strategic complementarities and mutual fund runsbschool.nus.edu/portals/0/images/camri/research...
TRANSCRIPT
Strategic Complementarities and Mutual Fund Runs
Meijun Qian* A. Başak Tanyeri**
National University of Singapore Bilkent University
February 2010
Abstract
Are self-fulfilling runs possible in mutual funds? This paper provides insights by investigating whether anticipation of adverse events can trigger runs in mutual funds. The adverse event in question is the litigations concerning market-timing and late trading practices that were filed in 2003 and 2004. We find that pre-event runs start as early as six months before litigation announcements. The size of pre-event runs is about half the size of the runs after litigation announcements. Investors, who run before litigation announcements, earn significantly higher risk- and peer- adjusted returns than do those who run after, especially in funds holding illiquid assets and in funds incurring large outflows. The return difference is driven by the fire-sale costs because event returns of firms held by implicated funds with negative flows are significantly negative. Our analysis suggests that pro-rata-ownership design may not suffice to prevent runs in the mutual funds. Return differences due to the timing of withdrawals suggest strategic complementarities in the fund industry, where investors have incentives to withdraw in anticipation of other investors doing so.
JEL: G23 G14
Keywords: Runs, mutual fund flows, returns, and strategic complementarities. --------------------------------------------------------------------------------------------------------------------------------- Meijun Qian is an Assistant Professor of Finance at NUS Business School. Tel: (65) 6516 8119; e-mail: [email protected] Basak Tanyeri is an Assistant Professor of Finance at Bilkent University. Tel: (90) 312-290-1871; e-mail: [email protected]
2
Strategic Complementarities and Mutual Fund Runs
Abstract
Are self-fulfilling runs possible in mutual funds? This paper provides insights by investigating whether anticipation of adverse events can trigger runs in mutual funds. The adverse event in question is the litigations concerning market-timing and late trading practices that were filed in 2003 and 2004. We find that pre-event runs start as early as six months before litigation announcements. The size of pre-event runs is about half the size of the runs after litigation announcements. Investors, who run before litigation announcements, earn significantly higher risk- and peer- adjusted returns than do those who run after, especially in funds holding illiquid assets and in funds incurring large outflows. The return difference is driven by the fire-sale costs because event returns of firms held by implicated funds with negative flows are significantly negative. Our analysis suggests that pro-rata-ownership design may not suffice to prevent runs in the mutual funds. Return differences due to the timing of withdrawals suggest strategic complementarities in the fund industry, where investors have incentives to withdraw in anticipation of other investors doing so.
JEL: G23 G14
Keywords: Runs, mutual fund flows, returns, and strategic complementarities.
3
1. Introduction
The first-come-first-served principle governing deposit withdrawals motivates
bank runs. Every depositor wants to withdraw before others do because those at the back
of the line may not recover their deposits (Diamond and Dybvig, 1983; Chari and
Jagannathan, 1988). In contrast, mutual funds allocate the proceeds from asset sales on a
pro-rata basis. The pro-rata design should shield mutual funds from runs. However,
mutual funds may prove susceptible to runs if the timing of redemptions affects the
returns to shareholders. Such a situation can happen when shareholders who redeem early
pass the cost of asset sales on to shareholders who redeem late1. If returns are higher for
early withdrawals than for late withdrawals, investors will have incentives to exit before
others do. This setting gives rise to strategic complementarities, where investors’
incentive to take an action increases if they believe more investors will take the same
action (Bulow et. al., 1985; Chen et. al., 2009). This paper aims to provide direct
evidence of such strategic complementarities in the mutual fund industry and an
economic rationale for fund runs.
We define a a pre-event run as concerted redemption of mutual fund shares in
anticipation of an adverse event and run as concerted redemption upon revelation of such
an event. The adverse event that this paper focuses on is the litigations of 2003 and 2004
alleging that certain mutual funds allowed some investors to engage in late trading and
1Mutual funds face two types of transaction costs when selling portfolio assets: trading costs and costs arising from dilution effects of flows (Edelen, 1996). Edelen (1999) shows that liquidity-motivated trading hurts fund performance. Chen, Goldstein, and Jiang (2009) argue that the costs of liquidity-motivated trading are partially borne by existing shareholders and may cause strategic (or payoff) complementarities in mutual fund shares.
4
market timing2. Investors who engage in late trading and market timing enjoy profits at
the expense of investors who do not engage in these practices. Upon the suspicion or the
revelation that fund managers do not serve the interests of all investors equally, investors
that are at a disadvantage may discipline implicated funds by withdrawing existing
investments and/or by withholding new investments.
We investigate whether there are pre-event runs and runs after litigation
announcements. We focus on pre-event runs to examine whether there are incentives in
addition to market discipline that might explain the redemptions of and/or lack of new
investments in implicated funds. If the timing of runs matters, e.g., it is beneficial to
withdraw before others; investors will run funds as long as they fear other investors will
do so regardless of whether there is revelation of adverse information. Consequently,
mutual fund industry may be exposed to financial fragility generated by strategic
complementarities.
We examine the return differences for withdrawals at different times to show
whether rationale of strategic complementarities exists. The concerted redemption and
the lack of new sales that follow litigation announcements would force funds to liquidate
assets quickly. Coval and Stafford (2007) find that large selling-volume by institutional
investors temporarily depresses underlying asset prices. Shareholders who redeem shares
at this time will suffer losses. Investors who can anticipate litigations and the redemptions
that would follow, have incentives to redeem shares before litigation announcements. By
2 Late trading is the purchase or sale of mutual fund shares after four p.m. at Net Asset Value (NAV) determined at four p.m. Market timing is the short-term trading of mutual fund shares to exploit price inefficiencies between mutual fund shares and underlying securities in the funds’ portfolios. Bank of America Nations Fund Securities Litigation Complaint is a representative case describing in detail the allegations of market timing and late trading (http://securities.stanford.edu/1028/BAC03-01/20030905_f01c_Lin.htm ).
5
exiting early, informed investors avoid the fire-sale costs caused by concerted future
withdrawals.
Furthermore, the return differences due to the timing of withdrawals will be larger
if the funds hold more illiquid assets or if the funds are likely to suffer from larger
outflows. Therefore, the incentive of early runs would also be greater for these funds.
Our paper supports the above arguments by providing empirical evidence for
these four questions. First, are there pre-event runs and runs post litigation
announcements? Second, do investors who run prior to announcements enjoy financial
benefits compared to investors who run post? Third, is the return difference larger in
funds with less liquid assets or large outflows? Finally, are the low returns to late
withdrawals caused by fire-sale costs?
We find fund runs both prior to and post litigation announcements. Pre-event runs
start as early as six months before litigation announcements. Flows to implicated funds
prove 2.28% lower than flows to non-implicated funds in the six months before litigation
announcements and are continuously lower for at least two years after. Second, investors
who run before litigation announcements earn significantly higher risk- and peer-
adjusted returns than do those who run after litigations. The difference in returns is as
high as 2.17% accumulated from six months before announcements to six months after.
Third, not all funds prove equal in their vulnerability to runs. Funds holding illiquid
assets and funds that are expected to suffer from large outflows, such as disreputable
funds (as measured by the history of Security Exchange Commission (SEC)
investigations) experience more severe runs both prior to and post litigation
announcements. Moreover, the return difference between investors who run before
6
litigation announcements and those who run after proves more pronounced in funds that
hold illiquid assets, funds that suffer from larger outflows, and funds with no prior history
of SEC charges. Finally, we show that the return difference between early and late
redemptions is driven by fire-sale costs. Event returns of firms held by implicated funds
with negative flows are negative and significant.
Our results indicate that mutual fund investors who anticipate negative flows
motivated by adverse events such as litigations have incentives to withdraw early and
avoid fire-sale costs. The incentives for early exit have an important implication in that
investors may run funds in expectation of other investors doing so. When the timing of
the action (runs) matters for payoff (returns), strategic complementarities exist. Strategic
complementarities can amplify the impact of adverse events on fundamentals and
generate financial fragility. However, we may not observe mutual fund runs to the extent
of bank runs unless there is a systematic liquidity shock to all fund investors (Chen,
Goldstein, and Jiang 2008). In the absence of such a liquidity shock, other investors will
purchase the assets in fire-sale and might correct the mispricing (Hanson, Hong, and
Stein, 2008)3.
The decision of the US Treasury to insure the holdings of eligible money-market
mutual funds in the wake of the turmoil caused by the run on the money-market mutual
fund Reserve Primary Fund in September 2008 showcases the financial fragility that the
mutual fund industry faces4. Our findings explain why runs can happen in mutual funds
3 Chen, Hanson, Hong, and Stein (2008) show that hedge funds that purchase funds’ underlying assets at the depressed price during fire-sale periods generate arbitrage profits similar to the profits of the short sellers. However, short selling is not allowed in most mutual funds. 4 The Reserve Primary Fund held debt securities of Lehman Brothers; following the bankruptcy of Lehman Brothers, redemptions totaled about two-thirds of Total Net Assets (Wall Street Journal, 2008; New York
7
and the events surrounding the demise of Lehman Brothers underline the financial
fragility of the mutual fund industry.
This paper is first to investigate and document runs in mutual fund industry in
anticipation of litigation announcements. Furthermore, we provide an economic rationale
-- to avoid fire-sale costs-- for why investors want to run before others do. These findings
contribute to our understanding of how liquidity associated trading costs may generate
strategic complementarities and financial fragility in the mutual fund industry.
The rest of the paper proceeds as follows: Section 2 develops the methodology.
Section 3 describes the data. Section 4 discusses empirical results. Section 5 concludes
the paper.
2. Research Methods
Empirically, we address four questions. The first question investigates whether
informed investors run implicated funds prior to litigation announcements. The second
question examines whether investors who run funds prior to the filing of lawsuits realize
higher returns than do those who run post. The third question analyzes whether some
types of funds are more susceptible to silent runs. The last question investigates whether
the low returns on withdrawals after litigation are caused by fire-sale costs of underlying
assets.
2.1. Detecting pre-event runs
Times, 2008). The liquidity crunch in the short-term credit market meant that non-redeeming investors would bear the fire-sale costs associated with asset sales to satisfy redemptions. The Treasury stated its concerns about the ensuing uncertainty in the mutual fund industry and in instating the guarantee program as follows: “…Maintaining confidence in the money market fund industry is critical to protecting the integrity and stability of the global financial system. …This action should enhance market confidence and alleviate investors' concerns about the ability for money market mutual funds to absorb a loss…” (US Treasury Department Press Release, 19 September 2008).
8
We need benchmarks of “normal” flow to document pre-event runs. The first
benchmark is flows to peers who are not named in the 2003 and 2004 lawsuits. We
construct three groups of funds. The first group consists of funds whose management
companies are not involved in the litigations of 2003 and 2004 (named as funds in non-
implicated families). The second group includes funds that are not named in the suits but
whose management companies are (non-implicated funds in implicated families). The
third group consists of the funds named in the suits (implicated funds).
We compute fund flows as follows:
Flowi,t = [TNAi,t –TNAi,t-1 *(1+ri,t)] / TNAi,t-1 , (1)
where Flowi,t is net flows of fund i in month t. TNAi,t-1 and TNAi,t are total net assets of
fund i in month t-1and t, respectively. ri,t is return of fund i in month t. We compare net
flows of the three groups around the litigation dates to detect whether implicated funds
have lower flows than do non-implicated funds.
The second benchmark for ‘normal’ flows is estimated net flows from a model
that tries to capture main determinants of fund flows. We review the empirical literature
to develop the flow model. Gruber (1996), Chevalier and Ellison (1997), Sirri and Tafuno
(1998), Zheng (2000), and Del Guercio and Tkac (2001, 2002) all show that past returns
predict future flows. Qian (2008) finds that industry-level and style-level flows explain
individual fund-level flows. We use a model of flows that includes variables for fund
characteristics, past returns, fund-level, and style-level flows. To detect pre-litigation and
post-litigation runs, we construct 25 event-window dummies.
The resulting model of flows is:
Flowi,t = a + ∑bj * fund characteristicsi,t j + ∑cj * past returnsi
j
9
+ ∑dj * aggregate flowst j + ∑γj * Event-window Dummiesi,t
j +εi,t, (2)
where fund characteristics include size, age, 12b-1 fees, rear, and front loads. Size is log
of TNA. Age is log of the days since the first offer date. 12b-1 fees are the annual fees
paid to financial advisors, measured as percentage of TNA. Front and rear loads are the
charges for purchases and redemption of shares measured as percentage of TNA. Past
returns include compounded returns in the past one (Ri,t-1), three ((1+ Ri,t-1)* (1+ Ri,t-2)*
(1+ Ri,t-3)-1), and six months ((1+ Ri,t-1)* (1+ Ri,t-2)* (1+ Ri,t-3)* (1+ Ri,t-4)* (1+ Ri,t-5)* (1+
Ri,t-6)-1). Aggregate flows include industry- and style-level flows. Industry-level flows are
the sum of flows in dollars (Σi (TNAi,t –TNAi,t-1*(1+Ri,t ))) to all funds in the sample
divided by the sum of lagged TNA (Σi (TNAi,t-1)). Style-level flows are the sum of flows
in dollars to all funds with the same investment style divided by the sum of lagged TNA.
We adopt the style classification of Pastor and Stambaugh (2002) and Ferson and Qian
(2005). There are eight styles: aggressive growth, growth-income, global equity, other
equity, bond funds, municipal funds, money market, and other. Event-window dummy (n
month) equals 1 if it is the nth month from the date of the litigation filing, and 0 otherwise
(n = -1, -2…12, 0, 1, 2…12).
2.2 Rationale for pre-event runs
What incentives exist for shareholders to run a mutual fund when proceeds from
asset sales are determined by the prices of underlying assets and are distributed pro-rata?
Investors may see abusive behavior as indicative of how faithfully fund managers serve
their best interests. As such, investors may want to redeem shares as soon as they are
informed, privately or publicly, of abusive practices such as market timing or late trading,
in the funds they invest in. If and when sufficient numbers of investors learn of abusive
10
behavior in a fund, a run may ensue. Mutual funds must liquidate assets quickly in order
to satisfy the redemption of shares. If large selling volumes temporarily depress
underlying asset prices, shareholders who redeem shares at this point of time would
realize negative abnormal returns.
We examine whether there are financial benefits to redeeming shares prior to the
revelation of abusive behavior. We develop two approaches to detect the return
differences between investors who run funds prior to lawsuits being filed and investors
who run post. The first approach benchmarks ‘normal’ returns using five different models
for returns and introduces indicators for pre- and post-event months to identify return
differences. The return models are the market model (Sharpe, 1964; Lintner, 1965), the
market model with lagged market returns (Scholes and William, 1977), the Fama-French
benchmarks (Fama and French, 1992 and 1993), the Fama-French benchmark with a
fourth factor that captures momentum (Jegadeesh and Titman, 1993; Carhart, 1997), and
the market model with a factor that captures liquidity (Pastor and Stambaugh, 2003). The
five models of returns are:
ri,t = α + β*r m.t + ∑αn * Dummyn+ ε i,t, (3)
ri,t = α + β1*r m.t + β2*rm.t-1 + ∑αn * Dummytn + ε i,t , (4)
ri,t = α + ∑βj * FFtj + ∑αn * Dummyt
n+ ε i,t, (5)
ri,t = α + ∑βj * FFtj + γ1* MOMt + ∑αn * Dummyt
n + ε i,t, (6)
ri,t = α + β*rm.t + γ2*LIQt+ ∑αn * Dummytn + ε i,t . (7)
where ri,t is the excess returns (net of the risk-free rate) of fund i on month t. rm.t is the
excess market return on month t. FFj include market returns, size (SMB) and value
(HML) factors. MOM is the momentum factor and LIQ is the liquidity factor. Event-
11
window dummy (n month) equals 1 if it is the nth month from the date of the litigation
filing, and 0 otherwise (n = -1, -2…-6, 0, 1, 2…6).
2.3. Impact of fund characteristics and liquidity on pre-event runs
Pre-event runs are motivated by suspicions of litigations and liquidation costs that
would arise to satisfy the redemptions following litigations. Factors that influence
investors’ belief or awareness of abusive behavior and factors that would increase fire-
sale costs would affect investors’ decision to run before the adverse event is confirmed.
Fund and investor characteristics such as management reputation and the ability of
investors to collect and process information may affect whether and when investors
become aware of abusive behavior. Ownership structure and a history of SEC charges
measure fund reputation. Investors may judge funds in conglomerate families to be less
likely to engage in abusive behavior since loss of reputation would hurt the abused fund
as well as other businesses of the conglomerate. Hence, the consequences of abusive
behavior may be larger for conglomerates than for fund families that only focus on
managing mutual funds. Past actions may predict future decisions. As such, investors
may judge funds with no history of abusive behavior to be less likely to engage in
abusive behavior in the future. We use differences in the distribution channels for fund
shares to measure the information collection and processing ability of investors. Investors
aided by financial advisors may be in a better position to judge which funds are more
likely to engage in abusive behavior. We expect investors who are assisted by financial
advisors to be more likely to anticipate abusive behavior and redeem shares in implicated
funds prior to lawsuits being filed.
12
To investigate whether fund and investor characteristics would influence the
susceptibility of funds to silent runs, we generate dummy variables for these
characteristics. Conglomerate, charge history, and 12b-1 fees indicators take on the value
1 if the fund is part of a conglomerate, has had a SEC investigation in the past eight
years, and charges 12b-1 fees, respectively and 0 otherwise. Retail fund and institutional
fund indicators take on the value 1 if the fund is a retail or institutional fund, respectively,
and 0 otherwise. We interact the dummy variables for fund and investor characteristics
with all the variables in Equation (2).
The economic rationale for silent runs is the liquidation cost (price depression)
that funds bear when they are forced to sell assets upon the revelation of an adverse
event. The liquidity costs increase with the illiquidity of underlying assets and with the
volume of redemptions. Investors in funds with illiquid assets, such as Real Estate
Investment Trusts (REITs), international assets, or municipal funds have stronger
incentives to run since benefits to running may be greater.
To investigate the impact of the liquidity of underlying assets on run incentives
and the benefits that investors realize from running early, we generate a dummy variable
(liquid) that identifies liquid funds. We classify funds as liquid and illiquid based on the
assets they invest in, as defined in the style classification. Liquid funds invest in large-cap
stock and Treasury bills. Illiquid funds invest in small-cap stocks, sector stocks,
international equity and bonds, and asset-backed securities. We interact the liquid dummy
with all the variables in Equations (3)-(7).
We conduct these analyses using a two-step fund-by-fund approach as well as
using a panel approach. The panel approach is efficient in the sense that it pools
13
information of all funds, however, may suffer from the problem that coefficients are
forced to be the same for all funds. In the fund-by-fund estimation, the first step estimates
the flow models and return models for each fund using time series observations only. The
five return models are the market model (Sharpe, 1964; Lintner, 1965), the market model
with lagged market returns (Scholes and William, 1977), the Fama-French benchmarks
(Fama and French, 1992 and 1993), the Carhart Four factor models that captures
momentum (Jegadeesh and Titman, 1993; Carhart, 1997), and the market model with a
factor that captures liquidity (Pastor and Stambaugh, 2003). The control variables for
flow analysis include accumulated returns in the past six, three, and one month, industry
level flows, and style level flows. In addition, the explanatory variables include two
indicators for six months pre- and post-event respectively. The coefficients on these
indicators estimate the silent runs and runs (from the flow-model estimation) and risk-
adjust returns (from the return-model estimation) six months pre- and post- event. The
second step compares the estimated silent runs, and risk-adjusted returns in the cross
section. We investigate their differences by groups of funds. The funds are grouped
according to their SEC charge history, ownership structure, distribution channels (proxied
by 12-b1 fees), investor clienteles, the liquidity of underlying assets, and the magnitude
of outflows in the post-event window.
2.4 Cost of fire-sales
A direct test of whether mutual funds bear costs associated with liquidating
portfolio positions involves analyzing the returns to underlying assets of fund portfolios.
To this end, we first identify implicated funds that face negative daily flows around
litigation announcements and further categorize them by whether the funds have negative
14
flows in every week of September 2003. Second, we compile the holdings of these funds
and catalog which shares they choose to sell and which shares to purchase. We measure
the changes of shares held by fund portfolio from the nearest quarterly statement prior to
September 2003 to the nearest statement post. Third, we calculate abnormal returns to
sold shares in the event windows surrounding the sale. Since the precise dates for when
funds trade is not available due to the quarterly reporting of fund holdings, we pick
September 2003 as the month in which the trades of implicated funds would most reflect
the withdrawals associated with the litigations.
To compute cumulative abnormal returns (CARs), we estimate the market model
for each firm using daily returns from 282 days to 30 days prior to litigation
announcements. The market model uses CRSP equally-weighted-portfolio as the market
portfolio. Weekly CARs are then aggregated using estimated daily abnormal returns for
the weeks of September 2-4, September 7-11, September 14-18, September 21-25, 2003.
3. Data
To identify funds and fund families implicated in the litigations concerning
market timing and late trading, we conduct a keyword search in the Financial Times5 and
the Wall Street Journal. We also search SEC litigation filings of Stanford Law School
Securities Class Action Clearinghouse6. Table 1 summarizes the results of the search
process. The table lists names of implicated fund-families, activities that they are indicted
5We use three key words --- investigation, mutual fund, and Spitzer--- to search the Financial Times and Wall Street Journal between September 3, 2003 and December 31, 2005. 6Stanford Law School Securities Class Action Clearinghouse (available online at http://securities.stanford.edu/index.html) compiles detailed information relating to the prosecution, defense, and settlement of federal class-action securities fraud litigations.
15
for, regulatory authorities involved, litigation announcement dates, and names of the
parent companies.
New York Attorney General Eliot Spitzer filed a complaint in the New York
Supreme Court on September 3, 2003, alleging that the mutual fund companies of Bank
of America Corp., Bank One Corp., Janus Capital Group Inc., and Strong Capital
Management Inc., allowed certain hedge fund managers to trade illegally in their fund
units. Mr. Spitzer’s complaint marked the beginning of a formal investigation into the
pricing practices of mutual fund companies. From September 2003 to August 2004,
SEC, the New York State Attorney General, and other regulatory authorities filed
litigations concerning funds in 25 mutual fund families.
Another two dates are also important to shred lights on how belief of adverse
events triggers silent runs and runs: the first date when information from trustable sources
triggers investors suspicion of abusive behavior and potential investigation in mutual
funds and the first date when information from trustable sources indicates the actual
investigation and possible SEC charges into the timing and late-trading activities in
funds. In fact, SEC was aware of the fair pricing problems in mutual fund as far back as
in 1997 and the probe of hedge fund trades that take advantage of such problems was
under the way since 2002. However, there was little suspicion on the active cooperation
from mutual fund management side. We identify a news article through Lexis-Nexus on
March 5, 2003 that indicated the possible active involvement of mutual fund
management. Moreover, by March 26, 2003 the pressure from congress to strengthen
mutual fund regulation peaks. Therefore, march 2006 is a reasonable time when investors
start to suspect abusive behavior and potential investigation in mutual funds. Although,
16
September 3rd , 2003 is the date when Spitzer filed the formal complaint. A wall street
journal article on September 1st, 2003 has already revealed the investigation going on and
formal complaints any time. Therefore, September 1st is when the public announcement is
actually made though relatively softly.
We rely on the CRSP mutual funds survival-bias-free database for the universe of
mutual funds (database provided by WRDS). The database provides monthly
observations of funds’ total net assets (TNA) and returns (R). We merge our list of
implicated funds with the CRSP universe of funds using ticker symbols. The resulting
sample identifies funds as implicated and non-implicated. We drop all funds with missing
ticker symbols. We also drop funds that are in the incubation period --- funds with fewer
than 12 months of observations --- and funds whose TNA is smaller than 5 million USD.
We observe outliers in flows, such as negative flows that are larger than TNA and
positive flows that are five times larger than TNA. We windsorize the sample at the first
and ninety-ninth percentiles for flows to reduce the effect of outliers. The observation
unit is a fund-month. The final sample covers 8,703 funds, of which 1,102 are implicated
funds and 1,003 are non-implicated funds in implicated families. There are 763,072 fund
months from February 1996 to December 2005.
Panels A and B of Table 2 present snapshots of funds in non-implicated
families, and implicated and non-implicated funds in implicated families as of December
2002 and December 2004, respectively. The panels show the number of funds, the mean,
and the total TNA of funds in each group. Sample funds manage 4.7 trillion USD as of
December 2002 and 5.5 trillion USD as of December 2004. Twenty percent and 23% of
total funds under management are controlled by implicated funds in December 2002 and
17
2004, respectively. This observation does not conflict with the shrinking of implicated
group, because the ratio is **% in June 2003. Furthermore, the average implicated fund is
larger than the average non-implicated fund.
The WRDS database provides information on fund characteristics such as expense
structure (12b-1 fees, rear and front loads), investment style, age (age), investor type
(retail and institutional funds). We use MorningStar’s data on funds’ intial buy restriction
to identify funds for large institutions. We also hand-collect data on some fund
characteristics. We identify whether the parent company is a conglomerate or an asset
management company using SEC EDGAR filings and firm websites. We check whether
funds have a prior history of SEC charges using SEC litigation filings. We compile
montly data for market returns (rm), risk-free rate (rf), the value (SMB), size (HML),
momentum (MOM), and liquidity (LIQ) factors using the Fama French, Momentum, and
Liquidity database from WRDS.
4. Empirical Results
This section discusses the empirical results of our investigation into the four
questions. First, we provide evidence that investors run implicated funds both prior to and
post litigation announcements. The size of silent runs proves both statistically and
economically significant. Second, we investigate whether investors who run prior to
litigation announcements earn higher risk-adjusted returns than do investors who run post
announcements. Third, we analyze how fund and investor characteristics and liquidity of
underlying assets may affect the timing and size of runs and how fund and investor
characteristics, liquidity of underlying assets, and the size of outflows may affect the
costs to investors who run after the public announcement of litigation, or in a relative
18
sense, the benefits that investors can reap from running early versus late. Finally, using
fund holding and stock return data, we empirically confirm that the cost of running late
is indeed from the fire-sale costs.
4.1 Detecting pre-event runs
We develop two benchmarks to detect silent runs. First, we benchmark flows of
implicated funds against flows of funds in non-implicated families using univariate
analysis. Second, we use multivariate analysis to benchmark flows of implicated funds
against flows estimated using a model of “normal” flows.
Figure 1 plots average monthly flows of funds in non-implicated families, non-
implicated funds in implicated families, and implicated funds from September 2001 to
September 2005. The straight line in Figure 1 indicates September 2003, which is the
month of the first litigation filing. The figure shows that flows of implicated funds are
either higher than or not different from flows of funds in non-implicated families before
April 2003, but consistently lower afterwards. The change of trend starts four months
before the first litigation filing. This pattern suggests that investors ran funds both before
and after the announcement of the first litigation.
Table 3 tests whether the flow differences observed in Figure 1 are statistically
significant. In months prior to September 2003, flows to implicated funds prove
statistically larger than flows to funds of non-implicated families, especially during
September 01 to august 2002. However, the trend reverses in the four months prior to
September 2003. Flows of funds to implicated funds prove smaller (but insignificantly
so) than flows to funds of non-implicated families. In the two years following September
2003, the trend reversal becomes even more pronounced. Flows to implicated funds
19
prove significantly lower than flows to funds of non-implicated families in all months but
two. Implicated funds that enjoyed large flows up to one year before the onset of
litigations, starts experience runs before September 2003 and statistically significant runs
after September 2003.
Table 3 also compares flows of non-implicated funds in implicated families to
flows of funds in non-implicated families. Flows of the former are significantly lower
than those of the latter in all months in the two years following September 2003.
Investors may regard involvement in these suits as an indication of fund family
managers’ failures to serve investor interests. Consequently, investors punish all funds in
implicated families regardless of whether the fund in question allowed abusive practices
or not, indicating a spill-over effect.
Table 4 estimates the model of flows described in Equation (2) that investigate
whether implicated funds realize abnormal flows around litigation dates. Monthly flows
are regressed on four sets of controls: fund characteristics, past returns, fee structures,
aggregate flows, and on dummy variables for event-window months extending 12 months
before and after litigation announcements7. Table 4 includes six specifications. The first
specification controls for fund characteristics and historic returns. The second and third
specifications introduce controls for fee structure and flow characteristics, respectively.
The last three specifications introduce post-announcement indicators into the first three
specifications. The observation unit is monthly flows from February 1996 to December
2005. Regressions use cluster-robust variance/covariance estimators, where the clusters
are funds.
7 We estimate Equation (2) using fund fixed-effects. The results are available upon request and remain qualitatively the same.
20
Table 4 confirms the presence of runs (silent and otherwise) that we first detect in
Figure 1 and Table 3. The table shows a significant outflow in implicated funds starting
as early as six months prior to litigation announcements in the fourth specification and as
late as two months in the third and sixth specifications. The significant outflows continue
in the 12 months post litigation announcements. The size of the runs ranges from -23 to -
63 basis points in the month prior to litigation announcements and ranges from -73 to -
103 basis points post announcements. The significant outflows indicate that investors run
implicated funds as soon as they suspect oncoming litigations in the case of pre-litigation
outflows and as soon as litigations are filed in the case of post-litigation outflows.
The four sets of controls prove significant. First, younger and larger firms enjoy
significantly higher flows than do their older and smaller counterparts. Second, investors
chase past returns. Third, funds with high transaction costs (as measured in loads) and
fees (as measured in 12b-1 fees) realize lower flows. Fourth, industry-level and style-
level flows matter. When the industry or the style is enjoying larger flows, so do the
individual funds.
4.2 Benefits of running early versus late
We investigate what benefits exist for investors who run implicated funds prior to
litigation announcements. Pooling all available data on implicated and non-implicated
funds, we estimate a model of “normal” returns to identify return differences of
implicated funds in the months surrounding litigation announcements8. Panel A of Table
8 estimates the models for returns described in Equations (3) through (7). Monthly
8 We employ an alternative approach to detect return differences. We estimate Equations (3) through (7) for each implicated fund seperately. We then test whether the coefficients of pre- and post-event months differ from each other. The differences in coefficients prove significant in the market model and insignificant in the other models.
21
returns from January 2000 to December 2005 are regressed on dummy variables for
event-window months and the risk factors9. We test for differences in the coefficients of
event-window dummies to detect return differences. All regressions use cluster-robust
variance-covariance estimators where the clusters are the mutual funds.
Panel A of Table 5 shows that investors who run implicated funds post litigation
announcements put up with low returns. The estimates from the market model indicate
that the cost of exiting implicated funds in the six months following litigation
announcements range from 3 basis points in the fifth month to 42 basis points in the
second month. In contrast, investors benefit from exiting implicated funds in three out of
the six months preceding litigation announcements. The results of the other four return
models prove qualitatively similar.
Investors who exit implicated funds before other investors do avoid the lower
returns that investors who exit after litigation announcements suffer from. This result is
consistent with Coval and Stafford’s (2007) argument that prices of underlying assets
become depressed when there is a large volume of asset sales. Table 4 shows that mutual
funds face large outflows following litigation announcements. Mutual funds may suffer
fire-sale costs when they try to liquidate their portfolios to satisfy the high redemption
volume. These fire-sale costs would explain the lower returns observed following
litigation announcements.
Panel B of Table 5 tests the hypothesis that investors benefit from exiting
implicated funds prior to litigation announcements. In the first rows for the five event
windows ranging from one month to five months, the panel shows the difference between
9 We estimate Equations (3) through (7) using fund fixed-effects. The results are available upon request and remain qualitatively the same.
22
the accumulated coefficients of event-month dummies before and after litigation
announcements. In the second rows, the panel reports the F-statistics for the test that the
difference is equal to 0. For the one-month window, the difference in coefficients pre-
and post-announcements ranges from 58 basis points to 63 basis points. For the six-
month window, the difference in accumulated coefficients pre- and post-announcements
ranges from 107 basis points to 237 basis points. The differences prove economically and
statistically significant.
4.3 Cross sectional difference in runs and returns
The degree of investors’ belief of abusive behavior in funds, size of concerted
redemption once the adverse event is confirmed, and the cost of fire-sale affects their
incentive to run the funds before other investors doing so. We conduct fund-by-fund
estimation on the size of runs and returns difference between runs before and after
litigation announcements to examine these effects. Two indictors are introduced in flow
and returns models (equations 2 to 7), one for six months pre- and the other, post- event.
The coefficients on these two indicators estimate silent runs and runs in the flow analysis
and risk-adjusted returns for the two period in the return analysis. These fund level
estimates are then summarized by groups with classification of whether there is a SEC
charge history, whether the management belongs to financial conglomerates, whether
there is actually 12b1 fees charged, retails vs. institutional funds, retail vs. funds large
institutions, liquidity of underlying assets. For summary of return difference, funds are
also classified by whether the outflows in post event window is above or below median.
23
Table 6 presents these fund-level estimations with panel A summarizing the
coefficients estimates on the indicators of six months pre- and post- event from the flow
model– estimates of silent runs and runs. The first column presents cross sectional mean
and t-statistics of the silent runs and runs for the full sample, The rest columns in panels
A presents the difference of runs and t-statistics of the difference cross groups. We can
see that abnormal flows in both windows are significantly negative implying both silent
runs and runs in funds involved in litigations. Abnormal flows in six-months before the
litigation are significantly more negative in funds with the SEC charge history or funds
that do not belong to financial conglomerates. Abnormal flows in six-months after the
litigation are significantly more negative in retail funds than funds for large institutions
and funds with liquid assets.
Panel B of table 9 compares the coefficient estimates on the indicators of six
months pre- and post- event from the returns models – estimates of return benefit of silent
runs. The first column of panel B presents the return benefits for the full sample. The rest
columns present the difference of return benefits and t-statistics of the difference cross
groups. We can see that the risk-adjusted returns (alpha) are significantly higher in the
pre-event window than those in the post-event window. The difference in difference of
alphas has little significance across fund characteristics, but significant cross the liquidity
of funds’ underlying assets and amount of flows occur during the post- event window.
These results show both silent runs and runs around the adverse event. Risk-
adjusted returns for investors who withdraw before the information become public are
significantly higher than those for investors who withdraw afterwards. Both runs and
return difference are affected by the fund reputation and the liquidity of underlying
24
assets, which are consistent with the payoff complementaries and liquidation cost
arguments.
The tests are also conducted with panel approach. We estimate the augmented
version of the flow model described in Equation (2) to determine whether funds differ in
their susceptibility to runs in term of timing and size due to investor clientele, fee
structure, or reputation effect such as whether funds are managed by financial
conglomerates, have a SEC charge history. The augmented models interact the dummy
variables for fund characteristic with every term in the original equation. Table 7
investigates whether fund reputation affects the decision of investors to run. The first two
columns report the results concerning management ownership type and the last two
columns report the results concerning the SEC charge records of the management. Both
sets of regressions use cluster-robust variance-covariance estimators where the clusters
are funds. Each regression generates two sets of coefficients with one for stand-alone
variables and the other for interaction terms.
The runs on implicated funds whose parents are conglomerates prove less
significant both prior to and post filing of litigations. Intuitively, fundsoperating under a
conglomerate may be more reputable since they acquire the backing of the conglomerate
and loss of reputation would hurt the abused fund as well as other businesses of the
conglomerate, therefore,investors may be less suspicious of abusive behavior by funds in
conglomerate families. Further, the reduced size of runs post litigation-announcements
indicates that investors do not punish funds in conglomerate families as severely as funds
in non-conglomerate families even when they learn of abusive behavior. Conglomeration
25
seems to protect funds from suspicions of abusive practices and from punishments for
abusive practices.
An SEC charge history affects the timing but not the size of the runs. In the three
months prior to litigation announcements, the incremental flows to implicated funds with
SEC charge histories cumulate to -1.63%, whereas in the three months post
announcements, the incremental flows cumulate to 1.70%. A lack of SEC investigations
protects funds from suspicions of abusive practices. However, results indicate that
reputation is lost as soon as investors learn of abusive practices. Even though fund
management may be innocent until proven guilty in the eye of the law, investors seem to
presume guilt as soon as they learn of litigations.
Table 8 analyzes whether investor clientele affects the decision to run and the
setting of the regressions are the same as in table 7. The first three columns report the
results for different investor types because there are two dummies indicating retail funds
and institutional funds indicators, respectively. The last two columns report the results
concerning distribution channels with a dummy indicator for 12-b1 fees. Both sets of
regressions use cluster-robust variance-covariance estimators where the clusters are the
mutual funds.
The information collection and processing skills of investors seem to matter for
detecting abusive behavior prior to such alleged behavior becoming public knowledge.
Investors who are aided by financial advisors (funds with 12b-1 fees) run funds more
both prior to and post filing of litigations. It is also possibly due to self-selection that
active investors who follow the financial news may be more likely to employ financial
26
advisors. As such, the larger runs might be explained by investor interest rather than the
benefits to retaining financial advisors.
The run size for institutional investors proves significantly lower. This may be
explained by agency problems (James and Karceski 2006). In the late analysis with fund
level estimates, we indentify institutional investors by requiring the fund’s initial
minimum purchase to be $100,000 or above. We find that large institutional do take
action when the funds they invest in allegedly allow abusive practices. This result
supports the agency-problem argument.
Finally, we look at the effect of underlying assets liquidity on the return
difference between withdrawals made prior and post announcements. The benefit of
silent runs is to avoid the liquidation cost (price depression) that investors bear when
funds are forced to sell assets upon revelation of an adverse event. Therefore, investors in
funds with illiquid assets, such as REITs, international assets, or municipal funds have
stronger incentives to run since benefits to running may be greater.
We estimates the augmented version of return model described in Equations (3) through
(7) to investigate whether liquidity of underlying assets affects the return differences
investors realize when they exit before versus after litigation announcements. The
augmented models interact the liquid fund indicator with every term in equations (3)
through (7). Monthly returns from January 2000 to December 2005 are regressed on
dummy variables for event-window months, the risk factors, and the interaction terms.
We report neither the coefficients on the standalone variables or interactions avoid
excessively long tables, although the main information deliverable from these
coefficients are that investors of liquid funds (compared to investors of illiquid funds)
27
enjoy higher returns in the four months surrounding litigation announcements. In all
specifications of the returns model, returns to illiquid funds are negative up to three
months prior to litigation announcements and remain negative up to four months post
announcements.
Instead, we test for differences in the accumulated coefficients of event-window
dummies and interaction with the liquid indicator,and present the results in table 9. In all
specifications and event-windows, Panel A finds significant return differences between
investors who exit before and after litigation announcements. Panel B shows that the
return differences between investors who exit pre- and post-announcements is less
pronounced in liquid funds. The results support the hypothesis that liquidation cost is
higher in illiquid funds.
To sum up, silent runs are more prominent in funds with bad reputation, illiquid
assets, and institutional investors. These findings are consistent with the payoff
complementary story, because investors are more likely to doubt these funds’ behavior,
withdrawals uponpublic announcement of misbehavior and price depressions during fire-
sale are likely to be larger in these funds.
4.4 Evidence from holding data
Talk about Table 10.
Talk about Table 11.
5. Conclusion
28
This is the first paper that documents silent runs in mutual funds. We find that
silent runs start as early as six months prior to announcements of litigations. The size of
silent runs before the event becomes public is half the size that it is after. Fund and
investor characteristics such as reputation affect the timing and size of runs. We also find
that investors who run funds prior to litigation announcements realize higher returns than
those who run afterwards, especially in the less-liquid funds, because concerted runs after
the announcement of adverse events trigger significantly fire-sale costs. These results
suggest that the pro-rata distribution of proceeds from asset sales is not sufficient to
prevent silent runs or ensure fairness among investors, since returns to investors differ
across the timing of withdrawals.
The rationale for exiting early has a critically important implication for the
stability of the fund industry. Once the timing of an action matters for payoff, payoff
complementary strategy will prevail. Investors may run funds in the expectation that
other investors will do so. It can amplify the impact of adverse events or random shocks
to fundamentals on financial markets. Mutual funds therefore may face the financial
fragility thatfear of liquidity-dry-up causes runs of liquidity. However, the devastating
consequences that a bank run would confer are not likely to manifest in the fund industry,
since depressed price during fire-sale can be soon recovered as long as the liquidity shock
does not cover all sectors. In case of fund run caused by mispricing set by the funds, it
will stop when price is reset with fair value.
29
References
J. Bulow, J. Geanakoplos, and P. Klemperer, 1985, Multimarket oligopoly: strategic
substitutes and strategic complements, Journal of Political Economy, 93, 488-
511.
Carhart, Mark M., 1997, On persistence in mutual fund performance, Journal of Finance,
52, 57-82.
Chari, V. V., and Ravi Jagannathan, 1988, Banking panics, information and rational
expectations equilibrium, Journal of Finance, 43, 749-761.
Coval, Joshua and Erik Stafford, 2007, Assets fire sales (and purchases) in equity market,
Journal of Financial Economics, 86, 479-512.
Chen, Qi, Itay Goldstein, and Wei Jiang, 2008, Payoff Complementarities and Financial
Fragility: Evidence from Mutual Fund Outflows, Working Paper, Wharton
School.
Chen, Joseph, Samuel Hanson, Harrison Hong, and Jeremy C. Stein, 2008, Do Hedge
Funds Profit From Mutual-Fund Distress? NBER Working Paper.
Del Guercio, Diane, and Paula A. Tkac, 2001. Star Power: the effect of Morningstar
ratings on mutual fund flows. Working paper, Federal Reserve Bank of Atlanta.
Del Guercio, Diane, and Paula A. Tkac, 2002. The determinants of the flow of funds of
managed portfolios: mutual funds versus pension funds. Journal of Financial and
Quantitative Analysis, 37.
Diamond, Douglas W. and Philip H. Dybvig, 1983, Bank runs, deposit insurance, and
liquidity, Journal of Political Economy, 91, 1983, 401-419.
30
Dodd, Peter and Jerold B. Warner, 1983. On corporate governance: a study of proxy
contests. Journal of Financial Economics 11, 401-438.
Fama, Eugene F. and Kenneth R. French, 1992, The cross-section of expected stock
returns, Journal of Finance, 67, 427-465.
Fama, Eugene F. and Kenneth R. French, 1993, Common risk factors in the returns on
stocks and bonds, Journal of Financial Economics, 33, 3-56.
Ferson, Wayne E., and Meijun Qian, 2004, Conditional Performance Evaluation,
Revisited. The Research Foundation of CFA Institute. (September).
Ferson, Wayne E., and Vincent A. Warther, 1996, Evaluating Fund Performance in a
Dynamic Market, 1996, Financial Analysts Journal 52, no. 6, pp.20-28.
Greenbaum, Stuart I. and Anjan V. Thakor, 2007 Contemporary Financial
Intermediation, Elsevier Inc.
Gruber, Martin, 1996. Another puzzle: the growth in actively managed mutual funds.
Journal of Finance 51, 783-810.
Hogue, T. and J. Wellman, 2005, Fallout from the Mutual Fund Trading Scandal, Journal
of Business Ethics, 62, 129-139.
Jagadeesh and Titman (1993). N. Jagadeesh and S. Titman , Returns to buying winners
and selling losers: implications for stock market efficiency. Journal of Finance,
48, 65–91.
James, Christopher and Jason Karceski , 2006, Investor Monitoring and Differences in
Mutual Fund Performance, Journal of Banking and Finance, October 2006, Vol.
30, 2787-2808.
31
Lintner, John, 1965, The valuation of risk assets and the selection of risky investments in
stock portfolios and capital budgets, Review of Economics and Statistics, 47, 13-
37.
Pastor, Lubos, and Robert F. Stambaugh, 2002, Mutual Fund Performance and Seemingly
Unrelated Assets, Journal of Financial Economics, vol. 63, no.3 (March): 315-
349.
Pastor, Lubos, and Robert F. Stambaugh, 2003, Liquidity Risk and Expected Stock
Returns, Journal of Political Economy, vol. 111, no. 3:642–685.
Scholes, Myron, and Joseph Williams, 1977, Estimating Betas from Nonsynchronous
Data, Journal of Financial Economics, Vol. 5, pp307-327.
Sharpe, William F., 1964, Capital assets prices: A theory of market equilibrium under
conditions of risk, Journal of Finance, 19, 425-442.
Sirri, Erik R. and Peter Tufano, 1998. Costly search and mutual fund flows. Journal of
Finance 53, 1589-1622.
Table 1 - List of fund families involved in the trading scandals
A firm is included only if the funds it manages are implicated. Hedge funds, brokerage firms, and other investment banking services are excluded. Some of the allegations are at the state level and some are informal. AG stands for Attorney General; WI stands for Wisconsin; MA stands for Massachusetts; NY stands for New York.
Fund family Practice under Investigation
Regulator involved Initial news date Parent firms
Alliance Bernstein Market timing Internal Probe 9/30/2003 Alliance Capital
Nations Market timing + Late trading
NY State AG 9/3/2003 Bank of America
One Group Market timing NY State AG 9/3/2003 Bank One
Columbia Trading Practice SEC 1/15/2004 Fleet Boston Financial
Federated Market timing + Late trading
SEC/ NASD /NY State AG
10/22/2003 Federated Investors
Franklin Templeton Market timing California AG 9/3/2003 Franklin Resources
Fred Alger & Co
Late trading NY State AG, NY Supreme Court
10/3/2003 Private
Fremont Market timing SEC 11/24/2003 Private
Heartland Advisors Trading Practice + Pricing violation
SEC 12/11/2003 Private
Invesco AIM Market timing SEC/ NY State AG AG/ Colorado AG
12/2/2003 Amvescap PLC
Janus Market timing NY State AG 9/3/2003 Janus Capital Group
Loomis Sayles & Co Market timing Internal Probe 11/13/2003 CDC Assets Management
MFS Market timing SEC 12/9/2003 Sun Life Financial
PBHG Funds market timing SEC/NY State AG 11/13/2003 Old Mutual PLC
Pimco/PEA Capital Market timing California AG 2/13/2004 Allianz Group
Putnam Investments Market timing SEC/ MA State Regulators 9/19/2003 Marsh & McLennan
Scudder Investments Market timing SEC 1/23/2004 Deutsche Bank
Strong Capital Market timing NY State AG/WI State Regulators 9/3/2003 Private
RS Investment Market Timing SEC/NY State AG 3/3/2003 Private
Excelsior Market Timing +late trading
Maryland AG 11/14/2003 Charles Schwab
ING Investment Market Timing + late trading
NY State AG 3/11/2004 ING Group
Evergreen Market Timing MA AG 8/4/2004 Wachovia
Seligman Trading practices +Market Timing
NY State AG 1/7/2004 Private
American Funds Market timing California AG 12/29/2003 Capital Group
Prudential Securities Market timing + late trading
SEC/NASD/NY State AG /MA State Regulators
11/4/2003 Prudential Securities
Sources: Money Management Executive Compilation, January 31, 2004, Wall Street Journal, “Fund Scandal Scorecard” April , 27th 2004, SEC press releases from September 2003 to December 2004. Financial Times 2003-2005. Stanford Law School Library Securities Class Action Clearing House.
33
Table 2 - Summary statistics: overview of sample funds Panels A and B present snapshots as of December 2002 and December 2004, respectively, for funds in non-implicated families and for implicated and non-implicated funds in implicated families. The table presents total number of funds, average TNA of each fund and total TNA of funds in the three groups. Funds in Non-
implicated Families
Non- implicated Funds in implicated families
Implicated Funds
Panel A: Snap shot at December 2002
Total # of funds 4,664 1,003 1,095
Average TNA of each fund ( million USD) 636 790 867
Total TNA ( million USD) 2,969,569 792,494 949,524
Panel B: Snap shot at December 2004
Total # of funds 4,333 817 1077
Average TNA of each fund ( million USD) 803 871 1,187
Total TNA ( million USD) 3,480,074 712,093 1,277,864
34
Figure 1 – Plot of flows to funds in non-implicated families, non-implicated funds in implicated families, and implicated funds The figure plots the trends of flows from September 2001 to September 2005 in three fund groups: funds in the non-implicated families, non-implicated funds in the implicated families, and implicated funds. Flowi,t is calculated as [TNAi,t –TNAi,t-1 *(1+Ri,t)] / TNAi,t-1.
Basak: Can you change the blue line into dashed-line? Looking at the print-outs of current one, it is very hard t tell the green from blue (red is fine because it is dark). I think this why the referee says that he cannot differentiate. Let’s change now, since journal graphs are in black and white anyway.
35
Table 3 - Flows to funds in non-implicated families, implicated and non-implicated funds in implicated families
This table averages monthly flows within non-implicated and implicated families from September 2001 to September 2005. Funds in implicated families are categorized as implicated funds for funds that are named in litigations and as non-implicated for funds that are not named. Event month is the first month litigation was announced: September 2003. Funds within the non-implicated families are benchmarks to test for flow differences against implicated and non-implicated funds in implicated families. ** and * denote significance levels at 1% and 5%, respectively.
Months
from September
2003
Date Non-implicated Families
Implicated Families
Non-implicated Funds
t-stat of difference
Implicated Funds
t-stat of difference
-12 Sep-02 -0.02% -0.55% -3.06** 0.33% 2.17*
-11 Oct-02 0.11% -0.19% -1.68 0.33% 1.31
-10 Nov-02 0.46% 0.68% 1.25 0.18% -1.68
-9 Dec-02 -0.20% -0.88% -3.68** -0.27% -0.41
-8 Jan-03 0.17% -0.25% -2.17* 0.20% 0.19
-7 Feb-03 0.15% -0.41% -3.42** 0.18% 0.15
-6 Mar-03 -0.05% -0.27% -1.31 0.14% 1.14
-5 Apr-03 0.04% -0.70% -4.22** 0.49% 2.78**
-4 May-03 0.16% 0.04% -0.73 0.09% -0.47
-3 Jun-03 0.36% 0.33% -0.13 0.31% -0.33
-2 Jul-03 0.20% 0.19% -0.06 0.00% -1.29
-1 Aug-03 -0.06% -0.49% -2.42** -0.14% -0.50
0 Sep-03 -0.35% -0.92% -3.38** -0.33% 0.15
1 Oct-03 0.12% -0.28% -2.24* -0.33% -2.87**
2 Nov-03 0.21% -0.25% -2.71** -1.09% -8.44**
3 Dec-03 -0.14% -0.97% -4.37** -1.03% -5.44**
4 Jan-04 0.49% -0.41% -4.43** -0.40% -5.04**
5 Feb-04 0.17% -0.41% -3.35** -0.47% -4.30**
6 Mar-04 -0.03% -0.59% -2.97** -0.74% -4.48**
7 Apr-04 -0.42% -1.22% -4.51** -0.93% -3.44**
8 May-04 -0.52% -0.61% -0.49 -1.32% -5.37**
9 Jun-04 -0.35% -0.82% -2.70** -1.11% -5.35**
10 Jul-04 -0.15% -0.74% -3.44** -0.97% -5.85**
11 Aug-04 -0.20% -0.66% -2.85** -0.85% -4.85**
12 Sep-04 -0.35% -1.01% -3.96** -0.72% -2.69**
36
Table 4 – Detecting silent runs: multivariate analysis of monthly flows The table runs six specifications of the flow model: Flow = a + ∑bj * fund characteristicsj + ∑cj * past returnsj + ∑dj * aggregate flowsj + ∑γj * Event-window dummiesj + ε. The dependent variable is computed as Flowi,t = [TNAi,t –TNAi,t-1 *(1+Ri,t)] / TNAi,t-1. Fund characteristics include size (log of TNA in million USD), age (log of days since first offer date), 12b-1 fees (annual fees paid to financial advisors, measured as percentage of TNA), rear and front loads (charges for purchasing and redeeming shares, as a percentage of TNA). Past returns include cumulative returns in the past one, three, and six months. Aggregate flows include industry- and style-level flows. Industry-level flows are the sum of flows in dollars (TNAi,t –TNAi,t-1
*(1+Ri,t )) to all funds in the sample divided by the sum of lagged TNA (TNAi,t-1). Style-level flows are the sum of flows in dollars to all the funds with the same investment style divided by the sum of lagged TNA. Event-window dummy (n month) equals 1 if it is the nth month to the date of litigation filing and 0 otherwise (n = -1, -2, - - -12, 0, 1, 2, - - -12). Observations are monthly and cover from February 1996 to December 2005. Robust t statistics are in brackets. * indicates significance at 5% and ** significance at 1%. (1) (2) (3) (4) (5) (6)
Dummies for Months -12 to -7 are controlled for.
Dummy (-6 month) -0.21 -0.09 0.09 -0.23* -0.13 0.04 [1.84] [0.75] [0.72] [2.06] [1.08] [0.35] Dummy (-5 month) -0.32** -0.32** -0.01 -0.34** -0.36** -0.06 [2.87] [2.97] [0.12] [3.10] [3.33] [0.54] Dummy (-4 month) -0.16 -0.07 0.07 -0.18 -0.12 0.03 [1.44] [0.62] [0.55] [1.68] [0.96] [0.23] Dummy (-3 month) -0.40** -0.23* 0.02 -0.42** -0.28* -0.02 [3.68] [2.00] [0.17] [3.91] [2.34] [0.20] Dummy (-2 month) -0.44** -0.43** -0.17 -0.47** -0.48** -0.22* [3.82] [4.27] [1.76] [4.04] [4.61] [2.16] Dummy (-1 month) -0.60** -0.52** -0.23* -0.63** -0.57** -0.28* [5.44] [4.61] [2.08] [5.66] [4.93] [2.45] Event month 0 -1.03** -0.95** -0.73** [9.80] [8.56] [6.62]
Dummy (+1 month) -1.77** -1.97** -1.76** [12.71] [11.80] [10.54] Dummy (+2 month) -1.58** -1.38** -1.07** [15.71] [13.50] [10.38] Dummy (+3 months) -1.32** -1.33** -1.14** [12.39] [12.13] [10.30] Dummy (+4 months) -1.05** -0.99** -0.74** [11.89] [11.68] [8.75] Dummy (+5 month) -1.04** -0.93** -0.75** [12.85] [10.53] [8.63] Dummy (+6 month) -0.77** -0.73** -0.48** [9.08] [9.39] [6.27] Dummy (+7 month) -0.99** -0.73** -0.48**
37
[12.35] [9.26] [6.11] Dummy (+8 month) -0.89** -0.73** -0.53** [11.39] [9.99] [7.49] Dummy (+9 month) -0.78** -0.67** -0.53** [9.91] [8.86] [7.05] Dummy (+10 month) -0.72** -0.77** -0.57** [8.22] [10.47] [7.71] Dummy(+11 month) -0.51** -0.55** -0.40* [5.14] [3.40] [2.46] Dummy (+12 month) -0.61** -0.77** -0.58** [8.38] [7.72] [5.76] Age (LogAge) -1.04** -1.18** -1.14** -1.03** -1.16** -1.12** [45.28] [36.87] [35.75] [44.82] [36.30] [35.33] Size (logTNA) 0.26** 0.23** 0.22** 0.26** 0.23** 0.23** [33.21] [21.49] [20.91] [33.36] [21.50] [20.94] Return in the 1.78** 1.40** 0.93** 1.77** 1.39** 0.95** last month [10.55] [7.28] [4.81] [10.50] [7.26] [4.93] Cumulative Returns 0.58** 0.27* 0.13 0.55** 0.23 0.1 in the past 3 months [5.27] [2.07] [0.99] [4.96] [1.77] [0.76] Cumulative Returns 4.55** 5.21** 4.41** 4.63** 5.29** 4.48** in the past 6 months [45.32] [41.25] [33.94] [45.65] [41.33] [34.07] Front+ Rear Load -0.03** -0.02** -0.03** -0.02** [4.44] [2.82] [4.27] [2.75] Actual 12b-1 Fess -0.71** -0.60** -0.67** -0.57** [13.13] [10.96] [12.19] [10.33] Industry-Normalized Flow 0.05* 0.03 [2.00] [1.13] Style-Normalized Flow
0.54** 0.53** [31.18] [31.11] Constant 6.93** 8.62** 8.08** 6.86** 8.47** 7.99** [39.85] [35.59] [33.47] [39.45] [34.93] [33.03] Observations 660,317 355,811 355,811 660,317 355,811 355,811 Adjusted R-squared 3.60% 5.96% 7.61% 3.70% 6.09% 7.69%
38
Table 5: Fund returns before and after litigation announcements Pooled regressions using CAPM, Fama-French, Cahart, and William and Scholes (1976), and Pastor Stambaugh (2000) models are run. Observations are from January 1, 2000 to December 31, 2005. The dependent variable is monthly fund returns (in %). A dummy (n month) equals 1 if it is the nth month before (-n) or after (+n) litigations are filed. For other months and non-indicted funds, the dummy takes on the value 0, n = -1, -2, - - - -6, 0 , 1, 2, - - - 6. Panel A presents the regression results. Robust t statistics are in brackets. * indicates significance at 5% and ** significance at 1%. Panel B tests for differences in the accumulated abnormal returns between D-n and D+n.
Market model
Fama-French
Carhart four
factors
Market model with
lagged returns
Market model with
liquidity factor
Panel A: Regression results
Dummy (-6 month) 0.36** 0.29** 0.29** 0.32** 0.37** [4.41] [3.57] [3.65] [3.91] [4.56] Dummy (-5 month) 0.46** 0.31** 0.32** 0.39** 0.47** [5.03] [3.37] [3.52] [4.21] [5.11] Dummy (-4 month) 0.00 -0.18* -0.18* -0.08 0.00 [0.02] [2.35] [2.32] [0.95] [0.04] Dummy (-3 month) -0.01 -0.12 -0.12 -0.07 0.00 [0.09] [1.53] [1.52] [0.90] [0.03] Dummy (-2 month) -0.08 -0.21** -0.21** -0.13 -0.08 [1.11] [2.80] [2.80] [1.70] [1.14] Dummy (-1 month) 0.54** 0.45** 0.45** 0.48** 0.57** [7.60] [6.24] [6.21] [6.67] [8.04] Dummy (0 month) 0.34** 0.30** 0.29** 0.31** 0.36** [4.89] [4.17] [4.09] [4.46] [5.21] Dummy (+1 month) -0.07 -0.13 -0.14* -0.12* -0.07 [1.26] [2.27]* [2.32] [2.08] [1.15] Dummy (+2 month) -0.40** -0.26** -0.25** -0.46** -0.41** [6.41] [4.25] [4.17] [7.41] [6.47] Dummy (+3 months) -0.15** -0.14** -0.14** -0.19** -0.15** [2.93] [2.66] [2.73] [3.70] [2.92] Dummy (+4 months) -0.08* -0.05 -0.05 -0.12** -0.11* [1.98] [1.23] [1.27] [2.75] [2.53] Dummy (+5 month) 0.00 0.11 0.12 -0.02 -0.03 [0.02] [1.78] [1.85] [0.29] [0.54] Dummy (+6 month) -0.19** -0.02 -0.02 -0.18** -0.23** [3.28] [0.37] [0.27] [3.10] [3.91]
Continue on the next page,
39
Table 5, continued.
Market Returns 0.49** 0.51** 0.51** 0.49** 0.49** [77.40] [83.19] [85.18] [77.53] [76.48] SMB 0.08** 0.07** [29.07] [30.28] HML 0.07** 0.07** [24.28] [23.71] Momentum 0.00** [3.41] Lagged market returns 0.02** [21.69] Liquidity factor 0.01** [21.41] Constant 0.10** -0.03** -0.03** 0.11** 0.15** [18.94] [8.85] [8.76] [20.02] [22.14] Observations 473,508 473,508 473,508 466,562 473,508 R-squared 30.73% 31.27% 31.28% 31.02% 30.77% Panel B: Performance Difference
Dummy(-1 month) 0.62 0.58 0.58 0.60 0.64 - Dummy (+1 month) 54.69 48.93 49.09 50.24 59.47 Accumulate (-1 to -2) 0.94 0.63 0.63 0.94 0.97 - Accumulate (+1 to +2) 71.57 33.44 33.11 70.28 75.01 Accumulate (-1 to -3) 1.09 0.65 0.65 1.06 1.12 - Accumulate (+1 to +3) 65.72 25.27 25.43 63.53 68.10
40
Table 6: Cross sectional difference in silent runs, runs, and return benefits This table presents the summary results of individual fund estimates. The analysis consists of two steps. In the first step, we run time series regressions of flows as in equation (2) in panel A and of returns as in equations (3) to (7) in panel B for each fund. In the second step, we compare these fund level estimates across groups. The fund groups are classified according to their SEC charge history, whether the management belongs to a financial conglomerates, whether there is actually 12b1 fees charged, retails vs. institutional funds, retail vs. funds large institutions, liquidity of underlying assets, and whether the outflows in post event window is above or below median.
Panel A presents the cross sectional mean and t-statistics of the coefficients on the indicators of six months pre- and post- event from the flow model– estimates of silent runs and runs. Panel B presents the difference in cross sectional mean and t-statistics of the coefficient on the indicators of six months pre- and post- event from the returns models – estimates of return benefit of silent runs. Panel A: Mean and t-statistics of the silent runs and runs for the full sample, and difference cross groups
Full Sample
Charge History (No–Yes)
Ownership (Other – Conglo-merates)
12-b1 fees (Without-With)
Clientele (Retail – Institution)
Clientele (Retail – large Institution)
Liquidity of Underlying Assets (Illiquid –Liquid)
Flows (silent runs) -0.42 0.56 -0.57 -0.07 0.09 0.48 0.15
(-6 to -1) -18.88 3.26 -3.08 -0.26 0.22 1.26 0.56
Flows (runs) -1.44 0.01 -1.03 0.29 -0.75 -0.69 0.62
(+1 to +6) -5.79 0.06 -5.39 0.95 -1.69 -1.88 2.29
Panel B: Mean and t-statistics of the return benefits of silent runs over runs for the full sample and difference cross groups.
Alpha (+1 to +6) – (-6 to -1)
Full Sample
Charge History (No–Yes)
Ownership (Other – Conglo-merates)
12-b1 fees (Without-With)
Clientele (Retail – Institution)
Clientele (Retail – large Institution)
Liquidity of Underlying Assets (Illiquid –Liquid)
Outflow (Large –Small)
Market model 0.32 0.09 -0.05 -0.05 0.14 0.23 0.25 0.19
13.34 1.66 -0.83 -0.63 0.96 2.5 3.41 4.12
Fama-French 0.09 0.12 -0.03 -0.07 0.05 0.1 0.21 0.1
4.13 2.26 -0.52 -1.05 0.47 1.15 3.39 2.43
Carhart Four 0.12 0.15 0.01 -0.07 0.07 0.16 0.23 0.08
Factors 6.03 3.16 0.26 -1.22 0.65 1.77 4.32 2.06
Market and lagged 0.28 0.1 -0.04 0.02 0.19 0.15 0.26 0.18
market returns 12.15 1.78 -0.72 0.29 1.45 1.62 3.63 3.95
Market and 0.34 0.09 -0.07 -0.08 0.13 0.3 0.27 0.22
liquidity factor 13.54 1.53 -1.19 -1.07 0.87 3.12 3.42 4.39
41
Table 7: The effect of reputation on silent runs
This table runs the augmented flow model (Flow = a + ∑bj * fund characteristicsj + ∑cj * past returnsj + ∑dj
* aggregate flowsj + ∑γj * Event-window dummiesj + ε), with characteristics dummy to interact with every term in the model. The first characteristics dummy equals one if funds managed by conglomerates and zero if stand-alone asset-management companies. The second characteristics dummy equals one if funds whose managements have been charged by SEC in the past eight years and zero if not. The dependent variable is computed as Flowi,t = [TNAi,t –TNAi,t-1 *(1+Ri,t)] / TNAi,t-1. Controls for fund characteristics include size (log of TNA in million USD), age (log of days since first offer date), 12b-1 fees (annual fees paid to financial advisors, measured as percentage of TNA), rear and front loads (charges for purchasing and redeeming shares, as a percentage of TNA). Past returns include cumulative returns in the past one, three, and six months. Aggregate flows include industry- and style-level flows. Industry-level flows are the sum of flows in dollars (TNAi,t –TNAi,t-1 *(1+Ri,t )) to all funds in the sample divided by the sum of lagged TNA (TNAi,t-1). Style-level flows are the sum of flows in dollars to all the funds with the same investment style divided by the sum of lagged TNA. Event-window dummy (n month) equals 1 if it is the nth month before or after the litigation is filed and 0 otherwise (n = -1, -2, - - -12, 0, 1, 2, - - -12). Observations are monthly and cover from February 1996 to December 2005. Robust t statistics are in brackets. * indicates significance at 5% and ** significance at 1%.
Regression with
conglomerate indicators Regression with charge-
history indicators
Stand-alone variable
Variables interacted with conglomerate indicators
Stand-alone variable
Variables interacted with charge- history indicators
Dummies for Months -12 to -7 are controlled for.
Dummy (-6 month) -0.30* 0.74** 0.10 -0.54* [2.32] [2.62] [0.63] [2.46] Dummy (-5 month) -0.37** 0.90** 0.11 -0.57* [2.89] [3.12] [0.74] [2.35] Dummy (-4 month) -0.38* 0.37 -0.13 -0.30 [2.56] [1.63] [1.02] [1.12] Dummy (-3 month) -0.66** 0.74** -0.24 -0.54* [5.82] [2.74] [1.79] [2.55] Dummy (-2 month) -0.60** 0.57* -0.24 -0.53* [4.29] [2.11] [1.47] [2.40] Dummy (-1 month) -0.64** 0.50 -0.28 -0.56* [4.77] [1.74] [1.74] [2.52] Event month 0 -1.12** 0.43 -0.99** 0.01 [8.98] [1.68] [6.78] [0.07] Dummy (+1 month) -2.30** 1.41** -2.30** 1.24** [12.15] [4.34] [10.71] [4.40] Dummy (+2 month) -1.87** 1.25** -1.61** 0.29 [14.79] [5.21] [10.79] [1.43] Dummy (+3 months) -1.69** 1.48** -1.30** 0.17
42
[12.98] [5.27] [9.08] [0.66] Dummy (+4 months) -1.35** 1.09** -1.03** 0.03 [12.01] [5.47] [8.64] [0.16] Dummy (+5 month) -1.22** 0.66** -1.02** 0.03 [11.34] [3.32] [9.18] [0.17] Dummy (+6 month) -1.01** 0.93** -0.81** 0.25 [10.50] [5.30] [7.64] [1.54] Dummy (+7 month) -1.01** 0.76** -0.96** 0.46** [10.06] [4.18] [8.43] [2.88] Dummy (+8 month) -0.95** 0.55** -0.98** 0.54** [10.82] [3.06] [9.78] [3.49] Dummy (+9 month) -0.87** 0.39 -0.86** 0.32 [10.61] [1.77] [8.36] [1.89] Dummy (+10 -0.71** 0.18 -0.62** -0.14 month) [7.76] [0.66] [4.96] [0.74] Dummy(+11 -0.50** 0.08 -0.46** 0.03 month) [3.57] [0.36] [4.08] [0.09] Dummy (+12 -0.68** 0.04 -0.66** 0.03 month) [7.14] [0.24] [7.42] [0.15] Age (LogAge) -1.00** -0.13* -1.05** 0.00 [26.64] [2.31] [28.73] [0.02] Size (logTNA) 0.22** 0.06** 0.23** 0.03 [18.03] [3.09] [19.02] [1.83] Return in the 1.96** -2.11** 0.21 1.70** last month [7.13] [5.46] [0.81] [4.32] Cumulative Returns 0.10 0.48 0.11 0.36 in the past 3 months [0.55] [1.82] [0.65] [1.36] Cumulative Returns 4.52** -0.89** 4.32** -0.34 in the past 6 months [26.66] [3.68] [25.35] [1.38] Front+ Rear Load 0.00 -0.05** 0.00 -0.03* [0.26] [3.69] [0.23] [2.27] Industry-Normalized 0.03 0.01 0.06 -0.03 Flow [1.11] [0.34] [1.84] [0.59] Style-Normalized 0.53** 0.03 0.50** 0.09** Flow [25.42] [0.84] [24.10] [2.81] Constant 6.77** 0.59 7.16** -0.38 [23.41] [1.39] [25.16] [0.89] Observations 447,219 447,219 Adjusted R-squared 6.14% 6.11%
43
Table 8 - The effect of investor type on runs
This table runs the augmented flow model (Flow = a + ∑bj * fund characteristicsj + ∑cj * past returnsj + ∑dj
* aggregate flowsj + ∑γj * Event-window dummiesj + ε) with characteristics indicator interact with all terms in the model. The first set of characteristics indicator include retail funds and institutional funds, The second characteristic indicator equals one if funds are distributed through financial advisory services and zero if not. The dependent variable is computed as Flowi,t = [TNAi,t –TNAi,t-1 *(1+Ri,t)] / TNAi,t-1. Controls for fund characteristics include size (log of TNA in million USD), age (log of days since first offer date), 12b-1 fees (annual fees paid to financial advisors, measured as percentage of TNA), rear and front loads (charges for purchasing and redeeming shares, as a percentage of TNA). Past returns include cumulative returns in the past one, three, and six months. Aggregate flows include industry- and style-level flows. Industry-level flows are the sum of flows in dollars (TNAi,t –TNAi,t-1 *(1+Ri,t )) to all funds in the sample divided by the sum of lagged TNA (TNAi,t-1). Style-level flows are the sum of flows in dollars to all the funds with the same investment style divided by the sum of lagged TNA. Event-window dummy (n month) equals 1 if it is the nth month before or after the litigation is filed and 0 otherwise (n = -1, -2, - - -12, 0, 1, 2, - - -12). Observations are monthly and cover from February 1996 to December 2005. Robust t statistics are in brackets. * indicates significance at 5% and ** significance at 1%.
Regression with retail and institutional fund indicators
Regression with 12b-1 fees indicators
Stand-alone variable
Variable interacted with indicator for retail fund
Variable interacted with indicator for institutional fund
Stand-alone variable
Variable interacted with indicator for 12b-1 fees
Dummies for Months -12 to -7 are controlled for.
Dummy (-6 month) -0.46 0.13 0.60* 0.18 -0.27 [0.92] [0.27] [2.43] [0.79] [1.04] Dummy (-5 month) -0.13 -0.24 0.49 0.32 -0.52 [0.31] [0.64] [1.85] [1.32] [1.96] Dummy (-4 month) 0.10 -0.36 0.37 0.21 -0.33 [0.27] [1.00] [1.51] [0.96] [1.31] Dummy (-3 month) -0.15 -0.20 0.16 -0.23 0.06 [0.34] [0.46] [0.68] [1.04] [0.24] Dummy (-2 month) 0.09 -0.49 0.21 0.28 -0.65* [0.20] [1.09] [0.88] [1.01] [2.20] Dummy (-1 month) -0.05 -0.65 0.48 -0.14 -0.28 [0.10] [1.36] [1.94] [0.59] [1.03] Event month 0 -0.67 -0.49 0.67** -0.50* -0.37 [1.53] [1.14] [2.88] [2.26] [1.50] Dummy (+1 month) -1.84** -0.32 1.20** -0.76** -1.15** [4.62] [0.93] [4.03] [3.04] [3.82] Dummy (+2 month) -1.22** -0.48 0.71** -1.26** 0.05 [3.32] [1.40] [3.12] [5.69] [0.18] Dummy (+3 months) -1.17** -0.48 1.07** -0.62** -0.66** [3.16] [1.38] [4.69] [2.73] [2.60] Dummy (+4 months) -0.70 -0.53 0.74** -0.58** -0.31 [1.85] [1.43] [3.93] [2.80] [1.39]
44
Dummy (+5 months) -0.54 -0.62 0.51** -0.72** -0.17 [1.63] [1.93] [2.94] [4.25] [0.88] Dummy (+6 months) -0.81* 0.01 0.53** -0.26 -0.35 [2.31] [0.02] [2.82] [1.31] [1.64] Dummy (+7 months) -0.90** 0.22 -0.04 -0.76** 0.16 [2.59] [0.66] [0.21] [4.37] [0.81] Dummy (+8 months) -1.51** 0.77* 0.33* -0.62** -0.04 [4.45] [2.34] [2.01] [3.44] [0.19] Dummy (+9 months) -1.10** 0.30 0.47** -0.43* -0.23 [3.16] [0.88] [2.89] [2.42] [1.20] Dummy (+10 months) -0.55 -0.17 0.29 -0.18 -0.52* [1.21] [0.38] [1.83] [0.93] [2.56] Dummy(+11 months) -0.31 -0.20 0.19 -0.18 -0.36 [0.99] [0.70] [0.85] [1.48] [1.76] Dummy (+12 months) -0.50 -0.16 0.28 -0.31** -0.42** [1.96] [0.68] [1.83] [3.09] [2.93] Age (LogAge) -0.82** 0.03 -0.07 -0.90** -0.19** [9.91] [0.36] [1.13] [32.28] [4.29] Size (logTNA) 0.17** 0.05 0.07** 0.26** -0.01 [6.11] [1.88] [3.39] [26.23] [0.41] Return in the 0.55 1.18 0.59 1.60** -0.64 last month [0.77] [1.69] [0.98] [5.76] [1.87] Cumulative Returns -1.38** 2.58** 1.69** 0.24 0.16 in the past 3 months [2.64] [5.10] [4.12] [1.41] [0.75] Cumulative Returns 1.87** 1.96** -0.52 2.97** 1.61** in the past 6 months [4.67] [5.05] [1.57] [18.20] [7.50] Front+ Rear Load 0.05 -0.06* -0.03* 0.01 -0.07** [1.77] [2.17] [2.45] [1.21] [5.04] Industry-Normalized 0.20** -0.26** -0.27** 0.03 0.01 Flow [2.85] [3.72] [4.27] [1.25] [0.19] Style-Normalized 0.35** 0.05 0.14** 0.61** -0.10** Flow [7.01] [1.12] [3.45] [37.85] [4.05] Constant 5.46** -0.48 0.30 5.74** 1.72** [8.49] [0.76] [0.59] [27.10] [5.09] Observations 452,939 660,317 Adjusted R-squared 4.91% 5.35%
45
Table 9 - The effects of asset liquidity on returns
This table runs the augmented return model: CAPM, Fama-French, Carhart, William and Scholes (1976) and Pastor and Stambaugh (2000), with liquidity indictor interacting with all terms in the model. Each regression generates two sets of coefficients: one set for the stand-alone variables and the other set for the interactions of variables and the indicator for liquid funds. We categorize growth-income and money-market funds as liquid funds and global equity, bond funds, municipal funds, and others (such as Ginnie Mae) as illiquid funds. The dependent variable is monthly fund excess returns in percentage. Event-window dummy (n month) equals 1 if it is the nth month before or after the litigation is filed and 0 otherwise (n = -1, -2, - - -12, 0, 1, 2, - - -12). Observations are monthly and cover from February 1996 to December 2005. Robust t statistics are in brackets. * indicates significance at 5% and ** significance at 1%. Panel A tests for differences in the accumulated coefficients between D-n and D+n. Panel B tests the difference in the accumulated coefficients between D-n and D+n interacted with the liquid indicator.
Market model
Fama-French
Carhart four factors
Market model with
lagged market returns
Market model with
liquidity factor
Panel A: Performance Difference (Stand-alone only) Dummy(-1 month) 1.10** 1.08** 1.09** 1.08** 1.13** - Dummy (+1 month) [10.27] [10.06] [10.11] [9.88] [10.56]Accumulate (-1 to -2) 1.35** 1.08** 1.06** 1.37** 1.38** - Accumulate (+1 to +2) [8.51] [6.97] [6.94] [8.60] [8.60]Accumulate (-1 to -3) 1.37** 0.98** 0.98** 1.36** 1.41** - Accumulate (+1 to +3) [6.74] [4.94] [5.06] [6.67] [6.79]Accumulate (-1 to -4) 1.56** 0.97** 1.00** 1.52** 1.62** - Accumulate (+1 to +4) [6.23] [3.95] [4.15] [6.06] [6.31]Accumulate (-1 to -5) 2.07** 1.23** 1.32** 1.99** 2.17** - Accumulate (+1 to +5) [7.17] [4.35] [4.77] [6.85] [7.36]Accumulate (-1 to -6) 2.53** 1.49** 1.59** 2.41** 2.68** -Accumulate (+1 to +6) [8.01] [4.83] [5.21] [7.56] [8.39]Panel B: Performance Difference (Interacted with liquid indicator)
Dummy(-1 month) 0.39** 0.35** 0.35** 0.39** 0.40** - Dummy (+1 month) [2.79] [2.65] [2.60] [2.77] [2.82]Accumulate (-1 to -2) 0.01 -0.02 -0.02 0.00 0.02 - Accumulate (+1 to +2) [0.00] [0.00] [0.10] [0.00] [0.10]Accumulate (-1 to -3) 0.21 0.24 0.23 0.18 0.22 - Accumulate (+1 to +3) [0.48] [0.61] [0.58] [0.42] [0.50]Accumulate (-1 to -4) 0.43 0.46 0.44 0.38 0.45 - Accumulate (+1 to +4) [0.92] [1.08] [1.01] [0.82] [0.94]Accumulate (-1 to -5) 2.07** 2.00** 1.95** 1.97** 2.11** - Accumulate (+1 to +5) [4.95] [5.18] [4.99] [4.74] [4.95]Accumulate (-1 to -6) 3.56** 3.39** 3.34** 3.43** 3.63** -Accumulate (+1 to +6) [8.93] [8.86] [8.67] [8.62] [8.99]
46
Table 10: Implicated fund holdings and firm CARs Held by at least
one implicated fund
No holding by implied funds t‐statistic p‐value
Firms for which aggregated change in holdings is negative
CAR (Sep 2‐5) ‐1.82% ‐0.11% ‐5.73 0.00
CAR (Sep 8‐12) ‐1.11% ‐0.21% ‐3.57 0.00
CAR (Sep 15‐19) ‐1.24% ‐0.96% ‐1.17 0.24
CAR (Sep 22‐26) 0.78% 0.16% 2.32 0.02
CAR (Sep 2‐26) ‐3.39% ‐1.11% ‐4.28 0.00
Number of observations 765 769
Firms for which aggregated change in holdings is positive
CAR (Sep 2‐5) ‐1.97% 0.64% ‐9.39 0.00
CAR (Sep 8‐12) ‐0.84% ‐0.23% ‐2.58 0.01
CAR (Sep 15‐19) ‐0.98% 0.07% ‐3.97 0.00
CAR (Sep 22‐26) 0.63% ‐0.54% 4.26 0.00
CAR (Sep 2‐26) ‐3.15% ‐0.04% ‐5.81 0.00
Number of observations 814 832
47
Table 11: Fund flows and firm CARs Panel A: Means Number of observations
T‐test for the difference between holdings of implied funds with negative flows and others
Holdings of all funds
Holdings of implicated funds with negative flows
Holdings of all other funds, (including implicated funds that have positive flows)
Holdings of all funds
Holdings of implied funds with negative flows
Holdings of all other funds
T‐statistic P‐value
CAR (Sep 2‐5) ‐0.78% ‐2.28% ‐0.48% 3,230 530 2700 6.50 0.00
CAR (Sep 8‐12) ‐0.59% ‐1.04% ‐0.50% 3,229 530 2699 2.30 0.02
CAR (Sep 15‐19) ‐0.75% ‐1.28% ‐0.64% 3,225 529 2696 2.58 0.01
CAR (Sep 22‐26) 0.24% 1.39% 0.01% 3,223 529 2694 ‐5.36 0.00
CAR (Sep 2‐26) ‐1.87% ‐3.21% ‐1.61% 3,230 530 2700 3.10 0.00
Panel B: Table 10 - Summary statistics: CARs of firms held in mutual fund portfolios
All firms
Number of observations
Firms for which change in holdings (aggregated across all funds) is:
T‐test for the difference between positive and negative change in holdings
Negative Positive t‐statistic p‐value
CAR (Sep 2‐5) ‐0.78% 3,230 ‐0.96% ‐0.61% ‐1.69 0.09
CAR (Sep 8‐12) ‐0.59% 3,229 ‐0.66% ‐0.52% ‐0.80 0.42
CAR (Sep 15‐19) ‐0.75% 3,225 ‐1.10% ‐0.43% ‐3.70 0.00
CAR (Sep 22‐26) 0.24% 3,223 0.47% 0.02% 2.33 0.02
CAR (Sep 2‐26) ‐1.87% 3,230 ‐2.25% ‐1.54% ‐1.86 0.06