a tale of two types: generalists vs. specialists in mutual funds
TRANSCRIPT
A Tale of Two Types: Generalists vs.Specialists in Mutual Funds Asset
Management
Rafael Zambrana∗ and Fernando Zapatero†
October 31, 2015
Abstract
We study the criteria that lead mutual funds to appoint specialists -managers whorun funds with a single investment style– or generalists -managers who run severalfunds with different investment styles. We identify managers with either stock-picking or market-timing ability. Managers who display stock-picking ability aremore likely to be specialists and managers with market-timing ability are morelikely to be generalists. In addition, we find that such assignments are optimal sincestock-pickers earn higher returns than other managers as specialists, and similarlymarket-timers as generalists. Finally, according to this optimal human capitalallocation, 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
We show that it is optimal to assign portfolio managers with market-timing ability
to generalist responsibilities, and managers with stock-picking ability to specialist re-
sponsibilities. Generalists are managers who run one or several funds comprising several
investment objectives, while specialists focus on just one investment objective.
One of the most controversial subjects in financial economics is the ability of asset
managers (or lack thereof) to achieve returns higher than the market on a risk-adjusted
basis. Authors going back to Jensen (1968) and before argue that actively managed
funds do not achieve higher returns after fees than passively managed funds. However,
a good part of the literature, especially more recently, argues that there is such a thing
as portfolio management ability, and it helps explains flows of funds –for example, the
influential work of Berk and Green (2004). In a step further, Kacperczyk, Nieuwerburgh
and Veldkamp (2014) study two different types of managerial ability, stock-picking and
market timing, and show that skilled portfolio managers display stock-picking ability in
economic expansions and market-timing ability in recessions.
Independently of this debate, we observe that within the asset management industry
there are different organizational structures. In particular, mutual fund families histor-
ically have assigned asset managers in what appears to be somehow different functions.
In particular, some asset managers run funds with a single investment objective1 while
other managers combine different objectives under their supervision. We will call spe-
cialists the fund managers who run either just one fund or several funds with the same
investment objective, and generalists the managers in charge of several funds with more
than one investment objective among them. There is already a literature that consid-
ers this distinction between specialists and generalists, but among CEOs. Murphy and
Zabojnık (2004) document that generalist skills, i.e., transferable across industries, have
become more important for CEOs. More recently, Custodio, Ferreira and Matos (2013),
show that generalist CEOs are paid a premium over specialist CEOs.
In this paper, using a version of one the standard methodologies, we identify portfolio
managers who have either of the two abilities, market timing or stock selection –many
seem to have neither and very few both– and study whether it is optimal for the manage-
ment company to deploy them as specialists or as generalists, depending on their type
of ability. We conjecture that managers with market-timing ability are more suited to
work as generalists, while those with stock-piking ability are a better fit for specialist
assignments. In addition to verifying if our conjecture is correct, we are interested in
studying whether funds assign managers according to this criterion and, if that is the
case, we want to quantify the effect.
1There are different possible classifications of investment objectives. To avoid possible selection problemsour study is based 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.
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To assess whether managers have stock picking or market timing ability 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. Of course, market-timing ability
and stock-pricing ability are both valuable and we find that managers with either of
these two skills outperform managers 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 assign market-timers to generalist roles outperform
other companies.
We conjecture that pickers, who are meant to perform fundamental analysis, narrow
down their focus into segments in which they have expertise, whereas timers possess a
more general view of the market and benefit from a wider access to information that
allows a better allocation among different security classes.
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 and/or quantitative
background, while specialist pickers are more likely to be MBAs with business related
studies. Gottesman and Morey (2006) study the effect of GMAT and school ranking on
performance. Our work also contributes to the literature on the organization and person-
nel decisions in mutual funds, especially the work on human capital assignment strategies
in mutual funds. 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 man-
agers are assigned to market segments that are less efficient where ability has a higher
expected payoff.
Overall, 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 assign managers depending on their type of skill achieve higher performance; (ii)
therefore, there seems to be an optimal strategy that consists in assigning managers
with timing ability to generalists positions and pickers to specialists positions; (iii) many
management companies follow this strategy.
The paper is organized as follows. First we describe the data. Then we introduce the
2Using novel measures of ability, a number of papers argue that some fund managers are better thanothers (Daniel, Grinblatt, Titman, and Wermers (1997), Cohen, Coval, and Pastor (2005), Bollen andBusse (2005), Kacperczyk, Sialm and 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 and Veldkamp (2014), Ferson and Mo (2015)).
2
notions of timers and pickers, as well as the functions of generalist and specialist, and
identify fund managers in our database accordingly. In the following section we present
our main results. Section 5 provides a number of robustness tests. 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 dataset to funds run
by a single manager, as opposed to a team.3 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.4 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
observations from different classes, grouping them at the fund level.5
Third, we use the NSAR forms required by the SEC to be filled 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.6 Although certain funds file reports starting in 1993, the
3Some new research focuses on individual managed funds (i.e Fang, Kempf and Trapp (2014), Kempf,Manconi and Spalt (2014).
4Some papers discuss the possible existence of an incubation bias (Evans, 2010).5We 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 expensesweighting 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, names or styles, we choose that of theoldest among all classes.
6A key variable we need for our analysis is investment objective of the fund. CRSP provides different
3
data appear 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 dataset contains
monthly-fund observations, from 3,005 U.S. open-ended domestic equity, 2,832 fixed in-
come, 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 during 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 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 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 included 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).7 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 individual portfolio man-
ager, the recent decrease in the number of funds in our sample is the result of the new
trend of mutual funds managed by a team rather than a single portfolio manager.8
classifications 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.
7A full description of these investment objectives is in the Appendix.8Bliss, Potter and Schwarz (2008) and Bar, Kempf and Ruenzi (2011) study the growth in team-managedfunds.
4
[Insert Table 1 here]
We also 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 totals (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 on 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.
[Insert Table 2 here]
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.9
[Insert Table 3 here]
9Past Manager Skill is the TNA-weighted cumulative return of the objective-adjusted before fees returnsof all the funds run by the manager during the past 24 months. We can see that specialists do not doa very good job, in general. However, as we will show later, their performance is substantially betterwhen they have some type of skill, especially if they are pickers. Table A1 in the appendix sectionprovides further tests.
5
3.2. Pickers and Timers
Next, we consider two possible skills of portfolio managers: stock-picking and market
timing. Assuming some portfolio managers have either of these skills, we want to analyze
if this affects their assignment as generalist or specialist in in the fund. First we need to
decide whether a 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 have 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)).10 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.11 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 , 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. Those 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,
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
10Many 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.
11From Kenneth French’s website.
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global factors.12
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.13 For portfolio managers who manage more than one fund
simultaneously, we use the TNA-weighted average of these coefficients.14
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.15
[Insert Table 4 here]
A possible explanation for this finding is that pickers are better suited to work as
specialists, while management companies can profit more from assigning timers to work
as generalists. 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 benefit from an
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 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 larger set of information
within the family and hence use it to invest across different assets.
12The 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.
13For 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.
14Breen, 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.
15Consistent 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.
7
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 also exists when we consider the performance of the whole family performance.
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.16
[Insert Table 5 here]
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 their 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)17 and TNAs,j,t is the total net assets managed by
16See 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.
17A full description of these investment objectives is in the Appendix.
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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 0.18 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 time
the manager has been affiliated with the portfolio.19 We also control for style of the
fund (dummy variables for each investment objectives) 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.20
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 similar funds and allocating market-
timers to manage funds with different investment styles.
[Insert Table 6 here]
18As we argued before, we will get 1 and 0, 0 and 1 and, very often, 0 and 0; 1 and 1 is exceptional.19We winsorize all the control variables at the 1% level.20These results are robust to different additional tests such as including continuous rather than dummy
variables, risk-adjusted performance measures, fund, family and manager fixed effects, clustering bytime, as well as Fama-MacBeth (1973) regressions. See Appendix for more details.
9
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
interactions Generalist×Timer and Generalist×Picker are 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.
[Insert Table 7 here]
4.3. Managerial Type and Performance: Subsamples
To achieve 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, is 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.21 Generalistj,t is a dummy variable that captures
whether the manager performs a generalist or a specialist role within the management
company. X is a vector of control variables at the fund, family and manager levels.22 We
include time and investment objective dummies (δt). We cluster standard errors at the
fund level and estimate equation (5) using Pooled OLS regression.23
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
21There 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.
22For a detailed description of these variables, see the appendix.23We apply the Petersen (2009) approach to estimate, in an efficient way, the standard errors of our
regression. The SE clustered by funds are dramatically larger than the white SE, while the SE clusteredby years are only slightly larger than the white SE. Besides, clusters by funds and years are similar toclusters by funds. Then, the importance of time effect (after including time dummies) is small, and inthe presence of a fund effect, white and Fama-MacBeth SE are significantly biased.
10
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. Unskilled portfolio managers seem to be performing
similarly, regardless of their managerial function. 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.
[Insert Table 8 here]
4.3.1. Portfolio Management Misallocation
According to our evidence, it seems optimal to assign timers to generalist positions,
and pickers to specialist roles. Yet, in some cases managers are not allocated according
to this rule. In this section we study what might drive suboptimal decisions, and 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,
and whether the family offers or demands sub-advising services, manager background
information (PhD, MBA, the number of prior positions, college reputation), 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 to a specialist role. 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 good 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
11
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 require the firm to assign timers to specialist functions. Lack of enough pickers
might lead 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.
[Insert Table 9 here]
4.3.2. Influence of skills on Promotions: Specialist to Generalist
We now study whether market-timing or stock-picking skills affect a portfolio man-
ager’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
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 include time and investment objective
dummies (δt) –year and style 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 go 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, a switch from specialist to generalists is, in general, a promotion
12
since, as we showed previously, generalists manage a significantly larger amount of assets
than specialists.24
These results suggest that management companies base a change from specialist to
generalist on the ability of the manager to predict market trends. Overall, these results
provide additional evidence on the importance of managerial skills in determining the
function of the manager; the results are also consistent with an optimal 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 management company.
[Insert Table 10 here]
4.3.3. Performance around Managerial Reassignment: Event Study
Next we study the effects on performance of reassignment of 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
particular, 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
improve significantly in the quarter after the manager went from specialist to generalist
–it could be the same timer or a different timer. This improvement seems to be persistent
even 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 improve
in the short-run after the fund is managed by a picker-generalist rather than specialist,
but it drops overtime and remains negative twelve months after the event. We find similar
results for funds run by unskilled managers. They get a significant improvement in the
first months after being run by a generalist, but this positive performance disappears in
the second quarter. Furthermore, we compute the difference in cumulative performance
before and after the event and its statistical significance.25 We find that funds managed by
specialists 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.
24Prior 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)).
25We compare the average performance of funds two months prior the switch and the performance twelvemonths after the event. We consider t-2 because the month right before the event might not be veryrepresentative of the managerial behavior due to the proximity of the switch. Similarly, we considert+12 as managers might need some time to be adapted to their new functions.
13
In Panel B of Table 11 we examine the effect on the performance of the manager.
So we compare the performance of the funds managed by the same managers before and
after their functions change. We classified each funds based on the type of skills (timer,
picker or unskilled) the manager had before being upgraded to generalist.26 We find
similar results, specialists who become generalists improve their overall performance only
they were timers. Pickers and Unskilled managers had an improvement in the short-run
that get reduced in the second quarter post the event.
[Insert Table 11 here]
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, and similar 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
funds and managers of the family; number of prior positions, length of time the manager
has been run 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
26Unlike in Panel A, this panel focus on funds managed by the same manager and allow for changes intheir skills after the managerial switching
14
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, specially 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.27
We have conjectured that the optimal portfolio manager allocation strategy is to
assign stock-picking skilled managers to specific investment objective funds and timer
skilled managers to different investment style funds. We would like to study who collects
the rents of the superior management: maybe the better managed funds charge higher
fees and the difference in performance is irrelevant for investors after we take fees into
account. In Table 12 we include the differences on net performance, that is, we use
returns after fees. Overall, the results in Panel A and B do not change substantially.
We conclude that investors are better off purchasing funds managed by generalists with
timing skills and specialists with stock-picking skills.
[Insert Table 12 here]
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,
this is not always the case and we can find families that only allocate one manager per
fund. These firms can have some policy of a manager per fund or simply because they
have too many managers for too few funds. Nevertheless, they will not be promoted
from specialist to generalist, not because of a lack of timing skills but because of some
other family characteristics. Thus, we are under a selection bias problem in which, only
27These results are also robust when we use the radius and stratification matching methods.
15
a subsample of managers will be specialist candidates to be promoted. To address this
issue, we conduct the Heckman selection model28, in which first we obtain the probability
that a manager runs several funds simultaneously, and in the second stage, we estimate
the probability of becoming a generalist.
Table 13 reports the regression results from the first-stage of the Heckman’s selection
estimation. In the this regression, we model the probability that a manager has more
than one fund using the same set of manager control variables previously used, and a
variable that measure the ratio of funds per manager that a family has on average29:
Prob(MFMi,t = 1) = φ(β0 + β1wf,t−1 + β2xi,t−1 + δt + εi,t) (8)
where φ(·) is the cdf (cumulative density function) of the standard normal distribution.
The dependent variable MFMi,t, Multi-Funds Manager, is a dummy that takes the value
1 if the fund i is managed by manager j who runs more than one fund, and 0 otherwise.
β0 is a constant and wf,t−1 is the variable Funds per Manager, defined as the number of
funds in the family f divided by the number of managers on 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 objective dummies (δt), and the standard errors
are clustered at the fund level. In the selection model of Table 13, we show that the
probability of managing more than one fund clearly depends positively on the ratio of
funds per manager of the family.
[Insert Table 13 here]
In Table 14, we show the estimates from the second stage Heckman’s two step proce-
dure presented in equation (8). We find in Model 5 that conditioned on the probability
of being a multi-fund manager, the 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 portfolio managers. A possible explanation is that becoming
a generalist is a promotion which rewards ability.
Additionally, we also 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, while top pickers are more likely to stay as specialist.
[Insert Table 14 here]
28The original Heckman Correction (1979) was used for continuous depend variable, in our case, thedependent variable is discrete and thus we are using a newer version of these procedure.
29The variable Fund per Manager clearly affects the probability that a manager will run more than afund, however there does not seem to be any theoretical reason why it will have any effect on thedecision to assign fund managers to generalist functions.
16
5.3. Family Expansion
One possible explanation behind the decision to reassign a manager to a generalist role
might be that it is efficient for firms that are trying to increase the range of investment
objectives they offer. In particular, a recent study on product differentiation and market
share concludes that fund families gain market share by offering a wider variety of fund
investment objectives (Khorana and Servaes, 2012). Thus, it is possible that firms that
are trying to increase their market share decide to offer a wider range of products. In
order to staff the new funds, the firms can assign some of their existing talent to manage
the new funds, and therefore naturally reassign some of their specialists to generalist
functions.
In Table 15 we again analyze the probability of a reassignment from specialist to gen-
eralist, but this time we divide our sample based on the concentration level of the family.
We sort families into four different groups every month and then group them into the low
concentrated ones if their Herfindahl index across investment objectives funds are within
the first quartile and highly concentrated if they are in the forth quartile. We show that
for funds that belong to families with either low or high levels of concentration, the timing
skill of the manager has a strong effect on the probability of switch to generalist, while
picking skills do not seem to matter for this decision. Therefore, family concentration is
an important characteristic to take into consideration. 30
[Insert Table 15 here]
5.4. Downturn Markets
Kacperczyk, Nieuwerburgh and Veldkamp (2014) provide evidence that outperforming
fund managers excel at stock picking in bull markets and at timing in recessions. We
wonder then if there is a cyclical component in the decision to switch a manager from
specialist to generalist, and that is the driving factor, and not the ability as timer. For
example, it could be that the management companies are trying to save costs by having
portfolio managers run different funds, possibly with different objectives.
For that reason, in Table 16 we estimate the probability of being transfered from
specialist to generalist for different market conditions. We classified bull and bear market
based on whether Chicago Fed National Activity Index (CFNAI) is on the forth or first
30In 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 a managers withno 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, but we cannot make the same claim for pickers.
17
quartile, respectively. 31 We can observe that timing ability is still the main factor
affecting the decisions of switching to generalist functions, independently on the market
being in recession or expansion. On the other hand, Table 16 shows that picking skills
matter only for bear market. 32
[Insert Table 16 here]
6. Conclusions
Our paper supports the literature that argues that there are actively managed mu-
tual funds that outperform passively managed funds. In particular, we identify portfolio
managers with two types of skills, stock-picking or market-timing –although we agree
that many portfolio managers do not display either type of skill. We argue that pick-
ers 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 an optimal allocation of human capital,
we find more timers among the generalists and more pickers among the specialists. In
addition, management companies tend to switch timers from specialists to generalists
functions, specially when they have been performing extremely well in the past. This is
considered as a promotion within the firm and we observe that overall manager perfor-
mance improve after such event. 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 MBA degree. Overall, management companies make rational decisions based on
measurable skills.
This result has important implications for the organization decisions of management
companies. Many studies have shown inefficiencies of this industry, in which portfolio
managers that are highly compensated are unable to outperform a given benchmark
or performance persists for poor managers but not for top performers. Our evidence
presents a picture in which funds management companies allocate their employees to
exert the maximum productivity. Further research might focus at the portfolio manager
level rather than a the fund level to understand the costs and benefits associated with
manager assignments given their skills as well as the role that human capital and industrial
organization play in this industry.
31The 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.
32In an unreported table, we verify that market conditions affect the probability of reassignment, butas in our previous tests, having some ability and specially the interaction between timing ability andprevious outstanding performance explains in a significant way the probability of reassignment.
18
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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)according to their investment objectives during the period 1996-2011. Funds are classified attending to the different categoriesprovided by the SEC for each investment company.
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, the number of funds managed by generalists, thetotal number of individual managers, number who are generalists, the total number of management companies and how manyhave employed generalist managers. The sample covers all equity, fixed income and international U.S open-end funds managed byindividual 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
25
Table 3: T-Test Analysis: Specialist vs Generalist
This table presents the mean of fund, family and manager characteristics for the samples of specialists (portfolio managers offunds within a single investment objective) and generalists (portfolio managers of funds from different investment objectives) andthe associated difference among the two samples. * denotes significance at the 10% level, ** denotes significance at the 5% leveland *** denotes significance at the 1% level. Panel A contains all the variables at the fund level, Panel B summarizes the familycharacteristics and Panel C the variables at the portfolio manager level. The description of each variable is defined in the appendixsection. 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 ∗∗∗
26
Table 4: Proportion of Generalist by Style and Skill
This table presents the proportion of funds managed by generalist managers, according to the investment objective and managerialskill (timing, picking or unskilled) for the period 1996-2011. A full description of the type of manager variables is provided in theappendix.
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 Types and Managerial Skills
This table presents the average investment objective (gross and net) returns of funds managed by generalist and specialist managers,according to their managerial skill (timing, picking or unskilled) for the period 1996-2011. A full description of the return measuresis provided in the appendix.
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
27
Table 6: Managerial Type and Performance (I)
This table presents the results of monthly Pooled OLS regressions of fund and manager investment objective-adjusted returns onfund, manager and family characteristics. Fund returns are the actual returns before deducting fees and expenses (gross) andmanager returns are the TNA-weighted average return of all the portfolios managed by the same manager at the same time. Thedependent variable are fund and manager performance, measured by substracting the median return of their investment objectivepeers, from the actual return of the fund and manager, respectively. Generalist is a dummy variable equals 1 if the fund is managedby manager that is in charge on funds from different investment styles. Timer is a dummy equals 1 if the fund is managed byportfolio manager that has been able to time the market during the past 24 months. Picker is a dummy variable equals 1 if thefund is managed by a manager that was able to pick stocks efficiently during the past 24 months. All variables are lagged oneperiod. A full description of the remaining variables is in the appendix. Time and investment objective dummies are included butnot reported; t-statistics are reported in parentheses. We adjust for serial correlation by clustering standard errors at the fundlevel. * 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 -0.030 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
28
Table 7: Managerial Type and Performance (II)
This table presents the results of monthly portfolio fixed effect (Panel A), manager (Panel B) and family fixed effect regressions(Panel C) of Objective-adjusted returns on fund, manager and family characteristics. Fund returns are the actual returns beforededucting 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 are fund and manager performance, measured by substracting themedian return of their investment objective peers, from the actual return of the fund and manager, respectively. Generalist is adummy variable equals 1 if the fund is managed by manager that is in charge on funds from different investment styles. Timer isa 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 past24 months. All variables are lagged one period. Control variables and time and investment style dummies are included but notreported; t-statistics are reported in 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 *** 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∗∗∗
(4.25) (4.16) (4.18) (4.10)Generalist × Picker -0.280∗∗∗ -0.260∗∗ -0.356∗∗∗ -0.341∗∗∗
(-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∗∗∗
(3.98) (3.85) (4.07) (3.95)Generalist × Picker -0.271∗∗∗ -0.252∗∗ -0.326∗∗∗ -0.312∗∗∗
(-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∗∗∗
(5.21) (5.14) (5.31) (5.24)Generalist × Picker -0.234∗∗∗ -0.221∗∗ -0.308∗∗∗ -0.298∗∗∗
(-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
29
Table 8: Managerial Skill and Performance: Subsamples
This table presents the results of monthly panel regressions of portfolio performance on manager type (generalist vs specialist)and other characteristics. Fund returns are actual returns before deducting fees and expenses (gross) and manager returns areTNA-weighted average returns of all the portfolios run by the same manager at the same time. The dependent variables are fund–columns (1)-(3)– and manager –(4)-(6)– performance, measured by subtracting the median return of their investment objectivepeers, from the actual return of the fund and manager, respectively. Observations are sorted into funds managed by Timers incolumns 1 and 4, Pickers in columns 2 and 5, and by Unskilled managers in column 3 and 6. Generalist is a dummy variable equalto 1 if the fund is run by a manager who is in charge on funds with different investment objectives. All variables are lagged oneperiod. A full description of the remaining variables is provided in the appendix. Time and Style dummies are included but notreported; 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.
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
30
Table 9: Portfolio Manager Mis-allocation
This table presents the results of monthly logistic regressions of portfolio manager misallocation within their management companieson fund, manager and family characteristics. The dependent variable is a dummy variable that equals 1 if a Picker is allocated asa Generalist (Generalist Misalloaction) or a Timer is allocated as a Specialist (Specialist Misallocation). All variables are laggedone period. A description of fund, manager and family variables is in the appendix. The sample contains all U.S. mutual funds runby an individual portfolio manager from 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. * denotes significance at the 10% level, **denotes significance at the 5% level and *** denotes significance at 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
31
Table 10: Specialist Manager Switching to Generalist
This table presents the monthly logistic regressions of manager switches from specialist to generalist on manager and othercharacteristics. The dependent variable is a dummy variable that equals 1 if a specialist becomes generalist in the next month, and0 otherwise. Timer is a dummy variable that takes the value of 1 if the manager has been timing the market significantly for thepast 24 months. Picker is a dummy variable that takes the value of 1 if the manager has been selecting stocks successfully for thepast 24 months, and 0 otherwise. Manager Past Skill is the TNA-weighted cumulative returns of the objective-adjusted returns ofall the funds run by the manager during the past 24 months. All variables are lagged one period. A description of the remainingvariables is in the appendix. The sample contains all U.S. mutual funds managed by a specialist from 1996 to 2011. Time andInvestment Objective dummies are included 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
32
Table 11: Performance around Managerial Switching: 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 manager goes from specialist to generalist. The performance is expressedin percentage and corresponds to the semi-annual cumulative objective-adjusted return before fees and expenses (gross). The analysis is for the entire sample from 1996 to 2011. Our sample is divided intofunds run by timers, pickers and unskilled portfolio managers. Panel A accounts for funds that go from being managed by a timer, picker or unskilled specialist to be managed by a generalist (the samemanager or new one) with the same type of skill. Panel B considers funds that keep the same manager that were specialist timer, picker or unskilled and become generalist (independently of the new skillthey might have gained). We also report differences between the cumulative performance before and after each event. * denotes significance at the 10% level, ** denotes significance at the 5% level and ***denotes significance 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
33
Table 12: Propensity Score Matching: Fund Performance and Manager Skill
In this table, we identify a control sample of funds managed by specialist with two different propensity score matching procedures:Nearest Neighbor of Rosenbaum and Rubin (1983), and the Kernel Matching of Heckman, Ichimura and Todd, (1997, 1998). Toestimate the propensity score we use the size, age, turnover, expenses, flows and past returns of the funds, the size and the numberof funds and managers of the family, and the number of prior positions, the length of time the manager has been managing the fund,the number of funds and the total amount of assets the manager has currently under management. We require that the differencebetween the propensity score of the funds managed by a specialist and the matching peer does not exceed 0.1% in absolute value.We then compare the fund performance between the two groups and report the value of the difference (Generalist-Specialist) andthe statistical significance using bootstrapped standard errors associated to that difference. Fund performance is the excess returnover the mean of the style, using the gross and net returns of the portfolio. We group the funds into quintiles according to thetiming ability (Panel A) and picking managerial skills (Panel B) during the period 1996-2011.* denotes significance at the 10%level, ** denotes significance at the 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∗∗∗
Table 13: Selection Bias: Heckman’s two-step procedure (1st Stage)
In this table, we show the estimates from the first stage Heckman’s two-step procedure. The model estimates the probability thata manager works for a firms with multi-funds policy. We present the monthly logistic regressions of managers running more thanone fund at the same time on fund and family characteristics. Funds per Manager is the total funds of the family divided by thenumber of manager of that firm. All variables are lagged one period. A complete description of the variables is provided in theappendix. The sample contains all U.S. mutual funds managed by a specialist portfolio manager from 1996 to 2011. Time andStyle dummies are included but not reported; t-statistics are reported in parentheses. Standard errors are clustered at the fundlevel. * denotes significance at the 10% level, ** denotes significance at the 5% level and *** denotes significance 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
34
Table 14: Selection Bias: Heckman’s two-step procedure (2nd Stage)
In this table, we show the estimates from the second stage Heckman’s two-step procedure to examine how different managercharacteristics affect the probability of a specialist being transfered to generalist, conditioned on being a multi-fund manager. Allvariables are lagged one period. A complete description of the variables is provided in the appendix. The sample contains allU.S. mutual funds managed by a specialist portfolio manager from 1996 to 2011. Time and Style dummies are included but notreported; 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.
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
35
Table 15: Family Expansion
This table presents the monthly logistic regressions of manager switches from being specialist to generalist on manager and other characteristics. The dependent variable is a dummy variable that equals 1 ifa specialist portfolio manager is transfered to generalist in the next month and 0 otherwise. Timer is a dummy variable that takes the value of 1 if the fund is managed by manager that has been significantlytiming the market during the past 24 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 during the past 24 months and 0otherwise. The sample is divided into quartiles based on the Herfindahl index across investment objectives of the fund within the family for each month. We consider Low and High concentrated firms basedon whether the management company is in the first or fourth quantile, respectively. All variables are lagged one period. A description of the remaining variables is provided in the appendix. The samplecontains all U.S. mutual funds managed by a specialist portfolio manager from 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. * denotes significance at the 10% 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
36
Table 16: Downturn Markets
This table presents the monthly logistic regressions of manager switches from being specialist to generalist on manager and other characteristics. The dependent variable is a dummy variable that equals 1 ifa specialist portfolio manager is transfered to generalist in the next month and 0 otherwise. Timer is a dummy variable that takes the value of 1 if the fund is managed by manager that has been significantlytiming the market during the past 24 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 during the past 24 months and0 otherwise. Manager Past Skill is measured as the TNA-weighted cumulative returns of the objective-adjusted returns of all the funds run by the manager during the past 24 months. Market Condition ismeasured with the Chicago Fed National Activity Index. All variables 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-statistics are reported in parentheses. Standard errors are clusteredat the fund level. * denotes significance at the 10% 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 1.135∗∗∗ 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
37
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.
38
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:
• 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
39
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]
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.
[Insert Table A2 here]
40
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
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.
41
[Insert Table A3 here]
In Table A4 we show that a manager moves to a different family –for the same
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.
[Insert Table A4 here]
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)33 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
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
33A full description of these investment objectives is in the Appendix.
42
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.
[Insert Table A5 here]
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.
[Insert Table A5 here]
A7. Risk-adjusted Returns
We replicate equation (4) using as dependent variable a risk-adjusted return.
[Insert Table A6 here]
A8. Fama-MacBeth (1973) regressions
We estimate prior equation following the Fama-MacBeth (1973) approach.
[Insert Table A7 here]
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.
43
Table A1: Proportion of Generalist by Styles and Size
This table summarizes the proportion of funds managed by generalist managers sorted by quintiles of portfolio sizes and accordingto the fund investment objective for all the U.S mutual funds 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 (Market Prediction Skills) and those unskilledin Panel A and the performance difference between fund managed by Pickers (Security Selection Skills) and the unskilled ones inPanel B. For each of the three asset classes (Domestic Equity, fixed income and international stocks), we sort the managers bySpecialist and Generalists and display the fund performance differences measured using the alpha of Carharts model augmentedby two more risk factors (to be more conservative as 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
44
Table A3: Transition Matrix (I): Role-switched Manager vs other Managers withinthat Role
This table presents the value difference of fund managed by managers that: 1) have switched from specialist to generalist role(and vice versa) within the same company, 2) between different firms and 3) simple changing the firm but keeping the role. Eachcolumn contains the difference between 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 reported on the last row. Full description ofall 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 0.166∗∗∗ 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 switched from specialist to generalist role(and vice versa) within the same company, 2) between different firms and 3) simple changing the firm but keeping the role. Eachcolumn contains the difference between the characteristics of the switched manager before and after the event of the switch. Thenumber of events within each category is also reported on the last row. Full description of all these variables is provided in theappendix.* 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
45
Table A5: Managerial Skill and Concentration
This table presents the results of monthly Pooled OLS regressions of fund and manager investment objective-adjusted returns onfund, manager and family characteristics. Fund returns are the actual returns before deducting fees and expenses (gross) andmanager returns are the TNA-weighted average return of all the portfolios managed by the same manager at the same time. Thedependent variable are fund and manager performance, measured by substracting the median return of their investment objectivepeers, from the actual return of the fund and manager, respectively. Generalist is a dummy variable equals 1 if the fund is managedby manager that is in charge on funds from different investment styles. Timing and Picking are the gamma and alpha coefficientsfrom estimating a modified version of the TM Timing model. Concentration is the Herfindahl index of concentration among alldifferent investment objectives of the funds of the manager. Timer is a dummy equals 1 if the fund is managed by portfolio managerthat 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 amanager that was able to pick stocks efficiently during the past 24 months. All variables are lagged one period. A full descriptionof the remaining variables is in the appendix. Time and investment objective dummies are included but not reported; t-statisticsare reported in parentheses. We adjust for serial correlation by clustering standard errors at the fund level. * denotes significanceat the 10% level, ** denotes significance at the 5% 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
46
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 effects regressions of fund investmentobjective-adjusted returns on fund, manager and family characteristics. Fund returns are the actual returns before deductingfees and expenses (gross). The dependent 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 if the fund is managed by portfoliomanager that has been able to time the market during the past 24 months. Picker is a dummy variable equals 1 if the fund ismanaged by a manager that was able to pick stocks efficiently during the past 24 months. Panel A contains only the subsampleof funds managed by generalist managers while Panel B considers only portfolios managed by Specialist managers. All variablesare lagged one period. A full description of the remaining variables is in the appendix. Control variables, time and investmentobjective dummies are included but 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, ** 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
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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 managerrisk-adjusted returns on fund, manager and family characteristics. Fund returns are the actual returns before deducting fees andexpenses (gross) and manager returns are the TNA-weighted average return of all the portfolios managed by the same manager atthe same time. The dependent variable is Fund Performance (obtained using the 6-factors model previously defined) and ManagerPerformance (TNA-weighted average alpha of all the funds managed by the same manager at the same time). Generalist is adummy variable equals 1 if the fund is managed by manager that is in charge on funds from different investment styles. Timer isa 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 past24 months. All variables are lagged one period. Control variables, time and investment objective dummies are included but notreported; t-statistics are reported in 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 *** 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∗∗∗
(3.38) (2.91) (3.25) (2.74)Generalist × Picker -0.242∗∗∗ -0.250∗∗∗ -0.243∗∗∗ -0.249∗∗∗
(-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
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Table A8: Managerial Type and Performance: Fama-MacBeth (1973)
This table presents the results of Fama-MacBeth regressions of fund and manager investment objective-adjusted returns on fund,manager and family characteristics. Fund returns are the actual returns before deducting fees and expenses (gross) and managerreturns are the TNA-weighted average return of all the portfolios managed by the same manager at the same time. Generalist is adummy variable equals 1 if the fund is managed by manager that is in charge on funds from different investment styles. Timer isa 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 past24 months. All variables are lagged one period. A full description of the remaining variables is in the appendix. Investmentobjective dummies are included but not reported; t-statistics are reported in parentheses. We adjust for serial correlation byapplying Newey-West (1987) estimates of standard errors with lags of order three. * 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∗
(2.30) (1.76) (2.50) (1.92)Generalist × Picker -0.311∗∗∗ -0.307∗∗∗ -0.305∗∗∗ -0.301∗∗∗
(-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
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