Understanding the Social, Cognitive, and Economic Debates

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Understanding the Social,
Cognitive, and Economic Debates
Edwin T. Burton
Sunit N. Shah
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Cover design: Leiva-Sposato
Copyright © 2013 by Edwin T. Burton and Sunit N. Shah. All rights reserved.
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Library of Congress Cataloging-in-Publication Data:
Burton, Edwin T.
Behavioral finance : understanding the social, cognitive, and economic debates / Edwin T. Burton
and Sunit N. Shah.
pages cm.—(Wiley finance series)
Includes index.
ISBN 978-1-118-30019-0 (cloth); ISBN 978-1-118-33410-2 (ebk);
ISBN 978-1-118-33521-5 (ebk); ISBN 978-1-118-33192-7 (ebk)
1. Investments—Psychological aspects. 2. Capital market—Psychological aspects. 3. Decision making. I. Title.
HG4521.B837 2013
Printed in the United States of America.
10 9 8 7 6 5 4 3 2 1
Part One
Introduction to Behavioral Finance
Chapter 1
What Is the Efficient Market Hypothesis?
Information and the Efficient Market Hypothesis
Random Walk, the Martingale Hypothesis, and the EMH
False Evidence against the EMH
What Does It Mean to Disagree with the EMH?
Chapter 2
The EMH and the “Market Model”
Risk and Return—the Simplest View
The Capital Asset Pricing Model (CAPM)
So What Is the Market Model?
Chapter 3
The Forerunners to Behavioral Finance
The Folklore of Wall Street Traders
The Birth of Value Investing: Graham and Dodd
Financial News in a World of Ubiquitous
Television and Internet
Part two
Noise Traders
Chapter 4
Noise Traders and the Law of One Price
The Law of One Price and the Case of Fungibility
Chapter 5
The Shleifer Model of Noise Trading
The Key Components of the Shleifer Model
Why the Shleifer Model Is Important
Resolving the Limits to Arbitrage Dispute
Chapter 6
Noise Trading Feedback Models
The Hirshleifer Model
The Subrahmanyam-Titman Model
Chapter 7
Noise Traders as Technical Traders
Technical Traders as Noise Traders
Herd Instinct Models
Part III
Chapter 8
The Rational Man
Consumer Choice with Certainty
Consumer Choice with Uncertainty
The Allais Paradox
Chapter 9
Prospect Theory
The Reference Point
The S-Curve
Loss Aversion
Prospect Theory in Practice
Drawbacks of Prospect Theory
Chapter 10
Perception Biases
Sunk Cost Bias
Chapter 11
Inertial Effects
Endowment Effect
Status Quo Effect
Disposition Effect
Chapter 12
Causality and Statistics
Conjunction Fallacy
Reading into Randomness
Small Sample Bias
Probability Neglect
Chapter 13
Illusion of Talent
Illusion of Skill
Illusion of Superiority
Illusion of Validity
Part IV
Serial Correlation
Chapter 14
Predictability of Stock Prices: Fama-French Leads the Way
Testing the Capital Asset Pricing Model
A Plug for Value Investing
Mean Reversion—The DeBondt-Thaler Research
Why Fama-French Is a Milestone for Behavioral Finance
Chapter 15
Fama-French and Mean Reversion: Which Is It?
The Month of January
Is This Just About Price?
The Overreaction Theme
Lakonishok, Shleifer, and Vishny on
Value versus Growth
Is Overreaction Nothing More Than a “Small Stock” Effect?
Daniel and Titman on Unpriced Risk in Fama and French
Summing Up the Contrarian Debate
Chapter 16
Short Term Momentum
Price and Earnings Momentum
Earnings Momentum—Ball and Brown
Measuring Earnings Surprises
Why Does It Matter Whether Momentum Is
Price or Earnings Based?
Hedge Funds and Momentum Strategies
Pricing and Earnings Momentum—Are They Real and
Do They Matter?
Chapter 17
Calendar Effects
January Effects
The Other January Effect
The Weekend Effect
Preholiday Effects
Sullivan, Timmermann, and White
Part V
Other Topics
Chapter 18
The Equity Premium Puzzle
Mehra and Prescott
What About Loss Aversion?
Could This Be Survivor Bias?
Other Explanations
Are Equities Always the Best Portfolio for the Long Run?
Is the Equity Premium Resolved?
Chapter 19
A Securities Market Is a Bid-Ask Market
Measuring Liquidity
Is Liquidity a Priced Risk for Common Stocks?
Significance of Liquidity Research
Chapter 20
Capuchin Monkeys
Innateness Versus Culture
Decisions Are Made by the Brain
Decisions versus Outcomes
Neuroeconomic Modeling
More Complicated Models of Brain Activity
The Kagan Critique
Chapter 21
Experimental Economics
Bubble Experiments
Endowment Effect and Status Quo Bias
Calendar Effects
And the Winner Is?
The Semi-Strong Hypothesis—Prices Accurately Summarize
All Known Public Information
Can Prices Change if Information Doesn’t Change?
Is the Law of One Price Valid?
Three Research Agendas
The Critics Hold the High Ground
What Have We Learned?
Where Do We Go From Here? (What Have We Not Learned?)
A Final Thought
his book was the product of five years of teaching “Behavioral Finance”
to over 1,800 undergraduates at the University of Virginia. The course
never had a textbook. In fact, the course was originally intended to be limited to, at most, 15 students due to the difficulty of the reading. By a strange
quirk of the registration process, the course limit in the online registration
system was altered to 300 and was quickly filled by eager students. It remains one of the most sought after courses at the University of Virginia.
Who would have guessed?
When I first decided to offer Behavioral Finance as a course, I was driven by the amount of space that the subject was occupying in the leading
finance journals. There was no book that I could find suitable for such a
course, so the initial reading list was comprised solely of original sources—
professional, academic journal articles. Somehow, this worked, and students
continue to pack into this course that is offered every spring at the University of Virginia.
It dawned on me that if this course proved useful to our students, perhaps I should write a book summarizing my thoughts on Behavioral Finance
in book form so that others might consider offering a similar course at their
institution. In this spirit, I dedicate this book to all of my students—past,
present, and future.
I would especially like to thank my co-author, Sunit Shah, whose brilliance and attention to detail has hopefully made up for much of my unintended carelessness. I would also like to thank my students Francesca Archila, Mu Chen, Qichen Wang, Grace Chuang, Samantha Rivard, and Patrick
Glading for help with this book. I would also especially like to thank my
daughter Lindsay Burton Sheehan for her help with numerous aspects of the
final version. My wife, Trish, and my daughter Elizabeth Burton have been a
constant source of encouragement toward the completion of this enterprise.
Finally, I am grateful to Wiley for their patience and support in getting this
book to print.
Edwin T. Burton
My fascination with financial markets was born with the execution
of my first trade at age 17. From that point forward, through forecasting
macro trends, to conducting actuarial analysis on life settlements, to creating predictive models around movements of credit spreads, that interest
has evolved into an ever-present curiosity as to how one might “beat the
market.” Its juxtaposition against my academic training at the University
of Virginia, presented mainly through the lens of the Efficient Markets Hypothesis, provided the contrast between the two sides of the Behavioral Finance debate. As such, this book has served as the perfect transition in my
life in finance, from academic setting to practice, from theory to application,
from avocation to full-time vocation.
To Ed’s sentiments, I’d simply like to add my heartfelt appreciation:
to Ed for the opportunity to join him on this endeavor, and for setting the
structure and organization to the topic that allowed our ideas to flow; to
all of the aforementioned students for all of their assistance in this book’s
creation; and to all of my friends and family, including my parents, Nitin
and Suhasini Shah, my sister, Vaishali Shah, and my niece, Kirsi Shah Chinn,
for their continued support along this journey, and throughout my life in
Sunit N. Shah
ehavioral finance is a subtopic of the broader subject of behavioral
economics. The behavioral in the name means that the behavior of participants in the actual economy is fundamentally different than what most
academic theorizing normally assumes. Behavioralists argue that the predictions of economics, finance in particular, must be modified to account for
how people actually behave in economic situations.
What is “commonly assumed” in economics and finance? The answer, in
a word, is rationality. The usual implementation of rationality is to assume
that individuals in the economy have a utility function that serves as a guide
to what makes them happy, happier, and less happy. That utility function
values various choices that a person could make subject to wealth, income,
or whatever constrains expenditures for a particular person. The rational
person maximizes utility (satisfaction, happiness, whatever the utility function is presumed to measure), staying within the bounds of what is possible
as constrained by wealth and liquidity.
The utility-maximizing exercise by agents (persons, businesses, etc.)
leads to predictable behavior and provides predictions about how markets
function in the real world. For example, rational behavior by individuals,
along with some other assumed conditions, implies that resources are allocated efficiently by the price mechanism both for the broader economy
and for financial markets in particular. Prices perform a signaling function
for the economy and, under these conditions, the prices direct agents to produce the “right amounts” and to buy and sell the “right amounts.” “Right
amounts” means, roughly, that the economy does not waste resources. The
result of the free interplay of market forces leads to results that are “right”
in the sense that it is not possible to make anyone better off without making
someone else worse off. This is the meaning of efficiency in economics and
in finance.
This does not mean that the result of free markets is the best of all
worlds—even in this highly theoretical exercise. The resulting income
distributions might be “unfair,” and such unfairness requires a separate
discussion. Behavioral economics and finance attack the foundations of
the argument that markets allocate resources efficiently, long before arguments arise about fairness or the lack thereof. The behavioralists argue that
markets may not produce efficient resource allocation, and it is generally
possible to improve the economic position of some individuals without
harming the economic position of other individuals.
Behavioral finance specifically questions the efficiency of financial markets. The prices of assets—usually the discussion is about stock prices—may
not really reflect value, argue the behavioralists. Even simple ideas in finance,
such as the idea that identical assets should sell at identical prices, have been
called into question by the behavioralists. The critique of received finance
theory by behavioral finance advocates is broad, deep, and extensive. Events
in the real world of finance, such as the 1987 stock market crash and the
2008 financial collapse in Western economies, have added fuel to the fire.
These events are difficult to reconcile with the efficient market point of view.
What follows is an effort to summarize the developments to date in the
behavioral finance debate. Numerous behavioral finance books have been
written for popular audiences in recent years, but they are mostly written
by true believers who are attempting to persuade the reader that behavioral
finance is the winner in its debate with more traditional finance. This is not
such a book. We are not sympathetic to the behavioral finance position and
this book takes a skeptical look at behavioral finance. But even skeptics,
such as ourselves, are today overwhelmed by the mountain of evidence that
is piling up for those who support the behavioral finance point of view and
the unexplained stock market behavior that is increasingly difficult to reconcile with the efficient market view.
Thus, this book represents a skeptic’s view with a grudging acceptance
that, at this point, the advocates of behavioral finance seem to have the
upper hand in the ongoing debate. This debate revolves around three main
discussions: (1) noise trader theory and models; (2) research in psychological behavior pioneered by Kahneman and Tversky; and (3) serial correlation patterns in stock price data. There are other discussions in behavioral
finance not captured in the three categories mentioned above, but the three
topics above are all on center stage in the ongoing debate.
We begin with a discussion of the efficient market hypothesis, which is
the central paradigm that behavioral finance seeks to attack. Then we move
on to consider each of the three main areas of attack set out in the preceding
paragraph. Finally, we conclude with thoughts about where this debate will
go from here.
Additional resources for professors can be found on Wiley’s Higher
Education website.
Introduction to
Behavioral Finance
What Is the Efficient
Market Hypothesis?
he efficient market hypothesis (EMH) has to do with the meaning and
predictability of prices in financial markets. Do asset markets “behave”
as they should? In particular, does the stock market perform its role as economists expect it to? Stock markets raise money from wealth holders and
provide businesses with that money to pursue, presumably, the maximization of profit. How well do these markets perform that function? Is some
part of the process wasteful? Do prices reflect true underlying value?
In recent years, a new question seems to have emerged in this ongoing
discussion. Do asset markets create instability in the greater economy? Put
crudely, do the actions of investment and commercial bankers lead to bubbles and economic catastrophe as the bubbles unwind? The great stock market crash of October 19, 1987, and the financial collapse in the fall of 2008
have focused attention on bubbles and crashes. These are easy concepts to
imagine but difficult to define or anticipate.
Bubbles usually feel so good to participants that no one, at the time,
really thinks of them as bubbles; they instead see their own participation
in bubbles as the inevitable payback for their hard work and virtuous
behavior—until the bubbles burst in catastrophe. Then, the attention turns
to the excesses of the past. Charges of greed, corruption, and foul play
accompany every crash.
If the catastrophe and the bubble that precedes it are the result of evil
people doing evil things, then there is no reason to suppose that markets
are themselves to blame. Simple correctives, usually through imposition of
legal reforms, are then proposed to correct the problem and eliminate future
bubbles and catastrophes. Casual empiricism suggests this approach is not
What if markets are inherently unstable? What if bubbles and their accompanying catastrophes are the natural order of things? Then what? If
prices do not, much of the time, represent true value and if the markets
Introduction to Behavioral Finance
themselves breed excessive optimism and pessimism, not to mention fraud
and corruption, then the very existence and operation of financial markets
may cause instability in the underlying economy. Prices may be signaling
“incorrect” information and resources may be allocated inefficiently. The
question of whether asset markets are efficiently priced, then, is a fundamental question. The outcome of this debate could shed light on the efficiency
of the modern, highly integrated economies in which a key role is played by
financial institutions.
It is important to agree on a definition of market efficiency, but there are
many such definitions. Practitioners in the everyday world of finance often
use market efficiency in ways that are different than the textbook definitions. We delimit the most common definitions in the next two sections of
this chapter.
Information and the Efficient Market Hypothesis
The EMH is most commonly defined as the idea that asset prices, stock prices in particular, “fully reflect” information.1 Only when information changes
will prices change. There are different versions of this definition, depending
on what kind of information is assumed to be reflected in current prices. The
most commonly used is the “semi-strong” definition of the EMH: Prices accurately summarize all publicly known information.
This definition means that if an investor studies carefully the companies
that he/she invests in, it will not matter. Other investors already know the information that the studious investor learns by painstakingly poring over public documents. These other investors have already acted on the information,
so that such “public” information is already reflected in the stock price. There
is no such thing, in this view, as a “cheap” stock or an “expensive” stock. The
current price is always the “best estimate” of the value of the company.
In particular, this definition implies that knowing past prices is of no
value. The idea that past stock price history is irrelevant is an example of the
weak form of the EMH: Knowledge of past prices is of no value in predicting future stock prices.
The semi-strong form implies the much weaker version of the EMH
embodied in the weak form of the EMH. It is possible that the weak form is
true but that the semi-strong form is false.
The weak form of the EMH is interesting because it directly attacks
a part of Wall Street research known as “technical” research. In technical
See Eugene Fama’s definition in “Random Walks in Stock Market Prices,” Financial
Analysts Journal 21, no. 5 (May 1965):55–59.
What Is the Efficient Market Hypothesis? 7
research, analysts study past prices and other historical data in an attempt
to predict future prices. Certain patterns of stock prices are said by “technicians” to imply certain future pricing paths. All of this means, of course, that
by studying past prices you can predict when stock prices are going to go up
and when they are going to go down. Put another way, technical research is
an attempt to “beat the market” by using historical pricing data. The weak
form says that this cannot be done.
Unlike other versions of the EMH, the weak form is especially easy to
subject to empirical testing, since there are many money managers and market
forecasters who explicitly rely on technical research. How do such managers
and forecasters do? Do they perform as well as a monkey randomly throwing
darts at a newspaper containing stock price names as a method of selecting
a “monkey portfolio”? Do index funds do better than money managers who
utilize technical research as their main method of picking stocks? These questions are simple to put to a test and, over the years, the results of such testing
have overwhelmingly supported the weak form version of the EMH.
The semi-strong version of the EMH is not as easy to test as the weak
form, but data from money managers is helpful here. If the semi-strong version is true, then money managers, using public information, should not beat
the market, which means that they should not beat simple indexes that mirror
the overall market for stocks. The evidence here is consistent and overwhelming. Money managers, on average, do not beat simple indexes. That doesn’t
mean that there aren’t money managers who seem to consistently outperform
over small time samples, but they are in the distinct minority and hard to
identify before the fact. Evidence from institutional investors, such as large
pensions funds and endowments, are consistent with the view that indexing
tends to produce better investment results than hiring money managers.
If this were all we knew, then the EMH would be on solid ground. But
we know more. There is growing evidence that there are empirical “regularities” in stock market return data, as well as some puzzling aspects of stock
market data that seem difficult to explain if one subscribes to the EMH.
We can identify three main lines of attack for critics of the semi-strong
form of the EMH:
1.Stock prices seem to be too volatile to be consistent with the EMH.
2.Stock prices seem to have “predictability” patterns in historical data.
3.There are unexplained (and perhaps unexplainable) behavioral data
items that have come to be known as “anomalies,” a nomenclature begun by Richard Thaler.2
See Richard Thaler, Winner’s Curse: Paradoxes and Anomalies of Economic Life
(New York: Free Press, 1992).
2 8
Introduction to Behavioral Finance
The evidence that has piled up in the past 20 years or so has created a
major headache for defenders of the EMH. Even though money managers
don’t necessarily beat the indexes, the behavioralists’ research suggests that
perhaps they should.
There is a third form of the EMH that is interesting but not easy to
subject to empirical validation. The third form is known as the strong form
of the EMH: Prices accurately summarize all information, private as well as
The strong form, of course, implies both the semi-strong and the weak
forms of the EMH. However, both the semi-strong and weak forms can be
true while the strong definition can be false. The strong form includes information that may be illegally obtained—or, perhaps, information that is
legally obtained but illegal to act upon. Needless to say, those breaking the
law are not likely to provide performance data to researchers attempting to
ascertain whether they are beating the market.
There seems to be a general consensus that the strong form of the EMH
is not likely to be true, but one should not rush to such a conclusion simply
because relevant data may be hard to come by. What little data we have
from those who have obtained illegal information and then acted upon it is
mixed. Sometimes crooks win, sometimes they appear to lose. When Ivan
Boesky, probably the most famous insider information trader in history,
concluded his investment activities and was carted off to jail, it was clear
that investors who owned index funds made better returns than investors in
Boesky’s fund, even before the legal authorities got wise to Boesky’s activities. If Boesky couldn’t beat the market with inside information, it does give
one pause.
Of the three informational definitions of the EMH, it is the semi-strong
hypothesis that commands most interest. It is widely believed that the weak
form is likely to be true, it is commonly assumed that the strong form is not
likely to be true, so interest focuses mainly on the semi-strong hypothesis.
Information determines prices and no one can really exploit publicly known
information—that is the content of the semi-strong EMH hypothesis.
Random Walk, the Martingale Hypothesis,
and the EMH
There is an alternative, mathematical view of the stock market related to
the EMH. The mathematical version begins with the idea that stock prices
follow a process known as random walk. The idea of the random walk is
sometimes taken by wary observers as the idea that stock price behavior is
simply arbitrary, but that is not what random walk means.
What Is the Efficient Market Hypothesis? 9
Imagine a coin flip where the coin is completely “fair” in the sense that
a heads or tails flip is equally likely to occur. Suppose you start with $100 in
wealth before beginning a series of coin flips. Suppose further that if you flip
a heads, you receive $1, and if you flip a tails, you have to give up $1. After
the first flip, for example, you will have either $101 (if you flip a heads) or
$99 (if you flip a tails).Your total wealth over time, in this simple example,
is following a process known as a random walk. A random walk is a process
where the next step (flip outcome, in this example) has a fixed probability
that is independent of all previous flips.
What does random walk rule out? If knowing the results of previous
coin flips is useful in predicting future coin flips, then the process is not a
random walk. Imagine that there have been five flips of heads in a row with
no flips of tails. Does this mean it is more likely that the next coin flip will
be tails? If so, then the process is not a random walk. The likelihood of a
heads or a tails on the next coin flip must be independent of the history of
previous flips for the process to be a random walk.
Does this mean, as some assume, that the results are arbitrary? No. We
know a lot about this process. What we can’t do, however, is predict the next
coin flip with any high degree of certainty. If the coin is a fair coin, the heads
or tails are equally likely on the next flip regardless of its history.
The coin-flipping game is a good example of a martingale. A martingale
has the following property:
E[Xt + s| X1, X2, . . ., Xt] = Xt for any t, s > 0
What does the above equation mean? Xt is the value at time t of some
variable X. It might be helpful to think of X as your wealth, so that Xt is the
value of your wealth at time t. Xt+s is then your wealth at some future date,
t+s. The E in the equation is the expectation operator. The simplest way to
think about E is that E[Xt+s| X1, X2, . . ., Xt] is what, on average, you expect
the value of your wealth to be at a future date, t+s, given your knowledge of
your wealth historically.
So, back to our example. You start on date t with $100 and you flip a
coin that is equally likely to be a heads flip as a tails flip. What do you expect
your wealth to be s periods from today, t? Since you are just as likely to gain
$1 as to lose $1 on each flip, your wealth at any future period is expected to
be the same as is today. Thus, this process satisfies the martingale property.
If your wealth is totally in stocks, and if stocks follow a martingale, so will
your wealth. On average, you will neither make nor lose money.
But this is not a very satisfying theory of how stocks behave. Why
would anyone own stocks if, on average, they could not be expected to increase their wealth? We need to modify our simple coin-flipping experiment
Introduction to Behavioral Finance
to allow for wealth to increase, but in a way consistent with our martingale
assumption. Suppose your wealth grows at $0.20 per period on average, so
that E[Xt + s| X1, X2, . . ., Xt] = Xt + $0.20 × s. Then, your wealth is no longer
a martingale.
To transform it into a martingale, define a new variable, Yt:
Yt = Xt – {t × $0.20}
Yt is a martingale since:
E[Yt+s] = E [Xt+s] – {(t + s) × $0.20}
= Xt + {s × $0.20} – {(t + s) × $0.20}
= Xt – {t × $0.20} = Yt
Even though wealth is growing over time, we have converted the wealth
variable into another variable that is a martingale.
If stock prices follow a random walk, then past stock prices cannot be
used to predict future stock prices. Random walk doesn’t mean we know
nothing or that the result of the process is arbitrary. Instead, one of the implications of random walk is that the outcome on any specific future date
cannot be known with certainty. By a simple conversion, similar to what
was shown earlier, we can convert the wealth accumulation process into a
Why all the effort? A martingale is a process whose value at any future
date is not predictable with certainty. While Xt is the best estimate of any
future value of X after Xt, we still cannot know with any degree of certainty
what that value will be.
The idea of a martingale captures the informational definitions given
in the previous section in a mathematical statement. Given the information
available today, the best estimate of a future stock price is today’s price
(possibly with a risk-adjusted trend over time).This process is described in
Figure 1.1.
Of course, the actual prices will not be on the solid line in Figure 1.1.
Instead, they will bound around randomly, but trend upward in a pattern
suggested by the bold solid line. The actual price movement might appear
(or be expected to appear) as the lighter line that bounces around the solid
line in Figure 1.2.
What makes the martingale an appropriate model for the EMH is that
on any date, past information offers no real clue to predicting future prices.
It is the absence of predictability that is the single most important feature of
the martingale process.
Stock Price
What Is the Efficient Market Hypothesis? Today
Future Date
Figure 1.1 Expected Future Stock Price
False Evidence against the EMH
Stock Price
There are always, at any point in time, legendary money managers who have
arguably beaten the market over their respective lifetimes. Warren Buffett
comes to mind as one of the more prominent examples. Is the existence of
money managers with long track records of having beaten indices evidence
against the EMH? To give this question some perspective, conduct a simple thought experiment. Imagine a group of 10,000 people engaged in a
Figure 1.2 Actual Future Stock Price
Future Date
Introduction to Behavioral Finance
coin-flipping experiment. In each period, each of these 10,000 people flips
a coin and notes the result. What would we expect if the coins were, in all
cases, fair coins? The likelihood of heads or tails is identical and equal to
50 percent on each and every coin toss.
In the first trial, you would expect, on average, about half of the 10,000
folks to flip heads and about half to flip tails. This would mean 5,000 flipped
heads and 5,000 flipped tails. This wouldn’t be the exact outcome, but it
serves as a useful approximation to the actual outcome. Now, flip again.
After the second trial, you would expect about one-fourth of the participants (2,500) to have flipped two heads in a row and one-fourth (2,500) to
have flipped two tails in a row. Continue on in this manner through eight
coin flips and what would you have? On average, you would expect about
39 flippers to have flipped eight heads in a row and about the same to have
flipped eight tails in a row. Are these 39 flippers evidence that there is something to the science of coin flipping?
What about the number of folks who flipped heads seven out of eight
times? There should be about 312 of those folks on average. That makes
over 350 people who flipped heads at least seven out of eight times. Isn’t
that evidence that these people are good head flippers?
No, clearly such evidence is useless. If coin flipping is completely random, with a 50 percent chance each time of either flipping heads or tails,
you will still get a significant number of extreme outcomes, even after repeated trials. In fact, failure to get the extremes of eight in a row or seven
out of eight a reasonable number of times would be evidence that the
flipping was not truly random. The same is true of evidence from money
management. If money management outcomes are completely random and
no one is really any good at stock picking, then a small percentage of
money managers will, nevertheless, appear to be good on the basis of their
track records.
One of the anomalies the behavioralists have uncovered is that things
that are random often appear not to be random.3 That is, they don’t look
random. There seems to be an expectation by observers that if a random
process is creating a data series, then that data series should have a random
appearance. It turns out that there are many more ways for the outcome of
a randomly generated data series to look like a pattern than there are ways
for it to look random. Put another way, output from a randomly generated process will typically exhibit trends, repetition, and other patterns even
though the results are generated by a truly random process.
See Chapter 12 for a broader discussion of this topic.