Myopic Investor Myth Debunked - swfa2015.uno.eduswfa2015.uno.edu/E_Institutional_Ownership/paper_82.pdf · Myopic Investor Myth Debunked: The Long-term Efficacy of Hedge Fund Activism

Myopic Investor Myth Debunked:

The Long-term Efficacy of Hedge Fund Activism

in the Boardroom

Shane Goodwin

Spears School of Business

Oklahoma State University

Ramesh Rao

Spears School of Business

Oklahoma State University

Abstract

Over the past two decades, hedge fund activism has emerged as new form of corporate

governance mechanism that brings about operational, financial and governance reforms to a

corporation. Many prominent business executives and legal scholars are convinced that the

American economy will suffer unless hedge fund activism with its perceived short-termism

agenda is significantly restricted. Shareholder activists and their proponents claim they function

as a disciplinary mechanism to monitor management and are instrumental in mitigating the

agency conflict between managers and shareholders. We find statistically meaningful empirical

evidence to reject the anecdotal conventional wisdom that hedge fund activism is detrimental to

the long term interests of companies and their long term shareholders. Moreover, our findings

suggest that hedge funds generate substantial long term value for target firms and its long term

shareholders when they function as a shareholder advocate to monitor management through

active board engagement.

JEL Classifications: D21, D22, D81, G12, G23, G32, G34, G35, G38, K22

Advocacy

Shareholder

1

I. INTRODUCTION

For society as a whole, further empowering [hedge funds] with short-term holding

periods subjects Americans to lower long-term growth and job creation…due to

excessive risk taking…when corporations maximize short-term profits.

Leo Strine, Chief Justice of the Delaware Supreme Court

Columbia Law Review, March 2014

Leo Strine, the Chief Justice of the Delaware Supreme Court, the country’s most

important arbiter of corporate law recently authored a 54-page article in the Columbia Law

Review with respect to his views about hedge funds and his belief that short-term investors are

detrimental to the long-term interests of American corporations, its long-term shareholders and

to society as a whole. Chief Justice Strine, many prominent business executives and legal

scholars are convinced that the American economy will suffer unless hedge fund activism with

its perceived short-termism agenda is significantly restricted. Shareholder activists and their

proponents claim they function as a disciplinary mechanism to monitor management and are

instrumental in mitigating the agency conflict between managers and shareholders.

In May 2014, the Delaware Court of Chancery denied Third Point, a well-known activist

hedge fund, the ability to increase its ownership above 10% in Sotheby’s. Third Point, the

company’s largest shareholder, sent a letter to Sotheby’s CEO in October 2013 expressing

concerns regarding the company’s strategic direction, shareholder misalignment and

recommended replacing the CEO. In response, Sotheby’s adopted a unique two-tier shareholder

rights plan1 (a poison pill) that purposely discriminated and severely punished any shareholder

that might have an agenda that conflicts with incumbent management2. Although the court

1 A shareholder rights plan, also known as a “poison pill”, is one of the most effective defense tactics available to publicly traded

corporations. A poison pill is designed to make a potential transaction extremely unattractive to a hostile party from an economic

perspective, compelling a suitor to negotiate with the target’s Board of Directors. A poison pill is effective because it dilutes the

economic interest of the hostile suitor in the target, making the transaction both economically unattractive and impractical if

pursued on a hostile basis. 2 The two-tier structure provided for a 10 percent trigger threshold for shareholders filing a Schedule 13D (for “active” investors)

and a 20 percent trigger for shareholders filing a Schedule 13G (available to “passive” investors). The rights plan also contained

a “qualifying offer” exception, which provided that the plan would not apply to an offer for all of Sotheby’s shares and expires in

one year unless it is approved by a shareholder vote.

2

viewed the two-tier trigger structure as “discriminatory” — differentiating between activist and

passive investors — the court found that Sotheby’s had a reasonable basis to believe that Third

Point posed a threat of exercising disproportionate control and influence over major decisions.

The court reached its decision notwithstanding the support from other large shareholders for

Third Point and despite the fact that Sotheby’s management and board owned less than 1% of the

company. Ironically and inexplicably, the court’s decision de facto authorized the incumbent

CEO and the board disproportionate control and influence over major decisions, notwithstanding

the objections of its shareholders.

Vice Chancellor Donald Parsons, Jr. stated in his opinion “…it’s important not to

overstate the way in which shareholders that file Schedule 13Ds differ from those who file

Schedule 13Gs.” The rationale for this decision coupled with the recent remarks by Chief Justice

Strine raise important policy questions about the value of hedge fund activism and its

disciplinary role as an active monitor of a firm’s management. In this paper, we suggest and

empirically test the following alternative hypothesis: hedge fund activism through board

representation is not detrimental to the long term operating performance of companies and does

not have an adverse effect on the target firm’s long term shareholders.

Agency conflict in publicly traded corporations with dispersed ownership is at the heart

of corporate governance literature, which focuses on mechanisms to discipline incumbent

management. One possible solution to mitigate agency cost is for shareholders to actively

monitor the firm’s management. However, while monitoring may reduce agency and improve

firm value, this effort is not without cost and the benefits from monitoring are enjoyed by all

shareholders (Grossman and Hart, 1980).

Shareholders that serve as active monitors of firm management to provide a disciplinary

mechanism is not a new concept. Earlier studies show that when institutional investors,

particularly mutual funds and pension funds, follow an activist agenda, they do not achieve

3

significant benefits for shareholders (Black (1998), Karpoff (2001), Romano (2001), and Gillan

and Starks (2007)). However, hedge funds have increasingly engaged in shareholder activism

and monitoring that differs fundamentally from previous activist efforts by other institutional

investors. Unlike mutual funds and pension funds, hedge funds are able to influence corporate

boards and managements due to key differences arising from their organizational form and the

incentive structures.

Hedge funds employ highly incentivized managers who manage large unregulated pools

of capital. Because they are not subject to regulation that governs mutual funds and pension

funds, they can hold highly concentrated positions in a small number of companies, and use

leverage and derivatives to extend their reach. In addition, hedge fund managers don’t

experience conflicts of interest since they are not beholden to the management of the firms

whose shares they hold. In sum, hedge funds are better positioned to act as informed monitors

than other institutional investors.

The growing literature with respect to shareholder activism identifies a significant

positive stock price reaction for targeted companies with the announcement of an activist

intervention ((Brav, Jiang and Kim, 2009), Clifford (2008) and Boyson and Mooradian (2011)).

Many critics of hedge fund activism concede that there are short-term positive value increases to

the target firm and its shareholders as a result of self-interested “myopic investors”. While this

“myopic investor” claim has been regularly invoked and has had considerable influence, its

supporters, including Chief Justice Strine, have thus far failed to support their position with

empirical evidence. However, the continued debate is about the long term efficacy on target

firms and the returns to all shareholders as result of hedge fund activism. Recent research by

Bebchuk, Brav and Jiang (2013) find statistically meaningful evidence that the operating

performance of target firms improves following activist interventions but no evidence to support

the claim that short-term improvement was at the expense of long-term performance.

4

The dataset used by Bebchuk, Brav and Jiang (2013) is consistent with the vast majority

of research with respect to shareholder activism. The sample of activist interventions is

primarily constructed from Schedule 13D filings, the mandatory federal securities law filings

under Section 13(d) of the 1934 Exchange Act. The law states that investors must file with the

SEC within 10 days of acquiring more than 5% of any class of securities of a publicly traded

company if they have an interest in influencing the management of the company. The

presumption is a shareholder that files a 13D is unequivocally motivated to change the strategic

direction of the company. However, we claim that is not always the case. In fact, some activist

campaigns are centered on corporate governance reforms (i.e., board declassification, removal of

shareholder rights plan, etc.) and not meaningful long-term strategic changes to the target firm.

We contend that any shareholder with sincere conviction to challenge the current strategic

direction of a firm would, ultimately, seek board representation if their demands were not

supported by the firm’s incumbent management. We view board representation as a signal of an

activist’s long term commitment to the firm. Additionally, board representation is a costly

endeavor that is not borne by all shareholders which further validates the activist’s credibility as

a long term shareholder of the firm.

The vast majority of shareholder activism literature is predicated on Schedule 13D

filings. However, we assert that the optimal dataset to empirically test the long-term effects of

shareholder activism should be based on board representation of target firms by a shareholder

activist. Therefore, we started with a much more expansive sample of activist interventions.

Figure I illustrates our comprehensive dataset of shareholder activist events, which includes

5,063 interventions from 1984-2013. Of those, 3,899 (77%) filed a 13D. However,

approximately 32% of all activist interventions were focused on board engagement, either

through a proxy contest (1,216) or a non-proxy contest dissident campaign that resulted in board

representation via private negotiations (418) with the target management team and its board of

5

directors. To be sure, over two-thirds (2/3) of activist interventions did not seek board

representation to actively monitor management. Moreover, GAMCO Asset Management, a

hedge fund founded by Mario Gabeli, has filed 478 13Ds since 1996. However, it has launched

only 18 proxy fights (3.8%) and won board representation only ten times (2.1%) to date. In

contrast, Carl Icahn has launched proxy fights and won board representation at eBay, Genzyme,

Time Warner and Yahoo! without filing a 13D.

Accordingly, we claim that there are numerous 13D filings of activist interventions that

otherwise include good performing companies with strong management that a dissident was not

compelled to seek board representation to actively monitor management and function as a

disciplinary mechanism. Additionally, there are over 90 activist interventions that led to board

representation without filing a 13D. Therefore, we assert that the optimal dataset to test

empirically the long-term effects of hedge fund activism should be based on board

representation of target firms by a shareholder activist and not merely the fact that a shareholder

crossed 5% ownership and might (not will) seek to influence strategic change at the target firm.

To be sure, an activist that is willing to incur significant financial cost that is not borne by all

shareholders, which Gantchev (2012) estimates is approximately $10 million per proxy contest,

has genuine conviction that the target firm requires strategic change that management is

unwilling to execute without shareholder interference.

We empirically test our manually constructed dataset of 448 activist interventions (the

“Treatment Group”) that resulted in at least one board seat granted to an activist hedge fund from

1996-2013 (see Table 1). A total of 843 board members (see Table 1) were elected at 398

unique target companies. This includes 225 unique activist hedge funds. Of the 448 activist

interventions in the Treatment Group, 243 (54%) target firms are still publicly-listed, 186 (42%)

were sold/merged and 19 (4%) target firms filed for bankruptcy. By compiling our own

6

database, we avoid some problems associated with survivorship bias, reporting selection bias,

and backfill, which are prevalent among other hedge fund databases.

To control for self-selection bias and endogeneity, we constructed a control group (the

“Control Group”) of all proxy fights campaigns that did not result in board representation during

the same period. Our dataset includes 595 target firms that experienced a proxy contest for

board representation. After we excluded certain events to reflect consistent sample parameters

with our Treatment Group, our Control Group includes 73 firms that were involved in a proxy

contest that the target firm incumbent management defeated the dissident shareholder during the

voting process. Therefore, we examined not only firms that granted at least one board seat to a

dissident shareholder and its ex post effects (the “Treatment Group”), but also companies that

were challenged by dissatisfied shareholders and did not suffer the ex post disciplinary effects by

the dissident (the “Control Group”).

During our investigation of abnormal returns during the review period, we employed

three standard methods used by financial economists for detecting stock return

performance. In particular, the study examines: first, whether the returns to targeted companies

were systematically lower than what would be expected given standard asset pricing models;

second, whether the returns to targeted companies were lower than those of the Control Group

that experienced a similar event; and, third, whether a portfolio based on taking positions in

activism targets and holding them for five years post the board seat grant date underperforms

relative to its risk characteristics. Additionally, we modeled an 18-year (1996-2013) buy-and-

hold abnormal returns (BHAR) portfolio of all shareholder activist interventions that resulted in

board representation and controlled for market risk, firm size and value tilt relative to the

Control Group.

Using the aforementioned financial and econometric models, we find no evidence that

target firms experience a “reversal of fortune” during the five-year period following the

7

intervention. The long-term underperformance asserted by supporters of the myopic activism

claim, and the resulting losses to long-term shareholders due to activist interventions, are not

found in the data.

Moreover, we find target firms that granted at least one board seat to an activist hedge

fund created positive abnormal returns (alpha) for all shareholders during short term event

windows and for a five year period ex post the activist joining the target firm board.

Additionally, those target firms increased certain operating performance measures that are

commonly used by financial economists, such as return on assets (ROA) and market value

relative to book value (Tobin’s Q) during the post event period. Further, the assertion that

myopically-focused activist investors only create value in the short-term at the expense of long-

term shareholders is not supported by the data. In fact, target firms that granted at least one

board seat to an activist hedge fund outperformed the Control Group with respect to firm

operating measures and positive abnormal returns for all shareholders during the five years ex

post the activist joining the board.

We find that target firms in our sample dataset underperformed their industry peers on

certain operating metrics prior to the activist intervention, which validates the value-oriented

characteristics of activist targets. However, those operating metrics progressively improved

during the review period subsequent to the board seat grant date. This is further evidence that

board representation by activist hedge funds lead to improved operating performance in the long

term. In contrast, target firms that won the proxy fight against the activist experienced

degradation in certain operating performance metrics compared to industry peers during the

review period.

Contrary to certain extant literature and to the prevailing narrative that activist hedge

funds frequently promote a “sell the company” agenda, we find that activists who seek board

representation do not promote such an objective. However, within two years of the dissident

8

shareholder joining the board, the CEO of the target firm had been replaced approximately 30%

of the time. Therefore, it is reasonable to assume from the data that hedge funds that seek board

representation are focused more on promoting change at the target firm via operating

improvements and changes to existing management rather than supporting a sale of the company

strategy. Our investigation and findings support the alternative hypothesis that hedge fund

activism is not detrimental to and does not have an adverse-effect on the long term interests of

target firms and their long term shareholders (see Graph 1 and Graph 2).

Our research fills the important void with respect to the long term efficacy of shareholder

activists serving as a disciplinary mechanism on the firm by actively seeking board

representation to monitor management. Additionally, we contribute to the literature regarding

shareholder activists as self-interested myopic investors at the expense of the long-term interest

of the company and its long term shareholders. Moreover, our findings have important policy

implications related to the ongoing debate on corporate governance and the rights and roles of

shareholders. Although some prominent legal commentators and presiding justices, such as

Chief Justice Strine, have called for restrictions on hedge fund activism because of its perceived

short-term orientation, our findings suggest that hedge fund activism generates substantial long

term value for target firms and its long term shareholders when they function as a shareholder

advocate to monitor management through active board engagement.

9

II. LITERATURE REVIEW

In this section, we review the extant literature with respect to shareholder activism. First,

we evaluate why shareholder activists are the optimal group to be effective monitors of firm

management to mitigate agency cost. Second, we will discuss why active board engagement is

the preferred path to create value rather than through passive shareholder proposals. Finally, we

will discuss how shareholders can intervene to effect change at a corporation via a proxy contest.

The separation of ownership and control in public firms gives rise to the possibility of

agency conflict between the firm’s managers and shareholders (Berle and Means (1932) and

Jensen and Meckling (1976)). To the extent that this agency cost is significant, it can have a

detrimental effect on shareholder value. The agency problem in publicly traded corporations

with dispersed ownership is at the heart of corporate governance literature, which focuses on

mechanisms to discipline incumbent management. One possible solution to mitigate agency cost

is for shareholders to actively monitor the firm’s management. However, while monitoring may

reduce agency and improve firm value, this effort is not without cost and the benefits from

monitoring are enjoyed by all shareholders (Grossman and Hart, 1980).

Shareholders that serve as active monitors of firm management to provide a disciplinary

mechanism is not a new concept. Gillan and Starks (2007) define shareholder activists as

“investors who, dissatisfied with some aspect of a company’s management or operations, try to

bring about change within the company without a change in control.” Tirole (2006) provides the

following definition: “Active monitoring consists in interfering with management in order to

increase the value of the investors’ claims.”

However, hedge funds have increasingly engaged in shareholder activism and monitoring

that differs fundamentally from previous activist efforts by other institutional investors. Earlier

10

studies show that when institutional investors, particularly mutual funds and pension funds,

follow an activist agenda, they do not achieve significant benefits for shareholders (Black

(1998), Karpoff (2001), Romano (2001), and Gillan and Starks (2007)). Unlike mutual funds

and pension funds, hedge funds are able to influence corporate boards and managements due to

key differences arising from their organizational form and the incentive structures.

Hedge funds employ highly incentivized managers who manage large unregulated pools

of capital. Because they are not subject to regulation that governs mutual funds and pension

funds, they can hold highly concentrated positions in a small number of companies, and use

leverage and derivatives to extend their reach. In addition, hedge fund managers don’t

experience conflicts of interest since they are not beholden to the management of the firms

whose shares they hold. In sum, hedge funds are better positioned to act as informed monitors

than other institutional investors.

Theory predicts that large shareholders should be effective monitors of the managers of

publicly listed firms, reducing the free-rider problem ((Shleifer and Vishny (1986) and Grossman

and Hart (1980)). Yet the evidence that large shareholders increase shareholder value is mixed.

In two recent surveys, Karpoff (2001) and Romano (2001) conclude that activism conducted by

large institutional shareholders (i.e., pension funds and mutual funds) has had little impact on

firm performance. Additionally, Karpoff, Malatesta, and Walkling (1996), Wahal (1996), and

Gillan and Starks (2000) report no persuasive evidence that shareholder proposals increase firm

values, improve operating performance or even influence firm policies. Therefore, hedge funds

are the best positioned to function as a shareholder advocates to monitor management through

active board engagement.

Brav, Jiang, Partnoy, and Thomas (2008) find that the announcement of hedge fund

11

activism generates abnormal returns of more than 7% in a short window around the

announcement. In addition, the authors document modest changes in operating performance

around the activism. Klein and Zur (2009) and Clifford (2007) also document significant

positive abnormal returns around the announcement of activism. Recent research by Bebchuk,

Brav and Jiang (2013) find statistically meaningful evidence that the operating performance of

target firms improves following activist interventions but no evidence to support the claim that

short-term improvement was at the expense of long-term performance.

When shareholders are dissatisfied with the performance of a corporation and its’ board

of directors, they can intervene via a proxy contest. The proxy contest process is a meticulously

regulated election mechanism which can be invoked when “one group, referred to as ‘dissidents’

or ‘insurgents’ attempt to obtain seats on the firm’s board of directors currently in the hands of

another group, referred to as ‘incumbents’ or ‘management’” (Dodd and Warner, 1983).

The objective is to displace incumbents with the dissidents’ preferred candidates in order

to bring about an overall improvement in enterprise financial performance and shareholder value.

Although dissident shareholders do not always obtain a majority of board seats, in many cases

they manage to capture some seats. Notwithstanding proxy contest outcome, there is evidence

that share price performance is positively and significantly associated with the proxy contest

process (Dodd and Warner, 1983).

Within three years of a proxy contest event, many publicly held firms experience major

changes including resignations of top management within one year of the contest followed by

sale or liquidation of the firm. Proxy contest shareholder gains derive largely from the dissident

linked sale of the corporation (DeAngelo and DeAngelo, 1989). These findings are consistent

with our investigation. Within two years of a dissident shareholder joining the board of a target

12

firm, the CEO resigned approximately 30% of the time and over the course of the five years 21%

of our target firms were sold/merged.

Mulherin and Poulsen (1998) determined that “on average, proxy contests create value.”

Research confirms that the bulk of shareholder wealth gains arise from firms that are acquired in

the period surrounding the contest. In contrast, management turnover in firms that are not

acquired results in a significant and positive effect on stock owners’ value proposition because

organizations engaged in management change out are more inclined to re-structure following a

proxy contest event.

The rate of management turnover for proxy contest challenged firms is much higher

compared to organizations not involved in proxy contest activity and is directly proportional to

the share of seats at the board won by proxy contenders. When the majority of seats are won by

proxy contesters, the highest management turnover is observed reflecting the importance of

intangible issues such as job security (Yen and Chen, 2005).

Bebchuk, Brav and Jiang (2013) found that contrary to the claim that hedge fund activists

adversely impact the long-term interests of organizations and their shareholders, there is

evidence that activist interventions lead to improved operating performance in the five years that

follow the interventions. Venkiteshwaran, Iyer and Rao (2010) conducted a detailed study of

hedge fund activist Carl Icahn’s 13D filings and subsequent firm performance and found

significant share price increases for the target companies (of about 10%) around the time Icahn

discloses his intentions publicly. Additionally, the author’s found a significant number (1/3) of

Icahn’s targets ended up being acquired or taken private within 18 months of his initial

investment. The shareholders of those companies earned abnormal returns of almost 25% from

the time of Icahn’s initial investment through the sale of the company. This finding is consistent

13

with the DeAngelo and DeAngelo (1989) research that shareholder gains derive largely from the

dissident linked sale of the corporation.

Our research fills the important void with respect to the long term efficacy of shareholder

activists serving as a disciplinary mechanism on the firm by actively seeking board

representation to monitor management. Additionally, we contribute to the literature regarding

shareholder activists as self-interested myopic investors at the expense of the long-term interest

of the company and its long term shareholders. Moreover, our findings have important policy

implications related to the ongoing debate on corporate governance and the rights and roles of

shareholders. Our findings suggest that hedge fund activism generates substantial long term

value for target firms and its long term shareholders when they function as a shareholder

advocate to monitor management through active board engagement.

14

III. DATA AND METHODOLOGY

There is no central database of activist hedge funds. Therefore, we constructed an

independent dataset of all activist interventions from 1984-2013 from various sources,

including Compustat, Capital IQ, FactSet, ISS Proxy Data, SharkRepellent and the SEC’s

EDGAR database. Additionally, our dataset includes Schedule 13D filings, the mandatory

federal securities law filings under Section 13(d) of the 1934 Exchange Act that investors must

file with the SEC within 10 days of acquiring more than 5% of any class of securities of a

publicly traded company if they have an interest in influencing the management of the

company. Our manually constructed database of shareholder activist events includes 5,063

interventions from 1984-2013 (see Figure I). Similar to Gillan and Starks (2007), we define

shareholder activist event as a purposeful intervention by “investors who, dissatisfied with some

aspect of a company’s management or operations, try to bring about change within the company

without a change in control.”

Our data collection comprised a multi-step procedure. The vast majority of shareholder

activism literature is predicated on Schedule 13D filings. However, we assert that the optimal

dataset to empirically test the long-term effects of shareholder activism should be based on

board representation of target firms by a shareholder activist. Therefore, we started with a

much more expansive sample of activist interventions. Our comprehensive dataset of

shareholder activist events includes 5,063 interventions from 1984-2013. Of those, 3,899

(77%) filed a 13D. However, approximately 32% of all activist interventions were focused on

board engagement, either through a proxy contest (1,216) or non-proxy contest dissident

campaigns that resulted in board representation via private negotiations (418) with the target

management team and board of directors. To be sure, over two-thirds (2/3) of activist

15

interventions did not seek board representation to actively monitor management. Moreover,

GAMCO Asset Management, a hedge fund founded by Mario Gabeli, has filed 478 13Ds since

1996. However, it has launched only 18 proxy fights (3.8%) and won board representation only

ten times (2.1%) to date. In contrast, Carl Icahn has launched proxy fights and won board

representation at eBay, Genzyme, Time Warner and Yahoo! without filing a 13D.

Accordingly, we claim that there are numerous 13D filings of activist interventions that

otherwise include good performing companies with strong management that an activist is not

compelled to seek board representation to actively monitor management and function as a

disciplinary mechanism. Additionally, there are over 90 activist interventions that led to board

representation without filing a 13D. Therefore, we assert that the optimal dataset to empirically

test the long-term effects of hedge fund activism should be based on board representation of

target firms by a shareholder activist and not merely the fact that a shareholder crossed 5%

ownership and might (not will) seek to influence strategic change at the target firm. To be sure,

an activist that is willing to incur significant financial cost that is not borne by all shareholders,

which Gantchev (2012) estimates is approximately $10 million per proxy contest, has genuine

conviction that the target firm requires strategic change that management is unwilling to

execute.

In our second step, we narrowed our time-frame from 1996-2013 and identified 1,039

activist interventions that resulted in board representation either through a proxy fight or private

negotiations. This sample set included 621 proxy fights and 418 activist interventions (non-

proxy contests) that resulted in board representation either through a settlement or concessions

between the target management and the dissident shareholder. Next, we excluded certain

events where: (1) the primary purpose of the filer is to be involved in the bankruptcy

16

reorganization or the financing of a distressed firm; and (2) the target is a closed-end fund or

other non-regular corporation. We excluded duplicate campaigns by multiple activists (i.e., the

wolf-pack) so the dataset includes information about the target firm only once with respect to a

specific campaign. If a target firm were to file for bankruptcy protection or liquidation, we

included financial information from the target firm up to the Chapter 11 or Chapter 7 filing

date. More specifically, the bankrupt firm would account for 100% loss as it relates to stock

return and portfolio analyses. In our final step, we included only hedge fund activist campaigns

at target firms with a market capitalization of $50 million or greater. Although, there are many

campaigns that are targeted at micro-cap companies, we determined that the trading liquidity

and financial data of these firms were not representative of the broader sample. Additionally,

we trimmed certain variables and financial data at the 1.0% and 99.0% in each tail to adjust for

outliers.

Our final dataset consists of 448 activist interventions (the “Treatment Group”) that

resulted in at least one board seat granted to an activist shareholder from 1996-2013 (see Table

1). A total of 843 board members were elected at 398 unique target companies. This includes

225 unique activist hedge funds. Of the 448 activist interventions in the Treatment Group, 243

(54%) target firms are still publicly-listed, 186 (42%) were sold/merged and 19 (4%) target

firms filed for bankruptcy. By compiling our own database, we avoid some problems

associated with survivorship bias, reporting selection bias, and backfill, which are prevalent

among other hedge fund databases. Table 4 provides descriptive statistics with respect to the

Treatment Group.

17

Table 2

As previously noted, extant literature determined that approximately 1/3 of certain

activist interventions led to a sale or merger of the target firm within 18 months of the

intervention. However, we find that hedge fund activists that seek board representation do not

promote a “sell the company” agenda consistent with other activist objectives. Table 3

illustrates that less than 7% of the target firms announced a sale of the company ex post the

dissident board seat grant date. Additionally, Table 3 highlights CEO changes at target firms

post a dissident joining the board after an activist campaign. Within two years of the

shareholder activist joining the board, the CEO had been replaced approximately 30% of the

time. Therefore, it is reasonable to assume from the data that hedge funds that seek board

representation are focused more on promoting change at the target firm via operating

improvements and changes to existing management rather than supporting a sale of the

company strategy.

Existing 243 54% Settled/Concessions Made 174 72%

Sold/Merged 186 42% Dissident 53 22%

Bankruptcy 19 4% Split 14 6%

Total 448 Total 241

Board Representation 235 52% Proxy Fight 241 54%

Board Control 69 15% Other Stockholder Campaign 165 37%

Maximize Shareholder Value 80 18% No Publicly Disclosed Activism 40 9%

No Publicly Disclosed Activism 40 9% Exempt Solicitation 2 0%

Other Activist Campaigns 24 5%

Total 448 Total 448

Proxy Fight WinnerCompany Status

Activism TypePrimary Campaign Type

18

Table 3

To control for self-selection bias and endogeneity, we constructed a control group (the

“Control Group”) of all proxy fights campaigns that did not result in board representation

during the same period. Our dataset includes 595 target firms that experienced a proxy contest

for board representation. After we excluded certain events to reflect consistent sample

parameters with our Treatment Group, our Control Group includes 73 firms that were involved

in a proxy contest that the target firm incumbent management defeated the dissident shareholder

during the voting process. Therefore, we examined not only firms that granted at least one

board seat to a dissident shareholder and its ex post effects (the “Treatment Group”), but also

companies that were challenged by dissatisfied shareholders and did not suffer the ex post

disciplinary effects by the dissident (the “Control Group”). Table 4 provides an overview of

certain characteristics the target firms and the respective shareholder activist tactics with respect

to each intervention for the Treatment Group (N=448) and the Control Group (N=73).

Timeframe

No. of

Target

Firms

% of Total

Board

Engagements

Timeframe

No. of

Target

Firms

% of Total

Board

Engagements

3-5 Years 19 4.2% 3-5 Years 36 8.0%

2-3 Years 17 3.8% 2-3 Years 27 6.0%

1-2 Years 27 6.0% 1-2 Years 46 10.3%

6-12 months 14 3.1% 6-12 months 37 8.3%

< Six (6) Months 17 3.8% < Six (6) Months 40 8.9%

Total 94 Total 186

CEO Change Ex Post Dissident Seat Date Announced Target Firm Sale or Divestiture

of Assets Ex Post Dissident Seat Date

20

Table 4

Descriptive Statistics of Dataset

Target Firm Characteristics Yes No Yes (%) No (%) Yes No Yes (%) No (%)

Classified Board 210 238 47% 53% 35 38 48% 52%

Cumulative Voting 34 414 8% 92% 3 70 4% 96%

Poison Pill Adopted in Response to Campaign 33 415 7% 93% 3 70 4% 96%

Poison Pill In Force Prior to Announcement 174 274 39% 61% 30 43 41% 59%

Shareholder Activist Tactics

13D Filer 400 48 89% 11% 47 26 64% 36%

Dissident Group Includes SharkWatch50 Member 261 187 58% 42% 38 35 52% 48%

Dissident Tactic: Nominate Slate of Directors 260 188 58% 42% 60 13 82% 18%

Dissident Tactic: Publicly Disclosed Letter to Board/Management 248 200 55% 45% 50 23 68% 32%

Proxy Fight 241 207 54% 46% 73 0 100% 0%

Standstill Agreement Special Exhibit Included 135 313 30% 70% 1 72 1% 99%

Publicly Disclosed Letter to Management 129 53 71% 29% 26 7 79% 21%

Dissident Seek Reimbursement 120 328 27% 73% 39 34 53% 47%

Dissident Tactic: Letter to Stockholders 115 333 26% 74% 61 12 84% 16%

Proxy Fight Went Definitive 114 334 25% 75% 73 0 100% 0%

Dissident's Fight Letter Special Exhibit Included 87 361 19% 81% 48 25 66% 34%

Short Slate 82 366 18% 82% 36 37 49% 51%

Dissident Tactic: Request Company Seek Buyer 75 373 17% 83% 13 60 18% 82%

Shareholder Proposals (Excluding Dissident Director Nominees) 71 377 16% 84% 24 49 33% 67%

Proxy Fight Went the Distance 67 381 15% 85% 73 0 100% 0%

Letter to Shareholders 66 116 36% 64% 30 3 91% 9%

Dissident Tactic: Threaten Proxy Fight 41 407 9% 91% 0 73 0% 100%

Dissident Tactic: Lawsuit 39 409 9% 91% 15 58 21% 79%

Dissident Tactic: Propose Binding Proposal 39 409 9% 91% 8 65 11% 89%

Dissident Tactic: Propose Precatory Proposal 32 416 7% 93% 12 61 16% 84%

Dissident Tactic: Remove Director(s) 27 421 6% 94% 5 68 7% 93%

Dissident Tactic: Unsolicited Offer 19 429 4% 96% 2 71 3% 97%

Threaten Proxy Fight 18 164 10% 90% 0 33 0% 100%

Hostile or Unsolicited Offer 14 168 8% 92% 0 33 0% 100%

Dissident Tactic: Remove Officer(s) 14 434 3% 97% 2 71 3% 97%

Special Meeting 13 435 3% 97% 10 63 14% 86%

Dissident Tactic: Take Action by Written Consent 12 436 3% 97% 0 73 0% 100%

Dissident Tactic: Call Special Meeting 11 437 2% 98% 4 69 5% 95%

Written Consent 11 437 2% 98% 0 73 0% 100%

Treatment Group: Target Firms that

Granted at least One Board Seat (N=448)

Control Group: Target Firm

Management Won Proxy Fight (No

Shareholder Activist Board

Representation, N=73)

21

IV. EMPIRICAL TESTS AND RESULTS

In this section, we address and empirically test the alternative hypothesis that hedge fund

activism is not detrimental to the long term operating performance of target firms or its long term

shareholders. To test the ex post disciplinary effects by dissident shareholders we examined and

measured firm performance and long term abnormal stock returns up to five years from the board

seat grant date. Additionally, we conducted similar tests during the five year period prior to the

intervention. We contrast those results with both Control Groups.

a. Measuring Cross-Sectional Firm Operating Performance

We measure firm performance by several empirical proxies: Tobin’s Q, returns on assets

(ROA), return on equity (ROE), return on invested capital (ROIC) and stock returns, which are

the most widely used and accepted firm performance proxies. Additionally, since the

conventional wisdom and the common narrative is that shareholder activists negatively affect

long term operating performance and decrease capital investment, we measured operating margin

(EBIT/Sales) and capital investment policy (CAPEX/Sales) of the target firms for the Treatment

Group and for both Control Groups. We report the industry adjusted differences for ROA and

Tobin’s Q for each target sample firm in the Treatment Group and both Control Groups

compared to similar matched firms (firms in the same four-digit SIC industry).

Tobin’s Q is named after the Nobel Prize winner James Tobin and is calculated as the

ratio of market value to asset replacement value (Yermack, 1996). Tobin’s Q is calculated as:

Tobin’s Q= (Market value of assets) / (Replacement cost of assets)

As an approximation, the market value of assets is computed as market value of equity

plus book value of assets minus book value of equity, following Brown and Caylor (2006). The

22

asset replacement value is taken as the book value of assets. A Tobin’s Q ratio greater than one

indicates the good quality of a firms investment decisions: it has invested in positive NPV

investment projects rather than in negative NPV investment projects and the returns meet or

exceed expectations. In contrast, Tobin’s Q lower than one suggests that the firm did not even

earn its returns expected from investors from the investment projects to cover the cost of capital.

Return on assets is calculated as earnings before interests, and taxes (EBIT) multiplied by

the reciprocal of the effective rate divided by the average of total assets for the year. Return on

assets (ROA) indicates how efficient management is at using its assets to generate earnings.

Calculated by dividing a company’s annual earnings by its total assets, ROA is generally

displayed as a percentage. Sometimes this is referred to as “return on investment”, an indicator

of how profitable a company is:

Return on assets (ROA) = (EBIT * (1-tax rate)) / ((Total Assets(t) + Total Assets(t-1)) / 2)

Return on invested capital (ROIC) is calculated as earnings before interest, and taxes

(EBIT) multiplied by the reciprocal of the effective rate divided by the average of total debt and

total common equity for the target firms. ROIC calculation is used to assess a firm’s efficiency

at allocating the capital under its control to profitable investments. The return on invested capital

measure gives a sense of how well a company is using its money to generate returns. The

calculation is as follows:

ROIC = (EBIT * (1-tax rate)) / ((Total Debt + Preferred Equity + Total Common Equity)(t)) +

((Total Debt + Preferred Equity + Total Common Equity)(t-1)) / 2)

Return on equity (ROE) is calculated as earnings after tax and interest (net income)

divided by the average of total common equity and total preferred equity plus minority interest

23

for the target firms. ROE calculation is used to assess a firm’s profitability by revealing how

much profit a company generates with the capital shareholders have invested. The calculation is

as follows:

ROE = Net Income / ((Total Common Equity + Preferred Equity + Total Minority))

Operating margin is calculated as earnings before interest and taxes divided by total

revenue generated by the target firms. A ratio used to measure a company’s pricing strategy

and operating efficiency. Operating margin is a measurement of what proportion of a

company’s revenue remains after paying for variable costs of production such as wages, raw

materials, etc. and provides an understanding of how much a company makes (before interest

and taxes) on each dollar of sales. The calculation is as follows:

Operating Margin = EBIT / Sales

Capital expenditures relative to sales (CAPEX / Sales) is calculated as total capital

expenditures divided by the firm’s revenues. This metric provides measure of how much the

firm is investing for future growth opportunities as well as maintaining existing fixed assets. The

calculation is as follows:

CAPEX / Sales = Total Capital Expenditures / Total Revenues

Our findings of target firm performance for companies that granted at least one board seat

to a dissident shareholder are presented in Panel A in Table 5 and our findings of target firm

performance for both Control Groups are presented in Panel B and Panel C in Table 5.

Consistent with the extant literature (Brav et al, 2008), we find that target firms’

operating performance for the Treatment Group and both Control Groups deteriorates prior to an

activist intervention (defined as the “Event”). For example, the median ROA for the Treatment

24

Group declines significantly from 3.46% five years prior to the Event date to 1.72% on the date

the board seat is granted to a dissident shareholder. The foregoing results were statistically

significant at the 1% level. The median ROA for the Treatment Group increased from 1.72% on

the grant seat date to 3.17% five years post the Event date, yielding a 13% CAGR increase

during the review period (see Graph 3). However, during the same period the Control Group

experienced a significant decline in ROA post the Event date (the announcement date of the

proxy contest results).

Graph 3

Return on Assets (ROA)

The mean Q Ratio for the Treatment Group declines significantly from 1.99% five years

prior to the Event date to 1.56% on the date the board seat is granted to a dissident shareholder.

The foregoing results were statistically significant at the 1% level. The mean Q Ratio for the

Treatment Group increased from 1.56% on the grant seat date to 1.77% five years post the Event

date, reflecting a 2.6% CAGR increase. During the same period, the Control Group experienced

a similar increase in its Q Ratio, albeit slightly less than the Treatment Group (See Graph 4).

1.72

3.17

3.53

1.35

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

1 2 3 4 5 6Event +1 +2 +3 +4 +5

Years

All Target Firms that granted at least one board seat to a dissident: 13% CAGR increase in ROA over 5 year period

All Target Firms that WON the proxy fight

against activist hedge fund: 18% CAGR decrease in ROA over 5 year period

Board Seat Grant Date

25

Graph 4

Q Ratio

ROIC and ROE reflects a similar pattern to the ROA and Q measures discussed above.

Our findings demonstrate a significant decline for both measures during the pre-Event Date

period of the activist intervention and a material improvement once a dissident shareholder

joined the board of the target firm. Although the Control Group experienced a comparable

degradation during the pre-Event Date period – it did not experience a similar increase in

improvement compared to the target firms that granted at least one board seat to a shareholder

activist.

Capital investment policy (CAPEX/Sales) of the target firms for the Treatment Group

and for the Control Group experienced a slight decline of fixed asset investment leading into the

Event. However, Panel A demonstrates that target firms that granted at least one board to a

dissident shareholder continued to experience a decline of CAPEX as a percentage of revenue ex

post the Event by approximately 40% of its capital spending prior to the Event. Panel C

illustrates that the Control Group increased its capital spending during Event +1 above the Event

period. Suggesting that those target firms may have under-invested in fixed assets leading into

1.56

1.77

1.71

1.87

1.45

1.55

1.65

1.75

1.85

1.95

2.05

2.15

1 2 3 4 5 6Event +1 +2 +3 +4 +5

Years

All Target Firms that granted at least one board seat to a dissident: 2.6% CAGR increase in Q Ratio over 5 year period


against activist hedge fund: 1.8% CAGR increase in Q Ratio over 5 year period


26

the Event to manage earnings – but had a “catch-up” period during Event +1 after the incumbent

management defeated the dissident in the proxy contest. Notwithstanding the decrease in capital

spending as a percent of sales, the target firms that granted at least one board seat to an activist

hedge fund experienced significant increases in both ROIC and ROE. In contrast, the Control

Group suffered a decline in comparable return measures.

A frequently invoked claim by opponents of hedge fund activism is that activist

interventions lead to deteriorating operating performance of the target firms ex post the activist

event. We empirically tested the operating performance of our data set by comparing target

firms in our Treatment Group to the Control Group (See Graph 5). After five years, the Target

Firms that granted at least one board seat experienced a slight improvement in operating margin,

whereas the target firms that the management team won the proxy contest against the dissident

shareholder experienced a -7% CAGR in operating margin.

Graph 5

Operating Margin

Our investigation and findings support the alternative hypothesis that hedge fund

activism is not detrimental to and does not have an adverse-effect on the long term interests of

4.33 4.35

4.81

3.38

3.00

3.50

4.00

4.50

5.00

5.50

6.00

6.50

7.00

1 2 3 4 5 6Event +1 +2 +3 +4 +5

Years

All Target Firms that granted at least one board seat to a dissident : Slight increasein Operating Margin over 5 year period)


against activist hedge fund: 7% CAGR decreasein Operating Margin over 5 year period


27

target firms and their long term shareholders.

We find that target firms in our sample dataset underperformed their industry peers on

certain operating metrics prior to the activist intervention, which validates the value-oriented

characteristics of activist targets. However, those operating metrics progressively improved

during the review period subsequent to the board seat grant date. This is further evidence that

board representation by activist hedge funds lead to improved operating performance in the long

term. In contrast, target firms that won the proxy fight against the activist experienced

degradation in certain operating performance metrics compared to industry peers during the

review period.

28

b. Event Study Methodology

Long-horizon event studies have an extensive history, including the original stock split

event study by Fama, Fisher, Jensen, and Roll (1969). As evidence inconsistent with the efficient

markets hypothesis started to accumulate in the late seventies and early eighties, interest in long-

horizon studies continued. Evidence on the post-earnings announcement effect (Ball and Brown,

1968, and Jones and Litzenberger, 1970), size effect (Banz, 1981), and earnings yield effect

(Basu, 1977 and 1983) contributed to skepticism about the CAPM as well as market efficiency.

This evidence prompted researchers to develop hypotheses about market inefficiency stemming

from investors’ information processing biases (DeBondt and Thaler, 1985 and 1987) and limits to

arbitrage (DeLong et al., 1990a and 1990b, and Shleifer and Vishny, 1997).

The “anomalies” literature and the attempts to model the anomalies as market

inefficiencies has led to a burgeoning field known as behavioral finance. Research in this field

formalizes (and tests) the security pricing implications of investors’ information processing

biases. Because the behavioral biases might be persistent and arbitrage forces might take a long

time to correct the mispricing, a vast body of literature hypothesizes and studies abnormal

performance over long horizons of one-to- five years following a wide range of corporate event s.

The events might be one-time (unpredictable) phenomena like an initial public offering or a

seasoned equity offering, or they may be recurring events such as earnings announcements.

Both cumulative abnormal returns (CAR) and buy-and-hold abnormal return (BHAR)

methods test the null hypothesis that abnormal performance is equal to zero. The abnormal

returns for an event are calculated as follows:

After calculating event CARs, we then calculate and report the cumulative

2

1

),( 21

t

tt

iti ARttCAR

29

average abnormal return (CAAR). Where, the CAAR is the arithmetic average of all

sample event CARs, and is calculated as follows:

2

11

21 ),(t

tt

it

N

j

i ARttCAAR

Under each method, the abnormal return measured is the same as the returns to a trading

rule which buys sample securities at the beginning of the first period, and holds through the end

of the last period. CARs and BHARs correspond to security holder wealth changes around an

event. Further, when applied to post-event periods, tests using these measures provide

information about market efficiency, since systematically nonzero abnormal returns following an

event are inconsistent with efficiency and imply a profitable trading rule (ignoring trading costs).

While post-event risk-adjusted performance measurement is crucial in long-horizon tests,

actual measurement is not straightforward. Two main methods for assessing and calibrating

post-event risk-adjusted performance are used: characteristic-based matching approach (also

known as BHAR) and the Jensen’s alpha approach, which is also known as the calendar-time

portfolio approach (Fama, 1998 or Mitchell and Stafford, 2000). Analysis and comparison of the

methods is detailed below.

c. Initial Market Reaction – Cumulative Abnormal Return

Table 6 presents our findings of the market reaction to news of that a target firm

either granted at least one board seat to a dissident shareholder (Treatment Group) or

that the target firm did not grant a board seat (Control Group). We examine the

market reaction in the 3-day, 5-day and 21-day windows around the board seat grant

date or announcement date via a press release of the results of the proxy contest. This

study employs a standard event study methodology and a standard market model to

measure normal performance. The market model, which improves on the constant

1

N

30

mean return model by controlling for Rmt, was calculated as follows:

The regression coefficients αi and βi are estimated in an ordinary least squares

(OLS) regression during the estimation period one year (255 trading days) prior to the

event period (event days -305 through -50). The event period consists of 21 trading

days centered on the product recall announcement event ( -10 through +10). Three

event windows were defined based on the event date, [ -10,10], [-2,+2] and [-1,+1]. As

proxy for the return for the market portfolio Rmt, both the Center for Research on Security

Prices (CRSP) value weighted index and the CRSP equal weighted index was used.

Under standard assumptions, OLS is a consistent estimation procedure for the market

model parameters. Under the assumption that asset returns are jointly multivariate

normal and independently and identically distributed, OLS is also efficient.

Panel A presents the market response for Target Firms that granted at least one

board seat to a dissident shareholder. The results in Panel A show significant

abnormal returns of 1.44%, 1.63% and 3.30% during the 3-day, 5-day and 21-day

windows, respectively, surrounding the announcement of board representation. The

results in Panel A are consistent with our other findings that investors perceive the

value associated with board representation of an activist hedge fund. However, Panel

B results indicate a significant negative market reaction of -4.33% during the 21-day

window around the announcement that a dissident shareholder did not obtain board

representation and the incumbent management team prevailed in a proxy fight against

an activist hedge fund. Overall, these results suggest that investors react positively to

the announcement that an activist hedge fund will function as a shareholder advocate

itmtiiit RR 2)(

0)(

iit

it

Var

E

Where,

31

to monitor management through active board engagement and will operate as a

disciplinary mechanism on the target firm. Interestingly, the significant negative

market reaction during the 21-day trading window when the incumbent management

team wins the proxy fight suggests that certain shareholders (perhaps other hedge

funds and arbitrageurs) are not supportive of the status quo.

Table 6

Cumulative Average Abnormal Return (CAAR)

Around Announcement of Board Engagement

Given the discernable difference between the market reactions around the

announcement of an activist hedge fund obtaining board representation vis-à-vis an

incumbent management team winning a proxy fight, we conducted a precision -weighted

cumulative average abnormal return (PWCAAR) between the respective groups and

tested the significance of the results. Graph 6 illustrates the statistically significant

Window N CAAR t-stat p-value Window N CAAR t-stat p-value

(- 1, 1) 409 1.44*** 3.94 0.001 (- 1, 1) 409 1.38*** 3.95 0.001

(- 2, 2) 409 1.63*** 3.20 0.001 (- 2, 2) 409 1.60*** 3.26 0.001

(- 10, 10) 410 3.30*** 3.94 0.001 (- 10, 10) 410 3.07*** 3.00 0.001

Window N CAAR t-stat p-value Window N CAAR t-stat p-value

(- 1, 1) 65 0.32 0.30 0.384 (- 1, 1) 65 0.29 0.27 0.936

(- 2, 2) 65 -1.03 -0.95 0.170 (- 2, 2) 65 -0.98 -0.94 0.173

(- 10, 10) 65 -4.33** -1.77 0.039 (- 10, 10) 65 -4.52** -1.89 0.029

Panel B: CAR for Target Firms that Management Won Proxy Fight

Market Model (Value Weighted) Market Model (Equal-Weighted)

Panel A: CAR for Target Firms that Granted at least One Board Seat

Market Model (Value Weighted) Market Model (Equal-Weighted)

*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

32

PWCAAR market reaction over a 21-day trading window surrounding the

announcement of board representation.

Graph 6

Precision Weighted Cumulative Average Abnormal Return (PWCAAR)

Around Announcement (period in days)

3.15%

-5.63%

-7.00%

-6.00%

-5.00%

-4.00%

-3.00%

-2.00%

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%All Target Firms that granted at least one

board seat to an activist hedge fund: 3.15%*** PWCAAR over 21 day Event window

All Target Firms that Management won the proxy fight: -5.63%**

PWCAAR over 21 day Event window

-10 -9 -8 7 -6 -5 -4 -3 -2 -1 EVENT +1 +2 +3 +4 +5 +6 +7 +8 +9 +10


33

d. Long Term Stock Returns

We examine long term stock returns for each individual target firm that granted at least

one board seat to an activist shareholder. We contrast those results with both Control Groups. To

identify whether stock returns are abnormally low or high we use a benchmark for comparative

purposes. Such benchmarks of comparison are provided by the Capital Asset Pricing Model

(CAPM), the Fama-French three factor model and the Fama-French-Carhart asset-pricing model.

These models provide a prediction of the return that “normally” would be expected for a given

security during a given period and, therefore, enable us to identifying “abnormal” returns.

In particular, using the CAPM, the standard procedure is to estimate an “alpha,” the

average excess return that is not explained by co-movement with the market. We estimate the

excess return on the market as the value-weight return of all CRSP firms incorporated in the US

and listed on the NYSE, AMEX, or NASDAQ that have a CRSP share code of 10 or 11 at the

beginning of month t, good shares and price data at the beginning of t, and good return data for t

minus the one-month Treasury bill rate from Ibbotson Associates. Specifically, we estimate for

each firm i an alpha using the following regression:

Similarly, using the Fama-French-three-factor model, the standard procedure is to

estimate an “alpha,” the average excess return that is not explained by the three market-wide

factors identified in by Fama and French (1993). We estimate for each firm i an alpha using the

following regression:

The Fama-French factors are constructed using the six value-weight portfolios formed on

size and book-to-market. SMB (Small Minus Big) is the average return on the three small

rit - rf = ai + β1RMRFt + Eit

rit - rf = ai + βi1RMRFt + βi2SMBt + βi3HMLt + Eit

34

portfolios minus the average return on the three big portfolios. HML (High Minus Low) is the

average return on the two value portfolios minus the average return on the two growth portfolios

and we estimate the excess return on the market similar to the methodology used in CAPM.

Additionally, using the Fama-French-Carhart four-factor asset pricing model, the standard

procedure is to estimate an “alpha,” the average excess return that is not explained by the four

market-wide factors identified by Fama and French (1993) and by Carhart (1997). We estimate

for each firm i an alpha using the following regression:

We use the same methodology as we used in the Fama-French Asset Pricing Model to

estimate the first three factors. We added an additional factor to construct a Momentum factor.

The momentum factor is the average return on the two high prior return portfolios minus the

average return on the two low prior return portfolios. We use six value-weight portfolios formed

on size and prior (2-12) returns to construct the MOM. The portfolios, which are formed daily,

are the intersections of two portfolios formed on size (market equity) and three portfolios formed

on prior (2-12) return. The daily size breakpoint is the median NYSE market equity. The daily

prior (2-12) return breakpoints are the 30th and 70th NYSE percentiles. The six portfolios used to

construct MOM each day include NYSE, AMEX, and NASDAQ stocks with prior return data.

To be included in a portfolio for day t (formed at the end of day t-1), a stock must have a price for

the end of day t-251 and a good return for t-21.

For each of the firms in the primarily dataset that granted at least one board seat, we

estimate a daily alpha, or abnormal return, for a five year period (annually) prior to date the board

seat was granted. In addition, we estimate daily alphas for a five year period (annually) following

one day post the board seat grant date. To the extent that firms delist from the sample we

rit - rf = ai + βi1RMRFt + βi2SMBt + βi3HMLt + βi4M0Mt + Eit

35

incorporate the financial returns up to the delisting date. Additionally, to the extent target firms

file for Chapter 11 bankruptcy protection, we incorporate the returns from the firm up to the date

of filing for bankruptcy.

Panel A in Table 7 provides results with respect to the risk-adjusted excess returns

(alphas) we calculated for all target firms that granted at least one board seat to a dissident

shareholder. Panel B provides our findings of the risk-adjusted excess returns (alphas) for the

Control Group.

Table 7

Long Term Risk-Adjusted Excess Returns

[Event -5] [Event -3] [Event -1] [Event +1] [Event +3] [Event +5]

α (Alpha)

Observations 442 442 442 344 208 128

Mean 20.63 *** -2.42 -1.93 0.77 38.08*** 78.21***

Median 10.45 -5.20 -2.75 2.46 36.57 43.05

t-Stat 4.64 -0.76 -0.88 0.24 3.67 4.00

Std. Dev. 0.07 0.09 0.18 0.23 0.20 0.18

α (Alpha)

Observations 442 442 442 344 208 128

Mean 6.28 -4.75 -0.99 0.21 30.45*** 64.75***

Median -0.90 -5.53 -2.56 3.89 29.54 32.86

t-Stat 1.42 -1.55 -0.46 0.07 2.97 3.27

Std. Dev. 0.07 0.09 0.18 0.23 0.20 0.18

α (Alpha)

Observations 442 442 442 344 208 128

Mean 6.22 -5.11* -0.95 -2.61 27.41*** 63.33***

Median 0.44 -5.26 -2.62 2.13 27.13 31.77

t-Stat 1.42 -1.72 -0.46 -0.82 2.68 3.20

Std. Dev. 0.07 0.08 0.17 0.23 0.20 0.18

Panel A: Target Firms that Granted at least One Board Seat

Periods (in Years)

Capital Asset Pricing Model (CAPM)

Three-Factor Asset Pricing Model (Fama-French)

Four-Factor Asset Pricing Model (Fama-French-Carhart)


36

For each of the periods, we provide both the median and average alpha for all the firms in

our sample. We also indicate the statistical significance of our results; however, as is now well-

known in the financial economics literature, the standard error of the average of the estimated

alphas understates the unobserved variability in performance, and the reported t-stats should thus

be treated as merely suggestive (Fama, 1998).

The first row in Table 8 provides our results concerning the risk adjusted excess return

(alpha) during each year for the five-year period preceding the board seat grant date. Using the

three asset pricing models, we find an alpha during the three-year period ex ante that is negative

and economically meaningful. These results, like those obtained with respect to target firm

operating performance, are consistent with the view that hedge fund activists target under-

performing companies. Additionally, target firms that granted at least one board seat

outperformed the Control Group.

[Event -5] [Event -3] [Event -1] [Event +1] [Event +3] [Event +5]

α (Alpha) Capital Asset Pricing Model (CAPM)

Observations 53 53 53 56 35 24

Mean 51.21*** 28.79*** 7.33 5.37 32.40 43.81

Median 36.77 32.29 11.99 4.12 15.92 6.26

t-Stat 3.59 3.19 1.32 0.94 1.05 0.79

Std. Dev. 0.08 0.09 0.16 0.17 0.24 0.21

α (Alpha) Fama-French Asset Pricing Model


Mean 34.14** 22.38*** 6.00 3.33 31.02 37.50

Median 24.35 18.66 9.58 4.96 16.39 6.64

t-Stat 2.52 2.59 1.03 0.60 1.03 0.68

Std. Dev. 0.08 0.08 0.17 0.17 0.24 0.21

α (Alpha) Fama-French-Carhart Asset Pricing Model


Mean 35.91*** 22.68*** 6.39 1.66 29.16 33.65

Median 26.86 16.80 13.58 3.41 16.01 4.28

t-Stat 2.65 2.59 1.15 0.30 0.98 0.61

Std. Dev. 0.08 0.08 0.16 0.17 0.23 0.21

Panel B: Management Won Proxy Fight (No Shareholder Activist Board Representation)

Periods (in Years)


37

What is more noteworthy, are the results with respect to the risk adjusted excess return

(alpha) during the five-year period following the board seat grant date. The average and the

median of the estimated alphas are positive and statistically significant when we use the CAPM

model, the Three Factor (Fama-French) Asset Pricing Model and the Four Factor Model. The

results provide no support for the negative returns during these periods hypothesized by

opponents of activism. More specifically, our results suggest that hedge fund activism generates

substantial long-term value for target firms and its long-term shareholders when they act as a

disciplinary mechanism to monitor management. Moreover, hedge fund activists that function as

disciplinary mechanism to monitor management generate higher risk-adjusted excess returns

compared to the Control Group.

e. Buy-and-hold abnormal returns (BHAR) Approach

In recent years, following the works of Ikenberry, Lakonishok, and Vermaelen (1995),

Barber and Lyon (1997), Lyon et al. (1999), the characteristic-based matching approach (or also

known as the buy-and-hold abnormal returns, BHAR) has been widely used. Mitchell and

Stafford (2000) describe BHAR returns as “the average multiyear return from a strategy of

investing in all firms that complete an event and selling at the end of a pre-specified holding

period versus a comparable strategy using otherwise similar nonevent firms.” An appealing

feature of using BHAR is that buy-and-hold returns better resemble investors’ actual investment

experience than periodic (monthly) rebalancing entailed in other approaches to measuring risk-

adjusted performance. The joint-test problem remains in that any inference on the basis of

BHAR hinges on the validity of the assumption that event firms differ from the “otherwise

similar nonevent firms” only in that they experience the event. The researcher implicitly assumes

38

an expected return model in which the matched characteristics (e.g., size and book-to- market)

perfectly proxy for the expected return on a security. Since corporate events themselves are

unlikely to be random occurrences, i.e., they are unlikely to be exogenous with respect to past

performance and expected returns, there is a danger that the event and nonevent samples differ

systematically in their expected returns notwithstanding the matching on certain firm

characteristics. This makes matching on (unobservable) expected returns more difficult,

especially in the case of event firms experiencing extreme prior performance.

Once a matching firm or portfolio is identified, BHAR calculation is straightforward. A

T- month BHAR for event firm i is defined as:

BHARi (t, T) = t = 1 to T (1 + Ri,t) - t = 1 to T (1 + RB,t)

Where RB is the return on either a non-event firm that is matched to the event firm i, or it

is the return on a matched (benchmark) portfolio. If the researcher believes that the Carhart

(1997) four- factor model is an adequate description of expected returns, then firm-specific

matching might entail identifying a non-event firm that is closest to an event firm on the basis of

firm size (i.e., market capitalization of equity), book-to-market ratio, and past one- year return.

Alternatively, characteristic portfolio matching would identify the portfolio of all non-

event stocks that share the same quintile ranking on size, book-to-market, and momentum as the

event firm (see Daniel, Grinblatt, Titman, and Wermers, 1997, or Lyon, Barber, and Tsai, 1997,

for details of benchmark portfolio construction). The return on the matched portfolio is the

benchmark portfolio return, RB. For the sample of event firms, the mean BHAR is calculated as

the (equal or value-weighted) average of the individual firm BHARs. Additionally, we tested the

significance of each coefficient using the following equation:

nBHARBHARt

or

nCARCARt

iiBHAR

iiCAR

//

//

39

We constructed two BHAR portfolios of stocks from 1997-2013 for the Treatment Group

and the Control Group. There were over 12,000 observations within the portfolios. We

calculated the daily abnormal returns for each target firm relative to the market benchmarks

discussed previously. Portfolio I, which is all target firms that granted at least one board seat to

an activist hedge fund generated approximately 8 bps/day or 20% annually of risk-adjusted

excess return (alpha) relative the market.

Consistent with our other findings, our Treatment Group outperformed the Control

Group. Moreover, Portfolio I (the Treatment Group) outperformed Portfolio II (the Control

Group that incumbent management won the proxy contest) by approximately 5 bps/day or 13%

annually. All portfolios had high exposure to small cap stocks and a high value tilt.

40

Table 9 (Portfolio I)

Buy-and-Hold Abnormal Return Portfolio of All Activist Board Governed Firms

Portfolio II

Buy-and-Hold Abnormal Return Portfolio of Control Group

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

α 0.08 0.02 4.55 <0.0001 0.04 0.11

βp, RM-RF 0.73 0.01 55.23 <0.0001 0.70 0.75


α 0.06 0.02 4.12 <0.0001 0.03 0.10

βp, RM-RF 0.74 0.01 60.80 <0.0001 0.72 0.76

βp, SMB 0.61 0.03 23.73 <0.0001 0.56 0.66

βp, HML 0.43 0.02 17.62 <0.0001 0.38 0.48


α 0.07 0.02 4.35 <0.0001 0.04 0.10

βp, RM-RF 0.72 0.01 55.80 <0.0001 0.69 0.74

βp, SMB 0.62 0.03 24.15 <0.0001 0.57 0.67

βp, HML 0.39 0.03 15.22 <0.0001 0.34 0.44

βp, MOM -0.09 0.02 -5.43 <0.0001 -0.13 -0.06

CAPM SUMMARY OUTPUT

THREE FACTOR MODEL (FAMA-FRENCH) SUMMARY OUTPUT

FOUR FACTOR MODEL (FAMA-FRENCH-CARHART) SUMMARY OUTPUT


α 0.076 0.026 2.89 <0.0001 0.02 0.13

βp, RM-RF 0.714 0.020 35.71 <0.0001 0.68 0.75


α 0.063 0.026 2.44 <0.0001 0.01 0.11

βp, RM-RF 0.690 0.020 34.78 <0.0001 0.65 0.73

βp, SMB 0.401 0.045 8.88 <0.0001 0.31 0.49

βp, HML 0.256 0.041 6.26 <0.0001 0.18 0.34


α 0.065 0.026 2.50 <0.0001 0.01 0.12

βp, RM-RF 0.668 0.022 30.70 <0.0001 0.63 0.71

βp, SMB 0.413 0.045 9.10 <0.0001 0.32 0.50

βp, HML 0.236 0.041 5.70 <0.0001 0.16 0.32

βp, MOM -0.069 0.028 -2.49 <0.0001 -0.12 -0.01

CAPM SUMMARY OUTPUT

THREE FACTOR MODEL (FAMA-FRENCH) SUMMARY OUTPUT

FOUR FACTOR MODEL (FAMA-FRENCH-CARHART) SUMMARY OUTPUT



41

f. Calendar-time portfolio approach (Jensen’s alpha)

The calendar-time portfolio or Jensen-alpha approach to estimating risk-adjusted abnormal

performance is an alternative to the BHAR calculation using a matched-firm approach to risk adjustment.

Jaffe (1974) and Mandelker (1974) introduced a calendar time methodology to the financial-economics

literature, and it has since been advocated by many, including Fama (1998), Mitchell and Stafford (2000)

and Brav and Gompers (1997).

The distinguishing feature of the most recent variants of the approach is to calculate calendar-time

portfolio returns for firms experiencing an event, and calibrate whether they are abnormal in a multifactor

regression. The estimated intercept from the regression of portfolio returns against factor returns is the

post-event abnormal performance of the sample of event firms.

We implemented the Jensen-alpha approach for a five year period during the pre-event (i.e., prior to

the dissident / activist board seat grant date) and then post-event annually for a five year period. In each

calendar month over the entire sample period, a portfolio was constructed comprising all firms

experiencing the event within the previous month. Since the number of event firms is not uniformly

distributed over the sample period, the number of firms included in a portfolio is not constant through

time. As a result, some new firms are added each month and some firms exit each month. Accordingly,

the portfolios are rebalanced each month and an equal or value-weighted portfolio excess return is

calculated. The resulting time series of monthly excess returns is regressed on the CAPM market factor

and the three Fama-French (1993) factors as follows:

Rpt – Rft = αp + βp (Rmt – Rft) + βp,SMBSMBt + βp,HMLHMLt + εpt

Where;

Rpt is the equal or value-weighted return for calendar month t for the portfolio of event firms that

experienced the event within previous T years,

Rft is the risk- free rate,

Rmt is the return on the CRSP value-weight market portfolio,

SMBpt is the difference between the return on the portfolio of “small” stocks and “big” stocks;

HMLpt is the difference between the return on the portfolio of “high” and “low” book-to-market

stocks;

αp is the average monthly abnormal return (Jensen alpha) on the portfolio of event firms over the T-

42

month post-event period,

βp is the beta (the sensitivities) of the event portfolio to the three factors.

Inferences about the abnormal performance are on the basis of the estimated αp and its statistical

significance. Since αp is the average monthly abnormal performance over the T- month post-event period,

it can be used to calculate annualized post-event abnormal performance. Table 8 reports statistics on long-

term abnormal returns associated with firms that granted at least one board seat to an activist/dissident

shareholder. We report regression estimates and t-statistics from value-weighted calendar-time portfolio

regressions. The portfolio holding period, was determined based on actual trading days and indicates the

holding period in years relative to the date that the board seat(s) was granted. For example, the portfolio

with holding period [Event +1], continually adds target firms that have added an activist/dissident

shareholder to their respective board during the year from the date the seat was granted. The portfolio

holds these firms until the earlier of December 31, 2013, a delisting date as a result of a Chapter 11 filing

or a sale/merger.

We report regression results separately for all targets in Panel A of Table 10. αp is the estimate of

the regression intercept from the factor model. βp,RM RF is the loading on the market excess return.

βp,SMB and βp,HML are the estimates of portfolio factor loadings on the Fama-French size and book-to-

market factors. We obtain the factor returns, market capitalization breakpoints, and monthly risk-free rates

from Ken French’s web site at Dartmouth College.

43

Table 10

Calendar-Time Series Regressions (Long-term Abnormal Returns)

During our investigation of the presence of abnormal returns during this period, we employed

three standard methods used by financial economists for detecting stock return underperformance.

In particular, the study examines whether the returns to targeted firms were systematically lower than

what would be expected given standard asset pricing models. Our findings demonstrate that the targeted

firms started to underperform relative to the market two-to-three years prior to the board representation.

More importantly, we find that those firms generated positive risk-adjusted excess returns (alpha) within

the first two-years after the dissident joined the board. Additionally, we find no evidence that target firms

[Event -3] [Event -2] [Event -1] [Event +1] [Event +2] [Event +3] [Event +4] [Event +5]

α -0.076 -0.101 -0.050 0.000 0.101 0.151 0.202 0.252

1.1 1.70* 1.48 NM 1.26 1.64* 1.35 1.25

βp, RM-RF 0.893 0.864 0.837 0.850 0.851 0.852 0.865 0.864

99.53*** 87.22*** 65.94*** 55.50*** 70.44** 75.23*** 79.43*** 79.16***

βp, SMB 0.750 0.716 0.673 0.668 0.682 0.677 0.671 0.660

40.67*** 35.04*** 25.50*** 20.72*** 26.82*** 28.36*** 29.29*** 28.74***

βp, HML 0.432 0.440 0.508 0.651 0.514 0.511 0.496 0.503

24.38*** 22.43*** 20.15*** 21.29*** 21.33*** 22.61*** 22.85*** 23.09***

Adjusted R2 70.21% 65.63% 53.73% 47.33% 58.63% 61.71% 64.05% 63.83%

F-Test 3881*** 2983*** 1717*** 1254*** 1977*** 2249*** 2485*** 2462***

Panel A: Target Firm Fama-French Calendar Time Series Regressions (N=443)

Holding Period (in Years)

[Event -3] [Event -2] [Event -1] [Event +1] [Event +2] [Event +3] [Event +4] [Event +5]

α 0.151 0.101 0.101 0.076 0.151 0.302 0.302 0.378

1.3 0.86 1.33 0.93 1.19 1.35 1.23 1.23

βp, RM-RF 0.939 0.981 0.933 0.918 0.879 0.843 0.823 0.822

43.38*** 34.85*** 27.08*** 30.57*** 31.43*** 31.37*** 30.37*** 30.06***

βp, SMB 0.759 0.790 0.607 0.602 0.593 0.546 0.528 0.525

18.64 13.63*** 8.24*** 9.48*** 10.36*** 10.13*** 9.83*** 9.69***

βp, HML 0.46 0.56 0.57 0.34 0.34 0.28 0.25 0.27

11.42*** 10.80*** 8.68*** 5.41*** 6.01*** 5.14*** 4.64*** 5.08***

Adjusted R2 59.36% 51.43% 35.53% 34.39% 37.04% 36.01% 35.30% 35.53%

F-Test 1960*** 1357*** 660*** 589*** 661*** 633*** 614*** 620***

Panel B: Target Firm Fama-French Calendar Time Series Regressions (N=73)

Holding Period (in Years)



44

experience a “reversal of fortune” during the five-year period following the activist intervention. The

long-term underperformance asserted by supporters of the myopic activism claim, and the resulting

losses to long-term shareholders due to activist interventions, are not found in the data.

CONCLUSION

Over the past two decades, hedge fund activism has emerged as new form of corporate governance

mechanism that brings about operational, financial and governance reforms to a corporation. Many

prominent business executives and legal scholars are convinced that the entire American economy will

suffer unless hedge fund activism with its perceived short-termism agenda is significantly restricted.

Shareholder activists and their proponents claim they function as a disciplinary mechanism to monitor

management and are instrumental in mitigating the agency conflict between managers and shareholders.

The vast majority of shareholder activism literature is predicated on Schedule 13D filings.

However, we assert that the optimal dataset to empirically test the long-term effects of shareholder

activism should be based on board representation of target firms by a shareholder activist.

We find statistically meaningful empirical evidence to reject the anecdotal conventional wisdom

that hedge fund activism is detrimental to the long term interests of companies and their long term

shareholders. Moreover, our findings suggest that hedge fund activism generates substantial long term

value for target firms and its long term shareholders when they function as a shareholder advocate to

monitor management through active board engagement.

Our research fills the important void with respect to the long term efficacy of shareholder activists

serving as a disciplinary mechanism on the firm by actively seeking board representation to monitor

management. Additionally, we contribute to the literature regarding shareholder activists as self-

interested myopic investors at the expense of the long-term interest of the company and its long term

shareholders. Moreover, our findings have important policy implications related to the ongoing debate on

corporate governance and the rights and roles of shareholders. Although some prominent legal

45

commentators and presiding justices, such as Chief Justice Strine, have called for restrictions on hedge

fund activism because of its supposedly short-term orientation, our findings suggest that hedge fund

activism generates substantial long term value for target firms and its long term shareholders when they

function as a shareholder advocate to monitor management through active board engagement.

46

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49

Graph I

Long Term Buy-and Hold Returns

Portfolio of Target Firms with Activist Board Representation

January 1, 1998 – December 31, 2013

-100

-50

0

50

100

150

200

250

300

350

400

450

Excess return on the market, value-weight return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ.

S&P 500 Index

All Target Firms that granted at least one board seat to a dissident (N=448).

Economic Recessions

Cu

mu

lati

ve

Ret

urn

s

Years

Appendix

50

Graph II

Long Term Cumulative Market Adjusted Returns (MAR)

(Periods in Years)

Figure I

Data Collection Methodology

38.8%

21.7%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Event +1 +2 +3 +4 +5

Years

All Target Firms that granted at least one board seat to an activist hedge fund: 38.8%Market Adjusted Excess Return after 5 years

from the board seat grant date

All Target Firms that WON the proxy fight against activist hedge fund: 21.7% Market Adjusted Excess Return after 5 years from

the Event date

Appendix Note: To control for self-selection bias and endogeneity, we constructed a control group (the “Control Group”) of all proxy fights campaigns that did not

result in a board representation during the same period. We used the market-adjusted return (MAR) model, which imposes the following joint

restrictions j=0 and j=1. Essentially, it includes market-wide factors but does not account for risk similar to the market model or CAPM.

The Control Group (N=73) is comprised of target firms that were involved in a proxy contest that the target firm incumbent management defeated the

dissident shareholder during the voting process. Therefore, we examined not only firms that granted at least one board seat to a dissident shareholder and its

ex post effects (the “Treatment Group”), but also companies that were challenged by dissatisfied shareholders and did not suffer the ex post disciplinary

effects by an activist hedge fund.

51

Figure 1

Data Collection Methodolgy

Notes:

There is no central database of activist hedge funds. Therefore, we constructed an independent dataset of all activist interventions from 1984-2013 from various sources. Our manually constructed database of shareholder activist events includes 5,063 interventions from 1984-2013. Similar to Gillan and Starks (2007), we

define shareholder activist event as a purposeful intervention by “investors who, dissatisfied with some aspect of a company’s management or operations, try to

bring about change within the company without a change in control.”

Our data collection comprised a multi-step procedure. Our comprehensive dataset of shareholder activist events includes 5,063 interventions from 1984-2013. Of those, 3,899 (77%) filed a 13D. However, approximately 32% of all activist interventions were focused on board engagement, either through a proxy contest

(1,216) or dissident campaigns that resulted in board representation via private negotiations (418) with the target management team and board of directors. In our

second step, we narrowed our time-frame from 1996-2013 and identified 1,039 activist interventions that resulted in board representation either through a proxy fight or private negotiations. This sample set included 621 proxy fights and 418 activist interventions (non-proxy contests) that resulted in board representation

either through a settlement or concessions between the target management and the dissident shareholder. Next, we excluded certain events and if a target firm were

to file for bankruptcy protection or liquidation, we included financial information from the target firm up to the Chapter 11 or Chapter 7 filing date.

Our final dataset consists of 448 activist interventions (the “Treatment Group”) that resulted in at least one board seat granted to an activist shareholder from 1996-2013 (see Table 1). A total of 843 board members (see Table 1) were elected at 398 unique target companies. This includes 225 unique dissident shareholders. Of

the 448 activist interventions in the Treatment Group, 243 (54%) target firms are still publicly-listed, 186 (42%) were sold/merged and 19 (4%) target firms filed

for bankruptcy. By compiling our own database, we avoid some problems associated with survivorship bias, reporting selection bias, and backfill, which are prevalent among other hedge fund databases. To control for self-selection bias and endogeneity, we constructed a Control Group from the set of all proxy fights

campaigns that did not result in a board representation during the same period (N=595). Similar to the primary sample set, we excluded certain events for

parameter consistency.

All Activist Events

1984-2013(N=5,063)

All Activist Events (Non-Proxy Contests) that resulted in Board

Representation

1996-2013(N=418)

All Proxy Contests1996-2013(N=1,216)

All Proxy Contests that resulted in Board Representation

1996-2013

(N=621)

All Activist Events that resulted in Board Representation

1996-2013 (N=1,039)

EXCLUDE SIC 6726 (Mutual Funds, etc), duplicate campaigns by multiple

Activists, Bankruptcy data post Filing Date, Missing data (N=300)

Treatment Group - Final Data Set

1996-2013(N=448 Firms)

(N=843 Board Members)

All Proxy Contests that did not result in Board Representation

1996-2013 (N=166 Firms)

Control Group - Final Data SetAll Proxy Contests MANAGEMENT

WON (Defeated Activist)1996-2013 (N=73 Firms)

All Proxy Contests that DID NOT result in Board Representation

1996-2013 (N=595)

EXCLUDE SIC 6726 (Mutual Funds, etc), duplicate campaigns by multiple

Activists, Bankruptcy data post Filing Date, Missing data (N=212)

CONTROL GROUP

FactSet

SharkRepellent

Compustat

ISS Proxy

S&P

CapIQ

CRSP

MergerMetrics

EDGAR Factiva

INCLUDE only activist campaigns by Hedge Funds at Target Firms with

Market Caps > $50mm

INCLUDE only activist campaigns by Hedge Funds at Target Firms with

Market Caps > $50mm

52

Table 1

Distribution of Shareholder Activist

Board Engagement Campaigns and Dissident Seats Granted

Board Engagements

(Completed)

Dissident Seats

Granted

Board Engagements

(Completed)

Dissident Seats

Granted

1996 1 1 - -

1997 2 2 2 2

1998 5 16 4 14

1999 14 28 6 10

2000 5 11 2 3

2001 26 54 6 11

2002 23 60 7 18

2003 25 57 10 17

2004 18 38 10 20

2005 23 59 15 46

2006 74 139 52 102

2007 68 128 52 95

2008 100 199 68 126

2009 90 166 49 86

2010 61 102 33 49

2011 51 88 34 55

2012 69 127 44 77

2013 84 179 54 112

Total 739 1454 448 843

All Board Engagements by Hedge Funds at

Target Firms with Market Caps over $50mmAll Board Engagements

53

Table 5 (Panel A)

[Event -5] [Event -4] [Event -3] [Event -2] [Event -1] [Event] [Event +1] [Event +2] [Event +3] [Event +4] [Event +5]

Return on Assets (ROA)

Observations 386 409 416 428 431 443 347 267 215 178 134

Mean 3.09*** 2.64*** 2.86*** 2.83*** 2.07*** 1.10*** 0.16 0.76* 0.95* 1.90*** 1.64**

Median 3.46 3.27 3.17 3.20 2.72 1.72 1.12 1.62 1.99 2.89 3.17

t-Stat 7.60 7.17 9.62 10.55 7.13 3.96 0.47 1.76 1.78 3.25 1.98

Std. Error 0.41 0.37 0.30 0.27 0.29 0.28 0.35 0.43 0.53 0.59 0.83

Industry Adjusted ROA

Observations 270 280 284 280 274 281 220 172 142 121 91

Mean 2.22*** 1.19* 1.47*** 1.36** -0.01 -0.97* -2.15*** -2.12*** -1.41* -1.36 -0.69

Median 1.24 0.60 0.44 -0.29 -0.32 -1.14 -1.61 -0.67 -1.12 0.04 0.55

t-Stat 3.20 1.74 2.80 2.20 -0.02 -1.80 -3.50 -3.11 -1.91 -1.57 -0.59

Std. Error 0.69 0.68 0.52 0.62 0.56 0.54 0.61 0.68 0.74 0.87 1.17

Tobin's Q

Observations 360 373 390 405 410 415 326 252 199 163 113

Mean 1.99** 1.83*** 1.88*** 1.82*** 1.67*** 1.56*** 1.51*** 1.62*** 1.72*** 1.73*** 1.77***

Median 3.17 1.50 1.48 1.46 1.42 1.29 1.27 1.19 1.27 1.38 1.38

t-Stat 1.98 25.08 29.43 27.35 29.48 28.94 34.23 28.80 22.89 17.44 17.40

Std. Error 0.83 0.08 0.06 0.07 0.06 0.06 0.05 0.05 0.07 0.10 0.10

Industry Adjusted Tobin's Q

Observations 278 287 308 320 327 332 257 199 155 127 85

Mean 0.51*** 0.35*** 0.24** 0.19*** 0.08*** 0.02*** 0.02*** 0.09*** 0.11*** 0.23*** 0.18***

Median 0.07 0.05 -0.03 -0.01 -0.07 -0.15 -0.10 -0.05 0.01 -0.05 -0.09

t-Stat 5.85 4.69 2.52 2.37 1.28 -0.34 0.37 0.92 0.91 1.39 1.37

Std. Error 0.09 0.07 0.10 0.08 0.06 0.06 0.06 0.09 0.12 0.16 0.13


Capex / Sales

Observations 443 442 441 437 439 441 348 267 214 179 136

Mean 8.82*** 8.48*** 7.72*** 8.43*** 8.49*** 7.80*** 6.77*** 5.58*** 6.78*** 4.93*** 4.44***

Median 3.80 10.88 10.72 11.21 10.89 10.83 9.92 9.75 7.55 7.97 6.92

t-Stat 10.84 19.80 20.80 21.80 22.80 23.80 24.80 25.80 26.80 27.80 28.80

Std. Error 0.81 0.78 0.72 0.75 0.78 0.72 0.68 0.57 0.90 0.62 0.64

Operating Margin

Observations 359 381 382 391 398 405 316 243 198 165 122

Mean 1.38 2.53** 3.68*** 2.51*** 1.35 3.77*** 2.69** 5.91*** 4.99*** 5.54*** 4.18***

Median 4.33 4.75 4.05 4.09 3.13 4.33 4.54 5.13 4.65 5.54 4.35

t-Stat 1.12 2.50 4.31 2.95 1.31 4.62 2.14 5.15 3.01 3.53 2.79

Std. Error 1.23 1.01 0.85 0.85 1.03 0.82 1.26 1.15 1.66 1.57 1.50

Return on Invested Capital (ROIC)

Observations 448 446 447 446 445 446 349 266 215 179 134

Mean 3.80*** 3.39*** 3.66*** 3.93*** 3.02*** 1.62*** 0.20 1.18* 1.41* 2.58** 1.61

Median 3.44 3.74 3.82 4.32 3.72 2.39 1.24 2.17 3.16 4.11 4.32

t-Stat 7.76 6.76 8.81 10.54 7.84 3.97 0.38 1.82 1.74 2.45 0.89

Std. Error 0.49 0.50 0.41 0.37 0.39 0.41 0.54 0.65 0.81 1.05 1.81

Return on equity (ROE)

Observations 379 397 397 412 414 427 329 252 204 167 116

Mean 1.61 -4.43** 1.00 0.47 -1.04 -7.29*** -8.54*** -7.10*** -9.24** -11.74** -5.03

Median 6.74 5.36 5.63 5.37 4.90 0.90 0.48 1.47 2.76 4.91 7.78

t-Stat 0.95 -1.81 0.71 0.39 -0.85 -4.20 -5.25 -3.65 -2.38 -2.21 -1.02

Std. Error 1.70 2.45 1.41 1.21 1.22 1.73 1.63 1.95 3.88 5.31 4.95


Periods (in Years)

Periods (in Years)


54

Table 5 (Panel B)


ROA

Observations 56 63 65 70 71 72 55 44 34 30 23

Mean 3.61*** 4.60*** 3.91*** 3.56** 2.61 2.24 3.29*** 3.59*** 3.95** 2.95 0.41

Median 4.23 4.48 5.09 5.82 4.32 3.53 3.94 3.38 2.57 2.24 1.35

t-Stat 3.52 5.16 2.68 2.04 1.28 1.44 3.99 3.07 2.34 1.42 0.23

Std. Error 1.03 0.89 1.46 1.90 2.04 1.56 0.83 1.27 1.70 2.08 1.75

Industry Adjusted ROA

Observations 33 35 38 41 43 42 35 25 17 14 15

Mean 3.04 1.95 0.20 4.88 5.21 6.97 14.89** 9.06* 9.39* 6.34 0.71

Median 2.80 3.02 2.39 1.92 1.91 1.15 1.22 0.54 0.19 -0.25 -1.64

t-Stat 1.24 1.07 0.07 0.79 0.82 1.12 2.04 1.66 1.61 1.01 0.22

Std. Error 2.45 1.82 2.66 6.17 6.36 6.25 7.28 5.46 5.84 6.25 3.19

Tobin's Q

Observations 54 58 59 65 66 67 52 41 32 27 22

Mean 2.14*** 2.43*** 2.13*** 3.19*** 1.67*** 1.71*** 1.90*** 2.02*** 2.06*** 2.11*** 1.87***

Median 1.76 1.41 1.44 1.55 1.32 1.43 1.52 1.59 1.75 1.93 1.57

t-Stat 10.22 5.30 8.81 3.66 15.08 15.78 9.67 6.94 8.35 6.39 6.06

Std. Error 0.21 0.46 0.24 0.87 0.11 0.11 0.20 0.29 0.25 0.33 0.31

Industry Adjusted Tobin's Q

Observations 34 36 40 47 47 47 37 28 22 18 15

Mean 0.77*** 1.19* 0.38* 1.35 0.13 0.03 0.10 0.18 0.58* 0.73* 0.41

Median 0.33 0.09 0.19 0.18 -0.03 -0.04 -0.04 -0.12 0.10 0.56 0.45

t-Stat 3.38 1.68 1.57 1.19 0.62 0.13 0.33 0.40 1.88 1.85 1.52

Std. Error 0.23 0.71 0.24 1.13 0.20 0.21 0.32 0.45 0.31 0.39 0.27


Capex / Sales

Observations 72 73 73 72 72 73 55 44 34 29 22

Mean 11.44*** 12.32*** 8.07*** 6.40*** 7.50*** 6.90*** 7.48*** 5.55*** 4.17*** 5.31*** 6.34***

Median 3.79 3.81 4.02 3.73 3.54 3.86 3.29 3.03 2.99 3.47 4.12

t-Stat 2.73 3.05 3.25 5.39 4.90 5.28 3.58 4.58 5.25 4.95 4.94

Std. Error 4.19 4.04 2.48 1.19 1.53 1.31 2.09 1.21 0.79 1.07 1.28

Operating Margin

Observations 50 56 58 60 61 62 47 40 30 27 21

Mean 0.21 1.29 6.04 7.17* 2.02*** 8.13*** 7.94*** 5.54*** 9.56*** 6.99** 5.69*

Median 4.23 3.04 4.75 4.76 4.06 4.81 5.63 5.15 6.14 4.43 3.38

t-Stat 0.04 0.31 1.26 4.03 0.75 3.08 2.47 2.06 3.47 2.50 1.83

Std. Error 4.94 4.14 4.78 1.78 2.68 2.64 3.21 2.69 2.75 2.80 3.10

Return on Invested Capital (ROIC)

Observations 73 73 73 73 72 72 55 44 34 30 23

Mean 3.51*** 5.30*** 4.45* 3.29 6.14*** 4.92*** 4.75*** 5.78*** 5.70** 3.36 -0.16

Median 4.09 4.85 5.17 6.36 5.72 4.51 5.61 6.40 3.21 3.12 2.05

t-Stat 3.13 4.85 1.94 0.63 6.93 5.71 4.14 3.35 2.37 0.98 -0.05

Std. Error 1.12 1.09 2.29 5.21 0.89 0.86 1.15 1.73 2.40 3.45 3.30

Return on equity (ROE)

Observations 54 60 63 66 68 69 53 42 31 28 23

Mean 7.47* 9.38* NM NM NM 9.76* 10.86** 4.39 1.57 -15.60 -23.27

Median 9.62 10.02 10.66 9.64 9.46 6.88 8.82 8.75 5.82 5.36 4.28

t-Stat 1.68 1.91 -0.98 2.05 1.75 1.68 2.14 0.75 0.33 -1.17 -1.34

Std. Error 4.46 4.92 NM 8.27 12.79 5.82 5.07 5.85 4.79 13.29 17.33


Periods (in Years)

Periods (in Years)


55

Table 8: Individual Target Firm Regressions (Long-term Abnormal Returns)

[Event -5] [Event -4] [Event -3] [Event -2] [Event -1] [Event +1] [Event +2] [Event +3] [Event +4] [Event +5]

α (Alpha)

Observations 442 442 442 442 442 344 263 208 173 128

Mean 20.63*** 4.90 -2.42 -6.14** -1.93 0.77 25.37** 38.08*** 56.34*** 78.21***

Median 10.45 -2.53 -5.20 -7.29 -2.75 2.46 21.05 36.57 38.98 43.05

t-Stat 4.64 1.30 -0.76 -2.14 -0.88 0.24 2.07 3.67 4.35 4.00

Std. Dev. 0.07 0.08 0.09 0.12 0.18 0.23 0.39 0.20 0.17 0.18

α (Alpha)

Observations 442 442 442 442 442 344 263 208 173 128

Mean 6.28 -3.38 -4.75 -5.60** -0.99 0.21 20.52* 30.45*** 48.39*** 64.75***

Median -0.90 -6.72 -5.53 -6.47 -2.56 3.89 18.68 29.54 29.42 32.86

t-Stat 1.42 -0.92 -1.55 -2.05 -0.46 0.07 1.70 2.97 3.54 3.27

Std. Dev. 0.07 0.08 0.09 0.11 0.18 0.23 0.39 0.20 0.18 0.18

α (Alpha)

Observations 442 442 442 442 442 344 263 208 173 128

Mean 6.22 -3.79 -5.11* -5.88** -0.95 -2.61 17.79 27.41*** 45.60*** 63.33***

Median 0.44 -7.51 -5.26 -5.42 -2.62 2.13 14.64 27.13 21.06 31.77

t-Stat 1.42 -1.06 -1.72 -2.21 -0.46 -0.82 1.44 2.68 3.34 3.20

Std. Dev. 0.07 0.07 0.08 0.11 0.17 0.23 0.40 0.20 0.18 0.18

[Event -5] [Event -4] [Event -3] [Event -2] [Event -1] [Event +1] [Event +2] [Event +3] [Event +4] [Event +5]

α (Alpha)

Observations 53 53 53 53 53 56 45 35 31 24

Mean 51.21*** 42.31*** 28.79*** 13.08* 7.33 5.37 15.63 32.40 30.28 43.81

Median 36.77 30.62 32.29 22.93 11.99 4.12 5.57 15.92 9.89 6.26

t-Stat 3.59 3.34 3.19 1.82 1.32 0.94 0.82 1.05 0.70 0.79

Std. Dev. 0.08 0.09 0.09 0.10 0.16 0.17 0.25 0.24 0.24 0.21

α (Alpha)

Observations 53 53 53 53 53 56 45 35 31 24

Mean 34.14** 31.28*** 22.38*** 10.03 6.00 3.33 15.72 31.02 25.69 37.50

Median 24.35 20.37 18.66 10.79 9.58 4.96 0.67 16.39 4.99 6.64

t-Stat 2.52 2.58 2.59 1.46 1.03 0.60 0.86 1.03 0.61 0.68

Std. Dev. 0.08 0.09 0.08 0.10 0.17 0.17 0.24 0.24 0.23 0.21

α (Alpha)

Observations 53 53 53 53 53 56 45 35 31 24

Mean 35.91*** 31.06*** 22.68*** 10.42 6.39 1.66 14.33 29.16 23.02 33.65

Median 26.86 20.71 16.80 11.73 13.58 3.41 2.09 16.01 -0.41 4.28

t-Stat 2.65 2.64 2.59 1.53 1.15 0.30 0.79 0.98 0.54 0.61

Std. Dev. 0.08 0.08 0.08 0.10 0.16 0.17 0.24 0.23 0.23 0.21


Periods (in Years)



Three-Factor Asset Pricing Model (Fama-French)

Four-Factor Asset Pricing Model (Fama-French-Carhart)


Fama-French Asset Pricing Model

Fama-French-Carhart Asset Pricing Model

Periods (in Years)


Documents

Myopic Investor Myth Debunked - swfa2015.uno.eduswfa2015.uno.edu/E_Institutional_Ownership/paper_82.pdf · Myopic Investor Myth Debunked: The Long-term Efficacy of Hedge Fund Activism