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Mutual Fund Starts: Performance, Characteristics andthe Relation with Stock Markets
15th September 2007
Abstract
This paper investigates the characteristics of new U.S. equity mutual funds and therelation between funds starts and stock markets. We document that, on average, newfunds have higher performance, higher fees, higher turnover, and attract higher netin�ows than existing funds. The degree of diversi�cation of new fund portfolios andthe liquidity of the stock positions they hold are comparable to old funds. We thenstudy the persistence of performance fund starts. Our comparisons over two subsequent12-months or 36-months windows show that top performing funds are more persistentthan poorly performing funds. A high proportion of new funds belong to either the top(winners) or bottom (losers) deciles and a non-negligible number of new funds migratedirectly from top performers to top losers or vice-versa, which suggests that new fundsadopt riskier investment strategies. Finally, we analyze the relation between fundsstarts and stock markets using holdings of funds. We �nd that new funds are highlyrelying on momentum strategies. Moreover, while the introduction of new funds doesnot a¤ect stock prices, it coincides with high IPO activity.
JEL Classi�cation: G11, G12, G14, G23KeyWords: mutual fund starts, stock markets, performance, persistence
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1 Introduction
A large number of new funds has emerged over the past decade. The total net asset value
managed by mutual funds worldwide increased from 9.6 trillion in 1998 to 17.8 trillion in
2005 and the number of funds increased from 50,266 to 56,863 over the same time span1. This
growth is not only observed in the U.S. mutual fund industry but worldwide. This raises the
question what triggers a fund start and whether new funds are di¤erent compared to existing
ones. We analyze the performance of new U.S. equity mutual funds, their characteristics and
the relation between the introduction of new funds and stock markets.
Khorana and Servaes (1999) study 1,163 fund starts over the period 1979-1992. Since
then the U.S. mutual fund industry has grown to a size more than six times the total net
asset values at the end of their sample period in 1992 and the number of mutual funds has
more than doubled2. Khorana and Servaes (1999) �nd that fund families more often start a
new fund when they have outperformed their peers. Moreover, large fund families and fund
families that have recently introduced new funds are more likely to start a new fund, and
smaller funds families tend to follow the large fund complexes when adding new funds. They
conclude that new funds starts tend to be driven by incentives to generate additional fee
income. Zhao (2002) stresses the importance to di¤erentiate between the decision to start
a new fund portfolio and the decision to introduce new share classes. He concludes that
fund families tend to introduce funds in investment objectives that had a poor performance
prior to the entry decision. New share classes are most often added for star portfolios with
good performance, high total net assets, high expense ratios, and a longer history. We
document that new funds tend to have higher fees and higher turnover. Using the industry
concentration index of Kacperczyk et al. (2005) and Amihud�s (2002) illiquidity ratio we
�nd that the diversi�cation and liquidity of new funds, on average, is comparable to old
funds.
While the literature discusses the determinants of fund starts, less attention has been
paid to the performance evaluation of new funds over the �rst months after their inception.
One notable exception for equity mutual funds is the work of Blake and Timmerman (1998)
which addresses the question whether the performance of newly created mutual funds in the
U.K. market is superior relative to existing funds. They �nd weak evidence for a higher
performance of new funds and an average, risk-adjusted excess return of 7 bps. Berzins
1Investement Institute Company (ICI), Factbook 2007, Tables 1 (p.93) and 9 (pp. 141/142).2Investement Institute Company (ICI), Factbook 2007, Table 1 (p.89).
2
(2006) analyzes institutional money managers and �nds that the performance of a newly
launched fund tends to fall into the same performance deciles than the existing funds of the
same family. For our sample of U.S. equity mutual funds we �nd that, on average, new
funds outperform old funds on a risk adjusted basis by 5 to 7 bps. When we rank old funds
into deciles and then assign the new funds to these deciles, we �nd that a high proportion
of new funds fall into the top performing deciles. We also document that top and bottom
performing new funds exhibit some persistence at the 12-months and 36-months horizon.
Finally, we study the characteristics of mutual fund starts and link them to the performance
over the �rst few months after inception.
Our work is also related to the literature that examines fund performance and age. Bauer
et al. (2002) �nd that young funds under perform old funds in a database containing 103
German, U.K. and U.S. ethical mutual funds. In contrast, analyzing the returns of 10,568
open-end, actively managed equity funds from 19 countries between 1999 and 2005, Ferreira
et al. (2006) show that young funds exhibit a better performance. Similarly, Otten and
Bams (2002) �nd a negative relationship between fund age and the risk-adjusted performance
using a survivorship bias controlled sample of 506 funds from the �ve most important mutual
fund countries (U.S., France, Italy, Netherlands, U.K.). Liang (1998) also �nds a negative
relationship between age and hedge fund performance. Our results are consistent with the
�ndings of these latter studies.
The last part of the paper addresses the relationship between fund starts and stock
markets. There are two main approaches to explain the correlation between mutual funds
and stock prices. The �rst one is the price pressure hypothesis that an increase in in�ows for
mutual funds will increase the demand of stocks and subsequently move up its price. The
second approach is the information revelation hypothesis which suggests that the common
investor follows the actions of mutual fund managers and mimics their portfolio rebalancing.
Edwards and Zhang (1998) examine the relationship between the aggregate monthly mutual
fund �ows and monthly stock and bond returns from 1961 to beginning of 1996. They �nd
that overall, with the exception of the 1971-1981 period, �ows into stock and bond funds
do not a¤ect security returns. On the other hand, Warther (1995) �nds a high correlation
of stock and bond returns with concurrent unexpected cash �ows into mutual funds, but no
relation to concurrent expected �ows. He also provides evidence of a positive impact of �ows
on subsequent returns3. All these contributions study the relationship between mutual funds
3Philippas (2002) conducts a study of the Greece market. He obtains a negative relationship betweenindex returns and lagged mutual fund �ows, but �nds no correlation between contemporary �ows and index
3
and stock markets at an aggregate level. They do not use fund portfolio holdings to study
the impact of mutual funds on stock markets. Addressing this shortcoming, Massa (2003)
shows how mutual fund competition a¤ects stock market liquidity. He provides evidence
that fund characteristics also a¤ect stocks characteristics. In our paper, we study abnormal
returns surrounding the introductions of new funds. In the event of a fund start one would
expect evidence for the price pressure and information hypothesis. Nonetheless, we do not
�nd a signi�cant change in abnormal stock returns around the introduction dates of new
funds. In contrast, we �nd that new funds are highly relying on momentum strategies and
are timing their starts. Moreover, we �nd a positive and signi�cant relationship between
initiations of U.S. equity funds and IPO activity on the major U.S. stock markets.
The remainder of the paper is organized as follows. Section 2 describes our sample. The
performance of new funds is analyzed in Section 3. The characteristics of these new funds
are documented in Section 4. Moreover, in Section 5, we do the link between Section 3 and
Section 4 by analyzing the determinants of the performance and the �ows of new mutual
funds using their characteristics. In Section 6, we investigate whether funds starts have an
impact on stock markets. Section 7 concludes.
[Table 1]
2 Data
We use three databases: the CRSP Survivor-Bias Free Mutual Funds Database (MFDB),
data provided by Morningstar, and the CRSP stock database. We use the CRSP mutual
fund database to get monthly returns, annual fees and annual turnover. The sample extends
from January 1962 to December 2005. Since we are studying the impact of the introduction
of new funds on stock markets, we work on equity mutual funds. We select funds based
on the information provided on CRSP classi�cations: Wiesenberger, Micropal/Investment
Company Data, Inc., Strategic Insight, and fund names. We use the same methodology as
Pastor and Stambaugh (2002a) to select funds. In their work, authors provide us the list
of ICDI (�ICDI_Obj�), Wiesenberger (�Obj�) and Strategic Insight (�SI_obj�) of equity
funds. Furthermore, we eliminate balanced funds, bond funds, �exible funds, international
funds, mortgage-backed funds, money market funds, multi-manager funds, and specialized
funds.
returns. However, he �nds no support for the price pressure hypothesis.
4
We use the Morningstar database to get quarterly holdings of funds and the general
industry classi�cation (GIC) for each stock held in portfolios. The CRSP survivor-bias free
mutual fund database initially contains 27720 funds (with share classes) for a period of
01/1962-12/2005, while the Morningstar database contains information for 4602 funds over
a period from 01/1991 to 01/2005. We merge the two databases and we succeed to �nd
a correspondence for 1374 funds in Morningstar and 3859 share classes in CRSP. In the
matching process, one fund in Morningstar may correspond to many share classes in CRSP
database. We eliminate share classes by keeping the one that has the longer history. This
allows us to have a one-by-one correspondence between funds in Morningstar and CRSP
databases. We also classify the 1374 funds by their family name obtaining a sample of
388 families. While the CRSP mutual fund database is free from survivorship bias, the
Morningstar database may su¤er from this bias. In the merged database we have some dead
funds, but it might not be comprehensive.
Table2 describes the distribution of styles in our sample. Most equity funds are composed
of Growth funds (30.86%). Table 3 summarizes characteristics of fund families taken in the
sample. Most of the families have a number of funds between 2 and 5. Only 26 families have
a number of funds exceeding 11. However, the sum of the TNA of the largest families (26
largest families) is superior to the sum of the TNA of small families (362) con�rming the
fact that mutual fund industry is dominated by large families.
[Table 2]
[Table 3]
We use the CRSP stock database to get stock characteristics: monthly price, monthly
volume and monthly returns. We have information for 27672 stocks for a period between
01/1962-12/2005. From these data, we have computed the illiquidity ratio of each stock using
illiquidity measure of Amihud (2002). We use also the Fama and French (1993) three factors
excess market returns (RMT), size (SMB) and book-to-market (HML) and we add also a
momentum factor (MOM) as it was speci�ed in Carhart (1997). We use the 3-months US
Treasury bill as a riskless asset. All these factors are obtained from the website of Kenneth
French.
Cooper, Gulen and Rau (2005) study the e¤ect of changing names on fund �ows. Each
fund is characterized by its ICDI number which is unique for each fund. Even though a fund
may change its name, it will keep its ICDI number. Since in our work, we rank funds based
on their ICDI number rather than on their names, we con�rm the fact that new ICDI number
5
are corresponding to new funds. Moreover, some funds may have a style name change while
the fund itself did not change. We also adjust for share classes by keeping only one share
class by fund (Zhao (2002)). However, it remains to see if newly created funds which have
a new ICDI number are really new or a product of many funds.
3 The performance of new mutual funds
3.1 Estimation of the performance of new funds
As stated in the related literature part, many articles advocate that young funds are among
top performers. In this part, we estimate the risk adjusted performance of new funds using
the multifactor model of Carhart (1997). We also test whether new funds exhibit a decline
in their performance for a subsequent time window after their start. In our sample, we have
1,327 equity fund introductions. For each fund introduction, we estimate the performance
for a �rst interval and a second one. Furthermore, we de�ne a �new fund�as a fund that
recently enters the market. It is the latest fund that joined the market at the time window
considered. We de�ne �old funds�as funds that already exist when the �new fund�enters the
market. For each fund start, we estimate the performance of the fund for the �rst interval
[0 t1]and the second one [t1 t2]
Rit = �i + �1iRmtt + �2iSMBt + �3iHMLt + �4iMOMt + �it (1)
0 t1 t2
αNew, [o,t1] αNew, [t1,t2]
Ho : �new;i;[t1;t2] � �new;i;[0;t1] = 0 (2)
H1 : �new;i;[t1;t2] � �new;i;[0;t1] 6= 0
After each fund start, we measure the performance for t1 �rst months of activity. We
obtain the performance of all the funds after t1 periods of their inception.. Then we compute
6
an average value of all the risk adjusted performance obtained. Furthermore, we vary the
length t1 and we see whether results are changing or not. Figure 1 shows that new mutual
funds have a risk adjusted return around spreading from -1 bps after 18 months to -4 bps
after 60 months. We also measure excess returns of new funds after t months from their
inception. The average excess return is decreasing as funds are getting aged, this result
con�rms the start e¤ect among new mutual funds.
[Insert Figure 1]
For each fund, we want to test the hypothesis of �good start�among new funds. At each
fund introduction, we compute the di¤erence between �new;i;[t1;t2]and �new;i;[0;t1] . We take
a time window of t1 = 60 months and we compute the kernel distribution of the di¤erence
between �new;i;[t1;t2]and �new;i;[0;t1]4. Figure 2 shows that a majority of the values is negative
indicating a decline in the performance of new funds in subsequent periods. In general,
new funds perform better in the �rst window of their existence than in the second one.
Moreover, we compare obtained results with a symmetric normal distribution that has the
same standard deviation and a zero mean. We observe that di¤erences in alphas distribution
have fatter negative tail and smaller positive tail. The mean di¤erence is equal to -8.99 bps
and the p-value is equal to 0.00.
[Insert Figure 2]
In a second step, we want to see how results are a¤ected by the size of the window
chosen. We vary the length of the interval used and we estimate the mean of the di¤erence
in alphas and the t-test. Figure 4 and Figure ?? show that the decline is more important aswe enlarge the size of the window. This �nding underlines the existence of the persistence in
the performance among new funds for the short run and not for the long run (>36 months).
New funds maintain high performance at their starts for at least 36 months. Most of the
decline in the performance occurs in the long run. Furthermore, we decompose the entire
sample into three sub-periods. We study fund introductions for periods: 1962-1980, 1981-
1990 and 1991-2005. During these periods, we have 105, 222 and 1,000 fund introductions
respectively. Our sample is dominated by the last period 1991-2005, in which we have
4Results for other values of t1 are available upon request. For brevity we just report the case of t1=60months.
7
registered a tremendous development in the mutual fund industry. For the period of 1962-
1980, the values of the di¤erences are positive but decreasing, suggesting that new funds are
improving their performance in subsequent periods. For the 1981-1990, most of the values
are negative, suggesting an absence of persistence for both short and long run. Results for
the overall sample are similar to those for the sub-sample of 1991-2005.
[Insert Figure 3]
[Insert Figure 4]
Furthermore, we measure the di¤erence in raw excess returns between two time windows.
There is a signi�cant decline in excess returns after 24 months. New funds are maintaining
high performance for at least 24 months as it is mentioned in Figure 5.
[Insert Figure 5]
3.2 �New funds�versus �old funds�
As we have already mentioned in the introduction, a large span of the literature supports that
small and young funds are performing better than old and large funds. Two methodologies
were developed to illustrate the relationship between performance and age. The �rst meth-
odology is a panel regression between age of funds and their performance (as in Otten and
Bams (2002)). This linear relationship is by de�nition too restrictive. If we can understand
that a fund that already start would perform better than a fund that is ten years old, we can
hardly understand why a fund that is 30 years old would necessarily perform worse than a 20
years old fund. The relationship is not necessarily linear and is also not necessarily correct
for the whole interval. The second approach is a grouping approach as it is displayed in Huij
and Verbeek (2007). After the estimation of the risk adjusted performance for the entire
sample (the entire time window), authors distinguish two groups: a �rst group containing
young funds (for example less than �ve years) and a second one containing old funds (more
than �ve years old). This approach is a snapshot and is a punctual estimation, because it
evaluates performance only at a one time window. Furthermore, it does not exactly take
the same time span for all funds. If we want to measure young fund performance, we must
compare them at the same time point of their existence.
Our approach is more robust and more general. We estimate the performance of each
fund at its start and rank it for each time window considered among the other funds. This
8
methodology gives us the entire historical start ranks for all funds available in our dataset.
Compared to the �rst methodology, our method is only a ranking method. Since it is a
nonparametric method it will by de�nition impose less assumptions on variables distribution
and subsequently on the type of the relationship. Compared to the second approach, we
simply repeat it 1,327 times, which is the number of fund starts considered in our sample.
Moreover, we exactly compare funds at the same time window after their launch.
In the previous section, we have estimated alphas of the new funds and we have tested
whether there is a decline for a subsequent time window. Nonetheless, another way to look
at the persistence is to take a relative measure of performance. We rank alphas of the new
funds among the existing funds for the t1 �rst months of activity [0 t1] and for the subsequent
time window [t1 t2]. We choose one example with t1=36 months . For each new fund, we
estimate the alpha of the new fund and the alphas of existing funds for this speci�c time
window. We divide the alphas of existing funds into deciles and we range the alpha of the
new fund among one of these deciles. We obtain the rank (i.e. the decile to which it belongs
to) of each new fund for the �rst and second time window. We give the histogram of the
ranks of new funds for the �rst window [0 t1] and the second one [t1 t2] in Figure 6.
0 t1 t2αNew ,[0,t1]
αOld ,[t1,t2]
0 t1 t2
αOld ,[0,t1]
αNew ,[t1,t2]
For the �rst interval [0 t1], there is a higher proportion of new funds that are among
top performers (tenth decile). A large number of new funds are among top performers for
at least the �rst 36 months of their existence. Furthermore, the histogram also shows a
U-shape, implying that a high proportion of new funds are belonging to either the tenth
(top winners) or the �rst decile (top losers). One might wonder that new fund managers are
targeting higher performance to attract more in�ows and to avoid liquidation. Moreover,
we perform a Chi-Deux test for a null hypothesis that all the proportions are equal. The
results of the test reject the null hypothesis with a p-value equal to 0.00 for the �rst interval
[0 t1]. However, for the second interval [t1 t2], we can not reject the null hypothesis and
the p-value is equal to 0.14. This con�rms the argument that new funds are starting with
higher performance.
9
[Insert Figure 6]
To verify whether new funds are taking riskier strategies we compute some risk measures.
We compute total risk of new funds using standard deviation of returns and standard de-
viation of �ows. Moreover, we estimate the systematic risk of the new funds using Carhart
loadings and unsystematic risk using R^2 of factors equation. We rank risk measures of new
funds among old funds. Results of this ranking are displayed in Figure 7 and Figure 8. To
verify that new funds have speci�c risk characteristics, we compute Chi-deux tests under
the null hypothesis of equality in proportions. Results of the test are displayed in Table 4.
Figure 7 shows that a high proportion of new funds have high return and �ows volatility.
Figure 8 shows that new funds have lower R^2 which is equivalent to a high idiosyncratic
risk. Moreover, �gure 8 highlights that new funds have SMB and MOM factors that are
belonging to extreme deciles. New funds are highly relying on SMB and MOM strategies.
This later strategy is facilitated since new funds can �ll their portfolios with every stock
they want while old funds, due to rebalancing restrictions, may not have this possibility.
Summarizing these results, this section suggests that new funds have higher performance
but also higher risk. The main advantage of new funds is the freedom to include any stock
they are targeting. Whereas for old funds, a full rebalancing of their portfolios remains
almost impossible. This advantage may be an incentive to liquidate or to merge poorer
performing funds rather than to operate a costly rebalancing of the portfolio.
[Insert Figure7]
[Insert Figure8 ]
However, while the proportions of funds across deciles are comparable between �rst and
second interval, it is not clear how new funds are migrating in their ranking. The Figure 9
explains how funds are changing from a decile to another.
[ Insert Figure 9]
Figure 9 shows the histogram of new fund ranks for the �rst and the second periods. For
example, square (2, 2) shows the number of new funds that were classi�ed in the second
decile for the �rst period and maintain the same decile in the second period. Figure 9b and
9d show the null hypothesis, i.e. if there were no change between the �rst time window and
the second one. We compute the change in the rank for two window sizes: for the short
run (T=12 months) and for the long run (T=36 months). At this stage, comparing Figure
10
6 and Figure 9, supports the existence of persistence in the tenth decile (highly performing
funds). However, there is less evidence of performance persistence among poorer performing
funds. Interestingly, we can observe that a non negligible proportion of funds are either
moving from the tenth decile to the �rst one or vice versa. These funds are taking riskier
positions to improve their performance. Except for highly performing funds, all other funds
have almost an equal probability to switch to any other decile for the next time period. This
�nding advocates the absence of skills among a large part of managers and supports the idea
of quasi-randomness of performance among funds.
Once we estimate adjusted performance of funds, we compare the performance of new
funds to existing ones. We choose a window of t1 observations after the launch of the fund.
Do new funds systematically outperform the average fund performance for the t1 �rst months
of activity? If it is the case, it would be economically worthwhile to invest in new funds
rather than in well established ones. We compare the performance of new funds with the
average performance of the existing funds after each start of a new fund.
Ho : �new;i;[0;t1] � �old;i;[0;t1] = 0 (3)
H1 : �new;i;[0;t1] � �old;i;[0;t1] 6= 0
Figure 10a shows that newly started funds outperform the average performance of existent
funds a large number of the time windows considered. However, the di¤erence is declining
as we extend the time window chosen as it is mentioned in Figure 10a. This also con�rms
that funds exhibit high performance at the beginning of their existence. Figure 10b shows
the t-test of the di¤erence between �new;i;[0;t1]and �old;i;[0;t1] ,the di¤erence is statistically
signi�cant regardless of the size of the time window considered.
[Figure 10]
[Table 5]
3.3 Estimation of the performance of �old funds�around the startsof other funds
Do new mutual fund starts have any impact on existing funds? We also verify if there is a
change in the performance of existing funds surrounding the addition of a new fund. In one
hand, we may expect a decrease in the performance of the established funds in the year of
the starting of new funds due to an increase of competition. On the other hand, if there are
11
t1 0 t1
αOld , [t1,0] αOld , [0,t1]
new funds entering the market, it would be a sign that there are still opportunities available.
We estimate alphas of the existing funds for a period of t1 observations before the launch of
a new competitor and t1 observations after. Moreover, we vary the value of t1 between 10
and 60 months. We then compute the mean and the t-test of the di¤erence in alphas:
Ho : �old;i;[0;t1] � �old;i;[�t1;0] = 0 (4)
H1 : �old;i;[0;t1] � �old;i;[�t1;0] 6= 0
Figure 11 shows that �old;i;[0;36] � �old;i;[�36;0] is statistically di¤erent from zero regardless
of the time window considered. On average, mutual funds� performance decreases after
the start of new competitors. This shows that new funds are not well timing their entry
in the market. Families are launching new funds when the fund industry is o¤ering higher
performance, but this later declines in subsequent periods. We can conclude that, on average,
families are not successful in �nding optimal time to launch funds. One advantage of our
study is that we are not estimating performance of funds at the end of a calendar year. We
are estimating the performance at any month. This point is likely to avoid window dressing
problems.
[Insert Figure 11]
4 Characteristics of new mutual funds
Data on holdings from Morningstar are available beginning with 01/01/1991. We take the
initial sample chosen in the previous sections and we restrict our focus on a time window
between 01/01/1991 and 31/12/2005. Holdings are available only at a quarterly frequency.
Fees and turnover are available at a yearly frequency. Whereas fund returns, fund TNA
and fund �ows are available at a monthly frequency. The di¤erences in the frequency of
12
the variables may to some extent bias the results as mentioned in Elton et al. (2006). In
this part, we want to look at the characteristics of new funds: fees, turnover, size, �ows,
number of stocks, ICI (industry concentration index), IR (illiquidity ratio). Since we have
showed that new funds have on average a higher performance, it is interesting to look for the
determinants of this performance. Table 4 gives descriptive statistics of the characteristics
of the entire sample of funds at di¤erent dates. We compute average fees, turnover, TNA,
�ows, ICI, and liquidity. We can observe that fees exhibit an upward trend. While turnover
is stable for the last twenty years. TNA is increasing which re�ects the development in the
mutual fund industry. Flows, ICI and liquidity are varying from one year to another and
they do not exhibit any trend.
4.1 De�nition of the variables
4.1.1 Fees and Turnover
Fund fees are one of the key elements for mutual fund managers. Setting up an optimal level
of fees will allow attracting an optimal volume of in�ows. Nonetheless, a high heterogeneity
in fees is observed in the sample. We think that new funds will have a propensity to set up
a high fees level. This latter is explained by an information asymmetry. New funds may
typically be less informed than old funds and they are protecting themselves from informed
investors. Moreover, setting up high fees will reduce the volatility of the �ows. Finally, new
funds may also have a small size at the beginning, they do not bene�t from scale economies.
We expect new funds to have higher fees than the average existing funds.
New funds may have higher turnover than average old funds. For instance, when they
already entered the market, new funds may not �nd stocks they are targeting. Adjusting
their optimal portfolio may take a period of time and will necessarily induce a higher number
of buying and selling. On the other hand, existing funds, may not have as much incentives
to rebalance their portfolios.
[Table 5]
[Table 6]
4.1.2 Size and Flows
As new funds enter the markets, it is obvious they are projecting to increase their TNA (total
net asset). Undoubtedly, we expect that new funds have smaller size than exiting funds.
13
We do not have the variable of fund �ows in our database. We compute fund �ows as
following:
Flowst =TNAt � (1 +Rt)TNAt�1
TNAt�1(5)
Rt: Return of the mutal fund at time t
TNAt: Total net asset of the fund at time t
This measure of �ows is in percentage and thus does not su¤er from size bias. As we
have found that new funds are performing among top performers, we expect that new funds
are attracting high in�ows.
4.1.3 Diversi�cation of the portfolio
We now analyze the holdings of new funds. The �rst element we are examining is the
diversi�cation. Do new funds have more or less diversi�ed portfolios? Since new funds are not
totally established in the market, we think that they will invest in a small number of stocks
and in a small number of industries. We have ten industries and we examine the holdings of
portfolios to verify the degree of their diversi�cation. To measure the diversi�cation of the
portfolio, we use the industry concentration index provided in Kacperczyk et al (2005).
ICIt =10Xj=1
(!j;t)2 (6)
!j;t: Weight of the mutual fund holdings in industry j :
ICI will, by construction, vary bewteen 1/12 and 1. If the portfolio is fully invested in
one industry, the ICI would be equal to one. The perfect diversi�cation implies equal weights
among di¤erent industries and the ICI would be equal to 0.1. The higher is the ICI, the
more concentrated (less diversi�ed) is the portfolio.
Another proxy for portfolio diversi�cation degree is the number of stocks. Even though
this proxy may be biased by the size of the fund, it gives an idea of the concentration of the
portfolio of the fund. We measure the number of stocks for each portfolio at each
4.1.4 Liquidity of the portfolio
The adjusted performance measured by alpha may not capture liquidity e¤ect and so part of
the performance of new funds may also be explained by placement made in illiquid stocks.
We propose to measure the liquidity of portfolios held by new funds and compare them to
14
old funds. We study the composition of new funds vs. existing funds in terms of liquidity.
Do new funds systematically hold more liquid stocks? As they are themselves facing more
uncertainty about their in�ows, we can think that new funds will primarily invest in very
liquid stocks. As their in�ows level is better known, they can invest in more illiquid stocks.
This is a cautious strategy that allows new funds to easily respond to in�ows and out�ows
movements. We use Amihud (2002) illiquidity ratio. We estimate this ratio for each stock
and after we estimate a value weighted liquidity index of the portfolio held by the mutual
fund.
IRit =jRitjTit � Pit
(7)
Rit: Return of the stock i at time t
Tit: Number of shares traded of stock i for month t
Pit: Price of the stock i at time t
The liquidity of the portfolio would be:
IRpt =Xi2p!itIRit (8)
Where !it =stk_mkt_shareitPstk_mkt_share is the weight of each stock in term of market capitalization i.e.
the proportion of portfolio invested in stock i at time t.
4.2 Persistence in the characteristics of new funds
We look at the average value of the characteristics of new funds after t months from their
inception. Fees, TNA and number of stocks in the portfolio are exhibiting an upward trend.
Turnover, �ows, ICI and illiquidity ratio have a downward trend. As funds are getting aged
they increase their size, add new stocks to their portfolio and diversify across industries.
Their portfolio is better established and they have less need to rebalance. However, they
will attract less �ow for subsequent periods compared to the �rst ones. They seem to
have higher preference for more liquid stocks in subsequent periods.Results are displayed in
Figure12
15
We look whether characteristics of new funds (FC) exhibit persistence or not. At the
start of the fund, managers may progressively adapt the characteristics of the funds to adjust
to market conditions. We want to see if there is a signi�cant change in fund characteristics
for two consecutive time windows after the inception date. Figure 13 shows that fees level
and turnover remain stable for the �rst years of activity. However, TNA is increasing which
is expected because in other case they would most probably leave the market. Furthermore,
the �ows are decreasing in the subsequent time window showing that new funds have a
capacity to attract high in�ows at their start but they do not maintain this high level of
growth. The ICI index decreases in subsequent period underlining the fact that new funds
gradually diversify their portfolios. Finally, the illiquidity ratio remains relatively stable.
Taken together, these �ndings show that new funds are diversifying their portfolios but are
not able to maintain high growth rate. We choose [0; t1] = [t1; t2] = 60 months
Ho : FCnew;i;[t1;t2] � FCnew;i;[0;t1] = 0 (9)
H1 : FCnew;i;[t1;t2] � FCnew;i;[0;t1] 6= 0
[Insert Figure 12]
[Insert Figure 13]
4.3 Characteristics of new funds vs. old funds
We use the ranking methodology as we did for fund performance. First, for each fund
start, we measure its characteristics for the �rst 36 months of existence. We also measure
characteristics of existent funds for this speci�c time window. Second, we compute deciles
of the distribution of fund characteristics of old funds. Then we assign the new fund the
rank of the decile to which it belongs to. Finally, we plot the histogram of all the rank of
the characteristics of new funds. Figure 12 shows that new funds have higher fees, slightly
higher turnover, smaller TNA and higher �ows. They have also slightly more concentrated
and illiquid assets. The histogram shows that new funds tend to have high fees and small
size. New funds do not take advantage from scale economies and consequently they are not
diversifying their portfolios to a great extent. Morevoer, we �nd a small evidence that new
funds have higher turnover and tend to invest in less liquid assets. For instance, new funds
are rebalancing their portfolios to reach an optimal composition. As they are new in the
markets, they may not �nd all the stocks they need and they are investing in stocks which
are less liquid.
16
As a second step, we compare the characteristics of new funds with average characteristics
of old funds. Figure 14 shows that new funds have higher fees and turnover than average
old funds. Moreover, new funds have smaller TNA but attract higher in�ows. Finally, new
funds are more concentrated and are investing in more liquid assets than the average old
funds.
[Insert Figure 14]
4.4 Estimation of the characteristics of old funds surroundingperiods of entry of new funds
In this section, we verify if the introduction of new funds has any impact on the character-
istics of old funds. We measure these latter surrounding dates of introduction of new funds.
We compute the following di¤erence: FCold;i;[0;t1] � FCold;i;[�t1;0] with t1=24 months. Figure14 shows that fees of funds are increasing following the introduction of new funds. Results
for turnover are quite mixed, some funds exhibit an increase while others a decrease. Unsur-
prisingly, TNA is increasing. On the other hand, fund �ows of existing funds are decreasing
after the introduction of new funds. Two explanation could be davanced for this �nding:
either new funds are successful to attract some of the �ows or the introduction of new funds
is operated in bust times. Moreover existing funds are increasing the diversi�cation of their
portfolios and increasing investments in liquid portfolios. Diversi�cation and size seem to
exhibit a time trend. As funds are getting more established in the market, they will diversify
and increase the size of their portfolios.
[Insert Figure 15]
5 The determinants of the performance and the �owsof new funds
As we have estimated the characteristics of the newly launched funds, it is interesting to see
whether these characteristics have an important role in the performance of the funds? The
�rst part of the paper tells us that new funds have a higher probability to perform better.
This part will tell us, among new funds, which funds to choose. Once we have estimated all
the ranks of new funds among exiting funds for both performance and characteristics, we
can run a multiple regression. Using the rank of the fund instead of the value of the variable
17
itself reduces the bias due to the heterogeneity of data sources and di¤erences in frequencies
(Elton and al. 2006).
Here is the equation below:
�i = 0 + 1Feesi + 2Turni + 3 Si zei + 4Flowsi + 5ICIi + 6Liqi + "i (10)
Explanatory variables Fees Turnover Size Flows ICI Illiquidity (IR)Expected Sign + + - + + +Observed Sign + + + + - -
Flowsi = 0 + 1Alphai + 2Feesi + 3Turni + 4 Si zei + 5ICIi + 6Liqi + "i (11)
Explanatory variables alpha Fees Turnover Size ICI Illiquidity (IR)Expected Sign + - + - + -Observed Sign + - + - + +
[Table 7]
Table 6 shows that turnover, �ows and TNA are signi�cant factors in the performance.
Funds that are attracting high �ows, that are larger and are rebalancing more often their
portfolios are among top performers. Even for new funds, the size of the portfolio man-
aged must reach a certain level to take advantage of scale economies and the possibility to
rebalance portfolios with less costs. Our �ndings, advocate the fact that new funds must
have a critical size at their starts. Fees also have a positive impact on fund performance
however, the coe¢ cient is not signi�cant. ICI and IR have a negative but insigni�cant e¤ect
on performance. Funds that diversify their portfolios and are investing in more liquid stocks
have higher performance. Summarizing all these �ndings, we think that the size of funds
is an important element to have high performance. Managers must estimate an optimal size
for their portfolios to get the highest performance.
6 Mutual fund starts and stock markets dependence
6.1 Mutual funds starts and IPOs
The growth in mutual fund industry and stock markets are both conditioned by economic
factors (Khorana, Servaes and Tufano, 2005). One might wonder a positive correlation
18
between the number of funds and the number of stocks in the market. Furthermore, we can
also expect a positive relationship between mutual fund starts and IPO�s. For example, a
high economic activity will induce a high number of IPO�s. Moreover, the increase in the
number of stocks will encourage fund managers to enlarge their portfolios. In this case,
managers either add new stocks in existing portfolios or create new ones. Shawky and Smith
(2005) highlight the existence of an optimal number of stocks held by a mutual fund. In
one hand, diversi�cation arguments encourages an increase in the number of stocks held.
On the other hand, a better analyst following of stocks suggests a decrease in the number
of stocks held. If we suppose the existence of an optimal number of stocks as it is speci�ed
by Shawky and Smith (2005), we can think that fund managers are creating new funds to
incorporate new stocks available on the market. Another explanation of the relationship
between mutual funds and stock markets would be as following: fund managers are creating
funds corresponding to a speci�c type of stocks. If managers notice a high IPO activity in a
speci�c type of stocks, they will create a new portfolio to target these stocks. This strategy
allows fund managers to have a diversi�ed o¤er.
This part is related to the work of Khorana and Servaes (1999). In their work, they use
family and fund industry characteristics to explain the number of fund starts in each style.
Moreover, Kaplan and Schoar (2005) explained the decision to open an equity partnership
at a family level. In our work, we simply try to link the number of funds with the number of
stocks as explained before. Other works underlined the correlation between IPO phenomenon
and mutual fund industry (Gaspar et al (2006), Reuter (2006)). They argue that IPOs are
strategically allocated to a speci�c type of funds to enhance the performance of these latter.
Fund families are favoring some funds in some speci�c periods using this IPO allocation. As
we are focusing on new funds, we may question whether new funds are betting on IPO to get
high returns at the �rst periods of their existence. Are new funds investing more in newly
launched stocks than the average existing funds? Is the time of launching the mutual fund
strategically determined to take into account IPOs that are taking place on the market?
While interesting, these questions are beyond the current purpose of our paper.
Figure14a shows the evolution of the number of funds and stocks 5. The number of
stocks in the US market has increased until mid 1998, then after it decreased. The internet
bubble was responsible for this decrease. Interestingly, we observe a common trend in the
number of funds and stocks as well. While a direct link may not exist between the both
5Equity funds are selected from CRSP Survivor-Bias Free Database (MFDB), while stocks are selectedfrom CRSP stocks database.
19
elements, many other factors can explain this relationship such as market state, exchange
rate, interest rates, scandals, economic activity. Pursuing the idea developed before, we also
think that a potential correlation may exist between the number of IPOs and the creation
of fund decisions. Figure14b shows the evolution of the number of funds starts and stock
launches. To con�rm the strength of the relationship between stock and fund markets,
we run two regressions as it is speci�ed in equation (12) and (13). Results from Table 5
show a signi�cant and positive relationship between the number of funds and stocks. We
obtain similar results for the regression between fund starts and IPOs suggesting that the
introduction of new funds coincides with high IPO activity.
Number of fundst = �0 + �1Number of stockst + �t (12)
Number of new fundst = �0
0 + �0
1Number of new stockst + �t (13)
[Table 8]
[Insert Figure 16]
6.2 Mutual fund starts and stock market prices
As we have underlined it in a previous section, mutual fund industry and stock markets
exhibit some connections. Both markets are a¤ected by some common factors. In this
section, we propose to study the interaction between mutual funds and stock markets. In
periods surrounding new mutual fund introduction, are there predictable reactions in stock
markets? Are there possibilities of arbitrage surrounding those dates? Can investors gain
systematic pro�ts by doing placements in funds just before a competitor starts? In order to
make pro�ts, we have to detect a systematic pattern in the reaction of the market that makes
it predictable at a high level. An investor can infer some information from the introduction
of new funds. For example, if for a given year, it is noticed a large number of new �growth
funds�introductions, it may be worthwhile to invest in �growth stocks�. Generally, a new
fund introduction may be informative even for investors which are not interested in mutual
funds. An investor can anticipate an increase in the demand for some speci�c stocks, as
a result of a new fund entry. He buys stocks and sells them after the new mutual fund
20
entry. If we anticipate the mutual funds�introduction, we can approximate their portfolio
composition (Warther 1995).
Institutional investors are getting large part and they are increasingly chosen by investors.
We look for some stocks held by new funds and see whether they exhibit an abnormal
(positive) return for this speci�c period. There are two main approaches to explain the
correlation between mutual funds and stock prices. The �rst one is the price pressure theory.
It supports the idea that an increase in in�ows for mutual funds will increase the demand
of the stocks and subsequently increases its price. The second approach is the information
revelation hypothesis. It suggests that mutual fund managers are followed by common
investors. Portfolios rebalancing operated by fund managers are in many cases herded by
other investors. Managers have a great in�uence on the behavior of other less informed
investors. The event of introduction of a new fund is a perfect example of the both approaches
explained before. First, the introduction of a new fund will certainly be confounded with
an increase in in�ows at least at short term. Investors will do placement in new funds. The
withdrawals from other funds, if any, would occur in second time. Second, the introduction of
new funds is interpreted as an anticipation of an upward trend in stock markets. In addition,
new funds are also herded by common investors who think that new funds�managers have
better information.
As a measure of returns, we consider average returns. We divide the sample into two
time windows. The �rst time window is t1 periods before the start of a new fund, and the
second time window is t1 periods after the start of new funds. We verify whether there is
a signi�cant change in the behavior of funds following a fund introduction. We have 1,327
funds in our database, and we study the impact of their introduction on stock returns. The
CRSP stock database contains also 27,672 stocks.
Ho : ARi;[0;t1] � ARi;[�t1;0] = 0 (14)
H1 : ARi;[0;t1] � ARi;[�t1;0] 6= 0
ARi : Abnormal return of the stock i.
We want to see the impact of fund introductions on stock prices. As we have a large
number of stocks, we randomly divide them into subgroups. Then, we compute for each
group the mean and the t-test of the di¤erence between average returns for the second and
the �rst period. We do not �nd on average a signi�cant e¤ect of fund introductions. We
21
AR1
t1 monthsafter
The month ofthe introduction
t1 monthsbefore
AR2
choose a value of t1=6 months. Figure 15 shows that the average t-test is zero. Stock prices
are not a¤ected by the introductions of mutual funds.
[Insert Figure 17]
[Insert Figure 18]
7 Conclusion
In this study, we analyze the returns of U.S. domestic equity mutual fund starts over the
period from 1962 to 2005 and their holdings beginning in 1991. More than a decade has
passed since the end of the sample period in 1992 in the pioneering work of Khorana and
Servaes (1999) on mutual fund starts. In fact, our sample is dominated by the more than
1,000 funds that have been introduced after 1992. We document that, on average, new U.S.
equity mutual funds outperform old funds over the �rst three to �ve years. However, there
are distinct patterns in the distribution of these excess returns. We compute the alphas of
all equity funds using Carhart�s (1997) four-factor model, sort the old funds into deciles,
and then assign the new funds to these deciles. We �nd that a larger number of new funds
fall into the top and, to some lesser extent, into the bottom deciles. This suggests that
the favourable excess returns of fund starts might also be the result of riskier strategies, a
hypothesis that we will study in more detail. Successful fund starts show some persistence
over two subsequent time windows, a result that holds for time windows from 12-months
to 36-months. On the other hand, a relatively large number of top performing funds in the
�rst period drop immediately to the bottom decile over the next period, again an indication
that some new funds might adopt relatively risky strategies. Analyzing the characteristics,
we �nd that new funds tend to have higher turnover and charge higher total fees. We do not
�nd any systematic di¤erences for the industry concentration and the liquidity of portfolio
22
holdings, which we measure using the industry concentration index of Kacperczyk et al.
(2005) and the illiquidity ratio of Amihud (2002). Finally, we study the relation of fund
starts and stock market movements. We �nd that many funds are introduced during periods
of high stock market IPO activity. On the other hand, we do not observe positive abnormal
returns around the launch dates of new funds that would indicate a price pressure e¤ect.
In order to investigate this question further, we plan to use the information from individual
portfolio positions of fund starts and analyze the impact on daily stock returns.
23
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26
8 Appendix
Figure 1: Average alpha of new funds after T months from their inception
After each fund start, we measure the performance for t1 �rst months of activity. We
obtain the performance of all the funds after t1 periods of their inception.. Then we compute
an average value of all the risk adjusted performance obtained. Furthermore, we vary the
length t1 and we see whether results are changing or not. Figure 1 shows that new mutual
funds have a risk adjusted return around spreading from -1 bps after 18 months to -4 bps
after 60 months. We also measure excess returns of new funds after t months from their
inception. The average excess return is decreasing as funds are getting aged, this result
con�rms the start e¤ect among new mutual funds.
27
Figure 2: Kernel density of the di¤erence between alphas of the second and �rsttime windows after the start of the fund
We measure alphas of new funds for two consecutive time windows (t=60 months). We
observe a decline in the performance of new funds. The kernel density of the di¤erence is
negatively skewed. Alphas are measured in basis points.
28
Figure 3: The di¤erence between alphas for di¤erent time windows lengths andfor di¤erent sub-samples
To verify whether the decline of the performance is in the short run or in the long run,
we modify the size of the window considered. We consider windows T = [0; t1] = [t1; t2]
from 10 months to 60 months. Results for the entire sample (Figure a), indicate that the
performance is declining beginning with the 36th month. There is a persistence only for the
short run (<36 months).
29
Figure 4: The T-test of the di¤erence between alphas for di¤erent time windowslengths and for di¤erent sub-samples
30
Figure 5: Mean di¤erence and T-test of the di¤erence between raw excess returnsfor di¤erent time windows lengths
We modify the size of the window considered to see whether the decline depends on the
time window considered. We consider windows T = [0; t1] = [t1; t2] from 20 months to 60
months. There is a decline in the performance beginning with 24 months. New funds will
maintain their performance for at least the �rst 48 months of activity.
31
Figure 6: Histogram of the rank decile of the alphas of new funds among oldfunds
For each new fund start, we compute the alpha of the new fund for a window of 36
months [0 t1]. We also compute the alphas of old funds for the same time window. We
compute deciles of the distributions of alphas of old funds. We rank the alpha of the new
fund among the old fund. We obtain the rank of each new fund for the �rst 36 months of
activity. We repeat the same steps for the subsequent tme window [t1 t2].
32
Figure 7: The rank decile of the systematic and idiosyncratic risk of the new fundamong old funds
For each new fund start, we compute the alpha, factor loadings and R^2of the new fund
and old funds for a window of 36 months after the start of the new fund. We compute deciles
of the distributions of old funds. We rank the the alpha, factor loadings and R^2 of the new
fund among the old fund. We obtain the rank of each new fund for the �rst 36 months of
activity.
33
Figure 8: Histogram of the rank decile of the new funds for mean returns, Sharperatio, standard deviation and volatility of �ows.
For each new fund start, we compute the mean return, Sharpe ratio, standard deviation
and the voltility of the �ows of the new fund and old funds for a window of 36 months after
the start of the new fund. We compute deciles of the distributions of old funds. We rank
the mean return, Sharpe ratio, standard deviation and the voltility of the �ows of the new
fund among the old fund. We obtain the rank of each new fund for the �rst 36 months of
activity.
34
Figure 9: The persistence of the performance among new mutual funds
We measure the change in the rank of the decile of new funds. We do this estimation for
two di¤erent windows sizes; 12 months (short run) and 36 months (long run). Figure a and
Figure b show the change in the rank decile for a time windows of 12 months. Figure c and
Figure d show the change in the rank decile for a time windows of 36 months. Figures b
and d show the ranking of funds under the hypothesis of no change (if they keep exactly the
same rank for two consecutive time windows)
35
Figure 10: The di¤erence between "alpha new" and average "alpha old" for dif-ferent time winodws lengths
At each fund start, we estimate the adjusted risk return for t1 �rst months of existence for
the new fund and the average alphas for old funds. We compute the di¤erence �new;i;[0;t1] ��old;i;[0;t1]. We obtain 1,327 di¤erences in alphas corresponding to all mutual fund starts
in our sample. Then, we compute the average of this value. We modify also the size of
the window considered to look for the sensitivity of the results. Figure a shows the mean
di¤erence whereas Figure b shows the t-test of this di¤erence.
36
Figure 11: The di¤erence in retruns of old funds before and after the starts ofnew funds
The mean of the di¤erence between "alpha old" before and after the start of new funds
for di¤erent interval windows is displayed in Figure a. The t-test of the di¤erence between
"alpha old" before and after the start of new funds for di¤erent interval windows is displayed
in Figure b.
37
Figure 12: Persistence of the characteristics of new mutual funds
After each fund start, we measure the fund characteristics (fees, turnover, TNA, �ows,
ICI, number of stocks, and liquidity) for t1 = 60 months of activity. We obtain the charac-
teristics of all the funds after t1 periods of their inception.. Then we compute an average
value of all these characteristics.
38
Figure 13: Kernel density of the di¤erence in new funds characteristics for twosubsequent time windows
We compute the di¤erence in the characteristics of new funds: FCnew;i;[t1;t2]�FCnew;i;[0;t1].Fees level and turnover remain stable for the �rst years of activity. However, TNA is in-
creasing which is expected because in other case they would most probably leave the market.
Furthermore, the �ows are decreasing in the subsequent time window showing that new funds
have a capacity to attract high in�ows at their start but they do not maintain this high level
of growth. The ICI index decreases in subsequent period underlining the fact that new funds
gradually diversify their portfolios. Finally, the illiquidity ratio remains relatively stable.
39
Figure 14: Rank decile of the characteristics of new funds: Fees, Turnover, TNA,Flows, ICI and Liquidity
New funds have higher fees, slightly higher turnover, smaller TNA and higher �ows. They
have also slightly more concentrated and illiquid assets.
40
Figure 15: Kernel density of the di¤erence in fees, turnover, TNA, �ows, ICI andliquidity after and before the starts of new funds
We verify if the introduction of new funds has any impact on the characteristics of old
funds. We measure these latter surrounding dates of introduction of new funds. We compute
the following di¤erence: FCold;i;[0;t1] � FCold;i;[�t1;0] with t1=24 months.
41
Figure 16: Relationship between fund starts and IPOs
The correlation between the number of equity funds and the number of stocks is displayed
in Figure a . The correlation between the number of equity funds starts and the number of
IPOs is displayed in Figure b.
42
Figure 17: The average price of stocks held by new funds
At each fund introduction, we measure the average price of stock held by the new fund
from 36 months before the start to 36 months after. Stock prices exhibit an increase before
the date of the start of the new fund and they are declining after. One explanation is that
new funds are highly implementing their portfolios using momentum stocks.
43
Figure 18: The average return of stocks held by new funds
At each fund introduction, we measure the average return of stocks held by the new fund
from 36 months before the start to 36 months after. Stock prices exhibit an increase before
the date of the start of the new fund and they are declining after. One explanation is that
new funds are highly implementing their portfolios using momentum stocks.
44
Table 1: Growth of the mutual fund industry in the U.S. and worldwideYears 1970 1980 1990 1998 1999 2000
Number of funds (World) n/a n/a n/a 50,266 52,746 51,692Number of equity funds (U.S.) 323 306 1,099 3,512 3,952 4,385Total Net Assets(World) n/a n/a n/a 9,594 11,762 11,871Total Net Assets (U.S.) 45 44 239 2,977 4,041 3 ,961
Years 2001 2002 2003 2004 2005Number of funds(World) 52,849 54,110 54,569 55,524 56,863Number of equity funds (U.S.) 4,716 4 747 4 599 4,547 4,586Total Net Assets(World) 11,654 11 324 14,048 16,164 17,771Total Net Assets(U.S.) 3,418 2 662 3,684 4,384 4,940
Table 1 gives information about the growth in mutual fund industry in the US market
and in the world. Both number of funds and total net asets have increased. Descriptive
statistics found in www.ICI.org. Total net assets is measured in billions of USD.
45
Table 2: Style distribution and TNA of the sample of mutual funds used
Type of the fund Number Percentage TNA(in millions of US$)of funds 1970 1980 1990 2005
Small Company Growth 248 18.05% 354.9 810.1 6,530.3 214,772.0Other Aggressive Growth 195 14.19% 1,964.3 1,757.9 11,974.1 153,412.3Growth 424 30.86% 7,592.1 6,418.3 63,544.5 676,040.0Income 48 3.49% 3,599.4 2,785.6 21,314.8 110,621.4Growth and Income 284 20.67% 14,941.5 13,079.0 64,155.3 719,644.4Maximum Capital Gains 0 0.00% 0 0 0 0Sector Funds 170 12.37% 514.9 491.4 9,613.7 102 947.1Not Speci�ed 5 0.36% 0 0 1,269.4 134.2
Total 1374 100.00% 28,967.3 25,342.6 178,402.3 1,977,571.4
We give the style distribution of our sample. We also compute the TNA of each style for
di¤erent years. Growth, Growth and Income, and Small Company Growth are the major
styles in the sample in terms of TNA.
46
Table 3: Size of Families
Number Number TNA(in millions of US$)of Portfolios of families 1970 1980 1990 20051 171 348.2 981.0 4,313.0 111,675.92-5 153 6,798.8 5,902.3 21,488.7 258,008.76-10 38 8,458.7 6,581.0 43,350.2 459,468.911-50 25 11,412.1 10,705.1 80,818.5 732,084.6>50 1 1,949.5 1,173 28,431.9 416,333,3Total 388 28,967.3 25,342.6 178,402.3 1,977,571.4
This table displays the number of funds held by each family. As we can see, only 26
families have more than 11 portfolios. Also, the largest 26 families have a TNA bigger than
the other 362 families. Mutual fund industry is dominated by large families.
47
Table 4: Systematic and idiosyncratic risk
Variables Khi-deux stat P-value
Performance measuresMean excess return** 23.41 0.0053Alpha** 80.15 0.0000Sharpe ratio** 31.8 0.0002
Risk measuresStandard deviation** 23.7 0.0047Flows volatility** 1275.2 0.0000
Systematic riskRMT 4.97 0.8367SMB** 33.43 0.0001HML 12.30 0.1968MOM 14.65 0.1009
Idiosyncratic riskR^2 48.10 0.0000
We rank the measure of the performance and risk if each new fund among old funds for
each start period. We also decompose the risk into two components: systematic and idiosyn-
cratic risk. New funds have higher SMB coe¢ cient and lower R2(i:e:highunsystematicrisk)
48
Table 5: Mutual fund characteristics
Fund Characteristics 1970 1980 1990 2000 2005Fees 0.0081 0.0099 0.0125 0.0122 0.0131Turnover 0.0131 0.7152 0.8758 1.0780 0.7702TNA 325.99 329.55 1,135.96 1,156.35 1,334.80Flows 0.0383 0.0002 0.1116 0.8689 0.0122ICI n/a n/a n/a 0.1996 0.2032Liquidity n/a n/a n/a 0.022 0.0021
This table displays some characteristics of the sample used. We compute average fees,
turnover, TNA, �ows, ICI, and liquidity. We can observe that fees exhibit an upward trend.
While turnover is stable for the last twenty years. TNA is increasing which re�ects the
development in the mutual fund industry. Flows, ICI and liquidity are varying from one
year to another and they do not exhibit any trend.
49
Table6:New
fundscharacteristics
Panela:Persistenceinthecharacteristicsofnewfunds
Variable
Meandi¤erenceT-StatP-value
Fees
-2.2640e-004
-1.06
0.28
Turnover
-0.0518
-1.10
0.26
TNA**
194.5540
6.47
0.00
Flows**
-0.1672
-3.37
0.00
ICI
-0.0029
-0.42
0.67
Numberofstocks*
14.5418
1.65
0.04
Illiquidity
-6.0207
-0.47
0.63
Panelb:
Characteristicsofnewfundsvs.oldfunds
Variable
Meandi¤erenceT-StatP-value
Variable
Chi-DeuxStat
P-value
Fees**
9.2656e-004
5.64
0.00
Fees**
70.01
0.00
Turnover**
0.1265
2.37
0.01
Turnover
13.57
0.13
TNA**
-467.13
-55.53
0.00
TNA**
233.95
0.00
Flows**
0.0889
9.70
0.00
Flows**
320.97
0.00
ICI**
0.0675
13.27
0.00
ICI
5.44
0.79
Numberofstocks**
-62.31
-6.36
0.00
Numberofstocks**
34.39
0.00
Illiquidity**
-57.6334
-4.39
0.00
Illiquidity*
14.53
0.10
Panelc:Characteristicsofoldfundssurroundingperiodsofentryofnewfunds
Variable
Meandi¤erenceT-StatP-value
Fees**
4.2112e-004
5.82
0.00
Turnover**
0.0265
5.35
0.00
TNA**
110.3104
11.83
0.00
Flows
0.9236
1.56
0.11
ICI**
-0.0071
-30.20
0.00
Numberofstocks**
11.8355
26.06
0.00
Illiquidity**
-21.8465
-15.75
0.00
50
We explain the performance of new funds using some characteristics. We use two steps:
First, we rank new funds among old funds for the performance and each of their character-
istics. Second, we run a cross-sectional regression between the rank of their performance and
the ranks of their characteristics.
*Signi�cant at 10%
**Signi�cant at 1%
51
Table 7: Relationship between the performance of new funds and their charac-teristics
Table 7a:Variable Coe¢ cient Std deviation T-Stat P-valueIntercept** 3.572 0.409 8.73 0.00Fees 0.037 0.038 0.99 0.32Turnover* 0.056 0.034 1.65 0.09Size** 0.258 0.035 7.30 0.00Flows** 0.131 0.032 4.04 0.00ICI -0.017 0.035 -0.50 0.61Liquidity -0.0375 0.033 -1.10 0.26
�i = 0 + 1Feesi + 2Turni + 3 Si zei + 4Flowsi + 5ICIi + 6Liqi + "i
Table 7b:Variable Coe¢ cient Std deviation T-Stat P-valueIntercept** 6.804 0.32 21.13 0.00Alpha** 0.105 0.02 4.09 0.00Fees -0.043 0.03 -1.41 0.15Turnover 0.002 0.02 0.09 0.92Size** -0.189 0.03 -6.03 0.00ICI 0.045 0.02 1.58 0.11Liquidity 0.024 0.02 0.87 0.38
Flowsi = 0 + 1Alpha+ 2Feesi + 3Turni + 4 Si zei + 5ICIi + 6Liqi + "i
52
Table 8: Relationship between fund industry and stock markets
Number of fundst = �0 + �1Number of stockst + �t
Variable Coe¢ cient Std.Error t-Stat P-value�1 4.22 61.95 0.06 0.94�2 0.064 0.01 5.93 0.00
Number of new fundst = �0
0 + �0
1Number of new stockst + �t
Variable Coe¢ cient Std.Error t-Stat P-value�01 0.487 0.343 1.41 0.15�02 0.047 0.005 8.07 0.00
The correlation between the number of funds and the number of stocks is positive and
signi�cant. Moreover, the correlation between fund starts and IPOs is also signi�cant and
positive.
53
