1
Fund Specific Determinants of Mutual Fund
Performance in the U.S. Market
Master thesis for MSc Finance (2012/13) Nan, Yi Student number: S2203596 Supervisor: Dr. B. A. Boonstra Number of words1: 9106 Date: 2013.06.21 Abstract
In this paper we use 2003 to 2012 panel data to study the relations between fund specific items (size, expense ratio, turnover ratio, age, and investment strategy) and the performance of 333 active open-end domestic equity mutual funds in the U.S. market. We find evidence that diminishing return to scale disappears in the period covering the financial crisis 2008. Mutual fund performance is negatively related with expense ratio and turnover ratio, the relation between fund age and fund performance is mixed, and the funds investing the small capitalization companies outperform the funds investing the mid and large capitalization companies in our sample period.
JEL classification: G11, G20, G23
Keywords: Mutual funds; Performance; Fund characteristics; Investment strategy; U.S. market; Financial crisis
2 1 Introduction
Mutual funds have become extremely popular over the past decades over the world. At the end of 2011, there are more than 14,000 mutual funds in the U.S. market, which have assets of $13 trillion under management. 23% of the household financial assets in the United States are located in the mutual funds. Mutual funds have been steadily growing in assets under management over the years. Although mutual fund assets fell in 2008 because of the credit crisis according to Money Magazine2, investors are still increasingly interested in investing in mutual funds3. According to Investment Company Fact Book4, Open-end mutual funds, which are most common type of mutual fund, have $11.6 trillion assets under management (AUM) out of $13 trillion. Hence open-end mutual fund selection is a popular study topic for the financial researchers.
Many researchers (Carhart, 1997; Chen et al., 2004; Gil-Bazo and Ruiz-Verdu, 2009; Kacperczyk, et al. 2005; Otten and Bams, 2002) have already tried to explain the performance of mutual funds with possible determinants in a fund characteristic perspective, including fund size, age, expense ratio, turnover ratio, loads, and flows. Most of the researchers study the performance of mutual funds before the 2008 credit crisis. And those researchers test the potential determinants using two-step regression, based on an estimated mutual fund performance (Jensen’s alpha). They estimate abnormal alpha by a market model, then regress the abnormal alpha with the determinants. It will cause a bad estimation of the relation between mutual fund performance and its determinants if the abnormal alpha is not significant so that it cannot stand for mutual fund performance well.
Besides fund characteristics, different investment strategies, like investing in large capitalization company, investing in mid capitalization company, or investing in
2 http://money.cnn.com/2009/05/13/pf/funds/mutual_funds.moneymag/
3 small capitalization company, also have effect on mutual fund performance. So we also take the investment strategy into our consideration.
In our study we investigate following factors as determinants of mutual fund performance, including fund size, expense ratio, turnover ratio, age and investment strategy. We define these factors as fund specific items in our research. It is interesting that we study the relation between fund specific items and the performance of mutual fund in a period that covers the financial crisis from 2007 to 2012. This crisis results in high of unemployment, collapse of large financial institutions, as well as worse performance in stock markets all over the world. It is reasonable to suspect that the crisis also influence the effectiveness of fund specific items on mutual fund performance. Then the research question is:
Do fund specific items influence the performance of open-end active mutual funds over 2003 to 2012 in U.S. market?
We study which fund specific items have impact on the performance of equity mutual funds in U.S. market over January 2003 to December 2012. 333 blend equity mutual funds5 are selected in this thesis. And they are all open-end funds, which means the fund issues new units or shares when more people invest. The financial crisis has a great impact on stock market, and the U.S. economy suffers from it. So we study mutual funds that mainly focus on domestic stocks in United States.
It is popular to use Jensen’s alpha (1968) as a performance measure, which determined by the constant in the regression of fund excess returns on the market factors. After that, the determinants are estimated in a regression of Jensen’s alpha. The estimation in second regression can be bad if the estimation is not good in first regression. And those dependent variables (Jensen’s alpha) are estimated data, which
4 is not consistent with regular Ordinary Least Squares.
To avoid a biased estimation of fund performance in two-step regression, we use one-step regression by adding dummy variables and independent variables into CAPM model, the three-factor model (Fama French, 1996), and the four-factor model (Carhart, 1997) to estimate the relations between fund specific items and total fund return with panel data. We use the similar model to do the regression on three mutual fund groups, including Blend – Large Cap, Blend – Mid Cap, and Blend – Small Cap, to estimate the effectiveness of the potential determinants in different investment strategy groups. In a robust test, we subtract data after July 2007, which has a direct impact of financial crisis, to test whether the model still holds.
This main purpose of the research is to help individual investors to choose well performing mutual funds by looking at fund specific items, including fund size, expense ratio, turnover ratio, age and investment strategy. All of these fund specific items have potential impact on fund performance. Fund size reflects the flexibility of the mutual fund. Expense ratio represents the cost of the mutual fund, which subtract from the return of mutual funds. Turnover ratio determines the investment activity of the mutual fund. Age is a proxy for the experience of the mutual fund manager. And different investment strategies definitely influence the fund performance.
It is interesting to study this topic because mutual funds are a popular investment choice for individual investors. It is crucial for individual investors to choose a good mutual fund. The research also investigates whether financial crisis has great impact on the selection of good mutual funds in the robust test. It tells us whether the selection of good mutual funds holds in different economy backgrounds.
5 2 Literature review
Some researchers choose mutual fund size, expense ratio, turnover ratio, and age as independent variables in their studies of the mutual fund performance, and a few investigate in investment strategy. A summary table of their results can be found in Appendix 1. More detail review and discussion are presented in the rest of this section.
According to Morningstar’s definition of net assets6, small usually means flexible, and large usually means less flexible, but can be less expensive. It seems that large size mutual funds have some advantages comparing to small size mutual funds. They have more information resources to do research so that they can analyze stocks more thoroughly. Because of economy of scale, they are cheaper. And because of the large size they have, they have more choices of investment. They have access to some investment that is not provided for some size funds.
Ferreira et al. (2013) report that large mutual funds tend to negotiate better prices, because their positions and trading volumes are relatively larger comparing to the small mutual funds. Brennan and Hughes (1991) also show that the size of trade has effect on the brokerage commissions and large size trade can have a discount. But large mutual funds also have some managerial problems. They are too large for mutual fund managers so that they cannot be managed well. They have to pay attention to wide range of investments they made. However small size mutual fund managers can focus on a few investment opportunities. While large size fund managers can find better investment opportunities due to large resources of information.
The evidence in the Cremers and Petajisto (2009) study shows the mutual funds with small size are more actively managed. And their performance is different with large funds’ performance, which is close to index funds. Furthermore, Chen et al.
6 (2004) show that fund size has negative impact on fund performance, because of liquidity constraints, which mean they suffer higher price to buy larger volumes of stock that is not available for such large volumes. This phenomenon always occurs in those mutual funds investing in illiquid stocks. They also explain the negative impact from a management perspective. When the mutual funds get larger, the organizational diseconomies become a crucial problem for them.
The results of empirical studies are quite mixed. Droms and Walker (1994) and Golec (1996) find there is no relation between fund size and performance of mutual fund. But Pollet and Wilson (2008) report that there is a negative relation between fund size and fund performance. Because the mutual funds will not diversify their portfolio with new assets but just invest more in their current positions when they receive inflows. Ferreira et al. (2013) show the U.S. evidence of diminishing returns to scale, but report that the relation between fund size and fund performance is positive for non-U.S. funds. Otten and Bams (2002) also find a positive relation between fund performance and fund size in some European countries. Overall, the evidence on the relation between fund size and performance of mutual fund is not uniform.
Another fund specific item is the expense ratio, which is defined as a fraction of total assets according to the definition on the Investopedia7 website. Fund expenses include operating expenses and management fees, which are paid for the services provided to investors. Fund managers with more information and professional knowledge render this asset management services. The expenses can vary among different mutual funds. It is reasonable that large mutual funds are cheaper, and international mutual funds may charge higher fees, which is found by Khorana, Servaes, and Tufano (2009).
Carhart (1997) and Gruber (1996) find that fund expense is negatively related
7 with the fund performance net of fee. But Chen et al. (2004) and Ippolito (1989) find no relationship between fund expense and fund performance. The results of Ferreira et al. (2013) show a negative relation between the fund expense ratio and net-of-fees performance, which is insignificantly for U.S. funds but significantly for non-U.S. funds. Dahlquist, Engstrom, and Soderlind (2000) and Otten and Bams (2002) make the same conclusion that expense is negatively related with fund performance for European funds. Sharpe (1966) also suggests a negative relation between fund expense and fund performance.
Fund turnover ratio measures the extent of mutual funds investment activities. Active funds do not necessarily outperform those funds with low turnover ratio. It really depends the stock-picking abilities.
Dahlquist et al. (2000) and Harlow and Brown (2006) and Wermers (2000) who have the same conclusion to Grinblatt and Titman (1994) find that there is positive relation between fund turnover ratio and fund performance. But there are also some opposite results from other literatures. Carhart (1997) and Elton et al. (1993) and Malkiel (1995) show that there is a negative relation between fund turnover ratio and fund performance, which means the stock-picking abilities of those mutual fund managers are poor. But Ippolito (1989) finds there is no significant relationship between fund performance and fund turnover ratio.
Ferreira et al. (2013) report that fund age measures a fund’s length of life and the ability of fund managers. But whether fund age influences the fund performance in positive way or negative way cannot be sure. It is really controversial, and each of them has his strong point. Although those younger mutual funds tend to face higher costs, they are more flexible and agile. While older mutual funds have rich experience.
8 conclusion made by Chen et al. (2004) but not in line with the result presented by Cremers and Petajisto (2009) who find a negative relation for U.S. mutual funds. In non-U.S. market Ferreira et al. (2013) find younger funds outperform older funds, which is consistent with the evidence of Otten and Bams (2002) in non-U.S. market, like U.K. and German market. Golec et al. (2004) and Prather et al. (2004) also find the relation between fund age and fund performance is negative, but not significantly.
Fund investment strategy describes what kind of companies a mutual fund invests in. If a mutual fund is a Large Cap fund, which means it invests the stock of large capitalization company, the performance of the mutual fund is positively related with the performance of the company. Bauman et al. (1998) show the stocks of smaller companies yield significantly higher returns than those of the larger companies. This evidence tells the Small Cap funds tend to perform well than Large Cap funds. Switzer (2010) also concludes that statistically significant abnormal performance is observed for small cap stocks in the U.S. and Canada.
Although the fund specific items seem to have some influence on fund performance based on literature review, the efficient market theory of Fama (1970) tells us that all information is already included in the prices. This means there is no opportunity for mutual fund managers to generate higher returns by active management, which means turnover is not necessary for better fund performance. And mutual fund managers cannot earn back the expenses, which means expense ratio has no positive effect on fund performance. If the market is efficient, the performance of mutual fund does not depend on the size, age, and investment strategy of the mutual fund. Furthermore, in Carhart (1997) four-factor model, which is an extension of Capital Asset Pricing Model, the asset returns are only determined by four market factors (market premium, SMB, HML, MOM). So that the fund specific items cannot determine the returns, which suggests our null hypothesis:
9 3 Methodology
We use a model that is based on Carhart (1997) four-factor model to test the fund specific items of mutual funds. We also use the CAPM model and Fama and French (1996) three-factor model as an additional test to confirm the results. The three-factor model improves the CAPM pricing biases by adding size and book-to-market factors, but this model does not control for the Jegadeesh and Titman (1993) momentum factor. Carhart (1997) proposes another factor to capture momentum effect. Hence, the four-factor model regression is given by
Rit – Rft = it + 0i * (Rmt – Rft) + 1i * SMBt
+ 2i * HMLt + 3i * MOMt + it, (1)
where Rit – Rft is the total return of mutual fund i minus the one-month U.S. Treasury
bill rate8 in month t. Rm
t – Rft is the risk premium on the market over the risk free
rate. The detailed definition of SMB, HML, and MOM can be found in Appendix 2. We use the four-factor model in equation (1), and add some other dummy variables and independent variables into the model to test the potential determinants (fund specific itmes), which gives the model,
Rit – Rft = it + 0i * (Rmt – Rft) + 1i * SMBt
+ 2i * HMLt + 3i * MOMt
+ 4i * D_S size + 5i * D_L size + 6i * Expense ratio
+ 7i * D_S turnover ratio + 8i * D_L turnover ratio
+ 9i * D_S age + 10i * D_L age
+ 11i * D_S cap + 12i * D_L cap + it, (2)
where D_S size and D_L size is the dummy variables for magnitude of size. We
10 define that 0-30 percentile of total fund sizes in a group is small size, 31-69 percentile is mid size, and 70-100 percentile is large size. If the size of a mutual fund is located in the 0-30 percentile, then D_S size equals to 1 and D_L size equals to 0. If it is located in the 70-100 percentile, then D_S size equals to 0 and D_L size equals to 1. If it is located in 31-69 percentile, then both of D_S size and D_L size equal to 0, which means we make the mid size as a benchmark to test the potential determinant (size). The principle is the same for turnover ratio and age. We also use D_S cap and D_L
cap as dummy variables to indicate what kind of investment strategy a mutual fund
has. We use monthly total return of mutual funds (net of expenses) in U.S. dollars from January 2003 to December 2012 to estimate the effectiveness of fund specific items.
To investigate the research question (Do fund specific items influence the performance of open-end active mutual funds over 2003 to 2012 in U.S. market?), we test the following hypotheses:
Ha, 0: Fund size does not influence fund performance ( 4 = 0, 5 = 0),
Hb, 0: Expense ratio of fund does not influence fund performance ( 6 = 0),
Hc, 0: Fund turnover ratio does not influence fund performance ( 7 = 0, 8 = 0),
Hd, 0: Fund age does not influence fund performance ( 9 = 0, 10 = 0),
and He, 0: Fund investment strategy does not influence fund performance ( 11 = 0, 12 = 0).
11 items (excluding investment strategy) remain the same in the mutual fund groups with different investment strategy. The model is given by
Rit – Rft = it + 0i * (Rmt – Rft) + 1i * SMBt
+ 2i * HMLt + 3i * MOMt
+ 4i * D_S size + 5i * D_L size + 6i * Expense ratio
+ 7i * D_S turnover ratio + 8i * D_L turnover ratio
+ 9i * D_S age + 10i * D_L age + it, (3)
where the variables are the same as those in equation (2), except that we kick out the dummy variables of investment strategy. We use the model to test the effectiveness of fund specific items (excluding investment strategy) in Large Cap group, Mid Cap group, and Small Cap group separately.
In order to check whether the results of our analysis on fund specific items hold, we subtract the sample period after the third quarter of 2007, which is the start of financial crisis, to perform the robust test for our hypotheses.
4 Data
In this section, we state the collection of our data sample, present descriptive statistics, describe the dummy setting and check for multicollinearity.
Data collection 4.1
The list of sample companies in this thesis is collected from Bloomberg website9, which provides active mutual funds information. We focus on all open-end mutual funds in U.S equity market in Blend Fund category, which includes Blend – Large
12 Cap, Blend – Mid Cap and Blend – Small Cap. The portfolios of Blend Fund are made up of a mix of value and growth stocks, which better represent the whole stock market. Data including monthly total return, net asset value (NAV), total asset/size, expense ratio, turnover ratio and inception date come from the Center for Research in Security Prices (CRSP) database. The following table shows the definition of those terms.
Table I: Definitions of mutual fund terms and fund specific items
This table shows the definitions of mutual fund terms that are used in this thesis. Fund specific items, including size, expense ratio, turnover ratio, age and investment strategy are potential determinants of mutual fund performance. (Source: www.morningstar.com)
Term Definition
Total return Returns by assuming that dividends are re-invested to purchase additional units of an equity or unit trust.
NAV NAV per share is calculated everyday based on the closing prices of the stocks in the fund's portfolio.
Size The size figure is the month-end net assets of the mutual fund, which is also total NAV.
Expense ratio The expense ratio is presented as the percentage of total asset value of a mutual fund paid for operating expenses and management fees, including administrative fees, 12b-1 fees, and all other relative costs incurred in the mutual fund.
Turnover ratio This is a measure of the fund's trading activity that is calculated by taking the lesser of purchases or sales and dividing by average monthly net assets.
Age The age measures the number of years since the founding of the mutual fund.
Investment strategy
Investment strategy tells a mutual fund invests in which level of capitalization company.
(Large capitalization company: Market value ≥ $ 10 billion; Small capitalization company: Market value < $ 2 billion; Mid capitalization company: Between $2 and $10 billion.)
13 data. We think expense ratio and turnover ratio are stable in a quarter, so that we assume they share the same quarterly data in one quarter. We translate the inception date of a mutual fund into age, which means the difference between each historical date points and inception date in years. Because our sample period is 10 years for active mutual funds, we eliminate those mutual funds that survive less than 10 years. As a result this sample includes 159 Blend – Large Cap, 57 Blend – Mid Cap and 117 Blend – Small Cap open-end equity funds, having 120 monthly observation points. Blend – Large Cap means those mutual funds invest in both value and growth companies with a market capitalization value of more than $10 billion. For Blend – Mid Cap, it ranges from $2 and $10 billion. And for Blend – Small Cap, it is between $300 million and $2 billion. The list of the mutual funds used in our research can be found in Appendix 3.
The data used in Fama – French three-factor model, and Carhart four-factor model are collected in Kenneth R. French website10. The data library in this website covers market return, SMB, HML, MOM, and risk free rate in U.S. market.
Descriptive statistics 4.2
The CRSP database provides mutual fund information of return and size in months, expense ratio and turnover ratio in quarters, as well as the inception date of mutual fund. The table in next page gives you a descriptive statistics of size, expense ratio, turnover ratio and age.
14 Table II: Description statistics of mutual fund characteristics
This table presents the descriptive statistics of mutual fund characteristics for 4 groups, which are Blend – Large Cap, Blend – Mid Cap, Blend – Small Cap, and the aggregation of these three groups over 2003.01.31 to 2012.12.31, using monthly data. The size measures the total asset that is the month-end net assets of the mutual fund, recorded in millions of dollars. The expense ratio presents as the percentage of fund assets paid for operating expenses and management fees, including administrative fees, and 12b-1 fees, and all other relative costs incurred in the mutual fund. Fund expenses are reflected in the fund's NAV. The turnover ratio is a measure of the fund's trading activity that is calculated by taking the lesser of purchases or sales and dividing by average monthly net assets. The age measures the number of years since the founding of the mutual fund. J-B stands for Jarque Bera statistics. * indicates significance at 1% level.
15 The average fund size of aggregation of groups of Blend – Large Cap, Blend – Mid Cap and Blend – Small Cap is $595 million. But the median fund size of aggregation group is only $97 million. So that the distribution of the fund size has an extremely positive skewness, which means most mutual funds have a small size. This feature is shared with Large Cap group, Mid Cap group and Small Cap group. And the fund size of Large Cap group is bigger than the fund size of Mid Cap group; the fund size of Mid Cap group is bigger than Small Cap group.
Large size usually means lower expenses. Large Cap group, which bearing a large size in our sample, has a lower expense ratio (1.22%) than the other two groups (1.32% for Mid Cap group, 1.40% for Small Cap group). The average of turnover ratio of aggregation group is 84.6%, and the average of age of aggregation group is around 12 years.
The Jarque-Bera statistic of fund size is extremely large. It is not possible to consider that the data of fund size is normal distributed. However, non-normal distributed data is in conflict with OLS assumption. To solve the problem, we decide to separate the fund size into three levels and set dummy variables instead of using the data of fund size directly. Because of the extremely large Jarque-Bera statistics of turnover ratio and age, we also use dummy variable technique for these two fund specific items. The detailed dummy setting is presented in Dummy setting section.
The distribution of expense ratio is not significant normal distribution, but it is quite good comparing with the distribution of the other fund specific items and it is acceptable for a regression test.
Dummy setting 4.3
16 assign D_S size, D_L size to the dummy variables of size. The principle is the same for turnover ratio and age. We also assign D_S cap and D_L cap to the dummy variables of investment strategy whether the mutual funds invest in the stock of large cap companies or small cap companies.
Table III: Criteria for dummy setting
This table presents the criteria for dummy setting of size, turnover ratio, and age of the mutual funds in 4 groups, which are Blend – Large Cap, Blend – Mid Cap, Blend – Small Cap, and the aggregation of these three groups over 2003.01.31 to 2012.12.31, using monthly data. 0-30 percentile is determined as small size; 31-69 percentile is determined as mid size; 70-100 percentile is determined as large size.
Aggregation Large Cap Mid Cap Small Cap 30% 70% 30% 70% 30% 70% 30% 70% Size ($million) 29.5 279.3 33.9 319.0 24.3 260.7 28.3 254.6 Turnover ratio (%) 49.0 104.0 48.0 95.0 53.0 124.0 47.0 112.0 Age (year) 8.0 14.0 8.0 14.0 7.0 13.0 7.0 12.0
In above table we find that the size of a mutual fund is not necessary determined by the investment strategy of the mutual fund. The 30 percentile point for small size in Mid Cap group (24.3) is smaller than the one in Small Cap group (28.3). Furthermore, the 30 percentile point and the 70 percentile point for turnover ratio in Mid Cap group are bigger than those points in the other three group, which means Mid Cap group is much more active than Large Cap and Small Cap group, and they are more flexible than the others.
Multicollinearity check 4.4
17 cap) for aggregation group to check the multicollinearity. The following table shows the correlation of the dummy variables of fund specific items.
Table IV: Correlation matrix of independent variables
This table presents the correlation coefficients of independent variables. All data are derived from the aggregation group over period 2003.01.31 to 2012.12.31.
# Correlation 1 2 3 4 5 6 7 8 9 1 D_S size 1.00 2 D_L size -0.43 1.00 3 Expense ratio 0.33 -0.34 1.00 4 D_S turnover ratio -0.04 0.05 -0.07 1.00 5 D_L turnover ratio 0.02 -0.03 0.03 -0.44 1.00 6 D_S age 0.16 -0.17 0.10 0.03 -0.02 1.00 7 D_L age -0.17 0.17 -0.16 -0.01 -0.03 -0.46 1.00 8 D_S cap 0.01 -0.03 0.15 0.01 0.07 0.05 -0.09 1.00 9 D_L cap -0.04 0.04 -0.15 0.01 -0.13 -0.06 0.09 -0.70 1.00
The table shows the correlation between independent variables. It is obvious that D_S and D_L are negatively related because of the dummy setting principle. We separate one potential determinant into dummy large, mid and small. So if the dummy for one mutual fund in particular date is not dummy large, it will properly be small.
Most of the correlation coefficients between D_S and D_L are around -0.45, except for the correlation coefficient between D_S cap and D_L cap, which is -0.70. This is because the dummy setting for size, turnover ratio and age is based on 30/70 percentile principle. For example, the number of small size and large size is almost the same. And the mid size occupies 40% of total. But it is not the case for D_S cap and D_L cap. The number of mutual funds in Mid Cap group is 57, only 17% of total. So if a mutual fund doesn’t invest in large cap companies, it is more likely to invest in small cap companies in our sample set.
18 age is negatively related with expense ratio and positively with D_L size, which states that the older mutual funds have lower expense ratio and larger size. The expense ratio of the mutual funds investing in large cap companies tend to be less expensive than those investing in small cap companies, because the correlation coefficient is -0.15 between expense ratio and D_L cap. Overall we find there is no large correlation between dummy variables of fund specific items. Hence we ignore the slight effect of multicollinearity in our research.
5 Empirical results
In this section we report the main findings from our analysis based on multiple linear regression model with dummy variables on panel data over January 2003 to December 2012 period and results of robust test over January 2003 to June 2007.
Main findings 5.1
19 Table V: Results of the multiple linear regression model
This table reports the results of 3 multiple linear regression of the excess total return of 333 open-end active domestic equity mutual funds in U.S. market by using panel data over January 2003 to December 2012 period. The dependent variable is the monthly excess return (difference between total return of a mutual fund minus one-month U.S. Treasury bill rate). The model (1), model (2), and model (3) represent CAPM model, Fama French three-factor model, and Carhart four-factor model respectively plus additional independent variables and dummy variables. The sample has 39873 observations, because of 87 missing data. T-statistics for the coefficient are in parentheses. * indicates significance at 10% level, ** indicates significance at 5% level, and *** indicates significance at 1%
level.
Model (1) Model (2) Model (3)
20 The above table shows that the adjusted R2’s of all the three models are quit big, which are 89.5%, 90.6% and 96.6% for model (1), model (2), and model (3) respectively. And all of the Durbin-Watson statistics are close to 2, which show there is no autocorrelation between residuals. Furthermore the significant F-statistics at 1% level also prove the strength of those three models.
The result reveals that the level of a fund size does not influence the performance of the mutual fund. Whatever level of a fund size is, small size or large size, it does not significantly contribute to the performance by making the performance of mid size fund as a benchmark. This indicates that we cannot reject the null hypothesis (Ha,0). This evidence is not in line with finding of Ferreira et al. (2013), who found fund size is negatively related to fund performance in U.S. market over period 2000 to 2007, and finding of Cremers and Petajisto (2009), who show that small funds are more active. The conflicting findings can be explained by that small mutual funds also perform poorly because of the financial crisis over 2007 - 2012. The mutual funds in this paper do experience this terrible crisis, but the mutual funds researched by Ferreira et al. (2013) do not.
The table shows that mutual fund performance is significantly negatively related with expense ratio at 5% significance level in model (2) and at 1% significance level in model (1) and (3). It indicates that we have to reject the null hypothesis (Hb,0), which means expense ratio indeed has a detrimental effect on fund performance. This result is documented in the many prior literature as well. The finding shows that there is no opportunity for mutual fund managers to gain extra return to compensate the expenses caused by active fund management.
21 better than mutual funds with high turnover ratio, which is supported by Fama (1970)’s assumption about efficient market. Furthermore, during the financial crisis, which is included in our research period, mutual fund mangers can hardly select right stocks to generate a positive return (net of transaction cost). Therefore keeping low turnover ratio can positively influence the performance of mutual funds. And the results shows there is no significant difference between the impacts of mutual funds with high turnover ratio and mid turnover ratio on fund performance. Overall we can only conclude that lower turnover ratio benefits the fund performance.
The result is mix for fund age. The result table shows that dummy variable D_S
age has significant influence on the performance of mutual fund, which means small
age funds perform better than mid age funds. This result is much more significant in model (1), which is 0.073 at 1% significance level. Young mutual fund captures more positive contribution to performance properly because those newer mutual funds are more agile and committed to perform better to get famous in public. The evidence also shows older mutual funds do not tend to perform badly. It has no significantly negative relation with the performance of mutual fund. It is even positively related with fund performance, but not significantly. This result properly because the older mutual funds have more experience about dealing with financial crisis, so they can seek return to compensate the negative influence of crisis.
22 companies is definitely a good choice. Small capitalization companies do tend to have higher return, but they are also more risky than larger capitalization companies, and all our data comes from active mutual fund, which means they are all survivors. Those dead mutual funds investing in small capitalization companies might not in our data set.
Overall we find that fund size has no influence on the performance of mutual fund. Fund expense ratio is negatively related with fund performance. Lower turnover ratio tends to have positive effect on the performance of mutual fund. Younger funds perform significantly better than mid age funds, and older funds also performance better than mid age funds but not significantly. And investment strategy also influences the performance of mutual fund. Those investing in smaller capitalization companies tend perform better than those investing in larger capitalization companies. To test whether the effects of size, expense ratio, turnover and age holds, we do the test by using the equation (3) to test those effects in different investment strategy group, which is Blend – Large Cap, Blend – Mid Cap, and Blend – Small Cap. The table of results can be found in Appendix 4.
We find some of the findings hold in this test, but some of them not. The conclusion for fund size remains the same. We do not find significant relation between fund performance and fund size. This finding is not in line with the study of Otten and Bams (2002), who report that fund size is significantly positively related with fund performance.
23 find a negative relation of fees with net-fee performance. But for Blend – Small Cap fund, this significant result disappeared. This properly can be explained that small capitalization companies are much volatile. And those mutual fund managers who invest in small capitalization companies successfully find the underpricing securities to cover their expenses. In the pervious studies, empirical findings on the relation between the performance of mutual fund and fund expense ratio is mixed. It might be the reason that the weights of Large Cap funds and Small Cap funds are different.
In this test of Large Cap group and Mid Cap group, we find lower turnover ratio contributes a positive effect on fund performance. Although the effect is not significant in Large Cap group, its t-statistic is also close to 1.65 (at 10% significance level). And the effect is significant at 5% level in Mid Cap group. For Small Cap group we find high level of turnover ratio has a significantly negative effect on fund performance, which is in line with the results of the other groups. Generally speaking, the test tells us that mutual fund with lower turnover ratio perform better than those with higher turnover ratio. This finding is aligned with Edelen et al. (2007).
The younger fund still has positive impact on fund performance in this test. We find small age funds are performing better than mid age, which is conflict with Ferreira et al. (2013) who find no relation between fund age and fund performance in U.S. market. But the Small Cap group behaves quite differently. The result shows that both older and younger funds perform better than mid age funds in Small Cap group, which is not in line with our findings that lower age benefits the fund performance.
Results of robust test 5.2
24 Table VI: Results of the robust test
This table reports the robust test results of 3 multiple linear regression of the excess total return of 333 open-end active domestic equity mutual funds in U.S. market by using panel data over January 2003 to June 2007 period. The dependent variable is the monthly excess return (difference between total return of a mutual fund minus one-month U.S. Treasury bill rate). The model (1), model (2), and model (3) represent CAPM model, Fama French three-factor model, and Carhart four-factor model respectively plus additional independent variables and dummy variables. The sample has 17895 observations, because of 87 missing data. T-statistics for the coefficient are in parentheses. * indicates significance at
10% level, ** indicates significance at 5% level, and *** indicates significance at 1% level.
Model (1) Model (2) Model (3)
Rm - Rf 1.089 (253.405) *** 0.962 (203.387) *** 0.991 (196.765) *** SMB 0.288 (52.676) *** 0.250 (42.086) *** HML 0.098 (14.086) *** 0.072 (10.200) *** MOM 0.063 (16.252) *** D_S size -0.003 (-0.116) -0.011 (-0.417) -0.006 (-0.227) D_L size 0.004 (0.154) 0.013 (0.520) 0.008 (0.315) Expense ratio -0.039 (-1.612) -0.037 (-1.670) * -0.038 (-1.727) * D_S turnover ratio -0.027 (-1.018) -0.025 (-0.989) -0.024 (-0.972) D_L turnover ratio 0.061 (2.242) ** 0.050 (1.977) ** 0.055 (2.197) ** D_S age 0.042 (1.535) -0.002 (-0.069) 0.009 (0.345) D_L age -0.010 (-0.269) -0.011 (-0.308) -0.006 (-0.186) D_S cap 0.107 (3.324) *** 0.108 (3.598) *** 0.108 (3.624) *** D_L cap -0.280 (-8.917) *** -0.289 (-9.869) *** -0.285 (-9.828) *** C 0.090 (1.713) * 0.068 (0.049) 0.070 (1.430) Observations 17895 17895 17895 R2 0.783 0.812 0.815 Adjusted R2 0.783 0.812 0.815 F-statistic 6455.993 *** 6448.553 *** 6060.411 *** Durbin-Watson stat 2.051 1.994 2.014
The model still performs well in the robust test. The adjusted R2’s of all the three models are approximately 80%, the Durbin-Watson statistics are close to 2, and the F-statistics are significant at 1% significance level.
25 we still have the result of significantly negative relation between fund performance and expense ratio. However, the significance level is 10%, which is larger than 1% in previous test for periods 2003 to 2012. It can tell that in a period without crisis the mutual fund managers can seek for the better investment, which is more profitable than those in a period including a crisis. But overall, those better investments still cannot compensate the fund expense. It is not surprise that we find the mutual funds with large turnover ratio perform better than the mutual funds with mid level of turnover ratio in a period without crisis, which is opposite to the results from period 2003 to 2012. It shows mutual fund managers have the skill to find underpricing stocks to make money in relative calm market. The result of age is in line with the conclusion of our previous research, which states no significantly negative relation between fund performance and fund age. It is interesting that the investment strategy still influences the performance of mutual fund quite significantly, which is at 1% significance level. The results in robust test and pervious tests tell us that in both periods with or without crisis, the mutual funds investing in small capitalization company significantly outperform those investing in large capitalization company.
6 Conclusion
26 relation between fund specific items and mutual fund performance by using a period of January 2003 to June 2007, which excludes the period since financial crisis starts.
The results show that some of fund specific items influence the performance of open-end active mutual funds over 2003 to 2012 in U.S. market, but some do not.
The phenomenon of diminishing returns to scale disappears. The mutual funds with large asset size and the mutual funds with small asset size do not have significant difference from those with mid asset size. Same result is given through the period 2003 – 2007 in our robust test. This evidence shows that open-end equity funds with small asset size might also has liquidity constraints during the financial crisis, so that the diminishing returns to scale do not be found in our test, in fact both returns of mutual funds with large size and small size are lower.
There is significantly negative relation between fund expense ratio and the performance of mutual fund remains in our test and robust test. This finding is consistent with the results of Carhart (1997), Gil-Bazo and Ruiz-Verdu (2009) in the U.S. market, and Dahlquist, Engstrom, and Soderlind (2000), Otten and Bams (2002) in European market.
Results also indicate that mutual fund with lower turnover ratio outperform those with higher turnover ratio, which is in line with the finding of Edelen et al. (2007). Furthermore, we find mutual funds investing in small capitalization companies suffer a lot from high turnover ratio. It is properly because the market in our sample period is bad due to financial crisis. And in the robust test, the result of the positive relation between fund performance and fund turnover ratio in periods without crisis also tells us that financial crisis indeed changes the relation.
27 relation between fund age and fund performance in all open-end equity funds. But we find there is a significantly negative relation in the Mid Cap fund group.
Finally, we find the significant difference of fund performance between mutual funds with different investment strategy. Small Cap funds tend to outperform Large Cap funds in both periods with and without crisis.
There are several limitations of the research. We just select five variables in our fund specific items, and some important variables for fund performance can be missing. There are 87 missing data in our panel, because some mutual funds data are not recorded in CRSP database. We test the distribution of the residual in equation (2). As you can see in Appendix 5, it has skewness of 0.12, which is good, but it also has excess kurtosis of 3.30. So the residual is not significantly normal distributed.
28 7 References
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31 8 Appendices
Appendix 8.1
Table VII: Summary of results in previous studies
This table reports the researchers and their results of studies on relationship between fund specific items (size, expense ratio, turnover ratio, age and investment strategy) and fund performance, as well as the market they research. Those results are collected from the literatures and articles that are used in this paper. “Int.” represents international market; “U.S.” represents the United States market; “Non-U.S.” represents the markets out of the United States. Symbol “n/a” represents not available; Symbol “+” represents positive relationship; Symbol “-” represents negative relationship; Symbol “n” represents no relationship.
Author (year) Market Size Expense
ratio
Turnover ratio
Age Investment strategy
Bauman et al. (1998) Int. n/a n/a n/a n/a -
Carhart (1997) U.S. n - - n/a n/a
Chen et al. (2004) U.S. - n n n n/a
Cremers and Petajisto (2009) U.S. - - n - n/a
Dahlquist et al. (2000) Non-U.S. - - + n/a n/a
Droms and Walker (1994) Int. n n n n/a n/a
Edelen et al. (2007) U.S. - - - n/a n/a
Elton et al. (1993) U.S. n - - n/a n/a
Ferreira et al. (2013) U.S. - - n/a n n/a
Ferreira et al. (2013) Non-U.S. + - + - n/a
Gil-Bazo and Ruiz-Verdu (2009) U.S. - - + - n/a
Golec (1996) Int. n - n n n/a
Grinblatt and Titman (1994) U.S. n n + n/a n/a
Ippolito (1989) U.S. n/a n n n/a n/a
Otten and Bams (2002) Non-U.S. + - n/a - n/a
Pollet and Wilson (2008) U.S. - - n - -
Prather et al. (2004) U.S. - - n n n/a
Sharpe (1966) U.S. n - n/a n/a n/a
32 Appendix
8.2
According to Kenneth R. French website, SMB, HML, and MOM are calculated as following:
SMB =1/3 * (Small Value + Small Neutral + Small Growth)
- 1/3 * (Big Value + Big Neutral + Big Growth), (4)
where SMBt (Small Minus Big) is the average return on the three small capitalization
portfolios minus the average return on the three big capitalization portfolios.
HML = 1/2 * (Small Value + Big Value)
- 1/2 * (Small Growth + Big Growth), (5)
where HML (High Minus Low) is the average return on the two value portfolios minus the average return on the two growth portfolios.
MOM = 1/2 * (Small High + Big High)
- 1/2 * (Small Low + Big Low), (6)
33 Appendix
8.3
Source: www.bloomberg.com
Fund Name CUSIP
number
Inception date
Blend – Large Cap
AIM Charter Fund; Class A Shares 001413103 19681126
AIM Charter Fund; Institutional Class 001413400 19910730
AIM Charter Fund; Class R Shares 001413442 20020603
AIM Charter Fund; Class B Shares 001413806 19950626
AIM Charter Fund; Class C Shares 001413814 19970804
Armada Core Equity Fund; I Shares 042086595 19970801
Armada Core Equity Fund; C Shares 042086157 20000120
Armada Core Equity Fund; A Shares 042086587 19970801
CCM Capital Appreciation Fund; Institutional Class 69338T864 19910308 CCM Capital Appreciation Fund; Administrative Class 69338T856 19960101 CCM Capital Appreciation Fund; Class A Shares 69338T476 19970120
Van Kampen Exchange Fund 024907107 19761216
Institutional Investors Capital Appreciation Fund, Inc 4579949A1 19530527 BNY Hamilton Equity Income Fund; Investor Shares 05561M408 19920810 BNY Hamilton Equity Income Fund; Institutional Shares 05561M770 19970401 Merrill Lynch Large Cap Core Fund; Class A Shares 59021R713 19991222 Merrill Lynch Large Cap Core Fund; Class B Shares 59021R739 19991222 Merrill Lynch Large Cap Core Fund; Class C Shares 59021R721 19991222 Merrill Lynch Large Cap Core Fund; Class R Shares 59021R697 20030103 Merrill Lynch Large Cap Core Fund; Class I Shares 59021R747 19991222
Burnham Fund; Class A Shares 122315203 19610213
34
Dreyfus Disciplined Stock Fund 261978340 19870101
Dreyfus Fund Incorporated 262003106 19470101
Prudential Tax-Managed Equity Fund; Class A Shares 74437B103 19990303 Prudential Tax-Managed Equity Fund; Class B Shares 74437B202 19990303 Prudential Tax-Managed Equity Fund; Class C Shares 74437B301 19990303 Prudential Tax-Managed Equity Fund; Class Z Shares 74437B400 19990303 Eaton Vance Equity Research Fund; Class A Shares 277911285 20011101 Evergreen Stock Selector Fund; Class I Shares 30023C657 19950221 Evergreen Stock Selector Fund; Class C Shares 30023C665 19990630 Evergreen Stock Selector Fund; Class A Shares 30023C681 19900228 Fidelity Advisor Large Cap Fund; Class T Shares 315805705 19960220 Fidelity Advisor Large Cap Fund; Class C Shares 315805747 19971103 Fidelity Advisor Large Cap Fund; Class B Shares 315805804 19960220 Fidelity Advisor Large Cap Fund; Class A Shares 315805861 19960903 Fidelity Advisor Large Cap Fund; Institutional Shares 315805887 19960220
Large Cap Stock Fund 315912402 19950622
Fidelity Destiny I; Class O Shares 316127109 19700710 Fidelity Destiny I; Class N Shares 316127307 19990430 Fidelity Growth & Income II Portfolio 31617F403 19981228
Fidelity Magellan Fund 316184100 19630502
FTI Large Capitalization Growth & Income Fund 302927801 19981211 Fundamental Investors, Inc; Class A Shares 360802102 19321215 Fundamental Investors, Inc; Class B Shares 360802201 20000315 Fundamental Investors, Inc; Class C Shares 360802300 20010315 Fundamental Investors, Inc; Class F Shares 360802409 20010315 Fundamental Investors, Inc; Class 529-A Shares 360802508 20020215 Fundamental Investors, Inc; Class 529-B Shares 360802607 20020219 Fundamental Investors, Inc; Class 529-C Shares 360802706 20020215 Fundamental Investors, Inc; Class 529-E Shares 360802805 20020307 Fundamental Investors, Inc; Class R-5 Shares 360802839 20020515 Fundamental Investors, Inc; Class R-4 Shares 360802847 20020725 Fundamental Investors, Inc; Class R-3 Shares 360802854 20020604 Fundamental Investors, Inc; Class R-2 Shares 360802862 20020521 Fundamental Investors, Inc; Class R-1 Shares 360802870 20020619 Fundamental Investors, Inc; Class 529-F Shares 360802888 20020923
US Equity Fund; Investment Class 36158T852 19971125
US Equity Fund; Service Class 36158T860 20010103
GMO US Core Fund; Class IV Shares 362008849 19980109
S&S Program Mutual Fund 369664107 19670101
35 Goldman Sachs CORE US Equity Fund; Class B Shares 38141W554 19960501 Goldman Sachs CORE US Equity Fund; Class A Shares 38141W620 19910524 Goldman Sachs CORE US Equity Fund; Class C Shares 38142B278 19970815 Hartford Growth & Income Fund; Class C Shares 416645547 19980801 Hartford Growth & Income Fund; Class Y Shares 416645653 19980430 Hartford Growth & Income Fund; Class B Shares 416645661 19980430 Hartford Growth & Income Fund; Class A Shares 416645679 19980430
Core Equity Fund; Class A Shares 92922W630 20000703
Core Equity Fund; Class B Shares 92922W622 20000711
Core Equity Fund; Class C Shares 92922W101 19920921
Core Equity Fund; Class Y Shares 92922W200 19950101
JP Morgan Institutional Tax Aware Disciplined Equity Fund 616920401 19970130 JP Morgan Disciplined Equity Fund; Class A Shares 616918363 20010928 JP Morgan Disciplined Equity Fund; Institutional Shares 616918793 19970103 JP Morgan Disciplined Equity Fund; Select Shares 616918512 20010910 MFS Union Standard Equity Fund; Class I Shares 55273W103 19940114 MFS Union Standard Equity Fund; Class A Shares 55273W400 19940101 MFS Union Standard Equity Fund; Class B Shares 55273W509 19970811 MFS Union Standard Equity Fund; Class C Shares 55273W608 19970811
MFS Research Fund; Class A Shares 552981102 19710101
MFS Research Fund; Class B Shares 552981201 19930907
MFS Research Fund; Class C Shares 552981508 19940103
\MFS Research Fund; Class I Shares 552981706 19970102
MP 63 Fund, Inc 553422106 19990302
Mosaic Foresight Fund 619442403 19930416
Fremont Structured Core Fund 357378405 19920814
Massachusetts Investors Trust; Class A Shares 575736103 19240715 Massachusetts Investors Trust; Class B Shares 575736202 19930907 Massachusetts Investors Trust; Class C Shares 575736301 19960701 Massachusetts Investors Trust; Class I Shares 575736400 19960101 Massachusetts Investors Trust; Class 529A Shares 575736608 20020731 Massachusetts Investors Trust; Class 529B Shares 575736707 20020731 Massachusetts Investors Trust; Class 529C Shares 575736806 20020731 Mellon Large Cap Stock Fund; M Class 58551X108 20001002 Mellon Large Cap Stock Fund; Investor Shares 58551X694 20010711 Oppenheimer Main Street Fund; Class A Shares 68380D108 19880203 Oppenheimer Main Street Fund; Class C Shares 68380D207 19930101 Oppenheimer Main Street Fund; Class B Shares 68380D801 19941003 Oppenheimer Main Street Fund; Class N Shares 68380D827 20010301 Oppenheimer Main Street Fund; Class Y Shares 68380D884 19961101
36
Equity Income Fund 701769101 19920831
Phoenix-Oakhurst Growth & Income Fund; Class A Shares 718939507 19970925 Phoenix-Oakhurst Growth & Income Fund; Class C Shares 718939705 19970925 Principal Partners Fund; Advisors Preferred Class Shares 74253J792 20001206 Principal Partners Fund; Advisors Select Class Shares 74253J818 20001206 Principal Partners Fund; Preferred Class Shares 74253J826 20001206 Principal Partners Fund; Select Class Shares 74253J834 20001206 Principal Partners Fund; Institutional Class Shares 74253J842 20001206 Principal Partners Fund; Class J Shares 74253M712 20010301 Putnam Research Fund; Class Y Shares 746802487 20000404 Putnam Research Fund; Class C Shares 746802628 19990201 Putnam Research Fund; Class M Shares 746802677 19980615 Putnam Research Fund; Class B Shares 746802685 19980615 Putnam Research Fund; Class A Shares 746802883 19951002 Putnam Investors Fund; Class A Shares 746809102 19251201 Putnam Investors Fund; Class B Shares 746809201 19930301 Putnam Investors Fund; Class C Shares 746809300 19990726 Putnam Investors Fund; Class M Shares 746809409 19941202 Putnam Investors Fund; Class Y Shares 746809508 19970107
Core Equity Portfolio 750869109 19940510
Core Equity Portfolio; Institutional Class 750869703 20020502 T Rowe Price Capital Opportunity Fund, Inc 77954P108 19940101 Tax-Managed Large Cap Fund; Class C Shares 782478309 19991201 Tax-Managed Large Cap Fund; Class E Shares 782478879 20001114 Tax-Managed Small Cap Fund; Class E Shares 782478887 20001206 Tax-Managed Large Cap Fund; Class S Shares 782493720 19961007
Schwab Core Equity Fund 808509806 19960701
Security Equity Series; Class A Shares 814219101 19620910 Security Equity Series; Class B Shares 814219200 19931019 Security Equity Series; Class C Shares 814219846 19990129 State Farm Equity Fund; Class A Shares 856852108 20001031 State Farm Equity Fund; Class B Shares 856852207 20001031 State Farm Equity Fund; Institutional Class Shares 856852751 20011101 AAL Capital Growth Fund; Institutional Shares 000357723 19971229 AAL Capital Growth Fund; Class A Shares 000357103 19870716
All American Equity Fund 911476604 19810304
Diversified Stock Fund; Class C Shares 926464157 20020301 Diversified Stock Fund; Class R Shares 926464421 19990326 Diversified Stock Fund; Class A Shares 926464603 19891020
Jamestown Equity Fund 969557602 19921201
37
Stralem Equity Fund 862594207 20000118
Blend – Mid Cap
AIM Mid Cap Core Equity Fund; Class R Shares 00141M598 20020603 AIM Mid Cap Core Equity Fund; Institutional Class Shares 00141M630 20020315 AIM Mid Cap Core Equity Fund; Class C Shares 00141M671 19990503 AIM Mid Cap Core Equity Fund; Class B Shares 00141M796 19930401 AIM Mid Cap Core Equity Fund; Class A Shares 00141M812 19870609 CCM Mid-Cap Fund; Institutional Class Shares 69338T849 19910827 CCM Mid-Cap Fund; Administrative Class Shares 69338T831 19940101
CCM Mid-Cap Fund; Class A Shares 69338T211 19970114
Dreyfus Premier Structured Mid Cap Fund; Class R Shares 26200C676 20010629 Dreyfus Premier Structured Mid Cap Fund; Class C Shares 26200C692 20010629 Dreyfus Premier Structured Mid Cap Fund; Class A Shares 26200C726 20010629
FPA Capital Fund, Inc 302539101 19660101
Fidelity Advisor Mid Cap Fund; Class T Shares 315805408 19960220 Fidelity Advisor Mid Cap Fund; Class B Shares 315805507 19960220 Fidelity Advisor Mid Cap Fund; Institutional Shares 315805606 19960220 Fidelity Advisor Mid Cap Fund; Class C Shares 315805713 19971103 Fidelity Advisor Mid Cap Fund; Class A Shares 315805879 19960903
Fidelity Mid-Cap Stock Fund 316128404 19940329
Technology Fund; Class A Shares 318530631 19940404
Technology Fund; Class Y Shares 318530649 19940404
Technology Fund; Class C Shares 318929429 20000201
Mid Cap Stock Fund; Class C Shares 42725G852 19971106 Mid Cap Stock Fund; Class A Shares 42725G860 19971106 Huntington Mid Corp America Fund; Trust Shares 446327546 20010301 Huntington Mid Corp America Fund; Investment A Shares 446327561 20010301 ING Index Plus Mid Cap Fund; Class O Shares 44981M136 20010801 ING Index Plus Mid Cap Fund; Class I Shares 44981M581 19980203 ING Index Plus Mid Cap Fund; Class C Shares 44981M599 19980630 ING Index Plus Mid Cap Fund; Class B Shares 44981M615 19990301 ING Index Plus Mid Cap Fund; Class A Shares 44981M623 19980203 JPMorgan Mid Cap Equity Fund; Select Shares 62826P502 19960101 JP Morgan Disciplined Equity Fund; Class A Shares 616918363 20010928 One Group Diversified Mid Cap Fund; Class A Shares 68231N701 19920501 One Group Diversified Mid Cap Fund; Class B Shares 68231N800 19960923 One Group Diversified Mid Cap Fund; Class C Shares 68231N883 19990322 One Group Diversified Mid Cap Fund; Class I Shares 68231N875 19910601
Mosaic Mid-Cap Fund 619442106 19830707
38 Mellon Mid Cap Stock Fund; M Class 58551X306 20001002 Munder Mid Cap Select Fund; Class Y Shares 626124242 19980624 Munder Mid Cap Select Fund; Class II Shares 626124267 20000714 Munder Mid Cap Select Fund; Class B Shares 626124275 20000705 Munder Mid Cap Select Fund; Class A Shares 626124283 20000703 MidCap Blend Fund; Advisors Preferred Class Shares 74251T222 20001206 MidCap Blend Fund; Advisors Select Class Shares 74251T230 20001206 MidCap Blend Fund; Preferred Class Shares 74251T248 20001206 MidCap Blend Fund; Select Class Shares 74251T255 20001206 MidCap Blend Fund; Institutional Class Shares 74253Q747 20010301
MidCap Blend Fund; Class J Shares 74253Q754 20010301
Mid-Cap Fund; Class A Shares 783925795 19930216
Dreyfus Premier New Leaders Fund, Inc; Class A Shares 26202E100 19850129 Dreyfus Premier New Leaders Fund, Inc; Class C Shares 26202E308 20021127 Dreyfus Premier New Leaders Fund, Inc; Class R Shares 26202E407 20021127 Strong Opportunity Fund; Advisor Class Shares 86335K209 20000224 Strong Opportunity Fund; Investor Class Shares 86335K100 19830101 Strong Opportunity Fund; Class K Shares 86335K845 20020830 Wright Selected Blue Chip Equities Fund; Standard Shares 98235F107 19830104
Blend – Small Cap
ABN AMRO/TAMRO Small Cap Fund; Class N Shares 00078H216 20001130 AIM Small Cap Equity Fund; Class R Shares 00141L509 20020603 ICM Small Company Portfolio; Institutional Class Shares 00758M220 19890417 FMA Small Company Portfolio; Institutional Class Shares 00758M246 19910101 AIM Small Cap Equity Fund; Class C Shares 008879470 20000831 AIM Small Cap Equity Fund; Class B Shares 008879488 20000831 AIM Small Cap Equity Fund; Class A Shares 008879496 20000831 Multi-Manager Series: CCM Emerging Companies Fund;
Institutional Class
69338T823 19930625 CCM Emerging Companies Fund; Administrative Class 69338T815 19960329
Small Company Fund; Advisor Class 02507M824 20000907
Small Company Fund; Inst Class 02507M832 19991001
Small Company Fund; Investor Shares 02507M840 19980731 BNY Hamilton Multi-Cap Equity Fund; Investor Shares 05561M564 19961210 Ultra-Small Company Tax Advantage Portfolio 108747403 19970731
Buffalo Small Cap Fund, Inc 119804102 19980414
39 Liberty Small Cap Fund; Class C Shares 531273357 20021118 Liberty Small Cap Fund; Class B Shares 531273365 19981101 Liberty Small Cap Fund; Class A Shares 531273373 19981101
Conestoga Small Cap Fund 207019100 20021001
US Small Cap Portfolio 233203843 19920319
Scudder Small Company Stock Fund; Class S Shares 460965791 20000717 Scudder Small Company Stock Fund; Class C Shares 460965569 20010625 Scudder Small Company Stock Fund; Class B Shares 460965577 20010625 Scudder Small Company Stock Fund; Class A Shares 460965585 20010625 Small Cap Contrarian Fund; Institutional Class Shares 24610B859 19981229 Small Cap Contrarian Fund; Class A Shares 24610B883 19981229 Diversified Institutional Special Equity Fund 255276479 20000911 Diversified Investors Special Equity Fund 255276800 19940103 Fidelity Small Cap Independence Fund 315912303 19930628 Fidelity Small Cap Retirement Fund 315912600 20000926 Small Cap Select Fund; Class Y Shares 318941481 19920506 Small Cap Select Fund; Class S Shares 318941499 19931231 Small Cap Select Fund; Class C Shares 318941515 20010924 Small Cap Select Fund; Class B Shares 318941523 19950101 Small Cap Select Fund; Class A Shares 318941531 19920506
Frontegra Horizon Fund 359033602 20020830
40 Prudential Small Company Fund, Inc; Class B Shares 743968208 19801113 Prudential Small Company Fund, Inc; Class C Shares 743968307 19940801 Prudential Small Company Fund, Inc; Class Z Shares 743968406 19960301 JPMorgan Trust Fund; Select Class Shares 62826P601 19960101 Kalmar 'Growth-With-Value' Small Cap Fund 483438206 19970411 Batterymarch US Portfolio; Inst Class 502082811 20000313 Lord Abbett Small-Cap Blend Fund; Class A Shares 54400M104 20010626 Lord Abbett Small-Cap Blend Fund; Class B Shares 54400M203 20010626 Lord Abbett Small-Cap Blend Fund; Class C Shares 54400M302 20010626 Lord Abbett Small-Cap Blend Fund; Class P Shares 54400M401 20010626 Lord Abbett Small-Cap Blend Fund; Class Y Shares 54400M500 20010626
Exeter Fund; Small Cap Series 301722104 19920430
DLB Small Company Opportunities Fund 232941807 19980720 Mellon Small Cap Stock Fund; M Class 58551X405 20001002 Mellon Small Cap Stock Fund; Investor Shares 58551X744 20010711 Nationwide Small Cap Fund; Institutional Service Class 366650307 19981102 Nationwide Small Cap Fund; Class C Shares 366645133 20010301 Nationwide Small Cap Fund; Class A Shares 366650109 19981102 Growth Discovery Fund; Class I Shares 653699660 19950712
Oberweis Micro-Cap Portfolio 674375209 19960101
Oppenheimer Main Street Small Cap Fund; Class A Shares 68381F102 19990802 Oppenheimer Main Street Small Cap Fund; Class B Shares 68381F201 19990802 Oppenheimer Main Street Small Cap Fund; Class C Shares 68381F300 19990802 Oppenheimer Main Street Small Cap Fund; Class Y Shares 68381F409 19990802 Oppenheimer Main Street Small Cap Fund; Class N Shares 68381F508 20010301 Phoenix-Kayne Small-Mid Cap Fund; Class A Shares 719087702 20020830 Phoenix-Kayne Small-Mid Cap Fund; Class X Shares 719087876 19961018 Phoenix-Kayne Small-Mid Cap Fund; Class C Shares 719087884 20020830 SmallCap Blend Fund; Advisors Preferred Class Shares 74253J271 20001206 SmallCap Blend Fund; Advisors Select Class Shares 74253J289 20001206 SmallCap Blend Fund; Preferred Class Shares 74253J297 20001206 SmallCap Blend Fund; Select Class Shares 74253J313 20001206 SmallCap Blend Fund; Institutional Class Shares 74253Q564 20010301 SmallCap Blend Fund; Class J Shares 74253Q572 20010301 T Rowe Price Small-Cap Stock Fund; Shares 779572106 19550101 T Rowe Price Small-Cap Stock Fund; Advisor Class 779572403 20000331
Equity II Fund; Class I Shares 782493209 19811228
Equity II Fund; Class E Shares 782493274 19990514
SA US Small Company Fund 78386T874 19990805
SSgA Small Cap Fund 784924706 19920701
41 Schroder US Opportunity Fund; Investor Shares 808088405 19930805 STAAR Investment Trust; Smaller Company Stock Fund 852314400 19970528 TIAA-CREF Fund; Retail Class Shares 87244W813 20021001 TIAA-CREF Fund; Retirement Class Shares 87244W821 20021001 TIAA-CREF Fund; Institutional Class Shares 87244W839 20021001
Babson Enterprise Fund II 056173107 19910805
AAL Small Cap Stock Fund; Institutional Shares 000357749 19971229 AAL Small Cap Stock Fund; Class A Shares 000357871 19960701 USAA Mutual Fund; Small Cap Stock Fund 903288850 19990802
UMB Scout Small Cap Fund 90280R102 19861218
