Electronic copy available at: http://ssrn.com/abstract=1675922Electronic copy available at: http://ssrn.com/abstract=1675922

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-5-2010 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 47405. E-mail:jhl8@indiana.edu

Electronic copy available at: http://ssrn.com/abstract=1675922Electronic copy available at: http://ssrn.com/abstract=1675922

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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.

2

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.

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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

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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 out-

performing 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

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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.

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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

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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 http://www.icifactbook.org/pdf/2010_factbook.pdf 7 See http://www.icifactbook.org/fb_data.html#section6 8 See www.sec.gov/rules/final/2006/33-8713.pdf

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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 www.sec.gov/rules/final/2006/33-8713.pdf

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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.

10

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/wp-

content/uploads/2008/05/hedge-funds-make-it-hard-to-say-goodbye-wsjcom.pdf)

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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 2008. According to the 2008 ICI

Fact Book,13 723 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 95th (90th) 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 www.icifactbook.org/pdf/2008_factbook.pdf 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: http://wrds.wharton.upenn.edu/news/sideitem/user2006/mutualfund_data.pdf

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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.

13

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 2007. 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 www.icifactbook.org/pdf/2008_factbook.pdf.

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total net assets of the underlying funds; for affiliated FoMFs, the top 90th 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 90th 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/wp-

content/uploads/2008/05/hedge-funds-make-it-hard-to-say-goodbye-wsjcom.pdf)

15

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

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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 funds20. 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.

17

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.

18

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 flow-

performance 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,

19

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

20

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 category-

the coefficient associated with the one year objective rank in the first column is 0.0552 (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 -0.0115 (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.

21

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 0.0643 (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 -0.0245 (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 http://www.sec.gov/rules/final/2006/33-8713.pdf.

22

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.

23

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

24

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.

25

number of mutual funds holdings is warranted.

The regression result of the subsample analysis is presented in Tables 4-(a) and (b). Each

table consists of two columns; the first column is assigned to below the sample median number

of mutual funds holdings group, while the second column is assigned to above the sample

median number of mutual funds holdings group. These tables show that the symmetric flow-

performance relationship is more apparent when FoMFs concentrate their holdings. More

precisely, a statistically significant relationship is only observed when the number of mutual

funds held by the FoMF is below the median. There can, therefore, be a tradeoff between the

diversification of investments and a lack of a proper monitoring mechanism. The over-

diversification decision can be suboptimal in the sense that FoMFs managers cannot keep a

constant eye on the performance of too many underlying funds. Furthermore, it appears that the

flow-performance insensitivity of unaffiliated FoMFs is largely driven by a high number of

mutual funds holdings.

Another direct consequence of the regulatory constraints is that UFoMFs might not be

able to enter or quickly exit large positions. Since I can observe UFoMFs’ trading activities at an

individual investor level, this point can be illustrated well by examining how UFoMFs respond

to their own flow shocks. To examine this, UFoMFs’ underlying funds are sorted into deciles

according to the relative size of their positions in the previous quarter. Then, I compute the

average percentage flows of UFoMFs to their holdings maintained, expanded, reduced, or

eliminated within each decile. The left side of Panel (a) in Table 5 shows the average response of

UFoMFs conditional on the size of their existing position in the previous quarter; in addition, the

right side of the Panel (a) displays the relative frequencies of the corresponding cases. Panel (b)

and (c) repeat this procedure, but the samples are limited to the cases where UFoMFs’ own flows

26

are in the top or bottom decile.

Several features of this analysis are worth noting. First, as one can see in Panel (b),

UFoMFs tend to expand the existing holdings more when they are experiencing a positive flow

shock. Furthermore, it is interesting to observe that the magnitude of increase is approximately

0.7% for both the top and bottom position deciles. However, if the constraints are binding, the

top position decile should not be able to respond to the flow shock. Combining this finding with

the observation that the median investment size in the top decile is about 0.9% of the underlying

fund’s size, the 3% SEC imposed rule does not appear to be binding. Second, as is suggested in

Panel (c), UFoMFs tend to reduce or eliminate the existing holdings more when they are

experiencing a negative flow shock. The results in Panel (a) are used as benchmarks for

comparison purposes. Considering the fact that the average size of the UFoMF investment is

about 0.5% and the 90th percentile of this investment size is about 1%, the maximum withdrawal

of 1% during the 30 days window imposed by the SEC regulation does not appear to be binding

either. Thus, there is no evidence that UFoMFs experiencing extreme flows are not freely

rebalancing their portfolios due to the regulatory constraints.

Additionally, I examine how UFoMFs’ funds holdings change conditional on the size

and the performance of their existing positions. To investigate this, UFoMFs’ underlying funds

are sorted into quintiles according to the size of their positions in the previous quarter. Also, they

are sorted into terciles according to the relative performance among the same style peer group in

the previous year. In the top position quintile, it is observed that capital flows are more

pronounced to the poorly performing fund group rather than the top performing fund group.

Furthermore, the total position is not likely to reach the 3% legal maximum since the median

investment size in the top quintile is about 0.65% of the underlying fund’s size.

27

Taken as a whole, the lack of sensitivity UFoMFs display to the underlying funds’

performance can be partly explained by the regulatory constraints. However, the evidence in

Table 5 suggests that the SEC rules designed to prevent the abuse of FoMFs arrangements do not

impose binding constraints on UFoMFs’ trading activities.

Another difference between AFoMFs and UFoMFs is the private information that they

might have. The affiliated FoMFs are “insiders” in the fund complex and might be better

informed with regard to the underlying funds’ future performance (or manager’s ability).

However, if this flow-performance relationship is related to the information advantage, the

subsequent investment outcome should also be investigated. The subsequent performance will be

examined in the next section.

D. Return Predictability

In the previous section, the results suggest that affiliated FoMFs instill a more

competitive environment by facilitating the fund family tournament. In other words, they churn

some of the under-performing funds and reward some of the out-performing funds (or their

managers) on a constant basis. Importantly, in the mutual fund industry, the majority of funds of

mutual funds are restricted to invest in their own families, that is, most FoMFs are affiliated

funds. Therefore, the main source of potential value to investors may be within the family

information advantage that affiliated funds of mutual funds enjoy. In this perspective, Gervais,

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. If this is true and they are able to select superior managers, then they

28

should have a very low turnover; once they identify a skilled manager, there is no need to leave

the fund. However, the fact that the summary statistics report average annual turnover of 40%

tends to contract this. How should an observer outside of the fund family interpret this result? Is

it an information-based investment decision, or is it simply momentum profits chasing or market

timing through frequent re-balancing?26 For either case, I ponder whether there should be

immediate disclosure to the public if affiliated FoMFs manager dumps shares in a fund owned by

his own company in order to buy another. In this section, I evaluate the outcome of FoMF’s

investment decision.27

The relationship between past flows and future fund performance is examined by the

recent literature on the smart money effect. For instance, 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. For example, Zheng (1999) reports an

annual three-factor alpha of 0.89% for positive fund cash flows and -0.32% for negative cash

flows. Sapp and Tiwari (2004) show that this effect is related to stock return momentum.

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. One possible explanation for the smart money

effect is that sophisticated investors have the ability to identify superior fund managers and

invest accordingly. This explanation is particularly relevant for affiliated FoMFs since they only

invest in the funds within their family, and, for this reason, they can be regarded as “insiders”

within the fund complex.

26 I thank Travis Sapp for raising this point. 27 It is hard to pin down the exact identity of information that FoMF managers have. I believe that this information advantage will manifest itself through a superior fund selection.

29

To investigate this issue, underlying funds of FoMFs are grouped into either the positive

cash-flow portfolio or the negative cash-flow portfolio based on the sign of the gross cash flow

from FoMFs during the previous quarter. Also, 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. These second flow groups

are labeled as “Other Flows” and will be used as a benchmark for evaluating the smartness of

FoMFs. 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 four factor model.

Table 6 presents results for cash-flow-weighted new-money portfolios. I first examine

affiliated FoMFs. The alpha of the positive cash-flow portfolio is an insignificant -15.3 basis

points per month. The negative cash-flow portfolio has a statistically significant alpha of - 44.9

basis points per month, or -5.39 % annually. When I take the difference between the positive

cash-flow and negative cash-flow portfolio alphas, I get about 30 basis points per month, or 3.6 %

annually, which has a p-value of 5.6%.28 Thus, unlike Sapp and Tiwari (2004), who document

the inability of aggregate mutual fund cash flows to predict future performance, at a disaggregate

level, flow decisions by AFoMFs show the predictability. As a benchmark, I also examine the

smartness of the flows entering or exiting the underlying funds after I exclude the flows from

FoMFs. The goal of this analysis is to investigate whether flows other than those of AFoMFs are

able to earn superior returns. The difference between the positive cash-flow and negative cash-

flow portfolio alphas is about 6 basis points per month, or 72 basis points annually. However, it

28 As was noted in Sapp and Tiwari (2004), the short selling of mutual funds is not allowed in most cases since most funds forbid such practice. This performance comparison merely illustrates the ability of FoMFs’ flows to predict future performance.

30

is not statistically significant.

I repeat the same analysis for unaffiliated FoMFs. The alpha of the positive cash-flow

portfolio is a significant -32.5 basis points per month. The negative cash-flow portfolio has a

statistically insignificant alpha of –14.8 basis points per month, or -1.78 % annually. When I take

the difference between the positive cash-flow and negative cash-flow portfolio alphas, I get

about -18 basis points per month, or -2.16 % annually, which has a p-value of 24.2%.

Furthermore, flows other than those of UFoMFs also do not show the predictability.

Overall, the results in this section provide evidence that supports the smart money effect.

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 may cause superior performance to

dissipate. This transitory persistence may explain the relatively frequent buying and selling

activities by FoMFs. In 2007, which is the last year of my sample, about 13% of all fund families

had such AFoMFs. These are nearly all large fund families, with an average family size of about

$113 billion dollars and, on average, 57 mutual funds per family. Therefore, 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 that they

enjoy appears to become a heavy burden since they have to overcome a search friction.

What is the source of this smartness? Even though the effect is often documented, the

smart money literature does not shed light on the source of this phenomenon. In Berk and Green

(2004), returns are not predictable if investors make full and rational use of the information about

funds’ past returns to learn about managerial ability. Therefore, fund flows compete away any

abnormal returns thereby ensuring that all returns are unpredictable. However, the literature

31

(Christoffersen and Musto (2002), Elton et al. (2004), and Berk and Tonks (2007)) claims that

there is heterogeneity in investors’ willingness to move their capital in response to information.

Heterogeneous responses (or performance sensitivities) across different groups of investors give

rise to sluggish capital flows, and fund size will not be sensitive enough to past performance.

This plausible explanation offers return persistence. Furthermore, a recent study by Glode et al.

(2009) documents that return predictability exists after periods of high market returns; thus, this

finding reflects that investors are not responsive enough to information about past performance,

relative to a fully rational setting assumed in Berk and Green (2004). Given the evidence of slow,

irrational capital flows by fund investors, AFoMFs may take advantage of this opportunity until

the other market participants fully react to the information in past performance. This investor

heterogeneity explanation indicates a weak form of selection skills since they make conditional

capital allocation decisions on the level of other market participants’ flows rather than actively

search for fund specific information. An alternative explanation, which is favored in Massa and

Rehman (2008)29, is that the affiliated FoMF is an “insider” in the fund complex and is better

informed with regard to the underlying funds’ future performance (or manager’s ability). Put

differently, organizational proximity imposed by the AFoMFs investment policy may yield better

information on the underlying funds’ future performance.30 This suggests that an information

advantage generated from unique organization structure may explain professional investors’

ability to predict returns. Since this return predictability is not observed in flows from

unaffiliated FoMFs, the information advantage of AFoMFs may also explain this result. These

two possibilities require further investigation.

29 Massa and Rehman (2008) suggest that membership in a financial group, such as a fund family, may give an information advantage to managers. 30 In a similar vein, Coval and Moskowitz (1999, 2001) examine the role of geographic proximity

32

E. Concentrated Funds Holdings

  FoMFs managers may decide to deviate from a well-diversified portfolio and

concentrate their fund holdings. Even among the affiliated FoMFs which display a predictive

ability, a considerable variation in the concentration of underlying holdings exists. Kacperczyk et

al. (2006) suggest that managers might want to hold concentrated portfolios if they have superior

information to select profitable stocks in specific industries. Also, Nanda et al. (2004) provide

evidence that fund families following more focused investment strategies across funds perform

better, likely due to their information advantages. Therefore, if managers with better ability or

information are more likely to pick concentrated portfolio, then they are likely to outperform

others.

In this section, I evaluate AFoFMs’ performance conditioned upon their fund holdings

concentration. The rationale for portfolio concentration as the conditioning variable is that better

informed AFoMF managers may exhibit superior performance by holding a more concentrated

portfolio to exploit their information advantages.31 To examine this, AFoMFs are divided into

the “Concentrated Holdings Group”, “Middle Group”, and “Diversified Holdings Group, based

on the number of mutual fund holdings. AFoMFs whose fund holdings number is below the 25th

percentile are assigned to the “Concentrated Holdings Group”. AFoMFs whose fund holdings

number is above the 75th percentile are assigned to the “Diversified Holdings Group”. For each

group, the underlying funds of the AFoMFs are divided into either the buy portfolio or the sell

portfolio based on the sign of the gross cash flow from FoMFs during the previous quarter. Then,

for each group, portfolio performance is evaluated based on the estimated portfolio alpha. Figure

2 displays the results.

31 I thank Jay Wang and John Knopf for raising this point.

33

I find that more concentrated AFoMFs display a more significant predictability. The buy

portfolio of the concentrated holdings group is far less negative than that of the diversified

holdings group. This pattern is also observed for the selling portfolios. When I compute the

difference between these two portfolios and its statistical significance, AFoMFs with the

concentrated holding display a much stronger evidence of predictability. This evidence is

consistent with the equity holdings level result from Kacperczyk et al. (2006).

F. Overall Performance

As a last step, in this section, I examine the overall performance of FoMFs. For the

performance comparison, the Sharpe ratio will be used as a main performance metric.

Since FoMFs provide significant diversification potential, an investor can reasonably

expect that the reward to volatility ratio is higher for FoMFs than it is for the average mutual

fund. Figure 3 confirms this expectation. More specifically, FoMFs offer consistently higher

Sharpe ratios in many of the years documented. When I examine the performance by using both

gross and net return data, the results are not qualitatively different. Especially, 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. This

explanation is supported by the summary statistics about the fee structure in Panel E (of table 1).

The summary statistics suggest either that FoMFs managers have done a particularly

good job at selecting superior mutual funds, or that FoMFs managers have been very successful

in decreasing the overall risk as evidenced by the bottom panels in Figure 3.

VI. Conclusion

Recently, funds of mutual funds (FoMFs) have emerged as an alternative structure to

advisory services. They provide a pre-packaged portfolio of mutual funds for investors. Virtually

34

non-existent in the 1990s, these funds have gained popularity in recent times. In this study, I

examine this competing structure to advisory services to see if they offer any value. First, I

examine the flow-performance relationship of FoMFs. My 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 outflows in the face of underperformance. Therefore, rather

than displaying cronyism, AFoMFs pound a competitive environment in the family by voting on

their co-worker’s ability. That is, AFoMFs pursue the objectives of their own investors by

investing in winners and de-investing in losers. 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.

I also examine the economic outcome of FoMFs’ rebalancing decision. The key issue is

whether their capital allocation decision is smart enough to generate positive future returns. This

relationship between past flows and future fund performance is examined by the recent literature

on the smart money effect. My results reveal that some professional investors (especially

AFoMFs) are indeed able to predict fund performance and invest accordingly. Unlike Sapp and

Tiwari (2004), this result is not driven by momentum effect and stands in sharp contrast with the

dumb money effects of retail investors documented by Frazzini and Lamont (2008).

Overall, professional oversight of funds operations and superior selection of constituent

funds are the value-additions that FoMFs provide for the investors. However, there is one

35

important question this paper does not answer. What is the source of this smartness? Even though

the effect is often documented, the smart money literature does not shed light on the source of

this phenomenon. AFoMFs may indeed be smart, in that they have a certain information

advantage with regard to the underlying funds’ performance. Alternatively, they might just be

smart about flows, in that they make conditional capital allocation decisions based on the level of

other market participants’ flows. The examination of this question is important for future

research.

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40

Table 1

Summary Statistics

Panel A: Description of Fund of Mutual Funds (FoMFs) Industry

This table provides the descriptive statistics regarding the FoMFs industry. The second and third columns show the size of the FoMFs industry and mutual funds

universe, respectively, from year 2002 to year 2007. The numbers in the second column are obtained from the Morningstar Principia October CDs in each year.

The numbers in the third column are from the entire CRSP mutual funds database. The fourth column shows the number of FoMFs available in each year from

Morningstar CDs. There are several cases where some FoMFs are not identified in CRSP mutual funds database. These observations are discarded in this section.

The fifth and sixth columns show the number of affiliated and unaffiliated FoMFs available in each year. Affiliated FoMFs are mutual funds that are constrained

to invest in funds within the same fund family. In contrast, unaffiliated FoMFs may invest in any fund in the mutual fund universe. The seventh column shows

the number of fund families which have FoMFs. The number in parenthesis is the total number of fund families in each year. Lastly, the eighth column shows the

average number of FoMFs available in each fund family.

_____________________________________________________________________________________________________________________Year Size of FoMFs Size of the MFs No. of FoMFs No. of AFoMFs No. of UFoMFs No. of Families No. of FoMFs Industry Industry with FoMFs in Each Family________________________________________________________________________________________________________________ 2002 30 5,602 87 69 18 32 (654) 3 2003 67 6,379 216 183 33 61 (649) 3 2004 144 6,844 295 254 41 71 (622) 4 2005 218 7,682 390 352 38 80 (627) 5 2006 336 9,090 560 504 56 98 (624) 7 2007 501 11,221 652 594 58 108 (688) 7 _____________________________________________________________________________________________________________________

(Unit: Billions) (Unit: Billions)

41

Panel B: Size of investments by FoMFs relative to the size of underlying funds

This table shows the investment size of FoMFs’ holdings as a percentage of the underlying funds size. This investment size is measured as the ratio of market

value of invested shares by FoMFs to the size of underlying funds. The first and second columns are for affiliated FoMFs from year 2002 to year 2007. The third

and fourth columns are for unaffiliated FoMFs from year 2002 to year 2007.

_____________________________________________________________________________________________________________________Affiliated FoMFs Unaffiliated FoMFs

All Funds Holdings Equity Funds Holdings All Holdings Equity Funds Holdings _____________________________________________________________________________________________________________________ MEAN 6% 4.9% 0.4% 0.5% 90 Percentile 17.6% 13.5% 1% 1.3% 95 Percentile 27.2% 22.8% 1.6% 1.8% No. of Obs. 43,106 17,752 9,362 2,229 _____________________________________________________________________________________________________________________

Panel C: Number of Mutual Funds Holdings in each FoMFs

This table shows the number of mutual funds holdings in each FoMFs. There are several cases where the underlying funds are not identified in the CRSP mutual

funds database. Those underlying funds are excluded.

_____________________________________________________________________________________________________________________Affiliated FoMFs Unaffiliated FoMFs

Year Mean Median Max Mean Median Max _____________________________________________________________________________________________________________________ 2002 9 8 26 18 14 64 2003 9 8 23 15 12 53 2004 9 9 22 17 14 44 2005 10 9 32 18 15 54 2006 11 10 33 20 14 63 2007 12 10 38 20 15 57 _____________________________________________________________________________________________________________________

42

YearDirect Expense

RatioTotal Gross

Expense RatioTotal Net

Expense RatioTurnover

RatioAge Expense Ratio Turnover Ratio Age

2002 0.91% N/A N/A 43.2% 5.9 1.01% 83.9% 11.82003 0.82% N/A N/A 51.8% 5.3 1.11% 86.7% 11.32004 0.66% N/A N/A 45.2% 4.8 1.07% 84.2% 10.62005 0.58% 1.99% 1.59% 33.3% 4.7 0.97% 83.9% 11.52006 0.56% 3.50% 1.49% 36.7% 4.3 0.92% 84.8% 112007 0.56% 2.35% 1.44% 41.1% 4.2 0.89% 85.2% 9.2

Funds of Mutual Funds Underlying Equity Funds

Year Total Gross Expense Ratio Total Net Expense Ratio Total Gross Expense Ratio Total Net Expense Ratio2005 1.97% 1.52% 2.72% 2.71%2006 3.84% 1.41% 2.43% 2.09%2007 2.13% 1.39% 2.39% 2.03%

AFoMFs UFoMFs

Panel D: Cross-Sectional Characteristics of the FoMFs and underlying equity funds sample

This sample includes FoMFs and their underlying actively managed U.S. equity funds from 2002 to 2007. First, age is measured by the years elapsed since the fund’s inception year. Second, FoMFs have a double layered expense structure. FoMFs’ total expense includes both the direct expense of FoMFs and the indirect expense of the acquired fund fees and expenses. However, in the CRSP mutual fund database, the expense ratio variable does not incorporate this indirect expense of acquired fund fees and expenses. The expense ratio in the second column denotes this direct expense reported from the CRSP mutual fund database. As of 2005, Morningstar CDs provide the total gross/net expense ratio of FoMFs, 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 regard to acquired funds fees and expenses (indirect expenses), total gross expense ratio covers the arithmetic sum of expenses that underlying funds can potentially charge; in contrast, total net expense ratio only captures the sum of expenses that underlying funds actually charge after rebates. For both FoMFs and the underlying funds, the numbers below are TNA (Total Net Assets) weighted statistics across different funds classes.

Panel E: Total Expense Ratio of the affiliated FoMFs and the unaffiliated FoMFs

This panel shows the total gross/net expense ratio of the affiliated and unaffiliated FoMFs. 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 regard to acquired funds fees and expenses (indirect expenses), total gross expense ratio covers the arithmetic sum of expenses that underlying funds can potentially charge; in contrast, total net expense ratio only captures the sum of expenses that underlying funds actually charge after rebates.

43

Inflows Outflows

Intercept 0.025** 0.0056*

(0.0031) (0.02)

Log(TNA) 0.0002 0.0002*

(0.7713) (0.0152)

Age -0.0003 0.0006

(0.7479) (0.0974)

Expense Ratio -0.0068 0.0012

(0.0661) (0.3982)

FoF's Own Flows 0.043** -0.0037

(0.0001) (0.6268)

log(# of competing funds) -0.0018 -0.0004

(0.1708) (0.1964)

Return Volatility -0.1451* -0.0188

(0.0203) (0.232)

Sector Flows -0.0298 0.0073

(0.5706) (0.6725)

Bottom Performance Group 0.0104 -0.0115

(0.6205) (0.1)

Middle Performance Group -0.0044 -0.0027

(0.1459) (0.0544)

High Performance Group 0.0552* 0.0045

(0.0408) (0.316)

No. Obs 15650 4331

Adj -R-Square 0.214 0.138

Independenat Variable% Changes in Weights

Table 2 Flow-Performance Sensitivity for the Full Sample This table examines the effect of relative performance by underlying funds on the flows decisions by the FoMFs. The model is estimated for inflows and outflows separately. The dependent variable is the change in flows by FoMF. This dependent variable is measured by the change in portfolio weight of each underlying fund invested by FoMF. For outflows case, I take the absolute value of the changes in portfolio weights. Each quarter (t), relative performance measure (fractional rank) is assigned to each underlying fund (j) according to the Carhart four factor risk adjusted returns estimated during the past 12 months. To find the fractional rank, the performance comparison group is confined to (a) mutual funds pursuing the same objective or (b) the entire actively managed domestic equity funds. Then, in each quarter, a piecewise regression is estimated by regressing change in portfolio weight on the relative performance measures and other control variables. The other control variables include (i) aggregate flows into underlying fund’s objective sector, (ii) flows to FoMF itself, (iii) total net asset of underlying funds, (iv) return volatility of underlying funds, (v) expense ratio of underlying funds, (vi) underlying fund age, and (vii) number of family funds within the underlying fund’s sector. As in Fama-Macbeth (1973), time series average coefficients and their corresponding t-statistics estimated by using Newey-West standard errors are reported. Also, to control for possible heteroskedasticity generated by the size variation of each underlying fund, weighted least squares approach is employed. P-values are given in parentheses. Statistical significance at the 5(1) % level is denoted by *(**).

44

Inflows Outflows

Intercept 0.0194** 0.0113**

(0.0005) (0.0003)

Log(TNA) 0.0004 0.0002

(0.3973) (0.3508)

Age -0.0003 0.0004

(0.7323) (0.4933)

Expense Ratio -0.005858** 0.0001

(0.0032) (0.9122)

FoF's Own Flows 0.0442** -0.0245*

(0.0001) (0.0222)

log(# of competing funds) -0.0008 -0.0001

(0.1385) (0.7405)

Return Volatility -0.204** -0.0779*

(0.0015) (0.0362)

Sector Flows -0.0736 0.0162

(0.3463) (0.5794)

Bottom Performance Group 0.009 -0.0245*

(0.4516) (0.0253)

Middle Performance Group -0.0037 -0.0016

(0.3561) (0.3174)

High Performance Group 0.0643* -0.0024

(0.0308) (0.8038)

No. Obs 14200 3552

Adj -R-Square 0.351 0.185

Independenat Variable% Changes in Weights

Table 3-(a) Flow-Performance Sensitivity for the Affiliated FoMFs This table examines the effect of relative performance by underlying funds on the flows decisions by the FoMFs. The model is estimated for affiliated FoMFs subsample. The dependent variable is the change in flows by FoMF. This dependent variable is measured by the change in portfolio weight of each underlying fund invested by FoMF. For outflows case, I take the absolute value of the changes in portfolio weights. Each quarter (t), relative performance measure (fractional rank) is assigned to each underlying fund (j) according to the Carhart four factor risk adjusted returns estimated during the past 12 months. To find the fractional rank, the performance comparison group is confined to mutual funds pursuing the same objective. Then, in each quarter, a piecewise regression is estimated by regressing change in portfolio weight on the relative performance measures and other control variables. The other control variables include (i) aggregate flows into underlying fund’s objective sector, (ii) flows to FoMF itself, (iii) total net asset of underlying funds, (iv) return volatility of underlying funds, (v) expense ratio of underlying funds, (vi) underlying fund age, and (vii) number of family funds within the underlying fund’s sector. As in Fama-Macbeth (1973), time series average coefficients and their corresponding t-statistics estimated by using Newey-West standard errors are reported. Also, to control for possible heteroskedasticity generated by the size variation of each underlying fund, weighted least squares approach is employed. Here, the weight is proportional to the portfolio weights. P-values are given in parentheses. Statistical significance at the 5(1) % level is denoted by *(**).

45

Inflows Outflows

Intercept 0.0215 0.0876

(0.082) (0.2276)

Log(TNA) 0.0047 -0.0017**

(0.0681) (0.0032)

Age -0.0124* -0.0066

(0.0225) (0.2037)

Expense Ratio -0.0075 -0.0021

(0.0584) (0.319)

FoF's Own Flows -0.0932 -0.0957

(0.3166) (0.2111)

log(# of competing funds) -0.004 0.0008

(0.3435) (0.7206)

Return Volatility 0.1688 -0.0119

(0.2086) (0.2272)

Sector Flows 0.0182 0.3885

(0.9395) (0.407)

Bottom Performance Group 0.1121 0.2458

(0.305) (0.0927)

Middle Performance Group -0.045 -0.1168

(0.3961) (0.2555)

High Performance Group 0.0607 0.1612

(0.4161) (0.1824)

No. Obs 1450 779

Adj -R-Square 0.167 0.158

Independenat Variable% Changes in Weights

Table 3-(b) Flow-Performance Sensitivity for the Unaffiliated FoMFs This table examines the effect of relative performance by underlying funds on the flows decisions by the FoMFs. The model is estimated for unaffiliated FoMFs subsample. The dependent variable is the change in flows by FoMF. This dependent variable is measured by the change in portfolio weight of each underlying fund invested by FoMF. For outflows case, I take the absolute value of the changes in portfolio weights. Each quarter (t), relative performance measure (fractional rank) is assigned to each underlying fund (j) according to the Carhart four factor risk adjusted returns estimated during the past 12 months. To find the fractional rank, the performance comparison group is confined to mutual funds pursuing the same objective. Then, in each quarter, a piecewise regression is estimated by regressing change in portfolio weight on the relative performance measures and other control variables. The other control variables include (i) aggregate flows into underlying fund’s objective sector, (ii) flows to FoMF itself, (iii) total net asset of underlying funds, (iv) return volatility of underlying funds, (v) expense ratio of underlying funds, (vi) underlying fund age, and (vii) number of family funds within the underlying fund’s sector. As in Fama-Macbeth (1973), time series average coefficients and their corresponding t-statistics estimated by using Newey-West standard errors are reported. Also, to control for possible heteroskedasticity generated by the size variation of each underlying fund, weighted least squares approach is employed. Here, the weight is proportional to the portfolio weights. P-values are given in parentheses. Statistical significance at the 5(1) % level is denoted by *(**).

46

Below Median Above Median

Intercept 0.0308** 0.0202*

(0.001) (0.0252)

Log(TNA) 0.0005 0.0006

(0.5037) (0.5675)

Age -0.0016 -0.0008

(0.3364) (0.644)

Expense Ratio -0.0037 -0.0025

(0.4416) (0.4289)

FoF's Own Flows 0.0369 0.0411**

(0.0567) (0.0081)

log(# of competing funds) -0.0013 -0.0022*

(0.4831) (0.0334)

Return Volatility -0.307* -0.1947**

(0.0427) (0.0013)

Sector Flows -0.2025 0.0528

(0.1514) (0.5259)

Bottom Performance Group 0.0328 0.0099

(0.3657) (0.6908)

Middle Performance Group -0.0141 -0.0008

(0.0589) (0.8997)

High Performance Group 0.0904* 0.0179

(0.05) (0.453)

No. Obs 7752 7898

Adj -R-Square 0.271 0.340

Independenat Variable% Changes in Weights

Inflows

Table 4-(a) Holdings Number Sub-Sample Analysis: Inflows This table examines the effect of relative performance by underlying funds on the flows decisions by the FoMF. The model is estimated for (i) FoMFs with more than the sample median number of MFs holdings and (ii) FoMFs with less than the sample median number of MFs holdings. The dependent variable is the change in inflows by FoMF. This dependent variable is measured by the change in portfolio weight of each underlying fund invested by FoMF. For outflows case, I take the absolute value of the changes in portfolio weights. Each quarter (t), relative performance measure (fractional rank) is assigned to each underlying fund (j) according to the Carhart four factor risk adjusted returns estimated during the past 12 months. To find the fractional rank, the performance comparison group is confined to mutual funds pursuing the same objective. Then, in each quarter, a piecewise regression is estimated by regressing change in portfolio weight on the relative performance measures and other control variables. The other control variables include (i) aggregate flows into underlying fund’s objective sector, (ii) flows to FoMF itself, (iii) total net asset of underlying funds, (iv) return volatility of underlying funds, (v) expense ratio of underlying funds, (vi) underlying fund age, and (vii) number of family funds within the underlying fund’s sector. As in Fama-Macbeth (1973), time series average coefficients and their corresponding t-statistics estimated by using Newey-West standard errors are reported. Also, to control for possible heteroskedasticity generated by the size variation of each underlying fund, weighted least squares approach is employed. Here, the weight is proportional to the portfolio weights. P-values are given in parentheses. Statistical significance at the 5(1) % level is denoted by *(**).

47

Below Median Above Median

Intercept 0.0145** 0.0032

(0.0001) (0.4238)

Log(TNA) -0.0002 0.0007

(0.3977) (0.0509)

Age 0.0008 0.0007

(0.1839) (0.3349)

Expense Ratio -0.0010 0.0016

(0.6371) (0.4255)

FoF's Own Flows -0.0317* 0.0106

(0.026) (0.0606)

log(# of competing funds) -0.0004 -0.0009

(0.2163) (0.1418)

Return Volatility -0.1119 -0.0551

(0.0813) (0.1542)

Sector Flows -0.0014 -0.0139

(0.9791) (0.5319)

Bottom Performance Group -0.0252** -0.0016

(0.0001) (0.9047)

Middle Performance Group -0.0005 -0.0028**

(0.7751) (0.0094)

High Performance Group -0.0134 0.1573

(0.1385) (0.4255)

No. Obs 2203 2128

Adj -R-Square 0.287 0.149

Independenat Variable% Changes in Weights

Outflows

Table 4-(b) Holdings Number Sub-Sample Analysis: Outflows This table examines the effect of relative performance by underlying funds on the flows decisions by the FoMF. The model is estimated for (i) FoMFs with more than the sample median number of MFs holdings and (ii) FoMFs with less than the sample median number of MFs holdings. The dependent variable is the change in outflows by FoMF. This dependent variable is measured by the change in portfolio weight of each underlying fund invested by FoMF. For outflows case, I take the absolute value of the changes in portfolio weights. Each quarter (t), relative performance measure (fractional rank) is assigned to each underlying fund (j) according to the Carhart four factor risk adjusted returns estimated during the past 12 months. To find the fractional rank, the performance comparison group is confined to mutual funds pursuing the same objective. Then, in each quarter, a piecewise regression is estimated by regressing change in portfolio weight on the relative performance measures and other control variables. The other control variables include (i) aggregate flows into underlying fund’s objective sector, (ii) flows to FoMF itself, (iii) total net asset of underlying funds, (iv) return volatility of underlying funds, (v) expense ratio of underlying funds, (vi) underlying fund age, and (vii) number of family funds within the underlying fund’s sector. As in Fama-Macbeth (1973), time series average coefficients and their corresponding t-statistics estimated by using Newey-West standard errors are reported. Also, to control for possible heteroskedasticity generated by the size variation of each underlying fund, weighted least squares approach is employed. Here, the weight is proportional to the portfolio weights. P-values are given in parentheses. Statistical significance at the 5(1) % level is denoted by *(**).

48

Maintained Expanded Reduced Eliminated Maintained Expanded Reduced Eliminated1(Lowest Position) 0 0.45 0 0 0.00 14.50 0.00 0.00

2 0 0.0031 -0.0005 -0.0009 3.07 2.09 0.32 0.023 0 0.0076 -0.002 -0.006 5.80 2.94 1.18 0.094 0 0.015 -0.005 -0.021 6.05 2.59 1.26 0.125 0 0.032 -0.012 -0.048 5.35 3.11 1.38 0.166 0 0.039 -0.029 -0.099 5.59 3.12 1.16 0.137 0 0.061 -0.052 -0.168 5.38 3.37 1.14 0.128 0 0.088 -0.0875 -0.303 5.46 3.02 1.43 0.099 0 0.179 -0.166 -0.572 4.64 3.71 1.50 0.16

10(Highest Position) 0 0.47 -0.609 -1.85 5.14 3.65 1.11 0.09

Avg Flows as % of Underlying Fund's Size % of the Observations

Table 5-(a) UFoMFs’ Trading Responses to Their Own Flow Shocks This table reports how UFoMFs’ funds holdings change conditional on the size of their existing positions in the previous quarter. UFoMFs’ underlying funds are

sorted into deciles according to the size of their positions in the previous quarter. Within each decile, Panel (a) reports the average percentage flows of UFoMFs

to their holdings maintained, expanded, reduced, or eliminated. Percentage flow is measured as a ratio of the dollar flow from UFoMFs to end-of-quarter total net

assets (TNA) of underlying fund. Panel (b) reports the average percentage flows of UFoMFs to their holdings maintained, expanded, reduced, or eliminated when

UFoMFs’ own flows are in the top decile. Panel (c) reports the average percentage flows of UFoMFs to their holdings maintained, expanded, reduced, or

eliminated when UFoMFs’ own flows are in the bottom decile.

(a) The whole sample

49

Maintained Expanded Reduced Eliminated Maintained Expanded Reduced Eliminated1(Lowest Position) 0 0.69 0 0 0.00 21.82 0.00 0.00

2 0 0.0024 -0.0008 0 0.97 4.03 0.16 0.003 0 0.008 -0.0027 -0.0088 2.31 5.27 0.86 0.054 0 0.032 -0.0073 0 1.93 4.30 1.13 0.005 0 0.046 -0.013 0 2.04 6.02 1.40 0.006 0 0.054 -0.034 0 2.47 6.23 1.24 0.007 0 0.082 -0.066 0 2.26 5.32 0.97 0.008 0 0.095 -0.098 0 2.63 6.13 1.24 0.009 0 0.233 -0.177 0 1.88 5.96 0.86 0.00

10(Highest Position) 0 0.671 -0.685 0 2.63 7.15 0.75 0.00

Avg Flows as % of Underlying Fund's Size % of the Observations

Maintained Expanded Reduced Eliminated Maintained Expanded Reduced Eliminated1(Lowest Position) 0 0.34 0 0 0.00 18.34 0.00 0.00

2 0 0.0024 -0.0005 -0.00004 4.96 1.28 0.80 0.053 0 0.004 -0.0017 -0.0043 8.58 2.40 2.19 0.164 0 0.013 -0.0041 -0.02 7.09 1.60 1.81 0.485 0 0.012 -0.0079 -0.051 5.28 1.49 2.61 0.376 0 0.067 -0.024 -0.098 4.74 0.96 1.33 0.437 0 0.076 -0.051 -0.163 4.05 1.71 1.44 0.538 0 0.142 -0.06 -0.3 5.01 1.28 2.56 0.329 0 0.142 -0.141 -0.564 3.09 2.03 2.83 0.59

10(Highest Position) 0 0.375 -0.333 -2.03 4.00 1.33 1.92 0.37

% of the ObservationsAvg Flows as % of Underlying Fund's Size

(b) Positive Flow Shock for UFoMFs

(c) Negative Flow Shock for UFoMFs

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Bottom Performance Group Middle Performance Group Top Performance Group1(Lowest Position) 0.31% 0.22% 0.33%

2 0.000003% 0.0007% 0.0023%3 0.008% 0.0076% 0.0126%4 0.113% 0.041% 0.078%

5(Highest Position) 0.3079% 0.14% 0.13%

Avg Flows as % of Underlying Fund's Size

Table 5-(b) UFoMFs’ Trading Responses to the Underlying Fund’s Performance This table reports how UFoMFs’ funds holdings change conditional on the size of their existing positions in the previous quarter and the relative performance

among the same style peer group in the previous year. UFoMFs’ underlying funds are sorted into quintiles according to the size of their positions in the previous

quarter. Also, UFoMFs’ underlying funds are sorted into terciles according to the relative performance among the same style peer group in the previous year.

Within each size quintile and performance tercile, Panel below reports the average percentage flows of UFoMFs. Percentage flow is measured as a ratio of the

dollar flow from UFoMFs to end-of-quarter total net assets (TNA) of underlying fund.

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(+) Flow Portfolio (-) Flow Portfolio (+) Flow Portfolio (-) Flow PortfolioAlpha -0.00153 -0.00449*** -0.00086 -0.00148**

(0.0009) (0.00123) (0.00081) (0.00062)RMRF 0.91077 0.90642 1.0428 0.9808

(0.0316) (0.0427) (0.0281) (0.0216)SMB 0.03749 -0.02005 0.0456 0.1347

(0.0434) (0.05874) (0.0387) (0.0297)HML 0.0233 0.12618 0.042 -0.0892

(0.0474) (0.06405) (0.0422) (0.0324)UMD 0.0203 0.1935 0.0103 0.0049

(0.0236) (0.03198) (0.021) (0.0161)No. Obs 69 69 69 69ADJ R^2 0.9482 0.8934 0.9683 0.9804

Difference in Alphas Difference in Alphas0.00296 (0.0015) 0.00062(0.001)

p-value:5.6% p-value: 54.6%

Four-Factor ModelAFoMFs' Flows Other Flows

Table 6 Performance of New-Money Portfolios For each quarter from June 2002 to Dec 2007, underlying funds of AFoMFs are grouped into either the positive cash-flow portfolio or the negative cash-flow

portfolio based on the sign of the gross cash flow from FoMFs during the previous quarter. These flow groups are labeled as “AFoMFs’ Flows”. Also, 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. These second flow groups are labeled as “Other Flows”. Then, portfolio performance is evaluated based on the estimated

portfolio alpha. The four-factor portfolio alpha is calculated as the intercept from the monthly time series regression of cash-flow weighted portfolio excess

returns on the market excess return (RMRF) and mimicking portfolios for size (SMB), book-to-market (HML), and momentum (UMD) factors. The same

procedure is repeated for UFoMFs. Panel below reports estimates for the new-money portfolios formed using cash-flow-weighted fund returns. The bottom part

of this panel also reports the difference in alphas between the positive cash-flow portfolio and the negative cash-flow portfolio. The t-statistics are reported in

parenthesis. Statistical significance is denoted only for alphas. **Statistical significance at 5% level; *** Statistical significance at 1% level.

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(+) Flow Portfolio (-) Flow Portfolio (+) Flow Portfolio (-) Flow PortfolioAlpha -0.00325*** -0.00148 -0.0008 -0.00129

(0.000898) (0.0012) (0.0008) (0.0010)RMRF 0.1563 0.2822 0.9959 0.9993

(0.03171) (0.0451) (0.0265) (0.0343)SMB -0.0301 -0.0075 0.0715 0.169

(0.039) (0.0556) (0.0349) (0.0451)HML 0.055 -0.0588 0.1209 -0.0729

(0.0475) (0.0677) (0.0418) (0.0541)UMD -0.0574 -0.1983 0.0962 -0.0576

(0.02266) (0.03227) (0.0212) (0.0274)

ADJ R^2 0.3948 0.6779 0.9637 0.6524Difference in Alphas Difference in Alphas

-0.00177(0.001499) 0.00049(0.00128)Not Significant Not Significant

Four-Factor ModelUFoMF's Flows Other Flows

Table 6- Continued

Performance of New-Money Portfolios For each quarter from June 2002 to Dec 2007, underlying funds of UFoMFs are grouped into either the positive cash-flow portfolio or the negative cash-flow

portfolio based on the sign of the gross cash flow from FoMFs during the previous quarter. These flow groups are labeled as “UFoMFs’ Flows”. Also, 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. These second flow groups are labeled as “Other Flows”. Then, portfolio performance is evaluated based on the estimated

portfolio alpha. The four-factor portfolio alpha is calculated as the intercept from the monthly time series regression of cash-flow weighted portfolio excess

returns on the market excess return (RMRF) and mimicking portfolios for size (SMB), book-to-market (HML), and momentum (UMD) factors. The same

procedure is repeated for UFoMFs. Panel below reports estimates for the new-money portfolios formed using cash-flow-weighted fund returns. The bottom part

of this panel also reports the difference in alphas between the positive cash-flow portfolio and the negative cash-flow portfolio. The t-statistics are reported in

parenthesis. Statistical significance is denoted only for alphas. **Statistical significance at 5% level; *** Statistical significance at 1% level.

53

-1.000

-0.500

0.000

0.500

1.000

1.500

Quantile 1 Quantile 2 Quantile 3 Quantile 4 Quantile 5

Weight

Change(

%)

Performance Fractional Ranking

Average Flow-Performace Relation

Inflows

Outflows

Figure 1 Flow-Performance Relation for AFoMFs Each quarter, underlying funds of AFoMFs are ranked into 5 groups by using their past one year risk-adjusted performance. I report average portfolio weight

change for each group over the subsequent quarter.

54

-0.04%

-0.21%

-0.40%

-0.28%

-0.42%

-0.68%-0.80%

-0.70%

-0.60%

-0.50%

-0.40%

-0.30%

-0.20%

-0.10%

0.00%

Concentrated Middle Diversified

ALPHA

Concentrated Holdings Dispersed Holdings

Alpha: Buy Portfolio Alpha: Sell Portfolio

Concentrated Holdings Middle Diversified HoldingsAlpha: Buy Portfolio -0.04% -0.21% -0.40%Alpha: Sell Portfolio -0.28% -0.42% -0.68%

Difference 0.23% 0.21% 0.28%P-value 8.67% 16.88% 28.13%

Figure 2 Predictability Comparison between Concentrated AFoMFs and Diversified AFoMFs Based on the number of mutual fund holdings, AFoMFs are divided into the “Concentrated Holdings Group”, “Middle Group”, and “Diversified Holdings Group.

AFoMFs whose fund holdings number is below the 25th percentile are assigned to the “Concentrated Holdings Group”. AFoMFs whose fund holdings number is

above the 75th percentile are assigned to the “Diversified Holdings Group”. For each group, the underlying funds of the AFoMFs are assigned into either the buy

portfolio or the sell portfolio based on the sign of the gross cash flow from AFoMFs during the previous quarter. Then, for each portfolio in each group,

performance is evaluated based on the estimated portfolio alpha. The four-factor portfolio alpha is calculated as the intercept from the monthly time series

regression of cash-flow weighted portfolio excess returns on the market excess return (RMRF) and mimicking portfolios for size (SMB), book-to-market (HML),

and momentum (UMD) factors. Panel below reports estimates for the new-money portfolios formed using cash-flow-weighted fund returns for each group.

55

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2003 2004 2005 2006 2007 2008

AV

G P

ER

CE

NT

ILE

Net Sharpe ratio ranking

mf

fof

unaff

aff

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

2003 2004 2005 2006 2007 2008

AV

G P

ER

CE

NT

ILE

Gross Sharpe ratio ranking

mf

fof

unaff

aff

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

2002 2003 2004 2005 2006 2007 2008

RAW

STD

Regular MFs FoMFs

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

2002 2003 2004 2005 2006 2007 2008

GROSS

STD

Regular MFs FoMFs

Figure 3 Performance and Volatility Comparison The average Sharpe ratio ranking is based on calculations for the corresponding year of data, and percentile ranking is based on the entire sample of funds. That

is, in each category (i.e., FoMFs, AFoFMs, UFoMFs, Ordinary Mutual funds, respectively), I compute the percentile ranking of each constituent fund and then

take the cross-sectional average in each year. For the gross Sharpe ratio ranking, I added back the annual expense ratio to raw return of each fund. Additionally,

two graphs at the bottom display the standard deviation in gross and net returns of both FoMFs and ordinary mutual funds across different years.