CHAPTER 5
Investor Psychology and Equity Market Anomalies

Hunter M. Holzhauer

Robert L. Maclellan and UC Foundation Associate Professor of Finance, University of Tennessee Chattanooga

INTRODUCTION

Richard Thaler, a founding father of behavioral finance, notes: “You assume that the agents in the economy are as smart as you are, and I assume that they are as dumb as me” (Harford 2019, p. 1). Thaler directed his comment to traditionalist Robert Barro during a National Bureau of Economic Research (NBER) conference. The quote perfectly contrasts the irrationality of investor decision-making explored in behavioral finance with the more rational framework of traditional finance models. However, behavioral finance is often and wrongly viewed as a direct competitor to traditional established theories like expected utility theory (EUT) (Bernoulli 1738; Neumann and Morgenstern 1944), modern portfolio theory (MPT) (Markowitz 1952), and the efficient market hypothesis (EMH) (Fama 1970, 1991). The most logical but false reason that many view behavioral finance as the polar opposite of traditional finance is that these well-established traditional theories assume that investors are always rational. Thus, many books erroneously claim that behavioral finance assumes that investors are always irrational. This assumption is simply not valid. Instead, behavioral finance assumes that investors are human but not always rational. This clarification is important because behavioral finance is not necessarily a substitute for traditional finance, but a complement. The two approaches are not mutually exclusive.

Instead, behavioral finance merely attempts to explain what traditional finance theories do not explain. In some cases, investors incorporate behavioral finance concepts into investment strategies that mostly disagree with traditional finance. However, the most basic tenets of behavioral finance aim to build on the existing foundation built by traditional finance. In other words, if traditional finance reveals most of the rational pieces of the market puzzle, behavioral finance attempts to reveal the final irrational missing pieces. Thus, any discussion on behavioral finance should begin with the understanding that most research in behavioral finance assumes that the market can be partially efficient, that investors can be partially rational, and that traditional finance theories do a remarkably accurate job of explaining a substantial portion of market behavior. In short, the bulk of behavioral finance explains why traditional finance theories do not always get market behavior right.

Having provided a brief synopsis on the relation between behavioral finance and traditional finance, the next two sections of this chapter focus primarily on investor psychology. More specifically, the following sections use two foundational concepts of behavioral finance – bounded rationality and prospect theory – as frameworks to provide reasons investors may not always be rational. The final section uses equity market anomalies as evidence for a detailed discussion on the limits of efficient markets. The chapter concludes by explaining why behavioral finance plays an important role in investor psychology and choice behavior.

INVESTOR PSYCHOLOGY: BOUNDED RATIONALITY

Much debate surrounds whether investors are rational. One concept that provides a logical framework for examining investor rationality is bounded rationality. Bounded rationality states that investor behavior may lead to mispriced stocks because investors are limited by the information they have, their cognitive abilities, and the time they have to make a decision (Simon 1978).

Information Limitations

Investors may have incomplete or inaccurate information. This ignorance gap can decrease the tractability of an investor's decision problem. For example, Baker and Nofsinger (2010) and Nofsinger (2018) discuss various biases that might create information limitations, including contamination bias, confirmation bias, omission bias, selection bias, sample size bias, and even time period–specific biases.

Cognitive Limitations

Even if the information is correct, investors may have cognitive limitations. Baker and Nofsinger (2010) and Nofsinger (2018) include different biases that indicate cognitive limitations, including framing, hindsight bias, memory recall issues, and cognitive dissonance. Other cognitive issues such as overconfidence can lead to scapegoating, the winner's curse, and the hot-hand illusion. Humans are also imperfect calculators. For example, they repeatedly make mistakes when calculating probabilities for two-stage problems, as highlighted by issues such as the isolation effect, the conjunction fallacy, and the gambler's fallacy.

Time Limitations

Even if the information is correct and processed correctly, investors may still behave irrationally, especially given a time constraint. Bounded rationality implies that investors have limited attention and use heuristics or mental shortcuts, which may lead to suboptimal-choice behavior. Baker and Nofsinger (2010) and Nofsinger (2018) discuss some of the more well-known heuristics, including affect, representativeness, availability, familiarity, ambiguity aversion, mental accounting, and anchoring and adjusting. Time limitations also create various inertia biases such as conservatism, normalcy bias, the ostrich effect, regret aversion, and the endowment effect. An example of an inertia bias is status-quo bias, which is a preference for the current state of affairs. This bias is important, especially in terms of retirement and health insurance planning, because it shows a tendency for individuals to stick with the default choice. Thus, authors such as Thaler and Sunstein (2009) recommend improving retirement planning by making automatic enrollment the default choice for pension plans and 401(k) plans rather than voluntary enrollment. Finally, all of these previously mentioned investor limitations, biases, and heuristics are relevant to investor psychology and merit additional discussion. However, for the sake of brevity, the following subsection uses prospect theory as a logical framework to focus on a few key issues that are extremely pertinent for investor psychology.

INVESTOR PSYCHOLOGY: PROSPECT THEORY

The basis of most traditional economic and financial models, such as the previously mentioned EUT, MPT, and EMH, is the assumption, or at least the implication, that investors are rational and, given choices, seek to maximize their utility. According to Kahneman and Tversky (1979, 1984), this primary assumption is flawed. Hence, they propose an alternative model called prospect theory (PT). PT assumes that investors value potential losses and gains differently, and maintains that investors' choices should be viewed more as a function of subjective decision weights than objective mathematical probabilities.

When Kahneman and Tversky (1979, 1984), introduced PT, it changed the way many investors and academics thought about choice behavior. First, academics often consider the PT approach more rigorous than its predecessor, namely, Simon's (1978) bounded rationality approach. Moreover, PT has stood the test of time and even paved the way for newer behavioral finance theories. Two notable theories include behavioral capital asset pricing theory (Shefrin and Statman 1994) and behavioral portfolio theory (Shefrin and Statman 2000). Behavioral capital asset pricing theory focuses primarily on the interaction between noise traders who commit cognitive errors and information traders who are free of cognitive errors. Behavioral portfolio theory suggests that a wide range of goals motivates investors, and these goals are not limited to simply maximizing the value of their portfolios. With respect to these newer theories and a growing stream of research in behavioral finance, PT remains the conventional wisdom for how to approach choice behavior. Thus, the final part of this section discusses PT and a few of its main contributions.

Reference Wealth

From a return perspective, a fundamental principle behind PT is that investors do not make decisions based on their potential gain or loss from a prospect (i.e., gamble), but rather on how their wealth may change compared to their reference wealth. In other words, PT assumes that choice decisions are based on a subjectively determined benchmark or reference wealth. However, EUT has no reference point. EUT assumes choices are independent of a reference wealth. In PT, the changes in wealth and not the level of end wealth matter. For example, given the same $1 million investment opportunity, a billionaire and a millionaire may behave differently because gaining or losing $1 million is unlikely to affect the billionaire's reference wealth as dramatically as it would the smaller reference wealth for the millionaire. Assume they both chose the investment but at different times. The millionaire goes first and doubles his wealth to $2 million. The billionaire goes second but loses $1 million. The millionaire is happier due to having a greater increase in utility than the billionaire, given this outcome. However, the billionaire is still better off financially. Thus, PT states that the change in wealth is more important for choice behavior than the final total amount of wealth. PT does not dispute that using end wealth is more rational, but rather, PT maintains that investors are not always rational, are often more short-term focused, and typically behave in terms of deviations from a reference point.

Risk versus Certainty

From a risk perspective, much of PT is based on the concept of risk aversion, which is the tendency to reduce or avoid uncertainty. Most investors are risk-averse, which means that they would rather avoid a loss than receive an equivalent gain. Risk aversion also helps explain the certainty effect, which states that investors often overweigh probable outcomes, especially small to medium probabilities of winning. Allais (1953) first notes the certainty effect when he coined the Allais paradox, which explains that investors' actual observed choices can violate the predictions of EUT. To illustrate both the certainty effect and the Allais paradox, assume an investor has to choose between a 100 percent chance of winning $25,000 or a 50 percent chance of winning $50,000. Although the expected payouts of these two gambles are equal, the certainty effect says a higher percentage of investors would choose the 100 percent or certain payout of $25,000.

Moreover, considering most investors are risk-averse, they would probably take even less than $25,000, such as $20,000, to avoid the risk involved in the 50 percent chance of winning $50,000. In this case, the utility of a certain payoff is higher for the investor than the uncertain payoff with a higher expected monetary value. However, if a certain payoff of $20,000 became an uncertain payoff (e.g., an 80 percent chance of winning $25,000), investors may now choose the 50 percent chance of winning $50,000 because neither choice provides the higher utility associated with certainty.

Other Effects of Prospect Theory

As further evidence of the subjective nature of investors, Kahneman and Tversky (1979, 1984) discuss two other pervasive effects that violate traditional theories like EUT: the reflection effect and the disposition effect. PT is largely based on the reflection effect, which explains that investors may have different preferences for gambles depending on the sign of the outcome (i.e., whether the outcome is a loss or a gain). In other words, investors are more likely to be risk-averse when they have something to gain and more likely to be risk-seeking when they have something to lose. The reflection effect may help explain the disposition effect. Two golden rules of investing from the well-known seventeenth-century economist David Ricardo are to cut short your losses and let your profits run (Zweig 2017). The opposite is the disposition effect, which is the tendency for investors to cut short their profits and to let their losses run (Shefrin and Statman 1985). Kahneman and Tversky (1979) explain the disposition effect in terms of risk aversion in that investors dislike losing much more than they enjoy winning. In other words, investors tend to sell assets that have increased in value because they do not want to experience the chance of the asset losing value. Likewise, they tend to hold on to assets that have dropped in value too long because they do not want to realize the loss. In short, PT presents many issues of why investors may be irrational. The next section looks at specific violations of the EMH.

EQUITY MARKET ANOMALIES: VIOLATIONS OF THE EMH

One of the more important concepts in finance is the efficient market hypothesis (EMH), which implies that stock prices fairly and accurately reflect all information available to investors (Fama 1970, 1991). The EMH has a heralded history of academic support (Kendall and Hill 1953; Cootner 1964; Fama 1965; and Malkiel 1973). Malkiel (1973, 2003), in particular, contends that stock prices evolve according to a random walk, which means price changes are random and cannot be predicted. Malkiel went as far as to say that “a blindfolded chimpanzee throwing darts at the Wall Street Journal could select a portfolio that would do as well as the experts.” That said, not everyone agrees. A growing list of EMH critics exists, such as Lo (1999), Lo and MacKinlay (2002), and Bhargave (2014), who present evidence of nonrandom trends in the stock market that suggest the stock market is somewhat predictable. One area of behavior finance in particular – equity market anomalies – provides abundant evidence that markets are not entirely efficient. Thus, the following section describes the limits of the EMH with a particular focus on market anomalies and how they violate the different forms of the EMH.

Fama (1970) proposes three forms or different views of the EMH: weak form, semi-strong form, and strong form.

  1. The weak form EMH asserts that the market has already incorporated all past trading (market) information such as price and volume into current stock prices. It also implies that investors cannot consistently outperform the market using technical analysis, which charts statistical trends in past trading behavior and forms trading strategies based on recurrent and predictable patterns in stock prices and volume levels.
  2. The semi-strong form EMH asserts that the market has not only priced in all past trading (market) information but has also incorporated all publicly available information. The added implication with the semi-strong EMH is that investors cannot consistently outperform the market using fundamental analysis, which analyzes all publicly available information, including financial statements, to measure a firm's intrinsic value based on relevant qualitative and quantitative factors.
  3. The strong form EMH asserts that the market has already incorporated all relevant information, including not only past and public information but also private information. Thus, the strong form implies that investors cannot consistently outperform the market even through insider trading, which is the usually illegal practice of trading a firm's stock based on access to private or material nonpublic information about the firm.

TABLE 5.1 Usefulness of Market Information for Different Views of Market Efficiency

Usefulness of Different Sources of Market Information and Analysis
Different Views of the Efficient Market Hypothesis (EMH) Past Information and Technical Analysis Public Information and Fundamental Analysis Private Information and Insider Trading
Not efficient Yes Yes Yes
Weak form EMH No Yes Yes
Semi-strong form EMH No No Yes
Strong form EMH No No No

This table breaks down the usefulness of different sources of market information and analysis based on increasing views of market efficiency. “Yes” means that this information and analysis may be useful because the market has not fully priced this information into stock prices. “No” means that this information and analysis is not useful because the market has already fully priced this information into stock prices.

Table 5.1 lists these different views of the EMH and which types of market information or analysis might be helpful to investors based on these respective views. In Table 5.1, “Yes” indicates that the market may not respond to this information in an accurate or timely manner and investors may be able to use the information or analysis to outperform the market consistently. Conversely, “No” indicates that the market has already incorporated this information into current security prices and investors are unable to use the information to outperform the market consistently. For example, look at the two extreme views. Table 5.1 shows that no market information or analysis is useful for outperforming the market in the strong form of EMH because the market has already priced this information into stock prices. However, if the market is inefficient, then technical analysis, fundamental analysis, and insider trading may all be useful because the market has not fully priced past, public, or private information into stock prices.

Violations of the Weak Form EMH

Having reviewed the different forms of the EMH, violations of each form are explored next. The weak form EMH involves several important violations: the momentum effect, the reversal effect, and calendar-related anomalies. The latter violation is a broad category of anomalies that focuses on different time periods associated with recurring patterns in stock prices. However, this section starts with a discussion of the first two violations, which are often incorporated into technical analysis and are essentially two different types of serial correlation.

Serial Correlation Serial correlation, sometimes called autocorrelation, is the relation or correlation between a variable and its lagged version at different time intervals. According to Bodie, Kane, and Marcus (2013), a serial correlation for stock market returns is the tendency for current stock market returns to be related to past stock market returns.

The two types are a positive serial correlation, as seen with the momentum effect, and a negative serial correlation, as seen with the reversal effect. Positive serial correlation implies a direct relation, meaning that positive stock returns tend to follow positive stock returns. For example, the momentum effect is the tendency for a stock's recent performance, good or bad, to continue in that same direction in the following period. Conversely, negative serial correlation implies an inverse relation, meaning that positive stock returns tend to follow negative stock returns. For example, the reversal effect is the tendency for a stock's recent performance, good or bad, to reverse or correct itself in the following period.

An important deviator between these two effects is time horizons. Research attributes the momentum effect to returns over short-term horizons and the reversal effect to returns over long-term horizons (DeBondt and Thaler 1985). For example, Jegadeesh and Titman (1993) investigate abnormal stock returns over 3- to 12-month holding periods and find evidence of both short- and intermediate-horizon price momentum. As for the long-term horizon, the authors rank-order stock returns over five years and compare their “loser” portfolio of the bottom 35 stocks with their “winner” portfolio of the 35 top stocks. They find significant negative autocorrelation as the loser portfolio outperformed the winner portfolio by an average of 25 percent cumulative return in the following three-year period. One behavioral explanation for both of these effects is that short-term momentum is often the result of temporary overreactions by investors, which may lead to a long-term reversal as investors over time recognize and correct past pricing mistakes.

Short-Term Calendar-Related Anomalies The final weak form EMH violation is calendar-related anomalies. Several short-term anomalies include weather patterns (Saunders 1993; Hirshleifer 2001; Hirshleifer and Shumway 2003), seasonality patterns (Kamstra, Kramer, and Levi 2003; Garrett, Kamstra, and Kramer 2004), daylight savings patterns (Kamstra, Kramer, and Levi 2000; Singal 2004), and holiday effects (Zweig 1986; Lakonishok and Smidt 1988; Ariel 1990). Academics have even examined the investment adage sell in May and go away, also known as the Halloween effect, which instructs investors to sell on the first trading day in May and buy on the sixth trading day before the end of October (Gultekin and Gultekin 1983; Keim and Ziemba 2000; Bouman and Jacobsen 2002; Doeswijk 2005). Dzhabarov and Ziemba (2011) provide evidence that using a sell-in-May-and-go-away strategy can offer superior returns compared to a standard buy-and-hold strategy. Interestingly, the reasons for this abnormal performance, and abnormal performance in most other calendar-related patterns, tend to focus on time period–specific differences in trading volume, investor mood, and market uncertainty.

Another example of a calendar-related anomaly that fits into this category is the weekend effect or day-of-the-week effect. This anomaly refers to the tendency of stock prices to decrease on Mondays and increase the most on Wednesdays and Fridays. Another, more substantiated, calendar-related anomaly is the turn-of-the-month effect, which is based on the tendency of stock prices to rise on the last trading day of the month and the first three trading days of the next month. The main explanation for this anomaly is that these four days represent the vast majority of monthly and quarterly announcements by firms and government reporting agencies. These four days also likely capture the bulk of trading activity regarding required portfolio rebalancing. Finally, perhaps the most significant and most substantiated calendar-related anomaly is the turn-of-the-year effect, which describes a pattern of increased trading volume and higher stock prices in the last week of December and the first two weeks of January.

Small-Firm-in-January Effect Some academics attribute the bulk of the turn-of-the-year effect to the small-firm-in-January effect. In other words, after breaking down the turn-of-the-year effect into different asset classes and time periods, one asset class and time period stands out in terms of abnormal returns – small-firm stocks in the first two to three weeks of January. The January effect is one of the most well-known anomalies, and certainly the most researched calendar-related anomaly, in financial markets. It is characterized by a seasonal increase in stock prices, especially for small firms, during January, with the bulk of the rally occurring primarily in the first two weeks of the month. Wachtel (1942) first documented the January effect with Banz (1981), adding the small-firm effect, which states that stocks of small firms earned abnormal risk-adjusted returns. As evidence, Bodie et al. (2013) stratify all New York Stock Exchange (NYSE) firms into 10 portfolios according to their size. They show that NYSE firms in the smallest-size decile outperformed the NYSE firms in the largest-size decile by 8.8 percent, on average, annually between 1926 and 2010. Later research by Reinganum (1983), Blume and Stambaugh (1983), and Keim (1993) provides conclusive data that the small-firm effect is restricted almost entirely to January. Both the January and small-firm effects are often combined and labeled as the small-firm-in-January effect.

Several important and potential explanations are available for the January effect. Some investors believe the January effect is a product of year-end bonuses, which allow investors to put large volumes of cash into the market. January is also a great time of the year from an investor psychology perspective to start new investment programs or to begin new investment-related New Year's resolutions. Another reason for the January effect could be “window dressing,” which entails portfolio managers selling lesser-known and underperforming small firms at the end of each year so that these firms do not appear on year-end reports. In other words, from an optics perspective, portfolio managers may prefer for their year-end statements to list larger and overperforming firms that their clients might recognize and consider as safer and more prudent investments. Although these rationales have some merit, most analysts and academics focus on taxes as the main reason for the January effect. They contend that many investors embrace tax-loss harvesting in December to offset any realized capital gains for the year.

Long-Term Calendar-Related Anomalies Finally, some investors may look for time-related patterns that stretch beyond the calendar year such as political effects or anomalies including the presidential premium, which suggests that the average stock market returns are significantly higher under Democratic presidential administrations compared to Republican presidential administrations (Santa-Clara and Valkanov 2003). However, investors should be careful using the presidential premium as an investment guide because several statistical issues need to be addressed. For example, Campbell and Li (2004) take a different statistical approach that controls for market volatility. They find that neither risk nor return is significantly different for Republican or Democrat presidential cycles. Statistically, many other inherent flaws need to be controlled when examining the presidential premium, including limited sample size, lag effects, announcement effects, and expectation effects of different administrations. Even measuring the actual impact of any president on the stock market is complicated. Thus, like many other calendar-related anomalies, research has explored some issues, but others remain a mystery. The only consistent theme is that both the predictability and the reasoning behind many of these anomalies remain highly debatable.

Violations of the Semi-Strong Form EMH

The semi-strong form EMH states that investors cannot consistently outperform the market using either past or publicly available information. However, when examining publicly available information, researchers find several noteworthy anomalies, including outperformance by stocks with high dividend yields, low price-to-earnings (P/E) ratios, and low price-to-book (P/B) ratios. Some of the most interesting anomalies are related to announcement effects, including initial public offerings, seasoned equity offerings, debt issuances, share repurchases, and dividend changes (Cohen, Lys, and Zach 2011). Three important announcement-related anomalies are linked respectively to firms' announcements of earnings, stock splits, and mergers and acquisitions (M&As).

Post-Earnings-Announcement Price Drift In a truly efficient market, a firm's stock price should immediately reflect any information or surprise concerning an earnings announcement. Much evidence indicates that stock prices respond immediately to earnings surprises. Nevertheless, after earnings announcements and the immediate market response, the firm's stock price tends to continue to drift in the same direction as the earnings surprise for days, weeks, and even months after the announcement (Ball and Brown 1968). For example, Foster, Olsen, and Shevlin (1984) show that firms with extreme positive and negative quarterly earnings surprises have average stock returns of 3.23 percent and –3.09 percent, respectively, for the 60 trading days following the announcement. Bernard and Thomas (1989) also document this post-earnings-announcement price spread. They find a spread of 4.20 percent for the same 60-day post-earnings trading period. Moreover, both studies find that this effect is far more prominent among smaller firms than larger firms. Thus, the market appears to continue to gradually adjust to the earnings surprise by creating a sustained period of abnormal returns, especially for smaller firms.

Stock Splits Another announcement-related anomaly is the stock-split effect. Theoretically, stock splits are merely a cosmetic change to the firm's stock price that should not affect the company's overall value. Still, empirical studies link significant price reaction directly to splits (Grinblatt, Masulis, and Titman 1984; Lamoureux and Poon 1987). According to Ikenberry, Rankine, and Stice (1996), investors use stock splits as signals. They find a 3.38 percent post-split announcement return for two-for-one stock splits. Strikingly, they maintain that this return is a market underreaction. In the subsequent one-year and three-year periods, these same stocks continue to earn post-split excess returns of 7.93 percent and 12.15 percent, respectively. The authors suggest that managers' decisions to split usually align with their expectations for higher future performance.

Interestingly, professional investors do not seem to agree with these managers. Dhar, Goetzmann, Shepherd, and Zhu (2005) find that individual investors make a higher percentage of post-split trades, whereas professional investors reduce their buying activity. Thus, stock splits may simply attract new and perhaps less sophisticated investors.

Mergers and Acquisitions Unlike stock splits, M&As represent important events that should affect a firm's value. Merger arbitrage involves trading stocks in companies that are subject to mergers or acquisitions. When firms announce a merger or takeover, the target firm's value being acquired tends to rise while the value of the bidding firm tends to fall. Arbitragers attempt to take advantage of the difference between the premium price the bidding firm is offering and the target firm's true intrinsic value. Research over various periods shows that after a merger, acquiring firms earn an average return of –7.00 percent for the following year (Schwert 1996), –4.00 percent for the following three years (Rau and Vermaelen [1998], and −10.26 percent for the subsequent five years following the merger (Agrawal, Jaffe, and Mandelker 1992). According to Schwert, this underperformance mainly results from abnormal outperformance by bidding firms in the years before the mergers.

Violations of the Strong Form EMH and Other Critiques of the EMH

The strong form EMH states that not only should all of the previous weak form and semi-strong form violations not exist, but that investors should also be unable to profit from insider trading. However, much research suggests that insider trading may represent the most profitable of all stock market anomalies and has delivered abnormal returns for insiders for the past 50 years (Lorie and Niederhoffer 1968; Jaffe 1974; Givoly and Palmon 1985; Seyhun 1988; Jeng, Metrick, and Zeckhauser 2003). Moreover, politicians seem to outperform even corporate insiders when it comes to insider trading. Ziobrowski, Cheng, Boyd, and Ziobrowski (2004) show that U.S. senators' stock purchases between 1993 and 1998 beat the market by 85 basis points per month while their stock sales lagged the market by 12 basis points per month.

Semi-Efficient Market Hypothesis Another competing idea with the EMH is the semi-efficient market hypothesis (SEMH), which is the idea that some stocks or areas of the market are priced less efficiently than the more recognized stocks found on the largest stock exchanges. Some examples of equities that are less covered by traditional research analysts include international stocks, small-cap stocks, and emerging markets. One anomaly that lends credence to the SEMH is the neglected-firm effect, which is the tendency for lesser-known firms to outperform more well-known firms. Arbel and Strebel (1983) use the neglected-firm effect as an interpretation of the previously mentioned small-firm effect. They propose that smaller firms – in contrast with larger firms – are less monitored and more neglected by large institutional investors, resulting in less publicly available information about smaller firms. This increased uncertainty about smaller firms makes them riskier investments that command higher returns.

Other Critiques of EMH In short, many different recurring violations of the EMH are available. Some investors even view stock market bubbles and infrequent events such as the 2000 dot-com bubble and the 2008 housing crisis as evidence that markets are not always efficient. Perhaps the most confirmative evidence for market inefficiency is that even Fama, the originator of the EMH, agrees that markets are not always efficient (Fama and Thaler 2016). He states that the EMH is just a model. The real issue is not whether markets are inefficient but rather how inefficient they are. Although market inefficiency is challenging to measure, anomalies do present empirical results that reflect either inefficiency in the market or inadequacies in the underlying asset pricing model.

Critiques of Anomalies The amount of research supporting anomalies varies dramatically, as does their timing, consistency, significance, magnitude, and robustness. Little evidence is available that investors can consistently profit from exploiting anomalies. First, considering that anomalies should decrease in efficient markets as arbitragers take advantage of them, no certainty exists that the past performance of anomalies has any predictive power regarding future performance. In other words, Fama (1998) maintains that if behavioral finance is real, it is insignificant and primarily based on data mining. He goes further to suggest that most long-term anomalies are fragile at best and that most short-term anomalies are simply due to chance, resulting from either short-term overreactions or underreactions. Second, if behavioral finance is real, Fama believes investors should be able to leverage these market anomalies into superior returns. However, many anomaly-based trading strategies are no longer profitable once returns are adjusted for trading costs and taxes. Third, researchers have examined few anomalies from a risk-adjusted return perspective, which would shed much-needed light on whether anomalies outperform relative to the market, their benchmark, or a model of “normal” return behavior.

SUMMARY AND CONCLUSIONS

Thaler states, “When an economist says the evidence is ‘mixed,’ he or she means that theory says one thing and data says the opposite” (Batista 2014, p. 42). The evidence and arguments for and against behavioral finance are certainly “mixed.” Interestingly, Thaler's quote could be taken in two different ways. For example, Thaler could argue that the evidence for efficient markets is “mixed” because EMH says one thing and data like anomalies says the opposite. On the other hand, others could counter that evidence for behavioral finance theories is also “mixed,” with theories like PT saying one thing and evidence against it saying the opposite (Levy and Levy 2002a, 2002b).

Furthermore, even if investors are irrational, do irrational investors lead to inefficient markets? Not necessarily. Rau (2010) lists three conditions needed for market inefficiency: (1) investors cannot always be rational, (2) their irrational biases must be correlated because random biases would effectively cancel each other out, and (3) arbitragers must be somewhat limited and cannot fully take advantage of these correlated biases. In other words, even if the first condition is assumed, the necessity of all three conditions for market inefficiency appears to support market efficiency. However, Schoenhart (2008) and Rau (2010) discuss several reasons for the prevalence of the latter two conditions, including herding effects among investors and additional risks and implementation costs for arbitragers.

In conclusion, even with only “mixed” support, behavioral finance continues to gain traction as an important field of finance that attempts to explain the many facets of investor psychology and to provide explanations for market inefficiencies like equity market anomalies. Such concepts as bounded rationality and PT are clearly contrary to more traditional finance ideas that assume investors are rational. Behavioral finance maintains that investors are not computers that can perfectly weigh expected probabilities. Even if investors properly use computers to make the best mathematical choices, limitations still exist involving available information and time constraints. Thus, investors' decisions are often biased, irrational, and driven by motivations other than maximizing their utility. Although behavioral finance is not meant to replace traditional finance, it is needed because it offers practical benefits. That is, behavioral finance is simply more adaptive for a real world with irrational investors and complex choice behavior than traditional finance. In the end, the goal of behavioral finance is to build better bridges from the theoretical world to the real world.

DISCUSSION QUESTIONS

  1. Discuss three conditions needed for market inefficiency and whether bounded rationality may address these conditions.
  2. List three different effects that relate to PT and how each effect can cause investors to make irrational decisions.
  3. List three violations of the weak form EMH and explain how they differ.
  4. List three announcement-related violations of the semi-strong form EMH and explain how they differ.

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