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Flows, Performance, and Managerial Incentives in the Hedge Fund Industry
Vikas Agarwal
Georgia State University
Naveen D. Daniel
Georgia State University
and
Narayan Y. Naik
London Business School
JEL Classification: G10, G19
This version: September 1, 2003
____________________________________________
Vikas Agarwal and Naveen D. Daniel are from Georgia State University, Robinson College of Business, 35, Broad
Street, Suite 1221, Atlanta GA 30303, USA: e-mail: [email protected] (Vikas) and [email protected] (Naveen) Tel:
+1-404-651-2699 (Vikas) +1-404-651-2691 (Naveen) Fax: +1-404-651-2630. Vikas Agarwal is a Research
Associate with EDHEC. Narayan Y. Naik is from London Business School, Sussex Place, Regent's Park, London
NW1 4SA, United Kingdom: e-mail: [email protected] Tel: +44-207-262-5050, extension 3579 Fax: +44-207-7243317. We would like to thank Nicole Boyson, Conrad Ciccotello, William Fung, Mila Getmansky, Paul Gompers,
William Goetzmann, Jason Greene, Roy Henriksson, David Hsieh, Robert Kosowski, Lalitha Naveen, Sebastien
Pouget, Stefan Reunzi, Krishna Ramaswamy, Ivo Welch, and participants at the Autumn seminar of INQUIRE
Europe in Stockholm, All-Georgia conference, EFA 2003 meetings in Glasgow, FMA European 2003 meetings in
Dublin, Georgia State University, London Business School and Wharton Hedge Fund conference for many helpful
comments and constructive suggestions on an earlier version of this paper. This paper was adjudged the best paper
on hedge funds at the European Finance Association (EFA) 2003 meetings in Glasgow. We are grateful for funding
from INQUIRE Europe. We are grateful to Josh Rosenberg of Hedge Fund Research Inc., Chicago, TASS
Investment Research Ltd., London and Zurich Capital Markets, Switzerland for providing us with the data on
individual hedge funds and hedge fund indexes. We are thankful to Otgontsetseg Erhemjamts, Purnendu Nath, and
Subhra Tripathy for excellent research assistance. We are responsible for all errors.
Flows, Performance, and Managerial Incentives in the Hedge Fund Industry
Abstract
Using a comprehensive database of individual hedge funds and funds of hedge funds, we
investigate the determinants of money-flow and performance in the hedge fund industry. We
have several important findings. First, good performers in a given year experience significantly
larger money-flows in the subsequent year and this performance-flow relation is convex. Second,
funds with persistently good (bad) performance attract larger (smaller) inflows compared to
those that show no persistence. Third, we find that money-flows are positively associated with
managerial incentives measured by the delta of the option-like incentive-fee contract offered to
hedge fund managers. Fourth, when we examine the relation between flows and future
performance, we find that larger hedge funds with greater inflows are associated with worse
performance in the future, a result consistent with decreasing returns to scale in the hedge fund
industry. Fifth, we find that funds with better managerial incentives (those with greater delta) are
associated with better performance in the future. Finally, we find that unlike individual hedge
funds, funds of hedge funds enjoy economies of scale. Overall, these results significantly
improve our understanding of the complex interaction between money-flows, performance, and
managerial incentives in the hedge fund industry.
2
Flows, Performance, and Managerial Incentives in the Hedge Fund Industry
In recent years, the hedge fund industry has emerged as an alternative investment vehicle
to the traditional mutual fund industry. It differs from the mutual fund industry in two important
ways. First, hedge funds are much less regulated compared to mutual funds, with limited
transparency and disclosure. Second, hedge funds charge performance-based incentive fees,
which help align the interests of manager and investors. These differences have important
implications for investment behavior of capital providers and the incentives of the hedge fund
managers to deliver superior performance. Therefore, in this paper, we investigate the complex
interaction between flows, performance, and managerial incentives in the hedge fund industry.
Due to legal restrictions placed on advertising by hedge funds and limited disclosure,
investors face significant search costs in selecting hedge funds. While we have a reasonable idea
of the factors investors consider before placing their money in mutual funds (Ippolito, 1992;
Chevalier and Ellison, 1997; Goetzmann and Peles, 1997; Sirri and Tufano, 1998), pension funds
(Del Guercio and Tkac, 2002) and private equity funds (Kaplan and Schoar, 2003), we have
limited understanding of the determinants of money-flows in the hedge fund industry. Given the
institutional differences between hedge funds and traditional asset management vehicles,
different factors may be influencing the investors’ fund selection process. This leads us to our
first research question. What are the determinants of money-flows in the hedge fund industry? In
particular, how do the money-flows relate to a fund’s past performance (returns and persistence
in returns), managerial incentives (call option-like incentive fee structure), and other fund
characteristics (size, volatility, and age)?
Active money management has long been considered as a puzzle to financial economists
(Gruber, 1996). For example, it is not clear why investors devote resources to evaluate past
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performance and chose funds on that basis, when future performance seems to be unrelated to
past performance. To resolve this puzzle, Berk and Green (2002) present a model of active
portfolio management where such behavior arises as a rational response on the part of the
investors. They argue that investors’ propensity to chase past performance drives the lack of
performance persistence found in the empirical literature. An important assumption underlying
their model is that the managers face decreasing returns to scale and therefore, money-flows into
better performing funds lower their abnormal returns in future. This leads us to our second
research question. What is the relation between money-flows and fund’s future performance
(returns and the likelihood of persistence in its future returns)? Furthermore, in case of hedge
funds, managerial incentives in the form of option-like incentive-fee contract play an important
role in motivating the managers to perform better. Therefore, we also examine the relation
between managerial incentives (proxied by the delta of the option-like incentive-fee contract)
and future performance.
Finally, we examine funds of hedge funds (henceforth FOFs), a rapidly growing segment
of the hedge fund industry. These intermediaries invest in other hedge funds. In order to search,
select, monitor, and track the performance of individual hedge funds, they charge a fixed
management fee and a performance-based incentive fee. Thus, an investor in FOFs encounters an
additional layer of agency. However, FOFs offer certain benefits including diversification, due
diligence, professional management, and potential access to star managers of hedge funds that
may be closed to new investors. This leads us to our third and final research question. What are
the determinants of money-flows and performance in FOFs and whether they are different from
those in case of individual hedge funds?
We conduct our investigation by merging three leading hedge fund databases: Hedge
Fund Research (HFR), TASS, and Zurich Capital Markets/Managed Accounts Reports
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(ZCM/MAR). Our database contains return histories for 1776 live and 1655 dead individual
hedge funds and 352 live and 187 dead FOFs. Using the merged database, we make four
important contributions to the hedge fund literature.
First, we conduct a comprehensive study of the determinants of money-flows into the
hedge fund industry. Goetzmann, Ingersoll, and Ross (2003) (henceforth GIR) is the only paper
to study this issue by conducting a univariate analysis of money-flows and past returns. We
believe that in addition to past returns, investors consider various other factors such as fund size,
age, volatility, and more importantly, managerial incentives in the form of degree of moneyness
of the manager’s option-like incentive-fee contract. For example, an investor may be reluctant to
invest his money in a small, young, and highly volatile fund, which is significantly below its
high-water mark.1 In addition to these factors, investors may also be paying attention to
consistency in performance of a fund. Arguably, investors prefer to place their money in funds
with persistently above-median returns. Therefore, we also examine the relation between flows
and persistence in past performance, a previously unstudied phenomenon.
Second, we make an important contribution to the extant literature by explicitly
measuring the managerial incentives through the delta of the manager’s option-like incentive-fee
contract. Performance-based compensation is an important tool to motivate the manager to
perform well.2 However, the incentive fee as a percentage of profits, per se, does not take into
account how far the fund is relative to its high-water mark. For example, Ackermann, McEnally,
and Ravenscraft (1999, pp. 860) acknowledge the limitations of using only incentive fees as a
metric for managerial incentives as follows: “The problem is that the relationship between high1
Fund size and age may proxy for managerial skill and hence, investors’ confidence in a particular fund.
Prior literature on hedge funds (Ackermann, McEnally, and Ravenscraft, 1999; Brown, Goetzmann, and Ibbotson,
1999; Liang, 1999; Edwards and Caglayan, 2001) has examined the relation between incentive fees and performance
2
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water marks, incentive fees, and volatility is complicated. The relationship should depend on
where the fund is relative to its high-water mark. This is further complicated by the fact that new
investors may have different high-water marks than original investors.” We specifically address
this issue and determine the managerial incentives by measuring the delta of the manager’s
incentive-fee option. For this purpose, we compute a fund’s yearly money-flows since the start of
a fund’s return history. We then estimate fund’s delta after applying the hurdle rate and highwater mark provisions to yearly money-flows from both new and original investors. We measure
delta as the dollar increase in the incentive-fee-based compensation of the manager for an
increase of one percent in the fund’s return. Clearly, delta depends on the degree of moneyness
of the options granted to the manager by annual inflows. The larger is the value of delta, the
greater is the incentive to deliver superior returns. This is the first attempt in the hedge fund
literature to empirically quantify the level of incentives, in dollar terms, offered by the
performance-based-compensation contracts.
Third, we examine the relation between current flows and future performance and
address the issue of decreasing returns to scale in the hedge fund industry. This is an important
issue that underpins Berk and Green’s (2002) model. In addition, as argued above, highpowered incentives should motivate the manager to perform better. Towards that end, we also
examine the relation between manager’s option-delta and future performance, an issue hitherto
unexplored in the hedge fund literature.
Finally, we make an important contribution to the literature by analyzing the
determinants of money-flows and performance for FOFs. Due to the double-fee arrangement,
Brown, Goetzmann, and Liang (2002) argue that FOFs face different incentives compared to
and, in general, has found a positive relation between the two. See Elton, Gruber and Blake (2003) for the
performance of mutual funds that charge incentive fees.
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individual hedge funds. Therefore, we also examine how money-flows and performance relate to
managerial incentives.
We have six main findings. First, we find money-flows chase returns and the
performance-flow relation is convex. This finding is consistent with that of Chevalier and Ellison
(1997), Goetzmann and Peles (1997), and Sirri and Tufano (1998) in the mutual fund industry.
Our finding, however, differs from that of GIR (2003), who find that the best performers
experience outflows. They argue that this may be due to good performers being reluctant to
accepting new money. It is important to note that presence of such managers in our sample
biases against finding evidence of money chasing good performance.
We believe that the differences in regulatory and market environments during the two
sample periods may be responsible for the differences in GIR’s and our results. For example, the
National Securities Markets Improvement Act of 1996 removed the restriction on the maximum
of 99 qualified investors. Further, during our sample period, data on individual hedge funds and
indexes started becoming widely available, which made it easier for investors to compare the
performance a hedge fund with its peer group. To assess the impact of these changes, we re-run
GIR’s univariate specification using only offshore funds from our composite database during
1989-1995. We find that instead of outflows, the top performers experience flows that are
indistinguishable from zero. This suggests that the nature of the hedge fund industry has indeed
changed over time, and in recent years it has started resembling the mutual fund industry.
Our second finding pertains to the relation between flows and persistence in performance.
Following Brown, Goetzmann, and Ibbotson’s (1999) approach, we label a fund as a winner
(loser) if its return in a given year is above (below) median return of peer-group funds. We
classify a fund as persistent winner (loser) if it is a winner (loser) for two successive years. We
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find that money-flows are significantly higher (lower) for funds that are persistent winners
(losers).
Our third finding concerns the relation between money-flows and managerial incentives.
We find a positive relation between flows and delta, which confirms that investors prefer to place
their money in funds where the manager has greater incentives to perform better. Since delta
incorporates information about the entire history of returns and money-flows, this finding
suggests that investors take into account historical returns as well as money-flows.
Our fourth finding pertains to the relation between current money-flows and future
performance. We find that flows into larger funds are associated with lower returns in the future.
Further, they are also associated with lower probability of those funds showing good persistence
in the future. This finding is consistent with the presence of decreasing returns to scale in the
hedge fund industry. It is also consistent with the findings of Chen et al (2002) for the mutual
fund industry.
Fifth, we find future performance to be positively related to managerial incentives. This
suggests that investors prefer funds where the manager has higher incentives to perform well in
the future, where good performance is reflected in higher returns and higher probability of
exhibiting good persistence.
Our final result relates to FOFs. When we analyze this intermediated sector of the hedge
fund industry, we find that FOFs with good performance (higher returns or good persistence)
attract greater money-flows. While this finding is similar to that for individual hedge funds, there
are two important differences. First, we find no significant relation between managerial
incentives, flows, and future performance. This suggests that investors’ preferences and funds’
performance in response to managerial incentives are different for FOFs. Second, we find that
larger flows into bigger FOFs are positively related to future returns and to the probability of
8
showing good persistence. This suggests that FOFs may be enjoying economies of scale. It is
also consistent with the notion that FOFs with substantial assets under management may have
greater bargaining power and enjoy better access to well-performing managers that may
otherwise be closed for new money.
The rest of the paper is organized as follows. Section I describes the data. Sections II and
III examine the relation between new money-flows, managerial incentives, and past
performance. Section IV and V investigate the relation between money-flows, managerial
incentives, and future performance. Section VI offers concluding remarks with suggestions for
future research.
I.
Data
A. Data Description
In this paper, we construct a comprehensive hedge fund database that is a union of three
large databases namely HFR, TASS, and ZCM/MAR.3 This enables us to resolve occasional
discrepancies among different databases as well as create a sample that is more representative of
the entire hedge fund industry. We consider both individual hedge funds as well as FOFs for the
purpose of our study. Our sample period extends from January 1994 to December 2000. We
focus on this period for three reasons. First, the number of funds prior to our sample period is
relatively few. Second, the databases do not extensively cover “dead” funds before 1994.4
Finally, publicly disseminated data on hedge fund indexes became available only since 1994,
3
In the past, researchers have used one or more of the three major hedge fund databases. For example, Fung and
Hsieh (1997) use TASS database, Ackermann, McEnally, and Ravenscraft (1999) use HFR and ZCM/MAR
databases, Agarwal and Naik (2000, 2003) and Liang (2000) uses HFR and TASS databases, while Brown,
Goetzmann, and Park (2001) use data from Offshore Funds Directory and TASS.
4
It is important to note that the word “dead” is misleading and “missing-in-action” may be a more appropriate term
as they include funds that are liquidated, merged/restructured, and funds that stopped reporting returns to the
database vendors but may have continued operations. However, in order to be consistent with previous research, we
9
which enabled investors to assess the relative performance of a fund more easily. Therefore, we
conduct our analysis using 1994-2000 data.
Table I (Panels A and B) provides the breakdown of funds from different data vendors. After
merging the three databases, we find that there are 1776 (352) live hedge funds (FOFs) and 1655
(187) dead hedge funds (FOFs).5 Figure 1 reports the overlap between the three databases via
Venn diagrams. The Venn diagrams in Figure 1 show that there is an overlap of 30% (41%) in
the number of hedge funds (FOFs) across the three databases, with HFR having the largest
coverage (54%). These Venn diagrams demonstrate that there are a large number of hedge funds
that are unique to different databases and thus, merging them helps in capturing a more
representative sample of the hedge fund industry.
Even though hedge funds market themselves as “absolute performers”, investors arguably
evaluate the performance of a hedge fund relative to its peers. Unfortunately, there is no
universally acceptable way of classifying hedge funds into different styles. Academic research
(Fung and Hsieh, 1997; Brown and Goetzmann, 2003) shows that there are five to eight distinct
style factors in hedge fund returns. Following these insights, we classify the reported hedge fund
strategies into four broad categories: Directional, Relative Value, Security Selection, and MultiProcess Traders. Appendix A describes the mapping between the data vendors’ classification and
our classification. Table I Panel C reports the distribution of hedge funds across the four broad
strategies.
Table II reports the means and medians of monthly net-of-fee returns, volatility of returns,
age, management and incentive fees for individual hedge funds and FOFs. We find that, in
continue to call them “dead” funds.
5
We exclude managed futures, natural resources, mutual funds, and ‘other’ hedge funds since these categories are
not usually considered as “typical” hedge funds. We also exclude long-only, Regulation D and funds with missing
strategy information.
10
general, FOFs have significantly lower returns and volatility, higher age and management fee but
lower incentive fee compared to individual hedge funds. Lower returns of FOFs can be
attributed, in part, to lesser biases compared to hedge funds (Fung and Hsieh, 2000) while the
lower volatility of FOFs can be explained by the diversified nature of their portfolios.
B. Computation of Money-Flows
We first compute dollar flows for fund i during month m as follows
Dollar Flowi , m = AUM i , m − AUM i , m −1 (1 + Ri , m )
(1)
where, AUM i ,m and AUM i ,m −1 are the size for fund i at the end of month m and month m-1
and Ri ,m is the return for fund i during month m.6 We aggregate the monthly flows during the
year t to estimate annual flows (Annual Dollar Flowi,t). As in Chevalier and Ellison (1997) and
Sirri and Tufano (1998), we scale annual dollar flows by beginning-of-year assets under
management to capture the change in size due to net money-flows.
Flowi,t =
Annual Dollar Flowi ,t
(2)
AUM i ,t −1
In Table III, we report the trend in assets under management (AUM) and money-flows in the
hedge fund industry. As can be seen from Table III, the total assets under management for hedge
funds have grown five-fold from $40 billion to $201 billion from December 1993 to December
2000. Over the same period, the total assets under management for FOFs have also grown from
$9 billion to $25 billion. We observe that, in general, more than 25% of funds experience
6
This formula assumes that the fund flows occur at the end of the month. For the sake of robustness, we also
compute money-flows assuming that they occur at the beginning of the month and find very similar results. When
AUM data is not available at a monthly frequency, we compute flows for coarser intervals.
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outflows. Further, the mean flows are systematically higher than the median flows suggesting
that some funds experienced significant growth in the assets under their management.
C. Computation of Managerial Incentives
One of the distinguishing differences between mutual funds and hedge funds is the
manager’s compensation contract. Unlike most mutual funds, hedge funds charge incentive fees
as a fixed percentage of the profits earned. Typically, incentive-fee contracts include hurdle rate
and high-water mark provisions. Hurdle rate (usually a rate of return on cash or LIBOR) is the
minimum return that the manager needs to achieve before claiming any incentive fees. Highwater mark requires the manager to make up for the previous losses before he can earn incentive
fees. Hence, incentive-fee contracts are essentially a call-option on the assets under management
with the exercise price depending on the hurdle rate and high-water mark provisions.
An incentive fee contract amounts to the investor having written a fraction of a call option on
the assets under management. For example, if incentive fee equals 20 percent, then it is
equivalent to the investor having written 0.2 of a call option on the money invested. When
money gets invested in a fund at different points in time, each investment is associated with its
own high-water mark. In addition, when a hurdle rate is specified, the manager must exceed it
before he can claim an incentive fee. Therefore, in general, incentive-fee contracts endow the
manager with a portfolio of call options. The value of the each of the call options depends on the
time when the money came in, its high-water mark and whether the return exceeds the hurdle
rate or not.
12
The option-like compensation provides the fund manager with incentives to deliver superior
returns.7 We proxy these incentives by the delta of the portfolio of options, which equals the
dollar change in the incentive fee for a one percent change in the fund’s return. The greater the
delta, larger is the incentive to deliver superior returns. We describe the procedure of computing
delta in Appendix B.
By definition, delta depends on the degree of moneyness of the portfolio of options generated
by money-flows over time, which in turn, depends on whether a fund has had a negative return in
a year or in previous years. Clearly, if the fund has had negative returns during a year, the fund
will be below its high-water mark. However, a fund with a positive return in a given year could
still be below its high-water mark if its cumulative negative returns in prior years exceed the
positive return in the current year.
We report the summary statistics of funds with negative returns and those below high-water
mark in Panel B of Table III. In case of individual hedge funds, we find that the percentage of
funds with negative returns has increased from 8.4% to 33.1% from December 1993 to
December 2000. The mean (median) losses also increased from 11.2% (7.9%) to 19.9% (15.0%)
over this period. Consistent with our discussion above, a larger proportion of funds are below
their high-water mark (50.2% and 51.3% in December 1993 and December 2000 respectively).
The shortfall below high-water mark for these funds will however be smaller than the magnitude
of loss in that year because it includes funds that have positive returns during that year and are
below high-water mark due to poor return history. As expected, the mean (median) shortfall
below high-water mark varies from 2.9% (0.3%) in December 1993 to 8.3% (1.2%) in December
2000. The results for FOFs are qualitatively similar to those for individual funds.
7
It is important to note that option-like payoffs also influence risk-taking incentives of hedge fund managers.
Agarwal, Daniel, and Naik (2003) examine these and find that fund volatility is positively related to both implicit
13
Finally, we report the summary statistics of delta in Panel C of Table III.8 Over the sample
period, we find that mean (median) delta across all hedge funds has increased from $130,000
($30,000) in December 1993 to $240,000 ($50,000) in December 2000. In contrast, across all
FOFs, the mean (median) delta has decreased from $170,000 ($20,000) to $80,000 ($10,000)
over the same period.9
II. Determinants of Money-Flows
In this section, we investigate the determinants of money-flows into and out of individual
hedge funds and FOFs. It may be that investors follow a top-down approach where they first
choose the broad strategies in which to invest, and then decide in which funds to invest.
Therefore, as a first step, we explore the performance-flow relation at a strategy level. For this
purpose, we sum up annual dollar flows across all funds within a strategy and obtain aggregate
strategy-level flows. We plot in Figure 2 the annual dollar flows against prior-year’s weighted
average returns of the funds following different strategies. We use both equally-weighted and
value-weighted (weighted by AUM) returns at the strategy level. Figure 2 suggests that, in
general, money-flows chase recent performance for all four strategies as well as for all the funds
considered together. We investigate this finding further at an individual fund level using our
composite database.
When investors infer a manager’s ability through past returns, one would expect to find that
funds that have performed especially well in the recent past would attract larger money-flows.
and explicit risk-taking incentives.
8
We measure delta in millions of dollars. However, it is simple to convert our dollar delta into the standard Black
and Scholes (1973) call option delta by dividing our dollar delta by (0.01*incentive fee*investors’ assets).
9
Like hedge fund managers, top executives of corporations receive option-like payoffs, which create similar
managerial incentives. It is interesting to compare the level of managerial incentives in these two industries. Coles,
Daniel, and Naveen (2003) report the mean (median) delta of executive stock options for the top 1500 firms in S&P
14
For poorly performing funds, one may not find significant outflows due to impediments unique
to the hedge fund industry such as lockup period, notice period, and redemption period. There
are two additional factors that may also affect the performance-flow relation. First, investors may
place less weight on the recent performance as some of the hedge fund managers could show a
good previous track record. Second, some of the well-performing hedge funds may be closed to
new investment. For these funds, we would find no money inflows despite their good
performance. This will lower the likelihood of finding that money-flows chase good
performance. Our empirical analysis captures the net effect of all these factors.
To examine the performance-flow relation, we need to categorize performance as good or
bad. While it would be useful to estimate risk-adjusted performance, this is a perilous task given
the non-normality in hedge funds returns and option-like dynamic trading strategies adopted by
hedge funds (Fung and Hsieh, 1997, 2001; Goetzmann et al, 2002; Agarwal and Naik, 2003).
Therefore, we use returns relative to one’s peer group as a measure of performance.
In order to compare our findings for hedge funds, with those of Sirri and Tufano (1998) for
mutual funds, we follow an identical empirical procedure. We classify each fund-year
observation into one of five quintiles based on performance. We find that the average inflow into
funds in the top (first) quintile is 63% compared to an average outflow of 3% for funds in the
bottom (fifth) quintile. We find a similar performance-flow relation for FOFs as well. In
particular, the top quintile performers attract inflow of 25% compared to outflow of 15% for the
bottom quintile performers.10
during 1992-2000 to be $584,000 ($196,000). See Murphy (1999) and Core, Guay, and Larcker (2003) for a survey
of literature on executive compensation.
10
For hedge funds, the t-test shows that the difference in the flows between all the adjacent quintiles (e.g., top and
second, second and third, and so on) is significantly different (p-value=0.000). For FOFs, the t-test shows that only
the difference in the flows between top and second, and fourth and bottom quintile is significantly different.
15
A. Performance-flow relation for individual Hedge Funds
Arguably, investors consider factors in addition to performance, such as size, volatility, age,
managerial incentives, and past flows before making their investment decisions. Therefore, we
examine the relation between flows and performance using a multivariate regression that controls
for other potential determinants of money-flows. We specify the following regression:
5
(
)
Flowi ,t = β 0 + ∑ β1j Qranki j,t −1 +β 2 ( Deltai ,t −1 ) + β 3 Sizei ,t −1 + β 4 (σ i ,t −1 ) + β 5 I ( AgeTopi ,t −1 )
j =1
3
+ β 6 I ( AgeBottomi ,t −1 ) + β 7 ( MFeei ,t ) + β 8 ( Flowi ,t −1 ) + β 9 ( Ri ,t ) + ∑ β10s I ( Strategyi , s ) + ε i ,t
s =1
(3)
where, Flowi ,t and Flowi ,t −1 are the money-flows in fund i in years t and t-1, Qranki ,jt −1 is the
fractional rank of fund i in quintile j for year t-1, Deltai ,t −1 is the natural logarithm of delta of the
managers’ incentive-fee-contract for fund i as of end of year t-1,11 Sizei ,t −1 is the size of the fund
measured as the natural logarithm of the AUM for fund i at time t-1, σ i ,t −1 is the standard
deviation of the monthly returns of fund i during year t-1, I ( AgeTopi ,t −1 ) is an indicator variable
that takes the value 1 if the fund age is in the top one-third of funds at the end of year t-1,
I ( AgeBottomi ,t −1 ) is an indicator variable that takes the value 1 if the fund age is in the bottom
one-third of funds at the end of year t-1, MFeei ,t is the management fees charged by fund i in
year t, Ri ,t is the return of fund i in year t, I ( Strategyi , s ) are strategy dummies that take the value
1 if fund i belongs to strategy s, and ε i ,t is the error term.
11
We measure delta in millions of dollars and take its natural logarithm to mitigate the outlier effect.
16
We construct the fractional rank quintiles, Qranki ,jt −1 as per Sirri and Tufano (1998). First,
each fund is given a fractional rank, Frank, from 0 through 1 based on returns in year t-1. This
fractional rank represents its percentile performance relative to other funds in the same period.
We estimate the coefficients on fractional ranks using piecewise linear regression framework
over five quintiles. Towards that end, we define Qrank 1 , the bottom quintile rank, to equal Min
(0.2, Frank), Qrank 2 = Min (0.2, Frank- Qrank 1 ) and so forth up to the highest performance
quintile, Qrank 5 , i.e., the top quintile. The slope coefficients on these piecewise decompositions
of fractional ranks represent the slope of the performance-flow relation over their range of
sensitivity.
Sirri and Tufano (1998) argue that the use of Fama and MacBeth (1973) procedure “produces
more conservative estimates of the significance levels” of their coefficient estimates compared to
those from pooled regression. Hence, we follow Fama and MacBeth (1973) procedure. For
robustness, we repeat our analysis using pooled regressions and obtain similar results. We report
the results of regression based on Fama-MacBeth (1973) procedure in Table IV.12
The results for individual hedge funds in Model 1 show that only the coefficients on the top
and the third quintile are significantly positive at the 1% level. This suggests that the wellperforming funds attract significantly higher flows compared to the poorly performing ones. The
coefficient on the top quintile (coeff=1.757) implies that if a fund moves 10 percentile in the
highest performance group, it will attract incremental inflows of 18%. The magnitude of the top
quintile coefficient is similar to that documented by Sirri and Tufano (1998) for mutual funds.
Also, the R-square of our model (about 13%) is of the same order of magnitude as Sirri and
12
To minimize the influence of outliers, we winsorize top 1% of the annual returns, standard deviation, assets under
management, and net flows.
17
Tufano (1998). Our R-square is considerably higher than R-square (about 4%) obtained from
univariate analysis in GIR (2003), confirming the importance of controlling for fund-specific
factors in addition to fund returns.
To examine if there is convexity in flow-performance relation, we conduct a Chow test on a
pairwise basis on adjacent performance quintiles. In case of individual hedge funds, we find that
only the third and fourth quintile coefficients are significantly different from each other (pvalue=0.08). This suggests that in case of individual hedge funds, the kink in the performanceflow relation occurs at the 40th percentile as compared to the 80th percentile kink documented by
Sirri and Tufano (1998) in case of mutual funds.13
Chevalier and Ellison (1997) document that the money-flows of older funds are less sensitive
to recent returns. Thus, we examine if the convexity of performance-flow relation varies across
age for individual hedge funds. For this purpose, we interact the terms for the five performance
quintiles and those for the two age dummies and re-estimate the regression in equation (3) after
including these interaction terms (not reported for the sake of brevity). Chow test results indicate
kinks at the 40th, 60th, and 80th percentile for younger funds, and none for the middle-age and
older funds. This suggests that similar to mutual fund industry, flows into the younger funds
appear to be more sensitive to recent performance.
We investigate the possibility that investors might consider coarser performance groups by
combining the middle three quintiles into one group as in Sirri and Tufano (1998).14 The results
in Model 2 of Table IV show that the coefficient of the top quintile (coeff=1.474) and the middle
three quintiles pooled together (coeff=0.743) are significantly positive at the 5% and 1% level
13
14
Interestingly, Kaplan and Schoar (2003) find a concave performance-flow relationship in private equity funds.
Here, the middle quintile rank is given by Min (0.6, Frank – Qrank1).
18
respectively, while the bottom quintile coefficient is indistinguishable from zero. These results
are once again consistent with those from the five-quintile specification in Model 1.
A.1 Relation between Flows and Managerial Incentives
As discussed earlier, performance-related fee provides strong incentives to the manager
to perform better. Managers whose funds are near or at their high-water marks face better
incentives compared to those substantially below their high-water marks. We capture these
incentives through delta. It is important to note that delta incorporates information about the
entire history of returns and money-flows. Greater the delta, greater are the managerial
incentives. Recognizing this effect, investors may prefer to invest in funds with greater delta.
Therefore, we expect to find a positive relation between flows and delta.
The results for individual hedge funds (in Models 1 and 2) show that the slope coefficient
for delta is positive and significant (coeff.=0.011). The impact of delta is economically
significant. An increase in the delta from 25th percentile ($10,000) to 75th percentile ($131,000)
is associated with an increase in annual flows by 2.8% compared to the median flow of 11.7%.
These findings suggest that investors recognize the importance of managerial incentives and
accordingly invest in funds where managers have better incentives, namely, funds with higher
delta.15
A.2 Relation between Flows and other factors
Next, we examine the relation between money-flows and other fund characteristics such as
size and age. From Table IV, we observe that the slope coefficient on fund size is significantly
15
For robustness, we examine if there is non-linear relation between flows and delta by including square of delta in
equation (3) and find the slope coefficient to be positive but not significant.
19
negative in both specifications, implying that smaller funds get higher flows. We also find that
the coefficient of younger funds dummy is significantly positive, implying that younger funds
experience higher flows than the middle-aged funds.
Prior literature on mutual funds (Gruber, 1996) has used lagged flows in addition to the fund
characteristics discussed above. Lagged flow is an important determinant of flows into hedge
funds for a number of reasons. First, lagged flows can proxy for non-performance variables such
as reputation and marketing efforts that can influence investors’ decisions. Second, investors
may want to stick to their prior decision and continue to invest in the same fund, behavior
deemed as status-quo bias. Finally, uninformed investors may herd with others and select funds
that have recently experienced larger flows. Therefore, we include lagged flow in equation (3)
and find that it is positively related to flows. Previous researchers (Chevalier and Ellison, 1997)
have also studied the response of money-flows in mutual funds to contemporaneous returns.
Hence, we also include returns Rt at time t in equation (3) and find that flows are positively
related to contemporaneous returns. One may argue that lagged flows and contemporaneous
returns may not be in the information set of investors. Hence, for robustness, we repeat our
analysis by excluding these variables from equation (3) and find that our results remain
unchanged.
Recent research in mutual funds suggest that there are significant spillover effects, i.e.,
“star” or well-performing funds within a fund family can lead to an increase in the flows to other
funds in that family (Khorana and Servaes, 2000; Massa, 2000; Ivkovich, 2001, Nanda, Wang,
and Zheng, 2002). We examine if there are similar spillover effects in the hedge fund industry.
Unlike mutual funds, multiple-fund families are rare in hedge funds. The average number of
funds per family across our sample period is 1.3 (minimum of 1 fund and a maximum of 11
funds per family) with 80% of the funds not belonging to a family. In order to examine spillover
20
effects, we include performance of other funds in a family as an additional explanatory variable
in equation (3). In unreported results, we find the slope coefficient on this variable to be positive
but not significant.
A.3 Comparison of results with Goetzmann, Ingersoll and Ross (2003)
Although our findings are consistent with those of Chevalier and Ellison (1997) and Sirri
and Tufano (1998) for mutual funds, they differ from those of GIR (2003). We believe that the
differences in the regulatory or market environments during the periods covered in the two
studies are responsible for the differences in the findings. For example, during our sample
period, the restriction on the maximum number of qualified investors was relaxed. Further, data
on individual hedge funds as well as a range of hedge fund indexes started becoming widely
available. These changes made it easier for investors to obtain information about hedge funds
and to compare their performance with peer group. To examine if the differences in the results
are indeed attributable to the changing nature of the industry, we repeat our analysis, as in GIR,
using only offshore funds from our sample during 1989-1995. Instead of GIR’s finding of
outflows for top performers, we find that the top performers experience flows that are
indistinguishable from zero (see Appendix C).16 This suggests that the nature of the hedge fund
industry has changed over time, and in recent years it has started resembling the mutual fund
industry.
B. Robustness checks
16
Please note that the two samples will still not be identical as GIR use Offshore Funds directory while we use
offshore funds included in the our composite database created by merging HFR, TASS, and ZCM/MAR databases.
21
For our performance-flow regression in equation (3), we need data on annual flows.
However, if a fund disappears from our database during a year, it will not have annual flows in
the year of its disappearance. Unlike mutual funds, where liquidation due to poor performance is
the only reason for fund’s disappearance, hedge funds may disappear for other reasons as well.
These reasons include delisting by the data vendor, non-reporting by fund, change in fund
names, and mergers (Fung and Hsieh, 2000). Goetzmann and Peles (1997) assign a flow of -100
percent in the last year for those funds that disappear due to poor performance. Following their
insights, we examine the robustness of our results by assigning a flow of -100 percent in the last
year for funds that disappear due to liquidation. We continue to find no relation between flows
and returns for poorly performing funds. This confirms that our results are not sensitive to
exclusion of last year’s flows for liquidated funds. This is consistent with the findings of Sirri
and Tufano (1998) for mutual funds.
Next, we investigate whether the investors consider longer-term performance for making
investment decisions. For this purpose, we analyze the fund flows based on the fund’s two-year
performance at time t-1. In particular, we form five fractional rank variables based on recent biannual performance. For the sake of brevity, we do not report the results here. We find that none
of the performance quintiles come out significant. However, delta continues to be positively
related to flows.17 At first sight, lack of significant relation between bi-annual performance
quintiles and flows may suggest that investors care more about recent performance. However, it
is important to note that delta incorporates the fund’s performance history and investors’
response (in terms of money-flows) to this history. Hence, our finding of positive and significant
17
In addition, size, lagged flows, and contemporaneous returns exhibit significant relation with flows as in
Models 1 and 2 in Table IV.
22
relation between flows and delta suggests that investors do take into account the long-term
performance of the funds.
C. Performance-flow relation for Funds of Hedge Funds
Since FOFs are an intermediated sector of hedge fund industry with an additional layer of
fees and agency issues, in this subsection, we separately investigate the performance-flow
relation for FOFs. For this purpose, we re-estimate regression in equation (3) for FOFs and
report our results in Table IV. Results for Model 1 show that the slope coefficient for top quintile
is positive and significant at the 5% level (coeff.=2.028). This implies that if a FOF moves 10
percentile in the highest performance group, it will attract incremental inflows of 20%. This
increase is of similar order of magnitude to that observed in case of individual hedge funds
(18%). Further, we investigate the possibility of investors considering coarser performance
groups by combining the middle three quintiles into one group. The results in Model 2 of Table
IV show that only the coefficient of the top quintile (coeff=1.850) is significantly positive at the
5% level.
These results suggest that the well-performing FOFs attract significantly higher flows than
the poorly performing ones in this intermediated sector of the hedge fund industry as well. We
conduct Chow test on the pairwise differences in the slope coefficients for adjacent performance
rank quintiles. Our results indicate that there are no kink points based on Model 1. However,
when we pool the middle three quintiles (Model 2), we obtain a kink at the 80th percentile. Thus,
our results using coarser performance groups suggest that there seems to be some evidence of
convex performance-flow relation for FOFs.
23
Similar to individual hedge funds, we find the slope coefficient for delta and lagged flows to
be positive, but not significant. Furthermore, like in case of hedge funds, we find flows are
negatively related to size and positively related to contemporaneous returns.
Overall, the findings in this section confirm that money-flows chase recent performance both
for individual hedge funds and FOFs. Arguably, in addition to recent performance, investors
consider consistently good performance before investing their money into hedge funds and
FOFs, an issue we examine in the next section.
III. Relation between flows and persistence in prior performance
A stronger signal of manager’s ability is persistence in performance and not simply one
year of superior performance. Investors therefore should direct more flows to managers who
show persistence. Therefore, in this section, we examine the relation between money-flows and
persistence in prior performance. To capture persistence, we follow Brown, Goetzmann, and
Ibbotson (1999) and Agarwal and Naik (2000) and define a fund to be a winner (loser) in year t,
if its returns are greater (less) than the return of the median fund in its peer group in that year.
The indicator variable I(Persistent Winneri,t-1) equals 1 if fund i is a winner in years t-1 and t-2,
and equals 0 otherwise. Similarly, the indicator variable I(Persistent Loseri,t-1) equals 1 if fund i
is a loser in years, t-1 and t-2, and equals 0 otherwise.
We investigate the persistence-flow relation by estimating the following regression:
Flowi ,t = γ 0 + γ 1 I ( PersistentWinneri ,t −1 ) + γ 2 I ( Persistent Loseri ,t −1 ) + γ 3 ( Deltai ,t −1 )
+γ 4 Sizei ,t −1 + γ 5 (σ i ,t −1 ) + γ 6 I ( AgeTopi ,t −1 ) + γ 7 I ( AgeBottomi ,t −1 ) + γ 8 ( MFeei ,t )
3
+γ 9 ( Flowi ,t −1 ) + γ 10 ( Ri ,t ) + ∑ γ 11s I ( Strategyi , s ) + ei ,t
s =1
(4)
24
We report the results from regression in equation (4) in Table V. Since, we have both Persistent
Winner and Persistent Loser indicator variables in the regression, the excluded category of funds
is the one that shows reversals, i.e., those who win in one year and lose in the other. We find that
the coefficient of Persistent Winner is significantly positive (coeff=0.205) for individual hedge
funds implying that the flow is 20.5% higher for funds which exhibit persistently good returns
compared to those that exhibit reversals. Similarly, the coefficient of Persistent Loser is
significantly negative (coeff=-0.137) for individual hedge funds. This implies that consistently
poorly performing funds experience 13.7% lower flows compared to the funds exhibiting
reversals. As before, slope coefficient on delta is significantly positive (coeff.=0.012) suggesting
that funds with better managerial incentives attract higher flows. An increase in delta from 25th
to 75th percentile is associated with 3.1% increase in annual flows. Finally, the slope coefficients
of control variables are similar to those in Table IV. In particular, we find that larger funds and
higher-volatility funds experience lower flows.
We repeat this analysis with FOFs and also report our results in Table V. Like hedge funds,
we find that FOFs that are persistent winners experience significantly higher flows compared to
those that exhibit reversals. However, unlike hedge funds, we do not find that persistent losers
experience lower flows. We also do not find a significant relation with delta.
Overall, these findings suggest that investors direct more flows towards persistent winners
and less towards persistent losers. Having seen that the flows are positively related to past
performance and persistence in past performance, we next examine the relation between flows
and future performance as well as persistence in future performance.
IV. Relation between flows, incentives, and future performance
25
New money-flows into a fund may hinder its future performance due to decreasing
returns to scale. 18 In this section, we shed light on this issue by examining the relation between
money-flows into a fund in a given year and its future performance.
A.
Flows, incentives, and future returns – OLS specification
We examine the flow-future performance relation by regressing a fund’s annual returns
on prior year’s flows, managerial incentives, size, and other control variables. In particular, we
estimate the following regression:
Ri ,t = λ0 + λ1 ( Flowi ,t −1 ) + λ2 Sizei ,t −1 + λ3  Sizei ,t −1 ∗ Flowi ,t −1 
+ λ4 ( Deltai ,t −1 ) + λ5 (σ i ,t −1 ) + λ6 I ( AgeTopi ,t −1 ) + λ7 I ( AgeBottomi ,t −1 )
(5)
3
+ λ8 ( MFeei ,t ) + ∑ λ9s I ( Strategyi , s ) + ξi ,t
s =1
If there are decreasing returns to scale in the hedge fund industry, one would expect this
effect to be especially prominent for larger funds experiencing higher inflows. In order to capture
this, we include interaction of size and flows  Sizei ,t −1 ∗ Flowi ,t −1  in our regression. As before,
we also include controls for age, volatility, and management fee. Since higher delta implies
higher incentives to perform better, we expect to see a positive relation between delta and future
performance. Table VI reports our findings from the regression in equation (5).
The OLS results (Model 1) in Table VI (Panel A) show that the coefficient on size is
significantly negative suggesting that larger funds are associated with lower returns in the
following year. Further, we find coefficient of interaction term between size and flow to be
significantly negative. As this is an interaction between two variables, we examine the effect of
the two individually as well as jointly. An increase in the size from 25th to 75th percentile is
18
Chen et al (2002) find that fund size erodes performance in the mutual fund industry. They attribute it to reasons
26
associated with a return decrease of 2.3% in the following year, compared to the median annual
return of 12.4%. A similar increase in flow is associated with a return decrease of 0.5%. This
suggests that compared to flow, size has a bigger impact on the future fund returns. Finally, an
increase in both size and flow from their respective 25th to 75th percentiles is associated with a
return decrease of 3.0%. Overall, these results suggest that larger flows into bigger funds are
associated with lower returns in the future. This suggests that there may be decreasing returns to
scale in the hedge fund industry.
When we examine the relation between managerial incentives and future performance,
we find that the slope coefficient on delta is positive and significant (coeff.=0.005), implying that
higher delta is associated with higher returns in the following year. The impact of delta is
economically significant. An increase in the delta from 25th percentile ($10,000) to 75th
percentile ($131,000) is associated with a return increase of 1.4% compared to the median annual
return of 12.4%. These findings confirm that higher delta provides greater incentives to the
managers for better performance.
Finally, we find that younger (older) fund dummy to be significantly positive (negative)
suggesting that younger (older) funds are associated with better (worse) returns. Since hedge
funds invest in relatively illiquid securities, it can induce serial correlation in monthly returns
(Getmansky, Lo, and Makarov, 2003). We believe this should not affect our analysis, which uses
annual returns. However, for robustness, we include lagged annual returns as a control variable
in equation (5). We find that the slope coefficient on lagged returns variable is not significant
and our results (not reported) remain unchanged.
B.
Flows, incentives, and future returns – Logisitic specification
such as liquidity and organizational diseconomies.
27
To allow for the possibility that the relation between flows and future performance may
be non-linear, we adopt a logistic regression approach. The dependent variable here is WINNER,
which takes the value 1 if a fund has above-median annual returns in its peer group.19 Thus, we
are interested in understanding how flows are associated with probability of the fund being a
winner in the following year. In particular, we estimate logistic regression as follows:
WINNERi ,t = φ0 + φ1 ( Flowi ,t −1 ) + φ 2 Sizei ,t −1 + φ3  Sizei ,t −1 ∗ Flowi ,t −1  + φ4 ( Deltai ,t −1 )
+φ4 (σ i ,t −1 ) + φ5 I ( AgeTopi ,t −1 ) + φ6 I ( AgeBottomi ,t −1 ) + φ7 ( MFeei ,t ) +
(6)
3
+ ∑ φ9s I ( Strategyi , s ) + ζ i ,t
s =1
We report the results from this regression for individual hedge funds in Model 2 of Table
VI (Panel A). The results for Model 2 show that the slope coefficients on both size as well as the
interaction term between size and flow are significantly negative, while that on flow to be
positive. Hereafter, for all the logistic regression results, we examine the economic significance
of a variable in the following way. We change the variable of interest from 25th to 75th percentile
value and then measure its impact on the dependent variable (probability). We then compare it to
the implied probability for a median fund.20 Increase in size is associated with a decrease in the
probability of being a winner by 4.3%, compared to an implied probability of 55%. In contrast,
increase in flow is associated with a decrease in the probability of being a winner by 0.8%. As in
the case of OLS results, these findings suggest that size has a bigger impact on fund’s future
performance compared to flows. Finally, an increase in both the size and flow is associated with
a decrease in the probability of being a winner by 7.1%. These results confirm that larger flows
into bigger hedge funds are associated with poor performance in the future.
19
For robustness, we also define a fund as a winner if its returns fall in the top quartile of its peer group and find our
results to be qualitatively similar.
20
We compute the implied probability for the median fund as the exponential of the sum-product of slope
coefficients and median values of independent variables divided by one plus the exponential of this sum-product.
28
As in case of OLS results, we continue to find a strong relation between delta and future
performance implying that funds with better incentives are more likely to be winners. An
increase from 25th to 75th percentile in delta is associated with an increase in the probability of
being a winner by 2.7%. This positive relation lends justification to investors’ action of directing
more money-flows to funds with greater delta.
C.
Flows, incentives, and future returns – Results for Funds of Hedge Funds
We estimate the OLS regression in equation (5) and report the results for FOFs in Table
VI (Panel B). Unlike in case of individual hedge funds, our results for Model 1 show that size is
positively related to future returns in case of FOFs. However, like in individual hedge funds, the
coefficient on interaction term (size*flow) is significantly negative, and coefficient on flow is
positive though not significant. An increase in the size is associated with a return increase of
3.0%, compared to the median annual return of 11.8%. A similar increase in flow is associated
with a return decrease of 0.7%. This suggests that although flows are negatively associated with
future returns in case of FOFs, size has a positive relation with future returns. This suggests that
larger size in case of FOFs is an advantage reflected in greater bargaining power and better
access to hedge funds with good performance. The overall impact of size and flow on returns is
positive and economically significant. An increase in both size and flow is associated with a
return increase of 1.7%. Finally, in contrast to individual hedge funds, we do not find any
relation between delta and future performance in case of FOFs.
We report the results of logistic regression for FOFs in Model 2 of Table VI (Panel B).
Again, like in the case of individual hedge funds, we find the slope coefficient for flow to be
positive and that for the interaction term (size*flow) to be negative. However, in contrast to the
results for individual hedge funds, we now find size coefficient to be positive and significant. An
29
increase in size is associated with an increase in the probability of being a winner by 10.9%,
compared to an implied probability of 61.2%. In contrast, an increase in flow is associated with a
decrease in the probability of being a winner by 0.7%. These findings confirm our OLS results of
flows being negatively related and size being positively related to future performance. Further, as
in case of OLS specification, size has a bigger economic impact compared to flows. Finally, an
increase in both the size and flow is associated with an increase in the probability of being a
winner by 9.4%. Overall, our results confirm that larger flows into bigger FOFs are associated
with better future performance and these results are mainly driven by size. Consistent with OLS
results, we do not find any relation between delta and future performance in case of FOFs for the
logistic regression.
Overall, these findings suggest that larger flows into bigger funds are associated with
poor performance implying decreasing returns to scale for individual hedge funds. Further, size
seems to have a bigger impact on future performance compared to flows. In contrast, there seem
to be economies of scale in case of FOFs where bigger size seems to be associated with better
performance. Also, managerial incentives captured by delta are positively related to future
returns. In the next section, we examine the relation between money-flows and persistence in
future returns.
V.
A.
Relation between flows, incentives, and persistence in future returns
Flows, incentives, and persistence in future returns – Results for individual hedge funds
The previous section documents that larger hedge funds with greater inflows are
associated with lower returns in the following year. This section examines whether the flows
show a relation with persistence in future performance using a logistic regression framework
given below:
30
PersistentWinneri ,t ( Loseri ,t ) = θ 0 + θ1 ( Flowi ,t −1 ) + θ 2 Sizei ,t −1 + θ 3  Sizei ,t −1 ∗ Flowi ,t −1  + θ 4 ( Deltai ,t −1 )
+θ 5 (σ i ,t −1 ) + θ 6 I ( AgeTopi ,t −1 ) + θ 7 I ( AgeBottomi ,t −1 ) + θ 8 ( MFeei ,t )
(7)
3
+ ∑θ 9s I ( Strategyi , s ) + π i ,t
s =1
where, Persistent Winner and Loser are as defined in Section III.
Panel A of Table VII reports the logistic regressions results for individual hedge funds. In
Model 1, the dependent variable is Persistent Winner. Based on our results from Model 1, we
find that the slope coefficient for size is significantly negative (coeff.=-0.14). The slope
coefficients on flow and the interaction term (size*flow) are positive and negative respectively,
but insignificant. An increase in size is associated with a decrease in the probability of a fund
being a Persistent Winner by 6.1% compared to an implied probability of 28.7%. In contrast, an
increase in flow is associated with a decrease in the probability by 0.7%. An increase in both the
size and flow is associated with a decrease in the probability by 7.0%. Overall, our results
suggest that the larger funds with higher flows are less likely to exhibit consistently good
performance indicative of decreasing returns to scale for individual hedge funds.
The coefficient on delta is positive and significant (coeff.=0.09) in Model 1, implying
that higher delta is associated with a higher probability of being a Persistent Winner. This
confirms our earlier results of delta being positively related with future performance. An increase
in delta is associated with an increase in the probability by 4.9%.
For Persistent Loser as a dependent variable, our results in Model 2 show significantly
positive coefficient on interaction term of size and flow (coeff.=0.048). The slope coefficients on
flow and size are negative and positive respectively, but insignificant. An increase in size is
associated with an increase in the probability of a fund being a Persistent Loser by 1.9%
compared to an implied probability of 23.4%. In contrast, an increase in flow is associated with
an increase in the probability by 0.2%. Once again, size dominates flow in its relation with future
31
performance. An increase in both the size and flow is associated with an increase in the
probability by 2.5%. Overall, our results suggest that the larger funds with higher flows are more
likely to exhibit consistently poor performance suggesting decreasing returns to scale in
individual hedge funds.
Further, we find the slope coefficient on delta is significantly negative (coeff.=-0.06). An
increase in delta is associated with a decrease in the probability of being a Persistent Loser by
2.8%. This suggests that better managerial incentives are associated with lower likelihood of a
fund being a persistent loser and confirms our earlier results of delta being positively related with
future performance.
B.
Flows, incentives, and persistence in future returns – Results for funds of hedge funds
We estimate the logistic regression in equation (7) for FOFs and report our results in
Panel B of Table VII. Results in Model 1 show that the slope coefficients for both size and flow
are significantly positive (coeffs.=0.396 and 0.568 respectively) while that for their interaction
term is significantly negative (coeff.=-0.18). An increase in size is associated with an increase in
the probability of a fund being a Persistent Winner by 16.3% compared to an implied probability
of 23.8%. In contrast, an increase in flow is associated with a decrease in the probability by
0.3%. An increase in both the size and flow is associated with an increase in the probability by
14.6%. We also do not find a significant relation between delta and probability of being a
Persistent Winner.
We re-estimate the logistic regression in equation (7) for the case of Persistent Loser and
report our results in Model 2 in Table VII (Panel B). We find the slope coefficient on size is
significantly negative (coeff.=-0.204) while that on flow is also negative, but not significant.
Further, the coefficient on interaction term (size*flow) is positive, though insignificant. An
32
increase in size is associated with a decrease in the probability of a fund being a Persistent Loser
by 5.7% compared to an implied probability of 14.9%. In contrast, an increase in flow is
associated with an increase in the probability by 0.3%. An increase in both the size and flow is
associated with a decrease in the probability by 5.3%. These findings confirm our earlier results
of economies of scale being a significant contributor to performance in the case of FOFs. We do
not find a significant relation between delta and probability of Persistent Loser.
Overall, these findings confirm our earlier OLS results, namely, larger flows into bigger
funds are associated with poor performance persistence for individual hedge funds. Again, size
seems to have a bigger impact on future persistence compared to flows. In contrast, there seem to
be economies of scale in FOFs. Larger FOFs seem to exhibit superior performance, which is
different from the result for individual hedge funds.
VI. Concluding Remarks
In this paper, we conduct an in-depth investigation of the determinants of money-flows in the
hedge fund industry and the relation between flows and future performance. We employ a
composite database of individual hedge funds and FOFs constructed by merging the HFR,
TASS, and ZCM/MAR databases. We contribute to the extant literature by constructing a proxy
for managerial incentives through delta of the manager’s option-like incentive-fee contract.
We have several new findings. First, money-flows chase good performance and the
performance-flow relation seems to be convex in the hedge fund industry. This finding is
different from that of Goetmann, Ingersoll, and Ross (2003) in hedge funds but it is consistent
with the findings of Chevalier and Ellison (1997), Goetzmann and Peles (1997), and Sirri and
Tufano (1998) in mutual funds. Second, money-flows are significantly higher (lower) for funds
that show good (bad) persistence. Third, funds with greater managerial incentives enjoy higher
33
flows. Since delta incorporates information about the entire history of returns and money-flows,
this finding suggests that investors take into account historical returns as well as money-flows.
Fourth, larger flows into bigger funds are associated with poor future performance in terms of
lower returns and lower likelihood of being a persistent winner. This finding is consistent with
the presence of decreasing returns to scale in the hedge fund industry and lends support to the
underlying assumption in Berk and Green’s (2002) model. Fifth, funds with higher delta exhibit
better performance, both in terms of returns and persistence in returns. Finally, we show how
FOFs differ from individual hedge funds in terms of the relation between money-flows and past
as well as future performance. Our findings suggest in contrast to hedge funds, there may be
economies of scale in FOFs. Overall, these findings significantly improve our understanding of
the behavior of investors and managers in the hedge fund industry.
*** *** ***
34
References
Ackermann, C., R. McEnally, and D. Ravenscraft, 1999, “The Performance of Hedge Funds: Risk,
Return and Incentives,” Journal of Finance, 54 (3), 833-874.
Agarwal, Vikas, Naveen D. Daniel, and Narayan Y. Naik, 2003, “Risk-taking Incentives and Hedge
Fund Volatility,” Working Paper, Georgia State University and London Business School.
Agarwal, Vikas, and Narayan Y. Naik, 2000, “Multi-Period Performance Persistence Analysis of
Hedge Funds,” Journal of Financial and Quantitative Analysis, 35 (3), 327-342.
Agarwal, Vikas, and Narayan Y. Naik, 2003, “Risks and Portfolio Decisions involving Hedge
Funds,” forthcoming Review of Financial Studies.
Berk, Jonathan B., and Richard C. Green, 2002, “Mutual Fund Flows and Performance in Rational
Markets,” NBER Working Paper No. 9275.
Black, Fischer, and Myron Scholes, 1973, “The Pricing of Options and Corporate Liabilities,”
Journal of Political Economy, 81(3), 637-654.
Brown, S.J., and W.N. Goetzmann, 2003, “Hedge Funds with Style,” Journal of Portfolio
Management, 29 (2), 101-112.
Brown, S.J., W.N. Goetzmann, and R.G. Ibbotson, 1999, “Offshore Hedge Funds: Survival and
Performance 1989-1995,” Journal of Business 72, 91-117.
Brown, S.J., W.N. Goetzmann, and Bing Liang, 2002, “Fees on Fees in Funds of Funds,” Working
Paper, Case Western Reserve University, New York University, and Yale School of Management.
Chen, Joseph, Harrison Hong, Ming Huang, and Jeffrey D. Kubik, 2002, “Does Fund Size Erode
Performance? Liquidity, Organizational Diseconomies and Active Money Management,”
Working Paper, Princeton University.
Chevalier, Judith, and Glenn Ellison, 1997, “Risk Taking by Mutual Funds as a Response to
Incentives,” Journal of Political Economy, 105, 1167-1200.
Coles, Jeffrey L., Naveen D. Daniel, and Lalitha Naveen, 2003, “Executive Compensation and
Managerial Risk-Taking,” Working Paper, Arizona State University and Georgia State
University.
Core, J.E., W.R. Guay, and David F. Larcker, 2003, “Executive Equity Compensation and
Incentives: A Survey,” Federal Reserve Bank of New York Economic Policy Review, 9(1), 27-50.
Del Guercio, D., and Paula A. Tkac, 2002, “The determinants of the flow of funds of managed
portfolios: mutual funds versus pension funds,” Journal of Financial and Quantitative Analysis,
37 (4), 523-557.
Elton, Edwin J., Martin J. Gruber and Christopher R. Blake, 2003, “Incentive Fees and Mutual
Funds,” Journal of Finance, 58 (2), 779-804.
Edwards, F. R., and M.O. Caglayan, 2001. “Hedge fund performance and manager skill,” Journal of
Futures Markets 21 (11), 1003-1028.
Fama, Eugene F., and James D. MacBeth, 1973, “Risk, Return and Equilibrium: Empirical Tests,”
Journal of Political Economy, 81 (3), 607-636.
Fung, W., and D.A. Hsieh, 1997, “Empirical characteristics of dynamic trading strategies: The case
of hedge funds,” Review of Financial Studies, 10, 275-302.
Fung, W., and D.A. Hsieh, 2000, “Performance Characteristics of Hedge Funds and CTA Funds:
Natural Versus Spurious Biases,” Journal of Financial and Quantitative Analysis, 35 (3), 291307.
Fung, W., and D.A. Hsieh, 2001, “The Risk in Hedge Fund Strategies: Theory and Evidence from
Trend Followers,” Review of Financial Studies, 14 (2), 313-341.
Getmansky, Mila, Andrew W. Lo, and Igor Makarov, 2003, “An Econometric Model of Serial
Correlation and Illiquidity in Hedge Fund Returns,” forthcoming Journal of Financial
Economics.
35
Goetzmann, W.N., J. Ingersoll, and S.A. Ross, 2003, “High-Water Marks and Hedge Fund
Management Contracts,” Journal of Finance, 58 (4), 1685-1718.
Goetzmann, W.N., J. Ingersoll, Matthew Spiegel, and Ivo Welch, 2002, “Sharpening Sharpe Ratios,”
Working Paper, Yale School of Management.
Goetzmann, W.N., N. Peles, 1997, “Cognitive Dissonance and Mutual Fund Investors,” Journal of
Financial Research, 20 (2), 145–58.
Gruber, Martin, 1996, “Another Puzzle: The growth in actively managed mutual funds,” Journal of
Finance 51, 783-810.
Ippolito, R.A., 1992, “Consumer reaction to measures of poor quality: evidence from the mutual fund
industry,” Journal of Law and Economics, 35 (1), 45-70.
Ivkovich, Zoran, 2001, “Is Blood Thicker than Water: Spillovers in Mutual Fund Families,” Working
Paper, Yale School of Management.
Jorion, Phillipe, 2000, “Risk Management Lessons from Long-Term Capital Management,”
European Financial Management, 6, 277-300.
Kaplan, Steve, and Antoinette Schoar, 2003, “Private Equity Performance: Returns, Persistence and
Capital Flows,” NBER Working Paper 9807.
Khorana, Ajay and Henri Servaes, 2001, “An examination of competition and investor behavior in
the mutual fund industry,” Working Paper, Georgia Institute of Technology and London Business
School.
Liang, Bing, 1999, “On the Performance of Hedge Funds,” Financial Analysts Journal, 55, 72-85.
Liang, Bing, 2000, “Hedge funds: The Living and the Dead,” Journal of Financial and Quantitative
Analysis, 35 (3), 309-326.
Liang, Bing, 2003, “The Accuracy of Hedge Fund Returns,” Journal of Portfolio Management,
Spring, 111-122.
Massa, Massimo, 2000, “Why so many mutual funds? Mutual fund families, market segmentation,
and financial performance,” Working Paper, INSEAD.
Murphy, K., 1999, “Executive compensation,” in Orley Ashenfelter and David Card (eds.),
Handbook of Labor Economics, Vol. 3b, Elsevier Science North Holland (1999), Chapter 38:
2485-2563.
Nanda, Vikram, Z. Jay Wang, and Lu Zheng, 2002, “Family Values and the Star Phenomenon,”
Working Paper, University of Michigan.
Sirri, Erik, and Peter Tufano, 1998, “Costly Search and Mutual Fund Flows,” Journal of Finance, 53,
1589-1622.
36
Table I: Breakdown of Funds by Data Sources
This table shows the number of hedge funds (Panel A) and funds of hedge funds (Panel B) from the three databases
namely HFR, ZCM/MAR, and TASS. Panel C provides the distribution of funds across different strategies. Live
funds are those that are operational as of Dec 2000. Dead funds are those that died during our sample period, 19942000.
Panel A: Hedge Funds
Source
HFR
TASS
ZCM/MAR
HFR and TASS
HFR and ZCM/MAR
ZCM/MAR and TASS
HFR, ZCM/MAR and TASS
Total
Live
395
348
244
205
154
121
309
1776
Dead
712
221
486
13
41
147
35
1655
ALL
1107
569
730
218
195
268
344
3431
Panel B: Funds of Hedge Funds
Source
HFR
TASS
ZCM/MAR
HFR and TASS
HFR and ZCM/MAR
ZCM/MAR and TASS
HFR, ZCM/MAR and TASS
Total
Live
61
101
0
34
59
23
74
352
Dead
156
0
0
0
0
15
16
187
ALL
217
101
0
34
59
38
90
539
Panel C: Distribution of Hedge Funds across Strategies
Live
21%
22%
45%
12%
Strategy
Directional Traders
Relative Value
Security Selection
Multi-Process
37
Dead
38%
21%
31%
10%
ALL
30%
21%
38%
11%
Table II: Cross-Sectional Fund Characteristics
This table shows the cross-sectional characteristics of hedge funds and funds of hedge funds during our sample
period, 1994-2000. Management fee and incentive fee do not change over time. We report the p-values for the
difference between means (Wilcoxon rank-sum test) and for the difference between medians (Median Two-Sample
test).
Hedge
Funds
Funds of
Hedge Funds
p-value
Mean
17.23
11.35
0.00
Median
13.26
11.80
0.00
Mean
4.72
3.17
0.00
Median
3.62
2.53
0.00
Mean
4.71
5.07
0.00
Median
3.91
4.34
0.00
Mean
1.22
1.29
0.00
Median
1.00
1.50
0.00
Mean
18.24
8.70
0.00
Median
20.00
10.00
0.00
Fund Characteristics
Returns (%)
Volatility (Standard Deviation) (%)
Age (years)
Management Fee (%)
Incentive Fee (%)
38
Table III: Trends in Assets under Management, Flows and Managerial Incentives
Panel A of this table shows the summary statistics of assets under management (AUM) and net flows while Panel B
provides the summary statistics of the funds that have negative returns and are below high-water mark (HWM).
Panel C provides the summary statistics of managerial incentives (delta) across hedge funds and funds of hedge
funds during the beginning, middle and end of the sample period, i.e. at the end of 1993, 1997, and 2000. Q1 and Q3
indicate the 25th and 75th percentiles.
AUM
Net
Flows
(%)
Panel A
Hedge Funds
12/93
12/97
12/00
12/93
12/97
12/00
Total ($bn)
40.7
136.5
201.0
9.1
13.9
24.8
Mean ($M)
115.0
120.2
135.3
181.0
86.1
90.3
Q1 ($M)
9.3
10.0
12.1
10.5
6.5
9.1
Median ($M) 23.3
30.0
38.4
36.6
21.0
28.0
Q3 ($M)
88.0
89.0
111.0
118.7
67.7
79.0
Mean
Q1
Median
Q3
101.1
-3.2
27.8
108.7
85.2
-11.1
14.3
75.9
53.9
-15.5
4.1
50.3
84.3
-4.9
8.6
152.3
45.0
-16.3
6.6
58.3
30.3
-14.0
6.7
35.6
Summary
Statistics
Negative Returns (%)
Funds of Hedge Funds
12/97
12/00
12/93
12/97
12/00
8.43
12.04
33.12
5.56
3.74
27.86
Mean
-11.24
-15.22
-19.85
-13.32
-7.62
-14.35
Median
-7.87
-8.77
-15.04
-13.10
-9.33
-9.29
50.20
43.07
51.29
31.58
31.30
37.44
Mean
-2.85
-3.99
-8.28
-2.10
-2.21
-3.25
Median
-0.28
-0.00
-1.21
-0.00
-0.00
-0.00
Funds below HWM (%)
Shortfall for funds below
HWM (%)
Panel B
Hedge Funds
12/93
Funds with negative
returns (%)
Delta ($M)
Funds of Hedge Funds
Summary
Statistics
Panel C
Hedge Funds
Funds of Hedge Funds
Summary
Statistics
12/93
12/97
12/00
12/93
12/97
12/00
Mean
0.13
0.17
0.24
0.17
0.09
0.08
Median
0.03
0.04
0.05
0.02
0.01
0.01
39
Table IV: Fund Flows and Past Performance
This table reports average OLS estimates (Fama-MacBeth) using flow as the dependent variable. Flow is the growth
rate in the AUM, defined as (AUMt-AUMt-1*(1+Rt))/AUMt-1, where AUMt are the AUM at time t and Rt is the fund
return in period t. The independent variables include lagged fractional rank quintiles (quintiles of Rankt-1),
logarithm of lagged delta (Deltat-1), lagged size computed as the logarithm of assets under management (Sizet-1),
lagged return volatility (Volatilityt-1), lagged age dummies for younger (bottom 33% of age) and older (top 33% of
age) funds, management fees, lagged flow (Flowt-1), contemporaneous returns (Returnt), and strategy dummies.
Figures marked with ***, **, and * are significant at the 1%, 5%, and 10% respectively.
Dependent variable: Flowt
Independent Variables
Intercept
Rankt-1 - Bottom Quintile
Rankt-1 - 4th Quintile
Rankt-1 - 3rd Quintile
Rankt-1 - 2nd-4th Quintile
Rankt-1 - 2nd Quintile
Rankt-1 - Top Quintile
Delta t-1
Sizet-1
Volatilityt-1
Younger Fund dummy
Older Fund dummy
Management Fee
Flowt-1
Returnt
Strategy dummies
Adjusted R2
Hedge Funds
Model 1
Model 2
0.269*
0.291*
0.981
0.609
0.139
1.467***
0.743***
0.186
1.757**
1.474**
*
0.011
0.011*
***
-0.130
-0.129***
-2.277
-2.303
0.129*
0.130*
0.002
0.002
-1.153
-1.348
0.053***
0.054***
***
0.674
0.680***
Yes
Yes
13.2%
13.3%
40
Funds of Hedge Funds
Model 1
Model 2
-0.085
-0.145
1.286
2.294
1.027
-0.548
-0.156
-0.678
2.028**
1.850**
0.008
0.008
-0.078***
-0.085**
-3.870
-2.918
-0.047
-0.046
-0.005
0.094
10.778
6.679
0.191
0.188
1.044**
1.072**
25.2%
23.1%
Table V: Fund Flows and Persistence in Past Performance
This table reports average OLS coefficient estimates (Fama-MacBeth) using flow as the dependent variable. Flow is
the growth rate in the assets under management (AUM), defined as (AUMt-AUMt-1*(1+Rt))/AUMt-1, where AUMt
are the AUM at time t and Rt is the fund return in period t. The independent variables include persistence at time t-1
and logarithm of lagged delta (Deltat-1). Persistent Winner (Loser) is an indicator variable that takes the value 1 if a
fund has annual returns above (below) median in its peer group during years, t-2 and t-1. Other control variables
include lagged size computed as the logarithm of assets under management (Sizet-1), lagged return volatility
(Volatilityt-1), lagged age dummies for younger (bottom 33% of age) and older (top 33% of age) funds, management
fees, lagged flow (Flowt-1), contemporaneous returns (Returnt), and strategy dummies. Figures marked with ***, **,
and * are significant at the 1%, 5%, and 10% respectively.
Dependent variable: Flowt
Independent Variables
Hedge Funds Funds of Hedge Funds
0.588**
0.452
Intercept
0.205**
0.125*
Persistent Winner dummyt-1
-0.137*
-0.037
Persistent Loser dummyt-1
0.012*
0.014
Delta t-1
-0.108***
-0.089
Sizet-1
-1.822**
-4.512
Volatilityt-1
0.060
-0.235
Younger Fund dummy
-0.008
0.102
Older Fund dummy
-1.747
1.653
Management Fee
**
0.151
0.132
Flowt-1
0.584**
0.843
Returnt
Yes
Strategy dummies
12.9%
9.1%
Adjusted R2
41
Table VI: Fund Flows and Future Returns
This table reports average OLS coefficient estimates (Fama-MacBeth) using the returns at time t as the dependent
variable. This table also reports the logistic regression of WINNER using pooled time-series data. WINNER takes
the value 1 if a fund has above-median annual returns in its peer group during year t. The independent variables
include the lagged size computed as the logarithm of assets under management (Sizet-1), lagged flow (Flowt-1),
interaction of lagged size and lagged flow (Sizet-1* Flowt-1), logarithm of lagged delta (Deltat-1), lagged return
volatility (Volatilityt-1), lagged age dummies for younger (bottom 33% of age) and older (top 33% of age) funds,
management fees, and strategy dummies. Figures marked with ***, **, and * are significant at the 1%, 5%, and 10%
respectively.
Panel A: Hedge Funds
Independent Variables
Intercept
Sizet-1
Flowt-1
Sizet-1 * Flowt-1
Delta t-1
Volatilityt-1
Younger Fund Dummy
Older Fund Dummy
Management Fee
Strategy Dummies
Adjusted / Pseudo R2
OLS
Model 1
Returnst
0.181***
-0.010**
0.011
-0.005*
0.005***
0.172
0.022*
-0.027**
-0.736
Yes
10.0%
Logistic
Model 2
WINNERt
0.60***
-0.08***
0.12**
-0.05***
0.06***
0.27
0.12
-0.35***
1.99
Yes
1.7%
Panel B: Funds of Hedge Funds
Independent Variables
Intercept
Sizet-1
Flowt-1
Sizet-1 * Flowt-1
Delta t-1
Volatilityt-1
Younger Fund Dummy
Older Fund Dummy
Management Fee
Adjusted / Pseudo R2
OLS
Model 1
Returnst
0.090
0.013*
0.052
-0.020**
-0.002
-0.868
0.009
-0.029
0.218
7.1%
42
Logistic
Model 2
WINNERt
0.187
0.209***
0.448*
-0.155**
0.018
-12.715**
-0.137
-0.210
-1.004
5.0%
Table VII: Fund Flows and Persistence in Future Returns
This table reports the results of logistic regression using the different performance persistence dummies at time t as
the dependent variable. Persistent Winner (Loser) is an indicator variable that takes value 1 if the fund is a winner
(loser) in two consecutive years and 0 otherwise, where a fund is a winner if it has above-median annual returns in
its peer group in that year. The independent variables include the lagged size computed as the logarithm of assets
under management (Sizet-1), lagged flows (Flowt-1), interaction of lagged size and lagged flow (Sizet-1* Flowt-1),
logarithm of lagged delta (Deltat-1), lagged return volatility (Volatilityt-1), lagged age dummies for younger (bottom
33% of age) and older (top 33% of age) funds, management fees, and strategy dummies. Figures marked with ***, **,
and * are significant at the 1%, 5%, and 10% respectively.
Panel A: Hedge Funds
Persistent Winner Persistent Loser
Model 1
Model 2
-0.14
-1.204***
Intercept
***
-0.14
0.045
Sizet-1
0.04
-0.148
Flowt-1
-0.03
0.048**
Sizet-1 * Flowt-1
0.09***
-0.060***
Delta t-1
-0.86
-7.451***
Volatilityt-1
**
0.24
-0.216*
Younger Fund Dummy
***
-0.41
0.287**
Older Fund Dummy
3.96
-5.373
Management Fee
Yes
Yes
Strategy Dummies
2
2.1%
3.2%
Pseudo R
Independent Variables
Panel B: Funds of Hedge Funds
Independent Variables
Intercept
Sizet-1
Flowt-1
Sizet-1 * Flowt-1
Delta t-1
Volatilityt-1
Younger Fund Dummy
Older Fund Dummy
Management Fee
Pseudo R2
Persistent Winner Persistent Loser
Model 1
Model 2
***
-2.308
-1.161*
***
0.396
-0.204*
**
0.568
-0.164
***
-0.184
0.063
-0.023
0.009
-6.349
-9.573
0.710*
0.437
0.199
0.915**
-5.624
22.835
7.3%
3.6%
43
Figure 1: Distribution of Funds by Data Sources
This table shows the percentage of hedge funds (Panel A) and funds of hedge funds (Panel B) from the three
databases namely HFR, ZCM/MAR, and TASS at the end of our sample period, i.e., at the end of 2000.
Panel A: Distribution of Number of Hedge Funds
ZCM/MAR
32%
6%
6%
10%
17%
21%
8%
HFR
TASS
Panel B: Distribution of Number of Funds of Hedge Funds
HFR
40%
6%
11%
17%
19%
7%
0%
TASS
ZCM/MAR
44
Figure 2: Flows in the Four Strategies as a function of past performance (1994-2000)
This figure plots the annual flows in millions of dollars with the lagged annual returns for all the funds belonging to
the four strategies and “All Funds” from 1994 to 2000.
Directional Traders: Annual Dollar Flows vs Lagged Annual Returns
5000
Relative Value: Annual Dollar Flows vs Lagged Annual Returns
50%
7000
40%
6000
3000
Value-Weighted Returns
30%
Equal-Weighted Returns
5000
2000
20%
1000
10%
Dollar Flows
1994
1998
1996
1999
2000
0%
1997
-1000
-10%
-2000
-20%
Annual Dollar Flows ($M)
Annual Dollar Flows ($M)
1995
0
Value-Weighted Returns
Equal-Weighted Returns
Lagged Annual Returns
Dollar Flows
4000
4000
3000
2000
1000
1999
0
1994
-3000
-30%
1996
1997
1998
-1000
Year
Year
Security Selection: Annual Dollar Flows vs Lagged Annual Returns
Multi-Process: Annual Dollar Flows vs Lagged Annual Returns
60%
8000
3500
7000
3000
50%
5000
40%
4000
Equal-Weighted Returns
3000
30%
Value-Weighted Returns
2000
20%
1000
1998
0
1994
1995
1996
1997
1999
2000
Dollar Flows
2500
Annual Dollar Flows ($M)
Dollar Flows
Lagged Annual Returns
6000
Annual Dollar Flows ($M)
1995
2000
Value-Weighted Returns
1500
Equal-Weighted Returns
1000
500
0
1994
1995
1996
1997
1998
1999
-500
10%
-1000
-1000
0%
-2000
-1500
Year
Year
All Funds: Annual Dollar Flows vs Lagged Annual Returns
16000
40%
14000
35%
Dollar Flows
10000
30%
Value-Weighted Returns
25%
8000
20%
Equal-Weighted
Returns
6000
15%
4000
10%
2000
5%
0
1994
1995
1996
1997
1998
1999
2000
0%
-2000
-4000
-5%
Year
45
Lagged Annual Returns
Annual Dollar Flows ($M)
12000
Appendix A: Classification of Hedge Fund Strategies
This table provides the mapping of the strategies provided by different data vendors with the four broad strategies
that we use in our study. It also provides a brief definition of each of the four broad strategies.
Source
HFR, ZCM/MAR and TASS
Vendor’s Strategy
Convertible Arbitrage
Broad Strategy
Relative Value
HFR, ZCM/MAR
Distressed Securities
Multi-Process
HFR, ZCM/MAR and TASS
Emerging Markets
Directional Traders
HFR, ZCM/MAR and TASS
Equity Hedge
Security Selection
HFR, ZCM/MAR
Equity Non-Hedge
Security Selection
HFR, ZCM/MAR and TASS
Event Driven
Multi-Process
HFR, ZCM/MAR and TASS
Fixed Income
Relative Value
HFR
Foreign Exchange
Directional Traders
ZCM/MAR
Global Established
Security Selection
ZCM/MAR
Global International
Security Selection
HFR, ZCM/MAR and TASS
Macro
Directional Traders
HFR, ZCM/MAR and TASS
Market Neutral
Relative Value
HFR, ZCM/MAR
Market Timing
Directional Traders
HFR, ZCM/MAR
Merger Arbitrage
Relative Value Traders
HFR, ZCM/MAR and TASS
Relative Value Arbitrage
Relative Value Traders
HFR, ZCM/MAR
Sector
Directional Traders
HFR, ZCM/MAR and TASS
Short Selling
Directional Traders
HFR, ZCM/MAR
Statistical Arbitrage
Relative Value
Directional Traders usually bet on the direction of market prices of currencies, commodities, equities, and
bonds in the futures and cash markets.
Relative Value strategies take positions on spread relations between prices of financial assets or
commodities and aim to minimize market exposure.
Security Selection managers take long and short positions in undervalued and overvalued securities
respectively and reduce the systematic market risks in the process. Usually, they take positions in equity
markets.
Multi-Process strategy involves multiple strategies employed by the funds usually involving investments
in opportunities created by significant transactional events, such as spin-offs, mergers and acquisitions,
bankruptcy reorganizations, recapitalizations and share buybacks. For example, the portfolio of some
Event-Driven managers may shift in majority weighting between Merger Arbitrage and Distressed
Securities, while others may take a broader scope.
46
Appendix B: Computation of Delta
We compute of the dollar flows from the investors for each fund and each year. For each year’s dollar
flow, we compute the option delta separately at the end of each year. We sum these deltas to determine
the total fund delta for the assets under management of each fund at the end of each year. The exercise
price of the option on each annual flow depends on its high-water mark level and hurdle rate. For dollar
outflows, we follow a FIFO (first-in, first-out) policy. Using Black and Scholes (1973) formula, we
estimate the dollar change in the manager’s performance-related incentive fee for a one percent change in
asset value. In particular, we compute delta as
delta = N(Z)*(S*0.01)*I
where Z
S
X
T
R
σ
N( )
I
= [ln(S/X) + T(R+σ2/2)]/[σ*T0.5]
= Spot price
= Exercise price
= Time to maturity of the option in years (one year)
= Risk-free rate of interest
= Volatility of monthly returns over the year
= Cumulative density function of a standard Normal distribution
= Incentive fee expressed as a fraction
47
Appendix C: Following GIR (2003) approach for only offshore funds in our sample
This table reports the regressions of flow on the performance in the previous year following Goetzmann, Ingersoll,
and Ross (GIR) (2003). As in GIR (2003), the results are for the sub-sample of offshore funds for the period, 19901995. Figures marked with ***, **, and * are significant at the 1%, 5%, and 10% respectively.
Independent
Variables
Intercept
1990
1991
1992
1993
1994
1995
F-M
Model 1 Model 2
-0.802*** -0.951*** -0.697** -0.606** -0.789* -0.173 -0.670*** -0.328*
Rankt-1 – Bottom
Quintile
-1.025
0.902
-0.472
1.144
1.999
1.398
0.658
1.023
Rankt-1 - 4th Quintile
1.704
2.402
2.641
-0.786
-0.133 -1.868
0.660
-0.007
Rankt-1 - 3rd Quintile
-0.085
-1.849
-0.340
3.070
-0.795 -0.339
-0.056
-0.005
Rankt-1 - 2nd Quintile
-0.289
0.767
1.475
-1.704
3.229
0.885
0.727
0.946
Rankt-1 -Top Quintile
0.181
1.490
-2.252
1.461
0.759
5.516
1.192
1.943
Lagged Return
0.106
Year Dummies
Adjusted R2
-0.029
-3.0%
1.6%
2.4%
-0.6%
48
2.3%
-0.1%
0.4%
Yes
Yes
2.4%
1.6%