Do Mutual Fund adoptions create value?

Transcription

1 Do Mutual Fund adoptions create value? Li-Wen Chen * National Chi-Nan University liwenchen@mail2000.com.tw Fan Chen Louisiana State University fchen4@lsu.edu Current edition: Aug 13th, 2004 Abstract: This study examines the determinants of mutual fund adoption and adoptee funds performance and shareholders wealth impact during the 2-year interval surrounding the adoptions. Result shows adoptee funds expense ratio and asset turnover rate drop significantly while net asset flow increases significantly during the adoptions. Shareholders wealth of adoptee funds enhances not merely through capital appreciation but better services adopter funds provide with lower expenses. Adoptee fund managers receive substantial increase in management fees from the positive net asset flow, supported by adopter funds reputation and strong sales network. Adopter fund managers increase operation efficiency through leveraging outside talent and offer diversified fund product lines to their shareholders. Mutual fund Adoption seems to be a win-win solution to the fund industry. Keywords: Mutual fund adoptions; Persistence performance; Fund flows and expenses * Department of Finance, National Chi Nan University, University Rd, Puli, Nantou Hsien, Taiwan 545; liwenchen@mail2000.com.tw Department of Finance, Louisiana State University, 2173 CEBA, Baton Rouge, LA Tel: ; fchen4@lsu.edu. The author thanks I-Hsuan Chiag for providing the mutual fund data and Ji-Chai Lin, Chip Ryan, Jeff Busse, Adam Lei, and Hsiao Fen Yang for helpful suggestion and comments. 1

2 Do Mutual Fund adoptions create value? Abstract: This study examines the determinants of mutual fund adoption and adoptee funds performance and shareholders wealth impact during the 2-year interval surrounding the adoptions. Result shows adoptee funds expense ratio and asset turnover rate drop significantly while net asset flow increases significantly during the adoptions. Shareholders wealth of adoptee funds enhances not merely through capital appreciation but better services adopter funds provide with lower expenses. Adoptee fund managers receive substantial increase in management fees from the positive net asset flow, supported by adopter funds reputation and strong sales network. Adopter fund managers increase operation efficiency through leveraging outside talent and offer diversified fund product lines to their shareholders. Mutual fund Adoption seems to be a win-win solution to the fund industry. 2

3 1. Introduction Motivated by market efficient hypothesis, signaling theory, and empirical evidence on the disagreement of mutual fund persistence performance, we examine the wealth creation effect surrounding the mutual fund adoption. Well known to the mutual fund industry, it is costly to build a sizable reputation asset management group and stay competitive. Through strategic alliances, small boutique investment companies (fund adoptees) can line up with industry powerhouses (fund adopters) by sharing their national-wide sales force and back office services. Adoptees share adopters reputation by rename their funds and list under adopters fund families. Consequently, adoptees receive positive asset flows in exchange for a small percentage of managing fees. On the other hand, adopters enhance their services by providing more diversified product lines through leveraging outside talent and achieving operation efficiency. This paper contributes to the fund industry by examining the motivation for fund management strategic alliance and shareholders wealth creation surrounding the fund adoptions. The strategic alliances seem to be perfect match, but according to Wall Street Journal s article on December 1 st, 2003, it does have some downside risk associated with the adoption. One of the many would be the control and ownership for the management of adoptee funds. If the adoptee funds underperform, could their managers get fired or lose the 3

4 control of ownership? For adopters, what would be the impact to the in house managers once the outside talent brining in? Besides, timing and investors preference are also important criteria when evaluating the chances of partnership. For example, Merrill Lynch brought in Turner large cap growth equity fund without marketing the fund. In addition, the aggressive-growth fund was out of investors favor. The strategic alliance was short-lived and the adoptee fund was returned to the adoptee. In 2003, after Matrix Advisors value funds shareholders voted for their funds adopted by Strong management group, Strong fund group was reported for timing (impropriate trading) scandal and the whole restructure plan was set aside. Successful adoptions require the adoptee management really get to know the adopting current management team. It started through understanding and accessing the investment team and not just reviewing historic record and numbers. This paper aims to provide solutions for the following concerns. First, do adoptee funds generate abnormal return (do adoptee funds perform differently) during the fund adoptions? Second, do adopters fund managers have better selection (forecast) skills to identify which funds will perform better in the short and long run? Third, and most important of all, what causes the abnormal return, if there is any, and who benefits through the fund adoptions? 4

5 Our result shows, on average, sample funds generate cumulative raw (market-adjusted) return of % (9.947%) with 5% (1%) significant while 80% (73%) of the sample funds generate positive raw (market-adjusted) return 12-month before the announcement. The adoptee funds have relative small fund sizes; with 80% of the sample funds have a total net asset less than 80 million at 13th month before the announcement. Our result indicates, at the month of adoption announced, net asset flow increased by an average (median) of million dollar (4.30 million) with 5% significant; expense ratio drop an average (median) of 86 (47) basis point compared with size-controlled sample during the 2-year interval. The annual asset turnover rate shows a significant drop on an average (median) of 51.34% (62.50%) at 10% significant compared with reputation-controlled sample 1 year after the adoption. It indicates positive net asset flows during the adoption reduce the portfolio turnover and the frequency of trading. Aligned with persistence performance literature, signaling hypothesis (reputation) and momentum strategy, sample funds show positive market-adjusted returns both in the short run and long run. In the short run, sample funds surpass the size-controlled matched funds by an average (median) of 1.206% (1.20%) monthly return at 5% (5%) significant one month after the announcement of the adoption. In the long run, through reputation-controlled match sample, during the 25-month of accumulative return, the adoptee funds show an average (median) return of 7.775% (8.735%) at 5% (5%) significant. From our result, we support the fund persistence performance holds 5

6 and signaling effect do play major roles to generate abnormal return both at the month of adoption announced and 2-year interval surrounding the adoptions. The synergy created through this strategic alliance for fund adoptions create significant value for both the fund managers, who receive the distribution fees (adopters) and managing fees (adoptees), and shareholders, who benefit through better services adopters provide, lower expenses and most important, a significant capital appreciation. 2. Existing Literature 2.1 Strategic alliance and joint venture There has been a tremendous and persistent growth in the mutual fund industry over the past 20 years. This is true whether one measures growth by assets under management, number of mutual funds, or the number of academic articles concerned with some aspect of the mutual fund industry. Total assets of mutual funds were less than $500 billion in 1985 and climbed to more than $7 trillion at the end of 2003, a compounded annual growth rate of 20%. However, the phenomenon of mutual fund adoption was not existed until late Although strategic alliance and joint venture among corporations have been addressed in the academic literature, little is known about the strategic alliance among mutual fund industry. Chan, Kensinger, Keown and Martin (1997) investigate share price responses to the formation of 345 non-equity strategic alliances from 1983 to The average stock price 6

7 response is positive and the partnering firms tend to display better operating performance than their industry peers over the 5-year period surrounding the year in which an alliance is formed. Among the samples, partners neither share equity control as in a minority equity investment nor create new organizational entity as in a joint venture but simply agree to pool resources. Among our sample funds, there are non-equity strategic alliances. If the adoptee funds generate abnormal return during the 2-year interval surrounding the adoption, we can conclude the strategic alliances among mutual fund entities do create value. Johnson and Houston (2000) conclude that horizontal joint ventures create synergistic gains that are shares by partners. Announcement of such ventures generate wealth gains that are positively correlated across the parties, which Berkovitch and Narayanan (1993) argue is consistent with synergy-sharing motive. 2.2 Persistence performance The existing literatures have been devoted lots to the persistence performance of mutual fund. William F. Sharpe (1966) presents the measurement and prediction of mutual fund performance by adapting capital theory and the behavior of stock market prices for the 34 open-end funds in He concludes capital market is highly efficient and that good managers concentrate on evaluating risk and providing diversification, spending little effort (and money) on the search for incorrectly priced securities. Early academic studies find that 7

8 mutual funds do not systematically outperform benchmark portfolio, such as the market indices. The practitioner literature sees things differently, expressing a consistent belief that active selection among actively managed funds can be profitable. Grinblatt and Titman (1988) show that the persistence performance effect is statistically significant over the five-year period. Grinblatt, Titman and Wermers (1995) find mutual funds that invested on momentum realized significantly better performance than other funds. Hendricks, Patel, and Zeckhauser (1993) show that the performance persistence phenomenon appears robust to a variety of risk-adjustment measures for the growth funds during 1974 to Brown and Coetzmann (1995) explore the persistence performance in equity mutual funds using absolute and relative benchmarks. Black, Elton and Gruber (1993) conclude that survivorship bias raises return by 27 basis points per annum for bond funds while Malkiel (1994) discover with survivor bias, all funds at the same period raise return by 150 basis points. Consequently, previous studies have somewhat overstate the return of mutual fund due to survivor bias. Carhart (1997) demonstrate common factors in stock returns and investment expenses almost completely explain persistence in equity mutual fund s mean and risk-adjusted returns. Elton, Gruber and Blake (1996) use free survivorship sample and reconfirm hot hands result that high return can predict high return in the short run. This is the same result as Hendricks, Patel and Zeckhauser (1993) conclude that this is a short run phenomenon. However, if using risk-adjusted returns with optimal weights from modern 8

9 portfolio theory, a past performance is predictive of future risk-adjusted performance on both the short run and long run. Detzel and Weigand (1998) point out previous research that use factor-mimicking portfolios and characteristic benchmarks to model fund performance fails to explain all the persistence in fund returns as there is little evidence of momentum in fund returns during the late 1980s and 1990s. They also suggest investors that instead of buying the best-performing funds from the prior periods, investors should identify the size and style characteristics of funds and research current market trends in these factors. Porter and Trifts (1998) use relative annual performance ranks find no evidence that superior relative performance of experienced managers measured over the 5-year period was not predictive of superior performance over the subsequent 5 years. Obvious, using different sample (different year) with different method create the disagreement for the market efficiency and the existence of persistence performance. Our result supports Carlson (1976) findings that mutual funds with above-median returns over the preceding year typically repeat their superior performance. It also strengthens Goetzmann and Ibbotson (1994) conclusion that the performance persistence phenomenon is present in raw and risk-adjusted returns from one month to three year. 2.3 Fund flows and return As Sirri and Tufano (1998) find out funds that generate high returns tend to attract 9

10 additional investment but poorly performing funds do not experience significant redemption. The asymmetric relationship between fund performance and fund asset flows may be attributed to the costly search. They conclude membership in a large fund complex is an important determinant of fund flows in the pre-1990 period because large fund complexes reduce consumers search costs for funds. Based on Sirri and Tufano (1998) argument, we hope to identify significant change of adoptee funds net asset flows as large fund complex (fund adopters) make the announcement of adoption. Warther (1995) finds aggregate security returns are highly correlated with concurrent unexpected cash flows into mutual funds, but unrelated to concurrent expected flows. An unexpected inflow equal to 1% of total stock fund assets ($4.75 billion) corresponds to a 5.7% increase in the stock price index. Further, fund flows are correlated with the returns of the securities held by the funds, but not with the returns of other types of securities. The author finds evidence of a positive relation between flows and subsequent returns and evidence of a negative relation between returns and subsequent flows. If we can identify a significant increase on the flows, then we directly support Warther s argument in explaining the abnormal return during the fund adoptions. Edelen and Warner (1999) use daily flow data to test the relationship between market return and aggregate flow into U.S equity mutual funds. They find aggregate flow follows market return with one-day lag and there exist positive association between aggregate daily flows and concurrent market returns. If we can identify the past return 10

11 attract the flows, we can support Edelen and Warner (1999) argument. 2.4 Mutual fund mergers Jayaraman, Khorana and Nelling (2002) uses 742 open-end mutual fund mergers during 1994 to 1997 to examine mutual fund mergers and shareholders wealth impact toward target and acquiring funds. The paper concludes a significant positive 1-year post-merger performance for the target fund and a significant negative post-merger performance for the acquiring funds. Expense ratio drops for the target fund while the net asset flows continue to remain negative for one year after the merger for the combined entities. In the pre-merger period, target funds usually have small asset size, higher expense ratio and perform poorly compared to the acquiring funds, suggesting that the fund mergers may be partly motivated by a desire to achieve economies of scale. Target fund shareholders appear to be the major beneficiaries of the mutual fund merger. McConnell and Namtell (1985) argue that joint ventures are similar to mergers, and therefore may also create synergistic gains for the partners. Our finding indicates that adoptee funds have similar characteristic of small fund size before the adoption and significant drop on expense ratio after the adoption. Adoptee fund shareholders are also major beneficiaries as target fund shareholders. Different from the target fund in the mutual fund merger, adoptee funds demonstrate a strong past performance. 11

12 2.5 Fund size and performance In our sample, we find 80% of our sample funds are less than 80 million in total net assets 13-month before the announcement of adoption. We are suspicious that besides control and ownership issues, does fund size play major role for adopters in determinants of adoptee funds? Chen, Hong, Huang and Kubik (2003) use data from 1962 to 1999 with CAPM-adjusted 4-quarter gross returns and conclude funds in the smallest fund size quintile outperform funds in the largest fund size quintile by an average of 2.07% over the next year. They also identify that fund performance is negatively correlated with total net assets. Clark (2003) looks at core, growth and value funds in 1991 to 2001 for 1, 3, and 5-year contiguous periods and find no contiguous periods where fund size affects performance on either a gross, net or risk-adjusted basis. Although he finds differences in the mean and median returns, he never finds quintile samples ever coming from different populations on a consistent basis. Consequently, there is probably nothing to be gained by filtering on fund size. 2.6 Signaling and reputation Slovin, Sushka and Hudson (1990) state the reputation (quality) of outside agents (accounting firm, commercial banks, and underwriter) can influence the market reaction of seasoned stock offerings. In the mutual fund adoption, adopters are fund management 12

13 companies with relative large and diversified product lines under management and higher reputation in the industry. We shouldn t be surprise if the adoptee funds have positive abnormal (market-adjusted) return. Booth and Smith (1986) develop a theory of the role of the underwriter in certifying that risky issue prices reflect potentially adverse inside information. The theory derives the literature on the use of reputational capital to guarantee product quality. Balvers, McDonald, and Miller (1998) develop a theoretical model that explicitly incorporates the relation between investment banker and auditor to provide a framework for testing the effect of auditor selection in the initial market for unseasoned equity issues. The theory suggests high reputation investment banker will more frequent use high reputation auditors, both reputation will help reduce underpricing. Mutual fund investors, as majority shareholders are non-sophisticate investors, treat the adoptee funds which were chosen by professional money managers, as strong positive signal to indicate a better future performance. 3. Data, sample description and methodology 3.1 Data To obtain our sample of funds that were being adopted, we search through both the Lexis/Nexis database (including the PR Newswire, Business Wire, Reuters) and the Dow Jones News Retrieval Service database (including the Dow Jones News Wire and the Wall 13

14 Street Journal) for all the available data till December 31 st, We used the key words mutual fund adoption and mutual fund strategic alliance to pull out all sample funds from those databases. The data on mutual fund universe is collected from Center for Research in Security Prices (CRSP) Survivor-Bias Free US Mutual Fund Database, which is based on the Standard & Poor s Micropal Database. We collect fund universe from 1996 to 2003 and maintain several of the 50 variables from the dataset for our analysis use. Variable contain funds icdi number, fund name, icdi obj, fund ticker, expense ratio, portfolio turnover, total net asset, net asset value, start date and end date, etc. For data in 2004, we collect individual fund through yahoo finance. Subject to the data availability, we include net asset value, dividend and split data, expense ratio, turnover and manager s starting date as portfolio manager. 3.2 Sample Descriptive Among those funds, data contains 22 equity funds, 8 international funds, 2 bond funds and 3 balanced funds. Since this paper will be focus on the issue of adoption for the diversified U.S. equity fund, we drop international funds, bond funds and balanced funds and narrow our search identifies to the 11 mutual fund families with 35 equity funds from 1999 to We then drop the long/short equity fund since this fund can profit from both bull and bearish market and can over-estimated the return of the sample funds. Consequently, we 14

15 drop Laudus Rosenberg US large/mid cap long/short equity fund, Laudus Rosenberg value long/short equity fund. We also drop Touchstone large cap growth fund since the fund has merged Navellier Millennium large cap growth fund before the adoption and may mislead the amount of total net asset and return. We maintain the Mercury select growth fund although we know this fund eventually returned to Turner group due to the disappointing performance. We drop Laudus Rosenberg US Large Cap Fund, Laudus Rosenberg US Large Cap Growth Fund, Pioneer Oak Ridge Large Cap growth and Pioneer Oak Ridge Small Cap Growth Funds due to data missing. The final sample contains 15 US diversified equity funds. From the identified sample funds, we then search the fund prospective and annual report to identify fund s adoption announcement date, shareholder meeting date and adoption effective data. Appendix shows all the sample funds we collect from 1999 to Methodology In order to avoid survivor bias in our data, we include funds net asset value in our sample to calculate return even the funds disappear or die later in order to create a survivor bias free data. Consistency with other mutual fund performance measurement, we employ market-adjusted return and market model with S&P 500 index as market index to estimate the fund abnormal return surrounding the adoptions for the performance of adoptee funds. Raw return from 1999 to 2003 is provided by CRSP Survivor Bias Free US Mutual Fund 15

16 Data Base, R t 1, t J D NAV X AM T K S t j X AM Tk = D S + NAV t 1 j = RE NAV j _ k = 1 RE _ NAV k where NAVt-1 stands for NAV at the end of the previous period and NAVt is the NAV at the end of the current period. J and K represent the number of dividend (capital gains) and NAV slits during the period. While X_AMTjD is the jth dividend or capital gains distribution during the period. RE_NAVjD is the NAV at which the jth dividend or capital gains distribution was reinvested. X_AMTkS is the number of new shares per RE_NAVkS of old shares investors received in the kth NAV split over the period; RE_NAVkS is the number of old shares investors traded in for X_AMTkS new shares in the kth NAV split. Fund return calculation in 2004, we collected daily NAV from yahoo finance and compute daily return by [(NAV t +Div t ) / NAV t-1 ]-1, and then compound the daily returns as the product of (1+daily return) for all daily returns within the month, and then subtract one at the end to match the raw return, which is identical as CRSP monthly return. For market adjusted return, we adapt S&P 500 as market index. In order to measure the statistical significant of the return for sample funds surrounding the adoption, we create a size-controlled match sample. To test whether Chen, Hong, Huang and Kubik (2003) small fund size outperform large fund size by an average of 2.07% over 1-year or Clark (2003) argument of nothing to be gained by filtering on fund size, we adopt size-controlled match sample. Size-controlled match sample is selected by first matched the sample fund characteristics (SI OBJ code) and then find the absolute minimum differences for the total net asset at 13-month before the announcement data. 16

17 To test whether Slovin, Sushka and Hudson (1990), Booth and Smith (1986) and Balvers, McDonald, and Miller (1998) conclusion of reputation affect the price, we employ size of product lines (reputation-controlled) matchesample. The selection process is followed by first choosing a same characteristic (SI OBJ) fund and then identifying the absolute minimum net difference of number of fund managed from the sample fund at 13- month before the announcement. Finally we pick absolute minimum differences for total net asset value from the control group to control the reputation factor and how it would affect the return of sample fund. For robustness, we employ robustness test by extending the size-controlled sample from single match sample to the 10 nearest total net asset funds at 13-month before the announcement to extent the control sample to 100 funds to control the sample outlier and non-normal distribution obstacles. We measure the magnitude of asset flows in the total net asset before and after the announcement date on a monthly basis. Asset flow is defined by the equation of (TNA t / TNA t-1 )/ TNA t-1 *100%, while TNA t is the current month total net asset, TNA t-1 is the previous month total net asset. The changes of expense ratio and portfolio turnover can only measured to the annually basis subject to the CRSP data sources. Performance is measured in raw return and market-adjusted return. Both T-test and Wilcoxon sign rank test is used to test whether the average return (median return) of the 15 sample funds is 17

18 significant different from 0 at 1%, 5% and 10% level. 4. Empirical Result Table I presents the descriptive statistics of the characteristic of sample funds by providing a mean, median, maximum and minimum statistic result for expense ratio, portfolio turnover rate, total net asset, number of funds managed and raw and market-adjusted return. The statistics result shows sample funds are relative small in size, with 80% of the sample funds total net assets less than 80 million at 13 month before the adoption. Adoptee funds are relative small fund companies with less fund products managed. Our result shows the median size for adoptee funds are 6-fund product lines under its management. This supports the argument from Chen, Hong, Huang and Kubik (2003) that size does matter for adopters when choosing the adoptee funds. Table II presents the 15 sample funds mean and median performance during the 2-year period surrounding the adoptions. Performance is measured in raw, market-adjusted and abnormal return (calculated by market model). From the sample funds characteristics, we find, on average, sample funds generate a % cumulative raw return in 12-month (from 12 month before the adoption till adoption) at 5% significant. Using market-adjusted return to measure, we still conclude an average of 9.947% cumulative 12-month return at 1% significant. This result answers the 1 st question we have, sample funds do significant perform 18

19 well at 1-year interval before the announcement. Using Wilcoxon sign rank test generate similar result, a 5% significant cumulative return for 12-month before the adoption on average is % and a median of %. We also find out that sample funds generate an average of 2.964% monthly return at 5% significant during the announcement month of the adoption. This indirect supports the argument that Slovin, Sushka and Hudson (1990), Balvers, McDonald, and Miller (1998) and Booth and Smith (1986) that reputation of the intermediate has positive price impact on the product (services). It also supports Chan, Kensinger, Keown and Martin (1997), Johnson and Houston (2000) argument the horizontal joint venture or strategic alliance create value for the shareholder, measure in stock price performance. Table III shows the performance measure of size-controlled match sample. Although showing a positive return before the adoption, the tendency is much smaller than the sample funds. Our result shows a past cumulative 12-month raw return for size-controlled match funds is % with 10% significant while a 6.64% cumulative market-adjusted return at 5% significant. At the month of fund adoptions, it generates an average of 2.373% monthly return with 1% significant while a sign rank test generates 2.467% monthly return with 1% significant. The result for performance of sample funds can be found on figure I while we plot size-controlled match sample funds holding period cumulative raw, market adjusted and abnormal return for the 2-year surrounding the adoptions. 19

20 Table IV presents the mean and median differences of the 15 sample funds and size-controlled match funds market-adjusted return during the 2-year period surrounding the adoptions. The result answers second and third questions we raise. In the short run, the sample fund beat the size-controlled match fund by an average (median) of 1.206% (1.20%) monthly market-adjusted return with 5% significant. We draw the comparison of holding period returns for sample fund to size-controlled match sample in figure II. We can find the discrepancy between return increases around the announcement. The structure change of the expense ratio, portfolio turnover rate and net asset flows seems to be the direct reason to explain the significant of return. It supports Carhart (1997) argument that fund expenses have a significant impact on mutual fund returns. It also confirms Hendricks, Patel, and Zeckhauser (1993) argument that performance persistence phenomenon appears for the growth funds. Although the literatures have mixes support for the persistence performance of well performance funds, our result supports the Grinblatt and Titman (1992) and Ibbotson and Goetzmann (1994) that winner repeated and positive performance persist. Table V shows mean and median of differences of the 15 sample funds and reputation-controlled match funds market-adjusted return during the 2-year period surrounding the adoptions. Since the reputation and number of product lines affect fund flows, we want to see with the control of reputation, can we still find the persistence performance hold. The result indicates from 1 year before the announcement to 1 year after 20

21 the announcement, fund sample beat the reputation-controlled match sample by an average of 7.675% cumulative market-adjusted return with 5% significant. The result also holds when we use Wilcoxon sign rank test to test the median of difference. A cumulative market-adjusted return of 9.235% within the 28-month surrounding the adoption with 5% significant supports our general conclusion that sample funds do perform better both in the short and long run. Figure IV shows under a reputation-controlled match sample, sample funds market-adjusted return dominate the match funds. Table VI demonstrates the result is robustness. We pick the 10 closest total net asset funds at AD-13 to form the control group that compose around 100 funds. The result for robustness of the two group s mean and median difference performance during the 2-year period surrounding the adoptions still hold. Figure III confirms the pattern is similar to the size-controlled match samples and reputation-controlled match sample that sample funds dominate the control sample in all time during the event window. Table VII demonstrates the expense ratio, turnover and asset flows during the 2-year change before and after the adoption. The result shows the expense ratio, on average, drops 86 basis point (47 basis point in median) compared with size-controlled sample during the 2-year interval. This support Elton et al (1993) and Carhart (1997) conclusion that high fees funds do not perform as well as low fees funds. The annual asset turnover also shows a significant drop an average (median) of 51.34% (62.50%) at 10% significant compared with 21

22 reputation-controlled sample for 1-year from the adoption, indicate the desired flow during the adoption reduce the portfolio turnover and the frequency of trading. Our result also indicates net asset flow increase by an average (median) of million (4.30 million) with 5% significant at the month of adoption announce. 5. Summary and implications for future research This study examines the determinants of mutual fund adoption and adoptee funds performance and shareholders wealth impact during the 2-year interval surrounding the adoptions. As an industry new phenomenon starting from late 1999, we are limited to a 15 U.S diversified equity fund samples. Result shows adoptee funds expense ratio and asset turnover rate drop significantly while net asset flow increases significantly during the adoptions. Shareholders wealth of adoptee funds enhances not merely through capital appreciation but better services adopter funds provide with lower expenses. Adoptee fund managers receive substantial increase in management fees from the positive net asset flow, supported by adopter funds reputation and strong sales network. Adopter fund managers increase operation efficiency through leveraging outside talent and offer diversified fund product lines to their shareholders. Mutual fund Adoption seems to be a win-win solution to the fund industry. Although this is the first for academic research exploring the mutual fund adoption, we 22

23 hope we motivate the future extension of this study. As more data becomes available, it would be interesting to examine the long term wealth effect to the shareholders. It would also be interesting to extent from U.S diversified open-end equity funds to bond funds and international funds to see the whether persistence performance still hold. The saturation effect of the adoptee funds to the adopter fund family s existing funds can also be addressed. Research can also extent to test whether outside talent affect the control and ownership for the current adopter managers and will adoptee fund managers get fired if they do lousy job? Id data is available, we can also extent the research to see if insiders trade frequently during the adoption. 23

24 REFERENCES Balvers, R., McDonald, B., Miller, R., 1998, Underpricing of new issues and the choice of auditors as a signal of investment banker reputation, The Accounting Review 4 Booth J., Smith J., Capital raising, Underwriting and the certification hypothesis, Journal of Financial Economics 15, Brown S. and Goetzmann, W., Performance persistence, Journal of Finance 50, Brown S., Goetzmann, W. Hiraki, T., Otsuki, T. and Shiraishi, N., The Japanese open-end fund puzzle, Journal of Business 74, Carhart, M., On persistence in mutual fund performance, Journal of Finance 52, Carlson, Robert S, 1970, Aggregate performance of mutual fund, Journal of Financial and Quantitative Analysis 5, 1-32 Chan, S., Kensinger, J., Keown, A., Martin, J., 1997, Do Strategic alliance create value? Jpurnal of Financial Economics 46, Chen, J., Hong, H., Huang, M. and Kubik J., 2003, Does Fund Size Affect Performance? The role of liquidity and organization, Organizational Diseconomies and Active Money Management? Working paper, University of Southern California Chen, Y. M., Liu, M. and Qian, J., Buy-side analysts, Sell-side analysts and Fund Performance: Theory and evidence, unpublished working paper, Boston College Chevalier, J. and Ellison, G Are some mutual fund managers better than others? Cross-sectional patterns in behavior and performance, Journal off Finance 54, Clark, A. 2003, Does fund size affect performance? Lipper Research Study, September 15, 2003 Detzel, L. and Weigand, R., Explaining persistence in mutual fund performance, Financial Services Review 7,

25 Elton, E., Gruber, M. and Black, C Modern Portfolio Theory and Investment Management, John Wilay and Sons, New York Elton, E., Gruber, M. and Black, C The persistence of risk-adjusted mutual fund performance, Journal of Business, 69, Fama, E., French, K., 1993, Common risk factors in the return on stocks and bonds, Journal of Financial Economics 33, 3-56 Grinblatt, M. and Titman, S The persistence of mutual fund performance, Journal of Finance, 47, Grinblatt, M., Titman, S. and Werners R Momentum investment strategies, portfolio performance, and herding: A study of mutual fund behavior, American Economic Review 85, Gruber, M., Another Puzzle: The growth in actively managed mutual funds, Journal of Finance 51, Hendricks, D., Patel, J and Zeckhauser, R., Hot Hands in Mutual Funds: Short-run persistence of relative performance, , Journal of Finance 48, Jayaram, N., Khorana, A., Nelling, E., An analysis of the determinants and shareholder wealth effects of mutual fund mergers 57, Johnson, S., Houston, M., A reexamination of the motives and gains in the joint venture. Journal of Financial and Quantitative Analysis, March Lakonishock, J., Shleifer, A., & Vishny, R., The structure and performance of the money management industry, Brooklings Papers on Economic Activity: Microeconomics, Lehmann, Bruce N., and Modest, David, 1987, Mutual fund performance evaluation: A comparison of benchmarks and a benchmark of comparisons, Journal of Finance, 21, Pozen, R., The Mutual Fund Business, 2nd Edition. Houghton Mifflin, Boston 25

26 Porter, G. and Trifts, J., Performance persistence of experienced mutual fund managers, Financial Services Review 7, Sharp, William F., Mutual fund performance, Journal of Business 39, Sirri, E., Tufano, P., Costly search and mutual fund flows, Journal of Finance 53, Slovin M., Sushka, M., Hudson, C., External monitoring and its effect on seasoned common stock issues, Journal of Accounting and Economics 12, Warther, Vincent A., Aggregate mutual fund flows and security returns, Journal of Financial Economics 39,

27 Figure I Sample holding period return during the event window CR: Cumulative raw return CMAR: Cumulative market-adjusted return CABR: Cumulative abnormal return (calculated by market model) % CR CMAR CABR Sample holding period return % 20 Size-controlled match sample holding period return 15 CR CMAR Note: the cumulative return in the graphs are drawn from AD-12 to AD+15 while X-axis stands for month and Y-axis represents rate of return in percentage. 27

28 Figure II Raw return from 1999 to 2003 is provided by CRSP Survivor Bias Free US Mutual Fund Data Base, J D NAV X AM T K S t j X AM Tk R t t = D S + NAV t j = RE NAV j where NAV t-1 stands for NAV at the, 1 1 _ k = 1 RE _ NAV k end of the previous period and NAV t is the NAV at the end of the current period. J and K represent the number of dividend (capital gains) and NAV slits during the period. While X_AMT j D is the j th dividend or capital gains distribution during the period. RE_NAV j D is the NAV at which the j th dividend or capital gains distribution was reinvested. X_AMT k S is the number of new shares per RE_NAV k S of old shares investors received in the k th NAV split over the period; RE_NAV k S is the number of old shares investors traded in for X_AMT k S new shares in the k th NAV split. Fund return calculation in 2004 was collected daily NAV from yahoo finance, used the actual and take the daily return as [(NAV t +Div) / NAV t-1 ]-1, and then compound the daily returns as the product of (1+daily return) for all daily returns within the month, and then subtract one at the end to match the raw return, which is identical as CRSP monthly return. For market adjusted return, we adapt S&P 500 as market index. This graph shows the market-adjusted return between the sample funds to the size-controlled match samples % 30 Cumulative market-adjusted return Sample funds V.S Size-controlled match sample funds Sample Matched sample

29 Figure III Raw return from 1999 to 2003 is provided by CRSP Survivor Bias Free US Mutual Fund Data Base, J D NAV X AM T K S t j X AM Tk R t t = D S + NAV t j = RE NAV j where NAV t-1 stands for NAV at the, 1 1 _ k = 1 RE _ NAV k end of the previous period and NAV t is the NAV at the end of the current period. J and K represent the D number of dividend (capital gains) and NAV slits during the period. While X_AMT j is the j th dividend or D capital gains distribution during the period. RE_NAV j is the NAV at which the j th dividend or capital gains S distribution was reinvested. X_AMT k is the number of new shares per RE_NAV S k of old shares investors received in the k th S NAV split over the period; RE_NAV k is the number of old shares investors traded in for X_AMT S k new shares in the k th NAV split. Fund return calculation in 2004 was collected daily NAV from yahoo finance, used the actual and take the daily return as [(NAV t +Div) / NAV t-1 ]-1, and then compound the daily returns as the product of (1+daily return) for all daily returns within the month, and then subtract one at the end to match the raw return, which is identical as CRSP monthly return. For market adjusted return, we adapt S&P 500 as market index. For robustness, we extent the size-controlled sample from a matched sample (one on one matched) to the 10 closest total net asset funds to compose the controlled group % 30 Cumulative market adjusted return Sample funds V.S 10 Size-controlled sample funds Sample Control sample

30 Figure IV Raw return from 1999 to 2003 is provided by CRSP Survivor Bias Free US Mutual Fund Data Base, J D NAV X AM T K S t j X AM Tk R t t = D S + NAV t j = RE NAV j where NAV t-1 stands for NAV at the, 1 1 _ k = 1 RE _ NAV k end of the previous period and NAV t is the NAV at the end of the current period. J and K represent the D number of dividend (capital gains) and NAV slits during the period. While X_AMT j is the j th dividend or D capital gains distribution during the period. RE_NAV j is the NAV at which the j th dividend or capital gains S distribution was reinvested. X_AMT k is the number of new shares per RE_NAV S k of old shares investors received in the k th S NAV split over the period; RE_NAV k is the number of old shares investors traded in for X_AMT S k new shares in the k th NAV split. Fund return calculation in 2004 was collected daily NAV from yahoo finance, used the actual and take the daily return as [(NAV t +Div) / NAV t-1 ]-1, and then compound the daily returns as the product of (1+daily return) for all daily returns within the month, and then subtract one at the end to match the raw return, which is identical as CRSP monthly return. For market adjusted return, we adapt S&P 500 as market index. This graph shows the cumulative market-adjusted return to sample funds versus the reputation-controlled match sample funds. Cumulative market adjusted return Sample funds V.S reputation-controlled match sample funds % Sample Reputation matched sample

31 Figure V Annual holding turnover is plotted a year before (-1) and a year after (+1) the announcement year (0) Annual holding turnover is plotted a year before (-1) and a year after (+1) the announcement year (0) % Annual holding turnover (%) Sample Size Reputation % 1.80 Annual expense ratio (%) Sample Size Reputation

32 Figure VI Asset flow is defined by the equation of (TNA t / TNA t-1 )/ TNA t-1 *100%, while TNA t is the current month total net asset, TNA t-1 is the previous month total net asset. This graph demonstrates the changes of accumulative flows from AD -12 to AD+15. Accumulative monthly changes of net asset flows (%) % Sample Reputation Size

33 Table I Descriptive Statistics The table presents descriptive statistics of 15 open-ended equity funds from 1999 to 2003 identified by Lexis/Nexis database and Wall Street Journal. It reports mean, median, maximum and minimum statistics of events. Total net aseet, expense ratio, turnover and number of funds management companies managed are reported at AD-13 (13-month before the announcement of adoption). Raw return from 1999 to 2003 is provided by CRSP Survivor Bias Free US Mutual Fund Data Base, J D NAV X AM T K S t j X AM Tk R t t = D S + NAV t j = RE NAV j where NAV t-1 stands for NAV at the, 1 1 _ k = 1 RE _ NAV k end of the previous period and NAV t is the NAV at the end of the current period. J and K represents the D number of dividend (capital gains) and NAV slits during the period. Whie X_AMT j is the j th dividend or D capital gains distribution during the period. RE_NAV j is the NAV at which the j th dividend or capital gains S distribution was reinvested. X_AMT k is the number of new shares per RE_NAV S k of old shares investors received in the k th S NAV split over the period; RE_NAV k is the number of old shares investors traded in for X_AMT S k new shares in the k th NAV split. Fund return calculation in 2004 was collected daily NAV from yahoo finance, used the actual and take the daily return as [(NAV t +Div) / NAV t-1 ]-1, and then compound the daily returns as the product of (1+daily return) for all daily returns within the month, and then subtract one at the end to match the raw return, which is identical as CRSP monthly return. For market-adjusted return, we adopt S&P 500 as market index. Statistics of events Mean Median Maximum Minimum Expense ratio(%) Turnover (%) Total net asset ( $million) Number of funds managed Market-adjusted returns 12-month month month month Raw returns 12-month month month month

34 Table II This table presents the 15 sample funds mean and median performance during the 2-year period surrounding the adoptions. Performance is measured in raw return, market-adjusted return and abnormal return (calculated by market model). T-test and (Wilcoxon sign rank test) is used to test whether the average return (median return) of the 15 sample funds is significant different from 0 at 1%, 5% and 10% level (the *, **, and *** denotes significant level at 1%, 5% and10%). A 0 in Start and 0 in End represents at the month of announcement. A -12 in Start and -1 in End represent the cumulative 12 months from 12 month before the announcement to 1 month before the announcement. Start End Performance of Sample funds around mutual fund adoptions Number of month Raw return Sample performance (%) Mean T-test Marketadjusted return T-test Abnormal return ** *** ** * ** * * ** ** *** * T-test Sample performance (%) Median Start End Number of month Raw return Wilcoxon sign rank test Marketadjusted return Wilcoxon sign rank test Abnormal return Wilcoxon sign rank test ** ** ** ** ** * *** *

35 Table III This table presents the 15 size-controlled match funds mean and median performance during the 2-year period surrounding the adoptions. Performance is measured in raw return and market-adjusted return. T- test (Wilcoxon sign rank test) is used to test whether the average return (median return) of the 15 sizecontrolled fund return is significant different from 0 at 1%, 5% and 10% level (the *, **, and *** denotes significant level at 1%, 5% and 10%). A 0 in Start and 0 in End represents at the month of announcement. A -12 in Start and -1 in End represent the cumulative 12 months from 12 months before the announcement to 1 month before the announcement. Performance of Size control matched funds around mutual fund adoptions Start Size control matched sample performance (%) Mean Number of End month Market-adjusted Raw return T-test return ** ** ** ** * ** *** Start Size control matched sample performance (%) Median Number of End month Market-adjusted Raw return T-test return * ** ** ** * ** *** * T-test T-test 35

36 Table IV This table presents the mean and median differences of the 15 sample funds and size-controlled match funds market-adjusted return during the 2-year period surrounding the adoptions. Performance is measured in market adjusted return and T-test (Wilcoxon sign rank test) is used to test whether the differences of average return (median return) of the 15 sample funds is significant different from 0 at 1%, 5% and 10% level (the *, **, and *** denotes significant level at 1%, 5% and 10%). A 0 in Start and 0 in End represents at the month of announcement. A -12 in Start and -1 in End represent the cumulative 12 months from 12 month before the announcement to 1 month before the announcement. Performance of Sample subtract Size-controlled match sample around adoptions Sample subtract size-controlled match funds performance (%) Mean Start End Number of month Marketadjusted return T-test ** Sample subtract size-controlled match funds performance (%) Start End Number of month Marketadjusted return Median Wilcoxon sign rank test **