The Effect of Closing Mutual Funds to New Investors on Performance and Size

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1 The Effect of Closing Mutual Funds to New Investors on Performance and Size Master Thesis Author: Tim van der Molen Kuipers Supervisor: dr. P.C. de Goeij Study Program: Master Finance Tilburg School of Economics and Management Department of Finance

2 Abstract This study investigates the effect that a closing of a mutual fund has on its performance and size. Mutual fund managers can decide to close their fund to new investors. They claim that this is done to prevent it from growing too big, and to maintain performance. I find evidence that existing investors in the fund will not withdraw their investments out of the fund after a closure, they will also not increase their investment in the fund. However, I also find evidence that the risk-adjusted performance will be lower after the closure. Therefore the fund will be less attractive than it was before the closure. The claim of the mutual fund managers that the closure is done in the best interest of the current shareholders, is therefore not correct. 1

3 Table of Contents Abstract Introduction Problem Definition Theoretical Framework The mutual fund industry The search for the fund that meets ones needs Performance of mutual funds Closing of Mutual Funds Role of existing investors in closed mutual funds Data and Methodology Dataset Model and Assumptions Calculations for differences in fund performances Calculations for Fund Inflow Abnormal Returns Cross Sectional Regressions Descriptive Statistics Empirical Findings Performance of Mutual Funds Before and After Closure Asset Inflow Before and After Closure Abnormal Inflows Cross Sectional Regressions Conclusion and Recommendations Appendices Appendix 1: References Appendix 2: List of tables

4 1. Introduction Mutual funds play a large role in the worldwide economy. They give investors the opportunity to invest in diversified portfolios. They are therefore very popular among investors for both savings and pensions. At the end of 2012, worldwide mutual funds had $26.8 trillion in assets. The US mutual fund industry is with $13 trillion in Assets the largest in the world (year-end 2012) 1. The mutual fund industry consists for the largest part of actively managed funds. By timing the market and stock picking, they try to outperform their benchmark. When funds become too large, they can suffer from liquidity problems and organizational diseconomies. To prevent mutual funds from becoming too large, and therefore suffer losses, fund managers can decide to close the fund to new investors. They argue that by closing the fund to new investors, they make sure that the fund remains profitable. A large increase of new investors in a fund can be explained by high positive earnings of the fund in the previous periods. An example of this is the statement of Bruce Herring, chief investment officer at Fidelity Management & Research Co. about the closure of two of their investment funds: Both of these funds have seen accelerating investor cash flows in recent months, and we believe that it s in the best interests of shareholders to closure them at this time. In the case of a closure of a mutual fund, investors that already have a stake in these funds still have the opportunity to increase their stake. Such mutual fund closures occur regularly, in 2013 the amount of closings was This research will focus on the decisions of investors that already have invested in a fund that has recently closed. If investors believe that the closure is done to make sure that performance remains positive, investors should increase their holdings in the fund, so the asset inflow should increase. Another aspect that will be studied is if the recently closed mutual funds have equal or higher performance compared to when they were open to new investors. The secondary research question is used to confirm the conclusions of Manakyan and Liano (1997) for a more recent data period. Using the results of both questions it can be determined how investors behave around the closure of a fund, and if the investors believe the statements of the fund managers. Since previous papers focused their research on the performance issue around the closure of the fund but not on the behavior of the current shareholders, this research will be a relevant addition. 1 The Investment Company Fact book This is the amount that is used for this research, see Chapter 3. 3

5 1.1 Problem Definition When a Mutual Fund decides to close to new investors, existing face the decision whether or not to hold their shares in the fund. These investors still have the opportunity to increase, decrease their stake in the fund or completely withdraw their money out of the fund. This paper will focus on the behavior of investors around a closure of a fund. If investors believe the statement of the fund manager that the closure is done to make sure that performance remains positive, investors should increase their share in the fund. In that case, the fund in flow after a closure should be positive. Otherwise, investors should sell shares of the fund, and then asset inflow should be negative. Also, this research will look if funds are performing better or equally after a closure. This will be used to see if investors are making the right choices around a closure of a mutual fund. 4

6 2. Theoretical Framework 2.1 The mutual fund industry Mutual Funds are investment companies which uses money from investors to buy stocks, bonds and other assets. Every investor in the mutual fund holds a part of the portfolio that the mutual fund has formed. In America, mutual funds are obliged to be registered with the Securities and Exchange Commission (SEC). The fund is managed by a registered investing professional and its actions are overseen by a board of directors. The funds considered for this research are open-ended funds, meaning that stockholders are always allowed to buy or sell shares of the fund. Closed-end funds, which have a fixed number of shares, are not taken into consideration for this research since fund inflow will be zero. Another type of fund is the exchange-traded fund (ETF) or index tracker. These funds try to follow the underlying index as close as possible. Mutual funds can be classified by their principal investments. These funds categories are: money market funds, stock/equity funds, bond/fixed income funds and hybrid funds. The reason investors invest in mutual funds is because of better diversification opportunities, low information and transaction costs and the liquidity of the investments. When an investor wants to construct a well-diversified portfolio, it will have to contain a large number of stocks. When investing with a relative small amount of cash, the constructing of such portfolio will be costly. Therefore investing in mutual funds could be a good choice for such investor since he can be more easily be well diversified and transaction costs to construct the portfolio will be lower. Every investor that holds shares in a mutual fund, will have to pay certain fees to cover the expenses of the fund. The fees can be classified into two categories: Annual fees and transaction fees. Transaction fees are paid when the shares in the mutual fund are bought or sold and are called loads. Yearly fees will cover the administrative, accounting, registration and transfer costs, and also the salaries for the fund managers (management fee) and the 12B-1 fee. These 12B-1 fees are charged to cover the costs for marketing and distribution costs of the fund. The expense ratio of a mutual fund is calculated as the expense fees as a percentage of the total net assets. The load fees are not included in the expense ratio of the fund. The expense fees are deducted from the assets on a daily basis. According to Sirri and Tufano (1998), investors are fee sensitive. Therefore mutual funds that lower their fees, will have a higher increase of new investments. Also, Carhart (1997) showed 5

7 that fund performance and load-fees are negatively correlated, so funds with high load-fees will have lower performance compared to funds with lower fees. 2.2 The search for the fund that meets ones needs With the large amount of funds available, investors have to search which fund is the best to invest in. According to Sirri and Tufano (1998), investors want to lower their search costs when looking for a fund to invest in. Funds with a high performance in previous periods are easily found, and consumers are therefore likely to invest in these funds. So if consumers believe that top performing funds will have good performance in the future, they will invest in these funds. Also, consumers will believe that poorly performing funds will also perform poorly in the future. However, as will be discussed in section 2.3, investing in funds that performed very well in the past, can lead to significant losses. To verify their hypotheses, they study the determinants of asset inflows into mutual funds. They proved that investors indeed chase returns: investors are willing to invest more money in funds that have performed well in the previous periods. However, consumers fail to move out from bad performing funds. Sirri and Tufano (1998) also found evidence that investors are sensitive to media coverage. When a certain mutual fund receives an increase in media attention, more investors are willing to invest in these funds. Also, flows into the fund will increase when the fund spends more money on the marketing of the fund (and therefore charges higher fees). Also Barber and Odean (2007) came to the same conclusion. They analyzed the trading data from several stock brokers to see what influence media attention has on the trading volumes. They conclude that when there are high search costs and many alternatives, individual investors are more likely to invest in funds that have caught their attention in some way. Within their subset of funds/stocks that are satisfy their needs, stocks or funds that had more media attention or more likely to be bought. This phenomenon is not present for the selling decisions since the investor will have less possible funds or stocks to sell and can therefore evaluate each fund. 2.3 Performance of mutual funds As discussed in section 2.2, Sirri and Tufano (1998) showed that there is a performance flow relationship, investors chase returns. However, Carhart (1997) showed that persistence of mutual fund performance does not exist. If a consumer would buy shares of a mutual fund in the top-decile 6

8 of last year and sell shares of a fund which was in the bottom-decile, he would make a significant loss. Most of this difference is explained by the market value and momentum of the stocks. Other findings of Carhart (1997) are that load fees (commissions that occur when investors purchase or sell shares in the fund), expense ratios and portfolio turnover are negatively related to performance. Gil-Bazo and Ruiz-Verdu (2009) also showed that there is a negative relation between fees and performance. In their research they used Carhart s four-factor model to estimate the risk-adjusted performance of the mutual funds. This relation between this risk-adjusted performance and the fund fees is estimated using an ordinary least squares regression. Using this regression, they showed that there is a negative relation between the fees that the fund charge to investors and the funds riskadjusted performance. A possible explanation for this phenomenon is that funds set their fees according to their estimated future performance and past performance. This is done since investors that still hold shares of bad performing funds are less sensitive to the performance of the fund. Since the demand of their shares did not change for this group, the fund is able to increase their fees (Christoffersen and Musto (2002). Another explantion is given by Gil-Gazo and Ruiz-Verdu (2008): since funds that have low performance cannot compete with high-performance funds, they set relatively higher fees to attract more performance insensitive investors. A third explanation could be that bad performing funds will have higher 12b-1 fees to cover their marketing costs. This could help to target more performance-insensitive investors. Grinblatt and Titman (1989) however argue that there are superior performing fund managers who could generate positive abnormal returns. They estimated the gross returns of mutual funds and formed a benchmark that corrected for passive strategies that could drive the results. Using their risk-adjusted performance measure (Jensens Measures), they show that some mutual funds have superior performance, especially for aggressive-growth and growth funds. But since the managers of these funds will charge higher fees to the investors, the abnormal returns of these funds will decrease. They conclude that abnormal returns can only be observed when all fees and costs are added back to the returns of the fund. Another important factor that influences mutual fund returns is size. Several studies show the negative relation between size and performance of mutual funds (see Perold and Salomon (1991), Indro et al. (1999) and Chen et al. (2004)). They conclude that the largest reasons for this negative relationship are liquidity and organizational diseconomies related to hierarchy costs. This effect will be primarily large for small company growth funds since they have to trade larger block of shares when their asset value increase and will therefore bid up the prices (Keim and Madhavan, 1996). 7

9 2.4 Closing of Mutual Funds Since several studies proved that there is a negative relation between size and performance, fund managers can decide to close funds to new investors when its size increases too rapidly. New investors will no longer be able to invest in that fund, but the fund will continue its operations with their current assets and new investments from existing shareholders. However, Manakyan and Liano (1997) show that the fund performances before the closure are better than after the closure. They also proved that the funds that were closed, outperformed control portfolios in the years before the closure but not after. Therefore it seems that the strategy of the fund managers to maintain performance is not successful. Chevalier and Ellison (1997) discussed the compensation scheme of mutual funds, which is assetbased: the amount of fees that are charged to shareholders of the fund is expressed as a percentage of a fund s asset. Therefore fund managers will try to increase the inflow of new investments. Considering the failing strategy of maintaining good performances by closing the fund and the positive relation between the fund s revenue and asset size, why would fund managers still decide to close down a fund to new investors? In case the portfolio manager s compensation depends on the performance of the fund, the portfolio manager could benefit from a closure. The closing decision is in most cases not done by the portfolio manager itself but by his superiors (Zhao, 2004). So therefore it is not likely that the fund manager s compensation is a good reason for a closure. Nanda et al (2004) discussed the spillover effect in mutual funds families. When a certain fund in a family has an exceptional strong performance, cash inflow will increase not only for the star fund but also to other funds in the family. Zhao (2004) found evidence that these spillover effects could play a role in the decision of closing a fund: After closing a good performing fund, inflow of new investments to other funds in the family increases. This could be because of signaling: by closing a fund, the fund manager shows that the fund performed exceptionally well. Therefore closing the fund can attract new investors to other funds in the family. 8

10 2.5 Role of existing investors in closed mutual funds After closing a fund to new investors, existing investors still have the opportunity to change their share in the fund. Therefore, even if the fund is closed, it is still possible for the fund to have a positive inflow of assets. Since the fund most likely closed because of the large increase in size which is caused by good performance in last periods, the chances that the fund could persist these good performances are low (Perold and Salomon (1991)). Also Manakyan and Liano (1997) showed that the funds are performing better before the closure than after. However, as already mentioned, investors are known to chase returns. By closing a fund, the fund manager signals that the fund performed exceptionally well and if investors indeed chase returns, there is a chance that existing shareholders will further increase their share in the fund. Combined with the spillover effect to other funds in the family, this could lead to an overall increase in assets of the funds family. Since the positive spillover effect is already discussed by Nanda et al (2004), this paper will focus on the asset inflow of the closed fund. Together with the findings of Nanda et al, it can be discussed if closing a fund could lead to an overall increase in assets in the fund family. 9

11 3. Data and Methodology 3.1 Dataset A dataset containing quarterly data from the beginning of the CRSP database till the third quarter of 2013 is created using the CRSP Survivor-Bias-Free mutual funds database. The dataset contains data of all equity and bond (or hybrid) funds. Funds are grouped using the CRSP fund number. Using the open to new investors identifier, funds that have been closed between the first quarter of 2000 and the third quarter of 2013 are determined. Only funds that are soft closed are used for this study. In order to be considered the fund needs to report earnings after closure. The other variables that are needed are Net Asset Value and total monthly return. Among the control variables are the Fama-French factors and Carhart s momentum factor. In section 3.3 I provide some descriptive statistics. 3.2 Model and Assumptions Calculations for differences in fund performances First, as described in the introduction, the performance of a closed fund are tested to find out if the strategy of mutual funds to maintain performance by closing the fund to new investors is successful. Performances of the closed funds are compared with their own performances before the closing. Following Manakyan and Liano (1997) this will been done using normal returns, Sharpe-ratio, Jensen s alpha and Treynor s index. The Sharpe Ratio is calculated using the following formula: To compare differences in the Sharpe-ratios of the funds before and after closure, the method of De Roon et al (2012) is used. They provide a formula for the limiting distribution if δ is the true difference between the Sharpe ratios of A and B. (( ) ) 10

12 In this formula, is equal to the limiting variance of the Sharpe ratios. To derive the formula for ( ), two moments are needed:. The first derivatives of these moments are needed to derive the limiting distribution: The first derivative of the second moment is calculated by The variance of the difference in Sharpe Ratios can be expressed by the following formula: [ ] ( ) ( ) Therefore, the standard error of the Sharp ratios can be estimated by [ ] The confidence interval of the difference between the Sharpe ratios, with a confidence level of 95%, can therefore be calculated by 11

13 3.2.2 Calculations for Fund Inflow Next, I want to determine if the fund inflow remains positive after a closure of a fund. Following Sirri and Tufano (1998), the following definition for fund inflows is used. ( ) Asset i,t = total assets of the fund i at the end of time t r i,t = holding period return of fund i during time t Hypotheses testes are done to see if the asset inflows increase or decrease after the closure of a fund. This is done with a 12, 6 and 2 month time period. To compare the asset inflow before and after the closure, the following hypotheses test is used: Also cumulative abnormal asset inflows (CAF) and cumulative abnormal returns (CAR) are calculated per investment fund. The cumulative abnormal inflows are calculated by subtracting the actual inflows from the predicted inflows per fund for each month. The predicted flows are estimated by calculating the average fund inflow in the period of 24 months before closure until 12 months before closure. To calculate the predicted asset inflow per month, the following formula is used The abnormal flows are calculated by subtracting the predicted flows from the observed inflows The cumulative abnormal asset inflows are calculated for two time periods: (-12,0) and (0,+12). Using these two time periods, the cumulative abnormal inflows before and after closure can be compared. The cumulative abnormal asset inflows are calculated using the following formula: 12

14 3.2.3 Abnormal Returns Next, the cumulative abnormal returns are calculated by subtracting the normal returns from the realized returns. The normal returns are estimated using a CAPM regression: ( ) Where alpha is the measure of risk-adjusted return of the portfolio, beta is the sensitivity to market risk and epsilon is the error term. The abnormal returns are calculated using the following formula: The Cumulative Abnormal Returns are calculated are calculated for two time periods, (-12,0) and (0,+12). Using these two time periods, the cumulative abnormal returns in both can be compared to see if there is a change in inflows before and after the closure. The Cumulative Abnormal Returns are calculated using the following formula: Using this formula, the Abnormal Returns from t1 till t2 are added up Cross Sectional Regressions Next, cross sectional regression analyses are conducted to investigate what factors drive the cumulative abnormal inflows into the fund. Also, a cross sectional regression on cumulative abnormal returns will be done. To investigate the factors driving the cumulative abnormal returns, I estimate the following regression equation: To investigate the factors driving the cumulative abnormal inflows, I estimate the following regression equation: 13

15 Where total net assets are the assets of the fund at month end in billions. Returns are monthly excess returns on the market. 12b-1 fees are marketing and distribution fees of the fund. Management fees is a charge by the fund manager for managing the fund. The turnover ratio is a percentage of the total holdings of the fund that have been replaced with other holdings. The equity, fixed income and mixed fund dummies are dummies for categorizing the investment funds. The Open to new investors dummy states if the fund is open or closed to new investors. 14

16 3.3 Descriptive Statistics Table 1 shows the amount of closings per year. Only closings with data of 12 months before and after closure are showed in this table. As can be seen, the amount of closings increased after 2006 and there were especially a high amount of closings during the recession period. Table 1 Amount of closings per year Year Amount of Closures Table 2 shows the descriptive statistics of the funds that are included in this research. On average, a fund has total net assets of 575 million, while monthly it increases with 0.13 million. The average management fee is 0.59 million per month and the average turnover ratio (the percentage of holdings of the portfolio that have been changed) is 65.37%. 15

17 0.05 Density.1 Density Frequency ,000 20,000 30, e+04 Table 2 Descriptive Statistics Descriptive Statistics Mean Std. Dev. Min Max Total Net Assets in mln Total Monthly Return 0.59% 4.66% % 45.22% Asset Inflow in mln Management Fee in Bl Turnover Ratio 65.37% Figure 1 shows the histograms of the descriptive statistics of the funds used for this research. Figure 1 Descriptive Statistics Amount of closures per fund type Return per share in % per month Equity Fund Mixed Fund Fixed Income Fund Other Fund log_mtna Management fee As can be seen in Figure 1, most of the funds are equity funds. A small proportion of the funds are mixed or other funds while there is no fixed income fund included in this research. Fixed income funds are known to almost never close because they mostly do not have any negative effects from becoming too big. 16

18 Most of the funds included in this research are relatively small. As can be seen in Figure 1, most funds that close have a net asset value of less than 1 billion, the average net asset value is 575 million as can be seen in Table 2. Since small funds will suffer more from the negative effects of large inflow of new assets than large funds, it is more likely that these smaller funds will close to new investors. The returns of the fund are, on average, positive. As can be seen in Figure 1, the returns are on average positive with some extreme outliers. The management fees are not normally distributed. Many funds have no management fee, while others have management fees higher than 1 million dollars per month. 17

19 4. Empirical Findings 4.1 Performance of Mutual Funds Before and After Closure Table 3 represents the performances of mutual funds before and after the closure. A window of 12 months before and after the closing is used for the calculations. The performance is measured using the raw returns, betas, Treynor s index, Jensen s Alpha and Sharpe s ratio. All data is monthly data. Raw return is used to see the difference in returns before and after closure. Beta is used to determine the systematic risk of the funds compared to the market. They Treynor s index and Sharpe s ratio are used to determine the risk adjusted performance of the funds and Jensen s alpha shows the abnormal returns of the funds. Table 3 Performance measures of mutual funds before and after closing Raw Beta Treynor Jensen Sharpe return Before 0.91%** 0.711** ** ** ** Closure After 0.66%** 0.744** ** ** ** Closure Difference 0.25%** * ** ** * Statistically different from zero at a 5% level ** Statistically different from zero at a 1% level Raw returns are in percentage per month. As can be seen, the average performance of the funds before the closure is better than after. The mean raw return before the closure is 0,25% per month higher than after the closure. This is significantly different from zero at a 1% level. The beta after the closure is on average higher than before the closure, this is however not statistical significant. Also, the Treynor s Ratio, Jensen s Alpha and Sharpe Ratio are lower after the closure than before. This suggest that the performance of the funds, adjusted for the risk, is lower after a closure than before. To determine if the difference of the Sharpe ratios significantly differs from zero, the formula s discussed in section are used: 18

20 ( ) ( ) Since zero lies not in the confidence interval, the difference in Sharpe ratios is significantly different from zero at a 95% confidence level. 4.3 Asset Inflow Before and After Closure Next the asset inflow before and after the closure is examined. The hypotheses test which is described in section 3 is used. The basic characteristics of the asset inflow before and after the closure using a 12 month window before and after the closure can be found in table 4. All numbers are in millions of dollars. Table 4 Comparison of asset inflow with a 12 month window, in millions Asset Inflow Mean Std. Error Std. Dev. [95% Conf. Interval] Open Funds Closed Funds Combined Difference t-statistic As can be seen, the average asset inflow per fund per month before the closure is $1709, while the average asset inflow per fund per month after the closure is $3193. When this is compared to the mean size of the funds (575 million), this is just a relatively very small amount. The test statistic has a value of t = which means H 0 cannot be rejected. Therefore it cannot be concluded that the asset inflow changes after the closing of a mutual fund. The same hypothesis test is done for a 6 month and 2 month interval before and after closure. The basic characteristics can be found in table 5 and table 6. Table 5 Comparison of asset inflow with a 6 month window, in millions Asset Inflow Mean Std. Error Std. Dev. [95% Conf. Interval] Open Funds Closed Funds Combined Difference t-statistic

21 Table 6 Comparison of asset inflow with a 2 month window, in millions Asset Inflow Mean Std. Error Std. Dev. [95% Conf. Interval] Open Funds Closed Funds Combined Difference t-statistic The t-statistic for the 6 month interval is t = and for the 2 month interval t = This means that also using the 6 month and 2 month interval, the H 0 hypothesis cannot be rejected. 4.4 Abnormal Inflows As discussed in section 4.3, there is no statistical difference between the asset inflow before and after the closure of the fund. To see if there are abnormal inflows in the fund before and after the closure, the cumulative abnormal inflows are calculated as explained in section 3.2. The results can be found in Table 7. Table 7 Cumulative Abnormal Inflows before and after closure, in millions Cumulative abnormal inflows Mean Std. Dev Before Closure After Closure The cumulative abnormal inflows before closure are not significantly different from zero, but after the closure the cumulative abnormal inflows are significantly different from zero. After the closure, the asset inflows are lower than expected. 4.5 Cross Sectional Regressions Next, cross sectional regressions analysis are done to see what factors influences the abnormal returns and the abnormal asset inflows. First, the pre-event period is analyzed by taking a period of 6 months till 1 month before the closing of the mutual fund. Also, regressions are done around the closing of the fund (1 month before and after) and the last regressions are done with time periods after the event. The results can be found in Table 8 (abnormal returns) and Table 9 (abnormal inflows). 20

22 Table 8 Cross sectional regressions on abnormal returns Totan Net Assets as of Month end in bln (1) (2) (3) (4) (5) CAR (-6,-1) CAR (-6,1) CAR (-1,1) CAR (0,1) CAR (1,5) (1.02) (0.92) (0.58) (0.88) (-0.85) Excess Return on the Market *** *** *** *** *** (16.97) (15.39) (18.28) (28.88) (12.46) 12b-1 Fee (1.20) (0.98) (1.00) (0.28) (0.08) Management Fee (1.30) (1.38) (1.36) (1.91) (0.50) Fund Turnover Ratio (-0.80) (-0.87) (-1.26) (-1.39) (0.19) Equity Fund (-1.20) (-0.76) (-1.15) (1.40) (0.67) o.fixed Income Fund (.) (.) (.) (.) (.) Mixed Fund (-1.12) (-0.81) (-0.65) (0.84) (0.40) Other Fund (-1.39) (-1.47) (-1.51) (-1.54) (-1.16) Constant *** * (-1.17) (-1.47) (-1.10) (-3.55) (-2.30) R N t statistics in parentheses p < 0.05, ** p < 0.01, *** p <

23 Table 9 Cross sectional regressions on abnormal inflows Totan Net Assets as of Month end in bln (1) (2) (3) (4) (5) CAF (-6,-1) CAF (-6,1) CAF (-1,1) CAF (0,1) CAF (1,6) (0.74) (0.77) (0.44) (0.87) (-0.32) Excess Return on the Market (1.29) (0.62) (0.79) (0.72) (0.07) 12b-1 Fee *** ** (-1.52) (-1.69) (-3.92) (-3.29) (-1.58) Management Fee (-0.35) (-0.40) (-0.27) (-0.70) (0.95) Fund Turnover Ratio * (0.08) (0.06) (-1.31) (-2.13) (1.48) Equity Fund * (-0.82) (-0.75) (-1.73) (-2.01) (-0.17) o.fixed Income Fund (.) (.) (.) (.) (.) Mixed Fund ** ** ** (-2.94) (-2.84) (-1.73) (-2.92) (-0.18) Other Fund ** ** * ** (-2.82) (-2.84) (-2.33) (-2.93) (-1.14) Constant ** ** *** *** ** (-3.24) (-3.24) (-4.45) (-7.39) (-2.63) R N t statistics in parentheses * p < 0.05, ** p < 0.01, *** p <

24 As can be seen in Table 8, the only variable that has consistent significant coefficients is Excess return on the market. Since this will be highly correlated to the abnormal returns, this was expected. There are no other variables that have any significant values. The cross sectional regressions on abnormal inflows can be found in Table 9. As can be seen, mixed and other funds have a negative, significant influence on the abnormal inflows. In regression 4, the equity fund dummy shows also a negative significant coefficient. In regression 4, the 12b-1 Fee and Turnover Ratio are also having a small, negative coefficient. Unfortunately the R 2 values of the regressions are low. While the regressions are showing some significant values, the economic significance of these values are very low. Since the amount of Fixed Income Funds and Other Funds in the dataset is very low, regressions are also done with only considering the Equity Funds and Mixed Funds. The last regressions are only done with Equity Funds. The results can be found in Table 10 and Table

25 Table 10 Cross sectional regressions on abnormal inflows with only Equity Funds and Mixed Funds Totan Net Assets as of Month end in bln (1) (2) (3) (4) (5) CAF (-6,-1) CAF (-6,1) CAF (-1,1) CAF (0,1) CAF (1,6) (0.59) (0.62) (0.18) (1.00) (-0.23) Excess Return on the Market (1.75) (1.03) (1.12) (-0.02) (0.17) 12b-1 Fee *** *** (-1.17) (-1.42) (-4.10) (-3.72) (-1.26) Management Fee (0.12) (0.04) (-0.55) (-1.36) (0.74) Fund Turnover Ratio * (1.45) (1.44) (-0.19) (-0.32) (2.26) Equity Fund (1.87) (1.83) (-0.01) (1.68) (-0.22) o.mixed Fund (.) (.) (.) (.) (.) Constant *** *** *** * (-5.57) (-5.42) (-0.50) (-8.96) (-2.16) R N t statistics in parentheses * p < 0.05, ** p < 0.01, *** p <

26 Table 11 Cross sectional regressions on abnormal inflows with only Equity Funds Totan Net Assets as of Month end in bln (1) (1) (1) (1) (1) CAF (-6,-1) CAF (-6,1) CAF (-1,1) CAF (0,1) CAF (1,6) (0.67) (0.76) (0.34) (0.79) (-0.32) Excess Return on the Market (1.09) (0.46) (0.70) (0.62) (0.02) 12b-1 Fee *** *** (-1.26) (-1.75) (-4.00) (-3.38) (-1.56) Management Fee (-0.09) (-0.13) (-0.89) (-1.23) (1.05) Fund Turnover Ratio * (0.56) (-0.33) (-1.59) (-2.47) (1.30) Constant *** *** *** *** ** (-4.07) (-4.87) (-6.03) (-9.88) (-3.02) R N t statistics in parentheses * p < 0.05, ** p < 0.01, *** p <

27 When comparing Table 10 to Table 9 it can be seen that there are no differences in the variables that are significant when considering all types of funds and when only Equity Funds and Mixed Funds are considered. The 12b-1 Fee (time period (-1,1) and (0,1) and Turnover ratio for the time period (1,6) are the only variables with small, significant coefficients. All other variables are not significant. In table 11, only Equity Funds are considered. The results do not differ much from the other regressions. In this regression, also the variables 12b-1 fees and Turnover ratio are the only variables that have some significant coefficients. Since the R 2 for both regressions are very low, there are no conclusions that can be drawn from these regressions. 26

28 5. Conclusion and Recommendations Closing a mutual fund can be done for several reasons, the main reason however is that the fund became too large to manage sufficiently. So the closure is done to maintain performance. According to my analysis, closing a fund will have a negative influence on the returns of that fund, while the riskiness of the portfolio increases. Therefore the fund will be less attractive than it was before the closure. According to the hypotheses test done in section 4.3 there are no statistical significant changes observed in the amount of asset inflows between before and after the closure. The cumulative abnormal inflows after the closure however is negative, which shows that there are less inflows after the closure than expected. An explanation for this could be that according to their performance (while it is still worse than before closure), it is still good enough that investors want to invest in the fund. Since the fund is closed to new investors, they are not able any more to invest in that fund. Apparently investors that already have invested in the fund are not willing to invest more after the closure. According to the hypotheses analysis, they are also not triggered to withdraw their money out of the fund after the closure. When the regression analyses are studied, it can be concluded that using these regressions, only a minor part of the asset inflow can be explained. Apparently there are more factors that influence the asset inflow, but they are not included in the regressions. Only the 12b-1 fees are having a small significant negative impact on the asset inflow in the period just after the closure. Using these results, it can be concluded that fund managers are making the right decision if they want to prevent the fund from becoming too large: the existing investors are not withdrawing their money out of the fund and are not triggered to invest more in the fund. However, the claim that the closure is done in the best interest of the shareholders it not correct. The risk-adjusted performance of the fund will be lower after the closure. These findings are in line with the paper of Manakyan and Liano (1997). Future research on closures of mutual funds could focus on funds that close within one year. For this research, these funds were not taken into account since I did not have access to enough data for these funds. Furthermore, the asset inflow into other funds in the fund family could be researched, since the closure of a mutual fund could cause investors to invest in other funds within the family. The closing of one fund could be a signal to investors to show that a certain fund performed 27

29 exceptionally well. Other funds in the fund family should then show an increase of new investments in the fund. 28

30 Appendices Appendix 1: References Barber, B.M., Odean, T. (2007). All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors. Review of Financial Studies, 21 (2), Berk, J.B., Green, R.C. (2002). Mutual Fund Flows and Performance in Rational Markets. National Bureau of Economic Research, working paper 9275 Carhart, M.M. (1997). On Persistence in Mutual Fund Performance. The Journal of Finance, 52 (1), Chen, J., Hong, H., Huang, M., Kubik, J.D. (2004). Does Fund Size Erode Mutual Fund Performance? The Role of Liquidity and Organization. The American Economic Review, 94 (5), Chevalier, J., Ellison, G. (1997). Risk taking by mutual funds as a response to incentives. Journal of Political Economy, 105, Christoffersen, S.E.K, Musto, D.K. (2002). Demand curves and the pricing of money management. Review of Financial Studies, 15, Fama, E.F., French, K.R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), Fama, E.F., French, K.R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1), Gil-Bazo, J., Ruiz-Verdu, P. (2008). When Cheaper is Better: Fee Determination in the Market for Equity Mutual Funds. Journal of Economic Behavior and Organization, 67, Gil-Bazo, J., Ruiz-Verdu, P. (2009). The Relation between Price and Performance in the Mutual Fund Industry. The Journal of Finance, 64 (5),

31 Grinblatt, M., Titman, S. (1989). Mutual Fund Performance: An Analysis of Quarterly Portfolio Holdings. The Journal of Business, 62 (3), Gruber, M.J. (1996). Another Puzzle: The Growth in Actively Managed Mutual Funds. The Journal of Finance, 51 (3), Indro, D., Jiang, C., Hu, M., Lee, W. (1999). Mutual fund performance: Does fund size matter? Financial Analysts Journal, 55, Keim, D.B., Madhavan, A. (1996). The Upstairs Market for Large-Block Transactions: Analysis and Measurement of Price Effects. The review of Financial Studies, 9 (1), 1-36 Lynch, A.W., Musto, D.K. (2003). How Investors Interpret Past Fund Returns. Journal of Finance, 57(5), Kempf, A., Ruenzi, S. (2008). Family Matters: Rankings Within Fund Families and Fund Inflows. Journal of Business Finance & Accounting, 35(1), Lo, A.W. (2002). The Statistics of Sharpe Ratios. Financial Analysts Journal, 58 (4), Manakyan, H., Liano, K. (1997). Performance of Mutual Funds Before and After Closing to New Investors. Financial Services Review, 6 (4), Nanda, V., Wang, J., Zheng, L. (2004). Family values and the star phenomenon. Review of Financial Studies, 17, Perold, A., Salomon, R. (1991). The right amount of assets under management. Financial Analysts Journal, 47, Roon de, F., Eiling, E., Gerard, B., Hillion, P. (2012). Currency Risk Hedging: No Free Lunch. Working paper no Retrieved from Sirri, E.R., Tufano, P. (1998). Costly Search and Mutual Fund Flows. The Journal of Finance, 53 (5),

32 Zhao, X (2004). Why are some mutual funds closed to new investors? Journal of Banking & Finance, 28,

33 Appendix 2: List of tables Table 1 Amount of closings per year Table 2 Descriptive Statistics Table 3 Performance measures of mutual funds before and after closing Table 4 Comparison of asset inflow with a 12 month window, in millions Table 5 Comparison of asset inflow with a 6 month window, in millions Table 6 Comparison of asset inflow with a 2 month window, in millions Table 7 Cumulative Abnormal Inflows before and after closure, in millions Table 8 Cross sectional regressions on abnormal returns Table 9 Cross sectional regressions on abnormal inflows Table 10 Cross sectional regressions on abnormal inflows with only Equity Funds and Mixed Funds 24 Table 11 Cross sectional regressions on abnormal inflows with only Equity Funds

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