Why do experienced hedge fund managers have lower returns?
Why do experienced hedge fund managers have lower returns?
Nicole M. Boyson*
November 3, 2003
Several theories of reputation suggest that managers’ career concerns affect their decisions. We
investigate these theories by studying the behavior of hedge fund managers over their careers. In
contrast with mutual fund managers who incur more risk over time, hedge fund managers take on
less risk over time. This finding is consistent with certain industry characteristics which imply
that experienced managers have “more to lose” in personal wealth, current income, and
reputation should their funds fail. Most important, the propensity of experienced managers to
reduce risk explains their underperformance. These results have implications for fund selection
and incentive contract design.
* Purdue University, Krannert Graduate School of Management, West Lafayette, IN 47907. E-mail:
firstname.lastname@example.org. Phone: (765) 496-7877. This paper is a revised version of an earlier paper entitled,
“How are hedge fund manager characteristics related to returns, risk, and survival?” I would like to thank Vikas
Agarwal, Richard Brealey, Stephen Brown, Steve Buser, Mike Cliff, Mike Cooper, Dave Denis, Diane Denis, Jean
Helwege, David Hirshleifer, Kewei Hou, Andrew Karolyi, Bing Liang, Bernadette Minton, John McConnell,
Narayan Naik, Karen Wruck, and René Stulz (my advisor), and seminar participants at The Ohio State University,
Purdue University, and London Business School's Centre for Hedge Fund Research and Education. All remaining
errors are my own.
A number of theoretical models hypothesize that managers will alter their risk-taking
behavior as their careers progress. Avery and Chevalier (1999) posit an increase in risk over
time, arguing that as managers gain experience they obtain more precise knowledge about, and
confidence in, their own abilities. Hence, older managers will take bold or aggressive actions
while younger managers will be more likely to mimic other managers, or to “herd.” Empirically,
in studies of mutual fund managers, security analysts, and macroeconomic forecasters
respectively, Chevalier and Ellison (1999b), Hong, Kubik, and Solomon (2000), and Lamont
(2002) report results consistent with this theory: old herd less than young.
By contrast, other models predict that risk-taking behavior will decrease as managers age
(see Prendergast and Stole (1996), hereafter PS, and Graham (1999)). The PS model argues that
managers wish to acquire a reputation for learning, and thus when faced with new information
each period, young managers overreact (to show they have good information) while old
managers underreact (so as not to signal that their actions in prior periods were wrong).
Additionally, a large labor economics and management literature supports the more general idea
that risk-aversion increases with age, causing a reduction in risky behavior.1 The empirical
results of Graham (1999) (investment newsletters) and Li (2002) (securities analysts) show a
decrease in risk over time. Also, in studies of CEOs and top managers, Katz (1982), Hambrick
and Mason (1984), Finkelstein and Hambrick (1990), and Jaggia and Thosar (2000) obtain
Perhaps the contradictory conclusions of these empirical studies owes to how well
various industries and firms provide incentives to overcome the effects of “career concerns.”
The “career concerns” literature, pioneered by Fama (1980), argues that principal-agent (agency)
problems (which occur when the incentives of investors and fund managers are misaligned) can
be mitigated by a manager's desire to keep his current job or obtain a better job (i.e., his “career
concerns”). However, sometimes career concerns can work too well, resolving some agency
problems while causing others (see Holmstrom (1982), and Holmstrom and Ricart i Costa
(1986)). An alternative view is that “career concerns” are themselves a type of agency problem,
For example, see Salancik (1977), Morin and Suarez (1983), Holmstrom and Milgrom (1987), Kanodia, Bushman,
and Dickhaut (1989), and Bernheim (1994), as well as related sociology literature (see Vroom and Pahl (1971),
Kiesler (1971), and Kahneman, Slovic, and Tversky (1982).
which sometimes mitigate and sometimes exacerbate existing problems. This paper studies the
implications of this literature in a new venue: the hedge fund industry.
Two features of the hedge fund industry motivate this study: low agency costs relative to
other money managers and career concerns that should change significantly over time. Hedge
fund managers face low agency costs for a number of reasons. First, they are not required to
publicly report their holdings, trades, or returns, which reduces their incentive to “window-
dress.”2 Second, hedge fund manager compensation is based on profits (20% is typical), rather
than on assets (1-2% is typical) as in the mutual fund industry. Arguably, this fee structure better
aligns shareholder and manager interests. The empirical literature supports this concept: in an
attempt to increase asset size, mutual fund managers increase risk when their funds underperform
in the first half of a year (see Brown, Harlow, and Starks (1996) and Chevalier and Ellison
(1997)). By contrast, hedge fund managers facing the same situation do not increase risk (see
Brown, Goetzmann, and Park (2001) (hereafter, BGP)).3
The third reason that agency costs are lower for hedge fund managers is that they invest
substantial personal assets in their funds. For example Bruce Cohen (who manages the SAC
Capital Advisors' fund) has about $1 billion of his own capital in the fund.4 An article in the
April, 2002 issue of Fortune notes that Crispin Odey and his team at Odey Asset Management
have at least $30 million of their own assets in the fund. By contrast, mutual fund managers
rarely have significant personal assets in their funds. Finally, hedge funds do not allow continual
investment and redemption of shares. These “lock-up” periods reduce agency costs by
encouraging managers to invest in longer-term strategies, and also reduce the amount of cash
that a manager must hold for withdrawals.
The second unique feature of the hedge fund industry is that career concerns are likely to
change significantly over time. There are at least three reasons we would expect this to happen.
First, hedge fund managers earn large salaries which are based on both the size and profitability
of their funds. Since older managers have much larger funds on average, they can earn high
Window-dressing refers to altering one's portfolio near a required reporting period in an attempt to attract new
customers. See Lakonishok, et. al. (1991).
Since hedge fund manager fees are asymmetrical (managers make money when their funds earn profits but do not
lose money when their funds incur losses), perhaps managers will take on too much risk. However, the literature
shows that these fees appear to encourage an “appropriate” amount of risk. See Starks (1987), Gibbons and Murphy
(1992), Carpenter (2000), and Das and Sundaram (2002).
See Institutional Investor, June, 2002.
salaries at low returns, which provides incentives for young managers to take on more risk, and
for old managers to reduce their risk levels as they age (and their funds grow in size).5 Second,
failed hedge fund managers rarely start new hedge funds (see BGP, (2001)). By contrast, about
67% of “failed” or terminated mutual fund managers remain in their industry. And, the pay
differential in the industries is tremendous: the average hedge fund manager in the sample has a
salary of about $4 million, while an average mutual fund manager made about $440,000 (in
2001). So, not only does a failed hedge fund manager have to leave the industry, but also, he
must take a very large pay cut if he moves to a different industry (for example, the mutual fund
industry). Arguably, older managers with larger funds are more affected by this situation.
Finally, experienced managers with larger funds probably have significant personal assets at
stake. Thus an older manager will likely have greater career concerns than a young manager.
For example, he has strong incentives to avoid failure in order to protect his wealth and
Low agency costs and high career concerns imply that the predictions of the PS and
Graham models are likely to be applicable to hedge fund managers. Specifically, if agency costs
are low, career concerns will be relatively more important, and to the extent that career concerns
increase over time, managers should become more risk-averse as their careers progress. This
paper finds results largely consistent with this theory.
With a large sample of hedge fund managers over the period 1994-2000, this paper tests
the opposing theories by examining how the risk-taking behavior of hedge fund managers
changes over time. Consistent with the predictions of PS, I find a strong negative relationship
between manager experience and risk-taking behavior; i.e., older managers are have career
concerns that induce them to take on less risk. This result is in direct contrast with previous
empirical findings for mutual fund managers; in mutual funds, more experienced managers face
In the sample, when managers are divided into thirds based on their tenure, the average fund size for a “young”
manager is about $10 million. By contrast, the average fund size for an “old” manager is over $250 million.
Clearly, young managers have a motivation to earn high returns, both to increase their current salaries and grow
their funds in order to increase future earnings. Old managers, by contrast, can earn much lower returns and still be
The opposing argument may also be made. A young manager arguably has more at stake in terms of expected
future earnings than an older manager. However, a young manager also will not have had the success and past
profitability of an old manager, so the transition from the hedge fund industry, to say, the mutual fund industry, may
be less difficult for him. Regardless, the empirical results of this paper support the idea that hedge fund manager
career concerns increase, rather than decrease, with age.
lower agency costs and have lower reputational concerns, leading to more risk-taking than their
less experienced counterparts (see Avery and Chevalier (1999) and Chevalier and Ellison
The remainder of the paper explains this negative relationship between manager age and
risk-taking behavior in light of the unique career concerns of older hedge fund managers (i.e.,
older managers have “more to lose” should their funds fail). I hypothesize that this negative
relationship is driven by older managers’ consciously reducing the types of risk that might be
associated with fund failure. Consistent with this hypothesis, I show that high levels of risk-
taking behavior do indeed lead to fund failure. These results are statistically and economically
Finally, this paper uses the finding that older managers reduce risk-taking behavior to
explain the empirical result that older managers have lower returns. (See Liang (1999) and
Edwards and Caglayan (2001).) While this difference cannot be explained by systematic
differences in “market beta” risk (measured as coefficients from regressions of fund returns on a
number of market factors), it can be explained by differences in volatility and “herding” behavior
documented above.7 These results are rather compelling. For example, risk-taking behavior
explains a large portion of the difference in returns at various levels of manager experience:
while controlling for “market beta” risk and fund characteristics, but without controlling for the
risk measures of interest (volatility and herding measures), the difference in returns between a 52
year-old manager and the average (47 year-old) manager is about 4.0% annually. However,
when controlling for volatility and herding risk, this difference drops to about 2.1% per year.
These results are consistent across all measures of volatility and herding risk, and robustness
checks confirm that they are not likely due to survival bias or decreasing manager ability over
The paper is organized as follows. Section 2 describes the data. Section 3 confirms the
negative relationship between hedge fund manager experience and fund returns, which holds for
both raw returns and when controlling for correlation with market indices. I hypothesize that
career concerns of older managers lead to a decrease in the types of risk that could lead the fund
I calculate regression coefficients for a number of proxies for the “market.” These include various passive indices,
hedge fund indices, and returns on dynamic trading strategies. While these factors are often relevant in explaining
hedge fund performance, the coefficients on these factors do not systematically differ among young and old
to fail, which causes the lower returns. The remaining sections confirm this hypothesis. First,
Section 4 shows that experienced managers do indeed reduce certain types of risk-taking
behavior. Next, Section 5 documents that increases in these types of risk-taking behavior are
positively related to fund failure – providing an explanation for the observed low risk-taking
behavior of older managers – and also finds that more experienced managers tend to have lower
failure rates than less experienced, implying that managers are responding to this incentive.
Finally, Section 6 shows that this decrease in risky behavior can explain a substantial portion of
the fall in performance of experienced managers. Section 7 provides a number of robustness
checks and Section 8 concludes.
Data was provided by Tremont Advisory Shareholders Services (TASS). TASS has been
collecting hedge fund data directly from managers since the late 1980's, and currently has over
2,400 funds in their database, both living and dead.8 The database includes monthly net-of-fee
returns, as well as expenses, fees, size, terms, age, and style of the funds. Many of the funds also
include a biographical sketch of the manager, providing information such as age, schooling,
professional designations, and prior work experience.
TASS categorizes funds as “dead” when they stop reporting their monthly information to
the database. Because the fund managers voluntarily report this data, there are a number of
reasons why funds stop reporting, including failure, merger, or a more arbitrary reason, such as
the fund manager's belief that being included in the database is not helping him raise additional
investment dollars. While some of these reasons are clearly associated with poor performance
(e.g., failure), others are less obvious. In most cases, TASS includes the reason for leaving the
database. Section 5 performs a detailed analysis of fund failure.9
Each fund must have 24 months of consecutive returns and at least $5 million in assets
during the period January, 1994 to December, 2000 for inclusion in the sample. Obviously, this
TASS has maintained data on dead funds since 1994.
Some managers might leave the sample because they are very successful and, from a marketing perspective, do not
need to report their data any longer. However, as a preview of results from the analysis in Section 5, most funds that
leave the sample are suffering from poor performance, and whether they actually “failed” at the time of leaving the
sample or at some time thereafter (and got out of the sample beforehand to save face), most of them were not
successful funds (they were smaller and had lower than average returns).
requirement represents a trade-off between sample size and ensuring that each fund has a long
enough time series for meaningful regression results. At the beginning of its existence, a hedge
fund undergoes an incubation period where the only significant investor is the fund manager. He
hopes to compile a good track record before making the fund available to the public. Thus, most
funds arrive in the database with a history of strong performance which was never available to
outside investors, biasing returns upward. This difference is often large – using the TASS
database, Fung and Hsieh (2000) calculate the bias as about 3.6% per year. TASS provides the
incubation period for each fund; thus, to control for this bias I drop the data for the initial
incubation period for each fund.
The final sample includes 982 funds (of which 285 failed at sometime during the sample
period). Variables included are fund returns and fund characteristics, including manager tenure.
A smaller sample of 271 funds includes fund returns, fund characteristics, and also includes
manager characteristics, such as age and education level. Since manager tenure is the variable
of interest in many of the following tests, the large sample is used throughout most of the paper,
with the smaller sample available for robustness tests. Table 1a includes summary statistics of
the return, manager and fund characteristics variables.
To facilitate the interpretation of test results from the large (982 fund) sample, Table 1b
converts the manager tenure variable to a manager age variable. Using the smaller (271 fund)
sample, the table divides the small sample into deciles by tenure, and then calculates average
manager age for each decile. The tenure percentile estimates for the small sample are within a
few months of the tenure percentile estimates for the large sample, so the age estimates appear to
As a brief summary, the average hedge fund manager is 47 years old, has 22 years of
work experience, manages a fund of about $113 million dollars, and comes from an
undergraduate institution with SAT scores about 300 points over the national average. About
half the managers in the sample have MBAs, and approximately 70% of the managers use
leverage and have their own money invested in their funds. Clearly, this is an experienced, well-
educated group. In comparison, the average mutual fund manager is about 3 years younger,
attended a school with average SAT scores about 200 points lower, and is slightly more likely to
have an MBA than the average hedge fund manager.10
2.1. Measures of risk-taking behavior
In order to understand how managers' risk-taking behavior changes over their careers, we
need a measure of risk. This paper uses three measures. The first is the total standard deviation
of a portfolio's returns. Standard deviation is an absolute measure in that it does not compare the
risk of a portfolio to the risk of a benchmark. Hence, it may or may not be correlated with the
coefficients estimated from a regression of returns on market indices, and as such, may contain
additional useful information regarding managerial risk-taking behavior. Additionally, since
hedge fund managers can engage in dynamic strategies, using leverage, options and other
derivative securities, they may have some control over the standard deviation of their funds'
While standard deviation measures total risk, this paper also uses two relative risk (or
“herding”) measures. The first is tracking error deviation measured relative to a fund-of-funds
(FOF) index. A fund-of-funds is a professionally managed hedge fund that invests in other funds.
Fung and Hsieh (2000) note that a fund-of-funds index is an appropriate benchmark for
measuring hedge fund performance, which motivates its use in this paper. Tracking error
measures the volatility in returns not explained by market volatility (specifically, it is the
standard deviation of the residuals in a time-series regression of a individual fund's returns on the
FOF index (and is calculated annually). Tracking error deviation measures how much a
manager's tracking error differs from that of the average manager in the same style category
(specifically, it is calculated by subtracting the average tracking error for a given fund’s style
from the tracking error of that fund). Since it is measured relative to other managers, it is a type
of herding measure.
The second relative risk measure is beta deviation, following Chevalier and Ellison
(1999b). Beta deviation uses each individual fund's time-series coefficient (beta) from a
regression of that fund's returns on the FOF index, and is calculated as the difference between
fund's beta on the FOF index and the average beta on the FOF index for all other funds in the
This data is obtained from Chevalier and Ellison (1999a).
same style category. It is also calculated annually.11 Like tracking error deviation, beta
deviation is also a herding measure.
2.2. Measures of performance
In addition to measuring risk-taking behavior of managers, we must also measure their
performance, which we estimate in three ways. The first measure uses simple excess-of-risk-
free-rate returns. The risk-free rate is the 30-day Treasury bill rate. The second performance
measure adjusts the returns for exposure to a number of passive indices, the factors used by
Carhart (1997), and a put option return as suggested by Agarwal and Naik (2003). The third
performance measure controls for exposure to hedge fund indices, in the spirit of Sharpe's style
analysis (see Sharpe (1992, 1994)), and as suggested by Fung and Hsieh (2001) and L'habitant
(2001). These indices should proxy for the specific risks faced by hedge funds.
The passive indices used in the second measure of performance are obtained from
Datastream and include: the US Trade Weighted Dollar index to capture currency risk, gold and
commodity indices, the Lehman Brothers Eurobond, 30-year Treasury bond, and U.S. aggregate
bond indices, and the S&P 500, Wilshire 5000, and MSCI Eafe stock market indices.
Additionally included are the Fama-French (1992,1993) SMB (a zero-investment portfolio
constructed by subtracting the returns of large market capitalization firms from the returns of
small capitalization firms) and HML (a zero investment portfolio constructed by subtracting the
returns of low book to market ratio stocks from the returns of high book to market ratio stocks)
factors, as well as Jegadeesh and Titman's (1993) momentum factor (MOM) – a zero-investment
portfolio constructed as the spread between the performance of stocks which were in the top 30%
of returns in the prior twelve months and those which were in the bottom 30%.12 Finally, the
regressions include a simple option trading strategy as suggested by Agarwal and Naik (2003).
For each passive index listed above, the return from investing each month in a one-month at-the-
money put option, holding the option for a month, and then reinvesting in next month's option is
This measure differs slightly from that used by Chevalier and Ellison (1999b). For their sample of equity mutual
funds, they calculate betas for each fund by regressing returns on a broad stock market index. For each fund, beta
deviation is the absolute value of the difference between a fund's beta and 1 (since a well-diversified equity mutual
fund would have a beta of about 1). For hedge funds, betas vary widely by style, and thus, the measure used in this
paper accounts for these style differences by measuring each fund's beta deviation relative to the average beta in its
These returns were obtained from the website of Kenneth French.
calculated, using the Black-Scholes model to estimate the option prices. Then, each month, an
equally weighted average of all the index option returns is calculated and used as a regressor.
As mentioned above, the third measure of performance uses hedge fund indices to control
for market exposure. These indices are published jointly by Credit Suisse First Boston (CSFB)
and TASS, and represent a number of hedge fund trading strategies. They are constructed so as
to minimize survivorship bias. For further detail about the indices used, see Appendix A. Table
2 reports summary statistics for all indices used in the paper.
For the second and third measures of performance (which control for exposures to
passive indices and hedge fund indices, respectively), I estimate two-step Fama-Macbeth (1973)
regressions. The first step performs time-series regressions to estimate each fund's loading on
each index. The second step uses these loadings as controls for market exposure in cross-
sectional regressions of each fund's return on fund and manager characteristics.
A well-known problem caused by two-stage regressions is that to the extent the
coefficients from the first-pass regressions are estimated with error, this error will then be
included in the second-pass regressions. (Fama and Macbeth (1973) refer to this as the “errors-
in-variables” problem.) They use portfolios of securities (or, in this case, hedge funds), rather
than individual securities (hedge funds), in the first-pass regression, to “average out” the
estimation error. Also, to maximize dispersion of coefficients among these portfolios, Fama and
French (1992, 1993) use firm size as well as individual fund beta to form the portfolios.13
I follow the Fama-Macbeth approach, with one exception. While they form portfolios in
one period (the formation period) and estimate the portfolio coefficients (betas) in a different
period (the estimation period), I use entire time-series of data for both formation and estimation.
Using separate periods reduces the likelihood of the “regression phenomenon”: when portfolios
are formed and betas are estimated in the same period, errors from the individual fund
regressions will be passed on to the portfolio regressions. However, due to the short time series
of data available, this paper cannot use separate formation and estimation periods. Thus, while
this approach of this paper addresses the “errors-in-variables” problem, it does not address the
One reason that Fama and French use fund size in addition to beta is that for individual firms, beta and fund size
are not highly correlated. Their finding also holds for hedge funds: I calculate correlations between the betas on
each index and hedge fund size as between -0.01 to 0.08.
To address this concern, I perform two robustness checks. First, I form portfolios based
on size only, as opposed to both size and beta. This approach completely eliminates the
“regression phenomenon,” since as noted above, estimated betas and hedge fund size have very
low correlations. Using this method, the main results of the paper remain unchanged. The
second robustness check sorts funds into portfolios based on the first two years of data, and
estimates portfolio betas using the next two years of data. Then, cross-sectional regressions are
performed using the remaining three years of data. Again, the results from this approach are
similar to those obtained when all the data is used in the formation, estimation, and testing
periods. These robustness checks provide comfort that the approach of using all the data for the
formation, estimation, and testing periods does not cause a significant “regression phenomenon”
The specifics of the process are as follows. First, I perform time-series regressions using
all the data, where I calculate “sorting” coefficients on each index for each fund.14 A single-
factor model estimates the time-series coefficient (beta) for each fund for each index, as follows:
r t = α t + bs t INDEX t + ε t (1)
where r it is fund i's return in period t (t=1 to 84 months), and (INDEXt) refers to the index used.
Next, I sort funds into quartile portfolios, based on this “sorting” beta (bst) and fund size.
This sort results in sixteen (16) portfolios for each index.15 Once the portfolios are formed, I
calculate equally-weighted averages of excess returns (net of the risk-free rate) for each set of
portfolios. Then, time-series regressions of the portfolio returns (rp) are performed for each
index as follows:
r pt = α pj + b pj INDEX t + ε t (2)
Recall that there are two separate sets of indices used as controls for market risk factors, passive and hedge fund.
Descriptive statistics of these indices may be found in Table 2.
The results are robust to using nine (9) or twenty-five (25) portfolios.
where r pt are monthly portfolio returns in excess of the risk-free rate (number of months, t=84),
and INDEX is the return on the INDEX used in the sorting process. Thus, sixteen (j=16)
regressions for each INDEX are performed (one for each of the sixteen portfolios). I then assign
the resulting coefficients (the bpj’s) to each fund in the sample, based on its portfolio
classification. This process results in each fund being assigned a beta coefficient for each index.
These coefficients are then used as market controls in the cross-sectional regressions in Section
3. The coefficients on the passive indices are used as market controls in the second measure of
performance, while the coefficients on the hedge fund indices are used as market controls in the
third measure of performance.
3. The relationship between returns and manager experience
As an overview, this paper tests the following hypothesis, which is motivated by the work
of PS (1996) and Graham (1999): Due to career concerns that increase over time, managers will
decrease risk as their careers progress. This reduction in risk-taking behavior can explain the
previous empirical finding that older managers have lower returns. Hence, we begin by
confirming that the negative relationship between returns and age that was documented in prior
studies also holds for this paper's data set. (See Liang (1999) and Edwards and Caglayan
(2001).) Our dataset differs from theirs: Liang uses a sample of 385 funds from the Hedge Fund
Research (HFR) database with at least three years of monthly returns for the period 1994 to
1996. Edwards and Caglayan use a sample of 836 funds with at least 24 months of returns from
the MAR/Hedge database for the time period January 1990 to August 1998.
I perform monthly cross-sectional regressions where the dependent variable is one of the
three different performance measures as described in Section 2.2, and the independent variables
are a number of manager and fund characteristics. The variable of interest in these regressions is
manager tenure (and/or manager age). In the first set of regressions, the dependent variable is
“excess” return, and no controls for market exposure are included. The second set of regressions
controls for exposure to “passive” market indices using the “passive” betas calculated in Section
2.2, while the third set of regressions controls for exposure to hedge fund market indices using
the “hedge fund” betas calculated in Section 2.2.
The regression for the first performance measure (excess returns) is as follows:
r t = α t + ∑ b j ,t M + ∑ c k ,t F k ,t + ε t
i i i
j =1 k =1
where the r t ’s are the monthly returns (in excess of the risk-free rate) of fund i in month t, the
M j ,t
’s are the manager characteristic variables of fund i in month t, and the F k ,t ’s are the fund
characteristics of fund i in month t.
The second and third cross-sectional regressions also include controls for market
exposure. Regression two includes passive market controls, where regression three includes
hedge fund market controls, as follows:
J K L
r t = α t + ∑ b j ,t M + ∑ c k , t F k ,t + ∑ d l , t E l ,t + ε t
i i i i
j =1 k =1 l =1
i i i i
where the r t ’s the M j ,t
’s and the F k ,t ’s are the same as in Equation (3) above, and the E l ,t ’s
are the market control coefficients assigned to each fund i from the time-series regressions on
passive (regression 2), or hedge fund (regression 3), indices as described in Section 2.2.
Table 3 reports the results from these regressions. In the table, regressions 1a and 1b use
excess return as the dependent variable as in equation (3) above, and do not control for exposure
to market indices. Regressions 2a and 2b control for exposure to passive indices, and regressions
3a and 3b control for exposure to hedge fund indices, as in equation (4) above. For all
specifications, regression a is performed on the small sample (271 funds), which includes all
manager, as well as all fund characteristics, while regression b is performed on the larger sample
(982 funds), which includes only fund characteristics and manager tenure.
Consistent with previous literature, manager tenure is strongly negatively related to
performance. This finding is robust to all specifications tested. The results are significant, both
economically and statistically. For each year of experience, annual returns decrease by about
0.8%. (For ease of interpretation, the coefficients on manager age and tenure are annualized in
Table 3.) Thus, a 52 year-old manager has returns about 4% lower than the average (age 47)
manager. Additionally, the coefficient on manager age is significant and negative, the same
direction as the manager tenure variable.16 Both tenure and age are meaningful proxies for
reputation, since a manager's reputation may have been developed before he became a hedge
Including the market control variables in regressions 2a and b and 3a and b provides
additional explanatory power; however, the coefficient on manager tenure remains significant
and negative. Most importantly, including these market control variables does not change the
manager tenure coefficient by very much: it drops from -0.8%/year to -0.7%/year. In other
words, although “market beta” risk explains a fair amount of the cross-sectional variation in
returns – the R2’s from the regressions that do not include market control variables are about
0.07, while the R2’s from the regressions that do include the market risk variables are about 0.30
– the negative relationship between manager tenure and returns still holds.
This section has confirmed the existence of a negative relationship between manager
tenure and hedge fund performance, one that is not explained by other fund characteristics or by
exposure to “market beta” risk. Now that this relationship has been confirmed, the next section
begins to investigate this paper’s main hypothesis: that, motivated by career concerns, managers
reduce risk as their careers progress, and that this reduction in risk can explain their lower
4. The relationship between manager tenure and risk-taking behavior
The first step in testing this hypothesis is to examine the relationship between manager
tenure and risk-taking behavior. Consistent with other empirical studies, we use manager tenure
(or age) as a proxy for manager reputation: as managers progress in their careers, their
reputations become more established. Another reasonable proxy for the importance of a
manager's reputation is fund size. As funds grow, their managers become better known and have
more at stake (with respect to both wages and status). Finally, the interaction of tenure and size
is another proxy: at the extremes, experienced managers with large funds should have “higher”
The number of years experience variable has coefficients and significance levels nearly identical to those found
for manager age, so results are not reported in Table 3.
reputational concerns than inexperienced managers with small funds. Hence, all three measures
(tenure, size, and the tenure/size interactions) are used as proxies below.
First, we perform nonparametric tests. For each of the 982 funds, we calculate the mean
values for manager tenure and fund size over the period 1994 to 2000. Funds are then sorted
into thirds as either young tenure, middle tenure, or old tenure. Additionally, funds are sorted
into three size categories: small, medium and large. Next, funds are cross-sorted into nine
tenure/size categories based on these classifications. Finally, means of the risk measures
(standard deviation, tracking error deviation, and beta deviation) are calculated for each tenure,
size, and tenure/size interaction category. Table 4 reports these means, and shows results from
Wilcox rank-sum tests for differences in means. Additionally, Figure 1 displays the risk
measures by reputation proxies. This Figure and Table 4 clarify the general pattern of risk-
taking behavior: managers reduce risk as they gain experience. The results are notable: for all
risk measures, the Wilcox tests of the difference in means between the extreme categories
(young and old, small and large, and small/young and old/large) are statistically significant at the
1% level. Figure 2 provides still more evidence: it plots the risk measures by the nine (9) fund
tenure/size categories. The pattern is even more dramatic and shows a nearly monotonic decrease
in risk by tenure/size category.
While these findings are suggestive, they do not control for heterogeneity in fund
characteristics, which could be influencing the results. Additionally, they aggregate the data
over each fund's entire existence during 1994-2000, so that time-series variation is not
considered. To address these issues, Table 5a presents results from a number of annual
regressions where the dependent variable is the risk measure (either standard deviation, tracking
error deviation or beta deviation) and the independent variable of interest is the measure of
reputation (either tenure, size, or the tenure/size interaction). These regressions control for a
number of fund characteristics listed in Table 1a.
Panel A of Table 5a reports results from regressions of standard deviation against
manager tenure and all fund characteristic variables (specification 1a), fund size and all fund
characteristic variables (regression 1b), and the manager tenure/fund size interaction variable and
all fund characteristic variables (regression 1c). Panels B and C (regressions 2a-3c) show results
from the same three regressions for each of the other risk measures. The results in Table 5a are
consistent with the results in Table 4: for all specifications and for all risk measures, tenure, fund
size, and tenure/size interactions are negatively related to risk-taking behavior. Managers with
more reputation at stake (as measured by tenure, size, or tenure/size interactions) incur less risk.
An additional result from Table 5a is worth investigating. The coefficient on “personal
capital invested” (a 0/1 indicator variable) is positive and significant for two of the three
regressions (standard deviation and beta deviation). This is puzzling, and contrary to our
hypothesis that managers with personal capital at stake will likely incur less risk in order to
preserve their assets. One reason might be that the “personal capital invested” variable is not
very informative. As a 0/1 variable, it says nothing about the magnitude of the manager's
investment or about the percentage of personal wealth that the investment represents. About
67% of managers answered “yes” to this question; their personal stakes in their funds likely vary.
To address this result, Table 5b reports results from additional regressions which include
additional independent variables: the reputation variable (either manager tenure, fund size, or
tenure/size interactions) is interacted with the “personal capital invested” variable. The focus is
on the personal capital interaction variables. A negative coefficient on these interaction variables
indicates that managers with greater reputational concerns that also have personal capital
invested incur less risk than managers with lower reputational concerns. The results from Table
5b, regression 1a, indicate that for the standard deviation risk measure, older managers with
personal capital invested do indeed take on less risk (this result is significant at the 1% level).
Also, for the beta deviation risk measure, the results in Table 5b, regressions 3a (and c) indicate
that managers having large funds (and older managers with large funds) and personal capital
invested also reduce risk. Thus, although the proxy for personal capital invested does not
precisely measure a manager’s personal capital at risk, there is support that managers with
greater career concerns and personal capital at stake do incur less risk.
5. Career concerns and risk-taking behavior
We hypothesize that older managers have greater career concerns than young managers;
as a result, older managers incur less risk to increase the probability of their funds’ (and their
careers’) survival. The prior section confirms that older managers do, indeed, incur less risk.
This section documents the link between risk-taking behavior and the likelihood of fund survival.
To study this relationship, we use a time-varying proportional hazards model which
allows for the complete use of the time-series variation in the sample.17 Time-varying
proportional hazards models (which are a category of the more general hazard functions) have
several advantages over the more commonly-used probit and logit models. First, they put fewer
distributional assumptions on the data; second, they calculate the conditional rather than the
absolute probability of failure (conditional upon not having failed in a prior period); and finally,
they do not introduce sample-selection bias into the data. Instead of using annual failure rates,
they allow for monthly failure times which reduces bias and adds precision to the estimates.
Recent work in finance has used this type of model. Examples include Helwege (1996),
who uses a proportional hazards model to examine the determinants of Savings and Loan failures
in the 1980's, Lunde, Timmerman, and Blake (1999), who use the model to examine the
determinants of mutual fund failures, and BGP (2001) who use this model to examine the
determinants of hedge fund failure, notably volatility and manager tenure.
5.1. Description of proportional hazards model
The time-varying proportional hazard model estimates the relationship between the
hazard rate (the likelihood of fund failure), λ(t), and a number of explanatory variables, z(t), that
are permitted to vary over time. The proportional hazard function is specified so that the
explanatory variables shift an underlying baseline hazard function up or down. The baseline
hazard function, λo(t), can follow any distribution for which proportionality holds. Examples
include the Weibull, exponential, and lognormal distributions. The time-varying proportional
hazard function is described by the following equation:
β z (t )
λ (t ; z (t )) = λ 0(t )e (5)
where β is the set of coefficients to be estimated.
Cox (1972) describes how β can be estimated by maximizing the partial likelihood
function of the probability of failure observed in the sample. β is estimated from inferences on
the conditional probability of failing in a given time period. Because of proportionality, the
Much of the following description closely follows Helwege (1996).
estimation ignores the baseline hazard function, which makes specifying a functional form for
the baseline unnecessary.
Assume that there is a sample of n hedge funds, k of which fail during the sample period
with failure times t1 < t2 < … < tk. The assumption of this model is that each failure occurs in a
different time period, and the failures are ordered from 1 to k chronologically.18 The remaining
n-k funds are censored and have no failure times during the sample period. However, these
funds could fail some time after the sample period ends. Assign δt equal to 1 if a fund in period i
fails and zero if it does not fail. Let zi(t) be z(t) for the fund with failure time ti and let zj(t) be z(t)
for each fund at risk at time ti. Ri is the set of funds at risk of failure in period i. The partial
likelihood function to be maximized is:
n e β z i(t i)
L( β ) = ∏
β z j (t )
i =1 ∑∈R e i
Intuitively, Equation (6) examines each hedge fund that fails (one per time period) and
compares its explanatory variables to the explanatory variables on the set of hedge funds that
could have failed during the period but did not. If the values of the explanatory variables for
those that failed differ from the values of the explanatory variables for those that survived, the
coefficients will be significantly different from zero.19
5.2. Estimation of proportional hazard model
In this section, I estimate a time-varying proportional hazard model, using the time that a
fund drops out of the sample as the failure time for that fund. As noted in Section 2, there are a
number of reasons that funds may be removed from the database. Some of the funds actually
fail, while others voluntarily stop reporting their data. Although TASS often provides the reason
This methodology assumes no ties (that is, no funds failed in the same period). In the sample of interest, there are
several tied failure times, which will be addressed later. For simplicity, the case with no ties is described here.
Maximum likelihood estimation is used to estimate the partial likelihood function. Since this process is
computationally demanding, an approximation (see Breslow (1974)) is usually used to save time and computer
resources. However, the Breslow approximation can be less accurate when there are many tied failure times (as is
the case in the hedge fund data.) Thus, following estimation uses the exact method (see Kalbfleisch and Prentice
that the fund has been removed, it is often difficult to interpret. Thus, the analysis below is
performed on two samples: one that considers as dead all 285 funds that TASS categorizes as
“dead” (out of the total sample of 982 funds), and another that considers as dead only the funds
that clearly state that the fund had failed or gone bankrupt. The results for both samples are
nearly identical, implying that most of the funds that left the sample had similar characteristics
consistent with actual failure. Thus, Table 6 reports the results using the larger sample.
Table 6 relates termination to risk-taking behavior. The regressions include controls for
fund characteristics (described in Table 1a and Section 4, above) and seasoning (year effects).
Including seasoning effects reflects the possibility that termination probabilities may vary from
year to year due to factors such as evaluations based on absolute performance or changes in the
labor market for hedge fund managers.
The first column, regression 1, examines the relationship between termination and
standard deviation. Columns 2 and 3 examine the relationship between termination and tracking
error deviation and beta deviation, respectively. All regressions include the fund characteristic
variables as well as a measure of fund performance (excess returns above the risk-free rate). In
the table, a negative coefficient indicates a positive likelihood of survival, while a positive
coefficient indicates a positive likelihood of failure. Examining the results in Table 6, two
patterns emerge. First, as expected, good performance is negatively related to failure. Second,
all three risk measures are positively related to failure. Funds that take on more risk have a
higher likelihood of failing. This finding confirms our hypothesis: risk-taking behavior
increases the likelihood of failure, which provides an incentive for managers with career
concerns to reduce these risk-taking behaviors.
The approach of this section has been to link various risk-taking behaviors to the
probability of fund failure. It provides evidence that engaging in risky behavior significantly
increases the probability of fund failure. Another way to think about risk-taking is to consider
fund failure itself as evidence of risk-taking behavior. If a fund fails, its manager likely incurred
some sort of risk, which may not be captured by the risk measures used in this paper. Therefore,
we next focus on the relationship between fund failure and manager tenure, without regard to the
pre-determined measures of risk. If the relationship between manager tenure and fund failure is
negative, this provides indirect evidence that older managers have been taking on less risk.
In order to capture the effect of manager tenure without regard to risk-taking behavior, I
re-estimate the model excluding the risk and performance measures. Column 4 of Table 6 shows
these results. The coefficient on the tenure variable is -0.06, which indicates that for each month
of manager experience, the average probability of failure decreases by 6%. Consistent with
results in columns 1 to 3, this finding suggests that older managers decrease risk over time to
reduce their likelihood of failure. To examine how this relationship varies by tenure and over
time, I estimate the shape of the survivor function (which is the inverse of the hazard function).
Holding all other variables constant at their averages, the function is estimated at nine levels of
the tenure variable, representing the 10th through the 90th percentile of tenure categories.
Figure 3 graphs the survival function at three percentiles: 10th, 50th, and 90th.
Examining this Figure, it is clear that holding all else constant, more experienced managers have
a much higher probability of survival. Interpreting fund failure as a proxy for risk, this Figure is
consistent with the results that older managers reduce risk over time. In summary, this section
provides evidence that risk-taking behavior leads to fund failure, establishing an implicit
incentive for older managers with career concerns to reduce risk.
6. Do low levels of risk-taking behavior cause low returns?
In Section 3, the negative relationship between hedge fund manager tenure and
performance was confirmed. Sections 4 and 5 show that older managers take on less risk than
young managers, and provide motivation for this behavior. This section provides a causal link
between the two results, thus confirming the hypothesis of this paper. Specifically, we show that
older managers’ propensity to reduce risk explains their lower realized returns.
Table 7 presents results from a number of cross-sectional regressions. For all the
regressions, the dependent variable is annual return less the risk-free rate, and the independent
variables include manager tenure, all fund characteristics, and one of the three measures of risk-
taking behavior -- standard deviation, tracking error deviation, and beta deviation. The
methodology is similar to that in Section 3 (and Table 3), but the regressions in this section are
performed annually instead of monthly, since the three risk-taking measures are estimated on an
annual basis. To establish the baseline relationship between returns and manager tenure (on an
annual level), the first set of regressions (Panel A) do not include the risk-taking variables as
regressors, so the first column (1a) includes as independent variables manager tenure and fund
characteristics only. The second column (1b) includes controls for exposure to passive indices,
while the third column of Panel A (regression 1c) also includes controls for exposure to hedge
fund indices. The next three sets of regressions (Panels B-D) are identical to regressions 1b and
1c, except they also include either standard deviation (Panel B), tracking error deviation (Panel
C), or beta deviation (Panel D) as independent variables. Table 7 indicates a positive relationship
between the each of the three risk measures and annual returns, confirming that the risk-return
relationship holds for this sample of hedge funds. In other words, risk-taking behavior is
rewarded with significantly higher returns.
Most important, the coefficient on the manager tenure variable decreases significantly
when risk-taking behavior is considered. These results are economically interesting: without
considering the impact of risk-taking behavior or correlations with “market beta” coefficients on
returns, the difference in returns for a 52 year old manager versus the average (47 year-old)
manager is about -4% per year. When “market beta” controls are included, this difference drops
to about -3.5% per year. However, when both the “market beta” controls and the risk-taking
measures are included, the difference drops to about -2% per year. Differences in risk-taking
behavior explain nearly half the differences in returns between old and young managers. The
remaining difference may be due to other risk factors that are more difficult to measure, or to
omitted variables that capture dynamic trading strategies of these managers. Thus, the results of
this section confirm the hypothesis that the negative relationship between manager tenure and
returns can be explained by reduced risk-taking behavior of older managers.
7. Robustness tests
This section includes two robustness tests. The first (see Section 7.1) attempts to rule out
the following explanation for the above findings: Perhaps older managers are not reducing risk
over time at all, but instead, they have taken on lower risk all along, which explains their
increased probability of survival. The second robustness test (see Section 7.2) relates fee income
to risk-taking to provide additional support for the idea that as managers' salaries increase, they
reduce risk to protect their ever-increasing incomes.
7.1 Do older managers take on less risk all along?
This section examines whether older managers have been able to survive because they
have taken on less risk throughout there careers. Examining this issue is difficult due to data
constraints. The first problem is that data prior to 1994 is not free of survivorship bias. Thus,
using this data to test this idea (perhaps with a fixed-effects regression) is not appropriate. The
managers of failed funds – which are not included in the sample – would likely have taken on
higher risk at the beginning of their careers. Using only the data after 1994 causes another
problem: some of the funds in the sample have been in existence since the early 1980’s, so the
early years of their existence (when their risk was likely to be the highest) are not included in the
sample. Given these caveats, the following tests were performed.
To test the alternative hypothesis (that older managers took on lower risk all along), all
funds in existence during the year 1994 are followed forward to the year 2000. The following
data is recorded: each of the risk measures (standard deviation, tracking error deviation, and beta
deviation) for each year, and a yes/no variable indicating whether the fund failed at some point
during the entire sample period. Then, the funds are sorted into deciles each year based on
manager tenure. For each year, for each decile, the funds are then sorted into two categories:
whether the fund was alive or dead at the end of the sample. The idea is to examine whether the
risk-taking behavior – at the beginning of the sample period – of the funds that eventually failed
differed from the funds that did not fail. If I find that the funds that eventually failed took on
more risk at the beginning of their lives than the funds that did not fail, the alternative hypothesis
is supported. However, if there is not a significant difference between risk-taking behavior for
extant and failed funds at the early stages of their lives, then this paper's argument that risk-
taking changes with age is supported.
Using Wilcox rank-sum tests, I find that the risk-taking behavior of young managers
(decile 1) at the beginning of the sample does not differ significantly between funds that survived
the entire sample and funds that eventually failed. This indicates that at the beginning of their
lives, funds have similar risk levels, which is consistent with the theory and empirical results
presented: funds reduce risk over time in order to survive. Additionally, for decile 10 managers
(those with the most experience) at the beginning of the sample, there is a significant difference
between levels of risk-taking behavior among surviving and failed funds: failed funds have
higher levels of tracking error deviation and standard deviation than surviving funds, indicating
that as these funds got older, those that reduced risk were able to survive, while those that did not
7.2 Fee income
One argument made earlier in this paper relates fee income to risk-taking behavior. The
idea is that successful hedge fund managers are so well-paid that they will go to great lengths to
protect their income. The opportunity set in the industry is such that hedge fund managers will
almost surely take a very large pay cut if they lose their jobs. However, if they reduce risk and
keep their jobs, they are able to maintain this earning potential, even if the reduction in risk leads
to lower returns. Since managers earn a percentage of profits, as long as their returns are
positive they are still paid. And, if their funds are large (as is likely the case with high fee
earners in the past), they can still earn exceptional salaries on mediocre investment returns.
To test this hypothesis, Table 8 regresses each of the three risk measures against a an
estimate of cumulative fee income for each manager. To estimate this variable, I multiply each
year's average fund size by the management fee percentage, and add the incentive fee, which I
estimate as the annual return for the fund times the incentive fee percentage times the average
total assets for the year. Each year, for each fund, I perform this calculation and sum the results
over the life of the fund. The estimates for this variable are then sorted into thirds, and assigned
0/1 indicator variables representing the magnitude (high, medium, or low) of cumulative fee
income. A number of caveats are in place: first, the fee income is not the only income managers
earn from their funds. The majority (67%) have some of their own assets invested in the fund,
which would increase the estimates of fee income. However, as noted above, this “personal
capital invested” variable is likely not very useful, as it does not provide information about the
magnitude of personal capital that managers have invested, nor what proportion of each
manager's net worth that personal capital comprises. Second, the cumulative measure is only
calculated from 1994 onward. Some of the funds in the sample have been in existence since
1982. However, since the data for these funds is not complete for these prior periods, the
variable is calculated from 1994 forward. Both of these issues likely cause the estimate of
cumulative fee income to be understated, which biases against finding any relationship in the
tests that follow. A final caveat is that some funds have “high water marks,” meaning that if the
net asset value of the fund falls below the level of the prior year, the manager has to make up the
difference before he can begin collecting fees again. This problem would cause the cumulative
fee income variable to be overstated.
Keeping these caveats in mind, Table 8 regresses each of the risk measures against the
fee income indicator variables for high cumulative fee income and low cumulative fee income
(the middle fee income variable is excluded). These regressions are performed in column a) of
each panel (Panel A, column 1a uses standard deviation as the dependent variable; panel B,
column 2a uses tracking error deviation as the dependent variable, and Panel C, column 3a
includes beta deviation as the dependent variable). For each of these regressions, manager
tenure, fund characteristics, and common risk and style factor controls are also included. For
both herding measures of risk (tracking error deviation and beta deviation), the results are
consistent with the hypothesis that managers with higher cumulative fee income take on the least
risk, while those with lower cumulative fee income most risk. The coefficients for standard
deviation are not significant.
Next, another variable is created that interacts the fee income indicator variables (low,
medium, and high) with the manager tenure variable (young, middle, and old). The idea is that
old managers with high fees likely have more at stake than young managers with low fees. If
this is true, we would expect to find negative coefficients on the old age/high fees variable.
Consistent with the results for cumulative fee income, for two of the three regressions (tracking
error deviation and beta deviation), the coefficients are as expected, indicating again that
managers with “more at stake” reduce risk. These findings provide further evidence that manager
career concerns are increasing with reputation: managers with high fee income have strong
incentives to keep their jobs; these incentives are more important to more experienced managers.
In this paper, I investigate whether differences in risk-taking behavior can explain why
experienced hedge fund managers have lower returns. I find that as managers age, their risk-
taking behavior changes: specifically, older managers reduce volatility and herd more than
younger managers. Managerial career concerns motivate this behavior: should their funds fail,
older managers have more to lose in terms of reputation, current income, and personal wealth
than do young managers. And, this reduction in risk-taking behavior can explain about half of
the differences in returns between young and old managers.
These empirical findings differ sharply from evidence from the mutual fund industry: in
mutual funds, old managers take on more risk than young, which is consistent with the agency
costs and career concerns of mutual fund managers. In the mutual fund industry, young
managers herd so as not to risk a poor performance outcome relative to their peers. As their
careers progress and they become more experienced, they are less likely to herd and take the
chance of an unfavorable outcome for at least three reasons: first, the probability of failure for a
poor outcome is much less than for young managers, second (in contrast to most experienced
hedge fund managers) they do not typically have substantial personal assets invested in their
funds, and finally, the flow/performance relationship indicates that the very best funds attract the
most new inflows, which are directly related to a manager’s compensation.20
Likewise, the findings in this paper are consistent with the agency costs and career
concerns of hedge fund managers. In hedge funds, older managers have “more to lose” than
young should their funds fail – they usually have significant personal assets in their funds, they
understand that risky behavior increases their probability of termination, and they know that it is
difficult to start a new hedge fund if their current fund fails – all of which induce them to take
less risk over time. Additionally, the pay structures in hedge funds encourage risk-taking among
younger managers versus old: since hedge fund managers are compensated both by incentive
fees (a percentage of profits) and management fees (a percentage of assets), young, smaller funds
focus on maximizing profits for two reasons: first, high profits increase their incentive fees
substantially, and second, high profits attract more assets to their funds, increasing their future
management fees. For older managers with larger funds, they earn significant fees from the
management fee component alone, which might induce them to take less risk
This paper adds to the growing literature about the effect of career concerns on
managerial behavior. The initial career concerns literature focused on how a the existence of an
outside labor market and a manager’s concern for his reputation would cause him to avoid
Chevalier and Ellison (1997) document an asymmetry in the flow/performance relationship. While the very best
funds attract significant inflows, the very worst funds do not experience significant outflows. Average funds
experience net inflows. See also Gruber (1996), Goetzmann and Peles (1997), and Sirri and Tufano (1998).
shirking or consuming excessive perquisites, thus reducing agency costs. (See Fama, 1980). The
results of this paper and others that study mutual funds, securities analysts, and macroeconomic
forecasters provide additional evidence regarding careers concerns: specifically that the existence
of career concerns can make a manager more or less risk averse depending on his compensation
structure, indirect incentives, and the odds of losing his job.
Additionally, the results of this paper indicate that differences in risk-taking behavior
explain about half of the differential in returns between young and old managers. These results
have implications for investors in hedge funds: young managers are substantially better than old,
which they achieve by using more aggressive trading strategies and avoiding herding behavior.
However, the increased probability of failure from young to old funds is striking: for a manager
with one year of tenure, the probability of surviving the next six years is only about 35%. For a
manager with four years of tenure, the probability of surviving the next six years is nearly 90%.
Due to the trade-off between returns, risk, and survival, fund selection by investors should take
into account this relationship: these results indicate that selecting very young or very old funds
are not likely in investors' best interests.
Finally, these results have ramifications for the appropriate design of incentive contracts.
While it has been thought that the incentive fee structure used by hedge fund managers
appropriately aligns the incentives of managers and investors, this paper provides evidence that,
at least in some cases, it does not. Particularly among older funds, the desire to survive and
remain in the industry can outweigh the desire to take the necessary risks to achieve superior
returns. Thus, as suggested by Dybvig, Farnsworth, and Carpenter (2001), perhaps contracts
should be designed that encourage managers to take on more risk. For younger managers, these
contracts reward them for actions they already are taking, and for older managers, perhaps these
contracts could provide incentive to take on more risk. This is a fruitful area for future research.
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Table 1a: Summary statistics: Return, manager and fund characteristics
Below are summary statistics for the return, manager, and fund variables used in the paper. Each statistic is first
calculated by fund, and then across funds. CFA, CPA, MBA, Ph.D., Other Advanced Degree, Law Degree,
Listed on Exchange, Onshore, Open to New Investment, Open to Non-Accredited Investors, Uses Leverage, and
Personal Capital Invested are (0/1) indicator variables set to one if the value is "yes" and zero if the value is "no".
The first column in each category shows statistics for the small sample (for which all manager and fund
characteristic variables are available), and the second shows statistics for the large sample (for which only
manager tenure and all fund characteristics are available.) HF market-adjusted. return is calculated by estimating
the market exposure for each fund for each hedge fund index (see Section 2.2 for details), weighting each of
these exposures by the performance of the hedge fund index, and subtracting these weighted exposures from the
simple excess return of the fund. Passive market-adjusted return is calculated in the same manner as HF market-
adjusted return, but passive indices are used instead of hedge fund indices. See Section 2.2 for detail on the
hedge fund and passive indices.
Table 1a (continued) : Summary statistics: Return, manager and fund characteristics
Mean Median Maximum Minimum
n=271 n=982 n=271 n=982 n=271 n=982 n=271 n=982
Fund features: location, risk (cont.)
Open to new investment 0.89 0.88 - - - - - -
Open to non-accredited investors 0.13 0.15 - - - - - -
Personal capital invested 0.73 0.67 - - - - - -
Uses leverage 0.74 0.72 - - - - - -
Fund policies and size
Lockup redemption period (months) 12 19 4 12 260 260 1 1
Lockup entrance period (months) 16 25 12 12 260 260 1 1
Minimum investment (millions) $.91 $.78 $.50 $.50 $25 $50 $.002 $.001
Size as of 12/31/00 (millions) $161 $113 $52 $30 $1,800 $1,893 $5 $5
Management fee (% of assets) 1.20% 1.30% 1.00% 1.00% 3.00% 6.00% 0.00% 0.00%
Incentive fee (% of profits) 18.85% 17.79% 20.00% 20.00% 33.00% 50.00% 0.00% 0.00%
n/a = not available
Table 1b: Conversion of tenure values to age estimates
Manager tenure is divided into percentiles based upon the large sample of 982 funds. Manager age is divided
into percentiles based on the smaller sample of 271 funds. The values shown are the means for each percentile.
Percentile Manager Tenure in Years Estimated Manager Age
10 1.2 41.6
20 1.9 42.4
30 2.5 43.3
40 3.1 44.0
50 3.8 45.0
60 4.7 46.0
70 5.8 46.9
80 7.3 48.9
90 11.0 51.2
Table 2: Summary statistics: Index returns
Below are mean buy and hold monthly returns and other summary statistics for the "market" indices used in the
paper. The period is January, 1994 to December, 2000. All returns are in excess of the risk-free rate. The
sources for the indices are Datastream (passive) and CSFB/Tremont (hedge fund). The returns on HML (a high
book value minus low book value stock portfolio), SMB (a small-capitalization minus large-capitalization stock
portfolio), and MOM (a momentum portfolio) were obtained from Kenneth French's website. Panel A shows
statistics for the passive indices, while panel B shows statistics for the hedge fund indices. For a detailed
description of the hedge fund indices, see Appendix A.
Panel A: Passive Indices
Mean Median Max Min Std Dev Skew Kurtosis
US Dollar Weighted Index -0.19% -0.24% 3.78% -5.18% 1.73% -0.06 0.14
Gold -0.73% -0.94% 20.51% -7.11% 3.69% 2.34 12.29
Commodities 0.42% 0.47% 16.81% -13.43% 5.40% 0.19 0.41
S&P 500 0.95% 1.56% 9.23% -14.98% 4.16% -0.79 1.38
LB Aggregate Bond -0.43% -0.26% 3.60% -3.66% 1.26% 0.00 0.81
LB 30 Yr. US T-bond 0.15% 0.10% 8.61% -7.78% 2.81% 0.00 0.70
SMB -0.36% -0.46% 14.23% -21.51% 4.93% -1.01 4.83
HML -0.28% -5.35% 15.40% -11.66% 3.83% 0.71 3.12
Momentum 1.56% 0.81% 18.23% 8.98% 4.43% 0.99 4.03
Panel B: Hedge Fund Indices
Mean Median Max Min Std Dev Skew Kurtosis
Managed Futures 0.08% -0.25% 9.56% -9.82% 3.32% 0.18 1.31
Convertible Arbitrage 0.45% 0.73% 3.13% -5.06% 1.44% -1.68 4.41
Emerging Markets 0.14% 0.43% 16.14% -23.41% 5.93% -0.45 2.40
Distressed Securities -0.33% -0.53% 22.32% -9.08% 5.52% 0.99 2.37
Market Neutral 0.54% 0.57% 2.89% -1.53% 0.98% 0.01 -0.16
Event Driven 0.56% 0.67% 3.43% -12.16% 1.91% -3.64 23.23
Fixed Income Arbitrage 0.14% 0.40% 1.61% -7.35% 1.25% -3.31 16.07
Global Macro 0.74% 0.83% 10.14% -11.94% 4.12% 0.00 0.68
Long/Short Equity 0.91% 0.95% 12.63% -11.82% 3.65% 0.00 2.21
Fund of Funds Index 0.24% -0.10% 4.92% -3.73% 1.83% 0.32 -0.18