1

Do Wealth Creating Mergers and Acquisitions Really Hurt Bidder Shareholders?*

Ronald W. Masulis† and Peter L. Swan

‡,

School of Banking and Finance, Australian School of Business, UNSW

First Version: November, 2010. Draft: February 5, 2015

ABSTRACT

We examine the expected economic benefits of mergers and acquisitions for a large dataset

with matched sample of public bidders and targets from seven large common law target

countries including the US. We conclude that both signaling and revelation biases associated

with bid announcements are responsible for the commonly reported finding that on average

takeovers announcement returns are negative, which researchers routinely interpret as

evidence that these bids are harmful to bidder shareholder wealth. After accounting for these

two biases in the bid announcement returns, we demonstrate that bidders on average benefit

from takeovers capturing roughly 40 percent of the economic gains in both stock and non-

stock bids. This increases to 44 percent when the small sample of contested acquisitions is

omitted. These economic gains are very large in magnitude, amounting to approximately

$266 million (11.9 percent) per successful deal. By including a number of robustness tests

including studying bids that fail for exogenous reasons and that are largely free of signaling

and revelation biases, we confirm the neoclassical view that takeovers are on average positive

net present value projects for a typical bidder.

Key Words: M&A, takeover bids, acquisition benefits, bidder gains, acquisition synergies,

targets, bidders, failed bids.

JEL Codes: G34, G14

* We thank the Australian Research Council (ARC) for financial support, Mark Humphery-Jenner and Cybele

Wong for exceptional research assistance, and Ken Ahern, Espen Eckbo, Emir Hrnjic, Paul Malatesta, Marco

Navone, Rex Thompson, Karin Thorburn and Moqi Xu for their many helpful

suggestions and to participants at

the Financial Intermediation Research Society (FIRS) Conference, Sydney 2011, China International

Conference in Finance (CICF), Wuhan 2011, Chulalongkorn Accounting and Finance Symposium, Bangkok,

November 2011 (CAFS), the Australasian Finance and Banking Conference (AFBC), Sydney, December 2011,

and the 2012 American Finance Association (AFA) Conference in January for their comments. Eugene Chua,

Nick Orlic, and Uwe Helmes provided assistance earlier on. Brett Tobiansky undertook extensive preliminary

tests of a number of these predictions for his honors thesis and we are very grateful for his input. † Email: ron.masulis@unsw.edu.au.

‡ Contact author. Department of Banking and Finance, ASB, UNSW Sydney NSW 2052 Australia. Tel: +61 (0)

2 9385 5871. Email: peter.swan@unsw.edu.au.

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1. INTRODUCTION

Many studies document that bidders systematically destroy shareholder wealth in mergers

and acquisitions (M&As), while targets benefit at their expense.1 Moeller, Schlingemann, and

Stulz (2005) find that shareholders of bidding firms over the 1998-2001 period lose 12 cents

per dollar of purchase price on takeover bid announcements and collectively lose a sizeable

$312 billion to conclude that there is a more negative reaction to a large bid. In their survey,

Betton, Eckbo, and Thorburn (2008, Table 9) find a significantly negative abnormal return of

-2.21 percent for large stock bidders of public targets, which shrinks to -0.3 percent for large

cash bidders of public targets. They find that small stock bidders approximately break-even,

while small cash bidders for public targets gain 3.06 percent. Surprisingly, small stock

bidders for private targets are favorably viewed by the market with a positive 6.46 percent

return. Thus, if one sets a zero NPV as the minimum expected return for a rational bidder in

an auction market, then only small bidders satisfy this rationality criterion. Given that M&A

investment plays a major role in the global economy2, constituting $2.1 trillion in the US in

just one year alone, or 15% of GDP (see Bao and Edmans (2011)), it is strange to find such

widespread M&A investment failures.

The virtually universal finding that acquiring shareholders do not gain from M&A activity

in listed companies is prominently mentioned in practically every major finance textbook

(see, for example, Damodaran (2001, pp.864-865), Brigham and Daves (2010, p.929), Ross,

Westerfield, and Jordan (2013, p.869), Copeland, Weston, and Shastri (2005, p.778) and Berk

and DeMarzo, (2011, p.915)). While the overall synergistic benefits measured by the net

change in the combined value of the bidder and target on a takeover announcement is on

average marginally positive, bidder shareholders are distinct losers, while target shareholders

are clear winners. Andrade, Mitchell and Stafford’s (2001) survey indicates a massive long-

event-window loss to acquirers that averages 3.8 percent, which they interpret as a possible

subsidy to targets that gain 23.8 percent overall.

1 For an early example of a study showing that bidder value falls at the time of takeover announcement see

Dodd (1980). Betton, Eckbo, and Thorburn (2008, Table 6) summarize 16 relatively recent large-sample studies

of bidder returns. Most report sizeable takeover samples in which the bidder’s share price reaction is negative. 2 By early November 2014, aggregate global M&A transactions exceeded $31 trillion (Dealogic) or 40 percent

of world GDP.

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These findings stand in stark contrast to what may be termed the neo-classical theory of

M&A (see Ahern and Weston (2007) and Ahern and Harford (2014)) which asserts that profit

motivated bidders naturally drive the ownership of assets to their highest value use. From this

perspective bidders (and their shareholders) expect on average to benefit from M&A

transactions, rather than suffering losses. Thus, if there are changes in technology, for

example due to innovation, regulatory changes, or shifts in demand for goods and services

such that the existing ownership of assets is no longer optimal, M&A activity should occur to

profit from a redistribution of the ownership and control of these assets from a target to a

bidder. This should reflect itself in a gain the overall value of the merged firms, which results

from the improved utilization of the combined firm’s assets.

In the last few years, several researchers have begun to question the conventional wisdom

that bidders destroy shareholder wealth and have reported some evidence that is consistent

with the neoclassical theory and our findings that are set out below. Ahern (2012) identifies

the appropriate metric for merger partner dollar gains, and finds that targets do not do a great

deal better than bidders, although bidders tend to be much larger in size. Moreover, in vertical

acquisitions a target’s relative scarcity and product market dependence help explain its share

of total merger gains. Cai, Song, and Walkling (2011) present evidence to show that some

bids come as more of a surprise to the market than others. In particular, the first bid within an

industry is a greater surprise to the market and has a higher return than subsequent bids. They

conclude that, after accounting for anticipation, bidding activity generates wealth.3

Betton, Eckbo and Thorburn (2008, Table 7) examine bidder cumulative abnormal returns

(CAR) from 41 days prior to the merger announcement until the date when the contest

outcome is known. They find a highly significant positive relationship with the magnitude of

the target’s stock price run-up and conclude that large run-ups mutually benefit both bidder

3Dimopoulos and Sacchetto (2013) structurally estimate the likelihood of preemptive bidding and target

resistance. They show that only rarely does a second bidder materialize, which means that most initial bids are

preemptive. Their simulations imply that initial bidder valuations of targets are roughly twice the pre-bid value

and the potential rival’s valuation is a far lower 58% above the pre-bid value. Thus, prospective synergistic

gains are likely to be the monopoly of the initial bidder, so that the main barrier to a bidder extracting most of

the gains is target resistance. Our findings are the first to justify the high initial bidder valuations that are

otherwise unexplained by their modeling. Furthermore, in their framework, deals fail endogenously because

synergistic gains are insufficient to overcome target resistance. Thus, endogenously failed deals yield fewer

synergistic benefits than exogenously failed deals, which is precisely what we find in our sample of

endogenously and exogenously failed deals.

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and target. They also note that the overall negative market reaction to control contests is

puzzling. Betton, Eckbo, Thompson, and Thorburn (2011) find strong empirical support for

the hypothesis that bidder abnormal stock returns are increasing in the target stock price run-

up to a merger announcement. This finding is predicted by their model in which takeover

rumors generate rational market anticipation of synergies from a pending takeover.

In this study, we present new evidence that there is no reason to think that M&A activity

typically occurs at considerable cost to bidder shareholders. On the contrary, we demonstrate

that not only are the net economic benefits of M&A activity large, but they are typically very

sizeable and appear to be shared roughly equally by bidder and target, consistent with

neoclassical theory (see, for example, Gort (1969), Mitchell and Mulherin (1996),

Maksimovic and Phillips (2001), Jovanovic and Rousseau (2002), and Harford (2005)). In

fact, from our calculations, bidder shareholders on average gain 35 percent of synergistic

gains in both typical stock-only transactions and non-stock deals, rising to 44 percent when

the relatively small proportion of contested bids is removed. Recent evidence of such mutual

gains comes from Hoberg and Phillips (2010) who identify product market synergies in

M&A activity and Bena and Li (2014) who provide direct evidence of synergistic benefits

based on technology transfers between bidder-target pairs.

These bidder gains are not obvious to most researchers since making a bid often releases

other bad news about a bidder’s stand-alone value. Thus, the prospective benefits of a typical

M&A bid can be masked by the negative news released about the bidder’s stand-alone value,

so that the net effect on a bidder’s equity value is positive, but small in the case of a non-

stock-only bid, while it falls in value by approximately 2.2 percent in the case of a stock-only

bid. However, we find that this fall in value for stock-only bids is more than offset by an

average 5.4 percent run-up in the bidder’s stock price as good news is typically released by

the bidder prior to the bid, perhaps to help lock-in better terms of trade. In fact, simply

including the full 120-day price run-up, while otherwise following the conventional

methodology, transforms the conventional estimate of a large bidder loss in a stock bid to a

sizeable average gain of 3.2% or $46 million in dollar terms.

Most recently, behavioral financial economists have proposed an alternative theory based

on the use of a bidder’s own stock, rather than cash as the method of payment (e.g., Shleifer

and Vishny (2003) and Dong, Hirshleifer, Richardson, and Teoh (2006)). This literature

argues that bidder managers can use their firms’ relatively overvalued stock to benefit their

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own shareholders at the expense of target shareholders, with no combined economic gain to

bidders and targets, while potentially generating huge losses to society as a whole, due to the

inefficient transfer of control of these assets.

We show the inference that bidder shareholders, and in particular large bidder

shareholders, fail to benefit from takeovers and often appear to become much worse off, is

due to a fundamental “revelation bias” which misleads researchers using standard event study

methodology to mis-measure the expected synergistic gains in bids, thereby incorrectly

assessing whether bidder shareholders are victims of manager-shareholder agency problems.

The logic for this conclusion follows.

First, bidder managers rewarded for short-term performance often instigate a “signaling

bias” by deliberately timing the “good news” of a M&A bid to coincide with the release of

bad news, such as unmet earnings targets (see Bhagat, Dong, Hirshleifer, and Noah (2005)).

This common practice, documented in the Wall Street Journal, ‘It’s Wall Street’s version of

‘Wag the Dog’, 1998, p. C1, confounds the information released about an acquisition’s value

to a bidder that is capitalized into a bidder’s stock price on the bid announcement, and results

in a concurrent downward revaluation of a bidder’s stand-alone value.

Second, the bid itself typically releases bad news about a bidder. For example, a bid may

reveal serious unprofitable empire-building tendencies on the part of incumbent management,

or a negative bidder management assessment of the firm’s profitable internal growth

opportunities, which leads them to switch to external investment (see Eckbo, Maksimovic

and Williams (1990), Fuller, Netter and Stegemoller (2002) and Hietala, Kaplan and

Robinson (2003)). More recently, Barraclough, Robinson, Smith, and Whaley (2013) use

option prices on a necessarily tiny sample of 167 takeover offers to argue that bids release

bad news about the bidder and good news about the target. They show that their sample is

biased towards a small overall synergistic gain estimate of 5.9 percent with gains equally split

between bidder and target. Their overall gain estimate is about half of our overall gain

estimate based on 5,588 takeover bids.

Third, as in the Malatesta and Thompson (1985) model, a bidder may have a long history

of generally successful bids, so that a new acquisition bid comes as no surprise to investors.

This leads to a near zero market reaction to such bids, even though beneficial acquisitions are

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the expectation as argued by Schipper and Thompson (1983). Serial bidders such as General

Electric, Cisco, GlaxoSmithKline and Capital One are possible examples of this activity.4

Fourth, given the conflict of interest between existing and new shareholders and manager

access to proprietary information and incentives to protect existing shareholders observed by

Myers and Majluf (1984), stock acquisitions that require large new equity issues, release bad

news about a stock’s true value. News of equity sales gives rise to substantial stock price falls

that can offset some or all the good news associated with a deal’s synergistic gains. Similarly,

stock issuance is likely to be the preferred M&A financing choice when a bidder’s stock is

overvalued (see Travlos (1987) and Shleifer and Vishny (2003)).

Fifth, bidders using stock as M&A currency have incentives to artificially inflate their own

stock price by releasing optimistic news in the lead up to a bid, when the exchange rate

between a bidder and target shares is set (Ahern and Sosyura (2014)). In a similar vein, He,

Lie, and Shu (2013) show that stock bidders manage analyst earnings forecasts downward a

considerable time prior to the bid so that they can announce a positive earnings surprise just

prior to setting the share exchange rate. Erickson and Wang (1999) find evidence that in the

lead up to a merger, bidders in stock mergers upwardly manage earnings. Louis (2004)

reports that acquirers inflate their earnings immediately prior to stock merger announcements,

in contrast to cash bidders who do not inflate earnings. The artificially inflated bidder stock

price is then likely to fall when the over-valuation motive is revealed by news of a stock bid.

Finally, bidder initial announcement returns can be downward biased because of a

significant likelihood of bid failure, perhaps due to potential entry by competing bidders,

regulatory disapproval, or insufficient synergies to overcome target resistance. In our sample,

the likelihood of failure is a sizeable 17 percent. Bhagat, Dong, Hirshleifer, and Noah (2005)

propose a probability scaling method (PSM) to estimate the probability of a successful bid

and the intervention method (IM) to assess the likelihood of competing bids. The PSM and

IM methods can be used to tackle two of the many dimensions of this complex problem.

Merger failures offer important insights into the merger process. Combining merger

partner assets to facilitate new positive net present value (NPV) projects create positive

excess (abnormal) returns for a bidder’s long term shareholders. Hoberg and Phillips (2010)

4 The point made here about expectations and share price reaction is quite distinct from the considerable

empirical literature on whether serial bidders are successful or not.

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show that the size of merger synergies is related to whether a target has skills or technologies

that can help a bidder differentiate its products from its rivals and hence improve

profitability. If a bid fails, then we expect to find the stock prices of the merger partners to

fall reflecting this loss of synergies.

Roll (1986) proposes the hubris hypothesis which argues that at least some managers

overpay for targets because they overestimate synergies, which leads to a loss in bidder value.

Similarly, Malmendier and Tate (2008) conjecture that overconfident CEOs overpay for

targets. If either hypothesis is valid, then bidder stock prices should systematically rise on

news of a bid failure as the overvalued bid is withdrawn. In fact, we see quite the opposite.

Several studies investigate M&A withdrawal announcements. A negative target share

price reaction to offer withdrawal announcements indicates the size of the loss of synergistic

benefits (Bradley, Desai, and Kim (1988) and Samuelson and Rosenthal (1986)). Bradley

Desai, and Kim (1988) find that after an offer withdrawal, a target’s share price on average

declines to it pre-offer level over the following year, if no subsequent acquisition bid occurs.

Bradley’s (1980) study of pure cash offers shows that bidders experience severe negative

reactions to offer failure, compared to positive reactions in successful offers.

Our study builds on the well-known Shleifer and Vishny (2003) study that puts forward an

explanation for the choice of stock acquisition bids over cash bids where only bidder

management is aware of the temporary overpricing of its stock. Bidder shareholders may gain

and target shareholders correspondingly lose from the resulting exchange of overvalued

bidder equity for relatively underpriced target equity. A bidder is assumed to be able to lock

in favorable terms for a target acquisition prior to the public release of bad news known

privately by the bidder. It follows that on a bid failure these price effects should be reversed.

In a recent contribution to this literature, Savor and Lu (2009) find that withdrawn stock

bidders suffer a more severe value reduction than completed stock bidders and that successful

stock bidders continue to outperform over the next three years. They attribute these finding to

the inability of unsuccessful bidders to swap their overvalued equity for fairly valued target

equity and conclude successful stock bidders realize sizable benefits for their shareholders

through the sale of overvalued shares to target shareholders. Under their market timing

hypothesis, bids create no synergistic benefits. As shown below, their hypothesis predicts that

on news of a stock bid failure, target share value rises by precisely the amount failed bidder

share value declines, so that the combined value of the two stocks is unchanged. Even prior to

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any market anticipation of bid failure, bidder and target stock prices are predicted to move in

opposite directions. One puzzle with their analysis is that while overpriced bidder shares is an

endogenous cause of bid failure, they nonetheless find a negligible difference in the relative

fall of bidder share prices in their “exogenously failed” and “all-failed” samples.

An important element in Savor and Lu‘s approach is their solution to the endogeneity

problem that arises from the inclusion of failed bids. Bidders with overvalued stock have

greater incentives to push for stock as the mode of payment, rather than cash. This

complicates matters as the bid may fail due to target shareholders discovering a bidder’s

overvaluation. Savor and Lu solve this endogeneity issue by selecting an “exogenous failed

subsample” where mergers fail for exogenous reasons outside of the control of a bidder or

target, and thus are unrelated to bidder or target valuation. Importantly, Savor and Lu restrict

their analysis to bidders only, so they are unable to directly test either the “no-synergy” or the

offsetting bidder and target price reaction predictions.

M&A researchers rarely use both failed and completed bids in their analysis. A key

property in our approach is that failed bids are subject to a much weaker “revelation bias”

relative to new bids, and this is especially true for stock bids. Bidder stock prices following

an initial bid announcement already reflects the endogenous valuation error associated with a

stock bid due to its perceived relative overvaluation, managerial empire-building incentives,

evidence of weak internal growth prospects, plus any other revelation problems. In contrast,

the induced change in a stock bidder’s value following an exogenously induced offer

withdrawal represents a much cleaner experiment, devoid of all of these endogenous effects

of initial bid announcements.

A fundamental insight of our study is that when failed bidders are matched with their

targets and deal failure is exogenously determined, we are able to impute the expected

synergies of the bid and the share of the gains captured by a successful bidder. We are also

able to more rigorously test the market timing hypothesis. We show that both bidder and

target are subject to large losses in value on a bid withdrawal which is triggered by an

exogenous cause, consistent with the bids being positive NPV investments and both parties

sustaining losses of synergistic benefits on bid failure. Furthermore, the more the target stock

falls, the more the bidder stock also falls, and vice versa, consistent with bidders and targets

jointly suffering a loss (or realizing a gain) of expected synergistic benefits. We also find that

bidder stock prices decline less in value from endogenously failed bidders than for

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exogenously failed bidders, indicating that endogenous failure is most likely due to a lack of

sufficient synergies, rather than an excessively high bidder stock price relative to its newly

revealed lower value. These findings contradict the market timing hypothesis.

Studying failed bids yields a great deal more insight than conventional approaches that

focus largely on successful bids. With failed bids, the subsequent long-term history of both

the bidder and target are observable, whereas in successful bids one cannot generally observe

either individual firm post-acquisition, only the combined firm. Thus, failed bids are a huge,

largely untapped mine of new information about the fundamental causes and consequences of

mergers and acquisitions. Just as a marriage counselor gains insights about what makes

successful marriages work from painful divorces, the same is true for merger proposals.

As a means of increasing the power of our analysis of matched bidders and targets when a

bid fails, we compile a much larger database of mergers and acquisitions drawn from seven

“Anglo” target countries with common law legal systems: Australia, Canada, Hong Kong,

New Zealand, Singapore, the United Kingdom, and the United States, and forty-seven bidder

countries, compared with most other studies that focus on a single country.5 With this

common law sample totaling 5,168 bids, we are able to analyze 4,663 successful bids, and

955 failed bids. Within this full sample, 3,530 bids, or approximately 63 percent, required

regulatory approval. For this subset, which is more likely to be subject to “exogenous”

failure, we are able to analyze 3,064 successful bids and 466 failed bids.

We conduct our analysis in two stages. First, using our entire sample of 5,588 observations

made up of bidders and targets, we conduct an investigation into the abnormal returns

around: (i) the initial announcement of a merger or acquisition, and (ii) the announcement

date of the bid outcome (success or failure). Specifically we find an average bidder stock

return in the run-up period 120 days before the bid announcement period of 5.4 percent for

stock bids relative to other bids combined with a stock bid announcement period return of

−2.2 percent, which yields an approximate net bidder gain of 3.2 percent. These run-up

findings indicating strong net acquirer gains are all significant at the 1% level. We believe

ours is the first study to fully recognize and incorporate this systematic run-up effect for stock

bids. There is no evidence of a run-up for other types of bid.

5 We are not the first to consider acquisitions outside of the United States. For example, Netter, Stegemoller, and

Wintoki (2011) examine a comprehensive set of mergers and acquisitions.

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Our longer pre-announcement event window is motivated by the analysis of Jegadeesh and

Tang (2010). They find evidence of significantly higher institutional buying pressure for

bidder shares in the period from six months to two months prior to the bid announcement and

particularly by bidder advisors starting five months prior to the bid. They also find that it is

only target advisor buying activity that raises target stock price, especially in the month prior

to the bid announcement, which exhibits a 3.7 percent return. Thus, trading activities of funds

tied to target advisors is fully consistent with the financial interests of their clients. While

bidder advisors help to lock-in better terms of trade for bidders, target broker advisors act to

improve a target’s terms of trade.6

These finding are also consistent with the evidence of Ahern and Sosyura (2014)

suggesting that stock bidders release a string of positive announcements in the run-up to an

M&A bid announcement, thereby boosting their share price. In their sample, merger talks

begin on average 64.5 days prior to a bid’s public announcement, but bidder advisors are

aware of the proposed offer well before merger talks begin. He, Lie, and Shu (2013)

demonstrate how bidders are able to manipulate analyst beliefs to help achieve a pre-bid

announcement stock price run-up. Erickson and Wang (1999) and Louis (2004) also find

evidence of earnings manipulation well before the bid announcement.

The second stage of our analysis utilizes a series of cross-sectional regression models to

investigate the creation, and subsequent loss of synergistic benefits. Using an innovative

method for examining synergistic benefits, we split our analysis into three sub-sections. The

first subsection investigates whether synergistic benefits are created using an event study of

initial announcements of mergers and acquisitions. We then explore other segments of the

120 day pre-announcement period through to after-the-bid outcome announcement separately

for the successful and failed bids.

Consistent with the survey by Andrade, Mitchell, and Stafford (2001), we find over a five-

day bid announcement window that bidder and target abnormal returns are −0.9 percent and

20.1 percent, respectively. Hence, our sample replicates the standard findings in conventional

event studies, which has led researchers to conclude that acquisitions are destructive of bidder

value, producing (apparent) negative bidder wealth effects. We find that on average the

6 A similar pattern of high ranked legal advisors helping their M&A clients improve their terms of trade is found

by Krishnan and Masulis (2013).

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combined bidder and target initial bid announcement return is positive (9.4 percent) and is

negative around the five-day announcement of a failed bid (−2.7 percent).

Bid failure is clearly anticipated well before its formal announcement as an 8.5 percent

value decline occurs for the combined entity after the bid announcement, but prior to the

formal bid failure announcement. Over the entire window, from 120 days prior to the bid to

two days after the bid outcome is known, the combined gain to successful acquisitions

relative to failed ones is 11.9 percent, which is significant at the 1% level. Thus, this evidence

yields strong support for the existence of overall synergistic gains in our sample.

Could reverse causality along the lines of Shleifer and Vishny (2003) explain the large

120-day bidder stock run-up of 5.4 percent prior to a bid announcement? In their argument,

firms whose stock price rises in an unjustifiable fashion have incentives to opportunistically

make stock bids to exploit this overvaluation. The evidence from a large number of studies

discussed above contradicts this conjecture, instead finding that a bidder and its advisors,

anticipating a stock bid, take actions over many prior months to lock-in favorable terms of

trade. Overwhelmingly, the evidence is that the prior price run-up is a consequence of a

planned stock bid. The proposition that stock bids are generally a consequence of random

price run-ups is not supported by the evidence.

A more difficult question is how can we rule out reverse causality in the case of failed

stock bids ‒ that the deal failed because the target wanted to trigger deal failure after it

updates its forecast of subsequent bidder performance and finds it to be much poorer than its

prior assessment? This could occur after a target due diligence investigation where it obtains

information of bidder overvaluation. We rule out this otherwise plausible story by

incorporating the target’s stock price reaction to a deal failure announcement when

examining bidder abnormal returns over an extended event window and vice versa when

examining target abnormal returns around the same announcement period.

If the reverse causality story is true, then both bidder and target stock prices would decline

after the release of bad news about the bidder, until the endogenous collapse of the bid. The

bidder’s stock value would continue to fall as it no longer benefits from purchasing a

relatively underpriced target, but the target’s stock would rise in value as its shareholders are

freed from the contractually agreed exchange of overvalued bidder shares. We do not find

evidence supporting this scenario. In fact, we find a statistically significant and economically

important positive relationship between bidder and target returns over the 120-day run-up

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period until two days prior to the announcement, over the announcement period itself, and

from two days post the bid announcement until two days post the bid outcome

announcement. These findings are consistent with both bidder and target advisors playing a

significant trading role to ensure that prospective shared synergy gains of a bid are captured

in the stock prices of both bidder and target and especially in the pre-announcement period.

We structure the remainder of the study as follows. Section 2 provides a simple theoretical

model of valuation changes around failed bids. Hypotheses are further developed in Section

3. Section 4 describes our data sources, acquisition sample and empirical methods. Section 5

contains the main empirical evidence and discusses its implications, Section 6 presents

estimates as to how synergistic gains are shared and Section 7 concludes.

2. SYNERGY MEASUREMENT AND DIVISION OF GAINS

2.1 Cash Bid for the Entire Target

Denote the mean initial equity values of the listed bidder and a listed target well before the

opening of M&A negotiations, at the time the bidder engages an advisor to identify a suitable

target as I

BE and ,I

TE respectively, and denote the overall synergistic gains of the deal as G ,

which is shared between the bidder and target in proportions denoted by BG and TG

respectively. Superscripts C and S denote cash and stock financing respectively to

distinguish these two important types of M&A currency. Thus ,C I

T T TG P E represents the

premium paid to the target in a cash bid, where TP represents the cash purchase price paid

(for simplicity, we assume there is no prior bidder toehold in target shares, no competing

bidder and neither the bidder nor the target is debt financed), and C

TG represents the dollar

value of the target’s share of the synergistic gain in a cash bid.

The market value of a cash bidder following a successful acquisition is denoted by ,

C

B SV ,

with subscripts orS F denoting success or failure respectively. Recognizing that the price

paid for the target TP , is simply the sum of target’s pre-bid valuation I

TE and its share of the

overall synergistic gain in a cash deal, C

TG , this market value simplifies to the sum of the

bidder’s initial pre-run-up value plus its share of the synergistic gain,C

BG :

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,

C I I C I I C C I I C C I C

B S B T T B T T T B T B BV E E G P E E G G E E G G E G . (1)

Equation (1) makes explicit that any run-up gain to the bidder forms part of the deal’s

synergistic gain. Once the terms of trade, TP , are fixed, the bidder’s value is increasing in the

combined synergistic gain, .CG

The target gain is conditional on bid success. If the bid is withdrawn due to exogenous

causes, i.e., fails due to (say) regulatory intervention, the bidder’s valuation conditional on

failure, ,

C

B FV , becomes simply its pre-bid value (or lower if the pre-bid target value was

raised by the market’s anticipation of a successful bid):

,

C I C C I

B F B B B BV E G G E . (2)

The successful bidder’s synergistic gain is given by the difference in the valuations of the

successful bidder and the equivalent failed bidder:

, ,

C C C

B B S B FG V V , (3)

since by assumption no new information is released about the bidder or the expected

synergistic gains, either by the bid itself, which is already public information, or by its

withdrawal.

During the pre-bid-announcement phase, any leakage of information about the bid will

influence the stock prices of both the bidder and target in a positive direction since they both

share in the combined synergistic gain if the bid succeeds. The same will be true after the bid

announcement. But if there is anticipation of bid failure or a deal failure is announced, then

both stock prices should fall reflecting the shared loss of expected synergistic gains. Hence,

the stock price of the bidder should move positively with that of the target and vice versa at

all times (see Hypothesis III below).

Let C

B represent the absolute value of the revelation loss in value due to any bad news

released about a bidder’s stand-alone market value in a cash bid, regardless of the bidder’s fit

with the target, i.e. independent of deal synergies. Hence, just prior to news of a bid’s

outcome, a cash bidder’s expected market valuation should equal:

14

, ,1

1 ,

C C C

B S B S S B F

I C C I C I C C

S B B B S B B B B S B

E V p V p V

p E G p E E p G

(4)

where Sp represents the probability of deal success and 1F Sp p is the probability of

failure. In our sample (and in the majority of M&A empirical studies), a negligible change is

found in a bidder’s value on a cash bid announcement, which is given byI C C

B B S BE p G .

Thus, prior to news being released to investors of bid success or failure, there is approximate

equality between a bidder’s value before and after news of a cash bid, I C C I

B B S B BE p G E .

Hence, on a bid announcement, a bidder suffers a revelation loss in value, which is

approximately equal to the bidder’s expected synergistic gain, C C

B S Bp G . This is formally

written as

, ,

CC C CBB B S B F

S

G V Vp

. (5)

The bidder’s synergistic gain still equals the absolute value of the bidder’s fall in value on

deal withdrawal announcement, as this change is not affected by any bad news released about

the bidder on the bid announcement. Thus, this bad news is reflected in the price of the bidder

at the time of the bid outcome, regardless whether or not the deal succeeds.

Furthermore, the cash bidder’s synergistic gain is lost if the bid fails, as is the target’s

gain. Note that the bad news coming to light at the bid has no effect on the size of the total

synergistic gain from the M&A deal or its distribution between bidder and target. This is

because both completed and withdrawn deals suffer these same losses. Nevertheless, an

economically significant negative news effect,C

B , that either exceeds the anticipated bidder

synergy gain, ps C

BG , or equals it, can make it appear (incorrectly) that the bidder either

overpaid for the target or that actual or potential competition from other bidders forced the

bidder to pass on all the anticipated synergistic gains to the target, respectively.

2.2 Stock Bid for the Entire Target

In pure stock bids, target shareholders are given bidder shares in exchange for their target

shares. We consider the case in which fixed deal terms are set just prior to the bid

announcement. In addition to any share that target shareholders receive in synergistic gains

due to the initial pre-bid exchange rate between bidder and target shares, target shareholders

15

are typically offered an additional takeover premium in the form of a more favorable

exchange rate specified in the bid itself. The terms of trade for the merger are set partially by

the bidder’s initial stock value (as already noted, superscript I ), perhaps prior to initiating

talks with the target in the stock bid (denoted by superscript S ),,I S

TE .

Importantly, for a typical stock bid there is a sizeable run-up in bidder stock price, which

is given by ,S I S

B BR E such that the pre-announcement valuation is , 1I S S

B BE R . If the initial

target valuation prior to the announcement is denoted,I S

TE , then the initial relative valuation

is

,

, 1

I SI T

I S SB B

E

E R

. Now suppose there is a takeover exchange premium offered to target

shareholderP such that , ,

, 1 1 1 ,S P I S P I I S S

T A T B BE E E R on substituting for the

initial target valuation, where the announced stock bid is denoted by subscript A. Hence, a

bidder’s announced terms of trade

,

,1

1

S

T A P I

I S S

B B

E

E R

is improved by a

bidder’s pre-offer price run-up.

Since a bidder’s incentive to stimulate a pre-bid run up is dependent on it making a stock

bid, if the stock bid is accepted, then the value of the combined entity is the sum of the stand-

alone valuations prior to the bidder’s run-up plus the combined synergistic gain:

, ,

,

S I S I S S

B T A B TE E E G . (6)

The residual share of the synergistic gain captured by a stock bidder is simply the overall gain

less the target’s share of the gain, which reflects the premium paid in a stock bid of bidder

shares in exchange for target shares:

,

, ,1S S I S S S S

B B B B S B FG G E R V V . (7)

This synergistic gain in a stock bid is just the conditional change in bidder value from a

successful deal and a failed deal. Similarly, the synergistic gain received by a target in a

successful deal is simply the takeover premium it receives in the form of bidder shares, which

equals the difference in target value conditional on deal success and failure:

, ,

S I S S

T B T S T FG E V V . (8)

16

The bidder’s market value following a stock bid announcement depends on its initial

valuation, ,I S

BE , plus its pre-bid announcement run-up in value, ,S I S

B BR E , less the fall in bidder

stock value on the bid announcement due to market recognition that a bidder’s stock price is

likely to be inflated to lock-in beneficial terms of trade, , .S I S

B BA E Thus, a bidder’s stock

market value following a stock bid announcement, but prior to knowing the bid outcome, is

,

S

B, S B, F

,

1 ,

1 1 ,

.

i

i

S I S S S

B B B B

S I S S S S

S S S B B S B

I S S S

B S B

E V E R A

p V p E p E G B p E B

E B p G

(9)

which is simply its share of the combined firm conditional on bid success, weighted by the

probability of success, plus the bidder valuation conditional on bid failure, weighted by its

likelihood. As in the case of the cash bid, there is unobserved bad information released about

the bidder’s true valuation, conditional on making a stock bid, denoted by SB . On solving

equation (9) for the synergistic gain accruing to the stock bidder, we have:

, , ,

S I S S

B B BS S S

B B S B F

S

B E R AG V V

p

(10)

with the bidder’s share of its synergistic gain given by its valuation fall on news of deal

failure, as is the case with a cash bid.

Unlike a cash bid, in a stock bid the bidder’s percentage share price reaction,S

BA , is

typically negative. In our dataset, it is also large in absolute value representing a fall of 2.2%

(see Table 4 below). Could this mean that bidders are more likely to overpay for stock bids

relative to cash bids? If a bidder systematically overpays, or overpays by more in a stock bid,

then stock bids would fall by less or even rise in value on the announcement of a bid failure,

since the expected loss from the bid is reversed. Yet, empirically, this is not observed.

Moreover, there is a much larger run-up in bidder stock price prior to announcements of stock

bids than for cash bids such that the apparently sizeable bidder loss on the deal announcement

is far more than offset by the additional run-up. Thus, it would appear that the announcement

price fall is simply the market’s negative reassessment of the stock bidder’s previously

released good news, once the market becomes aware of the bidder’s financial incentives to

lock-in favorable terms of trade in the proposed M&A deal. Given this large net stock price

17

run-up means that bidders retain a higher share of the synergistic gains in stock-bids than

they do in cash-bids, even though stock bid announcements are on average more negative.

3. HYPOTHESIS CONSTRUCTION

As explained above, despite an extensive body of research documenting the performance

of firms around bid announcements and exploring motives behind takeovers, the existing

methodologies have serious limitations due to the revelation and other bid related biases. To

circumvent these biases, we analyze the short- and long-term impacts of M&A offer failures

for both bidder and target shareholders. From this analysis, we fill some important gaps in

our understanding of merger and acquisition motives, while developing a new perspective on

and interpretation of M&A initial announcement effects. We first assess whether pursuit of

synergistic benefits is a plausible motive for bidders in choosing to make takeover bids.

Hypothesis I:

To overcome the revelation bias issue posed by Hietala, Kaplan, and Robinson (2003),

where the announcement of a takeover reveals information about the stand-alone value of the

bidder in addition to any potential synergies, including control benefits arising from the

combination of the two firms, we separately study failed and exogenously failed takeover

bids in our analysis. Based on the empirical evidence in the existing literature and Section 2.2

describing the outcome when deal failure occurs in the absence of any synergistic gains, we

propose the following hypothesis:

HI: On the announcement of an M&A bid failure, the combined value of the bidder and

target will fall, rejecting the null hypothesis of no net change in value.

We foresee that this result will prove robust to a variety of samples, methodological

approaches, and estimation techniques, revealing that on an M&A bid failure, there is a

decline in the combined bidder and target equity capitalization, reflecting the loss of expected

synergistic benefits.

HII: On average, bidder shareholders should gain from mergers and acquisitions, so that

their share prices should rise during the pre-bid announcement period due to information

leakage and fall on the announcement of bid failure. The fall in bidder stock value that

sometimes occurs on a bid announcement is due to the bid typically releasing other bad news

about the bidder. For example, the market realizes that the bidder artificially inflated its

18

stock price in the lead-up to a stock-financed bid to lock in a better exchange rate, which is

independent of any acquisition synergies.

Ahern and Sosyura (2014) show that stock-based bidders issue substantially more press

releases in the deal negotiating phase, promoting a substantial bidder price run-up, which

triggers a partial correction on a bid announcement, while He, Lie, and Shu (2013), Erickson

and Wang (1999), and Louis (2004) find evidence of positive bidder earnings forecasts and

earnings manipulation in this same event period.

HIII: Bidder shareholders gain a significant share of the expected synergistic benefits

created on the merger and acquisition announcement, and similarly, lose a significant

amount on the announcement of a bid failure.

To assess the validity of these propositions as well as the null hypothesis that bidders gain

little, if anything, we employ both event study analysis and cross-sectional regression models

to analyze the returns prior to and around initial bid announcements and at failure

announcements and in between. We seek to assess whether bidders gain a significant share of

the expected synergistic benefits on the initial M&A announcement. To test this proposition,

we examine whether on a bid failure announcement, bidder stocks lose their expected portion

of the synergistic benefits created at the bid announcement, at the same time as the paired

target stock also loses it corresponding share of the same overall gains.

The fourth hypothesis is a result of the modeling in Section 2 showing that bidder and

target valuations must always move together in the same direction as these movements reflect

either sharing in overall synergistic gains or both missing out if the deal fails:

HIV: The bidder stock price generally moves in the same direction as that of its paired

target during the run-up period, around bid announcement, over the period between

the initial bid announcement and the bid outcome announcement date, and when the

bid outcome becomes known, regardless of whether the bid succeeds or fails or which

type of M&A currency is chosen – cash or stock.

4. SAMPLE CONSTRUCTION

The purpose of the study is to highlight both the need to consider bidder returns

throughout the life of the deal (from announcement to completion or withdrawal) and to

examine the role of deal-failure on acquisition returns, thereby highlighting the potential

19

negative impact of deal failure on bidders. With this aim, we collect data on all the bids that

target a firm in ‘common law’ developed countries (the U.S., U.K., Australia, New Zealand,

Hong Kong, Canada, and Singapore) by a bidder from any country Both failed and successful

deals are studied, but both target and bidder must be publicly listed. The sample spans the

1983-2011 period. A deal’s value must exceed USD one million and the bidder must hold

than 50 percent of target shares before a bid announcement and seek to own more than 50

percent of target shares. We obtain data on deal-failure from Security Data Corporation

(SDC). We collect data on the announcement date and the ‘end’ date, which we define as the

date on which the bid is announced to be a failure or a success.

We assess takeover performance by analyzing bidder and target CARs and BHARs

immediately around the initial bid announcement date, and ‘end’ date, and over the period

from 120 days prior to the bid announcement period to immediately after the deal ‘end’ date.

We estimate CARs and BHARs using a market model whose parameters are estimated over a

250 trading ending 130 days before the bid announcement. We obtain data on stock returns

from Datastream. Market returns used in market model estimation are based on the relevant

local market index provided in Datastream.

We collect data on other control variables that the literature considers likely to influence

bid performance (see e.g. Moeller et al., 2004, 2005; Masulis et al., 2007; Harford et al.,

2013). Bidder characteristics used in the analysis are the logarithm of total assets, the debt to

asset ratio, operating performance measured by EBIT/Assets, indicators for whether (1) this

is a ‘serial’ bidder, (2) it pays cash dividends (to capture the presence of financial

constraints), (3) it is in a ‘high tech’ or ‘financial services’ industry and finally the ratio of

bidder to target Tobin’s Qs. We also control for whether the bidder or target is backed by a

private equity (PE) firm, as indicated in SDC M&A database. Further, we collect data on

deal-level factors that could influence bid performance, including indictors for cross-border

and multi-bidder deals, pure stock financed deals, and the ratio of target purchase price to

bidder equity capitalization.

The summary statistics are presented in Table 1. Bidder CARs are negative on average

around the bid announcement, consistent with prior studies of acquisitions of publicly listed

targets (Chang, 1998; Fuller, Netter, and Stegemoller, 2002), while target CARs are positive

and large in magnitude, consistent with the existence of a sizable takeover premium. The

target CAR over the five-day announcement window averages 20% and the buy and hold

20

abnormal returns (BHAR) over the entire M&A bid cycle window from 120 days prior to the

announcement until two days after the outcome is known averages 46.7%.

Indicative of positive synergistic gains, the combined bidder-target CARs around the bid

announcements are on average positive at 9.4%, suggesting that the typical acquisition bid is

expected to create economic value. Moreover, over the entire M&A cycle window, the

BHAR is 21.8%, indicating that overall synergistic gains are likely to be high. In fact, the

entire cycle gain is in excess of double the bid announcement period gain. Thus, the common

methodological practice of ignoring both run-up and post-announcement gains through to

when the outcome is known seriously understates the estimated combined synergistic gain.

Approximately 17% of our sample consists of failed bids and 32.5% are stock-only deals.

Cross-border deals represent 19% of the sample, while the mean relative target/bidder deal

size is 54.3%.

<< Insert Table 1 about here >>

The bivariate statistics are shown in Table 2. Failed bidders experience lower returns

around the announcements of both the initial bid and its failure. By contrast, bidder returns

from the bid announcement to the bid end are significantly lower for failed deals than for

successful deals. This highlights the importance of analyzing returns throughout a deal’s life.

Unsurprisingly, targets experience significantly lower returns in failed deals than in

successful deals. There are also significant differences in some firm- and deal-level

characteristics between failed deals and successful deals, which we control for using

multivariate regressions.

<< Insert Table 2 about here >>

We construct mean bidder-returns by bidder nation and by target nation. The figures in

relation to U.S. bidders are consistent with those reported in prior U.S. studies (see e.g.

Moeller et al. (2004); Masulis et al. (2007)). Similarly, the results for bidders outside the U.S.

are consistent with those in prior international studies (see e.g. Ferreira et al. (2010); Karolyi

and Taboada (2014); Humphery-Jenner (2012)). There are also differences in the number of

observations by country. While some countries only feature a small number of bidders, we

also include bidder fixed effects to control for bidder country-specific effects.7

7 To conserve space this table is not included, but is available on request.

21

Table 3 reports the sample composition by year. The CARs are relatively consistent across

time. There is significant variation in the number of observations over calendar time. This fits

with the idea that there is clustering in time of takeover bids (Harford (2005); Powell and

Yawson (2005), (2007), (2012); Ahern and Harford (2013)).

<< Insert Table 3 about here >>

5. ANALYSIS

This section contains our empirical analysis of the economic effects surrounding M&A

bids. The regressions serve three main purposes. First, we emphasize the importance of

analyzing returns from when the bidder is likely to have hired an advisor for a potential deal,

which is as early as 120 days prior to the bid announcement date through to the bid end date

(either as a failure or as a success). Second, in so doing, we demonstrate that failed deals

result in significantly lower bidder CARs and BHARs, emphasizing that the loss of synergies

from a failed bid can hurt bidder shareholders as well as target shareholders. Third, we

examine how bidder and target returns are correlated over different event intervals. Fourth,

we examine the factors that moderate the impact of deal failure on bidder-returns.

5.1 Do failed deals perform worse than do successful ones?

The first issue we examine is whether failed deals perform worse than successful ones and

to document the importance of examining returns over a deal’s life. We analyze bidder,

target, and combined CARs and BHARs for various time-horizons from 120 days prior to the

bid announcement to immediately after the bid end date (i.e., the deal withdrawal or

completion date). The results for bidder, target and combined CARs and BHARs are shown

in Tables 4, 5, and 6, respectively.

<< Insert Table 4 about here >>

While not shown in all three tables, interacting stock-only bids with bid failure has no

statistical significance. These are very important findings. For example, they indicate that,

contrary to the literature that maintains bidders use stock bids when they know that their

stock is overvalued, target share value does not rise when target shareholders escape having

to accept overvalued bidder equity for their target shares. Moreover, bidder share value does

22

not fall more following the failure of deals that involve potentially overvalued equity. We

also report several other important findings.

First, failed deals are associated with significantly lower bidder, target and combined

CARs and BHARs (when measured over a deal’s life). The target CAR and BHAR findings

in Table 5 are unsurprising; deal failure represents a lost takeover premium, which is not

mitigated by supposedly overvalued equity in the case of all-stock deals. The negative

relation between deal failure and bidder CARs and BHARs and combined CARs and BHARs

suggests that on average deal failure represents a loss of takeover synergies.

<< Insert Tables 5 and 6 about here >>

Second, we emphasize the importance of examining returns from deal announcement, or

before, until deal outcome. Bidder returns, realized immediately around the initial bid

announcement, or around the bid failure announcement, are not significantly influenced by

whether the deal is successful, as indicated by the insignificant coefficient on the failure

indicator in columns 1 and 6 of Table 4. However, if we examine bidder returns from the deal

announcement through to a deal’s end date, i.e., two trading days after the

completion/withdrawal announcement, shown in columns 5 and 10, we find significantly

different results; failed deals perform significantly worse than do successful ones. The

consequences of failure are worse in contested deals with competing bidders. This

emphasizes the need to examine a deal throughout its entire life in order to have complete

information on a bid’s value-implications.

The coefficient signs and significance levels for the control variables are generally

consistent with expectations. In relation to bidder CARs: deals that diversify across industries

or countries perform worse (as found in Moeller and Schlingemann (2005)). Competing bids

earn higher, i.e., less negative, returns on a failed deal announcement, a finding we explore in

the next section. This difference could reflects market ‘relief’ at the bid’s failure since

‘overbidding’ or ‘the winner’s curse’ is more likely to arise with multiple bidders. The larger

the bidder, the greater the outperformance over the entire M&A cycle, from 120 days prior to

the bid announcement to until two days after the bid outcome announcement. This finding

differs from the very time-specific finding of Moeller, Schlingemann, and Stulz (2005).

Acquisitions of relatively large targets earn lower returns over the entire life of the bid

contest, consistent with the idea that larger acquisitions can involve substantially larger

integration costs. The market-to-book ratio of the bidder relative to the target has a slightly

23

negative relation to the bidder returns over the event window from 120 days prior to the bid

announcement to two days after the bid outcome announcement, suggesting that acquisitions

by overvalued bidders tend to create less value (Fu et al. (2003); Harford et al. (2013)). We

find that larger bidders experience higher returns over the entire bid cycle, 120 days prior to

the announcement until two days after the bid end. In unreported analysis, we extend the

M&A cycle to 100 days post the bid outcome date and find that our major findings are further

strengthened. While this contrasts with the evidence in Moeller et al. (2004), it is consistent

with other evidence that large bidders at times originate ‘large gain’ deals (Fich et al. (2013)),

and that the impact of bidder size can vary across countries (Humphery-Jenner and Powell

(2011), (2014)).

These results emphasize both the importance of examining returns from well prior to the

deal announcement to deal end and demonstrate that failed deals earn significantly lower

returns, which is consistent with a loss of acquisition synergies. The following sections

examine type of factors that moderate the deal failure - bidder stock return relationship.

5.2 Bidders and targets share in synergistic gains over the M&A cycle

Hypothesis IV states that if bidder and target stockholders share the synergistic gains

approximately equally, then the abnormal returns for bidders and targets should consistently

move together at every stage of the M&A bid cycle. Alternatively, if the conventional belief

is correct that all the M&A gains are received by targets, with none captured by bidders, then

we should observe either no evidence of correlation between these two sets of returns or the

co-movements should be negatively related.

The evidence pertaining to Hypothesis IV is presented in Table 7. The table’s first three

columns displays bidder responses to target return movements over the three periods making

up the complete M&A bid cycle. These consist of the five-day bid announcement window as

measured by the cumulative abnormal return (CAR), the extended run-up period buy and

hold abnormal return (from 120 days prior to the bidder announcement until two days prior to

the bid announcement), and, finally, the buy and hold abnormal return from two days prior to

the bid announcement until two days after when the bid outcome is known. The

corresponding target returns over the same three periods in response to bidder returns are

displayed in the table’s remaining three columns.

<< Insert Table 7 about here >>

24

Average bidder responses to target return movements over the five-day CAR window is

shown in column 1 of Table 7 and similarly, the target response to the bidder return

movement over this same window is shown in column 4. Not only are both sets of responses

positive in sign, thus supporting the hypothesis, but both coefficients are significant at the 1%

probability level under robust clustered standard errors. The same positive and highly

statistically significant findings are observed for the 120 day run-up period for bidder BHARs

in column 2 and the corresponding target BHARs in column 5, and from just prior to the bid

announcement until two days after the bid outcome becomes known for bidder BHARs in

column 3 and for target BHARs in column 6. In each case, the size of the target response

coefficient to a bidder return in the last three columns is larger than the corresponding bidder

response to a target movement in the first three columns due to differences in the size of

bidder returns. As is well known, the target M&A bid responses in percentage terms are

greater in magnitude than the bidder responses given the typically smaller size of targets.

If the final window is extended until 100 days after the bid end date a similar positive and

significant co-movement is found (not reported). When the sample is split into successful and

failed deals, positive and significant coefficients are found for all interactions over all

windows, regardless of whether the bid succeeds or fails (not reported). Similarly, when deals

are split into purely stock deals and cash or a mixture of cash and stock deals, similar results

are again obtained (not reported). All coefficients are significant at the 1% level. The

magnitude of the pure stock bidder responses to movements in target stock price exceeds that

of the mixed cash and stock bidder responses. The deals are also split into purely cash and a

mixture of stock and cash with almost identical findings uncovered.

5.3 What factors moderate the impact of deal failure?

Failure and board size

The market should respond more favorably to deal failures when proposed by a bidder

with poor corporate governance. To test this proposition, we must restrict our sample to U.S.

bidders in order to obtain governance data from IRRC/Risk Metrics. As a first test, we

examine deals with a bidder whose board-size is in the top 50 percent of the sample. If larger

board sizes leads to weaker governance, then we should find a significant difference. The

regression results for the two board size categories are reported in Table 8. We find that

25

board-size is not itself significantly related to bidder returns. However, the (mostly) positive

and statistically significant coefficient on the interaction term “Failed Board Size Top

50%” suggests that the market responds positively to the failure of deals proposed by bidders

with a large board. This suggests a bidder shareholder benefit from the failure of potential

agency-motivated bids.

<< Insert Table 8 about here >>

5.2.1 Failure and ‘highly valued’ bidders

Bidder valuation might influence the impact of deal failure. Highly valued bidders could

use their high market valuations to pursue deals that create value for their shareholders; using

their highly valued stock to purchase a target that trades at a more realistic valuation (Dong et

al. (2006)). However, highly valued bidders can also use their overvalued equity to overpay

for targets, thereby potentially eroding bidder shareholder wealth (Fu et al. (2003); Harford et

al. (2012)). We analyze the relationship between bidder valuation, deal failure, and bidder

returns.

The relevant results are reported in Table 9. The main finding is that the interaction term

“Failed Bidder MTB” is significantly negatively related to returns earned over the deal’s

life. However, bidder valuation (i.e., “Bidder MTB”) is also significantly negatively related

to bidder returns. This suggests that bidders do generally use their overvalued equity to

engage in value-destroying bids. Further, upon deal failure, the market may recognize the

signs of bidder over-valuation, resulting in a greater bidder shareholder wealth loss.

Alternatively, the market can be adjusting to a bidder being unable to sell its overvalued

equity to target shareholders in a stock deal or that investors are writing down the anticipated

synergistic gain on the announcement of deal failure. The latter interpretation is suggested by

our finding that the bidder return is unrelated to stock deals when examined over the bid’s

entire life.

<<Insert Table 9 about here>>

5.2.2 Failure and cash flows

Bidders sometimes use free cash flows to engage in agency motivated bids that do not per

se create shareholder wealth (Jensen (1986)). Subsequently, we expect the market to respond

more favorably to the failure to consummate a deal that is motivated by high free cash flows

(incentives to empire build). We examine the interaction of bidder cash flows and deal failure

26

in Table 10. Consistent with expectations, the coefficient of the interaction term “Failed

Bidder EBIT/Assets” is significant and positive, whereas the coefficient of “Bidder

EBIT/Assets” alone is negative (albeit insignificant). These findings both suggest that cash-

flow-motivated bids do not per se create shareholder wealth, and that deal failure can prevent

an agency motivated bid from proceeding.

<< Insert Table 10 about here >>

5.2.3 Failure and regulatory intervention – exogenously failed deals

An important issue is whether bidder and target stock returns around takeover bid

announcements are endogenous with deal failure. The issue is that a bidder could react to a

negative bid announcement by withdrawing the proposed deal (following Liu and McConnell

(2013)). We address this concern by ensuring the results hold for the set of companies that

are more likely to have ‘exogenously’ failed bids for regulatory reasons (using an SDC

indicator for a deal requiring regulatory approval). In this situation, bid failure is less likely to

be a function of the market’s reaction to the bid and is much more likely to be a function of

independent regulatory decisions. The regression results that analyze this sub-sample of deals

are shown in Table 11. The main finding is that failed deals experience significantly lower

returns over the deal’s life (i.e., from deal start to end) than do successful deals. This suggests

that the results do not merely reflect endogeneity between failure and takeover returns.

<< Insert Table 11 about here >>

5.3 Additional robustness tests

In this section we carry out additional robustness tests addressing concerns over sensitivity

to outliers, failed bidders and bidder growth prospects.

5.3.1 Sensitivity to Outliers

Extreme deals could arguably bias our results. Large-loss deals (Moeller et al. (2005)) and

large-gain deals (Fich et al. (2012)) arguably account for a significant portion of value-

destructive and value creation bids. To assess the robustness of our findings to such deals, we

estimate quantile regressions (i.e., median regressions) and cluster-robust regressions (see

Cameron and Miller (2014)), both of which generate estimates that are less sensitive to outliers

than OLS estimates. The results are given in Table 12. The coefficients of the key variables

are consistent with our prior findings. Specifically, we find that failed deals continue to earn

significantly lower returns than successful deals, although the magnitude of the loss is lower.

27

<< Insert Table 12 about here >>

5.3.2 Failed bids and bidder growth prospects

One concern is that the relation between bid failure and takeover returns reflects a signal

about bidder growth prospects. That is, failed deals feature bidders who are in weak financial

positions; thus, deal failure is merely further evidence of the bidder’s poor growth prospects.

We adjust for this potential effect by examining sub-samples of bidders whose operating

performance (EBIT/Assets) two years after the acquisition announcement exceeds its

performance either one year or two years before the announcement. This sub-sample of

bidders is experiencing growth; and thus, deal failure is less likely to be merely a negative

signal about a bidder’s future profitability. The results are presented in Table 13 and are

largely consistent with those reported in the earlier tables. Specifically, we find that failed

deals are associated with significantly lower CARs calculated over the whole period from

before the bid announcement to after the bid outcome announcement.

<< Insert Table 13 about here >>

5.3.3 Summary of other robustness tests

In the Appendix we report additional robustness tests concerning deal competition and bid

failure, excluding toeholds and multiple bidder deals, omitting emerging market bidders,

excluding cross-border, and diversifying deals and using alternative event windows. In each

case we find that our earlier conclusions are essentially unaffected.

6. ESTIMATED RELATIVE SHARES OF SYNERGISTIC GAINS

The derivation of the estimated target and bidder synergistic gains is shown in Table 14. A

bidder’s mean initial market capitalization is US $1,447 million with a target to bidder

market capitalization ratio of 54.3 percent, which reflects a mean initial target market

capitalization of $785.7 million. In the overall M&A bid sample, a bid’s probability of

success is 82.9 percent and 32.5 percent of deals are financed with stock only. Perhaps the

best indication of the overall wealth loss consequences of deal failure is the loss measured

relative to the mean price gains in successful bids, measured over the entire M&A bid cycle

from 120 days prior to the bid until two days following the date at which the outcome

becomes known. The table shows a 6.6 percent (item 7 in Table 14) mean decline in a

bidder’s market value for a failed cash or mixed bid relative to a similar successful bid (non-

28

withdrawn bid). This fall increases to 8 percent (item 8) for the high proportion of non-

contested bids and is slightly lower at 7.2 percent (item 9) for exogenously failed bids. For

the matched combined bidder plus target, the loss on failure is 11.9 percent (item 10). All

these figures are statistically significant at the 1% level. The combination of the loss for

exogenously failed bids and for the matched combined bidder and target implies a loss on

failure for targets of 20.6 percent (item 11), which is less than loss for targets generally,

(targets not matched to bidders), of 24.7 percent (item 13).

<< Insert Table 14 about here >>

In Section 5.1 above it is reported that the interaction term between pure stock bids and

deal failure is consistently insignificant for bidders, targets, and matched bidder-targets. This

indicates that deal failures yield no statistical difference in the performance for stock versus

cash or mixed stock and cash deals. Thus, despite the substantial 2.2 percent mean fall in

bidder share price on the announcement of a pure stock bid, there is no difference in the

degree of synergistic gains lost when a stock bid fails relative to a cash or a mixed cash and

stock bid. This is true regardless of whether we take the perspective of bidders, targets, or

matched bidder-targets.

Using the values shown in Table 14 for exogenously failed bids and applying the formula

for cash and stock bids, equations (3) and (8) respectively, yields synergistic gains estimates

of $104 million for both stock and non-stock successful bidders, $162 million for targets of

stock and non-stock bids, and $266 million for combined bidder-target matched pairs (in

stock and non-stock bids) shown in items 18, 19, and 20 respectively of Table 14. It follows

that the estimated bidder’s synergistic gains average $104 million and the bidder’s share of

gains for successful takeovers is 39 percent.

In our sample, a vast majority of bids are preemptive (88.6 percent) with no competing

bidders. Table 14 (item 8) shows that without competing bidders, the expected loss due to bid

failure is 8 percent, hence raising the estimated of synergistic gain for such a bidder from

$104 million to $116 million, which represents 44 percent of the combined synergy gains.

The estimated value of the bad news released about a bidder’s stock value by a cash bid is

given by C C

B S Bp G , which is equal to $86 million (item 21) or 6 percent of the bidder’s

market capitalization.

29

The total negative news released about the value of a bidder stock on a stock bid

announcement is ,S S S I S

B S B B Bp G A E which is higher by about $32 million than for a cash

bid announcement at $118 million (8 percent) (item 22) due to the additional run-up

correction of 2.2 percent (item 15) for stock bids, which is not applicable to cash bids. These

findings contrast with our findings for cash bids of a mean bidder announcement effect of

zero, which is the typical estimate reported in most studies. The zero return finding is the

basis of the claim that bidder shareholders gain nothing from cash bids and realize a 2.2

percent fall on the announcement of a stock-only bid. These findings lead to the false

conclusion that stock bids are highly destructive of bidder shareholder wealth.

When conventional M&A gains methodology is used to infer an average stock bid

announcement effect, but when the event window is extended to include the stock price run-

up period, the result is a 3.2 percent net gain (item 17) which translates into a not-

inconsiderable synergistic gain estimate of $46m (item 23). This estimate is just under half of

the estimate based on bid failure of $104m, but it still clearly indicates that stock bidders

receive a sizeable component of the synergistic gains that M&A deals on average create.

7. CONCLUSIONS

We demonstrate that the net economic benefits of M&A activity are far larger than

previously believed. In fact, most studies find almost negligible overall synergies once the

losses to bidders are taken into account. In contrast, we identify gains of approximately $266

million per matched successful deal involving a public bidder and target. We also find that

bidder shareholders on average gain 39 percent (44 percent for non-competed bids) of the

large overall synergistic benefits in both typical cash and stock-only transactions. This

contrasts with the standard finding that bidder shareholders gain nothing in cash bids and lose

heavily in stock bids. These expected synergy gains are not obvious to researchers since a bid

announcement is often associated with the release bad news about a bidder’s stand-alone

value. We estimate the average value of this bad news to be $80 million (6 percent) for non-

stock bids and $118 million (8.2 percent) for stock-only bids. We show that these

conventional findings are due to a variety of bid “revelation bias”. In particular, we show that

the conventional finding of an additional 2.2 percent fall in bidder value on the

announcement of a stock bid is due to a large average 5.4% bidder stock price run-up prior to

the bid, which appears to represent bidder efforts to lock in advantageous terms of trade. The

30

net run-up gain for stock bids is 3.2 percent with a synergy estimate of $46 million and is

zero for non-stock deals. We conclude that the typical stock bid signals to the market likely

prior price manipulation which triggers a price fall to adjust for this presumed artificial price

run-up.

In this study, we overcome the problems in the existing empirical literature by devising a

new approach to assessing the economic benefits of M&A offers by using successful and

failed takeover deals in our analysis of both bidders and targets and by expanding the sample

of observations to multiple countries so as to include more failed bids. Moreover, instead of

focusing on the bid announcement, we instead examine the difference in return between

successful and failed bids over an all-inclusive M&A from 120 days prior to the

announcement until two days past when the bid outcome is known. We show theoretically

that the division of the synergistic gains between bidder and target are revealed by the

differences in return between successful and failed bids. Moreover, these differences are

largely free of the revelation biases that the standard event study methodology based on

short-period announcement returns is subject to.

This new methodology is complemented by analysis showing that in every sub-period of

the M&A cycle, the run-up, the bid announcement, and post bid announcement to success or

failure on deal outcome news that the market valuation of the bidder and target move together

in the same direction. This is because both share in considerable expected synergistic gains if

the bid is successful and also in the mutual loss of gains if the deal fails.

Finally, we show that our approach and findings are robust to a wide range of factors

inclusive of poor corporate governance as measured by large board size, highly valued

bidders, high free cash flow, regulatory intervention due to horizontal mergers that help to

explain “exogenous” failure, sensitivity to outliers, the effect of multiple bidders on deal

failure, toeholds and multiple bidder deals, excluding emerging market bidders, excluding

cross-border and diversifying deals and examining alternative event windows.

31

APPENDIX A.1 ADDITIONAL ROBUSTNESS ANALYSIS8

A.1.1 Deal-competition and bid failure

We first address how various deal-characteristics moderate the impact of deal failure

announcements on bidder CARs. We hypothesize that deal failure is relatively less

problematic for a bidder to fail in a multi-bidder deal. That is, if there are multiple bidders,

then deal-failure can prevent a bidder from over-bidding and exhibiting a ‘winner’s curse’.

The main variable of interest is the interaction term: ‘Failed Competed’. The coefficient of

this interaction term is positive and is sometimes significant. This lends some support to the

view that failing to complete a deal due to bidder competition can prevent over-bidding, so

that the market does not necessarily punish bid failure under these circumstances.

A.1.2 Excluding toeholds and multiple bidder deals

One concern is that if a bidder has a ‘toehold’ in the target, then the bid announcement

returns are more difficult to interpret. For example, a bidder with a toehold will both

experience a stock price change reflecting how the acquisition is expected to affect bidder

value and the revision in the value of the bidder’s fractional ownership of the target from

before to after the bid announcement (reflecting a target’s expected acquisition premium).

Subsequently, we check whether the results are robust to omitting a subset of deals that

involve competitive bids or a prior bidder toehold. The results are consistent with the earlier

findings; specifically failed deals earn significantly lower returns than do successful deals.

A.1.3. Omitting emerging market bidders

We take steps to mitigate concerns about the role of government-sponsored/ government-

motivated mergers. Such acquisitions can have significantly different characteristics from

ordinary acquisitions (Karolyi and Taboada (2013); Johan et al. (2013)). We control for this

by examining the subsample of deals where the bidder (in addition to the target) is in a

common law country (Australia, Canada, Hong Kong, New Zealand, Singapore, United

States, or United Kingdom). The results yield qualitatively similar results to those in the main

regressions; failed deals earn significantly lower returns over the deal’s life.

8 To conserve space the results in this appendix are not tabulated, but are available on request.

32

A.1.4 Excluding cross-border or diversifying deals

Cross-border deals are often more difficult to execute and there is some evidence that they

experience lower returns (Moeller and Schlingemann (2005)). We find that failed deals earn

significantly lower returns during the period from the beginning of the deal to its end, even

after excluding cross-border deals. Similarly, we find that the results are qualitatively similar

if we focus on the set of diversifying, or non-diversifying deals, where diversifying deals are

defined as acquisitions for a target in the same two-digit or three-digit industry.

A.1.5 Alternative event windows

The results are robust to examining alternative time-horizons after a deal’s failure. We

examine additional windows from 120 days before the deal announcement to 10 days through

to 90 days after the deal ends (either through success or failure). The results indicate that

deal-failure is negatively related to returns earned over these alternative time horizons. The

coefficients are of similar magnitude for all the time horizons. In particular, the result for the

‘tighter’ time horizon of 120 days before announcement to 10 days after finalization (success

or failure) highlights that our foregoing results are unlikely to merely reflect extraneous

information that emerges after the announcement of a bid failure.9

9 To conserve space these results are not tabulated, but are available on request.

33

Appendix A2. Variable Definitions

Performance variables Definition

CAR (Ann – 2, Ann + 2) The cumulative abnormal return obtained from two days before the

acquisition bid announcement to two days after the acquisition

announcement bid. We calculate the abnormal return on day 𝑡 as the

difference between the bidder’s return on day 𝑡 and the return

predicted by an OLS estimation of the market model estimated from

60 days before the acquisition announcement for a period of 250

trading days.

CAR (End – 2, End + 2) The cumulative abnormal return obtained from two days before the

announcement of the ‘end’ of the deal to two days after the ‘end’ of

the deal. We define the ‘end’ of the deal as the date on which the deal

is withdrawn or is completed, s applicable. We calculate the abnormal

return on day 𝑡 as the difference between the bidder’s return on day 𝑡 and the return predicted by an OLS estimation of the market model

estimated from 60 days before the acquisition announcement for a

period of 250 trading days.

BHAR (Ann – 120, End + 2) The buy and hold abnormal return earned from 120 days before the

acquisition announcement to 2 days after the end of the acquisition-

process. Abnormal returns are based on an OLS estimation of the

market model, computed from the period ending 130 days before the

acquisition announcement for t time-horizon of 250 days commencing

380 days prior to the announcement.

BHAR (Ann – 120, End + 2) The buy and hold abnormal return earned from 120 days before the

acquisition announcement to 2 days after the end of the acquisition-

process. Abnormal returns are based on an OLS estimation of the

market model, computed from the period 60 days before the

acquisition announcement for the time-horizon of 250 days.

BHAR (Ann – 60, Ann + 2) The buy and hold abnormal return earned from 60 days before the

acquisition bid announcement to 2 days after the acquisition bid

announcement. Abnormal returns are based on an OLS estimation of

the market model, computed from the period 60 days before the

acquisition announcement for t time-horizon of 250 days.

Core Variables

Failed An indicator that equals one if the deal fails and equals zero otherwise

Bidder Serial Bidder An indicator that equals one if the bidder has made a prior bid in the

last three years

Rel MTB The ratio of the bidder’s market-to-book value to the target’s market-

to-book value

Bidder ln(Assets) Natural log of bidder total book assets

Bidder Debt/Assets The bidder’s debt scaled by its assets

Tgt Debt/Assets The target’s debt scaled by its assets

Diversifying (SIC 3D) An indicator that equals one if the bidder and the target are in

different 3-digit SIC industries.

Bidder Pays Div An indicator that equals one if Worldscope records the bidder as

paying a dividend and equals zero otherwise.

Tgt Pays Div An indicator that equals one if Worldscope records the target as

paying a dividend and equals zero otherwise.

Bidder EBIT/Assets The bidder’s EBIT scaled by its assets

Tgt EBIT/Assets The target’s EBIT scaled by its assets

Stock Only An indicator that equals one if the deal was to be (or was) a pure

stock-for-stock deal

34

Bidder High Tech An indicator that equals one if the bidder is a high tech company. A

high-tech company is one with the 3-digit or 4-digit SIC codes 357,

737, 5045, or 5734.

Bidder Fin Services An indicator that equals one if the bidder is in the financial services

industry. A financial services company is one whose one-digit SIC

code is six.

Tgt High Tech An indicator that equals one if the target is a high tech company. A

high-tech company is one with the 3-digit or 4-digit SIC codes 357,

737, 5045, or 5734.

Tgt Fin Services An indicator that equals one if the target is in the financial services

industry. Financial services companies have one-digit SIC code of six.

Cross-border An indicator that equals one if the bidder and target are based in

different countries

Rel Deal Size The transaction value scaled by the bidder’s market value as recorded

in Worldscope.

Competed An indicator that equals one if there was more than one bidder.

Bidder PE Backed An indicator that equals one if the bidder was PE backed (as indicated

by the buy-side sponsor involvement flag in SDC).

Tgt PE Backed An indicator that equals one if the bidder was PE backed (as indicated

by the sell-side sponsor involvement flag in SDC).

Other Variables

Inst Own % The percentage of shares held (in the bidder) by institutions as

indicated by the Thomson 13f filings on WRDS.

Inst Own HHI

The HHI of institutional ownership (in the bidder) by institutions as

indicated by the Thomson 13f filings on WRDS.

BCF The Bebchuk et al. (2009) index of six key anti-takeover provisions as

obtained from IRRC/Risk Metrics

ln(CEO ownership) Natural log of the dollar value of the CEO’s ownership in the bidder.

Prop Exec Dir The proportion of the board that is made up of executive directors.

Prop Indep Dir The proportion of the board that is ‘independent’ (as opposed to

executive or gray)

CEO is Chair An indicator that equals one if the CEO is also the chairman of the

board of the bidder.

Board Size The number of directors on the bidder’s board.

At least one Bid in last 3 yrs

An indicator that equals one if the bidder has made at least one other

bid in the last three years

Num Bids Ann last year The number of bids that this bidder announced in the prior year.

Num Bids in last 3 yrs The number of bids that this bidder had made in the prior three years.

Ave Ann CAR in last 3 yrs The bidder’s average announcement CAR to acquisitions made in the

last three years

Num Failed in last 3 yrs Number of failed bids that a bidder has made in the last three years.

Failed Bids/ Total Bids in

last 3 yrs

The proportion of all bids in the last three years that failed.

Bidder MTB The bidder’s market-to-book ratio as reported in Worldscope.

Board Size Top 25% An indicator that equals one if the bidder’s board size is in the top

25% of the sample.

Board Size Top 50%

An indicator that equals one if the bidder’s board size is in the top

50% of the sample.

Board Size Bottom 25% An indicator that equals one if the bidder’s board size is in the bottom

25% of the sample.

Board Size Bottom 50% An indicator that equals one if the bidder’s board size is in the bottom

50% of the sample.

35

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39

Tables Table 1: Summary statistics Full sample Deals that involve regulators

Variable Obs Mean Median SD Obs Mean Median SD

Bidder CAR (Ann - 2, Ann + 2) 5,588 -0.009 -0.007 0.084 3,530 -0.014 -0.010 0.077

Bidder CAR (End - 2, End + 2) 5,588 0.002 0.001 0.071 3,530 0.004 0.001 0.067 Bidder BHAR (Ann - 60, Ann + 2) 5,588 0.006 -0.006 0.202 3,530 0.006 -0.005 0.188

Bidder BHAR (Ann - 120, Ann + 2) 5,587 0.005 -0.022 0.306 3,530 0.001 -0.021 0.278

Bidder BHAR (Ann - 120, End +2) 5,587 -0.027 -0.080 0.469 3,530 -0.035 -0.088 0.446

Tgt CAR (Ann - 2, Ann + 2) 5,587 0.201 0.148 0.255 3,530 0.212 0.163 0.250

Tgt CAR (End - 2, End + 2) 5,587 -0.005 -0.001 0.084 3,530 -0.004 -0.001 0.074

Tgt BHAR (Ann - 60, Ann + 2) 5,587 0.289 0.216 0.446 3,530 0.302 0.227 0.429

Tgt BHAR (Ann - 120, Ann + 2) 5,587 0.110 0.022 0.515 3,530 0.104 0.025 0.487

Tgt BHAR (Ann - 120, End +2) 5,587 0.467 0.195 1.242 3,530 0.476 0.207 1.225

Combined CAR (Ann - 2, Ann + 2) 5,587 0.094 0.062 0.146 3,530 0.099 0.068 0.145

Combined CAR (End - 2, End + 2) 5,587 -0.002 0.000 0.061 3,530 0.000 0.000 0.057

Combined BHAR (Ann - 60, Ann + 2) 5,587 0.145 0.100 0.273 3,530 0.152 0.105 0.264 Combined BHAR (Ann - 120, Ann + 2) 5,587 0.057 0.012 0.352 3,530 0.052 0.010 0.329

Combined BHAR (Ann - 120, End + 2) 5,587 0.218 0.067 0.759 3,530 0.218 0.064 0.748

Failed 5,588 0.171 0.000 0.376 3,530 0.132 0.000 0.339 Stock Only 5,588 0.325 0.000 0.468 3,530 0.334 0.000 0.472

Diversifying (SIC 3D) 5,588 0.492 0.000 0.500 3,530 0.467 0.000 0.499

Cross-border 5,588 0.190 0.000 0.392 3,530 0.195 0.000 0.397

Rel Deal Size 5,588 0.543 0.081 1.354 3,530 0.688 0.114 1.554

Bidder Assets 5,588 1,446,996 1,463,000 11.577 3,530 14.658 14.654 2.305

Bidder Debt/Assets 5,588 0.057 0.097 0.303 3,530 0.061 0.099 0.286 Bidder Pays Div 5,588 0.595 1.000 0.491 3,530 0.618 1.000 0.486

Bidder EBIT/Assets 5,588 0.046 0.071 0.205 3,530 0.056 0.069 0.180

Bidder High Tech 5,588 0.115 0.000 0.319 3,530 0.113 0.000 0.317 Bidder Fin Services 5,588 0.225 0.000 0.418 3,530 0.246 0.000 0.431

Tgt Assets 5,588 232,118 208,772 7.294 3,530 12.720 12.640 1.930

Tgt Debt/Assets 5,584 0.035 0.065 0.357 3,530 0.030 0.064 0.349 Tgt Pays Div 5,588 0.422 0.000 0.494 3,530 0.427 0.000 0.495

Tgt EBIT/Assets 5,523 -0.027 0.039 0.322 3,530 -0.012 0.035 0.287

Tgt High Tech 5,588 0.137 0.000 0.344 3,530 0.134 0.000 0.341

Tgt Fin Services 5,588 0.212 0.000 0.409 3,530 0.235 0.000 0.424

40

Table 2: bivariate statistics comparing failed and successful deals Sample Failed Not Failed Difference

Statistic Observations Mean Observations Mean Column [1] [2] [3] [4] [5] = [2]-[4]

Bidder CAR (Ann - 2, Ann + 2) 954 -0.011 4,633 -0.009 -0.002

Bidder CAR (End - 2, End + 2) 954 0.001 4,633 0.002 -0.001

Bidder CAR (Ann - 2, End + 2) 954 -0.105 4,633 -0.046 -0.059***

Bidder BHAR (Ann - 120, Ann + 2) 954 -0.003 4,633 0.006 -0.010

Bidder BHAR (Ann - 120, End +2) 954 -0.078 4,633 -0.017 -0.061***

Tgt CAR (Ann - 2, Ann + 2) 954 0.171 4,633 0.207 -0.036***

Tgt CAR (End - 2, End + 2) 954 -0.044 4,633 0.003 -0.047***

Tgt CAR (Ann - 2, End + 2) 954 0.094 4,633 0.261 -0.166***

Tgt BHAR (Ann - 120, Ann + 2) 954 0.139 4,633 0.104 0.035*

Tgt BHAR (Ann - 120, End +2) 954 0.482 4,633 0.465 0.017

Combined CAR (Ann - 2, Ann + 2) 954 0.081 4,633 0.096 -0.015***

Combined CAR (End - 2, End + 2) 954 -0.020 4,633 0.002 -0.022***

Combined CAR (Ann - 2, End + 2) 954 -0.004 4,633 0.101 -0.105***

Combined BHAR (Ann - 120, Ann + 2) 954 0.067 4,633 0.052 0.015

Combined BHAR (Ann - 120, End +2) 954 0.185 4,633 0.213 -0.029

Stock Only 955 0.350 4,633 0.320 0.030*

Diversifying (SIC 3D) 955 0.516 4,633 0.487 0.029

Cross-border 955 0.169 4,633 0.194 -0.026*

Rel Deal Size 955 0.679 4,633 0.515 0.164***

Bidder ln(Assets) 955 13.433 4,633 14.340 -0.906***

Bidder Debt/Assets 955 0.057 4,633 0.057 -0.001

Bidder Pays Div 955 0.532 4,633 0.608 -0.076***

Bidder EBIT/Assets 955 0.023 4,633 0.051 -0.028***

Bidder High Tech 955 0.096 4,633 0.118 -0.022*

Bidder Fin Services 955 0.177 4,633 0.235 -0.058***

Tgt ln(Assets) 955 12.432 4,633 12.339 0.092

Tgt Debt/Assets 955 0.056 4,633 0.030 0.025**

Tgt Pays Div 955 0.465 4,633 0.413 0.052***

Tgt EBIT/Assets 945 -0.033 4,633 -0.026 -0.007

Tgt High Tech 955 0.123 4,633 0.140 -0.018

Tgt Fin Services 955 0.155 4,633 0.224 -0.069***

41

Table 3: Bidder returns by year

This table contains the (average) bidder returns by year of bid-announcement. The figures are sample means. Year Obs. Bidder CAR Bidder CAR Bidder BHAR Bidder BHAR Bidder BHAR (Ann -2, Ann +2) (End -2, End +2) (Ann - 2, End +2) (Ann - 120, Ann +2) (Ann - 120, End +2)

1983 1 -0.002 0.002 -0.223 0.108 -0.134

1984 2 0.040 0.042 0.280 0.337 0.717

1985 18 0.030 -0.009 0.018 0.105 0.135

1986 40 0.002 -0.001 -0.039 -0.004 -0.031

1987 46 -0.002 -0.012 -0.105 -0.084 -0.175

1988 70 0.008 0.004 0.058 0.076 0.139

1989 64 -0.011 -0.003 -0.068 0.059 -0.012

1990 44 -0.020 -0.008 -0.101 -0.064 -0.151

1991 64 -0.008 0.006 0.004 0.138 0.167

1992 46 -0.005 0.001 -0.047 -0.052 -0.099

1993 42 -0.018 0.002 -0.029 0.090 0.080

1994 83 -0.004 -0.004 -0.044 -0.064 -0.076

1995 102 -0.016 0.005 0.007 0.055 0.073

1996 126 -0.009 0.003 -0.068 -0.034 -0.087

1997 224 -0.012 0.004 -0.051 0.014 -0.030

1998 383 -0.015 -0.001 -0.129 -0.070 -0.169

1999 491 -0.007 -0.001 -0.028 0.075 0.064

2000 480 -0.028 -0.007 -0.104 -0.032 -0.080

2001 367 0.000 0.001 -0.055 -0.034 -0.068

2002 237 -0.005 0.001 -0.109 0.004 -0.099

2003 276 -0.007 0.011 0.097 0.140 0.274

2004 284 -0.016 -0.004 -0.103 -0.059 -0.147

2005 295 -0.003 0.005 -0.010 0.016 0.008

2006 380 -0.006 0.001 -0.034 -0.007 -0.023

2007 376 -0.009 0.002 -0.133 -0.026 -0.143

2008 308 -0.030 0.007 -0.171 -0.063 -0.213

2009 270 0.013 0.013 0.184 0.236 0.490

2010 274 -0.006 0.000 -0.089 -0.083 -0.140

2011 194 -0.006 0.009 -0.109 -0.034 -0.127

42

Table 4: Bidder Returns

This table contains OLS regressions that analyze the drivers of bidder returns over various time-horizons (as indicated in the column header).

The models are OLS models that include year fixed effects, bidder nation fixed effects, and cluster standard errors by bidder. Brackets contain p-

values and superscripts ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively. Sample Full Sample Omitting Competing Bids

Dependent Variable Bidder CAR

Bidder CAR

Bidder BHAR

Bidder BHAR

Bidder BHAR

Bidder CAR

Bidder CAR

Bidder BHAR

Bidder BHAR

Bidder BHAR

(Ann -2,

Ann +2)

(End -2,

End +2)

(Ann -2,

End +2)

(Ann -120,

Ann +2)

(Ann -120,

End +2)

(Ann -2,

Ann +2)

(End -2,

End +2)

(Ann -2,

End +2)

(Ann -120,

Ann +2)

(Ann-120,

End +2) Column [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Failed -0.002 -0.004 -0.051*** -0.015 -0.066*** -0.004 -0.010** -0.068*** -0.018 -0.080***

[0.629] [0.254] [0.000] [0.249] [0.000] [0.400] [0.020] [0.000] [0.245] [0.000]

Bidder Serial -0.002 -0.002 -0.018** -0.004 -0.025** -0.002 -0.003 -0.020** -0.002 -0.024*

[0.346] [0.296] [0.021] [0.667] [0.044] [0.516] [0.170] [0.014] [0.824] [0.063]

Rel MTB -0.000* -0.000 -0.002*** -0.001 -0.003*** -0.000* -0.000 -0.002*** -0.001* -0.002**

[0.093] [0.944] [0.001] [0.137] [0.009] [0.073] [0.810] [0.005] [0.098] [0.017]

Bidder ln(Assets) -0.001 0.001* 0.013*** 0.003 0.012*** -0.001 0.001* 0.015*** 0.003 0.014***

[0.314] [0.073] [0.000] [0.203] [0.001] [0.256] [0.073] [0.000] [0.241] [0.001]

Bidder Debt/Assets 0.011** 0.001 -0.011 0.015 -0.009 0.016*** 0.002 0.005 0.014 0.008

[0.025] [0.888] [0.501] [0.444] [0.746] [0.002] [0.734] [0.757] [0.513] [0.771]

Diversifying -0.001 -0.001 -0.022*** -0.015* -0.036*** -0.001 -0.001 -0.025*** -0.016* -0.040***

[0.605] [0.611] [0.003] [0.060] [0.003] [0.627] [0.771] [0.002] [0.069] [0.002]

Bidder Pays Div 0.004 0.005* 0.030*** -0.002 0.017 0.002 0.004 0.023** -0.001 0.008

[0.173] [0.062] [0.002] [0.848] [0.254] [0.560] [0.125] [0.027] [0.924] [0.619]

Bidder EBIT/Assets -0.012 -0.015 0.015 0.015 0.026 -0.011 -0.015 0.013 0.018 0.034

[0.192] [0.124] [0.627] [0.691] [0.599] [0.250] [0.146] [0.689] [0.656] [0.488]

Stock Bid Only -0.022*** 0.002 -0.044*** 0.032*** -0.008 -0.022*** 0.002 -0.045*** 0.024** -0.014

[0.000] [0.304] [0.000] [0.001] [0.601] [0.000] [0.433] [0.000] [0.017] [0.362]

Bidder High Tech -0.007 -0.001 -0.033** 0.001 -0.041* -0.008 -0.001 -0.031** -0.002 -0.042*

[0.117] [0.897] [0.022] [0.965] [0.088] [0.120] [0.738] [0.039] [0.892] [0.089]

Bidder Fin Services 0.001 -0.004* -0.029*** -0.017* -0.052*** 0.000 -0.005** -0.032*** -0.018* -0.055***

[0.821] [0.063] [0.002] [0.070] [0.000] [0.938] [0.047] [0.001] [0.065] [0.000]

Cross-border -0.001 -0.005 -0.033*** -0.011 -0.044** -0.000 -0.008** -0.035*** -0.007 -0.044**

[0.699] [0.127] [0.004] [0.429] [0.027] [0.995] [0.018] [0.005] [0.682] [0.043]

Rel Deal Size -0.004*** -0.001 -0.016*** 0.002 -0.012*** -0.004*** -0.001 -0.019*** 0.002 -0.015***

[0.000] [0.431] [0.000] [0.517] [0.003] [0.000] [0.230] [0.000] [0.601] [0.001]

Competing Bid -0.000 0.009*** 0.026** 0.021 0.047**

[0.989] [0.006] [0.045] [0.119] [0.029]

Bidder PE Backed -0.022** 0.007 -0.029 0.038 0.011 -0.024** 0.006 -0.041 0.025 -0.010

[0.025] [0.465] [0.398] [0.350] [0.859] [0.020] [0.583] [0.235] [0.542] [0.880]

Tgt PE Backed 0.005 -0.006 0.023 0.003 0.022 0.002 -0.006 0.018 -0.007 0.012

[0.549] [0.412] [0.355] [0.901] [0.546] [0.866] [0.454] [0.495] [0.803] [0.769]

Constant -0.115*** 0.037*** -0.505*** 0.228*** -0.314*** 0.086*** -0.010 -0.112 0.047 0.032

[0.000] [0.000] [0.000] [0.000] [0.000] [0.002] [0.609] [0.147] [0.532] [0.790]

Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Bidder Nation

Fixed Effects

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 5,587 5,587 5,587 5,587 5,587 4,984 4,984 4,984 4,984 4,984

R-squared 0.052 0.020 0.124 0.079 0.140 0.058 0.021 0.129 0.078 0.142

43

Table 5: Target Returns

This table contains OLS regressions that analyze the drivers of target returns over various time-horizons (as indicated in the

column header). The models are OLS models that include year fixed effects, target nation fixed effects, and cluster standard

errors by target. Brackets contain p-values and superscripts ***, **, and * denote significance at 1%, 5%, and 10%,

respectively. Dependent Variable Tgt CAR Tgt CAR Tgt BHAR Tgt BHAR Tgt BHAR

(Ann -2, Ann +2) (End -2, End +2) (Ann - 2, End + 2) (Ann - 120, Ann +2) (Ann - 120, End + 2)

Column [1] [2] [3] [4] [5]

Failed -0.020** -0.055*** -0.213*** -0.033 -0.247***

[0.028] [0.000] [0.000] [0.101] [0.000]

Bidder Serial Bidder 0.032*** 0.001 0.012 0.007 0.009

[0.000] [0.545] [0.421] [0.599] [0.792]

Rel MTB -0.001** -0.000 -0.002** -0.001 -0.002

[0.046] [0.483] [0.013] [0.162] [0.354]

Tgt ln(Assets) -0.011*** 0.001 0.027*** 0.015*** 0.045***

[0.000] [0.195] [0.000] [0.009] [0.000]

Tgt Debt/Assets -0.039*** 0.001 -0.111*** -0.022 -0.130**

[0.002] [0.755] [0.000] [0.439] [0.042]

Diversifying (SIC 3D) 0.009 -0.001 0.002 0.018 0.036

[0.177] [0.618] [0.871] [0.205] [0.284]

Tgt Pays Div 0.001 0.005* -0.002 -0.007 -0.014

[0.948] [0.065] [0.898] [0.660] [0.721]

Tgt EBIT/Assets 0.022 -0.011 -0.024 0.028 -0.071

[0.167] [0.134] [0.504] [0.473] [0.408]

Stock Bid Only -0.061*** 0.002 -0.073*** -0.044*** -0.111***

[0.000] [0.512] [0.000] [0.004] [0.002]

Tgt High Tech -0.009 0.003 0.025 0.023 0.103

[0.449] [0.521] [0.386] [0.399] [0.134]

Tgt Fin Services -0.014 -0.005* -0.078*** -0.047*** -0.178***

[0.102] [0.068] [0.000] [0.003] [0.000]

Cross-border 0.025*** -0.007** 0.029 0.077*** 0.165***

[0.007] [0.012] [0.146] [0.000] [0.001]

Rel Deal Size -0.007*** -0.002** -0.041*** -0.016*** -0.072***

[0.002] [0.044] [0.000] [0.004] [0.000]

Competing Bid -0.010 0.026*** 0.202*** 0.247*** 0.977***

[0.278] [0.000] [0.000] [0.000] [0.000]

Tgt PE Backed 0.003 0.024** 0.088 -0.096** -0.013

[0.922] [0.013] [0.191] [0.013] [0.913]

Bidder PE Backed -0.009 -0.002 -0.023 -0.045 -0.117

[0.737] [0.842] [0.649] [0.239] [0.185]

Constant 0.093* -0.042** -0.841*** -0.626*** -2.047***

[0.086] [0.016] [0.000] [0.000] [0.000]

Year Fixed Effects Yes Yes Yes Yes Yes

Target Nation Fixed Effects Yes Yes Yes Yes Yes Observations 5,521 5,521 5,521 5,521 5,521

R-squared 0.073 0.066 0.081 0.088 0.125

44

Table 6: Combined Bidder and Target Returns

This table contains OLS regressions that analyze the drivers of combined bidder and target returns over various time-

horizons (as indicated in the column header). The models are OLS models that include year fixed effects, target nation fixed

effects, bidder nation fixed effects, and cluster standard errors by bidder. Brackets contain p-values and superscripts ***, **,

and * denote significance at 1%, 5%, and 10%, respectively.

Dependent Variable Combined CAR Combined CAR Combined BHAR Combined BHAR Combined BHAR

(Ann -2, Ann +2) (End -2, End +2) (Ann - 2, End + 2) (Ann - 120, Ann +2) (Ann - 120, End + 2)

Column [1] [2] [3] [4] [5]

Failed 0.003 -0.027*** -0.113*** -0.011 -0.119*** [0.532] [0.000] [0.000] [0.457] [0.000]

Bidder Serial Bidder 0.002 0.000 -0.013 -0.002 -0.028

[0.626] [0.877] [0.219] [0.858] [0.154] Rel MTB -0.001** -0.000 -0.003*** -0.001** -0.005***

[0.020] [0.878] [0.000] [0.043] [0.001] Bidder ln(Assets) 0.009*** 0.000 0.014*** 0.003 0.017**

[0.000] [0.816] [0.000] [0.332] [0.026]

Bidder Debt/Assets -0.002 -0.002 -0.039* 0.002 -0.063 [0.850] [0.691] [0.079] [0.922] [0.117]

Diversifying (SIC 3D) 0.001 -0.001 -0.015 0.000 -0.000

[0.784] [0.541] [0.129] [0.981] [0.990] Bidder Pays Div 0.016*** 0.002 0.044*** 0.011 0.056**

[0.003] [0.411] [0.001] [0.343] [0.026]

Bidder EBIT/Assets 0.030*** 0.001 0.063* 0.060 0.176*** [0.010] [0.871] [0.099] [0.114] [0.006]

Stock Bid Only -0.027*** 0.003* -0.041*** 0.004 -0.023

[0.000] [0.094] [0.000] [0.676] [0.298] Bidder High Tech 0.009 0.002 -0.004 -0.016 -0.018

[0.334] [0.648] [0.873] [0.506] [0.696]

Bidder Fin Services -0.011 -0.005 -0.030 0.015 -0.029 [0.179] [0.270] [0.221] [0.546] [0.551]

Cross-border -0.004 -0.008** -0.018 0.030* 0.067*

[0.521] [0.011] [0.278] [0.093] [0.088] Rel Deal Size 0.002 -0.002** -0.021*** -0.004 -0.027***

[0.220] [0.018] [0.000] [0.310] [0.000]

Competing Bid -0.008 0.018*** 0.104*** 0.122*** 0.478*** [0.200] [0.000] [0.000] [0.000] [0.000]

Bidder PE Backed 0.008 0.015* 0.032 -0.009 0.019

[0.641] [0.069] [0.410] [0.793] [0.797] Tgt PE Backed -0.000 -0.001 -0.002 -0.019 -0.051

[0.979] [0.907] [0.946] [0.471] [0.368]

Tgt ln(Assets) -0.016*** 0.002** 0.004 0.007 0.010 [0.000] [0.044] [0.355] [0.104] [0.276]

Tgt Debt/Assets -0.014* 0.000 -0.037* -0.026 -0.069*

[0.076] [0.962] [0.060] [0.186] [0.092] Tgt Pays Div -0.000 0.002 0.002 -0.006 0.002

[0.937] [0.233] [0.832] [0.590] [0.934]

Tgt EBIT/Assets 0.014 -0.006 -0.020 -0.015 -0.095* [0.121] [0.175] [0.386] [0.540] [0.077]

Tgt High Tech -0.008 -0.000 0.011 0.024 0.046

[0.404] [0.933] [0.587] [0.303] [0.290] Tgt Fin Services 0.006 0.001 -0.028 -0.049** -0.092*

[0.495] [0.860] [0.246] [0.047] [0.066]

Constant -0.047 -0.071* -0.350** -0.277* -0.586 [0.353] [0.070] [0.010] [0.076] [0.117]

Bidder Nation Fixed Effects Yes Yes Yes Yes Yes

Target Nation Fixed Effects Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes

Observations 5,521 5,521 5,521 5,521 5,521

R-squared 0.088 0.042 0.105 0.110 0.152

45

Table 7: Bidder returns explained by target returns and target returns explained by bidder returns over three different

horizons

This table displays OLS fixed-effect regressions and robust standard errors with three bidder return horizons as a function of the

matching target responses and a complete set of control variables (not displayed but corresponding to Table 4 above) and three target

return windows, once again with matching bidder responses (once again, not displayed but corresponding to Table 5 above). Column 1 shows the bidder cumulative abnormal returns over the five-day takeover announcement window, column 2 shows the bidder buy and

hold abnormal returns over the 120 day run-up period window prior to the announcement and column 3 shows the bidder buy and hold

abnormal returns over the window from two days prior to the takeover announcement until two days after the bid outcome is known. All bids, both completed and failed, are included in the regressions. Columns 4 to 6 inclusive display the corresponding event periods for

targets. Robust probability values are shown in brackets with *** p < 0.01, ** p < 0.05, * p < 0.1.

Dependent Bidder CAR Bidder BHAR Bidder BHAR Tgt CAR Tgt BHAR Tgt BHAR

Window Ann - 2 Ann - 120 Ann - 2 Ann - 2 Ann - 120 Ann - 2

Ann + 2 Ann - 2 End + 2 Ann + 2 Ann - 2 End + 2

Tgt CAR (ann-2,ann+2) 0.035***

[0.000]

Tgt BHAR (ann-120, ann-2)

0.157***

[0.000]

Tgt BHAR (ann-2, end +2)

0.178***

[0.000] Bidder CAR (ann-2,ann+2)

0.289***

[0.000]

Bidder BHAR (ann-120, ann-2)

0.438***

[0.000]

Bidder BHAR (ann-2, end +2)

0.655***

[0.000]

Year FE Yes Yes Yes Yes Yes Yes

Acq Nation FE Yes Yes Yes No No No

Bidder Nation FE No No No Yes Yes Yes

Control variables not reported Yes Yes Yes Yes Yes Yes

Observations 5,587 5,587 5,587 5,521 5,521 5,521

R-squared 0.062 0.142 0.226 0.082 0.151 0.188

46

Table 8: Bidder returns for large bidder boards

This table contains OLS regressions that analyze the drivers of bidder returns over various time-horizons (as indicated in the

column header). The models are OLS models that include year fixed effects, bidder nation fixed effects, and cluster standard

errors by bidder. Brackets contain p-values and superscripts ***, **, and * denote significance at 1%, 5%, and 10%,

respectively. Dependent Variable Bidder CAR Bidder CAR Bidder BHAR Bidder BHAR Bidder BHAR

(Ann -2, Ann +2) (End -2, End +2) (Ann - 2, End + 2) (Ann - 120, Ann +2) (Ann - 120, End + 2) Column [1] [2] [3] [4] [5]

Failed -0.012 0.006 -0.092*** -0.007 -0.107**

[0.236] [0.545] [0.005] [0.804] [0.026] Board Size Top 50% -0.002 0.008** -0.015 -0.002 -0.024

[0.724] [0.016] [0.341] [0.916] [0.307]

Failed x Board Size Top 50% 0.035** -0.013 0.079* 0.027 0.123* [0.025] [0.279] [0.063] [0.472] [0.057]

Serial Bidder 0.002 -0.005 -0.028** -0.007 -0.043*

[0.565] [0.172] [0.047] [0.592] [0.052] Rel MTB -0.003 0.002 0.006 0.003 0.013

[0.170] [0.156] [0.475] [0.600] [0.349]

Bidder ln(Assets) -0.001 -0.000 0.008 -0.004 0.005 [0.629] [0.830] [0.151] [0.489] [0.537]

Bidder Debt/Assets 0.001 0.003 0.052 0.028 0.049

[0.948] [0.781] [0.132] [0.385] [0.394] Diversifying (SIC 3D) -0.002 0.001 0.004 -0.013 -0.012

[0.591] [0.705] [0.792] [0.271] [0.540]

Bidder Pays Div 0.009* -0.000 0.013 0.013 0.026 [0.084] [0.994] [0.494] [0.445] [0.379]

Bidder EBIT/Assets 0.020 -0.026 -0.004 -0.091 -0.139

[0.274] [0.191] [0.967] [0.190] [0.334] Stock Bid Only -0.017*** 0.003 -0.030 0.008 -0.005

[0.002] [0.542] [0.117] [0.611] [0.864]

Bidder High Tech -0.006 -0.002 -0.002 0.029 0.016 [0.344] [0.644] [0.933] [0.207] [0.645]

Bidder Fin Services 0.002 -0.004 -0.031 -0.005 -0.045

[0.706] [0.283] [0.108] [0.784] [0.124] Cross-border -0.002 -0.009* -0.021 -0.042** -0.058*

[0.752] [0.055] [0.309] [0.034] [0.082] Rel Deal Size -0.007*** -0.001 -0.016*** 0.002 -0.014**

[0.000] [0.323] [0.003] [0.668] [0.028]

Competing Bid 0.002 0.001 0.050* 0.004 0.058 [0.749] [0.855] [0.065] [0.868] [0.196]

Bidder PE Backed -0.043 0.013 -0.010 -0.095 -0.023

[0.178] [0.635] [0.928] [0.245] [0.906] Tgt PE Backed 0.008 -0.001 0.003 0.033 0.031

[0.456] [0.931] [0.924] [0.368] [0.587]

Constant 0.018 0.017 -0.180** 0.053 -0.113 [0.491] [0.372] [0.036] [0.514] [0.392]

Year Fixed Effects Yes Yes Yes Yes Yes

Bidder Nation Fixed Effects Yes Yes Yes Yes Yes Observations 1,422 1,422 1,422 1,422 1,422

R-squared 0.072 0.022 0.121 0.074 0.140

47

Table 9: Failure and Highly Valued Bidders

This table contains OLS regressions that analyze the drivers of bidder returns over various time-horizons (as indicated in the

column header). The models are OLS models that include year fixed effects, bidder nation fixed effects, and cluster standard

errors by bidder. Brackets contain p-values and superscripts ***, **, and * denote significance at 1%, 5%, and 10%,

respectively. Dependent Variable Bidder CAR Bidder CAR Bidder BHAR Bidder BHAR Bidder BHAR

(Ann -2, Ann +2) (End -2, End +2) (Ann - 2, End + 2) (Ann - 120, Ann +2) (Ann - 120, End + 2)

Column [1] [2] [3] [4] [5]

Failed -0.003 -0.004 -0.042*** -0.009 -0.054*** [0.397] [0.315] [0.000] [0.488] [0.004]

Bidder MTB -0.007*** -0.008*** -0.026*** -0.031*** -0.044***

[0.000] [0.000] [0.000] [0.000] [0.000] Failed x Bidder MTB 0.018* -0.014 -0.170*** -0.128*** -0.234***

[0.069] [0.292] [0.000] [0.003] [0.000] Serial Bidder -0.002 -0.002 -0.016** -0.002 -0.021*

[0.388] [0.403] [0.039] [0.851] [0.077]

Rel MTB -0.000 0.000 -0.002*** -0.001 -0.002*

[0.102] [0.618] [0.009] [0.334] [0.062]

Bidder ln(Assets) -0.001 0.001 0.011*** 0.001 0.008**

[0.185] [0.310] [0.000] [0.722] [0.019] Bidder Debt/Assets 0.011** 0.000 -0.011 0.014 -0.010

[0.031] [0.950] [0.467] [0.472] [0.704]

Diversifying (SIC 3D) -0.001 -0.001 -0.021*** -0.015* -0.035*** [0.567] [0.627] [0.004] [0.070] [0.004]

Bidder Pays Div 0.004 0.005** 0.034*** 0.001 0.023

[0.161] [0.034] [0.000] [0.886] [0.133] Bidder EBIT/Assets -0.012 -0.021** -0.020 -0.016 -0.027

[0.205] [0.037] [0.510] [0.690] [0.589]

Stock Bid Only -0.022*** 0.002 -0.046*** 0.030*** -0.010 [0.000] [0.394] [0.000] [0.002] [0.483]

Bidder High Tech -0.008* -0.001 -0.035** -0.001 -0.044*

[0.098] [0.802] [0.016] [0.939] [0.066] Bidder Fin Services 0.001 -0.004* -0.029*** -0.017* -0.053***

[0.801] [0.059] [0.002] [0.061] [0.000]

Cross-border -0.002 -0.005 -0.032*** -0.011 -0.043** [0.660] [0.127] [0.004] [0.451] [0.030]

Rel Deal Size -0.004*** -0.000 -0.015*** 0.003 -0.011***

[0.000] [0.575] [0.000] [0.357] [0.006] Competing Bid 0.000 0.009*** 0.023* 0.019 0.043**

[0.988] [0.008] [0.073] [0.165] [0.045]

Bidder PE Backed -0.022** 0.008 -0.030 0.038 0.011 [0.031] [0.447] [0.386] [0.349] [0.866]

Tgt PE Backed 0.006 -0.006 0.023 0.004 0.023

[0.513] [0.438] [0.356] [0.881] [0.537] Constant -0.110*** 0.041*** -0.494*** 0.242*** -0.294***

[0.000] [0.000] [0.000] [0.000] [0.000]

Year Fixed Effects Yes Yes Yes Yes Yes Bidder Nation Fixed Effects Yes Yes Yes Yes Yes

Observations 5,587 5,587 5,587 5,587 5,587

R-squared 0.055 0.026 0.132 0.085 0.147

48

Table 10: Bidder returns controlling for bidder free cash flows

This table contains OLS regressions that analyze the drivers of bidder returns over various time-horizons (as indicated in the

column header). The models are OLS models that include year fixed effects, bidder nation fixed effects, and cluster standard

errors by bidder. Brackets contain p-values and superscripts ***, **, and * denote significance at 1%, 5%, and 10%,

respectively. Dependent Variable Bidder CAR Bidder CAR Bidder BHAR Bidder BHAR Bidder BHAR

(Ann -2, Ann +2) (End -2, End +2) (Ann - 2, End + 2) (Ann - 120, Ann +2) (Ann - 120, End + 2)

Column [1] [2] [3] [4] [5]

Failed -0.002 -0.005 -0.055*** -0.016 -0.069*** [0.642] [0.183] [0.000] [0.235] [0.000]

Failed x Bidder EBIT/Assets -0.000 0.039* 0.148** 0.051 0.140

[0.991] [0.081] [0.016] [0.503] [0.148] Serial Bidder -0.002 -0.002 -0.018** -0.004 -0.024**

[0.346] [0.299] [0.021] [0.668] [0.044] Rel MTB -0.000* -0.000 -0.002*** -0.001 -0.002**

[0.093] [0.990] [0.002] [0.144] [0.012]

Bidder ln(Assets) -0.001 0.001* 0.013*** 0.003 0.012***

[0.315] [0.088] [0.000] [0.214] [0.001]

Bidder Debt/Assets 0.011** 0.001 -0.010 0.015 -0.008

[0.025] [0.870] [0.516] [0.439] [0.757] Diversifying (SIC 3D) -0.001 -0.001 -0.022*** -0.015* -0.035***

[0.605] [0.630] [0.003] [0.062] [0.003]

Bidder Pays Div 0.004 0.005** 0.031*** -0.002 0.018 [0.174] [0.048] [0.001] [0.877] [0.229]

Bidder EBIT/Assets -0.012 -0.026** -0.026 0.001 -0.014

[0.291] [0.013] [0.464] [0.981] [0.811] Stock Bid Only -0.022*** 0.002 -0.045*** 0.031*** -0.008

[0.000] [0.348] [0.000] [0.001] [0.566]

Bidder High Tech -0.007 -0.001 -0.034** 0.000 -0.041* [0.117] [0.864] [0.019] [0.976] [0.084]

Bidder Fin Services 0.001 -0.004** -0.030*** -0.017* -0.053***

[0.821] [0.045] [0.001] [0.065] [0.000] Cross-border -0.001 -0.005 -0.033*** -0.011 -0.043**

[0.699] [0.142] [0.004] [0.439] [0.029]

Rel Deal Size -0.004*** -0.001 -0.016*** 0.002 -0.012*** [0.000] [0.426] [0.000] [0.518] [0.003]

Competing Bid -0.000 0.009** 0.023* 0.020 0.044**

[0.990] [0.012] [0.080] [0.143] [0.042] Bidder PE Backed -0.022** 0.007 -0.030 0.037 0.011

[0.025] [0.479] [0.381] [0.353] [0.868]

Tgt PE Backed 0.005 -0.007 0.022 0.003 0.021 [0.549] [0.392] [0.379] [0.912] [0.564]

Constant -0.115*** 0.041*** -0.488*** 0.234*** -0.298***

[0.000] [0.000] [0.000] [0.000] [0.000] Year Fixed Effects Yes Yes Yes Yes Yes

Bidder Nation Fixed Effects Yes Yes Yes Yes Yes

Observations 5,587 5,587 5,587 5,587 5,587 R-squared 0.052 0.022 0.127 0.079 0.141

49

Table 11: Exogenously failed bids

This table is confined to bids requiring regulatory approval (using an SDC indicator for a deal requiring regulatory

approval). Thus the 466 failed bids in this sample are more likely to have ‘exogenously’ failed for regulatory reasons. The

table contains OLS regressions that analyze the drivers of bidder returns over various time-horizons (as indicated in the

column header). The models are OLS models that include year fixed effects, bidder nation fixed effects, and cluster standard

errors by bidder. Brackets contain p-values and superscripts ***, **, and * denote significance at 1%, 5%, and 10%,

respectively. Dependent Variable Bidder CAR Bidder CAR Bidder BHAR Bidder BHAR Bidder BHAR

(Ann -2, Ann +2) (End -2, End +2) (Ann - 2, End + 2) (Ann - 120, Ann +2) (Ann - 120, End + 2) Column [1] [2] [3] [4] [5]

Failed 0.001 -0.004 -0.057*** -0.019 -0.072***

[0.894] [0.415] [0.001] [0.260] [0.005] Serial Bidder -0.004 -0.001 -0.015 -0.001 -0.021

[0.142] [0.699] [0.113] [0.949] [0.168]

Rel MTB -0.001*** 0.000 -0.003*** -0.002** -0.004*** [0.000] [0.886] [0.000] [0.020] [0.005]

Bidder ln(Assets) 0.000 0.001 0.013*** 0.001 0.013***

[0.671] [0.299] [0.000] [0.637] [0.004] Bidder Debt/Assets 0.012** -0.005 -0.002 0.042* 0.021

[0.043] [0.372] [0.925] [0.088] [0.562]

Diversifying (SIC 3D) 0.002 -0.001 -0.012 -0.014 -0.027* [0.493] [0.647] [0.184] [0.141] [0.058]

Bidder Pays Div 0.006* 0.007** 0.027** -0.016 0.004

[0.092] [0.025] [0.025] [0.193] [0.827] Bidder EBIT/Assets -0.007 -0.025* 0.025 0.015 0.025

[0.463] [0.079] [0.572] [0.778] [0.710]

Stock Bid Only -0.017*** 0.002 -0.039*** 0.012 -0.013 [0.000] [0.443] [0.001] [0.266] [0.473]

Bidder High Tech -0.001 0.000 -0.027 -0.018 -0.051*

[0.840] [0.965] [0.115] [0.375] [0.072] Bidder Fin Services 0.000 -0.006** -0.034*** -0.011 -0.058***

[0.885] [0.044] [0.004] [0.278] [0.001]

Cross-border 0.002 -0.004 -0.028* -0.004 -0.033 [0.701] [0.400] [0.072] [0.833] [0.186]

Rel Deal Size -0.004*** -0.001 -0.016*** 0.002 -0.012***

[0.000] [0.569] [0.000] [0.492] [0.008] Competing Bid -0.001 0.010** 0.011 0.015 0.018

[0.825] [0.023] [0.527] [0.340] [0.508] Bidder PE Backed -0.018 -0.000 -0.019 0.047 0.040

[0.129] [0.977] [0.695] [0.367] [0.643]

Tgt PE Backed 0.008 -0.011 0.027 0.036 0.054 [0.435] [0.212] [0.318] [0.204] [0.212]

Constant 0.092** -0.043* -0.034 0.117 0.164

[0.011] [0.082] [0.777] [0.203] [0.435] Year Fixed Effects Yes Yes Yes Yes Yes

Bidder Nation Fixed Effects Yes Yes Yes Yes Yes

Observations 3,530 3,530 3,530 3,530 3,530 R-squared 0.063 0.028 0.141 0.090 0.159

50

Table 12: Robust regressions and quantile regressions

This table contains robust regressions and quantile regressions (regressions through the median) that analyze the drivers of bidder returns over various time-horizons (as indicated in the column header). The

models include year fixed effects, bidder nation fixed effects, and cluster standard errors by bidder. Brackets contain p-values and superscripts ***, **, and * denote significance at 1%, 5%, and 10%,

respectively. Technique Robust Regression Quantile Regression

Dependent Variable Bidder CAR Bidder CAR Bidder BHAR Bidder BHAR Bidder BHAR Bidder CAR Bidder CAR Bidder BHAR Bidder BHAR Bidder BHAR

(Ann -2,

Ann +2)

(End -2,

End +2)

(Ann - 2, End + 2) (Ann - 120, Ann

+2)

(Ann - 120, End

+ 2)

(Ann -2,

Ann +2)

(End -2,

End +2)

(Ann - 2, End + 2) (Ann - 120, Ann

+2)

(Ann - 120, End

+ 2) Column [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Failed -0.001 0.001 -0.020** -0.013 -0.039*** 0.001 0.000 -0.018* -0.013 -0.027*

[0.788] [0.432] [0.020] [0.144] [0.002] [0.753] [0.835] [0.081] [0.266] [0.085]

Serial Bidder -0.003* -0.002* -0.010 0.002 -0.008 -0.003 -0.001 -0.006 0.002 -0.007 [0.072] [0.092] [0.131] [0.823] [0.405] [0.125] [0.309] [0.459] [0.841] [0.538]

Rel MTB -0.000 -0.000 -0.001*** -0.001** -0.002*** -0.000 0.000 -0.001** -0.001 -0.002*** [0.128] [0.768] [0.007] [0.030] [0.004] [0.329] [0.867] [0.031] [0.126] [0.009]

Bidder ln(Assets) 0.000 0.001*** 0.009*** 0.010*** 0.016*** 0.001 0.001* 0.009*** 0.004 0.013***

[0.423] [0.007] [0.000] [0.000] [0.000] [0.181] [0.057] [0.000] [0.121] [0.000] Bidder Debt/Assets 0.001 0.001 0.010 0.013 0.018 0.000 0.001 0.013 0.003 -0.004

[0.690] [0.728] [0.339] [0.236] [0.270] [0.943] [0.659] [0.335] [0.831] [0.835]

Diversifying 0.001 -0.001 -0.006 -0.013** -0.023** -0.000 0.000 -0.006 -0.009 -0.017 [0.590] [0.585] [0.327] [0.033] [0.011] [0.838] [0.744] [0.396] [0.260] [0.132]

Bidder Pays Div 0.005** 0.002 0.029*** 0.024*** 0.053*** 0.004 0.002 0.030*** 0.017 0.033**

[0.023] [0.180] [0.000] [0.003] [0.000] [0.148] [0.251] [0.001] [0.112] [0.019] Bidder EBIT/Assets -0.000 -0.002 0.123*** 0.165*** 0.198*** -0.004 -0.007** 0.045** 0.089*** 0.143***

[0.917] [0.533] [0.000] [0.000] [0.000] [0.385] [0.048] [0.018] [0.000] [0.000]

Stock Bid Only -0.018*** 0.001 -0.055*** 0.007 -0.046*** -0.015*** 0.000 -0.052*** 0.014 -0.039***

[0.000] [0.670] [0.000] [0.334] [0.000] [0.000] [0.744] [0.000] [0.128] [0.002]

Bidder High Tech -0.006** -0.001 -0.028*** -0.015 -0.036** -0.006* 0.001 -0.016 -0.013 -0.032*

[0.021] [0.718] [0.006] [0.157] [0.016] [0.063] [0.794] [0.182] [0.342] [0.086] Bidder Fin Services -0.002 -0.003* -0.013 0.002 -0.017 -0.001 -0.002 -0.026*** -0.006 -0.031**

[0.266] [0.080] [0.101] [0.786] [0.152] [0.589] [0.263] [0.008] [0.607] [0.037]

Cross-border 0.001 -0.000 -0.015 -0.019* -0.039*** 0.003 -0.001 -0.022* -0.008 -0.025 [0.641] [0.923] [0.137] [0.071] [0.010] [0.393] [0.513] [0.073] [0.559] [0.176]

Rel Deal Size -0.004*** 0.000 -0.013*** -0.002 -0.012*** -0.005*** 0.000 -0.011*** -0.001 -0.011**

[0.000] [0.561] [0.000] [0.408] [0.000] [0.000] [0.440] [0.000] [0.835] [0.011] Competing Bid -0.001 0.005** 0.007 0.024** 0.026* -0.001 0.004* 0.015 0.020 0.039**

[0.704] [0.019] [0.488] [0.027] [0.096] [0.863] [0.071] [0.226] [0.155] [0.044]

Bidder PE Backed -0.003 0.005 -0.040* 0.024 -0.008 -0.009 0.003 -0.032 0.042 0.022

[0.608] [0.278] [0.092] [0.333] [0.832] [0.234] [0.603] [0.267] [0.204] [0.613]

Tgt PE Backed -0.001 -0.009* 0.025 0.033 0.057* 0.001 -0.010** 0.050* 0.031 0.093**

[0.871] [0.055] [0.236] [0.128] [0.071] [0.827] [0.042] [0.055] [0.283] [0.018] Constant -0.127 0.029 -0.485 0.206 -0.305 0.035 0.024 0.290 0.383 0.541

[0.141] [0.650] [0.113] [0.518] [0.509] [0.679] [0.693] [0.374] [0.300] [0.276]

Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Bidder Nation Fixed

Effects

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 5,587 5,587 5,587 5,587 5,587 5,587 5,587 5,587 5,587 5,587 R-squared 0.057 0.0233 0.163 0.131 0.1961 0.026 0.012 0.062 0.043 0.079

51

Table 13: Bidder returns for growing bidders

This table contains OLS regressions that analyze the drivers of bidder returns over various time-horizons (as indicated in the

column header) and for various sub-samples based upon whether the bidder’s EBIT in year t+2 exceeds that in year t-1 or t-

2. The models are OLS models that include year fixed effects, bidder nation fixed effects, and cluster standard errors by

bidder. Brackets contain p-values and superscripts ***, **, and * denote significance at 1%, 5%, and 10%, respectively. Sample EBIT/Assets (t+2) > EBIT/Assets (t-1)

Dependent Variable Bidder CAR Bidder CAR Bidder BHAR Bidder BHAR Bidder BHAR

(Ann -2, Ann +2) (End -2, End +2) (Ann - 2, End + 2) (Ann - 120, Ann +2) (Ann - 120, End + 2) Column [1] [2] [3] [4] [5]

Failed 0.002 -0.005 -0.058*** -0.002 -0.071**

[0.735] [0.291] [0.001] [0.915] [0.019]

Serial Bidder -0.003 -0.004 -0.012 0.002 -0.008 [0.438] [0.256] [0.333] [0.904] [0.697]

Rel MTB -0.001* -0.000 -0.003*** -0.002* -0.003** [0.054] [0.479] [0.001] [0.082] [0.010]

Bidder ln(Assets) -0.001 0.000 0.013*** -0.002 0.007

[0.458] [0.751] [0.001] [0.595] [0.264]

Bidder Debt/Assets 0.020** -0.006 -0.031 -0.016 -0.079

[0.025] [0.486] [0.283] [0.638] [0.114]

Diversifying (SIC 3D) -0.002 -0.004 -0.029** -0.023* -0.044** [0.518] [0.228] [0.012] [0.082] [0.024]

Bidder Pays Div 0.004 0.010*** 0.053*** 0.004 0.049**

[0.374] [0.009] [0.000] [0.782] [0.042] Bidder EBIT/Assets -0.010 -0.016 0.032 0.113** 0.132**

[0.538] [0.250] [0.538] [0.046] [0.041]

Stock Bid Only -0.020*** 0.004 -0.052*** 0.046*** -0.001 [0.000] [0.286] [0.000] [0.004] [0.952]

Bidder High Tech -0.004 -0.001 -0.013 0.005 -0.040

[0.595] [0.854] [0.580] [0.859] [0.294] Bidder Fin Services -0.003 -0.005 -0.031** 0.005 -0.039*

[0.457] [0.139] [0.037] [0.731] [0.082]

Cross-border -0.005 -0.006 -0.055*** -0.006 -0.056 [0.474] [0.299] [0.004] [0.774] [0.106]

Rel Deal Size -0.005*** 0.001 -0.016*** -0.003 -0.014**

[0.000] [0.597] [0.001] [0.450] [0.025] Competing Bid -0.004 0.005 0.018 0.038* 0.067**

[0.458] [0.326] [0.387] [0.073] [0.049]

Bidder PE Backed -0.031 -0.001 -0.064 -0.040 -0.087 [0.120] [0.946] [0.267] [0.383] [0.293]

Tgt PE Backed -0.009 -0.008 0.018 0.016 0.020

[0.538] [0.676] [0.737] [0.731] [0.795] Constant 0.196*** -0.159*** -0.453*** 0.377*** -0.200**

[0.000] [0.000] [0.000] [0.000] [0.035]

Year Fixed Effects Yes Yes Yes Yes Yes Bidder Nation Fixed Effects Yes Yes Yes Yes Yes

Observations 2,159 2,159 2,159 2,159 2,159

R-squared 0.068 0.043 0.144 0.103 0.156

52

Table 14: Estimates of Synergistic Gains

Item Item Table

Value

(SM)

EB = Bidder Mkt Cap (Mean in $000s) 1 T1 (L21) $1,447

Relative Target/Bidder Market Cap. Ratio 2 T1 (L20) 54.3%

ET Target Mkt Cap (Mean in $000s) 3 Implied by ratio $785.7

Sp Probability of Deal Success 4 Imp. by deal failure 82.9%

Fp Probability of Deal Failure 5 T1 (L16) 17.1%

Proportion of Stock Only 6 T1 (L17) 32.5%

Failed Bidder BHAR (Ann-120, End +2) (Both Non-Stock and Stock) 7 T4 (C5, L1) -6.6%

Failed Bidder BHAR (Ann-120, End +2) (Both Non-Stock and Stock). Excluding Contested Bids 8 T4 (C10, L1) -8.0%

Failed Bidder BHAR (Ann-120, End +2) (Both Non-Stock and Stock). Exogenously Failed only 9 T11 (C5, L1) -7.2%

Failed Matched Comb. Bidder + Tgt BHAR (Ann-120, End +2) (All Bid Types) 10 T6 (C5, L1) -11.9%

Failed Tgt BHAR Relative to Success (Ann-120, End +2) Implied by I1, I3, I9 and I10 11 -20.6%

Failed Tgt BHAR Relative to Success (Ann-120, End +2) 12 T5 (C5, L1) -24.7%

Bidder's 5 day CAR on Deal Announcement 13 T4 (C1, L1) 0%

Tgt BHAR (Stock Bid) Relative to Non-Stock (Ann-120, End +2) 14 T5 (C5, L9) -11.1%

Stock Bidder Bid Announcement CAR 15 T4 (C1, L9) -2.2%

Stock Bidder BHAR Run-Up prior to (Ann-120, Ann-2) Relative to Non-Stock Bids 16 T4 (C4, L9 – C1, L9) 5.4%

Stock Bidder BHAR Run-Up to (Ann-120, Ann+2) Relative to Non-Stock Bids 17 T4 (C4, L9) 3.2%

CGB

= Bidder Synergistic Gain Estimate with Exogenous Failure for all Bid Types 18 Abs(I1 I9) $104m

CGT

= Target Synergistic Gain Estimate for all Bid Types 19 Abs(I3 I11) $162m

CGB

+ CGT

= Sum of Bidder + Target Gains for both Bid Types 20 I18 + I19 = Abs(I10 (I1+I3) $266m

C C

B S Bp G Estimate of Bidder Bad News Released by Non-Stock Bid Types 21 I4 I18 $86m

Stock Bid Only ,S S S I S

B S B B Bp G A E = Est. of Bidder Bad News Released by Stock Bids 22 I21+Abs(I1 I15) $118m

SBG = Bidder Syn. Gain Estimate: Conventional Methodology Including Run-up Period 23 (I1 I17) $46m