“Whom do you trust?” Investor-advisor relationships and mutual fund flows

Leonard Kostovetsky Simon School, University of Rochester

leonard.kostovetsky@simon.rochester.edu

Abstract I measure the value that investors place on trust and relationships in asset management by examining mutual fund flows around announced changes in the ownership of fund management companies. I find a decline in flows of around 7% of fund assets in the year following the announcement date, starting after announcement and accelerating after the closing date of the ownership change. A decomposition into inflows and outflows shows that the overall decrease in flows is entirely driven by increasing outflows with no change in inflows. Retail investors and investors in funds with higher expense ratios are most responsive to ownership changes, providing new evidence that such investors place a significant value on trust and are more likely to respond to a relationship disruption by withdrawing their assets. Alternative explanations such as changes in distribution network, reactions to expected fund closure, expected or past manager changes, or poor expected returns do not seem to explain the results.

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1. Introduction

An important unanswered question in the field of delegated asset management is how

much importance investors place on who is managing their money. Asset management

companies spend more than a billion dollars each year on advertising (Gallagher, Kaniel, and

Starks, 2006), much of it trying to persuade investors that their firm will provide them with

trustworthy and dependable financial advice (Mullainathan, Schwartzstein, and Shleifer, 2008).

Gennaioli, Shleifer, and Vishny (2012) propose that the well-documented empirical finding that

average active mutual fund alphas are negative (e.g., Jensen, 1968) is due to a “trust” premium,

which allows asset management firms to charge investors additional fees if there is a trusting

relationship between them. They write that trust can be established through “personal

relationships, familiarity, persuasive advertising, connections to friends and colleagues,

communication, and schmoozing,” all of which are likely to be disrupted by an exogenous

change in firm management.

In this paper, I measure the value of trustworthy relationships between investors and asset

management firms by examining mutual fund flows around management company ownership

changes. My main finding is that mutual fund flows turn negative in response to announced

changes in the parent company or ownership of a fund’s advisor. A reduction in flows begins

after the announcement date and is initially about 3% of assets (on an annualized basis), and then

accelerates after the closing date to total approximately 7% of assets over the twelve months

following the announcement date. The results are robust to controlling for fund characteristics

such as the past five years of returns, age, fund and family size, and style, as well as parent

company characteristics (for public parent companies) such as the parent’s market capitalization

and past year’s stock returns.

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An alternative empirical strategy would be to look at flows around individual manager

changes. The main problem with that strategy is that manager changes are highly correlated with

past fund performance (Chevalier and Ellison, 1999) and fund flows are also extremely sensitive

to past performance (Sirri and Tufano, 1998), making it difficult to disentangle performance-

driven outflows from outflows due to manager changes. In addition, there is a reverse causality

problem if managers can anticipate future flows and voluntarily depart the fund when they

expect fund outflows, and therefore reductions in assets under management and their own

compensation.

My empirical strategy begins with an examination of 185 events (covering 843 funds)

from 1995 through 2011, where there is a change in the ownership of the fund’s management

company (mergers and acquisitions involving the management company itself or its parent). One

example of such a merger occurred in 2001 when Deutsche Bank announced its purchase of

Zurich Scudder, the manager of the Scudder Funds, from Zurich Financial. In order to control for

parent company characteristics, I next restrict the sample to the 78 events (covering 391 funds)

involving ownership changes of U.S. public parent companies. While management company

ownership changes are less likely to be driven by a particular fund’s performance than manager

changes, it is still possible that they are related to the entire management company’s past

investment performance. In order to rule out such endogeneity concerns, I perform a more

rigorous test, restricting the event space to the 70 events (covering 295 funds) in which the

public parent company undergoing an ownership change derives a small share of revenues

(<10%) from its mutual fund operations, and find similar results.

Next, I test a number of different explanations for my main results. One explanation is

that a group of investors attach significant value to their relationship with the fund’s management

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company (e.g., due to advertising or past experience), and move their savings elsewhere when

the ownership change disrupts this relationship. However, new investors might also avoid

investing in the fund while its management is transitioning from one owner to another owner. I

decompose fund flows into inflows (purchases of shares by investors) and outflows (sales of

shares by investors), and find that while there is little change in inflows around the

announcement date, there is a large increase in outflows that leads to the reduction in total fund

flows.

The importance of trust and relationships should also be more important for less

sophisticated investors who don’t have the skills or resources to monitor the fund’s management.

I test the trust hypothesis by separately looking at the effect of ownership changes on retail class

flows and institutional class flows to examine whether investor sophistication is an important

factor. I find that outflows are driven by retail class investors, and that investors in institutional

classes do not react adversely to changes in ownership. This result might also explain why flows

only slowly react to announcement changes. Limited attention is well documented among retail

investors (Barber and Odean, 2008), which is why retail investors slowly find out about the

ownership change. After the closing date, the news of the ownership change is more likely to

filter through to retail investors, as it appears in the fund prospectus and other disclosure

documents.

In a similar vein, I separate my sample of funds into high-expense funds and low-expense

funds, and then examine the effect of ownership changes on the flows of each group. I find the

decline in flows from pre-announcement to post-announcement is anywhere from 25% to 100%

bigger for high-expense funds relative to those with low expense ratios. This supports the thesis

that a component of the expense ratio is a trust “premium”, since investors in funds with higher

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expense ratios are more likely to withdraw their money when their prior relationship is broken

due to a change in ownership.

Next, I examine whether characteristics of the acquiror or the purpose behind the

acquisition affect the fund flows after the announcement date. Interestingly, I find that investors

react in a more negative way when a bank acquires their fund’s management company than when

the acquiror is an insurance or securities firm. However, neither the purpose of the merger, nor

the past stock performance or past fund performance (either of the whole family or just funds in

the same style as the target fund) of the acquiring firm affects post-announcement fund flows.

This result might indicate that investors don’t believe the past performance of the acquiring firm

will necessarily carry through to their fund, and/or that the reason for their sale is the disruption

of their relationship with the prior organization that had been managing their fund.

I then test a number of alternative explanations for the paper’s main results. Changes in

ownership of management companies may also coincide with changes in distribution channels.

Del Guercio, Reuter, and Tkac (2010) document the various distribution channels used by

mutual fund families, and Bergstresser, Chalmers, and Tufano (2009) provide evidence on the

importance of brokers in portfolio decisions made by retail investors. A change in distribution

channel might lead brokers to counsel their clients to pull out money from the fund, an effect that

would have nothing to do with a disruption of trust between investors and fund management.

I test this hypothesis by controlling for the main distribution channel used by the fund,

and find that my results are robust to these controls. I then drop any funds that underwent both an

ownership change as well as a change in the primary type of distribution channel, leaving just

funds whose distribution channel remained the same after the announcement of the ownership

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change. Even among this subset of funds, there are economically and statistically significant

outflows after the announced ownership change.

The dot-com bubble presents some concerns as well. There is a significant clustering of

M&A events around the dot-com bubble since merger activity usually peaks during market

booms and the financial industry was undergoing consolidation at the time after the repeal of the

Glass-Steagall Act. I test whether my results are coming from this clustering by dropping all

fund-month observations from 1999 and 2000. I find that my main results are robust to exclusion

of the period around the dot-com bubble.

Another possible explanation is that advisor ownership changes are associated with an

increase in manager turnover and fund closures as the new owners tweak the array of offered

funds and the managers of those funds. Investors might be reacting to expected or realized

manager changes or announced fund closures by withdrawing money from the fund. I test this

hypothesis by including dummy variables that indicate whether the fund will close in the next six

months and whether the manager will change in the next six months or has changed in the prior

six months. My results are robust to inclusion of these controls. Another possibility is that the

decline in asset flows after an announced change in ownership is a rational reaction to

expectations of lower returns. For instance, during the period of transition, the management firm

might not be putting in maximum effort in fund management, and investors might temporarily be

leaving the fund to avoid this period of lower expected returns. I test whether performance is

affected by ownership changes, and find no evidence that mutual funds underperform in the year

following an announced ownership change.

This paper builds on the growing literature focusing on the importance of trust,

familiarity, and loyalty in investment. For instance, Guiso, Sapienza, and Zingales (2008)

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highlight the importance of trust in stock market participation. French and Poterba (1991), Coval

and Moskowitz (1999), and Huberman (2001) all provide evidence on the importance of

familiarity and geographic proximity for investment decisions. Cohen (2009) highlights the

effect of loyalty by studying employee decisions to invest in their company’s stock. My findings

complement this literature by highlighting and measuring the role that trust and familiarity play

in investors’ choices of asset managers, through the use of exogenous breaks in the adviser-

investor relationship.

This paper also contributes to prior research on the role of mutual fund parent companies.

Sialm and Tham (2011) find positive spillover effects from the performance of the parent

company’s stock to the ability of the mutual fund to attract investors. A number of papers

including Ferris and Yan (2009) and Adams, Mansi, and Nishikawa (2012) highlight the

importance of agency issues at advisory firms. Massa and Rehman (2008) show that information

flows from bank parent companies to affiliated mutual funds, allowing these mutual funds to

outperform on stock investments in companies that have borrowed from (and therefore provided

private information to) the bank.

My paper also uses mergers and acquisitions of financial institutions as exogenous

identification in a manner similar to Hong and Kacperczyk (2010). Most papers that have looked

at mergers in the context of mutual funds have focused on mergers between funds (e.g. Khorana,

Tufano, and Wedge, 2007), and not mergers at the family or adviser level. An important

exception is Allen and Parwada (2006) who look at a subset of parent company mergers for

mutual funds in Australia from 1995 to 1999, and also find evidence of negative outflow

reactions. However, their focus is on excessive size and its negative effect on performance as the

main culprit for investor adverse reaction to ownership changes. In contrast, my paper focuses on

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a much larger set of U.S. firms, and tests the hypothesis that trust and relationships between

investors and the advisor explain the empirical findings.

In summary, my paper uses an exogenous shock to the investor-advisor relationship to

measure investor reaction, and thus provides evidence of the significant value that investors

attach to this relationship. It underlines the notion that past (and expected future) performance

and expense ratios are not the only factors in how investors, especially retail investors, make

mutual fund investment decisions.

2. Data

The main data sources for this paper are the CRSP Survivor-Bias-Free US Mutual Fund

Database, annual Morningstar Principia CDs, and the SDC Platinum M&A database. Additional

data on fund advisers is downloaded from the SEC Investment Adviser Public Disclosure

(IAPD) database1 and SEC EDGAR, and stock-level data is collected from the CRSP/Compustat

database. Fund inflows and outflows are collected directly from NSAR filings on EDGAR. Data

on primary distribution networks is from Strategic Insight.

Mutual Fund Sample: The sample consists of all domestic, diversified, actively-managed,

equity mutual funds operating from 1995 through 2012. I construct this sample by merging

CRSP and Morningstar, using ticker symbols and (when ticker symbols are missing) fund names.

I then exclude all funds outside the nine main style boxes (e.g., smallcap value, largecap blend,

etc.) leaving only domestic diversified equity funds. Finally, I eliminate index funds by removing

all funds with the words “index”, “S&P”, “Dow Jones”, and “NASDAQ” in the fund name, and

by excluding all funds in the Dimensional Fund Advisors (DFA), Direxion, Potomac, ProFunds,

                                                                                                               1 The website for this service is: http://www.adviserinfo.sec.gov/IAPD/Content/Search/iapd_Search.aspx

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and Rydex fund families. ETFs are also excluded by removing all observations with the word

ETF in the fund name or funds with ticker symbols of four or fewer characters.

I aggregate funds across fund classes using the Morningstar portfolio identifier

(PORTCODE) or MFLinks variable (WFICN). I remove incubated funds by excluding funds that

were not contemporaneously reported in Morningstar or had a blank CRSP fund name at the start

of the calendar year. I also drop funds with less than $10 million in assets under management, as

flows in these funds are highly volatile and contaminated by “seeding” from the fund family.

This leaves 351,120 portfolio-month observations with the number of funds growing from 945

funds in January 1995 to 1,665 funds in December 2012.

Fund Advisers and Adviser Ownership Changes: Morningstar is the main source for

mutual fund advisers. I crosscheck the Morningstar adviser with the CRSP “Management

Company” identifier and find that they match for over 80% of observations. However, CRSP

sometimes reports the fund distributor as the management company, which is why I rely on

Morningstar for this variable.

I find the parent companies of mutual fund advisers by entering each adviser’s name into

the SEC’s IAPD online database and looking up the Schedule A of Form ADV, which lists all

direct owners and executive officers. For example, for Dreyfus Corporation, adviser to the

Dreyfus funds, the Form ADV Schedule A shows that Bank of New York Mellon is the sole

shareholder of this company. IAPD includes defunct fund advisers but it only began operations

in 2000 so fund advisers that went defunct prior to 2000 are not included. Therefore, I gather

ownership information on these companies by looking through mutual fund proxy documents on

SEC EDGAR.

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The IAPD database only includes current ownership information (or last reported

ownership information for defunct firms). Therefore, I manually look up all fund advisers and

their parent companies in the SDC Platinum M&A database, and note any changes in ownership

and the announcement and effective (closing) dates for each ownership change. I also gather

information from SDC Platinum on the identity, public status, and industry of the acquiring

company, as well as the purpose or purposes for the merger/acquisition.

Whenever Morningstar shows that a fund or fund family changes advisors or is merged

into another fund or fund family and I can find no corresponding ownership change in SDC

Platinum, I examine mutual fund proxy documents on SEC EDGAR to determine the reason for

the change. Overall, I find a total of 185 parent company changes that were announced from July

1995 through December 2011.2 Initial public offerings and management buyouts are not included

because they also coincide with the decisions to go public or private, which might have their own

implications for fund flows.

I use SDC Platinum to identify publicly traded parent companies (of acquirors and

targets) and match them to CRSP using CUSIPs. I also look up several foreign parent companies

on Google Finance to determine their public status. I define a fund as privately owned, with

Private firm (dummy) set to one, if that company is not in CRSP, it is not traded on a foreign

exchange, and it is not a mutual insurance company or non-profit organization. Privately owned

advisory companies make up approximately 40% of the funds in the sample, but manage over

half of the assets under management. This disparity is due to the fact that extremely large mutual

fund advisers such as Fidelity Management & Research (Fidelity Funds) and Capital

                                                                                                               2 Ownership changes announced in the first six months and last twelve months of the sample period are exclude because those months are required for studying fund flows around announcement dates.

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Management & Research (American Funds) are privately held. These estimates are consistent

with earlier research on public vs. private ownership of mutual fund management companies.

For public firms that are available in CRSP/COMPUSTAT, I gather data on market

capitalization and past stock returns from CRSP, and total revenues and segment revenues data

from COMPUSTAT. Overall, 78 of the 185 ownership changes involve acquisitions of public

(parent) companies. In “mergers of equals” such as the 1998 deal between Citicorp and

Travelers, the company that is delisted in CRSP (in that case, Citicorp) is the one that is deemed

to have a change in ownership.

In order to avoid possible endogeneity concerns, I also run tests on firms whose main line

of business is not in asset management, and whose change in ownership is therefore less likely to

be related to anything happening at the mutual fund family. For each fund advisor whose parent

company has revenues data in Compustat, I calculate the total estimated annual revenues of its

mutual fund family3 and divide by the parent company’s revenues in the same fiscal year to

calculate the Mutual fund revenues (%) variable. Non-asset management parent companies are

defined as having less than 10% of total revenues coming from estimated mutual fund revenues.

In addition, I check the Compustat Segments database to exclude all firms whose entire asset

management segments produce revenues greater than 20% of total revenues. In total, 70 of the

78 ownership changes involving public parent M&A happen at non-asset management firms,

mostly commercial and investment banks.

Table 1 presents summary statistics on ownership change announcements for mutual fund

advisory firms. Panel A displays the number of events, number of funds involved in each event,

and the assets under management of those funds, for each year. Columns 1 through 3 show a

                                                                                                               3 Estimated mutual fund revenues are defined as (1/12 × Annual Expense Ratio × Assets under Management) across all fund-month observations of a fund family in a particular fiscal year.

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total of 185 announced ownership changes from July 1995 through December 2011, involving

843 funds with $587 billion in assets under management at the time of the announcement. M&A

activity was strongest in the first six years of the sample period when the stock market was

booming in the late 1990s and the asset management business was also experiencing significant

growth. Columns 4 through 6 only include ownership changes due to public parent M&A, and

show a total of 78 events involving 391 funds managing $203 billion. Finally, Columns 7 though

9 show summary data on ownership changes due to non-asset management public parent M&A.

Among this subgroup, there are 70 events involving 295 funds managing $145 billion.

Panel B of Table 1 shows a breakdown of the merger types that make up the events used

in this paper. Among the entire sample of events, the merger types are fairly evenly distributed

between banks acquiring other banks, securities firms acquiring other securities firms, and

banks/insurance companies buying other securities firms. On the other hand, public parent M&A

in Columns 3 through 6 is mostly dominated by bank mergers, with over two-thirds of events

consisting of this merger type. Banks are larger and are therefore more likely to be publicly

traded than asset management firms, which is why there is such a dramatic change in merger

types. Appendix A shows fifteen examples of mergers used in this paper, with detailed

information on the acquiror and target, as well as the announcement date and effective date for

the merger.

Fund Characteristics: The main variable of interest for this paper is monthly mutual fund

flows. In order to calculate flows, I download data from the CRSP Mutual Fund database on

monthly assets under management and net returns. Fund flows ($mil) in month t is defined as:

(Eq.1) Fund flows ($) = Assets (end of t) – Assets (start of t) × (1 + Net Returns (over month t))

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Since this quantity is usually proportional to fund size, I standardize it by dividing by assets:

(Eq.2) Fund flows (%) = Fund flows ($)/Assets (start of t)

I adjust flows to eliminate assets added from another fund that was merged into the fund. Finally,

in order to eliminate the effect of outliers, fund flows (%) is winsorized at the 1% and 99% level.

Table 2 reports time-series averages of cross-sectional summary statistics for fund flow

variables. Flows over this sample period are fairly close to zero. Although firms had average

monthly inflows of approximately $0.3 million or 0.4% (4.8% on an annualized basis), the

median firm experienced slight outflows due to the fact that inflows tend to be concentrated

among the funds with the best past performance. Standard deviation of monthly flows, even after

winsorizing, is 4.5%, which highlights the significant cross-sectional variation in fund flows.

In most of the tests in the paper, I control for a number of fund variables that have been

shown in the past to predict fund flows. These variables include past fund performance, fund

assets under management (fund AUM), family assets under management (family AUM), fund age,

and expense ratio. The prior literature on fund flows found a non-linear relationship between past

performance and fund flows. In order to capture this non-linear relationship, I sort firms into

deciles for each year’s style-adjusted return from the past five years, and include five sets of past

return decile dummies as controls in all specifications. Newer funds that weren’t around for all

five years and therefore don’t have returns for a particular prior year are placed in a separate

bucket (in addition to the 10 decile groups) for that year, which has its own dummy variable.

The summary statistics in Table 2 show that the distributions of Fund AUM, Family

AUM, Fund age, and Expense ratio, are positively skewed so I transform them with the natural

logarithm and use the transformed variables as predictive variables in regression tests. Because

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the paper looks at shocks to the parent company of the fund’s adviser, I also collect parent

company characteristics. Table 2 shows that 42% of funds have a privately owned adviser, 35%

of funds have a publicly owned adviser whose parent company is not primarily an asset manager.

Public parent companies have average monthly returns over the prior year of just under 1%, and

derive 8% of revenues from mutual fund fees.

Fund Inflows and Outflows: For part of the analysis in the paper, I decompose fund flows

into inflows (dollar value of purchases of fund shares) and outflows (dollar value of sales of fund

shares). Data on inflows and outflows is included in the semiannual NSAR filing made by each

fund family. I use a script to download all NSAR filings from the EDGAR database, and match

them to funds using fund name. Because there are often slight variations in fund names, I attempt

to manually match any unmatched observations. NSARs also include assets under management

so I confirm matches using this variable. Using machine and manual matching, I obtain

inflow/outflow data for nearly 90% of the fund-month observations in my sample.

Table 2 includes summary statistics on inflows and outflows. The monthly inflows for a

typical fund are 3.8% of its assets under management at the start of the month, but 10% of funds

have inflows exceeding 8% of assets, confirming the skewed nature of inflows as investors put

new funds into the top past performers. Monthly outflows average 3.2% of assets under

management and are less skewed with the 90th percentile at 6%.

Distribution Channels: My main source for distribution channels is a dataset provided by

Strategic Insight. The Strategic Insight dataset includes current distribution channel data on each

fund class as well as archival data on distribution channels for defunct funds and families.

Generally, fund classes labeled A, B, C, and R are sold through brokers, fund classes labeled I

(Institutional) or Retirement are sold to institutions, and fund classes labeled N or Retail or with

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no class label are sold directly to investors (Direct). Brokers can be affiliated with the

management firm and are then classified depending on whether the parent company is a bank

(Bank Proprietary), insurance company (Insurance), or securities company (Proprietary), or not

affiliated with the management company at all (Non Proprietary). Families generally use only

one of these four distribution channels for non-direct and non-institutional sales. Finally, some

funds are sold to members of the fraternal, religious, or non-profit organization that runs the fund

(Other).

I aggregate the total assets for each type of distribution channel across fund classes and

then designate a fund portfolio’s main distribution channel as the channel that has the most assets

under management. Most portfolios (and fund families) distribute a significant proportion of

assets using one distribution channel. The average amount distributed by a portfolio’s top

distribution channel is 95% with a median value of 100%. Non-Proprietary is the most common

distribution channel used by approximately one-third of funds, followed by Direct distribution

used by one-quarter of funds, and Institutional used by about 20% of funds.

Other Variables: I collect a number of additional variables in order to test different

theories for the paper’s main results. For each fund class, I collect data from Morningstar on

whether it is only open to institutional investors or whether retail investors are also allowed to

invest in the class.4 About 20% of fund classes in the sample are only open to institutions.

Morningstar also reports manager names and tenure dates and is my source for the dates of

manager changes. I use CRSP for fund closure dates.

3. Main Results

                                                                                                               4 CRSP also has an institutional dummy variable but it is only available after 2000, which is why I use the Morningstar variable.

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Before studying how flows are affected by ownership change announcements, I examine

whether such announcements are actually exogenous events or whether they can be predicted by

(and are correlated with) past fund performance or other fund characteristics. In Table 3, I run

PROBIT regressions with an event announcement dummy, which equals one if there was an

announced ownership change of the fund’s adviser in the current month and zero otherwise, as

the dependent variable, and fund/parent company characteristics as the explanatory variables.

The announcements include completed mergers and one uncompleted merger, Zion’s Bancorp

attempted purchase of First Security Corp. (manager of the Achievement fund family) that was

rejected by Zion’s shareholders in 2000.

As with all the tests in this paper, I first run regressions for the sample of all ownership

changes of fund advisers (Columns 1 and 2), then restrict the sample to public ownership of fund

advisers where we have publicly available data on the parent companies (Columns 3 and 4), and

finally include only publicly owned advisers whose parent companies’ main line of business is

not asset management (Columns 5 and 6). The main advantage of the sample restrictions is that

the events are more likely to be exogenous to what’s happening at the mutual fund level, while

the main disadvantage is a reduction in the number of observations. Columns 1, 3, and 5, of

Table 3 include a simple measure of past performance, the average style-adjusted returns over

the past year. Columns 2, 4, and 6, use a more comprehensive measure, five sets of return decile

dummies for each of the past five years.

The main takeaway from Table 3 is that mutual fund and parent company characteristics

are generally not predictive of event announcements. This indicates that the announced

ownership changes are in fact exogenous and the windows around the changes can be used as a

laboratory to study the effect on flows. We can see that past performance measures (returns and

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flows) are not significant predictors of ownership changes. In fact, the coefficient on past style-

adjusted returns is actually positive (although insignificant) indicating that better-performing

funds are more likely to be subject to an adviser ownership change.

The only significant (at the 5% level) predictive variables in any of the specifications are

family size and parent company size, with the negative coefficients indicating larger fund

families and parent companies are less likely to have an adviser ownership change. This finding

may be due to capital constraints since there are very few investors or firms who can buy the

largest asset management firms or parent companies.

After confirming that ownership changes are largely unrelated to mutual fund

characteristics, I next calculate average flows in the event window around the ownership

changes. I define PREANN and POSTANN dummy variables for the timing of each observation

around the event window. PREANN is set to one for all fund-month observations in the 6 months

prior to the announcement date of an ownership change of the fund adviser, and zero otherwise.

POSTANN is set to one for all fund-month observations on the announcement date and for one

year after the announcement date of an ownership change of the fund adviser, and zero

otherwise.

Panel A of Table 4 shows the average value of monthly Fund flows (%) for all

observations in each event window. As in Table 3, I start with the entire sample of events in

Column 1 and restrict the sample to public parent companies in Column 2 and public non-asset

management parent companies that undergo M&A in Column 3. Prior to the announcement date

(PREANN=1), flows are not statistically different from zero. After the event announcement date

(POSTANN=1), we can see statistically significant outflows. For example, in the sample of all

events, the average value of Fund flows (%) in the post-announcement window is -0.548%

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(~6.6% on an annualized basis) with a t-statistic of 3.63. The results are very similar in the

sample of all public parent mergers (Column 2). Finally, in the smaller sample of non-asset

management mergers (Column 3), the average value of fund flows (%) in the post-announcement

window is -0.737% (~8.8% on an annualized basis) with a t-statistic of 5.96. Across all three

specifications, the pattern of statistically insignificant flows prior to the announcement and

strong outflows after the announcement is repeated.

Two possible explanations for the results in Panel A are that the announcements happen

to funds that are, for other reasons, likely to experience outflows, or that they are clustered in

periods prior to fund outflows such as market peaks. In order to test these explanations, I

construct a matched sample for each event-window observation and then calculate the average of

match-adjusted flows (fund flows relative to matched sample). The matched sample flows for a

particular fund-month observation are a weighted average of flows across all funds in the same

month and in the same fund style (and in the same restricted sample in Columns 2 and 3), where

the weights are proportional to closeness based on differences in size, past five years of returns,

and fund age. Appendix B at the end of the paper describes the matching algorithm.

Panel B of Table 4 presents average values of monthly match-adjusted Fund flows (%)

for observations in each event window. As in Panel A, flows are indistinguishable from zero

prior to the announcement date, but turn lower after the announcement date. The average flows

are higher for both the pre- and post-announcement periods (due to the match adjustment) but the

difference between pre- and post- remains very similar suggesting that it is not unique timing or

differences in characteristics that explain the downturn in flows after the announcement dates of

adviser ownership changes.

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Next, I use a “difference-in-difference” approach, comparing match-adjusted fund flows

in the post-announcement period to the pre-announcement match-adjusted fund flows, for each

fund involved in an event. Using only observations in the event window (from 6 months before

the announcement date to 12 months after the announcement date), I regress match-adjusted fund

flows on POSTANN and also include firm fixed effects. Panel C of Table 4 presents estimated

coefficients on the difference-in-difference estimator, POSTANN, in this regression. The

coefficients range from -0.490% (~6% annualized) in the sample of all events to -0.681% (~8%

annualized) in the sample of public non-asset management merger events, and all coefficients are

statistically significant at the 1% level. We can calculate economic significance by looking at the

total assets in funds undergoing events from Table 1. Across all funds, there were 185 events

with $587 billion in assets so 6% outflows means about $190 million (($587b/185) × 6%) of

outflows per event. Across the sample of public non-asset management merger events, there

were 70 events with $145 billion in assets so 8% outflows means about $165 million of outflows

per event.

It is also instructive to look at a graphical representation of monthly match-adjusted fund

flows from 6 months prior to the announcement date to 12 months after the announcement date.

Figure 1 provides this graphical representation (with each month’s average flows and confidence

intervals) for the entire sample of parent company changes, while Figure 2 depicts the same

results for ownership changes of public non-asset manager parent companies.5 Note that the

flows in the two figures are not cumulative. The figures show that match-adjusted flows are near

zero prior to the announcement date, and then decline slowly after the announcement date before

accelerating downward in the final six months. Clearly, this is not the type of picture we are used

                                                                                                               5 The graph for Column 2 of Table 4, which includes all public parent company mergers, looks very similar and is available upon request.

  19  

to seeing for event studies measuring market price reaction to events. However, it is not

surprising that fund flows (predominantly due to retail flows, as I will show later) are much

slower to react to new information than market prices, due to limited retail investor attention. It

is also possible that fund investors wait under the deal is closed (usually 3 to 6 months after

announcement) before reacting, or learn about the deal from intermittent fund disclosures. I

compare flows prior to and after the effective date of the ownership change in the context of a

regression in Table 12.

Another method of analyzing the effect of ownership changes on fund flows is by using a

multi-variable panel regression to control for an array of fund and parent company

characteristics. I regress monthly Fund flows (%) on event window dummy variables (PREANN

and POSTANN), fund-level controls, parent-level controls, style dummy variables, prior return

deciles dummy variables, and time dummy variables. Table 5 presents estimated coefficients

from this OLS 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 4 and Figures 1 and 2. Fund flows (%) are close to zero prior to

announcement dates, and negative and significant after the announcement dates.

Table 5 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

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

  20  

coefficient on expense ratios is positive, although it is not significant in Columns 4 and 6.

Expense ratios 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 firms) also positively predict flows, as was previously

documented by Sialm and Tham (2011).

4. Motivation for Decline in Fund Flows

Tables 4 and 5 provide evidence that a mutual fund management company ownership

change causes a decrease in fund flows. The next step is to examine why investors might behave

in this way. One possible explanation is that fund investors trust the previous management firm

(due to advertising or their experience with the firm) and this relationship is weakened or

destroyed as a result of the ownership change, leading to an increase in the redemption of fund

shares. Alternatively, 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

  21  

be temporarily accompanied by less emphasis on marketing, advertising, and distribution,

leading to a decline in new fund purchases.

I test these competing hypotheses by decomposing fund flows into inflows (purchases of

shares) and outflows (sales of shares) and examining whether the aggregate flow 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 makes it obvious

that the aggregate results are entirely 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, indicating an increase in inflows around the announcement date. However, for

the corresponding outflows (Column 2), the coefficients rise even more, from 0.442% prior to

the announcement to 1.332% after the announcement. The large increase in outflows

overwhelms the small increase in inflows leading to the aggregate decline in fund flows seen in

Table 5. The same results can be seen for the restricted samples in Columns 3 through 6. Overall,

any changes in inflows are small while the outflows increase dramatically, providing support for

the hypothesis that current fund investors are reacting to the ownership change by redeeming

their shares.

Still, while the increase in outflows is consistent with the trust hypothesis, it is not

conclusive as to why investors are selling shares in the fund. One way to get at their motivation

is to look at how different clienteles react to event announcements. The importance of trust and

relationships should be more important for less sophisticated investors who don’t have the skills

  22  

or resources to monitor the fund’s management. Therefore, I test the trust hypothesis by looking

at 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

aggregated to 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, parent-

level controls, style dummy variables, prior return deciles dummy variables, and time dummy

variables. Table 7 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. It is easy to see from Table 7 that

the aggregate results in Table 5 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.

These results are somewhat surprising because retail investors are thought to be less

attentive and more prone to inertia than institutional investors. In contrast, Table 7 shows that

retail investors’ reaction is much stronger to this particular news, suggesting that they attach

  23  

more value to their relationship with their fund advisor than institutional investors. For instance,

they might be clients of the parent company, obtaining banking, insurance, or other financial

services from that company, and then deciding to move their fund portfolio elsewhere if the

parent company is acquired by another financial services firm.

Another way to understand the motivation for investor reactions to ownership changes is

by looking at funds with different levels of expense ratios. Therefore, I next regress fund flows

on event window dummy variables and controls, separately for funds with above-median

expense ratios (high expense funds) and below-median expense ratios (low expense funds). If

investors are willing to pay higher expense ratios because they have a special (trusting)

relationship with the management company, we would expect to see more outflows at high-

expense funds than low-expense funds after ownership changes.

Table 8 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. In Table 8, we can see

significantly 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%. The

results in Table 8 confirm that investors willing to pay higher expenses (because they place some

intangible value on investing in the fund) are also the ones more likely to pull out as a result of

an announced change in ownership.

  24  

5. Cross-Sectional Variation in Fund Outflows in the Post-Announcement Window

The results so far suggest that the average fund undergoing a change in ownership suffers

a decline in flows, but there is significant cross-sectional variation in post-announcement flows.

There are also significant differences in acquiring companies, funds managed by the acquiror,

and purposes for acquisitions, leading to an intriguing question: Are investor reactions to

ownership changes affected by the acquiror or the reason for the acquisition or are they simply

reactions to the news that the prior management firm will be acquired? For instance, if the

acquiror is (or owns) a mutual fund management company with a strong track record, one might

expect more inflows to (or less outflows from) the target’s funds. Alternatively, if the acquiror

has little expertise in the mutual fund industry, investors might be even more likely to sell their

assets in response to the ownership change announcement. I test these competing hypotheses in

Table 10.

Before discussing the regressions, Table 9 presents the relevant summary statistics for the

explanatory variables used in Table 10. Panel A shows that there is no purpose available (in SDC

Platinum) for over 56% of the events in the paper. Of the remaining events, the top three reasons

for mergers are to strengthen the firm’s operations, create synergies, and expand presence into

new markets. These are standard explanations for M&A activity, so it does not seem like the

financial industry acquisitions used in this paper are very different from most mergers. Some

events have multiple purposes so the percentages do not add up to 100%.

Panel B shows the breakdown for the type of acquiring firms. Almost half are banks, one-

third are securities firms (broker-dealers, investment banks, asset managers, etc.), and the rest are

insurance companies. More than 6 in 7 acquiring companies are publicly traded, but of these

public firms, only 85% are in CRSP while the other 15% are traded over-the-counter or overseas.

  25  

Panel C shows some additional summary statistics for the acquiring firms. Their industry-

adjusted stock returns prior to the acquisition announcement are positive: 0.82% per month over

the prior year, and 0.39% per month over the prior three years. This is consistent with successful

firms being more likely to enter into acquisitions than struggling firms. On average, acquiring

firms get only 5% of revenues from mutual fund expenses, consistent with most of them being

banks and insurance companies. Finally, the style-adjusted returns of their mutual funds prior to

acquisition are not statistically different from zero.

In Table 10, I regress post-announcement match-adjusted fund flows for funds whose

management undergoes a change in ownership on acquiror characteristics and dummy variables

representing acquisition purposes. In Column 1 of Panel A, the predictive variables are indicators

for whether the acquiring firm is private, an insurance company, or a securities company (banks

are the omitted category). Interestingly, the coefficients on insurance and securities dummy

variables are positive and statistically significant, indicating that investors react more negatively

to acquisitions by banks than other financial firms. One possible explanation for this result is that

investors see banks as having less expertise in money management. Alternatively, since banks

are most likely to buy other banks, bank mutual fund investors might place a higher value on

trust than investors in funds run by insurance companies or securities firms.

In Columns 2 and 3 of Panel A, I also include past stock performance of the acquiring

firm, and in Column 4, I include the proportion of firm revenues from mutual funds. None of the

coefficients are significant so neither the past financial success of the acquiring firm, nor its

concentration on asset management, seems to matter for investors in funds run by the target firm.

Finally, in Column 5, I add dummies for the various possible purposes of the transaction. Only

COR (concentrate on core businesses) and ISV (increase shareholder value) are significantly

  26  

correlated with fund flows, with each having a negative coefficient. However, these two

purposes only represent 6% of transactions (that have a purpose given) so there is not much that

we can glean from these results.

In Panel B of Table 10, I regress match-adjusted fund flows on family size and

performance of the funds run by the acquiring firm. In Columns 1 and 2, performance is

measured by average monthly style-adjusted returns (in the prior 12 and 36 months, respectively)

of funds managed by the acquiror. In Columns 3 and 4, performance is measured as the average

monthly style-adjusted returns of funds (managed by the acquiror) that have the same style as the

fund whose manager is being acquired. The hypothesis behind this test is that if the acquiror has

a good track record of managing small-cap value funds, then investors in a small-cap value fund

that is being acquired might react positively (or less negatively) than they otherwise would.

However, the coefficients on the acquiror funds’ performance metrics are all insignificant, so the

data does not support this hypothesis.

In summary, Table 10 suggests that neither the recent financial performance of the

acquiring company nor the past returns of the mutual funds that it manages affects portfolio

decisions of investors in funds managed by the target. Thus, these investors are not performing a

Bayesian updating of future expected fund performance based on how well the acquiring firm

has done in the past. Instead, their adverse reaction seems to be due to the fact that their

relationship with the acquired firm is coming to an end.

6. Alternative Explanations and Robustness Checks

While the evidence so far seems to support the trust hypothesis, there are a number of

other possible explanations for investors selling their fund shares in response to an ownership

  27  

change. One such explanation is that the management change coincides with a change in the

primary distribution channel used by the fund. The main types of distribution channel are

through brokers, direct to investors (Direct), through institutional channels (Institutional) such as

retirement accounts, or to members of an organization that also runs the fund (Other). The

brokers can be affiliated with the bank (Bank Proprietary), insurance company (Insurance), or

securities firm (Proprietary) that run the fund or not affiliated with the management company at

all. If a mutual fund family that is distributed by non-affiliated brokers is purchased by a

company that has its own affiliated brokers, the original brokers can advise their clients to

withdraw their assets which would show up in the form of fund outflows.

In Table 11, I investigate the effect of distribution networks on fund flows. In Panel A, I

add distribution channel dummy variables to my main specifications, which control for the

primary type of distribution network used by each fund. The coefficients on the main variable of

interest (POSTANN) remain largely unchanged and are still statistically significant at the 1%

level. Insurance is the only type of distribution channel that is significantly different from the

“Other” distribution channel (the omitted category) and comes in with a positive coefficient.

In Panel B of Table 11, I exclude all observations where the fund that underwent an

ownership change also had a change in the primary type of distribution channel at any point in

the two years following the announcement date. This leaves a sample of funds where the primary

distribution channel remained the same so it is unlikely to be the cause for the outflows. Again,

the coefficients on POSTANN are of similar magnitude and remain statistically significant.

These findings are not too 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

after a change in ownership. This is because most of the mergers used for this study consist of

  28  

parent firms of similar types (Banks buying banks, insurance buying insurance). In most of the

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.

Figures 1 and 2 suggest that outflows are different prior to and after the effective date of

the ownership changes. I analyze the effect of the effective date of the ownership change by

running the regressions in Table 5 while 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 12 presents estimated coefficients

from a multi-variable panel regression of Fund flows (%) on the finer event window variables,

fund-level controls, parent-level controls, style dummy variables, prior return deciles dummy

variables, and time dummy variables.

In Column 1 of Table 12, which includes the entire sample of events, the outflows before

and after the effective date are of similar magnitudes and are both statistically significant at the

5%-level. However, as we move to the more restrictive sample of events in Columns 2 and 3, the

outflows prior to the effective date are smaller and no longer significant while those after the

effective date are larger and statistically significant. In the sample of public non-asset

management firms (Column 3), the coefficient on the post-effective date dummy variable,

POSTANN_POSTEFF, is -0.735%, which is more than three times as large as the coefficient on

the post-announcement, pre-effective date dummy variable. In summary, Table 12 suggests that

  29  

there are some investors who start to withdraw assets prior to the effective date but that the effect

intensifies after the closing date of the merger or acquisition.

Another concern for our study is that there is a large concentration of events in 1999 and

2000, which coincided with the creation and bursting of the dot-com bubble. Approximately one-

quarter of all events happen during this period of high volatility and unusual phenomena in the

financial markets. In Panel A of Table 13, I perform a robustness check by dropping all fund-

month observations during that period. The coefficients remain largely the same and are still

statistically significant, although the t-stats are slightly smaller due to fewer events.

An additional problem is that advisor ownership changes are also associated with an

increase in fund closures and manager turnover as the new ownership tweaks its array of offered

funds and the managers of those funds. Investors might react to expected or realized manager

change or announced fund closure by withdrawing money from the fund because they like the

current manager or because they don’t want to wait until the fund closes and their assets are

merged into a different fund. I test this explanation by generating Fund closure, a dummy

variable that equals one in the six months prior to a fund closure (and zero otherwise), and

Manager change, a dummy variable that equals one in the six months prior to and after a

manager change (and zero otherwise),

I regress monthly Fund flows on the standard event window dummy variables, Fund

closure, and the other standard controls from Table 5, and report the results in Panel B of Table

13. While the coefficients on POSTANN are slightly smaller than those in Table 5, the outflows

after the announcement date are still statistically significant. In Panel C, I repeat the same test as

in Panel B but include the Manager change dummy variable. Once again, the coefficients on the

variable of interest, POSTANN, are unchanged and remain statistically significant. In another

  30  

robustness check, I drop the funds with Manager change or Fund closure equal to one and find

similar results.

Another possible explanation is that advisor ownership changes might be associated with

lower future returns. During the ownership transition period, fund managers might be expending

effort on ensuring a smooth transition and putting in less effort on the actual management of the

fund leading to lower returns. Investors might be removing their money from the fund to avoid

these anticipated lower returns. I test this theory in Panel D of Table 13 by regressing fund

returns on the standard event window dummy variables and standard set of controls, and report

the results in Table 10. There is no evidence of return underperformance in the twelve months

following a management company ownership change.

7. Conclusion

Investors choose portfolio managers based not only on forecasts of future performance,

but also on factors such as trust and reliability (the ability to “sleep at night”) that are established

over long periods of interaction. In this paper, I examine how investors react when these

relationships are potentially broken due to mergers and acquisitions involving the fund’s

investment adviser. In spite of the fact that I find no detrimental effect on performance in the

wake of ownership changes, fund flows do deteriorate, only weakly after announcement but

more strongly after the change becomes effective. Funds suffer declines in flow equal to

approximately 7% of their assets in the year after announcements of ownership changes, which is

a significant economic cost for the new owners of the investment adviser.

There are several possible explanations for these findings. The results are driven by an

increase in redemption (outflows) rather than decrease in new purchases (inflows). Additional

  31  

tests suggest that familiarity and trust play a significant role. Retail investors’ reaction to fund

adviser ownership changes is stronger than that of institutional investors, which is consistent

with the story that trust is more important for less sophisticated investors in making investment

decisions. Investors in high-expense funds also have stronger outflows. This fits the story of

some investors willing to pay a premium (through higher expenses) for investing with a

trustworthy adviser, and then pulling their money out when the adviser is acquired by another

firm. Overall, the paper highlights the important role of intangible qualities and relationships

when individuals make investment decisions.

  32  

Appendix A

Appendix A includes 15 examples of mergers/acquisitions from the sample of 185 events

that make up this study. Panel A includes 5 events from mergers not involving public parent

companies. Panel B includes 5 events from mergers that involve acquisition of public parents

that are primarily asset managers. Finally, Panel C includes 5 events from mergers that involve

acquisitions of public parents that are primarily not asset managers. Within each panel, the

events are listed in chronological order (by announcement date). For each event, the appendix

includes the name of the acquiror and target, firm types (securities, insurance, or bank), whether

they are public or not and the country where they are listed, whether they are an asset manager,

and the name of the main fund family owned by each acquiror and target. I also list the

announcement date and effective date of each acquisition. The source for all data is the SDC

Platinum M&A database. The examples are chosen to illustrate the different types of mergers

that make up the sample.

Panel A: Mergers not involving acquisitions of public parent companies Acq: Franklin Resources Securities Public (U.S.) Asset Manager Franklin Templeton

Announced: 06/25/1996

Effective: 11/01/1996

Tgt: Heine Securities Securities Private (U.S.) Asset Manager Mutual Series

Acq: Allianz Insurance Public (Germany) Non Asset Mgr. PIMCO

Announced: 10/18/2000

Effective: 01/31/2001

Tgt: Nicholas-Applegate Securities Private (U.S.) Asset Manager Nicholas Applegate

Acq: Legg Mason Securities Public (U.S.) Asset Manager Legg Mason

Announced: 06/24/2005

Effective: 12/01/2005

Tgt: Smith Barney A.M. Securities subsidiary unit Asset Manager Smith Barney

Acq: Susquehanna Banc. Bank Public (U.S.) Non Asset Mgr. None

Announced: 01/02/2008

Effective: 04/30/2008

Tgt: Stratton Mgmt Securities Private (U.S.) Asset Manager Stratton

  33  

Acq: Guggenheim Ptnrs Securities Private (U.S.) Non Asset Mgr. None

Announced: 01/02/2008

Effective: 08/02/2010

Tgt: Security Benefit Insurance Private (U.S.) Non Asset Mgr. SGI (et al.) Panel B: Mergers involving acquisitions of public parent asset managers Acq: Reliastar Insurance Public (U.S.) Non Asset Mgr. Northstar

Announced: 07/22/1999

Effective: 10/29/1999

Tgt: Pilgrim Capital Securities Public (U.S.) Asset Manager Pilgrim America

Acq: UniCredit Bank Public (Italy) Non Asset Mgr. None

Announced: 05/15/2000

Effective: 10/25/2000

Tgt: Pioneer Group Securities Public (U.S.) Asset Manager Pioneer Acq: CDC Securities Private (France) Non Asset Mgr. CDC MPT+

Announced: 06/16/2000

Effective: 10/30/2000

Tgt: Nvest Securities Public (U.S.) Asset Manager Loomis Sayles (et al.)

Acq: Old Mutual Insurance Public (U.K.) Non Asset Mgr. None

Announced: 06/19/2000

Effective: 10/05/2000

Tgt: United Asset Mgrs Securities Public (U.S.) Asset Manager UAM (et al.)

Acq: Lehman Brothers Securities Public (U.S.) Non Asset Mgr. None

Announced: 07/22/2003

Effective: 10/31/2003

Tgt: Neuberger Berman Securities Public (U.S.) Asset Manager Neuberger Berman

Panel C: Mergers involving acquisitions of public parent non-asset managers Acq: U.S. Bancorp Bank Public (U.S.) Non Asset Mgr. First American

Announced: 12/15/1997

Effective: 05/01/1998

Tgt: Piper Jaffray Securities Public (U.S.) Non Asset Mgr. Piper

Acq: NationsBank Bank Public (U.S.) Non Asset Mgr. Nations

Announced: 04/13/1998

Effective: 09/30/1998

Tgt: BankAmerica Bank Public (U.S.) Non Asset Mgr. Pacific Horizon (et al.)

  34  

Acq: Manulife Financial Insurance Public (U.S. ADR) Non Asset Mgr. None

Announced: 09/29/2003

Effective: 04/29/2004

Tgt: John Hancock Insurance Public (U.S.) Non Asset Mgr. John Hancock

Acq: TD Bank Group Bank Public (Canada) Non Asset Mgr. TD

Announced: 08/26/2004

Effective: 03/01/2005

Banknorth Bank Public (U.S.) Non Asset Mgr. Banknorth

Acq: M&T Bank Bank Public (U.S.) Non Asset Mgr. MTB

Announced: 11/01/2010

Effective: 05/17/2011

Tgt: Wilmington Trust Bank Public (U.S.) Non Asset Mgr. WT (et al.)

  35  

Appendix B

Appendix B describes the matching procedure used to generate match-adjusted flows in

Section 3 of the paper.

For each fund-month observation that requires a matched sample, I take the set of all

funds in the same month and in the same fund style (e.g., small value, large blend, etc.). If the

match is for events including only public parent mergers, I also exclude non-public run funds

from the matched sample. If the match is for events including only public non-asset management

firms, I only include funds owned by such firms in the matched sample.

Once I have the matched sample, I calculate weights that depend on how close each

matched fund is to the fund that I am matching. Closeness is measured by past returns, size, and

age. For past returns, if my observation has one year of returns available then I match on past

year’s returns, if it has three years of returns, I match on past three year returns, and if it has five

years of returns, I match on past five year returns. I calculate the difference in returns between

each matched fund and the original fund, then standardize these differences by the standard

deviation of differences across the matched sample, then square this standardized quantity. For

size, I take the differences in log assets, then standardize these differences by the standard

deviation of differences across the matched sample, then square this standardized quantity.

Finally, for age, I take the differences in log age, then standardize these differences by the

standard deviation of differences across the matched sample, then square this standardized

quantity.

Finally, I add the three squared differences in past returns, size, and age, and set weight

equal to one divided by the sum of squared differences. The matched flows are calculated as the

weighted average of flows across the matched sample using the weights described above.

  36  

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659-680.

  37  

Jensen, Michael C., 1968, The performance of mutual funds in the period 1945-1964, Journal of Finance 23 (2), 389-416. Khorana, Ajay, Peter Tufano, and Lei Wedge, 2007, Board structure, mergers, and shareholder wealth: A study of the mutual fund industry, Journal of Financial Economics 85 (2), 571-598.

Massa, Massimo and Zahid Rehman, 2008, Information flows within financial conglomerates: Evidence from the banks – mutual funds relation, Journal of Financial Economics 89 (2), 288-306.

Mullainathan, Sendhil, Joshua Schwartzstein, and Andrei Shleifer, 2008, Coarse thinking and persuasion, Quarterly Journal of Economics 123 (2), 577-619.

Sialm, Clemens, and T. Mandy Tham, 2011, Spillover effects in mutual fund companies, working paper Sirri, Erik R., and Peter Tufano, 1998, Costly search and mutual fund flows, Journal of Finance 53, 1589–1622.

  38  

Table 1: Summary statistics for ownership change announcements of mutual fund management firms Table 1 displays summary statistics for announced ownership changes for the sample period from July 1995 to December 2011. For each year, Panel A reports the total number of events during that year, the number of actively-managed domestic equity mutual funds involved in those events, and the total assets under management of these funds. Panel B reports the percent of events involving different pairs of acquirer types and target types, where the three types are banks (SIC 6000-6199), securities firms (SIC 6200-6299), and insurance companies (SIC 6300-6499). Holding companies (SIC 6700-6799) are classified based on SDC Platinum industry classifications. Columns (1) through (3) of Panel A (and columns (1) and (2) of Panel B) display data for all announced changes in ownership, except for those involving initial public offerings or management buyouts. Columns (4) through (6) of Panel A (and columns (3) and (4) of Panel B) show summary statistics for a subset of events, where the parent company is a publicly traded company that is in CRSP, and where this public parent is to be acquired by another company. Columns (7) through (9) (and columns (5) and (6) of Panel B) show summary statistics for the subset of events in which the public parent company is not primarily an asset management firm (less than 10% of its revenues derives from mutual fund fees). The last line of Panel A shows the sum total of all events for 16.5 years of activity.

Panel A: Merger summary statistics by year All parent company changes All public parent changes All non-AM public parent chgs

Events # of funds AUM ($bil) Events # of funds AUM ($bil) Events # of funds AUM ($bil)

Year (1) (2) (3) (4) (5) (6) (7) (8) (9)

1995 9 17 $5.0 7 15 $4.8 7 15 $4.8 1996 7 30 $33.8 2 5 $0.6 2 5 $0.6 1997 27 69 $51.3 15 35 $25.3 15 35 $25.3 1998 18 50 $13.9 10 32 $9.7 9 28 $9.2 1999 11 44 $17.9 7 31 $13.3 5 14 $3.6 2000 29 135 $69.5 17 108 $54.5 13 43 $16.5 2001 18 84 $52.6 3 8 $2.1 3 8 $2.1 2002 9 19 $2.4 0 0 $0.0 0 0 $0.0 2003 5 51 $38.6 3 49 $38.5 2 39 $28.4 2004 10 55 $33.9 5 18 $15.1 5 18 $15.1 2005 8 38 $41.7 1 2 $0.4 1 2 $0.4 2006 9 84 $70.9 2 44 $23.6 2 44 $23.6 2007 2 19 $48.8 1 1 $0.0 1 1 $0.0 2008 6 41 $10.9 3 30 $8.5 3 30 $8.5 2009 7 66 $80.1 0 0 $0.0 0 0 $0.0 2010 8 38 $15.3 2 13 $6.8 2 13 $6.8 2011 2 3 $0.1 0 0 $0.0 0 0 $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 All non-AM public parent chgs

Merger Type % Merger Type % Merger Type % (1) (2) (3) (4) (5) (6)

Securities <-> Securities 32.4% Bank <-> Bank 69.2% Bank <-> Bank 77.1% Bank <-> Bank 30.8% Securities <-> Securities 10.3% Securities <-> Securities 8.6% Bank <-> Securities 17.8% Insurance <-> Securities 7.7% Bank <-> Securities 5.7% Insurance <-> Securities 13.0% Bank <-> Securities 6.4% Insurance <-> Insurance 4.3% Insurance <-> Insurance 3.8% Insurance <-> Insurance 3.8% All Other Types 4.3% All Other Types 2.2% All Other Types 2.6%

  39  

Table 2: Summary statistics for fund-level and parent-level variables Table 2 presents summary statistics for the main fund-level and parent company-level variables used in this study. First, I tabulate cross-sectional statistics by month, and then take the time-series average of each statistic across the 216 months of the sample period from January 1995 through December 2012. Fund flows ($) is the asset flows (in millions of $) into a fund for a particular month, which is defined as fund assets at the end of the month minus the product of fund assets at the beginning of the month and one plus the fund’s monthly return. Fund flows (%) is just Fund flows ($) divided by the fund’s assets at the start of the month, then winsorized for each month at the 1% and 99% levels. Inflows is the dollar amount of inflows (purchases of fund shares) for a particular month, divided by the fund’s assets at the start of the month. Outflows is the dollar mount of outflows (sales of fund shares) for a particular month, divided by the fund’s assets at the start of the month. Fund AUM is the fund’s assets (in millions of dollars) at the start of the month, and Log fund AUM is the natural logarithm of Fund AUM. Family AUM is the assets of the entire fund family at the start of the month, and Log family AUM is the natural logarithm of Family AUM. Fund age is equal to one plus the number of years since the fund began operations, while Log fund age is the natural logarithm of Fund age. Expense ratio is the fund’s expense ratio, while Log expense ratio is the natural logarithm of Expense ratio. Private firm is a dummy variable that equals one when the parent of the investment management company is not publicly traded, not a mutual insurance company and not a non-profit organization, and zero otherwise. Public non-a.m. firm is a dummy variable that equals one when the parent of the investment management company is public, has data in COMPUSTAT/CRSP, and earns less than 10% of its annual revenues from mutual fund fees, and zero otherwise. Log parent marketcap is only available for public parent companies that are also in CRSP, and equals the natural logarithm of their market capitalization at the start of the month. Stock returns is also only available for public parent companies that are also in CRSP, and equals their average monthly stock returns over the prior twelve months. Mutual fund revenues is also only available for public parent companies that are also in COMPUSTAT and equals the total annual expenses collected by all funds in the family (product of annual expense ratios and assets under management) divided by the parent company’s annual COMPUSTAT revenues.

# of Obs. Mean Median St.Dev. 10% 90% Variable (1) (2) (3) (4) (5) (6) Fund flows ($mil), monthly 348158 0.3 -0.3 67.6 -17.7 17.3 Fund flows (%), monthly 348158 0.4% -0.2% 4.5% -3.0% 4.4% Inflows (%), monthly 319285 3.8% 1.9% 7.0% 0.4% 8.1% Outflows (%), monthly 319284 3.2% 2.1% 5.4% 0.7% 6.0%

Fund AUM ($mil) 348158 1198 216 4482 26 2332 Log fund AUM 348158 5.5 5.4 1.7 3.3 7.7 Family AUM ($mil) 348891 60632 10807 155565 218 101807 Log family AUM 348891 8.7 9.0 2.4 5.3 11.4 Fund age (years) 347530 13.0 8.9 13.3 2.8 28.2 Log fund age 347530 2.2 2.2 0.9 1.0 3.3 Expense ratio (%) 338064 1.3% 1.2% 0.5% 0.8% 1.8% Log expense ratio 338064 -4.4 -4.4 0.4 -4.9 -4.0

Private firm (dummy) 351120 0.42 0.00 0.49 0.00 1.00 Public non-a.m. firm (dummy) 351120 0.35 0.00 0.47 0.00 1.00 Log parent marketcap 166554 2.8 2.8 0.1 2.6 2.9 Stock returns (avg over prior yr) 165921 1.1% 1.1% 1.9% -1.0% 3.4% Mutual fund revenues (%) 168953 8.4% 0.9% 15.5% 0.1% 31.1%

         

  40  

Table 3: Determinants of announced changes in ownership of mutual fund management companies  Table 3 presents estimated coefficients of PROBIT regressions of ownership change indicator variables on fund and parent company characteristics. The dependent variable in columns (1) and (2) is All Changes which is set to one for a fund-month observation if there was an announcement of a change in the ownership of the fund’s management firm in a particular month, and zero otherwise. For columns (3) and (4), the sample is restricted to funds whose management firms have a publicly-traded parent with data in CRSP, while for columns (5) and (6), the sample includes only funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1 for definition). Style-adjusted returns (prior year) is the average style-adjusted return for the prior twelve months. Fund flows (prior6mths) is the average value of the Fund flows (%) variable over the past six months. All other variables are defined in Table 2. All regressions include time dummies and fund style controls. Columns (2), (4), and (6) also include prior return decile dummies for each of the previous five years. The sample period for all regressions is from June 1995 to December 2011. T-statistics, using standard errors clustered at the management company level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Marginal effects are shown under the t-statistics.

Dep Var: Events All Changes Public Parent Public/Non AM

Regression: PROBIT PROBIT PROBIT PROBIT PROBIT PROBIT Predictor Variables (1) (2) (3) (4) (5) (6) Style-adjusted returns 1.612

6.060

1.346

(prior year) [0.57]

[1.60]

[0.32]

1.00%

2.91%

0.73%

Fund flows -0.693

-0.621

-0.917

-0.630

-0.976

-0.777 (prior 6 months) [1.29]

[1.06]

[1.51]

[0.94]

[1.19]

[0.90]

-0.43%

-0.38%

-0.44%

-0.28%

-0.53%

-0.39% Log fund AUM 0.009

0.010

-0.003

0.003

0.033

0.033

[0.66]

[0.67]

[0.15]

[0.14]

[1.38]

[1.27]

0.01%

0.01%

0.00%

0.00%

0.02%

0.02%

Log family AUM -0.025 * -0.025

-0.068 *** -0.070 *** -0.029

-0.027

[1.67]

[1.64]

[3.06]

[3.12]

[1.01]

[0.96]

-0.02%

-0.02%

-0.03%

-0.03%

-0.02%

-0.01% Log fund age (years) 0.024

0.000

-0.018

-0.033

-0.042

-0.028

[1.14]

[0.00]

[0.65]

[0.83]

[1.25]

[0.63]

0.01%

0.00%

-0.01%

-0.01%

-0.02%

-0.01%

Log expense ratio 0.091 * 0.093 * -0.033

-0.038

0.005

-0.006

[1.70]

[1.75]

[0.46]

[0.54]

[0.05]

[0.07]

0.06%

0.06%

-0.02%

-0.02%

0.00%

0.00% Private firm (dummy) -0.248 *** -0.239 ***

[3.17]

[3.13]

-0.15%

-0.14%

Log parent marketcap

-0.128

-0.139

-0.903 ** -0.904 **

[0.34]

[0.37]

[2.25]

[2.28]

-0.06%

-0.06%

-0.49%

-0.45% Stock returns (prioryr)

-3.433

-3.411

-4.150

-4.042

[1.38]

[1.41]

[1.35]

[1.34]

-1.65%

-1.51%

-2.25%

-2.01%

Mut. fund revenues (%)

0.333

0.367

-0.772

-0.803

[1.22]

[1.43]

[0.73]

[0.76]

0.16%

0.16%

-0.42%

-0.40% Observations 305334 308413 119639 120795 90098 90790

Time dummies YES YES YES YES YES YES Return decile dummies NO YES NO YES NO YES Fund style dummies YES YES YES YES YES YES Log-likelihood -5398.9 -5468.3 -2276.3 -2298.7 -1756.9 -1760.0

  41  

Table 4: Fund flows around changes in parent companies of fund management firms Table 4 presents average values of mutual fund flows around event windows (parent company changes of fund management firms). Panel A shows results for average (unadjusted) Fund flows (%), while Panel B displays average Fund flows (%) after adjusting for a matched sample of funds with the same style and similar age, size, and prior performance characteristics. Panel C shows fixed-effects regressions across the event window using match-adjusted Fund flows (%). PREANN is a dummy variable that equals one for all fund-month observations from months t–6 to t–1, where t is the announcement date of the event, and zero otherwise. POSTANN is a dummy variable that equals one for all fund-month observations from month t until t+12, where t is the announcement date of the event. Column (1) in each panel includes all ownership changes, while columns (2) and (3) include subsets of events (see Table 1 for definitions). T-statistics, using standard errors clustered at the event level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Monthly fund flows (unadjusted) around event windows

Changes: All Public Public

Changes Parent Non AM

Timing around event (1) (2) (3) PREANN (dummy) 0.038%

-0.058%

0.005%

t-stat [0.25]

[0.36]

[0.02] # of fund-month obs. 4601

2043

1533

POSTANN (dummy) -0.548% *** -0.483% ** -0.737% *** t-stat [3.63]

[1.99]

[5.96]

# of fund-month obs. 9443

4178

3106

Panel B: Monthly fund flows (match-adjusted) around event windows 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) -0.320% *** -0.197%

-0.395% **

t-stat [2.60]

[1.03]

[2.42] # of fund-month obs. 9443

4178

3106

Panel C: Fixed effect regression of Fund Flows (match-adjusted) around event

Changes: All Public Public

Changes Parent Non AM

Timing around event (1) (2) (3) POSTANN (dummy) -0.490% *** -0.542% *** -0.681% *** t-stat [3.78]

[2.87]

[4.07]

# of fund-month obs. 14044

6221

4639

Firm FE YES YES YES

  42  

Table 5: Regressions of fund flows on event window indicator variables Table 5 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fund-level and parent-level control variables. In each specification, the dependent variable is Fund Flows (%) and the variables of interest are PREANN, indicating observations in the six months prior to an announced ownership change and POSTANN, indicating observations on or in the twelve months after the event announcement date (see Table 4 for exact definitions). All controls are defined in Table 2. Columns (1) and (2) include all funds, while column (3) and (4) restrict the sample to funds (and events) with public management companies, and columns (5) and (6) only include funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1). All specifications include time dummies and prior return decile dummies for each of the previous five years. Columns (2), (4), and (6) also include fund style dummies, as well as additional fund and parent company controls. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered by fund family, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Dep.Var: Monthly Fund Flows (%)

Regression: OLS OLS OLS OLS OLS OLS Changes: All All Public Parent Public Parent Pub/Non AM Pub/Non AM

Predictor Variables (1) (2) (3) (4) (5) (6) PREANN (dummy) -0.169%

-0.125%

-0.186%

-0.066%

-0.030%

0.045%

[1.15]

[0.83]

[0.95]

[0.35]

[0.17]

[0.25]

POSTANN (dummy) -0.496% *** -0.438% *** -0.564% *** -0.579% *** -0.597% *** -0.540% ***

[4.23]

[3.64]

[3.43]

[3.29]

[3.67]

[3.08]

Log fund AUM

-0.042% **

-0.067% **

-0.005%

[1.98]

[2.06]

[0.14]

Log family AUM

0.046% **

0.119% ***

0.093% ***

[2.53]

[3.85]

[2.79]

Log fund age (years)

-0.372% ***

-0.273% ***

-0.284% ***

[9.59]

[5.69]

[4.76]

Log expense ratio

0.150% **

0.025%

0.035%

[2.19]

[0.25]

[0.34]

Private firm (dummy)

0.280% ***

[3.50]

Log parent marketcap

-0.543%

-0.913% *

[1.43]

[1.91]

Stock returns (prioryr)

5.077% ***

3.410% ***

[3.88]

[2.85]

Observations 344014 335118 163997 155647 119522 114524 Return decile dummies YES YES YES YES YES YES Time dummies YES YES YES YES YES YES Fund style dummies NO YES NO YES NO YES

     

  43  

Table 6: Regressions of fund inflows and outflows on event window indicator variables Table 6 presents estimated coefficients from OLS regressions of fund inflows and outflows on event window indicators and various fund-level and parent-level control variables. In columns (1), (3), and (5), the dependent variable is Inflows (%), while in columns (2), (4), and (6), the dependent variable is Outflows (%). In all specifications, the variables of interest are PREANN, indicating observations in the six months prior to an announced ownership change and POSTANN, indicating observations on or in the twelve months after the event announcement date (see Table 4 for exact definitions). All controls are defined in Table 2. Columns (1) and (2) include all funds, while column (3) and (4) restrict the sample to funds (and events) with public management companies, and columns (5) and (6) only include funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1). All specifications include time dummies and prior return decile dummies for each of the previous five years. Columns (2), (4), and (6) also include fund style dummies, as well as additional fund and parent company controls. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered by fund family, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Dep.Var: Monthly Fund Inflows and Outflows (%)

Regression: OLS OLS OLS OLS OLS OLS Changes: All All Public Parent Public Parent Pub/Non AM Pub/Non AM

Inflows/Outflows Inflows (%) Outflows (%) Inflows (%) Outflows (%) Inflows (%) Outflows (%) Predictor Variables (1) (2) (3) (4) (5) (6) PREANN (dummy) 0.181%

0.442% * 0.247%

0.337%

-0.132%

-0.214%

[0.67]

[1.89]

[0.47]

[0.84]

[0.34]

[0.75]

POSTANN (dummy) 0.323%

1.332% *** 0.372%

1.439% * -0.165%

0.639% *

[0.98]

[2.95]

[0.71]

[1.91]

[0.58]

[1.84]

Log fund AUM -0.242% *** -0.180% *** -0.356% *** -0.268% *** -0.349% *** -0.344% ***

[5.78]

[4.15]

[5.98]

[3.92]

[4.51]

[4.04]

Log family AUM 0.207% *** 0.146% *** 0.321% *** 0.174% *** 0.306% *** 0.196% ***

[6.65]

[5.40]

[6.39]

[2.91]

[5.16]

[4.06]

Log fund age (years) -0.654% *** -0.281% *** -0.329% *** -0.029%

-0.203%

0.142%

[7.79]

[3.35]

[2.63]

[0.20]

[1.35]

[0.81]

Log expense ratio 0.545% *** 0.266% ** 0.204%

0.040%

0.182%

-0.029%

[3.80]

[1.99]

[1.08]

[0.23]

[0.89]

[0.15]

Private firm (dummy) 0.105%

-0.114%

[0.74]

[0.87]

Log parent marketcap

-1.069%

-0.458%

-0.647%

0.241%

[1.16]

[0.50]

[0.75]

[0.35]

Stock returns (prioryr)

3.431% * -2.065%

2.261%

-0.696%

[1.82]

[0.99]

[1.38]

[0.42]

Observations 307545 307544 142229 142228 104921 104920 Return decile dummies YES YES YES YES YES YES Time dummies YES YES YES YES YES YES Fund style dummies YES YES YES YES YES YES

     

  44  

Table 7: Regressions of fund flows on event window indicator variables for retail vs. institutional classes Table 7 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fund-level and parent-level control variables. Unlike other regressions in this paper, the observations used in this table are at the fund class-month level, and Log fund AUM, Log fund age (years), and Log expense ratio are also calculated separately for each fund class. In columns (1), (3), (5), the sample consists of retail classes of mutual funds, while columns (2), (4), and (6), the sample includes classes open only to institutions. PREANN and POSTANN indicate timing around event windows (see Table 4 for precise definitions) and all other controls are defined in Table 2. Columns (1) and (2) include all funds, while columns (3) and (4) restrict the sample to funds (and events) with public management companies, and columns (5) and (6) only include funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1 for definition). Observations are weighted by the class’s percentage (using AUM) of the total fund’s AUM in that month so that each fund has the same weight. All specifications include time dummies, fund style dummies, and prior return decile dummies for each of the previous five years. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered by fund family, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Dep Var: Monthly Fund Flows (%) Changes: All Changes Public Parent Public/Non A.M.

Investor Class: Retail Institutional Retail Institutional Retail Institutional Predictor Variables (1) (2) (3) (4) (5) (6) PREANN (dummy) -0.077%

-0.062%

0.011%

0.109%

0.084%

0.098%

[0.41]

[0.25]

[0.04]

[0.40]

[0.39]

[0.27]

POSTANN (dummy) -0.429% *** -0.229%

-0.701% *** -0.207%

-0.692% *** -0.176%

[3.16]

[1.11]

[3.44]

[0.73]

[3.21]

[0.58]

Log fund AUM -0.078% *** 0.055% * -0.101% *** 0.040%

-0.047%

0.057%

[4.00]

[1.71]

[3.16]

[0.85]

[1.30]

[1.08]

Log family AUM 0.029% * 0.015%

0.099% *** 0.105% ** 0.068% ** 0.112% **

[1.76]

[0.49]

[3.19]

[2.31]

[1.99]

[2.29]

Log fund age (years) -0.456% *** -0.804% *** -0.381% *** -0.836% *** -0.377% *** -0.818% ***

[15.12]

[10.77]

[8.85]

[8.74]

[6.62]

[7.40]

Log expense ratio -0.239% *** -0.045%

-0.535% *** -0.093%

-0.450% *** -0.084%

[3.26]

[0.34]

[4.36]

[0.45]

[3.46]

[0.39]

Private firm (dummy) 0.293% *** 0.346% ***

[3.78]

[2.37]

Log parent marketcap

-0.638% * -0.596%

-0.681%

-1.073%

[1.75]

[0.72]

[1.47]

[1.36]

Stock returns (prioryr)

5.437% *** 4.206% * 2.743% ** 4.175% *

[3.74]

[1.90]

[2.10]

[1.84]

Observations 522002 138890 276283 80009 192133 62391 Return decile dummies YES YES YES YES YES YES Time dummies YES YES YES YES YES YES Fund style controls YES YES YES YES YES YES

     

  45  

Table 8: Regressions of fund flows on event window indicators for high vs. low expense funds Table 8 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fund-level and parent-level control variables. In columns (1), (3), (5), the sample is restricted to funds with above-median expense ratios (for the month), while columns (2), (4), and (6) only contain funds with below-median expense ratios (for the month). PREANN and POSTANN indicate timing around event windows, and all other controls are defined in Table 2. Columns (1) and (2) include all funds, while columns (3) and (4) restrict the sample to funds (and events) with public management companies, and in columns (5) and (6) only includes funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1 for definition). All specifications include time dummies, fund style dummies, and prior return decile dummies for each of the previous five years. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered at the management company level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.  

Dep Var: Monthly Fund Flows (%) Changes: All Changes Public Parent Public/Non A.M.

Investor Class: High Exp. Low Exp. High Exp. Low Exp. High Exp. Low Exp. Predictor Variables (1) (2) (3) (4) (5) (6) PREANN (dummy) -0.028%

-0.245%

-0.038%

-0.082%

0.205%

-0.019%

[0.12]

[1.61]

[0.14]

[0.38]

[0.88]

[0.08]

POSTANN (dummy) -0.440% *** -0.434% *** -0.600% ** -0.531% *** -0.614% ** -0.439% **

[2.72]

[3.32]

[2.25]

[2.95]

[2.30]

[2.35]

Log fund AUM -0.089% *** -0.003%

-0.134% *** -0.008%

-0.063%

0.041%

[3.22]

[0.12]

[3.79]

[0.18]

[1.42]

[0.88]

Log family AUM 0.074% *** 0.021%

0.146% *** 0.101% ** 0.113% *** 0.088% **

[3.67]

[0.86]

[4.45]

[2.54]

[3.13]

[2.06]

Log fund age (years) -0.410% *** -0.344% *** -0.257% *** -0.295% *** -0.261% *** -0.305% ***

[7.05]

[7.03]

[3.56]

[4.86]

[2.89]

[4.15]

Log expense ratio -0.279%

0.007%

-0.478%

-0.031%

-0.526%

-0.043%

[1.30]

[0.10]

[1.60]

[0.26]

[1.51]

[0.35]

Private firm (dummy) 0.366% *** 0.205% **

[3.67]

[2.17]

Log parent marketcap

-0.415%

-0.806%

-0.746%

-1.168% *

[0.97]

[1.43]

[1.44]

[1.79]

Stock returns (prioryr)

5.631% *** 4.027% ** 2.990% * 3.290% *

[3.36]

[2.45]

[1.90]

[1.82]

Observations 170911 164207 79010 76637 58163 56361 Return decile dummies YES YES YES YES YES YES Time dummies YES YES YES YES YES YES Fund style dummies YES YES YES YES YES YES

   

  46  

Table 9: Merger purposes and acquiror characteristics – summary statistics Table 9 provides summary statistics on the main purpose(s) behind the financial mergers that make up the events in this study, the types and public status of acquirors, and various additional acquiror characteristics. Panel A shows the proportion of events for which a purpose is not available and the proportion of mergers for which it is provided. It then shows a breakdown of the latter subset by listing the percentage of mergers with each type of purpose (out of the total number of mergers with a purpose provided). Some mergers have multiple purposes so the percentages do not add up to 100%. Panel B shows the proportion of mergers where the acquiror (or parent company of the acquiror) is a bank (SIC 6000-6199), an insurance company (SIC 6300-6499), and a securities firm (SIC 6200-6299). Panel B also shows the proportion of events where the acquiror is private versus public, and whether public acquirors are public and in CRSP, public and listed over the counter in the U.S., or public but listed only in foreign markets. Finally, Panel C shows summary statistics for acquiror characteristics. Acquiror adjusted stock returns, 12 months (%) is the acquiror’s average monthly stock returns over the 12 months before the announcement date, relative to other firms in the same sub-industry (Bank, Insurance, Securities). Acquiror adjusted stock returns, 36 months (%) is calculated in the same way but averaged over the prior 36 months. Both variables are only calculated for public acquirors that are in CRSP and have at least 12 (or 36) months of historical data. Acquiror mutual fund revenues (%) is the total annual expenses collected by all funds in the acquiror’s mutual fund family (product of annual expense ratios and assets under management) divided by the acquiror’s annual COMPUSTAT revenues. Acquiror adjusted family returns (avg.), 12 mths is the average style-adjusted monthly returns of funds in the acquiror’s mutual fund family over the 12 months prior to the announcement date. Acquiror adjusted family returns (avg.), 36 mths is calculated in the same way but averaged over the prior 36 months. The last three variables are only available for acquirors that manage their own mutual fund family prior to the acquisition.

Panel A: Merger purpose Merger purpose not available 56.1% Merger purpose available 43.9% Strengthen Operations (STR) 35.4% Synergies (SYN) 30.5% Expand Presence into New Markets (EXP) 28.0% Expand Presence in Primary Market (EPM) 18.3% Offer New Products and Services (PRD) 15.9% Acquire Competitors Assets (CMP) 11.0% General Strategy for Sound Investment (GEN) 9.8% Expand into New Geographic Areas (EPG) 6.1% Expand Presence in Secondary Market (ESM) 4.9% Increase Shareholder Value (ISV) 4.9% Other (OTH) 3.7% Concentrate on Core Businesses (COR) 1.2%

Panel B: Acquiror firm type and public status Acquiror firm type

Bank 49.7% Insurance 17.6% Securities 32.6%

Acquiror public status Private 13.9%

Public 86.1% Public - CRSP 85.1% Public - OTC 1.2% Public - Foreign 13.7%

  47  

Panel C: Acquiror summary statistics # of Obs. Mean Median St.Dev. 10% 90% Variable (1) (2) (3) (4) (5) (6) Acquiror adjusted stock returns (avg.), 12 mths 134 0.82% 0.73% 2.01% -1.33% 3.62% Acquiror adjusted stock returns (avg.), 36 mths 126 0.39% 0.32% 1.20% -0.92% 1.99% Acquiror mutual fund revenues (%) 116 5.4% 0.4% 13.2% 0.1% 17.2% Acquiror log family size 143 8.7 9.0 1.6 6.4 10.7 Acquiror adjusted family returns (avg.), 12 mths 142 -0.02% -0.03% 0.42% -0.47% 0.34% Acquiror adjusted family returns (avg.), 36 mths 140 -0.01% -0.01% 0.27% -0.27% 0.21%

  48  

Table 10: Effect of acquiror and merger characteristics on post-announcement fund flows Table 10 presents estimated coefficients from OLS regressions of match-adjusted (on style, fund age, size, and prior performance, as in Panels B and C of Table 4) fund flows on acquiror characteristics and merger purpose variables, where the sample consists of all fund-month observations in the twelve months after an announcement (POSTANN = 1). Independent variables in Panel A include acquiror types, merger purposes, and public acquiror stock returns prior to the announcement date. Independent variables in Panel B include acquiror types and acquiror family characteristics such as family size and family fund performance. Acquiror adj. same style returns, 12m (%) is the average style-adjusted monthly returns of funds in the acquiror’s mutual fund family that also use the same style as the fund (whose flows are being measured), over the 12 months prior to the announcement date. Acquiror adj. same style returns, 36m (%) is calculated in the same way but averaged over the prior 36 months. All other variables are defined in Table 9. T-statistics, using standard errors clustered at the event level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Effect of acquiror types, merger purposes, and public acquiror past stock performance Dep.Var: Monthly Fund Flows (%) - Match Adjusted

Regression: OLS OLS OLS OLS OLS Predictor Variables (1) (2) (3) (4) (5) Private acquiror (dummy) -0.356%

-0.700%

[0.89]

[1.08]

Insurance acquiror (dummy) 0.726% ** 0.411%

0.348%

0.031%

0.865% **

[2.01]

[0.99]

[0.75]

[0.08]

[2.12]

Securities acquiror (dummy) 0.534% ** 0.558% ** 0.584% ** 0.741% ** 0.602%

[2.12]

[2.27]

[2.28]

[2.37]

[1.60]

Acquiror adj. stock returns, prior 12m

-0.603%

[0.11]

Acquiror adj. stock returns, prior 36m

-0.226%

4.544%

[0.02]

[0.46]

Acquiror mutual fund revenues (%)

-0.922%

[0.35]

CMP purpose (dummy)

-0.054%

[0.14]

COR purpose (dummy)

-1.106% **

[2.09]

EPG purpose (dummy)

-0.409%

[1.27]

EPM purpose (dummy)

-0.047%

[0.12]

ESM purpose (dummy)

-0.448%

[1.47]

EXP purpose (dummy)

-0.276%

[0.74]

GEN purpose (dummy)

-0.631%

[0.68]

  49  

ISV purpose (dummy)

-0.725% ***

[2.65]

OTH purpose (dummy)

-1.017%

[1.43]

PRD purpose (dummy)

0.474%

[1.06]

STR purpose (dummy)

-0.415%

[1.27]

SYN purpose (dummy)

-0.160%

[0.53]

Observations 9443 6307 6048 5100 6598

Panel B: Effect of acquiror types, and aquiror family size and past performance Dep.Var: Monthly Fund Flows (%) - Match Adjusted

Regression: OLS OLS OLS OLS Predictor Variables (1) (2) (3) (4) Private acquiror (dummy) -1.069% ** -1.051% ** -1.101% ** -0.902%

[2.29]

[2.17]

[1.97]

[1.50]

Insurance acquiror (dummy) 0.823% ** 0.782% ** 0.734% ** 0.709% **

[2.19]

[2.00]

[2.35]

[2.10]

Securities acquiror (dummy) 0.929% *** 0.921% *** 0.781% ** 0.808% **

[3.21]

[3.09]

[2.50]

[2.31]

Acquiror log family size -0.143% * -0.141%

-0.186%

-0.234%

[1.74]

[1.63]

[1.43]

[1.55]

Acquiror adj. family returns, 12m -0.069

[0.24]

Acquiror adj. family returns, 36m

0.183

[0.46]

Acquiror adj. same style returns, 12m

0.014

[0.05]

Acquiror adj. same style returns, 36m

-0.245

[0.57]

Observations 6505 6442 4566 4299

  50  

Table 11: Regressions of fund flows on event window indicator with controls for distribution networks Table 11 presents estimated coefficients from OLS regressions of fund flows on event window indicators and dummy variables for the main type of distribution network used by each fund. The seven types of distribution network are Bank Proprietary, Direct, Institutional, Insurance, Non-Proprietary, Proprietary, and Other. Bank Proprietary, Insurance, and Proprietary distribution channels use brokers affiliated with the management company (bank, insurance, and securities respectively) to sell shares to retail investors. Non-Proprietary distribution channels use brokers unaffiliated with the management company to sell shares to retail investors. Funds use Direct distribution channels to sell directly to retail investors, and Institutional distribution channels to sell to institutional (high net worth or pension plan) clients. Other mostly consists of fund sales to investors who are members of a group or organization (often non-profit, fraternal, or religious) that owns or runs the fund’s management company. Panel A shows estimated coefficients on dummies for each type of distribution channel (where Other is the omitted class of distribution network) along with the standard fund and parent controls used in Table 5, time dummies, fund style dummies, and prior return decile dummies for each of the previous five years. Panel B runs the same regressions as in Panel A but excludes event window observations for funds that have a change in distribution network in the two years after the event announcement. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered at the management company level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Main regression with controls for main distribution channel Dep.Var: Monthly Fund Flows (%)

Regression: OLS OLS OLS Changes: All Public Parent Pub/Non AM

Predictor Variables (1) (2) (3) PREANN (dummy) -0.119%

-0.029%

0.091%

[0.82]

[0.16]

[0.52]

POSTANN (dummy) -0.426% *** -0.553% *** -0.504% ***

[3.53]

[3.14]

[2.88]

Bank Proprietary distrib. (dummy) -0.304%

0.029%

-0.015%

[1.51]

[0.16]

[0.08]

Direct distrib. (dummy) 0.078%

0.274%

0.310%

[0.39]

[1.23]

[1.56]

Institutional distrib. (dummy) -0.154%

0.074%

0.078%

[0.74]

[0.32]

[0.33]

Insurance distrib. (dummy) 0.655% *** 1.139% *** 1.109% ***

[3.07]

[4.19]

[3.78]

Non-Proprietary distrib. (dummy) 0.261%

0.400% * 0.359%

[1.27]

[1.76]

[1.61]

Proprietary distrib. (dummy) -0.155%

0.102%

0.076%

[0.66]

[0.39]

[0.26]

Observations 331598 154784 113769 Fund Controls YES YES YES Return decile dummies YES YES YES Time dummies YES YES YES Fund style dummies YES YES YES

  51  

Panel B: Excluding event observations for funds with change in distr. network Dep.Var: Monthly Fund Flows (%)

Regression: OLS OLS OLS Changes: All Public Parent Pub/Non AM

Predictor Variables (1) (2) (3) PREANN (dummy) -0.184%

-0.074%

0.047%

[1.27]

[0.38]

[0.25]

POSTANN (dummy) -0.445% *** -0.565% *** -0.511% ***

[3.42]

[3.06]

[2.72]

Bank Proprietary distrib. (dummy) -0.300%

0.033%

-0.013%

[1.50]

[0.18]

[0.07]

Direct distrib. (dummy) 0.080%

0.281%

0.313%

[0.40]

[1.26]

[1.57]

Institutional distrib. (dummy) -0.156%

0.073%

0.076%

[0.75]

[0.32]

[0.32]

Insurance distrib. (dummy) 0.658% *** 1.146% *** 1.114% ***

[3.08]

[4.22]

[3.79]

Non-Proprietary distrib. (dummy) 0.262%

0.408% * 0.367%

[1.27]

[1.80]

[1.64]

Proprietary distrib. (dummy) -0.162%

0.107%

0.080%

[0.69]

[0.41]

[0.28]

Observations 329916 154353 113400 Fund Controls YES YES YES Return decile dummies YES YES YES Time dummies YES YES YES Fund style dummies YES YES YES

  52  

Table 12: Regressions of fund flows on event window indicator variables – pre- and post-effective date Table 12 presents estimated coefficients from OLS regressions of fund flows on event window indicators and various fund-level and parent-level control variables. In each specification, the dependent variable is Fund Flows (%) and the variables of interest include PREANN (defined in Table 4), POSTANN_PREEFF, a dummy variable that equals one for all fund-month observations from announcement month t until T–1, where T is the effective date of the ownership change. Finally, POSTANN_POSTEFF is a dummy variable that equals one for all fund-month observations from month T until t+12. All controls are defined in Table 2. Column (1) includes all funds, while column (2) restricts the sample to funds (and events) with public management companies, and column (3) only include funds whose management firms have a publicly-traded parent whose primary business is not asset management (see Table 1 for definition). All specifications include time dummies, fund style dummies, and prior return decile dummies for each of the previous five years. The sample period for all regressions is from January 1995 to December 2012. T-statistics, using standard errors clustered at the management company level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Dep Var: Monthly Fund Flows (%)

Changes: All Public Public

Changes Parent Non AM

Predictor Variables (1) (2) (3) PREANN (dummy) -0.125%

-0.063%

0.047%

[0.83]

[0.34]

[0.26]

POSTANN_PREEFF (dummy) -0.361% ** -0.379%

-0.236%

[2.16]

[1.43]

[1.08]

POSTANN_POSTEFF (dummy) -0.480% *** -0.726% *** -0.735% ***

[3.68]

[4.22]

[4.01]

Log fund AUM -0.042% ** -0.067% ** -0.006%

[1.98]

[2.06]

[0.15]

Log family AUM 0.046% ** 0.119% *** 0.093% ***

[2.53]

[3.85]

[2.79]

Log fund age (years) -0.372% *** -0.272% *** -0.283% ***

[9.59]

[5.68]

[4.75]

Log expense ratio 0.150% ** 0.025%

0.035%

[2.19]

[0.25]

[0.34]

Private firm (dummy) 0.279% ***

[3.49]

Log parent marketcap

-0.528%

-0.887% *

[1.39]

[1.85]

Stock returns (prioryr)

5.062% *** 3.417% ***

[3.86]

[2.85]

Observations 335118 155647 114524 Return decile dummies YES YES YES Time dummies YES YES YES Fund style dummies YES YES YES

  53  

Table 13: Other alternative Explanations and robustness checks Table 13 present estimated coefficients from OLS regressions of fund flows (and fund returns in Panel D) on event window indicators, and various controls. In Panel A, fund-month observations from January 1999 through December 2000 are excluded to test whether the paper’s results can be explained by unusual phenomena surrounding the growth and bursting of the dot-com bubble. In Panel B, fund flows are regressed on Fund closure, a dummy variable that equals one in the six months prior to a fund closure, and zero otherwise. In Panel C, fund flows are regressed on Manager change, a dummy variable which equals one in the five months before, the month of, and six months after a manager change, and zero otherwise. In Panel D, the dependent variable is fund returns instead of fund flows. All specifications include standard fund and parent controls used in Table 5, time dummies, fund style dummies, and prior return decile dummies for each of the previous five years. The sample period for regressions in Panels B, C, and D is from January 1995 to December 2012. The same sample period, except for 1999 and 2000, is used in Panel A. T-statistics, using standard errors clustered at the management company level, are shown in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Excluding observations from dot-com bubble period (1999-2000) Dep.Var: Monthly Fund Flows (%)

Regression: OLS OLS OLS Changes: All Public Parent Pub/Non AM

Predictor Variables (1) (2) (3) PREANN (dummy) -0.075%

0.077%

0.029%

[0.47]

[0.41]

[0.15]

POSTANN (dummy) -0.364% *** -0.556% *** -0.543% ***

[2.95]

[3.13]

[2.95]

Observations 300439 139707 102980 Fund Controls YES YES YES Return decile dummies YES YES YES Time dummies YES YES YES Fund style dummies YES YES YES

Panel B: Controlling for upcoming fund closure

Dep.Var: Monthly Fund Flows (%) Regression: OLS OLS OLS

Changes: All Public Parent Pub/Non AM Predictor Variables (1) (2) (3) PREANN (dummy) -0.161%

-0.104%

0.008%

[1.08]

[0.56]

[0.05]

POSTANN (dummy) -0.381% *** -0.502% *** -0.444% **

[3.18]

[2.87]

[2.53]

Fund closure (dummy) -1.960% *** -1.822% *** -1.818% ***

[18.87]

[13.18]

[11.42]

Observations 335118 155647 114524 Fund Controls YES YES YES Return decile dummies YES YES YES Time dummies YES YES YES Fund style dummies YES YES YES

  54  

Panel C: Controlling for upcoming or recent manager change Dep.Var: Monthly Fund Flows (%)

Regression: OLS OLS OLS Changes: All Public Parent Pub/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% *** -0.575% *** -0.536% ***

[3.65]

[3.28]

[3.07]

Manager change (dummy) -0.316% *** -0.350% *** -0.261% ***

[6.95]

[5.69]

[3.94]

Observations 335118 155647 114524 Fund Controls YES YES YES Return decile dummies YES YES YES Time dummies YES YES YES Fund style dummies YES YES YES

Panel D: Effect of event announcement on fund returns

Dep.Var: Fund Net Returns (%) Regression: OLS OLS OLS

Changes: All Public Parent Pub/Non AM Predictor Variables (1) (2) (3) PREANN (dummy) 0.020%

0.116%

0.053%

[0.26]

[1.02]

[0.60]

POSTANN (dummy) 0.019%

-0.028%

-0.101%

[0.37]

[0.34]

[1.51]

Observations 335118 155647 114524 Fund Controls YES YES YES Return decile dummies YES YES YES Time dummies YES YES YES Fund style dummies YES YES YES

  55  

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.

-­‐1.2%  

-­‐1.0%  

-­‐0.8%  

-­‐0.6%  

-­‐0.4%  

-­‐0.2%  

0.0%  

0.2%  

0.4%  

0.6%  

0.8%  

-­‐6   -­‐5   -­‐4   -­‐3   -­‐2   -­‐1   0   1   2   3   4   5   6   7   8   9   10   11   12  

Fund

flow

s (%

)

Months prior to or after event

Monthly match-adjusted fund flows around events All parent company changes

  56  

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.

-­‐3.0%  

-­‐2.5%  

-­‐2.0%  

-­‐1.5%  

-­‐1.0%  

-­‐0.5%  

0.0%  

0.5%  

1.0%  

1.5%  

-­‐6   -­‐5   -­‐4   -­‐3   -­‐2   -­‐1   0   1   2   3   4   5   6   7   8   9   10   11   12  

Fund

flow

s (%

)

Months prior to or after event

Monthly match-adjusted fund flows around events Public non asset-managers only