Your SlideShare is downloading. ×
Performance changes and mgmt turnover khorana
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Introducing the official SlideShare app

Stunning, full-screen experience for iPhone and Android

Text the download link to your phone

Standard text messaging rates apply

Performance changes and mgmt turnover khorana

149
views

Published on

Published in: Economy & Finance, Business

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
149
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. 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
  • 2. 7/31/2001–coded–JFQA #36:3 Khorana Page 372 372 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.
  • 3. 7/31/2001–coded–JFQA #36:3 Khorana Page 373 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.
  • 4. 7/31/2001–coded–JFQA #36:3 Khorana Page 374 374 Journal of Financial and Quantitative Analysis 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.
  • 5. 7/31/2001–coded–JFQA #36:3 Khorana Page 375 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.
  • 6. 7/31/2001–coded–JFQA #36:3 Khorana Page 376 376 Journal of Financial and Quantitative Analysis 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.
  • 7. 7/31/2001–coded–JFQA #36:3 Khorana Page 377 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
  • 8. 7/31/2001–coded–JFQA #36:3 Khorana Page 378 378 Journal of Financial and Quantitative Analysis 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.
  • 9. 7/31/2001–coded–JFQA #36:3 Khorana Page 379 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.
  • 10. 7/31/2001–coded–JFQA #36:3 Khorana Page 380 380 Journal of Financial and Quantitative Analysis 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.
  • 11. 7/31/2001–coded–JFQA #36:3 Khorana Page 381 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-
  • 12. 7/31/2001–coded–JFQA #36:3 Khorana Page 382 382 Journal of Financial and Quantitative Analysis 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)
  • 13. 7/31/2001–coded–JFQA #36:3 Khorana Page 383 Khorana 383 TABLE 1 (continued) Performance Measures surrounding Fund Manager Turnover Panel B. Performance Characteristics—Changes in Levels Years with Respect to Managerial Turnover  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-
  • 14. 7/31/2001–coded–JFQA #36:3 Khorana Page 384 384 Journal of Financial and Quantitative Analysis 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.
  • 15. 7/31/2001–coded–JFQA #36:3 Khorana Page 385 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),
  • 16. 7/31/2001–coded–JFQA #36:3 Khorana Page 386 386 Journal of Financial and Quantitative Analysis TABLE 2 Performance Attribution Analysis (Stock Funds) Coefficients on Factor Mimicking Portfolios Years 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 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.
  • 17. 7/31/2001–coded–JFQA #36:3 Khorana Page 387 Khorana 387 TABLE 3 Performance Attribution Analysis (Bond Funds) Coefficients on Factor Mimicking Portfolios Years 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 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-
  • 18. 7/31/2001–coded–JFQA #36:3 Khorana Page 388 388 Journal of Financial and Quantitative Analysis 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 Years with Respect to Managerial 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. ***, **, 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]. 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.
  • 19. 7/31/2001–coded–JFQA #36:3 Khorana Page 389 Khorana 389 TABLE 5 Other Fund-Specific Characteristics surrounding Fund Manager Turnover Years with Respect to Managerial 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. ***, **, 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]. 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%
  • 20. 7/31/2001–coded–JFQA #36:3 Khorana Page 390 390 Journal of Financial and Quantitative Analysis (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
  • 21. 7/31/2001–coded–JFQA #36:3 Khorana Page 391 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 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.
  • 22. 7/31/2001–coded–JFQA #36:3 Khorana Page 392 392 Journal of Financial and Quantitative Analysis 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. References Blake, C. R.; E. J. Elton; and M. J. Gruber. “The Performance of Bond Mutual Funds.” Journal of Business, 66 (1993), 371–403. Brown, S. J., and W. N. Goetzmann. “Performance Persistence.” Journal of Finance, 50 (1995), 679–698. Brown, K. C.; W. V. Harlow; and L. T. Starks. “Of Tournaments and Temptations: An Analysis of Managerial Incentives in the Mutual Fund Industry.” Journal of Finance, 51 (1996), 85–110. Carhart, M. M. “On Persistence in Mutual Fund Performance.” Journal of Finance, 52 (1997), 57–82. Chevalier, J., and G. Ellison. “Career Concerns of Mutual Fund Managers.” Quarterly Journal of Economics, 2 (1999), 389–432. Coughlan, A. T., and R. W. Schmidt. “Executive Compensation, Managerial Turnover, and Firm Performance: An Empirical Investigation.” Journal of Accounting and Economics, 7 (1985), 43– 66. Denis, D., and D. K. Denis. “Performance Changes following Top Management Dismissals.” Journal of Finance, 50 (1995), 1029–1058. Elton, E. J.; M. J. Gruber; S. Das; and M. Hlavka. “Efficiency with Costly Information: A Re- Interpretation of Evidence from Managed Portfolios.” Review of Financial Studies, 6 (1993), 1–21. Fama, E. “Agency Problems and the Theory of the Firm.” Journal of Political Economy, 88 (1980), 288–307. Fama, E., and K. R. French. “Common Risk Factors in Returns on Bonds and Stocks.” Journal of Financial Economics, 33 (1993), 3–53. Fama, E., and M. Jensen. “Separation of Ownership and Control.” Journal of Law and Economics, 26 (1983), 301–325. Grinblatt, M.; S. Titman; and R. Wermers. “Momentum Investment Strategies, Portfolio Performance, and Herding: A Study of Mutual Fund Behavior.” American Economic Review, 85 (1995), 1088– 1105. Gruber, M. J. “Another Puzzle: The Growth in Actively Managed Mutual Funds.” Journal of Finance, 51 (1996), 783–810.
  • 23. 7/31/2001–coded–JFQA #36:3 Khorana Page 393 Khorana 393 Hendricks, D.; J. Patel; and R. Zeckhauser. “Hot Hands in Mutual Funds: Short-Run Persistence of Relative Performance, 1974–1988.” Journal of Finance, 48 (1993), 93–130. Ippolito, R. A. “Consumer Reaction to Measures of Poor Quality: Evidence from the Mutual Fund Industry.” Journal of Law and Economics, 35 (1992), 45–70. Jegadeesh, N., and S. Titman. “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Journal of Finance, 48 (1993), 65–91. Khorana, A. “Top Management Turnover: An Empirical Investigation of Mutual Fund Managers.” Journal of Financial Economics, 40 (1996), 403–427. Lakonishok, J.; A. Shleifer; R. Thaler; and R. W. Vishny. “Window Dressing by Pension Fund Man- agers.” AEA Papers and Proceedings, 81 (1991), 227–231. Malkiel, B. “Returns from Investing in Equity Mutual Funds: 1971 to 1991.” Journal of Finance, 50 (1995), 549–572. Morck, R.; A. Shleifer; and R. W. Vishny. “Alternative Mechanisms for Corporate Control.” American Economic Review, 89 (1989), 842–852. Sharpe, W. F. “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk.” Journal of Finance, 19 (1964), 425–442. Shleifer, A., and R. W. Vishny. “Large Shareholders and Corporate Control.” Journal of Political Economy, 95 (1986), 461–488. Sirri, E. R., and P. Tufano. “Costly Search and Mutual Fund Flows.” Journal of Finance, 53 (1998), 1589–1622. Warner, J. B.; R. L. Watts; and K. H. Wruck. “Stock Prices and Top Management Changes.” Journal of Financial Economics, 20 (1988), 461–492. Weisbach, M. S. “Outside Directors and CEO Turnover.” Journal of Financial Economics, 20 (1988), 431–460.
  • 24. 7/31/2001–coded–JFQA #36:3 Khorana Page 394