Behavioral Finance in Asset Management a Primer

FEATURE

Behavioral Finance in Asset Management A Primer

By Marcus Schulmerich, PhD, CFA®, FRM

Heuristic Simplification Biases inancial crises are taking place called behavioral biases. They played more frequently and their impact a central role in the development of Heuristics are experience-based tech- F is spreading across the globe— behavioral finance. niques that help us make decisions the latest examples are the 2007–2009 Slovic saw the relevance of behav- (Kahneman et al. 1982, 3–4). Three key Great Recession and stock market ioral concepts in finance and set out heuristics discussed in this article are crash. The need to better understand his thesis in two articles at the end of representativeness, anchoring/salience, and address the root causes of such the 1960s (Shefrin 2002, 7–8). Then, and loss aversion/prospect theory. crashes becomes ever more important. in 1974, Tversky and Kahneman intro- Loss aversion forms part of Kahneman Standard finance theory cannot duced the fundamental concept of heu- and Tversky’s prospect theory (1979, explain the phenomenon, but behav- ristics—the study of how people make 263–291, reviewed later in this article. ioral finance theory offers some com- decisions, rush to judgment, or solve In 2002, Kahneman and Smith were pelling explanations. Behavioral finance problems, often using their experiences awarded the Nobel Memorial Prize in supplements standard finance theory and biases. Since then, academics and Economic Sciences for their work in the by introducing behavioral and psycho- practitioners have incorporated heu- field of behavioral finance. logical criteria to help clarify investors’ ristics into behavioral finance and led Representativeness decision-making process. This article a vast research effort focusing on the outlines the background and key find- cause and effect of psychological biases Representativeness bias describes the ings of behavioral finance and examines in financial markets. tendency to evaluate a situation by the influence of behavioral biases in More recently, in 2001, David comparing it to generalities or stereo- stock market crashes. Hirshleifer5 published a concise over- types. It can be thought of as a mental view of the most important behavioral shortcut that helps decision makers An Introduction to Behavioral biases, as shown in table 1. The biases avoid the need to analyze similar pro- Finance are grouped into four categories: cesses again and again. In 1956, economist Vernon L. Smith1 • Self-deception (limits to learning) In investment decision-making, rep- was the first to introduce the concept • Heuristic Simplification resentativeness bias can arise either from of behavioral finance. At the time, the • Emotion/Affect base-rate neglect or sample-size neglect. investment community did not believe • Social Base-rate neglect: Investors attempt in the idea that human behavior influ- to determine the potential success of ences security prices. However, others Key biases are highlighted in table 1. an investment in a stock by contextual- such as the psychologists Paul Slovic,2 As examined below, these biases, in izing it in a familiar, easy-to-understand Amos Tversky,3 and Daniel Kahneman,4 large part, help explain the occurrence classification scheme. For example, an continued to analyze investors’ so- of stock market bubbles and crashes. investor might categorize a stock as a

TABLE 1: OVERVIEW OF BEHAVIORAL BIASES BY HIRSHLEIFER Behavioral Biases Heuristic Simplification Self-Deception (limits to learning) Emotion/Affect Social Representativeness Overoptimism Ambiguity Aversion Herding Anchoring/Salience Overconfidence Self-Control (hyperbolic discounting) Contagion Loss Aversion/Prospect Theory Confirmation Mood Imitation Framing Self-Attribution Regret Theory Cascades Availability Hindsight Cue Competition Cognitive Dissonance Categorization Conservation Source: Hirschleifer (2001, 1,533–1,597)

TABLE 2: TYPES OF REPRESENTATIVENESS BIASES Type of Representativeness Bias Examples 1. Investors can make significant financial errors when they examine a money manager’s track record. They look at that past few quarters or years and conclude, based on inadequate statistical data, that a fund’s performance is the result of skilled allocation and security selection. Base-rate neglect 2. Investors also make similar mistakes when investigating track records of stock analysts. For example, they look at the success of an analyst’s past few recommendations, erroneously assessing the analyst’s aptitude based on this limited data sample. Investors often fail to judge the likelihood of an investment outcome because they choose an inap- Sample-size neglect propriate sample size of data on which to base their judgments. They incorrectly assume that small sizes are representative of real data: Researchers call this phenomenon the “law of small numbers.”

Source: Pompian (2006, 66–67)

“value stock” and draw quick conclusions or irrelevant experience. Shiller (2005, tive assessment—their views can be sig- about the risks and rewards that follow 148) defines two types of anchors: quan- nificantly influenced by suggestions, e.g., from that categorization. This reason- titative and moral. how the numbers are presented. In this ing ignores variables that can impact Quantitative anchors are numbers example, the answers are strongly influ- the success of the investment. Investors or quantitative variables used to make enced by the first numbers that appear in often embark on this erroneous path estimations. One obvious example is the question. because they perceive it as an easy alter- past stock prices. When faced with investment decisions, native to the diligent and more compli- Moral anchors play an important investors seem to get anchored in past cated research that’s actually required role when people compare the intuitive price changes and the average prices of when evaluating an investment. force of stories and reasons to hold their assets (Shefrin 2005, 51). Figure 1 shows Sample-size neglect: Investors fail investments against their perceived that financial analysts also fall victim to to accurately consider the sample size of need to consume the wealth that the quantitative anchoring: Their forecasts the data on which they are basing their investments represent. of the S&P 500 index from June 1991 judgments and incorrectly assume that While heuristics can aid in decision to June 2004 were highly correlated to small sample sizes are representative making, they sometimes lead to severe the past prices of the index (Montier of populations. When people do not and systematic investment errors 2005, 11). initially comprehend a phenomenon because they rely on intuitive judgments In discussing moral anchors, Shiller reflected in a series of data, they quickly that are categorically different from the (2005, 151–152) recalls The Millionaire link assumptions based on only a few rational models on which investment Next Door, which was a best seller of the available data points. Individuals decisions should be based. during the stock market boom of the prone to sample-size neglect are quick Consider the following example 1990s (Stanley and Danko 1996). The to treat properties reflected in such from Montier (2007, 25): In performing book explained that most millionaires small samples as properties that accu- calculations, the way equations are in the United States are not exceptional rately describe universal pools of data. presented appears to have a significant income earners but merely frugal savers— However, the small sample examined influence on the result. people who are not interested in buy- may not be representative of the real Two versions of the same calculation ing a new car every year, for instance. data. This is also known as the “law of are shown below. The book’s enticing story about invest- small numbers.” ment millionaires, who save rather than (a) 1 × 2 × 3 × 4 × 5 × 6 × 7 × 8 As illustrated by the examples in spend their money, was just the kind (b) 8 × 7 × 6 × 5 × 4 × 3 × 2 × 1 table 2, both types of representativeness of moral anchor needed to help sustain bias can lead to significant investment In both cases the answer is 40,320. the unusual bull market during the mid mistakes. However, experiments have shown that to late 1990s. using different ways of presenting this Reasons to hold stocks or other Anchoring/Salience question of calculating the factorial of investments also can take on ethical as Anchoring is the tendency of decision eight produces different results: well as practical dimensions. Our culture makers to use “anchor” values or beliefs In (a) the median answer was 512. may supply reasons to hold stocks and as the basis for an assessment. When In (b) the median answer was 2,250. other savings vehicles that are related asked to form an assessment, they estab- This test shows that when people are to our sense of identity as responsible, lish a judgment based on inappropriate asked to make a decision—say a quantita- good, or level-headed people.

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Loss Aversion/Prospect Theory FIGURE 1: S&P 500 INDEX LEVEL COMPARED TO S&P 500 INDEX LEVEL Loss aversion is the third type of heu- FORECASTS ristic bias with significant implications 1600 for the financial markets. Kahneman and Tversky (1979) introduced prospect 1400 theory, which analyzes the value that individuals assign to gains and losses 1200

(Gärdenfors and Sahlin 1988, 183–187). 1000 Figure 2 illustrates prospect theory, showing that individuals assign a value 800 to gains and losses that yields a value 600 function. This value function is concave for gains (representing risk aversion) 400 Jun-92 Jun-94 Jun-96 Jun-98 Jun-00 Jun-02 Jun-04 and convex for losses (representing — Actual S&P 500 risk seeking). Moreover, because of — Forecast loss aversion, the value function gener- Source: Montier (2005, 11) ally is steeper for losses than for gains. Prospect theory supports the following FIGURE 2: PROSPECT THEORY generalizations: • People underweigh outcomes that Value are probable in comparison with outcomes that are expected to be obtained with certainty. • People discard decision-making

components that are shared by all $100 prospects under consideration. Losses Gains For example, when comparing two $100 cars—one blue, the other red—the evaluation will focus on color rather than the car itself because the decision maker assumes that cars have the same properties, even if they are very different from one model Source: SSgA to another (e.g., Porsche versus Volkswagen). FIGURE 3: CFO SURVEY (AS OF DECEMBER 2010) Self-Deception Biases 80 Self-deception, or limits to learning, is the other key component of Hirshleifer’s categorization in terms of influence on 70 investors’ decision-making. Three of these self-deceptions are overoptimism, 60 overconfidence, and confirmation bias.

Overoptimism 50 Overoptimism describes the mental state in which people believe that things 40 2003 2004 2005 2006 2007 2008 2009 2010 more likely will go well for them than — U.S. Optimism (economy) poorly. When playing a game, individu- — U.S. Optimism (own firm) als are more inclined to think they will win than they will lose (Pompian 2006, Source: Optimism Index, published in: Duke / CFO Magazine Global Business Outlook Survey 2010, see: www.cfosurvey.org. 51). Overoptimism also is the tendency

FIGURE 4: PSYCHOLOGISTS’ ANSWERS—ACCURACY VERSUS CONFIDENCE level of confidence at each of the four stages. 55 • The psychologists—and by exten- 50 sion experts in general, as research has shown—displayed overconfi- 45 dence, while in fact not producing 40 more-accurate responses (Montier

35 2007, 110). Percent

30 Institutional investors are consid- 25 ered experts in finance. Yet they sometimes tend to be overconfident (Shiller 20 Stage I Stage 2 Stage 3 Stage 4 2005, 152), as in the example with ■ Accuracy the psychologists above. Institutional ■ Confidence investors trade more than private investors because they think they Source: Kahneman et al. (1982, 287–288) possess special knowledge. They are thus overconfident, which leads to the of individuals to exaggerate their own Prediction overconfidence: People following types of common mistakes abilities. They suffer the illusion of con- estimate within confidence intervals (Pompian, 2006, 54): trol, believing they can influence out- that are too narrow. • Overestimation of their ability to comes over which they demonstrably Certainty overconfidence: People evaluate a company as a potential have no influence. are blind to adverse opinions. investment Overoptimism is a common bias in In one experiment, psychologists • High turnover or frequent trading, all stock-market speculative bubbles. were asked to answer questions regard- which leads to lower returns During periods of rising prices inves- ing a patient’s behavioral pattern • Underdiversification of portfolios, tors are overoptimistic about their (Kahneman et al. 1982, 287–288). The which leads to taking more risk and investments. Even in market crashes, study was separated into four stages. At underestimating the level of that risk overoptimism still can help to explain each stage, the psychologists received investor behavior. more information about the patient and Shiller’s survey about the 1987 crash Intuitively, professionals who have were asked to answer the same ques- showed that 47.9 percent of institu- experience in and broad knowledge of tions regarding the patient’s behavior tional investors thought, based on their fields should be more objective than pattern, attitudes, interests, and typical their expertise, that the market would laymen; they should learn from their reactions to real life events. The accu- rebound during the day of the crash experiences. Yet a 2010 Duke University racy of the psychologists’ answers was because of their expertise in managing survey of 500 U.S. chief financial officers measured in percentage terms versus assets (Shiller 2005, 152). (CFOs) regarding their optimism about the correct answers. At the end of each A strong positive relationship exists the economy and their own firms con- stage, the psychologists were asked to between overoptimism and overconfi- tradicts this assumption.6 Figure 3 shows state how confident they were about the dence. Research has shown, for example, that the CFOs surveyed were always correctness of their answers. that managerial decisions are affected by more optimistic about their own firms Figure 4 displays the psychologists’ a combination of manager overoptimism than about the economy as a whole. levels of confidence compared with the and overconfidence. Overoptimistic accuracy of their answers at each of the managers overvalue the probability of Overconfidence four stages in the experiment. their success and may employ aggressive Overconfidence refers to individuals’ The experiment highlighted that business and accounting strategies that faith in their abilities. Numerous psy- the psychologists showed significant lead to higher discretionary accruals. chological experiments have shown overconfidence: These aggressive strategies are the con- that people suffer from the illusion of • The more information they received sequence of overconfident behavior. knowledge—thinking they have better the more confident they became in Hence, overoptimism can lead to over information than is actually the case. their own judgments. confidence. Overconfidence resulting Various studies have documented two • The accuracy of the judgments does from overoptimism highlights that inves- types of overconfidence: not correlate with the psychiatrists’ tors are willing to pay very high prices

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Herding for stocks or items during speculative • When investors are bullish, they are bubbles like the dot-com boom. The herding effect is a fundamental willing to pay nearly any price while observation about human society sellers only want to sell at a higher price. Confirmation and describes the finding that people This combination pushes prices up. Confirmation bias is the tendency who regularly communicate with one • If every market participant is bearish, to prefer information that confirms another think similarly. The effect the opposite effect occurs, with prices hypotheses or former conceptions was first noted by Solomon Asch,7 an being pushed down. whether or not these are true. People acclaimed social psychologist. • Throughout the history of financial thus empower their beliefs and attitudes In 1952, Asch carried out an experi- markets, investors who did not fol- by selectively collecting new informa- ment that showed the immense power of low the mainstream were seen as fool- tion or evidence to legitimize those social pressure on individual judgment. ish, even if they had sound explana- beliefs (Bensley 1998, 137). He placed each subject in a group of tions for their actions. Herding has a In a famous experiment in between seven and nine people (the “con- major impact on at least three com- 1992, economists Forsythe, Nelson, federates”) who were unknown to the mon theories: Neumann, and Wright created a hypo- subject. Asch had coached the confeder- 1. The idea that all economic agents thetical stock market. While evaluating ates before the experiment. The individu- are independent of each other in the performance of traders, they found als in the group were asked to answer a making their decisions is false. that only a minority managed to gener- sequence of 12 questions related to the 2. The law of supply and demand, ate high profits. More interestingly, the length of the lines A, B, and C compared where higher prices are supposed best-performing traders were the ones to the length of the left line in figure 5. to attract more sellers and deter most able to resist confirmation bias buyers, is not immediate. (Hirshleifer 2001). FIGURE 5: THE SOLOMON ASCH 3. The idea that asset prices give This experiment highlights the diffi- EXPERIMENT pure information about funda- culty portfolio managers have in evalu- Is line A, B, or C of the same length as mentals is incorrect. Asset prices ating an investment while being bom- the left line? provide a mix of hard informa- barded by information. They already tion and softer information such will have formed certain beliefs about as the crowd’s mood, which are individual investments and often stick difficult to untangle. to them rather than properly judging Conclusion the information at hand. These types of bias can be very This brief review highlights the implica- dangerous for fundamental analysts tions of behavioral biases in investment A CB and portfolio managers who need decision-making, in particular that psy-

to be aware of the degree to which Source: Shiller (2005, 157–158.) chological mechanisms can lead to com- their judgment can be skewed by pre- mon investing mistakes. Unlike standard existing views. During the dot-com financial theory, behavioral finance the- bubble, for example, analysts appeared The answers were obvious, but the ory offers some persuasive explanations to need a large amount of new informa- confederates deliberately gave incorrect for stock market anomalies, stock market tion in order to change views about any answers to seven of the 12 questions. crashes, and financial bubbles. Internet stock even when the market One-third of the time the subject gave was heading downward. Yet, from the same wrong answers as the confed- Marcus Schulmerich, PhD, CFA®, an objective point of view, a change erates. In addition, the participant often FRM, is a vice president with State in opinion would have been justified showed signs of anxiety or distress, Street Global Advisors GmbH (SSgA) much earlier. In contrast to funda- suggesting that fear of being seen as dif- in Munich, and is a global portfolio mental asset managers, quantitative ferent or foolish by the rest of the group strategist responsible for Europe, managers cut out personal judgments swayed the participant’s judgment. Middle East, and Africa. He earned by using mathematical models to con- Herding clearly arises in financial BS and MS degrees in mathematics struct portfolios. markets and explains a number of inter- and an MBA from MIT Sloan School esting empirical observations. Most and a PhD from European Business Social Biases notably, herding results in stock market School EBS in Germany in quant Herding is the key social bias that per- speculative bubbles (Brunnermeier finance. Contact him at tains to investment decision-making. 2001, 165): [email protected].

Endnotes of Jack Hirshleifer (1925–2005), who was a Shefrin, H. 2002. Beyond Greed and Fear. New 1 Vernon Lomax Smith (1927– ) is profes- prominent UCLA economics professor. York: Oxford University Press. sor of economics at Chapman University’s 6 Optimism Index, published in Duke/CFO ———. 2005. Behavioral Approach to Asset Argyros School of Business and Economics Magazine Global Business Outlook Survey Pricing. Burlington, MA: Elsevier Academic and School of Law, a research scholar at 2010. This survey covers 500 CFOs of major Press. George Mason University Interdisciplinary U.S. companies and includes all sectors. See Shiller, R. 2005. Irrational Exuberance. Center for Economic Science, and a fel- www.cfosurvey.org. Princeton, NJ: Princeton University Press. low of the Mercatus Center in Arlington, 7 Solomon Eliot Asch (1907–1996) was a Stanley, Thomas J., and William D. Danko. Virginia. Smith shared the 2002 Nobel world-renowned American psychologist and 1996. The Millionaire Next Door: The Memorial Prize in Economic Sciences with pioneer in social psychology. He was born in Surprising Secrets of America’s Wealthy. Daniel Kahneman. He is the founder and Warsaw, Poland, and emigrated to the United Atlanta, GA: Longstreet Press. president of the International Foundation States in 1920. He earned a master’s degree in for Research in Experimental Economics 1930 and a PhD in 1932, both from Columbia Disclaimer: The views expressed in this and an adjunct scholar of the Cato Institute University. Asch was a professor of psychol- material are the author’s views through in Washington, DC. ogy at Swarthmore College for 19 years. the period ended November 30, 2011, and 2 Paul Slovic (1938– ) is a professor of psy- He became famous in the 1950s with the are subject to change based on market and chology at the University of Oregon and the Solomon Asch Experiment, which showed other conditions. The information provided president of the Decision Research group. that social pressure can make a person say does not constitute investment advice He earned a PhD in psychology from the something that is obviously incorrect. and it should not be relied on as such. All University of Michigan in 1964. Slovic has material has been obtained from sources References studied psychological heuristics and pub- believed to be reliable, but its accuracy is lished frequently with co-authors such as Bensley, D. A. 1998. Critical Thinking in not guaranteed. This document contains Daniel Kahneman and Amos Tversky. He Psychology: A Unified Skills Approach. certain statements that may be deemed was the first to theorize the affect heuristic Pacific Grove, CA: Brooks/Cole Publishing forward-looking statements. Please note and is considered one of the leading theorists Company. that any such statements are not guaran- and researchers in the risk-perception field. Brunnermeier, M. 2001. Asset Pricing under tees of any future performance and actual 3 Amos Nathan Tversky (1937–1996) was an Asymmetric Information: Bubbles, Crashes, results or developments may differ materi- American cognitive and mathematical psy- Technical Analysis, and Herding. Oxford, ally from those projected. Past performance chologist. He was a pioneer of cognitive sci- U.K.: Oxford University Press. is not a guarantee of future results. ence and a long-time collaborator of Daniel Gärdenfors, P., and N.-E. Sahlin. 1988. Decision, Kahneman, who is a key figure in the discov- Probability and Utility. Cambridge, UK: Investments & Wealth Monitor ery of systematic human cognitive bias and Cambridge University Press. handling of risk. Hirshleifer, D. 2001. Investor Psychology and Mobile App 4 Daniel Kahneman (1934– ) is an Israeli psy- Asset Pricing. Journal of Finance 56, no. 4 The Investments & Wealth Monitor App chologist and Nobel laureate, notable for (August): 1,533–1,597. is now available for Apple iPad and his work on the psychology of investors, the Kahneman, D., and A. Tversky. 1979. Prospect iPhone, Android devices, and Kindle Fire. decision-making process, behavioral eco- Theory: An Analysis of Decision under Risk. • Read current and past issues of nomics, and hedonic psychology. Econometrica 47, no. 2 (March): 263–291. Investments & Wealth Monitor, free for IMCA members 5 David Hirshleifer is an American econo- Kahneman, D., P. Slovic, and A. Tversky. 1982. • Download available issues and mist and professor who holds the Merage Judgment under Uncertainty: Heuristics access them offline at any time Chair in Business Growth at the University and Biases. Cambridge, UK: Cambridge • Read topical industry-related content on the go of California, Irvine. He previously held University Press. • Search the archive of available tenured positions at the University of Montier, J. 2005. Seven Sins of Fund issues Michigan, The Ohio State University, and Management. DrKW Macro Research, • Bookmark your favorite articles • Read articles in magazine-page or the University of California, Los Angeles. Global Equity Strategy (November 18): text formats He is known for his research on social 11. http://papers.ssrn.com/sol3/papers. • Share content with colleagues via e-mail and social media learning, behavioral finance, and informa- cfm?abstract_id=881760. tion cascades, which have contributed to a ———. 2007. Behavioral Investing. West Sussex, To download the free app, visit your device’s marketplace and search new understanding of why society is prone UK: John Wiley & Sons Ltd. “Investments & Wealth Monitor.” to fads and fashions, and why stock mar- Pompian, M. 2006. Behavioral Finance and kets over- versus under-react. His articles Wealth Management. Hoboken, NJ: John IMCA® have received several awards. He is the son Wiley & Sons, Inc.

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