ERASMUS UNIVERSITY ROTTERDAM
ERASMUS SCHOOL OF ECONOMICS
BSc Economics & Business Economics
Bachelor Thesis Financial Economics
The effects of idle cash on M&A performance in Europe
Theoretical and empirical research
Author:
S.T.R. van der Hart
Student number:
358044
Thesis supervisor:
Dr. J.J.G. Lemmen
Finish date:
June 27, 2014
PREFACE AND ACKNOWLEDGEMENTS
________________________________________________________
To start my thesis with, a word of thanks to the people who helped me throughout this process will
not be out of place. First of all, I would like to thank Dr. J.J.G. Lemmen for his professionalism and
help with my thesis. It was his idea to use (cumulative) abnormal returns instead of accounting
performance to evaluate the performance of the mergers and acquisitions researched in this thesis.
A hint which afterwards seems to have been precious for the continuation and rightfulness of the
research.
Also, Dr. R. Huisman deserves to be noticed and thanked for him making me enthusiastic for the
financial part of economics. Like it stimulated me to choose for a master in finance, it also stimulated
me to devote my thesis to an important part of the financial sector within economics; mergers and
acquisitions.
Lastly, I also would like to thank Sharon Klaassen for her individual help with the statistical analysis
using SPSS, as she helped me to choose which tests to use and with the preparation of data for this
specific tests.
NON-PLAGIARISM STATEMENT
By submitting this thesis the author declares to have written this thesis completely by himself/herself, and
not to have used sources or resources other than the ones mentioned. All sources used, quotes and
citations that were literally taken from publications, or that were in close accordance with the meaning of
those publications, are indicated as such.
COPYRIGHT STATEMENT
The author has copyright of this thesis, but also acknowledges the intellectual copyright of contributions
made by the thesis supervisor, which may include important research ideas and data. Author and thesis
supervisor will have made clear agreements about issues such as confidentiality.
Electronic versions of the thesis are in principle available for inclusion in any EUR thesis database and
repository, such as the Master Thesis Repository of the Erasmus University Rotterdam
1
ABSTRACT
________________________________________________________
This thesis focuses how M&As differ among two groups of companies; companies with higher and
companies with lower cash reserves. This research focuses on the behavioural aspects rather than
the already extensively researched basic aspects of M&A deals.
This research found, contradictory to the hypotheses, no significant difference in the contribution to
society deriving from M&As between the researched companies. Also, in serial acquisitions and the
monetary compensation aspect of the M&As is not found any significant difference between the
experimental and control group. This leads to the conclusions that having more idle cash has no
effect on M&A performance.
Keywords:
Mergers and Acquisitions, Overconfidence, (Cumulative) Abnormal Returns, Cash Rich and Poor,
Behavioural
JEL Classification Codes:
G01, G14, G34, C12, C92
2
TABLE OF CONTENTS
________________________________________________________
Preface and Acknowledgements
1
Abstract
2
Table of Contents
3
List of Tables
6
List of Figures
7
Chapter 1 – Introduction
8
1 – European M&A
9
2 – Aim of the study
10
3 – The shift from conventional to behavioural
10
4 – Research area
11
5 – Research specifics
12
6 – Link with existing literature
13
3
Chapter 2 – Literature Review
14
1 – Basics of M&A
14
1.1 – Types of M&As
14
1.2 – Advantages and disadvantages
16
1.3 – M&A motives
18
1.4 – Deal premium
18
2 – Literature Research
19
2.1 – Existence of overconfidence
19
2.2 - Ferris, Jayaraman and Sabherwel
19
2.3 – Returns
21
2.4 – Monetary Compensation
21
3 – Hypotheses description and derivation
23
4 – Hypotheses
25
Chapter 3 – Data and Methodology
1 – Data specifics
29
29
1.1 – Data criteria
29
1.2 – Data variables
30
2 – Derivation (C)ARs
32
2.1 – Capital Asset Pricing Model
32
2.2 – Abnormal Returns
35
3 – Methodology
3.1 – Event Study Tool
37
37
4
3.2 – T-Test
38
3.3 – ANOVA
40
4 – Research Method
42
Chapter 4 – Results
43
1 – Analysis
43
2 – Conclusion
46
Chapter 5 – Summary and Conclusion
48
1 – Research Question
48
2 – Discussion
49
3 – Recommendations for further research
50
Chapter 6 – Sources
51
Chapter 7 – Appendix
55
5
LIST OF TABLES
________________________________________________________
Table 2.1
Most important Contributions of different authors to the Literature Research 22
Table 3.1
Data Variables Specified
30-31
Table 3.2
Assumptions CAPM
32
6
LIST OF FIGURES
________________________________________________________
Figure 1.1
Value and number of M&As worldwide
8
Figure 1.2
Value and number of M&As in Europe
9
Figure 1.3
The most important acquiring countries
11
Figure 3.1
Security Market Line
33
Figure 3.2
ANOVA Summary
41
7
CHAPTER 1 – INTRODUCTION
________________________________________________________
Ever since the beginning of the financial crisis in 2007 the absolute number of mergers and
acquisitions (M&As) all around the world declined with nearly 30% (Institute of Mergers, 2013). Even
more steadily declining since is the monetary value of these deals, declining from 2007 from just
below 5.000 billion USD to a mere 2.000 billion USD total by the end of 2013 (Institute of Mergers,
2013). Also, these numbers are to be found in Figure 1.1 below (Institute of Mergers, 2013). This
strong decline is likely to be caused by the financial crisis. Other sources go beyond the simple
observation of the number and value of M&As declining and try to find a reason for this. One of the
explanations is that companies in the financial crisis have more trouble staying in business as before,
which sounds obvious, and therefore are not willing to take the additional risks of merging with or
acquiring another firm.
Figure 1.1: Value and number of M&As worldwide
8
1 - European M&A
As visible in Figure 1.2 below, Europe shows roughly the same patterns in deal value and number of
M&As when comparing with the ‘worldwide’ numbers above. Europe’s peak is also in 2007 with a
cumulative M&A value of nearly 1.800 billion EUR, distributed over just over 18.000 M&As (Institute
of Mergers, 2013). The same observation as in the previous graph can be made; the number of deals
does not seem to decline that much, it is the deal value that is the problem. When the deal value
drops this extreme, in comparison to the absolute number of M&As, this implies that the average
value of an M&A decreased in the last couple of years.
Figure 1.2: Value and number of M&As in Europe
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2 - Aim of the Study
The purpose of this thesis is to find out whether having more idle cash leads to more impulsive
reactions or overconfidence regarding M&As and therefore inferior choices. In other words, by
summing the abnormal returns (AR) for the acquiring and the target firm, the value added or
destroyed for society is measured. On the basis of this value added or destroyed, the research
question will be answered. Therefore, the research question of this thesis is as follows:
Do cash rich companies make inferior decisions regarding merger and acquisition deals in
comparison with other non cash rich companies, and if so, why?
H0: Companies with more idle cash make inferior M&A decisions in comparison with
companies with less idle cash.
3 - The shift from Conventional to Behavioural
In this thesis the focus will move from conventional explanations and research criteria to the possible
behavioural explanations. To be more precise, the effects of having more idle cash against the
situation of having less idle cash on M&As will be researched. The reason for this is that the idea
exists that companies who have less idle cash take more care with their decisions than companies
with more idle cash. This can probably be imputed to the fact that companies with more idle cash act
more impulsive, or think they are better, have better knowledge and more expertise than they
actually have, resulting in overconfidence (Stechyshyna, 2012). First of all, this thesis researches if
there are any differences between companies with more and companies with less idle cash. After
that, possible reasons, like the ones mentioned here, are researched.
10
4 - Research Area
For this research, the acquirer as well as the target firm have to be located in Europe. This is for the
desire to research European countries. This desire to research European companies arises from the
fact that a lot of studies researched American companies. Thinking it is important to try and find the
same negative relationship between overconfidence and inferior M&As in Europe as Malmendier and
Tate (2008) and Morck, Shleifer and Vishny (1990) found for American companies, the choice is made
for European companies. An important notice is that more than 50% of the researched acquirers are
from only six different countries, Switzerland, Finland, Germany, Sweden, the Netherlands and the
United Kingdom. This is not entirely accidental, as the first five countries mentioned are the top five
countries with the most competitive economies, according to the Global Competitiveness Report
(Schwab, 2013-2014). The sixth country is the United Kingdom, the country with the highest absolute
number of M&As (Kilduff, 2013). These countries are indicated in yellow in Figure 1. The target
countries are not concentrated in a few countries like the acquirers, they are more equally
distributed in the whole of Europe.
Figure 1.3: The most important acquiring countries (indicated in yellow)
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5 - Research Specifics
Under the current conditions, the M&As are more and more often becoming the last resort for the
owner of a company to continue its business in the way he envisions it (Marchan, 2013). All in all, the
financial crisis has massively changed the character of M&As, making a very clear difference in preand post-financial crises takeovers. This distinction is partially caused by the fact that after the initial
hit of the financial crisis the M&A market did not recover, this market is still feeling the impact from
the very start of the financial crisis. After a short revival around 2010 in both the absolute number
and value of the M&As, both these number started to decline even further. Nowadays, they are
nearly at the lowest point ever after the start of the financial crisis, indicating the market for M&As
still suffers heavily from the financial crisis (Mider, Campbell, & Chan, 2012).
The abovementioned facts are responsible for the fact that this thesis will focus on the years before
the crisis, and the most recent years of which data is available as years of comparison, as this is most
representative due to the change in reasons for M&As explained earlier. Therefore, the years this
thesis will focus on are 2006-2007, also partially because this are the peak years regarding absolute
number and value of the M&As, with more than 18.000 takeovers with a combined value of just
below 1.800 billion EUR in for example 2007. Also important for the research is to compare the
acquired results regarding 2006-2007 to other years. The years chosen for this purpose are 20112012, this will let the research cover four years. The most important reason for the choice to use the
years 2011-2012 is that it are the last possible years from which data is available without too much
data reduction.
Another important restriction of the research is the fact that banks, saving institutions and credit
institutions are left out. This is because this kind of firms are known to be different in terms of policy
and obligations to release information.
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6 - Link with Existing Literature
Traditionally, corporate finance studies assume that managers and investors have preferences and
beliefs that are fully rational. A relatively new stream of research in corporate finance relieves this
assumption and examines less-than-rational behaviour from the perspective of managers or
investors. Several papers, like the ones of Malmendier and Tate (2006), Doukas and Petmezas (2006),
Ferris, Jayaraman and Sabherwel (2012) as well as Morck, Shleifer and Vishny (1990) acknowledge
the presence of something called overconfidence by the CEO, or by the company. These CEOs, or
companies, overestimate their own ability to generate returns, resulting in overpaying for their
target firms and eventually having been involved in a value-destroying M&A (Malmendier & Tate,
2008). The paper of John A. Doukas and Dimitris Petmezas (2006) even presumes that overconfident
bidders realize lower returns on their investments than economically rational bidders, regarding
M&As (Doukas & Petmezas, 2006). However, not all the previous researchers agree in this aspect.
Hirshleiver, Low and Teoh (2012) found for example that at the same time, overconfidence makes
managers better innovators, which can be beneficial for companies operating in industries where
innovation is important (Hirschleiver, Low, & Teoh, 2012). Additionally, Roussanov and Savor (2012)
found that other factors, like marriage and status can also influence the performance of the CEOs
(Roussanov & Savor, 2012).
In another paper, from Stephan P. Ferris, Narayanan Jayaraman and Sanjiv Sabherwal (2012), it is
concluded that overconfidence leads to more M&As as opposed to the situation of a normal amount
of confidence. This higher than normal amount of M&As, logically, comes at the expense of quality
(Ferris, Jayaraman, & Sabherwal, 2012). In this context, Malmendier and Tate (2006) found that
overconfident CEOs more often choose for diversifying M&As. Exactly these diversifying M&As are
what results in negative announcement returns (Morck, Shleifer, & Vishny, 1990). All these papers,
and many more examples, suggest a negative relationship between the (cumulative) abnormal
returns ((C)ARs) of the M&As and overconfidence of the CEO or acquiring company. Overconfidence
may arise from having more idle cash. Therefore, the aim of this thesis is to define the relationship
between the amount of idle cash and performance as measured by (C)AR of the acquiring firms in
the M&As.
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CHAPTER 2 – LITERATURE REVIEW
________________________________________________________
In order to extend the short literature overview given in 1.6, a more extensive literature research will
be executed. A literature research in advance of the empirical research can be worthy, as some
earlier research results approve the null-hypotheses of the main research question and others
contradict .
Prior to the literature research, it can be helpful for the overall understanding to discuss and explain
some of the basics of M&As, this will include the discussion of types, advantages and disadvantages,
motives and an explanation about the deal premium.
At the end of this chapter, the hypotheses are derived from the literature and will be explained.
1 – Basics of M&As
1.1 – M&A types
There are three different kinds of M&As to distinguish (DeMarzo & Berk, 2011):
I.
Horizontal integration, where two similar companies in the same industry decide to continue
business together. This therefore concerns two companies at the same level in the value
chain, for example both manufacturing or transport companies. This horizontal integration
enables the company to share resources and most likely, to achieve economies of scale.
(DeMarzo & Berk, 2011)
II.
Vertical integration, where two companies who are not in the same industry, but are (in
different stages) in the same supply chain, decide to continue business together. This can for
example result in one company taking over the company that provided him with the semifinished products used in their production process. Obviously, this can be very beneficial as
14
the manufacturing company does no longer have to pay the margin over the production
costs of these semi-finished products. Therefore, this can lead to cost reduction, also in other
departments, like the possible reduction in transport costs for example. (DeMarzo & Berk,
2011)
III.
Conglomerate merger, where two completely strategically unrelated companies join forces.
Actually, the only utility of this kind of M&A is to create a presence in multiple markets.
(DeMarzo & Berk, 2011)
The difference between these three kinds of M&As is not important whatsoever in answering the
research question and therefore is not taken into account in the rest of the thesis, as all of these
three kinds of mergers will be present in the dataset.
Furthermore, a possible distinction between M&As can be made on the basis of the attitude of the
takeover. Some takeovers have a hostile attitude, where the managers of the target firm tries to
resist the buyers interest and does not want to join forces, but eventually has to because of the
current owners. Other takeovers have a friendly nature, where both companies are willing to merge.
(McClure, 2009) Also, this distinction is not taken into account in the rest of the thesis, as it is
unimportant to answer the research question as well.
15
1.2 – Advantages and Disadvantages
What is important are the reasons to merge with or acquire other companies. A takeover can bring
many advantages, to both companies. Just to mention a few:
I.
Economies of scale and scope; as mentioned before, this is common in horizontal M&As. It
basically means reducing costs by operating on a higher level. This may sound questionable,
but when one considers that the unit costs will stay equal, but the fixed costs can be divided
over a larger number of units, the costs will decrease. This can result in lower price and
maintaining the same margin, or maintaining the same prices and taking advantage of the
higher margin. It is possible to make these kind of cost reductions in for example marketing,
distribution etc. (DeMarzo & Berk, 2011)
II.
Expertise; with a takeover the acquirer brings possible experts and therewith expertise from
the target firm over to their firm. The knowledge of the experts the acquirer brings to their
company could otherwise have been used against them, when the expert had still been
employed in the target company. (DeMarzo & Berk, 2011)
III.
Gains in market share; when taking over a firm in the same industry the sales and the
consumer file will enlarge. This results in a gain in market share, which can be positive for the
company in numerous ways. In a more extreme case, when there is a low number of
competitors before the M&A, the M&A can possibly even result in the case of a monopoly.
(DeMarzo & Berk, 2011)
IV.
Tax savings; as a company is obliged to pay taxes over profits, but does not get a restitution
of tax when making losses, a M&A can be the solution. This is because, when making a profit
in one division, and making a loss in another division, these can be offset when merged and
little or no tax is paid. However, in the situation that the companies are not merged, tax over
the full profit has to be paid. (DeMarzo & Berk, 2011)
V.
Earnings growth, it is possible that after the merger of two companies the earnings per share
are higher than the two premerger earnings combined, even when the merger did not create
any economic value. (DeMarzo & Berk, 2011)
VI.
Another, different, advantage of a M&A can be the creation of shareholder value, shown by
Andrade, Mitchell and Stafford (2001). This most of time occurs on behalf of the target firms,
who accrues the most gains (Andrade, Mitchell, & Stafford, 2001).
16
The paper of Healy, Palepu and Rubak (1990) shows that these advantages are not only theoretical,
but also observable in practice. In their research of the 50 largest M&As they found results that
indicate that the merged firms have significant improvements in for example asset productivity
relative to their industries after the merger, resulting in higher cash-flows (Healy, Palepu, & Rubak,
1990).
Of course, no M&A comes without disadvantages:
I.
Costs; very simple, a M&A is expensive and has, on the moment of acquisition, yet to prove
to be worth the costs. This always carries a risk. (DeMarzo & Berk, 2011)
II.
Agency problems; as the concern grows more conflicting interests are going to be influential.
In small companies it is rather simple to keep a close eye on these problems, however, if the
company continues to grow this become more difficult. Inevitably, this will lead to higher
agency costs, which of course are not desirable. (DeMarzo & Berk, 2011)
III.
Employee retention; this is very common in horizontal mergers, where employees of the two
companies involved are likely to perform similar jobs. At the moment the M&A is announced,
they know that some of their jobs will be laid off and excess employees will be fired. This can
possibly result in employees seeking for another job, even before the actual takeover takes
place. (DeMarzo & Berk, 2011)
All together, it is the intention of a M&A that the advantages offset the disadvantages, otherwise the
M&A will not be done. However, not all advantages and disadvantages are perfectly predictable, and
therefore every M&A carries some risk. To reduce this risk expectations about the various factors are
tried to be made. Although, this also is not risk free, it does reduce the risk somehow (Besanko,
Dranove, Shanley, & Schaefer, 2010). When companies act more impulsive, these pre-research could
possibly be done less accurate and can result in an inferior M&A choice. A reason for a company to
act more impulsive is having more idle cash. Therefore, this thesis researches if cash rich companies
make inferior M&A decisions as opposed to companies with less idle cash.
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1.3 – M&A Motives
A M&A deal is made, as said earlier, when the advantages outweigh the disadvantages, but more
important is the reason why the firms would participate in a M&A at all. There are three main
motives for a company to be part of a M&A:
I.
Strategic motives; this is the most straightforward motive, as it is focused on the
improvement and development of the company. With this kind of M&As the acquirer is
trying to achieve a competitive advantage over other companies in the same business, when
regarding horizontal or vertical integration. (Johnson, Whittington, & Scholes, 2011)
II.
Financial motives; the company is focused on making the best use of the financial resources
to satisfy the shareholders to the best of their capabilities. Therefore, this motive is highly
concerned with the improvement of the financial performance by the M&A. (Johnson,
Whittington, & Scholes, 2011)
III.
Managerial motives; the managers are focused on self-interest. Actually, this is the worst
motive for a M&A to happen, as the managers only intend to use it in their best interest,
monetary or something different. The managers in M&As with this motive do not necessarily
perform this M&A for the interest of the shareholders. (Johnson, Whittington, & Scholes,
2011)
1.4 – Deal Premium
When a firm takes over another firm, the acquirer nearly always pays a premium (ϕ), more than the
value of the company, to the target firm (DeMarzo & Berk, 2011). Of course, when acquiring another
company, the acquirer thinks the advantages will offset the disadvantages. In that case, the net
result of the M&A is expected to be beneficial for the acquirer. When he announces his willingness to
take over the target firm, this target firm knows this and wants a monetary compensation for this;
the deal premium (Besanko, Dranove, Shanley, & Schaefer, 2010). This usually results in the target
company’s stock going up. This can also be used as an indicator of a good or bad M&A decision.
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2 – Literature Research
The abovementioned basics of M&As should be enough to understand the meaning and intention of
the research. Therefore, now will be proceeded with the official literature research. The results of
multiple researches on this subject will be discussed, as these results continue to contradict each
other. First, the very existence of overconfidence will be researched, after which the results of the
leading paper of Ferris, Jayaraman and Sabherwel will be compared to those of other researches.
2.1 – Existence of Overconfidence
Before the start of the literature research the very existence of overconfident firms has to be
acknowledged. This evidence is given in the paper of Yu (2013), where the main research included
the origin and very existence of CEO confidence. This paper concluded with the facts that there
definitely is something called CEO overconfidence and that is does influence the company in many
ways, some good, some not so good (Yu, 2013). Especially this overconfidence exist in entrepreneurs
and founder-managers (as opposed to new-venture managers) (Forbes, 2005).
2.2 - Ferris, Jayaraman and Sabherwal
The next important step is the analysis of the paper of Ferris, Jayaraman and Sabherwal (2012) who
did an extensive research to the effects of CEO overconfidence and came to many results, three of
which will discussed more detailed. Some of these results are approved by other papers, while some
others are disproving with the results found in their paper.
The first interesting finding in the their research is that the overconfidence of a CEO influences the
absolute number of M&As by a company in a positive way, meaning that companies with such an
overconfident CEO engage in more M&As than a company in which an economically rational CEO
makes the decisions regarding the M&As (Ferris, Jayaraman, & Sabherwal, 2012). This first
interesting result is approved by Bressane and Verini Maia (2010) in their paper on the impact of CEO
overconfidence on M&As, in which they made use of large sample of nearly 9.000 M&A deals. They
clearly state that they found a positive relationship between the overconfidence of a CEO and the
number of M&A deals for that specific firm or company (Bressane & Maia, 2010). Also, Malmendier
and Tate (2005) agree on this and state that overconfident CEOs are significantly more responsive to
19
cash flows (Malemendier & Tate, 2005). Also, a paper written by the University of Edinburgh, of
which the author is unknown, tested empirically for this and found the exact same results as
Bressane and Maia did (Unknown, 2008). However, El Allali (2009) found evidence against this result
found in the abovementioned papers. His paper suggests that there is no influence from an
overconfident CEO on the number of M&As (EL Allali, 2009).
The second main result of the paper of Ferris, Jayaraman and Sabherwal (2012) is that they found
strong evidence that overconfident CEOs engage in diversifying acquisitions with a higher frequency
than economically rational CEOs. As diversifying M&As induce fewer advantages when compared to
horizontal or vertical integration, this can be an indicator of inferior M&A decisions (Besanko,
Dranove, Shanley, & Schaefer, 2010). The finding of more diversifying M&As is confirmed by the
paper of Malmendier and Tate (2006), who also found this exact same result. They also proved
empirically what Besanko, Dranove, Shanley and Shaefer (2010) stated about the quantity of
advantages from the different kinds of M&As; diversifying M&As result in less advantages for the
concern as a whole (Malmendier & Tate, 2008). This last statement was the very subject of the paper
of Morck, Shleifer and Vishny (1990), who empirically researched the quality of the different kinds of
M&As and found that diversifying M&A deals resulted in lower returns (Morck, Shleifer, & Vishny,
1990).
This means that when the results of the papers of Malmendier and Tate (2006) and Morck, Shleifer
and Vishny (1990) are combined, one can induce that overconfident CEOs make M&A decisions
which result in lower returns. This can be explained by the fact that overconfident CEOs engage in
more diversifying M&As and that exactly these kind of M&As are responsible for lower returns
(Ferris, Jayaraman, & Sabherwal, 2012). However, there is some discussion around this.
The last main result deriving from the research of Ferris, Jayaraman and Sabherwal (2012) is that
they found that overconfident CEOs more often than economically rational CEOs use cash as the
method of payment in the M&A (Ferris, Jayaraman, & Sabherwal, 2012). This connects perfectly to
this research as it is researched if having more idle cash as opposed to situation of having less idle
cash influences the M&A decisions. As having more idle cash leads to more confidence, and
sometimes overconfidence, paying with cash may be an indicator of having more cash and therefore
the probability of being overconfident (Stechyshyna, 2012).
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2.3 - Returns
Another important step in the literature research is the discussion of the difference between M&As
executed by overconfident CEOs and M&As executed by economically rational CEOs regarding the
returns. Also in this area, the researchers are not at the same wavelength. Doukas and Petmezas
(2006) found that overconfidence in CEOs leads to lower returns (Doukas & Petmezas, 2006). This
finding confirmed the earlier findings, induced from the results of the researches of Malmendier and
Tate (2006) and Morck, Shleifer and Vishny (1990). Also, the paper of Ferris, Jayaraman and
Sabherwal (2012) confirms these lower returns with the M&As performed by overconfident CEOs,
despite the fact that they do not state this explicitly. They only induce it from stating that an
increasing amount of M&As will come at the expense of quality. As they confirmed this increase in
number of M&A deals, they indirectly state that M&As performed by overconfident CEOs are inferior
to those executed by economically rational CEOs (Ferris, Jayaraman, & Sabherwal, 2012). However,
as in all discussions, some disagree with this. Tarik El Allali (2009) found no direct evidence for lower
returns in his empirical research of M&A quality (EL Allali, 2009).
2.4 – Monetary Compensation
Another aspect of a M&A deal is the monetary equivalent of what is paid for the takeover. The term
monetary equivalent is used as it is possible to take over another firm with stocks or shares, then no
cash transactions would be involved. As this aspect of a M&A is researched, specifically in hypotheses
4, it is important to take into account and search for earlier results in this area. However, not much
research is done in the field of monetary compensation for a takeover, therefore this research will be
pretty controversial in that area. Only Malmendier and Tate (2006) found noteworthy results
regarding this, and they concluded that overconfident CEOs often overpay for their M&As as they
overestimate their own capacities (Malmendier & Tate, 2008). Also, important to note are the
findings of Roll (1986) with his hubris hypothesis. He states that hubris takes part of individual’s
decisions and can explain why bidding firms pay too much for their targets; it is because these firms
overestimate the positive aspects of the purchase and underestimate the negative ones (Roll, 1986).
All in all, it can be easily concluded that the literature research is indecisive.
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Table 2.1: Most important Contributions of different authors to the Literature Research
Author(s)
Most Important Contributions
(Yu, 2013)
Found evidence for the existence of overconfidence.
(Forbes, 2005)
Overconfidence is especially present in entrepreneurs and
founder-managers.
(Ferris, Jayaraman, &
Researched and evidenced that CEO overconfidence explains the
Sabherwal, 2012)
increase in number of M&As, frequency of diversifying
acquisitions, and the use of cash to finance a merger deal. They
also found that overconfidence does not say anything about the
quality of the M&As (they are only implying that more M&As
come at the cost of quality).
(Bressane & Maia, 2010)
Found evidence that overconfident CEOs are more likely to
perform M&As than economically rational CEOs.
University of Edinburgh
Evidenced that overconfident CEOs are more likely to conduct
(Unknown, 2008)
M&As than economically rational CEOs.
(EL Allali, 2009)
Found evidence that there is no significant influence of
overconfident CEOs on M&As in both number and quality of the
deals.
(Malemendier & Tate, 2005)
Proved that overconfident CEOs are significantly more responsive
to cash flows.
(Malmendier & Tate, 2008)
Found that CEO overconfidence explains the higher frequency of
diversifying acquisitions and is the reason why CEOs possibly
overpay for the M&A; overestimation of their own capacities.
(Morck, Shleifer, & Vishny,
Proved that diversifying M&As show lower returns.
1990)
(Stechyshyna, 2012)
Found evidence that having more idle cash can lead to
overconfidence.
(Doukas & Petmezas, 2006)
Found that overconfident CEOs achieve lower returns than
economically rational CEOs.
(Roll, 1986)
Found the Hubris hypothesis; makes bidding firms overestimate
the positive aspects and underestimate the negative aspects of
takeovers.
22
3 - Hypotheses Description and Derivation
Now, that literature research has been completed specific hypotheses can be derived from this. In
this thesis it is researched if companies with more idle cash make such decisions regarding M&As
that they suffer significant disadvantages when compared to companies with less idle cash, possibly
as a result of overconfidence. So, actually the outcome and results of the M&As between these two
groups are analysed and compared. This comparison is done on the basis (cumulative) abnormal
returns.
The underlying thought to this is that when there is more idle cash in a company, this company is
likely to be less cautious and more likely to take more rigorous decisions which are thought to be
inferior to well-thought decisions made by companies who act less impulsive; the companies with
less idle cash.
This research will start with finding the (C)AR for both the acquiring and target firm. The returns of
the two firms that belong together in the same M&A will be added, which results in a number
reflecting the value added for society as a whole. This number can either be positive or negative,
depending on if the M&A created value, or destroyed value. In the experimental and control group,
the average contribution to society is calculated and compared. This analysis will be done for the
years 2006-2007 and 2011-2012. This will result in making conclusions about the fact if the firms with
more idle cash make inferior choices regarding M&As as opposed to firms with less idle cash,
measured by (C)AR.
23
Although the expectation is that in both 2006-2007 and 2011-2012 the firms with more idle cash, the
experimental group, will make inferior decisions regarding M&As, as measured by the (C)ARs, it is
still important for the research to measure and analyse both these years. This is of such importance
that a hypothesis is dedicated to this part of the research, because with those results it will become
possible to make statements about the development of the performance in M&As by the firms with
more idle cash, as well for the firms with less idle cash. The latter is actually not of such importance
as the former, because the firms with less idle cash are expected to remain reasonably stable over
time in terms of value added to society by the M&As. In the experimental group, however, it is
expected that a trend towards less inferior decisions will be observed, possibly as a result of learning
from past failures and the fact that CEOs became more cautious. However, even though this
reasoning seems straight, Doukas and Petmezas (2007) found evidence of so called ‘serial acquirers’
performing worse (Doukas & Petmezas, 2007). They found that multiple acquisitions resulted in
lower wealth effects. As this contradicts the abovementioned thoughts, it will be valuable for the
research to include this. So, Doukas and Petmezas (2007) found evidence of serial acquirers
performing worse. This research actually reasoned that more M&As by the same acquirer will result
in better performance, possibly due to learning effects. Therefore, the third hypothesis includes this
part of the research.
The earlier discussed premiums are also an important step in the research. Therefore it will be
researched if the cash rich companies pay, compared to the cash poor companies, a higher deal
premium. As it is important to take the size of the M&A into account, the deal premium will be
divided by the deal value to obtain a ratio which is easy to work with. This aspect does not reflect the
‘quality’ of the target firm in the same way as the other components of the research does, but it does
reflect the ‘quality’ of the choice made by the management of the acquiring firm. As the research
question mentions, the research is about inferior decision, and as long as the monetary premium is
part of the decision, this also can lead to an inferior decision, for example in the case when the
acquirer overpays.
24
4 - The Hypotheses
This research will be done on the basis of the following hypotheses. After each hypothesis the
statistical representations, including explanations of the applied abbreviations, which eases testing,
are stated.
Hypothesis 1
This first hypothesis researches the situation in 2006-2007. It compares the value added or destroyed
to society from the experimental group to the control group. This value added or destroyed
component is calculated by taking the sum of the CAR from the acquirer and target firm. The
experimental group is expected to show less or more negative value added than the control group.
This results in the following hypothesis:
The total value added for society in M&A deals is lower in the experimental group than in the
control group in 2006-2007.
H10:
AVA2006-2007, 2 – AVA2006-2007,1 = 0
H11:
AVA2006-2007,2 – AVA2006-2007,1 > 0
Where;
AVA2006-2007,1
=
Average Value Added in 2006-2007 in the experimental group;
AVA2006-2007, 2
=
Average Value Added in 2006-2007 in the control group.
Hypothesis 1 implies the expectation of rejection of H10.
25
Hypothesis 2
This second hypothesis researches the exact same thing as hypothesis one, the only difference is in
the years researched, which is now 2011-2012. The underlying thought to do this is because it is
expected that there still is a disadvantage in the CARs to the experimental group. However, this
disadvantage regarding CAR is expected to be smaller. This results in the following hypothesis:
The total value added for society in M&A deals is lower in the experimental group than in the
control group in 2011-2012.
H20:
AVA2011-2012,2 – AVA2011-2012,1 = 0
H21:
AVA2011-2012,2 – AVA2011-2012,1 > 0
Where;
AVA2011-2012,1
=
Average Value Added in 2011-2012 in the experimental group;
AVA2011-2012,2
=
Average Value Added in 2011-2012 in the control group.
Hypothesis 2 implies the expectation of rejection of H20.
26
Hypothesis 3
In hypotheses one and two, it is researched if the summed CARs (the value added for society) in the
years 2006-2007 and 2011-2012 differ significantly between the two groups. It can also be valuable
for the research to compare the differences between the first and all later M&As in the experimental
and the control group. So, this will result in taking the differences between the value added to
society of the different M&As and compare these between the experimental and control group. The
expectation is that the difference is larger in the experimental group, because in the control group
the acquirers act more carefully and less impulsively, which induces the expectation that the value
added to society will be somewhat stable over time. In the experimental group, however, it is
expected that in the first M&A they act very impulse, because of their overconfidence, resulting in
low or negative CARs, which will make them, hopefully, more conscious about their decisions and
learned them to be more careful in their next ones. This is researched only, of course, for companies
who did multiple acquisitions in the given time periods. The value added for the first M&A is tested
against the value added of the following M&As performed by that same acquirer. Therefore, the
expectation is that there will be a significant difference in the experimental group, whereas the
control groups CARs are expected to remain stable.
The change in the value added between the first and the following M&As by the same acquirer is
larger in the experimental group than in the control group.
H30:
ΔAVA1 – ΔAVA2 = 0
H31:
ΔAVA1 –Δ AVA2 > 0
Where;
ΔAVA1 =
the change in Average Value Added in the experimental group between the
first and the following M&As by the same acquirer
ΔAVA2 =
the change in Average Value Added in the control group between the first and
the following M&As by the same acquirer
Hypothesis 3 induces the expectation of rejection of H30.
27
Hypothesis 4
As said earlier, the deal premium paid by the acquiring firm is important in the consideration if a
choice to merge or acquire a firm by the management is inferior in the experimental group as
opposed to the control group. This hypothesis will be tested on the basis of the data from 2006-2007
and 2011-2012 as opposed to data from only one time period, as this provides a larger sample, which
will be statistically more representative. A more mathematical representation:
(deal premium) = (deal value) – (number of outstanding stocks of target) * (value per stock before announcement) *
(percentage of shares acquired in transaction)
The intention should always be to keep this deal premium as low as possible in relation to the deal
value. Therefore, the expectation is that the acquiring firms in the experimental group will have a
higher deal premium to deal value ratio than the similar firms in the control group.
Premium Ratio = Deal Premium / Deal Value
This results in the hypothesis:
The premium ratio is higher in the experimental group than in the control group.
H40:
PR1 – PR2 = 0
H41:
PR1 – PR2 > 0
Where;
PR1
=
Premium ratio experimental group;
PR2
=
Premium ratio control group;
Hypothesis 4 induces the expectation of rejection of H40.
28
CHAPTER 3 – DATA AND METHODOLOGY
________________________________________________________
The empirical research in this thesis includes the analysis of the data on M&As extracted from
ThomsonOne (T1) with the Event Study Tool in DataStream. First, specifics about the data will be
provided after which the methodology used in this research is explained.
1– Data Specifics
1.1– Data Criteria
The gathering of the data started with the ThomsonOne database. From this database the basic
information needed for the M&A deals was collected. Multiple criteria for the data were set, because
it would benefice the research. The selected criteria were:
I.
Acquirer nation:
Europe
II.
Target nation:
Europe
III.
Acquirer public status:
Public
IV.
Target public status:
Public
V.
Date effective:
1/1/2006 – 12/31/2007 and 1/1/2011 – 12/31/2012
The choice to set both the acquirers and targets nation to ‘Europe’ was made, as said earlier, to try
and find the same results in European companies as Malmendier and Tate (2006) and Morck, Shleifer
and Vishny (1990) found in American companies. The choice to set both the acquirers and target
public status to ‘public’ was not much of a choice, as the key to the analysis in this research lies in the
CARs, which are acquired with the Event Study Tool, which only can calculate the ARs of public listed
firms and companies. Lastly, the chosen dates are explained earlier; the period 2006-2007 is chosen
because of the fact that it is the period just before the economic crisis (and the character of M&As
changed dramatically in this crisis), and the period 2011-2012 is chosen for comparison and on the
basis of that they are the last two years of which data is available without too much data reduction.
29
1.2– Data Variables
In the extraction of data from ThomsonOne multiple relevant variables were chosen, in order to
enable the continuation of the research. One variable of course being more important than the
other, below is a table discussing each variable briefly.
Table 3.1: Data Variables Specified
Variable
Explanation
Acquirer Name
To identify the acquirer.
Acquirer CUSIP
Database identifier, possibly useful for any
further research in other databases.
Acquirer Primary SIC Code
To identify the companies industry. SIC codes
between 6000-6999 are excluded from the
research as they are financial or investment
firms or companies.
Acquirer SEDOL
Formatted into compatible identifier to be
used in the Event Study Tool, in DataStream.
Acquirer Ticker Symbol
Database identifier, possibly useful for any
further research in other databases.
Target Name
To identify the target.
Target CUSIP
Database identifier, possibly useful for any
further research in other databases.
Target Primary SIC Code
To identify the companies industry. SIC codes
between 6000-6999 are excluded from the
research as they are financial or investment
firms or companies.
Target SEDOL
Formatted into compatible identifier to be
used in the Event Study Tool, in DataStream.
Target Ticker Symbol
Database identifier, possibly useful for any
further research in other databases.
Acquirer Cash LTM
The core of the research, as this is the variable
making
the
experimental
distinction
and
control
between
group.
the
It
is
30
researched if more idle cash as opposed to less
idle cash makes a difference in M&As, this is
the variable on the basis of which the
distinction is made.
Date Announced
Not much use to it, as the ‘date effective’
variable is the variable which determines if the
M&As are in the aforementioned time period.
Date Effective
The variable which determines if the M&As are
in the aforementioned periods.
% of Shares Acquired
Used for the calculations of the deal premium
(hypothesis 4).
% of Shares Owned after Transaction
Only situations in which a ‘substantial interest’
(>5%) is owned after transaction are taken into
account in this research.
Target Shares Outstanding
Target Closing Price 1 day after Announcement
Target Closing Price 1 week after Announcement
Used for the calculations of the deal premium
Target Share Price 1 day before Announcement
(hypothesis 4).
Target Share Price 1 week before Announcement
Value of the Transaction
31
Since the data has been specified now, the research can proceed by explaining the methodology. As
the core of the research concern the ARs and CARs of the M&As, a thorough explanation of the
derivation and underlying models of this is provided at first. Later on, specific instruments used, like
the EventStudy Tool, t-test and ANOVA, will be explained.
2 – Derivation (C)ARs
2.1 – Capital Asset Pricing Model
In this thesis abnormal returns (ARs) are used as measurement method. The basis of the calculation
of the AR is the expected return, without this, the AR cannot be calculated (see 2.6 – Abnormal
Returns). The calculations of these expected returns are based on the Capital Asset Pricing Model,
the CAPM. The CAPM was introduced by (Treynor, 1962), (Sharpe, 1964), (Lintner, 1965)
and (Mossin, 1966) (Perold, 2004). This model is used to calculate an expected return for an asset
(Chong, Jin, & Phillips, 2013). As most models do, the CAPM model has some assumptions which
have to be satisfied in order for the model to work appropriately. These assumptions are as follows
(Arnold, 2005):
Table 3.2: Assumptions CAPM (Arnold, 2005)
All investors:
I.
Aim to maximize economic utilities.
II.
Are rational and risk-averse.
III.
Are broadly diversified across a range of investments.
IV.
Are price takers.
V.
Can lend and borrow unlimited amounts under the risk free rate of interest.
VI.
Trade without transaction or taxation costs.
VII.
Deal with securities that are all highly divisible into small parcels.
VIII.
Have homogeneous expectations.
IX.
Assume all information is available at the same time to all investors.
32
The CAPM is used to calculate the expected returns regarding M&As. The CAPM makes use of the
security market line (SML) (Fama & French, 2004). This SML is a line representing that for higher
risks, investors want higher expected returns. On the x-axis the risk premium is displayed, and the
expected return on the y-axis. A property of the SML is that it is linear. When it would not have been
linear one could take advantage of that. This could be done by investing cleverly to get a higher
expected return to risk ratio than average. (Fama & French, 2004) To make this situation more clear,
graph 2.1 illustrates the situation. The intercept is determined by the risk free rate and the slope by
the markets risk premium (Geurts & Pavlov, 2006).
Figure 3.1: Security Market Line (SML) (Geurts & Pavlov, 2006)
To elaborate on how one could take advantage of the situation if the abovementioned ratios were
not equal to each other, lots of theories are developed. It will suffice to say that that specific
situation would result in arbitrage, which is a term meaning that one can make a profit, above the
risk free rate, without having any risk (Fama & French, 2004)!
33
The CAPM thus boils down to the following formulas: (Fama & French, 2004)
(𝐸(𝑅𝑖) − 𝑅𝑓)
= 𝐸(𝑅𝑚) − 𝑅𝑓
𝛽𝑖
𝐸(𝑅𝑖) = 𝑅𝑓 + 𝛽𝑖(𝐸(𝑅𝑚) − 𝑅𝑓)
Where;
E(Ri)
=
Expected return on the asset
Rf
=
Risk-free rate
Βi
=
Sensitivity of the expected asset returns to the expected market returns
E(Rm)
=
Expected return of the market
The risk premium can therefore be seen as (E(Ri)-Rf).
The importance in the knowledge behind the CAPM lies in the fact that the EventStudy Tool which
will be used in DataStream to calculate the ARs, and therewith the CARs, makes use of this model
when calculating the expected return factor.
34
2.2 – Abnormal Returns
In this thesis the concept AR is mentioned multiple times, but what exactly is an AR? And how is it
calculated? As AR stands for abnormal returns, this is a reliable measure to evaluate the impact of
certain events (MacKinley, 1997). All kinds of events, like M&As, can have influence on the returns of
a company. In the absence of such an event, the expectation of the returns of the company will be
conform the market model, however, when such an event takes place, this can be influential on the
returns. (Besanko, Dranove, Shanley, & Schaefer, 2010) In this case, the actual return deviates from
the expected return. The difference is what is called the AR. The basis for event studies is laid down
by the leading research of Mackinley (1997) and his findings will be used as the basis for this event
study related research. This controversial research defined event study based researches and also
introduced formulas regarding ARs. Formulas which are rehearsed and explained below: (MacKinley,
1997)
ARi,t = Ri,t – (E)Ri,t
ARi,t
=
Abnormal return i on day t
Ri,t
=
Actual return i on day t
(E)Ri,t =
Expected return i on day t
The expected return can be defined by (MacKinley, 1997):
(E)Ri,t = rf + ϕ
rf
=
Risk free market rate
ϕ
=
Risk Premium
This risk premium is a compensation for holding a risky market portfolio rather than holding a riskfree asset (Campbell, 2011). This results in the final formula for ARs (MacKinley, 1997):
ARi,t = Ri,t – (rf + ϕ)
To compute the CAR from the computed ARs, one just simply cumulates the ARs on the different
dates to obtain the CAR over those specific dates (MacKinley, 1997):
CARi = ΣARi,t
CARi
=
CAR of entity i over the event
ARi,t
=
AR i on day t
35
It is likely that the returns of both companies will be affected by the M&A. So, when adding the CARs
for the acquirer and the target, this will result in a figure representing the contribution for society
(Campbell, 2011).
CARa + CARt = CS
CARa
=
CAR acquirer
CARt
=
CAR target
CS
=
Contribution to Society
The best way to measure the impact of the M&A is to cumulate both the CARs for that specific M&A
deal. In that way, one will obtain information about the impact on society. It the total CAR of the
involved firms in a M&A is positive, this means that in total value is created for society. The opposite
is true in the case that the CAR is negative, value is destroyed for society. (Campbell, 2011) This
conclusion that can be made from the CAR of a M&A deal makes it a very powerful tool to evaluate
the performance of M&As.
As mentioned before, in this thesis the EventStudy Tool in DataStream will be used to calculate the
ARs. For reasons of completeness the full formula this tool uses for his calculations will be given and
explained below:
MarketModelAdjRetx = RetEvlDayx – α – β * RetIndEvlDayx
MarketModelAdjRet x =
The AR at day x
RetEvlDay x
=
Return at day x of the evaluation period
α
=
The intercept
β
=
Slope coefficient of RetIndEvlDay x
RetIndEvlDay x
=
Return of the index at day x of the evaluation period
All these variables together are thus able to calculate the AR (EDSC, 2010). Together with the
abovementioned formulas, the CARs and the contribution for society as a whole can be easily
computed.
36
3 – Methodology
For the analysis of the data, statistical software will be used. In this thesis will be made use of SPSS,
while always using a significance level (α) of 5%. Actually all four hypotheses boil down to comparing
means in the researched groups. As the first two hypotheses can be seen as a comparison of means
in multiple groups, an ANOVA can be used to prove the differences in means, or not. The third and
fourth hypotheses are not suitable to be included in this same ANOVA as they are dissimilar to the
first two. They first two namely research the exact same thing, but for different years, the last two
research completely different aspects. So, in order to test the last two hypotheses a t-test for both is
required.
3.1 – Event Study Tool
For the calculation, as mentioned earlier, the EventStudy Tool from DataStream is used. This tool
calculates the ARs. In doing so, it needs a forecast and an estimation window, identifiers with
corresponding event dates and an index with which it calculated the expected returns. The specific
choices and variables will be discussed briefly below.
First of all, the period window at the top of the macro; one has two fill in two periods. With the first
one, the tool calculates the expected return conform the market model, the second period is the
period for which it calculates the ARs. The chosen values for the first period, the forecast, are -60
until -50 days prior to the event. For the estimation period -1 until 1 is chosen.
Secondly, the identifiers used are SEDOL codes. There was not much choice to this as ThomsonOne
does not provide any other codes other than SEDOL which are recognized by DataStream. SEDOL
codes being the worst usable codes in DataStream, they needed some transformation. Without too
much problems the SEDOL codes were finally put into the tool.
Third and last, there was the choice for the underlying stock exchange in order to calculate the
expected returns conform CAPM. Although it had been possible to use every underlying individual
stock exchange for each company in each M&A, is chosen to use the universal MSCI Europe index.
This decision was made after advice from some employees of the Erasmus University, who reasoned
this to keep the complexity somewhat lower.
37
3.2 – T-Test
A t-test is a parametric statistical test which is usually used to determine differences in population
means (Moore, McCabe, Alwan, Craig, & Duckworth, 2011). Before one even gets started with a ttest (or an ANOVA), one has to check if the researched sample sticks to a normal distribution. This
distribution provides the highest accuracy for a t-test (Moore, McCabe, Alwan, Craig, & Duckworth,
2011). In this research the skewness criterion and Kurtosis criterion are used to determine the
normality of the datasets. In order to be considered normally distributed the skewness criterion is
not allowed to result in a value higher than 1.500 and the Kurtosis criterion is not allowed to be
larger than 3.000 (Klaasen, 2014). If on the first check these two criteria are not satisfied, there are
two ways of trying to acquire the desired normal distribution:
I.
Discard the outliers. Outliers are defined as data points with a z-score smaller than -3
or larger than +3. (Carter-Hill, Griffiths, & Lim, 2012)
II.
Take the log-transformation. This means that the logarithm naturalis (ln) will be
taken for every value in the problematic data set. Usually this results in a better
normally distributed data set. (Carter-Hill, Griffiths, & Lim, 2012)
After these procedures are undertaken the t-test will be executed. If the dataset still does not follow
a normal distribution, there is nothing left to do and the data set as is will be used. As in this research
the means of two different groups are researched on a significant difference, an independent t-test
on differences in means across groups is the best choice (Carter-Hill, Griffiths, & Lim, 2012). The
hypothesis and formula SPSS uses for the calculation of this statistic is as follows (Moore, McCabe,
Alwan, Craig, & Duckworth, 2011):
H0:
Means across researched groups are equal.
Ha:
Means across researched groups differ significantly.
38
The formula for an independent t-test is as follows (Moore, McCabe, Alwan, Craig, & Duckworth,
2011):
𝑋̅1 − 𝑋̅2
𝑡=
√(
(𝑁1 − 1)𝑠12 + (𝑁2 − 1)𝑠22
1
1
)( +
)
𝑁1 + 𝑁2 − 2
𝑁1 𝑁2
Where;
t
=
the researched t-statistic
𝑋̅x
=
mean of group x
Nx
=
number of observations in group x
sx
=
standard deviation group x
The outcome of the t-test is defined by the p-value. If this p-value is larger than alpha, the nullhypotheses will be considered true. If the p-value is smaller than alpha the null-hypotheses will be
rejected and a significant difference in means is present in the groups (Carter-Hill, Griffiths, & Lim,
2012).
39
3.3 – ANOVA
The name ANOVA is an abbreviation for Analysis of Variance. ANOVA is a testing procedure which
tests if the population means of two or more different groups actually differ (Carter-Hill, Griffiths, &
Lim, 2012). In SPSS this means that one has to make the desired amount of groups, containing the
data to be tested for that specific group and choose for an univariate analysis of variance. In advance
of any further explanation, it is important to know that the variances of the data groups to be tested
are equal. If they are not, an ANOVA is not significant. Testing on equal variances is usually done with
a ‘Levene’s test of Equality of Error Variances’. This test tests the null hypothesis that the error
variance of the dependent variable is equal across groups. (Moore, McCabe, Alwan, Craig, &
Duckworth, 2011)
H0: σ1 = σ2
Ha: σ1 ≠ σ2
This test basically is a F-test done multiple times. All in all, if the significance value (p-value) is larger
than alpha, the null hypothesis cannot be rejected and therefore equal variances are assumed. If this
p-value is smaller than alpha, the null hypothesis will be rejected, resulting in the fact that the
variances are not equal and rejection of the complete ANOVA test (Carter-Hill, Griffiths, & Lim, 2012).
After this Levene’s test one has to check the other of assumptions of the applicable Gauss-Markov
theorem (Moore, McCabe, Alwan, Craig, & Duckworth, 2011):
I.
Normality; as explained earlier in the theory about the t-test.
II.
No serial correlation; means that the error terms may not have any correlation with each
other, inducing a relationship of the variable with itself.
III.
No heteroskedasticity; means that the error variance has to be constant over all the
observations. This error term has to be homoskedastic, constant over all observations.
After the Levene’s test evidencing equal variances, normality is acknowledged and serial correlation
and heteroskedasticity are absent or one can continue with the remaining part of the ANOVA, where
it is researched if the group made have a significant effect. So, this means, if an observation is in
another group, does this affect its value? Thus, testing if the means across groups are different. This
is tested by evaluating the significance of the source ‘group’. If the p-value is larger than alpha, there
is no significant effect of group on the value, if the p-value is smaller than alpha, there is a significant
effect of the group on the value. (Moore, McCabe, Alwan, Craig, & Duckworth, 2011)
40
In this last situation, the researcher knows there are differences in his testing groups. Now,
obviously, he wants to know which groups differ in what ways. This analysis can be done with a posthoc test of multiple comparisons. So, when the source ‘group’ does have a significant effect and one
wants to know how and where the mean differences are, one uses a post-hoc test (Klaasen, 2014). In
this thesis a Bonferroni post-hoc test will be used as this is the most commonly accepted of its kind.
This test actually is very easy to understand as it provides confidence intervals for the differences in
means between all groups. If the value of 0 is in this confidence interval, one can conclude that there
is no significant difference in means between these two groups. By evaluating the confidence
intervals and finding the ones in which the value of zero is not included, one can identify the groups
which significantly differ and more importantly, how they differ, which group’s means is higher and
which’s is lower.
Figure 3.2: ANOVA Summary
This figure summarises exactly was is explained about the ANOVA. As explained in 5.2.2 – T-Test, the
groups need to be normally distributed (unless proved absolutely impossible after countermeasures),
displayed by the ‘bell-like’ form. Levene’s test researches the condition that all variation within
groups has to be equal, which is represented by the equal width of the normal distributions above.
Lastly, the ANOVA researches if the variation between groups is significant, indicated by the top of
the ‘bell-like’ shapes. (Klaasen, 2014)
41
4 – Research Method
A more specific and thorough explanation of the research approach per hypothesis will be given
now, in order to acquire a better understanding of how is researched.
Hypothesis 1 and 2:
These two are discussed together as they research the exact same thing, but for different years. First
of all, the basic M&A data was extracted from ThomsonOne. After this data was sorted on the
variable ‘cash’ and filtered (see table 3.1), a control and experimental group was made.
Subsequently, the data was prepared for the EventStudy Tool. After the tool ran, acquirer and target
were matched again and their CARs were summed to acquire the contribution to society as a whole.
This contribution to society file was exported to SPSS were a univariate analysis was performed,
continued by a Bonferroni post-hoc test to determine where the possible differences in means are
located.
Hypothesis 3:
As the third hypothesis researches the change over time in serial acquisitions, the value added to
society of the first M&A deal was calculated. After that, the average value added of the other M&A
deals performed by the same acquirer was calculated. These numbers were tested on a significant
difference in means using a independent t-test in SPSS.
Hypothesis 4:
The fourth and last hypothesis was relatively easy to research. The formulas mentioned before (see
2.4 – The hypotheses) were executed in excel to finally acquire the premium ratio. This value was,
again, exported to excel and a t-test on a difference in mean between the control and experimental
group was performed in SPSS. As no separation regarding years was made in this hypothesis,
premium ratios of both time periods were researched at once.
42
CHAPTER 4 – RESULTS
________________________________________________________
In this chapter the results deriving from the analysis will be interpreted and discussed. First the data
results will be analysed, after which conclusions are drawn and statements regarding the hypotheses
are made.
1 – Analysis of the data
The analysis of the data will be done in three steps, one step for each hypothesis where hypothesis 1
and 2 are combined as they research the exact same thing but for different years.
The analyses of the first two hypotheses involve the interpretation of the ANOVA results and of the
last two involve the interpretation of the performed t-tests. Before getting started with ANOVA or ttests, it was of great importance to check the conditions stated under 5.2.2 – T-test. The first step of
the analyses was to correct all data accordingly to continue the analysis without further delay later
on. In the appendix, table 1 shows the development of the skewness and Kurtosis criteria regarding
the data, after outliers had been removed and the log-transformation has been taken (only if
necessary of course). As evident from the figures in the appendix, all three data sets did not meet the
required standards of the Kurtosis criterium. After deleting outliers, the first two did. However, the
residuals/deal value ratio data set needed the log-transformation in order to become a correct and
usable dataset.
43
Hypothesis 1 and 2:
The total value added for society in M&A deals is lower in the experimental group than in the control
group in 2006-2007.
The total value added for society in M&A deals is lower in the experimental group than in the control
group in 2011-2012.
As mentioned before, these two hypotheses were research at once, using an ANOVA and a
Bonferroni post-hoc test. Before it is possible to start with an ANOVA, a Levene’s test is necessary.
This test is performed and, as evident from table 2 in the appendix, the error variances are to be
assumed equal, with a p-value of .103; therefore the ANOVA is legitimate.
Considering the ANOVA, the source group was not significant, with its p-value of .418. This leads to
the result that there is no significant difference in means between any of the researched four groups.
This implies that both the hypothesis are to be rejected as the total value added for society in M&A
deals is not lower in the experimental group than in the control group for any of the time periods;
there is no significant difference in this. Table 3 provides this information and also contains data
about the post-hoc test, which also confirms that the number zero is present in all in the 95%
confidence intervals, strengthening the abovementioned result of rejecting both hypotheses.
44
Hypothesis 3:
The change in the value added between the first and the following M&As by the same acquirer is
larger in the experimental group than in the control group.
This specific hypothesis is researched with an independent t-test. As mentioned before, this test
requires the data adjustments discussed earlier, which are executed appropriately. Like the ANOVA
required an additional Levene’s test which had to be ‘passed’ in order to consider the ANOVA
legimate, the t-test does require this too and therefore this Levene’s test is executed, ‘passed’ and to
found in appendix table 2 with its p-value of .840.
The independent t-test resulted in a very high p-value of .705, with which the null-hypothesis of the
t-test cannot be rejected. This results in rejecting the abovementioned hypothesis; the change in
value added between the first and following acquisitions is not larger in the experimental group than
in the control group. Again, the same conclusion as with the first two hypothesis; there is no
significant difference between them. The results of the t-test are to be found in appendix table 4.
This same conclusion can be drawn from interpreting the confidence interval, which do show a zero
in it, meaning that there is no significant difference between the groups.
Hypothesis 4:
The premium ratio is higher in the experimental group than in the control group.
The premium ratio as defined in the extensive explanation of hypothesis 4 was also examined with
an independent t-test. The same applies as to all earlier researched hypothesis, a Levene’s test was
necessary. This test is therefore executed appropriately and to be found in appendix table 2. The
result was a p-value of .934 which is, of course, high enough to accept the null-hypothesis of equal
variances.
Just like the independent t-test of hypothesis 3 this t-test results in a high p-value, namely .853,
resulting in not rejecting the null-hypothesis of the t-test. This implies the rejection of the hypothesis
stated above; the residue ratio is not higher in the experimental group than in the control group.
They are statistically equal; no significant difference in means. The t-test results are in table 4 of the
appendix and are strengthened by the interpretation of the confidence intervals, which, again, do
contain the value zero.
45
2 – Conclusion
After the analysis is performed conclusions can be drawn regarding the hypotheses. To ease this
process the earlier mentioned, more mathematical representations of the hypothesis will be
addressed again at the of the evaluation of that specific hypothesis.
Hypothesis 1 and 2
H10:
AVA2006-2007, 2 – AVA2006-2007,1 = 0
H11:
AVA2006-2007,2 – AVA2006-2007,1 > 0
Hypothesis 1 implies the expectation of rejection of H10.
H20:
AVA2011-2012,2 – AVA2011-2012,1 = 0
H21:
AVA2011-2012,2 – AVA2011-2012,1 > 0
Hypothesis 2 implies the expectation of rejection of H20.
Once again, hypothesis 1 and 2 are discussed together as they still research the exact same thing.
From the analysis of the ANOVA, the conclusion of not rejecting the H10 and H20 has to be drawn. As
mentioned before, there simply is no significant difference in means between any of the researched
groups. This results is mainly attributable to the fact that the data contained a high standard
deviation, which indirectly results in higher p-values. Therefore, the conclusion is that the value
added for society, as defined earlier, does not differ among the researched groups.
46
Hypothesis 3
H30:
ΔAVA1 – ΔAVA2 = 0
H31:
ΔAVA1 –Δ AVA2 > 0
Hypothesis 3 induces the expectation of rejection of H30.
Hypothesis 3 actually researched if there was a learning effect present in the behaviour of the CEOs
of the companies with more idle cash. This because of the expectation that these CEOs would take
inferior decisions regarding M&As. However, now it has become evident that there is no significant
difference is the value added to society between the two situations of having more against having
less idle cash, the expectations of rejecting H30 have tempered.
This tempering of expectations seems right as H30 cannot be rejected on the basis of the data
analysis. The conclusion is that the change in value added for society over multiple acquisitions does
not differ significantly between the experimental and control group, contradicting the hypothesis
and the findings of Doukas and Petmezas (2007).
Hypothesis 4
H40:
PR1 – PR2 = 0
H41:
PR1 – PR2 > 0
Hypothesis 4 induces the expectation of rejection of H40.
Hypothesis 4 is designed to research the monetary aspect of the involved M&As. The expectation
was a significant difference in the premium ratio, as mathematically represented before, between
the two researched groups. This ratio was to be expected higher in the experimental group, as all the
hypothesis were pointed towards the point of view in which M&As from the experimental group
were considered inferior. However, since the earlier hypothesis did not endorse this position, the
same is true as for hypothesis 3; the expectations of rejecting H40 has tempered.
And once again, this tempering seems right. H40 cannot and will not be rejected as the analysis
proves that there is no significant difference in the earlier specified ratios. Therefore, it can be
concluded that the premium ratio is statistically equal across the group with more and the group
with less idle cash.
47
SUMMARY AND CONCLUSION
________________________________________________________
To conclude this research with, the results of the individual hypotheses will be melted together in
order to provide an answer for the main research question. After that final conclusion, a short
discussion will follow in which the shortcomings of this research will be mentioned and
recommendations for further research are suggested.
1 - Research Question
Do cash rich companies make inferior decisions regarding merger and acquisition deals in
comparison with other non cash rich companies, and if so, why?
H0: Companies with more idle cash make inferior M&A decisions in comparison with
companies with less idle cash.
All four earlier discussed hypotheses were necessary to make statements and draw conclusions
about what it was all about; the research question.
As all hypotheses were extensions of the research question, which eased the research, all those
hypotheses were of the same beliefs as the research question. This means, they were all pointed into
a position in which they considered M&A deals performed by companies with more idle cash inferior.
The first two hypothesis researched the aspect of value added for society in terms of cumulative
abnormal returns. The third researched the change in value added for society as the expectation was
that the worse performing companies, the companies in the experimental group, will learn from their
faults and perform better in upcoming M&As. Finally, the last hypotheses researched the monetary
aspects, more specifically, if the cash rich firms did overpay the M&As or not.
48
After the results and conclusions of the hypothesis were analysed it became clear that there was no
significant difference in the contribution for society across the researched groups (hypotheses 1 and
2). Also, the change in value added for society for serial acquirers over time cannot be considered
different when comparing the experimental group with the control group (hypothesis 3). Lastly, the
monetary aspects as defined by the earlier mentioned ratio is also considered to be statistically equal
between the two researched groups (hypothesis 4). These findings all point in the same direction;
there being no significant difference in the evaluation of the M&As between the different groups. As
there is not a single indication to assume any differences in the M&As in the defined research
groups, the H0 of this research has to be rejected. The same negative relationship found by Morck,
Shleifer and Vishny (1990) between overconfidence and M&A performance in American companies
could not have been found in European companies.
Companies with more idle cash make no inferior M&A decisions in comparison with companies
with less idle cash.
2 – Discussion
Like every other research, this research obviously encourages some discussion. A first subject of this
discussion could be the fact that more factors than discussed in this research are influencing the
superiority or inferiority of the M&A deals. Not only the contribution to society, the change in value
added to society and the monetary aspect are important in researching this. There are countless
factors influencing this. However, in this research, the researched aspects were considered the most
important and chosen for that reason.
Also, the event window chosen in the EventStudy Tool in DataStream can give rise to some discussion
as others may have different opinions on what is necessary and what is helpful. Furtermore,
concerning
this
EvenStudy
Tool,
the
MeanAdjustedReturn
instead
of
the
MarketModelAdjustedReturn could have been chosen to obtain data regarding (C)ARs. Possibly,
different results and conclusions would have been drawn.
Lastly, the inference of the financial crisis is, of course, a factor. Who knows what the results would
have been in the absence of the financial crisis? This could obviously not have been tested now, but
maybe it can be interesting to do this in later researches.
49
3 – Recommendations for further research
The recommendations for further research logically follow from the discussion. The first one is to
take more or different influencing factors into account when researching the superiority or inferiority
of M&A deals between two groups. This could be aspects like payment method, acquirer’s or target’s
country and laws, differing economic environments etc.
Secondly,
it
could
be
interesting
to
use
the
MeanAdjustedReturn
instead
of
the
MarketModelAdjustedReturn in obtaining the data to calculate the value added for society from.
Also, setting a different event window in further research can lead to interesting results.
Lastly, as the financial crisis could have distorted the results, it could be interesting to do this same
research over years after the financial crisis. Therefore, that research should have to wait some
years, but can lead to different results.
50
CHAPTER 6 - SOURCES
________________________________________________________
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53
Figure 1.1
Value and number of M&As worldwide
http://www.imaainstitute.org/images/figure_announced%20mergers%20&%20acquisitions%20(worldwide).jpg
Figure 1.2
Value and number of M&As in Europe
http://www.imaainstitute.org/images/figure_announced%20mergers%20&%20acquisitions%20(europe).jpg
Figure 1.3
The most important acquiring countries
http://europatientrights.eu/ (bewerkt in Paint)
Figure 3.1
Security Market Line
http://www.themanagementor.com/enlightenmentorareas/finance/cfa/SecurityMarketLine.htm
Figure 3.2
ANOVA Summary
http://www.soc.napier.ac.uk/~cs181/Modules/CM/Statistics/Statistics%205.html
54
CHAPTER 7 - APPENDIX
________________________________________________________
Table 1
Development Descriptive Statistics
Contribution_to_society
Contribution_to_society_diff_time
Residuals
After removing outliers
Contribution_to_society
Contribution_to_society_diff_time
Residuals
After log-transformation (if necessary)
Contribution_to_society
Contribution_to_society_diff_time
Residuals
Skewness Kurtosis
0,807
6,342
0,661
3,212
-9,676
104,007
0,448
0,639
-9,518
2,293
1,867
100,624
0,448
0,639
0,170
2,293
1,867
0,924
Table 2
Levene’s Tests
F
Sig.
Hypothesis 1 and 2
2,075
0,103
Hypothesis 3
0,041
0,840
Hypothesis 4
0,007
0,934
55
Table 3
ANOVA Results
Source
Sig.
Corrected Model
.418
Intercept
.010
Group_year
.418
Post-Hoc Tests
95% Confidence Interval
(I) Group_year
(J) Group_year
Lower Bound
Upper Bound
control 2006
control 2011
-.0400499
.0343621
exp 2006
-.0271143
.0324103
exp 2011
-.0156530
.0571902
control 2006
-.0343621
.0400499
exp 2006
-.0320249
.0430087
exp 2011
-.0193788
.0666037
control 2006
-.0324103
.0271143
control 2011
-.0430087
.0320249
exp 2011
-.0186184
.0548596
control 2006
-.0571902
.0156530
control 2011
-.0666037
.0193788
exp 2006
-.0548596
.0186184
control 2011
exp 2006
exp 2011
56
Table 4
Independent t-tests
Hypothesis 3
Hypothesis 4
t
Sig. (2-tailed)
-0,380
-0,185
0,705
0,853
Lower
Bound
Upper
Bound
0,058785631 0,03984173
-0,38921
0,32228
57