How Smart are the Smart Guys?
A Unique View from Hedge Fund Stock Holdings
BY JOHN M. GRIFFIN AND JIN XU
*
April 3, 2006
Preliminary
John Griffin is an Associate Professor at the University of Texas at Austin and can be reached at
[email protected] or Ph# (512) 471-6621. Jin Xu is a Doctoral Candidate at the University of
Texas at Austin. We wish to thank Zhiwu Chen, David Hsieh (the FMA discussant), Roger Ibbotson, Owen
Lamont, Matt Spiegel, Will Goetzmann, Antti Petajisto, Geert Rouwenhorst, Laura Starks, Sheridan Titman,
Christian Tiu, and brownbag participants at the Financial Management Meetings in Chicago, University of
Lisboa in Portugal, University of Texas at Austin, and the Yale School of Management.
*
How Smart are the Smart Guys?
A Unique View from Hedge Fund Stock Holdings
Abstract:
We provide what we believe to be the first comprehensive examination of hedge fund stock
long-equity positions and the performance of these stock picks. Compared to mutual funds,
hedge funds act as if they provide value. They have high turnover, hold stocks that take
them further away from the market portfolio, and also prefer smaller opaque securities. In
aggregate, hedge funds do not exhibit the strong momentum tilt documented for mutual
funds but actually are overweight past loser stocks. Despite their active nature, we cannot
reject the null that hedge funds in general are any better at long stock picking or timing
sectors than mutual funds. However, hedge fund firms seem to have more differential ability
in stock picking than mutual funds with the best hedge fund outperforming in stock picking.
Overall, our study questions the ability of long-equity hedge funds to add value.
I. Introduction
An underlying assumption by many on Wall Street is that the best and brightest managers
migrate to the hedge fund industry. This assumption is clearly a contributor to the 20 fold
increase in hedge fund assets under management since 1990. While hedge funds typically are
known for investing in a variety of positions, roughly 1/3 of the industry simply invests in
equities. This paper examines the long equity positions of hedge fund managers and asks
whether these managers exhibit talent in stock picking and sector timing. We also examine
differential ability within the hedge fund industry. In the process, we shed some light on how
hedge funds seek to obtain profits in terms of the types of securities they hold and their
trading intensity.
Since hedge funds cater to sophisticated investors, they are afforded an extra level of
opacity by the SEC and are not required to report their holdings semi-annually in SEC N30D filings like mutual funds. Hence, the evaluation of hedge funds has largely been left to
measuring the effect of factor exposures to total returns. With the exception of an analysis of
the gaming behavior of forty Australian mangers by Brown, Gallagher, Steenbeek, and Swan
(2004), this lack of analysis of holdings data has led to almost no understanding of what type
of stocks hedge funds typically hold. Examinations of hedge fund returns can be beneficial
in that the total return for a fund is reported but the approach is also sensitive to the
benchmarking factors and approach. This, along with different samples, has led to widely
varying opinions regarding the performance and timing ability of hedge funds [Fung and
Hsieh (1997), Ackermann, McEnally, and Ravenscraft (1999), Brown, Goetzmann, and
Ibbotson (1999), Agarwal and Naik (2000), and Amin and Kat (2003)].
This paper backs out the stock holdings of 306 hedge fund firms from 1980 to
present through a unique and labor intensive process. It should be noted that hedge fund
1
firms often contain the activity of several hedge funds. Our approach yields quarterly hedge
fund activity in stocks during March 1980-September 2004 if we use our full data (Hedge A),
or March 1986-September 2004 if we adopt a more conservative approach, where we only
examine funds the year after they are available from one of our hedge fund data sources
(Hedge B). This approach will shed light on a variety of different facets of hedge fund
performance that have largely been ignored. To our knowledge no paper has used a
holdings-based approach to examine large-scale issues of hedge fund holdings and
performance. Brunnermeier and Nagel (2004) examine quarterly 13F holdings of hedge
funds as we do, but their focus is on whether 53 hedge fund firms increased or decreased
their positions and made money in high price-to-sales (P/S) stocks during the dot.com
bubble from 1998-2000.
Our first question concerns issues of how active hedge funds are in their trading and
their aggressiveness relative to the market. We find that although there is broad
heterogeneity in hedge fund turnover, the median hedge fund has about twice the turnover
of the median mutual fund. At the individual firm level, we find that hedge funds have
holdings that are less correlated with the market than the average mutual fund.
We next turn to examining what types of securities hedge funds hold. We do this
first by examining average holdings in 25 portfolios formed according to size and BE/ME
or size and momentum; and second, in a regression context with many firm characteristics.
Relative to mutual funds, hedge funds are overweight in the bottom three size quintiles and
underweight in the largest two size quintiles. Hedge funds tend to prefer small value stocks
but strongly avoid the largest quintile of value stocks. The comparison based on momentum
and size show that hedge funds are not heavy momentum players, and in fact, relative to
CRSP weights and mutual funds, they have larger holdings in past loser stocks.
2
Our cross-sectional regressions reveal that unlike mutual funds, which have a strong
and growing preference for stocks with analyst coverage, hedge funds in aggregate do not
seem to prefer stocks with high analyst coverage over those with low analyst coverage.
Hedge funds also have a much milder liquidity preference than mutual funds. They tend to
hold much smaller and somewhat more volatile securities than mutual funds as well. We find
that mutual funds throughout the mid 1990s to 2004 avoid high price-to-sales securities, but
hedge funds exhibit no aversion to these stocks. Overall, our holdings analysis suggests that
hedge funds, while more active traders, tend to prefer less traded, less analyzed, and smaller
securities.
We now turn to examining hedge fund performance. DGTW (1997) list many
benefits to holdings based approaches. The first main advantage is that characteristic
matching allows for benchmarks that explain more of the realized variance in returns than
those based on factor loadings and “should have more statistical power to detect abnormal
performance than factor models.” Second, the benchmark allows for a more comprehensive
and accurate return decomposition into the components of stock selection ability
[Characteristic Selectivity (CS), Characteristics Timing (CT), and Average Style (AS)].
However, for hedge funds this approach has even more advantages. Since hedge
funds report total returns and not the returns separately for their equity-only investments,
with total returns it is extremely difficult to answer whether hedge funds are able to earn
excess returns in stocks. Given the complex nature of the assets held by hedge funds, linear
factor models will have difficulties answering whether any perceived abnormal performance
is due to model mis-specification, unique asset mix, or true ability. Fung and Hsieh (2001)
find that trend-following strategies can fool standard linear-factor benchmarks and that even
benchmarks designed to capture trend-following strategies may fail without adequate
3
knowledge of fund operations. Additionally, ‘informationless’ strategies [Weismann (2002)]
using derivatives such as writing options on low probability events can increase a fund’s
Sharpe ratio at the expense of increasing downside risk, and these strategies will not be
detected using factor models. By focusing on the equity-only portion of investments, we
exclude markets where most of the informationless investing occurs. An important
limitation of our approach is that we only examine stock market performance through long
positions. We see this cost as necessary given our focus on the ability of hedge funds in the
stock markets and the paucity of evidence on this topic.
We follow DGTW (1997) and classify the return space using 125 benchmark
portfolios formed on size, BE/ME, and stock return momentum, due to the general
agreement that these patterns have been consistently related to past realized stock returns.
We find over the entire 1986-2004 period that hedge funds’ stock picking added 2.15 percent
per year in return value as compared to only 0.82 percent for mutual funds. Over the 1986 to
2004 period, hedge funds added a statistically insignificant 1.43 percent in stock selection
ability within the benchmarks. Most of this performance is generated in 1999 and 2000.
Even if the extra performance were significant, some simple extrapolation suggests that it is
unlikely to cover their fees. We hope future research will investigate holdings performance in
conjunction with hedge fund cost structure.
A major selling point of hedge funds is that they are said to contain the ability to
time stock picks across asset classes. At least in the long-equity stock universe, this assertion
is unfounded. Over the entire period there is no evidence that hedge funds are better at
rotating between sectors (with size, BE/ME, and momentum) portfolios than mutual funds.
Hedge funds tend to hold better average style portfolios than mutual funds over this 19922004 period as well (providing 0.92 percent in additional return).
4
We examine whether hedge funds that perform well one year outperform the next
year. We find that they do not. However, we find that the best hedge fund stock pickers do
seem to have talent at picking stocks despite finding that they are bad sector timers.
The remainder of our paper is as follows. Section II explains the sample construction
and displays some simple summary statistics. Section III examines the activeness of hedge
funds relative to mutual funds. Section IV displays the preference of hedge funds for firm
characteristics. Section V investigates whether mutual funds follow hedge funds. Section VI
and VII examine the performance of hedge funds and their performance persistence
followed by a conclusion in Section VIII.
II. Data
A. Data Collection Procedure
Here we describe the particulars of the labor intensive collection procedure for
compiling our hedge fund sample. Since 1978 all institutions with over $100 million under
management are required to fill out 13F forms quarterly for all U.S. equity positions where
they own greater than 10,000 shares or $200,000. Domestic and foreign hedge funds with
over $100 million of 13F securities under management are not exempted from these
requirements.1 A limitation of the 13F data is that the shorting activity of hedge funds is not
contained in these reports, so all of our results will be based on examining the long-side of
the portfolio strategy. Our overall task is to identify hedge funds from several sources, find
their management or holding company name, and match them up with the 13F holdings
1 The SEC website posts the commonly asked question of whether a foreign institutional investment manager
must file the 13F form and says: “Yes, if they: (1) use any means or instrumentality of United States interstate
commerce in the course of their business; and (2) exercise investment discretion over $100 million or more in
Section 13(f) securities.” A similar answer is posted for a non-SEC registered investment advisor. See
http://www.sec.gov/divisions/investment/13ffaq.htm.
5
data. Finally, we check the management company to find their main line of business and
exclude all funds whose primary business is not in the hedge fund industry, as we describe in
detail below.
We obtain hedge funds from six sources: AltVest, the MAR graveyard of dead funds,
firms from Hoovers.com (premium access, information as of 1997), firms in Table 2-4 in
Cottier (1997) that have management in excess of $500 million as of December 1995, hedge
funds listed in annual Nelson's directory books from 1988 to 2002, and TASS from February
1977 to May 2000 containing both alive and dead funds. Getmansky (2004) claims that
TASS is the industry’s only unbiased and most comprehensive database. Although TASS
includes the largest number of hedge funds, we find that other databases are equally
important in obtaining our final sample.
Our overall process of backing out hedge fund holdings from 13F filings is similar in
spirit to the pioneering approach of Brunnermeier and Nagel (2004). However, we use a
much more comprehensive list of hedge funds to start with and obtain a final sample which
over the entire period contains nearly six times as many hedge fund firms as the 52 hedge
fund firms analyzed in their sample. Many hedge funds have a holding company firm with a
different name from the hedge fund. It is the holding company firm name that we must
identify in the 13F database. An advantage of Nelson's directory is that it includes both
hedge funds and their affiliated (or holding) firm names and the fraction of hedge fund
business in their affiliated firms. We then use the holding company name (when they differ)
to match the firms up to the 13F institutional holdings database which match funds in each
of the above data sources. We manually check the matches and remove any mismatches. To
avoid spurious matches, for firms where the matches are less than perfect, we obtain
6
additional information from the websites of particular funds like the location and total net
asset to verify the fund.
After matching the holding firm names we then start to examine whether the holding
firm’s major line of business is hedge funds in using one of four main ways. First, we look
up the holding company names in Nelson’s Directory and only include firms with hedge
fund assets that constitute over 50 percent of a holding company’s total assets. Second, we
include firms whose major line of business is described as a hedge fund in Cottier (1997).
Third, if firms are unavailable from Nelson’s directory, we manually check the SEC ADV
forms and (like Brunnermeir and Nagel, 2004) require that a company have over 50 percent
of its investment listed as “other pooled investment vehicles (e.g., hedge funds)” or over 50
percent of its clients as “high net worth individuals.” In addition to this criterion we also
require that the fund charge performance-based fees. Fourth, for funds whose names are
found in 13f but are still unidentified in any of these ways we check their websites to see if
their primary business is hedge funds. Only those funds with websites that claim they are
hedge funds and only available to accredited investors are included in our sample.2 Funds
not identified in any of these four ways are not included in our sample. Finally, for all funds
that are identified as hedge funds in one of these four ways we perform a further check by
examining whether a mutual fund in the CDA/Spectrum database has a holding company of
the same name as one of the hedge funds. To avoid any delisting bias, we keep hedge fund
firms in our sample until they begin to offer mutual funds since hedge fund companies that
For a definition of Accredited Investors, please refer to SEC website: http://www.sec.gov/answer/
accred.htm.
2
7
perform well may later open mutual funds. The 13F database has some reporting issues
which we are aware of and carefully address.3
Mutual fund holdings are from Thomason Financial CDA/Spectrum S12 data. The
funds that we include have the following self-declared investment objective: Aggressive
Growth, Growth, Growth and Income, and Balanced--codes 2, 3, 4 and 7 respectively.
Sector, bond, preferred, international, and any fund with an investment objective that is not
oriented to general equity is excluded.
The main difference between the mutual fund (from N-30D) and hedge fund
holding (from 13F) databases are as follows:
1. Mutual fund holdings are at the fund level, whereas hedge fund holdings are at
the holding company or firm level.
2. There is no threshold that we are aware of on mutual fund reporting size,
whereas only hedge fund firms above $100 million must file.4
3. Hedge fund filings are required to be reported quarterly, whereas mutual funds
are only required to report semi-annually.5
4. Mutual funds are required to report all of their long stock holdings, whereas 13F
filings are only required on security positions that are greater than 10,000 shares
or $200,000.
These differences seem logical to lead to smaller mutual funds and smaller mutual fund
positions being reported. When equal-weighting performance across funds or firms, the
differences in capitalization between the groups should be important, but in value-weighted
3 These reporting problems and solutions to these problems are discussed recently in an appendix by Griffin,
Harris, and Topaloglu (2004).
4
However, Wermers (1999) notes that some small mutual funds do not report.
5 Nevertheless, there are missing quarters of data on the 13F database and some mutual funds voluntarily
report quarterly.
8
returns, the distinction between the two databases becomes less relevant. We explore
empirical differences in the datasets below.
B. Sample summary statistics
Panel A of Table I identifies how many hedge funds are available in each of our data
sources and how many of these funds end up in our final sample of hedge fund holdings.
Many funds are available from multiple sources but we identify a fund according to its
earliest reported source. The majority of our firms come from Nelson’s directory, Altvest,
TASS, and the MAR graveyard. It is important to note that many of our data sources end in
2000, and MAR and Nelson’s directory end in 2002. Updated versions of these databases
would likely add some newer hedge funds but these funds would only have limited track
records. In all, we are able to obtain quarterly stock positions of over 306 fund firms. It is
important to note that each of these fund firms may often have multiple hedge funds under
management so the sample may be representative over 1,000 funds. However, we have no
accurate method to judge the ultimate number of funds represented in our sample.
Because examining portfolio performance is one of our objectives, we are
particularly concerned with constructing a sample free of potential biases. Thus, we
construct two samples. The first sample, named Hedge A, is larger and more comprehensive
but may suffer from reporting biases while the second sample, named Hedge B, should be
relatively free from potential biases.
Since reporting of a hedge fund is voluntary, hedge funds may choose to report their
performance to standard data sources such as TASS after they have had a period of good
performance. Since we use these datasources to locate hedge funds, we are concerned about
examining their holdings prior to the period in which their holdings appear in our first
9
datasource.6 To control for this issue we also construct another sample, Hedge B, which only
contains information on funds in the years after we obtain their name from one of the
sources we list above. Thus, this database (Hedge B) should not suffer from self-selection
bias. 7 Hedge B is more applicable to measuring performance whereas both samples can
provide information about hedge fund holdings. Hedge A includes all hedge funds which are
in our database both before and after the time where we locate their name.
Annual summary statistics on both hedge fund samples are contained in Panel B of
Table I. In 1980 our Hedge A sample contains 25 hedge fund firms but it grows slowly
throughout the 1980s and 1990s to include 231 firms in 2000, before falling slightly to 191
firms in 2004.8 Panel B of Table I shows that Hedge B starts much later (because of the later
start of hedge fund data sources). Although we only have one Hedge B firm tracked in 1984
and 1985, the number of firms grows rapidly and includes 37 hedge fund holding companies
in 1992, 105 firms in 1995 and 193 firms in 2000. In addition, it is important to note that
the shorter period is longer than that in many other important studies.9
The number of funds and holdings of mutual funds from N-30D filings compiled by
the CDA/Spectrum Mutual Fund Holdings database is also reported for comparison
purposes. It is important to note that this is an unfair comparison in the sense that the hedge
fund firms are more likely to handle many hedge funds within the same firm whereas each
mutual fund is reported separately. Additionally, it is important to note that our hedge fund
sample is only a partial sample of the industry as we have excluded hedge funds with holding
6
Although this good performance could come from a variety of positions in other markets, performance in the
equity market is one factor that can drive positive performance.
7 The data could suffer from backfill bias to the extent that the original data reported by the vendors was filled
back in time. To counteract this effect we have two databases of dead funds.
8 Since our TASS sample ends in 2000 there are likely many new funds in 2001-2004 that are not included in
our sample. The exclusion of these funds should not result in any systematic biases.
9 For example, Brown, Goetzmann, and Ibbotson (1999) use a sample of only seven years, whereas 1992-2004
is 13 years.
10
companies having other primary businesses or also owning a mutual fund. Our sample
shows a decline in the number of mutual funds from 3092 in 1997 to 2229 in 2003.
We also examine the average and total number of stocks held in both hedge fund
samples and in mutual funds. As seen in Panel B of Table I, the average number of stocks
held by each Hedge A holding company is 120 in 1980, 131 in 1990, 169 in 2000, and 217 in
2004. The numbers for the Hedge B sample are slightly lower. The average mutual fund
holds fewer firms, particularly earlier in the sample. These differences in the number of firms
between mutual funds and hedge funds will have some relevance to the preciseness of
calculations performed later in the paper. The total number of stocks held by Hedge A
holding companies is 1,247 in 1980 which is 65.6 percent of the stocks with reported
holdings by mutual funds. However, this number grows to 83.5 percent in 1990 and 85.6
percent in 2000. Hedge B holdings are far below those of Hedge A, but the holdings grow
close by the mid 1990s.
III. How active are hedge funds?
We first examine how active traders of hedge funds are by first examining turnover
and then we look at the relation between their hedge fund weights and market weights to
judge the dispersion of their trading strategies.
A. Turnover
Hedge funds are not required to report their turnover like mutual funds. Thus, we
must estimate turnover from quarterly stock positions. This measure is by nature an
understatement of true turnover since intra-quarter trades will not be captured. Nevertheless,
examining turnover in this manner can serve as a useful comparison between hedge funds
and mutual funds. We compare the current holdings of a firm in stock X to its position at
11
the beginning of the quarter. So if the firm held 100,000 shares in stock X and it increased its
positions to 1,000,000, then the change in holdings would be 900,000 shares times the stock
price at the beginning of the period. We then sum this dollar value of trading across all
stocks. This is done separately for sells as well, and to avoid sales due to flows of capital, the
smaller of the buys and sells is picked and normalized by the firm’s beginning of the period
capitalization.10
Figure 1 reports the distribution of turnover for both hedge funds and mutual funds.
The distribution shows striking differences between the trading of mutual funds and hedge
funds. Most hedge funds have turnover above that of most mutual funds. The median
turnover using quarterly holdings for hedge funds of 1.02 is almost twice of the median
mutual fund turnover (0.63). The distribution is also right skewed for hedge funds and
similar results are obtained for both hedge fund samples. These findings reinforce the
conventional wisdom that hedge funds are active traders.
B. Weights relative to the market
We now turn to estimating the correlations between hedge fund positions and
market positions. Hedge funds may take positions in opposite directions that may lead to
different conclusions from examining individual hedge fund firm holdings than their
aggregate holdings. In every quarter for every hedge fund, we calculate the correlations of
individual hedge fund firm weights and market weights. We then average these correlations
across quarters for each fund and then compute annual averages across funds. As shown in
Figure 2, during early years of the sample hedge funds (Hedge A) have a stronger relative
correlation with market weights, but mutual fund correlations with the market grow in later
10
If the fund does not file for a particular quarter, then we carry over all the positions and no turnover occurs
that quarter. This procedure follows the way that the CRSP mutual fund database reports turnover and is
described in Chen, Jegadeesh and Wermers JFQA (2000, p349). Because CDA/Spectrum does not calculate
turnover, we calculate turnover in this manner for both hedge funds and mutual funds for consistency.
12
years of the sample period. In 2004 the average correlation of the value-weighted market
weights and those of the average mutual fund is 0.368 whereas it is 0.275 for hedge fund
sample A and 0.242 for sample B. In unreported results, we also calculate the correlation
between the aggregate holdings of mutual funds and the market. This aggregate correlation is
0.948 in 2004, as compared to 0.917 for aggregate hedge fund holdings (Hedge A).
We also compare weights using a deviation approach similar to Cremers and
Petajisto (2006) by taking the difference between the weights in each stock ($ holding in each
stock/$ total position in all stocks for a fund) and the weights of the CRSP market index in
each stock. For each fund, the value-weighted sum of these deviations in each stock is then
summed up across stocks and divided by two, since for every position over-weight there is
an offsetting under-weight.
Figure 3 presents the long-equity weight deviation distribution across funds where 0
indicates no deviation and 1 indicates a total deviation from either the CRSP value-weighted
market or S&P 500 index. Panel A shows that hedge fund firms generally have larger weight
deviations, except for the largest categories where some mutual funds deviate 100 percent
from the index. These are likely funds with other objectives, such as a small cap with other
benchmarks than the ones used here.
To further examine these deviations, we decompose the total deviation into
deviation across industries and that within industries. The cross-industry weights are the
fraction of capital that the firm designates to sectors that is different from, say, the S&P 500.
Hedge funds are generally much more aggressive in deviating from the industry, whereas
mutual funds exhibit higher within-industry weight deviations, suggesting that they are more
focused on localized stock-picking. In general, both through average weight correlations and
observing the distribution of weight deviations we learn that hedge funds generally deviate
13
more from the value-weighted market indices than mutual funds and that these deviations
usually come from taking larger cross-industry bets.
IV. What type of stocks do hedge funds prefer?
We examine whether hedge funds have preferences for stocks with certain
characteristics. We do this in two main ways. First, we compare hedge fund weights relative
to market weights on twenty-five size and book-to-market equity (BE/ME) portfolios and
twenty-five size and momentum portfolios. We then compare these weights relative to
mutual funds. Second, we perform cross-sectional regressions across firms of hedge fund
holdings on a variety of characteristics including firm age, analyst coverage, beta, turnover,
price-to-sales (P/S), standard deviation of returns, as well as size, BE/ME, and momentum.
We also compare the preferences of hedge funds for these characteristics to the preferences
of mutual funds.
A. Hedge fund weights in size, BE/ME, and momentum portfolios
Panel A and B of Figure 4 presents the positions of our Hedge B funds in stocks of a
particular size and book-to-market equity quintile. We measure their holdings first relative to
market weights (Panel A) and then relative to mutual funds (Panel B). The holdings are
calculated on an annual basis and then summed across time and the weights in each portfolio
are standardized by the market weights in each group so that percent weightings are
comparable across portfolios. Interestingly, hedge funds (sample A) hold more in mediumsized stocks than a market-weighted investor and hence hedge funds are generally
underweighted in the largest quintile of stocks relative to the market. The amount that the
weights vary in the large size quintile varies from a slight overweight of 1 percent in the large
growth portfolio to an underweighting of 18.3 percent in the large size value group 4
14
quintile. Hedge funds are also underweighted in stocks in the smallest size quintile (relative
to market weights).
The average preference for value/growth seems less clear. On average, hedge funds
seem to have a preference for both extreme value and growth stocks in the medium size
quintiles but not for small stocks. Preferences for these categories are likely to be highly
time-varying, which we will examine in more detail in the next sub-section.
Panel B compares hedge funds to mutual funds. Relative to mutual funds, hedge
funds actually have a strong positive preference for stocks in the smallest three quintiles. The
only exception is that hedge funds actually hold slightly more large growth stocks. Hedge
funds also tend to show a preference for value stocks in the smaller quintiles although they
are overweighted in growth stocks to a lesser degree in these quintiles as well.
Panel C and D of Figure 4 examines hedge fund holdings in excess of market
weights in securities sorted on size and momentum. Hedge funds prefer high momentum
stocks in the medium size groups but not in the smallest size quintile. However, they actually
have an even stronger preference for loser stocks in all size groups. Panel B shows that
relative to mutual funds the preference of hedge funds for high momentum stocks is even
more distinct. Past research has shown that mutual funds have a strong preference for stocks
with high past stock returns (Grinblatt, Titman, and Wermers, 1995). Panel B shows that
hedge funds have a weaker preference for winner stocks than do mutual funds. In addition,
hedge funds seem to have a strong preference for low momentum stocks, particularly those
with low BE/ME. The preference for low momentum stocks would seem to be a largely
losing strategy since Hong, Lim, and Stein (2000) show that small loser stocks have more
underperformance. However, it is important to note that they exclude stocks in the smallest
NYSE size quintile where there is little evidence of momentum. Relative to mutual funds,
15
hedge funds exhibit a positive bias towards smaller stocks, past losers, and value stocks. The
sorting approach has the advantage that it can allow for many non-linear patterns in
ownership but has the disadvantage in that only several characteristics can be examined
together. We now turn to an analysis of firm ownership on a broader set of firm
characteristics.
B. Regressions of stock ownership on firm characteristics
We seek to understand the preference of hedge funds for holding firms with a
variety of firm characteristics. Our approach to understanding the relation between
ownership and firm characteristics is similar to Falkenstein (1996) for understanding the
stock preference of mutual funds in 1991 and 1992. Each year, we estimate a cross-sectional
Tobit regression of 1+hedge fund ownership for a particular stock on firm characteristics
from the previous year. We use the yearly time-series to construct Fama/McBeth
coefficients and standard errors. The firm characteristics we consider are the age of the stock
measured by the number of months on the CRSP database, the number of IBES analysts,
the monthly Dimson betas, the BE/ME, turnover, firm market equity, price-to-sales (P/S),
the previous twelve month return (momentum), and a stock’s standard deviation. Gompers
and Metrick (2001) examine the preferences of institutions in general for firm characteristics
and Bennett, Sias, and Starks (2003) examine in detail how these preferences have changed
through time.
Table II reports regressions both for mutual funds and hedge funds. Hedge funds
seem to have a slight preference for younger securities, high beta securities, more liquid
securities with higher turnover, smaller stocks, and high past returns. Consistent with
Falkenstein’s (1996) main results, mutual funds tend to have a strong preference for stocks
16
with more analysts, more liquidity, and avoid stocks with low volatility. They also tend to
prefer stocks with high beta, low BE/ME, low P/S, and high past return.
To further trace the dynamics of these securities over time, we plot the annual crosssectional regression coefficients in Figure 5. Mutual funds exhibit a large and growing
preference for holding stocks with analyst coverage whereas hedge funds tend to not care
about analyst coverage. Mutual funds seem to vary their preference for beta much more over
time than do hedge funds (at least Hedge sample B), with a strong preference for beta in the
mid 1990s but a negative preference in the 2000s. Mutual fund preference for BE/ME also
tends to vary more across years although both mutual funds and hedge funds have tended to
prefer low BE/ME securities since the mid 1990s. Mutual funds have a strong and growing
preference for liquidity throughout the period, whereas the preference of hedge funds for
liquidity is consistently lower, and in the case of Hedge A this preference has slightly
decreased. Hedge funds had almost no preference for size in the 1990s, in contrast to mutual
funds where there was a large increase in their preference for firm size. In the 1990s, mutual
funds display an aversion to P/S that is not there for hedge funds. Mutual funds tend to
have a stronger preference for momentum through most of the period, although this
preference in both hedge funds and mutual funds seems to have disappeared in the last two
years. Hedge funds do not seem to display an aversion to standard deviation through the
entire period. In contrast, mutual funds avoid high standard deviation stocks until the last six
years of the sample.
Panel D compares the hedge fund sample to the mutual fund sample and further
clarifies the average statistical comparisons between mutual funds and hedge funds. Relative
to mutual funds, hedge funds prefer stocks with fewer analysts, less liquidity, smaller size,
lower past return, and higher standard deviation. These findings are robust to both hedge
17
fund samples with the exception being that Hedge B, which is free from self-selection bias
and starts later, also demonstrates a preference for high price-to-sales securities relative to
mutual funds. Overall, our findings indicate that hedge funds prefer less opaque firms that
may be more costly to trade and less analyzed. These results for stock selection are generally
consistent with the findings of Fung and Hsieh (1997) that the factor exposures of hedge
funds and mutual funds returns are dramatically different. Our findings indicate a potentially
important and different role for hedge funds in generating stock pricing efficiency.
V. Evaluation of fund performance
A. Performance
We use the methodology of Daniel, Grinblatt, Titman and Wermers (1997) to
separate out the performance of hedge funds into their excess return relative to 125 size,
industry adjusted BE/ME, and momentum benchmarks. 11 Characteristic selection (CT)
measures the ability of managers to pick stocks within the size, BE/ME, and momentum
benchmarks. The characteristic selectivity (CS) uses the change in the hedge fund weights
from the previous year to measure the ability of funds to time selection within the
benchmark portfolios. The average style (AS) uses the previous year’s portfolio weights to
measure the return due to a manager’s propensity to hold stocks of a particular style. The
combination of AS, CT, and CS equals a fund’s average return. We closely follow DGTW
(1997) and Wermers (2000) in calculating performance relative to the benchmarks. For
hedge fund firms we calculate the performance measures on an annual basis for each firm
and then perform equal-weighted averages across firms. For mutual funds we also calculate
equal-weighted averages across firms and thus the performance of the investors in the
The industry adjusted book-to-market equity is consistent with Wermers (2004). We thank Russ Wermers for
graciously providing the benchmark returns.
11
18
industry may differ because of the weighting. We examine performance measures for our
Hedge sample B that only examine stocks in the year after they appear in the database.
Because this database has fewer firms in early years of the sample, we begin reporting in
1986 when four hedge fund firms report. Our estimates quickly become more reliable in
later years of the sample when more hedge fund firms are available. We compare the
performance of our hedge funds to a CDA spectrum sample of actively managed mutual
funds.
Panel A of Table III reports year by year value-weighted performance measures and
Panel B reports equal-weighted. The gross return indicates that there is substantial variation
in the returns from hedge funds from both the market indices and mutual funds. We
examine the causes of that return through the benchmark decomposition. We first report
performance measures for hedge funds and mutual funds and then we compare differences
in the performance measures. In Panel B, the CS measure for hedge funds is positive in 13
of 19 years for hedge funds but 15 of 19 years for mutual funds. The largest deviation in the
characteristic selectivity measure for hedge funds is 16.02 percent in 1999 when hedge funds
outperformed mutual funds by 9.72 percent. This large outperformance may be due to the
high weights of tech stocks or high P/S stocks as documented by Brunnermeier and Nagel
(2004). Both hedge funds and mutual funds tend to generate negative characteristic timing in
many years of the sample although in many years hedge funds have worse timing than
mutual funds. In terms of average style it is not clear if mutual fund or hedge fund style
dominates.
Panel B also presents summary statistics for the sample from 1986-1994, from 1995
to 2004, and from 1986-2004. We choose to divide the sample here because our sample (B)
during later periods contains more hedge fund firms and thus performance statistics here are
19
much more reliable than those in early years of the sample. Overall, we find no evidence of
either hedge funds (sample B) or mutual funds engaging in successful characteristic
selectivity or timing in the earlier sub-period. However, both do seem to have positive
average style over this period with hedge funds delivering slightly lower style performance
than mutual funds.
Over the 1995-2004 period mutual funds deliver a characteristic selectivity return of
0.87 percent per year and hedge funds deliver 2.66 percent. In this period hedge funds
outperform mutual funds by a statistically significant 1.79 percent per year in stock picking.
Also, in terms of timing styles hedge funds have shown strong ability (1.27%) but the
difference from that of mutual funds is positive but not significant. In terms of average style
hedge funds pick an average style that delivers them an average return of 0.49 percent
greater than mutual funds over the 1995-2004 period. Over the whole 1986-2004 period
both hedge funds and mutual funds deliver positive and significant stock selection ability.
Hedge funds deliver an additional 1.65 percent in stock selection returns but this difference
is not statistically significant. Hedge funds tend to be slightly better timers and have a slightly
better style but the difference is not significant.
It is not clear whether we should rest our conclusions on the later 1995-2004 period
where there are more hedge funds available for judging their performance. If one does so
then you are left with the impression that hedge funds may be better at picking stocks and
have on average chosen a better average style. As discussed in DGTW (1997) it is not clear
whether to give managers credit for picking the style or if they fell into that style. Even if
one gives managers credit for picking the average style over the 1995-2004 period, it would
seem that hedge fund managers generated 2.75 [1.79 (CS) +0.51 (CT) + 0.49 (AS)] in value
20
over mutual funds. Recall that much of the CS return (shown in Panel A) is generated in
1999 and 2000 and that only marginal value is created in other years.
Since book-to-market benchmarks are inadequate for classifying tech stocks, we reexamine performance using ten price-to-sales deciles, similar to Brunnermeier and Nagel
(2004). We specifically wish to address whether hedge funds performance during the late
1990’s was confined to high tech stocks (high price-to-sales). Table IV shows that this is
largely the case—out of the total 9.72% performance difference between hedge funds and
mutual funds in 1999, 6.95% (1.40% + 1.25% + 4.30%) comes from the three highest priceto-sales deciles.
B. Discussion
The average outperformance of hedge funds over mutual funds is 1.48 percent
(Table III) over the 1986 to 2004 period. We are not able to address whether this is enough
to justify higher fees, since there is no reliable source on hedge fund leverage ratios available
that we are aware of.12 Nevertheless, it is clear that for a hedge fund to compensate for its
higher fees relative to a mutual fund, it would need significant leverage or to make more
profits on the short-side. However, shorting is on average a loosing proposition. It is also
true that a large selling point of hedge funds is the value they appear to create by investing in
various asset classes and timing capital between asset classes. While we can only examine
long stock positions here, the lack of hedge funds to create any positive returns in timing
characteristics (like picking growth, value or momentum at the right times) casts
considerable doubt on the assertion that hedge funds can time markets.
12
We thank David Hsieh for informing us of the various problems with leverage ratios in TASS and other
sources.
21
VI. Are some managers better than others?
Although the average hedge fund is not much better than mutual funds, there may
be more variation in ability for hedge funds, particularly given that they are less likely to
index as compared to mutual fund managers. Consistent with hedge fund firms taking
positions further away from the market index, Figure 6 shows that hedge fund firms have
wider tails or more dispersed performance than mutual funds. Figure 6 also shows that there
seems to exist a slightly larger group of hedge fund managers (in the right-hand tail) that
outperform than is found in the mutual fund industry. We turn to examining differential
ability first by looking at ‘hot hands’ and then by ranking managers according to their entire
history of performance.
A. Performance Persistence
We consider whether hedge funds have any ‘hot hands’ and whether managers that
perform well in one year also perform well in the next. We rank managers according to their
ability to generate gross return and then examine the three components of the return as well
as their gross return in the following year. Because of the limited number of hedge fund
firms in earlier years of the sample we begin the ranking in 1991 and examine returns from
1992-2004.
Panel A of Table V examines the gross return and return components of five
quintiles of past gross hedge fund or mutual fund return performance. Hedge funds with the
highest past quintile of fund performance in the previous year tend to outperform those
hedge funds with low past performance by 5.54 percent per year but this difference is not
statistically significant. Further, this difference (5.54 percent) may be due to superior stock
selection skill, but again there is no significant difference in the CS measure for past winner
and loser hedge fund firms.
22
We also examine these measures for mutual funds. Using the benchmark approach,
DGTW (1997) found that the previously identified patterns of ‘hot hands’ were largely due
to momentum and that mutual fund managers did not seem to exhibit ‘hot hands.’ Over a
much more recent period Table V confirms their findings. Mutual funds that performed well
in the previous year continue to outperform by about 553 basis points, but this difference is
not significant. None of the components of mutual fund performance are statistically
different between past winners and losers either. We also perform tests with semi-annual
rebalancing and although we find larger differences in hedge fund returns between winners
and losers, there is no significant difference in returns. Using simple strategies it does not
appear that past performing winner funds perform reliably better than loser funds.
B. Performance using each firm’s whole history
Table VI examines performance for hedge fund firms based on their entire past
picking of CS performance (Panel A) or stock picking and timing ability (Panel B). There is
some evidence that good stock pickers, continue to outperform in terms of stock picking
ability but they are bad market timers, resulting in insignificant total return differences. This
finding has important implications for the way fund-of-fund investors pick funds.
VII. Robustness Issues
A. Turnover
One potential problem with our findings is that many hedge funds may use stocks
not primarily to make money but for hedging purposes for either options or bonds.
Additionally, some hedge funds specialize in high frequency trading. To address these
concerns we stratify hedge funds into two groups based on whether the estimated turnover
in the previous year is less than or greater to one. Table VII presents value-weighted DGTW
23
performance numbers for both of these groups and shows that the aggregate performance
of the low and high turnover groups are very similar. Only looking at low turnover funds
does not alter our conclusions.
B. Aggregate Returns
Our results have shown some limited outperformance of hedge funds. One question
is whether using this information in aggregate is valuable. Chen, Jegadeesh, and Wermers
(2000) show that mutual funds purchases have statistically predictive power for future
returns. Hedge funds are a much smaller proportion of ownership and take more disparate
strategies than mutual funds, but we nevertheless examine a similar question of whether
aggregate hedge fund ownership can predict future returns. In cross-sectional regressions of
stock returns on the last quarter’s return, we find statistically significant evidence of past
hedge fund trading predicting future returns. However, this predictability is subsumed by
other variables that have been known to have predictive power for stock returns like lagged
mutual fund trades, a stock’s past return, past breadth of mutual fund ownership, and past
changes in institutional non-hedge fund ownership.
VIII. Conclusion
We provide the first comprehensive examination of the nature of hedge fund
holdings in U.S. stocks and a holdings-based analysis of the equity performance of hedge
funds. In comparison to mutual funds, hedge fund firms have much higher turnover and
deviate more from market positions. Despite this preference to trade more frequently,
however, hedge funds in comparison to mutual funds overweight stocks with fewer analysts,
less turnover, smaller size, and more standard deviation. Hedge funds also tend to strongly
buy small losers stocks and avoid winner stocks in comparison to mutual funds. These
24
patterns are inconsistent with the momentum preference found for mutual funds [Grinblatt,
Titman, and Wermers (1995)].
We also measure stock return performance and performance persistence. The use of
holdings to quantify performance is particularly important because unlike most mutual funds
who hold mostly positions in stocks and bonds, hedge funds hold many disparate asset
classes besides stocks and they do not typically report the performance of their stock-only
portfolios. Thus, even if accurate measures of a fund’s total performance are obtained, it is
difficult for an investor to assess whether a hedge fund’s abnormal performance is due to the
nature of its activity in derivative markets and/or through superior skill in traditional equity
market positions. We classify hedge fund performance by: a) the style of the fund, b) moving
in and out of styles before they become popular, or c) picking winners within styles that
simply perform better.
We find some evidence that hedge funds have some talent at stocks picking (by 2.15
percent per year for value-weighted portfolios and 1.65 for equal-weighted) over the 19862004 period. For value-weighted portfolios they are better stock pickers than mutual funds
by a statistically significant 1.32 percent per year. However, the ability of hedge funds to pick
styles is slightly worse, though not statistically different from mutual funds. Those hedge
funds that have stock picking ability in the past continue to pick stocks better than other
hedge funds, although they are poor market timers. Without the leverage positions of hedge
funds (which few report), we cannot give precise statements about whether the far tail of
equity hedge funds add enough value through their long positions to justify their higher fees
compared to mutual funds. However, even for the far tail of stock picking, our evidence
indicates that it would take significant leverage for these investors to earn abnormal returns
25
under standard hedge fund fee structures. Overall, our findings cast considerable doubt on
the long-equity performance of hedge funds.
We believe our examination of hedge fund holdings has shed more light on the
secrecy of hedge funds, but it also raises a host of interesting questions for future work. For
example, if hedge funds do not tend to make money through approaches that have worked
well in the past like using value and momentum, then what are their strategies? Given that
they trade more and in illiquid securities, how much does this act as a drag on the before
transactions costs performance numbers reported here? Finally, how do long-equity only
hedge funds convince investors that they add enough value to justify the higher fees? A role
for future research is to examine if hedge funds make more money on high frequency trades
or in short positions. We hope in future research to analyze the role of hedge funds and
market efficiency.
26
References
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Risk, Return, and Incentives, Journal of Finance 54, 833-874.
Agarwal, Vikas, and Narayan Y. Naik, 2000, Multi-Period Performance Persistence Analysis
of Hedge Funds, Journal of Financial and Quantitative Analysis 35.
Amin, G. S., and H. M. Kat, 2003, Hedge fund performance 1990-2000: Do the "money
machines" really add value?, Journal of Financial and Quantitative Analysis 38, 251-274.
Baker, Malcolm, Lubomir Litov, Jessica Wachter, and Jeffrey Wurgler, 2004, Can mutual
fund managers pick stocks? Evidence from their trades prior to earnings
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Impact of Dynamic Institutional Preferences. The Review of Financial Studies, 16, 12031238.
Brown, S. J., W. N. Goetzmann, and R. G. Ibbotson, 1999, Offshore Hedge Funds: Survival
and Performance, 1989-95, Journal of Business 72, 91-117.
Brunnermeier, M. K., and S. Nagel, 2004, Hedge Funds and the Technology Bubble, Journal
of Finance 59, 2013-2040.
Chen, Hsiu-Lang, Narasimhan Jegadeesh, and Russ Wermers, 2000, The value of active
mutual fund management: An examination of the stockholdings and trades of fund
managers, Journal of Financial and Quantitative Analysis 35, 343-368.
Cottier, Philipp, 1997. Hedge Funds and Managed Futures: Performance, Risks, Strategies, and Use in
Investment Portfolios (Verlag Paul Haupt, Bern).
Daniel, Kent, Mark Grinblatt, Sheridan Titman, and Russ Wermers, 1997, Measuring Mutual
Fund Performance with Characteristic-based Benchmarks, Journal of Finance 52, 10351058.
Falkenstein, E. G., 1996, Preferences for stock characteristics as revealed by mutual fund
portfolio holdings, Journal of Finance 51, 111-135.
Fung, William, and David A. Hsieh, 1997, Empirical Characteristics of Dynamic Trading
Strategies: The Case of Hedge Funds, The Review of Financial Studies 10, 275-302.
Getmansky, Mila, 2004, The Life Cycle of Hedge Funds: Fund Flows, Size and Performance,
Working Paper, MIT.
Griffin, John M., Jeffrey H. Harris and Selim Topaloglu, 2004, Why are IPO Investors Net
Buyers through Lead Underwriters?, University of Texas, working paper.
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Gompers, Paul A., and Andrew Metrick, 2001, Institutional investors and equity prices,
Quarterly Journal of Economics 116, 229-259.
Grinblatt, Mark, Sheridan Titman, and Russ Wermers, 1995, Momentum investment
strategies, portfolio performance, and herding: A study of mutual fund behavior,
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Hong, Harrison, Terence Lim, and Jeremy C. Stein, 2000, Bad News Travels Slowly: Size,
Analyst Coverage, and the Profitability of Momentum Strategies, Journal of Finance 55,
265-295.
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Measurement Bias, Journal of Portfolio Management 28, 80-91.
Wermers, Russ, 1999, Mutual fund herding and the impact on stock prices, Journal of
Finance 54, 581-622.
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1696.
28
Table I
Summary Statistics for Hedge Fund Firms and Mutual Funds
This table reports the summary statistics of our two hedge fund firm samples (Hedge A and
Hedge B). Hedge A is the sample with self-selection. Hedge B is constructed based on Hedge A
by only keeping firms from the year after they first appear in our data sources. Panel A presents
the eight data sources for the matched hedge fund firms. If a firm has funds appearing in more
than one source, we take the earliest sample as the source. In Panel B, for each year, we report
the average number of securities owned per hedge fund firm each quarter, the number of firm,
and the total number of securities which hedge fund firms own. For mutual funds the numbers
are presented on a fund basis. The CDA/Spectrum data is from March 1980 to September 2004.
In Panel C, we report the mean, median and s.i.q,r. (semi-interquartile range) of fund size in each
five-year period from 1980 to 2004. We also report the aggregate market value of common stock
holdings of all funds as a percentage of total market value of all CRSP common stocks (%CRSP).
Panel A: Source Distribution
Source
AltVest
Cottier
Hoovers
Mar Graveyard
Nelson's Directory
TASS Graveyard
TASS/Hedge World
Total
Total Number
1226
27
32
921
1010
501
2077
Hedge Fund Firms Matched
80
7
8
54
91
22
44
306
29
Start Year
1978
1995
1997
1978
1988
1978
1978
End Year
2003
1997
2003
2002
2002
2000
2000
Panel B: Year by Year Statistics
Year
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
Number of Funds
Hedge Hedge
Mutual
A
B
25
453
26
456
31
449
34
486
36
1
497
44
1
556
49
4
619
61
5
705
71
6
763
79
9
843
87
11
906
97
25
1045
106
37
1210
116
61
1952
125
78
2307
143
105
2479
152
113
2845
188
151
3092
202
162
3032
211
169
2773
231
193
2653
225
190
2493
214
181
2392
198
168
2229
191
162
2072
Average Number of Stocks
Owned Each Quarter
Hedge A
152
147
121
146
157
187
214
176
160
134
143
178
152
196
155
161
183
184
175
146
169
174
186
178
219
Hedge B
144
160
196
134
155
191
152
146
162
204
141
154
180
179
145
137
163
164
164
168
200
Mutual
43
48
47
56
60
56
62
91
64
66
75
68
85
81
84
97
96
94
96
98
97
119
107
116
113
Total Number of Stocks
Owned
Hedge Hedge
Mutual
A
B
1697
2493
1978
2757
1947
2844
2195
3427
2396
168
3634
2750
244
3947
3347
618
4225
3401
966
4238
3376
956
4218
3331
1245
4096
3271
1485
3891
3630
2006
3960
4113
3055
4185
4571
4021
5333
4812
4320
5823
5173
4890
6224
5718
5475
6744
6100
5900
6944
6047
5839
6745
5871
5677
6415
5443
5309
6221
4893
4780
5621
4410
4358
5098
4280
4234
4780
4215
4145
4640
Panel C: Fund Size Statistics
1980-1984
1985-1989
1990-1994
1995-1999
2000-2004
Mean
($ mil)
963
1471
1475
1958
2105
Hedge A
Median %CRSP
($ mil)
335
1.84%
511
2.87%
393
3.51%
434
3.10%
351
3.02%
Mean
($ mil)
396
1506
1984
1975
Hedge B
Median %CRSP
($ mil)
352
308
406
326
30
0.05%
1.27%
2.42%
2.42%
Mean
($ mil)
121
222
295
648
1058
Mutual Funds
Median %CRSP
($ mil)
40
3.49%
66
5.19%
68
8.22%
98
14.78%
178
16.63%
Table II
Fama-Macbeth Regressions of Hedge Fund and Mutual Fund Demand
The portfolio formation begins every June for the period 1980-2004. Explanatory variables were taken from December prior to the year in which portfolio holdings
were estimated except for Beta and Momentum. The stocks are a subset of all CRSP stocks during 1980-2004, which are subject to some criteria documented in the
paper. Age is the number of months at the time of fund holding since the stock was in CRSP stock database. Analyst is the number of analysts covering a stock in the
past year according to I/B/E/S Analyst database. Beta is the sum of coefficients on the current and lagged value-weighted CRSP return for the individual stocks
(Dimson betas). Beta is updated quarterly using the past 24-60 months depending on availability. BE/ME is the book to market equity ratio. D/P is the dividend yield.
Ln(Liq) is the natural logarithm of (liquidity + 1), where liquidity is defined as the average monthly trading volume divided by the shares outstanding. Ln(Size)
represents market equity. P/S is the price to sales ratio. Momentum is the prior 12 months’ net return from every June. Variance is the total variance of monthly
returns for the prior 2-5 years depending on availability, while Stdev is the square root of Variance. The Dependent variable is In(1+own), where own is the total
fraction of all funds ownership for a stock. All the dependent and independent variables are standardized. Every year, we do a cross–sectional OLS regression, and
then calculate the time serial average of the coefficients. Panel A reports regression results for mutual fund and hedge funds (Sample B) demand, Panel B reports the
loading difference of hedge funds (A and B) and mutual funds. In Panel A, t-statistics in parentheses are calculated by adjusting standard errors using the Newey-West
method up to 6 lags.
31
Panel A: Fama-Macbeth Regressions of Mutual Fund and Hedge Fund Firm (B) Demand
Age
Analyst
(1)
-0.03
(-1.02)
0.01
(0.22)
0.02
(2.46)
0.02
(1.49)
0.01
(2.33)
(0.10)
(2.48)
(0.10)
(2.11)
(0.10)
(3.52)
(4)
-0.03
(-0.99)
0.01
(0.22)
0.02
(1.50)
0.03
(2.86)
-0.04
(-8.16)
(0.09)
(2.51)
-0.21
(-2.77)
-0.20
(-1.67)
-0.22
(-2.60)
-0.21
(-2.80)
-0.01
(-0.33)
-0.02
(-0.39)
0.02
(0.44)
-0.01
(-1.08)
-0.01
(-0.32)
0.02
(0.35)
-0.01
(-0.45)
-0.01
(-0.30)
-0.01
(-0.52)
-0.02
(-0.41)
0.01
(0.15)
(5)
-0.02
(-1.10)
0.05
(1.19)
0.00
(0.14)
0.03
(9.86)
-0.04
(-11.01)
(0.10)
(3.69)
0.06
(1.27)
-1.22
(-1.15)
0.94
(0.96)
0.00
(0.05)
-0.02
(-0.49)
0.02
(0.58)
0.17
0.17
0.17
0.17
0.20
Beta
BE/ME
(2)
-0.04
(-1.16)
Hedge Funds B
(3)
-0.03
(-1.19)
0.01
(0.13)
0.02
(1.66)
0.01
(3.40)
D/P
Liquidity
In(Price)
In(Size)
In(Size)2
P/S
Momentum
Stdev
Average
Adjusted R2
32
-0.05
(-4.03)
0.02
(1.28)
-0.05
(-4.14)
Mutual Funds
(3)
0.00
(-0.21)
0.24
(9.62)
-0.01
(-0.70)
-0.03
(-2.34)
(0.34)
(44.59)
(0.40)
(44.41)
(0.27)
(15.82)
(4)
-0.01
(-1.12)
0.22
(10.67)
0.01
(1.19)
-0.02
(-2.38)
-0.09
(-3.97)
(0.34)
(51.03)
-0.05
(-0.54)
0.12
(1.16)
0.00
(0.02)
-0.04
(-0.51)
-0.08
(-4.05)
0.08
(4.45)
-0.16
(-4.76)
-0.09
(-4.17)
0.08
(4.15)
-0.20
(-6.60)
-0.10
(-3.56)
0.07
(4.51)
-0.07
(-3.35)
0.07
(4.01)
-0.20
(-8.39)
(5)
0.00
(0.11)
0.22
(10.34)
0.02
(2.54)
0.03
(3.69)
-0.08
(-5.52)
(0.33)
(39.43)
0.24
(17.17)
0.75
(3.52)
-0.92
(-6.00)
-0.09
(-3.42)
0.06
(2.75)
-0.10
(-3.88)
0.24
0.22
0.22
0.24
0.27
(1)
-0.02
(-1.91)
0.22
(10.41)
(2)
-0.04
(-6.15)
Panel B: The Difference of Demand on Stocks between Hedge Funds Firms and Mutual Funds
Age
Analyst
-0.15
(-12.57)
Hedge Fund A - Mutual Funds
(2)
(3)
(4)
-0.01
-0.03
-0.02
(-0.54)
(-2.56)
(-1.75)
-0.19
-0.16
(-9.16)
(-8.76)
0.00
0.02
0.00
(-0.13)
(1.55)
(0.01)
0.03
0.01
0.03
(2.73)
(1.24)
(2.89)
(0.00)
(0.04)
-0.20
-0.09
-0.15
(-17.10)
(-7.00)
(-12.80)
-0.04
(-1.02)
-0.16
(-3.93)
-0.08
(-1.95)
-0.04
(-0.95)
0.03
(2.32)
-0.05
(-3.36)
0.13
(6.51)
0.04
(2.76)
-0.05
(-3.32)
0.15
(7.95)
0.04
(2.79)
-0.04
(-3.05)
0.03
(2.41)
-0.05
(-3.26)
0.13
(7.08)
(1)
-0.02
(-1.98)
-0.16
(-8.85)
Beta
BE/ME
0.03
(2.79)
D/P
Liquidity
(5)
-0.02
(-2.27)
-0.16
(-9.32)
0.00
(-0.17)
0.01
(1.13)
(0.00)
(0.05)
-0.15
(-12.68)
(-0.12)
(-9.30)
-0.50
(-2.87)
(0.53)
(3.31)
0.04
(2.66)
-0.04
(-2.63)
0.08
(4.06)
In(Price)
In(Size)
In(Size)2
P/S
Momentum
Stdev
33
-0.25
(-11.38)
Hedge Fund B - Mutual funds
(2)
(3)
(4)
0.00
-0.03
-0.02
(0.16)
(-1.80)
(-1.09)
-0.24
-0.20
(-6.67)
(-6.09)
0.00
0.03
0.00
(-0.20)
(2.13)
(0.09)
0.06
0.04
0.05
(5.05)
(3.20)
(4.31)
(0.04)
(2.97)
-0.30
-0.17
-0.24
(-11.58)
(-9.67)
(-11.60)
-0.17
(-3.34)
-0.32
(-4.71)
-0.22
(-4.19)
-0.16
(-3.30)
0.08
(4.45)
-0.09
(-4.17)
0.18
(6.21)
0.08
(4.86)
-0.09
(-4.15)
0.21
(7.29)
0.09
(4.77)
-0.08
(-3.79)
0.07
(4.15)
-0.09
(-4.16)
0.20
(7.32)
(1)
-0.01
(-0.57)
-0.20
(-6.10)
0.06
(5.12)
(5)
-0.03
(-1.87)
-0.17
(-6.63)
-0.02
(-1.65)
0.00
(0.24)
(0.04)
(3.30)
-0.22
(-13.57)
(-0.18)
(-6.86)
-1.97
(-4.00)
(1.86)
(4.10)
0.09
(4.01)
-0.07
(-3.65)
0.12
(5.46)
Table III
DGTW Performance Measures of Hedge Funds and Mutual Funds
This table reports the value- and equal-weighted DGTW measures of the self-selection free hedge fund sample (Hedge B) and the actively managed mutual fund
sample. We report each year the annualized returns for the CRSP value-weighted (VW) and equal-weighted portfolios. For mutual funds sample (M) and hedge funds
sample B (H), three DGTW performance measures (CS, CT and AS) are reported. The differences (H-M) of measures between two samples are also reported.
Specifically, the CS (Characteristic Selectivity) measure is the difference between the quarter t return of the fund portfolio held at quarter t-1 and the quarter t return of
the quarter t-1 matching control portfolio. The CT (Characteristic Timing) measure is computed, for each fund, by matching stocks held at quarter t-5 and quarter t-1
with the proper control portfolios at quarter t-5 and quarter t-1, respectively. The “Average Style” (AS) measure is calculated, for quarter t, by matching each stock held
by a fund, at quarter t-5, with the proper control portfolio at quarter t-5. Panel A performance numbers that are value-weighted across funds and Panel B reports
performance number that are equal-weighted according to the market capitalization in equities at the end of the previous quarter.
34
Panel A: Value-Weighted Performance Measures
Year
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
CRSP w/Div
VW
EW
15.57
7.87
1.82
-8.47
17.55
18.82
28.43
11.84
-6.08
-21.61
33.64
51.60
9.06
26.84
11.59
26.88
-0.76
-5.08
35.67
30.23
21.16
17.93
30.35
20.16
22.30
-2.90
25.26
33.76
-11.04
-11.13
-11.27
22.10
-20.85
-10.94
33.15
72.60
13.01
21.77
Year
1986-1994
CRSP
w/Dividends
VW
EW
12.31
12.08
1995-2004
13.77
19.36
1986-2004
13.08
15.91
H
14.08
-1.02
24.44
31.12
-8.29
42.20
8.26
13.28
-0.54
36.91
22.48
28.67
23.69
43.73
0.17
-11.27
-21.83
35.28
14.92
Gross Return
M
16.97
2.61
17.64
29.27
-6.79
37.56
10.31
13.37
0.19
37.69
22.03
30.26
23.14
29.57
-5.48
-10.87
-21.33
32.14
12.09
H
13.73
(2.10)
17.28
(2.44)
15.59
(3.24)
Gross Return
M
13.46
(2.39)
14.92
(2.29)
14.23
(3.30)
H-M
-2.88
-3.64
6.80
1.85
-1.50
4.64
-2.05
-0.09
-0.73
-0.78
0.45
-1.60
0.56
14.16
5.65
-0.39
-0.49
3.14
2.83
H-M
0.27
(0.43)
2.35
(1.74)
1.36
(1.55)
CS Performance
H
M
H-M
3.44
0.07
3.37
1.23
0.63
0.60
3.30
-0.12
3.42
2.59
0.38
2.21
-1.32
0.16
-1.48
3.81
1.13
2.68
-1.71
0.70
-2.41
2.92
2.55
0.37
-0.61
0.13
-0.74
0.49
0.73
-0.24
0.93
0.23
0.70
-0.84
-0.97
0.13
2.70
-0.43
3.13
15.80
6.06
9.74
7.84
3.19
4.64
-0.45
1.29
-1.75
-1.87
-0.80
-1.07
1.06
0.43
0.63
1.52
0.29
1.23
CS Performance
H
M
H-M
1.52
0.63
0.89
(1.41)
(1.28)
(1.12)
2.72
1.00
1.71
(1.72)
(1.47)
(1.62)
2.15
0.82
1.32
(2.22)
(1.94)
(1.98)
35
CT Performance
H
M
H-M
-4.77
-0.18
-4.58
0.34
0.45
-0.11
-0.96
-0.99
0.03
-0.50
-0.71
0.21
0.18
0.30
-0.12
-0.16
-0.42
0.26
-1.36
-1.46
0.10
0.30
0.51
-0.21
-0.02
0.01
-0.03
-0.81
-0.13
-0.68
0.84
0.50
0.34
-0.93
0.09
-1.02
1.96
1.31
0.65
5.52
3.99
1.53
-0.33
-1.25
0.91
4.24
5.63
-1.39
1.18
1.63
-0.45
-1.31
-1.29
-0.03
1.13
0.15
0.98
AS Performance
H
M
H-M
16.62
15.47
1.15
2.21
1.58
0.63
17.19
18.05
-0.85
20.28
28.25
-7.96
-2.09
-6.09
4.00
26.24
34.05
-7.81
10.21
10.46
-0.25
3.97
8.67
-4.70
1.82
0.10
1.72
30.73
33.74
-3.00
18.92
19.55
-0.63
29.18
29.44
-0.27
18.14
21.81
-3.66
18.71
17.81
0.90
-6.40
-6.31
-0.09
-15.02
-16.40
1.38
-21.09
-20.17
-0.92
35.42
32.96
2.45
11.25
11.55
-0.30
CT Performance
H
M
H-M
-0.77
-0.28
-0.50
(-1.96)
(-1.17)
(-1.25)
1.15
1.06
0.08
(1.40)
(1.36)
(0.36)
0.24
0.43
-0.19
(0.50)
(1.00)
(-0.84)
AS Performance
H
M
H-M
10.72
12.28
-1.57
(2.85)
(2.46)
(-0.70)
11.99
12.40
-0.41
(1.95)
(2.04)
(-0.52)
11.38
12.34
-0.96
(3.10)
(3.14)
(-0.80)
Panel B: Equal-Weighted DGTW Measures
Year
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
CRSP w/Div
VW
EW
15.57
7.87
1.82
-8.47
17.55
18.82
28.43
11.84
-6.08
-21.61
33.64
51.60
9.06
26.84
11.59
26.88
-0.76
-5.08
35.67
30.23
21.16
17.93
30.35
20.16
22.30
-2.90
25.26
33.76
-11.04
-11.13
-11.27
22.10
-20.85
-10.94
33.15
72.60
13.01
21.77
Year
1986-1994
CRSP
w/Dividends
VW
EW
12.31
12.08
1995-2004
13.77
19.36
1986-2004
13.08
15.91
H
14.08
0.68
16.10
32.97
-14.33
45.97
13.73
14.64
-1.78
36.98
20.99
26.01
13.22
44.71
4.65
-4.43
-22.65
46.06
16.36
Gross Return
M
15.56
1.83
17.16
29.32
-7.04
40.38
11.20
13.14
-0.03
36.28
22.09
28.03
15.13
30.81
1.01
-7.03
-21.35
35.85
13.57
H
13.56
(2.02)
18.19
(2.46)
16.00
(3.20)
Gross Return
M
13.50
(2.32)
15.44
(2.37)
14.52
(3.32)
H-M
-1.47
-1.15
-1.06
3.65
-7.29
5.59
2.53
1.50
-1.75
0.70
-1.10
-2.03
-1.91
13.90
3.64
2.61
-1.29
10.21
2.78
H-M
0.06
(0.19)
2.75
(1.98)
1.48
(1.58)
CS Performance
H
M
H-M
3.44
0.07
3.37
1.08
1.20
-0.12
-2.32
-0.88
-1.44
3.42
1.39
2.03
-5.22
1.21
-6.43
3.26
1.80
1.47
-0.30
0.35
-0.66
2.66
1.33
1.32
-1.28
0.27
-1.56
1.10
0.10
0.99
0.12
0.82
-0.70
-1.40
-1.64
0.24
0.10
-1.06
1.17
16.02
6.30
9.72
7.48
4.39
3.09
1.29
0.59
0.70
-3.68
-1.73
-1.95
3.99
0.67
3.32
1.57
0.27
1.31
CS Performance
H
M
H-M
0.53
0.75
-0.22
(0.41)
(1.60)
(-0.17)
2.66
0.87
1.79
(1.65)
(1.20)
(1.74)
1.65
0.81
0.84
(1.56)
(1.85)
(1.11)
36
CT Performance
H
M
H-M
-4.77
-0.33
-4.44
1.10
0.60
0.49
-2.54
-1.43
-1.11
0.97
-0.42
1.39
-0.16
0.40
-0.56
0.72
-0.76
1.48
2.47
-1.15
3.62
-1.18
0.61
-1.79
-0.94
0.04
-0.98
-0.37
-0.27
-0.09
0.37
0.68
-0.31
-1.43
-0.15
-1.27
1.39
0.77
0.63
3.64
3.54
0.10
2.46
-0.44
2.90
3.51
3.29
0.22
1.08
0.93
0.16
1.13
-0.77
1.90
0.91
0.08
0.83
AS Performance
H
M
H-M
16.62
16.01
0.61
-2.58
0.47
-3.05
20.45
19.60
0.86
28.82
28.17
0.65
-8.42
-7.89
-0.54
42.28
39.21
3.07
14.00
12.06
1.94
12.45
11.03
1.41
0.19
-0.36
0.55
36.10
36.50
-0.40
20.22
20.53
-0.31
29.86
30.64
-0.79
13.54
16.18
-2.65
23.35
20.02
3.33
-4.27
-2.78
-1.49
-8.12
-11.31
3.19
-19.90
-20.38
0.48
39.01
36.00
3.01
13.70
13.14
0.56
CT Performance
H
M
H-M
-0.48
-0.27
-0.21
(-0.62)
(-1.07)
(-0.29)
1.27
0.76
0.51
(2.13)
(1.19)
(1.37)
0.44
0.27
0.17
(0.91)
(0.76)
(0.43)
AS Performance
H
M
H-M
13.76
13.14
0.61
(2.30)
(2.35)
(1.00)
14.35
13.85
0.49
(2.22)
(2.19)
(1.05)
14.07
13.52
0.55
(3.20)
(3.20)
(1.46)
Table IV
Decomposition of CS Performance
This table reports the decomposition of a fund’s CS performance into P/S deciles. All Stocks are divided into tow groups,
including a group with available sales information and one without sales information (missing or no sales). Within the group
that has sales information, we divide them into 10 deciles according to their most recent Price/Sales (P/S) ratios. Then we
decompose a fund’s total CS performance into the 11 bins. Reported below is annualized average across funds from the
quarterly performance. Due to the annual adjustment, performance from all the P/S groups does not have to sum up to the
total performance for a fund. H stands for Hedge Funds Sample B and M stands for Mutual Funds Sample. H-M is the
difference of sample average between hedge funds and mutual funds. T-statistics for differences are in parentheses.
Price/Sales Deciles
Year
1998
H
M
H-M
1999
H
M
H-M
2000
H
M
H-M
2001
H
M
H-M
2002
H
M
H-M
2003
H
M
H-M
2004
H
M
H-M
Others
p0
-0.07
-0.02
-0.06
(-0.63)
0.02
-0.02
0.05
(0.94)
-0.03
-0.03
0.00
(-0.03)
-0.02
-0.04
0.02
(0.08)
-0.22
-0.04
-0.19
(-2.58)
-0.02
-0.02
0.00
(0.05)
0.02
-0.07
0.09
(1.01)
Lowest
p1
0.22
0.02
0.20
(0.63)
-0.58
-0.38
-0.20
(-0.66)
0.26
-0.23
0.49
(1.37)
0.49
0.00
0.49
(2.27)
0.03
-0.12
0.14
(0.61)
-0.05
-0.21
0.16
(0.22)
0.54
0.16
0.38
(3.83)
p2
0.62
0.35
0.27
(0.92)
-0.45
-0.46
0.01
(-0.03)
-0.51
-0.17
-0.34
(-0.62)
-0.66
0.03
-0.68
(-1.68)
0.10
0.07
0.03
(0.08)
1.29
0.36
0.93
(1.39)
0.38
0.26
0.12
(0.41)
p3
-0.43
-0.49
0.06
(0.26)
-0.11
-0.46
0.35
(1.19)
0.06
-0.17
0.23
(0.87)
-0.34
-0.07
-0.26
(-1.21)
0.17
-0.37
0.54
(1.66)
0.62
0.36
0.26
(1.32)
0.41
0.37
0.05
(0.16)
p4
-0.73
-0.47
-0.26
(-1.15)
0.12
-0.35
0.47
(2.09)
0.86
0.65
0.21
(0.99)
0.27
0.14
0.13
(0.68)
0.37
0.22
0.15
(0.84)
0.40
0.07
0.33
(2.04)
1.08
0.97
0.11
(0.30)
p5
0.19
-0.46
0.64
(2.29)
0.00
-0.38
0.37
(1.53)
1.73
1.34
0.39
(0.81)
-0.39
-0.22
-0.16
(-0.68)
0.77
0.67
0.10
(0.39)
0.23
0.15
0.08
(0.33)
0.24
0.34
-0.11
(-0.20)
37
p6
-0.75
-0.69
-0.06
(-0.15)
0.94
-0.10
1.04
(2.08)
0.72
-0.14
0.86
(3.26)
-0.37
-0.20
-0.17
(-0.75)
0.59
0.24
0.36
(1.71)
-0.50
-0.80
0.29
(1.71)
0.12
0.23
-0.12
(-0.33)
p7
-2.03
-0.88
-1.15
(-2.25)
0.34
-0.13
0.47
(1.31)
0.31
0.35
-0.04
(-0.07)
0.30
0.14
0.16
(0.78)
-0.53
-0.12
-0.41
(-1.82)
0.28
0.36
-0.07
(-0.41)
0.14
0.23
-0.09
(-0.94)
p8
0.10
0.29
-0.19
(-0.88)
2.07
0.67
1.40
(1.17)
0.66
0.15
0.51
(0.98)
0.24
0.18
0.05
(0.14)
0.49
1.03
-0.54
(-1.50)
0.60
0.01
0.59
(3.08)
-0.44
-0.44
0.00
(-0.24)
p9
0.87
0.35
0.52
(1.23)
2.14
0.89
1.25
(1.94)
1.56
1.13
0.42
(0.62)
1.42
0.12
1.30
(0.76)
-1.62
-0.21
-1.41
(-2.85)
0.10
-0.15
0.24
(1.12)
-0.25
-0.58
0.33
(1.26)
Highest
p10
2.19
0.94
1.25
(1.71)
11.37
7.06
4.30
(3.05)
1.79
1.42
0.37
(0.26)
0.36
0.50
-0.14
(-0.21)
-3.84
-3.10
-0.74
(-0.95)
1.01
0.54
0.47
(1.08)
-0.68
-1.20
0.53
(1.73)
Total
0.10
-1.06
1.17
(0.85)
16.02
6.30
9.72
(3.66)
7.48
4.39
3.09
(1.83)
1.29
0.59
0.70
(0.35)
-3.68
-1.73
-1.95
(-1.59)
3.99
0.67
3.32
(2.65)
1.57
0.27
1.31
(1.54)
Table V
Performance Persistence of Hedge Funds and Mutual Funds
This table presents the average quarterly buy-and-hold gross return over 1992-2004 for equally weighted portfolios of mutual funds and
hedge fund firms (Sample B) which were ranked on their gross return, CS measure or CS+CT measure during the prior year. To select
these funds each period, all funds/firms existing during the entire prior period were ranked on their gross return, CS measures or CS+CT
measure of the prior period. Quintile portfolios were formed, and the gross return for the equally weighted portfolio of funds/firms in
each quintile was measured over the following year. All funds/firms existing during a given quarter were included in the following year
return calculation, even if the fund/firm did not survive the entire period. Then, the entire sort process was repeated for the following year.
Finally, the time series average return for each portfolio was calculated. We label as “Best” the quintile with the highest prior period return,
and as “Worst” the one with the lowest prior period return. The return of the portfolio that buys the winners and sells the losers is also
reported (P5-P1). Also presented are the three DGTW measures of performance for each quintile. T-statistics are in parenthesis. Panel A,
B and C report the result based on portfolio quintiles ranked by prior year gross return, CS performance measure, and CS+CT measure
respectively. Panel D report the result based on portfolio quintiles ranked by a fund’s gross return of its whole history during the
considered period.
Panel A: Portfolio quintiles ranked by prior year gross return
Hedge Funds
Gross Return
CS
CT
AS
Worst
P1
15.00
(2.18)
1.03
(0.54)
1.06
(1.11)
12.71
(2.17)
P2
15.63
(2.61)
1.90
(1.72)
0.38
(0.58)
13.12
(2.33)
P3
16.82
(2.73)
2.28
(1.65)
0.88
(1.45)
13.33
(2.41)
P4
18.87
(3.11)
3.28
(2.48)
1.16
(1.72)
13.95
(2.58)
Mutual Funds
Best
P5
20.54
(2.52)
5.37
(1.88)
0.19
(0.24)
14.38
(2.46)
P5-P1
5.54
(1.19)
4.34
(1.47)
-0.87
(-0.72)
1.67
(0.89)
38
Worst
P1
12.88
(2.06)
0.60
(0.61)
0.42
(0.62)
11.76
(2.02)
P2
13.33
(2.43)
0.51
(0.75)
0.76
(1.16)
11.95
(2.19)
P3
13.49
(2.55)
0.41
(0.84)
0.74
(1.17)
12.24
(2.29)
P4
15.89
(2.86)
1.31
(2.08)
0.91
(1.45)
13.45
(2.49)
Best
P5
18.41
(2.84)
2.54
(1.93)
0.74
(1.14)
14.75
(2.57)
P5-P1
5.53
(1.41)
1.93
(1.21)
0.32
(0.34)
2.98
(1.21)
Panel B: Portfolio quintiles ranked by prior year CS performance measures
Hedge Funds
Gross Return
CS
CT
AS
Worst
P1
16.78
(2.66)
1.24
(0.82)
1.05
(1.15)
14.24
(2.54)
P2
15.65
(2.79)
1.17
(1.34)
0.94
(1.32)
13.31
(2.41)
P3
17.25
(2.80)
2.91
(2.29)
1.17
(2.14)
12.76
(2.30)
P4
17.82
(2.70)
3.15
(1.70)
1.06
(1.71)
13.19
(2.33)
Mutual Funds
Best
P5
19.49
(2.32)
5.39
(1.80)
-0.48
(-0.59)
14.10
(2.40)
P5-P1
2.71
(0.58)
4.15
(1.41)
-1.53
(-1.32)
-0.15
(-0.13)
Worst
P1
14.75
(2.60)
0.58
(0.76)
0.65
(1.47)
13.40
(2.41)
P2
14.04
(2.66)
0.57
(1.05)
0.84
(1.39)
12.47
(2.34)
P3
13.89
(2.61)
0.54
(1.16)
0.83
(1.35)
12.40
(2.31)
P4
14.80
(2.64)
1.11
(1.69)
0.83
(1.33)
12.67
(2.32)
Best
P5
16.44
(2.33)
2.54
(1.50)
0.42
(0.65)
13.17
(2.26)
P5-P1
1.69
(0.45)
1.96
(1.04)
-0.23
(-0.40)
-0.23
(-0.15)
Best
P5
16.44
(2.33)
2.54
(1.50)
0.42
(0.65)
13.17
(2.26)
P5-P1
1.69
(0.45)
1.96
(1.04)
-0.23
(-0.40)
-0.23
(-0.15)
Panel C: Portfolio quintiles ranked by prior year CS+CT performance measures
Hedge Funds
Gross Return
CS
CT
AS
Worst
P1
16.78
(2.66)
1.24
(0.82)
1.05
(1.15)
14.24
(2.54)
P2
15.65
(2.79)
1.17
(1.34)
0.94
(1.32)
13.31
(2.41)
P3
17.25
(2.80)
2.91
(2.29)
1.17
(2.14)
12.76
(2.30)
P4
17.82
(2.70)
3.15
(1.70)
1.06
(1.71)
13.19
(2.33)
Mutual Funds
Best
P5
19.49
(2.32)
5.39
(1.80)
-0.48
(-0.59)
14.10
(2.40)
P5-P1
2.71
(0.58)
4.15
(1.41)
-1.53
(-1.32)
-0.15
(-0.13)
39
Worst
P1
14.75
(2.60)
0.58
(0.76)
0.65
(1.47)
13.40
(2.41)
P2
14.04
(2.66)
0.57
(1.05)
0.84
(1.39)
12.47
(2.34)
P3
13.89
(2.61)
0.54
(1.16)
0.83
(1.35)
12.40
(2.31)
P4
14.80
(2.64)
1.11
(1.69)
0.83
(1.33)
12.67
(2.32)
Table VI
Performance according to Entire Performance History of Hedge Funds and Mutual Funds
This table presents the average quarterly buy-and-hold gross return for equally weighted portfolios of mutual funds and hedge fund firms
(Sample B) which were ranked on their gross return, CS measure or CS+CT measure for their whole history. To select these funds each
period, all funds/firms existing during the entire prior period were ranked on their CS measures or CS+CT measure of the whole history.
Quartile portfolios were formed, and the gross return for the equally weighted portfolio of funds/firms in each quartile was measured over
the following year. All funds/firms existing during a given quarter were included in the following year return calculation, even if the
fund/firm did not survive the entire period. Then, the entire sort process was repeated for the following year. Finally, the time series
average return for each portfolio was calculated. We label as “Best” the quartile with the highest history return, and as “Worst” the one
with the lowest history return. The return of the portfolio that buys the winners and sells the losers is also reported (P4-P1). Also presented
are the three DGTW measures of performance for each quartile. T-statistics are in parenthesis. Panel A and B report the result based on
portfolio quartiles ranked by CS performance measure and CS+CT measure respectively.
Panel A: Portfolio quartiles ranked by CS measure of a fund’s whole history
Hedge Funds
Gross Return
CS
CT
AS
Worst
P1
16.95
(2.96)
-0.22
(-0.15)
2.64
(1.88)
14.21
(2.78)
P2
16.38
(3.11)
1.37
(1.31)
1.24
(2.08)
13.52
(2.70)
P3
16.98
(2.82)
2.95
(1.92)
0.58
(1.06)
13.07
(2.51)
Mutual Funds
Best
P4
18.63
(2.43)
4.88
(2.02)
-0.15
(-0.22)
13.44
(2.36)
P4-P1
1.68
(0.41)
5.10
(2.03)
-2.79
(-1.95)
-0.77
(-0.59)
40
Worst
P1
14.81
(3.08)
0.48
(0.85)
0.57
(1.17)
13.68
(2.78)
P2
14.19
(2.92)
0.66
(1.37)
0.80
(1.36)
12.61
(2.57)
P3
14.01
(2.70)
0.97
(1.78)
0.61
(1.03)
12.27
(2.40)
Best
P4
14.44
(2.22)
1.44
(1.09)
0.27
(0.45)
12.59
(2.26)
P4-P1
-0.36
(-0.13)
0.96
(0.68)
-0.30
(-0.49)
-1.09
(-0.71)
Panel B: Portfolio quartiles ranked by CS+CT measure of a fund’s whole history
Hedge Funds
Gross Return
CS
CT
AS
Worst
P1
16.65
(3.09)
-0.14
(-0.07)
3.50
(2.15)
12.90
(2.51)
P2
17.23
(3.15)
2.45
(1.93)
1.24
(2.06)
13.18
(2.62)
P3
16.90
(2.86)
2.66
(1.86)
0.17
(0.32)
13.78
(2.64)
Mutual Funds
Best
P4
17.54
(2.31)
3.15
(1.21)
0.01
(0.01)
14.03
(2.52)
P4-P1
0.89
(0.21)
3.29
(1.04)
-3.49
(-2.05)
1.13
(0.77)
41
Worst
P1
14.88
(3.02)
0.45
(0.79)
0.67
(1.51)
13.64
(2.76)
P2
14.72
(3.03)
0.95
(1.89)
0.75
(1.34)
12.88
(2.62)
P3
13.58
(2.64)
0.80
(1.50)
0.67
(1.04)
11.98
(2.34)
Best
P4
14.39
(2.25)
1.44
(1.14)
0.20
(0.32)
12.62
(2.29)
P4-P1
-0.49
(-0.18)
0.98
(0.75)
-0.47
(-0.78)
-1.02
(-0.70)
Table VII
Value-Weighted DGTW Performance Measures of Hedge Funds and Mutual Funds in Turnover Categories
This Table reports the DGTW measures of the self-selection free hedge fund sample (Hedge B) and the actively managed mutual fund sample with funds that fall into two
categories: high (>100% per year) and low (<= 100% per year) turnover category. For mutual funds sample (M) and hedge funds sample B (H), three DGTW performance
measures (CS, CT and AS) are reported. The differences (H-M) of measures between two samples are also reported. Specifically, the CS (Characteristic Selectivity) measure is
the difference between the quarter t return of the fund portfolio held at quarter t-1 and the quarter t return of the quarter t-1 matching control portfolio. The CT (Characteristic
Timing) measure is computed, for each fund, by matching stocks held at quarter t-5 and quarter t-1 with the proper control portfolios at quarter t-5 and quarter t-1, respectively.
The “Average Style” (AS) measure is calculated, for quarter t, by matching each stock held by a fund, at quarter t-5, with the proper control portfolio at quarter t-5. Both the
gross returns and three DGTW measures are quarterly value weighted average across funds (firms). The weights are the market value of reported equity holdings of funds at
each quarter end. This table reports year by year measures and the measures during the whole period. Panel A reports low turnover category and Panel B reports high turnover
category. Due to fewer hedge funds in late eighties, we report their performance since 1991.
Panel A: Value Weighted DGTW Performance Measures of Hedge Funds and Mutual Funds of Low Turnover
Year
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
1991-2004
Number
15
25
35
46
50
63
76
86
83
87
96
92
79
104
H
48.30
7.99
14.11
-0.16
36.73
23.35
31.69
24.45
37.30
0.58
-11.72
-21.94
32.77
13.61
16.93
(3.21)
Gross Return
M
36.52
10.19
12.42
0.15
37.76
21.89
30.54
22.76
27.87
-5.07
-10.28
-21.19
32.00
12.15
14.84
(3.06)
H-M
11.78
-2.21
1.69
-0.31
-1.03
1.46
1.15
1.69
9.43
5.65
-1.44
-0.75
0.77
1.46
2.10
(2.09)
CS Performance
H
M
H-M
5.01
0.69
4.32
-1.58
0.67
-2.24
3.15
1.94
1.21
-0.60
0.08
-0.69
0.33
0.70
-0.37
0.88
0.04
0.84
0.37
-0.88
1.24
1.93
-0.91
2.84
11.71
5.26
6.45
6.16
2.82
3.34
-0.27
1.39
-1.66
-1.50
-0.58
-0.92
0.13
0.38
-0.24
0.80
0.44
0.36
1.89
0.86
1.03
(1.90)
(1.84)
(1.47)
42
CT Performance
H
M
H-M
1.10
-0.45
1.55
-1.57
-1.51
-0.06
0.94
0.58
0.36
0.19
0.02
0.17
-1.10
-0.18
-0.92
1.17
0.51
0.66
-0.68
0.07
-0.75
1.98
1.31
0.66
5.48
4.02
1.46
0.05
-1.17
1.22
4.48
6.00
-1.52
0.79
1.68
-0.89
-2.11
-1.31
-0.80
1.25
0.14
1.11
0.85
0.69
0.16
(1.30)
(1.18)
(0.76)
H
38.24
11.61
9.08
2.09
35.99
19.57
31.67
20.22
18.91
-5.97
-16.54
-21.33
35.12
11.31
13.57
(2.80)
AS Performance
M
H-M
35.12
3.12
10.90
0.70
9.14
-0.06
0.24
1.85
35.13
0.86
19.99
-0.42
30.09
1.58
22.24
-2.01
17.58
1.33
-6.10
0.13
-16.42
-0.12
-20.38
-0.95
33.09
2.02
11.46
-0.15
13.01
0.56
(2.80)
(1.35)
Panel B: Value Weighted DGTW Performance Measures of Hedge Funds and Mutual Funds of High Turnover
Year
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
1991-2004
H
41.12
7.71
25.80
-1.33
37.06
20.77
20.20
22.27
67.22
0.14
-10.01
-21.59
43.59
17.50
19.32
(2.97)
Gross Return
M
49.05
10.60
19.71
0.10
36.75
23.44
27.26
27.43
43.72
-4.86
-16.99
-24.66
34.76
11.26
16.97
(2.83)
H-M
-7.93
-2.89
6.09
-1.43
0.30
-2.67
-7.06
-5.16
23.50
5.00
6.97
3.07
8.83
6.24
2.35
(1.28)
H
2.78
-4.45
11.99
-0.43
1.61
0.91
-4.76
6.05
30.80
11.31
-1.13
-3.18
4.15
2.53
4.16
(1.90)
CS Performance
M
6.11
0.84
7.36
0.26
0.67
1.81
-2.13
4.97
12.65
9.79
0.37
-5.58
1.57
-2.44
2.59
(2.07)
H-M
-3.32
-5.29
4.63
-0.69
0.95
-0.91
-2.63
1.09
18.15
1.52
-1.51
2.41
2.58
4.97
1.57
(1.02)
43
H
-1.37
-1.24
-2.38
-0.66
-0.52
0.25
-1.87
2.36
6.81
-1.07
4.06
2.50
1.28
1.14
0.66
(0.96)
CT Performance
M
0.16
-1.47
0.53
-0.13
0.33
0.54
0.25
1.39
4.50
-1.62
3.39
0.85
-0.94
0.44
0.59
(0.98)
H-M
-1.53
0.23
-2.91
-0.54
-0.85
-0.29
-2.12
0.97
2.31
0.55
0.67
1.66
2.22
0.70
0.08
(0.15)
H
39.81
13.11
13.94
1.87
34.82
18.92
28.97
13.31
24.95
-9.68
-12.48
-20.86
36.75
13.51
14.07
(2.77)
AS Performance
M
39.92
10.87
10.86
-0.04
34.35
19.77
28.54
19.87
22.79
-8.54
-19.71
-20.25
31.55
13.48
13.10
(2.67)
H-M
-0.11
2.24
3.08
1.91
0.47
-0.85
0.43
-6.56
2.16
-1.14
7.23
-0.60
5.20
0.03
0.96
(1.28)
Appendix A
Correlation Statistics on CRSP Securities Used in Fama-Macbeth Regressions for 1980-2004
The stocks are a subset of all CRSP stocks during 1980-2004, which are chosen based on the criteria provided in the paper. Age is the number of months at the time of
holding since the stock was in CRSP stock database. Analyst is the number of analysts covering a stock in the past year according to I/B/E/S Analyst database. Beta is
the sum of coefficients on the current and lagged value-weighted CRSP return for the individual stocks (Dimson betas), and is constructed using the returns of the past
24-60 months depending on availability. BE/ME is the book to market equity ratio. D/P is the dividend yield of a stock. Ln(Liq) is the natural logarithm of (liquidity +
1), where liquidity is defined as the average monthly trading volume divided by the shares outstanding. Ln(Size) represents market equity. P/S is the price to sales ratio.
Momentum is the prior year’s net return. Variance is the total variance of monthly returns for the prior 2-5 years depending on availability, while Stdev is the square
root of Variance.
Age
Analyst
Beta
BE/ME
D/P
ln(Liq)
In(Price)
In(Size)
In(Size)2
Momentum
P/S
Stdev
Age
1.00
0.35
-0.07
0.01
0.10
-0.18
0.36
0.42
0.44
0.00
-0.01
-0.32
Analyst
Beta
BE/ME
D/P
ln(Liq)
In(Price)
In(Size)
In(Size)2
Momentum
P/S
Stdev
1.00
-0.10
-0.13
0.08
0.11
0.50
0.74
0.75
0.01
-0.01
-0.22
1.00
0.10
0.00
-0.01
-0.20
-0.18
-0.18
-0.01
0.01
0.22
1.00
0.06
-0.07
-0.27
-0.28
-0.26
-0.12
-0.02
-0.01
1.00
-0.07
0.07
0.05
0.05
-0.01
0.00
-0.11
1.00
0.01
0.18
0.17
-0.02
0.02
0.40
1.00
0.81
0.79
0.15
-0.01
-0.52
1.00
0.99
0.07
0.00
-0.35
1.00
0.06
0.00
-0.34
1.00
0.00
0.03
1.00
0.04
1.00
44
Appendix B
Top 20 Hedge Funds in DGTW Performance
This table reports top 20 funds in DGTW performance. Panel A reports top 20 hedge funds in Characteristic
Selectivity (CS) measure, and Panel B reports top 20 hedge funds in Characteristic Timing (CT) measure.
Hedge funds with age less than 21 quarters are excluded from the rankings.
Panel A: Top 20 Hedge Funds in CS measure
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
PALANTIR CAPITAL INC
PRIVATE CAPITAL MGMT INC
CAMELOT MANAGEMENT CORPORATION
S SQUARED TECHNOLGY CORP
GRUBER & MCBAINE CAP MGT
APPALOOSA MANAGEMENT, L.P.
EMERGING GROWTH ADVISORS
TUDOR INVESTMENT CORP
DEERFIELD MANAGEMENT
BPI GLOBAL ASSET MGMT LP
PEQUOT CAPITAL MANAGEMENT INC.
CAMDEN ASSET MANAGEMENT, L.P.
GALLEON MANAGEMENT L.P.
MORGENS WATERFALL
DELTEC ASSET MANAGEMENT CORP.
GREENLIGHT CAPITAL INC
CROWN ADVISORS LTD.
APEX CAPITAL LLC
ORACLE INVESTMENT MANAGEMENT,
LANE CAPITAL MANAGEMENT, INC.
45
CS
21.66%
17.70%
16.01%
15.55%
14.74%
14.16%
13.77%
13.57%
13.44%
12.82%
11.83%
11.59%
11.27%
10.64%
10.54%
10.43%
9.99%
9.66%
9.61%
9.52%
Age
(in quarters)
29
48
28
46
49
33
22
31
36
28
34
47
28
43
40
24
37
31
39
46
Panel B: Top 20 Hedge Funds in CT measure
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
DAWSON SAMBERG CAP MGMT
EMERGING MARKETS MGMT
PARA ADVISORS INC
OZ MANAGEMENT LLC
TUDOR INVESTMENT CORP
MATADOR CAPITAL MGMT CORP.
JULIUS BAER INVT MGMT (NY)
DICKSTEIN PARTNERS INC
BAY ISLE FINANCIAL CORPORATION
BPI GLOBAL ASSET MGMT LP
HARVEST MANAGEMENT, L.L.C.
MARVIN & PALMER ASSOCS.
ODYSSEY PARTNERS
CHESAPEAKE PTNR MGMT CO. INC.
GREENLIGHT CAPITAL INC
GALLEON MANAGEMENT L.P.
KRAMER SPELLMAN, L.P.
S SQUARED TECHNOLGY CORP
CAMDEN ASSET MANAGEMENT, L.P.
JGD MANAGEMENT CORP
46
CT
10.98%
10.43%
9.10%
8.40%
7.78%
7.50%
7.20%
7.01%
6.29%
6.21%
5.93%
5.67%
5.65%
5.37%
5.20%
5.13%
5.09%
4.68%
4.43%
4.34%
Age
(in quarters)
31
23
26
28
31
28
26
32
31
28
35
49
46
22
24
28
32
46
47
30
Figure 1: Histogram of Trading Turnover of Hedge Funds and Mutual Funds
In this figure, we plot the histogram of the trading turnover of funds in the mutual funds sample and the two
hedge funds samples (A and B). Turnover is calculated quarterly as follows:
TURNOVERi,t = min(SALEi,t, BUYi,t)/HOLDINGSi, t-1
where SALEi,t is the total value of stocks sold by a fund i in quarter t, BUYi,t is the total value of stocks bought
by a fund i in quarter t, and HOLDINGSi,t-1 is the total equity holdings of fund i at quarter t-1 . The turnover
plotted below is annualized quarterly fund turnovers (100% per year).
25
20
Percent
15
10
5
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
Turnover (100% per year)
Hedge A
Hedge B
47
Mutual Funds
2.8
3
3.2
3.4
3.6
3.8
>4
Figure 2: Average Correlation of a Particular Fund’s Weights and Market Weights
The correlation is calculated at the fund level at each quarter end using the formula below:
n
r jm =
n
n
n
n∑ wij wim − (∑ wij )(∑ wim )
i =1
n
i =1
=
i =1
n
n
i =1
i =1
n
[n∑ wij − (∑ wij ) 2 ][n∑ wim − (∑ wim ) 2 ]
2
i =1
2
n∑ wij wim − 1
i =1
i =1
n
n
[n∑ wij − 1][n∑ wim − 1]
2
i =1
2
i =1
where rjm is the correlation of weights between fund j and the CRSP market portfolio m, wij and wim are the
weights of fund j and market portfolio m on stock i respectively, and n is the total number of stocks on CRSP at
the quarter end. We only include funds/firms that have at least 50 stocks in their portfolio. This figure depicts
average correlation between fund weights and market weights each year. Each year, we calculate the average
correlation across funds and then plot them on the chart.
0.45
0.35
0.30
0.25
Year
Hedge A
Hedge B
48
Mutual Fund
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
0.20
1980
Correlation
0.40
Figure 3: Weight Deviation from CRSP Market Index and S&P 500 Index
This figure depicts the histogram of weight deviations from CRSP Market Index and S&P 500. The deviation
of a fund at each quarter end is calculated as half of the sum of the absolute value of the difference between the
weight of a stock or industry in a fund and that in an index. The within industry deviation is the difference
between the stock deviation and the cross industry deviation. Panel A depicts the histogram of total weight
deviations from CRSP market portfolio and S&P 500 Index. Panel B depicts the histogram of Cross-Industry
deviations. Panel C depicts histogram of Within-Industry deviations.
Panel A: Total Weight Deviations
Percent
Total Deviations from CRSP Market Index
Total Deviations from S&P 500 Index
25
25
20
20
15
15
10
10
5
5
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
0.3
0.4
Deviation
0.5
0.6
0.7
0.8
0.9
1
Deviation
Panel B: Cross-Industry Weight Deviation
Percent
Cross Industry Deviation from CRSP Market Index
Cross Industry Deviation from S&P 500 Index
19
19
17
17
15
15
13
13
11
11
9
9
7
7
5
5
3
3
1
-1
1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-1
0
0.1
0.2
0.3
0.4
Deviation
0.5
0.6
0.7
0.8
0.9
1
0.9
1
Deviation
Panel C: Within-Industry Weight Deviation
Within-Industry Deviation from CRSP Market Index
Within-Industry Deviation S&P 500 Index
35
30
30
25
25
Percent
20
20
15
15
10
10
5
5
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
0.3
Deviation
0.4
0.5
0.6
0.7
0.8
Deviation
Hedge Fund A
49
Mutual Funds
Figure 4. Comparison between Hedge and Mutual Funds Weights Based on Size
and Book-to-Market or Size and Momentum Portfolios
This figure depicts the comparison between the weights of hedge funds (Sample A) and mutual funds based on
market equity and BE/ME portfolios (Panel A and B) and size and Momentum (Panels C and D). We sort all
the CRSP stocks according to NYSE size breakpoints into five size quintiles, then within each size quintiles we
further sort stocks into five smaller quintiles according to their previous year’s book-to-market equity ratios.
Thus we have 25 portfolios. We next calculate the weights of i) the market portfolio (all CRSP common stocks),
ii) actively managed mutual funds and iii) hedge funds (A) for those 25 portfolios. Next, for each portfolio we
divide the difference between fund weights and market weights by the market weights. Thus, the column in the
figures depict the percentage of fund weights deviating from market weights. Panel A and C presents weights
relative to the weights of the market portfolio and Panel B and D presents weights relative to mutual funds.
Panel A: The Weights of Hedge Funds Relative to the Market Portfolio Based on
Size and Book-to-Market Quintiles
0.5
0.4
Value
Percent
0.3
0.2
4
0.1
3
0
2
-0.1
-0.2
Growth
-0.3
Small
2
3
4
Size
50
Big
Book-to-Market
P e rc e nt
Panel B: The Weights of Hedge Funds Relative to Mutual Funds Based on Size and
Book–to-Market Quintiles
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
-0.05
-0.10
-0.15
-0.20
Value
4
3
2
Growth
Small
2
3
4
Size
51
Big
Book-to-Market
Panel C: The Weights of Hedge Funds Relative to Market Portfolio Based on Size
and Momentum Quintiles
Percent
0.5
0.4
0.3
0.2
High
4
0.1
0
-0.1
3
2
-0.2
-0.3
-0.4
Low
Small
2
3
4
Size
52
Big
Momentum
Panel D: The Weights of Hedge Funds Relative to Mutual Funds Based on Size and
Momentum Quintiles
1.0
0.8
High
P e rc e n t
0.6
4
0.4
3
0.2
0.0
2
-0.2
Low
-0.4
Small
2
3
4
Size
53
Big
Momentum
Figure 5: Funds’ Loadings on Various Stock Characteristics
This figure depicts the loadings of hedge funds (sample A and B) on various stock characteristics, where the
loadings are obtained through the year-by-year standardized FM regressions of funds’/firms’ ownership on the
following stock characteristics: age, number of analysts, beta, book to market equity ratio (BE/ME), liquidity, size,
D/P, Price to Sales ratio (P/S), prior year return (momentum), and historical 2-5 year standard deviation. Loadings
for a sample of actively managed mutual funds are also included.
Funds' Loadings on Age
Funds' Loadings on Analyst
0.15
0.1
0.05
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
0
-0.05
-0.1
-0.15
1999
2001
2003
1999
2001
2003
1999
2001
2003
1999
2001
2003
2001
2003
1997
1995
1993
1991
1989
Funds' Loadings on BE/ME
0.2
0.15
0.1
0.05
0
-0.05
-0.1
-0.15
0.15
0.1
0.05
1997
1995
1993
1991
1989
1987
1985
1983
1981
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
0
-0.05
-0.1
-0.15
Funds' Loadings on D/P
Funds' Loadings on Liquidity
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
-0.25
1997
1995
1993
1991
1989
1987
1985
1983
1981
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
Funds' Loadings on Log(Size)
Funds' Loadings on P/S
0.4
0.15
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
0.2
0
-0.2
-0.4
-0.6
1997
1995
1993
1991
1989
1987
1985
1983
1981
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
Funds' Loadings on Momentum
Funds' Loadings on Standard Deviation
0.2
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
0.1
0
-0.1
-0.2
1999
Hedge Fund B
1997
1995
1993
1991
54
1989
Hedge Fund A
1987
1985
1983
1981
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
-0.3
1987
1985
1983
1981
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
Funds' Loadings on Beta
Mutual Fund
Figure 6: The Distribution of Fund Performance
This figure depicts the distribution of hedge fund performance. Panel A is the distribution of funds’
Characteristic Selectivity (CS) measure, and Panel B is the distribution of funds’ Characteristic Timing (CT)
measure.
Panel A: Characteristics Selectivity Measure
40
Mutual Fund
Hedge Fund
Percentage of Funds
30
20
10
0
-40%
-20%
0%
20%
40%
60%
Characteristic Selectivity (CS)
55
80%
100
Panel B: Characteristic Timing Measure
60
Mutual Fund
Hedge Fund
Percentage of Funds
50
40
30
20
10
0
-50%
-40%
-30%
-20%
-10%
0%
Characteristic Timing (CT)
56
10%
20%
30%