Can Mutual Fund Families Affect the Performance of Their Funds.pdf by lovemacromastia

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									Can Mutual Fund Families Affect the Performance
                                    of Their Funds?

                            Ilan Guedj and Jannette Papastaikoudi∗
                               MIT - Sloan School of Management

                                    First Draft - August 2003
                                    This Draft - January 2004



                                               Abstract

           We examine whether mutual fund families affect the performance of the funds they
        manage. From a sample of funds belonging to large families we find that last year’s
        best performing funds outperform last year’s worst performing funds by 58 basis points.
        We also show that there exists persistence of performance of these funds inside their
        respective families. This persistent excess performance is related to the number of funds
        in the family which we interpret as a measure of the latitude the family has in allocating
        resources unevenly between its funds. Supporting these findings, we also show that the
        better performing funds in a family have a higher probability of getting more managers,
        one of the main resources available. This is consistent with the view that fund families
        allocate resources in proportion to fund performance and not fund needs.




  ∗
      We would like to thank John Chalmers, Joseph Chen, Denis Gromb, Dirk Jenter, Paul Joskow, Ajay
Khorana, Jonathan Lewellen, Stewart Myers, Stefan Nagel, Anna Pavlova, Steve Ross, Antoinette Schoar,
Henri Servaes, Peter Tufano, and the participants of the 2003 Transatlantic Conference at LBS for their
helpful comments. Corresponding Author: Ilan Guedj, MIT Sloan School of Management, E52-442, 50
Memorial Drive, Cambridge MA, 02142, USA or email: guedj@mit.edu


                                                    1
1    Introduction
The mutual fund industry has grown at an incredible rate in the past years, showing an ex-
plosive increase particularly over the last decade. However, the recent revelations about the
expropriation of investors’ trust and wealth by mutual fund families have shaken investors’
confidence and have brought the mutual fund industry under a lot of scrutiny. So far, we
have witnessed preferential treatment of certain clients by allowing market timing and after
hours trading. A legitimate question arises whether there have been other forms of prefer-
ential treatment that affect directly the observable performance of mutual funds. Towards
that end, we ask whether mutual fund families can and indeed do affect the performance of
their funds in a way that might systematically discriminate certain investors.
    One of the most extensively researched questions is whether mutual funds have persistent
abnormal returns. If so, this provides evidence for the existence of superior managerial
investment ability. In fact, persistence is well documented. Lehmann and Modest (1987),
Hendricks, Patel and Zeckhauser (1993) and Wermers (1997), among others, have found
evidence of persistence in fund performance over short horizons of one to three years. Yet,
Brown and Goetzmann (1995) conclude that even if there is some predictability it is quite
difficult to detect. Carhart (1997), Daniel, Grinblatt, Titman and Wermers (1997), Wermers
(2000) and Pastor and Stambaugh (2002) argue that most of this persistence is due to factors
other than managerial ability. In particular they show that positive abnormal returns can be
attributed to momentum in stock prices while negative abnormal returns can be attributed
to managerial expenses and transaction costs.
    In this paper we view mutual fund performance from a different perspective than the
majority of the existing literature. Instead of treating a mutual fund as a completely
independent entity, we view it as part of a larger group, the mutual fund family. Given
this dependence, differences might arise between the objectives of the fund and the family
it belongs to. Furthermore, these differences in objectives might potentially translate into
discrepancies between expected and observed performance. In particular, if it serves the
family’s interest, it could decide to follow a strategy of selectively allocating its limited
resources unevenly across its funds. The rationale behind such a strategy follows from both
the convexity of the performance-flow relationship and flow spillovers inside the family.
Spitz (1970), Chevalier and Ellison (1997) and Sirri and Tufano (1998) have documented
that abnormal positive returns generate disproportionately more inflows than abnormal


                                             2
negative returns would generate outflows. This implies that if the family had a choice
between managing two mediocre performing funds or managing one well performing fund
and one poorly performing fund, the family would prefer the latter combination. Apart
from this empirical finding, it has also been observed by Khorana and Servaes (2002) and
Nanda, Wang and Zheng (2003) that there exists a flow spillover within funds of families
that possess at least one fund with an excellent performance record. The implication of this
observation is equivalent to that of the convex performance-flow relationship: It is sufficient
for the family to only have some well-performing funds in order to experience a large inflow
in its assets under management.
       We therefore expect families to want to promote their funds selectively1 . However, in
order to act along these lines, the family needs to possess the latitude to do so, i.e. it
needs to have enough funds to be able to move resources from one to another. Hence,
we hypothesize that one should expect larger families to be more capable of affecting the
performance of their funds.
       To test our hypothesis, we use monthly open-end mutual fund data from the Center
for Research in Security Prices (CRSP) for the period of 1990-2002. Using a methodology
similar to Carhart (1997) we analyze the persistence of the performance of mutual funds that
belong to large families, defined either by the number of funds they hold or by their market
capitalization. We find a short-term persistence in mutual fund performance. The difference
in abnormal returns between a portfolio of funds which were last year’s winners and a
portfolio of funds which were last year’s losers is 58 basis points per month (statistically
significant at the 1% level), an annualized difference of 7.2%. In addition to these results, we
perform the persistence methodology on a relative scale, i.e. when the funds are placed into
portfolios with respect to their performance relative to other funds inside the same family.
Again, we find a statistically significant difference between the top and bottom portfolio of
53 basis points per month, which translates into a difference of 6.5% per year in abnormal
returns. The fact that persistence in fund performance is detected even within a mutual
fund family can be viewed as evidence that families are actively intervening in their funds’
performance.
   1
       Some anecdotal evidence about selective promotion of funds has been provided in a case study by Loeffler
(2003). He analyzes the case of the creation of a new class of pension funds. The three German mutual fund
companies he follows enhanced the performance of the new funds by allocating underpriced IPOs to their
portfolios. All that, in order to generate a favorable track record for the funds.



                                                       3
   If the family is actively promoting some funds over others, it can accomplish this by
unequal resource allocation. Managers (analysts) are one of the main resources families
have both in actual terms and in the eyes of the investors. Chevalier and Ellison (1999a,
1999b) show that personal characteristics of the fund manager can help predict superior
stock picking ability. Families seem to place serious consideration on this fact, since their
decisions on managerial turnover is linked to the managers’ past performance. Khorana
(1996, 2001) shows that a low performance of at least two years is necessary before a manager
is removed from the management of the fund. He also shows that in the post-replacement
period there is a significant improvement in the performance of the fund. Hu, Hall and
Harvey (2000) analyze promotions and demotions of managers and conclude that there
exists a positive relationship between the promotion probability and lagged fund returns.
All these findings indicate that mutual fund families take their fund management very
seriously and are not reluctant to intervene in their funds’ management, if needed. Hence,
we can expect to observe family intervention which can potentially lead to preferential
treatment of funds. In order to proxy an an attempt from the family to push certain funds,
we use the probability of adding another manager to a fund. If our hypothesis is wrong one
would expect that after controlling for fund characteristics, there would no longer be any
statistically significant difference in the probability of adding a manager between funds in
different groups of performance ranking.
   After using controls such as size and expenses we ask if within rankings, the probability
of adding another manager depends on abnormal returns and lagged abnormal returns.
We find that only funds in the top portfolio have a significant positive coefficient for their
alpha, which would indicate that the decision to add a new manager depends positively on
the fund’s performance only when it is among the top in its family. Second, we ask if the
probability of adding a manager given the funds’ past returns depends on the ranking group
of the fund, i.e. on its relative performance against its peers in the same family. We find
that the probability increases when a fund belongs to the top rank and decreases as the
rank of the fund decreases. Lastly, we identify cases where only one manager was in charge
of a fund and ask what the probability is of adding at least one new manager to such a fund.
This question seems to be a more precise proxy for a deliberate action by the family, since
the change from a single managed fund to a team managed fund is more substantial than
adding another manager to an existing team. We find that again, the probability increases
when a fund belongs to the top rank and decreases when the fund belongs to the bottom

                                             4
rank.
    The remainder of the paper proceeds as follows. In section 2 we develop our hypotheses
and methodology and describe the data. In section 3 we present our results and provide
alternative explanations. In section 4 we perform probit regressions to further enhance our
results. We conclude in section 5.


2     Hypotheses, Data and Methodology

2.1     Hypotheses

A mutual fund is not a stand alone entity, but belongs to a broader organizational structure,
the family. The family could impact the decisions of the fund, and thus could potentially
have a significant effect on the fund, its performance and the persistence of this performance
over time. In fact, there are two main reasons for the family to want to influence that
performance.
    The first reason is the performance-flow relationship. Chevalier and Ellison (1997) and
Sirri and Tufano (1998) have documented the existence of a convex relation between lagged
fund performance and present fund flows: abnormal positive returns generate disproportion-
ately more inflows than abnormal negative returns would generate outflows. This implies
that if the family had the choice between owning two mediocre performing funds or one
well performing fund and one poorly performing fund, the family would prefer the latter
combination. This is the case because the convexity of the performance-flow relationship
would translate into an increase of the net amount of assets under the family’s management.
Thus, it is reasonable to believe that faced with some better performing funds than others,
the family would consciously choose to offer preferential treatment to the better ones in
order to maintain their good track record, even if this would come at the expense of other
poorly performing funds in the family.
    The convex performance-flow relation is not the only reason why a family would want to
influence the performance of its funds. The second reason is that there exists evidence that
flows are not independent across funds of the same family, on the contrary, there seems to
exist a strong cross-sectional dependence. Khorana and Servaes (2002) find that a fund’s
market share within an investment objective is not only driven by its family’s policies within
that objective, there are important spillover effects from other funds within the same family


                                              5
as well. Nanda, Wang and Zheng (2003) find that there is a strong positive spillover from
a star performer to other funds within the same family. This spillover results in greater
cash inflows not only to the star fund but to other funds in the family as well. As such, the
cross sectional correlation between fund flows can increase aggregate inflows in a non-linear
fashion and could be another incentive for the family to ”push” the better performing funds
in order to maintain their performance.
       Therefore, both the convexity of the flow-performance relation and the cross sectional
correlation between fund flows imply that maximizing aggregate flows to the entire family is
not necessarily equivalent to maximizing flows to each and every fund individually. In fact,
and due to the cost of fund management, such a policy could even be sub-optimal. Instead,
it seems to be sufficient for a family to have a few funds that persist at outperforming
their peers; this would lead to increased flows to the entire family while being a more cost
efficient strategy. Such a strategy that focuses on improving performance selectively and
not to the entire universe of funds within a family might have direct implications on the
observed behavior and performance of individual funds.
       The relevant question is which funds to promote in order to obtain the desired aggregate
inflow effect to the family. Since investors look at past performance when deciding about the
allocation of their wealth between funds2 , a family, if capable, would deliberately attempt
to ”push” its currently well performing funds also in the following year (after they had
demonstrated good performance) in order for them to further increase their returns and
therefore increase flows to the entire family3 . The family has several resources it can use in
order to achieve this goal of ”pushing” certain funds at the expense of others. The existence
of a centralized research department, the waiving of management fees, and the ability to
move managers from one fund to another or even share them among funds are only some
of those resources that can be used and allocated not equitably between funds. Yet, even
if it may be in the best interest of every family to act along these lines, not all are capable
of adopting a fund promoting strategy, the main inhibition being the availability as well
as the flexibility and latitude in allocating those resources. Evidently, larger families that
offer a larger number and a greater variety of funds, have more flexibility in using their
   2
       Wilcox (2003) runs an experiment with investors and finds that performance is one of the most important
determinants of investor choice.
   3
     In fact, Nanda, Wang and Zheng (2003) find that the empirically documented spillover effect from a
star fund to other funds may induce lower quality families to pursue a star creating strategy.



                                                       6
resources in order to promote certain funds. Therefore, these families should exhibit a more
pronounced fund-promoting behavior.
      We thus hypothesize that larger families are better equipped and are expected to act
more along these lines than smaller families. Hence we expect to obtain two main findings.
First, that funds belonging to larger families have a more persistent performance than funds
belonging to smaller families. And second, given the strategy of the family to promote only
a few of its funds, we expect to find persistence in fund performance also within a family.
For example, even in a family where there isn’t even one fund that performs well when
measured in absolute terms, we still expect the family to ”push” its relatively better funds
to persist and improve.


2.2      Data Formation and Descriptive Statistics

2.2.1      Data

Our data originates from the Center for Research in Security Prices’s ”Survivor-Bias Free US
Mutual Fund Database” (CRSP). This database is considered free of survivorship bias since
it includes funds that no longer exist4 . Unfortunately it contains information on the fund
families only from 1992 onwards. Therefore, although the CRSP mutual fund database is
our primary source of data, we use complementary information to complete our study. Since
we focus on the mutual fund family, we need to obtain accurate information on the families
the funds belong to. Even though CRSP offers names of the fund family after 1992, it is
often not consistent in their documentation across time and across funds. The implications
thereof might affect adversely an analysis regarding families. As a precautionary measure
we revert to the use of an alternative mutual fund database that has been commonly used in
other mutual fund studies5 : Morningstar. We use the ”Morningstar Principia Pro”’s CDs
(Morningstar) for the years 1990-2002 to cross-check our information on the fund families
of our sample. This also allows us to extent the time period to 1990. If a fund does not
possess such information it is dropped out of our sample. In a few cases where Morningstar
does not provide the identify of the fund family, while CRSP possesses the information, the
latter source is used.
  4
      Though, some studies have disputed whether CRSP is indeed survivorship-bias free. See Elton, Gruber,
and Blake (2001) for an extensive analysis of this issue.
   5
     For example; Brown and Goetzmann (1997) Chevalier and Ellison (1999a, 1999b) and Hu, Hall, and
Harvey (2000) among others.


                                                     7
       In order to compare our results with the outstanding literature we keep only actively
managed equity funds, hence funds that have an objective of Growth, Aggressive Growth,
Growth and Income, Small Company and Equity Income6 . Therefore, we exclude Sector,
International, Balanced, Bond, and Municipal funds. In addition, for a fund to enter our
sample an additional requirement of one year of past reported returns is imposed. We use
Morningstar as a consistent source for identifying the objectives of the funds. For the time
period of interest we record 7,310 funds that meet these criteria. Next, we correct our
sample for multiple share classes7 . To construct return series for the funds after share class
aggregation, we value weight the returns of each individual class by class size. This reduces
our sample to 3,046 funds. In our sample, each fund possesses on average 2.32 classes,
48.4% of the funds only have one share class. From those funds that possess more than
one class, they possess on average 3.52 share classes. Our final sample incorporates 678
families across the years 1990-2002. Not all of them exist throughout the entire sample.
Acquisitions and mergers across families lead to some variation in the number of families
appearing each year.


2.2.2       Descriptive Statistics

In table 1 we provide annualized summary statistics of our sample. As one can note, the
number of funds has more than tripled between 1990 and 2000, while remaining almost
at a constant level during the recent years. This might be well due to the large amount
of consolidation in the mutual fund arena in the past few years, as well as the recent low
performance in the equity markets. The same pattern seems to appear for the average size
of assets under management. The average amount of assets under management has more
than quadrupled between 1990 and 2000. The table also includes some fund characteristics
such as loads, fees and turnover. Loads are fees charged to the investor by the mutual
fund, either when the fund is purchased (front-end load), when it is sold (rear-end loads)
or depend on the amount of time the investor holds the fund (deferred loads). 12-B1 is an
annual distribution charge (12b-1 fee) charged to the investor for marketing needs of the
funds. Loads and fees show little to no variation across time in contrast to turnover that
   6
       Funds with these objectives have been identified by Morningstar as pure equity funds.
   7
       Mutual funds very often offer different types of shares supported by the same underlying portfolio, that
differ only in the fee structure imposed to the investor. These different types are labeled as share classes
and given that they correspond to the same underlying portfolio, they have highly correlated returns.



                                                       8
has been increasing on average over the years.
       Table 2 contains summary statistics of the family across years. As in the case of indi-
vidual mutual funds, there is a steady increase in the number of fund families, yet slightly
dropping over the recent years, consistent with family consolidations. Though, in contrast
to the observed trend in the number of families, the average number of equity mutual funds
per family has been monotonically increasing over time reaching 5 funds per family in 2002.
Total net assets on the family level exhibit the same pattern as total assets on the fund level,
hence indicating that the recent drop in value is due to the downturn in the financial mar-
kets. Other parameters such as expense ratios, fees and loads do not exhibit a substantial
variation in time.


2.3       Measuring Fund Persistence

In order to test our hypotheses we perform a series of tests based on the standard mutual
fund persistence methodology8 .We restrict our attention to a sub-sample of funds that
belong to families with a larger number of funds (or assets) under management, in order to
test our hypothesis that funds belonging to larger families are more persistent.
       To measure abnormal returns, we use a four factor model. We use the Fama and French
(1993) three-factor model augmented with a momentum factor similar to Jegadeesh and
Titman (1993). This model has been shown in various contexts to provide explanatory
power for the observed cross-sectional variation in fund performance. We therefore apply
the following multi-factor model:

  rit = αi + bi M KT RFt + si SM Bt + hi HM Lt + pi M OMt +            it

                                                                                  t = 1, 2, ..., T   (1)

where rit is the excess monthly return for fund i. MKTRF is the excess monthly return on
the CRSP value-weighted stock index net of the one year Treasury-Bill. SMB and HML are
the Fama-French (1993) factor mimicking portfolios for size and book to market. MOM,
the momentum portfolio, is the equal-weighted average of firms with the highest 30 percent
eleven-month returns lagged one month minus the equal-weighted average of firms with the
   8
       See for example Grinblatt, Titman, and Wermers (1997), Carhart (1997), and Wermers (2000) among
others for a detailed description.




                                                   9
lowest 30 percent eleven-month returns lagged one month9 .
      At the beginning of every year, we form a sample of funds that belong to large families.
A family is considered large if the number of funds it owns (or its market capitalization)
exceeds a pre-specified threshold. Using prior twelve month returns, we regress each fund’s
monthly returns on the four factor model (equation 1) to obtain each fund’s alpha over the
prior year. Given this measure of performance we rank each fund by its alpha and assign the
funds to one of 10 portfolios based on these rankings. The composition of these 10 rank-
sorted portfolios remains unchanged for the following 12 months. Following the sorting
procedure, a value weighted return series is calculated for each portfolio. This process is
repeated every year and results in 10 time series which are then regressed on the four factor
model. A comparison of the alpha of the top portfolio (the portfolio of mutual funds that
had the highest alphas the year prior to the ranking) with the alpha of the bottom portfolio
(the portfolio of mutual funds that had the lowest alphas the year prior to the ranking)
gives an indication of the persistence of mutual fund performance.
      To further test our hypothesis that large families choose selectively which of their funds
to promote, we devise a second test that is a variation on the standard mutual fund persis-
tence methodology. At the beginning of every year, after selecting those funds that belong
to large families, we regress their prior twelve month returns on the four factor model (equa-
tion 1), thus obtaining each fund’s alpha. Then, we rank each fund by its alpha inside its
family and build 10 portfolios based on these rankings; this implies that for every family
of at least 10 funds each of its funds is allocated to one of the 10 portfolios. One has to
stress that contrary to the standard methodology, the result is not a portfolio of mutual
funds ranked by their absolute performance, but a portfolio of funds ranked by their relative
performance inside their respective families. We repeat this procedure for every year and
consequently obtain 10 time series for the portfolios which are subsequently regressed on the
four factor model. We then compare the alpha of the top portfolio (the portfolio of mutual
funds that had the highest alpha within their family the previous year) with the alpha of
the bottom portfolio (the portfolio of mutual funds that had the lowest alpha within their
family the previous year). The statistical and economic significance of the difference of the
alphas implies that funds exhibit persistence in their performance within their family.
  9
      We are grateful to Ken French for providing us with the SMB and HML factors, and to Mark Carhart
for providing us with the MOM factor.




                                                  10
3         Empirical Results

3.1         Fund Persistence

In table 3 we report the estimation results of the regression given in equation 1 for the 10
portfolios as outlined in section 2.3, while restricting ourselves to the sub-sample of funds
that belong to large families, i.e. families with 10 funds or more10 . Since in this paper we
take the viewpoint of the mutual fund family, we report our results using gross returns,
however, the same results hold for net returns too. The same characteristics of multi-factor
models regarding mutual fund performance reported in the literature also hold in our results.
First, the R-squared are above 90% for almost all portfolios. Second, beta is significant at a
1% level for all portfolios, and so is the loading on SMB. The loading on HML is significant
for most portfolios at the 1% significance level, except for portfolios 7 and 8. These are
by now standard results outlined by Lehmann and Modest (1987) and many others, that
a three factor model explains the main part of the return of mutual funds. Third, the
momentum factor is only significant for portfolios 9 and 10, also a well documented result.
One implication is that funds in portfolios with the highest alphas invest in momentum
stocks, which funds in the lesser performing portfolios seem not to follow.
         However, beyond these standard results, the findings of our family oriented methodology
differ from the standard results when analyzing the alphas of the portfolios. As can be seen
in table 3, we find that the top portfolio has a positive monthly alpha of 35 basis points, and
the bottom portfolio has a negative monthly alpha of -23 basis points. Thus, the portfolio
that consists of longing the top portfolio and shorting the bottom portfolio (referred to as
the 10-1 spread in all the tables) has a positive monthly alpha of 58 basis points (significant
at a 1% level). The statistical significance becomes even more important when considering
the economic significance of this difference, a monthly alpha of 58 basis points is equivalent
to an annual abnormal return of 7.2%.
         In table 4 we give the analogous results using the full sample11 .The spread in alphas
between deciles 1 and 10 is estimated at 35 basis points, and is statistically significant only
at the 10 percent level. These results for persistence are relatively weak. The marginal
statistical significance of the difference between winner and loser portfolios of funds has
    10
         An analysis of different definitions of large families is given in section 3.3.
    11
         These results are consistent with the standard results in the literature, such as Carhart (1997) and seem
to be robust to the different time period used in this study.



                                                          11
lead the literature to conclude that there is no true managerial ability in actively managed
mutual funds after accounting for the known risk factors in the market.
    Tables 3 and 4 are directly comparable. One notes immediately the larger difference in
alphas between deciles 1 and 10 for the full sample which is estimated at 35 basis points
per month with a t-stat of 1.75 compared to 58 basis points per month with a t-stat of 2.86.
This statistically significant difference of 23 basis points per month is the result we expected
to see given our hypothesis in section 2.1, bigger families seem to be able to maintain a
better persistence of their funds’ performance. The loadings on the factors are qualitatively
similar when comparing the two tables, although the loadings on the momentum factor are
higher in table 3, which implies that the winner funds in this sub-sample seem to hold more
momentum stocks than the funds in the full sample.
    Since these results are quite different from the standard persistence results, we start by
analyzing the 10 portfolios to see if they could provide us with some insight for these results.
Table 5 Panel A reports the descriptive statistics of the 10 portfolios using the full sample
and table 5 Panel B reports the descriptive statistics of the 10 portfolios using the sub-
sample. As in the case of the regression results, we can make a direct comparison between
the composition of the portfolios when considering the full sample and the sub-sample. The
first thing one can notice is that the average and median fund in each rank of the sub-sample
is consistently bigger than the average and median fund in the equivalent rank of the full
sample. This observation is consistent with the results of Chen, Hong, Huang, and Kubik
(2002) who show that even though fund size can adversely affect performance, family size
may actually improve performance. Within each panel and across portfolios one can observe
again the fact that the funds in the top and bottom portfolio are smaller than the funds in
the other portfolios; again in accordance to the findings of Chen, Hong, Huang, and Kubik
(2002). This is also consistent with the insight of Berk and Green (2002) who claim that
performance should deteriorate with an increase in flow and a consecutive increase in fund
size.
    The expense ratio and the loads don’t vary much across portfolios, although the loads
are higher in terms of mean and median than the loads on the whole sample. One might
suspect that one reason for the better performance of the sub-sample might be the higher
loads the funds charge, which deter investors from leaving or entering the fund and hence
avoid in- or out-flows that have an adverse effect on fund performance (Edelen (1999)). To
answer this, we calculate the average net flow in each portfolio and show that the mean

                                              12
and median net flow of each portfolio doesn’t differ significantly across the full sample and
the sub-sample as reported in panels A and B of table 5. This is inconsistent with the idea
that funds in the sub-sample that also charge higher fees should experience less flows than
funds in the entire sample. Another possible alternative explanation to our results could
be that the abnormal returns of the top portfolio could be due to higher spending on non
observable resources12 that contribute to the abnormal returns. Table 5 Panel B shows
that the average expense ratio of all the portfolios are quiet similar. The top portfolio has
an average expense ratio of 0.0139 while the average expense ratio of the bottom portfolio
is 0.0135. In addition, our persistence results also hold when using net returns. The 10-1
spread portfolio has a positive alpha of 53 basis points, significant at a 1% level. This shows
that our results are not driven by expenses.
       To see how long lived this fund persistence is, we perform the same methodology with
a varying estimation time period. We conclude that the above described persistence holds
only in the short term, observable at a one year horizon. With an estimation period of 2
years the 1-10 spread is reduced to 17 basis points per month.Using a 3 years estimation
period the statistical significance is lost.
       One alternative explanation to our results could be the presence of incubated mutual
funds in our sample. By incubating funds for a short period, in effect the family builds up
a considerable track record for its funds so that they show exceptional performance before
being launched to the public13 . There are several reasons why, although appealing, this
idea isn’t applicable to our results. First, the number of incubated funds is not significant
compared to the number of funds in our sample. Evans (2003) researches all the SEC
filings for 1995-2003 and finds only 60 such funds for the entire 9 years that were eventually
launched. To put it in perspective, each of our portfolios includes every year on average
more than 150 funds. Second, incubated funds have a very small size. Arteaga, Ciccotello,
and Grant (1998) find their average size to be $5 million. In our sample, the average size of
a fund in our top portfolio is $840 million which counters the notion of small funds entering
the portfolios. Third, Arteaga, Ciccotello, and Grant (1998) and Wisen (2002) seem to
conclude that incubation lasts one year. Since Evans (2003) finds that post incubation
funds do not perform better than other funds, thus, it seems clear that our predictions
  12
       Resources such as higher investments in research or in more capable and expensive fund managers.
  13
       This tactic could allow the discontinuation of poor performing funds and thus capture only the positive
side of the flow-performance relationship.



                                                       13
wouldn’t be affected since in our methodology the presence of incubated funds would affect
only the estimation period and not of the predictive period.
   According to our hypothesis, only families that have the ability to allocate resources
will do so, and by using the number of funds as a proxy for this latitude we find persistence
in fund performance. However, if we use the market capitalization of the family (although
highly correlated with the number of funds in the family) we expect to get weaker results
since even if it is a good proxy for the existence of resources we believe it is not that good a
proxy for the family’s latitude to allocate them in an unequal way between its funds. Market
capitalization is a much cruder measure, since a family with one big fund has no latitude in
allocating its resources compared to a family with 5 small funds. In table 6 we report the
results of the same methodology but instead of using the number of funds as the proxy for
family size we use the market capitalization of the family. We construct a sub-sample based
on family size, where the threshold to family capitalization will yield the same sub-sample
size as the prior analysis (that used the number of funds within the family), i.e. we use
a threshold level such that the resulting sub-sample has the same number of funds as was
generated by a threshold of 10 funds or more per family. The results in table 6 corroborate
our hypothesis. The 10-1 spread is 35 monthly basis points statistically significant only at
the 10% level, similar to the full sample. This result reinforces two claims. First, that it is
not the amount of resources the family has, but the latitude to allocate them unevenly. And
second, that it is not the mere statistical artifact of reducing the sample size that generates
the results but indeed the selection criterion.


3.2   Ranking within the family

As mentioned in section 2.1, given our hypothesis we not only expect to find an increased
persistence in larger families but also that this persistence holds inside the family. Since
the families have limited resources they are expected to favor certain funds over others. To
test this, we thus perform a ”within” analysis as detailed in section 2.3. Before analyzing
these results it is important to stress the main difference between the two methods and their
alternative interpretation. This analysis differs from what has been traditionally considered
to be an analysis of persistence of mutual fund performance, since we do not look at the
previous year’s absolute best performers and ask if they persist at doing so. Instead, we
ask whether, by taking into account information about the family of the fund ex-ante, one



                                              14
can make some predictive statements about the performance of the fund ex-post.
   In table 7 we report the estimation results of these regressions. To be consistent with
section 3.1 we use the same sub-sample and report the results for gross fund returns. Again
the results hold also for net returns. The characteristics of the multi-factor model are similar
to the one described in section 3.1. The loadings on the market, HML, SMB, and momentum
are similar in pattern and magnitude. However, beyond these standard results, the findings
of this within family methodology are quite interesting. As can be seen in table 7, we find
that the top portfolio has a positive monthly alpha of 32 basis points, and the bottom
portfolio has a negative monthly alpha of -21 basis points. Thus, the portfolio that consists
of longing the top portfolio and shorting the bottom portfolio has a positive monthly alpha
of 53 basis points (significant at a 1% level), equivalent to an annual abnormal return of
6.5%. These results are quite striking since they show that there is predictability of mutual
fund performance based on the ranking of these funds relative to other funds inside their
respective families. We believe that these predictability results are even more indicative
of the fact that the family has an instrumental influence on the future performance of its
funds than the results of section 3.1. If this were not the case then the 10-1 spread should
have been much smaller than the one in table 3. This sorting criterion introduces some
randomness into the portfolio ranking, since our methodology removes funds from the top
portfolio that performed well on an absolute scale, yet were not the best performers in their
families, and in return adds funds that didn’t perform well on an absolute scale yet were
the best performers in their family. Thus, it should have weakened the spread. The fact
that it didn’t, shows that the ranking criteria is indicative of an important phenomenon.
In section 4 we perform several tests to try and corroborate this claim.


3.3     Robustness Tests

We perform several robustness tests in order to check that indeed the results are a reflection
of our hypotheses and not a statistical artifact.


3.3.1    Fund Size

Table 5 Panel B includes an interesting result. There is a systematic pattern in the Mean
Total Assets across all the portfolios. The funds in the top and bottom portfolio have a
substantially lower average amount of assets under management than the funds in all other


                                              15
portfolios. This result is not surprising, it is well documented that size and performance
exhibit negative correlation. The only concern one can have while looking at these figures is
whether there could be a way that our results are driven by fund size. In order to investigate
this possibility, we perform the standard persistence methodology as described in section
3.1, where instead of sorting by alphas we sort by fund size (market capitalization).
   Using the same sub-sample and gross returns, at the beginning of every year, we rank
each fund by its size and assign the funds to one of 10 portfolios based on these rankings.
The composition of these 10 rank-sorted portfolios remains unchanged for the following 12
months. Following the sorting procedure a value weighted return series is calculated for
each portfolio. This process is repeated every year and results in 10 time series which are
then regressed on the four factor model. The results of this analysis are shown in table 8.
None of the 10 portfolios has an alpha that is statistically different from zero, neither is
the 10-1 spread portfolio. This shows that although our methodology generates portfolios
that comprise of different size mutual funds, size is not the element driving the positive
predictability results, but indeed it is the relative ranking inside the family.


3.3.2   Sample Size

The criterion we use for keeping only funds that belong to families that have 10 or more
funds reduces our sample to only 7% of all families, although in terms of funds it represents
38% of the full sample. Since the size of our sub-sample might be an issue, we apply the
methodology for alternative sizes of the family, i.e. for various numbers of funds within the
family, and obtain qualitatively similar results. When requiring that a fund belongs to a
family of more than 5 mutual funds, our sample encompasses 22% of all the families, and
63% of all the funds of the full sample. As an illustration one can look at table 9 where we
perform the same methodology for all the funds with 5 or more funds. As one can see the
results are very similar.
   Our results also hold when using 5 instead of 10 portfolios. Interestingly, when using
5 portfolios the results of the ”within” analysis gives better persistence results than the
standard methodology. We attribute this fact to issues of power of our tests when performing
the relative ranking procedure.




                                              16
3.4   Fund Correlations

Given our empirical findings so far, one might legitimately ask whether the observed persis-
tence in funds within a family is due to the fact that some families are better than others.
In other words, whether some families have better strategies which they employ in order to
enhance the performance of their funds. If this indeed were the case, then one would expect
to see a large correlation in returns of funds of the same family, as well as correlations
of their abnormal returns (alphas), assuming that the family implements the same invest-
ment strategy in all funds. To test for that possibility, we calculate the average pairwise
correlations of fund returns and abnormal returns within the same family. Correlations of
fund returns are high, the average full sample correlation for gross (net) returns is 75.9%
(75.8% respectively). This should not necessarily come as a surprise, since there is a lot of
co-movement in fund returns generated by the common underlying factors (Market, SMB,
HML, MOM). However, when we consider the correlations of the abnormal returns of the
funds within one family, we get a different picture. The results are presented in table 10.
Panel A provides characteristics of the distribution of average correlations of fund alphas
within each family. The numbers are provided for the full sample of families, as well as
for large and small families, i.e. for families with more (or respectively less) funds than a
pre-specified cutoff. The numbers are quite revealing. The average pairwise correlation of
fund alphas is 22.0% for the full sample. Yet, when we examine the correlations for the
sub-samples, we note that within larger families, the correlations are substantially lower
than within smaller families. The pairwise correlations of funds belonging to families with
more than 10 funds have a mean of 13.1%, compared to a mean of 21.9% for the comple-
mentary sample of families with less than 10 funds. In addition, the distribution of average
pairwise correlations has a higher standard deviation in the sample of smaller families than
in larger families. This result is even more striking in Panel B, where the absolute value of
the pairwise correlations of fund alphas within the same family is displayed. The average
correlation of the full sample is 30.2%, and in accordance with Panel A, the sub-samples of
small families has a much larger average pairwise correlation (30.6% for families with less
than 10 funds) than the sub-sample of large families (14.1% for families with less than 10
funds).
   These results do not provide evidence that supports the idea that bigger families main-
tain a better persistence of performance by applying a common strategy to all their funds.


                                             17
Instead, we actually note that there is a larger variation in investment styles within larger
families14 . This fact points towards the hypothesis of a limitation of resources a family
has, and implies that even if families have good investment ideas and strategies, these are
not fully scalable and hence cannot be applied to the entire universe of funds within one
family. Some selective allocation of these ideas has to occur. What we have claimed so far
is that the choice where to allocate them depends on the effects of the performance-flow
relationship and the implications thereof to aggregate inflows to the family.


4         The Role of the Family in Performance
If a mutual fund family decides to promote some of its funds more than others, it will
make sure that they exhibit an attractive performance record. One straightforward and
observable way to accomplish this is by allocating human resources to those funds. Man-
agers are one of the main resources families have both in actual terms and in the eyes of
the investors. Therefore, it follows that asking whether the probability of adding another
manager to a fund gives us an insight (and a good proxy) to an attempt from the family to
”push” certain funds. If our hypothesis is wrong one would expect that after controlling for
fund characteristics, there would no longer be any statistically significant difference in the
probability of adding a manager between funds in different groups of performance ranking.
         In this section, we analyze this point. We hypothesize that a fund family promotes its
best performing mutual funds by using its main resource, its managers, in a systematic and
predictable way. We use a logistic regression framework to estimate the probability of a
manager being added to a fund given that it is one of the best (respectively, one of the
worst) performing funds within the family.


4.1         Data and Summary Statistics on Managers

In order to test our hypothesis we use data on managers of the mutual funds that comprise
our database. Our main source on manager data is the ”Morningstar Principia Pro”’s CDs
(Morningstar), described in the data section 2.2.1. Although one can obtain manager names
at the fund level from CRSP, there exist some severe inaccuracies in the manager names
    14
         Our sample is comprised of funds that have an objective of Growth, Aggressive Growth, Growth and
Income, Small Cap and Equity Income; hence the investment strategies considered here are not very different
and therefore the sample of funds should not be that diversified.


                                                     18
as well as in the managing period. For instance, CRSP either misreports or leaves out
manager names that are active in mutual funds, is not consistent in the documentation of
the manager’s name or is not consistent in the managing period, often observing a name
of a manager dropping out and then reappearing after some time period. These drawbacks
are quite severe and might affect adversely an analysis of managerial turnover. For these
reasons we use Morningstar to obtain accurate information on the names of the managers
for the funds of our sample. During the period of 1990-2002 we identify 4,150 different
managers in our sample of 3,046 funds.
       Table 11 provides some summary statistics of manager characteristics and their evolution
over time. In panel A, one can note that the number of managers increases over time
although it is clear that the bad market conditions of the past years had an impact in the
evolution of the industry since this increase reverted in the last two years. Yet, contrary
to the slight decline in the number of managers of actively managed equity mutual funds,
the average number of funds under their control has been steadily increasing. This effect
is probably purely mechanical and predominately driven by the faster reduction in the
number of managers than in the number of mutual funds. The average size of the funds
under management also increases over time, although it exhibits a decline in the last three
years. Throughout their career, managers worked on average for 1.15 families. This is
an indication of managerial turnover, as well as of consolidation within the mutual fund
industry15 . In panel B, we look at the managers that manage more than one fund. As
we noted earlier, 46% of the families are families of one equity fund16 and therefore their
managers can rarely (except for subcontracting with another fund) manage more than one
fund. We can see that among those who manage more than one fund the number of funds
has been steadily increasing over the years and has always averaged above 2.5 funds per
manager. The descriptive statistics indicate that it is very prevalent to have more than
one manager per mutual fund. In addition, managers are quite mobile since they switch
between funds over time.
  15
       In a few cases, a merger or acquisition between two different mutual fund families results in the manager
effectively changing managing company. In our sample, we treat this as a switch of family.
  16
     Viewed at the fund level, 11% of the funds in our sample are the only funds in their respective family.




                                                       19
4.2       The Probability of Adding a Manager

In order to study the probability of adding a manager to a fund, we define a dichotomous
dependent variable in a logistic regression framework. The variable equals one when there
was at least one net managerial addition to the management team of a fund, and equals
zero when no net additions were made to the team17 . This methodology is similar to
Khorana (1996). Our main concern in this analysis is an issue of power. The low net
managerial turnover has to be analyzed using 10 portfolios, resulting in an even further
reduced managerial turnover by portfolio. If for consistency we were to perform our tests
on 10 portfolios, there would be not enough managerial movements to get any statistical
significance. To address this issue, we carry out all the logistic regressions using 5 portfolios
instead. As we have mentioned in section 3.3.2, our results on fund persistence within the
family hold also in a 5 portfolio framework, hence our hypothesis should also be testable in
this case. To test our hypothesis, we run the following regression for each portfolio:

  P rt+1 (Adding a manager) =
                 Λ [β1 + β2 αt + β3 αt−1 + β4 Log(T N A)t + β5 Log(T N A)t−1 + β6 Expenset ] (2)

where Λ [·] is the logistic cumulative distribution function. We estimate the specifications
using maximum likelihood. αt is the alpha of the fund, estimated from the four factor model
of equation 1. We account for the size of the fund by using the natural log of the total net
assets (TNA). This should control for the situation where the fund’s size increases and a
new manager is needed due to the increased workload. We also account for the expenses
of the fund in order to justify a situation where a manager is added in order to rationalize
to investors the higher fees they are being charged18 . The results of the regression can be
seen in table 12.
       The only portfolio where the alpha is significant is the top portfolio (portfolio 5). The
coefficient is statistically significant at a 1% level. As we have hypothesized, the probability
of adding a manager increases when a fund belongs to the top performing funds inside its
family. Size is statistically significant for portfolios 3 and 4. The result is not surprising,
since the largest funds are concentrated in the middle portfolios, hence they are be the ones
  17
       If the total number of managers in a team does not change even if there was managerial turnover, for
example, if one manager was replaced by another one, we do not count this as a net addition.
  18
     We also performed these regressions including other combinations of controls such as, turnover, change
in size and their lags. The main results were unchanged.


                                                     20
that would require additional managerial support when experiencing an increase in assets
under management19 . Expenses are significant for portfolios 3 and 4. Those portfolios are
comprised of funds that were relatively mediocre in their respective families, and the only
explanation for adding a manager is an increase (or a historical trend of increase) in size
and thus workload, or in order to justify the expenses of the funds.
       To account for the possibility that a family measures the performance of its funds from
their returns instead of their alphas, we estimate the probability of adding a manager to
a fund given prior returns (net or gross). We run the following regression where Ii is a
dummy variable for portfolio i:

  P rt+1 (Adding a manager) =
                                                                                                                  
              5                        5
Λ β1 +            β2,i Ii · Rett +         β3,j Ij · Rett−1 + β4 Log(T N A)t + β5 Log(T N A)t−1 + β6 Expenset 
             i=1                      j=1

                                                                                                                   (3)

where we let the dummy Ii interact with past returns. The results for net returns are pre-
sented in table 13 (we obtain similar results for gross returns). Interestingly, the interaction
terms are significant only for the top and bottom portfolios. For funds that belong to the
top relative performers, the probability of receiving an additional manager increases with
past returns, while for funds belonging to the bottom relative performers, the probability
of receiving a manager decreases with past returns. For funds belonging to intermediate
portfolios, returns do not matter in the decision whether to allocate an additional manager
of not to a fund.
       Finally, according to Khorana (1996), managerial turnover depends on the objective-
adjusted percentage change in a fund’s assets. To calculate this change in a fund’s assets
P CASSETt , we determine the average growth rate of other funds within the same invest-
ment objective and subtract it from the asset growth rate of the fund. The difference should
capture in- and out-flows from of a fund that are due to managerial performance and not
  19
       In the same way that we have calculated summary statistics in table 5 for 10 portfolios, the same results
hold for 5 portfolios: The middle portfolios possess larger funds on average, and expenses do not significantly
vary across portfolios.




                                                          21
to some aggregate investor sentiment. We run the following regression:

  P rt+1 (Adding a manager) =
                                                                                              
              5                          5
   Λ β1 +         β2,i Ii · Rett−1 +         β3,j Ij · P CASSETt + β4 Expenset + β5 T urnovert 
             i=1                        j=1

                                                                                                    (4)

   The results of the regressions can be seen in table 14. As in table 13, past returns
interacted with the dummy variable are significant for the top and bottom portfolio. Yet,
the change in asset growth rate does not affect the probability of adding a manager to funds
belonging to these groups. Families do not consider past objective adjusted asset growth
rate when deciding where to allocate more resources, instead they focus only on past per-
formance. The only statistically significant portfolio is the third. A possible interpretation
of this result would be that for mediocre funds it is more important how much inflows they
receive compared to their peers, since their performance is not outstanding anyways.


4.3   The Probability of Moving from One to Multiple Managers

Although we control for fund size and expenses, additions of managers to an already existing
team might also be due to other reasons than just the intention to further promote the fund.
As such, a manager might be added for a short period to fill in a temporary vacancy or
for training purposes, to obtain experience within an existing team. Therefore, we identify
cases where only one manager was in charge of a fund and ask what the probability is of
adding at least one new manager to such a fund. This question seems to be a more precise
proxy for a deliberate action by the family, since the change from a single managed fund
to a team managed fund is more substantial than adding another manager to an existing
team. Thus, for each portfolio we run the following regression:

  P rt+1 (Adding a manager to a single managed f und) =
             Λ [β1 + β2 αt + β3 αt−1 + β4 Log(T N A)t + β5 Log(T N A)t−1 + β6 Expenset ] (5)

We restrict ourselves to the subset of funds that are managed by only one manager and
regress the probability of adding a manager on each fund’s lagged alpha, controlling for size
and expenses. The results of the regression can be seen in table 15. The results are very

                                                      22
similar to the ones of equation 2. The only portfolio where the alpha is significant is the top
portfolio (portfolio 5). The coefficient is statistically significant at a 1% level. As we have
hypothesized, the probability of adding a manager increases when a fund belongs to the top
performing funds inside its family. The only other statistically significant parameters are
size for portfolios 3 and 4, and expenses for portfolios 3 and 4.
       We also perform tests similar to equation 3. The results can be seen in table 16. The
results are quite similar to table 13. Good past performance increases the probability of
converting a single-managed fund to a team-managed fund, only if the fund is one of the
top performers in its family. Contrary to table 13, we do not observe the reverse behavior
for funds in the bottom portfolio for lag t, instead the significance moves to lag t − 1. This
might be very well due to power issues, since the criterion of examining funds which were
initially single managed reduces our sample by approximately 60%.
       In this paper, we have provided evidence that mutual fund families use managerial
turnover as a means to promote some of their funds more than others. However, managers
are not the only resource mutual fund families have that they can allocate unevenly between
their funds. Reuter (2002), Gaspar, Massa, and Matos (2003), and Loeffler (2003) show
that families sometimes distribute their allocations of IPOs unevenly between their funds
in order to promote certain funds at the expense of others. Gaspar, Massa, and Matos
(2003) also mention the possibility of a cross-subsidization of fund performance based on
opposite trades of funds inside the family that would result in shifting bad performance
from one fund to the another. However compelling these claims might be, they have a
fundamental difference from our evidence on managerial turnover. Managers are the main
and permanent resource families have, hot IPOs could be an added value, but are conditional
on them being hot and available20 . The evidence on the managers shows that the families’
preferential treatment does not appear as an exploitation of a temporary opportunity, but
a more consistent and reoccurring behavior.




  20
       Loughran and Ritter (2003) among others show that the astronomical discount in IPO prices occurred
only during the 1999-2000 bubble period.


                                                    23
5    Conclusion
Mutual fund families have an incentive to selectively favor their well performing funds in
order for them to continue exhibit abnormal performance and thereby increase the inflows
accruing to the entire family. In this paper we hypothesize that larger families not only
have the incentive but also the means to do so.
    We show that funds that belong to larger families have a more persistent performance
than the entire universe of funds. We show that this persistence is directly related to the
number of funds in the family which we interpret as a measure of the latitude the family
has in allocating resources unevenly between its funds. We also show that there exists
persistence of performance of these funds inside their respective families. This is another
indication that the family is actively engaged in affecting the performance of its funds.
In order to support our hypothesis that the stronger persistence of performance in larger
families is due to a deliberate attempt by the family, we run a series of probit regressions
which estimate the probability of assigning additional managers to a fund. We show that
even after controlling for changes in size, expenses, and past performance, there still is
a higher probability of adding a manager to a fund that belongs to last year’s family’s
relative best performers. This implies that the family does not allocate resources (in this
case managers/analysts) proportionally according to the funds’ needs but in a way that
allows the family to promote certain funds, if this can help increase the inflows to the entire
family.
    The approach presented here has broader implications than just showing that contrary
to the outstanding literature there exists some persistence in mutual fund performance.
In particular, it contributes to the understanding of the organization of mutual funds.
Scharfstein and Stein (2000) and Rajan, Servaes and Zingales (2000), among others, have
shown that there exists a subsidization across divisions in an organization. When the mutual
fund family is viewed as the organization with the funds as its divisions, our empirical
findings would be consistent with a theory where some divisions are awarded more resources
than others given their high temporary output. Since managers compete to get more family
resources (and thus improve their returns and flows and by that their compensation) it might
create an incentive to distort the signal the managers send to the head of the organization.
In the context of mutual funds this could translate into risk shifting through the investment
in more volatile assets. Chevalier and Ellison (1999a) have shown that such risk shifting


                                             24
could be induced by career concerns of fund managers. However, our results imply that risk
shifting could be also induced by a rent-seeking behavior on the part of the fund managers.
   Our results have also a direct implication on the welfare of mutual fund investors. If
mutual fund families consciously promote some of their funds more than others, this will
result in a transfer of wealth from one group of investors to another one. By using the
management fees collected from all its funds to favor only a smaller sub-group of them, the
family is effectively shifting wealth between the majority of investors to those that invest
in the funds it promotes. The regulatory implications of such a behavior cannot be ignored
since it implies indirect investor discrimination and preferential treatment.




                                             25
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                                             28
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                                          29
                                                      Table 1: Descriptive Statistics - Funds
     This table reports summary statistics for the funds of our sample after accounting for the different share classes. The number of funds is the number of
     mutual funds that meet our selection criteria. Total Assets are the total assets under management at the end of the calendar year in millions of dollars.
     Monthly Return is the cross-sectional average of the annual averages of the monthly returns of the funds in our sample, expressed in percentage. Total
     Load is the total front-end load, deferred and rear-end charges as a percentage of new investments. 12-B1 is the annual distribution charge (12b-1 fee) in
     percentage of total assets. Turnover is the minimum of aggregate purchases of securities or aggregate sales of securities, divided by the average Total Net
     Assets of the fund. Standard deviations are reported in parentheses.

                                    Number of         Total       Monthly       Maximum        Other     Total
                           Year       Funds           Assets       Returns         Load        Loads     Loads      12-B1     Turnover
                           1990         796          260.33          0.10          2.09         0.00      2.09      0.002        0.74
                                                    (758.42)        (1.37)        (2.62)       (0.05)    (2.62)    (0.003)      (0.92)
                           1991         898          299.31          2.59          2.11         0.00      2.11      0.002        0.70
                                                    (901.25)        (1.58)        (2.58)       (0.05)    (2.58)    (0.003)      (0.95)
                           1992        1,072         343.60          0.84          2.19         0.61      2.80      0.002        0.66
                                                   (1,119.79)       (1.13)        (2.54)       (1.41)    (3.01)    (0.003)      (0.97)
                           1993        1,267         413.67          1.16          2.13         0.79      2.93      0.002        0.65
                                                   (1,412.39)       (2.15)        (2.46)       (1.60)    (3.25)    (0.003)      (0.77)




30
                           1994        1,447         432.38         -0.08          2.05         0.94      2.99      0.002        0.74
                                                    1,562.34         0.76          2.42         1.80      3.47      0.003        1.13
                           1995        1,617         499.40          2.23          2.10         1.20      3.30      0.002        0.71
                                                   (1,562.34)       (0.76)        (2.42)       (1.80)    (3.47)    (0.003)      (1.13)
                           1996        1,885         596.51          1.56          2.07         1.36      3.44      0.002        0.67
                                                   (2,351.61)       (0.91)        (2.44)       (2.06)    (3.96)    (0.003)      (1.40)
                           1997        2,091         722.62          1.73          2.10         1.41      3.51      0.002        0.72
                                                   (2,933.48)       (1.32)        (2.50)       (2.09)    (4.15)    (0.003)      (0.86)
                           1998        2,337         826.75          1.39          2.17         1.56      3.73      0.002        0.69
                                                   (3,563.00)       (1.97)        (2.56)       (2.18)    (4.36)    (0.003)      (1.22)
                           1999        2,471         984.65          2.45          2.28         1.68      3.96      0.002        0.97
                                                   (4,415.89)       (3.19)        (2.61)       (2.22)    (4.48)    (0.003)      (3.94)
                           2000        2,599        1,140.87        -0.06          2.42         1.85      4.27      0.002        1.19
                                                   (4,816.08)       (2.35)        (2.66)       (2.27)    (4.60)    (0.003)      (6.69)
                           2001        2,587         948.89         -0.70          2.47         1.94      4.41      0.002        1.19
                                                   (4,028.29)       (2.58)        (2.68)       (2.28)    (4.64)    (0.003)      (2.63)
                           2002        2,409         905.59         -3.31          2.49         1.98      4.48      0.002        1.21
                                                   (3,634.55)       (2.09)        (2.68)       (2.28)    (4.65)    (0.003)      (2.42)
                                                        Table 2: Descriptive Statistics - Families
     This table reports summary statistics for the families in our sample. The number of families is the number of fund families whose funds have met our
     selection criteria. Funds per family is the average number of funds each family has. TNA is the total assets under management of each family at the end
     of the calendar year in millions of dollars. Expense Ratio is the percentage of the total investment that shareholders pay for the mutual funds operating
     expenses. Total Load is the total front-end load, deferred and rear-end charges as a percentage of new investments. 12-B1 is the annual distribution charge
     (12b-1 fee) in percentage of total assets. Turnover is the minimum of aggregate purchases of securities or aggregate sales of securities, divided by the average
     Total Net Assets of the fund. Standard deviations are reported in parentheses.

                             Number of       Funds per       TNA per         Expense      Maximum         Other     Total
                    Year      Families         Family          Family         Ratio          Load         Loads     Loads      12-B1      Turnover
                    1990         293             2.51          651.92          1.25           1.84         0.00      1.85       0.001        0.62
                                               (3.86)        (2449.44)        (0.96)         (2.45)       (0.06)    (2.45)     (0.002)      (0.65)
                    1991         322             2.70          827.35          0.98           1.92         0.00      1.92       0.001        0.62
                                               (4.34)        (3277.09)        (0.81)         (2.41)       (0.06)    (2.41)     (0.002)      (0.93)
                    1992         366             2.76          969.63          1.44           1.93         0.33      2.25       0.002        0.61
                                               (4.26)        (4181.76)        (1.09)         (2.31)       (0.91)    (2.55)     (0.002)      (1.20)
                    1993         401             2.97         1239.73          1.40           1.87         0.39      2.26       0.001        0.62
                                               (4.62)        (5687.50)        (0.92)         (2.22)       (0.96)    (2.62)     (0.002)      (0.65)




31
                    1994         424             3.27         1470.02          1.40           1.74         0.46      2.20       0.001        0.76
                                               (4.98)        (6998.25)        (0.76)         (2.16)       (1.13)    (2.69)     (0.002)      (1.16)
                    1995         452             3.42         1782.89          1.44           1.75         0.65      2.40       0.001        0.70
                                               (5.14)        (9201.39)        (0.73)         (2.13)       (1.35)    (2.94)     (0.002)      (1.41)
                    1996         494             3.61         2270.75          1.43           1.70         0.73      2.43       0.002        0.67
                                               (5.54)       (12330.25)        (0.72)         (2.12)       (1.42)    (3.06)     (0.002)      (1.33)
                    1997         509             3.87         2961.27          1.44           1.59         0.80      2.39       0.001        0.69
                                               (5.93)       (16157.77)        (1.20)         (2.12)       (1.48)    (3.16)     (0.002)      (0.82)
                    1998         542             4.13         3545.65          1.46           1.59         0.90      2.49       0.002        0.66
                                               (6.38)       (19940.71)        (1.15)         (2.15)       (1.59)    (3.32)     (0.002)      (1.02)
                    1999         554             4.27         4356.66          1.48           1.68         0.97      2.65       0.002        0.82
                                               (6.74)       (25367.13)        (1.17)         (2.25)       (1.65)    (3.47)     (0.002)      (1.91)
                    2000         541             4.62         5461.84          1.47           1.74         1.07      2.81       0.002        1.39
                                               (7.21)       (30094.69)        (1.08)         (2.31)       (1.71)    (3.61)     (0.002)      (10.24)
                    2001         514             4.93         4765.06          1.51           1.67         1.08      2.74       0.002        1.21
                                               (7.78)       (25838.39)        (1.06)         (2.29)       (1.69)    (3.57)     (0.002)      (3.54)
                    2002         479             4.99         4523.02          1.52           1.64         1.13      2.76       0.002        1.24
                                               (7.98)       (23766.78)        (1.07)         (2.28)       (1.71)    (3.59)     (0.002)      (3.71)
Table 3: Portfolio of Mutual Funds Formed on Lagged 1-Year Alphas - Families With 10
or More Funds
Using mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At the
beginning of the year we drop all the funds that belong to a family that has less than 10 funds. Then, we regress
each fund’s monthly excess gross returns a four factor model to find each fund’s alpha. We then rank each fund by its
alpha. We build portfolios based on these rankings and keep them for a year. The procedure is then repeated every
year. This results in 10 time series which we regress on a four factor model. MKTRF is the excess return on the
CRSP value-weighted market proxy. SMB and HML are the Fama and French (1993) factor mimicking portfolios for
size and book to market equity. MOM is the momentum factor mimicking portfolio similar to Jegadeesh and Titman
(1993). The t-statistics are reported in parentheses.


           Portfolio      Alpha         MKTRF         SMB           HML            MOM           Adj R-Sq
           1              -0.0023       1.0707 ***    0.2316 ***    0.2008 ***     0.0080          0.880
                          -(1.49)       (26.13)       (5.77)        (3.99)         (0.35)


           2              -0.0014       1.0332 ***    0.1552 ***    0.1665 ***     0.0246          0.939
                          -(1.35)       (38.08)       (5.84)        (4.99)         (1.63)


           3              -0.0019 **    0.9962 ***    0.1088 ***    0.1300 ***     0.0007          0.959
                          -(2.32)       (46.57)       (5.20)        (4.95)         (0.06)


           4              -0.0015 **    0.9693 ***    0.0954 ***    0.1317 ***     -0.0129         0.965
                          -(2.11)       (50.19)       (5.05)        (5.55)         -(1.20)


           5              0.0002        1.0070 ***    0.0580 ***    0.1321 ***     -0.0062         0.970
                          (0.24)        (55.62)       (3.27)        (5.94)         -(0.62)


           6              -0.0003       0.9944 ***    0.0622 ***    0.0934 ***     0.0166 *        0.974
                          -(0.56)       (61.47)       (3.80)        (4.55)         (1.82)


           7              0.0003        0.9824 ***    0.1116 ***    0.0644 **      0.0161          0.954
                          (0.31)        (42.61)       (4.95)        (2.27)         (1.26)


           8              0.0015 *      0.9785 ***    0.1363 ***    -0.0109        0.0227 *        0.964
                          (1.88)        (46.05)       (6.55)        -(0.42)        (1.92)


           9              0.0023 **     1.0012 ***    0.3316 ***    -0.1325 ***    0.0893 ***      0.946
                          (2.07)        (33.11)       (11.20)       -(3.57)        (5.31)


           10             0.0035 ***    1.0456 ***    0.4562 ***    -0.2471 ***    0.1117 ***      0.949
                          (2.80)        (31.05)       (13.84)       -(5.97)        (5.97)


           10-1           0.0058 ***    -0.0250       0.2246 ***    -0.4478 ***    0.1036 ***      0.500
           spread         (2.86)        -(0.46)       (4.23)        -(6.72)        (3.44)

Note: *** denotes significance at the 1-percent level; ** denotes significance at the 5-percent level; * denotes signifi-
cance at the 10-percent level.




                                                         32
               Table 4: Portfolio of Mutual Funds Formed on Lagged 1-Year Alphas
Using mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At the
beginning of every year, we regress each fund’s monthly excess gross returns a four factor model to find each fund’s
alpha. We then rank each fund by its alpha. The procedure is then repeated every year. This results in 10 time series
which we regress on a four factor model. MKTRF is the excess return on the CRSP value-weighted market proxy.
SMB and HML are the Fama and French (1993) factor mimicking portfolios for size and book to market equity. MOM
is the momentum factor mimicking portfolio similar to Jegadeesh and Titman (1993). The t-statistics are reported in
parentheses.

           Portfolio      Alpha         MKTRF         SMB           HML           MOM            Adj R-Sq
           1              -0.0012       1.0433 ***    0.2418 ***    0.1786 ***    0.0059           0.905
                          -(0.97)       (30.85)       (7.06)        (4.16)        (0.31)


           2              -0.0019 **    1.0096 ***    0.1578 ***    0.2057 ***     0.0062          0.942
                          -(2.09)       (41.71)       (6.44)        (6.70)        (0.45)


           3              -0.0016 **    0.9765 ***    0.1152 ***    0.1467 ***     0.0006          0.966
                          -(2.29)       (53.86)       (6.28)        (6.38)        (0.06)


           4              -0.0013 **    0.9913 ***    0.1063 ***    0.1491 ***     -0.0017         0.969
                          -(2.00)       (57.04)       (6.04)        (6.77)        -(0.17)


           5              -0.0007       0.9690 ***    0.0709 ***    0.1186 ***    -0.0015          0.971
                          -(1.15)       (57.96)       (4.19)        (5.59)        -(0.16)


           6              0.0003        0.9658 ***    0.0713 ***    0.1068 ***    -0.0005          0.975
                          (0.45)        (62.89)       (4.59)        (5.49)        -(0.06)


           7              0.0003        1.0032 ***    0.1189 ***    0.0908 ***    0.0099           0.963
                          (0.47)        (50.82)       (5.95)        (3.63)        (0.88)


           8              0.0010        0.9698 ***    0.1510 ***    0.0423 *      0.0017           0.967
                          (1.46)        (51.78)       (7.96)        (1.78)        (0.16)


           9              0.0015        0.9966 ***    0.3140 ***    -0.0521 *     0.0494 ***       0.959
                          (1.63)        (42.02)       (13.07)       -(1.73)       (3.69)


           10             0.0023        0.9981 ***    0.4669 ***    -0.2832 ***   0.0867 ***       0.933
                          (1.65)        (27.46)       (12.68)       -(6.14)       (4.22)


           10-1           0.0035 *      -0.0452       0.2251 ***    -0.4618 ***   0.0808 ***       0.500
           spread         (1.72)        -(0.83)       (4.10)        -(6.72)       (2.64)

Note: *** denotes significance at the 1-percent level; ** denotes significance at the 5-percent level; * denotes signifi-
cance at the 10-percent level.




                                                         33
                               Table 5: Summary Statistics of the Ranks

Summary statistics of the ranks based on the two samples. Total Assets are the total amount of assets under

management by the fund in millions of Dollars. Expenses is the expense ratio of the fund as a percentage of total

net assets. Total Load is the total front-end load, deferred and rear-end charges as a percentage of new investments.

Turnover is the fraction of the portfolio that is replaced during a year. The flow is the monthly net flow of the fund

as a fraction of the assets under management.


                                                    Panel A:
                             Ranks based on the full samples - as described in table 4.
 Rank        Mean           Median       Mean         Median        Mean          Median         Mean      Mean     Median
          Total Assets   Total Assets    Expenses     Expenses    Total Load    Total Load     Turnover     Flow        Flow
 1           438.52          63.77         0.015        0.013         3.30           0.0         1.048     0.102        0.002
 2           777.20         121.00         0.013        0.012         3.61           1.0         0.888     0.050        0.002
 3          1025.51         141.13         0.013        0.012         3.79           1.0         0.687     0.050        0.003
 4          1211.48         155.85         0.012        0.011         3.68           1.0         0.804     0.048        0.005
 5          1115.95         160.45         0.012        0.011         3.62           1.0         0.616     0.045        0.008
 6          1221.77         169.23         0.011        0.011         3.49           0.5         0.586     0.047        0.006
 7          1065.03         150.63         0.012        0.012         3.73           1.0         0.657     0.045        0.007
 8           982.75         132.82         0.013        0.012         3.40           0.5         0.754     0.054        0.009
 9           808.86         131.81         0.014        0.013         3.40           0.0         0.820     0.049        0.012
 10          395.20          80.10         0.016        0.014         3.14           0.0         0.969     0.146        0.031




                                                    Panel B:
 Ranks based on the sample of funds that belong to families that have 10 or more funds - as described in table 3.
 Rank      Mean          Median          Mean       Median         Mean        Median         Mean       Mean Median
          Total Assets   Total Assets    Expenses     Expenses    Total Load    Total Load     Turnover     Flow        Flow
 1           920.75         184.80         0.014        0.013         5.08           5.0         0.956     0.094        0.002
 2          1513.98         293.16         0.012        0.012         5.06           5.0         0.907     0.065        0.003
 3          1919.74         318.29         0.012        0.012         5.28           5.5         0.720     0.037        0.003
 4          2361.45         360.08         0.011        0.011         4.98           5.0         0.660     0.049        0.006
 5          1879.79         391.68         0.011        0.011         4.87           4.5         0.643     0.040        0.010
 6          1860.23         320.85         0.011        0.011         4.47           3.0         0.657     0.049        0.007
 7          1715.35         331.87         0.012        0.012         4.91           4.8         0.712     0.034        0.008
 8          1982.90         328.02         0.012        0.012         4.52           3.0         0.812     0.042        0.009
 9          1454.30         292.87         0.013        0.012         4.61           4.0         0.882     0.053        0.011
 10          838.39         200.00         0.014        0.013         4.93           4.8         0.911     0.159        0.031




                                                         34
Table 6: Portfolio of Mutual Funds Formed on Lagged 1-Year Alphas - Families with a
Market Capitalization Above a Threshold
Using mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At the
beginning of the year we drop all the funds that belong to a family that has a market capitalization below a given
threshold. The threshold is such that the number of funds is equivalent to the number generated by a threshold
of 10 funds per family. Then, we regress each fund’s monthly excess gross returns a four factor model to find each
fund’s alpha. We then rank each fund by its alpha. We then rank each fund by its alpha inside its family. We build
portfolios based on these rankings and keep them for a year. The procedure is then repeated every year. This results
in 10 time series which we regress on a four factor model. MKTRF is the excess return on the CRSP value-weighted
market proxy. SMB and HML are the Fama and French (1993) factor mimicking portfolios for size and book to market
equity. MOM is the momentum factor mimicking portfolio similar to Jegadeesh and Titman (1993). The t-statistics
are reported in parentheses.

           Portfolio      Alpha         MKTRF         SMB           HML           MOM            Adj R-Sq
           1              -0.0008       1.0648 ***    0.2276 ***    0.1762 ***    0.0111           0.895
                          -(0.60)       (29.26)       (6.18)        (3.82)        (0.54)


           2              -0.0023 **    1.0489 ***    0.1610 ***    0.2115 ***     0.0183          0.936
                          -(2.26)       (39.47)       (5.98)        (6.28)        (1.22)


           3              -0.0016 **    0.9800 ***    0.1243 ***    0.1478 ***     0.0074          0.966
                          -(2.36)       (53.99)       (6.76)        (6.42)        (0.72)


           4              -0.0012 *     0.9911 ***    0.0958 ***    0.1540 ***    -0.0050          0.966
                          -(1.82)       (54.55)       (5.21)        (6.69)        -(0.49)


           5              -0.0005       0.9732 ***    0.0599 ***    0.1194 ***    -0.0040          0.968
                          -(0.77)       (55.50)       (3.37)        (5.37)        -(0.40)


           6              0.0001        0.9794 ***    0.0748 ***    0.1122 ***    0.0051           0.973
                          (0.20)        (61.00)       (4.60)        (5.51)        (0.56)


           7              0.0006        0.9979 ***    0.1120 ***    0.0627 **     0.0126           0.960
                          (0.72)        (47.99)       (5.32)        (2.38)        (1.07)


           8              0.0012 *      0.9864 ***    0.1706 ***    0.0357        0.0066           0.968
                          (1.71)        (52.35)       (8.94)        (1.49)        (0.62)


           9              0.0012        1.0036 ***    0.3432 ***    -0.0610 *     0.0550 ***       0.953
                          (1.22)        (38.61)       (13.04)       -(1.85)       (3.75)


           10             0.0026 **     1.0237 ***    0.4673 ***    -0.2842 ***    0.0956 ***      0.939
                          (2.00)        (29.08)       (13.11)       -(6.37)       (4.81)


           10-1           0.0035 *      -0.0411       0.2397 ***    -0.4604 ***   0.0845 ***       0.504
           spread         (1.68)        -(0.75)       (4.31)        -(6.61)       (2.73)

Note: *** denotes significance at the 1-percent level; ** denotes significance at the 5-percent level; * denotes signifi-
cance at the 10-percent level.


                                                         35
Table 7: Portfolio of Mutual Funds Formed on Lagged 1-Year Alphas - Analysis Within the
Family
Using mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At the
beginning of the year we drop all the funds that belong to a family that has less than 10 funds. Then, we regress
each fund’s monthly excess gross returns a four factor model to find each fund’s alpha. We then rank each fund by
its alpha. We then rank each fund by its alpha inside its family. We build portfolios based on these rankings and
keep them for a year. The procedure is then repeated every year. This results in 10 time series which we regress on
a four factor model. MKTRF is the excess return on the CRSP value-weighted market proxy. SMB and HML are
the Fama and French (1993) factor mimicking portfolios for size and book to market equity. MOM is the momentum
factor mimicking portfolio similar to Jegadeesh and Titman (1993). The t-statistics are reported in parentheses.


           Portfolio      Alpha         MKTRF         SMB           HML            MOM           Adj R-Sq
           1              -0.0021 *     1.0829 ***    0.2104 ***    0.2475 ***     0.0006          0.929
                          -(1.89)       (35.71)       (7.09)        (6.65)         (0.04)


           2              -0.0016       1.0171 ***    0.1798 ***    0.1638 ***     0.0123          0.913
                          -(1.28)       (31.30)       (5.65)        (4.10)         (0.68)


           3              -0.0014 *     0.9933 ***    0.0944 ***    0.1426 ***     -0.0018         0.962
                          -(1.84)       (48.60)       (4.72)        (5.68)         -(0.16)


           4              -0.0011       0.9895 ***    0.1222 ***    0.1259 ***     0.0179 *        0.969
                          -(1.55)       (53.72)       (6.78)        (5.56)         (1.75)


           5              -0.0009       0.9900 ***    0.0853 ***    0.0959 ***     0.0037          0.976
                          -(1.39)       (60.44)       (5.32)        (4.77)         (0.41)


           6              0.0003        1.0019 ***    0.0710 ***    0.0739 ***     0.0009          0.963
                          (0.34)        (50.16)       (3.51)        (2.92)         (0.08)


           7              0.0005        0.9831 ***    0.1347 ***    0.0315         0.0220 **       0.967
                          (0.65)        (49.28)       (6.90)        (1.29)         (1.98)


           8              0.0011        0.9903 ***    0.1680 ***    -0.0183        0.0461 ***      0.971
                          (1.50)        (50.93)       (8.83)        -(0.77)        (4.26)


           9              0.0018 **     1.0216 ***    0.3074 ***    -0.1216 ***    0.0801 ***      0.969
                          (2.11)        (44.73)       (13.75)       -(4.33)        (6.31)


           10             0.0032 ***    1.0430 ***    0.4503 ***    -0.1740 ***    0.0873 ***      0.949
                          (2.62)        (32.19)       (14.20)       -(4.37)        (4.85)


           10-1           0.0053 ***    -0.0399       0.2398 ***    -0.4215 ***    0.0867 ***      0.626
           spread         (3.19)        -(0.90)       (5.50)        -(7.71)        (3.50)

Note: *** denotes significance at the 1-percent level; ** denotes significance at the 5-percent level; * denotes signifi-
cance at the 10-percent level.



                                                         36
   Table 8: Portfolio of Mutual Funds Formed on Lagged 1-Year Market Capitalization
Using mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At the
beginning of the year we drop all the funds that belong to a family that has less than 10 funds. Then, we find the
market capitalization of each fund and rank them based on that. We build portfolios based on these rankings and
keep them for a year. The procedure is then repeated every year. This results in 10 time series which we regress on
a four factor model. MKTRF is the excess return on the CRSP value-weighted market proxy. SMB and HML are
the Fama and French (1993) factor mimicking portfolios for size and book to market equity. MOM is the momentum
factor mimicking portfolio similar to Jegadeesh and Titman (1993). The t-statistics are reported in parentheses.


             Portfolio      Alpha     MKTRF         SMB            HML           MOM           Adj R-Sq
             1              0.0015    0.9556 ***    0.2392 ***     0.0498        0.0494 ***      0.940
                            (1.45)    (35.45)       (9.07)         (1.51)        (3.30)


             2              0.0006    0.9971 ***    0.2132 ***     0.1319 ***    0.0111          0.946
                            (0.66)    (39.30)       (8.58)         (4.23)        (0.78)


             3              -0.0002   0.9758 ***    0.2104 ***     0.0751 ***    0.0194          0.959
                            -(0.22)   (44.32)       (9.76)         (2.78)        (1.58)


             4              -0.0006   1.0011 ***    0.2732         0.0177        0.0631 ***      0.975
                            -(0.83)   (55.21)       (15.39)        (0.80)        (6.26)


             5              0.0001    1.0238 ***    0.2712 ***     0.0721 ***    0.0555 ***      0.973
                            (0.16)    (53.74)       (14.54)        (3.08)        (5.24)


             6              -0.0004   1.0247 ***    0.2303 ***     0.1063 ***    0.0330 **       0.949
                            -(0.39)   (40.17)       (9.22)         (3.39)        (2.33)


             7              -0.0006   1.0309 ***    0.1402 ***     0.0346        0.0238 **       0.968
                            -(0.74)   (50.15)       (6.97)         (1.37)        (2.08)


             8              -0.0005   0.9990 ***    0.1336 ***     0.0255        0.0231 **       0.970
                            -(0.69)   (51.86)       (7.09)         (1.08)        (2.16)


             9              -0.0008   1.0115 ***    0.0374 **      0.0952 ***    -0.0012         0.970
                            -(1.21)   (54.09)       (2.04)         (4.14)        -(0.12)


             10             0.0008    1.0088 ***    0.0132         -0.0264       0.0072          0.977
                            (1.28)    (58.63)       (0.78)         -(1.25)       (0.76)


             10-1           -0.0006   0.0532        -0.2260 ***    -0.0762 *     -0.0421 **      0.308
             spread         -(0.50)   (1.57)        -(6.80)        -(1.83)       -(2.23)

Note: *** denotes significance at the 1-percent level; ** denotes significance at the 5-percent level; * denotes signifi-
cance at the 10-percent level.




                                                         37
Table 9: Portfolio of Mutual Funds Formed on Lagged 1-Year Alphas - Families With 5 or
More Funds
Using mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At the
beginning of the year we drop all the funds that belong to a family that has less than 5 funds. Then, we regress each
fund’s monthly excess gross returns a four factor model to find each fund’s alpha. We then rank each fund by its
alpha. We then rank each fund by its alpha. We build portfolios based on these rankings and keep them for a year.
The procedure is then repeated every year. This results in 10 time series which we regress on a four factor model.
MKTRF is the excess return on the CRSP value-weighted market proxy. SMB and HML are the Fama and French
(1993) factor mimicking portfolios for size and book to market equity. MOM is the momentum factor mimicking
portfolio similar to Jegadeesh and Titman (1993). The t-statistics are reported in parentheses.


           Portfolio      Alpha         MKTRF         SMB           HML            MOM           Adj R-Sq
           1              -0.0027 **    1.0738 ***    0.2388 ***    0.2207 ***     0.0070          0.895
                          -(1.98)       (29.74)       (6.53)        (4.82)         (0.34)


           2              -0.0018 *     1.0536 ***    0.1672 ***    0.2019 ***     0.0271 *        0.928
                          -(1.67)       (37.11)       (5.81)        (5.61)         (1.69)


           3              -0.0016 **    0.9751 ***    0.1273 ***    0.1329 ***     0.0082          0.965
                          -(2.38)       (53.26)       (6.87)        (5.72)         (0.80)


           4              -0.0012 *     0.9976 ***    0.0833 ***    0.1441 ***     -0.0002         0.968
                          -(1.81)       (56.26)       (4.64)        (6.41)         -(0.02)


           5              -0.0008       0.9645 ***    0.0556 ***    0.1160 ***     -0.0041         0.965
                          -(1.11)       (52.83)       (3.01)        (5.01)         -(0.40)


           6              -0.0001       0.9830 ***    0.0672 ***    0.1133 ***     0.0065          0.970
                          -(0.16)       (57.70)       (3.89)        (5.25)         (0.68)


           7              0.0006        0.9988 ***    0.0964 ***    0.0571 **      0.0168          0.952
                          (0.75)        (43.88)       (4.18)        (1.98)         (1.31)


           8              0.0006        0.9875 ***    0.1767 ***    0.0410         0.0097          0.961
                          (0.78)        (47.36)       (8.37)        (1.55)         (0.82)


           9              0.0011        0.9950 ***    0.3333 ***    -0.1238 ***    0.0727 ***      0.951
                          (1.12)        (37.01)       (12.24)       -(3.63)        (4.79)


           10             0.0031 ***    1.0364 ***    0.4610 ***    -0.2707 ***    0.1073 ***      0.946
                          (2.49)        (31.30)       (13.75)       -(6.45)        (5.74)


           10-1           0.0058 ***    -0.0374       0.2222 ***    -0.4914 ***    0.1003 ***      0.561
           spread         (3.04)        -(0.74)       (4.33)        -(7.64)        (3.50)

Note: *** denotes significance at the 1-percent level; ** denotes significance at the 5-percent level; * denotes signifi-
cance at the 10-percent level.




                                                         38
                  Table 10: Summary Statistics of Average Pairwise Correlations of Fund Alphas Within Each Family
     This table reports summary statistics of the average pairwise correlations of fund abnormal returns (alphas) within the funds’ respective families. Within each
     family we calculate the pairwise correlations between all the fund alphas for their overlapping periods and average the correlations. Thus, all the reported
     statistics are cross-sectional statistics across families. We report the correlations for the full sample (referred to as Cutoff - Full), and the sub-samples when
     we split by the number of funds in the family (referred to as Cutoffs bigger or smaller than either 5 or 10 funds in a family). Panel B reports the same
     statistics for the absolute values of the pairwise correlations.

                                                                               Panel A:
                                        Summary statistics of average pairwise correlations of fund alphas within each family.
      Cutoff     Mean     Median     Standard  Standard      Skewness       95th          80th          75th        60th             50th          35th         25th        15th          5th
                                      Error      Deviation                 Percentile    Percentile   Percentile    Percentile   Percentile    Percentile   Percentile   Percentile   Percentile
      Full      0.220     0.183       0.017        0.332        -0.115        0.835        0.466         0.391        0.240         0.183        0.096         0.044      -0.042       -0.254


      ≥ 10      0.131     0.109       0.014        0.118        0.587         0.366        0.218         0.203        0.148         0.109        0.071         0.056       0.021       -0.041
      < 10      0.219     0.180       0.018        0.338        0.055         0.866        0.480         0.404        0.250         0.180        0.083         0.016      -0.060       -0.269


      ≥5        0.154     0.127       0.013        0.159        1.504         0.428        0.247         0.217        0.152         0.127        0.085         0.060       0.029       -0.052




39
      <5        0.226     0.211       0.021        0.383        -0.033        0.897        0.564         0.480        0.307         0.211        0.063        -0.042      -0.153       -0.360




                                                                               Panel B:
                              Summary statistics of absolute value of average pairwise correlations of fund alphas within each family.
      Cutoff     Mean     Median Standard      Standard     Skewness        95th         80th          75th         60th         50th              35th         25th        15th          5th
                                      Error      Deviation                 Percentile    Percentile   Percentile    Percentile   Percentile    Percentile   Percentile   Percentile   Percentile
      Full      0.302     0.209       0.013        0.260        1.070         0.860        0.522         0.426        0.282         0.209        0.147         0.106       0.070        0.023


      ≥ 10      0.141     0.114       0.013        0.106        1.012         0.366        0.218         0.203        0.148         0.114        0.080         0.064       0.037        0.012
      < 10      0.306     0.223       0.014        0.262        1.000         0.879        0.530         0.446        0.301         0.223        0.144         0.096       0.056        0.019


      ≥5        0.168     0.135       0.011        0.144        2.117         0.428        0.247         0.217        0.157         0.135        0.091         0.072       0.049        0.023
      <5        0.353     0.277       0.015        0.271        0.788         0.902        0.596         0.531        0.349         0.277        0.193         0.141       0.081        0.029
          Table 11: Descriptive Statistics of Managers and the Funds they Manage

This table reports the descriptive statistics of the fund managers and the funds they manage. The Total Assets

Managed is the average total net assets of the funds each manager manages. Returns are expressed in percentages.



                                                    Panel A:
            Descriptive statistics of all the the managers and their funds in the sample set.
                 Year    Number of        Managers   Total Assets Monthly Net Monthly Gross
                         Managers     per Fund      Managed          Return           Return
                 1990       719          1.40         349.23            1.88           1.97
                 1991       854          1.44         493.53            5.62           5.69
                 1992      1037          1.46         555.76            0.85           0.88
                 1993      1230          1.51         633.82            1.24           1.22
                 1994      1420          1.54         612.49            2.60           2.51
                 1995      1600          1.59         784.69            0.93           0.95
                 1996      1799          1.73         953.78            1.62           1.62
                 1997      2020          1.73        1210.16            3.92           3.73
                 1998      2251          1.84        1378.11            -0.09          0.01
                 1999      2438          1.86        1549.20            2.22           2.20
                 2000      2519          1.89        1462.09            -2.48          -2.31
                 2001      2420          1.88        1338.84            2.56           2.62
                 2002      2254          1.86        1054.02            -1.79          -1.67




                                                    Panel B:
                  Descriptive statistics of all the the managers that manage more than one fund.

                                         Year    Number of     Managers
                                                 Managers      per Fund
                                         1990       184          2.58
                                         1991       246          2.54
                                         1992       316          2.52
                                         1993       383          2.64
                                         1994       469          2.63
                                         1995       578          2.64
                                         1996       743          2.76
                                         1997       830          2.77
                                         1998       1011         2.87
                                         1999       1088         2.93
                                         2000       1098         3.05
                                         2001       1038         3.05
                                         2002       961          3.03




                                                       40
  Table 12: Logistic Regression of the Probability of Adding at least One New Manager

We use mutual fund data from 1990 to 2002. At the beginning of the year we drop all the funds that belong to a

family that has less than 5 funds. Then, we regress each fund’s monthly excess gross returns a four factor model

to find each fund’s alpha. We then rank each fund by its alpha. We then rank each fund by its alpha. We build

portfolios based on these rankings and keep them for a year. We group all the funds by their portfolio and run the

following regression by rank:

P r(Adding a manager) = Λ [β1 + β2 αt + β3 αt−1 + β4 Log(T N A)t + β5 Log(T N A)t−1 + β6 Expenset ]

where α is the fund’s alpha as measured by a 4 factor model. Log(TNA) is the log of the total net assets of the fund,

and Expense is the expense ratio of the fund. Standard errors are reported in parentheses.

               Rank    Intercept   Alphat       Alphat−1       Log(TNA)t    Log(TNA)t−1      Expenset


               1       3.26 ***    -0.63        0.00           -0.06        0.00             8.56
                       (0.62)      (3.81)       (1.61)         (0.11)       (0.11)           (24.89)


               2       3.42 ***    0.07         1.66           0.01         -0.07            11.90
                       (0.42)      (3.28)       (4.04)         (0.10)       (0.10)           (17.32)


               3       4.42 ***    4.82         1.97           -0.26 **     0.09             -11.22 **
                       (0.30)      (3.18)       (3.18)         (0.10)       (0.10)           (4.76)


               4       3.39 ***    -0.73        -0.55          -0.38 ***    0.29 ***         41.13 *
                       (0.48)      (0.94)       (0.37)         (0.12)       (0.11)           (22.79)


               5       3.64 ***    60.84 ***    -10.84         0.00         -0.12            0.68
                       (0.69)      (21.45)      (13.85)        (0.14)       (0.14)           (24.69)

Note: *** denotes significance at the 1-percent level; ** denotes significance at the 5-percent level; * denotes signifi-

cance at the 10-percent level.




                                                          41
Table 13: Logistic Regression of the Probability of Adding at least One New Manager -
With Rank Dummies

We use mutual fund data from 1990 to 2002. At the beginning of the year we drop all the funds that belong to a

family that has less than 5 funds. Then, we regress each fund’s monthly excess gross returns a four factor model

to find each fund’s alpha. We then rank each fund by its alpha. We then rank each fund by its alpha. We build

portfolios based on these rankings and keep them for a year. This table reports results of the logistic regression using

dummy variables to account for these portfolios. Rett and Rett−1 are the fund’s past returns. All the return terms

are interacted with Ii which is a dummy variable for portfolio i. Log(TNA) is the log of the total net assets of the

fund, and Expense is the expense ratio of the fund.




                                        P rt+1 (Adding a manager) =
        h    P5                   P5                                                                      i
       Λ β1 + i=1 β2,i Ii · Rett + j=1 β3,j Ij · Rett−1 + β4 Log(T N A)t + β5 Log(T N A)t−1 + β6 Expenset


                              Variable           Estimate        StdErr   ChiSq     Prob


                              Intercept            3.8407        0.1979   376.63   0.0000


                              Rett I1              0.0424        0.8479     0.00   0.9601
                              Rett I2             -0.0492        0.6203     0.01   0.9368
                              Rett I3             -0.1330        0.4870     0.07   0.7848
                              Rett I4             -0.4539        0.3989     1.30   0.2551
                              Rett I5              0.1444        0.4820     0.09   0.7645


                              Rett−1 I1           -0.8277        0.5023     2.71   0.0994
                              Rett−1 I2            0.8195        0.5403     2.30   0.1293
                              Rett−1 I3            0.6506        0.4686     1.93   0.1650
                              Rett−1 I4            0.2996        0.4515     0.44   0.5070
                              Rett−1 I5            2.1822        0.7782     7.86   0.0050


                              Log(T N A)t          0.0483        0.0957     0.25   0.6138
                              Log(T N A)t−1       -0.1582        0.0938     2.84   0.0917
                              Expensest           -2.4479        5.4029     0.21   0.6505




                                                            42
Table 14: Logistic Regression of the Probability of Adding at least One New Manager to a
Single Managed Fund - With Rank Dummies and Asset Turnover

We use mutual fund data from 1990 to 2002. At the beginning of the year we drop all the funds that belong to a

family that has less than 5 funds. Then, we regress each fund’s monthly excess gross returns a four factor model

to find each fund’s alpha. We then rank each fund by its alpha. We then rank each fund by its alpha. We build

portfolios based on these rankings and keep them for a year. This table reports results of the logistic regression using

dummy variables to account for these portfolios. Rett and Rett−1 are the fund’s past returns. All the return terms

are interacted with Ii which is a dummy variable for portfolio i. PCASSET is the asset growth rate of the fund

adjusted by the average asset growth rate of the fund’s objective. Turnover is the fund’s average yearly turnover.

Expense is the fund’s yearly expense ratio.




                                         P rt+1 (Adding a manager) =
              h    P5                     P                                                    i
             Λ β1 + i=1 β2,i Ii · Rett−1 + 5 β3,j Ij · P CASSETt + β4 Expenset + β5 T urnovert
                                             j=1



                              Variable           Estimate        StdErr   ChiSq     Prob


                              Intercept            3.2547        0.1045   970.11   0.0000


                              P CASSETt I1         0.0631        0.0567     1.24   0.2660
                              P CASSETt I2         0.0312        0.0444     0.50   0.4813
                              P CASSETt I3        -0.0098        0.0045     4.85   0.0276
                              P CASSETt I4         0.0005        0.0084     0.00   0.9515
                              P CASSETt I5         0.0087        0.0302     0.08   0.7732


                              Rett−1 I1           -0.9579        0.5242     3.34   0.0677
                              Rett−1 I2            0.6091        0.5309     1.32   0.2512
                              Rett−1 I3            0.4265        0.4462     0.91   0.3391
                              Rett−1 I4           -0.0655        0.4232     0.02   0.8769
                              Rett−1 I5            1.9565        0.7536     6.74   0.0094


                              Expensest            0.2255        5.8227     0.00   0.9691
                              T urnovert           0.0483        0.0581     0.69   0.4060




                                                            43
Table 15: Logistic Regression of the Probability of Moving from a Single Managed Fund to
a Team Managed Fund

We use mutual fund data from 1990 to 2002. At the beginning of the year we drop all the funds that belong to a

family that has less than 5 funds. Then, we regress each fund’s monthly excess gross returns a four factor model

to find each fund’s alpha. We then rank each fund by its alpha. We then rank each fund by its alpha. We build

portfolios based on these rankings and keep them for a year. We group all the funds by their portfolio and run the

following regression:

P r(Adding a manager to a single managed f und) =

β1 + β2 αt + β3 αt−1 + β4 Log(T N A)t + β5 Log(T N A)t−1 + β6 Expenset + εt

where α is the fund’s alpha as measured by a 4 factor model. Log(TNA) is the log of the total net assets of the fund,

and Expense is the expense ratio of the fund. Standard errors are reported in parentheses.

               Rank     Intercept   Alphat      Alphat−1       Log(TNA)t    Log(TNA)t−1      Expenset


               1        3.26 ***    -0.65       0.01           -0.06        0.00             8.77
                        (0.62)      (3.83)      (1.61)         (0.11)       (0.11)           (24.98)


               2        3.42 ***    0.07        1.65           0.01         -0.07            11.87
                        (0.42)      (3.28)      (4.03)         (0.10)       (0.10)           (17.30)


               3        4.42 ***    4.83        1.96           -0.26 **     0.09             -11.22 **
                        (0.30)      (3.18)      (3.19)         (0.10)       (0.10)           (4.77)


               4        3.39 ***    -0.73       -0.55          -0.38 ***    0.29 ***         41.19 *
                        (0.48)      (0.94)      (0.37)         (0.12)       (0.11)           (22.78)


               5        3.64 ***    61.34 ***   -10.82         0.00         -0.12            0.11
                        (0.69)      (21.51)     (13.85)        (0.14)       (0.14)           (24.49)

Note: *** denotes significance at the 1-percent level; ** denotes significance at the 5-percent level; * denotes signifi-

cance at the 10-percent level.




                                                          44
Table 16: Logistic Regression of the Probability of Adding at least One New Manager to a
Single Managed Fund - With Rank Dummies

We use mutual fund data from 1990 to 2002. At the beginning of the year we drop all the funds that belong to a

family that has less than 5 funds. Then, we regress each fund’s monthly excess gross returns a four factor model

to find each fund’s alpha. We then rank each fund by its alpha. We then rank each fund by its alpha. We build

portfolios based on these rankings and keep them for a year. This table reports results of the logistic regression using

dummy variables to account for these portfolios. Rett and Rett−1 are the fund’s past returns. All the return terms

are interacted with Ii which is a dummy variable for portfolio i. Log(TNA) is the log of the total net assets of the

fund, and Expense is the expense ratio of the fund.




                             P rt+1 (Adding a manager to a single managed f und) =
        h    P5                     P                                                                   i
       Λ β1 + i=1 β2,i Ii · Rett + 5 β3,j Ij · Rett−1 + β4 Log(T N A)t + β5 Log(T N A)t−1 + β6 Expenset
                                      j=1



                              Variable           Estimate        StdErr   ChiSq     Prob


                              Intercept            4.1629        0.2414   297.40   0.0000


                              Rett I1             -1.9127        1.0711     3.19   0.0741
                              Rett I2              0.0061        0.8860     0.00   0.9945
                              Rett I3             -0.4316        0.6137     0.49   0.4819
                              Rett I4             -0.1286        0.6269     0.04   0.8374
                              Rett I5              0.6241        0.8185     0.58   0.4458


                              Rett−1 I1           -0.3922        0.8374     0.22   0.6395
                              Rett−1 I2            1.3661        0.7967     2.94   0.0864
                              Rett−1 I3            0.2532        0.6039     0.18   0.6750
                              Rett−1 I4            0.6109        0.6885     0.79   0.3749
                              Rett−1 I5            2.2477        1.1609     3.75   0.0528


                              Log(T N A)t          0.1580        0.1158     1.86   0.1722
                              Log(T N A)t−1       -0.1830        0.1151     2.53   0.1117
                              Expensest           -7.7294        4.1857     3.41   0.0648




                                                            45

								
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