Post-Apocalyptic: The Real Consequences of Activist Short-Selling

Yu Ting Forester Wong

Leventhal School of Accounting

Marshall School of Business

University of Southern California

yutingfw@marshall.usc.edu

Wuyang Zhao

Rotman School of Management

University of Toronto

wuyang.zhao13@rotman.utoronto.ca

March 25, 2017

AcknowledgementsWe are grateful to Colleen Honigsberg, Seil Kim, Clive Lennox, K. R. Subramanyam, Regina Wittenberg-Moerman, Han Wu, Mingyue Zhang, Aner Zhou, and participants of the USC faculty Brown Bag for valuable comments.

Post-Apocalyptic: The Real Consequences of Activist Short-Selling

ABSTRACT

This paper examines the real effects of a recent phenomenon commonly referred to as “activist short-

selling,” where short-sellers publicly talk down stocks to benefit their short positions. First, we show

that after firms are targeted by activist short-sellers, their investing, financing, and paying-out

activities on average drop by 7.2%, 24.5%, and 7.6%, respectively. Using a battery of empirical tests,

we find that our results are unlikely driven by the activists’ ability to select declining firms. Second,

we provide evidence of three different channels through which activist short-selling leads to real

changes in the firm: increased cost of capital, more monitoring, and feedback from stock prices.

Third, we find that firms that are more vulnerable to “short and distort” suffer from more drastic

declines in real activities. Fourth, our evidence indicates that on average, firms targeted by activist

short-sellers experience improvement in real efficiency. This study contributes to the literature on

activist investors and on short-selling, and sheds light on the policy debate over regulations on

activist short-selling.

Keywords: Activism, Short-Selling, Real Effects, Diff-in-Diff, Channel, Investment Efficiency

JEL Classifications: G31, G32, G35, M41

1. Introduction

In the past decade, a large stream of literature has been devoted to examining the real implications

that long-position activists have on their target firms (e.g., Clifford 2008; Brav, Jiang, Partnoy and

Thomas 2008; Klein and Zur 2009). Activist campaigns usually involve one or multiple institutional

investors who accumulate a small but influential percentage of shares and publicly voice their demands

(Wong 2016). Studies generally find that these activists are able to implement significant real changes in

many aspects of their target firms.

Recent years have witnessed a financial innovation where activist investors publicly talk down

stocks to benefit their short positions (Ljungqvist and Qian 2016), a phenomenon commonly referred to

as “activist short-selling” in the investing community (Zhao 2017). On top of commenting on popular

media and at investing conferences, the rising popularity of social media platforms has allowed short-

sellers to disseminate their short-theses to a massive crowd. They usually provide arguments and

evidence on why the targeted stocks are severely overvalued and should be shorted. Wide dissemination

combined with detailed research means that these activists usually have material stock price impact on

their target firms. For example, Zhao (2017) documents -1.56% announcement-date return across about

6,000 cases from 2006 to 2015. Unlike their counterpart long-position activists, who frequently obtain

board seats in their target companies and involve themselves with their target’s operation, these short-

sellers do not have incentives to monitor the firm, because their main objective is to depress stock prices

such that they can close out their positions with profits.

Interestingly, similar to their long-position counterparts, on top of having the ability to move the

market, anecdotal evidence also indicates that activist short-sellers can lead to drastic changes in the

targeted companies, although likely through different channels. For example, after being publicly shorted

by Citron Research in October 2015, Valeant Pharmaceuticals (NYSE: VRX) had to sell not only non-

1

core assets but even core assets to pay back their debts.1 However, as mentioned above, activist short-

sellers have very different incentives when compared to their long-position counterparts, and do not have

incentives to change the underlying firm. Therefore, ex-ante it remains unclear if activist short-selling

will have any significant real consequences on the target firm and if so what specific channels lead to

these consequences. Despite the growing popularity and interests in these short-sellers, we are unaware of

any studies that examine the real consequences of activist short-selling.2

In this paper, we examine the real effects of activist short-selling by addressing four questions.

First, our primary research question is whether activist short-selling triggers or accelerates real

consequences on the target firms, and if so, how big are the impacts? Second, what are the specific

channels that lead to these real consequences? Third, what types of firms are affected the most after being

targeted by these activists? And finally, can activists improve real efficiency of their target firms?

Following Zhao (2017), we investigate these questions using a large sample of activist short-selling

collected from Seeking Alpha (SeekingAlpha.com, SA hereafter) and Activist Shorts Research

(ActivistShorts.com, ASR hereafter). SA is the largest crowdsourced investing platform appealing to

non-celebrity short-sellers, while ASR tracks all influential activist short-selling events, providing a good

complement to the SA sample.

There are several reasons why activist short-selling affects corporate real decisions. First, these

short-selling campaigns create uncertainty in the firms’ future cash flow and stakeholders’ perception of

the future cash flow, leading to a higher bankruptcy risk and making it harder/more expensive for the

firm to obtain finance – we call this the cost-of-capital channel. Second, activist short-selling attracts

attention from market participants, including shareholders, who may increase their monitoring efforts in

disciplining managers – we call this the monitoring channel. Third, managers may be alarmed by and

1 See http://www.reuters.com/article/us-valeant-assets-idUSKCN0XG0CR. 2 For example, in 2011, activist short-seller Carlson Block of Muddy Waters was named as one of the most influential people in finance and investing by Bloomberg Markets Magazine.

2

learn from the price reaction after activist short-selling and in turn reduce investment and financing – we

call this the feedback channel. We focus on the aggregate effects in the main analyses and explore these

three channels in Section 5.

We follow Derrien and Kecskés (2013) and Grullon, Michenaud, and Weston (2015) and focus on

12 specific measures that fall under three broad sets of corporate real decisions: investing, financing, and

payout. We examine the change of these variables in the eight quarters after the activist short-selling

benchmarking on the eight quarters prior to it. The univariate analyses show that firms significantly

reduce investing, financing, and payout activities. Specifically, benchmarking on the average level in the

eight quarters prior to the quarter being targeted, firms reduce total investment by 7.2%, total financing

activities by 24.5%, and total payout activities by 7.6%. Despite the potential spillover effects caused by

activist short-selling, the results remain qualitatively similar after we include various control variables in

a difference-in-differences regression, where we match each target firm with an industry peer with the

closest market cap prior to being targeted.

The main diff-in-diff analyses control for time trends, industry-wide shocks, and concerns

associated with size such as political cost (Zimmerman 1983). However, to attribute the real

consequences to activist short-selling, we have to rule out several other alternative explanations based on

various selection explanations. Note that most of the documented real consequences occur within one

quarter of being targeted. Therefore, it’s unlikely that the activists are merely selecting firms whose real

activities are trending downwards; instead it’s more likely that the activists have triggered or at the very

least accelerated such declines. Nonetheless, we construct a battery of tests to examine this selection

issue.

First, an explicit selection possibility is that activist short-sellers select to talk down heavily

shorted stocks – it is the short-selling rather than activists’ public talking-down that causes real

3

consequences (Grullon et al. 2015). If this is the case, we should find the real consequences to target

firms would not be as severe as to those firms that witness the most short-selling. We match each target

firm with an industry peer with the highest increase in short-interest but find that all inferences remain.

Second, it is possible that activist short-sellers select a set of observable characteristics that are associated

with future decline of real activities. We match each target firm with another industry peer with the

closest propensity score of being targeted by activist short-sellers, based on a selection model in Zhao

(2017). All inferences remain the same with this matching procedure. Third, it remains possible that the

activists make their selection based on a set of unobservable characteristics. Therefore, we use two

additional approaches to provide further evidence on whether the activists’ public talking-down leads to

any real effects. The first approach shows that more influential activist short-selling (i.e., bigger price

drop) leads to more severe real consequences. The second approach shows that the latter half of the

sample, in which activist short-selling reaches a larger audience, is associated with larger real

consequences.

Taken together, the above results show that the documented real consequences are triggered or

accelerated by activist short-selling. Next, we move to three related questions that are not only interesting

by themselves but also corroborate our causal inferences and exemplify various policy implications.

First, we provide evidence consistent with all three channels (mentioned above): cost of capital,

monitoring, and feedback. Specifically, we find (1) that there is a decrease in bond price on the activist

short-selling date, an increase in interest rates, and a decrease in facility amount in the two years

following activist short-selling, indicating that target firms suffer from increased cost of capital; (2) that

there is a decrease in the probability of passing management proposals and an increase in the probability

of submitting shareholder proposals, indicating targets’ management is under closer monitoring; and (3)

that targets with more informative stock prices witness bigger real consequences after being targeted,

4

consistent with the feedback explanation that managers learning more from the stock price adjust more in

real decisions. These results provide additional support to our primary finding that activist short-selling

causes/accelerates the real consequences, as it is difficult to explain these channel results with a selection

story.

Next, we examine what type of firms will be more affected by activist short-selling. Alarmingly,

we find that firms more likely to be subjected to “short and distort,” a practice in which short-sellers

spread unverified bad news in an attempt to realize a profit, are also more likely to suffer from a more

drastic drop in real activities. In particular, firms that operate in a weaker information environment or

have a shorter history of being listed are associated with a higher decrease in real activities. These cross-

sectional results are consistent with our primary finding that activist short-selling causes/accelerates real

consequences, because firms that are easier for short-sellers to influence suffer more reduction in real

activities. Furthermore, having dedicated investors does not appear to alleviate these effects. In contrast,

firms that have a higher level of market sentiment display a smaller decrease in real activities.

Finally, we evaluate whether activist short-selling moves target firms towards or away from an

optimal level of real activities. To be brief, we focus on one real decision – investment. We have the

following findings: (1) treatment firms still have higher investment level than control firms even after the

reduction caused by activist short-selling; (2) treatment firms significantly overinvest prior to activist

short-selling but converge to expected (optimal) level afterwards based on the overinvestment model in

Richardson (2006); and (3) target firms’ investment is more responsive to growth opportunities but not to

free cash flow after activist short-selling. These results indicate that on average activist short-selling

moves firms towards optimality. However, we also find that some previously underinvesting firms

underinvest even more after activist short-selling, highlighting the heterogeneity of the real consequences

of activist short-selling. Also, this observation is more consistent with activists leading to a treatment

5

effect and less consistent with activists simply selecting mean-reverting firms (i.e., the mean-revert

hypothesis would predict that underinvested firms invest more).

This paper contributes to the literature in the following ways. We add to the vast short-selling

literature by documenting the real effects of activist short-selling. Closely related to this study, Grullon et

al. (2015) find that small firms reduce their investing and financing activities after the removal of short-

sale price tests (i.e., a type of short-selling constraint – selling short is prohibited when the price is going

down). Our study complements Grullon et al. (2015) in several ways. First, there are two different

components in which activist short-selling can lead to real effects: (1) the short position itself, and (2) the

public talking-down. The price test is a restriction on taking the short-position – and does not affect the

ability of the short-sellers’ public talking down a particular stock.3 We complement Grullon et al. (2015)

by showing that public talking-down has effects incremental to the short-position itself. Second, as

shown in Ljungqvist and Qian (2016), public talking-down is often used as a tool to overcome short-

selling constraints. Relatedly, Zhao (2017) shows that the firm characteristics that attract activist short-

selling are different from those that predict high short-interest. Therefore, the subset of firms (targeted by

activist short-sellers) that we examine are likely to be very different from the subset of firms that are

subjected to large short-position. Third, we focus on firm-specific shock created by activist short-sellers,

as opposed to a regulatory shock, which allows us to provide evidence in support of three channels and

explore welfare implications, while Grullon et al. do not address these issues. Fourth, the documented

real consequences in this paper are not limited to small firms.

We also contribute to the activism literature by being the first to document the real effects of

activist investors holding short-positions rather than long-positions, diverging from the prior literature on

3 If anything, relaxing short-selling constraints (e.g., removing the price tests) would reduce the motivation for activist short-selling (Ljungqvist and Qian 2016) because short-sellers feel it is less necessary to publicly talk down these firms if they can move price by taking a sufficiently big short-position.

6

how activist investors monitor the targets’ management (Clifford 2008; Brav, Jiang, Partnoy, and

Thomas 2008) and implement significant real changes in the target firms (Klein and Zur 2009).

Finally, our results may be of interest to regulators.4 Our paper highlights the possible severity of

public talking-down on target firms in this Internet-based society. On top of the potential accusation on

price manipulation, activist short-sellers can affect real resources allocation. Our results provide two

more reasons why regulators should pay attention to activist short-selling: (1) those firms that are

vulnerable to “short and distort” suffer particularly severe real consequences, and (2) activist short-selling

is not uniformly moving target firms towards optimality – even though the average effect is such a move.

When regulators decide on the appropriate regulatory action, they should also consider the real effects

that short-sellers have on the target firms in addition to considering factors related to investors’

protection.5

This paper is organized as follows. Section 2 reviews related literature. Section 3 examines the

first and primary research question – whether activist short-selling affects real decisions. Section 4

addresses the alternative explanations that activist short-sellers choose to target firms that will reduce real

activities for other reasons. Section 5 studies three possible channels. Section 6 investigates the cross-

sectional variations in the real consequences. Section 7 evaluates whether target firms move towards or

away from the optimal level (we focus on investment). Section 8 concludes.

2. Literature

2.1 Literature on Short-Selling

4 In a recent Bloomberg interview, the SEC chairwoman Mary Jo White mentioned that activist short-sellers have the SEC’s continuous attention, and implementing disclosure requirement for short-sellers has the SEC’s intense attention. See https://www.bloomberg.com/news/videos/2015-11-10/sec-chair-white-short-selling-has-legitimate-purpose. 5 Our evidence also casts doubt on the effectiveness of requiring short-position disclosure, since these activist short-sellers voluntarily disclose their short-theses anyway.

7

A huge bulk of literature on short-selling focus on the “passive” dimension of short-selling. Due

to the sophistication and the business model of profiting from price declines, short-sellers have unique

roles in the capital markets. Prior research has shown that short-selling activities and threats can not only

facilitate price discovery (Boehmer and Wu 2013), but also affect corporate decisions, including real

decisions regarding investing and financing activities (Grullon, Michenaud, and Weston 2015), executive

contracting outcomes (Chang, Lin, and Ma 2015), and accounting and disclosure decisions (Fang, Huang,

and Karpoff 2016; Li and Zhang 2015).

Prior literature on short-selling mainly explores its “passive” dimension, focusing on short-

interest, realized short-sales, and market-wide short-selling regulations (see the review by Reed 2013). As

Zhao (2017) summarizes, activist short-selling differs from passive short-selling in two important ways.

First, activist short-selling is likely to be more informative because (1) it is motivated by incentives

betting on the decline of stock prices, rather than by tax or hedging reasons (Brent, Morse, and Stice

1990); (2) it is considerably riskier than passive short-selling; and (3) it is largely unconstrained by the

supply in the equity-loan market (Ljungqvist and Qian 2016). Second, activist short-selling becomes a

public signal when the short-seller talks down stocks. According to Higher-Order Beliefs (HOB) theory

(Morris and Shin 2002; Allen, Morris, and Shin 2006; Gao 2008), a bearish public signal could push all

investors to worry that others may sell soon. As a result, the best strategy for everyone is to sell

immediately. Activist short-sellers are frequently accused of manipulating stock prices and causing

unjustified real consequences because they are likely to create panic among investors.6

Due to the lack of easily available data, we only have limited evidence on activist short-selling

(Ljungqvist and Qian 2016; Chen 2016; Zhao 2017). However, these papers do not systematically study

the real effects of activist short-selling.

6 For example, in October 2015, the then-CEO of Valeant accused Citron Research of the equivalent of yelling “fire!” in a crowded theater. See http://www.bloomberg.com/news/articles/2015-11-10/valeant-ceo-sees-significant-disruption-in-dermatology-business.

8

2.2 Literature on Real Effects

The last decade has witnessed great interest in real effects among researchers in economics,

finance, and accounting. Broadly speaking, real effects refer to the fact that an entity changes its

allocation of resources as a result of the event of interest (i.e., being targeted by activist short-selling in

this paper). The general setup of these studies is to show the causal impact of an event (i.e., the

explaining variable) on the real consequences (i.e., the dependent variable).

Researchers have examined the impacts of various explaining variables on different real effect

variables. A few examples include the impact of hedge fund activism on productivity of the targets (Brav,

Jiang, and Kim 2015) and the rival firms (Aslan and Kumar 2016), share repurchase on employment

(Almeida, Fos, and Kronlund 2016), tax reforms on employee compensation (Yagan 2015), tax

accounting on investment location and profit repatriation (Graham, Hanlon, and Shevlin 2011), and

exogenous price changes on takeover (Edmans, Goldstein, and Jiang 2012) and investment and

employment (Hau and Lai 2013).

Three theory papers illustrate how short-selling could lead to real consequences. The general story

is that after the short-selling leads to price declines, managers might (be forced to) take some actions that

later justify the decline of prices. For example, Goldstein and Guembel (2008) argue that managers learn

from the declined price and disregard the projects with positive NPVs – an action destroying values and

justifying the price declines; Brunnermeier and Oehmke (2014) focus on financial institutions and argue

that leverage constraints imposed by short-term creditors will force the institutions to fire-sell assets at a

discount after the initial price decline. Similarly, Liu (2014) argues that creditors are likely to have a bank

run and as a result, a sound financial institution can go bankrupt because of aggressive short-selling. Note

9

that these three theory papers are motivated to discuss the real impact of short-selling when the targets

are fundamentally sound to illustrate the possible damage of short-selling, but the arguments can apply to

situations in which targets are not fundamentally sound.

Two recent studies examine the real effects of short-selling empirically. For example, He and

Tian (2016) find that after Reg SHO removed short-sale price tests from one-third of Russell 3000 firms

(i.e., pilot firms), these firms witnessed improvement in the quality, value, and originality of patents they

produced, benchmarking on patents produced by the control firms that price tests were still in place. They

argue that the exposure to patenting-related litigation initiated by short-sellers is a plausible mechanism

through which short-sellers curb myopic behavior. Grullon et al. (2015) is closest to our study. Also

making use of the Reg SHO, they find that small pilot firms react to decline of prices after the removal of

price tests by reducing equity financing and investment. They argue but do not distinguish two channels

through which short-selling threats lead to real effects: overvaluation and feedback channels.7

3. Does Activist Short-Selling Affect Real Decisions?

3.1 Reasons Why Activist Short-Selling Affects Real Decisions

We identify three channels through which activist short-selling can affect corporate real decisions,

in particular, reducing investing, financing, and paying-out activities.

3.1.1 Cost of Capital

Activist short-sellers publicly accuse companies of overvaluation. In their short-theses, they

usually provide arguments and evidence to make the case. As short-sellers profit from the decline of

stock prices, their short-theses usually contain a considerable amount of negative information (or

misinformation). This situation is further complicated by the perceived conflict of interest that short-7 These theory and empirical papers focus on the impact of passive short-selling. But these theories and arguments also apply to activist short-selling. In addition, we investigate aspects that are unique to activist short-selling. For example, according to Kovbasyuk and Pagano’s (2015) theory, an advertising arbitrageur (e.g., activist short-seller) can attract investors’ limited attention and improve the disciplining role of the market.

10

sellers disclose negative information and they benefit from the incorporation of such information into the

stock prices. As ordinary investors are uncertain what they should believe, the information asymmetry

increases substantially after activist short-selling, leading to higher cost of capital.8

3.1.2 Monitoring

Based on Kovbasyuk and Pagano’s (2015) theory, it is demonstrated that the public disclosure of

the short-thesis attracts attention from other stakeholders, such as analysts, shareholders, debt holders,

suppliers, and regulators. Managers would be more scrutinized about all major corporate decisions. For

instance, Chhaochharia, Kumar, and Niessen-Ruenzi (2012) document that firms with higher local

institutional investors exhibit a lower propensity to engage in “empire building.” To the extent that target

firms have excessive level of real activities to begin with, we expect that they lower the activity level

afterwards under closer monitoring of other market participants.

3.1.3 Feedback

Recent developments in finance literature show that the stock market is not a “side-show” but has

real impacts on real economic activities because decision-makers can learn from the stock prices, which

aggregate different pieces of information through trades (see the review by Bond, Edmans, and Goldstein

2012). If the stock prices decline after activist short-selling, managers would interpret that the market

does not think the potential investment would create value for shareholders. A rational manager would

reconsider the investing plans (Goldstein and Guembel 2008).9

8 One may argue that, to the extent activist short-sellers are spreading rumors in their short-theses, there would be no or weaker consequences. This would be true if all market participants believe that the short-theses are invalid – a highly unlikely situation. As long as some investors are not sure about the validity of the short-theses, the disagreement and information asymmetry among the investors would increase. As a result, the validity of short-theses is not a necessary condition for (at least) the cost-of-capital channel.9 Similarly, in the activist short-selling setting, corporate managers can also learn directly from the short-thesis because it may contain concrete comments on corporate decisions, which short-sellers claim cause overvaluation. For example, in its first report on Valeant, Citron Research comments that “quality of acquisitions has waned” and “Valeant's ability to execute more acquisitions is now deeply impaired” (see http://www.citronresearch.com/wp-content/uploads/2015/10/Valeant-Part-II-final-b.pdf). It is possible that managers take such warnings seriously and adjust M&A strategies in the future. Also, note that the feedback effect does not require any information content of the short-theses. As the activist short-selling acts as a public signal, it can create panic among investors of “lest everyone else get out first.” As a result, the stock price can crash in a stampede. Indeed, Zhao (2017) finds that targeted firms’ short-term returns are more negative for firms whose

11

The above three arguments lead to the same prediction that activist short-selling reduces real

activities. However, there are also reasons it could not matter for corporate real decisions. For example,

it is possible that managers pay no attention to a short-selling campaign if it has little effect on stock

prices. In addition, some firms may have a very stable strategy and are robust to external shocks. We will

make use of such cross-sectional variation in ruling out some alternative hypotheses and focus on some

variations in firm-level characteristics in Section 6.

3.2 Sample Construction

Following Zhao (2017), we collect activist short-selling cases from Seeking Alpha (SA) and

Activists Shorts Research (ASR). Whereas SA is the largest crowdsourced investing platform and

therefore fits well for non-celebrity shorts, ASR tracks short-selling campaigns waged by prominent

traders. For those short-theses published on SA, we define activist short-selling as those articles in which

the authors disclose explicitly that they hold short-positions in the discussed stocks. Overall, we have

6,081 activist short-selling cases from 2006 to 2015. We explain how these cases are identified in

Appendix A.

We rely on quarterly financial accounting data to evaluate the real impacts. To do so, we collapse

the original activist short-selling dataset (i.e., at the firm-date-short-seller level) to the firm-quarter level.

Those firm-quarters in which the firm was targeted by activist short-sellers at least once are defined as

targeted firm-quarters. Table 1 illustrates the time distribution of targeted firm-quarters. We can see a

clear trend that more and more firm-quarters are targeted in the past decade, suggesting that activist

short-selling is an increasingly important phenomenon in the US equity market.

3.3 Real Decisions

investors face higher uncertainty and therefore are easier for short-sellers to influence. As long as the prices crash (regardless whether they crash based on information or misinformation), managers can learn from the price and reduce investing and financing activity. This is similar to the logic in Goldstein and Guembel (2008).

12

Following the prior literature on real impacts (Derrien and Kecskés 2013; Grullon, Michenaud,

and Weston 2015), we focus on three broad sets of corporate real decisions: investing, financing, and

paying-out.10 For investing decisions, we focus on five measures: Capital Expenditure (CAPX), Research

and Development Expenditure (R&D), Mergers and Acquisitions (M&A), Total Investment (the sum of

the previous three; TotalInvest), and Total Asset Growth (AssetGrowth). For financing decisions, we

focus on four measures: Short-Term Debt Issues (ShortDebtIssue), Long-Term Debt Issues

(LongDebtIssue), Equity Issues (EquityIssue), and Total Financing (the sum of the previous three;

TotalFinance). For paying-out decisions, we focus on three measures: Cash Dividend (Dividend),

Buyback (Buyback), and Total Pay-Out (the sum of the previous two; TotalPayout). All analyses are

conducted at the firm-quarter level.

3.4 Descriptive Statistics and Univariate Analyses

Figure 1 plots the time-series variation for the mean and median of each real-effect variable from

eight quarters before to eight quarters after a firm is targeted by activist short-sellers. We find a clear

trend that firms reduce investing (except in R&D), financing, and paying-out activities after activist

short-seller attacks.

Table 2 compares the real-effect variables of the treatment firms by aggregating all eight quarters

before and all eight quarters after the targeted firm-quarter. Again, we find that firms reduced 11 out of

12 real activities (except R&D), covering all three broad categories including investing, financing, and

paying-out. Further, the reduction of 12 variables are all significant at the 5% level or better. Note that

the drop is also economically substantial. For example, total investing activities reduce by 7.2%, total

financing activities by 24.5%, and total paying-out activities by 7.6%.

3.5 Difference-in-Differences Analyses

10 In untabulated tests, we find that firms also reduce cash-holding after they are targeted by activist short-selling.

13

3.5.1 Primary Matching Procedure: Closest Market Cap

Firms being targeted by activist short-sellers are clearly not based on random assignments. As a

result, the above univariate comparison can be interpreted alternatively. To the extent the control sample

represents the industry- or market-wide trend regarding corporate real decisions, we can separate the

treatment effects of activist short-selling with a diff-in-diff test. Note that using industry peers as a

benchmark could possibly underestimate the real impacts of activist short-selling if the impacts spillover

to industry peers.

Our primary control sample is constructed by matching each target firm with another firm in the

same Fama-French 48 industry with the closest market cap prior to the quarter the treatment firm is

targeted. In this way, we control for time trends caused by unknown industry- or market-wide factors and

concerns related to the size such as political cost (Zimmerman 1983). We successfully match 3,400

activist short-selling quarters with 3,382 control firm-quarters.11 Then we extend the matched firm-

quarters to eight quarters before and after the quarter in which the treatment firm is targeted by activist

short-selling and remove the quarter being targeted (i.e., Quarter 0). In so doing, we construct a panel

consisting of 16 quarters (i.e., eight quarters pre- and eight post- the event quarter) for both treatment and

control firms.

3.5.2 Difference-in-Differences Results

Table 3, Panel A presents the diff-in-diff univariate analyses for all 12 variables. The inferences

remain largely the same as those in Table 2. Except that of R&D, the diff-in-diff estimates are all

negative at the 5% level for the remaining 11 real-impact variables. Note that many diff-in-diff estimates

are less negative than those time-series differences in Table 2, consistent with the existence of industry-

11 We require control firms to be from the pool of firms that are not targeted by activist short-sellers throughout the sample period to avoid the situation that a firm appears in both the treatment and control sample.

14

wide spillover effects. However, it is interesting that diff-in-diff estimates of all paying-out variables and

several investing variables are more negative than their counterparts in Table 2.

3.5.3 Multivariate Difference-in-Differences

We estimate a standard diff-in-diff model as follows:

(1)

Where, RealDecision represents corporate real decisions including investing, financing, or

paying-out, as we explain in the above section. Treat is one for firms that are targeted by activist short-

sellers at least once and zero otherwise. Post is one for quarters that are after the targeted firm-quarter

(for treatment firms) or after the matched treatment firm’s targeted firm-quarter (for control firms), and

zero otherwise. Following Grullon et al. (2015), we also include several control variables, including Net

Cash Flow from Operations (CFO), Lagged Size (LagSize), Lagged Leverage (LagLev), and Lagged

Profitability (LagProfit).12 Note that as we control for Firm and Quarter fixed effects, we do not need to

include Treat and Post in the regression.

Table 3, Panel B reports multivariate diff-in-diff results based on regression analyses. The

coefficient of interest is that of Treat×Post. We can find that it is negative across all 12 columns. More

importantly, it is significant at the 5% level in eight columns except in CAPX, R&D, LongDebtIssue

(10% significant), and Buyback. These results confirm the inferences in univariate analyses that target

firms reduce investment, financing, and paying-out activities after activist short-selling.

4. Alternative Explanations Based on Selection

We propose a causal explanation of the real consequences documented in Section 3: activist short-

sellers trigger or accelerate the reduction in targets’ real activities. The diff-in-diff analyses address

concerns that the observed effects are caused by time trends, industry-wide shocks, or factors associated 12 Including short-interest as an additional control variable leads to little change to all coefficients and their significance levels.

15

with firm size. However, it is still possible that short-sellers pick firms that would have reduced their real

activities even in the absence of the activist short-sellers. Our results are unlikely to be purely driven by

selection given that a substantial drop in real activities happens in the quarter immediately after activist

short-selling. Nevertheless, we address such concerns in three steps. First, we focus on a specific

selection issue: activist short-sellers pick firms that have witnessed intense short-selling activities

recently, which led to real effects (Grullon et al. 2015). Second, we assume the selection is based on

several observable firm characteristics as modeled in Zhao (2017). Third, we use two cross-sectional tests

to address selection issues based on unobservable characteristics.

4.1 Selecting Heavily Shorted Firms

It is possible that activist short-sellers pick up heavily shorted stocks such that the real

consequences are driven by short-positions rather than the public talking-down. If this is the case, those

firms that are shorted the most should witness the biggest reduction in real activities. To address this

possibility, we construct a control sample consisting of similar industry peers of target firms that witness

the highest increase in short-interest.13 The matching procedure is as follows. For each targeted firm-

month, we match another firm in the same market-cap quintile in the same Fama-French 48 industry and

with the highest short-interest increase compared to the previous short-interest announcement.14 Then we

collapse this dataset into the firm-quarter level. If one firm is targeted in two months in the same quarter

and matched with two different firm-months, we use the matched firm-month with the higher increase in

short-interest as the control observation.15 Then we extend the matched firm-quarters to eight quarters

13 Zhao (2017) compares the market reactions to activist short-selling and to short-interest announcements of different industry peers. He finds that although the reaction to short-interest announcements are much smaller than the reaction to activist short-selling, the market reaction to announcements by industry peers with the highest short-interest increase is larger than the reaction to announcements by other firms. Note untabultated results indicate that all inferences remain if we use an industry peer with (1) closest increase in short-interest, (2) closest short-interest, or (3) highest short-interest. 14 The Financial Industry Regulatory Authority (FINRA) collects short-interest in individual securities on the settlement date twice-per-month (once-per-month before September 7, 2007) and the exchanges that list stocks publish the data at 4 PM eight business days later.15 The mean increase in short-interest for treatment sample is less than 0.0009, while the same statistic for the control sample is 0.0245 – 28 times larger than that in the treatment sample.

16

before and after the quarter in which the treatment firm is targeted by activist short-selling and remove

the quarter being targeted (i.e., Quarter 0).

Table 4, Panel A presents results based on the above matching procedure. To be brief, we only

tabulate results using the four aggregate measures. We find that the results are quantitatively similar to

those in Table 3, Panel B. Specifically, the coefficient of interest (i.e., Treat×Post) is negative for all

columns in both tables, and it is significant for all variables at the 1% level or better. These results

suggest that our documented results are unlikely driven by short-sellers’ short positions.

4.2 Selection Based on Observable Characteristics

It is also possible activist short-sellers strategically pick several firm characteristics that associate

with future reduction in real activities. To address this possibility, we construct a control sample based on

the propensity score matching using the determinant model in Zhao (2017).16 In Appendix C, we present

the logistic regression result used to calculate the propensity score, eight out of nine of the characteristics

are statistically significant with an overall pseudo R2 of 8.1%. For each firm-quarter, we calculate the

propensity score of being targeted, and then match each targeted firm-quarter with a non-targeted firm in

the same quarter and Fama-French 48 industry with the closest propensity score. Then we extend the

matched firm-quarters to eight quarters before and after the quarter in which the treatment firm is targeted

by activist short-selling and remove the quarter being targeted (i.e., Quarter 0).

Table 4, Panel B presents results based on the above matching procedure. The coefficient of

interest (i.e., Treat×Post) is negative for all columns in both tables, and it is significant for all variables at

the 10% level or better. These results corroborate our inference that activist short-selling affects corporate

real decisions.

16 We do not include quarter and industry fixed effects into the first stage. Instead, we require the matched observation is in the same quarter and industry as the treatment observation. The logistic regression results are tabulated in Appendix C.

17

4.3 Selection based on Unobservable Characteristics

The above two approaches address selection issues based on observable characteristics. However,

it remains possible that the activists select targets based on some unobservable characteristics. Therefore,

in this section, we provide further evidence on whether the real consequences are driven by selection

issues.

First, if the decrease in real effects is caused or accelerated by activist short-selling, we would

expect that more influential short-selling campaigns (or more informative short-theses) would lead to

more severe real consequences. In particular, we estimate the following regression at the firm-quarter

level, using only treatment observations:

(2)

We proxy for the information contained in the short-sellers’ theses, using ShortsInfo, which is the

cumulative abnormal returns in the first five trading days starting at the activist short-selling date. Table

5, Panel A presents results for equation (2). The coefficient of interest, ShortsInfo×Post, is negative cross

four columns, indicating that the more influential the short-thesis is, the bigger the real effects would be.

This is consistent with our prediction that activist short-selling drives real consequences.

Second, SA has become increasingly popular in recent years and ASR was started in 2014. The

more audience a short-selling campaign reaches, the more influential it will be. Therefore, it is natural to

predict that later campaigns are more influential than earlier ones, because the later ones reach a bigger

audience. It is not clear how a selection-based argument predicts the difference across time. We estimate

the following regression at the firm-quarter level, using only treatment observations:

(3)

18

We split the sample based on whether activist short-selling happened in the first five years (i.e.,

2006–2010) vs. in the last five years (i.e., 2011–2015). After2011 is defined as one if the activist short-

selling date is after January 1, 2011, and zero otherwise. Table 5, Panel B presents results for equation

(3). The coefficient of interest, After2011×Post, is negative across four columns, and it is significant at

the 5% level for TotalFinance. These results indicate that bigger real effects are observed in the period of

time in which activist short-selling is more visible in general. Again this is consistent with our story that

the results are driven by the treatment effect of activist short-selling.

5. Channels

In Section 3 we argue that activist short-selling leads to real consequences for three reasons: (1)

higher cost of capital, (2) closer monitoring, and (3) feedback effect. Our main analyses document the

aggregate effect. This section explores empirical support of these individual channels.

5.1 Cost of Capital

Activist short-selling increases information asymmetry among investors and thereby raises the

cost of capital. Since on average there is a negative stock price reaction on announcement day (Zhao

2017), the estimated cost of equity capital will increase mechanically. As a result, we cannot separate real

increase in cost of capital from measurement errors. However, cost of debt is largely free of this

measurement problem. Besides, as debt holders have priority over equity holders, a decrease in stock

price may not lower the cost of debt. Therefore, we focus our analysis on the cost of debt. In particular,

we obtain bond prices from the Trade Reporting and Compliance Engine (TRACE) and examine the

abnormal return on the announcement date of the short-thesis.

There are several econometric issues involved in calculating bond returns. First, as bond trading is

relatively thin, we eliminate firms whose bonds did not trade during the (-5, 0) window of activist short-

19

selling. Second, many firms have multiple bonds trading simultaneously. We take the average returns of a

firm’s multiple bond return. After applying these restrictions, we are left with 1,331 treatment firms with

bond return data.

In Table 6, Panels A and B, we present the results for our bond returns test. Panel A tabulates the

summary statistics for AbnBondRet, which is raw return for a target firm less the market return on that

day. On average the bond return is negative and statistically significant. Further, after sorting our sample

into quartiles by the equity returns of that day, the bond market reaction is strongest (i.e., most negative)

in the lowest quartile at -2.04% and weakest in the top quartile at 0.36%.

We also directly examine the actual change in cost of borrowing for the target firm. We search for

all syndicate loan contracts within DealScan and examine the actual interest rate within all loan facilities

of the target firms. Our findings in Panel B indicate that firms experience difficulties in obtaining

finances after being targeted by activist short-sellers. First, we find evidence consistent with an increase

in cost of borrowing. For facilities that started two years prior to the company being targeted, the average

interest rate is 286 bps above London Interbank Offered Rate (LIBOR). In the two years after being

targeted, the average interest rate is 332 bps. Furthermore, we find evidence indicating that target firms

have trouble obtaining financing. In the two years prior to being targeted, the total facility amount is $1.2

billion; however, this amount decreases to $0.8 billion in the post-period. All above evidence together

suggests that cost of capital is a significant channel through which activist short-selling leads to real

consequences.

5.2 Monitoring

Activist short-selling could attract attention from various market participants. As a result,

managers could be more disciplined given a higher probability of being identified as not improving

20

shareholders’ value. To the extent that managers engage in inorganic growth or empire building, better-

disciplined managers would reduce real activities.17

We examine whether managers become more disciplined through the lens of shareholder voting.

If managers are not perceived as disciplined, one potential way in which a shareholder can limit

management action is via shareholder votes. We obtain company voting results from Institutional

Shareholder Services’ (ISS) Voting Analytics and create four measures that examine various aspects of

shareholder votes: (1) PassMgn is the percentage of management proposal that is passed in that year; (2)

ShareholderProposal is an indicator that takes the value of one if there was at least one shareholder

proposal that year and zero otherwise; (3) PassShareholder is the percentage of shareholder proposal that

is passed in that year; and (4) Proxyfight is an indicator that takes the value of one if there was a proxy

fight that year and zero otherwise.

We identified 2,938 treatment firm-quarters with sufficient data in ISS Voting Analytics. In Table

6, Panel C we present the summary statistic for our four shareholder votes proxies. Overall, we find

evidence consistent with shareholders exercising their voting rights to limit management behavior. In row

(1) the percentage of management proposal passed is 0.3% lower in the post-period. Similarly, in row (2)

the probability that a shareholder proposal is raised increases by 0.93% in the post-period. However, we

find no evidence of change in the percentage of shareholder proposal that is passed and the probability of

proxy fight.

5.3 Feedback Effect

According to the feedback literature (i.e., Bond, Edmans, and Goldstein 2012), managers learn

from the stock price and therefore adjust their real decisions. If the price drops, the managers would

interpret that the market holds a pessimistic view on the planned investments and then reduce investment. 17 We assume that firms are doing real activities at a level more than optimal. We think this is a reasonable assumption because those firms are all targets of activist short-selling, while Zhao (2017) shows that activist short-sellers are more likely to target firms with overinvestment and higher past (unsustainable) growth. Section 7 provides further evidence supporting this assumption.

21

Based on the feedback-effect logic, managers should learn less from price if the price is uninformative

about the firm-level news, for example, highly synchronized with market- or industry-wide shocks. We

explore the variations in the targets’ price synchronicity before they are targeted by activist short-sellers.

Specifically, we follow Piotroski and Roulstone (2004) and regress each stock’s Wednesday-to-

Wednesday returns on the contemporaneous and one-week lagged market returns and industry returns

over each calendar year as follows:

(4)

where MarRet is the market-wide value-weighted return and IndRet is the value-weighted return

for the SIC-2 industry a firm belongs to. Synchronicity is calculated as log(R2/(1- R2)). Intuitively, this

measure captures the proportion of stock-return variation explained by the contemporaneous and lagged

market returns and industry returns. As result, stocks with lower synchronicity would be more

informative about firm-specific news, and therefore managers have more to learn from the stock price.

We create a dummy variable, Informative, defined as one for firms with lower-than-median

synchronicity values in the year prior to being targeted, and zero otherwise.18 Then we interact

Informative with Post for treatment sample. Table 6, Panel D presents results. The coefficient of interest,

Post×Informative, is negative across all five columns, and is significant at the 1% level for TotalInvest

and TotalFinance. In other words, for target firms with more informative stock prices, the real

consequences are stronger after these firms are targeted. These results suggest that managers learn from

the drop in stock prices – the feedback channel through which activist short-selling affects real

decisions.19

18 Here we implicitly assume that the informativeness of stock prices does not change substantially after activist short-selling. All results are qualitatively similar if we calculate synchronicity by quarter instead of by year or define Informative based on price synchronicity in the current year rather than the previous year. 19 However, it seems that the feedback channel is unlikely the full story. Again based on the feedback theory, if the price increases, the managers would increase investment. This is not the case. Our untabulated analyses show that, for those cases (around 20%) that surprisingly witness prices going up, their investment, financing, and paying-out also decreased. This fact indicates that the feedback channel is unlikely the only channel, and we now explore another two channels.

22

6. Firm Characteristics and Real Effects of Activist Short-Selling

The primary focus of this section is to investigate what types of targets suffer more from activist

short-selling. This is of particular interest to regulators. Note that the defining feature of activist short-

sellers is that they publicly talk down stocks. Clearly, if stakeholders are faced with a weaker information

environment – they are uncertain about the precision of the existing information signals – they would be

more easily convinced by the short-thesis. We predict that firms that are faced with a weaker information

environment are more likely to be influenced by activists. We measure a firm’s information environment

using the dispersion in analyst forecast error, and we define InfoEnv as an indicator variable that takes the

value of one if the standard deviation of analyst forecast is above the median of our sample, and zero

otherwise.20

Similarly, due to the lack of historical information, stakeholders may also consider young firms to

be more uncertain. We measure the age of the target firms by counting the number of years the firms

have been in CRSP. We define YoungFirm as an indicator variable that takes the value of one if their age

is below the median of all sample firms in that year, and zero otherwise.

However, not all stakeholders may be equally influenced by activist short-selling. For example,

certain institutional investors may be better able to process information and/or have access to better

information. Therefore, we predict that if firms have a larger number of dedicated investors as defined by

Bushee (1998), they would be less susceptible to short-seller attacks. In particular, we define DedicateInv

as one if the number of dedicated investors is higher than the median, and zero otherwise.

In contrast, short-sellers may target firms that have egregious overvaluation features and

ultimately stock price may decline. However, this decline may not necessarily affect the underlying real

operation of the firm. For example, the underlying operation of the firm may be healthy, but the stock

20 Note our inferences remain the same if we measure the information environment accounting for quality based on Francis, LaFond, Olsson, and Schipper (2005).

23

market may be too excited about the stock – high sentiment. After the activist attacks, the stock price of

the firm may drop back to its fundamental value without affecting the firm’s real activities. We identify

high-sentiment stocks as those stocks with a rapid price run-up in the last one year. We define Sentiment

as one if the raw return in the last one year ending in the quarter before the activist attack is higher than

the median, and zero otherwise.21

To examine the type of firms that may be more susceptible to activist short-selling, we estimate

the following regression at the firm-quarter level, using only treatment observations:

(5)

Table 7 presents our result for regression (3) using the full sample of short-selling events. Note

that we control for the first-week market reaction to activist short-selling.22 Our main variables of interest

are the interaction terms between Post and Determinant. As mentioned above, we examine four different

determinants: InfoEnv, YoungFirm, DedicateInv, and Sentiment. We find that younger firms and firms

with weaker information are more affected by short-theses. The coefficient on the interaction with

InfoEnv is negative and significant for total investment, total finance, and total payout. Similarly, the

coefficient on the interaction with YoungFirm is negative and significant for asset growth. We find no

evidence that dedicated investors are able to limit the impact that activist short-sellers have on the target

firm. In contrast, we find that firms with high sentiment are less affected by the short-theses across all

dimensions. The coefficient on the interaction with Sentiment is positive and significant for total

investment, asset growth, total finance, and total payout. It is unclear how these results can be explained

by a selection explanation, therefore providing additional support of our primary finding that activist

short-selling causes/accelerates real consequences.

21 Note our inferences remain the same if we measure sentiment with P/B ratio. 22 Untabulated results show that all inferences remain the same if we focus on the subset of targets who witness the most negative first-week market reactions.

24

7. Does Activist Short-Selling Improve or Impede Real Optimality?

Given the findings in prior sections that activist short-selling reduces real activities, the natural

question to ask next is whether target firms move towards or away from the optimal level. As we

researchers cannot observe the optimal level, we employ three different approaches to draw inferences.

To be brief, we focus on investing activities in this section.

First, to the extent that control firms (matched by industry and size) represent the equilibrium

level, we can evaluate whether activist short-selling moves target firms towards or away from the real

activity level of control firms. Table 8, Panel A shows that control firms remain largely the same

investment level from pre- to the post-period. Taking Table 2 and Table 8, Panel A together, we find that

although target firms reduce real activities, the post-period level of investing is still higher than the

control firms. In other words, after activist short-selling, the investing level of target firms is moving

towards control firms. We interpret this as evidence that activist short-selling improves real optimality.

The above approach implicitly assumes industry membership and size decide the optimal level of

real activities. Our second approach relaxes this assumption and follows Richardson (2006) to model the

expected level of investment based on a set of determinants. The residual of the expected regression is

labeled as Overinvestment. We provide a brief introduction to his methods in Appendix D. Table 8,

Panels B summarizes the Overinvestment statistic for both control and treatment firms in the pre- and

post-period of activist short-selling. Treatment firms exhibit significant overinvestment in pre-period

(Overinvestment = 0.0037; t = 5.68) while the pattern totally disappear in the post-period

(Overinvestment = -0.005; t = -1.34). Note that mean of 0.0037 in the pre-period is also economically

significantly – it is more than 10% of the new investment (INew – total investment excluding

maintenance). As a benchmark, the control firms are not significantly different from zero in both pre- and

25

post-periods. Panel C provides a more detailed picture on when target firms start to reduce

overinvestment. We can find a clear structural change: target firms exhibit significant overinvestment in

almost every quarter prior to being targeted (note the smallest t-value is 1.22), but in none of the quarters

afterwards (note the biggest t-value is 1.17). These provide evidence supporting the prediction that

activist short-selling reduces overinvestment and moves the target firms towards optimal level.

Our third approach does not rely on the assumption on the determinants of optimal investment

level. We follow McLean, Zhang, and Zhao (2012) and use the sensitivity between total investment

(TotalInvest) and growth opportunities, measured by VP ratio calculated by Richardson (2006), to

evaluate investment efficiency and free cash flow (FCF). The underlying rationale is that efficient

investments should respond more to growth opportunities than to available free cash flow. Table 8, Panel

D presents the results. Column 1 reports that for treatment firms, activist short-selling improves the

sensitivities of investment to growth opportunities. Column 2 shows that it has no impact on the

sensitivities of investment to growth opportunities for control firms. Also, activist short-selling seems to

have negligible impact on the sensitivities of investment to free cash flow. Taken together, after activist

short-selling, treatment firms’ investment is more responsive to growth opportunities but not more to free

cash flow benchmarking on control firms.

These results from the above three approaches are consistent with the prediction that activist

short-selling is moving the investment decisions towards the optimal level – i.e., improving the

investment efficiency. Next we take a non-parametric approach and examine the percentage of firms that

actually move towards optimality. We define Overinvesting (Underinvesting) firms as the cumulative

Overinvestment in the eight quarters in the pre-period is positive (negative). Then we check how many

firms increase or decrease overinvestment in the post-period. We find that 79% of overinvesting target

firms reduce overinvestment level, compared to 72% of overinvesting control firms. However, 60% of

26

underinvesting target firms underinvest even more afterwards, compared to 57% of underinvesting

control firms. These statistics provide two important messages. First, although activist short-selling

moves targets towards optimality on average, the effect is heterogeneous. Second, this pattern is also

inconsistent with the selection story based on mean reversal, providing additional support for our primary

finding that activist short-selling causes/accelerates such real consequences.

8. Conclusion

Prior literature on hedge fund activism has reached a consensus that long-position activists could

change the real decisions inside the target firms. However, the real effects of short-position activists –

activist short-sellers – are largely ignored by the literature, although activist short-selling is now on the

rapid rise. This paper fills the void in the literature and examines the real consequences of a large sample

of activist short-selling cases that happened in the US equity market from 2006 to 2015.

We have four sets of findings. First, we find that firms reduce real activities substantially after

activist short-selling. Specifically, they reduce investing by 7.2%, financing by 24.5%, and paying-out by

7.6%. A battery of tests suggest that such results are unlikely driven by the possibility that activist short-

sellers pick stocks that would have reduced real activities in the absence of activist short-selling. Second,

we explore three channels through which activist short-selling affects corporate real decisions. Most

importantly, we find preliminary evidence in support of all three channels: cost of capital, monitoring,

and feedback channels. Third, we explore the cross-sectional variations in the real consequences. We find

that firms that are ex-ante more vulnerable to “short and distort” are likely to reduce more in real

activities after being targeted. For example, firms with higher information uncertainty and a shorter

history of being listed are affected more by short-sellers’ public talking-down behavior. Finally, we

27

present evidence that activist short-selling on average moves target firms towards the optimal level of

real activities rather than away from it.

Our paper could be of interest to regulators. One key issue in the debate of regulating short-selling

activities is how their behavior affects the real economy. If activist short-selling does not affect corporate

real decisions, then the SEC and other regulators should care less about their controversial role. This

paper shows the opposite – activist short-selling has substantial real consequences and more so for firms

that are vulnerable, justifying the “intense attention” from the SEC on this increasingly popular market

phenomenon.

28

REFERENCES:

Allen, F., Morris, S., & Shin, H. S. (2006). Beauty contests and iterated expectations in asset markets. Review of Financial Studies, 19(3), 719-752.

Almeida, H., Fos, V., & Kronlund, M. (2016). The real effects of share repurchases. Journal of Financial Economics, 119(1), 168-185.

Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5(1), 31-56.

Aslan, H., & Kumar, P. (2016). The roduct market effects of hedge fund activism. Journal of Financial Economics, 119(1), 226-248.

Beneish, M. D., Lee, C. M., & Nichols, D. C. (2015). In short supply: Short-sellers and stock returns. Journal of Accounting and Economics, 60(2), 33-57.

Boehmer, E., & Wu, J. J. (2013). Short selling and the price discovery process. Review of Financial Studies, 26(2), 287-322.

Bond, P., Edmans, A., & Goldstein, I. (2012). The real effects of financial markets. The Annual Review of Financial Economics, 4, 339-360.

Brav, A., Jiang, W., & Kim, H. (2015). The real effects of hedge fund activism: Productivity, asset allocation, and labor outcomes. Review of Financial Studies, 28(10), 2723-2769.

Brav, A., Jiang, W., Partnoy, F., & Thomas, R. (2008). Hedge fund activism, corporate governance, and firm performance. The Journal of Finance, 63(4), 1729-1775.

Brent, A., Morse, D., & Stice, E. K. (1990). Short interest: Explanations and tests.  Journal of Financial and Quantitative Analysis, 25(2), 273-289.

Brunnermeier, M. K., & Oehmke, M. (2014). Predatory short selling. Review of Finance, 18(6), 2153-2195.

Bushee, B. J. (1998). The influence of institutional investors on myopic R&D investment behavior. The Accounting Review, 73(3), 305-333.

Chang, E. C., Lin, T. C., & Ma, X. (2015). Does short selling discipline overinvestment? University of Hong Kong working paper.

Chen, L. (2016). The informational role of Internet-based short sellers: The evidence from short sellers’ reports on US-listed Chinese firms. Journal of Business Finance & Accounting, 43(9-10), 1444-1482.

Chhaochharia, V., Kumar, A., & Niessen-Ruenzi, A. (2012). Local investors and corporate governance. Journal of Accounting and Economics, 54(1), 42-67.

Clifford, C. P. (2008). Value creation or destruction? Hedge funds as shareholder activists. Journal of Corporate Finance, 14(4), 323-336.

Corwin, S. A., & Schultz, P. (2012). A simple way to estimate bid‐ask spreads from daily high and low prices. The Journal of Finance, 67(2), 719-760.

Derrien, F., & Kecskés, A. (2013). The real effects of financial shocks: Evidence from exogenous changes in analyst coverage. The Journal of Finance, 68(4), 1407-1440.

Edmans, A., Goldstein, I., & Jiang, W. (2012). The real effects of financial markets: The impact of prices on takeovers. The Journal of Finance, 67(3), 933-971.

Fang, V. W., Huang, A. H., & Karpoff, J. M. (2016). Short selling and earnings management: A controlled experiment. The Journal of Finance,71(3), 1251-1294.

Francis, J., LaFond, R., Olsson, P., & Schipper, K. (2005). The market pricing of accruals quality. Journal of Accounting and Economics, 39(2), 295-327.

29

Gao, P. (2008). Keynesian beauty contest, accounting disclosure, and market efficiency. Journal of Accounting Research, 46(4), 785-807.

Goldstein, I., & Guembel, A. (2008). Manipulation and the allocational role of prices. The Review of Economic Studies, 75(1), 133-164.

Graham, J. R., Hanlon, M., & Shevlin, T. (2011). Real effects of accounting rules: Evidence from multinational firms’ Investment location and profit repatriation decisions. Journal of Accounting Research, 49(1), 137-185.

Grullon, G., Michenaud, S., & Weston, J. P. (2015). The real effects of short-selling constraints.  Review of Financial Studies, 28(6), 1737-1767.

Hau, H., & Lai, S. (2013). Real effects of stock underpricing. Journal of Financial Economics, 108(2), 392-408.

He, J., & Tian, X. (2016). Do short sellers exacerbate or mitigate managerial myopia? Evidence from patenting activities. Indiana University working paper.

Klein, A., & Zur, E. (2009). Entrepreneurial shareholder activism: Hedge funds and other private investors. The Journal of Finance, 64(1), 187-229.

Kovbasyuk, S., & Pagano, M. (2015). Advertising arbitrage. CFS working paper no. 84. Li, Y., & Zhang, L. (2015). Short selling pressure, stock price behavior, and management forecast

precision: Evidence from a natural experiment. Journal of Accounting Research, 53(1), 79-117.Liu, X. (2014). Short-selling attacks and creditor runs. Management Science, 61(4), 814-830.Ljungqvist, A., & Qian, W. (2016). How constraining are limits to arbitrage? Review of Financial

Studies, 29(8), 1975-2028.McLean, R. D., Zhang, T., & Zhao, M. (2012). Why does the law matter? Investor protection and its

effects on investment, finance, and growth. The Journal of Finance, 67(1), 313-350.Morris, S., & Shin, H. S. (2002). Social Value of Public Information. The American Economic

Review, 92(5), 1521-1534.Piotroski, J. D., & Roulstone, D. T. (2004). The influence of analysts, institutional investors, and insiders

on the incorporation of market, industry, and firm-specific information into stock prices. The Accounting Review, 79(4), 1119-1151.

Reed, A. V. (2013). Short selling. Annual Review of Financial Economics, 5, 245-258.Richardson, S. (2006). Over-investment of free cash flow. Review of Accounting Studies, 11(2-3), 159-

189.Richardson, S., Tuna, İ., & Wysocki, P. (2010). Accounting anomalies and fundamental analysis: A

review of recent research advances. Journal of Accounting and Economics, 50(2), 410-454.Wong, F. (2016). Wolves at the door: A closer look at hedge-fund activism. University of Southern

California working paper.Yagan, D. (2015). Capital tax reform and the real economy: The effects of the 2003 dividend tax cut. The

American Economic Review, 105(12), 3531-3563.Zhao, W. (2017). Activist short-selling. Rotman School of Management working paper. Zimmerman, J. L. (1983). Taxes and firm size. Journal of Accounting and Economics, 5, 119-149.

30

Appendix A: Sample Construction23

We follow Zhao (2017) combining information from Seeking Alpha (SA) and Activist Shorts

Research (ASR) to construct a large sample of activist short-selling from 2006 to 2015. SA is the most

popular crowdsourced platform for investment research, with broad coverage of stocks, asset classes,

ETFs, and investment strategy. As a crowdsourced platform, the articles on SA are written by

“contributors” and approved by its editorial board. After acceptance, the contributors can publish their

articles on the website and receive $35 per article plus further compensation based on page views. By the

end of 2015, there were 12,354 contributors in total. In 2006, SA opened a new section called “Short

Ideas,” where contributors write articles illustrating why they are short-selling or plan to short-sell certain

securities. We define activist short-sellers from SA as those investors who state that they have short-

positions in certain stocks, and analyze the reasons of shorting them. This leaves us 5,716 articles out of

15,072 published from February 13, 2006 to December 31, 2015, with 6,197 stock-article level

observations.

Activist Shorts Research (ASR) is an independent database dedicated to tracking activist short-

seller campaigns. By the end of 2015, ASR’s database includes 773 campaigns by 98 short-sellers. Zhao

(2017) collects another 172 campaigns that were waged by these short-sellers but not covered by ASR.

Out of 945 (i.e., 773+172) ASR activist short-selling cases, 341 are also available on the SA website. For

the combined 6,801 activist short-selling cases, 6,081 are matched with PERMNO and GVKEY and

comprise our final sample of activist short-selling.

23 More details can be found in Section 3 of Zhao (2017).

31

Appendix B: Variable Definitions

Following Beneish et al. (2015), all income statement and cash flow statement variables are trailing four quarters. Balance sheet variables are for the most recent quarter. Lagged income statement variables are for quarters t-7 to t-4. Lagged balance sheet variables are for quarter t-4.

Variables Definitions

Real-effect variables

CAPX Capital Expenditure invested in the quarter scaled by the opening total assets.R&D R&D expenditures invested in the quarter scaled by the opening total assets.M&A Mergers & Acquisitions made in the quarter scaled by the opening total

assets.TotalInvest The sum of CAPX, R&D, and M&A scaled by the opening total assets.AssetGrowth Ending total assets divided by the opening total assets minus one.ShortDebtIssue Change in short-term debt from the previous quarter scaled by the opening

total assets.LongDebtIssue Change in long-term debt from the previous quarter scaled by the opening

total assets.EquityIssue Equity issuance in the quarter scaled by the opening total assets.TotalFinance The sum of ShortDebtIssue, LongDebtIssue and EquityIssue.Dividend Dividend distributed in the quarter scaled by the opening total assets.Buyback Share repurchases made in the quarter scaled by the opening total assets.TotalPayout The sum of Dividend and Buyback. Other variables used in the main tests

Treat Indicator. One for firms that are targeted by activist short-sellers and zero for matched control firms.

Post Indicator. One for quarters that are after the targeted firm-quarter (for treatment firms) or after the matched treatment firm’s targeted firm-quarter (for control firms), and zero otherwise.

CFO Net cash flow from operations. LagSize The log of the opening total assets. LagLev The ratio of total debt to total assets at the beginning of the fiscal quarter.LagProfit The ratio of net income to opening total assets in the previous quarter.Variables used in the robustness testsShortsInfo The negative information content contained in the short-thesis, proxied by (-

1) * the market-adjusted cumulative abnormal return in the first five days starting from the activist short-selling date.

After2011 Indicator variable that takes the value of one if the activist short-selling happens after January 1, 2016, and zero otherwise.

Variables used in the channel tests

RET Wednesday-to-Wednesday weekly stock return.MarRet Wednesday-to-Wednesday weekly market-wide value-weighted return.

32

IndRet Wednesday-to-Wednesday weekly Fama-French 48 industry-wide value-weighted return.

Synchronicity log(R2/(1- R2)), where R2 is the R-square of the following regression for each stock in a year:

Informative Indicator. One for firms with lower-than-median synchronicity values in the year prior to being targeted, and zero otherwise.

AnbBondRet Difference between the daily bond return for the target firm and the market return.

PassMgn Percentage of management proposal that is passed in that year.ShareholderProposal Indicator variable which takes the value of one if there was at least one

shareholder proposal that year and zero otherwise.NShareholderProposal

The number of proposal proposed by shareholder in that year.

PassShareholder Percentage of shareholder proposal that is passed in that year.Proxyfight Indicator variable which takes the value of one if there was a proxy fight that

year and zero otherwise.Variables used in the cross-sectional tests

InfEnv Indicator variable that takes the value of one if the standard deviation of analyst forecast error for each analyst’s last EPS forecast prior to the earnings announcements of the quarter ended before activist short-selling is above the median, and zero otherwise. Forecast error is scaled by the closing price of the month prior to the earnings announcement.

YoungFirm Indicator variable that takes the value of one if the years by the quarter ended before activist short-selling, a firm has been in Compustat longer than the median, and zero otherwise.

DedicateInv Indicator variable that takes the value of one if the number of dedicated institutional investors classified by Bushee’s website (1998) in the quarter ended before activist short-selling is above the median, and zero otherwise.

Sentiment Indicator variable that takes the value of one if the raw return in the one year ending at the fiscal quarter ended before activist short-selling is above the median, and zero otherwise.

Variables used in the optimality tests

Overinvestment The residual from the investment model in Richardson (2006). Details can be found in Appendix D.

LagVP The V/P ratio in the beginning of the quarter – a proxy for growth opportunities. The calculation of V/P ratio can be found in Appendix D.

LagFCF Free cash flow from the previous quarter. The calculation of free cash flow can be found in Appendix D.

33

Appendix C: First-Stage Logistic Regression Results for the PSM Matching Sample

This table reports the first-stage logistic regression results in the PSM procedure. t statistics in parentheses are based on standard errors clustered by firm. * p < 0.1, ** p < 0.05, *** p < 0.01 (two-sided tests)As in Zhao (2017), all variables are defined as follows:

Variables DescriptionsTarget Indicator. For a given fiscal quarter, one if a firm is targeted by activist short-sellers at least once from

45 days after the fiscal end of the quarter to 45 days after the fiscal end of the next quarter, and zero otherwise.

Overvaluation The average of the following seven overvaluation variables that survive from the stepwise regression. Uncertain The average of the following six variables that survive from the stepwise regression.24

Size The log of total assets at the fiscal end of the data quarter.Leverage The ratio of total liabilities to total assets at the fiscal end of the data quarter.Illiquidity The mean of Amihud’s (2002) daily illiquidity measure in the data quarter, which is measured as the

log of one plus the ratio of absolute return and the dollar trading volume and scaled by 106.Volatility The quarterly standard deviation of daily return in the data quarter. LnAnalyst Log of 1 plus the number of analysts who provide EPS estimates for the data quarter prior to the

earnings announcements.ShortInterest The ratio of total shares in short-position to total shares outstanding based on the last settlement date

prior to the fiscal end of the data quarter.

(1)Target

Overvaluation 2.2382***(13.82)

Uncertainty 1.5826***(9.22)

Size 0.0557**(2.36)

Leverage 0.1062(0.82)

Illiquidity -1.8923***(-6.71)

Volatility 7.5212***(7.40)

LnAnalyst 0.5033***(11.15)

ShortInterest 2.5978***(15.42)

Constant -6.3333***(-31.69)

Observations 187,045Pseudo R2 0.081

24 For brevity, please refer to Zhao (2017) Appendix C for detailed definitions of Overvaluation and Uncertainty.

34

Appendix D: The Detailed Steps of Estimating Overinvestment Following Richardson (2006)

Richardson (2006) provides an accounting-based approach to estimate overinvestment and free cash

flow. The key is to estimate expected new investment given a set of predictors. We follow Richardson

(2006) as below. Note that Richardson uses annual data and we use quarterly data in this paper.

Step 1: Calculate INew = ITotal – IMaintenance = (CAPX + AQC + XRD – SPPE) – DP

Step 2: Calculate CFAIP = CFO – IMaintenance + XRD = OANCF – DP + XRD

Step 3: Estimate the following model:

Where variables are defined as follows:

Variables DescriptionsV/P The proxy for growth opportunity, defined as the ratio of intrinsic value to price at the end of the

fiscal quarter. The calculation of V is based on Ohlson model (Ohlson 1995; Feltham and Ohlson 1996), as defined as V = (1 – αγ)*CEQ + α(1+ γ)*OIADP – αγ*DVC, where α = ω/(1+ γ – ω); ω = 0.62; γ = 0.12. OIADP (DVC) is the rolling sum of quarterly values in the most recent four quarters (i.e., the current quarter and the previous three quarters).

Leverage The sum of the book value of short term debt (DLC) and long term debt (DLTT) deflated by the sum of the book value of total debt and the book value of equity (CEQ).

Cash The balance of cash and short term investments (CHE) deflated by total assets (AT) measured at the start of the quarter.

Age The log of the number of years the firm has been listed on CRSP as of the start of the quarter.Size The log of total assets (AT) measured at the start of the quarter.StockReturn The stock returns for the quarter prior to the investment quarter. It is measured as the change in

market value of the firm over that prior quarter.QTRIndicators A vector of indicator variables to capture quarterly fixed effects.IndustryIndicators A vector of indicator variables to capture Fama-French 48 industry fixed effects.

Step 4: Label Overinvestment = The residual of the above model;

Step 5: Calculate Free Cash Flow (FCF) = CFAIP – the fitted value of the above model.

Note CAPX, AQC, XRD, SPPE, DP, OANCF, CEQ, OIADP, DVC, DLC, DLTT, CHE, and AT are all

Compustat items.

35

Table 1: The Distribution of Targeted Firm-Quarters

This table presents the distribution of firm-quarters that are targeted by activist short-sellers by year and by quarter.

Year Q1 Q2 Q3 Q4 Total2006 4 11 21 33 692007 26 33 35 52 1462008 39 46 49 47 1812009 50 51 51 43 1952010 47 46 66 45 2042011 52 52 54 76 2342012 57 73 85 78 2932013 102 142 213 185 6422014 216 180 200 167 7632015 182 185 195 196 758Total 775 819 969 922 3,485% 22.2% 23.5% 27.8% 26.5% 100%

36

Table 2: Real Activities Eight Quarters before and after Activist Short-Selling

This table compares real-effect variables before (i.e., Pre-Targeted in column 2) and after activist short-selling (i.e., Post-Targeted in column 3) for treatment firms. Column 3 represents the differences between Pre-Targeted and Post-Targeted means and column 4 represents the differences as percentages of Pre-Targeted means. All variables are defined in Appendix B.

Panel A: Pre-Post Univariate comparison for treatment firms  Variables Pre-Targeted Post-Targeted Diff Percentage

Obs. Mean Obs. MeanTotal Investment 23,435 3.908 19,008 3.625 -0.283*** -7.2% CAPX 23,435 1.468 19,008 1.346 -0.122*** -8.3% R&D 23,435 1.569 19,008 1.590 0.0210 1.3% M&A 23,175 0.579 18,828 0.499 -0.081*** -13.8%Asset Growth 21,827 1.210 18,596 1.179 -0.032*** -2.6%Total Finance 23,435 3.877 19,008 2.928 -0.948*** -24.5% Equity Issue 23,435 2.511 19,008 1.866 -0.644*** -25.7% Long Debt Issue 23,435 1.289 19,008 1.124 -0.165*** -12.8% Short Debt Issue 22,762 0.208 18,464 0.167 -0.041*** -19.7%Total Payout 23,435 1.074 19,008 0.992 -0.082*** -7.6% Dividend 23,435 0.299 19,008 0.273 -0.026*** -8.7% Buyback 22,773 0.653 18,483 0.614 -0.039** -6.0%

37

Table 3: Difference-in-Differences Analyses Based on a Control Sample Matched on Market Cap

This table reports diff-in-diff results based on the primary matching procedure: treatment firms are matched with control firms that are in the same Fama-French 48 industry and have the closest market cap prior to the activist short-selling. Panel A presents univariate diff-in-diff results. Column 1 (2) represents the differences between Pre-Targeted and Post-Targeted means for the control (treatment) firms. Column 3 (4) represents the diff-in-diff estimates (t-statistic). Panel B presents multivariate diff-in-diff results using all 10 real-effect variables. All variables are defined in Appendix B. t-statistics in parentheses are based on standard errors clustered by firm. * p < 0.1, ** p < 0.05, *** p < 0.01 (two-sided tests)

Panel A: Univariate Difference-in-Differences

Control TreatmentMean Difference ([-8, -1] vs. [1, 8])

Mean Difference ([-8, -1] vs. [1, 8])

Mean Diff-in-Diff(Treat vs. Control)

t-stat for Diff-in-Diff

TotalInvest 0.0130 -0.283*** -0.296*** -4.37 CAPX -0.049*** -0.122*** -0.072*** -3.08 R&D 0.047* 0.0210 -0.026 -0.66 M&A 0.0180 -0.081*** -0.098*** -2.75AssetGrowth -0.015*** -0.032*** -0.016*** -3.15TotalFinance -0.242*** -0.948*** -0.706*** -4.54 EquityIssue -0.162*** -0.644*** -0.482*** -4.35 LongDebtIssue 0.0240 -0.165*** -0.190*** -3.39 ShortDebtIssue -0.00600 -0.041*** -0.035*** -2.96TotalPayout 0.044* -0.082*** -0.126*** -3.53 Dividend 0.00800 -0.026*** -0.034*** -3.03 Buyback 0.051*** -0.039** -0.090*** -3.40

38

Panel B: Multivariate Difference-in-Differences

(1) (2) (3) (4)TotalInvest AssetGrowth TotalFinance TotalPayout

Treat×Post -0.183*** -0.040*** -0.390*** -0.108**(-2.70) (-4.71) (-2.77) (-2.08)

CFO -0.146 0.102*** -9.246* 0.128(-0.23) (3.92) (-1.84) (0.75)

LagSize -1.911*** 0.128*** -5.731*** -0.047(-7.84) (6.73) (-11.97) (-0.31)

LagLev -1.183*** 0.027 -1.504 -0.943***(-2.78) (0.52) (-1.11) (-3.24)

LagProfit -0.781*** 0.126*** -4.446*** 0.003(-3.74) (3.90) (-5.29) (0.03)

Firm FE YES YES YES YESQTR FE YES YES YES YESConstant 16.596*** 0.645** 42.703*** 1.139

(9.33) (2.11) (12.76) (0.92)Observations 81,583 81,656 81,495 81,507Adjusted R2 0.450 0.396 0.237 0.425

Disaggregate Investment Disaggregate Finance Disaggregate Payout(5) (6) (7) (8) (9) (10) (11) (12)

CAPX R&D M&A EquityIssue

LongDebtIssu

e

ShortDebtIssu

e

Dividend Buyback

Treat × Post -0.037 -0.014 -0.081** -0.244** -0.114* -0.038*** -0.032** -0.064(-1.51) (-0.46) (-2.13) (-2.32) (-1.92) (-2.71) (-2.32) (-1.45)

CFO 0.137 0.014 -0.175 -3.762* -1.371 -0.568 0.039 0.057(1.28) (0.07) (-1.13) (-1.92) (-1.41) (-1.42) (0.87) (0.50)

LagSize -0.205* -0.78*** -0.52*** -3.311*** -0.979*** -0.057*** -0.051* -0.008(-1.95) (-7.07) (-5.64) (-9.01) (-7.37) (-2.71) (-1.78) (-0.07)

LagLev -0.42*** 0.388** -1.05*** 5.052*** -3.039*** -0.146** -0.077 -0.695***(-2.96) (2.13) (-5.62) (4.20) (-7.25) (-2.13) (-0.70) (-3.89)

LagProfit 0.134** -0.43*** 0.031 -1.824* -0.614*** -0.061* 0.031 0.014(2.03) (-3.10) (0.45) (-1.89) (-4.24) (-1.88) (0.82) (0.20)

Firm FE YES YES YES YES YES YES YES YESQTR FE YES YES YES YES YES YES YES YESConstant 3.013*** 6.392*** 4.246*** 21.860*** 8.925*** 0.935*** 0.620*** 0.500

(4.15) (8.31) (5.59) (8.75) (9.15) (2.77) (3.18) (0.47)Obs. 81,655 81,655 81,655 81,655 81,655 81,655 81,655 81,655Adj. R2 0.646 0.888 0.100 0.278 0.122 0.209 0.608 0.380

39

Table 4: Alternative Matching ProceduresThis table reports diff-in-diff results based on two alternative matching procedures: treatment firms are matched with control firms that are in the same Fama-French 48 industry and (1) have the highest increase in short-interest compared to the previous short-interest announcement, or (2) have the closest propensity score of being targeted as explained in Appendix C. For brevity only aggregate real-effect variables are tabulated. All variables are defined in Appendix B. t statistics in parentheses are based on standard errors clustered by firm. * p < 0.1, ** p < 0.05, *** p < 0.01 (two-sided tests)

Panel A: Diff-in-Diff Regressions Using Firms with the Highest Increase in Short-Interest as a Control Sample

(1) (2) (3) (4)TotalInvest AssetGrowth TotalFinance TotalPayout

Treat×Post -0.183*** -0.029*** -0.470*** -0.124***(-2.70) (-3.36) (-2.97) (-3.11)

CFO 0.004 0.095*** -9.353* 0.128(0.01) (3.76) (-1.82) (0.85)

LagSize -1.862*** 0.144*** -7.183*** -0.111(-7.96) (8.28) (-12.27) (-0.78)

LagLev -1.316*** -0.017 -2.916** -0.710***(-3.21) (-0.34) (-2.32) (-2.94)

LagProfit -0.796*** 0.136*** -5.353*** 0.052(-3.53) (3.87) (-5.39) (0.48)

Firm FE YES YES YES YESQTR FE YES YES YES YESConstant 14.639*** 0.336 47.742*** 1.186

(8.89) (0.65) (11.41) (1.24)Observations 79,101 79,165 78,958 78,987Adjusted R2 0.466 0.398 0.247 0.421

Panel B: Diff-in-Diff Regressions Using PSM-Matched Firms as a Control Sample(1) (2) (3) (4)

TotalInvest AssetGrowth TotalFinance TotalPayoutTreat×Post -0.120* -0.028*** -0.286** -0.099**

(-1.95) (-3.68) (-2.14) (-2.29)CFO -0.306 0.116*** -8.424** 0.175

(-0.54) (3.90) (-2.38) (1.02)LagSize -1.900*** 0.124*** -6.359*** 0.009

(-8.28) (6.42) (-11.83) (0.06)LagLev -0.535 0.034 0.421 -1.075***

(-1.35) (0.62) (0.23) (-3.65)LagProfit -0.703*** 0.139*** -4.536*** -0.109

(-3.76) (3.49) (-4.49) (-0.57)Firm FE YES YES YES YESQTR FE YES YES YES YESConstant 18.426*** 0.688** 41.033*** 1.319

(6.52) (2.23) (6.79) (1.33)Observations 81,390 81,456 81,306 81,336Adjusted R2 0.497 0.392 0.245 0.408

40

Table 5: More on the Selection Issue

This table employs two approaches to further address the selection issue. Panel A presents how the real consequences of activist short-selling vary with its initial impact measured by the raw return in the first week. Panel B shows how the real consequences vary with the visibility of activist short-selling campaigns in general. For brevity, only aggregate real-effect variables are tabulated. All variables are defined in Appendix B. t statistics in parentheses are based on standard errors clustered by firm. * p < 0.1, ** p < 0.05, *** p < 0.01 (two-sided tests)

Panel A: The Role of the Information Content in the Short-Theses(1) (2) (3) (4)

TotalInvest AssetGrowth TotalFinance TotalPayoutPost×ShortsInfo -0.952** -0.092 -2.691*** -0.533**

(-2.11) (-1.49) (-2.92) (-2.48)CFO -0.017 0.090*** -6.126* 0.161

(-0.03) (4.12) (-1.77) (0.93)LagSize -1.910*** 0.120*** -5.845*** 0.033

(-6.05) (5.81) (-9.88) (0.16)LagLev -0.329 -0.032 -0.642 -0.645*

(-0.67) (-0.49) (-0.45) (-1.86)LagProfit -0.634*** 0.109*** -3.951*** 0.018

(-3.22) (3.29) (-4.68) (0.14)Firm FE YES YES YES YESQTR FE YES YES YES YESConstant 15.329*** 1.429* 43.302*** 0.966

(5.84) (1.86) (10.34) (0.69)Observations 38,843 38,895 38,776 38,806Adjusted R2 0.500 0.424 0.248 0.412

Panel B: The Role of the Visibility of Activist Short-Selling(1) (2) (3) (4)

TotalInvest AssetGrowth TotalFinance TotalPayoutPost×After2011 -0.076 -0.009 -0.377** -0.060

(-1.02) (-0.87) (-2.10) (-1.28)CFO -0.018 0.090*** -6.180* 0.162

(-0.03) (4.09) (-1.77) (0.93)LagSize -1.916*** 0.119*** -5.860*** 0.033

(-6.07) (5.78) (-9.88) (0.16)LagLev -0.331 -0.033 -0.675 -0.645*

(-0.67) (-0.49) (-0.47) (-1.86)LagProfit -0.629*** 0.110*** -3.940*** 0.020

(-3.20) (3.30) (-4.68) (0.15)Firm FE YES YES YES YESQTR FE YES YES YES YESConstant 15.390*** 1.440* 43.430*** 0.969

(5.85) (1.88) (10.36) (0.69)Observations 39,113 39,167 39,043 39,073Adjusted R2 0.502 0.425 0.247 0.412

41

Table 6: Channels

This table presents preliminary results regarding three channels through which activist short-selling leads to real consequences: the cost-of-capital channel in Panels A and B, the monitoring channel in Panel C, and the feedback-effect channel in Panel D. Panel A reports a monotonic relation between bond market reaction and equity market reaction to the activist short-selling. Panel B shows the change in interest rate and facility amount of loans before and after activist short-selling. Panel C shows some evidence that shareholders reduce monitoring after activist short-selling. Panel D shows that managers could learn more from the price if the price is more informative about firm-specific shocks. For brevity only aggregate real-effect variables are tabulated. All variables are defined in Appendix B. t statistics in parentheses are based on standard errors clustered by firm. * p < 0.1, ** p < 0.05, *** p < 0.01 (two-sided tests)

Panel A: Cost-of-Capital as a Channel: Evidence from Average Bond Return on Activist Short-Selling Date

Announcement Date EquityReturn Quartile N Mean t-stat Median Z-Score

Full Sample 1,331 -0.0072 -2.8948 *** -0.01 -9.299 ***Quartile 1 332 -0.0204 -3.84272 *** -0.0162 -6.703 ***Quartile 2 333 -0.0075 -1.6873 * -0.0118 -6.337 ***Quartile 3 328 -0.0068 -1.6867 * -0.0081 -4.624 ***Quartile 4 338 0.00363 0.08 -0.0056 -0.999

Panel B: Cost-of-Capital as a Channel: The Amount and Pricing of Syndicate Loans

At facility-level Pre-Period N Post-Period N Diff t-statFacility Amount (Million) 304.1 1229 241.1 1211 -63.0 -3.58Interest Rate (bps above LIBOR) 286.0 1229 331.8 1211 45.8 6.44

At firm-QTR level Pre-Period N Post-Period N Diff t-statTotal Facility Amount (Million) 1207 276 828.9 310 -378 -2.00Avg Interest Rate (bps above LIBOR) 256.7 276 309.3 310 52.6 4.15

Panel C: Monitoring as a Channel: Evidence from Shareholder Votes

Pre Post Diff t-statProbability Management Proposal is passed 0.9809 0.9775 -0.0034 -2.95 **Probability of Shareholder Proposal 0.1694 0.1787 0.0093 2.29 **Probability Shareholder Proposal is passed 0.2067 0.2166 0.0099 1.03Probability of Proxy Fight 0.0078 0.0090 0.0011 1.17

42

Panel D: Feedback Effect as a Channel: Evidence from Stock Price Informativeness

(1) (2) (3) (4)TotalInvest AssetGrowth TotalFinance TotalPayout

Post×Informative -0.347*** -0.015 -0.782*** -0.073(-3.49) (-1.25) (-3.58) (-1.26)

CFO -0.000 0.090*** -6.055* 0.161(-0.00) (4.14) (-1.77) (0.92)

LagSize -1.887*** 0.120*** -5.842*** 0.025(-6.03) (5.94) (-9.89) (0.12)

LagLev -0.239 -0.027 -0.588 -0.650*(-0.49) (-0.41) (-0.41) (-1.85)

LagProfit -0.616*** 0.108*** -3.865*** 0.020(-3.17) (3.30) (-4.71) (0.15)

Firm FE YES YES YES YESQTR FE YES YES YES YESConstant 15.034*** 1.429* 42.971*** 0.999

(5.81) (1.86) (10.23) (0.71)Observations 38,065 38,118 37,998 38,028Adjusted R2 0.501 0.425 0.247 0.412

43

Table 7: Which Type of Firms Get Hurt the Most?

This table presents how the real consequences of activist short-selling vary with firm-level characteristics. For brevity, only aggregate real-effect variables are tabulated. All variables are defined in Appendix B. t statistics in parentheses are based on standard errors clustered by firm. * p < 0.1, ** p < 0.05, *** p < 0.01 (two-sided tests)

(1) (2) (3) (4)TotalInvest AssetGrowth TotalFinance TotalPayout

Post×InfoEnv -0.486*** 0.007 -1.073*** -0.255***(-4.04) (0.51) (-4.60) (-4.31)

Post×YoungFirm 0.006 -0.055*** 0.112 -0.074(0.04) (-3.70) (0.40) (-0.91)

Post×DedicateInv -0.094 -0.012 -0.210 -0.065(-0.92) (-1.13) (-1.00) (-1.17)

Post×PriceRunUp 0.427*** 0.054*** 0.669*** 0.128*(4.93) (4.70) (3.58) (1.86)

Post×ShortsInfo -0.939** -0.078 -2.521*** -0.351(-2.11) (-1.28) (-2.69) (-1.64)

CFO -0.016 0.089*** -6.122* 0.161(-0.03) (4.12) (-1.78) (0.92)

LagSize -1.987*** 0.121*** -6.000*** 0.009(-6.26) (5.81) (-10.08) (0.04)

LagLev -0.301 -0.023 -0.615 -0.637*(-0.63) (-0.35) (-0.43) (-1.85)

LagProfit -0.640*** 0.109*** -3.964*** 0.015(-3.28) (3.33) (-4.68) (0.11)

Firm FE YES YES YES YESQTR FE YES YES YES YESConstant 15.578*** 1.419* 43.732*** 0.967

(6.02) (1.85) (10.30) (0.70)Observations 38,843 38,895 38,776 38,806Adjusted R2 0.502 0.426 0.249 0.413

44

Table 8: Does Activist Short-Selling Move Targeted Firms towards Optimal Level of Real Activities?

This table examines whether target firms move towards or away from the optimal level of investment. Panel A presents univariate pre-post comparison for control firms. Panel B compares overinvestment for control and treatment firms in pre- and post-activist short-selling. Panel C reports average overinvestment by quarter. Panel D presents whether activist short-selling makes firms’ investment more responsive to investment opportunities. Panel E tabulates the proportion of firms that move towards/away from optimality conditional on whether they are overinvesting/underinvesting in the pre-period. The procedure of estimating optimal investment can be found in Appendix D. All other variables are defined in Appendix B. t statistics in parentheses are based on standard errors clustered by firm. * p < 0.1, ** p < 0.05, *** p < 0.01 (two-sided tests)

Panel A: Pre-Post Univariate Comparison for Control Firms

 Variables Pre-Targeted Post-Targeted Diff PercentageObs. Mean Obs. Mean

Total Investment 25013 3.113 19048 3.126 0.013 0.4% CAPX 25130 1.152 19134 1.102 -0.049*** -4.3% R&D 25130 1.162 19134 1.209 0.047* 4.0% M&A 25130 0.644 19134 0.661 0.018 2.8%

Panel B: Overinvestment for Control and Treatment Firms in Pre- and Post-Activist Short-Selling

Control Firms Treatment FirmsMean N t-stat Mean N t-stat

Pre-Period -0.0003 21594 -0.9697 0.0037 19765 5.6798Post-Period 0.0006 16610 1.183 -0.0005 16344 -1.3855

Panel C: Overinvestment for Control and Treatment Firms by Quarters

Control Firms Treatment FirmsQuarter Mean N t-stat Mean N t-stat-8 -0.0011 2570 -1.2435 0.0065 2259 2.4564-7 -0.0007 2611 -0.7865 0.0029 2311 2.0026-6 0.0005 2647 0.5433 0.0047 2371 1.8521-5 0.0006 2670 0.5800 0.0069 2429 2.7053-4 -0.0004 2710 -0.4401 0.0026 2485 1.9905-3 -0.0005 2756 -0.5713 0.0031 2563 2.3234-2 -0.0012 2793 -1.1981 0.0015 2637 1.2219-1 0.0000 2837 -0.0004 0.0020 2710 1.61340 0.0004 2870 0.3683 0.0017 2830 1.43481 0.0009 2696 0.8431 -0.0013 2691 -1.40992 -0.0007 2524 -0.7044 0.0002 2512 0.17003 -0.0009 2331 -0.9955 -0.0025 2318 -2.44524 0.0012 2151 1.0318 -0.0005 2131 -0.46135 -0.0003 1995 -0.3248 -0.0002 1946 -0.14036 0.0028 1814 0.9043 0.0001 1754 0.12337 0.0016 1645 0.8946 0.0014 1590 1.17848 0.0012 1454 0.9301 -0.0007 1402 -0.6025

45

Panel D: The Sensitivities of Investment to Growth Opportunities and Free Cash Flows

(1) (2)DV = TotalInvest Treatment Control Post -0.2388** -0.0530

(-2.56) (-0.45)LagV/P -0.6096*** -0.5432**

(-4.54) (-2.31)Post×LagV/P 0.2111* -0.0196

(1.79) (-0.13)LagFCF -0.0825 0.8517

(-0.73) (0.84)Post×LagFCF -0.0320 0.9059

(-0.03) (0.80)Firm FE YES YESQTR FE YES YESConstant 2.9578*** 3.6397***

(4.77) (11.98)Observations 35,293 37,718Adjusted R2 0.456 0.367

Panel E: Proportion of Firms that Move towards/away from Optimal InvestmentControl Firms Treatment Firms

Overinvested Firms in pre-period Less Overinvestment: 72.4% Less Overinvestment: 78.8%Underinvested Firms in pre-period More Underinvestment: 57.4% More Underinvestment: 60.1%

46

Figure 1: Detailed Real Variables around Activist Short-Selling (Quarter-by-Quarter)

This figure plots how the means of all 10 real-effect variables vary from the eight quarters prior to the quarter in which a firm is targeted by activist short-selling to eight quarters after that quarter. All variables are defined in Appendix B.

47