Investor Behavior and Capital Market Efficiency PDF

CH APTE R Investor Behavior and Capital Market Efficiency 13 AS FUND MANAGER OF LEGG MASON VALUE TRUST, WILLIAM NOTATION H. Mille...

CH APTE R Investor Behavior and Capital Market Efficiency 13 AS FUND MANAGER OF LEGG MASON VALUE TRUST, WILLIAM NOTATION H. Miller had built a reputation as one of the world’s savviest investors. Miller’s x i  portfolio weight of fund outperformed the overall market every year from 1991–2005, a winning investment in i streak no other fund manager came close to matching. But in 2007–2008, Legg Rs  return of stock or Mason Value Trust fell by nearly 65%, almost twice as much as the broader market. portfolio s While Legg Mason Value Trust outperformed the market in 2009, it lagged again r f  risk-free rate of from 2010 until Miller ultimately stepped down as manager and chief investment interest officer in 2012. As a result of this performance, investors in the fund since 1991 α s alpha of stock s effectively gave back all of the gains they had earned relative to the market in the β si  beta of stock s with intervening years and Miller’s reputation lay in tatters.1 Was Miller’s performance portfolio i prior to 2007 merely luck or was his performance in post-2007 the aberration? ε s  residual risk of According to the CAPM, the market portfolio is efficient, so it should be stock s impossible to consistently do better than the market without taking on additional risk. In this chapter, we will take a close look at this prediction of the CAPM, and assess to what extent the market portfolio is or is not efficient. We will begin by looking at the role of competition in driving the CAPM results, noting that for some investors to beat the market, other investors must be willing to hold port- folios that underperform the market. We then look at the behavior of individual investors, who tend to make a number of mistakes that reduce their returns. But while some professional fund managers are able to exploit these mistakes and profit from them, it does not appear that much, if any, of these profits make it into the hands of the investors who hold their funds. We will also consider evidence that certain investment “styles,” namely holding small stocks, value stocks, and stocks with high recent returns, perform better than predicted by the CAPM, indicating that the market portfolio may not be efficient. We explore this evidence, and then consider how to calculate the cost of capital if indeed the market portfolio is not efficient by deriving an alterna- tive model of risk—the multifactor asset pricing model. 1 T. Lauricella, “The Stock Picker’s Defeat,” Wall Street Journal, December 10, 2008. 483 M13_BERK6318_06_GE_C13.indd 483 26/04/23 7:04 PM  484 Chapter 13 Investor Behavior and Capital Market Efficiency 13.1 Competition and Capital Markets To understand the role of competition in the market, it is useful to consider how the CAPM equilibrium we derived in Chapter 11 might arise based on the behavior of individual inves- tors. In this section, we explain how investors who care only about expected return and variance react to new information and how their actions lead to the CAPM equilibrium. Identifying a Stock’s Alpha Consider the equilibrium, as we depicted in Figure 11.12 on pages 428–429, where the CAPM holds and the market portfolio is efficient. Now suppose new information ar- rives such that, if market prices remain unchanged, this news would raise the expected return of Walmart and Nike stocks by 2% and lower the expected return of McDonald’s and Tiffany stocks by 2%, leaving the expected return of the market unchanged.2 Figure 13.1 illustrates the effect of this change on the efficient frontier. With the new information, the market portfolio is no longer efficient. Alternative portfolios offer a higher expected return and a lower volatility than we can obtain by holding the market portfolio. Investors who are aware of this fact will alter their investments in order to make their portfolios efficient. To improve the performance of their portfolios, investors who are holding the market portfolio will compare the expected return of each security s with its required return from the CAPM (Eq. 12.1): rs = r f + β s × ( E [ R Mkt ] − r f ) (13.1) FIGURE 13.1 15% An Inefficient Market Portfolio Efficient Portfolio If the market portfolio is not (after news announcement) GE Tiffany equal to the efficient portfolio, then the market is not in the 10% Expected Return Market Portfolio Apple CAPM equilibrium. The figure Nike (efficient prior illustrates this possibility if to news) Amazon news is announced that raises the expected return of Walmart and Nike stocks and lowers the IBM 5% Walmart Molson-Coors expected return of McDonald’s and Tiffany stocks compared to the situation depicted in Newmont Mining Figure 11.12. McDonald’s T-Bills = effect of news See the CAPM tool in the eText- 0% book and MyLab Finance to 0% 5% 10% 15% 20% 25% 30% 35% 40% explore similar charts using Volatility (standard deviation) current data. 2 In general, news about individual stocks will affect the market’s expected return because these stocks are part of the market portfolio. To keep things simple, we assume the individual stock effects cancel out so that the market’s expected return remains unchanged. M13_BERK6318_06_GE_C13.indd 484 26/04/23 7:04 PM  13.1 Competition and Capital Markets 485 FIGURE 13.2 15% Security Market Line Deviations from the Security Market Line aTIF If the market portfolio is not ­efficient, then stocks will not Tiffany all lie on the security market 10% Nike Expected Return line. The distance of a stock above or below the security market line is the stock’s alpha. We can improve upon the Walmart Market Portfolio market portfolio by buying stocks with positive alphas and 5% selling stocks with negative alphas, but as we do so, prices = effect of news will change and their alphas McDonald’s will shrink toward zero. T-Bills = effect of trade 20.50 0.00 0.50 1.00 1.50 2.00 Beta Figure 13.2 shows this comparison. Note that the stocks whose returns have changed are no longer on the security market line. The difference between a stock’s expected return and its required return according to the security market line is the stock’s alpha: α s = E [ R s ] − rs (13.2) When the market portfolio is efficient, all stocks are on the security market line and have an alpha of zero. When a stock’s alpha is not zero, investors can improve upon the perfor- mance of the market portfolio. As we saw in Chapter 11, the Sharpe ratio of a portfolio will increase if we buy stocks whose expected return exceeds their required return—that is, if we buy stocks with positive alphas. Similarly, we can improve the performance of our portfolio by selling stocks with negative alphas. Profiting from Non-Zero Alpha Stocks Faced with the situation in Figure 13.2, savvy investors who are holding the market ­portfolio will want to buy stock in Walmart and Nike, and sell stock in McDonald’s and Tiffany. The surge of buy orders for Walmart and Nike will cause their stock prices to rise, and the surge of sell orders for McDonald’s and Tiffany will cause their stock prices to fall. As stock prices change, so do expected returns. Recall that a stock’s total return is equal to its dividend yield plus the capital gain rate. All else equal, an increase in the current stock price will lower the stock’s dividend yield and future capital gain rate, thereby lowering its expected return. Thus, as savvy investors attempt to trade to improve their portfolios, they raise the price and lower the expected return of the positive-alpha stocks, and they depress the price and raise the expected return of the negative-alpha stocks, until the stocks are once again on the security market line and the market portfolio is efficient. Notice that the actions of investors have two important consequences. First, while the CAPM conclusion that the market is always efficient may not literally be true, competition M13_BERK6318_06_GE_C13.indd 485 26/04/23 7:04 PM  486 Chapter 13 Investor Behavior and Capital Market Efficiency among savvy investors who try to “beat the market” and earn a positive alpha should keep the market portfolio close to efficient much of the time. In that sense, we can view the CAPM as an approximate description of a competitive market. Second, there may exist trading strategies that take advantage of non-zero alpha stocks, and by doing so actually can beat the market. In the remainder of this chapter we will ex- plore both of these consequences, looking at evidence of the approximate efficiency of the market, as well as identifying trading strategies that may actually do better than the market. CONCEPT CHECK 1. If investors attempt to buy a stock with a positive alpha, what is likely to happen to its price and expected return? How will this affect its alpha? 2. What is the consequence of investors exploiting non-zero alpha stocks for the efficiency of the market portfolio? 13.2 Information and Rational Expectations Under what circumstances could an investor profit from trading a non-zero alpha stock? Consider the situation in Figure 13.2 after the news announcement. Because Exxon Mobil has a positive alpha before prices adjust, investors will anticipate that the price will rise and will likely put in buy orders at the current prices. If the information that altered Exxon Mobil’s expected return is publicly announced, there are likely to be a large number of in- vestors who receive this news and act on it. Similarly, anybody who hears the news will not want to sell at the old prices. That is, there will be a large order imbalance. The only way to remove this imbalance is for the price to rise so that the alpha is zero. Note that in this case it is quite possible for the new prices to come about without trade. That is, the competi- tion between investors may be so intense that prices move before any investor can actually trade at the old prices, so no investor can profit from the news.3 Informed Versus Uninformed Investors As the above discussion makes clear, in order to profit by buying a positive-alpha stock, there must be someone willing to sell it. Under the CAPM assumption of homogeneous expectations, which states that investors have the same information, all investors would be aware that the stock had a positive alpha and none would be willing to sell. Of course, the assumption of homogeneous expectations is not necessarily a good ­description of the real world. In reality, investors have different information and spend varying amounts of effort researching stocks. Consequently, we might expect that sophisti- cated investors would learn that Exxon Mobil has a positive alpha, and that they would be able to purchase shares from more naïve investors. However, even differences in the quality of investors’ information will not necessar- ily be enough to generate trade in this situation. An important conclusion of the CAPM is that investors should hold the market portfolio (combined with risk-free investments), and this investment advice does not depend on the quality of an investor’s information or trading skill. Even naïve investors with no information can follow this investment advice, and as 3 The idea that prices will adjust to information without trade is sometimes referred to as the no-trade ­theorem. (P. Milgrom and N. Stokey, “Information, Trade and Common Knowledge,” Journal of Economic Theory 26 (1982): 17–27.) M13_BERK6318_06_GE_C13.indd 486 26/04/23 7:04 PM  13.2 Information and Rational Expectations 487 the following example shows, by doing so they can avoid being taken advantage of by more sophisticated investors. EXAMPLE 13.1 How to Avoid Being Outsmarted in Financial Markets Problem Suppose you are an investor without access to any information regarding stocks. You know that other investors in the market possess a great deal of information and are actively using that ­information to select an efficient portfolio. You are concerned that because you are less informed than the average investor, your portfolio will underperform the portfolio of the average investor. How can you prevent that outcome and guarantee that your portfolio will do as well as that of the ­average investor? Solution Even though you are not as well informed, you can guarantee yourself the same return as the average investor simply by holding the market portfolio. Because the aggregate of all investors’ portfolios must equal the market portfolio (i.e., demand must equal supply), if you hold the mar- ket portfolio then you must make the same return as the average investor. On the other hand, suppose you don’t hold the market portfolio, but instead hold less of some stock, such as Google, than its market weight. This must mean that in aggregate all other investors have over-weighted Google relative to the market. But because other investors are more informed than you are, they must realize Google is a good deal, and so are happy to profit at your expense. Rational Expectations Example 13.1 is very powerful. It implies that every investor, regardless of how little infor- mation he has access to, can guarantee himself the average return and earn an alpha of zero simply by holding the market portfolio. Thus, because investors can always earn a zero alpha, no investor should choose a portfolio with a negative alpha. However, because the average portfolio of all investors is the market portfolio, the average alpha of all investors is zero. If no investor earns a negative alpha, then no investor can earn a positive alpha, implying that the market portfolio must be efficient. As a result, the CAPM does not depend on the as- sumption of homogeneous expectations. Rather it requires only that investors have rational expectations, which means that all investors correctly interpret and use their own informa- tion, as well as information that can be inferred from market prices or the trades of others.4 For an investor to earn a positive alpha and beat the market, some investors must hold portfolios with negative alphas. Because these investors could have earned a zero alpha by holding the market portfolio, we reach the following important conclusion: The market portfolio can be inefficient (so it is possible to beat the market) only if a significant number of investors either 1.   Do not have rational expectations so that they misinterpret information and believe they are earn- ing a positive alpha when they are actually earning a negative alpha, or 2.   Care about aspects of their portfolios other than expected return and volatility, and so are willing to hold inefficient portfolios of securities. 4 See P. DeMarzo and C. Skiadas, “Aggregation, Determinacy, and Informational Efficiency for a Class of Economies with Asymmetric Information,” Journal of Economic Theory 80 (1998): 123–152. M13_BERK6318_06_GE_C13.indd 487 26/04/23 7:04 PM  488 Chapter 13 Investor Behavior and Capital Market Efficiency How do investors actually behave? Do uninformed investors follow the CAPM advice and hold the market portfolio? To shed light on these questions, in the next section we review the evidence on individual investor behavior. CONCEPT CHECK 1. How can an uninformed or unskilled investor guarantee herself a non-negative alpha? 2. Under what conditions will it be possible to earn a positive alpha and beat the market? 13.3 The Behavior of Individual Investors In this section, we examine whether small, individual investors heed the advice of the CAPM and hold the market portfolio. As we will see, many investors do not appear to hold an efficient portfolio, but instead fail to diversify and trade too much. We then consider whether these departures from the market create an opportunity for more sophisticated investors to profit at individual investors’ expense. Underdiversification and Portfolio Biases One of the most important implications of our discussion of risk and return is the benefit of diversification. By appropriately diversifying their portfolios, investors can reduce risk without reducing their expected return. In that sense, diversification is a “free lunch” that all investors should take advantage of. Despite this benefit, there is much evidence that individual investors fail to diversify their portfolios adequately. Evidence from the U.S. Survey of Consumer Finances shows that, for households that held stocks, the median number of stocks held by investors in 2001 was four, and 90% of investors held fewer than 10 different stocks.5 Moreover, these investments are often concentrated in stocks of companies that are in the same industry or are geographically close, further limiting the degree of diversification attained. A related finding comes from studying how individuals allocate their retirement savings accounts (401K plans). A study of large plans found that employees invested close to a third of their assets in their employer’s own stock.6 These underdiversification results are not unique to U.S. investors: A comprehensive study of Swedish investors documents that approximately one-half of the volatility in investors’ portfolios is due to firm-specific risk.7 There are a number of potential explanations for this behavior. One is that investors suffer from a familiarity bias, so that they favor investments in companies they are famil- iar with.8 Another is that investors have relative wealth concerns and care most about the performance of their portfolio relative to that of their peers. This desire to “keep up with the Joneses” can lead investors to choose undiversified portfolios that match those of their colleagues or neighbors.9 In any case, this underdiversification is one important piece of evidence that individual investors may choose sub-optimal portfolios. 5 V. Polkovnichenko, “Household Portfolio Diversification: A Case for Rank Dependent Preferences, Review of Financial Studies 18 (2005): 1467–1502. 6 S. Benartzi, “Excessive Extrapolation and the Allocation of 401(k) Accounts to Company Stock,” Journal of Finance 56 (2001): 1747–1764. 7 J. Campbell, “Household Finance,” Journal of Finance 61 (2006): 1553–1604. 8 G. Huberman, “Familiarity Breeds Investment,” Review of Financial Studies 14 (2001): 659–680. 9 P. DeMarzo, R. Kaniel, and I. Kremer, “Diversification as a Public Good: Community Effects in Portfolio Choice,” Journal of Finance 59 (2004): 1677–1715. M13_BERK6318_06_GE_C13.indd 488 26/04/23 7:04 PM  13.3 The Behavior of Individual Investors 489 Excessive Trading and Overconfidence According to the CAPM, investors should hold risk-free assets in combination with the market portfolio of all risky securities. In Chapter 12, we demonstrated that because the market portfolio is a value-weighted portfolio, it is also a passive portfolio in the sense that an investor does not need to trade in response to daily price changes in order to maintain it. Thus, if all investors held the market, we would see relatively little trading volume in financial markets. In reality, a tremendous amount of trading occurs each day. At its peak in 2008, for ex- ample, annual turnover on U.S. stock markets was nearly 500%, implying that each share of each stock was traded 5 times on average. While average turnover has declined dramatically in the wake of the financial crisis, as shown in Figure 13.3, it is still at levels far in excess of that predicted by the CAPM. Moreover, in a study of trading in individual accounts at a discount brokerage, Professors Brad Barber and Terrance Odean found that individual investors tend to trade very actively, with average turnover almost one and a half times the overall rates reported in Figure 13.3 during the time period of their study.10 What might explain this trading behavior? Psychologists have known since the 1960s that uninformed individuals tend to overestimate the precision of their knowledge. For example, many sports fans sitting in the stands confidently second guess the coaching deci- sions on the field, truly believing that they can do a better job. In finance we call this pre- sumptuousness the overconfidence bias. Barber and Odean hypothesized that this kind of behavior also characterizes individual investment decision making: Like sports fans, in- dividual investors believe they can pick winners and losers when, in fact, they cannot; this overconfidence leads them to trade too much. FIGURE 13.3 5x U.S. Stock Market Annual Share Turnover, Annual Turnover (past 12 months) 4x 1970–2021 The plot shows the annual share turnover (total value of shares traded / total market 3x capitalization of all stocks). Such high turnover is difficult to reconcile with the CAPM, 2x which implies that investors should hold passive market portfolios. Note the dramatic 1x spike in trading during the 2008 financial crisis. Most recently, trading increased by about 50% during the 0x 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 COVID-19 pandemic. Year Source: CRSP data 10 B. Barber and T. Odean, “Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors,” Journal of Finance 55 (2000): 773–806. M13_BERK6318_06_GE_C13.indd 489 26/04/23 7:04 PM  490 Chapter 13 Investor Behavior and Capital Market Efficiency An implication of this overconfidence bias is that, assuming they have no true ability, investors who trade more will not earn higher returns. Instead, their performance will be worse once we take into account the costs of trading (due to both commissions and b ­ id-ask spreads). Figure 13.4 documents precisely this result, showing that much investor trading appears not to be based on rational assessments of performance. As additional evidence, Barber and Odean contrasted the behavior and performance of men versus women.11 Psychological studies have shown that, in areas such as finance, men tend to be more overconfident than women. Consistent with the overconfidence hypothesis, they documented that men tend to trade more than women, and that their portfolios have lower returns as a result. These differences are even more pronounced for single men and women. Researchers have obtained similar results in an international context. Using an ­extraordinarily detailed database on Finnish investors, Professors Mark Grinblatt and Matti Keloharju find that trading activity increases with psychological measures of overconfi- dence. Interestingly, they also find that trading activity increases with the number of speed- ing tickets an individual receives, which they interpret as a measure of sensation seeking, or the individual’s desire for novel and intense risk-taking experiences. In both cases, the increased trading does not appear to be profitable for investors.12 FIGURE 13.4 Individual Investor Returns Versus Portfolio Turnover 20% 15% Annual Return 10% 5% 0% Q1 Q2 Q3 Q4 Q5 S&P 500 (lowest turnover) (highest turnover) The plot shows average annual return (net of commissions and trading costs) for indi- vidual investors at a large discount brokerage from 1991–1997. Investors are grouped into quintiles based on their average annual turnover. While the least-active investors had slightly (but not significantly) better performance than the S&P 500, performance declined with the rate of turnover. Source: B. Barber and T. Odean, “Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors,” Journal of Finance 55 (2000): 773–806. 11 B. Barber and T. Odean, “Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment,” Quarterly Journal of Economics 116 (2001): 261–292. 12 M. Grinblatt and M. Keloharju, “Sensation Seeking, Overconfidence, and Trading Activity,” Journal of Finance 64 (2009): 549–578. M13_BERK6318_06_GE_C13.indd 490 26/04/23 7:04 PM  13.4 Systematic Trading Biases 491 Individual Behavior and Market Prices Thus, in reality, individual investors are underdiversified and trade too much, violating a key prediction of the CAPM. But does this observation imply that the remaining conclu- sions of the CAPM are invalid? The answer is not necessarily. If individuals depart from the CAPM in random, idio- syncratic ways, then despite the fact that each individual doesn’t hold the market, when we combine their portfolios together these departures will tend to cancel out just like any other idiosyncratic risk. In that case, individuals will hold the market portfolio in aggregate, and there will be no effect on market prices or returns. These uninformed investors may simply be trading with themselves—generating trading commissions for their brokers, but without impacting the efficiency of the market. So, in order for the behavior of uninformed investors to have an impact on the market, there must be patterns to their behavior that lead them to depart from the CAPM in sys- tematic ways, thus imparting systematic uncertainty into prices. For investors’ trades to be correlated in this way, they must share a common motivation. Consequently, in Section 13.4, we investigate what might motivate investors to depart from the market portfolio, and show that investors appear to suffer from some common, and predictable, biases. CONCEPT CHECK 1. Do investors hold well-diversified portfolios? 2. Why is the high trading volume observed in markets inconsistent with the CAPM ­equilibrium? 3. What must be true about the behavior of small, uninformed investors for them to have an impact on market prices? 13.4 Systematic Trading Biases For the behavior of individual investors to impact market prices, and thus create a profit- able opportunity for more sophisticated investors, there must be predictable, systematic patterns in the types of errors individual investors make. In this section we review some of the evidence researchers have found of such systematic trading biases. Hanging on to Losers and the Disposition Effect Investors tend to hold on to stocks that have lost value and sell stocks that have risen in value since the time of purchase. We call this tendency to hang on to losers and sell winners the disposition effect. Professors Hersh Shefrin and Meir Statman, building on the work of psychologists Daniel Kahneman and Amos Tversky, posited that this effect arises due to investors’ increased willingness to take on risk in the face of possible losses.13 It may also reflect a reluctance to “admit a mistake” by taking the loss. Researchers have verified the disposition effect in many studies. For example, in a study of all trades in the Taiwanese stock market from 1995–1999, investors in aggregate were twice as likely to realize gains as they were to realize losses. Also, nearly 85% of individual investors were subject to this bias.14 On the other hand, mutual funds and foreign investors 13 H. Shefrin and M. Statman, “The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence,” Journal of Finance 40 (1985): 777–790; and D. Kahneman and A. Tversky, “Prospect Theory: An Analysis of Decision under Risk,” Econometrica 47 (1979): 263–291. 14 B. Barber, Y. T. Lee, Y. J. Liu, and T. Odean, “Is the Aggregate Investor Reluctant to Realize Losses? Evidence from Taiwan,” European Financial Management 13 (2007): 423–447. M13_BERK6318_06_GE_C13.indd 491 26/04/23 7:04 PM  492 Chapter 13 Investor Behavior and Capital Market Efficiency NOBEL PRIZE Prospect Theory, Mental Accounting, and Nudges In 2002, the Nobel Prize for Economics was awarded to rather than aggregating them into a single “bottom line.” For Daniel Kahneman for his development of Prospect Theory ­example, the disposition effect follows from Prospect Theory with fellow psychologist Amos Tversky (who would have by assuming investors evaluate their gains and losses on each surely shared the prize if not for his death in 1996). Prospect stock separately, rather than as an entire ­portfolio. Similarly, Theory provides a descriptive model of the way individu- long-term investors who check their portfolios frequently als make decisions under uncertainty, predicting the choices may experience disutility from short-term losses even if people do make rather than the ones they should make. It they are subsequently reversed. As a result, investors may posits that people evaluate outcomes relative to the status demand a higher risk premium on publicly traded securities quo or similar reference point (the framing effect ), will take on with daily price updates than on assets whose price changes risk to avoid realizing losses, and put too much weight on are more opaque. unlikely events. Thaler also has shown the importance of choice architec- Richard Thaler received the 2017 Nobel Prize in ture in public policy. Because many people fail to optimize ­Economics for his role in applying these behavioral models of and instead stick with the default outcome when making decision making in finance and economics. Thaler has high- decisions, it is possible to “nudge” investors into making lighted the role of mental accounting in decision ­making, better savings decisions by changing the default allocations in which individuals evaluate gains and losses separately in retirement savings programs. did not exhibit the same tendency, and other studies have shown that more sophisticated investors appear to be less susceptible to the disposition effect.15 This behavioral tendency to sell winners and hang on to losers is costly from a tax perspec- tive. Because capital gains are taxed only when the asset is sold, it is optimal for tax purposes to postpone taxable gains by continuing to hold profitable investments, delaying the tax payment and reducing its present value. On the other hand, investors should capture tax losses by selling their losing investments, especially near the year’s end, in order to accelerate the tax write-off. Of course, hanging on to losers and selling winners might make sense if investors forecast that the losing stocks would ultimately “bounce back” and outperform the win- ners going forward. While investors may in fact have this belief, it does not appear to be ­justified—if anything, the losing stocks that small investors continue to hold tend to under- perform the winners that they sell. According to one study, losers underperformed winners by 3.4% over the year after the winners were sold.16 Investor Attention, Mood, and Experience Individual investors generally are not full-time traders. As a result, they have limited time and attention to spend on their investment decisions, and so may be influenced by ­attention-grabbing news stories or other events. Studies show that individuals are more likely to buy stocks that have recently been in the news, engaged in advertising, experienced exceptionally high trading volume, or have had extreme (positive or negative) returns.17 Investment behavior also seems to be affected by investors’ moods. For example, sun- shine generally has a positive effect on mood, and studies have found that stock returns tend to be higher when it is a sunny day at the location of the stock exchange. In New York 15 R. Dhar and N. Zhu, “Up Close and Personal: Investor Sophistication and the Disposition Effect,” Management Science 52 (2006): 726–740. 16 T. Odean, “Are Investors Reluctant to Realize Their Losses?” Journal of Finance 53 (1998): 1775–1798. 17 See G. Grullon, G. Kanatas, and J. Weston, “Advertising, Breadth of Ownership, and Liquidity,” Review of Financial Studies 17 (2004): 439–461; M. Seasholes and G. Wu, “Predictable Behavior, Profits, and Attention,” Journal of Empirical Finance 14 (2007): 590–610; B. Barber and T. Odean, “All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors,” Review of Financial Studies 21 (2008): 785–818. M13_BERK6318_06_GE_C13.indd 492 26/04/23 7:04 PM  13.4 Systematic Trading Biases 493 City, the annualized market return on perfectly sunny days is approximately 24.8% per year versus 8.7% per year on perfectly cloudy days.18 Further evidence of the link between inves- tor mood and stock returns comes from the effect of major sports events on returns. One study estimates that a loss in the World Cup elimination stage lowers the next day’s stock returns in the losing country by about 0.50%, presumably due to investors’ poor mood.19 Finally, investors appear to put too much weight on their own experience rather than considering all the historical evidence. As a result, people who grew up and lived during a time of high stock returns are more likely to invest in stocks than people who experienced times when stocks performed poorly.20 Herd Behavior Thus far, we have considered common factors that might lead to correlated trading behavior by investors. An alternative reason why investors make similar trading errors is that they are actively trying to follow each other’s behavior. This phenomenon, in which individuals ­imitate each other’s actions, is referred to as herd behavior. There are several reasons why traders might herd in their portfolio choices. First, they might believe others have superior information that they can take advantage of by copying their trades. This behavior can lead to an informational cascade effect in which traders ignore their own information hoping to profit from the information of others.21 A second possibility is that, due to relative wealth concerns, individuals choose to herd in order to avoid the risk of underperforming their peers.22 Third, professional fund managers may face reputational risk if they stray too far from the actions of their peers.23 Implications of Behavioral Biases The insight that investors make mistakes is not news. What is surprising, however, is that these mistakes persist even though they may be economically costly and there is a relatively easy way to avoid them—buying and holding the market portfolio. Regardless of why individual investors choose not to protect themselves by holding the mar- ket portfolio, the fact that they don’t has potential implications for the CAPM. If individual investors are engaging in strategies that earn negative alphas, it may be possible for more sophis- ticated investors to take advantage of this behavior and earn positive alphas. Is there evidence that such savvy investors exist? In Section 13.5, we examine evidence regarding this possibility. CONCEPT CHECK 1. What are several systematic behavioral biases that individual investors fall prey to? 2. What implication might these behavioral biases have for the CAPM? 18 Based on data from 1982–1997; see D. Hirshleifer and T. Shumway, “Good Day Sunshine: Stock Returns and the Weather,” Journal of Finance 58 (2003): 1009–1032. 19 A. Edmans, D. Garcia, and O. Norli, “Sports Sentiment and Stock Returns,” Journal of Finance 62 (2007): 1967–1998. 20 U. Malmendier and S. Nagel, “Depression Babies: Do Macroeconomic Experiences Affect Risk- Taking?”, Quarterly Journal of Economics 126 (2011): 373–416. 21 For example, see S. Bikhchandani, D. Hirshleifer, and I. Welch, “A Theory of Fads, Fashion, Custom and Cultural Change as Informational Cascades,” Journal of Political Economy 100 (1992): 992–1026; and C. Avery and P. Zemsky, “Multidimensional Uncertainty and Herd Behavior in Financial Markets,” American Economic Review 88 (1998): 724–748. 22 P. DeMarzo, R. Kaniel, and I. Kremer, “Relative Wealth Concerns and Financial Bubbles,” Review of Financial Studies 21 (2008): 19–50. 23 D. Scharfstein and J. Stein, “Herd Behavior and Investment,” American Economic Review 80 (1990): 465–479. M13_BERK6318_06_GE_C13.indd 493 26/04/23 7:04 PM  494 Chapter 13 Investor Behavior and Capital Market Efficiency 13.5 The Efficiency of the Market Portfolio When individual investors make mistakes, can sophisticated investors easily profit at their expense? In order for sophisticated investors to profit from investor mistakes, two conditions must hold. First, the mistakes must be sufficiently pervasive and persistent to affect stock prices. That is, investor behavior must push prices so that non-zero alpha trading opportunities become apparent, as in Figure 13.2. Second, there must be limited competition to exploit these non-zero alpha opportunities. If competition is too intense, these opportunities will be quickly eliminated before any trader can take advantage of them in a significant way. In this section, we examine whether there is any evidence that individual or professional investors can outperform the market without taking on ad- ditional risk. Trading on News or Recommendations A natural place to look for profitable trading opportunities is in reaction to big news an- nouncements or analysts’ recommendations. If enough other investors are not paying at- tention, perhaps one can profit from these public sources of information. Takeover Offers. One of the biggest news announcements for a firm, in terms of stock price impact, is when it is the target of a takeover offer. Typically, the offer is for a signifi- cant premium to the target’s current stock price, and while the target’s stock price typically jumps on the announcement, it often does not jump completely to the offer price. While it might seem that this difference creates a profitable trading opportunity, in most cases there is usually remaining uncertainty regarding whether the deal will occur at the initially o­ ffered price, at a higher price, or fail to occur at all. Figure 13.5 shows the average response to FIGURE 13.5 Returns to Holding Target Stocks Subsequent to Takeover Announcements After the initial jump in the 90% stock price at the time of the 80% announcement, target stocks Subsequently do not appear to earn abnor- 70% Taken Over Cumulative Abnormal Return mal subsequent returns on 60% average. However, stocks that All Firms 50% are ultimately acquired tend to appreciate and have positive 40% alphas, while those that are not 30% acquired have negative alphas. Thus, an investor could profit 20% from correctly predicting the Not Taken 10% outcome. Over Source: Adapted from M. Bradley, 0% A. Desai, and E. H. Kim, “The Rationale Behind Interfirm Tender Offers: 210% Information or Synergy?,” Journal 220% of Financial Economics 11 (1983): 26 0 6 12 18 24 30 36 42 48 54 60 183–206. Months Relative to Announcement M13_BERK6318_06_GE_C13.indd 494 26/04/23 7:04 PM  13.5 The Efficiency of the Market Portfolio 495 many such takeover announcements, showing the target stock’s cumulative abnormal return, which measures the stock’s return relative to that predicted based on its beta, at the time of the event. Figure 13.5 reveals that the initial jump in the stock price is high enough so that the stock’s future returns do not outperform the market, on average. However, if we could predict whether the firm would ultimately be acquired, we could earn profits trad- ing on that information. Stock Recommendations. We could also consider stock recommendations. For ­example, popular commentator Jim Cramer makes numerous stock recommendations on his evening television show, Mad Money. Do investors profit from following these recommendations? Figure 13.6 shows the results of a recent study that analyzed the average stock price reaction to these recommendations, based on whether the recom- mendation coincided with a news story about the company. In the case where there is news, it appears that the stock price correctly reflects this information the next day, and stays flat (relative to the market) subsequently. On the other hand, for the stocks without news, there appears to be a significant jump in the stock price the next day, but the stock price then tends to fall relative to the market, generating a negative alpha, over the next several weeks. The authors of the study found that the stocks without news tended to be smaller, less liquid stocks; it appears that the individual investors who buy these stocks based on the recommendation push the price too high. They appear to be subject to an overconfidence bias, trusting too much in Cramer’s recommendation and not ­adequately taking into account the behavior of their fellow investors. The more interesting question is why don’t smart investors short these stocks and prevent the overreaction? In fact they do (the amount of short interest rises for these stocks), but because these small stocks are difficult to locate and borrow and therefore costly to short, the price does not correct immediately. FIGURE 13.6 Stock Price Reactions to Recommendations on Mad Money When recommendations 5% coincide with news, the initial stock price reaction 4% appears correct and future Cumulative Abnormal Return alphas are not significantly 3% News different from zero. Without news, the stock price appears 2% to overreact. While sophisti- No News cated investors gain by 1% shorting these stocks, costs of shorting limit their ability 0% to do so. 25 0 5 10 15 20 25 30 35 40 45 50 Source: Adapted from J. Engelberg, 21% C. Sasseville, J. Williams, “Market Madness? The Case of Mad Money,” 22% Management Science, 2011. 23% Days Relative to Recommendation M13_BERK6318_06_GE_C13.indd 495 26/04/23 7:04 PM  496 Chapter 13 Investor Behavior and Capital Market Efficiency NOBEL PRIZE The 2013 Prize: An Enigma? When the 2013 Nobel Prize in Economics was awarded difficult to predict in the short run, and that new informa- to three financial economists, most people were surprised. tion is very quickly incorporated into prices.... If prices The surprise was not that Eugene Fama, Robert Shiller, and are nearly impossible to predict over days or weeks, then Lars Peter Hansen had won the prize—most ­economists shouldn’t they be even harder to predict over several years? would agree they certainly deserved the prize for their The answer is no, as Robert Shiller discovered in the early ­contributions—rather it was that they won it together. After 1980s. He found that stock prices fluctuate much more than all, Fama is most well-known for what he termed the ­efficient corporate dividends, and that the ratio of prices to divi- market hypothesis, the assertion that markets are so competi- dends tends to fall when it is high, and to increase when it tive it is impossible to make money by trying to predict is low. This pattern holds not only for stocks, but also for stock price movements. On the other hand, Robert Shiller bonds and other assets. Lars Peter Hansen developed a sta- argued the opposite, that the excess volatility in markets tistical method that is particularly well suited to testing ratio- results from irrational behavior that can be exploited. Lars nal ­theories of asset pricing. Using this method, H ­ ansen and Peter ­Hansen is credited with developing statistical tools that other researchers have found that modifications of these can help d­ istinguish these opposing views. Here is how the theories go a long way toward explaining asset prices.” Nobel Prize committee justified its decision: “Beginning in the 1960s, Eugene Fama and several col- Source: “The Prize in Economic Sciences 2013—Press Release.” laborators demonstrated that stock prices are extremely Nobelprize.org. The Performance of Fund Managers The previous results suggest that though it may not be easy to profit simply by trading on news, sophisticated investors might be able to do so (for example, by being better able to predict takeover outcomes, or short small stocks). Presumably, professional fund manag- ers, such as those who manage mutual funds, should be in the best position to take advan- tage of such opportunities. Are they able to find profit-making opportunities in financial markets? Fund Manager Value-Added. The answer is yes. The value a fund manager adds by engaging in profit-making trades is equal to the fund’s alpha before fees (gross alpha) multiplied by the fund’s assets under management (AUM). The evidence shows that the average mutual fund manager is able to identify profitable trading opportunities worth ap- proximately $3 million per year, and for fund managers with at least five years experience, the number rises to almost $9 million per year (see Figure 13.7).24 Of course, the fact that the average mutual fund manager is able to find profitable trad- ing opportunities does not imply that all managers can do so. In fact, most cannot. The median mutual fund actually destroys value; that is, most fund managers appear to behave much like individual investors by trading so much that their trading costs exceed the profits from any trading opportunities they may find. But because skilled managers manage more money, the mutual fund industry as a whole has positive value added. Returns to Investors. Do investors benefit by identifying the profit-making funds and investing in them? This time the answer is no. As shown in Figure 13.7, the average fund’s alpha after fees (net alpha), which is the alpha earned by investors, is −0.34%. On average actively managed mutual funds don’t appear to provide superior returns for their investors 24 J. Berk and J. van Binsbergen, “Measuring Managerial Skill in the Mutual Fund Industry,” Journal of Financial Economics 118 (2015): 1–20. M13_BERK6318_06_GE_C13.indd 496 26/04/23 7:04 PM  13.5 The Efficiency of the Market Portfolio 497 FIGURE 13.7 $40 1.0% Manager Value Added and Value Added (Left Axis) Net Alpha (Right Axis) Investor Returns for U.S. $30 0.8% Mutual Funds (1977–2011) Value Added ($ millions per year) 0.6% Value added is alpha before $20 fees (gross alpha) times assets 0.4% Net Alpha (annual %) under management. Alpha is $10 Average 0.2% computed relative to available passive index funds. Net alpha $0 0.0% is the alpha earned by fund investors (the gross alpha net 20.2% 2$10 of fees). Results are averaged Average across all fund managers with 20.4% 2$20 at least five years experience 20.6% for each size quintile. While mutual fund managers do add 2$30 20.8% value on average, they capture this value through their fees, 2$40 20.10% so that investors do not earn 1 2 3 4 5 positive alphas. (Author’s cal- Smallest Largest culations using data provided Fund Size Quintile (assets under management) by CRSP and Morningstar.) compared to investing in passive index funds.25 The reason fund managers can add value but investors do not benefit is that on average the value added is offset by the fees the funds charge. While the average mutual fund does not provide a positive alpha to its investors, it is possible that some funds might. Can investors identify funds that consistently deliver positive alphas to their investors? Morningstar ranks fund managers each year based on their historical performance. For example, Morningstar named Legg Mason’s William Miller, whose performance we highlighted in the introduction to this chapter, as manager of the year in 1998 and manager of the decade the following year. As we have already noted, investors who were motivated to invest based on these awards saw poor performance over the next 10 years. Miller’s experience is not exceptional. At the end of each year Forbes publishes an Honor Roll of top mutual funds based on an analysis of the past performance and riskiness of the fund. In a famous 1994 study, Vanguard CEO John Bogle compared the returns from investing in the market index with the returns from investing each year in the newly announced Honor Roll funds. Over a 19-year period, the Honor Roll portfolio had an annual return of 11.2%, 25 Many studies report negative average alphas for investments in U.S equity mutual funds; see e.g., R. Kosowski, A. Timmermann, R. Wermers, and H. White, “Can Mutual Fund ‘Stars’ Really Pick Stocks? New Evidence from a Bootstrap Analysis,” Journal of Finance 61 (2006): 2551–2596; and E. Fama and K. French, “Luck versus Skill in the Cross Section of Mutual Fund Alpha Estimates,” Journal of Finance 65 (2010): 1915–1947. Using an expanded time period, and considering funds that hold international as well as domestic stocks, J. Berk and J. van Binsbergen find that alphas are not significantly different from zero (“Measuring Managerial Skill in the Mutual Fund Industry,” Journal of Financial Economics 118 (2015): 1–20). M13_BERK6318_06_GE_C13.indd 497 26/04/23 7:04 PM  498 Chapter 13 Investor Behavior and Capital Market Efficiency whereas the market index fund had an annual return of 13.1%.26 Thus, the s­ uperior past performance of these funds was not a good predictor of their future ability to outperform the market. Other studies have confirmed this result, and found little ­predictability in fund performance.27 While these results regarding mutual fund performance might seem surprising, they are consistent with a competitive capital market. If investors could predict that a skilled manager would generate a positive alpha in the future, they would rush to invest with this manager, who would then be flooded with capital. During Legg Mason manager William Miller’s meteoric rise, his capital under management grew from about $700 million in 1992 to $28 billion in 2007. But the more capital the manager has to invest, the harder it is to find profitable trading opportunities. Once these opportunities are exhausted, the manager can no longer produce better-than-average performance.28 Ultimately, as new capital arrives the fund’s returns should fall. The inflow of capital will cease when the fund’s alpha is no longer positive.29 Indeed, alphas could be somewhat negative to reflect other benefits these funds provide, or could result from overconfidence. Investors put too much confidence in their ability to select fund managers and thus commit too much capital to them. The argument above suggests that because skilled managers attract more capital, they will manage the largest funds. Consequently, fund size is a strong predictor of the future value added by fund managers.30 But while investors appear to be good at picking manag- ers, in the end they derive little benefit, because this superior performance is captured by the manager in the form of fees—mutual funds charge approximately the same percentage fee, so the larger funds collect higher aggregate fees. This result is exactly as we should expect: In a competitive labor market, the fund manager should capture the economic rents associated with his or her unique skill. In summary, while the profits of mutual fund managers imply it is possible to find profitable trading opportunities in markets, being able to do so consistently is a rare talent possessed by only the most skilled fund managers, and these managers earn fees commensurate with their talent. Researchers have obtained similar results when evaluating institutional fund managers responsible for managing retirement plans, pension funds, and endowment assets. A study investigating the hiring decisions of plan sponsors found that sponsors picked managers that had significantly outperformed their benchmarks historically (see Figure 13.8). Once 26 J. Bogle, Bogle on Mutual Funds: New Perspectives for the Intelligent Investor, McGraw-Hill, 1994. 27 See M. Carhart, “On Persistence in Mutual Fund Performance,” Journal of Finance 52 (1997): 57–82. One possible exception is fund fees—ironically, small funds that charge a higher percentage fee seem to ­generate predictably lower returns for their investors. 28 In Miller’s case most investors paid dearly for their confidence in him—although his losses post-2007 equaled his gains from 1992, most investors were not invested in 1992, and so they experienced overall performance that lagged the S&P 500. Not surprisingly, after 2007 he experienced large capital outflows, so by the end of 2008 he had only about $1.2 billion under management. 29 This mechanism was proposed by J. Berk and R. Green, “Mutual Fund Flows in Rational Markets,” Journal of Political Economy 112 (2004): 1269–1295. The following studies all find that new capital flows into funds that do well and out of funds that do poorly: M. Gruber, “Another Puzzle: The Growth in Actively Managed Mutual Funds,” Journal of Finance 51 (1996): 783–810; E. Sirri and P. Tufano, “Costly Search and Mutual Fund Flows,” Journal of Finance 53 (1998): 1589–1622; J. Chevalier and G. Ellison, “Risk Taking by Mutual Funds as a Response to Incentives,” Journal of Political Economy 105 (1997): 1167–1200. 30 J. Berk and J. van Binsbergen, “Measuring Managerial Skill in the Mutual Fund Industry,” Journal of Financial Economics 118 (2015): 1–20. M13_BERK6318_06_GE_C13.indd 498 26/04/23 7:04 PM  13.5 The Efficiency of the Market Portfolio 499 FIGURE 13.8 Before and After Hiring Returns of Investment Managers 4.0 Before Hiring Excess Return over Benchmark 3.5 After Hiring 3.0 2.5 (%/year) Hiring Date 2.0 1.5 1.0 Average 0.5 0.0 23 22 21 0 1 2 3 Years Relative to Hiring Date While plan sponsors tend to hire managers that have significantly outperformed their benchmarks historically, after-hiring performance is similar to the excess return of the average fund (0.64% on a value-weighted basis). Data based on 8755 hiring decisions of 3400 plan sponsors from 1994–2003, and returns are gross of management fees (which tend to range from 0.5%–0.7%/year). Sources: A. Goyal and S. Wahal, “The Selection and Termination of Investment Management Firms by Plan Sponsors,” Journal of Finance 63 (2008): 1805–1847 and with J. Busse, “Performance and Persistence in Institutional Investment Management,” Journal of Finance 65 (2010): 765–790. hired, however, the performance of these new managers looked very similar to the aver- age fund, with returns exceeding their benchmarks by an amount roughly equal to their management fees. The Winners and Losers The evidence in this section suggests that while it may be possible to improve on the market portfolio, it isn’t easy. This result is perhaps not so surprising, for as we noted in Section 13.2, the average investor (on a value-weighted basis) earns an alpha of zero, before including trading costs. So beating the market should require special skills, such as better analysis of information, or lower trading costs. Because individual investors are likely to be at a disadvantage on both counts, as well as subject to behavioral biases, the CAPM wisdom that investors should “hold the market” is probably the best advice for most people. Indeed, a comprehensive study of the Taiwan stock market found that individual investors there lose an average of 3.8% per year by trading, with roughly 1/3 of the losses due to poor trades and the remaining 2/3 due to transactions costs.31 31 Taiwan provides a unique opportunity to study how profits are distributed, because unlike the U.S., the identity of buyers and sellers is tracked for all trades. See B. Barber, Y. Lee, Y. Liu, and T. Odean, “Just How Much Do Individual Investors Lose by Trading?”, Review of Financial Studies 22 (2009): 609–632. M13_BERK6318_06_GE_C13.indd 499 26/04/23 7:04 PM  500 Chapter 13 Investor Behavior and Capital Market Efficiency The same study reported that institutions earn 1.5% per year on average from their trades. But while professional fund managers may profit due to their talent, information, and superior trading infrastructure, the results in this section suggest that little of those profits go to the investors who invest with them. CONCEPT CHECK 1. Should uninformed investors expect to make money by trading based on news announcements? 2. If fund managers are talented, why do the returns of their funds to investors not have posi- tive alphas? 13.6 S tyle-Based Techniques and the Market Efficiency Debate In Section 13.5, we looked for evidence that professional investors could profit at small investors’ expense and outperform the market. In this section, we will take a different tack. Rather than looking at managers’ profits, we will look at possible trading strate- gies. In particular, many fund managers distinguish their trading strategies based on the types of stocks they tend to hold; specifically, small versus large stocks, and value ver- sus growth stocks. In this section, we will consider these alternative investment styles, and see whether some strategies have generated higher returns historically than the CAPM predicts. Size Effects As we reported in Chapter 10, small stocks (those with smaller market capitalizations) have historically earned higher average returns than the market portfolio. Moreover, while small stocks do tend to have high market risk, their returns appear high even accounting for their higher beta, an empirical result we call the size effect. Excess Return and Market Capitalizations. To compare the performance of portfo- lios formed based on size, Professors Eugene Fama and Kenneth French32 divided stocks each year into 10 portfolios by ranking them based on their market capitalizations, and collecting the smallest 10% of stocks into the first portfolio, the next 10% into the second portfolio, up to the biggest 10% into the tenth portfolio. They then recorded the monthly excess returns of each decile portfolio over the following year. After repeating this pro- cess for each year, they calculated the average excess return of each portfolio and the beta of the portfolio; Figure 13.9 shows the result. As you can see, although the portfolios with higher betas yield higher returns, most portfolios plot above the security market line (SML)—all except one portfolio had a positive alpha. The smallest deciles exhibit the most extreme effect. Of course, this result could be due to estimation error; as the figure shows, the stan- dard errors are large and none of the alpha estimates is significantly different from zero. However, nine of the 10 portfolios plot above the SML. If the positive alphas were due purely to statistical error, we would expect as many portfolios to appear above the line as below it. Consequently, a test of whether the alphas of all 10 portfolios are jointly all equal to zero can be statistically rejected. 32 See E. Fama and K. French, “The Cross-Section of Stock Returns,” Journal of Finance 47 (1992): 427–465. M13_BERK6318_06_GE_C13.indd 500 26/04/23 7:04 PM  514 Chapter 13 Investor Behavior and Capital Market Efficiency When making a capital budgeting decision, the cost of capital is just one of several imprecise estimates that go into the NPV calculation. Indeed, in many cases, the impreci- sion in the cost of capital estimate is less important than the imprecision in the estimate of future cash flows. Often the least complicated models to implement are used. In this regard, the CAPM has the virtues of being simple to implement, theoretically justifiable, and reasonably consistent with investor behavior. CONCEPT CHECK 1. Which is the most popular method used by corporations to calculate the cost of capital? 2. What other techniques do corporations use to calculate the cost of capital? 3. What risk model is most consistent with investors’ choices in their mutual fund investments? Key Points 13.1 Competition and Capital Markets and Equations The difference between a stock’s expected return and its required return according to the secu- rity market line is the stock’s alpha: α s = E [ Rs ] − rs (13.2) While the CAPM conclusion that the market is always efficie