Behavioral Biases of Mutual Fund Investors

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1 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 , phone , fax , wbb1@cornell.edu; Alok Kumar, University of Miami, School of Business Administration, 514 Jenkins Building, Coral Gables, FL 33124, phone , fax , akumar@mail.utexas.edu; and David Ng, Wharton School, University of Pennsylvania, Steinberg Hall-Dietrich Hall, 3620 Locust Walk, Philadelphia, PA , and Dyson School of Applied Economics and Management, Cornell University, Ithaca, NY , phone , 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?

2 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.

3 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 This decline is matched by an increase in the holdings of open-end mutual funds, from 4.6% in 1980 to 32.4% in 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

4 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

5 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

6 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

7 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

8 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

9 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

10 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

11 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

12 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

13 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

14 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

15 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

16 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 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 14

17 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 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 These statistics are computed prior to 1% winsorizing which is employed throughout the balance of the paper. 15

18 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 % to a maximum of 6.437%. The median individual stock portfolio beta is a surprisingly high 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

19 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

20 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

21 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

22 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

23 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

24 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% ( ), 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