Do funds with few holdings outperform kaushik

Original Article

Do mutual funds with few holdingsoutperform the market?Received (in revised form): 24th October 2008

Abhay Kaushikis an assistant professor of finance at Radford University, Virginia. He received his MS in Economics and PhD in Finance from

Florida Atlantic University. His main areas of research include financial markets and exchange rate.

Scott W. Barnhartis an associate professor of finance at Florida Atlantic University. He received his MS in Economics from Florida State University

and PhD in Economics from Texas A&M University. Professor Barnhart is the Programme Director of both the MBA with Financial

Planning Track and the Certified Financial Plannert Certificate Programme at Florida Atlantic University.

Correspondence: Abhay Kaushik, Department of Accounting, Finance and Business Law, Radford University, Virginia 24142, USA

E-mail: [email protected]

ABSTRACT This paper investigates the performance of mutual funds that hold a small

number of stocks in their portfolio. Similar to results reported in the literature for the

average diversified mutual fund, our results indicate that the average small holding fund

does not outperform the S&P 500 index. On average, small holding funds under-perform

the market on a risk and investment style adjusted basis by about �20 basis points per

month, or by �2.40 per cent per year. We also find that there is a sharp contrast between

the performance of Winner and Loser portfolios. On average, Winner portfolios outperform

the S&P composite index by 410 basis points per month, or an astounding 49.2 per cent

per annum, whereas Losers under-perform by 320, or �38.4 per cent per annum, over the

same period. Cross sectional regressions indicate that Winner portfolio abnormal

performance is positively and significantly related to fund turnover and the per cent of

the fund’s assets invested in their top 10 most heavily weighted holdings. Results for Loser

portfolios show that abnormal performance deteriorates significantly with turnover,

concentration and expenses, but rises with Load and Size.

Journal of Asset Management (2009) 9, 398–408. doi:10.1057/jam.2008.39

Keywords: mutual fund performance; expense ratio; turnover ratio; holdings

INTRODUCTIONRecent academic research on actively

managed mutual fund performance has

shown that the average well-diversified

mutual fund under-performs passive

market benchmarks after adjusting for risk,

expenses and trading costs (see, for example,

Wermers (2000) among others). The

underperformance found is largely

explained by mutual fund expenses and

transactions costs, and to a lesser extent the

underperformance of non-stock holdings.

Moreover, Carhart (1997) shows that risk-

adjusted net returns from the average mutual

fund are negatively correlated with fund

expenses and portfolio turnover, both of

which have increased over time (Wermers

(2000)).1

In contrast to the results reported in

studies of broadly diversified mutual funds,

the financial press has frequently reported

that small, more concentrated or focused

398 & 2009 Palgrave Macmillan 1470-8272 Journal of Asset Management Vol. 9, 6, 398–408

www.palgrave-journals.com/jam/

portfolios, while perhaps not fully

diversified, may be a better investment bet.2

This is exactly the style of investing

advocated by Warren Buffett and used in his

phenomenally successful Berkshire Hathaway

fund.3 Recent research in this area has

proceeded down to two related paths: the

first investigates scale economies or the effect

of fund size on performance and the other

examines how portfolio concentration affects

fund performance.

In the first strand examining scale

economies, Berk and Green (2004)

demonstrate in their model that some

empirical regularities found in mutual fund

research, such as fund flow following

performance, and so on, result when they

assume that mutual fund manager costs are

an increasing function of the amount of

funds under management. They assume that

‘‘managerial talent is a scarce resource and is

dissipated as the scale of operations increases’’.

Empirically, Chen et al (2004) document

Berk and Green’s assumption, finding that

risk and fee-adjusted excess returns are

negatively related to size, measured by the

total net assets under management. In related

work, Shawky and Smith (2005) find a

quadratic relationship between risk-adjusted

fund returns and the number of fund

holdings, suggesting that there is a trade off

between diversification benefits and

increased transactions and monitoring costs.

In the second strand investigating fund

concentration, Kacperczyk et al (2005) show

that mutual funds that concentrate their

holdings within a few industries outperform

passive benchmarks by 1.58 per cent per year

after controlling for risk and style differences.

They attribute their findings to superior

stock selection by managers of concentrated

funds. Similarly, Nanda et al (2004) find that

fund families that have fewer or more

narrowly focused investment strategies

outperform families that have a wider variety

of strategies.

Although the argument in favour of

holding a fund whose assets are concentrated

in a small number of companies may be in

conflict with the common recommendation

of diversification, it is consistent with Warren

Buffett’s huge success and the notion that

some fund managers have informational

advantages over others. By holding fewer

stocks, as opposed to one or two hundred,

with larger percentages of the fund’s assets

concentrated among fewer companies, fund

managers can take more aggressive positions

in companies that they are more familiar

with, thereby magnifying potential gains

(and losses). Indeed, in the late 1990s, when

stock market index returns were driven

largely by a few highly valued companies in

the index, mutual fund companies

introduced a number of new funds with

concentrated holdings.4

These arguments raise a simple yet

important question: Do mutual funds with

fewer and more concentrated holdings

outperform broader based market

benchmarks, or do they suffer the same

underperformance of the average mutual

fund cited above? In this study we examine

the performance of mutual fund portfolios

that hold a small number of stocks. As a

consequence of holding few companies,

these funds also have concentrated holdings.

In a fashion similar to the existing literature,

we compare the performance of these funds

with passive portfolio benchmarks like the

S&P 500 index.

As no mutual fund trade association, such

as the Investment Company Institute (ICI),

or investment research firm, such as

Morningstar Inc., has defined a fund

category with the fund characteristics we

wish to investigate, that is, funds with a small

number of concentrated holdings, we rely on

definitions reported in the financial press and

in fund objective statements taken from

internet sources.5 Specifically, this study

defines a small, concentrated portfolio as a

mutual fund with holdings of 10–30 stocks.6

We investigate the performance of these

funds over the recent 2001–2006 year

period, a period that includes both recession

Do mutual funds with few holdings outperform the market?

399& 2009 Palgrave Macmillan 1470-8272 Journal of Asset Management Vol. 9, 6, 398–408

and expansion.7 During this period investors

have shown great interest in more narrowly

focused, non-diversified funds, such as sector

funds and exchange-traded funds. A case in

point, over the period of study, the growth in

the total net assets under management of the

funds we investigate grew at a compound

annual rate of 21.44 per cent, versus a rate of

8.33 per cent for the overall mutual fund

industry (Source: ICI fact book).

In addition to examining the overall

performance of these narrowly focused

funds, we also segment funds into Winning

and Losing portfolios in order to see their

upside and downside potentials. We then

examine the funds in a cross-sectional

analysis to see which fund characteristics,

such as expense ratio, turnover ratio, size,

concentration and load, explain their

abnormal performance.

In contrast to some of the literature cited

above, but in agreement with the findings for

the average mutual fund, we find that the

average, narrowly focused fund under-

performs market benchmarks on a risk-

adjusted basis by about �2.4 per cent per

year. Despite this finding, there are some

phenomenal successes (and failures) within

this fund group: the top quartile of Winning

portfolios outperforms on a risk-adjusted

basis by approximately 49.2 per cent per year,

whereas the Losing portfolios under-perform

by about �38.4 per cent per year. In the

cross-sectional analysis we find that fund

turnover and the concentration of the fund’s

assets in its top 10 most heavily weighted

holdings significantly and positively explain

Winning performance, whereas fund

expenses and the fund’s assets in its top 10

most heavily weighted holdings are the

major characteristics that significantly and

negatively explain Losing performance.

The remainder of the paper is organised

as follows: The next section discusses some

pros and cons of investing in narrowly

focused mutual funds. The subsequent

section describes the data sources and

discusses sample characteristics. The later

section discusses the methodology and the

penultimate section presents the empirical

results. The last section concludes the

paper.

THE PROS AND CONS OFHOLDING SMALL DIVERSIFIEDPORTFOLIOSFund managers who manage portfolios with

few holdings are conjectured to have a better

understanding of those stocks and, therefore,

are more informed to deliver higher returns

compared with managers of large-holding,

more diversified portfolios. This would be

the result of following the Warren Buffett

investment style. The obvious expected

benefit to this structure would be higher

returns, lower transactions costs and perhaps

expenses, because there will be fewer stocks

to trade and fewer stocks to research.

On the other hand, a smaller number

of holdings implies less diversification and

higher idiosyncratic risk. Given the convex-

option-like payoff in fund flows by investors

cited by Kacperczyk et al (2005), in which

outstanding fund performers attract huge

positive cash inflows but poor performers

do not experience outflows at the same

intensity, fund managers may be motivated

to place excessive bets on a few stocks. The

implication is that standard measures of risk

for these companies should be higher than

the average mutual fund in the industry,

and large losses may occur due to the rapid

decline in a few stocks.

DATAWe use the Morningstar Principia Pro

database to first select funds with holdings

of 10–30 stocks. Although we are interested

in examining the performance of funds with

small numbers of holdings that may be non-

diversified, we are not interested in all funds

of this type and certain other types of funds.

Therefore, we screened out index funds,

Kaushik and Barnhart


international funds, sector funds, specialty

funds, hybrid funds, bond funds and

quantitative funds. Following Shawky and

Smith (2005) among others, we include only

type A shares in the event that the fund offers

multiple share types.

Monthly returns over the period January

2001–December 2006 are obtained from the

Center for Research in Security Prices

(CRSP) using Wharton Research Data

Services (WRDS). From these sources we

obtain the returns of our fund sample, the

S&P 500 composite index, the Fama–French

factors (see Fama and French (1993)), SMB

(the difference in returns between small and

large capitalisation stocks), HML (the

difference in returns between high and low

book-to-market stocks), the Carhart

momentum factor, MOM (the difference in

returns between stocks with high and low

past returns) and the monthly risk-free

return. The resulting sample contains a total

of 197 funds over the period, consisting of

387 yearly fund observations and 4640

monthly fund observations.

Fund- and manager-specific variables

such as turnover ratio, expense ratio, load

charges, the fund’s investment in its top 10

most heavily weighted holdings, total net

assets and 12b-1 fees are taken from the

CRSP mutual fund database and

Morningstar Principia Pro database.

Table 1 contains a break-down of the

firms in our sample into the well-known

nine-box investment style and capitalisation

diagram, where all definitions are taken from

Morningstar Inc. It is clear from the chart

that the sample is dominated first by large

companies and second by growth companies;

however, there are some firms from all

categories.

Table 2 contains annual returns, annual

standard deviations of monthly returns and

the 6-year average Sharpe ratio for the

Table 1: Distribution of investment style and capitalisation

Large-Value

31 (15.7%)

Large-Blend

38 (19.3%)

Large-Growth

68 (34.5%)

Mid-Value

11 (5.58%)

Mid-Blend

11 (5.58%)

Mid-Growth

19 (9.64%)

Small-Value

5 (2.54%)

Small-Blend

4 (2.03%)

Small-Growth

10 (5.08%)

The table provides a distribution of sample funds based on style/capitalisation. Each box reports the number and(percentage) of funds in each category. Definitions are obtained from Morningstar Inc.: Large-valuea is defined asfunds that invest primarily in big US firms that are less expensive or growing more slowly than others; Large-blendfunds are portfolios that are fairly representative of the overall large-cap US stock market in size, growth, rates andprice; Large-growth portfolios invest primarily in big US firms that are projected to grow faster than other large-capstocks; Mid-valueb are portfolios that primarily invest in medium-sized firms or a mix of small, medium and largefirms; Mid-blend are portfolios that invest in various sizes and styles of medium-sized firms and tend to stay awayfrom high-priced growth stocks; Mid-growth funds primarily invest in mid-size firms that tend to grow faster thanother mid-cap stocks; Small-valuec funds are portfolios that primarily invest in small US companies with valuationsand growth rates below other small-cap peers; Small-blend funds tend to invest in various sizes and styles of smallsize firms or may use a mix of holdings with valuations and growth rates close to the small-cap averages; Small-growth tend to invest in faster growing companies whose shares are at the lower end of the market capitalisationrange.aLarge stocks are defined as firms with market capitalisation of over $10 billion.bMid stocks referred to firms with market capitalisation of between $2 billion and $10 billion.cSmall stocks are firms with capitalisation of between $300 million and $2 billion.



sample funds and the S&P 500 index. From

this table we see that the average fund in

the sample outperformed the S&P 500

in only one year, that is, 2003, and had a

larger standard deviation of returns in every

year in the study. The average annual return

and standard deviation over the 2001–2006

period for the S&P 500 is 2.16 and 11.59

per cent, respectively, whereas those for the

sample funds are 1.12 and 17.76 per cent.

Thus, consistent with our expectations for

narrowly focused funds, their risk as

measured by standard deviation is greater

than that for the S&P. However, the lower

average return for small holding funds was

not expected. The Sharpe ratios, calculated

as the return from the given portfolio minus

the Treasury-bill rate all divided by the

standard deviation of returns, provide a

measure of excess return per unit of risk.

Here we see that the S&P 500 has delivered

approximately triple the excess return per

unit of risk than the sample firms. Although

these results indicate that narrowly focused

funds may not deliver the returns expected of

more risky investments, we show below that

some of these funds reward investors

handsomely.

METHODOLOGYIn order to investigate the abnormal

performance of the funds in our sample more

thoroughly than with simple Sharpe ratios,

we first estimate the standard four-factor

model used in the literature by Carhart

(1997), among others. This model controls

for systematic risk and investment style

factors and is used commonly in the

literature. The model is

rit � rft ¼ ai þ b1i�RMRFt þ b2i�SMBt

þb3i�HMLtþb4i�MOMtþ eit

(1Þ

where rit�rft is the excess return on fund i in

month t minus the corresponding monthly

Treasury bill rate; ai is the monthly measure

of abnormal performance (alpha); RMRFt

the excess return on the market, that is,

the S&P 500 composite index return minus

the corresponding monthly Treasury bill

rate; bi is Beta, the measure of systematic

risk; SMBt the difference in returns between

small and large capitalisation stocks; HMLt

the difference in returns between high and

low book-to-market stocks; and MOMt the

difference in returns between stocks with

high and low past returns.

This model is estimated over the entire

2001–2006 period, consisting of 72 months

of data or 4640 monthly fund observations.

The intercept in the model, ai, is the

abnormal performance in excess of risk

premiums associated with the market, size,

book-to-market and momentum factors.

A positive alpha indicates that fund managers

in the sample are able to outperform the

market on a risk and investment style

adjusted basis, whereas a negative alpha

Table 2: Annual returns, standard deviations and the average Sharpe ratios for sample firms and the S&P 500index

YearSample firms S&P composite index

Return (%) Standard deviation (%) Return (%) Standard deviation (%)

2001 �16.10 30.40 �12.02 19.012002 �25.34 22.42 �24.35 19.742003 29.44 14.70 24.22 10.912004 7.51 13.35 8.88 6.952005 2.57 14.18 3.24 7.492006 8.63 11.50 12.99 5.46Average 1.12 17.76 2.16 11.59Sharpe ratio 0.1378 0.4276



indicates the opposite, and we examine these

below.

Once the Carhart (1997) four-factor

model is estimated, the parameters b1,y,b4

are then used to calculate the alphas using

the following equation:

ait ¼ rit � rft � b1i�RMRFt � b2i�SMBt

� b3i�HMLt � b4i�MOMt

(2Þ

The alphas are subsequently used in cross-

section regressions using the model in (3) in

an attempt to investigate the abnormal

performance across the sample funds using

fund-specific characteristics. Following the

existing literature, we estimate the following

model on a monthly basis across all funds

available in each month of the sample:

ait ¼ b0 þ b1�Expensesit

þ b2�Loadit þ b3�Sizeit

þ b4�Ttopit þ b5�Turnoverit

þ uit ð3Þ

where Expensesit is total annual management

and administrative expenses including 12b-1

fees divided by the average total net assets of

fund i at time t; Loadit is the total of

maximum front-end and deferred sales

charges as per cent of the total net assets of

fund i at time t; Sizeit the natural log of total

net assets of fund i at time t; Turnoverit the

minimum (of aggregated sales or aggregated

purchases of securities), divided by the

average 12-month total net assets of the fund,

also used as a proxy for transaction costs

associated with rebalancing the portfolio; and

Ttopit is per cent of the fund’s total net assets

in its top 10 most heavily weighted holdings

and is used as a proxy for concentration.

Note that all the variables above with the

exception of Size are divided by 12 to put

them on the same measurement basis as the

dependent variable, which is the monthly

alpha.

Previous research by Carhart (1997) and

Kacperczyk et al (2005) has shown that

expenses are negatively related to abnormal

performance, and Carhart has documented a

negative association between loads and

performance. However, these authors differ

in their findings regarding turnover. Carhart

(1997) finds a negative effect of turnover on

the average mutual fund’s performance,

whereas Kacperczyk et al (2005) and

Wermers (2000) find a positive effect of

turnover on abnormal performance. The

positive effect of turnover on abnormal

performance is attributed by Wermers (2000)

as the result of managers acting on

information and having superior stock

picking skills.

We examine both the performance results

in model (1) and the cross-sectional

regression results in model (3) over the entire

sample period, as well as in the top and

bottom quartiles of the sample, sorted by

excess return (fund return minus T-bill

return). This, of course, results in estimating

two distinct regression models, representing

two different regimes, one for the top

quartile of excess returns (Winners) and

the other for the bottom quartile (Losers).

We chose quartiles for Winners and Losers

arbitrarily based on similar segmentations

of firms reported in the literature.

Alternatively, we could have chosen them

using a regime switching or threshold

model, using panel data developed by

Hansen (1999), in which the regime break

points are estimated along with other

model parameters.8 Additionally, this

segmentation into Winners and Losers

reduces the number of fund months in

each of these quartiles to roughly 1000

observations, after months in which

independent variable data points were not

available are eliminated. Although this

reduction in sample size to the approximate

1000 observations in each regression is still

relatively large by conventional standards,

it is small by mutual fund standards, where

the typical regression may contain thousands

or tens of thousands of fund month

observations.



EMPIRICAL RESULTS

PerformanceRegression results from equation (1), the

Carhart (1997) four-factor model, are

reported in Table 3, panels a–c. The results in

panel a, for all funds in the sample, indicate

that, on average, small portfolios do not

outperform the S&P 500 index after

controlling for small stocks, book-to-market

and momentum. Results reported in panel a

show that monthly abnormal performance of

small portfolios relative to the S&P 500 index

is �0.002 (�2.4 per cent per annum) before

considering expenses and loads. All the

coefficients in the regression are highly

significant. Although the results above

indicate that smaller funds are more risky

than the S&P index, the beta coefficient

from panel a indicates that the funds are only

marginally more risky in terms of systematic

risk, with a beta slightly greater than 1.0.

The positive coefficients on SMB and MOM

indicate that small cap companies and

momentum stocks increase abnormal

performance, whereas the negative

coefficient on HML indicates that growth

stocks tend to detract.

It is intriguing to examine the

performance of the top and bottom

performers within the overall sample.

Therefore, we divide the sample into

Winners and Losers, defined as those funds

in the top and bottom quartiles based on

excess return (monthly fund return minus

corresponding month T-bill return). Each

year from 2001 to 2006, we sort the entire

sample of monthly fund excess returns,

labelling the top quartile Winners and the

bottom quartile Losers. We then re-estimate

the Carhart (1997) four-factor model using

these two sub-samples.

As one would expect for small, highly

concentrated portfolios, the differences in

the two quartiles are large. The Winner

group has a mean (median) monthly excess

return over the entire period of 6.2 per cent

(5.3 per cent), or 74.4 per cent annually,

whereas for the Loser group it is �6.4

per cent (�4.7 per cent), or �76.8 per cent

annually. The four-factor model results for

Winner and Loser portfolios are reported in

Table 3, panels b and c, respectively. Results

from the alpha coefficients indicate that the

average Winner fund outperforms the S&P

500 index on a risk-adjusted basis of 410

basis points per month (49.2 per cent per

annum), whereas Losers earn �320 (�38.4

per cent per annum). The larger positive

coefficient on SMB indicates that smaller

Table 3: Abnormal performance: (a) all funds;(b) Winner quartile and (c) Loser quartile

Variable Estimate t-value

Panel aa �0.002 �3.52***RMRF 1.075 59.1***SMB 0.288 15.0***HML �0.118 �5.02***MOM 0.081 5.89***Adj. R2 0.591 —Number of fund months 4640 —

Panel ba 0.041 20.15***RMRF 0.421 9.16***SMB 0.130 3.0***HML 0.018 0.38MOM �0.105 �4.89***Adj. R2 0.178 —Number of fund months 1,159 —

Panel ca �0.032 �19.28***RMRF 0.770 16.0***SMB 0.094 2.29**HML �0.313 �8.07***MOM �0.040 �1.13Adj. R2 0.458 —Number of fund months 1,160 —

The table summarises regression results for theCarhart (1997) four-factor model showing the abnormalperformance of sample firms, a, relative to market andstyle benchmarks. Results are obtained fromregressing the excess return of each fund (the monthlyfund return minus the corresponding month T-bill rate)against the excess return of the market, RMRF, theFama–French factors, SMB, which is the difference inreturns between small and large capitalisation stocks,HML, which is the difference in returns between highand low book-to-market stocks, and MOM, which isthe difference in returns between stocks with high andlow past returns.*, **, *** indicate statistical significance at the 10, 5 and1 per cent levels, respectively.



companies add more to Winner fund

abnormal performance, and the negative

coefficient on HML indicates that growth

stocks reduce excess returns in the Loser

funds.

Cross-section regressionsWe examine the funds in the sample using

cross-section regressions to assess which

fund-specific characteristics explain the

abnormal performance. The variables used to

explain the abnormal performance are those

contained in model (3) above.

Table 4 presents means and medians for

the variables used in the cross-sectional

regressions on a year-by-year basis. Several

items are noteworthy. First, the average size,

in terms of total net assets, has increased over

time. Second, the percentage of the fund

invested in the top 10 most heavily weighted

holdings is quite large and stays relatively

constant at about 55 per cent. Finally, the

average number of holdings in the funds has

increased slightly, whereas turnover has

declined over time.

The cross-sectional results for Winner and

Loser portfolios, reported in Tables 5a and b,

respectively, show a sharp contrast between

the two. The results for Winners in Table 5a

show that abnormal performance, a, is

significantly and directly associated with

turnover and the percentage of total net

assets invested in the fund’s top 10 most

heavily weighted holdings. Investment in the

Table 4: Descriptive statistics

Variable 2001 2002 2003 2004 2005 2006

Size (in millions) $49.23; $14.65 $120.65; $47.88 $89.93; $25.2 $107.66; $20 $116.19; $19.7 $128.49; $36.45

Number of stocks 23.85; 24 24.35; 25 24.65; 25 24.39; 25 24.68; 25 25.98; 27

Ttop

(top 10 holdings %)

58.08; 57.12 56.88; 55.68 54.24; 51.48 54.24; 52.56 57.96; 56.16 54.72; 53.52

Expense ratio (%) 1.73; 1.45 1.54; 1.44 1.98; 1.45 1.80; 1.49 1.83; 1.49 1.67; 1.35

Load (%) 2.25; 0.25 1.78; 0.25 2.23; 0.25 2.25; 0.25 1.97; 0.00 1.83; 0.00

12b-1 fee (%) 0.125; 0.00 0.24; 0.25 0.19; 0.16 0.21; 0.00 0.15; 0.00 0.18; 0.00

Turnover ratio (%) 177; 81.48 160; 57.96 97.44; 53.04 111.72;44.04 106.68; 63.96 84.48; 58.92

Observations 73; 874 54; 648 71; 852 63; 756 57; 684 69; 828

The table provides descriptive statistics of some of the key fund-specific variables of sample funds. For eachvariable, the mean value is followed by median value. Size is the annual total net assets under management andreported in millions of dollars; Number of stocks is the average number of stocks held by the funds during the year;Ttop is the percentage of the fund’s total net assets invested in its top 10 weighted holdings; Expense ratio isoperating expenses, management fees, 12b-1 fees, administrative fees and all other asset-based costs incurredby the fund as a percentage of total net assets; Load is the sum of fund’s front-end and deferred sales charges;12b-1 fee is the annual charge deducted from fund assets to pay for distribution and marketing expenses; Turnoverratio is the minimum (of aggregated sales or aggregated purchases of securities), divided by the average 12-monthtotal net assets of the fund. Yearly fund observations are followed by fund month observations.

Table 5: Cross-sectional regressions on fundcharacteristics: (a) Winner quartile and (b) Loserquartile

Variable Estimate T Value

(a)Intercept �0.013 �2.64***Expenses 0.672 0.79Load �0.243 �0.63Ttop 0.488 5.44***Turnover 0.021 3.94***Size �0.0002 �0.47Number of fund months 1,015 —Adj. R2 0.0398 —

(b)Intercept 0.004 0.79Expenses �2.761 �4.63***Load 0.844 2.08**Ttop �0.429 �5.09***Turnover �0.034 �6.24***Size 0.0022 4.37***Number of fund months 1,009 —Adj. R2 0.1351 —

The table reports results of model (3) regressingabnormal performance, a, against fund characteristicsfor Winner and Loser quartiles. Variable descriptionsare given in Table 4.*, **, *** indicate statistical significance at the 10, 5 and1 per cent levels, respectively.



top 10 most heavily weighted holdings

(Ttop) is also used as a proxy for

concentration, which, some have argued,

are also the best ideas of fund managers.

The 0.488 coefficient on Ttop implies that

for every 100 basis-point increase in funds

devoted to the top 10 most heavily held

companies, the annual abnormal return

increases by 48.8 basis points. This indicates

that fund managers in Winner portfolios

are making large bets on good stocks.

A smaller relationship is observed between

abnormal performance and the turnover

ratio. The turnover ratio coefficient of

0.021 suggests that for every 100 basis-point

increase in turnover, annual abnormal

performance increases by 2.1 basis points.

A positive relationship between turnover

ratio and abnormal performance is

consistent with the notion that the benefits

exceed the trading costs associated with

turnover if rebalancing of the portfolio is

a function of information arrival and not

churning activities. Studies by Chevalier

and Ellison (1999) and Wermers (2000)

demonstrate that turnover ratios can have

a positive and significant impact on the

performance of actively managed mutual

funds.

Results reported in Table 5b for Loser

funds show that expenses, turnover and Ttop

all negatively affect abnormal performance,

with expenses having a very large negative

effect. The �2.76 coefficient on expenses

implies that for every 100 basis-point

increase in Loser fund expenses, annual

abnormal performance declines by an

excessive 276 basis points. Thus, expenses

take a large toll on Loser portfolio returns.

Likewise, the �0.429 coefficient on Ttop

implies that for every 100 basis-point

increase in the Loser fund’s investment in

its top 10 most heavily weighted holdings,

annual abnormal performance drops by

approximately 43 basis points. Thus Loser

portfolio managers are making large bets

that affect their portfolios, but in poorly

performing stocks. Turnover also has a small

but significant negative impact on Loser

abnormal performance.

In contrast to the results for Winner

portfolios, the coefficients on load and size

have positive and significant effects on

Loser portfolio abnormal performance.

Load has the largest effect, with a coefficient

of 0.844, implying an 84 basis-point increase

in Loser portfolio abnormal performance

for every 100-point increase in Loads.

Although this may seem counter-intuitive

and in contrast to the results reported in

Chordia (1996), Carhart (1997) has shown

that loads, especially back-end loads and

redemption fees, dissuade investors from

frequently redeeming their mutual fund

positions, thereby allowing funds with

loads to keep less cash and invest more in

higher return stocks. Thus, higher loads

may in fact increase abnormal performance.

The 0.0022 coefficient on size indicates

that for every increase in total net assets

under management of US$1 million,

abnormal performance for Loser funds

increases by a paltry amount of $2200

or relatively small 0.22 basis points.

CONCLUSIONSThis paper investigates the performance of

mutual funds that hold a small number of

stocks in their portfolio. Following

definitions in the financial press and in

mutual fund-specific investment objective

statements, we limit our investigation to

funds that are concentrated in 10–30 stocks.

Our results indicate that, on average, fund

portfolios with few holdings do not

outperform the S&P 500 index. On average,

small portfolios under-perform the market

on a risk and investment style adjusted basis

by about �20 basis points per month or

�2.40 per cent per year.

We also find that there is a sharp contrast

between the performance of Winner and

Loser portfolios. Screening on excess return,

that is, fund return minus the T-bill rate, we

define Winners as funds in the top quartile



and Losers as those in the bottom quartile.

On average, Winner portfolios outperform

the S&P composite index by 410 basis points

per month or an astounding 49.2 per cent

per annum, whereas Losers under-perform

by 320 or �38.4 per cent per annum over

the same period.

To investigate this dramatic difference

between Winner and Loser portfolios, we

use fund-specific characteristics in cross-

sectional regressions to explain the abnormal

performance from each group. The results

indicate that Winner portfolio abnormal

performance is positively and significantly

related to the turnover ratio and the

percentage of the fund’s assets invested in

their top 10 most heavily weighted holdings.

Results for Loser portfolios show that

abnormal performance deteriorates

significantly with turnover, concentration

and expenses. On the other hand, Loser

portfolio abnormal performance is positively

related to Load and Size.

NOTES1. The funds analysed in these studies are characterised as

large, broadly diversified funds from various investment

styles that exclude sector funds, international funds, index

funds, quant funds and bond funds. The benchmarks

used to calculate risk-adjusted excess returns are those

used in this study and consist of the return on the S&P

500 index, the Fama–French HML and SMB factors

and Carhart’s Momentum factor, which are all defined

below.

2. ‘A fund with few holdings, called a focus fund, has a better

chance of beating the S&P 500, but it’s more likely that

one or two bad stocks can smack shareholders senseless’.

Source: Focus funds have big potential, if you dare. USA

Today, 11 November 2002.

‘Highly selective funds, with limited shares in the portfolio,

have become a popular way for investors to maximise their

chances of beating lackluster returns from the stock

market’. Source: Focus funds: A way of beating lacklustre

stock returns. Financial Times, 29 July 2005.

3. Two quotes attributed to Mr Buffett summarise his

investment philosophy: ‘If you are a know-something

investor, able to understand business economics and to find

five to ten sensibly priced companies that possess important

long-term competitive advantage, conventional

diversification makes no sense for you’. Source: Hagstrom

(1999). Additionally, ‘Wide diversification is only required

when investors do not understand what they are doing.

Why not invest your assets in the companies you really

likeyToo much of a good thing can be wonderful’. Source:

www.brainyquote.com.

4. Focus funds have big potential, if you dare. USA Today, 11

November 2002.

5. The following fund objective statement of the Janus 20

fund taken from the Fidelity website www.fideilty.com is

typical of the fund we are interested in: The fund seeks

long-term capital appreciation. The fund is non-diversified

and intends to achieve its objective by concentrating its

investments in the equity securities of a smaller number of

companies than more diversified funds. Typically invests in

15 to 35 firms at a time. The fund may invest in sectors or

foreign issuers.

6. The Financial Dictionary and investopedia define focus

funds as those that contain a small number of stocks, in

general: (a) those who hold a portfolio concentrated in

approximately 10–30 stocks, (b) those who concentrate

their holdings within 1–3 sectors and (c) those who hold a

large number of different stocks, but a large portion of their

total portfolio value is concentrated in a very small number

of stocks. (http://financial-dictionary.thefreedictionary.

com/Focused+Fund; http://www.investopedia.com/

terms/f/focusedfund.asp)

The Wall Street Journal defines focus funds as concentrated

portfolios that tend to make big bets on just a few

dozen stocks versus two to three times that amount

for a more diversified offering (Wall Street Journal,

28 November 2006).

7. According to the National Bureau of Economic Research

(NBER), the US economy underwent a recession in

March 2001 that ended in October of 2001. NBER

determined that the trough, which is also known as the

beginning of the expansion period, started in November

of 2001.

8. Hansen (1999) proposes estimating model parameters

and the threshold, g, using least squares. The overall

sample is then divided into regimes based on whether

the threshold variable, qi,t (or fund performance in our

case) is smaller or larger than the computed threshold g.The value of g is computed with the restriction that a

minimum percentage of observations must lie in each

regime. Hansen provides programs to run his analysis on

his website.

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Do funds with few holdings outperform kaushik

Economy & Finance

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