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Horses for courses: Fund managers and

organizational structures

Yufeng Han, Tom Noe, and Michael Rebello†

January 2008

Abstract

This paper considers the team management of mutual funds, fund manager ability, per-formance, and holdings. We find evidence suggesting there is a positive relation between performance and team management concurrent with a negative relation between managerial ability and the use of team management. Consistent with the notion that the team manage-ment suppresses portfolio eccentricity and leads to more generic trading strategies, thereby both increasing returns and making returns less informative of fund manager ability, we also find that team management is associated with less idiosyncratic portfolio holdings and a greater loading on large capitalization, low book-to-market, and momentum stocks.

JEL Classification: G11 Keyword:

The authors wish to thank Kelsey Wei for helpful comments and Susan Bergman for editorial help.Yufeng Han is at Freeman School of Business, Tulane University; Tom Noe is at Sa¨ıd Business School, University of Oxford, and Michael Rebello is at School of Management, University of Texas at Dallas.

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Horses for courses: Fund managers and

organizational structures

Abstract

This paper considers the team management of mutual funds, fund manager ability, per-formance, and holdings. We find evidence suggesting there is a positive relation between performance and team management concurrent with a negative relation between managerial ability and the use of team management. Consistent with the notion that the team manage-ment suppresses portfolio eccentricity and leads to more generic trading strategies, thereby both increasing returns and making returns less informative of fund manager ability, we also find that team management is associated with less idiosyncratic portfolio holdings and a greater loading on large capitalization, low book-to-market, and momentum stocks.

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1. Introduction

In a move suggesting that team management is attractive to investors, Fidelity Investments recently switched its marketing strategy to emphasize team-managed funds.1 Recent academic research confirms the growing popularity of team-management documenting a surge of inflows into these funds industrywide (see, e.g., Baer, Kempf, and Ruenzi, 2005; Bliss, Potter, and Schwarz,2007). At the same time, the popularity of team management is somewhat anomalous: The same research also demonstrates that team-managed funds perform no differently (Bliss, Potter, and Schwarz, 2007) or worse (Baer, Kempf, and Ruenzi, 2005; Massa, Reuter, and Zitzewitz,2006) than funds controlled by a single manager.

The aim of this paper is to explicate this anomaly: How can team management, a docu-mented underperforming institutional design, come to dominate the apparently superior, single-manager design in popularity? We propose that the superior performance of the single-single-manager structure is less a function of the actual superiority of the organizational design but it attractive-ness to superior managers. We develop and test this hypothesis that rationalizes the anomaly based on the endogenous, rational, and informative selection of fund design.

From our perspective, managers choose the fund structure, single or team-managed, under which they operate. Funds, however structured, aim to generate abnormal returns through trading strategies. Some strategies are “conventional,” i.e., based on well-known investment formulae, e.g., momentum trading; others are “unconventional,” i.e., dependent on manager-specific knowledge, for example, trading based on information gathered from private discussions with industry experts. Conventional strategies produce little information regarding the fund manager’s ability while unconventional strategies produce a great deal.

AsHolmstrom and Milgrom (1991) show, when an agent needs to divide his efforts amongst many tasks, the weight the agent places on any one task positively depends not only on the gain from the task but also on the facility with which the task signals the agent’s effort and ability. Thus, an unchecked single-manager, concerned about the value of his human capital, will have a tendency toward eccentric distortion, and overinvest time and effort in unconventional investment strategies. Team management mitigates this tendency in two ways. First, team

1

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management makes performance attribution more difficult for outsiders. Thus, it reduces the reputational gain from unconventional strategies. Second, because different managers will favor different unconventional strategies, reaching a consensus supporting any one strategy will be difficult. AsGomes and Novaes(2001) show, such conflicts between self-interested insider agents can lead to policies that more closely approximate the common good. For these reasons, we hypothesize that team management is (a) more efficient than individual management (b) less subject to eccentric distortion.

Thus, ceteris paribus, one would expect team management to dominate individual agement. However, the ceteris paribus assumption fails to hold because individual fund man-agement permits a manager to follow policies that better reveal her personal ability. For this reason managers with the highest ability will be willing to forgo some current rents and choose to operate within the less efficient framework, single management in exchange for the expected long-run gain in reputation. In short, single management serves as a costly ability signal. For this reason, we expect weaker managers will self-select into team-managed funds and, at the same time, team-managed funds will follow investment strategies which are less subject to eccentric distortion. The net effect of these two opposing forces on performance is unclear. However, we expect that, once we account for fund selection, team-managed funds will out-perform single managed funds. In addition, we expect team-managed funds to follow more conventional investment strategies.

We test this hypothesis on the 1993 - 2002 holdings and returns of a sample of mutual funds. We find that team-managed funds have better average performance (as measured by fund alpha) and that their returns load more on large capitalization, low book-to-market, and momentum, than single-manager funds. In addition, we find that within each style category, team-managed funds hold more conventional portfolios, more closely hewing to style average holdings associated with the style. The fact that team-managed funds put much less distance between their own holdings and fund averages than single-manager funds is consistent with our hypothesis that single management facilitates fund manager signaling of unique ability through unconventional portfolio holdings.

Once we control for style differences, however, we find, consistent with Baer, Kempf, and Ruenzi(2005), that the performance advantage of team-managed funds vanishes, and, in fact,

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managed funds on average underperform single managed funds. Thus, although team-managed funds typically adopted better overall investment strategies than single team-managed funds, investing more heavily in large cap, growth and momentum, on average they could not match the performance of single manager funds which adopted the same strategies. In fact, approximately 60% of team-managed funds failed to reach expected level of style-adjusted performance.

Next, as suggested by our hypothesis, we consider the effect of endogenous choice between team and single management by comparing team- and single-managed mutual funds while controlling both for style and the choice of fund management structure. We find that accounting for endogenous choice of structure essentially reverses the performance relation; now 70% of team-managed funds surpass expectations given selection and fund characteristics. This fairly striking reversal lends support for our hypothesis that single management is a costly signaling mechanism for managers and the cost is eccentric portfolio distortion. Also consistent with this result is our finding that team-managed funds exhibit inferior stock-picking ability. Further, team-managed fund trades exhibit a weaker response to public information. This result is consistent with our hypothesis that single-manager portfolios are subject to eccentric distortion if aggressive trading on public information is complementary to aggressive trading on private information.

The basic ideas we use to explicate the differences between team- and single-managed mutual funds are not novel. A large literature traces how eccentric strategies can be used by agents to signal quality (e.g.,Zwiebel,1995). These ideas have been applied to the empirical analysis of mutual fund behavior by Chevalier and Ellison(1999). A key assumption behind our analysis (and many other papers on mutual fund manager behavior) is that some managers are able to create value by following unobserved trading strategies in individual stocks. This assumption is validated by the research ofKacperczyk, Sialm, and Zheng (2005). Moreover, this paper is not the first paper to consider the apparent contradiction between the rush of funds into team-managed portfolios and the lack of any apparent performance advantage in these portfolios. Both Baer, Kempf, and Ruenzi (2005) and Bliss, Potter, and Schwarz (2007) have considered this issue. Massa, Reuter, and Zitzewitz(2006) have even recognized that managers may prefer a single management structure because it is more suited to signaling ability. Our point of

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departure is to attempt to rationalize this contradiction in terms of endogenous selection in a rational choice framework.

The remainder of this paper is organized as follows. In Section 2. we identify our data sources and describe our sample. In Section 3. we present our tests of fund management behavior and fund performance. In Section4.we try and pinpoint the causes for the difference in the performance of team- and single-managed funds. In Section 5. we demonstrate the robustness of our analysis. We conclude the paper with an overview of our investigation in Section6.

2. Data

We use the CRSP Survivorship Bias Free Mutual Fund Database, the Thompson Financial CDA/Spectrum holdings database, and the CRSP stock price database as our primary data sources. For our sample period, which extends from 1993 to 2002, we obtain fund returns, fund manager identities, management company information, total net assets, and other character-istics from the CRSP Survivorship Bias Free Mutual Fund Database. We obtain mutual fund stock holdings information from the Thompson Financial CDA/Spectrum holdings database, and use the Mutual Fund Links (MFLINKS) database to link these two databases. Although multiple share classes are listed as separate funds in CRSP, they have the same portfolio compo-sition and are managed by the same portfolio manager(s). Thus, using the MFLINKS tables we aggregate multiple classes of the same fund to avoid duplication. Specifically, we add together the total net assets under management (TNA) of the different share classes and report the sum as the TNA. For other quantitative attributes of funds such as returns, expenses, loads, and turnover, we take the average of the attributes of the individual share classes weighted by their TNAs. For fund age, we use the age of the oldest share class. Finally, we obtain stock prices, returns, and stock-related information for all stocks in our sample using the CRSP stock price database.

We employ the CRSP Mutual Fund database to classify funds as team-managed or single-manager funds. If funds are managed by an individual, the database reports the single-manager’s name. If funds are managed by two or more individuals, CRSP reports the names of the

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man-agers separated by the forward slash (/). In some cases, CRSP simply reports “team,” or “management team,” or “committee”; in other cases, CRSP reports the name of the manage-ment company. Sometimes, CRSP reports the leading manager of the fund and also notes it is team-managed. In several instances, the management structure of funds changes over time. For example, CRSP reports that AIM Global Utilities Fund was managed by Claude Cody and Robert Alley in 1992; by AIM Equity Management Team in 1993 and 1994; by Craig Smith in 1995 and 1996; and by Claude Cody, Robert Alley, and Craig Smith in 1997 and 1998. In 1999 and 2000, additional managers were reported by CRSP, and in 2001 and 2002, CRSP simply reports the fund is team-managed. Because these frequent changes in fund manage-ment structure may be the result of data errors, we depart from previous studies such asBaer, Kempf, and Ruenzi(2005);Massa, Reuter, and Zitzewitz(2006); andBliss, Potter, and Schwarz (2007); instead, we focus our analysis on funds in our sample that maintain their management structure throughout the sample period in our sample, and exclude any funds, such as the AIM Global Utilities Fund, that change their management structure during the sample period. We classify funds that are managed by a single-manager through our entire sample period as “Single-Managed” (SM) funds, and funds that are managed by two or more managers over the entire sample period as “Team-Managed” (TM) funds. In Section 5.we demonstrate that our results are not driven by sample selection bias arising from the exclusion of funds that change their management structures during our sample period.

Our final sample consists of 2171 funds out of which 1252 are single-manager funds and 919 are team managed.2 Figure 1 illustrates the distribution of funds in terms of their investment

styles. As the figure demonstrates, most of the funds in our sample invest in large capitalization stocks. They also have a marked tendency to specialize in growth stocks. Team-managed funds have a disproportionately large representation in both the growth and large capitalization categories. In fact, team-managed funds account for the majority of funds in the large cap growth, large cap value, and mid-cap growth categories.

Table 1 reports the summary statistics for characteristics of funds in our sample. The characteristics presented in the table are fund size (tna), cash holdings as a percentage of total

2

We do not exclude index funds because only a very small fraction of funds are index funds (4% in single-manager funds and 5% in team-managed funds).

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assets (cash), stock holdings as a percentage of total assets (stocks), the sum of the expense ratio and total loads (fees), the time since fund inception (age), and asset turnover (turnover). These summary statistics are reported for the entire sample, the sub-sample of single-manager funds and the sub-sample of team-managed funds. While each fund appears once in a given time period, it appears in the sample during several time periods. To arrive at the statistics presented in the table, we first calculate the average value for each fund characteristic over time for each fund. The table reports the mean and median values for the resulting distributions of average characteristics for each fund. For each characteristic, we report the difference between the mean values for the team-managed and single-manager funds, along with itst-statistic and p-value. The table also presents the median value of each characteristic and differences between the median values of the characteristics for team-managed and single-manager funds.

Funds in our sample have an average (median) of $400.9 mn ($60.1 mn) under management. Their cash balances and stock holdings average approximately 6% and 80% of assets under management, respectively. The median value for stock balances is slightly more than 91%, suggesting that some funds hold a relatively large proportion of their assets in cash and fixed income securities. Balanced funds are likely to account for most of these outlier funds. Fund fees average approximately 3.4%. On average, funds have been in existence for more than 10 years. Average asset turnover is relatively high at 1.36 while median asset turnover is 0.76. The marked difference between mean and median asset turnover suggests that some funds in the sample have extremely high turnover.

Single-manager funds are significantly larger with average (median) assets of $467.2 mn ($61.88 mn) against $310.3 mn ($58.31 mn) for team-managed funds. The smaller difference between the median values of assets under management suggests that our sample contains some extremely large single-manager funds. The size differences between these two sets of funds may be related to their age differences as the average age of single-manager funds of slightly less than 12 years compared with an average age of team-managed funds is slightly less than 9 years. Single-manager funds tend to maintain significantly higher average cash balances (6.6 % versus 5.0%). While single-manager funds invest significantly more of their assets in stocks on average (80.4% versus 79.2%), their median stock holdings are lower than the median stock holdings of team-managed funds. Fees charged by team-managed funds average 3.2% compared with 3.6%

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for single-manager funds. Team-managed funds display significantly higher average turnover (1.6 versus 1.2). The difference in median turnover is much smaller (0.77 versus 0.76). The difference between the average turnover and median turnover for team-managed funds suggests that team-managed funds account for the turnover outliers in our sample.3

3. Empirical Analysis

In this section we present our analysis of the effect of fund management structure. First we examine whether the proclivity for indexing varies with fund management structure. Then we analyze the relation between fund performance and management structure. In several instances, we employ regressions to model fund behavior and performance. In these regressions we use one-year lagged fund characteristics as control variables in the regressions. Every regression also controls for fund style and, when appropriate, time fixed effects. The t-statistics for all regressions are computed using robust standard errors that are adjusted to control for heteroscedasticity and for the cluster sample problem arising from the presence of multiple observations for funds in our sample.

In most instances we report both OLS estimates and regressions estimates that correct for endogeneity. We control for endogeneity by employing the Heckman methodology, where we first model the dummy variable TM, which takes the value of one if a fund is team managed and zero otherwise, using the following probit regression:

TM =β1+β2cash +β3stocks +β4 log(size) +β5fees +β6age +β7turnover +β8tot +β9tmpct +ǫ,

(1) where the variables tot and tmpct represent the total number of funds managed by the fund management company and the percentage of funds managed by the fund management company that are team managed, respectively. This regression also includes style and year dummies as controls. All our coefficients for this regression, with the exception of the coefficients for the variable tot and turnover, are statistically significant. We use this regression to calculate the

3

The turnover outliers in our sample are Rydex Banking fund with a turnover of 112.11 in 1999, Rydex Transportation fund with a turnover of 75.83 in 1999, and Rydex Financial Services fund with a turnover of 72.69 in 1999. All three were team managed.

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inverse Mills’ ratio (lambda) for each fund and use this ratio as an additional explanatory variable to control for endogeneity.

3.1. Indexing

We employ two approaches to examine the relation between fund management structure and the proclivity for indexing. First, we employ style regressions to capture the relation between the returns on benchmark indices and fund returns. Then we examine the variations between the weighting of stocks in benchmark indices and funds. Our results suggest that team-managed funds tend to follow their benchmark indices more closely than single-manager funds.

3.1.1. Style regressions

To capture a fund’s deviations from its benchmark indices, we run semi-strong style re-gressions using nine size and book-to-market portfolios formed with the NYSE break points as benchmark portfolios. We estimate the style regression for each fund using all monthly returns for the fund within the sample period. The coefficients in the style regressions are constrained to be positive. We use theR2from the style regression for each fund to capture the tendency of a fund to mimic its benchmark index. Each fund’s style is determined by the largest coefficient in the style regression. Table2 presents the average R2 from the style regressions for all funds in the sample, the single-manager funds in the sample, and the team-managed funds in the sam-ple. The table also presents tests for the difference between the average R2 of single-manager and team-managed funds.

On average, our style regressions account for 77.8% of the variation in fund returns. The regressions account for 82.8% of variation in the returns of growth funds and only 66.3% of the return variation in value funds. Similarly, the ability of the style regressions to account for returns variation drops with the market capitalization of the stocks that funds focus on. Large cap funds have an averageR2 of approximately 80% versus a 72.5% average for small cap funds. The R2 of single-manager and team-managed funds tend to mirror these variations. How-ever, growth, value, large cap, and mid cap team-managed funds tend to have a significantly largerR2 than their single-manager counterparts. While team-managed funds also have a larger

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R2 in the two remaining fund categories reported in the table, these differences are not statis-tically significant. These results suggest that team-managed funds have a higher proclivity for mimicking their benchmark indices than do their single-manager counterparts.

3.1.2. Variations in stock weightings

Now we examine funds’ stock holdings. First we assign each fund to a style based on the style regressions reported above. Then, we construct a proxy for the benchmark portfolio for each style group by averaging the weight of each stock held by a fund in the style group across all funds in that style group. To capture the deviation between the stock holdings of a fund and its proxy benchmark portfolio we first compute the squared deviation between the weighting of each stock it holds and the weighting of the stock in the benchmark portfolio for its style. Then, we normalize this measure by the variance of the style-adjusted stock holdings. Specifically, let wijt represent the weighting of stockj in fundiat timet,nit represent the number of distinct

stocks in fund iat time t, Kt represent the set of funds in fundi’s style group at time t, and

#(Kt) represent the number of funds in the style group Kt. Then our measure of a fund’s

average scaled weighting deviation from its style index is given bypct∆P OSit, where

pct∆P OSit= P jpct∆P OSijt nit , (2) where pct∆P OSijt= ∆P OSijt σjK2 t ×100%, ∆P OSijt= wijt− P k∈Ktwkjt #(Kt) 2 , σjK2 t = Vari∈Kt(wijt).

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each fund in our sample.

Model 1 (M1): pct∆P OSit=β0+β1TMi+γsStyle Dummies +γyYear Dummies; (3)

Model 2 (M2): pct∆P OSit=β0+β1TMi+βpFund Characteristics +γsStyle Dummies

+γyYear Dummies;

Model 3 (M3): pct∆P OSit=β0+β1TMi+βpFund Characteristics +βtTMi×Fund Characteristics

+γsStyle Dummies +γyYear Dummies.

The first model captures the effect of team management on fund holdings after we control for style and time fixed effects. The second model augments these controls by also accounting for the influence of fund characteristics. In light of the evidence in, for example,Bliss, Potter, and Schwarz (2007), suggesting that team-managed funds behave differently from single-manager funds, we arrive at Model 3 by augmenting Model 2 to also allow the fund characteristics to impact the holdings of team-managed funds differently than they do the holdings of single-manager funds.

Table 3 presents six sets of coefficients estimates along with their t-statistics. Regressions four, five, and six are identical to regressions one, two, and three, respectively, except for the inclusion of the inverse Mills’ ratio for each observation, computed using our estimates of Eq. (1), in the last three regressions. Because the coefficients for the inverse Mills’ ratios are statistically insignificant, indicating that the first three sets of coefficients are unbiased, we focus on the first three sets of coefficients.

The coefficients for TMin the first two regressions are negative and significant at the 10% level, suggesting that the stock weightings of team-managed funds are closer to their style benchmarks than the stockholdings of single-manager funds. Regression two also indicates that larger funds and funds that invest more of their assets in stocks tend to maintain positions that are closer to their style benchmarks while older funds and funds that have high turnover tend to deviate more from their benchmark holdings. The coefficient for TM is also negative in regression three but is not statistically significant. Further, only fund turnover appears to have a more muted effect on the indexing behavior of team-managed funds than on single-manager funds. Only 47% of our observations for team-managed funds have predicted weighting

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deviations that are larger than the predicted weighting deviations of single-manager funds with identical characteristics. Further, on average, team-managed funds have a predictedpct∆P OSit

score that is approximately 0.40 lower than the score for similar single-manager funds. Thus, our results on the holdings of team-managed funds support our earlier results which indicate that team-managed funds are more likely to index.

3.2. Fund performance

We now assess the relation between fund management structure and fund performance. First we document the relation between fund alphas and management structure. Then we examine the relation between fund alphas and fund characteristics. Finally, we examine the effect of the market environment on fund alphas.

3.2.1. Fund alphas

Table4reports the average alpha and betas from the CAPM, the Fama-French three-factor model, and the Carhart four-factor model. Average alphas and betas are reported for all funds in the sample, single-manager funds, and team-managed funds. To arrive at the figures presented in the table, for each month we first estimate alpha and betas using data from the previous 60 months. We then compute the average alpha and average betas of each fund from the time-series averages. The figures reported in Table4are computed from the cross-sectional distributions of these fund time-series averages.

The average monthly alpha for all funds in the sample is 4.87 basis points (bps) under the CAPM specification, and 2.22 bps under the Fama-French specification. In both cases the alpha estimate is statistically significant at the one percent level. The alpha estimate under the Carhart specification is -1.02 bps but is statistically insignificant. The alpha estimates for single-manager funds under the Fama-French and Carhart specifications are -2.14 bps and -4.19 bps, and statistically significant at the one percent level in both cases. The CAPM estimate for the alpha for single-manager funds is statistically insignificant. The average alphas for team-managed funds are positive and significant under all three specifications, and under all three specifications the average alpha estimates for team-managed funds are statistically greater than

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the corresponding alpha estimates for single-manager funds. Together these results suggest that fund managers generate positive alphas on average. However, this superior performance can be attributed almost entirely to team-managed funds.

The beta estimates in Table 4 also provide insight into the fund styles of single-manager and team-managed funds. Under the Fama-French specification, single-manager funds have a market beta of 0.92, a size beta of 0.22, and a growth beta of 0.02. All three estimates are significant at the one percent level and are virtually identical to their counterparts under the Carhart specification. Team-managed funds have a market beta of 0.90, a size beta of 0.20, and a growth beta of -0.03 under the Fama-French specification. Once again these estimates are statistically significant at the one percent level and virtually identical to their counterparts under the Carhart specification. Each of these three average betas is statistically smaller for team-managed funds than for single-manager funds under both the Fama-French and Carhart specifications. Our finding that team-managed funds have a smaller loading on the market portfolio is consistent with the evidence in Bliss, Potter, and Schwarz (2007). The other two sets of beta estimates corroborate the evidence in Figure1, which suggests that team-managed funds focus more on large stocks and stocks with low book-to-market (i.e., growth stocks). A comparison of the average momentum beta for team-managed funds with the average momen-tum beta for single-manager funds in the Carhart specification also suggests that team-managed funds tend to invest more heavily in stocks with superior performance in the recent past.

3.2.2. Fund performance and fund characteristics

We now describe a second set of tests designed to assess the relationship between fund performance and management structure while controlling for fund style. We first calculate the monthly abnormal returns for each fund using the Carhart four-factor model. As inKacperczyk and Seru(2007) andCarhart(1997), for each fund we first estimate the betas every month using the previous 60 months of fund returns and then subtract the expected returns calculated using these estimated pricing models from the observed returns; that is, the abnormal return for fund

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iin periodtis computed as follows:

arit=Rit−Rf t−βˆmt(Rmkt,t−Rf t)−βˆsmb,tRsmb,t−βˆhml,tRhml,t−βˆumd,tRumd,t, (4)

where arit is the abnormal return earned by fund i at month t, Rit, Rmkt,t, Rsmb,t, Rhml,t,

Rumd.t, and Rf t are returns on fundi, the market, SMB factor, HML factor, UMD factor, and

the risk-free asset, respectively.

Table 5 presents the results from regressing these estimates of fund abnormal returns on several fund characteristics while controlling for fund style and time fixed effects. The regression models we estimate are

Model 1 (M1) :ari,t=β0+β1TMi+γsStyle Dummies +γyYear Dummies;

Model 2 (M2) :ari,t=β0+β1TMi+βpFund Characteristics

+γsStyle Dummies +γyYear Dummies;

Model 3 (M3) :ari,t=β0+β1TMi+βpFund Characteristics +βtTMi×

Fund Characteristics +γsStyle Dummies +γyYear Dummies.

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Once again, the first model only controls for style and time fixed effects. The second model augments these controls by also accounting for the influence of fund characteristics; it is similar to the model estimated byChen, Hong, Huang, and Kubik(2004),Massa, Reuter, and Zitzewitz (2006), and Baer, Kempf, and Ruenzi (2005) to assess the effect of team management on performance. Because team-managed funds tend to behave differently from single-manager funds, the third model augments Model 2 by also allowing the fund characteristics to impact the holdings of team-managed funds differently than they do the holdings of single-manager funds. The table presents both OLS estimates of each model and estimates employing a correction for possible endogeneity bias in these OLS estimates.

Regardless of the estimation technique, for models M1 and M2 the coefficients for the dummy variable TM are statistically insignificant. The OLS estimate of model M2 suggests that team-managed funds underperform single-manager funds, but the performance difference is not statistically significant. When we allow the effect of fund characteristics on fund returns

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to vary with the fund’s management structure (model M3), we find that the coefficient for the variableTMis positive and significant. Further, when we include the inverse Mills’ ratio as an additional explanatory variable, we find that the coefficient associated with the inverse Mills’ ratio is negative and statistically significant, suggesting that the OLS estimates of M3 may be biased.

We use both sets of coefficients to assess the effect of team management on fund, as one would expect, returns and to assess the effect of the endogeneity bias on the OLS estimates. For each estimate of M3, we compute both the percentage of observations for team-managed funds that have higher predictedαs than single-manager funds with identical characteristics and the average difference between the predictedαs of these two fund types. Using the OLS estimates we find that only 33.5% of our observations for team-managed funds have higher predicted abnormal returns than single-manager funds after we control for fund characteristics. After we correct for endogeneity, this percentage rises to 68%. In both cases these frequencies are significantly different from 50%, which is what one would expect to see if team management has no influence on fund performance, at the one percent confidence level. We obtain similar results when we estimate the average abnormal return across the entire sample of team-managed funds. Using the OLS estimates, we find that, on average, the predicted αs of team-managed funds are lower than those of matched single-manager funds by 0.44 bps per month. After correcting for endogeneity we find that team-managed funds outperform matched single-manager funds by an average of approximately 15 bps per month. In both cases these return differences are significant at the 1% confidence level. These results are consistent with our hypothesis that single-manager funds tend to attract more able managers; because we are unable to directly control for manager ability, our OLS estimates understate the positive influence of adopting a team-management structure on fund performance.

Turning to the control variables and fund characteristics, we find the control variabledead, which indicates whether a fund has been liquidated, has a large negative and significant coef-ficient under all specifications. The percentage of assets committed to stocks tends to boost the performance of single-manager funds. Size, fees, fund age, and turnover negatively impact the abnormal returns generated by single-manager funds. The effect of fund fees is not surpris-ing, given that fund returns are computed net of fund fees. The estimates of the differences

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in the slope coefficients for team-managed and single-manager funds in M3 suggest that, with the exception of fund size, these factors tend to have a more muted effect on the performance of team-managed funds. The sum of lsz and TM×lsz is negative and significant, indicating that size also negatively influences the performance of team-managed funds. The sums of the coefficients forageand TM×age, andturnover andTM×turnover are positive and statistically significant, suggesting that, unlike single-manager funds, team-managed funds enjoy higher abnormal returns as they age and trade more actively.

3.2.3. Fund performance and market conditions

In Table6we present tests designed to assess whether differences between the performance of team-managed and single-manager funds is dependent on the market environment. To examine the effect of market conditions on fund performance, we estimate variants of model M3 with the Heckman’s correction presented in Table5. Instead of using year fixed effects, in each regression reported in Table6we include one of three variables to proxy for the overall market conditions: market volatility measured by volatility of returns on the S&P 500 index (spvol), market return measured by returns on the S&P 500 index (sprtrn), and a dummy variable (BOOM) that takes a value of one when the annual return on the S&P 500 is above 15%, and zero otherwise.

The coefficient for the Inverse Mills’ ratio is negative and significant in each of the six regression estimates, indicating that OLS estimates may be biased. The coefficient estimate for

spvolin regression M3a is positive but insignificant, indicating that market volatility does not have a significant effect on fund abnormal returns. Further, in regression M3b, the coefficient for

spvolis insignificant as is the sum of the coefficientsspvolandTM×spvol, a result that suggests that neither single-manager fund performance nor team-managed fund performance is affected by market volatility. The coefficients for sprtrn and BOOM are negative and significant in models M3c and M3e, indicating that funds tend to underperform the market when the market is doing well. These coefficient estimates are also negative and significant in models M3d and M3f, indicating that single-manager funds also tend to earn lower abnormal returns when the market performs well. However, the sums of the pairs of coefficients sprtrn and TM×sprtrn, andBOOM andTM×BOOMare statistically insignificant, indicating that the performance of

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team-managed funds is less sensitive to market conditions.

When we examine the relative performance of team-managed funds, we find that, across all six models, between 65% and 70% of our observations for team-managed funds have higher ab-normal returns than single-manager funds after controlling for fund characteristics and market conditions. Further, the average abnormal return for all our observations for team-managed fund is approximately 17 basis points. Thus, not only do team-managed funds generate supe-rior returns, but their performance is also less sensitive to market conditions. This may help explain the higher persistence in the performance of team-managed funds documented inBaer, Kempf, and Ruenzi(2005).

4. Identifying sources of team-management superiority

Given that team management is associated with superior performance as measured by fund alphas, we now try to identify the source of the difference between the performance of team-managed and single-manager funds. First, we examine the evidence for differences in stock-picking skills. Then we examine whether the performance differences can be attributed to momentum investing or market timing. The evidence presented in this section indicates that single-manager funds are better at picking stocks while team-managed funds rely more on momentum investing. Management structure appears to have little effect on market timing.

4.1. Stock picking

To determine if there is a relation between fund management structure and stock-picking skills, we first examineDaniel, Grainblatt, Titman, and Wermers’s (1997) CS measure for stock picking. Then we examine the risk-adjusted returns of stocks that are new additions to funds and stocks that funds cease to invest in.

4.1.1. Stock weightings and performance

To study whether stock picking skills of fund managers vary with fund organization, we construct Daniel, Grainblatt, Titman, and Wermers’s (1997) CS measure for funds in our sample. The CS measure for each fund-quarter is constructed as follows:

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CSit= nit X

j=1

wij,t−1(Rjt−Rbj,t−1,t), (6)

where wij,t−1 is the portfolio weight on stock j in fund i at the end of month t−1, Rjt is

the monthly return of stock j in month t, andRbj,t−1,t is the return of a benchmark portfolio,

denoted asbj,t−1, at montht, where the benchmark portfolio is the matching portfolio of stock

j at montht−1 based on the firm’s size, book-to-market ratio, and prior-year return.

We regress these measures against fund characteristics to assess whether fund organization structure has any effect on stock picking. These results are reported in Table 7. The table contains six regressions. Regressions four, five, and six are identical to regressions one, two, and three respectively, except that they also include the inverse Mills’ ratio from the first stage regression Eq. (1) to control for possible endogeneity biases in the first three regressions. However, the coefficients associated with the inverse Mills’ ratio are statistically insignificant, suggesting that the coefficient estimates in the first three regressions are unbiased. Thus, we focus our discussion on regressions one through three.

The coefficient forTMin regressions one and two are negative and significant. This suggests that team-managed funds are not as adept at picking stocks as single-manager funds. In regression three, the coefficient for TM is negative and significant once again. Further, with the exception of cash holdings, fund characteristics do not appear to have any differential effect on the stock-picking performance of team-managed funds. Consequently, we find that only 14% of the observations for team-managed funds have higher CS measures than comparable single-manager funds, and the average CS measure for team-managed funds is 1.86 lower than the CS measure for single-manager funds. Both these results are consistent with the result from regressions one and two and thus all three regressions indicate that team-managed funds are inferior stock pickers.4

4

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4.1.2. Opening and closing positions

For additional evidence on the relation between fund management structure and stock pick-ing skills we now examine the risk-adjusted abnormal returns on newly opened stock positions and recently closed positions. We employ the Carhart four-factor model and the previous 60 months’ returns to calculate abnormal return on stocks. In Table 8 we report estimates ob-tained by regressing fund characteristics and the dummy for team management on the abnormal returns on new additions to fund portfolios and stocks that have recently been eliminated from fund portfolios. These abnormal returns are estimated for a three-month horizon, a one-year horizon, and a two-year horizon.

With one exception, the regression for the one-year abnormal returns for closed positions, the coefficient for TM is statistically insignificant. Fund age is the only characteristic that is associated with a difference in performance between the new positions for single-manager and team-managed funds. The positive coefficients for this variable suggest that new positions established by older team-managed funds outperform those of single-manager funds with similar characteristics. However, for all three horizons, we find that only 5% to 15% of the new positions opened by team-managed funds outperform the positions opened by similar team-managed funds. The average abnormal return earned by the new positions of team-managed funds is between 0.16 and 0.47 basis points lower than for similar single-manager funds.

The significant positive coefficients associated with the sums of the coefficients for TM ×

cash, TM× fees, and TM × turnover in performance regressions for closed positions suggest that stocks sold by team-managed funds with larger cash balances, higher fees, and higher turnover, earn higher abnormal returns than the positions closed by single-manager funds with similar characteristics. However, for all three horizons, we find that the stocks that are closed out by team-managed funds tend to underperform those closed by similar single-manager funds 35% to 44% of the time, and the average abnormal return earned by stocks that are closed out by team-managed funds is between 0.07 and 0.21 basis points lower than the abnormal return for stocks closed out by single-manager funds.

Not surprisingly, we find evidence suggesting that stocks acquired by “dead” funds tend to underperform. We also find that stocks that are cut from single-manager portfolios tend

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to underperform when the funds have relatively high cash balances or high fees. A potential explanation is that these stock sales are prompted purely by valuation concerns rather than the need for liquidity. In contrast, consistent with the results presented in Table7, the sum of the coefficients associated withcashand TM×cashis positive and significant for closed positions. Stocks cut by single-manager funds tend to earn higher abnormal returns when the funds are large.

4.2. Momentum investing

To measure active momentum investing of team- and single-manager funds, we adopt the “lagged zero measure” (L0M) developed by Grinblatt, Titman, and Wermers (1995):

L0Mi = 1 3T T X t=1 3 X k=1 ni,3t X j=1 (wij,3t−wij,3(t−1))rj,3(t−1)+k, (7)

where wij,3t and wij,3(t−1) are the portfolio weights on security j in fund i at quarter t and

(t−1), respectively, ni,3t is the number of stocks held by fund i at quarter t, and rj,3(t−1)+k

is the monthly return of security j(j = 1,· · · , n) at month t+k, where k = 1,2,3. L0Mi

measures the degree to which fundiloads on stocks that experienced high returns and reduces its holdings of stocks that experienced low returns during the prior quarter. A positive value indicates that, on average, the fund has increased its holding in stocks with higher past returns. The portfolio weights of the past winner (loser) stocks increase (decrease) even if the number of shares held stays constant, in which caseL0M would indicate momentum investing for buy-and-hold investment strategies. To correct this bias on passive momentum strategies, we follow Grinblatt, Titman, and Wermers (1995) and calculate the beginning and ending weights for a stock during a given quarter using the average of the beginning and ending share prices.

Table 9 presents our analysis of the relation between the structure of fund management and momentum investing. The table presents the results from regressing the fund momentum measure defined above on the dummy variable TM and several fund characteristics. Regres-sions four, five, and six reported in the table are identical to regresRegres-sions one, two, and three, respectively except that they include the inverse Mills’ ratio from our estimate of (1) for each

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observation as an additional explanatory variable. The coefficients for the inverse Mills’ ratios in all three regressions are statistically insignificant, suggesting that our coefficient estimates for the first three regressions are unbiased. Thus, we focus our discussion on the first three regressions. The coefficients for TM in the first two regressions are positive but insignificant, indicating that there is no difference between the incidence of momentum investing behavior of team-managed and single-manager funds. In general, fund characteristics are not corre-lated with the momentum measure for single-manager funds. However, from regression three, it appears that higher cash balances lower the difference in momentum scores between team-managed funds and single-manager funds, while fund turnover has the opposite effect. Using the coefficient estimates for regression three we find that approximately 50% of the observations for team-managed funds have higher momentum measures than identical single-manager funds, and team-managed funds have an average momentum measure that is significantly higher than the momentum measure for similar single-manager funds. Thus, overall, our results support the view that team-managed funds rely more on momentum investing.

4.3. Market timing

To examine whether team-managed funds time the market better than the single-manager funds, we employ a quadratic regression approach ˜a la Treynor and Mazuy (1966). More specifically, we estimate the following three regressions to assess the differences between market timing by team-managed and single-manager funds:

rit=β0+β1TMi+β2rmkt,t+β3TMi×rmkt,t+β4rmkt,t2 +β5TMi×rmkt,t2 ; rit=β0+β1TMi+β2rmkt,t+β3rsmb,t+β4rhml,t+β5TMi×rmkt,t +β6TMi×rsmb,t+β7TMi×rhml,t+β8rmkt,t2 +β9TMi×rmkt,t2 ; rit=β0+β1TMi+β2rmkt,t+β3rsmb,t+β4rhml,t+β5rumd,t+β6TMi×rmkt,t +β7TMi×rsmb,t+β8TMi×rhml,t+β9TMi×rumd,t+β10rmkt,t2 +β11TMi×r2mkt,t, (8)

where rit is the excess return on fundi, rmkt,t is the market excess return, and rsmb,t, rhml,t,

rumd,t are returns on SMB, HML, and UMD factors respectively. The coefficients for the

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between the market timing ability of team-managed funds and single-manager funds. If the coefficient on this interaction term is significant, team-managed funds time the market better than the single-manager funds.

Table 10 presents the coefficient estimates for three regressions employing this quadratic approach. In all three regressions the interaction termTM×rmkt,t2 is positive but statistically insignificant. This suggests that there is no difference between the market timing ability of team-managed and single-manager funds. Interestingly, in the first regression the coefficient associated with the variablermkt,t2 and the sum of the coefficients for TMand TM×r2mkt,t are negative and statistically significant, suggesting that both single-manager and team-managed funds are poor at timing the market, a result consistent with previous studies (see, e.g.,Ferson and Schadt,1996;Goetzmann, Ingersoll, and Ivkovic, 2000; Jiang,2003). However, in the two remaining regressions, these coefficients are positive and significant, suggesting that, once we control for other factors that explain stock returns, mutual fund managers display positive timing ability, which is consistent withJiang, Yao, and Yu(2006) who use holdings to calculate fund betas.5

5. Other robustness tests

We now present other tests that provide further insight into the relation between fund management structure and investment behavior. These tests also address the robustness of the results presented thus far. First we examine how fund management structure can affect reliance on public information. Then we examine whether fund management structure affects window dressing activity. Finally, we demonstrate that our results are not sensitive to sample selection because we have excluded funds that switched their management structure during our sample period from our analysis.

5

We also construct Daniel, Grainblatt, Titman, and Wermers’s (1997) characteristic timing (CT) measure. The intercepts are insignificant, suggesting market timing does not exist. However, the coefficients forTMare

significantly negative, fewer than 3% of team-managed funds have higher CT measure than the single-manager funds with identical characteristics, and the average difference between the predicted CT measures of these two fund types is significantly negative.

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5.1. Reliance on public information

Our results thus far indicate that the investment behavior of team-managed funds is dif-ferent from that of single-manager funds. These differences must have their origins in the information employed by the funds to make investment decisions. We now follow Kacper-czyk and Seru(2007) to construct a Reliance on Public Information (RPI) measure using past stock recommendation changes by sell-side analysts obtained from I/B/E/S. Specifically, for each fund we estimate RPIs as the percentage variations of the percentage changes in a fund’s quarterly holdings attributed to changes in analysts’ past recommendations. To calculate the RPI for fundiat quarter t(RP Ii,t), we estimate the following cross-sectional regression:

∆Wi,j,t=β0,t+β1,i,t∆REj,t−1+β2,i,t∆REj,t−2+β3,i,t∆REj,t−3+β4,i,t∆REj,t−4+ǫi,j,t, (9)

j= 1,2,· · · , n, (10)

where ∆Wi,j,t denotes the percentage change in stock j’s split-adjusted holding in fundifrom

quartert−1 to t, and ∆REj,t−k, k= 1,2,3,5 measures the changes in the analysts’ consensus

recommendation of stockj from quartert−k−1 tot−kin the last five quarters. The RPI of fundiat quarter t is given by the unadjustedR2 of the above regression.

Table 11 contains averages of RPI measures for several sub-samples of funds. For each sub-sample the table presents the average RPI measure for the entire sub-sample, the set of single-manager funds in the sub-sample, the set of team-managed funds in the sub-sample and the difference between the RPI measures for single-manager and team-managed funds in the sub-sample. Each statistic in the table describes the cross-sectional distribution of the time-series averages of the RPI measures for individual funds.

On average, lagged analyst recommendation changes account for approximately 13% of changes in fund holdings. This is lower than the average RPI of 29% for funds in the Kacperczyk and Seru(2007) study, and the difference is likely driven by our exclusion of funds that switched their management structure from our analysis. The average RPI for single-manager funds is approximately 14%, which is significantly higher than the 11.56% RPI for team-managed funds. The average RPI varies with fund style. Growth funds have an average RPI of 11.31% compared with value funds that have an average RPI of 14.13%. This difference suggests that

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the trades of value funds are more sensitive to analyst recommendations. Similarly, trades of small capitalization funds that have an average RPI of 17.52% appear to be more sensitive to analyst recommendations than the trades of large cap funds that have an average RPI of 13.28%. Single-manager funds have a larger RPI for all sub-samples represented in the table. However, the difference between the RPI for single-manager and team-managed funds is not statistically significant for blend and small capitalization funds.

In Table 12 we present estimates obtained by regressing the RPI measure for a fund on its characteristics. These regressions provide some insight into the determinants of differences in RPI between single-manager and team-managed funds. The last three regressions include the inverse Mills’ ratio, computed using our estimates of 1, for each observation to control for possible endogeneity bias in the regression estimates. The coefficient for this variable is uniformly statistically insignificant, indicating that there is no need to control for endogeneity in these regressions. Thus, we focus our discussion on the first three regressions presented in this table. The negative and significant coefficients for the dummy variableTMin the first two regressions provides additional support for the results from Table11, which indicates that team-managed funds tend to rely less on the recommendations of sell-side analysts. Turning to the third regression, we find that after we control for fund characteristics, 56% of the observations for team-managed funds have lower RPI’s than single-manager funds and the average RPI for team-managed funds is 1.8% lower than the RPI for single-manager funds. In both cases, these statistics are significant at the 1% level.

The coefficients for com and lsz are negative and significant, suggesting that the holdings of funds that invest more heavily in stocks and larger funds tend to be less sensitive to sell-side analysts’ recommendations. The holdings of older funds, however, appear to be more sensitive to sell-side analyst recommendations. Based on the statistical insignificance of the sum of the coefficients forageandTM×age, fund age does not appear to affect the relation between fund holdings and lagged analysts’ recommendations. However, based on the statistical significance of the sum of the coefficients forfees and TM×fees, higher fees appear to be associated with higher RPIs for team-managed funds.

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5.2. Window dressing

Several of the results presented earlier are based on the stock holdings of funds. These stock holdings are observed once a quarter and because of window dressing by funds they may not be representative of fund holdings for much of the quarter. To the extent that window dressing activity varies systematically with fund management structure, our earlier results may not accurately reflect the effect of management structure on investment behavior. Thus, we now examine whether team-managed funds behave differently from single-managed funds in the aspect of window dressing and other unobserved actions. Following Kacperczyk, Sialm, and Zheng(2006), we calculate the return gaps as the difference between the investor returns and holdings returns. The holdings return is defined as the return of a hypothetical portfolio that invests in the most recently disclosed fund holdings. The hypothetical portfolio is rebalanced once the new fund holdings are disclosed:

rh,t=

n

X

j=1

wj,t−1rj,t. (11)

The weightwj,t−1depends on the number of shares held by the fund at the most recent disclosure

date at timeτk (Nj,τk) and the stock price at the end of the previous month (Pj,t−1).

wj,t−1 =

Nj,τkPj,t−1 Pn

j=1Nj,τkPj,t−1

, τk< t≤τk+1. (12)

The holdings returns also are adjusted for other non-equity holdings. SeeKacperczyk, Sialm, and Zheng(2006) for details. The return gap is defined as the difference between the investor return (rf,t) and the holdings return adjusted for the expenses:

rgap,t=rf,t−(rh,t−expf,t). (13)

Thus, the return gap captures the fund’s unobserved actions including window dressing. We present our analysis of the return gap measure in Table 13. The last three regressions include the inverse Mills’ ratio for each observation to control for possible endogeneity bias in the regression estimates. The coefficient for this variable is uniformly statistically insignificant,

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indicating that there is no need to control for endogeneity in these regressions. Thus, we focus our discussion on the first three regressions presented in this table. In the first two regressions, the coefficients associated with TM are statistically insignificant, suggesting that there is no difference between the window dressing activities of team-managed funds and single-manager funds. The coefficients forcomandfeesare positive and significant while that forlszis negative and significant. The first two results indicate that funds with more of their assets invested in equities and funds that charge higher fees may indulge in more window dressing, while the second result indicates that larger funds are less likely to window dress.

The coefficients for the products of fund characteristics andTMare uniformly insignificant in regression three, indicating that none of the fund characteristics we consider is related to the difference in window dressing between team-managed and single-manager funds. Further, the coefficient forTMis statistically insignificant in regression three. Not surprisingly, we find that exactly 50.2% of the observations for team-managed funds have higher window dressing scores than similar single-manager funds and that this statistic is statistically insignificant. Thus, overall, our analysis indicates that there is no systematic relation between fund organization and window dressing.

5.3. Sample selection and endogeneity

To focus on funds whose management structures remained stable through our sample period, 1993-2002, we excluded 1758 funds from our analysis. Table14presents characteristics of these funds. The funds that switched their management structures were larger and older than both single-manager funds and team-managed funds. Possibly because of their larger size, these funds also had lower asset turnover ratios. The remaining fund characteristics, cash balances, asset allocation to equity investment, and fees are lower than the respective averages for single-manager funds but greater than the averages for team-managed funds. This last group of statistics suggests that the investment strategies of the funds that switched their management structures lie somewhere between the investment strategies followed by team-managed funds and single-manager funds.

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man-agement structure on the four Carhart factors. The table also presents the factor loadings for both single-manager and team-managed funds. The funds that switched generated slightly higher alphas than single-manger funds and significantly lower alphas than team-managed funds. Their loading on the market portfolio also fell between the loadings of the other two fund types, and in this case both differences in the market portfolio loadings were statistically significant. The funds that switched have the lowest loading on the size portfolio, suggesting that, consistent with the size of their pool of assets, they focused more on large cap stocks than either single-manager or team-managed funds. The significantly lower loading on the momen-tum factor for the funds that switched is consistent with our earlier evidence on their lower asset turnover and suggests that these funds may rely less on momentum investing than either single-manager or team-managed funds.

Now we demonstrate that our results relating management structure to fund performance are robust when we control for possible sample selection bias arising from the exclusion from our analysis of the funds that switched management structures. To correct for sample selection bias induced by excluding funds that have experienced management structure changes during the sample period, we first define a dummy variableSW, which takes a value of one for a fund if the fund switched its management structure and is zero otherwise. We then model the dummy variableSWusing the following probit regression:

SW =γ1+γ2cash +γ3stocks +γ4 log(size) +γ5fees +γ6age +γ7turnover +γ8tot +γ9swpct +ǫ,

(14) whereswpctis the percentage of funds having experienced changes in their management struc-ture in a fund family. We also include style and year dummies as controls.

We use two modeling approaches to correct for the sample selection and endogeneity prob-lems. In the first approach, we model the two decisions as sequential choices; that is, the funds first decide whether to maintain stable management structures, and subsequently the funds that decide to maintain stable management structures decide whether to use a single-manager or to use team management. Thus, the second decision is conditioned on the outcome of the first decision, and like Ellis, Lawson, and Roberts-Thomson(2003) and Audas and Dolton (1998),

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who also use this approach to correct for both sample selection and endogeneity, we first regress the dummy variable SW against the fund characteristics using Eq. (14) and calculate the in-verse Mills’ ratio (slambda) for each fund. We then regress the dummy variable TM against the fund characteristics using the probit regression similar to Eq. (1), but with an additional instrumental variable,slambda:

TM =β12cash +β3stocks +β4 log(size) +β5fees +β6age +β7turnover +β8tot +β9tmpct +β10slambda +ǫ.

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We calculate the inverse Mills’ ratio (lambda) from this regression and use this ratio and

slambda in the subsequent performance regression. By including both inverse Mills’ ratios, we control for the exclusion of the funds that switched their management structures and the endogeneity of the dummy variableTM.

In the second approach, we model the two decisions as joint choices, meaning that the funds simultaneously decide whether to maintain stable structures and whether to use a single-manager or team management. Therefore, we model the two dummy variables,SWandTM, as bivariate probit model with correlated errors (Eq. (14) and (1)). We calculate two conditional inverse Mills’ ratios, one for each choice.6 Note that the difference between these two modeling

approaches is not timing per se, but information set (see, e.g.,Maddala,1983;Tunali,1986). In the sequential choice model, the information set of the second decision (decision of management structure) is larger than that of the first decision (decision of stable structure) because it includes the outcome of the first decision. By contrast, in the joint choice model, the information set is the same for both decisions. The second key difference is that the first model is a conditional model, which does not permit the second decision to have any effect on the first decision, that is, the choice between a single-manager and team management has no impact on the management stability decision.

Table 16 contains our estimates of the effect of team management on fund performance after controlling for sample selection.7 The coefficient for TM is uniformly positive and the

6

The inverse Mills’ ratios are different from the ones calculated in the single selection model. Please see

Maddala(1983) andTunali(1986) for details and formulas.

7

We also employ the instrumental variable approach described inSemykina and Wooldridge(2005) and obtain similar results. In this approach, we use the predicted probability from the probit model15in place of the dummy

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remaining coefficient estimates are similar to the Heckman-corrected estimates reported in Table 5. The control for selection bias, slambda, has a positive and significant coefficient in Models 2 and 3 when we employ the sequential choice approach, but is uniformly insignificant when we employ the simultaneous choice approach. Even in Model 3 where the coefficient forslambda is significant, for 59% of our observations for team-managed funds, the abnormal return exceeds that for matched single-manager funds. Further, the predicted abnormal return for team-managed funds is 10.22 bps higher than the predictedαs for matched single-manager funds. Thus, our results regarding fund performance are not an artifact of selection bias.

6. Conclusion

This paper documents performance and holdings differences between team-managed and single-manager funds. We find that, consistent with the literature, after we control for invest-ment style, team-managed funds underperform. However, we show that, conditioned on the endogenous selection of team management, team-managed funds outperform single-manager funds. The contrast between unconditional underperformance and selection-conditioned over-performance suggests that the best and brightest managers select into individual management. Consistent with this observation we document rather bland holdings and strategies for team-managed funds. These funds hold more conventional portfolios given their style and also tend to follow investment styles based on generic portfolio investment strategies, such as momen-tum, book-to-market, and large capitalization stock strategies. These results suggest that institutional design team management, per se, has a positive effect on fund performance. How-ever, this design’s very virtue, restricting managerial discretion, discourages the most talented managers from subjecting themselves to it, leading to the coexistence of team-managed and single-manager funds and only muted differences in performance between them.

The results in this paper suggest a number of avenues for future research. First, within the field of mutual fund research, other institutional features seem to have the same effect as team management in that they restrict the ambit of managerial discretion, such as active independent directors for the fund, fund rules restricting investment strategies, and so on. We variableTMand use this predicted value as well as its products with the fund characteristics in the subsequent

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conjecture that such features of institutional design restrictions would have a similar effect to team management—strong managers will select away from funds with these features while the features themselves will create value for the fund. Thus, the net effect of the restrictions on performance may be hard to predict but, conditioned on selection, the effect should be positive. More generally, and admittedly in a more speculative spirit, we believe our results have implications for governance beyond the realm of mutual funds. Just as team management restricts managerial discretion in a mutual fund, so, in a corporate setting, does a large outside shareholder act to restrict a CEO’s ability to follow novel strategies. If our conclusions generalize to a corporate setting, we expect that highly talented CEOs would seek employment in firms that lack a large outside blockholder, which exerts a negative effect on such firms’ performance, while blockholder monitoring per se would have a positive effect. Perhaps these offsetting effects may explain the generally weak conclusions of empirical studies of the effect of outside investor activism (see, e.g., Brav, Jiang, Thomas, and Partnoy,2006).

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Panel A 6* 6% 69 0* 0% 09 /* /% /9 6W\OH'LVWULEXWLRQVRI7HDPDQG6LQJOH0DQDJHG)XQGV 6LQJOH 7HDP Panel B 6* 6% 69 0* 0% 09 /* /% /9 0XWXDO)XQG0DQDJHPHQW'LVWULEXWLRQVE\6W\OH 6LQJOH 7HDP Figure 1:

Panel A compares the style distribution of single-manager and team-managed funds, and Panel B plots fund management distribution by style.

(35)

Table 1: Fund characteristics comparison

This table reports the summary statistics of fund characteristics for all funds (All), single-manager funds (Single), and team-managed funds (Team). For each fund, we calculate the time-series average for each fund characteristic. The statistics presented are for the distributions of these time-series averages of fund characteristics. For each fund characteristic, we report the difference between the team-managed and single-manager funds (Differ), along with its t-stat and p-value. The fund characteristics reported are the percentage of its assets that a fund holds in cash and stocks (cash and stocks, respectively), the fund assets in millions of dollars (tna), the sum of the fund expense ratio and loads (fees) in percentage of the total assets under management, the time since fund inception in months (age), and fund turnover (turnover).

Characteristics All Single Team Differ All Single Team Differ

Mean Median tna 400.93∗∗ 467.19∗∗ 310.31∗∗ -156.8960.06 61.88 58.31 -3.57 [9.87] [7.28] [7.89] [-2.09] cash 5.95∗∗ 6.63∗∗ 5.02∗∗ -1.61∗∗ 4.16 4.71 3.55 -1.16 [34.64] [26.61] [22.99] [-4.85] stocks 79.91∗∗ 80.39∗∗ 79.24∗∗ -1.15 91.05 89.73 92.57 2.84 [152.01] [125.41] [89.69] [-1.06] fees 3.42∗∗ 3.59∗∗ 3.17∗∗ -0.42∗∗ 2.40 2.95 1.85 -1.10 [62.92] [50.40] [38.12] [-3.84] age 128.42∗∗ 143.97∗∗ 107.17∗∗ -36.81∗∗ 100.00 111.00 91.00 -20.00 [52.31] [39.52] [37.67] [-7.96] turnover 1.36∗∗ 1.17∗∗ 1.63∗∗ 0.46∗∗ 0.76 0.76 0.77 0.01 [19.76] [20.03] [11.41] [2.98] ∗ p <0.05,∗∗ p <0.01

References

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