creating and appropriating value from mergers and
TRANSCRIPT
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
iii
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.
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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
xv
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
xviii
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
xx
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
xxi
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: [email protected]
JAN BARTHOLDY Aarhus School of Business
Fuglesangs Allé DK-8000 Aarhus C, Denmark
Tel: +45 8948 6338 E-mail: [email protected]
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
Chapter 1
30
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.
Chapter 1
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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
Chapter 1
34
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
Chapter 1
36
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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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.
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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: [email protected]
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: [email protected]
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.
Revisiting the returns to bidders in M&A
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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.
Revisiting the returns to bidders in M&A
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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
85
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
Chapter 2
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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
87
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
93
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
Revisiting the returns to bidders in M&A
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
97
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
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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: [email protected]
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.
Value creation, appropriation and destruction in M&A
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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
Value creation, appropriation and destruction in M&A
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-
Value creation, appropriation and destruction in M&A
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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
Value creation, appropriation and destruction in M&A
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.
Value creation, appropriation and destruction in M&A
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
Tab
le 1
: Des
crip
tive
stat
istic
s of t
he sa
mpl
e of
acq
uisi
tion
stra
tegi
es
Pane
l A: D
eal c
hara
cter
istic
s A
cqui
sitio
n st
rate
gies
Mea
n nu
mbe
r of
in
dust
ry a
cqui
sitio
ns
M
ean
num
ber
of
acqu
isiti
ons
Se
rial
acq
uire
rs
with
in se
quen
ce
R
elat
edne
ss
H
ostil
ity
U
se o
f sto
ck
paym
ent
All
32.8
4 1.
17
11.6
6%
73.6
0%
3.37
%
51.2
0
Out
of w
aves
17
.56
1.12
8.
22%
77
.63%
4.
25%
47
.78
In w
aves
55
.61
1.24
16
.79%
67
.59%
2.
04%
56
.29
Fi
rst-m
ovin
g 53
.11
1.39
22
.91%
68
.82%
2.
03%
55
.48
M
iddl
e-m
ovin
g 58
.37
1.26
20
.46%
68
.57%
1.
93%
57
.24
La
te-m
ovin
g 53
.42
1.06
4.
47%
64
.69%
2.
23%
55
.49
Jan
1st 1
981
– D
ec 3
1st 1
992
10.5
9 1.
12
8.32
%
73.8
2%
6.48
%
33.1
3
Jan
1st 1
993
– D
ec 3
1st 2
004
43.3
8 1.
19
13.2
5%
73.4
9%
1.89
%
59.7
5
P-va
lues
of t
he st
anda
rdiz
ed W
ilcox
on r
ank-
sum
test
In w
aves
= o
ut o
f wav
es
<0.0
01**
* <0
.001
***
<0.0
01**
* <0
.001
***
0.05
1* <0
.001
***
Firs
t-mov
ing
= m
iddl
e-m
ovin
g 0.
111
0.37
6 0.
528
0.89
4 0.
645
0.53
4
Mid
dle-
mov
ing
= la
te-m
ovin
g 0.
217
<0.0
01**
* <0
.001
***
0.52
6 0.
652
0.81
3
Late
-mov
ing
= fir
st-m
ovin
g
0.77
1 <0
.001
***
<0.0
01**
* 0.
785
0.64
6 0.
989
* 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.
165
Value creation, appropriation and destruction in M&A
Tab
le 1
: Des
crip
tive
stat
istic
s of t
he sa
mpl
e of
acq
uisi
tion
stra
tegi
es (c
ontin
ued)
Pa
nel B
: Acq
uire
r and
targ
et c
hara
cter
istic
s (do
llar v
alue
s in
$1,0
00)
Acq
uisi
tion
stra
tegi
es
M
ean
size
of
acqu
irer
rel
ativ
e to
ta
rget
(s)
M
ean
ex-a
nte
targ
et
firm
mar
ket v
alue
(s)
M
ean
ex-a
nte
acqu
irer
mar
ket
valu
e
M
ean
acqu
irer
re
turn
on
asse
ts
M
ean
acqu
irer
m
arke
t-to
-boo
k
M
ean
cash
&
equi
vale
nts t
o as
sets
All
42.2
0 1,
176,
482
8,02
4,77
8 18
.05%
1.
578
14.6
4%
Out
of w
aves
41
.54
952,
622
8,07
2,39
3 17
.47%
1.
532
14.4
7%
In w
aves
43
.18
1,51
0,07
1 7,
953,
825
18.9
4%
1.65
1 14
.92%
Fi
rst-m
ovin
g 15
.65
1,04
8,03
5 6,
672,
343
20.6
0%
1.61
4 15
.28%
M
iddl
e-m
ovin
g 48
.19
2,00
6,90
6 9,
243,
941
17.4
6%
1.73
5 16
.04%
La
te-m
ovin
g 62
.21
1,13
1,09
8 7,
051,
479
19.6
3%
1.54
2 12
.43%
Jan
1st 1
981
– D
ec 3
1st 1
992
20.6
6 83
2,35
2 4,
386,
280
18.4
1%
1.09
1 12
.17%
Jan
1st 1
993
– D
ec 3
1st 2
004
52.4
0 1,
339,
459
9,74
7,93
5 17
.87%
1.
814
15.8
5%
P-va
lues
of t
he st
anda
rdiz
ed W
ilcox
on r
ank-
sum
test
In w
aves
= o
ut o
f wav
es
<0.0
01**
* <0
.001
***
0.41
8 0.
045**
0.
033**
0.
895
Firs
t-mov
ing
= m
iddl
e-m
ovin
g 0.
055*
0.79
3 0.
307
0.08
2 0.
298
0.27
2
Mid
dle-
mov
ing
= la
te-m
ovin
g 0.
162
0.88
1 0.
202
0.89
8 0.
046**
0.
003**
*
Late
-mov
ing
= fir
st-m
ovin
g
0.10
0 0.
986
0.15
6 0.
725
0.05
8* 0.
008**
*
* si
gnifi
es st
atis
tical
sign
ifica
nce
at th
e 10
% le
vel,
** si
gnifi
es st
atis
tical
sign
ifica
nce
at th
e 5%
leve
l, **
* sign
ifies
stat
istic
al si
gnifi
canc
e at
the
1% le
vel.
166
Chapter 3
Figu
re 1
: Plo
t of i
nfla
tion-
adju
sted
dol
lar m
erge
r ret
urns
in th
e U
S 19
81-2
004
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003-2
00,00
0-1
50,00
0-1
00,00
0-5
0,000
050
,000
100,0
0015
0,000
$100
0 do
llars
Year$1
,000
,000
167
Value creation, appropriation and destruction in M&A
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
Pa
nel A
: Mer
ger v
alue
cre
atio
n in
acq
uisi
tion
stra
tegi
es (i
n $1
,000
) A
cqui
sitio
n st
rate
gies
Mea
n (m
edia
n) a
bsol
ute
dolla
r re
turn
s
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
abso
lute
do
llar
retu
rns
N
umbe
r of
st
rate
gies
Mea
n
M
edia
n
M
ean
M
edia
n
M
ean
M
edia
n
All
-1,0
16,1
65
3,25
0
-4.9
938
0.01
44
-0
.540
3 0.
0048
-1,6
72,6
07,1
39
1646
Out
of w
aves
-3
38,9
69
15,4
02
-3
.311
5 0.
0654
-0.0
021
0.01
45
-3
33,8
84,8
81
985
In w
aves
-2
,025
,298
-3
7,03
3
-7.5
007
-0.1
316
-1
.342
3 -0
.043
0
-1,3
38,7
22,2
58
661
Fi
rst-m
ovin
g -8
18,0
01
-19,
365
0.
1280
-0
.134
0
-0.2
733
-0.0
352
-1
46,4
22,1
40
179
M
iddl
e-m
ovin
g -3
,295
,249
-7
5,18
2
-14.
4128
-0
.203
5
-2.1
664
-0.0
820
-9
98,4
60,2
94
303
La
te-m
ovin
g -1
,082
,904
-1
2,26
1
-3.4
291
-0.0
482
-1
.016
3 -0
.009
9
-193
,839
,825
17
9
Jan
1st 1
981
– D
ec 3
1st 1
992
-20,
776
22,0
86
-0
.089
4 0.
0938
0.10
91
0.02
92
-1
0,99
0,42
1 52
9
Jan
1st 1
993
– D
ec 3
1st 2
004
-1,4
87,5
71
-15,
374
-7
.316
5 -0
.047
4
-0.8
478
-0.0
130
-1
,661
,616
,718
11
17
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.
208
Fi
rst-m
ovin
g 0.
221
Fi
rst-m
ovin
g 0.
232
Mid
dle-
mov
ing
= la
te-m
ovin
g La
te-m
ovin
g 0.
216
La
te-m
ovin
g 0.
146
La
te-m
ovin
g 0.
201
Late
-mov
ing
= fir
st-m
ovin
g La
te-m
ovin
g 0.
977
La
te-m
ovin
g 0.
727
La
te-m
ovin
g 0.
859
* 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.
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
) A
cqui
sitio
n st
rate
gies
Mea
n (m
edia
n) a
bsol
ute
dolla
r re
turn
s
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
-1,1
95,3
19
-35,
642
-5
.101
3 -0
.128
5
-0.5
621
-0.0
369
-1
,967
,494
,756
16
46
Out
of w
aves
-5
36,1
10
-19,
771
-3
.568
6 -0
.083
4
-0.1
334
-0.0
209
-5
28,0
68,7
27
985
In w
aves
-2
,177
,649
-9
0,11
9
-7.3
853
-0.2
457
-1
.200
9 -0
.089
9
-1,4
39,4
26,0
30
661
Fi
rst-m
ovin
g -9
75,5
97
-89,
182
-0
.078
5 -0
.285
8
-0.3
776
-0.0
816
-1
74,6
31,9
27
179
M
iddl
e-m
ovin
g -3
,461
,774
-1
16,5
25
-1
4.51
79
-0.2
769
-1
.808
8 -0
.131
7
-1,0
48,9
17,3
79
303
La
te-m
ovin
g -1
,206
,015
-5
7,48
6
-2.6
186
-0.1
369
-0
.995
3 -0
.058
2
-215
,876
,724
17
9
Jan
1st 1
981
– D
ec 3
1st 1
992
-219
,602
-1
6,60
9
-0.3
440
-0.0
554
-0
.088
8 -0
.021
1
-116
,169
,449
52
9
Jan
1st 1
993
– D
ec 3
1st 2
004
-1,6
57,4
09
-50,
314
-7
.354
3 -0
.191
1
-0.7
862
-0.0
535
-1
,851
,325
,308
11
17
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
0.00
3***
O
ut o
f wav
es
<0.0
01**
*
Firs
t-mov
ing
= m
iddl
e-m
ovin
g Fi
rst-m
ovin
g 0.
206
Fi
rst-m
ovin
g 0.
340
Fi
rst-m
ovin
g 0.
232
Mid
dle-
mov
ing
= la
te-m
ovin
g La
te-m
ovin
g 0.
202
La
te-m
ovin
g 0.
131
La
te-m
ovin
g 0.
136
Late
-mov
ing
= fir
st-m
ovin
g La
te-m
ovin
g 0.
961
Late
-mov
ing
0.49
3
La
te-m
ovin
g 0.
742
* 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.
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
dust
ry m
erge
r wav
es 1
981-
2005
A
cqui
sitio
n st
rate
gies
V
alue
cre
atio
n
Val
ue d
estr
uctio
n
Num
ber
of
acqu
isiti
on
stra
tegi
es
V
alue
cre
atio
n &
ac
quir
er v
alue
ap
prop
riat
ion
(1)
V
alue
des
truc
tion
& a
cqui
rer
valu
e ap
prop
riat
ion
(2)
V
alue
cre
atio
n an
d ac
quir
er v
alue
de
stru
ctio
n (3
)
V
alue
des
truc
tion
& a
cqui
rer
valu
e de
stru
ctio
n (4
)
A
ll 50
.85%
(8
37)
49.1
5%
(809
) 16
46
40
.52%
(6
67)
1.58
%
(26)
10
.33%
(1
70)
47.5
7%
(783
) O
ut o
f wav
es
54.4
2%
(536
) 45
.58%
(4
49)
985
42
.03%
(4
14)
1.83
%
(18)
12
.39%
(1
22)
43.7
6%
(431
) In
wav
es
45.5
4%
(301
) 54
.46%
(3
60)
661
38.2
8%
(253
)
1.21
%
(8)
7.26
%
(48)
53
.25%
(3
52)
Fi
rst-m
ovin
g 46
.37%
(8
3)
53.6
3%
(96)
17
9 39
.11%
(7
0)
0%
(0)
7.26
%
(13)
53
.63%
(9
6)
M
iddl
e-m
ovin
g 43
.23%
(1
31)
56.7
7%
(172
) 30
3 36
.96%
(1
12)
1.32
%
(4)
6.27
%
(19)
55
.45%
(1
68)
La
te-m
ovin
g 48
.60%
(8
7)
51.4
0%
(92)
17
9 39
.66%
(7
1)
2.23
%
(4)
8.94
%
(16)
49
.16%
(8
8)
Tes
ts o
f hom
ogen
eity
acr
oss s
ub-s
ampl
es
T
he 4
out
com
e ca
tego
ries
Val
ue c
reat
ion
V
alue
app
ropr
iatio
n
Pe
arso
n χ2
(3)
P-va
lue
M
cNem
ar’s
st
atis
tic S
P-
valu
e
McN
emar
’s
stat
istic
S
P-va
lue
In w
aves
= o
ut o
f wav
es
19.8
8 <0
.001
***
34
.57
<0.0
01**
*
1.23
0.
267
Firs
t-mov
ing
= m
iddl
e-m
ovin
g 2.
73
0.43
4
5.40
0.
020**
0.22
0.
641
Mid
dle-
mov
ing
= la
te-m
ovin
g 2.
72
0.43
6
6.82
0.
009**
*
0.65
0.
419
Late
-mov
ing
= fir
st-m
ovin
g
4.67
0.
198
0.44
0.
506
6.
28
0.01
2**
* si
gnifi
es st
atis
tical
sign
ifica
nce
at th
e 10
% le
vel,
** si
gnifi
es st
atis
tical
sign
ifica
nce
at th
e 5%
leve
l, **
* sign
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)
A
cqui
sitio
n st
rate
gies
Mea
n (m
edia
n) a
bsol
ute
dolla
r re
turn
s
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
1,62
6,25
1 19
6,56
9
6.90
23
0.66
05
0.
4623
0.
1699
1,36
1,17
2,29
3 83
7
Out
of w
aves
82
6,25
3 16
3,18
4
2.98
75
0.57
64
0.
3182
0.
1291
442,
871,
425
536
In w
aves
3,
050,
833
308,
163
13
.873
5 0.
8404
0.71
87
0.25
01
91
8,30
0,86
8 30
1
Fi
rst-m
ovin
g 2,
626,
304
407,
778
5.
5170
1.
0378
0.64
67
0.24
35
21
7,98
3,22
2 83
M
iddl
e-m
ovin
g 3,
069,
384
265,
366
13
.462
1 0.
7910
0.70
38
0.29
65
40
2,08
9,36
4 13
1
La
te-m
ovin
g 3,
427,
911
313,
369
22
.465
1 0.
9138
0.80
98
0.22
81
29
8,22
8,28
2 87
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
0.00
2***
In
wav
es
<0.0
01**
*
Firs
t-mov
ing
= m
iddl
e-m
ovin
g Fi
rst-m
ovin
g 0.
828
Fi
rst-m
ovin
g 0.
679
Mid
dle-
mov
ing
0.29
3
Mid
dle-
mov
ing
= la
te-m
ovin
g La
te-m
ovin
g 0.
658
La
te-m
ovin
g 0.
455
La
te-m
ovin
g 0.
477
Late
-mov
ing
= fir
st-m
ovin
g La
te-m
ovin
g 0.
901
La
te-m
ovin
g 0.
677
La
te-m
ovin
g 0.
813
* 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.
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)
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
1,38
8,32
7 96
,075
6.63
62
0.38
92
0.
2701
0.
0844
1,16
2,02
9,75
0 83
7
Out
of w
aves
60
1,33
5 66
,319
2.67
54
0.32
61
0.
1101
0.
0584
322,
315,
801
536
In w
aves
2,
789,
747
189,
217
13
.689
3 0.
6904
0.55
50
0.17
58
83
9,71
3,94
9 30
1
Fi
rst-m
ovin
g 2,
454,
397
261,
902
5.
2811
0.
7870
0.52
02
0.17
43
20
3,71
4,98
0 83
M
iddl
e-m
ovin
g 2,
752,
359
127,
345
13
.287
6 0.
5003
0.55
49
0.18
56
36
0,55
8,96
1 13
1
La
te-m
ovin
g 3,
165,
977
199,
066
22
.315
9 0.
7870
0.58
83
0.16
50
27
5,44
0,00
9 87
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.
276
Fi
rst-m
ovin
g 0.
438
Mid
dle-
mov
ing
0.98
4
Mid
dle-
mov
ing
= la
te-m
ovin
g La
te-m
ovin
g 0.
422
La
te-m
ovin
g 0.
348
La
te-m
ovin
g 0.
823
Late
-mov
ing
= fir
st-m
ovin
g La
te-m
ovin
g 0.
926
La
te-m
ovin
g 0.
697
La
te-m
ovin
g 0.
796
* 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.
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
ruct
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
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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
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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
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