Electronic copy available at: http://ssrn.com/abstract=2578262

Performance Persistence in Hedge Fund Activism

Nicole M. Boyson, Linlin Ma, and Robert M. Mooradian*

March 9, 2015

Abstract

Using 3-day announcement CARs, we find strong evidence of performance persistence in hedge fund activism in spite of increased competition. We also show that as hedge fund managers perform more activism, they commit a larger proportion of portfolio assets to activism, invest more money in each activism stock, and reduce the time between campaigns. Explaining both persistence and the increased commitment to activism, managers expand their activism opportunity sets by targeting larger firms, investing in a wider variety of industries, and employing increasingly aggressive activism tactics. Finally, despite stronger target firm opposition, activists more frequently achieve success in their stated activism goals and their target firms achieve better operating outcomes as hedge fund manager experience increases.

* All authors are from the D’Amore-McKim School of Business, Northeastern University Boston, MA 02115. Please send your comments to n.boyson@neu.edu, l.ma@neu.edu, or r.mooradian@neu.edu.

Electronic copy available at: http://ssrn.com/abstract=2578262

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Introduction

Hedge funds are run by highly sophisticated managers, and over the last several years

have received large capital inflows, growing to nearly $3 trillion in assets globally by the end of

2014. The number of managers starting new funds has also increased.1 Yet, in recent times,

hedge funds as an asset class have sharply underperformed the broader markets, leading

practitioners and academics to question whether these managers have skill. A recent report by

Bloomberg, using data from Hedge Fund Research, shows that in 8 of the last 10 years, hedge

funds have failed to outperform the S&P 500 index.2 While the hedge fund industry as a whole

has suffered disappointing performance, one hedge fund strategy – activist investing – has

performed quite well, leading all hedge fund styles in 2014, despite increased asset flows to this

category and an increase in the number of activist campaigns.3 In the light of increased flows and

a decreasing opportunity set, how have activist hedge funds managed to maintain their strong

performance? In this paper, we attempt to answer this question by investigating managerial skill

in the context of hedge fund activism. Examining performance of activist campaigns provides a

natural environment in which to examine this question free of many of the biases inherent in

commercial hedge fund databases. As our main test of skill, we investigate performance

persistence among hedge fund activists.

In general, performance evaluation for money managers has been the subject of extensive

debate in the literature. For example, many measures of performance have been proposed and

used to identify successful mutual fund managers, yet several studies question whether these

1 See “Hedge Flows Record Highest Inflows in 7 Years in 2014,” Reuters, http://www.reuters.com/article/2015/01/20/hedgefunds-size-idUSL6N0UZ39R20150120. 2 See “Hedge Funds are for Suckers,” Bloomberg Business Week, http://www.bloomberg.com/bw/articles/2013-07-11/why-hedge-funds-glory-days-may-be-gone-for-good 3 See Prequin Special Report: Activist Hedge Funds,”https://www.preqin.com/docs/reports/Preqin_Special_Report_Activist_Hedge_Funds_June_14.pdf.

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measures capture managerial skill given alternative explanations such as risk, model

misspecification, survivorship bias, or weak statistical power of empirical tests (Kacperczyk and

Seru, 2007). There is also mixed evidence on the value of active management in the hedge fund

industry. For example, Brown, Goetzmann and Ibbotson (1999) show that hedge fund returns do

not persist. Getmansky, Lo, and Makarov (2004) argue that illiquidity-induced serial correlation

in fund returns explain the persistence in hedge funds at quarterly horizons, demonstrated by

Agarwal and Naik (2000). In contrast, Jagannathan et al (2010) find that, over a 3-year horizon,

relative performance persistence among hedge fund managers while correcting for measurement

errors as well as for backfill, serial correlation, and look-ahead biases in the data. An important

complication from focusing on fund returns to address the question of skill is that in equilibrium

more money flows to managers with superior skill, leading to an erosion of performance over

time (Berk and Green, 2004). Consistent with the theory, Aggarwal and Jorion (2010) and

Boyson (2008), show that young (emerging) managers tend to outperform, and have better

persistence than, more experienced managers. Jagannathan et al (2010) find that funds

performing well attract new flows, and find an erosion of superior performance over time.

To avoid the potential biases that stem from focusing on hedge fund reported returns, our

paper takes a novel approach and looks for activist fund manager skill at the stock level, based

on the companies that activists target. This approach has two major advantages: first, target stock

returns are free from the measurement error and data biases associated with hedge fund returns in

commercially available databases. Second, target firm stock returns do not suffer from the issues

described by Berk and Green (2004) in which funds with superior returns attract additional fund

flows, eroding performance over time.

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Our first question is whether there is persistence in the performance in activist target

stocks. The answer to this question is a resounding yes. Given the significant resources allocated

to activism, the increasing number of hedge funds performing activism, and the large number of

corporations targeted by activists, this result is surprising. It is perhaps more plausible that

performance would erode – rather than persist – over time, for a number of reasons. First,

activism may merely be seen as a new strategy or technique, one that does not require superior

managerial skill. If this explanation is true, then following the argument of Glode and Green

(2011), profitability associated with hedge fund activism (the strategy) should erode with

imitation and competition. A second reason we might expect erosion in performance is a

declining opportunity set. It is rational for activists to target their best investments first, and

sequentially move from more desirable to less desirable targets, at some point running out of

opportunities.

This alternative hypothesis that performance ought to erode – not persist – over time

leads to our second question. How do hedge fund activists that face increased competition and a

declining opportunity set continue to perform well? Using regression models that include hedge

fund fixed effects, we first show that as hedge fund managers gain experience in activism they

allocate a larger proportion of their total portfolios to activism stocks, they increase the dollar

amount that they invest in each activism stock, and they reduce the amount of time between

activism events. These significant and aggressive portfolio changes indicate that hedge fund

managers do not back down when faced with imitators and greater competition. Rather, they

address the problem of a declining opportunity set by finding ways to expand this set.

Consistent with the existence of skill, we show that managers adjust their behavior in

several ways to expand their opportunity sets, again using regression models that include hedge

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fund manager fixed effects. First, as managers gain experience, they target larger firms. Second,

they expand the number of industries in which they invest, reducing their allocations to some

industries and increasing their allocations to others. Third, they become more aggressive in their

activism tactics with experience, by reducing the proportion of activism events in which they do

not state a specific purpose, and increasing the proportion of activism events in which they ask

for board representation, takeovers, capital structure, management related changes, or for the

firm to sell assets or do a spinoff. We also find that these aggressive tactics are met with

increased resistance from target firms. However, despite this increased resistance, more

experienced hedge fund managers achieve success in their stated activism goals more frequently

than managers with less experience. Finally, consistent with the idea that highly skilled managers

avoid stocks with deteriorating fundamentals (Nallareddy and Ogneva, 2013), we show that

target firms of more experienced hedge fund managers are more likely to achieve better

operating outcomes. Specifically, these firms are less likely to experience extremely poor

operating performance or to fail or delist in the 18 months following activism.

We are one of the first to study the question of performance persistence among hedge

fund activists. Our comprehensive dataset covers the period 2001-2013, and includes all known

activism events during this period, including stockholdings of 5% or more in a target firm that

are reported in SEC 13D filings, stockholdings of less than 5% in a firm that are reported in the

news, and proxy fights that may or may not involve a large stake in the target firm’s stock. We

argue that hedge fund activism is a promising venue in which to study the question of skill. One

reason is that hedge fund activism is economically important, receiving significant attention from

both academics and the media over the last several years. To date, approximately one in six firms

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has been targeted by hedge funds, with hedge funds targeting larger and more prominent firms as

time passes. Our sample includes activism campaign investments totaling over $350 billion.

Second, most prior studies of performance persistence in portfolio management focus on

overall returns of the fund. By contrast, we focus on persistence at the stock (activism campaign)

level. We can therefore examine whether there is persistence in successive activism campaigns

rather than aggregating performance by fund and over longer time periods. In this sense, our

paper most closely resembles studies of entrepreneurs (Gompers, Kovner, Lerner, and

Scharfstein (2010)), IPO investors (Chiang, Hirshleifer, Qian, and Sherman (2011)), and mergers

and acquisitions (see, for example, Ahern (2008), Aktas, de Bont, and Roll (2009, 2011, 2013),

Conn et. al. (2004), Croci (2005), Fuller et. al. (2004), Hayward (2002), Ismael (2008), and

Moeller et. al. (2004)). Importantly, our finding of performance persistence is consistent with

some studies (entrepreneurs) and inconsistent with others (IPO investors, mergers and

acquisitions). These latter studies conclude that investors face a declining opportunity set that

they are unable to overcome. By contrast, we provide direct and compelling evidence that hedge

fund activists are able to overcome their declining opportunity sets by changing their behavior as

they gain valuable experience.

Third, hedge fund activism is by nature, intended to make changes at target firms, as

opposed to the stock-picking investing strategies followed by most retail and institutional

investors. A growing literature in hedge fund activism argues convincingly that hedge funds

actually impact the firms they target, rather than being passive stock pickers. Therefore, our

finding of performance persistence among hedge fund activists – as opposed to more passive

investors – is more likely driven by hedge fund manager skill than by luck. Finally, our rich

hand-collected dataset allows us to carefully investigate the goals, outcomes, and target firm

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response to activists, so that we may draw more direct conclusions regarding the goals of,

persistence among, and efficacy of hedge fund activists.

Our work is related to a growing literature on hedge fund activism. Studies include Brav,

Jiang, Partnoy, and Thomas (2008), Klein and Zur (2009), Clifford (2008), Becht, Franks, Mayer,

and Rossi (2008), Boyson and Mooradian (2011), Greenwood and Schor (2009), Brav, Jiang, and

Kim (2010), Brav, Jiang, and Kim (2013), Bebchuk, Brav, and Jiang (2013), and Gantchev

(2013), among others. Our work is also related to the very large literature on performance

persistence, including Carhart (1997), Agarwal and Naik (2000) and Jagannathan, Malakhov,

and Novikov (2010).

The paper is organized as follows. Section 2 describes the data. Section 3 performs the

empirical analyses and Section 4 concludes.

2. Data

Our data on activism starts with Audit Analytics’ Shareholder Activism Database, which

provides 13D filing data for all SEC registrants that have filed a form 13D for the period 2001-

2013. The 13D filing data during the years 1994-2000 are directly obtained from the SEC’s

database, and for these filings we hand collect the relevant data. Section 13 (d) of the 1934

Securities Exchange Act requires investors who accumulate 5% or more of a publicly traded

company’s stock, and who have a current or future intention to influence corporate control, to

disclose their ownership and intent within 10 days of crossing the 5% threshold.4

We also gather data from FactSet’s Shark Repellant database. Shark Repellant’s stated

objective is to provide detail on all activism campaigns by hedge funds and other activists, and

covers the period 1994-present (our data ends in 2013). Importantly, Shark Repellant’s data

4 A shareholder that acquires greater than a 5% stake yet does not intend to influence the control of the issuer may elect to file a 13G form instead of a 13D form.

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includes a large number of activism events that are not captured in 13D filings. Campaigns that

are not included in 13D filings generally include proxy fights or other campaigns in which the

activist does not own 5% or more of the company's stock, and is thus not required to file a 13D

form. Including these filings is crucial in a study such as ours that attempts to measure

persistence in hedge fund performance, since we need to ensure that all past activism campaigns

are included in the analysis. We combine the Shark Repellant and 13D databases, a process that

is complicated by the lack of a common identifier between the two databases. Therefore, we

manually combine the filings, matching on target firm name, activism date, hedge fund name and

other descriptive information. We ensure that there are no duplicates in the dataset.

We then manually check the identity of each 13D filer and keep those that are hedge funds.

To filter out non-hedge fund filers, we use a two-step procedure, following Boyson, Helwege,

and Jindra (2013). As a first step, we check each filer’s SEC-required ADV filings to ensure that

they have at least 50% of their assets in hedge funds, or at least 50% of their assets owned by

high net worth individuals, and that they charge incentive fees. This approach sometimes results

in selecting private equity or venture capital firms. Thus, in our second step we read descriptions

of the filer type listed in Item 2 of 13D, Item D of the ADV filing, and an online review and

descriptions of these firms to confirm that the filer is indeed a hedge fund. Not all firms file

ADV forms; for these, we rely on a web search and eliminate those that we cannot conclusively

identify as hedge funds. Finally, we aggregate all filings at the hedge fund manager level.5 This

search yields a dataset running from 1994 to 2013 with over 5,000 separate hedge fund activism

events.

5 Each filer is assigned a unique Central Index Key (CIK) number by the SEC. Sometimes the same hedge fund manager will have more than one CIK; representing more than one individual hedge fund with the same manager. Since our analysis is done at the manager level, we manually collect manager names from the filings, and aggregate the data at the manager level.

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In most of our analyses, we measure hedge fund experience in activism. For these analyses,

we use the variable Order, which is a count variable that increases by one for each successive

activism event performed by the same hedge fund manager across the entire period from 1994 to

2013. For example, if a hedge fund manager has three activism events for three target firms

during the sample period, one in January 1994, one in January 1998, and one in January 2002,

the 1994 observation will have Order=1, and the 1998 and 2002 events will have Order=2 and 3,

respectively.

Since the SEC’s online dataset begins in 1994, activism data prior to this year is generally

not available, which could affect the calculation of the Order variable. To minimize the impact

of this problem, we only use data from 2001 onwards for the main analyses, and use the 1994-

2000 dataset as our “pre-estimation” period. Therefore, if a hedge fund manager has an activism

campaign between 1994 and 2000, this filing is considered when calculating Order, but the filing

itself is not included in the main analyses. If a hedge fund manager does not have an activism

campaign during the year 1994-2000, we consider its initial filing in 2001 or later as Order=1.

We believe this approach is reasonable for three reasons. First, there are likely very few hedge

fund managers that perform activism prior to 1994 based on the small number of activist events

between 1994 and 2000 (there are 156 events during this period). Second, for the managers

whose first event is observed in 2001 or later, even if they performed activism prior to 1994,

seven years (i.e., 1994-2000) is a long time between activism events, so it is probably

appropriate to count the first event in 2001 or later as their actual first event (i.e., Order=1).

Finally, in the main analyses Order is grouped into either four quartiles or ten deciles, such that

it is even less likely for activist events prior to 1994 to impact the variable of interest. Continuing

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the example from above, the only filing that would appear in our analyses is the 2002 filing, with

Order=3.

We match the sample of target firms with Compustat for accounting data and with CRSP for

stock price data. We drop all cases where the target firm is a closed-end fund. Our final sample

for the period 2001-2013 has 2,639 hedge fund campaigns, comprising 595 hedge fund managers

and 1,733 target firms. Importantly, even if the target firm is missing Compustat or CRSP data, it

is still counted in calculating the Order variable so that we are not biasing our results to only

include managerial experience obtained when targeting firms covered by CRSP and Compustat.

While Shark Repellant provides some textual data on the activism campaigns, including the

hedge fund’s stated goal and the target firm’s response, and provides some machine readable

data on the success rates in activism, Audit Analytics does not generally provide this level of

detail. For the campaigns that are in Audit Analytics but not in Shark Repellant, we therefore

hand collect data on the hedge fund’s stated goals, the target firm’s response, and the success

rates in activism by reading all the 13D filings and amendments that cover the entire activism

campaign, as well as news stories and regulatory filings by the target firm. For the Shark

Repellant dataset, we read the textual data on the activism campaigns to discern the target firm’s

response and additional goals of activism. For all activism campaigns we then create variables

related to stated goals, target firm response, and success rates in activism which we use in later

analyses. Given the significant amount of detail that we hand collect and the long time frame that

we cover, we believe our dataset is the largest and most comprehensive used in the literature.

Panel A of Table 1 reports the frequency of activism by year. Particularly in comparison to

the only 156 activism events from 1994-2000, activism grows quickly from 2001 to 2013, with a

small dip in 2009 but a quick rebound through 2013. The estimated dollars invested in activism

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uses the initial percentage owned by the hedge fund based on the first 13D filing or news report,

and does not include activism that we are unable to match with Compustat and CRSP, thereby

underestimating the total dollars eventually invested in activist stocks. Using this value, the total

dollars invested in activist stocks is nearly $360 billion from 2001-2013. The average target firm

market capitalization also increases dramatically over time, from $221 million in 2001 to about

$8 billion in 2013, again with a dip in 2009. Panel B of Table 1 reports the distribution of

activism by one digit SIC code. Manufacturing (codes 2 and 3) comprises the highest proportion

of activism events, at 36%, followed by services (codes 7 and 8) at 25% and Finance, Insurance,

and Real Estate at 15%.

Panel C presents the top 10 hedge fund activists by the total number of activist events from

1994 to 2013. These 10 hedge fund managers comprise about 2% of all managers (10 out of 595)

but nearly 20% of all activism. The number one activist in terms of total number of activist

events is Bulldog Investors (165 events), followed by Loeb Partners (133 events) and Steel

Partners (131 events). We also report the number of events from 2001-2013 that comprise the

final sample; this number is lower due to the shorter time period and the inability to match all

events to Compustat and CRSP. However, the proportion of the total number of events

represented by the top 10 activists is the same for both the total number of events from 1994-

2013 and the final sample of 2,639 events from 2001-2013.

Panel D presents detail on the stated purpose of activism. This purpose is gathered by

reviewing the entire activism campaign, not just the first activism filing or news report. Activists

often state multiple purposes in their campaigns. About half the events are coded as “Investment

purposes only” which means that the hedge fund did not state a specific purpose for activism at

any point during the activism campaign. These events include, for example, cases where the

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hedge fund states that the stock is undervalued, or the hedge fund states its intent to meet with

management, or the hedge fund lists a large number of non-specific concerns regarding the target

firm. While hedge funds do not state a specific purpose in these cases, we cannot rule out

behind-the-scenes communication with management or the board. We therefore believe it is

appropriate to include these events in determining a hedge fund manager’s total experience in

activism.

All other events we classify as purposeful, and categorize them into several categories:

Board representation includes all cases where the hedge fund campaigns for one or more seats

on the board of directors. For Takeover related, the hedge fund encourages the firm to sell itself,

either to a third party or sometimes to the hedge fund. Governance related encompasses a wide

range of governance related activity such as declassifying the board, changing supermajority

voting requirements, and rescinding a poison pill. For Capital structure related, the hedge fund

encourages changes to capital structure such as paying special dividends, issuing debt, or share

buybacks. For Management related, the hedge fund seeks to change management. Finally, for

Sell assets or do a spinoff, the hedge fund encourages the firm to sell assets or do a spinoff.

Other mostly includes agitating for a higher price in a merger or trying to block a merger or

acquisition. We do not include Other with Takeover related activism since the goals generally

have opposite intentions. The table also includes the proportion of times the hedge fund achieves

success in its stated goals. Overall, activists achieve their goals about half the time. Board

representation is the most common activist purpose, followed by takeover related activism.

Board representation is also the most successful activist purpose, with a 62% success rate.

Management related activism is least successful, with a 29% success rate. Success rates for all

others fall between 38% and 50%.

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Table 2 Panel A reports summary statistics for target firm characteristics prior to activism

and activism campaign characteristics. All variables from Panel A are winsorized at the 1% and

99% levels. Assets is total assets in $ millions. Tobin’s Q is defined as book value of debt plus

market value of equity divided by the sum of book value of debt and book value of equity.

Cash/assets is defined as cash plus cash equivalents scaled by assets. Leverage is defined as

book value of debt divided by the sum of book value of debt and book value of equity. Amihud is

an illiquidity measure calculated as in Amihud (2002) and is multiplied by 108. HHI is the

Herfindahl-Hirschman index by industry. CAR [-25,-5] is the cumulative abnormal return over the

period of 25 days before to 5 days before the activism filing date, estimated using the Carhart

(1997) four-factor model. BHAR [-250,-5] is the market-adjusted buy and hold return over the time

window [-250,-5]. Return on assets is calculated as operating income before depreciation and

amortization divided by average assets. Cash flow/assets is net income plus depreciation and

amortization divided by average assets. CAR [-1,1] is the cumulative abnormal return over the

period starting 1 day before and ending 1 day after the activism filing date and is estimated with

the Carhart (1997) four-factor model. The factor loadings are estimated with daily data from 250

days to 30 days before the activism event. We also calculate the initial percentage owned,

Ownership, based on either the 13D filing, the proxy filings, or news stories.

On average, activists target small to mid-cap firms, with a median market capitalization

of $330 million and a mean of $1.8 billion. A typical target firm in the sample has an average

Tobin’s Q of 1.89, Cash/assets of 0.22, ROA of 0.038, and Leverage of .23. There is significant

skewness in the Amihud ratio (higher values mean less liquid stocks), with a mean of 0.34 and a

median of 0.02. The average HHI index is 0.14, with a median of 0.10, suggesting that most

target firms are not from concentrated industries. For performance, the mean and median prior

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year stock performance is negative. The average CAR [-1,1] around the announcement of activism

is about 2.3% for the entire sample, and the median is 1.6%. Not all 3-day CARs are positive: the

25th percentile is about -1%. On average, hedge fund activists own about 13% of the target firms

based on market capitalization, with a median of about 9%.

Last, Table 2 Panel B provides statistics for the Order variable. This variable exhibits

some skewness, with a 25th percentile of 3, a median of 9, a mean of 22, and a 75th percentile of

30. The max value is 165, as shown in Table 1 Panel C. In the analyses that use the Order

variable, we group the sample into four quartiles. For robustness, we also create a decile variable

for Order. In all analyses, results using deciles and quartiles are similar.

3. Performance Persistence

In this section, we present our main tests examining whether hedge fund activists exhibit

performance persistence in CARs. We perform two sets of tests. First, in an OLS regression

setting, we regress activism CARs on the most recent lagged CAR, the average of the prior 6

months of CARs, and the historical CAR for the manager. Second, we perform two logit

regression analyses. In the first, the dependent variable is a dummy set to 1 if the manager’s

current CAR is in the top 25th percentile of CARs for that year. Independent variables include a

dummy variable set to 1 if the manager’s most recent CAR is in the top 25th percentile for that

year, a dummy variable set to 1 if the average of the manager’s most recent 6 month CARs is in

the top 25th percentile for that year, and a dummy variable set to 1 if the average of all the

manager’s prior CARs (historical CARs) is in the top 25th percentile for that year. A positive and

significant coefficient on these variables indicates evidence of performance persistence among

“winners.”

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The second logit regression analysis is identical to the first, except it models persistence

among “losers,” so that all dummy variables are set to 1 if the CARs are in the bottom 25th

percentile of CARs. A positive and significant coefficient on these variables indicates evidence

of performance persistence among “losers.” In all three regression analyses, standard errors are

clustered by time and by hedge fund. All regressions include the control variables from Table 2,

as well as dummy variables for time and industry, one digit SIC code.

Panel A of Table 3 presents results for the OLS regressions. For all three specifications

the coefficient for the lagged CAR variable is positive and statistically significant, providing

evidence of hedge fund manager performance persistence. However, from this first set of

regressions, we are unable to tell whether “winners” or “losers” drive this result. Therefore,

Panel B reports results from logit regressions described above, that separate the two categories.

Persistence tests for “winners” are in columns (1) – (3), and tests for “losers” are in columns (4)

– (6). For the first set of persistence regressions that examine persistence among “winners,” the

coefficients on the variables of interest are positive and significant, indicating performance

persistence among hedge fund managers. By contrast, for the second set of persistence

regressions that examine persistence among “losers,” the coefficients are statistically

insignificant. Taken together, these results provide strong evidence that hedge fund manager

performance persistence is driven by “winners” only. We should note that these results are in

contrast with much of the mutual fund literature which finds performance persistence mostly

concentrated among poor performing funds.

While we are the first to examine persistence in activism, consistent with our results,

Gompers et al (2010) finds performance persistence for serial entrepreneurs and some research

examining hedge fund returns finds evidence of performance persistence. Agarwal and Naik

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(2000) find some evidence of short-term performance persistence. Boyson (2008) finds

persistence that is concentrated among young and small hedge fund managers. Jagannathan et. al.

(2010) finds evidence of persistence among superior hedge funds.

The remainder of the paper is devoted to explaining the persistence that we document.

Given the large amount of assets devoted to activism, the increase in the visibility of hedge fund

activism, and the large number of stocks targeted by activist hedge funds, our finding of

performance persistence among “winners” is perhaps surprising. Therefore, we examine how

hedge fund managers adapt their activism portfolios as they gain experience. If activism is

merely a strategy that other hedge fund managers can imitate, we would not expect managers

facing greater competition and a reduced opportunity set in activism to increase the assets or

dollars they devote to activism. However, if managers find a way to adapt to the increased

competition and declining opportunity set of activist stocks, then we would expect managers to

increase their activism as they gain experience.

3.1. Persistence and Portfolio Choices

To investigate how managers adjust their portfolios as they gain activism experience,

Table 4 investigates five portfolio-related measures. First, a manager may invest a larger

proportion of his portfolio in activist stocks. Therefore, our first and second variables are the

proportion of a manager’s entire long equity portfolio that is held in activism stocks, based on

the number of stocks (the first measure) or the total dollar value of the stocks (the second

measure). To construct these data points, we gather data from 13F filings from the Thomson

Reuters database. Any investor with over $100 million in assets is required to file a Form 13F

with the SEC to report his stockholdings on a quarterly basis. To match our activism sample

filings with the SEC 13F filings, we use the fund’s Central Index Key (CIK) for the 595 funds in

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our sample, search the SEC database (www.sec.gov) for this CIK, get the manager’s filing name

from this website, and then manually match the name with the Thomson data to collect the

manager’s mgrno which is the unique identifier in the Thomson Reuters database. We hand

check all the names to ensure that the mgrno does not change over time, which is known to be an

issue in the Thomson Reuters database. We also ensure that there are not repeat observations in

the Thomson Reuters database by ensuring there is only one filing for each quarter. Finally, if the

filer names do not perfectly match, we check manager names and other information, such as city

and state of the filer, to verify that the match is correct.

In a form 13F, an investor is required to report all his long equity-related positions,

including U.S. exchange-traded stocks, shares of closed-end investment firms, and shares of

ETFs. Further, investors must report convertible debt securities, equity options, and warrants.

Thomson Reuters only reports common stocks, so that our analysis excludes these other types of

securities. Still, returns calculated from 13F filings exhibit high correlation with the returns

reported in hedge fund databases and are considered to be representative of hedge fund returns in

general (Gantchev 2013, p. 625). Other researchers that use 13F data for hedge funds include,

among others, Gantchev (2013) and Gantchev and Jotikasthira (2014), Griffin and Xu (2009),

Ben-David, Franzoni, and Moussawi (2013), and Boyson, Helwege and Jindra (2013).

The third portfolio-related measure is the estimated dollar amount initially invested in

each activist stock. To calculate this value, we multiply the percentage of a target firm’s market

capitalization purchased by the hedge fund by the firm’s total market capitalization. While the

first two measures capture the proportion of a hedge fund’s total portfolio in activism, this third

measure captures each hedge fund’s commitment per stock. An increase with experience implies

that hedge funds devote a larger dollar amount to each activism stock. Given an increase in the

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first two measures with manager experience, an increase in the third measure (dollar amount to

each activism stock), indicates that hedge funds increase both the number of firms targeted in a

particular period and the dollars invested in each firm. In contrast, an increase in the first two

measures with a decrease in the third implies that hedge funds simultaneously target more firms

in a particular period and decrease the dollars they invest in each firm. Either finding provides

evidence that hedge funds change their behavior with experience. The fourth portfolio measure is

the percentage of the target firm’s market capitalization that the hedge fund devotes to each

activism stock, and the fifth portfolio measure is the number of days between each successive

activism event.

Table 4 Panel A presents univariate analyses for these variables. Consistent with the idea

that skilled managers increase their activism activity with experience, the proportion (number) of

activist stocks in a manager’s portfolio increases from 5% to 20% between the lowest and top

Order quartile, while the dollar proportion of activist stocks increases from 10% to 24%. For the

third portfolio measure, activism dollars per stock, the results are similar, with a dollar increase

from $50 million to about $168 million from the first to fourth Order quartile. As hedge fund

managers gain experience, they not only increase their activism, but also, they change the nature

of activism by devoting more activism dollars to each target stock. The fourth measure, percent

of market capitalization in each activism stock does not change with Order, but the fifth measure

indicates that hedge fund managers sharply reduce their time between activism events from

about 1.5 years to less than 90 days as they move from Order quartile 1 to 4.

These results suggest that as managers gain experience in activism, they change their

portfolios dramatically. Managers increase both the proportion of their assets in activism and the

size of their activist positions, and perform activism far more frequently. However, since these

19

are univariate results, it could be the case that managers are not changing their behavior with

experience and these results are simply picking up cross-sectional differences between less and

more experienced managers. Therefore, to examine how manager behavior with respect to each

of the five portfolio-related measures changes with experience, Panel B performs regression

analyses for each of the five measures. To see how managerial behavior changes with experience,

the regressions include hedge fund manager fixed effects. With fixed effects in the regression,

the coefficients for Order Quartile may be interpreted as measuring how the behavior of a

particular hedge fund manager changes with experience.

Table 4 Panel B presents the results, which include all the control variables from Table 3,

industry dummies, and hedge fund manager fixed effects. Standard errors are clustered by time

and hedge fund. For all five portfolio-related measures, the results are consistent with those

presented in Panel A. Specifically, as hedge fund managers gain experience, they increase both

the number and dollar proportion of their portfolios devoted to activism, they increase the total

dollars devoted to each stock, and they reduce their time between activism events. The finding

that dollar amount per stock is increasing while the percentage of market capitalization is not

changing implies that activists target larger firms over time, a result that we will test more

formally in the next section. Overall, these results provide strong evidence that as managers gain

experience in activism, they become more committed to activism as a key component of their

funds, devoting more money and time to their activist positions. It is important to note that since

these regressions control for fixed effects, we are capturing how managerial behavior changes

with experience, and not a cross-sectional effect from including both less experienced and more

experienced managers in the sample. We conjecture that this increased commitment to activism

stems from the performance persistence that managers experience that confirms activism skill.

20

3.2 Persistence and Stock and Campaign Characteristics

In the prior section, we show that as they gain experience, hedge fund managers commit

more financial resources and time to activism. Given that these managers may face a declining

opportunity set as they and others conduct more and more hedge fund activism, we next

investigate how these managers maintain their performance persistence.nSpecifically, we

examine whether they change their activism campaign behavior with experience. We focus on

three potential observable measures of changing behavior: the characteristics of firms they target,

the industries they select, and their campaign characteristics.

The first measure that we examine is the target firm characteristics. Our approach is as

follows. For each of the firm characteristics that we measure, we estimate a fixed-effects OLS

regression in which the dependent variable is the firm characteristic and the independent variable

of interest is the Order quartile ordinal variable. The regressions include all the control variables

from the prior analyses, industry effects, and hedge fund manager fixed effects. If the coefficient

on the Order quartile ordinal is positive (negative) and statistically significant, this implies that

the hedge fund manager increases (decreases) his exposure to this variable with experience. In

Table 5, Panel A, the coefficient on Order quartile ordinal for the regression with log assets as

the dependent variable is positive and statistically significant at the 1% level, implying that

managers select larger firms as they gain experience. This result is consistent with the findings

from Table 4. None of the other coefficients is significant, implying that as hedge fund managers

gain experience, they target firms that are similar along the dimensions of Tobin’s Q, cash,

leverage, liquidity, and concentration.

Panel B presents logit regressions with fixed effects in which the dependent variable is a

dummy set to 1 for each of the one digit SIC codes, and 0 otherwise. As before, standard errors

21

are clustered by industry and time. Here, we show that hedge fund managers alter their activism

strategies over time. The positive and significant coefficients on SIC 1 (Mining) and SIC 3

(Manufacturing) imply that as they gain experience, hedge funds more frequently select these

industries, while the negative coefficients on SIC 6 (Financial institutions) and SIC 7 (Services)

imply that as they gain experience, hedge funds reduce their exposure to these industries. This

“switching” implies that hedge fund managers expand their opportunity sets to include more

industries outside of their initial areas of expertise. Furthermore, consistent with broad-based

skill, managers are not limited to a strategy with respect to a given industry; they are able to

apply their skill to multiple industries.

Finally, Panel C examines how hedge funds change their activism tactics with experience.

Similar to Panel B, each column represents a logit regression in which the dependent variable is

set to 1 if the hedge fund uses that tactic and 0 otherwise. As before, standard errors are clustered

by industry and time. The results from this Panel provide the most compelling evidence thus far

that hedge fund managers make changes as they gain activism experience. Specifically, they

significantly decrease the amount of investment-only activism that they perform, as indicated by

the negative and significant coefficient on this variable in Column (1). Further, Columns (2) –

(7) indicate that for 5 of the 6 purposeful activism tactics, hedge fund managers increase the

frequency of these events. Specifically, they increase the number of campaigns for board

representation, takeovers, capital structure changes, management changes, and selling assets or

doing a spin-off.

Taken together, these findings are consistent with the following story. As hedge fund

managers gain experience, performance persistence confirms their skill; they commit a larger

amount of their resources to activism and decrease the amount of time between successive

22

activist events. As they increase their commitment to activism, they also adapt their activism

techniques to seek out opportunities where they exist, thereby addressing a reduced opportunity

set. These techniques include changing the types of firms they target, targeting firms in a wider

variety of industries, and becoming far more aggressive in their activism tactics.

Given the results that as hedge fund managers gain experience, they make substantive

changes to their portfolios, to their target firm selection criteria, and to their stated goals in

activism, we next examine whether success in activism goals also increases with manager

experience.

3.3 Success in Stated Activism Goals

Thus far, we have documented that hedge fund managers exhibit performance persistence

in activism. We have also shown that as hedge fund managers gain experience, they allocate a

larger proportion of their portfolio assets to activism stocks, allocate more dollars per activism

stock, and reduce the time between successive activism events, despite facing a declining

opportunity set of potential target firms. We next show that as they gain experience, hedge fund

managers adapt to this reduced opportunity set by selecting larger target firms, by increasing the

variety of industries they target, and by becoming increasingly aggressive in activism over time.

Given their increased aggression in activism, this section investigates whether more experienced

hedge fund managers are more likely to achieve their stated goals in activism. Further, we also

investigate whether firms targeted by more experienced managers have better long term

operating outcomes.

To investigate activism success, Table 6 Panel A performs a logit regression in which the

dependent variable is set to 1 if the hedge fund manager achieves success in his activism goals,

and 0 otherwise. For Columns (1) and (2), we group together management, governance, sell

23

assets, capital structure, and takeover related activism and create a dummy variable set to 1 if

the hedge fund experiences success in any of these goals. It is very common for hedge funds to

state one or more of these activism goals in the same campaign, sometimes right at the outset of

activism but more frequently as the campaign evolves. Therefore, we want to use as broad a

measure of success as possible. All these goals further the broad mandate of “improving

shareholder value.”

We examine board representation separately in Columns (3) – (4). Since the sample

includes proxy fights, the goal of board representation is quite often the only goal an activist

states. It is also the most frequently stated goal. Finally, we do not report results for other

activism because the number of events is so small, and we do not combine it with the first set of

activism goals since it is often in conflict with the goals of management, governance, sell assets,

capital structure, and takeover related. In unreported results, we examine other activism

separately and find a negative but insignificant coefficient on the Order quartile and decile

experience measures. These regressions include the same controls as in Table 3, dummies for

time and one digit SIC, and standard errors clustered by time and hedge fund. The results in

Columns (1) and (2) show that as hedge fund manager experience increases, hedge fund

managers more frequently achieve success in their stated activism goals when the goals are

changes in management, governance, capital structure, takeover, or selling assets. For this

analysis, hedge funds are more likely to have activism success for larger firms with higher levels

of cash, and in less concentrated industries. For board representation (Columns (3) – (4)), there

is not a significant relationship between hedge fund manager experience and these goals.

However, many of the control variables help explain the incidence of board representation.

24

Board representation is more likely for larger firms, firms with less cash, lower leverage, lower

liquidity, worse prior year stock performance, and higher levels of hedge fund ownership.

In Columns (5) and (6) we perform a regression in which the dependent variable is set to

1 if the target firm worsens its governance (or resists activism in other ways) in response to

hedge fund activism. Since we have shown that with greater experience, hedge fund managers

become more aggressive in activism and are more successful in achieving most types of activism

goals, it is also helpful to examine the target firm’s response to activism. On one hand, having a

more experienced activist involved might lead the target firm to acquiesce more quickly, given

the reputation of the activist. On the other hand, a target firm might be more likely to fight fire

with fire, and actively resist the hedge fund activist. We gather this data from Shark Repellant’s

description of the activism events for the events in Shark Repellant, and hand collect additional

information by reading firm 8-Ks, 10-Ks, and other SEC filings and news stories. Resisting

activism/worsening governance includes among other actions, adopting a poison pill, classifying

the board, making voting requirements more restrictive, making it harder for shareholders to call

special meetings or act by written consent, and changing the board size in a way that is

prohibitive to hedge funds. The results from this regression indicate that target firms are more

likely to worsen governance against more experienced activists, which makes the success that we

document in activism in Columns (1) and (2) all the more impressive. Despite greater opposition

from target firms, hedge funds with more activism experience are more likely to achieve success

in their activism goals, providing additional evidence of skill among these hedge fund managers.

Finally, we investigate the relation between target long-term operating performance and

hedge fund manager experience. In unreported results, we investigate whether the change in

target cash flows increases with hedge fund manager experience, and find no evidence that this is

25

the case. However, the reason for finding no systematic evidence of a relation between target

cash flows and manager experience may be that there are a declining number of events available

for change in cash flows for 1, 2, and 3 years after activism, since a number of target firms leave

the sample during these years. The main reasons for leaving the sample include mergers (a good

outcome for target firm shareholders) or bankruptcy, liquidation, or delisting (a bad outcome for

target firm shareholders). Therefore, the unreported results that focus only on change in cash

flows are missing a number of other potential outcomes, both good and bad.

Therefore, to address this issue, we perform logit regressions in Panel B. The dependent

variable is set to 1 if the target firm has a bad outcome (as defined above) or its change in cash

flows is in the bottom 25th percentile of performance. The regression includes all control

variables from Panel A, and standard errors are clustered by hedge fund and year. In all

specifications, we find a negative and significant coefficient on the experience variable, implying

that as hedge fund managers gain experience, they are more able to avoid target firms that

experience subsequent bad outcomes. In additional unreported tests, we perform three other

analyses. First, we perform logit regressions where the dependent variable is set to 1 if the target

firm merges within 18 months of the onset of activism and 0 otherwise. The regressions show

positive but statistically insignificant coefficients on the experience variables (Order Quartile or

Order Decile Ordinal). Second, we perform logit regressions where the dependent variable is set

to 1 if the target firm merges within 18 months of the onset of activism or if the target firm has a

change in cash flows in the top decile. Similarly, the coefficients on the experience variables are

positive but generally statistically insignificant. The final regressions are logits where the

dependent variable is set to 1 if the firm delisted or failed and zero otherwise. Consistent with

Panel B, the coefficients on the experience variables are negative, but are only significant in one

26

of the three specifications. Taken together, the results of this section imply that as hedge fund

manager experience increases, managers have better success in activism despite stronger

resistance from target firms, and their target firms have better long-term operating outcomes.

Conclusion

To address the question of managerial skill, we begin this paper investigating whether

activist hedge fund managers exhibit persistence in performance for their activist events. We find

strong evidence of persistence among “winners” but no evidence of persistence among “losers.”

Since hedge fund managers face increasing competition and a declining opportunity set in

activism, we investigate these results further. First, we show that hedge fund managers actively

allocate more assets to activism as they gain experience, investing in larger stocks and

decreasing the time between activism events. Second, we show several ways in which hedge

funds respond to a declining opportunity set in activism. First, they begin to invest in larger firms.

Second, they expand the number of industries in which they invest. Third, they become far more

aggressive in their activism tactics, reducing dramatically the amount of “investment only”

activism and increasing activism for board representation, capital structure changes, management

changes, takeovers, and asset sales. These results provide strong evidence that managers

maintain their performance persistence by engaging in more aggressive activism behavior.

Further, we argue that performance persistence together with hedge fund manager ability to

adapt to increased competition and a declining opportunity set provide evidence of managerial

skill.

Finally, we link the persistence in activism to success in activism goals and changes in

operating performance. We find that as hedge fund manager experience increases, activists are

more likely to achieve success in five of six stated activism goals, despite increased target firm

27

resistance. Finally, as experience in activism increases, hedge fund managers are also able to

avoid subsequent bad operating performance, as well as bankruptcy, liquidation, or delisting.

With our unique and extensive hand collected dataset, we provide strong and comprehensive

evidence that hedge fund activists exhibit performance persistence. Further, we document the

likely sources of this persistence, as well as its ultimate impact on target firms. Overall, our

evidence is consistent with hedge fund managerial skill.

28

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Table 1: Frequency of Activism

Panel A reports the frequency of activism by year. Panel B reports the distribution of activism by industry. Panel C presents the distribution of activism by hedge fund. Panel D reports the distribution of activism by stated purpose, and the proportion of times that the hedge fund manager achieves success in his stated purpose. These sum to more than the total number of activism campaigns since hedge funds sometimes state multiple purposes in activism.

Panel A: Time Distribution of Activism

Year # of Events Percent Total Dollars

(billions) Avg. Market

Cap (millions) 2001 92 3.5% 3.0 227 2002 109 4.1% 3.3 235 2003 146 5.5% 4.8 321 2004 131 5.0% 9.5 1,132 2005 256 9.7% 29.1 1,108 2006 290 11.0% 36.0 1,810 2007 354 13.4% 58.7 1,505 2008 267 10.1% 48.2 1,538 2009 152 5.8% 8.1 526 2010 220 8.3% 36.5 1,964 2011 188 7.1% 38.6 2,597 2012 201 7.6% 36.6 4,130 2013 233 8.8% 40.3 8,203 Total 2,639 100% 352.7 N/A

Panel B: Targeting by One-Digit SIC

SIC Industry # of Events Percent 1 Mining 138 5.2% 2 Manufacturing 1 365 13.8% 3 Manufacturing 2 581 22.0% 4 Transportation, Communications, and Utilities 194 7.4% 5 Wholesale and Retail Trade 295 11.2% 6 Finance, Insurance, and Real Estate 383 14.5% 7 Services 1 517 19.6% 8 Services 2 136 5.2% 9 Public Administration and Non-Classifiable 30 1.1%

Totals 2,639 100%

Table 1: Frequency of Activism, continued

Panel C: Top 10 Activists

Hedge Fund Name Total # of Events

1994-2013 Events in Sample

2001-2013 Bulldog Investors 165 25 Loeb Partners 133 63 Steel Partners 131 43 Carl Icahn 129 70 Starboard/Ramius Capital 122 49 Tontine Partners 121 45 Millennium Partners 112 49 Elliott Associates 89 31 ValueAct Partners 87 66 Wynnefield Partners 81 30 Summary 1,170 471 All Other Activists 5,259 2,168 Top 10 as Percent of Total 18% 18%

Panel D: Stated Purpose

Purpose Number Percent

successful Investment purposes only 1,306 NA Purposeful activism Wants board representation 660 62% Takeover related 521 45% Governance related 313 40% Capital structure related 279 42% Management related 163 29% Sell assets or do a spinoff 166 38% Other 156 50% Purposeful total 2,258 48% Grand total 3,564 NA

Table 2: Summary Statistics

Panel A reports summary statistics for Order. Order is a count variable beginning in 1994 that increases by one for each successive activism event by the same hedge fund. Panel A also presents the frequency, means, and ranges for modified quartiles of the Order variable, where the first quartile (Q1) includes funds with Order=1, and the other quartiles are divided based on the 25th percentile (Q2), median (Q3), and the 75th percentile (Q4) of Order. Panel B reports summary statistics for target firm characteristics prior to the activism event and activism campaign characteristics. Assets is total assets in $ millions. Tobin’s Q is defined as book value of debt plus market value of equity divided by the sum of book value of debt and book value of equity. Cash/assets is defined as cash plus cash equivalents scaled by assets. Leverage is defined as book value of debt divided by the sum of book value of debt and book value of equity. Amihud is an illiquidity measure calculated as in Amihud (2002). HHI is the Herfindahl-Hirschman index by industry. CAR [-25,-5] is the cumulative abnormal return over the period of 25 days before to 5 days before the activism filing date. BHAR [-250,-5] is the market-adjusted buy and hold return over the time window [-250,-5]. CF/assets is net income plus depreciation and amortization divided by average assets. CAR [-1,-1] is the cumulative abnormal return over the period of 1 day before to 1 day after the activism filing date. Ownership (%) is the hedge fund’s ownership stake in the target firm. Purposeful is a dummy variable that is equal to one if the activism is purposeful as defined in Section III and zero otherwise. All variables except Order are winsorized at the 1% and 99% level.

Panel A: Summary Statistics for Other Characteristics

Variables Obs. Mean 25th

percentile Median 75th

percentile Std. Dev.

Target Characteristics Assets ($ Million) 2,639 1,774 98 330 1,061 5,512 Tobin’s Q 2,639 1.89 0.97 1.39 2.20 1.83 Cash/assets 2,639 0.22 0.04 0.13 0.33 0.23 Leverage 2,639 0.23 0.00 0.11 0.40 0.27 Amihud*108 2,639 0.340 0.003 0.021 0.153 1.046 HHI 2,639 0.14 0.05 0.10 0.17 0.13 CAR [-25,-5] (%) 2,639 0.41 -6.73 -0.18 7.23 13.17 BHAR [-250,-5] (%) 2,639 -3.67 -29.98 -5.34 14.84 43.56 Cash flow/assets 2,639 -0.006 -0.024 0.041 0.099 0.208

Campaign Characteristics CAR [-1, 1] (%) 2,639 2.29 -0.97 1.60 5.25 5.94 Ownership (%) 2,639 13.1 5.6 8.6 16.2 12.5

35

Table 2: Summary Statistics, continued

Panel B: Summary Statistics for Order

Variable Mean 25th

percentile Median 75th

percentile Std. Dev. Order 22 3 9 30 28

Quartiles, Ranking by Order # of Events Order Mean Order Range Q1 779 2 Order<=3 Q2 558 6 3<Order<=9 Q3 653 18 9<Order<30 Q4 649 64 Order>30

Deciles, Ranking by Order # of Events Order Mean Order Range D1 364 1 Order=1 D2 243 2 Order=2 D3 319 3 2<Order<=4 D4 201 5 4<Order<=6 D5 210 8 6<Order<=9 D6 260 12 9<Order<=15 D7 250 20 15<Order<=24 D8 273 31 24<Order<=38 D9 262 51 38<Order<=65 D10 261 92 Order>65

Table 3: Performance Persistence

This table presents OLS and logit regressions. In the OLS regressions, the dependent variable is the 3 day CAR. In the logit regressions, the dependent variable is set to 1 if the CAR for the period is in the top 25th percentile of CARs. Independent variables include a dummy set to 1 if one period lagged CAR is in the top 25th percentile, and similar dummy variables for the average twelve month and historical CARs. All regressions include industry and year fixed effects. The standard errors are clustered by firm and year, and t-values are reported below the coefficients in parentheses. Statistical significance at the 1%, 5%, and 10% levels are indicated with ***, **, and *, respectively.

Panel A: Continuous CARs

OLS: Dependent Variable: CAR Variables (1) (2) (3) Lagged CARt-1

0.049***

(3.19)

Avg. Lag. 6-mo. CARt-1

0.066*** (2.99) Avg. historical CARt-1

0.075* (1.66) Log assets 0.114 0.170 0.115

(0.97) (1.20) (0.98)

Tobin’s Q 0.031 0.142** 0.027

(0.50) (2.17) (0.42)

Cash/assets 0.686 1.420 0.662

(0.84) (1.59) (0.80)

Leverage 0.175 0.796 0.133

(0.23) (0.97) (0.18)

Amihud 0.134 0.082 0.135

(0.87) (0.80) (0.85)

HHI 1.040 1.743 1.016

(0.92) (1.19) (0.91)

CAR[-25,-5] -0.458 -2.690 -0.390

(-0.44) (-1.53) (-0.38)

BHAR[-250,-5] -1.669*** -2.294*** -1.671***

(-5.29) (-7.25) (-5.29)

CF 0.293 0.301 0.271

(0.32) (0.18) (0.29)

Ownership 0.021 0.013 0.021 (1.39) (0.86) (1.41) Board dummy 0.166 -0.070 0.148 (0.56) (-0.13) (0.51) Governance dummy 0.938** 0.264 0.912** (2.20) (0.45) (2.12) Capital dummy -0.051 -0.402 -0.076 (-0.12) (-0.74) (-0.18) Management dummy -0.684*** -0.901*** -0.685*** (-3.94) (-3.89) (-3.42) Sell assets dummy 0.352 0.844 0.392 (0.63) (1.21) (0.69) Takeover dummy 1.493*** 1.911*** 1.476*** (4.52) (5.86) (4.41) Investment only 0.094 -0.165 0.097

(0.21) (-0.23) (0.22)

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes Observations 2,118 1,327 2,118 Adjusted R2 0.048 0.091 0.048

37

Table 3: Performance Persistence, continued Panel B: Logits for top Quartile of CARs

Logit: CAR in top 25th

Percentile dummy Logit: CAR in bottom 25th

Percentile dummy Variables (1) (2) (3) (4) (5) (6) Lag CAR 25th pctl. dummy top/bottom 0.338*** 0.128

(2.60) (1.55)

Avg 6 mo lag CAR 25th pctl. dummy top/bottom 0.388*** 0.114 (2.49) (0.76) Avg hist. lag CAR 25th pctl. dummy top/bottom 0.486*** 0.134 (3.27) (1.46) Log assets 0.005 -0.017 0.001 -0.125*** -0.172*** -0.127***

(0.09) (-0.25) (0.02) (-3.12) (-2.88) (-3.27)

Tobin’s Q 0.003 0.030 -0.001 -0.007 -0.054 -0.006

(0.08) (1.13) (-0.03) (-0.20) (-1.50) (-0.20)

Cash/assets 0.076 0.455*** 0.028 0.265 0.104 0.259

(0.42) (3.58) (0.14) (0.75) (0.23) (0.73)

Leverage -0.034 0.265 -0.055 0.377 0.026 0.385

(-0.15) (1.39) (-0.25) (1.45) (0.11) (1.49)

Amihud 0.013 -0.020 0.016 0.000 -0.010 0.000

(0.26) (-0.43) (0.31) (0.01) (-0.47) (0.01)

HHI 0.660 0.828 0.633 0.148 -0.177 0.169

(1.42) (1.57) (1.34) (0.32) (-0.38) (0.36)

CAR[-25,-5] 0.144 -0.620 0.215 -0.836 -0.581 -0.854

(0.37) (-1.00) (0.57) (-2.50) (-1.08) (-2.58)

BHAR[-250,-5] -0.872*** -1.372*** -0.879*** 0.309*** 0.422*** 0.313***

(-6.58) (-7.27) (-6.54) (3.41) (3.33) (3.39)

CF/Assets -0.693* -0.360 -0.712** -0.696*** -0.554 -0.703***

(-1.93) (-0.58) (-2.01) (-3.07) (-1.13) (-3.14)

Ownership 0.008 0.005 0.006 -0.003 -0.003 -0.003 (1.44) (0.69) (1.21) (-0.48) (-0.53) (-0.53) Board dummy 0.076 -0.066 0.045 -0.327*** -0.327** -0.329*** (0.49) (-0.28) (0.30) (-2.58) (-1.96) (-2.56) Governance dummy 0.292 0.141 0.276 -0.130 -0.093 -0.129 (1.61) (0.64) (1.53) (-1.39) (-0.42) (-1.34) Capital dummy 0.004 -0.039 -0.002 -0.181 -0.178 -0.178 (0.03) (-0.15) (-0.01) (-1.18) (-0.72) (-1.17) Management dummy -0.019 -0.189 0.000 -0.171* -0.257 -0.169* (-0.11) (-0.78) (0.00) (-1.67) (-1.01) (-1.65) Sell assets dummy -0.064 0.086 -0.012 -0.036 -0.175 -0.035 (-0.30) (0.36) (-0.05) (-0.16) (-0.38) (-0.15) Takeover dummy 0.482*** 0.432* 0.457*** -0.362* -0.411* -0.364* (3.22) (1.90) (2.97) (-1.77) (-1.85) (-1.78) Investment only 0.010 -0.292 0.021 -0.278* -0.311 -0.272*

(0.05) (-1.10) (0.11) (-1.88) (-1.45) (-1.84)

Year Fixed Effects Yes Yes Yes Yes Yes Yes Industry Fixed Effects Yes Yes Yes Yes Yes Yes Observations 2,118 1,327 2,118 2,118 1,327 2,118 Pseudo R2 0.043 0.075 0.047 0.027 0.041 0.027

38

Table 4: Portfolio Choices over Time

This table examines how the proportion of activism stocks in the each manager’s portfolio, both number and dollar, the dollar value devoted to each activism stock, and the time elapsed between activism events changes as hedge fund managers gain activism experience. Panel A presents the univariate analysis with the sample sorted into four groups based on Order, and compares the-above mentioned characteristics across those groups. Panel B reports the estimated coefficients from regressions that include all the control variables from Table 3, industry fixed effects, and hedge fund manager fixed effects. The standard errors are clustered by firm and year. Statistical significance at the 1%, 5%, and 10% levels are indicated with ***, **, and *, respectively.

Panel A: Univariate Analysis

Aggregate Portfolio (13F) Campaign Characteristics

Order Quartiles

Proportion (Number) of

Activism Stocks

Proportion (Dollar) of Activism Stocks

Dollar Amount in

Each Activism Stock

Percent in Each Activism

Stock

Time (Days) Between Activism Events

Q1 4.8% 10.0% $50.4 13.2% 537 Q2 10.0% 16.2% 85.9 14.2% 327 Q3 15.9% 22.8% 102.7 13.6% 146 Q4 20.3% 23.8% 166.6 12.6% 72 Q4 – Q1 15.5%*** 13.8%*** 118.2*** -0.6% -465***

39

Table 4: Portfolio Choices over Time

Panel B: Multivariate Analysis with Hedge Fund Manager Fixed Effects

Portfolio Analysis (13F) Campaign Characteristics

Dependent Variable

Proportion (Number) of

Activism Stocks

Proportion (Dollar) of Activism

Stocks

Log Dollar Amount in

Each Activism Stock

Percent in Each Activism

Stock

Log Time Between Activism Events

Variables (1) (2) (3) (4) (5) Order Quartile Ordinal

0.050*** 0.072*** 0.284*** -0.006 -0.252***

(3.67) (4.85) (2.61) (-0.01) (-2.80)

Log Assets 0.004 0.016** -1.380*** 0.105***

(0.91) (2.23) (-4.48) (2.61)

Tobin’s Q -0.002 -0.001 0.102*** 0.251 -0.017 (-0.11) (-1.09) (3.18) (1.07) (-0.87) Cash/assets -0.024 -0.009 -0.556*** -3.132 0.316**

(-1.30) (-0.52) (-2.58) (-1.58) (2.09)

Leverage -0.006 -0.024 -0.266 0.391 -0.272**

(-0.34) (-0.93) (-1.24) (0.25) (-2.17)

Amihud 0.003 0.001 -0.340*** 0.322 0.034

(0.60) (0.30) (-4.32) (0.79) (0.87)

HHI -0.018 -0.006 -0.170 1.882 0.095

(-1.13) (-0.20) (-0.73) (0.82) (0.31)

CAR[-25,-5] 0.051** 0.051 0.223 1.232 0.238

(2.03) (1.46) (0.62) (0.39) (0.78)

BHAR[-250,-5] -0.017** -0.011 0.130 -2.277** 0.032

(-2.31) (-0.90) (1.44) (-2.27) (0.29)

CF/Assets 0.003 0.003 1.489*** -0.278 0.046

(0.27) (0.12) (8.66) (-0.13) (0.25)

Ownership 0.000 0.000 -0.002

(-0.39) (-0.98) (-0.55)

Board dummy 0.012 0.045*** 0.131* 1.457* -0.077 (0.95) (2.71) (1.70) (1.80) (-1.15) Governance dummy 0.006 0.016 0.169 1.260 0.026 (0.45) (1.04) (1.36) (1.30) (0.28) Capital dummy 0.010 0.021 -0.132 -1.054* -0.055 (0.79) (1.42) (-0.87) (-1.91) (-0.38) Management dummy -0.001 -0.007 0.482*** -2.821** -0.140 (-0.07) (-0.44) (3.65) (-2.38) (-0.81) Sell assets dummy -0.031** -0.008 -0.156** 1.206 0.052 (-2.40) (-0.56) (-2.31) (0.86) (0.36) Takeover dummy 0.000 -0.002 -0.236* -0.258 0.022 (-0.04) (-0.26) (-1.66) (-0.30) (0.19) Investment only -0.007 0.017 0.131* -0.345 -0.145 (-0.78) (1.22) (1.70) (-0.35) (-1.15) Industry Fixed Effects Yes Yes Yes Yes Yes Manager Fixed Effects Yes Yes Yes Yes Yes Observations 1,434 1,442 2,639 2,639 2,139 Adjusted R-squared 0.751 0.707 0.624 0.381 0.361

40

Table 5: How do Managers Adapt over Time? This table examines the impact of Order on target firm characteristics, industry choice, and tactic choice, in Panels A, B, and C respectively and reports estimated coefficients from OLS regressions (Panel A) and logits (Panels B and C) that include the control variables from Table 3 and hedge fund manager fixed effects. The standard errors are clustered by firm and year. Statistical significance at the 1%, 5%, and 10% levels are indicated with ***, **, and *, respectively.

Panel A: Firm Characteristics, OLS Clustered Fixed Effects Regressions Log assets Tobin’s Q Cash/Ass

et Leverage Amihud HHI CF

Variables (1) (2) (3) (4) (5) (6) (7) Order Quartile Ordinal

0.264*** 0.159 0.009 -0.018 0.038 -0.002 -0.015

(2.91) (1.54) (1.05) (-1.36) (0.55) (-0.30) (-1.41)

Log Assets -0.259*** -0.030*** 0.053*** -0.205*** 0.000 0.037*** (-3.89) (-6.96) (7.07) (-3.99) (0.00) (5.55) Tobin’s Q -0.103*** 0.011*** -0.005 -0.035 -0.002 -0.004

(-4.70) (2.80) (-1.18) (-1.13) (-0.86) (-0.51)

Cash/assets -1.023*** 0.992*** -0.018 -0.352*** -0.085*** -0.162***

(-7.10) (2.64) (-1.36) (-4.50) (-4.78) (-3.90)

Leverage 1.306*** -0.327 -0.226*** 0.425** 0.023 -0.121***

(6.49) (-1.25) (-11.32) (2.11) (1.13) (-4.36)

Amihud -0.262*** -0.113 -0.013*** -0.317*** 0.015** -0.009*

(-4.02) (-1.08) (-2.97) (-8.14) (2.00) (-1.76)

HHI -0.001 -0.302 -0.171*** 0.022*** 0.801 0.089***

(0.00) (-0.90) (-4.51) (3.44) (1.45) (2.84)

CAR[-25,-5] 0.238 -0.267 0.053 0.064 -0.245 -0.007 -0.084*

(1.12) (-0.92) (1.55) (1.13) (-1.18) (-0.36) (-1.76)

BHAR[-250,-5] -0.243*** -0.211** -0.031** 0.059 0.228** -0.006 0.037***

(-5.27) (-2.08) (-2.23) (1.33) (2.26) (-0.93) (2.62)

CF/Assets 1.375*** -0.342 -0.172*** 0.002 -0.272 0.047***

(6.22) (-0.51) (-3.92) (0.12) (-1.43) (2.69)

Ownership -0.014*** 0.006 -0.001* -0.181*** 0.003 0.000 0.000 (-3.93) (1.07) (-1.84) (-4.12) (0.70) (0.91) (-0.12) Ownership 0.003 -0.181 0.010 0.000 -0.075 -0.001 -0.002 (0.04) (-1.55) (0.71) (0.24) (-0.93) (-0.05) (-0.09) Board dummy 0.048 0.029 -0.015 -0.010 0.055 0.010 -0.024** (0.61) (0.17) (-1.34) (-0.61) (0.53) (0.97) (-1.97) Governance dummy 0.083 -0.124 0.049*** -0.006 0.044 -0.002 0.009 (0.83) (-0.74) (3.92) (-0.30) (0.94) (-0.16) (0.57) Capital dummy 0.019 -0.038 0.022 0.009 -0.035 0.006 0.016 (0.17) (-0.32) (1.21) (0.57) (-0.40) (0.28) (1.49) Management dummy

0.550*** 0.165 -0.036** -0.009 0.133** -0.036*** -0.003 (5.44) (1.59) (-2.03) (-0.44) (1.84) (-4.35) (-0.28) Sell assets dummy -0.210*** -0.155** -0.001 -0.035*** -0.016 0.009 0.028*** (-2.57) (-2.05) (-0.05) (-2.45) (-0.25) (0.63) (2.93) Investment Only -0.262** -0.118 -0.008 0.002 0.064 -0.003 -0.011 (-2.19) (-0.80) (-0.48) (0.14) (1.15) (-0.45) (-0.68) Industry Fixed Effects

Yes Yes Yes Yes Yes Yes Yes Manager Fixed Effects

Yes Yes Yes Yes Yes Yes Yes Observations 2,639 2,639 2,639 2,639 2,639 2,639 2,639 R-squared 0.663 0.248 0.504 0.433 0.285 0.154 0.445

41

Table 5: How do Managers Adapt over Time?, continued

Panel B: Industry Choice: Logit Fixed Effects Regressions SIC 1 SIC 2 SIC 3 SIC 4 SIC 5 SIC 6 SIC 7 SIC 8 SIC 9 Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) Order Quartile Ordinal 0.467* 0.159 0.260* 0.085 0.071 -0.521** -0.347*** 0.224 -0.836

(1.86) (0.97) (1.89) (0.31) (0.32) (-1.99) (-3.06) (1.12) (-0.69)

Log Assets 0.073 0.036 -0.174** 0.067 0.110 0.533*** -0.143** -0.313* 0.134 (0.37) (0.46) (-2.03) (0.66) (1.28) (3.08) (-1.99) (-2.03) (0.18) Tobin’s Q -0.170 0.053 -0.042 -0.096 0.046 0.036 0.012 0.025 -0.592*

(-1.48) (1.04) (-1.12) (-1.15) (0.52) (0.44) (0.41) (0.30) (-1.80)

Cash/assets -6.539*** 1.133** 0.002 -0.972 -4.655*** -0.282 1.203*** -1.789*** 2.261*

(-3.06) (2.16) (0.01) (-1.26) (-5.47) (-0.41) (3.14) (-2.47) (1.68)

Leverage 0.050 -0.135 -1.200*** 2.650*** -1.707*** 0.858 -0.132 0.813 -10.139***

(0.06) (-0.38) (-4.00) (7.69) (-3.93) (1.19) (-0.37) (1.05) (-2.80)

Amihud -0.082 0.012 0.055 -0.259 -0.267* 0.162 0.112 -0.173 -1.664***

(-0.23) (0.13) (0.96) (-1.61) (-1.82) (1.12) (1.58) (-1.25) (-3.01)

HHI -2.070 2.285*** 0.228 -3.026* 1.173* 1.496*** -7.039*** 3.683*** 11.266***

(-1.43) (2.98) (0.47) (-1.69) (1.81) (3.14) (-4.42) (4.31) (3.72)

CAR[-25,-5] -0.612 0.294 -1.161** -1.398 2.303*** -1.124 1.024*** -0.494 3.279

(-0.44) (0.73) (-2.01) (-1.26) (3.19) (-0.83) (6.03) (-0.60) (1.34)

BHAR[-250,-5] 0.333 0.343 -0.034 0.005 -0.342 0.006 -0.113 0.195 0.043

(0.74) (1.58) (-0.20) (0.02) (-1.44) (0.02) (-0.66) (0.49) (0.04)

CF/Assets 5.675*** -1.920*** 0.121 0.361 2.459*** -0.924 1.013* -0.155 -7.091***

(3.87) (-3.79) (0.36) (0.38) (2.64) (-1.14) (1.74) (-0.19) (-2.98)

Ownership -0.002 -0.008 -0.006 0.033*** -0.005 0.006 0.002 -0.001 0.032 (-0.15) (-1.15) (-0.79) (2.89) (-0.53) (0.55) (0.23) (-0.05) (1.03) Ownership 0.038 0.331** 0.390* 0.105 -0.173 -0.440 -0.078 -1.170** -3.328* (0.07) (2.14) (1.91) (0.30) (-0.59) (-1.41) (-0.55) (-2.12) (-1.72) Board dummy -0.117 -0.228 0.134 0.470 0.176 -0.237 0.049 0.151 - (-0.28) (-1.09) (0.68) (1.02) (0.46) (-0.51) (0.16) (0.27) - Governance dummy 0.271 -0.511 0.502 -0.238 0.353 -0.145 -0.104 -0.864* 2.086 (0.81) (-1.32) (1.64) (-0.53) (0.55) (-0.32) (-0.29) (-1.73) (1.00) Capital dummy 0.632 0.499 -0.179 -0.093 -0.213 -0.401 -0.091 -0.629 - (0.77) (1.34) (-0.48) (-0.14) (-0.32) (-0.67) (-0.34) (-0.81) - Management dummy

0.057 0.666*** -0.248 -0.527 0.058 -0.322 -0.066 -0.343 - (0.14) (4.06) (-0.91) (-0.98) (0.13) (-0.81) (-0.19) (-0.37) - Sell assets dummy 0.514 -0.184 0.024 -0.591 0.377 -0.091 0.209 -0.365 2.083 (1.04) (-0.62) (0.11) (-1.42) (1.06) (-0.22) (0.66) (-0.73) (0.87) Investment Only 0.920** -0.454** 0.165 -0.022 0.385 0.265 -0.156 -0.566 2.178 (2.01) (-2.09) (0.74) (-0.07) (0.94) (0.51) (-0.88) (-1.29) (0.81) Industry Fixed Effects

No No No No No No No No No Manager Fixed Effects

Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 827 1,479 1,738 1,323 1,429 1,241 1,610 1,008 279 Pseudo R-squared 0.319 0.191 0.140 0.266 0.244 0.308 0.194 0.185 0.499

42

Table 5: How do Managers Adapt over Time? continued

Panel C: Tactics, Fixed effects Logit regressions

Inv. Only Board Rep. Takeover Gov. Capital Mgmt Sell Assets

Variables (1) (2) (3) (4) (5) (6) (7) Order Quartile Ordinal -0.837*** 0.575*** 0.562*** 0.362 0.482* 0.688*** 0.491**

(-3.90) (4.06) (2.68) (1.49) (1.87) (2.70) (2.33)

Log Assets -0.294*** 0.194** 0.011 0.222*** 0.346*** 0.175* 0.745*** (-3.31) (2.10) (0.17) (2.91) (3.89) (1.75) (7.11) Tobin’s Q 0.021 -0.058 -0.064 0.019 -0.086 -0.047 0.046

(0.54) (-1.51) (-1.33) (0.25) (-1.15) (-0.61) (0.71)

Cash/assets -0.692 0.698 0.230 -0.104 2.186*** 1.069 -0.489

(-1.60) (1.58) (0.48) (-0.25) (4.05) (1.57) (-0.59)

Leverage -0.375* -0.080 0.201 -0.168 0.497 -0.258 -0.627*

(-1.71) (-0.27) (0.70) (-0.40) (1.25) (-0.68) (-1.71)

Amihud 0.115 -0.142* -0.081 0.027 0.003 -0.061 0.176

(1.57) (-1.85) (-0.89) (0.23) (0.04) (-0.41) (0.48)

HHI -0.351 0.266 0.586 0.808 -0.604 0.796 -3.819***

(-0.78) (0.34) (0.89) (1.27) (-0.59) (0.67) (-2.91)

CAR[-25,-5] 0.398 -0.043 -0.737 -1.792*** -1.618** -0.009 -1.385

(0.91) (-0.07) (-1.42) (-2.96) (-2.05) (-0.02) (-1.01)

BHAR[-250,-5] 0.080 -0.037 -0.358* -0.066 -0.161 0.015 -0.772**

(0.56) (-0.21) (-1.72) (-0.36) (-0.47) (0.05) (-2.28)

CF/Assets -0.893** 0.265 1.646*** -0.399 1.369 1.045** 1.495***

(-2.32) (0.48) (3.00) (-0.68) (1.61) (2.40) (2.50)

Ownership -0.010 0.016* -0.001 0.012** -0.011* -0.025** 0.018*** (-1.27) (1.84) (-0.09) (1.97) (-1.88) (-2.10) (2.62) Industry Fixed Effects Yes Yes Yes Yes Yes Yes Yes Manager Fixed Effects Yes Yes Yes Yes Yes Yes Yes Observations 2,020 1,790 1,779 1,273 1,472 1,060 947 R-squared 0.243 0.217 0.147 0.138 0.193 0.163 0.224

43

Table 6: Success in Activism and Hedge Fund Manager Experience

This table presents results for logit regressions of success in activism goals in columns (1) – (4) and of worsened governance in columns (5) and (6) on hedge fund manager experience and a number of control variables. Variables are described in Section 3 and Table 2. In the odd-numbered columns, the measure of hedge fund experience is Order Quartile Ordinal, which takes a value of 1 to 4 based on the quartile of Order. The even-numbered columns use Order Decile Ordinal, which takes a value from 1 to 10 based on the decile of Order. All regressions include industry and year fixed effects. The standard errors are clustered by firm and year, and t-values are reported below the coefficients in parentheses. Statistical significance at the 1%, 5%, and 10% levels are indicated with ***, **, and *, respectively.

Panel A: Success in Stated Activism Goals

Dependent variable:

Success in mgmt, gov, cap, takeover, or sell assets

Dependent variable: Success in board rep

Dependent variable: Firm worsens governance

Variables (1) (2) (3) (4) (5) (6) Order Quartile Ordinal 0.090* -0.143 0.226**

(1.82) (-1.42) (2.16)

Order Decile Ordinal 0.040* -0.050 0.087**

(1.69) (-1.24) (2.25)

Log Assets 0.154*** 0.150*** 0.160** 0.159** 0.100 0.098

(3.56) (3.31) (1.97) (1.98) (1.55) (1.48)

Tobin’s Q -0.011 -0.011 0.039 0.037 -0.045 -0.044

(-0.30) (-0.29) (1.57) (1.48) (-0.82) (-0.80)

Cash/assets 0.515 0.506 -0.652* -0.642* 0.788 0.766

(1.37) (1.35) (-1.76) (-1.71) (1.14) (1.11)

Leverage -0.279 -0.270 -0.581* -0.573* -0.567* -0.560*

(-1.50) (-1.47) (-1.83) (-1.89) (-1.88) (-1.81)

Amihud 0.100 0.101 0.232* 0.230* -0.032 -0.029

(0.78) (0.79) (1.73) (1.71) (-0.37) (-0.33)

HHI -1.087** -1.086** 1.279 1.278 -0.303 -0.308

(-2.08) (-2.07) (1.43) (1.43) (-0.38) (-0.38)

CAR[-25,-5] -0.219 -0.229 -0.253 -0.250 -0.146 -0.157

(-0.63) (-0.66) (-0.61) (-0.61) (-0.31) (-0.34)

BHAR[-250,-5] -0.610 -0.612 1.170 1.162 -0.875 -0.888

(-1.17) (-1.17) (1.57) (1.57) (-1.32) (-1.32)

CF/Assets 0.121 0.117 -0.421** -0.418** 0.214 0.215

(0.94) (0.90) (-1.98) (-1.96) (0.85) (0.85)

Ownership -0.005* -0.006* 0.031*** 0.031*** 0.011* 0.010*

(-1.79) (-1.81) (2.50) (2.50) (1.87) (1.87)

Year Fixed Effects Yes Yes Yes Yes Yes Yes Industry Fixed Effects Yes Yes Yes Yes Yes Yes Observations 1,144 1,144 663 663 1,364 1,364 Pseudo R-squared 0.050 0.050 0.102 0.098 0.084 0.081

44

Table 6: Success in Activism and Hedge Fund Manager Experience, continued

Panel B: Avoiding Bad Outcomes Bad Outcome: Dummy set to 1 if change in CF is in bottom quartile or firm drops or liquidates in 18 months

1 year 2 years 3 years Variables (1) (2) (3) (4) (5) (6) Order Quartile Ordinal

-0.106* -0.170*** -0.258***

(-1.91) (-2.57) (-3.78)

Order Decile Ordinal -0.036 -0.065*** -0.110*** (-1.59) (-2.51) (-4.32) Log Assets -0.202*** -0.204*** -0.210*** -0.211*** -0.190*** -0.186***

(-2.57) (-2.61) (-2.64) (-2.66) (-2.72) (-2.63)

Tobin’s Q 0.027 0.026 0.002 0.000 0.044 0.044

(0.83) (0.80) (0.09) (0.00) (1.51) (1.49)

Cash/assets 0.875*** 0.879*** 0.659*** 0.667*** 0.460* 0.468*

(4.21) (4.24) (2.89) (2.98) (1.69) (1.74)

Leverage 0.392** 0.396** 0.341 0.337 0.603** 0.586**

(2.03) (2.04) (1.50) (1.44) (2.32) (2.18)

Amihud 0.077 0.076 0.049 0.048 0.149* 0.148*

(1.08) (1.06) (0.61) (0.60) (1.88) (1.87)

HHI 0.522 0.524 0.688* 0.693* 0.876* 0.878*

(1.42) (1.42) (1.65) (1.66) (1.79) (1.76)

CAR[-25,-5] 1.435*** 1.438*** 1.392*** 1.401*** 0.011 0.033

(7.36) (7.47) (4.36) (4.40) (0.05) (0.16)

BHAR[-250,-5] -0.829* -0.831* -1.094* -1.094* -0.695 -0.704

(-1.65) (-1.66) (-1.73) (-1.73) (-1.25) (-1.27)

CF/assets -0.952*** -0.952*** -0.972*** -0.968*** -0.656** -0.645**

(-5.16) (-5.18) (-5.28) (-5.28) (-2.44) (-2.40)

Ownership 0.009*** 0.009*** 0.007** 0.008** 0.014*** 0.014***

(3.20) (3.20) (2.35) (2.41) (3.29) (3.36)

Board dummy 0.107 0.107 -0.221** -0.220** 0.070 0.070 (0.99) (0.98) (-1.99) (-1.96) (0.47) (0.46) Governance dummy -0.026 -0.027 0.284 0.284 0.023 0.020 (-0.15) (-0.15) (1.53) (1.52) (0.09) (0.08) Capital dummy 0.339** 0.342** 0.194** 0.196** -0.001 0.000 (1.95) (1.97) (2.29) (2.28) (0.00) (0.00) Management dummy 0.461** 0.458** 0.199 0.189 0.443* 0.430* (2.20) (2.18) (0.67) (0.63) (1.79) (1.72) Sell assets dummy 0.241 0.239 -0.354* -0.356* 0.030 0.033 (1.16) (1.15) (-1.79) (-1.82) (0.13) (0.14) Takeover dummy -0.136 -0.140 -0.188* -0.191* -0.329** -0.328** (-1.35) (-1.37) (-1.63) (-1.67) (-2.44) (-2.44) Investment only 0.094 0.091 -0.093 -0.091 0.063 0.078 (0.48) (0.47) (-0.86) (-0.86) (0.60) (0.75) Year F.E. Yes Yes Yes Yes Yes Yes Industry F.E. Yes Yes Yes Yes Yes Yes Observations 2,639 2,639 2,639 2,639 2,639 2,639 Adjusted R-squared 0.118 0.118 0.141 0.141 0.137 0.140