Electronic copy available at: https://ssrn.com/abstract=2978811

Managerial Structure and Performance-Induced Trading

Anastassia Fedyk, Saurin Patel, and Sergei Sarkissian∗

June 1, 2017

∗Fedyk is from Harvard University, Department of Economics and Harvard Business School, Boston, MA02163, USA. Patel is from the University of Western Ontario Ivey Business School, London, ON N6G0N1,Canada. Sarkissian is from the McGill University Faculty of Management, Montreal, QC H3A1G5, Canada,and Yerevan State University, Yerevan, Armenia (visiting). Fedyk may be reached at afedyk@hbs.edu. Patelmay be reached at spatel@ivey.uwo.ca. Sarkissian may be reached at sergei.sarkissian@mcgill.ca. We thankJohn Campbell and Jeremy Stein for discussions on this research project. We thank the representativesof Dodge & Cox Funds and Goldman Sachs Asset Management for sharing with us their investment deci-sion making process. The authors acknowledge �nancial support from the Social Sciences and HumanitiesResearch Council (SSHRC).

Electronic copy available at: https://ssrn.com/abstract=2978811

Managerial Structure and Performance-Induced Trading

Abstract

The literature �nds that investors increase portfolio turnover following high returns,

explaining it by either overcon�dence or skilled trading. This paper develops a theo-

retical model and shows empirically that team-managed funds trade less after good

performance than single-managed funds. The magnitude of this di�erential increases

with team size. Moreover, the change from single- to team-management structure de-

creases overcon�dence induced trading. In spite of more trading, the next-period risk-

adjusted returns of single-managed funds are no better than those of team-managed

funds. These �ndings indicate that team-management reduces overcon�dent trading.

Alternative channels cannot explain the drop in excessive trading in team-managed

funds.

JEL Classi�cation: D22; D70; G02; G23

Keywords: Behavioral bias, Excess turnover, Fund alpha, Portfolio optimization, Pos-terior belief

1

�What would I eliminate if I had a magic want? Overcon�dence.�

- Daniel Kahneman, Nobel Laureate

I. Introduction

In this paper, we study how team-management a�ects overcon�dence among mutual fund

managers. We deal with a particular type of overcon�dence: self-attribution bias, whereby

individuals tend to overly attribute outcomes, especially positive outcomes, to their own

skills. The self-attribution bias extends only to beliefs about one's own skills.1 This wedge

in attributing one's own versus others' outcomes to superior skill is the main building block

of our theoretical framework and empirical tests. Our main �nding is that team-based

managerial structure signi�cantly reduces mutual fund managers' overcon�dence as evidence

by reductions in excessive trading.

The extant literature �nds that investors often increase their portfolio turnover following

high returns, explaining this phenomenon by either overcon�dence or information-based

trading. When this excessive turnover results in some performance improvements, then

one can relate it to knowledge and skillful investing; otherwise, it is usually associated

with overcon�dence resulting from prior successes in money management (e.g., Gervais and

Odean, 2001). For example, Barber and Odean (2000, 2001), Glaser and Weber (2007),

Grinblatt and Keloharju (2009), Puetz, and Ruenzi (2011), Bailey, Kumar, and Ng (2011),

and Christo�ersen and Sarkissian (2011) all �nd that overcon�dence increases trading among

individual and professional investors without any gains in performance. Overcon�dence is

found to be detrimental in many other studies on �nancial and business decision making.2

Given such negative outcomes, understanding how �rms can mitigate overcon�dence among

1Another broad type of overcon�dence is miscalibration, when individuals overestimate the precision ofinformation (low signal volatility).

2Ben-David, Graham, and Harvey (2013) show that executives are severely miscalibrated, producing verynarrow (overcon�dent) distributions of expected returns. Scheinkman and Xiong (2003), Malmendier andTate (2005, 2008), Billett and Qian (2008), Gervais, Heaton, and Odean (2011), Malmendier, Tate, and Yan(2011), Schrand and Zechman (2012), Ahmed and Duellman (2013), and Malmendier and Taylor (2015)show that overcon�dent managers undertake suboptimal investments and value-destroying merger decisions,use less external �nance, and are prone to more intentional earnings misstatements. Camerer and Lovallo(1999) show in an experimental setting that overcon�dent people neglect the quality of their competitionand fail in business. Johnson and Fowler (2011) say that overcon�dence is prevalent among people in spiteof its many negative e�ects.

2

their decision makers is of paramount importance.3

Some studies in economics and psychology suggest that one channel through which �rms

can reduce overcon�dence in managers is collective decision-making. By working in teams,

managers avoid �bias blind spots� by catching others' behavioral mistakes, providing each

other with feedback, and thus, potentially reducing overcon�dence (Pronin, Lin, and Ross,

2002; Tetlock and Mitchell, 2009). Teams are generally smarter, more strategic, rational and

less �behavioral� than individuals which signi�cantly reduces chances of biased judgements

(Cooper and Kagel, 2005; Charness and Sutter, 2012).4 Yet, other studies �nd that group

decisions are less optimal than those of individuals.5 In particular, Sunstein and Hastie

(2014) argue that groups amplify various psychological biases, including overcon�dence, due

to incorrect informational signals from group members and reputational pressures. Therefore,

the jury is still out on how collective investment decisions a�ect the trading behavior of fund

managers.

Our theoretical model formalizes the following intuition. Individual fund managers are

subject to self-attribution bias, so that the manager proposing a particular strategy over-

attributes its success to his own skill, while other managers update their beliefs regarding

his skill rationally. Speci�cally, the model features one manager proposing a discretionary

trading strategy, and plays out over three periods. The �rst period reveals the strategy's past

3Few studies highlight positive e�ects of overcon�dence and/or downscale its extent. Galasso and Simcoe(2011) show that overcon�dent CEOs are more likely to take their �rms in a new technological direction.Gervais, Heaton, and Odean (2011) show that some overcon�dence helps managers in pursuing valuablerisky projects. Benoit and Dubra (2011) argue that much of the evidence on overcon�dence reveals only anapparent, not a true bias.

4The literature also reports the superiority of team-management for investors' performance and riskreduction. For example, Adams and Ferreira (2010) �nd that teams arrive to less extreme decisions. Sharpe(1981), Barry and Starks (1984), Sah and Stiglitz (1991), Bar, Kempf, and Ruenzi (2011) argue and �ndthat teams in the fund industry achieve diversi�cation of style and judgment that reduces portfolio risk.Empirical studies �nding a positive performance impact of teams are Hamilton, Nickerson, and Owan (2003)and Patel and Sarkissian (2016).

5For example, groups may act more aggressively and undertake riskier decisions than the average choicesof individuals in a group - phenomena known as �risky shifts� (see Wallach and Kogan, 1965; Stoner, 1968)and �group polarization� (Moscovici and Zavalloni, 1969; Sunstein, 2002). Janis (1982) develops the conceptof a �groupthink�, where people in groups accept suboptimal decisions to avoid con�icts with their colleagues.Moreover, papers, such as Prather and Middleton (2002), Chen, Hong, Huang, and Kubik, (2004), Bliss,Porter, and Schwarz (2008), Massa, Reuter, and Zitzewitz (2010), and Bar, Kempf, and Ruenzi (2011) �ndno performance di�erences between team-managed and single-managed funds. Cici (2012) �nds that whenfunds are managed by managerial teams, at the time of net out �ows they are selling substantially morewinners than losers.

3

performance, which is an imperfect signal of the manager's skill and the strategy's future

performance. In the second period, fund management decides how much capital to allocate

to the strategy going forward. The model ends in the third period, when the strategy's �nal

payo� is realized. Within this framework, single-managed funds are modeled as the single

manager, who initially proposes the strategy, deciding how much capital to allocate to it

in the second period. In team-managed funds, the second period allocation is determined

jointly by the proposing manager and his teammates.6

Coupled with any decision-making mechanism that places non-trivial weights on each

manager's opinion, this channel generates three key predictions. First, funds managed by

single individuals exhibit excessive performance-induced trading. Second, team-managed

funds exhibit less performance-induced trading than single-managed funds. Third, for team-

managed funds, performance-induced trading decreases in team size.

Our empirical tests are based on U.S. equity mutual funds data between 1991 and 2015.

Mutual fund industry is an ideal place to test our model predictions, because it provides

the largest comprehensive single source of occupational data with a rich mix of single- and

team-managed funds and a �standardized� task of generating maximum returns. In addition,

mutual fund managers perform security selection, which can be a di�cult task, and it is

precisely in such situations when people exhibit the greatest overcon�dence (Odean, 1999).

Lastly, several studies in the mutual fund literature use performance-induced trading as a

widely accepted empirical proxy for overcon�dence among fund managers which is readily

available. Following the literature, we proxy overcon�dence by performance-induced trading

which implies excessive trading by fund managers following superior performance. Figure

1 illustrates the relation between past fund performance quartiles and next period turnover

using objective-adjusted returns (OAR) of U.S. domestic equity funds.7

6Our communication with industry professionals reveals that funds are often managed by a committeevote. E�ectively, individual portfolio managers manage quasi-independent teams, where each team is re-sponsible for a speci�c strategy, but there are formal or informal cross-validation processes between theteams. For example, a more formal process might involve a junior member (not a portfolio manager) puttingtogether advocacy for a speci�c trade and proposing it to his immediate portfolio manager. If the portfoliomanager approves, then there is a more formal presentation to the senior committee with a subsequent vote.A larger group behind decision making implies that there are more people who can stop poor investmentideas or point out each others' biases.

7As the �gure shows, the fund turnover also increases after poor performance, but this increase cannot beassociated with overcon�dence; rather it results from fund managers actively changing their trading strategy

4

We begin by testing the key predictions of the model. Consistent with the model's intu-

ition, we �nd that performance-induced trading is signi�cantly lower among team-managed

funds compared to single-managed funds. That is, following strong returns, team-managed

funds increase their subsequent trading signi�cantly less than single-managed funds after

controlling for various fund and manager characteristics. This result is robust to di�erent

de�nitions of top performance (decile, quintile, or quartile) and various performance metrics,

and holds for both panel and cross-sectional regression methodologies. For example, based

on the Fama and French (2015) �ve-factor alpha and with the full set of control variables,

team-managed funds in the top quartile of performance increase their trading by about 9%

less than single-managed funds. Furthermore, consistent with another prediction of our

model, we �nd a negative relation between team size and turnover in team-managed funds

after good performance. In economic terms, two-member teams show a 7% reduction in

trading relative to single-managed funds, while funds with four or more members post a 12%

reduction in this measure.

We address the possibility of our results being driven by preexisting di�erences between

single- and team-managed funds through additional analyzes focusing on changes in fund

structure. In particular, we consider the samples of funds that switch from team- to single-

managed and vice versa, and construct matched samples of funds without changes in manage-

rial structure. The matched samples are constructed using propensity score matching based

on several fund and manager characteristics. Using the �ve closest matches, we �nd that

funds that switch from single- to team-managed see a 12% lower sensitivity of turnover to

past performance than funds that remain single-managed. Similarly, funds that switch from

team- to single-managed have 6% more performance-induced trading than their matched

counterparts that remain team-managed. In addition, we conduct placebo tests on manage-

rial structure changes and trading using middle performance quartiles and �nd no evidence

of the di�erence between team-managed and single-managed funds. This evidence helps

strengthen the causal interpretation of the relation between performance inducing trading

and team based managerial structure.

We rule out several alternative explanations that may potentially explain our results.

by replacing underperformed stocks with new securities.

5

First, we consider managerial experience. Several studies �nd that overcon�dence is per-

vasive among young and less experienced managers (e.g., Gervais and Odean, 2001; Seru,

Shumway and Sto�man, 2010; Menkho�, Schmeling and Schmidt, 2013), while others �nd

that overcon�dence may increase with experience (e.g., Heath and Tversky, 1991; Deaves,

Luders and Schroder, 2010). We show that managerial experience does not a�ect the impact

of teams on overcon�dence. Second, it is plausible that funds with superior past performance

receive higher than expected in�ows, which, in turn, force managers to trade more (e.g., Co-

val and Sta�ord, 2007; Pollet and Wilson, 2008). We address this concern by testing the

di�erential impact of net in�ows and out�ows on teams and performance-induced trading

separately. Again, we �nd no impact of fund in�ows on the relation between teams and

performance-induced trading. Third, studies show that males are more prone to overcon�-

dence than females (e.g., Lewellen, Lease, and Schlarbaum, 1977; Barber and Odean, 2001).

It is plausible then that the reduction in performance-induced trading among teams is due

to female team members. To address this concern, we test our model on male-only funds

and still �nd signi�cant reduction in performance-induced trading among male-only team-

managed funds. Finally, but very importantly, we also analyze how increased turnover a�ects

future fund performance depending on managerial structure of funds. These tests are aimed

at decoupling whether any increase in the fund turnover following superior performance is

information-based or simply re�ects overcon�dent trading. We observe that while single

managed funds trade more and, therefore, incur more costs after good returns, their next

period performance is no better and is often worse than that of team-managed funds. There-

fore, we rule out the information-based trading and conclude that single-managed funds are

more prone to overcon�dence bias.

Our paper's contribution is two-fold. First, we add to the theoretical and empirical lit-

erature that highlights the positive e�ects of �team production� on decision-making. For

example, studies show that team-based managerial structure helps overcome the problem

of e�ort coordination through peer pressure (Kandel and Lazear, 1992); diminishes extreme

and risky decisions through diversi�cation of opinions (Adams and Ferreira, 2010; Sharpe,

1981; Barry and Starks, 1984; Bar, Kempf, and Ruenzi, 2011); improves productivity and

performance (Hamilton, Nickerson, and Owan, 2003; Patel and Sarkissian, 2016). Second,

6

we contribute to the mutual fund literature that shows the prevalence of various behav-

ioral biases among fund managers. For example, studies document the disposition e�ect

(e.g., Frazzini, 2006; O'Connell and Teo, 2009; Jin and Scherbina, 2011; and Cici, 2012),

overcon�dence bias (e.g., Menkho�, Schmidt and Brozynski, 2006; Glaser and Weber, 2007;

Grinblatt and Keloharju, 2009; Puetz, and Ruenzi, 2011; Bailey, Kumar, and Ng, 2011;

and Christo�ersen and Sarkissian, 2011) and familiarity bias (e.g., Coval and Moskowitz,

1999; Pool, Sto�man and Yonker, 2012). Yet, instead of focusing on the existence of biases,

we examine the mechanism through which funds can mitigate these biases. Overall, we

interpret our �ndings as providing evidence on the bene�t of teamwork in overcoming the

overcon�dence bias among professional money managers.

The rest of the paper is organized as follows. Section 2 develops the theoretical model

of the relation between overcon�dence-induced trading and managerial structure. Section

3 describes the mutual fund data. It also presents the �rst evidence on the di�erences

in turnover response to past out-performance between single-managed and team-managed

funds. Section 4 deals with the main empirical tests. They include the examination of the

impact of managerial structure and its changes on fund turnover conditional on strong past

performance. Section 5 considers several alternative explanations for our results as well as

a series of robustness checks. Section 6 analyzes fund returns subsequent to increases in

turnover. Section 7 concludes.

II. Model of Overcon�dence and Team-Management

This section outlines a conceptual framework of decision making in mutual funds and de-

rives predictions for the relationship between trading and past performance. The key friction

in the model is overcon�dence, where an individual manager over-attributes good results to

his own skill. Due to overcon�dence, the individual manager trades more aggressively follow-

ing good past performance. Our model highlights the role of team-based decision making in

alleviating the adverse e�ects of overcon�dence: The more cautious beliefs of the manager's

teammates prevent him from overly aggressive trading. Hence, in team-managed funds, good

past performance does not induce as much subsequent trading as in individually-managed

funds.

7

A. Setup

We begin by presenting a parsimonious model of the mutual fund setting and describing

the fund managers' skills, beliefs, and objectives. The model runs over three periods, with

the time-line depicted below.

Period 1

observe signal z

+

update beliefsregarding M1's skill

Period 2

decision on how muchto trade into M1's

discretionary strategy

Period 3

payo� realized

Model time-line.

Consider a mutual fund managed by a single manager (M1) or a team of managers (M1

and Mj 6=1). The fund tracks a benchmark with return Rb. For simplicity, we assume that

Rb is a guaranteed return with no risk. In addition, the mutual fund manager M1 proposes

a discretionary trading strategy with a risky return Rd ∈ {0, R}.

The realization of the risky returnRd depends onM1's skill. In particular, the managerM1

can be either skilled or unskilled. The prior probability of a skilled manager is p. Additional

information regarding M1's skill arrives in the form of a noisy signal during period 1, and

is used by all fund managers to update their beliefs regarding the likelihood of M1 being a

skilled manager.

The signal about M1's skill is a binary outcome z ∈ {0, 1}. If the manager is skilled,

then the probability of a good signal (z = 1) is pH . If the manager is unskilled, then the

probability of a good signal is pL < pH . Thus, the joint distribution of M1's skill level and

the realization of the signal z is as follows:

(skill, z) =

(skilled, z=1) with probability p1 = ppH

(skilled, z=0) with probability p2 = p(1− pH)

(unskilled, z=1) with probability p3 = (1− p)pL(unskilled, z=0) with probability p4 = (1− p)(1− pL)

(1)

8

In the empirical analysis, we proxy for the skill signal z using past performance. In

particular, the interpretation of observing z = 1 is that the performance over the prior year

lies in the top quartile, quintile, or decile across all mutual funds.

The skill of manager M1 in�uences the future distribution of returns to his discretionary

trading. In particular, the returns from the new discretionary strategy Rd are realized in

period 3, and depend on M1's skill in the following manner. If the manager is skilled, then

the probability of a good return in period 3 is:

P{Rd = R|skilled} = q (2)

If the manager is not skilled, then the probability of a good return in period 3 is:

P{Rd = R|unskilled} = qL < q (3)

For convenience of exposition, we normalize qL = 0, which simpli�es the notation but does

not alter any of the results.

The key friction in the model is overcon�dence: the manager M1 does not correctly

update his beliefs regarding his own skill following the arrival of the signal z in period 1. In

particular, we de�ne managerial overcon�dence as follows.

De�nition 1 (Overcon�dence) Manager M1 is overcon�dent in that he overestimates the

joint probability of being skilled and receiving a good signal, and underestimates the joint

probability of being unskilled and receiving a good signal (z = 1). In particular, manager

M1 with overcon�dence parameter ∆ believes that: P{skilled, z = 1} = ppH + ∆ and

P{unskilled, z = 1} = (1 − p)pL − ∆. His beliefs conditional on a bad signal (z = 0)

are correct, as in (1).

This form of overcon�dence is consistent with a large body of empirical evidence on

the self-serving attribution bias in psychology: individuals tend to overly attribute positive

outcomes, to their own skill.8 Applications of the self-serving attribution bias to �nance

8See, for example, Langer and Roth (1975), Miller and Ross (1975), Winkler and Taylor (1979), andArkin, Appelman, and Burger (1980).

9

include short-term positive auto-correlation and long-term reversal in returns in �nancial

markets (see Daniel, Hirshleifer, and Subrahmanyam, 1998) and increased overcon�dence

among managers who have closed successful acquisition deals in the past (see Doukas and

Petmezas, 2007).

Managerial overcon�dence extends only to beliefs about one's own ability. Thus, if another

manager Mj 6=1 is present, he interprets the signal about M1's ability correctly according to

(1). This follows the extant literature in social psychology documenting the phenomenon

termed �bias blind spot": That individuals are, in general, more perceptive of others' biases

than of their own.9 The more correct beliefs about others than about oneself lead to relative

overcon�dence, i.e. more positive beliefs about oneself than about one's peers. Such relative

overcon�dence has been empirically documented in a variety of domains ranging from driving

to earning potential: the proportion of individuals who anticipate being above median is

substantially higher than 50%.10 Theoretically, relative overcon�dence and the bene�cial

e�ect of teamwork have been explored in Fedyk (2015) in the context of work assignments.

It is intuitive for a similar form of relative overcon�dence to exist in the mutual fund industry.

The remaining piece of the model setup is the investment decision. After observing the

signal and updating beliefs regarding M1's skill in period 1, the managerial team needs to

make the investment decision in period 2. Namely, the decision is how much to invest in

M1's proposed new discretionary trading strategy with stochastic return Rd, and how much

to invest in the benchmark with certain return Rb.

We model each manager's optimization problem, regardless of whether the fund is single-

managed or team-managed, as maximizing mean-variance utility in the �nal period 3 payo�,

with a risk aversion coe�cient A. In particular, if w denotes the portfolio weight on the dis-

cretionary trading strategy, then the optimization problem from the perspective of manager

i is given by:

maxw

{Ei{wRd + (1− w)Rb|z} −

A

2V ari{wRd + (1− w)Rb|z}

}, (4)

9See, for example: Pronin, Lin, and Ross (2002), Ehrlinger, Gilovich, and Ross (2005), and West, Meserve,and Stanovich (2012).

10See, for example, Svenson (1981) on relative overcon�dence about driving abilities, Weinstein (1980) onrelative overcon�dence about a variety of life events, and Alicke (1985) on overestimation of the likelihoodwith which positive adjectives are characteristic of oneself relative to one's peers.

10

subject to the no short selling constraint that the weight w ≥ 0. The expectation and

variance operators are subscripted with i to denote that the expectation and variance are

computed with respect to manager i's beliefs.

The way this optimization problem is solved by M1 in a single-managed fund is discussed

in Section 1.2 below, and the way the problem is handled jointly by {Mi}ni=1 in the team-

managed fund with n managers is described in Section 1.3. In order to ensure interior

solutions with positive discretionary trading in good circumstances, we assume that under

rational expectations, the expected return on the discretionary trading strategy conditional

on a good signal z = 1 is higher than the riskless benchmark return, i.e.:

ppHppH + (1− p)pL

qR > Rb (5)

Condition (5) simply ensures that the signal carries meaningful information � i.e., that the

optimal amount of discretionary trading is not identically equal to zero regardless of whether

the signal is good (z = 1) or bad (z = 0). The condition does not otherwise in�uence the

results.

B. Single-Managed Fund

We �rst consider the case of a single-managed fund. Here, managerial overcon�dence

induces excessive performance-based trading.

In a single-managed fund, the manager M1 solves the optimization problem (4) given his

beliefs upon observing the signal of his skill from past performance, z. In particular, given

M1's overcon�dence, his posterior beliefs are as follows:

Lemma 1 (M1's posterior beliefs) Upon observing the signal z regarding his skill, the

overcon�dent manager M1 holds the following posterior beliefs regarding the distribution of

the return Rd:

P1{Rd = 1|z = 1} =ppH + ∆

ppH + (1− p)pLq; P1{Rd = 1|z = 0} =

p(1− pH)

p(1− pH) + (1− p)(1− pL)q

(6)

E1{Rd|z} = P1{Rd = 1|z}R; V ar1{Rd|z} = P1{Rd = 1|z} (1− P1{Rd = 1|z})R2(7)

11

Proof. See Appendix.

Note that the manager's expectation of the returns to discretionary trading following a

good signal increases with his overcon�dence. Following a poor signal, however, the manager

correctly updates his beliefs regardless of his skill level. Thus, he takes too much credit for

a good signal, but correctly understands the relationship between skill and a poor signal.

As the manager factors the posterior beliefs from Lemma 1 into his optimization, he

chooses to trade more aggressively following a good signal. This is captured in the following

Proposition.

Proposition 1 (Trading in a single-managed fund) In a mutual fund managed by a

single manager with beliefs speci�ed in De�nition 1, there is more trading following a good

past performance signal (z = 1) than a poor one (z = 0), and this performance-induced

trading increases in the level of the manager's overcon�dence (∆).

Proof. See Appendix.

In our empirical setting, Proposition 1 translates to observing higher turnover following

good performance. We document this relationship in Section 4: Turnover in single-managed

funds is signi�cantly higher following performance in the top quartile, quintile, or decile than

following non-top performance. We refer to this increased turnover as �performance-induced

trading". Since we do not observe managerial overcon�dence, we do not directly test the

second part of Proposition 1; instead, we test for di�erential performance-induced trading

in single- versus team- managed funds, derived in the following subsection.

C. Team-Managed Fund

We now turn to the case of a mutual fund managed by multiple managers, and show that

the team management structure can serve to mitigate performance-induced trading.

First, we discuss the process of decision making within a team-managed fund with the

managerial team {Mi}ni=1. One of the managers, M1, proposes a discretionary trading strat-

egy with payo� Rd. The signal z is informative regarding M1's skill according to (1). Each

member of the managerial team updates his beliefs following the signal, and chooses his pre-

ferred solution to (4) according to his own beliefs. The managers then exchange their ideas

12

in an unmodeled bargaining process, and the �nal allocation to M1's discretionary trading

strategy is a weighted average of the individual managers' preferred allocations.

Formally, the team management process is de�ned as follows:

De�nition 2 (Team management process) In a fund managed by manager M1 in con-

junction with other managers Mj 6=1, the decision making process is as follows:

1. All managers observe the signal about M1's ability, z, and update their beliefs regarding

M1's skill. At the same time, M1 proposes a new discretionary trading strategy with

stochastic return Rd that depends on his skill according to equations (2)-(3).

2. Each manager Mi solves the optimization problem (4) given his updated beliefs, choos-

ing allocation weight w∗i |z. The managers discuss in an unmodeled bargaining process,

and the actual allocation weight is w∗|z =∑

i vI × (w∗|z), where vi is the weight allo-

cated by the team structure to manager Mi's preferences. All weights vi > 0.

3. As before, the payo� is realized and the game ends in period 3.

The unmodeled bargaining process in period 2 maps intuitively to a variety of natural

setups in real-life mutual funds mentioned in our conversations with industry professionals.

In some funds, the more junior portfolio managers perform the research and propose trading

strategies to the more senior team members, who give the �nal go-ahead on the trades. In

these situations, the decision weights vi favor heavily the non-proposing senior managers,

Mi 6=1; but to the extent that senior management relies on a junior manager's particular

expertise, his weight vi is also non-zero. In other funds, each manager is largely responsible

for his own asset class or strategy, but major trading decisions are approved via a majority

vote or veto by the entire group. In these situation, each manager has, at least in expectation,

a non-zero weight in the decision making process, with the realization of the weight depending

on whether his vote is pivotal. In all cases, it is reasonable to assume that in a team-managed

fund the preferences of both the strategy-proposing manager M1 and his colleagues non-

trivially enter the �nal decision, as modeled by De�nition 2.

In order to understand how the decision of the team di�ers from that of the single manager,

we highlight the way managers update their beliefs following the signal z. Recall from

13

De�nition 1 that managerM1 over-infers his own ability from a good signal z = 1. The other

managers, however, update their beliefs correctly. As a result, the implemented allocation,

which re�ects all managers' beliefs, mitigates the performance-induced trading documented

in the single-manager case. In particular, we establish the following result:

Proposition 2 (Trading in a team-managed fund) In a mutual fund managed by mul-

tiple managers, performance-induced trading is lower than in the single-managed fund. I.e.,

the di�erence in discretionary trading following a good signal (z = 1) versus a bad signal

(z = 0) is lower when the fund is team-managed.

Proof. See Appendix.

We test Proposition 2 empirically by identifying funds with single and multiple listed

managers as single- and team-managed, respectively. We observe which funds display per-

formance in the top quartile, quintile, or decile of the distribution over the previous year

(a proxy for the skill signal z), and then compare the sensitivity of turnover to the top

performance indicator for team-managed versus single-managed funds.

In order to evaluate the relation between performance-induced trading and the size of

the managerial team in team-managed funds, we make one additional assumption: that the

preferences of managers in a given team are weighted equally. In this case, the performance-

induced trading diminishes with team size. Since manager M1 is overcon�dent, his desired

allocation to his new proposed trading strategy is higher than the allocation preferred by

the other managers on the team. As the number of the other managers increases, the

equal-weighted average tilts further away fromM1's desired allocation, and the performance-

induced trading decreases. The decrease in performance-induced trading from an additional

team member is lower when the total number of managers in the team is already high. This

result can be summarized as follows:

Proposition 3 (Trading and team size) In a mutual fund managed by multiple man-

agers with equal weights, performance-induced trading monotonically decreases with the size

of the team. However, the e�ect of each additional team member becomes smaller as the

team grows.

14

We test Proposition 3 empirically by splitting the team-managed funds into those that

have two, three, and four or more managers. We compare the sensitivity of trading to past

performance in team-managed funds of each size against the sensitivity in single-managed

funds.

III. Mutual Fund Data

The mutual fund data is from MorningStar Direct. Our sample includes all domestic

U.S. equity mutual funds (excluding index and sector funds) for the 1991-2015 period. We

collect several fund and manager characteristics. The fund characteristics are: Turnover,

Size, Age, Family Size, Fees, Flows, Volatility, and two performance measures. Turnover

is the minimum of aggregated sales or aggregated purchases of securities during the year

divided by the average 12-month total net assets of the fund. Since the aggregate fund

turnover is known to change with market conditions and investment style, we also compute

excess turnover. Fund Size (in millions of dollars) is the total net assets under management

of a fund in a given year. Fund Age (in years) is the di�erence between the fund's inception

year and the current year. Family Size (in billions of dollars) is measured by the total net

assets under management of the fund complex to which the fund belongs at the end of the

calendar year. Fund Fees (percent per year) is the annual total expense ratio of the fund.

Return Volatility (percent per year) is the standard deviation of monthly gross fund returns

over the past 12 months. Flows is de�ned as the net growth in the total net assets of a

fund, as a percentage of the fund's total net assets, adjusted for the prior year return. To

reduce the in�uence of outliers, we trim Turnover and Excess Turnover, Fund Fees, and Fund

Flows at 1% and 99% levels. The two performance measures are: OAR, which is the annual

objective-adjusted gross fund return computed each year and α(FF5), which is the alpha

based on the Fama and French (2015) �ve-factor model that adds pro�tability (RMW) and

investment (CMA) factors to the Fama-French three factor model. Finally, we also compute

two managerial characteristics. The �rst is Manager Industry Tenure (in years), which is the

number of years the fund manager has been within the fund industry. The second is Female,

which is de�ned as the proportion of female managers in a fund.

Table 1 reports the summary statistics. Panel A shows the distribution of single-managed

15

and team-managed funds during the sample period and across di�erent managerial struc-

tures. There are more than 38,000 fund-year observations, and the number of observations

for team-managed funds is about 70% larger than single-managed funds. As documented

in earlier studies (see Patel and Sarkissian, 2016), the percentage of single-managed funds

has drastically decreased over time from almost 71% in 1991 to below 29% by 2015. Conse-

quently, the proportion of team-managed funds has increased, and the largest hike is observed

among teams consisting of four and more managers - almost six-fold from 4% to 23%.

Panel B of Table 1 reports the number of observations, means, and the standard deviation

of fund and manager characteristics of single-managed and team-managed funds. We can see

that on average, single-managed funds have signi�cantly larger turnover than team-managed

funds (86% versus 78%). Single-managed funds also are signi�cantly smaller, younger, and

are managed by managers with shorter industry tenure than team-managed funds. How-

ever, family size where single-managed funds operate is larger than that of team-managed

funds. Finally, single-managed funds on average outperform team-managed funds in terms

of objective-adjusted returns and the Fama-French �ve-factor alphas.

To give the �rst illustration on how fund managerial structure impacts performance-

induces trading in Figure 2 we show the relation between past fund OARs and subsequent

turnover for single-managed funds and team-managed funds of di�erent sizes. Plot A com-

pares the relation between single-managed funds and all team-managed funds. We can see

that on average the turnover of team-managed funds is lower than that of single-managed

funds across all performance quartiles. More importantly, we observe a divergence of turnover

for the highest return quartile, that is, single-managed funds increase their turnover following

strong performance much more than team-managed funds. Plots B, C, and D demonstrate

the same relation between single-managed and team-managed funds with two, three, and

four plus members, respectively. We can see that the di�erence between increased trad-

ing activity following previous out-performance between single-managed and team-managed

funds seems to be increasing with team size. This is generally consistent with the empirical

implications of our model and, in the absence of the next period gains, can well be explained

with overcon�dence resulting from past successful investments.

Figure 3 repeats the plots from Figure 2 but this time using the Fama-French �ve-factor

16

alphas as the fund performance measure. Again, Plot A deals with single-managed funds

and all team-managed funds, while Plots B, C, and D consider team-managed funds of

di�erent sizes. The patterns are similar to those in the corresponding plots in Figure 2.

However, we note that the turnover di�erence between single- and team-managed funds

for high-performing funds is larger than in Figure 2, while that for low-performing funds

(Quartile 1) is smaller or non-existent at all, as in the case of Plot C with three-member

teams. Therefore, Figures 2 and 3 document substantial turnover di�erences following strong

returns between single-managed and team-managed funds. In the next section, this �nding

is tested statistically in more rigorous empirical settings.

IV. Empirical Tests

A. Managerial Structure and Turnover

Our �rst set of empirical tests analyzes the impact of the current managerial structure of

a fund on its turnover. To properly account for genuine di�erences in fund trading intensity

over our sample period and across fund types, our main dependent variable is the excess

turnover, Turnoverex (percent per year). It is de�ned as the di�erence between the fund

turnover in a given year and the median turnover for all funds with the same fund investment

objective in that year. Since overcon�dence in �nancial markets is associated with increased

trading following superior performance, to examine the main empirical predictions of our

theoretical model, we use the following regression speci�cation:

Turnoverexi,t = β0 + β1Teami,t−1 × Perfi,t−1 + β2Perfi,t−1 + β3Teami,t−1

+ β4Controlsi,t−1 + β5FEi + ei,t, (8)

where Teami,t−1 is a dummy variable, which is equal to one if fund i is team managed

at the end of year t − 1, and zero otherwise, Perfi,t−1 is a dummy variable, which is equal

to one if fund i has the top performance rank, and zero otherwise. Our main performance

metric is the Fama-French �ve-factor alpha. We consider three top performance cut-o�s:

17

Top 25%, top 20%, and top 10%. Controlsi,t−1 is the set of control variables, which includes

all fund and manager characteristics from Panel (B) of Table 1; FEi are the fund �xed

e�ects, which include fund investment objective �xed e�ects and fund family �xed e�ects,

since average trading intensity is known to vary across funds with di�erent investment goals

and fund family culture. We also control for fund location with the inclusion of the �nancial

city indicator variable following Christo�ersen and Sarkissian (2011). Finally, we cluster

standard errors by fund and year.

Table 2 shows the results from estimating model (8) for three di�erent cut-o�s of the top

fund performance: Columns 1-3 for the top quartile performance, columns 4-6 for the top

quintile performance, and columns 7-9 for the top decile performance. The regressions in

columns 1, 4, and 7 do not include fund and manager controls. Intercept is included in each

regression but its estimates are not reported. Across all these estimations the coe�cient

on Perfi,t−1 is positive and signi�cant con�rming Proposition 1 of the model that single-

managed funds substantially increase trading following good returns. We also �nd that both

non-interactive and interactive Team coe�cients are negative and signi�cant, implying that

team-managed funds engage in much less trading overall, and especially after experiencing

strong prior year returns. The bottom of the table shows the di�erence in the coe�cients

between team-managed and single-managed funds, Di� (T-S), and the p-value of the corre-

sponding F-test. It reveals that the excess turnover of team-managed funds following their

top performance is about 13% lower than that of similarly performing single-managed funds,

as predicted by Proposition 2 of the model. This di�erence is statistically signi�cant at the

1% level across all three estimations. In fact, the share of this lower di�erence attributed

directly to funds being in the top performance bracket is about 8-9%, as one can infer from

the magnitude of the slope coe�cient on the term.

Regressions 2, 5, and 8 include the full set of controls. This inclusion reduces our sample

size substantially, by about 22%. In spite of this reduction in the number of observations,

we still �nd a negative and signi�cant coe�cient for the Team variables, especially on the in-

teractive term. The di�erence tests again show that team-managed funds trade signi�cantly

less than their single-managed peers following top performance results in the prior year. In

this case the economic magnitude of the di�erence ranges between 10% (using top 20% per-

18

formance) and 13% (using top 10% performance). In the last set of regressions (columns 3, 6,

and 9) we also add the fund family �xed e�ects. This alteration does not materially change

the observed relation between team-management and lower turnover subsequent to top fund

performance even further, and, if anything, the result becomes marginally stronger. Now

the di�erence tests show that the impact of team-management on reducing fund turnover

among last year's top decile performers exceeds 16% (column 9).

Thus, Table 2 shows that after achieving much better than average returns, team-managed

funds exhibit a signi�cantly lower propensity to subsequently increase their portfolio turnover

than single-managed funds, consistent with Propositions 1 and 2 of our theoretical model.

This result re�ects not only the lower trading activity of team-managed funds in general

as shown in Table 1, but, more importantly, the less excited reaction of these types of

funds to their past superior performance. Note that the negative relationship between team-

management and fund turnover conditional on performance can be associated not only with

overcon�dence but also with informed trading. If single-managed funds have better investing

skills, they may increase their trading activity following strong performance. We address this

issue later in the paper, showing that the single-managed funds' increased turnover does not

translate to better future performance.

Since our conclusions are immune to top fund performance cut-o� percentiles, in all

subsequent estimations we use the top quartile level. This choice implies a larger sample size

for our performance variable, increasing the power of the corresponding test statistics.

In Table 3 we repeat our estimations from Table 2 for the top performance quartile across

managerial teams of di�erent sizes. To conserve space, we report only the coe�cients and

their p-values for the team and performance variables. The last two lines of the table again

show the di�erence in the coe�cients between team-managed and single-managed funds and

its signi�cance. Columns 1-3 compare two-member team funds with single-managed funds,

columns 4-6 compare three-member team funds with single-managed funds, and columns

7-9 report a comparison of funds with four or more members against single-managed funds.

Across all regression speci�cations (without and with controls, and without and with fund

family �xed e�ects) and in spite of much smaller sample sizes, the excess turnover of team-

managed funds is signi�cantly lower than that of their single-managed counterparts irrespec-

19

tive of the team size. The only exception to this evidence is the statistically insigni�cant

di�erence in column 5, when three-manager funds are compared against single-managed ones

without accounting for fund family e�ects. Also, note that the coe�cients on the interac-

tive term, Teami,t−1 × Perfi,t−1, in the most comprehensive regressions for three and four

and more manager funds in columns 6 and 9, respectively, are about twice as large in mag-

nitude as the corresponding slope in column 3 for two-manager teams. This implies that

larger teams decrease the likelihood of excessive trading after good performance even more

than a managerial team composed of only two people, as suggested by Proposition 3 of our

theoretical model.

B. Changes in Managerial Structure and Fund Turnover

One limitation of the previous test results is that, due to the persistence in managerial

structure of funds and the three-year window metrics for our performance measures, they

could possibly be related to other fund di�erences not captured by the speci�cation of model

(1). A cleaner test would examine the performance-turnover relation immediately following

the changes in funds' managerial structure. The expectation is that the move to team-

management should decrease the likelihood of excessive trading after good performance,

while the move to single-management should increase it. To perform this test, we identify

all instances of funds that change their managerial structure during our sample period. There

are 233 funds that switch from single- to team-management and 162 funds that switch from

team- to single-management.

We repeat our model (8) estimations solely using fund-year observations for these two

very small sub-samples of the data and present the results in Table 4. In this table, we

replace the Team dummy with the change in managerial structure (∆MS) dummy. The �rst

three columns show the estimates in cases of funds becoming team-managed in year t-1. In

these cases, ∆MS is a dummy that takes the value of one if a fund is team-managed at time

t-1 but was single-managed at time t-2. We can see economically and, for the most part,

statistically signi�cant coe�cients on the interactive ∆MS term, similar to the results in

Table 2. The formal F-test on the di�erences in sensitivity of turnover to past performance

between team-managed and single-managed funds is signi�cant across all three regressions

20

with the weakest 10% level recorded in column 3. Note that these di�erences are again

economically comparable to those in Table 2 and their slightly weaker statistical signi�cance

re�ects solely the drastic sample size reduction in these estimations.

The last three columns of Table 4 show the estimates in cases of funds becoming single-

managed in year t− 1. For these estimations, ∆MS is a dummy that takes the value of one

if a fund is single-managed at time t − 1 but was team-managed at time t − 2. Now, the

coe�cient on the ∆MSi,t−1 × Perfi,t−1 term is large and positive in columns 5 and 6 � the

two most comprehensive regression speci�cations. The coe�cients and the di�erences are

insigni�cant statistically, again primarily because of the small sample size. However, in spite

of the loss of statistical power associated with much smaller samples relative to those in Table

2, the central message of our tests in Table 4 is clear � a change in the managerial structure of

funds has a large in�uence on how actively funds trade following good performance. In these

cases, the move towards team-management unambiguously reduces a fund's excess turnover.

To further re�ne our analysis, we proceed to compare the trading behavior of funds that

undergo changes in their managerial structure (treated sample) with otherwise similar funds

that see no alterations to their management (control sample). To create the control sample,

we apply the propensity score matching methodology using logistic regressions to identify

funds that share similar observable fund characteristics with the treatment group. Each fund

that switches its managerial structure is matched, with replacement, to one or more funds

with the closest propensity scores based on fund characteristics such as performance, size,

age, �ows, expenses, family size, and investment objective, as well as manager characteristics

such as industry tenure and gender composition in the same period. Then we calculate the

di�erence in excess turnover of funds that switch from single (team) to team (single) and

funds that are in the matched sample.

Table 5 shows how changes in managerial structure of funds in�uence the follow up excess

turnover after recording top 25% performance using the treated and matched fund samples.

The �rst three columns report the results for funds that become team-managed at time t-1.

The last three columns report the results for funds that become single-managed at time t-1.

The regression speci�cations are analogous to those in Table 4, and, as before, we do not

report the estimates of control variables and the intercept. Panel A shows the estimation

21

results for the one-to-one fund matching scheme. It leads to 466 observations for changes from

single- to team-management and 324 changes from team- to single-management, including

both the treated and the matched samples. We �nd a negative, very large in magnitude, and

statistically highly signi�cant coe�cient on the treated funds sample. In economic terms,

based on the regression with all �xed e�ects (column 3), the change to team structure leads

to a 21% drop in excess turnover in comparison to the control group of funds. On the other

side, the move towards single-management again increases excess turnover. The magnitude

of the treated group coe�cient is between 5% and 7%, depending on the speci�cation.

Panel B of Table 5 shows the estimation results for the one-to-�ve fund matching scheme.

In this case the samples are expectantly larger, as we have 792 observations for changes

from single- to team-management and 553 from team- to single-management, including the

matched samples. In this case, we again see that the slope coe�cients associated with the

treated funds are negative and highly signi�cant, albeit with relatively smaller magnitudes

than the corresponding estimates in Panel A. Also, similar to the output in Panel A, the

tests examining switches from team- to single-management produce consistently positive and

economically sizable coe�cients on the treated funds. In addition, due to the larger sample

sizes, now we also reach marginal signi�cance levels in columns 4 and 5.

Finally, we want to illustrate that the behavior of our samples of treated and control

groups in impacting the excess turnover documented in Table 5 is indeed limited to the top

performance funds. We can achieve this by considering placebo tests of the same scenarios as

in Table 5 but for middle performance quartiles. Table 6 shows the results of such tests. In

Panel A, both treated and control groups consist of funds in the second performance quartile

(between 25th and 50th percentiles). In Panel B, both fund groups consist of funds in the

third performance quartile (between 50th and 75th percentiles). All other speci�cations

and panel formats are the same as in Table 5. The results across both panels for changes

in the managerial structure from single- to team-management show that coe�cients on the

treated sample are not only statistically but also economically insigni�cant. Generally similar

patterns are observed in the cases of funds switching from team- to single-management, with

one marginal exception for funds in the third performance quartile: In column 6, based on

the full regression speci�cation with all �xed e�ects, the coe�cient on the treated funds is

22

relatively large (12%) and marginally signi�cant in spite of only 338 observations. Note that

this outcome is still consistent with the overall logic, since funds in the third performance

quartile (above the median) are considered as better performing institutions.

C. Cross-Sectional Tests

The literature documents that panel and cross-sectional regressions may produce point

estimates that di�er across estimators (see Sul, 2016). All of our previous tests are conducted

in panel regressions, which may have some issues arising from cross-sectionally correlated

standard errors even after accounting for clustering across funds and time (e.g., Pesaran,

2006). Therefore, it is important to illustrate that our �ndings hold irrespective of the

estimation methodology.

In Table 7, we show the results from standard Fama-Macbeth cross-sectional tests, where

the dependent variable is again excess turnover of fund i during each year t. All fund and

manager controls, as well as �xed e�ects, are the same as in previous panel tests. Estimation

in column 1 does not include control variables. Column 1 and 2 includes only objective �xed

e�ects. The full model speci�cation with the inclusion of all controls and both objective and

fund family �xed e�ects is presented in column 3. We again report only the main coe�cients

of interest. Overall, our results are similar to those in Table 2. Across all three speci�cations

the coe�cient on Teami,t−1 × Perfi,t−1 is negative and signi�cant at least at the 5% level,

implying that team-management reduces excess turnover resulting from previously reported

strong fund performance. Note that the inclusion of all controls and �xed e�ects increases

this coe�cient in magnitude. The last two rows of the table again show the di�erence in

excess turnover between team-managed and single-managed funds, as well as the p-value of

the corresponding F-test. These numbers are again very similar to the corresponding values

in Table 2.

V. Alternative Explanations and Speci�cations

In this sub-section we consider a range of alternative explanations that could potentially be

related to our evidence on the lower sensitivity of turnover to good past performance among

team-managed funds. The e�ects considered below could have a convoluted linkage with

23

team structure and, therefore, impact the estimation of the importance of team-management

for the performance-turnover relation.

A. Manager Experience

The �rst alternative is fund manager age and/or experience. Many studies �nd that over-

con�dence is especially pervasive among younger and less experienced people (e.g., Gervais

and Odean, 2001; Seru, Shumway and Sto�man, 2010; Christo�ersen and Sarkissian, 2011;

Menkho�, Schmeling, and Schmidt, 2013). However, other papers show that under certain

conditions, overcon�dence may also increase with age and experience (e.g., Heath and Tver-

sky, 1991; Deaves, Luders, and Schroder, 2010). Therefore, it is possible that our results

are driven largely by funds with certain management structures disproportionately tilting

towards either less or more experienced portfolio managers.

To address this concern, in columns 1 and 2 of Table 8, we split our sample by the fund

managers' experience level, considering separately funds whose managers have an average

of less than ten years of industry experience and those with average managerial experience

of ten years or more. In team-managed funds the industry experience is measured as the

average experience of all managers in a given fund. We then rerun our model (1) tests

similar to those in Table 2 with the full set of control variables and �xed e�ects. As we

can see, the coe�cient estimates on the interactive term, Teami,t−1 × Perfi,t−1, are negative

and signi�cant for funds managed by both less experienced and more experienced fund

managers. Moreover, the di�erence tests between team-managed and single-managed funds

at the bottom of the table for both sub-samples are again highly signi�cant. Among control

variables that increase turnover the most notable ones are the volatility and the �nancial

city dummy for less experienced managers, the latter being consistent with earlier results in

Christo�ersen and Sarkissian (2011).

B. Changes in Fund Flows

The second plausible alternative is that changes in trading intensity simply re�ect changes

in �ows to mutual funds. For example, Pollet and Wilson (2008) �nd that in response to

net fund in�ows, funds usually increase their investments in existing holdings. Then, top

24

performing team-managed funds may increase their turnover following good returns to a

lesser extent than single-managed funds due to weaker in�ows of investors' money rather

than due to lower overcon�dence. We address this concern in columns 3 and 4 of Table 8,

where we split our sample into funds with net in�ows and net out�ows. The coe�cient on

the interactive Team term is negative in both estimations, but it is much larger in magnitude

and statistically highly signi�cant only in the case of net out�ows. In addition, in the case

of in�ows, fund managers have a choice between immediately investing new money and

keeping it for a while in cash. Yet, in case of out�ows, funds are forced to reduce their

holdings immediately. Therefore, net investor �ows cannot explain our results.

C. Tournament Behavior

The third alternative explanation is based on mutual fund tournaments. For example,

Brown, Harlow, and Starks (1996) show that funds strategically shift risk levels by increasing

the volatility of their portfolios in the second half of the calendar year when they underper-

form in the �rst half to attract additional fund �ows. As a result, funds that engage in this

strategic risk-shifting end up trading signi�cantly more than funds that choose not to shift

risk (Huang, Sialm, and Zhang, 2011). Then, one may argue that a higher portfolio turnover

that we observe in single- managed funds compared to team-managed funds arises because

single-managed funds are more likely to engage in risk-shifting rather than trade as a result

of overcon�dence. To control for the impact of changes in risk levels on turnover, we add

the absolute change in volatility as a new explanatory variable in our regression speci�cation

in column 5 of Table 8. A positive coe�cient on absolute volatility implies that changes in

fund return volatility increase funds' portfolio turnover. This is indeed what we �nd � the

coe�cient on absolute change in volatility is positive. However, this has very little impact

on our coe�cient of interest, Teami,t−1 × Perfi,t−1, which is still negative and statistically

signi�cant in both economic and statistical terms. Therefore, the di�erences in portfolio

turnover after top past performance of single- and team-managed funds cannot be explained

by changes in risk levels due to tournament incentives.

25

D. Gender

Fourth, it is also possible that teams lead to less excessive trading after good performance

because larger manager groups have higher likelihood of including females, which are known

to be less overcon�dent than males (e.g. see Lewellen, Lease, and Schlarbaum, 1977; Bar-

ber and Odean, 2001). Therefore, in column 6 of Table 8, we re-estimate our full model

(1) speci�cation for male-only single- and team-managed funds. We still observe that the

interactive team coe�cient and the di�erence between team- and single-managed funds are

negative and statistically highly signi�cant. This implies that gender composition cannot

explain our results either. Thus, team-management itself appears to be the major driver for

the reduction of propensity of mutual fund managers to increase their trading after strong

performance.

E. Robustness Issues

Finally, in Table 9 we repeat our main panel tests on three alternative fund performance

metrics, namely: The objective-adjusted return, OAR, the Carhart (1997) four-factor alpha,

α(C4), and the Pastor and Stambaugh (2003) alpha, α(PS), that is computed from the

Cahrart (1997) model by adding to it the liquidity factor of Pastor and Stambaugh (2003).

In the last column of the table we also report the estimates using the Fama-French �ve-

factor alpha, α(FF5), but under alternative �xed e�ect settings � with fund �xed e�ects and

interactive objective and time �xed e�ects. The estimations for each of the other performance

metrics are conducted with and without family �xed e�ects. For brevity we report only the

di�erence in excess turnover between team-managed and single-managed funds for the top

25% performance quartile as in Table 2 as well as the p-value of the corresponding F-test.

We observe a consistent pattern across all estimation results, con�rming our earlier �nding:

Team-managed funds trade signi�cantly less than single-managed funds following strong

performance in the prior year. Even the point estimates of the di�erence are very similar

across almost all performance measures and model speci�cations with the widest di�erence

archived for the Fama-French �ve-factor alpha, when the regression includes interactive fund

investment objective and time �xed e�ects.

26

VI. Impact on Future Performance

As we mentioned earlier, funds may increase trading activity after posting strong returns

not only because of overcon�dence but also because of better investment knowledge. That

is, excess turnover would not be considered harmful if it leads to out-performance in the

subsequent period. In our setting, this implies that if single-managed funds increase their

turnover and report better returns than team-managed funds over the following period, then

at least a sizable part of their higher trading could be attributed to their managers' skills.

Note from Table 1 that single-managed funds have higher, on average, OARs than team-

managed funds, and there are more single-managed funds within larger fund families. It may

be that skillful single-managed funds with high turnover from the most established players

in the mutual fund industry are responsible for this average performance di�erence.

We examine the above issue directly in Table 10, which shows the relation between excess

turnover and future fund performance for single-managed and team-managed funds condi-

tional on past fund returns. We use all four performance metrics as our dependent variables.

Panel A conditions on the top quartile of past fund performance, while Panel B focuses on

the top decile. The main independent variable of interest is excess turnover. All fund and

manager controls are the same as in Table 2. Fixed e�ects include fund investment objective

times year �xed e�ects, as well as fund family �xed e�ects. The most important results we

observe in both panels is that the sign on excess turnover is negative for all risk-adjusted

returns irrespective of the managerial structure of funds. For OARs it is positive in both

panels but insigni�cant. Therefore, the �rst conclusion is that additional turnover following

strong prior performance is not predictive of another year of superior returns.

More importantly, however, is that the point estimates on the turnover term for single-

managed funds are lower than those for team managed funds for every risk-adjusted return

measure. While this di�erence is not statistically signi�cant (we do not report it explic-

itly), in economic terms the average spread in the trading impact on α(C4), α(PS), and

α(FF5) is 0.0320 in Panel A and 0.0229 in Panel B. This means that a 100% increase in

the current year trading of high-performing team-managed funds reduces their next period's

risk-adjusted return by 0.023-0.032 percent per month (28-39 basis points per year) less

27

than a similar trading increase of high-performing single-managed funds. In addition, the

only highly signi�cant (negative) impact of excess turnover on future return is recorded with

single-managed funds based on the α(PS) measure and top decile performance. Thus, we

conclude that the extra trading among single-managed funds relative to their team-managed

counterparts observed after top performance in the previous period is not bene�cial and

even damaging for their subsequent performance. This result provides additional empirical

evidence in support of our model that team structure of portfolio management in mutual

funds reduces overcon�dent and harmful trading.

VII. Conclusion

In this paper we establish a theoretical relation between the organizational structure of

mutual funds and the likelihood of their managers engaging in overcon�dent (excessive) trad-

ing following superior performance. Our model implies that team-managed funds exhibit less

overcon�dence than single-managed funds. Subsequent empirical tests support the model

predictions. Moreover, we show that the model predictions hold also when funds undergo

changes in their managerial structure. In particular, a shift from single- to team-management

signi�cantly decreases out-performance-induced trading, while a shift from team- to single-

management leads generally to the opposite result. Our �ndings are robust to various cut-o�s

of top fund performance and to the presence of both fund-speci�c and manager-level control

variables. They are also immune to fund performance metrics and other econometric alter-

ations, such as panel and cross-sectional regression methodologies. Moreover, we show that

our main �ndings cannot be accounted for by a range of potential alternative explanations.

28

References

Adams, R., and D. Ferreira, 2010, Moderation in groups: Evidence from betting on ice

break-ups in Alaska, Review of Economic Studies 77, 882-913.

Ahmed, A., and S. Duellman, 2013, Managerial overcon�dence and accounting conser-

vatism, Journal of Accounting Research 51, 1-30.

Alicke, M., 1985, Global self-evaluation as determined by the desirability and controlla-

bility of trait adjectives, Journal of Personality and Social Psychology 49, 1621-1630.

Arkin, R., A. Appelman, and J. Burger, 1980, Social anxiety, self-presentation, and the

self-serving bias in causal attribution, Journal of Personality and Social Psychology 38, 23-

35.

Bailey, W., A. Kumar, and D. Ng, 2011, Behavioral biases of mutual fund investors,

Journal of Financial Economics 102, 1-27.

Bar, M., A. Kempf, and S. Ruenzi, 2011, Is a team di�erent from the sum of its parts?

Evidence from mutual fund managers, Review of Finance 15, 359-396.

Barber, B., and T. Odean, 2000, Trading is hazardous to your wealth: The common stock

investment performance of individual investors, Journal of Finance 55, 773-806.

Barber, B., and T. Odean, 2001, Boys will be boys: Gender, overcon�dence, and common

stock investment, Quarterly Journal of Economics 116, 261-292.

Barry, C., and L. Starks, 1984, Investment management and risk sharing with multiple

managers, Journal of Finance 39, 477-491.

Ben-David, I., J. Graham, and C. Harvey, 2013, Managerial miscalibration, Quarterly

Journal of Economics 128, 1547-1584.

Benoit, J., and J. Dubra, 2011, Apparent overcon�dence, Econometrica 79, 1591-1625.

Billett, M., and Y. Qian, 2008, Are overcon�dent CEOs born or made? Evidence of

self-attribution bias from frequent acquirers, Management Science 54, 1037-1051.

Bliss, R., M. Porter, and C. Schwarz, 2008, Performance characteristics of individually-

managed versus team-managed mutual funds, Journal of Portfolio Management 34, 110-119.

Brown, K., W. Harlow, and L. Starks, 1996, Of tournaments and temptations: An analysis

of managerial incentives in the mutual fund industry, Journal of Finance 51, 85-110.

29

Camerer, C., and D. Lovallo, 1999, Overcon�dence and excess entry: An experimental

approach, American Economic Review 89, 306-318.

Carhart, M., 1997, On persistence in mutual fund performance, Journal of Finance 52,

57-82.

Charness, G., and M. Sutter, 2012, Groups make better self-interested decisions, Journal

of Economic Perspectives 26, 157-176.

Chen, J., H. Hong, M. Huang, and J. Kubik, 2004, Does fund size erode mutual fund

performance? The role of liquidity and organization, American Economic Review 94, 1276-

1302.

Christo�ersen, S., and S. Sarkissian, 2011, The demographics of fund turnover, Journal

of Financial Intermediation 20, 414-440.

Cici, G., 2012, The prevalence of the disposition e�ect in mutual funds' trades, Journal

of Financial and Quantitative Analysis 47, 795-820.

Cooper, D., and J. Kagal, 2005, Are two heads better than one? Team versus individual

play in signaling games, American Economic Review 95, 477-509.

Coval, J., and T. Moskowitz, 1999, Home bias at home: Local equity preference in do-

mestic portfolios, Journal of Finance 54, 2045-2073.

Coval, J., and E. Sta�ord, 2007, Asset �re sales (and purchases) in equity markets, Journal

of Financial Economics 86, 479-512.

Daniel, K., D. Hirshleifer, and A. Subrahmanyam, 1998, Investor psychology and security

market under and overreactions, Journal of Finance 53, 1839-1885.

Danz, D., K. Madarasz, and S. Wang, 2014, Information projection: An experimental

investigation, Working paper, London School of Economics.

Deaves, R., E. Luders, and M. Schroder, 2010, The dynamics of overcon�dence: evidence

from stock market forecasters, Journal of Economic Behavior and Organization 75, 402-412.

Doukas, J., and D. Petmezas, 2007, Acquisitions, Overcon�dent Managers and Self-

attribution Bias, European Financial Management 13, 531-577.

Ehrlinger, J., T. Gilovich, and L. Ross, 2005, Peering into the bias blind spot: People's

assessments of bias in themselves and others, Personality and Social Psychology Bulletin 31,

680-692.

30

Fama, E., and K. French, 2015, A �ve-factor asset pricing model, Journal of Financial

Economics 116, 1-22.

Fedyk, A., 2015, Overcoming overcon�dence: Teamwork and self-control, Working paper,

Harvard University.

Frazzini, A., 2006, The disposition e�ect and underraction to news, Journal of Finance

61, 2017-2046.

Galasso, A., and T. Simcoe, 2011, CEO overcon�dence and innovation, Management

Science 57, 1469-1484.

Gervais, S., J. Heaton, and T. Odean, 2011, Overcon�dence, compensation contracts, and

capital budgeting, Journal of Finance 66, 1735-1777.

Gervais, S., and T. Odean, 2001, Learning to be overcon�dent, Review of Financial Studies

14, 1-27.

Glaser, M., and M. Weber, 2007, Overcon�dence and trading volume, Geneva Risk and

Insurance Review 32, 1-36.

Grinblatt, M., and M. Keloharju, 2009, Sensation seeking, overcon�dence, and trading

activity, Journal of Finance 64, 549-578.

Hamilton, B., J. Nickerson, and H. Owan, 2003, Team incentives and worker heterogene-

ity: An empirical analysis of the impact of teams on productivity and participation, Journal

of Political Economy 111, 465-497.

Heath, C., and A. Tversky, 1991, Preference and belief: ambiguity and competence in

choice under uncertainty, Journal of Risk and Uncertainty 4, 5-28.

Huang, J., C. Sialm, and H. Zhang, 2011, Risk Shifting and Mutual Fund Performance,

Review of Financial Studies 24, 2575-2616.

Jin, L., and A. Scherbina, 2011, Inheriting loser, Review of Financial Studies 24, 786-820.

Janis, I., 1982, Groupthink: Psychological studies of policy decisions and �ascoes, Second

Edition, New York: Houghton Mi�in.

Johnson, D., and J. Fowler, 2011, The evolution of overcon�dence, Nature 477(7364),

317-320.

Kandel, E., and E. Lazear, 1992, Peer pressure and partnerships, Journal of Political

Economy 100, 801-817.

31

Langer, E., and J. Roth, 1975, Heads I win tails it's chance: The illusion of control as

a function of the sequence of outcomes in a purely chance task, Journal of Personality and

Social Psychology 32, 951-955.

Lewellen, W., R. Lease, and G. Schlarbaum, 1977, Patterns of investment strategy and

behavior among individual investors, Journal of Business 50, 296-333.

Malmendier, U., and G. Tate, 2005, CEO overcon�dence and corporate investment, Jour-

nal of Finance 60, 2661-2700.

Malmendier, U., and G. Tate, 2008, Who makes acquisitions? CEO overcon�dence and

the market's reaction, Journal of Financial Economics 89, 20-43.

Malmendier, U., G. Tate, and J. Yan, 2011, Overcon�dence and early-life experiences:

The e�ect of managerial traits on corporate �nancial policies, Journal of Finance 66, 1687-

1733.

Malmendier, U., and T. Taylor, 2015, On the verges of overcon�dence, Journal of Eco-

nomic Perspectives 29, 3-8.

Massa, M., J. Reuter, and E. Zitzewitz, 2010, When should �rms share credit with employ-

ees? Evidence from anonymously managed mutual funds, Journal of Financial Economics

95, 400-424.

Menkho�, L., M. Schmeling, and U. Schmidt, 2013, Overcon�dence, experience and pro-

fessionalism: an experimental study, Journal of Economic Behavior and Organization 86,

92-101.

Miller, D., and M. Ross, 1975, Self-serving Biases in the attribution of causality: Fact or

Fiction, Psychological Bulletin 82, 213-225.

Moscovici, S., and Zavalloni, M., 1969, The group as a polarizer of attitudes. Journal of

personality and social psychology 12, 125-131.

O'Connell, P., and M. Teo, 2009, Institutional investors, past performance, and dynamic

loss aversion, Journal of Financial and Quantitative Analysis 44, 155-188.

Pastor, L., and R. Stambaugh, 2003, Liquidity risk and expected stock returns, Journal

of Political Economy 111, 642-685.

Patel, S., and S. Sarkissian, 2016, To group or not to group? Evidence from mutual fund

databases, Journal of Financial and Quantitative Analysis forthcoming.

32

Pesaran, M., 2006, Estimation and inference in large heterogeneous panels with a multi

factor error structure, Econometrica 74, 967-1012.

Pollet, J., and M. Wilson, 2008, How does size a�ect mutual fund behavior?, Journal of

Finance 63, 2941-2969.

Pool, V., N. Sto�man, and S. Yonker, 2012, No place like home: Familiarity in mutual

fund manager portfolio choice, Review of Financial Studies 25, 2563-2599.

Prather, L., and K. Middleton, 2002, Are n+1 heads better than one? The case of mutual

fund managers, Journal of Economic Behavior and Organization 47, 103-120.

Pronin, E., D. Y. Lin, and L. Ross, 2002, The bias blind spot: Perceptions of bias in self

versus others, Personality and Social Psychology Bulletin 28, 369-381.

Puetz, A., and S. Ruenzi, 2011, Overcon�dence among professional investors: Evidence

from mutual fund managers, Journal of Business Finance & Accounting 38, 684-712.

Sah, R., and J. Stiglitz, 1991, The quality of managers in centralized versus decentralized

organizations, Quarterly Journal of Economics 106, 289-295.

Sharpe, W., 1981, Decentralized investment management, Journal of Finance 36, 217-234.

Scheinkman, J., and W. Xiong, 2003, Overcon�dence and speculative bubbles, Journal of

Political Economy 111, 1183-1220.

Schrand, C., and S. Zechman, 2012, Executive overcon�dence and the slippery slope to

�nancial misreporting, Journal of Accounting and Economics 53, 311-329.

Seru, A., T. Shumway, and N. Sto�man, 2010, Learning by Trading, Review of Financial

Studies 23, 705-739.

Statman, M., S. Thorley, and K. Vorkink, 2006, Investor overcon�dence and trading

volume, Review of Financial Studies 19, 1531-1565.

Stoner, J., 1968, Risky and cautious shifts in group decisions, Journal of Experimental

Social Psychology 4, 442-459.

Sul, D., 2016, Spurious cross-sectional and mindless panel regressions, Working paper,

University of Texas, Dallas.

Sunstein, C., 2002, The Law of group polarization, Journal of Political Philosophy 10,

175-195.

Sunstein, C., and R. Hastie, 2014, Making dumb groups smarter, Harvard Business Review

33

12, 90-98.

Svenson, O., 1981, Are we all less risky and more skillful than our fellow drivers?, Acta

Psychologica 47, 143-148.

Tetlock, P., and G. Mitchell, 2009, Implicit Bias and Accountability Systems: What must

organizations do to prevent discrimination?, Research in Organization Behavior 29, 3-38.

Wallach, M., and H. Kogan., 1965, The Roles of information, discussion, and consensus

in group risk taking, Journal of Experimental Social Psychology 1, 1-19.

Weinstein, N., 1980, Unrealistic optimism about future life events, Journal of Personality

and Social Psychology, 39, 806-820.

West, R., R. Meserve, and K. Stanovich, 2012, Cognitive sophistication does not attenuate

the bias blind spot, Journal of Personality and Social Psychology 103, 506.

Winkler, J. and S. Taylor, 1979, Preference, expectations, and attributional bias: Two

�eld studies, Journal of Applied Social Psychology 9, 183-197.

34

A Appendix

A. Proof of Lemma 1

Following a good signal z = 1, manager M1 with beliefs given by De�nition 1 forms the

following posteriors belief regarding his skill:

P1{skilled|z = 1} =P1{skilled, z = 1}

P1{skilled, z = 1}+ P1{unskilled, z = 1}=

ppH + ∆

ppH + ∆ + (1− p)pL −∆(A.1)

Since the discretionary trading strategy yields a payo� of r = R with probability q if and

only if the manager is skilled, and pays zero otherwise, the expected payo� conditional on

observing z = 1 is:

E1{Rd|z = 1} = P1{skilled|z = 1}qR =ppH

ppH + (1− p)pLqR (A.2)

And the perceived variance of the payo� is:

V ar1{Rd|z = 1} = E1{R2d|z = 1}−E1{Rd|z = 1}2 = P1{skilled|z = 1}qR2−P1{skilled|z = 1}2q2R2

=ppH

ppH + (1− p)pLq

(1− ppH

ppH + (1− p)pLq

)R

2(A.3)

Similarly, M1's posterior belief regarding his skill after a bad signal z = 0 is:

P1{skilled|z = 0} =P1{skilled, z = 0}

P1{skilled, z = 0}+ P1{unskilled, z = 0}=

p(1− pH)

p(1− pH) + (1− p)(1− pL)(A.4)

Correspondingly, M1's perception of the expected value and variance of the discretionary

trading strategy Rd following a bad signal z = 0 are given by:

E1{Rd|z = 0} = P1{skilled|z = 0}qR =p(1− pH)

p(1− pH) + (1− p)(1− pL)qR (A.5)

V ar1{Rd|z = 0} =p(1− pH)

p(1− pH) + (1− p)(1− pL)q

(1− p(1− pH)

p(1− pH) + (1− p)(1− pL)q

)R

2

(A.6)

35

�

B. Proof of Proposition 1

Manager M1 solves the portfolio optimization problem (4) subject to beliefs given by

Lemma 1.

The �rst order condition of the optimization problem is:

w∗1|z =E1{Rd|z} −Rb

AV ar1{Rd|z}=

P1{Rd = 1|z}qR−Rb

AP1{Rd = 1|z}q(1− P1{Rd = 1|z}q)R2 (A.7)

We �rst establish the following result:

Lemma 2 Whenever the optimal weight on discretionary trading, w∗1|z, is positive, it is

increasing in P1{Rd = 1|z}.

Proof. Assume that the optimal weight is positive. Then P1{Rd = 1|z}qR > Rb.

Now note that:

δw∗1|zδP1{Rd = 1|z}

∝ Aq2R3P1{Rd = 1|z}(1− P1{Rd = 1|z}q) (A.8)

−Aq2R3P1{Rd = 1|z}(1− 2P1{Rd = 1|z}q) + AqR2Rb(1− 2P1{Rd = 1|z}q)

= Aq2R2P1{Rd = 1|z}q − AqR2

RbP1{Rd = 1|z}q + AqR2Rb(1− P1{Rd = 1|z}q)

≥ AqR2(P1{Rd = 1|z}qR−Rb),

which is greater than zero if P1{Rd = 1|z}qR > Rb. Hence, whenever the optimal weight on

the discretionary trading is positive, it is increasing in P1{Rd = 1|z}.

Now, let us compare {w∗1|z = 1} against {w∗1|z = 0}. Condition (5) establishes that

P1{Rd = 1|z = 1}qR > Rb, and therefore that {w∗1|z = 1} is strictly positive. If {w∗1|z = 0}

is zero, then we have {w∗1|z = 1} > {w∗1|z = 0} and we are done with the �rst part of

Proposition 1. If {w∗1|z = 0} is positive, then we can establish that {w∗1|z = 1} > {w∗1|z = 0}

using Lemma 2 in conjunction with the following result:

Lemma 3 P1{Rd = 1|z = 1} > P1{Rd = 1|z = 0}.

36

Proof. Recall from the proof of Proposition 1 that:

P1{Rd = 1|z = 1} =ppH + ∆

ppH + (1− p)pLq; P1{Rd = 1|z = 0} =

p(1− pH)

p(1− pH) + (1− p)(1− pL)(A.9)

So we have:

P1{Rd = 1|z = 1} ≤ ppHppH + (1− p)pL

q; P1{Rd = 1|z = 0} =p

p+ (1− p) pLpH

q (A.10)

Since pH > pL, we have p + (1− p) qLqH

< p, and hence the expression in (A.10) is greater

than pq.

Meanwhile, since 1− pL > 1− pH , we have:

P1{Rd = 1|z = 0} =p(1− pH)

p(1− pH) + (1− p)(1− pL)q =

p

p+ (1− p) 1−pL1−pH

q < pq (A.11)

Therefore, P1{Rd = 1|z = 1} is greater than P1{Rd = 1|z = 0}.

This completes the proof of the �rst part of Proposition 1 � that the overcon�dent manager

M1 engages in more discretionary trading following a good signal z = 1.

Now, in order to establish the second part of the proposition � that the discretionary

trading after a good signal is increasing in the manager's overcon�dence � we consider the

following comparative static:

δP1{Rd = 1|z = 1}δ∆

=1

ppH + (1− p)pL> 0 (A.12)

Thus, the manager's beliefs regarding the likelihood of a positive payo� following a good

signal, P1{Rd = 1|z = 1}, increase in the level of overcon�dence ∆. Recall that by condition

(5), the chosen weight on the discretionary strategy following a good signal is strictly positive.

Hence, we can again invoke Lemma 2, yielding that the manager's chosen weight on the

discretionary trading strategy likewise increases in the level of his overcon�dence ∆.

�

37

C. Proof of Proposition 2

First, recall from Lemma 1 that manager M1's beliefs regarding his own skill, conditional

on the realization of the signal z, are:

P1{Rd = 1|z = 1} =ppH + ∆

ppH + (1− p)pL; P1{Rd = 1|z = 0} =

p(1− pH)

p(1− pH) + (1− p)(1− pL)(A.13)

Using the same logic as in the proof of Lemma 1, we can also arrive at the beliefs that

other managers Mj 6=1 hold regarding M1's skill, conditional on the realization of the signal

z:

Pj 6=11{Rd = 1|z = 1} =ppH

ppH + (1− p)pL;Pj 6=1{Rd = 1|z = 0} =

p(1− pH)

p(1− pH) + (1− p)(1− pL)(A.14)

Note that: (1) all managers' beliefs are the same following a bad signal; and (2) P1{Rd =

1|z = 1} > Pj 6=1{Rd = 1|z = 1}.

Point (1) implies that all managers choose the same allocation into the proposed new

strategy following a bad signal. Hence, the weighted average of the allocation in the team-

managed fund is exactly the same as M1's choice in the single-managed fund. Thus, the

team-managed fund behaves identically to the single-managed fund following a bad signal.

Point (2) combined with Lemma 2 implies that the other managers Mj 6=1 choose lower

allocations to the new proposed strategy than the managerM1. Hence, the weighted average

in the team-managed fund is likewise lower than in the single-managed fund.

Since the two types of funds trade identically following a bad signal and the team-managed

fund trades less following a good signal, performance-induced trading (di�erence between

trading following good versus bad signals) is lower in team-managed funds.

�

38

0.7

0.8

0.9

1.0

Q1(L) Q2 Q3 Q4(H)

Fund

Tur

nove

r

Past Fund Performance Quartiles

Figure 1

Relation between past fund OARs and subsequent turnover. This �gure shows the relation betweenquartiles of past objective-adjusted gross fund returns (OARs) and subsequent turnover. The sample includesall domestic U.S. equity mutual funds (excluding index and sector funds) for the 1991-2015 period. Turnoveris the minimum of aggregated sales or aggregated purchases of securities during the year divided by theaverage 12-month total net assets of the fund. Fund performance is based on the annual objective-adjustedgross fund returns computed each year.

39

0.7

0.8

0.9

1.0

1.1

Q1(L) Q2 Q3 Q4(H)

Fund

Tur

nove

r

Fund Past Performance Quartiles

Single Team

(a) Single vs Team Funds

0.7

0.8

0.9

1.0

1.1

Q1(L) Q2 Q3 Q4(H)

Fund

Tur

nove

r

Fund Past Performance Quartiles

Single 2FM Team

(b) Single vs Two Manager Funds

0.7

0.8

0.9

1.0

1.1

Q1(L) Q2 Q3 Q4(H)

Fund

Tur

nove

r

Fund Past Performance Quartiles

Single 3FM Team

(c) Single vs Three Manager Funds

0.7

0.8

0.9

1.0

1.1

Q1(L) Q2 Q3 Q4(H)

Fund

Tur

nove

r

Fund Past Performance Quartiles

Single 4FM+ Team

(d) Single vs Four+ Manager Funds

Figure 2

Relation between past fund OARs and subsequent turnover for teams of di�erent sizes. This�gure shows the relation between quartiles of past objective-adjusted gross fund returns (OARs) and sub-sequent fund turnover for di�erent managerial team sizes. The sample includes all domestic U.S. equitymutual funds (excluding index and sector funds) for the 1991-2015 period. Turnover is the minimum ofaggregated sales or aggregated purchases of securities during the year divided by the average 12-month totalnet assets of the fund. The team-managed funds are divided into two manager funds (2FM), three managerfunds (3FM), and four or more manager funds (4FM+).

40

0.0

0.1

0.2

0.3

0.4

Q1(L) Q2 Q3 Q4(H)

Fund

Exc

ess

Turn

over

Fund Past Alpha Quartiles

Single Team

(a) Single vs Team Funds

0.0

0.1

0.2

0.3

0.4

Q1(L) Q2 Q3 Q4(H)

Fund

Exc

ess

Turn

over

Fund Alpha Quartiles

Single 2FM Team

(b) Single vs Two Manager Funds

0.0

0.1

0.2

0.3

0.4

Q1(L) Q2 Q3 Q4(H)

Fund

Exc

ess

Turn

over

Fund Past Alpha Quartiles

Single 3FM Team

(c) Single vs Three Manager Funds

0.0

0.1

0.2

0.3

0.4

Q1(L) Q2 Q3 Q4(H)

Fund

Exc

ess

Turn

over

Fund Past Alpha Quartiles

Single 4FM+ Team

(d) Single vs Four+ Manager Funds

Figure 3

Relation between past fund alpha and subsequent turnover. This �gure shows the relation betweenquartiles of past fund alphas based on the Fama-French �ve-factor model and subsequent fund excess turnoverfor di�erent managerial team sizes. The sample includes all domestic U.S. equity mutual funds (excludingindex and sector funds) for the 1991-2015 period. Excess Turnover (percent per year) for each fund is de�nedas the di�erence between the fund turnover in a given year and the median turnover for all funds with thesame fund investment objective in that year. The team-managed funds are divided into two manager funds(2FM), three manager funds (3FM), and four or more manager funds (4FM+).

41

Table

1

SummaryStatistics

PanelA:Distribution

ofsingle-managed

andteam

-managed

funds

Single

Team

Twomanagers

ThreeManagers

Four+

Managers

Year

Total

Number

Percent

Number

Percent

Number

Percent

Number

Percent

Number

Percent

1991

564

400

70.9

164

29.1

102

18.1

386.7

244.3

1992

630

424

67.3

206

32.7

115

18.3

579.0

345.4

1993

748

492

65.8

256

34.2

144

19.3

709.4

425.6

1994

875

542

61.9

333

38.1

194

22.2

849.6

556.3

1995

999

616

61.7

383

38.3

228

22.8

959.5

606.0

1996

1,121

666

59.4

455

40.6

262

23.4

119

10.6

746.6

1997

1,278

716

56.0

562

44.0

336

26.3

120

9.4

106

8.3

1998

1,470

805

54.8

665

45.2

385

26.2

162

11.0

118

8.0

1999

1,673

870

52.0

803

48.0

454

27.1

198

11.8

151

9.0

2000

1,780

875

49.2

905

50.8

494

27.8

231

13.0

180

10.1

2001

1,929

894

46.3

1035

53.7

554

28.7

244

12.6

237

12.3

2002

1,990

880

44.2

1110

55.8

584

29.3

274

13.8

252

12.7

2003

1,970

849

43.1

1121

56.9

588

29.8

282

14.3

251

12.7

2004

1,966

821

41.8

1145

58.2

587

29.9

286

14.5

272

13.8

2005

1,952

749

38.4

1203

61.6

591

30.3

278

14.2

334

17.1

2006

2,064

709

34.4

1355

65.6

604

29.3

290

14.1

461

22.3

2007

2,075

683

32.9

1392

67.1

613

29.5

299

14.4

480

23.1

2008

2,051

652

31.8

1399

68.2

613

29.9

298

14.5

488

23.8

2009

1,859

592

31.8

1267

68.2

542

29.2

286

15.4

439

23.6

2010

1,744

516

29.6

1228

70.4

547

31.4

303

17.4

378

21.7

2011

1,656

477

28.8

1179

71.2

518

31.3

311

18.8

350

21.1

2012

1,554

455

29.3

1099

70.7

471

30.3

284

18.3

344

22.1

2013

1,465

401

27.4

1064

72.6

432

29.5

280

19.1

352

24.0

2014

1,429

403

28.2

1026

71.8

416

29.1

281

19.7

329

23.0

2015

1,386

397

28.6

989

71.4

416

30.0

251

18.1

322

23.2

Total

38,228

15,884

22,344

10,790

5,421

6,133

42

Panel B: Characteristics of single-managed and team-managed funds (Full Sample 1991 - 2015)

Single-Managed Funds Team-Managed FundsObs Mean SD Obs Mean SD Di� (Team-Single)

Turnover 14,748 0.8615 0.8155 21,464 0.7846 0.6507 -0.0769***Excess Turnover 14,465 0.2284 0.8219 21,235 0.1557 0.6454 -0.0728***Fund Size (millions) 15,774 1,143 4,284 22,276 1,295 5,405 151.5***Fund Age (years) 15,883 13.22 14.09 22,343 13.51 13.40 0.29**Family Size (billions) 15,665 39.14 109.06 22,243 20.45 55.95 -18.69***Fund Fees 15,383 0.0130 0.0051 21,878 0.0123 0.0043 -0.0007***Fund Flows 15,638 0.3689 1.5364 22,160 0.3021 1.4554 -0.0668***OAR (%/m) 15,884 0.0109 0.1309 22,344 0.0083 0.1126 -0.0026**α(FF5) (%/m) 13,595 0.0759 0.7229 19,757 0.0613 0.4282 -0.0145**Return Volatility 15,830 0.0476 0.0554 22,301 0.0471 0.0545 -0.0005Mgr Industry Tenure 15,252 16.4833 9.5375 21,490 16.6394 7.0002 -0.1561*Female (%) 15,306 0.0921 0.2800 22,151 0.0913 0.1842 -0.0008

This table shows the annual distribution of team-managed and team-managed funds (Panel A) and thesummary statistics of their fund and managerial characteristics (Panel B). The sample includes all domesticU.S. equity mutual funds (excluding index and sector funds) for the 1991-2015 period. Turnover is theminimum of aggregated sales or aggregated purchases of securities during the year divided by the average12-month total net assets of the fund. Excess Turnover (percent per year) of each fund is de�ned as thedi�erence between the fund turnover in a given year and the median turnover for all funds with the same fundinvestment objective in that year. Fund Size (millions of dollars) is the total net assets under managementof a fund in a given year. Fund Age (years) is the di�erence between the fund's inception year and thecurrent year. Family Size (billions of dollars) is measured by the total net assets under management of thefund complex to which the fund belongs at the end of the calendar year. Fund Fees (percent per year) isthe annual total expense ratio of the fund. Return Volatility (percent per year) is the standard deviationof monthly gross fund returns over the past 12 months. Flows is de�ned as the net growth in the total netassets of a fund, as a percentage of the fund's total net assets, adjusted for prior the year return. ExcessTurnover, Fund Fees, turnover, and fund �ows are winsorized at 1% and 99% levels. OAR is the annualobjective-adjusted gross fund returns computed each year. α(FF5) is the alpha based on Fama and French(2015) �ve-factor model which adds pro�tability (RMW) and investment (CMA) factors to the Fama andFrench (1993) three-factor model. Manager Industry Tenure (years) is the number of years the fund managerhas been within the fund industry. Female is de�ned as a proportion of female managers in a fund. Di�(Team-Single) is the di�erence between team-managed and single-managed funds. ***, **, and * denotesigni�cance at the 1%, 5%, and 10% levels, respectively.

43

Table

2

TopPerformanceandTurnoverRelation

Top

25th

PercentilePerform

ance

Top

20th

PercentilePerform

ance

Top

10th

PercentilePerform

ance

DV:Turnover

ex i,t

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Team

i,t−

1×

Perf i,t−1

-0.0923***

-0.0897***

-0.0863***

-0.0795**

-0.0730*

-0.0700**

-0.0858**

-0.1014*

-0.1159**

(0.005)

(0.008)

(0.006)

(0.026)

(0.053)

(0.041)

(0.018)

(0.076)

(0.034)

Perf i,t−1

0.1381***

0.0938***

0.0905***

0.1412***

0.0882**

0.0824***

0.1826***

0.1561***

0.1461***

(0.001)

(0.009)

(0.005)

(0.000)

(0.013)

(0.007)

(0.000)

(0.007)

(0.005)

Team

i,t−

1-0.0389*

-0.0227

-0.0399*

-0.0465**

-0.0307

-0.0473**

-0.0497**

-0.0349*

-0.0489**

(0.095)

(0.307)

(0.055)

(0.047)

(0.170)

(0.023)

(0.023)

(0.096)

(0.014)

Flows i,t−1

-0.0172*

-0.0143*

-0.0175**

-0.0145*

-0.0190**

-0.0155**

(0.055)

(0.064)

(0.049)

(0.060)

(0.035)

(0.045)

FF5 i

,t−1

-0.0155

-0.0112

-0.0150

-0.0101

-0.0203

-0.0133

(0.566)

(0.653)

(0.581)

(0.689)

(0.460)

(0.590)

Size i

,t−1

-0.0587***

-0.0546***

-0.0587***

-0.0546***

-0.0588***

-0.0545***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Volatilityi,t−

10.7566

0.6190

0.7566

0.6202

0.7448

0.6125

(0.291)

(0.309)

(0.289)

(0.307)

(0.294)

(0.311)

Expenses i,t−1

0.2082***

0.2109***

0.2080***

0.2110***

0.2053***

0.2089***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Age

i,t−

10.0009

0.0177

0.0007

0.0174

0.0011

0.0176

(0.950)

(0.210)

(0.958)

(0.217)

(0.935)

(0.212)

Fam

ilySize i

,t−1

0.0342***

0.0221**

0.0343***

0.0220*

0.0345***

0.0215*

(0.000)

(0.049)

(0.000)

(0.050)

(0.000)

(0.056)

FC

i0.0827***

0.0330

0.0827***

0.0329

0.0831***

0.0333

(0.000)

(0.357)

(0.000)

(0.359)

(0.000)

(0.353)

Tenure

i,t−

1-0.0106***

-0.0079***

-0.0106***

-0.0078***

-0.0106***

-0.0079***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Fem

ale i

,t−1

-0.0017

-0.0284

-0.0008

-0.0277

-0.0002

-0.0275

(0.970)

(0.514)

(0.986)

(0.526)

(0.997)

(0.529)

ObjectiveFE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Fam

ilyFE

No

No

Yes

No

No

Yes

No

No

Yes

Adj.R

20.026

0.083

0.184

0.026

0.083

0.183

0.029

0.084

0.184

Obs.

33,182

26,064

26,063

33,182

26,064

26,063

33,182

26,064

26,063

Di�

(Team-Single)

-0.1312***

-0.1124***

-0.1261***

-0.1260***

-0.1037***

-0.1173***

-0.1354***

-0.1364**

-0.1648***

p-value

(0.000)

(0.001)

(0.000)

(0.000)

(0.006)

(0.004)

(0.000)

(0.016)

(0.003)

44

Table 2 �(continued from previous page)

This table shows the e�ect of fund performance on subsequent excess fund turnover. The sample includes alldomestic U.S. equity mutual funds (excluding index and sector funds) for the 1991-2015 period. Three topperformance ranges, Perf, are considered: top 25% (�rst three columns), top 20% (second three columns),and top 10% (last three columns). Team is de�ned as a dummy variable that equals one if the fund hastwo or more fund managers and zero if the fund has only one fund manager at the end of the precedingcalendar year. FC is the indicator variable for a �nancial center, which is equal to one if a fund is locatedin the following six cities: Boston, Chicago, Los Angeles, Philadelphia, New York, and San Francisco. Allother characteristics are de�ned in Table 1. Intercept is included in each regression but its estimates are notreported. Fixed e�ects include fund investment objective and fund family �xed e�ects. Standard errors areclustered by fund and year. The last two rows show the di�erence in excess turnover between team-managedand single-managed funds, Di� (Team-Single), as well as the p-value of the corresponding F-test. ***, **,and * denote signi�cance at the 1%, 5%, and 10% levels, respectively.

45

Table

3

TopPerformanceandTurnoverRelationAcross

ManagerialTeamsofDi�erentSizes

Singlevs2FM

Team

Singlevs3FM

Team

Singlevs4+

FM

Team

DV:Turnover

ex i,t

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Team

i,t−

1×

Perf i,t−1

-0.0633*

-0.0712**

-0.0674**

-0.0914*

-0.0684

-0.1242***

-0.1373***

-0.1304***

-0.1201***

(0.080)

(0.047)

(0.043)

(0.060)

(0.176)

(0.002)

(0.000)

(0.005)

(0.005)

Perf i,t−1

0.1384***

0.0915**

0.0854***

0.1398***

0.0878**

0.0852***

0.1374***

0.0889**

0.0874***

(0.001)

(0.010)

(0.006)

(0.001)

(0.013)

(0.009)

(0.001)

(0.013)

(0.007)

Team

i,t−

1-0.0433

-0.0421*

-0.0515**

-0.0156

-0.0002

-0.0090

-0.0509*

-0.0086

-0.0114

(0.110)

(0.094)

(0.025)

(0.597)

(0.995)

(0.750)

(0.050)

(0.738)

(0.669)

Controls

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

ObjectiveFE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Fam

ilyFE

No

No

Yes

No

No

Yes

No

No

Yes

Adj.R

20.026

0.090

0.202

0.025

0.083

0.207

0.030

0.088

0.191

Obs.

22,251

17,357

17,356

18,176

14,105

14,104

19,657

14,948

14,947

Di�

(Team-Single)

-0.1066***

-0.1133***

-0.1261***

-0.1071**

-0.0686

-0.1332***

-0.1882***

-0.1390***

-0.1315***

p-value

(0.001)

(0.001)

(0.000)

(0.017)

(0.146)

(0.002)

(0.000)

(0.005)

(0.008)

Thistableshow

sthee�ectoffundperform

ance

onsubsequentexcess

turnover

across

fundmanager

teamsofdi�erentsizes.

Thesampleincludes

all

domesticU.S.equitymutualfunds(excludingindex

andsectorfunds)

forthe1991-2015period.Perfisanindicatorforthefund'sperform

ance

inyeart−

1fallingin

thetopquartile.

Team

isde�ned

asadummyvariablethatequalsoneifthefundhastwoormore

fundmanagersandzero

ifthe

fundhasonly

onefundmanager

attheendofthecalendaryear.

Controlsare

thesamefundandmanager

characteristics

asin

Table2.Interceptis

included

ineach

regressionbutitsestimatesare

notreported.Fixed

e�ectsincludefundinvestm

entobjectiveandfundfamily�xed

e�ects.Standard

errors

are

clustered

byfundandyear.

Thelast

tworowsshow

thedi�erence

inexcess

turnover

betweenteam-m

anaged

andsingle-m

anaged

funds,

Di�

(Team-Single),aswellasthep-valueofthecorrespondingF-test.***,**,and*denote

signi�cance

atthe1%,5%,and10%

levels,respectively.

46

Table 4

Changes in Managerial Structure and Turnover

Single to Team Team to SingleDV: ∆Turnoverexi,t (1) (2) (3) (4) (5) (6)

∆MSi,t−1 × Perfi,t−1 -0.1495*** -0.0929** -0.0598 -0.0257 0.0562 0.0521(0.002) (0.048) (0.140) (0.796) (0.598) (0.513)

Perfi,t−1 0.1325*** 0.1095*** 0.0676** 0.0241 0.0535 0.0148(0.007) (0.005) (0.045) (0.779) (0.650) (0.861)

∆MSi,t−1 0.0208 -0.0074 -0.0234 0.0281 0.0113 0.0003(0.521) (0.830) (0.467) (0.709) (0.877) (0.996)

Controls No Yes Yes No Yes Yes

Objective FE Yes Yes Yes Yes Yes YesFamily FE No No Yes No No YesAdj. R2 0.037 0.138 0.333 0.013 0.062 0.456Obs. 8,097 6,709 6,709 2,362 1,809 1,809

Di� (Team-Single) -0.1286*** -0.1001** -0.0832* 0.0024 0.0676 0.0524p-value (0.002) (0.032) (0.068) (0.982) (0.581) (0.576)

This table shows the e�ect of changes in managerial structure of funds on subsequent excess turnover. Thesample includes all domestic U.S. equity mutual funds (excluding index and sector funds) for the 1991-2015period. Perf is an indicator variable for the fund's performance in year t− 1 falling within the top quartile.∆MSi,t−1 is the change in managerial structure of fund i at time t− 1. For columns 1-3 ∆MS is a dummythat takes the value of one if a fund is team-managed at time t-1 but was single-managed at time t-2.For columns 4-6 ∆MS is a dummy that takes the value of one if a fund is single-managed at time t − 1but was team-managed at time t − 2. A fund is team-managed if it has two or more fund managers atthe end of the preceding calendar year. The �rst three columns report the results for funds that becometeam-managed at some point in the sample period. The last three columns report the results for funds thatbecome single-managed at some point in the sample period. There are 233 funds that switch from single-to team-management and 162 funds that switched from team- to single-management. Controls are the samefund and manager characteristics as in Table 2. Intercept is included in each regression but its estimatesare not reported. Fixed e�ects include fund investment objective and fund family �xed e�ects. Standarderrors are clustered by fund and year. The last two rows show the di�erence in excess turnover betweenteam-managed and single-managed funds, Di� (Team-Single), as well as the p-value of the correspondingF-test. ***, **, and * denote signi�cance at the 1%, 5%, and 10% levels, respectively.

47

Table 5

Matching Tests on Changes in Managerial Structure and Turnover

Panel A: Nearest neighbor (one-to-one matches)

Single to Team Team to Single

DV: ∆Turnoverexi,t (1) (2) (3) (4) (5) (6)

Treated Funds -0.1492** -0.1534*** -0.2105*** 0.0535 0.0558 0.0657

(0.014) (0.006) (0.000) (0.166) (0.133) (0.462)

Controls No Yes Yes No Yes Yes

Objective FE Yes Yes Yes Yes Yes Yes

Family FE No No Yes No No Yes

Adj. R2 0.024 0.099 0.325 0.028 0.037 0.143

Obs. 466 464 464 324 324 324

Panel B: Nearest �ve neighbors (one-to-�ve matches)

Single to Team Team to Single

DV: ∆Turnoverexi,t (1) (2) (3) (4) (5) (6)

Treated Funds -0.0948** -0.0875** -0.1156*** 0.0698* 0.0594* 0.0648

(0.019) (0.023) (0.001) (0.056) (0.088) (0.155)

Controls No Yes Yes No Yes Yes

Objective FE Yes Yes Yes Yes Yes Yes

Family FE No No Yes No No Yes

Adj. R2 0.011 0.072 0.172 0.012 0.035 0.206

Obs. 792 790 790 553 553 553

This table shows the e�ect of changes in managerial structure of funds on subsequent excess turnover usingthe treated and matched fund samples. The sample includes all domestic U.S. equity mutual funds (excludingindex and sector funds) for the 1991-2015 period. The �rst three columns report the results for funds thatbecome team-managed at time t-1. The last three columns report the results for funds that become single-managed at time t-1. There are 233 funds that switch from single- to team-management and 162 funds thatswitch from team- to single-management. To create the control sample, we use propensity score matchingapproach using logistic regressions to identify funds that share similar observable fund characteristics withthe treatment group. Each fund that switches its managerial structure is matched, with replacement, to thefund with the closest propensity score based on fund characteristics such as performance, size, age, �ows,expenses, family size, investment objective, as well as manager characteristics such as industry tenure andgender composition over the same period. We then calculate the di�erence in excess turnover of funds thatswitched from single (team) to team (single) management in the next period and funds that are in thematched sample. Panel A shows the estimation results for one-to-one fund matching, while Panel B displaysthe results for one-to-�ve fund matching. Team is de�ned as a dummy variable that equals one if the fundhas two or more fund managers and zero if the fund has only one fund manager at the end of the calendaryear. Controls are the same fund and manager characteristics as in Table 2. Intercept is included in eachregression but its estimates are not reported. Fixed e�ects include fund investment objective and fund family�xed e�ects. Standard errors are clustered by fund and year. ***, **, and * denote signi�cance at the 1%,5%, and 10% levels, respectively.

48

Table 6

Placebo Tests on Changes in Managerial Structure

Panel A: Between 25 to 50 Percentile (Quartile 2)

Single to Team Team to Single

DV: ∆Turnoverexi,t (1) (2) (3) (4) (5) (6)

Treated Funds -0.0032 -0.0086 -0.0359 0.0081 0.0101 0.0233

(0.944) (0.852) (0.602) (0.893) (0.860) (0.771)

Controls No Yes Yes No Yes Yes

Objective FE Yes Yes Yes Yes Yes Yes

Family FE No No Yes No No Yes

Adj. R2 0.001 -0.006 0.017 0.024 0.038 0.364

Obs. 618 617 617 335 334 334

Panel B: Between 50 to 75 Percentile (Quartile 3)

Single to Team Team to Single

DV: ∆Turnoverexi,t (1) (2) (3) (4) (5) (6)

Treated Funds 0.0108 0.0127 0.0244 0.0028 0.0180 0.1160*

(0.776) (0.742) (0.550) (0.962) (0.753) (0.072)

Controls No Yes Yes No Yes Yes

Objective FE Yes Yes Yes Yes Yes Yes

Family FE No No Yes No No Yes

Adj. R2 0.001 0.007 0.145 0.021 0.014 0.361

Obs. 576 573 573 340 338 338

This table shows the e�ect of changes in managerial structure of funds on subsequent excess turnover usingthe placebo tests on treated and matched fund samples. The sample includes all domestic U.S. equity mutualfunds (excluding index and sector funds) for the 1991-2015 period. Panel A shows the results for the secondpast performance quartile; Panel B - for the third quartile. The performance metric is based on the Fama-French �ve-factor alpha. The �rst three columns report the results for funds that become team-managedat time t-1. The last three columns report the results for funds that become single-managed at time t-1.Team is de�ned as a dummy variable that equals one if the fund has two or more fund managers and zero ifthe fund has only one fund manager at the end of the preceding calendar year. Controls are the same fundand manager characteristics as in Table 2. Intercept is included in each regression but its estimates are notreported. Fixed e�ects include fund investment objective and fund family �xed e�ects. Standard errors areclustered by fund and year. ***, **, and * denote signi�cance at the 1%, 5%, and 10% levels, respectively.

49

Table 7

Fama-Macbeth Regressions: Cross-Sectional Tests

DV: Turnoverexi,t (1) (2) (3)

Teami,t−1 × Perfi,t−1 -0.0492** -0.0764*** -0.0664**

(0.017) (0.002) (0.038)

Perfi,t−1 0.0908** 0.1312*** 0.1083***

(0.039) (0.000) (0.000)

Teami,t−1 -0.0533*** -0.0477*** -0.0556***

(0.000) (0.001) (0.000)

Controls Yes Yes Yes

Objective FE Yes Yes Yes

Family FE No No Yes

Adj. R2 0.035 0.138 0.625

Obs. 35,700 28,195 28,195

Di� (Team - Single) -0.1025*** -0.1225*** -0.1220***

P-value (F-test) (0.001) (0.000) (0.003)

This table shows the e�ect of fund performance on subsequent excess turnover using the Fama-MacBethcross-sectional regression method. The sample includes all domestic U.S. equity mutual funds (excludingindex and sector funds) for the 1991-2015 period. Perf is a dummy variable for past performance in the topquartile. Team is de�ned as a dummy variable that equals one if the fund has two or more fund managersand zero if the fund has only one fund manager at the end of the preceding calendar year. Controls arethe same fund and manager characteristics as in Table 2. Intercept is included in each regression but itsestimates are not reported. Fixed e�ects include fund investment objective and fund family �xed e�ects.The last two rows show the di�erence in excess turnover between team-managed and single-managed funds,Di� (Team-Single), as well as the p-value of the corresponding F-test. ***, **, and * denote signi�cance atthe 1%, 5%, and 10% levels, respectively.

50

Table 8

Alternative Explanations

Industry Experience Net Fund FlowsDV: Turnoverexi,t <10 years ≥10 years In�ows Out�ows Tournaments Males Only

Teami,t−1 × Top Perfi,t−1 -0.1061* -0.0693** -0.0536 -0.1349*** -0.0878*** -0.0854***(0.051) (0.049) (0.151) (0.000) (0.005) (0.009)

Top Perfi,t−1 0.1225*** 0.0660** 0.0751** 0.1445*** 0.0936*** 0.0925***(0.008) (0.048) (0.042) (0.000) (0.004) (0.005)

Teami,t−1 -0.1009*** -0.0200 -0.0384 -0.0502* -0.0402* -0.0331(0.001) (0.382) (0.124) (0.057) (0.053) (0.130)

Flowsi,t−1 -0.0058 -0.0169** 0.0016 -0.2594*** -0.0148* -0.0150*(0.674) (0.048) (0.778) (0.000) (0.059) (0.067)

FF5i,t−1 -0.0651 -0.0135 0.0215 -0.0577 -0.0103 -0.0093(0.163) (0.603) (0.266) (0.105) (0.662) (0.698)

Sizei,t−1 -0.0688*** -0.0522*** -0.0524*** -0.0467*** -0.0547*** -0.0604***(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Volatilityi,t−1 4.2573*** 0.3443 0.3424 2.5360*** 0.4969(0.000) (0.389) (0.359) (0.000) (0.349)

Expensesi,t−1 0.1901*** 0.2139*** 0.1729*** 0.2204*** 0.2132*** 0.2087***(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Agei,t−1 0.0168 0.0186 0.0061 0.0094 0.0170 0.0073(0.423) (0.223) (0.695) (0.583) (0.234) (0.664)

Family Sizei,t−1 0.0299* 0.0187 0.0267** 0.0147 0.0220** 0.0194(0.094) (0.108) (0.046) (0.262) (0.050) (0.140)

FCi 0.1166*** 0.0031 0.0403 0.0254 0.0321 0.0465(0.009) (0.940) (0.272) (0.559) (0.371) (0.231)

Tenurei,t−1 0.0015 -0.0063*** -0.0094*** -0.0070*** -0.0079*** -0.0067***(0.810) (0.000) (0.000) (0.000) (0.000) (0.000)

Femalei,t−1 -0.0646 -0.0271 0.0149 -0.0613 -0.0278(0.307) (0.575) (0.789) (0.157) (0.529)

AbsVoli,t 0.0800(0.667)

Objective FE Yes Yes Yes Yes Yes YesFamily FE Yes Yes Yes Yes Yes YesAdj. R2 0.261 0.188 0.189 0.197 0.182 0.195Obs. 5,176 20,887 11,421 14,642 26,063 20,785

Di� (Team-Single) -0.2069*** -0.0893** -0.0919** -0.1851*** -0.1280*** -0.1185***p-value (0.001) (0.012) (0.024) (0.004) (0.000) (0.000)

This table shows the e�ect of fund performance on subsequent excess fund turnover using alternative expla-nation settings. The �rst two columns split the sample based on manager industry experience. The third andfourth columns split the sample based on net fund in�ows and net fund out�ows. The �fth column estimationreplaces fund return volatility in year t with the absolute value of the changes in fund return volatility fromyear t-1 to t, AbsVoli,t. The last column uses a subsample of funds managed by male managers, both single-managed and team-managed. The other control variables are the same fund and manager characteristics asin Table 2. Fixed e�ects include fund investment objective and fund family �xed e�ects. Standard errors areclustered by fund and year. The last two rows show the di�erence in excess turnover between team-managedand single-managed funds, Di� (Team-Single), as well as the p-value of the corresponding F-test. ***, **,and * denote signi�cance at the 1%, 5%, and 10% levels, respectively.

51

Table

9

Robustness:AlternativePerformanceMeasuresandSpeci�cations

OAR

α(C4)

α(PS)

α(FF5)

DV:Turnover

ex i,t

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

F-test:Di�

(Team-Single)

-0.0657**

-0.0872***

-0.0658**

-0.0696**

-0.0770**

-0.0814**

-0.0679***

-0.1347***

P-value

(0.043)

(0.005)

(0.050)

(0.037)

(0.025)

(0.016)

(0.000)

(0.000)

Controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

ObjectiveFE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

Fam

ilyFE

No

Yes

No

Yes

No

Yes

No

Yes

Cluster

(Fund,Time)

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

FundFE

No

No

No

No

No

No

Yes

No

Objective×

TimeFE

No

No

No

No

No

No

No

Yes

Obs.

29,512

29,508

26,064

26,063

26,064

26,063

26,064

26,064

Thistableshow

sthee�ectoffundperform

ance

onsubsequentexcess

fundturnover

usingalternative

speci�cations.

Itonly

reportsthedi�erence

inexcessturnover

betweenteam-m

anaged

andsingle-m

anaged

funds,Di�

(Team-Single)aswellasthep-valueofthecorrespondingF-test.Thesample

includes

alldomesticU.S.equitymutualfunds(excludingindex

andsectorfunds)

forthe1991-2015period.Perfisanindicatorvariable

forpast

perform

ance

inthetopquartile.

OARistheobjective-adjusted

return;α

(C4)isthealphabasedontheCarhart

(1997)four-factormodel;α

(PS

5)

isthesimilarlycomputedrisk-adjusted

return

from

the�ve-factormodel,whichaddstheliquidityfactorofPastorandStambaugh(2003)to

the

Carhart

(1997)model.α

(FF

5)istheFama-French

�ve-factoralpha,asde�ned

inTable1.Team

isde�ned

asadummyvariablethatequalsoneif

thefundhastwoormore

fundmanagersandzero

ifthefundhasonly

onefundmanager

attheendoftheprecedingcalendaryear.

Controlsare

thesamefundandmanager

characteristics

asin

Table2.Interceptisincluded

ineach

regressionbutitsestimatesare

notreported.Fixed

e�ects

includefundinvestmentobjectiveandfundfamily�xed

e�ects.Speci�cation(7)replacesfundfamily�xed

e�ects

withindividualfund�xed

e�ects.

Speci�cation(8)replace

investmentobjective�xed

e�ects

withinvestmentobjectivetimes

year�xed

e�ects.Standard

errors

are

clustered

byfund

andyear.***,**,and*denote

signi�cance

atthe1%,5%,and10%

levels,respectively.

52

Table 10

Overcon�dence Induced Trading and Future Fund Performance

Panel A: Top Quartile Past Performance

Single-Managed Funds Team-Managed FundsDV: Fund Returns OARt α(C4)t α(PS5)t α(FF5)t OARt α(C4)t α(PS5)t α(FF5)t

Turnoverexi,t−1 0.0083 -0.0287 -0.0827 -0.0519 0.0025 -0.0227 -0.0449 0.0003(0.394) (0.572) (0.207) (0.171) (0.578) (0.567) (0.371) (0.992)

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Objective × Time FE Yes Yes Yes Yes Yes Yes Yes YesFamily FE Yes Yes Yes Yes Yes Yes Yes YesAdj. R2 0.733 0.130 0.062 0.075 0.834 0.170 0.166 0.106Obs. 3,135 2,997 2,994 3,021 4,620 4,612 4,611 4,384

Panel B: Top Decile Past Performance

Single-Managed Funds Team-Managed FundsDV: Fund Returns OARt α(C4)t α(PS5)t α(FF5)t OARt α(C4)t α(PS5)t α(FF5)t

Turnoverexi,t−1 0.0154 -0.1016 -0.1169*** -0.0889 -0.0033 -0.0661 -0.0828 -0.0897(0.280) (0.194) (0.001) (0.105) (0.593) (0.303) (0.314) (0.216)

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Objective × Time FE Yes Yes Yes Yes Yes Yes Yes YesFamily FE Yes Yes Yes Yes Yes Yes Yes YesAdj. R2 0.691 0.112 0.135 0.050 0.838 0.168 0.186 0.114Obs. 1,154 1,156 1,212 1,189 1,719 1,757 1,728 1,637

This table shows the relation between excess turnover and future fund performance, conditional on top pastperformance. The sample includes all domestic U.S. equity mutual funds (excluding index and sector funds)for the 1991-2015 period. OAR are objective-adjusted returns, α(C4) is the Carhart alpha, α(PS5) is thePastor-Stambaugh alpha, and α(FF5) is the Fama-French �ve-factor alpha, as de�ned in Table 1. ExcessTurnover (percent per year) of each fund is de�ned as the di�erence between the fund's turnover in a givenyear and the median turnover for all funds with the same fund investment objective in that year. Controlsare the same fund and manager characteristics as in Table 2. Intercept is included in each regression butits estimates are not reported. Fixed e�ects include fund investment objective times year �xed e�ects, aswell as and fund family �xed e�ects. Standard errors are clustered by fund and year. ***, **, and * denotesigni�cance at the 1%, 5%, and 10% levels, respectively.

53