how does investor short-termism affect mutual fund manager

How Does Investor Short-termism Affect Mutual Fund Manager Short-termism*

Li Jin

Harvard Business School

[email protected]

This draft: April, 2005

Abstract:

Mutual fund managers face large incentives to perform in the short run, as both the asset

under management and the firing decision depend on short-term performance. This paper

empirically demonstrates that fund managers facing higher short-term performance

pressure are more focused on short horizon investments. Further tests of causality suggest

that fund manager’s short investment horizons are caused by their investors’ short

horizons, but not the other way around.

Institutions’ focus on short horizon investment will have big implications for both asset

pricing and corporate finance. Excessive fund manager short-termism will likely affect

asset prices, by inflating the price of the most liquid assets, and dampening the price of

long term, illiquid investments. Institutional investor short-termism can also affect

corporate decision making, given that institutions constitute an increasingly important

investor class, and corporate managers will likely react to institutions’ excessive focus on

short-term performance by in turn exhibiting myopic behavior.

* I thank Brad Barber, Benjamin Esty, 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.

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“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.

The last several decades have witnessed significant developments in the mutual fund

industry.1 Concurrently, there has been a substantial decrease of the average holding

period of traded stocks, which is often attributed to “churning” of institutional investors.2

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 individual stock prices, especially during volatile markets.3

Existing research documents that mutual fund investors chase recent fund performance,

even though there is no systematic evidence that fund managers can persistently

outperform.4 As investors move money in response to recent fund performance, fund

managers might “feel the heat” of having to perform in the short-term and shun

investments that pay off only in the long run. Casual empiricism suggests 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

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 proportion of 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 Strickland (2002). 4 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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long-term and illiquid investment projects5. This paper empirically tests whether mutual

fund managers subject to severe pressure for short-term performance make

correspondingly short-horizon investment decisions.

While many mutual fund managers react to fund net inflow by immediately adjusting

their equity portfolio, there is no economic reason that fund managers have to shorten the

holding horizon of their equity portfolio in face of mild fluctuation of net fund inflow.

This is best illustrated by a simple “sources and uses of fund” relation for a mutual fund.

Fund inflow – redemptions + dividend reinvestment = change in cash + change in non-

cash securities holdings.

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.67. However, managerial career concern and compensation are directly

linked to asset under management, and this can prompt fund managers to make a

conscientious choice to alter the investment horizons of their equity holdings in response

to fund flow fluctuations. In particular, fund manager compensation is a function of asset

under management, thus fund flow directly affect compensation. In addition, manager

promotion and firing decision will also be a function of fund flow. The empirical results

in this paper demonstrate that fund managers facing higher short-term performance

pressure are more focused on short horizon investments. Further tests of causality suggest

5 In a related study, Coval and Stafford (2004) find that mutual funds sometimes get into fire selling as a result of large redemptions, and that stocks subject to fire selling might experience large fluctuation of valuation in the short run. This can have potentially large impact on the portfolio valuation of funds that hold such stocks. 6 Cash equitization refers to techniques that use derivatives such as S&P futures contract to approach a fully invested portfolio position and earn “market-like” returns on the investment’s cash balances. This technique affords fund managers a great opportunity to “park” their cash inflows until they find great investment opportunities, without hurting performance (relative to a benchmark) during the interim. 7 In addition, to directly address the possibility that large change in fund inflow might change the investment horizons of the equity portfolio, we control for previous period’s “surprise” in fund inflows in our regressions later on.

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that fund manager’s short investment horizons are caused by their investors’ short

horizons, but not the other way round.

There are several 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, institutions are often

argued to perform monitoring role and affect corporate governance. If institutions only

invest for the short run, they might not have much interest to monitor management or

participate in active governance. Furthermore, corporate managers might react to the

pressure of their institutional investors by pursuing myopic investment decisions.

The paper is organized as follows. Section 1 reviews related literature. 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 inflows to mutual funds respond 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. Given that fund managers’ compensation is an increasing function of

the asset under management, compensation increases as a result of good performance,

especially stellar performance.

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Brown, Harlow, and Starks (1996), Chevalier and Ellison (1997, 1999a), and Khorana

(1996) analyze the career concerns of fund managers and their implications to risk taking.

These researchers document strong performance-related turnover of fund managers,

particularly younger fund managers. For example, as reported in Chevalier and Ellison

(1999a), a move from 0% to -10% alpha increases the firing probability of younger

managers by more than 15%.

Thus fund managers face large incentives to perform in the short run. Such incentives

largely come in the form of increased fund inflow and thus asset under management on

the upside, and firing on the downside.

Researchers have also analyzed the relation between the level of fund openness and fund

returns. Stein (2004) argues that open-end mutual funds are ill-positioned 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.” 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.

This paper explicitly tests whether fund investment horizons are shortened when mutual

funds’ “degrees of openness” are higher. I analyze how cross-sectional variations in

managers’ investment horizons relate to the level of short-termism, measured by the

sensitivity of fund flow to short-term performance. When a fund is largely dominated by

hot money in pursuit of short-term profit managers cannot afford to ignore short-term

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performance. 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, as shown in Shleifer and Vishney (1990,

1997)8. This will also be consistent with the empirical literature that document that

liquidity might be priced, as in Amihud and Mendelson (1986), Brennan and

Subrahmanyam (1996), Brennan, Chordia, and Subrahmanyam (1998), Pastor and

Stambaugh (2003), among others.

There is also an extensive literature on how investor short-termism occasions myopic

investment decisions by corporate managers. Theoretical work includes that of Stein

(1988, 1989), Shleifer and Vishny (1990), Bebchuk and Stole (1993), and Bolton,

Scheinkman, and Xiong (2004). Empirical work includes that of Meulbroek et al. (1990),

Bushee (1998), and Verter (2003).

This paper has direct implications for the foregoing literature. This moves one level up in

the food chain in understanding why investors of corporations might exhibit short-

termism to begin with. Institutional investors such as mutual funds hold increasingly

large shares of public corporations, and managers likely react to the pressure from

institutional investors. Moreover, this study provides solid empirical evidence of the

theoretical models of myopic investment. Compared with corporate managers, fund

managers are under greater short-term pressure and their investment short-termism is

much easier to gauge.9

8 Shleifer and Vishny (1990), for example, 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 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 they have in mind are corporations, the intuition carries over to money management businesses. 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. 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 marked to market. These might be construed to mean that the short-termism problem goes away. But we do not think

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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. Given that fund managers’ compensation is an

increasing function of both performance and asset under management, 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 than corporate

investments. 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

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,

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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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 it is more complete and 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

described by 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

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. 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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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 holdings. We consequently hand match and merge

funds that differ only in fund class.

2.2. Definition of new money inflow

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 month, 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 month, the cash flow for fund i in month t

is given by:

i t i t i,t-1 i tNewmoney = TNA - TNA (1 + R ) (1)

where Rit is the rate of return of fund i in month 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.

itit

, 1

NewmoneyNewmoneygrowth = i tTNA −

(2)

2.3. Measuring performance

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, (3) a four-factor

model proposed by Carhart (1997), and (4) the median return for the fund style. 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:

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it it ft i mftFundperformance = Raw Return -R Rβ− (3)

where Rft is the return on one month T-bills, Rmft 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 βi is the beta of fund i (obtained through a market model regression of monthly

fund returns on the value-weighted market index return, using all the available data).

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 inflows

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 inflow), we include a quadratic term of the past performance in our

regression specification as below.

2

it 1 , 1 2 , 1Newmoneygrowth = i t i tFundperformance Fundperformance controlα β β− −+ + + (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-

to-performance sensitivity measure that uses past twelve month returns, a quadratic flow-

performance response function, and no additional control variables.

We construct the sensitivity of new fund flow to performance as the slope of the flow-to-

performance relationship, measured by the following “first derivative” term of equation

(4).

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it 1 2 , 1flow to performance sensitivity = 2 i tFundperformanceβ β −+ (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 inflow to performance, measures the proportion of the total

variation of the new money inflow explained by recent performance, using the regression

R2 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

Our first measure of fund managers’ investment horizons is the average remaining

holding periods of the securities in the fund. Since we have 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 they are sold12. We then take the value-

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. 12 For stocks held until the last period of the available data (Quarter 4 of 2003), we arbitrarily assume a remaining duration of another four quarters. This is consistent with an observed average turnover over the

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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 do not 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.15

Summary statistics for the variables used in the paper are provided in Table 1.

[Insert Table 1 about here.]

full sample of mutual funds of about once per year. Robustness checks using two or six quarters of remaning holding periods do not yield significantly different results. 13 In some instances assumptions have to be made about how fund managers sell stocks, such as first in first out (FIFO) or last in first out (LIFO). These turn 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 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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Table 2 presents the correlations between the measures of input and output short-

termism.


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 that, despite considerable

time series variations in both measures, both turnover and the volatility of new money

flow grow over time. All other measures of input and output short-termism exhibit

similar patterns.

[Insert Figure 1 about here.]

[Insert Figure 2 about here.]

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 do not 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.


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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

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 for

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autocorrelation in the coefficient estimates. 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 as

either 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.

, 1 , 1

2 i,t-1 3 , 1 4 i,t-1

_ ( _ _ ) + (fund performance )+ (log( ) ) (fund expense )

i t i t

i t

Output shortermism flow performance sensitivityTNA

α β

β β β−

−

= +

+(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,

which the previous literature argue would increase fund flows because marketing efforts

would make the good performance more salient. 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

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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.


The coefficient estimates reported in Table 4 for β1 are significantly positive when output

short-termism is measured by turnover (1.21 and 1.15 with t-statistics of 6.78 and 5.56

for columns (1) and (3), respectively) and significantly negative when output short-

termism is measured by the average remaining holding period -0.97 and -0.86 with t-

statistics of -2.64 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 0.035 (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 (0.025 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

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

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associated with shorter fund investment horizons. In deriving the results we control 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.


The results are consistent with those reported in Table 4. The coefficient estimates for

flow-to-performance sensitivity are significantly positive when output short-termism is

measured by turnover (0.79 and 0.91 with t-statistic of 3.94 and 7.00 for columns (1) and

(3), respectively) and significantly negative when output short-termism is measured by

average remaining holding period -0.64 and -0.94 with t-statistic of -5.46 and -3.42 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. Again the results hold 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

17

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 use fund flow-to-performance sensitivity obtained in regressions

with all historical fund flow and fund performance data, and the resulting measure of

investor short-termism has been assumed to be constant over time16. Arguably, because

fund characteristics can change over time, a constant level of investor short-termism may

be inappropriate. 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.


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 inflow and the statistical explanatory power of new money inflow

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

using past fund performance. New fund inflow volatility measures the degree of

unpredictability of fund inflows, which might cause managers to react by holding short

horizon investments. The statistical explanatory power of the flow-to-performance

relationship measures how well past performance can explain the new money inflow, and

thus measures the responsiveness of investors to short-term performance. The results of

these tests and of regressions run using both the PCSE and Fama-MacBeth methods are

reported in Tables 7 and 8.



Higher unexpected fund flow volatility does, indeed, increase turnover as reported in

Columns (1) and (3) of Table 7 (the coefficient on turnover is 0.28 (0.35) for the PCSE

(Fama-MacBeth) regression with t-statistic of 5.62 (3.68)) and decreases, as reported in

Columns (2) and (4), average remaining holding period (the coefficient on average

remaining holding period is -0.20 (-0.25) with t-statistic of -5.44 (-6.90). 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 do not 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-

termism can be determined endogenously and potentially by each other. We look for

19

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 an 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 funds19.

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 fund managers’ investment horizons we choose

marketing expenses, proxied by total fund fees, to be the instrumental variable for flow-

to-performance sensitivity21.

We follow the procedure in Chevalier and Ellison (1999a) in calculating manager age by

assuming managers to have been 21 upon graduating from college. Occasionally, when

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 There is a concern that there could be learning about manager ability, and investors react more to younger managers’ performance because there is more to learn about their abilities. In practice, however, investors are more likely to learn about funds, rather than fund managers. Existing fund rating agencies as well as funds themselves 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 fund manager 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

reported, we use managers’ birth years to calculate managers’ 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 the procedure in 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 = f1 (fund investor short-termism, manager age, other

controls);

and

Fund flow-to-performance sensitivity = f2 (fund investment short-termism, marketing

expense, other controls).

The results of the simultaneous equations regressions are reported in Table 9. We report

results where we measure investment short-termism by both average remaining holding

period and turnover, and investor short-termism by flow-to-performance sensitivity

measure estimated using past one year return, 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.


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. We also check whether adding the flow of money

(Newmoneygrowth) in (6) would affect the regression results. 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 paper23.

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

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 does 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 high incentives to perform in the short run. Such incentives largely

come in the form of increased fund inflow (and thus assets under management) on the

upside, and firing on the downside. As a consequence, fund managers might focus

excessively on short-term performance, at the cost of long term profits. Using mutual

fund holding and performance data, this paper finds that funds facing more short-term

performance pressure will invest for shorter horizons. Further causality tests demonstrate

investment short-termism to be caused by investor short-termism, but not vice versa.

“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. 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 R2,” namely, the difference between R2 with and without the return variables in a regression setting where all other variables of interest are already controlled for.

23

Such performance can have big implications for both asset pricing and corporate finance.

Excessive fund manager focus on short horizon investments will likely affect asset prices,

by inflating the price of the most liquid assets, which can be quickly resold without large

price impact. On the other hand, long term investments could be the “neglected asset

class” and thus might be less efficiently priced. Institutional investor short-termism can

also affect corporate decision making, given that institutional investors such as mutual

funds constitute an increasingly important investor class. Institutions are often argued to

perform monitoring role and affect corporate governance. If institutions only invest for

the short run, they might not have much interest to monitor management or participate in

active governance. Furthermore, corporate managers might react to the pressure of their

institutional investors by pursuing myopic investment decisions.

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 do not 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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Variable Name Number of Obs. mean median std. dev. skewness kurtosis minimum maximum

Measures of Investment Short-termism

Average Remaining Holding Period 17430 1.157 0.732 0.924 1.922 3.621 0.512 4.926Turnover 16792 0.831 0.680 0.656 1.504 2.775 0.020 3.440

Measures of Investor Short-termism

flow to performance sensitivity [1] 17430 49.500 49.000 28.860 0.000 -1.200 0.000 99.000flow to performance sensitivity [2] 17430 49.500 49.000 28.860 0.000 -1.200 0.000 99.000flow to performance sensitivity [3] 17430 49.500 49.000 28.860 0.000 -1.200 0.000 99.000flow to performance sensitivity [4] 17430 49.500 49.000 28.860 0.000 -1.200 0.000 99.000New Money Growth volatility 17379 0.084 0.056 0.123 5.384 32.646 0.015 0.973R2 [1] 17430 0.236 0.170 0.206 1.234 0.816 0.005 0.856R2 [2] 17430 0.364 0.306 0.240 0.763 -0.323 0.031 0.964R2 [3] 17430 0.286 0.212 0.238 1.081 0.393 0.004 0.959R2 [4] 17430 0.600 0.605 0.258 -0.152 -1.055 0.081 0.999

Control Variables

annual fund performance 17430 0.095 0.107 0.201 0.410 2.538 -0.760 1.826log(TNA) 17430 4.996 5.063 2.060 -0.279 0.669 -6.908 11.571expenses 17428 0.014 0.012 0.009 13.608 377.311 0.000 0.320

Table 1: Summary Statistics of Main Variables Used In Empirical Analysis

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

Holding Period

flow to performance sensitivity [1]



flow to performance sensitivity [4] R2 [1] R2 [2] R2 [3] R2 [4]

New Money Growth volatility

turnover 1.000 -0.194 0.072 0.070 0.127 0.086 0.163 0.084 0.164 0.137 0.084

Average Remaining -0.194 1.000 -0.062 -0.054 -0.078 -0.085 -0.083 -0.065 -0.088 -0.095 -0.064

0.072 -0.062 1.000 0.571 0.631 0.291 0.163 0.155 0.140 0.092 0.026

0.070 -0.054 0.571 1.000 0.365 0.463 0.244 0.211 0.252 0.285 0.066

0.127 -0.078 0.631 0.365 1.000 0.455 0.116 0.113 0.197 0.155 0.057

0.086 -0.085 0.291 0.463 0.455 1.000 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 0.211 0.113 0.033 0.502 1.000 0.309 0.338 0.068

R2 [3] 0.164 -0.088 0.140 0.252 0.197 0.069 0.347 0.309 1.000 0.497 0.083

R2 [4] 0.137 -0.095 0.092 0.285 0.155 0.160 0.161 0.338 0.497 1.000 0.081

New Money Growth 0.084 -0.064 0.026 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.



ending decile 1 2 3 4 5 6 7 8 9 10starting decile

1 0.69 0.17 0.06 0.03 0.01 0.01 0.00 0.01 0.01 0.012 0.19 0.44 0.18 0.08 0.04 0.03 0.02 0.01 0.01 0.013 0.06 0.23 0.36 0.17 0.07 0.04 0.02 0.03 0.01 0.014 0.04 0.10 0.19 0.33 0.15 0.09 0.04 0.03 0.01 0.015 0.02 0.05 0.10 0.18 0.33 0.15 0.09 0.05 0.02 0.026 0.01 0.02 0.04 0.10 0.19 0.33 0.13 0.09 0.04 0.037 0.01 0.02 0.04 0.06 0.10 0.16 0.33 0.16 0.09 0.038 0.01 0.01 0.02 0.03 0.06 0.10 0.16 0.35 0.18 0.099 0.01 0.01 0.02 0.02 0.04 0.05 0.11 0.19 0.41 0.14

10 0.01 0.00 0.00 0.01 0.02 0.03 0.05 0.07 0.17 0.64


1 0.42 0.17 0.09 0.07 0.05 0.05 0.05 0.04 0.05 0.042 0.20 0.23 0.17 0.11 0.08 0.05 0.04 0.05 0.04 0.033 0.11 0.17 0.18 0.16 0.10 0.08 0.07 0.05 0.04 0.044 0.07 0.15 0.16 0.14 0.13 0.10 0.08 0.07 0.05 0.055 0.06 0.11 0.11 0.15 0.14 0.11 0.09 0.09 0.09 0.056 0.06 0.06 0.08 0.11 0.14 0.15 0.13 0.12 0.08 0.077 0.03 0.05 0.06 0.09 0.13 0.14 0.15 0.15 0.12 0.088 0.05 0.04 0.06 0.07 0.10 0.11 0.14 0.17 0.14 0.129 0.03 0.04 0.05 0.06 0.07 0.11 0.12 0.17 0.19 0.16

10 0.04 0.03 0.05 0.06 0.07 0.09 0.07 0.10 0.15 0.33

Table 3 Transition probabilities of Selected Measures of Input and Output Short-termism

Panel A: Turnover: One Year Transition Probabilities

Panel B: Turnover: Three Year Transition Probabilities


1 0.52 0.21 0.08 0.05 0.04 0.02 0.02 0.01 0.02 0.032 0.20 0.26 0.19 0.14 0.09 0.04 0.03 0.02 0.02 0.023 0.08 0.18 0.17 0.19 0.15 0.10 0.05 0.03 0.03 0.034 0.05 0.11 0.18 0.17 0.18 0.11 0.08 0.04 0.05 0.035 0.04 0.09 0.12 0.16 0.19 0.16 0.09 0.07 0.03 0.046 0.02 0.07 0.08 0.13 0.15 0.18 0.15 0.12 0.05 0.067 0.02 0.04 0.07 0.09 0.08 0.17 0.20 0.18 0.11 0.048 0.02 0.04 0.04 0.06 0.08 0.11 0.18 0.19 0.20 0.099 0.01 0.03 0.03 0.04 0.05 0.06 0.12 0.21 0.27 0.19

10 0.02 0.02 0.02 0.04 0.05 0.05 0.06 0.10 0.20 0.44


1 0.35 0.19 0.08 0.06 0.07 0.06 0.05 0.05 0.04 0.042 0.16 0.19 0.13 0.13 0.10 0.08 0.06 0.06 0.06 0.043 0.09 0.13 0.16 0.14 0.12 0.11 0.08 0.07 0.05 0.054 0.07 0.10 0.14 0.14 0.14 0.12 0.10 0.07 0.05 0.065 0.06 0.09 0.14 0.15 0.14 0.11 0.12 0.09 0.06 0.056 0.06 0.09 0.08 0.11 0.13 0.13 0.14 0.12 0.08 0.077 0.05 0.08 0.09 0.09 0.10 0.11 0.13 0.16 0.11 0.088 0.06 0.06 0.06 0.09 0.10 0.09 0.13 0.14 0.17 0.119 0.05 0.06 0.07 0.08 0.08 0.10 0.09 0.12 0.19 0.17

10 0.04 0.05 0.06 0.06 0.08 0.08 0.08 0.11 0.15 0.31

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.

Panel D: Flow Volatility: Three Year Transition Probabilities

Panel C: Flow Volatility: One Year Transition Probabilities

(1) (2) (3) (4)

Turnover Maturity Turnover Maturity

intercept 0.85 0.71 0.81 0.71(26.98) (36.04) (27.68) (33.94)

flow to performance sensitivity [1] (x10-3) 1.21 -0.97 1.15 -0.86

(6.78) -(2.64) (5.56) -(3.64)

fund performance -0.02 0.09 -0.03 0.09-(0.71) (3.84) -(0.69) (3.37)

log(TNA) (x10-3) -15.33 -1.50 -15.68 -1.47-(5.45) -(0.78) -(5.43) -(0.79)

expenses 8.88 -2.20 6.79 -3.21(3.97) -(4.79) (3.79) -(6.09)

absolute value of lagged 0.24 -0.19 0.27 -0.16new money growth rate (3.29) (5.63) (2.70) (3.77)

Fund Style Fixed Effect Included Included Included Included

Year Fixed Effect Included Included Not Included Not Included

Number of Observations 16777 17407 16777 17407

Adjusted R-squared 0.08 0.06

PCSE Regressions Fama-MacBeth Regression

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 4: Regression of Turnover and Maturity on flow-to-performance sensitivity

Dependent Variable


(1) (2) (3) (4)

Intercept 0.83 0.78 0.66 0.58(30.14) (39.81) (6.49) (11.40)

flow to performance [2] (x10-3) 0.79 -0.64 0.91 -0.94(3.94) -(5.46) (7.00) -(3.42)

annual fund performance -0.02 0.09 -0.47 0.27-(0.70) (3.96) -(1.83) (2.98)

log(TNA) (x10-3) -15.49 1.01 -13.10 0.01-(5.05) (0.52) -(2.06) (0.00)

expenses 8.44 -2.04 11.06 -3.47(4.14) -(1.98) (5.15) -(3.46)





0.06 0.05

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.

Table 5: Regression of Turnover and Maturity on two-year flow-to-performance sensitivity

Dependent Variables

(1) (2) (3) (4)

Intercept 0.77 0.87 0.72 0.78(22.48) (21.34) (33.78) (29.83)

flow to performance sensitivity [3] (x10-3) 1.72 -0.92(8.13) -(4.68)

flow to performance sensitivity [4] (x10-3) 1.31 -0.84(5.33) -(3.30)

annual fund performance -0.04 -0.11 0.14 0.15-(1.05) -(2.20) (3.73) (4.24)

log(TNA) (x10-3) -16.74 -16.30 0.31 0.19-(5.31) -(4.19) (0.12) (0.07)

EXPENSES 7.08 6.11 -2.42 -2.61(8.82) (8.68) -(4.66) -(5.18)

absolute value of lagged 0.18 0.17 -0.14 -0.15new money growth rate (5.53) (3.59) (6.14) (5.18)


Year Fixed Effect Included Included Included Included


Table 6: Regression of Turnover and Maturity on year-by-year rolling estimate of flow-to-performance sensitivity

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.

Dependent VariableTurnover Maturity

(1) (2) (3) (4)


intercept 0.79 0.73 0.62 0.57(26.01) (38.02) (7.17) (9.79)

Fund Growth volatility 0.28 -0.20 0.35 -0.25(5.62) -(5.44) (3.68) -(6.90)


log(TNA) (x10-3) -9.07 1.42 -7.55 1.73-(3.50) (1.28) -(2.81) (1.40)

expenses 8.73 -3.04 11.18 -3.78(14.92) -(5.46) (4.67) -(4.34)







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 7: Regression of Turnover and Maturity on Standard Deviation of New Money Growth

Dependent Variable

(1) (2) (3) (4)


intercept 0.76 0.68 0.78 0.63(25.97) (34.07) (5.58) (13.31)

R2 [1] 0.14 -0.10 0.11 -0.09(6.38) -(4.40) (4.97) -(3.79)


log(TNA) (x10-3) -15.59 1.51 -10.25 -0.06-(3.82) (0.73) -(1.48) -(0.01)

expenses 8.21 -2.08 10.69 -4.03(3.61) -(1.79) (5.37) -(3.30)







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 8: Regression of Turnover and Maturity on Regression R-squared for the flow to performance relation

Dependent Variable

Equation 1: Investment Short-termism = f1 (Investor Short-termism, Manager Age, Controls);

Dependent Variable Avg Remaining Maturity Turnover

Intercept -13.09 Intercept -32.54-2.36 -4.11

-1.96 3.64-3.60 4.16

fund performance 0.15 fund performance -0.063.56 -1.07

log(TNA) (x10-3) -1.52 log(TNA) (x10-3) -20.42-0.34 -3.53

Manager Age (x10-3) 3.22 Manager Age -4.393.27 -3.43

Equation 2: Investor Short-termism = f2 (Investment Short-termism, Total Percentage Fees, Controls);

Dependent Variable

Intercept 1188.38 Intercept 1047.455.33 5.71

3.53 -4.340.60 -0.97

fund performance -1.75 fund performance -1.77-1.14 -1.10

log(TNA) (x10-3) 3427.20 log(TNA) (x10-3) 2968.5124.95 21.75

Total percentage Fees 3.58 Total percentage Fees 3.133.96 3.37

Table 9: Simultaneous Equations Estimate of Both Input and Output Short-termism

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 flow to performance sensitivity. T-statistics are reported in parentheses.

Average Remaining Maturity

flow to performance sensitivity [1] x 10 (-3)

Turnover

flow to performance sensitivity [1] x 10 (-3)



Figure 1: Mean and Median Turnover Over Time

Mean Turnover = 0.0147x Year - 28.602

Median Turnover = 0.0181x Year - 35.502

0.4

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1990 1992 1994 1996 1998 2000 2002 2004

Year

Turn

over

Mean Turnover Median TurnoverLinear (Mean Turnover) Linear (Median Turnover)

Figure 2: Mean and Median Flow Volatility Over Time

Mean Flow Volatility = 0.0036 x Year - 7.1598

Median Flow Volatility = 0.004 x Year - 7.8578

0

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0.03

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0.1

1990 1992 1994 1996 1998 2000 2002 2004Year

Flow

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atili

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Mean Flow Volatility Median Flow VolatilityLinear (Mean Flow Volatility) Linear (Median Flow Volatility)

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