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.1Some 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 their2Wilcox (2003) runs an experiment with investors and finds that performance is one of the most important

determinants of investor choice.3In 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.4Though, some studies have disputed whether CRSP is indeed survivorship-bias free. See Elton, Gruber,

and Blake (2001) for an extensive analysis of this issue.5For 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 that6Funds with these objectives have been identified by Morningstar as pure equity funds.7Mutual 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 + biMKTRFt + siSMBt + hiHMLt + piMOMt + εit

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 the8See 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.9We 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 has10An analysis of different definitions of large families is given in section 3.3.11These 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 predictions12Resources such as higher investments in research or in more capable and expensive fund managers.13This 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 names14Our 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.15In 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.16Viewed 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:

Prt+1(Adding a manager) =

Λ [β1 + β2αt + β3αt−1 + β4Log(TNA)t + β5Log(TNA)t−1 + β6Expenset] (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 ones17If 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.18We 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:

Prt+1(Adding a manager) =

Λ

β1 +

5∑

i=1

β2,iIi ·Rett +5∑

j=1

β3,jIj ·Rett−1 + β4Log(TNA)t + β5Log(TNA)t−1 + β6Expenset

(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

PCASSETt, 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 not19In 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:

Prt+1(Adding a manager) =

Λ

β1 +

5∑

i=1

β2,iIi ·Rett−1 +5∑

j=1

β3,jIj · PCASSETt + β4Expenset + β5Turnovert

(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:

Prt+1(Adding a manager to a single managed fund) =

Λ [β1 + β2αt + β3αt−1 + β4Log(TNA)t + β5Log(TNA)t−1 + β6Expenset] (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.

20Loughran 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

References

[1] Arteaga, Kenneth R., Ciccotello, C. S., and C. T. Grant, 1998, New Equity Funds:

Marketing and Performance, Financial Analysts Journal 6, 43-49.

[2] Berk, Jonathan B., and R. C. Green, 2002, Mutual Fund Flows and Performance in

Rational Markets, NBER working paper 9275.

[3] Brown, Stephen J., and W. N. Goetzmann, 1995, Performance Persistence, Journal of

Finance, 50 (2), 679-698.

[4] Brown, Stephen J. and W. N. Goetzmann, 1997, Mutual Fund Styles, Journal of

Financial Economics 43 (3), 373-399.

[5] Carhart, Mark M., 1997, On Persistence in Mutual Fund Performance, Journal of

Finance, 52 (1), 57-82.

[6] Chen, Joseph, Hong, H., Huang, M., and J. D. Kubik, 2003, Does Fund Size Erode

Mutual Fund Performance? The Role of Liquidity and Organization, working paper,

Princeton University.

[7] Chevalier, Judith and G. Ellison, 1997, Risk Taking By Mutual Funds as a Response

to Incentives, Journal of Political Economy 105 (6), 1167-1200.

[8] Chevalier, Judith and G. Ellison, 1999a, Career Concerns of Mutual Fund Managers,

Quarterly Journal of Economics 114 (2), 389-432.

[9] Chevalier, Judith and G. Ellison, 1999b, Are Some Mutual Funds Better than Others?

Cross-sectional Patterns in Behavior and Performance, Journal of Finance 54 (3), 875-

899.

[10] Daniel, Kent, Grinblatt, M., Titman, S., and R. Wermers, 1997, Measuring Mutual

Fund Performance with Characteristic-Based Benchmarks, Journal of Finance 52 (3),

1035-1058.

[11] Edelen, Roger, M., 1999, Investor Flows and the Assessed Performance of Open-End

Mutual Funds, Journal of Financial Economics 53, 439-466.

26

[12] Elton, Edwin J., Gruber, M. J., and C. R. Blake, 2001, A First Look at the Accuracy

of the CRSP Mutual Fund Database and a Comparison of the CRSP and Morningstar

Mutual Fund Databases, Journal of Finance 56 (6), 2415-2430.

[13] Evans, Richard B., 2003, Mutual Fund Incubation and Termination: The Endogeneity

of Survivorship Bias, working paper, University of Pennsylvania.

[14] Fama, Eugene F. and K. R. French, 1993, Common risk factors in the returns on stocks

and bonds, Journal of Financial Economics 33 (1), 3-56.

[15] Gaspar, Jose-Miguel, Massa, M., and P. Matos, 2003, Favoritism in Mutual Fund

Families? Evidence on Strategic Cross-Fund Subsidization, working paper, INSEAD.

[16] Grinblatt, Mark, Titman, S., and R. Wermers, 1995, Momentum investment strate-

gies, portfolio performance and herding: A study of mutual fund behavior, American

Economic Review, 85, 1088-1105.

[17] Hendricks, Darryll., Patel, J., and R. Zeckhauser, 1993, Hot Hands in Mutual Funds:

Short-Run Persistence of Relative Performance, 1974-1988, Journal of Finance, 48 (1),

93-130.

[18] Hu, Fand, Hall A. R., and C. R. Harvey, 2000, Promotion or demotion? An empirical

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

Duke University.

[19] Jegadeesh, Narasimhan and S. Titman, 1993, Returns to Buying Winners and Selling

Losers: Implications for Stock Market Efficiency, Journal of Finance 48, 93-130.

[20] Khorana, Ajay, 1996, Top Management Turnover - An Empirical Investigation of Mu-

tual Fund Manager, Journal of Financial Economics, 40, 403-427.

[21] Khorana, Ajay, 2001, Performance Changes Following Top Management Turnover: Ev-

idence from Open-End Mutual Funds, Journal of Financial and Quantitatie Analysis,

36, 371-393.

[22] Khorana, Ajay, and H. Servaes, 2002, Examination of Competition in the Mutual Fund

Industry, working paper, London Business School

27

[23] Lehmann, Bruce N., and D. Modest, 1987, Mutual Fund Performance Evaluation:

A Comparison of Benchmarks and Benchmark Comparisons, Journal of Finance, 42,

233-265.

[24] Loeffler, Gunter, 2003, Anatomy of a Performance Race, working paper, University of

Ulm.

[25] Loughran, Tim, and J. R. Ritter, 2003, Why Has IPO Underpricing Changed Over

Time?, working paper, Univerity of Florida.

[26] Nanda, Vikram, Wang, Z. J., and L. Zheng, 2003, Family Values and the Star Phe-

nomenon, forthcoming, Review of Financial Studies.

[27] Pastor, Lubos, and R. Stambaugh, 2002, Investing in Equity Mutual Funds , Journal

of Financial Economics, 63 (3), 351-380.

[28] Rajan, Raghuram, H. Servaes, and L. Zingales, 2000, The cost of diversity: The diver-

sification discount and inefficient investment, Journal of Finance 55, 35-80.

[29] Reuter, Jonathan, 2002, Are IPO Allocations For Sale? Evidence from the Mutual

Fund Industry, working paper, MIT.

[30] Scharfstein, David S., and J. C. Stein, 2000, The dark side of internal capital markets:

Divisional rent seeking and inefficient investment, Journal of Finance 55, 2537-2564.

[31] Sirri, Erik and P. Tufano, 1998, Costly Search and Mutual Fund Flows, Journal of

Finance 53 (5), 1589-1622.

[32] Spritz, A. E., 1970, Mutual fund performance and cash inflow, Applied Economics 2,

141-145.

[33] Wermers, Russ, 1997, Momentum Investment Strategies of Mutual Funds, Performance

Persistence, and Survivorship Bias, working paper, University of Colorado.

[34] Wermers, Russ, 2000, Mutual Fund Performance: An Empirical Decomposition into

Stock- Picking talent, Style, Transactions Costs, and Expenses, Journal of Finance, 55

(4), 1655-1695.

28

[35] Wilcox, Ronald T., 2003, Bargain Hunting or Star Gazing: Investors’ Preferences for

Stock Mutual Funds, Journal of Business, 76(4), 645-663.

[36] Wisen, Craig H., 2002, The Bias Associated with New Mutual Fund Returns, unpub-

lished thesis, Indiana University.

29

Tab

le1:

Des

crip

tive

Sta

tist

ics

-Funds

This

table

report

ssu

mm

ary

stati

stic

sfo

rth

efu

nds

of

our

sam

ple

aft

eracc

ounti

ng

for

the

diff

eren

tsh

are

class

es.

The

num

ber

of

funds

isth

enum

ber

of

mutu

alfu

nds

that

mee

tour

sele

ctio

ncr

iter

ia.

Tota

lA

sset

sare

the

tota

lass

ets

under

managem

ent

at

the

end

of

the

cale

ndar

yea

rin

millions

of

dollars

.

Month

lyR

eturn

isth

ecr

oss

-sec

tional

aver

age

of

the

annual

aver

ages

of

the

month

lyre

turn

sof

the

funds

inour

sam

ple

,ex

pre

ssed

inper

centa

ge.

Tota

l

Load

isth

eto

talfr

ont-

end

load,def

erre

dand

rear-

end

charg

esas

aper

centa

ge

of

new

inves

tmen

ts.

12-B

1is

the

annualdis

trib

uti

on

charg

e(1

2b-1

fee)

in

per

centa

ge

of

tota

lass

ets.

Turn

over

isth

em

inim

um

of

aggre

gate

purc

hase

sof

secu

riti

esor

aggre

gate

sale

sof

secu

riti

es,div

ided

by

the

aver

age

Tota

lN

et

Ass

ets

ofth

efu

nd.

Sta

ndard

dev

iati

ons

are

report

edin

pare

nth

eses

.

Num

ber

ofTot

alM

onth

lyM

axim

umO

ther

Tot

al

Yea

rFu

nds

Ass

ets

Ret

urns

Loa

dLoa

dsLoa

ds12

-B1

Tur

nove

r

1990

796

260.

330.

102.

090.

002.

090.

002

0.74

(758

.42)

(1.3

7)(2

.62)

(0.0

5)(2

.62)

(0.0

03)

(0.9

2)

1991

898

299.

312.

592.

110.

002.

110.

002

0.70

(901

.25)

(1.5

8)(2

.58)

(0.0

5)(2

.58)

(0.0

03)

(0.9

5)

1992

1,07

234

3.60

0.84

2.19

0.61

2.80

0.00

20.

66

(1,1

19.7

9)(1

.13)

(2.5

4)(1

.41)

(3.0

1)(0

.003

)(0

.97)

1993

1,26

741

3.67

1.16

2.13

0.79

2.93

0.00

20.

65

(1,4

12.3

9)(2

.15)

(2.4

6)(1

.60)

(3.2

5)(0

.003

)(0

.77)

1994

1,44

743

2.38

-0.0

82.

050.

942.

990.

002

0.74

1,56

2.34

0.76

2.42

1.80

3.47

0.00

31.

13

1995

1,61

749

9.40

2.23

2.10

1.20

3.30

0.00

20.

71

(1,5

62.3

4)(0

.76)

(2.4

2)(1

.80)

(3.4

7)(0

.003

)(1

.13)

1996

1,88

559

6.51

1.56

2.07

1.36

3.44

0.00

20.

67

(2,3

51.6

1)(0

.91)

(2.4

4)(2

.06)

(3.9

6)(0

.003

)(1

.40)

1997

2,09

172

2.62

1.73

2.10

1.41

3.51

0.00

20.

72

(2,9

33.4

8)(1

.32)

(2.5

0)(2

.09)

(4.1

5)(0

.003

)(0

.86)

1998

2,33

782

6.75

1.39

2.17

1.56

3.73

0.00

20.

69

(3,5

63.0

0)(1

.97)

(2.5

6)(2

.18)

(4.3

6)(0

.003

)(1

.22)

1999

2,47

198

4.65

2.45

2.28

1.68

3.96

0.00

20.

97

(4,4

15.8

9)(3

.19)

(2.6

1)(2

.22)

(4.4

8)(0

.003

)(3

.94)

2000

2,59

91,

140.

87-0

.06

2.42

1.85

4.27

0.00

21.

19

(4,8

16.0

8)(2

.35)

(2.6

6)(2

.27)

(4.6

0)(0

.003

)(6

.69)

2001

2,58

794

8.89

-0.7

02.

471.

944.

410.

002

1.19

(4,0

28.2

9)(2

.58)

(2.6

8)(2

.28)

(4.6

4)(0

.003

)(2

.63)

2002

2,40

990

5.59

-3.3

12.

491.

984.

480.

002

1.21

(3,6

34.5

5)(2

.09)

(2.6

8)(2

.28)

(4.6

5)(0

.003

)(2

.42)

30

Tab

le2:

Des

crip

tive

Sta

tist

ics

-Fam

ilie

s

This

table

report

ssu

mm

ary

stati

stic

sfo

rth

efa

milie

sin

our

sam

ple

.T

he

num

ber

of

fam

ilie

sis

the

num

ber

of

fund

fam

ilie

sw

hose

funds

have

met

our

sele

ctio

ncr

iter

ia.

Funds

per

fam

ily

isth

eaver

age

num

ber

of

funds

each

fam

ily

has.

TN

Ais

the

tota

lass

ets

under

managem

ent

of

each

fam

ily

at

the

end

of

the

cale

ndar

yea

rin

millions

of

dollars

.E

xpen

seR

ati

ois

the

per

centa

ge

of

the

tota

lin

ves

tmen

tth

at

share

hold

ers

pay

for

the

mutu

alfu

nds

oper

ati

ng

expen

ses.

Tota

lLoad

isth

eto

talfr

ont-

end

load,def

erre

dand

rear-

end

charg

esas

aper

centa

ge

ofnew

inves

tmen

ts.

12-B

1is

the

annualdis

trib

uti

on

charg

e

(12b-1

fee)

inper

centa

ge

ofto

talass

ets.

Turn

over

isth

em

inim

um

ofaggre

gate

purc

hase

sofse

curi

ties

or

aggre

gate

sale

sofse

curi

ties

,div

ided

by

the

aver

age

Tota

lN

etA

sset

softh

efu

nd.

Sta

ndard

dev

iati

ons

are

report

edin

pare

nth

eses

.

Num

ber

ofFu

nds

per

TN

Ape

rE

xpen

seM

axim

umO

ther

Tot

al

Yea

rFa

mili

esFa

mily

Fam

ilyR

atio

Loa

dLoa

dsLoa

ds12

-B1

Tur

nove

r

1990

293

2.51

651.

921.

251.

840.

001.

850.

001

0.62

(3.8

6)(2

449.

44)

(0.9

6)(2

.45)

(0.0

6)(2

.45)

(0.0

02)

(0.6

5)

1991

322

2.70

827.

350.

981.

920.

001.

920.

001

0.62

(4.3

4)(3

277.

09)

(0.8

1)(2

.41)

(0.0

6)(2

.41)

(0.0

02)

(0.9

3)

1992

366

2.76

969.

631.

441.

930.

332.

250.

002

0.61

(4.2

6)(4

181.

76)

(1.0

9)(2

.31)

(0.9

1)(2

.55)

(0.0

02)

(1.2

0)

1993

401

2.97

1239

.73

1.40

1.87

0.39

2.26

0.00

10.

62

(4.6

2)(5

687.

50)

(0.9

2)(2

.22)

(0.9

6)(2

.62)

(0.0

02)

(0.6

5)

1994

424

3.27

1470

.02

1.40

1.74

0.46

2.20

0.00

10.

76

(4.9

8)(6

998.

25)

(0.7

6)(2

.16)

(1.1

3)(2

.69)

(0.0

02)

(1.1

6)

1995

452

3.42

1782

.89

1.44

1.75

0.65

2.40

0.00

10.

70

(5.1

4)(9

201.

39)

(0.7

3)(2

.13)

(1.3

5)(2

.94)

(0.0

02)

(1.4

1)

1996

494

3.61

2270

.75

1.43

1.70

0.73

2.43

0.00

20.

67

(5.5

4)(1

2330

.25)

(0.7

2)(2

.12)

(1.4

2)(3

.06)

(0.0

02)

(1.3

3)

1997

509

3.87

2961

.27

1.44

1.59

0.80

2.39

0.00

10.

69

(5.9

3)(1

6157

.77)

(1.2

0)(2

.12)

(1.4

8)(3

.16)

(0.0

02)

(0.8

2)

1998

542

4.13

3545

.65

1.46

1.59

0.90

2.49

0.00

20.

66

(6.3

8)(1

9940

.71)

(1.1

5)(2

.15)

(1.5

9)(3

.32)

(0.0

02)

(1.0

2)

1999

554

4.27

4356

.66

1.48

1.68

0.97

2.65

0.00

20.

82

(6.7

4)(2

5367

.13)

(1.1

7)(2

.25)

(1.6

5)(3

.47)

(0.0

02)

(1.9

1)

2000

541

4.62

5461

.84

1.47

1.74

1.07

2.81

0.00

21.

39

(7.2

1)(3

0094

.69)

(1.0

8)(2

.31)

(1.7

1)(3

.61)

(0.0

02)

(10.

24)

2001

514

4.93

4765

.06

1.51

1.67

1.08

2.74

0.00

21.

21

(7.7

8)(2

5838

.39)

(1.0

6)(2

.29)

(1.6

9)(3

.57)

(0.0

02)

(3.5

4)

2002

479

4.99

4523

.02

1.52

1.64

1.13

2.76

0.00

21.

24

(7.9

8)(2

3766

.78)

(1.0

7)(2

.28)

(1.7

1)(3

.59)

(0.0

02)

(3.7

1)

31

Table 3: Portfolio of Mutual Funds Formed on Lagged 1-Year Alphas - Families With 10

or More FundsUsing mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At thebeginning of the year we drop all the funds that belong to a family that has less than 10 funds. Then, we regresseach fund’s monthly excess gross returns a four factor model to find each fund’s alpha. We then rank each fund by itsalpha. We build portfolios based on these rankings and keep them for a year. The procedure is then repeated everyyear. This results in 10 time series which we regress on a four factor model. MKTRF is the excess return on theCRSP value-weighted market proxy. SMB and HML are the Fama and French (1993) factor mimicking portfolios forsize 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 AlphasUsing mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At thebeginning of every year, we regress each fund’s monthly excess gross returns a four factor model to find each fund’salpha. We then rank each fund by its alpha. The procedure is then repeated every year. This results in 10 time serieswhich 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. MOMis the momentum factor mimicking portfolio similar to Jegadeesh and Titman (1993). The t-statistics are reported inparentheses.

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 ThresholdUsing mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At thebeginning of the year we drop all the funds that belong to a family that has a market capitalization below a giventhreshold. The threshold is such that the number of funds is equivalent to the number generated by a thresholdof 10 funds per family. Then, we regress each fund’s monthly excess gross returns a four factor model to find eachfund’s alpha. We then rank each fund by its alpha. We then rank each fund by its alpha inside its family. We buildportfolios based on these rankings and keep them for a year. The procedure is then repeated every year. This resultsin 10 time series which we regress on a four factor model. MKTRF is the excess return on the CRSP value-weightedmarket proxy. SMB and HML are the Fama and French (1993) factor mimicking portfolios for size and book to marketequity. MOM is the momentum factor mimicking portfolio similar to Jegadeesh and Titman (1993). The t-statisticsare 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

FamilyUsing mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At thebeginning of the year we drop all the funds that belong to a family that has less than 10 funds. Then, we regresseach fund’s monthly excess gross returns a four factor model to find each fund’s alpha. We then rank each fund byits alpha. We then rank each fund by its alpha inside its family. We build portfolios based on these rankings andkeep them for a year. The procedure is then repeated every year. This results in 10 time series which we regress ona four factor model. MKTRF is the excess return on the CRSP value-weighted market proxy. SMB and HML arethe Fama and French (1993) factor mimicking portfolios for size and book to market equity. MOM is the momentumfactor 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 CapitalizationUsing mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At thebeginning of the year we drop all the funds that belong to a family that has less than 10 funds. Then, we find themarket capitalization of each fund and rank them based on that. We build portfolios based on these rankings andkeep them for a year. The procedure is then repeated every year. This results in 10 time series which we regress ona four factor model. MKTRF is the excess return on the CRSP value-weighted market proxy. SMB and HML arethe Fama and French (1993) factor mimicking portfolios for size and book to market equity. MOM is the momentumfactor 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 FundsUsing mutual fund data from 1990 to 2002, we perform a persistence analysis similar to Carhart (1997). At thebeginning of the year we drop all the funds that belong to a family that has less than 5 funds. Then, we regress eachfund’s monthly excess gross returns a four factor model to find each fund’s alpha. We then rank each fund by itsalpha. 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 mimickingportfolio 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

Tab

le10

:Su

mm

ary

Stat

isti

csof

Ave

rage

Pai

rwis

eC

orre

lati

ons

ofFu

ndA

lpha

sW

ithi

nE

ach

Fam

ilyT

his

table

report

ssu

mm

ary

stati

stic

softh

eaver

age

pair

wis

eco

rrel

ati

onsoffu

nd

abnorm

alre

turn

s(a

lphas)

wit

hin

the

funds’

resp

ecti

ve

fam

ilie

s.W

ithin

each

fam

ily

we

calc

ula

teth

epair

wis

eco

rrel

ati

ons

bet

wee

nall

the

fund

alp

has

for

thei

rover

lappin

gper

iods

and

aver

age

the

corr

elati

ons.

Thus,

all

the

report

edst

ati

stic

sare

cross

-sec

tionalst

ati

stic

sacr

oss

fam

ilie

s.W

ere

port

the

corr

elati

ons

for

the

full

sam

ple

(ref

erre

dto

as

Cuto

ff-Full),

and

the

sub-s

am

ple

sw

hen

we

split

by

the

num

ber

of

funds

inth

efa

mily

(ref

erre

dto

as

Cuto

ffs

big

ger

or

smaller

than

eith

er5

or

10

funds

ina

fam

ily).

Panel

Bre

port

sth

esa

me

stati

stic

sfo

rth

eabso

lute

valu

esofth

epair

wis

eco

rrel

ati

ons.

Pan

elA

:Sum

mary

stati

stic

sofaver

age

pair

wis

eco

rrel

ati

ons

offu

nd

alp

has

wit

hin

each

fam

ily.

Cuto

ffM

ean

Med

ian

Sta

ndard

Sta

ndard

Skew

nes

s95th

80th

75th

60th

50th

35th

25th

15th

5th

Err

or

Dev

iati

on

Per

centi

lePer

centi

lePer

centi

lePer

centi

lePer

centi

lePer

centi

lePer

centi

lePer

centi

lePer

centi

le

Full

0.2

20

0.1

83

0.0

17

0.3

32

-0.1

15

0.8

35

0.4

66

0.3

91

0.2

40

0.1

83

0.0

96

0.0

44

-0.0

42

-0.2

54

≥10

0.1

31

0.1

09

0.0

14

0.1

18

0.5

87

0.3

66

0.2

18

0.2

03

0.1

48

0.1

09

0.0

71

0.0

56

0.0

21

-0.0

41

<10

0.2

19

0.1

80

0.0

18

0.3

38

0.0

55

0.8

66

0.4

80

0.4

04

0.2

50

0.1

80

0.0

83

0.0

16

-0.0

60

-0.2

69

≥5

0.1

54

0.1

27

0.0

13

0.1

59

1.5

04

0.4

28

0.2

47

0.2

17

0.1

52

0.1

27

0.0

85

0.0

60

0.0

29

-0.0

52

<5

0.2

26

0.2

11

0.0

21

0.3

83

-0.0

33

0.8

97

0.5

64

0.4

80

0.3

07

0.2

11

0.0

63

-0.0

42

-0.1

53

-0.3

60

Pan

elB

:Sum

mary

stati

stic

sofabso

lute

valu

eofaver

age

pair

wis

eco

rrel

ati

ons

offu

nd

alp

has

wit

hin

each

fam

ily.

Cuto

ffM

ean

Med

ian

Sta

ndard

Sta

ndard

Skew

nes

s95th

80th

75th

60th

50th

35th

25th

15th

5th

Err

or

Dev

iati

on

Per

centi

lePer

centi

lePer

centi

lePer

centi

lePer

centi

lePer

centi

lePer

centi

lePer

centi

lePer

centi

le

Full

0.3

02

0.2

09

0.0

13

0.2

60

1.0

70

0.8

60

0.5

22

0.4

26

0.2

82

0.2

09

0.1

47

0.1

06

0.0

70

0.0

23

≥10

0.1

41

0.1

14

0.0

13

0.1

06

1.0

12

0.3

66

0.2

18

0.2

03

0.1

48

0.1

14

0.0

80

0.0

64

0.0

37

0.0

12

<10

0.3

06

0.2

23

0.0

14

0.2

62

1.0

00

0.8

79

0.5

30

0.4

46

0.3

01

0.2

23

0.1

44

0.0

96

0.0

56

0.0

19

≥5

0.1

68

0.1

35

0.0

11

0.1

44

2.1

17

0.4

28

0.2

47

0.2

17

0.1

57

0.1

35

0.0

91

0.0

72

0.0

49

0.0

23

<5

0.3

53

0.2

77

0.0

15

0.2

71

0.7

88

0.9

02

0.5

96

0.5

31

0.3

49

0.2

77

0.1

93

0.1

41

0.0

81

0.0

29

39

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:

Pr(Adding a manager) = Λ [β1 + β2αt + β3αt−1 + β4Log(TNA)t + β5Log(TNA)t−1 + β6Expenset]

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.

Prt+1(Adding a manager) =

Λhβ1 +

P5i=1 β2,iIi ·Rett +

P5j=1 β3,jIj ·Rett−1 + β4Log(TNA)t + β5Log(TNA)t−1 + β6Expenset

iVariable Estimate StdErr ChiSq Prob

Intercept 3.8407 0.1979 376.63 0.0000

RettI1 0.0424 0.8479 0.00 0.9601

RettI2 -0.0492 0.6203 0.01 0.9368

RettI3 -0.1330 0.4870 0.07 0.7848

RettI4 -0.4539 0.3989 1.30 0.2551

RettI5 0.1444 0.4820 0.09 0.7645

Rett−1I1 -0.8277 0.5023 2.71 0.0994

Rett−1I2 0.8195 0.5403 2.30 0.1293

Rett−1I3 0.6506 0.4686 1.93 0.1650

Rett−1I4 0.2996 0.4515 0.44 0.5070

Rett−1I5 2.1822 0.7782 7.86 0.0050

Log(TNA)t 0.0483 0.0957 0.25 0.6138

Log(TNA)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.

Prt+1(Adding a manager) =

Λhβ1 +

P5i=1 β2,iIi ·Rett−1 +

P5j=1 β3,jIj · PCASSETt + β4Expenset + β5Turnovert

iVariable Estimate StdErr ChiSq Prob

Intercept 3.2547 0.1045 970.11 0.0000

PCASSETtI1 0.0631 0.0567 1.24 0.2660

PCASSETtI2 0.0312 0.0444 0.50 0.4813

PCASSETtI3 -0.0098 0.0045 4.85 0.0276

PCASSETtI4 0.0005 0.0084 0.00 0.9515

PCASSETtI5 0.0087 0.0302 0.08 0.7732

Rett−1I1 -0.9579 0.5242 3.34 0.0677

Rett−1I2 0.6091 0.5309 1.32 0.2512

Rett−1I3 0.4265 0.4462 0.91 0.3391

Rett−1I4 -0.0655 0.4232 0.02 0.8769

Rett−1I5 1.9565 0.7536 6.74 0.0094

Expensest 0.2255 5.8227 0.00 0.9691

Turnovert 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:

Pr(Adding a manager to a single managed fund) =

β1 + β2αt + β3αt−1 + β4Log(TNA)t + β5Log(TNA)t−1 + β6Expenset + 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.

Prt+1(Adding a manager to a single managed fund) =

Λhβ1 +

P5i=1 β2,iIi ·Rett +

P5j=1 β3,jIj ·Rett−1 + β4Log(TNA)t + β5Log(TNA)t−1 + β6Expenset

iVariable Estimate StdErr ChiSq Prob

Intercept 4.1629 0.2414 297.40 0.0000

RettI1 -1.9127 1.0711 3.19 0.0741

RettI2 0.0061 0.8860 0.00 0.9945

RettI3 -0.4316 0.6137 0.49 0.4819

RettI4 -0.1286 0.6269 0.04 0.8374

RettI5 0.6241 0.8185 0.58 0.4458

Rett−1I1 -0.3922 0.8374 0.22 0.6395

Rett−1I2 1.3661 0.7967 2.94 0.0864

Rett−1I3 0.2532 0.6039 0.18 0.6750

Rett−1I4 0.6109 0.6885 0.79 0.3749

Rett−1I5 2.2477 1.1609 3.75 0.0528

Log(TNA)t 0.1580 0.1158 1.86 0.1722

Log(TNA)t−1 -0.1830 0.1151 2.53 0.1117

Expensest -7.7294 4.1857 3.41 0.0648

45