How Does Investor Short-termism Affect Mutual Fund Manager Short-termism
*
Li Jin
Harvard Business School ljin@hbs.edu
First Draft: August 3, 2004
This draft: January 12, 2005
Abstract:
This paper studies short-termism of mutual funds, both short-termism of fund investors and short-termism of investments made by fund managers. The empirical results are consistent with the view that short investment horizons of fund managers (which we term output short-termism) are positively related to their investors’ short investment horizons
(which we term input short-termism). Further tests of causality suggest that fund manager investment short-termism is caused by investor short horizon, but not the other way round. The study contributes to a better understanding of why fund managers tend to exhibit short-term decision making. It also provides supporting evidence of theoretical models of managerial myopic reaction to investor short-termism as in Stein (1988, 1989) and Shleifer and Vishney (1990).
* I thank Brad Barber, Dwight Crane, Ray Fisman, Will Goetzmann, Ron Kaniel, Jon Lewellen, Jay Light,
Eddie Qian, Andre Perold, Anna Scherbina, Clemens Sialm, Laura Starks and Jeremy Stein for comments, and Russell Wermers and Morningstar for providing part of the data used in this paper. I acknowledge the excellent research assistance from Bryan Lincoln, Michael Sorrel and Deborah Strumsky, and editorial assistance by Jenn Chu and John Simon. I am especially gratefully to Peter Tufano for many helpful discussions. Research support from Harvard Business School Division of Research is gratefully acknowledged. All remaining errors are my own.

“In our business, there's tremendous pressure for performance. . . . That potentially drives dysfunctional behavior and way too short-term behavior.”
- Duncan Richardson, portfolio manager and chief of research staff at Eaton Vance
Management, quoted in Boston Globe , December 22, 2002.
This paper tests empirically whether mutual fund managers subject to severe short-term performance pressure make correspondingly short-horizon investment decisions.
The last several decades have witnessed significant developments in the mutual fund industry.
1 Concurrently, there has been a substantial increase in the overall turnover of traded stocks.
2 Although the latter might be explained partly by improvements in trading technologies and perhaps reflects greater informational efficiency, institutional investors’ pursuit of short-term trading profits has been alleged to cause the corporations in which they invest to pursue myopic goals and contribute thereby to wide fluctuations in stock prices, especially during volatile times.
3
Existing research provides considerable evidence that mutual fund investors chase recent fund performance, though there is no systematic evidence that fund managers can persistently outperform.
4 As investors focus on recent performance of funds and move money accordingly, do fund managers “feel the heat” of having to perform in the shortterm and shun investments that pay off only in the long run? Casual empiricism suggests
1 Zheng (1999) reports that mutual funds at the end of the first quarter of 1998 managed about $3.3 trillion in assets, exceeding total bank savings deposits. Moreover, according to the Investment Company Institute, the equity investment directly managed by households has declined, from approximately 63% in 1973 to
38.1% in 2003, and the number of stock mutual funds in the United States increased, from 399 in January
1984 to 4,601 in December 2003.
2 According to Verter (2003), on a value-weighted basis the annual share turnover of the average
NYSE/AMEX listed company climbed from approximately 13% in 1965 to more than 90% in 1999, a sevenfold increase.
3 For the former see Stein (1988, 1989), Jacobs (1991), and Porter (1992); for the latter see Dennis and
4
Strickland (2002).
Evidence that mutual fund outperformance is largely transient is provided by Gruber (1996) and Carhart
(1995, 1997). Brown, Harlow, and Starks (1996), Gruber (1996), Chevalier and Ellison (1997), Goetzmann and Peles (1997), Ippolito (1992), Nanda, Wang, and Zheng (2004a), Sirri and Tufano (1998), and Zheng
(1999), among many others, demonstrate that new money flows to mutual funds do chase recent fund performance, especially significant outperformance. Greene and Hodges (2002) argue that new fund flow might cause subsequent underperformance.
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that pressure of redemption is a serious concern for many types of money managers.
Hedge funds and private equity funds, for example, routinely adopt stringent minimum holding period restrictions to protect themselves from redemption, for fear of premature termination of long-term and illiquid investment projects.
That mutual fund managers might not need, in fact, to shorten their investment horizons in the face of pressure from short-term investors is best illustrated by the simple “source and use of fund” relation for a mutual fund.
Fund inflow – redemption + dividend reinvestment = change in cash + change in noncash securities holdings.
As is made obvious in the foregoing equation all the changes in net fund flow can usually be managed by changing cash positions. Combining cash position change with the increasingly popular practice of cash equitization, fund managers can maintain fund liquidity without jeopardizing performance.
5 Therefore there is little mechanical reason for fund managers to increase turnover in their equity portfolios
as investors move money in and out of the funds. However, as argued by theoretical models of Stein (1988, 1989) and Shleifer and Vishny (1990), and considered in greater detail in the following section, agency and behavioral reasons can prompt fund managers to make a conscientious choice to alter the investment horizons of their equity holdings in response to fund flow fluctuations. It is that choice that we analyze below.
What are the implications of fund managers shortening investment horizons in response to short-term performance pressure? First, long-horizon investments become the “avoided asset class” and might be less efficiently priced. Second, more liquid stocks might be relatively overpriced if fund managers systematically focus on stocks that can be resold at short notice without taking a big hit in prices. Third, and more broadly, this is directly related to the considerable literature on myopic corporate investment (e.g., Stein, 1988,
5 Cash equitization refers to techniques that use derivatives to approach a fully invested portfolio position and earn “market like” returns on the investment’s cash balances.
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1989 and Shleifer and Vishny, 1990). This paper provides not only supporting evidence for relevant theoretical models but also a rationale for corporate managers’ tendency towards short-termism: when institutional investors exhibit systematic preference for short-horizon investments, managers of the corporations, in which institutions invest, might as well have little interest for long term investments.
The paper is organized as follows. Section 1 reviews related literature and outlines the theoretical foundation for the empirical tests. Explanations of data, measures of variables, and descriptive statistics are provided in Section 2. Section 3 presents regression results that show that mutual fund investors’ short-term behavior impels fund managers to have short investment horizons. The robustness of these results is considered in Section 4.
Conclusions are offered in Section 5.
1. Literature and theoretical background
A substantial literature documents the relationship between fund flow and performance.
Empirical work such as that of Brown, Harlow, and Starks (1996), Chevalier and Ellison
(1997), Gruber (1996), Ippolito (1992), and Sirri and Tufano (1998) has demonstrated that new money flows to mutual funds are responsive to recent fund performance, especially significant outperformance. Berk and Green (2004) and Lynch and Musto
(2003) provide theoretical justification for the sensitivity of flow to performance in a rationale equilibrium context. Berk and Green (2004) assume a perfectly competitive capital market and decreasing return to skills in active management. Assuming the cost of managing a fund to be largely fixed and the benefit to be proportional to the value of the asset under management, they demonstrate a strong relation between past performance and the flow of funds even in the absence of persistent performance. Lynch and Musto
(2003) argue that the convex and positive relation between past returns and flows of mutual funds can be explained by the fact that funds discard precisely the strategies that underperform.
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Brown, Harlow, and Starks (1996), Khorana (1996), and Chevalier and Ellison (1997,
1999a) discuss the career concerns of fund managers and their implications for risk taking. These researchers document strong performance-related turnover of fund managers, particularly younger fund managers. This gives rise to two distinct sets of predictions. First, as suggested by the tournament literature (e.g., Brown, Harlow, and
Starks, 1996; Chevalier and Ellison, 1997), conditional on the performance of the first half of an evaluation period winners and losers will exhibit differences in risk taking behavior. Winners, for example, might take conservative positions to protect their leading positions, losers, being more desperate, assume more risk. Second, as suggested by
Chevalier and Ellison (1999a, 1999b), younger managers, being subject to greater performance pressure, might avoid unsystematic risk. Empirical evidence supports both sets of predictions.
Researchers have also analyzed the relation between liquidity and fund returns. Nanda,
Narayannan, and Warther (2000) show that in equilibrium, when there is relative scarcity of investors with low liquidity need, funds will have to trade off return with liquidity provision, and funds with more constraints on withdrawals might have to offset them by higher investor returns. Empirical work by Edelen (1999) and Rakowski (2003) suggests that providing liquidity is costly and thus lowers fund performance. Edelen (1999), for example, finds that a unit of liquidity-motivated trading, defined as an annual rate of trading equal to 100% of fund assets, is associated with an estimated 1.5%-2% decline in abnormal returns of mutual funds. Stein (2004) argues that open-end mutual funds are illpositioned to take on long-term arbitrage activities. “[Open-end] funds,” Stein maintains,
“will stick primarily to short-horizon strategies and earn low excess returns. In so doing, they will leave large long-horizon mispricings such as the internet bubble mostly untouched because attacking such mispricings aggressively would require a closed-end structure.”
This paper explicitly tests whether investment horizons are shortened when mutual funds’
“degrees of openness” are higher. Instead of asking how risk taking changes with recent performance, as in the tournament literature, this paper analyzes how cross-sectional
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variations in managers’ investment horizons relate to the sensitivity of fund flow to shortterm performance. When a fund is largely dominated by hot money in pursuit of shortterm profit managers cannot afford to ignore the short-term performance consequence of fund flow. Investments with long horizons, even if they promise to earn positive risk adjusted returns, tend to be avoided because they are less liquid, and the prices could deviate even further from the fundamental values for a long period of time, even if they eventually converge to fundamental values.
There is also an extensive literature on how investor short-termism occasions myopic investment decisions by corporate managers. Influential theoretical work includes that of
Stein (1988, 1989), Shleifer and Vishny (1990), and Bebchuk and Stole (1993). Empirical work includes that of Meulbroek et al. (1990), Bushee (1998), and Verter (2003). Stein
(1988, 1989), who models corporate managers’ responses to myopic investors, argues that when career concerns dispose managers towards preoccupation with the current level of their firms’ stock prices and imperfect information about firm investment levels leads equity investors to emphasize short-term results, managers might underinvest in positive
NPV long-term projects in an effort to boost current period earnings.
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Empirical tests of a relation between investor short-termism and corporate underinvestment are less conclusive. Meulbroek et al. (1990) find that firms that introduce shark repellents to protect management from takeover pressure decrease R&D intensity and Bushee (1998) finds that institutional investor short-termism in the forms of high turnover and momentum trading increases the likelihood that corporate managers will cut R&D to reverse an earnings decline, when such institutional investors are dominant shareholders. Verter (2003) finds that firms with short-horizon investor bases appear to overinvest.
The extant literature recognizes the difficulty of testing empirically the effect of investor short-termism on level
of investment. It is extremely difficult to determine whether these
6 Bebchuk and Stole (1993) argue that concerns about non-observable investment productivity can lead to overinvestment. The intuition is that every firm but the one with the least productive project will overinvest to signal that it has good long-term investment opportunities.
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soft investment decisions are value enhancing or wasteful. Underinvestment is frequently construed as forgoing profitable long-term investments for short-term earnings enhancements, but reducing investment might, in fact, better serve a company through the channel advocated by Jensen (1986).
7 This is particularly difficult to gauge for corporate investments because many are in intangibles and a forgone investment opportunity will typically be non-replicable. It might thus be impossible to know, ex post, whether a firm would have been better off in the long run if an investment had been made
(or had there been no investment).
Shleifer and Vishny (1990), who model the effects of investors’ short investment horizons on corporate managers’ investment decisions, argue that short-term arbitrage
(arbitrage on assets for which mispricing will disappear in the near future) is less costly and thus more frequently practiced. This leads to more mispricing of long-term assets for which mispricing can take a longer time to disappear. Severe underpricing of firm equity can put corporate managers at risk of being fired or their companies at risk of being taken over. Thus arbitrageurs’ short horizons leads to managers’ short horizons. Although the settings these authors have in mind are corporations, the intuition carries over to money management businesses.
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The study reported in this paper has direct implications for the foregoing literature. This moves one level up in the food chain, especially given that institutional investors such as mutual funds hold increasingly large shares of public corporations. Moreover, as argued below, it provides solid empirical evidence of the theoretical models of myopic
7 Implicit in some empirical tests of Stein (1988, 1989) is the assumption that investment increases the long-run value of the firm at the expense of its short-term valuation. Though plausible, this doesn’t have to be the only case. An investment can have negative NPV and nevertheless be favored by managers for reasons such as empire building. A misinterpretation of the quality of the investment might cause the market to overreact in the short run and thus temporarily boost share price. During the recent internet bubble the market seemed to react to many technology firms’ excessive (and ex post inefficient) investments with high short-term valuations. Early evidence in Kaplan (1989) suggests that firms involved in leveraged buyouts tend to reduce their capital expenditures. This is consistent with Jensen’s (1986) view
8 that leveraged buyouts serve to curtail wasteful (excessive) investment expenditures.
The corresponding intuition would be as follows. Fund managers might hesitate to invest in long-term assets, because arbitrage forces for those are likely lacking, thus for a long time such assets might remain underpriced or even become more underpriced and thereby depress overall portfolio value. That would increase the risk that a fund manager might be replaced or face performance-related fund outflow.
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investment. Compared with corporate managers, fund managers are under greater shortterm pressure and their investment short-termism is much easier to gauge.
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First, investor short-termism affects fund managers more than corporate managers.
Underperformance not only puts fund managers at risk of being fired, but also imposes the additional penalty that new fund flow is sensitive to performance and fund outflow constitutes automatic partial liquidation. Managers who focus on long-term payoff at the expense of short-term valuation might not survive to see the long-term profit realized and, even if they do, the assets under their management might be substantially reduced leaving them less well-positioned to benefit from any long-term profit opportunity that does materialize.
Fund manager investment short-termism, moreover, is more measurable. As noted earlier, whereas the level
of investment might not be indicative of short-termism, for fund managers we can directly measure the investment horizon
. In addition, we observe market prices and holding periods of mutual fund investments. We therefore can precisely measure investment performance and directly test whether the horizons of the investments made by fund managers are affected by investors’ short horizons, and any potential performance consequences.
2. Data, measures of variables, and descriptive statistics
In this section we identify the sources of our data and define the variables used and the means of measuring them.
2.1. Data
9 One question that might arise in tests of fund manager short-termism is that fund managers, unlike corporate managers, hold marketable securities that can be sold at any time and are constantly marketed to market. These might be construed to mean that the short-termism problem goes away. But we do not think so inasmuch as the existence of a market does not guarantee efficient pricing. As long as it is possible for short-term price to deviate substantially from long-term fundamental levels fund managers will continue to be concerned about the short-term price impact of holdings.
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Our empirical analysis relies primarily on the CRSP survivorship bias free mutual fund database, which provides monthly open-end mutual fund data from 1961 to 2003, including information about fund style, monthly returns and total net assets. Information about fund investment styles is available beginning in 1992. The analyses are performed on data from 1992 to 2003. We adopt as our fund investment style indicator the Strategic
Insight Objective Code because (1) it is more complete, and (2) it contains more detailed style information.
10 We emphasize fund styles with significant holdings in U.S. equities and our fund style classification is more refined than those used in existing empirical studies, which typically consist of crude classifications of a few investment style categories. All fund investment styles included in this analysis are identified in Appendix
A.
The CRSP mutual fund data is supplemented with detailed fund level stock holdings data from the CDA/Spectrum mutual fund holdings database, which contains quarterly stock holding data for all registered mutual funds filed with the SEC, plus 3,000 global funds, from 1980 to 2003. The data sets are linked using a file provided by Wharton Research
Data Services (WRDS).
For tests on causality between investor and investment short-termism we use an additional database that identifies fund managers’ dates of birth or years of graduation from college. This data set is used to construct measures of manager age via a procedure detailed in Chevalier and Ellison (1999a).
Whereas we use all available information to construct measures such as flow to performance sensitivity, in the regression analysis, we perform analysis at the annual frequency because of the possibility that seasonality that might affect fund flows, fees, and performance. Many investors, moreover, implicitly have an annual horizon for
10 CRSP provides several fund objective codes, the most recent being ICDI’s Fund Objective Codes and
Strategic Insight Fund Objective Codes. The raw data include 32,930 instances in which the ICDI objective code is missing but the Strategic Insight objective code is not and 806 cases in which the Strategic Insight objective code is missing but the ICDI objective code is not. The ICDI objective code covers 23 different investment types, the Strategic Insight objective code more than 100 investment types.
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planning and tax purposes. The existing literature (e.g., Brown, Harlow, and Starks,
1996; Chevalier and Ellison, 1997) supports this choice of frequency. As a robustness check we also perform the analysis at the quarterly frequency. Section 4 discusses the robustness of results.
The CRSP survivorship bias free mutual fund data delineate individually the different classes for each fund. Fund classes differ primarily by fee structure, but by definition share the same managements and investments. We consequently hand match and merge funds that differ only in fund class.
2.2. Definition of new money flow
Following the existing literature (e.g., Nanda, Wang, and Zheng, 2004b) new fund inflow is defined as the additional
money attracted by a fund in a given period, measured by the dollar change in Total Net Assets (TNA) net of the assets’ price appreciation. Assuming that new money is invested at the end of each period, the cash flow for fund i in period t is given by: i t i,t-1 i t
(1) where R it
is the rate of return of fund i in period t.
Normalizing the variable Newmoney by fund-level TNA at the beginning of the month gives the following measure for fund-level new money growth.
Newmoney it
TNA
(2)
2.3. Measuring performance
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Fund performance in a period is calculated as the risk-adjusted returns. Specifically, we adjust the risk by (1) a raw market return, (2) a CAPM one-factor model, and (3) a fourfactor model proposed by Carhart (1997). The results are not sensitive to these adjustment methods. For brevity, the results reported in this paper emphasize CAPM adjusted excess returns. In particular, for fund i in period t: it ft
− β i
R mft
(3) where R ft
is the return on a one month T-bill, R mft
is the market risk premium (taken to be the difference between the value-weighted market index return and the one month T-bill rate), and β i
is the beta of fund i (obtained through a market model regression of all monthly fund returns on the value-weighted market index return).
2.4. Measuring investor short-termism
Investor short-termism is measured by the sensitivity of fund flows to recent performance. Following the existing literature we run a regression of new money flows on past performance. To measure fund flow sensitivity to short-term performance, as required in the context of this study, we use a specification that focuses on the past one year fund performance. Also consistent with the literature, to account for the documented convex relation between new fund flow and performance (stellar performance draws more new money flow), a quadratic term of the past performance is included in our regression specification as below.
α β
1
Fundperformance
+ β
2
Fundperformance
2 + control
(4)
We run this regression for each of the funds in our sample using all available data from
1961 to 2003. We perform many robustness checks on the measure of the flow to performance sensitivity. Section 4 provides details of these robustness checks. The results are not qualitatively affected. The main results reported in the paper are based on a flow
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to performance sensitivity measure that uses past twelve month returns, a quadratic flowperformance response function, and no additional control variables.
We construct the sensitivity of new fund flow to performance as the slope of the flow-toperformance relationship, measured by the following “first derivative” term of equation
(4). flow to performance sensitivity = β
1
+ 2 β
2
Fundperformance
(5)
The raw measures are heterogeneous, exhibiting fat tails and high kurtosis. The regressions reported below use the percentile rank of flow sensitivity to performance, ranging from 0 to 99.
For robustness checks we construct two additional measures of investor short-termism.
One is the volatility of new fund flow growth, defined as the annualized standard deviation of the (seasonally adjusted) monthly growth rates of new money. As an alternative, consistent with the existing literature, we also define the volatility of fund flow as the standard deviation of total net asset growth rather than new money growth.
11
Using this alternative definition of fund flow volatilities yields slightly weaker but qualitatively similar results. Our second robustness check measure, the statistical sensitivity of new money flow to performance, answers the question: “In the flow to performance relation how much of the total variation of the new money flow is explained by recent performance?” The explanatory power of performance to new fund flow is captured by the regression R 2 in equation (4). The measure has the additional benefit of being bounded between 0 and 1 and thus more homogeneous.
2.5. Measuring investment short-termism
11 The difference is that new money growth adjusts for the mechanical fluctuation of fund total assets due to performance, whereas total net asset growth does not. The former is economically more sensible, as the truly unexpected fund flow volatility to which managers need to respond should not include the mechanical appreciation or depreciation of assets in place.
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The first measure of fund managers’ investment horizons that we consider seeks to answer the question: “At any given time what are the remaining holding periods of the securities in the fund?” Since we have data containing detailed holding information for each of the stocks in a fund portfolio, we estimate the time period that the stocks are bought and sold, and calculate, for all stocks currently in the portfolio at each point in time, what the remaining time periods are until which they are sold 12 . We then take the value-weighted average of these remaining holding periods.
13 Acknowledging the foregoing to be an ex post measure of average holding period, and that an ex ante measure would be more appropriate, we don’t have compelling reason to believe that the differences between ex-ante and ex-post measures are systematic, and therefore believe that the ex post measure adds noise, but not bias, to the regressions. In regression analyses the average remaining holding periods are sometimes abbreviated as “maturity.”
Our second measure of investment short-termism, fund level turnover as reported in the
CRSP survival bias free database, is intuitively the percentage of fund holdings that changes hands in a given period. The higher the turnover, the shorter a fund’s average investment horizon. To properly account for the impact of changes in fund flow the turnover ratio of the fund in CRSP is defined (following standard definition in the field, e.g., Grinold and Kahn, 1995) as the minimum of aggregate purchases or sales of securities divided by a fund’s average total net assets.
14 This measure is by definition fund specific.
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12 For stocks held until the last period of the available data (Q4 of 2003), we arbitrarily assume a remaining duration of another S periods, which, in implementation, we take to be equal to four quarters, a number consistent with an observed average turnover over the full sample of mutual funds of about once per year.
Robustness checks using S equal to two or six quarters do not yield significantly different results.
13 In some instances assumptions have to be made about how fund managers sell stocks (e.g., first in first out (FIFO) or last in first out (LIFO)). These turned out to not matter much as the resulting measures are at least 95% correlated. For brevity the results reported in the paper reflect the LIFO assumption. Various ways to construct the average remaining holding period measure are detailed in Appendix B.
14 A seemingly more straightforward measure, [absolute(buy)+absolute(sell)]/holdings, or one-half thereof to account for round trip trading, is subject to the criticism that if the fund experiences large cash inflows the measure can be arbitrarily high without any selling. For example, a fund that doubles its existing holdings will be calculated as having a “turnover” of 100%, though it might never sell any stocks at all.
Thus might a rapidly growing fund be misclassified as a high turnover fund.
15 There is also a stock level turnover measure (studied extensively in Lo and Wang, 2000), defined as a stock’s trading volume over shares outstanding. Verter (2003) uses this measure, which is invariant across all investors who hold a stock, to gauge firm level short-termism. The problem with this measure, as Verter points out, is that a high turnover for the stock could result either from a handful of people trading
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Summary statistics for the variables used in the paper are provided in Table 1.
[Insert Table 1 about here.]
Table 2 presents the correlations between the measures of input and output shorttermism.
[Insert Table 2 about here.]
All measures of short-termism are positively correlated. Flow to performance sensitivity is, for example, positively correlated with turnover, the sensitivity of flow to performance negatively correlated with average remaining holding period (which is an inverse measure of fund level investment short-termism). Similarly, the measure of new money growth volatility, a measure of input short-termism, is positively correlated with turnover and negatively correlated with the average remaining holding periods of fund investments. All correlations are statistically significant at the 1% level.
Figure 1 plots mean and median turnover for all mutual funds included in this study over the time period 1992 to 2002. Figure 2 plots the mean and median volatility of new money growth over the same time period. These figures reveal considerable time series variations in both measures, but overall a time trend: both turnover and the volatility of new money flow grow over time. Other measures of input and output short-termism exhibit similar patterns.
[Insert Figure 1 about here.]
[Insert Figure 2 about here.] extremely frequently or a majority of people trading less frequently. Thus, it might not be an appropriate measure of the true level of any particular investor’s trading activity. Our measure is fund rather than stock specific.
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Notwithstanding the time series patterns of increases in absolute levels of the measures of short-termism, relative levels of the short-termism measures exhibit considerable stickiness over time. Funds don’t change investment horizons overnight. Table 3 reports transition probabilities of turnover and fund flow volatility, which measure, respectively, short-termism at the output and input levels.
[Insert Table 3 about here.]
The measures of short-termism recorded in Table 3 are far from random. Panels A and C show that, one year on, the probability of staying in the same horizon decile remains considerably higher than the probability of switching to most other deciles. The pattern is particularly striking at the two extreme deciles: extremely high (low) turnover funds exhibit a strong tendency to maintain extremely high (low) turnover. The same pattern is observed in fund flow volatility.
This pattern persists even when the transition probabilities are measured three years out.
As can be seen in Panels B and D, the probability that a fund in the bottom (top) decile of turnover one year will continue to be in the bottom (top) decile three years later is 42%
(33%). Similarly, the probability that a fund in the bottom (top) decile of fund flow volatility will continue to be in the bottom (top) decile three years later is 35% (31%).
Similar patterns obtain for other measures of input and output short-termism.
3. Regression analysis
For most of the empirical analysis we estimate a pooled regression with panel-corrected standard errors (PCSE) and an AR (1) assumption about the error terms. The PCSE specification enables us to accommodate panel data with heteroskedasticity as well as autocorrelation and cross-correlation of the error terms (Beck and Katz, 1995). In addition, reflecting standard methodology in empirical finance we also report as robustness checks regressions run using the Fama-MacBeth (1973) method. In particular, we first run cross-sectional regressions for each year, reporting the time series average of
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the coefficient estimates, and use the time-series standard errors of the average slopes to draw inferences. The Fama-MacBeth methodology is a convenient and conservative way to account for potential cross-correlations in residuals. As reported by Fama and French
(2002), the Fama-MacBeth standard errors are often two to five times the OLS standard errors from pooled panel data regressions that ignore residual cross-correlation. Because the Fama-MacBeth procedure does not take into account autocorrelations we use the procedure described by Pontiff (1996) to adjust the time series standard deviations further upward should any autocorrelation in the coefficient estimate be present. Time fixed effects are included to control for possible time series effects. Fixed effects for fund investment style are also included to control for the possibility that different fund styles might inherently require different investment horizons.
3.1. Establishing the positive relation between input and output short-termism
We begin our regression analysis by examining how the flow to performance sensitivity measured in Equation (5) affects the output (investment) short-termism measured either as the average remaining holding period or the turnover, controlling for other fund attributes. Specifically, we use fund-level information to estimate the following pooled regression with panel-corrected standard errors.
Output
_ shortermism
α β
1
( flow
_ _
β β
TNA ) ) + β
4
)
(6)
Here, i is the index for fund and t the index for time period. The variable output_shorttermism can be either the maturity or the turnover. We control for other fund characteristics that might affect output short-termism over time. As demonstrated by
Brown, Harlow, and Starks (1996) and Chevalier and Ellison (1997), past fund performance might influence the degree of aggressiveness with which fund managers churn their portfolios. Fund managers’ ability to churn their portfolios quickly might be affected by fund size, larger funds likely having a more considerable market impact and thus being more difficult to turn over. We also control for fund-level expense ratios,
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which the previous literature uses to explain fund flows. Finally, we control for time and fund investment style fixed effects.
The regression results are presented in Table 4. Columns (1) and (2) report the results of the PCSE regression that permits the error terms to follow an AR (1) process over time.
Specifically, Column (1) reports the regression result of turnover on the sensitivity of flow to performance, Column (2) the regression result of average remaining holding period on the sensitivity of flow to performance. As a comparison, columns (3) and (4) report in the same order the results of the Fama-MacBeth regressions, which also use an
AR(1) correction to further adjust for potential serial correlation in the coefficient estimate.
[Insert Table 4 about here.]
The coefficient estimates reported in Table 4 for β
1
are significantly positive when output short-termism is measured by turnover (1.24 and 1.19 with t-statistics of 7.08 and 5.51 for columns (1) and (3), respectively) and significantly negative when output shorttermism is measured by the average remaining holding period -0.97 and -0.90 with tstatistics of -2.60 and -3.64 for columns (2) and (4), respectively). Both are consistent with the intuition that increased investor short-termism reduces investment horizons for mutual funds. Economically, a one standard deviation increase of the rank measure of the flow to performance sensitivity will increase the turnover by 0.036 (0.034 in the Fama-
MacBeth regression), or about 5% increase of the turnover around the median level of
0.68; a one standard deviation increase of the rank measure of the flow to performance sensitivity will decrease maturity by 0.028 (0.026 in the Fama-MacBeth regression), or about 4% decrease of the maturity measure around the median level of 0.732.
The coefficient estimates on the control variables are largely consistent with those reported in the previous literature. Turnover decreases with past performance, but only insignificantly. Conversely, remaining holding period increases with past performance and is reasonably significant. Consistent with intuition, fund size significantly reduces
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turnover, although the results are less than significant when output short-termism is measured by remaining holding period. As expected, the expense ratio increases turnover and reduces average remaining holding period.
In summary, empirical evidence suggests a positive and significant relation between input short-termism and output short-termism. Higher flow to performance sensitivity is associated with shorter fund investment horizons. This effect obtains after controlling for fund investment style and time fixed effects as well as for other factors that might affect fund-level investment horizons.
We perform and report many robustness checks of the results. First, we examine whether the results hold with longer investor horizons. We measure the flow sensitivity of funds to performance during the past 24 months (as opposed to 12 months) to determine whether longer period performance sensitivity might still predict fund managers’ investment horizons and, if so, to what extent.
We run the regressions specified in equation (4) to estimate flow to performance sensitivity, but using the past 24 month fund performance instead. The flow to performance sensitivity is correspondingly defined as the first derivative term.
Table 5 reports the regression results using the flow to performance sensitivities constructed using 24 months of past performance. Column (1) reports regression of turnover on flow to performance sensitivity, Column (2) regression of the average remaining holding period measure on flow to performance sensitivity.
[Insert Table 5 about here.]
The results are consistent with those reported in Table 4. The coefficient estimates for β
1 are significantly positive when output short-termism is measured by turnover (0.87 and
0.90 with t-statistic of 4.05 and 7.56 for columns (1) and (3), respectively) and significantly negative when output short-termism is measured by average remaining
17

holding period -0.67 and -0.93 with t-statistic of -5.17 and -3.45 for columns (2) and (4), respectively). The level of flow to performance sensitivity is thus positively related to the turnover, and negatively related to the average remaining holding period, of a mutual fund’s portfolio investment. This holds after controlling for other fund characteristics that might affect fund-level investment horizons such as past return, fund size, and fund expense ratio, as well as fixed effects on fund style and calendar time. Economically, a one standard deviation increase of the rank of the flow to performance sensitivity measure will increase the turnover measure by about 4%, and decrease the maturity measure by about 3-4%.
Thus far the analysis has measured fund flow to performance by running regressions using all historical fund flow and fund performance data and the resulting measure of investor short-termism has been assumed to be rather constant over time 16 . Arguably, because fund characteristics can change over time, a constant level of investor shorttermism might be an oversimplification. To address this concern we measure investor short-termism through a year-by-year regression of the flow to performance relation in
Equation (4) using up to 36 monthly observations up to the end of the last year. This measure is better able to capture whether and how much investor-level short-termism varies over time.
17
We use these year-by-year measures of short-termism to run additional tests on the relation between input short-termism and output short-termism, the results of which
(reported in Table 6) are consistent with the results using time-invariant measures of investor short-termism. The measures of investor short-termism are flow to performance sensitivity (3), a year-by-year measure of the sensitivity of new fund flow to past 12 month performance, and flow to performance sensitivity (4), a year-by-year measure of the sensitivity of new fund flow to past 24 month performance.
16 There is still some variation in the measure of the flow to performance sensitivity, as we are using all historical data up to the end of the last year in calculating the measure.
17 This approach is analogous to the common practice of using the past three years’ monthly stock return data to get a rolling beta estimate of a stock.
18

[Insert Table 6 about here.]
3.2. Tests with alternative measures of input short-termism
We rerun the tests using two alternative measures of investor level short-termism, the volatility of new fund flow and the statistical explanatory power of new money flow. The results of these tests and of regressions run using both the PCSE and Fama-MacBeth methods are reported in Tables 7 and 8.
[Insert Table 7 about here.]
[Insert Table 8 about here.]
Higher unexpected fund flow volatility does, indeed, increase turnover as reported in
Columns (1) and (3) of Table 7 (the coefficient on turnover is 0.30 (0.36) for the PCSE
(Fama-MacBeth) regression with t-statistic of 6.09 (3.97)) and decreases, as reported in
Columns (2) and (4), average remaining holding period (the coefficient on average remaining holding period is -0.22 (-0.23) with t-statistic of -5.20 (-6.48). Similarly, high statistical sensitivity of flow to performance (regression R2 of the flow-performance relationship) increases turnover and decrease average remaining holding period, as reported in Table 8.
3.3. Causality tests
Although we have established a positive relation between investor short-termism and fund manager short-termism we don’t know the direction of causality. It is as plausible that short-termism on the part of fund managers might attract short-term oriented investors as that investor impatience might occasion myopic behavior on the part of fund managers. We employ a simultaneous equations approach to address the causality question. We assume that both investor-level short-termism and investment short-
19

termism can be determined endogenously and potentially by each other. We look for instrumental variables that enable us to differentiate the causality and use the two-stage least squares method to estimate the relation between input and output short-termism.
18
We choose manager age to be the instrument for fund investment horizon. As demonstrated by Chevalier and Ellison (1999), younger managers are more subject to career concerns and thus under greater pressure to perform in the short run, thus, likely to be less willing to hold longer-horizon investments. Manager age should therefore be positively related to investment horizon. There is, on the other hand, no compelling reason to believe ex ante that manager age is directly related to the flow to performance sensitivity of funds 19 .
Sirri and Tufano (1998) and Jain and Wu (2000), among others, show that intensifying marketing activities both generates fund flow and makes it more sensitive to performance. The additional fund flow is more likely from hot money that comes in just for performance and will likely leave if disappointed.
20 In the absence of any compelling reason to link marketing expense to managers’ investment horizons we choose marketing expenses, proxied by total fund fees, to be the instrumental variable for flow to performance sensitivity 21 .
We follow Chevalier and Ellison (1999a) in calculating manager age by assuming managers to have been 21 upon graduating from college. Occasionally, when reported,
18 Using lagged explanatory variables as instruments for determining causality might be problematic because, as demonstrated above, both input short-termism and output short-termism change slowly over time. Endogeneity problems might thus plague lagged explanatory variables as much as contemporaneous ones. See, for example, the discussion in Himmelberg, Hubbard, and Palia (1999).
19 Existing fund rating agencies typically report fund performance, rather than the fund manager’s performance, and the star funds could have historically been managed by different managers. As a result, fund investors are more likely to buy into the funds, rather than the fund managers.
20 Recent work by Bergstresser, Chalmers, and Tufano (2004) finds a similar effect for investors who enter through brokered fund distribution channels in that they, too, aggressively chase short-term performance.
21 While in theory it is possible that a mutual fund runs an integrated strategy to manage investing horizons through the marketing and investment channels, in practice it would be hard, as most funds have distinct divisions for manufacturing and distribution, and the two are run separately.
20

we use managers’ birth years to calculate manager age.
22 Manager age is constructed from a separate data set provided by Morningstar. The sample size for this test is smaller than for the previous ones, not all managers in the previous regression analysis being identified in the Morningstar manager name database.
We follow Sirri and Tufano (1998) in calculating total fund fees to be expense plus 1/7 of load, load being amortized without discounting over seven years, the average holding period for an equity fund in their data. Total fees being highly correlated with the expense ratio used in the previous tables, to avoid multi-colinearity we drop the expense ratio from the causality test regression specification.
The simultaneous equations to be estimated are:
Fund investment horizon = f
1
(fund investor short-termism, manager age, other controls); and
Fund flow to performance sensitivity = f
2
(fund investment short-termism, marketing activity, other controls).
The results of the simultaneous equations regressions are reported in Table 9. We report results where we measure investment short-termism by either average remaining holding period or turnover, and investor short-termism by flow to performance sensitivity measure estimated using past two year returns, a quadratic flow-performance response function, and no additional control variables.
22 Chevalier and Ellison (1999a) consider and reject as an alternative measure of manager experience: the tenure of fund managers, as reported by Morningstar, because the measure is less meaningful and subject to more noise. Manager age, moreover, is quite different from fund age. In fact, Chevalier and Ellison (1997) study fund age, Chevalier and Ellison (1999a) manager age. Manager age is intuitively more related to a manager’s investment horizon.
21

The results of the causality test are in Table 9. Investor short-termism does, indeed, affect investment short-termism. Higher flow to performance sensitivity significantly decreases average remaining holding period and significantly increases fund turnover, after controlling for endogeneity of the measures of investor short-termism. The results are statistically as well as economically significant. There is, on the other hand, no significant evidence that investment short-termism causes investor short-termism.
[Insert Table 9 about here.]
4. Robustness checks
We outline here robustness issues related to our key measures, in particular, the robustness of our results to various alternative specifications of the flow to performance sensitivity measure.
We check whether adding a cubic term of past performance or dropping the quadratic term or allowing for asymmetric flow to performance relation in regression equation (4) causes the results to materially change. The results from these alternative definitions of flow to performance sensitivity confirm those reported in the paper.
We also perform tests allowing for an asymmetric flow to sensitivity relation (as in
Lynch and Musto, 2003). To do this we first allow the positive (benchmark adjusted) performance to affect new fund flow differently than negative performance. We then estimate the “slope” of the flow to performance relation by taking the average of the slopes for the positive and negative performance, and then take the rank measure of the resulting average slope. This yields similar results as reported in the paper 23 .
23 In our context, this yields similar measures to the ones estimated using a direct linear relation because the benchmark adjusted performance is largely symmetric, the smaller slope coefficient for the
“underperformance region” and larger slope coefficient for the “overperformance region” largely offset each other, and the resulting slope thus closely resembles the one estimated under a linear relation between fund flow and performance.
22

When we check for differences in the measure of short-term performance using past 12 month, 24 month or 36 month performance we find the results to be qualitatively similar.
In the main results the measures are constructed by regressing current period new money growth on past fund performance, without further controlling for other factors.
Alternatively, control variables can be added to the regression specification. Following the existing literature we include as control variables the log of fund size and expense ratio.
24 The resulting measure of flow to performance sensitivity do not significantly change our test results.
Finally, when we conduct the analysis at quarterly frequency, controlling for seasonality using quarter dummies, the results in the paper are borne out qualitatively.
All robustness check results are available upon request.
5. Conclusion
Fund managers face a dilemma: pursuing the best investment opportunity might dictate holding some assets for the long term [Shleifer and Vishny (1990) and Stein (2004)]. But ignoring short-term valuation in favor of long-term payoff might jeopardize short-term performance and a fund manager’s survival, in which case the long-term profit might not be realized or the assets under management might be substantially reduced leaving the manager less well-positioned to benefit from long term profit opportunities that do eventually materialize. This paper asks whether fund managers respond to their investors’ short-termism by choosing shorter investment horizons.
Our analysis of input (investor level) short-termism and output (investment) shorttermism in mutual funds reveals a strong link between the two after controlling for many
24 In such settings flow to performance sensitivity continues to be defined as in equation (5), but the statistical sensitivity of flow to performance is now measured as the “incremental R 2 difference between R 2 of interest are already controlled for.
,” namely, the
with and without the return variables in a regression setting where all other variables
23

other factors that affect investment horizons. Fund flows that are more sensitive to performance are found to exhibit higher turnover or shorter holding periods, which suggests a positive relation between the short-termism of mutual funds and that of the funds’ investors. Further tests demonstrate investment short-termism to be caused by investor short-termism, but not vice versa. The paper thus provides supporting evidence for the hypothesis that myopic behavior on the part of fund managers is a response to fund investor’s short horizons.
24

Appendix A: Fund investment styles used in the analysis
The following fund investment styles are included in the analysis by reason of having significant holdings in U.S. equity.
AGG: Aggressive Growth Funds seek maximum capital appreciation through investments that might include securities of start-up or emerging growth companies, special situations, or industries out of favor. Investment practices might include option writing, short-term trading, and leveraging.
BAL: Balanced Funds seek to realize current income, growth of income and principal, and principal preservation through a mixed portfolio of bonds, preferred stocks, common stocks, and money market securities, generally in fixed proportions.
EGG: Global Growth Funds invest primarily in equity securities worldwide (including the United States) for capital appreciation.
EGS: Global Small Company Funds invest primarily in equity securities of small capitalization companies worldwide (including the United States).
EGT: Global Total Return Funds invest primarily in equity securities worldwide
(including the United States) for capital appreciation and current or future income.
EGX: Global Equity Sector Funds invest primarily in equity securities of companies that are based in any part of the world but operate in a common sectorsuch as telecommunications or health. The natural resource and precious metal sectors are classified separately.
FLG: Flexible Global Funds are generally free to assign up to 100% of their assets across various asset classes including foreign and domestic equities, fixed-income securities, and money market instruments.
25

FLX: Flexible Portfolio Funds are generally free to assign up to 100% of their assets across various asset classes including domestic equities, fixed-income securities, and money markets instruments.
GMC: Growth MidCap Funds invest primarily in companies between $2 billion and $10 billion in total market capitalization.
GRI: Growth and Income Funds attempt to combine long-term capital appreciation with a steady stream of income by investing in companies that offer long-term earnings growth and have solid histories of dividend payments.
GRO: Growth Funds invest in well-established companies primarily for long-term capital gains rather than current income.
ING: Income - Growth Funds seek high current income and growth of income by investing the majority of their portfolios in equities.
SCG: Small Company Growth Funds invest primarily in companies of less than $2 billion in total market capitalization.
Investment style information is from the CRSP Mutual Fund Data manual.
26

Appendix B: Explanations of the algorithm used to calculate the value-weighted average remaining holding period of fund holdings
This appendix details the procedure used to create the alternative measure of investment short-termism, namely, the weighted average remaining holding period of fund holdings, using the quarterly mutual fund holding data obtained from CDA/Spectrum.
The measure is analogous to the remaining duration of a debt instrument, save that in the case of debt the timing of the realization of each coupon and of the principle cash flows are pre-determined. In this case the timing of the selling of the portfolio holdings is not known for the time period for which the measure is constructed (for example, at time t=5 we don’t know if a stock will be sold in period 6 and thus have a remaining holding period of 1). We use the ex post realized selling time to measure the remaining holding period as follows.
1) At any given time, for each stock held in the portfolio, we track the time that the stock is sold, then calculate the share number-weighted average remaining holding period of the holding in that stock.
For example, if for stock A at time period 3 we have the following three tranches: a) 10 stocks will be sold in period 4 leaving a remaining holding period of 1 b) 20 stocks will be sold in period 5 leaving a remaining holding period of 2 c) 10 stocks will be sold in period 6 leaving a remaining holding period of 3 the share number-weighted average remaining holding period of the holding in stock A will be:
(10*1 + 20*2 + 10*3)/(10+20+10)=2.
27

2) After calculating the remaining holding period of each of the stocks in the current holdings portfolio, we calculate the market value-weighted average remaining holding period of the portfolio.
For example, given the following two holdings: a) 40 shares of stock A, with current price of $10, with an average remaining holding period of 2 periods b) 20 shares of stock B, with current price of $30, with an average remaining holding period of 3 periods the portfolio value-weighted average remaining holding period is:
(40*10*2 + 20*30*3)/(40*10 + 20*30) = 2.6.
In measuring the remaining holding period of stocks in one time period we need to make certain assumptions, with respect to the selling of a stock in later periods, about which tranch of stocks is sold first. For example, we could assume that mutual funds always follow the rule of last in first out (LIFO), namely, the shares acquired most recently are always sold first. Alternatively, we could assume that the managers adopt the rule of first in first out (FIFO), in which case they sell first the shares acquired first. A third assumption is that managers sell various tranches of shares proportionally. All three selling patterns are tested in the construction of the portfolio value-weighted remaining holding period measure. The results using each measure are not qualitatively different. In fact, the measures have a correlation of more than 0.95. Consequently, we conclude that the choice of selling assumptions does not affect the final results. For brevity, we report the results in the paper using the assumption of LIFO.
28

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33

Table 1: Summary Statistics of Main Variables Used In Empirical Analysis
Number of Obs.
mean median std. dev. skewness kurtosis minimum maximum Variable Name
Measures of Investment Short-termism
Average Remaining Holding Period
Turnover
Measures of Investor Short-termism flow to performance sensitivity [1] flow to performance sensitivity [2] flow to performance sensitivity [3] flow to performance sensitivity [4]
New Money Growth volatility
R2 [1]
R2 [2]
R2 [3]
R2 [4]
Control Variables annual fund performance log(TNA) expenses
17430
16792
17430
17430
17430
17430
17379
17430
17430
17430
17430
17430
17430
17428
1.157
0.831
49.500
49.500
49.500
49.500
0.084
0.236
0.364
0.286
0.600
0.095
4.996
0.014
0.732
0.680
49.000
49.000
49.000
49.000
0.056
0.170
0.306
0.212
0.605
0.107
5.063
0.012
0.924
0.656
28.860
28.860
28.860
28.860
0.123
0.206
0.240
0.238
0.258
0.201
2.060
0.009
1.922
1.504
0.000
0.000
0.000
0.000
5.384
1.234
0.763
1.081
-0.152
0.410
-0.279
3.621
2.775
-1.200
-1.200
-1.200
-1.200
32.646
0.816
-0.323
0.393
-1.055
2.538
0.669
13.608
377.311
0.512
0.020
0.000
0.000
0.000
0.000
0.015
0.005
0.031
0.004
0.081
-0.760
-6.908
0.000
4.926
3.440
1.826
11.571
0.320
99.000
99.000
99.000
99.000
0.973
0.856
0.964
0.959
0.999
Note: Average remaining holding period is the value-weighted average of the remaining holding period before a stock is sold; turnover is the minimum of aggregate purchases of securities or aggregate sales of securities, divided by the total net assets; flow to performance sensitivity [1] (or [2]) is rank measure of the slope of the fund flow to past one year (or two years) performance relationship; flow to performance sensitivity [3] (or [4]) is rank measure of the slope of the fund flow to past one year (or two years) performance relationship; new money growth volatility is the standard deviation of the seasonally adjusted monthly new money growth rate during the last year; R2 [1] (or R2 [2]) is regression R-squared of the flow to past one year
(or two years) performance relationship, R2 [3] (or R2 [4]) is the R-squared of the year by year regression of new money growth to past one year (or two years) performance relationship. log(TNA) is natural log of total net asset, expenses is expense ratio.

Table 2: Correlation Between Measures of Output and Input Short-termism turnover
Average Remaining turnover
Average
Remaining
Holding
Period flow to performance sensitivity [1] flow to performance sensitivity [2] flow to performance sensitivity [3] flow to performance sensitivity [4]
1.000
-0.194
0.072
0.070
0.127
0.086
-0.194
1.000
-0.062
-0.054
-0.078
-0.085
flow to performance sensitivity [1]
0.072
-0.062
1.000
0.571
0.631
0.291
0.070
-0.054
0.571
1.000
0.365
0.463
flow to performance sensitivity [2] flow to performance sensitivity [3] flow to performance sensitivity [4]
R2 [1]
0.127
0.086
0.163
-0.078
-0.085
-0.083
0.631
0.291
0.163
0.365
0.463
0.244
1.000
0.455
0.116
0.455
1.000
0.057
R2 [2]
R2 [3]
R2 [4]
New Money Growth
0.084
0.164
0.137
0.084
-0.065
-0.088
-0.095
-0.064
0.155
0.140
0.092
0.026
0.211
0.252
0.285
0.066
0.113
0.197
0.155
0.057
0.033
0.069
0.160
0.072
R2 [1]
0.163
-0.083
0.163
0.244
0.116
0.057
1.000
0.502
0.347
0.161
0.057
R2 [2]
0.084
-0.065
0.155
R2 [3]
0.164
-0.088
0.140
0.211
0.113
0.033
0.502
1.000
0.309
0.338
0.068
0.252
0.197
0.069
0.347
0.309
1.000
0.497
0.083
R2 [4]
0.137
New
Money
Growth volatility
0.084
-0.095
0.092
-0.064
0.026
0.285
0.155
0.160
0.161
0.338
0.497
1.000
0.081
0.066
0.057
0.072
0.057
0.068
0.083
0.081
1.000
Note: Variables are defined as in Table 1. All correlation coefficients are significant at the 1% level.

Table 3 Transition probabilities of Selected Measures of Input and Output Short-termism
Panel A: Turnover: One Year Transition Probabilities starting decile
1
2
3
9
10
6
7
8
4
5 ending decile 1
0.69
0.19
0.06
0.04
0.02
0.01
0.01
0.01
0.01
0.01
2
0.17
0.44
0.23
0.10
0.05
0.02
0.02
0.01
0.01
0.00
3
0.06
0.18
0.36
0.19
0.10
0.04
0.04
0.02
0.02
0.00
4
0.03
0.08
0.17
0.33
0.18
0.10
0.06
0.03
0.02
0.01
5
0.01
0.04
0.07
0.15
0.33
0.19
0.10
0.06
0.04
0.02
6
0.01
0.03
0.04
0.09
0.15
0.33
0.16
0.10
0.05
0.03
7
0.00
0.02
0.02
0.04
0.09
0.13
0.33
0.16
0.11
0.05
8
0.01
0.01
0.03
0.03
0.05
0.09
0.16
0.35
0.19
0.07
9
0.01
0.01
0.01
0.01
0.02
0.04
0.09
0.18
0.41
0.17
10
0.01
0.01
0.01
0.01
0.02
0.03
0.03
0.09
0.14
0.64
Panel B: Turnover: Three Year Transition Probabilities starting decile
1
2
3
4
5
8
9
6
7
10 ending decile 1
0.42
0.20
0.11
0.07
0.06
0.06
0.03
0.05
0.03
0.04
2
0.17
0.23
0.17
0.15
0.11
0.06
0.05
0.04
0.04
0.03
3
0.09
0.17
0.18
0.16
0.11
0.08
0.06
0.06
0.05
0.05
4
0.07
0.11
0.16
0.14
0.15
0.11
0.09
0.07
0.06
0.06
5
0.05
0.08
0.10
0.13
0.14
0.14
0.13
0.10
0.07
0.07
6
0.05
0.05
0.08
0.10
0.11
0.15
0.14
0.11
0.11
0.09
7
0.05
0.04
0.07
0.08
0.09
0.13
0.15
0.14
0.12
0.07
8
0.04
0.05
0.05
0.07
0.09
0.12
0.15
0.17
0.17
0.10
9
0.05
0.04
0.04
0.05
0.09
0.08
0.12
0.14
0.19
0.15
10
0.04
0.03
0.04
0.05
0.05
0.07
0.08
0.12
0.16
0.33

Panel C: Flow Volatility: One Year Transition Probabilities starting decile
1
2
3
8
9
10
6
7
4
5 ending decile 1
0.52
0.20
0.08
0.05
0.04
0.02
0.02
0.02
0.01
0.02
2
0.21
0.26
0.18
0.11
0.09
0.07
0.04
0.04
0.03
0.02
3
0.08
0.19
0.17
0.18
0.12
0.08
0.07
0.04
0.03
0.02
4
0.05
0.14
0.19
0.17
0.16
0.13
0.09
0.06
0.04
0.04
5
0.04
0.09
0.15
0.18
0.19
0.15
0.08
0.08
0.05
0.05
6
0.02
0.04
0.10
0.11
0.16
0.18
0.17
0.11
0.06
0.05
7
0.02
0.03
0.05
0.08
0.09
0.15
0.20
0.18
0.12
0.06
8
0.01
0.02
0.03
0.04
0.07
0.12
0.18
0.19
0.21
0.10
9
0.02
0.02
0.03
0.05
0.03
0.05
0.11
0.20
0.27
0.20
10
0.03
0.02
0.03
0.03
0.04
0.06
0.04
0.09
0.19
0.44
Panel D: Flow Volatility: Three Year Transition Probabilities starting decile
1
2
3
4
5
6
9
10
7
8 ending decile 1 2 3 4 5 6 7 8 9 10
0.35
0.16
0.09
0.07
0.06
0.06
0.05
0.06
0.05
0.04
0.19
0.19
0.13
0.10
0.09
0.09
0.08
0.06
0.06
0.05
0.08
0.13
0.16
0.14
0.14
0.08
0.09
0.06
0.07
0.06
0.06
0.13
0.14
0.14
0.15
0.11
0.09
0.09
0.08
0.06
0.07
0.10
0.12
0.14
0.14
0.13
0.10
0.10
0.08
0.08
0.06
0.08
0.11
0.12
0.11
0.13
0.11
0.09
0.10
0.08
0.05
0.06
0.08
0.10
0.12
0.14
0.13
0.13
0.09
0.08
0.05
0.06
0.07
0.07
0.09
0.12
0.16
0.14
0.12
0.11
0.04
0.06
0.05
0.05
0.06
0.08
0.11
0.17
0.19
0.15
Note: to calculate the transition probabilities, we rank the measures of turnover (fund flow volatility) each year into 10 deciles, with decile one being the smallest and decile 10 being the largest. We then calculate the transition probabilities that the next year the fund would fall into each of the 10 turnover (fund flow volatilities) deciles, conditioning on this year's rank. Finally, we take the time series average across all years. The diagonal elements of the transition matrix are bolded.
0.04
0.04
0.05
0.06
0.05
0.07
0.08
0.11
0.17
0.31

intercept flow to performance sensitivity [1] (x10
-3
) fund performance log(TNA) (x10
-3
) expenses
Fund Style Fixed Effect
Year Fixed Effect
Number of Observations
Adjusted R-squared
Table 4: Regression of Turnover and Maturity on flow to performance sensitivity
PCSE Regressions
(1)
Turnover
0.81
(28.15)
(2)
Fama-MacBeth Regression
(3)
Dependent Variable
Maturity Turnover
0.72
(35.85)
0.81
(27.50)
(4)
Maturity
0.71
(35.30)
1.24
(7.08)
-0.02
-(0.72)
-15.35
-(5.53)
8.76
(4.04)
Included
Included
16777
-0.97
-(2.60)
0.09
(3.73)
-1.50
-(0.80)
-2.13
-(4.90)
Included
Included
17407
1.19
(5.51)
-0.03
-(0.69)
-15.05
-(5.52)
7.03
(3.70)
Included
Not Included
16777
0.07
-0.90
-(3.64)
0.09
(3.41)
-1.44
-(0.78)
-3.38
-(6.26)
Included
Not Included
17407
0.03
Note: All variables are as defined in Table 1. Columns (1) and (2) estimate the regressions with panel-corrected standard errors (PCSE) proposed by Beck and Katz (1995). The PCSE specification adjusts for the heteroskedasticity among fund returns as well as for the autocorrelation within each fund’s returns. We allow the error terms to have heterogeneous variances across funds and to follow a common AR(1) process over time.
Columns (3) and (4) estimate the regressions using the Fama-MacBeth methodology. In addition, we use the proceducre proposed by Pontiff (1996) with an AR(1) processon the error terms to adjust the time series standard errors of the coefficients to account for serial correlation. T-statistics are reported in parentheses.

Table 5: Panel Corrected Standard Error Regression, Using Only One Year Flow-to-Performance
Sensitivity
Intercept flow to performance [2] (x10 annual fund performance log(TNA) (x10 expenses
-3
)
Fund Style Fixed Effect
Year Fixed Effect
Number of Observations
-3
)
Turnover
(1)
0.78
(29.33)
0.87
(4.05)
-0.03
-(0.75)
-14.77
-(5.33)
8.70
(3.89)
Included
Included
16777
0.93
(0.49)
-2.24
-(2.11)
Included
Included
17407
Dependent Variables
Maturity Turnover
(2)
0.73
(36.97)
(3)
0.66
(6.40)
-0.67
-(5.17)
0.10
(3.70)
0.90
(7.56)
-0.44
-(1.67)
-12.81
-(2.19)
11.90
(4.79)
Included
Not Included
16777
0.06
Note: All variables are as defined in Table 1. We estimate the regressions with panel-corrected standard errors (PCSE) proposed by Beck and Katz (1995). The PCSE specification adjusts for the heteroskedasticity among fund returns as well as for the autocorrelation within each fund’s returns. All variables are as defined in Table 1 and Table 2. We allow the error terms to have heterogeneous variances across funds and to follow a common AR(1) process over time. T-statistics are reported in parentheses.
0.01
(0.00)
-3.65
-(3.67)
Included
Not Included
17407
0.03
Maturity
(4)
0.63
(11.90)
-0.93
-(3.46)
0.27
(3.11)

Table 6: Panel Corrected Standard Error Regression with Year by Year Rolling Estimate of Flow-to-Performance
Sensitivity
Turnover
Dependent Variable
Maturity
Intercept flow to performance sensitivity [3] (x10
-3 flow to performance sensitivity [4] (x10
-3
)
)
(1)
0.78
(21.85)
1.70
(8.27)
(2)
0.80
(23.31)
(3)
0.77
(33.51)
-0.89
-(4.37)
(4)
0.80
(32.90) annual fund performance log(TNA) (x10
-3
EXPENSES
)
Fund Style Fixed Effect
Year Fixed Effect
Number of Observations
-0.04
-(1.09)
-16.37
-(4.92)
6.51
(8.82)
Included
Included
16777
1.21
(5.81)
-0.11
-(2.35)
-15.87
-(4.42)
6.68
(9.28)
Included
Included
16777
0.13
(3.75)
0.34
(0.13)
-2.59
-(4.75)
Included
Included
16777
-0.83
-(3.60)
0.16
(4.00)
0.18
(0.07)
-2.68
-(4.98)
Included
Included
16777
Note: All variables are as defined in Table 1. We estimate the regressions with panel-corrected standard errors
(PCSE) proposed by Beck and Katz (1995). The PCSE specification adjusts for the heteroskedasticity among fund returns as well as for the autocorrelation within each fund’s returns. We allow the error terms to have heterogeneous variances across funds and to follow a common AR(1) process over time. T-statistics are reported in parentheses.

intercept
Fund Growth volatility fund performance log(TNA) (x10
-3
) expenses
Fund Style Fixed Effect
Year Fixed Effect
Number of Observations
Adjusted R-squared
Table 7: Regression of Turnover and Maturity on Standard Deviation of New Money Growth
PCSE Regressions
(1)
Turnover
0.78
(28.00)
(2)
Fama-MacBeth Regression
(3)
Dependent Variable
Maturity Turnover
0.75
(36.26)
0.64
(6.59)
(4)
Maturity
0.59
(10.41)
0.30
(6.09)
-0.21
-(1.56)
-0.22
-(5.20)
0.13
(3.27)
0.36
(3.97)
-0.41
-(1.78)
-0.23
-(6.48)
0.16
(2.70)
-9.20
-(3.82)
8.61
(14.43)
Included
Included
16758
1.33
(1.17)
-3.24
-(5.14)
Included
Included
17356
-8.16
-(2.80)
12.02
(5.10)
Included
Not Included
16758
0.06
1.83
(1.39)
-3.54
-(3.97)
Included
Not Included
17356
0.03
Note: All variables are as defined in Table 1. Columns (1) and (2) estimate the regressions with panel-corrected standard errors (PCSE) proposed by Beck and Katz (1995). The PCSE specification adjusts for the heteroskedasticity among fund returns as well as for the autocorrelation within each fund’s returns. We allow the error terms to have heterogeneous variances across funds and to follow a common AR(1) process over time.
Columns (3) and (4) estimate the regressions using the Fama-MacBeth methodology. In addition, we use the proceducre proposed by Pontiff (1996) with an AR(1) process on the error terms to adjust the time series standard errors of the coefficients to account for serial correlation. T-statistics are reported in parentheses.

Table 8: Regression of Turnover and Maturity on Regression R-squared for the flow to performance relation intercept
R2 [1] fund performance log(TNA) (x10
-3 expenses
)
Fund Style Fixed Effect
Year Fixed Effect
Number of Observations
Adjusted R-squared
PCSE Regressions
(1)
Turnover
0.70
(27.54)
(2)
Fama-MacBeth Regression
(3)
Dependent Variable
Maturity Turnover
0.70
(36.09)
0.72
(5.69)
(4)
Maturity
0.64
(12.53)
0.13
(6.23)
-0.03
-(0.68)
-0.10
-(4.24)
0.22
(3.76)
0.12
(4.97)
-0.48
-(1.61)
-0.09
-(3.96)
0.29
(3.45)
-14.95
-(4.05)
8.92
(3.47)
Included
Included
16777
1.39
(0.73)
-1.94
-(1.90)
Included
Included
17407
-9.80
-(1.56)
11.74
(5.18)
Included
Not Included
16777
0.06
-0.06
-(0.01)
-4.20
-(3.33)
Included
Not Included
17407
0.03
Note: All variables are as defined in Table 1. Columns (1) and (2) estimate the regressions with panel-corrected standard errors (PCSE) proposed by Beck and Katz (1995). The PCSE specification adjusts for the heteroskedasticity among fund returns as well as for the autocorrelation within each fund’s returns. We allow the error terms to have heterogeneous variances across funds and to follow a common AR(1) process over time.
Columns (3) and (4) estimate the regressions using the Fama-MacBeth methodology. In addition, we use the proceducre proposed by Pontiff (1996) with an AR(1) processon the error terms to adjust the time series standard errors of the coefficients to account for serial correlation. T-statistics are reported in parentheses.

Table 9: Simultaneous Equations Estimate of Both Input and Output Short-termism
Equation 1: Investment Short-termism = f
1
(Investor Short-termism, Manager Age, Controls);
Dependent Variable
Intercept flow to performance sensitivity [1] x 10 (
-3 fund performance
)
Avg Remaining Maturity
-13.09 Intercept
-2.36
-1.96
-3.60
flow to performance sensitivity [1] x 10 (
-3
0.15 fund performance
3.56
)
Turnover
-32.54
-4.11
3.64
4.16
-0.06
-1.07
log(TNA) (x10
-3
) -1.52 log(TNA) (x10
-3
)
-0.34
-20.42
-3.53
Manager Age (x10
-3
) 3.22 Manager Age
3.27
Equation 2: Investor Short-termism = f
2
(Investment Short-termism, Total Percentage Fees, Controls);
-4.39
-3.43
Dependent Variable
Intercept
Average Remaining
Maturity fund performance log(TNA) (x10
-3
)
Total percentage Fees flow to performance sensitivity [1]
1188.38 Intercept
5.33
3.53
0.60
Turnover
-1.75 fund performance
-1.14
3427.20 log(TNA) (x10
-3
)
24.95
3.58 Total percentage Fees
3.96
flow to performance sensitivity [1]
1047.45
5.71
-4.34
-0.97
-1.77
-1.10
2968.51
21.75
3.13
3.37
Note: All variables are as defined in Table 1. We estimate the simultaneous equations using manager age as an instrument for investment short-termism, and total fees as an instrument for the fund investor shorttermism. T-statistics are reported in parentheses.

Figure 1: Mean and Median Turnover Over Time
1
0.9
0.8
0.7
0.6
0.5
0.4
1990
1992
1994
Mean Turnover
Linear (Mean Turnover)
1996
Year
1998
Median Turnover
Linear (Median Turnover)
2000 2002 2004

0.1
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
1990
Mean Flow Volatility = 0.0036 x Year - 7.1598
1992
Mean Flow Volatility
Linear (Mean Flow Volatility)
1994
Median Flow Volatility = 0.004 x Year - 7.8578
Median Flow Volatility
Linear (Median Flow Volatility)
1996
Year
1998 2000 2002 2004