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Finance and the behavioral prospect

Risk, Exuberance, and Abnormal Markets


Quantitative Perspectives on Behavioral
Economics and Finance

Series Editor
James Ming Chen
College of Law
Michigan State University
East Lansing, Michigan, USA

Aims of the Series
The economic enterprise has firmly established itself as one of evaluating
human responses to scarcity not as a rigidly rational game of optimization, but as a holistic behavioral phenomenon. The full spectrum of social

sciences that inform economics, ranging from game theory to evolutionary psychology, has revealed the extent to which economic decisions and
their consequences hinge on psychological, social, cognitive, and emotional factors beyond the reach of classical and neoclassical approaches
to economics. Bounded rational decisions generate prices, returns, and
resource allocation decisions that no purely rational approach to optimization would predict, let alone prescribe.
Behavioral considerations hold the key to longstanding problems in
economics and finance. Market imperfections such as bubbles and crashes,
herd behavior, and the equity premium puzzle represent merely a few
of the phenomena whose principal causes arise from the comprehensible mysteries of human perception and behavior. Within the heterodox,
broad-ranging fields of behavioral economics, a distinct branch of behavioral finance has arisen.
Finance has established itself as a distinct branch of economics by applying the full arsenal of mathematical learning on questions of risk management. Mathematical finance has become so specialized that its practitioners
often divide themselves into distinct subfields. Whereas the P branch of
mathematical finance seeks to model the future by managing portfolios
through multivariate statistics, the Q world attempts to extrapolate the
present and guide risk-neutral management through the use of partial differential equations to compute the proper price of derivatives.
The emerging field of behavioral finance, worthy of designation by the
Greek letter psi (ψ), has identified deep psychological limitations on the
claims of the more traditional P and Q branches of mathematical finance.
From Markowitz’s original exercises in mean-variance optimization to the
Black-Scholes pricing model, the foundations of mathematical finance
rest on a seductively beautiful Gaussian edifice of symmetrical models and
crisp quantitative modeling. When these models fail, the results are often
The ψ branch of behavioral finance, along with other “postmodern”
critiques of traditional financial wisdom, can guide theorists and practitioners alike toward a more complete understanding of the behavior of
capital markets. It will no longer suffice to extrapolate prices and forecast
market trends without validating these techniques according to the full

range of economic theories and empirical data. Superior modeling and
data-gathering have made it not only possible, but also imperative to harmonize mathematical finance with other branches of economics.
Likewise, if behavioral finance wishes to fulfill its promise of transcending mere critique and providing a more comprehensive account of financial markets, behavioralists must engage the full mathematical apparatus
known in all other branches of finance. In a world that simultaneously lauds
Eugene Fama’s efficiency hypotheses and heeds Robert Shiller’s warnings
against irrational exuberance, progress lies in Lars Peter Hansen’s commitment to quantitative rigor. Theory and empiricism, one and indivisible,
now and forever.

More information about this series at

James Ming Chen

Finance and the
Behavioral Prospect
Risk, Exuberance, and Abnormal Markets

James Ming Chen
College of Law
Michigan State University
East Lansing, Michigan, USA

Quantitative Perspectives on Behavioral Economics and Finance
ISBN 978-3-319-32710-5
ISBN 978-3-319-32711-2 (eBook)
DOI 10.1007/978-3-319-32711-2
Library of Congress Control Number: 2016950218
© The Editor(s) (if applicable) and The Author(s) 2016
The author(s) has/have asserted their right(s) to be identified as the author(s) of this work
in accordance with the Copyright, Designs and Patents Act 1988.
This work is subject to copyright. All rights are solely and exclusively licensed by the
Publisher, whether the whole or part of the material is concerned, specifically the rights of
translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on
microfilms or in any other physical way, and transmission or information storage and retrieval,
electronic adaptation, computer software, or by similar or dissimilar methodology now
known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publication does not imply, even in the absence of a specific statement, that such names are
exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information
in this book are believed to be true and accurate at the date of publication. Neither the
publisher nor the authors or the editors give a warranty, express or implied, with respect to
the material contained herein or for any errors or omissions that may have been made.
Cover illustration © TongRo Images / Alamy Stock Photo
Printed on acid-free paper
This Palgrave Macmillan imprint is published by Springer Nature
The registered company is Macmillan Publishers Ltd. London

To Heather Elaine Worland Chen, with all my love


This book incorporates ideas from papers I have presented at the University
of Cincinnati, Florida State University, Georgetown University, Michigan
State University, the University of Pennsylvania, the University of Virginia,
and the Faculty of Economics of the University of Zagreb (Ekonomski
Fakultet, Sveučilište u Zagrebu). The International Atlantic Economic
Society and the ACRN Oxford Academic Research Network have provided multiple platforms for the work underlying this book. Along the
way, I have benefited from scholarly and professional interactions with
Anna Agrapetidou, Abdel Razzaq Al Rababa’a, Moisa Altar, Christopher
J. Brummer, Irene Maria Buso, Adam Candeub, Seth J. Chandler, Felix
B.  Chang, Tendai Charasika, César Crousillat, David Dixon, Robert
Dubois, John F.  Duffy, Daniel A.  Farber, Christopher C.  French,
Santanu K.  Ganguli, Tomislav Gelo, Periklis Gogas, Gil Grantmore,
Andy Greenberg, Losbichler Heimo, Hemantha Herath, Jesper Lyng
Jensen, Jagoda Kaszowska, Daniel Martin Katz, Yuri Katz, Imre Kondor,
Carolina Laureti, Cordell Lawrence Jr., Cordell Lawrence Sr., Matthew
Lee, Othmar Lehner, Heimo Losbichler, Gerry Mahar, Milivoj Marković,
L.  Thorne McCarty, Steven C.  Michael, Ludmila Mitkova, José María
Montero Lorenzo, Kevin Lynch, Laura Muro, Vivian Okere, Merav
Ozair, Elizabeth Porter, Mobeen Ur Rehman, Carol Royal, Bob Schmidt,
Jeffrey A. Sexton, Galen Sher, Ted Sichelman, Jurica Šimurina, Nika Sokol
Šimurina, Robert Sonora, Lisa Grow Sun, Elvira Takli, Peter Urbani,
Robert R.M. Verchick, Benjamin Walther, Karen Wendt, Gal Zahavi, and
Johanna F.  Ziegel. Christian Diego Alcocer Argüello of Michigan State
University’s Department of Economics provided very capable research



assistance. I am also grateful for contributions by several students at
Michigan State’s College of Law: Angela Caulley, Yuan Jiang, Morgan
Pitz, Emily Strickler, Paul M.  Vogel, and Michael Joseph Yassay. The
research services of the Michigan State University Law Library and administrative support by Marie Gordon were indispensable. Special thanks to
Heather Elaine Worland Chen.


1 The Structure of a Behavioral Revolution


2 Mental Accounting, Emotional
Hierarchies, and Behavioral Heuristics


3 Higher-Moment Capital Asset Pricing and Its
Behavioral Implications


4 Tracking the Low-Volatility Anomaly Across
Behavioral Space


5 The Intertemporal Capital Asset Pricing Model:
Hedging Investment Risk Across Time


6 Risk Aversion


7 The Equity Risk Premium and the Equity
Premium Puzzle


8 Prospect Theory






Specific Applications of Prospect Theory to
Behavioral Finance



Beyond Hope and Fear: Behavioral Portfolio Theory



Behavioral Gaps Between Hypothetical Investment
Returns and Actual Investor Returns


Irrational Exuberance: Momentum Crashes and
Speculative Bubbles


The Monster and the Sleeping Queen







The Structure of a Behavioral Revolution



This book represents one of the first two volumes in the series, “Quantitative
Perspectives on Behavioral Economics and Finance.” Its companion volume, Postmodern Portfolio Theory: Navigating Abnormal Markets and
Investor Behavior, addresses leading departures from the putative efficiency
of financial markets.1 Intense pressure on the conventional capital asset
pricing model gave rise to theoretical innovations such as Eugene Fama
and Kenneth French’s three-factor model. Postmodern Portfolio Theory
traces this story through the four statistical moments of the distribution of
financial returns: mean, variance, skewness, and kurtosis.
This book conducts a fuller exploration of behavioral phenomena in
finance, such as the low-volatility anomaly, the equity premium puzzle, and
momentum in stock returns. Mental accounting, persistent gaps between
hypothetical investment return and actual investor return, and alternatives
to modern portfolio theory and the conventional capital asset model contribute to the development of behavioral approaches to portfolio design
and risk management. Gaps in perception and behavioral departures from
rational decision-making appear to spur momentum, even irrational exuberance and speculative bubbles. Ultimately, this book hopes to explain
emotion-laden deviations from the strict rationality traditionally associated
with mathematical finance. Together with Postmodern Portfolio Theory, this

© The Editor(s) (if applicable) and The Author(s) 2016
J.M. Chen, Finance and the Behavioral Prospect, Quantitative
Perspectives on Behavioral Economics and Finance,
DOI 10.1007/978-3-319-32711-2_1




book synthesizes observations on abnormal markets and irrational investors into a coherent behavioral account of financial risk management.
Chapter 1 traces the rise of the behavioral revolution in portfolio theory
and, more generally, in mathematical finance. Like any other story in the
history and philosophy of science, the transformation of portfolio theory
begins with the identification of anomalies. Only after identifying anomalies and challenging an established paradigm can dissenters lay a credible
claim to a competing intellectual movement.
Behavioral accounting, arising from the irresistible human urge to keep
emotional score, undermines modern portfolio theory’s rational premises. The separation theorem and the two mutual fund theorem counsel
investors to consolidate all assets into a single portfolio along the efficient frontier. But individual and even institutional investors consistently
reject that sort of normative guidance. Assets and portfolios are imbued
with “affect,” and positive and negative emotions warp investment decisions. Chapter 2 explores two seemingly divergent but ultimately similar
manifestations of emotion in economics: Maslowian portfolio theory and
behavioral environmental economics.
Chapter 3 introduces some of the most important mathematical tools in
behavioral finance. After summarizing the conventional capital asset pricing
model, Chapter 3 presents a higher-moment approach to capital asset pricing as an outgrowth of the Taylor series expansion of logarithmic returns.
Finance proceeds from the assumption that risk and return are positively correlated. If investors are generally risk averse, they will presumably
demand a higher return in exchange for buying assets whose prices exhibit
higher variance. Departures from this relationship between risk and return
undermine this theoretical foundation of finance. In reality, some of the
highest returns are available on the stocks exhibiting the lowest levels of
volatility. Along with its analogue in accounting, Bowman’s paradox, the
low-volatility anomaly poses a serious challenge to the conventional financial narrative. Chapter 4 tracks the low-volatility anomaly in behavioral
space by examining beta on either side of mean returns and analyzing the
separate volatility and correlation components of beta. Chapter 5 introduces the intertemporal capital asset pricing model and the prospect of
explaining the low-volatility anomaly according to time as well as space.
Chapter 6 outlines a quantitative approach to risk aversion. It specifies the Arrow–Pratt measures of absolute and relative aversion, as well
as the famously tractable model of hyperbolic absolute risk aversion, as
a prelude to examining a more behaviorally sensitive account of human
responses to risk. Two paradoxes, Allais’s paradox and the St. Petersburg



paradox, suggest that conventional accounts of risk aversion do not
provide a comprehensive explanation of economic behavior in the face of
risk or uncertainty.
Risk aversion provides at least a partial explanation for the historic premium that equities have commanded over lower-risk investments such as
bonds. Though accounts vary, the equity risk premium rests in the neighborhood of 3–6% per year. The magnitude of this premium, however,
poses a formidable (and arguably, still unresolved) theoretical challenge
to conventional asset pricing models. Building on Chapter 6’s measures
of risk aversion, Chapter 7 explores both the equity risk premium and the
econometric puzzle to which that premium has given rise. The equity
premium puzzle is to behavioral finance as the low-volatility anomaly is to
modern portfolio theory and the conventional capital asset pricing model:
Without resolving contradictions of this magnitude, many of the theoretical suppositions of mathematical and behavioral finance will be squarely
contradicted by the behavior of real markets and real investors.
The final chapters of this book present two leading accounts of behavioral finance, prospect theory and SP/A (security-potential/aspiration)
theory. Those chapters also offer thoughts on speculative bubbles in
finance. Chapter 8 introduces prospect theory, arguably the most prominent manifestation of behavioral economics in finance. The theory’s fourfold pattern provides the most widely accepted account of risk-averse as
well as risk-seeking behavior. Prospect theory explains the financial impact
of fear and greed. Humans depart from purely rational utility in three
ways. First, humans heed reference points. Second, humans hate losing
more than they like winning. Third, humans grow less sensitive to economic changes as gains or losses increase. A “fourfold pattern” provides a
comprehensive account of risk-averse as well as risk-seeking behavior.
Chapter 9 applies prospect theory to a set of related problems and puzzles in finance. Affirmative risk-seeking, something not readily accommodated by expected utility theory, is predicted by prospect theory. Skewness
preference manifests itself across a large number of financial settings in
the form of investor demand for instruments that couple low expected
returns with high potential jackpots. The two-way mispricing of initial
public offerings—underpriced in the short run to issuers’ detriment, overpriced in the long run at the expense of investors—provides an especially
vivid illustration of this preference for lottery-like instruments. In addition, prospect theory supports a distinct body of proposed solutions to
Bowman’s paradox and the equity premium puzzle.



Chapter 10 presents a competing account of behavioral finance. SP/A
theory describes the competing forces of security, potential, and aspiration
within financial decision-making. SP/A theory transforms the dynamics of
hope and fear into a behavioral account of portfolio theory. Intriguingly,
behavioral portfolio theory extends the safety-first principle that inspired
the very first departures from the perfectly symmetrical and rational suppositions of modern portfolio theory. Behavioral portfolio also bears a
deep resemblance to value-at-risk analysis, albeit as a method for evaluating extreme positive outcomes.
Over time, investor behavior consistent with the predictions of prospect theory and SP/A theory has had a profound impact on financial markets. Cycles of fear and greed have systematically eroded investor returns.
Behavioral finance can measure those gaps in investor performance, relative to simple buy-and-hold strategies, through a new statistic, ψ. Chapter
11 presents a method for calculating ψ as the gap between hypothetical
investment returns and actual investor returns. A market where ordinary
investors—and many active fund managers—systematically underperform
buy-and-hold strategies is a market that exhibits both heterogeneity and
significant limits to arbitrage. Chapter 12 accordingly explores the behavioral origins of momentum as well as the rise of speculative bubbles.



How mathematical finance came to abandon its original, strictly rational and utilitarian suppositions and to adopt a sophisticated awareness of
investor behavior is by no means a unique story in science. Indeed, the
development of contemporary financial theory follows the usual progression of scientific progress. “Normal science does not aim at novelties of
fact or theory and, when successful, finds none.”2 But when “fundamental novelties of fact and theory” arise, “[d]iscovery commences with the
awareness of anomaly, i.e., with the recognition that nature has somehow
violated the paradigm-induced expectations that govern normal science.”3
Once an “awareness of anomaly ha[s] lasted so long and penetrated so
deep” as to plunge a scientific discipline into “a state of growing crisis,”
a succeeding “period of pronounced professional insecurity” over “the
persistent failure of the puzzles of normal science” prompts a fruitful
search for new rules.4
The quest for scientific understanding assumes even greater urgency in
finance, a field devoted to elaborating “uncertainty,” both in “theory and
[in] empirical implementation.”5 “The starting point for every financial



model is the uncertainty facing investors, and the substance of every financial
model involves the impact of uncertainty on the behavior of investors and,
ultimately, on market prices.”6 The “interplay between theory and empirical work” is a dialectic in which “[t]heorists develop models with testable
predictions” and empiricists “document ‘puzzles’,” or “stylized facts that
fail to fit established theories” and thereby “stimulate[] the development of
new theories.”7 What makes finance in general and asset pricing in particular
such fantastic instances of the scientific process is that the “random shocks”
that propel knowledge forward happen also to be “the subject matter” to
which these branches of economic theory devote themselves.8
The presence of “‘efficiency-defying anomalies’ … such as market swings
in the absence of new information and prolonged deviations from underlying asset values” invites challenges to the efficient markets hypothesis.9
Not all departures from market efficiency carry the same cognitive weight,
however. Chapters 4 and 7 of Postmodern Portfolio Theory distinguished
between the volatility and correlation components of beta partly on the
basis of differences in the way investors perceive, evaluate, and respond to
those quantifiable aspects of financial markets. Even in the shadow of highfrequency trading,10 contemporary markets exhibit meaningful differences
in the rate at which they absorb different types of information. Differences
in processing speed distinguish two basic models of human reasoning: a
speedy, intuitive mode prone to cognitive bias and mistakes in judgment,
and a slower, more rational mode that counterbalances humans’ innate
heuristics with comprehensive evaluation of evidence.
Behavioral finance reflects the interplay between the “fast” heuristics of
human behavior and the “slow” processing of rational evidence.11 Adopting
labels proposed in the psychological literature,12 Daniel Kahneman has
assigned the names System 1 and System 2, respectively, to these fast and
slow modes of thought.13 “System 1 operates automatically and quickly, with
little or no effort and no sense of voluntary control.”14 Intuitive, “fast thinking,” such as the “automatic[] and effortless[]” recognition of anger in a
human face, requires no work.15 “It just happen[s].”16 By contrast, “System
2 allocates attention to … effortful mental activities,” often those “associated with the subjective experience of agency, choice, and concentration.”17
Solving even a simple multiplication problem such as 17  ×  24 demands
“slow thinking.” Ponder, even for a second, whether the right answer to
that problem is 568 or 408.18 Slow thinking slogs “through a sequence of
steps” requiring “deliberate, effortful, and orderly” mental work.19
Complex financial computations presumably belong to the domain of
System 2.20 The common connection among the “highly diverse operations



of System 2” is their need for attention.21 Drawing attention away from a
task assigned System 2 will disrupt or even defeat this process of slow thinking.22 Successful discharge of System 2 responsibilities demands the commitment of constrained mental resources: “[Y]ou dispose of a limited budget of
attention that you can allocate to activities, and if you try to go beyond your
budget, you will fail.”23 In particular, “activities that impose high demands
on System 2 require self-control,” an exercise that “is depleting and unpleasant.”24 System 2’s dependence on “mental energy is more than a mere metaphor”;25 it literally commandeers and demands blood glucose.26
It is traditionally assumed that the slow rationality of System 2, albeit
imperfectly, curbs the fast heuristics and emotional excesses of System
1.27 The most complex financial calculations are assigned to System 2, the
place where “the conscious, reasoning self” of neoclassical economics and
mathematical finance carefully marshals its “beliefs, makes choices, and
decides what to think … and what to do.”28 But this compounds the usual
error of giving too much credit to rationality at the expense of instinct.
“Although System 2 believes itself to be where the action is, the automatic
System 1 is the [real] hero” of human cognition.29 The mind at work may
assign even “surprisingly complex patterns of ideas” to the “automatic
operations” and “the freewheeling impulses and associations of System
1.”30 For a chess master, finding a strong move constitutes an “automatic
activit[y] … attributed to System 1.”31
Moreover, the persistence of superstition and magical thinking, even
among educated and emotionally stable adults,32 suggests that System 2
may consist of two distinct processes: a moderately slow mechanism for
detecting cognitive errors committed by System 1, and an even slower
mechanism for correcting those errors.33 Instances where humans detect
their mistakes but choose not to correct them arguably represent an
entirely distinct response to uncertainty: acquiescence.34



It therefore behooves us to distinguish anomalies that might offer insight
into investor behavior from those that do little beyond identifying quantitative curiosities, at least within the limits of existing technology and psychological learning. Fluctuations in security prices according to the time
of year or even the day of the week undermine confidence that rationality
rather than human frailty rules the market.35
To earn genuine respect, however, technical considerations must supply practical investment advice, or at least inform financial decisions.



Should a rational investor really sell in May and go away?36 Maybe, or
maybe not.37 Although no less an authority than Eugene Fama has found
a “January effect,” whereby “stock returns, especially … on small stocks,
on are average higher in January,”38 no anomaly carries economic significance, let alone undermines the efficient markets hypothesis, unless it is
“strong enough to outperform a buy-and-hold strategy on a risk adjusted
basis.”39 To the extent that these calendar anomalies ever held sway, the
passage of time appears to have dissipated them, “or at least substantially
attenuated” their power.40 Presumably, savvy trading has exhausted any
arbitrage opportunity presented by the identification of the anomaly.41
If calendar anomalies are to offer any insight into investor behavior,
those anomalies must arise from factors affecting emotion and judgment. We do know that humans respond to news and environmental
stimuli. Reading sad rather than happy newspaper articles, for instance,
predisposes people to raise their estimates of the risk of various causes
of death and their levels of concern over those sources of mortality.42
Although the existence of the effect and its extent are contested,43
some studies have found that good weather positively influences stock
returns.44 Even geomagnetic storms are alleged to affect financial decisions.45 Some of these effects, if indeed they exist, are almost surely
attributable to the market impact of putatively random events with
emotional content, ranging from the trivial (sports events)46 to the
tragic (aviation disasters).47
By contrast, seasonal changes in climate may outweigh the potential
of ephemeral events such as the weather to exert a powerful and systematic influence on returns.48 “For everything there is a season,” said
the Preacher, “and a time for every matter under heaven.”49 Investors
evidently agree: Mutual funds flows around the world reveal an investor preference for safer funds in the fall and riskier funds in the spring.50
These preferences hold in Australia as well as in Canada and the USA.51
Because spring and fall are reversed on either side of the equator, these
three developed Anglophone markets demonstrate that risk-seeking
among investors rises with seasonal temperature, and not according to
fixed calendar dates.



I now present a markedly distinct illustration of the failure of markets to
satisfy the strict assumptions of modern portfolio theory—that returns be
normally distributed and new information be assimilated in frictionless



fashion into security prices.52 In tacit homage to the “fraud on the market” doctrine in federal securities law, we may call this phenomenon “law
on the market.”53 The Supreme Court of the USA routinely decides cases
involving publicly traded parties, or at least significant legal issues with
potential impact on security prices. Applying standard event study methodology,54 one survey of Supreme Court decisions from October Term
1999 through October Term 2013 (which ended in June 2014) found
79 decisions associated with abnormal returns on 118 securities.55 Those
79 decisions represented 5.5% of the Court’s docket during the relevant
time span.56 Share price changes in 118 securities in direct response to a
Supreme Court decision reached an estimated total of $140 billion.57
For our purposes, the crucial finding of this survey was the rate at which
new information from a Supreme Court decision diffused through the
securities market. In the algorithmically driven, high-frequency trading
environment of contemporary markets,58 security prices often move within
fractions of a second in response to central bank announcements,59 surveys of consumer sentiment,60 and other financial news.61 High-frequency
trades typically move “in the direction of permanent price changes,” which
presumably reflect “future efficient price moves,” and “in the opposite
direction of transitory pricing errors.”62 Although high-frequency traders
“impose adverse selection costs on other investors,” they “play a beneficial
role in price efficiency” and “supply liquidity in stressful times such as the
most volatile days and around macroeconomic news announcements.”63
By contrast, the full diffusion of Supreme Court decisions through
financial markets may take hours, even an entire trading day.64 In spectacular instances, the market affirmatively misinterprets a Supreme Court
decision and, at least initially, sends the prices of affected securities in the
wrong direction. In the 2012 case of National Federation of Independent
Business v. Sebelius,65 apparent misreporting on the actual nature of the
closely watched, hotly controversial “Obamacare” decision66 sparked very
high volatility in the stock prices of health insurance companies such as
Aetna (AET), Humana (HUM), and Anthem/WellPoint (WLP).67
Even more dramatically, the 2013 decision in Association for Molecular
Pathology v. Myriad Genetics Inc.68 accounted for a 10% abnormal increase
in the stock price of Myriad Genetics (MYGN) in the first hour of trading
after the 10 a.m. announcement of the decision, which was reversed into
a 10% abnormal decrease during the final two hours of the trading day.69
Over two trading days, the Supreme Court’s decision accounted for 20%
negative abnormal returns in the price of MYGN.70



Admittedly, the legal reasoning in the Sebelius and Myriad decisions
was highly complex. Expert legal analysts, let alone capital markets, had
evidently failed to anticipate that the high court could somehow reject
the government’s characterization of the Affordable Care Act as regulation of interstate commerce, but nevertheless uphold health care reform
as an exercise of Congress’s powers over taxation. Nor did the relevant
legal or financial actors appear to anticipate that the Court would invalidate Myriad’s patent claim to DNA mutations in the BRCA1 and BRCA2
genes (which are associated with a heightened risk of breast and ovarian cancer), but manage to uphold Myriad’s patent in complementary
DNA extracted from the same genetic material. The important implication for finance is that economically significant information from Supreme
Court decisions diffuses throughout markets over the course of minutes,
hours, or even entire trading days, a veritable eternity in the age of highfrequency trading.
The following heat map, covering two trading days after the announcement of 79 financially significant, “law on the market” decisions by the
Supreme Court, shows considerable amounts of blue, green, and yellow
to the left—colors indicating less than full assimilation of new information
by the securities market (Fig. 1.1).71
The behavioral implications of law on the market, if any, lie in the time
lags between the arrival of new information and the assimilation of that
information by a putatively efficient market. Barriers to the diffusion of
financially significant information from the Supreme Court, to say nothing
of less salient legal tribunals, appear to rise from legal complexity and the
nuance involved in interpreting the real economic impact of certain legal
decisions. I do not mean to suggest that law is immune to computational
analysis; algorithmic analysis promises new weaponry, for instance, against
tax evasion.72 But there remain meaningful differences in the speed with
which certain types of information are digested and diffused throughout
the market.
In finance, as in other domains, the quality of decision-making is a function of time pressure and information load.73 Among individual investors,
the speed and convenience accompanying the transition from phone-based
to online trading platforms led to more trading, more speculation, and
lower profits.74 Nearly instantaneous machine processing of announcements concerning macroeconomic variables and consumer confidence
fuels high-frequency trading strategies that move billions in market capitalization within seconds.75 Security-specific news from court decisions, at



S 6/25/2014
SBGI 6/25/2014
FOXA 6/25/2014
CBS 6/25/2014
HAL 6/23/2014
BHI 6/23/2014
XOP 6/23/2014
XLE 6/23/2014
XES 6/23/2014
C 6/23/2014
SANM 6/9/2014
CTS 6/9/2014
LLNW 6/2/2014
XLK 1/14/2014
XTL 12/10/2013
VOX 12/10/2013
F 12/2/2013
MA 6/20/2013
XPH 6/17/2013
ACT 6/17/2013
MYGN 6/13/2013
XLP 5/13/2013
XOP 4/17/2013
XLE 4/17/2013
PSO 3/19/2013
WLP 6/28/2012
MGLN 6/28/2012
HUM 6/28/2012
HNT 6/28/2012
HCA 6/28/2012
CI 6/28/2012
AET 6/28/2012
XLE 6/21/2012
KMI 6/21/2012
XHE 6/18/2012
STN 6/18/2012
XLV 4/17/2012
UBS 3/26/2012
DB 3/26/2012
CS 3/26/2012
GT 6/27/2011
XLY 6/20/2011
XES 6/6/2011
XLY 5/31/2011
JAH 5/31/2011
XLF 5/16/2011
XTL 4/27/2011
XPH 3/29/2011
XTL 3/1/2011
VOX 3/1/2011
GM 2/23/2011
WEX 1/24/2011
AXP 1/24/2011
XLI 4/21/2010
CDE 6/22/2009
CNA 6/18/2009
PFE 3/4/2009
DD 1/26/2009
MO 12/15/2008
XLY 6/23/2008
S 6/23/2008
XLV 2/20/2008
VOX 4/30/2007
TFX 4/30/2007
VOX 4/17/2007
GLBC 4/17/2007
MO 2/20/2007
EBAY 5/15/2006
MER 3/21/2006
ITW 3/1/2006
YRCW 6/20/2005
MRK 6/13/2005
XLP 5/16/2005
XLI 12/13/2004
XOM 6/24/2004
HES 6/24/2004
CVX 6/24/2004
UNH 6/21/2004
XLI 5/3/2004
V 4/21/2004
XLF 12/2/2003
XLY 6/26/2003
TRV 6/23/2003
XLP 6/9/2003
XLV 5/19/2003
PFE 5/19/2003
BMY 5/19/2003
AZN 5/19/2003
PHS 4/7/2003
XLI 3/10/2003
UNP 3/10/2003
NSC 3/10/2003
WLB 1/15/2003
XLY 5/20/2002
T 5/20/2002
T 5/13/2002
XLE 1/9/2002
GWO 1/8/2002
XLP 12/10/2001
XLI 12/10/2001
VGR 6/28/2001
XLI 6/4/2001
CC 3/21/2001
HMC 2/27/2001
GM 2/27/2001
XLV 2/21/2001
XLE 12/4/2000
XLF 6/12/2000
PLA 5/22/2000
KSU 4/17/2000
JNJ 4/3/2000
BMY 4/3/2000
ABT 4/3/2000
XOM 3/6/2000
CVX 3/6/2000

9:30 10:00

Day 1 Close




Day 2 Close



Fig. 1.1 Cumulative abnormal returns from Supreme Court’s decisions, 1999
through 2014, as a function of time



least under existing technology, still requires additional evaluation, both
legal and financial, before informed trading can take place. Given an extra
second, minute, or hour, would markets “dare/Disturb the universe?”76
“In a minute,” after all, “there is time/For decisions and revisions which
a minute will reverse.”77 The time it takes informed traders to act on law
on the market marks the temporal boundaries of the domain within which
instinct and calculation—action and reaction within the behavioral universe of finance—influence security prices.
From calendar anomalies to “law on the market,” we have now found
our focus within our quest for behavioral departures from strict rationality in the evaluation of financial risk. Cracks in the edifice of the efficient
markets hypothesis are the openings from which a more comprehensive and more accurate account of behavioral finance will emerge. Not
every anomaly has enough economic significance to provide material for
a workable trading strategy. And not every trading strategy arises from
cognitive biases and behavioral heuristics. These limitations safely consign slogans such as “sell in May and go away” to the domain of popular
financial journalism. By contrast, the financial impact of Supreme Court
decisions tantalizingly suggests a trading strategy whose temporal window
of opportunity may be orders of magnitude wider than that of the usual
high-frequency trading algorithm.78



Theories of behavioral finance become necessary only in the presence of
uninformed investors and noise traders.79 A “market composed solely of
information traders” is a market “where price efficiency and the CAPM
hold,” where “[r]isk premia are determined solely by beta and distribution
of returns on the market portfolio,” and where option prices80 and the
term structure of bonds81 follow mathematically beautiful models reflecting comparably rational assumptions about those corners of the financial
marketplace.82 “The actions of noise traders weaken the relation between
security returns and beta, but they create a positive conditional correlation between abnormal returns and beta.”83 As behavioral anomalies
exert “steady and forceful” pressure upon “the twin paradigms of price
efficiency and the CAPM,” a correspondingly compelling need arises for a
“behavioral theory of capital asset prices and the volume of trade.”84
At an even broader level of generality, behavioral limits undermine
the assumption of rationality that permeates not merely modern portfolio theory, but all of neoclassical economics.85 Real consumers and real



investors simply do not behave like the stylized actors of neoclassical economics’ rational expectations hypothesis.86 “We will never really understand important economic events unless we confront the fact that their
causes are largely mental in nature.”87 Risk, the prime mover in finance, is
experienced and understood in emotional terms.88 And the primary forces
that appeal to emotion take verbal, visual, and narrative form: “[M]uch of
the human thinking that results in action is not quantitative, but instead
takes the form of storytelling and justification.”89
And storytelling is gossip, “the steady deliverer of secrets, … , the carrier
of speculation and suspicion.”90 To “live without gossip is to forfeit the
perilous cost of being born human”:91
Gossip is theology translated into experience. In it we hear great stories of
conversion … as well as stories of failure.… When we gossip we are also
praying, not only for them but for ourselves.92

“[T]here is one story in the world, and only one ….”93 (Or perhaps as
many as seven, as we shall soon see.) Every individual,94 every organization,95 every country,96 every religion97 lays claim to some form of uniqueness: Cultures of all kinds “stress uniqueness and claim to be superior or
to offer the one true faith.”98 But claims to organizational uniqueness ultimately reduce to no more than seven stories, which are not unique at all,
but universal.99 The simultaneous recognition of these stories in diverse
domains resembles the scientific phenomenon of multiple discovery,100
which arguably portrays the typical way by which science advances.101
Social organizations, including businesses, thus repeat the human storytelling experience, which consists of seven basic plots.102 Of particular
interest to finance is the archetypical tale of rags to riches.103 Implicit in the
tale of successful rise from “obscurity, poverty and misery to a state of great
splendor and happiness”104 is “the ‘dark’ version of the Rags to Riches
plot,” in which failed or pyrrhically victorious protagonists reach “their
self-destructive [destiny] by precisely the same rules which govern” the
attainment of material and spiritual satisfaction.105 So perhaps, there is only
one story in the world after all, and we revel in telling it again and again.106
Unsurprisingly, the impact of language, down to the very words we use,
depends on its connection to the physical senses.107 When making financial
decisions, investors “weigh[] a story, which has no quantitative dimension,
against the observed quantity of financial wealth that they have available
for consumption.”108 Over time, the most cognitively appealing narratives
congeal into “conventional wisdom,”109 which investors, financial advisors,

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