Behavioral Biases of Mutual Fund Investors

Behavioral Biases of Mutual Fund Investors Warren Bailey Cornell University, Johnson Graduate School of Management Alok Kumar University of Miami & University of Texas at Austin David Ng University of Pennsylvania Wharton School & Cornell University 5th July 2010 Abstract We examine the effect of behavioral biases on the mutual fund choices of a large sample of U.S. discount brokerage investors using new measures of attention to news, tax awareness, and fund-level familiarity bias, in addition to behavioral and demographic characteristics of earlier studies. Behaviorally-biased investors typically make poor decisions about fund style and expenses, trading frequency, and timing, resulting in poor performance. Furthermore, trend-chasing appears related to behavioral biases, rather than to rationally inferring managerial skill from past performance. Beyond documenting the importance of behavioral factors in the delegated management setting of mutual funds, applying factor analysis to the individual behavioral bias measures and other characteristics identifies several investor stereotypes that we relate to mutual fund trading and performance. JEL Codes: G11, D03 Keywords: individual investors, mutual funds, trend chasing, behavioral biases, factor analysis. Address for Correspondence: Warren Bailey, Johnson Graduate School of Management, Cornell University, Sage Hall, Ithaca, NY 14853-6201, phone 607-255-4627, fax 607-255-4627, wbb1@cornell.edu; Alok Kumar, University of Miami, School of Business Administration, 514 Jenkins Building, Coral Gables, FL 33124, phone 305-284-1882, fax 305-284- 4800, akumar@mail.utexas.edu; and David Ng, Wharton School, University of Pennsylvania, Steinberg Hall-Dietrich Hall, 3620 Locust Walk, Philadelphia, PA 19104-6302, and Dyson School of Applied Economics and Management, Cornell University, Ithaca, NY 14853-7801, phone 607-279-7141, dtn4@cornell.edu. We thank an anonymous referee, Malcolm Baker (AFA discussant), Nick Barberis, Robert Battalio, Zahi Ben-David, Garrick Blalock, Charles Chang, Susan Christoffersen, George Korniotis, Lisa Kramer, Ulrike Malmendier (AFA session chair), Jay Ritter, Jeremy Tobacman, Jeff Wurgler, and seminar participants at BSI Gamma Foundation Conference (Frankfurt), Cornell, Federal Reserve Bank of Boston, Ohio State s Alumni Summer Conference, Northern Finance Association Meetings, McGill, and 2009 AFA Meetings (San Francisco) for comments and helpful discussions. We also thank Zoran Ivkovich and Lu Zheng for providing data for identifying the mutual funds in our sample. We are grateful to the BSI Gamma Foundation for financial support. All remaining errors and omissions are our own. Early presentations of this paper were entitled Why Do Individual Investors Hold Stocks and High-Expense Funds Instead of Index Funds?

Behavioral Biases of Mutual Fund Investors ABSTRACT We examine the effect of behavioral biases on the mutual fund choices of a large sample of U.S. discount brokerage investors using new measures of attention to news, tax awareness, and fundlevel familiarity bias, in addition to behavioral and demographic characteristics of earlier studies. Behaviorally-biased investors typically make poor decisions about fund style and expenses, trading frequency, and timing, resulting in poor performance. Furthermore, trend-chasing appears related to behavioral biases, rather than to rationally inferring managerial skill from past performance. Beyond documenting the importance of behavioral factors in the delegated management setting of mutual funds, applying factor analysis to the individual behavioral bias measures and other characteristics identifies several investor stereotypes that we relate to mutual fund trading and performance.

1. Introduction Previous studies of behavioral biases in the investment decisions of individual investors focus on the selection of individual stocks. Odean (1998, 1999), Barber and Odean (2001), and other empirical studies show that the stock-picking decisions of individual investors exhibit a variety of behavioral biases. However, little work has been done to link the decision-making biases of individuals to their mutual fund investments. Understanding the role of behavioral biases in individual mutual fund decisions is important for several reasons. First, individual investors increasingly use mutual funds to invest in the equity market rather than trading individual stocks. French (2008) reports that: Individuals hold 47.9% of the market in 1980 and only 21.5% in 2007. This decline is matched by an increase in the holdings of open-end mutual funds, from 4.6% in 1980 to 32.4% in 2007. Hence, it is increasingly important to understand how individual investors hold and trade mutual funds. Second, even though direct stock trading by individuals has declined, their mutual fund investment decisions can affect stock returns indirectly. Coval and Stafford (2007) argue that large flows force some mutual funds to trade heavily, causing price pressure for securities held across many funds. Previous papers document that mutual fund flows affect individual stock returns. Gruber (1996) and Zheng (1999) find that fund flows are followed by positive shortterm fund returns, perhaps due to a momentum effect. Frazzini and Lamont (2008) show that mutual fund flows appear to be dumb money : fund inflows are associated with low future returns, while outflows are associated with high future returns. Third, the manner in which individuals employ mutual funds cuts right to the heart of basic principles of financial management. Traditional portfolio choice models imply a simple investment strategy based on well-diversified, low expense mutual funds and minimal portfolio 1

rebalancing. Index funds, and other equity funds with low fees and low turnover, are cheap, convenient vehicles for individual investors to implement such a strategy. The extent to which individuals adhere to these principles in their use of mutual funds is an important measure of the rationality and effectiveness with which investors approach capital markets. The purpose of our paper is to test whether behavioral biases explain why the use of mutual funds varies substantially across individual investors and often departs from the simple strategies suggested by classic theories. The growing literature on behavioral finance has uncovered a variety of decision-making biases in how investors use individual common stocks. These behavioral forces should also have an impact on whether a particular investor uses mutual funds, and whether she uses them effectively. The mutual fund literature has already documented two specific anomalies. First, individual investors buy funds with high fees. Gruber (1996) and Barber, Odean, and Zheng (2005) document that many individual investors hold significant positions in high expense mutual funds. Even more puzzling is the finding of Elton, Gruber and Busse (2004) that substantial amounts have gone into index funds which charge high fees (over 2% per year) for passive holdings of broad indexes like the S&P500. Second, individual investors chase returns. Sirri and Tufano (1998), Bergstresser and Poterba (2002), and Sapp and Tiwari (2004) find that fund flows tend to chase funds with high past returns. This may be fostered by Morningstar s practice of rating funds based on past returns (Del Guercio and Tkac (2008)). Several explanations have been offered for these two anomalies. Carlin (2009) explains participation in high fee index funds using a model with search costs. Choi, Laibson and Madrian (2009) interpret their experiments on Wharton MBA students and participation in high fee funds as consistent with behavioral biases. Return-chasing has been ascribed to an agency problem that 2

induces fund managers to alter the riskiness of the fund to maximize investment flows instead of risk-adjusted expected returns (Chevalier and Ellison (1997)). It may also reflect inferring managerial skill from past returns (Sirri and Tufano (1998), Gruber (1996), Berk and Green (2004)). However, with the exception of the experimental data used by Choi, Laibson and Madrian (2009), these authors study aggregate fund flows rather than individual investor behavior. In contrast to previous studies, we link the decision-making biases of particular individual investors to their individual history of mutual fund investing using a database of tens of thousands of brokerage records of U.S. individual investors. The key to our experiment is the use of individual investor records of stock holdings and trading to estimate the behavioral bias proxies that previous authors have used to explain how investors trade individual stocks. These individual behavioral bias proxies are, in turn, related to the mutual fund holdings and trading of those individuals in a variety of empirical specifications that reveal different facets of mutual fund investor behavior. We can easily imagine behavioral biases affecting mutual fund selection. For example, the disposition effect (selling winners too quickly and holding losers too long) may lead some investors to overestimate expected holding periods and mistakenly select high front-end load funds. Investors with narrow framing bias (buying and selling individual assets without considering total portfolio effects), overconfidence (frequent trading plus poor performance), or a preference for speculative stocks may select funds that facilitate aggressive switching across asset classes without considering higher fees. Local bias (preference for stocks of companies geographically close to home) may induce the selection of locally-managed mutual funds without regard to cost or expected performance. Investors who view their portfolios in terms of 3

layers that serve different purposes (Shefrin and Statman (2000)) may demonstrate different behavior in their use of individual stocks versus mutual funds. For example, if mutual funds are viewed as substantially safer than selecting individual stocks on their own, investors may let their guard down and spend less time assessing fund performance and costs. Regardless of the type of behavioral bias, poor decisions about timing, holding periods, and choice of funds can combine with the substantial variety in mutual fund fee structures to yield poor performance. To examine the interactions and consequences of mutual fund choices and behavioral biases, we adopt two empirical viewpoints. First, we present tests across individual investors. Estimates of several dimensions of behavioral bias for each individual in our sample are used to explain individual investor choices across index funds, other types of mutual funds, and individual stocks. We also test whether behavioral biases influence associations between trading decisions and recent fund performance because those biases could cause some investors to misuse performance information. Second, we present tests across different types of funds. We summarize individual investor holding periods and returns across mutual funds classified by fee structure and by the extent of several behavioral biases of each fund s investors. Behaviorally-biased investors may cluster in particular types of funds, and demonstrate poor performance or very frequent trading. Furthermore, the fund industry s offerings may include some funds designed to attract and perhaps even exploit such investors. A large and growing number of mutual funds offer a variety of themes and fee structures to U.S. individual investors. Even across relatively generic index funds, there are many competing products that offer a wide range of fee structures and resultant performance (Elton, Gruber, and Busse (2004), Hortacsu and Syverson (2004)). It is plausible 4

that different types of funds attract different clienteles (Nanda, Wang, and Zheng (2009)), and some funds may have been designed specifically with behaviorally-biased clienteles in mind. 1 A handful of previous papers have examined specific dimensions of the mutual fund choices of individual investors. Barber, Odean, and Zheng (2005) find that investors are more sensitive to salient fees like front-end loads, but not as sensitive to hidden management fees. Christoffersen, Evans, and Musto (2006) consider how fund managers respond to the preferences of their investors. Malloy and Zhu (2004) show that investors who reside in less affluent and less educated neighborhoods tend to select high expense funds. Zhu (2005) shows that busy investors are more likely to invest in funds rather than individual stocks. Huang, Wei, and Yan (2007) characterize the effect of the information environment on the associations between fund flows and past performance. Bergstresser, Chalmers and Tufano (2009) study whether mutual fund brokers help educate investors and attenuate their behavioral biases, but conclude that brokers do not deliver tangible benefits for the fees they earn. Ivkovich and Weisbenner (2009) examine aggregate individual investor fund flows for tax effects. Our paper offers several substantial contributions. First, unlike earlier studies, we examine a combination of behavioral factors, plus controls for other likely influences on portfolio selection, to reveal the interactions between investor decisions, the characteristics of the mutual funds they select, and the consequences for portfolio performance. Second, because we employ proxies for a number of dimensions of investor behavior in our tests, we are also able to study the associations between different investor characteristics. In particular, applying factor 1 There is already some evidence that skilled capital market participants outsmart individual mutual fund investors. Money market funds appear to raise fees to exploit investors who are insensitive to fees and performance (Christoffersen and Musto (2002)). Weak associations between equity fund fees and performance may also reflect such behavior (Gil-Bazo and Ruiz-Verdu (2009)). Corporations are aware of patterns in mutual fund inflows and outflows and attempt to exploit them in timing equity issues (Frazzini and Lamont (2008)). Mutual fund inflows are attracted to seemingly high performance assessed against benchmarks that funds specify but which do not match fund styles (Sensoy (2009)). 5

analysis to the correlation structure of our investor characteristics reveals interesting overlaps among biases and other characteristics, and permits us to identify and profile five investor stereotypes that we label Gambler, Smart, Overconfident, Narrow-Framer, and Mature. Third, our tests take the viewpoints of both the investor, who may ignore or misuse mutual funds, and the mutual fund industry, which may design some of its products to exploit the poor decision-making skills of some investors. Last, we extend the empirical behavioral literature beyond the choice of individual stocks to decisions about professionally-managed portfolios. A summary of our results is as follows. We find that sophisticated investors (betterinformed, higher income, older, and more experienced) investors make good use of mutual funds, holding a high proportion of fund for long periods, avoiding high expense funds, and experiencing relatively good performance. However, investors with strong behavioral biases or lack of attention to firm-specific or macro-economic news are less likely to hold mutual funds, or select mutual funds for the wrong reasons. When they do buy mutual funds, they trade them frequently, tend to time their buys and sells badly, and prefer high expense funds and active funds rather than index funds. We also find that biased investors are more likely to chase fund performance, casting doubt on the idea that trend-chasing reflects rational fund selection decisions. Evidently, these decisions are sub-optimal because they are associated with lower overall returns. For instance, top-quintile narrow-framing investors have average mutual fund returns that are 2.16% lower than those in the bottom quintile, while top-quintile disposition effect investors have average returns that are 0.89% lower than those in the bottom quintile. In contrast, behavioral biases do not appear to affect the performance of index fund holdings. 6

Thus, our behavioral bias and news inattentiveness proxies, though crude, demonstrate that behavioral effects are at work in the mutual fund decisions of many investors and take a toll on performance. Furthermore, the bias and inattention to news proxies are themselves correlated in interesting ways that allow us to identify and study stereotypical investors. The five factors identified using factor analysis can explain over 75% of the variance of the behavioral factors and other investor characteristics. The intuitive combinations of investor characteristics that comprise these five factors relate to mutual fund trading habits and performance in an interesting and consistent manner. The rest of the paper is organized as follows. Section 2 describes our explanatory variables and test specifications. Section 3 describes the individual investor database and other data sources. We present our empirical results in Sections 4 and 5, and conclude in Section 6 with a brief discussion. 2. Measuring Investor Characteristics Our main objective is to relate mutual fund use and performance to behavioral factors that vary across our sample of investors. We begin by using each sample investor s record of common stock holdings and trading to estimate a set of variables that proxy for the behaviors evident in each investor s common stock portfolio. Recognizing that behavioral factors are unlikely to be the only determinant of mutual fund choices, we also construct controls for other drivers of mutual fund decisions suggested by the mutual fund and behavioral finance literatures. We use these variables in a variety of tests across individual investors and then across types of mutual funds. Detailed descriptions of behavioral factors, other investor characteristics, and references to supporting papers can be found in the Appendix. 7

2.1 Behavioral Bias Proxies We begin by estimating Disposition Effect and Narrow Framing, two mental accounting biases that have been explored extensively in the behavioral finance literature. The Disposition Effect is the propensity of an investor to sell winners too early and hold losers too long. As detailed in the Appendix, we measure each investor s peer-group adjusted disposition effect by comparing each investor s actual propensity to realize gains versus losses to a peer group s propensity to realize gains and losses. A positive value of our disposition effect proxy indicates that the investor sells a greater proportion of winners and a relatively smaller proportion of losers. Disposition Effect may be related to tax incentives. For example, selling winners but retaining losers is particularly costly for high-income U.S. individuals. In contrast, realizing losses in December instead of other months may represent a sophisticated tax minimization strategy. To distinguish disposition effect from tax loss selling, we construct a disposition effect times high income interaction variable (DE*High Income) and a disposition effect times no December tax loss selling interaction variable (DE*No Dec Tax Loss Selling). Selling winners too soon and holding losers too long is particularly costly for higher-income investors because they face higher marginal tax rates. Similarly, a cleaner measure of disposition effect may be isolated by identifying individuals who appear entirely unaware of the tax consequences of their trades. Therefore, both of these interaction terms are intended to isolate cleaner and severe facets of the disposition effect. Our second bias proxy, Narrow Framing, is the propensity of an investor to select investments individually, rather than considering the broad impact on her portfolio. Intuitively, the time interval between two consecutive decisions reflects the decision frame, with temporally- 8

separated decisions more likely to be framed narrowly than simultaneous decisions. Hence, investors who execute less-clustered trades are more likely to be using narrower decision frames. The Appendix describes how each investor s trade clustering measure is peer-group adjusted for portfolio size, number of stocks, and trading frequency. A low trade clustering measure indicates an investor who is more likely to use a narrow viewpoint in making investment choices. 2 Another important concept from the empirical behavioral finance literature is Overconfidence, an investor s propensity to trade frequently but unsuccessfully. Our overconfidence dummy variable is set to one for investors in the highest portfolio turnover quintile and lowest performance quintile for their individual common stock trading. 3 Since male investors typically exhibit overconfidence, we also use a male dummy as an additional proxy for overconfidence. Next, we compute a proxy for familiarity, as articulated by Merton (1987) and Huberman (2001). 4 Specifically, the Local Bias of an investor s common stock portfolio equals the mean distance between her home zip code and the headquarters zip codes of companies in her portfolio minus the mean distance to the companies headquarters in the market portfolio. 2 Odean (1998) computes Disposition Effect as proportion of losses realized minus proportion of gains realized, and notes that this measure is sensitive to portfolio size and trading frequency. For example, proportions are likely to be smaller for investors who hold larger portfolios and trade frequently because those portfolios contain a larger number of stocks with capital gains and capital losses. Thus, use of the original measure of the Disposition Effect in cross-sectional analysis is likely to induce mechanical associations with variables that are correlated with portfolio size and trading frequency. Similar issues apply to the Narrow Framing measure because the trade clustering measure used to proxy for narrow framing is correlated with portfolio size, number of stocks, and trading frequency. Further, there might be a mechanically induced relation between proxies for Narrow Framing and Disposition Effect. To minimize the potential influences of portfolio size, number of stocks, and trading frequency, we compute peer-group adjusted proxies of both Disposition Effect and Narrow Framing biases. Our stock-level and fund level local bias measures are adjusted with the means for the market. This does not affect estimation since the same constant is applied to all investors but this allows us to think about an investor s portfolio characteristics relative to a typical investor. 3 We measure the performance and turnover from the stock holdings of the investors for the entire period. We also constructed an alternative measure for performance and turnover using the first year of investors record. The results are very similar. 4 A related concept is home bias, the tendency for some investors to under-diversify their portfolios internationally. See Bailey, Kumar, and Ng (2008) for evidence that home bias may have its origins in behavioral biases. 9

Later in the paper, we introduce a new measure, Fund Level Local Bias, which equals the mean distance between the investor s home zip code and the headquarters of the mutual funds in her portfolio, minus the same measure aggregated across all funds held by all investors in the sample. We measure each investor s preference for gambling and speculation. Following Kumar (2009), Lottery Stocks Preference is the investor s mean portfolio weight (relative to the weight in the market portfolio) assigned to stocks that have low prices, high idiosyncratic volatility, and high idiosyncratic skewness. Last, we construct two indicators of whether a particular investor appears to ignore potentially relevant economic news. One variable captures inattention to earnings news while the other captures inattention to macroeconomic news. Both measures are computed using each individual s record of individual stock trades using the formula 1 (Number of investor trades around the event)/(total number of investor trades), where around the event is defined as days t 1, t, and t+1, where t is the earnings announcement date. To compute Inattention to Earnings News, earnings announcements for each stock held by the individual are collected from I/B/E/S/. To compute Inattention to Macroeconomic News, we collect dates of Fed Funds target rate changes, Non Farm Payroll reports, and Producer Price Index releases from relevant government web pages. 5 Note that the measures we construct are only proxies for behavioral biases. They do not correspond exactly to the definitions of decision-making biases in the psychology literature. Nonetheless, at the very least, these measures are indicators of sub-optimal stock investment decisions. They reflect portfolio management mistakes, and allow us to measure associations 5 Subsequent results shed light on whether inattention is a bias or part of a sensible passive strategy. For example, Barber and Odean (2008) find no evidence that trading based on other measures of news arrival is beneficial. 10

between an individual s propensity to make such mistakes, his use of mutual funds, and the consequences for portfolio performance. Furthermore, there are other ways to think about the behavioral bias proxies and our results. What we call behavioral bias proxies may simply represent each investor s financial literacy. Put another way, it is costly to continually acquire the skills and information needed to make successful investment decisions. While basic notions of portfolio management suggest that a simple buy-and-hold use of index funds is a sensible way to avoid incurring such costs, bounded rationality may lead some investors to other decisions. For example, an investor may display narrow framing bias if he elects not to incur the cost of thinking more carefully about investment decisions. Aside from recognizing that each investor may rationally strike a different balance between the costs and benefits of becoming a better investor, we must also consider preferences. While a preference for lottery-type stocks sounds suboptimal and, as we shall see, is associated with underperformance, it may simply represent skewness preference in the investor s objective function. Finally, some behavioral bias proxies may represent frictions in the investment process. For example, our overconfidence proxy identifies investors whose individual stock portfolio is high on turnover and low on return. While this may represent investors who are irrationally aggressive, it may also reflect a combination of small portfolio size, commission costs, and other frictions. With a portfolio of only a few stocks, rebalancing by trading just one stock yields high turnover, and even overconfidence if performance is poor. If such small investors recognize that mutual funds are particularly advantageous, this may even induce a correlation between 11

overconfidence and the propensity to use mutual funds. Our inclusion of portfolio size as a control variable in our regressions may not completely correct for such effects. 2.2 Control Variables Though we focus on the behavioral forces for which the previous section describes proxies, we also control for other factors that are likely to influence mutual fund choices. Specifically, we consider a set of demographic characteristics, which includes Age, Marital Status (a dummy set to one for married investors), Family Size (number of family members in the household), Professional Dummy (a dummy set to zero for investor in a blue collar profession, one otherwise), and Retired Dummy (a dummy set to one if the investor is retired). These factors may proxy for forces, such as the availability of time to study investments (Zhu (2005)), that can affect portfolio selection. Other control variables are more directly related to each individual s investment activities. Stock portfolio diversification is measured as the negative of Normalized Portfolio Variance (that is, the variance of the portfolio of individual domestic securities divided by the average variance of the individual common stocks in the portfolio). Investors who demonstrate awareness of the value of diversification in their portfolio of individual stocks are likely to extend that insight into their choice of mutual funds. Income (the total annual household income) and Portfolio Size (the sample-period natural log of the average market capitalization of the investor s common stock portfolio) identify investors who are more likely to understand the basic precepts of portfolio management and, therefore, tend to select index funds or other low expense funds, and hold them for relatively long periods. Investment Experience (years since the brokerage account was open) and a dummy for residence in a Financial Center may indicate 12

more experienced investors with easier access to information and opinions about investments (Christoffersen and Sarkissian (2009)). The Options Dummy equals one if the investor executes at least one option trade during the sample period. The Short Sale Dummy equals one if the investor executes at least one short trade during the sample period. 6 Stock Portfolio Performance (the intercept from the market model time series regression with the monthly common stock portfolio return as dependent variable) may identify particularly skillful, successful investors. Success may originate from a variety of strategies, ranging from selecting individual stocks to timing the market 7 No December Tax Loss Selling equals one minus the ratio of realized losses in December to both realized and paper losses in December. Holds Tax-Deferred Account is a dummy variable equal to 1 if the investor holds an IRA or Keogh account at the brokerage. Stock Portfolio Beta, Size, Value, and Momentum Factor loadings are computed with market or fourfactor regressions using monthly returns. 3. Data and Summary Statistics Having outlined the behavioral proxies and control variables that will support our study of multiple dimensions of investors mutual fund decisions, we now describe the data sets needed for the empirical tests. 3.1 Data Sources Our primary database is a six-year (January 1991 to November 1996) panel of trades and monthly portfolio positions of individual investors with accounts at a major U.S. discount 6 Options and short sale dummies may proxy for skill and experience, or may also reflect a tendency to speculate. See Campbell (2006) on the correlation between investor sophistication and investment mistakes. 7 For example, an informed investor may optimally focus on only a few stocks (Goetzmann and Kumar (2008), Ivkovich, Sialm, and Weisbenner (2008), Van Nieuweburgh and Veldkamp (2010)). 13

broker. 8 The database has been used by a number of other authors including Odean (1998) and Barber and Odean (2000). The database indicates the end-of-month portfolios of all investors, records all trades by these investors, and supplies demographic information (measured as of June 1997 and supplied to the brokerage house by Infobase) such as age, occupation, income, selfreported net worth, gender, marital status, and zip code. 9 We obtain the zip codes of the headquarters of a subset of mutual fund families from Professors Josh Coval and Zoran Ivkovich. We supplement this data set with additional information from the Lionshare database, 1996 Nelson s Directory of Investment Managers, and Google searches. We also obtain data from several standard sources. For each common stock and mutual fund in our sample, we obtain monthly returns data from the Center for Research in Security Prices (CRSP). We also use the CRSP mutual fund database to obtain information on fund characteristics such as the expense ratio and front-end load. Finally, we obtain the monthly timeseries of the three Fama-French factors and the momentum factor from Professor Kenneth French s data library. 10 3.2 Summary Statistics Table 1 provides summary statistics on individual investor trading and holding of mutual funds and, for comparison, individual stocks. Sample investors traded or held 1,492 different equity mutual funds (of which 33 are index funds) and close to 11,000 stocks. 32,122 investors have executed at least one mutual fund trade and 29,381 have held equity mutual funds at least 8 The brokerage firm has not made more recent data available. The time period covered largely excludes such phenomena as ETFs (WEBS) and high-frequency online day trading by individuals. 9 Each demographic variable is available for only a subset of the investors in the sample. For instance, both age and income is available for only 31,260 investors. Consequently, the number of observations in each cross-sectional regression depends upon the subset of demographic variables included. 10 The data library is available at http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/. 14

once. Among these, only 5,594 have executed at least one index fund trade and 4,432 have held index funds at least once. The balance of buys and sells suggests that, in contrast to individual stocks, mutual fund investors tend to buy and hold funds, rather than buying and selling more actively as with individual stocks. Trade sizes and quantities are typically modest. The mean (median) number of equity funds in a typical mutual fund portfolio is 3.51 (2.0) and number of trades executed is 19 (6.0). The mean (median) number of index funds held is 1.37 (1.0) and number of trades executed is 4 (2.0). In contrast, a typical investor holds 3.89 individual stocks (median is three) and executes 30 (median is 11) stock trades. Beyond what is reported in the table, the proportion of mutual funds in a typical equity portfolio that includes mutual funds is 23.78%. 11 This proportion increases slightly with equity portfolio size to about 26% in the highest size decile portfolios. The proportion of index funds in the aggregate mutual fund portfolio is quite low, varying between 5.30% and 8.39%, with a mean of only 6.54%. Nevertheless, among the investors who hold index funds, the proportion of index funds in the mutual fund portfolio is about 38%. Furthermore, there is much evidence that our sample of brokerage records represents typical U.S. individual investors. 12 In addition to detailed descriptions of each investor characteristic variable, the Appendix includes univariate summary statistics on those variables. 13 It is interesting to note some features of the data. For example, some of the behavioral bias proxies are skewed to the left (Disposition Effect, Narrow Framing) while others are skewed right with large positive outliers (Lottery Stocks 11 If we include all investors, not just those who hold mutual funds, this proportion is only 13.49%. Consistent with the common industry trend, it has grown steadily from 7.63% in January 1991 to 16.58% in November 1996. About 10% of all investors hold only mutual funds in their equity portfolio while about 17% hold more than three-fourths in equity mutual funds. 12 Ivkovic, Poterba, and Weisbenner (2005) find the distribution of stock holding periods is very similar across our sample and the general population reflected in tax returns. Zhu (2005), Goetzmann and Kumar (2008), and Ivkovich, Sialm, and Weisbenner (2008) confirm that our sample closely resembles the general U.S. individual investor population. Bailey, Kumar, and Ng (2008) document similarities with the Census Bureau s 1995 Survey of Income and Program Participation and the Fed s Survey of Consumer Finances of 1992 and 1995. 13 These statistics are computed prior to 1% winsorizing which is employed throughout the balance of the paper. 15

Preference). The median age of our sample investors is about 50 years, median income is $87,500 per year, and median family size is 2. Almost 90% of the accounts are held by males. The average (median) market risk-adjusted return on an investor s portfolio of individual stocks is an unflattering 0.378% ( 0.278%) per month, and ranges from a minimum of 11.474% to a maximum of 6.437%. The median individual stock portfolio beta is a surprisingly high 1.157. 4. Empirical Results We begin by examining our behavioral bias and news inattention proxies in more detail and, in particular, look for intuition from the associations among these proxies, and with other investor characteristics. Next, we study mutual fund participation and fund selection decisions across our sample investors. We then arrange information about these decisions by type of fund, rather than by individuals. In these tests, we examine the fees and expenses of funds chosen by the investors in our sample and whether there are associations with turnover, performance, and behavioral biases. We also investigate whether investors trend-chasing behavior is influenced by their behavioral biases. Further tests summarize the impact of individual investors mutual fund investment decisions on portfolio performance. Last, we report the results of various robustness checks. 4.1 Associations between Investor Characteristics The recent behavioral finance literature has proposed a number of behavioral factors. However, previous papers typically focus on only one behavioral factor. One of our contributions is to examine different behavioral factors jointly, and measure how they relate to each other and to other investor characteristics. 16

Table 2 presents correlations among the behavioral biases that we measure. A number of statistically significant associations are evident. Disposition Effect, Narrow Framing, Weight in Lottery Type Stocks, and Inattention to Earnings News often appear in the same individuals. These individuals time their trades poorly, make decisions in isolation, buy speculative stocks, and ignore firm-specific information. Although uncorrelated with Disposition Effect, Overconfidence is significantly positively correlated with Narrow Framing, Male Dummy, and Weight in Lottery Type Stocks, suggesting a class of particularly aggressive investors prone to speculation. Interestingly, some correlations for Local Bias suggest a cautious investor type (negative correlation with Overconfidence and Weight in Lottery Type Stocks). Inattention to Macro News is negatively correlated with Inattention to Earnings News, suggesting that some individuals invest on a top down basis and look at broad news, while ignoring firm-specific news. To save space, we do not report correlations among the other investor characteristics or between the behavioral biases and the other characteristics (they are available upon request). We summarize these correlations as follows. Many of the other investor characteristics are related in sensible ways. For example, Age is positively correlated with Marital Status, Retired Dummy, Investment Experience, and Stock Portfolio Size. Income is positively correlated with Family Size, Professional Dummy, and Financial Center Dummy. The use of options or short sales is correlated with Investment Experience and Financial Center Dummy. Financial sophistication is evident in correlations among Investment Experience, Options Dummy, Short Sale Dummy, Stock Portfolio Diversification, and tax minimization. A number of correlations are unexpected, such as no association between Investment Experience and Stock Portfolio Performance and negative association between Stock Portfolio Diversification and Stock Portfolio Performance. 17

Interestingly, high loadings of individual stock portfolios on market, size, value, and momentum factors are associated with poor performance. The (unreported) correlations between the behavioral bias variables and the other investor characteristics begin to suggest links between investment decision-making biases and more fundamental individual characteristics. For example, it is sensible that maturity and intelligence (represented by Age, Income, Professional Dummy, and Retired dummy) are typically uncorrelated or even negatively correlated with biases. Narrow-framing is more likely for young, relatively low-income investors, which is consistent with the findings of Kumar and Lim (2008). Lottery stock preference is associated with growth and value stocks (as proxied by SMB and HML factor exposures) and poor performance. Among the biases, only Local Bias is positively correlated with Stock Portfolio Performance, suggesting that familiarity bias is not necessarily detrimental. As we would predict given its definition, Narrow Framing tends to be negatively correlated with Stock Portfolio Diversification. While it is difficult to comprehensively grasp literally hundreds of individual crosscorrelations, some hint at effective investing, some suggest cautious behavior, and many imply that poor decision-making leads to inferior stock portfolio performance. To highlight these associations in a more formal and dramatic manner, Table 3 presents the results of factor analysis applied to the observed characteristics of the 21,542 investors in the database who traded individual stocks during the sample period. The first factor explains 21.8% of the variance of the investor characteristics. This factor has substantial positive loadings on Disposition Effect, Narrow Framing, and, especially, Lottery Stocks Preference. This suggests that this factor reflects investors with substantial behavioral biases, particularly a taste for risky stocks. We label this factor Gambler. Negative loadings on 18

Age, Income, Professional Dummy, Retired Dummy, Investment Experience, and Portfolio Size suggest that Gambler is relatively young, poor, unsophisticated, and inexperienced. The negative loading on Stock Portfolio Diversification indicates a tendency to plunge rather than spread risk. This is consistent with models (Mitton and Vorkink, 2007; Barberis and Huang, 20008) in which some investors take undiversified positions in skewed securities which appeal to their preferences. The loadings on risk factors indicate an appetite for high beta stocks, small stocks, value stocks, and trading against momentum. The negative loading on Stock Portfolio Performance suggests that Gambler typically suffers poor performance. This is consistent with the empirical finding in Kumar (2009) that investors with high Lottery Stocks Preference often select small value stocks that do not perform well. The second factor explains 18.1% of the variation of the investor characteristics. In contrast to Gambler, this factor represents investors who seem to do everything right, and earn good returns from individual stocks as a consequence. We label this factor Smart. Smart displays negative loadings on several behavioral biases, and has high income, professional status, and long investment experience. Smart s large, diverse individual stock portfolio has relatively modest loadings on market, size, value, and momentum risks, and reflects the value of December tax-loss selling. Among the first five factors, Smart is the most likely to maintain a tax-deferred brokerage account. This combination of good characteristics yields relatively high individual stock portfolio performance. Interestingly, Smart is likely to use short-selling, implying sophistication in investment tactics. The third factor explains 15.3% of the investor characteristics and puts cumulative variance explained above 55%. We label this factor Overconfident given the large positive loading on Overconfidence Dummy (which, by construction, is consistent with the large negative 19

loading on Stock Portfolio Performance). Overconfident is typically male, inclined to Lottery Stocks Preference, single, not retired, and inexperienced with investments. An association between male gender and overconfident investing mirrors the findings of Barber and Odean (2001). Overconfident s individual stock portfolio is poorly diversified and has a large loading on market risk. Interestingly, the use of options is associated with this ineffective decisionmaker, unlike the use of short sales which is associated with the successful Smart investor. The fourth factor explains 12.3% of the investor characteristics. We label it Narrow Framer given its particularly large loading on that bias. With significant positive loadings on three biases, youth, and low income, poor Stock Portfolio Diversification, and weak Stock Portfolio Performance, Narrow Framer is reminiscent of the Gambler and Overconfident stereotypes presented previously. Similar to the findings in Kumar and Lim (2008), Narrow Framer exhibits stronger disposition effect and hold less diversified portfolios. Narrow Framer does seem aware of tax issues, given the negative loading on No December Tax Selling, perhaps because he or she carefully accounts for each stock, though separately. The fifth factor explains 10.2% of variance and, given that it is the last factor with eigenvalue above one and puts cumulative variance explained above 75%, it is the final factor for which we offer detailed interpretation. 14 Given that this factor has a high loading on Age, Retired Dummy, and Investment Experience, a negative loading on behavioral biases, a large, welldiversified portfolio, and an understanding of tax-timing, we label it Mature. Unlike Smart, Mature s individual stock portfolio performance is not extraordinary, but successfully avoids the cost of obvious biases and mistakes. Caution is also reflected in Mature s relatively modest loadings on market, size, value, and momentum risks. Interestingly, Mature is less likely to hold 14 Given that we use factor analysis rather than principal components, a cut-off of one for the eigenvalue is conservative. Information on the sixth through tenth factors is unreported but available on request. 20

a tax-deferred account, perhaps because such accounts must be drawn down upon approaching retirement or are less valuable to relatively low income investors. Many of the characteristics of Mature parallel what Korniotis and Kumar (2010) report for older investors. To reconcile generally unbiased decision-making with mediocre performance, they suggest that aging is associated with deterioration in cognitive skills We recognize that the labels we have placed on the first five factors are at best speculative. Nonetheless, the clusters of characteristics they identify across tens of thousands of individual U.S. investors are intuitive. They validate the behavioral biases and other investor characteristics that the empirical behavioral finance literature has developed. We will employ these biases, and the factors we have extracted, in subsequent tests to understand how behavioral biases affect the use of equity mutual funds. 4.2 Participation in Open End Mutual Funds: Logit Regression Estimates Our next set of tests examines investors mutual fund participation decisions. We estimate logit regressions where the dependent variable is the fund participation dummy, which equals one for an investor who invests in mutual funds at least once during the sample period. The main independent variables of interest are the behavioral bias proxies, inattention measures, and tax-related interactives. The logit regression estimates are presented in the first four specifications of Table 4. The independent variables are standardized so that coefficient estimates can be easily compared within and across specifications. 15 In specifications (1) and (2) of Table 4, we explain the mutual fund participation dummy with behavioral bias proxies. Specification (2) also includes the control variables previously 15 To alleviate concerns about multi-collinearity, we check the variance inflation factor (VIF) for each explanatory variable. 21

described. Consistent with the presence of behavioral biases, negative slopes on disposition effect, narrow framing, lottery stocks preference, and inattention regarding earnings news indicate that investors who score high on these characteristics are less likely to invest in equity mutual funds. The negative slope on the interactive term for disposition effect and no December tax loss selling indicates that investors prone to both the disposition effect and lack of attention to tax issues are even less likely to invest in equity mutual funds. Somewhat surprisingly, we find that overconfident investors (that is, those who trade stocks more frequently, yet earn lower returns) are more likely to invest in mutual funds. This may reflect overconfidence in their ability to identify good funds. 16 In economic terms, the logit regression estimates indicate that the propensity to invest in mutual funds declines by 3.15% (0.126 25), 3.90%, 4.67%, and 0.95% when the level of disposition effect, narrow framing, lottery preference, or inattention to earnings news increases by one standard deviation, respectively. 17 The absolute size of slope coefficients is the largest for Lottery Stocks Preference, suggesting that the propensity to pick individual stocks is most likely to divert investment away from sensible strategies involving mutual funds. The finding for Lottery Stocks Preference is particularly significant as, unlike some of our other factors as discussed in Section 2.1, it is hard to characterize this factor as anything other than behavioral or, at best, skewness preference. These findings are robust to the inclusion of the control variables. Moreover, the estimated slopes on the control variables are intuitive. We find that investors who earn higher income, work as a professional, do not live near a financial center, are sufficiently sophisticated to use options, or who appear to value diversification in their stock portfolios are also more 16 Subsequent tests address this potentially puzzling finding. 17 Following Wooldridge (2003), we use a factor of 25% to interpret the logit regression results. 22

likely to invest in mutual funds. Those who ignore tax loss selling of their individual stocks or load high on market, size, or value risks are less likely to hold equity mutual funds. Specifications (3) and (4) repeat the tests described previously but for the index fund participation dummy, which is set to one only for those investors who invest in index funds at least once during the sample period. The decision to participate in index funds may be quite different from the decision to participate in mutual funds generally. The evidence on behavioral biases and index funds in specifications (3) and (4) largely echoes what we find for mutual funds generally in specifications (1) and (2). Investors who score high on disposition effect, narrow framing, inattention to earnings news, and disposition effect interacted with no December tax loss selling are more likely to avoid index funds. Once again, the importance of the propensity to trade risky individual stocks is evident: the strong aversion to mutual funds for those with Lottery Stocks Preference is heightened for index funds. Interestingly, the association between overconfidence and mutual fund investment disappears, perhaps indicating that overconfident investors confine themselves to actively-managed funds. Again, these findings are robust to the inclusion of the control variables. The estimates of the coefficients on the control variables also suggest that older investors, higher income investors, those with smaller stock portfolios, those who appear to value diversification, those who are cognizant of tax issues, those who do not live near a financial center, and those who avoid individual stocks with high loadings on market and size risks are more likely to value index funds. Thus, the clientele of index funds differs somewhat from the clientele of other mutual funds. However, behavioral biases appear to have a significant influence on the use of equity mutual funds regardless of type. In the following sections, we conduct additional tests to refine and extend these findings. 23

4.3 Extent of Fund Investment: Cross-Sectional Regression Estimates In our third set of tests, we estimate cross-sectional regressions with portfolio weights in mutual funds as dependent variables. Similar to the participation regressions, the independent variables are the behavioral factors that we focus on, plus control variables. One concern in such regressions is that the cross-correlation of individuals in decision-making may inflate the statistical significance of our regressions. For instance, some segment of investors may select very similar portfolios of funds and have correlated preferences for active, small cap, and industry funds. As a result, their fund choices may be correlated. We take the following steps to address such concerns for each of our cross-sectional regressions. First, clustered standard errors are intended to correct for correlation of residuals within each cluster (Petersen (2009)), though this method assumes independence across groups. 18 We do not know the exact nature of any cross-sectional dependence of returns residuals. Therefore, we try two different forms of clustered standard errors, by zip code (treating each investor within a zip code as one observation) and by peer group (same quintile of portfolio size, trading frequency and number of stocks). 19 Second, we construct risk-adjusted returns to remove the market-wide movement in returns that is common to all investors. Specifications (5) and (6) of Table 4 present the regression estimates. In specification (5), the dependent variable is the mean weight assigned to mutual funds in an investor s equity portfolio. The results parallel the findings from the participation regressions reported in Table 3. Individuals who score high on the disposition effect, narrow framing, lottery stocks preference, inattention to earnings news, or interaction between disposition effect and no tax loss selling 18 Kumar (2009) uses a similar method to account for potential cross-sectional dependence in performance across investors. 19 The results with peer group clustered standard errors are very similar. For brevity, we report the results with zip code clustered standard errors only. 24

typically put a smaller fraction of their portfolio in mutual funds, while overconfident investors typically allocate a larger proportion of their equity portfolio to mutual funds. In economic terms, a one standard deviation increase in narrow framing propensity is associated with a 1.94% lower allocation to mutual funds. The estimates of other statistically significant behavioral bias proxies are also economically significant. The estimates for the coefficients on the control variables show that investors who have higher income, are married, do not live near a financial center, understand short selling and diversification, have relatively small stock portfolios, understand tax issues, and have relatively low loadings on risk factors in their stock portfolios typically hold a higher proportion in mutual funds. Thus, similar forces drive the decision to participate in mutual funds and the extent of that participation. In specification (6), the dependent variable is the mean weight assigned to index funds. The cross-sectional regression results with index fund weight reinforce the findings from the index fund participation regressions. Investors with stronger behavioral biases typically allocate a smaller proportion of their equity portfolio to index funds, although the effect of overconfidence flips between specifications (5) and (6). Even though overconfident investors allocate a slightly larger weight to mutual funds, they allocate a smaller proportion of their equity portfolio to index funds. Thus, such investors focus more on actively-managed funds. Interestingly, the extent to which index funds are held goes up as individual stock portfolio performance goes down. 4.4 Behavioral Biases and Preference for Certain Types of Mutual Funds To better understand investor preferences for different types of funds, we examine three additional characteristics of investors mutual fund portfolios. Table 5, Panel A presents the 25

cross-sectional estimates. In specifications (1), (2) and (3), the dependent variable is the mean expense ratio, the mean front-end load, and the mean fund turnover respectively for each individual s mutual fund portfolio. Specification (1) shows that investors with stronger disposition effect, narrow framing, overconfidence, lottery stocks preference, inattention to earnings news, and interaction between disposition effect and no December tax loss selling tend to select mutual funds with higher expense ratios. Specification (2) examines front end loads and confirms that the same set of biases that drive investors to higher expense funds is also associated with choosing mutual funds with higher front end loads. Specification (3) shows that individuals who are overconfident, male, have lottery stocks preference, display inattention to earnings news, and have positive loading on measures of particularly severe disposition effect (Disposition Effect * High Income and Disposition Effect * No December tax loss selling interactive terms) tend to invest in funds with higher turnover. If we assume that funds with higher expense ratios, higher front-end loads, and high levels of turnover are poor choices, our evidence indicates that investors who demonstrate poor decision making with individual stocks also appear to make poor decisions about mutual funds. The slope coefficients on behavioral factors in specification (2) are particularly large, suggesting that behavioral biases are important in driving investors into high front end load funds. Interestingly, the slope coefficients on the control variables indicate that younger, poorer, less experienced, and less tax-savvy investors are more likely to elect these apparently poor choices. 4.5 A Closer Look at Fund-level Local Bias and Inattention 26

Why do some investors go against common wisdom and hold high front-end load funds? One possibility is that they are unaware of the load. 20 Alternatively, some investors may be more willing to pay a high load for funds they are familiar with. In particular, they may have more awareness of funds headquartered in their geographic area, perhaps due to localized marketing efforts. 21 As a result, they are willing to pay high fees for such funds. To investigate this thesis, we test whether investors with high Fund Level Local Bias are more likely to hold funds with high fees and expenses. Having employed proxies for local bias and inattention to news based on trading of individual stocks, we also investigate whether some investors concentrate their equity mutual fund trades around news. In Table 5, Panel B we introduce our Fund-Level Local Bias measure into the cross-sectional regression specification. This variable is distinct from individual equity local bias in that it measures the geographical proximity between an investor s home and the headquarters of mutual funds held by the investor, rather than the proximity of the headquarters of an individual listed company. We also introduce Fund-Level Inattention to the cross sectional regressions. This variable measures each individual s propensity to trade mutual funds around macroeconomic news events as 1 (Number of mutual fund trades around the event)/(total number of mutual fund trades). The estimates in specifications (1), (2), and (3) show that investors with stronger fundlevel local bias tend to select mutual funds with higher expense ratios, front end loads, and turnover, even after controlling for other behavioral biases. Indeed, Fund-Level Local Bias emerges as the variable with the largest economic and statistical significance compared to all other behavioral biases. Intriguingly, further correlation analysis (unreported but available upon 20 See Capon, Fitzsimons, and Prince (1996) for survey evidence that about 39% of mutual fund investors were unaware of the load charged by the funds they held. 21 Starks and Yates (2008) investigate a related familiarity-based hypothesis and find that individuals often cluster their choice of funds within the same family of funds. 27

request) shows that Fund-Level Local Bias is negatively correlated with age and positively correlated with the Retired dummy variable and stock portfolio size. This again suggests localized marketing efforts: older investors are typically cleverer and avoid Fund-Level Local Bias, but retired investors with large portfolios may be subjected to recommendations or marketing efforts from brokers, bankers, and social peers. Thus, investors who exhibit a stronger preference to hold local funds, which may be thought of as a familiarity effect, are more likely to buy funds with high fees, expenses, and turnover. Furthermore, Fund-Level Inattention is positive and significant in two of the three specifications, those for expense ratios and front end loads. Investors who pay less attention to news seem to select funds that impose higher expenses and loads on themselves. These findings suggest that behavioral biases can combine with ignorance to yield costly sub-optimal mutual fund investment decisions. 4.6 Behavioral Biases and Trend-Chasing Behavior Our next set of tests examines whether behavioral biases also play an important role in explaining individual investors trend-chasing behavior. Many explanations have previously been proposed for this robust pattern observed in mutual fund flow data. Chevalier and Ellison (1997) show that agency problems induce fund managers to alter the riskiness of the fund to maximize investment flows instead of risk-adjusted expected returns. Sirri and Tufano (1998) and Gruber (1996) propose that investors infer managerial skill from past returns. Berk and Green (2004) feature investors who infer managerial skill from past returns, and, therefore, chase returns. However, fund managers facing decreasing returns to scale in their active portfolios no longer outperform the index when more funds flow in, and, as a consequence, past performance 28

does not predict future returns. Rather than analyzing aggregate flows, our data allow us to study the relation between behavioral tendencies and trend-chasing behavior at the individual investorlevel. Table 6 examines trend-chasing in individual mutual fund portfolios. For each mutual fund purchase, we compute the return prior to the purchase, which is then averaged for each individual. Specification (1) uses one-year past returns as dependent variable while specification (2) uses the two-year past returns. The results from both specifications show that investors with certain behavioral biases, or inattention to macro news, tend to buy funds with more positive recent returns. Although the disposition effect does not seem to be associated with trend-chasing, the coefficients on the Disposition Effect * High Income and Disposition Effect * No December tax loss selling interactive terms are strongly significantly positive. Among the coefficients on the control variables, there is some evidence that sophisticated investors (those who are professionals, live near a financial center, trade options, or have well-diversified, wellperforming individual stock portfolios) are less likely to engage in trend-chasing. As was found previously (Table 5) for the propensity to select high-cost mutual funds, the size of slope coefficients suggest that Overconfidence and Lottery Stocks Preference are among the strongest predictors of whether a particular investor will trend-chase with mutual funds. This evidence suggests that trend-chasing is not a rational strategy. This interpretation is supported by the empirical results of previous authors concerning mutual fund flows and subsequent returns on individual stocks held by the funds. Frazzini and Lamont (2008) find relatively poor monthly returns on portfolios of individual stocks held disproportionately heavily by mutual funds that experience high inflows over the previous six months to three years. We find that it is more behaviorally-biased individuals who are responsible for trend-chasing inflows. 29

Thus, some of what they describe as the dumb money effect must be ascribed to a subset of investors who we have also identified as making poor decisions with their individual stock portfolios. The disposition effect result merits further discussion. In the classic form of this bias, investors sell well-performing individual stocks too quickly and hold poor-performing stocks too long. Trend-chasing by individuals who invest in mutual funds is broadly contradictory to a disposition effect in individual stocks: trend-chasers seek and then hold good performers, rather than selling them quickly. Our disposition effect interactive terms isolate investors who display a disposition effect that is likely to be particularly severe, and both terms earn a strongly significantly positive slope coefficient in the regressions of Table 6. Thus, individuals who display particularly damaging forms of the disposition effect in their individual stock portfolios tend to contradict themselves by displaying trend-chasing in their mutual fund choices. This implies that behavioral biases do not just vary across individuals but also across the components within a particular investor s portfolio, with professionally-managed assets handled in a radically different manner than individual stocks. This may be consistent with the idea that investors decompose their portfolios into layers that serve different purposes (Shefrin and Statman (2000)). Overall, our cross-sectional regression estimates reported in Tables 4 to 6 confirm that investors who are more behaviorally-biased on any of several dimensions or do not pay attention to salient news are more likely to display poor mutual fund investment decisions. They typically have a greater proportion of their equity investment in individual stocks rather than mutual funds, suggesting that they do not value diversification. When they buy funds, they prefer actively managed funds to index funds, tend to buy funds with high fees and loads, and chase funds with 30

high recent returns. The strength of one of our simplest behavioral bias measures, Lottery Stocks Preference, is particularly compelling. The missing link in our evidence and interpretations to this point is more explicit evidence on performance. While it appears that behavioral biases and ignoring news lead to poor choices, we must also document the consequences for performance. For example, individual investors typically avoid high front-end load funds (Barber, Odean, and Zheng (2005)), but some investors may be able to discriminate between good and bad quality front-end load funds, and enjoy superior portfolio performance from those high load funds that they do elect to hold. Thus, our next task is to examine the performance of investors mutual fund portfolios. 4.7 Performance of Mutual Fund Portfolios We again estimate cross-sectional regressions with the same behavioral proxies and controls as explanatory variables. Table 7, Panel A studies mutual fund performance for each investor s actual holdings. The dependent variables are four measures of the sample period performance of each investor s mutual fund portfolio, the raw performance measure (mean monthly portfolio return), the net-of-expenses performance measure (the net monthly return), the Sharpe ratio, and the market model alpha. We again use zip code clustered standard errors to compute the t-statistics because performance estimates are unlikely to be independent. 22 Specification (1) explains the mean monthly return. Disposition Effect, Narrow Framing, Overconfidence, Lottery Stock Preference, and both measures of inattention to news are associated with lower performance. For example, mean monthly return is lower by 0.041 per month for each standard deviation of increase in narrow framing. Because the highest and 22 As before, other forms of standard error clustering yield very similar results. 31

lowest quintiles of narrow framing differ by 4.3 standard deviations, this implies a 2.12% per year lower return for highest-quintile narrow framing investors compared to those in the lowest quintile. Similarly, highest-quintile disposition effect investors have returns 1.34% lower than those in the lowest quintile. 23 Thus, our behavioral proxies detect poor decision-making skills that reduce portfolio performance. Among the control variables, investment experience is significant, and the positive slope makes sense. The use of options or short sales is associated with better mutual fund performance, which is consistent with those variables reflecting skill or financial sophistication. Specification (2) examines net monthly returns and shows similar associations between behavioral biases and performance. Specification (3) examines the Sharpe Ratio. We again find broadly similar associations with the behavioral bias proxies, inattention measures, and control variables. Narrow Framing, Overconfidence, and, to a lesser extent, Disposition Effect are associated with lower performance. Results are similar when we account for potential cross-sectional dependence in performance induced by market-wide factors and consider a risk-adjusted performance measure as the dependent variable (Specification (4)). Collectively, the evidence in Table 7, Panel A shows that behavioral biases measured from individual stock selection are also associated with lower raw and risk-adjusted returns from mutual funds. Thus, poor decision-making in one domain appears to spill over into the performance experienced with other classes of investments. While Table 7, Panel A describes the actual realized returns of individual investors based on their total holdings at the end of each month, Panel B studies performance based on investor trades under both actual and hypothetical holding periods computed using daily fund returns data 23 Given that the highest and lowest quintiles of disposition effect differ by 4.13 standard deviations, their yearly performance difference is 1.34% ( 0.027% times 12 times 4.13). 32

from Morningstar. 24 Specifications (1) and (2) study actual holding period returns from trades. They confirm that investors with higher values on most of our behavioral bias proxies and inattention to news measures have significantly lower holding period returns and shorter holding period, in contrast to the buy-and-hold strategies prescribed by standard portfolio theory. Interestingly, Local Bias is associated with longer holding periods. Correlation analysis (unreported but available upon request) indicates that Local Bias is associated with poor diversification and mediocre performance in the individual stock portfolio, but Specification (2) reminds us that it may also yield sensible low turnover of mutual fund holdings. Specifications (3) and (4) adopt the alternative viewpoint of returns based on actual trades but standardized hypothetical holding periods. Following Odean (1999) and Kumar and Lee (2006), we calculate the subsequent k-month returns following each buy trade averaged over the trading history of an individual and subtract the subsequent k-month returns following each sell trade averaged over the trading history. The summary statistics on 1-month and 12-month post-trade buy-sell return differentials show that investors who score high on most of our behavioral and inattention proxies have lower post-trade buy-sell returns differentials. In other words, investors with strong behavioral biases tend to time their buys and sells poorly, and experience inferior performance relative to less-biased investors. The results are especially significant for 12-month returns differentials. Table 8 features interactions between investor portfolio characteristics and fund characteristics to explain performance. Individual household mutual fund performance is regressed on the behavioral biases and inattention measures previously employed, characteristics of the individual s mutual fund portfolio (the weight of the portfolio held in mutual funds, and 24 Partial sales are excluded from our calculations. Unlike Panel A, these calculations exclude any funds that were held prior to the start of our sample period. 33

the averages of the expense ratio, 12-B-1 fee, and front-end load on the funds held), interactive terms that combine behavioral and portfolio characteristics, and (unreported) control variables. The results confirm the negative impact of disposition effect, narrow framing, overconfidence, lottery stocks preference, and inattention to news on performance as documented previously. Among the mutual fund portfolio characteristics, investors with higher weight on mutual funds tend to enjoy superior fund performance, which is consistent with classic notions of portfolio management. Investors with higher weight on expenses, 12-B-1 marketing fees, and front-end load funds typically experience inferior fund performance. Among the interactive terms, we see particularly poor performance for high disposition effect investors who select funds with high 12-B-1 marketing fees or high front-end loads. This also appears to be the case for investors with strong framing effects or overconfidence. The coefficients for interactives of High Inattention and fees are uniformly significantly negative. Thus, investors with particularly high behavioral biases who choose to remain poorly informed may make particularly poor choices, stumbling into mutual funds with high expense ratios, high 12-B-1 marketing fees, or front-end loads. This echoes the finding in Table 5 that behavioral biases are particularly powerful in pulling investors into high front end load funds. This is also consistent with the possibility that the mutual fund industry positions certain products to exploit particularly biased individuals. In unreported results, we examine the performance differences among investors who use index funds. Interestingly, we do not find significant associations between the performance of individual index fund portfolios and individual behavioral biases. We consider different types of tests, including univariate sorts and multivariate regressions with and without controls or interaction terms. All our results consistently show that behavioral biases do not affect the 34

performance of investors index fund portfolios. This evidence indicates that investors can protect themselves from their own worst impulses by holding index funds and reinforces the classic intuition that most individual investors perform better if they stick to well-diversified index funds. Our findings also echo Kumar and Korniotis (2009) who show that the performance difference between smart and dumb investors is insignificant when both hold well-diversified stock portfolios, but is highly significant for those that choose concentrated portfolios, with smart investors outperforming by a wide margin. 25 4.8 Aggregating the Behavioral Bias Proxies and other Characteristics Next, we measure the combined effects of investor characteristics using both the factors constructed from the behavioral bias proxies and other investor characteristics, and an equallyweighted index that combines the behavioral bias proxies. Panel A of Table 9 summarizes regressions similar to those of Tables 4 to 7 but replacing the individual investor characteristics with the first five factors resulting from factor analysis described in Section 4.1 above. The first two columns study the first factor, which we previously labeled Gambler. The evidence in the table confirms this characterization. Gambler represents individuals who are less likely to use mutual funds, tend to select high expense funds, are more likely to trend-chase, and suffer significantly inferior mutual fund portfolio performance as a consequence. Put another way, Gambler employs mutual funds less than he probably should, but, when he does, he makes poor use of them. We previously identified the second factor as Smart, given that the individual stock portfolio of this stereotype avoids biases and displays relatively good performance. The evidence 25 This supports the notion that individual investors should be encouraged to make good decisions, as with retirement savings plan (Benartzi and Thaler (2007)). 35

in Panel A of Table 9 suggests that Smart s beneficial behavior extends to his use of mutual funds. The signs and significance of regression coefficients indicate that the Smart stereotype is more likely to use mutual funds, more likely to use funds with low expense ratios or loads, less likely to trend-chase, and enjoys significantly positive mutual fund performance based on all eight of the performance measures we examine. We previously labeled the third factor Overconfident based on trading of individual equities and other characteristics. The evidence on Overconfident s mutual fund portfolio confirms our impression that this stereotype is a poor decision-maker. Overconfident avoids participation in mutual funds and trend-chases to an even greater degree than Gambler, and also tends to select high expense, high load, and high turnover funds. Whether Overconfident s mutual fund performance is even worse than Gambler s varies across our eight performance measures. We labeled the fourth factor Narrow Framer. Narrow Framer s mutual fund participation is about as bad as Gambler s, though not as bad as Overconfident s. Small holdings of mutual funds, selection of high expense funds, trend-chasing, and consequent poor performance are also evident, though milder than for Gambler and Overconfident. Finally, the mutual fund use and performance represented by the fifth factor, Mature, mirrors what we reported earlier for Mature s individual stock portfolio. To Mature s credit, he participates and holds mutual funds to a greater extent than our other stereotypes, and avoids high-expense funds and trend-chasing to an even greater extent than Smart. However, there are other elements of Mature s decision-making about mutual funds that yield significant negatives on four of our eight performance measures. This finding is consistent with the evidence in Korniotis and Kumar (2010) who show that older investors are more likely to follow common 36

investing rules, but employ them less effectively and subsequently experience worse portfolio performance. One interesting observation from Panel A of Table 9 concerns the use of index funds. Unsurprisingly, Gambler, Overconfident, and Narrow Framer score negatively on both index fund participation and holdings. Their lack of interest in these useful and prudent funds is consistent with a pattern of bad decision-making in their use of other funds and individual stocks. Mature seems to participate in index funds as frequently as Smart, and holds an even greater proportion of such funds than Smart. However, this is not enough to overcome Mature s other decision-making problems and yield positive performance. As an alternative to the five named factors from factor analysis, Panel B of Table 9 presents similar results based on an equally-weighted behavioral index. 26 Specifically, we normalize each behavioral factor to have a mean of zero and a standard deviation of one, then average these normalized behavioral proxies for each individual in the sample. The table shows that in all cases the bias index is statistically significant and, more importantly, economically significant. In the discussion that follows, we infer the decisions of investors in the lowest and the highest bias quintiles. The average behavioral bias index values of investors in the extreme bias quintiles are 0.709 and 0.627. The standard deviation of the behavioral bias measure is 0.491, which indicates that the low and high behavioral bias quintiles are 2.721 standard deviations away from each other. In the participation regressions, the bias index estimates indicate that an investor who moves from the lowest to highest bias quintile reduces the probability of investing in mutual funds by 0.439 2.721 = 1.189%, while the propensity to invest in index funds drops by 26 This includes the five basic biases and the two inattention measures but excludes the two tax interactives. 37

1.933%. In the holdings regressions, we find that moving across the extreme bias quintiles reduces the weight assigned to mutual funds by 2.038%. This effect is even stronger (5.254%) for index funds. The other regressions summarized in Panel B of Table 9 paint a similar picture. Behavioral biases are associated with selecting higher expense funds, trend chasing with funds, and significant underperformance from fund holdings. In economic terms, the combined effects of all behavioral biases are moderately to strongly significant. 5. Additional Diagnostics In this section, we discuss additional tests that augment our main results by examining their robustness, considering alternative explanations for our findings, and offering additional evidence on the most biased investors. 5.1 Play Money Accounts? In our first set of additional tests, we test whether our results are driven primarily by a play money effect. We compute the average portfolio size-to-annual-income ratio for each investor, excluding investors in the lowest quintile. Unreported results indicate that our findings remain qualitatively similar even when we exclude investors who hold portfolios that are small relative to their annual income. For example, the coefficient estimate of the bias index in Table 4, Column (1) is 0.749 (t-statistic = 5.49) for the full-sample and 0.755 (t-statistic = 5.88) for the sub-sample that excludes potential play money. This evidence indicates that our results are unlikely to be induced by a subset of investors who maintain a small portfolio and trade it for irrational or frivolous reasons. 38

5.2 Mutual Fund Decisions for Retirement Accounts Many investors in our sample hold personal retirement accounts. About 42% of the accounts in our sample are retirement accounts (IRA or Keogh). 27 Thus, we examine whether 28 29 investors mutual fund choices vary between retirement and non-retirement accounts. It is plausible that the adverse effects of behavioral biases on mutual fund decisions are mainly concentrated in non-retirement accounts. Indeed, we may view a retirement account as the opposite of a play money account, and predict that it is managed in a more conservative manner. We define a taxable account only dummy, which is set to zero for investors who hold only retirement accounts in their equity portfolios and one otherwise. We include this dummy variable as an additional independent variable in our regression specifications. 30 We find that investors do not exhibit a greater propensity to hold mutual funds in their retirement accounts. The taxable account only dummy has an insignificant coefficient estimate ( 0.003 with z-statistic of 0.25). There is also no evidence of a stronger propensity to hold index funds for investors who hold retirement accounts. The taxable account only dummy has a coefficient estimate of 0.011 and z-statistic of 1.19. Even among investors who choose to hold mutual funds, there is no evidence that they allocate a larger proportion of their equity portfolio to mutual funds. The taxable account only dummy has statistically insignificant estimates in all specifications. 27 Among 158,031 accounts in our sample there are 64,416 IRA and 1,299 Keogh accounts. A typical household holds multiple accounts. Out of 77,995 households in the sample, 43,706 hold at least one retirement account. 28 See Sialm and Starks (2008) for evidence that funds directed at taxable investors appear more tax-efficient than funds directed at retirement accounts. 29 Note that this approach is distinct from our use of the holds tax deferred account dummy in earlier regressions, which identifies all accounts, regular or tax deferred, held by someone who holds at least one tax-deferred account. 30 All results are qualitatively similar when re-estimated over two subsamples: (i) investors who hold only retirement accounts and (ii) investors who hold retirement and non-retirement accounts. 39

Examining the characteristics of funds in the portfolios of investors who hold only retirement accounts, we find that they do not have lower expense ratios, lower front end loads, or lower turnover. Moreover, there is a greater tendency to engage in trend-chasing among these investors. When we re-estimate the trend chasing regressions of Table 6 with the taxable account only dummy variable, it has a significantly positive coefficient estimate (coefficient estimate = 0.029, t-statistic = 2.99). To examine whether retirement accounts only investors exhibit better performance, we re-estimate all the performance regressions with the taxable account dummy as an additional independent variable. In all specifications, this dummy variable has an insignificant coefficient estimate. Overall, we do not find evidence of superior mutual fund decisions when investors hold retirement accounts. The adverse effects of behavioral biases on mutual fund decisions are similar across both retirement and non-retirement accounts. Thus, behaviorally-biased investors do not manage retirement funds any more carefully than their regular accounts. 5.3 How Do the Most Severely Biased Investors Use Mutual Funds? Next, we consider whether the most severely behaviorally-biased investors tend to concentrate in particular types of funds, how often they trade those funds, and what consequences for performance result. We summarize unreported (but available on request) evidence on holdings, holding periods, and returns for the mutual funds owned by quintiles of investors who score highest on disposition effect, narrow framing, overconfidence, local bias, preference for lottery stocks, and inattention to news. Our primary prediction is that severelybiased investors are more likely to select higher expense funds, and avoid index funds. We also 40

expect the strongest Disposition Effect and Overconfidence investors to turn their mutual fund holdings over relatively frequently. There is much evidence to support such conjectures. For example, front load funds comprise 27.15% of the mutual fund holdings of typical investors, but we observe statistically significantly greater front holdings for the highest Disposition Effect (31.05%), Narrow Framing (26.69%), and Overconfidence (30.81%) cohorts. Interestingly, the mutual fund holdings of the highest Local Bias and Inattention Bias investors have, on average, about 2% less front load funds than typical investors. Holding periods for front end load funds are, on average, significantly low for highest Disposition Effect (215 days) and Overconfidence (233 days) investors and are significantly high for highest Narrow Framing (306 days), Local Bias (323 days), and Inattention Bias (327 day) investors. Somewhat similar, but weaker, results are observed for holdings of Back End Load funds and in comparing holdings of index funds and other funds. 6. Summary and Conclusions Using thousands of brokerage accounts of U.S. individual investors, we have shown that behavioral factors influence the decisions of individual investors to hold individual stocks as opposed to mutual funds, including passive index funds. As we might expect, investors with higher income, relatively higher educational level, and greater investment experience are more likely to use mutual funds and benefit from their choices. On the other hand, investors with strong behavioral biases tend to gravitate towards individual stocks and avoid low expense index funds. When they do invest in mutual funds, they tend to select high expense funds, trade funds frequently, avoid index funds, and time their buys and sells poorly, thereby damaging their 41

portfolio s performance. They also exhibit stronger trend-chasing behavior, suggesting that trend-chasing by mutual fund investors is not the result of rationally inferring managerial skill from past performance. When we use factor analysis to characterize associations among investor characteristics, we find interesting and intuitive patterns along multiple dimensions of bias and other characteristics that often crop up in the same individual. There is consistency across the behavioral biases, other characteristics, use of individual stocks, use of mutual funds, and resultant performance that our Gambler, Smart, Overconfident, Narrow Framer, and Mature stereotypes display. Our evidence on behavioral biases and mutual fund clienteles provides a new perspective on puzzles in mutual fund investment documented by previous authors. Several authors trace the mutual fund decisions of individual investors to such factors as excess focus on front-end loads, advertising, search costs, and complexity of fund features intended to exploit consumers. 31 Our evidence shows that investors who score high on behavioral biases tend to invest in funds with higher expense ratios and loads. They experience poor investment performance as a result. In his American Finance Association presidential address, Gruber (1996) notes several puzzling aspects of individual portfolio allocation decisions. He speaks of sophisticated investors who make decisions based on performance and disadvantaged investors who are susceptible to sales pressure or constrained by tax or institutional issues. In his presidential address, Campbell (2006) suggests that naïve investors may subsidize sophisticated investors in financial products such as mortgages. Our results echo the spirit of these ideas. A complex set of factors, some rational and some behavioral, appear to drive investors stocks versus funds decisions and their mutual fund choices after they decide to invest in mutual funds. Some types 31 See Barber, Odean, and Zheng (2005), Hortacsu and Syverson (2004), and Carlin (2008). 42

of investors appear to make effective choices that enhance portfolio performance, while others do not. Given the misuse of equity mutual funds, a public campaign to increase awareness of basic investment principles and the benefits and pitfalls of equity mutual funds is likely to help many types of individual investors make better decisions. Furthermore, the lack of attention to low cost or index funds suggests more explicit disclosure of fund expenses and turnover, perhaps even as prominent as the health warnings now displayed on packets of cigarettes. Finally, the reliance of mutual fund investors on broker-supplied information at the time a fund is selected and on delegated investment decisions afterwards suggests that even more explicit disclosure of fund characteristics be imposed on brokerage firms and fund managers. 43

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APPENDIX Panel A: Brief Description of Behavioral Proxies and Other Investor Characteristics Variable Description References Calculation Disposition Effect Investor s propensity to sell winners too early and hold losers too long. It is measured by the proportion of gains realized minus proportion of losses realized, adjusted for the peer group s disposition effect. Shefrin and Statman (1985), Odean (1998), Kumar and Lim (2008). Proportion of gains realized (PGR) = realized gains/(realized gains + paper gains) Proportion of losses realized (PLR) = realized losses/(realized losses + paper losses) A peer group of an investor is defined as those in the same quintile of portfolio size, trading frequency and number of stocks. Adjusted PGR = PGR of an investor Mean PGR of peer group. Adjusted PLR = PLR of an investor mean PLR in her peer group. Adjusted disposition effect = Adjusted PGR Narrow Framing Overconfidence Local Bias Lottery Stock Preference Investor s propensity to select investments individually rather than considering the broad impact on her portfolio. It is measured by the degree of trade clustering, adjusted for the peer group s framing propensity. Investor s propensity to trade frequently but unsuccessfully. It is measured with a dummy variable. Investor s propensity to select stocks with headquarters close to his geographical location. Investor s propensity to select stocks with lottery like features (low price, volatile and skewed returns). Kahneman and Lovallo (1993), Kahneman (2003), Kumar and Lim (2008). Odean (1999), Barber and Odean (2001). Huberman (2001), Coval and Moskowitz (1999), Grinblatt and Keloharju (2001), Zhu (2003), Ivkovich and Weisbenner (2005). Barberis and Huang (2008), Kumar (2009). Adjusted PLR. Trade clustering = 1 (number of trades/number of trading days). A peer group of an investor is defined as those in the same quintile of portfolio size, trading frequency, and number of stocks. Adjusted trade clustering = Trade clustering Mean trade clustering of the peer group. Overconfidence dummy variable = 1 for investors in the highest portfolio turnover quintile and lowest performance quintile for their individual common stock trading; 0 otherwise. Overconfidence is also captured by a gender dummy variable = 1 if the investor is male. Local bias of an investor s common stock portfolio = Mean distance between her home zip code and the headquarters zip codes of companies in her portfolio Mean distance between home zip code and the headquarters zip codes of companies in the market portfolio. Investor s mean portfolio weight (relative to the weight in the market portfolio) assigned to stocks that have bottom quintile prices, top quintile idiosyncratic volatility, and top quintile idiosyncratic skewness. 50

Inattention to Earnings News Inattention to Macroeconomic News Fund Level Local Bias Fund Level Inattention DE * No Dec Tax Loss Selling DE * High Income Degree to which investor does not trade a particular individual stock around earnings news. Degree to which investor does not trade any individual stocks around macroeconomic news events. Investor s propensity to select funds with headquarters close to his geographical location. Individual s propensity to trade mutual funds around macroeconomic news events. Extent of Disposition Effect for investor who ignores tax loss selling. Extent of Disposition Effect for investor with high income. New in this paper. New in this paper. New in this paper. New in this paper New in this paper. New in this paper. 1 (Number of investor trades around the event)/(total number of investor trades) on days t 1, t, and t+1 where t is the date of quarterly earnings announcement from I/B/E/S. Only trades around each firm s own earnings news are considered. 1 (Number of investor trades around the event)/(total number of investor trades) on days t 1, t, and t+1 where t is the date of Fed Funds target rate changes, Non Farm Payroll reports, and Producer Price Index announcements. Fund-level local bias = Mean distance between the investor s home zip code and the headquarters of the mutual funds in his portfolio the same mean distance averaged across all investors in the sample. 1 - (Number of mutual fund trades around the event)/(total number of mutual fund trades) Disposition Effect times dummy variable equal to 1 for investor with no December tax loss selling. Disposition Effect times High Income dummy Age Age of the investor. Self-reported. Age of the investor. Income Income of the investor. Self-reported. Annual income of investor. High Income Dummy Affluence of the household Graham and Kumar (2006) High Income dummy = 1 if the investor s average income exceeds $125,000, zero otherwise Marital Status Marital status of the investor. Self-reported. Marital status dummy = 1 if the investor is married, and 0 otherwise. Family Size Family size. Self-reported. Number of family members in the household. Professional Dummy A indicator whether an investor is a white collar or Self-reported. Professional dummy = 0 for investor in a blue collar profession, 1 otherwise blue collar worker. Retired Dummy Retirement status of investor. Self-reported. Retired dummy = 1 if the investor is retired, 0 otherwise. Investment Experience Financial Center Dummy Investment experience of investor. An indicator whether an investor lives near a financial center. Self-reported. Based on selfreported address. Years since the brokerage account was open Financial center dummy = 1 if the zip code of the investor s address is close to a metropolitan area, and zero otherwise. 51

Options Dummy Short Sale Dummy Stock Portfolio Diversification Stock Portfolio Size Stock Portfolio Performance No December Tax Loss Selling An indicator for whether the investor has ever traded an option in the investment account. An indicator for whether the investor has ever shorted a stock in the investment account. The extent to which the stock portfolio of the investor is diversified. The size of the investor s portfolio. Risk-adjusted excess returns of the investor s stock portfolios. An indicator if the investor fails to realized losses of his stock trade in December Based on investment record. Based on investment record. Based on investment record. Based on investment record. Based on investment record. Based on investment record. Options Dummy = 1 if the investor executes at least one option trade during the sample period, zero otherwise. Short Sale Dummy = 1 if the investor executes at least one short trade during the sample period. Negative of Normalized Portfolio Variance, i.e. variance of the portfolio of individual domestic securities divided by the average variance of the individual common stocks in the portfolio. Sample-period average market capitalization of the investor s common stock portfolio. The intercept, alpha, from the CAPM regression with the monthly common stock portfolio return as dependent variable. 1 Proportion of realized losses in December = 1 (Realized losses in December/Number of paper losses) Holds Tax- Deferred Account An indicator for whether the investor holds a tax deferred account in the brokerage. Based on investment record. Holds Tax Deferred Account Dummy = 1 if the investor holds an IRA or Keogh account in the brokerage. Stock Portfolio Market Factor (Beta) exposure Stock Portfolio SMB Factor (Size) exposure Stock Portfolio HML Factor (Value) exposure Stock Portfolio UMD Factor (Momentum) exposure The beta of the investor s stock portfolio. The loading of the stock portfolio on the small-minusbig factor in a four-factor model regression. The loading of the stock portfolio on the high-minuslow book-to-market factor in a four-factor model regression. The loading of the stock portfolio on the up-minusdown factor in a four-factor model regression. Based on investment record. Based on investment record. Based on investment record. Based on investment record. The loading of the stock portfolio on the market (RMRF) factor in a four-factor regression model with market, size, value, and momentum factors. All four factors come from Ken French s website. The loading of the stock portfolio on the size (SMB) factor in a four-factor regression model with market, size, value, and momentum factors. All four factors come from Ken French s website. The loading of the stock portfolio on the value (HML) factor in a four-factor regression model with market, size, value, and momentum factors. All four factors come from Ken French s website. The loading of the stock portfolio on the momentum (UMB) factor in a fourfactor regression model with market, size, value, and momentum factors. All four factors come from Ken French s website. 52

Appendix (Continued) Panel B: Univariate summary statistics on investor characteristics (21,542 observations) Variable Mean Std Dev Min 10th Pctl 25th Pctl Median 75th Pctl 90th Pctl Max Disposition Effect 3.719 112.197 100.00 100.00 11.111 12.609 66.667 100.000 100.000 Narrow Framing 0.010 0.155 0.683 0.207 0.081 0.038 0.131 0.181 0.440 Overconfidence Dummy 0.090 0.287 0.000 0.000 0.000 0.000 0.000 0.000 1.000 Male Dummy 0.898 0.282 0.000 0.899 1.000 1.000 1.000 1.000 1.000 Local Bias 0.273 0.395 1.323 0.204 0.058 0.272 0.542 0.773 0.996 Lottery Stocks Preference 12.025 17.206 0.000 0.000 0.000 4.265 18.510 33.644 100.000 Inattention to Earnings News 0.057 0.061 0.000 0.000 0.000 0.048 0.087 0.133 0.500 Inattention to Macro News 0.301 0.143 0.000 0.133 0.214 0.292 0.375 0.476 1.000 Fund Level Local Bias 0.000 0.703 1.249 0.854 0.468 0.97 0.394 1.015 4.171 Fund Level Inattention 0.303 0.107 0.000 0.250 0.304 0.304 0.304 0.333 1.000 Age 50.429 11.537 18.000 36.000 42.000 52.000 56.000 68.000 94.000 Income 89.358 60.381 7.500 35.000 62.500 87.500 112.500 250.000 250.000 High Income Dummy 0.241 0.427 0.000 0.000 0.000 0.000 0.000 1.000 1.000 Marital Status 0.736 0.386 0.000 0.000 0.736 1.000 1.000 1.000 1.000 Family Size 2.814 1.417 1.000 1.000 2.000 3.000 4.000 5.000 10.000 Professional Dummy 0.610 0.336 0.000 0.000 0.610 1.000 1.000 1.000 1.000 Retired Dummy 0.166 0.256 0.000 0.000 0.000 0.000 0.166 0.166 1.000 Investment Experience 9.809 3.190 5.255 5.880 6.915 9.630 12.019 13.964 22.373 Financial Center Dummy 0.327 0.469 0.000 0.000 0.000 0.000 1.000 1.000 1.000 Options Dummy 0.124 0.330 0.000 0.000 0.000 0.000 0.000 1.000 1.000 Short Sale Dummy 0.138 0.345 0.000 0.000 0.000 0.000 0.000 1.000 1.000 Stock Portfolio Diversification 0.422 0.135 0.966 0.598 0.514 0.422 0.323 0.245 0.000 Stock Portfolio Size 36.410 98.119 0.001 4.255 7.824 15.326 32.277 71.899 4079.582 Ln(Stock Portfolio Size) 2.797 1.159 7.082 1.448 2.057 2.729 3.474 4.275 8.314 Stock Portfolio Performance 0.378 1.460 11.474 2.111 1.116 0.278 0.468 1.253 6.437 No Dec Tax Loss Selling 0.818 0.386 0.000 0.000 1.000 1.000 1.000 1.000 1.000 Holds Tax-Deferred Account 0.490 0.500 0.000 0.000 0.000 0.000 1.000 1.000 1.000 Market Factor Exposure 1.196 0.557 1.911 0.555 0.850 1.157 1.521 1.895 3.901 SMB Factor Exposure 0.853 1.028 2.163 0.268 0.098 0.675 1.410 2.257 7.810 HML Factor Exposure 0.182 0.838 3.258 0.797 0.359 0.119 0.647 1.269 5.279 UMD Factor Exposure 0.331 0.667 3.898 1.182 0.704 0.267 0.089 0.410 2.986 53

Table 1: Summary Statistics on Mutual Fund Investments of Individual Investors This table summarizes the stock and mutual fund investment activities of our sample individual investors. The individual investor data are from a large U.S. discount brokerage house for the 1991 to 1996 period. The median numbers are indicated in parentheses. We identified a total of 136 index funds that were available to our sample of investors during this time period. The CRSP universe of individual stocks available during this time period is about 12,000. Statistic Equity Funds Index Funds Stocks Number of Assets 1,492 33 10,877 Sample-Period Trades Number of Investors With Trades 32,122 5,594 62,387 Number of Buys 405,376 (67.03%) 15,354 (73.66%) 1,015,735 (54.76%) Number of Sells 199,365 (32.97%) 5,491 (26.34%) 839,041 (45.24%) Mean (Median) Number of Trades 19 (6) 4 (2) 30 (11) Mean Buy Trade Quantity 2,787 470 634 Mean Buy Trade Size $9,929 $6,879 $11,251 Mean Sell Trade Quantity 4,226 964 694 Mean Sell Trade Size $15,744 $13,244 $13,684 End-of-Month Positions Number of Investors With Positions 29,381 4,432 59,387 Mean (Median) Portfolio Size $39,986 ($12,827) $13,659 ($5,200) $35,629 ($13,869) Mean (Median) Number of Assets 3.51 (2) 1.37 (1) 3.89 (3) 54

Table 2: Cross Correlations of the Behavioral Measures Computations are based on 21,542 individuals who have traded individual stocks during the sample period. Any correlation coefficient with t-statistic greater than or equal to 2.576 is presented in bold type to indicate strong statistical significance. All series are winsorized at the 1% level, and results throughout the paper are very similar for winsorizing at the 5% level. Disposition Narrow Overconfidence Male Local Lottery Inattention Inattention Fund Fund DE* DE* Effect Framing Dummy Dummy Bias Stocks to to Level Level No High Preference Earnings Macro Local Inattention Dec Selling Income News News Bias Disposition Effect 1.000 0.230 0.013 0.008 0.006 0.044 0.038 0.011 0.006 0.004 0.922 0.481 Narrow Framing 0.230 1.000 0.080 0.012 0.007 0.081 0.082 0.010 0.011 0.005 0.200 0.121 Overconfidence Dummy 0.013 0.080 1.000 0.019 0.039 0.062 0.015 0.010 0.007 0.012 0.001 0.006 Male Dummy 0.008 0.012 0.019 1.000 0.010 0.006 0.004 0.002 0.009 0.004 0.006 0.016 Local Bias 0.006 0.007 0.039 0.010 1.000 0.041 0.011 0.008 0.024 0.002 0.007 0.009 Lottery Stocks Preference 0.044 0.081 0.062 0.006 0.041 1.000 0.065 0.021 0.013 0.002 0.037 0.031 Inattention to Earnings News 0.038 0.082 0.015 0.004 0.011 0.065 1.000 0.060 0.005 0.001 0.028 0.023 Inattention to Macro News 0.011 0.010 0.010 0.002 0.008 0.021 0.060 1.000 0.003 0.043 0.008 0.009 Fund Level Local Bias 0.006 0.011 0.007 0.009 0.024 0.013 0.005 0.003 1.000 0.013 0.004 0.010 Fund Level Inattention 0.004 0.005 0.012 0.004 0.002 0.002 0.001 0.043 0.013 1.000 0.002 0.012 DE*No Dec Selling 0.922 0.200 0.001 0.006 0.007 0.037 0.028 0.008 0.004 0.002 1.000 0.444 DE*High Income 0.481 0.121 0.006 0.016 0.009 0.031 0.023 0.009 0.010 0.012 0.444 1.000 55

Table 3: Factor Analysis for the Behavioral Measures and Other Investor Characteristics Computations are based on 21,542 individuals who have traded individual stocks during the sample period. The varimax method is run for ten factors but only the first five are reported given variance explained. Factors Variables Gambler Smart Overconfident Narrow Framer Factor Characteristics Mature Eigenvalue 2.288 1.894 1.607 1.286 1.071 Variance Explained 0.218 0.181 0.153 0.123 0.102 Cumulative Variance 0.218 0.399 0.552 0.675 0.777 Explained Factor Loadings Disposition Effect 0.189 0.213 0.055 0.253 0.302 Narrow Framing 0.216 0.101 0.095 0.588 0.221 Overconfidence Dummy 0.055 0.058 0.472 0.090 0.232 Male Dummy 0.021 0.004 0.202 0.001 0.013 Local Bias 0.044 0.206 0.02 0.005 0.02 Lottery Stocks Preference 0.563 0.202 0.143 0.011 0.243 Inattention to Earnings News 0.058 0.011 0.090 0.196 0.013 Inattention to Macro News 0.029 0.007 0.052 0.011 0.008 Fund Level Local Bias 0.02 0.033 0.000 0.032 0.016 Fund Level Inattention 0.005 0.017 0.001 0.004 0.010 DE* No Dec Selling 0.023 0.028 0.028 0.015 0.015 DE* High Income 0.028 0.027 0.037 0.022 0.019 Age 0.335 0.067 0.026 0.202 0.458 Income 0.404 0.020 0.005 0.167 0.126 High Income 0.027 0.196 0.010 0.004 0.085 Marital Status 0.032 0.033 0.255 0.001 0.054 Family Size 0.008 0.04 0.023 0.001 0.104 Professional Dummy 0.155 0.332 0.002 0.000 0.589 Retired Dummy 0.342 0.055 0.331 0.008 0.890 Investment Experience 0.333 0.509 0.221 0.015 0.292 Financial Center Dummy 0.045 0.005 0.032 0.007 0.059 Options Dummy 0.066 0.094 0.301 0.010 0.029 Short Sale Dummy 0.014 0.332 0.014 0.012 0.011 Stock Portfolio Diversif. 0.323 0.723 0.333 0.41 0.403 Stock Portfolio Size 0.202 0.407 0.080 0.303 0.552 Stock Portfolio Performance 0.454 0.354 0.828 0.236 0.020 No Dec Tax Loss Selling 0.006 0.498 0.088 0.398 0.311 Holds Tax-Deferred Account 0.004 0.202 0.027 0.005 0.311 Market Factor Exposure 0.471 0.091 0.556 0.220 0.046 SMB Factor Exposure 0.806 0.125 0.150 0.023 0.044 HML Factor Exposure 0.594 0.121 0.213 0.110 0.059 UMD Factor Exposure 0.555 0.087 0.045 0.072 0.010 56

Table 4: Investor Characteristics and Mutual Fund Participation Decisions and Stock versus Funds Allocation The first four specifications in the table are logit regressions. In specifications (1) and (2), the dependent variable is one for investors who hold or trade mutual funds at least once during the sample period. In specifications (3) and (4), the dependent variable in the logit regression is one for investors who hold or trade index funds at least once during the sample period. Specifications (5) and (6) are cross-sectional regression estimates, where the proportion of mutual funds in the equity portfolio is the dependent variable. In specification (5), the dependent variable is the mean weight of mutual funds in the total equity (stocks and mutual funds) portfolio. In specification (6), the dependent variable is the mean weight of index funds only. The dependent variable is multiplied by 100. Independent variables are defined in the Appendix, and a constant term is included. They are standardized so coefficients can be compared within or across specifications. There is one observation per investor. An intercept is included but not reported. Robust zip code clustered standard errors are used to obtain the t-statistics. The individual investor data are from a large U.S. discount brokerage house for the 1991 to 1996 period. 57

Table 4 (Continued) Dependent Variable: Fund Participation or Portfolio Weight (One Observation Per Individual) Mutual Fund Participation Dummy (LOGIT) Mutual Fund Portfolio Weight All Mutual Funds Index Funds Only Mutual Fund Weight Index Fund Weight (1) (2) (3) (4) (5) (6) Independent Variables Coeff z-val Coeff z-val Coeff z-val Coeff z-val Coeff t-stat Coeff t-stat Behavioral Bias Proxies Disposition Effect 0.126 3.37 0.092 2.78 0.106 3.12 0.096 2.67 1.081 3.11 0.569 1.77 Narrow Framing 0.156 5.91 0.106 4.39 0.104 4.54 0.092 3.67 1.936 7.95 1.122 3.36 Overconfidence Dummy 0.057 2.19 0.060 3.30 0.005 0.63 0.004 0.62 0.790 3.67 0.800 2.22 Male Dummy 0.017 1.04 0.017 0.52 0.032 1.38 0.016 1.11 0.288 1.35 0.311 0.74 Local Bias 0.014 1.16 0.014 1.31 0.013 0.41 0.014 0.33 0.242 1.01 0.172 1.13 Lottery Stocks Preference 0.187 8.91 0.170 6.29 0.239 10.14 0.230 9.10 1.319 5.35 0.911 3.04 Inattention to Earnings News 0.038 2.17 0.044 2.11 0.047 2.49 0.057 2.11 0.580 2.30 0.690 2.55 Inattention to Macro News 0.019 1.18 0.013 1.09 0.014 1.14 0.013 1.03 0.452 1.78 0.206 1.42 DE * High Income 0.021 1.73 0.018 1.68 0.014 1.42 0.013 1.21 0.401 1.60 0.388 1.66 DE * No Dec Tax Loss Selling 0.091 2.98 0.081 2.11 0.074 3.14 0.069 2.84 0.327 3.10 0.430 2.74 Control Variables Age 0.022 1.19 0.186 4.01 0.488 1.60 1.355 3.25 Income 0.035 2.26 0.046 1.77 0.767 2.60 0.838 2.18 High Income Dummy 0.050 2.90 0.084 3.01 0.588 2.11 0.438 2.18 Marital Status 0.006 1.44 0.021 1.30 0.727 2.01 0.101 0.43 Family Size 0.024 0.70 0.003 0.31 0.208 0.70 0.055 0.22 Professional Dummy 0.032 1.99 0.030 1.20 0.454 1.86 0.200 1.11 Retired Dummy 0.009 0.22 0.028 1.55 0.071 0.41 1.011 2.91 Investment Experience 0.029 1.51 0.028 1.40 0.122 0.40 0.533 3.01 Financial Center Dummy 0.084 3.98 0.067 2.11 1.034 3.44 0.960 3.11 Options Dummy 0.066 3.01 0.016 1.11 0.101 1.53 0.188 0.76 Short Sale Dummy 0.033 1.17 0.025 1.55 0.717 2.01 0.142 0.61 Stock Portfolio Diversification 0.158 6.80 0.273 7.16 0.940 3.11 0.767 3.30 Stock Portfolio Size 0.022 0.98 0.160 2.11 1.399 10.02 0.594 2.55 Stock Portfolio Performance 0.035 1.70 0.020 1.40 0.105 0.36 0.409 2.21 No Dec Tax Loss Selling 0.047 2.52 0.036 1.95 1.013 2.06 1.322 3.44 Holds Tax-Deferred Account 0.135 9.08 0.105 7.11 2.452 7.46 0.650 2.29 Market Factor Exposure 0.041 2.60 0.031 3.01 0.980 2.93 0.148 1.70 SMB Factor Exposure 0.168 5.91 0.055 4.53 0.937 3.72 0.242 1.77 HML Factor Exposure 0.038 2.27 0.009 1.20 0.392 2.93 0.375 2.19 UMD Factor Exposure 0.017 1.68 0.010 2.09 0.462 2.09 0.400 2.13 Pseudo R 2 0.038 0.092 0.027 0.074 0.104 0.126 Number of Observations 22,984 21,542 22,984 21,542 21,542 21,542 58

Table 5: Characteristics of Investors and the Funds They Select This table reports cross-sectional regression estimates, where three different mutual fund portfolio characteristics are employed as dependent variables. In Panel A specifications (1)-(3), the mean expense ratio, the mean front-end load, and the mean turnover of the funds in the mutual fund portfolio is the dependent variable, respectively. In all specifications, the dependent variable is multiplied by 100. There is one observation per investor. Independent variables are defined in the Appendix, and an intercept term is included but not reported. In Panel B, we consider two additional independent variables. Zip code clustered standard errors are used to obtain the t- statistics. There is one observation per individual. Panel A: Mutual Fund Portfolio Characteristic Regression Estimates (1) Expense Ratio (2) Front-End Load (3) Fund Turnover Independent Variables Coeff t-stat Coeff t-stat Coeff t-stat Behavioral Bias Proxies Disposition Effect 0.012 3.02 0.033 3.11 0.004 0.55 Narrow Framing 0.019 3.55 0.041 2.42 0.004 1.01 Overconfidence Dummy 0.020 3.11 0.029 2.50 0.022 2.67 Male Dummy 0.005 1.05 0.012 1.22 0.018 2.19 Local Bias 0.003 0.18 0.021 1.70 0.009 1.40 Lottery Stocks Preference 0.024 3.95 0.033 2.29 0.017 2.66 Inattention to Earnings News 0.013 2.33 0.022 2.65 0.019 2.08 Inattention to Macro News 0.005 1.13 0.017 1.54 0.003 0.34 DE * High Income 0.007 1.60 0.011 1.69 0.021 2.80 DE * No Dec Tax Loss Selling 0.024 3.60 0.026 2.44 0.025 3.51 Control Variables Age 0.014 2.30 0.030 1.65 0.037 2.98 Income 0.007 1.51 0.011 1.00 0.023 1.71 High Income Dummy 0.003 0.90 0.015 0.69 0.034 2.30 Marital Status 0.005 1.70 0.008 0.50 0.005 0.55 Family Size 0.004 0.80 0.012 1.01 0.015 0.70 Professional Dummy 0.007 1.11 0.024 1.22 0.021 2.05 Retired Dummy 0.015 2.30 0.012 0.56 0.017 1.81 Investment Experience 0.014 2.59 0.025 2.51 0.033 3.00 Financial Center Dummy 0.008 1.41 0.004 1.22 0.027 2.67 Options Dummy 0.002 0.35 0.012 1.33 0.019 2.75 Short Sale Dummy 0.003 1.13 0.014 0.99 0.013 1.99 Stock Portfolio Diversification 0.001 0.11 0.003 0.90 0.013 1.09 Stock Portfolio Size 0.001 0.17 0.008 0.45 0.005 0.60 Stock Portfolio Performance 0.006 1.54 0.004 0.26 0.009 0.91 No Dec Tax Loss Selling 0.015 2.52 0.031 2.89 0.034 2.99 Holds Tax-Deferred Account 0.022 4.81 0.013 3.53 0.020 3.86 Market Factor Exposure 0.010 2.56 0.016 2.65 0.022 2.97 SMB Factor Exposure 0.018 3.17 0.012 2.26 0.024 3.39 HML Factor Exposure 0.003 0.93 0.012 2.39 0.001 0.23 UMD Factor Exposure 0.019 3.72 0.024 3.34 0.031 3.52 Adjusted R 2 0.071 0.054 0.066 Number of Observations 21,542 21,542 21,542 59

Table 5 (Continued) Panel B: Regression Estimates With the Fund-Level Local Bias and Inattentiveness Measures (1) Expense Ratio (2) Front End Load (3) Fund Turnover Independent Variables Coeff t-stat Coeff t-stat Coeff t-stat Behavioral Bias Proxies Disposition Effect 0.013 2.99 0.032 3.07 0.002 0.21 Narrow Framing 0.016 3.44 0.045 2.44 0.003 0.50 Overconfidence Dummy 0.018 3.24 0.025 2.32 0.022 2.43 Male Dummy 0.004 0.87 0.012 1.21 0.016 2.31 Local Bias 0.004 0.29 0.020 1.68 0.011 1.52 Lottery Stocks Preference 0.022 4.12 0.028 2.49 0.017 2.74 Inattention to Earnings News 0.011 2.19 0.022 2.49 0.015 2.58 Inattention to Macro News 0.008 1.85 0.019 2.68 0.002 0.22 DE * High Income 0.007 1.63 0.016 2.03 0.017 2.29 DE * No Dec Tax Loss Selling 0.022 3.49 0.022 2.36 0.023 3.44 Fund-Level Bias Proxies Fund-Level Local Bias 0.024 5.43 0.050 3.59 0.036 4.65 Fund-Level Inattention to Macro News 0.018 2.37 0.017 2.11 0.002 0.54 Control Variables Age 0.016 2.42 0.028 1.33 0.034 2.03 Income 0.007 1.23 0.011 1.05 0.021 1.60 High Income Dummy 0.003 0.50 0.015 0.65 0.030 2.13 Marital Status 0.006 1.70 0.007 0.70 0.005 0.51 Family Size 0.005 1.02 0.018 1.03 0.014 0.74 Professional Dummy 0.005 1.06 0.017 0.98 0.025 2.05 Retired Dummy 0.014 1.93 0.011 0.55 0.018 1.83 Investment Experience 0.015 2.61 0.016 2.12 0.025 2.62 Financial Center Dummy 0.004 1.06 0.005 1.35 0.024 2.63 Options Dummy 0.002 0.30 0.011 1.35 0.021 3.22 Short Sale Dummy 0.008 1.53 0.018 1.39 0.013 2.02 Stock Portfolio Diversification 0.004 0.76 0.004 0.91 0.011 1.06 Stock Portfolio Size 0.002 0.38 0.011 1.01 0.007 0.60 Stock Portfolio Performance 0.008 1.51 0.007 0.30 0.008 0.74 No Dec Tax Loss Selling 0.013 2.33 0.030 2.80 0.029 2.71 Holds Tax-Deferred Account 0.020 4.71 0.012 3.33 0.021 3.67 Market Factor Exposure 0.011 2.55 0.015 2.78 0.023 2.73 SMB Factor Exposure 0.018 3.11 0.012 2.21 0.023 3.32 HML Factor Exposure 0.003 0.90 0.013 2.43 0.001 0.21 UMD Factor Exposure 0.021 3.70 0.026 3.54 0.030 3.50 Adjusted R 2 0.072 0.056 0.069 Number of Observations 21,542 21,542 21,542 60

Table 6: Returns-Chasing and Fund Selection This table reports cross-sectional regression estimates with two different mutual fund portfolio performance measures as dependent variables, the 12 month past return and the 24 month past return. There is one observation per investor. The independent variables include behavioral bias proxies, control variables and an intercept term which is included but unreported. Independent variables are defined in the Appendix. Investors with fewer than 12 months of data are excluded. Zip code clustered standard errors are used to obtain the t-statistics. The individual investor data are from a large U.S. discount brokerage house for the 1991 to 1996 period. Dependent Variable: Mutual Fund Portfolio Characteristic (One Observation Per Individual) (1) 12 Month Past Return (2) 24 Month Past Return Independent Variables Coeff t-stat Coeff t-stat Behavioral Bias Proxies Disposition Effect 0.022 0.25 0.087 0.34 Narrow Framing 0.644 4.35 0.764 3.45 Overconfidence Dummy 1.370 5.04 1.604 6.87 Male Dummy 0.062 0.46 0.258 2.51 Local Bias 0.154 1.09 0.034 0.33 Lottery Stocks Preference 0.978 6.39 1.196 5.75 Inattention to Earnings News 0.199 1.62 0.291 1.85 Inattention to Macro News 0.581 2.18 0.492 2.84 DE * High Income 0.353 2.05 0.508 2.57 DE * No Dec Tax Loss Selling 0.480 2.16 0.390 2.06 Control Variables Age 0.329 1.66 0.886 2.11 Income 0.427 1.83 0.542 1.62 High Income Dummy 0.061 0.58 0.196 1.49 Marital Status 0.077 0.81 0.443 1.52 Family Size 0.149 1.22 0.609 1.76 Professional Dummy 0.629 2.32 1.052 2.92 Retired Dummy 0.487 2.91 0.152 1.92 Investment Experience 0.057 0.30 0.468 1.98 Financial Center Dummy 0.404 2.13 0.510 1.63 Options Dummy 0.347 2.71 0.492 2.63 Short Sale Dummy 0.024 0.12 0.070 0.46 Stock Portfolio Diversification 0.093 0.46 0.492 2.02 Stock Portfolio Size 0.139 0.68 0.103 0.71 Stock Portfolio Performance 0.558 3.46 0.768 2.13 No Dec Tax Loss Selling 0.380 1.92 0.407 2.93 Holds Tax-Deferred Account 0.062 0.61 0.168 2.38 Market Factor Exposure 0.862 3.87 0.565 3.54 SMB Factor Exposure 0.393 2.64 0.485 3.82 HML Factor Exposure 0.068 0.67 0.151 2.15 UMD Factor Exposure 0.228 2.40 0.264 2.51 Adjusted R 2 0.091 0.076 Number of Observations 21,542 21,542 61

Table 7: Investor Characteristics and Performance of Mutual Fund Investments This table reports cross-sectional regression estimates to explain two measures of mutual fund portfolio performance, position-based performance measures in Panel A and trade-based performance measures in Panel B. In Panel A, the dependent variables are (1) the mean monthly percent return (in percentage terms), (2) the net monthly return which equals the mean monthly return minus expenses (but not loads), (3) the Sharpe ratio of net returns multiplied by 100, and (4) the monthly market model alpha. In Panel B, the dependent variables in specifications (1)-(4) are the mean annualized holding period return, the mean holding period, the 1-month post trade buy-sell return differential, and the 12-month posttrade buy-sell return differential (PTBSD), respectively. Independent variables are defined in the Appendix. A constant term is included. Investors with fewer than 12 months of data are excluded. Zip code clustered standard errors are used to obtain the t-statistics. The individual investor data are from a large U.S. discount brokerage house from 1991 to 1996. Panel A: Position-Based Mutual Fund Portfolio Performance Regression Estimates (1) Mean Monthly Returns (2) Net Monthly Returns (3) Net Sharpe Ratio x 100 (4) Market model Alpha Independent Variables Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Behavioral Bias Proxies Disposition Effect 0.027 2.14 0.028 2.16 0.554 1.94 0.027 1.96 Narrow Framing 0.041 2.94 0.047 2.75 1.550 3.83 0.050 2.65 Overconfidence Dummy 0.025 2.17 0.031 2.60 1.502 2.39 0.033 2.06 Male Dummy 0.009 1.05 0.012 1.42 1.503 1.90 0.039 2.29 Local Bias 0.010 1.20 0.010 1.30 0.113 0.42 0.006 0.88 Lottery Stocks Preference 0.026 2.99 0.024 2.78 1.249 1.97 0.059 3.26 Inattention to Earnings News 0.014 2.18 0.015 2.48 1.041 2.13 0.026 2.82 Inattention to Macro News 0.022 2.22 0.024 2.45 1.010 2.03 0.019 1.92 DE * High Income 0.009 1.26 0.012 1.26 0.143 0.19 0.006 0.80 DE * No Dec Tax Loss Selling 0.016 1.66 0.016 1.62 0.155 0.51 0.028 2.58 Control Variables Age 0.007 1.59 0.008 1.45 0.448 0.65 0.003 0.72 Income 0.002 0.22 0.003 0.18 0.581 0.88 0.011 0.31 High Income Dummy 0.026 1.63 0.030 1.89 0.200 1.04 0.026 0.48 Marital Status 0.004 0.34 0.009 0.59 0.218 0.36 0.003 0.08 Family Size 0.004 0.34 0.004 0.40 0.679 0.97 0.039 1.91 Professional Dummy 0.002 0.12 0.017 0.87 0.308 0.40 0.002 0.43 Retired Dummy 0.003 0.21 0.003 0.18 0.046 0.08 0.034 1.54 Investment Experience 0.028 3.15 0.026 2.89 1.991 2.72 0.051 2.35 Financial Center Dummy 0.001 0.08 0.011 0.88 0.510 0.85 0.014 1.42 Options Dummy 0.034 2.51 0.043 1.79 1.517 2.62 0.062 3.12 Short Sale Dummy 0.051 2.36 0.021 1.55 0.978 1.64 0.035 1.33 Stock Portfolio Diversification 0.028 1.83 0.024 1.57 0.105 0.18 0.013 0.94 Stock Portfolio Size 0.023 1.39 0.023 1.43 1.288 2.03 0.011 0.28 Stock Portfolio Performance 0.032 1.29 0.033 2.07 0.672 1.17 0.001 0.31 No Dec Tax Loss Selling 0.003 0.29 0.002 0.18 0.614 1.66 0.018 1.47 Holds Tax-Deferred Account 0.003 0.59 0.003 0.58 0.168 1.02 0.017 1.62 Market Factor Exposure 0.021 2.78 0.019 2.43 0.595 3.08 0.009 0.51 SMB Factor Exposure 0.012 1.38 0.004 0.66 0.670 3.51 0.038 2.71 HML Factor Exposure 0.007 1.35 0.006 1.22 0.025 0.14 0.014 1.39 UMD Factor Exposure 0.031 3.35 0.024 2.98 0.449 2.75 0.016 1.68 Adjusted R 2 0.042 0.043 0.037 0.029 Number of Observations 21,542 21,542 21,542 20,142 62

Table 7 (Continued) Panel B: Trade-Based Mutual Fund Portfolio Performance (1) Holding Period Return (2) Holding Period (3) 1-Month PTBSD (4) 12-Month PTBSD Independent Variables Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Intercept 0.418 12.57 444.20 17.85 0.042 0.97 2.526 14.34 Behavioral Bias Proxies Disposition Effect 0.059 2.46 25.15 4.26 0.070 2.48 0.295 2.26 Narrow Framing 0.054 3.40 15.16 2.97 0.096 3.48 0.462 3.26 Overconfidence Dummy 0.066 3.06 42.61 7.75 0.090 2.17 0.569 2.99 Male Dummy 0.018 0.53 3.36 0.56 0.018 1.09 0.114 1.63 Local Bias 0.005 0.14 10.02 2.67 0.013 0.69 0.356 2.27 Lottery Stocks Preference 0.037 2.27 24.17 2.36 0.058 2.27 0.551 2.34 Inattention tos Earnings News 0.010 1.26 4.02 1.62 0.033 2.66 0.474 2.36 Inattention to Macro News 0.048 2.29 11.71 2.85 0.029 2.60 0.526 2.69 DE * High Income 0.065 3.17 19.66 2.67 0.061 2.55 0.485 2.07 DE * No Dec Tax Loss Selling 0.037 1.99 15.69 2.89 0.002 0.36 0.079 1.39 Control Variables Age 0.049 2.46 18.65 1.99 0.054 2.06 0.417 2.23 Income 0.015 0.22 2.27 0.25 0.005 0.15 0.084 0.72 High Income Dummy 0.014 0.94 4.58 1.39 0.024 1.63 0.257 1.26 Marital Status 0.025 1.09 5.02 0.59 0.008 0.46 0.183 1.61 Family Size 0.016 1.82 2.65 0.27 0.007 0.17 0.061 0.20 Professional Dummy 0.010 0.18 4.49 0.42 0.013 0.88 0.191 1.27 Retired Dummy 0.038 1.68 27.61 3.33 0.037 2.17 0.197 2.19 Investment Experience 0.038 2.21 8.67 1.58 0.089 3.50 0.587 3.14 Financial Center Dummy 0.006 0.31 16.56 2.03 0.020 1.31 0.051 0.31 Options Dummy 0.040 1.97 33.55 5.56 0.050 2.14 0.302 2.71 Short Sale Dummy 0.034 1.82 12.02 2.01 0.041 1.86 0.181 1.98 Stock Portfolio Diversification 0.013 0.73 41.47 3.88 0.016 1.01 0.091 1.33 Stock Portfolio Size 0.026 1.70 3.33 0.55 0.022 1.55 0.071 0.95 Stock Portfolio Performance 0.042 2.50 25.00 4.92 0.017 1.05 0.409 2.65 No Dec Tax Loss Selling 0.015 1.32 45.25 3.35 0.074 2.77 0.223 1.85 Holds Tax-Deferred Account 0.023 1.95 7.11 1.53 0.013 0.38 0.035 0.25 Market Factor Exposure 0.004 0.16 26.14 3.53 0.047 2.09 0.261 1.78 SMB Factor Exposure 0.035 1.98 23.73 3.86 0.013 0.95 0.457 3.13 HML Factor Exposure 0.015 1.21 2.36 0.51 0.025 1.65 0.234 1.68 UMD Factor Exposure 0.024 1.11 16.83 2.99 0.017 0.88 0.095 0.76 Adjusted R 2 0.045 0.093 0.040 0.064 Number of Observations 15,210 15,210 18,002 18,002 63

Table 8: Behavioral Biases, Mutual Fund Portfolio Characteristics, and Portfolio Performance This table reports cross-sectional regression estimates with two different mutual fund portfolio performance measures as dependent variables, (1) the mean net monthly percent return (in and (2.) the Sharpe ratio computed using net returns multiplied by 100. There is one observation per investor. The independent variables include behavioral bias proxies and inattention measures, mutual fund characteristics, bias-load interaction terms and control variables. Independent variables are defined in the Appendix. Mutual fund characteristics include the initial weight assigned to mutual funds in the equity (stocks and mutual funds) portfolio and three expense measures of the mutual fund portfolio: (i) the sample period mean expense ratio, (ii) the sample period mean 12-B-1 fee, and (iii) the sample period mean front-end load. Bias-load interaction terms equal the multiplication between each of three behavioral bias measures and each of three mutual fund expense ratio measures. The three behavioral bias measures are high disposition effect, strong framing effects and overconfidence. The inattention measure is the equally-weighted average of the two stock-level inattention measures. The three expense ratio measures are high expense ratios, high 12-B-1 fees and high front-end loads. The mutual fund portfolio weight is measured at the time an investor enters the sample or invests in mutual funds for the first time. High and low dummy variables are defined using the highest and the lowest quintile of the respective variable. Investors with fewer than 12 months of data are excluded. Zip code clustered standard errors are used to obtain the t- statistics. The individual investor data are from a large U.S. discount brokerage house for the 1991 to 1996 period. 64

Table 8 (Continued) Dependent Variable: Mutual Fund Portfolio Performance (One Observation Per Individual) (1) Net Monthly Return (2) Net Sharpe Ratio x 100 Independent Variables Coeff t-stat Coeff t-stat Intercept 1.320 13.14 40.156 21.80 Behavioral Bias Proxies Disposition Effect 0.025 2.01 0.556 1.98 Narrow Framing 0.043 2.90 1.565 3.67 Overconfidence Dummy 0.021 2.00 1.446 2.24 Male Dummy 0.010 1.12 1.498 1.81 Local Bias 0.009 0.56 0.100 0.22 Lottery Stocks Preference 0.033 3.11 1.301 1.99 Inattention to Earnings News 0.015 2.11 1.114 2.34 Inattention to Macro News 0.025 2.34 0.989 2.00 DE * High Income 0.011 1.55 0.101 0.16 DE * No Dec Tax Loss Selling 0.015 1.52 0.151 0.59 Mutual Fund Portfolio Characteristics Initial Weight in Mutual Funds 0.044 3.71 1.721 3.65 Mutual Fund Portfolio Expense Ratio 0.010 0.74 0.730 3.44 Mutual Fund Portfolio 12-B-1 Fee 0.051 3.55 2.234 4.36 Mutual Fund Portfolio Front-End Load 0.048 3.97 2.142 5.02 Bias-Load Interaction Terms High Disp Effect * High Exp Ratio 0.004 0.40 0.487 1.49 High Disp Effect * High 12-B-1 Fee 0.025 2.99 2.356 7.15 High Disp Effect * High Front-End Load 0.054 5.09 2.381 8.58 Strong Framing Effects * High Exp Ratio 0.008 0.86 0.268 0.79 Strong Framing Effects * High 12-B-1 Fee 0.015 1.91 2.298 6.93 Strong Framing Effects * High Front-End Load 0.055 7.09 2.464 8.71 Overconfident * High Exp Ratio 0.006 0.71 0.544 1.66 Overconfident * High 12-B-1 Fee 0.024 3.88 2.433 7.39 Overconfident * High Front-End Load 0.052 6.86 2.312 8.35 High Inattention * High Exp Ratio 0.017 3.88 1.119 2.83 High Inattention * High 12-B-1 Fee 0.022 2.46 2.106 4.73 High Inattention * High Front-End Load 0.063 4.97 0.927 2.21 Control Variables Coefficient estimates have been suppressed. Adjusted R 2 0.055 0.051 Number of Observations 21,542 21,542 65

Table 9: Associations between Aggregated Behavioral Biases and Other Characteristics, Fund Decisions, and Consequences Panel A of this table measures the combined effect of multiple bias proxies on mutual fund decisions using the five most important factors from factor analysis of the behavioral bias proxies and other investor characteristics. Panel B of this table measures the combined effect of multiple bias proxies on mutual fund decisions using an equally-weighted index of the behavioral bias proxies. The behavioral factors are defined in the Appendix, while the factor analysis is detailed in Table 3. This table summarizes estimates of the regressions of Tables 4 to 7 in which the behavioral proxies and other investor characteristics are replaced with the five most important factors from factor analysis. For brevity, only the coefficient estimates for the variable of interest are reported. Panel A: Estimates When the Dependent Variable is a Factor of the Behavioral Bias Proxies and other Investor Characteristics Regression Type Gambler Factor Coeff t- or z-stat Smart Factor Coeff t- or z-stat t- or z-stat Overconfident Factor Coeff Participation (Table 4) All Mutual Funds: Column (2) 0.339 3.77 0.125 2.24 0.722 3.75 0.350 2.73 0.258 3.11 0.059 21,542 Index Funds Only: Column (4) 0.229 2.93 0.171 2.59 0.402 2.68 0.311 2.60 0.174 2.81 0.051 21,542 Holdings (Table 4) Weight in All Mutual Funds: Column (5) 2.827 3.02 1.764 1.42 3.193 3.75 1.981 2.73 2.541 3.55 0.049 21,542 Weight in Index Funds Only: Column (6) 1.901 2.71 1.166 1.81 2.792 3.18 1.591 2.76 2.407 2.76 0.055 21,542 Portfolio Characteristics (Table 5) Expense Ratio: Column (1) 0.209 5.15 0.014 2.88 0.079 4.46 0.027 2.13 0.111 5.42 0.038 21,542 Front End Load: Column (2) 0.132 2.72 0.017 2.15 0.082 2.21 0.063 1.91 0.085 3.11 0.031 21,542 Fund Turnover: Column (3) 0.114 3.71 0.029 2.31 0.145 3.59 0.033 1.22 0.148 4.68 0.043 21,542 Trend Chasing (Table 6) 12 Month Lagged Fund Perf: Column (1) 1.180 2.93 0.096 1.23 1.729 3.02 0.863 1.97 1.367 2.65 0.071 21,542 24 Month Lagged Fund Perf: Column (2) 1.156 2.61 0.532 2.33 1.941 2.98 1.167 2.57 2.079 3.14 0.050 21,542 Portfolio Performance (Table 7) Mean Monthly Returns: Panel A, Column (1) 0.109 2.65 0.085 2.33 0.111 2.82 0.066 1.92 0.025 1.82 0.028 21,542 Net Monthly Returns: Panel A, Column (2) 0.122 2.60 0.076 2.51 0.189 3.77 0.058 1.98 0.019 1.31 0.026 21,542 Net Sharpe Ratio: Panel A, Column (3) 2.568 3.20 2.853 3.12 1.110 2.04 2.109 3.08 0.664 1.12 0.024 21,542 Four-Factor Alpha: Panel A, Column (4) 0.164 2.84 0.123 2.34 0.092 2.78 0.058 2.88 0.026 1.26 0.021 21,542 Holding Period Returns: Panel B, Column (1) 0.095 3.08 0.059 2.42 0.074 2.90 0.078 2.94 0.055 2.40 0.033 15,210 Holding Period: Panel B, Column (2) 27.904 3.07 18.514 2.38 15.504 2.27 8.211 2.58 21.675 2.99 0.065 15,210 One-Month PTBSD: Panel B, Column (3) 0.152 3.49 0.125 3.48 0.093 3.36 0.051 2.77 0.076 3.21 0.025 15,210 One-Year PTBSD: Panel B, Column (4) 0.916 3.37 0.936 3.70 0.691 3.28 0.544 2.82 0.722 2.87 0.050 15,210 Narrow Framer Factor Coeff t- or z-stat Mature Factor Coeff t- or z-stat Adj R 2 N 66

Table 9 (Continued) Panel B: Estimates When Dependent Variable is Equally-Weighted Index of Behavioral Bias Proxies t- or Regression Type Coeff z-stat Adj R 2 N Participation (Table 4) All Mutual Funds: Column (2) 0.439 7.11 0.033 21,542 Index Funds Only: Column (4) 0.719 7.41 0.065 21,542 Holdings (Table 4) Weight in All Mutual Funds: Column (5) 0.744 5.44 0.068 21,542 Weight in Index Funds Only: Column (6) 1.933 4.72 0.142 21,542 Portfolio Characteristics (Table 5) Expense Ratio: Column (1) 0.032 4.13 0.055 21,542 Front End Load: Column (2) 0.033 3.55 0.044 21,542 Fund Turnover: Column (3) 0.016 2.01 0.053 21,542 Trend Chasing (Table 6) 12 Month Lagged Fund Perf: Column (1) 1.441 4.90 0.083 21,542 24 Month Lagged Fund Perf: Column (2) 1.276 3.55 0.065 21,542 Portfolio Performance (Table 7) Mean Monthly Returns: Panel A, Column (1) 0.052 3.71 0.038 21,542 Net Monthly Returns: Panel A, Column (2) 0.062 3.47 0.042 21,542 Net Sharpe Ratio: Panel A, Column (3) 2.499 3.85 0.031 21,542 Four-Factor Alpha: Panel A, Column (4) 0.055 3.39 0.028 20,142 Holding Period Returns: Panel B, Column (1) 0.063 5.16 0.033 15,210 Holding Period: Panel B, Column (2) 21.175 4.44 0.080 15,210 One-Month PTBSD: Panel B, Column (3) 0.381 4.12 0.029 15,210 One-Year PTBSD: Panel B, Column (4) 0.622 4.23 0.048 15,210 67