 543-44, 553
magicians, 330
maintenance margin,
156
Major Market Index (MMI), 485
making a new market, 74
making the market,
74, 280
manager(s), 453-60
active,
197, 442,
443, 458
Buffet as most skilled, 463
choosing among, 460-62
closet indexers,
465
decisions about, 457
and horse race handicapping, 479
and insider trading, 595-97
in liquid markets, 466
luckiest, 462, 464
market-adjusted returns, 464
performance, 451, 453-57, 462,
477
skilled, 478-79
manager allocation problem, 211-12
manager timing,
437
Manne, Henry, 595-96
Manning Rule, 217
margin agreement,
169
marginal benefit,
207,
208
margin call,
156
margin requirements,
572,
575-76
margins,
36, 156
market(s)
brokered,
90, 95
-
96
consolidated,
524,
526-30,
533-35, 538-40
corrections, 559
definition of, 5
efficiency,
223, 240,
243, 250-51,
254
exposure costs, 389


## Page 645

632 • INDEX
market(s)
(Continued)
fragmented,
524,
526,530-35,
539-40
good, 202-19
hedging, 188-89
innovative, 527-28
international, 50,51
liquid,
70,
205
manipulation, 259-75
objectives for evaluating, 216-17
"pinging,"
92
quote-driven dealer,
90, 92,
93
regulation, 59-66
segmentation, 533-35
in similar instruments, 539
as statistical calculator, 226
trading, 44-59
Treasury, 54
U.S. by instrument class, 45
U.S. equity, 49
volume figures, 48
See also
market information
systems; market structures;
order-driven markets;
specific
markets, e.g.,
bond(s)
marketable limit orders,
73,
87
marketable orders,
126
market-adjusted returns,
448,
462,
464,468
market ask.
See
best offer
market-based economies,
208
-
9,
210,
212-13
market beta,
448
market bid.
See
best bid
market breadth.
See
width
market breaks.
See
crashes
market by price,
101, 504
market centers,
524
market-clearing price,
120,
508
market condition reports, 314
market data systems,
90, 98
market failure,
317
market-if-touched orders,
80,
87
market impact,
72,
73,437
market information systems, 97-110
accuracy in oral communications,
107-9
collection, 97
distribution, 98-99
order books,
109
-
10
order presentation,
105,
107
order routing, 102-5,106
ticker symbols, 99-101
transparency, 101-2
market makers,
195,
196,249,279,
401
-
2,
406
market manipulation,
259,
306
market meltdowns.
See
crashes
market microstructure,
3,
4
market-not-held orders,
82,
87,530
market offer.
See
best offer
market-on-close orders,
83,
84
market-on-open orders,
83,
84
market orders,
11,
13,71-72, 86,87,
381-82
market quotations,
70,
504-5
market reporters,
97
market resiliency,
340, 399
-
400,
403
market risk,
448
market structures, 89-111,483-599
definition of, 7,89
execution systems, 92-96
overview, 89-90
recent changes in, 202-3
trading sessions, 90-92
Market Systems Inc., 519
market-timing returns,
450
market value,
222, 284,
286-87
marketwide events,
556
matchmakers, 397
material information,
228,
289,315,
584
maturity spread,
358
mean absolute deviation,
414
mean-reverting variables,
350,
356-57,369,373
Mechanical Technology, 374
merchants, 278
Merchants' Exchange of St. Louis
(MESL), 56
merger and acquisition markets,
140
mergers, 365-66,367
Merrill Lynch, 16,18,19
Merton, Robert, 223
MESL.
See
Merchants' Exchange of
St. Louis
messaging systems,
105
method of paper portfolios,
424
Mexico, 468
MGE.
See
Minneapolis Grain
Exchange
microeconomic factors,
443
microprocessors, 540
Microsoft Corporation, 14-15,
26-27,547
MIDAM.
See
MidAmerica
Exchange
MidAmerica Exchange (MIDAM),
56
Midwest Stock Exchange, 47,52
minimum-or-none orders,
83


## Page 646

INDEX • 633
minimum price increments,
81, 114,
115, 500, 538
minimum price variations.
See
minimum price increments
Minneapolis Grain Exchange
(MGE), 56
missed trade opportunity costs,
421,
422
mixed offers,
362
MMI. See
Major Market Index
(MMI)
model risk,
363, 368
-
69
model timing,
437
momentum traders,
261
momentum trading strategies,
79,
232,
429
money.
See
currency; foreign
exchange
money flow,
423
monopolies, 298
Morgan Stanley, 16, 17-18, 19
morning sessions,
92
mortgage-backed securities,
41
mortgage pools,
41
multifactor risk models,
450
multilateral trades,
91
municipal bonds, 54
mutual funds, 472, 491-92
naked positions,
183
narrow spread,
280,
315
NASD.
See
National Association of
Securities Dealers
Nasdaq Stock Market
bubble, 569-70
demutualization, 35
as hybrid market, 96, 532
institutional stock trade, 19-20
levels of quotation, 105
Microsoft's listing, 547
and October 1987 crash, 563
options market, 52
OTC Bulletin Board, 107, 108
as quote-driven market, 93
retail stock trade, 14-15
Small Order Execution System,
14-15, 106, 391, 532
stocks, 48, 49
trading hours, 92
and volatility, 511
volume figures, 48
National Association of Securities
Dealers (NASD), 11, 64, 164
National Futures Association (NFA),
64, 164, 474
National Quotation Bureau, 107, 108
National Securities Clearing
Corporation (NSCC), 28, 35,
36, 37, 522
natural hedgers,
183
NBBO (national best bid and offer),
70
negative externalities, 7
net buyers,
270
net price basis,
144,
281
net sellers,
270
net settlement,
36
network externality,
145, 535
-
36
new issues, 39
news traders,
194,
196,
228
-
30,
231,
235, 239, 243
New York Board of Trade
(NYBOT), 55
New York Mercantile Exchange
(NYMEX)
contract volumes, 55
floor-based trading, 543
variation margin example, 42
New York Stock Exchange (NYSE)
After-hours Trading Session I,
132, 133
as "Big Board," 107
block trades, 323
bond market, 54
Exchange Stock Portfolio,
490
floor-based trading, 48, 543, 544
generally accepted accounting
principles, 314
history of, 64
as hybrid market, 96, 532
institutional stock trade, 15-19
listed stocks,
48
market-not-held orders, 530
options market, 52
program trades, 489
quantitative listing standards for
domestic companies, 46
retail stock trade, 11-14
Rule 80A, 577, 580-81
Rule 80B, 573, 578
specialists, 298, 494, 495, 496,
500, 510
SuperDot order-routing system,
13, 106, 489, 562
trading hours, 92
NFA.
See
National Futures
Association
noise,
223
noise traders,
194
noisy estimates,
427
nonlinear hedges,
186
nonstationary variables, 356-57


## Page 647

634 • INDEX
normal distribution,
467
normal-way settlement,
36
normative economic analyses,
203-4
notional size,
41
notional value,
41, 42
NSCC.
See
National Securities
Clearing Corporation
NYBOT.
See
New York Board of
Trade
NYMEX.
See
New York Mercantile
Exchange
NYSE.
See
New York Stock
Exchange
oats, 359
OCC.
See
Options Clearing
Corporation
Occidental Petroleum, 230
October 1989 Mini-Crash, 564-66
October stock market crashes, 567
odd/even gas rationing, 579
offensive strategies, 388
offering markets,
140
offering price.
See
offer prices
offer of liquidity,
68, 70,
113
offer prices,
69,
280
offers,
69,
70
off-floor traders,
104
off-track betting (OTB), 190
oil, 342, 352, 412, 579
OM Gruppen, 526
one-sided markets,
280
onion futures market, 65
on special,
157
on-the-run issues,
316
opaque markets,
101
open book markets,
109
open-end mutual funds, 491-92
open limit order books,
101
Open Market Operations Desk
(Federal Reserve), 58
open orders.
See
standing orders
open-outcry auctions.
See
oral
auctions
open-outcry rule,
113
open-outcry systems, 90
operationally fair markets,
544-45
Optimark trading system, 527, 543
option(s), 7
contracts,
42,
45,
75,
76, 100,
186-87, 354, 563
definition of, 42
dynamically created, 563
FLEX Options, 52
free, 526
hedging, 253
indexed, 212
in-the-money,
355
markets, 26-27, 50, 52-54
out-of-the-money,
355,
468
replication, 563
risks, 377
standing limit orders, 75-76
ticker symbols, 100
trade, 26-27
writer of,
42
Option Price Reporting Authority,
100
options class, 377
Options Clearing Corporation
(OCC), 50
options premium,
186
options put-call parity theorem, 187
option values,
75
oral auctions,
95,
107, 112-16
oral communications, 107-9
orange crop, 242, 316
order anticipators, 245-58
definiton of, 195, 197, 245
trading strategy, 196
order book officals,
500
order books,
90, 109-10,
118, 120,
126
order-driven markets, 112-38
and brokers, 141
and dealers, 205
definition of, 90, 94, 112
derivative pricing rule and crossing
networks, 132-37
discriminatory pricing rule and
continuous two-sided auctions,
126-32
oral auctions, 112-16
rule-based order-matching
systems, 116-20
structures, 94-95
uniform pricing rule and single-
price auctions, 120-25
order-driven trading systems,
34-35,
139, 303-10
order exposure, 528-29, 531
order exposure problem,
323,
324-25
order exposure rules,
505
order flow, 153, 155,
282-83,
434,
435, 517-21
order flow externality, 7, 8, 509,
526,
527-29, 535-36, 539
order flow markets,
140,
538
Order Handling Rules, 217
order indication.
See
indication of
interest
order list processing software,
489


## Page 648

INDEX • 635
order-matching systems, 35
See also
rule-based order-matching
systems
order precedence rules,
90, 94,
112,
113-15, 117-18
order preferencing,
161
-
63
order presentation systems,
90, 105,
107
order routing systems,
90, 102
-
5,
106, 534
orders, 68-88
definition of, 68
display instructions,
84
-
85
examples of, 69, 71, 78
exposure management of brokers,
146
limit,
72,
73-78, 87
market,
11,
13,
71
-
72,
87
market-if-touched,
80,
87
market-not-held,
82,
87
quantity instructions, 83-84
special settlement instructions,
85
spread,
84
substitution,
85
terminology, 69-71
tick-sensitive,
81,
87
undisclosed,
85
use of, 68-69
See also
stop orders
order submission strategies,
380,
389
Ortel (co.), 364
orthogonality,
237
OTB.
See
off-track betting
out-of-balance inventories,
283
out-of-the-money options, 468
outside spread,
340,
341-45, 403
outsizing the book,
333
out-trades,
35, 107
over-the-counter options contracts,
50
over-the-counter stocks,
48
over-the-counter trades, 35, 54
overvaluation,
177, 223
Oxley, Mike, 115
Pacific Exchange, 47, 92
Pacific Options Exchange Trading
System.
See
POETS
Pacific Stock Exchange, 47
package dealers,
489
-
91
package trading market,
490
pages,
98
painting the tape,
259
PairGain Technologies, 267
pairs trading,
358
-
60
palladium, 566, 568
Pandick Press, 586
paper portfolios,
191, 426
parasitic traders,
194,
195,
245, 383,
388
pari-mutuel betting,
91
Paris Bourse, 389, 495, 543
participation in trade,
117
par value, 40
passive liquidity suppliers,
400
passive managers,
197, 442, 488
passive traders, 248-50,
278,
396-97,
423
patience, 532
pattern recognition, 231, 246
payments for order flow,
155, 162,
166, 514
pending orders,
71
penny jumping.
See
quote matching
penny stocks, 255,
265
pension funds, 37, 252, 332
perfect foresight values,
223
perfectly competitive markets,
516,
517
perfectly hedged positions,
184
performance, 442-80
economic approaches to
prediction, 475-76
evaluation methods, 445-50
evaluation problems, 443-45, 448,
466-71
of management and trading firms,
478
of managers, 451, 453-57, 462,
477
past, 452
portfolio, 443, 451
prediction problem, 450-52
statistical evaluation, 452-71
unforseeable factors, 443-44
perishable goods,
412,
416
permanent price effect,
435
permanent spread component.
See
adverse selection spread
component
Perold's implementation shortfall,
424
Persian Gulf, 352
peso problem,
468
Pfizer (co.), 228
Philadelphia Stock Exchange, 589
physically convened markets,
90
physically settled contracts,
41
pinging the market,
92
Pink Sheets,
107,
108
pits.
See
trading pits
players, 32-34,
145


## Page 649

636 • INDEX
Plexus Group, 424, 437
Plug Power, 374
POETS (Pacific Options Exchange
Trading System), 27
poker, 268, 291
Poland, 50
political risks,
413
pollution, 213
pollution credits,
39
Pons, Stanley, 566
Ponzi schemes, 471
portfolio insurance, 253, 328,
560
-
61,
563
portfolio managers.
See
investment
advisers; managers
portfolio performance, 443, 451
portfolio trading market,
490
POSIT (POrtfolio System for
International Trading),
17,
19,
49, 132-34, 136, 325, 531
position limits,
572
position traders,
279
positive economic analyses,
203,
204
positive externalities, 7,
206
postal reply coupons, 470
posts.
See
trading posts
post-trade transparency,
101
power of test,
454
-
57
prearranged trading, 165,
166
precommitted liquidity suppliers,
400, 404,
406
preferencing, 161-63,
282, 514,
515,
520-22, 528-29
preferred stocks,
40
premiums,
75
pre-trade transparency,
101
price(s)
accelerator, 556
ask,
5, 69,
280, 295
benchmark,
422,
423-32, 433
bid,
5, 69,
70, 280, 295
clustering, 91
correlations, 8
dealer mistakes, 291-92
derivative, 132-37
discrimination, 247, 323, 325-26,
332
formation in index markets,
489-91
and fundamental values, 403
indexes,
484
-
86
inferior,
70
informative, 4, 206-14, 218,
222,
224, 235, 237-39, 241, 243
and limit orders, 76-77
manipulation, 135-37, 256
in market-based economies, 208-9
market-if-touched orders, 80
for perishable commodities, 416
predicting, 442
public benefits from informative,
206-14
reflection of information,
229
stock, 211-12
and stop orders, 78-79
terminology, 69-70
tick-sensitive orders, 81
trade,
70
and trading exposure, 385-86
unexpected increases, 370, 372
volatility,
76
price and sale feeds,
98
price characterization of arbitrage,
375
price concessions,
72,
324
price continuity,
497,
498
price convergence,
348,
350
price discovery process,
94, 284
price impact.
See
market impact
price improvement,
71,
72,
282,
515
price limits,
572,
573-75
price manipulators,
195,
196,
259
price priority,
113, 117,
334
price reversal, 432, 434,
497
price risk,
183
price-weighted index,
485,
486
primary capital markets,
209,
210,
211
primary government securities
dealers,
58
primary listing markets,
48,
49
primary markets, 39
primary order precedence rules,
117
primary spread determinants,
311,
312-13
Primex Auction System, 309, 515
principal-agent problem, 8,
159
principal trading, 149
principal value, 40
printing a trade,
333
private benefits,
205
-
6
private information,
223
private services, 538
proactive traders,
383,
384
production/allocation decisions,
206
-
7,
208
profit-motivated traders,
177,
194-97, 198, 205, 206
ProFunds Ultra OTC Fund, 447
program trading, 368,
489


## Page 650

INDEX • 637
proprietary orders,
70
proprietary traders,
32
proprietary trading,
32,149
pro rata allocation,
117,
134,447
proxies
asymmetric information, 314-15
for utilitarian trading interest,
316-17
volatility, 315-16
proxy variables,
312
pseudo-informed traders,
197,
229-30,
231
public benefits
definition of, 205
of exchange, 214
of hedging, 214
from informative prices, 206-14
of liquid markets, 214-16
of risk sharing, 214-15
of trading, 206
public commodity pools,
474
public goods,
494-95
public information,
223,
241-43
public liquidity preservation
principle,
499,
500
public order precedence rule, 113,
115,
500
public policy, 529-30,535
public services, 538
public traders, 310-11,313,498
pure arbitrageurs,
194
pure discount bonds.
See
zero-
coupon bonds
pure price-time precedence systems,
117
put-call parity theorem,
157,
187
put options,
42,
355
putting on the arbitrage,
348
pyramid schemes,
470-71
pyramid-shaped organizations,
227
QuantEX system, 103,309
quantities, 5
quantity characterization of arbitrage,
375
query services,
98
quotation feeds,
98
quotation midpoint,
423
quotation montage, 12,15,20
quote-driven markets,
90, 92,
93,
205
quote matching,
248-50,
386,502-3,
537
quotes,
69,
280-81,283,287-92,
504-5
random walk,
223, 357
reactive traders,
383,
384
real assets,
39, 178
real estate, 45,47,521
real estate investment trusts
(REITS),
40
real estate mortgage investment
conduits.
See
collateralized
mortgage obligations
realized alpha,
450
realized capital gains,
192
realized spread,
280, 424,425-26
real losses,
181
real risk-free interest rate,
180-81
real-time services,
98
REDIB ook, 35
reduced instruction set computing
(RISC) approach, 540
reference instrument,
348
regional exchange, 48-49
registered bond,
168
regression to mean,
474
Reg T margins.
See
speculative
margins
Regulated Environment View
(REV), 48,51
Regulation FD, 451,586
Regulation T margins.
See
speculative
margins
regulators, 60-66
and block trading, 333
by country, 62
competition among, 63
definition of, 59-60
and extreme volatility, 572-78
international, 62,66
miscellaneous private, 65
politics of intervention, 578-81
power allotted to, 540
self-regulatory organizations,
63-65
and specialists, 510-11
U.S. agencies, 60-63
REITS.
See
real estate investment
trusts
relative performance evaluations,
445
relative return,
447-48
relative spread,
312
relative value trades, 348
Reminiscences of a Stock Operator
(Livermore and Lefevre), 136
repayment,
178
replication, 188,253,316,563
required rate of return,
208
reserve orders.
See
undisclosed orders


## Page 651

638 • INDEX
residual risk,
349
resiliency,
400
responsive traders,
323
-
24
retail trading, 11-15, 517-18, 520,
522
retirement, 179
return smoothing, 469-70
Reuters, 59
REV.
See
Regulated Environment
View
risk-adjusted excess returns,
450
risk arbitrage,
357, 360,
362-63
risk aversion, 309
risk manager,
150
risk premium,
181
risk replication, 316
risk sharing, 214-15
rogue traders,
197
-
98
rolling a position,
353
Roll's serial covariance spread
estimator, 415-16,
434
rotation,
90
-
91
Rothschild, Nathan, 266
Rule 11Ac1-5/Ac1-6, 518
Rule 80A, 577, 580-81
Rule 80B, 573, 578
rule-based order-matching systems
definition of, 95, 116
in exchanges, 94
matching procedure, 118-19
and order books, 109
order precedence rules, 117-18
trade pricing rules, 120
rumormongers,
195,
196,
259
runners,
103
Russell 1000, 2000, and 3000 Index,
487
safe harbor,
154
St. Petersburg paradox,
469
sales brokers,
147
sales traders, 324
Salomon Brothers, 28, 149
sample selection bias, 470-75
avoiding, 472-74
definition of, 471
in mutual fund industry, 472
regression to mean, 474
S&P 500 Index,
186,
249, 328, 353,
361, 450, 486, 490, 565, 577
San Francisco Stock and Bond
Exchange, 47
SAXESS system, 526
scalable technology,
547
scalpers,
24,
279, 293
Scholes, Myron, 223
screen-based trading systems, 90,
99,
105,
107
Seagram Co. Ltd., 365-66
search engines,
396
seasoned issues,
316
seasoned securities, 39
SEC.
See
Securities and Exchange
Commission
secondary capital markets,
209,
211-12
secondary markets, 39
secondary precedence rules,
117,
536-38
secondary spread determinants,
312,
314-17
Securities and Exchange
Commission (SEC), 60-61
and best execution, 518
competition with Commodity
Futures Trading Commission,
63
functions of, 61
and insider trading, 585, 586
Order Handling Rules, 217
program trades, 489
Regulation FD, 451, 586
Rule 11Ac1-5/Ac1-6, 518
Securities Exchange Act, 61, 154
Securities Industry Automation
Corporation, 98
securities information processor,
98
Securities Investor Protection
Corporation (SIPC), 171
securities theft, 167-70
securitizing real estate, 40
security,
4
security registrar,
168
SEI (co.), 424
selective disclosure, 217
self-enforcing rule,
113
self-regulatory organization,
63
-
65
selling time,
309
sell side,
32,
33-34
sell uptick order,
81
semistrong-form efficient markets,
240
sentiment
-
oriented technical traders,
195,
196,
231, 245, 251,
252-54, 257
serial covariance,
434
settlement, 141, 142-43
settlement agents,
36
Shad, John, 63
Shanghai Stock Exchange, 52


## Page 652

INDEX • 639
shape of distribution,
467
shareholders, 46, 211, 212
sheep,
328
shipping arbitrage,
350,
351-53
shooting the moon,
256
shopping the block,
247,
324
short hedge,
349
short hedger,
349
short interest rebate,
155
-
57
short position,
32
short seller,
32,
169
side-by-side trading, 52
signaling, 90, 104, 108, 246
significance level,
454
silver, 164, 181
single-price auctions
definition of, 94, 120
example of, 120-21
supply and demand, 121-22
trader surpluses, 122-25
and uniform pricing rule, 120-25
SIPC.
See
Securities Investor
Protection Corporation
size,
5, 69,
281, 282, 398, 399,
530-31
size precedence,
117,
333
Sizzler International, 444
SLKC.
See
Spear, Leeds, Kellogg and
Co.
slogans, 525
slow price adjustment scenario,
373
small float,
255
Small Order Execution System
(SOES; Nasdaq), 14-15, 106,
391, 532
smoothing, 469-70
social welfare function,
204,
205
SOES.
See
Small Order Execution
System
SOES bandits,
391
soft commission, 153-54
soft dollar,
153
-
54,
191
soft prices,
69
soft quotes,
280
-
81
soybeans, 181, 356
SPDR (Standard & Poor's
Depository Receipt), 249,
504
Spear, Leeds, Kellogg and Co.
(SLKC), 19-20
specialist participation rate,
500
specialists, 494-513
affirmative obligations, 496-98
assignment of, 509-10
as auctioneers and exchange
officials, 501
as brokers, 500-501, 508
control of market quotes, 504-5
cream-skimming strategies, 503
and dealers, 279, 496-99, 508-9
definition of, 494
and market open, 508, 509
negative obligations, 198-99, 500
New York Stock Exchange, 298,
494, 495, 496, 500, 510
participation rates, 502
privileges, 501-9
quote-matching strategies, 502-3
regulatory issues, 510-11
and Rule 80A, 580
speculative strategies, 501-2
and stop orders, 503-4, 505-8
in U.S. stock markets, 48
specialist trading systems,
494,
495-96
specialization, 215
special settlement instructions,
85
specified price benchmark methods,
422,
423-27
speculative arbitrages, 356-63
speculative margins,
61
speculators, 6, 46, 177, 221-75
and bluffers, 265-67
definition of, 5, 181, 190, 194,
200, 206
and prediction of performance,
442
as profit-motivated traders,
194-95, 196, 206
speech recognition technology, 108
speed of execution, 515
Spider.
See
SPDR (Standard &
Poor's Depository Receipt)
spinning off holdings,
370
split orders, 428-29
spoiling the market,
324
spot currencies, 58
spot markets, 39,
182
spot prices, 416
spread orders,
84
spreads, 312, 357-58, 367
See also
bid/ask spread; outside
spread
spread traders,
279
squawk boxes,
149
squeezers,
195,
196,
245,
254-56,
257
stale information,
229
stale prices,
134,
135
Standard and Poor's.
See
S&P 500
Index


## Page 653

640 • INDEX
standard deviation,
414, 455
standard error of the mean,
453,
469-70
standardized futures contracts,
42
standing orders,
70,
74-76,77,
82-83,87,333
statistical arbitrageurs,
194, 360
staying power, 368
stealth trading,
226
stings,
388
stock(s)
bubbles and crashes, 558-71
certificate, 14
definition of, 39-40
diversified, 315
float,
255
institutional trade on Nasdaq,
19-20
institutional trade on NYSE,
15-19
international markets, 50,51
"January effect," 252
prices, 211-12
retail trade on Nasdaq, 14-15
retail trade on NYSE, 11-14
as small part of capital wealth, 45
stopping, 505-8
U.S. markets, 48-49
value versus growth, 315
very large block trade, 20-22
See also
exchanges
Stockholm Stock Exchange, 35
stock market crash of 1929,558-59
stock market crash of 1987,559-64,
578
stock market mini-crash of 1989,
564-66
stock offer,
362
stock price relative,
371
stop instruction,
78
stop limit order,
79
stop loss order,
78
stop orders, 78-80,87
definition of, 78
and limit orders, 79
and liquidity, 79-80
manipulation of, 256
and specialists, 503-4,505-8
stop price,
78
STP.
See
straight-through processing
straddle,
37, 192
straight bond,
40
straight-through processing (STP),
38
street name,
148, 169
strict time precedence,
117
strike price, 42,100,186,212
strong-form efficient markets,
240
structured products, 50
stub.
See
buying the stub
Student's t-statistic,
453,
454
subscribers,
143
subsequent traders,
507
subsidiaries, 370
substitution orders,
85
Sunpoint Securities, 170,171
sunshine traders,
247, 327
-
28
SuperDot order-routing system, 13,
106,489,562
SuperSoes.
See
Small Order
Execution System
supply and demand, 209
supply of liquidity.
See
offer of
liquidity
supply schedule,
121,
122-24
survival, 479
survivorship bias,
472
Swapnote, 43,189
swaps, 58-59
definition of, 42
hedging with, 187-88
yield curve, 43
swaptions,
43
sweeping the market,
534
symmetric distribution,
467
synthetic derivatives business, 50
synthetic short position,
157
synthetic stock,
407
System Open Market Account
(Federal Reserve), 58
system orders,
500
system timing,
437
taking off of arbitrage,
348
taking of liquidity,
68, 70,
113
taking the offer,
280
target firm,
360
target inventory,
283
taxes
avoiders, 192,193
Brazilian, 66
capital loss exclusion, 193
deferral, 192
Japanese, 192
timing strategies, 231
transaction,
572,
575-76
tax straddle,
192
t-distribution, 454
technical bankruptcy,
37
technical traders,
195, 230
-
32
technology, 524-26
Tel Aviv Stock Exchange, 52


## Page 654

INDEX • 641
telecommunications, 105
Telefonos de Mexico, S.A. de C.V.,
52
telegraph, 104,105
telephone, 104-5,108,109
Texas Gulf Sulfur, 585
theft, 167-70
third markets,
49
13F Holdings Reports, 326
tick.
See
minimum price increment
ticker symbols,
27,99-101
ticker tapes,
98, 498
tick-sensitive orders,
81,
87
tight spread,
280
time, 309
time-delayed services,
98
time precedence rules,
113
-
14,
117,
537
Time Slicing, 290
time travel, 180
timing of market,
450
timing option,
307,
308,506
Tokyo Stock Exchange, 92
tolls, 209
tomato forward contract, 41
top of the book,
101, 504
Toronto Stock Exchange, 35,99,389
totalizator system,
91
total return,
445
total volatility,
414
touch,
70
touch price,
80
toxic waste,
41
T + 3 settlement,
36
tracking errors,
486
trade prices,
70
trade pricing rules,
90, 94,
112,
115
-
16,
120
traders
accuracy in oral communications,
107-9
aggressive, 246,423
in brokered markets, 95
clean up after hours, 92
clearing and settlement among,
141
contrarian,
79,
429
credit checks, 142
definition of, 32
in order-driven markets, 95
orders, 68-88
profit-motivated,
177,
194-97,198
risks of standing limit orders, 77
self-regulatory associations, 64
taxonomy of types, 199
untrustworthy, 324,326
See also
informed traders; trader
surplus; trades and trading,
specific types
of traders, e.g.,
utilitarian traders
traders of last resort,
497
trader surplus
definition of, 123
examples of, 123,125
measuring, 124
in single price auction, 122-25
trader timing,
437
trades and trading
cash commodity and futures
market, 22-26
with dealers, 281-82
facilitators, 34-36,38,248
foreign exchange, 29-30
free entry and exit, 7-8
futures market, 22-26,39,46
industry, 32-67
information asymmetries, 7
institutional in Nasdaq stock,
19-20
institutional in NYSE stock,
15-19
magnitude of, 45-47
markets, 44-59
overview of, 5-6
players in, 32-34,145
private benefits of, 205
profits, 4,7
public benefits of, 206
reasons for, 176-201,252
retail in Nasdaq stock, 14-15
retail in NYSE stock, 11-14
rules,
7,94,
95,112,113
straddle,
37
structure of, 31-174
and technology, 524-26
very large stock block, 20-22
See also
bond(s); option(s); traders;
trading instruments
trading floors, 24-25,90,
116
trading forums,
90
trading halts,
572,
573-75,578
trading hours,
92
trading instruments
definition of, 38
derivative contracts, 41-42
financial assets, 39-41
gambling contracts, 43
hybrid contracts, 43-44
insurance contracts, 43
real assets, 39
trading pits, 24-25,90,
116
Trading
Places
(movie), 242


## Page 655

642 • INDEX
trading posts,
90,116
trading sessions,
89,
90-92
Trading System View (TSV), 48,51
trading window,
588
tranches,
41
Transaction Auditing Group, 424,
519
transaction costs, 4
and active managers, 488
components, 421-22
definition of, 180,421
econometric estimation methods,
422,423, 432,434
estimation methods, 422-23
estimator biases, 428-31
gaming problem, 430-31
and hedging, 188
implicit, 432,434
intelligent management, 439-40
as market frictions, 394
measurment, 420-41
missed trade opportunity costs,
434,436-38
and paying the spread, 71
prediction, 438-39
properties of price benchmark
estimators, 427-32,433
and public benefits of exchange,
214
specified price benchmark
methods,
422,
423-27
trade timing issues, 427-28
transaction cost spread component,
299
transaction fees,
494
transaction taxes,
572,
575-76
transitory exchange for physical,
334
transitory price changes,
299
transitory price effect,
435
transitory spread component.
See
transaction cost spread
component
transitory volatility,
410, 413-14
transparent markets,
101-2
t-ratio,
453,
454
Treasury bills,
40,
54,58,156
Treasury bonds,
40,
54,58
Treasury notes,
40,
54
Treynor, Jack, 424
trustworthiness,
8,
532-33
t-statistic,
453-54
TSV.
See
Trading System View
t-test,
453-54,
456,457,460,463,
467-69,471,475
turnover,
487-88
two-sided auctions,
95,
126-32,
342-43
two-sided markets,
280,
281
two-sided order flow,
284,
294
Type I error,
459
Type II error,
459
UAL Corporation, 564-66
unauthorized trading,
166-67
unconditional expected return,
180-81
underlying instruments,
41, 42,
253,
355,377
undervaluation,
177, 223
underwriters,
39
underwriting fees,
157-58
underwritten offerings,
39, 157-58
undisclosed orders,
85
uniform pricing rule, 120-25,
129-30
unilateral search,
395-96
uninformed liquidity demanders,
339-40,
373,376
uninformed traders, 6,237-38,241
and adverse selection, 303,304
and asymmetric information, 531
and block trading, 327,331
and dealers, 291,292
definition of, 177
and fundamental values, 239
reasons for trading, 252
United States Lime & Minerals
(USLM), 19-20
unlisted trading privileges,
49
unwinding of positions,
348
upstairs block market,
322,
328-32
uptick,
81,
423
USDA.
See
Department of
Agriculture (U.S.)
USLM.
See
United States Lime &
Minerals
utilitarian traders, 178-94,198,200,
205,216
asset exchangers,
181-82,
193
and bid/ask spreads, 313
cross-subsidizers,
191,
193
definition of, 177
fledglings,
190-91,
193
gamblers,
189-90,
193,200
hedgers, 182-89
investors and borrowers, 178-81,
193,200
Proxies for, 316-17
tax avoiders, 192
and transitory volatility, 575-76


## Page 656

INDEX • 643
validity and expiration instructions,
82
-
83,
84
value estimates, 6, 239, 309
ValueLine Index, 371, 486
value traders, 235, 239, 243, 338-46
and bluffers, 268-69
and confidence, 557
costs of trading, 343-44
and dealers, 294
definition of, 194, 226, 227, 338,
402
as liquidity suppliers, 338-40,
402-4, 406
outside spread,
340,
341-45
risks of, 340-43
trading strategies, 196
winner's curse,
338,
340, 341-43
value-weighted index,
485,
486, 487
variational margin payments,
42,
445
variation margin,
156
variation margin adjustments,
36
Vendor Display Rule,
99
vertically integrated firm,
183
Viagra, 228
victimized traders,
197
video display systems, 107
vigorish,
70,
93
Vinik, Jeffrey, 269
violation of confidentiality, 246
virtual shippers,
351
-
52,
353
Vivendi S.A., 365-66
volatility, 4, 410-16
definition of, 410
extreme, 555-83
fundamental,
410,
411-13, 416,
557-58
Internet, 343
measuring, 414-15
and Nasdaq, 511
primary spread determinants,
312-13
proxies, 315-17
return, 469-70
selling, 468
transitory,
410, 413
-
14,
557-58
value estimate, 309-10
Volkswagen, 181
volume-weighted average price
(VVVAP),
424
-
25,
426, 428-31
VWAP.
See
volume-weighted average
price
Wall Street Journal,
587
warrant,
44
Warsaw Stock Exchange, 50
wash trade,
259
weak-form efficient market,
240
welfare economics,
203
-
5,
216
well-informed speculators,
5,
6
well-informed traders.
See
informed
traders
WFE.
See
World Federation of
Exchanges
wheat, 183-85, 215, 255, 335, 352
wholesalers,
282
wide spread,
280
width,
398,
399
Winans, Foster, 587
winner's curse,
338,
340, 341-43
winner-take-all markets,
536
winning, 475, 476
wirehouses,
34,
329
wolf detectors,
328
wolves,
328
wolves in sheep's clothing,
327
working orders,
71
World Federation of Exchanges
(WFE), 48, 66
wrap accounts,
151
writers,
42,
75
Wunsch, R. Steven, 121
Yahoo.com
, 99
yen-euro, 357
yield curve spread,
358
zero-coupon bonds,
40,
206
zero downtick,
81
zero net supply,
41,
44, 225, 354
zero-sum game,
6,
8,
176,
205, 206,
458, 479, 488
zero tick,
81
Zip drive, 568
