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Wirtschaftswissenschaftliche Fakultät Department of Business Administration
and Economics
Merger Waves and Their Impact on Shareholder Returnin European Economies
Bernard Gudowski Piotr Zmuda Christoph J. Börner
Diskussionspapierzur Volkswirtschaftslehre, Finanzierung und Besteuerung Nr. 5/2010
Discussion Paper on Economics, Finance, and Taxation No. 5/2010
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Anmerkung: Beiträge zu dieser Diskussionspapierreihe sind vorläufige Papiere, die zur Diskussion und zu kritischen Anmerkungen anregen sollen. Die Analyse und Ergebnisse sind die des Autors (der Autoren) des jeweiligen Beitrages und spiegeln nicht unbedingt die Meinung anderer Mitglieder der Wirtschaftswissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf wider. Jede Reproduktion als Ganzes oder in Teilen in Form einer anderen Veröffentlichung, ob in gedruckter oder elektronischer Form, ist nur mit der schriftlichen Zustimmung des Autors/der Autoren erlaubt.
Note: Papers in this Discussion Paper Series are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the author(s) and do not indicate concurrence by other members of the Department of Business Administration and Economics at the Heinrich-Heine-University Dusseldorf. Any reproduction in the form of a different publication, whether printed or produced electronically, in whole or in part, is permitted only with the written authorisation of the author(s).
© Heinrich-Heine-Universität Düsseldorf 2010
ISSN 1867-2531
Merger Waves and Their Impact on Shareholder Return in European Economies
Bernard Gudowski, M.A. Dr. Piotr Zmuda Prof. Dr. Christoph J. Börner
maconda Corporate Development
Cologne/Germany
Cologne Business School,
Cologne/Germany
Heinrich-Heine-Universität
Düsseldorf/Germany
December 2010
AbstractIn our study we find that from the perspective of the acquirer as well as from the target´s perspective the optimal time to merge is during the time of market down-turn (off the wave). This confirms the common notion that the best buying opportu-nities arise in the times of economic depression. Additionally we verify the exis-tence of two recent merger waves in Europe. The first wave started in the second quarter of 1999 and lasted until the first quarter of 2001. The second wave lasted three years, starting in third quarter of 2005, ending in the third quarter of 2008.
Keywords: Mergers and Acquisitions, Event study, Shareholder returns, Acquirer returns, Corporate finance
JEL-Classification: G34
Corresponding author: Christoph J. Börner, Heinrich-Heine-Universität Düsseldorf, Faculty of Business Admin-istration and Economics, 40225 Düsseldorf, Phone: +49 211 8115258, [email protected]
4
TABLE OF CONTENT
Abstract ................................................................................................................................... 3�1� Introduction ....................................................................................................................... 5�2� Merger Waves ................................................................................................................... 6�
2.1� Theoretical Background ............................................................................................. 6�2.2� Data and Methodology ............................................................................................. 12�2.3� Results ...................................................................................................................... 17�
3� Event Study of Merger Performance .............................................................................. 21�3.1� Theoretical Background ........................................................................................... 21�3.2� Data and Methodology ............................................................................................. 22�3.3� Results ...................................................................................................................... 25�
4� Conclusion ...................................................................................................................... 32�Bibliography ......................................................................................................................... 34�
5
1 INTRODUCTION
The recent economic crisis has brought the European acquisitions market to a stand-
still, with cumulated deal values dropping from the peak of $1,6tr in 2007 to a mere $0,6tr
in 2009 (see Figure 1); marking the end of what was the largest merger wave in the Euro-
pean history.
In this paper we examine the profitability of mergers during the boom and bust periods
asking whether market contraction may pose an opportunity for companies willing to build
value through acquisitions in the time of economic downturn. Our analysis takes advantage
of recent econometric models for establishing and dating periods of merger waves, and
suppressed merger activity. These models are combined with classic event study analysis of
shareholder returns in M&A transactions.
In the first part of the paper we present the body of literature and econometric models
that have been applied to analyse the merger wave phenomenon concluding with the results
of the model for dating recent merger waves in Europe.
In the second part we briefly describe the event study methodology. Taking into con-
sideration the findings from the previous chapter, we analyse returns to merging parties
during and outside the merger waves.
6
Figure 1
European M&As 2000-2009
Source: Thomson Reuters.
2 MERGER WAVES
2.1 THEORETICAL BACKGROUND
Most researchers agree about the existence of merger wave phenomenon but fail to
deliver a general explanation of underlying causes. Brealey and Myers1 point that since the
end of the 19th century each sub sequential wave had different characteristics such as dif-
ferent types of merging sectors, different driving factors and methods of financing, or di-
verse institutions involved. It is interesting to understand that all of these booms were ac-
companied by a simultaneous rise in stock prices (see Figure 2). This consideration leads to
the hypothesis that the reason for this unpredicted rise in merger volumes has nothing to do
with economics but is likely to be a behavioural phenomenon, driven by the same “irra-
1 Brealey and Myers, Principles of Corporate Finance, 941.
0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
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l�val
ues�
in�tr
illio
n�$
European�M&As
7
tional exuberance” that Alan Greenspan referred to in his speech of December 5, 19962
when describing asset bubbles inflated by irrational human behaviour. Below we present
the body of literature and econometric models that have been applied to analyse the merger
wave phenomenon.
Figure 2
Merger activity vs. market valuation
Source: BvDEP ZEPHYR and Bloomberg.
One of the very early descriptions of merger waves comes from Nelson (1959), who
states, “An outstanding characteristics of mergers, which have been a basic force in mould-
ing our industrial structure, is the highly episodic nature of their existence”3. Since then,
2 The term was a part of famous A. Greenspan’s sentence: "But how do we know when irrational exuberance has unduly escalated asset values, which then become subject to unexpected and prolonged contractions as they have in Japan over the past decade?". This statement lead to a major drop on most of the world’s stock exchanges and was later used as the title for Robert J. Shiller’s book that predicted the dotcom bust in the 2000. 3 Nelson, Merger Movements in American Industry 1895-1956, 4.
050100150200250300350400450
0100200300400500600700800900
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8
the M&A literature often referred to periods of intense merger activity as waves. Although,
it is worth mentioning that the concept was not econometrically challenged until mid-1980s
when, thanks to advances in time series modelling, a number of techniques for identifying
and dating such waves has been developed.
Shughart and Tollison (1984) tested random walk and first-order autoregressive
hypotheses on series of US merger data covering the 1895-1979 time period. Using two
ARIMA models, they analysed the annual number of acquisitions, year-to-year changes in
inflation adjusted transaction values, and real values per merger. In the first two cases they
could not reject the random walk hypothesis at a 5% confidence level, while real values per
merger displayed strong characteristics of a stable first-order autoregressive process. Ob-
tained results suggested no influence of longer lags in the merger series, i.e. merger activity
depends strongly only on previous year values, displaying properties similar to changes in
stock prices. The authors admit that the data sample size might have been too small to de-
tect departures from Markovian behaviour, however they point out that based on the same
data sets the notion of merger waves was inferred.4
As an alternative method, Golbe and White used a nonparametric “runs” test com-
paring the allocation of high and low activity periods with randomly distributed ones. In all
analysed time spans, the numbers of positive and negative runs were significantly lower
than ones obtained from a randomly generated sample, creating interlaced clusters with
high and low frequencies of mergers, thus supporting the wave hypothesis.5
In their subsequent work the authors returned to the topic and provided a more ad-
vanced model, by fitting series of sine waves to annual time series on mergers in the US
economy. The authors used two approaches, one favouring internal data consistency (Fig-
ure 3), and the second emphasizing the length of the analysed period (Figure 4). They es-
4 Shughart and Tollison, “The Random Character of Merger Activity”, 509. 5 Auerbach, Corporate Takeovers, 293-296.
9
tablished that for both, number of mergers and inflation-adjusted value, a direct test of
wave hypothesis confirmed casual impression of mergers coming in waves.6
Figure 3
US Mergers 1919 – 1979
Source: Golbe and White, “Catch a Wave”, 498.
Figure 4
US Mergers 1895 - 1989
Source: Golbe and White, “Catch a Wave”, 498.
6 Golbe and White, “Catch a Wave”, 495-499.
10
A more concise definition of waves was introduced by Town (1992): “[…] a series
is said to behave in waves if it experiences epochs marked by large, discrete, and unsus-
tainable increases in expected value of the series, conditional on being in a wave epoch.”7
In his statistically thorough research based on Hamilton’s innovative approach to business
cycle analysis8, Town employs a univariate Markov regime-switching (MS) model to ex-
amine US and UK merger data from the 20th century. He argues that the MS model pro-
vides a superior fit over standard linear ARIMA models and he finds merger behaviour
alternating between two states: a high-mean-high-variance state and a low-mean-low-
variance state. These findings indicate that merger waves are endogenous phenomena.9
Linn and Zuh (1997) revisited Golbe and White’s and Shughart and Tollison’s
seminal papers testing whether merger activity occurs in waves or follows random walk.
The authors reject the random walk hypothesis and conclude that merger activity follows
two distinct AR(1) processes with laws of probability governing prevalence of one of the
regimes at any given time.10 Their work emphasises that wave testing should be conducted
on non-log-transformed series of data, as “Taking logs will tend to dampen any waves that
are present and make their identification more difficult” – a point earlier made by Golbe
and White. The analysis confirms that results obtained in previous research (i.e. Golbe and
White and Shughart and Tollison) are exactly what one would expect if the series were
governed by a two-state switching process.
An alternative for the MS model family is presented by Barkoulas et al. (2000). The
study characterises stochastic behaviour of M&A activity as long-memory or strongly de-
pendent processes. In this type of process a strong and hyperbolically declining dependence
exists even between distant observations. This long-memory dynamic co-dependence can
give rise to long lasting departures from the mean (waves), which occur without regularity
7 Town, “Merger Waves and the Structure of Merger and Acquisition Time-Series”, 85. 8 Hamilton, “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle”, 357-384. 9 Town, “Merger Waves and the Structure of Merger and Acquisition Time-Series”, 83-100. 10 Linn and Zhu, “Aggregate Merger Activity”, 130-146.
11
in time or duration. The author uses ARIFMA modelling (autoregressive fractionally inte-
grated moving average) with the same data that has been used by Town (1992), estimating
long-memory parameter by employing Robinson’s Gaussian semi parametric approach, and
Sowell’s exact maximum likelihood method. The results establish strong evidence of long-
term dependence in M&A time series11, ipso facto, challenging the low-memory quality of
the process, presumed by parameter switching models12. This seemingly strong divergence
has fewer consequences since our ability to better understand a wave, whether it was gener-
ated by long-term but unknown dependence or weakly dependent but discreetly switching
process, is equally low. However these findings have some theoretical implications,
namely, that shocks have long lasting effects on M&A activity.
Going beyond previously mentioned research by Town (1992) and Linn & Zhu
(1997); Halbheer and Gärtner (2006) put forward their own model, abandoning Hamilton’s
Maximum Likelihood approach, and building on a Bayesian framework employing the
Markov chain Monte Carlo simulation method. This allowed for an autoregressive process
to persist across regime switches, leading to a more gradual adoption of merger activity to
state transitions. The authors analysed a more recent and consistent dataset of log-
transformed US and UK transaction numbers from 1973:IV through 2003:IV, and 1969:I
through 2003:IV respectively. Employing Gibbs sampling method yielded interesting re-
sults, challenging the existence of the media hyped US wave of 1980s, and establishing two
UK merger waves dated 1971:I – 1973:IV and 1986:III – 1989:IV.13
One of the most recent papers on the subject is Chung-Hua et al. (2008). A distin-
guishable feature of this work is that unlike most research covering only UK and US data, it
extends analysis to 26 OECD countries. Building on previously discussed Town’s ap-
proach, the authors use a Markov switching-regime model applied to panel data (MSP) in-
vestigating co-waved behaviour of mergers in 1990:I – 2005:II period. Similarly to other
11 Barkoulas, Baum, et al., “Waves and persistence in merger and acquisition activity”, 243. 12 Halbheer and Gärtner, “Are There Waves in Merger Activity After All?”, 7. 13 Ibid., 27-28.
12
MS models two regimes are identified: the heated M&A activity (wave regime) and inert
M&A activity (normal regime). Additionally both are characterised by “long swing behav-
iour” implicating relative persistence of each regime, manifested as long lasting booms and
busts. The co-wave hypothesis on sampled countries couldn’t be refuted14, confirming exis-
tence of synchronised M&A cycles across OECD countries.
Summing the above, four distinct methods for testing cursory impressions of merger
waves have been developed e.g. (1) nonparametric runs test (Golbe and White), (2) sine
wave fitting (Golbe and White), (3) first-order autoregressive process with capability to
produce wave-like behaviour (Shughart and Tollison, Barkoulas et al.), and (4) Markov
parameter switching models (Town, Linn and Zhu; Halbheer and Gärtner; Chung-Hua et
al.). Most researchers confirm the common notion of merger waves, thus rejecting random
walk hypothesis.
Considering the academic consensus and statistical adequateness for characterising M&A
data sets15, we will employ Markov-switching model for our analysis – focusing on dating
of the merger waves, rather than ascertaining their existence through application of various
econometric modelling techniques.
2.2 DATA AND METHODOLOGY
There are three ways to measure merger activity in a time period: (1.) count the num-
ber of acquisitions, (2.) sum the values of transactions (nominal or real – inflation ad-
justed), and (3.) calculate the real value per merger (average merger value in given pe-
riod)16.
Most researchers use the number of acquisitions as basis for their analysis, because
of its relative availability – public domain databases provided by SEC, FTC, European
Commission or compilations like DOME offer consistency across longer time periods –
14 Chung-Hua Shen, “Common wave behavior for mergers and acquisitions in OECD countries?”, 7. 15 Town, “Merger Waves and the Structure of Merger and Acquisition Time-Series”, 83. 16 Shughart and Tollison, “The Random Character of Merger Activity”, 501-502.
13
and there is no need to adjust for inflation or the currency exchange rate. At the same time
this measurement suffers from bias caused by a large number of small transaction.
In the second approach (used i.a. by Golbe and White, and Shughart and Tollison) –
measuring activity by real value – distortion is caused by a small number of large transac-
tions, the exclusion of transactions with undisclosed value, and errors in deal value estima-
tions. R. J. Town writes: “Ideally one would measure M&A activity by the real value of
merged firms. However, this measure is subject to huge errors since the price paid for the
acquired firm is often not disclosed. In these cases the value of the acquisition is estimated
from publicly available information and generally understates the true value of the transac-
tion. This bias would represent simple shift in the mean of the series; however, the percent-
age of mergers where the transacted value is reported varies from period to period.[...]”17.
The third method combines two former by averaging real deal values with number
of transactions, this way reducing frequency and value bias.
To identify wave patterns we use a broad dataset from the Zephyr database, consist-
ing of 34000 known value M&A transactions from 1997 to the first quarter of 2009,
summed quarterly on finalization date, with target companies from the EU 27 member
countries (including mergers, acquisitions, MBOs, and BIMBOs with reported and esti-
mated value). The data has no cut-off point, meaning there is no minimal transaction value,
and no minimal acquisition stake.
17 Town, “Merger Waves and the Structure of Merger and Acquisition Time-Series”, S86.
14
Figure 5
Deal values vs. number of deals in Europe
Source: BvDEP ZEPHYR and Bloomberg.
Figure 5 illustrates both deal values and numbers of deals in Europe from 1997 until
2009. The large spike in 2000 deal values (caused by acquisition of Mannesmann by Voda-
fone Airtouch) is almost unnoticeable when looking at the cumulated number of deals. We
can observe that the deal numbers were more or less steadily building up from 1997 until
2007, and are noticeably less volatile than deal values. Summary statistics are shown in
Table 1.
Table 1
Summary statistics
Deal value �€bn� Deal number Mean 127.668 691,102 Median 110.899 707 Standard Deviation 71.997,8 147,357 Skewness 1,27217 -0,390881 Kurtosis 5,74285 2,47598 Jarqe-Bera statistic 28,5769 1,8084
01002003004005006007008009001000
050
100150200250300350400450
1997
�119
97�3
1998
�119
98�3
1999
�119
99�3
2000
�120
00�3
2001
�120
01�3
2002
�120
02�3
2003
�120
03�3
2004
�120
04�3
2005
�120
05�3
2006
�120
06�3
2007
�120
07�3
2008
�120
08�3
2009
�1
Num
ber�o
f�dea
ls
Dea
l�val
ues�
in�b
illio
n�€
Deal�value Number�of�deals
15
We analyse wave behaviour using Hamilton’s dynamic Markov regime-switching
model.18 As previously described, in this type of models, parameters of autoregressive
process (mean and/or variance) are determined by discrete-state the Markov process gov-
erning the probability of regime switches. For purposes of this paper we employ a simpli-
fied version of the general autoregressive model that was first applied to M&A activity by
Town (1992), who based his application on Hamilton’s (1989) real business cycle analysis.
The simplified version was devised by Engel and Hamilton (1990) in analysis of long
swings in currency exchange rates. The model provides a good fit for series displaying
wave-like behaviour and was successfully applied to M&A data i.a. by Chung-Hua et al.
(2008).
In accordance with Hamilton (1989) we assume that unobservable state variable St
follows first-order two state Markov process, with conditional probabilities governing the
transition state:
������ ��� � �� ��� (1.1)
������ ��� � �� ��� � � ��� (1.2)
������ ��� � �� ��� (1.3)
������ ��� � �� ��� � � ��� (1.4)
Where � ��denotes high mean regime (the wave state) and � ��denotes the low mean
regime (normal state).
The original Hamilton’s (1989) specification that has become a workhorse in re-
gime-switching modelling follows:
� ����� � ��� � � �������� � ! ", "#$����% &��� � (1.5)
A typical application of this model assumes first-order auto regression '(���, how-
ever based on Engel and Hamilton (1990), we employ specific case where (� )�, in ef-
18 Presented model borrows from Town (1992), Hamilton (1989), and Engel and Hamilton (1990).
16
fect, the explained variable depends only on current realization and mean determined by
discreet Markov process, the simplified equation takes the form:
� �*� ! " (1.6)
The model assumes two states of heightened and suppressed merger activity, con-
trary to Engel and Hamilton, we assume that only mean is conditional on the regime, and
variance is kept constant across both regimes, so that
� ��draws from $���% &��
� ��draws from $���% &��
The model allows for three particular types of behaviour e.g.
� high activity periods are short and sharp (�� large and positive, ���small) and low
activity periods slow and gradual (�� negative and small, ��� large),
� merger activity could be modelled as random walk, where current period is inde-
pendent of the regime in previous period if ��� � � ���,
� alternatively the activity could be characterized by long swing behaviour where
���% ��� are large and ��% �� are opposite in signs and values19.
19 Engel and Hamilton, “Long Swings in the Dollar”, 692.
17
2.3 RESULTS
Table 2 summarizes estimation results for the univariate Markov model. High
���% ���values and opposite signs of ��% �� confirm long swing behavior of merger waves
i.e. both regimes are relatively persistent, with expected duration of the high regime
�+�� � ����� , and low regime �+�� � ����� �- periods20 for 49 analysed. This trans-
lates into slightly over two years of estimated duration of a merger wave. Ergodic probabil-
ity of being in a wave state is calculated . �� � ����+�� � ��� ! �� � �����and equals
0.39, implying that, unconditionally on current realization, there’s 40% likelihood of high
merger activity. Proposed specification explains about 60% of changes in standardized
merger value, T statistics are significant at 5% confidence level.
20 Hamilton, Time Series Analysis, 697.
18
Table 2
Estimation results
Markov�Transition�Probabilities���������������������P(.|1)�������P(.|2)�P(1|.)�������������0.89394�����0.066828�P(2|.)�������������0.10606������0.93317��������������������������������Estimate��Std.�Err.���t�Ratio��p�Value�Logistic,�t(1,1)���������������2.13165�����1.0291�������������������Logistic,�t(1,2)���������������2.63646������0.711�������������������Mean,�Regime�1�����������������0.79864����0.39386�����2.028��0.049�Mean,�Regime�2�����������������0.59474����0.23057�����2.579��0.013�Error�Variance^(1/2)�����������0.72458�����0.1613�������������������������������������������Log�Likelihood�=��61.4014���������������������Schwarz�Criterion�=��71.1309����������������Hannan�Quinn�Criterion�=��68.1958����������������������Akaike�Criterion�=��66.4014������������������������Sum�of�Squares�=�22.9119�����������������������������R�Squared�=��0.6388�������������������������R�Bar�Squared�=��0.6059���������������������������Residual�SD�=��0.7206���������������������Residual�Skewness�=��2.2355���������������������Residual�Kurtosis�=�11.0488����������������������Jarque�Bera�Test�=�173.079�����{0}�Box�Pierce�(residuals):���������Q(12)�=�27.5382�{0.006}�Box�Pierce�(squared�residuals):�Q(12)�=��1.4283�����{1}�Covariance�matrix�from�robust�formula.
In the Figure 6 we can see filtered probabilities that are the by-product of MLE es-
timation21 (Hamilton’s filter), and the smoothed probabilities computed using Kim’s algo-
rithm22.
Dating of the merger waves follows original Hamilton specification23, the decision
regarding whether dependent variable yt is in-the-wave state is governed by ��� �� /)0-, the decision line is plotted in Figure 7.
21 Engel and Hamilton, “Long Swings in the Dollar”, 693. 22 Kim and Nelson, State-Space Models with Regime Switching, 68-70.
19
Figure 6
Distribution and probabilities
Obtained results prove that Europe experienced two major merger waves since the
beginning of 1997. First one lasting from 1999:II through 2001:I, and second one from
2005:III through 2008:III. The start of the first wave corresponds with findings of Chung-
Hua et al.(2008), however the wave lasted shorter than in aforementioned research. Sudar-
sanam (2003)24 dates corresponding merger wave from 1995 through 2001, however he
draws from significantly wider dataset (1984 – 2002), and doesn’t employ as econometric
model for inference about merger waves.
23 Hamilton, “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle”, 374. 24 Sudarsanam, Creating value from mergers and acquisitions, 19.
-2
-1
0
1
2
3
4
1998 2000 2002 2004 2006 2008
Actual DealValue_StFitted
-1.5-1
-0.5 0
0.5 1
1.5 2
2.5 3
3.5
1998 2000 2002 2004 2006 2008
Residuals for DealValue_St2SE Bands
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
1998 2000 2002 2004 2006 2008
Filter Probabilities of Regime 1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
1998 2000 2002 2004 2006 2008
Smoothed Probabilities of Regime 1
20
Figure 7
Smoothed probabilities and merger waves
For comparison the red line in Figure 7 represents the smoothed probability of being in
the wave state as measured by average deal value. Similarly to cumulated deal values we
can see two peaks, however the second one is offset to the right, and fails to properly cap-
ture the expected beginning and end of the second wave.
0
0,2
0,4
0,6
0,8
1
1997�1
1997�3
1998�1
1998�3
1999�1
1999�3
2000�1
2000�3
2001�1
2001�3
2002�1
2002�3
2003�1
2003�3
2004�1
2004�3
2005�1
2005�3
2006�1
2006�3
2007�1
2007�3
2008�1
2008�3
2009�1
Waves Smoothed�probability�(deal�value)
Smoothed�probability�(avg�deal�value) 0.5�Rule
21
3 EVENT STUDY OF MERGER PERFORMANCE
3.1 THEORETICAL BACKGROUND
The origins of the event study methodology can be traced back to James Dolley
(1933). In its modern form it was introduced in the seminal paper by Fama, Fisher, Jensen
and Roll (1969). Since then, event study analysis was extensively used to examine the im-
pact of new information on stock prices. A census of literature conducted by Kothari and
Warner, reports total of 565 event studies published in 5 leading financial journals for years
1974 through 2000.25 The essence for the application of event studies in merger profitabil-
ity analysis lies in the assumption that stock prices represent discounted value of the firm
future profits. Therefore measuring changes in equity price following an important event
such as merger announcement, dividend payoff or a stock split, approximates additional
future profits/losses that accrue due to the event. More directly consequent changes in stock
price, measure changes in shareholder wealth. The key is to compare the observable – price
fluctuation related to the event, with the unobservable – stock price had the event not hap-
pened. The impact of the event is measured in terms of abnormal or excess return. It is done
by comparing actual returns on the stock, with normal or expected return. Models for esti-
mating normal returns include: Market Model, Mean Adjusted Returns Model, and Market
Adjusted Returns or Index Model. Excess returns can be calculated by CAPM, and it’s
modifications i.e. the three-factor Fama-French Model, or the four-factor momentum aug-
mented model, to name a few.
The body of research can be separated on the basis of the horizon length. The ad-
vantage of a short-run (less than one year) approach is that daily-expected returns are close
to zero, therefore abnormal returns aren’t burdened with large errors caused by estimation
of expected returns. Thus application of short-horizon event studies is pretty strait forward
and predictions of the model are quiet reliable. Short window studies typically assume that
the market quickly responds to new information so there’s little lag in the price adjust-
25 Eckbo, Handbook of Corporate Finance, Volume 1, 6-8.
22
ment.26 Duso, Gugler and Yurtoglu (2007) find that abnormal returns calculated for 25 to
50 days before an event are highly significant and positively correlated with future profit-
ability of up to 5 years as measured by accounting data.27 However Fama (1998) challenges
this view and argues that one must examine returns over a long horizon to obtain the full
picture.28 That said, long-run studies (one year and more), suffer from the lack of reliable
significance metrics, and are the subject of ongoing debate, for that reason their results have
to be approached with extreme caution.
3.2 DATA AND METHODOLOGY
Following Brown and Warner (1985) the Market Model is used:
(12 314 ! 516(7 (1.7)
where (8� is the expected return of firm i at day t, (7 is the market return from Eurostoxx
600, and where 314 and 516 are OLS values from estimation period.29
'(19 (� � (12 (� � 314 � �16(7 (1.8)
Abnormal return '(19 is the difference between stock return (� and expected return (8�.
:'(19�;�% ;�� < '(19=>
=?(1.9)
Cumulative abnormal returns :'(19 for period ;� to ;� are the sum of daily abnormal re-
turns. Summing across N firms we obtain cumulative average abnormal returns :'(@@@@@@ (also
denoted as CAAR, equation 1.7).
26 Fama, “Market efficiency, long-term returns, and behavioral finance1”, 284. 27 Duso et al, Is the Event Study Methodology Useful for Merger Analysis?, 9. 28 Fama, “Market efficiency, long-term returns, and behavioral finance1”, 284. 29 Brown and Warner, “Using daily stock returns”, 7.
23
�:'(@@@@@@�;�% ;�� �$<:'(19�;�% ;��
A
� �(1.10)
Merger data was obtained from the Zephyr database and stock quotes come from
Bloomberg Financial. The empirical analysis is based on a final sample of 240 unmatched
merger announcements over the period 1997-2009. All 98 targets and 142 acquirers are
publicly traded companies from EU-27 countries and Switzerland. Table 3 provides infor-
mation on sample distribution across EU member countries. Germany, France, Great Brit-
tan and Italy account for the largest proportion, approximating their share of domestic
economy in the European Union. These four countries represent about 62% of the whole
sample.
Table 3.
Distribution of the number of M&A announcements by country and wave regime
Targets Acquirers Country: Austria 5 6 Belgium 0 7 Czech Republic 0 1 Denmark 2 2 Finland 0 6 France 10 31 Germany 20 15 Great Brittan 20 16 Greece 2 5 Hungary 0 4 Italy 17 20 Lithuania 2 1 Netherlands 5 7 Poland 7 3 Portugal 2 1 Slovenia 0 3 Spain 2 9 Sweden 4 2 Switzerland 0 3 Wave regime:
24
Targets Acquirers On-the-wave 60 54 Off-the-wave 38 88
The deals with at least 10% acquisition stake are included; sample mean is 60% and
average deal value of €760M. As shown in Table 6 a majority of deals took place in finan-
cial services followed by construction, chemicals, publishing and machinery. Over 60% of
transactions were paid entirely in cash, share and mix deals account for about 18% each.
Table 4
Deal characteristics by method of payment and major sectors
Deal characteristics Method of payment
Cash 148 Converted Debt 1 Debt assumed 4 Deferred payment 2 Mixed 42 Shares 43
Major sector
Agriculture, Mining 10 Chemicals 23 Construction 24 Education, Health 1 Financial services 49 Food, beverages, tobacco 8 Gas, Water, Electricity 7 Hotels, restaurants 1 Insurance companies 9 Machinery 20 Metals 8 Other services 40 Publishing, printing 21 Textiles 3 Transport 5
25
Deal characteristics Wholesale, retail trade 11
The estimation period spans from 31 to 280 days before the announcement date. For
infrequently treaded companies a minimum of 180 quotes over 250 trading days was estab-
lished. Due to the incomplete data for domestic bourses, Eurostoxx 600 broad index was
used as reference for the market model. Six different windows were considered, three an-
nouncement period windows: [-1, +1], [-5,+5], [-30,+30], and three post announcement
windows [-1,+60], [-1,+90], [-1,+180]. Significance tests were calculated following Lyon,
Barber, Tsai (1999) using skewness adjusted bootstrapped t-statistics.30 The method origi-
nally developed for improved testing of long-run abnormal returns, reports higher p-values
in short window estimations than usual t-tests, however it was applied in short-horizon re-
search31, and appears more appropriate for longer event windows.
3.3 RESULTS
Table 5 reports average cumulative abnormal returns to target and acquirer share-
holders over different event windows. Results are presented across the regimes and for on-
the-wave and off-the-wave sub samples. For the whole duration, short-widow [-1,+1] re-
turns for both targets and acquirers are in line with other Pan-European studies (Campa &
Hernando, 2004 report 3.93% and 0.44% to targets and acquirers respectively)32 and less
dispersed than most of US studies (Andrade Stafford 2001 report 16% for targets and -0.7%
for acquirers)33, following the general consensus that short window abnormal returns for
targets are positive, and negative or close to zero for acquirers. When we extend the win-
dow to [-5,+5] and [-30,+30] the difference between targets and acquirers grows. Returns to
acquirers become increasingly negative, and while it is significant at 5% confidence level,
30 Lyon, Barber, et al., “Improved Methods for Tests of Long-Run Abnormal Stock Returns”. 31 Campa, Hernando, “Shareholder Value Creation in European M&As”, 64. 32 Ibid. 33 Andrade, Mitchell, and Stafford, “New Evidence and Perspectives on Mergers”, 110.
26
the reported loss of over -4% contrasts with comparable research of Campa & Hernando
(2004) who find that over [-30,+30] window CAARs are close to zero but positive34,
Groegen & Renneboog (2004) report -0.48% over [-60,+60] window35. Returns to target’s
shareholders are similar to those reported by Campa (8.9%)36 and much lower than reported
by Groegen (23.43%)37. Longer horizon post announcement returns for acquirers are statis-
tically significant and decrease with time showing progressive erosion of shareholder value
reaching -12.19% 180 trading days after the announcement. P-values for target returns are
high and insignificant at any customary level, average cumulative abnormal returns vary
from around 4% to 0%. A more comprehensive review of literature on cumulative abnor-
mal returns can be found in Jensen and Ruback (1983)38, Campa and Hernando (2004)39,
Gregoriou and Renneboog (2007)40.
Table 5
Cumulative average abnormal returns and p-values for various window lengths. On-the-wave and off-the-wave announcements segregated on the basis of Markov regime switching model from the previous chapter.
All regimes On-the-wave Off-the-wave Targets Announcement period
[-1,+1] CAAR 2.85%* 3.84%** 1.28% p-value 0.063 0.029 0.65 [-5,+5] CAAR 6.13%** 8.37%** 2.59% p-value 0.011 0 0.53 [-30,+30] CAAR 6.60% 5.21% 8.79% p-value 0.143 0.31 0.361
Post announcement [-1,+60] CAAR 4.19% 0.95% 9.28% p-value 0.286 0.815 0.225
34 Campa, “Shareholder Value Creation in European M&As”, 64. 35 Ibid., 37. 36 Ibid., 64. 37 Goergen and Renneboog, “Shareholder Wealth Effects of European Domestic and Cross-border Takeover Bids”, 37. 38 Jensen and Ruback, “The market for corporate control”, 14. 39 Campa, “Shareholder Value Creation in European M&As”, 51. 40 Gregoriou and Renneboog, International Mergers and Acquisitions Activity Since 1990, 9.
27
All regimes On-the-wave Off-the-wave [-1,+90] CAAR -0.38% -1.56% 1.49% p-value 0.967 0.748 0.91 [-1,+180] CAAR 3.53% -1.07% 11.30% p-value 0.609 0.898 0.439
Acquirers Announcement period
[-1,+1] CAAR 0.77% 0.60% 0.87% p-value 0.128 0.491 0.123 [-5,+5] CAAR -0.70% -0.55% -0.80% p-value 0.408 0.664 0.45 [-30,+30] CAAR -4.15%** -8.95%** -1.21% p-value 0.019 0.002 0.558
Post announcement [-1,+60] CAAR -3.58%** -6.74%** -1.64% p-value 0.041 0.027 0.408 [-1,+90] CAAR -5.64%** -9.61%** -3.20% p-value 0.019 0.044 0.217 [-1,+180] CAAR -12.19%** -22.32%** -5.98%* p-value 0.001 0.003 0.098
*/** denotes significance at 10% / 5% level.Results on target returns classified by regime persistent during merger announce-
ment lack significance, presumably, due to reduction in sample size. However some pat-
terns can be identified. Short-horizon abnormal returns are higher for on-the-wave an-
nouncements, and exceed whole sample returns by more than 2% in the [-5,+5] window. As
we extend the event window the relationship starts to change, and on-the-wave abnormal
returns plunge to about 5% compared to 8.79% for off-the-wave abnormal returns. While
the relative difference between on-the-wave and off-the-wave acquiree’s return changes at
particular windows, off-the-wave targets outperform on-the-wave targets all the way
through 180 days after the announcement date.
As for acquirers, the differences between on-the-wave and off-the-wave acquisitions
are more pronounced and more significant. In the short-window [-1,+1] on-the-wave ac-
quirers do slightly worse 0.6% compared with 0.87% for off-the-wave. In the longer win-
dow [-30,+30] abnormal returns during hyped merger periods fall to -9%, while mergers in
the whole sample yield on average -4.15%. That ratio is maintained in the short post event
28
windows [-1,+60] where average return is -4% and on-the-wave bidders lose -6.74%. 180
days post-merger announcement, off-the-wave abnormal returns fall to -6% compared to
on-the-wave returns of -22.32% maintaining significance at a 10% confidence level.
Figure 8 through Figure 10 illustrate changes in cumulative abnormal returns with the
change of event window span from [-30, 0] through [0,0] , and [0,0] through [0, +60]. In
Figure 8 we can observe the fluctuation of CAARs for the whole period grouped by acquir-
ers and targets. A typical drop of the target’s abnormal return, around announcement date is
present.41 Further the results from Table 5 are confirmed. Cumulative abnormal returns for
target companies are positive, while returns for buying companies oscillate around 0% in
the short window [0,-20] and turn negative when a longer event window is considered.
Figure 8
Cumulative average abnormal returns to acquirer and target shareholders. For preannouncement window BC ;% )D and post announcement windowB)% !;D.
41 Chatterjee, “Types of Synergy and Economic Value”, 134.
Targets
Bidders
-4
-2
0
2
4
6
% C
umul
ativ
e av
erag
e ab
norm
al re
turn
s
-30 -20 -10 0 10 20 30 40 50 60Days relative to announcement of merger
29
Figure 9 groups target cumulative average abnormal returns by regime persistent
during the merger announcement. Initially on-the-wave mergers outperform suppressed
period mergers by about 2% on the announcement date. The relationship begins to change
in the post event period and around 20 days after the announcement date, off-the-wave ac-
quisitions begin to outperform on-the-wave acquisitions. In the longer post event window
on-the-wave returns oscillate around zero per cent, while off-the-wave returns are strongly
positive.
Figure 9
Cumulative average abnormal returns to target shareholders for on-the-wave and off-the-wave regimes
Analogous analysis for the acquirer’s returns is depicted in Figure 10 both on- and
off-the-wave returns are negative for most of the analysed period. However off-the-wave
acquirers perform significantly better, and are able to maintain positive CAARs in the ini-
tial post event window.
On the wave targets
Off the wave targets
-5
0
5
10
% C
umul
ativ
e av
erag
e ab
norm
al re
turn
s
-30 -20 -10 0 10 20 30 40 50 60Days relative to announcement of merger
30
Figure 10
Cumulative average abnormal returns to acquirer shareholders for on-the-wave and off-the-wave regime.
Figure 11 shows the distribution of cumulative abnormal return in on-the-wave and
off-the-wave periods for shorter [-1,+1] and longer [-30,+30] event windows. The target’s
returns exhibit higher variance than the acquirer’s, while the variance for the short window
is higher in the on-the-wave period, and lower for the longer window. Acquirer’s returns
are slightly less volatile in the suppressed activity period for both window lengths.
On the wave bidders
Off the wave bidders
-8
-6
-4
-2
0
2
% C
umul
ativ
e av
erag
e ab
norm
al re
turn
s
-30 -20 -10 0 10 20 30 40 50 60Days relative to announcement of merger
31
Figure 11
Distribution of cumulative abnormal returns (in %) for [-1,+1] and [-30,+30] event windows to target and acquirer shareholders. Areas between doted lines represent periods of hyped merger activity.
-200
-100
0
100
200
19971998
19992000
20012002
20032004
20052006
20072008
2009 19971998
19992000
20012002
20032004
20052006
20072008
2009
Targets Acquirers
-200
-100
0
100
200
19971998
19992000
20012002
20032004
20052006
20072008
2009 19971998
19992000
20012002
20032004
20052006
20072008
2009
Targets Acquirers
32
4 CONCLUSION
The application of Hamilton's Markov switching model to European merger activity
for the period 1997 through 2009 verifies the existence of two merger waves. The first
wave started in the second quarter of 1999, and lasted till the first quarter of 2001. The sec-
ond wave lasted three years, starting in the third quarter of 2005, ending in the third quarter
of 2008.
Event study analysis shows that mergers announced during the boom periods yield on
average higher short-horizon abnormal returns for targets, and lower short-run abnormal
returns for acquirers, as compared with announcements during low activity periods. Long-
horizon abnormal returns are strictly lower during hyped merger activity for both targets
and acquirers, thus confirming the initial hypothesis.
Looking across both regimes, acquisitions result in positive abnormal returns to the
target’s shareholders, and close to zero or negative returns to the acquirer’s shareholders.
The findings are in line with the academic consensus of acquisitions being a questionable
strategy from bidder’s shareholders wealth perspective.42 However it’s important to high-
light that the negative effect is largely offset in off-the-wave acquisitions.
Further, results find support in the Shleifer and Vishny (2003) market timing hypothe-
sis, predicting that bidder’s long-run returns are likely to be negative in stock acquisitions,
and positive in cash acquisitions.43 As the proportion of stock financed mergers is higher
during the wave periods, the excess returns will be lower, as compared to the low activity
periods, when the majority of mergers are financed by cash.
Our results should encourage companies to actively pursue acquisitions during the bear
market as a sensible growth strategy that has a positive shareholder value impact. It may
also serve as guidance for private equity funds to utilise downmarket periods for building
42 Andrade, Mitchell, and Stafford, “New Evidence and Perspectives on Mergers”, 118. 43 Shleifer and Vishny, “Stock market driven acquisitions”, 297.
33
and extending their portfolios. In contrast the mergers undertaken during hyped market
epochs should be approached with caution and greater scrutiny from shareholders of both
acquiring and target companies.
34
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Die folgenden Diskussionspapiere wurden seit Juni 2008 veröffentlicht:
The following Discussion Papers have been published since June 2008:
1 2008 Finanzierungsentscheidungen mittelständischer Christoph Börner Unternehmer – Eine empirische Analyse des Dietmar Grichnik Pecking-Order-Modells Franz Reize
Solvig Räthke
2 2008 Credit Default Swaps and the Stability of the Frank Heyde Banking Sector Ulrike Neyer
3 2008 Are Rating Splits a Useful Indicator for the Opacity Achim Hauck of an Industry? Ulrike Neyer
4 2008 Gewährträgerhaftung im öffentlich-rechtlichen Uwe Vollmer Bankensektor: Konsequenzen für die Unterneh- Achim Hauck mensfinanzierung
1 2009 Wirtschaftliche Wirkungen der Freistellung Guido Förster ausländischer Betriebsstätteneinkünfte unter Progressionsvorbehalt
2 2009 Exchange Traded Funds in Deutschland: Andreas Wiesner simply buying the Index? Heinz-Dieter Smeets
3 2009 A Lender of Last Resort for Public Banks? Achim Hauck Theory and an Application to Japan Post Bank Uwe Vollmer
4 2009 Dynamik der Staatsverschuldung Heinz-Dieter Smeets
5 2009 Finanzkrise: Ursachen, Wirkungen und Heinz-Dieter Smeets (wirtschafts-)politische Reaktionen
1 2010 Optimierung der Tarifvergünstigungen für Dirk Schmidtmann außerordentliche Einkünfte und des negativen Progressionsvorbehalts durch die Schedulenbesteuerung gem. § 34a EStG und §§ 32d, 43 Abs. 5 EStG
2 2010 The Euro Area Interbank Market and the Liquidity Achim Hauck Management of the Eurosystem in the Financial Crisis Ulrike Neyer
3 2010 Government Interventions in Banking Crises: Diemo Dietrich Assessing Alternative Schemes in a Banking Model Achim Hauck of Debt Overhang
4 2010 Peer-to-Peer-Kredite: Eine empirische Überprüfung der Odelia Johnen Signaling-Wirkung auf die Kreditvergabe Daniel J. Goebel
5 2010 Merger Waves and Their Impact on Shareholder Return Bernard Gudowski in European Economies Piotr Zmuda Christoph J. Börner