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How Much is Too Much? Large Termination Fees and Target Distress

Jordan Neyland1 University of Melbourne and

Financial Research Network (FIRN) jordan.neyland@unimelb.edu.au

Chander Shekhar

University of Melbourne and Financial Research Network (FIRN)

c.shekhar@unimelb.edu.au

August 2016

We provide evidence that large termination fees mitigate contracting problems in acquisitions of targets with high information asymmetry. Large fees are more common if targets face financial constraints or distress. Deals with large termination fees are more likely to attract a competing bid, consistent with large fees allowing acquirers to recover bidding costs when facing a high risk of bid failure. We correct for the endogenous selection of large termination fees and present evidence that managers negotiate large fees in exchange for higher premiums. This contrasts prior evidence that suggests large fees result from managerial self-interest and harm target shareholders.

Keywords: merger; acquisition; termination fee; information asymmetry

JEL codes: G30, G34, G38

1 Corresponding author. Faculty of Business and Economics at The University of Melbourne, 198 Berkeley St., Carlton, Victoria, Australia 3010, phone: +61 3 9035 3763, fax: +61 3 8344 6914. For helpful comments, we thank Gil Aharoni, Anna Faelten, Michael Keefe, Lubo Litov, Antonio Macias, Spencer Martin, Nguyet Nguyen, Ronan Powell, Alireza Touani-Rad, and David Yermack, as well as participants at the 2013 FMA meetings, the 2013 Auckland Finance Meeting, the 2014 Conference on Empirical Legal Studies at UC Berkeley, and the 2014 Australasian Finance and Banking Conference, as well as Mengchen Yang for research assistance.

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1. Introduction

How much power should a board have to resist an unwanted takeover bid? Acquirers

often secure merger agreements with contractual provisions that protect the deal from

competing bidders. One such provision, a termination fee, promises a payment to the acquirer

if the target breaks the agreement. This payment increases the cost of terminating the deal,

which provides an opportunity for self-interested managers to secure an agreement that

provides personal benefits and inhibits higher value competing bids. In spite of the potential

harm to shareholders, termination fees are common, found in over 90% of bids. Proponents

suggest these fees allow acquirers to recover their bidding costs after termination, which

provides incentive to sink these costs and make a formal offer.2

Prior research provides evidence on whether termination fees are harmful or

beneficial to target shareholders. Bates and Lemmon (2003) and Officer (2003) find that

acquirers are willing to pay a higher price to target shareholders in exchange for the greater

certainty that termination fees provide. However, Jeon and Ligon (2011) suggest that larger

fees (measured as a percentage of deal value) indicate agency problems. The 2014 Comverge

case in the Delaware courts epitomizes this criticism. In light of a low bid price and

allegations that management received favorable employment packages, the court argued that

a fee of 7% of deal value, in addition to other payments, could create an unreasonable barrier

to competing bidders, breaching the target directors’ duties to shareholders.3 However,

Comverge’s management contended that they faced severe liquidity constraints and had no

alternative offers, which created a “perfect storm” that resulted in such extreme negotiation

outcomes.

2 Statistic is from 2011 data. By comparison, other contractual provisions such as earn-outs, lock ups, toeholds, and go shop provisions are present in only about 1%, 0%, 5%, and 10% of sample bids in 2011. 3 In re Comverge, Inc. Shareholders Litigation, 2014 WL 6686570 (Del. Ch. Nov. 25, 2014).

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In this paper we examine the role of large termination fees in acquisitions,

specifically, whether such fees are motivated by managerial self-interest or the desire to

attract the best offer for target shareholders. We posit that large fees aid in the acquisition of

targets facing severe problems of information asymmetry, as proxied by target distress or

financial constraints. We focus on these acquisitions, because bidders must invest more

relative to the size of the target to learn about the viability of the target’s assets, uncover any

undisclosed liabilities, and investigate the claims of other stakeholders on the target’s assets.

Potential acquirers would be hesitant to sink these substantial costs without the promise of

recovery provided by a termination fee.

We find that distress and financial constraints are significant determinants of the

choice of large termination fees, defined as fees above 6% of deal value.4 Targets are more

likely to use large termination fees if they are distressed based on CHS scores, Ohlson’s O-

scores, interest coverage ratios, and debt ratios. Large fees are also more likely when the

target suffers from poor performance, as measured by equity returns, free cash flow, or ROA.

Similarly, large termination fees are more common in deals for targets that score poorly on

common measures of financial constraints—the Kaplan-Zingales index, Whited-Wu index,

and SA index. These results are consistent with large fees helping attract bids for targets with

high information-gathering costs.

The alternative view suggests that agency problems dictate the use of large fees in

acquisitions. Such problems are heightened in firms with financial difficulties, as managers

often have poor incentive alignment due to lower pay, out-of-the-money options, and

increased turnover risk (Hotchkiss, John, Mooradian, and Thorburn, 2008). Due to the

4 We distinguish between small and large termination fees as fees less than and greater than 6% of deal value, respectively. Our choice of 6% is motivated by prior literature, and our results are generally robust to 5% and 10% definitions. Bates and Lemmon (2003) use a 10% cut-off to study “jumbo” fees. Jeon and Ligon (2011) examine “high” fees in the top third of the distribution, and courts and legal practitioners suggest fees above 6% are unreasonably large (Panagopoulos, 2005).

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increased incentive problems, Roosevelt (2000) posits that managers of insolvent targets have

greater incentives to use termination fees to secure agreements with personal benefits, such as

severance payments or employment with the acquirer.5

We test for the impact of agency problems in several ways. First, we re-examine the

relation between bid premiums and large termination fees found in prior research. Bates and

Lemmon (2003) and Jeon and Ligon (2011) show a large and significant negative association

in the cross section between large termination fees and bid premiums, consistent with

managers accepting lower premiums in exchange for personal benefits. We then examine the

endogenous nature of this relation and correct for the endogeneity using Delaware

incorporation as an instrument. After this correction, we find a positive, albeit insignificant,

relation between premiums and large fees in two-stage least squares estimates. This result

suggests target shareholders do not fare worse in bids with large termination fees compared to

bids with smaller termination fees.

We next examine the impact of large termination fees on bid competition. While

termination fees increase the cost to the target of abandoning a merger agreement to accept a

competing bid, we find that large fee bids are more competitive on average. Targets in deals

with large fees have lower completion rates by 4.3% to 8.4%, and they are 2.9% to 6.5%

more likely to attract a challenging bid in multivariate analysis. This result suggests large fees

do not lock-in management-friendly bidders but is consistent with bidders contracting for

higher fees when competing bids are expected and bidding costs are high.

5 The Delaware courts also suggest that large termination fees outside of a “conventionally accepted range,” (Answers Corp.) result from agency conflicts, unreasonably restrict bid competition, violate managers’ duties to shareholders, and push the limits of deal protection beyond its “breaking point” (Phelps). In re Answers Corp. Shareholders Litigation, C.A. No. 6170-VCN (Del. Ch. Apr. 11, 2011); Phelps Dodge Corp. v. Cyprus Amax Minerals Co., C.A. No. 17398 (Del. Ch. Sept. 27, 1999). We also note that the Delaware courts generally take a permissive attitude toward smaller termination fees. In In re Cogent, Inc. Shareholder Litigation, 7 A. 3d 487 (Del. Ch. Oct. 05, 2010) the court stated “a termination fee of 3% is generally reasonable.” In In re Topps Co. Shareholders Litigation, 926 A.2d 50 (Del. Ch., 2007) the court stated a termination fee of 4.3% is not “likely to have deterred a bidder.”

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We also find no direct evidence of self-interested managerial bargaining on the part of

managers in large fee deals. Following Hartzell, Ofek, and Yermack (2004), we study the

personal benefits of managers by hand-collecting data on post-acquisition employment and

compensation for target managers in large fee bids and for a matched sample of target

managers in bids without large fees. Target managers that negotiate high fees are not more

likely to receive greater compensation or employment with the acquirer than a matched

sample. The lack of personal benefits is inconsistent with the idea that managers of distressed

targets have greater agency and incentive problems.

This paper contributes to the existing literature in several ways. Primarily, our results

contrast prior evidence that suggests large fees harm target shareholders. Rather, we show

large fees are common in the sale of distressed and constrained targets, i.e., deals in which the

costs of bidding are large relative to the size of the deal. This is consistent with prior research

showing that target managers use termination fees and, more generally, deal protections to

incentivize bidding and increase target managers’ bargaining power in negotiations.6 We

similarly add to a broader literature on the motivations for contractual devices and

antitakeover provisions, which posits that managerial self-interest or wealth maximization

can motivate defensive measures and restrictions on bid competition.7 We contribute to this

literature by showing that even seemingly excessive protections can be used to maximize

shareholder value and aid in the sale of a distressed target.

Our results also complement recent evidence on the motivations of acquisitions of

targets with financial constraints (Almeida, Campello, and Hackbarth, 2011; Erel, Jang, and

6 Prior literature on termination fees generally supports an efficient contracting hypothesis for typical fees but is critical of larger fees. See Bates and Lemmon (2003), Berkovitch and Khanna (1990), Coates and Subramanian (2000); Jeon and Ligon (2011). For evidence on efficient contracting with deal protections, see Bulow and Klemperer (2009), Cramton and Schwartz (1991), and Romano (1992). 7 For managerial self-interest, see Bebchuk and Cohen (2005), Bertrand and Mullainathan (2003), and Masulis, Wang, and Xie (2007). Bargaining power is analyzed in Bates, Becher, and Lemmon (2008), Comment and Schwert (1995), and Schwert (2000).

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Weisbach, 2014) and distress (Clark and Ofek, 1994; Hotchkiss and Mooradian, 1994; Meier

and Servaes, 2014), as well as a larger literature on the financial motivations of mergers.8

Due to the evidence that large fees aid in the reallocation of assets of troubled targets, our

results also complement prior literature that managers use contracts to overcome capital

market frictions (e.g. Chava and Roberts, 2008; Hoshi, Kashyap, and Scharfstein, 1990).

The paper proceeds as follows. In Section 2, we discuss related literature and develop

the hypotheses, followed by data selection and sample description in Section 3. Section 4

provides the multivariate results. Section 5 concludes the paper.

2. Related Literature and Hypotheses

Our focus on termination fees is motivated by prior studies on the role of information

gathering, bidding costs, and termination fees in acquisitions. Fishman (1988) and French and

McCormick (1984) predict that the costs of bidding, including the costs of investigating and

gathering information about a target, can deter potential acquirers from making a bid.

Berkovitch and Khanna (1990) show that target managers can induce bidding with defensive

strategies, such as termination fees. Under their model, a bidder must bear the costs

associated with search and learning about a potential target. Fraidin and Hanson (1994)

suggest that competing bidders, who observe an initial bid, gain information about the initial

bidder’s value of the target, and can bid at lower cost by free-riding on this information.

Hence, initial bidders are potentially disadvantaged relative to other bidders and may not

initiate bids for potentially valuable targets.

Termination fees compensate bidders for their sunk costs by promising a payment

from the target to the bidder in the event of a failed bid. This promise can induce bidding by

hesitant bidders reluctant to sink these costs, while also increasing the cost of a competing 8 Prior literature on the financial benefits of mergers includes Lewellen (1971), Smith and Kim (1994), Palepu (1986), and Devos, Kadapakkam, and Krishnamurthy (2009).

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bid, as rational target shareholders will not accept any rival bid unless it is higher than the

sum of the initial premium plus the cost of the termination fee. Due to their ability to limit

competing bids by increasing the minimum price, Cramton and Schwartz (1991) suggest

termination fees allow target managers to negotiate higher premiums in exchange for greater

certainty of bid completion. Bates and Lemmon (2003) and Officer (2003) find empirical

evidence that premiums paid to targets are higher and the probability of a competing bid is

lower in bids with termination fees. Boone and Mulherin (2007) report a positive relation

between the presence of termination fees and the pre-bid number of potential bidders

contacted, consistent with the threat of a competing bid motivating the use of termination

fees. This evidence suggests bidders trade-off higher premiums for the greater certainty that

they will ultimately acquire the target in the presence of free-riding competitors.

We suggest that similar motivations drive the use of large termination fees, although

the trade-off is nuanced due to the financial circumstances surrounding the sale of the firm.

For example, the sale of a distressed target likely includes several additional costs relative to

the sale of other firms, such as determining if the target is financially or economically

distressed, valuing complex liabilities (e.g., unfunded pensions), and negotiating with other

claimants on the firm’s cash flows. Related work by Lemmon, Ma, and Tashjian (2009)

shows that the degree of financial distress relative to economic distress of a firm can impact

the decision to redeploy the assets of distressed firms, and Gilson, Hotchkiss, and Osborn

(2015) find evidence that for firms in bankruptcy, senior creditors impact the decision to sell

assets of distressed firms, as creditors exert their influence to increase recovery rates.

Similarly, the costs of bidding are likely high for targets facing financial constraints.

Prior literature emphasizes the role financial, non-operational motivations for targets with

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insufficient capital.9 Almeida, Campello, and Hackbarth (2011) find that access to lines of

credit facilitate acquisitions of firms within the same industry that lack liquidity. Erel, Jang,

and Weisbach (2014) provide direct evidence on the link between finance and investment in

mergers and find that targets exhibit improved investment and decreased reliance on cash

flow for their financing needs after acquisitions. Because financial constraints result from an

inability of the firm to contract on its future cash flows due to information asymmetry, we

expect that bidders must invest relatively more to value a constrained target. With greater

uncertainty and higher costs of bidding, constrained targets likely face difficulty in attracting

bids. We formalize this intuition as follows:

H1: The incidence of large termination fees is higher in deals with targets

facing financial constraints or distress, due to the larger costs of bidding

associated with these deals.

Alternatively, extant research suggests the largest termination fees harm target

shareholders. Bates and Lemmon (2003) find a significant negative relation between

termination fees above 10% of deal value and bid premiums. Jeon and Ligon (2011) examine

the choice of the size of termination fees and find that the largest fees are associated with

lower premiums, suggesting that these fees result from managerial self-interest. For example,

Hartzell, Ofek, and Yermack (2004) study the personal benefits obtained by CEOs whose

firms are acquired. They find that target managers who receive special cash bonuses or

favorable employment contracts with the acquirer receive lower premiums, consistent with

self-interested target managers negotiating for personal benefits at the expense of target

shareholders. If self-interested target managers set termination fees sufficiently high, they

9 Lewellen (1971) posits that mergers can increase the merging partners’ ability to access financing and allows firms to invest in more positive net present value projects. Similarly, Myers and Majluf (1984) suggest that mergers can create value if an acquirer (target) with financial slack covers the investment of a “slack-poor” target (acquirer) by providing cash or increasing access to capital markets.

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could secure friendly bids and receive personal benefits while locking out higher value rival

bidders

Due to the potentially detrimental effects of large fees, Coates and Subramanian

(2000) suggest that “breakup fees above 3% of deal value should be given a particularly hard

look,” and Delaware courts have scrutinized high termination fees for their potential to harm

target shareholders. In the Phelps case, the court stated that a fee of 6.3% of deal value

pushed the definition of reasonableness beyond its breaking point.10 The Delaware Chancery

Court reaffirmed its position in Toys "R" Us, condemning the termination fee in the Phelps

case, as "a more than reasonably explicable barrier to a second bidder".11 Overall, large fees

receive criticism due to the perception that large fees limit bid competition by excessively

increasing the cost of terminating a deal.

Moreover, target distress can exacerbate agency conflicts from a lack of incentives.

Eckbo, Thorburn, and Wang (2014) find that bankruptcy is associated higher turnover risk

and large losses in compensation. They also find the likelihood of forced departure is higher

with stronger creditor rights.12 Gilson and Vetsuypens (1993) find that CEOs entering a debt

restructuring receive a substantial pay cut. Chen, Hill, and Ozkan (2014) find that new CEOs

of distressed firms in the U.K. have less experience, earn less total compensation and receive

lower equity compensation as a fraction of total compensation. Roosevelt (2000) argues that

the threat of managerial opportunism in an acquisition is higher in bankruptcy, as the value of

managers’ stock and options is underwater, and managers are not likely to gain much from

maximizing share price. Due to the decreased incentive alignment and reduced long-term

prospects for managers of distressed firms, we propose the following hypothesis:

10 Phelps Dodge Corp. v. Cyprus Amax Minerals Co., 1999 WL 1054255 (Del.Ch. Sept.27, 1999). 11 In re Toys "R" Us, Inc. Shareholder Litigation, 877 A.2d 975 (Del.Ch. Jun 22, 2005). 12 We expect agency issues between creditors and managers also impact merger negotiations as shareholders and creditors have differing incentives with respect to the firm (John and Senbet, 1998).

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H2: Large termination fees are positively related to the distress and financial

difficulties of the target, as managers face reduced incentives and negotiate

personal benefits from a preferred bidder.

Both hypotheses predict a positive relation between the presence of large termination

fees and target distress or financial constraints. The rationale underlying these hypotheses

motivates our study, and hence, we focus our empirical analysis on distinguishing whether

large fees result from agency conflicts or efficient contracting to determine the ultimate effect

on target shareholders.

3. Data and Summary Statistics

3.1. Data

Thomson Securities Data Corporation’s (SDC) merger and acquisition database

provides the sample of transactions announced between January 1989 and December 2011.

We limit our sample to deals after 1989, because Officer (2003) reports that termination fee

data is limited before 1988 in SDC. We restrict the SDC sample to bids with the following

restrictions. (1) The target is a U.S. public target. (2) The form of the deal is defined as a

“merger” or “acquisition” by SDC. (3) The status of the deal is either “completed” or

“withdrawn”. (4) The deal value must be equal to or greater than one million dollars. (5) The

percentage of shares held by a bidder 6 months prior to the announcement is less than 50%.

We also drop deals in which SDC identifies the acquirer as an “Investor”, “Investor Group”,

“Shareholders”, or “Creditors”, as well as deals in which the acquirer and the target share the

same parent.

These restrictions leave a sample of 8,742 bids. We require target firms to have non-

missing returns data from CRSP for the year preceding the merger and data for book assets

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from Compustat for the fiscal year preceding the merger announcement. These data

restrictions reduce the final sample to 6,732 deals.13 Table 1 contains descriptions of all

variables and details on their construction.

3.2. Descriptive statistics - termination fees

Table 2 reports statistics on the frequency and magnitude of target termination fees.

Panel A of Table 2 reveals that target termination fees are found in 59% (3,979 bids) of

sample bids (6,732 bids). Panel A also reveals significant variation in the magnitude of

termination fees. The median target termination fee is about 3.13% of deal value, but the 99th

percentile is as high as 14.65% of deal value.

We highlight the significance of the largest fees by comparison to other common

contractual provisions. Lockups, which are agreements for the target (bidder) to sell stock or

assets to the bidder (target) in the event that a bid fails, resemble termination fees, but target

and bidder lockups are only found in 10% and 2% of deals, respectively (unreported). Go

shop provisions, which can help ensure a competitive sale of the target, are found in about

only 2% of sample transactions. Earn-out agreements, which help overcome information

asymmetry and moral hazard in acquisitions, are only present in about 3.9% of transactions

(Cain, Denis, and Denis, 2011). With a frequency of over 90% in 2011, the most recent

sample year, termination fees rival the ubiquity of MAC clauses, which are found in about

99% of bids (Denis and Macias, 2013). By comparison, large termination fees, defined as

fees above 6% of deal value, comprise 3.3% (219 bids) of the 6,732 sample bids or 5.5% of

bids with termination fees (3,979 bids). 14

13 The actual sample size in regressions is reduced by data availability. In particular, missing observations for target free cash flow and acquirer returns reduce the sample size. 14 In our sample bidder termination fees are found in 17% (1,121 bids) of sample bids and also show significant variation with a median of about 3.04% and a 99th percentile of almost 17%. Although we control for the

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We further explore variation in fee size in Panel B of Table 2 with a focus on target

distress. In Panel B, we split our sample of transactions into two groups based on target firm

characteristics related to distress and financial constraints. First, we use the model developed

by Campbell, Hilscher, and Szilagyi (2008) to estimate the distress risk of target firms and

create a “CHS score” for each target. We classify the top CHS score decile of targets as “high

distress” and the bottom 90% of targets as “no distress”. We then compare the use of

termination fees across the two groups of targets.

We find that target termination fees are significantly larger in bids on distressed

targets. The average distressed target has a termination fee 2.57%, while the non-distressed

targets have a target fee equal to 1.96% of deal value, a relative increase of 31% (2.57/1.96),

which is statistically significant at the 1% level. The difference is even more striking if we

exclude deals without target termination fees. Distressed targets with non-zero termination

fees have an average fee size of 4.89%, compared to 3.27% for targets that are not distressed.

This is a relative increase of almost 50% (4.89/3.27) that is significant at the 1% level with a

t-statistic of -15.94.

We also segment targets into two groups by their level of financial constraints, as

proxied by the “SA index” from Hadlock and Pierce (2010). We group targets into the top

decile and bottom 90% by their SA index and compare the magnitude of termination fees

across the two groups in Panel B of Table 2. Similar to differences between healthy and

distressed targets, financially constrained targets contract with significantly larger termination

fees than unconstrained targets. The average constrained target agrees to a target termination

fee of 2.23% while the average unconstrained target agrees to a fee of only 2%, and this

difference is significant in t-tests at the 1% level. If we exclude deals without termination fees

presence of bidder fee in empirical analyses, a comprehensive study of these fees is beyond the scope of this paper.

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(i.e. a termination fee of zero), the results are more striking, as the average termination fee is

4.59% for constrained targets and 3.31% for unconstrained targets. This is an increase of

about 39% (4.59/3.31) for distressed targets, which is significant at the 1% level with a t-

statistic of -12.05. Bidder termination fees also vary with the target’s level of financial

constraints, although only excluding deals with bidder fees of zero. Bidder termination fees

are higher by 1.14% (4.64% vs. 3.50%) in bids with constrained targets. This difference is

significant at the 1% level.

Panel C of Table 1 presents the incidence of termination fees by year. We split the

sample bids into small (below 6% of deal value) and large (above or equal to 6% of deal

value) termination fees for comparison. Coates and Subramanian (2000) show a growing

incidence of termination provisions from 1988 to 1998. Officer (2003) reports that

termination fees were almost never used in the 1980s, but nearly 60% of bids included a

termination fee in 1998. Consistent with prior literature, we find termination fees are more

frequently used across time. Termination fees are present in less than 15% of sample bids in

1989, the first year of our sample, but they are used in over 90% of deals in 2011, the final

year of our sample.

We next examine the time trends in the use of target fees, separated into small and

large fees in Panel C. The use of large termination fees increases with time, similar to the use

of small termination fees, but there are some differences in the relative use of large

termination fees across time. Large termination fees are relatively more common in the early

1990’s, early 2000’s and between 2008 and 2010. These years roughly correspond to the

recession of the early 1990’s, the internet bubble bust, and the financial crisis. That is, large

target termination fees are relatively common in times of distress, and, in fact, they comprise

almost ten percent of the sample bids in 2009, the height of the financial crisis. While this is

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only suggestive evidence, it is consistent with the notion that large fees are related to greater

acquisition costs associated with financial constraints and distress.

Finally, Boone and Mulherin (2007) reveal that the data provided by SDC

underreports the incidence of termination fees, potentially biasing estimates of the relation

between termination fees and time. We take two steps to reduce concerns of sample bias in

our data. First, our comparisons primarily focus on the differences between deals with small

and large termination fees. If the impact underreporting is similar across fee sizes, these

comparisons remain relatively consistent. Second, Boone and Mulherin (2007) and Jeon and

Ligon (2011) report that bias is relatively small after 1997. We also check the robustness of

our results by limiting the data to years after 1997 and find that our results remain

qualitatively unchanged for the smaller sample.

3.3. Descriptive statistics - deal and target characteristics

Table 3, Panel A provides descriptive statistics of the sample bids and targets. We

define large termination fees as fees above 6% and split the bids into three groups according

to target fee size–no fee, small fee, and large fee. This threshold is based off prior literature

and Delaware court cases examining termination fee size, but this threshold is admittedly

arbitrary. In multivariate analysis we check the robustness of results to definitions of large

fees as fees above 5% and 10% of deal value, which roughly correspond to the 95th and 99th

percentile of the distribution of termination fees. Panel A also indicates that for about 96.8%

of observations with termination fees, the fee is between zero and 6% of deal value. The

remaining 219 deals (3.3%) have termination fees at or above 6% of transaction value, but

only 73 observations involve fees in excess of 10% of transaction value.

We find the mean small (large) target termination fee in dollar value is $38.49

($13.87) million. The mean small (large) fee as a percentage of deal value is 3.08% (9.15%).

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The maximum termination fee grant for targets in our sample is $2.5 billion payable by Sprint

Nextel to MCI Worldcom in a $115 billion proposed merger announced in October of 1999.

The maximum target termination fee in relative terms is the $2.5 million (215%) fee payable

by Envirosource Inc. to Greenwich Street Capital in a $1.16 million merger announced in

June of 2001. This maximum is an extreme outlier related to unique characteristics of the

assets and liabilities acquired in the deal. Because smaller deals could include extreme

observations, we winsorize control variables at the 1% level.

We observe that the average firm with a large termination fee is smaller than the

average firm with a lower termination fee by deal value ($1,405.90 vs. $192.21 million),

market value ($933.50 vs. $171.50 million), and the size of book assets ($1,659.28 vs.

$531.22 million). In fact, the average deal size monotonically decreases within termination

fees size quintiles, excluding firms without termination fees (results untabulated). This

observation is consistent with the idea that bidding costs do not scale directly with target size,

and smaller target firms have higher bidding costs as a percentage of deal value. These higher

costs can arise from reduced economies of scale in the sale of the target in, for example,

investment bank fees. McLaughlin (1992) shows that investment bank fee contracts contain

fixed and variable components that depend on the size of the acquisition.

Alternatively, we propose there are higher costs related to greater information

gathering costs for acquirers bidding on targets with financial distress, constraints, or other

frictions. We find the average market to book ratio is higher in deals with low termination

fees, suggesting that targets in deals with large termination fees have lower growth

opportunities (Smith and Watts, 1992) or are suffering from poor performance. Consistent

with this observation, the mean one year return prior to the takeover is significantly lower

(negative) for large fee targets relative to low fee targets with a prior 1-year return of about -

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30%. Similarly, targets in deals with large termination fees exhibit significantly lower free

cash flow and ROA, suggesting significant performance problems.

Deals without large termination fees are significantly different from deals with large

fees on several other dimensions. While prior literature finds that termination fees have a

negative (Bates and Lemmon, 2003) or insignificant (Boone and Mulherin, 2007) relation to

bidder toeholds, deals with large fees exhibit higher likelihood of a bidder toehold, relative to

low-fee bids. They are also more likely to be financed by cash, which, together with

increased use of toeholds, may suggest that bidders are using large fees as part of a strategy to

pre-empt competing bidders (Fishman, 1989).

We further examine the financial characteristics of target firms in Panel B of Table 3.

Primarily, we examine the financial distress, constraints, and performance of target firms

with several proxies. Following Campbell, Hilscher, and Szilagyi (2008) we construct a

“CHS score” based on their distress prediction model, which Mansi, Maxwell, and Zhang

(2012) suggest provides the best prediction model for distress. In addition, we construct the

common Ohlson’s O-score (Ohlson, 1980).

Compared to targets with smaller termination fees, we find that targets with large

termination fees have significantly worse CHS scores (t-statistic = -16.16), Ohlson O-scores

(t-statistic = -10.15), and these targets also have higher debt ratios (t-statistic = -3.50) and

lower interest coverage ratios (t-statistic = -1.77). Targets with large fees also exhibit

relatively poor performance as they experience lower prior year’s return, lower free cash

flow, and lower ROA when compared to targets with small fees, and all differences are

statistically significance at 1% level in t-tests and non-parametric rank-sum tests.

Evidence suggests that targets in bids with large termination fees are significantly

more financially constrained. We measure financial constraints with three common indexes–

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the SA index (Hadlock and Pierce, 2010), the Whited-Wu index (Whited and Wu, 2006), and

the KZ index (Kaplan and Zingales, 1997). Similar to the measures of distress, targets with

large termination fees are significantly more constrained based on the SA index (t-statistic = -

9.60) and Whited-Wu index (t-statistic = -8.89). These targets are more constrained using the

KZ index (t-statistic = -1.61) but this difference is only significant in non-parametric tests (z-

statistic = -5.97).

Several deal outcomes vary with termination fee size. Notably, bidder abnormal

returns in the three days surrounding the announcement of the bid are 1.70% higher (t-

statistic = -4.19) in bids with large termination fees, compared to bids with small fees, but

target abnormal returns are lower by 6.21% (t-statistic = 3.56). This is consistent with bidders

paying lower premiums, which are lower by 7.56% (t-statistic = 2.07) in bids with large fees.

The reduced benefits received by target shareholders do not seem to result from reduced

competition in bids with large fees, as bids with large fees are less likely to complete.15

Similarly, auctions of targets with large fees are more likely to include a competing bid and

have a higher number of bidders on average, although these differences are not statistically

significant at conventional levels.16 Taken together, these univariate results suggest that large

termination fee provisions are more common with distressed targets, and these targets fare

worse in negotiations of deal premiums. This outcome could indicate appropriate pricing for

illiquid, risky assets or the fact that agency problems are more severe in troubled firms.17 It is

then necessary to disentangle these two motivations to determine if large fees are beneficial

to target shareholders.

15 We note that Jeon and Ligon (2011) also find higher completion rates for higher fee deals in univariate analysis. This differs from our result, likely due to different definitions of large termination fees. We define large at a minimum of 5, 6, and 10% of deal value. However, Jeon and Ligon (2011) use a more general cut-off and define all bids in the top 33% of termination fees as large, with a sample average of 4.9% of deal value. 16 We define an auction as all bids on a target firm within a 365-day rolling window. 17 In related papers, Clark and Ofek (1994) and Hotchkiss and Mooradian (1997) have examined the efficacy of mergers as a means to restructure financially distressed firms.

18

4. Results

We extend our analysis to a multivariate setting to examine the choice between small

and large target termination fees to provide further evidence on the motivations for large fees.

We primarily model the choice of termination fee size using probit regressions in which the

dependent variable equals one if the merger agreement contains a large fee, zero otherwise.

We exclude bids with no fee due to the fact that over 90% of bids contain termination fees in

recent years, suggesting that the choice is not whether or not to have a fee but, rather, how

large to make it. In Appendix Table 1, we incorporate bids with no termination fee into

multinomial logit regressions that partition the sample among mutually exclusive discrete

choices: no termination fee, small termination fee (< 6%), and large termination fee (≥ 6%).

In the multinomial logits in Appendix Table A1, deals without termination fees are the base

category. The interpretations of results from the simple binary choice model are economically

and statistically similar to the multinomial framework.

4.1. Determinants of large termination fees

The first set of probit regressions is presented in Panel A of Table 4. In column (1),

we re-examine previously documented determinants of termination fees. We report marginal

effects with t-statistics in parentheses. Bates and Lemmon (2003) find a significant positive

relation between the use of bidder and target termination fees. We find that the use of a large

target termination fee is negatively and significantly related to the use of a bidder termination

fee, after controlling for the presence of a large bidder termination fee. Large bidder fees are

positively and significantly related to the use of large target termination fees with a p-value of

less than 1%.18 We posit that this relation is evidence of a reciprocal bargaining trade-off.

18 We similarly define bidder-payable termination fees as “large”, if they are greater than or equal to 6% of the value of the deal.

19

That is, the bidder or target managers may offer a large fee in exchange for receiving a large

fee from the other party.

The probit models control for the presence of a target lockup agreement but find no

significant impact of lockups on the choice of target fee size. The lack of relation is not

surprising given the fact that lockups are no longer present in the sample after 2005. There is

also an indicator for deal hostility, but it is not significantly related to fee size, as deals with a

negotiated termination fee are not likely to be hostile. Consistent with a lack of hostility, bids

with large termination fees a not more likely to be structured as tender offers.

We find that the presence of a toehold is positively related to the presence of a large

termination fee. Prior literature suggests that termination fees are negatively (Bates and

Lemmon, 2003) or insignificantly related (Boone and Mulherin, 2007) to toeholds, as

toeholds can signal bidder hostility. Given the lack of hostility in bids with large termination

fees, the greater use of toeholds potentially indicates a strategy to pre-empt competing

bidders when there is uncertainty surrounding the value of the target and when a bid could

signal the quality of the target’s assets. Consistent with this idea, large termination fees are

positively, significantly associated with cash as a form of payment, which can also be used as

a strategy to pre-empt competing bidders, as it signals that the initial bidder has a high

valuation of the target (Fishman, 1989).

We control for the target’s firm size with the log of its market capitalization.19 The

market value is negatively related to the presence of a large target fee, significant at the 1%

level. Similarly, the target’s market to book ratio is negatively related to large fees,

significant at the 5% level. These negative correlations are consistent with large termination

fees negotiated as part of the sale of a distressed firm with a depressed market value.

19 We control for firm size with the log of fixed assets in regressions including the SA index and Whited-Wu index, which already incorporate firm size (total assets).

20

We control for industry effects with an indicator for a same industry bid and

indicators for each industry based on two-digit acquirer SIC codes. We follow prior literature

(Coates and Subramanian, 2000; Jeon and Ligon, 2011) and control for time trends in the use

of termination fees with three indicators representing three significant Delaware court

decisions that affected the limits to which managers can contract with termination fees. These

cases are Paramount (1994), Brazen (1997), and Phelps (1999).20 For robustness, we note

that our results are generally unaffected by including year fixed effects instead of indicators

for these judicial decisions.

We further test the hypothesis that large termination fees aid in the sale of targets with

financial difficulties in Table 4. Columns (1) through (4) incorporate proxies of financial

distress. In column (1) we include an indicator, High CHS score, that equals one if the

target’s CHS score is above the sample median, zero otherwise. This indicator is positively,

significantly related to the use of large termination fees at the 1% level. We use above-

median indicators of Ohlson’s O score, debt ratio, and interest coverage ratio as alternative

proxies of financial distress. Results suggest that distress is significantly related to the use of

large termination fees with all proxies. These results are consistent with a greater need for

large termination fees when there are higher costs of bidding associated with the information

risk and uncertainty of distressed targets.

In columns (5) through (7), we include proxies for targets’ financial constraints based

on the Whited-Wu, Kaplan-Zingales, and SA indices. For each index, a target is defined as

financially constrained if it is above the sample median on an index. The results paint a

consistent picture. Higher financial constraints are significantly more common in bids with

large fees. If financial constraints are related to an inability to access capital markets due to

20 Brazen v. Bell Atlantic Corp., 695 A.2d 43, 50 (Del. 1997); Paramount Communications, Inc. v. Time, Inc., 637 A.2d 34 (Del. 1994); Phelps Dodge Corp. v. Cyprus Amax Minerals Co., C.A. No. 17398 (Del. Ch. Sept. 27, 1999).

21

market frictions, such as information asymmetry, then we expect constraints to be positively

related to the information gathering costs involved in bidding.

Finally, we present evidence that target performance is inversely related to

termination fee size. We create indicators for performance equal to one if the target has above

median stock returns, free cash flow, or ROA. These indicators are negatively related to the

use of large fees in columns (8) to (10), consistent with large fees aiding the sale of targets

with financial troubles. The negative relation is significant at the 5% level for stock returns

and ROA, although not significant for free cash flow. Overall, this evidence is consistent with

distressed and constrained targets using larger termination fees to entice bidders to undertake

the costs of bidding.

4.2. Firm performance

Table 5 presents regressions of measures of firm performance around the

announcement of the sample acquisitions. We examine this evidence to provide evidence on

whether fees are motivated by the need to overcome large bidding costs or by agency

conflicts. We expect lower premiums in bids with large termination fees if target managers

use large fees to secure deals with management-friendly acquirers and trade-off lower

premiums for personal benefits. If the information gathering costs dominate the choice of

termination fee size, we expect that target managers negotiate large termination fees in

exchange for a higher premium, which the bidder is willing to pay due to greater bid

certainty.

The first three columns in Table 5 regress premiums paid on target and deal

characteristics. We include indicators for bidder termination fees, large bidder termination

fees, lockups, prior bids, hostile bids, toeholds, tender offers, all-stock bids, and same

industry bids. We control for the log of firm size, the target’s market to book, debt to asset

22

ratio, and pre-announcement stock price run-up. We also include dummies for 2-digit

acquirer SIC industries and years to control for industry and time effects, respectively. All

models also contain a target termination fee indicator variable as well as an indicator variable

that captures whether the fee is large (above 5%, 6%, or 10%).

Similar to prior literature (e.g., Officer, 2003), termination fees are significantly,

positively related to premiums with coefficients ranging from 2.8% to 3.7%, suggesting

target managers gain concessions from bidders when granting a termination fee. After

controlling for the presence of a termination fee, the presence of a large termination fee

(either 5%, 6%, or 10%) is negatively related to premiums at the 1% level with a large

reduction in premium ranging from 11.1% of target value to 23.6% for the most extreme fees

above 10% of deal value. As presented by Bates and Lemmon (2003) and Jeon and Ligon

(2011), the reduced premiums are consistent with self-interested managerial bargaining.

We test the impact of large fees on shareholders by examining target and acquirer

abnormal returns. Cumulative abnormal returns are measured from a market model of returns

in the three days surrounding the bid announcement (-1, 1), where the market return is

proxied by the CRSP value-weighted index. The estimation period for the market model is

from 250 days to 30 days prior to the bid announcement date. Results for target

announcement cumulative abnormal returns (TCAR) are presented in columns (4) through (6)

in Table 5. In these models the impact of large termination fees is negative and significant,

similar to premium regressions. Target returns are lower by 5.1% to 12.3% on average in

deals with larger termination fees, controlling for the presence of a termination fee of any

size. These results again suggest that target shareholders do not gain more concessions from

the bidder in deals with large fees.

23

In the last three columns of Table 5, we conduct a similar analysis for acquirer

announcement cumulative abnormal returns (ACAR), to examine if acquirers gain in deals

with large fees. We find that the estimated coefficients on large termination fees are positive

and statistically significant at the 5% level, suggesting that acquirers obtain much higher

announcement returns, between 0.8% and 2.3% when deals include a large target termination

fee. Taken together, bidders receive higher returns while target shareholders receive lower

returns and premiums, consistent with a wealth transfer from target to bidder shareholders

and with managers bargaining in their self-interest.

4.3. Endogenous choice and premiums

The results presented above do not account for the endogenous choice of termination

fee size. We cannot rule out the possibility that premiums are lower in bids with large fees if

large fees are correlated with a compromised bargaining position related to target distress.

That is, low premium targets may be more likely to contract with a large termination fee. In

this case, lower premiums can represent an efficient outcome for target firms.

Related literature suggests that target managers of firms facing financial difficulties

enter negotiations in a relatively disadvantaged position. Masulis and Simsir (2014) find that

distressed firms are more likely to initiate merger discussions and that target initiation

reduces the ability of target managers to negotiate a high price, as relatively uninformed

bidders fear purchasing a “lemon” (Ackerlof, 1970). Meier and Servaes (2014) find that

acquirers of distressed assets receive abnormally high returns, but target returns are low,

consistent with a wealth transfer to bidders from targets. Oh (2013) also finds that distressed

targets receive lower acquisition prices, suggesting acquirers exploit target firms’ bargaining

disadvantage. When targets face complex financial circumstances, large fees may be

24

necessary to entice bidders to enter into negotiations and secure an offer, in spite of the

targets limited bargaining power.21

An appropriate correction would account for the fact that various characteristics

related to target distress or financial constraints can influence premiums. We correct for

endogenous selection of fee size using two stage least squares.22 Similar to the probit models

on the determinants of fee size, we limit our analysis to the choice of a small (<6% of deal

size) or large (>=6% of deal size), given that the vast majority of deals include a fee. In the

first stage, we model the choice of a high or low fee, and in the second stage we use the

instrumented choice of fee size in a regression of premiums.

Because our endogenous variable is a binary outcome based on fee size, we adopt the

three stage procedure of Adams, Almeida, and Ferreira (2009). We first model the choice of

large termination fee with a probit model, including an instrument. We then include the

predicted value from this probit model with the other control variables in the first stage of a

typical two-stage least squares framework, i.e., the predicted value is the instrument in the

two-stage model. This three step procedure allows us to incorporate the dichotomous nature

of the endogenous variable into our estimation, while ensuring the two-stage least squares

estimates remain consistent.

Our instrument for the choice of large fees is an indicator for Delaware incorporation

of the target. We expect that being subject to Delaware law influences the choice of large

termination fees, because Delaware courts are critical of large fees. Columns (1), (3), and (5)

of Table 6 present the results of the probit regressions of the determinants of large fees,

defined as fees above 5%, 6%, and 10% of deal value, respectively. In columns (1) and (3), 21 Andre, Khalil, and Magnan (2007) present evidence consistent with the view that the size of termination fees is related to the magnitude of costs of bidding, as bidders contract to recover their sunk costs in the event of a failed bid. 22 We do not estimate the choice of no fee, but results are qualitatively similar if we model the choice of high fee against a low fee including a fee of zero in switching regressions.

25

Delaware incorporation is significantly and negatively related to the use of large fees at the

5% level, validating the relevance criteria for our choice of instrument. When we define a

large fee as a fee above 10% of deal value in column (5), we find a negative but insignificant

relation between Delaware incorporation and the choice of large fees, suggesting any results

be interpreted cautiously with a 10% definition of large, as results may suffer from a problem

of weak instruments.

In terms of the exclusion restriction, we are limited in our ability to statistically show

a lack of relation between our instrument and the error term of the model of premiums.

However, we argue that the choice of state of incorporation of a firm is likely made well in

advance of a proposed acquisition, prior to any knowledge of the sale of the firm or the

financial conditions of the target at the time of merger negotiations. Hence, we expect that the

choice of state of incorporation and accompanying legal environment has little impact on

merger negotiations beyond the contractual provisions that state law allows managers to

include in merger agreements. We control for several contractual provisions and other

determinants of premiums, which reduces the likelihood that other variables related to state of

incorporation drive our results. We also note that our results are robust to the inclusion of

controls for state headquarters locations, suggesting the results are not related to geographic

effects. Overall, this suggests the instrument is relatively exogenous to premiums and other

bid outcomes, satisfying the exclusion requirement.

With proper identification, we can then model and the effect of large termination fees

on premiums. We estimate three different models in Table 6 in which we define large fees as

fees above 5% (Model 1), 6% (Model 2), or 10% (Model 3) of deal value. The results are

generally consistent across all three models. Notably, after we correct for endogenous

selection of fee size, the sign flips on the relation between large termination fees and

premiums. In contrast to the negative relation found in prior literature and in Table 5, we find

26

that the use of large termination fees is positively related to the premiums target shareholders

receive. The second column in Table 6 reveals that negotiating a termination fee above 5% of

deal value increases premiums by about 1% on average. In column 4, termination fees above

6% of deal value increases premiums by about 3%, and column 6 shows that fees above 10%

increase premiums by almost 17%. Although coefficients are not significant, the results

suggest that bidders are willing to sacrifice premiums in exchange for the assurance that they

will be able to recoup their bidding costs, when such costs are large relative to the size of the

deal.

These higher premiums are consistent with large fees aiding managers in the sale of

targets that require greater upfront investment in terms of investigation by the acquiring firm

to overcome uncertainty about the value of the target. The fact that bidders pay more in such

deals reveals that targets benefit for agreeing to compensate the bidder the costs of

investigation, which is consistent value maximization by managers, rather than agency

conflicts. Overall, our evidence is inconsistent with prior literature and case law that suggests

large fees are harmful to target shareholders.23

4.4. Deal completion

In Table 7, we examine the effect of large termination fee provisions on bid

completion with probit regressions. In the first three columns, the dependent variable is an

indicator equal to one if a bid is eventually consummated, zero otherwise. We again use three

proxies for large termination fees based on whether the size of the fee is greater than 5%, 6%,

23 We also model the endogenous choice using a switching model, which allows coefficients to vary for all control variables in small-fee and large-fee deals. Golubov, Petmezas, and Travlos (2012) use a similar estimation technique in their study of the effect of investment bank adviser choice on bidder wealth. The results are presented in Appendix Table A2 and show that the expected premium would be significantly lower for firms that contracted large fees if these targets had contracted small termination fees, consistent with two-stage least squares results.

27

or 10% of deal value to check our results are robust to different definitions of size. We report

marginal effect estimates along with t-statistics in parentheses.

The results in Table 7 indicate that the inclusion of a termination fee significantly

increases the probability of a bid completing, consistent with prior literature (Bates and

Lemmon, 2003). However, after controlling for the presence of a termination fee of any size,

large termination fees have a significant, negative relation with completion rates, suggesting

these bids are more competitive. While the indicator for a 6% termination fee does not have a

significant effect, marginal effect estimates on indicators for large termination fees above 5%

and 10% of deal value suggest that large fees are related to a 4.3% to 8.4% lower probability

of completion, and these effects are significant at the 5% level.

The regressions in columns (4) to (6) of Table 7 use auction-level data to model the

probability that an initial bid for a target is challenged by a competing bidder. We include all

bids for a target within a 365 day rolling window in a single auction. This reduces 6,732 bids

into 6,226 auctions. Deal characteristics from the first bid in an auction are used to construct

variables of interest and controls. Results suggest that large termination fees above 6% and

10% of deal value are positively related to the probability of a challenging bid. Marginal

effect estimates suggest the probability is higher by 2.9% to 6.5%, significant at the 5% and

1% level for fees larger than 6% and 10%, respectively.

We then model the total number of bidders within an auction in columns (7) to (9)

using Poisson regressions to account for the fact the dependent variable is count data. Similar

to probit regressions with proxies of bid competition, large termination fees are positively

related to the number of bidders for a target. This relation is significant at the 10% level for

fees above 6% of deal value and at the 1% level for fees above 10% of deal value. These

results are inconsistent with the notion that target managers agree to large termination fees as

28

a means of locking-in a management friendly bidder to receive personal benefits. To the

contrary, our results suggest that managers use large termination fees in expectation of bid

competition, when the likelihood of a competing bidder free-riding on the information

revealed by an initial bidder is high and initial bidders are reluctant to make an offer.

4.5. Target manager employment and compensation

To test the relation between managerial agency problems and large termination fees,

we examine the post-merger employment and compensation arrangements for target CEOs

and senior executives in deals with large termination fees and a matched sample of deals.

Hartzell, Ofek, and Yermack (2004) present results suggesting that there is a negative relation

between premiums paid and benefits, such as financial bonuses and appointments in the

acquiring firm, accorded to the CEOs of target firms. We gather compensation and post-

acquisition employment data for the executive of the 219 large termination fee targets. This

data comes from proxy statements, tender offer statements, S-4’s, and other filings available

through the SEC’s EDGAR database. Due to a lack of electronic filings before 1995 and

other data constraints, we are not able to find employment and compensation data for each

target firm. However, we are able to confirm employment and compensation data for 190 of

the deals with termination fees above 6%.

We form a matched sample of bids for comparison. We categorize each bid by its one-

digit SIC code, year of announcement, and size tercile based on book assets. For each large

termination fee bid, we randomly draw a sample bid from the same industry, year, and size

tercile with replacement.24 Out of the sample of 219 matched observations, we find

compensation and employment data for 174 targets. We search the filings for employment

and compensation data and create variables indicating if 1) the CEO receives a job with the

24 Only 8 bids are duplicate observations in the matched sample due to replacement.

29

acquirer, 2) another top executive receives a job with the acquirer, 3) the CEO receives a

severance package as a result of the change in control, and 4) the CEO has options that vest

as a result of the acquisition.

Table 8 presents employment statistics for the large termination fee bids and the

matched sample. The data indicate that there is no statistically significant difference in the

probability that a target CEO receives employment with the acquirer between the large fee

deals and the matched sample. Additionally, other non-CEO executives in large fee deals are

no more likely to receive a position with the acquirer than the matched sample non-CEO

executives.

CEOs’ compensation incentives are also not significantly different in large fee deals.

Target CEOs in large termination fee deals are just as likely to receive a severance or to have

options that will vest in the event of a merger. Hartzell, Ofek, and Yermack (2004) suggest

that negotiated increases in severance packages are negatively related to the probability of

receiving a job and negatively related to premiums. We also hand-collect data on negotiated

increases in severance for the large fee and matched samples in unreported analysis. We limit

our search to post-2006, when SEC required greater disclosure of severance changes in item

402 of Regulation S-K. We find no evidence that CEOs in large fee bids are more likely to

receive an increased severance. However, our data are restricted to less than two dozen

observations, which limits the statistical interpretation of our results.

We also compare both deal and financial characteristics of the large fee bids and the

matched sample. The characteristics of the sub-sample are similar on average with those

reported in Table 3 for the full sample, suggesting that our conclusions regarding target CEO

and executive post-merger employment are generalizable. Large fee targets are significantly

associated with smaller deal sizes, lower market to book ratios, and larger bidder termination

30

fees. Large fee targets in Table 8 are also associated with a greater use of cash payment and

toeholds, although the differences are not significant at conventional levels, possibly due to

the reduced sample size, relative to the full sample in Table 3. Overall, these results suggest

that CEOs and other executives’ personal incentives are not related to the decision to

negotiate a larger termination fee. Rather, the univariate evidence about compensation and

employment supports the idea that large termination fees are related to contracting costs

associated with acquisition of these firms.

5. Conclusion

Target termination fees have been widely used in recent years. We ask what role they

have in the sale of firms with high bidding costs and information asymmetry. We find that

termination fees are larger in deals with targets that are distressed or constrained, consistent

with large fees compensating bidders for undertaking the costs associated with learning about

risky targets. We therefore suggest that large termination fees are part of a process by which

target firms attract and formalize acquisition agreements with reluctant bidders who may be

unwilling to otherwise sink the necessary costs to investigate the benefits of an acquisition.

We provide new evidence on the motivations of the use of large fees, as prior

literature suggests large fees result from agency problems. We present evidence that

managers use large fees to maximize premiums, after accounting for endogeneity. This

highlights the importance of endogeneity concerns in studies of corporate decisions and firm

value (e.g., Roberts and Whited, 2012), and it supports the idea that managers use protective

devices to maximize shareholder value.

From a policy perspective, we question the criticisms of large termination fees and,

more generally, protective devices. Boone and Mulherin (2007) show that increases in the use

of caps on deal protections with variable payouts coincided with years in which there were

31

two landmark decisions on deal protections, Paramount and Brazen.25 This evidence suggests

that court decisions can have real effects on the use of protective devices. While restrictions

on the use of protections can limit agency problems related to managerial self-interest, they

can also hinder managers’ ability to negotiate the sale of distressed targets, which are most in

need of the capital that acquisitions provide. These concerns are highlighted in recent case

law, Comverge, in which the court rejected target managers’ use of large fees and other deal

protections as detrimental to target shareholders. This decision contrasts with our evidence, as

we find no evidence of personal benefits accruing to managers in the form of increased

compensation or employment opportunities and that premiums would be lower in deals with

large fees if target managers were unable to freely contract with bidders. We also show that

large fees are positively associated with bid competition, suggesting these deal protections

reflect a more competitive sale environment. In general, our research supports arguments that

restrictions on deal protections reduce efficient sales of targets and deter potential bidders

from entering an auction.26

25 Brazen v. Bell Atlantic Corp., 695 A.2d 43, 50 (Del. 1997); Paramount Communications, Inc. v. Time, Inc., 637 A.2d 34 (Del. 1994). 26 For example, see Omnicare, Inc. v. NCS Healthcare, Inc. 818 A.2d 914 (Del. 2003)

32

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Table 1 – Variable Definitions Variable Description Acquirer Abnormal Returns The cumulative abnormal returns to bidders for the three days surrounding the

announcement of a bid, (-1, 1). Abnormal returns come from a market model of returns with an estimation window of 200 days starting 260 days before the announcement of a bid.

Bidder Lockup An indicator equal to one if a bidder lockup agreement is included in a bid,. Bidder Termination Fee An indicator equal to one if the deal includes a bidder-payable termination fee. CAPX The target's capital expenditures prior to the acquisition, scaled by the beginning of

period property, plant, and equipment. CEO Has Vesting Options An indicator equal to one if the target CEO has options that vest as a result of the

merger and zero otherwise. CEO Receives Severance An indicator equal to one if the target CEO receives a severance package. Challenged Bid An indicator equal to one if a competing bid is made while the original bid is

pending and zero otherwise. CHS Score An index defined as -9.16 - 20.26×nimtaavg + 1.42×tlmta - 7.13×exretavg +

1.41×sigma - 0.045×rsize - 2.13×cashmta + 0.075×mtob - 0.058×price. Nimtaavg is a weighted average of the past four quarters' net income scaled by firm value. Tlmta is the debt ratio of the firm. Exretavg is the weighted average of abnormal return relative to the S&P 500 Index. Sigma is the annualized volatility of target firm daily stock returns from the past three months. Rsize is the log ratio of the target's market capitalization relative to the S&P 500. Cashmta is the ratio of cash and marketable securities relative to firm value. Mtob is the ratio of the market value of the firm divided by the book value of assets. Price is the log of the firm's share price, truncated above $15. Accounting data are quarterly. See Campbell, Hilscher, and Szilagyi (2008) for details on variable construction.

Completed An indicator variable equal to one if the bid is eventually completed. Deal Value The value of the transaction in millions of dollars. Delaware Incorporation An indicator equal to one if the target firm is incorporated in Delaware. Firm Size Equal to the log of market capitalization of the target forty-two days prior to the

bid announcement. Hostile Deal An indicator equal to one if the deal attitude is hostile, as defined by SDC. Interest Coverage Ratio The ratio of EBIT divided by interest expense. Kaplan-Zingales Index The index is defined as KZ = -1.001990×Cashflow/K + 0.2826389×Tobin’s Q +

3.139193×Debt/Total Capital – 39.3678×Dividends/K – 1.314759×Cash/K. Cashflow/K is calculated from Compustat as (ib+dp)/ppent. Tobin’s Q is calculated as (at+CRSPmcap+ceq)/at. CRSPmcap is the firm’s market capitalization as reported by CRSP at the fiscal year end. Debt/Total Capital is (dltt+dlc)/(dltt+dlc+seq). Dividends/K is (dvc+dvp)/ppent. Cash/K is che/ppent. Deferred taxes are not included in the calculation of Tobin’s Q due to the large number of missing observations in Compustat. Measures of capital (ppent) represent capital at the beginning of the period.

Large Target (Bidder) Termination Fee (5%/6%/10%)

Large fees are target (bidder) termination fees that are greater than or equal to 6% of the deal value, unless otherwise designated at greater than 5% or 10%.

Number of Bidders A count variable of the number of bidders for a target. Ohlson's O-score The O-score is defined similar to prior literature as O = -1.32 - 0.407×log(at) +

6.03×lt/at - 1.43× ((act-lct)/at) + 0.0757× (lct/act) - 2.37× (ni/at) - 1.83×((ebit+dp)/lt) + 0.285×intwo - 1.72×negeq - 0.521×chin, where act is current assets; lct is current liabilities; at is the book value of assets; ni is net income; ebit is earnings before interest and taxes; dp is depreciation; intwo is equal to one if net income was negative in the previous two years and zero otherwise; lt is total liabilities; negeq is a binary variable equal to one if shareholders' equity is negative; and chin is the dollar change in net income divided by the sum of absolute values of net income and lagged net income.

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Table 1 - Continued Variable Description Premium Premiums are defined as the total value of consideration offered to the target

divided by the market capitalization of the target 42 days before the announcement of the bid less one. Similar to Officer (2003), we adjust for outliers below zero or above two (200%). If the premium is above (below) 2 (0), we calculate premium as the price offered per share as provided by SDC divided by the price of target stock 42 days prior to the bid announcement less one. If this per share measure is also above two or below zero, premium is missing.

Prior Bid An indicator equal to one if there was a preceding bid for the target firm within 365 days of the announcement of the current bid.

Prior Return (1 year) The stock return of the target firm, net of market returns, for the 252 trading days beginning 312 days before bid announcement and ending 61 days before bid announcement.

R&D Indicator An indicator equal to one if the target engages in R&D and zero otherwise. Run-up Run-up is the percentage change in price for the target firm from 42 days before the

announcement of the bid to two days before announcement. SA Index This index proxies for financial constraints following the work of Hadlock and

Pierce (2010) and is defined as SA = -0.737×Size + 0.043×Size2 – 0.040×Age. Size is the inflation-adjusted value of the log of book assets. Age is the number of years a firm has non-missing Compustat data, at the time of the acquisition. Size and Age are winsorized at log of $4.5 billion and 37 years, respectively.

Same SIC An indicator for the target and acquirer sharing the same two-digit SIC code. Stock Deal An indicator equal to one if the consideration offered is all stock or a mix of cash

and stock. Target Abnormal Returns The cumulative abnormal returns to targets for the three days surrounding the

announcement of a bid, (-1, 1). Abnormal returns come from a market model of returns with an estimation window of 200 days starting 260 days before the announcement of a bid.

Target Assets The book value of the target's assets in millions. Target CEO (Other Executive) Receives Job

An indicator equal to one if the target CEO (other executive) receives a job with the bidder subsequent to the merger.

Target Debt to Assets The sum of long-term and short-term debt of the target divided by the book value of target assets.

Target Free Cash Flow Operating income before depreciation less interest expense, income taxes, and capital expenditure, scaled by the book value of assets.

Target Lockup An indicator equal to one if the bidder has a lockup provision, an option to purchase target shares or other assets, in the merger agreement.

Target Market to Book The market value of the target divided by the book value of target assets, as defined by Compustat.

Target ROA The return on assets of a firm, defined as net income divided by book asset value. Target Termination Fee An indicator equal to one if the merger agreement includes a target termination fee

and zero otherwise. Tender Offer An indicator equal to one if the bid is a tender offer for the target's shares. Target Market to Book The market value of the target divided by the book value of target assets, as defined

by Compustat. Target ROA The return on assets of a firm, defined as net income divided by book asset value. Target Termination Fee An indicator equal to one if the merger agreement includes a target termination fee,

zero otherwise. Tender Offer An indicator equal to one if the bid is a tender offer for the target's shares. Toehold Indicator An indicator equal to one if the bidder owns shares in the target firm prior to the

announcement of the bid. Whited-Wu Index The index is defined as WW = -0.091×CF – 0.062×DIVPOS + 0.021×TLTD –

0.044×LNTA + 0.102×ISG – 0.035×SG. CF is defined using Compustat variables as (ib+dp)/at. DIVPOS is an indicator if a firm pays cash dividends. TLTD is dltt/at. LNTA is the log of assets (at). ISG is industry sales growth at the 3-digit SIC code level. SG is the firm’s sales growth (sale/lag of sale).

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Table 2 Termination Fee Univariate Statistics This table presents the distribution of termination fee provisions for the 6,732 sample bids from the SDC database for the years 1989 to 2011. The indicator for target fees shows the frequency of fee provisions in the sample. The magnitude of these fees as a percentage of deal value is reported for deals that include fees. Panel A full sample statistics. Panel B splits the sample into the top 10%/ bottom 90% of target distress and financial constraints based on the targets’ CHS scores and SA Index values, respectively. Panel C shows the percentage of deals with no fee, small fees, and large fees by year. Large termination fees are fees greater than or equal to 6% of deal value. *, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively. Panel A – Termination Fee Statistics

N Mean Median

Target Termination Fee Indicator 6,732 0.59 1.00 Target Fee Size (% of Deal Value) 3,979 3.42 3.13 Panel B – Termination Fee Statistics by Constraints and Distress

N Mean Median N Mean Median T-statistic

No Distress (CHS) High Distress (CHS)

Target Fee Size (% of Deal Value) 6,058 1.96 2.08 674 2.57 1.34 -6.79*** Target Fee Size (% of Deal Value, > 0) 3,625 3.27 3.09 354 4.89 3.91 -15.94***

Unconstrained (SA Index) Constrained (SA Index)

Target Fee Size (% of Deal Value) 6,058 2.00 2.10 674 2.23 0.00 -2.59*** Target Fee Size (% of Deal Value, > 0) 3,652 3.31 3.09 327 4.59 3.91 -12.05***

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Panel C - Termination Fees by Year and Fee Size Year N No Termination Fee Small Termination Fee Large Termination Fee 1989 263 84.41% 14.45% 1.14% 1990 131 88.55% 10.69% 0.76% 1991 110 82.73% 16.36% 0.91% 1992 103 71.84% 25.24% 2.91% 1993 129 71.32% 26.36% 2.33% 1994 286 64.69% 32.52% 2.80% 1995 385 67.27% 31.17% 1.56% 1996 382 69.11% 29.06% 1.83% 1997 505 37.23% 58.61% 4.16% 1998 558 37.46% 59.68% 2.87% 1999 581 38.38% 58.86% 2.75% 2000 484 35.33% 59.92% 4.75% 2001 359 23.12% 70.19% 6.69% 2002 216 37.04% 58.33% 4.63% 2003 256 49.61% 47.66% 2.73% 2004 230 14.78% 82.17% 3.04% 2005 261 19.16% 78.93% 1.92% 2006 307 18.57% 80.13% 1.30% 2007 334 15.62% 82.58% 1.80% 2008 237 26.16% 69.20% 4.64% 2009 179 28.49% 62.01% 9.50% 2010 245 17.96% 77.14% 4.90% 2011 191 9.42% 86.39% 4.19% Total 6,732

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Table 3 This table presents sample statistics for the 6,732 sample bids from the SDC database for the years 1989 to 2011. Panel A presents statistics target characteristics related to target distress, financial constraints, investment, and deal outcomes. Panel B shows means and medians of deal and target characteristics by fee type. Large termination fees are greater than or equal to 6% of deal value. T-tests and rank-sum tests provide tests of significant differences of the means and medians of the small termination fee and large termination fee deals. Variables are winsorized at the 1% level. Details on the construction of variables and their definitions are in Table 1. Panel A – Deal Characteristics

No Termination Fee Small Termination Fee Large Termination Fee Small-Large Statistical Tests

N Mean Median N Mean Median N Mean Median T-statistic Z-statistic

Deal Characteristics Target Fee Size ($mil) 2,753 0.00 0.00 3,760 38.49 9.34 219 13.87 3.00 4.25*** 10.58***

Target Fee Size (% of Deal Value) 2,753 0.00 0.00 3,760 0.03 0.03 219 0.09 0.08 -68.85*** -24.91*** Bidder Termination Fee 2,753 0.03 0.00 3,760 0.26 0.00 219 0.32 0.00 -1.95* -1.95* Bidder Fee Size (% of Deal Value) 2,753 0.00 0.00 3,760 0.01 0.00 219 0.02 0.00 -11.39*** -3.93*** Target Lockup 2,753 0.13 0.00 3,760 0.08 0.00 219 0.07 0.00 0.75 0.75 Bidder Lockup 2,753 0.02 0.00 3,760 0.01 0.00 219 0.00 0.00 1.09 1.09 Prior Bid 2,753 0.10 0.00 3,760 0.06 0.00 219 0.08 0.00 -1.36 -1.36 Hostile Deal 2,753 0.08 0.00 3,760 0.01 0.00 219 0.00 0.00 0.59 0.59 Toehold Indicator 2,753 0.13 0.00 3,760 0.04 0.00 219 0.07 0.00 -2.33** -2.32** Tender Offer 2,753 0.15 0.00 3,760 0.23 0.00 219 0.21 0.00 0.55 0.55 Stock Deal 2,753 0.61 1.00 3,760 0.59 1.00 219 0.47 0.00 3.34*** 3.33*** Deal Value 2,753 709.66 102.00 3,760 1,405.90 321.00 219 192.21 30.02 6.03*** 17.58*** Same SIC 2,753 0.56 1.00 3,760 0.58 1.00 219 0.57 1.00 0.51 0.51 Target Characteristics

Target Market Capitalization 2,753 478.67 64.70 3,760 933.50 195.92 219 171.50 23.82 5.12*** 16.11*** Target Assets 2,753 1,455.68 160.33 3,760 1,659.28 260.84 219 531.22 66.91 3.25*** 10.14*** Target Market to Book 2,753 0.83 0.49 3,760 1.26 0.83 219 0.72 0.39 5.59*** 8.18*** Run-up 2,748 0.03 0.01 3,759 0.07 0.04 219 0.04 0.00 1.71* 2.48**

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Panel B – Target Distress, Financial Constraints, Investment, and Deal Outcomes No Termination Fee Small Termination Fee Large Termination Fee Small-Large Statistical Tests N Mean Median N Mean Median N Mean Median T-statistic Z-statistic Distress CHS Score 2,753 -7.13 -7.30 3,760 -7.45 -7.62 219 -6.37 -6.70 -16.16*** -13.19*** Ohlson's O-score 1,780 -0.15 -0.57 2,867 -1.05 -1.25 169 1.33 0.83 -10.15*** -9.46*** Target Debt to Assets 2,726 0.23 0.17 3,743 0.22 0.15 218 0.27 0.19 -3.50*** -3.11*** Interest Coverage Ratio 1,907 -2.79 0.64 2,788 -0.14 1.40 173 -17.09 -0.87 1.77* 7.63*** Financial Constraints SA Index 2,753 -3.20 -3.25 3,760 -3.31 -3.29 219 -2.90 -2.88 -9.60*** -8.57*** Whited-Wu Index 2,524 -0.19 -0.18 3,535 -0.21 -0.20 213 -0.15 -0.13 -8.89*** -8.64*** Kaplan-Zingales Index 2,359 -10.91 -0.85 3,434 -11.41 -1.73 190 -7.22 0.25 -1.61 -5.97*** Performance Prior Return (1 year) 2,753 -0.10 -0.14 3,760 -0.04 -0.11 219 -0.30 -0.37 7.61*** 8.82*** Target Free Cash Flow 2,675 -0.04 0.02 3,633 -0.03 0.02 214 -0.12 -0.01 6.86*** 6.58*** Target ROA 2,753 -0.05 0.01 3,760 -0.03 0.02 219 -0.20 -0.02 10.20*** 9.04*** Deal Outcomes Bidder Abnormal Return 1,784 -0.01 -0.01 2,621 -0.02 -0.01 134 0.01 0.00 -4.19*** -3.29*** Target Abnormal Return 2,747 0.18 0.13 3,759 0.23 0.18 219 0.17 0.11 3.56*** 4.11*** Premium 2,334 0.60 0.49 3,486 0.64 0.54 154 0.57 0.40 2.07** 3.12*** Completed 2,753 0.65 1.00 3,760 0.93 1.00 219 0.91 1.00 0.93 0.93 Number of Bidders 2,489 1.09 1.00 3,536 1.05 1.00 201 1.06 1.00 -0.52 -1.17 Challenged Bid 2,489 0.07 0.00 3,536 0.04 0.00 201 0.06 0.00 -1.19 -1.19

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Table 4 Probit Regressions of the Choice between Small and Large Target Termination Fees This table presents the results of probit regressions modelling the probability that a bid includes a small termination fee (dependent variable = 0) or large termination fee (dependent variable = 1). The sample bids come from the SDC database for the years 1989 to 2011. Large termination fees are fees equal to or greater than 6% of deal value. Variables are winsorized at the 1% level. Variable definitions are in Table 1. Marginal effect estimates are reported for the determinants of termination fee size as well as estimates of the impact of proxies of target distress and financial constraints on fee size. The proxies of interest are indicator variables for high distress, constraints, and investment. “High” signifies that the target is above the sample median for the variable of interest. Two-digit acquirer SIC indicators control for industry-fixed effects. Indicators for years following 1994, 1997, and 1999 correspond to important court cases related to termination fees (Paramount, Brazen, and Phelps) and control for the impact of time. Standard errors are clustered by acquirer industry. *, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively. T-statistics for marginal effect estimates are reported in parentheses.

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(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Bidder Termination Fee -0.006*** -0.003*** -0.007*** -0.003** -0.010*** -0.011*** -0.004*** -0.004*** -0.007*** -0.003*** (-3.49) (-2.67) (-3.29) (-2.54) (-3.41) (-3.52) (-2.89) (-2.65) (-3.64) (-2.64) Large Bidder Termination Fee 0.026*** 0.012*** 0.030*** 0.012*** 0.041*** 0.043*** 0.018*** 0.015*** 0.029*** 0.014*** (5.73) (4.41) (6.14) (5.65) (7.58) (7.63) (6.68) (6.01) (6.21) (5.85) Target Lockup 0.001 0.001 0.002 0.001 0.001 0.001 0.002 0.001 0.003 0.001 (0.48) (0.29) (0.77) (0.46) (0.19) (0.27) (0.94) (0.60) (0.94) (0.69) Prior Bid 0.001 -0.001 0.001 0.000 0.001 0.002 0.001 0.000 0.001 0.000 (0.45) (-1.14) (0.49) (0.36) (0.49) (0.69) (0.39) (0.04) (0.43) (0.05) Hostile Deal 0.005 0.004 0.006 0.004 0.005 0.007 0.005 0.005 0.006 0.005 (0.57) (0.90) (0.61) (0.90) (0.39) (0.43) (0.77) (0.82) (0.63) (0.85) Toehold Indicator 0.004** 0.001 0.005** 0.002** 0.007*** 0.004 0.002 0.002 0.005** 0.001 (2.20) (0.92) (2.56) (1.98) (3.30) (1.43) (1.39) (0.96) (2.49) (0.93) Tender Offer -0.002 -0.001 -0.002 -0.001 -0.003 -0.003 -0.001 -0.001 -0.001 -0.000 (-1.33) (-1.22) (-1.10) (-0.90) (-1.30) (-1.40) (-0.99) (-0.60) (-0.85) (-0.59) Stock Deal -0.003** -0.001* -0.003** -0.001* -0.004 -0.004 -0.001 -0.001 -0.002* -0.001 (-2.52) (-1.68) (-2.05) (-1.90) (-1.62) (-1.54) (-1.48) (-1.19) (-1.78) (-1.23) Log of Firm Size -0.005*** -0.003*** -0.006*** -0.003*** -0.004*** -0.004*** -0.004*** -0.003*** -0.006*** -0.003*** (-11.46) (-7.48) (-13.45) (-10.44) (-3.64) (-3.28) (-12.04) (-11.36) (-13.24) (-11.86) Target Market to Book -0.001** -0.001*** -0.002** -0.001** -0.009*** -0.009*** -0.001** -0.001** -0.002** -0.001** (-2.07) (-3.19) (-2.24) (-2.26) (-4.57) (-4.67) (-2.56) (-2.30) (-2.13) (-2.12) Same SIC 0.000 -0.000 0.001 -0.000 -0.001 -0.001 -0.000 -0.000 0.000 -0.000 (0.26) (-0.42) (0.30) (-0.35) (-0.41) (-0.35) (-0.06) (-0.28) (0.27) (-0.24) Distress Variables High CHS 0.007***

(6.66)

High O-score 0.002***

(3.18)

High Debt Ratio 0.003**

(2.02)

High Interest Coverage Ratio -0.002***

(-3.35)

Financial Constraint Variables High SA Index 0.005*

(1.82)

High Whited-Wu Index 0.005**

(1.99)

High KZ Index 0.002** (2.15)

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Table 3 - Continued (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Performance Variables High Prior Return

-0.003**

(-2.03) High Free Cash Flow

-0.001

(-0.88) High ROA

-0.003**

(-2.28) Time Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,979 3,036 3,961 2,961 3,803 3,979 3,748 3,346 3,953 3,346 Pseudo R2 0.327 0.344 0.316 0.339 0.238 0.237 0.326 0.327 0.318 0.327

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Table 5 Large Termination Fees and Firm Performance This table presents the results of regressions of deal outcomes on indicators of termination fees, large termination fees, and control variables. The sample bids come from the SDC database for the years 1989 to 2011. Acquirer abnormal returns (ACAR) are the cumulative abnormal return to the bidder for the three days surrounding bid announcement, (-1,1). Abnormal returns are estimated from a market model of returns. Target abnormal returns (TCAR) are similarly defined. Premium is the price offered by the bidder divided by the target’s value forty-two days prior to the bid announcement. Further details on variables are available in the Table 1. An indicator for a target termination fee controls for the presence of any termination fee. Large Termination Fee (5%, 6%, and 10%) are indicators equal to one if the target termination fee is above 5%, 6%, and 10%, respectively. Variables are winsorized at the 1% level. Year and two-digit SIC indicators control for industry and year fixed effects. Standard errors are clustered by acquirer industry. *, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively. T-statistics are reported in parentheses below coefficient estimates.

46

Premium

TCAR ACAR (1) (2) (3)

(4) (5) (6)

(7) (8) (9)

Large Termination Fee (5%) -0.111***

-0.051***

0.008** (-4.34)

(-3.13)

(2.01)

Large Termination Fee (6%)

-0.163***

-0.097***

0.013*

(-5.14)

(-4.94)

(1.88)

Large Termination Fee (10%)

-0.236***

-0.123***

0.023**

(-4.57)

(-3.62)

(2.27)

Target Termination Fee 0.037*** 0.033** 0.028** 0.061*** 0.062*** 0.058*** -0.003 -0.002 -0.002 (2.74) (2.47) (2.07) (4.97) (4.94) (4.52) (-1.16) (-1.11) (-0.94) Bidder Termination Fee -0.029** -0.029** -0.028**

-0.045*** -0.045*** -0.044***

-0.008 -0.008 -0.008

(-2.26) (-2.28) (-2.20)

(-3.78) (-3.86) (-3.86)

(-1.46) (-1.45) (-1.45) Large Bidder Termination Fee (6%) -0.044 -0.030 -0.072**

-0.028 -0.012 -0.039*

0.024* 0.021* 0.025**

(-1.21) (-0.71) (-2.01)

(-1.35) (-0.55) (-1.72)

(1.85) (1.68) (2.00) Target Lockup 0.010 0.011 0.010

0.026*** 0.026*** 0.025***

-0.009*** -0.009*** -0.009***

(0.43) (0.46) (0.43)

(2.88) (2.96) (2.95)

(-3.22) (-3.23) (-3.19) Prior Bid -0.054*** -0.054*** -0.054***

-0.078*** -0.077*** -0.078***

-0.006 -0.006 -0.006

(-3.31) (-3.34) (-3.34)

(-8.91) (-8.86) (-8.99)

(-1.66) (-1.64) (-1.63) Hostile Deal 0.109*** 0.108*** 0.107***

0.037** 0.037** 0.037**

-0.011 -0.011 -0.011

(4.87) (4.83) (4.84)

(2.25) (2.27) (2.27)

(-1.62) (-1.62) (-1.62) Toehold Indicator -0.143*** -0.143*** -0.144***

0.003 0.004 0.002

0.001 0.001 0.002

(-8.85) (-8.88) (-8.95)

(0.23) (0.28) (0.15)

(0.30) (0.29) (0.37) Tender Offer 0.085*** 0.085*** 0.085***

0.087*** 0.087*** 0.087***

0.010*** 0.010*** 0.010***

(6.31) (6.33) (6.31)

(10.05) (9.97) (9.66)

(3.62) (3.63) (3.68) Stock Deal 0.124*** 0.124*** 0.124***

-0.045*** -0.045*** -0.045***

-0.018*** -0.018*** -0.019***

(6.64) (6.64) (6.67)

(-7.61) (-7.64) (-7.56)

(-4.33) (-4.34) (-4.36) Log of Firm Size -0.040*** -0.039*** -0.038***

-0.009*** -0.009*** -0.008***

-0.004*** -0.004*** -0.004***

(-7.58) (-7.38) (-7.44)

(-3.49) (-3.82) (-3.41)

(-5.07) (-5.01) (-5.27) Target Market to Book -0.018** -0.018** -0.018**

-0.017*** -0.017*** -0.017***

-0.003** -0.003** -0.003**

(-2.37) (-2.38) (-2.41)

(-10.76) (-10.63) (-10.43)

(-2.10) (-2.11) (-2.10) Target Debt to Assets 0.281*** 0.281*** 0.279***

-0.037** -0.035** -0.037**

0.004 0.004 0.005

(11.86) (12.00) (12.18)

(-2.34) (-2.31) (-2.36)

(0.60) (0.57) (0.62) Same SIC 0.009 0.009 0.009

-0.009 -0.009 -0.009

-0.001 -0.001 -0.001

(0.57) (0.61) (0.56)

(-1.08) (-1.02) (-1.06)

(-0.62) (-0.66) (-0.63) Run-up 0.704*** 0.704*** 0.706***

-0.119*** -0.120*** -0.119***

0.010* 0.010* 0.010*

(28.10) (28.22) (28.75)

(-7.30) (-7.51) (-7.49)

(1.86) (1.87) (1.86) Time Controls Yes Yes Yes

Yes Yes Yes

Yes Yes Yes

Industry Controls Yes Yes Yes

Yes Yes Yes

Yes Yes Yes N 5,934 5,934 5,934

6,678 6,678 6,678

4,509 4,509 4,509

Adjusted R2 0.269 0.270 0.269

0.134 0.136 0.134

0.072 0.073 0.073

47

Table 6 Large Termination Fees and Premiums with Two-Stage Least Squares This table reports estimated marginal effect estimates of probit models two-stage least square estimates. The sample includes bids that include termination fees from the SDC database for the years 1989 to 2011. An indicator for the Delaware incorporation of the target firm serves as an instrument in the selection of a large termination fee. Columns (1), (3), and (5) present the results of probit regressions of the determinants of large termination fees above 5%, 6%, and 10% of deal value, respectively. Columns (2), (4), and (6) report the second-stage estimates from two-stage least squares premium regressions with instrumented fees above 5%, 6%, and 10% of deal value, respectively. The predicted value of the probit regressions serves is included in the first stage of the two stage least squares. Premium is the price offered by the bidder divided by the target’s value 42 days prior to the bid announcement. Further details on variables are available in the Variable Appendix. Large Termination Fee (5%, 6%, and 10%) is an indicator equal to one if the target termination fee is above 5%, 6%, and 10% of deal value, respectively. Variables are winsorized at the 1% level. Year and two-digit SIC indicators control for industry and year fixed effects. Standard errors are clustered by acquirer industry. *, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively. T-statistics are reported in parentheses below estimates.

48

Model 1 Model 2 Model 3

Large Fee (5%) Premium Large Fee

(6%) Premium Large Fee (10%) Premium

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

Large Termination Fee (5%)

0.009

(0.09)

Large Termination Fee (6%)

0.029

(0.30)

Large Termination Fee (10%)

0.166

(1.06)

Delaware Incorporation -0.014**

-0.013**

-0.004

(-2.34)

(-2.03)

(-0.95)

Bidder Termination Fee -0.018 -0.037** -0.021** -0.037** -0.008 -0.037**

(-1.39) (-2.40) (-2.04) (-2.39) (-1.51) (-2.37)

Large Bidder Termination Fee (6%) 0.172*** -0.092* 0.120*** -0.098** 0.012 -0.094**

(7.90) (-1.90) (8.15) (-2.00) (1.51) (-2.41)

Target Lockup 0.011 0.011 0.016* 0.011 0.008 0.010

(0.54) (0.45) (1.84) (0.43) (1.22) (0.41)

Prior Bid 0.007 -0.066** 0.005 -0.066** 0.006 -0.068***

(0.33) (-2.55) (0.54) (-2.55) (1.08) (-2.61)

Hostile Deal 0.091* 0.230*** 0.055 0.229*** -0.058*** 0.229***

(1.90) (3.40) (1.42) (3.40) (-7.95) (3.39)

Toehold Indicator -0.014 -0.115*** -0.002 -0.115*** -0.003 -0.115***

(-0.87) (-3.48) (-0.18) (-3.48) (-0.34) (-3.47)

Tender Offer -0.023** 0.084*** -0.008 0.084*** -0.009** 0.086***

(-2.25) (4.84) (-0.82) (4.91) (-2.17) (4.99)

Stock Deal -0.020*** 0.149*** -0.012** 0.150*** -0.002 0.150***

(-2.84) (9.76) (-2.29) (9.84) (-0.81) (9.87)

Log of Firm Size -0.044*** -0.039*** -0.028*** -0.039*** -0.010*** -0.038***

(-12.80) (-6.88) (-9.42) (-8.01) (-4.68) (-8.76)

Target Market to Book 0.001 -0.015*** -0.003 -0.015*** -0.003* -0.015***

(0.37) (-2.82) (-1.27) (-2.81) (-1.83) (-2.75)

Same SIC 0.006 0.018 0.008 0.017 0.002 0.017

(0.78) (1.30) (1.20) (1.28) (0.67) (1.26)

Run-up -0.008 0.767*** -0.012 0.767*** -0.001 0.766***

(-0.56) (27.68) (-0.84) (27.66) (-0.28) (27.58)

Target Debt to Assets 0.075*** 0.255*** 0.043*** 0.255*** 0.014* 0.252***

(3.35) (7.30) (3.63) (7.38) (1.94) (7.32)

Time Controls Yes Yes Yes Yes Yes Yes

Industry Controls Yes Yes Yes Yes Yes Yes

N 3,623 3,623 3,623 3,623 3,623 3,623

Adjusted R2 N/A 0.300 N/A 0.299 N/A 0.295

Pseudo R2 0.278 N/A 0.378 N/A 0.436 N/A

49

Table 7 Large Termination Fees, Bid Competition, and Bid Completion This table presents the results of regressions of proxies of bid competition on indicators of termination fees, large termination fees, and control variables. The sample bids come from the SDC database for the years 1989 to 2011. In the first three models, Completion, an indicator variable equal to one if a bid is completed, is regressed on deal characteristics in probit regressions. In Poisson regressions, Number of Bidders is a count variable, which is regressed on deal characteristics. Challenged Bid is an indicator equal to one if a bid has a competing bid, and probit regressions model the probability of receiving another bid. Only the first bid in an auction is used in regressions of Number of Bidders and Challenged Bid. An auction is defined as all bids on a target within a 365 day rolling window of a prior bid. An indicator for target termination fees is equal to one if the merger agreement includes a target termination fee. Large Termination Fee (5%, 6%, and 10%) are indicators equal to one if the target termination fee are above 5%, 6%, and 10%, respectively. Variables are winsorized at the 1% level. Standard errors are clustered by acquirer industry. *, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively. T-statistics are reported in parentheses below marginal effect estimates.

50

Completion Challenged Bid

Number of Bidders

(1) (2) (3)

(4) (5) (6)

(7) (8) (9)

Large Termination Fee (5%) -0.043**

0.009

0.012 (-1.98)

(0.84)

(0.46)

Large Termination Fee (6%)

-0.028

0.029**

0.056*

(-0.99)

(2.04)

(1.73)

Large Termination Fee (10%)

-0.084**

0.065***

0.132***

(-2.14)

(3.09)

(3.26)

Target Termination Fee 0.244*** 0.240*** 0.241*** -0.028*** -0.029*** -0.029*** -0.063*** -0.065*** -0.066*** (23.64) (23.81) (24.20) (-4.16) (-4.61) (-4.45) (-3.81) (-4.14) (-4.08) Bidder Termination Fee -0.024* -0.023 -0.023

0.022** 0.022** 0.022**

0.038 0.039 0.039

(-1.65) (-1.60) (-1.63)

(2.04) (2.10) (2.07)

(1.36) (1.42) (1.42) Large Bidder Termination Fee (6%) 0.047 0.040 0.037

-0.018 -0.024 -0.020

-0.053 -0.067 -0.060

(1.32) (1.11) (1.03)

(-0.67) (-0.89) (-0.77)

(-0.78) (-0.94) (-0.88) Target Lockup 0.295*** 0.295*** 0.295***

-0.067*** -0.067*** -0.066***

-0.094*** -0.094*** -0.093***

(10.93) (10.93) (10.91)

(-4.18) (-4.16) (-4.10)

(-2.77) (-2.76) (-2.73) Hostile Deal -0.160*** -0.161*** -0.160***

0.055*** 0.055*** 0.054***

0.084*** 0.083*** 0.082***

(-7.96) (-7.99) (-7.95)

(4.60) (4.55) (4.43)

(3.28) (3.27) (3.21) Toehold Indicator -0.080*** -0.081*** -0.081***

0.022* 0.021* 0.022*

0.022 0.022 0.022

(-5.38) (-5.40) (-5.43)

(1.79) (1.77) (1.80)

(0.89) (0.88) (0.89) Tender Offer 0.127*** 0.127*** 0.127***

0.004 0.004 0.005

-0.004 -0.004 -0.003

(9.34) (9.38) (9.33)

(0.39) (0.40) (0.45)

(-0.18) (-0.18) (-0.14) Stock Deal -0.009 -0.009 -0.009

-0.010* -0.010* -0.010*

-0.027* -0.027* -0.026*

(-0.89) (-0.87) (-0.89)

(-1.75) (-1.74) (-1.78)

(-1.87) (-1.87) (-1.86) Log of Firm Size -0.007*** -0.006** -0.006**

0.008*** 0.008*** 0.008***

0.020*** 0.021*** 0.021***

(-2.64) (-2.44) (-2.51)

(4.70) (4.82) (5.08)

(4.59) (4.67) (4.84) Target Market to Book 0.004 0.004 0.004

-0.014*** -0.013*** -0.013***

-0.039*** -0.038*** -0.038***

(0.90) (0.93) (0.91)

(-3.60) (-3.52) (-3.54)

(-4.25) (-4.17) (-4.19) Same SIC 0.025** 0.026** 0.026**

0.019*** 0.019*** 0.019***

0.052*** 0.052*** 0.052***

(2.51) (2.52) (2.52)

(2.95) (2.90) (2.93)

(3.09) (3.07) (3.08) Prior Bid -0.134*** -0.134*** -0.134*** (-10.24) (-10.23) (-10.24) Time Controls Yes Yes Yes

Yes Yes Yes

Yes Yes Yes

Industry Controls Yes Yes Yes

Yes Yes Yes

Yes Yes Yes N 6,732 6,732 6,732

6,226 6,226 6,226

6,226 6,226 6,226

Pseudo R2 0.233 0.233 0.233

0.106 0.107 0.108

0.638 0.639 0.640

51

Table 8 Target Executive Post-merger Employment Arrangements, Compensation, and Deal Characteristics This table presents mean statistics of the post-merger employment and compensation outcomes of target managers. Out of the 222 sample bids with large termination fees, 190 have employment information. A matched sample of 222 bids without large fees is collected for comparison, and this matched sample has data on employment and compensation in 168 bids. Large termination fees are greater than or equal to 6% of deal value. T-statistics report the results from t-tests of differences between the large fee and small fee deals for each variable. Z-statistics report the results from non-parametric rank-sum tests of differences in variables across the large fee and matched samples. Variable definitions are in Table 1. Matched Sample Large Termination Fee Statistical Tests N Mean N Mean T-statistic Z-statistic Personal Benefits

Target CEO Receives Job 168 0.506 190 0.495 0.21 0.21 Other Executives Receive Job 168 0.595 190 0.558 0.71 0.71 CEO Receives Severance 168 0.375 190 0.416 -0.79 -0.79 CEO has Vesting Options 168 0.339 190 0.400 -1.19 -1.19 Deal Characteristics

Termination Fee Size ($mil) 168 7.250 190 14.829 -1.51 -7.07*** Target Fee Size (%) 168 0.022 190 0.091 -24.79*** -16.39*** Bidder Termination Fee 168 0.185 190 0.342 -3.4*** -3.36*** Bidder Fee Size (%) 168 0.008 190 0.026 -5.06*** -4.05*** Target Lockup 168 0.071 190 0.068 0.11 0.11 Bidder Lockup 168 0.000 190 0.005 -0.94 -0.94 Prior Bid 168 0.089 190 0.079 0.35 0.35 Hostile Deal 168 0.012 190 0.005 0.69 0.69 Toehold Indicator 168 0.077 190 0.074 0.13 0.13 Tender Offer 168 0.161 190 0.221 -1.44 -1.44 Stock Deal 168 0.494 190 0.458 0.68 0.68 Deal Value 168 258.098 190 208.742 0.46 3.97*** Same SIC 168 0.470 190 0.563 -1.76* -1.76* Target Characteristics

Target Market Capitalization 168 223.443 190 187.206 0.30 2.45** Target Assets 168 408.111 190 531.135 -0.43 0.62 Target Market to Book 168 0.971 190 0.692 2.34** 3.01*** Run-up 168 0.024 190 0.030 -0.16 0.37

52

Appendix Table A1 Multinomial Logits of the Determinants of Small and Large Target Termination Fees This table presents the results of multinomial logit regressions modelling the probability that a bid includes a small termination fee, large termination fee, or no termination fee. The base outcome is no termination fee. The sample bids come from the SDC database for the years 1989 to 2011. Large termination fees are fees equal to or greater than 6%. Variables are winsorized at the 1% level. Variable definitions are in Table 1. Panel A reports coefficient estimates for all determinants of termination fees. Panel B presents the results of four separate multinomial logits, each with one of four proxies for target distress. Panel C includes three proxies for the level of target financial constraints. Panel D includes three proxies of target performance. In Panels B, C, and D, “high” designates that the indicator variable is set equal to 1 if the firm characteristic is above the sample median, 0 otherwise. The models in Panels B, C, and D include all variables included in Panel A, but coefficient estimates are suppressed for brevity. Two-digit acquirer SIC indicators control for industry-fixed effects. Standard errors are clustered by acquirer industry. Indicators for years following 1994, 1997, and 1999 correspond to important court cases related to termination fees (Paramount, Brazen, and Phelps) and control for the impact of time. *, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively. Marginal effects are reported under coefficient estimates in brackets. T-statistics for marginal effects are reported in parentheses below coefficient estimates. In Panel A, chi-squared statistics are reported for Wald tests that test for the equality of coefficient estimates across columns (1) and (2).

53

Panel A – Multinomial Logits of the Determinants of Target Termination Fee Size Small Fee Large Fee Wald Test Chi-squared (1) (2) (3) Bidder Termination Fee 2.443*** 1.650*** 7.66*** [0.570***] [0.001] (12.84) (0.46) Large Bidder Termination Fee (6%) -0.673 3.014*** 58.51*** [-0.173] [0.025***] (-0.99) (7.02) Target Lockup -0.456 -0.135 0.79 [-0.107] [0.001] (-1.21) (0.40) Prior Bid -0.535*** -0.271 1.12 [-0.125***] [0.000] (-3.74) (0.27) Hostile Deal -2.864*** -2.367** 0.19 [-0.667***] [-0.004] (-8.67) (-0.56) Toehold Indicator -1.175*** -0.334 20.91*** [-0.276***] [0.003*] (-7.82) (1.89) Tender Offer 0.978*** 0.901*** 0.14 [0.227***] [0.002] (13.48) (1.45) Stock Deal 0.097* -0.172 2.37 [0.024**] [-0.002] (1.96) (-1.32) Log of Firm Size 0.300*** -0.391*** 204.21*** [0.073***] [-0.004***] (8.61) (-14.05) Target Market to Book 0.064* -0.157 4.98** [0.016*] [-0.001**] (1.75) (-2.27) Same SIC -0.011 0.059 0.14 [-0.003] [0.000] (-0.19) (0.36) Time Controls Yes Yes Industry Controls Yes Yes N 6,732 Pseudo R2 0.289

54

Panel B – Multinomial Logits of Termination Fee Size Including Proxies for Target Distress

Small Fee Large Fee Chi-squared Small Fee Large Fee Chi-squared Small Fee Large Fee Chi-squared Small Fee Large Fee Chi-squared

Distress (1) High CHS Score (2) High Ohlson's O (3) High Debt to Assets (4) High Interest Coverage Ratio -0.271*** 0.835*** 35.66*** -0.083 0.404** 8.47*** -0.042 0.274* 3.81* 0.058 -0.593*** 12.12*** [-0.068***] [0.007***] [-0.019] [0.001***] [-0.011] [0.002**] [0.014] [-0.002***] (-4.56) (7.01) (-1.17) (3.02) (-0.60) (1.99) (0.79) (-3.52) Bid Controls Yes Yes Yes Yes Yes Yes Yes Yes Time Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry Controls Yes Yes Yes Yes Yes Yes Yes Yes N 6,732 4,816 6,687 4,868 Pseudo R2 0.293 0.305 0.290 0.305 Panel C – Multinomial Logits of Termination Fee Size Including Proxies for Target Financial Constraints Small Fee Large Fee Chi-squared Small Fee Large Fee Chi-squared Small Fee Large Fee Chi-squared Financial Constraints (5) High SA Index (6) High Whited-Wu Index (7) High Kaplan-Zingales Index -0.282*** 0.088 3.06* -0.234*** 0.243 4.96**

-0.193** 0.296 6.98***

[-0.067***] [0.002]

[-0.056***] [0.004*]

[-0.045***] [0.002**] (-3.00) (1.17)

(-2.74) (1.89)

(-2.60) (2.31)

Bid Controls Yes Yes

Yes Yes

Yes Yes Time Controls Yes Yes

Yes Yes

Yes Yes

Industry Controls Yes Yes

Yes Yes

Yes Yes N 6,732

6,272

5,995

Pseudo R2 0.269 0.265

0.291 Panel D – Multinomial Logits of Termination Fee Size Including Proxies for Target Performance

Small Fee Large Fee Chi-squared Small Fee Large Fee Chi-squared Small Fee Large Fee Chi-squared

Performance (8) High Prior Return (1 year) (9) High Free Cash Flow (10) High ROA 0.264*** -0.175 4.94** 0.211*** 0.102 0.71 -0.004 -0.529** 7.23*** [0.063***] [-0.002*] [0.050***] [-0.000] [0.001] [-0.004***] (4.46) (-1.93) (3.25) (-0.22) (0.05) (-2.94) Bid Controls Yes Yes Yes Yes Yes Yes Time Controls Yes Yes Yes Yes Yes Yes Industry Controls Yes Yes Yes Yes Yes Yes N 6,732 6,522 6,732 Pseudo R2 0.291 0.290 0.290

55

Appendix Table A2 Large Termination Fees and Expected Premiums in Switching Regressions This table presents the results of switching regressions of premiums for bids with termination fees for the years 1989 to 2011. In Panel A, the selection model estimates the probability that a bid includes a large termination fee greater than 6% of deal value. Regressions of premiums for bids with small and large termination fees are then estimated with corrections for selection bias. Premium is the price offered by the bidder divided by the target’s value forty-two days prior to the bid announcement. Rho estimates the correlation between error terms in the selection model and the respective premium model. Further details on variables are available in Table 1. Variables are winsorized at the 1% level. Indicators for relevant case law and two-digit SIC indicators control for time- and year-fixed effects. Standard errors are clustered by acquirer industry. *, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively. T-statistics are reported in parentheses below coefficient estimates. In panel B, mean predicted premiums are reported for large and small fee bids in addition to counter-factual mean predicted premiums of large (small) fee bids switching to the small (large) fee regime. The actual (realized) mean premium is reported in parentheses. Panel A – Switching Regressions of Bid Premiums and Fee Size Choice Selection Small Fee Bid Premium Large Fee Bid Premium (1) (2) (3) Bidder Termination Fee -0.109 -0.040** 0.149 (-0.90) (-2.45) (1.16) Large Bidder Termination Fee

1.563*** -0.189*** -0.269

(8.84) (-4.21) (-1.09) Target Lockup 0.194 -0.015 0.190 (1.40) (-0.58) (1.53) Prior Bid 0.316** -0.077*** 0.096 (2.30) (-2.83) (0.87) Hostile Deal 0.414 0.186*** 0.647 (1.17) (2.64) (1.47) Toehold Indicator 0.106 -0.106*** -0.154 (0.56) (-3.04) (-1.15) Tender Offer -0.073 0.096*** -0.041 (-0.70) (5.34) (-0.41) Stock Deal -0.285*** 0.157*** 0.044 (-3.18) (9.84) (0.51) Log of Firm Size -0.277*** -0.021*** -0.043 (-6.60) (-4.92) (-0.95) Target Market to Book -0.021 -0.010* -0.084** (-0.48) (-1.85) (-2.07) Same SIC 0.012 0.010 0.032 (0.14) (0.69) (0.44) Run-up -0.507*** 0.756*** 0.881*** (-2.62) (25.44) (6.49) Target Debt to Assets -0.160 0.242*** 0.357** (-0.95) (7.36) (2.42) Delaware Incorporation -0.212*** (-2.89) Rho -0.959*** -0.288 (-43.96) (-0.62) Time Controls Yes Yes Yes Industry Controls Yes Yes Yes N 3,623 3,470 153

56

Panel B – Estimated Mean Premiums across Small and Large Fee Regimes Expected and Actual Premiums Across Different Regimes Small Fee Deals Large Fee Deals Expected Actual Expected Actual Small Fee Regime 64.69% 64.17% 3.47% N/A Large Fee Regime 63.56% N/A 56.32% 56.32% Difference 1.13%

52.85%

T-statistic 3.21***

31.91***