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

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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 STATEMENTThe 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.

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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

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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

4

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 29

1 – Data specifics 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 37

3.1 – Event Study Tool 37

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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

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LIST OF TABLES

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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

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LIST OF FIGURES

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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

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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

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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.

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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 pre-

and 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 2011-

2012, 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

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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 semi-

finished products used in their production process. Obviously, this can be very beneficial as

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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.

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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).

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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

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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, &

Sabherwal, 2012)

Researched and evidenced that CEO overconfidence explains the

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

(Unknown, 2008)

Evidenced that overconfident CEOs are more likely to conduct

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,

1990)

Proved that diversifying M&As show lower returns.

(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.

3 - Hypotheses Description and Derivation

24

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.

25

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.

26

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.

27

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.

28

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.

29

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.

30

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.

31

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 distinction between the

experimental and control group. It is

researched if more idle cash as opposed to less

32

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

Used for the calculations of the deal premium

(hypothesis 4).

Target Closing Price 1 day after Announcement

Target Closing Price 1 week after Announcement

Target Share Price 1 day before Announcement

Target Share Price 1 week before Announcement

Value of the Transaction

33

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.

34

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)!

35

The CAPM thus boils down to the following formulas: (Fama & French, 2004)

(E (Ri)−Rf )βi

=E (Rm )−Rf

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.

36

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 risk-

free 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

37

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.

38

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.

39

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 t-

test (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.

40

The formula for an independent t-test is as follows (Moore, McCabe, Alwan, Craig, & Duckworth,

2011):

t= X1−X 2

√( (N1−1 ) s12+(N 2−1 ) s2

2

N 1+N 2−2 )( 1N 1+ 1N2 ¿)¿

Where;

t = the researched t-statistic

Xx = 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 null-

hypotheses 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).

41

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)

42

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 post-

hoc 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)

43

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.

44

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 t-

tests, 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.

45

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.

46

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.

47

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.

48

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.

49

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.

50

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.

51

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.

52

CHAPTER 6 - SOURCES

________________________________________________________

Andrade, G., Mitchell, M., & Stafford, E. (2001). New Evidence and Perspective on Mergers. Journal of Economic Perspectives, 15 , 103-120.

Arnold, G. (2005). Corporate Financial Management. Harlow, England: Financial Times.

Besanko, D., Dranove, D., Shanley, M., & Schaefer, S. (2010). Economies of Strategy, fifth edition. River Street, Hoboken, New-Jersey: Wiley and Sons.

Bressane, B. P., & Maia, M. V. (2010). CEO Overconfidence and the Impact on M&A Activity. SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1674467.

Campbell, H. (2011). NASDAQ. Opgeroepen op Mei 18, 2014, van NASDAQ.com: http://www.nasdaq.com/investing/glossary/r/risk-premium

Carter-Hill, Griffiths, & Lim. (2012). Principles of Econometrics, fourth edition. River Street, Hoboken, New-Jersey: Wiley Inc.

Chong, J., Jin, Y., & Phillips, M. (2013). The Entrepreneur's Cost of Capital: Incorporating Downside Risk in the Buildup Method. MacroRisk Analytics.

DeMarzo, P., & Berk, J. (2011). Corporate Finance, second edition. Stanford: Pearson.

Doukas, & Petmezas. (2007). Acquisitions, overconfident managers and self attribution bias. European Financial Management, 13 , 531-577.

Doukas, J., & Petmezas, D. (2006). Acquisitions, Overconfident Managers and Self-Attribution Bias. Durham.

EDSC. (2010). EDSC Tools. Opgeroepen op Mei 29, 2014, van http://www.eur.nl/fileadmin/ASSETS/edsc/Handleidingen/Datastream/Datastream_Event_Study_Tool.pdf

53

EL Allali, T. (2009). CEO Overconfidence in het M&A besluitvormingsproces. Rotterdam: Erasmus Universiteit Rotterdam.

Fama, E. F., & French, K. R. (2004). The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives, 18 (3) , 25-46.

Ferris, S., Jayaraman, N., & Sabherwal, S. (2012). CEO Overconfidence and International Merger and Acquisition Activity. SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1982286.

Forbes, D. (2005). Are some entrepeneurs more overconfident than others? Journal of Business Venturing, 20 , 623-640.

Geurts, & Pavlov. (2006). Calculating the Cost of Capital for REITs.

Healy, P., Palepu, K., & Rubak, R. (1990). Does corporate performance improve after mergers? JFEC, 31 , 135-176.

Hirschleiver, Low, & Teoh. (2012). Are confident CEOs better innovators? Journal of Finance, 67 , 1457-1498.

Institute of Mergers, A. a. (2013). Institute of Mergers, Acquisitions and Alliances. Opgeroepen op May 1, 2014, van imaa-institute: http://www.imaa-institute.org/statistics-mergers-acquisitions.html#MergersAcquisitions_Worldwide

Johnson, Whittington, & Scholes. (2011). Exploring Strategy. Opgeroepen op Mei 19, 2014, van Slideshare: http://www.slideshare.net/tutor2u/buss4-2012-research-bullet-1

Kilduff, J. (2013, December 12). UK companies dominate European M&A in 2013. Opgeroepen op May 22, 2014, van http://www.towerswatson.com/en-GB/Press/2013/12/UK-companies-dominate-European-M-and-A-in-2013

Klaasen, S. (2014, June 5). (S. v. Hart, Interviewer)

Lintner, J. (1965). The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives, 18 (3) , 25-46.

MacKinley, W. (1997). Event Studies in Economics and Finance. Journal of Economic Literature, 35 , 13-39.

Malemendier, U., & Tate, G. (2005). CEO Overconfidence and Corporate Investment. European Financial Management, 11 , 649-659.

Malmendier, U., & Tate, G. (2008, May 22). Who makes acquisitions? CEO overconfidence and the market's reaction. Journal of Financial Economics, 89 , 20-43.

Marchan, D. (2013). Analyzing How Financial Crisis Has Changed M&A Deals. Moscow: The Moscow Times.

54

McClure, B. (2009, February 25). Mergers and Acquisitions. Opgeroepen op June 16, 2014, van Investopedia: http://www.investopedia.com/university/mergers/

Mider, Z., Campbell, M., & Chan, C. (2012, September 27). M&A Slumps to Lowest Level Since Financial Crisis’s Nadir. Opgeroepen op June 12, 2014, van BloomBerg: http://www.bloomberg.com/news/2012-09-27/m-a-slumps-to-lowest-level-since-financial-crisis-s-nadir.html

Moore, McCabe, Alwan, Craig, & Duckworth. (2011). The practice of statistics for business and economics, third edition. USA: W.H. Freeman and Company.

Morck, R., Shleifer, A., & Vishny, R. (1990). Do managerial objectives drive bad acquisitions. The Journal of Finance, 45 (1) , 31-48.

Mossin, J. (1966). The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives, 18 (3) , 25-46.

Perold, A. (2004). The Capital Asset Pricing Model. Journal of Economic Perspectives, 18 (3) , 25-46.

Roll, R. (1986). The Hubris Hypothesis of Corporate Takeovers. Journal of Business, 59 , 197-216.

Roussanov, N., & Savor, P. (2012). Status, Marriage and Managers' Attitudes to Risk. Unpublished Working Paper.

Schwab, K. (2013-2014). The Global Competitiveness Report. World Economic Forum.

Sharpe, W. (1964). Capital Asset Prices – A Theory of Market Equilibrium Under Conditions of Risk. The Journal of Finance, 19 (3) , 425-443.

Stechyshyna, N. (2012). Manager's overconfidence and risk taking attitutes and the cross industry acquisitions.

Treynor, J. (1962). Toward a Theory of Market Value of Risky Assets. SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=628187.

Unknown. (2008). CEO Overconfidence in M&A Decision. Edinburgh: University of Edinburgh.

Yu, C.-F. (2013). CEO Overconfidence, CEO Compensation and Earnings Manipulation. Adelaide: University of Adelaide.

55

Figure 1.1 Value and number of M&As worldwide

http://www.imaa-institute.org/images/figure_announced%20mergers%20&%20acquisitions

%20(worldwide).jpg

Figure 1.2 Value and number of M&As in Europe

http://www.imaa-institute.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

56

CHAPTER 7 - APPENDIX

________________________________________________________

Table 1

Development Descriptive Statistics

Skewness Kurtosis

Contribution_to_society 0,807 6,342

Contribution_to_society_diff_time 0,661 3,212

Residuals -9,676 104,007

After removing outliers

Contribution_to_society 0,448 2,293

Contribution_to_society_diff_time 0,639 1,867

Residuals -9,518 100,624

After log-transformation (if necessary)

Contribution_to_society 0,448 2,293

Contribution_to_society_diff_time 0,639 1,867

Residuals 0,170 0,924

Table 2

Levene’s Tests

57

F Sig.

Hypothesis 1 and 2 2,075 0,103

Hypothesis 3 0,041 0,840

Hypothesis 4 0,007 0,934

Table 3

ANOVA Results

Source Sig.

Corrected Model .418

Intercept .010

Group_year .418

Post-Hoc Tests

(I) Group_year (J) Group_year

95% Confidence Interval

Lower Bound Upper Bound

control 2006 control 2011 -.0400499 .0343621

exp 2006 -.0271143 .0324103

exp 2011 -.0156530 .0571902

control 2011 control 2006 -.0343621 .0400499

exp 2006 -.0320249 .0430087

exp 2011 -.0193788 .0666037

exp 2006 control 2006 -.0324103 .0271143

control 2011 -.0430087 .0320249

58

exp 2011 -.0186184 .0548596

exp 2011 control 2006 -.0571902 .0156530

control 2011 -.0666037 .0193788

exp 2006 -.0548596 .0186184

Table 4

Independent t-tests

t Sig. (2-tailed)Lower Bound

Upper Bound

Hypothesis 3 -0,380 0,705 0,058785631 0,03984173Hypothesis 4 -0,185 0,853 -0,38921 0,32228