The Role of the Media in Hedge Fund Activism†

Xinjie Wang

Southern University of Science and Technology

Email: xinjie.wang@sustech.edu.cn

Ge Wu

University of Richmond

Email: gwu@richmond.edu

January 12, 2020

Keywords: sentiment, media, hedge fund activism, merger, acquisition, governance

JEL Classification: G30, G34

† We are grateful for the valuable comments from Tom Arnold, Suman Banerjee, Mitch Conover, Pat Fishe, William

Johnson, Simi Kedia, Tingting Liu, Yaxuan Qi, Cassandra Marshall, Vikram Nanda, David North, and Jerry Stevens.

All remaining errors are our own. Xinjie Wang acknowledges financial support from Southern University of Science

and Technology (Grant No. Y01246210, Y01246110).

The Role of the Media in Hedge Fund Activism

Abstract

Using a large set of hedge fund 13D filing and news data for the period of 2000 to 2019, we

document that firms with more negative media sentiment are more likely to be targeted by hedge

funds. The media sentiment of target firms reverts to normal level after the 13D filing dates. Our

analysis of media sentiment for different corporate events shows that event-specific media

sentiment is associated with the specific objectives of activism campaigns. The firm’s fundamental

information captured by media sentiment drives the hedge fund targeting event and such targeting

generates higher long-term stock returns. Our findings provide support to the notion that hedge

fund activism creates value for shareholders.

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1 Introduction

Hedge fund activism in the recent past has become an important corporate governance

mechanism that facilitates significant changes in the operation and governance of target firms. (see,

e.g., Brav, Jiang, and Kim, 2010). Hedge fund activists create substantial value for target firms’

shareholders. Prior studies show that target firms experience positive returns on the announcement

of activism as well as long term returns (Brav, Jiang, Partnoy, and Thomas, 2008; Becht, Franks,

Mayer, and Rossi, 2008; Clifford, 2008; Klein and Zur, 2009; Greenwood and Schor, 2009).

Though hedge fund activism has become more acceptable, there is little research on which

firms do activists target. One notable exception is the seminal work by Brav, Jiang, Partnoy, and

Thomas (2008), who use a matched sample to examine the characteristics of target firms and find

that firms targeted by hedge fund activists tend to be small, profitable and “value” firms with lower

payout ratio, more takeover defenses and higher CEO compensation. But there is still not enough

understanding of firm characteristics that make them more susceptible to hedge fund activism.

This probably arises because hedge funds seek a variety of different changes in firms, from pushing

firms to sell themselves, to stopping a planned merger, initiating restructuring, making governance

changes and returning cash to shareholders. The myriad goals of hedge fund activists make it

difficult to ascertain what influences the likelihood of being targeted. Though it is difficult to

evaluate firms along all potential reasons that might motivate hedge fund activism, one common

factor across all is the negative perception among investors of the relevant firm activities. The

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PWC report says that a firm is more likely to be targeted for shareholder activism if it has received

media/analyst criticism.1

In this paper, we study the information environment of a firm to investigate whether negative

investor response to the news being disclosed is associated with a higher likelihood of hedge fund

activism. Specifically, we use the sentiment in the media reporting of the firm to capture the

direction of the information that is being revealed about the firm. If the average news report is

negative, it suggests that the news surrounding the firm, whether it be about governance or product

launches is not being well received by the markets. We predict that such firms are more likely to

be targeted, and such targeting will generate higher returns.

We first use the average news sentiment scores from RavenPack to examine the information

environment of the target firms around hedge fund activism events. The media sentiment measure

and negative press coverage have been used in prior literature to capture the amount of information

contained in a publicly released news article and proxy for investor and public attention (e.g., Lin,

Massa and Zhang 2014; Kolasinski, Reed, and Ringgenberg 2013; Massa, Qian, Wu and Zhang

2015; Core, Guay and Larker 2008). We develop a sample of 1,046 target firms over the 2000 -

2019 period. We find that target firms’ news sentiment is significantly lower in the three quarters

prior to being targeted relative to their prior news sentiment and other firms’ news sentiment. We

also find that target firms’ negative news coverage is significantly higher in the three quarters prior

to being targeted relative to their prior negative news coverage and other firms’ negative news

coverage. These results hold for the novel news stories, which are the first coverage on a firm’s

corporate event in the past 24-hour window.

1 As quoted by in a Wall Street Journal article, “Companies need to grow listening ears.” These external listening

posts can take various forms besides social media. Understanding the concerns of customers and investors can attune

the company to concerns that lead them to be targeted by hedge fund activists.

3

If the media sentiment captures the public’s perceptions, then we would expect that a low

sentiment score could then reflect shareholders and investors are more likely to push for changes.

Thus, we examine if a firm with more negative news coverage is more likely to be targeted by

hedge fund activists. In multivariate regressions, we control for other firm level variables which

have been shown in prior literature to impact targeting and we also include year, quarter and

industry fixed effects. The level of news sentiment prior to targeting is negative and statistically

significant. We also find economic significance. A one standard deviation decrease in the level of

news sentiment is associated with a 0.15%-0.18% increase in the probability of being targeted

(22%-27% of the unconditional probability of 0.66%). We also find a positive association between

the drop in news sentiment prior to targeting and the likelihood of hedge fund activism. The results

are both statistically and economically significant. A one standard deviation decrease in the news

sentiment is associated with a 0.18%-0.19% increase in the probability of being targeted (27%-29%

of the unconditional probability of 0.66%). Our results are robust to different news sentiment

measures and to different regression specifications.

We then examine if the negative news sentiment captures the firm’s fundamental information

which attracts hedge fund activists’ attention. We select the following events which are likely to

be the issues targeted by hedge fund activists: mergers and acquisitions, earnings announcements,

analyst and credit ratings, dividend payouts, and regulatory events. And we calculate the abnormal

news sentiment for those corporate events prior to targeting. We find that if the firm makes some

bad acquisition decisions or if it is a potential acquisition target, then it’s more likely to be targeted

by hedge fund activists. We also find that firms with lower earnings, analyst ratings or credit

ratings related news sentiment are more likely to be targeted by hedge fund activists. In summary,

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we have shown that the sentiment in the media reporting of the firm captures the firm’s

fundamentals and it drives the hedge fund targeting event.

Next, we examine if there is any relation between the pre-targeting negative news sentiment

and the stated objectives in hedge fund activism campaigns. We use Factset’s SharkRepellent

database to collect three primary requests for change made by hedge fund activists: merger related

requests, governance related requests and capital structure requests. We find that earnings and

regulatory news sentiment are negatively associated with governance-related and capital structure-

related activism, respectively. We also find a positive correlation between M&A news sentiment

and merger-related activism.

Lastly, we show that such targeting will generate higher stock returns. We examine the short-

term performance of the target firm around the announcement of the activism by estimating

cumulative abnormal returns (CARs). The result shows that target firms with lower media

sentiment prior to the event have significant higher short-term returns. We also examine longer

term returns to the activism using buy and hold abnormal returns, and find that target firms with

lower prior media sentiment have significant higher long run abnormal returns.

Our paper contributes to the growing literature on hedge fund activism, especially the

characteristics of target firms. Hedge fund activists tend to target generalizable issues to lower the

marginal cost of launching a new activism campaign (Black, 1990). Kahn and Winton (1998) show

that investors are more likely to target issues that are well-understood by the market. The potential

problems are related to firm fundamentals such as dividend payout and leverage ratio (Brav, Jiang,

and Kim 2010), or market characteristics such as institutional ownership and stock liquidity

(Greenwood and Schor, 2009; Edmans, Fang, and Zur, 2013; Norli, Ostergaard, and Schindele

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2014; Kedia, Starks, and Wang, 2019).2 However, there is still not enough understanding of all

factors that might motivate hedge fund activism. Our paper fills in this gap by examining one

common factor which drives hedge fund activism: the negative market perception of firm activities

and its impact on the success of the activist’s campaign. Our evidence is consistent with the

predictions in Black (1990) and Kahn and Winton (1998).

Our paper is also related to the growing literature on the informational and governance roles

of media in financial markets. Norden and Strand (2018) find in a sample of Swedish firms that

institutional investors are more likely to target large firms with more media coverage and higher

institutional ownership. They argue that the reason to target firms with more coverage is to get

more media attention for themselves. Liu, Sherman and Zhang (2014) examine media coverage in

the pre-IPO period and find that it is related to analyst coverage, institutional ownership etc. Da,

Engelberg and Gao (2011) also look at media coverage (i.e. google searches) and find that it is

associated with higher first day returns and lower long run performance. They argue that this

reflects attention by retail investors. You, Zhang and Zhang (2017) find that negative coverage by

media not only increases the likelihood of forced top executive turnover but also ties the sensitivity

of the likelihood of top executive turnover to firm performance. Complementary to these studies,

we find that negative coverage by the press increases the likelihood of being targeted by activists

and the profits generated from activism events.

The next section describes the construction of our sample and our proxy variables for public

perception. Section 3 examines the impact of negative media sentiment on the likelihood of being

targeted. Section 4 discusses the information content of negative media sentiment. Section 5

2 There are many other studies of hedge fund activism. Important examples include Brav, Jiang, Partnoy, and

Thomas (2008), Clifford (2008), Klein and Zur (2011), Cheng, Huang, Li, and Stanfield (2012), Sunder, Sunder, and

Wongsunwai (2014), Bebchuk, Brav and Jiang (2015), Appel, Gormley, and Keim (2018), among others.

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examines the effect of negative media sentiment on the success of the hedge fund activism

campaign. Section 6 reports robustness analyses. Finally, Section 7 concludes.

2 Data Description

We begin with a sample of hedge fund activism events. This sample is obtained from Brav et

al. (2010) and SharkRepellent.net. Factset’s SharkRepellent has been used in several prior studies

(e.g., Gantchev 2013; Gantchev and Jotikasthira 2017; Boyson, Gantchev, and Shivdasani 2017).

Investors that acquire 5% of a public company with the intention of influencing its operations or

management are required to file a beneficial ownership filing, which is a Form 13D, within ten

days of the event. The original hedge fund 13D filing sample consists of 4,329 activism events

with 3,119 target firms over the period from 2000 to 2019. To avoid overlapping events for the

same firm, we select only the first targeting event for each target firm.

We obtain corporate news data from RavenPack News Analytics. RavenPack collects real-

time firm news from Dow Jones Newswires, regional editions of the Wall Street Journal, and

Barron's. The database assigns “sentiment scores” (discussed below) to the news stories on a 100-

point scale (the lower the score is, the more negative the coverage of the firm is). To filter out

noisy news stories, we exclude 2,438,781 neutral news articles, 1,770,403 news articles related to

insider-trading and 660,504 news articles related to order-imbalance. This gives us a sample of

12,018 firms and 4,090,128 news articles over the 2000 – 2019 period. Then we merge the news

data with the activism event sample by matching target firms’ CUSIP codes and company names

(i.e. exact name matching). We aggregate the news data at the quarterly frequency.

Finally, we obtain firm characteristics variables from CRSP, Compustat, Thomson Reuters

and IBES. We merge the news data and the activism event sample with the firm characteristics

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variables. We require that all variables are available for regression analysis. The final sample

consists of 122,383 firm-quarter observations on 1,046 target firms and 3,731 non-target firms

over the period from 2000 to 2019.

2.1 Proxy of public signal

As discussed earlier, RavenPack conducts linguistic analyses and proprietary algorithms to

process news articles and assigns sentiment scores (i.e. ESS) to the news. We standardize the news

tones to the unit interval, which ranges from -1 (very negative news) to 1 (very positive news),

with 0 being neutral. The sentiment scores cover more than 330 different types of news events,

including product recalls, earnings announcements, layoffs, mergers and acquisitions (M&A)

activity, and so on.

News sentiment from RavenPack is also used by Lin, Massa and Zhang (2014) and Kolasinski,

Reed, and Ringgenberg (2013) as the measure of the amount of positive or negative information

contained in a publicly released new article. Massa, Qian, Wu and Zhang (2015) use another

interesting variable, which is defined as the number of negative news articles for a firm in one

month minus the average number of negative news for the same firm in the preceding three months,

to proxy for short-sellers’ attention. Core, Guay and Larker (2008) find that media tends to take a

negative tone with firms that are out of favor with public opinion. A low sentiment score could

then reflect shareholders and investors that are more likely to push for changes.

Table 2 provides summary statistics on the news sentiment of target firms and non-target firms.

The first news sentiment measure Average ESS is the quarterly average value of our standardized

event sentiment score (i.e. ESS). The second measure Average AES is the quarterly average value

of our standardized aggregate event sentiment (i.e. AES). AES is constructed as the ratio of positive

events (i.e. ESS > 0) reported on a firm compared to the total count of events (excluding news with

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ESS = 0) measured over a rolling 91- day window. The third measure Average Negative News is

the percentage of negative news stories (i.e. ESS < 0) over one quarter. For a firm that was targeted

in quarter Q, the average ESS of all news reports about this firm in the three quarters prior to being

targeted (i.e. from Q-3 to Q-1) is 0.1013, and the median ESS is 0.1060. They are significantly

lower than the average and the median ESS of all news reports about the target firm in all the prior

quarters (i.e. Q-4 and prior). They are also significantly lower than the average and the median

ESS of all news reports about the non-target firm over our sample period. In addition, the average

and the median ESS of all news reports about the non-target firm over our sample period are

significantly lower than the average and the median ESS of all news reports about the target firm

in all the prior quarters (i.e. Q-4 and prior). Similar patterns are also seen when we use average

and median AES and average and median negative news as news sentiment measures. We find

that the target firms’ news sentiment drops from Q-4 to the three quarters prior to being targeted

and the average news article is more negative for targeted firms in the three quarters priors.

The news sentiment measures developed in this paper are proxies for public signal or

perception among investors of the relevant firm activities. We construct them based on all news

stories. RavenPack also provides the event novelty score (ENS) which represents how novel a news

article is. The ENS variable allows users to isolate and focus on the first news article in a chain of

similar articles on a given news event. ENS has a range of values between 0 and 100, with a high

value indicating the more recent release of a given news event. We define novel news as news

stories with ENS of 100, which means that no similar story about the company has been reported

in the past 24-hour window. We find that for all novel news, the average sentiment three quarters

prior to targeting is low and the number of negative news stories is high. This is consistent with

the results using all news events.

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<Table 2>

Figure 1 provides a graphical representation for the average media sentiment in each quarter

prior to and post targeting for both target firms and non-target firms. We create fake targeting

events / pseudo-events for non-target firms in our sample. We first calculate the frequency

distribution of the targeting events from the first quarter in 2000 to the second quarter in 2019 (i.e.

78 quarters). Then, for each non-target firm, we generate a random number and set it to the pseudo-

event quarter based on the frequency distribution. In Panel A and Panel B, we find that the level

of target firms’ sentiment is lower comparing to non-target firms and target firms’ sentiment drops

prior to being targeted and comes back up after the event quarter. In Panel C, we find that the

percentage of negative news stories is higher for target firms comparing to non-target firms and

the target firms’ negative news increases prior to the targeting event and drops after the event

quarter. This result is consistent with our summary statistics.

<Figure 1>

Panel C of Table 2 provides summary statistics on the media coverage for target firms in our

sample. A target firm has on average 11.45 (7.89) (novel) news stories in the quarters prior to the

targeting event. Among those (novel) news stories, 4.15 (2.79) of them have negative sentiment

scores and 7.30 (5.10) of them have positive sentiment scores. The change in the number of (novel)

news is 0.64 (0.40) on average and it’s mainly driven by the increase in negative (novel) news

stories.

2.2 Control variables

Our control variables come from the prior literature. First, we include Firm Size, which is

defined as the firm’s market capitalization. We then include the valuation variable Tobin’s q,

which is defined as the market to book ratio. The next set of control variables concern profitability

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and growth: we include ROA, defined as the ratio of EBITDA to lagged assets. And we also include

Sales Growth, which is the firm’s growth rate of sales over the previous year. We include control

variables that proxy for firm’s capital structure. These variables are: the ratio of book value of debt

to the sum of book value of debt and book value of equity (Leverage) and the ratio of common

dividend to the market value of common stocks (Dividend Yield). We also control for firm’s

investment activities. Accordingly, we have control variable R&D, which is the firm’s R&D scaled

by lagged assets, and HHI, which is measured as the Herfindahl index of sales in different business

segments as reported in Compustat. We include #Analysts, defined as the natural logarithm of the

number of analysts covering the company from I/B/E/S, and INSOWN, defined as the proportion

of the firm’s share held by institutions to proxy for shareholder sophistication. The last two control

variables proxy for firm’s trading liquidity: Amihud, which is the yearly average of

1000√|𝑅𝑒𝑡𝑢𝑟𝑛|/(𝐷𝑜𝑙𝑙𝑎𝑟 𝑇𝑟𝑎𝑑𝑖𝑛𝑔 𝑉𝑜𝑙𝑢𝑚𝑒), and MFOWN, defined as the annual average of

quarterly change in ownership of all mutual funds.

As seen in Table 1, hedge fund targets in our sample have similar characteristics to those

reported in the hedge fund activism literature. Compared to the non-target firms in our sample,

target firms are smaller, have lower Tobin’s q, lower leverage, lower sales growth, higher

institutional ownership, lower returns and more negative net institutional trading volume measured

by the average change in quarterly mutual fund holdings.

<Table 1>

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3 Empirical Results

3.1 Likelihood of being targeted

In this section, we use the sentiment in the media reporting of the firm to capture the direction

of the information that is being revealed about the firm and examine whether firms with negative

news sentiment are more likely to be targeted by hedge fund activists.

The main variables of interest are the information environment surrounding the firms prior to

being targeted. Our first proxy for a firm’s information environment ESS[Q-3, Q-1] is the firm’s

average quarterly standardized ESS from Q-3 to Q-1, where Q is the hedge fund targeting event

quarter. Our second proxy AES[Q-3, Q-1] is the firm’s average quarterly standardized AES from

Q-3 to Q-1. Our third proxy ESS[Q-4, Q-1] is the change in firm’s quarterly average standardized

ESS from Q-4 to Q-1. Our fourth proxy ESS[Q-4, Q-1] is the change in firm’s quarterly average

standardized AES from Q-4 to Q-1. The dependent variable takes the value of one if the firm was

targeted during the quarter. We include the following firm level control variables: firm size (total

assets), Tobin’s Q, return on assets, leverage (ratio of book value of long term debt to total assets),

dividend yield, the change in sales over the prior years, the ratio of R&D expenses to sales, the

number of analysts following the firm and average change in quarterly mutual fund holdings. All

variables are defined in Appendix B and their measurement follows closely Brav et al. (2010),

Edmans et al. (2013) and Gantchev and Jotikasthira (2017). We also include year, quarter and

industry fixed effects.

Table 3 reports the effects of media sentiment on the likelihood of being targeted by activist

hedge funds. We begin by examining the impact of the level of news sentiment prior to targeting

on the likelihood of the firm being targeted by activists. The coefficients for our two proxy

variables are negative and significant (Column 1 and 2). Further, there is economic significance as

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well. We find that a one standard deviation decrease in the value of ESS[Q-3, Q-1] is associated

with a 0.18% increase in the probability of being targeted (Column 1). The marginal probabilities

are substantial relative to the 0.66% unconditional probability of a firm being targeted in a quarter.

The results are qualitatively similar when we use the other measure, AES[Q-3, Q-1]. A one

standard deviation decrease in the value of AES[Q-3, Q-1] is associated with a 0.15% increase in

the probability of being targeted (Column 2). We then test for whether the drop in news sentiment

prior to targeting is associated with a higher likelihood of hedge fund activism. We find that the

effects are statistically and economically significant. A one standard deviation decrease in the

value of ESS[Q-4, Q-1] is associated with a 0.18% increase in the probability of being targeted

(Column 3). A one standard deviation decrease in the value of AES[Q-4, Q-1] is associated with

a 0.19% increase in the probability of being targeted (Column 4).

<Table 3>

In summary, the lower news sentiment surrounding the firm in the pre event quarter

significantly increases the likelihood of being targeted by hedge fund activists.

3.2 Information content of negative media sentiment

In the prior section, we find a positive association between the negative investor perception

of the firm and the hedge fund activist targeting event. The next important question for our study

is: What does negative news sentiment capture? Does it capture the firm’s fundamental problems

which attract hedge fund activists’ attention? Prior studies on hedge fund activism show that hedge

funds tend to focus on issues such as “general undervaluation/maximize shareholder value”,

“capital structure”, “business strategy”, “sale of target company” and “governance”. Focusing on

those generalizable issues helps hedge funds to lower the marginal cost of launching a new

activism campaign and get support from fellow shareholders in order to implement the changes.

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We make use of “news tag” created by RavenPack, which recognizes a particular type and property

of an entity-specific news event. We report summary statistics on media coverage and media

sentiment of different news events for all firms and target firms in the pre-event quarters in Table

A of Appendix. For example, all firms in our sample have 948,646 earnings related news articles

while the target firms have 16,329 news articles in the pre-event quarters. The percentage of

earnings-related news with negative sentiment scores is 31.71% for all firms and 41.96% for target

firms in the quarters prior to targeting. The average standardized ESS for all firms’ earnings-related

news is 0.15. This is in comparison to 0.07, the average standardized ESS for target firms’

earnings-related news in the pre-event quarters. The average standardized AES for all firms’

earnings-related news is 0.38. This is in comparison to 0.30, the average standardized AES for

target firms’ earnings-related news in the pre-event quarters. We select the following news events

that are related to firm’s fundamentals: earnings, analyst ratings, credit ratings, mergers and

acquisitions, dividends, regulatory events and industrial accidents. We then study whether the

fundamental news has an impact on the hedge fund targeting event.

<Table A>

3.2.1 Earnings related news

Previous literature shows that hedge fund activists target issues that are related to firms’

growth and profitability. Target firms tend to be low-growth firms but more profitable than

comparable firms.

To study the impact of earnings related news sentiment on the likelihood of being targeted,

we construct three variables of interest: ESS Earnings Dummy is the dummy variable that takes

the value of one if the firm has any earnings related news from Q-3 to Q-1, where Q is the hedge

fund targeting event quarter. ESS Earnings is our first measure for abnormal earnings-related news

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sentiment. It is defined as the difference between the firm’s average earnings related ESS from Q-

3 to Q-1 and the average earnings related ESS in Q-4 for all firms. ESS Earnings / Stdev ESS

Earnings [Q-4] is our second measure for abnormal earnings-related news sentiment. It is defined

as the difference between the firm’s average earnings related ESS from Q-3 to Q-1 and the average

earnings related ESS in Q-4 for all firms, scaled by the standard deviation of earnings related ESS

in Q-4. The dependent variable takes the value of one if the firm was targeted during the quarter.

The other variables are the same as in the previous analysis. We also include year, quarter and

industry fixed effects.

We report the results in column (1) of Table 4. The coefficients for our two measures for

abnormal earnings-related news sentiment are negative and significant (Column 2 and 3). We find

that a one standard deviation decrease in the value of ESS Earnings is associated with a 0.11%

increase in the probability of being targeted (Column 2). We also find that a one standard deviation

decrease in the value of ESS Earnings / Stdev ESS Earnings [Q-4] is associated with a 0.12%

increase in the probability of being targeted (Column 3). In sum, the evidence suggests that firms

with lower earnings-related news sentiment are more likely to be targeted by hedge fund activists.

3.2.2 Analyst and credit ratings related news

Hedge fund activists only hold a small portion of the target firms’ shares and they need the

support from other institutional investors to launch successful activism campaigns. Target firms

tend to have more analyst coverage, which is often used as a proxy for shareholder sophistication.

To study the impact of analyst or credit ratings related news sentiment on the likelihood of

being targeted, we construct three variables of interest: ESS Ratings Dummy is the dummy variable

that takes the value of one if the firm has any analyst or credit ratings related news from Q-3 to Q-

1, where Q is the hedge fund targeting event quarter. ESS Ratings is our first measure for abnormal

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analyst or credit ratings related news sentiment. It is defined as the difference between the firm’s

average analyst or credit ratings related ESS from Q-3 to Q-1 and the average analyst or credit

ratings related ESS in Q-4 for all firms. ESS Ratings / Stdev ESS Ratings [Q-4] is our second

measure for abnormal analyst or credit ratings related news sentiment. It is defined as the

difference between the firm’s average analyst or credit ratings related ESS from Q-3 to Q-1 and

the average analyst or credit ratings related ESS in Q-4 for all firms, scaled by the standard

deviation of analyst or credit ratings related ESS in Q-4. The dependent variable takes the value

of one if the firm was targeted during the quarter. The other variables are the same as in the

previous analysis. We also include year, quarter and industry fixed effects.

We report the results in column (2) of Table 4. The coefficients for our two measures for

abnormal analyst or credit ratings related news sentiment are negative and significant (Column 2

and 3). We find that a one standard deviation decrease in the value of ESS Ratings Abn is associated

with a 0.07% increase in the probability of being targeted (Column 2). We also find that a one

standard deviation decrease in the value of ESS Ratings / Stdev ESS Ratings [Q-4] is associated

with a 0.07% increase in the probability of being targeted (Column 3). In sum, the evidence

suggests that firms with lower analyst or credit ratings related news sentiment are more likely to

be targeted by hedge fund activists.

3.2.3 Mergers and acquisitions related news

One of the important activist events involves activism urging the sale of the target. Hedge

funds attempt either to force a sale of the target company to a third party, or, in a small minority

of the cases, to acquire the company themselves. Greenwood and Schor (2009) show a strong

association between activism and takeovers and they also show that the positive returns generated

by activism events are largely explained by hedge funds’ success at getting target firms taken over.

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To study the impact of mergers and acquisitions related news sentiment on the likelihood of

being targeted, we construct three variables of interest: ESS M&A Dummy is the dummy variable

that takes the value of one if the firm has any M&A related news from Q-3 to Q-1, where Q is the

hedge fund targeting event quarter. ESS M&A is our first measure for abnormal M&A related news

sentiment. It is defined as the difference between the firm’s average M&A related ESS from Q-3

to Q-1 and the average M&A related ESS in Q-4 for all firms. ESS M&A / Stdev ESS M&A [Q-4]

is our second measure for abnormal M&A related news sentiment. It is defined as the difference

between the firm’s average M&A related ESS from Q-3 to Q-1 and the average M&A related ESS

in Q-4 for all firms, scaled by the standard deviation of M&A related ESS in Q-4. The dependent

variable takes the value of one if the firm was targeted during the quarter. The other variables are

the same as in the previous analysis. We also include year, quarter and industry fixed effects.

We report the results in column (3) of Table 4. The coefficients for our two measures for

abnormal M&A related news sentiment are positive and significant (Column 2 and 3). We find

that a one standard deviation increase in the value of ESS M&A is associated with a 0.13% increase

in the probability of being targeted (Column 2). We also find that a one standard deviation increase

in the value of ESS M&A / Stdev ESS M&A [Q-4] is associated with a 0.12% increase in the

probability of being targeted (Column 3). In sum, the evidence suggests that firms with higher

M&A related news sentiment are more likely to be targeted by hedge fund activists.

3.2.4 Dividends related news

To study the impact of dividends related news sentiment on the likelihood of being targeted,

we construct three variables of interest: ESS Divd Dummy is the dummy variable that takes the

value of one if the firm has any dividends related news from Q-3 to Q-1, where Q is the hedge

fund targeting event quarter. ESS Divd is our first measure for abnormal dividends related news

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sentiment. It is defined as the difference between the firm’s average dividends related ESS from

Q-3 to Q-1 and the average dividends related ESS in Q-4 for all firms. ESS Divd Abn / Stdev ESS

Divd [Q-4] is our second measure for abnormal dividends related news sentiment. It is defined as

the difference between the firm’s average dividends related ESS from Q-3 to Q-1 and the average

dividends related ESS in Q-4 for all firms, scaled by the standard deviation of all firm’s average

dividends related news ESS in Q-4. The dependent variable takes the value of one if the firm was

targeted during the quarter. The other variables are the same as in the previous analysis. We also

include year, quarter and industry fixed effects.

We report the results in column (4) of Table 4. The coefficients for our two measures for

abnormal dividends related news sentiment are positive and significant (Column 2 and 3).

3.2.5 Regulatory related news

To study the impact of regulatory related news sentiment on the likelihood of being targeted,

we construct three variables of interest: ESS RegFail Dummy is the dummy variable that takes the

value of one if the firm has any regulatory related news from Q-3 to Q-1, where Q is the hedge

fund targeting event quarter. ESS RegFail is our first measure for abnormal regulatory related news

sentiment. It is defined as the difference between the firm’s average regulatory related ESS from

Q-3 to Q-1 and the average regulatory related ESS in Q-4 for all firms. ESS RegFail / Stdev ESS

RegFail [Q-4] is our second measure for abnormal regulatory related news sentiment. It is defined

as the difference between the firm’s average regulatory related ESS from Q-3 to Q-1 and the

average regulatory related ESS in Q-4 for all firms, scaled by the standard deviation of regulatory

related ESS in Q-4. The dependent variable takes the value of one if the firm was targeted during

the quarter. The other variables are the same as in the previous analysis. We also include year,

quarter and industry fixed effects.

18

We report the results in column (5) of Table 4. The coefficients for our two measures for

abnormal regulatory related news sentiment are positive and significant (Column 2 and 3).

3.2.6 More results on mergers and acquisitions related news

To understand what M&A related information are targeted by hedge fund activists, we

recreate our event dummy variable and abnormal news sentiment variables based on two sub-

groups of M&A related news events: acquiree related news and acquirer related news. We define

an acquiree related news story as if the firm announces its shares are being acquired by another

firm or sells part of its stocks to another firm, or its unit or division is acquired by another firm.

We define an acquirer related news story as if the firm announces it will acquire all the shares of

another firm or buy part of the stocks of another firm, or acquires a unit or division of another firm.

In Table 5, we rerun the logit regressions where the variable of interests are the event dummy

variables and abnormal news sentiment variables based on the sub-groups of M&A related news

events. In columns (1) of Panel A / B, we report the effects of having any target / acquirer related

news events on the likelihood of being targeted by activist hedge fund. In columns (2) -(4) of Panel

B, we report the effects of target / acquirer related abnormal sentiment variables on the likelihood

of being targeted by activist hedge fund. We find that if the firm makes some bad acquisition

decisions (i.e. significant negative coefficients for Neg ESS M&A Acquirer) or it is a potential

acquisition target (i.e. significant positive coefficient for ESS M&A Acquiree Dummy), then it’s

more likely to be targeted by hedge fund activists.

<Table 5>

3.3 Sentiment effect on the objectives of the hedge fund activism campaign

To further investigate Greenwood and Schor (2009) and Brav, Jiang, and Kim (2010) report

the objectives which activist funds provide when they launch their activism campaigns. From

19

Factset’s SharkRepellent database, we collect all requests for change made by the hedge fund

activists. The requests are categorized into the following three primary areas: merger related

requests, governance related requests and capital structure requests. Merger related requests

include the sale of target firms, mergers, liquidation, potential acquisitions, breakup of target firms

and divesting assets. Governance related requests include adding independent directors, board

representation, executive compensation, CEO turnover, removing takeover defenses, social,

environmental or political issues. Capital structure requests include share buybacks, dividends, or

increasing leverage. The dummy variable MERGREQ / GOVREQ / CSREQ identifies the merger

related / governance related / capital structure request hedge fund activists made to the target firm

management. The objectives are available in SharkRepellent for all campaigns announced since

1/1/2007 and select campaigns prior to that date. Therefore, we drop all cases before 2007 in our

sample.

In Table 6, we examine the effects of different news events sentiment on the requests made

to target firm management by hedge fund activists over the 2007-2019 sample period. We find a

negative correlation between the value of ESS Earnings / ESS RegFail and the probability of

having a governance related / capital structure request in the activism campaign (Column 2 and 3).

We also find a positive association between the value of ESS M&A and the probability of having

a merger related request in the activism campaign (Column 1). In sum, the evidence suggests that

the pre-targeting sentiment for different corporate events is driving the specific objectives of

activism campaigns. This is consistent with our information hypothesis.

<Table 6>

20

3.4 Sentiment effect on the success of the hedge fund activism campaign

To further investigate the economic mechanism of our findings, we study the impact of the

negative media sentiment on the hedge fund’s activist campaign. If the negative sentiment captures

the investor’s behavioral bias, we should not observe any positive correlation between activism

performance measures and prior negative media sentiment. However, if it is not the case, then it’s

consistent with the view that negative media sentiment captures fundamental related news and it

catches the attention of hedge fund activists. Therefore, target firms with negative market response

will have higher value improvement which is measured by short-run and long-run stock abnormal

returns.

We first examine the short-term performance of the target firm around the announcement of

the activism, defined as the 13D filing, by estimating cumulative abnormal returns (CARs) using

the trading day window [-20, +20]. The CARs are calculated in excess of the CRSP value weighted

index. We run cross sectional regressions where the dependent variable is the CAR with the

variables of interest being the news sentiment prior to targeting (i.e. ESS[Q-3, Q-1] and AES[Q-3,

Q-1] ). We control for firm size and firm leverage. We also include year, quarter and industry fixed

effects. The results are shown in Table 7. We find that target firms with lower media sentiment

prior to the event have significant higher short-term returns. This is consistent with our information

hypothesis.

<Table 7>

Although returns around announcements capture the market view of the potential value to be

created through the activism campaign, it is possible that the market’s expectations are not borne

out. Thus, we also examine longer term returns to the activism. We use buy and hold abnormal

returns to measure long-term abnormal performance. The benchmark is the value weighted CRSP

21

index and the holding period is 36 months. As shown in Table 8, we add the same firm-level and

campaign-level control variables and we also include year, quarter and industry fixed effects. The

evidence suggests that target firms with lower prior media sentiment have significant higher long

run abnormal returns, which is consistent with our information hypothesis.

<Table 8>

4 Robustness Tests

To test the robustness of our main results, we conduct an additional set of analyses. Given that

the probability of being targeted by hedge fund activist is low, we first re-estimate our logit model

using a matched sample. The matched firms for each target company are assigned from the same

year, same industry and with the smallest total percentage difference in the value of total assets

and the book to market ratio in the year prior. The results are reported in Panel A of Table 9. All

the variables in this analysis are the same as the ones in Table 3. The results are also qualitatively

similar to those in Table 3. We find that the firm’s prior media sentiment is negatively associated

with the probability of being targeted by hedge fund activists.

We then estimate the logit model at the firm-year level. The dependent variable is the same as

in the previous analysis. The news sentiment variables are estimated at the firm-year level. The

first variable ESS[Q-3, Q-1] is still the average quarterly ESS from Q-3 to Q-1 for target firms.

But for non-target firms, it is the annual average ESS in T-1, where T is the hedge fund targeting

event year. The second variable AES[Q-3, Q-1] is still the average quarterly AES from Q-3 to Q-

1 for target firms. But for non-target firms, it is the annual average AES in T-1, where T is the

hedge fund targeting event year. We also include year and industry fixed effects. The results

reported in Panel B of Table 9 are qualitatively similar to those in Table 3. The coefficients of all

news sentiment variables are negative and significant.

22

<Table 9>

5 Conclusion

In this paper, we use media sentiment to study the information environment of a firm around

hedge fund activism events. We examine whether negative market perception to a firm’s news

report is associated with a higher likelihood of hedge fund activism and whether it has an impact

on the success of the hedge fund activism campaign. We find both the level of the news sentiment

and the drop in news sentiment prior to targeting are positively associated with the likelihood of

hedge fund targeting. We also find that the sentiment in the media reporting of the firm captures

the firm’s fundamentals such as mergers and acquisitions, earnings, analyst ratings and credit

ratings. Lastly, we find that such targeting will generate higher short- and long-term stock returns.

We contribute to the literature on the role of media in financial markets and the growing

literature on hedge fund activism by documenting that the importance of public perception for the

hedge fund activism campaign and the profits generated from activism.

23

References

Appel, Ian R, Todd A Gormley, and Donald B Keim, 2018, Standing on the Shoulders of Giants:

The Effect of Passive Investors on Activism, Review of Financial Studies 32, 2720–2774.

Bebchuk, Lucian A, Alon Brav, and Wei Jiang, 2015, The Long-term effects of hedge fund

activism, Columbia Law Review 115, 1085–1155.

Becht, M., Franks, J., Mayer, C., Rossi, S., 2008. Returns to shareholder activism: Evidence from

a clinical study of the Hermes UK Focus Fund. Review of Financial Studies 22, 3093–3129.

Black, B.S., 1990. Shareholder passivity reexamined. Michigan Law Review, 89(3), pp.520-608.

Boyson, N.M., Gantchev, N. and Shivdasani, A., 2017. Activism mergers. Journal of Financial

Economics, 126(1), pp.54-73.

Brav, A., Jiang, W. and Kim, H., 2010. Hedge fund activism: A review. Foundations and Trends

in Finance, 4(3), pp.185-246.

Brav, Alon, Wei Jiang, Frank Partnoy, and Randall Thomas, 2008. Hedge fund activism, corporate

governance, and firm performance. The Journal of Finance 63(4), 1729-1775.

Cheng, C S Agnes, Henry He Huang, Yinghua Li, and Jason Stanfield, 2012, The Effect of Hedge

Fund Activism on Corporate Tax Avoidance, The Accounting Review 87, 1493–1526.

Clifford, Christopher P., 2008. Value creation or destruction? Hedge funds as shareholder activists.

Journal of Corporate Finance 14(4), 323-336.

Core, J.E., Guay, W. and Larcker, D.F., 2008. The power of the pen and executive compensation.

Journal of Financial Economics, 88(1), pp.1-25.

Da, Z., Engelberg, J. and Gao, P., 2011. In Search of Attention. The Journal of Finance, 66(5), pp.

1461-1499.

Edmans, A., Fang, V.W. and Zur, E., 2013. The effect of liquidity on governance. The Review of

Financial Studies, 26(6), pp.1443-1482.

Gantchev, N., 2013. The costs of shareholder activism: Evidence from a sequential decision model.

Journal of Financial Economics, 107(3), pp.610-631.

Gantchev, N. and Jotikasthira, C., 2017. Institutional trading and hedge fund activism.

Management Science, 64(6), pp.2930-2950.

Greenwood, R. and Schor, M., 2009. Investor activism and takeovers. Journal of Financial

Economics, 92(3), pp.362-375.

Kahn, C. and Winton, A., 1998. Ownership structure, speculation, and shareholder intervention.

The Journal of Finance, 53(1), pp.99-129.

24

Kedia, S., Starks, L. and Wang, X., 2019. Institutional Investors and Hedge Fund Activism.

Working paper.

Klein, April, and Emanuel Zur, 2011, The Impact of Hedge Fund Activism on the Target Firm’s

Existing Bondholders. Review of Financial Studies 24, 1735–1771.

Kolasinski, A.C., Reed, A.V. and Ringgenberg, M.C., 2013. A multiple lender approach to

understanding supply and search in the equity lending market. The Journal of Finance,

68(2), pp.559-595.

Lin, C., Massa, M. and Zhang, H., 2014. Mutual funds and information diffusion: The role of

country-level governance. The Review of Financial Studies, 27(11), pp.3343-3387.

Liu, L.X., Sherman, A.E. and Zhang, Y., 2014. The long-run role of the media: Evidence from

initial public offerings. Management Science, 60(8), pp.1945-1964.

Massa, M., Qian, W., Xu, W. and Zhang, H., 2015. Competition of the informed: Does the presence

of short sellers affect insider selling? Journal of Financial Economics, 118(2), pp.268-288.

Norden, L. and Strand, T., 2011. Shareholder activism among portfolio managers: rational

decisions or 15 minutes of fame? Journal of Management & Governance, 15(3), pp.375-

391.

Norli, Ø., Ostergaard, C. and Schindele, I., 2014. Liquidity and shareholder activism. The Review

of Financial Studies, 28(2), pp.486-520.

PWC Report, 2015, Shareholder Activism, Who, What, When and How?

Sunder, Jayanthi, Shyam V Sunder, and Wan Wongsunwai, 2014, Debtholder Responses to

Shareholder Activism: Evidence from Hedge Fund Interventions, Review of Financial

Studies 27, 3318–3342.

You, J., Zhang, B. and Zhang, L., 2017. Who captures the power of the pen? Review of Financial

Studies, 31(1), pp.43-96.

25

Appendix A: Description of Variables

This table describes all variables used in the paper.

Panel A: Sentiment variables

Variable Definition

ESS Standardized event sentiment score = (RavenPack’s Event Sentiment Score – 50)/50,

RavenPack’s Event Sentiment Score is a granular score between 0 and 100 that represents

the news sentiment for a given entity by measuring various proxies sampled from the news.

We exclude neutral news, news related to insider trading and order imbalance.

AES Standardized event sentiment score (i.e. AES) = (RavenPack’s Aggregate Sentiment Score

– 50)/50, RavenPack’s Aggregate Sentiment Score is a granular score between 0 and 100

that represents the ratio of positive events reported on an entity compared to the total count

of events (excluding neutral ones) measured over a rolling 91- day window.

Percentage of Negative News The ratio of negative news (i.e. ESS<50) compared to the total number of news (excluding

neutral ones) measured over a quarter.

ESS[Q-3, Q-1] The quarterly average ESS from Q-3 to Q-1, where Q is the hedge fund targeting event

quarter.

AES[Q-3, Q-1] The quarterly average AES from Q-3 to Q-1, where Q is the hedge fund targeting event

quarter.

ESS[Q-4, Q-1] Drop in the quarterly average ESS from Q-4 to Q-1, where Q is the hedge fund targeting

event quarter.

AES[Q-4, Q-1] Drop in the quarterly average AES from Q-4 to Q-1, where Q is the hedge fund targeting

event quarter.

ESS[Q-4] The quarterly average ESS for in Q-4, where Q is the hedge fund targeting event quarter.

AES[Q-4] The quarterly average AES for in Q-4, where Q is the hedge fund targeting event quarter.

ESS[T-1] The yearly average ESS in T-1, where T is the hedge fund targeting event year.

AES[T-1] The yearly average AES in T-1, where T is the hedge fund targeting event year.

ESS M&A The firm’s average M&A related news ESS from Q-3 to Q-1 – All firm’s average M&A

related news ESS in Q-4.

ESS M&A Acquirer The firm’s average M&A ACQUIRER related news ESS from Q-3 to Q-1 – All firm’s

average M&A ACQUIRER related news ESS in Q-4.

Neg ESS M&A Acquirer Negative M&A ACQUIRER related abnormal news sentiment (i.e.

ess_ma_acquirer_abn_v1).

ESS M&A Acquiree The firm’s average M&A ACQUIREE related news ESS from Q-3 to Q-1 – All firm’s

average M&A ACQUIREE related news ESS in Q-4.

Neg ESS M&A Acquiree Negative M&A ACQUIREE related abnormal news sentiment (i.e.

ess_ma_acquiree_abn_v1).

ESS M&A Other The firm’s average OTHER M&A related news ESS from Q-3 to Q-1 – All firm’s average

OTHER M&A related news ESS in Q-4.

Neg ESS M&A Other Negative OTHER M&A related abnormal news sentiment (i.e. ess_ma_merger_abn_v1).

ESS Earnings The firm’s average earnings-related news ESS from Q-3 to Q-1 – All firm’s average

earnings related news ESS in Q-4.

ESS Ratings The firm’s average analyst or credit ratings related news ESS from Q-3 to Q-1 – All firm’s

average analyst or credit ratings related news ESS in Q-4.

ESS Divd The firm’s average dividends related news ESS from Q-3 to Q-1 – All firm’s average

dividends related news ESS in Q-4.

ESS RegFail The firm’s average industrial-accidents, regulatory related news ESS from Q-3 to Q-1 –

All firm’s average industrial-accidents or regulatory related news ESS in Q-4.

26

Panel B: Control variables (all variables are lagged by one year) Variable Definition

Firm Size The firm’s market capitalization in millions of dollars.

Tobin’s q (Book value of debt + market value of equity) / (book value of debt + book value of

equity).

ROA The firm’s return on assets, defined as EBITDA / ASSETS (LAG).

Leverage The firm’s book leverage ratio defined as debt / (debt + book value of equity).

Dividend Yield The firm’s dividend yield, defined as common dividend / market value of common

stocks.

Sales Growth The firm’s growth rate of sales over the previous year.

R&D The firm’s R&D scaled by lagged assets.

HHI The firm’s Herfindahl index of sales in different business segments as reported by

Compustat.

#Analysts The natural logarithm of the number of analysts covering the company from I/B/E/S.

INSOWN The proportion of the firm’s share held by institutions.

Amihud The Amihud (2002) illiquidity measure, defined as the yearly average (using daily data)

of 1000√|𝑅𝑒𝑡𝑢𝑟𝑛| (𝐷𝑜𝑙𝑙𝑎𝑟 𝑇𝑟𝑎𝑑𝑖𝑛𝑔 𝑉𝑜𝑙𝑢𝑚𝑒)⁄

MFOWN Annual average of quarterly change in ownership of all mutual funds.

Return The firm’s abnormal stock buy and hold returns.

27

Appendix B

Table A. Media coverage and media sentiment of different types of news events

This table reports summary statistics on media coverage and media sentiment of different types of

news events for all firms and target firms in the pre-event quarters over the 2000-2019 sample

period. Panel A reports the summary statistics for all firms in the sample. Panel B reports the

summary statistics for target firms from three quarters to one quarter prior to targeting. Column 1,

2, 3 and 4 report Number of News, Percentage of Negative News, Average Standardized ESS and

Average Standardized AES respectively.

28

Panel A: All Firms

Number of News

Percentage of

Negative News

Average

Standardized ESS

Average

Standardized AES

earnings 948,646 31.71% 0.15 0.38

technical-analysis 484,463 40.23% 0.03 0.31

products-services 438,704 4.60% 0.32 0.49

labor-issues 415,835 23.71% 0.02 0.37

acquisitions-

mergers 291,926 63.82% 0.14 0.26

revenues 279,265 25.20% 0.16 0.42

equity-actions 266,324 41.72% 0.10 0.28

analyst-ratings 261,173 36.98% 0.16 0.36

credit-ratings 146,982 50.41% -0.07 0.22

stock-prices 141,917 44.71% 0.05 0.31

credit 71,838 16.79% 0.10 0.36

assets 66,024 20.26% 0.20 0.39

partnerships 63,938 1.66% 0.21 0.49

legal 59,554 65.82% -0.24 0.26

dividends 45,821 5.58% 0.37 0.53

marketing 42,246 0.53% 0.14 0.69

price-targets 36,275 39.02% 0.14 0.37

regulatory 11,104 89.99% -0.44 0.07

investor-relations 6,198 0.00% 0.02 0.48

bankruptcy 5,368 86.23% -0.71 -0.25

indexes 2,108 10.01% 0.42 0.50

corporate-

responsibility 1,216 0.00% 0.09 0.47

exploration 1,066 7.22% 0.47 0.48

industrial-accidents 1,009 97.32% -0.36 0.13

security 497 93.96% -0.49 0.21

war-conflict 204 58.33% -0.11 0.35

transportation 123 100.00% -0.28 0.28

crime 105 100.00% -0.44 0.26

balance-of-

payments 102 29.41% 0.30 0.27

civil-unrest 72 80.56% -0.15 0.29

taxes 18 0.00% 0.50 0.38

public-opinion 4 0.25 0.25 0.5

pollution 3 0 0.26 0.28

29

Panel B: Target Firms in [Q-3, Q-1]

Number of News

Percentage of

Negative News

Average

Standardized ESS

Average

Standardized AES

earnings 16,329 41.96% 0.07 0.30

labor-issues 8,137 24.69% 0.02 0.32

products-services 7,046 4.44% 0.32 0.43

technical-analysis 6,622 36.50% 0.05 0.30

revenues 5,808 32.99% 0.11 0.31

equity-actions 4,483 39.21% 0.11 0.24

acquisitions-

mergers 4,108 66.04% 0.12 0.20

analyst-ratings 4,045 45.02% 0.09 0.26

stock-prices 2,514 55.29% 0.01 0.16

credit-ratings 2,380 65.59% -0.17 0.09

credit 1,327 18.46% 0.12 0.27

assets 1,290 20.78% 0.20 0.32

partnerships 997 1.10% 0.22 0.43

legal 962 60.50% -0.19 0.18

dividends 402 12.44% 0.30 0.38

price-targets 265 78.49% -0.38 -0.02

regulatory 192 88.02% -0.41 0.01

marketing 167 0.60% 0.15 0.61

bankruptcy 164 83.54% -0.68 0.01

investor-relations 120 0.00% 0.02 0.51

indexes 32 18.75% 0.33 0.42

industrial-

accidents 8 100.00% -0.31 0.45

corporate-

responsibility 8 0.00% 0.10 0.37

exploration 8 0.00% 0.56 0.51

war-conflict 4 25.00% 0.45 0.01

balance-of-

payments 3 0.00% 0.62 0.38

civil-unrest 1 100.00% -0.12 -0.64

security 1 100.00% -0.70 0.68

30

Figure 1: Graphical presentation of the media sentiment measures

This figure presents three media sentiment measures in each quarter prior to and post targeting for

both target firms and non-target firms. All variables are defined in Appendix. Panel A presents the

average ESS in each quarter prior to and post targeting for both target firms and non-target firms.

Panel B presents the average AES in each quarter prior to and post targeting for both target firms

and non-target firms. Panel C presents the Percentage of Negative News in each quarter prior to

and post targeting for both target firms and non-target firms. For each non-target firm, the pseudo-

targeting event quarter is created by generating a random number based on the frequency

distribution of all targeting event quarters for our sample over the period of 2000 - 2019.

Panel A: Average ESS around mergers

Panel B: Average AES around mergers

0.08

0.10

0.12

0.14

0.16

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

Average ESS

Target Non-Target

0.28

0.30

0.32

0.34

0.36

0.38

0.40

0.42

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

Average AES

Target Non-Target

31

Panel C: Percentage of negative news around mergers

0.28

0.30

0.32

0.34

0.36

0.38

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

Negative News

Target Non-Target

32

Table 1: Characteristics of target firms and non-target firms

This table reports summary statistics of firm characteristics for the target and non-target firms in

our final sample over the period of 2000-2019. All variables are defined in Appendix. *, **, and

*** refer to statistical significance (of the difference in means or medians) at 10%, 5%, and 1%

levels, respectively. All variables are winsorized at 1% and 99% levels.

Target Firms Non-Target Firms Difference

Mean Median Mean Median Mean Median

Firm Size 5.4766 5.3980 6.2697 6.2929 -0.7931*** -0.8949***

Tobin’s q 1.7023 1.2797 2.0175 1.3846 -0.3152*** -0.1049**

ROA 0.0167 0.0625 0.0438 0.0814 -0.0271*** -0.0189***

Leverage 0.2081 0.1228 0.2234 0.1704 -0.0153* -0.0476***

Dividend Yield 0.0094 0.0000 0.0158 0.0000 -0.0064*** 0.0000

Sales Growth 0.1125 0.0395 0.1377 0.0726 -0.0252* -0.0331***

R&D 0.0494 0.0000 0.0268 0.0000 0.0226*** 0.0000

HHI 0.0681 0.0572 0.0645 0.0524 0.0036* 0.0048**

#Analysts 1.4246 1.3863 1.6508 1.7918 -0.2262*** -0.4055***

INSOWN 0.5608 0.5661 0.5153 0.5487 0.0455*** 0.0174

Amihud 0.3374 0.1370 0.3382 0.0807 -0.0008 0.0563***

MFOWN 0.0058 0.0031 0.0099 0.0057 -0.0041*** -0.0026***

Return -0.1150 -0.1458 0.0335 -0.0169 -0.1485*** -0.1289***

33

Table 2: Measures of media sentiment

This table reports summary statistics on the three measures of media sentiment for target firms and

non-target firms over the 2000-2019 sample period. Standardized event sentiment score (i.e. ESS)

= (RavenPack’s Event Sentiment Score – 50)/50. RavenPack’s Event Sentiment Score is a granular

score between 0 and 100 that represents the news sentiment for a given entity by measuring various

proxies sampled from the news. All neutral news (i.e. ESS = 50), news related to insider trading

or order imbalance are excluded. Standardized aggregate sentiment score (i.e. AES) =

(RavenPack’s Aggregate Sentiment Score – 50)/50. RavenPack’s Aggregate Sentiment Score is a

granular score between 0 and 100 that represents the ratio of positive events reported on an entity

compared to the total count of events (excluding neutral ones) measured over a rolling 91- day

window. The Percentage of Negative News is the ratio of negative news (i.e. ESS<50) compared

to the total number of news (excluding neutral ones) measured over a quarter. Panel A (Panel B)

reports the summary statistics on the basis of all (novel) news stories. In column 1, all three

measures are calculated for target firms from three quarters to one quarter prior to targeting. In

column 2, all three measures are calculated for non-target firms over all quarters in our sample. In

column 3, all three measures are calculated for target firms over all quarters which are at least four

quarters prior to targeting. Panel C reports summary statistics on the media coverage for target

firms prior to targeting over the 2000-2019 sample period. Column (1) (column 2) reports the

summary statistics on the basis of all news (novel) stories. *, **, and *** refer to statistical

significance (of the difference in means or medians) at 10%, 5%, and 1% levels, respectively.

Panel A: All News

(1) (2) (3) (1)-(2) (1)-(3) (2)-(3)

Target [Q-3, Q-1] Non-Target Target Q-4 and prior

Average ESS 0.1013 0.1394 0.1394 -0.0381 *** -0.0380 *** 0.0001

Average AES 0.3095 0.3793 0.3676 -0.0698 *** -0.0581 *** 0.0117 ***

Average % Negative News 0.3519 0.3107 0.3189 0.0412 *** 0.0330 *** -0.0082 ***

Median ESS 0.1060 0.1514 0.1550 -0.0454 *** -0.0490 *** -0.0036***

Median AES 0.3600 0.4600 0.4400 -0.1000 *** -0.0800 *** 0.0200 ***

Median % Negative News 0.3333 0.2500 0.2778 0.0833 *** 0.0555 *** -0.0278 ***

Panel B: Novel News

(1) (2) (3) (1)-(2) (1)-(3) (2)-(3)

Target [Q-3, Q-1] Non-Target Target Q-4 and prior

Average ESS 0.1022 0.1391 0.1421 -0.0369 *** -0.0398 *** -0.0030 ***

Average AES 0.3102 0.3796 0.3685 -0.0694 *** -0.0583 *** 0.0111 ***

Average % Negative News 0.3498 0.3096 0.3149 0.0402 *** 0.0348 *** -0.0054 ***

Median ESS 0.1050 0.1486 0.1569 -0.0436 *** -0.0519 *** -0.0083 ***

Median AES 0.3650 0.4633 0.4450 -0.0983 *** -0.0800 *** 0.0183 ***

Median % Negative News 0.3333 0.2667 0.2857 0.0666 *** 0.0476 *** -0.0190 ***

34

Panel C: Measures of media coverage

All News Novel News

(1) (2)

Average Number of News in [Q-3, Q-1] 11.45 7.89

Average Number of Positive News in [Q-3, Q-1] 7.30 5.10

Average Number of Negative News in [Q-3, Q-1] 4.15 2.79

Change in Number of News from Q-4 to Q-1 0.64 0.40

Change in Number of Positive News from Q-4 to Q-1 0.03 -0.02

Change in Number of Negative News from Q-4 to Q-1 0.61 0.42

35

Table 3: Effects of media sentiment on the likelihood of being targeted

This table reports the effects of media sentiment on the likelihood of being targeted by activist

hedge funds. The logit regression is running at the firm-year-quarter level. The dependent variable

is a dummy variable equal to one if there is hedge fund activism targeting the company during the

quarter. All independent variables are defined in Appendix. All firm characteristic variables are as

of the end of the prior year and are winsorized at 1% and 99% levels. T-statistics based on standard

errors clustered at the firm level are in parentheses. All specifications include industry, year and

quarter fixed effects. *, **, and *** refer to statistical significance at 10%, 5%, and 1% levels,

respectively.

(1) (2) (3) (4)

ESS[Q-3, Q-1] -0.0102***

(-4.5280)

AES[Q-3, Q-1] -0.0033***

(-3.7807)

ESS[Q-4, Q-1] -0.0064***

(-4.0699)

AES[Q-4, Q-1] -0.0031***

(-4.5360)

ESS[Q-4] 0.0014 -0.0063***

(0.8796) (-3.1374)

AES[Q-4] 0.0007 -0.0029***

(1.0251) (-3.4415)

Firm Size -0.0019*** -0.0019*** -0.0019*** -0.0019***

(-7.5860) (-7.5249) (-7.5945) (-7.4361)

Tobin’s q -0.0013*** -0.0013*** -0.0013*** -0.0013***

(-7.3412) (-7.5146) (-7.3847) (-7.4710)

ROA 0.0016 0.0010 0.0007 0.0008

(0.6921) (0.4447) (0.2960) (0.3256)

Leverage 0.0030* 0.0030* 0.0030* 0.0030*

(1.8375) (1.8889) (1.8835) (1.8606)

Dividend Yield -0.0308* -0.0287 -0.0281 -0.0274

(-1.7401) (-1.6310) (-1.5707) (-1.5363)

Sales Growth 0.0001 -0.0001 -0.0000 -0.0001

(0.1461) (-0.0982) (-0.0400) (-0.1014)

R&D 0.0161*** 0.0166*** 0.0158*** 0.0162***

(3.0852) (3.1897) (3.0273) (3.1032)

HHI -0.0004 -0.0004 -0.0002 -0.0000

(-0.0474) (-0.0519) (-0.0284) (-0.0053)

#Analysts -0.0006 -0.0005 -0.0005 -0.0006

(-1.1939) (-1.1154) (-1.1323) (-1.1698)

INSOWN 0.0090*** 0.0090*** 0.0093*** 0.0092***

(6.6670) (6.6633) (6.8587) (6.8383)

Amihud -0.0016 -0.0015 -0.0012 -0.0012

(-1.3639) (-1.3177) (-0.9926) (-1.0030)

MFOWN -0.0375*** -0.0391*** -0.0352** -0.0363**

(-2.6046) (-2.7136) (-2.4505) (-2.5295)

Return -0.0067*** -0.0071*** -0.0068*** -0.0069***

(-10.4984) (-11.2063) (-10.6180) (-10.8649)

Observations 122,383 122,383 121,575 121,575

R-squared 0.05 0.05 0.05 0.05

Industry FE YES YES YES YES

Year FE YES YES YES YES

Quarter FE YES YES YES YES

36

Table 4: Effects of different news events sentiment on the likelihood of being targeted

This table reports the effects of different news events sentiment on the likelihood of being targeted

by activist hedge funds. Panel A reports regression results for earnings related news sentiment.

Panel B reports regression results for analyst or credit ratings related news sentiment. Panel C

reports regression results for mergers and acquisitions related news sentiment. Panel D reports

regression results for dividends related news sentiment. Panel E reports regression results for

regulatory or industrial accident related news sentiment. Panel F reports regression results for all

news events sentiment. The logit regression is running at the firm-year-quarter level. The

dependent variable is a dummy variable equal to one if there is hedge fund activism targeting the

company during the quarter. All independent variables are defined in Appendix. All firm

characteristic variables are as of the end of the prior year and are winsorized at 1% and 99% levels.

T-statistics based on standard errors clustered at the firm level are in parentheses. All specifications

include industry, year and quarter fixed effects. *, **, and *** refer to statistical significance at

10%, 5%, and 1% levels, respectively.

(1) (2) (3) (4) (5)

ESS Earnings -0.0054**

(-2.2116)

ESS Ratings -0.0024**

(-1.9934)

ESS M&A 0.0067***

(2.7769)

ESS Divd -0.0018

(-0.8329)

ESS RegFail -0.0002

(-0.0831)

Observations 122,121 93,845 54,518 49,660 104,032

R-squared 0.05 0.06 0.09 0.10 0.05

Industry FE YES YES YES YES YES

Year FE YES YES YES YES YES

37

Table 5: Effects of different M&A related news events sentiment on the likelihood of being

targeted

This table reports the effects of different mergers and acquisitions related news events sentiment

on the likelihood of being targeted by activist hedge funds. Panel A report regression results for

acquiree related news sentiment. Panel B report regression results for acquirer related news

sentiment. The logit regression is running at the firm-year-quarter level. The dependent variable is

a dummy variable equal to one if there is hedge fund activism targeting the company during the

quarter. All independent variables are defined in Appendix. All firm characteristic variables are as

of the end of the prior year and are winsorized at 1% and 99% levels. T-statistics based on standard

errors clustered at the firm level are in parentheses. All specifications include industry, year and

quarter fixed effects. *, **, and *** refer to statistical significance at 10%, 5%, and 1% levels,

respectively.

Panel A: Acquiree related news

(1) (2) (3) (4)

ESS M&A Acquiree Dummy 0.0041***

(4.8174)

ESS M&A Acquiree -0.0044

(-0.7746)

Pos ESS M&A Acquiree 0.0009

(0.0232)

Neg ESS M&A Acquiree 0.0038

(0.2656)

Observations 122,383 15,408 11,550 3,858

R-squared 0.05 0.21 0.23 0.48

Industry FE YES YES YES YES

Year FE YES YES YES YES

Panel B: Acquirer related news

(1) (2) (3) (4)

ESS M&A Acquirer Dummy 0.0002

(0.3927)

ESS M&A Acquirer -0.0516

(-1.4923)

Pos ESS M&A Acquirer 0.0059

(0.1381)

Neg ESS M&A Acquirer -0.1611**

(-1.9745)

Observations 122,383 46,600 35,925 10,675

R-squared 0.05 0.10 0.11 0.25

Industry FE YES YES YES YES

Year FE YES YES YES YES

38

Table 6: Effects of different news events sentiment on the objectives of activism campaigns

This table reports the effects of different news events sentiment on the requests made to target firm

management by hedge fund activists over the 2007-2019 sample period. The requests are

categorized into three primary areas. Merger related requests include the sale of target firms,

mergers, liquidation, potential acquisitions, breakup of target firms and divesting assets.

MERGREQ is a dummy variable equal to one if hedge fund activists made a merger related request

to the target firm management. Governance related requests include adding independent directors,

board representation, executive compensation, CEO turnover, removing takeover defenses, social,

environmental or political issues. GOVREQ is a dummy variable equal to one if hedge fund

activists made a governance related request to the target firm management. Capital structure

requests include share buybacks, dividends, or increasing leverage. CSREQ is a dummy variable

equal to one if hedge fund activists made a capital structure request to the target firm management.

T-statistics based on standard errors clustered at the firm level are in parentheses. *, **, and ***

refer to statistical significance at 10%, 5%, and 1% levels, respectively.

(1) (2) (3)

MERGREQ GOVREQ CSREQ

ESS M&A 0.8912**

(2.28)

ESS Earnings -0.6639*

(-1.78)

ESS RegFail -1.0461***

(-2.67)

Observations 239 624 586

R-squared 0.05 0.00 0.02

39

Table 7: Effects of media sentiment on short term abnormal returns (CARs)

This table reports the effects of media sentiment on the CAR around the 13D filing date. The

dependent variable is the target firm’s cumulative abnormal returns over event day –20 to event

day +20, where event day 0 is the 13D filing date. All independent variables are defined in

Appendix. All firm characteristic variables are as of the end of the prior year and are winsorized

at 1% and 99% levels. T-statistics based on standard errors clustered at the firm level are in

parentheses. All specifications include industry, year and quarter fixed effects. *, **, and *** refer

to statistical significance at 10%, 5%, and 1% levels, respectively.

(1) (2)

ESS[Q-3, Q-1] -0.1657*

(-1.8579)

AES[Q-3, Q-1] -0.0772*

(-1.7627)

Num[Q-3, Q-1] 0.0006 0.0005

(0.8481) (0.7458)

Firm Size -0.0016 -0.0003

(-0.1809) (-0.0392)

Tobin’s q 0.0124 0.0118

(0.6618) (0.6317)

Leverage -0.1796 -0.1770

(-0.9991) (-0.9854)

Return -0.1512*** -0.1539***

(-4.4277) (-4.3405)

Observations 1,311 1,311

R-squared 0.05 0.05

Industry FE 863 863

Year FE YES YES

Quarter FE YES YES

40

Table 8: Effects of media sentiment on long term abnormal returns (BHARs)

This table reports the effects of media sentiment on the 36 month BHARs that are market adjusted.

The dependent variable is the target firm’s 36-month market adjusted BHARs after the 13D filing

date. All independent variables are defined in Appendix. All firm characteristic variables are as of

the end of the prior year and are winsorized at 1% and 99% levels. T-statistics based on standard

errors clustered at the firm level are in parentheses. All specifications include industry, year and

quarter fixed effects. *, **, and *** refer to statistical significance at 10%, 5%, and 1% levels,

respectively.

(1) (2)

ESS[Q-3, Q-1] -1.3164***

(-2.7126)

AES[Q-3, Q-1] -0.5245**

(-2.5216)

Num[Q-3, Q-1] 0.0091 0.0087

(0.9990) (0.9445)

Firm Size -0.0355 -0.0332

(-0.7297) (-0.6817)

Tobin’s q -0.0259 -0.0235

(-0.3595) (-0.3200)

Leverage 0.1516 0.2647

(0.3177) (0.5606)

Return 0.6277** 0.5353*

(2.2116) (1.8740)

Observations 767 767

R-squared 0.06 0.06

Industry FE 577 577

Year FE YES YES

Quarter FE YES YES

41

Table 9: Effects of media sentiment on the likelihood of being targeted

This table reports the effects of media sentiment on the likelihood of being targeted by activist

hedge funds. The regressions in Panel A are based on a matched sample. The regressions in Panel

B follows the logit model at the firm-year level. The control firms are industry, size and book to

market ratio matched firms. The dependent variable is a dummy variable equal to one if there is

hedge fund activism targeting the company during the quarter. All independent variables are

defined in Appendix. All firm characteristic variables are as of the end of the prior year and are

winsorized at 1% and 99% levels. T-statistics based on standard errors clustered at the firm level

are in parentheses. All specifications include industry, year and quarter fixed effects. *, **, and

*** refer to statistical significance at 10%, 5%, and 1% levels, respectively.

Panel A: Matched sample

(1) (2) (3) (4)

ESS[Q-3, Q-1] -1.7662***

(-4.4874)

AES[Q-3, Q-1] -0.6289***

(-3.9306)

ESS[Q-4, Q-1] -0.9478***

(-3.5605)

AES[Q-4, Q-1] -0.3628***

(-3.0590)

Control variables Yes Yes Yes Yes

Observations 3,102 3,102 3,038 3,038

Pseudo R-squared 0.52 0.52 0.52 0.52

Panel B: Firm-year level regressions

(1) (2) (3) (4)

ESS[T-1] -0.0288***

(-3.1845)

AES[T-1] -0.0057*

(-1.7295)

ESS[Q-3, Q-1] -0.0621***

(-5.9213)

AES[Q-3, Q-1] -0.0159***

(-4.4218)

Control variables Yes Yes Yes Yes

Observations 31,708 31,708 31,708 31,708

R-squared 0.06 0.06 0.06 0.06

Industry FE YES YES YES YES

Year FE YES YES YES YES

Quarter FE YES YES YES YES