Fund performance persistence and competition keswani

The Journal of Financial Research • Vol. XXIX, No. 3 • Pages 349–366 • Fall 2006

MUTUAL FUND PERFORMANCE PERSISTENCE AND COMPETITION:
A CROSS-SECTOR ANALYSIS

Aneel Keswani
Cass Business School

David Stolin
Toulouse Business School

Abstract

Existing work on mutual fund performance persistence obtains diverse results,
depending on the group of funds studied. We examine whether performance per-
sistence within a peer group of competing mutual funds depends on the group’s
composition. The U.K. mutual fund industry is ideal for such an examination be-
cause funds compete within strictly defined sectors. We consider several attributes
related to the intensity of competition within a sector and use them to explain
sector-level persistence. We find robust evidence that persistence is higher in
sectors where concentration of assets under management is higher.

JEL Classification: G23

I. Introduction

It is well established in the industrial organization literature that the structure of
a sector affects its competitiveness. In more competitive sectors we expect to see
few firms doing persistently well and those performing poorly being forced to exit
the sector. This reasoning is tested by Waring (1996), who finds a strong negative
relation between competitiveness within an industrial sector and the persistence of
profitability for firms in that sector.
We translate this logic to the mutual fund context. To use the terminology of
industrial organization, mutual funds compete with each other using a combination
of price and nonprice competition strategies. Price competition involves funds

We thank Vladimir Atanasov, Andrew Clare, Zsuzsanna Fluck, Gordon Gemmill, Brian Kluger,
Tobias Kretschmer, Gordon Midgley, Kenneth Moon, Dennis Stanton, Dylan Thomas, Giovanni Urga, and
especially William T. Moore (former editor) and Jonathan Fletcher (the referee) for insightful comments.
We also acknowledge comments received from participants at the 2003 Financial Management Association
meeting in Denver, and seminars at Cass Business School and the universities of Porto, Reading, Warwick,
and Oxford. We thank Benjamin Kogan, Jan Steinberg, James Sullivan, the Allenbridge Group, and the
Investment Management Association for help with data. Part of the research reported here was conducted
while Stolin was visiting at the Stockholm Institute for Financial Research. All errors and omissions are
ours.

349

350 The Journal of Financial Research

varying the fees they charge to obtain a competitive advantage. Nonprice com-
petition involves (among other things) funds competing to produce superior invest-
ment performance. Numerous studies show that higher investment returns have an
important influence on fund market share (e.g., Siggelkow 2003).
We expect funds from more competitive sectors to compete more aggres-
sively for abnormal returns. This should result in the exit of funds that underperform
and a low probability of remaining funds doing repeatedly well. Competing funds
should be able to close the performance gap on “star” funds by devoting more
resources to researching investment opportunities, by learning to imitate the best
performers, or even by poaching their managers. Thus, in more competitive sectors
we expect to see less persistence in funds’ performance relative to their rivals (i.e.,
less relative persistence).
To examine the influence of competition on investment performance per-
sistence, we focus on the U.K. unit trust (open-ended mutual fund) industry. This
environment is ideal for our purpose because U.K. mutual funds compete in a
large number of unambiguously defined peer groups (sectors), whose membership
is monitored and enforced by the industry trade body. This is unlike the United
States, where multiple sector definitions coexist and managers are free to game
their sector affiliations (Cooper, Gulen, and Rau 2005).
Although research into fund performance persistence has a long history,
Brown et al. (1992) show that early studies exaggerate the extent of persistence by re-
lying on survivorship-biased data sets. Carhart (1997) finds that in his survivorship-
free sample of U.S. equity funds, persistence largely disappears after accounting
for momentum in stock returns. However, recent studies argue that after properly
considering fund styles, there is persistence in U.S. equity mutual funds (Ibbotson
and Patel 2002; Teo and Woo 2001; Wermers 2003).
Outside the United States, there has been debate as well. In the United
Kingdom, it has involved academics (Blake and Timmermann 1998; Allen and Tan
1999; Fletcher and Forbes 2002), practitioners (Quigley and Sinquefield 2000),
the trade association (Giles, Wilsdon, and Worboys 2002), and the regulatory body
(Rhodes 2000; Blake and Timmermann 2003). This literature agrees that perfor-
mance persistence is an important issue but disagrees on whether and to what extent
persistence is present.
The preceding studies all focus on funds investing in domestic equity secu-
rities. The availability of well-accepted benchmarks for risk adjustment is a major
reason for this focus. Several studies examine persistence for funds investing in
other asset classes (e.g., see Blake, Elton, and Gruber 1993 for bond mutual funds)
and obtain diverse results, depending on the period used and the fund sector stud-
ied. This raises the possibility that levels of persistence may vary depending on the
economic circumstances. In particular, the market structure of a mutual fund sector
may influence funds’ ability to perform consistently.
Our research examines how mutual fund performance persistence at the
fund sector level is influenced by competition within the sector. A few studies

Mutual Fund Performance Persistence 351

consider determinants of persistent performance at the individual fund level (e.g.,
Volkman and Wohar 1995). Several other studies note persistence differences across
sectors or fund objectives (Blake and Timmermann 1998; Kosowski et al. 2003;
Wermers 2003). No study, however, conducts a sector-level statistical analysis of
persistence, and none investigates the effect of competition on persistence. Massa’s
(2003) empirical demonstration that sector-level variables related to competition ex-
plain sector-level performance suggests that such an analysis is potentially fruitful.
We construct several variables to capture the intensity of competition in
a sector. These include the number of funds in a sector, the proportion of mature
funds, and the Herfindahl index of asset concentration. We find robust evidence that
persistence is higher in sectors where concentration of assets under management
is higher. Our results suggest that the degree of persistence exhibited by a sector’s
investment managers depends on how competitive that sector is.

II. Data and Method

The U.K. Mutual Fund Industry
Unlike in the United States, a survivorship-bias-free electronic database of mutual
funds does not exist in the United Kingdom. To conduct our study, we therefore
manually collected data from 11 consecutive editions of the annual Unit Trust
Yearbook.1 Our data span from 1991 to 2001 and include names of funds and their
management groups, annual returns (including reinvested income and excluding
fees), fund assets under management, launch dates, and of course the name of the
sector to which each fund belongs. We use fund names and an index of name changes
to link fund data across years. We consider mergers between funds as creating a
new fund.
As our primary focus is at the sector level (rather than at the individual
fund level), we track the evolution and membership of official fund sectors as
defined by the Association of Unit Trust and Investment Funds (AUTIF) and by its
successor, the Investment Management Association (IMA).2 To do this, we use data
on fund movement across sectors, as well as historical announcements by AUTIF
and IMA. Appendix A summarizes the history of U.K. mutual fund sectors. We use

1
In the editions corresponding to 2000 and 2001 year-ends, the yearbook had a new publisher, and
several smaller fund families did not supply information on their funds. However, there is no survivorship bias
due to selective reporting of funds. Post-2001 data are unavailable as the yearbook has been discontinued.
2
In the United Kingdom, all information providers use the official classification system. The IMA
enforces its sector definitions, and if the asset allocation of a fund contravenes the allocation rules of its
current sector, the IMA will warn the fund to change its allocation if it does not wish to change sectors. If
the fund does not comply, the IMA will move the fund to a new sector reflecting its new asset allocation.
By contrast, in the United States there is a proliferation of methods for assigning funds to a peer group.
This ambiguity allows fund managers to “game” their objectives (Cooper, Gulen, and Rau 2005) and makes
objective-level measures of competition less meaningful.


official sector descriptions to group sectors into four broad categories: domestic
equity, global equity, domestic nonequity, and global nonequity. The appendix paints
a picture of substantial innovation at the sector level—with numerous instances
of sectors being opened, discontinued, redefined, or merged—consistent with an
industry seeking to respond to changing conditions. Additionally, there is much
variability in the number of funds within a sector. Our sample period thus captures
an industry in transition, which is helpful for our analysis of the role of market
structure characteristics.

Measurement of Persistence
Measures of performance persistence quantify to what extent performance in one
period (the “ranking” period) continues into the subsequent period (the “evaluation”
period). In this study, we focus on persistence at the one-year frequency (i.e., our
ranking and evaluation periods each equals one year). There are several reasons
for this choice. First, researchers who find evidence of persistence generally find
it for one-year horizons. Second, investors and fund managers tend to evaluate
performance over annual periods. Third, tests of performance persistence require
return availability for both ranking and evaluation periods. This leads to a look-
ahead bias, which can influence how much persistence is detected (Brown et al.
1992; Ter Horst, Nijman, and Verbeek 2001). Lengthening the horizon over which
persistence is measured makes this bias more severe. Over one-year periods, Ter
Horst, Nijman, and Verbeek (2001) find the look-ahead bias to be negligible.
Performance persistence can be measured using both absolute and relative
performance. We measure persistence using relative performance for two reasons.
First, existing research highlights that in determining mutual fund money flows,
relative performance matters beyond absolute performance. Second, measures of
absolute performance persistence depend on the volatility of securities invested
in by a given sector, making comparisons of absolute persistence across sectors
misleading.
To measure relative performance persistence, we use raw and not risk-
adjusted returns. In our context, examining persistence on a risk-adjusted basis is
problematic for two reasons. First, we do not have access to monthly returns for
existing and extinct U.K. mutual funds. Second, and more important, the quality of
any risk adjustment would inevitably vary across sectors. For example, domestic
equity returns can be analyzed with well-researched multifactor models, whereas
this is less likely for global or nonequity funds. This means that sector characteristics
related to our ability to risk-adjust would have a spurious effect on a cross-sectional
analysis of persistence in risk-adjusted returns. Persistence measured on the basis of
raw returns, on the other hand, is important in its own right. Numerous information
providers such as Money Management, Unit Trust Yearbook, Standard & Poor’s Web
site, and others rank funds based on raw returns within a sector. Indeed, evidence


on the return-flow relation indicates that investors react to raw returns. Moreover,
implicit in looking at within-sector persistence, as we do, is a peer-group adjustment
of fund returns.
Commonly used statistics for studying relative persistence within a peer
group include the Spearman rank-correlation coefficient, and quantities based on
2 × 2 winner/loser contingency tables. The latter include the log-odds ratio and
the chi-squared statistic. The chi-squared statistic is disqualified for our purpose
(which is to explain the extent of persistence) because high values correspond
to either persistence or reversal of performance. The advantage of the Spearman
correlation over the log-odds ratio is that the latter uses the performance rank
of each fund rather than just its winner/loser status. This generally means more
powerful tests for persistence (Carpenter and Lynch 1999). The advantage of the
log-odds ratio is that it has a more straightforward economic interpretation, as
we show shortly. We use both the log-odds ratio and the Spearman correlation as
our measures of persistence (equations are given in Appendix B). To avoid our
results being influenced by the small-sample properties of these statistics, we use
only sector-years with at least 20 funds in existence over both years for which
performance is measured.
Table 1 shows the extent of relative performance persistence across U.K.
mutual fund sectors based on raw returns over consecutive years. In Panel A,
we present the distribution of the Spearman correlation coefficient and of the
log-odds ratio by type of sector. The first group of eight rows pertains to the
log-odds ratio. In column 1, for all sector-years combined (162), the average log-
odds ratio is 0.357. The null hypothesis that the mean log-odds ratio is zero can
be rejected ( p-value < .001) based on applying Student’s t-test to our set of 162
sector-years. The median sector-year has a log-odds ratio of 0.405, and the dis-
tribution ranges from −2.837 to 4.317. The log-odds ratio is positive for 62% of
the sector-years. Furthermore, the table reports the proportion of sector-years for
which the hypothesis of no persistence is rejected in favor of the one-sided alter-
native of positive persistence. For the log-odds ratio, this is the case for 29% of the
sector-years at the .05 confidence level, and for 17% of the sector-years at the .01
confidence level.
The next eight rows characterize the distribution of the Spearman corre-
lation across sector-years. For all sector-years combined, the average Spearman
correlation is 0.143 and is significantly different from zero. We note that even at
the .01 confidence level, the hypothesis of no persistence is rejected in favor of
the hypothesis of positive persistence for 29% of sector-years. This suggests the
Spearman-based test is more powerful than the log-odds ratio.
To ensure that performance persistence in our sample is not driven by a
particular subperiod, we separately consider sector-years for which the evaluation
years are 1992 through 1996, and those for which the evaluation years are 1997
through 2001 (results not reported in a table). The average log-odds ratio for the


TABLE 1. Relative Performance Persistence Across Sectors.

All Sectors
Domestic Other Than Global Domestic Global
All Equity Domestic Equity Nonequity Nonequity
Sectors Sectors Equity Sectors Sectors Sectors
Variable (1) (2) (3) (4) (5) (6)
Panel A. Sector-Year Statistics
Number of sector-years 162 36 126 63 30 33
Log-odds ratio by sector-year
Average 0.357 0.448 0.331 0.173 0.565 0.421
p-value for H0 : mean = 0 0.000 0.006 0.002 0.192 0.019 0.057
Median 0.405 0.382 0.405 0.365 0.555 0.525
Minimum −2.837 −1.168 −2.837 −2.837 −1.455 −2.485
Maximum 4.317 2.711 4.317 2.398 3.008 4.317
Proportion positive 0.623 0.639 0.619 0.571 0.667 0.667
Proportion positive and 0.290 0.306 0.286 0.270 0.367 0.242
significant at .05 level
Spearman correlation by sector-year
Average 0.143 0.147 0.142 0.097 0.180 0.191
p-value for H0 : mean = 0 0.000 0.004 0.000 0.017 0.005 0.001
Median 0.162 0.135 0.166 0.133 0.257 0.176
Minimum −0.642 −0.477 −0.642 −0.642 −0.544 −0.476
Maximum 0.817 0.804 0.817 0.672 0.656 0.817
Proportion positive 0.691 0.583 0.722 0.698 0.733 0.758

Panel B. Aggregate Statistics
Fund-years in winner-winner 2,724 900 1,824 1,240 286 298
category
Fund-years in loser-loser 2,672 895 1,777 1,219 273 285
category
Fund-years in winner-loser 2,276 728 1,548 1,090 225 233
category
Fund-years in loser-winner 2,296 740 1,556 1,096 229 231
category
Aggregate log-odds ratio 0.331 0.402 0.297 0.235 0.416 0.456
p-value for aggregate log-odds 0.000 0.000 0.000 0.000 0.001 0.000
ratio
Frequency of repeat performance 0.541 0.550 0.537 0.529 0.552 0.557

Note: This table reports descriptive statistics for measures of relative performance persistence across U.K.
mutual fund sectors, 1991–2001. Sector-years are included if at least 20 funds had returns available in the
ranking and evaluation years. Panel A reports the distribution of the Spearman correlation coefficient and
the log-odds ratio across sector-years. Both the Spearman correlation coefficient and the log-odds ratio are
based on raw annual returns in consecutive calendar years (formulae are given in Appendix B). In Panel B,
sector-years are pooled into an aggregate contingency table.


earlier (later) period is 0.358 (0.357), and the average Spearman correlation coef-
ficient is 0.134 (0.152). All of these averages are statistically significant at the .01
level. Moreover, the average log-odds ratio and the average Spearman correlation
coefficient are not significantly different between the two periods ( p-values = .99
and .70, respectively).
Because research on mutual fund performance persistence tends to focus
on domestic equity funds, we separately report results for these sectors in the second
column. The persistence measures are positive and, despite a sample size of only 36
sector-years, highly statistically significant. The average log-odds ratio, at 0.448, is
slightly lower than the 0.516 average log-odds ratio in Fletcher and Forbes (2002),
which is based on raw annual returns for U.K. equity mutual funds from 1982 to
1996. The average Spearman correlation, at 0.147, is slightly lower than the 0.188
reported by Allen and Tan (1999) for raw annual returns of U.K. equity mutual
funds from 1989 to 1995.
Column 3 presents results for sectors other than domestic equity. The level
of persistence is comparable to that in the preceding column. In fact, unreported
tests show that differences between the two columns are never significant. The
last three columns further disaggregate sectors other than domestic equity into
global equity, domestic nonequity, and global nonequity. In each category, perfor-
mance persistence is positive and significant, at least for the Spearman correlation
coefficient.
As further evidence on the level of performance persistence in our sample,
in Panel B we pool data from different sector-years to present an aggregate con-
tingency table. In 2,724 (2,672) instances, funds are two-period winners (losers)
in their respective sectors, and in 2,276 (2,296) instances, funds win in the rank-
ing (evaluation) period and lose in the evaluation (ranking) period. The resulting
aggregate log-odds ratio equals ln((2724 × 2672)/(2276 × 2296)) = 0.331 and is
highly significant. The aggregate log-odds ratios for the different sector groups in
columns 2 through 6 are all significant at the .01 level.
The economic significance of performance persistence in our sample is
perhaps best addressed through the probability that a fund’s winner/loser status
carries over from the ranking period to the evaluation period. This probability of
repeat performance can be estimated as the number of fund-years corresponding
to two-period winners or two-period losers divided by the total number of fund-
years. For all sectors together, this quantity (reported in the last row of the table)
equals (2,724 + 2,672)/(2,724 + 2,672 + 2,276 + 2,296) = 54.1%, as compared
to the 50.0% that one would expect in the absence of performance persistence or
performance reversal.3

3
Because we define a winner (loser) as a fund that places in the top (bottom) half in its sector in a
given year, the cell counts in the contingency table are not independent. In fact, if there were no ties and
if the number of funds in a sector were always divisible by four, the winner-winner fund count would be


Overall, there is strong evidence that the U.K. mutual fund industry exhibits
persistence in relative investment performance. If at least some of this persistence is
due to sector-level attributes, in particular to those related to sector competitiveness,
a cross-sector analysis may reveal this. Such analysis is conducted in the next
section.

III. Determinants of Sector-Level Persistence

Sector Attributes
Broadly speaking, systematic differences in persistence between sectors can be due
to differences in the composition of sector membership, or to differences in the
types of assets sector members invest in. We construct several variables designed to
quantify how competitive a sector is, and the distribution of these variables is given
in Panel A of Table 2. N is simply the number of funds in a sector at the end of the
ranking year. The largest number of funds in a sector is 302, corresponding to the
UK All Companies sector in 1999 (after sectors dedicated to domestic “growth” and
“growth and income” stocks were merged). Because we drop sectors comprising
fewer than 20 funds with recorded returns, the minimum number of funds in a sector
is 24, the median is 79, and the average is 87.4 It is reasonable to conjecture that
consistent performance is harder to attain in a more crowded sector. For example, in
studying fund performance Siggelkow (2003) regards the number of mutual funds
in a category as “a measure of general competition, for instance, for mis-priced
securities” (p. 133).
We recognize, however, that in such competition, small funds may have rel-
atively little effect. We therefore also use the Herfindahl index, which is commonly
considered as a measure of intra-industry rivalry. Specifically, HERFINDAHL is
the concentration index of assets under management. Because several funds from
a single family of funds can coexist within a sector, we aggregate assets by fam-
ily to calculate this measure. Thus, the value of HERFINDAHL for each sector is
the sum across families of the square of each family’s assets as a proportion of
a sector’s total assets. Although we use only sectors with at least 20 funds, there
is substantial variation in the value of the Herfindahl index, ranging from 0.027
to 0.629 (by construction, the smallest possible value of the Herfindal index is 0

exactly equal to the loser-loser count, and the winner-loser count would be exactly equal to the loser-winner
count. Using this insight, it is straightforward to show that the probability of repeat performance can be
obtained directly from the log-odds ratio (L) as 1/(1 + e−L/2 ). For example, using the aggregate log-odds
ratio of 0.331, the probability of repeat performance is 1/(1 + e−0.331/2 ) = 0.541. We subsequently use this
conversion to assess the economic significance of our regression results.
4
For comparison, Massa (2003) uses several data providers’ fund descriptions to assign U.S. mutual
funds to 1 of 23 categories. The numbers of funds in his categories range from 14 to 1,149, the median is
343, and the average is 411.


TABLE 2. Descriptive Statistics for Sector-Level Variables.
Panel A. Moments
Variable Mean Standard Deviation Median Minimum Maximum

N 87 50 79 24 302
MATURITY 0.552 0.188 0.626 0.000 0.846
HERFINDAHL 0.088 0.070 0.068 0.027 0.629

Panel B. Correlations
Variable N MATURITY HERFINDAHL LATER

DOMESTIC EQUITY 0.420 0.420 −0.247 −0.007
N 0.476 −0.464 −0.163
MATURITY −0.586 −0.040
HERFINDAHL −0.078

Note: This table contains descriptive statistics for the set of sector-year explanatory variables. Sector-years
are included if at least 20 funds had returns available in the ranking and evaluation years: 162 sector-years
meet this requirement. The variables are as follows. N is the number of funds within the sector. MATURITY
is the proportion of sector funds that are at least five years old. HERFINDAHL is the Herfindahl index
measuring the concentration of fund assets within the sector, where funds from the same family are
aggregated. DOMESTIC EQUITY is a dummy variable equal to 1 if sector funds are primarily invested in
U.K. equities, and 0 otherwise.

and the largest possible value is 1). We hypothesize that less concentrated (more
competitive) sectors exhibit lower persistence.
Finally, MATURITY is the proportion of funds that are at least five years
old. In the average sector, most funds are “seasoned,” but the minimum value of 0
for the maturity variable indicates that for some sector-years, all of the funds are
relatively recent entrants. Berk and Green (2004) give a powerful reason why mutual
fund performance persistence should decrease with fund vintage. If investment
management returns to scale are decreasing, managers have differential ability, and
investors channel money to best performers, then superior funds grow to the point
where outperformance is no longer possible. Empirically, Waring (1996) finds that
earnings persistence in an industrial sector tends to decay over time, as competitive
forces have acted over a longer period.
Panel B of Table 2 shows a correlation matrix for the preceding sector
attributes and for a dummy variable indicating sector membership in the U.K.
equity category (DOMESTIC EQUITY ), as well as a dummy variable that equals
1 for the second half of our sample period (evaluation years from 1997 to 2001),
and 0 otherwise (LATER). DOMESTIC EQUITY is included because most studies
of performance persistence focus on domestic equity sectors. LATER controls for
the possibility that the level of persistence may have changed in more recent years.
We note that pairwise correlations between N, MATURITY, and HERFINDAHL are
high in magnitude: sectors with more funds in them tend to be more mature, and


assets invested in these sectors are more dispersed across fund families. Therefore,
in the regressions to follow, we enter these three variables one at a time.

Regression Results
Table 3 presents the results of a pooled regression of sector-level measures of relative
persistence on our set of sector-level explanatory variables. Persistence is measured
over years T (the ranking year) and T+1 (the evaluation year). Explanatory variables
are measured as of the end of year T (with one exception explained below). Thus,
we examine whether sector characteristics observed at the end of year T tell us to
what extent year T performance of the sector’s funds persist into year T+1. We
do not require that funds remain in the same sector until the end of year T+1 (or,
indeed, that the sector itself continue to exist until the end of year T+1) because
doing so would constitute a look-ahead bias.
In regressions (1) through (3) we use the log-odds ratio as the measure of
persistence and proxy for sector competitiveness with N, MATURITY, or
HERFINDAHL, respectively. Although the number of funds in a sector is not signif-
icantly related to persistence, maturity of funds is significant (t-statistic = −2.71),
as is the concentration of assets under management (t-statistic = 3.01). In other
words, sectors that are less mature and have more concentrated assets—that is,
sectors that may be described as less competitive—are characterized by greater
persistence. The other variables are not statistically significant.
Regressions (4) through (6) parallel regressions (1) through (3) but include
an additional control variable. CROSSRET is defined as the product of average
sector returns in years T and T+1. Although we do not adjust for differences in
fund exposure to different risk factors, these differences can generate persistence in
raw returns when there is persistence in factor realizations. CROSSRET is intended
to capture spurious persistence due to ex post momentum for the sector as a whole.
The regression results confirm this intuition in that CROSSRET is positive and
highly significant ( p-value < .001). Its only other influence is to enhance slightly
the significance of MATURITY and HERFINDAHL (t-statistics = −2.80 and 3.24,
respectively).
In regressions (7) through (12), the Spearman correlation coefficient is the
dependent variable. The results are similar to those based on the log-odds ratio.
Once again, MATURITY and HERFINDAHL are statistically significant at the .01
level, and CROSSRET continues to capture persistence due to momentum in all
specifications.

Robustness Checks
The preceding subsection presents evidence that HERFINDAHL and MATURITY
are sector attributes that are systematically related to the persistence exhibited by
the sector. We now report on the robustness of our results to alternative sample-
selection criteria, econometric methods, and other variations.

TABLE 3. Explaining Sector-Level Persistence.

Dependent Variable: Log-Odds Ratio Dependent Variable: Spearman Correlation Coefficient
Explanatory Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Intercept 0.586 1.097 0.020 0.568 1.040 −0.070 0.214 0.348 0.048 0.208 0.328 0.016
(2.93)∗∗∗ (3.90)∗∗∗ (0.12) (2.90)∗∗ (3.76)∗∗∗ (−0.40) (3.82)∗∗∗ (4.41)∗∗∗ (0.98) (3.84)∗∗∗ (4.29)∗∗∗ (0.34)
DOMESTIC EQUITY 0.224 0.302 0.270 0.257 0.306 0.279 0.038 0.057 0.051 0.050 0.058 0.054
(0.98) (1.40) (1.29) (1.14) (1.45) (1.36) (0.59) (0.93) (0.87) (0.80) (0.99) (0.96)
N −0.003 −0.004 −0.001 −0.001
(−1.13) (−1.48) (−1.21) (−1.66)
MATURITY −1.297 −1.315 −0.353 −0.359
(−2.71)∗∗∗ (−2.80)∗∗∗ (−2.63)∗∗∗ (−2.76)∗∗∗
HERFINDAHL 3.780 3.975 1.104 1.172
(3.01)∗∗∗ (3.24)∗∗∗ (3.15)∗∗∗ (3.47)∗∗∗
LATER −0.225 −0.226 −0.139 −0.175 −0.168 −0.075 −0.060 −0.058 −0.033 −0.041 −0.037 −0.011
(−1.25) (−1.30) (−0.80) (−0.99) (−0.98) (−0.44) (−1.16) (−1.18) (−0.68) (−0.84) (−0.78) (−0.22)
CROSSRET 4.271 4.062 4.272 1.489 1.422 1.485
(2.80) (2.73)∗∗∗ (2.89)∗∗∗ (3.53)∗∗∗ (3.45)∗∗∗ (3.64)∗∗∗
R2 0.017 0.053 0.063 0.063 0.090 0.110 0.015 0.047 0.064 0.087 0.114 0.137

Note: This table contains the results of regressing measures of persistence in relative investment performance on sector-level variables. Sector-years are included if at least
20 funds have returns available in the ranking and evaluation years: 162 sector-years meet this requirement. The explanatory variables are as follows. N is the number of funds
within a sector. MATURITY is the proportion of sector funds that are at least five years old. HERFINDAHL is the Herfindahl index measuring the concentration of fund assets
Mutual Fund Performance Persistence

within a sector, where funds from the same family are aggregated. DOMESTIC EQUITY is a dummy variable equal to 1 if sector funds are primarily invested in U.K. equities,
and 0 otherwise. LATER is a dummy variable equal to 1 when the evaluation year is 1997 or later, and 0 otherwise. CROSSRET is the product of average sector returns in the
ranking and evaluation years. The t-statistics are shown in parentheses.
∗∗∗
Significant at the 1% level.
∗∗
Significant at the 5% level.
359


Because both the log-odds ratio and the Spearman correlation are estimated
with differing degrees of precision across sectors, the resulting heteroskedasticity in
our regression may lead to inefficient estimation. We therefore use the inverse of the
standard error of the log-odds ratio and of the Spearman correlation coefficient as
weights in a generalized least squares regression using these measures as dependent
variables. The results are not significantly different from those reported earlier.
We also investigate whether time-series correlation affects our results. First,
we test for serial correlation in a panel but fail to find evidence of this. Second, we
include lagged persistence measures in our regressions. This reduces the number of
observations from 162 to 136. HERFINDAHL and MATURITY remain significant at
the .05 level or better, and N remains insignificant. The lagged persistence measure
itself is never statistically significant.
To check that our results using Spearman correlation are not influenced
by having a dependent variable limited to the [+1,−1] range, we estimate our
model using the Papke and Wooldridge (1996) generalized linear approach, which
is designed for estimating models with a fractional dependent variable. Our results
are broadly unchanged. All coefficient estimates that are significant using ordinary
least squares at the .05 level and above are also significant using the new approach,
and the signs of all significant coefficient estimates remain the same as before.
To address the possibility that our results may be influenced by the small-
sample properties of our persistence measures, we exclude sector-years with fewer
than 30 funds. This reduces the number of sector-years to 124. When we do this,
MATURITY becomes insignificant regardless of the econometric method used.
The statistical significance of HERFINDAHL, however, is .05 or better in all
specifications.
As the coverage of the last two editions of the Unit Trust Yearbook (corre-
sponding to calendar years 2000 and 2001) is reduced because of nonreporting by
several fund families, we repeat our regressions after omitting these years. We also
conduct Fama-MacBeth regressions, drop outlier observations, and use different
ranges to winsorize our persistence measures. Our results remain basically un-
changed: HERFINDAHL is always significant at least at the .10 level and generally
at the .05 level. None of our other variables is consistently significant.

Economic Significance
Our results indicate that the concentration of funds’ assets is statistically signifi-
cantly related to the persistence level in that sector. We now assess the economic
significance of this relation. Consider a fund sector not restricted to U.K. equities
(DOMESTIC EQUITY = 0) in the second half of our sample period (LATER =
1). When HERFINDAHL is set to its full-sample 10th percentile value of 0.038,
using the estimated coefficients in regression (3) of Table 3, the fitted value of
the log-odds ratio equals 0.025. Using the conversion formula in footnote 3, this


translates into a 50.3% probability that a fund’s winner or loser status is retained
from the ranking period to the evaluation period. This probability exceeds by only
0.3% the corresponding probability that one would expect by mere chance in the
absence of any persistence.
We now reset HERFINDAHL to its 90th percentile value of 0.162. The
corresponding fitted value of the log-odds ratio is 0.493, which translates into a
56.1% probability of repeat performance, or 6.1% higher than would be expected in
the absence of persistence. In other words, if a sector goes from the 10th to the 90th
percentile of concentration of assets, the excess (relative to the no-persistence case)
probability of remaining in the same half of performance rankings increases from
0.3% to 6.1%. These numbers indicate that the effect of sector-level concentration
on performance persistence is substantial in economic terms.

IV. Longer Term Persistence

Because we find a link between sector characteristics and persistence, we check
whether the results hold when persistence is measured over a longer period. To do
this, rather than examining adjacent ranking and evaluation periods as we did in
the preceding section, we use a lagged ranking period (as in Teo and Woo 2001).
In other words, one year is allowed to pass between the end of the ranking period
and the start of the evaluation period.
Recall that when the ranking period is not lagged, the average (across
sector-years) log-odds ratio is 0.357 and highly statistically significant. When we
lag the ranking period by one year, the average log-odds ratio becomes −0.013 and
is not significant. Likewise, the average Spearman coefficient drops from 0.143 to
−0.012 and is no longer significant.
Even though the average level of longer term persistence across sector-
years is close to zero, it is still possible that variation in longer term persistence is
related to sector competitiveness. We therefore repeat our regression analysis when
the dependent variable is the longer term measure of persistence.5 Consistent with
the notion that our sample exhibits little or no persistence at the longer horizons,
HERFINDAHL does not have a significant effect on longer term persistence, and
neither do the other sector-level variables.6

5
These results are available from the authors on request.
6
Although we find it important to document that our significant results are limited to the one-year
horizon, we note that tests for the existence of persistence, and by extension tests for the association
of our sector-level variables with persistence, are weaker when the horizon is longer. First, survivorship
conditioning becomes more serious when the horizon is longer, and depending on the characteristics of the
fund attrition process, this can either strengthen or weaken persistence. Second, measurement of persistence
is noisier when the horizon is longer (e.g., because fund characteristics change over time). Indeed, few studies
detect persistence beyond the one-year horizon.


V. Conclusion

Performance persistence is important to all parties connected with fund manage-
ment. Its existence has been the subject of an intense and ongoing debate. We
contribute to this debate by studying variation in performance persistence across
peer groups. The focus of our study is the U.K. mutual fund industry, where official
sectors unambiguously define such peer groups.
We study the effect of several sector-level variables on sector-level persis-
tence. Our choice of variables is based on the notion that the more competitive a
sector is, the less likely it is to be characterized by persistence in its funds’ perfor-
mance. The variables used to capture intra-sector rivalry are: the number of funds
in the sector, the concentration of fund family assets under management in the
sector, and the proportion of mature funds in the sector. We additionally control for
the types of assets in which the sector’s funds are invested. Only the concentration
index of fund family assets is consistently significant: the less dispersed the sector’s
assets are, the more persistence is observed. In all, our results indicate that the com-
petitiveness of a fund sector influences the persistence in the relative performance
of its members. The exact channels through which the competitive environment
affects investment managers’ performance are a subject for future research.

APPENDIX A
Evolution of U.K. Unit Trust Sectors, 1991–2001


APPENDIX B
Calculation of Performance Persistence Statistics

Spearman Rank-Correlation Coefficient
First, funds that existed in years T (the ranking year) and T+1 (the evaluation year)
are identified. Define N to be the size of this sample. For each fund in the sample,
the difference d i in the rank of fund i between years T and T+1 is calculated. The
Spearman rank-correlation statistic is defined as

N
rs = 1 − 6 di (N 3 − N )
i=1

and lies between −1 and +1. For sufficiently large N, it is appropriate to test for
the statistical significance of rs using a t-test where the critical t-statistic is given
by


N −2
ts = r s
1 − rs2
and has N−2 degrees of freedom.

Log-Odds Ratio
Funds within a sector are classified as winners (W ) (losers [L]) if their returns are
in the top (bottom) half of funds for each of the years T and T+1. WW denotes the
number of two-period winners, LW denotes the number of losers in the first year
and winners in the second year, WL reverses this order, and LL denotes the number
of two-period losers. The log-odds ratio is defined as
WW ∗ LL
ln .
WL ∗ LW
The log-odds ratio is asymptotically normally distributed with mean zero and stan-
dard error given by

1 1 1 1
σ = + + + .
WW WL LW LL
The z-statistic of the log odds ratio refers to the log-odds ratio divided by its standard
error.

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