Download Boyson persistence

Do hedge funds exhibit performance persistence? A new approach
Nicole M. Boyson*
October, 2003
Abstract
Motivated by prior work that documents a negative relationship between manager experience
(tenure) and performance, we design a new approach to detect persistence. While a portfolio
of funds selected on past performance alone shows no persistence, a portfolio that is long low
tenure/past good performers and short high tenure/past poor performers displays quarterly
persistence. Consistently poor performance among high tenure/past poor performers drives
this finding, and occurs because young managers are much more likely than old to be
terminated for poor performance. This termination/performance asymmetry provides
additional support for previous research showing greater risk-taking behavior among young
managers.
*Purdue University, Krannert Graduate School of Management, West Lafayette, IN 47907. E-mail:
[email protected]. Phone: (765)496-7877. I would like to thank Vikas Agarwal, Mike Cooper, Yong
Chen, Jean Helwege, Ravi Jagannathan, Andrew Karolyi, Narayan Naik, Karen Wruck, and René Stulz (my
advisor). Ashour Yacoub and John Shelbourne provided excellent research assistance. All remaining errors are
my own.
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1. Introduction
In selecting a hedge fund for investment, is it helpful to consult the manager’s prior
performance record? If past performance is indicative of future results, there is value to
investors in this information. If not, then investors may be better off selecting a manager
based on his reputation, investment style, or trading costs. Recent research regarding this
issue finds consistent results: there is some evidence of short term (one to three month)
persistence among individual hedge funds. (See Agarwal and Naik (1999, 2000), Bares,
Gibson, and Gyger (2002), and Baquero, ter Horst, and Verbeek (2002).) This persistence is
not driven by the existence of survivorship bias. At longer time horizons (semi-annual or
beyond), however, persistence largely disappears; see e.g., Brown, Goetzmann, and Ibbotson
(1999), and Brown and Goetzmann (2001).
With a more rigorous approach that controls for common risk and style factors in
hedge fund returns, this paper finds no evidence of persistence (short or long-term) when
funds are selected based on past performance alone. Style factors explain the previous
findings of short-term persistence, consistent with the work of Brown and Goetzmann (2001)
who show that certain styles perform well in certain periods; in other periods, these same style
do not perform as well. Thus, controlling for style is important in an analysis of performance
persistence among hedge funds.
However, while controlling for style casts doubt upon the previous findings of
persistence, there is another important factor that should be considered in constructing a test
of performance persistence – manager tenure. Boyson (2003) shows that less experienced
managers (hereafter referred to as “young” or “low tenure” managers) significantly
outperform more experienced managers (hereafter referred to as “old” or “high tenure”
managers). Specifically, in a sample of hedge funds for the period 1994 to 2000, she finds
that after controlling for common risk and style factors, the annual difference in performance
between young and old managers drops by about 0.75% for each year of experience. That is,
a manager who is 52 years of age has annual performance about 4% lower than the average
(47 year old) manager in the sample. Her results suggest the following: since low tenure
managers are better, then a bad return for a low tenure manager is more likely to be due to bad
luck than for a high tenure manager. Likewise, a good return for a high tenure manager is
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more likely to be due to good luck than for a low tenure manager. In other words, good (bad)
returns for low tenure managers are likely to be due to superior manager skill (bad luck); good
(bad) returns for high tenure managers are likely to be due to good luck (lack of manager
skill). Thus, properly accounting for manager tenure when performing a persistence analysis
should detect performance persistence among the young versus the old hedge fund managers.
The remainder of the paper designs a more powerful test of performance persistence,
taking into account manager tenure. I construct a portfolio that takes a long position in low
tenure/past good performers and a short position in high tenure/past poor performers, which
by design, should maximize the likelihood of finding persistence.
And, this portfolio
demonstrates quarterly persistence: controlling for risk and style factors, the excess
performance is about 9% annually, which is both economically and statistically significant.
This result is driven primarily by persistent underperformance among old, past poor
performers.
Next, we explain the concentration of persistence among old past performers with the
following hypothesis: that the termination relationship is more performance-sensitive for
young managers. If this is the case, then old, poor performers have a low probability of being
terminated and thus are more likely to persist in the next period. This hypothesis is motivated
by theoretical literature that suggests that young managers will be punished more severely for
poor performance than are old.1
It is also motivated by empirical results for mutual fund
managers and security analysts. (See Chevalier and Ellison (1999b) and Hong, Kubik, and
Solomon (2000)).
A conditional survival analysis documents the following results:
conditional on having been a poor past performer (in the bottom third of returns), young
managers are significantly more likely to be terminated than old.
Also, conditional upon
having been a “middle” performer (in the middle third of performance) young managers are
still significantly more likely to be terminated than old. Only when we condition upon having
been a past good, performer (top third of returns) is there no difference in survival rates
1
See, for example, Zwiebel (1995) and Holmstrom (1999) who describe the process by which investors find out
about managers with a learning model. Each period, investors observe a new performance outcome (in this case,
a monthly return) by which they learn about manager ability. Since there are more observations for older
managers than young, this implies that the sensitivity of a manager’s reputation is less dependent on the most
recent observation. Hence, old managers are less likely to be assessed as inferior based on a recent bad outcome
than are young.
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among young and old managers. Thus, being in the bottom two-thirds of performance
significantly hurts young managers relative to old. The second survival analysis (this time,
conditional upon manager tenure) establishes the following result: conditional on being
young, past poor performers are more likely to be terminated than past good performers.
However, conditional on being old, there is no difference in survival rates between past poor
and past good performers. These findings support each other are broadly consistent with the
idea that investors are more likely to tolerate poor performance from managers with moreestablished reputations (i.e., more experienced managers). Thus, this finding helps to explain
the continued poor performance among old, past poor performers.
This paper makes two contributions to the literature. First, it takes advantage of the
empirical result that young managers outperform old to design a test that detects risk- and
style-adjusted performance persistence at the quarterly level. While selecting funds based on
past performance alone results in a finding of no performance persistence, the more powerful
approach of choosing funds based on both past performance and manager tenure does result in
a finding of persistence.
This persistence is mostly concentrated among the old, past poor
performers. To our knowledge, this is the first paper in the literature to test for performance
persistence in this manner.
Second, this paper explains this finding of persistence (notably, that it is concentrated
among the old, past poor performers) as being driven by differences in termination rates
among young and old managers. Specifically, there is an interesting asymmetry in the shape
of the termination and age/performance relationship: the termination process is much more
performance-sensitive for young managers than for old. At first glance, this relationship
appears similar to that in the mutual fund industry: Chevalier and Ellison (1999b) also find
that young mutual fund managers are more likely to be terminated than old for poor
performance. However, there is a key difference between their results and the results of this
paper. They find that for young mutual fund managers, the probability of termination
decreases steeply with performance when managers have negative excess returns, but it is
fairly insensitive to performance differences at positive excess return levels.2 As long as a
young manager’s returns are positive, his probability of failure does not differ from that of an
2
Chevalier and Ellison (1999b), page 391.
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older manager.
By contrast, in the hedge fund industry the performance threshold is much
higher, and is measured relative to other managers rather than based on absolute performance:
unless a young hedge fund manager’s returns are in the top third of managers, his probability
of failure is significantly higher than that of an older manager. In other words, young hedge
fund managers have to beat two-thirds of other managers to reduce their probability of failure
to the same as that of older managers.
Clearly, this high threshold of performance in the hedge fund industry sets up a
different incentive structure for young mutual fund versus young hedge fund managers: while
young mutual fund managers concerned about survival need only avoid posting a negative
excess return (which gives them an incentive to “play it safe” and avoid idiosyncratic risk),
young hedge fund managers that are concerned with survival need to post returns in the top
third of all performers (which gives them an incentive to make “bold” investment decisions
relative to other managers.) The empirical evidence for hedge fund managers is completely
consistent with this implied incentive: Boyson (2003) shows that young managers “herd” less
and take on more idiosyncratic risk than old.
While this paper contributes to a relatively small and recent literature in the hedge
fund industry, researchers have been studying persistence in the mutual fund and pension fund
industries for many years, with mixed results. An early study by Jensen (1968) finds no
support for persistence. Papers supporting persistence over five to ten year periods include
Grinblatt and Titman (1992), Elton, Gruber, Das and Hlavka (1993), and Elton, Gruber, Das
and Blake (1996), who attribute this persistence to manager stock-picking ability. Support for
shorter-term (one to three year) persistence comes from Hendricks, Patel, and Zeckhauser
(1993), Goetzmann and Ibbotson (1994), Brown and Goetzmann (1995), and Wermers
(1999). Carhart (1997) shows that the one-year momentum effect of Jegadeesh and Titman
(1993) accounts for much of the performance persistence found by Hendricks, Patel, and
Zeckhauser (1993), and that differences in mutual fund expenses and trading costs can explain
nearly all of the remaining persistence. Christopherson, Ferson, and Glassman (1998) apply
conditional performance evaluation techniques to a sample of pension funds, and show that
the conditional approach is better able to detect persistence and predict future performance
than “unconditional” (linear) methods. This persistence is mostly concentrated among the
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worst performers. More recently, using a Bayesian approach with daily mutual fund data,
Bollen and Busse (2002), and Busse and Irvine (2002) find evidence of quarterly performance
persistence that is not explained by momentum.
This paper is organized as follows. Section 2 describes the data. Section 3 describes
the performance measures and portfolio formation process used in Section 4. Section 4
performs the first analysis of persistence, and shows that when common risk and style factors
are properly accounted for, there is no evidence of quarterly persistence when funds are
selected based on past performance only. Section 5 motivates and designs a more powerful
test of persistence that incorporates manager tenure, and shows evidence of persistence at the
quarterly level. Section 6 performs a detailed survival analysis to explain the patterns in
persistence found in Section 5. Section 7 discusses the results of the survival analysis in light
of the career concerns literature. Section 8 concludes.
2. Data
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.3 The database includes monthly
net-of-fee returns, as well as expenses, fees, size, terms, age, and style of the funds. For the
quarterly persistence tests, we require that each fund have at least 6 months of consecutive
returns for inclusion in the sample. For the semi-annual persistence tests, each fund must
have at least 12 consecutive months of returns for inclusion, and for the annual persistence
tests, each fund must have at least 24 months of consecutive returns. For all time frames
(quarterly, semi-annual, and annual) each fund must have at least $5 million in assets during
the period January, 1994 to December, 2000.
In constructing the sample, an important issue must be considered: “backfilling” or
“instant history” bias (see Edwards and Park (1996). On the date that TASS adds a new fund
to their database, they “backfill” historical returns. Typically, a hedge fund manager will
start his fund with a limited amount of personal capital before selling shares to the public. He
hopes to compile a good track record so as to eventually attract outside investors. Thus, most
3
TASS has maintained data on dead funds since 1994.
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funds arrive in the database with a history of strong performance which was never available to
the public, which biases returns upward. This difference is often large -- using the TASS
database, Fung and Hsieh (2000) calculate the bias as about 3.6% per year. The average
“incubation” period in our sample is about one year; thus, to control for this bias we drop the
incubation period for each fund.
The final sample used to perform the quarterly persistence tests includes 1,659 funds
with at least 6 months of returns, $5 million in assets, and all of the fund characteristic
variables. Table 1a includes summary statistics of the return and fund characteristic variables.
The final sample for the semi-annual persistence tests has 1,503 funds, and the final sample
for the annual persistence tests has 982 funds. Summary statistics for these samples do not
differ materially from the quarterly sample. As a brief summary, the average fund in the
sample is about three years old, has about $80 million in net assets, and annualized excess
returns of about 8%. More than half the managers use leverage and have personal capital
invested, and the styles of US Equity, Relative Value, Event Driven, and Emerging Markets
make up over 60% of the funds in the sample.4 About 30% of the sample consists of funds
that failed at some time during the sample period.
With respect to failed funds, a number of researchers have emphasized the importance
of including dead as well as extant funds in an analysis of performance.5 Not including
defunct funds in the sample can bias returns upwards. In an earlier study of offshore hedge
funds, Brown, Goetzmann, and Ibbotson (1999) find that not including defunct funds in the
sample biases returns upwards by 3% per year. Liang (2000) compares two major hedge fund
databases (Hedge Fund Research (HFR) and TASS), and finds that the TASS database tends
to contain more dead funds, and thus, should be more appropriate for the type of study in this
paper.
He finds survivorship bias in the TASS data of about 2% per year, which is
approximately what we calculate using the sample of 1,659 funds.
4
See Appendix B for a description of the fund styles.
In the mutual fund literature, see, for example, Brown, Goetzmann, Ibbotson, and Ross (1992), Wermers
(1996), and Carhart (1997).
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3. Performance measures and portfolio formation process
3.1. Performance measures
This paper uses a multi-factor model to control for common risk factors in hedge fund
performance. Since hedge funds have exposures to a number of markets, and can engage in
dynamic trading strategies (such as using options, futures, and leverage), using a broad set of
indices is appropriate (see Fung and Hsieh (1997) and Ben Dor, Jagannathan, and Meier
(2003) for further discussion). The passive indices are obtained from Datastream and include:
the US Trade Weighted Dollar index to capture currency risk, gold and commodity indices,
and the Lehman Brothers 30-year Treasury bond and US aggregate bond indices. For stock
market risk, the Value-Weighted CRSP index (obtained from WRDS) is used.
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%.6
Table 1b reports summary statistics for the passive indices and other
risk factors (HML, SMB, and MOM) used in the paper.
Since a number of researchers have stressed the importance of considering style in a
study of hedge fund performance (see, e.g., Fung and Hsieh (1997), Brown, Goetzmann, and
Ibbotson (1999), Ibbotson and Patel (2002), and Ben Dor, Jagannathan, and Meier (2003)),
the model includes “style” as well as common risk factors. These “style factors” are hedge
fund indices are published jointly by Credit Suisse First Boston (CSFB) and TASS, and
represent a number of hedge fund trading strategies or styles. They are constructed so as to
minimize survivorship bias. For further detail about the indices used, see Appendix A. Table
1b reports summary statistics for all hedge fund style indices used in the paper.
The model is as follows:
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These returns were obtained from the website of Kenneth French.
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K
D
k =1
d =1
r pt = α pT + ∑ b pkT F kt + ∑ b pdT H dt + ε t
(1)
where rpt is portfolio p’s return in month t in excess of the risk-free rate (t = 1 to T months),
the Fkt’s are each of the passive index returns and HML, SMB, and MOM (k = 1 to K) in
month t, and the Hdt’s are each of the hedge fund index returns (d = 1 to D) in month t.
3.2. Portfolio formation process
This section describes the methodology by which hedge funds are sorted into
portfolios to evaluate performance persistence. Similar to HPZ (1993), and Carhart (1997),
we form portfolios on lagged hedge fund returns and test for both short-term and longer-term
persistence.7 As noted earlier, quarterly (but not longer) persistence in hedge funds has been
documented by Agarwal and Naik (2000), Baquero, ter Horst, and Verbeek (2002), and Bares,
Gibson, and Gyger (2002). Following Carhart (1997), funds are sorted into decile portfolios
based on lagged returns, which are net of fees and expenses and in excess of the risk-free rate.
They span the period January, 1994 to December, 2000 for a maximum time series per fund
of 84 months. Not all funds have all 84 months of returns available. Some funds fail before
the end of the sample period, and other funds do not begin until some time after January,
1994; however, as long as a fund has at least 6 consecutive monthly returns, it is included in
the sample.8 Funds are sorted into portfolios based on three different time frames: three
months, six months, and one year.9 This initial period is called the formation period, and for
the persistence analysis, each portfolio is held for a length of time equal to its formation
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When we form portfolios based on lagged returns, we do this in two different ways. The first method is to
form portfolios based on lagged excess-of-risk-free rate returns. The second method forms portfolios based on
their returns in excess of their style average. For both methods, the raw returns of the portfolios are then
regressed against a number of common risk factors AND style factors as in Equation (1). We find that the
results in the paper are robust to whether the portfolios are formed based on excess-of-risk-free-rate or excess-ofstyle-category returns. Thus, only the results from the excess-of-risk-free rate are reported throughout.
8
We require six months of returns for quarterly tests, 12 months for semi-annual tests, and 24 months for annual
tests.
9
Since in all the tests performed, funds never exhibit persistence at the semi-annual or annual level, this paper
only reports results for quarterly examination periods. Results for other time horizons are available from the
author by request.
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period. For example, at the three month time horizon, funds are first sorted into portfolios
using the prior quarter's return. These portfolios are held for three months, and equalweighted portfolio returns are calculated for each of the three months. Every three months,
portfolios are re-formed. This yields a time-series of 81 monthly returns for each portfolio
(the first three months are used in the initial formation period). For the six-month formation
period, there are 78 monthly portfolio returns, and for the one-year formation period, there are
72 monthly portfolio returns.
As noted above, the process of using portfolios of funds to study persistence has been
used by HPZ (1993) and Carhart (1997).
Another common methodology examines
persistence using individual funds (rather than portfolios of funds). See, for example, Brown
and Goetzmann (1992), Goetzmann and Ibbotson (1994), and Agarwal and Naik (2000).
While the individual fund approach is appropriate in certain cases, this paper uses the
portfolio approach, since this approach is better-suited to a study of hedge funds. The main
reason is that the portfolio approach allows for a risk- and style- adjusted analysis of
persistence while using a minimum number of time-series observations. As described above,
in the portfolio approach, each period a portfolio of funds is created for which an average
return is calculated, resulting in a long time-series of portfolio returns which may then be
adjusted for common risk and style factors. In this case, only the returns used in the initial
formation period are not analyzed for persistence, and persistence may be examined for a
larger number of funds over any time frame (e.g., monthly, quarterly, semiannually, etc.). By
contrast, when persistence among individual funds is studied, properly adjusting for risk and
style factors is typically accomplished by calculating intercepts (alphas) from time-series
regressions over the period being analyzed. These alphas are then compared to each other on
a period-over-period basis to test for persistence. If one wishes to use a multi-factor model to
control for a number of risk and style factors, this necessarily lengthens the time frame over
which persistence may be analyzed. For example, this paper uses an model with eighteen (18)
independent variables. A study of persistence among individual funds that uses an 18-factor
model will not be able to test for persistence at the quarterly, semiannual, or even annual
level, since alphas could not be calculated for periods shorter than 18 months due to the
degrees of freedom constraint. Additionally, funds would be required to have a much longer
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time-series of returns than in the portfolio approach, which would reduce the sample size
significantly. Thus, while the individual fund approach is well-suited to mutual fund studies
(which have much longer time series of returns and typically have exposures to a smaller
number of risk and style factors), it is less suited to a study of hedge funds, with their short
time-series of returns and exposures to a large number of risk and style factors. Hence, the
portfolio approach is used in the analyses that follow.
4. Do hedge funds exhibit risk and style-adjusted persistence?
In this section, we test for performance persistence when controlling for fund exposure
to common risk and style factors. Controlling for style is accomplished by including hedge
fund style indices as independent variables, using Equation (1) from Section 3.1. A slightly
different way to control for the effect of style on performance would be to model the return
process for each fund style. Fung and Hsieh (2001) refer to this approach as developing
“asset-based style factors.” For example, Fung and Hsieh (1997) show that the returns of the
“trend-following” style (which is probably most closely related to the “Global Macro” style in
this paper) can be modeled as a look-back straddle on the S&P 500 index. Mitchell and
Pulvino (2001) show that the returns to merger arbitrage hedge funds closely resemble short
positions in put options. Finally, Agarwal and Naik (2003) show that a good deal of variation
in hedge fund returns can be explained with simple option buying/writing strategies. While
these approaches are very helpful in understanding the return processes for each hedge fund
style, there are not yet “asset-based” factors developed for each fund style. Since we wish to
control for as much of the style exposure as possible, we use-reported styles as regressors.10
As described in Section 3.2 above, decile portfolios are formed based on a fund's
lagged quarterly returns in excess of that fund’s style’s average return.
Then for each
portfolio, equally-weighted monthly returns are calculated and regressed against a number of
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In unreported results, we include the option-based returns developed by Agarwal and Naik (2003) as
additional independent variables. When these returns are included separately (without including the hedge fund
style index returns), the portfolios load significantly on these factors, and the results are consistent with Agarwal
and Naik (2003) in that the returns of hedge funds are similar to short positions in put options. However, when
the option returns are included in addition to the hedge fund style index returns, they never receive significant
loadings. This result occurs because the option returns are fairly highly correlated with a number of the hedge
fund styles. Regardless of whether the option returns are included, the intercepts from the regressions (in which
we are most interested) are quite similar.
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passive indices, the HML, SMB, and MOM factors, and a number of hedge fund indices (style
factors) using Equation (1), above. Results from this regression are in Table 2. The first
column shows the monthly excess-of-risk-free rate returns and standard deviations for the
formation period portfolios. The second column shows the monthly excess-of-risk-free rate
returns and standard deviations for the lagged decile portfolios.
For the lagged decile
portfolios, average monthly returns are fairly monotonic, increasing from -0.66% for decile 1
(worst) to 0.49% for decile 10 (best). Standard deviation is highest among the best and worst
deciles and lowest in the middle deciles.
Examining the alphas (intercepts) from the
regressions, the intercept on the best minus worst (10-1) portfolio is positive, but not
statistically significant. In addition to the alphas, the coefficients from the most statistically
significant independent variables are shown.
This analysis provides evidence that once common risk and style factors are
considered, there is no evidence of quarterly persistence. This result is in direct contrast with
the results of other hedge fund studies, which find some persistence at the quarterly level.
There are at least two reasons why the results of this paper contrast with theirs. First, most
previous studies examine individual fund performance and define persistence as a fund’s
being in the top half of returns for two consecutive periods. This paper sets a more difficult
standard for persistence, requiring that funds be in the top 10% of performers (rather than the
top 50%) for two consecutive periods. Second, while the other studies control for common
risk factors, they do not control for style in the same way as in this paper. To control for style
effects, other studies compare fund returns in excess of style average, but do not adjust these
“net” returns for exposures to other style indices.
To examine the incremental effect that
controlling for style indices has on the ability to find persistence, I re-perform the above
analysis controlling only for common risk factors (and not style indices). With this approach
(in unreported results), I do find evidence of performance persistence at the quarterly (but not
longer) time horizon. Thus, this paper provides evidence that style factors account for much
of the persistence found in prior studies.
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5. Manager tenure as a predictor of persistence
While the results of the previous section indicate that prior research findings of
quarterly performance persistence can be largely explained by omitted “style” factors, there is
another factor systematically linked to performance that has been ignored in tests of
performance persistence. This factor is manager tenure – the length of time that a manager
has been overseeing his fund.
Boyson (2003) shows that, controlling for common risk and
style factors, manager tenure is related to performance. Specifically, more experienced (e.g.,
older or higher tenure) managers underperform less experienced (e.g., younger or lower
tenure) managers by approximately -0.75% for each year of tenure. This difference is both
economically and statistically significant. If young managers are more skilled than old, this
result should be of use in designing a more powerful test of performance persistence. The
idea is that young managers with good returns likely achieved those returns due to skill, while
old managers with good returns have a higher probability of having achieved those returns
due to good luck. The converse also should hold: young managers with poor returns likely
experienced bad luck, while old managers with poor returns are likely to have experienced
those returns due to lack of skill. Thus, a persistence test that selects funds based both on past
performance and on manager tenure should be able to detect persistence.
Thus, this result is used to design the following test of performance persistence.
Funds are sorted into thirds based on two factors: first, they are sorted into thirds based on
prior period returns, and then these portfolios are again sorted into thirds based on manager
tenure. These sorts result in nine portfolios, ranging from old, past poor performers (portfolio
1, the “worst” portfolio) to young, past good performers (portfolio 9, the “best” portfolio). As
before, a portfolio that is long the best portfolio (young, past good) and short the worst
portfolio (old, past bad), is formed (this is referred to as the 9-1 portfolio). Again as before,
these portfolios are held for the three months (six months, one year), and equal-weighted
portfolio returns are calculated for each of the three months (six months, one year). Every
three months, portfolios are re-formed. This yields a time-series of 81 (78, 72) monthly
returns for each portfolio (the first three (six, twelve) months are used in the initial formation
period).
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The quarterly persistence results are in Table 3. The intercept from the 9-1 portfolio is
positive and significant at the 5% level (t-value = 2.05). The annualized excess return from
investing in this portfolio is about 9%/year, which is economically significant as well. To
conserve space, this table shows coefficients and related t-statistics only for the dependent
variables which are statistically significant in at least one of the portfolio regressions. While
investing in the 9-1 portfolio results in significant quarterly persistence, there is no persistence
at the semi-annual and annual levels (which is consistent with prior research).
An examination of the coefficients on the explanatory variables indicates some
interesting patterns in the data. First, the worst portfolios load positively and significantly on
the value-weighted CRSP index, while the best portfolios do not have significant exposure to
this factor.
This could be interpreted as “herding” behavior by the worst (and oldest)
managers, which is consistent with the findings of Boyson (2003).
Additionally, the best
portfolios have positive exposure to the currency and commodity indices, while the worst
portfolios have negative exposure to these indices. Also, the worst portfolios have negative
exposure to bond indices, while the best have positive exposure.
Finally, style plays an
important role in explaining the return differences; managed futures appear to have been out
of favor during the time frame, while the long-short equity style was very successful during
this time. Thus, it appears that the best managers were successful in both short and long
positions in the equity market (as evidenced by their negligible exposure to the VW CRSP
index) while the worst managers had a more pronounced long exposure (as evidenced by their
significant exposure to the VW CRSP index and insignificant exposure to the long/short
equity style index).
While there is evidence of quarterly performance persistence, it appears that poor
performance among old, past bad managers is driving this result. The net annualized return of
9% for the 9-1 portfolio attributes about -5.5% to the poor performance of the old, past bad
managers (which is statistically significant at the 10% level) and about +3.5% to the good
performance of the young, past good managers (which is not statistically significant at
conventional levels). Thus, there appears to be an asymmetry in persistence: old, past bad
managers continue to perform quite badly, while young, past good managers continue to
perform fairly well, although at levels that are not statistically significant from zero. Due to
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the lack of statistical significance, it is probably most accurate to say that while young, past
good managers may not continue their past good performance, the are at least able to avoid
future poor performance. The next section investigates the likely cause of this asymmetry in
more detail.
6. Why do old, past bad returns persist?
The persistence test in Section 5 indicates persistence at the quarterly level when funds
are selected for investment based on both manager tenure and past performance.
This
persistence is concentrated among old, past poor performers. This section investigates the
likely cause of this continued poor performance.
We consider the following hypothesis:
young managers are fired more often than old for poor performance. If this is true, then old
managers with past poor performance are less likely than young to fail, and thus are more
likely to show (poor) performance persistence.
This idea comes from models that relate termination to a learning process where
investors learn about a manager’s ability over time. (See, for example, Jovanovic (1979),
Zwiebel (1995), and Holmstrom (1999)). Early in a manager’s career, when his reputation is
not well-established, investors put more weight on his most recent performance (in the case of
a hedge fund manager, his most recently reported monthly return).
Eventually, as his
reputation becomes more established, each subsequent monthly return has less and less impact
on his assessed reputation.
The implication of this process is that the sensitivity of
termination to the most recent performance evaluation should decrease over time as managers
gain reputation.
Another reason is noted by Chevalier and Ellison (1999b): since more
experienced managers have survived a selection process, the market’s assessment of their
abilities may be further away from the threshold level at which it becomes efficient to fire the
manager.
Thus, we examine whether the termination process is more performance sensitive for
young managers. In studying this relationship, we repeat and augment the analysis of Boyson
(2003), who performs an unconditional survival test and shows that age and manager ability
are both positively related to the likelihood of a manager’s survival. Here, we extend her
work by performing a conditional survival analysis.
15
In this analysis, we follow her approach, which uses a time-varying proportional
hazards model to study the relationship between manager termination and a number of
factors. Intuitively, this model 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. 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, the
more flexible proportional-hazards model allows for more frequent failure times which
reduces bias and adds precision to the estimates.11
Table 4 performs a number of specifications of the model. Panel A reports results from
unconditional regression specifications that include as dependent variables a number of
lagged quarterly returns and the manager tenure variable. For ease of interpretation, the
coefficient on the manager tenure variable is annualized. In the table, a negative coefficient
implies a positive probability of survival, while a positive coefficient implies a positive
probability of failure. The results are consistent with Boyson (2003) who uses a smaller,
though similar, sample: both current and past returns, as well as manager tenure, are strongly
negatively related to failure. All else equal, good funds and old managers are more likely to
survive than bad funds and young managers.
Panel B begins the first conditional analysis; in this case, conditional upon past
performance. For certain of the portfolios described in Section 5, dummy variables are
created as follows. The first regression (Column 1) models the probability of survival by
tenure, given that the fund’s past performance was in the bottom third of returns. This
11
For details on the model and a thorough description of the estimation process, see Boyson (2003). For
technical details, see Cox (1972) and Kalbfleisch and Prentice (1980). Finally, for related finance/economics
literature that uses this methodology, see Helwege (1996), Lunde, Timmerman, and Blake (1999), and Brown,
Goetzmann, and Park (2001).
16
category corresponds to portfolios 1,2, and 3 from Table 3. If a fund is in portfolio 1 (high
tenure managers with poor past performance) it is assigned a value of one (1). If a fund is in
portfolios 2 or 3 (middle tenure managers with poor past performance, and short tenure
managers with poor past performance, respectively), it is assigned a value of zero (0). Since
this analysis is focusing on past poor performers, portfolios 4-9 are excluded. Hence, the
proportional hazards model is comparing the probability of failure for managers in portfolio 1
(old and bad) to the probability of failure for managers in portfolios 2 and 3 (middle tenure
and bad, and young tenure and bad, respectively). The negative and statistically significant
coefficient on the dummy variable indicates that old and bad funds have a 26.5% lower
probability of failure due to poor performance than do young and middle tenure bad funds.
The next column repeats the analysis of the first column, this time modeling the
probability of failure for managers in portfolio 4 from Table 3 (middle third of performance
with high tenure) against the probability of failure for managers in portfolios 5 and 6 (middle
third of performance with middle tenure, and middle third of performance with low tenure,
respectively). This time, portfolios 1-3 and 7-9 are excluded from the analysis. The results
are similar to those in column 1: the negative and statistically significant coefficient on this
variable indicates that old and middle-third performing funds have a 36% lower probability of
failure than do young and middle tenure, middle-third performing funds.
The final analysis is performed in column 3, which models the probability of failure
for managers in portfolio 7 from Table 3 (top third of performance with high tenure) against
the probability of failure for managers in portfolios 8 and 9 (top third of performance with
middle tenure, and top third of performance with short tenure, respectively). In this case, the
coefficient is positive but not statistically significant. This indicates that for good managers,
failure probabilities do not vary systematically by manager tenure. To summarize the results
of Panel B, termination is much more performance-sensitive for young than for old managers.
Young managers must perform in the top third of all managers to significantly reduce their
likelihood of being terminated.
Next, Panel C of Table 4 estimates another set of survival functions, this time
conditional upon manager tenure. From Panel B, it is clear that the termination relationship is
highly performance-sensitive for young managers. Panel C provides additional evidence that
17
supports this result. Specifically, column 1 examines the relationship between termination
and performance, conditional upon being a young manager. In this case, the managers in
column 9 (the young, past good performers) are assigned a dummy variable value of one (1),
while the managers in columns 3 and 6 (the young, past poor performers and the young, past
middle third performers) are assigned a dummy variable value of zero (0). This analysis
models the probability of fund failure for young managers in the top third of performance
against the probability of failure for young managers in the bottom two-thirds of performance.
The negative and significant coefficient indicates that young, past good performers are about
45% more likely to survive than young managers in the bottom two-thirds of performance.
The last analysis in Panel C examines only the old managers (columns 1, 4, and 7 of
Table 3) for differences in termination probability that are related to performance. In this
case, managers in column 7 (old tenure and good past performance) are assigned a dummy
variable of one (1), while managers in columns 1 and 4 (old tenure and bottom two-thirds of
performance) are assigned a dummy variable of zero (0)). The statistically insignificant
coefficient on this variable indicates that there are not differences in termination probabilities
related to performance for older managers. Thus, the results of Panel C support the results of
Panel B above: young managers can increase their probability of survival significantly by
being in the top third of performers, while among old managers performance is unrelated to
the likelihood of survival.
As noted above, these results provide evidence that fund survival is much more
performance-sensitive for young than for old managers. This asymmetry in the terminationperformance relationship drives the result from the previous section that performance
persistence is concentrated among old, past poor performers. Old managers survive more
often, regardless of performance. Thus, there is a greater likelihood of seeing persistence
among old, past poor performers (since they are unlikely to drop out of the sample) than
among young, past good performers (since these managers have to continue to have very
strong performance in order to survive).
18
7. Relationship of termination and performance persistence to managerial career
concerns
This section relates the results of Sections 3.5 and 3.6 to previous hedge fund and
mutual fund literature regarding the effect of a manager’s reputational concerns (or his “career
concerns”) on his behavior and ultimately, on his performance.
The career concerns
literature discusses the idea that a manager’s concern for keeping his current job or obtaining
a better job can mitigate potential agency problems which occur as a result of misaligned
incentives between managers and investors. See Fama (1980). It is reasonable to think that
these concerns will change over a manager’s career and affect his behavior, specifically his
propensity to take on risk.
For example, Chevalier and Ellison (1999b) study the behavior of mutual fund
managers, and show that these managers increase risk-taking behavior as their careers
progress (they tend to increase idiosyncratic risk and mimic other managers less (or “herd”
less)).
Their explanation is that there are implicit incentives in the mutual fund industry –
which relate to a manager’s likelihood of losing his job – that cause young managers to be
more risk-averse than old. As evidence, they model the termination/performance relationship
and show that for excess performance below zero, termination is more likely for young than
for old managers. However, for excess performance above zero, termination rates do not
differ among young and old managers.
Additionally, the termination/performance
relationship is fairly flat at all levels of return for old managers: termination is much less
performance dependent for old managers. Thus, the implicit incentives are clear: a young
manager that wishes to avoid termination will avoid risks that could lead to negative excess
performance. That is, he will avoid unsystematic risk and herd with other managers. And,
since termination is not dependent on performance for old managers, these managers will take
on more risk (they will herd less) in order to increase the possibility that their returns will end
up in positive territory.
By contrast, Boyson (2003) argues the opposing case for hedge fund managers. She
shows that hedge fund managers reduce volatility risk and herd more as they age. She
attributes this behavior to career concerns that increase over time in the hedge fund industry
(by contrast, Chevalier and Ellison (1999b) argue that career concerns decrease over time in
19
the mutual fund industry). Specifically, she argues that hedge fund managers have more to
lose in terms of reputation, personal capital, and current income than do hedge fund managers,
should their funds fail. One piece of evidence that supports this argument is the empirical
regularity cited in Brown, Goetzmann, and Park (2001) that a failed hedge fund manager is
unlikely to start a successful hedge fund in the future. By contrast, mutual fund managers that
fail often are hired by other fund companies. Other support for her argument is that hedge
fund managers have tremendously high salaries (often 10 or more times that of a comparable
mutual fund manager), and have their own capital invested in their funds. These factors all
imply that failure would be much less desirable for old than for young managers: their funds
tend to be larger, so their incomes and personal capital invested are higher, and finding
another job would result in a much larger pay cut than for a younger manager with a smaller
fund.
Next, Boyson (2003) shows that, all else equal, risk-taking behavior leads to
termination.
Both the existence of high career concerns and the higher probability of
termination for risk-taking behavior motivate old managers to reduce their risk-taking
behavior, which they do. Finally, she links this reduction in risk-taking behavior to lower
returns for old hedge fund managers: since old managers take on less volatility risk and herd
more, this behavior results in lower returns for these managers.
Thus, the different results of Boyson (2003) and Chevalier and Ellison (1999b) can be
reconciled by differences in career concerns in the hedge fund and mutual fund industries.
These results are also consistent with the finding of Section 5 of this essay that old, past poor
performers have persistently worse returns than young, past good performers. However, there
is one finding of this current essay that is on the surface, puzzling. In Section 6 we show that
young managers are more likely to be terminated for poor performance than are old.
At first
glance, this result looks very similar to that of Chevalier and Ellison (1999b): they also find
that termination is more performance-sensitive for young than for old managers. But a closer
look at these results shows a very important difference:
while for young mutual fund
managers, termination is only more performance-sensitive for excess returns below zero, for
young hedge fund managers, termination is more performance-sensitive at a larger range of
returns: specifically, if a young manager’s returns are not in the top third of all returns, he is
20
significantly more likely to fail than an old manager. Thus, the bar is set much higher in the
hedge fund industry: young managers must strive to be in the top third of returns in order to
continue in the industry.
This finding suggests the following story, which is completely consistent with Boyson
(2003): for young hedge fund managers who wish to continue in the industry, they must
achieve returns higher than one-third of all managers. To maximize their probability of doing
this, they must take on more volatility risk and herd less than other managers. Boyson (2003)
shows that this type of risk-taking behavior is indeed associated with higher returns. Old
hedge fund managers who wish to survive in the industry face a much lower hurdle. Since
survival rates for old managers do not vary much with performance (see Section 6), they have
strong incentives to herd and reduce risk so as to avoid the possibility of a very bad return that
would increase their probability of failure (and would likely result in a loss of personal assets
as well). Thus, young managers strive for high returns by taking on more risk, while old
managers strive for average (or even below average) returns by taking on less risk.
8. Conclusions
This paper designs a more powerful test for performance persistence that is able to
detect quarterly persistence among hedge fund managers. This test is motivated by the results
of Boyson (2003), who shows that young managers outperform old, on average, which
implies that for young managers, good results are driven by skill, while for old managers,
good results are driven by luck. If young managers are more skilled, they should show
persistence.
And this is indeed true:
at the quarterly time horizon, young, past good
managers outperform old, past poor managers by about 9%/year. This result is driven
primarily by the propensity of old, past poor managers to continue underperforming.
Additionally, we perform a survival analysis to investigate the result that old, past
poor managers have persistence underperformance, and find the following asymmetric
relationship. Young managers must perform in the top third of all managers to have survival
probabilities that are the same as those of old managers. That is, young managers are
punished (by fund failure) significantly more often than old if their returns are in the bottom
21
two-thirds of managers. Thus, a larger number of old, past poor performers survive from one
period to the next, which leads to this persistence.
Finally, these results are linked to the career concerns study of Boyson (2003).
Boyson finds that old managers take on less risk than young. She argues that this results from
greater career concerns of older managers: since older managers have more to lose in terms of
reputation, personal capital, and current income than young, they reduce volatility risk and
herd more to increase their probability of survival (or more precisely, to decrease their
probability of an extremely poor performance which would decrease their probability of
survival). By contrast, young managers take on more risk and herd less than old, which
increases their returns. The evidence from this essay provides additional support: the “higher
bar” that younger managers face in terms of survival (they must achieve returns in the top
third of all managers to have the same survival probabilities of old managers) provides an
additional incentive for young managers to take more risk than old. And, the relatively flat
relationship between termination and performance for old managers, coupled with their desire
to protect their current job and personal assets invested in their funds, leads old managers to
take on less volatility risk and herd more than their younger counterparts.
These results have broad implications for hedge fund investors. Specifically, the
result of quarterly persistence among young, past good performers over old, past poor
performers might provide a way for investors to achieve higher returns. However, for many
hedge fund investors, it is not possible to trade hedge funds as often as quarterly, due to
lockup periods (most hedge fund managers do not allow very frequent entrance and
redemption of assets). There is one class of investors, however, that could theoretically
benefit from this finding: fund of funds (FOF) investors. A fund of funds is a hedge fund that
invests in other hedge funds, and hedge fund managers will often waive lockup periods for
FOF investors.
However, there is another problem with implementing this investment strategy.
Specifically, the persistence is concentrated among the worst, rather than the best, performers.
Currently, there is no way to short a hedge fund. While investing in a long-only portfolio of
the best performers (young, past good managers) is possible, the excess return from this
strategy would have been 3.5%/year, which is not statistically significant, and arguably, not
22
very economically significant either. The very high failure rates of young hedge funds lead to
this result: even good young hedge funds fail at higher rates that old funds, so selecting a
portfolio of young, past good performers is no guarantee of performance persistence since
many of these funds are likely to fail in the next period, due simply to their age.
These results are broadly similar to many studies of mutual funds which find
persistence among the worst performers. Evidence for why this is true can be found in
Agarwal, Daniel, and Naik (2003) who show that, similar to mutual funds, good hedge funds
attract significant inflows while bad past performers do not experience as significant outflows.
Thus, the bad performers persist while the good performers may not. A more detailed look
into this relationship is an interesting area for future research.
Finally, an additional area for future research is to model the termination/performance
relationship in much more detail. We show that there is a large asymmetry: for young
managers, their returns must be in the top third of all managers to have the same survival
probabilities as for old funds. However, this conclusion is based on a simple sort of managers
into thirds based on age and performance. A closer look at the shape of the relationship can
provide better evidence regarding incentives and the subsequent risk-taking behavior of hedge
fund managers.
23
LIST OF REFERENCES
Ackerman, Carl, Richard McEnally, and David Ravenscraft, 1999, The performance of hedge
funds: Risk, return, and incentives, The Journal of Finance 54(3), 833-873.
Agarwal, Vikas and Narayan Y. Naik, 1999, On taking the alternative route: Risks, rewards,
style, and performance persistence of hedge funds," The Journal of Alternative
Investments 2(4), 6-23.
Agarwal, Vikas and Narayan Y. Naik, 2003, Risks and portfolio decisions involving hedge
funds, forthcoming, The Review of Financial Studies.
Asness, Clifford, Krail, Robert, and John Liew, 2001, Do hedge funds hedge?, The Journal of
Portfolio Management, Fall 2001, 6-19.
Avery, Christopher N. and Judith A. Chevalier, 1999, Herding over the career, Economics
Letters 53, 327-333.
Baquero, Guillermo, ter Horst, Jenke, and Mario Verbeek, 2002, Survival, look-ahead bias,
and the persistence in hedge fund performance, Working paper, ERIM Report Series
Research in Management, www.erim.eur.nl.
Bares, P.A., R. Gibson, and S. Gyger, 2002, Performance in the hedge funds industry: An
analysis of short and long-term persistence, Working paper, Institute of Theoretical
Physics Swiss Federal Institute of Technology Lausanne EPFL, Switzerland.
Bikhchandani, Sushil, Hirshleifer, David, and Ivo Welch, 1992, A theory of fads, fashion,
custom, and cultural change as information cascades, The Journal of Political
Economy 100(5), 992-1026.
Breslow, N., 1974, `Covariance analysis of censored survival data, Biometrics 20, 89-99.
Brown, Keith C., and V.W. Harlow, 2002, Staying the course: The impact of investment style
consistency on mutual fund performance, Working paper, University of Texas.
Brown, Keith, Harlow W.V., and Laura T. Starks, 1996, `Of tournaments and temptations: An
analysis of managerial incentives in the mutual fund industry, The Journal of Finance
51(1), 85-110.
Brown, Stephen and William Goetzmann, 1995, Performance persistence, The Journal of
Finance 50(2), 679-698.
24
Brown, Stephen and William Goetzmann, 2001, Hedge funds with style, NBER Working
Paper 8173, www.nber.org/papers/w8173.
Brown, Stephen, William Goetzmann and Roger Ibbotson, 1999, Offshore hedge funds:
Survival and performance 1989-1995, The Journal of Business 72, 91-117.
Brown, Stephen, William Goetzmann, and James Park, 2001, Conditions for survival:
Changing risk and the performance of hedge fund managers and CTA's, The Journal
of Finance 56(5), 1869-1886.
Brown, Stephen, William Goetzmann, Roger G. Ibbotson, and Stephen A. Ross, 1992,
Survivorship bias in performance studies, The Review of Financial Studies 5(4), 553580.
Carhart, Mark M., 1997, On persistence in mutual fund performance, The Journal of Finance
52(1), 57-82.
Carhart, Mark M., Jennifer N. Carpenter, Anthony W. Lynch, and David K. Musto., 2002,
Mutual fund survivorship, The Review of Financial Studies 15(5), 1439-1463.
Carpenter, Jennifer, ``Does option compensation increase managerial risk appetite?, 2000,
The Journal of Finance 55, 2311-2331.
Chevalier, Judith and Glenn Ellison, 1997, Risk-taking by mutual funds as a response to
incentives, The Journal of Political Economy 105(6), 1167-1200.
Chevalier, Judith and Glenn Ellison, 1999a, Are some mutual fund managers better than
others?, The Journal of Finance 54(3), 875-899.
Chevalier, Judith and Glenn Ellison, 1999b, Career concerns of mutual fund managers,
Quarterly Journal of Economics 114(2), 389-432.
Christopherson, Jon A., Ferson, Wayne E., and Debra A. Glassman, 1998, Conditional
measures of performance and persistence for pension funds, Research in Finance 16,
1-46.
Cox, D, 1972, Regression models and life tables (with discussion.), Journal of the Royal
Statistical Society 34, 187-220.
Das, Sanjiv and Rangarajan Sundaram, 2003, Fee speech: Signaling, risk-sharing, and the
impact of fee structure on investor welfare, forthcoming The Review of Financial
Studies.
Diamond, Douglas, 1991, Reputation acquisition in debt markets, The Journal of Political
Economy 97, 828-862.
25
Edwards, Franklin R. and James M. Park, 1996, Do managed futures make good
investments?, Journal of Futures Markets 16(5), 475-517.
Edwards, Franklin R. and Mustafa Caglayan, 2001, Hedge fund performance and manager
skill, Journal of Futures Markets 21(11), 1003-1028.
Elton, Edwin, Gruber, Martin J., and Christopher R. Blake, 1996, Survivorship bias and
mutual fund performance, The Review of Financial Studies 9(4), 1097-1120.
Elton, Edwin, Gruber, Martin J., and Christopher R. Blake, 1996, The persistence of riskadjusted mutual fund performance, The Journal of Business 69(2), 133-157.
Fama, Eugene, 1980, Agency problems and the theory of the firm, The Journal of Political
Economy 88(2), 288-307.
Fama, Eugene F. and James D. MacBeth, 1973, Risk, return, and equilibrium: Empirical tests,
The Journal of Political Economy 81, 607-636.
Fama, Eugene and Kenneth R. French, 1992, The cross-section of expected stock returns, The
Journal of Finance 47, 427-465.
Fama, Eugene and Kenneth R. French, 1993, Common risk factors in the returns on bonds and
stocks," Journal of Financial Economics 33, 3-53.
Fung, William and David A. Hsieh, 1997, Empirical characteristics of dynamic trading
strategies: The case of hedge funds, The Review of Financial Studies 10(2), 275-302.
Fung, William and David A. Hsieh, 1999, A primer on hedge funds, Journal of Empirical
Finance 6, 309-331.
Fung, William and David A. Hsieh, 2000, Performance characteristics of hedge funds and
commodity funds: Natural versus spurious biases, Journal of Financial and
Quantitative Analysis 35, 291-307.
Fung, William and David A. Hsieh, 2001, The risk in hedge fund strategies: Theory and
evidence from trend followers, The Review of Financial Studies 14(1), 313-341.
Fung, William and David A. Hsieh, 2003, Asset-based hedge fund styles and portfolio
diversification, forthcoming Financial Analysts Journal.
Gibbons, Robert and Kevin J. Murphy, 1992, Optimal incentive contracts in the presence of
career concerns: Theory and evidence, The Journal of Political Economy 100(3), 468505.
26
Glosten, Lawrence and Ravi Jagannathan, 1994, A contingent claim approach to performance
evaluation, Journal of Empirical Finance 1, 133-160.
Goetzmann, William N. and Roger G. Ibbotson, 1994, Do winners repeat?, The Journal of
Portfolio Management, Winter 1994, 9-18.
Goetzmann, William N. and Nadev Peles, 1997, Cognitive dissonance and mutual fund
investors, The Journal of Financial Research 20(2), 145-158.
Graham, John R., 1999, Herding among investment newsletters: Theory and evidence, The
Journal of Finance 54(1), 237-268.
Grinblatt, Mark and Sheridan Titman, 1989, Adverse risk incentives and the Design of
performance-based contracts, Management Science 35(7), 807-822.
Grinblatt, Mark and Sheridan Titman, 1989, Portfolio performance evaluation: Old issues and
new insights, The Review of Financial Studies 2(3), 393-421.
Grinblatt, Mark and Sheridan Titman, 1992, The persistence of mutual fund performance, The
Journal of Finance 47(5), 1977-1984.
Grinblatt, Mark and Sheridan Titman, 1994, A study of monthly mutual fund returns and
performance evaluation techniques, Journal of Financial and Quantitative Analysis
29(3), 419-444.
Grinblatt, Mark, Titman, Sheridan, and Russ Wermers, 1995, Momentum investment
strategies, portfolio performance, and herding: A study of mutual fund behavior, The
American Economic Review 85, 1088-1105.
Gruber, Martin J., 1996, Another puzzle: The growth in actively managed mutual funds, The
Journal of Finance 51(30), 783-810.
Hambrick, Donald and Phyllis Mason, 1984, Upper echelons: The organization as a reflection
of its top managers, The Academy of Management Review 9(2), 193-206.
Helwege, Jean, 1996, Determinants of savings and loan failures: Estimates of a time-varying
proportional hazard function, Journal of Financial Services Research 10, 373-392.
Hendricks, Darryll, Patel, Jayendu, and Richard Zeckhauser, 1993, Hot hands in mutual
funds: Short-run persistence of relative performance 1974-1988, The Journal of
Finance 48(1), 93-130.
Hendricks, Darryll, Patel, Jayendu, and Richard Zeckhauser, 1997, The j-shape of
performance persistence given survivorship bias, The Review of Economics and
Statistics 74(2), 161-170.
27
Hirshleifer, David and Anjan V. Thakor, 1992, Managerial conservatism, project choice, and
debt, The Review of Financial Studies 5(3), 437-470.
Holmstrom, Bengt, 1982/1999, Managerial incentive problems -- A dynamic perspective,
NBER Working Paper #6875.
Holmstrom, Bengt and Paul R. Milgrom, 1987, Aggregation and linearity in the provision of
intertemporal incentives, Econometrica 55, 303-328.
Holmstrom, Bengt and Joan Ricart I Costa, 1986, Managerial incentives and Capital
management, Quarterly Journal of Economics 101(4), 835-860.
Hong, Harrison, Kubik, Jeffrey D., and Amit Solomon, 2000, Security analysts' career
concerns and herding of earnings forecasts, RAND Journal of Economics 31(1), 121144.
Huddart, Steven, 1999, Reputation and performance fee effects on portfolio choice by
investment advisers, Journal of Financial Markets 2, 227-271.
Ineichen,
Alexander,
2000,
In
www.ubswarburg.com/researchweb.
search
of
alpha,
UBS
Warburg,
Ippolito, R., 1989, Efficiency with costly information: A study of mutual fund performance
1965-1984, Quarterly Journal of Economics 104, 1-23.
Jaggia, S. and Satish Thosar, 2000, Risk aversion and the investment horizon: A new
perspective on the time diversification debate, Journal of Psychology and Financial
Markets 1(3), 101-125.
Jegadeesh, Narisimhan and Sheridan Titman, 1993, Returns to buying winners and selling
losers: Implications for stock market efficiency, The Journal of Finance 48, 93-130.
Jensen, Michael C. and Kevin J. Murphy, 1990, Performance pay and top-management
incentives, The Journal of Political Economy 98(2), 225-264.
Jovanovic, Boyan, 1979, Job matching and the theory of turnover, Journal of Political
Economy 87, 972-990.
Kahneman, D., Slovic, P., and Tversky, A., 1982. Judgment under Uncertainty: Heuristics
and Biases. Cambridge, NY: Cambridge University Press.
Kalbfleisch, J. and R. Prentice, 1980. The Statistical Analysis of Failure Time Data. New
York: John Wiley & Sons.
28
Kanodia, Chandra, Bushman, Robert, and John Dickhaut, 1989, Escalation errors and the
sunk cost effect: An explanation based on reputation and information asymmetries,
Journal of Accounting Research 27, 59-77.
Katz, Ralph, 1982, The effects of group longevity on project communication and
performance, Administrative Science Quarterly 27, 81-104.
Kiesler, C., 1971. The Psychology of Commitment. New-York: Academic Press.
Lakonishok, J., A. Shliefer, R. Thaler, and R. Vishny 1991, Window dressing by pension fund
managers, America Economic Review, Papers and Proceedings 81, 227-231.
Lamont, Owen, 2002, Macroeconomic forecasts and microeconomic forecasters, Journal of
Economic Behavior and Organization 48, 265-280.
Lehmann, Bruce N. and David M. Modest, 1987, Mutual fund performance evaluation: A
comparison of benchmarks and benchmark comparisons, The Journal of Finance
42(2), 233-265.
L'habitant, François-Serge, 2001, Assessing market risk for hedge funds and hedge fund
portfolios, Working paper, HEC University of Lausanne.
Li, Xi, 2002, Performance, herding, and career concerns of individual financial analysts,
Working paper, Vanderbilt University.
Liang, Bing, 1999, On the performance of hedge funds, Financial Analysts Journal 55(4), 7285.
Liang, Bing, 2000, Hedge funds: The living and the dead, Journal of Financial and
Quantitative Analysis 35(3), 309-336.
Lo, Andrew, 2001, Risk management for hedge funds: Introduction and overview, Financial
Analysts Journal 57(6), 16-33.
Lunde,
Asger, Allan Timmermann, and David Blake, 1999, The hazards of
underperformance: A Cox regression analysis, Journal of Empirical Finance 6, 121152.
Malkiel, Burton G., 1995, Returns from investing in equity mutual funds 1971 to 1991, The
Journal of Finance 50(2), 549-572.
Mitchell, Mark and Todd Pulvino, 2001, Characteristics of risk and return in risk arbitrage,
The Journal of Finance 56(6), 2135-2175.
29
Morin, Roger A. and A. Fernandez Suarez, 1983, Risk aversion revisited, The Journal of
Finance 38(4), 1201-1216.
Prendergast, Canice and Lars Stole, 1996, Impetuous youngsters and jaded old-timers:
Acquiring a reputation for learning, The Journal of Political Economy 194(6), 11051134.
Salancik G.R., Commitment and the control of organizational behavior and belief, in B. Staw
and G.R. Salancik (eds.), New Directions in Organizational Behavior, St. Clair Press,
Chicago, 1977, 1-54.
Scharfstein, David S. and Jeremy C. Stein, 1990, Herd behavior and investment, The
American Economic Review 80(3), 465-479.
Shumway, Tyler, 2001, Forecasting bankruptcy more accurately: A simple hazard model,
Journal of Business 74(1), 101-124.
Sirri, Erik R. and Peter Tufano, 1998, Costly search and mutual fund flows, The Journal of
Finance 53(5), 1589-1622.
Starks, Laura T., 1987, Performance incentive fees: An agency theoretic approach, Journal of
Financial and Quantitative Analysis 22, 17-32.
Vroom, Victor and Bernd Pahl, 1971, The relationship between age and risk-taking among
managers, Journal of Applied Psychology 35, 399-405.
Weismann, Andrew, 2001, Conservation of volatility and the interpretation of hedge fund
performance, Alternative Investment Management Association, June/July 1998.
Welch, Ivo, 2000, Herding among security analysts, Journal of Financial Economics 58, 369396.
Wermers, Russ, 1997, Momentum investment strategies of mutual funds, performance
persistence, and survivorship bias, Working paper, University of Maryland.
Wermers, Russ, 2000, Mutual fund performance: An empirical decomposition into stockpicking talent, style, transaction costs, and expenses, The Journal of Finance 55(4),
1655-1695.
Zitzewitz, Eric, 2001, Opinion-producing agents: Career concerns and exaggeration, Working
paper, Stanford Graduate School of Business.
Zwiebel, Jeffrey, 1995, Corporate conservatism and relative compensation. The Journal of
Political Economy 103(1), 1-25.
30
Table 1a: Summary statistics for sample of 1659 funds
Below are averages of fund and manager characteristics over the period 1994 to 2000. Funds must have at least
six months of consecutive returns to be included in the sample. Monthly Net Return is net of all expenses, fees,
and is in excess of the 1-month Treasury Bill rate. Manager Tenure is the number of months that the manager has
been overseeing the fund. Size is in millions of dollars. Listed on Exchange is a 0/1 indicator variable set to one
(1) if the fund is listed on a stock exchange. Onshore is a 0/1 indicator variable set to one (1) if the fund is
headquartered in the United States. Open to New Investment is a 0/1 indicator variable set to one (1) if the fund
is accepting new investors. Open to Non-Accredited Investors is a 0/1 indicator variable set to one (1) if the
fund is open to non-accredited investors. Personal Capital Invested is a 0/1 indicator variable set to one (1) if the
manager reports that she has invested her own money in the fund. Uses Leverage is a 0/1 indicator variable that
is set to one (1) if the manager reports that she uses leverage. Redemption frequency measures the number of
months that an investor must keep his money in the fund before withdrawing it. Entrance frequency measures
how often new investors may enter the fund. Minimum investment is the minimum initial investment required
and is reported in millions of dollars. Management fee is the annual fee charged by the fund, measured as a
percentage of assets. Incentive fee is the annual incentive fee that may be charged by the fund, and is measured
as a percentage of annual (positive) profits. US Equity Style, Europe Equity Style, Relative Value Style, and
Event Driven Style are 0/1 indicator variables that are set to one (1) if the self-reported fund style is that style.
Live funds are those in operation at the end of the sample period. Dead funds are those that ceased operations
before the end of the sample period.
Variable
All Funds
Live Funds
Dead Funds
0.64%
1.11%
-0.14%
35
38
29
Fund Size (millions)
$82.2
$119.7
$21.5
Listed on Exchange?
16%
15%
17%
Onshore
44%
46%
40%
Open to New Investment
84%
75%
97%
Open to Non-Accredited Investors
13%
8%
21%
Personal Capital Invested
59%
57%
63%
Uses Leverage
74%
70%
80%
Redemption Frequency (months)
12
11
14
Entrance Frequency (months)
25
22
31
Minimum Investment (millions)
$0.83
$1.03
$0.48
Management Fee (% of assets)
1.38%
1.21%
1.65%
18.17%
18.28%
17.99%
22%
27%
12%
5%
7%
2%
Relative Value Style
14%
15%
12%
Event Driven Style
10%
14%
4%
Number of Funds
1659
1027
632
Monthly Net Return
Manager Tenure (months)
Incentive Fee (% of profits)
U.S. Equity Style
European Equity Style
31
Table 1b: Summary statistics for passive and hedge fund indices
Below are mean buy-and-hold monthly returns and other summary statistics for the passive indices and
hedge fund style 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 and WRDS (passive) and
CSFB/Tremont (hedge fund style). 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 style indices. For a detailed
description of the hedge fund indices, see Appendix A.
Panel A: Passive Indices
US Dollar Weighted
Index
Gold
Commodities
CRSP Value Weighted
LB Aggregate Bond
LB 30 Yr. U.S. Treasury
Bond
SMB
HML
Momentum
Std.
Dev.
Mean
Median
Max.
Min.
Skew.
Kurtosis
-0.19%
-0.73%
0.42%
0.95%
-0.43%
-0.24%
-0.94%
0.47%
1.56%
-0.26%
3.78%
20.51%
16.81%
9.23%
3.60%
-5.18%
-7.11%
-13.43%
-14.98%
-3.66%
1.73%
3.69%
5.40%
4.16%
1.26%
-0.06
2.34
0.19
-0.79
0.00
0.14
12.29
0.41
1.38
0.81
0.15%
-0.36%
-0.28%
1.56%
0.10%
-0.46%
-5.35%
0.81%
8.61%
14.23%
15.40%
18.23%
-7.78%
-21.51%
-11.66%
8.98%
2.81%
4.93%
3.83%
4.43%
0.00
-1.01
0.71
0.99
0.70
4.83
3.12
4.03
Panel B: Hedge Fund Style Indices
Managed Futures
Convertible Arbitrage
Emerging Markets
Distressed Securities
Market Neutral
Event Driven
Fixed Income Arbitrage
Global Macro
Long/Short Equity
Fund of Funds Index
Mean
Median
Max.
Min.
0.08%
0.45%
0.14%
-0.33%
0.54%
0.56%
0.14%
0.74%
0.91%
0.24%
-0.25%
0.73%
0.43%
-0.53%
0.57%
0.67%
0.40%
0.83%
0.95%
-0.10%
9.56%
3.13%
16.14%
22.32%
2.89%
3.43%
1.61%
10.14%
12.63%
4.92%
-9.82%
-5.06%
-23.41%
-9.08%
-1.53%
-12.16%
-7.35%
-11.94%
-11.82%
-3.73%
32
Std.
Dev.
3.32%
1.44%
5.93%
5.52%
0.98%
1.91%
1.25%
4.12%
3.65%
1.83%
Skew.
0.18
-1.68
-0.45
0.99
0.01
-3.64
-3.31
0.00
0.00
0.32
Kurtosis
1.31
4.41
2.40
2.37
-0.16
23.23
16.07
0.68
2.21
-0.18
Table 2: Quarterly persistence analysis when funds are selected based on prior performance
Hedge funds are sorted at the beginning of each quarterly period from the second quarter in 1994 to the final quarter in 2000 into decile portfolios based on
their previous quarter's return less the risk-free rate. The portfolios are equally weighted quarterly so the weights are readjusted whenever a fund disappears.
Funds with the highest past quarterly returns in excess of the risk-free rate comprise decile 10 and funds with the lowest past quarterly returns comprise decile
1. The dependent variable is the portfolio's excess monthly return. The dependent variables are the passive indices and the hedge fund indices described in
Table 1b. To save space, only the indices with the most statistically significant coefficients are shown. Alpha is the intercept of the model. t-statistics are in
parentheses. Number of funds is 1659.
Portfolio
Formation Period
Monthly
Monthly
Excess
Standard
Return
Deviation
Testing Period
Monthly Monthly
Excess
Standard
Return
Deviation
INDEPENDENT VARIABLES
Int’cept
(Alpha)
VW
CRSP
US $
Trade
Agg.
Bond
Long
Bond
EM
MF
LS
Adj.
R2
0.510
(1.79)
-0.469
(-3.98)
-0.059
(-0.24)
-0.379
(-2.70)
0.317
(4.43)
0.130
(0.98)
0.146
(1.01)
0.681
33
1 (worst)
-7.87%
6.19%
-0.66%
4.79%
-0.90%
(-2.48)
2
-2.96%
3.70%
-0.80%
3.46%
-0.46%
(-2.18)
0.424
(3.51)
-0.069
(-0.86)
-0.134
(-0.97)
-0.208
(-3.39)
0.108
(2.91)
0.151
(2.62)
0.124
(1.56)
0.780
3
-1.64%
2.20%
-0.29%
2.65%
0.05%
(0.36)
0.270
(3.98)
-0.027
(-0.46)
0.034
(0.55)
-0.092
(-2.10)
0.054
(2.22)
0.140
(3.73)
0.040
(0.91)
0.792
4
-0.84%
1.71%
-0.16%
3.07%
-0.01%
(-0.05)
0.179
(2.94)
-0.029
(-0.55)
-0.086
(-1.27)
-0.082
(-1.49)
0.019
(0.82)
0.069
(1.83)
0.030
(0.68)
0.769
5
-0.25%
1.32%
-0.17%
1.40%
-0.02%
(-0.17)
0.142
(2.76)
0.018
(0.44)
-0.067
(-1.15)
-0.031
(-0.69)
0.015
(0.62)
0.033
(0.90)
0.057
(1.28)
0.838
6
0.29%
1.82%
-0.07%
2.06%
0.06%
(0.60)
-0.018
(-0.27)
0.055
(1.27)
-0.168
(-3.04)
0.040
(1.26)
0.056
(2.59)
0.064
(2.44)
0.113
(2.26)
0.826
7
0.78%
1.20%
-0.31%
1.92%
0.16%
(1.30)
0.080
(0.90)
0.110
(1.53)
-0.188
(-1.80)
0.040
(1.00)
0.062
(2.21)
0.079
(2.02)
0.191
(2.37)
0.803
8
1.57%
1.75%
0.05%
2.41%
0.24%
(1.60)
-0.023
(-0.22)
0.141
(1.76)
-0.205
(-2.49)
0.092
(1.84)
0.049
(1.92)
0.048
(1.11)
0.413
(5.28)
0.797
9
2.78%
2.24%
0.30%
2.41%
0.20%
(0.81)
-0.132
(-0.87)
0.225
(1.96)
-0.247
(-1.85)
0.079
(1.10)
0.043
(1.05)
0.052
(0.99)
0.717
(6.75)
0.795
10 (best)
7.14%
5.24%
0.49%
4.11%
-0.28%
(-0.62)
-0.097
(-0.34)
0.322
(1.59)
-0.266
(-1.14)
0.133
(0.91)
0.067
(0.87)
0.134
(1.14)
1.136
(5.08)
0.687
10-1 (bestworst)
15.01%
8.55%
1.14%
6.25%
0.62%
(1.05)
-0.607
(-1.50)
0.791
(2.61)
-0.207
(-0.61)
0.512
(2.50)
-0.250
(-2.25)
0.990
(3.36)
0.429
0.007
(0.04)
Table 3: Quarterly persistence analysis when funds are selected based on prior performance and manager tenure
Hedge funds are sorted at the beginning of each quarter from the second quarter in 1994 to the final quarter in 2000 into thirds portfolios based on their
previous quarterly return less the risk-free rate. The portfolios are equally weighted quarterly so the weights are readjusted whenever a fund disappears.
These portfolios are then cross-sorted based on the quarter-end value of manager tenure into three additional portfolios: young, middle, and old. There are
nine portfolios, ranging from poor and old to good and young. A tenth portfolio is created which is long the good and young managers and short the poor and
old managers. The dependent variable is the portfolio's monthly return in excess of the risk-free rate. The independent variables are the passive and hedge
fund indices described in Table 1b. To save space, only the indices with the most frequent statistically significant coefficients are shown. Alpha is the
intercept of the model. t-statistics are in parentheses. Number of funds is 1659.
Formation Period
Testing Period
INDEPENDENT VARIABLES
Monthly
Monthly Monthly Monthly
Excess
Standard
Excess
Standard Int’cept
VW
US $
Agg.
Long
Adj.
Portfolio
Return
Deviation
Return
Deviation (Alpha) CRSP Trade
Bond
Bond
EM
MF
LS
R2
34
1 (poor perf./
old age)
-2.54%
2.98%
0.27%
2.47%
-0.41%
(-1.86)
0.409
(2.51)
-0.165
(-1.82)
0.119
(0.88)
-0.218
(-2.65)
0.084
(1.82)
0.172
(2.84)
0.010
(1.36)
0.151
(1.82)
0.687
2 (poor perf./
middle age)
-2.70%
3.22%
0.25%
3.09%
0.418
(2.94)
-0.174
(-1.79)
-0.065
(-0.43)
-0.254
(-3.39)
0.189
(4.44)
3 (poor perf./
young age)
-2.67%
3.18%
0.38%
2.85%
0.51%
(-2.48)
-0.30%
(-1.24)
0.060
(0.62)
0.756
0.53%
1.32%
0.50%
1.42%
0.10%
(0.90)
-0.203
(-2.49)
0.108
(1.78)
-0.216
(-1.44)
4 (middle
perf./old age)
0.297
(2.34)
0.077
(1.06)
-0.064
(-0.73)
-0.165
(-2.30)
0.032
(0.89)
0.184
(4.77)
0.030
(1.04)
0.112
(1.62)
0.104
(1.50)
0.783
0.075
(1.93)
0.022
(0.49)
0.751
5 (mid. perf./
middle age)
0.52%
1.26%
0.49%
1.56%
-0.13%
(-1.08)
0.058
(1.07)
-0.192
(-2.72)
-0.015
(-0.34)
6 (mid. perf./
young age)
0.56%
1.35%
0.65%
1.47%
0.11%
(1.47)
0.133
(2.09)
0.075
(1.86)
0.023
(0.77)
0.076
(1.90)
0.118
(1.73)
0.764
-0.039
(-1.26)
-0.190
(-3.89)
-0.55
(-1.57)
0.043
(2.32)
-0.005
(-0.11)
0.034
(1.26)
0.131
(3.78)
0.891
7 (good perf./
old age)
3.76%
3.20%
0.71%
3.10%
-0.13%
(-0.57)
-0.103
(-0.71)
8 (good perf./
middle age)
3.92%
3.06%
0.97%
2.85%
0.10%
(0.46)
-0.039
(-0.23)
0.257
(2.17)
0.207
(1.85)
-0.250
(-2.30)
-0.231
(-1.79)
0.180
(2.13)
0.057
(0.83)
0.136
(2.01)
0.689
(6.13)
0.803
0.115
(2.05)
-0.002
(-0.03)
0.521
(5.07)
0.772
9 (good perf./
young age)
4.21%
3.28%
1.48%
3.34%
0.29%
(1.03)
-0.081
(-0.48)
0.208
(1.62)
-0.165
(-1.05)
0.069
(0.83)
0.850
(6.47)
0.760
9-1
6.75%
3.62%
1.21%
2.90%
0.69%
(2.05)
-0.490
(-2.09)
0.373
(2.12)
-0.284
(-1.33)
0.287
(2.58)
-0.174
(-1.98)
0.699
(4.45)
0.405
0.061
(1.64)
0.108
(2.23)
0.024
(0.36)
Table 4
Conditional time-varying proportional hazards models
A time-varying proportional hazards model is estimated below. This model estimates the relationship between
the hazard rate and certain explanatory variables 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.
See Section 6 for a complete description of the model. Maximum likelihood estimation is used to estimate the
model. In the following table, a negative coefficient indicates a positive likelihood of survival, while a positive
coefficient indicates a positive likelihood of failure. P-values from a chi-squared test are shown in parentheses.
Number of total funds is 1659, while number of defunct funds is 632. Only coefficients on the variables of
interest are reported.
Specification
Current qtr. excess return
1
-2.790
(<0.001)
2
3
4
5
Panel A: Unconditional Estimation
-2.822
-1.810
-1.435
(<0.001)
(<0.001)
(<0.001)
One qtr. lagged excess
return
-1.888
(<0.001)
Two qtr. lagged excess
return
-1.467
(<0.001)
-2.306
(<0.001)
-2.021
(<0.001)
-1.884
(<0.001)
Manager tenure
-0.070
(<0.001)
-0.074
(<0001)
-0.086
(<0.001)
-0.098
(<0.001)
Panel B: Conditional on Past Performance
-0.265
(0.05)
Middle third past
performance and old tenure
-0.357
(0.05)
Top third past performance
and old tenure
0.142
(0.35)
Panel C: Conditional on Manager Tenure
Young tenure and good
past performance
Old tenure and good past
performance
-1.414
(<0.001)
-1.776
(<0.001)
Three qtr. lagged excess
return
Bottom third past
performance and old tenure
6
-0.448
(0.01)
0.124
(0.42)
35
-0.097
(<0.001)
Appendix A: Description of hedge fund indices
(Source: www.hedgeindex.com)
The methodology utilized in the CSFB/Tremont Hedge Fund Index starts by defining the universe it is
measuring. Credit Suisse First Boston Tremont Index LLC uses the TASS database, which tracks over 2,600
funds. The universe consists only of funds with a minimum of US $10 million under management and a current
audited financial statement. Funds are separated into primary sub-categories based on their investment style.
The Index in all cases represents at least 85% of the assets under management in the universe. CSFB/Tremont
analyzes the percentage of assets invested in each sub-category and selects funds for the Index based on those
percentages, matching the shape of the Index to the shape of the universe. The Index is re-balanced monthly.
Funds are re-selected on a quarterly basis as necessary. Funds must meet the Credit Suisse First Boston Tremont
Index LLC reporting requirements. Funds are not removed from the Index until they are liquidated or fail to
meet the financial reporting requirements. The objective is to minimize survivorship bias.
CONVERTIBLE ARBITRAGE
This strategy is identified by hedge investing in the convertible securities of a company. A typical investment is
to be long the convertible bond and short the common stock of the same company. Positions are designed to
generate profits from the fixed income security as well as the short sale of stock, while protecting principal from
market moves.
DEDICATED SHORT BIAS
Dedicated short sellers were once a robust category of hedge funds before the long bull market rendered the
strategy difficult to implement. A new category, short biased, has emerged. The strategy is to maintain net short
as opposed to pure short exposure. Short biased managers take short positions in mostly equities and derivatives.
The short bias of a manager’s portfolio must be constantly greater than zero to be classified in this category.
EMERGING MARKETS
This strategy involved equity or fixed income investing in emerging markets around the world. Because many
emerging markets do not allow short selling, nor offer viable futures or other derivative products with which to
hedge, emerging market investing often employs a long-only strategy.
EQUITY MARKET NEUTRAL
This investment strategy is designed to exploit equity market inefficiencies and usually involved being
simultaneously long and short matched equity portfolios of the same size within a country. Market neutral
portfolios are designed to be either beta or currency neutral, or both. Well-designed portfolios typically control
for industry, sector, market capitalization, and other exposures. Leverage is often applied to enhance returns.
EVENT DRIVEN
This strategy is defined as ‘special situations’ investing designed to capture price movement generated by a
significant pending corporate event such as a merger, corporate restructuring, liquidation, bankruptcy or
reorganization. There are three popular sub-categories in even-driven strategies: risk (merger) arbitrage,
distressed/high yield securities, and Regulation D.
FIXED INCOME ARBITRAGE
The fixed income arbitrageur aims to profit from price anomalies between related interest rate securities. Most
managers trade globally with categories including interest rate swap arbitrage, US and non-US government bond
arbitrage, forward yield curve arbitrage, and mortgage-backed securities arbitrage. The mortgage-backed market
is primarily US-based, over-the-counter and particularly complex.
36
Appendix A, continued
GLOBAL MACRO
Global macro managers carry long and short positions in any of the world’s major capital or derivative markets.
These positions reflect their views on overall market direction as influenced by major economic trends and/or
events. The portfolios of these funds can include stocks, bonds, currencies, and commodities in the form of cash
or derivatives instruments. Most funds invest globally in both developed and emerging markets.
LONG-SHORT EQUITY
This directional strategy involves equity-oriented investing on both the long and short sides of the market. The
objective is not to be market neutral. Managers have the ability to shift from value to growth, fro small to
medium to large capitalization stocks, and from a net long position to a new short position. Managers may use
futures and options to hedge. The focus may be regional, such as long/short US or European equity, or sector
specific, such as long and short technology or healthcare stocks. Long/short equity funds tend to build and hold
portfolios that are substantially more concentrated than those of traditional stock funds.
MANAGED FUTURES
This strategy invests in listed financial and commodity futures markets and currency markets around the world.
The managers are usually referred to as Commodity Trading Advisors, or CTA’s. Trading disciplines are
generally systematic or discretionary. Systematic traders tend to use price and market specific information
(often technical) to make trading decisions, while discretionary managers use a judgmental approach.
37
Appendix B
Investment style categories
(Source: TASS)
US Long/Short Equity: The investment manager takes long and short positions in U.S. equities.
European Equity Hedge: Same as above, with the European equities as the major focus.
Global/International Equity Hedge: Same as above, with an international focus.
Event Driven: The investment manager typically takes long or short positions in equities or debt instruments in
anticipation of an even (i.e., corporate restructuring, planned joint venture, etc.) expected cause substantial price
movement.
Distressed Securities: The investment manager invests in the securities of bankrupt companies in “Chapters 11
Status” in the U.S.
Risk Arbitrage/Deal Arbitrage: Strategy involves the simultaneous purchase of stock in a company being
acquired a sale of stock in the acquiring company. Also called takeover arbitrage and merger arbitrage.
Special Situations: The investment focus is on takeover situations, as well as distressed or financially troubled
securities. The manager looks for events that characteristically happen very rarely in the case of a
company/issuer.
Relative Value: The manager looks to establish offsetting long and short positions in related primary or
derivative markets based on the belief that one instrument or security is undervalued in terms of risk, liquidity
and/or return relative to another.
Market Neutral: Strategies that, in theory, do not depend on directional movement in markets traded.
Investment managers take offsetting long and short positions in related primary and derivative markets with the
intention of capturing pricing inequities. While resulting profits can be impacted by market direction, positions
should generate positive returns in either up or down markets.
Convertible Arbitrage: The investment manager simultaneously establishes long and short positions in
different forms of convertible securities from the same corporate issuer, and in so doing, captures pricing
inefficiencies between the different securities.
Statistical Arbitrage: The investment manager establishes long and short positions in related securities based
on quantitative models that identify pricing inequities.
Fixed Income Arbitrage: The investment manager establishes long and short positions in related debt
securities or derivative instruments.
Global Macro Discretionary: The investment manager utilizes fundamental and/or technical analysis to
establish directional positions in any publicly traded market around the world. Typically, managers follow a
“top down” analysis that attempts to identify the largest economic forces within the global economy and position
accordingly through the debt, equity, currency or commodity markets.
Global Macro Systematic: The investment manager uses technical systems to establish directional positions in
major primary and derivative markets around the world. Typically, investment decisions are generated by
proprietary computer programs that dictate the specific buy and sell strategies.
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Appendix B, continued
Dedicated Short Seller: The investment manager attempts to identify securities that are overprice or which it is
believed will decrease in value in the near future and establishes short positions.
Pure Currency Fund: The strategy is dedicated to trading currencies only. Different currency funds will
employ various investment approaches, either fundamental/discretionary or technical/systematic. The strategy
can be directional or arbitrage or both.
Pure Futures Fund: The strategy is implemented primarily in futures markets, though many managers will
carry foreign exchange positions in the interbank market.
Pure Emerging Markets: The fund invests exclusively in the emerging market debt or equity markets.
Emerging Market funds are the only “long only” funds listed in the TASS Database.
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