Do Funds of Mutual Funds Add Any Value?

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1 Do Funds of Mutual Funds Add Any Value? Jung Hoon Lee Kelley School of Business Indiana University Abstract This paper examines funds of mutual funds (FoMFs) as a competing structure to advisory services to see if they offer any value to investors. First, the flow-performance relationship associated with FoMF buys and sells suggests that affiliated FoMFs (i.e., funds that only invest in funds within their own family) instill a competitive environment in the family by increasing inflows to good performing funds and withdrawing investments from poorly performing funds. These affiliated FoMFs, who are insiders within the fund complex, also tend to buy/sell the right funds and therefore display a superior fund selection skill. In other words, the affiliated FoMFs capital allocation decision is smart and generates positive abnormal returns. However, unaffiliated FoMFs show no sensitivity to performance nor do they display fund selection ability. This result is partly driven by the strict regulatory constraint unaffiliated FoMFs face. Overall, professional oversight of fund operations and superior selection of constituent funds are the value-additions that FoMFs provide for the investors. Current Draft: Nov Key Words: Fund of mutual funds, flow-performance relationship, smart money effect, fund family JEL classification: G10, G11, G20, G23 Acknowledgement: I am very grateful to Charles Trzcinka for his careful comments on current paper. I also thank Jonathan Berk, Utpal Bhattacharya, Eitan Goldman, Jay Wang, Tobias Mühlhofer, Craig Holden, Noah Stoffman, Neal Stoughton, Tom Berglund, Wolfgang Bühler, Travis Sapp, Vikas Agarwal, Jayant Kale, Rasha Ashraf, Xiaoxia Lou, David Cicero, John Knopf, Franklin Allen, and participants at the European Finance Association (EFA) annual meeting in Frankfurt, Germany and the Financial Management Association (FMA) doctoral consortium in New York for their insightful comments and suggestions. I especially thank Veronika Pool for extensive feedback, helpful advice, and constant encouragement. All remaining errors are mine. The earlier version of this paper was circulated under the title The Information Content of Professional Investors Mutual Fund Flows. Address for correspondence: Finance Department, Kelley School of Business, Indiana University, 1309 East Tenth Street, Bloomington, IN Electronic copy available at:

2 I. Introduction Investors often use a middleman and seek costly financial advice to select a portfolio of mutual funds. The role of these middlemen is a controversial issue in the money management literature. First, studies document potential conflicts of interest in fund recommendations. 1 Second, even when the financial intermediaries are committed to their fiduciary role, fund selection appears to be a daunting task. Despite the high cost and inferior performance associated with the intermediated distribution channels, many investors prefer to use them over picking the funds themselves. Recently, funds of mutual funds (FoMFs) have emerged as an alternative structure to advisory services. FoMFs are investment funds which use an investment strategy of holding a portfolio of other investment funds. That is, they provide a pre-packaged portfolio of mutual funds for investors. Moreover, these advisors may be better aligned with their clients because their compensation is a direct function of the performance of the selected portfolio. As is well known in the hedge fund literature, the major disadvantage of the fund of funds arrangement is the cost to the investor. In addition to the fees funds of funds charge, they pass on to the investor all fees charged by the constituent funds. In return for high expenses, funds of hedge funds (FoHFs) provide non-performance based benefits. For instance, FoHFs offer due diligence that is otherwise impossible at the level of the individual investor and allow investors to hold a diversified portfolio of managers that is otherwise prohibitively expensive. Additionally, since little information exists about fund operations and holdings, FoHFs might be better at overcoming search frictions. However, the mutual funds industry is much more transparent. What do fund of mutual fund investors earn in exchange for fees on fees? 1 For instance, Bergstresser, Chalmers, and Tufano (2009) find that broker sold funds underperform funds sold through direct channels. 1 Electronic copy available at:

3 In this study, I examine FoMFs as a competing structure to advisory services to see if they offer any value. First, I examine the relation between FoMF investment (capital flow) and the past performance of the portfolio funds; that is, the flow-performance sensitivity of FoMFs. 2 There are two types of FoMFs: affiliated and unaffiliated. Affiliated FoMFs (AFoMFs) are mutual funds that, by law, can only invest in other mutual funds within the family. In 2007, about 13% of all fund families had such AFoMFs; these are nearly all large fund families. In contrast, unaffiliated FoMFs (UFoMFs) may invest in any fund in the mutual fund universe. It is important to note that there is a tension between two alternative dynamics with regard to AFoMFs capital allocation decisions. First, since the average investment by AFoMFs account for 6 % of the total assets that underlying managers run, member funds in the family may compete with each other to become a part of AFoMFs portfolio. In this perspective, Kempf and Ruenzi (2007) suggest that fund managers engage in a family tournament. 3 Therefore, AFoMFs can instill a competitive environment in the family by rewarding winners and penalizing losers and pursue the objective of their investors. Alternatively, since AFoMFs and underlying fund managers belong to the same fund complex, they might be subject to cronyism. That is, AFoMFs may face peer pressure to help the poorly performing member funds since the underlying managers may run into a trouble if the AFoMFs pull out too much money. However, unlike AFoMFs, UFoMFs are not subject to organizational constraints. Next, I examine whether FoMFs rebalancing decisions represent smart money. The relationship between past flows and future performance is often examined in the literature. The 2 Existing evidence on the flow-performance relation is largely limited to retail investors. Also, the empirical mutual fund literature widely documents a convexity: retail investors chase good performing funds, but are much less willing to withdraw their money when the fund underperforms. While retail investors fail to respond to negative performance in the face of limited experience and personal resources, FoMFs, who are professional investors, may act differently. 3 I thank Vikas Agarwal for raising this point. 2

4 key issue is whether FoMFs are able to buy or sell the right funds and deliver positive abnormal returns for the investors. Interestingly, the majority of funds of mutual funds are affiliated funds. Therefore, the main source of potential value to investors is within the family information advantage. In this perspective, Gervais, Lynch, and Musto (2005) argue that families know more about their funds and managers than outside investors do. This explanation is particularly relevant for AFoMFs since they can be regarded as insiders within the fund complex. Thus, if AFoMFs are better informed about funds future prospects, this information advantage will manifest itself through a superior fund selection. Unlike AFoMFs, UFoMFs are not expected to have information advantage. FoMFs offer a unique setting to test the above questions. These investors of mutual funds are mutual funds themselves. Thus, tracking the composition of their portfolios over time provides essentially account level gross flow (inflow and outflow separately) information. To the best of my knowledge, the only other existing studies that investigate flow-performance sensitivity, using data at the individual investor level, are Johnson (2009) and Ivkovich and Weisbenner (2009), who examine retail investors. My first result reveals that AFoMFs reward good performance with additional capital inflows. This is consistent with the behavior documented for total net investor flows in previous studies. However, I find that affiliated FoMFs also respond to bad performance by redeeming their investments; hence, they penalize poorly performing underlying funds. This evidence runs contrary to the results of the previous literature, which documents slow net (retail) outflows in the face of underperformance. This symmetric flow-performance relation provides support for the family tournament explanation. Rather than displaying cronyism, AFoMFs pound a competitive environment in the family by voting on their co-worker s ability. That is, AFoMFs 3

5 pursue the objectives of their own investors by investing in winners and de-investing in losers. This provides a strong incentive for underlying managers to engage in a family tournament, as evidenced by Kempf and Ruenzi (2007). Next, unlike AFoMFs, UFoMFs display different flow response results. In this group, I do not find significant evidence that flows are sensitive to performance. Unaffiliated funds do not appear to reward good performers, nor do they vote with their feet when performance is lagging. What drives the lack of response for the unaffiliated FoMFs? One possible conjecture is that it is due to the severe regulatory constraints unaffiliated FoMFs face. Since the regulatory limit on the size of UFoMF holdings is small, they are forced to have a small position in many funds. A sub-sample analysis based on the number of mutual fund holdings shows that the lack of sensitivity UFoMFs display to the underlying funds performance can be partly explained by the over-diversification of holding too many funds and thus the difficulty of proper monitoring. However, an additional analysis suggests that the SEC rules designed to prevent the potential abuse of FoMF arrangements do not impose binding constraints on UFoMFs trading activities. Thus far, the results suggest that AFoMFs constantly rebalance their own portfolios. In other words, they churn some of the under-performing funds and reward some of the outperforming funds on a constant basis. As was stated before, the majority of FoMFs are affiliated funds; therefore, the source of potential value to investors may be within the family information advantage. If this is true and they can select superior managers, then they should have a low turnover; once they identify a skilled manager, there is no need to leave the fund. However, a frequent rebalancing and the average turnover of 40% tend to contradict this. How should an observer outside of the fund family interpret this frequent rebalancing result? Is it an information-based investment decision, or is it simply momentum profits chasing through 4

6 frequent re-balancing? 4 To make the distinction, I investigate the subsequent investment outcome. The relationship between past flows and future fund performance is examined by the recent literature on the smart money effect. Gruber (1996) and Zheng (1999) report that investors at an aggregate level have selection ability, in that the short-term performance of funds that experience net cash inflow appears to be significantly better than the short-term performance of funds that experience net cash outflow. Sapp and Tiwari (2004) show that this effect is related to stock return momentum. 5 That is, after controlling for the Carhart (1997) momentum factor, the smart money effect disappears. Thus, in the domain of U.S. mutual fund investments, there is no robust evidence that investors have fund selection ability. To investigate whether FoMF money is smart, I use a unique laboratory setup in which to compare the fund selection outcome of different investor groups on the same set of underlying funds. First, I form portfolios at the beginning of each quarter based on whether the FoMFs bought or sold the underlying funds, respectively. Underlying funds that are bought comprise the positive flow portfolios, while those that are sold during the previous quarter are placed in the negative flow portfolio. In addition, underlying funds are also grouped into either the positive cash-flow portfolio or the negative cash-flow portfolio, based on the sign of the net cash flow after excluding flows from FoMFs (i.e., these represent flows from other investors). This second set of flow groups will be used as a benchmark to investigate whether other flows are able to earn superior returns. After forming these portfolios, the subsequent quarter s returns are 4 I thank Travis Sapp for raising this point. 5 Keswani and Stolin (2008) argue that, after controlling for stock return momentum, there is still a smart money effect by U.K mutual funds data. 5

7 evaluated by using the Carhart (1997) four factor model. My results reveal that AFoMFs display superior performance and this performance is not driven by momentum. When I take the difference between the positive cash-flow and negative cash-flow portfolio alphas, I get about 30 basis points per month. However, flows other than those of AFoMFs fail to earn significant returns. I also repeat the same analysis for unaffiliated FoMFs. When I take the difference between the positive cash-flow and negative cash-flow portfolio alphas, I get about -18 basis points per month, which has a p-value of 24.2%. Furthermore, flows excluding those of UFoMFs also do not earn superior returns. Overall, the results provide evidence that supports the information advantage story. However, this effect is transitory since there is a quarterly rebalancing of buying and selling portfolios of FoMFs. As Keswani and Stolin (2008) suggest, price pressure from fund inflows, growing fund size, and imitation of the fund s strategy cause superior performance to dissipate. This transitory persistence may explain the relatively frequent buying and selling activities by FoMFs. Additionally, the sub-sample analysis suggests that this effect is more pronounced when AFoMFs have concentrated fund holdings. Overall, for AFoMFs, the benefits from information advantage appear to exceed the cost of investment constraints since they mostly belong to large fund families. For UFoMFs, the freedom of investments appears to become a heavy burden since they have to overcome a search friction. Lastly, my final analysis documents that AFoMFs dominate ordinary mutual funds or UFoMFs on the Sharpe ratio basis. The superior after-fee performance of AFoMFs might be explained by the nature of the fee arrangement since AFoMFs get a great deal of discounts and rebates through large within-family transactions. My paper proceeds as follows. Section II discusses the funds of mutual funds industry 6

8 and the recent change in regulation, respectively. Section III discusses the main hypotheses. Section IV describes my data and basic summary statistics, and presents the empirical methodology used in this paper. I discuss the main and sub-sample results in Section V. Section VI concludes the paper. II. FoMFs Industry and Regulatory Background A. FoMFs Industry "Funds of mutual funds" (FoMFs) are investment funds that use an investment strategy of holding a portfolio of other investment funds rather than investing directly in securities. According to the 2008 Investment Company Institute (ICI) Fact Book, it is estimated that over $ 640 billion is now managed by the funds of mutual funds industry in 2007, with substantial additional money flowing in each year. 6 Considering that total net assets of mutual funds held in institutional accounts are about $1.6 trillion in 2007, FoMFs constitute an important segment in the mutual fund industry. 7 One interesting observation is that the large proportion of FoMFs assets is held in retirement accounts. This increased demand from the company pension plan facilitates the rise of this sector in the mutual fund industry. The industry s rapid growth can also be explained by changes in legislation by Congress and the SEC that amend the Investment Company Act of 1940, to enhance the ability of investment companies to invest in shares of other investment companies under FoMFs arrangements. 8 Importantly, there are two different types of FoMFs: affiliated and unaffiliated funds of funds. Affiliated FoMFs are mutual funds that are constrained to invest in funds within the 6 See 7 See 8 See 7

9 same fund family. Thus, there are non-economic constraints on their investment opportunity sets. In contrast, unaffiliated FoMFs may invest in any fund in the mutual fund universe. These two types of fund of funds also face different regulatory constraints. Next section describes these differences in detail. B. The SEC Imposed Regulation Previously, the federal securities laws substantially restricted the ability of funds to invest in shares of other funds. These restrictions were designed to prevent fund of funds arrangements that have been used in the past to enable investors in an acquiring fund to control the assets of an acquired fund and use those assets to enrich themselves at the expense of acquired shareholders. 9 In 1996, the SEC adopted new rules under the Investment Company Act of 1940 that address the ability of an investment company ( fund ) to acquire shares of another fund. This adoption loosens the rules governing FoMF structures and broadens the ability of FoMF to invest in shares of another fund. Briefly summarizing the new rules, first, for affiliated fund of mutual funds arrangements, section 12(d)-(1)-(G) permits a registered open-end fund to acquire an unlimited amount of shares of other registered open-end funds that are part of the same group of investment companies (typically known as a fund complex). Second, for unaffiliated fund of funds arrangements, section 12(d)-(1)-(F) permits a registered fund to take small positions in other funds. More precisely, unaffiliated fund of funds may acquire no more than 3% of another fund s outstanding stock. Furthermore, they are restricted in their ability to redeem shares of the 9 See 8

10 acquired fund. 10 In addition, the fund s adviser would not be able to influence the outcome of shareholder votes in the acquired fund. Naturally, these two classes of FoMFs face different constraints and, as a result, may also exhibit different flow-performance sensitivities. In 2006, the rules were loosened further by allowing affiliated FoMFs to acquire securities of funds that are not part of the same group of investment companies, subject to the limit in section 12(d)-(1)-(F). In other words, affiliated FoMFs can invest up to 3% of another fund s outstanding shares even when this underlying fund is outside of their own fund complex. III. Hypothesis Since AFoMFs are mutual funds that, by law, can only invest in other mutual funds within the family, they can juggle between competition and cooperation among their member funds within a family. Kempf and Ruenzi (2007) examine whether fund managers engage in a family tournament. They especially focus on the influence of the competitive situation within fund families. In a similar vein, considering the fact that the average investments by AFoMFs account for 6% of the total assets that underlying managers run, member funds in the family may compete with each other to become a part of AFoMFs portfolio. 11 Therefore, AFoMFs can instill a more competitive environment in the family by investing in winners and de-investing in losers. Additionally, in the mutual fund industry, the majority of funds of mutual funds are affiliated funds. Therefore, the main source of potential value to investors is within the family information advantage that affiliated funds of mutual funds enjoy. In this perspective, Gervais, 10 A fund whose shares are acquired pursuant to section 12(d)(1)(f) is not obligated to redeem more than 1 percent of its total shares outstanding during any period of less than 30 days. 11 I thank Vikas Agarwal for raising this point. 9

11 Lynch, and Musto (2005) argue that families know more about their funds and managers than outside investors do. Consistent with this argument, Massa and Rehman (2006) find significant information flow among members of financial conglomerates. Moreover, Coval and Moskowitz (2001) show that the geographic proximity of the investment opportunities results in greater investment performance. This explanation is particularly relevant for affiliated FoMFs since they can be regarded as insiders within the fund complex, and can be better informed with regard to the underlying funds future performance (or manager s ability). In other words, organizational proximity imposed by the AFoMFs investment policy may yield better information on the underlying funds performance and lead managers to increase capital flows to winners and discard the losers. Alternatively, the family structure may also distort the behavior of FoMF managers. In particular, being affiliated, AFoMFs may solve the family s optimization problem rather than maximizing value for their own shareholders. This means that, though they do not have regulatory constraints, they have organizational constraints. Especially, since AFoMFs managers and underlying fund managers belong to the same fund complex, they might be subject to cronyism. Given the anecdotal evidence that flows from funds of hedge funds can be a big threat to underlying hedge funds upon their redemptions, 12 AFoMFs may face peer pressure to help the poorly performing member funds since the underlying managers may run into a trouble if the AFoMFs pull out too much money. However, unlike AFoMFs, UFoMFs are not subject to organizational constraints, since there is no restriction in their investment opportunity set. 12 A Wall Street Journal article suggested that sudden outflows by the Funds of Hedge Funds can leave a big hole in the invested hedge funds. Furthermore, there is a high chance of liquidation for these hedge funds. Similarly, a big redemption by FoMFs can be a threat to underlying funds. (http://www.miamiflorida.com/wpcontent/uploads/2008/05/hedge-funds-make-it-hard-to-say-goodbye-wsjcom.pdf) 10

12 IV. Data, Summary Statistics, and Methodology A. Data To identify the list of FoMFs, I use the Morningstar Principia CDs. These CDs are issued on a monthly basis. In this paper, I record FoMFs holdings in January, April, July, and October. The sample period starts in October 2002 and ends in January According to the 2008 ICI Fact Book, FoMFs are available in 2007 and are managing $640 billion. Panel A in Table 1 shows that the Morningstar Principia CDs provide a good coverage of the FoMFs industry. After identifying the list of FoMFs from the Morningstar Principia CDs, I extract detailed holdings information for these funds from the same data source. While portfolio holdings information is available at a quarterly frequency for most funds, there are several cases where holdings information is available at a semi-annual frequency or less. 14 The 95 th (90 th ) percentile of the number of months between the previous portfolio date and current portfolio date is 6 (3). Thus, I only include cases where the two consecutive reporting dates are no more than three months apart. Additionally, I focus entirely on actively managed US equity funds among the FoMFs holdings because, relative to other types of funds, equity funds offer the most widely accepted benchmarks and risk-adjusted approaches. Therefore, I screen underlying funds to exclude index, international, and fixed income funds. 15 The holdings information includes each underlying 13 See 14 There are also cases where the difference between two consecutive portfolio dates is one month. This is probably because there is 60-day maximum delay in the public release of fund holdings following the end of fiscal quarter. Also, there might be more frequent voluntary disclosure by some funds. Jin and Scherbina (2005) stated that the integrity of voluntary reporting solely relies on the good faith of the fund companies. 15 I use the investment objective classification explained in the following link: 11

13 fund s name, portfolio weight, number of shares held by the FoMFs, corresponding market value, share amount change relative to the previous portfolio date, and current (and previous) portfolio date. Thus, tracking the composition and changes in the FoMFs portfolios during a reporting period provides gross flow (inflow and outflow separately) information at the individual investor (fund of funds) level. Morningstar also contains basic information about the FoMFs, which I also extract. I then hand-match each FoMFs and all of its mutual fund holdings to the corresponding funds in the CRSP mutual funds database by fund name. In some cases, I was unable to find a corresponding CRSP fund number. These holdings are, therefore, discarded. Additionally, to ensure that the empirical results are not driven by outliers, I eliminate the bottom 1% and top 1% of inflow and outflow observations which are defined below. Applying the criteria outlined above results in 17,752 fund-quarter observations over the sample period. 16 After identifying the CRSP fund number for each underlying fund, I draw information on monthly fund returns as well as fund characteristics (such as TNA and expense ratio) from the CRSP mutual funds database. In a few cases, (i) previous portfolio dates are missing, (ii) the underlying mutual funds are not identified in the CRSP mutual funds database, or (iii) FoMFs are not identified in the CRSP mutual funds database; thus, their CRSP fund numbers are not available. These observations are eliminated from the sample. 17 B. Summary Statistics One important aspect of this study is that it provides the first detailed description about the fund of mutual funds industry. Existing studies on FoMFs examine funds of hedge funds, but since the hedge fund industry is largely opaque due to less regulation, existing studies cannot 16 In total, there were 43,106 fund-quarter observations in the full sample. 17 CRSP Survivor-Bias-Free database do not cover the funds whose asset sizes are below $25 millions. Therefore, the newly introduced FoMFs are likely to be excluded from CRSP database during the first few years after their inceptions. 12

14 provide a comprehensive picture about overall industry characteristics. In particular, no information is available on fund of hedge funds holdings. In contrast, this section describes mutual fund FoMFs portfolio holdings in detail. First, Panel A in Table 1 provides concise snapshots of the FoMF industry including fund size, structural forms, and their funds families. Although my analyses in the rest of the paper focus on fund of mutual funds equity fund holdings, Panel A reports information about the entire asset pool of FoMFs. More specifically, Panel A in Table 1 shows the total net assets managed by the FoMFs industry and the total net assets managed by the entire mutual fund universe in the CRSP mutual fund database. From 2002, there is a continuous increase in the FoMFs market share. In 2007, FoMFs market share is about 5% in the U.S. mutual fund industry. 18 There is also an exponential increase in the number of FoMFs such that more than 650 FoMFs are operating in the mutual funds industry in Furthermore, as shown in Panel A, most of these FoMFs take the structural form of investing in the funds of the same fund family. After 2005, the composition between affiliated FoMFs and unaffiliated FoMFs becomes quite stable with affiliated FoMFs accounting for about 90% of the entire FoMFs sample. It is also observed that more fund families begin to offer FoMFs, and more FoMFs become available in each fund family. For instance, in 2007, more than 100 mutual funds families have funds of funds products in their product lineup; in addition, each fund family has about seven different FoMFs. Second, Panel B and Panel C provide a detailed description of FoMFs holdings. Briefly summarizing these characteristics, Panel B shows the size of investments by FoMFs relative to the size of the underlying funds. On average, the investments by AFoMFs account for 6% of the 18 This number is a bit different from the number from 2008 ICI Factbook. It appears that the Morningstar Principia CDs fail to capture some of the newly introduced, small FoMFs. For detailed information, visit 13

15 total net assets of the underlying funds; for affiliated FoMFs, the top 90 th percentile group accounts for an economically significant portion of total net assets managed by the underlying funds. Given the anecdotal evidence that flows from FoMFs can be a big threat to underlying funds upon their redemptions, 19 the large flows from FoMFs can serve a role to mitigate potential agency problems. The investments by UFoMFs, on average, account for 0.4% of the total net assets of the underlying funds; additionally, the top 90 th percentile group accounts for about 1% of total net assets managed by the underlying funds. Panel C indicates that while AFoMFs hold about 12 mutual funds on average, UFoMFS hold about 20 mutual funds. This well-diversified holdings pattern is likely to be caused by the small regulatory limit of UFoMFs. Third, Panel D describes the cross-sectional characteristics of the FoMFs and the underlying funds. Since FoMFs are an emerging type of mutual funds in the U.S. mutual fund industry, their age is about half of the underlying funds age. The important point in this panel is the expense ratio. The SEC now requires all registered open-end funds investing in shares of another fund to include in the fee table of their prospectus an additional line item titled Acquired Fund Fees and Expenses under the section that discloses total annual fund operating expenses. However, in the CRSP mutual fund database, the expense ratio variable does not incorporate this indirect expense of acquired fund fees and expenses. In Panel D, the expense ratio in the second column denotes this direct expense reported from the CRSP mutual fund database. Fortunately, as of 2005, I am able to extract the total gross/net expense ratio of FoMFs 19 A Wall Street Journal article suggested that sudden outflows by the Funds of Hedge Funds can leave a big hole in the invested hedge funds. Furthermore, there is a high chance of liquidation for these hedge funds. Similarly, a big redemption by FoMFs can be a threat to underlying funds. (http://www.miamiflorida.com/wpcontent/uploads/2008/05/hedge-funds-make-it-hard-to-say-goodbye-wsjcom.pdf) 14

16 from the Morningstar CDs, and these total expense ratios include both the direct expense of FoMFs and the indirect expense of the acquired funds fees and expenses (i.e., the double layered fees). With respect to acquired funds fees and expenses, the total gross expense ratio covers the arithmetic sum of expenses that underlying funds can potentially charge; in contrast, the total net expense ratio only captures the sum of expenses that underlying funds actually charge after rebates. On the surface, just by examining the direct expense ratio in Panel D, the expense ratio of FoMFs is much lower than that of the underlying funds. However, when focusing on the total net expense ratio measure, FoMFs expense ratio is, on average, about 40 to 50% higher than that of underlying funds. Fourth, Panel E describes the total gross (net) expense ratio for AFoMFs and UFoMFs, respectively. It is observed that total net expense ratio has been decreasing over time for both types of FoMFs. However, AFoFMs display much lower total net expense ratio; for example, in 2007, AFoMFs expense ratio is, on average, about 40% lower than that of UFoMFs. This is probably because fund fees are not linear but quantity based. C. Methodology First, to examine how FoMFs respond to mutual fund performance, I employ gross flows by FoMFs. Existing studies rely upon net flows data to examine how investors respond to mutual fund performance. Net flows, however, are differences of capital inflows and capital outflows, which might show different patterns; furthermore, aggregation into net flows may preclude the development of more detailed insights. Recognizing this data limitation, recent research studies the relationship between gross flows and fund performances. According to Ivkovich and Weisbenner (2009), aggregation into net flows may blur different patterns that inflows and outflows follow. Since I can track the composition and changes in the FoMFs mutual fund 15

17 portfolios, I consider inflows and outflows separately. To conduct a formal analysis, the flow-performance sensitivity is estimated by using a piecewise linear regression between current flows and past returns of the funds. To measure the mutual fund flow, I use the change in portfolio weight of each underlying fund. The Morningstar CDs provide information about the portfolio weight of each underlying fund held by the FoMFs on the current portfolio date. Also, they provide information about the number of shares held by the FoMFs and the change in the share amount relative to the previous portfolio date. Since I can infer information about the number of shares on the previous portfolio date, I can find the change in the portfolio weight purely attributable to new flows in the following way: W,, W,, Share Amount,, NAV, TNA, 2 Here, subscript i denotes each FoMFs (i), subscript j denotes each underlying fund held by FoMFs (i), and subscript t denotes the current month-end portfolio date. Next, following Sirri and Tufano (1998), I measure the relative performance of each mutual fund by using fractional performance ranks. While retail investors may primarily pay attention to raw-returns rather than risk-adjusted returns, FoMFs managers, who are sophisticated professional investors, may focus more on risk-adjusted performance when allocating their investments across funds 20. For this reason, this paper employs the Carhart (1997) four-factor model to evaluate the performance of underlying mutual funds. After finding each equity fund s prior one year risk-adjusted performance, I rank it among the universe of funds pursuing the same investment objective. Following the literature, a fractional rank ranging from 0 to 1 is assigned to each underlying equity fund. Next, I divide performance fractional ranks into terciles. The fractional rank at portfolio date t for underlying funds (j) in the bottom 20 As is suggested in Busse et al. (2009), a clear consensus is not reached yet with respect to the proper benchmark. 16

18 performance group (LowPerf) is defined as Min (Fractional Rank (j,t),0.2). For the medium performance group, the fractional rank (MidPerf) is defined as Min (0.6, Fractional Rank (j,t)- LowPerf). For the top performance group, the fractional rank is defined as Fractional Rank (j,t)- MidPerf-LowPerf. Once the fractional rank and the resulting terciles are assigned, I use the Fama and Macbeth (1973) method with Newey and West (1987) robust standard errors to estimate the following piecewise linear regression: w,, Fn Performance Rank,, Return Volatility,,Expenses,,Flows to FoMF,, Sector Flows,, Log # of Competing Funds,,LogTNA,, LogAge, 21 4 Another important consideration is that each underlying fund is not equal within a given FoMF portfolio. Some underlying funds have higher portfolio weights than others; these funds contribute more to the aggregate performance and are more important for a given FoMF. Thus, to control for heteroskedasticity generated by the size variation of each fund holding, I use the weighted least squares approach. Here, the weight is proportional to the current period portfolio weight of each underlying fund. The regression model above also includes several characteristics of the underlying funds. First, Chen et al. (2002) provide evidence that size erodes performance; similarly, the size of the underlying fund in the previous period may impact the flow decision of the FoMF. Thus, the natural log of TNA is included as a control variable. Second, Huang et al. (2007) provide evidence that the return volatility of an underlying fund can impact flows. To control for this possibility, the volatility of the underlying fund is included in the model. Third, FoMFs have a 21 Here, the sub-index i refers to FoMF, the sub-index j refers to underlying funds held by FoMF, and the index t refers to current portfolio date. 17

19 double-layered expense structure. As shown in Brown et al. (2004) and Ang et al. (2008), management fees for FoMFs are typically higher than those of traditional investment funds because they include part of the management fees charged by the underlying funds. Due to this structural uniqueness, FoMFs might be very sensitive to the expense of underlying funds. To consider this feature, I include the expense ratio of the underlying fund to account for this possibility. Fourth, since flows to younger funds are more sensitive to performance (Chevalier and Ellison (1997)), the log of one plus fund age is added to capture this extra sensitivity. Fifth, I include the number of family funds within the underlying fund s sector as a measure of competition within a fund family to attract investments from FoMFs. Furthermore, two flow-related variables are included for the following reasons. First, FoMFs are unique mutual fund investors in that we observe the budget constraints they face, that is, their own investor flows. Since any flow shock FoMFs experience, in turn, affects their flows to the underlying funds, a methodological innovation to examine the first research question is that I can isolate the confounding effect of investors liquidity shock on the flow-performance relationship by controlling for flows to FoMFs themselves. Thus, the true, unconstrained flowperformance sensitivity can be examined by controlling the idiosyncratic liquidity needs of FoMFs investors (or FoMFs own flows). For this reason, flows to FoMFs themselves will be added as a concrete measure of the flow shock or budget constraint. Second, since FoMF managers may attempt to time the market by moving their capital to rising sectors, sector flows are added as an explanatory variable. These sector flows represent the growth of the fund objective category in period t. Second, to evaluate the outcome of FoMFs investment decisions, I form portfolios at the beginning of each quarter based on whether the FoMFs bought or sold the underlying funds, 18

20 respectively. Underlying funds that are bought comprise the positive flow portfolios, while those that are sold during the previous quarter are placed in the negative flow portfolio. I rebalance those portfolios every quarter, that is, I form portfolios at the end of the first quarter, keep these funds in the appropriate portfolios for the next three months, then, at the end of the three months, I reallocate each holding to reflect the direction of the FoMFs trade during the second quarter. In addition, underlying funds are grouped into either the positive cash-flow portfolio or the negative cash-flow portfolio based on the sign of the net cash flow after excluding flows from FoMFs during the previous quarter. That is, I repeat the same procedure as the above by using the net cash flows of outside investors after excluding FoMFs. This second set of flow groups will be used as a benchmark to investigate whether other flows (excluding FoMFs flows) are able to earn superior returns in the subsequent period. This unique experimental setting enables me to test funds selection ability of two investor groups on the same set of underlying funds. After forming these portfolios, the subsequent quarter s cash-flow weighted returns are evaluated by using the Carhart (1997) four factor model. V. Empirical Results A. Flow-Performance Regressions: Full Sample To test my first research question, I examine the flow-performance relation in a regression framework. Since this paper employs gross flow data, I analyze gross inflow and outflow separately. In analyzing the flow-performance sensitivity, I estimate a piecewise linear regression between current fund flows by FoMFs and the past returns of the underlying funds. This piecewise structure enables me to separately estimate the sensitivity of institutional flows to performance in each of three performance terciles. Here, the current fund flows are measured by the change in portfolio weights of the underlying funds attributable to a change in flows by 19

21 FoMFs. For outflows, I take the absolute value of the change in portfolio weights. 22 As a measure of performance, I use each underlying fund s four factor risk-adjusted return ranking relative to the universe of funds pursuing the same investment objective. As a starting point, I report the full sample results of relating flows to a range of regressors in Table 2. Table 2 consists of two columns; the first column is assigned to inflow data while the second column is assigned to outflow data. The bottom portion of Table 2 focuses on performance measures. First, for inflows, consistent with previous evidence from the extant literature, good performance by the underlying funds results in additional inflows from FoMFs in the next quarter. For top performers-those in the top terciles of funds in their objective categorythe coefficient associated with the one year objective rank in the first column is (p-value of 4.08%), suggesting that performance is associated with economically and statistically significant inflows. For other performance groups, I do not find statistically significant evidence that performance is associated with flows. Second, for outflows, I find weak evidence that FoMFs penalize poorly performing funds by redeeming their shares. For poor performers-those in the bottom terciles of funds in their objective category-the coefficient associated with the one year objective rank in the second column is (p-value of 10%), suggesting that performance is weakly associated with outflows. B. Flow-Performance Regressions: Affiliated and Unaffiliated FoMFs Up to this point, my analyses disregard the regulatory constraints and assume that FoMFs are free to invest in the underlying funds without any restrictions. However, as emphasized in the introduction, the two types of FoMFs have different regulatory constraints. According to the SEC rule, while affiliated FoMFs are permitted to acquire an unlimited amount 22 This does not change inferences from the model. By taking the absolute value, it is easier to interpret the results. 20

22 of shares of other funds within the same fund complex, unaffiliated FoMFs are only permitted to take small positions in other funds. 23 In contrast, while affiliated FoMFs are constrained to invest in funds within their fund family, unaffiliated FoMFs do not face any constraints in their investment opportunity sets. In order to examine whether the above mentioned constraints have a material impact on professional flows, I re-estimate the model for the affiliated and unaffiliated FoMFs subsamples. The results for the affiliated FoMFs subsample, presented in Table 3-(a), are different from the full sample results. For inflows, good performance by the underlying funds results in additional inflows from affiliated FoMFs in the next quarter. For top performers, the coefficient associated with the one-year objective rank in the first column is (p-value of 3%), suggesting that good performance is rewarded with economically and statistically significant inflows. For outflows, poor performance results in redemption by the affiliated FoMFs in the next quarter. More precisely, the coefficient on the performance rank in the second column is (p-value of 2.53%), suggesting that bad performance is penalized with economically and statistically significant outflows. One caveat to this finding is that the flow-performance sensitivity is not linear. The asymmetry still exists to some extent since the slope in the worst performance tercile is flatter than the slope in the best performing tercile. That is, even though the degree to which AFoMFs respond to negative performance is statistically significant, the magnitude of rewarding good performing funds is far larger. Overall, the result provides support for the family tournament hypothesis. Rather than displaying cronyism activity, AFoMFs pound a competitive environment in the family by voting 23 More precisely, unaffiliated fund of mutual funds may acquire no more than 3 percent of another fund s outstanding stock; in addition, it is restricted in its ability to redeem shares of the acquired fund. For more detailed information, see 21

23 on their co-worker s ability. This provides a strong incentive for underlying managers to engage in a family tournament as evidenced by Kempf and Ruenzi (2007). Additionally, this result is also consistent with the information advantage hypothesis. Additionally, my result stands in contrast to the evidence from recent studies (Johnson (2009) and Ivkovich and Weisbenner (2009)) which focus on retail flows from account-level data. Johnson (2009) documents that gross outflows by retail investors are not sensitive to performance, and he concludes that mutual fund redemptions are idiosyncratic and based upon investors liquidity needs. However, what becomes obscure by focusing on retail flows is that for sophisticated professional investors (i.e., FoMFs), there exists a strong link between flows and (relative) performance. I also examine the above result graphically in Figures 1. As a preliminary examination, I repeat the analysis of Figure 1 in Sirri and Tufano (1998). 24 In each quarter, based on their past one year risk-adjusted performance, mutual funds are ranked among the funds pursuing the same investment objective and then assigned to one of five performance groups. I then compute the average portfolio weight change for each group over the subsequent quarter. The result is presented in Figure 1. For inflows, the result of this simple analysis is consistent with the previous evidence of the non-linear flow-performance relationship. Good performance results in additional inflows to underlying funds next quarter. However, for outflows, the result from this simple bivariate plot runs counter to evidence from the extant literature. Thus, affiliated FoMFs respond to poor performance by redeeming their investments. The results for the unaffiliated subsample, shown in Table 3-(b), are somewhat surprising. In this subsample, there is virtually no relationship between historical performance 24 One slight difference is that rather than using twenty performance buckets, I use five performance groups. 22

24 and flows by unaffiliated FoMFs: even good performers are not rewarded by additional flows. It seems quite puzzling to observe such different responses from these two types of FoMFs. The result may even seem counterintuitive since unaffiliated FoMFs do not face constraints in their investment opportunity sets. One possible reason behind this non-responsiveness is the strict regulatory constraint that UFoMFs face in their allocation of investments across funds. For instance, since their maximum position cannot exceed 3% of underlying fund size, UFoMFs may not be able to respond to good performance once they reach the legal maximum. Furthermore, since their maximum withdrawal cannot exceed 1% of the total shares outstanding over the 30 days window, this might also impose a binding constraint for UFoMFs investments decision. This issue will be discussed further in the sub-sample analysis below. The flow-performance regressions presented in Table 2 and 3 also provide some interesting auxiliary results. First, expenses for FoMFs are typically higher than those of traditional investment funds because their fees include the management fees and expenses charged by the underlying funds. I find that the expense ratio variable is negatively associated with the inflow decision in the affiliated FoMFs subsample results. This evidence indicates that affiliated FoMFs are reluctant to invest in funds with expensive fee structures. With respect to the role of the expense ratio in the flow decisions, Barber et al. (2005), using net flow data, do not find a significant relation, but Ivkovich and Weisbenner (2009), using gross retail flow data, find that inflows are positively related to the expense ratio. Therefore, focusing solely on gross institutional flows yields different insights regarding the effect of investment costs. Second, Chevalier and Ellison (1997) show that flows to younger funds are more sensitive to performance. Similarly, inflow decisions are highly sensitive to the underlying fund s age in the unaffiliated FoMFs subsample. Possibly due to the incentive effects generated from the 23

25 reputation concern of younger funds, UFoMFs managers prefer younger funds when they make inflow decisions. Third, volatile return history is negatively associated with the inflow decision for the affiliated FoMFs. This evidence is consistent with Huang et al. (2007) For the regressions in Table 2, the most important flow-related control variable is flow to the FoMFs itself. Since the spurious influence of the FoMFs flow shock will unduly impact the true flow-performance sensitivity, controlling for this variable is very important. In the full sample results, flow shocks have a larger influence on the inflow decision rather than the outflow decision. However, this pattern is largely driven by the unaffiliated sub-sample since, for both outflows and inflows, the affiliated FoMF subsample shows symmetric responses to FoMFs own flows. Furthermore, it is not surprising to observe this symmetric response only from the affiliated FoMFs since their counterparts cannot fully respond to large flow shocks due to regulatory constraints. C. Regulatory Constraints In the previous analysis, I document the non-responsiveness of UFoMFs to the underlying funds performances. What drives the lack of response for unaffiliated FoMFs? One possible conjecture is that despite the freedom of investments in any mutual funds, unaffiliated FoMFs are subject to severe regulatory constraints. Since the regulatory limit in the size of their holdings is small, 25 unaffiliated FoMFs are forced to have a small position in many different funds. When there are a large number of holdings in managers portfolios, FoMFs managers are likely to spread their attention widely and, consequently, their monitoring ability may decrease. On the contrary, when FoMFs managers concentrate their holdings, they can closely track the performance of each underlying fund. Thus, an additional sub-sample analysis based on the 25 For more information on these rules, see the section II about the new regulation in FoMFs industry. 24

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