the role of the media in hedge fund activism · 2020. 1. 12. · the role of the media in hedge...
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The Role of the Media in Hedge Fund Activism†
Xinjie Wang
Southern University of Science and Technology
Email: [email protected]
Ge Wu
University of Richmond
Email: [email protected]
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
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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