A Tale of Two Types: Generalists vs. Specialists in Asset ... · Rafael Zambrana and Fernando Zapateroy January 31, 2016 Abstract We observe that some managers run funds with a single

A Tale of Two Types: Generalists vs.

Specialists in Asset Management

Rafael Zambrana∗ and Fernando Zapatero†

January 31, 2016

Abstract

We observe that some managers run funds with a single investment style –specialists–

while others run several funds with different investment styles –generalists. We

study if funds families use specific criteria in assigning managers to specialist or

generalist positions. We find that managers who display stock-picking ability are

more likely to be specialists and managers with market-timing ability are more

likely to be generalists. In addition, we find that such assignments are optimal

since stock-pickers earn higher returns than other managers as specialists, and so do

market-timers as generalists. Similarly, families that assign managers accordingly

earn higher returns. Finally, consistent with this optimal human capital allocation,

specialists with timing ability are more likely to switch to generalists.

Keywords : Mutual Fund, Asset Management, Human Capital, Portfolio Manager, Spe-

cialist, Generalist.

JEL classification: G20, G23, J24, M51.

∗Nova School of Business and Economics, Lisboa, Portugal. E-mail: [email protected]†FBE, Marshall School of Business, USC. E-mail: [email protected]

1. Introduction

The majority of the vast literature on mutual funds has focused on a few topics,

especially related to the performance of funds, and the incentives of the fund managers

and their alignment with the incentives of the investors. In this paper we consider a

different dimension of mutual funds that has been mostly overlooked by the literature:

the allocation of human capital within fund families. In particular, we show that there

is an optimal assignment of responsibilities among fund managers depending on their

abilities. In addition, we observe that many funds families assign their talent accordingly,

and that has a positive effect on their returns. Also, we bring into the analysis of mutual

funds management the notion of different types of workload profiles. In particular, we

sort out managers of funds run by a single manager into specialits and generalists, a

distinction used before for CEOs, but new to the literature on mutual funds managers.

Jensen (1968) already questioned the performance of mutual funds and opened the

door to two big lines of debate. On one hand, whether mutual funds are actually able

to provide a risk-adjusted return higher than that of an index –i.e., return achieved by

holding a portfolio more or less representative of the market– and how to measure that

performance. The debate on whether mutual funds perform above or below a simple

index is still open. Some influential papers argue that mutual funds underperform the

market on average –for example, Gruber (1996). However, another school of thought

supports the existence of outstanding ability in the mutual funds industry –for example,

Berk and Green (2004).

A parallel, different –but not fully unrelated– line of research has focused on the incen-

tives of fund managers and whether they are aligned with the objectives of the investors

–the agency problem resulting from the delegation of the portfolio allocation decision

from the investor to the fund manager. For example, Brown, Harlow and Starks (1996)

document that the relative performance evaluation of mutual fund managers amounts to

a tournament-like type of competition as a result of which managers who lag their peers

might undertake levels of risk that are not in the best interest of the investors. Key in the

literature that analyzes the incentives of fund managers is the work of Sirri and Tufano

1

(1998) and Chevalier and Ellison (1999a). These papers show that mutual fund managers

effectively face convex incentives –as an option– because relative underperformance does

not have substantial effects on the assets under management, but outperformance leads

to an inflow of funds –piece-wise linear function with a flat segment up to a threshold,

and an increasing part above the threshold. A growing amount of literature studies the

practical consequences of these types of incentives –for example, Basak, Pavlova and

Shapiro (2007).

This paper is not oblivious to the previous literature, but focuses on a different aspect

of the mutual funds industry. Only recently a growing number of papers has started to

analyze organizational issues in mutual funds –for example, Kuhnen (2009) studies the

effects of networking among managers and governance of the mutual funds family on the

agency problems. A topic that is central to the analysis of organizations in general, the

allocation of human capital, seems to have been mostly overlooked by the mutual funds

literature so far. This paper tries to help in filling that void. In particular, we observe

different types of assignments within the category of fund managers: some managers

concentrate on portfolios within a single investment style, while other managers run

portfolios with different investment styles, regardless of the investment styles categories

used for the analysis.1 Throughout the paper we will call specialists the fund managers

who run either a single fund –which according to the categories used by the SEC implies

a single investment style– or several funds but all with the same investment style, and

generalists the fund managers in charge of several funds with more than one investment

style across them. Our terminology is based on a strand of the literature in financial

economics that considers the distinction between specialists and generalists, but among

CEOs. Murphy and Zabojnık (2004, 2007) argue that the large increase in CEO pay

compared to the 1970s is at least in part the result of a change in the type of skills expected

from CEOs. In particular, general managerial skills –“transferable across companies or

even industries”– have become relatively more important than firm-specific knowledge.

1There are different possible classifications of investment objectives. To avoid possible selection problemswe will base our analysis on the classification established by the SEC. All mutual funds must declare theirobjective according to this classification in the NSAR form they are legally required to file semi-anually.

2

More recently, Custodio, Ferreira and Matos (2013), introduce the particular terminology

of generalists and specialists and test directly whether the composition of managerial skills

has an effect on CEO pay.

The observation that there are different types of functions among fund managers

raises the question of whether the allocation of particular individuals to a certain set

of responsibilities is random –difficult to believe in an industry that moves trillions of

dollars– or if there are specific reasons that explain it. We conjecture and test that the

different portfolio management abilities identified in the literature make the candidates

for assignment a better fit for the specific functions. In particular, in this paper we show

that it is optimal to appoint managers with market-timing ability as generalists, and

managers with stock-picking ability as specialists.

As we argued before, one of the most controversial subjects in financial economics is

the ability (or lack thereof) of asset managers to achieve returns higher than the market,

on a risk-adjusted basis. Some recent literature, however, argues that there is such a thing

as portfolio management ability, and it helps explains flows of funds –the influential work

of Berk and Green (2004). The fundamental types of management ability studied in

the literature are stock-picking and market timing –see Bollen and Busse (2005) for a

detailed review of that literature. In a recent paper, Kacperczyk, Nieuwerburgh and

Veldkamp (2014) show that skilled portfolio managers display stock-picking ability in

economic expansions and market-timing ability in recessions.

This paper embraces the school of thought that believes fund managers have outstand-

ing ability. We also focus on the two types of abilities just mentioned. Using a version

of one the standard methodologies, we identify portfolio managers who have at least one

of the two types of ability –many seem to have neither and very few both– and study

whether it is optimal for the management company to deploy them as specialists or as

generalists, depending on their type of ability. In addition to verifying if our conjecture is

correct, we explore whether fund companies assign managers according to this criterion

and, if that is the case, what is the effect on the performance of the funds.

We use monthly data on funds run by a single manager since 1996. Over the years,

3

there has been a trend towards multi-manager oversight of individual funds. However, we

still have in our sample a large number of funds run by a single manager. Also, individual

managers often oversee multiple funds. Specialists are substantially more frequent than

generalists, yet there is a sizable amount of generalists in our sample. We find that, on

average, generalists manage funds that are smaller and younger, and work for smaller

companies. Also, specialists are more likely to work in families that supply advising to

other companies.

In the first stage of our analysis, we need to assess whether managers have stock

picking or market timing ability. With that goal, we use the Treynor-Mazuy (1966)

market-timing model, augmented with multi-risk factors.2 We find more managers with

market timing ability among the set of generalists and more managers with stock picking

ability among specialists.3 Of course, market-timing ability and stock-pricing ability are

both valuable and we find that managers with either of these two skills outperform man-

agers that lack them, regardless of whether they are specialists or generalists. However,

the difference in performance is significantly higher -both in economic and statistical

terms– when market timers are generalists and when stock pickers are specialists. We

also find that companies are more likely to re-assign a market-timer working as specialist

to a generalist position than a non-market-timer. In addition, management companies

that appoint market-timers to generalist assignments, outperform other companies.

We explore whether there are individual characteristics associated with each type of

ability. We find that generalist-timers are more likely to have a PhD or quantitative

background, while specialist-pickers are more likely to be MBAs with business related

studies. Previous literature has already analyzed the effect of academic credentials on

performance. For example, Gottesman and Morey (2006) study the effect of GMAT and

school ranking on performance. Our work also contributes to the nascent literature on

2Overall, our analysis is consistent with a recent and fast growing literature that uses novel measures ofability, and argues that some fund managers have higher ability than the rest (Daniel, Grinblatt, Titman,and Wermers (1997), Bollen and Busse (2005), Cohen, Coval, and Pastor (2005), Kacperczyk, Sialmand Zheng (2005), Kacperczyk and Seru (2007), Cremers and Petajisto (2009), Baker, Litov, Wachter,and Wurgler (2010), Berk and van Binsbergen (2012), Koijen (2012), Kacperczyk, Nieuwerburgh andVeldkamp (2014), Ferson and Mo (2015)).

3Merton (1980) suggests another approach to identify and measure picking and timing abilities. Weverify that our results are robust to this alternative methodology.

4

the organization and personnel decisions in mutual funds, especially on human capital

assignment strategies. Evans (2009, 2010) argues that companies use measures of risk-

adjusted performance to promote or demote their managers. Massa, Reuter and Zitzewitz

(2010) study the trade-offs between publicizing the names of their fund managers and

keeping them anonymous. Fang, Kemp and Trapp (2014) document that the most skilled

managers are assigned to market segments that are less efficient, where ability has a higher

expected payoff.

Our interpretation of the results –and the reason that justifies our analysis– is that

pickers, who arguably perform what is informally known as fundamental analysis, narrow

down their focus into segments in which they have expertise whereas timers, who possess

a more general view of the market, use, at least partially, what is known as quantitative

analysis, and benefit from access to broader information that allows a better allocation

among different security classes. This would be consistent with the notion that specialists

are better served with a MBA training –and the accounting, valuation and other tools

that come with it– along with the previous –to their MBA– experience on a particular

industry –either as financial analysts, or as employed in an company in the industry. On

the other hand, a PhD and/or quantitative education is necessary for the quantitative

analysis we conjecture is better suited for the role of generalist.

Our paper reinforces the literature that argues that some –but not all– portfolio

managers have outstanding ability, and can beat the market. Furthermore, our findings

are consistent with the idea that there is a certain degree of efficiency in the mutual funds

industry. In particular, we find that: (i) mutual funds that decide managerial assignments

depending on the type of skill of the managers –when the managers display special ability–

achieve higher performance; (ii) therefore, there is an optimal strategy that consists in

appointing managers with timing ability to generalist positions and pickers to specialists

positions; (iii) many management companies follow this optimal strategy, and specialists

with timing ability are more likely to become generalists.

The paper is organized as follows. First we describe the data. Then we introduce

the notion of timers and pickers, as well as the functions of generalist and specialist,

5

and identify fund managers in our database accordingly. In this section we study the

main characteristics of each type of manager (pickers and timers). The following section

includes our main results: we show that pickers display better performance as specialists

and timers as generalists; also, families that assign managers accordingly perform better.

Section 5 provides a number of robustness tests addressing possible endogeneity problems.

We close the paper with some conclusions.

2. Data Description

We use three sources. First, the CRSP Survivorship-bias free Mutual Funds Database.

It provides names of the money managers, funds returns, total net assets, funds inceptions,

turnover, expenses, and other fund and family characteristics. Since it is not clear how

the skill of the team members translates into the skill of a team and our focus is on the

ability and specific role of each individual manager, we restrict our sample to funds run

by a single manager.4 We filter manager names manually, since in some cases they appear

under their middle name, a shortened first name, or simply by their family name. We

manually correct manager names with different spellings and code them with a unique

identifier.

Next, we merge this information with Morningstar Direct. This database provides

comprehensive information about both professional and academic backgrounds of the

portfolio managers. To merge them we use text matching and we check manually those

unmatched. We also examine managers’ websites and web-search for managers’ resumes

when necessary.

We exclude index funds, funds with less than $5 million in assets under management,

and funds in which the observation date is prior to the inception date.5 The CRSP

database has information about multiple share classes issued by a particular fund. These

classes have the same underlying portfolio and the main difference among them is the

fee structure. Thus, for mutual funds with different share classes, we aggregate all the

4Some new research focuses on individually managed funds (i.e Fang, Kempf and Trapp (2014), Kempf,Manconi and Spalt (2014)).

5Some papers discuss the possible existence of an incubation bias (Evans, 2010).

6

observations from different classes, grouping them at the fund level.6

Third, we use the NSAR forms required by the SEC to be submitted by all U.S.

mutual funds and other regulated investment management companies. Mutual funds file

this form every six months. NSAR filings provide a substantial amount of information

about the Management Company, advisory arrangements, fund investment objectives,

and fund compensation characteristics.7 Although certain funds file reports starting in

1993, the data appears to be more reliable for all funds after mandatory disclosure begins

in 1996. We merge NSAR filings with CRSP by text matching and check manually. To

mitigate any possible selection bias, our time series starts in 1996. Our final sample

contains monthly-fund observations, from 3,005 U.S. open-ended domestic equity, 2,832

fixed income, 349 balanced and 897 international mutual funds. This corresponds to a

total of 521 management companies and 4,625 portfolio managers from 1996 to 2011.

3. Functions and Types of Portfolio Managers

Our primary objective is to study if mutual funds allocate portfolio managers to

different functions depending on their skills. In particular, we focus on two different

functions, generalists and specialists, and two different abilities, stock-picking and market-

timing. Generalists are managers that in a given period manage funds with more than

one investment objective –which we will proxy by the style reported by the fund; we

discuss this later. Specialists either manage just one fund or manage several funds with

the same investment style. With respect to the abilities, we call managers who have

extraordinary ability at picking stocks “pickers” and managers with extraordinary ability

at timing the market “timers.” Of course, some managers are neither. Pickers and timers

are the types, as opposed to the functions. We will show evidence that pickers perform

6We group data by observation at the fund level, following the literature (i.e., Nanda, Narayan andWarther (2000) or Gaspar, Massa and Matos (2006)). We aggregate returns, turnover and expenses,weighting each class by their total net assets (TNA) where the fund TNA is the sum of TNA over allclasses. For the qualitative attributes of the funds such as age, name or style, we choose that of theoldest among all classes.

7A key variable we need for our analysis is the investment objective of the fund. CRSP provides differentclassifications of fund style. However, we believe that NSAR filings are more reliable since they providethe actual investment objective described in the prospectus. In the appendix, we describe in detail theobjectives included in NSAR filings.

7

better as specialists and timers perform better as generalists, and mutual funds improve

their performance when they allocate managers accordingly.

3.1. Specialists and Generalists

Table 1 describes our sample. Portfolios are classified attending to 9 different in-

vestment objectives as defined in the NSAR filings and reported in the fund prospectus

(capital appreciation, growth, income, total return, government short-term debt, govern-

ment long-term debt, corporate debt, balance and international stocks).8 While equity

funds seem to be more concentrated on capital appreciation and growth objectives, the

most frequent fixed-income funds invest in government long-term debt, followed by funds

that invest in government short-term. For each investment objective, the number of funds

in our sample seems to follow a similar pattern of growth, increasing until 2003-2004 and

decreasing afterwards. Since we focus on funds managed by an single portfolio manager,

the recent decrease in the number of funds in our sample is the result of the new trend

among mutual funds to be managed by a team rather than an individual.9

[Insert Table 1 here]

We provide more information about our sample in Table 2. In particular, we report

the number of funds run by a generalist, the number of families that have funds run

by generalists, and the number of generalists, compared to the total (generalists plus

specialists) in each category. The total number of funds grew to over 2000 funds by 2004,

and subsequently dropped to under 1500 by 2010. We observe a similar pattern on the

number of funds managed by generalist managers; it reaches 561 in 2002, is above 400

until 2004, and decreases to below 300 by 2010. The total number of managers in our

sample starts at 1176 and ends at 657, with a maximum of 1390 in 2000; meanwhile, the

number of generalists starts at 133 and finishes at 60, with a maximum of 175. Finally,

the number of management companies starts at 261 in 1996 and ends at 197 in 2011.

Out of them, 89 in 1996 and 44 in 2011, were offering funds run by generalists.

8A full description of these investment objectives is in the Appendix.9Bliss, Potter and Schwarz (2008) and Bar, Kempf and Ruenzi (2011) study the growth in team-managedfunds.

8


In Table 3, we present characteristics and differences between generalists and spe-

cialists, as well as between the funds they run and the fund families to which they are

affiliated. In Panel A, we show the average characteristics of funds managed by special-

ists and generalists, as well as the magnitude and significance of their differences. On

average, generalists run funds that are smaller, younger and cheaper, with higher flows

and turnover, and with similar cumulative past returns. Panel B shows that smaller

management companies (less assets, fewer funds and fewer managers) are more likely

to employ generalist managers. Specialist managers are more likely to be working for

companies that offer their management services to other firms (sub-advisors). This is

consistent with the literature on outsourcing portfolio management decisions that find

that sub-advising contracts allow fund families to gain market share by partnering with

specialized external management firms (Cashman and Deli, 2009; Moreno, Rodriguez and

Zambrana, 2015). Panel C summarizes the mean and differences between specialist and

generalist characteristics. Specialists are more likely to hold a MBA degree and have held

more jobs in the past, while managers with PhD studies are more likely to be generalists.

On average, generalists manage a larger number of funds and have a higher volume of

assets under management. They manage their funds longer, have been affiliated with the

management company longer and have shown a better past performance track record.10


3.2. Pickers and Timers

Next, we consider two possible skills of portfolio managers: stock-picking and market

timing. When portfolio managers have either of these skills, we want to analyze if it

affects their assignment as generalists or specialists. First we need to identify whether a

10Past Manager Skill is the cumulative return calculated as the TNA-weighted average cumulative,objective-adjusted returns before fees of all the funds run by the manager during the past 24 months.We see that specialists do not do a very good job, in general. However, as we will show later, theirperformance is substantially better when they have some type of skill, especially if they are pickers.Table A1 in the appendix section provides further tests.

9

fund manager has picking or timing ability. For that purpose, we run the Treynor-Mazuy

(1966) market-timing model (hereafter referred to as TM), augmented with multi-risk

factors, and sorted by asset class. Prior research has also considered a multi-factor

version of the Treynor-Mazuy (1966) and the Henriksson and Merton (1981) approach

(i.e. Bollen and Busse, (2001, 2005)).11 In particular, for the equity funds we use the

following model:

rit = αi + βrm,irmt + γrm2,irm2t + βsmb,ismbt + βhlm,ihlmt + βmom,imomt + εit (1)

where rit is equity fund i’s before-expense return in month t in excess of the 30-day risk-

free interest rate; rmt is the market portfolio return in excess of the risk-free rate; smbt,

hlmt and momt are the size, book-to-market and momentum factors commonly used in

the literature.12 For the fixed income funds we use:

rit = αi + βrm,iABt + γrm2,iAB2t +Bj,iBFt + εi,t for j = 5 (2)

where rit is bonds fund i’s before-expense return in month t in excess of the 30-day risk-

free interest rate; ABt is the U.S. Aggregate Bond Index return in excess of the risk free

rate and its squared value; BF stands for “bond factors”. We follow the methodology

in Blake, Elton, and Gruber (1993) and add six bond index returns, all in excess of the

1-month treasury rate. These bond indices include three for government bond (Barclays

U.S. Treasury Long, Barclays U.S. Treasury Intermediate, and Barclay U.S. Treasury Bill

36m), two for corporate bonds (Barclays U.S. Corp Investment Grade, and Barclays U.S.

High-Yield Composite), and one for agency bonds (Barclays GNMA 30-Year). Finally,

11Many other studies use portfolio holdings to determine timing and selection ability. However, given theshortcomings of the existing databases, we choose to follow the basic TM model of portfolio returns.In particular, the database with mutual fund portfolio holdings most frequently used by academics isThomson Reuters, however it does not serve our purposes. Thompson provides holdings only for equityfunds, but we need to observe the ability of managers across all the different asset classes. Besides, itonly reports portfolio holdings quarterly.

12From Kenneth French’s website.

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for the international stock funds we use this model:

rit = αi + βrm,iGMt + γrm2,iGM2t +Bj,iGFt + εi,t for j = 3 (3)

which is similar to (2), with the only difference that fund returns GM , for “global mar-

kets,” are from international stocks funds, and risk factors, GF , are the Fama-French

global factors.13

Next, we classify as pickers the managers of funds for which αi is greater than 0

and statistically significant, and as timers the managers of funds for which γi is positive

and statistically significant.14 For portfolio managers who manage more than one fund

simultaneously, we use the TNA-weighted average of these coefficients.15

In Table 4, we present the proportion of funds managed by generalists sorted by the

investment objective. We list the proportion for each style when the manager has picking

ability, timing ability or no ability at all, as well as the total. We find that a higher

proportion of generalists run total return and balanced funds, regardless of their ability.

Also, all balance funds are managed by generalists; this is not surprising, as this category

allows funds to invest in both equity and fixed-income assets. We also observe that

generalists are more frequent among timers across all categories, except for government

long-term and foreign funds. Thus, it seems that generalists are more likely to be timers

than pickers, and very unlikely that they are unskilled.16


A possible explanation for this finding is that pickers are better suited to work as

13The global factors include all 23 countries in the four regions: Australia, Austria, Belgium, Canada,Denmark, Finland, France, Germany, Greece, Hong Kong, Ireland, Italy, Japan, Netherlands, NewZealand, Norway, Portugal, Singapore, Spain, Switzerland, Sweden, United Kingdom, United States.

14For each period, we estimate all the coefficients using data covering the previous 24 months (with aminimum of 20 observations). As a robustness check, we also estimate them using 36 months, with aminimum of 30 observations, and results remain unchanged.

15Breen, Jagannathan and Ofer (1986) show that the Henriksson and Merton (1981) regression mayexhibit heteroscedasticity and therefore might be less accurate than the TM approach, both in termsof size and power. Nevertheless, we replicate of our test using the Henriksson and Merton (1981)approach and the main results remain unchanged.

16Consistent with Kacperczyk, Nieuwerburgh and Veldkamp (2014), portfolio managers display eithersecurity selection or market timing abilities, but not both at the same time. We find that very fewmanagers are both timers and pickers simultaneously.

11

specialists, while timers are a better match to generalist roles. That could explain why

a substantial number of mutual funds assign them accordingly. The argument seems

straightforward in the case of specialists: by definition, specialists have to invest within a

narrow class of securities, and they capitalize on their ability to choose the best performers

within that class. Generalists, on the other hand, manage several funds and have a wider

range of securities to cover. That role might suit timers better. Since they manage a

large and diverse number of securities, their ability to predict market trends might allow

them to shift money across groups of assets with different cyclical characteristics, instead

of selecting individual securities. Their strategy would rely on predicting market trends

and decide across the different funds on what sets of securities to bet. Besides, timers

might benefit from access to a broader range of information within the family and hence

use it to invest across different assets.

We explore our conjecture in Table 5. In Panel A we show that stock-picking skilled

managers working as specialists produce better results than those working as generalists.

While, on average, funds managed by specialist pickers have 11.1 (11.3) bps of gross

(net) return per month higher than the average fund in that style, funds managed by

generalist pickers only show a 3.9 (5) bps of gross (net) excess return. This difference

is even larger when we compute the average performance over all the funds run by the

manager and it is also there when we consider the performance of the whole family.

In Panel B we study portfolios with managers with market-timing ability. When the

managers are specialists, their average style-excess return is a mere 0.1 (gross) and 0.3

(net) bps. These numbers, though, go up to 4.8 and 4.5 bps, respectively, when the

managers are generalists. Furthermore, the average manager performance of generalist

timers is about 11.7 (12.5) bps of monthly gross (net) investment objective-adjusted

return. Finally, Panel C displays average performances of funds managed by unskilled

managers. Predictably, these figures are negative or close to zero. Also, it seems that

unskilled managers are less harmful as specialists than as generalists.17


17See Table A2 in the appendix for further tests about differences in fund performance between managerswith timing or stock-picking ability versus those without any skill.

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4. Empirical Results

4.1. Managerial Type and Performance

Our main hypothesis is that portfolio managers with a certain skill (timing or picking)

are better suited to perform a specific function -generalist or specialist. To test this, we

estimate the following model:

OARi,t = a0 + a1Gj,t + a2MSj,t + a3Gj,t ×MSj,t + a4Xi,t−1 + δt + ei,t (4)

where OARi,t, the fund performance, measures the investment objective-adjusted return

of fund i at time t, using the excess return of the portfolio over the average return

of all funds in its style. Gj,t –for “Generalist”– measures the level of diversification of

fund i run by manager j in month t (i might represent several funds, if the manager

runs more than one). In particular, G is a dummy variable equal to 1 if the Herfindahl

index,∑9

s=1

(TNAs,j,t

TNAj,t

)2

is below 1, and 0 otherwise. The subindex s corresponds to

the “fund style” as defined in the NSAR-B filings (capital appreciation, growth, income,

total return, government short-term debt, government long-term debt, corporate debt,

balance and international stocks)18 and TNAs,j,t is the total net assets managed by

manager j according to investment style s at time t. Therefore, for funds managed by

specialist managers, who manage funds in a single investment style, G = 0. MSj,t–for

“Manager Skill”– denotes whether the manager has timing or picking ability; we use a

dummy variable for each of these abilities, with values 1 or 0.19 X is a vector of fund,

manager and family-specific control variables, including size, age, turnover, expenses,

flows and past return of the portfolio, size and number of funds within the family, number

of managers in the family, whether the family offers or demands sub-advising services,

manager background information (PhD, MBA, the number of prior positions, college

type), number of funds and assets the manager is currently managing, as well as the

length of time the manager has been affiliated with the portfolio.20 We also control for

18A full description of these investment objectives is in the Appendix.19As we argued before, we get 1 and 0, 0 and 1 and, very often, 0 and 0; 1 and 1 is exceptional.20We winsorize all the control variables at the 1% level.

13

style of the fund (dummy variables for each investment objective) and the period (year) in

which the manager is evaluated (δt) to rule out the possibility that the results are driven

by a correlation between a given fund style or time period and the fund performance –that

is, style and year fixed effects. We estimate equation (4) using Pooled-OLS regressions.

We also adjust for serial correlation by clustering standard errors at the fund level.21

Table 6 shows the results depending on whether we measure the performance at the

portfolio i or manager j level. Columns 1 to 3 result from OAR computed at the portfolio

level. Columns 4 to 6 consider as dependent variable the OAR of the manager: TNA-

weighted average gross (before deducting fees and expenses) style-adjusted return of all

the funds managed by that manager. Whereas the relationship between generalist (as

opposed to specialist) and fund performance is practically nonexistent, there is a strong

negative relation for funds managed by generalist pickers, and significantly positive for

funds managed by generalist timers. In economic terms, generalist managers with market-

timing ability have an abnormal fund (manager) return of 324 (215) bps per year (.27 and

.179 monthly, respectively) greater than those managed by specialists. On the other hand,

generalist managers with stock-picking ability yield a fund (manager) performance of 247

(377) yearly bps (.206 and .314 monthly, respectively) lower than those with a specialist

role. This means that management companies can achieve better performance by allowing

managers with picking ability to manage funds with similar styles and allocating market-

timers to manage funds with different investment styles.


4.2. Managerial Type and Performance: Fixed Effects

To reinforce our previous conclusions, we repeat the analysis with additional fixed

effects. In Panel A of Table 7 we control for fund fixed effects, which allows us to compare

differences in performance across different portfolio managers with different skills (timers

or pickers) and different assignments (specialists or generalists). The coefficients of the

21These results are robust to different additional tests such as including continuous, rather than dummyvariables, risk-adjusted performance measures, fund, family and manager fixed effects, clustering bytime, as well as Fama-MacBeth (1973) regressions. See Appendix for more details.

14

interactions Generalist × Timer and Generalist × Picker become even larger. Funds

managed by Generalist-Timers or Specialist-Pickers return on average about 353 and 312

bps more per year than Specialist-Timers and Generalist-Pickers, respectively. We also

control for manager and family fixed effects to rule out the possibly that the results are

driven by specific portfolio managers or management company characteristics. Panels B

and C of Table 7 confirm the results.


4.3. Managerial Type and Performance: Subsamples

To gain further insight in our results, we split our sample into funds managed by

timers, funds managed by pickers and funds managed by unskilled portfolio managers,

and we estimate the following model for each subsample:

OARi,t = a0 + a1Generalistj,t + a2Xi,t−1 + δt + ei,t (5)

The dependent variable OARi,t, represents performance, measured at different levels:

fund and manager. For fund performance, we use the investment objective-adjusted

return described in the previous section. Manager performance is the TNA-weighted

average OAR across all the funds run by the manager.22 Generalistj,t is a dummy

variable that captures whether the manager has a generalist or a specialist assignment

within the management company. X is a vector of control variables at the fund, family

and manager levels.23 We include time and investment objective dummies (δt). We

cluster standard errors at the fund level and estimate equation (5) using Pooled OLS

regression.24

22There is a wide range in the number of funds managed by the same person. In our sample they varyfrom 1 to 26, with a mean of 3.05 and a standard deviation of 3.5.

23For a detailed description of these variables, see the appendix.24We apply the Petersen (2009) approach to estimate, in an efficient way, the standard errors (SE) of

the regression. The SE clustered by funds are dramatically larger than the white SE, while the SEclustered by years are only slightly larger than the white SE. Besides, clusters by funds and years aresimilar to clusters by funds. Then, the importance of time effect (after including time dummies) issmall, and in the presence of a fund effect, white and Fama-MacBeth SE are significantly biased.

15

Table 8 shows the results of estimating (5) in two parts –columns (1)-(3) and (4)-

(6), respectively. On the left side we estimate the objective-adjusted performance at the

fund level, and on the right side at the portfolio manager level. Each part sorts the

sample into funds managed by timers, pickers and funds managed by unskilled portfolio

managers. Whereas funds managed by generalists with timing skills perform about 19.9

bps per month better than specialists with timing skill, funds managed by specialist with

stock-picking ability yield 14.3 bps per month more than other generalists with equivalent

stock-picking ability. There is no substantial difference in performance among unskilled

portfolio managers assigned as generalists or specialists. We find similar results when

we consider manager performance. Thus, we conclude that pickers are better suited to

manage funds with a single investment objective and timers to portfolios from different

styles, because they both contribute to improve the performance of the funds they run,

as well as the overall performance of all their portfolios.


4.3.1. Portfolio Management Misallocation

According to our evidence, it seems optimal to select timers as generalists and pickers

as specialists. Yet, in some cases, companies deviate from this rule. In this section we

analyze what might drive suboptimal decisions. With that goal, we estimate the following

logistic model:

Prob(Msai,t = 1) =exp(a0 + a1Xi,t−1 + δt + ei,t)

1 + exp(a0 + a1Xi,t + δt + ei,t)(6)

The dependent variable Msaj,t –for misallocation – represents funds that are run

by either a generalist with stock-picking ability (left panel of table 9) or a specialist

with market-timing skill (right panel). X is a set of fund, manager and family-specific

explanatory variables, including size, age, turnover, expenses, flows and past return of the

portfolio, size and number of funds within the family, number of managers in the family;

it also includes whether the family offers or demands sub-advising services, manager

background information (PhD, MBA, the number of prior positions, college reputation),

16

number of funds and amount of assets under management, as well as the time the manager

has been affiliated with the fund.

Table 9 presents the results of estimating specification (6) for all the U.S. open-

end funds in our dataset. The unconditional probability of misallocating a picker as

a generalist is 5.4% and 8.9% for a timer as specialist. The probability of assigning a

stock-picker as a generalist is larger for small funds, with high turnover and good past

performance. These managers usually have a graduate degree (MBA or PhD), manage

a fair number of funds, amounting to a high value of assets, and work in families with

few managers. It is possible that small firms lack employees with timing ability and use

their more qualified pickers to manage several small funds. On the other hand, managers

without a PhD degree, running a relatively low total of assets spread out across several

expensive funds with poor past performance, are more likely to be specialists with timing

ability. This type of misallocation seems more frequent among families with a large value

of assets under management in which managers are in charge of several funds and the firm

offers its services as sub-advisor. Providing specialized managing services as sub-advisors

might force the firm to assign timers to specialist functions. Shortage of pickers might

lead them to employ the less quantitatively qualified timers to manage expensive funds

with a poor past record.

Overall, human capital misallocation seems to be associated with a lack of qualified

portfolio managers to run the volume of assets the firms control.


4.3.2. Influence of skills on Promotions: Specialist to Generalist

We now study whether market-timing or stock-picking skills affect the probability

of a portfolio manager’s switch from specialist to generalist. We estimate the following

logistic model:

Prob(yi,t = 1) =exp(βfzi)

1 + exp(βfzi)(7)

where βfzi = (a0+a1Skillj,t+a2MPSj,t+a3Xi,t−1+δt+ei.t). The dependent variable (yi,t)

is a dummy variable, equal to 1 when portfolio manager j in charge of fund i switches from

17

specialist at t to generalist at t+1, and equal to 0 when the manager remains a specialist.

Skillj,t stands for the two different types of portfolio manager ability: Timerj,t if manager

j successfully timed the market from t-25 to t-1 and Pickerj,t if manager j showed stock-

picking skill during the prior 24 months. We also include MPSj,t (“Manager Past Skill”)

as the past 24 months cumulative OAR of the manager (TNA-weighted average of the

objective-adjusted return of all the funds run by the manager j). X is a vector of manager-

related control variables lagged one period. We control for year and investment objective

fixed effects and cluster standard errors at the fund level.

Table 10 shows the results of estimating (7). We observe in Models 3 and 4 that both

timers and pickers are more likely to switch from specialist to generalist than unskilled

managers. The marginal effects of the Timer and Picker coefficients are about 0.3% and

0.2%, whereas the unconditional probability of the change from specialist to generalist

is 1.3%. Therefore, these managers are about (0.003/0.013) 23% and (0.002/0.013) 15%

more likely to switch to generalist than unskilled specialists. In Model 4 we interact

the type of managerial skill with the overall abnormal return of the manager during the

past 24 months. We find that the probability of changing from specialist to generalist

for top performers increases in a significant way when they are timers and decreases for

top pickers. In economic terms, an increase of one standard deviation on MPS (4.611)

makes a timer (0.003 + 0.001 ∗ 4.611)/0.013 = 58.5% more likely to switch than other

specialists. Arguably, some switches from specialist to generalists might be promotions

since, as we showed previously, generalists manage a significantly larger amount of assets

than specialists.25

These results suggest that management companies often decide a change from special-

ist to generalist based on the ability of the manager to predict market trends. Overall,

these results provide additional evidence about the importance of managerial skills in

determining the function of the manager; the results are also consistent with an opti-

mal assignment of managers to functions. Top specialist-pickers, although less likely to

switch from specialist to generalist, are likely to be highly compensated by the manage-

25Prior research has classified promotions and demotions based on total assets under management (i.e.Chevalier and Ellison (1999a), Hu, Hall, and Harvey (2000), and Baks (2003)).

18

ment company.


4.3.3. Performance around Managerial Reassignment: Event Study

Next we study the effects on performance of reassigning specialists to generalists.

We conduct an event study and average the fund performance during the six months

before and twelve months after the switch. We measure performance using the 6-months

cumulative objective-adjusted return (OAR) before deducting expenses and fees. We

divide our sample into funds managed by timers, funds managed by pickers, and funds

run by unskilled managers.

Table 11 Panel A displays the results of reassignment on funds performance. In par-

ticular, we present the effect on return of a change of management from specialist to

generalist –the manager of the fund becomes a generalist or is replaced with a generalist

with the same type of skill. On average, the performance of the funds run by a timer

improves significantly in the quarter after the manager changes from specialist to gener-

alist –it could be the same timer or a different timer. The improvement persists twelve

months after the switch. On the other hand, when the manager has picking skill, the

performance in the quarter before the switch is positive and might even increase in the

short-run after the fund is managed by a picker-generalist rather than specialist, but it

drops overtime and is negative twelve months after the event. We find similar results for

funds run by unskilled managers. There is a significant improvement in the first months

after the manager becomes a generalist, but this positive performance disappears in the

second quarter. Also, we compute the difference in cumulative performance before and

after the event and its statistical significance.26 We find that funds managed by special-

ists experience a significant improvement after the manager becomes a generalist only

for funds managed by timers. The return of funds managed by pickers decreases. The

change in return of funds run by unskilled managers is not significant.

26We compare the average performance of funds two months prior to the switch and the performancetwelve months after the event. We consider t-2 because the month right before the event might not bevery representative of the managerial behavior due to the proximity of the switch. Similarly, we uset+12 as managers might need some time to be adapted to their new functions.

19

In Panel B of Table 11 we examine the effect on the performance of the manager. In

particular, we compare the performance of the funds managed by the same person before

and after a change from specialist to generalist. We classify each fund according to the

skill (timer, picker or unskilled) of the manager before the switch.27 We find similar

results, specialists who become generalists improve their overall performance only when

they are timers before the switch. Pickers and unskilled managers have an improvement

in the short-run, but it disappears two quarters after the event.


5. Robustness and Alternative Interpretations

5.1. Ability or Selection: Propensity Score Matching

We want to rule out that timers might perform better as generalists for a reason other

than their timing skill; similarly, for pickers as specialists. To eliminate this concern we

carry out a propensity score matching exercise. We use two different propensity matching

techniques: the Nearest Neighbor procedure of Rosenbaum and Rubin (1983), and the

Kernel Matching of Heckman, Ichimura and Todd (1997, 1998). We first identify a con-

trol sample of funds managed by specialist-timers that exhibit no observable differences

in characteristics relative to the funds managed by generalist-timers. Thus, each pair

of matched funds is almost identically to one another, except for the main variable of

interest: the function of the manager. Similarly, we also identify pairs of funds managed

by generalist-pickers that are identical to specialist-pickers, except for the type of ability

of the managers.

More explicitly, we calculate the probability (i.e., the propensity score) that a fund

with certain characteristics is managed by a generalist. To calculate the propensity score

we use characteristics of the fund, management company and portfolio manager. In

particular, we estimate this probability as a function of the following factors: size, age,

turnover, expenses, flows and past returns of funds; volume of assets and number of

27The type of skill might change as well, but we do not explore that possibility in this panel.

20

funds and managers of the family; number of prior positions, length of time the manager

has been running the fund, number of funds and total amount of assets the manager

has currently under management. We require that the maximum difference between the

propensity scores of the funds does not exceed 0.1% in absolute value.

Next, we compare fund performance between the two groups of matched funds. As

the control funds are a set of peers almost identical in terms of observable characteristics,

unless timing ability matters, the funds managed by generalist-timers should perform at

a level similar to the funds managed by specialist-timers. Similarly for the group of funds

run by portfolio managers with stock picking skills. We calculate fund performance as

the excess return over the mean of the style. We use returns before and after fees.

In Table 12 we compare performance of the two groups and report the value of the

difference (Generalist-Specialist) and the statistical significance using bootstrapped stan-

dard errors associated to that difference. We also group the portfolios into quintiles

based on the timing and picking skills of the managers running the funds during the

period 1996-2011.

Panel A contains all funds sorted into quintiles according to timing ability, from

lowest to highest. The matching resulting from the propensity score methodology uses

a control fund from the same quintile. In every quintile, funds managed by generalist–

timers outperform their specialist peers, especially in the highest quintile in which we

observe that such outperformance averages 42.2 and 68.5 bps per month -depending on

the propensity score method. Panel B reports the differences between generalist and

specialist managers depending on different levels of stock-picking ability. As expected,

stock pickers are more effective at managing funds within the same investment objectives.

The greater the picking skill, the greater the differences between specialist and generalist

managers. For the top timing quintile the difference averages 89.4 bps and 73.8 bps per

month, depending on the score propensity measure.28 We also include in Table 12, the

differences in net performance, after fees –it is possible that funds collect all the rents of

the superior performance. The results using net performance are very similar.

28These results are also robust to the use of the radius and stratification matching methods.

21


5.2. Selection Bias: Heckman’s (1979) two-step procedure

By definition, a generalist has to manage more than one fund at a time. However,

there are fund families that only allocate one manager per fund. Maybe these firms have

a policy of a manager per fund, maybe they they have too many managers for too few

funds. Either way, it could be that they are not reassigned from specialist to generalist

despite their timing skills because of some family characteristics. This would present a

bias selection problem because only a subsample of specialists would be candidates to

become generalists. To address this issue, we use the two-stage method of Heckman.29.

According to the method, we first estimate the probability that a manager runs several

funds simultaneously; in the second stage, we estimate the probability of becoming a

generalist.

Table 13 reports the results from the first-stage. We estimate the probability that a

manager runs more than one fund using the same set of manager control variables we

have used so far, plus we add a variable that represents the average number of funds per

manager in the family:30

Prob(MFMi,t = 1) = φ(β0 + β1wf,t−1 + β2xi,t−1 + δt + εi,t) (8)

where φ(·) is the cumulative density function of the standard normal distribution. The

dependent variable MFMi,t, Multi-Funds Manager, takes the value 1 if manager j, who

runs fund i, manages more than one fund. β0 is a constant and wf,t−1 is the variable

Funds per Manager, defined as the number of funds in family f divided by the number of

managers in that family. Xi,t−1 is the set of fund, family and manager control variables

for each portfolio we have used previously. We also include year and investment style

29The original Heckman Correction (1979) was designed for continuous dependent variables. In our case,the dependent variable is discrete, thus we use a version of the original model.

30The variable Fund per Manager clearly has to affect the probability that a manager will run more thana fund, however there does not seem to be any theoretical reason why it should have any effect on thedecision to reassign a specialist to generalist.

22

fixed effects (δt), and cluster standard errors at the fund level. In Table 13 we show that

the probability of managing more than one fund clearly depends on the ratio of funds

per manager in the family.


In Table 14, we show the estimates from the second stage. In Model 5 we find that,

conditional on the probability of being a multi-fund manager, specialist-timers are about

60% (0.012/0.020) more likely to become generalists than other specialists. Similarly,

specialist pickers are 45% more likely than unskilled managers to become generalists. A

possible explanation could be that becoming a generalist is a promotion, and it rewards

ability.

Additionally, we interact the type of skill with the cumulative past performance record

of the manager. The higher the quality of the timer, the greater the probability of

reassignment to generalist. Meanwhile, top pickers are more likely to remain specialists.


5.3. Family Focus

Some families of funds as a whole focus on a small range of investment styles, while

other families offer the full spectrum. A recent study on product differentiation and

market share concludes that fund families increase their share by offering a wider variety

of investment objectives (Khorana and Servaes, 2012). These families that cover more

investment styles might need more generalists and, therefore, be more likely to reassign

specialists to generalists. We explore that possibility in Table 15. We estimate again the

probability of a reassignment from specialist to generalist, but this time we divide our

sample according to the concentration of investment styles of the family. We sort fami-

lies into four different groups every month and then group them into low-concentration

families if their Herfindahl index of funds’ investment objectives is within the first quar-

tile, and high-concentration families if they are in the fourth quartile. We show that for

funds that belong to families with either low or high levels of concentration, the timing

23

skill of the manager has a strong effect on the probability of switch to generalist, while

picking skills do not seem to matter. Therefore, family concentration is an important

characteristic that we need to take into consideration. 31


5.4. Downturn Markets

Kacperczyk, Nieuwerburgh and Veldkamp (2014) show that outperforming fund man-

agers excel at stock picking in bull markets and at timing in recessions. Then, we would

like to know if there might be a cyclical component in the decision to switch a manager

from specialist to generalist. For example, it is possible that in a downturn management

companies try to save costs by laying off some managers and assigning others to run

several funds simultaneously.

To study this, in Table 16 we estimate the probability of reassignment from specialist

to generalist under different market conditions. We identify time periods as bull or bear

markets depending on whether the Chicago Fed National Activity Index (CFNAI) is on

the fourth or first quartile, respectively.32 We conclude that timing ability is still the main

factor affecting the switch to generalist, regardless of whether the market is in recession

or expansion. On the other hand, Table 16 shows that picking skills matter only for bear

markets. 33


31In an unreported table we show that the level of concentration affects negatively the probability of areassignment to generalist. However, after controlling for this factor, timing and picking abilities arestill statistically relevant. We observe again that timing ability (23% more likely than for managerswith no ability) is a stronger predictor of the switch than picking ability (15% more likely). Our resultsalso show that specialists with timing skills and outstanding performance record are more likely to bereassigned than managers without ability; however, pickers with outstanding record are not more likelyto switch from specialists to generalists.

32The CFNAI is a coincident indicator of national economic activity comprising 85 existing macroe-conomic time series. It is constructed to have an average value of zero and a standard deviation ofone. As in Kacperczyk, Nieuwerburgh and Veldkamp (2014), we use the headline three-month movingaverage to measure the market conditions.

33In an unreported table, we verify that market conditions affect the probability of reassignment, butas in our previous tests, any ability, and specially the interaction between timing ability and previousoutstanding performance, carries a significant weight in the probability of reassignment.

24

6. Conclusions

Our paper falls within the school of thought that argues that some actively managed

mutual funds outperform passively managed funds. In particular, we identify portfolio

managers with two types of skills, stock-picking or market-timing –although we also

find that many portfolio managers do not display either type of skill. We find that

pickers are a better fit for positions as specialists –managers who run funds with a single

investment style– while timers perform better as generalists –running several funds with

different investment styles. Consistent with the optimal allocation of human capital, we

find more timers among generalists and more pickers among specialists. In addition,

management companies often switch timers from specialists to generalists, especially

when they have shown outstanding performance in the past. We also observe that overall

manager performance improves after this switch. On a side note, we find that market-

timers are more likely to have a PhD degree and a quantitative background, while stock-

pickers tend to have a MBA degree. Overall, management companies make rational

decisions based on measurable skills.

Overall, our study presents evidence of optimal strategic decisions in the organization

of management companies, in contrast with the strand of the literature that is skeptical

of the performance of actively managed funds.

25

References

Baker, M., Litov L., Wachter, J. and J. Wurgler, 2010, Can mutual fund managers

pick stocks? Evidence from their trades prior to earnings announcements, Journal of

Financial and Quantitative Analysis 45, 1111–1131.

Baks, K., 2003, On the Performance of Mutual Fund Managers, working paper, Emory

University.

Bar, M., A. Kempf and S. Ruenzi, 2011, Is a Team Different from the Sum of its Parts?

Evidence from Mutual Fund Managers, Review of Finance 15, 359–396.

Basak, S., Pavlova, A. and A. Shapiro, 2007, Optimal Asset Allocation and Risk Shifting

in Money Management, Review of Financial Studies 20, 1583–1621.

Berk, J. and R. Green, 2004, Mutual fund flows and performance in rational markets,

Journal of Political Economy 112, 1269–1295.

Berk, J. and J. Xu, 2005, Persistence and fund flows of the worst performing mutual

funds, working paper, UC Berkeley.

Berk, J. and J. van Binsbergen, 2012, Measuring managerial skill in the mutual fund

industry, working paper, Stanford University.

Blake, C. , E. Elton and M. Gruber, 1993, The performance of bond mutual funds, The

Journal of Business 66, 371–403.

Blake, D., Rossi, A., Timmermann, A., Tonks, I. and R. Wermers, 2013, Decentralized

investment management: evidence from the pension fund industry, Journal of Finance

68, 1133–1178.

Bliss, R., Potter, M. and C. Schwarz, 2008, Performance Characteristics of Individually-

Managed versus Team-Managed Mutual Funds, The Journal of Portfolio Management

34, 110–119

26

Bollen, Nicolas, and Jeffrey Busse, 2001, On the timing ability of mutual fund managers,

Journal of Finance 56, 1075-1094

Bollen, Nicolas, and Jeffrey Busse, 2005, Short-term persistence in mutual fund perfor-

mance, Review of Financial Studies 18, 569–597.

Boyson, N. , 2003, Why do experienced hedge fund managers have lower returns?,

working paper, Purdue University.

Bradley, M., Brav, A., Goldstein, I. and Wei Jiang, 2005, Costly communication, share-

holder activism, and limits to arbitrage, working paper, Duke University

Brauer, Gregory, 1984, Open-ending closed-end funds, Journal of Financial Economics

13, 491-507.

Brauer, Gregory, 1988, Closed-end fund shares’ abnormal returns and the information

content of discounts and premiums, Journal of Finance 43, 113–128.

Breen, W., Jagannathan, R. and A. Ofer, 1986, Correcting for Heteroscedasticity in

Tests for Market Timing Ability, Journal of Business 59, 585–98.

Brickley, James and James Schallheim, 1985, Lifting the lid on closed-end investment

companies: A case of abnormal returns, Journal of Financial and Quantitative Anal-

ysis 20, 107–117.

Brown, Stephen and William Goetzmann, 1995, Performance persistence, Journal of

Finance 50, 679–698.

Brown, K., Harlow, W. and L. Starks, 1996, Of Tournaments and Temptations: An

Analysis of Managerial Incentives in the Mutual Fund Industry, Journal of Finance

51, 85–110.

Bollen, N. andd J. Busse, 2005, Short-Term Persistence in Mutual Fund Performance,

Review of Financial Studies 18, 569–597.

27

Carhart, Mark, 1997, On persistence in mutual fund performance, Journal of Finance

52, 57–82.

Cashman, George and Daniel Deli, 2009, Locating decision rights: Evidence from the

mutual fund industry, Journal of Financial Markets 12, 645–671.

Chen, Hsiu-Lang, Jegadeesh, Narasimhan and Russ Wermers, 2000, The value of active

mutual fund management: An examination of the stockholdings and trades of fund

managers, Journal of Financial and Quantitative Analysis 35, 343–368.

Chevalier, J. and G. Ellison, 1999a, Are Some Mutual Fund Managers Better than

Others? Cross-Sectional Patterns in Behavior and Performance, Journal of Finance

54, 875–899.

Chevalier, J. and G. Ellison, 1999b, Career Concerns of Mutual Fund Managers, Quar-

terly Journal of Economics 105, 1167–1200.

Chordia, T., 1996, The Structure of Mutual Fund Charges, Journal of Financial Eco-

nomics 41, 3–39.

Cohen, Randolph, Coval, Joshua and Lubos Pastor, 2005, Judging fund managers by

the company they keep, Journal of Finance 60, 1057–1096.

Cremers, Martijn and Antti Petajisto, 2009, How active is your fund manager? A new

measure that predicts performance, Review of Financial Studies 22, 3329–3365.

Custodio, C., Ferreira, M. and P. Matos, 2013, Generalists Versus Specialists: Lifetime

Work Experience and Chief Executive Officer Pay, Journal of Financial Economics

108, 471–492.

Daniel, K., Grinblatt, M., Titman, S. and R. Wermers, 1997, Measuring Mutual Fund

Performance with Characteristic-Based Benchmarks, Journal of Finance 52, 1035–

1058.

Ding, B. and R. Wemers, 2004, Mutual Fund “Stars”: The Performance and Behavior

of U.S. Fund Managers, working paper, University of Maryland.

28

Ding, Bill and Russell Wermers, 2012, Mutual fund performance and governance struc-

ture: The role of portfolio managers and boards of directors, working paper, University

of Maryland.

Drazin, R. and H. Rao, 2002, Harnessing Managerial Knowledge to Implement Product

line Extensions: How Do Mutual Fund Families Allocate Portfolio Managers to Old

and New funds? The Academy of Management Journal 45, 609–619.

Evans, R., 2009, Does Alpha Really Matter? Evidence from Mutual Fund Incubation,

Termination and Manager Change, working paper, University of Virginia.

Evans, R., 2010, Mutual fund incubation, Journal of Finance 65, 1581–1611.

Fang, Jieyan, Alexander, Kempf and Monika Trapp, 2014, Fund Manager Allocation,

Journal of Financial Economics 111, 661–674.

Fee, C. and C. Hadlock, 2003, Raids, Rewards, and Reputations in the Market for

Managerial Talent, Review of Financial Studies 16, 1315–1357.

Ferreira, D. and R. Kumar Sah, 2012, Who gets to the top? Generalists versus specialists

in managerial organizations, RAND Journal of Economics 43, 577–601.

Ferson, Wayne, and Haitao Mo, 2015, Performance measurement with selectivity, market

and volatility timing, Journal of Financial Economics, forthcoming.

Fortin, Rich, Michelson, Stuart and James Jordan-Wagner, 1999, Does mutual fund

manager tenure matter?, Journal of Financial Planning, August Issue, 72–79.

Frydman, C., 2009, Rising through the ranks: the evolution of the market for corporate

executives, working paper, Boston University.

Gallaher S., Kaniel, R. and L. Starks, 2006, Madison Avenue Meets Wall Street: Mutual

Fund Families, Competition, and Advertising, working paper, UT Austin.

Gaspar, J., Massa, M. and P. Matos, 2006, Favoritism in Mutual Fund Families? Evi-

dence on Strategic Cross-Fund Subsidization, Journal of Finance 61, 73–104.

29

Golec, J., 1996, The Effects of Mutual Fund Managers’ Characteristics on their Perfor-

mance, Risk and Fees, Financial Services Review 5, 133–147

Gottesman, A. and M. Morey, 2006, Manager Education and Mutual Fund Performance,

Journal of Empirical Finance 13, 145–182.

Gruber, M., 1996, Another Puzzle: The Growth in Actively Managed Mutual Funds,

Journal of Finance 51, 783–810.

Jain, P. and J. Wu, 2000, Truth in Mutual-Fund Advertising: Evidence on Future

Performance and Fund Flows, Journal of Finance 55, 937–958.

Jensen, Michael, 1968, The performance of mutual funds in the period 1945-1964, Jour-

nal of Finance 23, 389–416.

Heckman, J., 1979, Sample selection bias as a specification error, Econometrica 47,

153–61.

Heckman, J., Ichimura, H. and P. Todd, 1997, Matching As An Econometric Evaluation

Estimator: Evidence from Evaluating a Job Training Programme, Review of Economic

Studies 64, 605–654.

Heckman, J., Ichimura, H., and P. Todd, 1998, Matching as an Econometric Evaluation

Estimator, Review of Economic Studies 65, 261–294.

Henriksson R. and R. Merton, 1981, On Market Timing and Investment Performance.

II. Statistical Procedures for Evaluating Forecasting Skills, Journal of Business 54,

513–533.

Hu, Fan, Hall, Alastair and Campbell Harvey, 2000, Promotion or demotion? An em-

pirical investigation of the determinants of top mutual fund manager change, working

paper, Duke University.

Jensen, M., 1968, The Performance of Mutual Funds in the Period 19451964, Journal

of Finance 50, 549-572.

30

Kacperczyk, Marcin, Sialm, Clemens and Lu Zheng, 2005, On the industry concentration

of actively managed equity mutual funds, Journal of Finance 60, 1983–2011.

Kacperczyk, Marcin, and Amit Seru, 2007, Fund manager use of public information:

New evidence on managerial skills, Journal of Finance 62, 485–528.

Kacperczyk, M., van Nieuwerburgh, S. and L. Veldkamp, 2014, Time-Varying Fund

Manager Skill, Journal of Finance 69, 1455–1484.

Kempf, E., Manconi A. and O. Spalt, 2014, Learning by Doing: The Value of Experience

and the Origins of Skill for Mutual Fund Managers, working paper, Tilburg University.

Khorana, A., 1996, Top Management Turnover: An Empirical Investigation of Mutual

Fund Managers, Journal of Financial Economics 40, 403–427.

Khorana, A., Wahal, S. and M. Zenner, 2002, Agency conflicts in closed-end funds: The

case of rights offerings, Journal of Financial and Quantitative Analysis 37, 177–200

Khorana, A. and H. Servaes, 2012, What Drives Market Share in the Mutual Fund

Industry?, Review of Finance 16, 81–113.

Koijen, R., 2012, The cross-section of managerial ability, incentives, and risk preferences,

Journal of Finance 69, 1051–1098.

Kuhnen, C., 2009, Business Networks, Corporate Governance and Contracting in the

Mutual Fund Industry, Journal of Finance 64, 2185–2220.

Lazear, E., 2009, Firm-specific human capital: a skill-weights approach, Journal of

Political Economy 117, 914-940.

Litov, L., Baker, M., Wachter, J. and J. Wurgler, 2005, Can mutual fund managers pick

stocks? Evidence from their trades prior to earnings announcements, working paper,

New York University.

Malmendier, U. and G. Tate, 2009, Superstar CEOs, Quarterly Journal of Economics

124, 1593–1638.

31

Mamaysky, H. and M. Spiegel, 2001, A Theory of Mutual Funds: Optimal Fund Objec-

tives and Industry Organization, working paper, Yale University.

Marris, R., 1963, A model of the managerial enterprise, Quarterly Journal of Economics

77, 185–209.

Massa, M., 2003, How do Family Strategies Affect Fund Performance? When

Performance-Maximization is not the Only Game in Town, Journal of Financial Eco-

nomics 67, 249–304.

Massa, M., Reuter, J. and E. Zitzewitz, 2010, When Should Firms Share Credit with

Employees? Evidence from Anonymously Managed Mutual Funds, Journal of Finan-

cial Economics 95, 400–424.

Merton, Robert, 1980, On estimating the expected return on the market: An exploratory

investigation, Journal of Financial Economics 8, 323–361.

Moreno, David, Rodriguez, Rosa and Rafael Zambrana, 2013, Management sub-

advising: Mutual fund industry, working paper, Universidad Carlos III.

Murphy, K. J. and J. Zbojnk, 2004, CEO Pay and Appointments: A Market-Based

Explanation for Recent Trends, American Economic Review 94, 192–196.

Murphy, K. J. and J. Zbojnk, 2007, Managerial Capital and the Market for CEOs,

working paper. University of Southern California.

Nanda, V., Narayanan M. and V. Warther, 2000, Liquidity, Investment Ability, and

Mutual Fund Structure, Journal of Financial Economics 57, 417–443.

Rosenbaum, P. and D. Rubin, 1983, The Central Role of the Propensity Score in Ob-

servational Studies for Causal Effects, Biometrika 70, 41–55.

Schultz, P., 2010, Rational cross-sectional differences in market efficiency: Evidence from

mutual fund returns, Journal of Financial and Quantitative Analysis 45, 847-881.

32

Sirri, E. and P. Tufano, 1998, Costly Search and Mutual Fund Flows, Journal of Finance

53, 1589-1622.

Treynor, J. and K. Mazuy, 1966, Can Mutual Funds Outguess the Market?, Harvard

Business Review 44, 131–136.

Warner, J., Watts R. and K. Wruck, 1988, Stock prices and top management changes,

Journal of Financial Economics 20, 461–492.

Wermers, Russ, Youchang Wu and Josef Zechner, 2005, Portfolio performance, discount

dynamics, and the turnover of closed-end fund managers, working paper, University

of Maryland.

33

Table 1: Funds by Investment Objectives

Table 1 displays the number of U.S open-end funds managed by an individual manager (team-managed funds are excluded) for each investment objective during the period 1996-2011. Theinvestment objectives are reported by the funds according to the nine categories defined by theSEC.

Capital Growth Income Return Gov ST Gov LT Corporate Balance Foreign1996 254 137 141 35 355 552 84 61 1861997 254 144 120 40 366 586 90 61 1931998 337 193 152 58 374 549 108 75 2511999 360 205 136 56 351 556 118 73 2572000 465 230 159 78 376 626 115 83 2382001 494 234 143 74 356 558 110 82 1992002 495 283 135 74 432 497 97 84 2362003 453 251 111 62 458 515 96 65 2002004 400 236 119 70 405 481 100 78 1842005 331 196 105 68 268 432 72 58 1482006 345 163 96 69 275 455 73 70 1442007 348 173 84 76 243 460 73 61 1512008 387 175 88 82 199 393 62 57 1642009 387 189 87 65 208 362 69 42 1972010 320 164 72 53 170 342 74 32 2102011 256 120 49 29 83 255 57 16 172

Table 2: Distribution of Funds and Managers by year

Table 2 displays the total number of funds managed by individual managers, out of those,the number managed by generalists, the total number of individual managers, number whoare generalists, the total number of management companies and how many have employedgeneralist managers. The sample covers all equity, fixed income and international U.S open-endfunds managed by individual managers during 1996-2011.

Funds Generalist Funds Managers Generalist Managers Firms Generalist Firms1996 1807 395 1176 133 261 891997 1860 444 1165 144 269 961998 2105 433 1361 142 311 1151999 2118 478 1359 150 299 1142000 2386 541 1390 175 407 1432001 1955 403 1202 138 358 1052002 2341 561 1307 169 339 1212003 2216 456 1241 144 333 1132004 2079 446 1171 138 321 1092005 1681 373 977 113 283 962006 1693 353 943 104 266 882007 1670 331 944 96 247 842008 1608 366 891 102 252 752009 1611 380 901 107 260 732010 1441 265 846 89 255 632011 1037 159 657 60 197 44

34

Table 3: T-Test Analysis: Specialist vs Generalist

This table presents the mean of fund, family and manager characteristics for the samples of spe-cialists (portfolio managers of funds with a single investment objective) and generalists (portfoliomanagers of multiple funds with different investment objectives) and the corresponding differ-ence in the measure of the characteristic between the two samples. * denotes significance at the10% level, ** denotes significance at the 5% level and *** denotes significance at the 1% level.Panel A contains all the variables at the fund level, Panel B summarizes the characteristicsfor the whole family, and Panel C displays the variables at the portfolio manager level. Thedescription of each variable is in the appendix. The data covers the period 1996 to 2011.

Panel A: Fund CharacteristicsSpecialist Generalist Difference

Fund Size 4.956 4.805 0.150∗∗∗

Fund Age 10.570 9.834 0.736∗∗∗

Fund Turnover 92.681 99.819 -7.138∗∗∗

Fund Expenses 1.152 1.122 0.030∗∗∗

Fund Flows 0.399 0.469 -0.071∗∗∗

Past Year Return 0.073 0.074 -0.001

Panel B: Family Characteristics

Specialist Generalist DifferenceFamily Size 8.120 7.793 0.327∗∗∗

Family Funds 26.689 21.175 5.514∗∗∗

Family Managers 9.789 8.397 1.392∗∗∗

Demand Advising 0.393 0.395 -0.002Supply Advising 0.654 0.579 0.075∗∗∗

Panel C: Manager Characteristics

Specialist Generalist DifferenceIvy League 0.210 0.211 -0.001MBA 0.464 0.423 0.041∗∗∗

PhD 0.028 0.035 -0.007∗∗∗

Past Positions 2.419 2.390 0.029∗∗∗

Manager Size 5.743 6.492 -0.748∗∗∗

Manager Funds 2.683 4.350 -1.667∗∗∗

Fund Affiliation 5.391 5.518 -0.126∗∗∗

Past Manager Skill -0.158 0.600 -0.758 ∗∗∗

35

Table 4: Proportion of Generalists by Style and Skill

This table presents the proportion of funds managed by generalist managers sorted by invest-ment objective (raws) and managerial skill (columns). The description of each variable is in theappendix. The data covers the period 1996 to 2011.

Picker Timer Unskilled TotalCapital 0.33 0.34 0.18 0.20Growth 0.36 0.41 0.26 0.27Income 0.48 0.54 0.33 0.35Return 0.51 0.60 0.40 0.40Gov ST 0.14 0.28 0.24 0.18Gov LT 0.42 0.23 0.13 0.17Corporate 0.32 0.38 0.20 0.23Balance 1.00 1.00 0.42 0.44Foreign 0.37 0.23 0.12 0.14

Table 5: Objective Adjusted Returns by Functions and Managerial Skills

This table presents the average investment objective returns (gross and net) of funds managedby generalists and specialists sorted by managerial skill (timing, picking or unskilled). for theperiod 1996-2011. The description of each variable is in the appendix. The data covers theperiod 1996 to 2011.

Gross Objective-Adj Return Net Objective-Adj ReturnPanel A: Picker Fund Family Manager Fund Family Manager

Specialist 0.111 0.117 0.131 0.113 0.123 0.143Generalist 0.039 0.067 0.035 0.050 0.078 0.044Panel B: Timer Fund Family Manager Fund Family ManagerSpecialist 0.001 0.006 0.098 0.003 0.013 0.106Generalist 0.048 0.013 0.117 0.045 0.017 0.125Panel C: Unskilled Fund Family Manager Fund Family ManagerSpecialist -0.015 -0.017 0.005 -0.013 -0.011 0.015Generalist -0.029 -0.029 0.004 -0.024 -0.019 0.014

36

Table 6: Managerial Type and Performance (I)

This table presents the results of monthly Pooled OLS regressions of fund and manager invest-ment objective-adjusted returns on fund, manager and family characteristics. Fund returns arethe actual returns before fees and expenses (gross) and manager returns are the TNA-weightedaverage returns of all the portfolios managed by specific managers at the same time. Dependentvariables are the return of the fund and manager after substracting the median return of theirinvestment objective peers. Generalist is a dummy variable equal to 1 if the fund manager runsmultiple funds with different investment styles. Timer is a dummy equal to 1 if the fund man-ager has been able to time the market during the past 24 months. Picker is a dummy variableequal to 1 if the fund manager was able to pick stocks efficiently during the past 24 months. Allvariables are lagged one period. A full description of the remaining variables is in the appendix.Time and investment objective dummies are included but not reported; t-statistics are reportedin parentheses. We adjust for serial correlation by clustering standard errors at the fund level.* denotes significance at the 10% level, ** denotes significance at the 5% level and *** denotessignificance at the 1% level.

Fund Performance Manager Performance(1) (2) (3) (4) (5) (6)

Generalist -0.030 0.037 -0.009 -0.028 0.046 0.016(-0.88) (1.03) (-0.24) (-0.87) (1.41) (0.45)

Timer -0.090∗ -0.067 -0.082∗ -0.061(-1.80) (-1.35) (-1.68) (-1.24)

Generalist × Timer 0.286∗∗∗ 0.270∗∗∗ 0.199∗∗∗ 0.179∗∗

(3.45) (3.25) (2.70) (2.43)Picker 0.294∗∗∗ 0.290∗∗∗ 0.305∗∗∗ 0.302∗∗∗

(5.43) (5.35) (5.62) (5.54)Generalist × Picker -0.214∗∗∗ -0.206∗∗ -0.320∗∗∗ -0.314∗∗∗

(-2.63) (-2.53) (-4.14) (-4.06)Fund Size 0.027∗∗ 0.026∗∗ 0.027∗∗ 0.020∗ 0.019∗ 0.020∗

(2.28) (2.26) (2.34) (1.73) (1.70) (1.74)Fund Age -0.001 -0.000 -0.000 0.000 0.000 0.000

(-0.35) (-0.09) (-0.14) (0.05) (0.30) (0.27)Fund Turnover 0.016∗ 0.015∗ 0.015 0.004 0.004 0.004

(1.69) (1.65) (1.61) (0.57) (0.56) (0.55)Fund Expenses 0.146∗∗∗ 0.136∗∗∗ 0.138∗∗∗ 0.126∗∗∗ 0.117∗∗∗ 0.119∗∗∗

(4.49) (4.16) (4.23) (4.15) (3.81) (3.86)Fund Flows 0.069∗∗∗ 0.067∗∗∗ 0.067∗∗∗ 0.064∗∗∗ 0.063∗∗∗ 0.063∗∗∗

(4.54) (4.45) (4.46) (4.43) (4.34) (4.34)Past Year Return -0.458∗∗∗ -0.488∗∗∗ -0.492∗∗∗ -0.451∗∗∗ -0.481∗∗∗ -0.485∗∗∗

(-3.52) (-3.74) (-3.77) (-3.45) (-3.67) (-3.70)Family Size 0.016∗ 0.016∗ 0.016∗∗ 0.020∗∗ 0.021∗∗ 0.021∗∗

(1.89) (1.95) (1.97) (2.41) (2.47) (2.48)Family Funds 0.000 0.000 0.000 0.000 0.000 0.000

(0.60) (0.45) (0.43) (0.89) (0.69) (0.68)Family Managers -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

(-1.14) (-1.05) (-1.00) (-1.48) (-1.45) (-1.42)Supply Advising -0.045 -0.053∗ -0.051∗ -0.056∗∗ -0.063∗∗ -0.062∗∗

(-1.51) (-1.77) (-1.70) (-2.01) (-2.27) (-2.22)Demand Advising -0.014 -0.009 -0.009 -0.001 0.004 0.004

(-0.60) (-0.39) (-0.39) (-0.05) (0.18) (0.19)MBA 0.009 0.007 0.006 0.005 0.004 0.004

(0.34) (0.29) (0.26) (0.19) (0.17) (0.15)PhD -0.072 -0.072 -0.068 -0.066 -0.062 -0.060

(-1.40) (-1.40) (-1.32) (-1.34) (-1.26) (-1.22)Past Positions -0.012 -0.012 -0.012 -0.011 -0.011 -0.011

(-1.28) (-1.33) (-1.30) (-1.24) (-1.32) (-1.30)Ivy League 0.019 0.019 0.021 0.028 0.028 0.028

(0.59) (0.59) (0.63) (0.90) (0.90) (0.93)Manager Funds 0.003 0.003 0.003 0.001 0.000 0.000

(1.24) (1.16) (1.01) (0.28) (0.11) (0.10)Manager Size 0.009 0.004 0.003 0.009 0.005 0.004

(0.77) (0.35) (0.29) (0.78) (0.41) (0.39)Fund Affiliation -0.001 -0.002 -0.002 0.000 -0.000 -0.000

(-0.49) (-0.69) (-0.62) (0.10) (-0.07) (-0.02)Constant -0.374∗∗∗ -0.362∗∗∗ -0.363∗∗∗ -0.303∗∗∗ -0.298∗∗∗ -0.298∗∗∗

(-3.63) (-3.51) (-3.52) (-3.03) (-2.97) (-2.98)Time Dummies Yes Yes Yes Yes Yes YesStyle Dummies Yes Yes Yes Yes Yes YesObservations 80059 80059 80059 80059 80059 80059r2 0.022 0.022 0.022 0.022 0.022 0.022

37

Table 7: Managerial Type and Performance (II)

This table presents the results of monthly portfolio (Panel A), manager (Panel B), and family(Panel C), fixed effect regressions of objective-adjusted returns on fund, manager, and familycharacteristics. Fund returns are actual returns before fees and expenses (gross) and managerreturns are the TNA-weighted average returns of all the portfolios simultaneously managed spe-cific managers. Dependent variables are the return of the fund and manager after substractingthe median return of their investment objective peers. Generalist is a dummy variable equal to1 if the fund manager runs multiple funds with different investment styles. Timer is a dummyequal to 1 if the fund manager has been able to time the market during the past 24 months.Picker is a dummy variable equal to 1 if the fund manager was able to pick stocks efficientlyduring the past 24 months. All variables are lagged one period. A full description of the re-maining variables is in the appendix. Time and investment objective dummies are includedbut not reported; t-statistics are reported in parentheses. We adjust for serial correlation byclustering standard errors at the fund level. * denotes significance at the 10% level, ** denotessignificance at the 5% level and *** denotes significance at the 1% level.

Panel A: Fund Fixed EffectFund Performance Manager Performance

Generalist -0.121∗∗ -0.037 -0.089 -0.115∗∗ -0.030 -0.069(-2.15) (-0.70) (-1.58) (-2.10) (-0.57) (-1.26)

Timer -0.082 -0.061 -0.083 -0.062(-1.43) (-1.06) (-1.45) (-1.09)

Generalist × Timer 0.315∗∗∗ 0.294∗∗∗ 0.252∗∗∗ 0.227∗∗∗

(3.23) (3.01) (2.96) (2.64)Picker 0.284∗∗∗ 0.279∗∗∗ 0.284∗∗∗ 0.279∗∗∗


(-2.67) (-2.47) (-3.45) (-3.28)r2 0.043 0.044 0.044 0.043 0.043 0.043

Panel B: Manager Fixed EffectFund Performance Manager Performance

Generalist -0.170∗∗∗ -0.085∗ -0.137∗∗ -0.161∗∗∗ -0.078 -0.118∗∗

(-3.17) (-1.67) (-2.52) (-3.12) (-1.59) (-2.29)Timer -0.105∗ -0.085 -0.104∗ -0.084

(-1.89) (-1.53) (-1.88) (-1.52)Generalist × Timer 0.324∗∗∗ 0.303∗∗∗ 0.261∗∗∗ 0.237∗∗∗

(3.48) (3.25) (3.21) (2.90)Picker 0.255∗∗∗ 0.248∗∗∗ 0.263∗∗∗ 0.255∗∗∗


(-2.72) (-2.53) (-3.31) (-3.15)r2 0.039 0.039 0.039 0.040 0.040 0.040

Panel C: Family Fixed EffectFund Performance Manager Performance

Generalist -0.064∗ 0.003 -0.038 -0.063∗ 0.008 -0.022(-1.73) (0.08) (-0.98) (-1.82) (0.25) (-0.61)

Timer -0.074 -0.053 -0.072 -0.051(-1.42) (-1.02) (-1.42) (-1.01)

Generalist × Timer 0.257∗∗∗ 0.242∗∗∗ 0.201∗∗∗ 0.182∗∗

(2.96) (2.78) (2.61) (2.35)Picker 0.285∗∗∗ 0.281∗∗∗ 0.292∗∗∗ 0.289∗∗∗


(-2.69) (-2.53) (-3.62) (-3.48)r2 0.028 0.028 0.028 0.029 0.029 0.029

Control Variables Yes Yes Yes Yes Yes YesTime Dummies Yes Yes Yes Yes Yes YesStyle Dummies Yes Yes Yes Yes Yes YesObservations 80059 80059 80059 80059 80059 80059

38

Table 8: Managerial Skill and Performance: Subsamples

This table presents the results of monthly panel regressions of portfolio performance on managerfunction (generalist or specialist) and other characteristics. Fund returns are actual returnsbefore fees and expenses (gross) and manager returns are the TNA-weighted average returns ofall the portfolios simultaneously managed specific managers. Dependent variables are the fundreturn –columns (1)-(3)– and manager return –(4)-(6)–, both after substracting the medianreturn of their investment objective peers. Observations are sorted into funds managed byTimers in columns 1 and 4, Pickers in columns 2 and 5, and Unskilled managers in column3 and 6. Generalist is a dummy variable equal to 1 if the fund manager runs multiple fundswith different investment styles. All variables are lagged one period. A full description of theremaining variables is in the appendix. Time and investment objective dummies are includedbut not reported; t-statistics are reported in parentheses. We adjust for serial correlation byclustering standard errors at the fund level. * denotes significance at the 10% level, ** denotessignificance at the 5% level and *** denotes significance at the 1% level.

Fund Performance Manager PerformanceTimer Picker Unskilled Timer Picker Unskilled

Generalist 0.199∗∗ -0.143∗ 0.004 0.146∗ -0.195∗∗ 0.008(2.45) (-1.83) (0.11) (1.96) (-2.44) (0.24)

Fund Size 0.025 0.054∗ 0.039∗∗∗ 0.017 0.039 0.028∗∗

(0.92) (1.79) (2.87) (0.59) (1.33) (2.09)Fund Age -0.006 0.002 -0.001 -0.004 0.003 -0.000

(-1.57) (0.39) (-0.38) (-1.15) (0.64) (-0.15)Fund Turnover 0.071∗∗ 0.042∗∗∗ -0.001 0.043 0.012 0.001

(2.20) (4.00) (-0.08) (1.54) (0.90) (0.07)Fund Expenses 0.200∗∗ 0.146 0.157∗∗∗ 0.147∗ 0.131 0.139∗∗∗

(2.53) (1.56) (4.14) (1.94) (1.51) (3.77)Fund Flows 0.060∗ 0.061∗∗∗ 0.064∗∗∗ 0.047 0.054∗∗ 0.065∗∗∗

(1.82) (2.69) (4.22) (1.51) (2.50) (4.43)Past Year Return -0.154 -1.761∗∗∗ -0.364∗∗∗ -0.062 -1.586∗∗∗ -0.368∗∗∗

(-0.46) (-4.97) (-3.18) (-0.20) (-4.44) (-3.27)Family Size 0.010 -0.040∗ 0.026∗∗∗ 0.028 -0.014 0.027∗∗∗

(0.42) (-1.79) (2.73) (1.23) (-0.63) (2.87)Family Funds 0.003∗∗ 0.003∗ -0.001 0.003∗ 0.002∗ -0.001

(2.13) (1.96) (-1.58) (1.72) (1.72) (-1.07)Family Managers -0.007∗∗ 0.000 0.000 -0.007∗∗ 0.000 -0.000

(-2.19) (0.07) (0.10) (-2.36) (0.20) (-0.29)Supply Advising -0.116 0.033 -0.088∗∗∗ -0.094 -0.005 -0.097∗∗∗

(-1.29) (0.36) (-2.62) (-1.18) (-0.05) (-2.98)Demand Advising -0.134∗∗ -0.144∗∗ 0.031 -0.055 -0.150∗∗ 0.039

(-1.98) (-2.03) (1.18) (-0.89) (-2.20) (1.53)MBA -0.115 -0.033 0.020 -0.183∗∗ -0.059 0.026

(-1.48) (-0.47) (0.73) (-2.58) (-0.88) (0.94)PhD -0.277 -0.077 -0.035 -0.239 -0.065 -0.026

(-1.58) (-0.55) (-0.65) (-1.44) (-0.47) (-0.50)Past Positions 0.035 -0.051∗∗ 0.000 0.046∗ -0.047∗∗ -0.002

(1.24) (-2.13) (0.03) (1.83) (-2.07) (-0.16)Ivy League 0.028 -0.048 0.016 0.114 -0.029 0.009

(0.32) (-0.54) (0.42) (1.43) (-0.33) (0.26)Manager Funds 0.004 0.001 0.002 -0.008 -0.013∗∗ 0.003

(0.63) (0.15) (0.69) (-1.55) (-1.97) (0.99)Manager Size 0.059∗∗ 0.002 -0.008 0.052∗ -0.002 -0.004

(2.01) (0.08) (-0.59) (1.79) (-0.07) (-0.29)Fund Affiliation -0.001 -0.010 -0.001 0.008 -0.006 -0.000

(-0.09) (-1.11) (-0.39) (0.85) (-0.68) (-0.13)Constant -0.836∗∗∗ 0.998∗∗∗ -0.596∗∗∗ -0.757∗∗ 0.955∗∗∗ -0.525∗∗∗

(-2.65) (3.37) (-5.20) (-2.52) (3.37) (-4.71)Time Dummies Yes Yes Yes Yes Yes YesStyle Dummies Yes Yes Yes Yes Yes YesObservations 11197 15473 57158 11197 15473 57158r2 0.036 0.032 0.019 0.035 0.031 0.019

39

Table 9: Portfolio Manager Mis-allocation

This table presents the results of monthly logistic regressions of portfolio manager misalloca-tion within their management companies on fund, manager and family characteristics. Thedependent variable is a dummy variable that equals 1 if a Picker is allocated as a Generalist(Generalist Misalloaction) or a Timer is allocated as a Specialist (Specialist Misallocation). Allvariables are lagged one period. A description of fund, manager and family variables is in theappendix. The sample contains all U.S. mutual funds run by an individual portfolio managerfrom 1996 to 2011. Time and investment objective dummies are included but not reported; t-statistics are reported in parentheses. Standard errors are clustered at the fund level. * denotessignificance at the 10% level, ** denotes significance at the 5% level and *** denotes significanceat the 1% level.

Generalist Mis-allocation Specialist Mis-allocationCoef/t Mfx/Std Coef/t Mfx/Std

Fund Size -0.248∗∗∗ -0.006∗∗∗ 0.042 0.003(-4.677) 1.792 (1.192) 1.791

Fund Age -0.002 -0.000 0.004 0.000(-0.185) 9.491 (0.784) 9.144

Fund Turnover 0.086∗∗∗ 0.002∗∗∗ 0.004 0.000(3.593) 1.683 (0.282) 1.700

Fund Expenses -0.116 -0.003 0.261∗∗∗ 0.017∗∗∗

(-0.634) 0.537 (3.076) 0.535Fund Flows 0.017 0.000 -0.008 -0.001

(1.412) 2.275 (-0.584) 2.302Past Year Return 0.285∗ 0.007∗ -1.326∗∗∗ -0.085∗∗∗

(1.782) 0.196 (-7.568) 0.198Family Size -0.131∗∗∗ -0.003∗∗∗ 0.044∗∗∗ 0.003∗∗∗

(-4.133) 2.565 (2.896) 2.554Family Funds -0.011∗∗∗ -0.000∗∗∗ 0.001 0.000

(-3.454) 38.983 (0.947) 39.404Family Managers -0.024∗∗∗ -0.001∗∗∗ -0.001 -0.000

(-5.134) 18.511 (-0.248) 18.590Supply Advising -0.114 -0.003 0.022∗∗ 0.001∗∗

(-0.668) 0.473 (2.064) 0.471Demand Advising 0.134 0.003 0.101 0.007

(0.863) 0.492 (1.442) 0.492MBA 0.330∗∗ 0.008∗∗ 0.116 0.007

(2.160) 0.499 (1.591) 0.499PhD 0.570∗∗ 0.015∗∗ -0.384∗ -0.025∗

(2.003) 0.179 (-1.771) 0.176Past Positions -0.021 -0.001 0.019 0.001

(-0.409) 1.364 (0.677) 1.352Ivy League -0.257 -0.007 -0.046 -0.003

(-1.385) 0.428 (-0.563) 0.430Manager Funds 0.051∗∗∗ 0.001∗∗∗ 0.066∗∗∗ 0.004∗∗∗

(3.098) 4.443 (8.617) 4.500Manager Size 0.772∗∗∗ 0.020∗∗∗ -0.094∗∗∗ -0.006∗∗∗

(14.985) 1.846 (-2.754) 1.835Fund Affiliation 0.002 0.000 0.005 0.000

(0.098) 4.446 (0.559) 4.393Time Dummies Yes YesStyle Dummies Yes YesObservations 80059 80059Pseudo R2 0.180 0.091Baseline Predicted Prob 0.054 0.089

40

Table 10: Specialist Reassigned as Generalist

This table presents the monthly logistic regressions of manager reassignments from specialist togeneralist, on manager and other characteristics. The dependent variable is a dummy variablethat equals 1 if a specialist becomes generalist in the next month, and 0 otherwise. Timer is adummy variable that takes the value of 1 if the manager has been timing the market significantlyfor the past 24 months, and 0 otherwise. Picker is a dummy variable that takes the value of 1if the manager has been selecting stocks successfully for the past 24 months, and 0 otherwise.Manager Past Skill is the TNA-weighted average of the objective-adjusted returns of all thefunds run by the manager during the past 24 months. All variables are lagged one period. Adescription of the remaining variables is in the appendix. The sample contains all U.S. mutualfunds managed by a specialist from 1996 to 2011. Time and investment objective dummies areincluded but not reported; t-statistics are in parentheses. Standard errors are clustered at thefund level. * denotes significance at the 10% level, ** denotes significance at the 5% level and*** denotes significance at the 1% level.

Model 1 Model 2 Model 3 Model 4Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/Std

Timer 0.711∗∗∗ 0.003∗∗∗ 0.806∗∗∗ 0.003∗∗∗ 0.876∗∗∗ 0.003∗∗∗

(4.800) 0.310 (5.454) 0.310 (5.067) 0.314Picker 0.679∗∗∗ 0.002∗∗∗ 0.769∗∗∗ 0.003∗∗∗ 0.766∗∗∗ 0.002∗∗∗

(4.681) 0.326 (5.288) 0.326 (4.358) 0.332Manager Past Skill 0.004 0.000

(0.962) 17.683Timer × Manager Past Skill 0.021∗∗∗ 0.001∗∗∗

(3.265) 4.611Picker × Manager Past Skill -0.028∗∗∗ -0.001∗∗∗

(-3.915) 10.278Fund Size -0.273∗∗∗ -0.001∗∗∗ -0.276∗∗∗ -0.001∗∗∗ -0.273∗∗∗ -0.001∗∗∗ -0.262∗∗∗ -0.001∗∗∗

(-5.680) 1.773 (-5.733) 1.773 (-5.680) 1.773 (-4.710) 1.810Fund Age -0.001 -0.000 0.001 0.000 0.001 0.000 0.008 0.000

(-0.112) 9.062 (0.242) 9.062 (0.155) 9.062 (1.044) 9.211Fund Turnover 0.040∗ 0.000∗ 0.039∗ 0.000∗ 0.041∗ 0.000∗ 0.033 0.000

(1.844) 1.408 (1.824) 1.408 (1.921) 1.408 (1.166) 1.331Fund Expenses -0.434∗∗∗ -0.002∗∗∗ -0.400∗∗ -0.001∗∗ -0.452∗∗∗ -0.002∗∗∗ -0.517∗∗∗ -0.002∗∗∗

(-2.590) 0.533 (-2.418) 0.533 (-2.699) 0.533 (-2.649) 0.534Fund Flows 0.004 0.000 0.001 0.000 0.002 0.000 0.009 0.000

(0.216) 2.488 (0.065) 2.488 (0.141) 2.488 (0.554) 2.335Past Year Return 0.305 0.001 -0.019 -0.000 0.113 0.000 0.180 0.001

(1.467) 0.196 (-0.090) 0.196 (0.521) 0.196 (0.564) 0.188Family Size -0.148∗∗∗ -0.001∗∗∗ -0.144∗∗∗ -0.001∗∗∗ -0.146∗∗∗ -0.001∗∗∗ -0.145∗∗∗ -0.000∗∗∗

(-4.992) 2.542 (-4.754) 2.542 (-4.895) 2.542 (-4.219) 2.560Family Funds -0.004∗ -0.000∗ -0.004∗ -0.000∗ -0.004∗ -0.000∗ -0.003 -0.000

(-1.812) 40.412 (-1.820) 40.412 (-1.810) 40.412 (-1.417) 39.002Family Managers -0.002 -0.000 -0.002 -0.000 -0.002 -0.000 -0.004 -0.000

(-0.601) 18.531 (-0.575) 18.531 (-0.580) 18.531 (-1.016) 17.831Supply Advising 0.076 0.000 0.034 0.000 0.043 0.000 0.193 0.001

(0.527) 0.470 (0.237) 0.470 (0.297) 0.470 (1.096) 0.474Demand Advising 0.397∗∗∗ 0.001∗∗∗ 0.446∗∗∗ 0.002∗∗∗ 0.419∗∗∗ 0.001∗∗∗ 0.459∗∗∗ 0.002∗∗∗

(3.153) 0.491 (3.492) 0.491 (3.310) 0.491 (3.051) 0.493MBA 0.139 0.001 0.141 0.001 0.130 0.000 0.285∗ 0.001∗

(1.045) 0.499 (1.067) 0.499 (0.977) 0.499 (1.759) 0.499PhD 0.364 0.001 0.317 0.001 0.335 0.001 -0.136 -0.000

(1.154) 0.170 (0.964) 0.170 (1.029) 0.170 (-0.294) 0.158Past Positions 0.027 0.000 0.022 0.000 0.019 0.000 -0.051 -0.000

(0.620) 1.353 (0.514) 1.353 (0.454) 1.353 (-0.960) 1.328Ivy League -0.050 -0.000 -0.071 -0.000 -0.064 -0.000 -0.179 -0.001

(-0.326) 0.427 (-0.461) 0.427 (-0.414) 0.427 (-0.921) 0.434Manager Funds 0.052∗∗∗ 0.000∗∗∗ 0.054∗∗∗ 0.000∗∗∗ 0.050∗∗∗ 0.000∗∗∗ 0.063∗∗∗ 0.001∗∗∗

(2.901) 4.667 (3.116) 4.667 (2.816) 4.667 (3.130) 5.176Manager Size 0.643∗∗∗ 0.002∗∗∗ 0.633∗∗∗ 0.002∗∗∗ 0.622∗∗∗ 0.002∗∗∗ 0.572∗∗∗ 0.002∗∗∗

(12.471) 1.806 (12.055) 1.806 (12.066) 1.806 (9.451) 1.812Fund Affiliation -0.136∗∗∗ -0.000∗∗∗ -0.136∗∗∗ -0.000∗∗∗ -0.137∗∗∗ -0.000∗∗∗ -0.148∗∗∗ -0.001∗∗∗

(-5.904) 4.309 (-5.931) 4.309 (-5.945) 4.309 (-5.461) 4.286Time Dummies Yes Yes Yes YesStyle Dummies Yes Yes Yes YesObservations 62235 62235 62235 62235Pseudo R2 0.116 0.116 0.122 0.140Baseline Predicted Prob 0.012 0.012 0.013 0.013

41

Table 11: Performance around Managerial Switch: Event Study

We present average fund performance in Panel A and manager performance in Panel B over 6 months before and 12 months after the managerswitches from specialist to generalist. The performance is expressed in percentage and corresponds to the semi-annual cumulative objective-adjustedreturn before fees and expenses (gross). The analysis is for the entire sample from 1996 to 2011. Our sample is divided into funds run by timers,pickers and unskilled portfolio managers. Panel A displays funds that go from being managed by a timer, picker or unskilled specialist to bemanaged by a generalist (the same manager or a new one) with the same type of skill. Panel B displays funds that keep the same manager whowas specialist timer, picker or unskilled, and becomes generalist (independently of the new skill status). We also report differences between thecumulative performance before and after each event. * denotes significance at the 10% level, ** denotes significance at the 5% level and *** denotessignificance at the 1% level.

Panel A: Fund Performancet-6 t-5 t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9 t+10 t+11 t+12

Timer -1.39 -1.14 -0.92 -2.34 -2.52 -0.07 0.92 -3.66 -2.44 -0.47 1.51 1.34 0.05 2.79 7.14 0.99 1.00 2.90 3.68Picker 0.10 -0.08 -0.16 0.30 0.11 0.57 -0.04 0.02 0.49 0.94 0.31 -0.36 -0.84 -0.36 -0.67 -0.94 -0.99 -1.17 -2.10Unskilled -0.95 -0.76 -0.32 -0.54 -0.89 -0.44 -0.96 -0.56 0.15 2.50 2.09 1.24 -0.69 -0.51 -0.02 -0.52 -1.34 -1.10 -2.01

Cumulative Performance DifferencePrior Event - Post Event

Value t-statTimer -6.194 -2.57Picker 2.211 2.32

Unskilled 1.113 1.64

Panel B: Manager Performancet-6 t-5 t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9 t+10 t+11 t+12

Timer -0.61 0.19 0.17 -1.12 -1.70 -0.08 0.77 -0.21 1.04 2.51 3.29 2.66 1.36 1.18 1.20 0.57 0.16 0.43 1.89Picker 0.09 0.25 0.11 0.16 0.17 0.47 -0.03 0.14 0.14 0.88 0.79 -0.25 -1.25 -0.77 -1.03 -1.21 -1.24 -1.32 -1.76Unskilled -0.62 -0.80 -0.35 -0.45 -0.89 -0.43 -0.47 -0.09 0.63 2.28 2.64 1.70 -0.07 0.07 0.12 -0.47 -1.24 -0.70 -2.00

Cumulative Performance DifferencePrior Event - Post Event

Value t-statTimer -3.587 -2.33Picker 1.933 2.15

Unskilled 1.109 1.79

42

Table 12: Propensity Score Matching: Fund Performance and Manager Skill

In this table, we identify a control sample of funds managed by specialists with two differentpropensity score matching procedures: Nearest Neighbor of Rosenbaum and Rubin (1983), andthe Kernel Matching of Heckman, Ichimura and Todd, (1997, 1998). To estimate the propensityscore we use the size, age, turnover, expenses, flows and past returns of the funds, the size andthe number of funds and managers of the family, and the number of prior positions, the lengthof time the manager has been in charge of the fund, the number of funds and the total amountof assets the manager has currently under management. We require that the difference betweenthe propensity score of the funds managed by a specialist and the matching peer does not exceed0.1% in absolute value. We then compare the fund performance between the two groups andreport the value of the difference (Generalist-Specialist) and the statistical significance usingbootstrapped standard errors associated to that difference. Fund performance is the excessreturn over the mean of the style, using the gross and net returns of the portfolio. We groupthe funds into quintiles according to the timing ability (Panel A) and picking ability (Panel B)during the period 1996-2011.* denotes significance at the 10% level, ** denotes significance atthe 5% level and *** denotes significance at the 1% level

Generalist vs SpecialistPanel A:Timing Q1 Q2 Q3 Q4 Q5

Nearest Kernel Nearest Kernel Nearest Kernel Nearest Kernel Nearest KernelGross Ret 0.110 0.013 0.179 0.278 0.072 0.034 0.314∗∗ 0.375∗∗∗ 0.422∗∗∗ 0.685∗∗∗

Net Ret 0.111 -0.011 0.158 0.250 0.067 0.024 0.137 0.189 0.455∗∗∗ 0.720∗∗∗

Panel B:Picking Q1 Q2 Q3 Q4 Q5

Nearest Kernel Nearest Kernel Nearest Kernel Nearest Kernel Nearest KernelGross Ret 0.342 0.232 -0.010 -0.023 -0.017 -0.032 - 0.136 -0.209 -0.894∗∗∗ -0.738∗∗∗

Net Ret 0.351 0.240 -0.002 -0.031 -0.037 -0.010 -0.139∗∗∗ -0.218∗∗∗ -0.851∗∗∗ -0.716∗∗∗

43

Table 13: Selection Bias: Heckman’s two-step procedure (1st Stage)

In this table, we display estimates from the first stage Heckman’s two-step procedure. Themodel estimates the probability that a manager works for a firm with multi-funds policy. Wepresent the monthly logistic regressions of managers running multiple funds on fund and familycharacteristics. Funds per Manager is the total number of funds of the family divided bythe number of managers. All variables are lagged one period. A complete description of thevariables is provided in the appendix. The sample contains all U.S. mutual funds managedby specialists from 1996 to 2011. Time and investment objective dummies are included butnot reported; t-statistics are in parentheses. Standard errors are clustered at the fund level. *denotes significance at the 10% level, ** denotes significance at the 5% level and *** denotessignificance at the 1% level.

Multi-Funds Policy=1

Coef/t Mfx/StdFund Size 0.003 -0.001

(0.180) 1.762Fund Age -0.000 0.000

(-0.090) 9.232Fund Turnover -0.031 0.000

(-1.614) 1.498Fund Expenses -0.065 -0.004

(-0.921) 0.542Fund Flows 0.001 0.000

(0.179) 2.286Past Year Return -0.060 0.003

(-0.896) 0.201Funds per Manager 0.007∗∗∗ 0.001∗∗∗

(2.707) 8.684Time Dummies YesStyle Dummies YesObservations 90955

44

Table 14: Selection Bias: Heckman’s two-step procedure (2nd Stage)

This table displays estimates from the second stage Heckman’s two-step procedure, that weuse to examine how different manager characteristics affect the probability of a specialist re-assignment as generalist, conditioned on being a multi-fund manager. All variables are laggedone period. A complete description of the variables is provided in the appendix. The samplecontains all U.S. mutual funds managed by specialists from 1996 to 2011. Time and investmentobjective dummies are included but not reported; t-statistics are in parentheses. Standard errorsare clustered at the fund level. * denotes significance at the 10% level, ** denotes significanceat the 5% level and *** denotes significance at the 1% level.

Model 1 Model 2 Model 3 Model 4Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/Std

Timer 0.215∗∗∗ 0.017∗∗∗ 0.257∗∗∗ 0.015∗∗∗ 0.309∗∗∗ 0.012∗∗∗

(3.235) 0.298 (4.096) 0.298 (4.359) 0.293Picker 0.233∗∗∗ 0.013∗∗∗ 0.265∗∗∗ 0.014∗∗∗ 0.267∗∗∗ 0.009∗∗∗

(3.863) 0.316 (4.313) 0.316 (3.829) 0.317Manager Past Skill 0.003 0.000

(1.544) 18.341Timer*Manager Past Skill 0.010∗∗∗ 0.002∗∗∗

(2.941) 5.148Picker*Manager Past Skill -0.015∗∗∗ -0.001∗∗∗

(-4.839) 9.557Fund Size -0.027 -0.002 -0.030 -0.002 -0.029 -0.002 -0.025 -0.001

(-1.266) 1.755 (-1.380) 1.755 (-1.344) 1.755 (-1.028) 1.762Fund Age 0.005 0.000 0.005 0.000 0.005 0.000 0.007∗ 0.000∗

(1.356) 9.096 (1.390) 9.096 (1.386) 9.096 (1.783) 9.232Fund Turnover 0.024 0.002 0.021 0.001 0.021 0.001 0.014 0.000

(1.354) 1.478 (1.257) 1.478 (1.283) 1.478 (0.812) 1.498Fund Expenses -0.084 -0.006 -0.088 -0.005 -0.100 -0.006 -0.129∗ -0.004∗

(-1.022) 0.540 (-1.215) 0.540 (-1.361) 0.540 (-1.723) 0.542Fund Flows 0.003 0.000 0.002 0.000 0.002 0.000 0.006 0.000

(0.433) 2.260 (0.301) 2.260 (0.340) 2.260 (1.011) 2.286Past Year Return 0.116 0.009 0.028 0.002 0.060 0.003 0.089 0.003

(1.332) 0.203 (0.313) 0.203 (0.678) 0.203 (0.686) 0.201Family Size -0.051∗∗∗ -0.004∗∗∗ -0.050∗∗∗ -0.003∗∗∗ -0.051∗∗∗ -0.003∗∗∗ -0.056∗∗∗ -0.002∗∗∗

(-3.718) 2.598 (-3.793) 2.598 (-3.862) 2.598 (-3.741) 2.614Family Funds -0.002∗∗∗ -0.000∗∗∗ -0.002∗∗∗ -0.000∗∗∗ -0.002∗∗∗ -0.000∗∗∗ -0.002∗∗ -0.000∗∗

(-2.786) 40.769 (-2.803) 40.769 (-2.710) 40.769 (-2.118) 40.481Family Managers 0.002 0.000 0.002 0.000 0.002 0.000 0.002 0.000

(1.595) 17.998 (1.537) 17.998 (1.503) 17.998 (0.948) 17.766Supply Advising -0.018 -0.001 -0.027 -0.002 -0.026 -0.001 0.016 0.001

(-0.295) 0.471 (-0.445) 0.471 (-0.429) 0.471 (0.226) 0.472Demand Advising 0.168∗∗∗ 0.013∗∗∗ 0.196∗∗∗ 0.011∗∗∗ 0.183∗∗∗ 0.010∗∗∗ 0.220∗∗∗ 0.007∗∗∗

(2.933) 0.490 (3.562) 0.490 (3.358) 0.490 (3.521) 0.491MBA 0.084∗ 0.007∗ 0.081 0.004 0.080 0.004 0.119∗ 0.004∗

(1.658) 0.499 (1.544) 0.499 (1.516) 0.499 (1.883) 0.499PhD -0.034 -0.003 -0.062 -0.003 -0.044 -0.002 -0.316 -0.010

(-0.245) 0.177 (-0.425) 0.177 (-0.298) 0.177 (-1.390) 0.167Past Positions 0.012 0.001 0.011 0.001 0.009 0.000 -0.010 -0.000

(0.684) 1.340 (0.592) 1.340 (0.493) 1.340 (-0.484) 1.341Ivy League 0.037 0.003 0.038 0.002 0.035 0.002 -0.004 -0.000

(0.632) 0.424 (0.618) 0.424 (0.575) 0.424 (-0.056) 0.426Manager Funds 0.001 0.000 0.001 0.000 -0.000 -0.000 0.005 0.000

(0.117) 3.911 (0.226) 3.911 (-0.051) 3.911 (0.633) 3.935Manager Size 0.126∗∗∗ 0.010∗∗∗ 0.124∗∗∗ 0.007∗∗∗ 0.120∗∗∗ 0.007∗∗∗ 0.125∗∗∗ 0.004∗∗∗

(4.248) 1.834 (4.737) 1.834 (4.636) 1.834 (4.264) 1.848Fund Affiliation -0.038∗∗∗ -0.003∗∗∗ -0.040∗∗∗ -0.002∗∗∗ -0.039∗∗∗ -0.002∗∗∗ -0.043∗∗∗ -0.001∗∗∗

(-4.284) 4.205 (-4.896) 4.205 (-4.816) 4.205 (-4.591) 4.206Time Dummies Yes Yes Yes YesStyle Dummies Yes Yes Yes YesObservations 90955 90955 90955 90955Baseline Predicted Prob 0.051 0.034 0.035 0.020

45

Table 15: Family Expansion

This table presents the results of monthly logistic regressions of manager switches from specialists to generalists on manager and other characteristics.The dependent variable is a dummy variable that equals 1 if a specialist portfolio manager is reassigned as generalist in the next month and 0otherwise. Timer is a dummy variable that takes the value of 1 if the fund is managed by a manager who has been timing the market in a statisticallysignificant way during the past 24 months. Picker is a dummy variable that takes the value of 1 if the fund is managed by a manager who has beenselecting stock successfully during the past 24 months, and 0 otherwise. The sample is divided into quartiles based on the Herfindahl index acrossfunds’ investment objectives within the family for each month. We consider Low and High concentrated firms based on whether the managementcompany is in the first or fourth quantile, respectively. All variables are lagged one period. A description of the remaining variables is providedin the appendix. The sample contains all U.S. mutual funds managed by specialists from 1996 to 2011. Time and Investment Objective dummiesare included but not reported; t-statistics are reported in parentheses. Standard errors are clustered at the fund level. * denotes significance at the10% level, ** denotes significance at the 5% level and *** denotes significance at the 1% level.

Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/StdTimer 1.265∗∗∗ 0.002∗∗∗ 1.205∗∗∗ 0.002∗∗∗ 1.320∗∗∗ 0.003∗∗∗ 1.292∗∗∗ 0.002∗∗∗

(4.615) 0.334 (3.256) 0.285 (4.910) 0.334 (3.502) 0.285Picker 0.196 0.000 0.312 0.001 0.396 0.001 0.525 0.001

(0.600) 0.341 (0.723) 0.333 (1.225) 0.341 (1.196) 0.333Fund Size -0.287∗∗∗ -0.001∗∗∗ -0.296∗ -0.001∗ -0.284∗∗∗ -0.001∗∗∗ -0.313∗ -0.001∗ -0.284∗∗∗ -0.001∗∗∗ -0.307∗ -0.001∗

(-3.728) 1.954 (-1.708) 1.589 (-3.586) 1.954 (-1.777) 1.589 (-3.662) 1.954 (-1.744) 1.589Fund Age 0.002 0.000 0.001 0.000 0.002 0.000 0.002 0.000 0.003 0.000 0.002 0.000

(0.177) 10.959 (0.044) 7.014 (0.146) 10.959 (0.081) 7.014 (0.255) 10.959 (0.091) 7.014Fund Turnover -0.105 -0.000 0.081 0.000 -0.115 -0.000 0.080 0.000 -0.139 -0.000 0.086 0.000

(-0.551) 1.533 (1.465) 1.509 (-0.582) 1.533 (1.401) 1.509 (-0.683) 1.533 (1.438) 1.509Fund Expenses -0.190 -0.000 -0.086 -0.000 -0.165 -0.000 -0.059 -0.000 -0.166 -0.000 -0.125 -0.000

(-0.514) 0.448 (-0.281) 0.599 (-0.421) 0.448 (-0.196) 0.599 (-0.447) 0.448 (-0.403) 0.599Fund Flows -0.256∗ -0.000∗ 0.046∗∗∗ 0.000∗∗∗ -0.300∗ -0.001∗ 0.042∗∗∗ 0.000∗∗∗ -0.265∗ -0.001∗ 0.043∗∗∗ 0.000∗∗∗

(-1.735) 2.189 (3.679) 3.225 (-1.687) 2.189 (3.475) 3.225 (-1.758) 2.189 (3.617) 3.225Past Year Return -0.293 -0.001 0.647∗ 0.001∗ -0.557 -0.001 0.446 0.001 -0.306 -0.001 0.575 0.001

(-0.476) 0.173 (1.802) 0.253 (-0.979) 0.173 (1.109) 0.253 (-0.498) 0.173 (1.554) 0.253Family Size -0.217∗∗∗ -0.000∗∗∗ -0.019 -0.000 -0.201∗∗∗ -0.000∗∗∗ -0.009 -0.000 -0.220∗∗∗ -0.000∗∗∗ -0.019 -0.000

(-3.877) 2.399 (-0.159) 2.358 (-3.514) 2.399 (-0.079) 2.358 (-3.918) 2.399 (-0.154) 2.358Family Funds -0.000 -0.000 -0.002 -0.000 -0.000 -0.000 0.001 0.000 -0.000 -0.000 -0.002 -0.000

(-0.149) 56.310 (-0.085) 12.960 (-0.154) 56.310 (0.081) 12.960 (-0.165) 56.310 (-0.085) 12.960Family Managers 0.008 0.000 -0.015 -0.000 0.010∗ 0.000∗ -0.019 -0.000 0.008 0.000 -0.006 -0.000

(1.564) 27.565 (-0.089) 3.770 (1.764) 27.565 (-0.117) 3.770 (1.553) 27.565 (-0.039) 3.770Supply Advising -0.001 -0.000 -0.767∗∗ -0.001∗∗ -0.161 -0.000 -0.766∗ -0.001∗ -0.007 -0.000 -0.797∗∗ -0.001∗∗

(-0.002) 0.402 (-1.994) 0.500 (-0.442) 0.402 (-1.869) 0.500 (-0.018) 0.402 (-2.070) 0.500Demand Advising 1.041∗∗∗ 0.002∗∗∗ 0.579 0.001 1.210∗∗∗ 0.002∗∗∗ 0.576 0.001 1.022∗∗∗ 0.002∗∗∗ 0.570 0.001

(3.441) 0.469 (1.425) 0.479 (3.725) 0.469 (1.396) 0.479 (3.383) 0.469 (1.403) 0.479MBA -0.139 -0.000 -0.039 -0.000 -0.162 -0.000 0.070 0.000 -0.151 -0.000 -0.053 -0.000

(-0.517) 0.499 (-0.106) 0.500 (-0.596) 0.499 (0.186) 0.500 (-0.563) 0.499 (-0.142) 0.500PHD 0.108 0.000 0.692 0.001 -0.059 -0.000 0.701 0.001 0.063 0.000 0.660 0.001

(0.201) 0.160 (1.076) 0.209 (-0.111) 0.160 (1.067) 0.209 (0.114) 0.160 (1.064) 0.209Past Positions 0.030 0.000 0.050 0.000 0.004 0.000 0.078 0.000 0.024 0.000 0.055 0.000

(0.351) 1.320 (0.407) 1.300 (0.048) 1.320 (0.624) 1.300 (0.274) 1.320 (0.446) 1.300Ivy League 0.057 0.000 -0.416 -0.001 0.051 0.000 -0.519 -0.001 0.086 0.000 -0.480 -0.001

(0.213) 0.491 (-0.761) 0.370 (0.191) 0.491 (-0.894) 0.370 (0.322) 0.491 (-0.831) 0.370Manager Funds -0.026 -0.000 0.336∗∗∗ 0.001∗∗∗ -0.019 -0.000 0.345∗∗∗ 0.001∗∗∗ -0.026 -0.000 0.332∗∗∗ 0.001∗∗∗

(-1.108) 7.265 (2.631) 2.100 (-0.793) 7.265 (2.690) 2.100 (-1.111) 7.265 (2.627) 2.100Manager Size 0.764∗∗∗ 0.001∗∗∗ 0.519∗∗∗ 0.001∗∗∗ 0.789∗∗∗ 0.002∗∗∗ 0.545∗∗∗ 0.001∗∗∗ 0.755∗∗∗ 0.001∗∗∗ 0.520∗∗∗ 0.001∗∗∗

(6.449) 1.756 (2.687) 1.666 (6.276) 1.756 (2.834) 1.666 (6.322) 1.756 (2.684) 1.666Fund Affiliation -0.260∗∗∗ -0.001∗∗∗ -0.097∗ -0.000∗ -0.263∗∗∗ -0.001∗∗∗ -0.100∗ -0.000∗ -0.263∗∗∗ -0.001∗∗∗ -0.097∗ -0.000∗

(-5.304) 4.400 (-1.901) 4.539 (-5.287) 4.400 (-1.911) 4.539 (-5.290) 4.400 (-1.912) 4.539Family Concentration Low High Low High Low HighTime Dummies Yes Yes Yes Yes Yes YesStyle Dummies Yes Yes Yes Yes Yes YesObservations 19234 12082 19234 12082 19234 12082Pseudo R2 0.210 0.190 0.195 0.177 0.212 0.192Baseline Predicted Prob 0.011 0.034 0.011 0.033 0.011 0.035

46

Table 16: Downturn Markets

This table presents the results of monthly logistic regressions of manager reassignments from specialists to generalists on manager and othercharacteristics. The dependent variable is a dummy variable that equals 1 if a specialist becomes generalist in the next month and 0 otherwise.Timer is a dummy variable that takes the value of 1 if the fund is run by a manager who has been timing the market significantly during the past24 months. Picker is a dummy variable that takes the value of 1 if the fund is managed by manager that has been efficiently selecting stock duringthe past 24 months and 0 otherwise. Manager Past Skill is measured as the TNA-weighted cumulative returns of the objective-adjusted returns ofall the funds run by the manager during the past 24 months. Market Condition is measured with the Chicago Fed National Activity Index. Allvariables are lagged one period. A description of the remaining variables is provided in the appendix. The sample contains all U.S. mutual fundsmanaged by a specialist portfolio manager from 1996 to 2011. Time and Investment Objective dummies are included but not reported; t-statisticsare reported in parentheses. Standard errors are clustered at the fund level. * denotes significance at the 10% level, ** denotes significance at the5% level and *** denotes significance at the 1% level.

Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/Std Coef/t Mfx/StdTimer 1.135∗∗∗ 0.006∗∗∗ 0.926∗∗∗ 0.007∗∗∗ 1.269∗∗∗ 0.006∗∗∗ 0.912∗∗∗ 0.007∗∗∗

(4.850) 0.288 (3.243) 0.333 (5.248) 0.288 (3.172) 0.333Picker 0.757∗∗∗ 0.004∗∗∗ -0.248 -0.002 0.940∗∗∗ 0.004∗∗∗ -0.097 -0.001

(2.844) 0.311 (-0.709) 0.343 (3.446) 0.311 (-0.274) 0.343Fund Size -0.241∗∗∗ -0.001∗∗∗ -0.496∗∗∗ -0.004∗∗∗ -0.218∗∗∗ -0.001∗∗∗ -0.507∗∗∗ -0.004∗∗∗ -0.215∗∗∗ -0.001∗∗∗ -0.496∗∗∗ -0.004∗∗∗

(-3.119) 1.755 (-5.622) 1.781 (-2.684) 1.755 (-5.651) 1.781 (-2.716) 1.755 (-5.630) 1.781Fund Age 0.000 0.000 0.011 0.000 0.003 0.000 0.012 0.000 0.002 0.000 0.011 0.000

(0.044) 8.622 (1.093) 9.113 (0.300) 8.622 (1.175) 9.113 (0.210) 8.622 (1.071) 9.113Fund Turnover -0.024 -0.000 0.132∗∗ 0.001∗∗ -0.010 -0.000 0.115∗∗ 0.001∗∗ -0.027 -0.000 0.131∗∗ 0.001∗∗

(-0.314) 1.289 (2.451) 1.273 (-0.129) 1.289 (2.228) 1.273 (-0.344) 1.289 (2.448) 1.273Fund Expenses -0.210 -0.001 -0.284 -0.002 -0.105 -0.001 -0.324 -0.002 -0.220 -0.001 -0.279 -0.002

(-0.683) 0.528 (-1.086) 0.521 (-0.345) 0.528 (-1.239) 0.521 (-0.706) 0.528 (-1.062) 0.521Fund Flows -0.006 -0.000 0.023∗ 0.000∗ -0.020 -0.000 0.021∗ 0.000∗ -0.018 -0.000 0.023∗ 0.000∗

(-0.227) 2.630 (1.923) 2.626 (-0.644) 2.630 (1.785) 2.626 (-0.635) 2.630 (1.958) 2.626Past Year Return 1.388∗∗∗ 0.007∗∗∗ 0.600 0.004 0.966∗∗∗ 0.005∗∗∗ 0.533 0.004 1.195∗∗∗ 0.006∗∗∗ 0.627 0.005

(5.940) 0.233 (1.148) 0.169 (3.764) 0.233 (0.997) 0.169 (4.882) 0.233 (1.129) 0.169Family Size 0.006 0.000 -0.181∗∗∗ -0.001∗∗∗ -0.003 -0.000 -0.178∗∗∗ -0.001∗∗∗ 0.001 0.000 -0.181∗∗∗ -0.001∗∗∗

(0.098) 2.536 (-3.428) 2.540 (-0.048) 2.536 (-3.331) 2.540 (0.022) 2.536 (-3.427) 2.540Family Funds -0.004 -0.000 -0.008∗∗ -0.000∗∗ -0.006 -0.000 -0.008∗∗ -0.000∗∗ -0.006 -0.000 -0.008∗∗ -0.000∗∗

(-1.051) 36.516 (-2.312) 42.494 (-1.335) 36.516 (-2.303) 42.494 (-1.268) 36.516 (-2.306) 42.494Family Managers -0.007 -0.000 0.005 0.000 -0.005 -0.000 0.003 0.000 -0.005 -0.000 0.005 0.000

(-0.962) 17.192 (0.780) 18.647 (-0.595) 17.192 (0.530) 18.647 (-0.654) 17.192 (0.759) 18.647Supply Advising -0.351 -0.002 0.893∗∗∗ 0.007∗∗∗ -0.351 -0.002 0.958∗∗∗ 0.007∗∗∗ -0.364 -0.002 0.901∗∗∗ 0.007∗∗∗

(-1.467) 0.471 (3.446) 0.468 (-1.425) 0.471 (3.647) 0.468 (-1.519) 0.471 (3.481) 0.468Demand Advising 0.748∗∗∗ 0.004∗∗∗ -0.335 -0.002 0.875∗∗∗ 0.004∗∗∗ -0.268 -0.002 0.797∗∗∗ 0.004∗∗∗ -0.336 -0.002

(3.497) 0.497 (-1.398) 0.488 (4.048) 0.497 (-1.110) 0.488 (3.690) 0.497 (-1.403) 0.488MBA 0.104 0.001 -0.107 -0.001 0.069 0.000 -0.079 -0.001 0.078 0.000 -0.104 -0.001

(0.508) 0.500 (-0.496) 0.500 (0.335) 0.500 (-0.362) 0.500 (0.377) 0.500 (-0.485) 0.500PHD 0.552 0.003 -2.261∗∗∗ -0.017∗∗∗ 0.671 0.003 -2.217∗∗ -0.017∗∗ 0.533 0.002 -2.238∗∗ -0.016∗∗

(0.970) 0.171 (-2.583) 0.173 (1.195) 0.171 (-2.553) 0.173 (0.924) 0.171 (-2.530) 0.173Past Positions 0.020 0.000 0.013 0.000 0.010 0.000 0.019 0.000 0.008 0.000 0.013 0.000

(0.290) 1.406 (0.169) 1.308 (0.150) 1.406 (0.238) 1.308 (0.107) 1.406 (0.167) 1.308Ivy League 0.201 0.001 0.132 0.001 0.180 0.001 0.122 0.001 0.225 0.001 0.135 0.001

(0.873) 0.428 (0.531) 0.434 (0.769) 0.428 (0.482) 0.434 (0.960) 0.428 (0.541) 0.434Manager Funds 0.009 0.000 0.059∗∗ 0.000∗∗ 0.012 0.000 0.078∗∗∗ 0.001∗∗∗ 0.015 0.000 0.059∗∗ 0.000∗∗

(0.376) 5.192 (2.256) 4.622 (0.530) 5.192 (2.821) 4.622 (0.650) 5.192 (2.279) 4.622Manager Size 0.620∗∗∗ 0.003∗∗∗ 0.728∗∗∗ 0.005∗∗∗ 0.629∗∗∗ 0.003∗∗∗ 0.731∗∗∗ 0.006∗∗∗ 0.590∗∗∗ 0.003∗∗∗ 0.731∗∗∗ 0.005∗∗∗

(7.246) 1.770 (7.948) 1.818 (6.613) 1.770 (7.565) 1.818 (6.745) 1.770 (7.817) 1.818Fund Affiliation -0.155∗∗∗ -0.001∗∗∗ -0.112∗∗∗ -0.001∗∗∗ -0.159∗∗∗ -0.001∗∗∗ -0.113∗∗∗ -0.001∗∗∗ -0.155∗∗∗ -0.001∗∗∗ -0.111∗∗∗ -0.001∗∗∗

(-4.781) 4.459 (-2.977) 4.173 (-4.799) 4.459 (-2.909) 4.173 (-4.773) 4.459 (-2.951) 4.173Market Bear Bull Bear Bull Bear BullTime Dummies Yes Yes Yes Yes Yes YesStyle Dummies Yes Yes Yes Yes Yes YesObservations 12167 6902 12167 6902 12167 6902Pseudo R2 0.183 0.213 0.175 0.204 0.191 0.213Baseline Predicted Prob 0.023 0.049 0.025 0.049 0.025 0.049

47

Supplementary Appendix

A1. Variable definitions

Variable DefinitionFund Characteristics

Fund Size Natural logarithm of TNA under management in millions of dollars.Fund Age Number of years the fund has been offered.Fund Turnover Minimum of aggregate purchases and sales of securities divided by

average TNA over the calendar year.Fund Expenses Total annual expenses and fees divided by year-end TNA.Fund Flow Percentage of new inflows of the fund over the previous year.Alpha 6F Intercept from estimating Carharts model augmented by MSCI World

Index return and the U.S. Aggregate Bond Index return (in excess ofrisk-free).

Objective-adj Returns Portfolio gross return minus the median value of the return of all thefunds within the same investment objective.

Net Return Objective-adj return using net returns instead of gross (before fees).Family Characteristics

Family Size Logarithm of TNA of all funds in the family, excluding the fund itself.Family Funds Logarithm of the number of funds within the fund family.Family Managers Number of portfolio managers within the fund family.Demand Advising Dummy variable equal 1 if the family has at least one fund outsourced

to an unaffiliated firm.Supply Advising Dummy variable equal 1 if the family is managing at least one fund

from an unaffiliated firm.Funds Per Manager Number of total funds of the family divided by total number of man-

agers within the family.Family Concentration Herfindahl index across investment objectives of the fund of the family.

Portfolio Manager CharacteristicsIvy league Dummy variable equals 1 if the manager graduated from an Ivy

League university.MBA Dummy variable equals 1 if the manager holds a MBA degreePhD Dummy variable equals 1 if the manager holds a PhD degreePast Positions Number of prior job positions of the managerManager Funds Number of funds managed simultaneously by a portfolio manager.Manager Size Natural logarithm of the sum of TNA of all the funds the manager is

managing in that period.Fund affiliation Number of years the manager is managing the fund.Family affiliation Number of years the manager is managing funds from the family.Picker Dummy variable equals 1 if the portfolio manager that has been effi-

ciently selecting stock during the past 24 months.Timer Dummy variable equals 1 if the portfolio manager that has been effi-

ciently predicting the market during the past 24 months.Concentration Herfindahl index of concentration among all different investment ob-

jectives of manager funds.Generalist Manager with Concentration variable less than 1.Specialist Manager with Concentration variable equal than 1.Manager Performance TNA-weighted average return of all the funds managed by the same

manager in one period.Manager Past Skill TNA-weighted cumulative return of the objective-adjusted returns of

all the funds run by the manager during the past 24 months.Picking Alpha coefficient for estimating a modified version of the TM Model.Timing Gamma coefficient for estimating a modified version of the TM Model.Multi-Funds Manager Dummy variable equals 1 if the manager is managing more than 1

fund simultaneously.Other Variables

Specialist To Generalist Equals 1 if the manager is specialist in t and generalist in t+1.Generalist To Specialist Equals 1 if the manager is generalist in t and specialist in t+1.Market Condition The Chicago Fed National Activity Index.Generalist Mis-allocation Equals 1 if a manager with picking ability is allocated as generalist.Specialist Mis-allocation Equals 1 if a manager with timing skill is allocated as specialist.

48

A2. Asset classes and investment styles definitions

Under the Investment Act of 1940, an investment company has to register with the

Securities and Exchange Commission (SEC). All U.S. mutual funds and other regulated

investment management companies are required to file Form NSAR (along with other

documents) on a semi-annual basis. According to this Form, the filer must classify the

funds attending to different Asset Classes and Investment Objective. Here is the definition

of these categories that the SEC gives to the registrant and that we will use to classify

the funds of our database:

ASSET CLASS:

• Equity: invests in equity securities, options and futures on equity securities, indices

of equity securities or securities convertible into equity securities.

• Fixed Income: invests primarily in debt securities, including convertible debt secu-

rities, options and futures on debt securities or indexes of debt securities.

• International: have more than 50% of its net assets at the end of the current period

invested in securities located primarily in countries other than the United States.

INVESTMENT STYLE

1. Equity Funds:

• Capital: primarily and regularly seeks short and intermediate-term return

by investing in moderate to high-risk securities, with little or no concern for

receipt of income.

• Growth: seeks long-term growth, with a moderate degree of risk. Receipt of

income may be considered to some degree in selecting investments.

• Income: primarily and regularly makes low risk investments with the objective

of capital growth and income production.

• Return: portfolio includes a varying mix of equity and debt securities.

2. Fixed Income Funds:

49

• Government Short-Term: Short-Term Maturities of U.S. Treasury, U.S. Gov-

ernment Agency and State and municipal tax-free.

• Government Long-Term: Intermediate & Long-Term Maturities of U.S. Trea-

sury U.S. Government Agency, State and Municipal tax-free.

• Corporate: Intermediate & Long-Term Maturities of Corporate assets.

– Short-term maturities are defined for purposes of this form as securities

with maturities of 12 months or less. Securities having variable or floating

interest rates or subject to a demand feature should be considered short-

term if the interest rate adjustment period or the demand period is 12

months or less. Intermediate and long-term maturities include all other

debt securities.

3. Balance: at least 25% of the value of the assets fund should be invested in either

debt securities, preferred stock, or some combination of both. If convertible senior

securities are included in the required 25%, only that portion of their value at-

tributable to their fixed income characteristics may be used in calculating the 25%

figure.

4. Foreign: Invest more than 50% of its net assets in securities located primarily in

countries other than the United States.

A3. Generalist vs Specialist

In this section, we also examine the frequency of generalists across the different in-

vestment objectives sorted by quintiles of portfolio sizes. We find a larger proportion of

generalists among smaller income funds (42%), larger return funds (47%) and above me-

dian balance funds (53%-54%). On the other hand, the largest proportions of specialists

are within large short-term debt funds and international funds.

[Insert Table A1 here]

50

We further sorts our sample of managers into specialists and generalists and shows

the difference in fund performance between managers with timing ability or stock-picking

skill against those without any type of skill. Since we have fixed income and international

stocks, in addition to domestic equity funds, we measure fund performance using the al-

pha of Carhart’s model augmented by two risk factors –the MSCI World Index return

and the U.S. Aggregate Bond Index return, both in excess of the risk free rate. By defi-

nition, pickers and timers are better performers than managers lacking these skills. More

interesting is the fact that their performance advantage is greater for pickers among spe-

cialists and timers among generalists, except for the international category. For example,

for equity funds, a specialist with stock picking skill delivers an extra 91.2 bps per month,

while a generalist with similar skill only achieves 42.2 bps per month. On the other hand,

timing ability means an extra 12.5 bps per month for generalists, but only 8.1 bps for

specialists. The exception is international funds, where timing ability seems to be very

profitable: specialists who manage international funds achieve an average of 37 bps extra

per month. Intuitively, international funds invest in a broad range of assets and a timer

is in a good position to run them, using the argument we just stated.


A4. Switching Between Generalist and Specialist

In Tables A3 and A4 we study the characteristics of the funds run by managers who

switch roles. Table A3 compares the characteristics of the funds managed by managers

who have just switched roles with those of managers who have not switched and have the

same function (specialist or generalist) as the manager in question after the switch. In

Table 10 we compare the difference in characteristics between the funds managed before

and after the switch by a given manager.

In our sample, we have 1149 intra-firm manager switches; in 561 of these cases, a

specialist becomes generalist, and in 588, a generalist becomes specialist. Table A3 shows

that managers reassigned from specialist to generalist within the firm have higher tenure

at the company and hold a PhD degree and a quantitative background, manage funds

51

with large volume, and on average show higher turnover and flows and lower fees than

those run by other generalists at the time of the switch. In addition, their firms are also

more likely to outsource funds –prior literature has shown that management companies

outsource funds as an attempt to offer a wider variety of investment choices. Thus, there

is a higher probability of switch to generalist in firms that usually demand sub-advisory

services. A possible explanation is that management firms that demand sub-advisory

services are considering an expansion of the number of objectives they cover in-house;

when they decide to start a fund with a new objective, a former specialist is charged

with the management of the new fund, without dropping the funds managed up to that

point; the management firm uses existing in-house talent, instead of hiring outside. On

the other hand, managers who just switched from generalist to specialist run fewer funds

and during shorter periods, and these funds are bigger and older. These moves are more

likely to take place in firms with a larger number of funds that offer external sub-advisory

services.

Between-firms changes of management function are less frequent: In our sample,

we have 165 moves from specialist to generalist and 162 from generalist to specialist.

Changes between firms -without a change in the type of management– are more common

for specialists (1349 times) while there are only 306 changes for generalists. In general

these transfers are more likely for managers that had a shorter tenure at smaller families

and were managing less assets and funds. The funds they were managing were smaller,

younger and more expensive. Those who change companies to be specialists, whether

generalists or specialists previously, are more likely to hold MBA degrees and have more

experience in past positions, while they were managing funds receiving larger flows. Those

who change to be generalists are more likely to have graduated in an Ivy school and end up

in firms that own a wider variety of products for which they demand sub-advising services–

again, consistent with our argument that they adopt a generalist role in a management

firm that is growing.


In Table A4 we show that a manager moves to a different family –for the same

52

or different function– on average will run younger funds, especially when changing to

specialist. The firms of destination, are smaller and offer fewer funds. Of course, new,

growing firms, are more likely to have to hire outside talent. In the same spirit, managers

who change functions within the family end up managing more funds, and when the

change is to generalist, they manage more assets as well.


A5. Managerial Skill and Concentration

Our main hypothesis is that portfolio managers with a certain skill (timing of picking)

are better suited to exert different functions -generalists or specialists. In order to test

this, we replicate equation (4) using continuous variables instead of dummies for manager

function and skill. Generalist is a dummy variable equals 1 if the fund is managed by

manager that is in charge on funds from different investment styles. Timing and Picking

are the gamma and alpha coefficients from estimating a modified version of the TM

Timing model. Concentration measures the level of diversification of fund i managed by

j in month t (i might represents several funds, if the manager runs more than one). In

particular, this variable is a Herfindahl index:∑9

s=1

(TNAs,j,t

TNAj,t

)2

, with s the “fund style”

as defined in the NSAR-B filings (capital appreciation, growth, income, total return,

government short-term debt, government long-term debt, corporate debt, balance and

international stocks)34 and TNAs,j,t total net assets managed by manager j according

to investment style s at time t. Therefore, the higher the index, the more focused the

portfolio is. Timer is a dummy equals 1 if the fund is managed by portfolio manager that

has been able to time the market during the past 24 months. Picker is a dummy variable

equals 1 if the fund is managed by a manager that was able to pick stocks efficiently

during the past 24 months.

Table A5 shows the results of monthly Pooled OLS regressions of fund and manager

investment objective-adjusted returns on fund, manager and family characteristics. Hav-

ing picking skill is always associated to higher fund and manager performance, while

34A full description of these investment objectives is in the Appendix.

53

market-timing ability is positive related to performance only for generalist managers.

Additionally, the relationship between concentration and fund performance is highly

positive for funds managed by pickers, and has no effect for funds managed by timers. In

economic terms, one standard deviation increase in concentration (0.15) leads to an ab-

normal return increase of 108 bps per year in fund performance and 153 bps on manager

performance for funds managed by stock pickers. This means that management compa-

nies can obtain a greater output by allowing managers with picking ability to manage

similar funds.


A6. Managerial Skill by Portfolio Manager Type

We have established that managers with stock picking ability are better suited to work

as specialists, while managers with timing ability are better as generalists. We want to

analyze further the effect of managerial skills on performance. With that goal, we split

our sample into funds managed by generalists and funds managed by specialists, and we

estimate the following model for each subsample:

OARi,t = a0 + a1Timerj,t + a2Pickerj,t + a3Xi,t + δt + ei,t (9)

Table A5 shows the results of estimating (9) using pooled OLS, fund, manager and

family fixed effects, divided in two different Panels. Panel A sorts the sample into funds

managed by generalist and Panel B funds managed by specialist. Generalist with picking

skills do not affect performance while those with timing skills result in an increase in

fund performance from 20.8 bps per month to 30.4 bps per month. Specialist with

timing ability has no influence on fund performance, similar managers with picking skills

contribute to an increase in fund performance that ranges from 26.2 bps to 32.4 bps per

month. Thus, we conclude that pickers are better suited to manage funds with a single

investment objective because they contribute to improve the performance of the funds

they run, whereas timers do a better job at generalist functions.

54


A7. Risk-adjusted Returns

We replicate equation (4) using as dependent variable a risk-adjusted return.


A8. Fama-MacBeth (1973) regressions

We estimate prior equation following the Fama-MacBeth (1973) approach.


Overall findings point in the direction that management companies that assign pick-

ers to specialist functions and timers to generalist functions improve their performance

regardless the approach followed.

55

Table A1: Proportion of Generalist by Styles and Size

This table summarizes the proportion of funds managed by generalist managers sorted byquintiles of portfolio sizes and according to the fund investment objective for all the U.S mutualfunds managed by individual managers during 1996-2011.

Small (1) (2) Medium (3) (4) Large (5)Capital 0.23 0.22 0.19 0.16 0.17Growth 0.26 0.29 0.26 0.29 0.23Income 0.42 0.31 0.33 0.29 0.32Return 0.31 0.36 0.43 0.38 0.47Gov ST 0.26 0.21 0.17 0.15 0.09Gov LT 0.17 0.14 0.14 0.19 0.19Corporate 0.28 0.25 0.19 0.18 0.24Balance 0.34 0.35 0.53 0.54 0.46Foreign 0.18 0.13 0.12 0.12 0.13

Table A2: T-Test Analysis: Managers’ role and Skill

This table reports the performance differences between portfolio managed by timers (MarketPrediction Skills) and those unskilled in Panel A and the performance difference between fundmanaged by Pickers (Security Selection Skills) and the unskilled ones in Panel B. For each ofthe three asset classes (Domestic Equity, fixed income and international stocks), we sort themanagers by Specialist and Generalists and display the fund performance differences measuredusing the alpha of Carharts model augmented by two more risk factors (to be more conservativeas our sample also contains fixed income and international stocks).

Panel A Panel BPicking vs Unskilled Timing vs Unskilled

Specialist Generalist Specialist GeneralistEquity 0.912∗∗∗ 0.422∗∗∗ 0.081∗∗∗ 0.125∗∗∗

Debt 0.085∗∗∗ 0.070∗∗∗ 0.014∗∗∗ 0.027∗∗∗

Foreign 0.911∗∗∗ 0.170∗∗∗ 0.370∗∗∗ 0.007

56

Table A3: Transition Matrix (I): Role-switched Manager vs other Managers within that Role

This table presents the value difference of fund managed by managers that: 1) have switchedfrom specialist to generalist role (and vice versa) within the same company, 2) between differentfirms and 3) simple changing the firm but keeping the role. Each column contains the differencebetween the characteristics of the switched manager and all the other managers within the samerole over the period 1996-2011. The number of switches within each category is also reportedon the last row. Full description of all these variables is provided in the appendix.* denotessignificance at the 10% level, ** denotes significance at the 5% level and *** denotes significanceat the 1% level.

Intra-Family Between-FamilySpecialist to Generalist to Specialist to Generalist to Specialist to Generalist toGeneralist Specialist Generalist Specialist Specialist Generalist

Fund Size 0.166∗∗∗ 0.235∗∗∗ -0.428 -0.591∗∗∗ -0.521∗∗∗ -0.512∗∗

Fund Age -0.363 0.871∗∗∗ -3.206∗∗ -2.391∗∗∗ -2.587∗∗∗ -0.644Fund Turnover 0.208∗∗∗ 0.098 0.038 0.173 0.058 -0.047Fund Expenses -0.103∗∗∗ -0.092∗∗∗ 0.125 0.228∗∗∗ 0.204∗∗∗ 0.150∗

Fund Flows 0.251∗∗∗ -0.115 -0.141 1.021∗∗ 0.302∗∗ -0.345Family Size 0.452∗∗∗ 0.619∗∗∗ -0.886∗ -0.929∗∗∗ -1.162∗∗∗ -1.101∗∗∗

Family Funds 0.259∗∗∗ 0.295∗∗∗ -0.617∗∗ -0.615∗∗∗ -0.626∗∗∗ -0.632∗∗∗

Family Managers 0.093∗∗∗ 0.107∗∗∗ -0.207 -0.186∗ -0.310∗∗∗ -0.169Demand Advising 0.021∗∗ 0.013 0.156∗ 0.017 0.002 0.089Supply Advising 0.056∗ 0.088∗∗∗ -0.041 0.009 -0.071∗∗∗ 0.001Ivy League 0.013 0.046∗∗∗ 0.106 -0.061 -0.046∗∗∗ 0.159∗∗

MBA -0.007 -0.004 -0.029 0.111∗∗ 0.044∗∗ 0.029PhD 0.017∗∗∗ -0.008 -0.028 0.033∗ 0.000 -0.035Past Positions -0.029 0.084 -0.893∗∗∗ 0.366∗∗ 0.301∗∗∗ -0.390Manager Size 1.073∗∗∗ -0.252∗∗∗ -0.105 -2.189∗∗∗ -1.245∗∗∗ -1.216∗∗∗

Manager Funds 1.607∗∗∗ -0.768∗∗∗ -0.586 -3.283∗∗∗ -1.598∗∗∗ -2.320∗∗∗

Fund Affiliation -0.387∗∗∗ 0.243∗ -0.816 -2.211∗∗∗ -1.652∗∗∗ -1.929∗∗∗

Number of Events 561 588 165 162 1349 306

Table A4: Transition Matrix (II): Before and after Role-switched Manager

This table presents the value difference of fund managed by managers that: 1) have switchedfrom specialist to generalist role (and vice versa) within the same company, 2) between differentfirms and 3) simple changing the firm but keeping the role. Each column contains the differencebetween the characteristics of the switched manager before and after the event of the switch.The number of events within each category is also reported on the last row. Full descriptionof all these variables is provided in the appendix.* denotes significance at the 10% level, **denotes significance at the 5% level and *** denotes significance at the 1% level.

Intra-Family Between-FamilySpecialist to Generalist to Specialist to Generalist to Specialist to Generalist toGeneralist Specialist Generalist Specialist Specialist Generalist

Fund Size 0.174 -0.630∗∗ -0.354 -0.673 -0.252∗∗∗ -0.324Fund Age -0.128 -1.416 -2.562 -3.219 -2.366∗∗∗ -1.638Fund Turnover 0.183 0.387 -0.168 -1.130∗∗ -0.060 -0.153Fund Expenses -0.198∗∗∗ -0.261∗∗∗ 0.029 -0.064 0.019 -0.131Fund Flows -0.127 0.267 -0.118 1.421 0.246 -0.028Family Size 0.889∗∗∗ 0.443 0.097 -1.323 -0.295∗∗∗ -0.452Family Funds 0.531∗∗∗ 0.069 -0.319 -0.597 -0.281∗∗∗ -0.264Family Managers 0.219∗∗∗ 0.437∗∗ -0.008 -0.413 -0.032 -0.123Demand Advising 0.024 -0.061 0.021 -0.088 -0.117∗∗∗ -0.081Supply Advising 0.072∗∗∗ -0.006 0.022 -0.287 -0.036∗ -0.028Ivy League 0.025 -0.012 0.088 0.149 -0.007 0.223MBA -0.045 -0.191∗∗ -0.059 -0.466 -0.004 0.006PhD -0.005 0.027 -0.013 0.068 0.009 0.000Past Positions -0.165 -0.013 -0.884 -0.744 0.001 -1.190Manager Size 1.816∗∗∗ -0.182 0.759∗∗ -1.075 -0.250∗∗∗ -0.292Manager Funds 3.248∗∗∗ 2.011∗∗∗ 1.097∗∗∗ -0.433∗∗∗ 0.013 0.018Fund Affiliation 0.025 0.212 -1.812∗ -2.101 -2.015∗∗∗ -2.153∗∗

Number of Events 561 588 165 162 1349 306

57

Table A5: Managerial Skill and Concentration

This table presents the results of monthly Pooled OLS regressions of fund and manager invest-ment objective-adjusted returns on fund, manager and family characteristics. Fund returns arethe actual returns before deducting fees and expenses (gross) and manager returns are the TNA-weighted average return of all the portfolios managed by the same manager at the same time.The dependent variable are fund and manager performance, measured by substracting the me-dian return of their investment objective peers, from the actual return of the fund and manager,respectively. Generalist is a dummy variable equals 1 if the fund is managed by manager thatis in charge on funds from different investment styles. Timing and Picking are the gamma andalpha coefficients from estimating a modified version of the TM Timing model. Concentrationis the Herfindahl index of concentration among all different investment objectives of the fundsof the manager. Timer is a dummy equals 1 if the fund is managed by portfolio manager thathas been able to time the market during the past 24 months. Picker is a dummy variable equals1 if the fund is managed by a manager that was able to pick stocks efficiently during the past24 months. All variables are lagged one period. A full description of the remaining variablesis in the appendix. Time and investment objective dummies are included but not reported;t-statistics are reported in parentheses. We adjust for serial correlation by clustering standarderrors at the fund level. * denotes significance at the 10% level, ** denotes significance at the5% level and *** denotes significance at the 1% level.

Fund Performance Manager Performance Fund Performance Manager PerformanceGeneralist 0.020 0.006

(0.65) (0.22)Timing -0.003 -0.003

(-1.62) (-1.53)Picking 0.033∗∗∗ 0.034∗∗∗

(5.40) (5.64)Generalist × Timing 0.021∗∗∗ 0.022∗∗∗

(4.39) (4.78)Generalists × Picking -0.003 -0.013

(-0.23) (-1.11)Concentration -0.154 -0.257

(-1.44) (-0.73)Picker -0.315∗ -0.565∗∗∗

(-1.74) (-3.59)Timer 0.133∗∗∗ 0.181∗∗∗

(3.65) (3.49)Concentration × Picker 0.599∗∗∗ 0.849∗∗∗

(2.97) (4.69)Concentration × Timer -0.130 0.083

(-0.58) (0.44)Fund Size 0.023∗∗ 0.016 0.029∗∗ 0.023∗∗

(1.98) (1.46) (2.47) (2.02)Fund Age 0.000 0.001 -0.000 0.000

(0.11) (0.47) (-0.12) (0.27)Fund Turnover 0.018∗∗ 0.006 0.015 0.003

(2.04) (0.92) (1.62) (0.46)Fund Expenses 0.133∗∗∗ 0.114∗∗∗ 0.137∗∗∗ 0.120∗∗∗

(4.03) (3.69) (4.21) (3.90)Fund Flows 0.063∗∗∗ 0.059∗∗∗ 0.067∗∗∗ 0.063∗∗∗

(4.39) (4.27) (4.46) (4.35)Past Year Return -0.659∗∗∗ -0.655∗∗∗ -0.481∗∗∗ -0.473∗∗∗

(-5.02) (-4.92) (-3.68) (-3.61)Family Size 0.016∗ 0.021∗∗ 0.017∗∗ 0.021∗∗

(1.92) (2.44) (1.99) (2.51)Family Funds 0.000 0.000 0.000 0.000

(0.32) (0.60) (0.42) (0.68)Family Managers -0.001 -0.001 -0.001 -0.001

(-1.02) (-1.37) (-0.98) (-1.35)Supply Advising -0.047 -0.057∗∗ -0.047 -0.056∗∗

(-1.60) (-2.07) (-1.60) (-2.04)Demand Advising -0.004 0.009 -0.008 0.005

(-0.19) (0.41) (-0.34) (0.22)MBA 0.016 0.012 0.008 0.005

(0.66) (0.49) (0.32) (0.21)PhD -0.060 -0.050 -0.071 -0.063

(-1.14) (-0.99) (-1.39) (-1.28)Past Positions -0.013 -0.012 -0.013 -0.012

(-1.46) (-1.41) (-1.41) (-1.41)Ivy League 0.017 0.026 0.020 0.029

(0.53) (0.84) (0.62) (0.95)Manager Funds 0.004 0.001 0.002 0.000

(1.44) (0.38) (0.97) (0.09)Manager Size 0.006 0.005 0.002 0.001

(0.52) (0.48) (0.14) (0.05)Fund Affiliation -0.002 -0.000 -0.002 0.000

(-0.72) (-0.10) (-0.57) (0.07)Constant -0.283∗∗∗ -0.214∗∗ -0.219∗ -0.055

(-2.76) (-2.15) (-1.68) (-0.45)Time Dummies Yes Yes Yes YesStyle Dummies Yes Yes Yes YesObservations 80059 80059 80059 80059r2 0.023 0.023 0.022 0.022

58

Table A6: Managerial Skills by Portfolio Manager type: Generalist and Specialist

This table presents the results of monthly Pooled OLS, Fund, Manager and Family fixed effectsregressions of fund investment objective-adjusted returns on fund, manager and family charac-teristics. Fund returns are the actual returns before deducting fees and expenses (gross). Thedependent variable are fund performance, measured by substracting the median return of theirinvestment objective peers, from the actual return of the fund. Timer is a dummy equals 1 ifthe fund is managed by portfolio manager that has been able to time the market during thepast 24 months. Picker is a dummy variable equals 1 if the fund is managed by a managerthat was able to pick stocks efficiently during the past 24 months. Panel A contains only thesubsample of funds managed by generalist managers while Panel B considers only portfoliosmanaged by Specialist managers. All variables are lagged one period. A full description ofthe remaining variables is in the appendix. Control variables, time and investment objectivedummies are included but not reported; t-statistics are reported in parentheses. We adjust forserial correlation by clustering standard errors at the fund level. * denotes significance at the10% level, ** denotes significance at the 5% level and *** denotes significance at the 1% level.

Panel A: Generalist SamplePooled OLS Fund Fixed Effect Manager Fixed Effect Family Fixed Effect

Timer 0.208∗∗∗ 0.282∗∗∗ 0.304∗∗∗ 0.206∗∗∗

(3.10) (3.31) (3.64) (2.73)Picker 0.106∗ -0.012 -0.048 0.034

(1.67) (-0.13) (-0.54) (0.46)Control Variables Yes Yes Yes YesTime Dummies Yes Yes Yes YesStyle Dummies Yes Yes Yes YesObservations 19833 19833 19833 19833r2 0.024 0.058 0.046 0.039

Panel B: Specialist SamplePooled OLS Fund FE Manager FE Firm FE

Timer -0.053 -0.035 -0.067 -0.041(-1.04) (-0.60) (-1.21) (-0.79)

Picker 0.324∗∗∗ 0.281∗∗∗ 0.262∗∗∗ 0.293∗∗∗

(6.25) (4.40) (4.26) (5.59)Control Variables Yes Yes Yes YesTime Dummies Yes Yes Yes YesStyle Dummies Yes Yes Yes YesObservations 60226 60226 60226 60226r2 0.020 0.042 0.039 0.027

59

Table A7: Managerial Type and Performance: Risk-adjusted Returns

This table presents the results of monthly Pooled OLS (Panel A) and Family Fixed Effect(Panel B) regressions of fund and manager risk-adjusted returns on fund, manager and familycharacteristics. Fund returns are the actual returns before deducting fees and expenses (gross)and manager returns are the TNA-weighted average return of all the portfolios managed bythe same manager at the same time. The dependent variable is Fund Performance (obtainedusing the 6-factors model previously defined) and Manager Performance (TNA-weighted averagealpha of all the funds managed by the same manager at the same time). Generalist is a dummyvariable equals 1 if the fund is managed by manager that is in charge on funds from differentinvestment styles. Timer is a dummy equals 1 if the fund is managed by portfolio manager thathas been able to time the market during the past 24 months. Picker is a dummy variable equals1 if the fund is managed by a manager that was able to pick stocks efficiently during the past 24months. All variables are lagged one period. Control variables, time and investment objectivedummies are included but not reported; t-statistics are reported in parentheses. We adjust forserial correlation by clustering standard errors at the fund level. * denotes significance at the10% level, ** denotes significance at the 5% level and *** denotes significance at the 1% level.

Panel A: Pooled OLSFund Performance Manager Performance

(1) (2) (3) (4) (5) (6)Generalist -0.021 0.014 0.004 -0.031∗ 0.001 -0.009

(-1.13) (0.75) (0.21) (-1.81) (0.08) (-0.51)Timer -0.015 0.025 -0.011 0.031

(-0.76) (1.28) (-0.53) (1.63)Picker 0.519∗∗∗ 0.521∗∗∗ 0.533∗∗∗ 0.535∗∗∗

(16.85) (16.98) (16.82) (16.98)Generalist × Timer 0.070∗∗ 0.049∗ 0.069∗∗ 0.048∗

(2.18) (1.74) (2.50) (1.78)Generalist × Picker -0.316∗∗∗ -0.313∗∗∗ -0.310∗∗∗ -0.307∗∗∗

(-7.76) (-7.70) (-7.94) (-7.87)Observations 70425 70425 70425 70425 70425 70425r2 0.102 0.151 0.152 0.103 0.159 0.159

Panel B: Family Fixed EffectFund Performance Manager Performance

(1) (2) (3) (4) (5) (6)Generalist 0.007 0.029∗∗ 0.027∗ -0.011 0.010 0.007

(0.50) (2.13) (1.85) (-0.73) (0.71) (0.53)Timer -0.016 0.031 -0.011 0.037

(-0.60) (1.08) (-0.41) (1.31)Picker 0.469∗∗∗ 0.475∗∗∗ 0.490∗∗∗ 0.498∗∗∗

(9.88) (10.04) (9.39) (9.56)Generalist × Timer 0.088∗∗∗ 0.079∗∗∗ 0.084∗∗∗ 0.075∗∗∗


(-5.55) (-5.72) (-4.96) (-5.12)Observations 70425 70425 70425 70425 70425 70425r2 0.202 0.249 0.258 0.202 0.256 0.266

Time Dummies Yes Yes Yes Yes Yes YesStyle Dummies Yes Yes Yes Yes Yes Yes

60

Table A8: Managerial Type and Performance: Fama-MacBeth (1973)

This table presents the results of Fama-MacBeth regressions of fund and manager investmentobjective-adjusted returns on fund, manager and family characteristics. Fund returns are theactual returns before deducting fees and expenses (gross) and manager returns are the TNA-weighted average return of all the portfolios managed simultaneously by a manager. Generalistis a dummy variable that equals 1 if the fund is run by a manager in charge of multiple funds withdifferent investment styles. Timer is a dummy that equals 1 if the fund is run by a manager whohas timed the market during the past 24 months. Picker is a dummy variable that equals 1 if themanager that was able picked stocks successfully during the past 24 months. All variables arelagged one period. A full description of the remaining variables is in the appendix. Investmentobjective dummies are included but not reported; t-statistics are in parentheses. We adjust forserial correlation by applying Newey-West (1987) estimates of standard errors with lags of orderthree. * denotes significance at the 10% level, ** denotes significance at the 5% level and ***denotes significance at the 1% level.

Fund Performance Manager Performance(1) (2) (3) (4) (5) (6)

Generalist -0.021 0.017 0.008 -0.035∗∗ 0.001 -0.008(-1.15) (0.99) (0.44) (-2.04) (0.05) (-0.48)

Timer -0.019 0.019 -0.016 0.023(-0.95) (0.99) (-0.83) (1.20)

Picker 0.486∗∗∗ 0.488∗∗∗ 0.499∗∗∗ 0.501∗∗∗

(16.30) (16.42) (16.32) (16.48)Generalist × Timer 0.070∗∗ 0.053∗ 0.065∗∗ 0.048∗


(-8.11) (-8.02) (-7.99) (-7.89)Fund Size 0.024∗∗∗ 0.024∗∗∗ 0.024∗∗∗ 0.013∗ 0.012∗ 0.013∗

(2.81) (2.90) (2.93) (1.66) (1.72) (1.75)Fund Age -0.004∗∗∗ -0.003∗∗∗ -0.003∗∗∗ -0.003∗∗∗ -0.002∗∗∗ -0.002∗∗∗

(-4.25) (-3.70) (-3.74) (-3.45) (-2.92) (-2.95)Fund Turnover 0.004 0.003 0.003 0.004 0.002 0.002

(0.47) (0.33) (0.31) (0.45) (0.30) (0.27)Fund Expenses -0.023 -0.031 -0.030 -0.017 -0.025 -0.025

(-0.84) (-1.15) (-1.14) (-0.63) (-0.96) (-0.95)Fund Flows 0.029∗∗∗ 0.026∗∗∗ 0.026∗∗∗ 0.027∗∗∗ 0.024∗∗∗ 0.024∗∗∗

(4.52) (4.32) (4.32) (4.54) (4.33) (4.33)Family Size 0.011∗∗∗ 0.010∗∗∗ 0.010∗∗∗ 0.013∗∗∗ 0.012∗∗∗ 0.012∗∗∗

(2.77) (2.58) (2.61) (3.26) (3.09) (3.12)Family Funds 0.000 0.000 0.000 0.000 0.000 0.000

(0.91) (0.96) (0.96) (0.98) (1.04) (1.03)Family Managers -0.001 -0.000 -0.000 -0.001 -0.001 -0.001

(-0.85) (-0.39) (-0.37) (-1.20) (-0.70) (-0.68)Supply Advising -0.012 -0.018 -0.018 -0.024 -0.030 -0.029

(-0.38) (-0.59) (-0.58) (-0.77) (-1.01) (-1.00)Demand Advising 0.010 0.019 0.018 0.015 0.024 0.023

(0.47) (0.95) (0.91) (0.76) (1.30) (1.25)MBA -0.051∗∗∗ -0.050∗∗∗ -0.050∗∗∗ -0.055∗∗∗ -0.054∗∗∗ -0.054∗∗∗

(-2.67) (-2.73) (-2.73) (-2.91) (-2.99) (-2.99)PhD -0.020 -0.027 -0.023 -0.034 -0.042 -0.038

(-0.42) (-0.57) (-0.48) (-0.75) (-0.91) (-0.82)Past Positions 0.010 0.007 0.008 0.009 0.006 0.006

(1.38) (1.08) (1.10) (1.25) (0.90) (0.92)Ivy League 0.032 0.036 0.035 0.038∗ 0.042∗∗ 0.041∗

(1.38) (1.64) (1.61) (1.70) (1.99) (1.96)Manager Funds -0.015∗∗∗ -0.013∗∗∗ -0.014∗∗∗ -0.014∗∗∗ -0.013∗∗∗ -0.014∗∗∗

(-4.26) (-3.86) (-3.99) (-4.51) (-4.11) (-4.27)Manager Size 0.035∗∗∗ 0.024∗∗∗ 0.024∗∗ 0.044∗∗∗ 0.033∗∗∗ 0.033∗∗∗

(3.63) (2.60) (2.58) (4.84) (3.82) (3.80)Fund Affiliation -0.001 -0.001 -0.001 0.002 0.001 0.002

(-0.26) (-0.35) (-0.30) (0.69) (0.63) (0.68)Family Affiliation -0.005∗ -0.005∗∗ -0.005∗∗ -0.007∗∗∗ -0.007∗∗∗ -0.007∗∗∗

(-1.82) (-2.02) (-2.04) (-2.86) (-3.12) (-3.15)Constant -0.471∗∗∗ -0.436∗∗∗ -0.436∗∗∗ -0.476∗∗∗ -0.438∗∗∗ -0.438∗∗∗

(-5.71) (-5.56) (-5.56) (-5.86) (-5.71) (-5.71)Style Dummies Yes Yes Yes Yes Yes YesObservations 70425 70425 70425 70425 70425 70425r2 0.207 0.246 0.247 0.213 0.257 0.257

61

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