Investing in talents mager characteristics and hegde fund performance

Investing in Talents: Manager Characteristics and
Hedge Fund Performances
Haitao Lia , Xiaoyan Zhangb , and Rui Zhaoc
March 2008

a Li is from the Stephen M. Ross School of Business, University of Michigan, Ann Arbor, MI 48109. b Zhang is from
the Johnson Graduate School of Management, Cornell University, Ithaca, NY 14853. c Zhao is from BlackRock Inc.,

40 East 52nd Street, New York, NY 10022. We thank Andrew Ang, Warren Bailey, Lauren Cohen, Jed Devaro,
Ravi Jagannathan, Wei Jiang, Maureen O’Hara, Gideon Saar, Clara Vega, and seminar participants at Columbia
University, Cornell University, and the University of Wisconsin at Milwaukee for helpful comments. We thank
David Hsieh for making the lookback straddle returns available on his Web site and Ken French for making the
Fama-French factor portfolios available on his Web site. We are responsible for any remaining errors.

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Investing in Talents: Manager Characteristics and Hedge Fund Performances
Abstract
Using a large sample of hedge fund manager characteristics, we provide one of the first comprehensive studies on the impact of manager characteristics, such as education and career concern, on
hedge fund performances. We document differential ability among hedge fund managers in generating risk-adjusted returns and flow-chasing-return behaviors among hedge fund investors. In
particular, we find that managers from higher-SAT undergraduate institutes tend to have higher
raw and risk-adjusted returns, more inflows, and take less risks. Our results provide supporting
evidence to some of the assumptions and implications of the rational theory of active portfolio
management of Berk and Green (2004). However, in contrast to the results for mutual funds, we
find a rather symmetric relation between hedge fund flows and past performance, and that hedge
fund flows do not have a significant negative impact on future performance.
JEL: G23, G11, G12.
Keywords: hedge fund performance, manager characteristics, hedge fund flows.

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An investment in a hedge fund is really an investment in a manager and the specialized talent
he possesses to capture profits from a unique strategy.–– Sanford J. Grossman, The Wall Street
Journal, September 29, 2005
Hedge funds have experienced tremendous growth in the past decade. According to the SEC
and various hedge fund research companies, the amount of assets under management by hedge
funds has grown from about $15 billion in 1990 to about $1 trillion by the end of 2004, and
the number of existing hedge funds is about 7,000 to 8,000. Some industry experts even predict
that hedge fund assets could exceed $3.2 trillion globally by 2009. As a result, hedge funds have
attracted enormous attention from a wide range of market participants and academics in recent
years.
Hedge funds differ from mutual funds in the ways they operate and how their managers are
compensated. For example, hedge funds are not subject to the same level of regulation as mutual
funds and thus enjoy greater flexibility in their investment strategies. As a result, hedge funds
frequently use short selling, leverage, and derivatives, strategies rarely used by mutual funds, to
enhance returns and/or reduce risk. While mutual funds charge a management fee proportional to
assets under management (usually 1-2%), most hedge funds charge an incentive fee, typically 15%
to 20% of profits, in addition to a fixed 1-2% management fee. Moreover, hedge fund managers
often invest a significant portion of their personal wealth in the funds they manage; and many
funds have a high watermark provision, which requires managers to recoup previous losses before
receiving incentive fees.
Hedge funds also differ from mutual funds in the economic functions they perform in the economy. As pointed out by Sanford J. Grossman (2005) in a recent Wall Street Journal commentary,
while mutual funds enable small investors to pool their money and invest in diversified portfolios,
“a hedge fund is a vehicle for acquiring the specialized talents of its manager.” Grossman observes
that, “Hedge funds are typically managed by an entrepreneur, and hedge fund returns are the
outcome of an entrepreneur activity.” As a result, Grossman emphasizes that a “fund’s return
will be no better than its management and the economic environment in which it produces its
product. An investor should understand the product being produced and the manager producing
it.” Grossman’s observation suggests that the performance of a hedge fund depends crucially on
both the investment strategies it follows and the talents of its manager(s) in implementing such
strategies.

Though great progress has been made in understanding the risk and return properties of many
hedge fund strategies,1 only limited analysis has been done on the impact of manager talents on
hedge fund performances in the literature. Just like any entrepreneur activity, it is entirely possible
1

See, for example, the interesting works of Agarwal and Naik (2004), Fung and Hsieh (1997, 2001), and Mitchell

and Pulvino (2001), among others.

1
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that some hedge fund managers are better than others in making investment decisions. Given the
billions of dollars poured into hedge funds from pension funds, endowments, and other institutional
investors each year, identifying manager characteristics that lead to superior performance could be
very helpful to potential investors in selecting hedge fund managers and also could have profound
welfare implications.
In addition to the practical value of identifying superior managers, understanding the impact
of manager talents on hedge fund performances also provides a way of testing some of the assumptions and implications of the rational theory of active portfolio management of Berk and Green
(2004).2 For example, one important assumption of Berk and Green (2004) is that mutual fund
managers have differential ability in delivering positive risk-adjusted returns. However, if the theory of Berk and Green (2004) is true, then it could be difficult to identify cross-sectional differences
in risk-adjusted returns in equilibrium using mutual fund data, because most mutual funds might
have increased their sizes to the extent that their risk-adjusted returns have disappeared. Moreover, due to established investment process and team-oriented approach to portfolio management
in many mutual fund families, the impact of individual managers on mutual fund performances
is likely to be small as well. Consistent with this view, Chevalier and Ellison (1999a) find that
although mutual fund managers from higher-SAT institutes tend to have higher raw returns, their
results become much less significant for risk-adjusted returns.
In contrast, the unique structure of hedge funds suggests that manager talents might be more
important for hedge fund performances. Since a significant part of hedge fund compensation
comes from incentive fees, hedge fund managers may not want to grow their funds to the extent

that all risk-adjusted returns disappear. In addition, many hedge funds have a high watermark
provision, and many hedge fund managers have personal wealth invested in their funds. As a
result, inferior hedge fund returns could be really costly for these managers. Therefore, even in
equilibrium there might be an optimal fund size at which abnormal returns still exist. In addition,
the entrepreneur nature of hedge fund operations suggests that hedge fund performance should
depend more significantly on individual managers. Therefore, hedge fund data provide a unique
opportunity for testing the theory of Berk and Green (2004).
In this paper, we provide a comprehensive empirical analysis of the impact of manager characteristics on hedge fund performances. We conjecture that everything else equal, a manager who
is more talented and more devoted to his/her job is more likely to have better performance. We
use intelligence and education as proxies for manager talents. We use manager career concern as
2

Berk and Green’s (2004) model combines three elements: competitive provision of capital by investors to

mutual funds, differential ability to generate high average returns across managers but decreasing returns to scale
in deploying these abilities, and learning about managerial ability from past returns. The theory predicts that
mutual fund managers increase the size of their funds, and their own compensation, to the point at which expected
returns to investors do not outperform passive benchmarks in equilibrium.

2
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a proxy for manager job commitments. The rationale is that a manager who is under pressure
to establish his/her career at an early stage might be willing to put in more effort than a more
established manager.
We first construct probably the most comprehensive dataset on manager characteristics based
on more than 4,000 hedge funds covered by TASS between 1994 and 2003. Boyson (2003, 2004)
studies hedge fund performance and manager career concerns using a much smaller sample of
about 200 funds up to 2000. In contrast, our dataset covers a wide range of information on personal, educational, and professional backgrounds of managers of 1,002 hedge funds up to 2003.
Specifically, we collect information on the following six characteristics of the lead manager of each

fund if such information is available: the composite SAT score for the manager’s undergraduate
institute (SAT), whether the manager has a CPA or CFA, whether the manager has an MBA degree, the total number of years of working (WORK), the number of years of working at the specific
hedge fund (TENURE), and the manager’s age (AGE). Broadly speaking, the six characteristics
can be divided into two groups: SAT, CFA/CPA and MBA dummies represent intelligence and
education; WORK, TENURE, and AGE could represent working experience and career concern.
We also conduct a careful analysis on risk adjustments for hedge fund returns to obtain hedge
fund abnormal performance. Many studies have shown that due to the dynamic trading strategies
and derivatives used by hedge funds, traditional linear asset pricing models could give misleading
results on hedge fund performance. Given that there are no well-established risk-adjustment
methods for hedge fund returns, we choose a wide variety of models to ensure the robustness of
our results. Specifically, in addition to the traditional Fama and French (1993) (hereafter FF)
three-factor model, to capture the nonlinearity in hedge fund returns, we also consider a wide
variety of models that include returns on various hedge fund indices and options as factors. In
particular, we consider the model of Agarwal and Naik (2004) and the seven-factor model first
proposed by Fung and Hsieh (2004) and used recently by Fung, Hsieh, Naik, and Ramadorai
(2006) (hereafter FHNR). As a further robustness check, we consider two specific hedge fund
strategies whose risk and return properties have been carefully examined and thus are reasonably
well understood in the literature. These are the trend following strategy studied by Fung and
Hsieh (2001) and the risk arbitrage strategy studied by Mitchell and Pulvino (2001).
Based on the new dataset on manager characteristics and various risk-adjustment methods, we
document a strong impact of manager education on different aspects of hedge fund performances,
such as fund risk-taking behaviors, raw and risk-adjusted returns, and fund flows. Specifically,
we find that managers from higher-SAT institutes tend to take less (overall, systematic, and
idiosyncratic) risks and have higher raw and risk-adjusted returns. In our analysis, risk-adjusted
returns include both alpha and appraisal ratio (the ratio between alpha and residual volatility).
We also find that managers from higher-SAT institutes tend to attract more capital inflows. On

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the other hand, we find some weak evidence that managers with longer years of working tend
to have lower raw and risk-adjusted returns and take less risks. These results are very robust to
the different risk-adjustment benchmarks, sample periods, and types of funds (funds of funds vs.
regular hedge funds) we consider.
Although we document differential ability among hedge fund managers in generating riskadjusted returns and flow-chasing-return behaviors among hedge fund investors, we find mixed
results in our tests of other implications of Berk and Green’s theory. For example, unlike the
convex relation between flows and lagged returns documented for mutual funds, we find that
hedge fund flows react to lagged returns rather symmetrically. We also find a significant and
robust negative relation between hedge fund flows and both fund age and lagged fund size. This
suggests that there might be an optimal fund size beyond which hedge fund managers start to
take less inflows. Finally, in contrast to the results for mutual funds, we do not find a significant
negative impact of current fund flows on future fund performances for hedge funds.
Our paper contributes to the fast-growing literature on hedge funds by providing (i) one
of the first systematic studies on the impact of manager characteristics on the cross-sectional
differences in hedge fund performances and (ii) an empirical test of Berk and Green’s (2004)
theory using hedge fund data. Our paper also complements and extends FHNR (2006), the first
study that tests Berk and Green’s (2004) theory using hedge fund data.3 While both FHNR
(2006) and our paper show that some hedge fund managers are indeed better than others, our
study traces superior hedge fund performances to important manager characteristics, such as
education and career concern. Therefore, our paper provides an economic explanation for the
existence of superior performances as well as a guidance on how to identify superior hedge fund
managers based on manager characteristics. Our results on flow-return relation also are broadly
consistent with that of FHNR (2006). While FHNR (2006) show that fund flows negatively affect
the transition probability of have-alpha funds to remain in have-alpha category, the effect of flows
on future risk-adjusted returns is not statistically significant. Collectively, the results of our paper
and FHNR (2006) suggest that the basic mechanisms of Berk and Green’s (2004) model are also
at work in the hedge fund industry. However, because of the unique compensation structure of
hedge funds, hedge fund managers do not have the same incentives as mutual fund managers in
growing the size of their funds. Therefore, the negative impact of fund flows on future returns
for hedge funds may not be as strong as that for mutual funds, and hedge funds may still exhibit

positive abnormal returns even in equilibrium.4 Our results strongly suggest that hedge funds are
3

Using data on funds of funds, FHNR (2006) show that some hedge fund managers are able to deliver better

alphas than others. They further show that the alpha producing funds of funds (denoted as have-alpha funds)
experience greater and steadier capital inflows than the other funds that fail to produce alphas (denoted as betaonly funds).
4
This view is also consistent with the findings of Kosowski, Naik, and Teo (2007). Using powerful bootstrap

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very different from mutual funds, and a manager’s talents and motivations should be important
considerations in selecting hedge fund managers.
The remainder of the paper proceeds as follows. In section I, we introduce our data on hedge
fund returns and manager characteristics. In section II, we introduce a wide variety of riskadjustment benchmarks for hedge fund returns. In section III, we examine the relation between
different aspects of hedge fund performance and manager education/career concerns. In Section
IV, we specialize our analysis to two special hedge fund strategies whose risk and return properties
have been relatively well understood in the literature. In Section V, we study the behaviors of
fund flows and the impact of fund flows on future fund performances. Section VI concludes.
I. Data on Hedge Fund Returns and Manager Characteristics
The data on hedge fund returns and manager characteristics are obtained from TASS. Among
all the datasets that have been used in the existing hedge fund literature, the TASS database is
probably the most comprehensive one. TASS builds its dataset based on surveys of hedge fund
managers. Funds report to TASS mainly for marketing purposes, because they are prohibited
from public advertisements. Overall, TASS covers more than 4,000 funds from November 1977
to September 2003. All funds are classified into “live” and “graveyard” categories. “Live” funds
are those that are active as of September 2003. Once a fund is considered no longer active,
it is transferred to the “graveyard” category.5 The “graveyard” database did not exist before

1994. Thus, funds that became inactive before 1994 were not recorded by TASS. To mitigate
the potential problem of survivorship bias, we include both “live” and “dead” funds and restrict
our sample to the period between January 1994 and September 2003, yielding a sample of 4,131
funds.
Our analysis focuses on different aspects of hedge fund performances to obtain a more complete
picture. These include fund risk-taking behaviors (measured by overall, systematic, and idiosyncratic risks), raw and risk-adjusted returns, and fund flows. We make these choices because
we believe that managers would devote their time and effort to improve performance measures
that could lead to higher compensations, which could come from management/incentive fees and
personal wealth invested in their funds. For example, Goetzmann, Ingersoll, and Ross (2003)
argue that both returns and capital flows are important for hedge fund manager compensation,
although the relative importance depends on market condition and is time-varying. The monthly
returns provided by TASS are net of management/incentive fees and other fund expenses, and are
and Bayesian methods, the authors show that the abnormal performance of top hedge funds cannot be attributed
to luck and that hedge fund abnormal performance persists at annual horizons.
5
A fund is in “graveyard” because either it had bad performance or it had stopped reporting to TASS. For
instance, a fund might have done well and attracted enough capital, and it no longer has any incentive to report to
TASS.

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closely related to actual returns received by investors. TASS also provides data on several fund
characteristics, such as management and incentive fees, whether a fund has a high watermark,
and whether its managers have personal wealth invested in the fund.
Other than returns and fund characteristics, TASS also provides rich information on personal,
educational, and professional backgrounds of managers of most funds. Although the return data
of TASS have been extensively studied in the literature, our paper is one of the first that examines
the impact of manager characteristics on hedge fund performance. Specifically, we identify a lead
manager of a particular fund and construct a dataset on the characteristics of this manager.6 For

educational background, we identify the undergraduate college the manager attended, the SAT
score of the college from U.S. News and Princeton Review of 2003,7 whether the manager has
an MBA degree, and whether the manager has a CFA or CPA. For professional background, we
obtain the years the manager has worked (WORK) either directly from the dataset or assume that
the manager started working right after MBA if he/she has one. However, if neither information
is available, then WORK is missing. We also obtain the number of years the manager has worked
at a particular fund, which we refer to as manager tenure (TENURE). For personal information,
we obtain the age of the manager (AGE), which is either reported in the dataset or inferred based
on the assumption that the manager was 21 upon graduation from college. Generally speaking,
SAT, MBA, and CPA/CFA dummies could capture either the intelligence or education of the fund
manager, while WORK, TENURE, and AGE could capture the working experience and career
concern of the manager.
Out of the 4,000 funds covered by TASS, we are able to identify most of the characteristics of
the lead manager for 1,002 funds. Panel A of Table 1 provides summary statistics on quarterly
returns, and fund and manager characteristics for the 1,002 hedge funds.8 For fund characteristics,
we report incentive and management fees, whether the fund has a high watermark, whether the
manager has personal wealth invested in the fund, the age and asset value of the fund, and
the number of managers of the fund. For manager characteristics, we include SAT, MBA, and
CFA/CPA dummies, AGE, WORK, and TENURE. To be consistent with the Fama and MacBeth
(1973) regression approach used in later analysis, we report time series averages of cross-sectional
distributions of each individual variable. That is, at each quarter, we calculate the mean, standard
deviation, minimum, first quartile, median, third quartile, and maximum of the distribution of
6

We choose the founder of a fund as the lead manager, and for funds with multiple founders we choose the one

that is in charge of investment strategies or for whom the characteristics information is available.
7
We repeat our analysis using SAT scores in 1973, 1983, and 1993 obtained from Lovejoy’s college guide and
U.S. News and reach very similar results. The general level of SAT scores has increased from early 1970s to 2003

by about 100 points.
8
We use quarterly returns mainly because, as documented in Getmansky, Lo, and Makarov (2003), quarterly
returns might be more precisely measured than monthly returns for hedge funds due to liquidity issues.

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each variable. Then we report the time series averages of each of the above quantities over all
quarters in our sample period.
The average raw and excess quarterly returns are 3.33% and 2.28% respectively, with a wide
dispersion. The lowest return is around -17% and the highest is more than 26% per quarter. In
terms of fund characteristics, we find that most funds charge a 20% incentive fee and a 1-1.5%
management fee. About 40% of the funds have a high watermark, and managers of 60% of the
funds have personal wealth invested in their own funds. The mean and median ages of funds are
about 4 and 3 years, respectively. The mean and median fund sizes are about $86 million and
$31 million, respectively. Although the majority of the funds are run by one or two managers,
certain funds have more than 10 managers. The SAT scores range from the lowest of 878 to the
highest of 1,511 with a mean/median around 1,300. In results not reported, about 30% of the
managers graduated from Ivy league universities. About 17% of the managers have either a CFA
or CPA, and 47% of the managers have an MBA degree, while the rest fail to report on this item.9
For many funds, the age variable is missing and in total we only have around 7,351 quarter-fund
observations with age information. For those funds with age information, the mean and median
manager ages are about 44 and 42.5 years, respectively, with the youngest of 27 years and the
oldest of more than 72 years.10 Out of the 1,002 funds, we directly observe the WORK variable
for 899 funds. For the rest of the funds, we construct WORK based on the finishing date of MBA
degree. On average, managers have close to 20 years of working experience, with the shortest of
4 years and the longest of 50 years. The average tenure with current fund is about 3 to 4 years,
with the shortest of less than one quarter and the longest of 20 years.
Panel B of Table 1 reports the correlations among fund excess returns and various fund

and manager characteristics. We find a positive correlation between fund excess returns and
SAT, which provides preliminary evidence that managers from higher-SAT colleges are more
likely to have better performance. On the other hand, we find negative correlations between
excess returns and fund age and several working experience variables. This provides preliminary
evidence that younger funds and managers with less working experience tend to have better
performance. We find a strong positive correlation of 0.93 between fund age and manager tenure,
which is consistent with the typical structure of hedge funds: They are usually established by a few
important managers who tend to stay with the fund.11 Chevalier and Ellison (1999b) argue that
9

A zero value of an MBA or CFA/CPA dummy variable does not necessarily mean that the manager does not

have an MBA or CFA/CPA, respectively. It could be that the manager fails to report this information.
10
We do not include age in our regressions because age is missing for about 40% of the funds. However, due to
the high correlation between age and years of working, we will not lose much information by omitting age in our
analysis.
11
This result has important implications for interpreting the causality of our later finding that smarter managers
tend to have higher risk-adjusted returns. Although we interpret this result as evidence that smarter managers can
deliver better returns, an alternative interpretation is that smarter managers are attracted to better-performing

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years of working is a better proxy for working experience than manager tenure. In our empirical
analysis, we use WORK as a proxy for working experience or career concern, and we always
include fund age and lagged fund size as fund characteristics controls. We also find significant
positive correlations between fund size and SAT/WORK, suggesting that manager characteristics
affect not only the returns but also the sizes of hedge funds.

Due to the nature of currently available hedge fund datasets, most empirical studies of hedge
funds potentially face various selection biases in their data.12 To minimize the impact of survivorship bias, we restrict our sample to the period between 1994 and 2003 which include both
graveyard and live funds. Panel C of Table 1 provides a comparison between graveyard and live
funds. The summary statistics of graveyard and live funds are constructed in a similar way as
that in Panel A. Consistent with conventional wisdom, we find that live funds tend to have higher
raw/excess returns and more assets under management. Although there are some differences between graveyard and live funds in terms of fund and manager characteristics, these differences are
not very significant.13 Panel D of Table 1 compares the funds with manager characteristics with
the rest of the funds covered by TASS. In general, we find that funds with manager characteristics
tend to be younger, have higher returns and less assets under management than funds without
manager characteristics.
II. Risk-Adjustment Benchmarks for Hedge Fund Returns
The rich dataset constructed in the previous section allows us to examine the relation between
hedge fund performance and manager characteristics. One challenge we face in this analysis is that
risk adjustments for hedge fund returns are much more difficult due to their use of derivatives
and dynamic trading strategies. Many studies have shown that standard linear asset pricing
models fail to adequately capture the risk and return properties of most hedge funds, and it is fair
to say that there is no well-established method for hedge fund risk adjustments in the existing
literature. Therefore, to ensure robust findings, we consider two broad classes of models to obtain
risk-adjusted hedge fund returns.
In the first class of models, we use various hedge fund indices as benchmarks to adjust for risks
in hedge fund returns. The basic idea behind this approach is that these indices might be able to
capture the risk exposures of average hedge funds and automatically adjust for the nonlinearity
hedge funds. Though this interpretation could be true for mutual funds, the 0.93 correlation coefficient suggests
that the hedge funds in our sample are most likely started by their current managers.
12
13

See Ackermann, McEnally, and Ravenscraft (1999) for a taxonomy of potential biases in hedge fund datasets.
One reason that live and graveyard funds have similar SAT scores is that graveyard funds include funds that

have done poorly as well as funds that have done well and stopped reporting to TASS. In results not reported, we
divide the graveyard funds into finer sub-categories and find the liquidated funds on average have lower SATs than
the graveyard funds that have done well.

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in hedge fund returns. One advantage of this approach is that we do not need to explicitly
model the risk-taking behavior of hedge funds. Another advantage is that this approach is easy
to implement: Investors can easily compare returns of individual hedge funds with that of broad
hedge fund indices. We obtain the risk-adjusted returns as the intercept term of regressions of
individual hedge fund returns on the returns of the indices, and the risk exposures as the regression
coefficients or the loadings of the indices.
Among the three indices we consider, the first one (INDEX) is the broad hedge fund index
(a weighted average of returns of all hedge funds) provided by TASS. We also consider the index
of funds of funds (FoF), which is a weighted average of returns of funds of funds. Fung and
Hsieh (2002) argue that returns of funds of funds are more accurately measured than that of
regular hedge funds and could better reflect true hedge fund performance. The above two indices,
however, might not be able to capture the cross-sectional differences in hedge funds strategies. For
example, TASS reports around a dozen widely followed investment styles whose risk and return
properties differ from each other dramatically. Brown and Goetzmann (2003) argue that styles
capture most of the cross-sectional differences in hedge fund returns. Therefore, in addition to
the above two indices, we also use style indices (STYLE), which is the weighted average returns
of all funds within each style, to adjust the risks of hedge funds in that specific style.
The second class of benchmarks we consider include the Fama-French three-factor model (FF),
the model of Agarwal and Naik (2004) (AN), and the seven-factor model used in FHNR (2006).
The FF model is well-established in the asset pricing literature and has been successfully applied
to returns of stocks, stock portfolios, and mutual funds. The FF model has three factors: a market factor which is the excess return of the market portfolio (MKT), a size factor which captures
return difference between small and big firms (SMB), and a book-to-market factor which captures
return difference between value and growth firms (HML). Agarwal and Naik (2004) propose to

include option returns in traditional asset pricing models to capture the nonlinearities in hedge
fund returns due to dynamic trading strategies and derivatives. The AN model has two factors:
a market factor as in FF, and an option factor which is the excess return of an out-of-money
put option on market index (OPT). We obtain the option data from CBOE. Agarwal and Naik
(2004) show that the AN model is relatively successful in capturing hedge fund returns. One
caveat we need to keep in mind is that option returns tend to be very volatile and could lead to
noisy parameter estimates. The seven factors included in the FHNR model are the excess return
on the S&P 500 index (SNPMRF); a small minus big factor (SCMLC); the excess returns on
portfolios of lookback straddle options on currencies (PTFSFX), commodities (PTFSCOM), and
bonds (PTFSBD); the yield spread of the US ten-year Treasury bond over the three-month T-bill,
adjusted for the duration of the ten-year bond (BD10RET); and the change in the credit spread
of the Moody’s BAA bond over the ten-year Treasury bond, adjusted for duration (BAAMTSY).

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Fung and Hsieh (2004) and FHNR (2006) have shown that these factors have considerable explanatory power for fund of funds and hedge fund returns.
Based on the above benchmark models, we run time series regressions for each fund to estimate
its risk exposures to the various factors and the risk-adjusted returns. Then we take the estimated
risk loadings and risk-adjusted returns as independent variables, and run Fama-MacBeth regressions on various manager characteristics. More specifically, at the end of each quarter we use the
past 24 monthly returns to run the following regression:
0
Ft + εi,t ,
Ri,t = αi + βi,q

(1)

where Ri,t is the raw return of fund i over month t, βi,q (generally a vector) represents the risk
exposures of fund i at quarter q to the various factors, and Ft (also generally a vector) is the
monthly value of different factors. In the same regression, we also calculate the quarterly residual

volatility, σ
ˆi,q , as
0
σ
ˆi,q = [var (ˆ
εi,t )]1/2 with εˆi,t = Ri,t − α
bi − βbi,q
Ft ,

(2)

0
Fq ,
α
ˆ i,q = Ri,q − βbi,q
α
ˆ i,q
d i,q =
,
AR
σ
ˆi,q (ε)

(3)

0 are estimated in equation (1). In addition, we compute alpha (ˆ
where both α
bi and βbi,q
αi,q ) and
d i,q ) of fund i at quarter q, respectively, as:

appraisal ratio (AR

(4)

where Ri,q is the raw return of fund i for quarter q, and Fq is the value of the various factors in
quarter q.
d i,q to
ˆi,q , and AR
Since the regression is done every quarter, we implicitly allow α
ˆ i,q , βˆi,q , σ

be time-varying. This allows us to capture potential variations over time in trading strategies
of hedge funds under study. While βˆi,q measures a fund’s exposures to various systematic risk
ˆ i,q measures a fund’s
factors, σ
ˆi,q measures the amount of idiosyncratic risks a fund takes. While α
d
abnormal return, ARi,q measures abnormal return for per unit of idiosyncratic risk taken.14
To explore the relation between hedge fund performance and manager characteristics, the

empirical analysis in this paper is mainly based on the Fama-MacBeth regression. As an alternative, we also conduct estimation using panel data regression with clustering and obtain similar
results. Let yi,q represent one particular measure of hedge fund performance, which could be
overall return volatility, factor loadings, raw excess returns, alpha, residual volatility, appraisal
ratio, or fund flows of fund i at quarter q.15 Let SATi be the composite SAT score of fund i’s
14
15

We thank the referee for the suggestion of using residual volatility as a measure of fund performance.
We delete the top and bottom 1% observations on independent variables to avoid potential recording errors.

We do not conduct the bootstrap procedure of FHNR (2006) due to the small number of funds that exhibit manager
characteristics.

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manager’s undergraduate institute, and W ORKi,q be years of working of the manager for fund
i at quarter q, and let Controli,q be a vector of control variables for fund i at quarter q. Given
that the performance of hedge funds could depend on the size and age of the fund, we choose lag
fund size and age as control variables.16 Then all empirical analysis in our paper is based on the
standard Fama-MacBeth regression approach with the following benchmark regression for each
quarter q :
yi,q = b0 + b1 SATi + b2 W ORKi,q + b03 Controli,q + ui,q .

(5)

III. Education, Career Concern, and Hedge Fund Performance
In this section, we examine the relation between hedge fund performance and manager education and career concern, measured by SAT and WORK, respectively.
A. Results Based on Raw Returns
Table 2 first reports the Fama-MacBeth regressions of raw excess returns on SAT and WORK
as described in equation (5). The regression results reveal a strong positive relation between
raw excess returns and SAT. The coefficient of SAT is highly significant and equals 0.091. We
also document a strong negative relation between raw excess returns and WORK, where the
coefficient is -0.027 and highly significant. The parameter estimates of SAT and WORK suggest
that everything else being the same, a manager from an undergraduate institute with a 200point higher SAT (for instance, from George Washington University with an SAT of 1280 to Yale
University with an SAT of 1480) can expect to earn an additional 0.73% raw excess return per
year, and a manager with 5 years less working experience can expect to earn an additional 0.54%
raw excess return more per year. Given the relatively low volatility of hedge fund returns (16%
per year), the difference of 0.5-0.7% in excess returns is economically important.
We also examine the risk-taking behaviors of fund managers by using fund total return volatility as the dependent variable in equation (5). Fund total return volatility is calculated as the

volatility of monthly returns over the past 12 months and is updated every quarter. Given that
certain hedge fund investors care about absolute performance, total return volatility is a reasonable measure of fund risk and has the advantage of being model free. We find a significant
negative relation between fund total return volatility and SAT, suggesting that managers from
higher-SAT institutes tend to take less risks. We also find that managers with longer working
experiences take significantly less risks.
The above results are consistent with the hypothesis that better-educated managers are better
at their jobs and thus can achieve higher returns at lower risk exposures. They are also consistent
16

Though it is easier to manage a smaller fund, a larger fund may have advantages in transactions costs and

economy of scale. Thus it is possible that there might be an optimal fund size. As pointed out by Getmansky
(2004), there is also a life-cycle effect in hedge fund performance.

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with the career concern hypothesis that less established managers have stronger incentives to
work hard at their jobs and are more willing to take risks, and consequently tend to have better
performance than more established managers. In the above regressions, we find that the control
variable fund size is negatively related to raw excess returns and total return volatility. This result
is consistent with the assumption of Berk and Green (2004) that it is more difficult to manage a
larger fund given the limited number of arbitrage opportunities in the market. Larger funds are
usually more established and thus may have less incentives to take excessive risks. Larger funds
also can invest in more securities, which may lead to less overall volatility due to the additional
diversification benefits.
B. Results Based on Risk-Adjusted Returns
Although the results in Table 2 are strong and significant, raw hedge fund returns could be
due to either risk taking or manager’s ability in identifying mispriced securities. For investors who
are interested in selecting managers with positive abnormal performance, it is more interesting to

study the relation between risk-adjusted returns and manager characteristics. In this section, we
relate hedge fund risk-taking behaviors and risk-adjusted returns to manager education and career
concern. To control for systematic risk, we use factor loadings and alpha as measures of risk-taking
behaviors and abnormal returns, respectively. On the other hand, to control for idiosyncratic risk,
we use residual volatility and appraisal ratio as measures of risk-taking behaviors and abnormal
returns, respectively.
Before we examine the cross-sectional differences in abnormal returns of hedge funds, we first
provide some distributional statistics on alphas under different benchmark models in Panel A of
Table 3. At each quarter, we calculate the alpha of each hedge fund as in equation (3) using the
six risk-adjustment models we consider. Then for each quarter, we calculate the mean, standard
deviation, and 5, 25, 50, 75, and 95 percentiles of the alphas under each model of all hedge funds.
The time series averages of all the above quantities are reported for each model in the table.
Under each of the six models, the average alphas are positive, and a high percentage of hedge
funds produce positive alphas. Given the wide range of risk-adjustment models we consider, this
result seems to be quite robust. In addition, this result is consistent with the findings of FHNR
(2006) that there are a significant number of funds of funds that produce positive risk-adjusted
returns.
Panel B of Table 3 reports the results of Fama-MacBeth regressions of hedge fund alpha on
SAT, WORK, fund age, and lagged fund size. We find a strong positive relation between alpha
and SAT, which is very robust to different risk-adjustment benchmarks we use. The coefficients
of SAT in all six models range from about 0.077 to 0.174, and are mostly significant at the 5%
level. The one (the AN model) that is not significant at the 5% level is significant at the 10%
level. The parameter estimates suggest that everything else equal, a manager who graduates from

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a college with a 200-point higher SAT will earn between 0.62% and 1.39% additional abnormal
return per year. We also find a negative relation between alpha and WORK, which also is very
robust to the different risk-adjustment benchmarks. The coefficients of WORK range from -0.013

to -0.027, and most of them are highly significant. The only exception is that of the AN model,
in which the large standard errors could be driven by the volatile option factor. The parameter
estimates suggest that a manager with 5 years less working experience can earn between 0.26%
and 0.54% additional abnormal return per year.
Panel C of Table 3 contains the results of Fama-MacBeth regressions of estimated fund risk
loadings under different models on SAT, WORK, fund age, and lagged fund size. Though the
dependence of factor loadings on SAT and WORK is not as uniform as that of alpha, we do find a
strong negative impact of SAT on the risk-taking behaviors of hedge funds. For example, a higher
SAT leads to significantly lower factor loadings of eleven out of the fifteen risk factors included in
the six benchmark models. Although the results for WORK are not as strong and uniform as that
for SAT, they still point to a general negative relation between factor loadings and WORK. A
higher WORK leads to significantly smaller factor loadings for eight out of the fifteen risk factors.
The results for most other factors are negative, but not statistically significant.
Panels D and E of Table 3 contain the results of Fama-MacBeth regressions of residual volatility and appraisal ratio under different models on SAT, WORK, fund age, and lagged fund size,
respectively. We find a strong negative relation between residual volatility and both SAT and
WORK, although the impact of WORK is much smaller than that of SAT. We also find a strong
positive relation between appraisal ratio and SAT. On the other hand, we find a negative relation
between appraisal ratio and WORK. Therefore, better-educated managers not only take less idiosyncratic risks, they also earn higher abnormal returns for per unit of idiosyncratic risk taken. In
contrast, although more established managers take less idiosyncratic risk, they earn less abnormal
returns for per unit of idiosyncratic risk taken as well.
The SAT score of a manager’s undergraduate institute could represent different qualities of the
manager. For example, higher SAT could mean the manager is more intelligent, more ambitious
and driven, more competitive, has better work ethics, or better educated, etc.17 Given the highly
competitive nature of the hedge fund industry and the complexity of hedge fund strategies, our
results on hedge fund performance and SAT are consistent with the conjecture that managers
with better educational backgrounds might be able to understand, design, and implement these
strategies better than others, either because they are smarter and better educated, or because
they are more devoted to their jobs.
Manager working experience could measure a manager’s knowledge and experience about
17

SAT also could measure how closely connected graduates from a certain university are. We use endowments

per student for each university as a proxy for connection and find that it has no significant impact on performance.

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the industry, as well as the manager’s incentive to work hard at his/her job. On one hand, a
more experienced manager might be able to earn higher returns due to his/her experience and
knowledge. On the other hand, because such a manager is more likely to be better established,
he/she also may have less incentive to work hard than a manager who still needs to establish
his/her career. The negative relation between hedge fund performance and WORK seems to
suggest that the impact of career concern dominates that of working experience.
Collectively, the results in Table 3 suggest that better-educated and more established managers
tend to take less systematic and idiosyncratic risks than their peers. Moreover, better-educated
(more established) managers also earn higher (lower) abnormal returns for per unit of systematic
and idiosyncratic risks taken. These patterns strongly suggest that certain managers are indeed
better than others and, no matter seeking superior absolute or relative performance, investors are
better off by selecting less established managers with better educational backgrounds, everything
else the same. FHNR (2006) also document significant cross-sectional differences in risk-adjusted
returns of funds of funds. Our results extend FHNR (2006) by relating differences in hedge fund
performances to education and career concern, and therefore provide a guidance on identifying
superior hedge fund managers based on manager characteristics.
C. Robustness Checks
In this section, we conduct additional analysis to further examine the robustness of the relation
between hedge fund performance and manager characteristics documented in the previous section.
We also consider the impact of gender and professional training, measured by CFA/CPA, MBA
and Ph.D. dummies, on hedge fund performance. We do not report those results here because
none of the above factors have any significant effect on hedge fund performances.

First, we repeat all the analysis in the previous section using data on funds of funds. According
to TASS, about 10% of hedge funds belong to the so-called funds of funds. Though regular hedge
funds make direct investments in different markets, funds of funds invest in a group of other hedge
funds. By doing this, funds of funds can diversify away idiosyncratic risks in individual hedge
funds and thus achieve more stable returns. The incentive fees of funds of funds (typically around
10%) are also significantly lower than that of regular funds (typically 15%-20%). One important
advantage of funds of funds, as pointed out by Fung and Hsieh (2004) and FHNR (2006), is
that their returns are less susceptible to survivorship bias than regular hedge fund returns might
have.18 Although our previous analysis includes both live and dead funds, results based on funds
18

Fung and Hsieh (2000) and FHNR (2006) argue that returns of funds of funds are less susceptible to survivorship

bias issues than that of regular hedge funds. Suppose the returns of a regular hedge fund that goes out of business
are not recorded in a dataset. Then analysis based on the returns of regular hedge funds contained in the dataset
would suffer from survivorship bias. On the other hand, the returns of a fund of funds that has invested in the
defaulted hedge fund would partially reflect the negative returns of the defaulted fund. Because of the more
diversified investments of funds of funds, they are more likely to survive than regular funds, and survirorship bias

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of funds further ensure that our results are not driven by survivorship bias.
The results for the 122 funds of funds in our sample in Panel A of Table 4 are generally
consistent with that of the regular hedge funds in Tables 2 and 3.19 For example, we find a
significant positive (negative) relation between excess returns (residual volatility) and SAT. The
negative-but-not-significant relation between total volatility and SAT could be due to the fact
that funds of funds are generally pretty well diversified. We also find a positive relation between
both alpha and appraisal ratio and SAT. Although the coefficients of SAT in both regressions are
not significant at the 5% confidence level, they are significant at the 10% level. We find a much

weaker negative impact of WORK on alpha, residual volatility, and appraisal ratio for funds of
funds than for regular funds: The Fama-MacBeth coefficients of WORK are not significant in all
three regressions. The potential financial rewards from the high incentive fees of regular funds
might provide stronger incentives for less established managers to work harder in the early stage
of their careers. This could lead to a bigger decline in performance when these managers start to
slow down after they become more established. On the other hand, because such incentives for
less established managers are weaker for funds of funds, the declines in performance as managers
become more established also could be less dramatic.
Second, we repeat all the analysis using value-weighted average returns of all hedge funds
managed by the same manager. Sometimes the same manager may start more than one fund
with different fund structures.20 One of these funds may be used as a “showcase” fund, which has
low assets under management but great performance. Unfortunately, these funds are closed and
are used as a marketing tool to attract capital in other less well-performing funds launched by the
same manager. Our previous analysis would treat these funds as multiple observations, although
in reality they are managed by the same manager. To control for this effect, we study the impact
of manager characteristic on the weighted average returns of all funds managed by the same
manager. The results of this analysis, reported in Panel B of Table 4, are broadly consistent with
that of our original analysis. We find a strong positive impact of SAT on excess return, alpha, and
appraisal ratio. Given that a portfolio of hedge funds is generally better diversified than a single
fund, the impact of SAT on volatility is weaker for individual managers. For example, although
we find a negative relation between SAT and residual volatility, the coefficient is significant only
at the 10% but not at the 5% level. Moreover, we do not find any significant relation between
overall volatility and SAT. We also find weaker impact of WORK on various aspects of hedge
fund performance for individual managers than regular hedge funds.
is a lesser concern for funds of funds.
19
Though we only report results based on the FHNR model, we obtain generally similar results for all other
models.
20
Out of the 607 managers in our sample, 371 managers handle only one fund, and 236 managers handle more

than one fund. The mean and median number of funds managed by the 236 managers are 2.67 and 2, respectively.

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Finally, we repeat all the analysis for two different subperiods during our sample to control
for time-varying risk exposures in hedge fund returns. Due to the dynamic nature of hedge fund
business, it is highly possible that hedge fund strategies, risk exposures, and risk-adjusted returns
all change over time. Moreover, recent studies of Fung and Hsieh (2004) and FHNR (2006) show
that there are structural breaks in hedge fund returns caused by LTCM crisis (around October
1998) and bursting of the Internet bubble (April 2000). Although our Fama-MacBeth regressions
explicitly allow for time-varying risk-adjusted returns and risk exposures, sub-period analysis
provides further assurance that our results are robust to these structural breaks. Specifically, we
repeat our analysis for the following two subperiods in which hedge fund returns are relatively
stable: Q1.1995 to Q2.1998 and Q3.2000 to Q3.2003. We ignore the period between Q3.1998 and
Q1.2000, because the few quarters of observations during this period makes it difficult to conduct
Fama-MacBeth regressions. For brevity, we only report results based on the FHNR model in
Panel C of Table 4. We find the results in the two subperiods are generally consistent with each
other. For example, we find a significant negative (positive) relation between residual volatility
(appraisal ratio) and SAT in both sub-periods. Although we find a significant positive relation
between alpha and SAT in the first sub-period, the coefficient of SAT in the second sub-period
is significant only at the 10% level. Furthermore, the SAT coefficients on alpha and appraisal
ratio in the second sub-period are much smaller than that in the first sub-period. This result
is consistent with the finding of FHNR (2006) that most hedge funds perform quite poorly and
exhibit small cross-sectional differences in their performances during the second sub-period.21
IV. Two Special Cases
Although the relations between hedge fund performance and SAT/WORK are pretty robust to
various risk-adjustment benchmarks, these models could still be misspecified. In this section, we
specialize our analysis to two specific hedge fund strategies whose risk-return characteristics have
been reasonably well understood in the literature. These two strategies are the trend following

strategy studied by Fung and Hsieh (2001) and the risk arbitrage strategy studied by Mitchell
and Pulvino (2001).
A. Trend Followers
The trend following strategy has been widely used by commodity trading advisors, who hope
to identify trends in prices and then either buy on upward or sell on downward trend. The
returns on trend followers tend to be large and positive during the best and worst periods of
market performance. Due to this nonlinear feature, the returns on trend followers have low
correlations with standard equity, bond, currency, and commodity indices. The important work
of Fung and Hsieh (2001) shows that returns of trend followers resemble closely that of a lookback
21

We thank the referee for recommending this explanation of the result to us.

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straddle. They further demonstrate that the following model, which includes lookback straddles
from several major markets (stock, bond, commodity, interest rate, and foreign currency), can
explain the returns of trend followers very well:
Ri,t = αi + βi,stock P T F Sstock,t + βi,bond P T F Sbond,t + βi,currency P T F Scurrency,t
+βi,interest P T F Sinterest,t + βi,com mod ity P T F Scom mod ity,t + ei,t ,

(6)

where PTFS stands for lookback straddle returns, and the subscripts represent the market for
which the straddles are constructed.
We apply the model of Fung and Hsieh (2001) to obtain the risk-adjusted returns of the 90
live and dead funds in the TASS dataset that have a “trend following” investment focus and for
which we can identify the manager characteristics. In addition to the model of Fung and Hsieh
(2001), we also construct a trend following index based on the value weighted average returns of

the 90 trend following funds. This index allows us to measure whether an individual fund takes
more or less style risk than the average trend following fund.
In Panel A of Table 5, we estimate three different versions of the model for the average returns
of the 90 trend following funds. In the first model, which includes the lookback straddles from all
five markets, the adjusted R2 is 27.8% and the coefficients of currency and commodity straddles are
highly significant. The next two models use only a subset of the five straddle factors. Consistent
with Fung and Hsieh (2001), we find that the incremental contributions of stock and interest rate
straddles over the other three are very small. We obtain similar results in later analysis using all
three models and report results based on the second model which has the highest adjusted R2 .
Panel B of Table 5 reports the results of Fama-MacBeth regressions of excess returns, factor
loadings, alpha, residual volatility, and appraisal ratio based on the Fung and Hsieh (2001) model
on SAT and WORK. The relation between excess returns and manager characteristics for trend
followers becomes somewhat weaker. For example, we do not find significant dependence of
excess returns on either SAT or WORK. However, consistent with previous results, we find a
strong positive effect of SAT on both alpha and appraisal ratio. For example, the SAT coefficient
in alpha regression, which is 0.365, suggests that a manager from an undergraduate institute with
a 200-point higher SAT can earn a higher alpha of almost 2.9% per year. On the other hand, we
do not find a strong relation between alpha and WORK for trend followers.
The results in Panel C of Table 5 based on the trend following style index are generally
consistent with that in Table 3. We find managers from higher-SAT institutes take significantly
less style risk than the average trend following fund. These managers also earn significantly higher
alpha and appraisal ratio. Although we find a positive relation between residual volatility and
SAT in both Panels B and C, the SAT coefficients are not significant in either case.
Although the model of Fung and Hsieh (2001) serves a good risk-adjustment benchmark for
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trend followers, there are some caveats we need to keep in mind when interpreting the results.
First, due to the relative small number of trend following funds in our sample, we may not have
enough information to obtain statistically significant estimates. Second, the returns on straddles

tend to be very volatile, which also could introduce noises in parameter estimates. Indeed we
find a weaker relation between hedge fund performance and manager characteristics for trend
followers. Despite that, we still find manager educational background to be positively related to
risk-adjusted returns. Therefore, the overall results of Table 5 suggest that the relation between
hedge fund performance and manager characteristics documented for regular hedge funds generally
hold for trend following funds.
B. Risk Arbitragers
Risk arbitrage, or the so-called merger arbitrage, has become very popular among hedge funds.
Generally speaking, in a merger or takeover the acquiring company’s stock price tends to go down
and the acquired company’s stock price tends to go up. To take advantage of this pattern, risk
arbitragers typically short the acquiring company and long the acquired company. Although the
strategy is called merger arbitrage, it is not a pure arbitrage opportunity. The main risk is that
the deal may not go through, in which case the arbitragers would lose money.
Mitchell and Pulvino (2001) have conducted a careful analysis on the risk and return properties
of merger arbitrage. They examine a return index of all the merger deals in the past few decades
and returns of hedge funds that specialize in this strategy. They find that returns on merger
arbitrage resemble that of a shorted put option. That is, the strategy makes money if the market
goes up or at least stays flat, but loses money if the market goes down. Mitchell and Pulvino
(2001) use the following piecewise linear regression to capture the returns of the risk arbitrage
strategy,
Ri,t = (1 − δ)(αi,low + βi,low M KTt )I(M KTt < −4%)

+δ(αi,high + βi,high M KTt )I(M KTt > −4%) + εi,t

(7)

where Ri,t is the excess return on asset i at time t, δ is a weighting coefficient, αi,low (αi,high ) is
the intercept when market return is below (above) -4%, βi,low (βi,high ) is the market risk loading
when market return is below (above) -4%, M KTt is the excess return of the market portfolio, and
I(·) is an indicator function, which equals one if the condition in the parenthesis is true and zero

otherwise. Mitchell and Pulvino (2001) show that by allowing different exposures to the market
factor in up and down markets, the above model captures the returns of merger arbitrage very
well.
We apply the model of Mitchell and Pulvino (2001) to the 150 funds in the TASS dataset that
have an investment focus of “risk arbitrage” and for which we can identify most of the manager
characteristics. Although the sample period of Mitchell and Pulvino (2001) is between 1963 and
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1998, their model works equally well in our sample period. In addition to the model of Mitchell
and Pulvino (2001), we also construct a risk arbitrage style index based on the value weighted
average returns of the 150 risk arbitrage funds. This index allows us to measure whether an
individual fund takes more or less style risk than the average risk arbitrage fund.
Panel A of Table 6 compares the average returns of the 150 risk arbitrage funds with the
returns on the market portfolio in each year of our sample. As predicted by Mitchell and Pulvino
(2001), the risk arbitrage strategy performs very well in up markets but very poorly in down
markets. For example, between 1995 and 1999, when the market has above 30% returns, the
returns on risk arbitrage are also very high.22 On the other hand, between 2000 and 2002 and in
1994 when the market was doing poorly, the risk arbitrage returns were substantially lower.
In Panel B of Table 6, we estimate the model of Mitchell and Pulvino (2001) using the average
returns of all risk arbitrage funds in our sample. Even though we use different threshold levels,
we obtain very similar results as that in Mitchell and Pulvino (2001). It is interesting to see that
the estimates of βlow in down markets are between 0.2 and 0.3, and the estimates of βhigh in up
markets are always close to 0.1. This clearly indicates that the betas are different under different
market conditions. The high adjusted R2 s in all models suggest that the piecewise regression
model captures the returns of risk arbitrage funds reasonably well. Since the results are very
similar for different thresholds, we adopt the same value used in Mitchell and Pulvino (2001),
-4%, in our risk-adjustment analysis.
Panel C of Table 6 reports the results of Fama-MacBeth regressions of excess returns, factor
loadings, alpha, residual volatility, and appraisal ratio based on the Mitchell and Pulvino (2001)

model on SAT and WORK. Consistent with existing results, we document a strong positive effect
of SAT on both excess returns and alpha. We also document a significant negative, but much
weaker, effect of WORK on both excess returns and alpha. However, neither SAT nor WORK
has any significant effect on appraisal ratio. In terms of risk-taking behavior, while for general
hedge funds, managers from higher-SAT institutes tend to take less systematic and idiosyncratic
risks, we find the opposite results for risk arbitrage funds. The results based on the risk arbitrage
style index in Panel D of Table 6 are generally consistent with that in Panel C. We find managers
from higher-SAT institutes take more style and residual risks than the average risk arbitrage fund.
Meanwhile, we still find a significant positive effect of SAT on alpha and there is also a positive
relation between appraisal ratio and SAT.
The results of Table 6 suggest that risk arbitrage funds behave somewhat differently from
other hedge funds in their risk-taking behaviors. However, despite this finding, we still find a
strong positive relation between alpha and SAT, suggesting that our main conclusions in Tables
22

The low return in 1998 is an outlier and could be a result of the hedge fund crisis caused by the collapse of

Long Term Capital Management.

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2 and 3 are quite robust.
V. Fund Flows and Future Fund Performances
Our previous results provide strong support for one important assumption of the model of
Berk and Green (2004). That is, there exists differential ability among hedge fund managers
in generating risk-adjusted returns. According to Berk and Green (2004), since investors infer
managerial ability from past performance and direct more capital toward funds with superior
performance, there should be zero abnormal returns in equilibrium. In this section, we provide
further analysis of the remaining implications of Berk and Green’s (2004) theory using hedge

fund data. In particular, we examine (i) the relation between fund flows and manager/fund
characteristics and past fund returns, and (ii) the impact of current fund flows on future fund
performance.
We investigate the relation between fund flows and manager/fund characteristics and past
fund returns by using the general regression in equation (5). In addition to fund age and size,
we also include lagged flow and current return in our regressions, because there could be serial
correlations in flows and flows might comove with current returns. Our measure of fund flows is
the standard flow growth rate:
Fi,q =

Ai,q − Ai,q−1 (1 + reti,q )
,
Ai,q−1

(8)

where Ai,q is the asset under management and reti,q is the raw return for fund i at quarter q.
The same flow measure has been used in many other studies, such as FHNR (2006).
Panel A of Table 7 contains the results of Fama-MacBeth regressions of fund flows on various
explanatory variables. We first focus on the benchmark regression in the second column of the
table. Consistent with the prediction of Berk and Green (2004), we find a significant positive
relation between fund flows and past fund returns. Thus, flow-chasing-return behavior exists
among both mutual fund and hedge fund investors. Interestingly, even after controlling for fund
size, current and past returns, and past flows, we still find a significant positive relation between
fund flows and SAT. Everything else equal, a 200-point increase in SAT leads to about 1.91%
higher growth rate in assets under management per quarter. Following the logic of Berk and Green
(2004), this result implies that investors might be more confident that past superior performances
of better-educated managers are due to true ability than luck, and therefore are more willing
to invest with such managers. Even though there is a negative relation between fund flows and
WORK, the coefficient is not statistically significant.

In the same benchmark regression, we find a significant negative relation between fund flows
and both lagged fund size and fund age. This result is consistent with the idea that there might be
an optimal size of assets under management for a given hedge fund: Since a significant part of the
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compensation of a hedge fund manager comes from incentive fees, the manager may not want to
increase the fund size beyond a certain level due to the diminishing return to scale effect pointed
out by Berk and Green (2004).23 In the same regression, we also find a positive serial correlation
between current and lagged fund flows as well as between current fund flow and returns.
In addition to the benchmark regression, we also consider dummy variable regressions to
examine potential interactions between manager characteristics and other explanatory variables
in explaining fund flows. Specifically, we introduce an additional variable in the benchmark
regression which is the product of one of the explanatory variables (i.e., lagged flow, lagged size,
current and lagged return, and fund age) and a dummy variable. The dummy variable for SAT
equals one if SAT is bigger than 1321 (median value of SAT for all funds) and zero otherwise,
and the dummy variable for WORK equals one if WORK is bigger than 18.5 years (median value
of WORK for all managers) and zero otherwise. The dummy variable regressions show that SAT
reduces the negative impact of lagged fund size and age on current fund flows. This suggests
that everything else equal, higher-SAT managers are less affected by the capacity constraint of
Berk and Green (2004) and can remain profitable by managing larger funds. The dummy variable
regressions also show that WORK increases (reduces) the negative impact of lagged fund size (fund
age) on current fund flows. This suggests that everything else equal, more established managers
are more affected by the capacity constraint, and past superior performance of more established
managers induce less current fund inflows. It is possible that more established managers are not
as motivated as less established ones and therefore are less able to handle additional inflows. It
is also possible that investors are more certain about the abilities of more established managers,
and as a result, past returns contain less new information on manager ability and lead to less
significant inflows.
There is a well-known convex relation between mutual fund flows and past returns: Funds with

superior past performances tend to attract huge inflows, but funds with inferior past performances
do not suffer as much in outflows. Next we examine whether a similar convex flow-return relation
exists for hedge funds by considering one modification of the benchmark regression in Panel B of
Table 7. We introduce an additional term which is the product of lagged return and a dummy
variable. The dummy variable equals one if lagged (raw or risk-adjusted) return is negative and
zero otherwise. If there is a convex flow-return relation for hedge funds, then the coefficient of the
dummy variable should be significantly negative. Our empirical results in Panel B of Table 7 show
that the prediction is not true in the data. The coefficient of the dummy variable is positive and
23

FHNR (2006) reports the growing presence of institutional investors among hedge fund investors since the burst

of the Internet bubble. The distribution of fund age in Table 1 suggests that our sample is probably biased toward
younger funds. Therefore, the negative relation between fund flows and fund age could be due to the fact that
older and more established funds simply run out of capacity in a demand-driven industry. We thank the referee for
pointing out this possibility.

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insignificantly different from zero. This suggests that hedge fund flows react to lagged returns
rather symmetrically. This is consistent with anecdotal evidence that hedge funds with poor
performances tend to lose their capitals very quickly.24
Finally, we examine one of the most important implications of the theory of Berk and Green
(2004), i.e., higher current fund inflows lead to deteriorating future fund performances. The compensation for mutual fund managers mainly comes from management fees, which are proportional
to assets under management. Combined with the convex flow-return relation, this suggests that
mutual fund managers have strong incentives to accept additional capitals to increase their compensations. Our flow results suggest that this may not be the case for hedge funds. A significant
part of compensation of hedge fund managers comes from incentive fees. Many hedge funds also
have a high watermark, and many managers have their personal wealth invested in the funds
they manage. Combined with the rather symmetric flow-return relation for hedge funds, inferior

returns can be extremely costly to hedge fund managers. As a result, hedge fund managers might
not have the same incentives as mutual fund managers in increasing their assets under management. Instead, there seems to be an optimal fund size, beyond which hedge funds start to take
less inflows.
The results of Fama-MacBeth regressions of future fund performances on past fund flows in
Panel C of Table 7 confirm the above intuition. Because hedge fund investors probably care
about both absolute and relative performances, we include both raw returns and risk-adjusted
returns using the FHNR model in our regressions.25 We first regress raw returns and alpha on
lagged fund flows controlling for fund age and lagged fund size. Then we include interaction terms
between lagged fund flow and SAT/WORK in our regressions to examine the impact of manager
characteristics on flow-return relation. We do not find any significant and uniform results from
these regressions. Though lagged flow negatively affects future raw returns, the coefficient is not
statistically significant in both regressions. Although the impact of lagged flow on future alpha is
positive and statistically significant in the original regression, it becomes negative and statistically
insignificant when the interaction terms are included. Similarly, the coefficients of most of the
interaction terms are not statistically significant either.
Our results on flow-return relation are broadly consistent with that of FHNR (2006), which
is the first study that tests the theory of Berk and Green (2004) using hedge fund data. FHNR
(2006) do not find much significant effects of past flows on future performances for beta-only
funds. While FHNR (2006) show that past flows have a significantly negative effect on the
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The result that hedge funds with poor performance tend to lose their capital very quickly is striking given

that this is occurring despite the longer lock-up period of investor’s capital, the cumber notification period for
withdrawing from hedge funds, and the existence of quite hefty redemption penalties. We thank the referee for this
interesting observation.
25

We obtain similar results using other risk-adjustment benchmarks.

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transition probability of have-alpha funds to remain have-alpha funds, the effect of fund flows
on future risk-adjusted returns of have-alpha funds are not statistically significant. Therefore,
it is fair to say that there exists no evidence in the current literature that hedge fund inflows
have statistically significant negative impacts on future risk-adjusted returns. At a deeper level,
this result is consistent with the cross-sectional differences in risk-adjusted hedge fund returns
documented in both FHNR (2006) and this paper: If Berk and Green’s theory works perfectly
for hedge funds, then it would probably be difficult to find such strong cross-sectional results for
hedge funds. Collectively, both FHNR (2006) and our paper show that the basic mechanisms of
Berk and Green’s theory are at work in hedge funds. However, due to the unique compensation
structure of hedge funds, the negative impacts of fund flows on future performances for hedge
funds are not as strong as that for mutual funds. Therefore, it is possible that even in equilibrium
some hedge funds can still deliver positive risk-adjusted returns, although such funds might have
already been closed to investors.
VI. Conclusion
Hedge funds differ from mutual funds in fundamental ways. These differences raise challenges
as well as opportunities for studying the important issue of delegated portfolio management.
Although the existing literature has mainly focused on the unique investment strategies of hedge
funds, we provide one of the first comprehensive studies on the impact of manager characteristics,
such as education and career concern, on hedge fund performances. Our analysis also provides a
test of the rational theory of active portfolio management of Berk and Green (2004) using hedge
fund data.
We document differential managerial ability among hedge fund managers in generating riskadjusted returns and flow-chasing-return behaviors among hedge fund investors. Specifically, we
find that managers from higher-SAT undergraduate institutes tend to have higher raw and riskadjusted returns, more inflows, and take less risks. We also find some weaker evidence that more
established managers tend to have lower returns and take less risks. However, in contrast to
the results for mutual funds, we find a rather symmetric relation between hedge fund flows and
past performance, and that hedge fund flows do not have a significant negative impact on future
performance. Our results strongly suggest that hedge funds are very different from mutual funds,

and that a manager’s talents and motivations should be important considerations in selecting
hedge fund managers.

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