“Whom do you trust?”
Investor-advisor relationships and mutual fund flows
Leonard Kostovetsky1
Simon School, University of Rochester
leonard.kostovetsky@simon.rochester.edu
Abstract
I investigate 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 an average 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 redemptions by existing investors. Prior legal controversies
at the acquiring firm exacerbate outflows from the target firm’s mutual funds. Retail investors
and investors in funds with higher expense ratios are more responsive to ownership changes,
consistent with the notion that such investors place a higher 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.
1
The author can be reached at the following address:
Simon Graduate School of Business, University of Rochester, Rochester, NY 14627.
Phone: (585) 275-3956, Email: leonard.kostovetsky@simon.rochester.edu
1. Introduction
The importance of trust in delegated asset management has not received much attention
in the academic literature. However, a 2013 CFA Institute/Edelman survey found that 75% of
investors believe the most important attribute for choosing an investment manager is trust (or
ethics), while only 17% believe it is the ability to generate high returns.2 Consistent with this
survey evidence, 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.
This paper attempts to 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 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
2
Description and results of the CFA Institute & Edelman study can be found at:
http://www.cfainstitute.org/learning/future/getinvolved/Pages/investor_trust_study.aspx
1 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.
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 performancedriven 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 an acquisition occurred in 2001 when Deutsche Bank announced its purchase of
Zurich Scudder, the manager of the Scudder Funds, from Zurich Financial (Appendix A provides
other examples). 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 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 past
investment performance at the entire management company.
2 In order to minimize endogeneity concerns, I therefore further restrict 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 (defined as less than 10%) from its mutual fund
operations. Asset management is a small part of these firms’ financial activities so the motivation
for the ownership change is unlikely to be anything related to mutual fund operations or
performance. Media articles covering these events usually don’t mention mutual funds at all,
instead discussing geographic expansion and returns to scale in the primary business such as
commercial/investment banking or insurance as the main reasons for the transaction. I find
declines in fund flows for this subsample of events that is of similar magnitude to the entire
sample.
Still, the decline in flows is only circumstantial evidence that investors are redeeming
their shares due to a lack of trust in the new owner. Therefore, I attempt to explicitly connect the
overall decline in flows to trust problems, as opposed to lower expected future performance or
some other reasons, by collecting data on prior legal controversies at acquiring and target firms. I
find that the decline in mutual fund flows around the announcement date is more than twice as
large for firms whose acquirers had legal issues in the previous three years. This result shows
that investors at funds managed by acquired firms are looking at the identity and ethical
reputation of the acquiring firm and responding more strongly to any deficiencies.
Next, I test a number of different explanations for the paper’s main result. My hypothesis
is that some investors attach significant value to their relationship with the fund’s management
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
3 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 is also more likely to be important for less
sophisticated investors who don’t have the skills or resources to monitor the fund’s management.
I separately look at the effect of ownership changes on retail class flows and institutional class
flows to examine whether investor sophistication is an important factor in the main effect. I find
that retail class investors drive the overall decline in flows, and that investors in institutional
classes do not react adversely to changes in ownership. This result is also consistent with my
finding that flows slowly react to announcement changes. Limited attention is well documented
among retail investors (Barber and Odean, 2008), so many such investors might not immediately
find out about the ownership change. After the closing date, when the news of the ownership
change appears in the fund prospectus and other disclosure documents, it is more likely to filter
through to those retail investors.
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 result is consistent
with the model of Gennaioli, Shleifer, and Vishny (2012), where a component of the expense
ratio comes from a premium that investors are willing to pay to their trusted fund advisors.
Another possibility is that ownership changes of management companies may also
coincide with changes in distribution channels. Del Guercio, Reuter, and Tkac (2010) document
4 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 examine how the decline in flows around announcement of an ownership change varies
by the fund’s primary distribution channel type. Post-announcement flows decline, to various
degrees, for all types of distribution channels. The difference between funds with affiliated
channels (i.e., where the distributors are affiliated with the management company) and
unaffiliated channels is not significant, and the outflows are significant even for funds that did
not change their primary type of distribution channel around the event window. I also control for
distribution channel in a panel regression, and find that my results are robust to these controls.
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 past or future 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
5 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 don’t find 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)
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. Brown,
Goetzmann, Liang, and Schwarz (2012) use a sample of due diligence reports to study how
investors react to hedge fund operational risk. 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 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 (2013) highlight the
importance of agency issues at advisory firms. Massa and Rehman (2008) show that information
6 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 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. Two important exceptions are
Allen and Parwada (2009) and Park (2013), who do look at management company M&A events.
Allen and Parwada (2006) examine 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 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. Park (2013) uses a two-sided matching model to explain
mutual fund company mergers and then examine their consequences, but the events are identified
using the change in mutual fund advisor rather than parent ownership changes, and postannouncement performance is measured using asset growth at the combined firm rather than
fund flows at the target firm. These methodological differences between my paper and Park
(2013) originate from two different research questions and lead to different results.
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
7 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) database3 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. Evidence on legal controversies is
drawn from the SEC litigation releases website, item 77E of NSAR filings on EDGAR, and
Factiva media reports.
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,
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
3
The website for this service is: http://www.adviserinfo.sec.gov/IAPD/Content/Search/iapd_Search.aspx
8 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.
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
9 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 2011.4 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
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
4
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.
10 (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 minimize 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 family5 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
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
5
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.
11 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 ($) = Assets (end of t) – Assets (start of t) × (1 + Net Returns (over month t))
Since this quantity is usually proportional to fund size, I standardize it by dividing by assets:
(Eq.2) Fund flows (%) = Fund flows ($)/Assets (start of t)
12 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.
Panel A of 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 Panel A of 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 the paper looks at shocks to the parent company of the fund’s adviser, I also collect
parent company characteristics. 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
13 companies have average monthly returns over the prior year of just under 1%, and derive 8% of
revenues from mutual fund fees.
Panel B of Table 2 shows average values of several key fund characteristics for funds
whose management companies underwent an ownership change (in Column 1). For each such
fund, I then calculate a comparison value of each fund variable, which is the average value of the
variable for all other funds of the same style at the same date. Column 2 shows the average
comparison value across funds, while Column 3 provides the average difference between event
fund values and comparison fund values. The main takeaway from this panel is that there are no
significant differences in past performance, fund size, expense ratio, and fund age between funds
whose management firms were acquired and comparison firms (firms in same style at same
date). This result does not prove that the events are exogenous, but it does provide some comfort
that the control and treatment sets have very similar pre-announcement observable
characteristics.
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.
Panel A of 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
14 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
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.
15 Legal Controversies: I use three different data sources to collect a comprehensive dataset
on legal controversies at mutual fund management companies. My first source is the SEC’s
litigation website6, which lists all litigation releases by the SEC. I search for keywords such as
“asset management”, “mutual fund(s)”, and close variants, and manually read through all hits to
find any litigation disclosures related to mutual fund management firms. I record the date of the
legal problem as the date of the SEC release, and I only use the first release for any ongoing
litigation to avoid duplication.
My second data source is information provided by mutual funds on item 77E on NSAR
filings. According to the instructions for the NSAR form, mutual funds must use item 77E to
“briefly describe any material legal proceedings, other than routine litigation incidental to the
business, to which the registrant or any of its subsidiaries has become a party or of which any of
their property has become the subject.” For each legal issue, I record the date of the first NSAR
filing that mentions that particular issue.
My third data source is Factiva, a database of media articles. I search Factiva articles for
one or more of the following words: “fraud, scandal, lawsuit, investigation, enforcement, violate,
violation, settlement” and simultaneous mentions of “asset management” or “mutual fund(s).” I
manually read through all hits to find any issues, and record the date that the article was
published, again only including the first mention of a particular issue. Since my three data
sources usually overlap in terms of references to the same legal controversy, I only use the first
time an issue is mentioned across all three sources. Finally, I only include issues arising from
inappropriate or illegal behavior by individuals or groups in charge of asset management
functions (as well as senior management), not brokers, distributors, or other unrelated
professions. Some examples of legal controversies are described in Appendix B.
6
SEC’s litigation releases can be found at: http://www.sec.gov/litigation/litreleases.shtml
16 Other Variables: I collect a number of additional variables in order to test alternative
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.7 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
I start the analysis by calculating average monthly fund flows before and after
announcements of management company ownership changes. In order to measure the effect of
these announcements, I define two event dummy variables, PREANN and POSTANN, for the
timing of each observation around the event window. PREANN is set to one for all fund-month
observations in the six months prior to the announcement date of an ownership change of the
fund adviser, and zero otherwise. Fund-month observations with PREANN equal to one are a
logical control group because they tell us how fund flows behave prior to an announced event.
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. This variable defines the treatment group for this study. Fund-month observations
outside the event window have both PREANN and POSTANN equal to zero.
Panel A of Table 3 shows the average value of monthly Fund flows (%) for observations
within event windows. I start with the entire sample of events in Column 1, restrict the sample to
only public parent companies in Column 2 and only public non-asset management parent
7
CRSP also has an institutional dummy variable but it is only available after 2000, which is why I use the
Morningstar variable.
17 companies in Column 3. Prior to the announcement date (PREANN=1), flows are not statistically
different from zero. After the event announcement date (POSTANN=1), flows are negative and
statistically different from zero. In the sample of all events, the average value of Fund flows (%)
in the post-announcement window is -0.548% (about 6.6% on an annualized basis) with a tstatistic 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% (about 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. The number of observations in each specification is greater than the
number of funds undergoing events because each fund-month observations is included
separately, rather than combined across months. I cluster standard errors by event to take into
account any correlation in error terms across months and funds for the same event.
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. Therefore, I construct a matched sample for
each event-window fund-month observation, and use it to calculate 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 C provides a more detailed explanation of the matching algorithm.
18 Panel B of Table 3 presents average values of monthly match-adjusted Fund flows (%)
for observations in each event window. As in Panel A, fund 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-announcement and post-announcement periods due to the match
adjustment but the difference between periods 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.
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, I regress matchadjusted fund flows on POSTANN and also include fixed effects for each fund. Panel C of Table
3 presents estimated coefficients on the difference-in-difference estimator, POSTANN, in this
regression. The coefficients range from -0.489% (about 6% annualized flows) in the sample of
all events to -0.680% (about 8% annualized flows) in the sample of public non-asset
management merger events, and all coefficients are statistically significant at the 1% level. Thus,
the overall annualized decline in flows is in the neighborhood of 6% to 8%.
There are several ways we can illuminate the economic significance of these events. One
method is to convert these percent flows into actual dollars lost due to the ownership changes.
Across all funds, there are 185 events with $587 billion in pre-announcement assets so 6%
outflows equates to $190 million (($587b/185) × 6%) of outflows per event, for a total of $35
billion lost for all the studied events. Across the subsample of public non-asset management
merger events, there are 70 events with $145 billion in assets so 8% outflows means about $165
million of outflows per event, for a total of $12 billion lost due from this subset of events.
19 Another method of evaluating economic significance is to find another series of events as
a barometer for investor reactions. A natural set of events is the market timing scandals of 20032004, the worst scandals to hit the industry in its history, which produced daily headlines of
newly-implicated fund complexes and over a billion of total fines levied on firms implicated in
any malfeasance. Two papers, Choi and Kahan (2007) and McCabe (2009) study investor
reactions to funds and families implicated in these scandals. They find an average decline in
flows of around 10%-15% in the year after the media reported that a particular fund or firm was
involved, with a 19% decline from funds in which market timing took place, and 8% decline
from funds that were not affected themselves but which were managed by companies that had
other affected funds. I find smaller investor reactions for my sample of events, but they are in the
same ballpark, and my set of ownership changes is not associated with any of the improper
behavior or bad publicity that occurred during the market timing scandals.
It is also useful to look at a graphical representation of Table 3, with monthly matchadjusted fund flows from six months prior to the announcement date to twelve 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.8
The flows in the two figures are not cumulative. The figures are consistent with the regression
results from Table 3, showing 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.
8
The graph for Column 2 of Table 3, which includes all public parent company mergers, looks very similar and is
available upon request.
20 Interestingly, this is not the type of picture we are used to seeing for event studies
measuring market price reaction to events. However, these are investor reactions (predominantly
retail investors, as I show later in the paper) so it is not surprising that fund flows are much
slower to react to new information than market prices. 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 fund disclosures which lag the announcement date of the parent company
ownership change. I compare flows prior to and after the effective date of the ownership change
in a regression in Table 10.
A more comprehensive way to measure 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 the event window dummy variables
(PREANN and POSTANN), fund-level controls, parent-level controls, style dummy variables,
prior return deciles dummy variables, and time dummy variables. Table 4 presents estimated
coefficients from this panel regression for all funds (Column 1 and 2), funds whose advisers are
owned by public parent companies (Column 3 and 4), and fund whose advisers are owned by
non-asset management public parent companies (Column 5 and 6). The findings are broadly
consistent with those in Table 3 and Figures 1 and 2. Fund flows (%) are close to zero prior to
announcement dates, and negative and significant after the announcement dates. Magnitudes of
coefficients are also very similar to those found in Table 3, since the event windows dummy
variables are largely uncorrelated with other fund characteristics.
Table 4 also allows us to examine the effects of other characteristics on fund flows. There
is a negative coefficient on fund size (Log fund AUM), since many large funds close to new
assets because they are unable to trade without a large and costly price impact. The coefficient
21 on family size (Log family AUM) is positive, perhaps because large fund families have more
exposure and have bigger advertising budgets. The coefficient on Fund age is negative as newer
funds attract more flows, including seed money from the fund family itself. Surprisingly, the
coefficient on expense ratios is positive, although it is not significant in Columns 4 and 6.
Expense ratios often include 12b-1 expenses (for advertising and promoting the fund) so these
results do suggest that spending more on marketing works in attracting flows.
There are two parent-level variables that also have explanatory value in predicting fund
flows. Funds with privately held advisers have significantly higher flows: 0.27% per month, or
approximately 3.2% per year. This might be due to the fact that privately held firms are more
likely to focus on asset management while public parent companies are mostly banks or
insurance firms that also offer mutual funds. In addition, Ferris and Yan (2009) find that mutual
funds with public parents underperform and suffer from more agency issues, while Adams,
Mansi, and Nishikawa (2012) find more management changes at public parents. Both of these
papers might explain the lower level of flows at funds managed by public companies. In
addition, past stock returns (of public parent firms) also positively predict flows, consistent with
the findings of Sialm and Tham (2011).
4. Role of trust and alternative explanations
In this section, I investigate the cause for the post-announcement decline in flows shown
in Tables 3 and 4. I start by examining whether the ethical reputation of the new (and prior)
owner of the mutual fund management company makes any difference for how investors react to
the event announcement. Using the dataset on legal proceedings at investment management
companies, I create two dummy variables for each event: ACQLEGAL equals one if the
22 acquiring company had legal problems with their asset management operations in the three years
before the start of the event window, and zero otherwise. TARLEGAL is defined in the same way
for the acquired company. The three-year horizon is chosen because it is the typical length of
litigation, from filing to judgment, in this field. However, the results are similar with longer
horizons.
Table 5 presents estimated coefficients from the same fixed effects regressions from
Panel C of Table 3, but also including interaction terms of the POSTANN dummy with
ACQLEGAL and with TARLEGAL. The coefficients on the non-interacted event dummy variable
are negative and significant so fund flows decline even when there are no legal issues.
Interestingly the coefficients on the interaction term, POSTANN × ACQLEGAL, are also
negative and significant, indicating that investors (at funds of the acquired firm) react stronger
for events where there are legal issues at the acquiring firm. The magnitudes of the coefficients
on the interaction term are actually larger than those on the non-interacted term, so outflows
more than double when there were past legal problems at the acquirer. This result suggests that
investors react negatively when their previous relationship is being severed, but react even
stronger if they worry about the identity and trustworthiness of their new mutual fund
management firm.
I also include interaction terms with TARLEGAL as a placebo test. Any investor who
worried about legal problems at his or her fund’s management firm could have reacted
beforehand by redeeming shares, so the change in flows in reaction to the announcement of an
acquisition should not be affected by prior legal issues at the target firm. Consistent with this
hypothesis, I find that the interaction term between POSTANN × TARLEGAL is not statistically
different from zero.
23 One possible alternative explanation to the trust hypothesis is that investors might be
reluctant to invest new money in the fund as a result of uncertainty arising from the change in
management ownership. The ownership change could also be temporarily accompanied by less
emphasis on marketing, advertising, and distribution, leading to a decline in new fund purchases.
I test this hypothesis by decomposing fund flows into inflows (purchases of shares) and outflows
(sales of shares) and examining whether the aggregate effects are due to a decline in inflows or
an increase in outflows.
In Table 6, inflows (Columns 1, 3, and 5) and outflows (Columns 2, 4, and 6) are
regressed on event window dummy variables (PREANN and POSTANN), fund-level controls,
parent-level controls, style dummy variables, prior return deciles dummy variables, and time
dummy variables. A comparison of columns indicates that the aggregate results are driven by
increasing outflows. For instance, in Column 1, the coefficient changes from 0.181% prior to the
announcement to 0.323% after the announcement, with both coefficients not statistically
different from zero. However, for the corresponding outflows (Column 2), the coefficients rise
from 0.442% prior to the announcement to 1.332% after the announcement. The large increase in
outflows overwhelms the insignificant change in inflows leading to the overall decline in fund
flows seen in Tables 3 and 4. The same results can be seen for the event subsamples in Columns
3 through 6. Changes in inflows are insignificant while outflows increase dramatically, providing
support for the idea that it is the current fund investors that are causing the decline in flows by
redeeming their shares.
While the evidence so far supports the trust hypothesis, another possibility comes from
the fact that the management change sometimes coincides with a change in distribution channel
used by the fund. If the acquiring company has its own affiliated brokers, the previous brokers
24 can advise their clients to withdraw their assets, which might show up in the lower fund flows
that I document in this paper. Using data from Strategic Insight on mutual fund primary
distribution channels, I examine whether the decline in flows around the event window varies by
type of distribution channel, by whether the primary distributor is affiliated with the management
company, and by whether there was a change in distribution channels in the year after the event.
Panel A of Table 7 presents estimated coefficients from fixed effects regressions, similar
to those in Panel C of Table 3. However, it also includes interaction terms of the POSTANN
dummy with various measures of distribution channels. In Column 1, POSTANN is interacted
with AFFILIATED, a dummy variable that equals one if the primary distributor is affiliated with
the management company (i.e., Bank Proprietary, Proprietary, and Insurance). The coefficient on
the interaction term is not significant so there is no statistical difference in outflows between
funds with an affiliated primary distributor and funds with a non-affiliated primary distributor.
In Column 2 of Panel A, POSTANN is interacted with each type of distribution channel.
Except for the “Other” distribution channel category (a very small group, which is also the
omitted category), funds using all other types of distribution channels undergo a decline in flows
around the event window, with varying magnitudes. Statistical significance also varies because
some of the distribution channel groups have few observations. The strongest decline in fund
flows occurs at insurance-distributed funds while funds in the other-distribution category have a
small and insignificant inflow. More importantly, there is no particular category or type of
distribution channel that is driving the overall result.
In Column 3 of Panel A, I interact POSTANN with CHANNEL_CHG, a dummy variable
that equals one if the fund underwent a change in primary distribution channel in the year
following the announcement date. While the coefficient on the interaction term is negative, it is
25 not significantly different from zero. Furthermore, the coefficient on the non-interacted term
(measuring the decline in flows for those funds not undergoing a change in primary distribution
channel) is negative and statistically significant, which suggests that the overall decline in flows
is not due to funds that simultaneously changed distribution channels.
In Panel B of Table 7, I include distribution channel dummy variables in the panel
regression that includes all fund-month observations, to control for the primary type of
distribution network used by each fund. The coefficients on the variable of interest, POSTANN,
remain largely unchanged and are still statistically significant at the 1% level. Interestingly,
during my sample period, Insurance was the only type of distribution channel that generated
significantly different fund flows from the “Other” distribution channel (the omitted category)
and outperformed in terms of flows.
Overall, the results in Table 7 are not surprising given the event space for this study. In
my sample, only a small fraction of funds (around 10%) undergo a change in main distribution
channel type coinciding with a change in ownership. This is because most of the mergers used
for this study consist of parent firms of similar types, i.e., banks buying banks, insurance buying
insurance. For most of these observations, the fund’s main distributor has the same parent as the
management company, is also acquired as part of the acquisition, and then becomes part of the
acquiring company. Therefore, it has no incentive to steer investors out of the fund. In addition, I
would argue that distribution channels are much more important in marketing the fund to new
investors rather than keeping investors from pulling out their money. Yet, as we saw in Table 6,
the results in this study are entirely driven by higher outflows rather than changes in inflows.
If it is not due to the broker or distributor, then why are investors reacting negatively to
announcements of ownership changes? One way to get at investor motivation is to look at how
26 different clienteles react to event announcements. My hypothesis is that trust is more important
for less sophisticated investors who don’t have the skills or resources to monitor the fund’s
management. Therefore, I test the trust hypothesis by comparing how investors of different
levels of sophistication respond to the events in question.
Morningstar provides an institutional dummy variable for each fund class, which equals
one if only institutions are allowed to invest in that class, and zero otherwise. In order to use the
heterogeneity in fund classes, I run regressions with observations at the fund class level, not at
the portfolio level. For each fund class, I calculate flows using Equations 1 and 2, and also take
the size, age, and expense ratio of the fund class instead of the portfolio-level weighted average
used in previous tests. Finally, since some funds have multiple fund classes, I attach a weight to
each observation equal to the assets of the fund class divided by the total assets of the entire
fund, which ensures that each fund portfolio has the same weight in these regressions.
I regress fund class flows on event window dummy variables, fund-level controls, parentlevel controls, style dummy variables, prior return deciles dummy variables, and time dummy
variables. Table 8 shows estimated coefficients for different event samples using retail versus
institutional fund classes. The dependent variables are flows of retail classes in Columns 1, 3,
and 5, and flows of institutional classes in Columns 2, 4, and 6. Consistent with my hypothesis,
the results in Table 8 indicate that the aggregate results are being driven by retail class flows.
The coefficients on POSTANN for institutional investors (Columns 2, 4, and 6) are small and
statistically insignificant, while the coefficients on POSTANN for retail investors (Columns 1, 3,
and 5) are two to four times larger and statistically significant at the 1% level.
Gennaioli, Shleifer, and Vishny (2012) write down a model where a component of the
expense ratio comes from a premium that investors are willing to pay to their trusted fund
27 advisors. Motivated by this model, I regress fund flows on event window dummy variables and
controls, separately for funds with above-median expense ratios (high expense funds) and belowmedian expense ratios (low expense funds). My hypothesis is that if investors are willing to pay
higher expense ratios because they have a special (trusting) relationship with the management
company, then outflows should be higher at high-expense funds than low-expense funds after
ownership changes.
Table 9 presents estimated coefficients from OLS regressions for different event samples
using high-expense vs. low-expense funds. Columns 1, 3, and 5 only include high-expense funds,
while the remaining three columns only include low-expense funds. We can see larger declines
in flows from pre-announcement to post-announcement for high-expense funds compared to
low-expense funds. For instance, in Column 5 (high-expense funds), we see a decrease of about
0.8% from the PREANN dummy to the POSTANN dummy variable, while the corresponding
decline in Column 6 (low-expense funds) is only about 0.4%. These results suggest that investors
willing to pay higher expenses are also more likely to pull out as a result of an announced change
in ownership.
The final test in this section examines the influence of the effective date (when the
transaction closes) of the ownership change by re-running the regressions in Table 4 but
including separate event dummies for the post-announcement date, pre-effective date period
(POSTANN_PREEFF)
and
the
post-announcement
and
post-effective
date
period
(POSTANN_POSTEFF). Table 10 presents estimated coefficients from a multi-variable panel
regression of Fund flows (%) on the finer event window variables, fund-level controls, parentlevel controls, style dummy variables, prior return deciles dummy variables, and time dummy
variables.
28 In Column 1 of Table 10, 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. Especially for acquisitions of public
parent firms, it seems like that there are many investors who only react to the closing date of the
merger or acquisition.
5. Robustness Checks
In this section, I run several robustness checks and test some possible concerns about the
validity of my main findings. The results of these tests are reported in Table 11. One concern 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 11,
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
29 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 creating the variable 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 all the other standard controls from Table 4, and report the results in Panel B of
Table 11. While the coefficients on POSTANN are slightly smaller than those in Table 4, the
outflows after the announcement date are still negative and 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 unreported robustness check, I drop the funds with Manager change or
Fund closure equal to one and find similar results.
Another possible concern 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 by regressing fund returns on the standard event
window dummy variables and standard set of controls, and report the results in Panel D of Table
11. There is no evidence of significant underperformance in the twelve months following a
management company ownership change.
30 6. 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
tests suggest that familiarity and trust play a significant role. Legal issues at the acquiring firm
exacerbate outflows by investors in funds of the acquired firm. Retail investors’ reaction to fund
adviser ownership changes is stronger than that of institutional investors, which is consistent
with trust being more important for less sophisticated investors in making investment decisions.
Investors in high-expense funds also have stronger outflows. Overall, the paper highlights the
important role of intangible qualities and relationships when individuals make investment
decisions.
31 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
Announced: 06/25/1996
Effective: 11/01/1996
Heine Securities Securities Private (U.S.)
Tgt:
Asset Manager
Allianz
Acq:
Insurance
Announced: 10/18/2000
Tgt:
Nicholas-Applegate Securities
Legg Mason
Acq:
Securities
Announced: 06/24/2005
Tgt:
Smith Barney A.M. Securities
Acq:
Susquehanna Banc. Bank
Announced: 01/02/2008
Tgt:
Stratton Mgmt
Securities
Franklin Templeton
Mutual Series
Public (Germany) Non Asset Mgr. PIMCO
Effective: 01/31/2001
Private (U.S.)
Asset Manager Nicholas Applegate
Public (U.S.)
Asset Manager
Effective: 12/01/2005
subsidiary unit
Asset Manager
Legg Mason
Smith Barney
Public (U.S.)
Non Asset Mgr. None
Effective: 04/30/2008
Private (U.S.)
Asset Manager Stratton
32 Acq:
Guggenheim Ptnrs Securities
Announced: 01/02/2008
Tgt:
Security Benefit
Insurance
Private (U.S.)
Non Asset Mgr. None
Effective: 08/02/2010
Private (U.S.)
Non Asset Mgr. SGI (et al.)
Panel B: Mergers involving acquisitions of public parent asset managers
Reliastar
Acq:
Insurance
Public (U.S.)
Non Asset Mgr. Northstar
Announced: 07/22/1999
Effective: 10/29/1999
Pilgrim Capital
Tgt:
Securities
Public (U.S.)
Asset Manager Pilgrim America
UniCredit
Acq:
Bank
Announced: 05/15/2000
Tgt:
Pioneer Group
Securities
Acq:
CDC
Securities
Announced: 06/16/2000
Tgt:
Nvest
Securities
Acq:
Old Mutual
Insurance
Announced: 06/19/2000
Tgt:
United Asset Mgrs Securities
Acq:
Lehman Brothers
Securities
Announced: 07/22/2003
Tgt:
Neuberger Berman Securities
Public (Italy)
Non Asset Mgr. None
Effective: 10/25/2000
Public (U.S.)
Asset Manager Pioneer
Private (France)
Non Asset Mgr. CDC MPT+
Effective: 10/30/2000
Public (U.S.)
Asset Manager Loomis Sayles (et al.)
Public (U.K.)
Non Asset Mgr. None
Effective: 10/05/2000
Public (U.S.)
Asset Manager UAM (et al.)
Public (U.S.)
Non Asset Mgr. None
Effective: 10/31/2003
Public (U.S.)
Asset Manager Neuberger Berman
Panel C: Mergers involving acquisitions of public parent non-asset managers
U.S. Bancorp
Acq:
Bank
Public (U.S.)
Non Asset Mgr. First American
Announced: 12/15/1997
Effective: 05/01/1998
Piper
Jaffray
Tgt:
Securities
Public (U.S.)
Non Asset Mgr. Piper
NationsBank
Acq:
Bank
Announced: 04/13/1998
BankAmerica
Tgt:
Bank
Public (U.S.)
Non Asset Mgr. Nations
Effective: 09/30/1998
Public (U.S.)
Non Asset Mgr. Pacific Horizon (et al.)
33 Acq:
Manulife Financial Insurance
Announced: 09/29/2003
Tgt:
Acq:
John Hancock
TD Bank Group
Insurance
Bank
Announced: 08/26/2004
Acq:
Banknorth
Bank
M&T Bank
Bank
Announced: 11/01/2010
Tgt:
Wilmington Trust
Bank
Public (U.S. ADR) Non Asset Mgr. None
Effective: 04/29/2004
Public (U.S.)
Non Asset Mgr. John Hancock
Public (Canada)
Non Asset Mgr. TD
Effective: 03/01/2005
Public (U.S.)
Non Asset Mgr. Banknorth
Public (U.S.)
Non Asset Mgr. MTB
Effective: 05/17/2011
Public (U.S.)
Non Asset Mgr. WT (et al.)
34 Appendix B
Appendix B lists 20 examples of legal issues from a hand-gathered sample of 155 such
cases, including one from each year from 1991 through 2010. For each issue, the Appendix
provides the main source (as well as either the title of the Factiva article or the hyperlink to the
SEC litigation release or NSAR form), the implicated asset management company, and the date.
Hyperlink or Article Title
Implicated Company
Date
Source
"Top Manager Price Becomes Tangled In Legal Dispute"
Heine Securities
06/04/1991 Factiva
"Ex-Smith Barney Analyst Pleads Guilty To Fraud"
Keystone Investments
05/21/1992 Factiva
"USAA Investment Censured, Fined $50,000 In SEC Settlement"
USAA Investment Management
01/22/1993 Factiva
"Strong/Corneliuson in Talks With SEC Over Inquiry Relating to Cross Trades"
Strong Capital Management
03/12/1994 Factiva
"Ex-Piper Capital Mgmt CEO Lands Job At Money Mgmt Firm"
Piper Capital Management
02/24/1995 Factiva
"Founders' Chief Borgen Gives Writing Sample In Lawsuit"
Founders Funds
06/13/1996 Factiva
https://www.sec.gov/litigation/admin/ia1630.txt
Alliance Capital Management
04/28/1997 SEC
http://www.sec.gov/litigation/admin/3-9796.txt
Meridian Funds
12/28/1998 SEC
"Lawsuit Claims Morgan Stanley Misled Mutual Fund Customers"
Morgan Stanley
02/23/1999 Factiva
"Texas Mutual Fund Settles SEC Fraud Charges"
Rupay-Barrington
07/10/2000 Factiva
http://www.sec.gov/Archives/edgar/data/809586/000100472601500159/exhibitshai.txt
Heartland Advisors
06/30/2001 NSAR
https://www.sec.gov/litigation/admin/ia-2079.htm
Gintel Asset Management
11/08/2002 SEC
"Putnam Subpoenaed in Timing Case"
Putnam Investments
10/22/2003 Factiva
http://www.sec.gov/litigation/admin/ia-2294.htm
Bridgeway Capital Management
09/15/2004 SEC
http://www.sec.gov/Archives/edgar/data/102756/000010275609000017/legal.txt
Value Line Securities
06/15/2005 NSAR
35 https://www.sec.gov/litigation/admin/2006/ia-2548.pdf
Gartmore Mutual Funds
09/07/2006 SEC
http://www.sec.gov/litigation/admin/2007/33-8774.pdf
Kelmoore Investment Company
01/18/2007 SEC
https://www.sec.gov/litigation/litreleases/2008/lr20539.htm
Gabelli Funds
04/24/2008 SEC
http://www.sec.gov/Archives/edgar/data/898745/000132535811000252/legalproceedings.htm
Principal Funds
10/28/2009 NSAR
http://www.sec.gov/Archives/edgar/data/1137393/000128769512000335/bfk77e.htm
Blackrock Advisors
07/27/2010 NSAR
36 Appendix C
Appendix C 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.
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39 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 main types are banks (SIC
6000-6199), securities firms (SIC 6200-6299), and insurance companies (SIC 6300-6499). Holding companies (SIC 67006799) 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. 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
Events # of funds AUM ($bil) Events # of funds AUM ($bil)
Year
(1)
(2)
(3)
(4)
(5)
(6)
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
9
7
27
18
11
29
18
9
5
10
8
9
2
6
7
8
2
17
30
69
50
44
135
84
19
51
55
38
84
19
41
66
38
3
$5.0
$33.8
$51.3
$13.9
$17.9
$69.5
$52.6
$2.4
$38.6
$33.9
$41.7
$70.9
$48.8
$10.9
$80.1
$15.3
$0.1
7
2
15
10
7
17
3
0
3
5
1
2
1
3
0
2
0
15
5
35
32
31
108
8
0
49
18
2
44
1
30
0
13
0
$4.8
$0.6
$25.3
$9.7
$13.3
$54.5
$2.1
$0.0
$38.5
$15.1
$0.4
$23.6
$0.0
$8.5
$0.0
$6.8
$0.0
7
2
15
9
5
13
3
0
2
5
1
2
1
3
0
2
0
15
5
35
28
14
43
8
0
39
18
2
44
1
30
0
13
0
$4.8
$0.6
$25.3
$9.2
$3.6
$16.5
$2.1
$0.0
$28.4
$15.1
$0.4
$23.6
$0.0
$8.5
$0.0
$6.8
$0.0
Total
185
843
$586.9
78
391
$203.3
70
295
$144.9
Panel B: Merger summary statistics by merger type
All parent company changes
All public parent changes
Merger Type
%
Merger Type
%
(1)
(2)
(3)
(4)
Securities <-> Securities
Bank <-> Bank
Bank <-> Securities
Insurance <-> Securities
Insurance <-> Insurance
All Other Types
All non-AM public parent chgs
Events # of funds AUM ($bil)
(7)
(8)
(9)
32.4%
30.8%
17.8%
13.0%
3.8%
2.2%
Bank <-> Bank
Securities <-> Securities
Insurance <-> Securities
Bank <-> Securities
Insurance <-> Insurance
All Other Types
69.2%
10.3%
7.7%
6.4%
3.8%
2.6%
All non-AM public parent chgs
Merger Type
%
(5)
(6)
Bank <-> Bank
Securities <-> Securities
Bank <-> Securities
Insurance <-> Insurance
All Other Types
77.1%
8.6%
5.7%
4.3%
4.3%
Table 2: Summary statistics and comparison of fund variables for treated funds and similar untreated funds
Table 2 presents summary statistics for the main fund-level and parent company-level variables, as well as a comparison of
fund variables for treated funds (undergoing M&A) and similar untreated funds. In Panel A, 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. In Panel B, for each fund undergoing an event, I create a comparison value of each fund
variable by averaging that variable for all funds in the same month and style that did not undergo an event. Column (1) of
Panel B shows averages across all funds going through an event, column (2) shows averages across all comparison values,
and column (3) shows the difference between columns (1) and (2), with a t-statistic, using standard errors adjusted for
clustering by event, in brackets.
Panel A: Summary statistics for fund-level and parent-level variables
# of Obs.
Mean
Median
Variable
(1)
(2)
(3)
Fund flows ($mil), monthly
348158
0.3
-0.3
Fund flows (%), monthly
348158
0.4%
-0.2%
Inflows (%), monthly
319285
3.8%
1.9%
Outflows (%), monthly
319284
3.2%
2.1%
St.Dev.
(4)
67.6
4.5%
7.0%
5.4%
10%
(5)
-17.7
-3.0%
0.4%
0.7%
90%
(6)
17.3
4.4%
8.1%
6.0%
Fund AUM ($mil)
Log fund AUM
Family AUM ($mil)
Log family AUM
Fund age (years)
Log fund age
Expense ratio (%)
Log expense ratio
348158
348158
348891
348891
347530
347530
338064
338064
1198
5.5
60632
8.7
13.0
2.2
1.3%
-4.4
216
5.4
10807
9.0
8.9
2.2
1.2%
-4.4
4482
1.7
155565
2.4
13.3
0.9
0.5%
0.4
26
3.3
218
5.3
2.8
1.0
0.8%
-4.9
2332
7.7
101807
11.4
28.2
3.3
1.8%
-4.0
Private firm (dummy)
Public non-a.m. firm (dummy)
Log parent marketcap
Stock returns (avg over prior yr)
Mutual fund revenues (%)
351120
351120
166554
165921
168953
0.42
0.35
2.8
1.1%
8.4%
0.00
0.00
2.8
1.1%
0.9%
0.49
0.47
0.1
1.9%
15.5%
0.00
0.00
2.6
-1.0%
0.1%
1.00
1.00
2.9
3.4%
31.1%
41 Panel B: Comparison of fund-level variables for treatment group and control group
Funds at Event Date
Comparison Funds
Fund Variable
(1)
(2)
Style-adjusted returns
0.020%
0.016%
(prior year)
Style-adjusted returns
(prior 3 years)
-0.005%
0.004%
-0.009%
[0.38]
5.47
5.47
0.00
[0.02]
Expense ratio (%)
1.286%
1.250%
0.036%
[1.63]
Fund age (years)
13.1
12.3
0.80
[1.15]
Log fund AUM
Difference
(3)
0.004%
[0.08]
42 Table 3: Fund flows around changes in parent companies of fund management firms
Table 3 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 [-6,12]
Changes:
All
Public
Changes
Parent
Timing around event
(1)
(2)
PREANN (dummy)
0.038%
-0.058%
t-stat
[0.25]
[0.36]
# of fund-month obs.
4601
2043
POSTANN (dummy)
t-stat
# of fund-month obs.
-0.548%***
[3.63]
9443
-0.483%**
[1.99]
4178
Public
Non AM
(3)
0.005%
[0.02]
1533
-0.737%***
[5.96]
3106
Panel B: Monthly fund flows (match-adjusted) around event windows [-6,12]
Changes:
All
Public
Public
Changes
Parent
Non AM
Timing around event
(1)
(2)
(3)
PREANN (dummy)
0.116%
0.263%*
0.223%
t-stat
[0.84]
[1.92]
[1.23]
# of fund-month obs.
4601
2043
1533
POSTANN (dummy)
t-stat
# of fund-month obs.
-0.320%***
[2.60]
9443
-0.197%
[1.03]
4178
-0.395%**
[2.42]
3106
Panel C: Fixed effects regression of fund flows (match-adjusted) around event [-6,12]
Changes:
All
Public
Public
Changes
Parent
Non AM
Timing around event
(1)
(2)
(3)
POSTANN (dummy)
-0.489%***
-0.542%***
-0.680%***
t-stat
[3.76]
[2.87]
[4.06]
Observations
Fund-Event FE
14044
YES
6221
YES
4639
YES
43 Table 4: Regressions of fund flows on event window indicator variables
Table 4 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fundlevel 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 3 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:
Regression:
Changes:
Predictor Variables
PREANN (dummy)
POSTANN (dummy)
(1)
-0.169%
[1.15]
(2)
-0.125%
[0.83]
Monthly Fund Flows (%)
OLS
OLS
Public Parent
(3)
(4)
-0.186%
-0.066%
[0.95]
[0.35]
-0.496%***
[4.23]
-0.438%***
[3.64]
-0.564%***
[3.43]
OLS
OLS
All Changes
Log fund AUM
-0.042%**
[1.98]
Log family AUM
Log fund age (years)
-0.067%**
[2.06]
-0.372%***
[9.59]
-0.273%***
[5.69]
-0.284%***
[4.76]
0.280%***
[3.50]
Log parent marketcap
Stock returns (prioryr)
-0.005%
[0.14]
0.093%***
[2.79]
Private firm (dummy)
0.025%
[0.25]
0.035%
[0.34]
-0.543%
[1.43]
-0.913%*
[1.91]
5.077%***
[3.88]
344014
YES
YES
NO
-0.540%***
[3.08]
0.119%***
[3.85]
0.150%**
[2.19]
-0.597%***
[3.67]
0.046%**
[2.53]
Log expense ratio
Observations
Return decile dummies
Time dummies
Fund style dummies
-0.579%***
[3.29]
OLS
OLS
Public Non AM
(5)
(6)
-0.030%
0.045%
[0.17]
[0.25]
335118
YES
YES
YES
163997
YES
YES
NO
155647
YES
YES
YES
3.410%***
[2.85]
119522
YES
YES
NO
114524
YES
YES
YES
44 Table 5: Fund flows around changes in parent companies with interaction terms for legal issues
Table 5 presents estimated coefficients from fixed effects regressions across the event window [-6,12] of match-adjusted
Fund flows (%) on the post-announcement dummy variable, POSTANN, and interaction terms of POSTANN with dummy
variables indicating prior legal controversies at the acquiring firm and the target firm. 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.
ACQLEGAL equals one if the acquiring firm in an event had a legal controversy in the three years prior to the start of the
event window and zero otherwise. TARLEGAL equals one if the target firm in an event had a legal controversy in the three
years prior to the start of the event window and zero otherwise. Non-interacted versions of these legal controversy variables
are constant for each fund across the event window so they drop out due to the fixed effects. Column (1) 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.
Fixed effects regression of fund flows (match-adjusted) around event [-6,12]
Changes:
All
Public
Changes
Parent
Timing around event
(1)
(2)
POSTANN (dummy)
-0.518%***
-0.406%*
[3.41]
[1.73]
POSTANN × ACQLEGAL
-0.569%**
[2.35]
POSTANN × TARLEGAL
0.370%
[1.10]
Observations
Fund-Event FE
14044
YES
Public
Non AM
(3)
-0.552%**
[2.29]
-0.887%***
[2.78]
-0.741%**
[2.28]
-0.249%
[0.84]
-0.103%
[0.34]
6221
YES
4639
YES
45 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 3 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:
Changes:
Inflows/Outflows
Predictor Variables
PREANN (dummy)
POSTANN (dummy)
Log fund AUM
Monthly Fund Inflows and Outflows (%)
All Changes
Public Parent
Public Non AM
Inflows (%)
Outflows (%)
Inflows (%)
Outflows (%)
Inflows (%)
Outflows (%)
(1)
(2)
(3)
(4)
(5)
(6)
0.181%
0.442%*
0.247%
0.337%
-0.132%
-0.214%
[0.67]
[1.89]
[0.47]
[0.84]
[0.34]
[0.75]
0.323%
[0.98]
1.332%***
[2.95]
0.372%
[0.71]
1.439%*
[1.91]
-0.165%
[0.58]
0.639%*
[1.84]
-0.242%***
[5.78]
-0.180%***
[4.15]
-0.356%***
[5.98]
-0.268%***
[3.92]
-0.349%***
[4.51]
-0.344%***
[4.04]
0.207%***
[6.65]
0.146%***
[5.40]
0.321%***
[6.39]
0.174%***
[2.91]
0.306%***
[5.16]
0.196%***
[4.06]
-0.654%***
[7.79]
-0.281%***
[3.35]
-0.329%***
[2.63]
Log expense ratio
0.545%***
[3.80]
0.266%**
[1.99]
Private firm (dummy)
0.105%
[0.74]
Log family AUM
Log fund age (years)
Stock returns (prioryr)
-0.203%
[1.35]
0.142%
[0.81]
0.204%
[1.08]
0.040%
[0.23]
0.182%
[0.89]
-0.029%
[0.15]
-1.069%
[1.16]
-0.458%
[0.50]
-0.647%
[0.75]
0.241%
[0.35]
-2.065%
[0.99]
2.261%
[1.38]
-0.696%
[0.42]
-0.114%
[0.87]
Log parent marketcap
Observations
Return decile dummies
Time dummies
Fund style dummies
-0.029%
[0.20]
3.431%*
[1.82]
307545
YES
YES
YES
307544
YES
YES
YES
142229
YES
YES
YES
142228
YES
YES
YES
104921
YES
YES
YES
104920
YES
YES
YES
46 Table 7: Fund flows for different types of distribution networks
Table 7 shows results of fixed effects regressions around the event window [-6,12] and panel regressions, of flows on event
dummies and dummy variables representing categories of primary fund distribution network. 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 also 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 from fixed
effects regressions across the event window [-6,12] of match-adjusted Fund flows (%) on the post-announcement dummy
variable, POSTANN, and interaction terms of POSTANN with different distribution channel variables. AFFILIATED is a
dummy variable that equals one if the fund’s primary distribution channel is affiliated with the advisor (Bank Proprietary,
Insurance, Proprietary), and zero otherwise. CHANNEL_CHG equals one if the fund changed its primary distribution
channel in the year after the event, and zero otherwise. Columns (1) and (2) only include funds that did not change primary
distribution channel while column (3) includes all funds undergoing an event. Panel B 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 4, time dummies, fund style dummies, and prior return decile dummies for
each of the previous five years. The sample period for all regressions runs from January 1995 to December 2012. Tstatistics, 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: Fixed effects regression of fund flows (match-adjusted) around event [-6,12]
Changes:
All
All
All
Changes
Changes
Changes
Timing around event
(1)
(2)
(3)
POSTANN (dummy)
-0.537%***
0.107%
-0.485%***
[3.63]
[0.37]
[3.96]
POSTANN × AFFILIATED
0.181%
[0.72]
POSTANN × CHANNEL_CHG
POSTANN × Bank Proprietary
-0.502%
[1.34]
POSTANN × Direct
-0.220%
[0.49]
POSTANN × Institutional
-0.771%*
[1.82]
POSTANN × Insurance
-1.169%*
[1.78]
POSTANN × Non-Proprietary
-0.778%**
[2.36]
POSTANN × Proprietary
-0.231%
[0.47]
Observations
Includes changes in dist channel?
Fund-Event FE
-0.178%
[0.41]
12502
NO
YES
12502
NO
YES
13842
YES
YES
47 Panel B: OLS panel regression including controls for primary distribution channel
Dep.Var:
Monthly Fund Flows (%)
Changes:
All
Public Parent Pub. Non AM
Predictor Variables
(1)
(2)
(3)
PREANN (dummy)
-0.119%
-0.029%
0.091%
[0.82]
[0.16]
[0.52]
POSTANN (dummy)
-0.426%***
[3.53]
Bank Proprietary (dummy)
-0.304%
[1.51]
0.029%
[0.16]
-0.015%
[0.08]
0.078%
[0.39]
0.274%
[1.23]
0.310%
[1.56]
-0.154%
[0.74]
0.074%
[0.32]
0.078%
[0.33]
Direct (dummy)
Institutional (dummy)
0.655%***
[3.07]
1.139%***
[4.19]
1.109%***
[3.78]
Non-Proprietary (dummy)
0.261%
[1.27]
0.400%*
[1.76]
0.359%
[1.61]
-0.155%
[0.66]
0.102%
[0.39]
0.076%
[0.26]
Observations
Fund Controls
Return decile dummies
Time dummies
Fund style dummies
-0.504%***
[2.88]
Insurance (dummy)
Proprietary (dummy)
-0.553%***
[3.14]
331598
YES
YES
YES
YES
154784
YES
YES
YES
YES
113769
YES
YES
YES
YES
48 Table 8: Regressions of fund flows on event window indicator variables for retail vs. institutional classes
Table 8 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fundlevel 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 3 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:
Changes:
All Changes
Investor Class:
Retail
Institutional
Predictor Variables
(1)
(2)
PREANN (dummy)
-0.077%
-0.062%
[0.41]
[0.25]
POSTANN (dummy)
-0.429%***
[3.16]
Log fund AUM
-0.078%***
[4.00]
Log family AUM
0.029%*
[1.76]
-0.229%
[1.11]
Monthly Fund Flows (%)
Public Parent
Retail
Institutional
(3)
(4)
0.011%
0.109%
[0.04]
[0.40]
Public Non AM
Retail
Institutional
(5)
(6)
0.084%
0.098%
[0.39]
[0.27]
-0.701%***
[3.44]
-0.207%
[0.73]
-0.692%***
[3.21]
0.055%*
[1.71]
-0.101%***
[3.16]
0.040%
[0.85]
0.015%
[0.49]
0.099%***
[3.19]
0.105%**
[2.31]
0.068%**
[1.99]
0.112%**
[2.29]
-0.047%
[1.30]
-0.176%
[0.58]
0.057%
[1.08]
Log fund age (years)
-0.456%***
[15.12]
-0.804%***
[10.77]
-0.381%***
[8.85]
-0.836%***
[8.74]
-0.377%***
[6.62]
-0.818%***
[7.40]
Log expense ratio
-0.239%***
[3.26]
-0.045%
[0.34]
-0.535%***
[4.36]
-0.093%
[0.45]
-0.450%***
[3.46]
-0.084%
[0.39]
-0.638%*
[1.75]
-0.596%
[0.72]
-0.681%
[1.47]
-1.073%
[1.36]
Private firm (dummy)
0.293%***
[3.78]
0.346%***
[2.37]
Log parent marketcap
Stock returns (prioryr)
Observations
Return decile dummies
Time dummies
Fund style controls
5.437%***
[3.74]
522002
YES
YES
YES
138890
YES
YES
YES
276283
YES
YES
YES
4.206%*
[1.90]
80009
YES
YES
YES
2.743%**
[2.10]
192133
YES
YES
YES
4.175%*
[1.84]
62391
YES
YES
YES
49 Table 9: Regressions of fund flows on event window indicators for high vs. low expense funds
Table 9 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fundlevel 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:
Changes:
All Changes
Investor Class: High Exp.
Low Exp.
Predictor Variables
(1)
(2)
PREANN (dummy)
-0.028%
-0.245%
[0.12]
[1.61]
Monthly Fund Flows (%)
Public Parent
High Exp.
Low Exp.
(3)
(4)
-0.038%
-0.082%
[0.14]
[0.38]
POSTANN (dummy)
-0.440%***
[2.72]
-0.434%***
[3.32]
-0.600%**
[2.25]
-0.531%***
[2.95]
-0.614%**
[2.30]
Log fund AUM
-0.089%***
[3.22]
-0.003%
[0.12]
-0.134%***
[3.79]
-0.008%
[0.18]
-0.063%
[1.42]
0.074%***
[3.67]
0.021%
[0.86]
0.146%***
[4.45]
0.101%**
[2.54]
0.113%***
[3.13]
0.088%**
[2.06]
-0.257%***
[3.56]
-0.295%***
[4.86]
-0.261%***
[2.89]
-0.305%***
[4.15]
-0.478%
[1.60]
-0.031%
[0.26]
-0.526%
[1.51]
-0.043%
[0.35]
-0.415%
[0.97]
-0.806%
[1.43]
-0.746%
[1.44]
-1.168%*
[1.79]
Log family AUM
Log fund age (years)
-0.410%***
[7.05]
Log expense ratio
-0.279%
[1.30]
Private firm (dummy)
0.366%***
[3.67]
-0.344%***
[7.03]
0.007%
[0.10]
Stock returns (prioryr)
170911
YES
YES
YES
-0.439%**
[2.35]
0.041%
[0.88]
0.205%**
[2.17]
Log parent marketcap
Observations
Return decile dummies
Time dummies
Fund style dummies
Public Non AM
High Exp.
Low Exp.
(5)
(6)
0.205%
-0.019%
[0.88]
[0.08]
164207
YES
YES
YES
5.631%***
[3.36]
4.027%**
[2.45]
79010
YES
YES
YES
76637
YES
YES
YES
2.990%*
[1.90]
3.290%*
[1.82]
58163
YES
YES
YES
56361
YES
YES
YES
50 Table 10: Regressions of fund flows on event window indicator variables – pre- and post-effective date
Table 10 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fundlevel and parent-level control variables. In each specification, the dependent variable is Fund Flows (%) and the variables
of interest include PREANN (defined in Table 3), POSTANN_PREEFF, a dummy variable that equals one for all fundmonth 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.
Dep Var:
Changes:
Predictor Variables
PREANN (dummy)
All
Changes
(1)
-0.125%
[0.83]
POSTANN_PREEFF (dummy)
-0.361% **
[2.16]
-0.379%
[1.43]
-0.236%
[1.08]
POSTANN_POSTEFF (dummy)
-0.480% ***
[3.68]
-0.726%***
[4.22]
-0.735%***
[4.01]
Log fund AUM
-0.042% **
[1.98]
-0.067%**
[2.06]
-0.006%
[0.15]
Log family AUM
Log fund age (years)
0.046% **
[2.53]
0.119%***
[3.85]
0.093%***
[2.79]
-0.372% ***
[9.59]
-0.272%***
[5.68]
-0.283%***
[4.75]
Log expense ratio
0.150% **
[2.19]
Private firm (dummy)
0.279% ***
[3.49]
Log parent marketcap
Stock returns (prioryr)
Observations
Return decile dummies
Time dummies
Fund style dummies
Monthly Fund Flows (%)
Public
Public
Parent
Non AM
(2)
(3)
-0.063%
0.047%
[0.34]
[0.26]
335118
YES
YES
YES
0.025%
[0.25]
0.035%
[0.34]
-0.528%
[1.39]
-0.887%*
[1.85]
5.062%***
[3.86]
3.417%***
[2.85]
155647
YES
YES
YES
114524
YES
YES
YES
51 Table 11: Other alternative explanations
Table 11 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 4, 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 (%)
Changes:
All
Public Parent Public Non AM
Predictor Variables
(1)
(2)
(3)
PREANN (dummy)
-0.075%
0.077%
0.029%
[0.47]
[0.41]
[0.15]
POSTANN (dummy)
Observations
Fund controls
Return decile dummies
Time dummies
Fund style dummies
Panel B: Controlling for upcoming fund closure
Dep.Var:
Changes:
Predictor Variables
PREANN (dummy)
-0.364%***
[2.95]
-0.556%***
[3.13]
-0.543%***
[2.95]
300439
YES
YES
YES
YES
139707
YES
YES
YES
YES
102980
YES
YES
YES
YES
All
(1)
-0.161%
[1.08]
Monthly Fund Flows (%)
Public Parent Public Non AM
(2)
(3)
-0.104%
0.008%
[0.56]
[0.05]
POSTANN (dummy)
-0.381%***
[3.18]
-0.502%***
[2.87]
-0.444%**
[2.53]
Fund closure (dummy)
-1.960%***
[18.87]
-1.822%***
[13.18]
-1.818%***
[11.42]
Observations
Fund controls
Return decile dummies
Time dummies
Fund style dummies
335118
YES
YES
YES
YES
155647
YES
YES
YES
YES
114524
YES
YES
YES
YES
52 Panel C: Controlling for upcoming or recent manager change
Dep.Var:
Monthly Fund Flows (%)
Changes:
All
Public Parent Public Non AM
Predictor Variables
(1)
(2)
(3)
PREANN (dummy)
-0.118%
-0.048%
0.053%
[0.79]
[0.25]
[0.30]
POSTANN (dummy)
-0.439%***
[3.65]
-0.575%***
[3.28]
-0.536%***
[3.07]
Manager change (dummy)
-0.316%***
[6.95]
-0.350%***
[5.69]
-0.261%***
[3.94]
335118
YES
YES
YES
YES
155647
YES
YES
YES
YES
114524
YES
YES
YES
YES
Observations
Fund controls
Return decile dummies
Time dummies
Fund style dummies
Panel D: Effect of event announcement on fund returns
Dep.Var:
Changes:
All
Predictor Variables
(1)
PREANN (dummy)
0.020%
[0.26]
POSTANN (dummy)
Observations
Fund controls
Return decile dummies
Time dummies
Fund style dummies
0.019%
[0.37]
335118
YES
YES
YES
YES
Fund Net Returns (%)
Public Parent Public Non AM
(2)
(3)
0.116%
0.053%
[1.02]
[0.60]
-0.028%
[0.34]
155647
YES
YES
YES
YES
-0.101%
[1.51]
114524
YES
YES
YES
YES
53 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
0.8% 0.6% 0.4% Fund flows (%)
0.2% 0.0% -­‐6 -­‐5 -­‐4 -­‐3 -­‐2 -­‐1 0 1 2 3 4 5 6 7 8 9 10 11 12 -­‐0.2% -­‐0.4% -­‐0.6% -­‐0.8% -­‐1.0% -­‐1.2% Months prior to or after event
54 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.
Monthly match-adjusted fund flows around events
Public non asset-managers only
1.5% 1.0% Fund flows (%)
0.5% 0.0% -­‐6 -­‐5 -­‐4 -­‐3 -­‐2 -­‐1 0 1 2 3 4 5 6 7 8 9 10 11 12 -­‐0.5% -­‐1.0% -­‐1.5% -­‐2.0% -­‐2.5% -­‐3.0% Months prior to or after event
55