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Takeover avoidance culture and the market for corporate control

Ivan Obaydina, Ralf Zurbrueggb and Grant Richardsonb

Abstract

We conjecture that the employment of anti-takeover provisions (ATPs) is a function of a

firm’s takeover avoidance culture. Viewed in this light, the absolute number of ATPs a firm

has is of less consequence than the relative number compared to its peers in determining

board and management attitudes towards being a takeover target. By constructing a relative

entrenchment index we find an economically significant, inverted U-shape relationship

exists between the relative number of ATPs a firm has and the probability of being a target

that can also explain the probability that a firm will encounter a hostile versus friendly bid.

This Draft: June, 2016

Please do not cite without author permission

Keywords: anti-takeover provisions, corporate control transactions, relative E-Index

JEL Classification: G32, G34

The authors wish to thank Paul Brockman, Vidhan Goyal, Jarrad Harford, Alfred Yawson

and David Yermack, along with participants at a University of Adelaide School of

Accounting and Finance research seminar.

a Flinders University Business School

b University of Adelaide Business School

Please send all comments to Ralf.Zurbrugg@adelaide.edu.au

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“There is no possibility of any condition, at any price that PeopleSoft will be sold to

anyone. No condition.”

Craig Conway, Former CEO and Director, PeopleSoft

Introduction

In June 2003 Oracle, a resource planning software company, placed a bid to purchase

the competitor PeopleSoft. At the time it did not go down well with the CEO and Director

of PeopleSoft, Craig Conway, which led to the firm closing the proverbial drawbridge by

introducing a number of anti-takeover provisions (ATPs) that would limit Oracle’s ability

to successfully take over the company without significant additional costs. Proceeding the

initial bid, both firms began suing each other and it was not for another 18 months, during

which time the CEO of PeopleSoft was ousted by the board, before support for a revised

bid by Oracle was endorsed and the takeover completed.

Whilst not a unique takeover story, it does demonstrate the key to an important

argument we make in this paper that the employment of ATPs will be a function of the

takeover avoidance culture of the firm, which itself will be a combination of both board and

management attitude towards takeovers. Whilst some firms may prefer to use ATPs to

entrench themselves, others may find value in utilizing them to enhance shareholder value.

Unfortunately, the takeover avoidance culture of the firm is not directly observable.

However, if we consider measuring the relative number of ATPs a firm has to comparable

firms, then perhaps this can proxy for it. The underlying assumption being that if there are

a cohort of firms that are similar, but one firm clearly has more or less ATPs than the others,

it likely says something about the attitude of either or both of the board and management

towards corporate transactions.

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As ATPs transfer, or limit, rights from minority shareholders and block-holders to

firm incumbents, it is possible that this can improve the negotiating capacity of management

involved in a takeover, yet at the same time they can used to facilitate entrenchment and

extract private benefits of control. This leads to questions of how successful ATPs are in

limiting prospective takeovers and whether these ATPs have a detrimental impact on firm

value. In the latter case, numerous studies have empirically examined this issue and whilst

not unanimous1, the majority of research, such as by Gompers, Ishi and Metric (2003), Chi

(2005), Harford, Mansi and Maxwell (2008), and Bebchuk Cohen and Farrell (2009) find

that firm value is negatively associated with the number of ATPs a firm has at its disposal.

This suggests investors generally have a dim view of firms adopting ATPs, which partly

will be due to the perceived interference it can have on the efficient functioning of the

market for corporate control that helps ensure management are acting in the best interests

of its shareholders.

Surprisingly, studies2 focusing on the direct impact that individual provisions, such

as classified boards and poison pills, have on takeover likelihood is ambiguous.

Furthermore, if these provisions are indeed able to enhance the negotiating capacity of target

firm management, as suggested by Straska and Waller (2010) who find ATPs increase firm

value where management bargaining power is low, one would expect that these provisions

should have some impact on takeover dynamics. Despite this, empirical research finds no

support for these provisions, collectively, having any impact on takeover frequencies (Core,

Guay and Rusticus, 2006; Sokolyk, 2011). Even the notion of ATPs enhancing management

1 Work by Kadyrzhanova and Rhodes-Kropf (2011) and Stráska and Waller (2010) find that

ATPs can, under certain conditions, contribute positively to firm value.

2 Heron and Lie, 2006; Bates, Becher and Lemmon, 2008; Giroud and Mueller, 2010;

Kadyrzhanova and Rhodes-Kropf, 2011; Sokolyk, 2011

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bargaining power is questionable given the lack of consensus on this matter in the literature

(Field and Karpoff, 2002; Kadyrzhanova and Rhodes-Kropf, 2011; Sokolyk, 2011).

The above findings are puzzling given the evidence that suggest ATPs affect firm

value, which one must assume is at least partly related to the impact ATPs have on the

market for corporate control. This motivates our study as we take a different perspective

from the extant literature that examines the impact ATPs have in affecting the firm’s

probability of being a takeover target. We argue that when it comes to takeovers, a potential

acquirer will almost certainly assess a firm’s suitability as a takeover target based on a

comparison with its peers. In which case it is not the absolute number of provisions a firm

has that will be relevant, but rather the relative number of provisions it has compared to

alternative takeover targets. Importantly, the usage of these provisions within a firm that has

a strong takeover avoidance culture will be different to a firm whose board and management

are more focused on the interests of their shareholders. We conjecture that without

consideration of this, and the fact that takeover targets must be considered relative to

alternative possible acquisitions, can lead to an erroneous conclusion that there is no

relationship between the number of ATPs a firm has and the probability of it being targeted.

Our view is that if the attitudes of the board and management are relatively more

focused on maximizing shareholder wealth, rather than extracting private benefits of

control, then they are less likely to adopt ATPs to mitigate disciplinary takeovers. We

therefore expect that these firms will employ provisions sparingly and have relatively few

provisions compared to their peers. Although this may seem to make them easier takeover

targets, they are in fact targeted less as disciplinary bidding for these firms will be lower. In

addition, whilst it may be possible for the board and management to effectively entrench

themselves if they employ a relatively large number of ATPs, in smaller numbers the

prevalence of ATPs within a firm may otherwise do nothing but signal to the market that

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the firm is attempting to entrench itself, which consequently attracts acquirers interested in

pursuing a disciplinary takeover. This leads to our hypothesis that the relationship that exists

between the relative number of provisions a firm has is nonlinear and having an inverted U-

shape relationship with the probability of being a takeover target.

Using takeover data spanning a thirteen year period from 1997 until 2009, we focus

on two primary methods to compare the relative number of provisions a firm has against

others with the probability of being an ex-post takeover target. Both methods rely on

utilizing Bebchuk et al.’s (2009) entrenchment index (E-Index). We focus on the E-Index

due to its popularity in corporate finance research to capture the level of management

entrenchment within firms3. Also, relative to other indexes, such as Gompers, Ishi and

Metric (2003) governance index, it excludes provisions that are unlikely to have an impact

on limiting takeover action. Although the importance and impact of ATPs on firm value and

takeover likelihood will always, to a greater or lesser degree, be contingent upon the

individual firm’s conditions, the popularity of the E-Index can be partially attributed to its

simplicity in calculation that provides a general gauge of the level of entrenchment within

the firm.

The first method we use is to construct a Relative E-Index to provide a proxy for the

takeover avoidance culture of the firm. Although this culture is not directly observable, we

can construct a regression model to estimate the number of ATPs a firm is expected to have

relative to other firms based on its managerial characteristics, board characteristics and other

firm-specific factors that may influence the decision of a firm to adopt ATPs. Given that

having more ATPs is associated with a reduction in firm value, if the employment of ATPs

is a function of the culture towards being taken over then this relative measure should act

3 See Straska and Waller (2014) for a review of the literature.

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as a good proxy for a firm that is either more interested in avoiding takeovers or maximizing

shareholder value. We estimate the Relative E-Index by recording the deviation between the

expected to actual number of provisions a firm has in our model. Our second method uses

a one-to-one peer matching process using propensity scores from a logistic model to

compare differences in the number of provisions a firm has with like-for-like firms.

Specifically, on an annual basis, we match firms with a high E-Index against firms with

similar characteristics but with a relatively low E-Index. The difference in the number of

provisions between each matched pair of firms becomes our second measure of the relative

number of provisions a firm has.

Using the above two approaches we then examine what explanatory power the

relative number of provisions a firm has in determining firm value and takeover likelihood.

Whilst our results are congruent with Bebchuk et al. (2009) in finding a negative relationship

with firm value, we also find a robust relationship exists between the relative number of

provisions a firm has and takeover likelihood. In alignment with our hypothesis, an inverted,

U-shape relationship materializes. The results from our logistic regression find that firms

with a Relative E-Index close the zero (in other words firms whose expected number of

ATPs are close to the actual number) have a probability of being a takeover target, at 4.3

percent, that is slightly below the average, of 4.9 percent, for our entire sample of firms.

However, firms on each tail of the distribution that have the maximum number of either six

more of less provisions than expected, reduce their takeover likelihood by approximately

70 and 86 percent, respectively.

To further support these results we then proceed to show that the Relative E-Index

is a good indicator of the type of takeover that will likely occur. We find that hostile

takeovers are less likely to occur if firms have a low Relative E-Index. This is supportive of

our hypothesis as there is less reason why a hostile takeover will occur if the firm is already

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acting in the best interests of the shareholder. As the Relative E-Index rises, the probability

of experiencing a hostile takeover increases. Our peer matching method also reveals a

nonlinear relationship as firms that are substantially entrenched, represented by having a

very high Relative E-Index, have a lower probability of experiencing a hostile takeover. This

is indicative of a successfully entrenched board or management that leads to the only way a

takeover can proceed is if they ultimately support it.

For robustness, we show results from adopting an instrumental variable approach in

analyzing our headline results. Given that we argue deviations in the number of provisions

a firm has from the expected effectively is capturing takeover avoidance culture within the

firm, it is possible that this culture is influenced by the prospect of being targeted. This

raises a reverse causality issue of whether takeovers impact takeover avoidance culture or

that this culture has an impact on a firm becoming a takeover target? To deal with this

endogeneity concern we instrument the Relative E-Index by using a measure that captures

the political culture within the state that a firm is headquartered in and show our results still

hold. Specifically, we adopt Sharkansky’s (1969) adaption of Elazar’s (1966) political

subculture index that has been shown to be correlated with a number of protectionist and

participatory measures of community involvement and attitudes. The higher the index, the

more traditionalist and conservative the attitudes of its citizens are towards change. In

regions where citizens are keen on preserving the status quo, we posit that there will also be

a greater proclivity towards the board and senior management of a firm to entrench

themselves, leading to a positive relationship with the index. In showing that it satisfies the

relevancy condition for an IV in the results of our first stage of the IV approach, it also

meets the requirement of the exclusion condition, as we do not believe that takeover

likelihood of a firm can affect a whole region’s political subcultural attitudes.

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We also examine three other issues. We show our results remain strong when we

sub-sample our data to match a treatment group of target firms with a control group of non-

target firms to control for a possible self-selection bias that may exist in our previous results,

as takeover targets are not randomly selected (see Kadyrzhanova and Rhodes-Kropf, 2011).

We also show what happens when we focus on the impact that one specific provision can

have on firm value and takeover likelihood when examining it against a firm’s Relative E-

Index. We find that the impact of a firm having a classified board provision only affects

firms with a high Relative E-Index by reducing both firm value and takeover likelihood.

Whilst research by Field and Karpoff (2002), Faleye (2007) and Sokolyk (2011) find that

classified boards also have a negative impact on firm value, we show that it is limited to

firms that are more likely to be pursuing takeover avoidance strategies. Finally, we also

conduct analyses on the association between the relative number of provisions a firm has

and the impact it has on bid premiums. The bargaining hypothesis stipulates that ATPs can

improve the negotiating capacity of management, leading to higher bid premiums and we

therefore test to see if we can find any evidence of this. We find only weak evidence when

using our peer matching approach that there is a positive relationship between higher bid

premiums and firms with a high, relative to a low, E-Index.

Our paper contributes to the research examining the impact that ATPs have on the

market for corporate control. In contrast with the extant literature, we focus on the relative

differences in the number of provisions a firm has to other firms. Importantly, given that

ATPs can on the one hand be used for entrenchment purposes, and on the other hand they

can also be used to enhance shareholder value, we contribute to the takeover literature by

highlighting the impact that ATPs, collectively, have on takeover likelihood is a function of

the takeover avoidance culture of the firm. This leads us to hypothesize and demonstrate

that a nonlinear relationship exists between our proxy for this takeover avoidance culture

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and the probability of being a takeover target. Additionally, we contribute to the extant

knowledge on what leads to a firm experiencing a hostile versus friendly takeover, as we

find evidence to support that it is contingent on whether the firm is using ATPs to maximize

shareholder wealth or to entrench either the board and/or management. Finally, we also

contribute to the literature that examines the effectiveness of individual provisions (such as

classified boards) in reducing the likelihood of being a takeover target, as we show that it is

is, again, dependent on the takeover avoidance culture of the firm.

The rest of the paper is organized as follows. In Section II we provide a literature

review and formerly present our hypotheses. Section III details the data and research design.

In Section IV we present our empirical results and in Section V we provide some robustness

results. Section V concludes the paper.

II. Literature review and hypotheses development

A. A review of the anti-takeover provisions literature

Given the nature of modern corporations where ownership is separated from control

rights, agency problems may arise when the interests of the principal (i.e. directors of a

company) do not align with that of the agent (i.e. shareholders). To protect the interest of

shareholders, and help to align the interests of both parties, internal and external governance

mechanisms exist to curtail non-value maximizing behavior by incumbents (Core, Guay and

Rusticus, 2006). Importantly, as Jensen (1993) highlights, given the failings of internal

governance controls, external governance is essential if the interests of management are to

be aligned with that of shareholders. Accordingly, it may be argued that any impediments

to the external disciplining mechanism are detrimental to firm value. This raises the question

as to whether mechanisms such as ATPs are either an impediment or a benefit for the firm.

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In addressing this question, Gompers et al. (2003) develop an equally weighted

index (hereafter GIM-Index) for the number of anti-takeover provisions a firm has at its

disposal to proxy for corporate governance quality. As these provisions are expected to

transfer rights from shareholders to incumbents, they show that the GIM index has a

negative relationship with firm value. They also find that firms with many provisions, which

they call dictatorship firms, exhibit sub-standard market performance relative to firms with

fewer provisions.

Bebchuk et al. (2009) re-examine the GIM index and refine it by identifying a subset

of provisions that should matter the most. Through empirical testing, interviews with

leading M&A legal practitioners and reviews of shareholder precatory resolutions, they

establish that not all provisions are of material importance. They identify only six provisions

that are substantive to explaining the inverse relationship between ATPs and firm value

uncovered by Gompers et al. (2003). Using these six provisions, Bebchuk et al. (2009)

construct a new governance index (E-Index). Four of the six provisions impose

‘constitutional limitations’ on shareholder voting rights. The remaining two provisions are

often regarded as ‘takeover readiness’ measures. If such provisions facilitate managerial

entrenchment, by reducing the effectiveness of the market for corporate control, firm value

would likely fall. Similar to Gompers, et al. (2003), Bebchuk et al. (2009) also find a

negative relationship between their E-Index and firm value.

There are, however, some studies that show ATPs can have a positive effect on firm

value. Stráska and Waller (2010), for example, show that firm value, measured using

Tobin’s Q, is positively related to the GIM index where management have low bargaining

power. Kadyrzhanova and Rhodes-Kropf (2011) also find Tobin’s Q is positively related to

the prevalence of certain ATPs that can delay a takeover in concentrated industries.

Therefore, although the bulk of the established literature (Gompers et al., 2003; Chi, 2005;

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Faleye, 2007; Harford, Mansi and Maxwell, 2008 and Bebchuk et al., 2009) suggests the

relationship between firm value and the incidence of ATPs is negative, the result can be

context-dependent.

With regards to takeover likelihood, the research to-date has produced mixed results

in terms of its relationship with the number of provisions a firm has. Early work by Pound

(1987) and Field and Karpoff (2002) that examine the availablity of poison pills and

classified boards, respectively, find a negative relationship. These studies suggest that

provisions lead to managerial entrenchment. However, other research which applies the

GIM index finds contrary results that do not support the entrenchment hypothesis. Core et

al. (2006) do not find that takeover frequencies differ between high and low GIM index

firms. Consistent with this, Sokolyk (2011) also shows that there is no relationship between

the index and takeover likelihood. When examining the components of the GIM index, he

does find that a subset of the provisions have a notable impact on takeover likelihood and

transaction outcomes. For instance, poison pills and classified board provisions decrease

takeover likelihood, whereas golden parachutes and compensation plans increase it.

Along with research that suggests ATPs support managerial entrenchment, research

has shown it facilitates management to extract private benefits of control (DeAngelo and

Rice, 1983; Masulis, Wang and Xie, 2007; Bates et al., 2008; Harford, Humphery-Jenner

and Powell, 2012). Giroud and Mueller (2011) examine the impact of high GIM index firms

and find that ATPs do increase managerial slack if the firm operates in a non-competitive

(i.e. concentrated) industry. This finding suggests that ATPs may be used to facilitate

agency problems, but only if external pressures do not force management to make optimal

investment decisions.

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Several studies have also considered whether ATPs are used to maximize the pay-

off target firm shareholders receive in the event of a takeover contest (Stulz, 1988; Field

and Karpoff, 2002; Kadyrzhanova and Rhodes-Kropf, 2011; Sokolyk, 2011). As first

articulated by Stulz (1988), takeover defenses which limit shareholder rights have the

potential to enhance takeover premiums by deterring opportunistic bidding that may

otherwise occur. Contrary to this proposition, Field and Karpoff (2002) find no evidence to

suggest that firm-level provisions, or state level takeover-laws, that empower management

are related to takeover bid premiums. Heron et al. (2006), on the other hand, find that ATPs

can help management with low ownership to negotiate for more favorable merger terms.

Similarly, Kadyrzhanova and Rhodes-Kropf (2011) also find evidence to suggest that

certain ATPs have a positive impact on offer premiums, when controlling for the economic

environment in which a firm operates.

B. Hypotheses development

Our hypothesis relates to the association that ATPs have with the market for

corporate control. Whilst we would expect that the number of ATPs a firm adopts has an

impact on firm value, when it comes to the takeover market we argue that what matters

more is not how many provisions a firm has, per se, but rather how many provisions a firm

has relative to its peers. As acquirers will likely compare similar firms when choosing a

target, we believe it is more relevant to examine the relative surplus or deficit number of

provisions a firm has.

A couple of possibilities arise as to how this relationship will look like. It could be

argued that as the number of surplus provisions a firm has increases, and assuming these

ATPs are effective in facilitating the entrenchment of either or both the board and

management, the probability of becoming a takeover target would decrease. In other words,

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incumbents may be deploying these provisions in a manner aimed at impairing the

disciplinary function of the market for corporate control. As such, an acquirer who is in

search of a new target would likely choose a firm with less anti-takeover provisions than

one with more, if all else is the same. In cases where too few provisions have been ratified,

a firm may be unable to defend itself against opportunistic bidders seeking to exploit

temporary stock mispricing or shareholder myopic behavior; increasing takeover likelihood.

Based on this line of reasoning, the expected relationship between takeover likelihood and

the number of surplus provisions a firm has will be negative.

However, it is also possible to posit that a positive relationship exists between

takeover likelihood and the number of surplus provisions a firm has. If these provisions

provide insight into the takeover avoidance culture of the firm, then firms with a relative

excess number of provisions may act as a signal to the market that incumbents within the

firm are entrenching their positions and potentially not acting in the interests of their

shareholders, leading to the likelihood of disciplinary takeovers to increase. Therefore, a

positive relationship between a surplus number of provisions and takeover likelihood can

be posited. Likewise, a firm with very few ATPs may be indicative of management focused

on maximizing shareholder wealth and therefore will not need to concern themselves with

the prospect of disciplinary takeovers. This provides the basis for our first hypothesis:

H1: A positive, linear relationship exists between takeover likelihood and the number

of surplus provisions.

There is, however, an extension to this line of logic if one allows for a non-linear

relationship to exist between takeover likelihood and the number of surplus provisions. If

ATPs are ineffective in deterring takeovers, then their prevalent usage across the corporate

sector would be hard to explain. What may be the case is that ATPs entrench management

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in some instances, but not others. It might be, for example, that a substantive number of

anti-takeover provisions need to be employed before any form of entrenchment strategy is

successful in reducing the likelihood of being targeted. It is therefore possible that these

ATPs are successful in reducing the likelihood of a takeover only where there are large

deviations in the number of ATPs one firm has relative to others. This is represented in

Figure 1 for firms that are on the far right of the horizontal axis. An inflexion point arises

where additional provisions begin to reduce the probability of being a takeover target. At

the other end of the spectrum (the left side of the horizontal axis in Figure 1) firms with

relatively few ATPs are more likely to be focused on shareholder-wealth maximization and

thus will be less likely to be targeted for disciplinary or opportunistic bidding.

How ATPs affect the market for corporate control, therefore, is expected to be

conditional on the firm’s attitude towards being acquired that potentially can be captured by

looking at the relative number of provisions a firm has. This leads us to our second

hypothesis that a non-linear relationship exists between the number of surplus provisions a

firm has and takeover likelihood:

H2: A nonlinear, inverted U-shape relationship exists between takeover likelihood and

the number of surplus provisions.

III. Data and Methodology

A. Data

As we collect data from a number of sources our final sample period is based on

matching the common start and end dates across several databases. Specifically, we examine

a period from 1997 to 2009. All firm-level ATPs are extracted from the Institutional

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Shareholder Services (ISS) governance database (formerly Risk Metrics). To complement

this, we utilize Compustat data to provide firm-level information concerning the asset

structure of each firm and other control variables. The complete set of variables employed

in our study is provided in the Appendix. SDC Platinum is used to identify takeover targets,

along with other relevant takeover transaction related information such as deal value and

method of payment.

We also collect data on managerial characteristics, ownership and board structure

from Execucomp, Thomson Reuter’s 13F-filings and ISS Directors databases, respectively.

Where data on governance provisions are not available, we follow the method employed by

Gompers et al. (2003) to populate missing year observations. Specifically, we use the

previous year (that is available) to proxy for missing year governance provisions. We also

assume that data on governance provisions in 2006 are valid for a further three years.

When merging the different databases we drop dual-class firms from our sample.

Dual class firms are not considered given that management can stop unwanted takeover

attempts via their often substantial voting rights (Gompers et al., 2003; Masulis, Wang and

Xie, 2009). We also exclude utilities, financials and miscellaneous industries. Utilities and

financials are excluded because of differences in laws, in comparison to non-utilities and

non-financial stocks, governing the operation and acquisition activities of such firms

(Daines and Klausner, 2001; Kadyrzhanova and Rhodes-Kropf, 2011). Industry assignment

is based on the Fama and French (1997) 12-industry classification codes.

Our final sample consists of 11,989 firm-year observations which is made up of

1,797 individual firms of which 597 unique firms are subject to a takeover bid. We only

consider takeovers with transaction values in excess of $10 million. In addition to this, bids

that have transaction values in excess of the 99th percentile are trimmed from the sample.

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To avoid double counting targeted firms (Kadyrzhanova and Rhodes-Kropf, 2011) we only

consider initial bids in our analysis. In line with Bates et al. (2008), follow-on bids (defined

as bids that occur within 365 days of a previous bid announcement for the target) are

dropped from our sample.

B. Determining the relative number of provisions

We use one of the most widely used measures in the literature to capture the level of

takeover defenses a firm has at its disposal to generate our relative measures. Bebchuk et

al.’s (2009) entrenchment index (E-Index) is based on the number of provisions a firm has

that they identify can substantively influence takeover contests. Specifically, there are six

provisions that can constitute the E-Index, all of them having the potential to impact

corporate control transactions, with four specifically related to takeovers.4

Our Relative E-Index is constructed by first running a cross-sectional regression to

explain variations in the E-Index across all firms in our sample. For each firm i in year t we

estimate the expected number of provisions it has based on managerial characteristics, board

characteristics and controls that may influence the decision to adopt provisions. Together,

we expect these factors to capture the takeover avoidance culture of the firm:

E-Indexi,t = f (Managerial Attributesi,t + Board Characteristicsi,t +

Firm Characteristicsi,t) (1)

4 The six provisions that constitute the E-Index are classified boards, poison pills, golden parachutes,

supermajority voting requirements for mergers, limits to shareholder bylaw changes and charter amendments.

The first four will have a direct bearing on the outcome of a takeover whilst the remaining may have depending

on what the specific provisions relate to.

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For managerial attributes and board characteristics we use Field and Karpoff’s (2002) set of

variables which they apply to establish the determinants of IPO firms’ use of takeover

defenses. This includes CEO compensation, tenure and age. For monitoring and control we

use board independence, board size plus CEO and chair duality. Industry-adjusted book

value of equity and industry-adjusted leverage (debt to assets) are also added as control

variables as they may also influence the firm’s decision to adopt ATPs. The Appendix

details how each variable is constructed. To establish a firm’s relative number of surplus,

or deficit, provisions we subtract the number of expected provisions a firm should have from

estimating equation (1) and subtract it from the actual number of provisions a firm employs.

This provides us with a Relative E-Index score for each firm.

The advantage of the above measure is that it provides a simple score of the number

of provisions a firm has relative to the rest of the sample. The disadvantage is that we are

not directly comparing like-for-like firms. Our second approach deals with this by

identifying the differences in the number of provisions a firm has through a one-to-one peer

matching process. We achieve this by first splitting our sample of firms into two groups.

One cohort contains firms with an E-Index score above the median in the sample, and

another cohort with an E-Index value below the sample median. We then run a logistic

model similar to equation (1), except that this time the dependent variable is either equal to

one if the firm is placed in the cohort that have a large number of provisions (a high E-Index

score), and zero otherwise. From the regression we obtain a propensity score for each firm

which we use to match firms between the two cohorts. The matching is done on a one-to-

one basis without replacement and with a calipher threshold set at 1%. Firms that cannot be

matched are discarded. Importantly, our matches are done on an annual basis and only

between firms within the same industry. This leaves us with a new sub-sample of firms

where for each firm with a small number of provisions, there is another with a large number

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that is similar in terms of the industry they are in plus managerial, board and firm

characteristics. The difference in provisions between each pair of matched firms we call

PeerMatchDiff.

C. Summary Statistics

Descriptive statistics for the mean, median and standard deviation of the primary

variables used in our empirical analysis are provided in Table I. Firms are split between

non-target and target firms, with the last two columns displaying the mean difference

between the two subsets and the p-value for the test in differences in these means. The first

set of variables are of the governance and entrenchment indexes, namely the E-Index and

GIM Index. We include the GIM Index for comparison purposes. The mean and median

values of the E-Index for our sample are 2.44 and 2, respectively. It is worth highlighting

that Masulis et al. (2007) differentiate between good and poorly governed firms by using an

E-Index value of 2 as the cut-off. If firms have an E-Index score that is greater than 2, they

are categorized as having poor governance. Using this criterion our data is approximately

evenly split between good and poorly governed firms. In addition, it is interesting to note

there is no significant difference between target and non-target firms for the E-Index,

implying it has no explanatory power, at least on an individual basis, in determining whether

a firm is going to be a takeover target or not.

Whilst there is no statistical difference between target and non-target firms insofar

as the number of the E-Index, there are in terms of their managerial attributes. Specifically,

the CEO cash compensation of targeted firms is higher (at the 1% level), plus the tenure and

age of the CEO is lower (at the 10% and 5% levels, respectively). Whilst there are no

significant differences for our measures of monitoring and control, some differences do

exist within our list of additional variables used for estimating our E-Index that also serve

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as controls in our subsequent regressions. Specifically, at the 1% level, targeted firms in our

sample are smaller, have lower return on assets (ROA), and lower Tobin’s Q. These last

two features may suggest that the sample of target firms are not performing as well, relative

to the non-acquisition sample of firms. These firms also tend to be younger and have higher

institutional ownership (at the 1% level).

[INSERT TABLE I]

Pearson correlation coefficients are reported in Table II for the variables that we use

to estimate the E-Index, as well as with the GIM Index for comparison purposes. The

correlation of the E-Index with the GIM-Index (0.736) is positive, implying that firms with

a higher E-Index are also likely to have more of the other provisions that Bebchuk et al.

(2009) omit. We also include firm value (Tobin’s Q) in the correlation matrix that has a

negative correlation coefficient of -0.156 with the E-Index, which is consistent with the

majority of the literature that show ATPs lower firm value (Gompers et al., 2003; Bebchuk

et al., 2009).

[INSERT TABLE II]

In Table III we provide summary statistics on the corporate control transactions

within our sample. The number of corporate control transactions, relative to the total number

of firm-year observations, is 4.98%. However, 35.44% of all firms in our sample are

involved in a corporate control transaction at some stage during the sample period. Of the

597 initial bid transactions considered in this study, 80.54% were completed. The average

transaction value is $353.80 million, whereas the median is $136.15 million. Clearly, there

is significant skewness in the distribution of deal values in our sample. This is due to a

limited number of large-scale takeover transactions in the sample. Average bid premiums

are 21.94%, and largely in line with that of previous studies (see Kadyrzhanova and Rhodes-

20

Kropf, 2011). Given that the average market capitalization is $1.51 billion for our sample

of firms, this translates to an average bid premium value of approximately $300 million.

The average cumulative abnormal return that target firm shareholders realize over the

duration of a bid is 32.57%, which represents a substantial gain in target firm shareholder

wealth over a relatively short period of time.

In terms of how deals are structured, the predominant method of payment is cash.

All cash bids represent 45.20% of the transactions in our sample. Stock bids make up

38.67%, with the remainder of bids (16.13%) having both a cash and stock component.

Interestingly, few bidders choose to acquire a toehold in the target prior to launching the

official bid. In fact, only in 6.15% of the bids did the acquirer hold a toehold stake. Hostile

bids make up 10.76% of the sample.

[INSERT TABLE III]

In Table IV we report governance, board and firm-level characteristics associated

with low and high Relative E-Index firms. Firms are split into low and high cohorts based

on the median of the Relative E-Index. We also use a similar process for our peer matched

firms which are split by the median value of the E-Index in our sample. There are differences

when one looks at the Relative E-Index split compared to the PeerMatchDiff split in terms

of which variables are significantly different across the high and low cohorts. However,

what is common in both cases is that firm value (Tobin’s Q) is always negatively related to

having a higher number of provisions, indicative of ATPs destroying firm value.

[INSERT TABLE IV]

21

IV. Empirical results

A. The relative number of provisions and firm value

We begin our empirical analyses by examining the relationship our two approaches

to measuring the relative number of provisions a firm has with firm value. Our a priori

expectation is that regardless of whether an absolute number of provisions are used or a

relative number, there should be a negative relationship between the more ATPs a firm has

and firm value. In Table V we set the dependent variable to be Tobin’s Q. To provide a

benchmark with what happens when we apply Bebchuk et al.’s (2009) original E-Index, in

column (1) we regress firm value against the original E-Index term and a set of firm specific

controls that may also explain firm value. Our selection of control variables is based on the

common firm-specific factors that other papers that examine ATPs with firm value use.

These controls include the sales growth of the firm, return on assets, free cash flow, leverage

– measured as the proportion of debt to equity in the firm, the natural logarithm of the firm’s

market capitalization, a block holder dummy that has the value of zero if an institutional

investor owns more than 5% of the firm, and zero otherwise, the proportion of intangible

assets to total assets, and the concentration of the industry the firm is in. We also include

industry and year fixed effects to account for any further unobservable factors that we have

not explicitly accounted for. In column (2) we also include the quadratic form of the E-

Index to check for a possible nonlinear relationship between the number of provisions a firm

has and firm value.

Consistent with the literature, we find that the coefficients for the E-Index in both

regressions are negative and statistically significant at the 1% level, with no evidence of a

22

nonlinear relationship. Based on the estimated coefficient value for the E-Index in column

(2), each unit rise in the E-Index leads to an 8.8% decline in firm value.

In columns (3) and (4) we repeat the first two regressions but replace the E-Index

with our Relative E-Index measure. The results are similar with the coefficient for Relative

E-Index being negative and significant in both regressions at the 5% level. The interpretation

is slightly different as now the variable of interest expresses the relative number of

provisions a firm when accounting for certain board and management characteristics. Based

on the estimated coefficient value for the Relative E-Index, for each additional provision a

firm has relative to what is expected leads to firm value declining by 7.03%. Columns (5)

and (6) show the results from a reduced sample of peer-matched firms. The results are very

similar to our earlier regression results, showing again that a significant negative

relationship, at the 1% level, exists between the number of provisions employed by a firm

and firm value.

[INSERT TABLE V]

Taken together, the results from Table V are in alignment with the existing literature

on ATPs and firm value. We are able to show that our two approaches capture the same

negative relationship that an absolute measure of the number of ATPs a firm has on firm

value. As a robustness check, we also use the Fama and MacBeth (1973) two-stage approach

to examine the relationship between the relative number of ATPs a firm employs and firm

value. This approach avoids clustering the standard errors by firms. In the first stage, we

run a series of cross-sectional regressions pooled by year for the firms in our sample. We

then average the coefficients from these regressions across years to obtain the Fama-

MacBeth factor loadings for our second-stage regressions. The results from this approach,

23

using Newey-West (1987) adjusted standard errors that are robust to serial correlation across

firms, are qualitatively the same as the results we report in Table V.

B. The relative number of provisions and the market for corporate control

Although our independent variables remain the same to what we used for examining

firm value, the dependent variable is now a binary which is set to one if a firm is targeted,

and zero otherwise. Table VI shows the results from probit regressions using this dependent

variable, and as with Table V, we first present the results obtained from using the standard

E-Index first to set a benchmark against our measures of the number of relative provisions

a firm employs. The results from columns (1) and (2) show that neither the coefficient for

the E-Index nor its quadratic form are significantly related to the probability of a firm being

targeted.

The above results are in contrast to what we find when we measure the relative number

of provisions a firm has against either our whole sample using the Relative E-Index

(columns 3 and 4) or when we use our peer-matched sample of firms (columns 5 and 6). In

column (1) we find that the coefficient for the Relative E-Index is positive and significant

at the 1% level with a coefficient value of 0.0208. This result is contrary to the argument

that if ATPs are effective in entrenching management as this implies the more provisions a

firm employs, the probability of being a takeover target should decline. Instead, we find

evidence that supports our first hypothesis that having less provisions leads to a reduction

in takeover likelihood. Firms with relatively fewer provisions are more likely to be less

averse to protecting themselves against takeovers as they are focused on shareholder wealth

as opposed to entrenching their own positions. As a result, they are less likely to face the

prospect of disciplinary takeovers.

24

When we turn our attention to the nonlinear case, tabulated in column (4), we find that

the estimated coefficient for the Relative E-Index remains positive and significant, as well

as the size being similar (0.0239). However, the coefficient of the squared Relative E-Index

term is also significant at the 1% level. The coefficient is negative, and the magnitude of the

coefficient is smaller (-0.0182) than the coefficient for the linear term, leading to an inverted

U-shape relationship forming between the number of excess provisions a firm employs and

takeover likelihood. We interpret this result as suggesting that where firms employ a number

of ATPs beyond a certain threshold, entrenchment begins to be successful. This

entrenchment threshold is reached just after the firm has more than one additional provision

than what is expected. In Figure 2, the probability of being a takeover target is inferred from

our baseline probit model, estimated and reported in model (4) of table six, and plotted

against the Relative E-Index score. To derive the expected probability of a firm being

targeted we set all control variables to the sample average and then extract the probability

of being targeted by changing the Relative E-Index score, in one unit increments, from -6

(the lowest possible score) to +6 (the highest possible score). A firm, for example, that is

expected to have no ATPs from the entrenchment index (E-Index), yet has all six provisions,

reduces the likelihood of being a takeover target, in a given year, by 70.01%, to a value of

1.29% percent. On the other hand, firms with fewer provisions than expected also reduce

the likelihood of being a takeover target. Those firms that are expected to have six

provisions, but instead have none, reduce their probability of being a takeover target, in a

given year, by 86.05%, to 0.60%. Right in the middle, for firms that have the same amount

of ATPs as to what is expected, have a probability of being targeted (4.3%) that is slightly

below the mean for our entire sample (4.86%).

The results from our peer matched sample provide a similar story, showing a significant

quadratic relationship exists between the number of ATPs a firm has and takeover

25

likelihood. As such, our results match our second hypothesis and suggest our approaches to

capturing the relative number of provisions a firm has, as opposed to the absolute value, can

explain the probability of a firm being targeted. We also believe these results are consistent

with the results from Table V that show firms with a relative deficit number of provisions

also have higher firm value. Firms with relatively few provisions are more likely to be run

in the interests of the shareholder and therefore reduce the possibility of disciplinary

takeovers.

[INSERT TABLE VI]

Although not tabulated, we conduct a number of other tests to check the reliability

and consistency of using our Relative E-Index when we change how it is estimated. We

consider what happens if we group each firm into its respective industry classification and

then run a series of rolling regressions (by year for each industry) to determine the

Relative E-Index. We also consider what happens when we further filter and drop some of

our explanatory variables. For example, to avoid capturing the attitude of a departed CEO

in influencing firm culture, we restrict the sample to firms with a CEO that has a minimum

tenure of two years. Our results, however, remain qualitatively the same, showing a strong

nonlinear relationship exists between the Relative E-Index and the probability of being

targeted.

C. Robustness tests

In this section we conduct a number of robustness checks and auxiliary analysis to

complement our baseline results on the relationship that the number of ATPs have with

takeover likelihood. To start, we address a possible endogeneity concern. If the relative

number of ATPs a firm adopts is related to the takeover avoidance culture of the firm, then

26

it raises the question of whether takeover likelihood can also influence the firm’s culture

towards being taken over? To deal with this reverse-causality concern, and concerns that

our Relative E-Index is proxying for unknown, omitted variables within our econometric

modeling that lead to a spurious association between our measures with takeover likelihood,

we instrument it with Sharkansky’s (1969) adaption of Elazar’s (1966) political subculture

index that has been shown to be correlated with a number of protectionist and participatory

measures of community involvement and attitudes. Sharkansky (1969) shows that the higher

the index, the more traditionalist and conservative the attitudes of its citizens are towards

change. In regions where citizens are keen on preserving the status quo, we posit that there

will also be a greater proclivity towards the board and senior management of a firm to

entrench themselves, as a desire to avoid change. This should lead to a positive relationship

with the index. Also, as one cannot argue that the takeover likelihood of a firm will affect a

whole region’s political subcultural attitudes, the IV meets the exclusion condition for being

a suitable instrument. For every firm, we associate it with a Sharkansky score based on the

state where it is headquartered.

Table VII presents the results of estimating the probability of being a takeover target

using a two-stage least squares probit approach when applying the Sharkansky index to

instrument for our measures of the relative number of provisions a firm has. In column (1)

we show the first-stage regression results, where the instrumental variable (Sharkansky

Score) has the expected, positive sign and is significant at the 1% level. When we use the

predicted values from the regression of column (1) to replace the Relative E-Index variable

in column (2), we find it is significant at the 10% level and also has the correct, hypothesized

sign.

In columns (3) and (4) we repeat the regressions when adding the quadratic term. As

we now require a second IV, we follow Wooldridge (2002) and estimate a second, first-

27

stage regression where we use the squared predicted values obtained from the first

regression in column (1) to predict the square of the Relative E-Index. Column (3) shows

the regression results where the coefficient for the square of the predicted values obtained

from column (1) is significant, at the 1% level, along with having a positive relationship

with the probability of being a takeover target. Using the predicted values from this

regression for the square of the Relative E-Index we then estimate our second-stage

quadratic model with the results tabulated in column (4). We show that for our two

instrumented variables, one being the Relative E-Index and the other for its square term,

their estimated coefficients are both significant at the 10% and have the expected signs that

lead to an inverted, U-shape relationship materializing. In columns (5) to (8) the analysis is

repeated for our peer matched sample where we use the Hostile Experience variable to

instrument for the E-Index and the results are stronger, as our instrumented Relative E-

Index, and its quadratic term, are now significant at the 1% level in column (6), and the 5%

levels in column (8).

[INSERT TABLE VII]

Another issue that arises when examining takeover likelihood determinants is that

targets are not randomly selected (Kadyrzhanova and Rhodes-Kropf, 2011). To address this

particular issue, we utilize propensity score matching (PSM) to match a treatment group of

target firms with a control group of non-target firms. We first run a logit regression to

determine the propensity scores for each firm in our sample based on the same set of control

variables used in the baseline regression of Table VI. We then match each target firm,

without replacement, with a non-target firm where the caliper does not exceed 0.01 standard

deviations. This reduces our sample size from 11,989 to 2,160. Table VIII presents the

results from re-running our baseline regressions presented in Table VI. In terms of statistical

28

significance, sign and the relative magnitudes between our ATP measures and its square

term, nothing substantially changes.

[INSERT TABLE VIII]

We next consider if capturing the relative number of provisions a firm has can also predict

the probability that a firm will experience a hostile, versus a negotiated / friendly takeover.

If it is true that, for example, our Relative E-Index is a proxy for the takeover avoidance

culture of the firm, then we should find firms with a low Relative E-Index should experience

less disciplinary (hostile) takeovers given that they are more likely using ATPs for the

benefit of the shareholder rather than for board and/or management entrenchment. In Table

X we regress the probability of receiving a hostile bid against the standard E-Index, the

Relative E-Index and our peer-matched sample of firms. We use a two-stage Heckman

probit model where in the first stage the regressor is the probability of receiving a bid, and

in the second stage it is whether the bid is hostile or not. In the second stage we also add a

number of deal characteristics that the literature has shown to influence bid attributes. These

deal characteristics include whether there is an established toehold or not, whether the bid

is an all cash payment and whether the bid is a tender offer or not.

The results show that if we use the benchmark E-Index (columns 1 and 2), no

relationship can be found between the absolute number of ATPs a firm has and the

probability that the firm experiences a hostile bid. However, when we use the Relative E-

Index (columns 3 and 4), we find evidence of a significant positive, linear relationship. If

we focus on the peer matched sample of firms (columns 5 and 6) the significance of the

results become stronger and we also find evidence of a nonlinear relationship. As the

coefficient for the squared E-Index term in column (6) is significant and negative (but with

a smaller coefficient size than the coefficient for the E-Index term), it suggests an inverted

U-shape relationship exists between the probability of experiencing a hostile bid and the

29

relative number of ATPs a firm has. We believe this is a result of the fact that if the board

and / or management have successfully entrenched themselves the only way a bid will be

successful is if it in negotiated. The case in point would be our example with PeopleSoft.

At the end of the day a negotiated settlement was reached.

[INSERT TABLE X]

In Tables XI and XII we examine the impact that one specific provision, classified boards,

can have on the likelihood of being a takeover target. This provision limits the ability for an

acquirer to clear out the acquired firm’s board as board members serve overlapping terms

and cannot be completely replaced following a takeover. Whilst on the one hand this

provision may be utilized to entrench board members and make it less appealing for

acquirers to target the company (Daines and Klausner, 2001; Bebchuk, Coates and

Subramanian, 2002; Bebchuk and Cohen, 2005; Faleye, 2007), it can also be argued that

classified boards, under certain conditions, contribute positively to firm value by promoting

board stability (Wilcox, 2002; Ahn and Shrestha 2013; Johnson, Karpoff and Yi, 2015). For

this reason, examining the impact that this provision can have on firm value and takeover

likelihood is a good case study as its impact may well be firms-specific. If it is true that

firms with a relatively small number of provisions are less likely to be employing ATPs for

entrenchment purposes, then investors should not react negatively to the employment of this

provision, as well as it not having any impact on takeover likelihood. Conversely, if firms

with a relatively large number of provisions is indicative of firms entrenching the board and

management then both firm value and takeover likelihood should decline.

To test the above we split our sample into a cohort of firms with a low Relative E-

Index (column 2 of each table) versus those with a high Relative E-Index (column 3 of

each table). For Table XI the dependent variable is our firm value measure, Tobin’s Q,

which is regressed against our usual set of control variables plus a dummy variable that is

30

equal to one if the firm has a classified board provision and zero otherwise. When we

examine the regression results from using the full sample (column 1) the estimated

coefficient for the classified board dummy is significant at the 5% level and negative,

implying firm value declines if this specific provision is present within the firm. However,

when we look at the regression results from the two subsamples, they suggest that the

impact on firm value is only present for firms with a high Relative E-Index. Whilst

research by Field and Karpoff (2002), Faleye (2007) and Sokolyk (2011) find that

classified boards also have a negative impact on firm value, our results show that it is

limited to firms that are more likely to be pursuing takeover avoidance strategies. Similar

results appear when we examine the probability of being a takeover target in Table XII, as

a significant relationship only exists for high Relative E-Index firms.

[INSERT TABLE XI and XII]

As a final extension to our analyses of takeover likelihood, Table XIII examines whether

measuring the relative number of provisions a firm has can affect bid premiums. As part of

this we also need to account for a possible self-selection bias takeover premiums may, in

part, reflect the market’s expectation of a takeover bid. We deal with this issue by employing

a two-stage Heckman (1979) approach where in the first state we use the original probit

regressions from Table VI to determine the likelihood of being targeted. To ameliorate

multicollinearity concerns, FIRM AGE is used as an instrument for takeover likelihood in

this first-stage regression. Similar to the argument presented by Kadyrzhanova and Rhodes-

Kropf (2011), we use FIRM AGE at the time of the firm’s initial public offering as it is

correlated with takeover likelihood (i.e. firm exit) but not takeover premiums. We then

compute the inverse Mills ratio and include it in the second stage OLS regression that is

reported in Table XIII. In addition to our usual set of control variables, we also add deal-

specific controls used in Table X. The results, however, are not particularly strong as we

31

only find evidence that the relative number of provisions a firm has positively affects bid

premiums for the matched sample of firms in columns (5) and (6). The estimated coefficient

for the E-Index in column (5) is significant at the 5% level. To double-check whether there

is any stronger result if we look at takeover contest returns, we redo the above analysis but

replace the dependent variable with auction cumulative returns but fail to find, again,

consistent significant results between our different approaches for capturing the relative

number of provisions a firm has.

[INSERT TABLE XIII]

V. Conclusion

In this paper we contribute to the established literature by demonstrating the

importance of considering the relative number of anti-takeover provisions a firm employs

in determining the relationship it has with takeover likelihood. We relate deviations in the

actual number of provisions a firm has against an expected number, based on board and

management characteristics plus other firm-specific factors. Our argument is that when it

comes to an acquirer determining which firm it will bid for, the impact of the number of

ATPs a firm has must be considered to other comparable firms. Moreover, the relative

deficit or surplus number of provisions a firm has can proxy for a firm’s takeover avoidance

culture. If the board and / or management of the firm are more concerned with private

benefits of control, then they are more likely to encourage the proliferation of these

provisions that will lead them to have a relative surplus number of provisions. This will

facilitate their entrenchment within the firm. On the other hand, management that are more

focused on maximizing shareholder wealth will not need to employ these provisions to

mitigate disciplinary takeovers, likely leading to the employment of fewer provisions

relative to its peers.

32

By examining the relative number of provisions a firm has, we show that it has a

significant relationship with takeover likelihood. Whereas the prior literature does not find

strong evidence of an association (Core et al., 2006), we find an inverted U-shaped

relationship exists where firms that have either a relative surplus or deficit number of

provisions are less likely to be targeted. In the former case, we argue that this is because the

board and management are acting in the interest of shareholders, whereas in the latter case

it is due to them successfully entrenching their positions. Additionally, we demonstrate that

by focusing on the relative number of provisions a firm has, as opposed to the absolute

number, it can also determine whether the firm is likely to experience a hostile takeover.

We also examine whether the inclusion of a specific provision for classified boards will

have a detrimental impact on firm value and takeover likelihood. In alignment with our main

hypothesis, we find that only for firms with a strong takeover avoidance culture (high

Relative E-Index) do we see that the presence of a classified board provision leads to a

decline in firm value and rise in takeover likelihood.

Overall, our results suggest that it is important to capture the takeover avoidance

culture of the firm. Whether ATPs have a positive or negative impact will be dependent on

the view that the board and management have towards shareholder wealth creation versus

the desire to entrench their positions. It may therefore be beneficial for future research to

explore additional dimensions of board and management attitude to better understand the

market for corporate control.

33

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36

Figure 1. The hypothesized relationship between the Relative E-Index and the probability of

being a takeover target.

Figure 2. The estimated probabilties of being a takeover target against the Relative E-Index.

In the following diagram, the relationship between takeover likelihood (derived from model (4) of

Table VI) and Relative E is illustrated. In estimating the takeover probablities, all covariates, other

than the Residual E, were set to the sample average.

37

Table I Descriptive statistics of target and non-target firms

In this table we report descriptive statistics for our full sample, non-target sample (i.e. firms not subject to a takeover contest) and target sample (i.e. firms subject to a takeover

contest) of firms. In column diff, differences in Mean values between non-target and target firm variables are reported. Reported P-values are based on a one-sided univariate

t-test. Variable definitions are provided in Appendix A.

Full Sample Non-Targets Targets T-Test

Mean Med. p25 p75 Mean Med. p25 p75 Mean Med. p25 p75 diff P-value

Panel A: Governance Index

E-Index 2.44 2.00 1.26 2.00 3.00 2.44 2.00 1.26 2.00 3.00 2.44 2.00 1.20 2.00 3.00 0.00 0.92

GIM 9.22 9.00 2.60 7.00 11.00 9.24 9.00 2.60 7.00 11.00 9.00 9.00 2.54 7.00 11.00 0.23 0.03

Panel B: CEO Characteristics

LN(Cash Compensation) 7.41 6.91 1.96 6.44 7.50 7.38 6.91 1.91 6.44 7.50 7.94 6.89 2.64 6.45 7.71 -0.56 0.00

Tenure 6.54 4.00 7.18 2.00 9.00 6.57 4.00 7.20 2.00 9.00 6.08 4.00 6.74 1.00 8.00 0.49 0.09

Age 54.94 55.00 7.28 50.00 60.00 54.98 55.00 7.28 50.00 60.00 54.29 55.00 7.19 49.00 60.00 0.68 0.02

Panel C: Monitoring and Control

Board Independence 0.55 0.67 0.31 0.43 0.80 0.55 0.67 0.31 0.43 0.80 0.54 0.63 0.30 0.43 0.78 0.01 0.42

Log of Board Size 1.77 2.08 0.88 1.79 2.30 1.77 2.08 0.88 1.79 2.30 1.75 2.08 0.85 1.79 2.30 0.02 0.53

CEO/Chair Duality 0.51 1.00 0.50 0.00 1.00 0.51 1.00 0.50 0.00 1.00 0.53 1.00 0.50 0.00 1.00 -0.02 0.37

Panel D: Control Variables

Q 1.73 1.32 1.37 0.93 2.02 1.74 1.32 1.38 0.93 2.02 1.53 1.23 1.05 0.88 1.83 0.22 0.00

Ln(Market Cap) 14.25 14.14 1.65 13.17 15.24 14.28 14.16 1.65 13.19 15.28 13.76 13.74 1.49 12.85 14.61 0.52 0.00

Book Value of Assets 7.29 7.11 1.46 6.26 8.21 7.31 7.14 1.46 6.27 8.24 6.90 6.76 1.32 5.97 7.63 0.41 0.00

Leverage (D/A) 0.23 0.21 0.19 0.06 0.34 0.22 0.21 0.18 0.07 0.33 0.23 0.22 0.20 0.04 0.35 -0.01 0.43

Asset Tangibility 0.72 0.78 0.22 0.60 0.89 0.72 0.78 0.22 0.60 0.89 0.72 0.80 0.23 0.60 0.91 -0.01 0.51

Sales Growth 0.08 0.08 0.21 0.00 0.16 0.08 0.08 0.21 0.00 0.16 0.08 0.08 0.22 0.00 0.17 0.00 0.64

Return on Assets 0.09 0.09 0.11 0.05 0.14 0.09 0.09 0.11 0.05 0.14 0.07 0.08 0.11 0.04 0.12 0.02 0.00

Free Cash Flow 0.02 0.04 0.13 -0.01 0.08 0.02 0.04 0.13 -0.01 0.08 0.01 0.03 0.13 -0.01 0.07 0.01 0.04

Block Holder 0.21 0.00 0.40 0.00 0.00 0.20 0.00 0.40 0.00 0.00 0.34 0.00 0.47 0.00 1.00 -0.14 0.00

Firm Age 2.39 2.40 0.38 2.20 2.71 2.39 2.40 0.38 2.20 2.71 2.30 2.30 0.40 2.08 2.64 0.09 0.00

Industry Concentration 0.08 0.06 0.06 0.05 0.08 0.08 0.06 0.06 0.05 0.08 0.08 0.06 0.06 0.05 0.08 0.00 0.17

Observations 11,989 11,392 597

38

Table II Correlation matrix of key variables used in the estimation of the Relative E-Index.

Pearson correlation coefficients for the variables used to derive measurements of management attitude are reported below. We also include our measure of firm value, Tobin’s

Q, in the correlation matrix plus, for comparison purposes, the GIM-Index. Variable definitions are provided in Appendix A. P-values are reported in parentheses.

E-Index GIM-

Index CEO Comp. CEO Tenure

CEO

Age

Board

Indep. Board Size CEO & Chair Book Value Leverage State law

DE

Incorp.

Tobin’s

Q

E-Index 1.0000

GIM-Index 0.7357 1.0000

(0.00)

CEO Compensation -0.0332 -0.0482 1.0000

(0.00) (0.00)

CEO Tenure -0.1013 -0.1083 0.0176 1.0000

(0.00) (0.00) (0.04)

CEO Age 0.0432 0.0781 -0.0002 0.4122 1.0000

(0.00) (0.00) (0.98) (0.00)

Board Independence 0.1053 0.1673 -0.3131 -0.0292 0.0629 1.0000

(0.00) (0.00) (0.00) (0.00) (0.00)

Board Size 0.0700 0.1635 -0.3176 0.0022 0.0979 0.8688 1.0000

(0.00) (0.00) (0.00) (0.79) (0.00) (0.00)

Duality 0.0650 0.1204 -0.1167 0.2189 0.2073 0.4612 0.4853 1.0000

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Book Value 0.0094 0.1906 0.0440 -0.0714 0.1094 0.2200 0.2681 0.1897 1.0000

(0.26) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Leverage 0.1020 0.1090 0.1113 -0.0694 0.0223 -0.0292 0.0015 0.0591 0.2097 1.0000

(0.00) (0.00) (0.00) (0.00) (0.01) (0.00) (0.86) (0.00) (0.00)

State law 0.1475 0.2083 0.0307 -0.0694 -0.0198 0.0028 -0.0072 0.0113 0.0676 0.0656 1.0000

(0.00) (0.00) (0.00) (0.00) (0.02) (0.74) (0.40) (0.18) (0.00) (0.00)

Delaware Incorporation -0.1099 -0.1116 0.0702 -0.0673 -0.0345 -0.0205 -0.0650 -0.0219 0.0438 0.0358 0.2685 1.0000

(0.00) (0.00) (0.00) (0.00) (0.00) (0.02) (0.00) (0.01) (0.00) (0.00) (0.00)

Tobin's Q -0.1555 -0.1355 -0.0002 0.0572 -0.0762 0.0123 0.0289 0.0143 -0.0580 -0.1607 -0.0493 0.0432 1.0000

(0.00) (0.00) (0.98) (0.00) (0.00) (0.14) (0.00) (0.09) (0.00) (0.00) (0.00) (0.00)

39

Table III Descriptive statistics of corporate control transactions

Initial bid deal characteristics for our sample of firms are reported below. % of Firms Targeted

represents the proportion of firms in our sample that receive a takeover bid. Initial Bids Completed

is an indicator variable that is set to one if the initial bid is completed, and zero otherwise.

Transaction Value is the deal dollar value as reported by SDC Platinum. Target run-up is the

summation of cumulative abnormal returns over a ten day event window as defined in Appendix

A; Bid Premium is based on cumulative abnormal returns over an event window that begins (ends)

five days before (after) the initial bid announcement date and is further defined in Appendix A; All

Cash Bid is an indicator variable set to one if the method of payment is all cash, and zero otherwise;

All Stock Bid is an indicator variable set to one if the method of payment is all stock, and zero

otherwise; Mixed Payment is an indicator variable set to one if the method of payment has both a

stock and cash component, and zero otherwise. Toehold Flag is a binary variable set to one if the

acquiring firm has a toehold (defined as a holding of 5% or more at the bid announcement date),

and zero otherwise; Target Termination Fee is an indicator variable set to one if a target termination

agreement has been initiated between the bidder and target, and zero otherwise; Hostile Bid is set

to one if SDC flags the deal as hostile, and zero otherwise; Tender flag is a binary variable set to

one if the deal is a tender offer, and zero otherwise.

Mean Standard

Deviation

25th

Percentile Median

75th

Percentile

% of Firms Targeted 0.0486 0.2151

Initial Bids Completed 0.8054 0.3962

Transaction Value ($100 mil) 3.5380 7.1256 0.5329 1.3615 3.2524

Target Run-up 0.0304 0.1573 -0.0786 0.0163 0.1289

Bid Premium 0.2194 0.1697 0.0893 0.2030 0.3244

All Cash Bid 0.4520 0.4980

All Stock Bid 0.3867 0.4873

Mixed Payment 0.1613 0.3681

Toehold 0.0615 0.2403

Target Termination Fee 0.7875 0.4094

Hostile Bid 0.1076 0.3100

Tender 0.2407 0.4278

40

Table IV Comparative statistics between firms with relative low and high anti-takeover provisions.

In this table we report the univariate statistics for both the full sample of firms and the PeerMatchedDiff sample of firms. Low (high) Relative E-Index firms have an

estimated Relative E-Index that is below (above) the sample median. Similarly, low (high) E-Index firms have E-Index scores that are below (above) the median E-Index

for the full sample of firms. Variable definitions are provided in Appendix A.

Full Sample PeerMatchedDiff Sample

Low Relative E-Index High Relative E-Index Low E-Index High E-Index

Mean Median STD Mean Median STD diff Pvalue Mean Median STD Mean Median STD diff Pvalue

E-Index 1.28 1.00 0.77 3.25 3.00 0.75 1.97 0.00 0.70 1.00 0.46 2.79 3.00 0.83 2.09 0.00

State Law 0.95 1.00 0.21 0.96 1.00 0.20 0.00 0.42 0.96 1.00 0.21 0.94 1.00 0.23 -0.01 0.03

Delaware Inc. 0.61 1.00 0.49 0.61 1.00 0.49 0.00 0.90 0.60 1.00 0.49 0.57 1.00 0.50 -0.03 0.01

Leverage (Debt to assets) 0.22 0.20 0.19 0.23 0.22 0.18 0.02 0.00 0.21 0.19 0.20 0.20 0.19 0.18 -0.01 0.09

Book Value of Assets 7.27 7.05 1.57 7.30 7.18 1.33 0.03 0.18 7.30 7.03 1.66 7.30 7.17 1.33 0.00 0.98

Industry Concentration 0.08 0.06 0.07 0.08 0.06 0.06 0.00 0.49 0.08 0.06 0.06 0.08 0.06 0.06 0.00 0.18

CEO Tenure 6.61 4.00 7.02 6.48 4.00 7.34 -0.13 0.37 7.03 5.00 7.39 7.54 5.00 8.29 0.51 0.01

LN(CEO Cash Comp.) 7.42 6.87 2.06 7.39 6.94 1.84 -0.03 0.34 7.51 6.86 2.20 7.68 7.00 2.21 0.18 0.00

LN(CEO Age) 4.00 4.01 0.14 4.00 4.01 0.13 0.00 0.10 3.99 4.01 0.14 3.98 3.99 0.14 -0.01 0.02

Board Independence 0.54 0.64 0.31 0.57 0.67 0.30 0.02 0.00 0.51 0.60 0.31 0.46 0.56 0.31 -0.05 0.00

CEO Chair Duality 0.49 0.00 0.50 0.53 1.00 0.51 0.04 0.00 0.48 0.00 0.50 0.42 0.00 0.50 -0.05 0.00

LN(Board Size) 1.73 2.08 0.90 1.80 2.08 0.86 0.07 0.00 1.68 2.08 0.91 1.56 1.95 0.95 -0.12 0.00

Sales Growth 0.08 0.08 0.21 0.07 0.08 0.20 -0.01 0.15 0.08 0.08 0.23 0.08 0.08 0.21 0.00 0.63

Return on Assets 0.09 0.09 0.12 0.09 0.09 0.10 0.00 0.11 0.08 0.10 0.12 0.09 0.09 0.10 0.00 0.25

Free Cash Flow 0.02 0.04 0.14 0.02 0.04 0.13 0.00 0.11 0.01 0.04 0.16 0.02 0.04 0.12 0.01 0.03

LN(Market Capitalisation) 14.29 14.13 1.77 14.21 14.15 1.52 -0.08 0.01 14.34 14.13 1.88 14.30 14.22 1.50 -0.05 0.31

Block Holder Flag 0.21 0.00 0.40 0.20 0.00 0.40 0.00 0.80 0.22 0.00 0.42 0.22 0.00 0.41 -0.01 0.55

Firm Age 2.39 2.40 0.37 2.39 2.40 0.40 -0.01 0.25 2.37 2.40 0.37 2.35 2.40 0.40 -0.02 0.04

Tangible Assets 0.72 0.78 0.22 0.72 0.78 0.22 -0.01 0.13 0.73 0.79 0.22 0.73 0.80 0.22 0.00 0.96

Tobin's Q 1.84 1.37 1.47 1.62 1.27 1.26 -0.22 0.00 1.90 1.40 1.55 1.75 1.32 1.40 -0.16 0.00

41

Table V Multivariate results for firm value.

In this table we report OLS regression results for industry adjusted Tobin’s Q on our variables that capture unwarranted

provisions. The Relative E-Index and Relative E-Index Squared variables, used in regressions (3) and (4), proxy for

management attitude and are derived from the residual of equation (1) defined in section III.B. In regressions (5) and (6),

a one-to-one propensity score matching (PSM) procedure was used to construct a pool of peer-matched firms. Using

propensity scores derived from a logistic regression, low E-Index firms (E-Index is below the median E-Index) were

matched with high E-Index firms (E-Index is above the median E-Index). Firms with no successful match (i.e. the

difference in propensity scores exceeded a calliper of 0.01 standard deviations) were dropped from the sample. Using this

sample of peer-matched firms, we postulate that any remaining differences between low and high E-Index firms are due

to variations in management attitudes. Variable definitions are reported in Appendix A. Industry and year fixed effects

are included in all regressions. Robust standard errors clustered by industry, based on the Fama and French (1997) 12

industry classification scheme, are reported in parentheses. Significance levels are denoted by *, **, and *** for the 10%,

5% and 1% levels, respectively.

Standard Relative E-Index PeerMatchedDiff

(1) (2) (3) (4) (5) (6)

E-Index -0.0884*** -0.0877*** -0.0907*** -0.0974***

(0.0168) (0.0166) (0.0115) (0.0141)

E-Index Squared 0.0152 0.0117

(0.0113) (0.0086)

Relative E-Index -0.0703*** -0.0700***

(0.0167) (0.0168)

Relative E-Index Squared -0.0060

(0.0099)

Sales Growth 0.5068*** 0.5076*** 0.5231*** 0.5223*** 0.6616*** 0.6629***

(0.1021) (0.1021) (0.1028) (0.1029) (0.1134) (0.1135)

Return on Assets 4.3034*** 4.3001*** 4.3054*** 4.3056*** 4.6375*** 4.6332***

(0.3825) (0.3817) (0.3830) (0.3831) (0.3218) (0.3212)

Free Cash Flow -1.3555*** -1.3538*** -1.3625*** -1.3628*** -1.3998*** -1.3978***

(0.1922) (0.1918) (0.1925) (0.1926) (0.2194) (0.2189)

Leverage (Debt to assets) -0.4876*** -0.4845*** -0.5229*** -0.5225*** -0.6944*** -0.6953***

(0.1473) (0.1474) (0.1474) (0.1475) (0.1165) (0.1162)

Ln(Market Cap) 0.1915*** 0.1908*** 0.1919*** 0.1923*** 0.1967*** 0.1963***

(0.0169) (0.0168) (0.0170) (0.0170) (0.0122) (0.0122)

Block holder 0.0857 0.0810 0.0879 0.0897 0.0252 0.0231

(0.0836) (0.0835) (0.0840) (0.0839) (0.0871) (0.0871)

Firm Age -0.2999*** -0.2975*** -0.3026*** -0.3035*** -0.3263*** -0.3264***

(0.0673) (0.0671) (0.0676) (0.0675) (0.0526) (0.0525)

Asset Tangibility 0.4692*** 0.4667*** 0.4700*** 0.4718*** 0.3473*** 0.3474***

(0.1029) (0.1025) (0.1029) (0.1028) (0.0694) (0.0694)

Industry Concentration -0.5715* -0.5781* -0.5415* -0.5368* -0.5114** -0.5085**

(0.3065) (0.3071) (0.3056) (0.3058) (0.2426) (0.2425)

Intercept -2.4962*** -2.7138*** -2.6949*** -2.6910*** -2.4293*** -2.5835***

(0.2943) (0.3035) (0.3023) (0.3023) (0.2239) (0.2243)

Industry Fixed Effects Yes Yes Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes Yes Yes

Number of Observations 11,989 11,989 11,989 11,989 5,734 5,734

Adjusted R2 0.2641 0.2646 0.2610 0.2610 0.2835 0.2836

42

Table VI Multivariate results for the probability of being a takeover target.

In this table we report the results of probit regressions that model the likelihood of a firm being targeted. The dependent

variable in models (1) – (3) is set to one if during the year a firm is subject to a takeover contest, and zero otherwise. The

Relative E-Index and Relative E-Index Squared variables, used in regressions (3) and (4), proxy for management attitude

and are derived from the residual of equation (1) defined in section III.B. In regressions (5) and (6), a one-to-one

propensity score matching (PSM) procedure was used to construct a pool of peer-matched firms. Using propensity scores

derived from a logistic regression, low E-Index firms (E-Index is below the median E-Index) were matched with high E-

Index firms (E-Index is above the median E-Index). Firms with no successful match (i.e. the difference in propensity

scores exceeded a calliper of 0.01 standard deviations) were dropped from the sample. Using this sample of peer-matched

firms, we postulate that any remaining differences between low and high E-Index firms are due to variations in

management attitudes. Variable definitions are reported in Appendix A. Industry and year fixed effects are included in

all regressions. Robust standard errors clustered by industry, based on the Fama and French (1997) 12 industry

classification scheme, are reported in parentheses. Significance levels are denoted by *, **, and *** for the 10%, 5% and

1% levels, respectively.

Standard RelativeE-Index PeerMatchedDiff

(1) (2) (3) (4) (5) (6)

E-Index 0.0148 0.1015 0.0368*** 0.0586***

(0.0104) (0.0619) (0.0128) (0.0097)

E-Index Squared -0.0180 -0.0335**

(0.0132) (0.0135)

Relative E-Index 0.0208** 0.0239**

(0.0099) (0.0098)

Relative E-Index

Squared -0.0182**

(0.0090)

Sales Growth 0.0140 0.0148 0.0118 0.0112 -0.0512 -0.0514

(0.1603) (0.1589) (0.1617) (0.1603) (0.1435) (0.1420)

Return on Assets -0.7245** -0.7301** -0.7238** -0.7254** -1.2524*** -1.2444***

(0.2997) (0.2985) (0.2999) (0.2966) (0.2586) (0.2540)

Free Cash Flow 0.5799** 0.5856** 0.5791** 0.5812** 0.9841*** 0.9854***

(0.2629) (0.2650) (0.2622) (0.2634) (0.3185) (0.3190)

Leverage 0.1054 0.1054 0.1082 0.1104 0.0006 0.0012

(0.1306) (0.1309) (0.1290) (0.1302) (0.1739) (0.1728)

Ln(Market Cap) -0.1068*** -0.1064*** -0.1070*** -0.1066*** -0.1115*** -0.1118***

(0.0128) (0.0128) (0.0130) (0.0133) (0.0204) (0.0206)

Block Holder 0.8201*** 0.8222*** 0.8180*** 0.8225*** 0.9111*** 0.9119***

(0.1092) (0.1080) (0.1086) (0.1079) (0.1807) (0.1781)

Firm Age -0.1534*** -0.1548*** -0.1505*** -0.1520*** -0.1942** -0.1928**

(0.0533) (0.0537) (0.0540) (0.0545) (0.0868) (0.0867)

Asset Tangibility 0.0294 0.0318 0.0290 0.0342 0.1005 0.0993

(0.1882) (0.1888) (0.1880) (0.1901) (0.2742) (0.2741)

Industry Concentration -0.1618 -0.1460 -0.1654 -0.1515 -0.1188 -0.1280

(0.5846) (0.5798) (0.5828) (0.5853) (0.6582) (0.6680)

Intercept -0.7167*** -0.7946*** -0.6834*** -0.6633*** -0.6734 -0.5501

(0.2503) (0.2510) (0.2462) (0.2467) (0.5083) (0.4992)

Industry Fixed Effects Yes Yes Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes Yes Yes

Number of Observations 11,989 11,989 11,989 11,989 5,734 5,734

Pseudo R2 0.0578 0.0584 0.0580 0.0584 0.0736 0.0751

43

Table VII. An instrumental variables approach for the probability of being a takeover target.

In this table we report the result for takeover likelihood and Relative E/Relative E2 where Relative E/Relative E2 is instrumented by the Sharkansky Score to address endogeneity

concerns. For the Relative E-Index, OLS first stage regression results are reported in models (1) and (3) where the dependent variables are Relative E and Relative E2, respectively. The

predicted values for Relative E and Relative E2 (derived from models (1) and (3)) then enter as independent variables in probit models (2) and (4) to instrument for Relative E and

Relative E2, respectively. The dependent variable used in models (2) and (4) is binary and set to one if the firm is targeted and zero otherwise. For the PeerMatchedDiff approach, first

stage OLS regression results are reported in models (5) and (7). The predicted values of E-Index and E-Index2 then enter as Instrumented Relative E and Instrumented Relative E2 in

probit models (6) and (8). The dependent variable in models (6) and (8) is binary and set to one if the firm is targeted and zero otherwise.

Relative E-Index PeerMatchedDiff

(1) (2) (3) (4) (5) (6) (7) (8)

Dependent Variable: Relative E-Index target(0,1) Relative E-Index2 target(0,1) E-Index target(0,1) E-Index2 target(0,1)

Sharkansky Score IV 0.0445*** 0.0407***

(0.0067) (0.0094)

(Predicted Relative E-Index)2 IV 0.6059*** 0.9897***

(0.1267) (0.2892)

Instrumented Relative E-Index 0.6580* 0.6821* 0.6178*** 3.0106**

(0.3377) (0.3644) (0.1416) (1.1970)

Instrumented Relative E-Index2 -0.5937* -0.6947**

(0.3512) (0.3031)

Sales Growth 0.0069 0.0827 0.0154 0.0386 -0.0859 0.0092 -0.0767 -0.0541

(0.0645) (0.1389) (0.0947) (0.1561) (0.0848) (0.1100) (0.3706) (0.1683)

Return on Assets 0.5662*** -1.1738*** 0.3505 -1.0439** 0.3936* -0.8475** 0.5315 -0.8051

(0.1947) (0.4047) (0.2858) (0.4891) (0.2308) (0.3408) (1.0540) (0.5160)

Free Cash Flow 0.2728* 0.4673 -0.1863 0.3918 0.2455 0.4310 -0.0008 0.6898*

(0.1606) (0.3619) (0.2358) (0.4109) (0.1803) (0.3398) (0.8043) (0.4180)

Leverage 0.4411*** -0.0658 0.1949* 0.0736 0.0301 0.0035 0.1763 0.1398

(0.0741) (0.1916) (0.1086) (0.2253) (0.0972) (0.1277) (0.4192) (0.1981)

LN(Market Capitalization) 0.0247 -0.1534* -0.2256*** -0.2820** -0.1286** -0.0523 -0.1499 -0.2235*

(0.0388) (0.0845) (0.0565) (0.1185) (0.0549) (0.0956) (0.2853) (0.1289)

Block Holder -0.1182*** -0.0297 0.0707*** 0.0206 -0.0160 -0.0717** 0.0271 -0.0954***

(0.0082) (0.0467) (0.0121) (0.0597) (0.0113) (0.0296) (0.0493) (0.0263)

Firm Age 0.0255 0.7660 -0.0193 0.7770 0.0262 0.5153** 0.2709 0.9610***

(0.0325) (1.0655) (0.0475) (1.1398) (0.1092) (0.2575) (0.4668) (0.3177)

Asset Tangibility -0.0978 0.1867 -0.2966*** 0.0519 0.0472 -0.0873 0.2693 0.0858

(0.0604) (0.1511) (0.0888) (0.1910) (0.0951) (0.1287) (0.4104) (0.2111)

Industry Concentration 0.1644 -0.5203 0.6552** -0.0819 0.0092 -0.2275 -0.1909 -0.4607

(0.2014) (0.4280) (0.2959) (0.5779) (0.2710) (0.3778) (1.1582) (0.5572)

Intercept 1.2706*** 0.6580 0.9611*** -1.2985 1.9350*** -1.5011*** 1.3483 -2.6867**

(0.1592) (0.3377) (0.2226) (1.3889) (0.2534) (0.4509) (1.7018) (1.2954)

Number of Observations 9,293 9,293 9,293 9,293 5,451 5,451 5,451 5,451

44

Table VIII. Results for the probability of being a takeover target using propensity score matching.

In this table we report the probit model regression results using the propensity score matching procedure. The dependent

variable is binary and set to one if the firm is targeted, and zero otherwise. We match each target firm with three non-

target firms. All variables are defined in Appendix A. Significance levels are denoted by *, **, and *** for the 10%,

5% and 1% levels, respectively.

Relative E-Index PeerMatchedDiff

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

Relative E-Index 0.0367** 0.0420**

(0.0146) (0.0165)

Relative E-Index Squared -0.0285**

(0.0118)

E-Index 0.0354 0.2331***

(0.0248) (0.0608)

E-Index Squared -0.0473***

(0.0159)

Sales Growth -0.1034 -0.1057 -0.1502 -0.1533

(0.2191) (0.2171) (0.2645) (0.2598)

Return on Assets -0.5516 -0.5654 -1.0230*** -1.0385***

(0.5156) (0.5094) (0.3729) (0.3435)

Free Cash Flow 0.3115 0.3293 0.5618* 0.5893*

(0.4455) (0.4507) (0.3189) (0.3209)

Leverage 0.0605 0.0619 0.1707 0.1777

(0.1697) (0.1741) (0.2340) (0.2380)

Ln(Market Cap) -0.0089 -0.0095 -0.0043 -0.0072

(0.0137) (0.0139) (0.0161) (0.0167)

Block holder 0.1468*** 0.1637*** 0.1534** 0.1748***

(0.0431) (0.0430) (0.0647) (0.0649)

Firm Age 0.0059 0.0047 -0.0200 -0.0116

(0.0633) (0.0659) (0.0992) (0.0943)

Asset Tangibility 0.0049 0.0102 -0.0322 -0.0206

(0.2701) (0.2705) (0.4041) (0.4043)

Industry Concentration 0.1383 0.1661 -0.0479 -0.0336

(0.7105) (0.7213) (1.0962) (1.1021)

Intercept -0.5969** -0.5610** -0.6088 -0.7485

(0.2404) (0.2382) (0.5646) (0.5561)

Industry Fixed Effects Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes

Number of Observations 2,160 2,160 1,001 1,001

Pseudo R2 0.0048 0.0058 0.0070 0.0096

45

Table IX Probability of experiencing a hostile bid reception

In this table we report the 2nd stage Heckman Probit Model results where the dependent variable is probability of

receiving a hostile bid (as defined by SDC). To control for the likelihood of a firm being initially targeted, we model

the likelihood of receiving a takeover bid in the 1st stage regression. The first stage regressions are reported in Table VI

and align with the model numbers given below. To ensure the 2nd stage regressions are correctly identified, deal-specific

characteristics are included in the second stage regressions (Toehold, All Cash Payment and Tender Bid) but not in the

first stage.

Standard Relative E-Index PeerMatchedDiff

(1) (2) (3) (4) (5) (6)

E 0.0924 0.4242 0.2018** 0.4877***

(0.2008) (0.3498) (0.0901) (0.1422)

E Squared -0.0676 -0.0646**

(0.0713) (0.0291)

Relative E-Index 0.1244* 0.1317**

(0.0646) (0.0642)

Relative E-Index Squared -0.0228

(0.0445)

Toehold 1.1558 1.0168** 1.0114** 0.9809** 1.4101*** 1.4588***

(1.2072) (0.4055) (0.4528) (0.3924) (0.4552) (0.4962)

All Cash Payment 0.3083 0.2622* 0.2607 0.2442* -0.1562 -0.1465

(0.2958) (0.1438) (0.1598) (0.1314) (0.2823) (0.2919)

Tender Bid 0.2418 0.1951 0.2124* 0.2027 0.3153*** 0.3018***

(0.3917) (0.1274) (0.1212) (0.1236) (0.1223) (0.1084)

Sales Growth -0.2783 -0.2183 -0.2627 -0.2508 -0.4878 -0.4654

(0.2369) (0.3321) (0.3626) (0.3384) (0.6147) (0.6263)

Return on Assets -2.4534 -2.7143*** -2.5970** -2.5827*** -2.0361* -2.0755*

(6.2369) (0.9864) (1.0130) (0.9631) (1.0673) (1.0803)

Free Cash Flow 0.5852 0.9287 0.8146 0.8489 0.5992 0.5726

(3.1442) (0.7452) (0.7402) (0.7282) (0.9321) (0.9664)

Leverage 0.7629 0.6914* 0.7429 0.7187* 0.7235 0.6989

(1.1957) (0.4091) (0.4610) (0.4136) (0.8265) (0.8202)

LN(Mkt Cap) 0.1511 0.0566 0.0675 0.0548 0.0385 0.0320

(0.4958) (0.0917) (0.1175) (0.0939) (0.0853) (0.0845)

Block Holder -1.7869 -0.9661 -1.0625 -0.9405 -0.7854* -0.7872*

(2.8047) (0.8090) (1.0137) (0.8019) (0.4514) (0.4574)

Asset Tangibility -1.1542 -0.9474*** -0.9959** -0.9359*** 0.1583 0.1364

(0.9356) (0.3293) (0.4164) (0.2937) (0.5524) (0.5372)

Industry Concentration 0.6603 0.5251 0.4098 0.4106 0.5415 0.4388

(0.6527) (0.8834) (0.7958) (0.7856) (1.6802) (1.6691)

Intercept -1.4674 -2.6803*** -2.1790* -2.1833** -5.1245*** -5.2422**

(11.1227) (0.9603) (1.1527) (1.0501) (1.9474) (2.0644)

Industry Fixed Effects Yes Yes Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes Yes Yes

Number of Observations 11,989 11,989 11,989 11,989 5,734 5,734

Log Pseudo likelihood -2392.505 -2391.325 -2391.489 -2391.347 -1086.838 -1084.923

46

Table XII Multivariate results for bid premiums and takeover contest returns using the two-stage

Heckman selection model

In this table we report the results for examining bid premiums and takeover contests from using the two-stage Heckman

selection model to control for self-selection bias. The dependent variable is initial bid premium as defined in Appendix A.

Ln(MktCapt-42) is the market capitalization of the target firm 42 days prior to the initial bid announcement date. All other

variables are defined in Appendix A. The inverse Mills ratio is derived from the first stage probit regression that estimates

the likelihood of a takeover bid materializing (see Table VII). In the second stage OLS regressions (reported below), the

inverse Mills ratio is included to ameliorate the selection bias. Significance levels are denoted by *, **, and *** for the

10%, 5% and 1% levels, respectively.

Standard Relative E-Index PeerMatchedDiff

(1) (2) (3) (4) (5) (6)

E-Index 0.8195 0.9572 2.1752** 2.1037*

(0.6062) (0.5986) (0.9236) (1.1199)

E-Index Squared -0.6083 0.0595

(0.4075) (0.5820)

Relative E-Index 0.8375 0.9826

(0.6231) (0.6303)

Relative E-Index Squared -0.4309

(0.4609)

LN(Mktcapt-42) -1.6326 -1.6255 -1.6096 -1.6112 -3.5042** -3.4871**

(1.3589) (1.3623) (1.2178) (1.3437) (1.6674) (1.6697)

Target Run-up -0.1130** -0.1149** -0.1123** -0.1162** -0.1652** -0.1631**

(0.0500) (0.0498) (0.0467) (0.0499) (0.0763) (0.0762)

Tangible Assets -0.4476 -0.4257 -0.6851 -0.6736 3.2111 3.2717

(3.7947) (3.8003) (3.7596) (3.7659) (5.3807) (5.4447)

Industry Concentration 0.5395 1.6845 -0.2563 0.4276 5.9693 5.9196

(11.5051) (11.3717) (11.5640) (11.3843) (15.1083) (15.0317)

Free Cash Flow 0.0260 0.5077 -0.2112 -0.0142 -1.4653 -1.5614

(6.1302) (6.1719) (5.8868) (6.1299) (9.8231) (9.8447)

Hostile Bid -1.8665 -1.9938 -1.9154 -1.8964 -0.9850 -0.9635

(1.9946) (1.9958) (2.2180) (1.9933) (2.8890) (2.9145)

Toehold -0.0509 -0.0476 -0.0490 -0.0448 0.0735 0.0715

(0.1083) (0.1094) (0.1339) (0.1090) (0.1168) (0.1166)

All Cash 6.5721*** 6.6596*** 6.5028*** 6.5018*** 4.1066* 4.1470*

(1.6338) (1.6304) (1.5689) (1.6325) (2.1451) (2.1486)

Public Acquirer 4.5429*** 4.8388*** 4.5843*** 4.7522*** 6.6929*** 6.6423***

(1.5213) (1.5253) (1.4860) (1.5284) (2.1555) (2.1612)

Block Holder 6.0698 5.6047 5.9553 5.7005 7.1534 7.2129

(9.4670) (9.5838) (9.6755) (9.5125) (10.7909) (10.8994)

Inverse Mills Ratio -2.0975 -2.8714 -2.1416 -2.5912 6.4647 6.5455

(10.6504) (10.6670) (9.9975) (10.5521) (11.6965) (11.8203)

Intercept 37.2168** 41.9510** 39.3208** 41.1887** 40.0211** 43.2523**

(17.4601) (17.1514) (17.9427) (17.1315) (20.2097) (20.2134)

Number of Obs. 604 604 604 604 284 284

R2 0.1662 0.1705 0.1661 0.1678 0.2882 0.2886

Adjusted R2 0.1179 0.1209 0.1178 0.1181 0.1942 0.1914

47

Table X Firm value and Classified boards

OLS regressions modelling the relationship between firm value and the classified board provision are

reported below. The dependent variable is industry adjusted Tobin’s Q. In model (1) the full sample of

firms are used. In models (2) and (3) we group firms into low and high Relative E-Index firms,

respectively. If the Relative E-Index of a given firm is less than the industry median Relative E-Index,

it is defined as a Low Relative E-Index firm. Similarly, if the Relative E-Index of a given firm is greater

than the industry median Relative E-Index, it is defined as a High Relative E-Index firm. Standard errors

are clustered by industry.

Full Sample Low Relative E-Index High Relative E-Index

(1) (2) (3)

Classified Board -0.1083** 0.0227 -0.1521*

(0.0462) (0.0638) (0.0846)

Sales Growth 0.3488*** 0.4870*** 0.1423

(0.1168) (0.1690) (0.1185)

Return on Assets 5.9554*** 6.4363*** 5.4580***

(0.4635) (0.6363) (0.6037)

Free Cash Flow -1.7512*** -2.1413*** -1.2973***

(0.2572) (0.4261) (0.2501)

Leverage -0.8186*** -0.7689*** -0.8636***

(0.1656) (0.2328) (0.1803)

LN(Market Capitalization) 0.1819*** 0.1801*** 0.1787***

(0.0168) (0.0225) (0.0206)

Block holder 0.0388 0.1528 -0.1712

(0.1412) (0.1840) (0.2076)

Firm Age -0.2674*** -0.3778*** -0.2003**

(0.0785) (0.1308) (0.0803)

Asset Tangibility 0.3918*** 0.5375*** 0.2735**

(0.1079) (0.1629) (0.1207)

Industry Concentration -0.5506 -0.4702 -0.5441

(0.3350) (0.5118) (0.3331)

Intercept -2.4916*** -2.5455*** -2.2221***

(0.3316) (0.4544) (0.4162)

Industry Fixed Effects Yes Yes Yes

Year Fixed Effects Yes Yes Yes

Number of Observations 9,412 4,588 4,824

R2 0.3316 0.3442 0.3286

Adjusted R2 0.3294 0.3397 0.3242

48

Table XI Takeover Likelihood and Classified Boards

Probit regressions modelling the likelihood of a firm being targeted are report below. The dependent

variables are set to one if the firm is targeted in a given year, and zero otherwise. In model (1) the full

sample of firms are used. In models (2) and (3) we group firms into low and high Relative E-Index

firms, respectively. If the Relative E-Index of a given firm is less than the industry median Relative E-

Index, it is defined as a Low Relative E-Index firm. Similarly, if the Relative E-Index of a given firm is

greater than the industry median Relative E-Index, it is defined as a High Relative E-Index firm.

Full Sample Low Relative E-Index High Relative E-Index

(1) (2) (3)

Classified Board -0.0708* -0.0675 -0.2182***

(0.0417) (0.0833) (0.0427)

Sales Growth 0.0246 0.1304 -0.1189

(0.1424) (0.1829) (0.1334)

Return on Assets -0.8135** -0.5953* -1.0084**

(0.3373) (0.3572) (0.4114)

Free Cash Flow 0.6194 0.4714 0.7805***

(0.3914) (0.5712) (0.2941)

Leverage 0.1763 0.1092 0.2797**

(0.1389) (0.2180) (0.1176)

Ln(Market Capitalisation) -0.1142*** -0.1223*** -0.1033***

(0.0148) (0.0198) (0.0210)

Block holder 0.7494** 0.6179** 3.6768***

(0.2910) (0.2852) (0.1196)

Firm Age -0.1864*** -0.2316*** -0.1577***

(0.0421) (0.0893) (0.0424)

Asset Tangibility 0.0525 -0.0382 0.0895

(0.2172) (0.2672) (0.2472)

Industry Concentration -0.3923 -1.3008*** 0.2046

(0.4924) (0.5030) (0.5897)

Intercept -0.3874 0.0773 -3.4935***

(0.4758) (0.5516) (0.4323)

Industry Fixed Effects Yes Yes Yes

Year Fixed Effects Yes Yes Yes

Number of Observations 9,309 4,589 4,720

Pseudo R2 0.0639 0.0819 0.0599

49

Appendix Variable Definitions

Except where noted, all variables are constructed using one year lagged values to mitigate issues

associated with a look-ahead bias. All variables are also winsorized at the 1% and 99% levels.

Variable Name Definition

Firm-level Control Variables

Book Value Natural logarithm of total assets.

Ln(Market Capitalization) Natural logarithm of market capitalization, where market

capitalization is based on the average monthly market cap over

the current fiscal year.

Sales Growth Percentage increase in sales over the previous fiscal year.

Return On Assets Earnings before interest and tax (EBIT) divided by total assets.

Free Cash Flow Summation of net income plus depreciation and amortization

minus capital expenditure, divided by total assets.

Intangible Assets One minus the ratio of property, plant and equipment to total

assets.

Leverage Book value of debt divided by book value of assets.

Tobin’s Q Market value of assets divided by book value of assets, where

market value of assets is defined as book value of assets minus

book value of equity plus the market value of common stock at

the fiscal year end.

Firm Age The number of years that have elapsed since first appearing on

Compustat.

Industry Concentration Based on the Herfindahl-Hirschman Index.

Blockholder An indicator variable set to one if an institutional investor holds

5% or more of a company’s common stock, and zero otherwise.

CEO and Chair Characteristics

Ln(Cash Compensation) Defined as the natural logarithm of a CEO’s cash (salary +

bonus) compensation for a given fiscal year.

CEO Chair Duality Indicator variable set to one if the CEO is also the chairperson in

the current fiscal year, and zero otherwise.

CEO Age CEO Age is reported on Execucomp. If CEO Age is missing,

Capital IQ is then used to fill in missing observations (where

possible).

CEO Tenure CEO Tenure is a count variable where it represents the number

of years a CEO has been at a given firm. This variable is

constructed using data from Execucomp and Capital IQ.

50

Board Size Board size is a count of all directors on a firm’s board in a given

fiscal year.

Board Ownership Percentage of outstanding shares owned by the board of directors

in the current fiscal year.

Board Independence Percentage of board members that are classified as independent

directors in the current fiscal year.

Missing Board Flag Indicator variable set to one if board data is missing in the current

fiscal year, and zero otherwise.

Deal Characteristics

All Cash Indicator variable set to one if method of payment is all cash, and

zero otherwise.

All Stock Indicator variable set to one if method of payment is all stock,

and zero otherwise.

Mixed Payment Indicator variable set to one if method of payment has both a

cash and stock component, and zero otherwise.

Toehold Indicator variable set to one if the bidding firm owns 5% or more

of the target firm’s common stock on the bid announcement date,

and zero otherwise.

Target Termination Fee Indicator variable set to one if a target termination payment is

applicable, and zero otherwise.

Hostile Bid Indicator variable set to one if the bidder is hostile (based on

SDC’s classification), and zero otherwise.

Tender Indicator variable set to one if the deal is a tender offer, and zero

otherwise.

Target Run-Up

This is defined as the summation of cumulative abnormal returns

over a ten day event window starting 11 days prior to the initial

target bid announcement. The market model parameters that are

used to infer abnormal returns is estimated using a 100-day time

period beginning 152 days prior to the initial announcement of a

takeover bid. For more details regarding the event study

methodology used in this paper, please refer to MacKinlay

(1997).

Bid Premium

Target firm bid premiums are based on cumulative abnormal

returns over an event window that begins (ends) five days before

(after) the initial bid announcement date. The time period we use

to estimate the market model parameters begins (ends) 152 days

(42 days) prior to the initial bid announcement date. For more

details regarding the event study methodology used in this paper,

please refer to MacKinlay (1997).