Performance changes and mgmt turnover khorana

7/31/2001–coded–JFQA #36:3 Khorana Page 371

JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS VOL. 36, NO. 3, SEPTEMBER 2001
COPYRIGHT 2001, SCHOOL OF BUSINESS ADMINISTRATION, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195

Performance Changes following Top
Management Turnover: Evidence from
Open-End Mutual Funds
Ajay Khorana£

Abstract
I examine the impact of mutual fund manager replacement on subsequent fund perfor-
mance. Using a sample of 393 domestic equity and bond fund managers that were replaced
over the 1979–1991 period, for the underperformers, I document significant improvements
in post-replacement performance relative to the past performance of the fund. On the
other hand, the replacement of overperforming managers results in deterioration in post-
replacement performance. I find evidence supporting the presence of strategic risk shifting
in the fund portfolios prior to replacement. Furthermore, consistent with the notion of win-
dow dressing, I document that the level of portfolio turnover activity decreases significantly
in the post-replacement period. Lastly, the replacement of poor performers is preceded by
significant decreases in net new inflows in the fund.

I. Introduction
The academic literature has devoted considerable attention to understand-
ing the effectiveness of various corporate governance mechanisms, ranging from
shareholder activism to monitoring activities on the part of boards of directors and
large blockholders. Past research on regulating the behavior of corporate man-
agers has also focused on the disciplinary forces of the external product market,
the takeover market, and the managerial labor market. 1 In addition, the literature
on executive compensation has attempted to examine the effect of incentives on
managerial behavior.
While the linkages among various stakeholders in a corporation have been
extensively examined along with the impact of incentives on managerial behavior,
£ DuPree College of Management, Georgia Institute of Technology, Atlanta, GA 30332-0520, e-
mail: ajay.khorana@mgt.gatech.edu. I thank Lipper Analytical Services Inc. and Morningstar Inc. for
providing part of the data used for this study and Stephen Brown (the editor), Jin-Wan Cho, Melissa
Frye, Edward Nelling, Ajay Patel, Henri Servaes, Sunil Wahal, and David Yermack (the referee) for
helpful comments and Melissa Frye and Robert Craddock for valuable research assistance. I also thank
Shane Corwin for programming assistance and Mark Carhart for providing access to the factor-model
database.
1 Fama (1980), Fama and Jensen (1983), and Shleifer and Vishny (1986), among others, are im-
portant contributors to this area of research.
371


372 Journal of Financial and Quantitative Analysis

very few studies have examined mutual fund organizations. In a notable excep-
tion, Brown, Harlow, and Starks (1996) argue that in an attempt to maximize their
expected compensation, rational managers may revise their portfolio composi-
tion depending upon their relative performance during the year. Specifically, fund
managers most likely to be losers will seek to increase their portfolio risk relative
to the group of likely winners.
In an effort to understand the effectiveness of internal and external control
mechanisms in the mutual fund industry, Khorana (1996) studies the relation be-
tween managerial replacement and prior fund performance. He finds evidence
supporting the presence of an inverse relation between the probability of fund
manager replacement and past performance. 2 In addition, he documents that the
magnitude of underperformance that investment advisors are willing to tolerate is
positively related to the volatility of the underlying assets being managed by fund
managers. Chevalier and Ellison (1999) reexamine the performance replacement
relation with special focus on the age of the fund manager. They find that younger
managers are more likely to experience replacement if the fund’s systematic or un-
systematic risk deviates from the average risk level of other funds in the matched
investment objective.
The objective of this paper is to shed additional light on the effectiveness of
internal and external control mechanisms in mutual fund organizations by ana-
lyzing the consequences of fund manager replacement on subsequent fund per-
formance. This idea is similar in spirit to Denis and Denis (1995), who examine
the impact of CEO turnover on the post-replacement performance of the firm.
For the subsample of managers experiencing forced replacement, they document
significant improvements in post-replacement operating performance. However,
they find that forced turnovers occur after prolonged periods of poor performance,
which leads to a substantial loss in shareholder wealth.
Understanding the post-replacement effects in a mutual fund setting is use-
ful for a number of constituents: i) fund advisors, who are compensated based on
the percentage of outstanding assets, may be interested in knowing whether man-
agerial replacement dramatically alters the pattern of asset inflows in the post-
replacement period; ii) fund investors may want to know whether managerial re-
placement alters future fund performance; and iii) regulators, such as the SEC,
may want to examine the pre- vs. post-replacement performance effects to obtain
a better understanding of the effectiveness of internal and external disciplinary
forces operating at the level of the mutual fund.
Specifically, in this paper, I examine whether underperforming funds in the
pre-replacement period are able to turn around their performance and, if so, how
long it takes to be a part of the winning group of managers. In contrast, does the
departure of a manager at an overperforming fund adversely affect performance
in the post-replacement years? In addition, I examine the relation between man-
agerial replacement and asset flows into the fund. I also analyze whether there is
a dramatic shift in the risk profile of funds across the pre- and post-replacement

2 For corporate CEOs, Coughlan and Schmidt (1985) and Warner, Watts, and Wruck (1988) have
documented the presence of an inverse relation between managerial turnover and firm performance.
Weisbach (1988) finds that the magnitude of this effect is positively related to the number of indepen-
dent outsiders on the board of directors.


Khorana 373

periods. Finally, I examine whether there is any perceptible shift in managerial
behavior with regard to the change in portfolio turnover rates and expense ratios
in the years surrounding managerial replacement.
Using a sample of 393 domestic equity and bond fund managers experienc-
ing replacement over the 1979–1991 period, I document that in the post-replace-
ment period, the sample of funds with negative pre-replacement performance con-
tinue to exhibit negative abnormal performance based on the single-factor CAPM
and the Carhart (1997) four-factor model. However, in comparison with the fund’s
own poor pre-replacement performance, the new fund managers exhibit dramatic
performance improvements in the post-replacement period. Median objective-
adjusted fund returns improve from 2.4% in year 1, i.e., the year preceding
the replacement year, to 0.5% in the third year (+3) after replacement. The cor-
responding figures for the sample of funds experiencing positive abnormal per-
formance (in the pre-replacement period) are 1.9% and 0.4% in years 1 and
+3, respectively. Hence, the replacement of the superior managers results in a
significant deterioration in post-replacement performance.
Performance attribution tests conducted to ascertain the source of perfor-
mance indicate that equity fund managers with superior abnormal performance
in the pre-replacement period subject their funds to a positive momentum fac-
tor. In addition, these fund managers tend to hold a greater proportion of smaller
capitalization stocks.
In the presence of a positive flow performance relation, there should be a
perceptible decline in pre-replacement asset flows for underperforming managers.
Multivariate regression results are indeed supportive of significantly negative pre-
replacement asset flows for the poorly performing fund managers. This result
provides direct evidence on the importance of managerial replacements for the
fund’s investment advisors. Reversing the trend of declining asset inflows can
lead to economies of scale and generate additional fee income for the fund. This
evidence also suggests that both existing and prospective shareholders pay close
attention to the managerial replacement decision and exercise strong discretion in
deciding when to “vote with their feet.”
For the underperforming sample, I find an increase in portfolio risk in the
pre-replacement period followed by a reduction in total portfolio risk in the post-
replacement period. This result is consistent with the tournaments model of
Brown, Harlow, and Starks (1996) where fund managers most likely to be losers
tend to increase their portfolio risk relative to the group of likely winners. Con-
sistent with window dressing behavior, I also document higher levels of portfolio
turnover activity in the pre-replacement period followed by significant decreases
in the post-replacement period.
The remainder of the paper is organized as follows. Section II describes
the data sources and the sample selection procedure. Section III outlines the un-
derlying hypotheses and methodology used for the study. Section IV provides a
discussion of the empirical results and Section V concludes.



II. Data and Survivorship Issues
A. Data Sources and Sample Description

The sample of replaced fund managers is constructed from Morningstar’s
database (preceding the end of 1992) by supplementing it with information on the
year and the month in which the current fund manager commenced overseeing
the operations of the fund and thus the year in which the previous manager was
replaced. The information on the month and year of managerial replacement is
obtained by directly contacting the fund families and from Morningstar. From this
sample, domestic equity and bond funds with at least three years of performance
history preceding the month of managerial replacement in a particular year and
at least one year of post-replacement data are selected. This screening criterion
is critical for the empirical tests since I need to follow the same set of mutual
funds in the pre- and post-replacement periods. Based on the above criteria, the
final managerial replacement sample comprised 393 funds. Of these, there are
171 equity funds and 222 bond funds.
To measure the returns performance of individual fund managers, monthly
returns data are obtained from both Lipper Analytical Services Inc. and Morn-
ingstar Inc.; the information on other fund-specific variables is accessed from
Morningstar. Returns are computed by adding to the change in net asset value
(NAV), both the income and capital gains distributions during the period, and then
dividing by the beginning of period NAV. The reinvestment of dividend distribu-
tions is computed at the ex-date. These returns are not adjusted for sales charges,
front/back end load, and redemption fees. This database is supplemented with
other data sources such as the Wiesenberger Mutual Fund Updates, S&P Quar-
terly Stock Guide, and The Wall Street Journal. As a precautionary measure, the
data used from the respective databases are cross-checked with other sources that
make available the same information. The monthly returns on the value-weighted
market index are obtained from the monthly CRSP files, total returns on the Trea-
sury bond and corporate bond indices are obtained from Lehman Brothers, and
returns on the Carhart (1997) factors are obtained from Mark Carhart. Note that
for all variables except returns, only annual data are available.
An important caveat on managerial replacement is that it may occur due to
the dismissal of underperforming managers or voluntary departure of average or
overperforming managers. Even though both forms of departure will be reflected
in managerial turnover, the factors leading to replacement are different in the two
cases. However, the lack of any publicly available information for a large majority
of the fund managers precludes knowledge of the exact sequence of events that
may be responsible for managerial replacement. 3 Only high profile replacements
are reported in the popular press. Hence, I am unable to distinguish explicitly
among the various reasons for replacement. The traditional corporate finance
research uses the age of the manager as a proxy for forced or voluntary turnover.
However, this is not a plausible alternative for my study since the mean (median)
3 A Wall Street Journal article dated April 7, 1994, indicates that since the SEC came out with a
ruling that the names of mutual fund managers must be disclosed to fund investors, mutual funds have
actually started hiding the names of their managers. This is motivated by the desire on the part of fund
organizations to make it more difficult to link fund performance with the portfolio manager.


Khorana 375

age of the replaced fund manager is 41 (42) years with the oldest managers being
62 years. Furthermore, since age data is available for only a small subset of the
managers, it cannot be used for conducting the sample decomposition.
Hence, as a proxy for the reason behind replacement, I decompose the sam-
ple of 393 fund managers based on their objective-adjusted fund performance in
the 36-month period preceding replacement. Funds exhibiting negative objective-
adjusted performance are placed in the negative performance sample (NP) and
those exhibiting positive objective-adjusted performance are placed in the positive
performance sample (PP). The subsequent portfolio performance and other fund
management characteristics of these separate groups of managers are examined
in the post-replacement period. In the absence of publicly available information
on the rationale behind replacement, such a portfolio decomposition approach
serves as the next best alternative. This sample decomposition yields 239 funds in
the negative performance (NP) sample and 154 funds in the positive performance
(PP) sample. Out of the 239 funds in the NP sample, there are 117 equity funds
and 122 bond funds. The similar breakdown for the PP sample is 54 and 100
funds in the equity and bond categories, respectively.
In additional robustness tests, I decompose the sample using the one-factor
and four-factor models, the percentile rank of the fund relative to other funds in the
corresponding investment objective, and the percentile rank of the fund relative
to all funds in existence during the year. The qualitative nature of the results
is similar for these alternative performance measures. Hence, for the sake of
brevity, only results using the objective-adjusted return decomposition approach
are reported.

B. Adjusting for Survivorship Bias
One potential drawback of this data set is that it only includes surviving
funds. As a result, total inﬂows into an objective are understated, while perfor-
mance measures are likely to be overstated (assuming poorly performing funds
are terminated). 4 This could bias my ﬁndings. Hence, the sample is supple-
mented with data on non-surviving funds from the largest 100 families measured
by total assets at the end of 1992. I focus on the 100 largest families to keep the
data collection process manageable. These families account for 93.3% of total
mutual fund assets in the sample at the end of 1992.
Data on non-surviving funds are collected using the following procedure. I
start with a list of all funds that survive through 1992 for the largest 100 fund
families in my sample. I then compare my replacement and control sample with
the funds listed in the Wiesenberger Investment Companies books (for each year)
for these families. This produces an initial list of 251 potentially non-surviving
funds; these are funds listed in Wiesenberger but missing from my sample. It is
possible, however, that information is missing because the fund changed its name
or because it operates in an investment objective excluded from my sample. Us-
ing the Wiesenberger publications, I follow each of these 251 funds from 1979 or
inception through 1994. I check through 1994 to ensure that Wiesenberger did not
simply omit the fund for a year or two or delay the reporting of a name change. In
4 Malkiel (1995) shows that non-surviving funds underperform funds that survive.



addition, Wiesenberger’s list of name changes is not always complete; thus, cer-
tain name changes are identified by matching performance and other fund-specific
data. Of the 251 funds, 124 simply changed their names during the sample period.
Forty-two of the remaining 127 funds are in investment objectives excluded from
the analysis. Thus, I expand my initial sample by 85. The net asset value, asset
base, and the return data on these funds is obtained from Wiesenberger and is
used to correct for biases in the underlying benchmarks. I was not able to obtain
fund-specific information on the annual portfolio turnover and expense ratio of
each fund since this information is not available on a consistent basis.

III. Hypotheses and Methodology

A. Fund Performance

In light of the extremely competitive nature of the mutual fund industry
where the market has a tendency to penalize poorly performing funds via a sys-
tematic loss in market share to superior performers (Ippolito (1992)), 5 the invest-
ment advisors have the major responsibility of revitalizing the fund by attracting
superior managers. In fact, the potential disciplinary role of external product mar-
kets for mutual funds is atypical in the sense that fund shareholders can directly
redeem their proportional ownership interest with the fund’s management. As
a result, the degree of control exercised by fund shareholders is far greater than
shareholders of regular corporations who can only liquidate their holdings in the
secondary market. Hence, if poor fund performance in the pre-replacement period
is attributable to managerial abilities rather than bad luck, and if the fund’s board
and investment advisors are able to attract good managerial talent, one would ex-
pect an improvement in post-replacement performance for the NP sample. For
the PP sample, on the other hand, the post-replacement performance will depend
on the ability of the new manager to sustain superior performance. If the new
manager is successful, it will result in persistence of superior fund performance.
On the other hand, any deterioration in post-replacement performance may be
indicative of the superior skill set and abilities of the fund manager in the pre-
replacement period. 6
Based on recent academic studies on fund performance, I analyze perfor-
mance using a series of different performance measures: i) a one-factor and
a four-factor abnormal performance measure, ii) an objective-adjusted perfor-
mance, iii) a matched sample approach, and iv) the percentile performance rank-
ings of the fund. In the following section, I describe each of these performance
measures in detail.
5 In a related study examining the flow of funds, Sirri and Tufano (1998) document that mutual
fund investors direct new capital toward the most recent overperformers but fail to take assets away
from underperformers.
6 Carhart (1997) documents that funds generating higher one-year returns are able to do so because
they happen to hold relatively large positions in last year’s winners. Grinblatt, Titman, and Wermers
(1995) find that funds following short-term momentum strategies realize superior performance be-
fore fees and transactions costs, but Carhart (1997) shows that the superior performance based on a
momentum-based stock investment strategy disappears after adjusting for transaction costs.


Khorana 377

1. Abnormal Performance based on the One-Factor and Four-Factor Models
Consistent with other fund performance measurement studies, I employ
Sharpe’s (1964) one-factor Capital Asset Pricing Model and Carhart’s (1997)
four-factor model. The four-factor model includes the three-factor model of Fama
and French (1993) and Jegadeesh and Titman’s (1993) momentum factor. Specif-
ically, for equity funds, the following model specifications are examined in the
paper,
Rit «it + ¬1 it VWRFt + it
Rit «it + ¬1 it RMRFt + ¬2 it SMBt + ¬3 it HMLt + ¬4 it PR1YRt + it

where Rit is the fund return in excess of the monthly T-bill return; VWRF is the
excess return on the CRSP value-weighted index; RMRF is the value-weighted
market return on all NYSE/AMEX/NASDAQ firms in excess of the risk-free rate;
SMB (small minus big) is the difference in returns across small and big stock
portfolios controlling for the same weighted average book-to-market equity in
the two portfolios; HML (high minus low) is the difference in returns between
high and low book-to-market equity portfolios; PR1YR is the momentum factor
computed in Carhart (1997) by subtracting from the equally-weighted return of
firms with the highest 30% 11-month return lagged one month, the corresponding
return for firms with the lowest 30% 11-month return, which is also lagged one
month.
For bond funds, I also use a one-factor model and a four-factor model to
compute the risk-adjusted excess return for each fund. The following model spec-
ifications are employed,
Rit «it + ¬1 it GOVCORPt + it
Rit «it ¬1 it GOVCORPt + ¬2 it MBSt + ¬3 it LONGGOVTt
+ ¬4 it INTGOVTt + it
where Rit is the fund return in excess of the monthly T-bill return; GOVCORP
is the excess return on the Lehman Brothers Government/Corporate bond index
and is a weighted market average of government and investment grade corporate
issues that have more than one year until maturity; MBS is the excess return on the
Lehman Brothers Mortgage-Backed securities index; LONGGOVT is the excess
return on the Lehman Brothers Long Term Government Bond index; INTGOVT
is the excess return on the Lehman Brothers Intermediate Term Government Bond
index. These model specifications are consistent with Blake, Elton, and Gruber
(1993). For both the equity and bond fund regressions, I use 24 months of return
data to estimate the regression parameters.
In addition to improving the average pricing errors of the single-factor model,
the four-factor model is also used to conduct performance attribution analysis for
ascertaining the source of performance and the underlying investment strategies
pursued by portfolio managers.

2. Objective-Adjusted Performance
To complement measures of abnormal fund performance based on single-
and multi-factor models, I examine the pre- and post-replacement changes in



objective-adjusted performance. The use of an objective-adjusted performance
measure is consistent with the argument that, in making their managerial replace-
ment decisions, a firm benchmarks a manager’s performance against other firms
in the industry (Morck, Shleifer, and Vishny (1989)).
The objective-adjusted return (OAR) of a fund is measured as the annual
holding period fund return in excess of the annual holding period return on the
benchmark portfolio of other funds within the matched investment objective.
Hence, the OAR is computed as follows,
12 12
OAR ´1 + Ri t µ 1 ´1 + Ro t µ 1
t 1 t 1

where Ri t is the return of firm i in month t, and R o t is the monthly return on
the benchmark portfolio. Thus, the OAR measures fund performance relative
to other managers in the peer group. This measure absolves the manager from
sector, industry, or style-specific effects that may exogenously affect all managers
in the same investment category. The returns of the non-surviving funds are used
to correct the objective benchmarks for the underlying survivor bias.

3. Matched Sample Performance Measurement Approach 7
To ascertain whether any post-replacement improvement or deterioration in
fund performance is related to true managerial ability rather than a mere artifact
of the tendency of security return to exhibit mean reversion, I employ a matched
sample based performance measurement approach. I construct a sample of poten-
tial matching firms for each fund in the replacement sample by identifying funds
with similar performance histories and the same investment objective as the re-
placement sample firm. However, unlike the replacement sample, the investment
advisors of these potential control firms choose not to replace their managers.
To match the performance histories of the replacement and control sample firms,
objective-adjusted as well as risk-adjusted performance is measured using the 36-
month period preceding the managerial replacement month.
Two separate matching procedures are employed in the analysis. In the first
approach, a particular firm can be used as a match only for a single (unique)
replacement sample firm. Hence, once a matching firm has been selected, it is
not used as a potential firm for another replacement sample firm with the same
investment objective. The second approach allows a potential firm to be used as a
match for multiple funds in the replacement sample. However, in instances where
the same firm is used as a match for multiple replacement sample firms, data over
different time periods is used (due to differing managerial replacement dates for
the replacement sample firms).
After identifying a unique matching firm for each replacement sample firm, I
subtract the annual holding period return of the control firm from the correspond-
ing holding period return for the replacement sample firm. This is referred to as
the matched sample adjusted return (MSAR). I eliminate 10% of the observations
to obtain a matched sample adjusted return close to zero in the pre-replacement
7I thank David Yermack (the referee) for suggesting the matched sample approach.


Khorana 379

years. Then, I examine the performance pattern of this matched sample adjusted
return for managers in both the negative performance (NP) and positive perfor-
mance (PP) samples. If the managerial replacement event truly adds value to the
shareholders of a poorly performing fund, one would expect to find a performance
reversal in this adjusted return series. Such a test would provide an explicit de-
termination of whether post-replacement performance changes can be attributed
to “true” managerial ability or whether they are merely the result of the mean re-
version phenomenon observed in security prices. For the sake of brevity, only the
results of the multiple matching approach are reported.

4. Percentile Rankings
As another measure of performance, I report the mean and median percentile
performance ranking of funds (as computed by Morningstar) in the sample rela-
tive to other funds in the same investment objective in a given year.

B. Changes in Portfolio Risk
Since fund managers are evaluated within a tournaments framework where
their performance is benchmarked against other managers within the peer group
(Brown, Harlow, and Starks (1996)), such tournaments create certain risk shifting
incentives among managers. Specifically, managers in the bottom half of their
performance group may undertake more risk in an effort to be a part of the top
half of managers. To explicitly test this notion of tournaments and its risk shift-
ing implications, I examine the time-series pattern of the beta and the standard
deviation of monthly returns for each of the six years surrounding replacement.

C. Changes in Portfolio Turnover and Expense Ratios
In an attempt to prevent dismissal, poorly performing fund managers may
engage in window dressing behavior by rebalancing their portfolios to closely re-
semble the portfolios of other overperforming mangers in their peer group (Lakon-
ishok, Shleifer, Thaler, and Vishny (1991)). This would result in significantly
larger pre-replacement portfolio turnover rates. However, to the extent that the
new fund manager does not have a track record of persistent poor performance,
the manager has a lesser need to engage in window dressing behavior. Hence,
one would expect a significant decline in a fund’s portfolio turnover in the post-
replacement period. On the other hand, for the overperforming managers, there
are no conclusive priors with regard to the pattern of portfolio turnover rates in
the pre- vs. post-replacement years.
The likely pattern of expense ratios would depend on the price sensitivity of
the fund investors and the desire on the part of fund families to pass along any
savings that accrue due to the economies of scale from operating a larger fund. A
higher level of price sensitivity and greater scale benefits would lead to a reduction
in average fund expenses over time. 8
8 A recent study by the Investment Company Institute (“Mutual Fund Costs,” Vol. 5, No. 4, Septem-
ber 1999) indicates that expenses for equity (bond) mutual funds have declined by 91 (45) basis points
over the 1980–1998 period. A study by the United States General Accounting Office (June 2000) on
mutual fund fees corroborates the findings of the ICI study.



D. Impact of Asset Flows on Managerial Turnover
As a direct test of whether external product markets play an active role in
disciplining fund managers, I examine whether shareholders redirect money flows
away from managers’ experiencing negative performance in the pre-replacement
period. This analysis is interesting for several reasons. Since the primary source
of income for investment advisors is the advisory fee received for managing the
fund (which is usually a fraction of assets under management), it is extremely
critical for the investment advisor of a poorly performing fund to generate im-
provements in post-replacement performance. In addition, examining the pre- vs.
post-replacement relation between performance and asset flows for the overper-
forming sample provides evidence on market participants’ beliefs with regard to
the fund’s ability to exhibit performance persistence. 9 This empirical framework
also provides a test of whether investors redeem assets in response to the depar-
ture of the superior manager. Since Sirri and Tufano (1998) document a weak
performance asset flow sensitivity for poor performers, I reexamine this relation
conditional on the replacement of the fund manager.
There is an important caveat in determining the magnitude of asset flows.
Since most flow data are reported as total assets of the fund at the end of the
year, these figures could be affected by both the returns generated by the portfolio
manager during the year and by actual (net) asset inflows/outflows. Hence, to
compute inflows net of returns, i.e., (NETFLOW i t ), I use the following approach,

NETFLOWi t ASSETSi t ASSETSi t 1 £ ´1 + Ri t µ ASSETSi t 1

where ASSETSi t is total assets in fund i at the end of year t, and R i t is the return
of fund i during year t. Based on the above computation, the NETFLOW variable
measures the growth in assets over and above the change in value of the fund’s
asset base (existing at the beginning of the year), partly due to the fund manager’s
portfolio management decisions.
In a multivariate regression framework, I examine the relation between net
inflows to a fund in a given year and fund performance using the following general
model,

NETFLOWi t f Objective Flowst ; Fund performance i t 1; Riski t 1 ;
Expensesi t 1 ; Log(Assets)i t 1 ;
Negative performance indicator variable;
Pre-replacement indicator variable;
Interaction effects

Objective flows are used to control for the effect of flow variations in a particular
investment objective. Lagged fund performance is included to capture the effect
9 Using a sample of no-load growth funds, Hendricks, Patel, and Zeckhauser (1993) find evidence
that persistence of superior fund performance is a short-run phenomenon, i.e., it lasts for up to four
quarters. Carhart (1997) finds that funds with hot hands rarely demonstrate a repetition in their su-
perior performance. However, he documents evidence of performance persistence among funds with
extreme underperformance. Similarly, Brown and Goetzmann (1995) find evidence of persistence in
risk-adjusted performance in funds that lag the S&P 500 index.


Khorana 381

of fund performance on subsequent inflows. Performance is measured based on
alphas from one-factor and four-factor models. Risk is measured as the standard
deviation of 12 monthly returns. A negative relation between the risk level of
the fund and asset flows is hypothesized. Lagged fund expenses are also likely
to be inversely related to fund flows since higher expenses are likely to deter
new investors from investing in the fund. The size of the fund in the previous
period is included as a control variable since the larger funds will receive a lesser
percentage flow for the same dollar flow than smaller funds.
In addition to the above controls, I include a number of indicator variables in
the regression specifications to capture differences in the performance flow rela-
tion across the pre- and post-replacement period and across the samples of nega-
tive performance (NP) and positive performance (PP) managers. Specifically, NPI
is the negative performance indicator variable that equals one if the fund belongs
to the negative performance group (NP), and zero if the fund is a part of the pos-
itive performance group (PP). PRE is the pre-replacement indicator variable that
equals one for years 2, 1, and year zero (the managerial replacement year),
and equals zero otherwise. I include an interaction term, i.e., [NPI£PRE] in the
model specifications to ascertain if the asset flow relation is different for underper-
forming funds in the pre-replacement period and whether asset flows change after
the replacement of the fund manager. To determine the relative impact of posi-
tive vs. negative performance, I also construct a positive (negative) risk-adjusted
performance variable where all positive (negative) alpha values are retained and
negative (positive) alpha values are set equal to zero.

IV. Results
A. Performance Changes surrounding Replacement

The impact of managerial turnover on fund performance is examined based
on the levels and changes in various performance measures during the period two
years preceding and three years following the replacement event. As mentioned
earlier, fund performance is measured using a one-factor model and a four-factor
model, based on objective-adjusted returns, the matched sample return approach,
and the percentile ranking of funds. These results are provided separately for the
negative (NP) and positive performance (PP) sample of fund managers. Changes
in both mean and median performance measures across various event windows
are also reported in Table 1.
Given the sample decomposition procedure, it is not surprising that in the
pre-replacement period, managers in the NP sample exhibit significant under-
performance. Based on the performance estimates from the CAPM (one-factor
model), managers in the NP sample exhibit significantly negative mean (median)
monthly abnormal returns of 20 (13) basis points in year 2 (i.e., two years pre-
ceding managerial replacement), 25 (17) basis points in year 1, and 33 (23)
basis points in the year of managerial replacement (Table 1, panel A). This trans-
lates into an annualized return of 2.4% ( 1.6%), 3.0% (2.0%), and 4.0%
( 2.8%) in the three years, respectively. In the pre-replacement period, on the
other hand, managers in the PP sample exhibit marginally positive abnormal an-



nual performance ranging from 1.3%–2.0% (based on means) and 0.5%–1.3%
(based on medians).
I find similar results when abnormal returns are measured based on a four-
factor model. Fund managers in the NP sample exhibit mean (median) monthly
abnormal underperformance of 27 (17) basis points in year 2, with the magni-
tude of underperformance increasing to 30 (25) basis points in year 0. Hence,
consistently negative and increasing underperformance results in managerial dis-
missal. These findings are consistent with Khorana (1996). The presence of sig-
nificant underperformance in the pre-replacement period is also manifested in the
fact that managers in the NP sample are, on average, in the bottom 35th percentile
of performance when benchmarked against the subsample of funds in the matched
investment objective.
In additional tests, I measure annual fund performance i) in comparison with
the performance of the underlying investment objective, i.e., objective-adjusted
return (OAR) and ii) relative to a control sample of funds with similar perfor-
mance characteristics but which choose not to replace their managers, i.e., the
matched sample adjusted return (MSAR). For the NP sample, the mean (median)
annual OARs for years 2, 1, and zero are 2.5% ( 1.5%), 4.1% ( 2.4%),
and 4.7% ( 3.2%), respectively.

TABLE 1
Performance Measures surrounding Fund Manager Turnover

Panel A. Performance Characteristics—Levels
Years with Respect to Managerial Turnover

2 1 0 +1 +2 +3
One-factor alpha NP 0.204 0.249 0.332 0.291 0.026 0.097
(in %) [ 0.133] [ 0.172] [ 0.226] [ 0.217] [ 0.040] [0.043]
PP 0.112 0.166 0.124 0.015 0.004 0.010
[0.044] [0.111] [0.073] [0.006] [ 0.003]
[ 0.020]
Four-factor alpha NP 0.271 0.230 0.298 0.285 0.028 0.017
(in %)
[ 0.169]
[ 0.196]
[ 0.250]
[ 0.250] [ 0.045]
[ 0.015]
PP 0.078 0.045 0.019 0.021 0.002 0.053
[ 0.012]
[ 0.019]
[ 0.048]
[ 0.025] [ 0.074] [ 0.096]
Objective-adjusted NP 0.025 0.041 0.047 0.001 0.004 0.009
return [ 0.015] [ 0.024] [ 0.032] [0.001] [0.004] [0.005]
PP 0.033 0.033 0.011 0.009 0.015 0.002
[0.021] [0.019] [0.008] [0.006] [0.009] [0.004]
Matched sample NP 0.002 0.004 0.006 0.006 0.029 0.027
adjusted return
[ 0.002]
[ 0.002]
[ 0.003] [0.001] [0.018] [0.014]
PP 0.004 0.006 0.003 0.006 0.004 0.011
[0.001] [0.001] [ 0.001] [0.003] [0.001]
[ 0.007]
Percentile rank NP 35.71 30.16 36.10 51.93 51.65 53.93
(within objective) [30.00] [25.00] [30.00] [54.00] [54.00] [59.00]
PP 61.94 59.31 53.19 54.65 53.82 56.20
[65.50] [61.50] [56.50] [57.00] [55.00] [60.00]
Sample size NP 239 239 239 239 233 161
PP 154 154 154 154 148 120
(continued on next page)


Khorana 383

TABLE 1 (continued)
Performance Measures surrounding Fund Manager Turnover

Panel B. Performance Characteristics—Changes in Levels
2 to 1 2 to 0 1 to 0 1 to +1 1 to +2 1 to +3
One-factor alpha NP 0.045 0.128** 0.083* 0.042 0.222*** 0.403***
(in %) [ 0.019] [ 0.076]*** [ 0.055]** [ 0.018] [0.160]*** [0.343]***
PP 0.054 0.013 0.042 0.150*** 0.171***
0.199***
[ 0.007] [ 0.021] [ 0.013] [ 0.109]***
[ 0.052]*** [ 0.107]***
Four-factor alpha NP 0.040 0.025 0.069 0.055 0.199*** 0.206***
(in %) [ 0.002] [ 0.032]** [ 0.058]** [ 0.020] [0.159]*** [0.177]***
PP 0.123 0.099 0.026 0.023* 0.046 0.140*
[ 0.033] [ 0.044] [ 0.010] [ 0.004]
[ 0.001]
[ 0.057]***
Objective-adjusted NP 0.016*** 0.022*** 0.006 0.040*** 0.044*** 0.056***
return [ 0.005]** [ 0.012]*** [ 0.007] [0.018]*** [0.026]*** [0.038]***
PP 0.001 0.022*** 0.022*** 0.023***
0.019*** 0.033***
[ 0.003] [ 0.013]*** [ 0.006]***
[ 0.009]*** [ 0.015]**
[ 0.019]***
Matched sample NP 0.002 0.004 0.002 0.010*** 0.020*** 0.028***
adjusted return [ 0.003] [ 0.005]* [ 0.003] [0.007]** [0.012]*** [0.010]***
PP 0.002* 0.007 0.009** 0.000 0.002 0.018***
[ 0.001] [ 0.005]* [ 0.001]*
[ 0.001]
[ 0.004]
[ 0.009]**
Percentile rank NP 5.56** 0.39 5.93** 21.77*** 19.33*** 22.19***
(within objective) [ 3.00]** [1.00] [3.00]** [20.00]*** [18.00] [20.00]***
PP 2.63 8.75*** 6.12** 4.66 3.57 0.68
[0.00] [ 7.00]*** [ 6.50]**
[ 5.00]
[ 3.00] [0.00]
Table 1 reports the mean [median] pre- and post-replacement performance of a sample of 393 mutual
funds experiencing managerial turnover between 1979 and 1991. Performance is measured based on
the one-factor CAPM (one-factor alpha) and Carhart’s four-factor model (four-factor alpha), the objective-
adjusted holding period return (relative to all funds in the matched investment objective), the matched
sampled adjusted return, and the percentile ranking of the fund relative to other funds within the matched
investment objective. Year 0 refers to the managerial replacement year. The NP (negative performance)
(PP (positive performance)) samples comprise funds exhibiting negative (positive) objective-adjusted
performance in the 36-month period preceding managerial replacement. The NP and PP samples in-
clude 239 and 154 funds, respectively. To obtain a matched sample adjusted return close to zero (in
the pre-replacement years), 10% of the observations in the sample are eliminated. Panel A reports the
actual values of the respective variables in each year and panel B reports the changes in levels across
various event windows surrounding managerial replacement.
***, **, and * indicate that the mean [median] coefﬁcient is statistically signiﬁcant at the 1%, 5%, and 10%
levels, based on a paired t -test [Wilcoxon sign rank test].

The mean (median) matched sample adjusted returns for the negative perfor-
mance (NP) sample are 0.2% ( 0.2%), 0.4% ( 0.2%), and 0.6% ( 0.3%)
for years 2, 1, and zero, respectively. The small return differences using
the matched sample approach validate the fact that the control sample closely
matches the performance behavior of the replacement sample. Hence, examin-
ing the post-replacement pattern of performance will provide useful insights in
determining whether the replacement of poorly performing managers is truly a
value-generating activity.
Even though the sample decomposition procedure leads to negative pre-
replacement performance in the NP sample, the more interesting issue is to as-
certain the magnitude and direction of performance in the post-replacement pe-
riod. With regard to managerial replacements of corporate managers, Denis and
Denis (1995) document that forced resignations are followed by large improve-



ments in post-replacement operating performance. They also find that in the post-
replacement years, these firms undertake significant corporate downsizing activ-
ity and are subjected to the external market for corporate control in the form of
takeover attempts, leveraged buyouts, and large block investments in the shares
of the firm.
The overall results in Table 1, panel B indicate a monotonic and statistically
significant decrease in fund performance for the NP sample in the pre-replacement
period, followed by a statistically significant increase in performance in the post-
replacement period. The statistically and economically significant change in per-
formance between the [ 1, +2] and [ 1, +3] event windows, is robust across var-
ious performance measures. For instance, the mean (median) increase in monthly
abnormal performance is 22 (16) basis points in the [ 1, +2] event window, based
on the single-factor CAPM, and 20 (16) basis points, based on the four-factor
model. These performance improvements are statistically significant at the 1%
level. I obtain similar results for the changes in the objective-adjusted return
(OAR) and matched sample adjusted return (MSAR) performance measures. The
mean (median) change in objective-adjusted returns, i.e., OAR, is 4.4% (2.6%),
5.6% (3.8%) across the [ 1, +2] and [ 1, +3] event windows, respectively. The
corresponding performance changes for the MSAR are 2.0% (1.2%) and 2.8%
(1.0%), respectively. All performance changes are significant at the 1% level.
The improvements in post-replacement MSARs are particularly noteworthy since
the matched sample approach compares performance with respect to other funds
that exhibit similar pre-replacement performance as the main sample but choose
not to replace their managers. As mentioned earlier, the MSAR allows one to dis-
tinguish between mean reversion and true performance improvement attributable
to the new manager.
These performance improvements are also reflected in a significant increase
in the percentile rankings of funds in the negative performance (NP) sample. In
the post-replacement period, the average manager in the NP sample performs bet-
ter than 50% of other managers in the same investment objective. This results in
an improvement of 19–22 (18–20) points in the mean (median) percentile perfor-
mance rankings across the pre- and post-replacement years.
Despite significant performance improvements relative to the pre-replace-
ment period, it is important to recognize that funds in the negative performance
(NP) sample continue to exhibit underperformance in the post-replacement pe-
riod, when performance is measured using alphas from single- and multi-factor
models.10 For instance, the mean (median) monthly abnormal returns for the neg-
ative performance (NP) sample based on the four-factor model continue to remain
negative 28 (25) and three (five) basis points for year +1 and +2, respectively.
However, in year +3, the average alpha becomes marginally positive whereas
the median alpha continues to remain negative. Despite these negative alphas,
10 A widely documented result in the academic literature is that, on average, mutual fund managers
tend to underperform standard benchmarks on a risk-adjusted basis. For instance, Elton, Gruber,
Das, and Hlavka (1993) find evidence of negative fund performance. Gruber (1996) documents that,
based on various performance measures such as returns relative to the market, risk-adjusted returns
from a single-index model, or risk-adjusted returns from a four-index model, mutual funds exhibit
underperformance. Using the single-index model, he estimates the magnitude of underperformance to

be 1.56% per year.


Khorana 385

it is important to reiterate that, relative to their own past, these funds experience
dramatic improvements in post-replacement performance. In other words, there
is evidence of reversion in fund performance after managerial replacement, at-
tributable specifically to the managerial replacement event.
In contrast to the negative performance (NP) sample, funds in the positive
performance (PP) sample experience deterioration in the post-replacement per-
formance. The mean (median) decline in fund performance over the [ 1, +3]
event window is significant for all performance measures. The mean (median)
performance decline is 20 (11) and 14 (six) basis points per month based on al-
phas from the one-factor and four-factor models, respectively. These performance
changes are significant at the 1% level.
The overall findings support the hypothesis that the internal market for cor-
porate control in the mutual fund industry is effective in disciplining poorly per-
forming fund managers. By hiring a new manager, investment advisors are able
to reverse performance to more normal levels. Hence, the fund begins to perform
in line with the average fund in the industry. On the other hand, the departure
of overperforming managers leads to deterioration in fund performance in the
post-replacement period. Nevertheless, these funds continue to remain median
performers in their peer group.

B. Performance Attribution Analysis

In addition to providing an alternative performance measure, the four-factor
model allows one to determine the proportion of a fund’s return that is attributable
to various portfolio investment strategies. Specifically, for equity funds, the major
strategies include investments in large vs. small capitalization stocks, high vs. low
book-to-market stocks (i.e., value vs. growth stocks), and the use of contrarian vs.
momentum-based investment strategies. For bond funds, superior/inferior perfor-
mance is based on the ability of the manager to change the portfolio’s duration
based on interest rate expectations.
In Table 2 (for equity funds), I report the mean and median coefficients of
the four-factor model for each year surrounding managerial replacement. I report
results separately for managers in the negative performance (NP) and positive
performance (PP) samples to ascertain whether there is any significant time-series
and cross-sectional variation in the perceived use of various investment strategies
pursued by the equity fund managers in the sample.
In the pre-replacement period, the positive momentum factor (PR1YR) for
the superior managers in the PP sample relative to the underperforming sample
of NP managers may partly explain the large performance differential across the
two groups of managers. The inability to identify and invest in these momentum
stocks can be detrimental to fund performance. This also provides a rationale
for why fund managers are reluctant to get left behind the crowd. However, in
the presence of poor performance and a need to window dress their portfolio,
managers may buy into these momentum stocks of the past ex post. Interestingly,
in the post-replacement period, the coefficient on the momentum factor declines
for the PP sample from a mean (median) value of 0.085 (0.063) in year zero to
0.009 (0.022) in year +3. The overall findings are consistent with Carhart (1997),



TABLE 2
Performance Attribution Analysis (Stock Funds)

Coefficients on Factor Mimicking Portfolios
2 1 0 +1 +2 +3
RMRF NP 0.864 0.880 0.903 0.906 0.909 0.953
[0.888] [0.901] [0.920] [0.915] [0.942] [0.993]
PP 0.873 0.845 0.865 0.881 0.923 0.903
[0.922] [0.821] [0.888] [0.928] [0.934] [0.953]
SMB NP 0.239 0.240 0.272 0.261 0.256 0.251
[0.192] [0.185] [0.215] [0.169] [0.194] [0.172]
PP 0.254 0.308 0.274 0.218 0.220 0.194
[0.198] [0.332] [0.224] [0.163] [0.174] [0.107]
HML NP 0.174 0.204 0.171 0.145 0.144 0.113

[ 0.177]
[ 0.139]
[ 0.161]
[ 0.132]
[ 0.095]
[ 0.063]
PP 0.094 0.101 0.137 0.161 0.164 0.183
[ 0.069] [ 0.089] [ 0.115] [ 0.206] [ 0.169] [ 0.142]
PR1YR NP 0.008 0.014 0.023 0.040 0.044 0.084
[0.039] [0.022] [0.045] [0.029] [0.041] [0.087]
PP 0.075 0.135 0.085 0.060 0.063 0.009
[0.075] [0.116] [0.063] [0.052] [0.037] [0.022]
Table 2 reports the mean [median] values of coefficients obtained from fund regressions where the RMRF,
SMB, HML, and PR1YR factors are used to ascertain the proportion of a fund’s performance attributable
to each of the above factors. RMRF is the excess return on the value-weighted market proxy, SMB is
the difference in returns across small and big stock portfolios controlling for the same weighted aver-
age book-to-market equity in the two portfolios, HML is the difference in returns between high and low
book-to-market equity portfolios, and PR1YR is the momentum factor computed by subtracting from the
equally-weighted return of firms with the highest 30% 11-month return lagged one month, the corre-
sponding return for firms with the lowest 30% 11-month return, which is also lagged one month. Year 0
refers to the managerial replacement year. The NP (negative performance) (PP (positive performance))
samples comprise funds exhibiting negative (positive) objective-adjusted performance in the 36-month
period preceding managerial replacement. The NP and PP samples include 117 and 54 funds, respec-
tively.

who documents that returns for the top decile of funds based on performance
exhibit a strong positive correlation with the one-year momentum factor.
In the pre-replacement period, the positive performance (PP) sample seems
to have a greater exposure to small stocks, as evidenced by larger SMB (small mi-
nus big) loadings. This finding also conforms with Carhart (1997); he documents
that funds in the top performance decile tend to hold more small capitalization
stocks than funds in lower performance deciles.
In Table 3, I report results of the performance attribution analysis for bond
funds. I find that the median factor loadings for both the long-term (LONGGOVT)
and the intermediate-term government bond (INTGOVT) indices are more nega-
tive for the positive performance (PP) sample in the pre-replacement period. This
suggests that managers in the PP sample have lower exposure to the interme-
diate and long-end of the Treasury yield curve, which consequently implies a
lower portfolio duration. The fact that these managers did not underperform other
managers in their peer group may be partly reflective of the fact that the pre-
replacement period was characterized by rising interest rates, which consequently
had a less adverse capital loss effect on the positive performance (PP) sample,
given its smaller exposure to the long/intermediate end of the yield curve.


Khorana 387

TABLE 3
Performance Attribution Analysis (Bond Funds)

Coefficients on Factor Mimicking Portfolios
2 1 0 +1 +2 +3
GOVCORP NP 6.785 5.794 5.435 5.684 6.607 7.347
[4.568] [3.814] [2.899] [2.364] [3.075] [3.629]
PP 5.177 4.934 4.552 5.375 5.254 5.898
[3.903] [4.111] [3.783] [3.909] [2.919] [4.785]
MBS NP 0.149 0.599 0.243 0.709 0.336 0.179
[0.047] [0.246] [0.266] [0.427] [0.324] [0.099]
PP 0.444 0.101 0.246 0.134 0.265 0.349
[0.000] [0.345] [0.145] [0.042] [0.174] [0.165]
LONGGOVT NP 1.479 1.178 1.064 1.136 1.324 1.536

[ 0.852]
[ 0.463]
[ 0.504]
[ 0.381]
[ 0.486]
[ 0.610]
PP 0.882 0.852 0.687 0.873 0.908 0.999
[ 0.894] [ 0.542] [ 0.558] [ 0.498] [ 0.421] [ 0.631]
INTGOVT NP 4.783 4.942 4.145 4.758 5.059 5.441
[ 2.203] [ 2.292] [ 1.731] [ 1.772] [ 2.148] [ 2.553]
PP 3.685 3.522 3.589 3.942 3.885 4.775
[ 2.409] [ 2.579] [ 2.478] [ 2.602] [ 1.983] [ 2.808]
Table 3 reports the mean [median] values of coefficients obtained from fund regressions where the GOV-
CORP, MBS, LONGGOVT, and INTGOVT factors are used to ascertain the proportion of a fund’s perfor-
mance attributable to each of the above factors. GOVCORP is the excess return on the Lehman Brothers
Government/Corporate bond index and is a weighted market average of government and investment
grade corporate issues that have more than one year until maturity, MBS is excess return on the Lehman
Brothers Mortgage-Backed securities index, LONGGOVT is the excess return on the Lehman Brothers
Long Term Government Bond index, and INTGOVT is the excess return on the Lehman Brothers Inter-
mediate Term Government Bond index. The NP (negative performance) (PP (positive performance))
samples comprise funds exhibiting negative (positive) objective-adjusted performance in the 36-month
period preceding managerial replacement. The NP and PP samples include 122 and 100 funds, respec-
tively.

C. Portfolio Risk Characteristics

In this section, I examine both cross-sectional and time-series differences in
managerial risk-taking behavior. Specifically, I examine levels and changes in
a fund’s total risk (i.e., sigma) and systematic risk (i.e., beta) during the years
surrounding managerial replacement.
The results in panels A and B, Table 4 indicate a marginal but statistically
significant increase in the median level of total fund risk (as measured by the stan-
dard deviation of monthly returns) for managers in the negative performance (NP)
sample in the pre-replacement period. Over the [ 2, 0] event window, the average
portfolio risk increases by 0.6% (0.3%). These risk changes are statistically sig-
nificant at the 5% level. In the post-replacement period, however, the mean (me-
dian) portfolio risk of the NP sample declines by 0.7% (0.7%) and 0.6% (0.6%)
over the [ 1, +2] and [ 1, +3] event windows, with these volatility decreases
being statistically significant at conventional levels (p-value 0.05). These re-
sults are consistent with Brown, Harlow, and Starks (1996), who document that,
in an attempt to maximize their expected compensation, underperforming fund
managers will increase the overall volatility of their portfolio. In addition, the
systematic risk of managers in the negative performance (NP) sample, as repre-



sented by the beta from the single-factor CAPM, exhibits an upward trend with a
mean (median) value of 0.789 (0.876) in year 2 and 0.791 (0.883) in year 1.
The corresponding beta value for year zero is 0.815 (0.860). The change in the
beta across the various pre-replacement windows is not significant at conventional
levels. This suggests that fund managers tend to maintain a relatively steady level
of systematic risk; however, they increase a fund’s residual risk in the years pre-
ceding managerial replacement. The increase in residual risk may be an attempt
on the part of fund managers to increase portfolio returns at the expense of less
than perfect portfolio diversification.

TABLE 4
Risk Measures surrounding Fund Manager Turnover

Panel A. Risk Characteristics—Levels
2 1 0 +1 +2 +3
Sigma NP 0.034 0.036 0.040 0.035 0.034 0.036
[0.032] [0.033] [0.035] [0.034] [0.034] [0.034]
PP 0.036 0.032 0.031 0.034 0.030 0.029
[0.029] [0.025] [0.025] [0.027] [0.024] [0.022]
Beta NP 0.789 0.791 0.815 0.897 0.922 0.907
[0.876] [0.883] [0.860] [0.928] [0.939] [0.982]
PP 0.993 0.901 0.883 0.971 0.986 0.892
[0.958] [0.918] [0.895] [0.926] [0.949] [0.884]
Panel B. Risk Characteristics—Changes in Levels
2 to 1 2 to 0 1 to 0 1 to +1 1 to +2 1 to +3
Sigma NP 0.002 0.006** 0.004** 0.001 0.007*** 0.006***
[0.001] [0.003]** [0.003]** [ 0.004] [ 0.007]*** [ 0.006]***
PP 0.004 0.005* 0.002 0.002 0.003 0.005

[ 0.001]
[ 0.004]**
[ 0.001]
[ 0.001] [ 0.004] [ 0.004]*
Beta NP 0.002 0.026 0.024 0.107*** 0.130*** 0.057**

[ 0.005]
[ 0.018]* [0.005] [0.017]** [0.024]** [0.043]**
PP 0.091 0.109 0.019 0.069 0.084 0.030
[ 0.036] [ 0.059]*
[ 0.022]
[ 0.024]
[ 0.001]
[ 0.028]
Table 4 reports the mean [median] figures for the fund’s portfolio standard deviation (sigma) and beta
computed using monthly returns. Year 0 refers to the year in which replacement occurred. Sigma is
the 12-month standard deviation of returns. Beta is the parameter estimate of the market model regres-
sions (based on a single-index CAPM model) obtained from 24 months of data (which includes the year
under consideration and the immediate preceding year) with the value-weighted index being used as
the relevant benchmark for equity funds and the Lehman Brothers aggregate bond index as the bench-
mark for bond funds. The NP (negative performance) (PP (positive performance)) samples comprise
funds exhibiting negative (positive) objective-adjusted performance in the 36-month period preceding
managerial replacement. Panel A reports the actual values of the respective variables in each year and
panel B reports the changes in levels across various event windows surrounding managerial replace-
ment.

D. Other Fund Characteristics surrounding Replacement

In panels A and B, Table 5, I report univariate statistics on levels and changes
in a fund’s portfolio turnover rate and expense ratios for each year between 2
and +3.


Khorana 389

TABLE 5
Other Fund-Specific Characteristics surrounding Fund Manager Turnover

Panel A. Fund Management Characteristics—Levels
2 1 0 +1 +2 +3
Portfolio turnover NP 106.40 106.00 102.20 95.75 92.49 81.43
(in %) [78.00] [72.00] [75.00] [70.00] [73.00] [72.00]
PP 107.96 96.55 93.76 87.27 79.99 71.79
[72.00] [77.00] [68.50] [73.00] [65.00] [56.00]
Expense ratio NP 1.24 1.27 1.30 1.26 1.20 1.19
(in %) [1.01] [1.04] [1.06] [1.08] [1.01] [0.96]
PP 0.97 0.97 0.99 0.99 0.99 0.95
[0.92] [0.93] [0.89] [0.90] [0.92] [0.87]
Panel B. Fund Management Characteristics—Changes in Levels
2 to 1 2 to 0 1 to 0 1 to +1 1 to +2 1 to +3
Portfolio turnover NP 0.40 4.21 3.82 10.25 9.25 13.18***
(in %) [2.00] [ 1.00] [ 2.00] [ 4.00] [ 4.00] [ 3.00]
PP 11.41 14.20 2.80 9.28 12.24* 24.97***
[ 6.00] [ 6.00] [ 5.00] [0.00] [ 1.00] [ 14.00]**
Expenses NP 0.027 0.062 0.028 0.013 0.020 0.041
(in %) [0.000] [0.020] [0.010] [0.010] [0.010] [0.010]
PP 0.001 0.021 0.018 0.019 0.051 0.060
[0.000] [0.000] [0.000]
[ 0.010] [0.000]
[ 0.010]
Table 5 reports the mean [median] figures for the annual portfolio turnover rate and annual expense ratio
for each year over the six-year period commencing two years prior to the managerial replacement year
to three years after the actual year of dismissal. Year 0 refers to the year in which replacement occurred.
Portfolio turnover is a measure of the fund’s trading activity and is measured as the total turnover ex-
perienced by the fund in each year. Expenses refer to the proportion of a fund’s assets that are used
to pay for operating expenses, management fees, and 12b-1 fees, excluding sales charges. The NP
(negative performance) (PP (positive performance)) samples comprise funds exhibiting negative (posi-
tive) objective-adjusted performance in the 36-month period preceding managerial replacement. Panel
A reports the actual values of the respective variables in each year and panel B reports the changes in
levels across various event windows surrounding managerial replacement.

For the negative performance (NP) sample of managers, the time series of
portfolio turnover rates indicate a relatively steady level of portfolio turnover
activity, but a significant decline in mean portfolio turnover rates in the post-
replacement period. Specifically, the portfolio turnover rate decreases from a
mean level of 106.4% in year 2, to 102.2% in year zero, and 81.43% in year
+3. These results provide evidence to suggest that prior to being replaced, poorly
performing managers will tend to engage in relatively higher levels of portfolio
turnover activity. This may be a reflection of both window dressing effects and
the inability on the part of the fund manager to identify the appropriate assets in
the first place. Funds in the positive performance (PP) sample also experience a
decline in portfolio turnover activity in the post-replacement period.
Consistent with the notion that the average mutual fund investor has become
more price-sensitive primarily due to greater competition in the fund industry, I
find that the time-series pattern of expense ratios for both the NP and PP sam-
ple exhibits a downward drift over time. For instance, the mean (median) ex-
pense ratios for the NP sample in years 2 and +3 are 1.24% (1.01%) and 1.19%



(0.96%), respectively. The corresponding expense figures for the PP sample are
0.97% (0.92%) and 0.95% (0.87%), respectively. The decrease in expenses is also
reflective of the economies of scale resulting from an increase in the average fund
size.

E. Relation between Asset Flows and Managerial Replacement

Using a multivariate regression approach, I examine the relation between
asset flows and managerial replacement after controlling for lagged fund returns,
the risk level of the fund, expense ratios, fund size, and contemporaneous flows
in the matched investment objective. I employ indicator variables to distinguish
between pre- vs. post-replacement flows and differences in flows across funds in
the negative performance (NP) and positive performance (PP) samples.
Similar to Sirri and Tufano (1998), I find that asset inflows to a fund are
positively related to contemporaneous flows in the investment objective and to
lagged fund performance measured using risk-adjusted returns from the one- and
four-factor models. In addition, higher past return volatility and higher fund ex-
penses have a negative and statistically significant impact on net asset flows. Fur-
thermore, smaller funds tend to attract larger net asset flows. These results are
reported in Table 6.
More importantly, however, I find that underperforming funds receive lower
flows than their overperforming counterparts, based on significantly lower coeffi-
cients on the negative risk-adjusted performance variable relative to the positive
risk-adjusted performance variable (in model iii relative to model ii and in model
vi relative to model v). In addition, in model vii, I find that the [NPI£PRE] inter-
action variable is negative and statistically significant at the 5% level, suggesting
that underperforming funds in the pre-replacement period experience even lower
asset inflows. Overall, these results suggest that the replacement of poorly per-
forming fund managers is preceded by significantly lower asset flows that can
adversely impact the ability of investment advisors to earn advisory fees (which
are based on a percentage of net assets).

V. Conclusion
This paper documents the impact of fund manager turnover on the subse-
quent performance and asset flows in the fund. Using a sample of 393 domestic
equity and bond fund managers experiencing replacement between 1979–1991,
I document that the dismissal of poorly performing managers leads to substan-
tial improvements in post-replacement performance relative to the past perfor-
mance of the fund. However, based on alphas from a one-factor and a four-factor
model, these fund managers continue to exhibit underperformance in the post-
replacement period. On the other hand, the sample of overperforming funds in the
pre-replacement period experiences a significant deterioration in subsequent fund
performance. These results are consistent with the argument that internal and/or
external monitoring mechanisms are effective in reversing the performance of a
poorly performing fund (relative to its own past), but new fund managers do not


Khorana 391

TABLE 6
Flow Regressions

Model: Netflowi t f Óbjective flowt ; Fund performancei t 1 ; Riski t 1 ; Expensesi t 1 ;
Log(Assets) i t 1 ; Negative performance indicator variable;
Pre-replacement indicator variable; Interaction effects µ

Explanatory Variables Model i Model ii Model iii Model iv Model v Model vi Model vii
Intercept 1.272 1.224 1.264 1.303 1.233 1.307 1.238
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Objective flowt 0.993 0.993 0.977 0.985 0.995 0.974 0.976
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Standard deviationt 1 0.058 0.072 0.059 0.069 0.078 0.061 0.059
(0.01) (0.00) (0.01) (0.00) (0.00) (0.01) (0.01)
Expensest 1 0.139 0.157 0.160 0.125 0.157 0.135 0.154
(0.04) (0.02) (0.02) (0.06) (0.02) (0.05) (0.02)
Log(Assets)t 1 0.157 0.154 0.150 0.155 0.151 0.153 0.154
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
One-factor «t 1 0.228 — — — — — —
(0.04)
Four-factor «t 1 — — — 0.332 — — —
(0.00)
Positive risk-adjusted — 0.674 — — 0.655 — —
performance variable (0.00) (0.00)
Negative risk-adjusted — — 0.061 — — 0.289 —
performance variable (0.72) (0.07)
Negative performance — — — — — — 0.035
indicator variable [NPI] (0.77)
Pre-replacement year — — — — — — 0.447
indicator variable [PRE] (0.02)
[NPI] £ [PRE] — — — — — — 0.486
(0.05)
Adjusted R 2 0.04 0.04 0.04 0.04 0.04 0.04 0.04
Regression p-value 0.00 0.00 0.00 0.00 0.00 0.00 0.00
N 1750 1750 1750 1750 1750 1750 1750
Table 6 contains the results of cross-sectional time-series multivariate OLS regressions of the net annual
inflows into a fund on: objective flows, fund performance, volatility of the fund’s returns, fund expenses,
size of the fund, indicator variable to distinguish between the NP (negative performance) and PP (positive
performance) samples, and an indicator variable to distinguish between the pre- and post-replacement
years. Net inflow is measured as follows: Netflowi t Assetsi t £
Assetsi t 1 ´1 + Ri t µ Assetsi t 1 ,
where Assetsi t is total assets in fund i at the end of year t and Ri t is the return of fund i during year t .
Objective flow is the average asset inflow in the matched investment objective. Performance is measured
using returns based on the one-factor (one-factor «) and the four-factor (four-factor «) models. The
positive (negative) risk-adjusted performance variable is constructed by retaining all positive (negative)
performance values and setting the negative (positive) values to zero. This variable is constructed using
the one-factor « for Models ii and iii and the four-factor « for Models v and vi. Risk, i.e., the fund’s
volatility, is measured as the standard deviation of 12-monthly returns (standard deviation). Expenses
refer to the proportion of a fund’s assets that are used to pay for operating expenses, management fees,
and 12b-1 fees, excluding sales charges. The size of the fund in the previous year is measured by Log
(Assets). NPI is an indicator variable that equals one if the fund in the 36-months preceding managerial
replacement exhibited a negative objective-adjusted return, and zero if the fund exhibited a positive
objected-adjusted return. PRE is an indicator variable that equals one for years 2, 1, and zero (the
managerial replacement year), and zero otherwise. The p-values of the regression coefficients are in
parentheses.



possess abilities to generate significantly superior performance relative to stan-
dard performance benchmarks.
The performance flow relation suggests that replacement of the poorly per-
forming fund managers is preceded by significantly lower asset flows, hence
limiting the ability of funds to earn higher investment advisory fees in the pre-
replacement years. These findings also suggest that external product markets can
play an important role in affecting the managerial replacement decision.
I also document that underperforming fund managers tend to increase overall
portfolio risk in the years preceding managerial replacement. However, in the
post-replacement period, the actions undertaken by the new fund manager lead
to a reduction in total portfolio risk (as measured by the standard deviation of
monthly returns). These results are consistent with the notion that managers with
the worst interim performance tend to undertake larger increases in portfolio risk
compared to winning managers in a given performance assessment period.
Since altering the fund’s portfolio turnover rate is an important action that
the new fund manager can undertake, I examine the time-series behavior of port-
folio turnover to gain additional insights into any perceptible shifts in managerial
behavior. The significantly higher pre-replacement portfolio turnover activity and
a subsequent reversal in the post-replacement period provide evidence in favor of
the window dressing argument.
In summary, the replacement of poorly performing managers tends to be a
value-enhancing activity for both the investment advisors and shareholders of the
fund.

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