Creating and Appropriating Value from Mergers and Acquisitions – A Merger Wave Perspective

By Benjamin W. Blunck

A dissertation submitted to

the Faculty of Social Sciences, University of Aarhus

in partial fulfilment of the requirements of

the PhD degree in

Economics and Management

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TABLE OF CONTENTS

PREFACE III

DANSK RESUMÉ (DANISH SUMMARY) V

INTRODUCTION AND SUMMARY VII

CHAPTER 1 3

What Drives Private and Public Merger Waves in Europe?

CHAPTER 2 63

Revisiting the Returns to Bidding Firms in Mergers and Acquisitions: The Nature of Synergies and the Market for Corporate Control

CHAPTER 3 111

Value Creation, Appropriation and Destruction in Mergers and Acquisitions: An Industry Merger Wave Perspective

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PREFACE

This dissertation was written in the period from September 2004 to October 2008

during my studies at the School of Economics & Management at the University of Aarhus

and during my stay at the Fisher College of Business, The Ohio State University.

I would like to thank the School of Economics & Management for providing me with

the administrative support necessary to complete this journey. I especially appreciate the

generous financial support of my participation in numerous courses, workshops and

conferences abroad which have been beneficial to my continued learning. I would also like to

thank the School for giving me the chance to design and teach my own Master’s level course.

Among the support staff at the School, Bibiana Paluszewska deserves special thanks for

helping me with many ad-hoc issues, as does Thomas Stephansen for proofing my work. In

addition, I am grateful to Søren Staunsager for granting my numerous special IT requests,

even the tricky ones.

I am particularly grateful to my advisors, Ole Ø. Madsen and Jan Bartholdy for

guiding me through this meandering process. Ole has offered me critical support and advice

the past five years, and his loyalty has been invaluable to me. Jan and I have had many

inspiring academic discussions in our attempt to grasp the drivers of European merger

activity. I also owe a debt of gratitude to my informal advisor, Jay Anand, who gave me the

opportunity to visit Fisher College and work with him, and with whom I have had many fun

and illuminating conversations.

My stay at Fisher College was enormously beneficial for me from both an academic

and personal perspective, and I would like to thank the many people with whom I interacted.

I am very grateful to the organizations which funded my stay – Fonden Erik Hoffmeyers

Rejselegat, Augustinus Fonden, Axel Nielsens Mindelegat, Det Danske Handelskammers

Fond and Oticon Fonden.

The ‘predefence’ of this dissertation was held on February 24th, 2009. I would like to

thank the committee – Professors Bechmann, Overgaard and Sudarsanam – for their

comments and suggestions. They have greatly improved the paper and I look forward to

incorporating more of their suggestions in the future.

I would also like to thank a number of people who have taken the time to offer me

excellent advice, give me valuable academic feedback and generally assist my academic

progress. For the sake of completeness, the list is here, in alphabetical order: Andrew Ang,

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Jay Barney, B. Espen Eckbo, Tom Engsted, Erik Hoffmeyer, Svend Hylleberg, Peter G.

Klein, Joe Mahoney, Niels Peter Mols, Randall Mørck, Lars Nautrup, Per B. Overgaard,

Johannes Raaballe, Torben B. Rasmussen, Carina Sponholtz, Rene Stulz and Margarita

Tsoutsoura. I would especially like to single out David G. Skovmand and Peter T. Larsen for

taking an active interest in my work (sometimes under slight duress!), and for the fun times

we had back in the day. Finally, I owe more pints of beer to Thomas Poulsen than anyone

else. He has helped me enormously during the last couple of years and shared with me his

good nature and his ability to inspire and be inspired.

My present and former PhD colleagues deserve credit for sharing with me their own

thoughts on this process and its ups and downs. I especially owe a debt of gratitude to my

officemate Jesper R. Hansen, who has offered me his ear on many an occasion. I still cling to

the hope that one day I shall see him clean up his desk!

Finally, I would like to thank my friends and family for their love and support and

good times. And to Anne Sofie and Svetlana: I am quite sure that all of this would have

seemed a lot more trivial had I not had you in my life.

But of all, my parents of course deserve the most credit for all the obvious reasons.

Your love, support and encouragement throughout the many years of my education have been

a solid base on which to build success.

Benjamin W. Blunck, Aarhus, May 2009

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DANSK RESUMÉ (DANISH SUMMARY)

Denne afhandling består at tre selvstændige papirer, der beskæftiger sig med årsagen

til og profitabiliteten af virksomhedsopkøb og -fusioner med udgangspunkt i et opkøbsbølge-

perspektiv.

Første papir tilbyder to unikke bidrag til den empiriske forskning i

virksomhedsopkøb. Der gennemføres den første komplette empiriske undersøgelse af de

forklarende faktorer bag 90’ernes opkøbsbølge i EU-15. Samtidig sammenlignes de

forklarende faktorer bag bølgerne for hhv. de børsnoterede selskaber og de ikke-børsnoterede

selskaber, som er forskellige med hensyn til aktionærpræferencer, selskabsledelse (’corporate

governance’) samt informationsmiljøet. Undersøgelsen viser, at bølgen af opkøb mellem

ikke-børsnoterede selskaber var drevet af rationelle, økonomiske faktorer, der sandsynligvis

skabte værdi for aktionærerne. Bølgen af opkøb involverende børsnoterede selskaber var til

gengæld drevet af ledelsens ønske om at fastholde den høje vækst, som aktiemarkedet

forventede af virksomhederne. Dette bør tages in mente af både virksomhedsledere,

bestyrelsesmedlemmer og aktionærer.

Andet papir undersøger teoretisk, hvordan konkurrencen i markedet for

virksomhedskontrol – hvor virksomheder købes og sælges – ændrer sig, når man tager højde

for synergiens karakter. Vi fokuserer på, hvordan synergiens karakter påvirker værdien af

opkøbsmålet samt opkøberens rivaler, og derved ændrer det forventede udfald i markedet

samt opkøberens afkast. Blandt andet finder vi, at opkøberen risikerer at skulle betale mere

end den fulde værdi af synergien, såfremt rivalerne står til at miste værdi som følge af

opkøbet. Hvis selskaber, som står uden for fusionen, står til at opnå en gevinst som følge af

opkøbet, burde den potentielle opkøber slet ikke købe virksomheden. Hvis den gør det, vil

den opnå en meget begrænset gevinst. Papirets konklusioner er af stor betydning for en

forståelse af årsagerne til og konsekvenserne for virksomhedsopkøb.

Tredje papir undersøger empirisk, hvorvidt og i hvor stor grad amerikanske

børsnoterede opkøbere opnår et positivt afkast i industriopkøbsbølger kontra i perioder

udenfor bølger. Undersøgelsen viser, at flere opkøb indenfor bølger er drevet af rationaler,

der begrundes af ledelsesmæssige forhold og derved ikke er forbundet med værdiskabelse for

det opkøbende selskabs aktionærer. Dog finder jeg, at de opkøb, der er drevet af et

værdiskabelsesmotiv, dvs. af muligheden for synergitilvækst og ikke ledelsesmæssige

forhold, opnår et højere afkast indenfor opkøbsbølger end udenfor. Samtidig vil de opkøb, der

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er drevet af ledelsesmæssige forhold til gengæld miste mere, når de gennemføres indenfor

bølger. For virksomhedsledere betyder det, at gevinsten ved at gennemføre et veltænkt og

velmotiveret opkøb er størst indenfor opkøbsbølger. For aktionærer og

bestyrelsesmedlemmer betyder det, at man i perioder med høj opkøbsaktivitet i højere grad

skal gardere sig mod opkøb foretaget med udgangspunkt i ledelsens egen interesse og

kognitive begrænsninger.

Det første papir har været præsenteret til European Financial Management

Association 2007 Meeting (Wien, Østrig) og 2008 Financial Management Association

Meeting (Texas, USA). Andet papir skal præsenteres på 2009 Academy of Management

Annual Meeting i Chicago, USA, i august 2009. Tredje papir skal præsenteres på 29th

Strategic Management Society Annual International Conference i Washington D.C, USA,

oktober 2009.

vii

INTRODUCTION AND SUMMARY

What drives mergers and what determines their performance? These are the

fundamental research questions in the corporate strategy and finance literature on mergers

and acquisitions (M&A). They have been addressed from a multitude of angles, but until

recently, they have rarely been addressed beyond the firm or transaction levels.

This dissertation adopts a merger wave perspective on the cause and consequences of

merger activity, which provides two sets of implications beyond those of the firm level.

Firstly, the existence of merger waves implies that individual mergers are in part driven by

factors at the industry and economy-wide levels. Specifically, these higher-level effects have

a potential to influence the cause of mergers; firms with common characteristics such as

industry affiliation, organization of firm ownership and strategic profile may share both the

same drivers and underlying motives for merger. Secondly, the ensuing interdependency

between mergers and acquisitions at these levels implies that individual mergers are

conducted in sequences as part of a competitive game against rival firms. Hence, causal

factors above the firm level introduce a specific course to merger activity, which again

affects their fundamental causes and consequences, as well as the characteristics of the

transactions, such as method of payment, bid premium etc. In all, the introduction of causal

effects at a level above the firm and transaction levels implies an underlying framework for

the merger decision as well the resulting value creation, appropriation or destruction in a

given merger or sequence of mergers.

The dissertation consists of three chapters which build on the above implications to

further push the research frontier on the cause and consequences of M&A. Although each

chapter offers a contribution to the issues at hand, there is no explicit linkage between them.

Note that while the first chapter is written with a corporate finance audience in mind, the

second and third chapter are written within the research stream of strategic management.

The first chapter adds directly to the merger wave literature in financial economics by

analyzing the drivers of merger waves in the EU-15 in the late 90s. So far, only anecdotal

evidence has touched on the drivers of European merger activity. Even more importantly, we

analyze both the drivers of merger waves involving publicly held firms and privately held

firms. To our knowledge, ours is the first work to analyze theoretically and empirically the

drivers of private merger waves.

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The second chapter adds to the strategic management research on M&A by further

delineating the conceptual link between the creation of synergy and M&A performance.

Acknowledging that M&A often occurs on the basis of a shared synergy potential, we argue

how different sources of synergies confer spillovers on rival bidding firms, and how the

expected returns to M&A change when we take into account the ensuing competitive

dynamics in the market for corporate control.

The third chapter adds to both the strategic management research on M&A and

merger wave literature by investigating empirically the returns to M&A within industry

merger waves compared to out of waves. As opposed to existing research, I identity and

examine two separate research questions – the incidence of value creating and destroying

motives and the extent of value creation, appropriation and destruction – which together

make up expected M&A performance. Unlike previous research, I use a modified event study

approach to take into account the interdependency of M&A within waves.

The remainder of this introduction places each chapter within its research stream, and

provides a summary of the chapter and its main contributions.

What Drives Merger Waves?

At least 5 Great US Merger Waves have taken place since the 1890s – in 1895-1905,

1920s, 1960s, 1980s and 1990s. The last two are often regarded as a ‘new’ type of merger

activity (Jensen, 1993, 2005; Sudarsanam 2003) on account of modern deal design and anti-

trust regulation, as well as specific changes in business models and competition brought on

by the shareholder value paradigm, ongoing computerization, deregulation and globalization

(Bruner, 2004; Jensen, 1993; Sudarsanam, 2003). In short, M&A has appeared to become a

greater part of corporate strategy. This, along with the increase in US merger activity which

occurred in the 1980s and continued with the unprecedented merger activity of the 1990s, has

lead to the re-emergence within the past 15 years of merger wave theory1. Brealey and Myers

(1996) note that the lack of a general hypothesis to explain merger waves is considered one of

the 10 major ‘puzzles’ in corporate finance theory.

The ‘new’ merger activity lead to two merger waves within the US – 1983-1989 and

1993-2000. The European Union has experienced a small and a large merger wave in 1987-

1992 and 1995-2001, respectively (Sudarsanam, 2003). Notably, both the US and the EU

1 Early literature on merger wave theory is Nelson (1959), Gort (1969) and McGowan (1971).

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waves of the 80s were regional in nature2. In comparison, the 5th Great US Merger Wave was

more global in nature. Despite the emergence of a European merger market comparable in

size to the US market (Goergen & Renneboog, 2004) and an increasingly global merger

market, no theoretical, and only sparse empirical research has been directed outside the US

and the UK.

In essence, the current literature is in broad agreement on a few well-established facts,

but in general disagreement on the theoretical explanation for these facts. In general, it is

accepted that aggregate merger waves are pro-cyclical, i.e., they take place when stock

market valuations are high and interest rates are low. A second fact is that aggregate waves

are generally made up of merger waves at the industry level3 which cluster but still show

inter-industry variation within the aggregate merger wave. These facts have lead to 4 broad

views on merger waves, which differ on their assumptions relating to two main dimensions –

the efficiency of the stock market and the efficiency of control mechanisms, i.e., the ability of

shareholders to motivate managers to maximize shareholder value (see table 1).

Table 1: The general assumptions of merger wave theories Shareholder efficient manager

(efficient control)

Shareholder inefficient manager (inefficient control)

Efficient stock market Neoclassical view (Harford 2005; Jovanovic and Rousseau, 2001, 2002a,b; McGowan, 1971; Mitchell & Mulherin, 1996; Weston, 2001)

Managerial discretion view (Gorton, Kahl, & Rosen, 2005; Jensen 1986, 1993, 2005)

Inefficient stock market Market driven view (Rhodes-Kropf & Viswanathan 2004; Shleifer & Vishny 2003)

Institutional/behavioural view (Auster & Sirower 2002; Stearns & Allan, 1996)

The neoclassical view covers theories in which shareholder value-maximizing

managers use M&A to reorganize assets in response to broad and specific shocks which

change the fundamental economics of industries. Ad-hoc theories are often used to explain

merger waves in individual industries. The neoclassical view argues that merger waves in

2 The 4th Great US Merger Wave is known for its refocusing of large US conglomerate firms and an increased use of leveraged buyouts conducted by way of hostile tender offers (Bruner, 2004; Sudarsanam, 2003). The European wave began towards the decline of the US market, as European corporations battled to position themselves to take advantage of the EEC ‘free market’ initiated in 1987 (Sudarsanam, 2003). 3 Lamoureaux (1985), Eis (1969), Mitchell & Mulherin (1996) and Harford (2000) document inter-industry variation of merger activity in the 1st, 2nd, 4th and 5th Great Merger Wave, respectively. Notably, there is no clear statistical evidence of industry clustering in the 3rd Great (Conglomerate) Merger Wave of the 1960s. Mitchell & Mulherin (1996) fail to reject the null hypothesis that there was no inter-industry variation in US merger activity 1962-1971.

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individual industries cluster in time (Mitchell & Mulherin, 1996) when sufficient cheap

financing is available, creating aggregate merger waves (Harford, 2005). Economic industry

changes may be brought on by broader economy-wide changes such as changes in merger

legislature or regional liberalization (e.g., the advent of the European common market), or

trickle down from widespread technological changes (Jovanovic & Rousseau, 2001, 2002a,

2002b).

The managerial discretion view argues that merger waves occur when economic

conditions give managers a greater incentive or opportunity to pursue their own objectives as

opposed to those of shareholders. In regards to the former, the changes in economic

conditions preceding waves may increase directly or indirectly the managerial pay-off to

M&A4. For instance, changes in risk could increase the value of diversifying managerial

wealth (e.g., Amihud & Lev, 1983) or ‘empire building’ through M&A (e.g., Mueller, 1969).

Alternatively, Gorton, Kahl, & Rosen (2005) argue that the onset of a merger wave may lead

managers to acquire other firms purely to reach a size that makes them indigestible to other

firms. As for the latter, the economic changes occurring before and during the merger wave

may loosen the constraints placed on managers by shareholder control. Specifically, the

control of managers provided by the shareholders, the equity and debt markets and the

product market becomes more lax when firm performance is high (Jensen, 1993)5. Jensen

(2005) notes that stock market overvaluation may also provoke agency driven acquisitions as

managers take advantage of the desire of the board and the investors to maintain high growth

levels to satisfy overoptimistic investor predictions.

The institutional view further removes merger waves from assumptions of efficiency

by arguing that shareholders and managers suffer from behavioural afflictions. Merger waves

occur due to economic changes, but the synergies are only attainable by a few firms.

However, the coercive, mimetic and normative isomorphic processes lead to a legitimacy

regarding specific acquisition strategies, which managers follow in a ‘bandwagon effect’

(Auster & Sirower, 2002), despite the fact that most acquirers will not be able to achieve

similar gains. A high degree of uncertainty is a necessary condition for these processes to

4 Note also that if merger activity in general serves managerial objectives, then any easing of financial constraints will trivially lead to agency cost dominated waves (Jensen, 1993). 5 This builds on the idea that managers are fundamentally self-serving, but constrained (or incentivized) from following managerial objectives to the degree that external and internal control systems are efficient. External control mechanisms consist of the discipline of external markets such as the equity, debt and product markets, while the internal controls systems consists of the board of directors, compensation packages etc. (Jensen, 1993; Sudarsanam, 2000). As an example of the effect of the economic environment on shareholder control, Jensen (1986) argues a ‘free cash flow’ theory in which managers of cash-strapped firms in stable industries are able to conduct empire-building and diversifying acquisitions as opposed to paying out cash to its shareholders.

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unfold. As a result, merger waves are generally value destroying, and they continue until the

poor acquisition performance becomes sufficiently evident and legitimacy breaks down.

The market driven view argues that aggregate and industry merger waves take place

as persistent stock market overvaluation allows the most overvalued firms to conduct

acquisitions which are paid in overvalued shares, providing them with a long term gain

proportional in size to the overvaluation of their shares. Consequently, the increase in merger

activity correlates with an increased use of stock payment, and not with an increase in

average merger synergies. Notably, this requires not only overvaluation of firms, but also an

overestimation of the potential value of synergies in order to ensure that the gains from

overvaluation are not dissipated in the terms of exchange or any subsequent market re-

evaluation of the share price (Rhodes-Kropf & Viswanathan 2004). In other words, a

corroborating ‘synergy story’ is needed.

Bruner (2004) summarizes quite well the extant evidence on the causes of aggregate

and industry merger waves.

“Research lends some speculative answers to these questions. The explanations should be approached with caution since they are not mutually exclusive and more research remains to be done.” (Bruner, 2004: p. 75, 27-30)

Although there are numerous major and minor empirical predictions proposed by the

4 broad views, there are currently 4 main empirical arenas under dispute: a) the timing of

merger waves, b) the consideration structure of mergers, c) the characteristics of acquirer and

targets, and d) the post-merger performance of mergers in merger waves. The role of

alternative corporate adjustment methods (such as internal investment and strategic alliances)

during merger waves is a related empirical arena, but as yet only indirectly so (see, e.g.,

Andrade & Stafford, 2004).

In general, all perspectives argue that favourable financial conditions are a necessary

condition for merger waves, implying that the correlation between merger activity and low

interest rates on one hand and high stock market values on the other does not offer any basis

for discerning theories. However, the market driven view stands alone in predicting the

correlation between merger activity and the increased use of stock payment. It is widely

reported that the waves of the 60s and 90s display such an increase, while Andrade, Mitchell,

& Stafford (2001) report that the proportion of M&A using some stock or all stock in the 80s

does not differ from that of the 70s.

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Harford (2005) cites unpublished research by Verter (2002) which shows that

historical aggregate merger activity is generally determined both by stock market values and

the dispersion in stock market values, both of which are supportive of the market driven

perspectives. However, Jovanovic & Rousseau (2002a) show that Tobin’s Q, which proxies

for growth opportunities, does the same. They argue this as an empirical result in favour of

their (neoclassical) theory of merger waves in which technological change causes efficient

mergers between high and low Tobin’s Q of acquirers and targets, respectively.

Firm-level evidence on the characteristics of acquirers confirms the aggregate effects;

high market-to-book (or Q) acquirers take over low market-to-book targets. In an attempt to

separate the neoclassical and market driven hypotheses, Rhodes-Kropf, Robinson, &

Viswanathan (2005), Dong, Hirschleifer, Richardson, & Teoh (2006) and Ang & Cheng

(2003) offer three alternative methods of measuring temporary misevaluation, and they all

conclude that overvalued acquirers take over less overvalued target firms. The differences

between acquirers paying in stock and acquirers paying in cash offer convincing evidence

that the choice to acquire with stock is influenced by stock overvaluation.

Mitchell & Mulherin (1996) and Harford (2005) back up previous anecdotal evidence

on the importance of industry economic conditions, by statistically showing the link between

broad and specific economic industry ‘shocks’ and the timing of industry merger waves.

Harford (2005) finds an index of economic shocks coupled with a proxy for capital liquidity

can explain the onset of US industry merger waves 1981-2001, leaving no explanatory power

for measures of stock market values and their dispersion. This, in principle, supports all

merger wave theories except the market driven view and Jensen’s agency costs of

overvaluation (Jensen, 2005). However, Rhodes-Kropf et al. (2005) show that the industry

component of firm misvaluation also has independent explanatory power in explaining the

same US industry and aggregate merger waves.

The performance implications of the theories are quite clear; only the neoclassical

view expects that mergers within merger waves are efficient – i.e., better performing than

similar non-merging firms – and that acquirers appropriate any of the synergies. Numerous

surveys on M&A conclude that short run announcement returns to acquisitions are positive

for the acquirer and target combined, but slightly negative or zero for acquirers (e.g., Andrade

et al., 2001), which offers no overwhelming support for neoclassical theory. In fact, this and

similar evidence is often used to argue that merger waves are value destroying (Auster and

Sirower, 2002). The performance implications of market driven theories and Jensen’s agency

costs of overvaluation are difficult to test using short-run returns, since they assume stock

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market inefficiency. However, Moeller, Schlingemann, & Stulz (2005) note that the pattern

of short-run returns to acquisitions conducted by large-loss firms in the US at the turn of the

millennium indicate support for an agency costs story as opposed to a market driven story.

Specifically, the announcement of acquisitions by the firms during the last years of the 90s

merger wave lead the stock market to re-evaluate the fundamental value of firms. This was

confirmed by the subsequently very negative long run returns of theses acquirers. Harford

(2005) also presents evidence of long-run returns to stock bidders (as well as pre-wave

returns) which do not offer convincing evidence for the stock movements implied by the

market driven view.

Studies of operating performance are often open to interpretation depending on how

they take into account the unobservable benchmark (Sudarsanam, 2003). When necessary

industry, size and performance matching are used, the results are often insignificant.

However, Bouwman, Fuller, & Nain (2007) find that both cash and stock mergers achieve

similar improvements in both high and low valuation periods during the 80s and 90s, which

goes against the market driven view. However, acquirers achieve better performance in low

valuation periods than high valuation periods, which would seem to support the institutional

view (Bouwman et al. refer to theories of ‘managerial herding’) and Jensen’s agency costs of

overvaluation (2005). Harford (2005) uses analyst forecasts as the unobservable benchmark

and finds that mergers within waves are no worse than mergers out of waves. In all, the

evidence supports neither view conclusively, but adds weight to the expectation that merger

waves are not decidedly value destroying.

In conclusion, merger waves lie quite firmly on a neoclassical foundation with regards

to timing, but the value destruction of some acquirers – especially in the 90s – adds a clear

indication of an influence of value destruction in the form of agency costs of overvaluation or

institutional effects. And while the use of stock as payment is clearly not the foundation of

US merger waves, stock market overvaluation nevertheless affects managerial decision-

making and hence, the pairings of merger partners.

Blunck & Bartholdy (2009). The first chapter in this dissertation offers two major

contributions towards the building of a broader perspective on merger activity and merger

waves.6 The first unique contribution of this paper is to provide evidence of merger waves

and the factors driving them in Europe. Although recent research has addressed the

consequences and characteristics of mergers in Europe in the 90s (Campa & Hernando, 2004; 6 Blunck & Bartholdy (2009) has been presented at the European Financial Management Association 2007 Meeting in Vienna, Austria, and the 2008 Financial Management Association Meeting in Dallas, Texas.

xiv

Goergen & Renneboog, 2004; Martynova, Oosting & Renneboog, 2006; Martynova &

Renneboog, 2006a, 2006b), research is yet to address the industry and economy-level drivers

of European merger activity. We analyse the cause, characteristics and consequences of EU-

15 merger activity in and around the European Merger Wave of the 90s – specifically, 1995-

2004 – from the perspective of recent advances in merger wave theory. The second unique

contribution is to analyse theoretically and empirically the applicability of existing merger

wave theory to mergers involving private firms. According to Thomson Financial’s SDC

Platinum M&A Database, roughly three-quarters of the combined merger activity of the US

and the EU-15 involves a private acquirer or target firm. The consequences of public

acquirers buying private and public targets have been analysed in both the US (see for

example Chang, 1998; Ang & Kohers, 2001; Bargeron, Schlingemann, Stulz, & Zutter,

2007), the UK (Draper & Paudyal, 2006) and the EU as a whole (Martynova et al., 2006).

However, deals involving private acquirers buying private targets have not been addressed,

nor has the role of private acquirers and targets in merger waves. We believe that the study of

the drivers of merger waves should include all mergers regardless of the organization of

corporate ownership. Thus, we identify and compare industry merger waves involving private

and public firms and analyse their drivers separately.

Mergers involving private firms are less likely to be susceptible to the overvaluation

theories. Private firm acquisitions imply a different impact of a) acquirer and target

shareholder preferences, e.g., it is unlikely that public firm shareholders will accept privately

held stock as payment, b) corporate governance, e.g., private shareholdings are more

concentrated reducing agency conflict, and c) the informational environment, e.g., search

costs for private targets are higher. Thus, it is not clear if the same factors drive merger waves

for both public and private firms. In all, we conclude that merger waves involving purely

private firms (from here on: private-private) are the least likely to be driven by overvaluation,

while merger waves of private acquirers and public targets (from here on: private-public),

and public acquirers and private targets (from here on: public-private) are more likely to be

driven by overvaluation. However, merger waves involving purely public firms (from here

on: public-public) are still the most likely to be driven by overvaluation theories.

We use a methodology based on Harford (2005) to identify private-private, public-

public, private-public, and public-private industry merger waves in the 10 years in and

around the European Merger Wave of the 90s (1995-2004). We find a significant inter-

industry variation in merger activity which suggests that the driving factors of the European

Merger Wave of the 90s are to be found at the industry level. This corresponds to evidence on

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the US merger waves of the 80s and 90s (see Mitchell & Mulherin, 1996, and Harford, 2005).

To test whether neoclassical or overvaluation theories explain private and public merger

waves, we investigate theoretical predictions on the pattern of consideration structure, post-

merger operating performance and timing of industry merger waves.

The neoclassical theory implies that the impact of the shock is independent of

ownership structure. Thus, there should be no difference in timing and performance of

industry merger waves involving private and public firms in the economic shock theory.

However, we find very little overlap between private and public merger waves. In other

words, while we confirm previous evidence that the existence and timing of waves differs

across industries, we also show that the existence and timing of waves differs across the

private and public ownership of acquirers and targets. Since we expect that the private-private

sample is the one most likely to be driven by neoclassical theory, the lack of similarity

exhibited by the 3 other samples is primae facie evidence of an influence of overvaluation.

Alternatively, other unspecified theories could be in play within these subsamples.

We investigate the use of stock payment in private and public industry merger waves

to see if stock payment increases as predicted by the market driven theory. However, only the

public-public subsamples use stock to any considerable degree. And while there is a small

increase from 42.8% to 49.6% in the proportion of mergers using stock payment during

industry merger waves, it is only weakly significant, and the proportion of mergers paid fully

in stock does not increase, nor does the average proportion of stock consideration in an offer.

We therefore reject the market driven explanation across all 4 sub-samples.

Univariate and multivariate analyses of the determinants of the timing of industry

merger waves reveals that economic shock factors are not important in explaining merger

waves involving publicly held firms, whereas they are the prime drivers of merger waves

involving privately held firms. Specifically, logistic modelling on the onset of industry

merger waves shows that an index of economic shock variables (Harford, 2005) explains all 4

subsamples except the public-public sample. Thus, the economic shock theory can explain

private merger waves but not public merger waves. However, public-public industry merger

waves are explainable by the 1-year ex-ante industry stock returns, which proxies for short-

run stock market overvaluation. Thus, it would seem that public-public waves are driven by

overvaluation. Since the use of stock payment is similar to periods of lower industry merger

activity, this points to the influence of agency costs of overvaluation. The public-private and

private-private samples also show an influence of overvaluation, although we argue that this

xvi

is due to the industry valuations measures picking up the increased growth opportunities in

the industry.

To validate the cumulative evidence, we turn to a study of post-merger operating

performance. The neoclassical view sees mergers as engines of efficient asset restructuring,

whereas the agency costs perspective argues that the mergers, at best, serve to prolong

ultimately unprofitable business activities. Therefore, the neoclassical perspective predicts

that mergers in merger waves lead to a post-merger operating performance which is better (or

no worse) than the unobservable benchmark, whereas the agency costs perspective expects

performance which is worse (or no better). We use the two-step procedure of Ghosh (2001) to

find pairwise matching firms for each acquirer and target in our sample on the dimensions of

industry, size and performance in order to calculate a benchmark measure of post-merger

operating performance. The change in operating performance adjusted for benchmark

performance shows some evidence that private-private mergers outperform the other 3

samples, and to slight extent their own benchmark. These results should be seen against the

backdrop of European and US studies of operating performance, which fail to find evidence

of abnormal merger performance when compared to firms matched on industry, size and

performance (see e.g., Sudarsanam 2003). In all, the performance study suggests that private-

private merger waves generally restructure assets in an efficient manner. Public-public

merger waves seem to be less efficient, although they do not significantly underperform

compared to matched firms. The same seems to be true for the private-public and public-

private samples.

In all, the combined evidence presented in this paper leads us to conclude that public-

public merger waves are driven by the agency costs of overvaluation, while neoclassical

reasoning drives private-private merger waves. The evidence is less clear for private-public

and public-private merger waves, which we find to be driven by economic shocks, but

showing operating performance which borders on value destruction. Nevertheless, agency

costs or other non-efficiency considerations may affect the motivation behind these mergers

even though they are triggered by economic shocks. We leave it to future research to explore

the additional factors driving these merger waves.

Revisiting the Returns to Bidding Firms in Mergers and Acquisitions

The pursuit of synergy is widely regarded as the raison d’être of M&A within the

business world. When synergies between two firms present themselves, a merger has the

potential to create value for both acquiring and selling firm shareholders. And as the size of

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synergies increase, it is expected that the gains to the acquirer and target firm will also

increase. Therefore, if an acquirer achieves a poor or negative gain from an acquisition, this

implies that the deal rationale was weak and/or that the acquirer has overpaid. Such

conclusions are routinely drawn in the business press. In fact, together with the critique that

synergies are generally illusory or not attainable (Sirower, 1997), this conclusion is a

cornerstone of the pessimists’ view on M&A, which is equally widespread in the business

world.

However, the simplistic perspective on the link between synergy and performance

expressed in these conclusions does not take into account the mediating factor which is the

market for corporate control (Barney, 1988; Bradley, Desai, & Kim, 1988; Capron & Pistre,

2002; Jensen & Ruback, 1983). The market for corporate control is the marketplace where

firms are bought and sold (Manne, 1965). Since this market allows many potential bidders

and many potential target firms to meet, it is inaccurate to understand an acquisition as a deal

between just one target and one acquirer. Thus, a merger deal is not simply the result of a

negotiation between the acquirer and target, in which we may expect the acquirer and target

firm to share the synergistic gains. When several potential acquirers can create synergies in a

merger with a given target firm, there is a state of excess demand for the target firm.

Consequently, negotiations are not exclusive and a bidding process is likely to ensue. Such

excess demand seems especially likely when the sources of synergies are driven by broad or

specific economic changes at the industry level. Imagine a case where synergies are identical

across bidders. Unless there are structural imperfections in the market for corporate control,

or one bidder is able to implement bidding strategies which create or exploit market

imperfections to set aside competitive dynamics, the price which fully reflect the value of

synergies and the acquirer will achieve no share, and hence, no gains (Hirschleifer, 1980).

In all, an acquirer should only expect gains to acquisitions if it can quell the

competitive dynamics by creating unique or privately known, valuable synergies (Barney,

1988) or by taking advantage of market imperfections in its bidding strategy (Hirschleifer,

1995). These two avenues spawn two isolated, but complementary research streams within

the literature on the market for corporate control.

The latter focuses on uncovering the sources and consequences of structural

imperfections in the bidding process, i.e., financial transaction costs, asymmetric information

etc. This literature, sometimes referred to as the ‘theory of M&A’ (Bradley et al., 1988),

primarily builds on insights from financial economics and auction theory adapted to the

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specific case of M&A and the legislature surrounding M&A (e.g., Hirschleifer, 1995). As

such, it has a price focus.

The former – which is the foundation of chapter 2 of this dissertation – focuses on

synergy. It builds on insights in strategic management. Specifically, it focuses on how market

imperfections in the product and resource markets create ‘privately appropriable’ synergies in

excess of other potential bidders. Whether a potential bidder can create inimitable (privately

appropriable) synergies depends on its endowment of assets vis-à-vis the source of synergies,

i.e., how firm assets match the opportunities and threats of the external economic

environment (Barney, 1991; Peteraf, 1993). Additionally, firms may be endowed with private

knowledge of the synergy potential, implying that they can create privately known (and

privately appropriable) value (Barney, 1988). This dichotomy between privately and

‘publicly’ appropriable synergies implies that acquisitions which make no economic profit

for acquirers may simply imply a certain nature of synergies. Thus, firms should not engage

in acquisitions unless they are able to avoid the full competitive dynamics. And research

should take into account the mediating factor of competitive dynamics when linking the

drivers of potential synergies to acquirer returns (Barney, 1988).

Blunck & Anand (2009). The second chapter of this dissertation further delineates

how the nature of synergies affects the outcome of acquisitions.7 Our paper extends the

existing strategy research on the market for corporate control by introducing the effect of

competitive spillovers to acquisitions on the valuations held by bidders and the ensuing

outcome of the competitive market dynamics. ‘Competitive spillovers’ refers to the effect of

acquisition synergies (or more generally, their strategic foundation) on the fundamental value

of rival firms. Generally speaking, a (rival) firm is affected by an acquisition if it is

competing for the same ‘economic value’ (Brandenburger & Stuart, 1996). The size and sign

of spillovers depend on the specific source(s) of acquisition value (Chatterjee, 1986). Broadly

speaking, spillovers are negative when the acquisition creates a competitive advantage for the

acquirer and a subsequent competitive disadvantage for the rival firm (Bradley, Desai, &

Kim, 1983). Spillovers are positive if the acquisition increases industry profitability to the

benefit of all industry firms (e.g., Porter, 1980). Competitive spillovers affect the price that

bidders are willing to pay for a given target firm in the sense that a given potential bidder will

avoid negative spillovers (or miss out on positive spillovers) if it succeeds in winning the bid.

7 Blunck & Anand (2009) has been accepted for presentation at the 2009 Academy of Management Annual Meeting in Chicago, Illinois, August 2009.

xix

This change in bidder valuations affects the competitive dynamics in the market for corporate

control and the subsequent expected returns to acquirer, target and rival firms.

We use a simple model of the market for corporate control (based on insights by

Aktas, de Bodt, & Roll, 2009; Boone & Mulherin, 2007; Bruner, 2004; Lippman & Rumelt,

2003) to analyze conceptually how different sources of synergies lead to different expected

outcomes in the market for corporate control when the valuation effects of competitive

spillovers are taken into account. In doing so, we obtain a series of novel and, at times,

counterintuitive propositions. Firstly, when synergies stem from a competitive advantage and

are imitable, all potential bidders will bid beyond the value of their synergies to avoid the

negative competitive spillovers from a competitive disadvantage. This rational overbidding

will lead to negative returns for the winning bidder. This means that there is a negative

relationship between the extent of synergies from imitable competitive advantages and the

returns to M&A. At very low levels of spillovers, the returns to imitable synergies (in

isolation) are negative, but close to zero; at very high levels of spillovers, such as those

implied by a horizontal acquisition in a concentrated industry, they can be highly negative.

Even inimitable synergies from competitive advantages can be heavily discounted if they

create significant competitive disadvantage for one or a few potential rival bidders. When

acquisitions provide both imitable and inimitable synergies from competitive advantage, the

negative returns from the imitable source of synergies may outweigh the positive returns

from the inimitable source. In short, inimitability is no longer a sufficient condition for

positive returns. However, if a bidder has sustainable private knowledge of the existence of

synergies, i.e., if neither the potential rival bidders nor the target firm know that they exist,

negative competitive spillovers will have no effect on the returns to the acquirer. Private

knowledge as a source of private gains will therefore lead to higher returns than asset

inimitability.

Secondly, when taking into account the effect of positive spillovers from increased

industry profitability on the incentive to bid and sell in the market for corporate control, the

price may exhaust all synergies – leading to zero acquirer returns. This is so because the

target firm does not have any incentive to accept the offer unless it receives the full value of

the positive spillovers it would otherwise gain should it remain independent. A similar ‘free-

rider’ problem can affect the incentive of an individual bidder to bid for a target as it would

be unwilling to pay for economic value which it could receive without acquiring. Therefore,

unless the acquisition of such a target firm offers sufficient inimitable (or privately known)

synergistic value, no shareholder value-maximizing bidder will rationally choose to acquire

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the target. So, the acquisition will not occur despite the existence of significant synergies. In

all, the link between synergy and acquirer returns is critically mediated by the specific nature

of the synergies.

Thirdly, while the managerial motivation of acquirers is often used to explain the

variation in acquirer returns and the zero average return (e.g., Roll, 1986), we show that this

link is also oversimplified when significant competitive spillovers are present. When a high

valuation bidder competes in the market for corporate control against a lower valuation rival

who has additional, managerially motivated incentives to overbid, the high valuation bidder

may bid and win at a price beyond the value of the synergies in order to avoid the negative

spillovers from the rival’s acquisition. Conversely, if the higher valuation bidder faces

positive spillovers from the rival’s acquisition – which occurs if the competitive abilities of

the rival firm are harmed by the merger – the higher valuation bidder may choose to bid less

than the value of synergies, essentially leaving money ‘on the table’. In short, similar to the

link between synergy and acquirer returns, the link between managerial motivations and

acquirer returns depends more broadly on the direct and indirect effects of the acquisition

value held by both the winning bidder and rival bidders.

Our conceptual analysis and propositions extend the strategy perspective on M&A

(Barney, 1988; Capron et al., 1998; Chatterjee, 1986; Singh & Montgomery, 1987) by

delineating the returns which can be expected given the source(s) of synergies in a given

context. It follows that various merger contexts provide not only a different scope for

sustainable value creation from M&A, but also a different scope for value appropriation. In

this sense, our paper presents an even stronger argument than Barney (1988) that the study of

acquisitions is conducted at a too aggregated level and that future research should not

compare the efficacy of acquisitions involving fundamentally different synergy phenomena.

In extension, our propositions cast a new light on existing empirical research. Firstly,

the cross-variation found in empirical studies of the announcement returns to acquisitions

might simply reflect differences in the competitive dynamics of the market for corporate

control. Secondly, the instance of negative returns does not necessarily stem from the

managerial motivations of the acquiring firm. It may instead stem from the effects of the

competitive dynamics involving several bidding firms and/or the indirect effects of the

managerial motivations held by rival bidders. Thirdly, our study suggests that contextual

variables used as determinants of value creation are also likely to be direct or indirect

determinants of value appropriation depending on the specific merger context. For instance,

researchers cannot find evidence of positive value effects from market power in horizontal

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M&A using measures such as industry concentration (Eckbo, 1983; Eckbo, Maksimovic, &

Phillips, 1990). This inability may stem from the confounding of co-existing negative

spillovers. Future research on the determinants of the gains to acquisitions must therefore

take into account the underlying nature of synergies.

Our propositions provide managers with a framework from which to judge the

potential outcome of mergers and acquisitions whether their firm is a potential bidder, target

firm or both. It is clear that firms should understand the acquisition value held by potential

rival bidders and targets in order to judge the effects of announced or proposed mergers

(Barney, 1988). Our analysis further implies that firms should understand more specifically

the source of their synergistic value to uncover the associated competitive impact on firms

within the bidder’s peer set. This true ‘acid test’ of the acquisition decision is one of

simultaneous creation and appropriation. In this regard, our paper asserts that managers of

firms with scarce resources and several distinct acquisition strategies (or alternatives) to

choose from should consider the full implications of the nature of the synergies before

deciding on a strategy.

Finally, our framework and its propositions also offer several insights for the broader

understanding of strategic factor markets and the resource-based perspective (e.g., Barney,

1986). Since the market for corporate control in essence concerns the purchase of a specific

bundle of resources, the principles and insights of our conceptual model also extend to

strategic factor markets more generally. Negative and positive spillovers in other strategic

factor markets may thus lead to similar effects and outcomes, although their impact may not

be as great as in the corporate control setting presented here.

Value Creation, Appropriation and Destruction in Mergers and Acquisitions

No research in strategic management has directly addressed the importance of the

merger wave context on the potential for value creation, i.e., synergy gains, and the value

appropriation, i.e., the gains appropriated by the acquirer, in M&A. However, the corporate

managers, analysts and the general business press routinely acknowledges the existence of

merger waves and periods of low merger activity, which may correspondingly be referred to

a ‘industry merger trough’ (Carow, Heron, & Saxton, 2004). However, on the question of

which context provides greater opportunity for acquiring firms to create value for their

shareholders, there seems to be two opposing myths.

On one hand, industry merger waves come at time when several analysts and

managers have proclaimed the advent of a new economic reality, which reflect changes in the

xxii

external industry environment, such as political, economic, social and technical changes

(Sudarsanam, 2003), or changes driven by internal industry competition such as product,

process and business model innovations. M&A is often lauded by managers to be the tool that

best responds to these opportunities and threats. However, this industry-level foundation also

implies significant competition among rival potential bidders. In this regard, firms may have

to ‘time’ their acquisitions to stay ahead of the industry learning curve and to avoid engaging

in bidding wars in a competitive market for corporate control. On the other hand, when

merger activity falls as the source of synergies and target firms dry up, perhaps following a

drop in industry demand, analysts and managers change their perspective. They argue the

greater potential for ‘bargains’ in the deflated market for corporate control, and talk about the

ability of individual firms to gain a decisive competitive edge by conducting acquisitions

which create uniquely valuable synergies. Clearly, these two myths differ on the relative

importance of acquirer returns of the broad vs. narrow foundation of synergies and the high

vs. low competition for synergies.

In addition, the role of M&A has a well-reported shadow side. The highly anticipated

synergy gains may be illusory (Sirower, 1997) and there is an increased chance that managers

conduct acquisitions purely not to feel left behind by their more acquisitive, glamorous peers.

In addition, managers and the boards that control them face numerous cognitive limitations in

their understanding of the changes unfolding before them (e.g., Auster & Sirower, 2002). In

this regard, it is clear to both researchers and the business press that merger waves and the

underlying economic changes may be conducive to self-serving and/or misconceived

acquisitions strategies. The business press talks of a merger ‘frenzy’, ‘bandwagon’ or similar

catchy expressions such as ‘mania’ (Fortune Magazine, 1994). The resulting mergers may

lead to value destruction as opposed to creation and appropriation.

Note that while the merger wave theory introduced in this dissertation argues that

periods of high merger activity are driven by a fundamental change in the content of merger

motives, theory has yet to hypothesize how and why value creating and value destroying

motives may appear simultaneously. Most existing theory is written from the perspective that

merger waves increase the number of mergers motivated by either one of the other (Gorton et

al., 2005, is the exception). This, despite that numerous empirical studies and surveys have

documented the co-existence of both value creating and value destroying motivations within

populations of mergers (e.g., Berkovitch & Naranayan, 1993; Moeller et al., 2005), implying

that the underlying drivers of merger waves may in fact cause both kinds of mergers to occur.

Consequently, no theory is currently able to provide a theoretical explanation for the relative

xxiii

incidence of value creating and value destroying motives in and out of industry merger

waves.

Recent work finds that mergers within US industry merger waves on average create

more value than mergers out of waves, and similarly, that the value appropriated by acquirers

within waves on average exceeds that of acquirers out of waves (Harford, 2003). In addition,

there is weak evidence that acquirers who are first movers in the industry merger wave on

average are able to create more synergies (Carow et al., 2004; Harford, 2003; McNamara,

Haleblian, & Dykes, 2008). Similarly, there is some evidence which shows that acquirers at

the end of industry waves fare worse than acquirers at other stages (Harford, 2003), which is

taken as evidence of value destroying motives at the end of the wave. However, these studies

have not taken into account the co-existence of two fundamentally different types of

acquirers: those motivated by value creating, synergistic motives and those motivated by

value destroying, managerial motives. As a result, it is unknown whether the higher level of

value creation and appropriation within waves and in the early stages of waves is due to a

greater extent of value creation and appropriation, i.e., the ability of acquirers to create and

appropriate more synergies, or whether it is merely due to the incidence of more value

creating motives, i.e., that a greater portion of acquirers within waves conduct acquisitions on

account of self-interested motives or their cognitive limitations.

Blunck (2009). The third chapter in this dissertation investigates simultaneously the

incidence and extent of value creation, appropriation and destruction to avoid the

confounding of co-existing motives, as well as to investigate the often-ignored, separate role

of value destruction by itself.8 By separating these concepts, the question of whether

performance is higher in or out of waves essentially breaks down to two separate research

questions. Firstly, is the incidence of value creating (or alternatively, value destroying)

motives higher in or out of waves? Secondly, is the extent of value creation and appropriation

higher in or out of waves? Our primary focus is on the second question, which is the primary

research question in M&A research in strategic management. The answer to this question is

clearly vital not only for the academic conversation on the returns to M&A and its

determining factors, but also for business practice.

Theoretically, I offer separate hypotheses on the incidence and extent of value

creation, value appropriation, and value destruction across the merger wave context and the

8 Blunck (2009) has been accepted for presentation at the 29th Strategic Management Society Annual International Conference in Washington, DC, October 2009.

xxiv

stages of the wave. These hypotheses are distilled from existing theories of mergers and

merger waves as well as resource-based theory.

To measure acquisition returns empirically, I employ a revised short-run

announcement return methodology which takes into account the workings of the stock market

during industry merger waves. Existing empirical work on industry merger waves primarily

uses the traditional short-run event methodology, which is based on the acquisition being an

unexpected event (Campbell, Lo, & MacKinlay, 1997). However, the economic changes

underlying industry merger waves may create partial anticipation of the returns to future

merger activity (Malatesta & Thompson, 1985), and this partial anticipation is revised as firm

and industry rival acquisitions occur throughout the wave. More importantly, should the firm

conduct more than one acquisition, it is impossible to separate the effect of the individual

acquisitions in any meaningful way, since they all serve to respond to a common, underlying

shock. Therefore, I measure returns at the higher level of acquisition strategy, meaning the

acquisition(s) conducted by acquirers during merger waves to respond to the economic

changes. I use a regression parameter approach (Eckbo, 2005) which sums the firm returns to

both its own acquisitions within a wave as well as those of industry rival firms. In order to

compare correctly the returns to acquisitions within waves with the returns to acquisitions out

of waves, I design a similar methodology for out-of-wave mergers.

I examine the relation between my measure of returns and the returns provided by

traditional event study methodology. I find evidence of significant partial anticipation and

ongoing revision of acquisition returns within industry merger wave, while this is not an issue

out of waves. Although I cannot rule out the importance of unknown economic or non-

economic factors, I argue that this validates my approach.

I investigate a sample of US mergers 1980-2005 similar to previous work on industry

merger waves. Using non-parametric Wilcoxon tests, I find that the average acquirers within

industry merger waves both destroy merger value and experience negative returns. In

comparison, the average acquirer out of waves creates merger value, but experiences negative

acquirer returns as well. Going beyond these aggregate statistics, I test my hypotheses on the

incidence of value appropriation, creation and destruction by evaluating the distribution of

merger outcomes in and out of waves. As expected from previous research (e.g., Moeller et

al., 2005), I find that both synergistically and managerially motivated acquisitions co-exist

within merger waves and in merger troughs. I confirm the expectation of institutional merger

wave theory (e.g., Auster & Sirower, 2002) that acquisitions within waves are more likely to

be motivated by value destruction. Surprisingly, I find that only 45.6% of acquisition

xxv

strategies within waves create value, compared with 56.4% of acquisition strategies out of

waves. The returns at different stages of the wave do not show signs that first-moving

acquirers and late-moving acquirers are more likely to be motivated by value creating or

value destroying motives. This goes counter to the expectation of institutional theory (Auster

& Sirower, 2002).

I then separate value creating and value destroying strategies and conduct univariate

and multivariate analyses on the extent of value creation, appropriation and destruction. As

expected by merger wave theories of value creation and value destruction, I find that in-wave

acquisitions both create and destroy more value than their out of wave counterparts. In other

words, acquirers who are synergistically motivated are able to create more merger value in

waves than out of waves, while acquirers who are managerially motivated destroy more

merger value in waves than out of waves. Looking at the value appropriated by

synergistically motivated acquirers, I see that the advantage held by in-wave acquirers over

out-of-wave acquirers remains. Thus, although there is smaller incidence of value

appropriation within waves, the acquirers which do appropriate value do so to a greater

degree than their out of wave counterparts. Of the managerially motivated acquisitions, the

acquirers within waves destroy the most acquirer value, i.e., they have the highest negative

value appropriation. Surprisingly, our results are materially unaffected by the timing of the

acquisition strategy within waves. Thus, when taking into account the effect of the merger

wave context on the returns to acquisitions, the hypothesized first-mover advantages and late-

mover disadvantages disappear.

In all, this paper uncovers a completely new angle on existing theoretical and

empirical research. In-wave acquisitions are both more value creating and value destroying,

depending on whether the acquisition is driven by primarily synergistic or managerial

motivations. I believe that the approach and results presented here will help guide future

empirical research on the performance of acquisitions as well as provide a foundation for

building a more complete ‘theory of mergers and merger waves’ (Weston, Chung & Hoag,

1990) which can explain the central questions concerning M&A – its cause, course and

consequences.

xxvi

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CHAPTER 1

What Drives Private and Public Merger Waves in Europe?

WHAT DRIVES PRIVATE AND PUBLIC MERGER WAVES IN EUROPE?

BENJAMIN W. BLUNCK School of Economics and Management

Aarhus Universitet Building 322, Bartholins Allé 10

DK-8000 Aarhus C, Denmark Tel: +45 8942 1524

E-mail: bblunck@econ.au.dk

JAN BARTHOLDY Aarhus School of Business

Fuglesangs Allé DK-8000 Aarhus C, Denmark

Tel: +45 8948 6338 E-mail: jby@asb.dk

This version: May 11th, 2009

Acknowledgements The authors are grateful for the commentary provided by Thomas Poulsen, Carina Sponholtz, Rene Stulz, and the participants at the European Financial Management Association 2007 Meeting, the 2008 Financial Management Association Meeting, and the Danish Doctoral School of Finance Workshop 2007. Any remaining errors are our own.

Chapter 1

4

Abstract

Our paper conducts the first full analysis of the drivers of the European Merger Wave of the

90s, and we offer the first analysis of the drivers of merger waves involving private firms. We

argue that private merger waves are less likely to be driven by the inefficiencies relating to

overvaluation which are the foundation of market driven theory and the agency costs of

overvaluation. Our empirical investigation confirms that industry merger waves involving

private firms represent efficient responses to economic shocks in which overvaluation

theories do not play a major part, while public merger waves seem to be driven by agency

issues related to the stock market overvaluation of the EU in the late 90s. Firstly, we show

that private industry merger waves occur at different times than public firm industry merger

waves. Secondly, we find that these differences are attributable to the significant influence of

economic industry shocks on the timing private firm merger waves and a significant

influence of stock market overvaluation on the timing of public merger waves. Thirdly, our

study of post-merger (accounting) performance confirms that private firm merger waves are

more efficient than public firm merger waves, although the statistical significance is weak.

What drives merger waves in Europe?

5

1. Introduction

What drives private and public merger waves in Europe? Despite that the size of the

European Mergers and Acquisitions market has risen to rival that of the US over the past 10+

years (Goergen and Renneboog, 2004), research on merger waves has focused primarily on

mergers involving publicly held (or listed) firms in the US (see, e.g., Mitchell and Mulherin,

1996; Harford, 2005; Rhodes-Kropf, Robinson, and Viswanathan, 2005).

The first unique contribution of this paper is to provide evidence of merger waves and the

factors driving them in Europe. Although recent research has addressed the consequences and

characteristics of mergers in Europe in the 90s (Campa and Hernando, 2004; Goergen and

Renneboog, 2004; Martynova, Oosting and Renneboog, 2006; Martynova and Renneboog,

2006a, 2006b), research is yet to address the industry and economy-level drivers of European

merger activity. We analyse the cause, characteristics and consequences of EU-15 merger

activity in and around the European Merger Wave of the 90s – specifically, 1995-2004 –

from the perspective of recent advances in merger wave theory.

The second unique contribution is to analyse theoretically and empirically the applicability of

existing merger wave theory to mergers involving private firms. According to Thomson

Financial’s SDC Platinum M&A Database, roughly three-quarters of the combined merger

activity of the US and the EU-15 involves a private acquirer or target firm. The consequences

of public acquirers buying private and public targets have been analysed in both the US (see

for example Chang, 1998; Ang and Kohers, 2001; Bargeron, Schlingemann, Stulz, and

Zutter, 2007), the UK (Draper and Paudyal, 2006) and the EU as a whole (Martynova et al.,

2006). However, deals involving private acquirers buying private targets have not been

addressed, nor has the role of private acquirers and targets in merger waves. We believe that

the study of the drivers of merger waves should include all mergers regardless of the

organization of corporate ownership. Thus, we identify and compare industry merger waves

involving private and public firms and analyse their drivers separately.

Two empirical observations form the foundation of theories of merger waves. The first

observation is that waves often happen around major economic events in the economy (e.g.,

Mitchell and Mulherin, 1996) – for example technological or regulatory changes – leading to

a neoclassical theory in which merger waves are efficient organizational responses to changes

in industry economic conditions (McGowan, 1971; Mitchell and Mulherin, 1996; Harford,

Chapter 1

6

2005). The second observation is that waves also coincide with times of high valuation in

stock markets (e.g., Nelson, 1959; Gort, 1969; Rhodes-Kropf et al., 2005) giving rise to

theories which argue that stock market overvaluation fuels merger waves. According to the

market driven perspective, the acquiring manager maximizes the long-run return to

shareholders by using stock swaps to buy less overvalued targets (Shleifer and Vishny, 2003;

Rhodes-Kropf and Viswanathan, 2004). According to the agency costs of overvaluation, the

acquiring manager conducts acquisitions sanctioned by the board in order to sustain the short

run overvaluation by maintaining high growth rates (Jensen, 2005).

Mergers involving private firms are less likely to be susceptible to the overvaluation theories.

Private firm acquisitions imply a different impact of a) acquirer and target shareholder

preferences, e.g., it is unlikely that public firm shareholders will accept privately held stock

as payment, b) corporate governance, e.g., private shareholdings are more concentrated

reducing agency conflict, and c) the informational environment, e.g., search costs for private

targets are higher. Thus, it is not clear if the same factors are driving merger waves for both

public and private firms.

We find a significant inter-industry variation in merger activity which suggests that the

driving factors of the European Merger Wave of the 90s are to be found at the industry level.

This corresponds to evidence on the US merger waves of the 80s and 90s (see Mitchell and

Mulherin, 1996, and Harford, 2005). To test whether neoclassical or overvaluation theories

explain private and public merger waves, we investigate theoretical predictions on the timing,

pattern of consideration structure and post-merger operating performance of industry merger

waves.

The economic shock or neoclassical theory implies that the impact of the shock is

independent of ownership structure. Thus, there should be no difference in timing and

performance of industry merger waves involving private and public firms in the economic

shock theory. However, we find very little overlap between private and public merger waves.

In other words, while we confirm previous evidence that the existence and timing of waves

differs across industries, we also show that the existence and timing of waves differs across

the private and public ownership of acquirers and targets. Further empirical analysis of the

timing of industry merger waves reveals that economic shock factors are not important in

explaining merger waves involving publicly held firms, whereas they are the prime drivers of

What drives merger waves in Europe?

7

merger waves involving privately held firms. Thus, the economic shock theory can explain

private merger waves but not public merger waves.

We find that a proxy for the overvaluation of firms explains the timing of public industry

merger waves, lending support to the market driven view. However, a key prediction of this

theory is that the use of stock payment should increase significantly during merger waves,

since mergers must be paid fully or mostly in stock to take advantage of overvaluation. Since

the use of stock payment is similar to periods of lower industry merger activity, we reject the

market driven perspective. Post-merger accounting performance is marginally wealth

destroying, supporting Jensen’s agency costs perspective on the drivers of public merger

waves. On the contrary, we argue and confirm that the mechanisms in behavioural and

agency reasoning do not drive mergers in which private firms are involved, especially when

private acquirers purchase private targets. Very few private deals involve stock swaps,

proxies for overvaluation have limited explanatory power and the post-merger performance

borders on wealth improving. Hence, both the market driven perspective and agency costs of

overvaluation are rejected for private firms. We also investigate the drivers of merger waves

involving private acquirers and public targets, and public acquirers and private targets. The

results are mixed and point to other determining factors than those hypothesized here.

The main conclusion of the paper is therefore that private merger wave in the 90s represents

an (optimal) response to economic shocks in which overvaluation theories do not play a

major part, while the public merger wave seems to be driven by agency issues related to the

stock market overvaluation of the EU in the late 90s.

The rest of the paper proceeds as follows: The second section presents the three theories,

summarizing existing evidence and offering predictions on their applicability to private and

public merger waves. The third section describes our merger sample and introduces the

European Merger Wave of the 90s. The fourth section identifies industry merger waves and

compares public and private waves. The fifth section conducts testing on the consideration

structure, timing and performance of industry merger waves and summarizes the combined

empirical evidence. The sixth section concludes.

Chapter 1

8

2. Literature review

The past 25 years have seen two merger waves within the US – 1983-1989 and 1993-2000 –

while the European Union has experienced a small and a large merger wave in 1987-1992

and 1995-2001, respectively (Sudarsanam, 2003). However, while there is a long tradition for

research on US merger waves dating back to Nelson (1959), research on European merger

waves has largely been anecdotal and national in nature (Sudarsanam, 2003). The exceptions

are Schoenberg and Reeves (1999) and Powell and Yawson (2005), who focus on the

individual economic factors determining UK industry merger activity in 1991-1995 and

1986-2000, respectively. Similarly, the role of privately held firms in merger waves has

received very sparse commentary. Hence, both established empirical facts and theory have

been formed solely on the basis of merger activity involving publicly held, US firms.

There are two well-established facts about US public merger waves. Firstly, merger activity

varies across industries (e.g., Gort, 1969; Mitchell and Mulherin, 1996; Mulherin and Boone,

2000; Harford 2005). Secondly, both aggregate and industry merger waves occur during

periods of favourable financial conditions, i.e., when stock market levels are high (e.g., Gort,

1969, Rhodes-Kropf et al., 2005) and when interest rates are low (e.g., Melicher, Ledolter,

and D’Antonio, 1983; Harford, 2005).

Although there is wide consensus regarding the above “facts”, there is less theoretical

agreement as to why merger waves occur. Neoclassical reasoning takes its departure from the

inter-industry variation in merger activity. On the other hand, Market driven (or,

behavioural) and agency costs of overvaluation reasoning centres on the correlation between

stock market overvaluation and merger activity. We detail the theories below, focusing on the

merger gains proposed and the theoretical assumptions and factors supporting the existence

of merger waves. We then turn to argue the relevance of these assumptions and factors for

private acquirers and targets, and hence, for the existence of merger waves involving private

firms.

2.1 Theories of merger waves

2.1.1 Neoclassical perspective

Neoclassical theories argue that merger waves occur at the industry level as firms respond to

significant changes in industry economic conditions caused by broad economic shocks or

more specific economic industry ‘shocks’ such as industry deregulation or technological

What drives merger waves in Europe?

9

change. Such economic shocks generate a much increased need for major restructuring of

assets within the industry. M&A is often the cheapest method of adjustment compared with

alternatives such as internal investment (Weston, 2001) since it avoids excess capacity

(McGowan, 1971; Mitchell and Mulherin, 1996). Neoclassical theory assumes that external

control and managerial incentive systems are sufficient to ensure that managers maximize the

long-term values of both acquirer and targets shares. Consequently, firms are bought and sold

in the market for corporate control only when the transaction increases shareholder value for

both target and acquirer shareholders, i.e., when there are positive synergies.

The occurrence of an aggregate merger wave across industries requires either an economy-

wide shock, e.g., the introduction of the “free market in the EEC” in 1992, or the independent

clustering of shocks in several industries at the same time (Mitchell and Mulherin, 1996).

Harford (2005) adds that industry and aggregate merger waves can occur only when there is

sufficient availability of ‘capital liquidity’. Specifically, periods of tight liquidity constrain

the ability of firms to restructure through capital-intensive activities such as M&A, forcing

them to postpone these activities or deal with the economic changes in other ways. Periods

with amble liquidity set lighter financial constraints which are conducive to merger activity.

Therefore, the neoclassical perspective predicts that economic industry shocks will precede

individual industry merger waves which will cluster in time when the level of capital liquidity

is high. Notably, the neoclassical perspective also predicts a correlation – but no causation –

between stock market values and merger activity. Specifically, the low interest rates prevalent

in periods of amble liquidity will increase share and asset (collateral) values. This in turn

makes it easier (and cheaper) to borrow, thus relaxing financing constraints and further

increasing merger activity and stock market values.

The empirical foundation of the economic shock theory is mostly anecdotal in nature (see

surveys by Sudarsanam, 2003; Bruner, 2004; Weston, Mitchell, and Mulherin, 2004)

although Harford (2005) confirms that economic industry shocks and high capital liquidity

preceded the industry merger waves in the US in the period 1981-2000. Accounting and stock

market evidence does not generally support the underlying hypothesis that industry

restructuring activities are more efficient or profitable than the alternative strategies chosen

by non-merging rivals (e.g., Harford, 2005), although it also does not suggest that they are

any less so. While some studies of short run stock returns offer evidence that the US merger

wave of the 1980s was efficient and profitable for acquirers (e.g., Mitchell and Mulherin,

Chapter 1

10

1996; Moeller, Schlingemann, and Stulz, 2005), the merger wave of the 1990s has yet to

provide clear evidence of the same (Sudarsanam, 2003). Similarly, while a series of studies

on samples in and around the aggregate European Merger Wave of the 90s generally find that

the overall value creation of mergers (i.e., synergies) was positive, there is only mixed

evidence that acquirers attain a share of the gains (Campa and Hernando, 2004; Goergen and

Renneboog, 2004; Martynova and Renneboog, 2006a, 2006b).

2.1.2 Market driven perspective

The market driven perspective argues from behavioural finance that periods of persistent

irrational investor sentiment may cause firm market values to deviate significantly from their

true values (see Baker, Ruback, and Wurgler, 2005, for a review). This allows the most

temporarily overvalued firms to increase their long run value by acquiring less overvalued

firms and paying with stock. Cash acquisitions on the other hand do not allow the acquirer to

take advantage of the stock market mispricing. Therefore, periods of prolonged overvaluation

lead to an increase in mergers conducted using stock swaps which essentially causes merger

waves. Notably, target firm shareholders suffer losses equivalent to the gains appropriated by

the acquiring firm shareholders1. Shleifer and Vishny (2003) and Rhodes-Kropf and

Viswanathan (2004) offer two distinct theories on why target managers accept overvalued

stock swap offers.

Shleifer and Vishny (2003) argue that acquiring and target managers have full knowledge of

the overvaluation of their firms, and that target managers therefore knowingly overestimate

the synergy component in the transaction. For a deal to be made, the two managers must then

have a ‘coincidence’ of opposing objectives; acquiring firm managers must maximize long-

term shareholder value, whereas the target firm manager has a short term horizon. Target firm

managers may push for such a short-term sale to realize their executive stock options while

overvaluation is still high, or to move on to a better job within the new firm. Essentially, the

target firm manager is “bought off”. The target shareholders allow this because they are

1 To illustrate the consequences of a market driven transaction, consider the case of two firms, A and T. They both have 100 shares outstanding and fundamental values of 100. However, both firms are temporarily overvalued; their current prices are 130 for A and 105 for T. Now A makes a stock-swap offer for T at a price of 115.5 (a premium over current market value of 10%). The stock-swap implies that A offers approximately 88.85 (115.5/1.3) shares for the 100 shares in T. Assuming that T accepts, the long-run value of the merged firm will drop to its fundamental value, 200, but each share in A’s stake will be valued at 105.9. Thus, although A’s stock value drops roughly 19% following the deal, it would have eventually dropped 23% to 100 without the transaction. The long-run gain to A’s shareholders comes at the expense of target shareholders, whose stake in the merged company will be worth only 94.1.

What drives merger waves in Europe?

11

considered as irrational as outside investors. Conversely, we may offer that target firm

shareholders themselves have pushed for the maximization of the short-term and not long-

term shareholder value.

Rhodes-Kropf and Viswanathan (2004) argue that both the acquiring manager and the target

firm manager are working in the interest of long-term shareholders. However, acquiring

managers have a knowledge advantage over target firm managers. This causes the target firm

manager to systematically underestimate the overvaluation present in a stock offer and

overestimate the synergy in the deal. Specifically, while the target firm manager is fully

aware of the overvaluation of her own firm, and uses this information to address the extent of

acquirer overvaluation, she is still unsure to which degree the remaining, unknown

component constitutes additional overvaluation or synergies. Target firm managers attribute

some probability to both scenarios, leading them to accept more stock offers during periods

of overvaluation.

Aggregate merger waves occur when the entire market is overvalued leading to an increase in

stock-swap deals across all industries. Equivalently, at the industry level a merger wave

requires overvaluation of stocks in the industry (Rhodes-Kropf et al., 2005). The market

driven view argues that both aggregate and industry merger waves are preceded by an

increase in stock market valuations. Without overvaluation, the incentive and/or opportunity

to conduct market-driven stock-swap mergers does not occur. Waves may be more or less

pronounced in individual industries depending on industry-specific sensitivity to the source

of overvaluation (Shleifer and Vishny, 2003), and conceivably also on the specific ‘synergy

story’ relevant to each industry. Economic shocks play no explicit role, although they may do

so indirectly in two ways: New technologies may lead to overvaluation of the involved firms

and economic shocks may provide the required input to the ‘synergy story’ that markets (and

the target managers in Rhodes-Kropf and Viswanathan, 2004) supposedly believe in.

Importantly, transactions can only occur if there is sufficient dispersion in the valuations of

firms2. Such dispersion can be either intra-industry or inter-industry, although the ‘synergy

story’ may limit the extent to which overvalued firms can conduct market driven acquisitions

across industry boundaries. In the sense that there are no synergies present in the deal, or at 2 Note that in both Shleifer and Vishny (2003) and Rhodes-Kropf and Viswanathan (2004), merger waves can also be triggered by stock market undervaluation. However, the theories then imply among other things that the payment method is cash. Since the 90s were an era of overvaluation and not undervaluation, we focus on the overvaluation side to the story.

Chapter 1

12

least, less synergies than in non-market driven acquisitions, stock mergers in waves should

perform worse than cash mergers.

Andrade, Mitchell, and Stafford (2001) and many others note that the use of stock payment

increases during aggregate US merger waves, implying that market driven reasoning has an

inescapable influence on merger activity. Consistent with this aggregate evidence, empirical

studies using varying proxies for overvaluation show that a short-run deviation from ‘true’

value increased the probability of a firm being a stock bidder in the past two US merger

waves (e.g., Rhodes-Kropf et al., 2005; Dong, Hirschleifer, Richardson, and Teoh, 2006).

Rhodes-Kropf et al. (2005) also report that there is evidence of an important influence of

sector-specific overvaluation on the timing of industry merger waves. Furthermore, Dong et

al. (2006) argue that the evidence of the market driven view is most persuasive in the 90s US

merger wave. However, Bouwman, Fuller, and Nain (2007) show that stock mergers in the

US during the 80s and 90s lead to the same performance improvement as cash mergers,

which does not support the prediction of the market driven view that synergies all else equal

should be lower in stock mergers.

2.1.3 Agency costs perspective

Jensen (2005) argues that the overvaluation of the stock markets in the late 90s was driven by

irrational investor-confidence in novel, but fundamentally flawed business models, e.g.,

World-Com, and that managers conducted board-sanctioned acquisitions in an attempt to live

up to their promise of sustained top-line growth. Unlike the other perspectives, this implies

that acquirers destroy shareholder value in the long run.

Agency costs imply that the manager has the incentive and opportunity to conduct self-

serving investments (see for example, Jensen, 1986). In the 90s, the incentive to choose

growth-by-acquisition was the realization of short-term executive compensation packages,

perhaps supported by the managerial desire to build an empire to achieve prestige and higher

future base pay (Mueller, 1969). Notably, this requires not only that the external and internal

control systems systemically break down, but also that they lead to additional, adverse

effects. The counterproductive effect of executive compensation packages in the late 90s was

largely possible due to the lack of external control from debt and equity markets.

Furthermore, anecdotal evidence supports that the internal control performed by the board of

directors were similarly afflicted by agency costs (Jensen, 2005). Both managers and

What drives merger waves in Europe?

13

directors were subject to a professional environment and media coverage, which could have

spread the irrational exuberance of the stock market to the corporate headquarters (Auster and

Sirower, 2002). Finally, we might again add that corporate decision making could instead be

seen as managers simply implementing the extant short-term focus of some of the acquiring

firm shareholders.

The agency costs perspective thus predicts that merger waves are undertaken when markets

are overvalued. However, the overvaluation of the acquiring firm is not an instrument as

such. Rather, it is an indication that the mergers conducted and their accompanying ‘synergy

stories’ are fundamentally wealth destroying, or at least that acquirers overpay for whatever

synergy there is. Since the underlying source of overvaluation and synergies are the same,

industry merger waves follow perfectly ex-ante industry overvaluation. Neither economic

shocks nor the dispersion in valuations need drive merger activity. In the sense that

overvaluation coincides with low interest rates which relax financial constraints, both cash,

debt and stock mergers may be abundant. Hence, the agency theory provides no prediction on

the consideration structure chosen during waves.

2.2. Private firm merger waves

Firms under private ownership are fundamentally different from publicly held firms on the

dimensions which lead to mergers driven by overvaluation. Therefore, while the economic

shock perspective translates seamlessly to private firms, the overvaluation perspectives do

not. Specifically, private organization of firm ownership affects among other things a)

acquirer and target shareholder preferences, b) corporate governance and c) the informational

environment surrounding the firm. Below the overvaluation theories represented by the

market driven perspective (Shleifer and Vishny, 2003; Rhodes-Kropf and Viswanathan,

2004) and the agency costs perspective of Jensen (2005) are discussed with respect to private

acquirers and targets.

2.2.1 Market driven perspective

2.2.1.1 Privately held acquirers

The preferences of private firm shareholders lower the potential acquirer gains to market

driven mergers. Firstly, ownership of private firms is more concentrated, implying that major

shareholders face a significant control loss if they swap stock. Faccio and Masulis (2005)

support the disinclination of European firms with major shareholders to finance acquisitions

Chapter 1

14

with stock. Secondly, from the perspective of a target firm shareholder, swapping stock with

a private firm implies that the gain cannot be realized in the short run unless the private firm

is readying an IPO, which makes selling to a private acquirer less attractive, ceteris paribus.

In addition, private firms do not have a listed value on which to base the exchange of shares.

One possible consensus estimate of the value of private acquirers could be the firm value

according to industry average stock market multiples, which would tend to reduce the amount

of overvaluation and therefore decrease the gains from paying in stock. In all, private

acquirers are much less likely to use stock swaps to try to exploit overvaluation.

2.2.1.2 Privately held targets

The market driven theories of Shleifer and Vishny (2003) and Rhodes-Kropf and

Viswanathan (2004) differ on the reason why target managers accept overvalued stock-swap

offers, and the two theories therefore provide quite different predictions on the relative

potential of privately held target firms to accept overvalued stock-swap offers compared with

publicly held target firms.

Shleifer and Vishny (2003) rely on the incentive and opportunity of target firm managers to

sell their firm at a price they know to be below its true value in order to cash out or take a

new position in the merged firm. Since target firm shareholders are irrational and do not

know the true value of the firm, they do not stop them. However, it seems less likely that

managers of private firms know something that their owners do not. Private firm owners are

not comparable to shareholders of widely held firms in the sense that their concentrated

ownership gives them the incentive and opportunity to monitor their managers more closely

(Bolton and von Thadden, 1998). Also, since private firm managers are more likely to hold a

significant stake in the firm, they are likely to have a lower incentive to ‘sell out’ compared

with public firm managers. In the extreme case of owner-managers with a long-term horizon,

there is no foundation for a target firm manager to accept an overvalued stock-swap offer,

since all associated costs are internalized. However, if private firm owners have a short-term

horizon, i.e., they wish to sell out their full holdings, then selling to market driven acquirers

may be their best option during times of overvaluation, since cash acquirers would not want

to pay the inflated price unless there were sufficiently large synergies.

What drives merger waves in Europe?

15

Rhodes-Kropf and Viswanathan (2004) assumes that target firm managers work in the

interest of shareholders but are not able to differentiate between the overvaluation of stock

acquirers and the potential synergies offered by the deal. Notably, private target firm

managers have less information than their public firm counterparts on which to estimate the

overvaluation of the stock offer. While they can adjust for general industry overvaluation by

observing the valuations of industry firms, they are unable to factor in the market signal of

their own relative overvaluation. In terms of Rhodes-Kropf and Viswanathan (2004), this lack

of information is in fact likely to increase the overestimation of the synergy component

present in stock swap offers relative to cash offers.

Finally, note that the shareholders of privately held firms are much less likely to prefer stock

to cash. They prefer the liquidity of cash payment since the decision to sell is often a

purposeful realization of (large) private firm stock holdings (Faccio and Masulis, 2005). This

would strain the validity of Shleifer and Vishny (2003) in a private target setting, while

heavily diminishing the potential influence of Rhodes-Kropf and Viswanathan (2004). In all,

private target firms are much less likely to accept stock-swap mergers according to either

variant of the market driven perspective.

2.2.2 Agency costs perspective

2.2.2.1 Privately held acquirers

Neither corporate governance nor shareholder preferences in privately held firms support the

same degree of agency costs as in public firms. Hence, privately held acquirers are less likely

to conduct agency driven acquisitions. Firstly, private firm managers lack the executive

compensation packages, which arguably bred the short-termism of the 90s (Jensen, 2005),

although other growth incentives such as empire building (Mueller, 1969) or job protection

(Gorton, Kahl, and Rosen, 2005) could affect private and public firm managers in an equal

manner. Secondly, private firm managers face increased monitoring and active ownership on

the part of the large private shareholders. These shareholders also have a much longer

investment horizon than public firm shareholders leading them to discourage quick growth

‘fixes’ such as acquisitions. However, even private firm boards, where they exist, may buy in

to the exuberant expectations of a ‘naïve’ stock market as growth strategies and risky

business models are legitimized by professional advisers, media coverage and industry

associations (Auster and Sirower, 2002). Nevertheless, we would still expect the agency costs

of overvaluation to be somewhat lower compared with public firms.

Chapter 1

16

2.2.2.2 Privately held targets

There are two reasons why agency costs of overvaluation are less likely to be a strong

motivator of purchases of privately held target firms. Firstly, since they are not constrained

by the doctrine of shareholder value maximization, they would take the path of least

resistance if given the opportunity. This implies that the most accessible firms – which are

likely to be the publicly held firms – would be preferred. Secondly, the short-term managerial

goals of growth, empire-building and prestige are better catered to by the relatively larger and

well-known publicly held firms (Ang and Kohers, 2001).

2.2.3 Summary

Table 1 summarizes the likelihood of observing overvaluation influences in deals involving

private and public acquirers and targets.

< Insert table 1 about here >

Merger waves involving private acquirers and targets (from here on: private-private) are the

least likely to be driven by the effects of overvaluation. Merger waves involving publicly

held acquirers and targets (from here on: public-public) are the most likely to be driven by

them. It is much less certain to which extent merger waves involving private acquirers and

public targets (from here on: private-public) or private targets and public acquirers (from here

on: public-private) will be affected by overvaluation. Our analysis implies that they are much

less likely to be driven by the market driven theories, while it seems relatively more likely

that the agency costs of overvaluation may influence these sub-samples. Of the two, public-

private mergers offer the most obvious potential for overvaluation effects driven by agency

costs, even though the lower accessibility of private target firms means that the effect is likely

to be lower compared with public target firms. We recognize that several factors beyond the

merger wave theories described here may affect the drivers of private-public and public-

private merger waves.

3. The European Merger Wave of the 90s

3.1 Aggregate merger activity

In this paper, we focus on merger activity in the EU-15, which comprises established Western

European economies bound together by a) a free market, b) a partly shared regulatory agency

to mitigate anti-competitive mergers, and c) a shared economic monetary system. European

economic unification has been building up in the 1990s and was affirmed with the

implementation of the ‘Eurozone’ monetary area on the December 22nd, 1998, which brought

What drives merger waves in Europe?

17

together 12 countries under one currency and one central bank (Austria, Belgium, Finland,

France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal and Spain),

while 3 members (United Kingdom, Sweden and Denmark) maintained independent – but

tightly connected – monetary systems.

Figure 1 shows the gross number of completed mergers in the US and EU-15 reported by the

Thomson Financial’s SDC Platinum database 1991-2005, in which we have excluded only

newly created joint ventures, mutual fund activity and government mergers. The SDC

database has been very widely used in both US and European research on mergers (see e.g.,

Harford, 2005, and Martynova and Renneboog, 2006a, 2006b, respectively). Throughout this

paper, we define a merger as a transaction in which a controlling stake changes ownership;

we therefore exclude partial-firm transactions (i.e., subsidiary/divisional transactions). While

partial-firm acquisitions may be an important part of a restructuring strategy (Weston, 2001;

Andrade and Stafford, 2004; Harford, 2005), we focus on the drivers of merger activity

involving independent publicly and privately held firms. We include all acquisitions

regardless of transaction value. Transaction values are less likely to be reported in

acquisitions involving private firms, especially when there is both a private acquirer and

target. This is because they are not faced with the same disclosure requirements as public

firms.

< Insert Figure 1 here >

There is a clear, aggregate merger wave in the EU-15 from 1995 to 2001, in which the

number of mergers increases to a higher level, then peaks dramatically in 1999-2000, before

dropping to the pre-1995 level in 2002. The US experiences a similar wave, although merger

activity begins earlier – in 1993 – and sustains a more constant peak from 1998-20003. We

notice that the oft-cited co-movement between aggregate merger activity and stock market

levels holds true for the EU-15; the correlation between EU-15 merger activity and the

industry median market-to-book ratio is 0.83. Overall, we have no reason to dispute the

established fact that merger activity correlates with stock market values and favourable

economic conditions.

3 The similarity becomes even more apparent if we were to include cross-border transactions between the US and the EU-15.

Chapter 1

18

3.2 Private and public firm merger activity

Table 2 summarizes the proportion of mergers involving private and public acquirers and

targets according to the SDC Platinum Database.

< Insert table 2 here >

First, we see that there is close to an equal number of acquisitions conducted by private and

public acquirers, which far exceeds that of the US, in which public acquirers outnumber

private acquirers four to one according to the SDC database. Secondly, less than a third of the

activity involving a public acquirer is directed at public targets. In fact, the bulk of merger

activity involves private targets (80%). In all, private-private transactions occur three times as

much as public-public acquisitions (37% vs 12%), which reverses the roles played by these

transactions in the US, where public-public transactions are twice as frequent as private-

private transactions (at 26% and 14%, respectively).

Figures 2a and 2b depict EU-15 merger activity 1991-2005 split on private-private and

public-private, and private-public and public-public transactions, respectively.

< Insert figures 2a and 2b here >

We see in figure 2a that during the aggregate merger wave 1995-2001 the acquisition of

private targets by public acquirers increases more than the acquisition of private targets by

private acquirers. On the other hand, private-private activity experiences a local peak in 1995,

and then falls until 1998-2001, where activity is at its peak. Figure 2b shows that public and

private acquisitions of public targets show a similar activity, although the peak seems to

occur roughly one year earlier.

Thus, at the aggregate level, public acquirers generally seem to experience a more

pronounced, longer merger wave than private acquirers. Nevertheless, the correlation

between mergers involving public acquirers and mergers involving private acquirers is 0.79.

In general, it would seem that broadly similar forces drive private and public merger waves at

the aggregate level. However, there is US evidence that forces at the industry-level drive

merger activity (cf. section 2). If merger waves involving private and public firms are

founded on similar motivations and opportunities to acquire and sell, then their industry

merger waves should be similar on the dimensions of the timing, characteristics and merger

performance. We turn to analyze the timing, characteristics and performance of industry

merger waves involving private and public ownership.

What drives merger waves in Europe?

19

4. The timing of industry merger waves

4.1 Industry merger wave identification

To identify and analyze the drivers of industry merger waves, we follow a methodology

based on Harford (2005) in which we focus our investigation on a 10-year period of merger

activity involving private and public firms in and around the aggregate merger wave of the

90s, specifically January 1st, 1995 to December 31st, 2004 – a sample of 9,196 mergers.

Unlike previous studies on public firms, we maintain both public and private mergers

regardless of transaction size.

We define industries according to the 48 industry groups defined by Fama and French (1997)

according to the SIC industry codes reported by the acquirers and targets. These industry

groups cover the full spectrum of industries relating to non-governmental corporate activity.

We remove the ‘Miscellaneous’ industry, since it serves as a residual group. To allocate

merger activity to industries (from hereon we use the term ‘industry’ and ‘industry group’

interchangeably), we conceive an allocation procedure which accommodates the more

widespread use of pyramidal ownership structures in the EU-15 (La Porta, Lopez-De-Silanes

and Shleifer, 1999; Faccio and Lang, 2002). Specifically, we wish to take into account that a

higher-level corporate entity may acquire private and public firms on behalf of a lower-level

(subsidiary) firm. Should the high-level and low-level entity be engaged in different

activities, we run the risk of misallocating the merger.

Our four-level allocation procedure utilizes the reporting of firm secondary industries by the

SDC database at the time of the transaction. First, if the primary industries of the acquirer and

target firm match up, we consider this a fully related merger within the shared primary

industry. Second, if the primary industries do not match up, we compare the primary industry

with the secondary industries of the target, allocating the merger to the shared industry if

there is a match. Third, we attempt to match the primary industry of the target with the

secondary industries of the acquirer. Lastly, we compare the secondary industries of both

firms and allocate the merger to the industry in which we find the most overlaps. Only if

there is no match on any of these three levels do we define the merger as unrelated and

allocate it to both the industry of the acquirer and the target (Harford, 2005). Our expanded

procedure leads to an increase in related mergers in our sample from 50.3% to 63.4%, of

which 4.8%, 7.3% and 1.0% are attributable to mergers related between the acquirer primary

Chapter 1

20

and a target secondary industry, the target primary and acquirer secondary industry, and

acquirer secondary and target secondary industry, respectively.

To identify potential and statistically significant industry merger waves, we first define a

potential merger wave within an industry as the 24-month period with the highest

concentration of merger activity within our 10-year period. Secondly, we simulate 1000

distributions of the actual number of mergers within the industry in the 10-year period, and

then determine the 24-month period with the highest concentration of activity in each of the

1000 simulated distributions. This leaves us with 1000 simulated industry merger waves for

each industry. We then rank the 1000 simulated industry merger waves by the number of

mergers and record the 95th percentile. If the number of mergers in the potential industry

merger wave exceeds this 95th percentile, we identify it as a statistically significant industry

merger wave.

4.2 Comparing private and public industry merger waves

Tables 3a to 3e show the placement in time and significance of the potential waves for each

industry. Significant industry merger waves are depicted for whole, private-private and

public-public samples in table 3a. These are followed by depictions of the potential and

significant merger waves of the private-private, public-public, public-private and private-

public samples in tables 3b to 3e. There are 32 significant waves in the full sample, whereas

there are 24, 21, 29 and 17 significant industry merger waves in the private-private, public-

public, public-private and private-public samples, respectively.

< Insert tables 3a-e here >

In table 3a, we first notice that there seems to be significant variation in the timing of waves

across industries, implying that the drivers of merger waves – whether neoclassical,

behavioural or agency related – are to be found on the industry-level, or at the very least have

a strong industry component. Thus, the European Merger Wave of the 90s is in fact more of a

clustering of industry merger waves at different times in some, but not all industries. This

corresponds to the US evidence from both the 80s (Mitchell and Mulherin, 1996) and 90s

(Mulherin and Boone, 2000; Harford, 2005). Note that this also means that the clear link

between economy-wide stock market levels and merger activity may not hold on the industry

level. However, it is too soon to rule out the potential influence of stock market

overvaluation, since individual industries may experience overvaluation of different degrees

and at different times (Shleifer and Vishny, 2003).

What drives merger waves in Europe?

21

Comparing the significant industry merger waves in the private-private and public-public

samples (table 3a), we see little overlap and numerous differences in timing. While some

private-private and public-public waves are reasonably close (e.g., Business Services

(‘BusSv’) and Personal Services (‘PerSv’)), the private-private wave is more than 12 months

apart from its public-public counterpart across more than half of the industries. Overall,

tables 3b to 3e seem to show significant differences between the four samples. This implies

that industry-level forces are pulling the industry waves in different directions depending on

the private and public ownership of merger firms. Note that our theoretical analysis of the

drivers of merger waves leads us to expect that private-private waves would be most likely to

be driven by neoclassical reasoning. From that perspective, the timing of private-private

waves is a ‘benchmark’ from which to judge whether the remaining samples exhibit a

‘neoclassical’ timing of waves. Thus, the lack of similarity with the private-private waves

may be primae facie evidence of an influence of overvaluation, or other unspecified theories,

on the other three samples. We move to investigate statistically the strength of similarities in

the timing of potential and significant waves.

Table 4a notes the average distance in months between the potential industry merger waves

across the four sub-samples, as well as their pairwise (non-parametric) Spearman rank-order

correlation between samples. The latter is calculated by ranking the timing of each industry

merger wave within a given sample and measuring the correlation of industry rankings

between samples using the Spearman correlation coefficient. The Spearman correlation

coefficient is

)1(

61 2

1

2

, −−=

∑=

nn

dn

ii

yxρ

n is the number of industry waves, and d is the difference between the ranking of industry

wave i in sample x and the ranking of industry wave i in sample y.

< Insert table 4a here >

It turns out that the potential industry merger waves in each sample are on average at least 20

months apart from the other samples. The private-private and public-public samples are 20.9

months apart, and their correlation is insignificant at 0.17. Only the private-private and

public-private samples are correlated to a significant degree (0.56). Despite this high

correlation, the average distance between industry merger waves in these two samples is 21.4

months. Observing the timing of the two samples in tables 3b and 3d, respectively, we see

Chapter 1

22

that the public-private waves on average occur earlier than the private-private waves,

implying a correlated, but lagged relationship between the two.

Table 4b notes the average distance between the significant merger waves across the four

sub-samples, the pairwise Spearman rank-order correlation, and the number of industries in

which the pairwise samples simultaneously experience a significant wave.

< Insert table 4b here >

The results tell a similar story to that of the potential waves, although it is striking how few

overlaps there are between significant industry merger waves across samples. Only 10 of a

possible 21 public-public significant industry merger waves are simultaneously significant in

the private-private sample. Although the correlation between them increases to 0.41, it is

statistically insignificant. However, the private-private and public-private waves exhibit a

statistically significant, positive correlation, and 19 waves out of a possible 24 waves are

simultaneously significant. Nevertheless, the mean distance between the waves in these two

samples is still too high to lead us to expect that they are driven by common motivations and

opportunities to acquire and sell. Therefore, if we assume that the private-private serves as a

neoclassical ‘benchmark’, it would seem that the other samples are subject to a significant

influence from other factors, which may or may not involve overvaluation.

In all, we find that industries do not often experience merger waves across all combinations

of private and public ownership, and even when two samples experience a wave within the

same industry, it is often at quite different points in time. Besides the correlation between the

private-private and private-public samples, these differences cannot be dismissed as lagged

(i.e., aggregate) effects. Therefore, while we see in tables 4a and 4b that the full sample

correlates with both the private-private, public-public and public-private samples

individually, there is no actual aggregate merger wave. Thus, while we confirm previous

evidence that the existence and timing of waves differs across industries, we also show that

the existence and timing of waves differs across the private and public ownership of acquirers

and targets. In the following section, we will attempt to determine the factors driving these

differences in timing.

What drives merger waves in Europe?

23

5. The drivers of private and public industry merger waves

From our review of merger wave theories in section 2, we offer empirical hypotheses on the

drivers of industry merger waves relating to a) the determinants of the timing of industry

merger waves, b) the consideration structure of mergers within industry merger waves and c)

the post-merger operating performance of mergers. With regards a), the neoclassical

perspective expects that the onset of an industry merger waves will be preceded by high

values of economic industry shock variables. However, this may potentially only occur when

capital liquidity is ‘loose’ compared to ‘tight’. The market driven perspective postulates that

the timing of industry merger waves can be predicted by high ex-ante industry stock market

valuations as well as a high intra-industry dispersion of industry stock market valuations

(Shleifer and Vishny, 2003; Rhodes-Kropf and Viswanathan, 2004). On the other hand, the

agency costs perspective does not require intra-industry dispersion of stock valuations,

merely a period of prolonged overvaluation (Jensen, 2005).

With regards b), both the neoclassical and agency costs perspectives have no prediction.

However, the market driven perspective is based on overvalued acquirers paying for

relatively undervalued targets fully (or at least partially) in stock. Therefore, the use of stock

payment should increase during waves.

With regards c), the neoclassical perspective views mergers as engines of efficient asset

restructuring, whereas the agency costs perspective argues that the mergers, at best, serve to

prolong ultimately unprofitable business activities. Therefore, the neoclassical perspective

predicts that mergers in merger waves lead to a post-merger operating performance which is

better (or no worse) than the unobservable benchmark, whereas the agency costs perspective

expects performance which is worse (or no better). The market driven perspective argues that

stock mergers lead to less synergies than cash mergers (if any at all), and thus, stock mergers

should have poorer performance than cash mergers. Table 5 summarizes these predictions.

< Insert table 5 about here >

5.1 The use of stock payment in industry merger waves

Table 6 presents the differences between the out-of-wave and in-wave periods on the use of

stock payment in mergers. Consistent with previous findings (c.f. section 2), we document

that the proportion of public-public mergers financed at least partially with stock increases

within industry merger waves, from 42.8% to 49.6%. However, this hardly constitutes the

Chapter 1

24

economically significant increase which is required to make the case for market driven

theory. Furthermore, there is no accompanying increase in the average proportion of stock

payment in mergers within industry merger waves. Nor is there any significant increase in the

proportion of mergers fully financed with stock. Therefore, we cannot attribute the cause of

public-public merger waves to market driven reasoning. Similarly, although we find a

significant increase in both the proportion of public-private mergers financed fully with stock

(from 5.9% to 7.7%) and the proportion of stock payment in these deals (from 11.7% to

15.0%), the use of stock payment seems far too limited to offer support for a market driven

explanation of public-private industry merger waves. Note that these results do not mean that

stock financing does not have an influence on the depth of public-public and public-private

merger activity, i.e., merger activity measured in dollar values. But it does not seem to have

an effect on the breadth of merger activity, i.e., the number of mergers (Bruner, 2004), which

is the metric of merger activity in this paper.

As expected, we see that neither private-private nor private-public acquisitions use stock

payment to any real degree. Notably, the degree of cash payment in private-private

transactions is also low at 10-14%, implying that much of the consideration in these deals

consist of payment other than stock or cash. However, since private-private acquisitions do

not face the same disclosure requirements as deals involving public acquirers or targets, this

third category may simply be picking up payment components which are left undefined.

< Insert table 6 here >

In sum, despite the high correlation between economy-wide stock market levels and

aggregate merger activity, we reject the market driven perspective as an explanation for

merger waves involving private and public firms, and hence, for the European Merger Wave

of the 90s. Specifically, the use of stock payment in industry merger waves does not support

such an influence on any subsample of private and public ownership. Therefore, we now have

only two viable theories to consider – the neoclassical and agency costs perspectives. We

move on to test their predictions on the determinants of the timing of industry merger waves,

as well the ensuing operating performance of merged firms.

5.2 The determinants of the timing of private and public industry merger waves

We follow generally the univariate and multivariate procedure of Harford (2005) in testing

whether preceding economics shocks, capital liquidity and valuation factors can explain the

timing of industry merger waves. The univariate procedure shows whether predictive

What drives merger waves in Europe?

25

variables are generally high or low prior to industry merger waves. Logistic modelling of the

timing of industry merger waves then tests their predictive power in a multivariate setting.

5.2.1 Predictive variables

5.2.1.1 Economic shocks

Economic industry shocks are shocks to the very fundamentals of the industry. Following

Harford (2005), we seek to use as proxies for various economic shocks the ex-ante change in

7 accounting measures – cash flow margin, return on assets, sales growth, employee growth,

asset turnover, capital expenditures and research and development expenses. A given

economic shock is calculated as the industry median absolute change from period t-1 to t.

Our shock measures are based on data retrievals from accounting data held in the Bureau van

Dijk AMADEUS database, which covers both currently active and inactive private and public

firms dating back to at least 1992. The AMADEUS database is built on state-enforced,

publicly disclosed financial reporting within the EU-15. While we have no means of

validating the quality of the handling of data by Bureau van Dijk, we consider the integrity of

the database to be subject only to misrepresentation by the individual firms themselves. To

our knowledge, there is no other accounting database which can provide us with a similar

range of times series accounting data. The database has only recently been used in research.

For instance, Martynova et al. (2006) use AMADEUS to study the post-merger operating

performance of selected acquisitions in Europe 1997-2001.

We extract data from the AMADEUS database using two separate sources. Firstly, we use a

set of DVDs purchased from Bureau van Dijk which provide annual ‘snapshots’ of the full

information held on the top 1,000,000 firms. The DVDs provide us snapshots from February

of each year 2000-2005. Secondly, we extract all data from the Bureau van Dijk AMADEUS

web-based interface. This dual extraction ensures that we include any information which may

have been removed over time due to national restrictions on data access. In comparison,

Martynova et al. (2006) use only the web-based interface, which also limits the reach of their

accounting data to 1995-2004. Throughout, we limit our accounting sample to firms with

more than 50 employees in at least one year within the period 1992-2004. Note that

AMADEUS does not offer accounting data on banks, so we drop the Banking industry

(‘Bank’) from our analyses.

Chapter 1

26

We choose to use financial data from unconsolidated firms as the basis for our economic

industry shocks, since unconsolidated firms are the lowest level of corporate aggregation.

Hence, they more accurately portray the fundamentals of the industry they serve, whereas

consolidated reports may reflect the economic changes experienced by several corporate

subsidiaries across various industries. We exempt the R&D shock variable since expenses for

research and development are not reported by AMADEUS. Also, we redefine Harford’s

profitability measures so that it uses profits from operations. That way we lessen the

contamination of our data by revenues or expenses from non-operational activities and profit

flows between corporate entities. We define Harford’s combined economic industry shock

variable which encompasses the variation of the remaining 6 shock variables. Specifically,

we calculate the first principal component of the covariance matrix, and create a linear

combination of the shock variables using the weights of the principle component. This

measure captures roughly 80% of the combined variation in the 6 variables. Table 7 notes the

definition of the economic shock measures. Assigning each firm-year accounting observation

to one of the remaining 46 industry groups provides us with approximately 1.4 million firm

industry memberships4.

5.2.1.2 The ease of financing

We implement the capital liquidity factor pioneered by Harford (2005) – the spread between

the yearly average interest rate on commercial & industrial loans and the central bank rate –

to capture the ‘ease of financing’ within the EU-15. When the measure is high, it means that

capital liquidity is low, i.e., financing is expensive.

We define our EU-15 measure as the spread between the GDP weighted average lending rate

on commercial & industrial loans and the GDP weighted average central bank marginal

lending rate. However, the calculation of this measure implies several discontinuities over

time. Firstly, an EU-15 commercial bank lending rate similar in purpose to the average

lending rate on commercial & industrial loans published by the US Federal Reserve (used in

Harford, 2005) was not initiated until January 2003 upon the successful implementation of

the EU-15 harmonization of interest rate statistics5. For the years preceding 2003, we

substitute the Eurozone rate for a synthetic version constructed by Thomson Financial’s 4 Some firms change their industry membership within the period – this means that they are counted once per industry membership. 5 The ECB collects rates from appointed monetary financial institutions (MFIs) to be used in future ECB monetary policy analyses (Christoffersen and Jakobsen, 2003).

What drives merger waves in Europe?

27

Datastream. Our central bank rates are collected from Eurostat, Datastream and the websites

of individual national banks where necessary. For the years prior to the inception of the Euro,

i.e., pre-1999, we simply construct a weighted average of the marginal lending rates of the

EU-15 national banks, readjusting the weighting in the few places where Eurostat displays

missing values. We acknowledge that the practical changes in interest rate definitions

throughout the period will invariably create some noise in the capital liquidity proxy.

5.2.1.3 Valuation factors

We follow Harford (2005) in choosing the industry median 1-year and 3-year stock returns

and the median market-to-book ratio at the yearend as our stock market variables. To cover

the dispersion in stock market values, we choose the intra-industry standard deviation of

these three series. We retrieve stock data from Datastream for all active and inactive public

firms from 1989 to 2004 and allocate each firm year to the industry of its primary SIC code.

Having removed those firm industry memberships which provide less than 3 consecutive

years of data, we are left with 4,859 firm industry memberships. However, in light of the

results of Rhodes-Kropf et al. (2005), we also include their industry time-series error variable

(specifically, the second of their three model specifications) as an alternative measure of

industry overvaluation. Their method involves using a fitted values technique to decompose

the market-to-book ratio into three valuation errors; cross-sectional, time-series and long-run

errors. The first and second valuation errors capture the misvaluation attributable to the firm

and sectoral levels, respectively. Following Rhodes-Kropf et al. (2005), we calculate these

valuation errors for each firm year using the 12 sectors as defined by Fama and French6. We

then define the industry time series error as the average firm times series error in a given

industry (using our classification of 46 industry groups).

Some industries have very few public firms to provide the necessary data. We therefore

exclude industries where less than 5 firms provide stock return data in a given sample year

(‘Coal’, ‘Ships’, ‘Defs’, and ‘Gold’ ), leaving us with 42 remaining industries.

Note that we of course cannot measure the de facto stock market valuation of private firms

since they have no stock price. Thus, the stock market overvaluation in an industry as well as

6 The sectors are ‘Consumer nondurables’, ‘Consumer durables’, ‘Manufacturing’, ‘Energy’, ‘Chemicals’, ‘Computers, software etc.’, ‘Telephone and TV’, ‘Utilities’, ‘Wholesale’, ‘Medical’, ‘Finance’, ‘Everything else’ (definitions are available on http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).

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the dispersion in valuations must be measured solely by the population of public firms.

However, since private firms conduct the same industry activities as public firms, the

valuation levels and dispersion in valuations within an industry suggest the benchmark for

overvaluation of private firms as well as the dispersion in valuations between them.

5.2.2 Univariate results

If a given variable does in fact predict the onset of an industry merger wave, we expect it to

be high in the year prior to an industry merger wave. Therefore, we assign a quartile rank to

the value of the predictive variables in each industry year and test whether the mean rank of

the predictive variables in each year preceding an industry merger wave is significantly above

the average rank value of 2.5. If the mean rank is significantly higher (lower) than 2.5, it

means that high (low) values of the variable are more likely to precede industry merger

waves. All of the variables introduced above are predicted to be higher than 2.5, except the

proxy for capital liquidity, which is predicted to be low prior to an industry merger wave.

Table 7 provides the mean rank value of the predictive variables in each year preceding an

industry merger wave for all four sub-samples, as well as for the whole sample.

< Insert Table 7 here >

In general, all neoclassical variables except capital liquidity show significant, high values.

The economic shock variables are especially significant in the private-private and public-

private sub-samples, whereas it is less so in the private-public sub-sample. However, the

public-public sub-sample does not display one single, strongly significant neoclassical factor.

Even capital liquidity is significantly higher than 2.5, which contradicts our theoretical

expectation. Thus, public-public industry merger waves appear not to be univariately

influenced by neoclassical factors. This stands in stark contrast to findings in the public-

private sub-sample, which offers mean ranks for the economic shock variables which are

higher even than the private-private sample.

Looking at measures of industry stock market valuations, a different picture emerges. While

the public-public sample is not influenced by economic shocks, it does show significantly

high ranks for the market-to-book ratio as well as the 1-year stock returns. The 3-year stock

returns are only weakly significant. Interestingly, the public-private sample shows a similar

degree of influence from stock market values, even though we saw above that it is also highly

influenced by economic shocks. We see that even the private-private sample is affected by

the industry 3-year stock returns. Though not predicted, this is not intuitively surprising since

What drives merger waves in Europe?

29

ex-ante industry returns are likely not only to pick up stock market overvaluation within an

industry, but also the higher marginal investment profitability of growth opportunities within

the industry (Klasa and Stegemoeller, 2005). In addition, ex-ante stock returns may reflect the

availability of cheap capital. Our capital liquidity proxy does not indicate any importance of

such financing concerns, but this could be due to its noisiness.

Regarding the variables measuring the dispersion in industry stock market valuations, we see

that neither the public-public, nor the private-public sample show consistent significance.

Only the dispersion in the market-to-book ratio is significant in the public-public sample, and

the public-private sample shows no real significance at all. On the other hand, the private-

private sample shows a significant influence of the dispersion in stock market valuations

measured by the market-to-book ratio and the 3-year returns. Again, this may refer to the role

of firm stock market valuations as measures of firm growth opportunities and/or managerial

quality (Jovanovic and Rousseau, 2002; Harford, 2005). Specifically, the scope for

efficiency-increasing merger activity during times of economic change increases when some

firms have (or create) better growth opportunities than rival firms, or alternatively, when

firms employ technology which is more efficient (Maksimovic and Phillips, 2001). Despite

the significance of other overvaluation measures on our samples, the time series error of

Rhodes-Kropf, Robinson and Viswanathan et al. (2005) is insignificant in all samples except

the private-public sample. We now turn to a logistic regression model to see whether this

univariate evidence carries over to a multivariate setting. We will focus only on the market-

to-book ratio and the 1-year and 3-year returns as our measures of overvaluation.

5.2.3 Multivariate results

Since we have a binary response variable, i.e., a given industry year either experiences a

merger wave or not, we use logistic regression modelling (Wooldridge, 2002). Our dependent

variable is coded one if a merger wave begins in a given industry year, and zero if no industry

merger wave begins. This regression mirrors that of Harford 2005, but our analysis deviates

in two ways. Most importantly, we recognize that, in logistic modelling, the maximum

likelihood parameter estimates cannot be interpreted as the marginal effects of the

explanatory variables – in fact, they are the log-odds ratios. Therefore, we calculate and

report the true marginal effects on the probability of an industry merger wave using the ‘delta

method’ detailed in Wooldridge (2002). We also take care to account for the complications

arising as a result of our use of interaction effects. Since marginal effects in a logistic

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equation are a function of the specific values of the explanatory variables, we need to pick a

specific vector of variable values on which to evaluate the marginal effects. We choose to

evaluate them at their mean values. Additionally, unlike Harford, we use the value of the

capital liquidity proxy in a given year, as opposed to using its lagged value. Since it proxies

directly for the ease of financing, it seems more correct to compare it to the specific time for

that activity as opposed to historical values. Finally, as proxies for overvaluation we maintain

both the 1-year and 3-year stock returns and the dispersion in these, as well as the mean and

dispersion of market to book values. This allows us to capture the potentially varying

importance of overvaluation proxies across sub-samples. As in Harford (2005), we use our

combined economic shock variable as opposed to the individual economic shock variables to

avoid multicollinearity and to facilitate the interpretation of a general economic shock

influence. Table 8 reports the results for the full sample and the four sub-samples.

< Insert table 8 here >

The first column shows that the full sample is driven by the combined economics shocks, and

not by any other variables. Turning to the individual subsamples in the other columns, we see

that this general influence of economic shock values is driven by the heavy significance of

the combined economic shock variable on the private-private and public-private samples.

This is in stark contrast to the lack of significance of the economic shock variable on the

public-public sample. Again, we see that the timing of the aggregate wave obscures

differences in underlying factors across samples of private and public ownership.

The capital liquidity proxy is not significant in the private-private and public-private samples

which is somewhat surprising. We partially attribute this to the noise brought on by the

discontinuities in our interest rate measures. However, the private-private sample shows that

the univariate influence of the 3-year stock returns carries over to our multivariate setting. As

already suggested, this implies that the returns pick up the increased growth opportunities

which accompany periods of economic changes as well as the positive market value effects of

the favourable financial conditions, which our capital liquidity measure does not capture7.

The private-public sample seems to be influenced in a similar way to the private-private

sample, although capital liquidity in this case does explain the timing of waves.

7 Notably, the variable denoting the effect of the shock index during periods of tight capital is insignificant across all samples. At face value, this implies that the importance of economic shocks has not been dampened by years of less favourable financial conditions. However, it could also be the case that our time series is too short to correlate ‘real’ periods of low liquidity with industry merger waves, and/or that our capital liquidity proxy is too noisy.

What drives merger waves in Europe?

31

In lieu of any neoclassical influence, the public-public sample seems to be explained only by

the 1-year stock return. Hence, we conclude that the lack of a correlation in the timing of

public-public and private-private industry merger waves is driven by the lack of influence of

economic shocks on the onset of public-public industry merger waves, and a corresponding

strong influence of short-term stock market values. Notably, the public-private sample does

not exhibit any influence of stock returns; it is only explained by economic shocks.

In all, we see that the timing of private-private and public-private industry merger waves –

which we found to be correlated in section 4 – displays a highly significant influence of

economic shocks. The private-private sample also displays an influence of growth

opportunities expressed through high industry stock market valuations. The private-public

sample is comparable to the private-private sample. Therefore, there is strong multivariate

evidence that the timing of these three samples is determined by neoclassical factors. On the

other hand, the timing of public-public industry merger waves is not driven by neoclassical

factors, but by short-term overvaluation. Both of these conclusions support our theoretical

conjecture that private-private and public-public industry merger waves are the most likely to

be driven by neoclassical and overvaluation reasoning, respectively. We have documented

that public-public transactions in waves do not increase the use of stock payment in any

economically significant way. Therefore, this multivariate evidence is indicative of an agency

costs of overvaluation explanation.

Our results differ somewhat from the multivariate analysis of Harford (2005), who finds no

influence of overvaluation on his sample of public-public US mergers 1980-2001, which

seem to be explainable purely by economic shocks and capital liquidity. At face value, this

would imply a difference in the fundamental drivers of the two samples. However, as noted

above, we have chosen to test the marginal effects as opposed to simply looking at the

significance of the log-odds ratios. Without replicating Harford’s study using the marginal

effects, we can thus not be sure that Harford’s conclusions hold for the true marginal effects.

We move on to see whether the operating performance realized in these industry merger

waves offers supporting evidence of shareholder value destroying, agency motives in public-

public samples, and evidence of shareholder value creating, neoclassical motives in the

remaining samples.

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5.3 The performance of private and public industry merger waves

Less than a handful of studies have examined the profitability of M&A in select European

countries in terms of operating performance. Mueller (1980) examines mergers in the 50s,

60s and 70s across UK, Germany, France, Belgium, Netherlands and Sweden. Gugler,

Mueller, Yurtoglu, and Zulehner (2003) examine European mergers 1981-1998 as part of a

broader international comparison. Powell and Stark (2005) examine 191 UK mergers 1985-

1993. Martynova et al. (2006) examine 155 European mergers involving at least one listed

firm in 1997-2001. Only the two latter studies adopt an adequate empirical methodology for

measuring changes in post-merger operating performance (see below); Powell and Stark

(2005) find a significant increase in operating performance compared with matched firms,

while Martynova et al. (2006) finds no significant difference. None of these studies adopts an

industry merger wave perspective, and thus they provide no direct evidence on the drivers of

merger waves, let alone the European Merger Wave of the 90s.

5.3.1 Measuring operating performance

5.3.1.1 Methodology

We base our measure of operating performance on cash flow from operations in order to

mitigate the impact of accrual accounting, the financing of the transaction and the method of

accounting for the transaction (see, e.g., Healy, Palepu, and Ruback, 1992, Ghosh, 2001,

Powell and Stark, 2005; Martynova et al., 2006). Specifically, we measure operating

performance as earnings before interest, taxes, depreciation and amortization (EBITDA)

scaled by operating revenue. For the year preceding the merger, we aggregate the data for the

target and the acquirer to obtain a pro forma pre-merger performance of the combined firm.

For the years following the merger, post-merger performance is obtained from the values

reported by the merged firm.

We need to adjust the change in performance by a ‘benchmark’ of expected performance

(Healy et al., 1992). Since our study concerns industry merger waves, it seems relevant to

compare the change in operating performance to the change experienced by firms who

negotiate the industry environment using other adjustment methods. However, Barber and

Lyon (1996) find that test statistics are well specified only when sample firms are matched to

control firms with similar pre-merger performance. We therefore use the two-step procedure

of Ghosh (2001) to find pairwise matching firms on the dimensions of industry, size and

performance for each acquirer and target in our sample. Specifically, we find all firms in the

What drives merger waves in Europe?

33

industries of the acquirer and the target that are within 25% and 200% of their size measured

by the book value of assets in the year before the merger. For both the acquirer and target we

choose among these potential matches the firm which has the most similar performance in the

year before the merger. We then aggregate the data of the pair of matching firms to calculate

the matched-firm pre-merger performance (the year before the merger) and post-merger

performance in the 3 years following the merger. We use the book value of the assets of the

acquiring and target firm one year prior to the merger as weights.

Abnormal performance for a given year is then calculated by subtracting the benchmark

performance from the actual performance. The change in operating performance used in our

analysis is the post-merger abnormal performance less pre-merger abnormal performance,

where post-merger abnormal performance is the median abnormal performance over the three

years following the merger (Ghosh, 2001).

5.3.1.2 Merger and accounting data

To carry out our study of accounting performance on private and public mergers, we need to

match merger observations in the SDC database with our AMADEUS accounting data.

However, there is no unique identifier between the two databases. Therefore, for the sake of

our performance study, we substitute the SDC database for the Bureau van Dijk ‘Zephyr’

European M&A database, which has the Bureau van Dijk identifiers needed to link mergers

to the accounting data in AMADEUS. We did not use the Zephyr database in our previous

analyses because its data range does not support a wave analysis; it does not begin until 1997,

and it reports only sparse activity until ca. 2000. However, since it is the only one of the two

databases to offer a firm-level link between mergers and accounting data, we use it to

perform our study of post-merger operating performance. Similar to AMADEUS, the role in

research of the Zephyr database is growing as the reach of its time series is extended. For

instance, Martynova et al. (2006) use a combined sample of the SDC database and the Zephyr

database as the foundation of their empirical analysis of the accounting performance of

European M&As 1997-2001.

We use the same criteria to sample mergers from the Zephyr database as we did when

sampling mergers from the SDC database. Our methodology requires acquirer and target firm

financial data one year prior to the merger and acquirer financial data in the 3 years following

the merger. We match the acquirers and targets of mergers reported in Zephyr 1997-2004

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with consolidated accounting data in our AMADEUS data, which provides us with a sample

of 345 mergers8. 271 of these mergers occur within industry merger waves.

5.3.2 Results

Table 9, panel A, presents the mean change in abnormal performance for each sample and

tests their significance using the Wald test. We use a dummy regression approach which

allows us to calculate and use White’s heteroskedasticity-consistent standard errors (White,

1980). The first row in panel A reports the mean change in abnormal performance for the full

sample of 345 mergers, i.e., the full period, whereas the second row only reports mergers

conducted during significant industry merger waves.

< Insert table 9 here >

We see in the first row that the mean change in operating performance in private-private

mergers over the full period is positive at 2.29% and close to significance (P-value = 0.11).

This implies that private-private mergers in our sample period are wealth creating, albeit they

do not convincingly outperform the control sample matched on industry, size and

performance. In comparison, all of the other samples exhibit wealth destruction on average,

although the significance is above 10%. Panel B compares these three samples to the private-

private sample, and we see that they are all significantly wealth destroying in comparison.

Importantly, both the public-public and private-public samples are strongly significant (P-

values are 0.029 and 0.030, respectively). It would thus seem that the private-private sample

can be considered the neoclassical benchmark across the full period.

The second row of panel A focuses on the mean change of operating performance in industry

merger waves only. Although the results are qualitatively unchanged, the significance level

drops for the private-private and public-public samples, which are now no longer close to

being significantly positive and negative, respectively. Thus, we cannot validate the 8 In principle, we could also link acquirers and targets to the unconsolidated accounts in the AMADEUS database to capture acquirers and targets who are single-entity firms, and hence, do not have a consolidated account. If we could be sure that a firm with only an unconsolidated report for a given year was a single-entity firm, the use of such unconsolidated reports would cause us no problems, as the unconsolidated report would show the full activities of the firm. However, while there is no conceptual reason not to extend the match to the unconsolidated accounts in the AMADEUS database, there is a practical problem; we have no means of verifying whether a firm that has only an unconsolidated account is in fact a single-entity firm. The consolidated accounts may just be missing or subject to national accounting data constraints. If we were to use unconsolidated accounts for firms which were multiple-entity firms, but simply missing their consolidated reports, we would risk creating a bias in our measures of the pre-merger and post-merger performance of the acquirer, target and merged firms. Were we to use unconsolidated reports anyway, our sample size would increase to 1,313. Martynova et al. (2006) – who use the AMADEUS database as well – do not comment on the consolidation level of the company accounts they use.

What drives merger waves in Europe?

35

neoclassical prediction in its strong form for private-private mergers in waves; they do not

outperform their matched firms. However, we also cannot reject the weak form prediction

that they fare no worse than non-merging firms. Similarly, we cannot confirm the agency

prediction in its strong form for public-public mergers in waves; they do not significantly

underperform their matched firms. Furthermore, while panel B shows that the mean change

in performance in the public-public, public-private and private-public samples is still clearly

lower than that of the private-private sample; only the public-private sample has significance

below 10%. Thus, there is less conclusive evidence that the private-private sample can be

seen as the neoclassical benchmark for mergers within industry merger waves.

The lack of evidence of abnormal post-merger performance when firms are matched on

industry, size and performance corresponds to the recent results of Martynova et al. (2006) as

well as a broader range of studies of accounting performance mainly conducted on the US

(see, e.g., Sudarsanam, 2003). We offer two factors relating to empirical testing which

independently or in unison may lead to a lack of significance in the in-wave sample. Firstly,

the reduction of the size of the sample from 345 to 271 has reduced the power of our tests.

Secondly, we have chosen an empirical methodology which assumes that any operational

improvements should be reflected in operating performance within a few years. Increasing

the time frame of the post-merger performance study could perhaps accentuate our results,

although doing so would simultaneously increase the ‘noise’ from the performance effects of

other corporate decisions (Sudarsanam, 2003).

5.4 Summary of results

In section 4, we conclude that the timing of industry merger waves across ownership samples

differed substantially. Public-public and private-public samples are uncorrelated with the

private-private sample, which we consider to be our neoclassical ‘benchmark’, theoretically

speaking. Only the public-private industry merger waves seem to be correlated with the

private-private industry merger waves, although they are still far apart on average. Despite

these timing differences, our study of the timing of industry merger waves implies that

private-private, private-public and public-private industry merger waves are driven by

economic industry shocks, whereas only public-public industry merger waves are clearly

driven by overvaluation. The lack of an economically significant increase in the use of stock

payment during industry merger waves across all ownership types suggests that the market

driven view was not a significant driver of any sample, including that of public-public

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industry merger waves. Thus, it would seem that public-public industry merger waves are

driven by agency costs of overvaluation.

Our study of operating performance offers evidence both supportive of, and contradictory to,

our analyses of the timing and payment characteristics of industry merger waves. We see that

private-private mergers perform slightly better than their matched firms do and better than

other types of mergers, implying that they generally restructure assets in an efficient manner,

as suggested by neoclassical theory. In particular, our performance study suggests that public-

public mergers are not driven by the same wealth creating motives as the private-private

sample, although the evidence is just beyond statistical significance. Thus, public-public

mergers, to a higher degree than private-private mergers, are driven by a deal rationale which

does not efficiently restructure firm assets. And these deal rationales coincide with periods of

higher industry stock market valuations. In all, the combined evidence presented in this paper

is supportive of the conclusion that public-public merger waves are driven by agency costs of

overvaluation, while neoclassical reasoning drives private-private merger waves.

Our results offer a somewhat different picture of public-public industry merger waves than

Harford (2005), who shows that US public-public merger waves are driven by neoclassical

factors and not by valuation factors. Future research might revisit Harford’s study and

calculate the marginal effects in his multivariate timing regressions to see whether these

results in fact hold true. If they do, there is obviously a major need to consider why the

drivers of US and European public-public merger waves should differ so.

Our paper also provides new evidence relating to a merger wave level perspective on the

cause, characteristics and consequences of private-public and public-private mergers,

although these results are more mixed and do not clearly point in one direction. We initially

note that their timing differs compared with the private-private ‘benchmark’ sample, but our

analysis of the underlying determinants finds that economic shocks were triggers of both

public-private and private-public waves. However, the performance study provides weak

contradictory evidence that mergers within these waves on average are not efficient responses

to economic industry shocks. Thus, we would seem to have insufficient evidence to clearly

support an explanation based on either neoclassical motives or the agency costs of

overvaluation. Nevertheless, the lack of an influence from overvaluation does not rule out

that agency costs of different origin or other non-efficiency considerations may affect the

What drives merger waves in Europe?

37

motivation behind these mergers even though they are triggered by economic shocks. We

believe that a firm-specific analysis is required in order for future research to come to terms

with this mixed evidence. Note also the difference in timing between public-private merger

waves and public-public merger waves. This incongruence suggests that public firms may

choose public and private targets for different reasons and hence, at different times. We leave

it to future research to explore the additional factors driving public-private and private-public

merger waves as well as the implicit choice between private and public targets.

Finally, our study implies two additional avenues for future research. Firstly, our results on

the differences between the timing of private-private and public-public industry merger

waves suggests that it would be fruitful to conduct a similar study of the timing of US

industry waves across ownership types. If the public-public industry waves turn out to be

uncorrelated with the private-private neoclassical benchmark, we would have additional

indirect evidence on the drivers of US public-public merger waves. Note that a full

replication of our study must wait until accounting data on US privately held firm is

available. Secondly, while our study has focused on full corporate acquisitions, future

research can look at how European acquirers used partial-firm (i.e., subsidiary/divisional)

acquisitions. Subsidiary acquisitions are different phenomena from full corporate

acquisitions, but they can be used by firms to adjust to fundamental economic changes in

much the same way as mergers (Weston, 2001; Andrade and Stafford, 2004; Harford, 2005).

Harford (2005) finds that both stock and cash US acquirers are more likely to conduct partial-

firm acquisitions (and pay for them in cash) than non-acquirers. It would be interesting to see

how and when acquirers in our European sample conduct subsidiary acquisitions.

6. Conclusion

Our paper conducts the first full analysis of the drivers of the European Merger Wave of the

90s, and we offer the first analysis of the drivers of merger waves involving private firms. We

argue theoretically that the differences between private and public firms make private merger

waves less likely to be driven by the inefficiencies relating to overvaluation which are the

foundation of market driven theory and the agency costs of overvaluation. Our empirical

investigation finds that industry merger waves involving private firms represent efficient

responses to economic shocks in which overvaluation theories do not play a major part, while

public merger waves seem to be driven by agency issues related to the stock market

overvaluation of the EU in the late 90s. Firstly, we show that private industry merger waves

Chapter 1

38

occur at different times than public firm industry merger waves. Secondly, we find that these

differences are attributable to the significant influence of economic industry shocks on the

timing private firm merger waves and a significant influence of stock market overvaluation

on the timing of public merger waves. Thirdly, our study of post-merger (accounting)

performance confirms that private firm merger waves are more efficient than public firm

merger waves, although the statistical significance is weak. The evidence for merger waves

involving public acquirers and private targets, and private acquirers and public targets is less

clear. However, our study of post-merger operating performance offers weak evidence that

these merger waves are not efficient.

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What drives merger waves in Europe?

39

Faccio, M., Masulis, R. W., 2005. The choice of payment method in European mergers and acquisitions. Journal of Finance 60, 1345–1388. Fama, E., French, K., 1997. Industry costs of equity. Journal of Financial Economics 43, 153–193. Ghosh, A., 2001.Does operating performance improve following corporate acquisitions? Journal of Corporate Finance 7, 151–178. Goergen, M., Renneboog, L., 2004. Shareholder wealth effects of European domestic and cross-border takeover bids. European Financial Management 10, 9–45. Gort, M., 1969. An economic disturbance theory of mergers. Quarterly Journal of Economics 83, 623–642. Gorton, G., Kahl, M., Rosen, R., 2005. Eat or be eaten: A theory of mergers and merger waves. Unpublished working paper. University of Pennsylvania, Philadelphia, PA. Gugler, K., Mueller, D. C., Yurtoglu, B. B., Zulehner, C., 2003. The effects of mergers: an international comparison. International Journal of Industrial Organization 21, 625–653. Harford, J., 2005. What drives merger waves? Journal of Financial Economics 77, 483–702. Healy, P., Palepu, K., Ruback, R., 1992. Does corporate performance improve after mergers? Journal of Financial Economics 31, 135–175. Jensen, M. C., 1986. Agency costs of free cash flow, corporate finance and takeovers. American Economic Review 76, 323–329. Jensen, Michael C., 2005. Agency costs of overvalued equity. Financial Management 34, 5–19. Jovanovic, B., Rousseau, P., 2002. The Q-theory of mergers. American Economic Review 92, 198–204. Klasa, S., Stegemoller, M., 2005. Takeover activity as a response to time-varying changes in investment opportunity sets: Evidence from takeover sequences. Unpublished working paper, University of Arizona. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 1999. Corporate ownership around the world. Journal of Finance 54, 471–517. Maksimovic, V., Phillips, G., 2001. The market for corporate assets: Who engages in mergers and asset sales and are there efficiency gains? Journal of Finance 56, 2019–2065. Martynova, M., Oosting, S., Renneboog, L., 2006. The long-term operating performance of European mergers and acquisitions. ECGI Working Paper Series in Finance. Martynova, M., Renneboog, L., 2006a. The performance of the European market for corporate control: Evidence from the 5th takeover wave. ECGI Working Paper Series in Finance. Martynova, M., Renneboog, L., 2006b. Mergers and acquisitions in Europe. ECGI Working Paper Series in Finance. McGowan, J. J., 1971. International comparisons of merger activity. Journal of Law and Economics 14, 233–250. Melicher, R. W., Ledolter, J., D'Antonio, L. J., 1983. A time series analysis of aggregate merger activity. Review of Economics and Statistics 65, 423–430. Mitchell, M., Mulherin, J. H., 1996. The impact of industry shocks on takeover and restructuring activity. Journal of Financial Economics 41, 193–229. Moeller, S., Schlingemann, F., Stulz, R., 2005. Wealth destruction on a massive scale? A study of acquiring–firm returns in the recent merger wave. Journal of Finance 60, 757–782. Mueller, D. C., 1969. A theory of conglomerate mergers, Quarterly Journal of Economics 83, 643–59. Mueller, D. C. (Ed.), 1980. The determinants and effects of mergers: an international comparison. Oelgeschlager, Gunn & Hain, Cambridge, MA.

Chapter 1

40

Mulherin, H., Boone, A., 2000. Comparing acquisitions and divestitures. Journal of Corporate Finance 6, 117–139. Nelson, R. L., 1959. Merger movements in American industry, 1895-1956. Princeton University Press, NBER, Princeton, NJ. Powell R. G., Stark, A. W., 2005. Does operating performance increase post-takeover for UK takeovers? A comparison of performance measures and benchmarks. Journal of Corporate Finance 11, 293–317. Powell, R. G., Yawson, A., 2005. Industry aspects of takeovers and divestitures: Evidence from the UK. Journal of Banking & Finance 29, 3015–3040. Rhodes-Kropf, M., Viswanathan, S., 2004. Market valuation and merger waves. Journal of Finance 59, 2685–2718. Rhodes-Kropf, M., Robinson, D., T., Viswanathan, S., 2005. Valuation waves and merger activity: The empirical evidence. Journal of Financial Economics 77, 561–603 Schoenberg, R., Reeves, R., 1999. What determines acquisition activity within an industry? European Management Journal 17, 93–98. Shleifer, A., Vishny, R. W., 2003. Stock market driven acquisitions. Journal of Financial Economics 70, 295–311. Sudarsanam, S., 2003. Creating value from mergers and acquisitions. Prentice Hall, Malaysia. Weston, J. F., 2001. Merger and acquisitions as adjustment Processes, Journal of Industry, Competition and Trade 1, 395–410. Weston, J. F., Mitchell, M. L., Mulherin, J. H., 2004. Takeovers, restructuring, and corporate governance (4th, international edition). Pearson Education, Upper Saddle River, N.J. White, H., 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48, 817–38. Wooldridge, J. M., 2002. Econometric analysis of cross section and panel data. The MIT Press, Cambridge, MA.

What drives merger waves in Europe?

41

Table 1: The impact of the organization of firm ownership on the likelihood of a specific behavioural influence Public-public refers to mergers involving a public acquirer and target. Public-private refers to mergers involving a public acquirer and a private target. Private-public refers to mergers involving a private acquirer and public target. Private-private refers to mergers involving a private acquirer and private target.

Public-public Public-private

Private-public

Private-private

Agency costs (Jensen, 2005)

Conducive A little less conducive

Less conducive Less conducive

Market driven (Shleifer and Vishny, 2003)

Conducive Much less conducive

Much less conducive

Least conducive

Market driven (Rhodes-Kropf and Viswanathan, 2004)

Conducive Much less conducive

Much less conducive

Least conducive

Chapter 1

42

Figure 1: Merger activity and the average industry market-to-book ratio 1991-2005 Merger activity is sampled from the Thomson Financial SDC Platinum database, and contains all completed control-shifting transactions, though not newly created joint ventures, mutual fund activity and government mergers, from the EU-15 and the US. Mergers are maintained regardless of transaction size, and they are allocated to the year of the announcement date. Following the later use of industry median market-to-book values in our empirical investigation, this measure is defined as the yearly average of those values, lagged one period.

What drives merger waves in Europe?

43

Table 2: The proportion of mergers involving privately held and publicly held firms within the EU-15 1991-2005 Merger activity is sampled from the Thomson Financial SDC Platinum database, and contains all completed control-shifting transactions involving privately and publicly held firms. Mergers are maintained regardless of transaction size. Due to rounding errors, the total does not sum to 100%.

Private target

Public target Total

Private acquirer

37.13% 7.46% 44.59%

Public acquirer

43.01% 12.39% 55.40%

Total 80.14%

19.85% 100.00%

Chapter 1

44

Figure 2a: EU-15 private-private and public-private merger activity 1991-2005 and the average industry market-to-book ratio Merger activity is sampled from the Thomson Financial SDC Platinum database. The graph depicts completed control-shifting transactions involving private acquirers and private targets, and public acquirers and private targets, in the EU-15 from 1991 to 2005. Mergers are maintained regardless of transaction size, and they are allocated to the year of the announcement date. Following the later use of industry median market-to-book values in our empirical investigation, this measure is defined as the yearly average of those values, lagged one period.

What drives merger waves in Europe?

45

Figure 2b: EU-15 private-public and public-public merger activity 1991-2005 and the average industry market-to-book ratio Merger activity is sampled from the Thomson Financial SDC Platinum database. The graph depicts completed control-shifting transactions involving private acquirers and public targets, and private acquirers and public targets, in the EU-15 from 1991 to 2005. Mergers are maintained regardless of transaction size, and they are allocated to the year of the announcement date. Following the later use of industry median market-to-book values in our empirical investigation, this measure is defined as the yearly average of those values, lagged one period. .

Chapter 1

46

Table 3a: The timing of statistically significant industry merger waves within the EU-15 1995-2004 Statistically significant industry merger waves are defined in section 4. They are depicted for the whole sample and the private-private and public-public sub-samples. The black rectangles signify significant waves in the full sample, while the dark and light greys signify private-private firm and public-public significant waves, respectively. The two columns on the right note the specific starting and ending point of the potential industry merger waves.

What drives merger waves in Europe?

47

Table 3a: The timing of statistically significant industry merger waves within the EU-15 1995-2004 (continued – page 2)

Chapter 1

48

Table 3a: The timing of statistically significant industry merger waves within the EU-15 1995-2004 (continued – page 3)

What drives merger waves in Europe?

49

Table 3b: The timing of private-private industry merger waves within the EU-15 1995-2004 Industry merger waves and their statistical significance are defined in section 4. Significant industry merger waves are depicted for the private-private sub-sample. The black rectangles signify significant waves in the sample, while the light grey rectangles signify non-significant private-private waves. The two columns on the right note the specific starting and ending point of the industry merger waves.

Chapter 1

50

Table 3c: The timing of public-public industry merger waves within the EU-15 1995-2004 Industry merger waves and their statistical significance are defined in section 4. Significant industry merger waves are depicted for the public-public sub-sample. The black rectangles signify significant waves in the sample, while the light grey rectangles signify non-significant public-public waves. The two columns on the right note the specific starting and ending point of the industry merger waves.

What drives merger waves in Europe?

51

Table 3d: The timing of public-private firm industry merger waves within the EU-15 1995-2004 Industry merger waves and their statistical significance are defined in section 4. Significant industry merger waves are depicted for the public-private sub-sample. The black rectangles signify significant waves in the sample, while the light grey rectangles signify non-significant public-private waves. The two columns on the right note the specific starting and ending point of the industry merger waves.

Chapter 1

52

Table 3e: The timing of private-public firm industry merger waves within the EU-15 1995-2004 Industry merger waves and their statistical significance are defined in section 4. Significant industry merger waves are depicted for the private-public sub-sample. The black rectangles signify significant waves in the sample, while the light grey rectangles signify non-significant private-public waves. The two columns on the right note the specific starting and ending point of the industry merger waves.

What drives merger waves in Europe?

53

Tab

le 4

: The

cor

rela

tions

bet

wee

n po

tent

ial a

nd si

gnifi

cant

indu

stry

mer

ger

wav

es in

volv

ing

com

bina

tions

of p

riva

te a

nd p

ublic

acq

uire

rs a

nd ta

rget

s Pa

nel A

repo

rts th

e pa

irwis

e (n

on-p

aram

etric

) Spe

arm

an ra

nk-o

rder

cor

rela

tion

betw

een

pote

ntia

l ind

ustry

mer

ger w

aves

in e

ach

sam

ple.

Sig

nific

ant i

ndus

try m

erge

r wav

es

are

iden

tifie

d fo

r eac

h su

b-sa

mpl

e as

det

aile

d in

sect

ion

4. T

he c

orre

latio

n is

cal

cula

ted

by ra

nkin

g th

e tim

ing

of e

ach

pote

ntia

l ind

ustry

mer

ger w

ave

with

in a

giv

en sa

mpl

e an

d m

easu

ring

the

corr

elat

ion

of in

dust

ry ra

nkin

gs b

etw

een

sam

ples

usi

ng th

e Sp

earm

an c

orre

latio

n co

effic

ient

. N re

fers

to th

e nu

mbe

r of i

ndus

tries

in w

hich

bot

h gi

ven

sam

ples

hav

e a

pote

ntia

l ind

ustry

mer

ger w

ave,

whi

ch o

ccur

s whe

n th

ere

is m

ore

than

one

mer

ger o

bser

vatio

n. M

ean

dist

ance

in m

onth

s is t

he a

vera

ge d

iffer

ence

bet

wee

n po

tent

ial i

ndus

try m

erge

r wav

es in

two

give

n sa

mpl

es. P

-val

ues f

rom

the

t-tes

t are

repo

rted

in p

aren

thes

is. **

* , ** , *

mar

ks si

gnifi

canc

e at

the

1%, 5

%, a

nd 1

0% le

vel,

resp

ectiv

ely.

Pa

nel A

:

Priv

ate-

priv

ate

Publ

ic-p

ublic

Pu

blic

-pri

vate

Fu

ll sa

mpl

e

C

orre

latio

n N

M

ean

dist

ance

in

m

onth

s

C

orre

latio

n N

M

ean

dist

ance

in

m

onth

s

C

orre

latio

n N

M

ean

dist

ance

in

m

onth

s

C

orre

latio

n N

M

ean

dist

ance

in

m

onth

s

Priv

ate-

priv

ate

0.36

9**

(0.0

12)

46

13.5

4

Publ

ic-p

ublic

0.

174

(0.2

57)

44

20.9

3

0.36

9**

(0.0

11)

46

14.7

8

Publ

ic-p

riva

te

0.56

3***

(<0.

001)

46

21

.39

0.

203

(0.1

80)

45

21.9

6

0.58

8***

(<0.

001)

47

12

.70

Priv

ate-

publ

ic

0.19

1 (0

.251

) 38

22

.26

-0

.063

(0

.711

) 37

27

.22

-0

.008

(0

.963

) 38

33

.3

0.

079

(0.9

37)

38

26.3

2

Chapter 1

54

Tab

le 4

: The

cor

rela

tions

bet

wee

n po

tent

ial a

nd si

gnifi

cant

indu

stry

mer

ger

wav

es in

volv

ing

com

bina

tions

of p

riva

te a

nd p

ublic

acq

uire

rs a

nd ta

rget

s (co

ntin

ued)

Pa

nel B

repo

rts th

e pa

irwis

e (n

on-p

aram

etric

) Spe

arm

an ra

nk-o

rder

cor

rela

tion

betw

een

sign

ifica

nt in

dust

ry m

erge

r wav

es in

eac

h sa

mpl

e. S

igni

fican

t ind

ustry

mer

ger w

aves

ar

e id

entif

ied

for e

ach

sub-

sam

ple

as d

etai

led

in se

ctio

n 4.

The

cor

rela

tion

is c

alcu

late

d by

rank

ing

the

timin

g of

eac

h si

gnifi

cant

indu

stry

mer

ger w

ave

with

in a

giv

en sa

mpl

e an

d m

easu

ring

the

corr

elat

ion

of in

dust

ry ra

nkin

gs b

etw

een

sam

ples

usi

ng th

e Sp

earm

an c

orre

latio

n co

effic

ient

. N re

fers

to th

e nu

mbe

r of s

imul

tane

ousl

y si

gnifi

cant

indu

stry

m

erge

r wav

es b

etw

een

two

give

n sa

mpl

e. M

ean

dist

ance

in m

onth

s is t

he a

vera

ge d

iffer

ence

bet

wee

n si

gnifi

cant

indu

stry

mer

ger w

aves

in tw

o gi

ven

sam

ples

. P-v

alue

s fro

m

the

t-tes

t are

repo

rted

in p

aren

thes

is. **

* , ** , *

mar

ks si

gnifi

canc

e at

the

1%, 5

%, a

nd 1

0% le

vel,

resp

ectiv

ely.

Pa

nel B

:

Pr

ivat

e-pr

ivat

e

Publ

ic-p

ublic

Pu

blic

-pri

vate

Fu

ll sa

mpl

e

C

orre

latio

n N

M

ean

dist

ance

in

m

onth

s

C

orre

latio

n N

M

ean

dist

ance

in

mon

ths

C

orre

latio

n N

M

ean

dist

ance

in

mon

ths

C

orre

latio

n N

M

ean

dist

ance

in

m

onth

s

Priv

ate-

priv

ate

0.82

3***

(<0.

001)

22

/2

4 12

.93

Publ

ic-p

ublic

0.

413

(0.2

35)

10/

21

14.4

0

0.54

2**

(0.0

37)

15/

21

6.95

Publ

ic-p

riva

te

0.57

5***

(0.0

10)

19/

24

13.2

0

0.00

8 (0

.979

)

13/

21

22.4

6

0.81

7***

(<0.

001)

23

/ 29

6.

22

Priv

ate-

publ

ic

-0.0

48

(0.9

09)

8/

17

22.7

5

0.03

0 (0

.944

) 8/

17

21

.62

-0

.190

(0

.651

) 8/

17

34

.60

0.

130

(0.7

03)

11/

17

23.0

0

What drives merger waves in Europe?

55

Table 5: Predictions on the timing, pattern of consideration structure and post-merger operating performance of industry merger waves

Neoclassical

Market driven

Agency costs of overvaluation

The timing of industry merger waves

High ex-ante values of economic shock factors

High or sufficient level of capital liquidity

High ex-ante stock market valuations High ex-ante dispersion in valuations

High ex-ante stock market valuations

Consideration structure

No prediction

More stock payment No prediction

Post-merger operating performance

High relative to benchmark Cash mergers outperform stock mergers

Low relative to benchmark

Chapter 1

56

Table 6: The use of stock payment in private and public industry merger waves within the EU 1995-2004 The portion of various payment types are reported. Significant industry merger waves are identified for each sub-sample as detailed in section 4. Out of wave periods are all industry periods when there is no significant industry merger wave. P-values for a t-test of the difference in payment between the out-of-wave and in-wave periods are reported in parenthesis. ***, **, * marks significance at the 1%, 5%, and 10% level, respectively.

Some stock

payment

Full stock payment

No stock payment

Proportion of stock payment

Proportion of cash payment

Private-private Out-of-wave 0.004 0.003 0.994 0.004 0.143 In-wave 0.004 0.003 0.993 0.005 0.106 Difference (P-values)

-0.000 (0.892)

0.001 (0.785)

-0.001 (0.779)

0.001 (0.673)

-0.037*** (0.003)

Public-public Out-of-wave 0.141 0.287 0.572 0.375 0.463 In-wave 0.164 0.332 0.504 0.426 0.430 Difference (P-values)

0.023 (0.383)

0.045 (0.178)

-0.068* (0.058)

0.050 (0.130)

-0.333 (0.321)

Public-private Out-of-wave 0.164 0.059 0.777 0.117 0.337 In-wave 0.166 0.077 0.757 0.150 0.231 Difference (P-values)

0.001 (0.909)

0.018** (0.029)

-0.020 (0.157)

0.033*** (0.001)

-0.106*** (<0.001)

Private-public Out-of-wave 0.006 0.028 0.965 0.032 0.655 In-wave 0.000 0.025 0.975 0.025 0.778 Difference (P-values)

-0.006* (0.083)

-0.003 (0.842)

0.009 (0.534)

-0.007 (0.626)

0.122*** (0.002)

What drives merger waves in Europe?

57

Tab

le 7

: Mea

n ra

nk v

alue

s of t

he p

redi

ctiv

e va

riab

les p

rece

ding

sign

ifica

nt in

dust

ry m

erge

r w

aves

with

in th

e E

U-1

5 19

95-2

004

Mea

n ra

nk v

alue

s (w

ith s

igni

fican

ce in

par

enth

esis

) fo

r th

e pr

edic

tive

varia

bles

in th

e ye

ar p

rior

to in

dust

ry m

erge

r w

aves

are

rep

orte

d fo

r th

e w

hole

sam

ple

and

the

sub-

sam

ples

. Sig

nific

ant i

ndus

try m

erge

r wav

es a

re id

entif

ied

for e

ach

sub-

sam

ple

as d

etai

led

in s

ectio

n 4.

The

sam

ples

are

: all

mer

gers

; mer

gers

of p

rivat

ely

held

acq

uire

rs a

nd

targ

ets;

mer

gers

of p

rivat

e ac

quire

rs a

nd p

ublic

targ

ets;

mer

gers

of p

ublic

acq

uire

rs a

nd p

rivat

e ta

rget

s; m

erge

rs o

f pub

lic a

cqui

rers

and

targ

ets.

Ran

ks a

re b

ased

on

quar

tiles

co

nstru

cted

usi

ng th

e sa

mpl

e tim

es se

ries o

f eac

h va

riabl

e. S

ee se

ctio

n 5.

2.2

for d

etai

ls. S

igni

fican

ce is

bas

ed o

n th

e t-t

est o

f the

nul

l hyp

othe

sis t

hat m

ean

rank

equ

als 2

.5. **

* ,

**, *

mar

ks si

gnifi

canc

e at

the

1%, 5

%, a

nd 1

0% le

vel,

resp

ectiv

ely.

V

aria

bles

Def

initi

on

Mea

n ra

nk:

All

mer

gers

M

ean

rank

: Pr

ivat

e-pr

ivat

e m

erge

rs

Mea

n ra

nk:

Priv

ate-

publ

ic

mer

gers

Mea

n ra

nk:

Publ

ic-p

riva

te

mer

gers

Mea

n ra

nk:

Publ

ic m

erge

rs

Eco

nom

ic sh

ock

(All

med

ian

abso

lute

cha

nges

)

C

apita

l liq

uidi

ty

Ave

rage

rate

on

com

mer

cial

and

indu

stria

l lo

ans t

- the

cen

tral b

ank

rate

t 2.

40

(0.5

61)

2.27

(0

.313

) 2.

19

(0.1

90)

2.58

(0

.704

) 3.

39**

* (0

.002

)

Cas

h flo

w m

argi

n O

pera

tiona

l pro

fitt /

Sal

est

3.43

***

(<0.

001)

3.41

***

(<0.

001)

3.

19**

* (0

.005

) 3.

71**

* (<

0.00

1)

2.83

(0

.117

)

Asse

t tur

nove

r Sa

les t

/ Tot

al a

sset

s t-1

3.20

***

(<0.

001)

3.23

***

(<0.

001)

2.

94*

(0.0

79)

3.54

***

(<0.

001)

2.

83

(0.1

93)

Retu

rn o

n as

sets

O

pera

tiona

l pro

fitt /

Tot

al a

sset

s t 3.

17**

* (<

0.00

1)

3.18

***

(0.0

01)

2.94

(0

.159

) 3.

54**

* (<

0.00

1)

2.83

(0

.193

)

Empl

oyee

gro

wth

(N

o. o

f em

ploy

ees t

– N

o. o

f em

ploy

ees t-

1) /

No.

of e

mpl

oyee

s t-1

2.97

***

(0.0

05)

2.91

**

(0.0

28)

3.06

* (0

.064

) 3.

04**

* (0

.005

) 2.

06*

(0.0

76)

Sale

s gro

wth

(S

ales

t – S

ales

t-1) /

Sal

est-1

3.

37**

* (<

0.00

1)

3.36

***

(<0.

001)

3.

13**

(0

.013

) 3.

67**

* (<

0.00

1)

2.78

(0

.326

)

Cap

. exp

endi

ture

s C

apita

l exp

endi

ture

s t / T

otal

ass

ets t-

1

3.40

***

(<0.

001)

3.55

***

(<0.

001)

3.

63**

* (<

0.00

1)

3.46

***

(<0.

001)

2.

72

(0.2

70)

Econ

omic

shoc

k in

dex

Firs

t prin

cipa

l com

pone

nt o

f Cas

h flo

w

mar

gin,

RO

A, E

mpl

oyee

gro

wth

, Ass

et

turn

over

, Sal

es g

row

th &

Cap

. exp

endi

ture

s

3.30

***

(<0.

001)

3.

36**

* (<

0.00

1)

3.06

**

(0.0

29)

3.58

***

(<0.

001)

2.

78

(0.3

26)

Chapter 1

58

Tab

le 7

– c

ontin

ued

– pa

ge 2

V

aria

bles

Def

initi

on

Mea

n ra

nk:

All

mer

gers

M

ean

rank

: Pr

ivat

e-pr

ivat

e m

erge

rs

Mea

n ra

nk:

Priv

ate-

publ

ic

mer

gers

Mea

n ra

nk:

Publ

ic-p

riva

te

mer

gers

Mea

n ra

nk:

Publ

ic m

erge

rs

Mar

ket d

rive

n / a

genc

y co

sts

M

arke

t to

book

M

arke

t cap

italiz

atio

n t-1

to S

hare

hold

er

Equi

ty t-

1

3.13

***

(0.0

01)

2.82

(0

.171

) 2.

88

(0.2

11)

3.38

***

(<0.

001)

3.

28**

* (0

.001

)

Dis

pers

ion

in m

arke

t to

book

In

tra-in

dust

ry d

ispe

rsio

n in

mar

ket-t

o-bo

ok

valu

es

3.03

**

(0.0

10)

3.18

***

(0.0

02)

3.00

* (0

.056

) 2.

88

(0.1

25)

3.28

***

(0.0

03)

1 ye

ar re

turn

s M

edia

n in

dust

ry 1

- yea

r cum

ulat

ive

retu

rns

2.

50

(1.0

00)

2.64

(0

.565

) 2.

56

(0.8

39)

2.79

* (0

.100

) 3.

33**

* (0

.003

)

3 ye

ar re

turn

s M

edia

n in

dust

ry 3

-yea

r cum

ulat

ive

retu

rns

3.

03**

* (0

.001

)

3.14

***

(0.0

05)

3.13

**

(0.0

28)

3.25

***

(<0.

001)

2.

89*

(0.0

84)

Dis

pers

ion

in 1

yea

r re

turn

s In

tra-in

dust

ry d

ispe

rsio

n of

the

1-ye

ar

cum

ulat

ive

retu

rns

2.73

(0

.233

) 3.

09**

(0

.013

) 2.

75

(0.3

88)

2.50

(1

.000

) 2.

44

(0.8

01)

Dis

pers

ion

in 3

yea

r re

turn

s In

tra-in

dust

ry d

ispe

rsio

n of

the

3-ye

ar

cum

ulat

ive

retu

rns

2.90

**

(0.0

48)

3.45

***

(<0.

001)

3.

06*

(0.0

64)

2.88

* (0

.089

) 2.

50

(1.0

00)

Indu

stry

tim

e se

ries

er

ror

Ver

sion

2 o

f the

tim

e se

ries e

rror

in R

hode

s-K

ropf

et a

l. 20

05

2.73

(0

.203

)

2.64

(0

.579

) 2.

56

(0.7

99)

3.00

**

(0.0

20)

2.61

(0

.636

)

What drives merger waves in Europe?

59

Table 8: Logistic regression modelling of significant industry merger waves within the EU-15 1995-2004 This table shows a logistic regression modelling of the occurrence of an industry merger wave. Significant industry merger waves are identified for each sub-sample as detailed in section 4. The samples are: all mergers; mergers of private acquirers and targets; mergers of private acquirers and public targets; mergers of public acquirers and private targets; mergers of public acquirers and targets. The dependent variable is a binary variable. It is coded ‘1’ if a statistically significant industry merger wave begins in a given industry year. If not, it is coded ‘0’. Each sample counts 420 industry years. The explanatory variables are the industry median 1-year and 3-year stock returns, and the industry dispersion in these stock returns, the economic shock index, capital liquidity and tight capital interacted with the shock index, as well industry median market to book and the industry dispersion in market to book. Variables are further defined in section 5.2. The marginal effects of the explanatory variables – evaluated at their sample mean – are reported (the intercept has no marginal effect), along with their significance (of a χ2-test) in parenthesis. ***, **, * marks significance at the 1%, 5%, and 10% level, respectively. Variable All mergers Private-

private mergers

Private-public mergers

Public-private mergers

Public-public mergers

1 year stock returnt-1 -0.077 (0.449)

0.052 (0.435)

-0.042 (0.349)

0.001 (0.995)

0.148** (0.013)

Dispersion in 1 year stock returnt-1

-0.010 (0.612)

-0.017 (0.305)

0.003 (0.691)

-0.009 (0.617)

-0.009 (0.699)

3 year stock returnt-1 0.039 (0.493)

0.071** (0.049)

0.074*** (0.005)

0.027 (0.524)

-0.015 (0.645)

Dispersion in 3 year stock returnt-1

0.008 (0.192)

0.002 (0.671)

-0.004 (0.506)

0.002 (0.702)

-0.005 (0.649)

Market to bookt-1 0.002 (0.933)

-0.026 (0.148)

-0.021 (0.169)

-0.012 (0.568)

-0.004 (0.823)

Dispersion in market to bookt-1

0.008 (0.775)

0.030 (0.104)

-0.009 (0.586)

0.023 (0.285)

0.012 (0.452)

Economic shock indext-1 0.541*** (0.002)

0.317** (0.010)

0.164* (0.053)

0.429*** (0.002)

-0.034 (0.739)

(Economic shock indext-1) * (Tight capitalt)

0.263 (0.906)

0.381 (0.628)

0.092 (0.919)

-0.006 (0.998)

-0.095 (0.664)

Capital liquidityt -0.014 (0.516)

-0.012 (0.359)

-0.018** (0.042)

0.000 (0.991)

0.023* (0.097)

Correlation of prediction with waves

0.138 0.256 0.245 0.188 0.131

Chapter 1

60

Table 9: The change in operational performance following mergers within the EU 1995-2004 Panel A shows the mean change in abnormal operating performance for mergers in the 4 samples. The 4 samples are: mergers of private acquirers and targets; mergers of private acquirers and public targets; mergers of public acquirers and private targets; mergers of public acquirers and targets. The procedure for calculating abnormal performance is described in section 5.3.1.1. The full sample has 345 observations, and the in wave sample has 271 observations. P-values for the Wald test based on robust (White consistent) standard errors are reported in parentheses. Panel B tests the mean change in abnormal operating performance for the public-public, private-public and the public-private samples relative to the private-private sample. ***, **, * marks significance at the 1%, 5%, and 10% level, respectively. Panel A: Mean change in abnormal operating performance

Private-private

Private-public

Public-private

Public-public

Full period 2.29 (0.110)

-11.29 (0.110)

-1.95 (0.137)

-2.21 (0.132)

In-wave 1.41

(0.313) -17.05 (0.149)

-2.12 (0.128)

-1.35 (0.252)

Panel B: Mean change in abnormal operating performance relative to the private-private sample Full period

In-wave

Diff. P-value Diff.

P-value

Public-public

-4.50** 0.029 -2.75 0.132

Private-public

-13.58* 0.060 -18.45 0.122

Public-private

-4.24** 0.030 -3.52* 0.075

CHAPTER 2

Revisiting the Returns to Bidding Firms in Mergers and Acquisitions:

The Nature of Synergies and the Market for Corporate Control

REVISITING THE RETURNS TO BIDDING FIRMS IN MERGERS AND ACQUISITIONS: THE NATURE OF SYNERGIES AND THE MARKET FOR CORPORATE

CONTROL

BENJAMIN W. BLUNCK School of Economics and Management

Aarhus Universitet Building 322, Bartholins Allé 10

DK-8000 Aarhus C, Denmark Tel: +45 8942 1524

E-mail: bblunck@econ.au.dk

JAIDEEP ANAND Fisher College of Business

Ohio State University 2100 Neil Avenue

Columbus, OH 43210-1144, USA Tel: (614) 247-6851 Fax: (614) 292-7062

E-mail: anand_18@fisher.osu.edu

This version: May 11th, 2009

Acknowledgements The authors are grateful for the commentary provided by Jay Barney, the participants of the Strategy Seminar Series at Fisher College of Business, The Ohio State University, the Management Seminar Series at the School of Economics & Management at the University of Aarhus, and the students in my ‘4089: Mergers & Acquisitions’ Master’s level course, as well as three anonymous reviewers of the 2009 Academy of Management Annual Meeting. Any remaining errors are our own.

Chapter 2

64

ABSTRACT

Our paper extends the existing strategy research on the market for corporate control

by introducing the effect of competitive spillovers to acquisitions on the valuations held by

bidders and the ensuing outcome of the competitive market dynamics. Specifically, we use a

simple model of the market for corporate control to demonstrate conceptually how different

sources of synergies affect the incentive of rival firms to compete for the acquisition value.

When synergies stem from a competitive advantage and are imitable, potential bidders will

bid beyond the value of their synergies to avoid the negative competitive spillovers from a

competitive disadvantage. When acquisitions provide both imitable and inimitable synergies

from competitive advantage, the negative returns from the imitable source of synergies may

even outweigh the positive returns from the inimitable source. Also, when taking into account

the effect of positive spillovers from increased industry profitability on the incentive to bid

and sell in the market for corporate control, the price may exhaust all synergies – leading to

zero acquirer returns. Unless the acquisition of such a target firm offers sufficient inimitable

(or privately known) synergistic value, no shareholder value-maximizing bidder will

rationally choose to acquire the target. In all, the link between synergy and acquirer returns is

critically mediated by the specific nature of the synergies. We also show that the link between

managerial motivations and acquirer returns depends more broadly on the direct and indirect

effects of the acquisition value held by both the winning bidder and rival bidders.

Revisiting the returns to bidders in M&A

65

INTRODUCTION

Mergers and acquisitions (M&A) often provides a necessary tool to reconfigure firm

resources and capabilities to address the opportunities and threats created by changes in the

economics of industries (Capron, Dussauge, & Mitchell, 1998; Lubatkin, 1983; Weston,

2001). Nevertheless, the performance of acquiring firms is generally viewed as disappointing,

since acquirers seem to overpay and on average receive no or little gain (Lubatkin, 1983;

Singh & Montgomery, 1987; Sudarsanam, 2003). The substantial cross-sectional variation in

acquirer performance has lead to a diverse range of theoretical explanations. Most prominent

are theories which argue that acquiring managers are cognitively biased (e.g., suffer from

‘hubris’, as in Hayward & Hambrick, 1997) or self-serving (e.g., Jensen, 1986).

Alternatively, it is well established that the expected returns to acquiring firms are first and

foremost determined by the competitive dynamics of the market for corporate control

(Barney, 1988; Bradley, Desai, & Kim, 1988; Capron & Pistre, 2002; Jensen & Ruback,

1983). The market for corporate control is the market in which firms and divisions are bought

and sold (Manne, 1965). In the presence of several bidding firms, target firms are likely to

appropriate the bulk of the synergistic gains. Only bidders with inimitable assets or private

knowledge of potential synergies can avoid severe price competition (Barney, 1988).

Therefore, the competitive dynamics in the market for corporate control provides a simple,

yet forceful prediction of low or zero returns to a large portion of acquisitions (Capron &

Pistre, 2002).

Our paper extends the existing strategy research on the market for corporate control

by introducing the effect of competitive spillovers to acquisitions on the valuations held by

bidders and the ensuing outcome of the competitive market dynamics. ‘Competitive

spillovers’ refers to the effect of acquisition synergies (or more generally, their strategic

foundation) on the fundamental value of rival firms. Generally speaking, a (rival) firm is

affected by an acquisition if it is competing for the same ‘economic value’ (Brandenburger &

Stuart, 1996). The size and sign of spillovers depend on the specific source(s) of acquisition

value (Chatterjee, 1986). Broadly speaking, spillovers are negative when the acquisition

creates a competitive advantage for the acquirer and a subsequent competitive disadvantage

for the rival firm (Bradley, Desai, & Kim, 1983). Spillovers are positive if the acquisition

increases industry profitability to the benefit of all industry firms (e.g., Porter, 1980).

Competitive spillovers affect the price that bidders are willing to pay for a given target firm

in the sense that a given potential bidder will avoid negative spillovers (or miss out on

Chapter 2

66

positive spillovers) if it succeeds in winning the bid. This change in bidder valuations affects

the competitive dynamics in the market for corporate control and the subsequent expected

returns to acquirer, target and rival firms.

Our paper uses a simple model of the market for corporate control to analyze

conceptually how different sources of synergies lead to different expected outcomes in the

market for corporate control when the valuation effects of competitive spillovers are taken

into account. In doing so, we obtain a series of novel and, at times, counterintuitive

propositions. Firstly, when synergies stem from a competitive advantage and are imitable, all

potential bidders will bid beyond the value of their synergies to avoid the negative

competitive spillovers from a competitive disadvantage. This rational overbidding will lead

to negative returns for the winning bidder. This means that there is a negative relationship

between the extent of synergies from imitable competitive advantages and the returns to

M&A. At very low levels of spillovers, the returns to imitable synergies (in isolation) are

negative, but close to zero; at very high levels of spillovers, such as those implied by a

horizontal acquisition in a concentrated industry, they can be highly negative. Even

inimitable synergies from competitive advantages can be heavily discounted if they create

significant competitive disadvantage for one or a few potential rival bidders. When

acquisitions provide both imitable and inimitable synergies from competitive advantage, the

negative returns from the imitable source of synergies may outweigh the positive returns

from the inimitable source. In short, inimitability is no longer a sufficient condition for

positive returns. However, if a bidder has sustainable private knowledge of the existence of

synergies, i.e., if neither the potential rival bidders nor the target firm know that they exist,

negative competitive spillovers will have no effect on the returns to the acquirer. Private

knowledge as a source of private gains will therefore lead to higher returns than asset

inimitability.

Secondly, when taking into account the effect of positive spillovers from increased

industry profitability on the incentive to bid and sell in the market for corporate control, the

price may exhaust all synergies – leading to zero acquirer returns. This is so because the

target firm does not have any incentive to accept the offer unless it receives the full value of

the positive spillovers it would otherwise gain should it remain independent. A similar ‘free-

rider’ problem can affect the incentive of an individual bidder to bid for a target as it would

be unwilling to pay for economic value which it could receive without acquiring. Therefore,

unless the acquisition of such a target firm offers sufficient inimitable (or privately known)

synergistic value, no shareholder value-maximizing bidder will rationally choose to acquire

Revisiting the returns to bidders in M&A

67

the target. So, the acquisition will not occur despite the existence of significant synergies. In

all, the link between synergy and acquirer returns is critically mediated by the specific nature

of the synergies.

Thirdly, while the managerial motivation of acquirers is often used to explain the

variation in acquirer returns and the zero average return, we show that this link is also

oversimplified when significant competitive spillovers are present. When a high valuation

bidder competes in the market for corporate control against a lower valuation rival who has

additional, managerially motivated incentives to overbid, the high valuation bidder may bid

and win at a price beyond the value of the synergies in order to avoid the negative spillovers

from the rival’s acquisition. Conversely, if the higher valuation bidder faces positive

spillovers from the rival’s acquisition – which occurs if the competitive abilities of the rival

firm are harmed by the merger – the higher valuation bidder may choose to bid less than the

value of synergies, essentially leaving money ‘on the table’. In short, similar to the link

between synergy and acquirer returns, the link between managerial motivations and acquirer

returns depends more broadly on the direct and indirect effects of the acquisition value held

by both the winning bidder and rival bidders.

Our conceptual analysis and propositions extend the strategy perspective on M&A

(Barney, 1988; Capron et al., 1998; Chatterjee, 1986; Singh & Montgomery, 1987) by

delineating the returns which can be expected given the source(s) of synergies in a given

context. It follows that various merger contexts provide not only a different scope for

sustainable value creation from M&A, but also a different scope for value appropriation. In

this sense, our paper presents an even stronger argument than Barney (1988) that the study of

acquisitions is conducted at a too aggregated level and that future research should not

compare the efficacy of acquisitions involving fundamentally different synergy phenomena.

In extension, our propositions cast a new light on existing empirical research. Firstly,

the cross-variation found in empirical studies of the announcement returns to acquisitions

might simply reflect differences in the competitive dynamics of the market for corporate

control. Secondly, the instance of negative returns does not necessarily stem from the

managerial motivations of the acquiring firm. It may instead stem from the effects of the

competitive dynamics involving several bidding firms and/or the indirect effects of the

managerial motivations held by rival bidders. Thirdly, our study suggests that contextual

variables used as determinants of value creation are also likely to be direct or indirect

determinants of value appropriation depending on the specific merger context. Future

Chapter 2

68

research on the determinants of the gains to acquisitions must therefore take into account the

underlying nature of synergies.

Our propositions provide managers with a framework from which to judge the

potential outcome of mergers and acquisitions whether their firm is a potential bidder, target

firm or both. It is clear that firms should understand the acquisition value held by potential

rival bidders and targets in order to judge the effects of announced or proposed mergers

(Barney, 1988). Our analysis further implies that firms should understand more specifically

the source of their synergistic value to uncover the associated competitive impact on firms

within the bidder’s peer set. This true ‘acid test’ of the acquisition decision is one of

simultaneous creation and appropriation. In this regard, our paper asserts that managers of

firms with scarce resources and several distinct acquisition strategies (or alternatives) to

choose from should consider the full implications of the nature of the synergies before

deciding on a strategy.

Finally, our framework and its propositions also offer several insights for the broader

understanding of strategic factor markets and the resource-based perspective (e.g., Barney,

1986). Since the market for corporate control in essence concerns the purchase of a specific

bundle of resources, the principles and insights of our conceptual model also extend to

strategic factor markets more generally. Negative and positive spillovers in other strategic

factor markets may thus lead to similar effects and outcomes, although their impact may not

be as great as in the corporate control setting presented here.

BACKGROUND

Acquirer Returns from Acquisitions

Previous research has established that two conditions underlie positive acquirer

returns to acquisition strategies. Firstly, the acquisition must create synergistic value – i.e.,

the net present value of the cash flows generated by the combined firm assets must exceed

that of the sum of the value of the two independent firms (Bradley et al., 1988; Seth, 1990):

V – (A + T) = S > 0 (1)

V is the value of the merged firm ex-post, A and T are the ex-ante values of the

independent acquiring and target firms, and S is the synergistic value1. The foundation for

increasing shareholder value through M&A is creating economic value by reconfiguring

1 Some of the extant literature defines ’synergies’ purely in the context of value from revenue enhancement. We will equate this term to synergistic value creation irrespective of its strategic foundation.

Revisiting the returns to bidders in M&A

69

assets – profitably exploiting, improving and/or transforming assets in the dyad – to better

meet the demands of the external environment (Barney, 1991; Brandenburger & Stuart, 1996;

Peteraf, 1993). Broadly speaking, profitable reconfigurations imply cost savings, enhance

revenues and/or create growth options (Sudarsanam, 2003). A number of exogenous factors

may limit which firms can merge and the synergies involved, such as the path-dependent

endowment of firm assets (Barney, 1988), indigestibility of some firms or simple infeasibility

due to legal barriers (Reuer, 1999), or the reluctance of self-interested or cognitively biased

managers to relinquish control of their firm (Auster & Sirower, 2002; Jensen, 1993).

Secondly, the acquiring firm must pay the target firm less than the sum of the

cumulative synergistic gains and the stand-alone value of the target, i.e. the price premium P

must be below that of the synergistic value S, producing positive returns R:

R = S – P > 0 ↔ P < S (2)

The deal premium is first and foremost determined by competitive bidding dynamics

implied by factors of supply and demand in the market for corporate control (Aktas, de Bodt,

& Roll, 2009; Jensen & Ruback, 1983).

Acquirer Returns in the Market for Corporate Control

When two or more potential bidders are equally able to recombine assets of the target

firm, a structurally competitive market for corporate control will trade away the potential

synergistic value of a merger to the target firm shareholders, leaving none for the winning

bidder (Barney, 1988; Chatterjee, 1986; Jensen & Ruback, 1983; Lubatkin, 1983). This is

likely to occur when synergistic value is driven by the valuable scarcity of assets residing in a

given target firm and when few (if any) other potential target firms can provide potential

bidders with an alternative (Capron & Pistre, 2002). Even if more than one target firm can

offer the same assets, the result of subsequent acquisitions carried out by other potential

bidders would be materially similar as long as excess demand persists (Barney, 1988).

The potential for acquirer returns then depends on whether a given bidding firm can

create and take advantage of imperfections in the market for corporate control. Barney (1988)

specifies two antecedents of such market failure. Firstly, the assets of a bidder may provide a

valuable and inimitable advantage. More precisely, rival firms do not have the ability to apply

the necessary assets to achieve the same acquisition value from the given target. Either they

do not possess them at the time of the merger, or barriers to imitation internal or external to

the firm prevent them from building, organizing, or acquiring the required type and capacity

of assets at the same cost and in a timely fashion. Secondly, the bidder may have private

Chapter 2

70

knowledge of synergies, implying that rival bidders would not have the knowledge to

compete in the market for corporate control, or alternatively, the market for required

complementary assets (Conner, 1991; Priem & Butler, 2001).2 However, the information

processing role of the market before, during, and after the deal process, along with the

incentives of the target firm to alert rival bidders to the source of synergies, facilitate the

dissemination of information and the weakening of acquirer private knowledge. It is

especially unlikely that private information can be sustained if synergies are imitable

(Barney, 1988).

These two necessary conditions mirror the necessary conditions for abnormal returns

to trade in resource markets in general (Lippman & Rumelt, 2003b). Either of them implies

that competitive bidding dynamics will not amount to the full value of the potential

synergistic value, and hence, the acquirer will realize positive abnormal bidder returns. We

choose to refer to synergistic value which is appropriable by only one bidding firm as

privately appropriable value.

THE NATURE OF SYNERGIES AND COMPETITIVE SPILLOVERS FROM

ACQUISITIONS

Barney notes: “It is not difficult to imagine a set of similar firms pursuing the same

strategy in an industry all becoming interested in a particular type of acquisition to implement

that strategy” (1988: 73). In other words, firms that compete for similar existing economic

value and have similar growth options are likely to conduct similar acquisitions of similar

target firms. And similar target firms are then likely to be among the potential targets. In

essence, this implies that a set of rival firms may experience competitive spillovers to

acquisition (Chatterjee, 1986; Fridolfsson & Stennek, 2005). When an acquirer achieves a

competitive advantage, it may lead to negative spillovers for a rival firm, whereas gains from

increased industry profitability imply that a rival may share in the value, leading to positive

spillovers. We turn to argue when synergies from competitive advantage and increased

industry profitability confer competitive spillovers on rival firms.

2 Asset knowledge advantages evolve from the path-dependent heterogeneity of firms (Denrell, Fang, & Winter, 2003; Dierickx & Cool, 1989) or pure luck (Barney, 1986), but they can also be influenced by their information acquisition strategies and capabilities (Makadok & Barney, 2001), perhaps even specialized in the acquisition selection and acquisition identification capabilities of the firm (Mitchell, Capron, & Anand, 2006).

Revisiting the returns to bidders in M&A

71

Competitive Advantages

Acquisitions create synergies from competitive advantage when the recombination of

the merged assets is inimitable or scarce and enables the firm to appropriate value from rival

firms (Bradley et al., 1983), who then hold a competitive disadvantage and face strategic

liabilities (Arend, 2004). Consequently, it is likely that several rival firms will experience

negative spillovers.

Competitive advantages can drive acquisitions under various strategies. The specific

acquisition strategy determines how the assets are recombined, and hence, the set of firms

from whom the merger appropriates value as well as the extent. If the acquisition were

horizontal, it would imply that the two merging firms transfer their same-industry assets

between them (unilaterally or reciprocally) to appropriate value from product market rivals

(Capron et al., 1998), and perhaps also from firms in customer and supplier industries (Porter,

1980). As an example of such competitive advantages, take the acquisition by PepsiCo of

Quaker Oats in 2000. Both PepsiCo and Quaker Oats were present in the beverages and

snacks industries, implying horizontal synergies in the form of scope economies in the

bottling and distribution activities of the two firms. Furthermore, PepsiCo conceivably

increased its firm-specific bargaining power vis-à-vis supplier and customer firms (Mergers

and Acquisitions, 2001).

Vertical acquisitions may confer negative spillovers on the product market rivals of

the acquirer and the target firm if the asset transfers create competitive advantages for both

firms (Fee & Thomas, 2004). Similarly, related acquisitions may create competitive

advantages across several products markets, potentially conferring negative spillovers on

rivals in all of the related markets (Markides & Williamson, 1994). As an example, note that

PepsiCo planned to achieve related-industry synergies from its acquisition of Quaker Oats by

using umbrella brands in the related markets of beverages and snacks (Mergers and

Acquisitions, 2001).

The spillovers from competitive advantages may go beyond the more immediate,

‘static’ gains to merging firms. Specifically, the option value of future competitive moves

within and between industries can be an important source of acquisition value for a bidder.

Like the more ‘static’ value, this option value may confer negative spillovers on rival firms to

the extent that it reduces the option values held by these firms (Smit & Trigeorgis, 2004). For

example, the option value of PepsiCo further expanding its umbrella brands to Quaker Oats’

Chapter 2

72

main industry, cereals, may reduce the existing option value of other firms planning to

establish brand leadership within that industry.

Increased Industry Profitability

Acquisitions create synergies from increased industry profitability when the

recombination of assets leads to the appropriation of value from nearby industries. In doing

so, the acquirer and the remaining firms within the industry share in the gains from increased

industry profitability, creating a positive externality for all industry firms.

Increased industry profitability is created in a horizontal merger when the

recombination of assets rationalizes excess industry capacity (Dutz, 1989), and/or facilitates

collusion (Eckbo, 1983) or the improvement of bargaining power in the value chain

(Galbraith, 1952). The acquisition of Quaker Oats by PepsiCo increased the concentration of

both the beverages and snacks industries, which may have increased the market power of

these industries vis-à-vis suppliers and buyers. The extent of increased industry profitability

generally depends on the level of concentration within the industry and its ability to sustain it

through entry barriers (Eckbo, 1983; Eckbo, 1985; Stigler, 1964)3, but it also depends on the

concentration of the affected industries in the value chain and their ability to ‘countervail’ the

market power by conducting acquisitions themselves (Fee & Thomas, 2004; Galbraith, 1952).

In principle, the distribution of gains from increased industry profitability across

industry firms may be uneven, depending on the specific industry structure and their source.

In a traditional stylization of increased industry profitability, the distribution of gains from

increased industry profitability may be shared equally among industry firms of similar size

(in the vein of Eckbo, 1983; Porter, 1980), implying that reducing the number of industry

firms through acquisitions will confer equal positive spillovers on the rival bidding firms

within the same industry.

While vertical mergers can also realize market power gains from improving

bargaining power, they are more likely to be specific to the merged firm, creating a

competitive advantage vis-à-vis rival firms (Fee & Thomas, 2004).

3 Depending on the specific source of value, various measures of industry concentration co-determine the size of the value effect. For instance, in the case of collusion on the part of the whole industry or just a coalition of firms, the number of independent producers determines the extent of gains, since fewer producers imply lower costs of monitoring collusion (Eckbo, 1983; Stigler, 1964). Also, the reduction in output achieved by a collusive or market power merger provides higher value the more monopolistic the industry (Eckbo, 1992). When acquisitions lower excess industry capacity, the value effect is likely related to the size of excess industry assets (Dutz, 1989).

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THE NATURE OF SYNERGIES AND THE MARKET FOR CORPORATE

CONTROL

The prospect of competitive spillovers from acquisitions makes up an additional

component of the reservation price of a given market participant. Negative (positive)

spillovers increase (decrease) a bidder’s valuation of a target firm, while negative (positive)

spillovers will decrease (increase) the target’s minimum reserve price, i.e., the lowest price it

will accept given its value as a stand-alone firm. Hence, the synergistic value that a potential

bidder can achieve is no longer the sole determinant of its valuation of a target firm, nor is the

potential target firm’s selling strategy driven purely by the upside potential of a sale.

In all, a given bidder i values the target firm at its stand alone value (T) plus the

synergistic value it can create (Si) and the net value of competitive spillovers (Ci, which is

defined in terms of costs). Expressing the valuation of a given bidder i in terms of the

premium over stand alone value – a term we refer to as the reservation (price) premium

(BRPi) – we have:

BRPi = Si + Ci (3)

We use suffixes 1, 2 etc. to designate the ranking of the potential bidders with regards

to their reservation premia. Ci denotes the total spillovers (in terms of costs) to bidder i of an

acquisition conducted by the highest ranked rival bidder. Expressing the ‘walk away’ price of

the target firm t in terms of the premium over stand alone value – a term we refer to as the

minimum reserve (price) premium (SRPt) – we have:

SRPt = -Ct (4)

Ct denotes the total spillovers (in terms of costs) to the target in question of an

acquisition conducted by the highest ranked bidder.

A Simple Model of the Market for Corporate Control

To take advantage of an opportunity to buy and sell a firm requires a well-functioning

market for corporate control. The ‘rules’ of this market are defined by a variety of regulations

depending on the incorporation, legal status, listing status of the firm and the choice of

takeover vehicle (Sudarsanam, 2003). These factors essentially create the potential for market

imperfections, which may lead to specific bidding and selling strategies on the part of bidders

and targets and affect the outcome of a given acquisition in a non-trivial way. The potential

consequences of these issues for the market for corporate control as well as actual examples

of their impact are discussed in numerous papers (e.g., Bradley et al., 1988; Hirschleifer,

1995) and in the business press.

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It is our objective succinctly to demonstrate the effect of competitive spillovers on the

baseline outcome of the market for corporate control. In this sense, our focus lies on the

importance of the context of synergies, rather than on the bidding strategies set in place by

firms to take advantage of market imperfections and improve the outcome to their favour. We

therefore choose a simple representation of the market for corporate control based on

dimensions of the takeover process highlighted by recent research.

The market mechanism in resource markets (of which the market for corporate control

is one variant) has often been depicted as an implicit or explicit open English auction process

(e.g., Conner, 1991; Makadok, 2001). This is especially true of the market for corporate

control, although most of this attention derives from auction theorists and the economics

literature (see, e.g., Hirschleifer, 1995). The market for corporate control in Barney (1988)

maintains an open English auction process with perfect information and costless bidding.

Essentially, this implies that the winning bidder only has to pay a price which is marginally

above the reservation price of the second bidder. The winning bidder does not have to bid

beyond this to secure the target, since the target passively accepts the outcome of the auction.

However, Lippman & Rumelt (2003b) note that the English auction model is just one of

many potential solution concepts to a resource market game with excess demand for a

resource bundle. We note two key issues in the market for corporate control which do not

easily fit the choice of using a simple auction model as the underlying market mechanism: the

observed heavy use of exclusive negotiations in place of auctions, and the target’s

unwillingness to passively accept the highest auction bid. These issues need to be taken into

account when building a model of the market for corporate control.

Firstly, the takeover process is as often an exclusive negotiation between a bidder and

the target as it is an auction between several bidders. This is true even for acquisitions

involving listed firms (Boone & Mulherin, 2007)4. Thus, the market mechanism does not

always explicitly work as a competitive auction. However, Aktas et al. (2009) find that the

premium paid for a given target in the market for corporate control increases with the ex-ante

bidding competition regardless of whether the takeover process proceeds as an auction or an

exclusive negotiation. So, the market for corporate control is competitive regardless of

whether competition remains implicit as in the case of negotiations or materializes explicitly

4 When considering both the initial, ‘hidden’ and later, public part of the takeover process, Boone & Mulherin (2007) report that roughly half of acquisitions involving US listed firms 1989-1999 imply an auction process and the other half proceed as de facto bilateral negotiations. 39% were auctions initiated by targets, and 46% were negotiations initiated by targets. 11% were auctions initiated by bidders, whereas only 4% were negotiations initiated by bidders. And of the auctions, only 24% led to more than one public bid.

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in the form of a competitive auction. In the case of negotiations, we may think conceptually

of an exclusive (bilateral) negotiation as taking place after an (implicit) auction process has

produced a sufficiently superior bidder5.

Secondly, it is not obvious that a target firm is bound to accept the highest bid which

emerges from an auction. This seems especially true when only one bidder emerges from the

more informal, private part of the takeover (auction) process. In the Boone & Mulherin

(2007) sample of mergers between listed firms this occurs 76% of the time. If there are no

contractual binds to accept the highest bid, the emergence of a single remaining bidder

merely creates a bilateral negotiation situation between this bidder and the target for the

synergistic value which has not yet been competed away by the presence of the other bidders.

Essentially, this value is ‘idiosyncratic bilateral synergy’ (Mahoney & Pandian, 1992) in the

sense that neither firm has any alternative merger partner with which to create the same

value. The target may therefore be able to negotiate a revised, higher bid. Alternatively, the

first public bid may itself already be the result of a negotiation with the target firm.

Consequently, Bruner (2004) writes that the process can be considered a “hybrid” between an

auction and a negotiation. To this effect, Hirschleifer (1995) adds that modelling in auction

theory in general needs to address the potential for bilateral bargaining between the bidder

and the target6.

To implement a simple model which encompasses both auctions and negotiations and

the incentive of the target to negotiate with a single remaining bidder, we choose a notional

two-step model in the vein of Lippman & Rumelt (2003b). This model essentially amounts to

a Nash bargaining game in which the range for bilateral bargaining (~ the ‘core’ of a game) is

determined by the degree of competition in the market for corporate control7. Firstly, we

assume that the competition between multiple bidders initially follows an open English

auction process with no bidding costs. For now, we assume perfect information for all

players. This means that each potential bidder will be willing to bid until the point where the

price rises above its reservation price, or until it is the only bidder left. Notably, since there

5 A rational target firm would, under normal assumptions, never choose to conduct an exclusive negotiation as opposed to a competitive auction, since auctions lead to greater payoffs (Bülow & Klemperer, 1996). However, Boone & Mulherin (2007) argue that negotiations are chosen over auctions when the indirect auctions costs to the target (such as information spillage) outweigh the gains to increasing the number of bidders. Thus, when indirect auction costs are high and/or the target figures that the single bidder is sufficiently superior, it can be fully rational to carry out an exclusive negotiation. 6 Note that the target firm management may forego negotiation of the premium to negotiate private benefits, or to achieve a quick resolution in the case of a ‘fire-sale’, in which the target may simply accept any offer above a minimum reserve price. 7 Specifically, our model is akin to the ‘patent game’ described in Lippman & Rumelt (2003b).

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are neither bidding costs nor any passage of time, this process is of course instantaneous.

Secondly, the remaining synergistic value – the difference between the reservation price of

the second ranked bidder and the reservation price of the remaining bidder – is subject to a

bilateral negotiation between the remaining bidder and the target firm. While the solution to

this negotiation game is ‘formally indeterminate’, any point within the negotiation range is a

potential solution (Lippman & Rumelt, 2003b). Assuming shareholder value-maximizing

managers – and barring any other determinants on the division of gains – the bidder and

target firm are likely to divide the synergies equally according to the Nash bargaining

solution (Lippman & Rumelt, 2003b; Nash, 1953).8

The outcome to our simple Nash bargaining model (assuming no excess supply) is

expressed as follows9:

If BRP1 ≥ BRP2 > SRPt then P = ½(BRP1+BRP2) (5)

If BRP1 > SRPt ≥ BRP2 then P = ½(BRP1+SRPt) (6)

If BRP1 ≤ SRPt then P = Ø (7)

Equation (5) implies that the second highest ranked bidder bids up the price to its

reservation price, and the highest ranked bidder and the target split the remaining value

defined as the differential in the reservation prices of bidders 1 and 2. Equation (6) notes that

the lower bound of the negotiation range becomes the minimum reserve price of the target if

the second ranked bidder does not have a reservation price beyond this. Equation (7) notes a

case where the bidder has a reservation price above or equal to the minimum reserve price of

the target, which implies an ‘empty’ core to the game and a no-acquisition outcome.

We turn to examine how competitive spillovers influence competitive dynamics in the

market for corporate control and consequently, the returns to the acquirer, target and rival

firms. We first analyze the outcome of synergies based on competitive advantage and

increased industry profitability, respectively. We then analyze the outcome in more realistic

settings where multiple sources of synergies co-exist, which significantly affects the

outcomes to synergies in isolation. Finally, we address the effect of the presence of

acquisition value from managerial utility on the identity of the winning bidder and the returns

to acquisitions. In these regards, our objective is to delineate how the nature of acquisition

value – its appropriability and competitive spillovers on rival firms – affects acquisition

decisions and outcomes.

8 In this paper, we choose the realistic assumption of no coalition-building and subsequent side payments among bidders; hence, we do not consider the Shapley (1953) solution discussed by Lippman and Rumelt (2003b). 9 Assuming excess supply would create a symmetrical model in which the targets compete for the acquirer.

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Synergies from Competitive Advantage

The competitive bidding dynamics and outcomes associated with synergistic value

from a given competitive advantage will depend critically on whether other potential bidders

have the asset base to achieve the same synergies, i.e., the degree to which acquisition

synergies are imitable. However, if potential bidders and target firms are unaware of the

existence of synergies, the competitive dynamics will not unfold. We proceed to propose

outcomes associated with competitive advantages of different degrees of imitability and

public knowledge, and offer illustrative numerical examples.

Fully imitable synergies. Consider initially the situation where there is more than

one bidder which can create the same synergies with the same target firm, i.e., S1 = Sj = S for

some bidder(s) j ≠ 1. In this case there is excess demand. This implies that the synergistic

value in the acquisition is driven by the unique value and scarcity of the assets of the target

firm, and the synergies are consequently fully imitable. The competitive advantage achieved

by the winning bidder implies negative competitive spillovers for any remaining bidder j,

which is left with a competitive disadvantage, as some of its assets turn into strategic

liabilities (Arend, 2004). Since the synergistic value is equal across potential bidders, we

assume for simplicity that the competitive spillovers are also equal across firms (C1 = Cj =

C), so BRP1 = BRP2 = S + C. The two or more bidders will bid up the price to the full

acquisition value (Barney, 1988; Capron & Pistre, 2002), i.e., P = ½(BRP1 + BRP2) = ½(S +

C + S + C) = S + C. Trivially, we note:

Proposition 1. When synergistic value from a competitive advantage is fully imitable and publicly known, the returns to the winning bidder (R1 = S – P = –C) will be negative. The returns to rival potential bidders will be equally negative (Rj = –C < 0 for j ≠ 1).

Hence, when the synergies from competitive advantage are fully imitable and involve

negative competitive spillovers, the price will fully exhaust not only the synergies S as noted

in Barney (1988), but also the savings from avoiding the negative competitive synergies C.

This is essentially ‘rational’ overbidding; should a bidder choose to drop out of the bidding

race before the premium reaches the sum of synergistic value and the spillovers to

acquisition, it would stand to lose even more value. The potential for overpayment due to

negative spillovers is also noted by Jehiel & Moldovanu (1996) and Akdogu (2009)10. Note

10 Note that Jehiel & Moldovanu (1996) argue that such a situation where bidders have equal valuations may lead bidder to choose “strategic non-participation” in the bidding process. They would be better off not bidding for the target firm at all. However, this requires that the bidders can coordinate their non-bidding strategy, which

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that the winning bidder does in fact achieve a sustainable competitive advantage in the

product market and will over time experience increased profits (e.g., due to an increase in

market share). Conversely, the losing bidders achieve a corresponding competitive

disadvantage, which implies that their profits will drop over time (e.g., due to a loss in market

share). However, due to the public appropriability of the synergies and the negative

competitive spillovers, the gains to the winning bidder are given away immediately in the

acquisition premium.

To see this, consider a numerical example in which three potential bidders are capable

of achieving the same competitive advantage from the target firm (S1 = S2 = S3 = S = 100),

and assume that each remaining firm j within the industry will experience negative

competitive spillovers equal to Cj = C = 10. Figure 1 illustrates the effect of bidding

competition as the premium P increases, noting the price range in which bidders would bid,

as well as the final price premium. All potential bidders would bid until the premium equals

the sum of synergistic value plus spillovers, i.e., P = S + C = 110. The target firm

appropriates more than the full potential synergistic value; we see how the imitability of

synergies completely removes the scope for any bidders to negotiate bilaterally with the

target. Due to overbidding, both the winning bidder and the remaining industry firms lose

value equal to the value of the spillovers (R1 = R2 = R3 = –10).

------------------------------ Insert Figure 1 about here ------------------------------

The above setting captures the outcome when synergies reside in a single target firm.

When other target firms can provide similar synergies to the same potential bidders, a series

of acquisition will occur, but the rational overbidding outcome will occur for each additional

acquisition as long as excess demand exists. Notice that even though an acquirer in such a

‘me-too’ acquisition may avoid the product market spillovers from rival acquisitions, rational

overbidding in the market for corporate control will also dissipate the full value of this gain.

Therefore, on the announcement of the initial acquisition in the series, the value of the future

me-too acquiring firms would decrease in the same way as the value of non-merging firms.

The bidding war between Tesco and Sainsbury for the Scottish supermarkets group

William Low illustrates how competitive spillovers may force overbidding. Both Sainsbury

and Tesco were looking to increase their market share in Scotland. Tesco made a cash offer

of £154m, Sainsbury fought back with a £210m offer. Finally, Tesco won with a £247m would be highly specific to the structure of the game chosen. Also, it is likely that the potential target will impede the potential for coordination in practice by strategizing the pre-bidding process.

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offer, more than 60% above its own initial offer. Tesco’s share price fell around 2% when it

became clear that it had won the bid (Independent, 1994), suggesting that the stock market

believed it had overpaid.

Fully inimitable synergies. It may also be the case that there is only one target and

one bidder. This would occur if the assets underlying the competitive advantage were both

unique and fully inimitable, or if there were an exogenous constraint on the set of potential

bidders and targets. Without considering spillovers, this would imply a simple two-way split

of the synergistic value according to the Nash bargaining solution, since there are no

alternative bidders. In other words, the expected price would be found at the midpoint

between the bidder reservation price and the target’s walk-away price (which trivially is the

zero premium price), i.e., P = ½(BRP1 – SRPt) = ½(S1 – 0) = ½S1. However, considering that

some rival firms will experience negative competitive spillovers, it becomes clear that each of

these rival firms now has a positive reservation price premium, making them all potential

rival bidders, even though they cannot create any synergies themselves. Negative spillovers

create (implicit or explicit) competition where there earlier was none, shifting the bargaining

power from the bidding firm to the target firm by decreasing the range for bilateral

negotiation, thus increasing the price and lowering the return (R1 = S1 – P = ½(S1 – C2)). The

specific outcome will depend on the specific distribution of synergies and spillovers; if the

negative competitive spillovers are heavily forced on just a few firms, the price could

increase quite significantly. However, if negative competitive spillovers are spread across

many firms, we would not expect the returns of the acquirer to suffer markedly. For example,

if the merger has a synergistic value of S1 = 100 and rival firms j ≠ 1 suffer equally from

bidder 1’s competitive advantage with Cj = 10, their reservation premia are BRPj = Sj + Cj =

0 + 10 and the price would increase from P = 50 to P = 55. In general, we have:

Proposition 2. When synergistic value from a competitive advantage is fully inimitable and publicly known, the returns to the winning bidder (R1 = S1 – P = ½(S1 – C2)) will be positive as long as C2 < S1. The returns to rival bidders will be negative (Rj = –Cj < 0 for j ≠ 1).

Partially inimitable synergies. A more likely scenario than the above rather stylized

cases is the situation where several bidders have synergistic value potential, but the extent of

this potential varies across firms. Such heterogeneity may owe to variation in asset quality, or

the instance of bidders with different sources of competitive advantage. In this case, the

inimitability of bidder 1’s synergies only provides a residual portion of privately appropriable

value. Therefore, the price premium would be bid up to at least the reservation price of the

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second ranked bidder, after which bidder 1 and the target firm would negotiate the division of

the residual value.

As in the case of fully inimitable synergies from competitive advantage, negative

competitive spillovers increase the reservation price of other bidders and force the lower

bound of the negotiation range upwards. Since bidder 1 also faces negative competitive

spillovers if a rival bidder acquirers the target, the upper bound of the negotiation range also

increases. If we did not take into account the effect of negative spillovers, the acquirer would

seem guaranteed to experience positive returns (Barney, 1988). However, both the size and

the sign of acquirer returns depend on the distribution of negative spillovers. The price will

be P = ½(BRP1 + BRP2) = ½(S1 + C1 + S2 + C2). If the bidder with the second highest

reservation price (i.e., the de facto bidder 2) faces negative spillovers such that C2 > S1 – C1 –

S2, the returns to bidder 1 will turn negative. Thus, we note:

Proposition 3. When synergistic value from a competitive advantage is partially inimitable and publicly known, the returns to the winning bidder (R1 = S1 – P = ½(S1 – C1 – S2 – C2)) will be positive as long as C2 < S1 – C1 – S2. The returns to rival bidders will be negative (Rj = –Cj < 0 for j ≠ 1).

To illustrate, assume that bidders 2 and 3 are able to extract some, but not all of the

synergistic value potential of bidder 1 ((S1, S2, S3) = (100, 80, 60)). Without spillovers, the

price premium would be bid up to 80, after which bidder 1 and the target firm would

negotiate the division of the residual value (from S2 = 80 to S1 = 100), implying an expected

price premium of P = 90 and an acquirer return of R1 = 10. Assume now that any given non-

merging firm faces spillovers equal to 10% of the synergistic value achieved by the winning

bidder. So, bidder 3 would bid up to BRP3 = S3 + C3 = 60 + 10 = 70, and bidder 2 would bid

until BRP2 = S2 + C2 = 80 + 10 = 90. The spillovers to bidder 1 are equal to the loss it would

face if bidder 2 won the bid, since bidder 2 is the highest ranked rival bidder. Therefore,

BRP1 = S1 + C1 = 100 + 8 = 108. The outcome would be a negotiation between bidder 1 and

the target in the range between 90 and 108 and the expected price premium would be P = 99,

implying a return of just R1 = 1. Figure 2 illustrates the outcome.

------------------------------ Insert Figure 2 about here ------------------------------

In all, the presence of these negative spillovers has reduced a value gain of 20% of the

premium to just 1%. Clearly, the role of negotiating plays a large part in decreasing the gains

so much; the returns would be less affected if the bidder were able to avoid negotiating over

the full remaining value. However, if S2 > 90, the residual value component would be

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negative, and rational overbidding and subsequent negative returns would occur even without

any subsequent negotiation with the target.

We can conclude that the private appropriability of synergies is not a sufficient

condition to expect abnormal returns; rather, the size of the residual value component must

now also be sufficient to exceed the competitive dynamics driven by the increased bidding

incentive held by all rival bidders on account of negative spillovers. However, note that even

if bidder 1 experiences negative absolute returns, it would still achieve positive relative

returns – and hence, would still wish to acquire – until the price reaches its full acquisition

value, which in the example in figure 2 is BRP1 = 108.

Since the synergistic value resides (at least partially) within inimitable acquirer assets,

it is not unlikely that more than one target firm can cater to the value generating potential of

acquirer assets (Capron & Pistre, 2002). If we were to generalize our simple model to several

targets (while maintaining excess demand), we would have a simultaneous game in which the

many potential bidders could bid for several target firms. Note that a stable equilibrium in

such a game requires that the price of all target firms, k, is the same. As we would expect,

when we increase the number of targets – leaving the bidder synergies unchanged – the price

decreases, improving the gains to acquirers11 and lowering the gains to target firms.12 As is

the case when there is only one target, this price is found within the core of the game, which

in generalized form is bounded at the bottom by the reservation premium of the k+1’th

bidder, and at the top by the reservation premium of the k’th bidder13. Note that this core

implies a k-way bilateral negotiation. If we were to extend the Nash bargaining solution to the

case of k target firms, the price of all acquisitions would be the price at the midpoint of the

core, although this solution concept may be somewhat stretched under these more

complicated circumstances.14 The existence of competitive spillovers will have materially the

11 Although there is still excess demand, the k-1 highest bidders in effect experience a local excess supply. As in the one target case, bidders whose synergistic value is not above the negotiation range may still achieve negative returns in equilibrium on account of the negative spillovers. 12 Note that if we add an extra target firm without keeping bidder synergies constant, we would trivially assume that each acquisition would create less value than it would in isolation, since the competitive advantage gained in one acquisition would (at least partly) dissipate the gains to the other. Whether adding extra targets would improve the gains to a specific bidder overall depends on the number of potential bidders and targets and their distribution of synergies and spillovers. A detailed generalization of our example when there are two potential targets and three potential bidders is available from the authors upon request. 13 To see that the price is bounded at the top, note that the addition of an extra target creates competition among targets to sell to the bidder with highest synergies, which in our example, is bidder 1; if either target firm demanded a price from bidder 1 above the synergistic value of bidder 2, the other target would undercut it. 14 Note that the extended use of Nash bargaining would deviate from the acquisition outcome schema used by Barney (1988) and Conner (1991) in situations with more than one target. Barney states that the “… negotiated price is likely to fall somewhere between the value of targets for firms with the highest value combined cash

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same effect on bidding competition in the several target case as we have described in the one

target case; both the lower and higher bounds of the core move upwards15. When the

synergistic value potential of bidders becomes more homogeneous, i.e., competitive

advantages become more imitable, the equilibrium converges to the outcome of acquisitions

driven by imitable synergies, in which rational overbidding leads all bidders to lose equally

and to the same degree as non-merging rival firms.

Private knowledge. Barney (1988) notes that ex-ante private knowledge can affect

acquisition outcomes by creating additional privately appropriable value. In a deal with

private appropriable value from private knowledge the bidder only has to bid marginally (by

some ε > 0) above the value held by the second ranked bidder to win the bid. In our simple

model, in which there is room for bilateral negotiation with the target firm, private knowledge

can also affect the division of value between the bidder and the target firm. The target cannot

negotiate over value that it is not aware of, allowing the bidder to appropriate almost all of

the privately appropriable value16. Therefore, value subject to private knowledge is likely to

lead to higher returns than an asset advantage can achieve by itself.

The existence of competitive spillovers may amplify these effects of private

knowledge. Firstly, if rival firms are unaware of synergies from competitive advantages, they

will not know to bid up the price to reflect the value of spillovers either. Secondly, the range

of bilateral negotiation between the bidder and the target would not shift upwards because of

the spillovers. In all, when a bidder has full private knowledge of the synergy potential of a

competitive advantage, the competitive spillovers will have no effect on the outcome at all.17

Proposition 4. When synergistic value from a competitive advantage is privately known, the returns to the winning bidder will amount to almost the full synergistic value and will not be affected by competitive spillovers (R1 = S1 – P = S1 – ε). The returns to rival bidders will be negative (Rj = –Cj < 0 for j ≠ 1).

flows and the value of targets for other bidding firms” (1988: 76). Conner (1991) states that the price will be equal to the value of the target to the ‘marginal bidder’, i.e., the k+1’th bidder. 15 In our example in figure 2, if there were two target firms, i.e., k = 2, the core without spillovers would lie between the reservation premia of bidder 2 and 3, while it would rise to between 70 and 90 when spillovers are present. The premium would be P = ½((80 + 10) + (60 + 10)) = 80 as opposed to 70. 16 Note that a similar effect will occur if the target is unaware that the bidder has no alternative target firms. 17 Inspired by the auction theory literature, Makadok (2001) follows a different conceptual perspective than Barney (1988) on private knowledge in which it is private information concerning the precision of the signal(s) received about resource value. In his paper, signals of value follow a normal distribution, and private information is operationalized as a lower signal variance. Improved information allows the bidder is to make a more informed decision of when to bid higher for a more valuable resource or lower for a less valuable resource. Adding negative competitive spillovers to his model would imply creating a separate component of value for spillovers, which together with synergistic value could be assumed to follow a bivariate normal distribution. Private information of synergistic value and spillovers would then trivially imply as before that the bidder can make a more informed decision, bidding higher or lower as warranted.

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Observe the outcome of the example depicted in figure 2 if the residual value

component was privately known. Regardless of whether competitive spillovers were present

or not, bidder 2 would bid BRP2 = 80 at the most, after which bidder 1 would merely have to

bid marginally more to win the bid (P = 80 + ε).

Synergies from Increased Industry Profitability

If a bidder can only achieve synergies from an increase in industry profitability, then

all potential rival bidders face positive spillovers. Therefore, no potential bidder has the

incentive to bid beyond the stand alone value of the target, since it would gain positive

spillovers equivalent to the value of its synergies if another bidder acquired the target. Only

when synergies are fully inimitable would a bidder have an incentive to pay for the synergies.

We would expect these synergies (e.g, synergies from removing excess industry capacity) to

be fully imitable unless regulatory issues or the like lead to the infeasibility of certain

mergers.

Since these types of synergies are likely to be generic in nature, several potential

targets may exist. The existence of several potential targets implies that each potential target

firm will agree to a deal only if the price reflects the full value of its positive spillovers, i.e., P

= –Ct (which is positive as C is defined in costs). If the price is lower than this, the target

would receive more if it remained independent. Due to this price pressure, the bidder will

then achieve small, zero or negative returns depending on the size of the target’s positive

spillovers compared to its own share of the increase in industry profitability (R1 = S1 – P =

½(S1 + Ct)), while each rival firm j ≠ 1 would achieve its share of the increase in industry

profitability.

This lack of bidding and selling incentive of potential bidders and targets,

respectively, essentially constitutes a two-sided ‘free-rider’ problem. As a result, when there

are several potential bidders and several potential targets, the core of the game is empty and

there is no Nash bargaining solution (P = Ø). This result remains true whether there are an

equal number of bidders and targets, or more potential bidders than targets, or vice-versa.

Under these circumstances, acquisition can only ever be optimal if we were to allow side

payments between firms.

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Proposition 5. When synergistic value from increased industry profitability is publicly known and A) the value is imitable, no potential bidder will acquire. B) there is more than one potential target firm, the returns to acquisition will diminish significantly (R1 = S1 – P = ½(S1 + Ct)), turning non-positive if –Ct ≥ S1. The returns to rival bidders will be positive (Rj = –Cj > 0 for j ≠ 1).18

To exemplify how this proposition works, assume for simplicity that three potential

bidders can achieve the same synergies from increased industry profitability (S1 = S2 = S3 =

100) from more than one target firm and that all industry firms j (including all potential target

firms) share the industry gains equally (Cj = C1 = C2 = C3 = Ct = –100), but no other

synergies exist. Figure 3 shows that the price range in which potential bidders would bid, and

the price range in which the potential targets would accept a bid, do not overlap; the

minimum reservation premium of a potential target is SRPt = –Ct = 100, while the

reservation premium of the potential bidders is BRPi = Si + Ci = 100 – 100 = 0. In this case,

there would be no acquisition. Note that if there were just one potential bidder, it would be

indifferent between acquiring a target at the required price (P = –Ct = 100) and not acquiring,

since the returns would be zero either way.

------------------------------ Insert Figure 3 about here ------------------------------

Note that if synergies for an exogenous reason (such as regulatory infeasibility) are

not imitable by other bidders, i.e., there is only one possible pairing of bidder and target

firms, the free-riding problems disappear and the acquisition would go through and reach the

outcome which we would expect prior to taking the positive spillovers into consideration.

Specifically, the positive spillovers become zero, and a bilateral negotiation of the synergistic

value would ensue, leading to an expected price premium and return of P = R1 = 50.

Therefore, the effect of the free-rider problem on the incentive to sell leads to the counter-

intuitive result that if there were more than one target firm available to a sole bidder, the

returns would disappear as opposed to increasing. Similarly, the free-problem on the

incentive to bid means that if there is more than one bidder available to a single target, no one

would want to acquire it. In all, taking into account the positive spillovers from increased

industry profitability completely alters the workings of excess supply or demand.

18 Note that when there is more than one potential target firm, a sole bidder will acquire only if S1 – CT > 0 for some target T.

Revisiting the returns to bidders in M&A

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The Co-Existence of Multiple Sources of Acquisition Value

Consistent with corporate stories in the business press as well as empirical research

(e.g., Capron et al., 1998), our analysis of the different sources of acquisition value implies

that the recombination of assets will likely consist of multiple sources of acquisition value.

This means that co-existing, yet distinct, value effects will make up the acquisition returns.

As an example of co-existing sources of synergies, we have noted how the acquisition of

Quaker Oats would likely offer synergies from increased industry profitability to all industry

participants, while also giving PepsiCo competitive advantages from cost savings and

revenue enhancements.

A common misperception remains that a higher synergy potential in general translates

to increased acquirer returns. Barney (1988) argues that this will hold only for privately

appropriable value components. We will demonstrate that adding imitable synergies will in

fact encroach on existing acquirer returns from privately appropriable value, while adding

synergies from increased industry profitability may cause a bidder to walk away from

otherwise appropriable synergies. Similarly, previous literature has linked the managerial

incentives of acquirers to negative or low acquirer returns. We will demonstrate that this is

not necessarily so.

Inimitable and imitable synergies from competitive advantages. When we add

synergies from a publicly available competitive advantage (Sna) to synergies from a privately

appropriable competitive advantage (Sca), the overbidding for the former will encroach on the

return to the private value component in proportion to the negative spillovers associated with

the imitable competitive advantage (Cna). However, the ‘relative’ returns remain positive, and

unless the firm has a more profitable non-acquisition response strategy, it will still carry out

the acquisition.

Imagine a proposed vertical merger in which bidder 1 wishes to acquire a lone target

firm to capitalize on a fully inimitable competitive advantage to the value of Sca = 20, which

would in turn confer negative spillovers, Cca = 2, on each of its industry competitors. This

uniquely valuable recombination of assets could be due to an interfirm complementarity in

technological capabilities. In itself, this synergistic value would lead to a bilateral negotiation

of the price range between 2 and 20 with an expected price premium and return of P = 11, R1

= 9.

Now imagine that the recombining of the assets of the target firm with one of many

potential bidders would simultaneously create a simple cost advantage (for instance, through

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the lowering of transaction costs) to the tune of Sna = 80 with Cna = 8. The reservation

premium of bidder 1 would increase to BRP1 = S1 + C1 = Sca + Sna + Cna = 20 + 80 + 8 =

108 and the rival bidders j ≠ 1 would now have corresponding reservation premia of BRPj =

Sj + Cj = Sna + (Cca + Cna) = 80 + 2 + 8 = 90. The price premium is P = ½(BRP1 + BRP2) =

½(Sca + 2Sna + 2Cna + Cca) = 99 and the return to bidder 1 is R1 = S1 – P = ½(Sca – Cca –

2Cna) = 1. This dual deal rationale brings us back to the situation in figure 3.19 We note:

Proposition 6. When imitable synergistic value from a competitive advantage is added to inimitable synergistic value (and both are publicly known), the returns to the winning bidder (R1 = S1 – P = ½(Sca – Cca – 2Cna)) will decrease in proportion to the size of the spillovers from the imitable synergies. When spillovers are sufficiently large (Cca + 2Cna > Sca), the returns will be negative. The return to rivals will be negative (Rj = –(Cca + Cna) < 0 for j ≠ 1).

Notice how the return to privately appropriable value is lowered (in our example,

from 9 to 1) on account of the co-existence of publicly appropriable value; specifically, the

existence of publicly appropriable value with negative competitive spillovers. It seems likely

that many vertical – but especially horizontal – mergers would offer such generic advantages,

although the relative size of the two value components would depend on the strategic industry

factors (Amit & Schoemaker, 1993; Arend, 2004). In general, we can conclude that the

potential for realizing abnormal returns depends on not only the asset advantage of the

acquiring firm in relation to the specific source of synergistic value, but also on the more

generic acquirer, target and industry assets and how acquisition would alter them.

As an example, in the battle for Quaker Oats, both PepsiCo and Coca-Cola arguably

had potential synergies from an imitable cost advantage. But PepsiCo expected additional,

inimitable synergies from redeploying its umbrella brands from beverages within Quaker

Oats’ cereals business (Mergers and Acquisitions, 2001), making it the superior bidder.

However, in the three days surrounding the announcement of the deal, PepsiCo’s market

value fell more than 2%, suggesting that PepsiCo had overpaid. It is possible that the

competitive spillovers relating to the imitable cost advantage lead to a price pressure which

went beyond the value of PepsiCo’s combined synergies.

19 If bidder private knowledge had been the foundation of private appropriability, the return would have been R1

PI = 20 – ε without the publicly appropriable value (there would be no competition from rival bidders and no bilateral negotiation), and R1

PI = 12 – ε with the publicly appropriable value (since the full synergies would be bid up to P = 88 + ε).

Revisiting the returns to bidders in M&A

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Imitable, generic synergies from increased industry profitability and inimitable

synergies from competitive advantages. As we have shown under certain assumptions,

imitable, generic synergies driven purely by increased industry profitability – such as those

provided by the removal of excess industry capacity – will not in themselves lead to

acquisition. Conversely, a lone, privately appropriable value component will always make

acquiring optimal. However, when these two effects co-exist, this is no longer the case; the

private value component must have a sufficient size to make acquisition optimal, because an

increase in imitable, generic synergies from increased industry profitability drives the

reservation prices of the bidder and the target firm apart. If this value becomes too large

relative to the privately appropriable value, the bidder will choose to ‘free-ride’ in spite of the

potentially large private gains to acquisition. Note that the decision to acquire does not

depend on how much value the bidder can appropriate from the privately appropriable value

component – it depends only on the relative size of the value created by this component.

Assume that a number of potential bidders can conduct an acquisition of a few

potential target firms and in doing so achieve synergies from increased industry profitability

(Smp,j). Assuming that gains to increased industry profitability are distributed depending on

the size of a given industry firm, each potential bidder will experience the same gains

regardless of whether the target is acquired by the potential bidder itself or by one of its

rivals, i.e., there are positive spillovers to the increase in industry profitability such that Cmp,j

= Smp,j. In addition, assume that bidder 1 can create inimitable synergistic value from a

competitive advantage (Sca) by acquiring one specific industry firm, target t. Such an

acquisition would confer a loss of Cca,j on each of the remaining rival industry firms j ≠ 1.

Assuming that bidder 1’s acquisition goes through, the target’s minimum reserve price is the

sum of its positive spillovers from increased industry profitability and the negative spillovers

from bidder 1’s competitive advantage, i.e. SRPt = –Ct = –(–Smp,t + Cca,t). The reservation

premium of bidder 1 is the sum of the synergistic value from increased industry profitability

and competitive advantage and the positive spillovers from increased industry profitability,

i.e. BRP1 = S1 + C1 = Smp,1 + Sca – Smp,1 = Sca. Generally, as long as SRPt > BRP1 => Sca <

Smp,t – Cca,t, there can be no acquisition on account of the double-sided free-rider problem.

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Proposition 7. When synergistic value from an inimitable competitive advantage co-exists with imitable, generic synergies from increased industry profitability, a bidder will bid only if the value of the former is sufficiently large to overcome the net spillovers conferred on the target firm (Sca > Smp,t – Cca,t). If it does acquire, the returns will be positive and equal to the returns to the synergies from the inimitable competitive advantage in isolation, while the return to industry rivals (Rj = –Cj = Smp,j – Cca,j for j ≠ 1) is positive if Smp > Cca.

As an example, if the negative spillovers from the inimitable competitive advantage

affect all N industry firms equally such that Cj = Cca = Sca/N for j ≠ 1, and we assume that Smp

= 20 and N = 10, then only when Sca ≥ 18.18 will a bidder have the incentive to acquire a

target firm. Even though there is excess supply for the entire synergistic value, the free-rider

problem still removes the incentive to acquire.

The Presence of Acquisition Value from Managerial Value

In agency theory, acquisitions can increase managerial value by way of risk

diversification (Amihud & Lev, 1981; Lane, Cannella, & Lubatkin, 1998), ‘empire building’

(Mueller, 1969) and job protection (Gorton, Kahl, & Rosen, 2005) among others. A

managerially motivated acquirer may therefore win the bid at a premium beyond the

synergistic value, and consequently earns negative or low abnormal returns (Jensen, 1986). In

our simple model, this can occur in two separate ways. Firstly, the manager of the superior

bidding firm may have a private interest in acquiring a given target (implying a managerial

value component, M1), and this will increase the upper bound of the negotiation range,

leading to a higher price20. Secondly, the manager of a less superior bidding firm may have a

private interest that causes it to outbid the superior rival bidder and acquire the target firm at

an inflated price. In the case of Quaker Oats, Coca Cola almost ended up as a bidder in the

second category. It surpassed the initial bid offered by the superior bidder (PepsiCo), but its

own board eventually overthrew the offer (Mergers and Acquisitions, 2001).

However, in the presence of competitive spillovers to acquisition, the often-assumed

link between the extent of managerial motivations and the size of the acquisition premium

and the abnormal returns can be broken. We offer two novel ways in which acquisition value

from managerial utility affects competitive dynamics and the resulting outcome. Firstly, the

highest ranked bidder may be pushed to rationally overpay due to the managerial motivation

of a lower ranked bidder to bid beyond its synergistic value. Secondly, higher ranked bidders

20 Note that acquisition value from managerial utility is only guaranteed to lead to this outcome if there is one target firm available. If there is more than one target firm, there will be either general or local excess supply, in which case the managerial utility of a superior bidder will not affect the price.

Revisiting the returns to bidders in M&A

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may forego acquiring the target if they stand to gain from a competitive disadvantage

achieved by a managerially motivated, lower ranked bidder.

Lower ranked bidders motivated by managerial value. If a lower ranked bidder

has a managerial motivation to bid up the price, the spillovers to acquisition may force the

synergistically motivated, highest ranked bidder to overpay. Imagine synergistic value of S1 =

100 (with Cj = 10 if bidder 1 should win the bid), and a managerially motivated bidder with

S2 = 80 (with Cj = 8 if bidder 2 should win the bid) and M2 = x, so BRP2 = S2 + M2 + C2 =

80 + x + 10. With no managerial value (x = 0), the premium is bilaterally negotiated in the

price range of 90 to 108, i.e. P = 99, R1 = 1. However, when x > 2, bidder 1 will end up

paying more than the value of synergies to win the target firm. Therefore, managerial value

on the part of a rival bidder can change a healthy acquirer return to a smaller or even negative

acquirer return. Generally, we have:

Proposition 8. When a less superior bidder j adds managerially motivated acquisition value, Mj, such that its reservation price is A) higher than that of bidder 2, but lower than the synergistic value held by bidder 1 (S2 + C2 – (Sj + Cj) < Mj < S1 + C1 – (Sj + Cj)), bidder 1 still wins the bid, but the returns to the winning bidder will decrease by ½(Mj + Sj + Cj – (S2 – C2)). B) higher than that of bidder 2 and the synergistic value held by bidder 1, but lower than bidder 1’s reservation price (S1 – (Sj + Cj) < Mj < S1 + C1 – (Sj + Cj)), the returns will be negative.

Note that we have now argued two separate antecedents of a materially similar

outcome; both the existence of a managerially motivated rival bidder and the existence of

imitable synergies can drive rational overbidding. Therefore, we cannot trivially link

observed overpayment to either the likelihood of a managerial motivated rival bidder, or the

imitable nature of synergies. Furthermore, it is also clear that we cannot even know whether

it is the spillovers to acquisition or a similar managerial motivation on the part of the winning

bidder, which leads it to overpay.

Lower ranked bidders with negative synergies from competitive disadvantage. A

second novel outcome occurs when we realize that a managerially motivated acquisition may

in fact give rise to negative synergies as the result of the poor fit between the bidder and

target firms (i.e., a bidder competitive disadvantage). This may confer positive competitive

spillovers on rival bidders, which will lower their reservation premia. If these competitive

spillovers are high enough, a superior bidder may choose not to acquire the target firm even

though the resulting premium may be below its synergistic value.

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As an example, imagine a setting in which three bidders within the same industry

contest a target firm. Bidders 1 and 2 have similar growth options to expand their operations

to a related industry, whereas bidder 3 does not have the requisite asset base. The target firm

offers complementary assets which can increase the value of these growth options for bidder

1 (S1 = 100). However, bidder 2 does not have the assets to acquire this specific target firm

and faces a competitive disadvantage (due to a loss in option value) if bidder 1 wins the bid

(S2 = 0, C2 = 20). Since bidder 3 has neither the necessary growth options nor the necessary

assets, it has neither positive synergies, nor any negative spillovers to face (C3 = 0). In fact, if

it were to acquire the target firm, it would be a poor asset fit and lead to a competitive

disadvantage within its home industry (S3 = –100). However, we endow the managers of

bidder 3 with the incentive and opportunity to increase the size of their firm regardless. The

managers therefore argue that the acquisition is ‘strategic’ in the sense that bidder 1 must be

stopped from securing a competitive advantage vis-à-vis their firm, although we know that

bidder 3 will in fact not be affected. If bidder 3 wins the bid, the managers achieve

managerial value (M3 = 200), but it decreases the value of the firm and passes on positive

spillovers to its industry rivals (Cj = –20 for j ≠ 3). Without the managerially motivated

bidder 3, the price would be P = ½*(100 + 20) = 60, implying that bidder 1 would make a

return of R1 = 100 – 60 = 40, bidder 2 would lose value (R2 = –20), and bidder 3 would

sustain its status quo (R3 = 0).

When we add the managerially motivated bid from bidder 3, the reservation premia

change as the expected winner changes. The reservation premia of bidder 1 and 3 are then

respectively BRP1 = S1 + C1 = 100 – 20 = 80 and BRP3 = M3 + S3 + C3 = 200 – 100 + 0 =

100. See how bidder 1 would bid 20% below its full synergistic value because of the gain it

would achieve if bidder 3 fulfils its managerial motivations. Note also that bidder 2 now has a

negative reservation premium (BRP2 = S2 + C2 = –20), since it also stands to gain from

bidder 3’s competitive disadvantage. Bidder 3 is now the highest ranked bidder, and the price

will be P = 90. The returns will be R1 = R2 = 20 and R3 = –190. Despite the positive return to

bidder 1 – who would have been the acquirer had bidder 3 not been managerially motivated –

it will seem to outside observers that bidder 1 has walked away leaving money on the table.

However, we know that this is because of the positive spillovers conferred on bidder 1 by

bidder 3’s competitive disadvantage. Notably, despite experiencing positive spillovers, bidder

1 is worse off in this alternative equilibrium; the advent of the managerially motivated bidder

3 has lowered its returns from 40 to 20. Figure 4 shows the outcome of this example.

Revisiting the returns to bidders in M&A

91

------------------------------ Insert Figure 4 about here ------------------------------

Thus, we can conclude generally:

Proposition 9. When a less superior bidder j has managerially motivated acquisition value, Mj, and bidder 1 has sufficiently positive spillovers to the acquisition carried out by bidder j (C1 < (Mj + Sj + Cj) – S1), bidder 1 will choose not to acquire even though the price will be below its synergistic value. It will receive positive returns, as will the industry rivals (Ri = –Ci > 0 for i ≠ j).

As a practical example of the effect of positive synergies on rival bidders, note the

case of the proposed acquisition of hearing aid maker GN ReSound by its rival Phonak.

Although their mutual rivals Siemens and William Demant had a larger market share then

Phonak – arguably making them superior bidders – they chose not to bid beyond Phonak’s

offer of $2.65bn (HearingReview, 2006). Phonak’s share price dropped on the announcement

of the acquisition, while William Demant’s share price increased (Børsen, 2006; Forbes,

2006). William Demant CEO Niels Jacobsen argued that the eroding of the value of the

Resound and Phonak brands was the cause of the increase in William Demant’s market value

(Børsen, 2006).

The novel outcomes of propositions 8 and 9 show how managerial utility can affect

bidding behaviour in non-trivial ways when we recognize the existence of competitive

spillovers. In all, the outcome of a given acquisition is highly dependent on the nature of

acquisition value – both synergistically and managerially motivated – held by the set of

potential bidders.

CONCLUSION AND DISCUSSION

Our conceptual model of the market for corporate control infuses the incentive to

compete for synergistic acquisition value with the corresponding competitive spillovers. The

result is an expanded framework from which to judge the competition dynamics and the

subsequent acquisition premium and returns associated with a specific transaction.

To conceptualize the value of these spillovers – and synergistic value in general – we

have drawn on the frameworks of the resource-based view, industrial organization and related

theories of strategic investment. Our application of the resource-based view is based on an

exogenously determined resource value, and an appropriation scheme centred on relative

resource scarcity and imitability, both in kind to Barney (1988) and Conner (1991). Our

intuitive merger equilibria essentially derive from the cooperative game theory approach of

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Lippman & Rumelt (2003b), in which the creation of relatively scarce and inimitable

resource complementarities leads the acquirer and target to negotiate over acquisition value

which would otherwise be fully priced in the market.

Our theoretical revision reveals several stylized results to inform theory on the

outcome of acquisitions. Some of these run counter to previous explicit or implicit

expectations of the link between acquisition value and performance. In general, taking into

consideration spillovers to acquisition implies that while the presence of synergies increases

value creation, only privately appropriable synergies from competitive advantages can lead to

acquirer value appropriation.

Firstly, synergies from competitive advantages which are imitable by other firms will

lead to rational overbidding. This may even be the case for competitive advantages stemming

from the existing asset base as well as firm growth options, as both encroach on the value of

rival firms. When these imitable synergies co-exist with private synergies, they will create

value, but decrease acquirer value appropriation. In this sense, acquisition returns are not just

a function of the inimitability of acquirer assets, but also a function of the more generic

acquirer assets which offer (potentially incidental) imitable synergies.

Secondly, while previous research has noted that imitable increased industry

profitability shared by industry firms is not a sufficient motivation for one firm to conduct a

horizontal acquisition, we show that the implications of positive spillovers on competitive

dynamics exacerbate this free-rider problem. When increased industry profitability is imitable

and co-exists with privately appropriable synergies, it will either offer no additional acquirer

returns, or it will deter acquisition, depending on the relative size of the two value

components.

Thirdly, the existence of acquirer private knowledge makes a substantial difference,

and in general, the advantage of private knowledge over the inimitability of assets as a driver

of value appropriation increases with the presence of spillovers. In all, these conclusions

should not be lost on future research.

Finally, note some of the potential changes in outcome that may occur when a bidder

has a co-existing managerially motivated value component. We have argued that bidders who

have both managerially motivated acquisition value and potential synergies from competitive

advantages may force other bidders with private synergies to rationally overbid for their

acquisition value on account of the negative spillovers from the competitive advantage. Also,

positive spillovers from a competitive disadvantage brought on by managerial motivations

Revisiting the returns to bidders in M&A

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may lead potential bidders with private synergies to underbid and hence, potentially choose

not to acquire a target firm.

Our study contributes to the M&A literature by increasing the understanding of how

bidders appropriate value from M&A by delineating the conditions under which bidders and

targets compete for value in the market for corporate control. Surveys and meta-studies of

M&A research have consistently concluded that there is insufficient research on the concept

of synergy (Datta, Pinches, & Narayanan, 1992; King, Dalton, Daily, & Covin, 2004;

Trautwein, 1990). Singh & Montgomery (1987) noted that the link between synergies and

acquirer performance was “underspecified”. Seminal work by Barney (1988) identified the

conceptual link between the existence of uniquely valuable synergies in M&A and bidding

competition in the market for corporate control. While this link remains true, our work is the

first that comprehensively models the impact of specific sources of synergistic value on the

incentives of firms to compete in the market for corporate control.

Future theoretical and empirical research on M&A cannot deny the implications of the

revised endogenous relationship between the nature of synergistic value and the competitive

dynamics in the market for corporate control. Specifically, addressing the outcome of a given

acquisition in most contexts requires taking into consideration the competitive dynamics

associated with the specific source(s) of the acquisition value. In this sense, our paper

presents an even stronger case than Barney (1988) for arguing that the study of acquisitions is

conducted at a too aggregated level and that future research should not compare the efficacy

of acquisitions and acquisition decision-making involving fundamentally different acquisition

phenomena.

Research on accounting based and stock based return measures to M&A has often

concluded that acquisition gains are small or non-existent (Anand & Singh, 1997; Andrade,

Mitchell, & Stafford, 2001; Bradley et al., 1988; Jensen & Ruback, 1983; Singh &

Montgomery, 1987), and that they display significant cross-sectional dispersion (e.g.,

Moeller, Schlingemann, & Stulz, 2005). Existing explanations of these mixed results range

from the theoretical and/or empirical inadequacy of the synergy (~’relatedness’) construct

(e.g., Seth, 1990), to the widespread hypotheses that low or negative acquirer returns are

driven by the objectives of self-interested managers (e.g., Jensen, 1986; Mueller, 1969;

Shleifer & Vishny, 1989) and/or misperceptions of acquiring firm managers, such as

‘managerial hubris’ (Hayward & Hambrick, 1997; Roll, 1986). Organizational perspectives

often attribute poor ex-post realization of synergies to poor organizational integration of the

merging firms (e.g., Larsson & Finkelstein, 1999). However, we argue that these results may

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be due to variation across deals in the source of synergies and the associated competitive

bidding dynamics.

As shown in table 1, our framework provides a general typology linking the

performance of acquiring, target and rival firms to the main source of synergistic value.

----------------------------- Insert Table 1 about here -----------------------------

Although these predictions and interpretations are yet to be tested or applied, a few

papers offer exploratory event study evidence which implies that a portion of negative returns

are due to rational overbidding as opposed to value destruction. Akdogu (2009) reports

summary evidence consistent with a significant influence of forced overpayment in

horizontal mergers during times of heavy industry merger activity. In addition, Akdogu finds

a positive relationship between the size of acquirer returns and the size of rival returns,

especially when acquirer returns are negative, implying that overpayment occurs when there

are high negative spillovers. However, this evidence does not distinguish between a

synergistic motivation to overpay and a managerial motivation to overpay which is shared

among potential industry bidders. The inability in event studies to adequately separate the

intertwined effects of economic consequences and the signalling of future consequences

further clouds the interpretation of this and similar studies.

A crucial point of ours – the co-existence of different value components – obviously

makes it difficult to pinpoint the full palette of motivations of a given acquisition, let alone

individual components. In addition, numerous confounding effects exist in both accounting

performance and event study returns methodology (e.g., Fridolfsson & Stennek, 2005), and

any empirical work attempting to utilize our typology for empirical prediction should take

these into account.

Our analysis has also identified new or often disregarded roles of contextual variables.

Specifically, variables often cited as determinants of value creation are also likely to be direct

or indirect determinants of value appropriation, implying a two-dimensional and often

contextually sensitive role in acquisition performance. For instance, in a horizontal merger,

the concentration of a given industry determines not only the strength of positive increased

industry profitability gains to acquirers and its industry rivals (Eckbo, 1985); it also

influences the bidding competition driven by the negative spillovers from any co-existing

synergies from a competitive advantage. Eckbo (1985) and Eckbo, Maksimovic, & Phillips

(1990) find a significant, negative relationship between industry concentration and the returns

to acquirers and industry rivals from horizontal mergers. In our framework, this does not

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95

preclude that increased industry profitability gains from market power exist, but instead

suggests that any such gains are overwhelmed by the negative direct and indirect effects of an

imitable, acquirer competitive advantage.

Note that our conceptual model also adds to our understanding of the micro-

foundations of strategy analysis (e.g., Lippman & Rumelt, 2003a, 2003b) in that its derived

insights are applicable to other resource investment settings. Specifically, if a given

investment provides a firm with a competitive advantage, the spillovers associated with this

investment may increase prices in strategic factor markets (in the case of negative spillovers)

and/or decrease the incentive to invest in strategic factors (in the case of positive spillovers).

Consequently, negative and positive spillovers may in their extremes also lead to rational

overbidding and the free-rider problem, respectively, in strategic factor markets, although

their relevance may be less forceful than in the corporate control setting analyzed here.

Prescriptive Implications

Our framework and stylized results provide managers with a foundation for

considering the potential outcome of mergers and acquisitions whether they are a potential

bidder, target firm or both. In doing so, it echoes the implications of Barney (1988) that firms

should understand the acquisition value held by potential rival bidders and targets to judge

the effects of announced or proposed mergers. However, our analysis implies further that

firms should understand more specifically the source of their synergistic value to uncover the

associated competitive impact on firms within the bidder’s peer set. Depending on the source

of synergistic value, the returns to acquisition – and even the choice to acquire – may vary.

Managers should be especially wary not to pursue synergies from increased industry

profitability, and they should realize that the bidding affects of imitable synergies from more

generic advantages would eat into privately appropriable returns.

Prior to beginning an acquisition strategy, managers must not mistake value which

(all else equal) is up for ‘grabs’ in bilateral merger negotiations with value that is bound to be

bid away by market competition and/or target incentives, especially when there are heavy

competitive spillovers. Similarly, managers should realize the costs or gains of not becoming

a target firm when they themselves do not have the assets to be an acquirer. In this sense, our

framework proposes that a firm can – but sometimes, must – respond to synergistic

opportunities or preceding mergers that have changed the value dynamics of the firm and its

industry. Staying small or staying independent is not necessarily always an economically

viable option.

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Our analysis of different sources of synergies implies that managers should not

necessarily view negative acquirer returns as value destruction, nor should they necessarily

jump at the chance of positive synergies. This becomes especially true when managers expect

similar (or worse) outcomes in substitute markets, i.e. alternative markets resources. Because

while previous research has identified several conditions under which the alternatives to

acquisitions cannot provide the same value creation (Capron et al., 1998; Dyer, Kale, &

Singh, 2004; Villalonga & McGahan, 2005), the ‘acid test’ of acquisition is one of

simultaneous creation and appropriation.

In all, we can revise the conclusion in Barney (1988) that managers should only

consider acquisition strategies in cases where the market for corporate control is imperfectly

competitive. Instead, we would argue that a firm with scarce resources and several distinct

acquisition strategies (or alternatives) to choose from should consider the nature of the

synergies before deciding on one strategy over the other.

Limitations and Future Research

While our framework has dealt with the effect of the nature of synergistic value on the

availability of target firms, we have not explicitly treated the constraints imposed by self-

interested target firm managers and shareholders on the incentive of target firms to sell.

Target managers and shareholders may take steps to avoid a sale or change the terms of sale

to their own benefit (Moeller, 2005). In our simple framework, such steps would increase the

de facto reservation price of the target firm and/or affect the outcome of the bilateral

negotiation. Both existing academic research, and stories in the business press, support the

need for a deeper understanding of how these motivations interact with the motivations and

mechanisms which we have treated here.

It is clearly contentious whether our simple model can go further and provide a useful

simplification of the outcome to a multi-target, multi-bidder setting, especially since

sequences of acquisitions are likely to imply timing dynamics. Existing work in industrial

organization adopts a variety of assumptions regarding the non-cooperative/cooperative

nature of the game between sequential bidders and the industry economic model in its

attempts to disentangle how and when sequences of acquisitions occur (e.g., Gowrisankaran,

1999; Gowrisankaran & Holmes, 2004; Salant, Switzer, & Reynolds, 1983; Horn & Persson,

2001). Currently, there is to the knowledge of the authors no model of acquisition sequences

which endogenizes the price premium paid in the market for corporate control. Future

research is required to address this.

Revisiting the returns to bidders in M&A

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In the broader, cross-disciplinary literature on M&A, our framework complements

work on the effect of structural imperfections in the market for corporate control and the

effect of ownership structure on bidding competition, as well as the outcomes associated with

tender offers and acquisitions in general (see Hirshleifer, 1995 for a survey). We have not

taken into consideration these effects – of which there are many – focusing instead on

enhancing the strategy perspective on M&A. A potentially very fruitful avenue of future

research is the relative importance and interaction of these distinct effects in different

contexts.

We also recognize that the existence of multiple, and interdependent ‘outside options’

(i.e., decision alternatives) among bidders and targets offers added decision complexity

(Lippman & Rumelt, 2003b). Our conceptual framework suggests a need for modelling the

broader decision context faced by managers.

An intriguing follow-up question to our revisiting the pattern of returns in the market

for corporate control is the degree to which the market for firm cooperative arrangements, i.e.

alliances, behaves in comparison to the market for corporate control. We would expect

important differences between the two markets because of the differences in the content of

these agreements.

We believe that our conceptual work provides ample basis for not only continuing and

furthering research on the existence as well as the determinants and limitations of different

types of gains from mergers, but also for the continued advancement of the understanding of

M&A and related resource investments from a strategy perspective. More specifically, our

research has highlighted the role of different competitive spillovers in the market for

corporate control.

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FIGURE 1

Competitive Dynamics of Imitable Synergies from Competitive Advantage

S = 0

All bidders bid

All bidders bid – target refuses

Premium

S + C = 110 = P

S = 100

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FIGURE 2

Competitive Dynamics of Partially Inimitable Synergies from Competitive Advantage

S = 0

All bidders bid

All bidders bid – target refuses

S1 + C1 = 108

Premium

S2 + C2 = 90

S3 + C3 = 70

Bidders 1and 2 bid

Bidder 1 and target negotiate outcome

S2 = 80

S3 = 60

S1 = 100 P = 99

Revisiting the returns to bidders in M&A

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FIGURE 3

Competitive Bidding Dynamics of Synergies from Increased industry Profitability

Si + Ci = 0

All bidders bid – target refuses

-Ct = 100

Premium

No bidders bid – target accepts

No bidders bid

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FIGURE 4

Competitive Bidding Dynamics when a Managerially Motivated Rival Bidder with

Negative Synergies Outbids a Bidder with Partially Inimitable Synergies from

Competitive Advantage

S2 + C2 = -20

All bidders bid – target refuses

S3 + M3 + C3 = 100

Premium

S1 + C1 = 80

Bidders 1and 3 bid

Bidder 3 and target negotiate outcome

P = 90

Revisiting the returns to bidders in M&A

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TABLE 1

Empirical Prediction and Interpretation of the Outcomes of Various Types of

Acquisition Value

Absolute returns to the winning bidder

Absolute returns to the losing potential bidder

Partially or fully inimitable synergies from competitive advantage

– / +

–

Imitable synergies from competitive advantage

– –

Shared synergies from increased industry profitability

0

+

Value from managerial utility

– 0

Negative synergies from value from managerial utility

– +

CHAPTER 3

Value Creation, Appropriation and Destruction in Mergers and Acquisitions: An Industry Merger Wave Perspective

VALUE CREATION, APPROPRIATION AND DESTRUCTION: AN INDUSTRY MERGER WAVE

PERSPECTIVE

BENJAMIN W. BLUNCK School of Economics and Management

Aarhus Universitet Building 322, Bartholins Allé 10

DK-8000 Aarhus C, Denmark Tel: +45 8942 1524

E-mail: bblunck@econ.au.dk

This version: May 11th, 2009

Acknowledgements I am grateful for the commentary provided by Jay Anand, Jan Bartholdy, Peter T. Larsen, Ole Ø. Madsen, Torben B. Rasmussen, David Skovmand, the participants of the TOMS Seminar Series and the Management Seminar Series at the School of Economics & Management at the University of Aarhus, and the students in my ‘4089: Mergers & Acquisitions’ Master’s level course, as well as an anonymous reviewer at the 29th Strategic Management Society Annual International Conference. Any remaining errors are my own.

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ABSTRACT

Existing research on periods of high industry merger activity reports that acquisitions

within these industry merger ‘waves’ on average lead to both higher value creation and

higher value appropriation for the acquiring firm than acquisitions out of waves – especially

when they occur in the beginning of the wave. However, these studies have not taken into

account the co-existence of two fundamentally different types of acquirers: those motivated

by value creating, synergistic motives and those motivated by value destroying, managerial

motives. Using a novel empirical approach to measure short-run returns to acquisition

strategies more correctly, I investigate separately the incidence and extent of value creation,

appropriation and destruction. I show first that the incidence of value destroying acquisition

strategies is in fact higher in waves than out of waves, and that value destroying acquirers

within waves destroy more value than their out-of-wave counterparts. However, the in-wave

acquirers which are synergistically motivated create and appropriate more value than their

out-of-wave counterparts. The timing of acquisition strategies within industry merger waves

has no effect on either of these relationships. Overall, the study advocates a revised

perspective on the potential incidence and extent of value creation, appropriation and

destruction in acquisitions.

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INTRODUCTION

Mergers and acquisitions (M&A) occur during times of heavy industry merger activity

or during moderate or low industry merger activity. The former is often referred to an

‘industry merger wave’ (e.g., Sudarsanam, 2003), the latter may correspondingly be referred

to an ‘industry merger trough’ (Carow, Heron, & Saxton, 2004). Although research has not

paid much attention to the potential for varying motives and consequences of these two

arguably different phenomena, corporate managers, analysts and the general business press

routinely refer to this dichotomy. However, on the question of which context provides greater

opportunity for acquiring firms to create value for their shareholders, there seems to be two

opposing myths.

Industry merger waves come at times when analysts and managers proclaim the

advent of a new economic reality, which reflects changes in the external industry

environment, such as political, economic, social and technical changes (Sudarsanam, 2003),

or changes driven by internal industry competition such as product, process and business

model innovations. M&A is often assumed the most appropriate tool to reorganize firm and

dyadic assets to take advantage of product and resource market disequilibria (Andrade,

Mitchell, & Stafford, 2001; Lubatkin, 1983; McNamara, Haleblian, & Dykes, 2008; Mitchell

& Mulherin, 1996; Sudarsanam, 2003, Weston, 2001). However, the broad-based nature of

the underlying economic changes suggests that firms face significant competition from their

industry competitors in taking advantage of the different sources of synergies (Toxværd,

2004). In this regard, firms may have to ‘time’ their acquisitions to stay ahead of the industry

learning curve and to avoid engaging in bidding wars in a competitive ‘market for corporate

control’, where firms are bought and sold (Manne, 1965).

On the other hand, when merger activity falls as the source of synergies and target

firms dry up, perhaps following a drop in industry demand, analysts and managers change

their perspective. They argue the greater potential for ‘bargains’ in the deflated market for

corporate control, and talk about the ability of individual firms to gain a decisive competitive

edge by conducting acquisitions which create uniquely valuable synergies.

Clearly, these two myths differ on the relative importance for acquirer returns of the

broad vs. narrow foundation of synergies and the high vs. low competition for synergies. This

paper seeks to determine whether the potential for value creation, i.e., synergy gains, and

value appropriation, i.e., the gains appropriated by the acquirer, is higher within industry

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merger waves or out of waves. The answer to this question is vital not only for the academic

conversation on the returns to M&A and its determining factors, but also for business

practice.

However, M&A may also play a value destroying role. It is well established in both

research and practice that managers may use M&A to build empires or otherwise follow

personal goals (e.g., Jensen, 1986). In addition, managers and their boards face numerous

cognitive limitations in their understanding of the changes unfolding before them (e.g.,

Auster & Sirower, 2002). Such self-serving and cognitively limited managerial decision-

making may affect the value creation and appropriation from M&A significantly. In this

regard, it is clear to both researchers and the business press that merger waves and the

underlying economic changes may be especially conducive to self-serving and/or

misconceived acquisitions strategies. The business press talks of a merger ‘frenzy’,

‘bandwagon’ or ‘mania’ (Fortune Magazine, 1994). Research in agency theory argues that

these inefficient acquisition strategies may be driven by managers who want to boost the size

of their company to avoid becoming a target themselves (Gorton, Kahl, & Rosen, 2005).

Similarly, institutional theory argues that managers employ acquisition strategies because

they are deemed legitimate strategic responses by the informal and formal institutions which

surround them (Auster & Sirower, 2002). The result is value destruction as opposed to

creation and appropriation.

Recent work finds that mergers within US industry merger waves on average create

more value than mergers out of waves, and similarly, that the value appropriated by acquirers

within waves on average exceeds that of acquirers out of waves (Harford, 2003). In addition,

there is weak evidence for a resource-based theory that acquirers who are first-movers in the

industry merger wave on average are able to create more synergies (Carow et al., 2004;

Harford, 2003; McNamara et al., 2008). Similarly, there is some evidence which shows that

acquirers at the end of industry waves fare worse than acquirers at other stages (Harford,

2003), which is taken as evidence of agency or behavioural motives. However, these studies

have not taken into account the co-existence of two fundamentally different types of

acquirers: those motivated by value creating, synergistic motives and those motivated by

value destroying, managerial motives. As a result, it is unknown whether the higher level of

value creation and appropriation within waves and in the early stages of waves is due to a

greater extent of value creation and appropriation, i.e., the ability of acquirers to create and

appropriate more synergies, or whether it is merely due to the incidence of more value

creating motives, i.e., that a greater portion of acquirers within waves conduct acquisitions on

Value creation, appropriation and destruction in M&A

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account of self-interested motives or their cognitive limitations. I argue that the incidence and

extent of value creation, appropriation and destruction must be investigated separately in

order to judge the effect of the merger wave context and its stages.

This paper simultaneously gauges the potential for value creation, appropriation and

destruction to avoid the confounding of co-existing motives, as well as to investigate the

often-ignored, separate role of value destruction by itself. Theoretically, I offer separate

hypotheses on the incidence and extent of value creation, value appropriation, and value

destruction across the merger wave context and the stages of the wave. I distil these

hypotheses from existing theories of mergers and merger waves as well as the resource-based

view of strategy (Barney, 1991). To measure acquisition returns empirically, I employ a

revised short-run announcement return methodology which takes into account the workings

of the stock market during industry merger waves. Existing empirical work on industry

merger waves primarily uses the traditional short-run event methodology, which is based on

the acquisition being an unexpected event (Campbell, Lo, & MacKinlay, 1997). However, the

economic changes underlying industry merger waves may create partial anticipation of the

returns to future merger activity (Malatesta & Thompson, 1985), and this partial anticipation

is revised as firm and industry rival acquisitions occur throughout the wave. More

importantly, should the firm conduct more than one acquisition, it is impossible to separate

the effect of the individual acquisitions in any meaningful way, since they all serve to

respond to a common, underlying shock. Therefore, I measure returns at the higher level of

acquisition strategy, meaning the acquisition(s) conducted by acquirers during merger waves

to respond to the economic changes.

I investigate a sample of US mergers 1980-2005 similar to previous work on industry

merger waves. Using non-parametric Wilcoxon tests, I find that the average acquirers within

industry merger waves both destroy merger value and experience negative returns. In

comparison, the average acquirer out of waves creates merger value, but experiences negative

acquirer returns as well. Going beyond these aggregate statistics, I test my hypotheses on the

incidence of value appropriation, creation and destruction by evaluating the distribution of

merger outcomes in and out of waves. As expected from previous research (e.g., Moeller et

al., 2005), I find that both synergistically and managerially motivated acquisitions co-exist

within merger waves and in merger troughs. I confirm the expectation of institutional merger

wave theory (e.g., Auster & Sirower, 2002) that acquisitions within waves are more likely to

be motivated by value destruction. Surprisingly, I find that only 45.6% of acquisition

strategies within waves create value, compared with 56.4% of acquisition strategies out of

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waves. The returns at different stages of the wave do not show signs that first-moving

acquirers and late-moving acquirers are more likely to be motivated by value creating or

value destroying motives. This goes counter to the expectation of institutional theory (Auster

& Sirower, 2002).

I then separate value creating and value destroying strategies and conduct univariate

and multivariate analyses on the extent of value creation, appropriation and destruction. As

expected by merger wave theories of value creation and value destruction, I find that in-wave

acquisitions both create and destroy more value than their out of wave counterparts. In other

words, acquirers who are synergistically motivated are able to create more merger value in

waves than out of waves, while acquirers who are managerially motivated destroy more

merger value in waves than out of waves. Looking at the value appropriated by

synergistically motivated acquirers, I see that the advantage held by in-wave acquirers over

out-of-wave acquirers remains. Thus, although there is smaller incidence of value

appropriation within waves, the acquirers which do appropriate value do so to a greater

degree than their out of wave counterparts. Of the managerially motivated acquisitions, the

acquirers within waves destroy the most acquirer value, i.e., they have the highest negative

value appropriation. Surprisingly, our results are materially unaffected by the timing of the

acquisition strategy within waves. Thus, when taking into account the effect of the merger

wave context on the returns to acquisitions, the hypothesized first-mover advantages and late-

mover disadvantages disappear.

In all, this paper uncovers a completely new angle on existing theoretical and

empirical research. In-wave acquisitions are both more value creating and value destroying,

depending on whether the acquisition is driven by primarily synergistic or managerial

motivations. I believe that the approach and results presented here will help guide future

empirical research on the performance of acquisitions as well as provide a foundation for

building a more complete ‘theory of mergers and merger waves’ (Weston, Chung & Hoag,

1990) which can explain the central questions concerning M&A – its cause, course and

consequences.

BACKGROUND

Broadly speaking, existing research on merger motives can be split up in two groups,

depending on whether they posit value creating or value destroying motives (Harford, 2003;

Trautwein, 1990). Contemporary strategy content thinking, such as the resource-based view

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(e.g., Barney, 1991), argues that M&A can lead to value creation if the merging firms can

take advantage of the potential for reorganizing assets across firms (e.g., Capron, 2001). On

the other hand, agency and behavioural perspectives on managerial decision-making argue

that managerial motivations or managerial misperceptions may lead to value destroying

acquisitions (e.g., Jensen, 1986).

Creating and Appropriating Value from Acquisitions

Acquisitions create value in the form of synergies when the merged firm achieves

cash flows in excess of the two independent firms (e.g., Anand & Singh, 1997; Barney, 1988;

Lubatkin, 1983; Seth, 1990) by recombining assets so as better to meet the demands of the

external environment (e.g., Barney, 1991; Brandenburger & Stuart, 1996; Carow et al., 2004,

Peteraf, 1993). Acquirers appropriate value when they pay a price premium lower than the

synergistic value created. Whether a given firm combination leads to value creation and

appropriation or not depends on the nature of the potential synergies. Synergistic value can

both come in the form of a competitive advantage (e.g., Bradley, Desai, & Kim, 1983) and/or

increased industry profitability (e.g., Porter, 1980). Unlike the former, the latter benefits all

industry firms. However, the potential for a competitive advantage is a necessary condition

for shareholder value maximizing managers to conduct an acquisition strategy (Blunck &

Anand, 2009).

If a given acquirer can create a competitive advantage by combining inimitable

acquirer assets with the more or less generic assets held by potential target firms, the

associated synergistic value is privately appropriable, and rival bidders will not bid up the

price beyond the value of the competitive spillovers which they face (Blunck & Anand,

2009). However, if other rival firms have the necessary asset base and knowledge to acquire

similar target firms and implement a similar strategy, the synergistic value is instead publicly

appropriable, and the potential gains may be competed away in a competitive product

market. Even if publicly appropriable gains can be sustained in competition, these may be bid

away ex-ante in the ‘market for corporate control’ (Manne, 1965; Jensen & Ruback, 1983) as

the many potential bidders fight over the few available targets (Barney, 1988; Capron &

Pistre, 2002; Singh & Montgomery, 1987). In fact, the threat of negative competitive

spillovers if a rival bidder should acquire the target firm will likely lead to a ‘rational

overbidding’ outcome, in which merger value is created, but the acquirer experiences

negative returns to the same extent as non-merging rivals (Blunck & Anand, 2009). In all,

both the synergistic value created and the value appropriated by bidding firms depend

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crucially on the existence of other potential asset combinations. The market for corporate

control is made up of potential acquisitions which are driven by the same sources of

synergies, implying that they are interdependent on both synergistic gains and price.

Destroying Value through Acquisitions

Agency and behavioural theory argues that mergers and acquisitions can destroy

value if the combination of firms creates de facto negative synergies (e.g., Mørck, Shleifer, &

Vishny, 1990). Negative synergies may stem from a poorly conceived or poorly implemented

acquisition strategy (Sirower, 1997). Consequently, even a zero acquisition premium would

lead to value destruction, i.e., negative value appropriation, for acquiring shareholders.

Notably, acquirers may destroy shareholder value even in the presence of substantial

synergies if the price exceeds the value of these synergies, i.e., there is deal overpayment.

Regardless of whether acquirer value destruction stems from negative synergies or deal

overpayment, the underlying foundation is the existence of acquisition value which derives

from non-shareholder managerial incentives or misperceptions of the synergy potential.

In agency (or managerial) theory, managers are not thought to conduct acquisitions to

appropriate a share of synergies on behalf of their shareholders. They do it to satisfy their

inherently self-interested nature whenever the opportunity presents itself (Jensen, 1986;

Shleifer & Vishny, 1986; Walsh & Seward, 1990). Acquisitions may provide managerial

utility by facilitating risk diversification of the managerial portfolio (Amihud & Lev, 1981;

Eisenhardt, 1989; Lane, Cannella, & Lubatkin, 1998). They may also aid ‘empire building’ to

increase perquisites and monetary compensation (e.g., Mueller, 1969), and job security

(Gorton et al., 2005; Shleifer & Vishny, 1989). Often, the pursuit of managerial goals comes

at a (agency) cost to firm shareholders (Berle & Means, 1932; Jensen, 1986). The opportunity

to follow managerial goals ultimately requires an inefficiency of the firm’s internal and

external control mechanisms. The former consist of the executive compensation program, the

active ownership of shareholders and the monitoring conducted by the board of directors,

while the latter consists of the market for corporate control as well as product, debt and

equity markets (Jensen, 1986, 1993, 2005; Sudarsanam, 2000; Walsh & Seward, 1990).

Behavioural perspectives, on the other hand, argue that the irrationality of managers,

and of the board members and shareholders who control them, leads to acquisition strategies

which have no (or poor) economic foundation. This irrationality can be attributed to the

instance of one, or the interaction of many behavioural biases such as managerial ‘hubris’

(Hayward & Hambrick, 1997; Roll, 1986), ‘overconfidence’/‘optimism’ (Heaton, 2002;

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Malmendier & Tate, 2005), ‘self-attribution’ (Billett & Qian, 2007) and ‘escalation of

commitment’ (Duhaime & Schwenk, 1985; Haunschild, Davis-Blake, & Fichman, 1994).

Managers may also mistakenly attribute the synergy potential of rival firms to resources

which their own firm possesses, even though these synergies in reality are specific to the rival

firms. Auster & Sirower (2002) argue that the fundamental root of managerially motivated

acquisitions is often the perceptions of the formal and informal institutions which surround

managers and guide their decision-making.

Similar to the value created and appropriated, the value destroyed by mergers and

acquirers depends on the existence of other synergistically or managerially motivated

acquirers in the market for corporate control. The acquisitive activities of other firms affect

the value destroyed by a merger and the acquirer by affecting the competitiveness of the

product and corporate control market contexts (Blunck & Anand, 2009).

In this paper I argue that to analyze the extent of value creation and value

appropriation in M&A requires uncovering, and separating the incidence and extent of value

destruction. This is perhaps especially appropriate when dealing with industry merger waves

which, according to recent research in both agency and institutional perspectives, present a

fertile ground for value destroying influences. The next sections present a series of

hypotheses relating to the relative influence of value creating and value destroying motives,

and the extent of value creation, appropriation and destruction in and out of merger waves.

HYPOTHESIS DEVELOPMENT

Is Value Created or Destroyed In and Out of Merger Waves?

Recent research on the merger wave phenomenon has argued that periods of high

merger activity are driven by a fundamental change in the content of merger motives (Auster

& Sirower, 2002; Blunck & Bartholdy, 2009; Harford, 2003, 2005; Mitchell & Mulherin,

1996). However, theory has yet to hypothesize how and why value creating and value

destroying motives may appear simultaneously. Existing theory is written from the

perspective that merger waves increase the number of mergers motivated by either one of the

other (Gorton et al., 2005, is the exception). This, despite that numerous empirical studies and

surveys have documented the co-existence of both value creating and value destroying

motivations within populations of mergers (e.g., Berkovitch & Naranayan, 1993; Moeller et

al., 2005), implying that the underlying drivers of merger waves may in fact cause both kinds

of mergers to occur. Consequently, no theory is currently able to provide a theoretical

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explanation for the relative incidence of value creating and value destroying motives in and

out of industry merger waves.

Merger wave theories of value creation – often referred to under the umbrella of

‘neoclassical theory’ (Harford, 2005) – argue that the increases in industry merger activity

observed across industries in the 80s and 90s can be traced back to numerous economic

‘shocks’ at the industry and economy-wide level. These ‘shocks’ create an increased potential

for reorganizing (or, recombining, reconfiguring, restructuring) dyadic and industry assets

through M&A (Andrade et al., 2001; Comment & Schwert, 1995; Harford, 2003, 2005;

Jensen, 1993; Mitchell and Mulherin, 1996). These theories implicitly argue that mergers

within merger waves are more likely to be driven by value creating motives than merger out

of waves (see e.g., Harford, 2003, 2005). Since periods without economic industry shocks do

not provide a broad economy-wide or industry-wide impetus for merger activity, the resulting

out-of-wave mergers are ceteris paribus more likely to be value destroying1.

Merger wave theories of value destruction argue that the cause and course of merger

waves can be traced back to fundamental changes in managerial incentives and/or

perceptions.

In institutional (behavioural) theory, periods of increased economic uncertainty may

lead to the emergence of a specific legitimate, but ineffectual strategic response (Auster &

Sirower, 2002). This legitimacy and the accompanying misperception are bred and

maintained by the three isomorphic processes in institutional theory – coercive, mimetic and

normative (DiMaggio & Powell, 1983). In the context of an industry merger wave, the

economic uncertainty is created by the direct and indirect consequences of the ongoing

economic changes. Therefore, firms occupying similar institutional environments (such as

industries) undertake acquisition strategies, essentially creating a ‘bandwagon’ effect

(McNamara et al., 2008; Stearns & Allan, 1996). However, despite the increased legitimacy

of acquisition strategies, the gains to acquisition are in fact specific to a few initial, value

creating acquirers, implying that a significant number of acquisitions fail to duplicate these

sources of acquisition value (Auster & Sirower, 2002)2. On the other hand, acquisitions out of

industry merger waves occur in a time without the economic upheaval required to trigger the

uncertainty which sets off the bandwagon effect. Consequently, institutional theory implies

1 However, note how standard theory of the resource-based view would oppose this by arguing that firms are inherently different (e.g., Barney, 1991), and that acquirers out of waves may simply have a strategic profile which allows them to distil value creation from M&A at times when their competitors cannot. 2 The institutional theory described here provides a course for merger waves similar to models of herd behavior introduced elsewhere (see, e.g., Scharfstein & Stein, 1990).

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that these acquisitions are relatively more likely to be motivated by firm-specific synergies

and thus, to be value creating3.

In general, agency theory does not imply a different proportion of value destroying

mergers in industry waves compared with that of periods of low merger activity4. However, it

seems plausible that managers would be less inclined to carry out latent agency motivations

during waves since their opportunity costs increase. Firstly, the firm may increase managerial

payoffs significantly by conducting one or more profitable, synergistic acquisitions and/or

becoming a target itself (Gorton et al., 2005). Secondly, the increased probability of being

subject to a disciplinary takeover as a ‘bad bidder’ (Mitchell & Lehn, 1990) may curb

inefficient acquisition strategies. Thus, in such an opportunity cost perspective on agency

costs, it seems more likely that relatively fewer agency motivated acquisitions occur within

waves. However, according to Gorton et al. (2005), the threat of being taken over may

instead lead self-serving managers to acquire other firms to entrench their positions within

the firm by making it an indigestible target. This variant of the ‘job security’ motive may in

fact increase the proportion of agency-motivated acquisitions within waves; the aggregate

effect will depend on the specific managerial payoffs.

In all, we see that the merger wave theories of value creation and value destruction

provide contradictory predictions. Despite this, I expect that the value destroying motives

relating to the institutional bandwagon and the managerial entrenchment of the job security

motive will lead to a higher (lower) proportion of value destroying (creating) within industry

merger waves compared with out-of-wave periods.

Hypothesis 1A: Acquirers in industry merger waves are more likely to be managerially motivated than acquirers out of waves

In addition, I note that the institutional and agency (job security) theory differ on

where to find the greatest proportion of managerially motivated acquisitions. Gorton et al.

3 However, note that we could easily engineer an alternative behavioural perspective to provide an equally, or more dismal perspective on mergers out of waves. Specifically, while a given merger within an industry merger wave may merely be a jump onto the shared ‘bandwagon’, acquisitions conducted at a time when few or none of the acquirer’s rivals are doing the same suggests that the acquirer is not even copying other, successful acquirers. Rather, it would imply that the manager is so overconfident (or otherwise irrational) that she chooses a risky strategy which its rivals would do not have the knowledge or assets to attempt. Thus, from this perspective, the mergers out of waves may be the most likely to destroy value. 4 I would still expect an increase in the absolute number of managerially motivated acquisitions, since some existing firm managers are likely to be harbouring latent managerial motivations (Toxværd, 2004). Yet, an increase in the relative proportion of agency-motivated acquisitions would require a change in the incentive and/or opportunity of a typical industry firm manager to conduct self-serving acquisitions. As an example, a decrease in industry growth prospects could lead to a greater managerial incentive to employ growth-by-acquisition strategies (Jensen, 1986).

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(2005) argue that the job security motive will lead to value destroying, pre-empting

acquisition strategies. The broad institutional theory of Auster & Sirower (2002) argues that

later movers are more likely to be value destroying (bandwagon) mergers.

Hypothesis 1B: Late-moving acquirers in industry merger waves are more likely to be managerially motivated than other acquirers in industry waves Hypothesis 1B-alt: First-moving acquirers in industry merger waves are more likely to be managerially motivated than other acquirers in industry waves

The following sections offer hypotheses on how the merger wave context influences

the potential for value creation, appropriation and destruction from acquisitions. This

involves separately judging a) the potential for (merger) value creation and (acquirer) value

appropriation in and out of waves for those acquisitions which are driven by value creating

motives, and b) the potential for merger value destruction and acquirer value destruction in

and out of waves for those acquirers who are driven by value destroying motives.

Value Creation and Appropriation In and Out of Industry Merger Waves

I expect the value created in acquisitions motivated by shareholder value, i.e., the

absolute and relative size of potential synergies, to be greatest within industry merger waves.

This follows trivially from the assumption that the synergistic potential is created by newly

arisen (or newly discovered) opportunities for recombining assets at the dyadic and industry

level. These many broad and/or specific economic drivers arguably create significant sources

of disequilibrium in resource and product markets (Weston, 2001). Nevertheless, a high

synergistic potential will only lead to high value creating acquisitions if the heterogeneity

among acquiring firms allows them to sustain their competitive advantages in the face of rival

firm acquisitions. In this regard, the high level of merger activity itself counteracts the

expectation of high value creation by threatening the permanence of synergies (Sudarsanam,

2003).

Periods of low industry merger activity imply that there is a smaller potential for asset

reconfiguration within a given industry (Harford, 2005). In such a case, synergies are not so

much driven externally by broader industry changes, but internally by firm-specific, path-

dependent assets of the acquirer and/or target firm. Thus, acquirers out of waves have a much

narrower (though potentially deeper) synergistic value potential. So, while I expect higher

value creation within waves, I note that the inverse represents a viable alternative hypothesis.

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Hypothesis 2A: Assuming synergistically motivated managers, acquirers in industry merger waves create more value than acquirers out of waves

Given the common foundation of synergistic value within waves, it seems plausible

that synergies for a portion of acquirers will be more publicly than privately available, i.e.,

the synergies are more likely to be imitable and publicly known. This will create a situation

of excess demand in the market for corporate control where bidders fight for the relatively

scarce assets held by target firms. Assuming a structurally efficient market, this competition

will transfer value to the target firm shareholders by way of the price mechanism (Jensen &

Ruback, 1983). In fact, in those situations where no one potential bidder can create

sufficiently high privately appropriable synergies from competitive advantage, bidding

competition may force the winning bidder to bid beyond the value of synergies, leading to

negative value appropriation (Blunck & Anand, 2009).

On the other hand, bidding competition should be lower out of industry merger

waves, where the number of potential bidders is lower5. In fact, there may even be an excess

supply of targets. Nevertheless, it only takes one additional potential bidder per target firm to

create a competitive market (Barney, 1988), and the difference in competitive outcomes may

therefore be slight. Also, it is well established in the business press and research that target

firm shareholders and managers are generally unwilling to sell their firm at the market price

(Moeller, 2005). To this effect, the bulk of listed firms in the US have installed takeover

defences such as the ‘poison pill’ to increase their bargaining power vis-a-vis potential

acquirers (Bruner, 2004).

I do not expect the potentially increased bidding competition within waves to offset

the positive effect of higher merger value creation on the value appropriated by acquirers.

Hypothesis 3A: Assuming synergistically motivated managers, acquirers in industry merger waves appropriate more value than acquirers out of waves

The Effect of Timing on Value Creation and Appropriation within Industry Merger

Waves

It is commonly assumed that the path dependent nature of the evolution of firms

creates significant heterogeneity in firm asset bundles (Dierickx & Cool, 1993) and

information sets (Denrell, Fang, & Winter, 2003; Makadok & Barney, 2001) even if firms

5 Note that we are not arguing that non-acquiring rival firms do not have any recourse to compete with the acquiring firm; simply that they choose non-acquisition strategies to do so, which lowers the de facto bidding competition somewhat.

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compete within the same industries and have similar strategic profiles (Barney, 1991). This

heterogeneity implies two potential influences on the pattern of merger value creation within

an industry merger wave. Firstly, although the economic changes associated with the merger

wave affect all firms to a similar degree, some combinations of potential acquirers and targets

are able to create a greater degree of privately available synergies (Barney, 1988; Capron &

Pistre, 2002). Secondly, some acquirers have better knowledge of how their assets and

potential target firm assets can be reorganized to fit the changed demands of the external

environment, allowing them to move faster in resource and asset markets (Conner, 1991;

Peteraf, 1993; Denrell et al., 2003; Dierickx & Cool, 1993; Priem & Butler, 2001).

Previous empirical and theoretical research argues that acquisitions founded on

private information and/or superior acquirer assets will predominantly show up as first mover

advantages (Carow et al., 2004; McNamara et al., 2008; Toxværd, 2004). Specifically, these

firms are first in line to acquire the target assets which are most likely to create the valuable

and inimitable asset combinations which underlie competitive advantages. Carow et al.

(2004) and Harford (2003) find evidence that first-moving acquisitions on average create

more (merger) value than acquisitions at other points in the wave. While this could be taken

to imply that first-moving acquisitions achieve a greater extent of value creation, we should

be mindful that it could also simply mean that the incidence and extent of value destruction is

lower in the beginning of waves compared with later (as per hypothesis 1B).

Since the value created by superior, first-moving acquisitions is more likely to be

founded on private knowledge or inimitable asset combinations, the value appropriated by

the first-moving acquirers should be higher as well. The value is then privately appropriable

and the market for corporate control is less likely to be perfectly competitive (Blunck &

Anand, 2009). Carow et al. (2004) do not confirm this. However, they find that a small subset

of first-movers who conduct related acquisitions paid in cash during times of economic

growth – referred to as ‘strategic pioneers’ – do in fact appropriate more value than other in-

wave acquirers. Both Harford (2003) and McNamara et al. (2008) find clearer evidence that

the acquirer returns are higher for acquisitions conducted earlier in the wave, which may

reflect higher value appropriation, but could again reflect the incidence and extent of value

destruction increases throughout the wave.

I further add to this that first-movers who have the foresight and/or the existing asset

base to begin their acquisition strategy early in the wave should achieve a significant asset

advantage enabling them to build on existing synergies through later, superior acquisitions

(e.g., Makadok, 2001). Therefore, the source of superior acquirer returns should not be first-

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moving acquisitions, but rather first-moving acquisition strategies. Previous research has not

taken this straightforward implication from the resource-based view into account.

In contrast, I argue that late-moving acquirers have inferior knowledge of the ongoing

changes or inferior asset bases (McNamara et al., 2008). Although late-moving acquirers may

benefit from observing the actions and reactions of previous acquirers, it is doubtful that this

learning will be rewarded to the extent of the value creation and appropriation of earlier-

moving, superior acquirers (Carow et al., 2004). Quite simply, late-movers will have lower,

more publicly available synergies as well as less target firms to choose from (Anand & Singh,

1997), which will increase bidding competition and further depress acquirer returns (Carow

et al., 2004). Given the potential for rational overbidding, late-moving acquisitions may even

result in negative acquirer returns even though they are motivated by value creation (Blunck

& Anand, 2009). In all, I therefore expect acquirers in the beginning of a wave to both create

and appropriate more value than other acquirers. By the same logic, the value created and

appropriated by acquirers throughout the wave should drop, and late-moving acquisitions

should create the least value.

Hypothesis 2B: Assuming synergistically motivated managers, first-moving acquirers in industry waves create the most value, while late-moving acquirers in industry merger waves create the least value Hypothesis 3B: Assuming synergistically motivated managers, first-moving acquirers in industry waves appropriate the most value, while late-moving acquirers in industry merger waves appropriate the least value

Value Destruction In and Out of Industry Merger Waves

I argue that managerially motivated acquirers within waves are likely to be more

value destroying than their out-of-wave counterparts. The industry upheaval present during

periods of industry merger waves offers not only greater synergistic potential which can

improve a firm’s competitive standing vis-a-vis its rivals, but also the greatest opportunity

cost – and real cost – should managerial misperceptions and incentives lead to value

destroying decisions. The cost comes at the hands of the successful acquirers, but also at the

hands of non-merging firms.

Note that we cannot completely rule out the opposite prediction that value destroying

acquirers out of waves destroy more value than their in-wave counterparts: if a sufficient

portion of industry firms are involved in poor acquisitions, each of the acquiring firms may

lose value, but their competitive losses would be dampened because many of their rivals

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conduct poor acquisitions too. On the other hand, managerially motivated acquirers out of

waves have cognitive biases or incentives which are not shared by their rivals. These rivals

may therefore all be able to dominate the acquirer strategically to a significant degree,

causing more profound merger value destruction. This could well be the case within waves if

a significant portion of the population of firms is involved in takeovers (Mitchell & Mulherin,

1996). Although, existing evidence does not seem to support that a major portion of acquirers

within waves are motivated by value destruction. I therefore expect acquirers within waves to

destroy the most value.

Hypothesis 4A: Assuming managerially motivated managers, acquirers in industry merger waves destroy more merger value than acquirers out of waves

Value destroying acquirers face much tougher bidding competition if they acquire

within waves. They will have to fight similarly motivated rivals as well as acquirers

motivated by value creation for the relatively scarce target firm assets. And acquisition

premia are likely to reflect the higher potential for value creation within waves (as per

hypothesis 2A). On the other hand, managerial motivations out of waves are likely to be more

specific to the individual acquiring firm and/or manager, implying that an acquirer out of

waves can choose between several firms and face lower bidding competition.

Assuming that managerially motivated mergers within waves destroy more value than

their out of wave counterparts (i.e., hypothesis 4A), the expected intense bidding competition

within waves is likely to further push down acquirer returns. Acquirers within waves would

then experience the worst negative acquirer returns, i.e., the highest negative value

appropriation.

Hypothesis 5A: Assuming managerially motivated managers, acquirers in industry merger waves destroy more acquirer value than acquirers out of waves

The Effect of Timing on Value Destruction within Industry Merger Waves

I follow extant literature in institutional theory in promoting the expectation of a late-

mover disadvantage among managerially motivated acquirers (Auster & Sirower, 2002).

Specifically, the legitimacy of the lauded acquisition strategies increases throughout the

wave, creating a ‘bandwagon’ effect (McNamara et al., 2008; Stearns & Allan, 1996) which

does not dissipate until disintegrating forces such as increased evidence of acquisition failure

become abundant (Auster & Sirower, 2002).

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Hypothesis 4B: Assuming managerially motivated managers, late-moving acquirers in industry merger waves destroy the most merger value

In addition, we would naturally expect the late-moving, bandwagon acquirers to

overpay more than earlier acquirers as the number of firms actively seeking target firms

increases (Auster & Sirower, 2002).

Hypothesis 5B: Assuming managerially motivated managers, late-moving acquirers in industry merger waves destroy the most acquirer value

MERGER DATA AND THE IDENTIFICATION OF WAVES

I use transactions announced from January 1st, 1981 to December 31st, 2004 in the

Thomson Financial SDC Platinum M&A database as the basis for the empirical investigation.

The sample is the set of mergers involving acquirers and target firms listed in the US. I define

a merger as a transaction which involves a shift in firm control (defined as an ownership

stake above 50%). I leave out all deals below $50m to maintain focus on the reaction of firms

to significant deals. The sample from which to define periods of industry merger waves and

the corresponding non-wave periods consists of 3,421 acquisitions.

The study requires that I a) allocate acquisitions to industries, and b) determine the

bounds of an industry merger wave.

Determining Industry Merger Activity

I follow a scheme similar to that of Blunck & Bartholdy (2009) in allocating mergers

to industry memberships reported by the SDC database. This scheme argues that it is not

clear which industries are the foundation(s) for the merger synergies when an acquirer and a

target firm are involved in several industries. Industry groupings are based on the 48 industry

groups defined in Fama & French (1997). I drop the ‘Financial Services’ and ‘Bank’ industry

groups, as these firms compete under different competitive structures than other industries

(Carow et al., 2004). I also drop the ‘Miscellaneous’ industry group, since it serves purely as

a residual group.

If the primary industry of the acquirer and the target match up, I assume that it is a

closely related (perhaps horizontal) acquisition regardless of any other industry activities. If

this is not the case, but there is a match between the primary industry of the acquirer and a

secondary industry of the target, I allocate the merger to this shared industry. Otherwise, I

attempt to match the primary industry of the target with the secondary industries of the

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acquirer. Lastly, I attempt to match secondary industries. In the case where there is no

overlap whatsoever – which could be either a case of related or unrelated diversification – I

attribute the merger to both industries.

Determining the Bounds of Industry Merger Waves

Previous work has used varying methodologies for determining the existence, timing

and length of industry merger waves. Only Harford (2003, 2005) conducts analyses on both

the in and out of wave periods; Carow et al. (2004) and McNamara et al. (2008) focus on the

effect of timing with industry merger waves.

Harford (2003, 2005) identifies within each decade (the 80s and the 90s) the 2-year

peak in industry merger activity. These ‘potential merger waves’ are then characterized as

actual merger waves if they are ‘statistically significant’. This method implies simulating

1000 industry merger waves for each potential industry wave and testing whether the amount

of mergers in the actual merger wave exceeds the 95th percentile of the simulated merger

waves. Carow et al. (2004) argues that industry merger waves may naturally differ in their

duration and the specific distribution of merger activity. They define the beginning of an

industry merger wave as the year when the merger activity is three times as high as the

preceding year (given that it exceeds a certain threshold), and the final year of the wave as the

year preceding a year where it drops to a third of the previous year. As a consequence,

merger waves may never end, which increases the chance that out of wave years may be

counted as in-wave years. McNamara et al. (2008) use the same methodology as Carow et al.

(2004), although they use the simulation method of Harford (2003, 2005) to ensure that the

wave is significant. This creates the opposite problem that if in-wave periods are (wrongly)

extended beyond their time, they will not survive the randomness test. Hence, industry

merger waves which are otherwise valid (albeit in a shorter form) are discarded.

This paper uses a novel approach to determine endogenously the timing and length of

potential industry merger waves. It builds on observations from previous literature, but with

an increased sensitivity for the meaning of the dichotomous periods in and out of waves.

Firstly, we note that Mitchell & Mulherin (1996) find that an average of 50% of the merger

activity within a given industry occurs within a fourth of their sample period. Secondly,

Carow et al. (2004) highlight that industries may experience waves of quite varying duration

and distribution of merger activity. Thirdly, Harford (2005) correctly argues that observed

peaks in merger activity should qualify as being statistically different from random clustering

of mergers within the industry.

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I implement the following identification scheme for two separate, 12-year periods: the

first beginning on January 1st, 1980 and ending December 31st, 1992, and the second

beginning January 1st, 1993 and ending December 31st, 2004. First, I identify the shortest

period in which the concentration of industry merger activity reaches 50% of the total

industry merger activity within that 12-year period. This provides me with a potential

industry merger wave in each period. I argue that a given industry merger wave must be

between 2 and 6 years long. A period above 6 years implies that more than one wave exists,

which would void the empirical testing of propositions. A wave shorter than 2 years would

seem to focus purely on the time around the peak activity and less on the beginning and end

of a wave. Second, I then implement the simulation method of Harford (2005) to confirm

whether potential industry merger waves are statistically different from waves which could

occur at random. Per its nature, this method is more likely to reject longer, less intense waves.

Thirdly, to increase the power of the tests on the differences between periods in and out of

waves, I remove those industries which do not experience an industry merger wave at any

time within the full sample period.

I list the statistically significant industry merger waves in the appendix. There are 14

and 21 such waves in the 1981-1992 and 1993-2004 periods, respectively, occurring in 23 out

of the 45 industries. Roughly half of the industries experiencing a merger wave observe a

wave in each sample period. The average length of a wave is 34.4 months, compared with

29.7 and 37.5 months in the two sub-samples, respectively.

MEASURING ABNORMAL RETURNS TO ACQUISITIONS

Event study methodology has been widely applied in finance and strategy research to

study the returns to mergers and acquisitions (Sudarsanam, 2003). Arguably, it remains the

most popular empirical methodology by which to study the phenomenon (Datta, Pinches, &

Narayanan, 1992; King, Dalton, Daily, & Covin, 2004). However, in the following section, I

argue that the context of a merger wave strains the conceptual framework underlying the

traditional event study methodology. A modified conceptual approach is warranted to answer

research questions regarding the value created, appropriated and destroyed in industry merger

waves. I offer an empirical specification of the returns to acquisitions in and out of merger

waves which is adapted to these issues, and I argue that it compares favourably to existing

variations of the event study methodology.

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The Conceptual Specification of the Returns to Acquisitions

Stock market reactions to the economic consequences of acquisitions in industry

merger waves. Event study analysis is based on the assumption of an efficient stock market

(in the semi-strong sense) which will “…reflect all publicly available information about the

future prospects for the respective stock issues”, and in which “...prices change quickly to

incorporate the economic consequences of new information” (Eckbo, 1988: 5). Under this

assumption, the stock market reaction to an unexpected event over an event window of a few

days constitutes the bulk of the expected economic consequences of this event (Campbell et

al., 1997).

The advent and course of an industry merger wave implies quite clearly that the

concurrent merger activity is not unexpected by the stock market. Specifically, the merger

wave context changes our conceptual understanding of the returns to acquisitions in 3 ways.

Firstly, the existence of industry merger waves is predicated on the occurrence of a specific

or broad industry shock(s) (Harford, 2003, 2005; Mitchell & Mulherin, 1996), which creates

a shared economic foundation for the resulting industry merger activity (Harford, 2005;

Weston, 2001). News of these evolving or sudden economic changes creates prior

anticipation of merger events in the stock market (Harford, 2003).

Secondly, the shared economic foundation implies that the merger activity within the

entire industry merger wave is a strategic response by industry firms to the economic changes

(Sudarsanam, 2003). In this sense, a series of acquisitions conducted by the same firm

become interdependent; their individual effects are tied inexplicably to each other, and it is

not possible to isolate them in a meaningful way. Consequently, it makes sense to view their

contribution to firm returns at a higher level of aggregation – the level of acquisition strategy.

By this, I mean that the firm has chosen a certain strategy of acquisition(s) as its response to

the underlying economic changes. Note that while we can characterize this strategy on

dimensions such as single/multiple acquisition, related/unrelated, cash/stock etc., we do of

course not know the precise underlying strategic rationale(s).

Thirdly, since the true unexpected event – the underlying economic shock – affects

the rival firms as well, it makes sense that they might also respond by attempting a series of

acquisition strategies. In the sense that rival acquisitions provide information on the

probability of firm merger activity, the announcement of these acquisitions provides some

additional prior anticipation of later firm acquisitions or sales (Harford, 2003). Similarly,

merger activity will cause competitive actions and reactions of rival firms. Since these are

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likely to affect the firm, they will lead to an ongoing revision of the equilibrium economic

consequences of the firm’s acquisition strategy. The revision continues until the industry

merger wave is complete.

None of the previous studies on the returns to acquisitions in industry merger waves

deal with these conceptual issues, even though Harford (2003) goes some way in dealing with

the issues of partial anticipation. Therefore, I conclude that the methodological foundations of

Harford (2003), Carow et al. (2004) and McNamara et al. (2008) are conceptually inadequate

to judge value creation, appropriation and destruction from acquisitions in industry merger

waves effectively. As such, they are also not suited to comparing the returns to acquisitions in

and out of waves. I now offer an empirical specification of abnormal returns which meets this

critique.

The Empirical Specification of Abnormal Returns

Measuring abnormal returns within waves. I implement the conceptual framework

using a modified ‘regression parameter approach’ (Eckbo, 2005; Malatesta & Thompson,

1985) to abnormal returns. This novel approach is designed to isolate the economic

consequences of acquisitions by a) summing returns of acquisitions used in the acquisition

strategy, b) mitigating the effect of partial anticipation of merger activity at the beginning of

the industry merger wave, and c) adding the ongoing revision of the economic consequences

of the acquisition strategy given rival responses. Furthermore, as a consequence of the merger

wave setting, I deviate from previous literature in using dollar returns as the foundation of the

acquisition return calculations.

In its standard form, the foundation of the regression parameter approach to event

studies is to run a regression of the daily returns on a return-generating model over an

estimation window which includes the event date. The (average) daily return to the firm event

is then extracted by including in the model an event dummy variable which is coded one for

the duration of the event window (Eckbo, 2005). In comparison, the traditional event study

methodology estimates the regression parameters of the return-generating model on an

estimation window preceding the event date and calculating the cumulative daily ‘abnormal’

return over the event days (Campbell et al., 1997). Using the CAPM model as the return-

generating model – and adding the Fama-French factors (HMLt and SMLt) (Fama & French,

1993) and the ‘momentum’ factor (MOMt) to soak up otherwise unexplainable market risk

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factors which are missing6 – the standard regression parameter approach for a given firm i

becomes:

(Rit – rft) = αi + βi(Rmt – rft) + β2iHMLt + β3iSMBt + β4iMOMt + γiEventit + εit (1)

αi is a constant, Rit is the individual firm return, rft, the risk-free rate, Rmt is the return

to the market portfolio. HMLt is the factor return to a portfolio that is long in high market-to-

book stocks and short in low market-to-book stocks, SMBt is the factor return to a portfolio

that is long in small firms and short in large firms. MOMt is the factor return to a portfolio

long in stocks which are high performers over the short term and short in the stocks which are

low performers over the short term. Eventit is the dummy for firm i’s acquisition event – it is

coded 1 for each of the event days, otherwise it is 0. εit is the error term. γi then measures the

average firm event return over the chosen event window of length τ. The (cumulative)

abnormal return of firm i to the event produced by the simple regression parameter approach

CAPM model is:

CARi = γi * τ (2)

If the firm event is completely unexpected and αi and the βji‘s remain stable before

and during the event window, this regression parameter approach provides an event return

identical to the simpler event study analysis method of cumulating the firm return on the

event days (Eckbo, 2005). However, unlike the traditional event study approach, the

regression parameter approach is able to deal with ex-ante partial anticipation of the

acquisition strategy. Assuming that this partial anticipation is priced at the beginning of the

estimation period and would disappear gradually throughout the estimation period should the

anticipated event not materialize, it is soaked up by the constant αi in the model (Malatesta &

Thompson, 1985). Therefore, the (dummy) event return(s) would no longer be a biased

estimate of the economic effects on account of the event already being partially priced by the

stock market, as would be the case in the traditional event study method (Harford, 2003;

Malatesta & Thompson, 1985).

Since I argue that acquisitions conducted within an industry merger wave are

interdependent, I add a firm event dummy for each firm acquisition j. Upon extracting the

return for each firm acquisition, I cumulate them to achieve the returns to the firm acquisition

strategy. When calculating this return, I measure the value created by each event in dollar

terms. Note that an alternative would be to simply code the one existing firm event dummy

equal to one on the event days surrounding all of the J firm events. γi would measure the 6 I take these factors along with the return to the market portfolio (Rmt) directly from Ken French’s homepage (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).

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average firm event return over the chosen event window of all firm events, and the combined

return to these events would be CARi = γji * τ * J. However, when the ‘event’ in question is

not a single announcement, but rather a series of announcements, events may be cumulated

unevenly. Specifically, the market value of the firm – which is the denominator in the event

abnormal return measure – changes from one announcement to the next. Assuming that the

acquisition announcements generally increase (decrease) firm value, the use of a single

acquisition dummy implies that the size of the cumulated return will decrease (increase) as

the economic effects of the acquisition strategy are spread out over a higher number of

announcements. Furthermore, firm market value may change throughout the wave on account

of issues which do not concern the firm’s response to the industry merger wave. For instance,

a diversified firm may experience events relating to a business segment which is unrelated to

the industry in question. Then the value of a later acquisition event may seem smaller than

that of previous events even though their economic impact in dollar terms may be

comparable. This change is due to a change in the denominator of acquisition returns (the

increased market value of the firm) and not the size of the returns per se. Therefore, I

measure the dollar return to each acquisition separately before aggregation, and denominate

them by the value of the firm at the beginning of the wave.

However, I also need to take into account that the stock market revises its

expectations of future merger activity and the economic consequences of past merger activity

when industry rivals conduct their own acquisitions. Harford (2003) argues that rival

acquisitions which precede a firm’s acquisition provide additional prior anticipation of the

probability and nature of the acquisition. Using the regression parameter approach, he adds

an event dummy which is coded one for the duration of the event window of the industry

rival acquisitions occurring prior to the firm acquisition. However, I have argue more

generally that the interdependence of the economic consequences of firm and rival

acquisitions implies that the announcements of all industry acquisitions provide either prior

anticipation or ongoing revision of the information held by the market. Thus, the ‘rival-event’

dummy should cover all industry acquisitions occurring within the estimation period. And as

before, I add a dummy variable for each event.

In all, I add a series of event and rival-event dummy variables to the regression.

Eventjit is the dummy for firm i’s acquisition j – it is coded 1 for each of the event days,

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otherwise it is 0. Rival-Eventkit is coded one on each day of the event window around the

announcement of the k’th industry rival acquisition7. In all,

(Rit – rft) = α + β1i(Rmt – rft) + β2iHMLt + β3iSMBt + β4iMOMt + ∑γjiEventjit + ∑γkiRival-

Eventkit + εit (3)

In this paper, this regression runs from a year (252 trading days) prior to the

beginning of the first acquisition in an industry merger wave, to the trading day following the

last acquisition in the wave. I choose an event window of 3 days surrounding the

announcement date of an acquisition (i.e., equal to a CAR(-1,1) specification), implying that

the event dummies Eventjit and Rival-Eventkit are coded 1 on these 3 days, and otherwise are

coded 0. This length has been widely used in empirical event study research on M&A (e.g.,

Harford, 2003; Moeller et al., 2005). It is thus recognized as an acceptable compromise

between the loss of power of statistical testing implied by extending the event window and

the gain from capturing a greater share of the returns in the semi-efficient market (Campbell

et al., 1997). Note that the empirical specification of returns does not easily allow us to

extend this window any further, since the risk of overlaps between the event windows

surrounding industry acquisition announcements would hurt our empirical testing.

This paper adapts the above specification to calculate returns in (inflation-adjusted)

dollar returns. The abnormal (dollar) returns appropriated by firm i’s acquisition strategy

when acquiring J target firms in a wave where rivals conduct K acquisitions is then:

ΠiA = ∑(γji * 3 * mji) + ∑(γki * 3 * mki) (4)

mji is the market capitalization of firm i at the beginning of the event window around

the announcement of the j’th acquisition. mki is the market capitalization of firm i at the

beginning of the event window around the announcement of the k’th rival firm acquisition.

The returns to the target firm are the same, although I cut off their estimation period the day

after the announcement of their sale.

To calculate the abnormal dollar returns created by the acquirer’s acquisition strategy,

I simply add together the dollar returns appropriated by the acquirer (ΠiA) and the returns

appropriated by the j = 1,...,J firms which are acquired (ΨjA):

ΠiC = ΠiA + ∑ ΨjA (5)

Although it is an unquestioned practice in the extant literature to measure value

creation and appropriation relative to the acquiring firm’s market value, this choice involves a 7 To avoid multicollinearity of the merger event dummies, we can allow only one firm acquisition announcement and one industry acquisition announcement per trading day. In the case of same-day observations, we keep the acquisition with the largest deal value as reported by the SDC, leading to a sample reduction resulting in 3,234 acquisitions.

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certain conceptual bias. To illustrate the importance of choosing between a market value

denomination and simple dollar returns, imagine that a $1bn bidder purchases a $50m target,

creating $20m in synergistic value at a price of $60m. Value creation deflated by acquirer

value would be 2%. Imagine now that a $100m rival bidder instead acquires the target at a

price of $65m, creating the same synergies, i.e., appropriating $5m compared to the larger

acquirer’s $10m. Value creation deflated by acquirer value would then be 20%; seemingly

much higher than the 2% created by the larger bidder, although the value creation is actually

the same in dollar values. At the same time, the value appropriated by the smaller firm would

be 5% relative to its own market value. It would only be 1% for the larger bidder although he

would have appropriated more value from the acquisition in both absolute and relative terms.

Reliance on this relative measure implies that the results depend on the distribution of the

size of acquirers and targets over time; when acquisitions are conducted by larger firms, the

value created and appropriated will seem smaller. Using dollar returns, however, implies the

opposite; when acquisitions are conducted by larger firms, the value created and appropriated

will seem larger. Also, dollar returns are likely to be noisier than relative returns. Hence, for

robustness, I will conduct the tests with inflation-adjusted dollar returns as well as dollar

returns deflated by both target firm market value and acquirer market value.

The validity of the empirical specification. Given the novelty of the approach we

first note the issues involved in using the specification. Firstly, it assumes that the merger

wave and all its economic effects (and all associated uncertainty) are resolved within a day

following the end of the wave. This is not the case if partial anticipation of future merger

activity remains in the stock price or if the wave is actually not over yet. Therefore, the

validity of our specification of empirical returns is closely linked to the correct identification

of industry merger waves.

Secondly, in using the modified regression parameter approach, the estimation of the

parameters αi (or rather, the portion of αi unrelated to partial anticipation) and βi in the return-

generating model differs from that in the traditional event study methodology. Specifically,

the estimation of the parameters become generalized across the longer estimation period from

1 year before the industry merger wave until the end of the wave as opposed to being

estimated in a short period prior to the event. Since each firm acquisition and industry rival

acquisition may change the true parameter values, my estimation of the returns to the

individual acquisition events, which make up the returns to the acquisition strategy, may

therefore be biased compared to the ‘true’ parameter values. The impact of this bias depends

on the true (unobservable) evolution of parameter values throughout the merger wave. I

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consider both of the above issues to be unavoidable infractions given the fundamental change

in conceptual understanding of the returns to acquisitions implied by my approach. Also, the

use of a traditional event study approach would imply a different, potentially worse bias in

parameter values8.

Comparison with alternative event study methodologies. Two alternatives to our

approach clearly present themselves: using a variant of the long-run return methodology and

summing the (simple) cumulative abnormal returns. Note first that either existing empirical

methodology can trivially be adapted to a dollar denomination of returns to facilitate the

‘correct’ aggregation of returns. However, both have significant deficiencies in the merger

wave context.

Long returns are based on the assumption of a much less efficient market which

slowly incorporates information on the economic consequences of events over time (Eckbo,

2005; Sudarsanam, 2003). While this assumption is arguably incorrect – or at least, we have

assumed so in choosing our methodology – long run returns have the additional advantage in

practice that they are able to incorporate the information and economic consequences carried

by the announcement of competitive reactions by competitors. To include partial anticipation,

though, the long-run returns would have to be measured from the beginning of the merger

wave period. However, long-run returns are more severely hindered by the ‘bad model’

problem, i.e., it is difficult to specify the correct return-generating model (Sudarsanam,

2003). In our setting, this is of paramount importance and creates two major problems. It

means that we cannot credibly measure the absolute value created and appropriated – at best,

we can compare the biased excess return measures for acquisitions in and out of waves. But

more importantly, the broad shifts occurring in asset prices around times of industry merger

waves suggest that the bad model problem is likely to affect the two types of acquisitions

differently9. Without understanding this difference, the return measures have little value.

Note that in the methodology chosen here, I do not argue market inefficiency. Rather,

I argue that the market is semi-efficient, and that the ‘event’ is spread out over several

8 The traditional event study approach estimates parameters across an estimation window prior to the event, e.g., (-120,-2) (Campbell et al., 1997). Using this model in the context of an industry merger wave would imply a clear bias; the continuous announcements of firm and industry rival acquisition events make it unlikely that there would exist an ex-ante estimation period which is untainted by these ongoing and interdependent events. An alternative solution would be to estimate the parameters prior to the industry merger wave itself. However, this would imply using historical parameters which do not take into account that the risk profile of the firm is likely to change during the wave. 9 When long-run returns are measured over several years, they may periods of industry merger waves and/or out-of-wave periods. This suggests a further feeding of the potential difference between the ‘true’ return-generating model underlying the return to acquirers in and out of wave.

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announcements occurring across a longer period and involving relevant reactive strategies on

the part of rival firms. Essentially, my methodology falls between these two polar opposites

of short and long run returns. It meets some of the opposition held against short run returns in

favour of long run returns. However, generally it does not accept market inefficiency; the

economic consequences of acquisition (strategies) are found in short run announcement

returns, the difference being that all firm and rival acquisition announcements are relevant to

the return measure.

Instead of the regression parameter approach, I could also sum cumulative short-run

returns (in dollar returns) to the firm and industry rival acquisitions within the merger wave

period. This would mirror the methodology of summing the returns to event dummies and

thus deal with the ongoing revision of partial anticipation and the economic consequences of

acquisitions (conceptual issues 2 and 3). Yet, the estimation of the parameters would be

missing the effect of the resolution of ex-ante partial anticipation (conceptual issue 1). As

noted above, the ex-ante estimation windows implied by the traditional event study approach

may also lead to an increased bias in the estimated CAPM parameter values.

Measuring abnormal returns out of waves. To compare returns within waves with

the returns to acquisitions out of waves, I must use a similar methodology to calculate the

returns to acquisition strategies out of waves (Harford, 2003). Even though there is no wave

per empirical definition – and therefore no apparent need for a methodology different from

traditional event study analysis – acquisitions may still occur in smaller sequences. In other

words, an acquirer may still make a couple of interdependent acquisitions, or industry rivals

may conduct acquisitions in response to an acquisition. In those cases, my revised

methodology is conceptually more accurate, although interdependent acquisitions are

unlikely to occur as often as within industry waves. However, not taking this in account

means a potential comparison of ‘apples with oranges’. In other words, it is necessary to

create a comparable estimation period – a ‘pseudo’ merger wave – for each out-of-wave

acquisition strategy in order to compare the return to in and out-of-wave acquisition

strategies.

The construction of a ‘pseudo’ merger wave is not trivial, since we have no

conceptual basis for determining whether a given focal acquisition occurs as part of a

sequence involving several or only its own acquisitions and/or acquisitions carried out by

industry rivals. In other words, we have no basis for deciding when a pseudo merger wave

begins and ends. I choose a methodology which lets the merger activity speak for itself in this

matter. Firstly, I look for the first acquisition carried out by a given firm. I then define a time

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window around that acquisition as a pseudo merger wave. Since the average length of the

industry merger waves in my sample is roughly 3 years, I consider this to be an appropriate

window. So, I set this as the duration of a potential out-of-wave merger wave/sequence. Thus,

I define the pseudo merger wave period as beginning 1½ years (126 trading days) prior to the

focal acquisition and ending 1½ years (126 trading days) following the focal acquisition.

Therefore, I implement the return-generating model detailed above on the period beginning

one year (252 trading days) prior to this starting point and ending on the last day of the

pseudo merger wave. Secondly, all industry rival acquisition k occurring within the merger

wave period are represented by a rival-event dummy variable Rival-eventki. Thirdly, should

the focal firm conduct another acquisition within the pseudo merger wave period, the period

is extended to at least 1½ years (378 trading days) following this event. This involves the

inclusion of an additional event dummy in the model and adding more rival-event dummies

should more industry rival acquisitions occur in the prolonged period.

I concede that a period of 3 years is somewhat arbitrary in terms of the specific

corporate strategy setting underlying a given acquisition out of waves. It is possible that a)

two acquisitions carried out by the same firm are not strategically interdependent or b) that

the strategic foundation of an industry rival acquisition occurring within the wave period is in

fact independent of the acquisition strategy conducted by the firm. In neither cases should

these acquisitions be taken into account when calculating returns to the acquisition strategy in

question. However, with regards to a), I would argue that two acquisitions conducted within

1½ years of each other in most cases are sufficiently strategically similar to constitute a

general acquisition strategy. With regards to b), I argue that the effect on the acquirer strategy

return is likely to be negligible, or purely create noise in the cross-section of acquisitions

strategy returns.

Note that if a given firm only conducts one acquisition in the time period, and there is

no rival acquisition in the 3 years surrounding it, the conceptual approach essentially reverts

to the classic regression parameter approach in Eckbo (2005), which produces returns

identical to the classic event study methodology under the assumption of constant αi and βi.

Stock Return Data

The empirical approach to acquisition strategies requires stock return data for each

acquirer and target for the duration of the model estimation window. I therefore collect stock

return data from the Center for Research in Security Prices (CRSP) in the period beginning

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378 trading days prior to January 1st, 1981, and ending 378 trading days after December 31st,

2004.

I consult CUSIP records held by CRSP to associate the acquirer and target CUSIPs

reported throughout the years by SDC to the CRSP data. However, I am unable to find the

necessary stock data for a number of acquisitions. This could be due to missing observations

in CRSP, but it could also be a product of misclassification of public listings by SDC. There

are also several firms which can only provide daily returns to cover part of the acquisition

strategy period. This does not necessarily imply missing data; it could be because some firms

go public during the estimation period. I choose to remove any acquisition strategies in which

the acquirer or a target firm has less than a year’s worth of daily return data (252 trading

days) within the estimation period10.

Overall, I am left with 1,646 acquisition strategies11, of which 661 occur within the

wave, while 985 occur out of waves. These acquisition strategies involve 851 and 1,101

acquisitions, respectively.

The Relation between Simple Event Returns and the Returns to Acquisition Strategies

Before presenting the returns to acquisition strategies and conducting the main

analyses, I digress to examine the relation between my measure of returns and the returns

provided by traditional event study methodology. Specifically, I wish to compare the ‘full’

acquisition strategy returns (given by equation (4)) to the sum of the ‘simple’ event returns to

the acquisition events within a given strategy, i.e., the CARs (which make up the first half of

equation (4)). My conceptual framework establishes that specification (4) is needed to

accurately measure the true value creation and value appropriation of acquisition strategies

within waves, while it is less likely to be needed out of waves. I move to examine the

correlation between the two components of equation (4) – the firm event returns (the first half

of equation (4)) and the firm returns to industry events in and out of waves (the second half of

equation (4)) – as well as their relative size. This will provide intuitive evidence of whether

my revised empirical framework is warranted within waves and less warranted out of waves

(as expected).

10 Note that even though we remove a given acquisition strategy due to insufficient data, we still keep the individual acquisition(s) as an industry event for the sake of the remaining acquisition strategies, since they are still affected by it. 11 Notably, we have removed 33 acquisition strategies in which the acquirer was later acquired itself.

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The correlation between firm event returns and the firm returns to industry events. If

industry events do not lead to partial anticipation or ongoing revision of firm events, I expect

to find that the ‘simple’ firm event returns (the first half of equation (4)) are uncorrelated with

the firm returns to industry events (the second half of equation (4)) within the relevant

estimation period. For strategies within waves, this means comparing the firm returns to its

acquisitions (for acquirers) or its sale (for targets) to the firm returns to the industry rival

acquisitions carried out within the same industry merger wave. For strategies out of waves,

this means comparing the firm returns to acquisitions (for acquirers) and sale (for targets) to

the firm returns to the industry rival acquisitions carried out during the estimated pseudo

merger wave.

When looking at out-of-wave strategies, the correlation between target event returns

and the target returns to industry events is very low and insignificant at 0.014. The same is

true for the correlation between acquirer event returns and acquirer returns to industry events,

which is 0.032. This lack of correlation between firm event returns and the firm returns to

industry events suggests that industry events do not lead to partial anticipation or ongoing

revision of the economic consequences of firm acquisitions/sale out of waves. In fact, this

implies that firm events out of industry waves are founded on a separate economic foundation

from that of the industry events which occur within a similar time frame. Thus, the

conceptual framework assumed by traditional event study analysis would therefore seem

sufficiently adept for acquisitions and sales out of waves.

When I observe the correlation between firm event returns and firm returns to

industry events within waves, a different pattern emerges. The target event returns and the

target returns to industry events are significantly negatively correlated at -0.211. I interpret

this to mean that firms whose sale is highly anticipated ex-ante do not achieve as high event

returns as firms whose sale is less highly anticipated. This correlation supports the validity of

the ‘acquisition probability’ hypothesis of Song & Walking (2000) which states that the stock

market responds to the signals provided by other industry acquisitions that a given firm may

become a future target. The acquirer event returns are also correlated with the acquirer

returns to industry events, but positively so at 0.245. It would therefore seem that the market

anticipates and revises acquisition returns (whether gains or losses) throughout industry

merger waves.

The relative size of firm event returns and the firm returns to industry events.

The merger wave context also has a significant impact on the relative size of the firm event

returns and firm returns to industry events. Remember in equation (4) that the sum of the two

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makes up the ‘full’ returns to an acquisition strategy. When I look at the target event returns

out of waves, they contribute on average 141% of the full returns (the median is 92%),

implying that the economic consequences of a target event are well captured by the short

event window. The stock market does not seem to update its expectations (or lack thereof) of

a target event on the basis of industry events which occur within a similar time frame.

However, share of the target event returns falls to 39% when I look at strategies within waves

(the median is 63%), implying that the partial anticipation present in industry merger waves

moves much of the expectation of the economic consequences of a target event to the days

surrounding the announcement of industry events.

A similar, but more influential effect occurs on the acquirer returns. While on average

26% (the median is 21%) of the full returns to an out-of-wave acquisition strategy are

contributed by the acquirer event returns, this changes to -5% within waves. The median is

positive at 7%. This means that the acquisition strategies put in place by rivals during

industry merger waves have a significant effect on the expected economic consequences of

firm acquisitions, and that this effect is not captured by the simple acquirer event returns. In

my conceptual framework these effects must be taken into account, since they present real

economic effects of the ongoing restructuring which – in response to fundamental economic

industry changes – moves the industry from one competitive equilibrium to the next.

I believe that the correlation measures and the relative size of the return components

in equation (4) present a case for my conceptual approach, although I cannot rule out the

importance of unknown economic or non-economic factors. Nevertheless, the choice of

whether to accept this novel approach in place of a traditional event study approach remains a

matter of whether one accepts – and wishes to take into account – the partial anticipation of

acquisitions and the interdependency between acquisitions which cluster as a consequence of

a common industry event. In this regard, I favour my ‘equilibrium approach’.

EMPIRICAL ANALYSES

Descriptive Statistics

Acquisition strategies. Table 1 provides descriptive statistics of the sample of

acquisition strategies, noting the difference between the wave and non-wave periods. I also

note the differences between the first 25% acquisition strategies, the last 25%, and the

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strategies in between, which I refer to as first-movers, late-movers and middle-movers,

respectively12. Pairwise comparisons of sub-samples use the Wilcoxon rank-sum test.

To report the deal characteristics relating to stock payment, relatedness, hostility and

stock payment at the acquisition strategy level, I construct them as weighted averages, using

the values of the acquired firms (measured at the beginning of the wave) as weights. For

example, if an acquirer has acquired two target firms of equal size, one of them completely

with cash and the other one completely with stock, the measure of stock payment would be

0.5. If the first target firm were twice as large as the second target firm, the variable would be

equal to 0.33.

------------------------------ Insert Table 1 about here ------------------------------

I see from the deal characteristics in panel A that acquisition strategies are on average

73.60% related to its given industry. This is above normal compared with previous research

since our methodology expands the definition of relatedness. I see that in-wave acquisition

strategies are significantly less related than out-of-wave strategies (67.59% vs. 77.63%, P-

value < 0.001).

Not surprisingly, there are more serial acquirers and more acquisitions per acquirer

within waves. First-movers are the most frequent acquirers, although the difference is only

significant compared with later-movers.

There is a higher degree of stock payment within waves (56.29% vs. 47.78%, P-value

< 0.001), which corresponds to previous reports by Andrade et al. (2001) and numerous other

authors. The use of stock payment is constant across the timing of acquisition strategies, and

it is primarily a 90s phenomenon – only on average 33.13% of acquisition strategies in the

first sample period are paid in stock13.

The firm characteristics noted in panel B, which are collected from Standard & Poor’s

COMPUSTAT database of US accounting data, show no marked differences between in-

wave and out-of-wave acquisition strategies bar two. Firstly, the relative size of the acquirer

12 Note that unlike previous research, we measure first-moving acquisition strategies as opposed to first-moving acquisitions. This means that acquisitions conducted by a serial acquirer which initiates its strategy early in the wave are all considered part of a first moving acquisition strategy. This is consistent with the logic underlying the theoretical propositions. 13 A strand of research argues that the use of stock payment is an especially important issue in merger wave theory. Specifically, stock payment may heavily affect the decision to acquire, and it may allow an acquirer to appropriate value from target firms by conducting stock-swap mergers on advantageous terms (Shleifer & Vishny, 2003; Rhodes-Kropf, & Viswanathan, 2004). I do not wish to examine this issue directly, since this paper focuses on synergistic gains (or lack thereof). However, I will control for the influence of payment choice on returns.

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to the target(s) is lower for out-of-wave strategies than in-wave strategies, expect for first-

movers. Secondly, both the market-to-book ratio and returns on assets of acquirers are higher

within waves. There is also some evidence that later-movers are valued lower and have less

free cash compared with other in-wave acquirers.

Value creation, appropriation and destruction from acquisition strategies. Table

2, panel A, presents the value created in acquisition strategies across the whole sample and

separately on the 80s and 90s sample. Dollar values are inflation-adjusted and reported at

2004 values.

------------------------------ Insert Table 2 about here ------------------------------

Table 2 shows quite clearly that there is extraordinary value destruction in the 1993-

2004 period. In fact, over the whole period $1,672bn is destroyed through acquisition

strategies, and $1,661bn stems from the period 1993-2004. These values are of course

completely outrageous, and most likely the highest figure ever reported for the 90s merger

value destruction in any research, which owes to the empirical method applied. Moeller et al.

(2005) examine the returns to US acquisitions conducted from 1980 through 2001. They

measure acquisition returns using a traditional 3-day CAR methodology and report an

aggregate $90.2bn loss in 1991-2001 compared with an aggregate $11.6bn gain in the 80s. In

contrast, I conclude that value was destroyed in the 1981 to 1993 period ($11.0bn in total).

Splitting up mergers on in-wave and out-of-wave periods, I see that both contexts

destroy value overall, to the tune of $1,339bn and $333.9bn respectively. As a consequence

of this value destruction, the mean dollar return to an acquisition strategy in the full sample

period is -$1.0bn. Figure 1 plots the dollar merger returns (i.e., value creation) for the sample

of acquisition strategies by the first year of the acquisition strategy.

------------------------------ Insert Figure 1 about here ------------------------------

Figure 1 shows quite clearly that a series of highly value destroying acquisition

strategies as well as highly value creating acquisition strategies are conducted in the 90s,

although the former is more prominent than the latter. Beyond these eye-catching returns, the

bulk of merger activity achieves returns distributed around zero. The median dollar return is

positive at $3.25m, which confirms that the extreme aggregate dollar losses noted above are

not representative of the full sample. Since it is my endeavour to examine the importance of

the merger wave context on the ‘representative’ acquisition – and not the extremes – I turn to

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explain where these extreme values come from; how they affect the sample; and how I treat

them subsequently in the paper.

Moeller et al. (2005) find that the ‘massive’ wealth destruction reported in their study

stems from 87 large-loss acquisitions in the period 1998-2001. Since I use the same database,

these acquisitions are also present in my sample. It is therefore likely that the large-loss

acquirers in Moeller et al. (2005) conduct many of the acquisition strategies experiencing

very poor returns in figure 1. Moeller et al. report that these acquirers are highly valued prior

to their acquisitions. They note that the acquisition returns may in fact not reflect the

economic consequences of the acquisition, but rather the stock market’s re-evaluation of the

fundamental value of the firm. Specifically, they suggest that it is “... highly likely that part of

the loss is attributable to a reassessment of the future cash flows of the acquirer as a stand-

alone firm” (p. 765: 39-41). Since this reassessment is obviously not related to the economic

consequences of an acquisition strategy, the returns reported here and in Moeller et al. (2005)

overestimate the actual economic value destruction relating to these acquisitions and their

acquirers. Furthermore, the effect is likely to be exacerbated in my methodology, which

includes the firm returns to industry rival acquisitions as well. This would explain the

extreme losses in the 1993-2004 sample. Since it is my goal to judge the value creation,

appropriation and destruction from M&A – and not how some acquisitions signal the market

to re-evaluate the stand alone value of the acquirers – I seek to understand, and deal with this

‘signalling’ effect in my empirical testing.

I argue from popular knowledge that the observed value destruction in the 90s is a by-

product of the bubble market which rose – and broke – in the period 1997-2001 on the basis

of the ‘New Economy’ revolution. And the sample seems to validate this. Of the 207 firm

acquisition strategies which succeed in gaining or losing more than their own market value

(measured at the beginning of the industry merger wave), 154 come from the ‘Electronic

Equipment’, ‘Computers’, ‘Communication’ and ‘Business Services’ industries, and 146 of

these acquisition strategies stem from the period 1993-2004. The ‘Business Services’ industry

– a depository for a number of ‘dot-com’ or internet related firms – is the main contributor

with 80 of the 207 acquisitions. The eight most value destroying strategies from industry

merger waves beginning in 1997 – which can be seen in clearly figure 1 – are courtesy of

acquirers in ‘Business Services’.14 Overall, table 2 shows significant variation in returns on

14 CMGI (College Marketing Group Information) Inc., a dot-com firm in the ‘Business Services’ industry, provides a poignant example of the pattern of returns to firm and industry acquisitions when these acquisitions signal the need for revising expectations of industry and firm future cash flows. CMGI conducted four

Value creation, appropriation and destruction in M&A

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account of the 90s bubble market. However, it is also clear from table 2 that signalling effects

do not as heavily affect the acquisition strategies 1981-1992, which is also in line with the

results in Moeller et al. (2005).

It is in general impossible to separate the ‘true’ economic effect of acquisitions from

these signalling effects. As opposed to narrowing or otherwise redefining the sample of

acquisition strategies, I choose to use statistical techniques in my univariate and multivariate

analyses to account for the potential effect of these extremes.

Having set aside these issues, I focus my attention on describing the median values,

which per definition offer a more representative look at the samples. The median acquisition

strategy creates value of $3.25m. However, the non-parametric Wilcoxon sign test and the

signed rank test do not show that the mean is significantly different from zero, i.e., I have no

evidence that the average acquisition is value creating. In fact, the signed rank statistic is

negative and weakly significant (P-value = 0.054). Thus, I do not replicate the general result

that acquisitions on average create value (Andrade et al., 2001; Bradley, Desai & Kim, 1988;

Moeller et al., 2005).

The full sample masks a clear difference between the value creation of mergers in

waves and out of waves. Panel A shows that median out-of-wave acquisition strategy creates

$15.4m, while the median in-wave acquisition strategy destroys $37m. According to the

Wilcoxon rank-sum test, the difference is highly significant (P-value < 0.001). The result

carries over to the acquisition returns relative to the acquirer and target firm values. Most

notably, while the median acquisition strategy out of wave creates roughly 1.5% of acquirer

value, the corresponding in-wave acquisition strategy destroys just above 4.3%. Wilcoxon

sign and signed rank tests show that in-wave acquisition strategy returns are significantly

negative at the 5% level for in-wave mergers, while they are positive for out-of-wave

acquisition strategies, although only the sign test is significant (not reported).

I move on to statistics of value appropriation in Panel B, which essentially displays

the same mean value destruction. Observing the sample medians, I see that acquirers destroy

value both out of waves and within waves (-$19.8m and -$90.1m respectively), and the

Wilcoxon tests are statistically significant at the 1% level (not reported). Relative to the value

acquisitions in the 40 months span of the 90s wave in ‘Business Services’, and succeeded in losing 181 times its own value in the process! At the beginning of the wave in August 1997, its stock market value was $151.5m. But when it conducted its first acquisition on September 20th, 1999, it was valued at $9.5bn, which increased to a staggering $29.1bn by the time it conducted its last acquisition less than 5 months later. Before the bubble burst for CMGI, its stock reached $163 pr. share ($40bn); in 2002, it dropped to below $1 (Wikipedia). According to the empirical methodology, it experienced a loss of $2.0bn (nominal) in event returns and a loss of $23.2bn in returns to acquisitions conducted by industry rivals.

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of the acquirer and the value to the target(s), the in-wave sample also presents the highest

acquirer value destruction (i.e. negative value appropriation). And the relative values are

quite high: an acquisition strategy in (out of) a merger wave destroys 9.0% (2.1%) of

acquiring firm value.

Overall, these results are more pessimistic than we might expect given the extant

literature. Many studies support the idea of slight value creation coupled with zero value

appropriation, especially within the 1981-2004 sample period (Andrade et al., 2001; Bradley

et al., 1988). However, it seems that taking into account partial anticipation and ongoing

revision of the value of acquisition strategies reveals negative value appropriation. In

addition, this univariate evidence reverses previous weak evidence that merger waves imply

higher value creation and higher value appropriation (Harford, 2003). In fact, they destroy

value. If I were to conclude on this aggregate sample, I would argue that the potential for

value creation and appropriation was less within merger waves.

Notably, there is no evidence of any timing advantages or disadvantages in value

creation or appropriation. Middle-movers are generally below both first-movers and late-

movers, but the difference is never close to significance. This is somewhat surprising

compared with previous research (Carow et al., 2004; Harford, 2003; McNamara et al.,

2008).

I argue that none of the above results tell us whether the extent of value creation and

appropriation is higher in or out or waves, or for first-moving or last-moving strategies etc.

Like many other acquisition studies, the sample displays a great deal of dispersion in returns

around a near-zero median (e.g., Moeller et al., 2005). This suggests that both value creating

and value destroying motivations co-exist within the sample. Since value destroying

acquisitions are arguably driven by managerial motivations founded in agency theory or

behavioural theory, these acquisitions have little to say about the potential for a

synergistically motivated acquisition to create value, and the potential for such acquirers to

appropriate this value. Therefore, it is clear that I must separate acquisitions which are

synergistically motivated from those which are managerially motivated to uncover the true

potential for value creation and appropriation within waves; only by isolating value creation,

appropriation and destruction can I answer my hypotheses.

The change in perspective essentially splits the research question into two separate

questions. First, it asks whether value creating or value destroying motives drive acquisitions

in and out of mergers waves. Second, it asks to which degree synergistically motivated

acquirers create and appropriate value in and out of merger waves. I refer to these two

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147

research question combined as: what is the incidence and extent of value creation and

appropriation in and out of merger waves? I move on to examine in turn the incidence and

extent of value creation and appropriation.

The Incidence of Value Creation, Appropriation and Destruction

Examining the relative incidence of synergistically motivated and managerially

motivated acquisition strategies in and out of waves will answer hypotheses 1A and 1B and

provide the two classes of acquisition strategies on which to examine the extent of value

creation, appropriation and destruction.

There are essentially four distinct outcomes to merger activity, each of which implies

certain determining factors. When the combined returns to the acquirer and its target(s) are

positive, the merger has created value. In this case, if the acquirer return is positive, then

there is acquirer value creation and appropriation due to at least partially privately

appropriable from a competitive advantage and/or industry market power gains (Blunck &

Anand, 2009). If both acquirer and combined returns are negative, then I conclude that

acquirers destroy value because of a managerial motivation founded in agency or institutional

theory. In between these two clear-cut outcomes are two less clear outcomes. Firstly, if the

combined returns are positive, but the acquirer returns are negative, then there is value

creation, but also acquirer overpayment due to the effect of competitive spillovers and/or

managerial motivations on competitive bidding strategies (Blunck & Anand, 2009). I cannot

immediately distinguish between these two explanations. Secondly, it could also be the case

that there is negative merger value creation, but acquirer value appropriation. Although it is

somewhat unlikely, it may still occur if there is a sufficient excess supply of target firms.

I tabulate the frequency of the four acquisition outcomes across the in-wave and out-

wave contexts in table 3. To my knowledge, this represents the first overview offered of the

outcomes to acquisitions in and out of merger waves. I trivially confirm that the dispersion in

returns is driven by the co-existence of different motives to acquisition. The bulk or

acquisition strategies lead to either value creation and value appropriation, or value

destruction and negative acquirer value appropriation.

------------------------------ Insert Table 3 about here ------------------------------

In the bottom of table 3 I test whether frequencies are homogenous across in-wave

and out-of-wave mergers and across the wave. Pearson’s Chi-square test strongly rejects the

hypothesis that the distribution of outcomes in and out of waves samples are statistically

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associated (χ2(3) = 19.88 (P-value < 0.001)). Similarly, using McNemar’s test for 2x2 tables,

I also find that the incidence of merger value creation (and value destruction) is

heterogeneous across waves (P-value < 0.001). In fact, only 45.6% of in-wave acquisitions

create value compared with 54.4% out of waves. Therefore, I confirm hypothesis 1A.

Specifically, in-wave mergers are more likely to be value destroying, which I interpret as

evidence that in-wave mergers are more likely to be driven by managerial motivations.

At the same time, adding up outcomes 1 and 2, I see that 43.9% of acquirers out of

waves appropriate value, while this is the case for only 39.5% of acquirers within waves.

However, the difference is not significant according to McNemar’s test (P-value = 0.267).

The relatively low incidence of value creation is not driven by large differences across

the timing of acquisition strategies; value creation is below 50% throughout. Homogeneity

testing shows that I cannot reject the hypothesis that the outcomes of acquisitions at different

points in the wave are statistically related. This suggests that acquirers are similarly

motivated throughout waves. However, when I look at the value creation outcomes, (1) and

(3), I see that both first-movers and late-movers have a significantly higher incidence of value

creation than the middle-movers. This is surprising, since hypotheses 1B and 1B-alt expected

late-movers and first-movers, respectively, to be the most likely to be value destroying. This

suggests that agency or behavioural motivations less obvious to existing theory have a

general effect on industry merger waves. Therefore, I reject hypotheses 1B and 1B-alt.

Notably, it would seem that a larger portion of late-move acquirers appropriate value

than first-movers, which is quite surprising and counter-intuitive. I can offer no clear

explanation for this. It goes against the conclusion of Harford (2003) that first-moving

acquirers are more likely to be driven by efficiency motives rather than hubris and agency,

and similarly, that late-moving acquirers are more likely to be driven by behavioural and

agency motives rather than efficiency.

In all, there is less (more) chance of an acquisition being driven by value creating

(value destroying) motives in waves. There is also less (more) chance of an in-wave acquirer

appropriating (destroying) value, although the difference is not significant. However, this

does not say anything about the extent of value creation and appropriation. I move on to

examine whether the incidence of more value creating destroying motives out of waves

implies that there is also a greater extent for value creation, appropriation and destruction out

of waves. To do this, I analyze separately the value creating and value destroying acquisition

strategies.

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The Extent of Value Creation, Appropriation and Destruction

Univariate evidence. Table 4 displays the extent of value creation, appropriation and

destruction in the value creating and value destroying sub-samples, measured by the mean

and median returns to acquisition strategies. Again, I report returns in absolute terms, relative

to the value of the target(s) at the beginning of the wave, and relative to the market value of

the acquirer at the beginning of the wave.

------------------------------ Insert Table 4 about here ------------------------------

Table 4, panel A shows that both the means and medians of the merger value created

are higher within waves. The average value creation by an in-wave (out-of-wave) merger is

$3.1bn ($826m), but there is also a very high level of dispersion, since the median is $308m

($163m). The median value created relative to target market value is 84% and 58% in and out

of waves, respectively, and it is 25% and 13% when measured relative to acquirer market

value. All together, in-wave mergers with positive value creation outperform the out-of-wave

mergers by nearly 2 to 1, and the Wilcoxon rank-sum test is highly significant (P-value <

0.001), providing strongly significant, univariate support for hypothesis 2A. The Wilcoxon

tests show that the higher value creation in waves is independent of whether the acquisition

strategy is among the first, middle or last of the wave strategies, which leads me to reject

hypothesis 2B. While I did not find a first-mover or late-mover effect on the aggregate

sample, it is still surprising that splitting up of the sample does not lead to evidence of timing

effects on the extent of value creation. On the basis on this univariate evidence, we cannot

confirm the existence of a first-mover advantage in value creation as found by Harford

(2003).

Panel B of table 4 shows materially similar results for value appropriation. In-wave

(out-of-wave) acquirers appropriate $2.8bn ($601m) of the gains on average with a median of

$189m ($66m). The median relative to the size of the target firm(s) and the acquiring firm is

69% and 18%, respectively. This compares to 33% and 6% out of waves. The Wilcoxon

rank-sum test is highly significant (P-value < 0.001). Again, there is no significant difference

with regards to the timing of acquisitions within waves. In all, the univariate evidence on

value appropriation supports hypothesis 3A but not hypothesis 3B. The lack of timing effects

is again surprising and we cannot find support for a first-mover advantage in value

appropriation as found by Harford (2003) and McNamara et al. (2008).

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The counterbalancing force to the higher value creation and appropriation within

waves is the very significant, negative effect of the wave context on the value destruction

sample. Panel C documents that of the staggering $3,034bn destroyed in the sample of value

destroying acquisition strategies, the in-wave sample contributed ‘only’ $777bn. Both the

mean and median value destruction is 3 times as high within waves, and the Wilcoxon rank-

sum test is highly significant. Hence, there is strong univariate evidence supporting

hypothesis 4A. Once again, there is no marked difference across the stages of the industry

merger waves, although there is some evidence that middle-moving acquisitions destroy more

value than first-movers, who destroy the least. However, the difference is only significant on

the returns relative to acquirer market value. And it does not support hypothesis 4B that the

value destruction should be highest among late-movers.

The results in panel D on the acquirer returns to value destroying mergers are close to

synonymous with the above result for the merger value destruction, and I therefore have

univariate support for hypothesis 5A, but no support for 5B.

Overall, I see strong univariate evidence that the in-wave mergers both create more

value and lead to higher value appropriation, while they also destroy more merger value and

acquirer value than acquisitions out of waves. Thus, this univariate evidence nuances the

aggregate evidence in table 2 that acquisition strategies within waves were generally more

value destroying than acquisition strategies out of waves. However, the separation of value

creating and value destroying samples has not unearthed any clear differences between

merger wave acquisition strategies which are initiated at different times. The lack of evidence

goes against my hypotheses as well as previous literature (Carow et al., 2004; Harford, 2003;

McNamara et al., 2008).

Multivariate regression. I move to multivariate regression with dummy variables to

certify the univariate results. I use the inflation-adjusted dollar returns and the returns relative

to acquirer market value as my return (dependent) variables. I use a wave dummy (Wave)

which is coded one for acquisition strategies within waves and zero otherwise, and I define

dummy variables for first-movers and late-movers as well (First-mover and Late-mover,

respectively). To split up the sample on synergistically and managerially motivated

acquisition strategies, I use a dummy variable (Sign) which is coded one for acquisition

strategies with positive value creation and zero for negative value creation (i.e. destruction).

To secure the validity of the findings, I add the necessary control variables identified

in previous literature (see McNamara et al., 2008 and many others). I include the variables

tabulated in the descriptive statistics section: ROA, Cash (& equivalents)/assets, Market-to-

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book, Acquirer market value and Target market value. Sequential acquirer is a dummy coded

one for firms which acquire more than one target firm during the estimation period.

Relatedness, Hostility and Stock payment are weighted averages of the acquisition strategy

deal characteristics as described in the ‘Descriptive statistics’ section. Also, I add variables to

control for the empirical irregularities relating to stock market expectations which I identified

earlier. Firstly, I add the number of industry events occurring during the estimation period

(Industry events) to control for those acquisition strategies in which fundamental firm market

value may be subject to several re-evaluations. Secondly, I include a dummy for the sample

period 1993-2004 (Period 1993-2004), as well as industry dummies for the individual

industry merger waves. These variables control for the potential effects of the bubble market

of the 90s. However, I have also run the regressions without the 90s industry merger waves in

‘Electronic Equipment’, ‘Computers’, ‘Communication’ and ‘Business Services’. The results

are materially unchanged. I do not tabulate industry merger wave dummies. F-tests show that

their combined statistical significance is never below 0.6 in any regression. The data required

for constructing control variables decreases the sample of acquisition strategies from 1,646 to

1,445 for the tests of merger value creation and 1,451 for the tests of acquirer value

appropriation.

Table 5 and 6 reports the regressions for the determinants of value creation and the

determinants of acquirer value appropriation, respectively. I first run regressions without the

First-mover and Later-mover dummy variables to test the general influence of the merger

wave context for each return measure (columns 1 and 2). I then include these two dummies

and their interaction with Sign in columns 3 to 4, which means that the estimate of the Wave

dummy changes to signify the marginal effect of being a middle-mover. I calculate the

statistical significance of regression parameter estimates using the F-test based on White’s

heteroskedasticity-consistent covariance matrix (White, 1980).

------------------------------ Insert Table 5 about here ------------------------------

Columns 1 and 2 in panel A show that multivariate regressions confirm the univariate

results concerning the influence of the wave context on value creation and value destruction.

Specifically, the Wave dummy, which captures the effect of being in a wave given that the

strategy is managerially motivated, is significantly negative. However, the level of

significance is strongest when I use absolute returns as the dependent variable (P-value =

0.034%); it is only weakly significant when I use returns relative to acquirer value (P-value =

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0.096%). In all, I accept hypothesis 4A that the extent of value destruction is higher within

waves.

To see how the returns to synergistically motivated acquisition strategies changes

within an industry merger wave, I need to calculate the sum of the marginal effect of the

interaction between the dummy variables Sign and Wave and the marginal effect of the Wave

dummy. I report the test in (a) in panel B. It is highly significant for both return measures. I

therefore accept hypothesis 2A that the extent of value creation is higher within waves.

Table 6 shows that the effect of the wave context is the same for value appropriation.

The wave context leads to greater value appropriation for synergistically motivated

acquisition strategies (P-values are below 0.01). It seems that the conditions in the market for

corporate control during waves do not materially affect the ability of acquirers to appropriate

the higher value. Value destruction for managerially motivated acquisition strategies is also

greater within waves, although the evidence is not as strong using absolute or relative dollar

values (P-values are 0.059 and 0.067, respectively). Thus, I accept hypotheses 3A and 5A.

------------------------------ Insert Table 6 about here ------------------------------

Columns 3 and 4 in tables 5 and 6 introduce the timing variables. The marginal effect

of timing is represented in the following way. First-mover and Late-mover capture the

marginal effect of being a first-mover and a late-mover, respectively, compared with being a

middle-mover, given that the strategy is value destroying. The marginal effect of being a

first-mover and a late-mover compared with being a middle-mover, given that the strategy is

value creating, is calculated by summing First-mover and First-mover*Sign, and Late-mover

and Late-mover*Sign, respectively.

Neither table 5 nor table 6 shows any timing advantages or disadvantages among

synergistically motivated strategies. Test (b) in panel B of both tables 5 and 6 shows that

there is no difference between middle-movers and first-movers, in that the sum of First-

mover and First-mover*Sign is insignificant. Similarly, test (c) shows that later-mover

advantages also do not have an advantage over middle-movers. And finally, test (e) shows

that there is no difference between first-movers and late-movers, in that the sum of First-

mover and First-mover*Sign is not statistically different from the sum of Late-mover and

Late-mover*Sign. In all, value creation and appropriation in merger waves is independent of

timing and I can reject hypothesis 2B and 3B.

Similarly, there is no evidence that late-movers both destroy and negatively

appropriate more value, and I reject hypotheses 4B and 5B. However, there is minor evidence

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153

that first-movers destroy less merger and acquirer value when I use returns relative to

acquirer value as the dependent variable. First, First-mover is positive in both tables 5 and 6

and close to the 5% significance level when I use returns relative to acquirer market value,

suggesting that first-movers destroy less merger and acquirer value than middle-movers.

Also, test (d) in panel B show that the marginal effect of being a first-mover vs. a late-mover

is weakly significant in table 5 (P-value = 0.068) and borders on significance in table 6 (P-

value = 0.113). In all, this provides weak evidence suggesting that managerially motivated

acquirers who move first do not destroy as much value or achieve as high negative returns as

middle-movers and later-movers. Theoretically, this could imply that even among

managerially motivated acquirers, there may be a first-mover advantage in being able to

choose among more and better targets, although there is no corresponding late-mover

disadvantage. However, that this evidence does not appear in the absolute returns weakens it

significantly.

To summarize, I can accept hypotheses 2A and 3A that synergistically motivated

acquisition strategies create and appropriate more value within waves, while I can reject the

existence of first-moving differences (hypotheses 2B and 3B), implying that both value

creation and appropriation are independent of timing. I accept hypotheses 4A and 5A that

managerially motivated acquisition strategies destroy more value and lead to lower returns

within waves. However, I reject the existence of any additional later-moving disadvantages in

this regard (hypotheses 4B and 5B).

Overall, I have shown not only that acquisitions within waves are more likely to be

managerially motivated, but also that the extent of value destruction is greater within waves

than out of waves. However, although in-waves acquisitions are less likely to be

synergistically motivated, I nonetheless conclude that the extent of value creation and value

appropriation in these mergers is higher than out of waves. Table 6 summarizes my

hypotheses and the results of the empirical testing.

------------------------------ Insert Table 7 about here ------------------------------

DISCUSSION AND CONCLUSIONS

This paper draws on novel conceptual insights to investigate the returns to M&A in

merger waves and merger troughs as well as the returns to M&A at different stages of the

waves. Firstly, both value creating and value destroying motives co-exist and research should

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therefore separately judge theoretically and empirically the extent of value creation,

appropriation and destruction. This implies the emergence of two separate research questions

relating to the incidence and extent of value creation, appropriation and destruction.

Secondly, the returns to individual acquisitions which respond collectively or competitively

to a common industry economic shock fundamentally intertwine, and traditional empirical

methodologies should be revised in kind. Beyond changing the empirical specification of

abnormal returns, this means focusing on the returns to acquisition strategies as opposed to

the simple returns to individual acquisitions.

This paper accepts empirically some theoretical hypotheses distilled from existing

merger wave theory, while rejecting others. Firstly, drawing on agency and

institutional/behavioural merger wave theory, I argue that acquirers within industry merger

waves are more likely to be motivated by managerial incentives or misperceptions than

acquirers out of industry waves. I confirm this by observing that acquisition strategies within

waves are more likely to destroy than create value, while acquisitions out of waves are more

likely to create value. Secondly, I argue from the resource-based view that synergistically

motivated acquirers within industry merger waves are likely to create and appropriate more

value than similarly motivated acquirers out of waves. Thirdly, I argue from institutional

theory that managerially motivated acquirers within industry merger waves are more likely to

destroy more value and achieve worse negative returns than such acquirers out of waves. I

accept these hypotheses in both univariate and multivariate testing. Throughout this series of

tests, there is no material difference between the incidence and extent of value creation,

appropriation and destruction across the first, middle and later stages of the industry merger

wave. Hence, there is no evidence supportive of the first-mover advantages in value creation

and appropriation professed by the resource-based view or the later-mover disadvantages in

value destruction expected by institutional theory. These results hold whether I measure value

in inflation-adjusted dollar returns or relative to acquirer firm values.

This paper contributes to the existing theoretical and empirical research in several

ways. Firstly, by redrafting the fundamental research questions and removing the

confounding effect of changes in the relative frequency of managerial motivations, I am able

to more correctly propose and test hypotheses relating to the potential for value creation and

value appropriation. These are the central questions in the strategic management research in

M&A.

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155

Secondly, the separation of value creation and value destruction allows the latter to

finally be addressed in its own right. More work is needed to explain the pattern of

managerial motivations over time.

Thirdly, the conceptual specification of the returns to acquisitions highlights that first-

mover advantages in industry merger waves should be addressed at the level of acquisition

strategy, not individual acquisitions. It makes little sense to argue a resource-based

explanation of competitive advantages without understanding how building on both existing

and recently acquired resources promotes such advantages.

Finally, the dual focus on value creation and destruction allows me to juxtapose

merger theories to decide to which degree they explain the incidence and extent of value

creation, appropriation and destruction in and out of merger waves. Delineating their

importance relative to each other is a key requirement for moving towards building a theory

of mergers and merger waves, which can serve as a foundation for future theoretical and

empirical research on the central questions concerning M&A – its cause, course,

characteristics and consequences (Weston, Chung, & Hoag, 1990). In this regard, a clear

avenue for future research is the focus on value creation, appropriation and destruction from a

merger trough perspective. There is little theoretical basis for understanding the cause and

consequences of acquisitions and acquisition strategies out of waves.

The empirical outcome of this study provides some answers to the relative importance

of the facets of theories of mergers and merger waves, but it also raises several questions. The

lack of evidence of timing effects in value creation, appropriation and destruction is an

obviously contentious point, since they constitute an important corollary of both theories of

value creation and theories of value destruction. Beginning with the latter, both the job

protection theory of Gorton et al. (2005) and the institutional theory of Auster and Sirower

(2002) play heavily on timing differences. Especially the lack of a late-mover advantage as

expected by the latter theory is surprising15. Note that value destroying acquisitions in waves

may just be driven by general, ‘latent’ managerial motivations which are realized in response

to broader economic changes and looser capital constraints.

Equally intriguing is the lack of the first-mover advantage in value creation and

appropriation expected by resource-based reasoning and prior evidence. I note a theoretical

and empirical explanation why returns are independent of the timing of acquisition strategies.

15 McNamara et al. (2008) note that there may be a slight curvilinear relationship between value appropriation and the stages of the wave. They find some evidence of this using traditional event study methodology, while it does not fit my results well.

Chapter 3

156

First, the concept of a first-mover advantage in waves derives from the opportunity and/or

need of superior acquirers to conduct their acquirers quickly to avoid missing out on critical

assets and/or overpaying for them. However, superior acquisitions could occur at any time

throughout the wave if the competitive advantages sought in acquisitions generally build on

longer-lasting firm-specific resources or private knowledge held by bidding firms. This

would mean that bidders build or acquire the assets prior to the wave on account of

pioneering foresight or perhaps just luck (Barney, 1986, 1988). Additional empirical work is

required to accept this explanation. Second, I can point to the empirical difficulty in locating

first-movers (Carow et al., 2004). There may be fewer of them or they may acquire in the

months leading up to the beginning of the merger waves. Either way, it is not clear that any

method of merger wave identification – including mine – should succeed effortlessly in

identifying them. Future work could investigate more finely the returns to first-moving

acquisition strategies.

The novel returns methodology employed in this paper is clearly pivotal to the

missing timing advantages and disadvantages. I have argued that previous studies on the

returns across the merger wave context are inadequate in effectively compiling the economic

consequences of M&A. Notably, while my approach takes into account partial anticipation

and ongoing revision, the previous short run event studies were essentially ‘raw’ to these

influences. I believe that existing evidence of timing effects in industry merger waves owe

partially to the inadequacy of these studies to take into account the patterns of partial

anticipation and revision of the economic consequences of future acquisition activity.

The extent of value destruction showed in this paper also deserves additional

discussion. As noted, the extent of value destruction displayed here is beyond anything

shown by previous research. This is likely due to the chosen empirical methodology, in that it

picks up information about the economic consequences of acquisition strategies released on

the announcement days of rival acquisitions. When firm or rival acquisitions signal the need

for a negative re-evaluation of firm future cash flows, the returns will be biased downwards. I

confirm that this heavily affects some ‘New Economy’ firms and industries. In this sense, I

expand on the empirical analysis of Moeller et al. (2005), which focuses on the 89 largest

losing US acquisitions 1997-2001. However, I am able to show that the incidence and extent

of value destruction is generally higher in US industry merger waves 1980-2005. This is true

even when controlling for these signalling effects.

Value creation, appropriation and destruction in M&A

157

Prescriptive Implications

The results rewrite the prescriptive implications of previous studies on M&A in and

out of industry merger waves. Unlike McNamara et al. (2008), I see no reason to recommend

that managers cease from acquisitive activity if an industry merger wave is already underway

without them. Rather, I could say that if managers see a synergistic opportunity to acquire,

they should take it at any point in the wave. Also, I can add that a manager should not shy

away from conducting acquisitions when no one else in the industry is doing so. In fact, the

only condition to obey, whether conducting an acquisition in or out of waves, is to avoid self-

interested behaviour, and especially, to guard against the behavioural biases which the merger

wave context may allow to prosper. This prescription is perhaps most poignant for the board

of directors, whose task it is to coordinate the control systems set in place to curb managerial

motivations.

Limitations and Future Research

I have argued – and shown – that industry merger waves present a context in which it

is necessary to modify traditional short-run event study methods to take into account both

partial anticipation and the ongoing revision of acquisition returns. Future research can use a

more detailed analysis of the parameters of my empirical methodology in conjunction with

existing short-run and long-run event study methods to further uncover the pattern of

anticipation, revision and the true economic effects. In this regard, I believe that my empirical

methodology can also assist in the delineation (and following treatment) of the signalling

effects observed in the late 90s as reported here and in Moeller et al. (2005). In all, I believe

that my empirical approach could be valuably employed in other studies on the returns to

M&A, and I look forward to future research which validates or re-moulds the empirical

framework.

As regards the main results of this paper, it is clear that future research should build

on these results to delineate the importance of the specific industry economic context on

value creation, appropriation and destruction in acquisitions. Carow et al. (2004) and

McNamara et al. (2008) hypothesize some effects of the economic context on the returns to

acquisitions in industry merger waves and at different stages of the wave. I also note that the

type of competition and value creation within an industry would presumably affect the

potential for sustainable asset advantages, while the impact and duration of the underlying

economic changes would affect the specific number and timing of first movers. The theories

of value destruction present similar potential for differences across industries. In general, this

Chapter 3

158

paper’s theoretical and empirical contributions open up for a more detailed approach to

uncover the specific workings of industry merger waves and the role of different types of

firms. However, such a study requires a theoretical and empirical foundation which can

separate the many distinct, yet intertwined effects relating to ‘static’ industry characteristics –

such as the nature of industry competition – and the ‘dynamic’ effects initiated by the

economic changes leading to the merger wave. I leave this for future research.

Importantly, research has yet to fully embrace acquisitions as a tool among numerous

other ‘adjustment’ methods to handle fundamental economic changes (Villalonga &

McGahan, 2005; Weston, 2001). In fact, there are many complementary and substitutable

tools available to managers, such as internal growth and strategic alliances etc, and theories

of mergers and merger waves should actively integrate them as such. In this regard, I argue

the need for both theoretical and empirical studies to try to include these choices and the

ensuing interactions in corporate strategies.

In these endeavours, I believe that a dual focus on the incidence and extent of value

creation, appropriation and destruction will serve to provide the basis for guiding future

empirical research and moulding a more complete theory of mergers and merger waves.

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164

Chapter 3

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vel,

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tical

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ifica

nce

at th

e 1%

leve

l.

168

Chapter 3

Tab

le 2

: Val

ue c

reat

ion,

app

ropr

iatio

n an

d de

stru

ctio

n in

acq

uisi

tion

stra

tegi

es c

ondu

cted

by

US

publ

icly

hel

d fir

m 1

981-

2005

(con

tinue

d)

Pane

l B: A

cqui

rer v

alue

app

ropr

iatio

n in

acq

uisi

tion

stra

tegi

es (i

n $1

,000

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n st

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n (m

edia

n) a

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r re

turn

s

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n (m

edia

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169

Value creation, appropriation and destruction in M&A

Tab

le 3

: The

inci

denc

e of

val

ue c

reat

ion,

app

ropr

iatio

n an

d de

stru

ctio

n in

and

out

of U

S in

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ry m

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r wav

es 1

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sitio

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Num

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er v

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de

stru

ctio

n (3

)

V

alue

des

truc

tion

& a

cqui

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valu

e de

stru

ctio

n (4

)

A

ll 50

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37)

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46

40

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67)

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ifica

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vel,

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e 5%

leve

l, **

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ifies

stat

istic

al si

gnifi

canc

e at

the

1% le

vel.

170

Chapter 3

Tab

le 4

: Val

ue c

reat

ion,

app

ropr

iatio

n an

d de

stru

ctio

n in

acq

uisi

tion

stra

tegi

es st

ratif

ied

by m

erge

r val

ue c

reat

ion

Pane

l A: M

erge

r val

ue c

reat

ion

in a

cqui

sitio

n st

rate

gies

with

pos

itive

mer

ger v

alue

cre

atio

n (in

$1,

000)

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n st

rate

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n (m

edia

n) a

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ute

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s

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n (m

edia

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455

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677

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te-m

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813

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ifies

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istic

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l, **

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istic

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e at

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vel,

*** si

gnifi

es st

atis

tical

sign

ifica

nce

at th

e 1%

leve

l.

171

Value creation, appropriation and destruction in M&A

Tab

le 4

: Val

ue c

reat

ion,

app

ropr

iatio

n an

d de

stru

ctio

n in

acq

uisi

tion

stra

tegi

es st

ratif

ied

by m

erge

r val

ue c

reat

ion

(con

tinue

d)

Pane

l B: A

cqui

rer v

alue

app

ropr

iatio

n in

acq

uisi

tion

stra

tegi

es w

ith p

ositi

ve m

erge

r val

ue c

reat

ion

(in $

1,00

0)

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uisi

tion

stra

tegi

es

M

ean

(med

ian)

abs

olut

e do

llar

retu

rns

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n (m

edia

n) d

olla

r re

turn

s re

lativ

e to

targ

et fi

rm v

alue

(s)

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n (m

edia

n) d

olla

r re

turn

s re

lativ

e to

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r fir

m v

alue

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of d

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s

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umbe

r of

st

rate

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n

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edia

n

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ean

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edia

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ean

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edia

n

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te-m

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istic

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gnifi

canc

e at

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leve

l, **

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ifies

stat

istic

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gnifi

canc

e at

the

5% le

vel,

*** si

gnifi

es st

atis

tical

sign

ifica

nce

at th

e 1%

leve

l.

172

Chapter 3

Tab

le 4

: Val

ue c

reat

ion,

app

ropr

iatio

n an

d de

stru

ctio

n in

acq

uisi

tion

stra

tegi

es st

ratif

ied

by m

erge

r val

ue c

reat

ion

(con

tinue

d)

Pane

l C: M

erge

r val

ue c

reat

ion

in a

cqui

sitio

n st

rate

gies

with

neg

ativ

e m

erge

r val

ue c

reat

ion

(in $

1,00

0)

Acq

uisi

tion

stra

tegi

es

M

ean

(med

ian)

abs

olut

e do

llar

retu

rns

Mea

n (m

edia

n) d

olla

r re

turn

s re

lativ

e to

targ

et fi

rm v

alue

(s)

Mea

n (m

edia

n) d

olla

r re

turn

s re

lativ

e to

acq

uire

r fir

m v

alue

Sum

of d

olla

r re

turn

s

N

umbe

r of

st

rate

gies

Mea

n

M

edia

n

M

ean

M

edia

n

M

ean

M

edia

n

All

-3,7

50,0

36

-317

,809

-17.

3017

-1

.079

1

-1.5

775

-0.2

037

-3

,033

,779

,431

80

9

Out

of w

aves

-1

,729

,969

-2

19,4

45

-1

0.83

11

-0.8

325

-0

.384

4 -0

.120

9

-776

,756

,305

44

9

In w

aves

-6

,269

,509

-6

08,8

06

-2

5.37

19

-1.6

739

-3

.065

5 -0

.356

8

-2,2

57,0

23,1

26

360

Fi

rst-m

ovin

g -3

,795

,889

-5

41,7

69

-4

.531

3 -1

.299

4

-1.0

687

-0.3

009

-3

64,4

05,3

61

96

M

iddl

e-m

ovin

g -8

,142

,731

-7

16,7

51

-3

5.64

30

-1.7

989

-4

.352

5 -0

.413

6

-1,4

00,5

49,6

58

172

La

te-m

ovin

g -5

,348

,566

-6

53,1

33

-2

7.91

60

-1.5

648

-2

.743

2 -0

.345

9

-492

,068

,107

92

Stan

dard

ized

Wilc

oxon

ran

k-su

m te

sts

H

ighe

r sam

ple

P-va

lue

H

ighe

r sam

ple

P-va

lue

H

ighe

r sam

ple

P-va

lue

In w

aves

= o

ut o

f wav

es

Out

of w

aves

<0

.001

***

O

ut o

f wav

es

<0.0

01**

*

Out

of w

aves

<0

.001

***

Firs

t-mov

ing

= m

iddl

e-m

ovin

g Fi

rst-m

ovin

g 0.

133

Fi

rst-m

ovin

g 0.

202

Fi

rst-m

ovin

g 0.

024**

Mid

dle-

mov

ing

= la

te-m

ovin

g La

te-m

ovin

g 0.

771

La

te-m

ovin

g 0.

550

La

te-m

ovin

g 0.

200

Late

-mov

ing

= fir

st-m

ovin

g La

te-m

ovin

g 0.

323

La

te-m

ovin

g 0.

689

La

te-m

ovin

g 0.

487

* sign

ifies

stat

istic

al si

gnifi

canc

e at

the

10%

leve

l, **

sign

ifies

stat

istic

al si

gnifi

canc

e at

the

5% le

vel,

*** si

gnifi

es st

atis

tical

sign

ifica

nce

at th

e 1%

leve

l.

173

Value creation, appropriation and destruction in M&A

Tab

le 4

: Val

ue c

reat

ion,

app

ropr

iatio

n an

d de

stru

ctio

n in

acq

uisi

tion

stra

tegi

es st

ratif

ied

by m

erge

r val

ue c

reat

ion

(con

tinue

d)

Pane

l D: A

cqui

rer v

alue

app

ropr

iatio

n in

acq

uisi

tion

stra

tegi

es w

ith n

egat

ive

mer

ger v

alue

cre

atio

n (in

$1,

000)

A

cqui

sitio

n st

rate

gies

Abs

olut

e do

llar

retu

rns

Dol

lar

retu

rns r

elat

ive

to

targ

et fi

rm v

alue

(s)

Dol

lar

retu

rns r

elat

ive

to

acqu

irer

firm

val

ue

Sum

of d

olla

r re

turn

s

N

umbe

r of

st

rate

gies

Mea

n

M

edia

n

M

ean

M

edia

n

M

ean

M

edia

n

All

-3,8

68,3

86

-384

,419

-17.

2451

-1

.207

2

-1.4

231

-0.2

322

-3

,129

,524

,506

80

9

Out

of w

aves

-1

,893

,952

-2

98,2

98

-1

1.02

24

-1.0

819

-0

.424

1 -0

.155

2

-850

,384

,528

44

9

In w

aves

-6

,330

,944

-6

02,1

38

-2

5.00

61

-1.7

632

-2

.669

1 -0

.371

0

-2,2

79,1

39,9

79

360

Fi

rst-m

ovin

g -3

,941

,114

-5

41,9

89

-4

.712

3 -1

.388

0

-1.1

539

-0.3

461

-3

78,3

46,9

07

96

M

iddl

e-m

ovin

g -8

,194

,630

-6

37,7

15

-3

5.69

54

-1.9

287

-3

.609

0 -0

.414

7

-1,4

09,4

76,3

39

172

La

te-m

ovin

g -5

,340

,399

-6

23,7

04

-2

6.19

79

-1.3

931

-2

.492

8 -0

.323

0

-491

,316

,733

92

Stan

dard

ized

Wilc

oxon

ran

k-su

m te

sts

H

ighe

r sam

ple

P-va

lue

H

ighe

r sam

ple

P-va

lue

H

ighe

r sam

ple

P-va

lue

In w

aves

= o

ut o

f wav

es

In w

aves

<0

.001

***

In

wav

es

<0.0

01**

*

In w

aves

<0

.001

***

Firs

t-mov

ing

= m

iddl

e-m

ovin

g Fi

rst-m

ovin

g 0.

312

Fi

rst-m

ovin

g 0.

492

Fi

rst-m

ovin

g 0.

136

Mid

dle-

mov

ing

= la

te-m

ovin

g La

te-m

ovin

g 0.

869

La

te-m

ovin

g 0.

662

La

te-m

ovin

g 0.

208

Late

-mov

ing

= fir

st-m

ovin

g La

te-m

ovin

g 0.

434

La

te-m

ovin

g 0.

993

La

te-m

ovin

g 0.

965

* sign

ifies

stat

istic

al si

gnifi

canc

e at

the

10%

leve

l, **

sign

ifies

stat

istic

al si

gnifi

canc

e at

the

5% le

vel,

*** si

gnifi

es st

atis

tical

sign

ifica

nce

at th

e 1%

leve

l.

Chapter 3

174

Table 5: The determinants of value creation in acquisition strategies Panel A: Multivariate regression analysis (dollar values in $1,000,000)

Explanatory variables

Absolute dollar returns

Dollar returns relative to

acquirer value

Absolute dollar returns

Dollar returns relative to

acquirer value

Intercept

-1117.806 (0.100)

0.051 (0.931)

-747.655 (0.316)

0.355 (0.439)

Sign

2500.498*** (<0.001)

0.541*** (<0.001)

2507.227*** (<0.001)

0.546*** (<0.001)

Wave

-2338.969** (0.034)

-1.354* (0.096)

-3754.715** (0.016)

-3.109* (0.077)

Wave*Sign

6948.791*** (<0.001)

2.744*** (0.005)

8887.066*** (<0.001)

4.601** (0.023)

First-mover

3396.894* (0.098)

3.669* (0.055)

Late-mover

1769.534 (0.491)

2.763 (0.129)

First-mover*Sign

-4673.865 (0.124)

-3.646* (0.060)

Late-mover*Sign

-2168.762 (0.515)

-3.208 (0.126)

Returns on assets

4262.291** (0.036)

2.855 (0.216)

4015.510** (0.043)

2.582 (0.246)

Cash reserves/assets

3425.450 (0.144)

-3.137 (0.157)

3333.565 (0.156)

-3.171 (0.151)

Market-to-book

-0.646 (0.122)

0.00007 (0.715)

-0.627 (0.128)

0.00008 (0.669)

Acquirer market value

0.055 (0.413)

0.00001 (0.103)

0.055 (0.418)

0.00001 (0.116)

Target market value.

-0.806** (0.035)

-0.00001 (0.770)

-0.793** (0.037)

0.00001 (0.742)

Relative size of acquirer to target

-10.965*** (0.002)

-0.00032 (0.224)

-10.851*** (0.002)

-0.0002 (0.440)

Sequential acquirer

-2175.094 (0.183)

-1.681 (0.155)

-2071.869 (0.193)

-1.603 (0.158)

Stock payment

1.117 (0.871)

-0.006* (0.078)

0.848 (0.903)

-0.006* (0.081)

Hostility

-3158.432** (0.015)

-0.313 (0.372)

-3093.865** (0.019)

-0.272 (0.420)

Relatedness

197.514 (0.719)

0.182 (0.666)

-255.260 (0.567)

-0.167 (0.441)

Industry events

-60.625* (0.035)

-0.025* (0.091)

-60.682** (0.033)

-0.024 (0.141)

Period 1993-2004

1896.776*** (0.024)

0.417 (0.177)

2167.270** (0.015)

0.667 (0.121)

R2 21.72% 5.63% 22.03% 6.53%

Value creation, appropriation and destruction in M&A

175

Table 5: The determinants of value creation in acquisition strategies (continued) Panel B: Additional hypothesis testing (P-values)

(1)

(2)

(3)

(4) (a) Wave + Wave*Sign

<0.001*** 0.012** <0.001*** 0.028** (b) First-mover + First-mover*Sign

0.552 0.946 (c) Late-mover + Late-mover*Sign

0.839 0.395 (d) First-mover = Late-mover

0.457 0.068* (e) First-mover + First-mover*Sign = Late-mover + Late-mover*Sign

0.432 0.478

* signifies statistical significance at the 10% level, ** signifies statistical significance at the 5% level, *** signifies statistical significance at the 1% level.

Chapter 3

176

Table 6: The determinants of value appropriation in acquisition strategies Panel A: Multivariate regression analysis (dollar values in $1,000,000)

Explanatory variables

Absolute dollar returns

Dollar returns relative to

acquirer value

Absolute dollar returns

Dollar returns relative to

acquirer value Intercept

-1088.012 (0.108)

-0.231 (0.570)

-747.783 (0.314)

0.053 (0.864)

Sign

2429.568*** (<0.001)

0.391*** (<0.001)

2436.286*** (<0.001)

0.395*** (<0.001)

Wave

-2109.218* (0.059)

-0.890* (0.067)

-3395.740** (0.029)

-2.149** (0.046)

Wave*Sign

6832.228*** (<0.001)

2.299*** (<0.001)

8611.606*** (<0.001)

3.704*** (0.009)

First-mover

3151.283 (0.127)

2.692* (0.051)

Late-mover

1498.520 (0.586)

1.977 (0.111)

First-mover*Sign

-4332.877 (0.154)

-2.667* (0.059)

Late-mover*Sign

-1929.112 (0.577)

-2.514* (0.092)

Returns on assets

4138.714** (0.043)

2.709 (0.192)

3915.246* (0.051)

2.507 (0.207)

Cash reserves/assets

3474.454 (0.137)

-2.364 (0.116)

3381.673 (0.147)

-2.398 (0.110)

Market-to-book

-0.640 (0.140)

0.00003 (0.833)

-0.621 (0.145)

0.00004 (0.784)

Acquirer market value

0.056 (0.408)

0.00001* (0.053)

0.055 (0.413)

0.00001* (0.057)

Target market value

-0.927** (0.012)

-0.00003 (0.341)

-0.916** (0.012)

-0.00001 (0.639)

Relative size of acquirer to target

-11.013*** (0.002)

-0.0004 (0.104)

-10.908*** (0.002)

-0.0003 (0.168)

Sequential acquirer

-2292.691 (0.165)

-1.897* (0.079)

-2213.214 (0.167)

-1.844* (0.075)

Stock payment

1.133 (0.870)

-0.005** (0.030)

0.903 (0.897)

-0.005** (0.032)

Hostility

-3248.186** (0.011)

-0.310 (0.311)

-3189.175** (0.014)

-0.274 (0.346)

Relatedness

88.151 (0.872)

0.232 (0.482)

-335.030 (0.452)

-0.097 (0.524)

Industry events

-64.214** (0.026)

-0.025* (0.062)

-63.662** (0.026)

-0.026* (0.087)

Period 1993-2004

1991.680** (0.017)

0.574* (0.059)

2235.542** (0.012)

0.765* (0.050)

R2 22.80% 7.24% 23.06% 8.09%

Value creation, appropriation and destruction in M&A

177

Table 6: The determinants of value appropriation in acquisition strategies (continued) Panel B: Additional hypothesis testing (P-values)

(1)

(2)

(3)

(4) (a) Wave + Wave*Sign

<0.001*** 0.006*** 0.001*** 0.013** (b) First-mover + First-mover*Sign

0.580 0.935 (c) Late-mover + Late-mover*Sign

0.824 0.205 (d) First-mover = Late-mover

0.483 0.113 (e) First-mover + First-mover*Sign = Late-mover + Late-mover*Sign

0.469 0.765

* signifies statistical significance at the 10% level, ** signifies statistical significance at the 5% level, *** signifies statistical significance at the 1% level.

178

Chapter 3

Tab

le 7

: Sum

mar

y of

hyp

othe

ses,

empi

rical

test

s and

resu

lts

Hyp

othe

ses

Em

piri

cal e

vide

nce

Tab

le

Tes

t

P-va

lues

Res

ult

The

inci

denc

e of

val

ue d

estr

uctio

n

H1A

H

ighe

r in

wav

es th

an o

ut o

f wav

es

3 In

wav

es <

Out

of w

aves

<0

.001

***

Acc

epte

d H

1B

Hig

her i

n la

te-m

ovin

g ac

quis

ition

s 3

Late

-mov

er <

Firs

t-mov

er a

nd

Late

-mov

er <

Mid

dle-

mov

er

(> F

irst-m

over

) and

(> M

iddl

e-m

over

) R

ejec

ted

H1B

-al

t H

ighe

r in

first

-mov

ing

acqu

isiti

ons

3

Firs

t-mov

er <

Lat

e-m

over

and

Fi

rst-m

over

< M

iddl

e-m

over

0.50

6 an

d (>

Mid

dle-

mov

er)

Rej

ecte

d

D

olla

r ret

urns

Rela

tive

retu

rns

Valu

e cr

eatio

n

H2A

H

ighe

r in

wav

es th

an o

ut o

f wav

es

5 Si

gn*W

ave

+ W

ave

> 0

<0.0

01**

* 0.

012**

A

ccep

ted

H2B

H

ighe

r in

first

-mov

ing

acqu

isiti

ons a

nd

low

er in

late

-mov

ing

acqu

isiti

ons

5 Si

gn*F

irst-m

over

+ F

irst-m

over

> 0

and

Si

gn*L

ater

-mov

er +

Lat

e-m

over

< 0

(<

0) a

nd 0

.839

0.

946

and

0.39

5 R

ejec

ted

Valu

e ap

prop

riat

ion

H3A

H

ighe

r in

wav

es th

an o

ut o

f wav

es

6 Si

gn*W

ave

+ W

ave

> 0

<0.0

01**

* 0.

006**

* A

ccep

ted

H3B

H

ighe

r in

first

-mov

ing

acqu

isiti

ons a

nd

low

er in

late

-mov

ing

acqu

isiti

ons

6 Si

gn*F

irst-m

over

+ F

irst-m

over

> 0

and

Si

gn*L

ater

-mov

er +

Lat

e-m

over

< 0

(<

0) a

nd 0

.824

0.

935

and

0.20

5 R

ejec

ted

Mer

ger v

alue

des

truc

tion

H4A

H

ighe

r in

wav

es th

an o

ut o

f wav

es

5 W

ave

< 0

0.03

4**

0.09

6* A

ccep

ted

H4B

H

ighe

r in

late

r-m

ovin

g ac

quis

ition

s

5 La

te-m

over

< 0

and

Lat

e-m

over

< fi

rst-m

over

(> 0

) and

0.4

57

(> 0

) and

0.0

68*

Rej

ecte

d

Acqu

irer

val

ue d

estr

uctio

n

H5A

H

ighe

r in

wav

es th

an o

ut o

f wav

es

6 W

ave

< 0

0.05

9* 0.

067*

Acc

epte

d H

5B

Hig

her i

n la

ter-

mov

ing

acqu

isiti

ons

6 La

te-m

over

< 0

and

Lat

e-m

over

< fi

rst-m

over

(> 0

) and

0.4

83

(> 0

) and

0.1

13

Rej

ecte

d

* sign

ifies

stat

istic

al si

gnifi

canc

e at

the

10%

leve

l, **

sign

ifies

stat

istic

al si

gnifi

canc

e at

the

5% le

vel,

*** si

gnifi

es st

atis

tical

sign

ifica

nce

at th

e 1%

leve

l.

179

Value creation, appropriation and destruction in M&A

App

endi

x: In

dust

ry m

erge

r wav

es w

ithin

the

US

1981

-200

4 In

dust

ries a

re b

ased

on

the

48 in

dust

ry g

roup

s of F

ama

& F

renc

h (1

997)

. The

ann

ounc

emen

t dat

e an

d in

dust

ry m

embe

rshi

p of

a g

iven

mer

ger i

s ide

ntifi

ed b

y th

e SD

C

data

base

, and

a m

erge

r is d

efin

ed a

s a tr

ansa

ctio

n w

hich

lead

s to

a sh

ift in

the

cont

rolli

ng st

ake

of a

firm

. Onl

y m

erge

rs w

ith a

tran

sact

ion

valu

e eq

ual t

o or

abo

ve $

50m

are

se

lect

ed. T

he m

etho

d us

ed to

iden

tify

thes

e m

erge

r wav

es is

det

aile

d in

the

‘Mer

ger D

ata

and

the

Iden

tific

atio

n of

Wav

es’ s

ectio

n. T

he d

ates

for t

he b

egin

ning

and

the

end

are

the

tradi

ng d

ay o

f the

firs

t and

last

mer

ger i

n th

e w

ave,

resp

ectiv

ely.

Not

e th

at th

e w

aves

in ‘A

ircra

ft’ a

nd ‘B

usin

ess S

uppl

ies’

dur

ing

the

perio

d 19

81-1

992

are

belo

w 2

4 m

onth

s in

leng

th d

ue to

lack

of a

ctiv

ity a

t the

end

of t

he w

ave.

In th

is c

ase,

the

end

of th

e w

ave

is b

roug

ht fo

rwar

d.

Indu

stry

Beg

inni

ng

of w

ave

(198

1-19

92)

End

of w

ave

(1

981-

1992

)

Wav

e le

ngth

in

mon

ths

(198

1-19

92)

Num

ber

of

mer

gers

(1

981-

1992

)

Beg

inni

ng

of w

ave

(199

3-20

04)

End

of w

ave

(1

993-

2004

)

Wav

e le

ngth

in

mon

ths

(199

3-20

04)

Num

ber

of

mer

gers

(1

993-

2004

)

Num

ber

of

mer

gers

(1

981-

2004

) Ai

rcra

ft 12

-06-

1984

23

-10-

1985

17

14

11

25

Busi

ness

Ser

vice

s 03

-11-

1986

25

-06-

1990

44

64

07

-08-

1997

27

-11-

2000

40

37

6 44

0 Bu

sine

ss S

uppl

ies

16-1

2-19

85

11-0

9-19

87

22

14

24

38

C

hem

ical

s 03

-01-

1983

18

-07-

1985

31

15

02

-01-

1998

08

-05-

2000

29

31

46

C

omm

unic

atio

n

33

17-0

7-19

97

13-0

3-20

00

33

144

177

Com

pute

rs

28

02

-09-

1998

30

-10-

2001

38

95

12

3 C

onst

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ion

Mat

eria

ls

09-0

7-19

84

14-1

1-19

86

29

20

12-0

9-19

96

19-0

1-19

99

29

25

45

Con

sum

er G

oods

02

-10-

1985

25

-01-

1988

28

25

02

-03-

1998

24

-05-

2001

39

25

50

El

ectr

onic

Eq.

09

-07-

1986

07

-06-

1988

24

30

19

-11-

1997

24

-05-

2001

43

13

4 16

4 En

ergy

30

-09-

1983

20

-02-

1986

30

40

10

-06-

1997

23

-05-

2001

48

97

13

7 En

tert

ainm

ent

10-0

8-19

87

10-0

4-19

89

21

12

31-1

0-19

96

22-0

2-20

00

41

41

53

Hea

lthca

re

22

03

-10-

1995

14

-12-

1998

39

72

94

In

sura

nce

25

09

-09-

1994

04

-11-

1997

39

10

4 12

9 M

achi

nery

17

-04-

1986

24

-07-

1989

40

36

03

-02-

1997

18

-01-

2000

36

61

97

M

easu

ring

& C

ontr

ol E

q.

15

22

-04-

1999

23

-05-

2002

38

33

48

M

edic

al E

q.

11

22

-05-

1995

30

-11-

1998

43

83

94

Ph

arm

aceu

tical

Pro

duct

s 01

-03-

1985

11

-01-

1988

35

22

05

-10-

1998

25

-02-

2003

53

81

10

3 Re

stau

rant

s, H

otel

s, M

otel

s 12

-09-

1983

07

-05-

1986

33

19

11

-01-

1996

23

-03-

1998

27

34

53

Re

tail

25-0

5-19

84

21-0

8-19

87

40

36

08-0

7-19

96

04-0

3-19

99

33

82

118

Stee

l Wor

ks E

tc.

13

18

-11-

1996

14

-03-

2000

41

27

40

Tr

ansp

orta

tion

22-0

8-19

85

27-0

5-19

87

22

30

06-1

0-19

97

22-0

8-20

01

47

36

66

Util

ities

23

18-1

2-19

97

31-0

1-20

00

26

98

121

Who

lesa

le

28

11-0

9-19

96

19-1

0-19

98

26

60

88

SCHOOL OF ECONOMICS AND MANAGEMENT UNIVERSITY OF AARHUS - UNIVERSITETSPARKEN - BUILDING 1322

DK-8000 AARHUS C – TEL. +45 8942 1111 - www.econ.au.dk

PhD Theses: 1999-4 Philipp J.H. Schröder, Aspects of Transition in Central and Eastern Europe. 1999-5 Robert Rene Dogonowski, Aspects of Classical and Contemporary European Fiscal

Policy Issues. 1999-6 Peter Raahauge, Dynamic Programming in Computational Economics. 1999-7 Torben Dall Schmidt, Social Insurance, Incentives and Economic Integration. 1999 Jørgen Vig Pedersen, An Asset-Based Explanation of Strategic Advantage. 1999 Bjarke Jensen, Five Essays on Contingent Claim Valuation. 1999 Ken Lamdahl Bechmann, Five Essays on Convertible Bonds and Capital Structure

Theory. 1999 Birgitte Holt Andersen, Structural Analysis of the Earth Observation Industry. 2000-1 Jakob Roland Munch, Economic Integration and Industrial Location in Unionized

Countries. 2000-2 Christian Møller Dahl, Essays on Nonlinear Econometric Time Series Modelling. 2000-3 Mette C. Deding, Aspects of Income Distributions in a Labour Market Perspective. 2000-4 Michael Jansson, Testing the Null Hypothesis of Cointegration. 2000-5 Svend Jespersen, Aspects of Economic Growth and the Distribution of Wealth. 2001-1 Michael Svarer, Application of Search Models. 2001-2 Morten Berg Jensen, Financial Models for Stocks, Interest Rates, and Options: Theory

and Estimation. 2001-3 Niels C. Beier, Propagation of Nominal Shocks in Open Economies. 2001-4 Mette Verner, Causes and Consequences of Interrruptions in the Labour Market. 2001-5 Tobias Nybo Rasmussen, Dynamic Computable General Equilibrium Models: Essays

on Environmental Regulation and Economic Growth.

2001-6 Søren Vester Sørensen, Three Essays on the Propagation of Monetary Shocks in Open Economies.

2001-7 Rasmus Højbjerg Jacobsen, Essays on Endogenous Policies under Labor Union

Influence and their Implications. 2001-8 Peter Ejler Storgaard, Price Rigidity in Closed and Open Economies: Causes and

Effects. 2001 Charlotte Strunk-Hansen, Studies in Financial Econometrics. 2002-1 Mette Rose Skaksen, Multinational Enterprises: Interactions with the Labor Market. 2002-2 Nikolaj Malchow-Møller, Dynamic Behaviour and Agricultural Households in

Nicaragua. 2002-3 Boriss Siliverstovs, Multicointegration, Nonlinearity, and Forecasting. 2002-4 Søren Tang Sørensen, Aspects of Sequential Auctions and Industrial Agglomeration. 2002-5 Peter Myhre Lildholdt, Essays on Seasonality, Long Memory, and Volatility. 2002-6 Sean Hove, Three Essays on Mobility and Income Distribution Dynamics. 2002 Hanne Kargaard Thomsen, The Learning organization from a management point of

view - Theoretical perspectives and empirical findings in four Danish service organizations.

2002 Johannes Liebach Lüneborg, Technology Acquisition, Structure, and Performance in

The Nordic Banking Industry. 2003-1 Carter Bloch, Aspects of Economic Policy in Emerging Markets. 2003-2 Morten Ørregaard Nielsen, Multivariate Fractional Integration and Cointegration. 2003 Michael Knie-Andersen, Customer Relationship Management in the Financial Sector. 2004-1 Lars Stentoft, Least Squares Monte-Carlo and GARCH Methods for American

Options. 2004-2 Brian Krogh Graversen, Employment Effects of Active Labour Market Programmes:

Do the Programmes Help Welfare Benefit Recipients to Find Jobs? 2004-3 Dmitri Koulikov, Long Memory Models for Volatility and High Frequency Financial

Data Econometrics. 2004-4 René Kirkegaard, Essays on Auction Theory.

2004-5 Christian Kjær, Essays on Bargaining and the Formation of Coalitions. 2005-1 Julia Chiriaeva, Credibility of Fixed Exchange Rate Arrangements. 2005-2 Morten Spange, Fiscal Stabilization Policies and Labour Market Rigidities. 2005-3 Bjarne Brendstrup, Essays on the Empirical Analysis of Auctions. 2005-4 Lars Skipper, Essays on Estimation of Causal Relationships in the Danish Labour

Market. 2005-5 Ott Toomet, Marginalisation and Discouragement: Regional Aspects and the Impact

of Benefits. 2005-6 Marianne Simonsen, Essays on Motherhood and Female Labour Supply. 2005 Hesham Morten Gabr, Strategic Groups: The Ghosts of Yesterday when it comes to

Understanding Firm Performance within Industries? 2005 Malene Shin-Jensen, Essays on Term Structure Models, Interest Rate Derivatives and

Credit Risk. 2006-1 Peter Sandholt Jensen, Essays on Growth Empirics and Economic Development. 2006-2 Allan Sørensen, Economic Integration, Ageing and Labour Market Outcomes 2006-3 Philipp Festerling, Essays on Competition Policy 2006-4 Carina Sponholtz, Essays on Empirical Corporate Finance 2006-5 Claus Thrane-Jensen, Capital Forms and the Entrepreneur – A contingency approach

on new venture creation 2006-6 Thomas Busch, Econometric Modeling of Volatility and Price Behavior in Asset and

Derivative Markets 2007-1 Jesper Bagger, Essays on Earnings Dynamics and Job Mobility 2007-2 Niels Stender, Essays on Marketing Engineering 2007-3 Mads Peter Pilkjær Harmsen, Three Essays in Behavioral and Experimental

Economics 2007-4 Juanna Schrøter Joensen, Determinants and Consequences of Human Capital

Investments 2007-5 Peter Tind Larsen, Essays on Capital Structure and Credit Risk

2008-1 Toke Lilhauge Hjortshøj, Essays on Empirical Corporate Finance – Managerial Incentives, Information Disclosure, and Bond Covenants

2008-2 Jie Zhu, Essays on Econometric Analysis of Price and Volatility Behavior in Asset

Markets 2008-3 David Glavind Skovmand, Libor Market Models - Theory and Applications 2008-4 Martin Seneca, Aspects of Household Heterogeneity in New Keynesian Economics 2008-5 Agne Lauzadyte, Active Labour Market Policies and Labour Market Transitions in

Denmark: an Analysis of Event History Data 2009-1 Christian Dahl Winther, Strategic timing of product introduction under heterogeneous

demand 2009-2 Martin Møller Andreasen, DSGE Models and Term Structure Models with

Macroeconomic Variables 2009-3 Frank Steen Nielsen, On the estimation of fractionally integrated processes 2009-4 Maria Knoth Humlum, Essays on Human Capital Accumulation and Educational

Choices 2009-5 Yu Wang, Economic Analysis of Open Source Software 2009-6 Benjamin W. Blunck, Creating and Appropriating Value from Mergers and

Acquisitions – A Merger Wave Perspective