do funds of mutual funds add any value?...negative flow portfolio. in addition, underlying funds are...
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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:[email protected]
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.
3
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.
6
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
8
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
9
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.
14
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
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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.