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 John.griffin@mccombs.utexas.edu 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.

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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 N-

30D 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

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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.

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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

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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 1992-

2004 period as well (providing 0.92 percent in additional return).

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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.

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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

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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

2 For a definition of Accredited Investors, please refer to SEC website: http://www.sec.gov/answer/ accred.htm.

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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.

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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

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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.

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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

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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.

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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

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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 medium-

sized 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

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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,

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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

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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 cross-

sectional 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

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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 11 The industry adjusted book-to-market equity is consistent with Wermers (2004). We thank Russ Wermers for graciously providing the benchmark returns.

19

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

20

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

21

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 re-

examine 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 price-

to-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.

22

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.

23

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

24

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

25

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 1986-

2004 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

26

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.

27

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and Performance, 1989-95, Journal of Business 72, 91-117. Brunnermeier, M. K., and S. Nagel, 2004, Hedge Funds and the Technology Bubble, Journal

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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

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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.

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strategies, portfolio performance, and herding: A study of mutual fund behavior, American Economic Review 85, 1088-1105.

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29

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 Total Number Hedge Fund Firms Matched Start Year End Year AltVest 1226 80 1978 2003 Cottier 27 7 1995 1997 Hoovers 32 8 1997 2003 Mar Graveyard 921 54 1978 2002 Nelson's Directory 1010 91 1988 2002 TASS Graveyard 501 22 1978 2000 TASS/Hedge World 2077 44 1978 2000 Total 306

30

Panel B: Year by Year Statistics

Number of Funds Average Number of Stocks

Owned Each Quarter Total Number of Stocks

Owned

Year Hedge

A Hedge

B Mutual Hedge A Hedge B Mutual Hedge

A Hedge

B Mutual

1980 25 453 152 43 1697 24931981 26 456 147 48 1978 27571982 31 449 121 47 1947 28441983 34 486 146 56 2195 34271984 36 1 497 157 144 60 2396 168 36341985 44 1 556 187 160 56 2750 244 39471986 49 4 619 214 196 62 3347 618 42251987 61 5 705 176 134 91 3401 966 42381988 71 6 763 160 155 64 3376 956 42181989 79 9 843 134 191 66 3331 1245 40961990 87 11 906 143 152 75 3271 1485 38911991 97 25 1045 178 146 68 3630 2006 39601992 106 37 1210 152 162 85 4113 3055 41851993 116 61 1952 196 204 81 4571 4021 53331994 125 78 2307 155 141 84 4812 4320 58231995 143 105 2479 161 154 97 5173 4890 62241996 152 113 2845 183 180 96 5718 5475 67441997 188 151 3092 184 179 94 6100 5900 69441998 202 162 3032 175 145 96 6047 5839 67451999 211 169 2773 146 137 98 5871 5677 64152000 231 193 2653 169 163 97 5443 5309 62212001 225 190 2493 174 164 119 4893 4780 56212002 214 181 2392 186 164 107 4410 4358 50982003 198 168 2229 178 168 116 4280 4234 47802004 191 162 2072 219 200 113 4215 4145 4640 Panel C: Fund Size Statistics

Hedge A Hedge B Mutual Funds Mean Median %CRSP Mean Median %CRSP Mean Median %CRSP ($ mil) ($ mil) ($ mil) ($ mil) ($ mil) ($ mil)

1980-1984 963 335 1.84% 121 40 3.49% 1985-1989 1471 511 2.87% 396 352 0.05% 222 66 5.19% 1990-1994 1475 393 3.51% 1506 308 1.27% 295 68 8.22% 1995-1999 1958 434 3.10% 1984 406 2.42% 648 98 14.78% 2000-2004 2105 351 3.02% 1975 326 2.42% 1058 178 16.63%

31

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.

32

Panel A: Fama-Macbeth Regressions of Mutual Fund and Hedge Fund Firm (B) Demand Hedge Funds B Mutual Funds (1) (2) (3) (4) (5) (1) (2) (3) (4) (5) Age -0.03 -0.04 -0.03 -0.03 -0.02 -0.02 -0.04 0.00 -0.01 0.00 (-1.02) (-1.16) (-1.19) (-0.99) (-1.10) (-1.91) (-6.15) (-0.21) (-1.12) (0.11) Analyst 0.01 0.01 0.01 0.05 0.22 0.24 0.22 0.22 (0.22) (0.13) (0.22) (1.19) (10.41) (9.62) (10.67) (10.34) Beta 0.02 0.02 0.02 0.00 0.02 -0.01 0.01 0.02 (1.49) (1.66) (1.50) (0.14) (1.28) (-0.70) (1.19) (2.54) BE/ME 0.02 0.01 0.01 0.03 0.03 -0.05 -0.05 -0.03 -0.02 0.03 (2.46) (2.33) (3.40) (2.86) (9.86) (-4.03) (-4.14) (-2.34) (-2.38) (3.69) D/P -0.04 -0.04 -0.09 -0.08 (-8.16) (-11.01) (-3.97) (-5.52) Liquidity (0.10) (0.10) (0.10) (0.09) (0.10) (0.34) (0.40) (0.27) (0.34) (0.33) (2.48) (2.11) (3.52) (2.51) (3.69) (44.59) (44.41) (15.82) (51.03) (39.43) In(Price) 0.06 0.24 (1.27) (17.17) In(Size) -0.21 -0.20 -0.22 -0.21 -1.22 -0.05 0.12 0.00 -0.04 0.75 (-2.77) (-1.67) (-2.60) (-2.80) (-1.15) (-0.54) (1.16) (0.02) (-0.51) (3.52) In(Size)2 0.94 -0.92 (0.96) (-6.00) P/S -0.01 -0.01 -0.01 -0.01 0.00 -0.08 -0.09 -0.10 -0.07 -0.09 (-0.33) (-1.08) (-0.45) (-0.52) (0.05) (-4.05) (-4.17) (-3.56) (-3.35) (-3.42) Momentum -0.02 -0.01 -0.01 -0.02 -0.02 0.08 0.08 0.07 0.07 0.06 (-0.39) (-0.32) (-0.30) (-0.41) (-0.49) (4.45) (4.15) (4.51) (4.01) (2.75) Stdev 0.02 0.02 0.01 0.02 -0.16 -0.20 -0.20 -0.10 (0.44) (0.35) (0.15) (0.58) (-4.76) (-6.60) (-8.39) (-3.88) Average Adjusted R2 0.17 0.17 0.17 0.17 0.20 0.24 0.22 0.22 0.24 0.27

33

Panel B: The Difference of Demand on Stocks between Hedge Funds Firms and Mutual Funds Hedge Fund A - Mutual Funds Hedge Fund B - Mutual funds (1) (2) (3) (4) (5) (1) (2) (3) (4) (5) Age -0.02 -0.01 -0.03 -0.02 -0.02 -0.01 0.00 -0.03 -0.02 -0.03 (-1.98) (-0.54) (-2.56) (-1.75) (-2.27) (-0.57) (0.16) (-1.80) (-1.09) (-1.87) Analyst -0.16 -0.19 -0.16 -0.16 -0.20 -0.24 -0.20 -0.17 (-8.85) (-9.16) (-8.76) (-9.32) (-6.10) (-6.67) (-6.09) (-6.63) Beta 0.00 0.02 0.00 0.00 0.00 0.03 0.00 -0.02 (-0.13) (1.55) (0.01) (-0.17) (-0.20) (2.13) (0.09) (-1.65) BE/ME 0.03 0.03 0.01 0.03 0.01 0.06 0.06 0.04 0.05 0.00 (2.79) (2.73) (1.24) (2.89) (1.13) (5.12) (5.05) (3.20) (4.31) (0.24) D/P (0.00) (0.00) (0.04) (0.04) (0.04) (0.05) (2.97) (3.30) Liquidity -0.15 -0.20 -0.09 -0.15 -0.15 -0.25 -0.30 -0.17 -0.24 -0.22 (-12.57) (-17.10) (-7.00) (-12.80) (-12.68) (-11.38) (-11.58) (-9.67) (-11.60) (-13.57) In(Price) (-0.12) (-0.18) (-9.30) (-6.86) In(Size) -0.04 -0.16 -0.08 -0.04 -0.50 -0.17 -0.32 -0.22 -0.16 -1.97 (-1.02) (-3.93) (-1.95) (-0.95) (-2.87) (-3.34) (-4.71) (-4.19) (-3.30) (-4.00) In(Size)2 (0.53) (1.86) (3.31) (4.10) P/S 0.03 0.04 0.04 0.03 0.04 0.08 0.08 0.09 0.07 0.09 (2.32) (2.76) (2.79) (2.41) (2.66) (4.45) (4.86) (4.77) (4.15) (4.01) Momentum -0.05 -0.05 -0.04 -0.05 -0.04 -0.09 -0.09 -0.08 -0.09 -0.07 (-3.36) (-3.32) (-3.05) (-3.26) (-2.63) (-4.17) (-4.15) (-3.79) (-4.16) (-3.65) Stdev 0.13 0.15 0.13 0.08 0.18 0.21 0.20 0.12 (6.51) (7.95) (7.08) (4.06) (6.21) (7.29) (7.32) (5.46)

34

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.

35

Panel A: Value-Weighted Performance Measures CRSP w/Div Gross Return CS Performance CT Performance AS Performance

Year VW EW H M H-M H M H-M H M H-M H M H-M

1986 15.57 7.87 14.08 16.97 -2.88 3.44 0.07 3.37 -4.77 -0.18 -4.58 16.62 15.47 1.15 1987 1.82 -8.47 -1.02 2.61 -3.64 1.23 0.63 0.60 0.34 0.45 -0.11 2.21 1.58 0.63 1988 17.55 18.82 24.44 17.64 6.80 3.30 -0.12 3.42 -0.96 -0.99 0.03 17.19 18.05 -0.85 1989 28.43 11.84 31.12 29.27 1.85 2.59 0.38 2.21 -0.50 -0.71 0.21 20.28 28.25 -7.96 1990 -6.08 -21.61 -8.29 -6.79 -1.50 -1.32 0.16 -1.48 0.18 0.30 -0.12 -2.09 -6.09 4.00 1991 33.64 51.60 42.20 37.56 4.64 3.81 1.13 2.68 -0.16 -0.42 0.26 26.24 34.05 -7.81 1992 9.06 26.84 8.26 10.31 -2.05 -1.71 0.70 -2.41 -1.36 -1.46 0.10 10.21 10.46 -0.25 1993 11.59 26.88 13.28 13.37 -0.09 2.92 2.55 0.37 0.30 0.51 -0.21 3.97 8.67 -4.70 1994 -0.76 -5.08 -0.54 0.19 -0.73 -0.61 0.13 -0.74 -0.02 0.01 -0.03 1.82 0.10 1.72 1995 35.67 30.23 36.91 37.69 -0.78 0.49 0.73 -0.24 -0.81 -0.13 -0.68 30.73 33.74 -3.00 1996 21.16 17.93 22.48 22.03 0.45 0.93 0.23 0.70 0.84 0.50 0.34 18.92 19.55 -0.63 1997 30.35 20.16 28.67 30.26 -1.60 -0.84 -0.97 0.13 -0.93 0.09 -1.02 29.18 29.44 -0.27 1998 22.30 -2.90 23.69 23.14 0.56 2.70 -0.43 3.13 1.96 1.31 0.65 18.14 21.81 -3.66 1999 25.26 33.76 43.73 29.57 14.16 15.80 6.06 9.74 5.52 3.99 1.53 18.71 17.81 0.90 2000 -11.04 -11.13 0.17 -5.48 5.65 7.84 3.19 4.64 -0.33 -1.25 0.91 -6.40 -6.31 -0.09 2001 -11.27 22.10 -11.27 -10.87 -0.39 -0.45 1.29 -1.75 4.24 5.63 -1.39 -15.02 -16.40 1.38 2002 -20.85 -10.94 -21.83 -21.33 -0.49 -1.87 -0.80 -1.07 1.18 1.63 -0.45 -21.09 -20.17 -0.92 2003 33.15 72.60 35.28 32.14 3.14 1.06 0.43 0.63 -1.31 -1.29 -0.03 35.42 32.96 2.45 2004 13.01 21.77 14.92 12.09 2.83 1.52 0.29 1.23 1.13 0.15 0.98 11.25 11.55 -0.30

CRSP

w/Dividends Gross Return CS Performance CT Performance AS Performance Year VW EW H M H-M H M H-M H M H-M H M H-M

1986-1994 12.31 12.08 13.73 13.46 0.27 1.52 0.63 0.89 -0.77 -0.28 -0.50 10.72 12.28 -1.57 (2.10) (2.39) (0.43) (1.41) (1.28) (1.12) (-1.96) (-1.17) (-1.25) (2.85) (2.46) (-0.70)

1995-2004 13.77 19.36 17.28 14.92 2.35 2.72 1.00 1.71 1.15 1.06 0.08 11.99 12.40 -0.41 (2.44) (2.29) (1.74) (1.72) (1.47) (1.62) (1.40) (1.36) (0.36) (1.95) (2.04) (-0.52)

1986-2004 13.08 15.91 15.59 14.23 1.36 2.15 0.82 1.32 0.24 0.43 -0.19 11.38 12.34 -0.96 (3.24) (3.30) (1.55) (2.22) (1.94) (1.98) (0.50) (1.00) (-0.84) (3.10) (3.14) (-0.80)

36

Panel B: Equal-Weighted DGTW Measures CRSP w/Div Gross Return CS Performance CT Performance AS Performance

Year VW EW H M H-M H M H-M H M H-M H M H-M

1986 15.57 7.87 14.08 15.56 -1.47 3.44 0.07 3.37 -4.77 -0.33 -4.44 16.62 16.01 0.61 1987 1.82 -8.47 0.68 1.83 -1.15 1.08 1.20 -0.12 1.10 0.60 0.49 -2.58 0.47 -3.05 1988 17.55 18.82 16.10 17.16 -1.06 -2.32 -0.88 -1.44 -2.54 -1.43 -1.11 20.45 19.60 0.86 1989 28.43 11.84 32.97 29.32 3.65 3.42 1.39 2.03 0.97 -0.42 1.39 28.82 28.17 0.65 1990 -6.08 -21.61 -14.33 -7.04 -7.29 -5.22 1.21 -6.43 -0.16 0.40 -0.56 -8.42 -7.89 -0.54 1991 33.64 51.60 45.97 40.38 5.59 3.26 1.80 1.47 0.72 -0.76 1.48 42.28 39.21 3.07 1992 9.06 26.84 13.73 11.20 2.53 -0.30 0.35 -0.66 2.47 -1.15 3.62 14.00 12.06 1.94 1993 11.59 26.88 14.64 13.14 1.50 2.66 1.33 1.32 -1.18 0.61 -1.79 12.45 11.03 1.41 1994 -0.76 -5.08 -1.78 -0.03 -1.75 -1.28 0.27 -1.56 -0.94 0.04 -0.98 0.19 -0.36 0.55 1995 35.67 30.23 36.98 36.28 0.70 1.10 0.10 0.99 -0.37 -0.27 -0.09 36.10 36.50 -0.40 1996 21.16 17.93 20.99 22.09 -1.10 0.12 0.82 -0.70 0.37 0.68 -0.31 20.22 20.53 -0.31 1997 30.35 20.16 26.01 28.03 -2.03 -1.40 -1.64 0.24 -1.43 -0.15 -1.27 29.86 30.64 -0.79 1998 22.30 -2.90 13.22 15.13 -1.91 0.10 -1.06 1.17 1.39 0.77 0.63 13.54 16.18 -2.65 1999 25.26 33.76 44.71 30.81 13.90 16.02 6.30 9.72 3.64 3.54 0.10 23.35 20.02 3.33 2000 -11.04 -11.13 4.65 1.01 3.64 7.48 4.39 3.09 2.46 -0.44 2.90 -4.27 -2.78 -1.49 2001 -11.27 22.10 -4.43 -7.03 2.61 1.29 0.59 0.70 3.51 3.29 0.22 -8.12 -11.31 3.19 2002 -20.85 -10.94 -22.65 -21.35 -1.29 -3.68 -1.73 -1.95 1.08 0.93 0.16 -19.90 -20.38 0.48 2003 33.15 72.60 46.06 35.85 10.21 3.99 0.67 3.32 1.13 -0.77 1.90 39.01 36.00 3.01 2004 13.01 21.77 16.36 13.57 2.78 1.57 0.27 1.31 0.91 0.08 0.83 13.70 13.14 0.56

CRSP

w/Dividends Gross Return CS Performance CT Performance AS Performance Year VW EW H M H-M H M H-M H M H-M H M H-M

1986-1994 12.31 12.08 13.56 13.50 0.06 0.53 0.75 -0.22 -0.48 -0.27 -0.21 13.76 13.14 0.61 (2.02) (2.32) (0.19) (0.41) (1.60) (-0.17) (-0.62) (-1.07) (-0.29) (2.30) (2.35) (1.00)

1995-2004 13.77 19.36 18.19 15.44 2.75 2.66 0.87 1.79 1.27 0.76 0.51 14.35 13.85 0.49 (2.46) (2.37) (1.98) (1.65) (1.20) (1.74) (2.13) (1.19) (1.37) (2.22) (2.19) (1.05)

1986-2004 13.08 15.91 16.00 14.52 1.48 1.65 0.81 0.84 0.44 0.27 0.17 14.07 13.52 0.55 (3.20) (3.32) (1.58) (1.56) (1.85) (1.11) (0.91) (0.76) (0.43) (3.20) (3.20) (1.46)

37

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

Others Lowest Highest Year p0 p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 Total

1998 H -0.07 0.22 0.62 -0.43 -0.73 0.19 -0.75 -2.03 0.10 0.87 2.19 0.10 M -0.02 0.02 0.35 -0.49 -0.47 -0.46 -0.69 -0.88 0.29 0.35 0.94 -1.06 H-M -0.06 0.20 0.27 0.06 -0.26 0.64 -0.06 -1.15 -0.19 0.52 1.25 1.17 (-0.63) (0.63) (0.92) (0.26) (-1.15) (2.29) (-0.15) (-2.25) (-0.88) (1.23) (1.71) (0.85)

1999 H 0.02 -0.58 -0.45 -0.11 0.12 0.00 0.94 0.34 2.07 2.14 11.37 16.02 M -0.02 -0.38 -0.46 -0.46 -0.35 -0.38 -0.10 -0.13 0.67 0.89 7.06 6.30 H-M 0.05 -0.20 0.01 0.35 0.47 0.37 1.04 0.47 1.40 1.25 4.30 9.72 (0.94) (-0.66) (-0.03) (1.19) (2.09) (1.53) (2.08) (1.31) (1.17) (1.94) (3.05) (3.66)

2000 H -0.03 0.26 -0.51 0.06 0.86 1.73 0.72 0.31 0.66 1.56 1.79 7.48 M -0.03 -0.23 -0.17 -0.17 0.65 1.34 -0.14 0.35 0.15 1.13 1.42 4.39 H-M 0.00 0.49 -0.34 0.23 0.21 0.39 0.86 -0.04 0.51 0.42 0.37 3.09 (-0.03) (1.37) (-0.62) (0.87) (0.99) (0.81) (3.26) (-0.07) (0.98) (0.62) (0.26) (1.83)

2001 H -0.02 0.49 -0.66 -0.34 0.27 -0.39 -0.37 0.30 0.24 1.42 0.36 1.29 M -0.04 0.00 0.03 -0.07 0.14 -0.22 -0.20 0.14 0.18 0.12 0.50 0.59 H-M 0.02 0.49 -0.68 -0.26 0.13 -0.16 -0.17 0.16 0.05 1.30 -0.14 0.70 (0.08) (2.27) (-1.68) (-1.21) (0.68) (-0.68) (-0.75) (0.78) (0.14) (0.76) (-0.21) (0.35)

2002 H -0.22 0.03 0.10 0.17 0.37 0.77 0.59 -0.53 0.49 -1.62 -3.84 -3.68 M -0.04 -0.12 0.07 -0.37 0.22 0.67 0.24 -0.12 1.03 -0.21 -3.10 -1.73 H-M -0.19 0.14 0.03 0.54 0.15 0.10 0.36 -0.41 -0.54 -1.41 -0.74 -1.95 (-2.58) (0.61) (0.08) (1.66) (0.84) (0.39) (1.71) (-1.82) (-1.50) (-2.85) (-0.95) (-1.59)

2003 H -0.02 -0.05 1.29 0.62 0.40 0.23 -0.50 0.28 0.60 0.10 1.01 3.99 M -0.02 -0.21 0.36 0.36 0.07 0.15 -0.80 0.36 0.01 -0.15 0.54 0.67 H-M 0.00 0.16 0.93 0.26 0.33 0.08 0.29 -0.07 0.59 0.24 0.47 3.32 (0.05) (0.22) (1.39) (1.32) (2.04) (0.33) (1.71) (-0.41) (3.08) (1.12) (1.08) (2.65)

2004 H 0.02 0.54 0.38 0.41 1.08 0.24 0.12 0.14 -0.44 -0.25 -0.68 1.57 M -0.07 0.16 0.26 0.37 0.97 0.34 0.23 0.23 -0.44 -0.58 -1.20 0.27 H-M 0.09 0.38 0.12 0.05 0.11 -0.11 -0.12 -0.09 0.00 0.33 0.53 1.31 (1.01) (3.83) (0.41) (0.16) (0.30) (-0.20) (-0.33) (-0.94) (-0.24) (1.26) (1.73) (1.54)

38

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 Mutual Funds Worst Best Worst Best P1 P2 P3 P4 P5 P5-P1 P1 P2 P3 P4 P5 P5-P1

Gross Return 15.00 15.63 16.82 18.87 20.54 5.54 12.88 13.33 13.49 15.89 18.41 5.53 (2.18) (2.61) (2.73) (3.11) (2.52) (1.19) (2.06) (2.43) (2.55) (2.86) (2.84) (1.41) CS 1.03 1.90 2.28 3.28 5.37 4.34 0.60 0.51 0.41 1.31 2.54 1.93 (0.54) (1.72) (1.65) (2.48) (1.88) (1.47) (0.61) (0.75) (0.84) (2.08) (1.93) (1.21) CT 1.06 0.38 0.88 1.16 0.19 -0.87 0.42 0.76 0.74 0.91 0.74 0.32 (1.11) (0.58) (1.45) (1.72) (0.24) (-0.72) (0.62) (1.16) (1.17) (1.45) (1.14) (0.34) AS 12.71 13.12 13.33 13.95 14.38 1.67 11.76 11.95 12.24 13.45 14.75 2.98 (2.17) (2.33) (2.41) (2.58) (2.46) (0.89) (2.02) (2.19) (2.29) (2.49) (2.57) (1.21)

39

Panel B: Portfolio quintiles ranked by prior year CS performance measures Hedge Funds Mutual Funds Worst Best Worst Best P1 P2 P3 P4 P5 P5-P1 P1 P2 P3 P4 P5 P5-P1

Gross Return 16.78 15.65 17.25 17.82 19.49 2.71 14.75 14.04 13.89 14.80 16.44 1.69 (2.66) (2.79) (2.80) (2.70) (2.32) (0.58) (2.60) (2.66) (2.61) (2.64) (2.33) (0.45) CS 1.24 1.17 2.91 3.15 5.39 4.15 0.58 0.57 0.54 1.11 2.54 1.96 (0.82) (1.34) (2.29) (1.70) (1.80) (1.41) (0.76) (1.05) (1.16) (1.69) (1.50) (1.04) CT 1.05 0.94 1.17 1.06 -0.48 -1.53 0.65 0.84 0.83 0.83 0.42 -0.23 (1.15) (1.32) (2.14) (1.71) (-0.59) (-1.32) (1.47) (1.39) (1.35) (1.33) (0.65) (-0.40) AS 14.24 13.31 12.76 13.19 14.10 -0.15 13.40 12.47 12.40 12.67 13.17 -0.23 (2.54) (2.41) (2.30) (2.33) (2.40) (-0.13) (2.41) (2.34) (2.31) (2.32) (2.26) (-0.15) Panel C: Portfolio quintiles ranked by prior year CS+CT performance measures Hedge Funds Mutual Funds Worst Best Worst Best P1 P2 P3 P4 P5 P5-P1 P1 P2 P3 P4 P5 P5-P1

Gross Return 16.78 15.65 17.25 17.82 19.49 2.71 14.75 14.04 13.89 14.80 16.44 1.69 (2.66) (2.79) (2.80) (2.70) (2.32) (0.58) (2.60) (2.66) (2.61) (2.64) (2.33) (0.45) CS 1.24 1.17 2.91 3.15 5.39 4.15 0.58 0.57 0.54 1.11 2.54 1.96 (0.82) (1.34) (2.29) (1.70) (1.80) (1.41) (0.76) (1.05) (1.16) (1.69) (1.50) (1.04) CT 1.05 0.94 1.17 1.06 -0.48 -1.53 0.65 0.84 0.83 0.83 0.42 -0.23 (1.15) (1.32) (2.14) (1.71) (-0.59) (-1.32) (1.47) (1.39) (1.35) (1.33) (0.65) (-0.40) AS 14.24 13.31 12.76 13.19 14.10 -0.15 13.40 12.47 12.40 12.67 13.17 -0.23 (2.54) (2.41) (2.30) (2.33) (2.40) (-0.13) (2.41) (2.34) (2.31) (2.32) (2.26) (-0.15)

40

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 Mutual Funds Worst Best Worst Best P1 P2 P3 P4 P4-P1 P1 P2 P3 P4 P4-P1

Gross Return 16.95 16.38 16.98 18.63 1.68 14.81 14.19 14.01 14.44 -0.36 (2.96) (3.11) (2.82) (2.43) (0.41) (3.08) (2.92) (2.70) (2.22) (-0.13) CS -0.22 1.37 2.95 4.88 5.10 0.48 0.66 0.97 1.44 0.96 (-0.15) (1.31) (1.92) (2.02) (2.03) (0.85) (1.37) (1.78) (1.09) (0.68) CT 2.64 1.24 0.58 -0.15 -2.79 0.57 0.80 0.61 0.27 -0.30 (1.88) (2.08) (1.06) (-0.22) (-1.95) (1.17) (1.36) (1.03) (0.45) (-0.49) AS 14.21 13.52 13.07 13.44 -0.77 13.68 12.61 12.27 12.59 -1.09 (2.78) (2.70) (2.51) (2.36) (-0.59) (2.78) (2.57) (2.40) (2.26) (-0.71)

41

Panel B: Portfolio quartiles ranked by CS+CT measure of a fund’s whole history Hedge Funds Mutual Funds Worst Best Worst Best P1 P2 P3 P4 P4-P1 P1 P2 P3 P4 P4-P1

Gross Return 16.65 17.23 16.90 17.54 0.89 14.88 14.72 13.58 14.39 -0.49 (3.09) (3.15) (2.86) (2.31) (0.21) (3.02) (3.03) (2.64) (2.25) (-0.18) CS -0.14 2.45 2.66 3.15 3.29 0.45 0.95 0.80 1.44 0.98 (-0.07) (1.93) (1.86) (1.21) (1.04) (0.79) (1.89) (1.50) (1.14) (0.75) CT 3.50 1.24 0.17 0.01 -3.49 0.67 0.75 0.67 0.20 -0.47 (2.15) (2.06) (0.32) (0.01) (-2.05) (1.51) (1.34) (1.04) (0.32) (-0.78) AS 12.90 13.18 13.78 14.03 1.13 13.64 12.88 11.98 12.62 -1.02 (2.51) (2.62) (2.64) (2.52) (0.77) (2.76) (2.62) (2.34) (2.29) (-0.70)

42

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

Gross Return CS Performance CT Performance AS Performance

Year Number H M H-M H M H-M H M H-M H M H-M

1991 15 48.30 36.52 11.78 5.01 0.69 4.32 1.10 -0.45 1.55 38.24 35.12 3.12 1992 25 7.99 10.19 -2.21 -1.58 0.67 -2.24 -1.57 -1.51 -0.06 11.61 10.90 0.70 1993 35 14.11 12.42 1.69 3.15 1.94 1.21 0.94 0.58 0.36 9.08 9.14 -0.06 1994 46 -0.16 0.15 -0.31 -0.60 0.08 -0.69 0.19 0.02 0.17 2.09 0.24 1.85 1995 50 36.73 37.76 -1.03 0.33 0.70 -0.37 -1.10 -0.18 -0.92 35.99 35.13 0.86 1996 63 23.35 21.89 1.46 0.88 0.04 0.84 1.17 0.51 0.66 19.57 19.99 -0.42 1997 76 31.69 30.54 1.15 0.37 -0.88 1.24 -0.68 0.07 -0.75 31.67 30.09 1.58 1998 86 24.45 22.76 1.69 1.93 -0.91 2.84 1.98 1.31 0.66 20.22 22.24 -2.01 1999 83 37.30 27.87 9.43 11.71 5.26 6.45 5.48 4.02 1.46 18.91 17.58 1.33 2000 87 0.58 -5.07 5.65 6.16 2.82 3.34 0.05 -1.17 1.22 -5.97 -6.10 0.13 2001 96 -11.72 -10.28 -1.44 -0.27 1.39 -1.66 4.48 6.00 -1.52 -16.54 -16.42 -0.12 2002 92 -21.94 -21.19 -0.75 -1.50 -0.58 -0.92 0.79 1.68 -0.89 -21.33 -20.38 -0.95 2003 79 32.77 32.00 0.77 0.13 0.38 -0.24 -2.11 -1.31 -0.80 35.12 33.09 2.02 2004 104 13.61 12.15 1.46 0.80 0.44 0.36 1.25 0.14 1.11 11.31 11.46 -0.15

1991-2004 16.93 14.84 2.10 1.89 0.86 1.03 0.85 0.69 0.16 13.57 13.01 0.56 (3.21) (3.06) (2.09) (1.90) (1.84) (1.47) (1.30) (1.18) (0.76) (2.80) (2.80) (1.35)

43

Panel B: Value Weighted DGTW Performance Measures of Hedge Funds and Mutual Funds of High Turnover

Gross Return CS Performance CT Performance AS Performance Year H M H-M H M H-M H M H-M H M H-M

1991 41.12 49.05 -7.93 2.78 6.11 -3.32 -1.37 0.16 -1.53 39.81 39.92 -0.11 1992 7.71 10.60 -2.89 -4.45 0.84 -5.29 -1.24 -1.47 0.23 13.11 10.87 2.24 1993 25.80 19.71 6.09 11.99 7.36 4.63 -2.38 0.53 -2.91 13.94 10.86 3.08 1994 -1.33 0.10 -1.43 -0.43 0.26 -0.69 -0.66 -0.13 -0.54 1.87 -0.04 1.91 1995 37.06 36.75 0.30 1.61 0.67 0.95 -0.52 0.33 -0.85 34.82 34.35 0.47 1996 20.77 23.44 -2.67 0.91 1.81 -0.91 0.25 0.54 -0.29 18.92 19.77 -0.85 1997 20.20 27.26 -7.06 -4.76 -2.13 -2.63 -1.87 0.25 -2.12 28.97 28.54 0.43 1998 22.27 27.43 -5.16 6.05 4.97 1.09 2.36 1.39 0.97 13.31 19.87 -6.56 1999 67.22 43.72 23.50 30.80 12.65 18.15 6.81 4.50 2.31 24.95 22.79 2.16 2000 0.14 -4.86 5.00 11.31 9.79 1.52 -1.07 -1.62 0.55 -9.68 -8.54 -1.14 2001 -10.01 -16.99 6.97 -1.13 0.37 -1.51 4.06 3.39 0.67 -12.48 -19.71 7.23 2002 -21.59 -24.66 3.07 -3.18 -5.58 2.41 2.50 0.85 1.66 -20.86 -20.25 -0.60 2003 43.59 34.76 8.83 4.15 1.57 2.58 1.28 -0.94 2.22 36.75 31.55 5.20 2004 17.50 11.26 6.24 2.53 -2.44 4.97 1.14 0.44 0.70 13.51 13.48 0.03

1991-2004 19.32 16.97 2.35 4.16 2.59 1.57 0.66 0.59 0.08 14.07 13.10 0.96 (2.97) (2.83) (1.28) (1.90) (2.07) (1.02) (0.96) (0.98) (0.15) (2.77) (2.67) (1.28)

44

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 Analyst 0.35 1.00 Beta -0.07 -0.10 1.00 BE/ME 0.01 -0.13 0.10 1.00 D/P 0.10 0.08 0.00 0.06 1.00 ln(Liq) -0.18 0.11 -0.01 -0.07 -0.07 1.00 In(Price) 0.36 0.50 -0.20 -0.27 0.07 0.01 1.00 In(Size) 0.42 0.74 -0.18 -0.28 0.05 0.18 0.81 1.00 In(Size)2 0.44 0.75 -0.18 -0.26 0.05 0.17 0.79 0.99 1.00 Momentum 0.00 0.01 -0.01 -0.12 -0.01 -0.02 0.15 0.07 0.06 1.00 P/S -0.01 -0.01 0.01 -0.02 0.00 0.02 -0.01 0.00 0.00 0.00 1.00 Stdev -0.32 -0.22 0.22 -0.01 -0.11 0.40 -0.52 -0.35 -0.34 0.03 0.04 1.00

45

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

CS Age

(in quarters) 1 PALANTIR CAPITAL INC 21.66% 29 2 PRIVATE CAPITAL MGMT INC 17.70% 48 3 CAMELOT MANAGEMENT CORPORATION 16.01% 28 4 S SQUARED TECHNOLGY CORP 15.55% 46 5 GRUBER & MCBAINE CAP MGT 14.74% 49 6 APPALOOSA MANAGEMENT, L.P. 14.16% 33 7 EMERGING GROWTH ADVISORS 13.77% 22 8 TUDOR INVESTMENT CORP 13.57% 31 9 DEERFIELD MANAGEMENT 13.44% 36

10 BPI GLOBAL ASSET MGMT LP 12.82% 28 11 PEQUOT CAPITAL MANAGEMENT INC. 11.83% 34 12 CAMDEN ASSET MANAGEMENT, L.P. 11.59% 47 13 GALLEON MANAGEMENT L.P. 11.27% 28 14 MORGENS WATERFALL 10.64% 43 15 DELTEC ASSET MANAGEMENT CORP. 10.54% 40 16 GREENLIGHT CAPITAL INC 10.43% 24 17 CROWN ADVISORS LTD. 9.99% 37 18 APEX CAPITAL LLC 9.66% 31 19 ORACLE INVESTMENT MANAGEMENT, 9.61% 39 20 LANE CAPITAL MANAGEMENT, INC. 9.52% 46

46

Panel B: Top 20 Hedge Funds in CT measure

CT Age

(in quarters) 1 DAWSON SAMBERG CAP MGMT 10.98% 31 2 EMERGING MARKETS MGMT 10.43% 23 3 PARA ADVISORS INC 9.10% 26 4 OZ MANAGEMENT LLC 8.40% 28 5 TUDOR INVESTMENT CORP 7.78% 31 6 MATADOR CAPITAL MGMT CORP. 7.50% 28 7 JULIUS BAER INVT MGMT (NY) 7.20% 26 8 DICKSTEIN PARTNERS INC 7.01% 32 9 BAY ISLE FINANCIAL CORPORATION 6.29% 31

10 BPI GLOBAL ASSET MGMT LP 6.21% 28 11 HARVEST MANAGEMENT, L.L.C. 5.93% 35 12 MARVIN & PALMER ASSOCS. 5.67% 49 13 ODYSSEY PARTNERS 5.65% 46 14 CHESAPEAKE PTNR MGMT CO. INC. 5.37% 22 15 GREENLIGHT CAPITAL INC 5.20% 24 16 GALLEON MANAGEMENT L.P. 5.13% 28 17 KRAMER SPELLMAN, L.P. 5.09% 32 18 S SQUARED TECHNOLGY CORP 4.68% 46 19 CAMDEN ASSET MANAGEMENT, L.P. 4.43% 47 20 JGD MANAGEMENT CORP 4.34% 30

47

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).

0

5

10

15

20

25

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 2.8 3 3.2 3.4 3.6 3.8 >4

Turnover (100% per year)

Perc

ent

Hedge A Hedge B Mutual Funds

48

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:

]1][1[

1

])(][)([

))((

1

2

1

2

1

2

11

22

11

2

1 1 1

−−

−=

−−

−=

∑∑

∑

∑∑∑∑

∑ ∑ ∑

==

=

====

= = =

n

iim

n

iij

n

iimij

n

iim

n

iim

n

iij

n

iij

n

i

n

i

n

iimijimij

jm

wnwn

wwn

wwnwwn

wwwwnr

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.

0.20

0.25

0.30

0.35

0.40

0.45

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004Year

Corre

latio

n

Hedge A Hedge B Mutual Fund

49

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

Panel B: Cross-Industry Weight Deviation

Panel C: Within-Industry Weight Deviation

Total Deviations from CRSP Market Index

0

5

10

15

20

25

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Deviation

Per

cent

Total Deviations from S&P 500 Index

0

5

10

15

20

25

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Deviation

Cross Industry Deviation from CRSP Market Index

-1

1

3

5

7

9

11

13

15

17

19

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Deviation

Per

cent

Cross Industry Deviation from S&P 500 Index

-1

1

3

5

7

9

11

13

15

17

19

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Deviation

Within-Industry Deviation from CRSP Market Index

0

5

10

15

20

25

30

35

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Deviation

Per

cent

Within-Industry Deviation S&P 500 Index

0

5

10

15

20

25

30

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Deviation

Hedge Fund A Mutual Funds

50

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

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Perc

ent

Small 2 3 4 Big

Growth

2

3

4

Value

Size

Book-to-Market

51

Panel B: The Weights of Hedge Funds Relative to Mutual Funds Based on Size and Book–to-Market Quintiles

-0.20-0.15-0.10-0.050.000.050.100.150.200.250.300.35

Perc

ent

Small 2 3 4 Big

Growth

2

3

4

Value

Size

Book-to-Market

52

Panel C: The Weights of Hedge Funds Relative to Market Portfolio Based on Size and Momentum Quintiles

-0.4-0.3-0.2-0.1

00.10.20.30.40.5

Perc

ent

Small 2 3 4 Big

Low

2

3

4

High

Size

Momentum

53

Panel D: The Weights of Hedge Funds Relative to Mutual Funds Based on Size and Momentum Quintiles

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Perc

ent

Small 2 3 4 Big

Low

2

3

4

High

Size

Momentum

54

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

-0.15-0.1

-0.050

0.050.1

0.15

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Funds' Loadings on Analyst

-0.4-0.3-0.2-0.1

00.10.20.30.4

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Funds' Loadings on Beta

-0.15-0.1

-0.050

0.050.1

0.150.2

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Funds' Loadings on BE/ME

-0.15-0.1

-0.050

0.050.1

0.15

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Funds' Loadings on D/P

-0.25-0.2

-0.15-0.1

-0.050

0.050.1

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Funds' Loadings on Liquidity

-0.3-0.2-0.1

00.10.20.30.40.5

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Funds' Loadings on Log(Size)

-0.6

-0.4

-0.2

0

0.2

0.4

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Funds' Loadings on P/S

-0.2-0.15-0.1

-0.050

0.050.1

0.15

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Funds' Loadings on Momentum

-0.3

-0.2

-0.1

0

0.1

0.2

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Funds' Loadings on Standard Deviation

-0.4-0.3-0.2-0.1

00.10.20.3

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Hedge Fund A Hedge Fund B Mutual Fund

55

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

0

10

20

30

40

-40% -20% 0% 20% 40% 60% 80% 100

Characteristic Selectivity (CS)

Per

cen

tage

of F

un

ds

Mutual FundHedge Fund

56

Panel B: Characteristic Timing Measure

0

10

20

30

40

50

60

-50% -40% -30% -20% -10% 0% 10% 20% 30%

Characteristic Timing (CT)

Per

cen

tage

of

Fu

nd

sMutual FundHedge Fund