“Whom do you trust?” Investor-advisor relationships and mutual fund flows Leonard Kostovetsky Simon School, University of Rochester [email protected]Abstract I measure the value that investors place on trust and relationships in asset management by examining mutual fund flows around announced changes in the ownership of fund management companies. I find a decline in flows of around 7% of fund assets in the year following the announcement date, starting after announcement and accelerating after the closing date of the ownership change. A decomposition into inflows and outflows shows that the overall decrease in flows is entirely driven by increasing outflows with no change in inflows. Retail investors and investors in funds with higher expense ratios are most responsive to ownership changes, providing new evidence that such investors place a significant value on trust and are more likely to respond to a relationship disruption by withdrawing their assets. Alternative explanations such as changes in distribution network, reactions to expected fund closure, expected or past manager changes, or poor expected returns do not seem to explain the results.
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“Whom do you trust?” Investor-advisor relationships and mutual fund flows
Leonard Kostovetsky Simon School, University of Rochester
Abstract I measure the value that investors place on trust and relationships in asset management by examining mutual fund flows around announced changes in the ownership of fund management companies. I find a decline in flows of around 7% of fund assets in the year following the announcement date, starting after announcement and accelerating after the closing date of the ownership change. A decomposition into inflows and outflows shows that the overall decrease in flows is entirely driven by increasing outflows with no change in inflows. Retail investors and investors in funds with higher expense ratios are most responsive to ownership changes, providing new evidence that such investors place a significant value on trust and are more likely to respond to a relationship disruption by withdrawing their assets. Alternative explanations such as changes in distribution network, reactions to expected fund closure, expected or past manager changes, or poor expected returns do not seem to explain the results.
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1. Introduction
An important unanswered question in the field of delegated asset management is how
much importance investors place on who is managing their money. Asset management
companies spend more than a billion dollars each year on advertising (Gallagher, Kaniel, and
Starks, 2006), much of it trying to persuade investors that their firm will provide them with
trustworthy and dependable financial advice (Mullainathan, Schwartzstein, and Shleifer, 2008).
Gennaioli, Shleifer, and Vishny (2012) propose that the well-documented empirical finding that
average active mutual fund alphas are negative (e.g., Jensen, 1968) is due to a “trust” premium,
which allows asset management firms to charge investors additional fees if there is a trusting
relationship between them. They write that trust can be established through “personal
relationships, familiarity, persuasive advertising, connections to friends and colleagues,
communication, and schmoozing,” all of which are likely to be disrupted by an exogenous
change in firm management.
In this paper, I measure the value of trustworthy relationships between investors and asset
management firms by examining mutual fund flows around management company ownership
changes. My main finding is that mutual fund flows turn negative in response to announced
changes in the parent company or ownership of a fund’s advisor. A reduction in flows begins
after the announcement date and is initially about 3% of assets (on an annualized basis), and then
accelerates after the closing date to total approximately 7% of assets over the twelve months
following the announcement date. The results are robust to controlling for fund characteristics
such as the past five years of returns, age, fund and family size, and style, as well as parent
company characteristics (for public parent companies) such as the parent’s market capitalization
and past year’s stock returns.
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An alternative empirical strategy would be to look at flows around individual manager
changes. The main problem with that strategy is that manager changes are highly correlated with
past fund performance (Chevalier and Ellison, 1999) and fund flows are also extremely sensitive
to past performance (Sirri and Tufano, 1998), making it difficult to disentangle performance-
driven outflows from outflows due to manager changes. In addition, there is a reverse causality
problem if managers can anticipate future flows and voluntarily depart the fund when they
expect fund outflows, and therefore reductions in assets under management and their own
compensation.
My empirical strategy begins with an examination of 185 events (covering 843 funds)
from 1995 through 2011, where there is a change in the ownership of the fund’s management
company (mergers and acquisitions involving the management company itself or its parent). One
example of such a merger occurred in 2001 when Deutsche Bank announced its purchase of
Zurich Scudder, the manager of the Scudder Funds, from Zurich Financial. In order to control for
parent company characteristics, I next restrict the sample to the 78 events (covering 391 funds)
involving ownership changes of U.S. public parent companies. While management company
ownership changes are less likely to be driven by a particular fund’s performance than manager
changes, it is still possible that they are related to the entire management company’s past
investment performance. In order to rule out such endogeneity concerns, I perform a more
rigorous test, restricting the event space to the 70 events (covering 295 funds) in which the
public parent company undergoing an ownership change derives a small share of revenues
(<10%) from its mutual fund operations, and find similar results.
Next, I test a number of different explanations for my main results. One explanation is
that a group of investors attach significant value to their relationship with the fund’s management
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company (e.g., due to advertising or past experience), and move their savings elsewhere when
the ownership change disrupts this relationship. However, new investors might also avoid
investing in the fund while its management is transitioning from one owner to another owner. I
decompose fund flows into inflows (purchases of shares by investors) and outflows (sales of
shares by investors), and find that while there is little change in inflows around the
announcement date, there is a large increase in outflows that leads to the reduction in total fund
flows.
The importance of trust and relationships should also be more important for less
sophisticated investors who don’t have the skills or resources to monitor the fund’s management.
I test the trust hypothesis by separately looking at the effect of ownership changes on retail class
flows and institutional class flows to examine whether investor sophistication is an important
factor. I find that outflows are driven by retail class investors, and that investors in institutional
classes do not react adversely to changes in ownership. This result might also explain why flows
only slowly react to announcement changes. Limited attention is well documented among retail
investors (Barber and Odean, 2008), which is why retail investors slowly find out about the
ownership change. After the closing date, the news of the ownership change is more likely to
filter through to retail investors, as it appears in the fund prospectus and other disclosure
documents.
In a similar vein, I separate my sample of funds into high-expense funds and low-expense
funds, and then examine the effect of ownership changes on the flows of each group. I find the
decline in flows from pre-announcement to post-announcement is anywhere from 25% to 100%
bigger for high-expense funds relative to those with low expense ratios. This supports the thesis
that a component of the expense ratio is a trust “premium”, since investors in funds with higher
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expense ratios are more likely to withdraw their money when their prior relationship is broken
due to a change in ownership.
Next, I examine whether characteristics of the acquiror or the purpose behind the
acquisition affect the fund flows after the announcement date. Interestingly, I find that investors
react in a more negative way when a bank acquires their fund’s management company than when
the acquiror is an insurance or securities firm. However, neither the purpose of the merger, nor
the past stock performance or past fund performance (either of the whole family or just funds in
the same style as the target fund) of the acquiring firm affects post-announcement fund flows.
This result might indicate that investors don’t believe the past performance of the acquiring firm
will necessarily carry through to their fund, and/or that the reason for their sale is the disruption
of their relationship with the prior organization that had been managing their fund.
I then test a number of alternative explanations for the paper’s main results. Changes in
ownership of management companies may also coincide with changes in distribution channels.
Del Guercio, Reuter, and Tkac (2010) document the various distribution channels used by
mutual fund families, and Bergstresser, Chalmers, and Tufano (2009) provide evidence on the
importance of brokers in portfolio decisions made by retail investors. A change in distribution
channel might lead brokers to counsel their clients to pull out money from the fund, an effect that
would have nothing to do with a disruption of trust between investors and fund management.
I test this hypothesis by controlling for the main distribution channel used by the fund,
and find that my results are robust to these controls. I then drop any funds that underwent both an
ownership change as well as a change in the primary type of distribution channel, leaving just
funds whose distribution channel remained the same after the announcement of the ownership
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change. Even among this subset of funds, there are economically and statistically significant
outflows after the announced ownership change.
The dot-com bubble presents some concerns as well. There is a significant clustering of
M&A events around the dot-com bubble since merger activity usually peaks during market
booms and the financial industry was undergoing consolidation at the time after the repeal of the
Glass-Steagall Act. I test whether my results are coming from this clustering by dropping all
fund-month observations from 1999 and 2000. I find that my main results are robust to exclusion
of the period around the dot-com bubble.
Another possible explanation is that advisor ownership changes are associated with an
increase in manager turnover and fund closures as the new owners tweak the array of offered
funds and the managers of those funds. Investors might be reacting to expected or realized
manager changes or announced fund closures by withdrawing money from the fund. I test this
hypothesis by including dummy variables that indicate whether the fund will close in the next six
months and whether the manager will change in the next six months or has changed in the prior
six months. My results are robust to inclusion of these controls. Another possibility is that the
decline in asset flows after an announced change in ownership is a rational reaction to
expectations of lower returns. For instance, during the period of transition, the management firm
might not be putting in maximum effort in fund management, and investors might temporarily be
leaving the fund to avoid this period of lower expected returns. I test whether performance is
affected by ownership changes, and find no evidence that mutual funds underperform in the year
following an announced ownership change.
This paper builds on the growing literature focusing on the importance of trust,
familiarity, and loyalty in investment. For instance, Guiso, Sapienza, and Zingales (2008)
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highlight the importance of trust in stock market participation. French and Poterba (1991), Coval
and Moskowitz (1999), and Huberman (2001) all provide evidence on the importance of
familiarity and geographic proximity for investment decisions. Cohen (2009) highlights the
effect of loyalty by studying employee decisions to invest in their company’s stock. My findings
complement this literature by highlighting and measuring the role that trust and familiarity play
in investors’ choices of asset managers, through the use of exogenous breaks in the adviser-
investor relationship.
This paper also contributes to prior research on the role of mutual fund parent companies.
Sialm and Tham (2011) find positive spillover effects from the performance of the parent
company’s stock to the ability of the mutual fund to attract investors. A number of papers
including Ferris and Yan (2009) and Adams, Mansi, and Nishikawa (2012) highlight the
importance of agency issues at advisory firms. Massa and Rehman (2008) show that information
flows from bank parent companies to affiliated mutual funds, allowing these mutual funds to
outperform on stock investments in companies that have borrowed from (and therefore provided
private information to) the bank.
My paper also uses mergers and acquisitions of financial institutions as exogenous
identification in a manner similar to Hong and Kacperczyk (2010). Most papers that have looked
at mergers in the context of mutual funds have focused on mergers between funds (e.g. Khorana,
Tufano, and Wedge, 2007), and not mergers at the family or adviser level. An important
exception is Allen and Parwada (2006) who look at a subset of parent company mergers for
mutual funds in Australia from 1995 to 1999, and also find evidence of negative outflow
reactions. However, their focus is on excessive size and its negative effect on performance as the
main culprit for investor adverse reaction to ownership changes. In contrast, my paper focuses on
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a much larger set of U.S. firms, and tests the hypothesis that trust and relationships between
investors and the advisor explain the empirical findings.
In summary, my paper uses an exogenous shock to the investor-advisor relationship to
measure investor reaction, and thus provides evidence of the significant value that investors
attach to this relationship. It underlines the notion that past (and expected future) performance
and expense ratios are not the only factors in how investors, especially retail investors, make
mutual fund investment decisions.
2. Data
The main data sources for this paper are the CRSP Survivor-Bias-Free US Mutual Fund
Database, annual Morningstar Principia CDs, and the SDC Platinum M&A database. Additional
data on fund advisers is downloaded from the SEC Investment Adviser Public Disclosure
(IAPD) database1 and SEC EDGAR, and stock-level data is collected from the CRSP/Compustat
database. Fund inflows and outflows are collected directly from NSAR filings on EDGAR. Data
on primary distribution networks is from Strategic Insight.
Mutual Fund Sample: The sample consists of all domestic, diversified, actively-managed,
equity mutual funds operating from 1995 through 2012. I construct this sample by merging
CRSP and Morningstar, using ticker symbols and (when ticker symbols are missing) fund names.
I then exclude all funds outside the nine main style boxes (e.g., smallcap value, largecap blend,
etc.) leaving only domestic diversified equity funds. Finally, I eliminate index funds by removing
all funds with the words “index”, “S&P”, “Dow Jones”, and “NASDAQ” in the fund name, and
by excluding all funds in the Dimensional Fund Advisors (DFA), Direxion, Potomac, ProFunds,
1 The website for this service is: http://www.adviserinfo.sec.gov/IAPD/Content/Search/iapd_Search.aspx
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and Rydex fund families. ETFs are also excluded by removing all observations with the word
ETF in the fund name or funds with ticker symbols of four or fewer characters.
I aggregate funds across fund classes using the Morningstar portfolio identifier
(PORTCODE) or MFLinks variable (WFICN). I remove incubated funds by excluding funds that
were not contemporaneously reported in Morningstar or had a blank CRSP fund name at the start
of the calendar year. I also drop funds with less than $10 million in assets under management, as
flows in these funds are highly volatile and contaminated by “seeding” from the fund family.
This leaves 351,120 portfolio-month observations with the number of funds growing from 945
funds in January 1995 to 1,665 funds in December 2012.
Fund Advisers and Adviser Ownership Changes: Morningstar is the main source for
mutual fund advisers. I crosscheck the Morningstar adviser with the CRSP “Management
Company” identifier and find that they match for over 80% of observations. However, CRSP
sometimes reports the fund distributor as the management company, which is why I rely on
Morningstar for this variable.
I find the parent companies of mutual fund advisers by entering each adviser’s name into
the SEC’s IAPD online database and looking up the Schedule A of Form ADV, which lists all
direct owners and executive officers. For example, for Dreyfus Corporation, adviser to the
Dreyfus funds, the Form ADV Schedule A shows that Bank of New York Mellon is the sole
shareholder of this company. IAPD includes defunct fund advisers but it only began operations
in 2000 so fund advisers that went defunct prior to 2000 are not included. Therefore, I gather
ownership information on these companies by looking through mutual fund proxy documents on
SEC EDGAR.
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The IAPD database only includes current ownership information (or last reported
ownership information for defunct firms). Therefore, I manually look up all fund advisers and
their parent companies in the SDC Platinum M&A database, and note any changes in ownership
and the announcement and effective (closing) dates for each ownership change. I also gather
information from SDC Platinum on the identity, public status, and industry of the acquiring
company, as well as the purpose or purposes for the merger/acquisition.
Whenever Morningstar shows that a fund or fund family changes advisors or is merged
into another fund or fund family and I can find no corresponding ownership change in SDC
Platinum, I examine mutual fund proxy documents on SEC EDGAR to determine the reason for
the change. Overall, I find a total of 185 parent company changes that were announced from July
1995 through December 2011.2 Initial public offerings and management buyouts are not included
because they also coincide with the decisions to go public or private, which might have their own
implications for fund flows.
I use SDC Platinum to identify publicly traded parent companies (of acquirors and
targets) and match them to CRSP using CUSIPs. I also look up several foreign parent companies
on Google Finance to determine their public status. I define a fund as privately owned, with
Private firm (dummy) set to one, if that company is not in CRSP, it is not traded on a foreign
exchange, and it is not a mutual insurance company or non-profit organization. Privately owned
advisory companies make up approximately 40% of the funds in the sample, but manage over
half of the assets under management. This disparity is due to the fact that extremely large mutual
fund advisers such as Fidelity Management & Research (Fidelity Funds) and Capital
2 Ownership changes announced in the first six months and last twelve months of the sample period are exclude because those months are required for studying fund flows around announcement dates.
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Management & Research (American Funds) are privately held. These estimates are consistent
with earlier research on public vs. private ownership of mutual fund management companies.
For public firms that are available in CRSP/COMPUSTAT, I gather data on market
capitalization and past stock returns from CRSP, and total revenues and segment revenues data
from COMPUSTAT. Overall, 78 of the 185 ownership changes involve acquisitions of public
(parent) companies. In “mergers of equals” such as the 1998 deal between Citicorp and
Travelers, the company that is delisted in CRSP (in that case, Citicorp) is the one that is deemed
to have a change in ownership.
In order to avoid possible endogeneity concerns, I also run tests on firms whose main line
of business is not in asset management, and whose change in ownership is therefore less likely to
be related to anything happening at the mutual fund family. For each fund advisor whose parent
company has revenues data in Compustat, I calculate the total estimated annual revenues of its
mutual fund family3 and divide by the parent company’s revenues in the same fiscal year to
calculate the Mutual fund revenues (%) variable. Non-asset management parent companies are
defined as having less than 10% of total revenues coming from estimated mutual fund revenues.
In addition, I check the Compustat Segments database to exclude all firms whose entire asset
management segments produce revenues greater than 20% of total revenues. In total, 70 of the
78 ownership changes involving public parent M&A happen at non-asset management firms,
mostly commercial and investment banks.
Table 1 presents summary statistics on ownership change announcements for mutual fund
advisory firms. Panel A displays the number of events, number of funds involved in each event,
and the assets under management of those funds, for each year. Columns 1 through 3 show a
3 Estimated mutual fund revenues are defined as (1/12 × Annual Expense Ratio × Assets under Management) across all fund-month observations of a fund family in a particular fiscal year.
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total of 185 announced ownership changes from July 1995 through December 2011, involving
843 funds with $587 billion in assets under management at the time of the announcement. M&A
activity was strongest in the first six years of the sample period when the stock market was
booming in the late 1990s and the asset management business was also experiencing significant
growth. Columns 4 through 6 only include ownership changes due to public parent M&A, and
show a total of 78 events involving 391 funds managing $203 billion. Finally, Columns 7 though
9 show summary data on ownership changes due to non-asset management public parent M&A.
Among this subgroup, there are 70 events involving 295 funds managing $145 billion.
Panel B of Table 1 shows a breakdown of the merger types that make up the events used
in this paper. Among the entire sample of events, the merger types are fairly evenly distributed
between banks acquiring other banks, securities firms acquiring other securities firms, and
banks/insurance companies buying other securities firms. On the other hand, public parent M&A
in Columns 3 through 6 is mostly dominated by bank mergers, with over two-thirds of events
consisting of this merger type. Banks are larger and are therefore more likely to be publicly
traded than asset management firms, which is why there is such a dramatic change in merger
types. Appendix A shows fifteen examples of mergers used in this paper, with detailed
information on the acquiror and target, as well as the announcement date and effective date for
the merger.
Fund Characteristics: The main variable of interest for this paper is monthly mutual fund
flows. In order to calculate flows, I download data from the CRSP Mutual Fund database on
monthly assets under management and net returns. Fund flows ($mil) in month t is defined as:
(Eq.1) Fund flows ($) = Assets (end of t) – Assets (start of t) × (1 + Net Returns (over month t))
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Since this quantity is usually proportional to fund size, I standardize it by dividing by assets:
(Eq.2) Fund flows (%) = Fund flows ($)/Assets (start of t)
I adjust flows to eliminate assets added from another fund that was merged into the fund. Finally,
in order to eliminate the effect of outliers, fund flows (%) is winsorized at the 1% and 99% level.
Table 2 reports time-series averages of cross-sectional summary statistics for fund flow
variables. Flows over this sample period are fairly close to zero. Although firms had average
monthly inflows of approximately $0.3 million or 0.4% (4.8% on an annualized basis), the
median firm experienced slight outflows due to the fact that inflows tend to be concentrated
among the funds with the best past performance. Standard deviation of monthly flows, even after
winsorizing, is 4.5%, which highlights the significant cross-sectional variation in fund flows.
In most of the tests in the paper, I control for a number of fund variables that have been
shown in the past to predict fund flows. These variables include past fund performance, fund
assets under management (fund AUM), family assets under management (family AUM), fund age,
and expense ratio. The prior literature on fund flows found a non-linear relationship between past
performance and fund flows. In order to capture this non-linear relationship, I sort firms into
deciles for each year’s style-adjusted return from the past five years, and include five sets of past
return decile dummies as controls in all specifications. Newer funds that weren’t around for all
five years and therefore don’t have returns for a particular prior year are placed in a separate
bucket (in addition to the 10 decile groups) for that year, which has its own dummy variable.
The summary statistics in Table 2 show that the distributions of Fund AUM, Family
AUM, Fund age, and Expense ratio, are positively skewed so I transform them with the natural
logarithm and use the transformed variables as predictive variables in regression tests. Because
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the paper looks at shocks to the parent company of the fund’s adviser, I also collect parent
company characteristics. Table 2 shows that 42% of funds have a privately owned adviser, 35%
of funds have a publicly owned adviser whose parent company is not primarily an asset manager.
Public parent companies have average monthly returns over the prior year of just under 1%, and
derive 8% of revenues from mutual fund fees.
Fund Inflows and Outflows: For part of the analysis in the paper, I decompose fund flows
into inflows (dollar value of purchases of fund shares) and outflows (dollar value of sales of fund
shares). Data on inflows and outflows is included in the semiannual NSAR filing made by each
fund family. I use a script to download all NSAR filings from the EDGAR database, and match
them to funds using fund name. Because there are often slight variations in fund names, I attempt
to manually match any unmatched observations. NSARs also include assets under management
so I confirm matches using this variable. Using machine and manual matching, I obtain
inflow/outflow data for nearly 90% of the fund-month observations in my sample.
Table 2 includes summary statistics on inflows and outflows. The monthly inflows for a
typical fund are 3.8% of its assets under management at the start of the month, but 10% of funds
have inflows exceeding 8% of assets, confirming the skewed nature of inflows as investors put
new funds into the top past performers. Monthly outflows average 3.2% of assets under
management and are less skewed with the 90th percentile at 6%.
Distribution Channels: My main source for distribution channels is a dataset provided by
Strategic Insight. The Strategic Insight dataset includes current distribution channel data on each
fund class as well as archival data on distribution channels for defunct funds and families.
Generally, fund classes labeled A, B, C, and R are sold through brokers, fund classes labeled I
(Institutional) or Retirement are sold to institutions, and fund classes labeled N or Retail or with
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no class label are sold directly to investors (Direct). Brokers can be affiliated with the
management firm and are then classified depending on whether the parent company is a bank
(Bank Proprietary), insurance company (Insurance), or securities company (Proprietary), or not
affiliated with the management company at all (Non Proprietary). Families generally use only
one of these four distribution channels for non-direct and non-institutional sales. Finally, some
funds are sold to members of the fraternal, religious, or non-profit organization that runs the fund
(Other).
I aggregate the total assets for each type of distribution channel across fund classes and
then designate a fund portfolio’s main distribution channel as the channel that has the most assets
under management. Most portfolios (and fund families) distribute a significant proportion of
assets using one distribution channel. The average amount distributed by a portfolio’s top
distribution channel is 95% with a median value of 100%. Non-Proprietary is the most common
distribution channel used by approximately one-third of funds, followed by Direct distribution
used by one-quarter of funds, and Institutional used by about 20% of funds.
Other Variables: I collect a number of additional variables in order to test different
theories for the paper’s main results. For each fund class, I collect data from Morningstar on
whether it is only open to institutional investors or whether retail investors are also allowed to
invest in the class.4 About 20% of fund classes in the sample are only open to institutions.
Morningstar also reports manager names and tenure dates and is my source for the dates of
manager changes. I use CRSP for fund closure dates.
3. Main Results
4 CRSP also has an institutional dummy variable but it is only available after 2000, which is why I use the Morningstar variable.
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Before studying how flows are affected by ownership change announcements, I examine
whether such announcements are actually exogenous events or whether they can be predicted by
(and are correlated with) past fund performance or other fund characteristics. In Table 3, I run
PROBIT regressions with an event announcement dummy, which equals one if there was an
announced ownership change of the fund’s adviser in the current month and zero otherwise, as
the dependent variable, and fund/parent company characteristics as the explanatory variables.
The announcements include completed mergers and one uncompleted merger, Zion’s Bancorp
attempted purchase of First Security Corp. (manager of the Achievement fund family) that was
rejected by Zion’s shareholders in 2000.
As with all the tests in this paper, I first run regressions for the sample of all ownership
changes of fund advisers (Columns 1 and 2), then restrict the sample to public ownership of fund
advisers where we have publicly available data on the parent companies (Columns 3 and 4), and
finally include only publicly owned advisers whose parent companies’ main line of business is
not asset management (Columns 5 and 6). The main advantage of the sample restrictions is that
the events are more likely to be exogenous to what’s happening at the mutual fund level, while
the main disadvantage is a reduction in the number of observations. Columns 1, 3, and 5, of
Table 3 include a simple measure of past performance, the average style-adjusted returns over
the past year. Columns 2, 4, and 6, use a more comprehensive measure, five sets of return decile
dummies for each of the past five years.
The main takeaway from Table 3 is that mutual fund and parent company characteristics
are generally not predictive of event announcements. This indicates that the announced
ownership changes are in fact exogenous and the windows around the changes can be used as a
laboratory to study the effect on flows. We can see that past performance measures (returns and
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flows) are not significant predictors of ownership changes. In fact, the coefficient on past style-
adjusted returns is actually positive (although insignificant) indicating that better-performing
funds are more likely to be subject to an adviser ownership change.
The only significant (at the 5% level) predictive variables in any of the specifications are
family size and parent company size, with the negative coefficients indicating larger fund
families and parent companies are less likely to have an adviser ownership change. This finding
may be due to capital constraints since there are very few investors or firms who can buy the
largest asset management firms or parent companies.
After confirming that ownership changes are largely unrelated to mutual fund
characteristics, I next calculate average flows in the event window around the ownership
changes. I define PREANN and POSTANN dummy variables for the timing of each observation
around the event window. PREANN is set to one for all fund-month observations in the 6 months
prior to the announcement date of an ownership change of the fund adviser, and zero otherwise.
POSTANN is set to one for all fund-month observations on the announcement date and for one
year after the announcement date of an ownership change of the fund adviser, and zero
otherwise.
Panel A of Table 4 shows the average value of monthly Fund flows (%) for all
observations in each event window. As in Table 3, I start with the entire sample of events in
Column 1 and restrict the sample to public parent companies in Column 2 and public non-asset
management parent companies that undergo M&A in Column 3. Prior to the announcement date
(PREANN=1), flows are not statistically different from zero. After the event announcement date
(POSTANN=1), we can see statistically significant outflows. For example, in the sample of all
events, the average value of Fund flows (%) in the post-announcement window is -0.548%
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(~6.6% on an annualized basis) with a t-statistic of 3.63. The results are very similar in the
sample of all public parent mergers (Column 2). Finally, in the smaller sample of non-asset
management mergers (Column 3), the average value of fund flows (%) in the post-announcement
window is -0.737% (~8.8% on an annualized basis) with a t-statistic of 5.96. Across all three
specifications, the pattern of statistically insignificant flows prior to the announcement and
strong outflows after the announcement is repeated.
Two possible explanations for the results in Panel A are that the announcements happen
to funds that are, for other reasons, likely to experience outflows, or that they are clustered in
periods prior to fund outflows such as market peaks. In order to test these explanations, I
construct a matched sample for each event-window observation and then calculate the average of
match-adjusted flows (fund flows relative to matched sample). The matched sample flows for a
particular fund-month observation are a weighted average of flows across all funds in the same
month and in the same fund style (and in the same restricted sample in Columns 2 and 3), where
the weights are proportional to closeness based on differences in size, past five years of returns,
and fund age. Appendix B at the end of the paper describes the matching algorithm.
Panel B of Table 4 presents average values of monthly match-adjusted Fund flows (%)
for observations in each event window. As in Panel A, flows are indistinguishable from zero
prior to the announcement date, but turn lower after the announcement date. The average flows
are higher for both the pre- and post-announcement periods (due to the match adjustment) but the
difference between pre- and post- remains very similar suggesting that it is not unique timing or
differences in characteristics that explain the downturn in flows after the announcement dates of
adviser ownership changes.
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Next, I use a “difference-in-difference” approach, comparing match-adjusted fund flows
in the post-announcement period to the pre-announcement match-adjusted fund flows, for each
fund involved in an event. Using only observations in the event window (from 6 months before
the announcement date to 12 months after the announcement date), I regress match-adjusted fund
flows on POSTANN and also include firm fixed effects. Panel C of Table 4 presents estimated
coefficients on the difference-in-difference estimator, POSTANN, in this regression. The
coefficients range from -0.490% (~6% annualized) in the sample of all events to -0.681% (~8%
annualized) in the sample of public non-asset management merger events, and all coefficients are
statistically significant at the 1% level. We can calculate economic significance by looking at the
total assets in funds undergoing events from Table 1. Across all funds, there were 185 events
with $587 billion in assets so 6% outflows means about $190 million (($587b/185) × 6%) of
outflows per event. Across the sample of public non-asset management merger events, there
were 70 events with $145 billion in assets so 8% outflows means about $165 million of outflows
per event.
It is also instructive to look at a graphical representation of monthly match-adjusted fund
flows from 6 months prior to the announcement date to 12 months after the announcement date.
Figure 1 provides this graphical representation (with each month’s average flows and confidence
intervals) for the entire sample of parent company changes, while Figure 2 depicts the same
results for ownership changes of public non-asset manager parent companies.5 Note that the
flows in the two figures are not cumulative. The figures show that match-adjusted flows are near
zero prior to the announcement date, and then decline slowly after the announcement date before
accelerating downward in the final six months. Clearly, this is not the type of picture we are used
5 The graph for Column 2 of Table 4, which includes all public parent company mergers, looks very similar and is available upon request.
19
to seeing for event studies measuring market price reaction to events. However, it is not
surprising that fund flows (predominantly due to retail flows, as I will show later) are much
slower to react to new information than market prices, due to limited retail investor attention. It
is also possible that fund investors wait under the deal is closed (usually 3 to 6 months after
announcement) before reacting, or learn about the deal from intermittent fund disclosures. I
compare flows prior to and after the effective date of the ownership change in the context of a
regression in Table 12.
Another method of analyzing the effect of ownership changes on fund flows is by using a
multi-variable panel regression to control for an array of fund and parent company
characteristics. I regress monthly Fund flows (%) on event window dummy variables (PREANN
In Column 1 of Table 12, which includes the entire sample of events, the outflows before
and after the effective date are of similar magnitudes and are both statistically significant at the
5%-level. However, as we move to the more restrictive sample of events in Columns 2 and 3, the
outflows prior to the effective date are smaller and no longer significant while those after the
effective date are larger and statistically significant. In the sample of public non-asset
management firms (Column 3), the coefficient on the post-effective date dummy variable,
POSTANN_POSTEFF, is -0.735%, which is more than three times as large as the coefficient on
the post-announcement, pre-effective date dummy variable. In summary, Table 12 suggests that
29
there are some investors who start to withdraw assets prior to the effective date but that the effect
intensifies after the closing date of the merger or acquisition.
Another concern for our study is that there is a large concentration of events in 1999 and
2000, which coincided with the creation and bursting of the dot-com bubble. Approximately one-
quarter of all events happen during this period of high volatility and unusual phenomena in the
financial markets. In Panel A of Table 13, I perform a robustness check by dropping all fund-
month observations during that period. The coefficients remain largely the same and are still
statistically significant, although the t-stats are slightly smaller due to fewer events.
An additional problem is that advisor ownership changes are also associated with an
increase in fund closures and manager turnover as the new ownership tweaks its array of offered
funds and the managers of those funds. Investors might react to expected or realized manager
change or announced fund closure by withdrawing money from the fund because they like the
current manager or because they don’t want to wait until the fund closes and their assets are
merged into a different fund. I test this explanation by generating Fund closure, a dummy
variable that equals one in the six months prior to a fund closure (and zero otherwise), and
Manager change, a dummy variable that equals one in the six months prior to and after a
manager change (and zero otherwise),
I regress monthly Fund flows on the standard event window dummy variables, Fund
closure, and the other standard controls from Table 5, and report the results in Panel B of Table
13. While the coefficients on POSTANN are slightly smaller than those in Table 5, the outflows
after the announcement date are still statistically significant. In Panel C, I repeat the same test as
in Panel B but include the Manager change dummy variable. Once again, the coefficients on the
variable of interest, POSTANN, are unchanged and remain statistically significant. In another
30
robustness check, I drop the funds with Manager change or Fund closure equal to one and find
similar results.
Another possible explanation is that advisor ownership changes might be associated with
lower future returns. During the ownership transition period, fund managers might be expending
effort on ensuring a smooth transition and putting in less effort on the actual management of the
fund leading to lower returns. Investors might be removing their money from the fund to avoid
these anticipated lower returns. I test this theory in Panel D of Table 13 by regressing fund
returns on the standard event window dummy variables and standard set of controls, and report
the results in Table 10. There is no evidence of return underperformance in the twelve months
following a management company ownership change.
7. Conclusion
Investors choose portfolio managers based not only on forecasts of future performance,
but also on factors such as trust and reliability (the ability to “sleep at night”) that are established
over long periods of interaction. In this paper, I examine how investors react when these
relationships are potentially broken due to mergers and acquisitions involving the fund’s
investment adviser. In spite of the fact that I find no detrimental effect on performance in the
wake of ownership changes, fund flows do deteriorate, only weakly after announcement but
more strongly after the change becomes effective. Funds suffer declines in flow equal to
approximately 7% of their assets in the year after announcements of ownership changes, which is
a significant economic cost for the new owners of the investment adviser.
There are several possible explanations for these findings. The results are driven by an
increase in redemption (outflows) rather than decrease in new purchases (inflows). Additional
31
tests suggest that familiarity and trust play a significant role. Retail investors’ reaction to fund
adviser ownership changes is stronger than that of institutional investors, which is consistent
with the story that trust is more important for less sophisticated investors in making investment
decisions. Investors in high-expense funds also have stronger outflows. This fits the story of
some investors willing to pay a premium (through higher expenses) for investing with a
trustworthy adviser, and then pulling their money out when the adviser is acquired by another
firm. Overall, the paper highlights the important role of intangible qualities and relationships
when individuals make investment decisions.
32
Appendix A
Appendix A includes 15 examples of mergers/acquisitions from the sample of 185 events
that make up this study. Panel A includes 5 events from mergers not involving public parent
companies. Panel B includes 5 events from mergers that involve acquisition of public parents
that are primarily asset managers. Finally, Panel C includes 5 events from mergers that involve
acquisitions of public parents that are primarily not asset managers. Within each panel, the
events are listed in chronological order (by announcement date). For each event, the appendix
includes the name of the acquiror and target, firm types (securities, insurance, or bank), whether
they are public or not and the country where they are listed, whether they are an asset manager,
and the name of the main fund family owned by each acquiror and target. I also list the
announcement date and effective date of each acquisition. The source for all data is the SDC
Platinum M&A database. The examples are chosen to illustrate the different types of mergers
that make up the sample.
Panel A: Mergers not involving acquisitions of public parent companies Acq: Franklin Resources Securities Public (U.S.) Asset Manager Franklin Templeton
Announced: 06/25/1996
Effective: 11/01/1996
Tgt: Heine Securities Securities Private (U.S.) Asset Manager Mutual Series
Acq: Allianz Insurance Public (Germany) Non Asset Mgr. PIMCO
Announced: 10/18/2000
Effective: 01/31/2001
Tgt: Nicholas-Applegate Securities Private (U.S.) Asset Manager Nicholas Applegate
Acq: Legg Mason Securities Public (U.S.) Asset Manager Legg Mason
Announced: 06/24/2005
Effective: 12/01/2005
Tgt: Smith Barney A.M. Securities subsidiary unit Asset Manager Smith Barney
Acq: Susquehanna Banc. Bank Public (U.S.) Non Asset Mgr. None
Acq: Guggenheim Ptnrs Securities Private (U.S.) Non Asset Mgr. None
Announced: 01/02/2008
Effective: 08/02/2010
Tgt: Security Benefit Insurance Private (U.S.) Non Asset Mgr. SGI (et al.) Panel B: Mergers involving acquisitions of public parent asset managers Acq: Reliastar Insurance Public (U.S.) Non Asset Mgr. Northstar
Announced: 07/22/1999
Effective: 10/29/1999
Tgt: Pilgrim Capital Securities Public (U.S.) Asset Manager Pilgrim America
Acq: UniCredit Bank Public (Italy) Non Asset Mgr. None
Announced: 05/15/2000
Effective: 10/25/2000
Tgt: Pioneer Group Securities Public (U.S.) Asset Manager Pioneer Acq: CDC Securities Private (France) Non Asset Mgr. CDC MPT+
Announced: 06/16/2000
Effective: 10/30/2000
Tgt: Nvest Securities Public (U.S.) Asset Manager Loomis Sayles (et al.)
Acq: Old Mutual Insurance Public (U.K.) Non Asset Mgr. None
Announced: 06/19/2000
Effective: 10/05/2000
Tgt: United Asset Mgrs Securities Public (U.S.) Asset Manager UAM (et al.)
Acq: Lehman Brothers Securities Public (U.S.) Non Asset Mgr. None
Announced: 07/22/2003
Effective: 10/31/2003
Tgt: Neuberger Berman Securities Public (U.S.) Asset Manager Neuberger Berman
Panel C: Mergers involving acquisitions of public parent non-asset managers Acq: U.S. Bancorp Bank Public (U.S.) Non Asset Mgr. First American
Announced: 12/15/1997
Effective: 05/01/1998
Tgt: Piper Jaffray Securities Public (U.S.) Non Asset Mgr. Piper
Acq: NationsBank Bank Public (U.S.) Non Asset Mgr. Nations
Announced: 04/13/1998
Effective: 09/30/1998
Tgt: BankAmerica Bank Public (U.S.) Non Asset Mgr. Pacific Horizon (et al.)
34
Acq: Manulife Financial Insurance Public (U.S. ADR) Non Asset Mgr. None
Announced: 09/29/2003
Effective: 04/29/2004
Tgt: John Hancock Insurance Public (U.S.) Non Asset Mgr. John Hancock
Acq: TD Bank Group Bank Public (Canada) Non Asset Mgr. TD
Announced: 08/26/2004
Effective: 03/01/2005
Banknorth Bank Public (U.S.) Non Asset Mgr. Banknorth
Acq: M&T Bank Bank Public (U.S.) Non Asset Mgr. MTB
Announced: 11/01/2010
Effective: 05/17/2011
Tgt: Wilmington Trust Bank Public (U.S.) Non Asset Mgr. WT (et al.)
35
Appendix B
Appendix B describes the matching procedure used to generate match-adjusted flows in
Section 3 of the paper.
For each fund-month observation that requires a matched sample, I take the set of all
funds in the same month and in the same fund style (e.g., small value, large blend, etc.). If the
match is for events including only public parent mergers, I also exclude non-public run funds
from the matched sample. If the match is for events including only public non-asset management
firms, I only include funds owned by such firms in the matched sample.
Once I have the matched sample, I calculate weights that depend on how close each
matched fund is to the fund that I am matching. Closeness is measured by past returns, size, and
age. For past returns, if my observation has one year of returns available then I match on past
year’s returns, if it has three years of returns, I match on past three year returns, and if it has five
years of returns, I match on past five year returns. I calculate the difference in returns between
each matched fund and the original fund, then standardize these differences by the standard
deviation of differences across the matched sample, then square this standardized quantity. For
size, I take the differences in log assets, then standardize these differences by the standard
deviation of differences across the matched sample, then square this standardized quantity.
Finally, for age, I take the differences in log age, then standardize these differences by the
standard deviation of differences across the matched sample, then square this standardized
quantity.
Finally, I add the three squared differences in past returns, size, and age, and set weight
equal to one divided by the sum of squared differences. The matched flows are calculated as the
weighted average of flows across the matched sample using the weights described above.
36
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38
Table 1: Summary statistics for ownership change announcements of mutual fund management firms Table 1 displays summary statistics for announced ownership changes for the sample period from July 1995 to December 2011. For each year, Panel A reports the total number of events during that year, the number of actively-managed domestic equity mutual funds involved in those events, and the total assets under management of these funds. Panel B reports the percent of events involving different pairs of acquirer types and target types, where the three types are banks (SIC 6000-6199), securities firms (SIC 6200-6299), and insurance companies (SIC 6300-6499). Holding companies (SIC 6700-6799) are classified based on SDC Platinum industry classifications. Columns (1) through (3) of Panel A (and columns (1) and (2) of Panel B) display data for all announced changes in ownership, except for those involving initial public offerings or management buyouts. Columns (4) through (6) of Panel A (and columns (3) and (4) of Panel B) show summary statistics for a subset of events, where the parent company is a publicly traded company that is in CRSP, and where this public parent is to be acquired by another company. Columns (7) through (9) (and columns (5) and (6) of Panel B) show summary statistics for the subset of events in which the public parent company is not primarily an asset management firm (less than 10% of its revenues derives from mutual fund fees). The last line of Panel A shows the sum total of all events for 16.5 years of activity.
Panel A: Merger summary statistics by year All parent company changes All public parent changes All non-AM public parent chgs
Events # of funds AUM ($bil) Events # of funds AUM ($bil) Events # of funds AUM ($bil)
Panel B: Merger summary statistics by merger type All parent company changes All public parent changes All non-AM public parent chgs
Merger Type % Merger Type % Merger Type % (1) (2) (3) (4) (5) (6)
Securities <-> Securities 32.4% Bank <-> Bank 69.2% Bank <-> Bank 77.1% Bank <-> Bank 30.8% Securities <-> Securities 10.3% Securities <-> Securities 8.6% Bank <-> Securities 17.8% Insurance <-> Securities 7.7% Bank <-> Securities 5.7% Insurance <-> Securities 13.0% Bank <-> Securities 6.4% Insurance <-> Insurance 4.3% Insurance <-> Insurance 3.8% Insurance <-> Insurance 3.8% All Other Types 4.3% All Other Types 2.2% All Other Types 2.6%
39
Table 2: Summary statistics for fund-level and parent-level variables Table 2 presents summary statistics for the main fund-level and parent company-level variables used in this study. First, I tabulate cross-sectional statistics by month, and then take the time-series average of each statistic across the 216 months of the sample period from January 1995 through December 2012. Fund flows ($) is the asset flows (in millions of $) into a fund for a particular month, which is defined as fund assets at the end of the month minus the product of fund assets at the beginning of the month and one plus the fund’s monthly return. Fund flows (%) is just Fund flows ($) divided by the fund’s assets at the start of the month, then winsorized for each month at the 1% and 99% levels. Inflows is the dollar amount of inflows (purchases of fund shares) for a particular month, divided by the fund’s assets at the start of the month. Outflows is the dollar mount of outflows (sales of fund shares) for a particular month, divided by the fund’s assets at the start of the month. Fund AUM is the fund’s assets (in millions of dollars) at the start of the month, and Log fund AUM is the natural logarithm of Fund AUM. Family AUM is the assets of the entire fund family at the start of the month, and Log family AUM is the natural logarithm of Family AUM. Fund age is equal to one plus the number of years since the fund began operations, while Log fund age is the natural logarithm of Fund age. Expense ratio is the fund’s expense ratio, while Log expense ratio is the natural logarithm of Expense ratio. Private firm is a dummy variable that equals one when the parent of the investment management company is not publicly traded, not a mutual insurance company and not a non-profit organization, and zero otherwise. Public non-a.m. firm is a dummy variable that equals one when the parent of the investment management company is public, has data in COMPUSTAT/CRSP, and earns less than 10% of its annual revenues from mutual fund fees, and zero otherwise. Log parent marketcap is only available for public parent companies that are also in CRSP, and equals the natural logarithm of their market capitalization at the start of the month. Stock returns is also only available for public parent companies that are also in CRSP, and equals their average monthly stock returns over the prior twelve months. Mutual fund revenues is also only available for public parent companies that are also in COMPUSTAT and equals the total annual expenses collected by all funds in the family (product of annual expense ratios and assets under management) divided by the parent company’s annual COMPUSTAT revenues.
Table 3: Determinants of announced changes in ownership of mutual fund management companies Table 3 presents estimated coefficients of PROBIT regressions of ownership change indicator variables on fund and parent company characteristics. The dependent variable in columns (1) and (2) is All Changes which is set to one for a fund-month observation if there was an announcement of a change in the ownership of the fund’s management firm in a particular month, and zero otherwise. For columns (3) and (4), the sample is restricted to funds whose management firms have a publicly-traded parent with data in CRSP, while for columns (5) and (6), the sample includes only funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1 for definition). Style-adjusted returns (prior year) is the average style-adjusted return for the prior twelve months. Fund flows (prior6mths) is the average value of the Fund flows (%) variable over the past six months. All other variables are defined in Table 2. All regressions include time dummies and fund style controls. Columns (2), (4), and (6) also include prior return decile dummies for each of the previous five years. The sample period for all regressions is from June 1995 to December 2011. T-statistics, using standard errors clustered at the management company level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Marginal effects are shown under the t-statistics.
Dep Var: Events All Changes Public Parent Public/Non AM
Time dummies YES YES YES YES YES YES Return decile dummies NO YES NO YES NO YES Fund style dummies YES YES YES YES YES YES Log-likelihood -5398.9 -5468.3 -2276.3 -2298.7 -1756.9 -1760.0
41
Table 4: Fund flows around changes in parent companies of fund management firms Table 4 presents average values of mutual fund flows around event windows (parent company changes of fund management firms). Panel A shows results for average (unadjusted) Fund flows (%), while Panel B displays average Fund flows (%) after adjusting for a matched sample of funds with the same style and similar age, size, and prior performance characteristics. Panel C shows fixed-effects regressions across the event window using match-adjusted Fund flows (%). PREANN is a dummy variable that equals one for all fund-month observations from months t–6 to t–1, where t is the announcement date of the event, and zero otherwise. POSTANN is a dummy variable that equals one for all fund-month observations from month t until t+12, where t is the announcement date of the event. Column (1) in each panel includes all ownership changes, while columns (2) and (3) include subsets of events (see Table 1 for definitions). T-statistics, using standard errors clustered at the event level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Monthly fund flows (unadjusted) around event windows
Changes: All Public Public
Changes Parent Non AM
Timing around event (1) (2) (3) PREANN (dummy) 0.038%
Table 5: Regressions of fund flows on event window indicator variables Table 5 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fund-level and parent-level control variables. In each specification, the dependent variable is Fund Flows (%) and the variables of interest are PREANN, indicating observations in the six months prior to an announced ownership change and POSTANN, indicating observations on or in the twelve months after the event announcement date (see Table 4 for exact definitions). All controls are defined in Table 2. Columns (1) and (2) include all funds, while column (3) and (4) restrict the sample to funds (and events) with public management companies, and columns (5) and (6) only include funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1). All specifications include time dummies and prior return decile dummies for each of the previous five years. Columns (2), (4), and (6) also include fund style dummies, as well as additional fund and parent company controls. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered by fund family, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Dep.Var: Monthly Fund Flows (%)
Regression: OLS OLS OLS OLS OLS OLS Changes: All All Public Parent Public Parent Pub/Non AM Pub/Non AM
Observations 344014 335118 163997 155647 119522 114524 Return decile dummies YES YES YES YES YES YES Time dummies YES YES YES YES YES YES Fund style dummies NO YES NO YES NO YES
43
Table 6: Regressions of fund inflows and outflows on event window indicator variables Table 6 presents estimated coefficients from OLS regressions of fund inflows and outflows on event window indicators and various fund-level and parent-level control variables. In columns (1), (3), and (5), the dependent variable is Inflows (%), while in columns (2), (4), and (6), the dependent variable is Outflows (%). In all specifications, the variables of interest are PREANN, indicating observations in the six months prior to an announced ownership change and POSTANN, indicating observations on or in the twelve months after the event announcement date (see Table 4 for exact definitions). All controls are defined in Table 2. Columns (1) and (2) include all funds, while column (3) and (4) restrict the sample to funds (and events) with public management companies, and columns (5) and (6) only include funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1). All specifications include time dummies and prior return decile dummies for each of the previous five years. Columns (2), (4), and (6) also include fund style dummies, as well as additional fund and parent company controls. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered by fund family, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Dep.Var: Monthly Fund Inflows and Outflows (%)
Regression: OLS OLS OLS OLS OLS OLS Changes: All All Public Parent Public Parent Pub/Non AM Pub/Non AM
Table 7: Regressions of fund flows on event window indicator variables for retail vs. institutional classes Table 7 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fund-level and parent-level control variables. Unlike other regressions in this paper, the observations used in this table are at the fund class-month level, and Log fund AUM, Log fund age (years), and Log expense ratio are also calculated separately for each fund class. In columns (1), (3), (5), the sample consists of retail classes of mutual funds, while columns (2), (4), and (6), the sample includes classes open only to institutions. PREANN and POSTANN indicate timing around event windows (see Table 4 for precise definitions) and all other controls are defined in Table 2. Columns (1) and (2) include all funds, while columns (3) and (4) restrict the sample to funds (and events) with public management companies, and columns (5) and (6) only include funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1 for definition). Observations are weighted by the class’s percentage (using AUM) of the total fund’s AUM in that month so that each fund has the same weight. All specifications include time dummies, fund style dummies, and prior return decile dummies for each of the previous five years. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered by fund family, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Dep Var: Monthly Fund Flows (%) Changes: All Changes Public Parent Public/Non A.M.
Table 8: Regressions of fund flows on event window indicators for high vs. low expense funds Table 8 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fund-level and parent-level control variables. In columns (1), (3), (5), the sample is restricted to funds with above-median expense ratios (for the month), while columns (2), (4), and (6) only contain funds with below-median expense ratios (for the month). PREANN and POSTANN indicate timing around event windows, and all other controls are defined in Table 2. Columns (1) and (2) include all funds, while columns (3) and (4) restrict the sample to funds (and events) with public management companies, and in columns (5) and (6) only includes funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1 for definition). All specifications include time dummies, fund style dummies, and prior return decile dummies for each of the previous five years. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered at the management company level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Dep Var: Monthly Fund Flows (%) Changes: All Changes Public Parent Public/Non A.M.
Investor Class: High Exp. Low Exp. High Exp. Low Exp. High Exp. Low Exp. Predictor Variables (1) (2) (3) (4) (5) (6) PREANN (dummy) -0.028%
Table 9: Merger purposes and acquiror characteristics – summary statistics Table 9 provides summary statistics on the main purpose(s) behind the financial mergers that make up the events in this study, the types and public status of acquirors, and various additional acquiror characteristics. Panel A shows the proportion of events for which a purpose is not available and the proportion of mergers for which it is provided. It then shows a breakdown of the latter subset by listing the percentage of mergers with each type of purpose (out of the total number of mergers with a purpose provided). Some mergers have multiple purposes so the percentages do not add up to 100%. Panel B shows the proportion of mergers where the acquiror (or parent company of the acquiror) is a bank (SIC 6000-6199), an insurance company (SIC 6300-6499), and a securities firm (SIC 6200-6299). Panel B also shows the proportion of events where the acquiror is private versus public, and whether public acquirors are public and in CRSP, public and listed over the counter in the U.S., or public but listed only in foreign markets. Finally, Panel C shows summary statistics for acquiror characteristics. Acquiror adjusted stock returns, 12 months (%) is the acquiror’s average monthly stock returns over the 12 months before the announcement date, relative to other firms in the same sub-industry (Bank, Insurance, Securities). Acquiror adjusted stock returns, 36 months (%) is calculated in the same way but averaged over the prior 36 months. Both variables are only calculated for public acquirors that are in CRSP and have at least 12 (or 36) months of historical data. Acquiror mutual fund revenues (%) is the total annual expenses collected by all funds in the acquiror’s mutual fund family (product of annual expense ratios and assets under management) divided by the acquiror’s annual COMPUSTAT revenues. Acquiror adjusted family returns (avg.), 12 mths is the average style-adjusted monthly returns of funds in the acquiror’s mutual fund family over the 12 months prior to the announcement date. Acquiror adjusted family returns (avg.), 36 mths is calculated in the same way but averaged over the prior 36 months. The last three variables are only available for acquirors that manage their own mutual fund family prior to the acquisition.
Panel A: Merger purpose Merger purpose not available 56.1% Merger purpose available 43.9% Strengthen Operations (STR) 35.4% Synergies (SYN) 30.5% Expand Presence into New Markets (EXP) 28.0% Expand Presence in Primary Market (EPM) 18.3% Offer New Products and Services (PRD) 15.9% Acquire Competitors Assets (CMP) 11.0% General Strategy for Sound Investment (GEN) 9.8% Expand into New Geographic Areas (EPG) 6.1% Expand Presence in Secondary Market (ESM) 4.9% Increase Shareholder Value (ISV) 4.9% Other (OTH) 3.7% Concentrate on Core Businesses (COR) 1.2%
Panel B: Acquiror firm type and public status Acquiror firm type
Bank 49.7% Insurance 17.6% Securities 32.6%
Acquiror public status Private 13.9%
Public 86.1% Public - CRSP 85.1% Public - OTC 1.2% Public - Foreign 13.7%
Table 10: Effect of acquiror and merger characteristics on post-announcement fund flows Table 10 presents estimated coefficients from OLS regressions of match-adjusted (on style, fund age, size, and prior performance, as in Panels B and C of Table 4) fund flows on acquiror characteristics and merger purpose variables, where the sample consists of all fund-month observations in the twelve months after an announcement (POSTANN = 1). Independent variables in Panel A include acquiror types, merger purposes, and public acquiror stock returns prior to the announcement date. Independent variables in Panel B include acquiror types and acquiror family characteristics such as family size and family fund performance. Acquiror adj. same style returns, 12m (%) is the average style-adjusted monthly returns of funds in the acquiror’s mutual fund family that also use the same style as the fund (whose flows are being measured), over the 12 months prior to the announcement date. Acquiror adj. same style returns, 36m (%) is calculated in the same way but averaged over the prior 36 months. All other variables are defined in Table 9. T-statistics, using standard errors clustered at the event level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Effect of acquiror types, merger purposes, and public acquiror past stock performance Dep.Var: Monthly Fund Flows (%) - Match Adjusted
Table 11: Regressions of fund flows on event window indicator with controls for distribution networks Table 11 presents estimated coefficients from OLS regressions of fund flows on event window indicators and dummy variables for the main type of distribution network used by each fund. The seven types of distribution network are Bank Proprietary, Direct, Institutional, Insurance, Non-Proprietary, Proprietary, and Other. Bank Proprietary, Insurance, and Proprietary distribution channels use brokers affiliated with the management company (bank, insurance, and securities respectively) to sell shares to retail investors. Non-Proprietary distribution channels use brokers unaffiliated with the management company to sell shares to retail investors. Funds use Direct distribution channels to sell directly to retail investors, and Institutional distribution channels to sell to institutional (high net worth or pension plan) clients. Other mostly consists of fund sales to investors who are members of a group or organization (often non-profit, fraternal, or religious) that owns or runs the fund’s management company. Panel A shows estimated coefficients on dummies for each type of distribution channel (where Other is the omitted class of distribution network) along with the standard fund and parent controls used in Table 5, time dummies, fund style dummies, and prior return decile dummies for each of the previous five years. Panel B runs the same regressions as in Panel A but excludes event window observations for funds that have a change in distribution network in the two years after the event announcement. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered at the management company level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Main regression with controls for main distribution channel Dep.Var: Monthly Fund Flows (%)
Regression: OLS OLS OLS Changes: All Public Parent Pub/Non AM
Observations 329916 154353 113400 Fund Controls YES YES YES Return decile dummies YES YES YES Time dummies YES YES YES Fund style dummies YES YES YES
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Table 12: Regressions of fund flows on event window indicator variables – pre- and post-effective date Table 12 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fund-level and parent-level control variables. In each specification, the dependent variable is Fund Flows (%) and the variables of interest include PREANN (defined in Table 4), POSTANN_PREEFF, a dummy variable that equals one for all fund-month observations from announcement month t until T–1, where T is the effective date of the ownership change. Finally, POSTANN_POSTEFF is a dummy variable that equals one for all fund-month observations from month T until t+12. All controls are defined in Table 2. Column (1) includes all funds, while column (2) restricts the sample to funds (and events) with public management companies, and column (3) only include funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1 for definition). All specifications include time dummies, fund style dummies, and prior return decile dummies for each of the previous five years. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered at the management company level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Table 13: Other alternative Explanations and robustness checks Table 13 present estimated coefficients from OLS regressions of fund flows (and fund returns in Panel D) on event window indicators, and various controls. In Panel A, fund-month observations from January 1999 through December 2000 are excluded to test whether the paper’s results can be explained by unusual phenomena surrounding the growth and bursting of the dot-com bubble. In Panel B, fund flows are regressed on Fund closure, a dummy variable that equals one in the six months prior to a fund closure, and zero otherwise. In Panel C, fund flows are regressed on Manager change, a dummy variable which equals one in the five months before, the month of, and six months after a manager change, and zero otherwise. In Panel D, the dependent variable is fund returns instead of fund flows. All specifications include standard fund and parent controls used in Table 5, time dummies, fund style dummies, and prior return decile dummies for each of the previous five years. The sample period for regressions in Panels B, C, and D is from January 1995 to December 2012. The same sample period, except for 1999 and 2000, is used in Panel A. T-statistics, using standard errors clustered at the management company level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Excluding observations from dot-com bubble period (1999-2000) Dep.Var: Monthly Fund Flows (%)
Regression: OLS OLS OLS Changes: All Public Parent Pub/Non AM
Observations 335118 155647 114524 Fund Controls YES YES YES Return decile dummies YES YES YES Time dummies YES YES YES Fund style dummies YES YES YES
Panel D: Effect of event announcement on fund returns
Dep.Var: Fund Net Returns (%) Regression: OLS OLS OLS
Changes: All Public Parent Pub/Non AM Predictor Variables (1) (2) (3) PREANN (dummy) 0.020%
0.116%
0.053%
[0.26]
[1.02]
[0.60]
POSTANN (dummy) 0.019%
-0.028%
-0.101%
[0.37]
[0.34]
[1.51]
Observations 335118 155647 114524 Fund Controls YES YES YES Return decile dummies YES YES YES Time dummies YES YES YES Fund style dummies YES YES YES
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Figure 1 Figure 1 depicts (in blue) average match-adjusted flows for all events starting six months prior to the announcement date and ending twelve months after the announcement date. Upper and lower confidence boundaries (for a 5% level of statistical significance) are also shown. Note that these are not cumulative flows but average flows in each event window month.
Monthly match-adjusted fund flows around events All parent company changes
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Figure 2 Figure 2 depicts (in blue) average match-adjusted flows for ownership changes involving non asset-management public parent companies starting six months prior to the announcement date and ending twelve months after the announcement date. Upper and lower confidence boundaries (for a 5% level of statistical significance) are also shown. Note that these are not cumulative flows but average flows in each event window month.