The Devil is in the Details: Firm-Specific or Market Information in Shareholder Activism

Duo Pei

Accounting & Information Systems

Rutgers Business School

1 Washington Park

Newark, NJ, 07102

Draft October 30, 2020

Abstract

This study measures how shareholder activism may change market participants’ processing and

incorporation of different types of information. Specifically, I examine the earnings response

coefficient (ERC), price delay, and probability of informed trading (PIN), which capture the

usage of firm-specific public information, public market-wide information, and firm-specific

private information, respectively. I find an increase in ERC, price delay, and PIN during

shareholder activism. I also find an influx of attention-based trading in the 2 quarters

immediately after 13D filing, which is subsequently replaced by information-based trading. The

findings are consistent with a slower reflection of publicly available market-wide information

and investors engaging in more firm-specific information processing. Investors appear to

substitute more general information with focused information about activist targets in their

trading decisions.

Keywords: Shareholder activism, price response, investor attention, firm-specific information,

private information, public information

The Devil is in the Details: Firm-Specific or Market Information in Shareholder Activism

1. Introduction

Activist shareholders who try to influence management strategies have been a noticeable

presence in financial markets. Although the empirical evidence on the long-term benefits of

activism remain mixed (Karpoff et al., 1996; Cremers et al., 2015; Mulherin and Poulsen, 1998;

Bebchuk et al., 2015; Brav et al., 2008; Klein and Zur, 2009), activist interventions constitute

major events for firms and their shareholders. Activists commonly prepare detailed reports about

their due diligence; allege accounting fraud, management incompetence, and poor governance;

and lay out plans to revitalize the firm. One of the benefits of activists exercising the right of

voice is they can attempt to influence the rest of the market to side with their objectives. In this

study, I examine if there is value and if so, what type of value shareholder activist events have on

market perceptions about the firm. Specifically, I focus on how perceptions may change through

changes in the information set.

An area the literature has overlooked is whether and to what extent the market

incorporates different types of information into prices. Verrecchia (1980) shows that investors

will incur the costs necessary to process information which they perceive as most precise.

Shareholder activism is a unique setting to study how an event, in this case made public through

an SEC 13D filing, may change investor perceptions, and subsequently, their processing of

information. I posit that activist intervention can influence such perceptions through its impact

on the interpretation of public information and private information.

I examine three measures that reflect information processing by capital market

participants to determine how shareholder activism changes the information set of investors. I

classify these measures by if they are publicly available or private information, and along

dimensions of generalizability – market-wide or firm-specific. The earnings response coefficient

(ERC) is the price response to largely firm-specific information issued by the manager (Collins

and Kothari, 1989). Price delay is the speed of incorporation of market-wide information into

firm specific returns (Hou and Moskowitz, 2005). In addition to public sources of information, I

also investigate how the probability of informed trading (PIN) changes after shareholder activist

entrance, which measures the expected amount of firm-specific private information incorporated

in prices (Easley et al., 1996; Easley et al., 1997; Easley et al., 2002).

I focus on one type of activist event, Schedule 13D filings by investors with potential

activist intentions who acquire at least 5% of a security within ten days. Using a sample of 744

13D filings from 2000 to 2019, I find an increase in ERC, price delay, and PIN during activist

intervention relative to control firms using a difference-in-difference model. The results suggest

investors are more willing to use firm-specific information, both public and private, in their

pricing decisions. The market is slower to incorporate market-wide public information. This

higher price delay is most prevalent for targets which have activist directors. I also find an

attention effect to shareholder activism, which drives up volume of shares traded in the 2

quarters after 13D filing. However, attention-based trading dissipates over the course of activist

presence at the target, while information-based trading exists throughout the period of activist

activity. I interpret the results as evidence investors interested in activist targets are wary about

low-cost and possibly imprecise public information. Instead, they are willing to spend time and

effort either processing private information or analyzing firm-specific information which is

disseminated directly through management.

This paper makes several contributions to the literature. It is unique in its focus on

investors’ response to different types of information following activism intervention. Prior

literature examines the role of institutional shareholders in activism events (Norli et al, 2015;

Edmans et al., 2013; Coffee and Palia, 2015; Brav et al., 2018; McCahery et al., 2016; Becht et

al., 2008; Wong, 2019; Kedia et al., 2016a; Gantchev and Jotikasthira, 2017), changes in

operations (Gantchev et al., 2017; Brav et al., 2015; Brav et al., 2018b; Grewal et al., 2016) or

managerial disclosure (Chen and Jung, 2015; Ng et al., 2017; Khurana et al., 2017; Cheng et al.,

2015; Bourveau and Schoenfeld, 2017; McDonough and Schoenfeld, 2020). By studying the

market response to different sources of disclosure, I provide further evidence regarding the

effects of shareholder activism on dissemination of information and disclosure behavior.

Second, it is an empirical test of Verrecchia (1980), who shows investors process

information based on its perceived precision. To the best of my knowledge, other than Peng and

Xiong (2005), this is the only study which focuses on tradeoffs investors make between firm-

specific and general information. However, different from Peng and Xiong (2005), I study a

specific event, i.e., Schedule 13D filing and subsequent shareholder intervention, which results

in this change in investor behavior.

Finally, it provides an information-based explanation to the value of shareholder

activism, irrespective of shareholder activists’ stock-picking ability (Cremers et al. 2015;

Bebchuk et al. 2015). Shareholder activism encourages firm-specific information processing,

which increases price efficiency (Durnev et al. 2003). I find increases in both incorporation of

managerial firm-specific information, proxied by the ERC, and private information, proxied by

PIN, in prices.

The remainder of the paper is organized as follows; Section 2 outlines prior literature

relating shareholder activism to information response and hypotheses development. Section 3

explains the research design and the data. Section 4 describes the empirical results and

robustness checks and Section 5 provides a concluding discussion.

2. Literature Review and Hypotheses Development

2.1 Shareholder Activism, Firm Value, and Information

Active investing, a strategy popularized by “corporate raiders,” such as Carl Icahn and

Nelson Peltz in the 1980s, involves letters to shareholders, shareholder proposals, proxy fights,

and occasionally, hostile takeovers. Supporters of activism viewed them as a corrective market

mechanism to potential agency problems (Jensen and Meckling, 1976). Critics, on the other

hand, expressed the belief that activists were filling their own coffers with gains won from the

firms’ acquiescence to their demands, often at the expense of other stakeholders of the firm.

The extant literature on shareholder activism and information has provided mixed evidence on

the influence of activism on firm values. Karpoff et al. (1996) do not detect any significant stock

price reaction around the initial press announcement of the shareholder proposal or annual

meeting (voting) date. On the other hand, Mulherin and Poulsen (1998) find that proxy contest

targets that are subsequently acquired generate positive abnormal returns during and after the

proxy period. With hedge funds as instigators, researchers mostly find positive short-term returns

and long term performance (Bebchuk et al., 2015; Brav et al., 2008; Klein and Zur, 2009) for

activist targets. Brav et al. (2008) documents that the market reacts positively to the

announcement of activism with 7-8% abnormal returns. The positive returns to activism also

persists over the long horizon with improvements in return on assets and return on sales. Klein

and Zur (2009) find positive results for firms that are campaigned against by both hedge funds

and other activists. Firms targeted by hedge funds or other activists experience CARs of 10.2%

or 5.1%, respectively, around 13D filing and 11.4% or 17.8%, respectively, for the subsequent

year. On the other hand, Cremers et al. (2015) show that long term gains to activism may not be

as acute when compared to like peers. Moreover, shareholder activists may also pursue

objectives that benefit themselves, at the expense of debtholders (Sunder et al., 2014; Liu and

Wu, 2017).

There is also mixed evidence on the influence of activism on quality and quantity of

information. Ng et al. (2017) opine that activist proposals may improve governance at the firm in

the form of lower discretionary accruals. Chen and Jung (2015) observe that in retaliation to

activist intervention, firms limit disclosure which subsequently results in reduced transparency.

Khurana et al. (2017) expand on Chen and Jung (2015) and conclude firms opportunistically

withhold bad news while not changing disclosure patterns for good news. In contrast, Cheng et

al. (2015) challenge the theory of managerial opportunism and show that conditional

conservatism, i.e. more timely reporting of bad news than good news, increases after activism.

Furthermore, in contradiction to the aforementioned studies of less information output, Bourveau

and Schoenfeld (2017) find that firms not targeted but in the same industry as activist targets are

likely to increase information when they become aware of the increased risk of activist

engagement, perhaps to ward off any attempts.

Prior research has focused on the type of information. This study focuses on what types

of information is reflected in trading patterns. An expanse of information about the firm is

available at any time, but I focus on the information set used for firm valuation purposes. The

change in information disclosed following shareholder activism could lead to a change in how

the market processes information for price formation. Shareholder activism is a worthwhile

setting to study information processing as there is a public announcement marking its

commencement in the form of the SEC 13D. Subsequently, investors may react in various ways.

They may incorporate more publicly available information into trading because of differing

interpretations of public information (Kandel and Pearson, 1995; Harris and Raviv, 1993).

Investors may also trade more on information they have privately gathered. This could happen if

the private information is of heterogeneous degrees of precision (Kim and Verrecchia, 1991) or

if public information makes the private information more informative by completing an

information mosaic (Cheynel and Levine, 2020). Alternatively, the shareholder intervention may

have encouraged private information incorporation (Grossman and Stiglitz, 1980). Theory on

attention effects (Hirshleifer and Teoh, 2003; Peng and Xiong, 2005) states there is a limit to the

amount of information investors can process at one time. Therefore, the types of information

used and tradeoffs between information used by the investor in price formation is an empirical

question.

I focus on price response to different types of information, including (i) the earnings

response coefficient, i.e., the price response to firm-specific earnings information prepared by

management, (ii) price delay, i.e., the speed of reflection of publicly available information about

general market conditions in the stock price using (Hou and Moskowitz, 2005), and (iii) the

probability of informed trading (PIN), i.e., the incorporation of private information.

2.2 Earnings Response Coefficient (ERC)

I use the earnings response coefficient (Collins and Kothari, 1989), or ERC, to gauge

market’s reaction to managerial disclosure during shareholder activist presence. ERC represents

the sensitivity of short-term unexpected returns to unexpected earnings surprises. Shareholder

activism could influence ERC through its impact on the market’s beliefs about earnings

disclosure. For example, if shareholder activism increases audit quality, then there may be a

higher level of confidence that the earnings numbers inform about future expected earnings,

resulting in a greater ERC (Teoh and Wong, 1993). Investors may also want to transact more

because of increased attention to earnings announcements (Hirshleifer and Teoh, 2003) and a

more varied investor base (Kandel and Pearson, 1995; Harris and Raviv, 1993). On the other

hand, shareholder activists may intervene because of governance issues at the target firm.

Boubaker et al. (2014) show that when a large shareholder has substantial power over a firm, less

firm-specific information is revealed. In such cases, I expect investors to rely less on earnings

numbers because of lower quality. Similar to Wilson (2008), who explores the effect of

misstatements on the ERC, activism may signify deficiencies in the financial reporting process

(Ng et al., 2017; Cheng et al., 2015). Wilson (2008) finds that ERC is temporarily depressed,

presumably by investors’ loss of confidence in the accounting numbers after misstatements. If

shareholder activism alerts the market to overlooked governance issues (Gow et al., 2014; Fos

and Tsoutsoura, 2014; Ng et al., 2017; Cheng et al., 2015), ERC may also decrease because of

increased suspicion of the target’s accounting statements.

Further, the ERC is also influenced by market’s perception of the margin of error in beliefs

revision. If the error in the earnings process is large so that future earnings can not be predicted

with accuracy, investors could be less willing to react to earnings and trade. Analysts express

increased uncertainty (Chen and Shohfi, 2018; Flugum and Howe, 2020) coinciding with

shareholder activism. If there is more uncertainty about a unit of change in current earnings for

future expected earnings, shareholder activism may also increase the variance of unexpected

earnings. This could result in a lower ERC because of the increased risk of projected earnings

(Collins and Kothari, 1989; Dhaliwal and Reynolds, 1994). Taken together, the change in ERC

for shareholder activist targets is an empirical question. I present Hypothesis 1 in the null form.

Hypothesis 1: There is no difference in the earnings response coefficient for shareholder activist

targets for intervention and non-intervention time periods.

2.3 Price Delay

While the ERC focuses on how the market reacts to information which originates from

the firm, Hou and Moskowitz’s (2005) price delay measures the speed at which market-wide

information is absorbed into prices. Both the ERC and price delay measure how the market

responds to publicly available information. Price delay has been shown to correlate with opacity

of the information environment (Callen et al., 2013), information processing costs (Dong et al.,

2016), and investor attention (Hou and Moskowitz, 2005). Since activist presence has been

documented to encourage more precise and timely information (Ng et al., 2017; Khurana et al.,

2017; Cheng et al., 2015), shareholder activism may make it easier for the market to compare

target information to publicly available sources, decreasing the price delay (Callen et al., 2013;

Dong et al., 2016). Moreover, shareholder activists could release more information themselves

through proactive information sharing (McDonough and Schoenfeld, 2020), which decreases

price delay. Shareholder activism could also attract investor attention and in turn, trading

(Kandel and Pearson, 1995; Harris and Raviv, 1993).

On the other hand, shareholder activism direct investor attention from easily attainable

firm-specific information to potentially more valuable firm-specific information (Peng and

Xiong, 2005). This would result in increased price delay from slower incorporation of industry

and macroeconomic information into prices. Market may also decide information asymmetry is

sufficiently high that fewer players overall would trade: Flugum and Howe (2020) find analysts

issue less forecasts overall and for those still issuing, there are more hold recommendations.

With less aggregate trading, price delay increases (Hou and Moskowitz, 2005). In summary,

because of the interplay of attention and information processing effects of shareholder activism,

the direction of price delay after shareholder activism is uncertain.

Hypothesis 2: There is no difference in price delay for shareholder activist targets for

intervention and non-intervention time periods.

2.4 Probability of Informed Trading (PIN)

The last measure I use to explore information response of prices is the probability of

informed trading, which captures the presence of private information in trades (Easley et al.,

1996; Easley et al., 1997). Order arrival rates and its direction is used to estimate the probability

of informed trading within a period. Some studies document that activists have already

accumulated sizeable holdings in the target by the 13D filing date (Collin-Dufresne and Fos,

2015; Wong, 2019; Norli et al., 2015), in such cases activists’ information may have already

been incorporated into prices, resulting in lower PIN after the 13D filing date until activist exit.

Shareholder activism may also bring additional noise to the target firms, resulting in

proportionately less informed trading (Easley et al., 1997). Contrarily, activist trading and

information may spur more trading as part of an information “mosaic” (Cheynel and Levine,

2020; Kim and Verrecchia, 1991) or as perceived benefits to information search increase

(Grossman and Stiglitz, 1980), which could increase PIN. The effect that shareholder activism

has on PIN is an open question.

Hypothesis 3: There is no difference in PIN for shareholder activist targets for intervention and

non-intervention time periods

3. Methodology

3.1 Difference-in-Differences Specification

The treatment sample are firms which have been targeted by shareholder activism,

proxied by the filing of an SEC 13D on the firm. There does not exist a natural control group for

firms which are targeted by shareholder activists, as it is not readily apparent which factors

activists consider for their decision to initiate. However, Brav et al. (2008) have explored firm

characteristics which increase the probability of targeting by activist. I use these factors in a

logistic regression to calculate the propensity score of being an activist target (Rosenbaum and

Rubin, 1983). I then match each activist target to a control firm which had the most similar

propensity score in the year before activist intervention.

Following Brav et al. (2008)1, I estimate the probability of targeting based on t-1 values

of firm size, market-to-book, return on assets, debt-to-equity ratio, cash, Tobin’s Q, number of

analysts, institutional ownership using logistic regression:

𝐿𝑜𝑔𝑖𝑡(𝐴𝐶𝑇𝐼𝑉𝐸) = 𝛽1𝑀𝑇𝐵 + 𝛽2𝐶𝐴𝑆𝐻 + 𝛽3𝑅𝑂𝐴 + 𝛽4𝑆𝐼𝑍𝐸 + 𝛽5𝑇𝑂𝐵𝐼𝑁𝑄 + 𝛽6𝐷𝐸 + 𝛽7𝐴𝑁𝐴𝐿𝑌𝑆𝑇𝑆 +

𝛽8𝐼𝑁𝑆𝑇𝑂𝑊𝑁 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝐸 + 𝑌𝑒𝑎𝑟 𝐹𝐸 + 휀 (1)

I also control for year and Fama-French Industry code. Size, Tobin’s Q, analyst

following, and institutional ownership are significant correlates with activist intervention. Table

1 outlines the parameters to calculate the propensity score for each firm. In total, I match 744

activist target firms (ACTIVE = 1) to control firms (ACTIVE = 0). I include all firm-years with

data availability for the 744 target-control pairs.

1

[Insert Table 1]

3.2 Earnings Response Coefficient

To test the earnings response coefficient for activism and non-activism quarters, I regress

cumulative abnormal returns around the earnings announcement windows on unexpected

earnings. Following Wilson (2008) for the ERC model, I specify the following difference-in-

differences specifications

𝐶𝐴𝑅 = 𝛼1 + 𝛽1𝐴𝐶𝑇𝐼𝑉𝐸 + 𝛽2𝑃𝑂𝑆𝑇 + 𝛽3𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 + 𝛽4𝑈𝐸 + 𝛽5𝑈𝐸 × 𝐴𝐶𝑇𝐼𝑉𝐸 +

𝛽6𝑈𝐸 × 𝑃𝑂𝑆𝑇 + 𝛽7𝑈𝐸 × 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 휀 (2a)

𝐶𝐴𝑅 = 𝛽1𝑃𝑂𝑆𝑇 + 𝛽2𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 + 𝛽3𝑈𝐸 + 𝛽4𝑈𝐸 × 𝐴𝐶𝑇𝐼𝑉𝐸 + 𝛽5𝑈𝐸 × 𝑃𝑂𝑆𝑇 +

𝛽6𝑈𝐸 × 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝐹𝑖𝑟𝑚 𝐹𝐸 + 𝑌𝑒𝑎𝑟 𝐹𝐸 + 휀 (2b)

In (2b), the effect of being a firm targeted by activists is subsumed by the firm fixed

effects. Thus, ACTIVE is dropped from the regression. Similarly, the intercept is not needed

because of the firm and year fixed effects present. POST represents the period of activist

intervention starting with the filing of the SEC 13D. POST = 1 for any quarters where the

earnings announcement date falls after the 13D filing date and POST = 0 for any quarters where

the earnings announcement date falls before the 13D filing date. The return is calculated as the 3-

day cumulative abnormal return starting from 1 trading day before announcement. Abnormal

return is calculated as the difference between daily firm return and the value-weighted market

return. Prior research has shown that most earnings information is incorporated into the price by

day +1 after the earnings announcement (Foster, 1977). I measure unexpected earnings as the

amount of actual reported earnings over the median of all analyst forecasts issued from 60 days

before the earnings announcement until announcement date, scaled by price at quarter end. 𝛽6 is

the coefficient of interest, which translates to the additional change in ERC during the period of

shareholder activism at activist targets compared to the matched sample.

NONLINEAR is a control added to mitigate the effect of ERC diminishing for more

extreme values of unexpected earnings (Burgstahler and Chuk, 2017). I add additional controls

for the predictability of a firm’s earnings series, size, market-to-book, and CAPM beta of the

firm, and indicators for loss and 4th quarters. I also include the interaction between all controls

and unexpected earnings. A more predictable earnings series is associated with a higher ERC

(Lipe, 1990). I use the average of the past two year’s unexpected earnings over analyst forecasts

as predictability of earnings. Market-to-book is a proxy for growth, which has been shown to be

positively related to ERC (Collins and Kothari, 1989). Beta is negatively related to ERC (Collins

and Kothari, 1989). Loss quarters are shown to elicit less of a reaction from the market (Hayn,

1995) and similarly for the 4th quarter, which often coincides with information from fiscal year

end and the issuance of annual reports (Mendenhall and Nichols 1988; Salamon and Stober,

1994), therefore, I expect the ERC to be negatively related to both variables.

3.3 Price Delay

For activism’s relationship with price delay, I use Hou and Moskowitz’s (2005) measure.

I first regress the firm-specific daily return on daily market returns from the past five days for

each day in the firm-quarter and get the coefficient of determination 𝑅2 from the following

model,

𝑟𝑡 = 𝛼 + 𝛽1𝑅𝑚,𝑡 + ∑ 𝛽𝑖 ∗ 𝑅𝑚,𝑡−𝑖6𝑖=2 + 휀𝑡. (3)

Subsequently, assuming the coefficients on lagged market returns being zero, I get the

coefficient of determination, 𝑅𝛽2,3,4,5,6=02 , from a restricted model including only concurrent daily

market returns,

𝑟𝑡 = 𝛼 + 𝛽1𝑅𝑚,𝑡 + 휀𝑡. (3’)

I calculate the delay proxy as

𝐷𝑒𝑙𝑎𝑦 = 1 −𝑅𝛽2,3,4,5,6=0

2

𝑅2 (4)

If the lagged market returns explain variation in the firm-specific return of the current day (i.e.,

𝑅𝛽2,3,4,5,6=02 < 𝑅2), then the firm’s prices are “late” in reflecting market-wide information from

earlier periods. The greater 𝑅2 is compared to 𝑅𝛽2,3,4,5,6=02 , the smaller the fraction and greater the

price delay.

The delay measure for every firm-quarter is regressed on indicators of activist presence,

𝐷𝑒𝑙𝑎𝑦 = 𝛽1𝐴𝐶𝑇𝐼𝑉𝐸 + 𝛽2𝑃𝑂𝑆𝑇 + 𝛽3𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 휀 (5a)

𝐷𝑒𝑙𝑎𝑦 = 𝛽1𝑃𝑂𝑆𝑇 + 𝛽2𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝐹𝑖𝑟𝑚 𝐹𝐸 + 𝑌𝑒𝑎𝑟 𝐹𝐸 + 휀 (5b)

Similar to the previous models, POST represents the period of activist intervention starting with

the filing of 13D and periods after activist exit are excluded from the regression. Illustrations of

what constitutes an activism quarter is in Figure 1. Specifically, if the beginning and end of an

activism event falls entirely within the quarter, then the quarter is considered POST = 1. If the

activist event started or ended at any time within the quarter, then the quarter is also coded as

POST = 1. The coefficient on 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇, 𝛽2, is the difference in change of price delay for

activist targets and peers. Similar to model (2b), I drop the indicator ACTIVE and the intercept

due to firm and year fixed effects in model (5b). Following Hou and Moskowitz (2005), I add

controls for size and market-to-book, as larger firms and firms with higher market valuation have

higher market participation, resulting in more frequent trading in their stocks, which may reduce

price delay. I also control for share turnover (Hou and Moskowitz, 2005; Callen et al., 2013), a

proxy for liquidity, reflecting if a stock is bought and held more often in a time period, then

information may be incorporated into the stock price at a faster rate. Finally, I control for

institutional ownership (Hou and Moskowitz, 2005; Callen et al., 2013) as shareholder activists

are a type of blockholder. Their entry as an investor could possibly affect the percentage

ownership by institutions, which as an attention measure, may result in lower price delay.

3.4 Probability of Informed Trading (PIN)

Unlike public information proxies, the possibility of large benefits to be reaped by trading

on private information means its direct role in decision-making often can not be observed, only

inferred. Easley et al. (1997) posit that an uninformed trader is indifferent between submitting a

buy or a sell order while the informed trader may receive information to trade in a certain

direction. As such, a day where there is excess buy or sell orders for a stock which normally sees

a balance of buys and sells would imply higher likelihood of an information event for the firm. In

a microstructure model (Easley et al., 1996; Easley et al., 1997), the probability of trading by

informed participants can be estimated from trade data on buys and sells. In particular, the

likelihood function for buys and sells can be derived assuming the arrival rate of these buy and

sell orders follow independent Poisson processes (Easley et al., 2010; Lin and Ke, 2011). The

likelihood function specifies buys and sells are based on parameters 𝛼, the probability of an

information event, 𝛿, the probability of a high state signal, 𝜇, the probability that informed trader

decides to submit an order, and 휀𝑏 , 휀𝑠, probabilities of an uninformed trader buying and selling,

respectively. I use the Lee and Ready (1991) test on TAQ trade and quote data to estimate buys

and sells each day. I estimate parameters 𝛼 , 𝛿 , 𝜇 , 휀𝑏, and 휀𝑠 by maximizing the log likelihood

function for buys and sells employing non-linear optimization. With estimates of the parameters,

I calculate PIN as:

𝑃𝐼𝑁 = 𝛼𝜇

𝛼𝛿+ 𝑏+ 𝑠. (6)

I observe the correlation between shareholder activism and PIN using the same

difference-in-differences specification:

𝑃𝐼𝑁 = 𝛽1𝐴𝐶𝑇𝐼𝑉𝐸 + 𝛽2𝑃𝑂𝑆𝑇 + 𝛽3𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 휀 (7a)

𝑃𝐼𝑁 = 𝛽1𝑃𝑂𝑆𝑇 + 𝛽2𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝐹𝑖𝑟𝑚 𝐹𝐸 + 𝑌𝑒𝑎𝑟 𝐹𝐸 + 휀 (7b)

Intercept and coefficient on ACTIVE are dropped because of the presence of firm and year fixed

effects. The determination of POST is identical to the price delay in model (6). 𝛽2, the coefficient

on 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇, is of primary interest as it represents the incremental change to PIN for

activist targets compared to matched peers.

I control for market-to-book, size, and turnover, which may affect the number of

informed traders at a firm (Easley et al., 2002; Brown and Hillegeist, 2007). Institutional

ownership may bring more private information to the firm, which I add as a control (Jiambalvo

et al., 2002). I also control for analysts’ following and dispersion as activism could result in more

uncertainty for analysts (Flugum and Howe, 2020), whose activity correlates with the amount of

informed trading (Piotroski and Roulstone, 2014). I control for leverage, which makes trading on

private information more profitable (Boot and Thakor, 1993).

4. Empirical Results

4.1 Data description

I extract 13D filings from Audit Analytics. I also hand collect firms and individuals who

engage in shareholder activism from the NIRI list of top 200 activist hedge funds2, 13D

monitor3, and internet searches. Some shareholder activists do not cross the 5% threshold when

engaging in activism, such as proxy contests and shareholder proposals relating to Section 14 of

2 https://www.niri.org/resources/resource-libraries/corporate-governance-resource-library/shareholder-activism 3 https://www.13dmonitor.com/

the Securities and Exchange Act of 1934, and thus, these events will not be recorded in the 13D

database unless they voluntarily elected to file a SEC 13D. I categorize each instance of

shareholder activism as bookended by filing of an SEC 13D with shareholding above 5% and

filing of an SEC 13D amendment (SEC 13D/A) with shareholding dropping below 5%. In the

case of “wolf packs”, or multiple activists engaging with the same target I determine the start of

an event to be the first SEC 13D filed, relaxing the requirement of 5% shareholding4. This is

because members of a wolf pack may secretly coordinate but intentionally remain below the 5%

shareholding to circumvent SEC scrutiny (Wong 2019). Therefore, by measuring the 13D filing

of the first activist engaging with the target, I can more accurately capture the start of wolf pack

activism events. As mentioned in an earlier section, I group activism events with less than a year

between interventions as one occurrence. This is also for clearer identification of wolf pack

events.

After removing recent observations with unavailable data for the targets, I find 744

events initiated by 247 different activists that can be matched to similar control firms using

propensity matching based on logistic model (1). I obtain analyst and earnings announcement

variables from I/B/E/S, institutional ownership from the Thomson Reuters 13F database, the

number of words and uncertainty words in the 10-K report from a publicly available dataset

provided by Loughran and McDonald (2011),5 PIN using trade and quote data from the TAQ

database, financial information from Compustat, firm professionals from Capital IQ and returns

from CRSP.

4 I provide additional statistics which separates the 5% or more filers from the less than 5% wolf pack filers in the

robustness section. 5 https://sraf.nd.edu/textual-analysis/resources/

4.2 Descriptive Statistics

Table 2 gives a brief overview of the shareholder activism events captured in the sample.

Panel A shows the distribution of activism events by the first year firms are targeted by activists.

In accordance with prior research (Boyson et al., 2017; Chen and Jung, 2016; Bourveau and

Schoenfeld, 2017; Klein and Zur, 2009), the frequency of activism appears to follow economic

cycles where activism events reach peaks around 2007 and 2015 and decreases following

recessionary pressures in 2009.

Interest in initiating new activism campaigns appears to be waning in the most recent

years despite favorable stock market performance. Most of the events have only one activist in

participation (72%), about 23% have two activists targeting one firm at the same time, and the

rest have three or more (Panel B). Panel B also shows about 15% of targets have directors who

also work for the activist firm at the same time. Panel C lists shareholder activism events by

industry, with financial, business services, and healthcare being popular industries. This is not

surprising as healthcare is a litigious industry while shareholder activists are likely to be more

knowledgeable about industries such as financial services which operate similarly to their own

firm (Agarwal et al., 2013).

[Insert Table 2]

Table 3 gives the descriptive statistics for activism firms and their matched sample as

well as tests of difference in means. Earnings predictability, unexpected earnings, dispersion, and

market-to-book have extreme outliers. Therefore, these variables are reported after winsorized at

1%. Table 3 Panel A shows that activist targets experience higher share volume, price delay, and

PIN than matched firms. Activism targets also have more institutional ownership, more losses,

fewer analysts following the firm, and higher leverage. As shown in Table 3 Panel B, activism

targets also produce longer 10-Ks with more words expressing uncertainty, and more income

decreasing special items.

[Insert Table 3]

4.3 ERC and Shareholder Activism

Table 4 contains results on the ERC with and without fixed effects. I focus on Column 3,

which includes both firm and year fixed effects. Compared to control firms, investors respond

less to earnings at activist firms, shown through the negative coefficient (coefficient = -0.16, t-

statistic = -2.66) on 𝑈𝐸 × 𝐴𝐶𝑇𝐼𝑉𝐸. After activist intervention becomes public information

through the SEC 13D, ERC is now higher at targets than control firms. The coefficient on

𝑈𝐸 × 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 is positive and significant (coefficient = 0.207, t-statistic = 2.4). For

robustness, I also restrict the sample to no more than 8 quarters before and after SEC filing

quarter in Column 4. I use 8 quarters as prior research (Brav et al. 2008) has found the 75th

percentile of targets experiences activist exit in about 2 years. The results are similar for

𝑈𝐸 × 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇. This is consistent with a shift in market attention to firms experiencing

shareholder activist intervention. I investigate the attention hypothesis further by examining price

response to other sources of information.

[Insert Table 4]

4.4 Price Delay and Shareholder Activism

In the descriptive statistics, price delay appears to be higher for the activism sample, but

institutional ownership is also higher for activist targets. Hou and Moskowitz (2005) find

negative correlation between institutional ownership and price delay. Therefore, it is not clear if

the observed increase in price delay would persist controlling for greater institutional holdings.

Table 5 describes the interaction between shareholder activism and price delay. After adding

controls, the results in the difference-in-differences specification corroborate the difference in

means test. Compared to peers, activist targets experience significantly more price delay. The

coefficient on 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 in Column 3 with firm and year fixed effects is 0.025 (t-statistic

= 5.8). The other specifications also corroborate this finding. The regression result suggests there

is an incremental association between activist intervention and higher price delay, taking into

consideration the depression effect of institutional ownership on price delay. This coefficient is

economically significant as it shows daily target returns reflecting approximately 2.5% more past

weeks’ market return than matched peers. Price delay measures market participants’ usage of

publicly available market-wide and macroeconomic information. The positive association

between shareholder activism and price delay implies market is slower to incorporate publicly

available common information for target firms when activists hold shares of the target firm6.

[Insert Table 5]

For the control variables, size and institutional ownership coefficients are negative and

significant, conforming to expectations that they are signs of higher investor participation, and

thus, correlated with a lower price delay. Turnover and market-to-book is positive and

significant. However, in untabulated results, both have a negative and significant correlation with

price delay and running the same difference-in-differences regression without the size variable

results in the rest of the control variables taking the expected relationship with price delay while

6 For robustness I also replace price delay with price synchronicity (Roll, 1988; Durnev et al., 2003), which

measures the amount of correlation between firm-specific returns and concurrent market returns. Price synchronicity

is another indicator of how much public market information is reflected in a firm’s prices. The results are similar. I

find price synchronicity also decreases for targets during shareholder activist intervention, suggesting less public

market-wide information is reflected for intervention quarters.

the coefficient of interest on 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 remains positive and significant. The longer price

delay but higher ERC suggests investors discriminate between types of public information they

are willing to use in decision-making. They incorporate more managerial firm-specific

information but are hesitant about market-wide information. Aside from publicly available

information, disclosures may also encourage the incorporation of private information into prices

(Kim and Verrecchia, 1991; Kandel and Pearson, 1995).

4.5 PIN and Shareholder Activism

As investors knowledgeable about the financial markets, shareholder activists may have

access to exclusive information, or their engagement may change information usage of other

traders. The final main analysis concerns how shareholder activism may change the reflection of

private information in prices. Table 6 shows the estimates of regressing PIN on shareholder

activist intervention using a difference-in-differences specification. Shareholder activism is

associated with significantly greater PIN (Column 3 difference-in-differences coefficient =

0.014, t-statistic = 2.31). It is also economically significant as the mean amount of informed

trading of targets and matched firms is about 0.24, while an activist target would experience an

additional 0.014 probability of informed trades on average over its matched peers, or about a 5%

increase. Columns 1 and 2 with no fixed effects and with only firm fixed effect, respectively,

also show a positive a significant relationship between PIN and shareholder activist intervention.

In Column 4, when firm-quarters are restricted to within 8 quarters before and after filing date,

the difference-in-differences coefficient is no longer significant. However, this may be a result of

the much smaller sample size compared to the other regressions. For the control variables, size

and turnover are in the expected direction, market-to-book is not. The increasing coefficient on

PIN shows that informed trading increases even after the first activist has assumed position

within a target. This is in accordance with filing of the SEC 13D attracting more informed

trading (Kim and Verrecchia, 1991; Cheynel and Levine, 2020).

[Insert Table 6]

Thus far, the results show that target firm prices are slower to reflect publicly available

common information when the activist is a large shareholder of the target. At the same time,

market participants are more likely to trade on firm-specific information, both from the firm and

from their own private processing activities. This is in accordance with Grossman and Stiglitz

(1980). Activist presence alerts investors to the opportunity to improve information about targets,

which motivates them to focus on these firms and use more firm-specific information.

Supplemental Analyses

4.6 Words and Uncertain words in 10-K disclosures

I also examine two aspects of 10-Ks: length and uncertainty (Loughran and McDonald,

2011). Length is a representation of how much detail management is willing to give about firm

operations after 13D filing. Uncertainty, on the other hand, reveals management’s own

uncertainty about what has been disclosed. Following shareholder activism, managers may

decide to disclose more and increase the length of 10-Ks in acknowledgement of activist

concerns or to mollify the rest of the investor base. However, managers may also disclose less

and reduce information for fear of litigation or mass sales of shares from deficiencies uncovered

through activism (Chen and Jung, 2015; Khurana et al., 2017). Similarly, managers could report

with either more or less uncertainty following shareholder activism. They could choose more

uncertainty if they have doubts about the activist’s intentions or want to intentionally obfuscate

results; they could also report with less uncertainty in an attempt to reduce information

asymmetry (Ng et al., 2017; Cheng et al., 2015).

The literature has shown that certain types of information increase while others decrease.

It is thus possible for managers to choose more information and less uncertainty in 10-Ks, less

information and more uncertainty, or any combination of length of information and uncertainty.

I examine the change in the amount of disclosure and uncertainty of disclosure in 10-Ks

using a difference-in-differences specification.7

10𝐾𝑑𝑖𝑠𝑐𝑙𝑜𝑠𝑢𝑟𝑒 = 𝛽1𝑃𝑂𝑆𝑇 + 𝛽2𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝐹𝑖𝑟𝑚 𝐹𝐸 + 𝑌𝑒𝑎𝑟 𝐹𝐸 + 휀 (2)

The dependent variable, 10Kdisclsoure, represents the variable Words, the natural

logarithm of the total number of words in 10-K or Uncertain Words, the natural logarithm of the

total number of uncertain words. The indicator variable, POST, equals one for observations after

13D filings until the exit of the shareholder activist, identified by 13D filing amendment when

shareholding falls below 5%, and zero otherwise. Post-activism periods after activist exit are

excluded from the difference-in-differences sample. Illustrations of what constitutes an activism

year is in Figure 2. Specifically, if the beginning and end of an activism event falls entirely

within the year, then the year is considered post-activism period8. If the beginning of an event

falls less than 180 days from the beginning of the entry year and the end of an event falls less

than 180 days from the end of the exit year, then all years in between including the entry and exit

year are considered POST = 1 activism years. I group activism events with less than a year

between interventions as one occurrence whose start date is the filing of the SEC 13D for the

7 The difference-in-difference specification is used instead of just comparing activist targets to non-targets in order

to provide a little more granularity into the specific period of intervention where the activist is present at the target

compared to matched non-targets.

8 For robustness, I also re-run regressions on number of words and number of uncertain words excluding the

activism events which begin and end in the same year (6 events). The results are similar and significance does not

change.

first activist and end date is exit of the last activist with no further 13D filings by any firm within

the next year. Determination of POST=1 then follows from above. Specifically, POST=1 for all

years between start and end date, including start year and end year if event is present for a

majority of the year.

The coefficient of interest is 𝛽2, which represents the change for activist targets compared

to matched peers in the period (in this case, year) when activist is a large shareholder according

to SEC 13D records. I control for size and market-to-book as they are related to the probability

of targeting (Brav et al., 2008) and the complexity of the annual report (Loughran and

McDonald, 2011). I control for earnings and return volatility which may influence management’s

report of activism events (Li, 2008). I also control for number of business and geographic

segments (Li, 2008), as activism has been shown to be associated with reorganization of product

lines (Brav et al., 2018b; Brav et al., 2015). Finally, I add firm age and the number of special

items as controls since they may affect the number of words in the 10-K (Guay et al., 2016; Li,

2008). Details on how the variables are calculated are included in the appendix. Similar to the

main models, ACTIVE is dropped from the regression and intercept is suppressed.

Table 7 presents how shareholder activism affects managerial disclosure. Contrary to the

difference in means results in the descriptive statistics, the multivariate regression shows 10-K

reports to contain less words and less uncertainty words during the period when shareholder

activists are active at the target. The difference-in-differences coefficient on activism firm-year is

negative and significant for both the total number of words (coefficient = -0.045, t-statistic = -

2.53) and number of uncertainty words (coefficient = -0.050, t-statistic = -3.49). This suggests

that target firms limit disclosure and disclosure related to uncertainty compared to control firms,

consistent with less disclosure as a protection mechanism (McDonough and Schoenfeld, 2020;

Chen and Jung, 2015; Khurana et al., 2017). Firms also use fewer uncertainty words, perhaps to

reduce chances investors will find and question indicators of risk in the financial statements.

Together with the findings that ERC increases, the results support a policy of disclosure

prudence. Managers may be aware that investors will be more alert to their disclosure (Bourveau

and Schoenfeld, 2017), which makes them more careful and strategic about what they disclose.

[Insert Table 7]

4.7 Volume

I further investigate the nature of information processing. I complete additional analysis

on share volume. I use the same difference-in-differences specification as the main tests to

examine whether trading is information-based or motivated by frenzy. Table 8 includes the

results of regressing share volume on the coefficient of interest, 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇, with

additional controls which correlate with liquidity and activism (Norli et al. 2015; Pástor and

Stambaugh 2003). Panel A Column (1) shows compared to non-activism quarters, activism

quarters do not have significantly different share volume. This is driven mostly by increased

amounts of private trading. In Column (3) and (4), PIN has a positive significant relationship

with volume and without it, the coefficient on 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 is negative and significant.

Panel B reports the difference between activism and non-activism quarters for the short interval

of [-2,+2] quarters around SEC filing date. Column A shows there is a significant increase in

share volume (difference-in-differences coefficient = 0.093, t-statistic = 3.49). This increased

trading intensity is not fueled by information as neither price delay nor PIN (Columns 2-4)

explain the variation in volume. Instead, it is a representation of general interest. In conclusion, I

find 13D filing of activist intentions generates a quick peak of investor sentiment, which quickly

dissipates (Peress and Schmidt, 2019). Over the course of shareholder intervention, sentiment-

based trading is replaced by information-based trading.

[Insert Table 8]

4.8 Activist Directors

Shareholder activists are an eclectic group with heterogeneous campaign tactics

(Gantchev, 2013), demands (Brav et al., 2008), and levels of antagonism (Boyson and Pichler,

2018). They are also likely to inspire varying degrees of price response to their interventions.

Therefore, I examine activist targets which have activist employees serve on the board of

directors. I expect activists to exert the greatest degree of control on these firms. If the market

considers activists to have a positive effect on information, then targets with activists as directors

should have more information incorporated into their prices. I identify the professionals from the

Capital IQ database who held both a position at the activist firm and a directorial position at the

target during activist intervention. The most frequent title held by the professionals at their

original activist firm is investment professional whereas the most frequent titles at the target are

member of theaudit committee, compensation committee, corporate governance committee, or

nominating committee. I use the same regressions with difference-in-differences coefficient as

the main tests on ERC, price delay, and PIN. I add interactions with indicator for activist

directors, 𝐴𝐶𝑇𝐷𝐼𝑅. The coefficient of interest is 𝑃𝑂𝑆𝑇 × 𝐴𝐶𝑇𝐷𝐼𝑅9, which represents the

incremental effect of activist directors targets compared to the rest of the activist targets. The

coefficient 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 represents the effect of activists on targets which do not have

9 The interaction 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝐴𝐶𝑇𝐷𝐼𝑅 and indicator 𝐴𝐶𝑇𝐷𝐼𝑅 are equivalent. By definition, 𝐴𝐶𝑇𝐼𝑉𝐸=1 for all

𝐴𝐶𝑇𝐷𝐼𝑅=1 firms. Therefore, the incremental coefficient, 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 × 𝐴𝐶𝑇𝐷𝐼𝑅, is replaced by

𝑃𝑂𝑆𝑇 × 𝐴𝐶𝑇𝐷𝐼𝑅.

activist directors compared to the matched sample not targeted by activism. Table 9 outlines the

results. In the ERC regression, the positive relationship between shareholder activism and ERC

becomes stronger (UE×ACTIVE×POST coefficient = 0.252, t-statistic = 2.74). Market also pays

less attention to targets which have activist directors (UE ×POST×ACTDIR coefficient = -0.196,

t-statistic = -1.65). Similarly, as in the main regression, shareholder activism increases delay

(ACTIVE×POST coefficient = 0.022, t-statistic = 4.8). Activists taking directorial positions

further increases the price delay of information (POST×ACTDIR coefficient = 0.026, t-statistic =

2.86) as compared to activist intervention without representation on the board10. There is no

significant difference in private information trading. Altogether, the estimates suggest while

investors incorporate more firm-specific information into target prices during shareholder

activism, it is more of an attention effect than shareholder governance. In the cases where

activists can more easily enact governance as a director, usage of publicly available information

decreases. Current returns of targets with activist directors reflect less earnings information and

slower market information compared to other targets.

[Insert Table 9]

4.9 Additional Robustness Checks

I add some robustness tests to alleviate the concern that the results are spurious.

Researchers have documented increased stock activity in the 60 days before 13D filing (Norli et

al., 2015). Since the matching procedure is on t-1 end of year values, volatility in the quarters

after the match date but before filing quarter may be driving the observed differences in price

10 For robustness, I also split the sample and complete two sets of tests. One with activist director targets and their

matched controls and one with the rest of the activist targets and their matched controls. The results are similar.

Particularly, activist targets which do not have an activist director experience higher ERC, PIN, and price delay.

Activist director targets also experience greater ERC and price delay than non-director targets.

delay and PIN. To address this issue, I re-run the regressions without the 4 quarters before

intervention. The value of the coefficients of interest on 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇 in Table 10 are similar

to the main results and do not change in significance.

[Insert Table 10]

As mentioned in the Data section, for firms which have been targeted by more than one

activist, I do not require the 5% threshold for the filing of the initial SEC 13D, instead, any first

13D filing is considered as a new event, except in the case where the end of the preceding

activist intervention (identified by SEC 13D/A documenting shares of the filing activist dropping

to below 5%) was within one year of the new 13D filing. If it is within one year, it is considered

a continuation of the preceding activist intervention until all shareholder activist of the wolf pack

have exited with less than 5% shareholding. Wong (2019) opines member of the wolf pack may

purposefully hold less than 5% shares to avoid legal responsibility. By not requiring 5%

shareholding for wolf packs, I can better capture the length of wolf pack interventions and the

number of members. However, firms are not required to file a SEC 13D until they meet the 5%

ownership threshold and they may also file for more specialized reasons such as to participate in

reorganization of distressed firm (Brav et al., 2008). It is less certain if the less than 5% filers

have the same activist motivations as the rest of the sample. I address this partially by identifying

only SEC 13Ds filed by known activists, but to add some clarity into the sample, I also separate

the SEC 13Ds filed at less than 5% from the others. Out of the 744 activism events in the sample,

79 are linked to 13D filings with less than 5% ownership. I run the same regressions as the

supplemental analyses where I replace 𝐴𝐶𝑇𝐷𝐼𝑅 with targets linked to these types of filings. The

coefficients of interest for shareholder activist intervention firms, 𝐴𝐶𝑇𝐼𝑉𝐸 × 𝑃𝑂𝑆𝑇, in other

words activist target firms with 5% or more shareholding, have similar direction and significance

to the main analyses for ERC, price delay, and PIN11.

[Insert Table 11]

5. Conclusion

Academic literature on shareholder activism by hedge funds have maintained an overall

positive assessment of its effect on future firm performance (Brav et al., 2008; Klein and Zur,

2009; Bebchuk et al., 2015) while industry experts are doubtful about its investment viability12.

Despite a number of studies on shareholder activism, activists’ motivations and strategies remain

shrouded in secrecy. I approach the subject from the angle of how shareholder activism affects

information processing, if at all. Specifically, I investigate how the market responds with their

subsequent information gathering needs through measuring the earnings response coefficient,

price delay in reflecting market-wide information, and the probability of informed trading at

activism targets compared to non-targets.

In the period of activist intervention, investors incorporate more private and

public firm-specific information and are slower to act on common market-wide information,

measured as increasing ERC, increasing PIN, and increasing price delay. This is consistent with

shareholder activism directing investor attention toward targets (Hirshleifer and Teoh, 2003) and

thereby increasing their needs for information. Additionally, volume of the target’s stock also

significantly increases in the 2 quarters after 13D filing by the activist compared to the 2 quarters

11 Also similar results for ERC, price delay, and PIN when the sample is split on less than 5% filer activist targets

and their matched sample and 5% filer activist targets and their matched sample. Shareholder activist targets for

only 5% or more activist filers have significantly greater ERC, price delay, and PIN as compared to matched firms

which did not experience shareholder activism. 12 https://corpgov.law.harvard.edu/2018/02/01/the-changing-face-of-shareholder-activism/

before, suggesting more interest in the target’s shares. I also find the slower reflection of market

information is incrementally significant for activist targets which have activist directors on the

board.

This paper outlines a less direct method through which shareholder activism may

influence stock price. Shareholder activism brings a wave of immediate interest to the target, but

over time as the market contemplates sometimes conflicting disclosures from activists and

management (McDonough and Schoenfeld, 2020), uncertainty and differences of opinion arise.

Noise traders trade less because their costs are higher. However, the uncertainty for targets

encourages investors to incorporate private information and use more firm-specific public

information. With the shift to firm-specific information, price efficiency is increased (Durnev et

al. 2003).

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Figure 1

Figure 2

Table 1: Probability of Being an Activist Firm-Year

Determinants of shareholder activism are taken from Brav et al. (2008). Firm-years are between 2000-2019.

Regressions include year and Fama-French 48 Industry fixed effect. All variables are measured at t – 1 while

activism is measured at time t. MTB is the market-to-book ratio, CASH is ratio of the cash balance to total assets,

ROA is the return on assets, SIZE is measured as the log of the market value. DE is the debt-to-equity ratio,

TOBINQ is Tobin’s Q for the firm, ANALYSTS is the number of analysts issuing annual forecasts, and INST OWN

is percentage of ownership by institutions at the firm. ***, **, * denotes significance at the 1%, 5%, and 10% level,

respectively.

Variable Estimate Standard Error

Intercept -16.920 109.500

MTB 0.000 0.000

CASH 0.094 0.128

ROA 0.042 0.079

SIZE -0.200*** 0.017

TOBINQ -0.130*** 0.018

DE 0.000 0.000

ANALYSTS -0.024*** 0.004

INST OWN 1.079*** 0.077

Industry FE Yes

Year FE Yes

Firm-Years 71,840

Adjusted R2 0.135

Table 2: Descriptive Statistics for Activism Targets

Sample contains 744 shareholder activism events at 723 activist targets initiated by 247 activists.

Panel A: Activism Events by Year

Start Year Number of Events

2000 1

2001 31

2002 27

2003 30

2004 41

2005 50

2006 64

2007 65

2008 54

2009 13

2010 39

2011 45

2012 42

2013 38

2014 44

2015 51

2016 39

2017 48

2018 18

2019 4

Panel B: Activism Events by Type

Type Number of Events

1 Activist 538

2 Activist 171

3 or more activist 35

Total events 744

Activist director 118

Less than 5% filer 79

Panel C: Activism Events by Industry

Industry Number of Events

Business Services 111

Consumer Durables 58

Consumer Nondurables 19

Financial 92

Healthcare 102

Manufacturing 71

Natural Resources 43

Other 57

Other Services 31

Technology 72

Telecommunications 14

Utilities 9

Wholesale and Retail 65

Table 3: Descriptive Statistics for Firms Targeted by Activists

Panel A: Activist Intervention Firm-Quarters

Activism sample includes intervention firm-years of the 744 activist events and its matched pair matched using 1:1

optimal matching on propensity score, which minimizes the total absolute difference in scores. Since matches are

completed by year, an activist target could have more than one activist event if it was targeted by activists more than

once in different time periods. Target denotes all activist target firm-quarters during shareholder activist

intervention. Matched Sample denotes all firm-quarters for which the matched pair’s corresponding target firm

experienced shareholder activism. Quarters after activist exit are eliminated from the sample. PRED, UE,

DISPERSION, and MTB are winsorized at 1% because of extreme outliers. Variable descriptions are included in

Appendix 1. ***, **, * denotes significance at the 1%, 5%, and 10% level, respectively.

Mean Median STD 25% 75%

Target

VOLUME 0.484*** 0.114 1.61 0.021 0.4

DELAY 0.45*** 0.375 0.317 0.166 0.733

PIN 0.248** 0.235 0.155 0.133 0.331

UE -0.004*** 0.001 0.044 -0.002 0.003

UR 0.003** 0.002 0.107 -0.045 0.05

MTB 2.807*** 1.754 4.677 1.045 3.196

SIZE 5.961 5.826 1.827 4.612 7.291

INST OWN 0.622*** 0.666 0.305 0.388 0.864

LOSS 0.287*** 0 0.453 0 1

Q4 0.246 0 0.431 0 0

ANALYSTS 3.328*** 2 3.771 1 4

DISPERSION 0.011** 0.037 0.634 0 0.129

LEVERAGE 0.564*** 0.546 0.303 0.352 0.742

BETA 1.018 0.987 0.779 0.538 1.446

PREDICT 0.416*** 0.079 6.069 0.038 0.16

Matched

Sample

VOLUME 0.41 0.092 1.738 0.021 0.275

DELAY 0.435 0.346 0.32 0.151 0.722

PIN 0.244 0.228 0.155 0.129 0.329

UE -0.001 0.001 0.036 -0.001 0.003

UR -0.001 0 0.092 -0.045 0.046

MTB 2.722 1.877 3.796 1.117 3.257

SIZE 5.959 6.016 1.915 4.617 7.233

INST OWN 0.578 0.628 0.331 0.289 0.862

LOSS 0.202 0 0.401 0 0

Q4 0.245 0 0.43 0 0

ANALYSTS 3.611 2 4.379 1 4

DISPERSION 0.046 0.039 0.554 0.009 0.115

LEVERAGE 0.511 0.486 0.368 0.297 0.693

BETA 1.026 1.018 0.828 0.582 1.463

PREDICT 3.882 0.054 80.803 0.029 0.107

Panel B: Activist Intervention Firm-Years

Activism sample includes intervention firm-years of the 744 activist events and its matched pair matched using 1:1

optimal matching on propensity score, which minimizes the total absolute difference in scores. Since matches are

completed by year, an activist target could have more than one activist event if it was targeted by activists more than

once in different time periods. Target denotes all activist target firm-years during shareholder activist intervention.

Matched Sample denotes all firm-years for which the matched pair’s corresponding target firm experienced

shareholder activism. Years after activist exit are eliminated from the sample. Variable descriptions are included in

Appendix 1. ***, **, * denotes significance at the 1%, 5%, and 10% level, respectively.

Mean Median STD 25% 75%

Target

WORDS 6.444*** 6.482 0.497 6.176 6.759

UNCERTAIN WORDS 10.75*** 10.733 0.508 10.456 11.034

AGE 19.595 16 15.888 8 26

SPI -0.022*** -0.003 0.09 -0.017 0

EARN VOL 85.269** 15.898 435.406 4.925 50.702

RET VOL 0.132** 0.106 0.128 0.074 0.158

BUS SEGS 1.171 1.099 0.914 0 1.946

GEO SEGS 1.284 1.099 1.019 0 2.197

Matched

Sample

WORDS 6.349 6.404 0.537 6.091 6.7

UNCERTAIN WORDS 10.656 10.646 0.499 10.376 10.944

AGE 19.923 17 14.748 9 26

SPI -0.016 -0.001 0.08 -0.011 0

EARN VOL 64.078 11.463 339.768 4.245 32.659

RET VOL 0.125 0.103 0.11 0.074 0.149

BUS SEGS 1.143 1.099 0.882 0 1.792

GEO SEGS 1.243 1.099 1.046 0 2.197

Table 4: ERC and Activism

The table reports the incremental change in ERC from before activist intervention to during activist intervention for

activist targets compared to matched sample. Firm-quarters are between 2000-2019. POST = 1 for any quarters

where the earnings announcement date falls after the 13D filing date and POST = 0 for any quarters where the

earnings announcement date falls before the 13D filing date. ACTIVE = 1 if firm has been the target of a

shareholder activist event between 2000-2019. Determination of POST for matched firms follow its activist target

counterpart. Quarters after activist exit are eliminated from the sample. Dependent variable is UR, 3-day cumulative

abnormal return starting from 1 trading day before announcement. Abnormal return is computed as the excess return

from the Carhart four factor model (1997), where factor betas are of the most recent quarter. UE is the unexpected

earnings of the firm, calculated as the difference between the actual earnings on announcement date and the median

of analyst forecasts from 60 days before to 1 day before announcement date, difference is scaled by price at quarter

end. NONLINEAR, SIZE, MTB, LOSS, Q4, BETA, and PREDICT and their interactions with UE are included as

controls in the regression but not tabulated for ease of interpretation. PRED, UE, and MTB are winsorized at 1%

because of extreme outliers. Variable descriptions are included in Appendix 1. Regressions include year and firm

fixed effects. ***, **, * denotes significance at the 1%, 5%, and 10% level, respectively.

UR UR UR UR

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

Intercept 0.011***

(3.03)

ACTIVE -0.005***

(-3.04)

POST -0.005*** -0.002 0.000 0.000

(-2.66) (-0.64) (0.14) (-0.06)

ACTIVE×POST 0.009*** 0.005 0.005 0.013***

(3.61) (1.48) (1.52) (2.69)

UE 0.579*** 0.556*** 0.561*** 0.651***

(5.13) (4.64) (4.67) (3.79)

UE×ACTIVE -0.177*** -0.158*** -0.160*** -0.107

(-3.16) (-2.63) (-2.66) (-1.2)

UE× POST -0.298*** -0.256*** -0.258*** -0.348***

(-4.77) (-3.75) (-3.78) (-3.62)

UE× ACTIVE ×POST 0.255*** 0.205** 0.207** 0.407***

(3.16) (2.37) (2.4) (3.34)

Controls Yes Yes Yes Yes

Firm FE No Yes Yes Yes

Year FE No No Yes Yes

Quarters [-8,+8] No No No Yes

Firm-quarters 22,286 22,286 22,286 8,167

R2 0.032 0.084 0.085 0.166

Table 5: Price Delay and Activism

The table reports the incremental change in price delay from before activist intervention to during activist

intervention for activist targets compared to matched sample. Firm-quarters are between 2000-2019. The sample is

restricted to firms with institutional ownership less or equal to 1. If the beginning and end of an activism event falls

entirely within the quarter, then the quarter is considered POST = 1. If the beginning of an event falls anytime in the

entry quarter and the end of an event falls anytime in the exit quarter, then all quarters in between including the

entry and exit quarter are considered POST = 1 (see Figure 1 for details). If quarter ends before beginning of activist

event then POST = 0. Determination of POST for matched firms follow its activist target counterpart. ACTIVE = 1

if firm has been the target of a shareholder activist event between 2000-2019. Quarters after activist exit are

eliminated from the sample. Dependent variable is DELAY, price delay calculated using Hou and Moskowitz’s

(2005) regression of daily market and firm-specific returns, delay measure is for the fiscal quarter. MTB is

winsorized at 1% because of extreme outliers. Variable descriptions are included in Appendix 1. Regressions

include year and firm fixed effects. ***, **, * denotes significance at the 1%, 5%, and 10% level, respectively.

Delay

(1)

Delay

(2)

Delay

(3)

Delay

(4)

Intercept 0.995***

(272.59)

ACTIVE 0.013***

(4.92)

POST -0.014*** -0.028*** 0.001 -0.013**

(-4.66) (-8.79) (0.29) (-1.97)

ACTIVE×POST 0.018*** 0.028*** 0.025*** 0.019***

(4.33) (6.1) (5.8) (2.78)

MTB 0.003*** 0.003*** 0.002*** 0.001

(10.64) (8.89) (5.36) (0.82)

SIZE -0.069*** -0.062*** -0.074*** -0.048***

(-92.42) (-40.05) (-46.79) (-11.29)

TURNOVER -0.001*** 0.000 0.001** 0.003***

(-5.3) (-1.12) (2.42) (6.74)

INST OWN -0.283*** -0.272*** -0.194*** -0.164***

(-62.39) (-39.31) (-27.68) (-9.3)

Firm FE No Yes Yes Yes

Year FE No No Yes Yes

Quarters [-8,+8] No No No Yes

Firm-quarters 63,443 63,443 63,443 17,520

R2 0.357 0.446 0.483 0.562

Table 6: PIN and Activism

The table reports the incremental change in PIN from before activist intervention to during activist intervention for

activist targets compared to matched sample. Firm-quarters are between 2000-2019. The sample is restricted to firms

with institutional ownership less or equal to 1. If the beginning of an event falls anytime in the entry quarter and the

end of an event falls anytime in the exit quarter, then all quarters in between including the entry and exit quarter are

considered POST = 1 (see Figure 1 for details). If quarter ends before beginning of activist event then POST = 0.

Determination of POST for matched firms follow its activist target counterpart. ACTIVE = 1 if firm has been the

target of a shareholder activist event between 2000-2019. Quarters after activist exit are eliminated from the sample.

Dependent variable is PIN, Probability of Informed Trading, calculated from estimating the likelihood function (Lin

and Ke, 2011; Easley et al., 2010) of informed orders. Buy and sell estimates are from Lee and Ready (1991).

DISPERSION and MTB are winsorized at 1% because of extreme outliers. Variable descriptions are included in

Appendix 1. Regressions include year and firm fixed effects. ***, **, * denotes significance at the 1%, 5%, and

10% level, respectively.

PIN

(1)

PIN

(2)

PIN

(3)

PIN

(4)

Intercept 0.361***

(53.04)

ACTIVE -0.001

(-0.22)

POST -0.033*** -0.034*** -0.000 0.004

(-9.13) (-7.63) (-0.03) (0.46)

ACTIVE×POST 0.010* 0.011* 0.014** 0.013

(1.85) (1.75) (2.31) (1.33)

MTB 0.001** 0.001*** 0.001*** 0.000

(2.28) (3.87) (2.95) (0.41)

SIZE -0.015*** -0.028*** -0.020*** -0.015**

(-16.63) (-12.78) (-8.24) (-2.24)

LEVERAGE 0.012** -0.019** 0.011 -0.005

(2.33) (-1.98) (1.16) (-0.18)

NUM ANALY 0.000 0.000 0.000 -0.001

(-1.43) (-0.32) (0.53) (-0.68)

DISPERSION 0.004* 0.004 0.003 0.004

(1.67) (1.59) (1.36) (0.97)

TURNOVER 0.000** -0.001*** -0.001** -0.001

(-2.19) (-3.08) (-2.56) (-1.11)

INST OWN -0.038*** -0.036*** -0.009 -0.008

(-6.64) (-4.34) (-1.01) (-0.34)

Firm FE No Yes Yes Yes

Year FE No No Yes Yes

Quarters [-8,+8] No No No Yes

Firm-quarters 13,057 13,057 13,057 3,914

R2 0.051 0.206 0.224 0.363

Table 7: Words in 10-K and Activism

The table reports the incremental change in 10-K word counts from before activist intervention to during activist

intervention for activist targets compared to matched sample. Firm-years are between 2000-2019. If the beginning

and end of an activism event falls entirely within the year, then the year is considered POST = 1. If the beginning of

an activist event falls less than 180 days from the beginning of the entry year and the end of an event falls less than

180 days from the end of the exit year, then all years in between including the entry and exit year are considered

POST = 1 (see Figure 2 for details). If beginning of year is more than 180 days before the beginning of activist event

then POST = 0. Determination of POST for matched firms follow its activist target counterpart. ACTIVE = 1 if firm

has been the target of a shareholder activist event between 2000-2019. Years after activist exit are eliminated from

the sample. Dependent variable is WORDS or UNCERTAIN WORDS, natural logarithm of total number of words

and total number of uncertainty words, respectively, in the 10-K using Loughran and McDonald’s (2011) dataset.

Variable descriptions are included in Appendix 1. Regressions include year and firm fixed effects. ***, **, *

denotes significance at the 1%, 5%, and 10% level, respectively.

(1) WORDS (2) UNCERTAIN WORDS

POST 0.035** 0.030***

(2.49) (2.64)

ACTIVE×POST -0.045** -0.050***

(-2.53) (-3.49)

SIZE -0.000 0.012**

(-0.02) (2.45)

MTB -0.000 0.000

(-1.31) (0.03)

AGE 0.006*** 0.004**

(3.01) (2.44)

SPI -0.138*** -0.130***

(-3.67) (-4.32)

EARN VOL 0.000 0.000

(0.55) (0.86)

RET VOL 0.032 0.065***

(1.09) (2.73)

NUM BUS SEG 0.047*** 0.044***

(5.08) (5.94)

NUM GEO SEG -0.010 0.001

(-1.06) (0.18)

Firm FE Yes Yes

Year FE Yes Yes

Firm-years 10,101 10,098

R2 0.609 0.761

Table 8: Investor Attention and Activism

The table reports the incremental change in volume from before activist intervention to during activist intervention

for activist targets compared to matched sample. Firm-quarters are between 2000-2019. The sample is restricted to

firms with institutional ownership less or equal to 1. If the beginning and end of an activism event falls entirely

within the quarter, then the quarter is considered POST = 1. If the beginning of an event falls anytime in the entry

quarter and the end of an event falls anytime in the exit quarter, then all quarters in between including the entry and

exit quarter are considered POST = 1 (see Figure 1 for details). If quarter ends before beginning of activist event

then POST = 0. Determination of POST for matched firms follow its activist target counterpart. ACTIVE = 1 if firm

has been the target of a shareholder activist event between 2000-2019. Quarters after activist exit are eliminated

from the sample. Dependent variable is VOLUME, share volume, measured as total quarterly volume (in

100,000,000s). Market-to-book, size, institutional ownership, past abnormal performance, beta, and past volatility

are included as controls in the regression following Norli et al. (2015) and Pástor and Stambaugh (2003) but not

tabulated for ease of interpretation Panel A includes all quarters. Panel B includes only the 4 quarters immediately

before and immediately after the intervention quarter, identified by 13D filing. Column (1) for each panel is the

difference-in-difference specification with volume as the dependent variable. Column (2-4) for each panel adds price

delay and PIN as controls. Variable descriptions are included in Appendix 1. Regressions include year and firm

fixed effects. ***, **, * denotes significance at the 1%, 5%, and 10% level, respectively.

Panel A: Volume All Quarters

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

POST -0.018 -0.017 -0.005 -0.005

(-1.33) (-1.26) (-0.38) (-0.34)

ACTIVE×POST -0.024 -0.028* -0.045*** -0.048***

(-1.47) (-1.73) (-2.6) (-2.78)

DELAY 0.271*** 0.245***

(15.11) (13.25)

PIN 0.084*** 0.073***

(3.22) (2.78)

Controls Yes Yes Yes Yes

Q = [-2, +2] No No No No

Firm FE Yes Yes Yes Yes

Year FE Yes Yes Yes Yes

Firm-quarters 63,873 63,873 54,933 54,933

R2 0.600 0.602 0.608 0.609

Panel B: Volume Quarters around SEC 13D

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

POST -0.003 -0.003 -0.040** -0.039*

(-0.16) (-0.13) (-1.97) (-1.93)

ACTIVE×POST 0.093*** 0.093*** 0.072*** 0.071***

(3.49) (3.48) (2.85) (2.84)

DELAY 0.155*** 0.154***

(3.88) (4.22)

PIN -0.069 -0.067

(-1.28) (-1.25)

Controls Yes Yes Yes Yes

Q = [-2, +2] Yes Yes Yes Yes

Firm FE Yes Yes Yes Yes

Year FE Yes Yes Yes Yes

Firm-quarters 4,580 4,580 4,034 4,034

R2 0.907 0.908 0.911 0.912

Table 9: Activist Directors

The table compares changes in ERC, price delay, and PIN for activist targets with activist directors, activist targets,

and matched sample. Firm-quarters are between 2000-2019. For ERC quarters, POST = 1 for any quarters where the

earnings announcement date falls after the 13D filing date and POST = 0 for any quarters where the earnings

announcement date falls before the 13D filing date. For price delay and PIN quarters, if the beginning and end of an

activism event falls entirely within the quarter, then the quarter is considered POST = 1. If the beginning of an event

falls anytime in the entry quarter and the end of an event falls anytime in the exit quarter, then all quarters in

between including the entry and exit quarter are considered POST = 1 (see Figure 1 for details). If quarter ends

before beginning of activist event then POST = 0. Determination of POST for matched firms follow its activist

target counterpart. ACTIVE = 1 if firm has been the target of a shareholder activist event between 2000-2019. For

price delay and PIN regressions, the sample is restricted to firms with institutional ownership less or equal to 1.

ACTDIR = 1 for target if an activist representative held a position on target’s board of directors at anytime during

the activist intervention. PRED, UE, DISPERSION, and MTB are winsorized at 1% because of extreme outliers.

Variable descriptions are included in Appendix 1. ***, **, * denotes significance at the 1%, 5%, and 10% level,

respectively.

ERC Price Delay PIN

POST 0.000 0.001 -0.000

(0.15) (0.3) (-0.04)

ACTIVE×POST 0.005 0.022*** 0.014**

(1.61) (4.8) (2.19)

ACTDIR 0.002 -0.016 -0.001

(0.07) (-0.38) (-0.02)

POST× 𝐴𝐶𝑇DIR -0.003 0.026*** 0.003

(-0.48) (2.86) (0.21)

UE 0.568***

(4.73)

UE×ACTIVE -0.159**

(-2.41)

UE× POST -0.261***

(-3.82)

UE× ACTIVE ×POST 0.252***

(2.74)

UE×ACTDIR 0.000

(0)

UE×POST× 𝐴𝐶𝑇DIR -0.196*

(-1.65)

Controls Yes Yes Yes

Firm FE Yes Yes Yes

Year FE Yes Yes Yes

Firm-quarters 22,286 63,443 13,057

R2 0.086 0.483 0.224

Table 10: Robustness: Excluding Immediate 4 Quarters Before SEC 13D

Firm-quarters are between 2000-2019. For ERC quarters, POST = 1 for any quarters where the earnings

announcement date falls after the 13D filing date and POST = 0 for any quarters where the earnings announcement

date falls before the 13D filing date. For price delay and PIN quarters, if the beginning and end of an activism event

falls entirely within the quarter, then the quarter is considered POST = 1. If the beginning of an event falls anytime

in the entry quarter and the end of an event falls anytime in the exit quarter, then all quarters in between including

the entry and exit quarter are considered POST = 1 (see Figure 1 for details). If quarter ends before beginning of

activist event then POST = 0. Determination of POST for matched firms follow its activist target counterpart.

ACTIVE = 1 if firm has been the target of a shareholder activist event between 2000-2019. For price delay and PIN

regressions, the sample is restricted to firms with institutional ownership less or equal to 1. Quarters in the

regression excludes the 4 quarters before the quarter of activist intervention, this is applicable for activist targets and

its matched pair. ***, **, * denotes significance at the 1%, 5%, and 10% level, respectively.

ERC Price Delay PIN

POST -0.002 0.005 0.003

(-0.54) (1.2) (0.54)

ACTIVE×POST 0.002 0.027*** 0.013**

(0.58) (5.8) (1.97)

UE 0.485***

(3.64)

UE×ACTIVE -0.138

(-1.93)

UE× POST -0.277***

(-3.65)

UE× ACTIVE ×POST 0.225**

(2.34)

Controls Yes Yes Yes

Firm FE Yes Yes Yes

Year FE Yes Yes Yes

Firm-quarters 19,938 58,708 11,976

R2 0.089 0.483 0.232

Table 11: Less than 5% Filers

The table compares changes in ERC, price delay, and PIN for firms targeted by individual and wolf pack activists

with at least 5% shareholding at filing date of the first SEC 13D compared to activist targets of wolf packs which did

not have 5% shareholding at first filing date. Firm-quarters are between 2000-2019. For ERC quarters, POST = 1 for

any quarters where the earnings announcement date falls after the 13D filing date and POST = 0 for any quarters

where the earnings announcement date falls before the 13D filing date. For price delay and PIN quarters, if the

beginning and end of an activism event falls entirely within the quarter, then the quarter is considered POST = 1. If

the beginning of an event falls anytime in the entry quarter and the end of an event falls anytime in the exit quarter,

then all quarters in between including the entry and exit quarter are considered POST = 1 (see Figure 1 for details).

If quarter ends before beginning of activist event then POST = 0. Determination of POST for matched firms follow

its activist target counterpart. ACTIVE = 1 if firm has been the target of a shareholder activist event between 2000-

2019. For price delay and PIN regressions, the sample is restricted to firms with institutional ownership less or equal

to 1. LESSFIVE = 1 if first SEC 13D is filed by an activist with less than 5% shareholding. PRED, UE,

DISPERSION, and MTB are winsorized at 1% because of extreme outliers. Variable descriptions are included in

Appendix 1. ***, **, * denotes significance at the 1%, 5%, and 10% level, respectively.

ERC Price Delay PIN

POST 0.000 0.001 0.000

(0.14) (0.28) (-0.05)

ACTIVE×POST 0.005 0.028*** 0.016***

(1.41) (6.12) (2.62)

LESSFIVE 0.000 0.007 0.006

(-0.03) (0.46) (0.29)

POST× 𝐿𝐸𝑆𝑆𝐹𝐼𝑉𝐸 0.002 -0.019** -0.021

(0.32) (-2.01) (-1.56)

UE 0.555***

(4.62)

UE×ACTIVE -0.152**

(-2.48)

UE× POST -0.258***

(-3.78)

UE× ACTIVE ×POST 0.177**

(2.01)

UE×LESSFIVE -0.076

(-0.61)

UE×POST× 𝐿𝐸𝑆𝑆𝐹𝐼𝑉𝐸 0.327*

(1.85)

Controls Yes Yes Yes

Firm FE Yes Yes Yes

Year FE Yes Yes Yes

Firm-quarters 22,286 63,443 13,057

R2 0.086 0.483 0.225

Table 12: ERC and Activism All Regression Variables

UR

POST 0.000

(0.14)

ACTIVE×POST 0.005

(1.52)

UE 0.561***

(4.67)

UE×ACTIVE -0.160***

(-2.66)

UE× POST -0.258***

(-3.78)

UE× ACTIVE ×POST 0.207**

(2.4)

NONLINEAR 1.372***

(7.22)

SIZE -0.008***

(-5.95)

MTB 0.000***

(2.89)

LOSS -0.019***

(-8.84)

Q4 0.000

(0.25)

BETA 0.002

(1.5)

PREDICT 0.000

(-1.31)

SIZE×UE 0.075***

(4.44)

MTB×UE -0.004

(-1.13)

LOSS×UE -0.378***

(-5.91)

Q4×UE -0.355***

(-7.95)

BETA×UE 0.080***

(3.99)

PREDICT×UE 0.000*

(-1.67)

Firm FE Yes

Year FE Yes

Firm-quarters 22,286

R2 0.085

Appendix 1: Variable Definitions

Activism

ACTIVE ACTIVE = 1 if firm has been the target of a shareholder activist

event between 2000-2019 from Audit Analytics.

POST For ERC quarters, POST = 1 for any quarters where the earnings

announcement date falls after the 13D filing date and POST = 0

for any quarters where the earnings announcement date falls

before the 13D filing date.

For price delay and PIN quarters, if the beginning and end of an

activism event falls entirely within the quarter, then the quarter is

considered POST = 1. If the beginning of an event falls anytime in

the entry quarter and the end of an event falls anytime in the exit

quarter, then all quarters in between including the entry and exit

quarter are considered POST = 1 (see Figure 1 for details). If

quarter ends before beginning of activist event then POST = 0.

For fiscal years, if the beginning and end of an activism event falls

entirely within the year, then the year is considered POST = 1. If

the beginning of an activist event falls less than 180 days from the

beginning of the entry year and the end of an event falls less than

180 days from the end of the exit year, then all years in between

including the entry and exit year are considered POST = 1 (see

Figure 2 for details). If beginning of year is more than 180 days

before the beginning of activist event then POST = 0.

Determination of POST for matched firms follow its activist target

counterpart.

ACTDIR ACTDIR = 1 for target if an activist representative, defined as a

professional of activist firm, is also a director of target, defined by

any position title including “board” or “committee” at anytime

during the activist intervention. ACTDIR = 0 otherwise.

Professionals data from the Capital IQ Professional database.

LESSFIVE LESSFIVE = 1 for activist target if first SEC 13D is filed by an

activist with less than 5% shareholding. This applies only in the

case of the wolf pack sample. All firms targeted by individual

activists must have 5% or more shareholding to be included in the

sample.

Probability of Activist Target

CASH Ratio of the cash balance to total assets from Compustat.

ROA Return on assets of firm, measured as net income over total assets

from Compustat.

DE Debt-to-equity ratio of firm, measured as total liabilities over

common equity.

TOBINQ Tobin’s Q of firm, measured as total assets plus market value of

equity subtracted by book value of equity, then divided by total

assets. Market value of equity calculated as share price at calendar

year end multiplied by shares outstanding at year-end. Book value

of equity calculated as sum of stockholders’ equity, deferred taxes,

and investment tax credit, subtracted by preferred stock. All

variables are from Compustat.

ANALYSTS Number of analysts issuing a forecast for the year.

Controls

SIZE Size of firm measured as the natural logarithm of the market value

from Compustat.

MTB The ratio of market value to common equity of the firm from

Compustat.

INST OWN Percentage of institutional ownership at the firm, measured from

13F filings.

TURNOVER Share turnover, measured as total quarterly volume (in

100,000,000s) over shares outstanding (in 1,000,000,000s) from

CRSP.

BETA Beta from the market model estimated from regression of daily

firm-specific return on daily value-weighted market return.

Estimation is on a quarterly basis.

NUM ANALY Number of analysts issuing a forecast.

Quarters

LOSS Indicator = 1 for quarters whose earnings were negative.

Q4 Indicator = 1 for the 4th quarter.

Earnings Response Coefficient (ERC)

UR 3-day cumulative abnormal return starting from 1 trading day

before announcement. Abnormal return is computed as the excess

return from the Carhart four factor model (1997). Factor betas are

calculated as of the most recent quarter.

UE Unexpected earnings of the firm, calculated as the difference

between the actual earnings on announcement date and the median

of analyst forecasts from 60 days before to 1 day before

announcement date, difference is scaled by price at quarter end.

NONLINEAR Captures non-linearity in the earnings response coefficient,

calculated as UE*UE.

PREDICT Predictability of a firm’s earnings series, measured as the average

unexpected earnings of the last 8 quarters with a minimum of at

least 4 quarters, where unexpected earnings is calculated using the

median of analyst forecasts up to 60 days before the

announcement date.

Price Delay

DELAY Price delay calculated using Hou and Moskowitz’s (2005)

regression of daily market and daily firm-specific returns,

including 5 lags of daily market returns from CRSP.

Probability of Informed Trading (PIN)

PIN Trade data is extracted from the TAQ database and classified into

buys and sells following Lee and Ready (1991). Quarterly PIN is

estimated from daily buy and sell data with non-linear

optimization using the Newton-Raphson Method. The likelihood

function employs the Lin and Ke (2011) correction for floating-

point exceptions.

LEVERAGE Leverage of firm, measured as total liabilities divided by total

assets.

DISPERSION Dispersion of analyst forecasts, measured as standard deviation of

analyst forecasts scaled by mean of analyst forecasts.

Words in 10-K

WORDS Natural logarithm of total number of words in the 10-K using

Loughran and McDonald’s (2011) dataset.

UNCERTAIN WORDS Natural logarithm of total number of uncertainty words in the

10-K using Loughran and McDonald’s (2011) dataset.

AGE Age of firm measured as years since its first appearance in CRSP

SPI Special items of the firm from Compustat.

EARN VOL Earnings volatility of the firm, measured as the standard deviation

of the past 5 years of earnings.

RET VOL Returns volatility of the firm, measured as the standard deviation

of the 12-month monthly returns beginning two years from fiscal

year end and ending one year from fiscal year end.

NUM BUS SEG Natural logarithm of number of business segments reported from

the Compustat historical segments file.

NUM GEO SEG Natural logarithm of number of geographic segments reported

from the Compustat historical segments file.

Volume

VOLUME Share volume, measured as total quarterly volume (in

100,000,000s).

ABNPERF Past abnormal performance measured as mean of daily abnormal

returns of the past quarter. Abnormal return is calculated as the

difference between daily firm return and the value-weighted

market return.

VOLATILITY Past volatility, measured as standard deviation of daily returns of

the past quarter.