Mutual Fund Stars: The Performance and Behavior of U.S. Fund Managers Bill Ding Department of Finance Leeds School of Business University of Colorado at Boulder Boulder, CO 80309 [email protected]Russ Wermers Department of Finance Robert H. Smith School of Business University of Maryland at College Park College Park, MD 20742-1815 Phone: (301) 405-0572 [email protected]July 2002 Web address: http://www.rhsmith.umd.edu/Finance/rwermers/. Wermers gratefully acknowledges research support from INQUIRE-UK for this project. Also, we thank Morningstar and Thomson Wiesenberger for providing mutual fund manager data.
Transcript
Mutual Fund �Stars�:The Performance and Behavior of U.S. Fund Managers
Bill DingDepartment of FinanceLeeds School of Business
University of Colorado at BoulderBoulder, CO 80309
Web address: http://www.rhsmith.umd.edu/Finance/rwermers/. Wermers gratefully acknowledges research support
from INQUIRE-UK for this project. Also, we thank Morningstar and Thomson Wiesenberger for providing mutual
fund manager data.
Mutual Fund �Stars�:The Performance and Behavior of U.S. Fund Managers
Abstract
Do mutual fund �star� managers exist? Past studies of mutual fund performance have ignored
the role of managers in the performance of funds. Our study assembles the most complete database
of U.S. fund managers to date, and merges this manager database with a comprehensive database
of fund stockholdings, net returns, and other characteristics. This merged database allows us to
investigate several issues related to whether talent resides at the manager level, including the role
of managerial experience and stockpicking track-record in predicting the future performance of a
manager�this unique database, which extends from 1985 to 2000, allows the creation of several new
measures that provide insights into these issues.
We Þnd that experience matters, but only for growth-oriented fund managers�the most expe-
rienced growth managers have substantially better stockpicking skills than their less-experienced
colleagues. We also show that the career stockpicking track-record of a fund manager holds the
most signiÞcant predictive power for future fund manager performance�managers with the best
career records choose portfolios that beat their style benchmarks by almost two percent per year,
while managers with the worst records provide an insigniÞcant level of performance�thus, manage-
rial talent strongly persists. Finally, we Þnd that the replacement of a fund manager, for whatever
reason, has an impact on fund performance, but only because the new manager has a substantially
better track record than the replaced manager. During the year of replacement, fund managers
underperform their counterparts by about one percent per year�this underperformance vanishes af-
ter the manager is replaced. Our results add several new insights to the mutual fund performance
and performance persistence literature by highlighting the role of the manager in generating fund
performance.
I Introduction
A good deal of attention is focused on professionals who manage money, in the form of television
interviews, best-selling books, and frequent articles in the popular press. The media often focuses
on the investment results of a few �star� mutual fund managers, such as Bill Miller of the Legg-
Mason Value Trust Fund or Scott Schoelzel of the Janus 20 Fund. The implication of the media
spotlight on star managers is that experienced managers, or managers with a good track-record,
outperform other managers in addition to passively managed funds on a consistent basis. However,
do star fund managers really exist?
Over the past few decades, several papers have analyzed the performance of mutual funds,
ignoring (in general) the role of the fund manager. Overall, these studies Þnd that, adjusted
for the return premia earned from loading either on the overall stock market (relative to Þxed-
income investments), or on certain equity �style characteristics,� mutual funds have provided a
slightly negative level of abnormal returns. Examples of papers that examine this issue using net
returns data include Malkiel (1995) and Carhart (1997), and examples that examine the issue
using stockholdings data include Grinblatt and Titman (1989, 1993) and Wermers (2000). These
papers indicate that our view of the average mutual fund�s performance depends on whether style
investing represents a systematic risk: if we do not deduct the return premia for loading on style
characteristics, the average manager, rather than exhibiting a negative abnormal return, exhibits
an abnormal return of about zero. However, if talent resides at the manager level rather than
at the fund level, then all prior tests may lack power in detecting stockpicking ability. With a
couple exceptions, prior work has not considered the role of the fund manager in generating fund
performance.
Our paper provides fresh evidence on the role of managers in both the characteristics and the
performance of mutual funds. Chevalier and Ellison (1999), using a sample of mutual funds over
a short time period, are the Þrst to analyze the impact of experience on fund performance. Baks
(2001) examines managers in the CRSP Mutual Fund database over the 1992 to 1999 period to
separate the impact of the fund manager from the impact of the non-manager characteristics of a
fund on the fund�s performance.
Our paper contributes to the literature in several ways. First, our manager database covers
the 1985 to 2000 period, which is the longest time-series of manager data assembled to date. The
manager data is compiled from several sources, and includes basic information about a manager,
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such as the starting and ending dates of the manager with each fund managed over her career.
Second, we merge the manager database with an updated version of the merged Thomson/CDA
mutual fund stockholdings and CRSP mutual fund net returns and characteristics database that
is Þrst examined in Wermers (2000).1 And, third, the nature of our merged database allows us to
design several new measures of manager and fund characteristics, such as the career stockpicking
record of a manager and the level of �style drift� experienced by a fund. These new measures
provide us with the ability to investigate several determinants of manager and fund characteristics,
which, in turn, allow us to measure the correlates of these characteristics with fund performance
and performance persistence.
We study three basic issues in this paper to determine whether mutual fund star managers
exist. First, we examine whether the experience of a fund manager, over her entire career, has
any impact on the performance of the fund. There are several reasons why we may believe that
seasoned fund managers have superior talents�these reasons include the increasing ability of the
fund manager to interpret the research provided by internal and external stock analysts as well as
the increasing access that fund managers may gain to corporate managers as the fund managers�
careers progress.2
Second, we measure the past stockpicking record of a fund manager to investigate whether man-
agers with past success have persistent stockpicking skills, independent of their level of experience
at a certain date. And, third, we examine mutual fund performance during the time surrounding
the replacement of a manager, which provides a sharp test of whether stockpicking talent resides
at the manager level.
Our results provide several interesting insights. First, we Þnd that managerial experience is
an important predictor of future stockpicking success for growth-oriented fund managers, but not
for income-oriented managers. This Þnding indicates that experience is important for success in
picking growth stocks, perhaps because of the difficulty in accurately forecasting earnings growth
for these stocks, relative to value stocks. Growth-oriented managers may either develop specialized
1This merged database, along with our new manager database, provides several advantages over past work. Forexample, we are able to more precisely measure the stockpicking talents of managers by using portfolio holdings data�these data also allow us to provide a complete attribution analysis for each mutual fund, before and after tradingcosts and other fund expenses.
2This increasing access to corporate managers may result from several inßuences, including an increase in the sizeof positions in stocks that may result as a result of seasoned managers taking on the responsibility for larger funds. Inaddition, a relationship with a corporate manager may develop over time, as the fund manager potentially becomes a�long-term� shareholder of the Þrm. Regulation FD, implemented by the SEC to prevent an information advantagefor institutions and other shareholders, was not in effect during the majority of our sample period.
2
skills over time, or, alternatively, they develop valuable relationships with corporate managers that
give them access to private information on future earnings.
Second, we Þnd that the past stockpicking track record of a fund manager is the most important
predictor of the future performance of the fund. Managers with the best past stockpicking records
outperform those with the worst records by almost two percent per year, even though these �star�
managers do not have appreciably greater experience levels than their counterparts. SpeciÞcally, the
signiÞcance of the track record variable remains strong when experience is added in a multivariate
regression setting. This Þnding indicates that managerial talent persists for multiple-year periods,
which is consistent with the Þndings of Wermers (2002b).
We also Þnd that the replacement of a manager has a substantial effect on fund performance, but
only because the new manager has a substantially better track record than the replaced manager.
While the pre-replacement benchmark-adjusted return of a fund (before expenses and trading costs)
is reliably lower than that of other funds, this difference vanishes after the manager is replaced.
Thus, our paper indicates that managerial talent does persist over long time periods, and that
the labor market for fund managers appears to work efficiently by replacing managers when their
stockpicking talents have Þnally faded.
Our Þnal tests look at the role of managerial aversion to risk in explaining fund performance.
SpeciÞcally, we add two proxies for managerial risk aversion to determine whether managers who
trade more aggressively on their private information exhibit a level of performance different from
other managers. Adding these two proxies to the multivariate setting above, we Þnd no evidence
that risk aversion inßuences future performance. This Þnding indicates that, if risk-aversion matters
in generating portfolio performance, it is highly correlated with the other variables in our regressions
(i.e., experience or track-record).
The remainder of this paper is organized in four sections. The construction of our database is
discussed in Section II, while our measures of manager characteristics and fund performance and
costs are outlined in Section III. Section IV presents empirical Þndings. We conclude the paper in
Section V.
II Data
Our mutual fund characteristics data is drawn from the merged CDA�CRSP mutual fund database
of Wermers (2000). For each U.S. equity fund portfolio that exists anytime between January 1975
3
and December 2000, CDA�CRSP contains data on various fund statistics, such as the monthly
net return, total net assets, annual expense ratio, annual turnover ratio, and quarterly stock hold-
ings of each fund. This database is the longest time-series having both stockholdings and net
returns/characteristics information that has been assembled to date. See Wermers (2000) for more
information on the construction and limitations of an earlier version of this database.
We merge the CDA-CRSP database with a newly constructed database of mutual fund managers
that covers the 1985 to 2000 (inclusive) time period. In constructing our database of managers, we
focus on U.S. equity funds, that is, funds having a self-declared investment objective of aggressive
growth (AG), growth (G), growth and income (GI), income or balanced (I or B) at the beginning
of a given calendar quarter. The fund manager data is assembled from three separate sources of
manager data: the 2001 Morningstar Principia Pro database, the CRSP Survivor-Bias Free Mutual
Fund Database, and a database of fund managers that was purchased from Thomson/Wiesenberger
in 1999. We combine the fund manager data from these three sources based on the manager�s name
and the name of the managed fund to ensure that we create a manager database that is as complete
as possible.3 SpeciÞcally, for each fund manager, we collect her name, the names of funds managed
by her during her career, the start and end dates for that manager at each fund over her career,
and other manager characteristics, including CFA designation, universities attended, prior analyst
experience, and other items such as marital status and personal interests. The fund manager
data are then matched with the CDA�CRSP database of portfolio holdings, net returns, and fund
characteristics. In conducting our study, we focus our attention on the lead manager of each mutual
fund, assuming that this manager has the greatest decision-making power for that fund. As a proxy
to identify the lead manager, we choose the manager with the longest tenure at a given fund (if
team managed) to decide on which manager is the lead manager.4
Counts of our sample of lead managers over the entire 1985 to 2000 period, as well as counts
at the beginning of 1985, 1990, 1995, and 2000 are presented in Table I. There are a total of 2,272
CDA�CRSP funds and 2,229 lead managers in our sample. Growth funds account for the majority
of the fund universe, and about 80% of the fund managers have experience in managing at least one
3We note that in some (rare) cases there are inconsistencies in the manager�s Þrst name abbreviation (e.g. Robertand Bob) and name suffix (e.g. none vs. Jr.) among the three fund manager data sources. In these cases, we useother information, such as the historical fund manager name, managed fund name, and start and end dates to ensurethe accuracy of matching.
4If there is tie in the start date, we use the total career experience as the tie-breaker, i.e., we pick the currentlyactive fund manager who becomes a fund manager (of any fund) at the earliest date.
4
growth fund during 1985 to 2000. Not surprisingly, the number of funds and fund managers grows
rapidly with the expansion of the whole fund industry in our sample period. The average number
of funds lead-managed by a given fund manager increases gradually from 1.27 at the beginning of
1985 to 1.57 at the beginning of 2000.
To check the completeness of our matched manager/fund database, we further examine the
CDA�CRSP funds that fail to be matched with any fund manager, and report the results in panels
C and D of Table I. Overall, we are able to identify at the lead manager for almost 94 percent of
funds in our CDA�CRSP database. In addition, more than 85 percent of all fund-months during
1985 to 2000 in the merged CDA�CRSP database contain information about the lead manager.
A close look at the number of missing managers at four different points in time reveals more
detailed information. Thirty-three percent of the funds that exist at the beginning of 1985 are
unable to be matched with a manager during 1985, but this fraction steadily declines over our
sample period to 6.1 percent and 4.8 percent during 1995 and 2000, respectively. One reason that
post-1995 manager data is noticeably more complete than pre-1995 data is that our data sources, in
general, begin to formally collect manager data in the Þrst half of the 1990s, and probably backÞlled
previous manager data. In Panel D, a further comparison is provided between funds with complete
manager data and funds that have missing manager data. This panel presents data on the total
net assets under management and the net return, in excess of the S&P 500 index return, between
funds having manager data and funds with missing manager data at the beginning of each Þve-year
period, as well as for the entire sample period of 1985 to 2000. We Þnd that funds with missing
manager data tend to be smaller and perform somewhat worse than those with complete manager
data.
III Methodology
A Measures of Manager Characteristics
Since the fund manager is the unit of analysis for our study, we construct measures that quantify
various manager characteristics, such as experience, track record in picking stocks, attitude toward
risk-taking, and aggressiveness in trading stocks. The richness of our fund characteristics and port-
folio holdings data available from CDA�CRSP allow us to design several measures that accurately
capture these proxies for various attributes that might be associated with superior stockpicking
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skills. In this subsection, we describe these measures, and then present summary statistics on the
measures over the sample period.
The Þrst manager characteristic of interest is experience, which we is simply deÞned as the
total number of months that an individual has served as a fund manager over her entire career.
To capture the track record of a fund manager, we develop three measures. The Þrst track record
variable is the time-series average of monthly net return in excess of the S&P500 index return, or
TrackRecordi1,t =1
t− ti0tX
τ=ti0
(Riτ −RS&P500τ ) (1)
where ti0 is the month at which manager i Þrst becomes a lead manager for any fund. Riτ and
RS&P500τ are fund i�s return and the S&P500 index return for month τ , respectively. We choose
the S&P 500 index as our Þrst benchmark, since this benchmark is the most common one used by
the U.S. fund industry.
The second measure that we use to proxy for the track record of a fund manager is the time-
series average of monthly investment objective-adjusted returns, which is deÞned as the manager�s
net return minus the average return of all funds with the same investment objective as the managed
fund during the same time period. This measure for manager i at month t is
TrackRecordi2,t =1
t− ti0tX
τ=ti0
(Riτ −RKτ ) (2)
where K is the investment objective of fund i and RKτ is the average return across all funds with
objective K at month τ . The rationale of using the average investment objective return as a second
benchmark is that managers likely have an incentive to outperform their peer funds, regardless of
their performance relative to the S&P 500 index.
The third track record that we use is the stockpicking talent of the fund manager, as de-
Þned by the Characteristic Selectivity measure of Daniel, Grinblatt, Titman, and Wermers (1997)
(henceforth, DGTW), where mutual fund performance is evaluated against characteristic-based
benchmarks. SpeciÞcally, we use the time-series average of a manager�s Characteristic Selectivity
(CS) measure (henceforth, CS measure), over the entire career of the manager, to measure the
manager�s track record in picking stocks. The CS track record measure (CST ) for manager i at
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month t is calculated as
CST it =1
t− ti0tX
τ=ti0
JτXj=1
wj,τ (Rj,τ −Rbj,ττ ) (3)
where wj,τ is manager i�s portfolio weight on stock j at the end of the calendar quarter just
preceding month τ ; Rj,τ is the month τ return of stock j; Rbj,ττ is the month τ return of stock
j�s characteristic-matched portfolio (matched on market capitalization, the ratio of book-equity to
market-equity, and the prior one-year return on stocks); Jτ indicates the number of stocks held in
the fund(s) managed by manager i at the end of the quarter preceding month τ . An advantage of
the CS measure is that it uses portfolio holdings information, which DGTW argue provides a more
precise measurement of performance relative to regression-based methods. Further information on
the construction of this measure is given in the next section, when we further describe this measure.
A manager�s risk attitude may determine her choice of stocks to hold in the managed fund
portfolio, and, thus, may affect fund performance. In some cases, managers may take on, or avoid,
risk in response to labor-market incentives (see, for example, Chevalier and Ellison (1997) or Brown,
Harlow, and Starks (1996)). The measures we use to characterize a fund manager�s risk attitude
are, respectively, the standard deviation of her monthly excess return and the standard deviation
of her monthly investment objective-adjusted return, i.e.,
RiskAttitudei1,t =
1
t− ti0tX
τ=ti0
(Riτ −RS&P500τ − TrackRecordi1,t)2
12
(4)
RiskAttitudei2,t =
1
t− ti0tX
τ=ti0
(Riτ −RKτ − TrackRecordi2,t)2
12
(5)
Some managers may be more aggressive in trading stocks than others, perhaps because they
have better private information about stock values than others, because they believe they have
superior stock-picking skills (perhaps due to overconÞdence), or because they are simply less risk-
averse than other fund managers in using their private information. We would believe that such
aggressiveness would lead to higher trading frequency and volume. As such, a manager�s aggressive-
ness in managing her portfolio is measured as the time-series average turnover ratio of the fund(s)
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managed by her.5 The expression for the aggressiveness of manager i through month t is
Aggressivenessit =1
t− ti0tX
τ=ti0
TURNOV ERiτ . (6)
Our Þnal manager characteristic measure captures the tendency of the manager to shift between
different equity investing styles through time. For example, some managers may be less focused
in their style of investing, and believe that they can Þnd underpriced stocks in several different
style categories (�bottom-up� investing). Alternatively, Brown, Harlow, and Starks (1996) show
that managers that underperform their peers during early periods may later move to investments
that are more risky in order to attempt to �catch up� to their peers. This risk-taking behavior
may involve shifting to a different style category, relative to the manager�s peers. In any case, we
measure the tendency of the manager to actively shift between different styles with
ActiveStyleDriftit =3Xj=1
¯̄̄dij,t
¯̄̄, (7)
where dij,t equals the drift of manager i in style dimension j (j=size, value/growth, or momen-
tum/contrarian) during year t. To measure the active drift in a given style dimension, we measure
the difference in the portfolio-weighted style number between the current portfolio, at the end of
June of year t, and that of the portfolio that would have resulted, had the manager passively held
the prior-year�s June 30th portfolio. Thus, the active style drift (ASD) measure captures move-
ments in style that are solely due to active trades during the year ending on June 30th. Following
Wermers (2002a) and DGTW, we use a non-parametric characterization of each stock in three
dimensions: the market capitalization, the ratio of the industry-normalized book-equity to market-
equity, and the prior-year return of the stock. Further details on the assignment of style dimension
numbers to each stock during each year are provided in Section B.1 below, as well as in DGTW.
Details on the computation of the style drift of funds is given in Wermers (2002a). The sum of the
absolute values of the active drift in each style dimension, during a year t, is our measure of the
active style drift that results from a manager�s actions during that year.
5The annual turnover ratio of a fund is deÞned, by CRSP, as the lesser of securities purchased and sold, dividedby average monthly total net assets during the year.
8
B Measures of Mutual Fund Performance and Costs
In this study, we use several measures that quantify the ability of a mutual fund manager to choose
stocks, as well as to generate superior performance at the net return level. These measures, in
general, decompose the return of the stocks held by a mutual fund into several components in order
to both benchmark the stock portfolio and to provide a performance attribution for the fund.
The measures used to decompose fund returns include:
1. the portfolio-weighted return on stocks currently held by the fund, in excess of returns
(during the same time period) on matched control portfolios having the same style characteristics
(selectivity)
2. the execution costs incurred by the fund
3. the expense ratio charged by the fund
4. the net returns to investors in the fund
5. the benchmark-adjusted net returns of the fund.
The Þrst component, which measures the style-adjusted return of a given mutual fund before
any trading costs or expenses are considered, is brießy described next.6,7 We estimate the execution
costs of each mutual fund during each quarter by applying recent research on institutional trading
costs to our stockholdings data�we also describe this procedure below. Data on expense ratios and
net returns are obtained directly from the merged CDA-CRSP mutual fund database. Finally, we
describe the Carhart (1997) and Ferson and Schadt (1996) regression-based performance measures,
which we use to benchmark-adjust net returns.
B.1 The Characteristic Selectivity Measure
The Þrst component of performance measures the stock-picking ability of the fund manager dur-
ing a given month, controlling for the particular style used by that manager.8 This measure of
6This measure is developed in Daniel, Grinblatt, Titman, and Wermers (1997), and is more fully described there.In that paper, the authors argue that decomposing performance with the use of benchmark portfolios matchedto stocks on the basis of the size, book-to-market, and prior-year return characteristics of the stocks is a moreprecise method of controlling for style-based returns than the method of decomposing performance with factor-basedregression techniques that is used by Carhart (1997).
7Due to the limited frequency (usually quarterly) of our holdings database, this component of performance assumesthat a fund manager holds a portfolio (a buy-and-hold strategy) from the date of the holdings data, until the nextholdings data become available.
8This study does not take a position on whether fund managers should be rewarded for holding stocks withcertain characteristics (e.g., momentum stocks) during long periods of time when those stocks outperform the market.However, we provide an accurate decomposition of the returns of winners and losers into style-based returns and style-adjusted returns to allow the reader (and investors) to draw their own conclusions about which method to use to
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stock-picking ability, which is called the �Characteristic-Selectivity� measure (CS) (which was also
described earlier in our measure of career stockpicking talent), is developed in DGTW, and is
computed during quarter t as
CSt =NXj=1
�wj,t−1( �Rj,t − �Rbj,t−1t ), (8)
where �wj,t−1 is the portfolio weight on stock j at the end of quarter t − 1, �Rj,t is the quarter tbuy-and-hold return of stock j, and �R
bj,t−1t is the quarter t buy-and-hold return of the characteristic-
based benchmark portfolio that is matched to stock j at the end of quarter t− 1.To construct the characteristic-based benchmark portfolio for a given stock during a given
quarter, we characterize that stock over three characteristics�the size, book-value of equity to
market-value of equity ratio, and past returns of that stock. Benchmarking a stock proceeds as
follows�this procedure is based on Daniel, Grinblatt, Titman, and Wermers (1997), and is described
in more detail in that paper. First, all stocks (listed on NYSE, AMEX, or Nasdaq) having book
value of equity information in Compustat, and stock return and market capitalization of equity
data in CRSP, are ranked, at the end of each June, by their market capitalization. Quintile
portfolios are formed (using NYSE size quintile breakpoints), and each quintile portfolio is further
subdivided into book-to-market quintiles, based on their book-to-market data as of the end of the
December immediately prior to the ranking year. Finally, each of the resulting 25 fractile portfolios
are further subdivided into quintiles based on the 12-month past return of stocks through the end
of May of the ranking year. This three-way ranking procedure results in 125 fractile portfolios,
each having a distinct combination of size, book-to-market, and momentum characteristics.9 The
three-way ranking procedure is repeated at the end of June of each year, and the 125 portfolios are
reconstituted at that date.
Value-weighted returns are computed for each of the 125 fractile portfolios, and the benchmark
for each stock during a given quarter is the buy-and-hold return of the fractile portfolio of which
that stock is a member during that quarter. Therefore, the benchmark-adjusted return (also called
the �DGTW-adjusted return�) for a given stock is computed as the buy-and-hold stock return
minus the buy-and-hold value-weighted benchmark return during the same quarter. Finally, the
rank mutual funds.9Thus, a stock belonging to size portfolio one, book-to-market portfolio one, and prior return portfolio one is a
small, low book-to-market stock having a low prior-year return.
10
Characteristic Selectivity measure of the stock portfolio of a given mutual fund during quarter t,
CSt, is computed as the portfolio-weighted DGTW-adjusted return of the component stocks in the
portfolio, where the stock portfolio is normalized so that the weights add to one.
B.2 Execution Costs
Wermers (2000) uses past literature on the trading costs of institutional investors to construct an
equation that describes the total trading costs of a fund manager in a given stock. This method
is based on the empirical results of Keim and Madhavan (1997) and Stoll (1995), and should be
viewed as an approximation of the expected total trading cost faced by a fund manager from the
time a trade decision is made by a fund manager to the time it is fully executed. Thus, this trading
cost estimate includes both the price impact (pre-trade price drift and trade price concession) and
the explicit brokerage commission paid by the fund. This equation captures the cross-sectional
dependence of total institutional trading costs on the market in which a stock is traded (i.e., NYSE
or AMEX vs. Nasdaq), the size of the trade, the market capitalization and price of the stock, and
whether the trade was a �buy� or a �sell.� SpeciÞcally, the equation for estimating the total cost
of executing a purchase of stock i during quarter t, as a percentage of the total value of the trade,
Here, Rj,t−RF,t equals the excess net return of fund j during month t (the fund net return minus T-bills); RMRFt equals the month t return on a value-weighted aggregate market proxy portfolio; and
SMBt, HMLt, and PR1Y Rt equal the month t returns on value-weighted, zero-investment factor-
mimicking portfolios for size, book-to-market equity, and one-year momentum in stock returns.
We use the Carhart (1997) regression measure of performance, α, to estimate the performance of
mutual funds from their net return time-series data.
B.4 The Ferson-Schadt Measure
Ferson and Schadt (FS, 1996) develop a returns-based performance measure that controls for return
predictability using dynamically evolving public information on relevant economic variables. In
essence, the measure identiÞes a fund manager as providing value to shareholders if the manager
provides excess net returns that are signiÞcantly higher than the fund�s matched factor benchmarks,
both unconditional and conditional. These conditional benchmarks control for any predictability
of the factor return premia that is due to evolving public information. Managers, therefore, are
only labeled as superior if they possess superior private information on stock prices. FS also
Þnd that these conditional benchmarks help to control for the response of consumer cashßows to
mutual funds. For example, when public information indicates that the market return will be
unusually high, consumers invest unusually high amounts of cash into mutual funds, which reduces
the performance measure, �alpha,� from an unconditional model (such as the Carhart model).
This reduction in alpha occurs because of the unconditional model does not control for the �market
timing� inherent in using the public information to decide when to invest cash into the market�it
is well-known that unconditional models exhibit a downward-biased alpha for funds with market
timing �abilities� (see, for example, Treynor and Mazuy (1966)).
Since the FS measure controls for the effect of public information, it also provides a control for
12
the effect of consumer cashßows on fund performance. The version of the FS model used in this
paper starts with the unconditional Carhart four-factor model and adds a market factor that is
conditioned on the Þve FS economic variables. This model is described as,
where zi,t−1 is the deviation of information variable i from its unconditional (time-series) mean at
time t − 1, and Bj,i is the response of fund manager j�s loading on the market factor, RMRFt,in response to the observed realization of zi,t−1.10,11 The intercept of the model, αj, is the FS
performance measure for fund j.
C Summary Statistics on Funds and Fund Managers
Table II provides four �snapshots� (at the beginning of 1985, 1990, 1995, and 2000) and the full-
sample (1985 to 2000) summary statistics of manager characteristics. These characteristics are
presented in two ways: the characteristics of the manager over that manager�s career with the
current fund (only), and the full-career characteristics of that manager. The average manager
career experience is roughly consistent throughout our sample period�average career experience is
7.4 years at the beginning of 1985 and 7.6 years at the beginning of 2000.
Consistent with the Þndings of Wermers (2000), the mean and median manager track records,
measured as the return in excess of the S&P 500 index (�Excess Return�), the return in excess
of the same investment-objective average fund return (�Objective-Adjusted Return�), or as the
CST measure (�DGTW�), are mostly positive. This is also consistent with the Þnding in Khorana
(1996) that underperforming managers are more likely to be replaced than the average manager.
Interestingly, fund managers take on somewhat higher portfolio risk in the Þve-year post-1995
period than in the pre-1995 period. For example, at the beginning of the year 2000, the mean career
risk-tolerance, measured as the time-series standard deviation of excess return relative to the S&P
500 index or the standard deviation of investment objective-adjusted return are 10.2 percent and
8.9 percent, respectively, which are higher than their levels at the beginning of 1995. This higher
10The public information variables of FS include (1) the lagged level of the one-month T-bill yield, (2) the laggeddividend yield of the CRSP value-weighted NYSE and AMEX stock index, (3) a lagged measure of the slope of theterm structure, (4) a lagged quality spread in the corporate bond market, and (5) a dummy variable for January.11Note that, to maintain model simplicity, we use only the market equity premium to construct conditional factors.
However, it is likely that the majority of public information concerns the return on the broad market, versus thereturn premia due to various styles.
13
average risk level is likely due to the increasing style specialization of mutual funds over this time
period, and the subsequent increased volatility corresponding to the decreased style diversiÞcation
of funds.
Finally, the mean and median career aggressiveness of fund managers has risen gradually during
the 15-year period. As noted by Wermers (2000), this increased trading activity is likely due to
the substantially lower trading costs at the end of our sample period, compared with earlier years.
More aggressive trading over time may also reßect more frequent portfolio adjustments required
because of the increased market volatility toward the end of our sample period.
IV Results
A Does Experience Matter?
We begin with an analysis of the effect of manager experience on mutual fund characteristics and
performance. The extant literature, in general, has not examined whether more seasoned managers
have better skills in picking stocks. We might believe that a manager gains skills in picking stocks
as her career progresses, from perhaps several sources. For example, it may take some time for the
manager to assemble and train her stock analysts, or to learn how to best use the analysts already
in place at a fund complex. Also, over time, managers may develop relationships with corporate
managers that provide them with privileged information on the prospects of Þrms. Chevalier
and Ellison (1999) study the impact of the experience on the managerial stock-picking behavior,
approaching the issue from the perspective of career concerns of fund managers. They Þnd that
young managers are more risk averse and more likely to herd in picking stocks; however, the short
time-series contained in their database of managers prevents them from following individual fund
managers over their entire careers.12
To test the effect of manager experience on the performance and characteristics of a mutual
fund, we sort all funds, at the end of each calendar year, on the level of career experience of the
�lead manager� of the fund. We then measure the characteristics and performance of each ranked
fractile of funds during the following calendar year�the process is repeated at the end of each year,
starting December 31, 1985 and ending December 31, 1999. For a mutual fund with only one
12In addition, their database, which is obtained from Morningstar, has a large number of missing managers duringthe time period under study. By contrast, our manager database contains the vast majority of managers, especiallyduring the last 10 years of our sample period.
14
manager, that manager, by construction, is our lead manager. For funds that are team-managed,
the lead manager is deÞned as the manager with the earliest start date as manager of the fund. We
base our proxy for experience on the lead manager�s career experience because we believe that this
manager probably has the biggest role in the decision-making process of the fund. If, on the other
hand, the non-lead managers play a huge part of the decision-making process of a mutual fund,
this will simply add noise to our tests.
For example, for the year ending December 31, 1985, we rank all funds having an investment
objective (at that date) consistent with holding mainly U.S. equities on the number of months of
career experience of their lead managers.13 Then, funds are placed in quintile, decile, or ventile
portfolios. Various average characteristics and measures of performance are computed for these
fractile portfolios during the following �test� year. In computing test-year measures for statistics
that are available at least quarterly (such as net returns or performance measures), we compute,
for each test-year calendar quarter, the equal-weighted measure across all funds in a given fractile.
If a fund disappears during the test year, we include it in the appropriate fractile portfolio until
the beginning of the quarter in which the fund disappears, then we rebalance the fractile portfolio
for the next quarter. For return or performance measures, we compound these rebalanced equal-
weighted measures over all four quarters in the test year. For non-return characteristics, such
as managerial turnover, the quarterly measures are cumulated over the test year. In computing
test-year measures for statistics that are available only annually (such as portfolio turnover), we
compute the equal-weighted average measure across all funds having data for that measure during
the test year. The reader should note that all tables that follow will use these procedures for
computing test-year average measures.
Table III shows the results of our ranking on career lead manager experience. Panel A of that
table shows the characteristics of the fractile portfolios over the year following the sort of funds on
career manager experience. SpeciÞcally, the table shows the number of funds in each fractile, the
average total net assets of funds in each fractile, the coefficients from a regression of the EW-average
excess net return on the four Carhart factors, and the EW-average (over all event years): career
aggressiveness of the lead manager (the average portfolio turnover level over all funds managed
over her career), career experience of the lead manager, lead manager turnover level (percentage
of lead managers that are replaced), portfolio turnover level, and active style drift (the sum of the
13That is, funds must have a self-reported investment objective of �aggressive growth,� �growth,� �growth andincome,� �income,� or �balanced� at the end of a given ranking year.
15
absolute values of the active style movements of the fund over the test year).
The third column of the panel shows the average level of career experience of the sorted fractiles.
The most experienced managers (the Top 5% fractile) have 343 months of experience, while the
least experienced managers (the Bottom 5% fractile) have only 18 months. The panel also shows
that more experienced lead managers oversee much larger pools of mutual fund assets than their
less-experienced counterparts. For example, the Þve percent of managers with the most experience
manage, on average, funds that are about seven times the size of funds managed by the least
experienced Þve percent of managers ($2.2 billion vs. $321 million, respectively). The coefficients
for the Carhart regressions show that more experienced managers have slightly less exposure to the
broad stock market, small-capitalization stocks, and value stocks. All fractiles of fund managers
show similar exposures to momentum stocks. Overall, as previously shown by Carhart (1997) and
Wermers (2000), mutual fund managers hold about 90 percent of their assets in the stock market
(vs. Þxed income and other investments), hold more small stocks than the broad market, and have
a slight value and momentum tilt.
The career aggressiveness measure shows the average portfolio turnover of each lead-manager
fractile over the managers� entire careers. More experienced fund managers exhibit much lower
levels of career aggressiveness than less experienced managers�this may either be due to these
managers trading less frequently as their careers progress (to avoid trading and other costs), or
to these managers holding much larger portfolios than their less-experienced counterparts during
the latter parts of their careers. These managers may simply be avoiding high levels of turnover of
their large positions in order to avoid large trading impacts by their actions. The test year portfolio
turnover column conÞrms that these managers trade much less frequently than their counterparts
during this stage of their careers.
The Þnal two columns of panel A show the percentage of managers that are replaced, and the
level of active style drift during the test year, respectively. Both relatively experienced and relatively
inexperienced managers are replaced at a higher rate than their mid-career counterparts. We would
expect that many of the most experienced managers leave a fund either to retire or to manage a
larger fund, while many of the least experienced managers may either be Þred, or (if successful)
may leave to manage a larger fund. These two groups of managers also have a higher tendency
to exhibit �active style drift� (ASD) than their mid-career peers. Less-experienced managers, who
manage smaller portfolios consisting of heavier holdings of small-capitalization stocks, relative to
16
their counterparts, may move around in the style dimensions in an attempt to outperform their
peers. Or, these managers may need to move across style categories due to the limited number
of liquid small-capitalization stocks in a particular style category. For example, perhaps a small-
capitalization growth manager Þnds it necessary to invest in some small-capitalization value stocks
in response to large cash inßows from fund shareholders.
Panel B of Table III presents a performance attribution for each manager-experience fractile.
Experienced managers hold portfolios of stocks with slightly lower returns, both before and after
trading costs and fund expenses, as shown by the �Gross Return� and �Net Return� columns.
However, manager talent is best measured by the CS measure of stockpicking talent�here, expe-
rienced managers show a level of talent that is not statistically distinguishable from their rookie
counterparts. SpeciÞcally, the most experienced managers, those in the Top 5 percent fractile, ex-
hibit a CS measure of 1.9 percent per year, while those managers in the Bottom 5 percent fractile
exhibit a CS measure of one percent per year. The difference between these two measures is not
signiÞcant.
Of interest is the level of expenses charged by experienced managers, as we might expect that
experienced managers charge higher expenses for their presumed greater skills. However, the ex-
penses of experienced managers, which average 1.2 percent per year for experienced managers, are
actually slightly lower than the expenses of inexperienced managers. To some degree, this reduc-
tion in expense ratios might be related to the economies-of-scale in running funds, and experienced
managers may still be capturing a larger net fee than other managers.
Inßows from consumers are signiÞcantly higher for the most experienced fractile of managers,
relative to the least experienced. However, this appears to be mainly driven by the reluctance of
consumers to invest in funds managed by the most inexperienced managers (the Bottom 5 percent
fractile), as none of the other inßow differences are signiÞcant. Finally, the Carhart and Ferson-
Schadt alphas indicate that all fractile groups exhibit negative performance, net of all costs and
expenses (except load fees and taxes), but none of these alphas are signiÞcant.
In unreported tests, we repeat the sorting procedure of this section, limited to funds having
a growth-oriented investment objective (an investment objective, at the end of a given ranking
year, of either �aggressive growth� or �growth�). The results are consistent with our baseline
results for all funds above: experienced managers exhibit no higher level of stockpicking talent
than inexperienced managers.
17
To summarize our results from this section, experienced managers tend to manage much larger
funds, and exhibit lower levels of trading activity than inexperienced managers. However, our
results provide no support for the hypothesis that the stockpicking skills of fund managers improve
over their careers. This Þnding seems somewhat surprising, since we might reasonably believe that
a manager without stockpicking talents would be forced to leave the industry before the latter
part of her career, as investors become more certain from the longer time-series of manager returns
available, that the manager does not have talent. Apparently, the labor market for fund managers
does not function effectively in this dimension.
B Does Past Performance Matter?
While our last section rejects the notion that experience is correlated with talent, we are also
interested in whether some managers, at any experience level, have persistent stockpicking skills.
In this section, we investigate this issue by examining whether lead fund managers with the best
career stockpicking records have skills that persist in the future. We measure career stockpicking
talent using our characteristic selectivity track record (CST ) for each manager, as described by
Equation (3). Analogous to the ranking procedure of the last section, we sort all fund managers,
at the end of each calendar year starting December 31, 1985 and ending December 31, 1999, on
their CST measure at the end of that year. Then, we measure the following-year characteristics
and performance of each fractile that results from this sorting procedure.
In Panel A, we present the characteristics of these manager career-record fractiles. The panel
shows that managers with the best track records do not have substantially more experience (92
months) than the average fund manager (108 months), although managers with the worst track
records do have substantially less experience than average (52 months). Thus, experience, by itself,
does not appear to be associated with career stockpicking talent; consistent with the results of the
prior section, the majority of experienced managers appear to have no stockpicking talents.
The results also show that managers with extreme stockpicking track records (either good or
poor) tend to be more aggressive traders than the average fund manager. This Þnding holds both
for their entire careers (up to and including the test year�shown in the �Career Aggressiveness�
column) and during the test year alone (shown in the �Portfolio Turnover� column). Managers with
the best track records may know that their talents will persist, and, therefore, may trade frequently
to capitalize on their talent. Alternatively, these managers may be exhibiting overconÞdence based
18
on their past success, which would result in unnecessary costly trading of stocks in the future. On
the other hand, managers with poor track records may be trading frequently in order to try to
reverse their fortunes, or, alternatively, to appear to have stockpicking skills.
Finally, managers with extreme stockpicking track records (either good or poor) experience
higher managerial replacement rates and higher levels of active style drift. For managers with the
best records, we would expect that they depart from a fund to either retire or to manage a larger
fund, based on their past success. For managers with the worst records, we would expect a large
proportion of dismissals or transfers to smaller funds. Baks (2001) studies this issue and provides
Þndings that are consistent with this.
The high levels of active style drift that we observe among successful managers may occur
because these managers have talents that span across more than one style category. In contrast,
the high levels of active style drift that we observe among unsuccessful managers may be due
either to the difficulty of maintaining a style focus with a fund that invests in smaller-capitalization
stocks, or to the manager taking active �bets� in order to attempt to outperform her counterparts
(by luck).
In Panel B, we provide a performance attribution for each track-record fractile of fund managers.
The evidence shows that fund managers with the best career records have persistent stockpicking
skills�for example, the Top 5% fractile of managers�those with the very best career stockpicking
records�hold stocks that outperform their characteristic benchmarks by two percent per year. Fund
managers in the bottom 40% of fractiles, by contrast, have no ability to pick underpriced stocks.
In addition, the difference in stockpicking talents between managers with the best and worst career
records is large and statistically signiÞcant. For example, the top decile of managers hold stocks
that outperform the stocks held by the bottom decile of managers, adjusted for their characteristics,
by a statistically signiÞcant 1.7 percent, averaged over all test years.
Consistent with the higher portfolio turnover levels found earlier for the extreme fractiles (Panel
A), execution costs are somewhat higher for these fractiles (Panel B) than for the average fund. In
addition, the management companies of these extreme fractile funds charge higher average expense
ratios�to some extent, this is due to the smaller portfolios managed by the top and bottom fractile
managers. As shown by Collins and Mack (1999), strong economies-of-scale exist in the mutual
fund industry, resulting in expense ratios that are inversely related to the level of assets under
management.
19
An examination of consumer inßows to the various fractiles provides some interesting results.
While managers with good track records have only slightly higher net returns than other managers,
these �star� managers attract much higher levels of cash inßows. For example, the top quintile of
managers experience an average yearly inßow equal to 25 percent of the beginning-of-year TNA of
their funds, while the manager of the average fund attracts only 17 percent. This Þnding indicates
that consumers appear to prefer to invest their money in a fund managed by a �star,� independent
of the immediate past net return of the fund.
Finally, the panel shows the net return alphas of each equal-weighted fractile of funds. Both the
Carhart and Ferson-Schadt alphas are insigniÞcant for all fractiles, except for the fractile of funds
that are managed by the managers with the very worst track records. These managers continue to
perform poorly, underperforming their benchmarks by about two percent, on average over all test
years.
C The Impact of Managerial Replacement on Fund Characteristics and Per-
formance
As discussed by Baks (2001), the replacement of a manager provides a unique opportunity to
study the impact of the manager on the performance of a fund, independent of the fund�s other
characteristics. In this section, we examine the characteristics and returns of funds during the
periods immediately before and after a lead manager is replaced.
Each year, we separate funds into those having a lead manager change during the year, and
those with no change in lead manager. Then, we measure the returns and characteristics of the
equal-weighted portfolio of funds in each group, during the year of the potential change, and during
the three following years. Table V presents the results of this test.
Panel A shows that managers are replaced during years when their stockpicking talents are
signiÞcantly worse than those of all other managers. SpeciÞcally, the characteristic selectivity
measure during the year that the manager is replaced is insigniÞcant, compared to a measure of
0.5 percent (which is statistically signiÞcant) for all funds with no managerial change. Further, the
arrival of a new fund manager is very good news for a fund: the new manager brings stockpicking
talents that are statistically indistinguishable from the talents of all other managers during the
three years following the managerial change. Panel B, which measures the net returns of funds
with managerial turnover vs. all other funds, shows similar results�underperformance during the
20
year of manager replacement, followed by improved performance during the following years. Finally,
Panel C shows that the appears to reduce the level of trading, relative to the replaced manager. In
particular, the average portfolio turnover drops from a level of 99 percent, during the managerial
replacement year, to 89 percent during the third year following the managerial replacement. These
results indicate that the manager who is replaced may be engaging in heavy trading during the
Þnal year of her tenure at a fund in an attempt to �gamble� as a last resort.
D Multivariate Regressions
Our results of the prior sections indicate correlations between stockpicking talents and manager
characteristics�speciÞcally, managerial experience, career stockpicking record, and managerial re-
placement. In this section, we test whether our prior sorting results, which test the relation between
current talent and manager characteristics, still hold in a multivariate setting. Here, we conduct
Fama-McBeth (1973)-type multivariate regressions to conduct these tests.
For each year, starting with 1986 and ending with 2000, we run a cross-sectional regression
of a fund�s CS measure, averaged across all four quarters of that year, on the manager�s level of
experience and the manager�s stockpicking track-record (CST ), both measured at the end of the
prior year. A dummy variable is also added to indicate whether the manager is replaced during
the prior year. We then average the coefficient estimates over all years, and report this average, as
well as the time-series t-statistic.
The resulting regressions (1) and (2) in Table VI show that experience, alone, does not explain
future stockpicking success, but that the career stockpicking track-record does. Regression (3)
includes both experience and career track-record as regressors, and shows that the track-record
remains signiÞcant, controlling for any correlation between these two variables. Thus, the Þrst
three regressions conÞrm the results of our sorting tests of the prior sections: the past stockpicking
record of a manager helps to predict her future stockpicking success, but experience does not matter,
either alone or in combination with the track-record.
In the fourth regression, we add a manager replacement dummy that equals one, if a manager
is replaced during the prior year. This speciÞcation shows that managerial turnover does not help
to explain future stockpicking success, when experience and stockpicking talent are included as
regressors. In the last section, we found that manager replacement, alone, is a strong predictor of
improved fund performance. Thus, manager replacement provides predictive power only because it
21
serves as a proxy for career stockpicking record�that is, a new manager enters a fund with a strong
track record, and it is that variable that predicts the future success of the manager. This Þnding
is consistent with Khorana (1996), who Þnds that new fund managers have substantially better
records than the managers that they replace.
In Table VII, we repeat these Fama-McBeth regressions on growth-oriented funds only. For
example, the cross-sectional regression for 1986 includes only managers of funds having a self-
declared investment objective of either �aggressive growth� or �growth� at the end of 1985. The
results show some interesting contrasts with the full-sample results of Table VI. SpeciÞcally, man-
agerial experience provides signiÞcant explanatory power, by itself (regression (1)), in combination
with stockpicking record (regression (3)) and in combination with both stockpicking record and
the replacement dummy (regression (4)). Thus, experience appears to a strong inßuence in pick-
ing growth stocks, perhaps because it is much more difficult to accurately forecast the growth in
earnings of growth stocks, relative to value stocks. Growth-oriented managers may either develop
specialized skills over time, or, alternatively, they develop valuable relationships with corporate
managers that give them access to private information on future earnings.
Table VIII repeats these regressions on income-oriented funds, which are deÞned as funds having
a self-declared investment objective of �growth and income,� �income,� or �balanced� at the end
of the year prior to the regression year. For these managers, none of the variables are signiÞcant�
experience, track record, or managerial replacement. Although this Þnding is somewhat surprising,
it is consistent with prior work by Chen, Jegadeesh, and Wermers (2000), who show that income-
oriented funds exhibit no abnormal returns, while growth-oriented funds do. Thus, income-oriented
funds appear to provide style-based return premia, but nothing else.
E The Role of Managerial Risk-Aversion
Our Þnal tests explore whether fund managers with lower levels of risk-aversion are better able to
exploit their stockpicking talents (if any) to generate higher average levels of fund performance.
Regression (5) in Tables VI, VII, and VIII add two proxies for managerial risk tolerance to address
the role of this characteristic in generating performance. The Þrst proxy, �Career Risk Tolerance,�
is the standard deviation of the manager�s S&P500-adjusted monthly return over her career, prior
to a given year, while the second proxy, �Career Aggressiveness,� is the turnover ratio of all funds
managed, averaged over the manager�s career prior to the given year.
22
Table VI, regression (5) shows that these risk tolerance proxies are not signiÞcant inßuences on
future performance. However, the career CST record is now insigniÞcant, which indicates that there
is substantial multicollinearity between risk tolerance and track-record. Indeed, in unreported tests,
we Þnd cross-sectional Pearson correlations of 0.25 and 0.11 (both signiÞcant at the one percent
level) between the CST measure and the career risk tolerance and career aggressiveness variables,
respectively. These correlations are measured for all managers at the end of 1999.
A different result holds for managers of growth-oriented funds, as shown in regression (5) of
Table VII. Here, the inßuence of experience and track-record remain after adding the risk-tolerance
proxies. However, the risk-tolerance variables are still insigniÞcant for these managers, which
indicates that any inßuence of risk-tolerance on performance is already captured by the experience
or CST track-record variables.
V Conclusion
In this paper, we have presented evidence on the role of mutual fund managers in generating mutual
fund performance. This topic has received relatively little attention in the academic literature, with
the exception of Chevalier and Ellison (1999) and Baks (2001). Our study uses the longest cross-
sectional database of fund managers available to date, extending from 1985 to 2000, and includes
both the stockholdings, net returns, and other characteristics of each managed fund. This database
allows us to investigate several issues of interest regarding the role of managers, including the
importance of experience and past track record in generating future performance.
We Þnd that experience is an important indicator of stockpicking talent, but only for growth-
oriented fund managers. The stockpicking track record of a fund manager, however, is a stronger
indicator of manager talent for all types of fund managers. Thus, manager talent strongly persists.
We also Þnd that the replacement of a manager is good news for a fund, as the pre-replacement
performance of the fund is reliably lower than its counterpart funds, while the post-replacement
performance is statistically indistinguishable from the counterpart performance. However, the
signiÞcance of this variable disappears, once we include both the stockpicking track record and
manager replacement in a multivariate regression setting.
Our study, while providing new insight on the performance and performance persistence issues
that have been a focus of academic research for decades, also opens up possible new studies on the
behavior of fund managers. Our database allows the study of these behavioral issues though an
23
analysis of the stock trades of fund managers having various characteristics. We believe that this
is an important new direction for future research.
24
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[4] Collins, Sean, and Phillip Mack, 1999, �Some Evidence on Scope, Scale, and X-Efficiency in
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Paper.
Tabl
eI:
Sum
mar
yS
tatis
tics
ofM
utua
lFun
dsan
dF
und
Man
ager
s
Thi
sta
ble
pres
ents
the
sum
mar
yst
atis
tics
ofm
utua
lfun
dsan
dfu
ndm
anag
ers
inou
rfun
d-m
anag
ersa
mpl
ebe
twee
n19
85an
d20
00(in
clus
ive)
.O
urm
utua
lfu
ndda
taar
edr
awn
from
the
mer
ged
CD
A–C
RS
Pm
utua
lfu
ndda
taba
se(C
DA
–CR
SP
).A
nea
rlyve
rsio
nof
CD
A–C
RS
Pis
used
inW
erm
ers
(200
0),
whi
chal
soco
ntai
nsa
deta
iled
desc
riptio
nof
the
cons
truc
tion
ofC
DA
–CR
SP.
The
fund
man
ager
data
are
colle
cted
from
thre
em
ostu
sed
mut
ualf
und
data
sour
ces:
the
Mor
ning
star
Prin
cipi
aP
ro(J
anua
ry20
01),
the
CR
SP
Mut
ualF
und
Dat
aB
ase
(200
0Q3)
,an
dW
iese
nber
ger.
Pan
elA
repo
rts
the
num
ber
ofm
utua
lfun
dsex
istin
gdu
ring
1985
,19
90,
1995
,an
d20
00,
asw
ella
sdu
ring
the
who
lesa
mpl
epe
riod,
1985
–200
0,fo
rth
ew
hole
fund
univ
erse
asw
ell
asea
chof
the
follo
win
gfo
urse
lf-de
clar
edin
vest
men
tob
ject
ive
cate
gorie
s—ag
gres
sive
grow
th(A
G),
grow
th(G
),gr
owth
and
inco
me
(GI)
,in
com
eor
bala
nced
(Ior
B).
Sel
f-de
clar
edin
vest
men
t-ob
ject
ive
data
are
colle
cted
from
CD
A.
Afu
nd’s
inve
stm
ent
obje
ctiv
efo
ra
repo
rtin
gpe
riod
isth
eon
efir
stre
port
edin
the
perio
d.P
anel
Bpr
esen
tsth
eco
unts
ofle
adm
anag
ers
and
the
aver
age
num
ber
offu
nds
lead
man
aged
bya
lead
man
ager
durin
g19
85–2
000
asw
ella
sdu
ring
1985
,19
90,
1995
,an
d20
00.
Inca
seof
team
man
agem
ent,
the
lead
man
ager
isde
fined
asth
eac
tive
man
ager
who
star
tsto
man
age
the
fund
earli
est.
Toca
lcul
ate
the
num
ber
offu
nds
lead
man
aged
bya
fund
man
ager
durin
ga
repo
rtin
gpe
riod,
we
divi
deth
eto
taln
umbe
rof
fund
-mon
ths
she
lead
man
ages
byth
eto
taln
umbe
rof
mon
ths
whe
nsh
eis
ale
adm
anag
er.
The
aver
age
num
ber
offu
nds
lead
man
aged
bya
fund
man
ager
isca
lcul
ated
byta
king
the
cros
s-se
ctio
nala
vera
geac
ross
allm
anag
ers
ina
grou
p.A
fund
man
ager
isco
unte
das
ale
adm
anag
erof
AG
fund
sfo
ra
give
npe
riod
ifsh
eis
the
lead
man
ager
ofat
leas
tone
AG
fund
durin
gth
epe
riod.
Man
ager
sof
G,G
I,an
dIo
rB
fund
sar
esi
mila
rlyde
fined
.S
ince
som
em
anag
ers
lead
-man
age
mul
tiple
fund
sw
ithdi
ffere
ntin
vest
men
tob
ject
ives
for
agi
ven
perio
d,th
esu
mof
the
num
bers
ofle
adm
anag
ers
ofA
G,
G,
GI,
and
Ior
Bfu
nds
may
begr
eate
rth
anth
eto
taln
umbe
rof
lead
man
ager
s.P
anel
Cre
port
sth
enu
mbe
rof
fund
sm
issi
ngm
anag
ers.
The
1985
(199
0,19
95,2
000)
colu
mn
inP
anel
Cre
port
sth
efu
nds
that
exis
tin
1985
(199
0,19
95,2
000)
butd
ono
thav
em
anag
ers
mat
ched
in19
85(1
990,
1995
,200
0).
The
1985
–200
0co
lum
nin
Pan
elC
repo
rts
the
fund
sth
atex
ista
tone
poin
toft
ime
durin
g19
85–2
000
butd
ono
thav
ea
mat
ched
man
ager
thro
ugho
utth
esa
mpl
epe
riod.
The
perc
ento
ffun
dsm
issi
ngm
anag
ers
isca
lcul
ated
asth
eth
enu
mbe
rof
fund
sm
issi
ngm
anag
ers
toth
enu
mbe
rof
fund
sin
the
sam
epe
riod.
Pan
elD
prov
ides
aco
mpa
rison
ofm
edia
nto
taln
etas
sets
(TN
A)
and
mea
nex
cess
retu
rns
betw
een
the
fund
sth
atar
em
atch
edw
itha
man
ager
and
the
fund
sth
atdo
noth
ave
any
mat
ched
fund
man
ager
.T
he19
85(1
990,
1995
,200
0,19
85–2
000)
colu
mn
inP
anel
Dre
port
sth
efu
nds
exis
ting
in19
85(1
990,
1995
,200
0,19
85–2
000)
.A
fund
’sto
taln
etas
sets
in19
85,
1990
,19
95,
or20
00is
defin
edas
itsye
ar-e
ndT
NA
.A
fund
’sto
taln
etas
sets
over
1985
–200
0is
the
time-
serie
sav
erag
eof
itsm
onth
lyto
taln
etas
sets
betw
een
1985
and
2000
.T
hem
edia
nT
NA
sar
eex
pres
sed
inm
illio
nsof
year
2000
dolla
rs.
The
exce
ssre
turn
ofa
fund
for
agi
ven
year
isco
mpu
ted
bysu
btra
ctin
gth
ean
nual
S&
P50
0re
turn
from
the
fund
’san
nual
net
retu
rn.
The
exce
ssre
turn
ofa
fund
over
1985
–200
0is
the
time-
serie
sav
erag
eof
annu
alex
cess
retu
rns
inth
epe
riod
itex
ists
in19
85–2
000.
The
mea
nex
cess
retu
rns
offu
nds
are
expr
esse
din
perc
ent.
Pan
elA
:Cou
nts
ofM
utua
lFun
dsIn
vest
men
tO
bjec
tive
1985
1990
1995
2000
1985
–200
0A
llF
unds
352
621
1513
1683
2272
AG
8311
816
412
921
7G
151
294
943
1072
1507
GI
9314
225
833
036
6Io
rB
2567
148
152
182
Pan
elB
:Cou
nts
ofLe
adM
anag
ers
ofM
utua
lFun
ds19
8519
9019
9520
0019
85–2
000
N
Avg
.N
o.of
Fun
dsLe
adM
anag
edN
Avg
.N
o.of
Fun
dsLe
adM
anag
edN
Avg
.N
o.of
Fun
dsLe
adM
anag
edN
Avg
.N
o.of
Fun
dsLe
adM
anag
edN
Avg
.N
o.of
Fun
dsLe
adM
anag
edA
llM
anag
ers
202
1.27
334
1.38
1116
1.43
1256
1.57
2229
1.34
Man
ager
sof
AG
Fun
ds48
1.52
871.
6416
51.
8812
62.
1837
21.
62M
anag
ers
ofG
Fun
ds10
21.
4216
91.
4676
31.
5487
91.
7016
611.
40M
anag
ers
ofG
IFun
ds64
1.41
781.
5124
51.
7032
31.
9156
31.
54M
anag
ers
ofIo
rB
Fun
ds20
1.70
411.
6514
31.
9114
52.
1928
91.
62
Pan
elC
:Cou
nts
ofM
utua
lFun
dsM
issi
ngM
anag
ers
1985
1990
1995
2000
1985
–200
0
NP
erce
ntof
Tota
lFun
dsN
Per
cent
ofTo
talF
unds
NP
erce
ntof
Tota
lFun
dsN
Per
cent
ofTo
talF
unds
NP
erce
ntof
Tota
lFun
dsA
llF
unds
116
33.0
102
16.4
936.
180
4.8
142
6.3
AG
3238
.614
11.9
42.
43
2.3
156.
9G
4731
.149
16.7
646.
852
4.9
885.
8G
I32
34.4
3222
.513
5.0
133.
923
6.3
Ior
B5
20.0
710
.412
8.1
127.
916
8.8
Pan
elD
:Com
paris
onof
Fun
dsR
epor
ting
Lead
Man
ager
san
dF
unds
Mis
sing
Man
ager
s19
8519
9019
9520
0019
85–2
000
Med
ian
TN
A
Mea
nE
xces
sR
etur
nM
edia
nT
NA
Mea
nE
xces
sR
etur
nM
edia
nT
NA
Mea
nE
xces
sR
etur
nM
edia
nT
NA
Mea
nE
xces
sR
etur
nM
edia
nT
NA
Mea
nE
xces
sR
etur
nA
llF
unds
174.
4-3
.85
108.
8-2
.69
134.
5-7
.46
244.
68.
4310
9.0
-3.1
9F
unds
Rep
ortin
gLe
adM
anag
ers
187.
8-2
.83
138.
0-2
.46
143.
9-7
.36
249.
78.
5411
7.3
-2.9
7F
unds
Mis
sing
Man
ager
s13
1.4
-6.1
345
.3-3
.98
64.5
-9.1
417
9.6
6.19
28.7
-7.3
0
Tabl
eII:
Sum
mar
yS
tatis
tics
ofLe
adM
anag
erC
hara
cter
istic
s
Thi
sta
ble
pres
ents
the
sum
mar
yst
atis
tics
ofle
adm
anag
erch
arac
teris
tics,
incl
udin
gex
perie
nce,
trac
kre
cord
,ris
kat
titud
e,an
dag
gres
sive
ness
,with
curr
ent
fund
asw
ella
sov
erca
reer
,att
hebe
ginn
ing
of19
85,1
990,
1999
,and
2000
,as
wel
las
for
1985
–200
0.T
he“C
aree
r”(“
Cur
rent
Fun
d”)
expe
rienc
eof
ale
adm
anag
eris
defin
edas
the
time
elap
sed
sinc
esh
efir
stbe
com
esa
fund
man
ager
(sin
cesh
ebe
com
esth
ele
adm
anag
erof
the
curr
ent
fund
).In
calc
ulat
ing
the
rest
ofm
anag
erch
arac
teris
tics
with
“Cur
rent
Fun
d,”
we
star
tfro
mw
hen
the
man
ager
beco
mes
the
lead
man
ager
ofth
efu
nd.
Toco
mpu
teth
e“C
aree
r”m
easu
res,
we
star
tfr
omw
hen
the
fund
man
ager
first
beco
mes
ale
adm
anag
er.
Thr
eepr
oxie
sar
eem
ploy
edto
mea
sure
afu
ndm
anag
er’s
trac
kre
cord
:ex
cess
retu
rn(t
ime-
serie
sav
erag
em
onth
lyne
tret
urn
inex
cess
ofth
eS
&P
500
retu
rn),
obje
ctiv
e-ad
just
edre
turn
(tim
e-se
ries
aver
age
mon
thly
obje
ctiv
e-ad
just
edre
turn
),an
dst
ockh
oldi
ngch
arac
teris
tics-
base
dD
GT
Wm
easu
refo
llow
ing
Dan
iel,
Grin
blat
t,T
itman
,an
dW
erm
ers
(199
7).
We
use
the
stan
dard
devi
atio
nof
mon
thly
exce
ssre
turn
and
mon
thly
obje
ctiv
e-ad
just
edre
turn
topr
oxy
for
the
risk
attit
ude
ofa
fund
man
ager
.T
heag
gres
sive
ness
ofa
fund
man
ager
ispr
oxie
dby
the
time-
serie
sav
erag
etu
rnov
erra
tioof
the
man
aged
fund
(s).
Afu
nd’s
turn
over
ratio
isde
fined
asth
ele
sser
ofits
secu
ritie
ssa
les
and
purc
hase
divi
ded
byth
eav
erag
em
onth
lyto
taln
etas
sets
.A
fund
man
ager
’sch
arac
teris
tics
for
1985
–200
0is
her
char
acte
ristic
sw
hen
she
leav
esth
esa
mpl
e(e
ither
onth
esa
mpl
een
dda
teD
ecem
ber
31,2
000
orw
hen
she
depa
rts
the
last
fund
man
aged
byhe
r).
The
expe
rienc
eis
expr
esse
din
year
sw
hile
allt
hetr
ack
reco
rdan
dris
kva
riabl
esar
ean
nual
ized
and
expr
esse
din
perc
ent.
Agg
ress
iven
ess
isal
soan
nual
ized
.
1985
1990
1995
2000
1985
–200
0M
ean
Med
ian
Mea
nM
edia
nM
ean
Med
ian
Mea
nM
edia
nM
ean
Med
ian
Exp
erie
nce
(In
Yea
rs)
With
Cur
rent
Fun
d6.
34.
75.
23.
14.
42.
14.
42.
24.
83.
3C
aree
r7.
46.
16.
23.
96.
54.
97.
66.
27.
76.
1T
rack
Rec
ord
(Exc
ess
Ret
urn,
%P
erY
ear)
With
Cur
rent
Fun
d4.
865.
960.
550.
972.
762.
440.
90-1
.45
1.00
0.20
Car
eer
4.86
5.98
0.74
1.18
2.77
2.46
0.06
-1.5
90.
550.
10
Tra
ckR
ecor
d(O
bjec
tive-
Adj
uste
dR
etur
n,%
Per
Yea
r)W
ithC
urre
ntF
und
1.03
0.76
0.30
0.97
0.63
0.38
1.10
0.40
-0.1
3-0
.12
Car
eer
0.86
0.47
0.69
0.76
0.50
0.34
0.87
0.40
0.14
0.15
Tra
ckR
ecor
d(D
GT
W,%
Per
Yea
r)W
ithC
urre
ntF
und
1.39
1.87
0.22
0.71
0.47
0.53
0.58
-0.1
11.
700.
52C
aree
r0.
901.
600.
500.
880.
520.
570.
58-0
.01
1.19
0.63
Ris
kA
ttitu
de(S
td.
Dev
.of
Exc
ess
Ret
urn,
%P
erY
ear)
With
Cur
rent
Fun
d8.
467.
797.
867.
266.
896.
1610
.60
9.18
11.5
19.
26C
aree
r8.
617.
798.
087.
487.
126.
3710
.23
8.63
11.1
69.
05
Ris
kA
ttitu
de(S
td.
Dev
.of
Obj
ectiv
e-A
djus
ted
Ret
urn,
%P
erY
ear)
With
Cur
rent
Fun
d7.
186.
666.
545.
665.
514.
869.
088.
0310
.02
8.24
Car
eer
7.27
7.23
6.31
5.60
5.72
4.90
8.91
7.66
9.59
7.85
Agg
ress
iven
ess
With
Cur
rent
Fun
d0.
740.
590.
800.
640.
860.
660.
900.
700.
950.
74C
aree
r0.
720.
570.
800.
660.
860.
660.
890.
690.
930.
74
Table IIIA Decomposition of Returns for Experienced vs. Inexperienced Managers
A decomposition of mutual fund returns and costs is provided below for the merged manager, CDA holdings, and CRSP mutual fund characteristics/net returns databases. At the end of eachcalendar year, starting December 31, 1985 and ending December 31, 1999, we rank all mutual funds in the merged database that existed during the entire prior 12-month period (and had a completedata record during that year) on the level of experience of the lead fund manager (the months of career experience, with any fund, of the manager starting at a given fund at the earliest date) atthe end of that year (the �ranking year�). Then, fractile portfolios are formed, and we compute average return measures (e.g., net returns) for each fractile portfolio during the following year (the�test year�). In computing the average return measure for a given test year, we Þrst compute quarterly buy-and-hold returns for each fund that exists during each quarter of the test year, regardlessof whether the fund survives past the end of that quarter. Then, we compute the equal-weighted (EW) average quarterly buy-and-hold return across all funds for each quarter of the test year.Finally, we compound these returns into an annual return that is rebalanced quarterly. Panel A presents several characteristics of these sorted fractiles during the Þrst year following the rankingyear: the number of funds in each fractile, the average career experience of the lead fund manager, the average total net assets of funds, the coefficients from a regression of the EW-average excessnet return on the four Carhart factors, and the EW-average (over all event years): career aggressiveness of the lead manager (the average portfolio turnover level over all funds managed over hercareer), portfolio turnover level, lead manager turnover level (percentage of lead managers that are replaced), and active style drift (the sum of the absolute values of the active style movements inthe three style dimensions of the fund over the test year). Panel B presents a decomposition of fund returns and costs during the test year. SpeciÞcally, the panel presents the EW-average annual:characteristic selectivity measure (CS), estimated transactions costs, expense ratio, net reported return, fund inßows, Carhart net return alpha, and Ferson and Schadt net return alpha. Both panelsof this table present test year statistics, averaged over all test years. In forming all portfolios in this table, we limit our analysis to funds having a self-declared investment objective of �aggressivegrowth,� �growth,� �growth and income,� �income,� or �balanced� at the beginning of the test year.
Panel A. Fractile Characteristics (Test Year)
Ranking Variable = Experience Career Avg Career Portfolio Manager
∗ SigniÞcant at the 90% conÞdence level.∗∗ SigniÞcant at the 95% conÞdence level.∗∗∗ SigniÞcant at the 99% conÞdence level.
Table IVA Decomposition of One-Year Future Returns for �Star Career Stockpickers�
A decomposition of mutual fund returns and costs is provided below for the merged manager, CDA holdings, and CRSP mutual fund characteristics/net returns databases. At the end of eachcalendar year, starting December 31, 1985 and ending December 31, 1999, we rank all mutual funds in the merged database that existed during the entire prior 12-month period (and had a completedata record during that year) on the level of career stockpicking talent, as measured by experience of the lead fund manager (the months of career experience, with any fund, of the manager startingat a given fund at the earliest date) at the end of that year (the �ranking year�). Then, fractile portfolios are formed, and we compute average return measures (e.g., net returns) for each fractileportfolio during the following year (the �test year�). In computing the average return measure for a given test year, we Þrst compute quarterly buy-and-hold returns for each fund that exists duringeach quarter of the test year, regardless of whether the fund survives past the end of that quarter. Then, we compute the equal-weighted (EW) average quarterly buy-and-hold return across allfunds for each quarter of the test year. Finally, we compound these returns into an annual return that is rebalanced quarterly. Panel A presents several characteristics of these sorted fractiles duringthe Þrst year following the ranking year: the number of funds in each fractile, the average total net assets of funds in each fractile, the coefficients from a regression of the EW-average excess netreturn on the four Carhart factors, and the EW-average (over all event years): career aggressiveness of the lead manager (the average portfolio turnover level over all funds managed over her career),career experience of the lead manager, lead manager turnover level (percentage of lead managers that are replaced), portfolio turnover level, and active style drift (the sum of the absolute values ofthe active style movements of the fund over the test year). Panel B presents a decomposition of fund returns and costs during the test year. SpeciÞcally, the panel presents the EW-average annual:pre-trade cost and pre-expense return on the stock portfolio of the funds (Gross Return), characteristic selectivity measure (CS), estimated transactions costs, expense ratio, net reported return,Carhart net return alpha, and Ferson and Schadt net return alpha. Both panels of this table present test year statistics, averaged over all test years. In forming all portfolios in this table, we limitour analysis to funds having a self-declared investment objective of �aggressive growth,� �growth,� �growth and income,� �income,� or �balanced� at the beginning of the test year.
Panel A. Fractile Characteristics (Test Year)
Ranking Variable = Career CST Career Avg Career Portfolio Manager
∗ SigniÞcant at the 90% conÞdence level.∗∗ SigniÞcant at the 95% conÞdence level.∗∗∗ SigniÞcant at the 99% conÞdence level.
Table VMutual Fund Performance and Characteristics Surrounding Manager Replacement
Selected mutual fund measures are provided below for the merged CDA holdings and CRSP mutual fund characteristics/net returns databases.At the end of each calendar year starting December 31, 1985 and ending December 31, 1999, we separate all mutual funds in the merged databasethat existed during the entire prior 12-month period and had an investment objective at the end of that year of �aggressive growth,� �growth,��growth and income,� �income,� or �balanced,� into those funds that experienced a change in lead manager (the manager with the most careerexperience at that fund) during the prior year (the �ranking year�). Then, fractile portfolios are formed, and we compute average measures (e.g.,net returns) for each fractile portfolio during the following year (the �test year�). In computing the average measure for a given test year, we Þrstcompute the quarterly buy-and-hold measure for each fund that exists during each quarter of the test year, regardless of whether the fund survivespast the end of that quarter. Then, we compute the equal-weighted (EW) cross-sectional average quarterly buy-and-hold measure across all fundsfor each quarter of the test year. Finally, we compound these measures into an annual measure that is rebalanced quarterly. Presented in this tableare the EW-average annual: characteristic selectivity measure (Panel A), net return (Panel B), and turnover level (Panel C). The table presentstest year statistics over years 0-3 relative to the ranking year, averaged over all event dates. The table also shows the time-series average numberof funds within each category. Time-series inference tests are shown, where appropriate.
Panel A. Characteristic Selectivity Measure (percent per year)
Avg Avg Year Year Year Year
Fractile No TNA 0 +1 +2 +3
Manager Change (1) 108 731 -0.2 0.6 0.8 0.3
No Manager Change (2) 810 980 0.5∗∗ 0.5∗ 0.5 0.4∗
(1) Minus (2) � � -0.7∗∗ 0.1 0.3 -0.1
All Funds 918 951 0.4 0.5 0.5 0.4
Panel B. Net Return (percent per year)
Avg Avg Year Year Year Year
Fractile No TNA 0 +1 +2 +3
Manager Change (1) 108 731 15.8 16.3 17.5 17.6
No Manager Change (2) 810 980 16.7 15.7 15.6 16.7
(1) Minus (2) � � -0.9∗∗ 0.6 1.9∗∗∗ 1.0
All Funds 918 951 16.6 15.8 15.8 16.8
Panel C. Portfolio Turnover (percent per year)
Avg Avg Year Year Year Year
Fractile No TNA 0 +1 +2 +3
Manager Change (1) 108 731 99.0 99.7 96.1 88.6
No Manager Change (2) 810 980 75.3 73.7 72.1 70.5
(1) Minus (2) � � 23.7∗∗∗ 26.0∗∗∗ 24.0∗∗∗ 18.1∗∗∗
All Funds 918 951 78.1 76.8 74.9 72.6
∗ SigniÞcant at the 90% conÞdence level.∗∗ SigniÞcant at the 95% conÞdence level.∗∗∗ SigniÞcant at the 99% conÞdence level.
Table VICross-Sectional Regressions of Fund Characteristic Selectivity (CS)
Measure on Manager Characteristics
This table reports the time-series average OLS coefficient estimates, with associated t-statisticsbeneath the coefficient estimates, from annual cross-sectional regressions of fund CS measureson year-beginning manager characteristics. A regression is computed each year, starting in 1986and ending in 2000. Manager characteristics include manager: career experience, career averageCS performance measure (CST ), risk tolerance (time-series standard deviation of S&P 500-adjusted returns over the manager�s career), career manager aggressiveness (time-series averagemanager career turnover ratio), and a dummy variable that indicates whether a manager wasreplaced during the year prior to the given year. In all cases, the manager characteristic (e.g.,experience or risk tolerance) is measured only up to the end of the year prior to the year ofthe regression. Also reported are the time-series average sample size and time-series averageadjusted R2 of the cross-sectional regressions. SigniÞcance levels are indicated by ***, **, and*, which denote 1 percent, 5 percent, and 10 percent levels, respectively.
Table VIICross-Sectional Regressions of Fund Characteristic Selectivity (CS)Measure on Manager Characteristics (Growth-Oriented Funds)
This table reports the time-series average OLS coefficient estimates, with associated t-statisticsbeneath the coefficient estimates, from annual cross-sectional regressions of fund CS measureson year-beginning manager characteristics. Included in these regressions, for a given year,are all funds having a self-declared investment objective of �aggressive growth� or �growth�at the end of that year. A regression is computed each year, starting in 1986 and ending in2000. Manager characteristics include manager: career experience, career time-series averageCS performance measure (CST ), risk tolerance (time-series standard deviation of S&P 500-adjusted returns over the manager�s career), career manager aggressiveness (time-series averagemanager career turnover ratio), and a dummy variable that indicates whether a manager wasreplaced during the year prior to the given year. In all cases, the manager characteristic (e.g.,experience or risk tolerance) is measured only up to the beginning of the year of the regression.Also reported are the time-series average sample size and time-series average adjusted R2 ofthe cross-sectional regressions. SigniÞcance levels are indicated by ***, **, and *, which denote1 percent, 5 percent, and 10 percent levels, respectively.
Table VIIICross-Sectional Regressions of Fund Characteristic Selectivity (CS)Measure on Manager Characteristics (Income-Oriented Funds)
This table reports the time-series average OLS coefficient estimates, with associated t-statisticsbeneath the coefficient estimates, from annual cross-sectional regressions of fund CS measureson year-beginning manager characteristics. Included in these regressions, for a given year,are all funds having a self-declared investment objective of �growth-income,� �income,� or�balanced� at the end of that year. A regression is computed each year, starting in 1986 andending in 2000. Manager characteristics include manager: career experience, career time-seriesaverage CS performance measure (CST ), risk tolerance (time-series standard deviation ofS&P 500-adjusted returns over the manager�s career), career manager aggressiveness (time-series average manager career turnover ratio), and a dummy variable that indicates whethera manager was replaced during the year prior to the given year. In all cases, the managercharacteristic (e.g., experience or risk tolerance) is measured only up to the beginning of theyear of the regression. Also reported are the time-series average sample size and time-seriesaverage adjusted R2 of the cross-sectional regressions. SigniÞcance levels are indicated by ***,**, and *, which denote 1 percent, 5 percent, and 10 percent levels, respectively.
