Download Successful Activist Board Engagement

Myopic Investor Myth Debunked:
Shareholder Advocacy
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
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.
1
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
2
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.
3
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
4
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.
5
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 (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-andhold 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
6
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 longterm 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
7
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.
8
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
9
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
10
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
11
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
12
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.
13
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
14
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 (nonproxy 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
15
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.
16
Table 2
Company Status
Existing
Sold/Merged
Bankruptcy
Total
243
186
19
448
54%
42%
4%
Primary Campaign Type
Board Representation
Board Control
Maximize Shareholder Value
No Publicly Disclosed Activism
Other Activist Campaigns
Total
235
69
80
40
24
448
52%
15%
18%
9%
5%
Proxy Fight Winner
Settled/Concessions Made
174
Dissident
53
Split
14
Total
241
Activism Type
Proxy Fight
Other Stockholder Campaign
No Publicly Disclosed Activism
Exempt Solicitation
241
165
40
2
Total
448
72%
22%
6%
54%
37%
9%
0%
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.
17
Table 3
Announced Target Firm Sale or Divestiture
of Assets Ex Post Dissident Seat Date
3-5 Years
No. of
Target
Firms
19
% of Total
Board
Engagements
4.2%
2-3 Years
17
1-2 Years
CEO Change Ex Post Dissident Seat Date
3-5 Years
No. of
Target
Firms
36
% of Total
Board
Engagements
8.0%
3.8%
2-3 Years
27
6.0%
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
Timeframe
Timeframe
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).
18
Table 4
Descriptive Statistics of Dataset
Treatment Group: Target Firms that
Granted at least One Board Seat (N=448)
Target Firm Characteristics
Classified Board
Cumulative Voting
Poison Pill Adopted in Response to Campaign
Poison Pill In Force Prior to Announcement
Control Group: Target Firm
Management Won Proxy Fight (No
Shareholder Activist Board
Representation, N=73)
Yes
210
No
238
Yes (%)
47%
No (%)
53%
Yes
35
No
38
Yes (%)
48%
No (%)
52%
34
33
414
415
8%
7%
92%
93%
3
3
70
70
4%
4%
96%
96%
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%
20
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
21
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
22
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
23
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)
4.50
4.00
3.50
Board Seat
Grant Date
All Target Firms that granted at least one
board seat to a dissident: 13% CAGR
increase in ROA over 5 year period
3.53
3.17
3.00
2.50
2.00
1.72
All Target Firms that WON the proxy fight
against activist hedge fund: 18% CAGR
1.50
decrease in ROA over 5 year period
1.35
1.00
Event
1
+1
2
+2
3
+3
4
+4
5
6 +5
Years
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).
24
Graph 4
Q Ratio
2.15
Board Seat
Grant Date
2.05
1.95
All Target Firms that WON the proxy fight
against activist hedge fund: 1.8% CAGR
increase in Q Ratio over 5 year period
1.85
1.87
1.77
1.75
1.71
1.65
All Target Firms that granted at least one
board seat to a dissident: 2.6% CAGR
increase in Q Ratio over 5 year period
1.56
1.55
1.45
1
Event
2
+1
3
+2
4
+3
5
+4
6+5
Years
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
25
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
7.00
6.50
6.00
All Target Firms that granted at least one
board seat to a dissident : Slight increase
in Operating Margin over 5 year period)
Board Seat
Grant Date
5.50
5.00
4.81
4.50
4.33
4.35
4.00
All Target Firms that WON the proxy fight
against activist hedge fund: 7% CAGR decrease
3.50
3.38
in Operating Margin over 5 year period
3.00
1
Event
2
+1
3
+2
4
+3
5
+4
6+5
Years
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
26
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.
27
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 longhorizon 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:
t2
CARi (t1 , t2 )   ARit
t t1
After calculating event CARs, we then calculate and report the cumulative
28
average abnormal return (CAAR). Where, the CAAR is the arithmetic average of all
sample event CARs, and is calculated as follows:
1
CAARi (t1 , t 2 )  
N
N
t2
j 1
t  t1
  AR
it
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
29
mean return model by controlling for R mt , was calculated as follows:
Rit   i  i Rmt   it
Where,
E ( it )  0
Var ( it )   i2
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 R mt , 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 sh ow 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
30
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
Panel A: CAR for Target Firms that Granted at least One Board Seat
Market Model (Value Weighted)
Window
Market Model (Equal-Weighted)
N
CAAR
t-stat
p-value
(- 1, 1)
409
1.44***
3.94
0.001
(- 2, 2)
409
1.63***
3.20
(- 10, 10)
410
3.30***
3.94
Window
N
CAAR
t-stat
p-value
(- 1, 1)
409
1.38***
3.95
0.001
0.001
(- 2, 2)
409
1.60***
3.26
0.001
0.001
(- 10, 10)
410
3.07***
3.00
0.001
Panel B: CAR for Target Firms that Management Won Proxy Fight
Market Model (Value Weighted)
Market Model (Equal-Weighted)
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
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
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
31
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)
4.00%
3.00%
All Target Firms that granted at least one
board seat to an activist hedge fund: 3.15%***
PWCAAR over 21 day Event window
3.15%
2.00%
1.00%
0.00%
-10
-9
-8
7
-6
-5
-4
-3
-2
-1
EVENT +1
+2
+3
+4
+5
+6
+7
+8
+9
+10
-1.00%
-2.00%
-3.00%
-4.00%
-5.00%
-6.00%
All Target Firms that Management
won the proxy fight: -5.63%**
PWCAAR over 21 day Event window
-5.63%
-7.00%
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
32
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:
rit - rf = ai + β1 RMRFt + Eit
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:
rit - rf = ai + βi1 RMRFt + βi2 SMBt + βi3 HMLt + Eit
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
33
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:
rit - rf = ai + βi1 RMRFt + βi2 SMBt + βi3 HMLt + βi4 M0Mt + Eit
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
34
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]
α (Alpha)
Observations
Mean
Median
t-Stat
Std. Dev.
442
20.63 ***
10.45
4.64
0.07
α (Alpha)
Observations
Mean
Median
t-Stat
Std. Dev.
442
6.28
-0.90
1.42
0.07
α (Alpha)
Observations
Mean
Median
t-Stat
Std. Dev.
442
6.22
0.44
1.42
0.07
Panel A: Target Firms that Granted at least One Board Seat
Periods (in Years)
[Event -3]
[Event -1]
[Event +1]
[Event +3]
Capital Asset Pricing Model (CAPM)
442
442
344
208
-2.42
-1.93
0.77
38.08***
-5.20
-2.75
2.46
36.57
-0.76
-0.88
0.24
3.67
0.09
0.18
0.23
0.20
Three-Factor Asset Pricing Model (Fama-French)
442
442
344
208
-4.75
-0.99
0.21
30.45***
-5.53
-2.56
3.89
29.54
-1.55
-0.46
0.07
2.97
0.09
0.18
0.23
0.20
Four-Factor Asset Pricing Model (Fama-French-Carhart)
442
442
344
208
-5.11*
-0.95
-2.61
27.41***
-5.26
-2.62
2.13
27.13
-1.72
-0.46
-0.82
2.68
0.08
0.17
0.23
0.20
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
35
[Event +5]
128
78.21***
43.05
4.00
0.18
128
64.75***
32.86
3.27
0.18
128
63.33***
31.77
3.20
0.18
α (Alpha)
Observations
Mean
Median
t-Stat
Std. Dev.
α (Alpha)
Observations
Mean
Median
t-Stat
Std. Dev.
α (Alpha)
Observations
Mean
Median
t-Stat
Std. Dev.
Panel B: Management Won Proxy Fight (No Shareholder Activist Board Representation)
Periods (in Years)
[Event -5]
[Event -3]
[Event -1]
[Event +1]
[Event +3]
[Event +5]
Capital Asset Pricing Model (CAPM)
53
53
53
56
35
24
51.21***
28.79***
7.33
5.37
32.40
43.81
36.77
32.29
11.99
4.12
15.92
6.26
3.59
3.19
1.32
0.94
1.05
0.79
0.08
0.09
0.16
0.17
0.24
0.21
Fama-French Asset Pricing Model
53
53
53
56
35
24
34.14**
22.38***
6.00
3.33
31.02
37.50
24.35
18.66
9.58
4.96
16.39
6.64
2.52
2.59
1.03
0.60
1.03
0.68
0.08
0.08
0.17
0.17
0.24
0.21
Fama-French-Carhart Asset Pricing Model
53
53
53
56
35
24
35.91***
22.68***
6.39
1.66
29.16
33.65
26.86
16.80
13.58
3.41
16.01
4.28
2.65
2.59
1.15
0.30
0.98
0.61
0.08
0.08
0.16
0.17
0.23
0.21
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
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 wellknown 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 underperforming companies.
Additionally, target firms that granted at least one board seat
outperformed the Control Group.
36
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 riskadjusted 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
37
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 nonevent 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
t

 CAR /  CAR
/
CAR
i
i
significance of each coefficient using
the following
equation:
or

n

t BHAR  BHARi /  BHARi  / n
38

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.
39
Table 9 (Portfolio I)
Buy-and-Hold Abnormal Return Portfolio of All Activist Board Governed Firms
CAPM SUMMARY OUTPUT
α
βp, RM-RF
Coefficients Standard Error
0.08
0.02
0.73
0.01
t Stat
4.55
55.23
P-value
<0.0001
<0.0001
Lower 95%
0.04
0.70
Upper 95%
0.11
0.75
THREE FACTOR MODEL (FAMA-FRENCH) SUMMARY OUTPUT
α
βp, RM-RF
βp, SMB
βp, HML
Coefficients Standard Error
0.06
0.02
0.74
0.01
0.61
0.03
0.43
0.02
t Stat
4.12
60.80
23.73
17.62
P-value
<0.0001
<0.0001
<0.0001
<0.0001
Lower 95%
0.03
0.72
0.56
0.38
Upper 95%
0.10
0.76
0.66
0.48
FOUR FACTOR MODEL (FAMA-FRENCH-CARHART) SUMMARY OUTPUT
α
βp, RM-RF
βp, SMB
βp, HML
βp, MOM
Coefficients Standard Error
0.07
0.02
0.72
0.01
0.62
0.03
0.39
0.03
-0.09
0.02
t Stat
4.35
55.80
24.15
15.22
-5.43
P-value
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
Lower 95%
0.04
0.69
0.57
0.34
-0.13
Upper 95%
0.10
0.74
0.67
0.44
-0.06
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Portfolio II
Buy-and-Hold Abnormal Return Portfolio of Control Group
CAPM SUMMARY OUTPUT
α
βp, RM-RF
Coefficients Standard Error
0.076
0.026
0.714
0.020
t Stat
2.89
35.71
P-value
<0.0001
<0.0001
Lower 95%
0.02
0.68
Upper 95%
0.13
0.75
THREE FACTOR MODEL (FAMA-FRENCH) SUMMARY OUTPUT
α
βp, RM-RF
βp, SMB
βp, HML
Coefficients Standard Error
0.063
0.026
0.690
0.020
0.401
0.045
0.256
0.041
t Stat
2.44
34.78
8.88
6.26
P-value
<0.0001
<0.0001
<0.0001
<0.0001
Lower 95%
0.01
0.65
0.31
0.18
Upper 95%
0.11
0.73
0.49
0.34
FOUR FACTOR MODEL (FAMA-FRENCH-CARHART) SUMMARY OUTPUT
α
βp, RM-RF
βp, SMB
βp, HML
βp, MOM
Coefficients Standard Error
0.065
0.026
0.668
0.022
0.413
0.045
0.236
0.041
-0.069
0.028
t Stat
2.50
30.70
9.10
5.70
-2.49
P-value
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
40
Lower 95%
0.01
0.63
0.32
0.16
-0.12
Upper 95%
0.12
0.71
0.50
0.32
-0.01
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,SMB SMB t + βp,HMLHML t +
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 T41

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 longterm 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-tomarket factors. We obtain the factor returns, market capitalization breakpoints, and monthly risk-free rates
from Ken French’s web site at Dartmouth College.
42
Table 10
Calendar-Time Series Regressions (Long-term Abnormal Returns)
Panel A: Target Firm Fama-French Calendar Time Series Regressions (N=443)
α
[Event -3]
-0.076
[Event -2]
-0.101
[Event -1]
-0.050
1.1
1.70*
1.48
Holding Period (in Years)
[Event +1]
[Event +2]
0.000
0.101
NM
1.26
[Event +3]
0.151
[Event +4]
0.202
[Event +5]
0.252
1.64*
1.35
1.25
βp, RM-RF
0.893
99.53***
0.864
87.22***
0.837
65.94***
0.850
55.50***
0.851
70.44**
0.852
75.23***
0.865
79.43***
0.864
79.16***
βp, SMB
0.750
40.67***
0.716
35.04***
0.673
25.50***
0.668
20.72***
0.682
26.82***
0.677
28.36***
0.671
29.29***
0.660
28.74***
βp, HML
0.432
24.38***
70.21%
3881***
0.440
22.43***
65.63%
2983***
0.508
20.15***
53.73%
1717***
0.651
21.29***
47.33%
1254***
0.514
21.33***
58.63%
1977***
0.511
22.61***
61.71%
2249***
0.496
22.85***
64.05%
2485***
0.503
23.09***
63.83%
2462***
Adjusted R2
F-Test
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel B: Target Firm Fama-French Calendar Time Series Regressions (N=73)
α
βp, RM-RF
βp, SMB
βp, HML
Adjusted R2
F-Test
Holding Period (in Years)
[Event +1]
[Event +2]
0.076
0.151
0.93
1.19
[Event -3]
0.151
1.3
[Event -2]
0.101
0.86
[Event -1]
0.101
1.33
[Event +3]
0.302
1.35
[Event +4]
0.302
1.23
[Event +5]
0.378
1.23
0.939
43.38***
0.981
34.85***
0.933
27.08***
0.918
30.57***
0.879
31.43***
0.843
31.37***
0.823
30.37***
0.822
30.06***
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***
0.46
11.42***
59.36%
1960***
0.56
10.80***
51.43%
1357***
0.57
8.68***
35.53%
660***
0.34
5.41***
34.39%
589***
0.34
6.01***
37.04%
661***
0.28
5.14***
36.01%
633***
0.25
4.64***
35.30%
614***
0.27
5.08***
35.53%
620***
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
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
43
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.
44
Although some prominent legal
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.
45
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48
Appendix
Graph I
Long Term Buy-and Hold Returns
Portfolio of Target Firms with Activist Board Representation
January 1, 1998 – December 31, 2013
450
All Target Firms that granted at least one
board seat to a dissident (N=448).
400
350
Economic Recessions
Cumulative Returns
300
250
Excess return on the market, value-weight
return of all CRSP firms incorporated in the US
and listed on the NYSE, AMEX, or NASDAQ.
200
150
100
50
0
S&P 500 Index
-50
-100
Years
49
Graph II
Long Term Cumulative Market Adjusted Returns (MAR)
(Periods in Years)
45.0%
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
40.0%
35.0%
38.8%
30.0%
25.0%
21.7%
20.0%
15.0%
10.0%
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
5.0%
0.0%
-5.0%
Event
+1
+2
+3
+4
+5
-10.0%
Years
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
Appendix
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.
Figure I
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
Collection
Methodology
dissident shareholder during the voting process. Therefore,Data
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.
50
Figure 1
Data Collection Methodolgy
FactSet
S&P
Compustat
EDGAR
Factiva
ISS
Proxy
CapIQ
CRSP
MergerMetrics
All Activist Events
1984-2013
(N=5,063)
SharkRepellent
All Proxy Contests
1996-2013
(N=1,216)
All Activist Events (Non-Proxy
Contests) that resulted in Board
Representation
1996-2013
(N=418)
CONTROL GROUP
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)
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=300)
EXCLUDE SIC 6726 (Mutual Funds, etc),
duplicate campaigns by multiple
Activists, Bankruptcy data post Filing
Date, Missing data (N=212)
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
Control Group - Final Data Set
All Proxy Contests MANAGEMENT
WON (Defeated Activist)
1996-2013 (N=73 Firms)
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)
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 19962013 (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.
51
Table 1
Distribution of Shareholder Activist
Board Engagement Campaigns and Dissident Seats Granted
All Board Engagements by Hedge Funds at
Target Firms with Market Caps over $50mm
All Board Engagements
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Total
Board Engagements
(Completed)
1
2
5
14
5
26
23
25
18
23
74
68
100
90
61
51
69
84
739
Dissident Seats
Granted
1
2
16
28
11
54
60
57
38
59
139
128
199
166
102
88
127
179
1454
52
Board Engagements
(Completed)
2
4
6
2
6
7
10
10
15
52
52
68
49
33
34
44
54
448
Dissident Seats
Granted
2
14
10
3
11
18
17
20
46
102
95
126
86
49
55
77
112
843
Table 5 (Panel A)
Return on Assets (ROA)
Observations
Mean
Median
t-Stat
Std. Error
Industry Adjusted ROA
Observations
Mean
Median
t-Stat
Std. Error
Tobin's Q
Observations
Mean
Median
t-Stat
Std. Error
Industry Adjusted Tobin's Q
Observations
Mean
Median
t-Stat
Std. Error
Capex / Sales
Observations
Mean
Median
t-Stat
Std. Error
Operating Margin
Observations
Mean
Median
t-Stat
Std. Error
Return on Invested Capital (ROIC)
Observations
Mean
Median
t-Stat
Std. Error
Return on equity (ROE)
Observations
Mean
Median
t-Stat
Std. Error
Panel A: Target Firms that Granted at least One Board Seat
Periods (in Years)
[Event -2]
[Event -1]
[Event]
[Event +1]
[Event +2]
[Event -5]
[Event -4]
[Event -3]
386
3.09***
3.46
7.60
0.41
409
2.64***
3.27
7.17
0.37
416
2.86***
3.17
9.62
0.30
428
2.83***
3.20
10.55
0.27
431
2.07***
2.72
7.13
0.29
270
2.22***
1.24
3.20
0.69
280
1.19*
0.60
1.74
0.68
284
1.47***
0.44
2.80
0.52
280
1.36**
-0.29
2.20
0.62
274
-0.01
-0.32
-0.02
0.56
360
1.99**
3.17
1.98
0.83
373
1.83***
1.50
25.08
0.08
390
1.88***
1.48
29.43
0.06
405
1.82***
1.46
27.35
0.07
278
0.51***
0.07
5.85
0.09
287
0.35***
0.05
4.69
0.07
308
0.24**
-0.03
2.52
0.10
[Event -5]
[Event -4]
443
8.82***
3.80
10.84
0.81
[Event +4]
[Event +5]
347
0.16
1.12
0.47
0.35
267
0.76*
1.62
1.76
0.43
215
0.95*
1.99
1.78
0.53
178
1.90***
2.89
3.25
0.59
134
1.64**
3.17
1.98
0.83
281
-0.97*
-1.14
-1.80
0.54
220
-2.15***
-1.61
-3.50
0.61
172
-2.12***
-0.67
-3.11
0.68
142
-1.41*
-1.12
-1.91
0.74
121
-1.36
0.04
-1.57
0.87
91
-0.69
0.55
-0.59
1.17
410
1.67***
1.42
29.48
0.06
415
1.56***
1.29
28.94
0.06
326
1.51***
1.27
34.23
0.05
252
1.62***
1.19
28.80
0.05
199
1.72***
1.27
22.89
0.07
163
1.73***
1.38
17.44
0.10
113
1.77***
1.38
17.40
0.10
320
0.19***
-0.01
2.37
0.08
327
0.08***
-0.07
1.28
0.06
332
0.02***
-0.15
-0.34
0.06
257
0.02***
-0.10
0.37
0.06
199
0.09***
-0.05
0.92
0.09
155
0.11***
0.01
0.91
0.12
127
0.23***
-0.05
1.39
0.16
85
0.18***
-0.09
1.37
0.13
[Event -3]
[Event -2]
[Event -1]
Periods (in Years)
[Event]
[Event +1]
[Event +2]
[Event +3]
[Event +4]
[Event +5]
442
8.48***
10.88
19.80
0.78
441
7.72***
10.72
20.80
0.72
437
8.43***
11.21
21.80
0.75
439
8.49***
10.89
22.80
0.78
381
2.53**
4.75
2.50
1.01
382
3.68***
4.05
4.31
0.85
391
2.51***
4.09
2.95
0.85
448
3.80***
3.44
7.76
0.49
446
3.39***
3.74
6.76
0.50
447
3.66***
3.82
8.81
0.41
446
3.93***
4.32
10.54
0.37
379
1.61
6.74
0.95
1.70
397
-4.43**
5.36
-1.81
2.45
359
1.38
4.33
1.12
1.23
397
1.00
5.63
0.71
1.41
412
0.47
5.37
0.39
1.21
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
398
1.35
3.13
1.31
1.03
443
1.10***
1.72
3.96
0.28
[Event +3]
441
7.80***
10.83
23.80
0.72
348
6.77***
9.92
24.80
0.68
267
5.58***
9.75
25.80
0.57
214
6.78***
7.55
26.80
0.90
179
4.93***
7.97
27.80
0.62
136
4.44***
6.92
28.80
0.64
405
3.77***
4.33
4.62
0.82
316
2.69**
4.54
2.14
1.26
243
5.91***
5.13
5.15
1.15
198
4.99***
4.65
3.01
1.66
165
5.54***
5.54
3.53
1.57
122
4.18***
4.35
2.79
1.50
445
3.02***
3.72
7.84
0.39
446
1.62***
2.39
3.97
0.41
349
0.20
1.24
0.38
0.54
266
1.18*
2.17
1.82
0.65
215
1.41*
3.16
1.74
0.81
179
2.58**
4.11
2.45
1.05
134
1.61
4.32
0.89
1.81
414
-1.04
4.90
-0.85
1.22
427
-7.29***
0.90
-4.20
1.73
329
-8.54***
0.48
-5.25
1.63
252
-7.10***
1.47
-3.65
1.95
204
-9.24**
2.76
-2.38
3.88
167
-11.74**
4.91
-2.21
5.31
116
-5.03
7.78
-1.02
4.95
53
Table 5 (Panel B)
ROA
Observations
Mean
Median
t-Stat
Std. Error
Industry Adjusted ROA
Observations
Mean
Median
t-Stat
Std. Error
Tobin's Q
Observations
Mean
Median
t-Stat
Std. Error
Industry Adjusted Tobin's Q
Observations
Mean
Median
t-Stat
Std. Error
[Event -5]
[Event -4]
56
3.61***
4.23
3.52
1.03
63
4.60***
4.48
5.16
0.89
65
3.91***
5.09
2.68
1.46
70
3.56**
5.82
2.04
1.90
71
2.61
4.32
1.28
2.04
72
2.24
3.53
1.44
1.56
55
3.29***
3.94
3.99
0.83
44
3.59***
3.38
3.07
1.27
33
3.04
2.80
1.24
2.45
35
1.95
3.02
1.07
1.82
38
0.20
2.39
0.07
2.66
41
4.88
1.92
0.79
6.17
43
5.21
1.91
0.82
6.36
42
6.97
1.15
1.12
6.25
35
14.89**
1.22
2.04
7.28
54
2.14***
1.76
10.22
0.21
58
2.43***
1.41
5.30
0.46
59
2.13***
1.44
8.81
0.24
65
3.19***
1.55
3.66
0.87
66
1.67***
1.32
15.08
0.11
67
1.71***
1.43
15.78
0.11
34
0.77***
0.33
3.38
0.23
36
1.19*
0.09
1.68
0.71
40
0.38*
0.19
1.57
0.24
47
1.35
0.18
1.19
1.13
47
0.13
-0.03
0.62
0.20
47
0.03
-0.04
0.13
0.21
[Event -4]
[Event -3]
[Event -2]
[Event -1]
[Event -5]
Capex / Sales
Observations
Mean
Median
t-Stat
Std. Error
Operating Margin
Observations
Mean
Median
t-Stat
Std. Error
Return on Invested Capital (ROIC)
Observations
Mean
Median
t-Stat
Std. Error
Return on equity (ROE)
Observations
Mean
Median
t-Stat
Std. Error
Panel B: Management Won Proxy Fight (No Shareholder Activist Board Representation)
Periods (in Years)
[Event -3]
[Event -2]
[Event -1]
[Event]
[Event +1]
[Event +2]
[Event +3]
[Event +4]
[Event +5]
34
3.95**
2.57
2.34
1.70
30
2.95
2.24
1.42
2.08
23
0.41
1.35
0.23
1.75
25
9.06*
0.54
1.66
5.46
17
9.39*
0.19
1.61
5.84
14
6.34
-0.25
1.01
6.25
15
0.71
-1.64
0.22
3.19
52
1.90***
1.52
9.67
0.20
41
2.02***
1.59
6.94
0.29
32
2.06***
1.75
8.35
0.25
27
2.11***
1.93
6.39
0.33
22
1.87***
1.57
6.06
0.31
37
0.10
-0.04
0.33
0.32
28
0.18
-0.12
0.40
0.45
22
0.58*
0.10
1.88
0.31
18
0.73*
0.56
1.85
0.39
15
0.41
0.45
1.52
0.27
[Event +2]
[Event +3]
[Event +4]
[Event +5]
Periods (in Years)
[Event]
[Event +1]
72
11.44***
3.79
2.73
4.19
73
12.32***
3.81
3.05
4.04
73
8.07***
4.02
3.25
2.48
72
6.40***
3.73
5.39
1.19
72
7.50***
3.54
4.90
1.53
73
6.90***
3.86
5.28
1.31
55
7.48***
3.29
3.58
2.09
44
5.55***
3.03
4.58
1.21
34
4.17***
2.99
5.25
0.79
29
5.31***
3.47
4.95
1.07
22
6.34***
4.12
4.94
1.28
50
0.21
4.23
0.04
4.94
56
1.29
3.04
0.31
4.14
58
6.04
4.75
1.26
4.78
60
7.17*
4.76
4.03
1.78
61
2.02***
4.06
0.75
2.68
62
8.13***
4.81
3.08
2.64
47
7.94***
5.63
2.47
3.21
40
5.54***
5.15
2.06
2.69
30
9.56***
6.14
3.47
2.75
27
6.99**
4.43
2.50
2.80
21
5.69*
3.38
1.83
3.10
73
3.51***
4.09
3.13
1.12
73
5.30***
4.85
4.85
1.09
73
4.45*
5.17
1.94
2.29
73
3.29
6.36
0.63
5.21
72
6.14***
5.72
6.93
0.89
72
4.92***
4.51
5.71
0.86
55
4.75***
5.61
4.14
1.15
44
5.78***
6.40
3.35
1.73
34
5.70**
3.21
2.37
2.40
30
3.36
3.12
0.98
3.45
23
-0.16
2.05
-0.05
3.30
54
7.47*
9.62
1.68
4.46
60
9.38*
10.02
1.91
4.92
63
NM
10.66
-0.98
NM
66
NM
9.64
2.05
8.27
68
NM
9.46
1.75
12.79
69
9.76*
6.88
1.68
5.82
53
10.86**
8.82
2.14
5.07
42
4.39
8.75
0.75
5.85
31
1.57
5.82
0.33
4.79
28
-15.60
5.36
-1.17
13.29
23
-23.27
4.28
-1.34
17.33
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
54
Table 8: Individual Target Firm Regressions (Long-term Abnormal Returns)
[Event -5]
[Event -4]
[Event -3]
α (Alpha)
Observations
442
442
Mean
Median
t-Stat
Std. Dev.
20.63***
10.45
4.64
0.07
442
4.90
-2.53
1.30
0.08
-2.42
-5.20
-0.76
0.09
α (Alpha)
Observations
Mean
Median
t-Stat
Std. Dev.
442
6.28
-0.90
1.42
0.07
442
-3.38
-6.72
-0.92
0.08
442
-4.75
-5.53
-1.55
0.09
α (Alpha)
Observations
Mean
Median
t-Stat
Std. Dev.
442
6.22
0.44
1.42
0.07
442
-3.79
-7.51
-1.06
0.07
442
-5.11*
-5.26
-1.72
0.08
[Event -5]
[Event -4]
α (Alpha)
Panel A: Target Firms that Granted at least One Board Seat
Periods (in Years)
[Event -2]
[Event -1]
[Event +1]
[Event +2]
Capital Asset Pricing Model (CAPM)
442
442
344
263
[Event +3]
208
[Event +4]
173
[Event +5]
128
-6.14**
-1.93
0.77
25.37**
-7.29
-2.75
2.46
21.05
-2.14
-0.88
0.24
2.07
0.12
0.18
0.23
0.39
Three-Factor Asset Pricing Model (Fama-French)
442
442
344
263
-5.60**
-0.99
0.21
20.52*
-6.47
-2.56
3.89
18.68
-2.05
-0.46
0.07
1.70
0.11
0.18
0.23
0.39
Four-Factor Asset Pricing Model (Fama-French-Carhart)
38.08***
36.57
3.67
0.20
56.34***
38.98
4.35
0.17
78.21***
43.05
4.00
0.18
208
30.45***
29.54
2.97
0.20
173
48.39***
29.42
3.54
0.18
128
64.75***
32.86
3.27
0.18
442
-5.88**
-5.42
-2.21
0.11
208
27.41***
27.13
2.68
0.20
173
45.60***
21.06
3.34
0.18
128
63.33***
31.77
3.20
0.18
442
-0.95
-2.62
-0.46
0.17
344
-2.61
2.13
-0.82
0.23
263
17.79
14.64
1.44
0.40
Panel B: Management Won Proxy Fight (No Shareholder Activist Board Representation)
Periods (in Years)
[Event -3]
[Event -2]
[Event -1]
[Event +1]
[Event +2]
[Event +3]
Capital Asset Pricing Model (CAPM)
Observations
Mean
Median
t-Stat
Std. Dev.
53
51.21***
36.77
3.59
0.08
53
42.31***
30.62
3.34
0.09
53
28.79***
32.29
3.19
0.09
53
13.08*
22.93
1.82
0.10
α (Alpha)
Observations
Mean
Median
t-Stat
Std. Dev.
53
34.14**
24.35
2.52
0.08
53
31.28***
20.37
2.58
0.09
53
22.38***
18.66
2.59
0.08
53
10.03
10.79
1.46
0.10
α (Alpha)
Observations
Mean
Median
t-Stat
Std. Dev.
53
35.91***
26.86
2.65
0.08
53
31.06***
20.71
2.64
0.08
53
22.68***
16.80
2.59
0.08
53
10.42
11.73
1.53
0.10
[Event +4]
[Event +5]
53
56
7.33
5.37
11.99
4.12
1.32
0.94
0.16
0.17
Fama-French Asset Pricing Model
45
15.63
5.57
0.82
0.25
35
32.40
15.92
1.05
0.24
31
30.28
9.89
0.70
0.24
24
43.81
6.26
0.79
0.21
53
56
6.00
3.33
9.58
4.96
1.03
0.60
0.17
0.17
Fama-French-Carhart Asset Pricing Model
45
15.72
0.67
0.86
0.24
35
31.02
16.39
1.03
0.24
31
25.69
4.99
0.61
0.23
24
37.50
6.64
0.68
0.21
45
14.33
2.09
0.79
0.24
35
29.16
16.01
0.98
0.23
31
23.02
-0.41
0.54
0.23
24
33.65
4.28
0.61
0.21
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
53
6.39
13.58
1.15
0.16
55
56
1.66
3.41
0.30
0.17