Preliminary version. Comments welcome. Please do not quote or circulate without permission.

Identifying skilled managers: Evidence from mutual fund short sales

Honghui Chen Department of Finance

College of Business Administration University of Central Florida

Orlando, FL 32816 (407) 823 0895

honghui.chen@bus.ucf.edu

Hemang Desai Edwin L. Cox School of Business

Southern Methodist University Dallas, TX 75275-0333

214 768-3185 hdesai@mail.cox.smu.edu

Srinivasan Krishnamurthy School of Management

SUNY – Binghamton University Binghamton, NY 13902

(607) 777 6861 srinik@binghamton.edu

1

Abstract

In this paper, we provide a first look at the short positions established by 75 mutual funds that used short sales of US domestic stocks as an investment strategy. We document that mutual funds tend to establish short positions in the larger and more liquid stocks, likely to minimize the possibility of a short squeeze. We also find that the shorted stocks have low equity BM ratios, higher total accruals, and higher prior sales growth, and that the shorted stocks earn an abnormal return of between -3.3% and -9.1% on an annualized basis. This suggests that the fund managers are able to use valuation and financial indicators and identify stocks that do poorly.

We use the portfolio holdings data and show that the mutual funds earn significant abnormal returns on both the short and the long portfolios. The average alpha for the short portfolio (using the Carhart (1997) four-factor model) ranges between 4.8% and 5.9% on an annualized basis. The corresponding abnormal return on the long portfolio ranges between 1.9% and 2.6%. Using a total net assets-matched control fund approach, the incremental alpha ranges between 2.9% to 4.1% annually. Overall, the result that mutual fund managers using short sales exhibit superior performance is consistent with the theoretical prediction (e.g., Diamond and Verrecchia (1987)) that only informed investors will sell short.

2

In this paper, we extend the existing literature on mutual funds by conducting a detailed

analysis of the short positions held by mutual funds. To the best of our knowledge, no prior study

has analyzed the mutual funds’ short sales. Specifically, we investigate whether both the

characteristics and performance of the shorted stocks support the notion that skilled mutual fund

managers are more likely to use short selling as an investment strategy. Our approach is

motivated by the prior theoretical literature on short sales (e.g., Diamond and Verrecchia

(1987)), which posits that given the significant costs associated with shorting a stock, only

informed investors with negative information will engage in short selling.

Diamond and Verrecchia (1987) argue that liquidity investor who needs resources

quickly would prefer to liquidate other assets rather than short selling securities about which they

are uninformed since the restrictions associated with short selling prevent them from

immediately using the proceeds from short sales. Furthermore, if the costs of short selling are

sufficiently high, this would deter investors with marginally negative information from shorting.1

Since holding short positions is more restrictive than holding long positions, it is likely that

classifying managers based on whether or not they undertake short sales would separate skilled

managers from the population of all fund managers. Consequently, mutual fund shorting activity

may be a good alternative setting to evaluate the ability of mutual fund managers to generate

abnormal returns.

Earlier studies have examined the ability of mutual fund to earn an abnormal return on its

investments (long positions), with mixed results. In an early, influential paper, Jensen (1968)

concludes that mutual funds do not display evidence of superior performance. Consistent with

Jensen (1968), other studies (e.g., Malkiel (1995) and Carhart (1997)) find that superior

1 Several researchers (Asquith and Meulbroek (1995) and Desai et al (2002)) find that heavily shorted stocks underperform, though Asquith, Pathak, and Ritter (2005) show that this relationship has weakened in recent times.

3

performance is not persistent, and conclude that mutual fund managers have little or no stock

picking skills. But, Grinblatt and Titman (1989, 1993), Chen, Jegadeesh, and Wermers (1999),

Wermers (1999), and Baker, Litov, Wachter, and Wurgler (2005), among others, examine the

performance of stocks that are actively purchased and sold by mutual funds and conclude that

fund managers have stock picking skill. Other recent studies (e.g., Brunnermeier and Nagel

(2004) and Griffin and Xu (2005)) investigate the performance of hedge funds using portfolio

data from 13F filings. But, their analysis is also restricted to the hedge funds’ long portfolio since

short positions are not reported in the 13F filings. Even though the evidence is mixed on whether

mutual fund managers exhibit stock picking skill or not, one salient feature that is common to

these studies is that they focus only on the long side of the mutual fund’s portfolio. In contrast to

these studies, we focus on the stocks shorted by mutual funds, a feature that has not been

explored in the extant literature.

Our research is facilitated by the fact that in recent times, mutual funds have been

increasingly engaging in short sales. Prior to 1997, mutual funds were subject to the ‘short-short’

rule that limited gains from short-term positions to less than 30% of their total income. Since

gains from short sales were considered short-term irrespective of how long the position was held,

this rule limited the extent to which mutual funds were able to hold short positions. The

Taxpayer Relief Act of 1997 repealed this rule, and relaxed the constraints on the ability of

mutual funds to undertake short positions. Consequently, mutual funds started using short selling

as an active component of their investment strategy.2 Almazan, Brown, Carlson, and Chapman

(2004) provide evidence confirming this trend. About one-third of the domestic equity mutual

funds in their sample state that the fund’s investment policy guidelines allow short selling, and

this fraction has increased near-monotonically from 26.7% in 1994 to about 34% in 2000. More 2 “Funds that tame bull and bear”, January 26, 1998, BusinessWeek, pg. 98.

4

interestingly, 75 funds actually used short selling in our sample period of 2003-2005, which is

about 4% of the total number of actively managed domestic equity funds that existed in 2004.3

Since prior academic literature has not examined short selling by mutual funds, the first

part of this study is descriptive in nature. Our sample of 75 domestic (US) equity mutual funds

that established short positions in US stocks includes both large and small funds. For example,

the total net assets including all securities averages $461 million, but the median is much smaller

at $44 million. In the first period that the fund reports short positions, they short sell an average

of 38 stocks with a market value of $18 million. In the same period, the average long position is

$278 million invested in 81 stocks, indicating that the average size of the short position per stock

is smaller than that for long positions ($0.47 million versus $3.44 million). The mean expense

ratio is 2.17% of assets and the portfolio turnover is about 277% per year.

We also investigate whether mutual funds exhibit preferences for specific stock

characteristics in their short portfolios. This investigation is motivated by the evidence that

mutual funds prefer investing in stocks with particular characteristics and systematically avoid

other stocks (e.g., Falkenstein (1996)). Compared to stocks in their long portfolios, stocks in the

short portfolio are significantly different along several dimensions. Mutual funds prefer to

establish short positions in the larger, more liquid stocks, likely to minimize the possibility of a

short squeeze. Both the equity beta and the standard deviation of returns are higher for the

shorted stocks, indicating that they are subject to higher levels of information asymmetry.

Further, compared to stocks in the long portfolios, the shorted stocks have lower equity book to

market ratio and prior momentum and higher industry-adjusted total accruals and prior sales

growth. Overall, this evidence suggests that compared to long positions, mutual funds choose to

3 We identify sample funds from the CRSP mutual fund database, which begins reporting portfolio holdings on a regular basis from 2003 onwards.

5

establish short positions in glamour stocks with greater degree of information asymmetry and

poor earnings quality. This is consistent with prior research which documents that such firms

underperform. However, to minimize the likelihood of a short squeeze that may force them to

close out short positions prematurely, the funds prefer to short larger and more liquid stocks.

The second part of the study examines the performance of mutual funds engaging in short

selling activities using three complementary approaches. First, if skilled mutual fund managers

are more likely to short stocks than other managers, then we expect the shorted stocks to

underperform on a risk-adjusted basis. Using calendar time regressions and the Carhart (1997)

four factor model, we find that on an annualized basis, portfolios of the shorted stocks

underperform by between 3.3% (equally-weighted) to 9.1% (weighted by the intensity of mutual

fund shorting activity). The statistically significant and large negative abnormal return confirms

that mutual fund managers are able to identify stocks that decline in value and establish short

positions in such firms. This result also corroborates the findings from earlier papers that

document the underperformance of highly shorted stocks.

The second series of tests extends this analysis and investigates whether the short

portfolio of these mutual funds also earns negative abnormal returns. Unlike prior literature on

short sales that commonly uses short interest data, the portfolio-level analysis directly tests the

assertion that sophisticated investors such as mutual fund managers are able to establish and

unwind short positions at a profit. The use of short interest data imposes two limitations that do

not permit reliable inferences about whether on average, informed short sellers earn abnormal

returns. First, the publicly available data consists of monthly short interest reported by the NYSE

and Nasdaq for each security. This data aggregates short interest that is due to either valuation

reasons or arbitrage reasons, making it difficult to separately identify valuation-based short

6

selling. Second, detailed portfolio holdings of informed arbitrageurs engaging in short sales are

not publicly available to researchers. Hence, it is difficult to establish when the short positions

are initiated and closed. The analysis in this study circumvents this limitation because individual

mutual funds periodically disclose their complete portfolio holdings, including short positions.

This allows us to estimate the gain or loss at the individual portfolio level for one group of

informed investors, i.e., mutual funds, that engages in short selling.4 Consistent with the stock

return evidence, the average abnormal return on the short portfolios of individual mutual funds is

also negative. The mean abnormal return ranges between -4.8% to -5.9% on an annualized basis,

and are statistically significant at conventional levels. This result further confirms that mutual

fund managers are able to establish and close their short positions profitably.

Finally, if mutual fund managers that use short selling as part of their investment strategy

are indeed more likely to be skilled managers, then we would expect their skill to be reflected in

their investments (long positions) also. The long portfolio of these mutual funds would also

generate positive abnormal returns. The estimates of abnormal returns earned on the long

portfolio further corroborate this line of reasoning. The mean abnormal return varies between

+0.16% and +0.21% per month, and is statistically significant at the five-percent level or better.

The results also hold up when we use the abnormal return earned by size-matched control funds

as the benchmark abnormal return. Thus, mutual fund managers who choose to undertake short

positions are able to earn significant abnormal returns on both their short and long portfolios.

Collectively, the evidence from analyses of both short and long positions consistently

suggests that fund managers engaging in short sales are more likely to be better informed/skilled

managers. While the empirical evidence is robust, we urge care in interpreting the results since

4 Since the portfolio holdings are available at discrete points of time (usually every three months), we cannot identify the exact dates within the quarter when the mutual funds sell short and when they buy back the stock to cover their short positions.

7

the time period that we examine is relatively short. However, given that mutual funds are

increasingly utilizing short sales as part of their investment strategy, we expect this methodology

to be more applicable in the future. As more machine-readable data becomes available, future

research could also examine whether the persistence of performance is more prevalent for

managers that use short sales.

The rest of the paper is organized as follows. Section I describes the sample selection and

provides summary statistics. Section II compares the characteristics of stocks in the short

portfolio with both stocks in the long portfolio and other CRSP-listed stocks. Section III

examines the performance of the mutual funds’ short positions. Section IV investigates the

performance of the long positions of our sample funds and tests whether it is different from that

of the other mutual funds that do not undertake short positions. Section V concludes the paper.

I. Sample and Descriptive Statistics

A. Sample

From the Center for Research in Security Prices (CRSP) Mutual Fund database, we

identify all actively managed mutual funds that focus on domestic equity. We eliminate bond

funds, global and international equity funds, index funds, asset allocation funds and money

market funds. This database provides all the mutual fund data used in the study, including fund

objective, fees, loads, portfolio holdings and turnover. Since the availability of portfolio holdings

of individual mutual funds on the CRSP database is sparse prior to the second quarter of 2003,

we focus on portfolio holdings from April 2003 to December 2005. We aggregate different

classes of the same portfolio into one observation. If the portfolio holdings indicate that a fund

held short positions in publicly traded domestic (U.S.) stocks with a share code of 10 or 11, then

8

it is considered a sample mutual fund that engages in short sales.5 The final sample includes 75

mutual funds that had short positions in domestic US stocks.6 All other actively managed

domestic equity funds are considered as potential control funds, including funds that may have

shorted only international stocks, ADRs, or other non-common stock securities.

Some of our empirical tests use the performance of ‘control funds’ as the benchmark to

infer the abnormal performance of the sample funds. Berk and Green (2004) present a model

where investors assign high ability to a manager and invest in the fund after observing high

returns. If there are decreasing returns to scale in deploying the manager’s ability to generate

abnormal returns, then the fund (which is now larger, due to higher inflows) would earn only

normal returns. Consistent with this model, Chen, Hong, Huang, and Kubik (2004) find that

larger funds experience lower returns. Following this literature, we select five control funds that

are closest in total net assets to the sample fund and use the abnormal return on these control

funds as the benchmark to estimate the abnormal return of the long portfolio of the sample funds.

We note that this approach is feasible only for assessing the performance of the long portfolio of

the sample mutual funds. We cannot use this benchmark for estimating the performance of the

short portfolio since all funds that use short sales are part of the sample and none of the control

funds would have short positions.

The portfolio holdings data include the effective date of the portfolio holdings, number of

shares held in either the long or the short portfolio, the name of the firm, and the CRSP identifier

‘permno’. For a random sample, we manually verify the portfolio holdings data for shorted

5 We note that our sample includes only funds that had short positions outstanding at the portfolio report date. Funds that initiated and covered all their short positions within two adjacent portfolio reporting dates are not considered sample funds if they did not have outstanding short positions at the portfolio reporting dates. Thus, our sample represents a lower bound on the prevalence of shorting among mutual funds. 6 We exclude one fund (Merger Fund) that engaged in short sales as part of merger arbitrage transactions, since these are not considered valuation shorts.

9

stocks with the data from Form N-Q filed with the Securities and Exchange Commission (SEC).

In all instances that we checked, we found no discrepancies between the portfolio holdings

available from CRSP and those in the SEC filing. We link the portfolio holdings data to the

CRSP stock database using the permno as the link variable, and collect returns and shares

outstanding data from CRSP. We only retain portfolio holdings of domestic US stocks (CRSP

share code 10 or 11), and exclude all other securities. We use the merged CRSP-Compustat file

to link the portfolio holdings data with Compustat. All financial data are from Compustat.

B. Descriptive Statistics

The sample consists of both small and large funds since there is considerable variation in

the size of the mutual funds that use short sales. The average total net assets for the 75 sample

funds is $461 million, and the median is $44 million (not tabulated). This is comparable to the

values reported in the prior literature.7 Table I summarizes the characteristics of the sample

mutual funds in more detail. In the first reporting period during 2003-2005 when these mutual

funds reported short positions outstanding, they short sell an average of 38 stocks. The average

market value of the short positions, calculated using the stock price on the portfolio reporting

date, is $18 million. In the same quarter, the average market value of long positions is $278

million. However, the average number of stocks in the long portfolio is about twice as large (81

stocks versus 38 stocks), indicating that the average size of the short position per stock ($0.47

million) is smaller than that for long positions ($3.45 million). The mean long position for the

control funds in the same quarter is $402 million invested in 90 stocks.8

7 The mean total net assets in Chen et al. (2004) is $282 million, and in Kacperczyk, Sialm, and Zheng (2005) is $623 million. 8 We note that the control funds are matched on total net assets at the start of the period when the abnormal return on the long portfolio is computed, which is different from the first quarter when the fund first reported short positions.

10

The mean total load is 2.54% for the sample funds and is 1.93% for control funds, but the

difference is not statistically significant. However, both the average 12b1 fees attributed to

marketing and distribution costs and the overall operating expenses (including the 12b1 fees) are

significantly higher for the sample funds than for control funds (0.30% and 2.17% versus 0.2%

and 1.49%, respectively). The average turnover of the fund assets is 277% for the sample funds

and 193% for the control funds. These values are also higher than the values for actively

managed equity funds reported in prior literature. For example, Kacperczyk, Sialm, and Zheng

(2005) report that for their sample of 1,771 funds during 1984 to 1999, the average expense ratio

was 1.26% and the mean turnover was 88.3%.

II. Stock Characteristics

In this section, we provide a detailed characterization of the stocks that the mutual funds choose

to short. Every year (July t to June t+1), we identify all domestic common stocks that were shorted

at least once by any domestic equity mutual fund from the portfolio holdings data reported

during this time period. We construct two comparison samples and compare the characteristics of

the shorted stocks to the characteristics of stocks in the two comparison samples. The first

includes all stocks held in the long portfolio by these same mutual funds that had reported, at

least once, a short position in US domestic stocks. The second includes all CRSP-listed stocks,

excluding the stocks in the mutual funds’ short portfolios. For ease of exposition, we term these

samples the ‘shorted stocks’, ‘long stocks’, and ‘CRSP stocks’, respectively.

This analysis is motivated by the evidence in Falkenstein (1996), that mutual funds avoid

investing in small, low-priced stocks, and in illiquid stocks with little information. On the other

hand, Dechow, Hutton, Meulbroek, and Sloan (2001) find that stocks with high short interest are

11

more likely to have low fundamentals (e.g., cash flow, book value, earnings) to price ratios, and

conclude that short sellers take positions in such stocks. D’Avolio (2002) examines stock loan

data and concludes that small, illiquid firms are difficult to short. Our results are consistent with

mutual funds using valuation and fundamental indicators to establish short positions that are

significantly different from long positions.

A. Shortability

We match the stocks with characteristics measured at the end of June in year‘t’, and

present the results in Table II. The results suggest that mutual fund managers take the possibility

of a short squeeze into account when they select which stocks to short. Specifically, the shorted

stocks are larger and more liquid than the long stocks. The average equity market value,

measured as the product of the number of shares outstanding and share price at the end of June,

from CRSP), is $6,104 million, and the median is $1,016 million. This is significantly higher

than the mean (median) size of $4,497 million ($696 million) for the long stocks at the one-

percent significance level. The mean (median) size decile for the shorted stocks is 4.86 (4.00),

and is significantly higher than that for the long stocks (4.18 and 3.00, respectively).9 We define

the annual turnover as the sum (over the prior twelve months) of the share volume in each month

divided by the total number of shares outstanding at the end of the month. The mean annual

turnover for the shorted stocks is larger than that for the long stocks (2.43 versus 1.98). A similar

pattern obtains when we use the other CRSP-listed stocks as the benchmark – the shorted stocks

are significantly larger and more liquid than a typical CRSP-listed stock.

Since institutional investors prefer investing in large, liquid stocks (e.g., Falkenstein

(1996)), these results have a straightforward interpretation. Given that institutions are responsible 9 The decile breakpoints are based on NYSE stocks and are downloaded from Ken French’s website.

12

for the bulk of the stock lending, ceteris paribus, the shorted stocks are more likely to be those

where institutional investments are anticipated. Mutual funds prefer to establish short positions

in stocks where locating a lender is easier, to minimize the likelihood of a short squeeze.

B. Performance Indicators

The mean prior momentum (buy and hold raw return over the eleven months ending in

May of year ‘t’) for the short stocks is 17%, and is significantly smaller than both the average

momentum of stocks in their long portfolios (23%) and that of the other CRSP stocks (20%).

Consistently, both the mean and median momentum decile for the shorted stocks (5.25, 5.00) are

smaller than for the long stocks (5.77, 6.00) and the other CRSP stocks (5.56, 6.00).

However, the shorted stocks tend to be glamour stocks with high total accruals and high

prior sales growth. The equity book to market ratio (BE/ME) is calculated as the equity book

value from the fiscal year ending in calendar year ‘t-1’, divided by equity market value

calculated from CRSP at the end of December t-1. The average BE/ME for the shorted stocks is

0.50, and is significantly smaller than that of 0.61 for the long stocks. The mean and median

BE/ME decile for the shorted stocks is also smaller than for the long stocks (3.97 and 3.00 versus

4.81 and 5.00, respectively). The differences are magnified when compared to a typical CRSP

firm. The mean and median BE/ME (BE/ME decile) for the other CRSP firms are 0.76 and 0.57

(5.60 and 6.00), and are significantly different from the values for the shorted stocks at less than

the one-percent significance level. For the shorted stocks, the mean industry-adjusted total

accruals is 3.5% and mean industry-adjusted sales growth is 11.6%.10 These are larger than the

10 We calculate the industry-adjusted values by subtracting the median for all the firms in the same two-digit SIC industry from the value for individual firms.

13

corresponding values for the long stocks (2.4% and 8.4%, respectively) and the other CRSP

stocks (-0.3% and 6.4%, respectively) at the one-percent significance level.

These results are consistent with mutual fund managers using the information in

valuation and earnings quality indicators to identify appropriate stocks to short. For example,

Sloan (1996) and Fama and French (2006) find that firms reporting high accruals experience low

future returns. Consistently, Richardson, Sloan, Soliman, and Tuna (2006) conclude that extreme

accruals are likely due to temporary accounting distortions such as earnings manipulation. Given

that glamour (low BE/ME) firms with high prior sales growth are more likely to manage their

financial results and experience poor subsequent performance, our findings suggest that mutual

fund managers are sensitive to the information contained in such indicators of performance.

C. Risk

We find some differences in the risk characteristics of the shorted stocks compared to the

other stocks. The average equity beta for the shorted stocks (estimated using upto 60 monthly

returns and using both the current and lagged value-weighted market return as explanatory

variables) is 1.43, and the median is 1.18, suggesting that the shorted stocks have above-market

risk. The return standard deviation (estimated using monthly returns over the prior 60 months)

averages 0.17. These values are significantly higher than for the long stocks (mean beta 1.33,

median beta 1.05, and mean standard deviation 0.16) at the one-percent level. While the average

beta for the shorted stocks is similar when compared to the other CRSP stocks, the median beta

remains significantly higher for the shorted stocks. This suggests that mutual funds select more

risky and volatile stocks in which to establish short positions. It is likely that their informational

14

advantage would be higher in these stocks, rather than in the less risky, informationally

transparent stocks.

To summarize, the stocks in the short portfolio differ predictably from the long stocks

and the other CRSP stocks along several dimensions. The shorted stocks (a) are larger and more

liquid, (b) have low BE/ME, and whose current performance seems to be unsustainable, and (c)

are more risky and volatile.

III. Performance of Mutual Funds’ Short Positions

This section analyzes the profitability of the mutual fund’s shorting decisions. Following

Carhart (1997), several studies estimate abnormal returns as the intercept in calendar time

regressions of monthly portfolio returns on selected risk factors. We also adopt this framework to

estimate abnormal performance. The first set of tests examines whether mutual funds are

successful in selecting appropriate stocks to short. The second group of tests analyzes whether

the individual fund manager’s short portfolio as a whole is profitable. The results indicate that

fund managers are able to identify stocks that do poorly, and profit from their shorting decisions.

A. Stock Return Analysis

If fund managers are able to accurately identify overpriced stocks to include in their short

portfolio, then a portfolio of stocks shorted by mutual funds will earn negative abnormal returns.

Empirically, we proceed as follows. We aggregate the shorted stocks into portfolios using

several combinations of weighting schemes and assumed holding periods and calculate the

monthly raw return on this portfolio using the individual stock returns from CRSP. The resultant

monthly portfolio return (in excess of the risk free rate) is regressed on the four Carhart (1997)

15

factors – RmRf, SMB, HML, and MOM.11 The intercept from calendar time regressions of the

monthly portfolio return on the four Carhart (1997) factors is an estimate of the monthly

abnormal return for the shorted stocks.

In the baseline analysis, we assume a holding period of six months and construct equally

weighted portfolios. Every calendar month, all the stocks that are shorted by at least one mutual

fund in the relevant holding period (the prior six months) are aggregated into an equally-

weighted portfolio. The following example illustrates the procedure. For a given month (say

August 2004), we first identify all portfolio holdings that are reported in the preceding six

months (February to July 2004). All US common stocks that are shorted in these reported

portfolio holdings are included in the portfolio. The equally weighted portfolio return for the

month is then obtained. The time series of these monthly returns is then used as the dependent

variable in the calendar time regressions. The results are reported in Table III. The shorted stocks

load positively on the market factor RmRf with a coefficient of 1.21, suggesting that the shorted

stocks have slightly greater risk than the aggregate market. The coefficient on SMB is also

positive and significant, but the coefficients on both HML and MOM are not significantly

different from zero. Importantly, the intercept is -0.30%, and is significantly different from zero

at the five-percent level (t-value of -2.14). This annualizes to an abnormal return of -3.6%. The

results are similar when we assume a holding period of twelve months instead of six months. The

loadings on the four factors are similar to those obtained earlier assuming a six month holding

period. The magnitude of the abnormal return now is -0.28% per month or -3.3% on an

annualized basis. This abnormal return is significant at the five-percent level (t-value of -2.12).

The equal weighting scheme does not reflect the intensity of shorting activity by mutual

funds. Presumably, stocks that attract higher levels of relative short interest from fund managers 11 We downloaded the factor returns from Ken French’s website.

16

should be more likely to be overpriced. Ceteris paribus, if a particular stock is significantly

overvalued, many fund managers would consider the stock an attractive short candidate, and

their collective shorting would result in higher relative short interest. In contrast, stocks that

attract low levels of relative short interest from managers are not likely to be overvalued by

much. We use an alternative weighting scheme that incorporates this notion. Assuming a six

month holding period (or twelve months), we calculate the relative short interest for each stock

as follows. For a given month (say August 2004), we identify all short positions that are reported

in the preceding six months (February to July 2004). For every fund-stock pair, we calculate the

relative short interest ratio (RSI) as the sum of the ratio of the number of shares shorted by the

fund during the holding period divided by the total number of shares outstanding. The individual

RSIs for a given stock are then summed across all mutual funds. We use this summed RSI as the

weight in calculating the monthly portfolio return.

As expected, the magnitude of the abnormal return is much larger when the intensity of

shorting activity is taken into account. The abnormal return is -0.76% per month or -9.1% on an

annualized basis, and is statistically significant at the one-percent level (t-value of -3.61). The

abnormal return is similarly large and significant (-0.65% per month with a t-value of -3.48)

when the holding period is extended to twelve months instead of six months. Similar to the

equally weighted case, the coefficients of the four risk factors do not change by much.

Collectively, this evidence strongly supports the view that fund managers carefully select

the stocks to short. This may be due to the potentially large penalty associated with shorting a

stock that increases in value instead of declining. In such instances, fund managers would be

forced to cover their positions at a significant loss. Further, the high cost of shorting a stock may

prevent uninformed managers from using short sales, leaving only the more informed / skilled

17

managers to undertake short positions. The magnitude of this abnormal return is also

economically significant when compared to the results in prior short sales literature. For

example, Asquith, Pathak, and Ritter (2005) show that during 1988-2002, highly shorted stocks

(short interest greater than 10% of shares outstanding) underperform by -0.78% per month on an

equally weighted basis and an insignificant -0.27% per month on a value-weighted basis.

B. Portfolio Analysis

The results presented in Table III, while suggestive, do not provide direct evidence on

whether the individual mutual fund’s short portfolios generate abnormal returns. This is because

the stock return analysis does not consider the timing of the individual fund manager’s trading

decisions. It is possible that while the shorted stocks may experience negative abnormal returns

over the subsequent six or twelve months, the fund manager may either unwind their short

positions before the negative performance begins, or hold on to their short positions long after

the negative returns have materialized. Hence, even though the shorted stocks earn negative

abnormal returns, poor timing of the shorting and/or subsequent buying trades could result in a

fund manager not benefiting from this underperformance. We estimate calendar time regressions

at the individual mutual fund portfolio level to address this question.

For every sample mutual fund that reported short positions, we use the portfolio holdings

of short positions and calculate the monthly return. In order to allow sufficient degrees of

freedom and to enable accurate estimation of the coefficients, we require at least twenty-four

months (two years) of non-missing returns. The baseline estimation assumes a maximum holding

period of six months. In other words, we hold the portfolio composition constant for six months,

unless the fund reports updated portfolio holdings. For example, suppose that a fund reports

18

portfolio holdings with short positions in February 2004. If the next portfolio holdings report is

on August 2004, then the February 2004 short positions would be held until August 2004 (six

months holding period) , and the new portfolio holdings would be used starting in September

2004. But, if the next holdings report is on May 2004, then the portfolio composition would be

updated in June 2004, before the six months holding period expires. The portfolio return is set to

missing in the months after the assumed holding period if no short positions are reported, and

these months are not included in the calendar time regressions. Calendar time regressions are

estimated for individual funds, and the cross-sectional means of the coefficients are reported in

Table IV. The results show that the average fund earns an abnormal return of -0.45% per month,

and is statistically significant at the five-percent level (t-value of -2.12). We re-estimate the

model by increasing the holding period to twelve months rather than six months, and find similar

results. The intercept is a significant -0.40% per month (t-value of -2.11).

As an alternative specification, we re-estimate the abnormal returns, but only include

funds that shorted five or more stocks at least once. This restriction on the number of shorted

stocks eliminates funds that use short sales infrequently and never shorted five or more stocks in

any reporting period. We find that the results are unchanged. The intercept remains negative (-

0.49% and -0.42% per month for the six and twelve month holding period), and significant at the

five-percent level. Further, the coefficients on the four risk factors are similar in magnitude

across all four models. The coefficient on the market factor RmRf and the size factor (SMB) are

always positive and significant, and that on momentum (MOM) is never statistically significant.

The equity book to market ratio factor (HML) is negative but is significant only in two models.

These results provide confirmatory evidence that fund managers are able to initiate short

positions and unwind them profitably. Taken together with the stock return results discussed in

19

section III.A, the evidence supports the theoretical prediction that informed / skilled fund

managers are more likely to engage in short sales. We conduct an additional test to verify this

result. If these are indeed skilled managers, evidence of their stock picking skill should be

apparent in their long positions also. In other words, they should be able to earn positive

abnormal returns on their long portfolios, and the magnitude of abnormal return should be larger

than that earned by other fund managers that manage similar sized funds. In the next section, we

provide further evidence to test this claim.

IV. Performance of Mutual Funds’ Long Positions

The analysis presented in this section further examines whether mutual funds that include

short selling as a component of their investment strategy are indeed the better managed funds.

Similar to the analysis in section III.B, we test whether the portfolio of long stocks of the sample

funds earns a positive abnormal return. We also test whether the abnormal return is higher than

that earned by other funds with comparable total net assets (TNA). We select control funds based

on TNA since prior literature (e.g., Chen, Hong, Huang, and Kubik (2004)) finds evidence that

fund size erodes fund performance. The results corroborate the findings in Section III.B that fund

managers that engage in short sales are skilled and are able to earn significantly positive

abnormal returns.

As in Table IV, the baseline model estimates the four-factor calendar time regression for

individual funds that have at least twenty-four monthly returns available, assuming a six month

holding period. Panel A in Table V reports the cross-sectional mean of the regression

coefficients. The intercept averages 0.20% per month or 2.4% on an annualized basis, and is

statistically significant at the one-percent level (t-value of 2.77). The results are similar when we

20

use a twelve month holding period instead of six months, or when we exclude funds that use

short sales infrequently and never shorted five or more stocks in any reporting period. In all three

cases, the abnormal return averages between 0.16% per month to 0.21% per month, and all are

significant at the five-percent level or better. In all four models, the coefficient on the market risk

factor, SMB and MOM are significantly positive. In contrast, the coefficient on the HML factor

is statistically significant (positive) in only one of the four models.

While the results presented above are consistent with skilled fund managers using short

sales as an investment strategy, they may be an artifact of fund TNA. Chen et al. (2004) find

evidence that an increase in an individual fund’s TNA reduces subsequent performance. They

conduct additional tests to show that this relation is stronger for funds that invest in small stocks,

likely due to the larger funds incurring higher trading costs arising from liquidity and price

impact. Since Table II documents that the median size of an individual stock in the long portfolio

of our sample funds is $696 million (NYSE size decile 3), it is possible that the estimated

abnormal returns may be inflated. In other words, the superior performance of our sample funds

may be similar to that of other funds that have similar total net assets. In Panel B, we refine the

results in panel A by comparing the performance of our sample funds with that of actively

managed funds of similar size that invest in domestic equity, but do not engage in short sales.

We generate a time series of monthly control fund returns using the first control fund, replacing

it with the second best match if it ceases to exist, and so on. For both the sample and control

funds, we only use returns when the sample fund returns are available.

In the baseline case reported in panel B of Table VI, we require the funds to have at least

twenty-four monthly returns and assume a six month holding period, as before. The average

abnormal return earned by the control funds is -0.08% per month, and the mean difference in

21

abnormal return (sample – control) is 0.28% per month, which is statistically significant at the

one-percent level (t = 2.97). As in panel A, the results are unchanged when we use a twelve

month holding period instead of six months, or when we exclude funds that use short sales

infrequently and never shorted five or more stocks in any reporting period. In all three cases, the

difference in abnormal return (sample – control) averages between 0.24% per month to 0.34%

per month, and all are significant at the one-percent level.12 Further, in all instances, the mean

difference in fund size is around $1 million and the difference is never statistically significant,

confirming that the matching process works well.

The results in Table V provide strong confirmatory support for the theoretical prediction

that skilled fund managers are more likely to undertake short positions. Stated differently, sorting

fund managers on the basis of whether or nor they use short selling as a component of their

investment strategy helps identify skilled managers. The magnitude of abnormal returns (before

expenses) ranges between 1.9% and 2.6% on an annualized basis, which is comparable to that

documented in other settings. For example, Kacperczyk et al. (2005) find that mutual funds with

above average industry concentration earn an average abnormal return of 1.6% per year, before

expenses.

V. Conclusions

In contrast to prior research that focuses on the long positions held by mutual funds, this

paper extends the mutual fund performance evaluation literature by providing a first look at the

performance of mutual funds that use short selling as an investment strategy. The rationale for

12 In unreported tests, we replicate the analysis using the monthly return reported by the CRSP mutual fund database, rather than using the portfolio holdings data to calculate the returns. The former includes the effect of expenses and other investments also, not just US common stocks. However, the mean difference in performance (sample – control) remains positive and is statistically significant at the five-percent level or better.

22

using short selling as the criteria for identifying skilled managers is that only informed investors

are likely to use short sales. This follows from the theoretical model in Diamond and Verrecchia

(1987), which suggests that the costs associated with shorting would drive out the uninformed

investors, leaving only investors with bad news to sell short. Our approach complements those in

other recent studies that use geographic proximity (e.g., Coval and Moskowitz (2001)),

correlation of investments with other successful managers (e.g., Cohen, Coval, and Pastor

(2005)), degree of industry concentration (Kacperczyk, Sialm, and Zheng (2005)), and the extent

to which the fund portfolio deviates from its benchmark index (Cremers and Petajisto (2006)) as

ex-ante measures to identify stock picking skill among mutual fund managers.

We find that the mutual funds prefer to establish short positions in the larger and more

liquid stocks, where the likelihood of a short squeeze is smaller. However, the shorted stocks are

glamour stocks with poor earnings quality, and exhibit poor subsequent performance. The mutual

funds are able to earn statistically significant and economically large average abnormal returns

on both their short and long portfolios. Overall, the evidence is consistent with the theoretical

prediction that it is possible to identify skilled fund managers based on whether or not they use

short sales as a component of their investment strategy.

23

References

Almazan, Andres, Keith C. Brown, Murray Carlson, and David A. Chapman, 2004, Why

constrain your mutual fund manager?, Journal of Financial Economics 73, 289–321.

Asquith, Paul, Parag Pathak, and Jay R. Ritter, 2005, Short interest, institutional ownership, and

stock returns, Journal of Financial Economics 78, 243-76.

Asquith, Paul, and Lisa Muelbroek, 1996, An empirical investigation of short interest, Working

paper, MIT.

Baker, Malcolm, Lubomir Litov, Jessica A. Wachter, and Jeffrey Wurgler, 2005, Can mutual

fund managers pick stocks? Evidence from their trades prior to earnings announcements,

Working paper, Harvard Business School.

Berk, Jonathan, and Richard C. Green, 2004, Mutual fund flows and performance in rational

markets, Journal of Political Economy 112, 1269-1295.

Brunnermeier, Markus K., and S. Nagel, 2004, Hedge funds and the technology bubble, Journal

of Finance 59, 2013-2040.

Carhart, Mark M., 1997, On persistence in mutual fund performance, Journal of Finance 52, 57-

82.

Chen, Joseph, Harrison Hong, Ming Huang, and Jeffrey Kubik, 2004, Does fund size erode

performance? Liquidity, organizational diseconomies and active money management,

American Economic Review 94, 1276-1302.

Chen, Hsiu-Lang, Narasimhan Jegadeesh, and Russ Wermers, 2000, The value of active mutual

fund management: An examination of the stockholdings and trades of fund managers,

Journal of Financial and Quantitative Analysis 35, 343-368.

Cohen, Randolph B., Joshua D. Coval, and Lubos Pastor, 2005, Judging fund managers by the

company they keep, Journal of Finance 60, 1057-1096.

Coval, Joshua D., and Tobias J. Moskowitz, 2001, The geography of investment: Informed

trading and asset prices, Journal of Political Economy 109, 811-841.

Cremers, Martijn, and Antii Petajisto, 2006, How active is your fund manager? A new measure

that predicts performance, Working paper, Yale University.

24

D'Avolio, Gene, 2002, The market for borrowing stock, Journal of Financial Economics 66,

271–306.

Dechow, Patricia, Amy Hutton, Lisa Meulbroek, and Richard Sloan, 2001, Short sellers,

fundamental analysis, and stock returns, Journal of Financial Economics 61, 77-106.

Desai, Hemang, S. Ramu Thiagarajan, K. Ramesh, and Bala V. Balachandran, 2002, An

investigation of the informational role of short interest in the Nasdaq market, Journal of

Finance 57, 2263-2287.

Diamond, Douglas W., and Robert E. Verrecchia, 1987, Constraints on short-selling and asset

price adjustment to private information, Journal of Financial Economics 18, 277-312.

Falkenstein, Erik, 1996, Preferences for stock characteristics as revealed by mutual fund

holdings, Journal of Finance 51, 111–136.

Fama, Eugene F., and Kenneth R. French, 2006, Dissecting anomalies, Working paper,

University of Chicago.

Griffin, John M., and Jin Xu, 2005, How smart are the smart guys? A unique view from hedge

fund stock holdings, Working paper, University of Texas at Austin.

Grinblatt, Mark, and Sheridan Titman, 1989, Mutual fund performance: An analysis of quarterly

portfolio holdings, Journal of Business 62, 393-416.

Grinblatt, Mark, and Sheridan Titman, 1993, Performance measurement without benchmarks: An

examination of mutual fund returns, Journal of Business 66, 47-68

Jensen, Michael C., 1968, The performance of mutual funds in the period 1945-1964, Journal of

Finance 23, 389-416.

Kacperczyk, Marcin, Clemens Sialm, and Lu Zheng, 2005, On the industry concentration of

actively managed equity mutual funds, Journal of Finance 60, 1983-2012.

Malkiel, Burton G., 1995, Returns from investing in equity mutual funds 1971 to 1991, Journal

of Finance 50, 549-572.

Richardson, Scott A., Richard G. Sloan, Mark T. Soliman, and Irem Tuna, 2006, The

implications of accounting distortions and growth for accruals and profitability, The

Accounting Review 81, 713-743.

25

Sloan, Richard G., 1996, Do stock prices fully reflect information in accruals and cash flows

about future earnings?, The Accounting Review 71, 289–315.

Wermers, Russ, 1999, Mutual fund herding and the impact on stock prices, Journal of Finance

54, 581-622.

Table I. Summary characteristics of the funds that use short sales This table reports the characteristics for the domestic equity funds that short. Fund characteristics are obtained when a fund first reported a short position in some CRSP common stocks during our sample period. We obtain both the total dollar amount short and number of short positions for the funds with short positions in CRSP stocks, as well as the total dollar amount of and number of long positions. Other fund characteristics such as expenses, fees, and loads are also obtained for the fund. If there are several funds associated with one portfolio, the total net asset value (TNA) weighted average is used for the portfolio. We also compare our sample fund characteristics with the average characteristics for a group of five matching funds that are closest in TNA to our sample funds. In addition, we compare our sample fund characteristics with those of other non-index domestic equity funds in June 2004. The differences in mean and median are tested using t-tests and Wilcoxon signed rank tests, respectively.

Funds Statistics Amount

Short ($million)

Positions Short

Amount Long

($million)

Positions Long

Expenses(%)

Front Load (%)

Rear Load (%)

12b1 Fees (%) Turnover Mgmt

Fees (%)

Mean 17.72 38 278.06 80.7 2.17 1.61 0.93 0.30 2.77 1.21

Median 3.23 5 40.04 49.5 1.94 0.18 0.91 0.25 1.73 1.03 Sample Funds

N 75 75 72 72 74 75 75 75 70 75

Mean 401.65a 89.62 1.49a 1.19 0.74 0.20a 1.93 0.77a

Median 46.05a 77.25a 1.47a 1.12 0.68 0.19a 1.16 0.78a Match Funds

N 72 72 74 75 75 75 70 75

Mean 796.94 102.4b 1.50b 1.37 0.87 0.23c 1.27a 0.78a

Median 120.43a 66.0a 1.40a 0.02 0.39 0.15 0.71a 0.75a

Other Active Domestic Equity Funds

N 2,187 2,187 2,134 2,142 2,178 2,187 2,128 2,187

a, b, and c denote significant difference from the characteristics of the sample funds at 1, 5, and 10 percent levels, respectively.

Table II. Characteristics of shorted, long, and other CRSP stocks This table reports the characteristics for stocks shorted or longed by the domestic equity funds that short. We look at the market characteristics as of June of each year for all the positions held by any sample fund from the end of June through end of May of the next year. Each stock is included only once for either the short or long side, regardless how many funds have the same position and how many times a position is held. Size is measured as the end of June market capitalization. BE/ME at year t is the ratio of book equity for the fiscal year ending in the previous year to the market equity at the end of year t-1. Momentum is cumulative returns over the previous 11 months ending in May. The decile breakpoints for size, BE/ME, and momentum are based on NYSE stocks, and downloaded from French’s website. Standard deviation and beta are obtained from preceding 60 months of returns, and at least 24 months of returns are required for the estimates to be included in the analysis. Beta is obtained from a regression on the con current and one lagged value weighted market return. The differences in mean and median sample characteristics between our sample funds and other funds are tested using t-tests and Wilcoxon signed rank tests, respectively.

Characteristics

Shorted stocks

(1)

Long stocks

(2)

CRSP stocks, excluding

shorted stocks(3)

Test statistics (1)-(2)

Test statistics (1)-(3)

Shortability:

Size 6,104.45 1,016.11

4,496.94 696.29

910.29 115.99

4.64 14.22

22.65 63.00

Size Decile 4.86 4.00

4.18 3.00

2.08 1.00

12.65 13.57

66.89 61.24

Turnover 2.43 1.74

1.98 1.38

1.24 0.68

9.34 15.48

26.39 49.63

Performance Indicators:

Momentum 0.17 0.06

0.23 0.12

0.20 0.11

-4.97 -9.15

-2.24 -4.90

Momentum Decile 5.25 5.00

5.77 6.00

5.56 6.00

-9.10 -9.03

-5.40 -5.11

BE/ME 0.50 0.40

0.61 0.49

0.76 0.57

-10.94 -14.72

-19.86 -28.12

BE/ME Decile 3.97 3.00

4.81 5.00

5.60 6.00

-16.45 -16.23

-31.93 -30.93

Total Accruals Raw

0.023 0.012

0.014 0.006

0.003 0.006

2.735 3.190

5.685 4.922

Total Accruals Industry Adjusted

0.035 0.021

0.024 0.013

-0.003 0.000

3.464 4.602

10.829 12.883

Sales Growth Raw

0.181 0.087

0.153 0.080

0.170 0.092

3.280 1.905

1.306 -1.532

Sales Growth Industry Adjusted

0.116 0.023

0.084 0.010

0.064 -0.004

3.769 3.458

6.163 9.650

Risk:

Beta 1.43 1.18

1.33 1.05

1.39 1.02

4.47 4.97

1.21 5.80

Return Standard Deviation

0.17 0.15

0.16 0.14

0.18 0.15

3.88 4.94

-5.08 0.74

Table III. Abnormal returns to stocks that are shorted by domestic equity funds. In this table, we evaluate the returns to portfolios of stocks that are shorted by domestic equity funds. For each month from 200307 through 200512, all the stocks that are shorted by at least one domestic equity funds over the preceding holding period (3-month, 6-month, or 12-month) are included in the portfolio. RSI-weighted portfolio returns are obtained by assigning each stock with the proportion of each stock that is shorted by each fund. When there are several funds shorting the same stock, each fund is given different weight in the analysis. For equally-weighted portfolio returns, each stock is only included once no matter how many funds take short positions in the stocks. The 30-month time series of portfolio returns are then regressed on the Carhart four factors, and the results are reported below. In each cell, the top row is the regression coefficient, and the bottom row is the corresponding t-statistic, testing the null hypothesis that the coefficient is zero.

Assumed Holding Period

Weighting Scheme Intercept RmRf SMB HML MOM Adj. R2

Equal -0.297 b -2.138

1.215 20.046

0.564 7.300

-0.028 -0.328

-0.085 -1.476 0.976

6-months

RSI -0.756 a -3.606

1.344 14.672

0.789 6.751

0.079 0.622

-0.116 -1.328 0.961

Equal -0.276 b -2.120

1.185 20.870

0.569 7.850

0.028 0.352

-0.069 -1.273 0.978

12-months

RSI -0.650 a -3.480

1.261 15.449

0.804 7.721

0.087 0.769

-0.114 -1.459 0.966

a, b, and c denote significance at 1, 5, and 10 percent levels, respectively.

Table IV. Abnormal returns to short portfolios of domestic equity funds We evaluate the performance of the short positions taken by the domestic equity funds. Starting from the second quarter of 2003, we construct a portfolio of short positions for each domestic equity fund that has established short positions, and trace the portfolio return until the hypothetical holding period ends, or until the next portfolio holdings information becomes available, whichever occurs first. The time-series of these individual portfolio returns are then regressed on the four factors from Carhart model, the averages of the regression coefficients, along with their corresponding t-statistics are then reported. In order for a portfolio to be included in the average, we require that at least 24-month of returns data be available for the estimation of the regression model. In each cell, the top row is the regression coefficient, and the bottom row is the corresponding t-statistic, testing the null hypothesis that the coefficient is zero.

Holding Period

# of Funds intercept rmrf smb hml mom Adj.

R2 # Stocks Shorted

6 24 -0.453 b -2.117

1.169 a 12.483

0.496 a 3.207

-0.135 -1.609

-0.020 -0.173 0.679 > 1

12 27 -0.401 b -2.114

1.162 a 13.945

0.537 a 3.522

-0.162 b -2.064

-0.042 -0.387 0.683 > 1

6 23 -0.491 b -2.229

1.240 a 19.586

0.426 a 2.954

-0.132 -1.502

0.018 0.154 0.697 > 5

12 25 -0.417 b -2.065

1.230 a 20.873

0.411 a 3.041

-0.162 c -1.909

0.018 0.165 0.711 > 5

a, b, and c denote significance at 1, 5, and 10 percent levels, respectively.

Table V. Abnormal returns to long positions for the domestic equity funds that use short sales We evaluate performance of the long positions taken by the domestic equity funds that take short positions. Starting from the second quarter of 2003, we construct a portfolio of long positions for each domestic equity fund that have established short positions at least once over our sample period, and trace the portfolio return until the hypothetical holding period ends, or until the next portfolio holdings information becomes available, whichever occurs first. The time-series of these individual portfolio returns are then regressed on the four factors from Carhart model, the averages of the regression coefficients, along with their corresponding t-statistics are then reported. In order for a portfolio to be included in the average, we require at least 24-month of returns data. The performance of our sample funds is compared with the performance of their corresponding TNA-matched funds. In each cell, the first number is the regression coefficient, and the second number is the corresponding t-statistics for testing the null hypothesis that the coefficient is zero. Panel A:

Holding Period

# of Funds intercept rmrf smb hml mom Adj.

R2 # Stocks Shorted

6 48 0.200 a 2.771

1.047 a 24.669

0.345 a 7.426

-0.009 -0.115

0.082 c 1.917

0.747 > 1

12 55 0.160 b 2.497

1.078 a 26.969

0.339 a 8.305

-0.036 -0.544

0.086 b 2.298

0.755 > 1

6 27 0.215 a 3.127

1.015 a 25.488

0.388 a 6.773

0.151 1.642

0.112 b 2.384

0.787 > 5

12 29 0.211 a 3.308

1.017 a 29.012

0.382 a 6.967

0.140 c 1.702

0.116 b 2.697

0.799 > 5

Panel B:

Four Factor Carhart (1997) Alpha Total Net Assets

Holding Period

# of Funds Sample Matches Paired

Difference Sample Matches Paired Difference

# Stocks Shorted

6 48 0.200 a 2.771

-0.076 -1.284

0.276 a 2.975

464.454 3.146

463.369 3.143

1.085 1.226 > 1

12 55 0.160 b 2.497

-0.083 -1.567

0.243 a 2.895

455.942 3.413

455.038 3.411

0.903 1.165 > 1

6 27 0.215 a 3.127

-0.104 -1.426

0.319 a 3.291

437.796 2.141

436.878 2.135

0.918 0.693 > 5

12 29 0.211 a 3.308

-0.127 c -1.969

0.338 a 3.780

424.841 2.229

423.976 2.223

0.865 0.702 > 5

a, b, and c denote significance at 1, 5, and 10 percent levels, respectively.