Performance changes and mgmt turnover khorana

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

Performance Changes following TopManagement Turnover: Evidence fromOpen-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 replacedover the 1979–1991 period, for the underperformers, I document significant improvementsin post-replacement performance relative to the past performance of the fund. On theother hand, the replacement of overperforming managers results in deterioration in post-replacement performance. I find evidence supporting the presence of strategic risk shiftingin 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 significantlyin the post-replacement period. Lastly, the replacement of poor performers is preceded bysignificant 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 fromshareholder activism to monitoring activities on the part of boards of directors andlarge 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 literatureon executive compensation has attempted to examine the effect of incentives onmanagerial behavior.

While the linkages among various stakeholders in a corporation have beenextensively examined along with the impact of incentives on managerial behavior,

�DuPree College of Management, Georgia Institute of Technology, Atlanta, GA 30332-0520, e-mail: [email protected]. I thank Lipper Analytical Services Inc. and Morningstar Inc. forproviding part of the data used for this study and Stephen Brown (the editor), Jin-Wan Cho, MelissaFrye, Edward Nelling, Ajay Patel, Henri Servaes, Sunil Wahal, and David Yermack (the referee) forhelpful comments and Melissa Frye and Robert Craddock for valuable research assistance. I also thankShane Corwin for programming assistance and Mark Carhart for providing access to the factor-modeldatabase.

1Fama (1980), Fama and Jensen (1983), and Shleifer and Vishny (1986), among others, are im-portant contributors to this area of research.

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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 theirexpected compensation, rational managers may revise their portfolio composi-tion depending upon their relative performance during the year. Specifically, fundmanagers most likely to be losers will seek to increase their portfolio risk relativeto the group of likely winners.

In an effort to understand the effectiveness of internal and external controlmechanisms in the mutual fund industry, Khorana (1996) studies the relation be-tween managerial replacement and prior fund performance. He finds evidencesupporting the presence of an inverse relation between the probability of fundmanager replacement and past performance.2 In addition, he documents that themagnitude of underperformance that investment advisors are willing to tolerate ispositively related to the volatility of the underlying assets being managed by fundmanagers. Chevalier and Ellison (1999) reexamine the performance replacementrelation with special focus on the age of the fund manager. They find that youngermanagers 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 matchedinvestment objective.

The objective of this paper is to shed additional light on the effectiveness ofinternal 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 examinethe impact of CEO turnover on the post-replacement performance of the firm.For the subsample of managers experiencing forced replacement, they documentsignificant 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 onthe 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 obtaina better understanding of the effectiveness of internal and external disciplinaryforces operating at the level of the mutual fund.

Specifically, in this paper, I examine whether underperforming funds in thepre-replacement period are able to turn around their performance and, if so, howlong it takes to be a part of the winning group of managers. In contrast, does thedeparture of a manager at an overperforming fund adversely affect performancein 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 isa dramatic shift in the risk profile of funds across the pre- and post-replacement

2For corporate CEOs, Coughlan and Schmidt (1985) and Warner, Watts, and Wruck (1988) havedocumented 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.

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periods. Finally, I examine whether there is any perceptible shift in managerialbehavior with regard to the change in portfolio turnover rates and expense ratiosin 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 CAPMand the Carhart (1997) four-factor model. However, in comparison with the fund’sown poor pre-replacement performance, the new fund managers exhibit dramaticperformance improvements in the post-replacement period. Median objective-adjusted fund returns improve from�2.4% in year�1, i.e., the year precedingthe 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 asignificant deterioration in post-replacement performance.

Performance attribution tests conducted to ascertain the source of perfor-mance indicate that equity fund managers with superior abnormal performancein 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 smallercapitalization stocks.

In the presence of a positive flow performance relation, there should be aperceptible 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 resultprovides direct evidence on the importance of managerial replacements for thefund’s investment advisors. Reversing the trend of declining asset inflows canlead to economies of scale and generate additional fee income for the fund. Thisevidence also suggests that both existing and prospective shareholders pay closeattention to the managerial replacement decision and exercise strong discretion indeciding when to “vote with their feet.”

For the underperforming sample, I find an increase in portfolio risk in thepre-replacement period followed by a reduction in total portfolio risk in the post-replacement period. This result is consistent with the tournaments model ofBrown, Harlow, and Starks (1996) where fund managers most likely to be loserstend to increase their portfolio risk relative to the group of likely winners. Con-sistent with window dressing behavior, I also document higher levels of portfolioturnover activity in the pre-replacement period followed by significant decreasesin the post-replacement period.

The remainder of the paper is organized as follows. Section II describesthe data sources and the sample selection procedure. Section III outlines the un-derlying hypotheses and methodology used for the study. Section IV provides adiscussion 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’sdatabase (preceding the end of 1992) by supplementing it with information on theyear and the month in which the current fund manager commenced overseeingthe operations of the fund and thus the year in which the previous manager wasreplaced. The information on the month and year of managerial replacement isobtained by directly contacting the fund families and from Morningstar. From thissample, domestic equity and bond funds with at least three years of performancehistory preceding the month of managerial replacement in a particular year andat least one year of post-replacement data are selected. This screening criterionis critical for the empirical tests since I need to follow the same set of mutualfunds in the pre- and post-replacement periods. Based on the above criteria, thefinal managerial replacement sample comprised 393 funds. Of these, there are171 equity funds and 222 bond funds.

To measure the returns performance of individual fund managers, monthlyreturns data are obtained from both Lipper Analytical Services Inc. and Morn-ingstar Inc.; the information on other fund-specific variables is accessed fromMorningstar. Returns are computed by adding to the change in net asset value(NAV), both the income and capital gains distributions during the period, and thendividing 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 withother data sources such as the Wiesenberger Mutual Fund Updates, S&P Quar-terly Stock Guide, andThe Wall Street Journal. As a precautionary measure, thedata used from the respective databases are cross-checked with other sources thatmake available the same information. The monthly returns on the value-weightedmarket index are obtained from the monthly CRSP files, total returns on the Trea-sury bond and corporate bond indices are obtained from Lehman Brothers, andreturns on the Carhart (1997) factors are obtained from Mark Carhart. Note thatfor all variables except returns, only annual data are available.

An important caveat on managerial replacement is that it may occur due tothe dismissal of underperforming managers or voluntary departure of average oroverperforming managers. Even though both forms of departure will be reflectedin managerial turnover, the factors leading to replacement are different in the twocases. However, the lack of any publicly available information for a large majorityof the fund managers precludes knowledge of the exact sequence of events thatmay be responsible for managerial replacement.3 Only high profile replacementsare reported in the popular press. Hence, I am unable to distinguish explicitlyamong the various reasons for replacement. The traditional corporate financeresearch 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)

3A Wall Street Journal article dated April 7, 1994, indicates that since the SEC came out with aruling that the names of mutual fund managers must be disclosed to fund investors, mutual funds haveactually started hiding the names of their managers. This is motivated by the desire on the part of fundorganizations to make it more difficult to link fund performance with the portfolio manager.


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age of the replaced fund manager is 41 (42) years with the oldest managers being62 years. Furthermore, since age data is available for only a small subset of themanagers, 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 inthe 36-month period preceding replacement. Funds exhibiting negative objective-adjusted performance are placed in the negative performance sample (NP) andthose exhibiting positive objective-adjusted performance are placed in the positiveperformance sample (PP). The subsequent portfolio performance and other fundmanagement characteristics of these separate groups of managers are examinedin the post-replacement period. In the absence of publicly available informationon the rationale behind replacement, such a portfolio decomposition approachserves as the next best alternative. This sample decomposition yields 239 funds inthe 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 fundsand 122 bond funds. The similar breakdown for the PP sample is 54 and 100funds in the equity and bond categories, respectively.

In additional robustness tests, I decompose the sample using the one-factorand four-factor models, the percentile rank of the fund relative to other funds in thecorresponding investment objective, and the percentile rank of the fund relativeto all funds in existence during the year. The qualitative nature of the resultsis similar for these alternative performance measures. Hence, for the sake ofbrevity, only results using the objective-adjusted return decomposition approachare reported.

B. Adjusting for Survivorship Bias

One potential drawback of this data set is that it only includes survivingfunds. As a result, total inflows into an objective are understated, while perfor-mance measures are likely to be overstated (assuming poorly performing fundsare terminated).4 This could bias my findings. Hence, the sample is supple-mented with data on non-surviving funds from the largest 100 families measuredby total assets at the end of 1992. I focus on the 100 largest families to keep thedata collection process manageable. These families account for 93.3% of totalmutual fund assets in the sample at the end of 1992.

Data on non-surviving funds are collected using the following procedure. Istart with a list of all funds that survive through 1992 for the largest 100 fundfamilies in my sample. I then compare my replacement and control sample withthe funds listed in the Wiesenberger Investment Companies books (for each year)for these families. This produces an initial list of 251 potentially non-survivingfunds; these are funds listed in Wiesenberger but missing from my sample. It ispossible, however, that information is missing because the fund changed its nameor 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 orinception through 1994. I check through 1994 to ensure that Wiesenberger did notsimply omit the fund for a year or two or delay the reporting of a name change. In

4Malkiel (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-specificdata. 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 fromthe analysis. Thus, I expand my initial sample by 85. The net asset value, assetbase, and the return data on these funds is obtained from Wiesenberger and isused to correct for biases in the underlying benchmarks. I was not able to obtainfund-specific information on the annual portfolio turnover and expense ratio ofeach 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 industrywhere 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 attractingsuperior managers. In fact, the potential disciplinary role of external product mar-kets for mutual funds is atypical in the sense that fund shareholders can directlyredeem their proportional ownership interest with the fund’s management. Asa result, the degree of control exercised by fund shareholders is far greater thanshareholders of regular corporations who can only liquidate their holdings in thesecondary market. Hence, if poor fund performance in the pre-replacement periodis attributable to managerial abilities rather than bad luck, and if the fund’s boardand investment advisors are able to attract good managerial talent, one would ex-pect an improvement in post-replacement performance for the NP sample. Forthe PP sample, on the other hand, the post-replacement performance will dependon the ability of the new manager to sustain superior performance. If the newmanager is successful, it will result in persistence of superior fund performance.On the other hand, any deterioration in post-replacement performance may beindicative 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 anda 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 performancemeasures in detail.

5In a related study examining the flow of funds, Sirri and Tufano (1998) document that mutualfund investors direct new capital toward the most recent overperformers but fail to take assets awayfrom underperformers.

6Carhart (1997) documents that funds generating higher one-year returns are able to do so becausethey 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 amomentum-based stock investment strategy disappears after adjusting for transaction costs.


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1. Abnormal Performance based on the One-Factor and Four-Factor Models

Consistent with other fund performance measurement studies, I employSharpe’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 Famaand French (1993) and Jegadeesh and Titman’s (1993) momentum factor. Specif-ically, for equity funds, the following model specifications are examined in thepaper,

Rit = �it + �1;itVWRFt + "it;

Rit = �it + �1;itRMRFt + �2;itSMBt + �3;itHML t + �4;itPR1YRt + "it;

whereRit is the fund return in excess of the monthly T-bill return; VWRF is theexcess return on the CRSP value-weighted index; RMRF is the value-weightedmarket 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 stockportfolios controlling for the same weighted average book-to-market equity inthe two portfolios; HML (high minus low) is the difference in returns betweenhigh and low book-to-market equity portfolios; PR1YR is the momentum factorcomputed in Carhart (1997) by subtracting from the equally-weighted return offirms with the highest 30% 11-month return lagged one month, the correspondingreturn for firms with the lowest 30% 11-month return, which is also lagged onemonth.

For bond funds, I also use a one-factor model and a four-factor model tocompute the risk-adjusted excess return for each fund. The following model spec-ifications are employed,

Rit = �it + �1;itGOVCORPt + "it;

Rit = �it�1;itGOVCORPt + �2;itMBSt + �3;itLONGGOVTt

+ �4;itINTGOVTt + "it;

whereRit is the fund return in excess of the monthly T-bill return; GOVCORPis the excess return on the Lehman Brothers Government/Corporate bond indexand is a weighted market average of government and investment grade corporateissues that have more than one year until maturity; MBS is the excess return on theLehman Brothers Mortgage-Backed securities index; LONGGOVT is the excessreturn on the Lehman Brothers Long Term Government Bond index; INTGOVTis the excess return on the Lehman Brothers Intermediate Term Government Bondindex. These model specifications are consistent with Blake, Elton, and Gruber(1993). For both the equity and bond fund regressions, I use 24 months of returndata 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 forascertaining the source of performance and the underlying investment strategiespursued 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 performancemeasure is consistent with the argument that, in making their managerial replace-ment decisions, a firm benchmarks a manager’s performance against other firmsin the industry (Morck, Shleifer, and Vishny (1989)).

The objective-adjusted return (OAR) of a fund is measured as the annualholding period fund return in excess of the annual holding period return on thebenchmark portfolio of other funds within the matched investment objective.Hence, the OAR is computed as follows,

OAR =

"12Y

t=1

(1 + Ri;t)� 1

#�

"12Y

t=1

(1 + Ro;t)� 1

#;

whereRi;t is the return of firmi in month t, andRo;t is the monthly return onthe benchmark portfolio. Thus, the OAR measures fund performance relativeto other managers in the peer group. This measure absolves the manager fromsector, industry, or style-specific effects that may exogenously affect all managersin the same investment category. The returns of the non-surviving funds are usedto 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 infund performance is related to true managerial ability rather than a mere artifactof the tendency of security return to exhibit mean reversion, I employ a matchedsample based performance measurement approach. I construct a sample of poten-tial matching firms for each fund in the replacement sample by identifying fundswith similar performance histories and the same investment objective as the re-placement sample firm. However, unlike the replacement sample, the investmentadvisors 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 firstapproach, 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 isnot used as a potential firm for another replacement sample firm with the sameinvestment objective. The second approach allows a potential firm to be used as amatch for multiple funds in the replacement sample. However, in instances wherethe same firm is used as a match for multiple replacement sample firms, data overdifferent time periods is used (due to differing managerial replacement dates forthe replacement sample firms).

After identifying a unique matching firm for each replacement sample firm, Isubtract 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 asthe matched sample adjusted return (MSAR). I eliminate 10% of the observationsto 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.


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years. Then, I examine the performance pattern of this matched sample adjustedreturn for managers in both the negative performance (NP) and positive perfor-mance (PP) samples. If the managerial replacement event truly adds value to theshareholders of a poorly performing fund, one would expect to find a performancereversal in this adjusted return series. Such a test would provide an explicit de-termination of whether post-replacement performance changes can be attributedto “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 theresults of the multiple matching approach are reported.

4. Percentile Rankings

As another measure of performance, I report the mean and median percentileperformance 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 wheretheir performance is benchmarked against other managers within the peer group(Brown, Harlow, and Starks (1996)), such tournaments create certain risk shiftingincentives among managers. Specifically, managers in the bottom half of theirperformance group may undertake more risk in an effort to be a part of the tophalf 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 standarddeviation 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 mayengage in window dressing behavior by rebalancing their portfolios to closely re-semble the portfolios of other overperformingmangers in their peer group (Lakon-ishok, Shleifer, Thaler, and Vishny (1991)). This would result in significantlylarger pre-replacement portfolio turnover rates. However, to the extent that thenew 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, thereare no conclusive priors with regard to the pattern of portfolio turnover rates inthe pre- vs. post-replacement years.

The likely pattern of expense ratios would depend on the price sensitivity ofthe fund investors and the desire on the part of fund families to pass along anysavings that accrue due to the economies of scale from operating a larger fund. Ahigher level of price sensitivity and greater scale benefits would lead to a reductionin average fund expenses over time.8

8A 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 pointsover the 1980–1998 period. A study by the United States General Accounting Office (June 2000) onmutual 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 indisciplining fund managers, I examine whether shareholders redirect money flowsaway from managers’ experiencing negative performance in the pre-replacementperiod. This analysis is interesting for several reasons. Since the primary sourceof income for investment advisors is the advisory fee received for managing thefund (which is usually a fraction of assets under management), it is extremelycritical 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 tothe fund’s ability to exhibit performance persistence.9 This empirical frameworkalso 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 weakperformance asset flow sensitivity for poor performers, I reexamine this relationconditional 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 theyear, these figures could be affected by both the returns generated by the portfoliomanager during the year and by actual (net) asset inflows/outflows. Hence, tocompute inflows net of returns, i.e., (NETFLOWi;t), I use the following approach,

NETFLOWi;t = [ASSETSi;t � ASSETSi;t�1 � (1 + Ri;t)] =ASSETSi;t�1;

where ASSETSi;t is total assets in fundi at the end of yeart, andRi;t is the returnof fund i during yeart. Based on the above computation, the NETFLOW variablemeasures the growth in assets over and above the change in value of the fund’sasset base (existing at the beginning of the year), partly due to the fund manager’sportfolio management decisions.

In a multivariate regression framework, I examine the relation between netinflows to a fund in a given year and fund performance using the following generalmodel,

NETFLOWi;t = ffObjective Flowst; Fund performancei;t�1; Riski;t�1;

Expensesi;t�1; Log(Assets)i;t�1;

Negative performance indicator variable;

Pre-replacement indicator variable;

Interaction effectsg:

Objective flows are used to control for the effect of flow variations in a particularinvestment objective. Lagged fund performance is included to capture the effect

9Using a sample of no-load growth funds, Hendricks, Patel, and Zeckhauser (1993) find evidencethat persistence of superior fund performance is a short-run phenomenon, i.e., it lasts for up to fourquarters. 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 withextreme underperformance. Similarly, Brown and Goetzmann (1995) find evidence of persistence inrisk-adjusted performance in funds that lag the S&P 500 index.


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of fund performance on subsequent inflows. Performance is measured based onalphas from one-factor and four-factor models. Risk is measured as the standarddeviation of 12 monthly returns. A negative relation between the risk level ofthe fund and asset flows is hypothesized. Lagged fund expenses are also likelyto be inversely related to fund flows since higher expenses are likely to deternew investors from investing in the fund. The size of the fund in the previousperiod is included as a control variable since the larger funds will receive a lesserpercentage flow for the same dollar flow than smaller funds.

In addition to the above controls, I include a number of indicator variables inthe 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, NPIis the negative performance indicator variable that equals one if the fund belongsto 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 thatequals 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 themodel specifications to ascertain if the asset flow relation is different for underper-forming funds in the pre-replacement period and whether asset flows change afterthe replacement of the fund manager. To determine the relative impact of posi-tive vs. negative performance, I also construct a positive (negative) risk-adjustedperformance variable where all positive (negative) alpha values are retained andnegative (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 basedon the levels and changes in various performance measures during the period twoyears preceding and three years following the replacement event. As mentionedearlier, fund performance is measured using a one-factor model and a four-factormodel, based on objective-adjusted returns, the matched sample return approach,and the percentile ranking of funds. These results are provided separately for thenegative (NP) and positive performance (PP) sample of fund managers. Changesin both mean and median performance measures across various event windowsare also reported in Table 1.

Given the sample decomposition procedure, it is not surprising that in thepre-replacement period, managers in the NP sample exhibit significant under-performance. Based on the performance estimates from the CAPM (one-factormodel), 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 theother 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) monthlyabnormal 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 thefact that managers in the NP sample are, on average, in the bottom 35th percentileof performance when benchmarked against the subsample of funds in the matchedinvestment objective.

In additional tests, I measure annual fund performance i) in comparison withthe performance of the underlying investment objective, i.e., objective-adjustedreturn (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., thematched 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 0.009return [�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 0.027adjusted 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 mutualfunds experiencing managerial turnover between 1979 and 1991. Performance is measured based onthe 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 matchedsampled adjusted return, and the percentile ranking of the fund relative to other funds within the matchedinvestment objective. Year 0 refers to the managerial replacement year. The NP (negative performance)(PP (positive performance)) samples comprise funds exhibiting negative (positive) objective-adjustedperformance 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 (inthe pre-replacement years), 10% of the observations in the sample are eliminated. Panel A reports theactual values of the respective variables in each year and panel B reports the changes in levels acrossvarious 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 usingthe matched sample approach validate the fact that the control sample closelymatches the performance behavior of the replacement sample. Hence, examin-ing the post-replacement pattern of performance will provide useful insights indetermining whether the replacement of poorly performing managers is truly avalue-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 andDenis (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 oftakeover attempts, leveraged buyouts, and large block investments in the sharesof the firm.

The overall results in Table 1, panel B indicate a monotonic and statisticallysignificant decrease in fund performance for the NP sample in the pre-replacementperiod, 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 monthlyabnormal performance is 22 (16) basis points in the [�1, +2] event window, basedon the single-factor CAPM, and 20 (16) basis points, based on the four-factormodel. 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. Themean (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. Thecorresponding 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 sincethe matched sample approach compares performance with respect to other fundsthat exhibit similar pre-replacement performance as the main sample but choosenot to replace their managers. As mentioned earlier, the MSAR allows one to dis-tinguish between mean reversion and true performance improvement attributableto the new manager.

These performance improvements are also reflected in a significant increasein the percentile rankings of funds in the negative performance (NP) sample. Inthe 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 inan 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-factormodels.10 For instance, the mean (median) monthly abnormal returns for the neg-ative performance (NP) sample based on the four-factor model continue to remainnegative 28 (25) and three (five) basis points for year +1 and +2, respectively.However, in year +3, the average alpha becomes marginally positive whereasthe median alpha continues to remain negative. Despite these negative alphas,

10A widely documented result in the academic literature is that, on average, mutual fund managerstend 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 returnsfrom a single-index model, or risk-adjusted returns from a four-index model, mutual funds exhibitunderperformance. Using the single-index model, he estimates the magnitude of underperformance tobe�1.56% per year.


Khorana 385

it is important to reiterate that, relative to their own past, these funds experiencedramatic improvements in post-replacement performance. In other words, thereis 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 positiveperformance (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 performancechanges 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 ableto reverse performance to more normal levels. Hence, the fund begins to performin line with the average fund in the industry. On the other hand, the departureof overperforming managers leads to deterioration in fund performance in thepost-replacement period. Nevertheless, these funds continue to remain medianperformers in their peer group.

B. Performance Attribution Analysis

In addition to providing an alternative performance measure, the four-factormodel allows one to determine the proportion of a fund’s return that is attributableto various portfolio investment strategies. Specifically, for equity funds, the majorstrategies include investments in large vs. small capitalization stocks, high vs. lowbook-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 durationbased on interest rate expectations.

In Table 2 (for equity funds), I report the mean and median coefficients ofthe four-factor model for each year surrounding managerial replacement. I reportresults separately for managers in the negative performance (NP) and positiveperformance (PP) samples to ascertain whether there is any significant time-seriesand cross-sectional variation in the perceived use of various investment strategiespursued by the equity fund managers in the sample.

In the pre-replacement period, the positive momentum factor (PR1YR) forthe superior managers in the PP sample relative to the underperforming sampleof NP managers may partly explain the large performance differential across thetwo groups of managers. The inability to identify and invest in these momentumstocks can be detrimental to fund performance. This also provides a rationalefor why fund managers are reluctant to get left behind the crowd. However, inthe 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 declinesfor the PP sample from a mean (median) value of 0.085 (0.063) in year zero to0.009 (0.022) in year +3. The overall findings are consistent with Carhart (1997),



TABLE 2

Performance Attribution Analysis (Stock Funds)

Coefficients on Factor Mimicking PortfoliosYears with Respect to Managerial Turnover

�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 attributableto each of the above factors. RMRF is the excess return on the value-weighted market proxy, SMB isthe 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 lowbook-to-market equity portfolios, and PR1YR is the momentum factor computed by subtracting from theequally-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 0refers to the managerial replacement year. The NP (negative performance) (PP (positive performance))samples comprise funds exhibiting negative (positive) objective-adjusted performance in the 36-monthperiod 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 performanceexhibit a strong positive correlation with the one-year momentum factor.

In the pre-replacement period, the positive performance (PP) sample seemsto 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 documentsthat funds in the top performance decile tend to hold more small capitalizationstocks than funds in lower performance deciles.

In Table 3, I report results of the performance attribution analysis for bondfunds. 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. Thissuggests that managers in the PP sample have lower exposure to the interme-diate and long-end of the Treasury yield curve, which consequently implies alower portfolio duration. The fact that these managers did not underperform othermanagers in their peer group may be partly reflective of the fact that the pre-replacement period was characterized by rising interest rates, which consequentlyhad 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 PortfoliosYears with Respect to Managerial Turnover

�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 BrothersGovernment/Corporate bond index and is a weighted market average of government and investmentgrade corporate issues that have more than one year until maturity, MBS is excess return on the LehmanBrothers Mortgage-Backed securities index, LONGGOVT is the excess return on the Lehman BrothersLong 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-monthperiod 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 inmanagerial risk-taking behavior. Specifically, I examine levels and changes ina fund’s total risk (i.e., sigma) and systematic risk (i.e., beta) during the yearssurrounding managerial replacement.

The results in panels A and B, Table 4 indicate a marginal but statisticallysignificant 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 averageportfolio 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 decreasesbeing 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 fundmanagers will increase the overall volatility of their portfolio. In addition, thesystematic 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 amean (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 thebeta across the various pre-replacement windows is not significant at conventionallevels. This suggests that fund managers tend to maintain a relatively steady levelof 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 attempton the part of fund managers to increase portfolio returns at the expense of lessthan perfect portfolio diversification.

TABLE 4

Risk Measures surrounding Fund Manager Turnover


Panel A. Risk Characteristics—Levels

�2 �1 0 +1 +2 +3Sigma 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 betacomputed using monthly returns. Year 0 refers to the year in which replacement occurred. Sigma isthe 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 yearunder consideration and the immediate preceding year) with the value-weighted index being used asthe 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 comprisefunds exhibiting negative (positive) objective-adjusted performance in the 36-month period precedingmanagerial replacement. Panel A reports the actual values of the respective variables in each year andpanel 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 changesin a fund’s portfolio turnover rate and expense ratios for each year between�2and +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 ratiofor each year over the six-year period commencing two years prior to the managerial replacement yearto 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 usedto 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. PanelA reports the actual values of the respective variables in each year and panel B reports the changes inlevels across various event windows surrounding managerial replacement.


For the negative performance (NP) sample of managers, the time series ofportfolio turnover rates indicate a relatively steady level of portfolio turnoveractivity, but a significant decline in mean portfolio turnover rates in the post-replacement period. Specifically, the portfolio turnover rate decreases from amean 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, poorlyperforming managers will tend to engage in relatively higher levels of portfolioturnover activity. This may be a reflection of both window dressing effects andthe inability on the part of the fund manager to identify the appropriate assets inthe first place. Funds in the positive performance (PP) sample also experience adecline in portfolio turnover activity in the post-replacement period.

Consistent with the notion that the average mutual fund investor has becomemore price-sensitive primarily due to greater competition in the fund industry, Ifind 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 are0.97% (0.92%) and 0.95% (0.87%), respectively. The decrease in expenses is alsoreflective of the economies of scale resulting from an increase in the average fundsize.

E. Relation between Asset Flows and Managerial Replacement

Using a multivariate regression approach, I examine the relation betweenasset flows and managerial replacement after controlling for lagged fund returns,the risk level of the fund, expense ratios, fund size, and contemporaneous flowsin the matched investment objective. I employ indicator variables to distinguishbetween pre- vs. post-replacement flows and differences in flows across funds inthe negative performance (NP) and positive performance (PP) samples.

Similar to Sirri and Tufano (1998), I find that asset inflows to a fund arepositively related to contemporaneous flows in the investment objective and tolagged fund performance measured using risk-adjusted returns from the one- andfour-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 arereported in Table 6.

More importantly, however, I find that underperforming funds receive lowerflows than their overperforming counterparts, based on significantly lower coeffi-cients on the negative risk-adjusted performance variable relative to the positiverisk-adjusted performance variable (in model iii relative to model ii and in modelvi 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, suggestingthat underperforming funds in the pre-replacement period experience even lowerasset inflows. Overall, these results suggest that the replacement of poorly per-forming fund managers is preceded by significantly lower asset flows that canadversely impact the ability of investment advisors to earn advisory fees (whichare 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 domesticequity 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-factormodel, these fund managers continue to exhibit underperformance in the post-replacement period. On the other hand, the sample of overperforming funds in thepre-replacement period experiences a significant deterioration in subsequent fundperformance. These results are consistent with the argument that internal and/orexternal monitoring mechanisms are effective in reversing the performance of apoorly performing fund (relative to its own past), but new fund managers do not


Khorana 391

TABLE 6

Flow Regressions

Model: Netflowi;t = f (Objective 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)

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 — — — — — — �0.035indicator variable [NPI] (0.77)

Pre-replacement year — — — — — — 0.447indicator 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 0.04Regression p-value 0.00 0.00 0.00 0.00 0.00 0.00 0.00N 1750 1750 1750 1750 1750 1750 1750

Table 6 contains the results of cross-sectional time-series multivariate OLS regressions of the net annualinflows 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 (positiveperformance) samples, and an indicator variable to distinguish between the pre- and post-replacementyears. Net inflow is measured as follows: Netflowi;t = [Assetsi;t � Assetsi;t�1 � (1 + Ri;t)]=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 measuredusing returns based on the one-factor (one-factor �) and the four-factor (four-factor �) models. Thepositive (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 usingthe one-factor � for Models ii and iii and the four-factor � for Models v and vi. Risk, i.e., the fund’svolatility, is measured as the standard deviation of 12-monthly returns (standard deviation). Expensesrefer 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 managerialreplacement exhibited a negative objective-adjusted return, and zero if the fund exhibited a positiveobjected-adjusted return. PRE is an indicator variable that equals one for years �2, �1, and zero (themanagerial replacement year), and zero otherwise. The p-values of the regression coefficients are inparentheses.



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, hencelimiting the ability of funds to earn higher investment advisory fees in the pre-replacement years. These findings also suggest that external product markets canplay an important role in affecting the managerial replacement decision.

I also document that underperforming fund managers tend to increase overallportfolio risk in the years preceding managerial replacement. However, in thepost-replacement period, the actions undertaken by the new fund manager leadto a reduction in total portfolio risk (as measured by the standard deviation ofmonthly returns). These results are consistent with the notion that managers withthe worst interim performance tend to undertake larger increases in portfolio riskcompared to winning managers in a given performance assessment period.

Since altering the fund’s portfolio turnover rate is an important action thatthe new fund manager can undertake, I examine the time-series behavior of port-folio turnover to gain additional insights into any perceptible shifts in managerialbehavior. The significantly higher pre-replacement portfolio turnover activity anda subsequent reversal in the post-replacement period provide evidence in favor ofthe window dressing argument.

In summary, the replacement of poorly performing managers tends to be avalue-enhancing activity for both the investment advisors and shareholders of thefund.

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