The Determinants of Flows into Retail Bond Funds

Transcription

1 The Determinants of Flows into Retail Bond Funds Xinge Zhao * School of Business Administration P.O. Box 8795 College of William & Mary Williamsburg, VA Phone: (757) Fax: (757) xinge.zhao@business.wm.edu October 2003 Abstract Unlike equity fund investors, bond fund investors do not flock into funds with the highest recent raw returns. Instead, they pay more attention to risk-adjusted performance. In addition, bond fund investors, municipal bond fund and government security fund investors in particular, appear to be very sensitive to expenses, such as operating expenses and sales loads. Due to the high volatility of fund returns, high yield bond fund investors do not chase absolute performance and are most sensitive to risks. Investors are more likely to treat investments in government security funds, Ginnie Mae funds, and high quality bond funds as swing components in their total asset mix, and increase their investments in these funds when they flee a prolonged bear equity market. * xinge.zhao@business.wm.edu, School of Business Administration, P.O. Box 8795, College of William & Mary, Williamsburg, VA I would like to thank Julie Agnew, Audra Boone, John Boschen, Erik Lie, George Oldfield, Chris Taber, and Wanda Wallace for very helpful comments. Any errors are my responsibility.

2 The Determinants of Flows into Retail Bond Funds October 2003 Abstract Unlike equity fund investors, bond fund investors do not flock into funds with the highest recent raw returns. Instead, they pay more attention to risk-adjusted performance. In addition, bond fund investors, municipal bond fund and government security fund investors in particular, appear to be very sensitive to expenses, such as operating expenses and sales loads. Due to the high volatility of fund returns, high yield bond fund investors do not chase absolute performance and are most sensitive to risks. Investors are more likely to treat investments in government security funds, Ginnie Mae funds, and high quality bond funds as swing components in their total asset mix, and increase their investments in these funds when they flee a prolonged bear equity market.

3 I. Introduction The prolonged bear market since the stock market meltdown in March 2000 has evidenced the importance of bonds in individual investors asset allocation. However, the high trading costs and minimum transaction size requirements by brokers, as well as the inconvenience of trading bonds, make it almost infeasible for the vast majority of individual investors to create a diversified portfolio using individual bonds. 1 Consequently, bond mutual funds provide the only sensible opportunity for most individual investors to have access to fixed-income instruments. The growing interest in bond funds is shown by the $87.7 billion net flows into such funds in 2001, almost three times the net investments in equity funds ($31.9 billion) for the same period. 2 However, despite the dramatic growth in the investments into bond funds, little research has been done to study the determinants of flows into bond funds, or, ultimately, the behaviors of bond fund investors, such as whether they chase past performance or whether they are sensitive to expenses. 3 Even though there is a large literature on the determinants of flows into equity funds, bond funds differ from equity funds in both the characteristics of holdings and the profile of investors. 4 As a result, the behaviors of bond fund investors might differ from those of equity fund investors and entail separate investigation. This paper intends to fill this void in the current literature. In addition, different bond funds might focus on different subcategories of fixedincome instruments, such as government securities or high yield bonds. Therefore, their investors might exhibit different behaviors in their investments. Thus, in addition to studying the determinants of flows into bond funds as a whole, this paper makes a further contribution to the literature by disaggregating bond funds according to their investment 2

4 objectives and studying the determinants of flows for funds with each type of investment objective separately. The investment objectives analyzed in this paper include municipal bond funds, government security funds, Ginnie Mae funds, high quality bond funds, high yield bond funds, and global bond funds. By further disaggregating bond funds, this paper makes possible a comparison of the behaviors of investors of bond funds with different investment objectives. This paper first finds a negative relationship between bond fund size and dollar flows. Therefore, following Del Guercio and Tkac (2002), this paper focuses on dollar flows in the study of the determinants of bond fund flows. This paper reveals that, unlike equity fund investors, bond fund investors do not flock into funds with the highest recent raw returns. Instead, risk-adjusted performance has a much stronger effect on bond fund flows. In addition, bond fund investors, municipal bond fund and government security fund investors in particular, appear to be very sensitive to expenses. They tend to stay away from funds with high operating expenses and funds that charge sales loads. Due to the high volatility of fund returns, high yield bond fund investors do not chase absolute performance and are most sensitive to risks. This study also finds that the investors of government security funds, Ginnie Mae funds, and high quality bond funds are more likely to treat such funds as swing components in their total asset mix. The investments in these funds are more likely to be affected by investors general asset allocation strategies. As a matter of fact, investors do increase their investments in these funds when they flee a prolonged bear equity market. However, investors will only treat government security funds as a safe haven when they face temporary equity market drops. 3

5 The remainder of the paper is organized as follows. Section II outlines the data, the determinants, and the methodology used. Section III discusses the estimation results. Section IV concludes. II. Data, Determinants, and Methodology A. Data Using the CRSP Survivor-Bias Free US Mutual Fund Database, I create a new data set of quarterly data from the first quarter of 1992 to the third quarter of 2001 of 5,554 open-end bond funds. The time frame is selected because the Investment Company Data, Inc. (ICDI) s Fund Objective Code, fund family, and 12b-1 fee data are only available after 1992 in the CRSP mutual fund database. 5 All bond funds are categorized into the following eight investment objectives based on the ICDI s Fund Objective Code, which indicates the fund s investment strategy as identified by Standard & Poor s Fund Services: High Quality Municipal Bond, Single State Municipal Bond, High Yield Municipal Bond, Government Security, Ginnie Mae, High Quality Bond, High Yield Bond, and Global Bond. 6 [Insert Figure 1 about here] Figure 1 shows the distribution of the number of bond funds among these investment objectives from the beginning of 1992 to the end of the third quarter of 2001, with the three municipal bond objectives combined into one investment objective of municipal bond funds. The market shares among various investment objectives appear to 4

6 be fairly stable over this ten-year period. Municipal bond funds, the dominant investment objective, consistently account for around 50 percent of the total number of bond funds. High quality bond funds are a distant second, accounting for about 20 percent of bond funds. Government security funds and Ginnie Mae funds have experienced slight erosions in their market shares, giving ground to high yield bond funds and global bond funds. The data include fund name, fund family (management company), inception date, fund age (months), quarterly return, NAV (net asset value), expense ratio, turnover ratio, fund loads (front-end load, back-end load, and 12b-1 fees), and total assets. More than 60 percent of the funds are different share classes of a common portfolio. 7 To examine the effects of loads, 12b-1 fees, and operating expenses, which are specific to each share class, on flows, following Greene and Hodges (2002), this paper studies flows to each share class instead of each portfolio. About 78 percent of all funds target retail investors, and these retail bond funds can be disaggregated into four categories by load types: frontend load funds, back-end load funds, level-load funds, and no-load funds. 8 Load funds are generally sold through brokers and financial advisors, while no-load funds largely rely on direct sales to investors. B. Potential Determinants The determinants of flows into equity funds have been the subject of a growing number of academic studies. Many of these determinants might also apply to bond funds. For instance, Gruber (1996) finds that equity fund investors chase past performance. Chevalier and Ellison (1997) and Sirri and Tufano (1998) not only 5

7 corroborate this finding but also detect the non-linearity in the performance-flow relationship: equity fund investors flock to funds with the highest recent returns, but fail to flee from poor performers. Sirri and Tufano (1998) also find equity fund investors are fee-sensitive in that funds with higher total fees (expense ratio plus amortized load assuming a seven-year holding period) have lower flows. However, Sirri and Tufano (1998) also realize that some components of total fees, such as loads and 12b-1 fees, are used to compensate salespersons and to pay for distribution expenses and therefore might help increase flows. Unfortunately, due to data limitations, they are unable to perform a full sample investigation of the effects of different fee components. Using more recent data, Barber, Odean, and Zheng (2002) study the effects of front-end loads, 12b-1 fees, and other operating expenses separately. They find a negative relationship between load fees and fund flows, no relationship between total operating expenses and fund flows, as well as a positive relationship between 12b-1 fees and fund flows. They argue that equity fund investors are more sensitive to salient in-your-face fees, such as loads, than operating expenses. Wilcox (2003) draws similar conclusions using a conjoint experiment. Sirri and Tufano (1998), Nanda, Wang, and Zheng (2002), and Ivkovic (2002) all study the spillover effects a fund might enjoy higher flows if its fund family has larger size, a star fund with superior performance, or impressive overall family performance. In addition, the effects of other factors, such as previous flows, fund age, turnover ratio, and fund risk, have also been studied in the above-mentioned papers and other studies (Jain and Wu (2000), Del Guercio and Tkac (2002), Bergstresser and Poterba (2002), and James and Karceski (2002)). 6

8 In addition to the determinants already studied in previous research, this paper introduces two new factors to investigate the effects of fund families and investment objectives on the flows into bond funds. First, this paper considers the number of investment objectives (including equity investment objectives) offered in the fund family. By offering more investment objectives, the fund family provides investors with greater flexibility to switch among funds and a better opportunity to execute asset allocation strategies. Therefore, the number of investment objectives offered by a fund family might influence investor behavior. Second, since this paper follows Sirri and Tufano (1998) in measuring fund performance as its percentile performance relative to other funds with the same investment objective in the same period, the asset-weighted average raw return of the corresponding investment objective is also included to test whether investors chase absolute performance. C. Measurement of Flows The vast majority of current literature measures fund flows as the fund asset growth rate net of fund holding period returns (percentage flows), rather than the dollar amount of net flows (dollar flows) to control for fund size. This measure is based on the presumption that larger funds tend to have larger net dollar flows. However, the univariate correlation between dollar flows and fund size for bond funds is a statistically significant 0.194, indicating that larger bond funds actually receive smaller net dollar flows. Contrary to the presumption behind using percentage flows, this finding apparently does not support the use of such a measure. 7

9 In addition, regardless of the relationship between fund size and dollar flows, as argued by Del Guercio and Tkac (2002), using dollar flows as the dependent variable while controlling for a potential size effect in a multiple regression format, rather than by scaling the flows, preserves this information for analysis. Consequently, following Del Guercio and Tkac (2002), this paper focuses on dollar flows in the study of the determinants of bond fund flows and, in particular, tests the relationship between dollar flows and fund size for bond funds. Results using percentage flows are also reported for comparison. D. Definitions of Variables Flows Consistent with the literature, I define dollar flows (FLOW) as the change in total assets in excess of appreciation. 9 I especially follow Zheng (1999) in also removing the increase in total assets due to merger so that the flow measure clearly represents only net new investments made by investors: 10 (1) FLOW i,t = ASSET i,t ASSET i,t-1 (1+ R i,t ) MASSET i,t where ASSET i,t is the total assets of fund i at the end of quarter t, R i,t is the holding period return of fund i during quarter t, and MASSET i,t is the assets added to fund i during quarter t through acquisition of other funds. I also follow Del Guercio and Tkac (2002) in excluding observations from funds closed to new investors, since these funds flows are artificially restricted. 11 8

10 dollar flows: I then define percentage flows (PFLOW) as the asset growth rate of a fund due to (2) PFLOW i,t = FLOW i,t / ASSET i,t-1 Fund Size When FLOW i,t (dollar flows) is used as the dependent variable, ASSET i,t is used to represent the size of a fund; when PFLOW i,t (percentage flows) is used as the dependent variable, LASSET i,t, which is the natural log of ASSET i,t, is used instead. Performance and Risk Following Sirri and Tufano (1998), I measure the performance of a fund as its fractional performance rank (RANK i,t ), which represents the percentile of its raw return (RAW) relative to other funds with the same investment objective in the same quarter. To apply a piecewise linear regression to test any non-linearity in the flow-performance relationship, I continue to follow Sirri and Tufano (1998) to create three performance range variables defined as follows using splines: (3) LOWPERF i,t = min [RANK i,t, 0.2] MIDPERF i,t = min [RANK i,t - LOWPERF i,t, 0.6] HIGHPERF i,t = min [RANK i,t - LOWPERF i,t - MIDPERF i,t, 0.2] 9

11 LOWPERF i,t represents the bottom performance quintile, MIDPERF i,t represents the middle three performance quintiles, and HIGHPERF i,t represents the top performance quintile. Since the above-mentioned performance variables measure the relative performance of a fund in its investment objective, I also include a new performance variable, OAWRET i,t, which is the asset-weighted average of the raw holding period returns of all bond funds with the same investment objective, to test whether investors might chase absolute performance. I also follow Sirri and Tufano (1998) in using the standard deviation (SDRET) of monthly raw returns of fund i in the past 12 months to measure the risk of a fund and to study the effect of risk on fund net flows. I also measure the risk-adjusted performance of a fund using the Sharpe ratio (SHARPE), which is computed as: (4) SHARPE = R R i f SDRET i where Ri and R f are the average monthly raw return of fund i and risk-free rate in the past 12 months, respectively, and SDRET i is the standard deviation of the monthly raw returns of fund i in the past 12 months. 12 Performance ranks and performance range variables LOWSHARPE i,t, MIDSHARPE i,t, and HIGHSHARPE i,t are computed in the same fashion as in Equation (3), and used to study the effect of risk-adjusted performance on flows. 10

12 Furthermore, I also employ the Risk-3 index model introduced by Blake, Elton, and Gruber (1993) to compute an alternative measure of risk-adjusted performance. I calculate this measure using monthly fund returns over the previous 24 months. (5) Rit = α i + β i1govcreditt + β i2mbst + β i3highyieldt + ε it where R it is the bond fund return in excess of the monthly T-bill return; GOVCREDIT is the excess return on the Lehman Brothers Government/Credit Bond Index and is a weighted market average of government and investment grade corporate issues that have more than one year until maturity; MBS is the excess return on the Lehman Brothers Mortgage-Backed Securities Index; and HIGHYIELD is the excess return on the Lehman Brothers Corporate High Yield Bond Index. 13 After obtaining the estimates for α, the alternative risk-adjusted performance measure, I still follow Sirri and Tufano (1998) in generating performance range variables LOWALPHA i,t, MIDALPHA i,t, and HIGHALPHA i,t in the same fashion as in Equation (3), and study their effects on bond fund flows. Expenses and Load Dummies Since information on 12b-1 fees (12B) is available in the data set used by this paper, as in Barber, Odean, and Zheng (2002), I subtract 12b-1 fees from the expense ratio to create a new variable, NON12B, which only represents operating expenses not related to distribution efforts. I include both variables in the analysis to test if they might have distinct relationships to fund flows. 11

13 To test whether any type of load bond funds might receive higher flows than noload bond funds, I create three load fund dummy variables, FLDUMMY, BLDUMMY, and LLDUMMY, which take the value of one if the fund is a front-end load fund, back-end load fund, and level load fund, respectively, and zero otherwise. 14 Fund Age and Turnover Ratio The age (AGE) and turnover ratio (TURNOVER) of a fund are also included in the analysis to control for their possible effects. Number of Investment Objectives in the Fund Family NUMOBJ represents the number of investment objectives offered in the fund family. This variable is included to capture the spillover effects within a fund family from a different angle. E. Summary Statistics [Insert Table 1 about here] I compute the medians of various characteristics of bond funds with different investment objectives (all three municipal bond investment objectives are combined as one single objective) and report the results in Table 1. High yield bond funds have by far the largest median size ($ million), while municipal bond funds and global bond funds are the smallest in median size. Surprisingly, municipal bond funds have the 12

14 highest median raw return (1.522 percent), followed by high quality bond funds (1.510 percent). Global bond funds and high yield bond funds, on the other hand, experience the lowest raw returns. It should be noted that this ranking is the exact opposite to their ranking in terms of expenses, both 12b-1 fees and other expenses. While global bond funds and high yield bond funds, charge the highest fees, municipal bond funds and high quality bond funds appear to be the low cost leaders. High yield bond funds and global bond funds also have the most volatile returns, as shown by their standard deviations, which are more than 50 percent higher than those of funds with other investment objectives. With a combination of low raw returns and high risks, it is not surprising to find that funds with these two investment objectives also have the lowest Sharpe ratios. With the help of their low standard deviation, Ginnie Mae funds boast the highest Sharpe ratios. Global funds have their portfolios replaced most often, while a median municipal bond fund has a fairly stable portfolio, as shown by their respective turnover ratios. Somewhat unexpected, high yield bond funds have lower turnover ratios than both government security funds and high quality bond funds. Another surprise is that, high yield bond funds have the highest median dollar and percentage flows, followed by high quality bond funds under both measures. [Insert Table 2 about here] Table 2 reports the pairwise correlations of various bond fund characteristics. The correlations between dollar flows and all other variables, which are reported in the first column, serve as a univariate analysis of the relationship of these variables to dollar 13

15 flows. As noted earlier, larger bond funds appear to have smaller dollar flows. In addition, dollar flows are positively correlated to returns, while negatively correlated to other variables, such as expenses and turnover ratios. F. The Statistical Model To study the determinants of dollar flows into bond funds, I estimate the following random effects panel regression using the full sample of retail bond funds: 15 (6) FLOW i,t = α+ β1 ASSET i,t-1 + β 2 FLOW i,t-1 + β 3 FLOW i,t-2 + β 4 FLOW i,t-3 + β 5 LOWPERF i,t-1 + β 6 MIDPERF i,t-1 + β 7 HIGHPERF i,t-1 + β 8 NON12B i,t-1 + β 9 12B i,t- 1 + β 10 AGE i,t-1 + β 11 TURNOVER i,t-1 + β 12 SDRET i,t-1 + β 13 NUMOBJ i,t-1 + β 14 OAWRET i,t- 1 + β 15 FLDUMMY i + β 16 BLDUMMY i + β 17 LLDUMMY i ui + + ε i, t where all variables are as defined in Section II. D, and u i is the random disturbance characterizing the i th fund and is constant through time. Since Warther (1995) shows that aggregate flows follow an AR (3) process, I also include FLOW i,t-2 and FLOW i,t-3 in the estimation. The effects on percentage flows are also investigated. When PFLOW i,t is used as the dependent variable, LASSET i,t-1 and the lags of PFLOW are used to represent fund size and flows in the previous quarters instead. 16 In separate regressions, LOWPERF, MIDPERF, and HIGHPERF are replaced by LOWSHARPE, MIDSHARPE, and HIGHSHARPE or LOWALPHA, MIDALPHA, and HIGHALPHA as alternative performance measures. The panel regression method is used to account for the fact that observations from the same fund are not independent relative to one another in this time-series cross- 14

16 sectional (panel) data set. The random effects model is chosen over a fixed effects model due to the existence of the load fund dummy variables. Like the fixed effects, the dummy variables are also fund-specific and time invariant and therefore cannot be distinguished from the fixed effects. Consequently, a fixed effects model cannot be estimated with such dummy variables. 17 As a robustness check, I also apply the Fama-MacBeth method and estimate the coefficients for each of the 38 quarters separately. Then I calculate the coefficients and t-statistics from the vector of quarterly results, as in Fama and MacBeth (1973). The same qualitative results (not reported) are obtained for almost all of the variables. 18 III. Estimation Results A. Determinants of Flows for All Retail Bond Funds Table 3 reports the results of random effects panel estimation using the full sample of retail bond funds. Results using both dollar flows and percentage flows are reported. Model 1 uses performance measures based on raw returns, while Model 2 and Model 3 use risk-adjusted performance measures based on Sharpe ratios and Risk-3 model alphas, respectively. [Insert Table 3 about here] It should be noted first that, regardless of the performance measures used, after controlling for other fund characteristics, the estimates of the coefficient of fund size (ASSET) are shown to be significantly negative. 19 The negative relationship between 15

17 fund size and dollar flows is shown to be driven by the results from load funds, which account for more than 75 percent of the observations. In results not reported here, load fund investors are more likely to be directed to smaller funds by brokers and financial advisors. The negative relationship between fund size and dollar flows, consistent with the univariate result in Table 2, corroborates that, contrary to the presumption behind the use of percentage flows, larger funds actually receive lower dollar flows. Consequently, following Del Guercio and Tkac (2002), I focus on estimates from models using dollar flows, although similar results can be observed for many variables in models using percentage flows. Blake, Elton, and Gruber (1993) show that a bond fund s past performance does not predict future performance. It then becomes interesting to see whether bond fund investors chase past performance, a behavior well documented for equity fund investors. This study first reveals that bond fund investors apparently chase absolute performance, flocking into investment objectives with high average raw returns. A one percentage point (100 basis points) increase in objective return on average is associated with around $600,000 extra flows into a bond fund. However, bond fund investors appear to chase raw-return-based relative performance only in the middle range of performance, as shown by the significantly positive estimate for MIDPERF and the insignificant estimates obtained for LOWPERF and HIGHPERF. Similar to equity fund investors, bond fund investors do not punish funds with the worst raw returns with huge outflows. However, they do not invest disproportionately more in the raw return leaders in each investment objective, either, which is in sharp contrast to equity fund investors

18 Bond fund investors appear to be very sensitive to the risks involved in their investments. Even though the estimate for SDRET, a direct measure of fund risk, is only marginally negative, bond fund investors risk aversion can be apparently shown by their attitudes towards performance measures based on Sharpe ratios and Risk-3 model alphas, which both measure risk-adjusted performance. Model 2 and Model 3 both show a significantly positive and convex relationship between flows and performance percentile ranks based on risk-adjusted performance measures, except for funds in the bottom performance quintile. The estimates from the piecewise regression of the performance ranges show that, the same increase in performance percentile ranks leads to three to four times as many dollar flows in the top performance quintile as in the middle three quintiles. Apparently, risk-adjusted performance exerts much stronger influence on bond investors investment decisions than raw returns. Blake, Elton, and Gruber (1993) and Elton, Gruber, and Blake (1995) both find that a percentage-point increase in expenses approximately leads to a percentage-point decrease in bond fund performance. Consequently, they urge individual investors with no forecasting ability to select a low-expense bond fund. Contrary to the mixed results on expense-sensitivity for equity fund investors, bond fund investors appear to have a better understanding of the erosion effects of expenses on bond fund returns. A one percentage point (100 basis points) increase in operating expenses on average is associated with a reduction of flows by around one million dollars. It should also be noted that higher 12b-1 fees actually marginally lead to higher flows. Since 12b-1 fees are primarily used to pay for distribution expenses, this result indicates some positive effects on investors of the distribution efforts by bond funds. This result also shows the 17

19 importance of studying the effects of 12b-1 fees and operating expenses separately, since neither effect might be detected had the two types of expenses been combined in one expense ratio. Other findings also show that bond fund investors are sensitive to expenses. For example, all else being equal, a load bond fund receives between two and four million dollars fewer flows, depending on the specific load type, than a no-load bond fund. This finding indicates that bond fund investors tend to stay away from load funds. Considering that, compared to equity funds, bond funds tend to have relatively smaller upside potential in returns, the negative effect of loads on load-adjusted returns should be more severe for bond fund investors. In addition, higher turnover ratios also marginally discourage investments into bond funds, presumably because the associated higher trading costs exert a strong deterrent effect on bond fund investors. Furthermore, bond funds from fund families investing in a greater number of investment objectives tend to receive higher flows. This positive spillover effect from offering more investment objectives in the fund family indicates that investors select bond funds as an asset class in their total asset allocation strategies and do value the potential option of switching within the fund family. In addition, bond fund flows are found to be highly autocorrelated, as shown by the significantly positive estimates for all three lags of flows. The autocorrelation decreases over time, though, as evidenced by the fact that the coefficient of the third lag is only about one fifth of that of the first lag in magnitude. 18

20 B. Determinants of Flows for Retail Bond Funds with Different Investment Objectives Different bond funds might focus on various fixed-income instruments, such as municipal bonds or global bonds. These various fixed-income instruments have very different return and risk characteristics and tax treatments, and might appeal to different investors. For instance, municipal bond funds are especially attractive to wealthy investors because income from municipal bonds is exempt from federal personal income tax. As a result, certain factors might not have the same effect on the flows into bond funds with different investment objectives. In addition, Goetzmann, Massa, and Rouwenhorst (2003) and Agnew and Balduzzi (2003) both document that investors rebalance among different asset classes. Their findings suggest that flows into bond funds might also be affected by equity market returns. Considering the different return and risk characteristics of various fixed-income instruments, the effects of equity market returns on bond fund flows might also vary by bond fund investment objectives and should be studied separately. I adopt the Fama/French benchmark factor RM, which is the value-weighted return on all NYSE, AMEX, and NASDAQ stocks, as the measure of equity market returns. 21 Using quarterly RM, I construct RM8QT as the compounded return in the past eight quarters. Adding RM8QT, I re-estimate the models in Section III.A for each bond fund investment objective (the three municipal bond objectives are once again combined into one investment objective of municipal bonds) separately and compare the results, which are reported in Table 4, Table 5, and Table Table 4 reports results using performance measures based on raw returns, while Table 5 and Table 6 use risk-adjusted performance 19

21 measures based on Sharpe ratios and Risk-3 model alphas, respectively. Only results using dollar flows are reported in all three tables. [Insert Table 4 about here] [Insert Table 5 about here] [Insert Table 6 about here] It is first noted that, flows into government security funds, Ginnie Mae funds, and high quality bond funds are all significantly negatively correlated to long-term equity market returns. These findings show that investors increase their investments in bond funds when they flee a prolonged bear equity market. However, in results not reported here, if quarterly RM is used instead, only flows into government security funds are significantly negatively correlated to short-term equity market returns. Apparently, among all bond funds, investors will only treat government security funds, which primarily invest in securities backed by the federal government, as a safe haven when they face temporary equity market drops. Only a few factors have consistent effects across all investment objectives. Among them, flows are almost all significantly positively correlated to lagged flows. 23 In addition, bond fund investors do not seem to punish funds with the worst raw returns or chase funds with the highest raw returns in any of the six investment objectives, as shown by the insignificant estimates for LOWPERF and HIGHPERF across all investment objectives. Table 5 and Table 6 show that the strong influence of risk-adjusted performance on flows can be observed across all investment objectives, although maybe 20

22 not for both the middle and high performance ranges. Inconsistent with the finding for bond funds as a whole, 12b-1 fees do not show a significant effect on flows for any of the investment objectives, except for high yield bond funds and global bond funds in Table 6. This result casts doubt on the effect of distribution efforts by bond funds on flows. Some of the variables exert consistent effects on flows across all investment objectives with only one anomaly. For instance, the negative relationship between fund size and flows holds for all investment objectives except high quality bond funds, for which the relationship is significantly positive. This is shown to be driven by the fact that, in addition to no-load fund investors, front-end load fund investors also invest more in larger high quality bond funds (not reported). Furthermore, bond fund investors appear to chase absolute performance in all investment objectives by flocking into the objectives with high returns, with the exception of high yield bond funds, for which the effect of past objective return on flows appears to be insignificant. Given the volatile nature of high yield bond returns (Table 1 shows high yield bond funds have the highest standard deviation of returns among all investment objectives), their investors apparently are reluctant to chase absolute performance. For most variables, their effects on flows for bond funds as a whole can only be observed for a subset of the six investment objectives. Apparently, the same factor might play different roles in the investments into bond funds with different investment objectives. This corroborates the importance of studying the determinants of flows for each investment objective separately. Even though bond fund investors in general still appear to chase raw-return-based relative performance in the middle range of performance, such behavior cannot be 21

23 observed for municipal bond fund, government security fund, or high-yield bond fund investors. For these three investment objectives, raw returns do not seem to affect investors decisions at all for any performance range. Municipal bond funds and government security bonds hold relatively homogeneous instruments, which make the discrepancy in performance among different funds fairly narrow. As a result, it becomes less meaningful for investors to select these funds based on raw returns. The narrow range of performance difference also makes evident the erosion effects of expenses on returns, which makes lower expenses the primary driver behind flows into such funds. For instance, a one percentage point (100 basis points) increase in operating expenses on average is associated with a reduction of flows by more than one million dollars for these funds. Global bond fund investors also appear to be sensitive to operating expenses, apparently because global bond funds charge the highest operating expenses among all investment objectives, as shown in Table 1. The fact that high yield bond investors do not chase relative performance based on raw returns is consistent with their insensitivity to absolute performance. The high volatility of high yield bond fund returns is more likely to make a performance-chasing momentum strategy detrimental to their investors. The high risk also explains why high yield bond fund investors are the most sensitive to risks. A one percentage point (100 basis points) increase in the standard deviation of returns on average might reduce flows by more than two million dollars. A clear distinction can also be seen among different investment objectives as to whether the potential to alter asset allocation within the fund family affects investors decisions. Those who invest in government security funds, Ginnie Mae funds, and high 22

24 quality bond funds apparently value such options, as shown by the significantly positive effects on flows of the number of investment objectives offered by a fund family across all three tables. This finding indicates that the investors of these funds, which have a more general appeal, are more likely to treat such funds as swing components in their total asset mix and the investments in these funds are more likely to be affected by investors general asset allocation strategies. On the other hand, investors of the more specialized municipal bond funds, high yield bond funds, and global bond funds pay less attention to other options in the fund family and apparently make their investments in these funds for their own specific reasons. As noted earlier, tax avoidance is the main reason for municipal bond fund investments. High yield bond funds and global bond funds give investors exposure to the more volatile junk bond and global fixed-income security markets, respectively. Investors show stronger avoidance of load funds for almost all investment objectives except high yield bond funds. Most bond funds tend to invest in relatively homogenous instruments, making the service of brokers and financial advisors less valuable and the loads paid a pure loss. On the other hand, the estimates of all three load dummies are positive, some even significant in certain tables, for high yield bond funds. This result indicates that investors are more likely to be subject to the influence of brokers and financial advisors in this riskier investment objective. 23

25 IV. Conclusion This paper studies the determinants of net flows into retail bond funds both as a whole and by investment objective, using a new data set of all retail bond funds from 1992 to This paper reveals that, unlike equity fund investors, bond fund investors do not flock into funds with the highest recent raw returns. Instead, risk-adjusted performance has a much stronger effect on bond fund flows. In addition, bond fund investors, municipal bond fund and government security fund investors in particular, appear to be very sensitive to expenses. They tend to stay away from funds with high operating expenses and funds that charge sales loads. Except for high quality bond funds, smaller bond funds receive higher net dollar flows. Due to the high volatility of fund returns, high yield bond fund investors do not chase absolute performance and are most sensitive to risks. This study also finds that the investors of government security funds, Ginnie Mae funds, and high quality bond funds are more likely to treat such funds as swing components in their total asset mix. The investments in these funds are more likely to be affected by investors general asset allocation strategies. As a matter of fact, investors do increase their investments in these funds when they flee a prolonged bear equity market. However, investors will only treat government security funds as a safe haven when they face temporary equity market drops. Bond funds exhibit increasing importance for individual investors to create a truly diversified portfolio. As the first comprehensive study of the determinants of flows into bond funds, this paper sheds light on the behaviors of bond fund investors with various investment objectives. 24

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28 James, C., and J. Karceski. Captured Money? Differences in the Performance Characteristics of Retail and Institutional Mutual Funds. Working paper, University of Florida (2002). Kihn, J. The Financial Performance of Low-Grade Municipal Bond Funds. Financial Management, 25 (1996), LaPlante, M. Influences and Trends in Mutual Fund Expense Ratios. Journal of Financial Research, 24 (2001), McLeod, R. W., and D. K. Malhotra. The Effect of Rule 12b-1 on Bond Fund Expense Ratios. Journal of Economics and Finance, 20 (1996), Morey, M. R., and E. S. O Neal. Window Dressing and Bond Mutual Funds. working paper, Pace University and Wake Forest University (2001). Nanda, V.; Z. J. Wang; and L. Zheng. Family Values and the Star Phenomenon. Review of Financial Studies (forthcoming 2003). O Neal, E. S. Purchase and Redemption Patterns of US Equity Mutual Funds. Working paper, Wake Forest University (2002). Sirri, E. R., and P. Tufano. Costly Search and Mutual Fund Flows. Journal of Finance, 53 (1998), Warther, V. A. Aggregate Mutual Fund Flows and Security Returns. Journal of Financial Economics, 39 (1995), Wilcox, R. T. Bargain Hunting or Star Gazing? Investors Preferences for Stock Mutual Funds. Journal of Business (forthcoming 2003). Zheng, L. Is Money Smart? A Study of Mutual Fund Investors Fund Selection Ability. Journal of Finance, 54 (1999),

29 1 Even discount brokers such as E*Trade and Schwab charge commissions of about $40 to $50 for each bond transaction, considerably higher than what they charge for trading stocks. Individual investors also incur large bid-ask spreads for trading smaller numbers of bonds. Some brokers such as Fidelity require a $5,000 minimum for bond transactions. In addition, even many discount brokers such as Ameritrade and Quick & Reilly require that individual investors consult with financial advisors or service representatives before engaging in bond transactions. Adding additional inconvenience, the reinvestment of coupon payments from various bond holdings requires separate attention. 2 See 2002 Mutual Fund Fact Book by Investment Company Institute. 3 The vast majority of the limited literature on bond funds focuses on the performances of bond funds as a whole (Blake, Elton, and Gruber (1993) and Elton, Gruber, and Blake (1995)) or a certain subcategory of bond funds, such as high yield bond funds (Cornell and Green (1991)), global bond funds (Detzler (1999)), and low-grade municipal bond funds (Kihn (1996)). The remaining papers study issues such as the expense ratios (McLeod and Malhotra (1996) and LaPlante (2001)) and window dressing (Morey and O Neal (2001)) of bond funds. 4 The literature on the determinants of flows into equity funds will be discussed in detail in Section II. B. Compared to equity, fixed-income securities are on average less risky. Thus, investors with a lower level of risk tolerance and closer to retirement are more likely to invest in bond funds (for example, see the asset allocation suggestions from TIAA-CREF at In addition, bond fund investors are on average wealthier than equity fund investors. The household income and financial assets of an average bond fund investor are both about 25 percent higher than those of an average equity fund investor (see 2001 Profile of Mutual Fund Shareholders by Investment Company Institute.) 5 No investment objective information is available in the CRSP Mutual Fund Database for bond funds before 1990, making it impossible to incorporate the observations from earlier time periods. However, not being able to use data from a longer time period should not affect the estimation results, though. Considering that bond funds were rare compared to equity funds in the 1970s and 1980s, and that the 28

30 number of bond funds tripled from the beginning of 1992 to the end of the third quarter of 2001, any estimation results using data including observations before 1992 will still be dominated by observations from 1992 to I use quarterly data so that an adequate number of time periods (38 quarters) are available to apply the Fama-MacBeth method as a robustness check. I also use annual data from 1992 to 2000 and find the same qualitative results for all analyses. I refrain from using higher frequency data because this paper intends to study the long-term behaviors of bond fund investors. 6 According to Appendix A to the CRSP Survivor-Bias Free US Mutual Fund Database Guide, High Quality Municipal Bond Funds invest in municipal securities rated BBB or better; Single State Municipal Bond Funds invest in municipal bonds of a single state; High Yield Municipal Bond Funds invest in municipal securities rated BB or lower; Government Security Funds invest in securities backed by the federal government; Ginnie Mae Funds invest primarily in Government National Mortgage Association securities; High Quality Bond Funds invest in corporate bonds rated BBB or better; High Yield Bond Funds invest in corporate bonds rated BB or lower; and Global Bond Funds invest primarily in fixedincome securities with at least 25 percent of the portfolio invested in securities traded outside the United States. 7 For example, the following four funds Dreyfus Premier Core Bond Fund A, Dreyfus Premier Core Bond Fund B, Dreyfus Premier Core Bond Fund C, and Dreyfus Premier Core Bond Fund R share the same portfolio, that of Dreyfus Premier Core Bond Fund. Using fund name, NAV, return, and turnover ratio, I identify the portfolio for each fund. 8 Front-end load funds charge a front-end load and a 12b-1 fee but not a back-end load; back-end load funds charge a back-end load and a 12b-1 fee but not a front-end load; level-load funds charge a much smaller back-end load (less than two percent) and a 12b-1 fee (generally greater than that of a back-end load fund) but not a front-end load; and, no-load funds charge neither a front-end load nor a back-end load, but may charge a 12b-1 fee (if any) less than 25 basis points. The loads and 12b-1 fees are used primarily to compensate brokers and financial advisors and to pay for distribution expenses. Some all-load funds charge both a front-end load and a back-end load. Considering that such bond funds only account for 2.79 percent of all bond funds (155 out of 5,554 bond funds), they are not included in this study. 29

31 9 Studying new purchases and redemptions separately instead of net flows might provide more insight. However, data limitations preclude such a full-sample study of all bond funds. The only known source of such information is the N-SAR form filed by mutual funds semiannually with the SEC. However, since mutual funds with multiple share classes only file one N-SAR form for each fund, instead of for each share class, they report only aggregate new purchases and redemptions from all share classes. Considering that various load funds, which account for percent of all bond funds, are primarily different share classes from funds with multiple share classes, studying their new purchases and redemptions becomes infeasible when such information is not available for each share class. This problem is apparent by examining item 28 of the N-SAR form, available at and confirmed with the Division of Investment Management of the SEC. Furthermore, the strong positive correlation between new purchases and redemptions detected in O Neal (2002) also justifies the use of net flows, which capture the net effect of new purchases and redemptions. 10 Del Guercio and Tkac (2002) also try to control for any effect to flows due to merger. 11 As a result, 992 observations are excluded, which account for 0.93 percent of all observations. 12 Goetzmann and Kumar (2002) calculate the Sharpe ratio in the same fashion. 13 I thank Lehman Brothers for kindly providing the data on Lehman Brothers Indices. The Lehman Brothers Government/Credit Bond Index used to be named the Lehman Brothers Government/Corporate Bond Index, as used in Blake, Elton, and Gruber (1993). In addition, while the Risk-3 model in Blake, Elton, and Gruber (1993) employed Blume/Keim High-Yield Index, I use the Lehman Brothers Corporate High Yield Bond Index instead. 14 An alternative would be to use the actual level of front-end or back-end load and examining their coefficients. However, two offsetting effects might be combined in such a procedure and cannot be distinguished from one another. First, better-informed investors might stay away from any load fund, regardless of the actual load level. However, for the clientele who do invest in load funds, the stronger incentives due to the higher compensation to brokers and financial advisors from higher loads might actually lead to higher flows. Consequently, in this paper, I only include various load fund dummy variables instead of actual load levels to test whether any type of load funds receives higher flows. 30