GHENT UNIVERSITY

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION

ACADEMIC YEAR 2013 – 2014

Acquisition motives and methods of financing

Master dissertation submitted to obtain the degree of

Master of Science in Business Economics

Toon Borghgraef

under the supervision of

Prof. dr. ir. Sophie Manigart and Virginie Mataigne

GHENT UNIVERSITY

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION

ACADEMIC YEAR 2013 – 2014

Acquisition motives and methods of financing

Master dissertation submitted to obtain the degree of

Master of Science in Business Economics

Toon Borghgraef

under the supervision of

Prof. dr. ir. Sophie Manigart and Virginie Mataigne

PERMISSION

The undersigned declares that the content of this master dissertation may be consulted and/or

reproduced, provided that the source is acknowledged.

Toon Borghgraef

Summary in Dutch

Er werd reeds veel onderzoek gevoerd naar de financiering van overnames, met name de

determinanten van de financieringskeuze. Deze masterproef onderzoekt in welke mate het

overnamemotief een invloed heeft op de methode van financiering.

In het algemeen kan men vier overnamemotieven onderscheiden. Het meest voor de hand liggende

motief is het benutten van synergiën. Hoewel overnemers graag de synergiën in een overname

benadrukken, kunnen ook drie andere drijfveren voor overnames onderscheiden worden. Zo kan het

management een overname doen om hun persoonlijke macht te vergroten, ten koste van de

aandeelhouders. Deze overnames worden gekenmerkt door informatie-asymmetrieën tussen

aandeelhouders en management en zijn het resultaat van agency-problemen. Andere overnames zijn

het resultaat van overmoedigheid van het management: potentiële synergiën worden overschat. Deze

motivatie wordt als hubris bestempeld. Ten slotte kunnen ook marktinefficiënties leiden tot overnames.

Management heeft gedurende zulke periodes een incentive om overnames doen van relatief

ondergewaardeerde bedrijven, wat als market timing beschouwd wordt.

Overnames kunnen gefinancierd worden met (combinaties van) drie financieringsbronnen: interne

middelen, eigen vermogen of schulden. Op basis van een steekproef van Europese beursgenoteerde

bedrijven werd in dit onderzoek empirisch achterhaald in welke mate het overnamemotief de

financieringsmethode beïnvloedt.

Uit de resultaten blijkt dat overnames gemotiveerd door synergiën bij voorkeur gefinancierd worden

met een combinatie van eigen vermogen en schulden. Ook in overnames gekenmerkt door overmoed is

een combinatie van eigen vermogen en schulden de gekozen financieringsmethode. Op basis van deze

resultaten zou men kunnen zeggen dat het voor de financieringsmethode niet uitmaakt of de

voorspelde waardecreatie realistisch is, of het resultaat van overmoed. Wanneer agency-problemen de

drijfveer zijn, werd financiering met interne middelen verwacht op basis van de literatuurstudie. De

bevindingen voor agency zijn echter niet statistisch significant, waardoor geen onderbouwde conclusies

kunnen getrokken worden voor dit motief. Voor het market timing-motief bleek eigen vermogen de

financieringsmethode bij uitstek: de overgewaardeerde aandelen worden als goedkope munteenheid

gebruikt bij overnames van relatief ondergewaardeerde bedrijven.

Zoals eerder onderzoek reeds uitwees, zijn er nog talloze andere determinanten van de

financieringsmethode. De impact van grootte, marktwaarderingen, risico en specifieke incentives om

cash te gebruiken wordt bevestigd in deze masterproef. Daarnaast hebben ook offertes,

grensoverschrijdende en diversifiërende overnames een invloed op de financieringsmethode.

I

Preface

I would like to thank all the people who assisted and supported me. First of all, I am truly grateful to

prof. Sophie Manigart and Virginie Mataigne for giving me the chance to study this very interesting

subject. Your guidance was excellent, as well as the highly appreciated feedback. Second, I really

appreciate the support of my friends and family, who helped me each in their own way.

Finally, I would like to thank my parents for their unconditional support and the opportunity to finish my

studies.

II

Table of contents

1 Introduction .......................................................................................................................................... 1

2 Motives ................................................................................................................................................. 2

2.1 Synergy ......................................................................................................................................... 2

2.2 Agency .......................................................................................................................................... 3

2.3 Hubris ........................................................................................................................................... 3

2.4 Market timing ............................................................................................................................... 4

3 Methods of financing ........................................................................................................................... 5

3.1 Synergy ......................................................................................................................................... 6

3.2 Agency .......................................................................................................................................... 7

3.3 Hubris ........................................................................................................................................... 8

3.4 Market timing ............................................................................................................................... 9

4 Methodology ...................................................................................................................................... 10

4.1 Data ............................................................................................................................................ 10

4.2 Event study ................................................................................................................................. 10

4.3 Identification of the motives ...................................................................................................... 13

4.3.1 Synergy ............................................................................................................................... 13

4.3.2 Agency ................................................................................................................................ 14

4.3.3 Hubris ................................................................................................................................. 14

4.3.4 Market timing ..................................................................................................................... 15

4.4 Control variables ........................................................................................................................ 18

5 Analysis and results ............................................................................................................................ 20

5.1 Sample description ..................................................................................................................... 20

5.2 Bivariate analysis ........................................................................................................................ 21

5.3 Multivariate analysis .................................................................................................................. 28

5.4 Sensitivity analysis ...................................................................................................................... 32

5.4.1 Effect of extreme values ..................................................................................................... 32

5.4.2 Classification of motives ..................................................................................................... 34

III

6 Conclusion .......................................................................................................................................... 37

6.1 Findings and implications ........................................................................................................... 37

6.2 Limitations .................................................................................................................................. 38

6.3 Further research ......................................................................................................................... 39

7 References .......................................................................................................................................... VII

8 Appendix .............................................................................................................................................. XI

Appendix A: Variable definitions ............................................................................................................. XI

Appendix B: Correlation matrix .............................................................................................................. XII

Appendix C: Multinomial logit model for (-2,+2) CAR window ............................................................. XIII

Appendix D: Multinomial logit model for (-10,+10) CAR window ......................................................... XIV

Appendix E: Multinomial logit model with 2% significance threshold ................................................... XV

IV

List of abbreviations

CAR Cumulative abnormal return

CF Cash flow

M&As Mergers and acquisitions

MTB ratio Market-to-book ratio

RI Return index

RoA Return on assets

SIC Standard Industrial Classification

Transval Transaction value

UK United Kingdom

USD United States Dollar

VIF Variance inflation factor

V

List of tables

Table 1: Event study results by financing sources ...................................................................................... 12

Table 2: Motives and associated patterns of correlation .......................................................................... 13

Table 3: M&A sample by acquirer and target country ............................................................................... 21

Table 4: Bivariate analysis of motives and methods of financing .............................................................. 22

Table 5: Composition of dummy control variables in sample .................................................................... 25

Table 6: Ratio-level control variables for method of financing .................................................................. 26

Table 7: Market-to-book ratio depending on M&A motive ....................................................................... 27

Table 8: Multinomial logit model predicting the method of financing ...................................................... 29

Table 9: Deal characteristics in the subsample of takeovers financed with stock and debt. .................... 31

Table 10: Multinomial logit model for sensitivity analysis ......................................................................... 33

Table 11: Multinomial logit model for (-2,+2) CAR window ....................................................................... 35

Table 12: Multinomial logit model for (-10,+10) CAR window ................................................................... 35

Table 13: Multinomial logit model with 2% significance threshold ........................................................... 36

VI

List of graphs

Graph 1: Year of M&A announcement and completion ............................................................................ 18

Graph 2: Sensitivity of motive identification .............................................................................................. 34

1

1 Introduction

This paper adds to the literature on mergers and acquisitions (M&As) by studying the influence of M&A

motives on the method of financing. Several papers provide a comprehensive overview of determinants

for the financing method (e.g. Martynova & Renneboog, 2008; Haleblian et al., 2009), although some

questions remain unanswered. The latter suggest integrating all established drivers to assess their

importance and contingency characteristics (Haleblian et al., 2009). Furthermore, they conclude that the

question whether acquisitions are driven by value creation or managerial self-interest remains

unanswered. This paper attempts to address both issues by first identifying the underlying motives and

subsequently studying their influence, controlling for the other established determinants. The research

question of this paper is:

Do acquisition motives influence the method of financing?

The method of financing has received a lot of attention in the literature on M&As, mainly because it

seems to affect stock prices. Several studies find significantly different bidder announcement-date

returns for cash offers compared to stock offers (Martin, 1996). These corrections are attributed to

asymmetric information in the market. If the bidding firm’s managers possess information about the

intrinsic value of their firm which is not fully reflected in the stock price, they will act accordingly to

finance the acquisition in the most profitable way for the existing stockholders (Travlos, 1987).

Following Travlos (1987), new information about the true value of the company is signalled through the

method of financing the M&A, which leads to the revaluations observed in the market. In this paper, an

event study is conducted to identify underlying motives based on these revaluations. Subsequently, the

influence of M&A motives on the method of financing is analysed to determine its statistical

significance.

Chapter 2 provides an overview of the motives; chapter 3 zooms in on the sources of financing and the

hypotheses. Chapter 4 outlines the methodology, and results are presented in chapter 5. Chapter 6

provides the general conclusion and limitations of the research.

Throughout this paper, M&As will be referred to as acquisitions, takeovers or mergers, which are

considered to be synonymous. The classification as merger or acquisition is mostly arbitrary, and is not

particularly relevant for the purpose of this research.

2

2 Motives

Most bidding firms try to emphasize the synergy gains in M&A announcements. However, it is

frequently observed that takeovers do not create value for acquirers (Haleblian et al., 2009). This may

be the result of over-optimistic forecasts by the bidding management or the fact that the merger or

acquisition was initiated for entirely different reasons than announced (Goergen & Renneboog, 2004). In

this section, a broad overview of takeover motives is provided.

Some M&As are indeed motivated by synergies of operational, financial or informational nature.

However, three additional motives can be identified (Martynova & Renneboog, 2008). First of all,

acquirer management might be affected by hubris, leading them to overestimate synergies and

consequently overbid. In other cases, takeovers are characterised by agency problems. These deals are

made to satisfy the private benefits of management instead of creating shareholder value. Finally, in

M&As taking place during market booms, acquirers take advantage of temporary market overvaluation

to expropriate wealth from target shareholders.

2.1 Synergy

In a takeover where there truly are synergies, the combined value of target and acquirer is greater than

that of the separate entities. By gaining control over the target, the acquirer is able to combine the

resources in a way that creates additional value (Bradley, Desai, Kim, 1988). This added value can be

created through a combination of several factors. Synergies can include increased market power and

efficiency, through economies of scale and scope (Haleblian et al., 2009). Some synergies, such as the

creation of an internal capital market or tax optimization, result in financial gain. Another source for

synergies is improvement of the resource utilization of targets (Asquith, 1983). This inefficient

management hypothesis is supported by target firms displaying large negative returns prior to a merger

bid (Asquith, 1983). Especially in the case of industry shocks and market discipline, resource

redeployment and increased effectiveness can create value (Haleblian et al., 2009).

3

2.2 Agency

Management does not always act in their shareholders’ best interests, and may pursue takeovers that

benefit themselves at the expense of shareholder value. This motive for takeovers was suggested by

Jensen (1986), describing these bids as arising from agency conflicts (hence the term agency). Agency

conflicts are mainly the result of compensation schemes, which are frequently tied to the amount of

assets management has under their control. Therefore, managers are more likely to seek higher rates of

growth in assets than profits (Marris, 1964) and are more interested in maximizing size than value

(Morck, Shleifer, Vishny, 1990). Managers are incentivised to pursue excessive growth and go for

‘empire building’ (Martynova & Renneboog, 2008). Because the agency motivation focuses on the self-

interest of managers, ‘managerialism’ is sometimes used as a synonym.

If the interests of shareholders and management are not aligned in a proper compensation scheme,

M&A activity is not always in the best interest of shareholders. When agency problems arise, managers

are likely to minimize risk to enhance corporate survival and thus protect their own positions. In that

sense, diversification can be considered as a form of agency, motivated by managers’ wish to decrease

their companies’ earnings volatility (Amihud & Lev, 1981). Agency conflicts also lead managers to

entrench themselves through M&As: Shleifer and Vishny (1989) find that some acquisitions are made to

increase the dependence of the firm on the skills of the acquiring managers.

2.3 Hubris

The hubris motive was brought forward by Roll (1986), who pointed to the large gains for target

shareholders and low to non-existent gains for acquirer shareholders generally found in the M&A

literature, inconsistent with wealth-creation motives. He suggests this is due to overconfident managers

overestimating the gains from the takeover and overpaying for the privilege of accessing the possibly

non-existing gains (Hodgkinson & Partington, 2008). In Roll’s view, the gains to target shareholders do

not result from synergies, but represent wealth transfers from acquiring firms’ shareholders. Since there

is no actual wealth creation for the combined entity, there are no total gains in the takeover.

Hubris may be stimulated by the herding-phenomenon, namely that firms tend to mimic the actions of a

leader. If successful takeovers encourage other companies to engage in takeovers as well, some of these

takeovers suffer from managerial hubris (Martynova & Renneboog, 2008). On average, firms that

acquire early in a takeover wave achieve positive returns, whereas the market reacts negatively to later

acquirers (McNamara, Haleblian, Dykes, 2008). The negative reaction is consistent with the presence of

hubris in these M&As.

4

It is possible that M&As are motivated by synergy as well as hubris. The most obvious reason for this is

the fact that it is not uncommon that offers are improved in cases where target management mounts a

strong defence or a rival acquirer emerges (Hodgkinson & Partington, 2007). Bids initiated with a

synergy motive might show traces of hubris when managers are reluctant to walk away in such cases

and end up paying more than the synergy is worth (Hodgkinson & Partington, 2007).

Both hubris- and agency-motivated takeovers act against shareholder interests by destroying value.

However, in agency motivated takeovers, managers knowingly overpay for takeovers to maximize their

own benefits. In contrast, management under the influence of hubris may have honourable intentions,

but their bids are also destroy value because of incorrect estimates of target firm value (Seth, Song,

Pettit, 2000).

2.4 Market timing

Takeovers are motivated by market timing when managers try to take advantage of temporary

overvaluation during financial market booms (Myers & Majluf, 1984). Financial bull markets tend to

overvalue stocks in the short-run, with the degree of overvaluation varying significantly across

companies due to inefficiencies (Shleifer & Vishny, 2003). During such market booms, the true value of

firms is highly uncertain, so better-informed bidders can exploit their informational advantage at the

expense of less-informed targets (Martynova & Renneboog, 2008). In takeovers motivated by market

timing, overvalued bidders try to take advantage of the mispricing by buying targets that are less

overvalued than the bidder (Dong, Hirshleifer, Richardson, Teoh, 2006).

One might wonder why target management is willing to accept such bids. Two possible explanations are

provided in the literature. Shleifer and Vishny (2003) assume that target managers maximize short-term

private benefits. On the other hand, in the model of Rhodes-Kropf and Viswanathan (2004), target

management accepts such offers because they tend to overvalue potential takeover synergies as a

consequence of overpricing. In a sense, these theories imply that target management is either affected

by agency or by hubris, respectively.

In contrast with hubris, takeovers motivated by market timing are characterized by inefficient markets

and rational managers. Hubris-driven takeovers are characterized by irrational managers and market

efficiency (Roll, 1986).

5

3 Methods of financing

In the corporate finance literature, three different methods of financing are generally identified. When

financing comes from internal sources, typically financial slack in the form of accumulated retained

earnings, it is labelled cash. External financing can be either debt or equity (stock). These broad

categories can be split up into several forms of debt financing (such as bank credits, loan notes or

bonds) and equity financing (public or private equity placement), but the three main methods of

financing suffice for the purpose of this research. Combinations are also considered, leading to seven

possible financing scenarios. In this chapter, an overview of the literature regarding financing decisions

is provided, followed by the development of hypotheses regarding the particular M&A motives

discussed in chapter 2.

It is important to stress that the method of financing should not be confused with the method of

payment, which is not necessarily the same. Some papers have treated them as synonyms, even though

one third of all-cash offers are at least partially financed with external funds (Martynova & Renneboog,

2008). Throughout this paper, offers that are discussed (e.g. a cash offer) indicate a means of payment.

There are a number of possible theories to predict financing decisions. According to the pecking order

theory (Myers & Majluf, 1984), companies prefer the method of financing with the least information

sensitivity. Therefore, according to the pecking order theory, investments are preferably financed with

internal financing. If these funds are insufficient, external financing will be used, with a preference for

debt rather than new stock (Deloof, Manigart, Ooghe, Van Hulle, 2012). The static trade-off theory, or

‘MM theory’ referring to Modigliani and Miller (1958), predicts that managers balance the tax savings

from debt financing against the costs of financial distress (Myers, 1993). Jensen and Meckling (1976)

introduced the motivation theory based on the agency relationship. This theory predicts that the

marginal agency costs of debt and equity play a central role in financing decisions. The free cash flow

theory (Jensen, 1986) builds further on the agency relationship, and predicts that M&As motivated by

empire building will be financed with internally generated funds.

In addition to the predictions of these general theories, the effect of capital gains taxes (cash is taxed

immediately, stock only when sold) and corporate control (dilution of acquirer shareholder control

when offering stock) should be taken into account when evaluating financing decisions (Eckbo, 2009).

The type of financing to fund takeovers may also be influenced by market inefficiencies and preferences

for a specific payment method (Martynova & Renneboog, 2009).

6

One final driving force for the financing decision is the signal that particular decision would send to the

financial markets. It is assumed that if the bidding firms' managers possess information about the

intrinsic value of their firm which is not fully reflected in the stock price, they use this information to

finance the acquisition in the most profitable way for the existing stockholders (Travlos, 1987). Hence,

new information about the value of the company is signalled through the payment method and sources

of financing, which leads to revaluations. The perception created by these signals should be taken into

account when formulating hypotheses, because it may have an influence on the financing decision.

Several scholars have suggested that M&A motives might drive means of payment (Hodgkinson &

Partington, 2008), and therefore also the method of financing. Bidder financial condition, corporate

control threat and deal characteristics can explain up to 23% of the portion of cash payment in M&As

(Faccio and Masulis, 2005). The general theories in corporate finance literature are not always useful to

predict M&A financing since they do not take the specific circumstances surrounding a takeover into

account. Below, the relevance of these general theories is discussed for each M&A motive.

3.1 Synergy

Much research has been done to determine the preferred financing method for synergy-motivated

takeovers. By definition, value-creating mergers show positive total returns. Asquith, Bruner and Mullins

(1990) find that these positive returns occur only for cash offers; the opposite being true for equity

offers. The empirical literature supports the view that the market expects cash offers to create more

value, with acquirers who offer cash experiencing higher abnormal returns than acquirers who offer

equity (Sudarsanam & Mahate, 2003). Several explanations are brought forward.

Acquirers will prefer cash offers if they believe that their firm is undervalued, stock offers will be

preferred if they believe their firm is overvalued (Myers & Majluf, 1984). Accordingly, the market

participants interpret a cash offer as good news about the bidding firm's true value (Travlos, 1987). It

may also signal the bidder’s confidence in successfully exploiting the potential synergies. If the acquirer

is well informed about the high value of the target, it will offer cash to avoid sharing these gains with the

target (Franks, Harris, Mayer, 1988). Cash also pre-empts other firms from bidding (Martin, 1996), which

may signal that there are significant potential gains. Linn and Switzer (2001) find that the change in

performance of merged firms is significantly larger for deals which offered cash. Unsurprisingly, cash-

paid M&A deals are more often associated with drastic changes in the target firm's management (Denis

& Denis, 1995). However, as previously stated, these studies are mostly based on payment method

instead of sources of financing. While cash offers generally convey a positive signal, this result seems to

be driven by debt-financed deals.

7

Studies by Lang, Stulz, Walkling (1991) and Schlingemann (2004) find a significant negative correlation

between internally generated funds and announcement returns in cash-paid M&As. Martynova and

Renneboog (2009) confirm these findings: acquisitions financed with internally generated funds

underperform debt-financed deals. Debt financing is expected to trigger a positive reaction by the

financial markets, in contrast to the findings for internally generated funds. While cash offers in general

are viewed as positive signals that the firm’s shares are not overvalued, debt financing conveys an

additional signal that the takeover is profitable and generates a tax shield (Martynova & Renneboog,

2009). Externally raised funds also reduce agency problems because of closer monitoring by financial

markets (Dickerson, Gibson, Tsakalotos, 2000). Another important aspect, especially in a European

context where debt capital is typically raised via borrowing from a bank (Martynova & Renneboog,

2009), is the positive signal conveyed by the bank's decision to provide funding. Martynova and

Renneboog (2009) argue that this is because banks use their superior information and evaluation

capabilities to identify bad acquisitions and therefore they fund only value-creating deals.

These findings imply that when acquirers are convinced of the synergy gains, they will accept the

monitoring by banks to send a positive signal to the markets. Loans will only be provided if banks can be

convinced of the value-creation effects, so they act as a signal proving the M&A potential. In a way, this

could be seen as an application of the static trade-off theory. Managers balancing the advantages and

disadvantages of using debt should, all else equal, be more inclined to use debt financing if they expect

large synergy gains.

Hypothesis 1: Acquisitions motivated by synergy will most likely be financed with debt.

In order to provide the necessary detail to uncover nuances embedded in the results, synergy-motivated

M&As with traces of hubris or market timing are tested separately. In these cases, although synergies

are created for the combined firms, value is destroyed for the acquirer or target firm, respectively.

Because the total gains are nonetheless positive (hence, synergy is still the dominant motive), the same

method of financing is expected for all synergy-motivated M&As. When discussing hypothesis 1 in the

strict sense only the takeovers creating value for both parties are considered, whereas all synergy

motives are considered in the broad sense.

3.2 Agency

According to the free cash flow theory (Jensen, 1986), M&As motivated by empire building will be

financed with internally generated funds. Industry-shocks or stock market booms often lead to excessive

cash at the discretion of managers. In the case of agency problems, self-interested managers use these

free cash flows to go for empire building instead of returning them to the shareholders.

8

Managers may prefer to maximise corporate growth rather than corporate value as their private

benefits tend to increase in line with firm size (supra, p. 3). Excessive internal funds make it possible for

managers to make poor acquisitions when they have run out of good ones (Martynova & Renneboog,

2008). Empirical evidence confirms the free cash flow theory (Goergen & Renneboog, 2004).

Hypothesis 2: Acquisitions motivated by agency will most likely be financed with cash.

3.3 Hubris

The hubris motive implies overconfident acquirer management. For these M&As, cash offers are the

most obvious choice since managers would want to avoid the negative signal associated with equity

offers.

The market reacts negatively to equity offers because it interprets them as a signal that bidding

managers believe their firm’s shares are overvalued (Goergen & Renneboog, 2004). Furthermore, it

assumes that the equity being offered is more overvalued than the target assets being acquired (Dong

et al., 2006). Advantages of equity are that there are no financing constraints associated with it,

supported by a greater frequency of stock use in financially troubled firms (Faccio & Masulis, 2005), and

the fact that it allows for risk sharing. Asymmetrical information between acquirer and target makes it

hard to properly value the target. Acquirers can make an equity offer to reduce the problem of adverse

selection as target firm shareholders bear some of the risk (Sudarsanam & Mahate, 2003). Therefore,

equity offers may be interpreted by the market as a negative signal about the target firm's quality and

uncertainty surrounding potential synergies (Martynova & Renneboog, 2009). In aggregate, it is likely

that the advantages of stock financed deals only play a significant role in cross-border acquisitions

(Dutta, Saadi, Zhu, 2013) because of the negative signals associated with stock offers.

These cash offers may be financed through internal or external funds. Equity financing is less likely

under the assumption that the market will recognize this source of financing and react negatively as it

would in equity offers. On the one hand one might expect the same method of financing as for

synergies, since management is convinced that their takeover qualifies as synergetic. On the other hand,

debt financing requires management to convince lenders who may be more sceptical about the value

creation of the takeover in question and see through the exaggerated optimism. This is confirmed by

Malmendier and Tate (2008), who find that overconfident CEOs are most likely to make diversifying

takeovers that do not require external financing. This is consistent with the pecking order theory.

Hypothesis 3: Acquisitions motivated by hubris will most likely be financed with cash.

9

3.4 Market timing

As stated above, equity offers lead to a more negative market reaction than other sources of financing,

ceteris paribus. However, one major advantage of equity is that its managers can take advantage of

temporarily overvalued equity during market booms (Myers & Majluf, 1984). During periods of

overvaluation, which may be deliberately done through earnings manipulation (Shleifer & Vishny, 2003),

acquirers enjoy lower takeover costs because they can pay with more highly valued stock (Rossi &

Volpin, 2004) due to the mispricing premium. Dong et al. (2006) find that stock bidders have higher

valuations than cash bidders on average, which confirms the preference for stock during periods of

overvaluation.

Stock acquisitions are used specifically by overvalued bidders who expect a negative share price

correction. Acquirer management tries to minimize the correction by acquiring less overvalued targets

(Shleifer & Vishny, 2003) and converting overvalued equity into real assets. Even if acquirers are not

that overvalued, target overvaluation provides an incentive to leave part of the overpricing on the

shoulders of their shareholders by paying with stock rather than cash (Dong et al., 2006). Shleifer and

Vishny (2003) provide three conditions which increase the likelihood of stock financing: market

overvaluations, perceived synergies and target management under the influence of short-termism or

pay-offs.

The empirical literature provides evidence supporting the view that mispricing is an important motive

for choosing equity as a means of payment (Martynova & Renneboog, 2008). Over- and undervaluation

has also been studied in terms of the financing preferences of firms with high market-to-book ratios

(glamour firms) versus firms with lower MTB ratios (value firms). Glamour acquirers tend to have higher

past returns compared to value acquirers (Sudarsanam & Mahate, 2003), thus for glamour firms it is

more likely market inefficiencies lead to overvaluation. Rau and Vermaelen (1998) find that 54% of the

glamour acquirers use equity as a means of payment while it is only 38% for the takeovers by value

acquirers. The dominant theory for market timing is the pecking order theory. In general, the pecking

order is based on increasing information asymmetries leading to an increasing cost of capital. In the

rather unusual case of market timing, the advantage of using overvalued equity is greater than the

disadvantage of choosing the financing method with the highest information asymmetry. Therefore,

hypothesis 4 is considered to be in line with the pecking order theory, with the pecking order based on

the cost of capital in this case.

Hypothesis 4: Acquisitions motivated by market timing will most likely be financed with equity.

10

4 Methodology

Chapter 4 discusses the data, the identification of the motives and it concludes with an overview of the

control variables.

4.1 Data

To test the hypotheses outlined in chapter 3, I obtained a dataset from the Faculty of Economics and

Business Administration from Ghent University. The information originated from Thomson and Factiva,

and methods of financing were hand-collected. The dataset consists of 407 completed M&A deals

between listed European companies in the period 1997-2010. This period comprises the 1993-2000

merger wave (Goergen & Renneboog, 2004) ended by the collapse of the internet bubble, from January

1999 to March 2000 (Schultz & Zaman, 2001), and the 2003-2007 merger wave, followed by the

financial crisis (Alexandridis, Mavrovitis and Travlos, 2012). No information regarding the underlying

motives was available in the dataset. Additional data needed to identify the motives was gathered from

Thomson Datastream, as well as market-to-book ratios.

4.2 Event study

The identification of M&A motives is not a straight-forward task. Going through M&A announcements to

obtain them would be a tedious task, not to mention the fact that this data would be biased and often

incomplete (Nguyen, Yung, Sun, 2012). A common solution to this problem is using ex-post market data

in an event study to infer the underlying motives. Using ex-post data, results are reliable and the

definitions are clear. However, various methodological approaches have been used, which makes it hard

to compare results (Bruner, 2004). Furthermore, the use of longer or shorter event windows can even

have a significant impact on the results (Hodgkinson & Partington, 2008). Nguyen et al. (2012) suggest

that event studies could be biased because the abnormal returns of bidders could be correlated due to

the occurrence of merger waves.

Notwithstanding these disadvantages, many scholars consider abnormal returns as the most effective

technique to measure acquisition performance (Haleblian et al., 2009). There is a body of literature that

identifies synergy, agency and hubris using short-term wealth effects based on abnormal returns (Roll,

1986; Berkovitch & Narayanan, 1993; Seth, Song, Pettit, 2000; Goergen & Renneboog, 2004; Hodgkinson

& Partington, 2008). Therefore, to identify the motives, this study also infers motives based on a short-

term event study for bidding and target firms.

11

To distinguish between the different motives, the sign of the correlation coefficient between target and

total gains is measured, using the same methodology as the papers cited above. Whereas these studies

apply the methodology to datasets, the research design of this paper requires motives to be identified

on the individual firm level. This approach, necessary for the purpose of this research, does not allow

much flexibility for coexisting motives which may have a different impact on the preferred method of

financing. However, it is likely that the dominant motive will also be the dominant determinant of the

method of financing.

For the event study, historical data for the return index (RI) and share price (in U.S. Dollar), number of

shares (x 1000) and the market index were collected from Thomson Datastream. The event study is

based on the market model, a statistical model which compares the stock returns on an individual level

to the return of a market index. RIs are used to measure individual stock returns because they show a

theoretical growth in the value of a share, adjusting for dividends and stock splits. The market index

deemed most appropriate for the sample is Standard & Poor's Europe 350. For any security i, the market

model is:

where Rit and Rmt are the period-t returns on security i and the market portfolio, respectively, and Ɛit is

the disturbance term (MacKinlay, 1997). Expected returns are calculated using the market model, based

on a period 300 trading days prior to the event date until 60 trading days (-300, -60) prior to the event

date (i.c. the M&A announcement) to avoid contamination of the estimated parameters by rumours or

insider trading. Bids with less than 240 trading days’ data are allowed, as long as they provide a

minimum of 100 days’ data. In 16 cases, less than 240 trading days were used to estimate the market

model. On average, these cases provided information for 177 trading days. Abnormal returns (ARs) are

calculated as the difference between the actual and the expected returns.

Empirical literature shows a variety of event windows used to measure short-term wealth effects (for an

overview, see table 2 in Martynova & Renneboog, 2008). Narrow events might not capture the effects of

leakage, whereas starting the window too early risks overstating returns in the case of M&As following

positive movements in the acquirer’s stock price (Goergen & Renneboog, 2004). For the main analyses

in this paper, five days prior to the event until five days after the event (-5,+5) was deemed an

appropriate window which balances these two effects and follows several studies as showcased in the

overview. Cumulative abnormal returns (CARs) are calculated as the sum of abnormal returns in this

11-day event window.

12

In the next step, target, acquirer and total gain were derived from the CARs, necessary to differentiate

between the motives. Target gain is defined as the target’s CAR multiplied by the market value of the

target firm’s equity at t-6, minus the value of the target shares already held by the acquirer. Likewise,

acquirer gain is defined as the acquirer's CAR multiplied by the market value of the acquiring firm at t-6

(Bradley et al., 1988).

The initial sample of 407 companies is reduced by 3 deals which lack the minimum required amount of

trading days’ data for the market model and 1 deal with no stock price changes. One more deal with no

known target holdings prior to the takeover, one deal with no share price information and one deal

showing up twice in the dataset are also excluded. After dropping these cases, the final sample consists

of 400 deals. Table 1 summarizes the results.

It is surprising that the average target gains are negative (not significant) in some cases, whereas the

CAR are positive. This can be explained by smaller companies having large positive CARs, and bigger

companies having small negative CARs, which have a greater impact on average U.S. Dollar gains. This

would not be surprising, since earlier research finds that the gains identified in M&A are mainly driven

by large deals (Bruner, 2004). High standard deviations for the total sample, and significant differences

between the financing methods for acquirer and total gains indicate that the motives based on these

gains may explain some of the variation in financing sources.

Table 1: Event study results by financing sources

CAR acquirer Acquirer gains CAR target Target gains Total gains

% s.d. USD (mln)

s.d. % s.d. USD (mln)

s.d. USD (mln)

s.d.

Cash 0.27 1.21 69.4 248.4 3.65 1.73 1.5 3.5 70.9 248.0

Stock -2.43 1.04 -191.3 128.9 1.52 1.51 -33.1 47.2 -224.4 162.3

Debt -0.39 1.09 -374.4 172.3 3.08 1.72 -28.5 31.9 -403.0 188.3

Cash & debt -0.63 1.07 -233.2 173.7 2.91 1.44 42.7 40.5 -190.5 166.6

Cash & stock -0.58 1.48 -86.7 70.2 0.69 1.88 -28.6 30.3 -115.3 72.6

Stock & debt 0.19 1.07 119.1 71.8 4.55 1.20 240.1 128.2 359.2 150.1

Cash, stock &

debt

1.84 1.46 34.5 91.6 2.26 1.17 19.3 10.4 53.8 92.4

All financing -0.63 9.38 -107.7 1 288 2.68 12.73 25.9 479.8 -81.8 1 476

F-stat 0.342 0.053* 0.599 0.142 0.021**

This table reports the means and standard deviations for a number of event study characteristics. F-stats are reported for the difference in means between the different sources of financing. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Source: own computations.

13

4.3 Identification of the motives

After calculating target, acquirer and total gains, motives could be identified based on the expected

signs of these coefficients. In the empirical literature, this analysis is based on correlations between

target and acquirer gains, and target and total gains. Therefore, the expected signs as well as

correlations are provided. For each motive, the expected signs are outlined in table 2, based on the

assumptions outlined below.

Table 2: Motives and associated patterns of correlation

Acquirer gains Target gains Target gain & Acquirer gain correlation

Target gain & Total gain correlation

Positive total gains

Synergy + + + +

Synergy/market timing + - - -

Synergy/hubris - + - +

Zero total gains†

Hubris - + - 0†

Negative total gains

Agency - + - -

Market timing - +

† Total gains that are not material are considered to be zero. This table reports the expected patterns of correlation. A plus sign corresponds with a positive value (in the case of gains) or sign (in the case of correlations), a minus sign corresponds with a negative value or sign. Source: literature review in section 4.3

4.3.1 Synergy

The synergy motive assumes that managers of targets and acquirers intend to maximize shareholder

wealth and would engage in takeover activity only if it would benefit the shareholders of both parties.

Therefore, it follows that the gains to both target and acquirer shareholders would be positive

(Berkovitch & Narayanan, 1993), resulting in a positive total gain.

Some M&As create value in total, but destroy value for either acquirer or target. In order to obtain a

nuanced overview of the financing of synergy-motivated deals, these particular scenarios are analysed

separately. Because of the secondary motives associated with acquirer or target value destruction, I

label these cases synergy/hubris and synergy/market timing, respectively. Hodgkinson, Partington

(2007) and Seth et al. (2000) also recognize the former motive and expect the same result for target and

acquirer gains.

14

4.3.2 Agency

If acquirer management pursues personal objectives other than maximization of shareholder value, it is

willing to pay more for targets than they are worth to the acquirers' shareholders (Morck et al., 1990).

Fu, Lin & Officer (2013), confirm this empirically: high premiums paid by acquirers in M&As that

generate negative returns for acquirer shareholders are accompanied by acquirer CEOs extracting

considerable rewards for themselves. Thus, these acquisitions result in the extraction of value from the

acquirer shareholders by acquirer management and target shareholders. The reason why the latter also

extract value is because targets realise their value to the acquirer management and try to take

advantage of the situation. The amount targets can appropriate increases with their relative bargaining

power and the severity of the agency problem (Berkovitch & Narayanan, 1993).

The net result of this value extraction is that acquirers experience a negative gain, targets experience a

positive gain, and the total gain is negative. While the target gain is merely a transfer of gains, having no

impact on total gains, acquiring shareholder’s gains are further reduced by acquirer management’s

appropriation which results in lower total gain. (Berkovitch & Narayanan, 1993; Seth et al., 2000;

Goergen & Renneboog, 2004; Hodgkinson & Partington, 2007)

4.3.3 Hubris

When overconfident managers overestimate the gains from a takeover, they overpay for the privilege of

accessing the possibly non-existing gains (Malmendier & Tate, 2008). In this case, the gains to the target

shareholders would simply represent wealth transfers from acquirer to target (Hodgkinson &

Partington, 2007). Therefore, hubris predicts that, around a takeover, the value of the bidding firm

should decrease and the value of the target should increase (Roll, 1986). Since these acquisitions entail

nothing more than a transfer of value from the acquirer to the target, there should be no correlation

between total gains and the wealth gains to the target because total gains would be zero on average

(Berkovitch & Narayanan, 1993; Seth et al., 2000).

As stated earlier, prior studies analyse the correlations on a dataset level, whereas I try to identify

motives on the firm level. The problem that arises in converting this methodology to the firm-level is

that average effects cannot be studied, i.c. whether the average total gains are significantly different

from zero. Correlations can be tested for statistical significance for multiple data points, but individual

data does not allow for statistical analysis.

15

This problem is circumvented by creating a firm-level measure for significance. In this paper, negative

gains are considered to be material (hence, significant) when they exceed 10% of the acquirer’s pre-

M&A total assets1. The advantage of using total assets is that this measure remains relatively stable over

time, and that it is frequently used as a measure for materiality (e.g. Gleason & Mills, 2002).

4.3.4 Market timing

As mentioned in the introduction, there is a body of literature identifying the synergy, agency and hubris

motives. However, identification of the market timing motive based on abnormal returns is lacking and

therefore further research is required. The predictions outlined below might be a first step towards the

development of a theory and a methodology to identify market timing based on abnormal returns.

Basically, negative target gains are expectedt in market timing M&As. I arrive at this conclusion by

finding that targets are bid up prior to mergers and that target management accepts expropriative

offers. This is expected to trigger negative announcement-date revaluations.

“The biggest reason AOL Time Warner has been such a dog for investors is that the deal creating the

company was done on terms that were insane. And the really painful part is that this was perfectly clear

at the time. *…+ Trouble was, AOL stock was ridiculously overvalued. [...] So don't blame Case for what

has happened. He chose the moment, almost to the day, when his stock was most valuable and then

used it as currency. He served his shareholders well. It was Time Warner that sold itself for wampum.”

– Geoffrey Colvin2

The market timing motive comprises takeovers where financial market booms increase uncertainty

about the true value of firms, and better-informed bidders can exploit their informational advantage at

the expense of less-informed targets (Martynova & Renneboog, 2008). Apart from market- or industry-

wide overvaluation, targets may be additionally overvalued because the probability of a firm being a

target may be better recognized than the probability of a firm being a bidder (Houston & Ryngaert,

1994). This prediction is confirmed by Rhodes-Kropf, Robinson and Viswanathan (2005), who find that

stock targets are overvalued, and Asquith’s (1983) findings that pre-announcement stock prices of most

target firms are higher, due to the fact that they are potential targets. Rumours are another cause for

target stock price build-up prior to a merger.

1 In section 5.4.2, I study the effect of using another threshold as part of the sensitivity analysis.

2 Geoffrey Colvin, "Time Warner, don't blame Steve Case", February 3, 2003, Fortune.

16

Martynova & Renneboog (2008) discuss six studies reporting that the run-up premium is between 13.3%

and 21.8% in the month prior to the bid and often exceeds the announcement effect itself3. According

to them, this implies that the bids are anticipated, and result from rumours, information leakages or

insider trading. Target stock price reactions generally commence more than 40 working days prior to the

initial announcement of a bid (Schwert, 1996; Goergen & Renneboog, 2004).

Target overvaluation encourages target management to voluntarily accept expropriative offers (Dong et

al., 2006). Target management could be persuaded through stock options (which could be very valuable

if the target is overvalued), severance pay, or by ensuring top positions for target management (Shleifer

& Vishny, 2003). This last argument is confirmed by Haleblian et al. (2009), who find that managers of

target firms obtain significant wealth in acquisitions, and Hartzell et al. (2003), who find that personal

treatment leads to lower acquisition premia. Lower premia may also be a result of the fact that targets

are more likely to overestimate synergies when the market is overvalued (Rhodes-Kropf et al., 2005).

High-valuation targets receive, on average, lower premia, and earn lower announcement-date abnormal

returns (Dong et al., 2006). In an overvalued market, targets expecting their stock to fall may be more

eager to accept a merger proposal to reduce the impact on the target when the market corrects

(Rhodes-Kropf & Viswanathan, 2004). This proposal might trigger more careful valuations of the bidder,

which will have a negative impact on stock prices (Dong et al., 2006). Ironically, targets accepting a

merger proposal to reduce the impact of market corrections might trigger more careful valuations and

thus provoke a correction of their stock price. This supports the view that target abnormal returns

should be quite low in a market timing-motived M&A, and might even be negative.

Negative target abnormal returns have not received much attention in literature, mainly because

empirical evidence suggests that target abnormal returns are positive and significant on average.

However, many of the papers studied by Bruner (2004) show that a fraction of M&As show non-positive

target abnormal returns, ranging from 5% to 40% of the sample studied. Furthermore, Goergen and

Renneboog (2004) find no significant correlation between target and total gains for value-destroying

M&As, suggesting that both positive and negative target gains are observed4.

3 I observe a similar effect. In the (-30,-1) window, the average acquirer CAR is -0.39%, which is in support of a

correct specification of the market model. In the same window, the average target CAR is 25.14%, consistent with the findings of Martynova & Renneboog (2008). 85% of the targets have a positive CAR, and the target CAR is higher than 10% in 75% of the cases. These findings are almost identical in the (-46,-1) window, indicating that rumours are mainly relevant in the month leading up to the announcement in the sample studied in this paper. 4 If targets always gain in M&As, even value-destroying ones, one would expect a significant negative correlation

between target and total gains. However, the coefficient is insignificant and even positive. This could also be due to lack of data, but in the same subsample of value-destroying M&As, correlation between target and bidder gains is statistically significant, suggesting otherwise.

17

In the model of Rhodes-Kropf & Viswanathan (2004), the target's and acquirer's market price could

either rise or fall on the announcement of a merger. Their rationale for the fact that empirical works find

that, in general, the acquirer’s stock price falls while the target’s stock price rises, is mainly due to

market expectations that the acquirer is overvalued and the target undervalued. In the case of market

timing however, target overvaluation is expected, which lends support to the hypothesis that the

target’s stock price should fall upon announcement. Empirical evidence is not conclusive whether this is

the case, most of the studies discussing overvaluation only report lower abnormal returns and do not

discuss whether or not they become negative. However, it is important to consider the speculation

spread, defined as the difference between the bid price and market price after the initial announcement

(Jindra & Walkling, 2004), accounting for uncertainty around the merger. When the speculation spread

is also taken into account, one could reasonably expect negative announcement-date abnormal returns

for targets in market timing-motivated M&As5.

Taken together, these predictions make it appear that quite often, takeovers destroy value. In most

cases however, negative gains are not a result of deliberate value destruction, but because acquirers are

revealing information about their true market value, provoking a share price correction (Rhodes-Kropf &

Viswanathan, 2004).

5 This expectation is not inconsistent with a target realising positive abnormal returns due to its takeover potential.

On the contrary, in a scenario where the share price of a potential takeover target is bid up in advance, some uncertainty surrounding the M&A is included in the share price. When all details are released at the announcement date, a share price correction is likely. Prior share price gains during the bid-up period may largely offset the negative announcement-date abnormal returns, but since only short-term wealth changes are measured, negative CARs are expected.

18

4.4 Control variables

An analysis of the possible relationship between the acquisition motive and its method of financing

should take into account other variables which may influence this relationship. A detailed description of

the control variables can be found in appendix A. First of all, dummy variables are introduced to control

for hostility, domestic deals, intra-industry deals and tender offers. In hostile bids and tender offers,

equity payments decrease the probability of a bid’s success (Martynova & Renneboog, 2009), which may

be reflected in a lower probability of equity financing6. The reduced information asymmetries in

domestic or intra-industry deals make equity payments, and hence equity financing, more likely in those

cases (Faccio & Masulis, 2005). I also control for time periods, identifying the M&A wave (1997-1999)

and collapse (2000-2001) caused by the internet bubble following Martynova & Renneboog (2009).

2002-2005 is identified as the recovery phase, followed by a second stock market boom in 2006-2007

and recovery from 2008 until 2010.

Graph 1: Year of M&A announcement and completion

Graph 1 reports the evolution of the number of takeovers over time. No data are missing. Source: own computations.

6 This is not necessarily the case: if the increase in stock-financed cash offers outnumbers the reduced amount of

stock offers, equity financing could increase as a whole.

0

10

20

30

40

50

60

70

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Nu

mb

er o

f ta

keo

vers

Year

Year of M&A announcement and completion

Announcement

Completion

19

Acquirer age, profitability, market value and relative value capture risk and size effects. Due to

information asymmetries, one would expect that younger firms are more likely to employ equity

financing, whereas larger firms should have easier access to debt financing, mostly due to their proven

business model. Profitability is measured to capture another risk factor: firms reporting losses are less

likely to use internal sources of financing since this would increase their bankruptcy risk. Debt financing

may also be more difficult to obtain for these firms. The measure for acquirer profitability is return on

assets. Because asymmetric information problems increase with the relative value of the target

(Hansen, 1987), the likelihood of equity financing should increase as well. This effect may be countered

by the potential dilution for existing shareholders, but is strengthened by the reduced financing

constraints resulting from the increase in equity (Faccio & Masulis, 2005). Empirical literature finds that

large acquisitions are most likely financed externally (Dickerson et al., 2000).

Three variables measure valuation effects: the market-to-book ratio, Tobin’s Q and the share price

runup of acquirers. Higher acquirer MTB ratios and Q ratios should make their stock more attractive for

targets (Faccio & Masulis, 2005; Martin, 1996). The same goes for share price run-up, which makes

equity financing increasingly more interesting (Martynova & Renneboog, 2009).

To capture firm-specific incentives to use cash, stock or debt, seven more variables are considered. For

the cash-related incentives, acquirer cash holdings and cash flow are measured relative to the

transaction value. To consider debt-related incentives, acquirer collateral and leverage are measured.

Bidder creditor rights and enforcement of these rights are also considered, assuming that the likelihood

of debt increases with better creditor protection. Finally, to capture incentives for stock financing,

shareholder rights and enforcement of these rights are measured. Better acquirer shareholder

protection should increase the likelihood of stock financing. The difference between acquirer and target

shareholder protection is also considered because stock is more acceptable for targets if acquirer

shareholder protection is relatively better (Rossi & Volpin, 2004).

20

5 Analysis and results

To test the hypotheses from chapter 3, bivariate and multivariate analyses are conducted based on the

methodology outlined in chapter 4. After discussing the results, this chapter concludes with a sensitivity

analysis testing the robustness of the findings. First, a general description of the sample is provided.

5.1 Sample description

All 400 M&As remaining in the sample involve takeovers between listed, European companies in the

period 1997 - 2010. A detailed overview of the geographical dispersion of the European M&A sample is

provided in table 3. It is clear that the sample is dominated by UK deals. This is not surprising, since

continental Europe has less developed capital markets (La Porta, Lopez‐de‐Silanes, Shleifer, Vishny,

1998) and weaker investor protection (La Porta, Lopez‐de‐Silanes, Shleifer, Vishny, 1997) in comparison

to the UK. A closer look at the legal origin of acquirers as well as targets reveals that around 58% have

English origin, 25% French, 9% Scandinavian and 7% have German legal origin. The majority of the deals

are domestic takeovers: 73% of all M&A deals occur within the same nation.

Graph 1 already showed that the historical pattern of these M&As clearly confirms the trends identified

in the literature. First of all, the internet bubble can clearly be identified, with its peak in 2000. The

resulting decline is interrupted by a new merger wave in the middle of the decennium, abruptly stopped

by the financial crisis in 2007-2008.

We identify some additional characteristics that may be of importance for the analysis. Nearly all M&As

were friendly (92%), with 15 cases classified as neutral and only 14 hostile takeovers. In four cases, the

acquirer was a so-called white knight. In more than three quarters of the M&A deals, the original offer

was not successful: only 23% of the sample is a new deal. Furthermore, 73% of the M&A deals were

tender offers.

Information about the Standard Industrial Classification or SIC-codes can be used to study the extent to

which takeovers are diversifying. In 35% of the cases, target and acquirer 4-digit-SIC codes are identical.

When considering the industry group level (3-digit-SIC-codes), 48% of M&As occur within the same

industry. In broad terms, on a 2-digit-SIC-code level, 60% of takeovers occur in the same industry group,

indicating that at least 40% of takeovers are diversifying (including vertical integration).

21

Table 3: M&A sample by acquirer and target country

Acquirer Target

# % # %

English origin 221 55.25 233 58.25

Ireland 8 2.00 5 1.25

United Kingdom 213 53.25 228 57.00

French origin 107 26.75 102 25.50

Belgium 6 1.50 6 1.50

France 46 11.50 43 10.75

Greece 5 1.25 5 1.25

Italy 14 3.50 7 1.75

Luxembourg - - 2 0.50

Netherlands 22 5.50 28 7.00

Portugal - - 1 0.25

Spain 14 3.50 10 2.50

German origin 32 8.00 24 6.00

Austria 2 0.50 2 0.50

Germany 30 7.50 22 5.50

Scandinavian origin 37 9.25 34 8.50

Denmark 10 2.50 7 1.75

Finland 9 2.25 5 1.25

Sweden 18 4.50 22 5.50

2004 EU Accession 3 0.75 7 1.75

Czech Republic - - 2 0.50

Lithuania 1 0.25 - -

Poland 2 0.50 5 1.25

This table reports the number and proportion of takeovers by acquirer and target country. Source: own computations.

5.2 Bivariate analysis

We conduct bivariate analyses to test whether the influence of a single variable on the method of

financing is significant. However, these results should be interpreted with caution, since bivariate

analyses do not control for the influence of other variables.

22

Table 4 reveals that financing methods are not equally distributed among M&A motives. Especially for

all-cash and all-stock financed takeovers, the differences are statistically and economically significant. A

closer look at the motives for the whole sample reveals that nearly half of them can be classified as

synergy, and a third even creates value for target as well as acquirer shareholders. Of the M&As that do

not create value, the majority are motivated by market timing: 33% of the total sample. In M&As where

synergy is the primary motive, 22% show traces of market timing and 11% indicate management was

under the influence of hubris. Takeovers motivated entirely by hubris account for 13% of the sample.

Agency motives account for 6% of the M&As, which is rather low compared to the 59% identified as

hubris or agency in the sample of Nguyen et al. (2012), but they do account for multiple motives. This

may indicate that most agency problems are not severe enough to encourage management to pursue

value-destroying takeovers. To summarize: in more than 90% of the cases, acquirer management is

trying to create value for shareholders, and in three out of four takeovers, they succeed7.

Table 4: Bivariate analysis of motives and methods of financing

Cash Stock Debt Cash & debt

Cash & stock

Stock & debt

Cash, stock, debt

All financing Stock Debt

Proportion of deals employing this method of financing # % Transaction value derived from this

source of financing

Synergy 12% 26% 13% 11% 5% 19% 13% 131 33% 44% 33%

Synergy/ market timing

36% 12% 21% 14% 5% 5% 7% 42 11% 20% 46%

Synergy/hubris 18% 32% 5% 14% 0% 23% 9% 22 6% 49% 14%

Total synergy

18% 24% 14% 12% 5% 16% 11% 195 49% 39% 34%

Hubris 19% 19% 12% 15% 6% 17% 12% 52 13% 32% 35%

Agency 17% 39% 13% 9% 13% 9% 0% 23 6% 55% 18%

Market timing 11% 41% 12% 12% 6% 10% 8% 130 33% 58% 28%

All motives # 63 118 52 49 23 56 39 400

% 16% 30% 13% 12% 6% 14% 10% 100%

F-stat 0.005*** 0.002*** 0.524 0.955 0.585 0.078* 0.440 0.000*** 0.070*

This table reports the proportion of takeovers in a certain financing category (left panel) and mean weight of a certain financing source (right panel) for each of the methods of financing. F-stats are reported for the difference between the motives. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Reliable data was not available for cash financing. One should be cautious in estimating the level of cash financing, since in some cases the total financing obtained is greater than the M&A financing need. This means that all three sources of financing combined could provide more than 100% of the transaction value. Source: own computations.

7 In the case of market timing, the value is created by a reduction in the negative target gains when the share price

correction occurs.

23

Looking at the financing source for the whole sample, it is notable that nearly 30% of M&As are financed

solely with equity. However, all methods of financing are used commonly: 59% of M&As are financed

with some equity, 49% with some debt and 44% with some cash. These results generally are in line with

previous studies, as is the case in the recent European M&A study of Martynova & Renneboog (2009),

where 43% was cash financed, 34% financed solely with stock, 13% solely with debt and 10% with a

mixture of debt and stock. However, the proportions of cash and debt financing are significantly

different when taking the underlying motives into account.

About 16% of takeovers are financed only with cash. The overall picture of synergy-motivated M&As

shows that all-cash offers are slightly more frequently observed in those cases, but the detailed analysis

reveals large differences. For purely synergetic takeovers, cash is the only source of financing in just 12%

of the cases. However, when elements of market timing appear to be driving a value-creating takeover,

this figure is three times as high. Synergy-motivated M&As with some hubris characteristics are not

particularly associated with cash-only financing, indicated by the average occurrence of all-cash

financing in these cases (18%).

In M&As motivated by agency and hubris, all-cash financing was predicted to be the preferred method

of financing. However, in these deals, all-cash financing is employed only slightly more than average

(17% and 19%, respectively), which does not really support hypotheses 2 and 3. For market timing, stock

financing would prevail according to the hypothesis. As expected, the use of all-cash financing is nearly a

third below average for these M&As (11%).

In general, the frequency of all-stock financed takeovers is almost twice as high (30%) compared to all-

cash financing. Again, significant differences can be found amongst the motives. The value-increasing

M&As generally have similar proportions, except for the cases where the motive is synergy/market

timing. In these cases, only 12% of the deals are financed solely with equity. Whereas the share of all-

stock takeovers is lower for hubris takeovers (19%), consistent with the predictions, the opposite is true

for agency-motivated M&As (39%). Finally, in line with hypothesis 4, market timing has the highest

proportion of takeovers where stock is the only source of financing.

24

The only other financing method where significant differences are observed is debt and stock, albeit

only at the 10% significance level. Financing with a combination of stock and debt is relatively more

associated with the hubris motive than agency or market timing. This is exactly the opposite of what can

be observed for stock-only financing. As was the case for the other financing methods, the findings for

M&As characterised by synergy/market timing are not consistent with those of the other synergetic

takeovers. Whereas the combination of stock and debt occurs relatively more often in most of the

value-increasing takeovers, this is certainly not the case for synergy/market timing, which confirms the

finding that, in these cases, all-cash financing is preferred. The findings for synergetic M&As are very

mixed and do not fully support hypothesis 1 in the broad sense.

To get a sense of the influence the control variables may have on the results, a bivariate analysis of the

dummy variables is conducted. The results, reported in table 5, show that geography plays an important

role: for deals where the acquirer is UK-based, the proportions in financing sources are significant

different from those in takeovers by continental European acquirers. UK deals are relatively

underrepresented in the deals financed solely with cash. Domestic acquisitions are overrepresented in

subsamples of financing with a combination of cash and stock or only stock; and underrepresented in

the all-cash and all-debt subsamples.

Intra-industry, hostile or new deals are not associated with a particular method of financing. However,

for tender offers the all-cash source of financing is underrepresented. When looking at the correlation

between tender offers and UK deals, it is not surprising that these have similar effects on financing

characteristics: 86% of UK deals are tender offers, compared to 73% in the total sample (p 0.000).

Industry-effects (not reported) only have significant deviations from the norm when considering 4-digit

SIC codes.

25

Table 5: Composition of dummy control variables in sample

Whole sample

Cash Stock Debt Cash & debt

Cash &

stock

Stock &

debt

Cash, stock, debt

P(pearson χ²)

# % Proportion of deals with this method of financing

Hostile 14 4% 0% 3% 6% 2% 0% 9% 3% 0.162

UK deal 213 53% 32% 58% 56% 43% 65% 68% 54% 0.002***

Domestic 292 73% 60% 84% 63% 55% 96% 80% 72% 0.000***

Same industry 139 35% 32% 40% 35% 31% 22% 39% 31% 0.614

Tender offer 293 73% 52% 69% 83% 82% 74% 84% 82% 0.000***

New deal 91 23% 29% 27% 27% 16% 9% 23% 10% 0.116

1997-1999 88 22% 2% 5% 3% 4% 1% 5% 3% 0.043**

2000-2001 88 22% 4% 6% 3% 4% 1% 3% 2% 0.886

2002-2005 101 25% 5% 8% 3% 2% 2% 2% 2% 0.090*

2006-2007 74 19% 3% 4% 4% 2% 1% 3% 2% 0.649

2008-2010 49 12% 2% 5% 1% 1% 1% 2% 1% 0.783

This table reports the means of the control variables for all financing sources. The percentages report the cases where the value indicated 1, being the positive outcome. Pearson Chi²-tests are reported for the difference between the methods of financing. The null hypothesis is: equal proportions for every source of financing. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Source: own computations.

Table 6 provides information for the ratio-level control variables that might influence the relationship.

Younger and smaller firms seem to be associated with stock financing. The average age of acquirers

employing stock finance is 178 (even 14 for deals financed with cash and stock), for cash and debt deals

the average age is 32 and 35, respectively. Larger acquiring firms, both in real and relative terms, use

cash more often as a source of financing: the average acquirer size is 3.09 billion U.S. Dollar for cash

financing. Stock is associated with the smallest acquirer value (513 million USD). When the relative value

of the transaction is considered, the difference is even more striking: the transaction value for acquirers

financing deals with cash is on average 9% of their market value, compared to the sample-wide average

of 61%.

8 The average age of 17 can be computed as 10^1.24. Computations are similar for the other values reported.

26

Table 6: Ratio-level control variables for method of financing

All cash All stock All debt Cash & debt Cash & stock Stock & debt Cash, stock, debt F-stat

Av. s.d. Av. s.d. Av. s.d. Av. s.d. Av. s.d. Av. s.d. Av. s.d.

Log(Age) 1.51 0.06 1.24 0.05 1.55 0.08 1.38 0.07 1.16 0.11 1.35 0.08 1.39 0.09 0.001***

Market value 6.49 0.12 5.71 0.08 6.07 0.13 6.08 0.14 5.58 0.21 5.99 0.11 5.64 0.15 0.000***

Relative value 0.09 0.02 0.95 0.18 0.30 0.04 0.28 0.06 0.51 0.20 1.21 0.33 0.50 0.09 0.000***

MTB ratio 2.95 0.81 4.34 0.83 -10.73 9.52 3.46 0.45 2.68 0.31 4.88 1.66 2.54 0.28 0.123

Tobin’s Q 2.45 0.48 5.42 1.83 3.84 0.91 2.89 0.47 2.60 0.39 6.08 2.28 3.46 0.75 0.330

CAR run-up 0.01 0.02 0.05 0.03 -0.05 0.02 0.02 0.02 -0.00 0.04 0.01 0.02 0.04 0.04 0.094*

RoA 0.07 0.02 0.03 0.02 0.12 0.01 0.11 0.01 0.07 0.02 0.11 0.02 0.10 0.02 0.001***

CF/transval 18.81 4.10 0.31 0.06 1.46 0.37 3.50 1.56 1.57 0.55 0.30 0.05 0.41 0.10 0.000***

Cash/transval 28.79 7.16 0.58 0.08 1.84 0.66 1.91 0.56 2.42 1.13 0.23 0.05 0.35 0.06 0.000***

Collateral 0.29 0.02 0.25 0.02 0.33 0.03 0.30 0.03 0.25 0.05 0.34 0.04 0.26 0.03 0.203

Leverage 0.24 0.02 0.30 0.02 0.30 0.02 0.25 0.02 0.18 0.03 0.33 0.03 0.19 0.02 0.000***

Credit. rights 2.71 0.17 3.05 0.12 3.10 0.18 3.15 0.23 3.30 0.29 3.35 0.18 3.28 0.18 0.170

Shareh. rights 27.15 1.20 31.32 0.91 30.73 1.41 27.50 1.63 33.34 1.96 33.70 1.08 32.46 1.36 0.001***

Acq – tar rights 13.30 1.65 16.63 1.11 15.61 1.64 15.47 1.62 21.92 2.20 18.80 1.62 20.81 1.95 0.011**

This table reports the mean values of the ratio-level variables measured to control for their influence on the method of financing. All coefficients are significantly different from zero, except for the variable run-up, RoA in the all-stock subsample and MTB ratio in the all-debt subsample. F-stats are reported for the difference between the methods of financing. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Source: own computations.

The measures to capture valuation effects are not significantly different across the M&A motives in the

bivariate analysis, but it is interesting to note that the share price run-up is largest for stock-financed

deals, in line with hypothesis 4. The theoretical framework underlying the market timing motive

assumes a share price run-up (+5% in this case), the opposite is true for debt financed deals, which is

indeed confirmed by the data (-5%).

The significance of the measure for profitability reveals that financial condition may have an influence

on the financing methods. Acquirers using debt financing have an average return on assets of 12%, for

cash this is 7% and for stock the return on assets is not significantly different from zero9. These findings

support hypothesis 1, which predicted that profitable, successful firms can sustain a tax shield and are

therefore more likely to use debt than stock.

9 I also measure profitability by return on equity, and the results remain quantitatively unchanged.

27

Several variables capture the firm-specific incentives to use cash, stock or debt. Cash financing is

associated with the presence of (relatively) high cash flow generation and cash holdings, found to be

highly statistically and economically relevant. In cash-financed M&As, cash flow generation is almost 19

times higher than the transaction value, in contrast to the average sample ratio of 4. The same picture

emerges for cash holdings, where these ratios are respectively 29 and 5. For debt financing, collateral,

leverage and creditor rights are the control variables. Acquirer leverage is found to be significant: cash

acquirers seem to have lower leverage than their counterparts. Takeovers financed solely with either

debt or stock are associated with an average leverage of 30%, for mergers where some form of cash

financing is involved, the average is always below 25%. Unsurprisingly, the results indicate that stock

financing is associated with relatively higher shareholder protection. In cases where cash is used as a

source of finance and no stock is used, the protection of the acquiring firm’s shareholders is

considerably lower.

Finally, further information can be deducted from year effects. Whereas single years are not significant

on their own, the clustered periods do yield significant results for the period 1997-1999 and 2002-2005.

Seemingly, stock was more frequently used during these periods.

As an alternative test of hypothesis 4, an additional ANOVA is employed to test whether the market

timing motive truly drives acquirers with high MTB ratio’s. Although the model is significant, and

acquirers timing the market have higher MTB ratios on average, the Bonferroni multiple comparison

tests (not reported) do not identify any significant differences. The high MTB ratio’s observed in agency-

motivated takeovers may be explained by the young average firm age associated with the agency

acquisition motive (13 years, in contrast to over 20 years for market timing).

Table 7: Market-to-book ratio depending on M&A motive

Synergy Synergy/hubris Synergy/market timing Hubris Agency Market timing

MTB ratio (Av.) 2.76 2.32 2.04 2.38 4.16 3.19

MTB ratio (s.d.) 2.49 2.11 1.51 1.56 3.11 2.71

F-stat 0.008***

This table reports the means and standard deviation of the acquirer’s MTB ratio for the different motives. The F-stat is reported for the difference between the motives. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Source: own computations.

In general, the bivariate results are in line with the findings of Martynova & Renneboog (2009).

28

5.3 Multivariate analysis

For the multivariate analysis, a multinomial logit model estimates the preference for certain financing

sources. The model disentangles the effects of the control variables, and thus reveals the true influence

of the M&A motives and other determinants of the financing method. In table 8, the probability of using

a certain financing source, relative to another financing source (the base category), is reported. Stock is

the base category for the financing source and market timing is the default motive. The model is

significant, and makes it possible to analyse the aggregate effect of all independent variables. No

multicollinearity problems are reported: the maximum VIF is 3.35. The dummy variables UK deal, which

has a 0.87 correlation with shareholder protection, and New deal, which has a -0.72 correlation with the

difference between acquirer and target shareholder protection, were left out to avoid collinearity

issues. The correlation matrix can be found in appendix B.

First of all, the model shows that motives have a significant influence on the method of financing, even

when controlling for several other determinants. In takeovers motivated only by synergy, financing with

stock and debt (model 5) is more likely than financing with stock in comparison to M&As motivated by

market timing. This supports hypothesis 1 in the strict sense. However, there is no consistency among

the different types of synergetic takeovers. The synergy/hubris subsample does not seem to indicate a

significant preference for a certain source of financing, in comparison to market timing. In takeovers

characterised by synergy/market timing however, there is very strong evidence that cash-only financing,

as well as debt-only financing are preferred over stock, in comparison to market timing. The mixed and

contradictory findings lead to a rejection of hypothesis 1 in the broad sense. It seems that only the

purely synergetic M&As indicate a preference for debt, combined with stock, financing.

There is no statistical indication that agency-motivated takeovers are associated with a more frequent

use of a particular source of financing. Therefore, no conclusions regarding the validity of hypothesis 2

are drawn. The lack of statistical power for synergy/hubris- and agency-motivated takeovers could be

due to the relatively smaller amount these takeovers identified in the sample. Insignificant results

indicate that the coefficient is highest for all-cash financing, so future research employing a bigger

subsample of agency takeovers may be able to confirm this hypothesis.

29

Table 8: Multinomial logit model predicting the method of financing

N = 400

VIFmax = 3.35

Cash

(vs stock)

(1)

Debt

(vs stock)

(2)

Cash & debt

(vs stock)

(3)

Cash & stock

(vs stock)

(4)

Stock & debt

(vs stock)

(5)

Cash, stock, debt

(vs stock)

(6)

Coeff. P>z Coeff. P>z Coeff. P>z Coeff. P>z Coeff. P>z Coeff. P>z

Motives

Synergy -.03 0.957 .32 0.509 -.05 0.916 -.07 0.901 1.48*** 0.002 .48 0.340

Synergy/hubris .71 0.442 -1.72 0.213 .23 0.782 -13.74 0.987 1.15 0.135 .43 0.653

Synergy/

market timing

2.23*** 0.006 1.60** 0.037 1.21 0.130 .90 0.383 .94 0.334 .77 0.399

Agency .69 0.494 -.16 0.872 -.47 0.626 .67 0.469 -.05 0.957 -13.88 0.984

Hubris .00 0.998 -.02 0.972 -.13 0.842 .10 0.907 1.74*** 0.008 .65 0.347

Dummy variables

Hostile -12.66 0.987 1.06 0.275 -.70 0.597 -13.99 0.992 .87 0.291 -.92 0.499

Domestic -.90 0.122 -1.01** 0.050 -1.22** 0.017 1.22 0.275 .19 0.712 -.99* 0.081

Same industry -.88* 0.072 -.68 0.105 -.74* 0.083 -1.22** 0.044 -.09 0.817 -.66 0.136

Tender offer -.16 0.756 .95* 0.069 1.37*** 0.008 .59 0.339 .49 0.328 .76 0.155

Risk measures

Log(Age) .36 0.417 1.08*** 0.009 .37 0.332 -.16 0.739 .04 0.911 .55 0.196

Market value .20 0.480 -.14 0.569 -.05 0.845 -.24 0.459 .41* 0.074 -.23 0.411

Relative value -2.62** 0.024 -1.36** 0.016 -.94* 0.055 -.06 0.822 .07 0.443 -.75* 0.074

Valuation measures

MTB ratio -.04 0.306 -.10* 0.073 -.06* 0.085 -.03 0.650 .01 0.644 -.03 0.495

Tobin’s Q -.01 0.821 -.02 0.520 -.01 0.637 -.05 0.595 .00 0.645 -.01 0.760

CAR run-up .27 0.835 -3.30*** 0.010 .06 0.956 -.49 0.713 -1.52 0.169 .62 0.464

Incentives to use cash

RoA -.24 0.878 7.96*** 0.005 3.57 0.162 1.60 0.425 2.91* 0.088 4.24* 0.091

CF/transval .34** 0.021 .16 0.314 .36** 0.017 .28 0.110 -.23 0.475 -.05 0.873

Cash/transval .37** 0.020 .35** 0.032 .22 0.179 .35** 0.038 -.83* 0.057 -.70 0.132

Incentives to use debt

Collateral 1.02 0.365 1.11 0.253 .31 0.762 1.38 0.285 1.42 0.122 .48 0.665

Leverage -1.90 0.244 2.22 0.134 -.63 0.664 -2.29 0.225 -.98 0.412 -3.44** 0.038

Creditor rights .22 0.267 .16 0.342 .17 0.281 .20 0.356 .17 0.287 .10 0.577

Incentives to use stock

Shareh. rights .02 0.528 -.01 0.682 -.03 0.224 -.01 0.760 .01 0.534 .01 0.678

Acq-tar rights -.02 0.442 .01 0.633 .01 0.612 .02 0.584 -.00 0.920 .00 0.980

Year dummies

1997-1999 .47 0.589 .49 0.543 .90 0.222 .59 0.531 .00 0.993 .89 0.284

2000-2001 .37 0.638 -.11 0.888 .35 0.623 .17 0.854 -.23 0.730 .25 0.764

2002-2005 .68 0.385 .33 0.668 -.18 0.812 .84 0.341 -1.03 0.132 .38 0.641

2006-2007 .10 0.908 1.13 0.145 .42 0.589 1.12 0.222 -.06 0.920 .94 0.261

This table reports the coefficients estimated by a multinomial logit model for the choice of financing method. Each coefficient represents the probability of choosing the method of financing in that column relative to the probability of choosing stock financing, which is the benchmark. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Source: own computations.

30

For the hubris motivation, the model provides strong evidence that takeovers financed with a

combination of stock and debt are preferred over stock as the only source of finance, in comparison to

market timing. This finding leads to the rejection of hypothesis 3. A possible explanation for the fact that

overconfident managers can - in contrast to the expectations - obtain debt financing is that the creditor

does not recognize the overconfidence. Another explanation is that the management’s overconfidence

coincides with creditor overconfidence. Finally, it is possible that the management is able to convince

creditors by financing partly with stock, to mitigate the creditor’s risk.

All the significant results point towards the fact that motives other than market timing employ relatively

less stock financing than the latter. This supports the expected preference for stock-financing in market

timing-motivated M&As. Therefore, hypothesis 4 cannot be rejected.

The results do raise the question whether takeovers specified as ‘synergy/market timing’ are correctly

labelled. The findings confirm that synergy-motivated mergers are financed with debt (and stock), and

the market timing motive is associated with the use of stock financing. This is not consistent with the

fact that synergy/market timing has a significant preference of cash financing over stock and debt

financing. Therefore, the value destruction for target shareholders may be due to factors other than

market timing.

Looking at the other variables significantly impacting the financing decision, the model provides

evidence of a home country bias (Faccio & Masulis, 2005), in the sense that deals within the same

country are less likely to be financed with debt and/or cash compared to stock as cross-border deals.

This effect also applies to takeovers in the same industry. Furthermore, the type of deal has an influence

on the financing decision: cash and debt are preferred over stock financing in tender offers.

The variables used to proxy for risk are significant, underlying this factor’s importance for the financing

decision. In line with the results from the bivariate analysis, highly profitable acquirers are more likely to

use debt financing, compared to stock financing. Older firms, having already proven their business

model, are more likely to use debt financing for M&As as well. Size also has an impact on the sources of

financing: the likelihood of cash and debt financing decreases with the relative value of the M&A

transaction.

Regarding the effects of valuation, highly valued acquirers see an increased use of stock financing. For

acquirers with a high market-to-book ratio the use of cash & debt is less likely. The likeliness of debt

financing also decreases with the share price run-up before the takeover announcement.

31

As one would expect, cash-financed offers are more likely than equity-financed offers for acquirers

generating or stockpiling relatively large amounts of cash. To a lesser extent, this is also true for debt

financing. The data reveals that acquirers stockpiling cash have a tendency to use it along stock

financing, whereas acquirers generating a lot of cash have a tendency to use it along debt financing.

Since debt financing requires stable and relatively high cash flows, this is not surprising. Firm-specific

incentives to use debt or equity financing do not appear to have a distinct influence on the financing

source.

These determinants of the financing method are not always in line with the findings of Martynova &

Renneboog (2009). Perhaps due to the fact that they do not control for M&A motives and use less

financing categories, various variables which have a negligible effect in the models reported above are

significant in their model. This is the case for collateral, Q ratio and the hostility of bids. However, the

influence of size, market valuations and cash flow is recognized in both models.

Since synergy and hubris seem to have a similar preference for the combination of stock and debt, the

subsample of deals financed this way is more closely analysed in table 9 to see whether some

differences can be identified. The proportions of debt financing and stock financing are not statistically

different between these two motivations. However, the transaction value of deals motivated by synergy

is on average more than 10 times as large as that of deals motivated by hubris. Therefore, in real terms,

synergy-motivated takeovers attract a lot more debt financing. Furthermore, in hubris takeovers, the

use of stock as payment method is three times lower than for the synergy M&As. A possible explanation

for this phenomenon is the fact that targets recognize the overconfidence of acquirer management, and

are therefore reluctant to obtain a stake in that company. It is also apparent that the financing raised in

synergy-motivated M&As is nearly 20% more than the financing needed for the transaction value. The

fact that this phenomenon is not observed for hubris takeovers may be an indication that debt financing

is harder to obtain for overconfident managers. This is in line with the higher relative amount of debt

financing for synergy-motivated M&As, although the difference is not statistically significant.

Table 9: Deal characteristics in the subsample of takeovers financed with stock and debt.

N = 56 Synergy Hubris F-stat

Av. s.d. Av. s.d.

% of debt financing 71% 9.95 56% 6.21 0.178

% of stock financing 47% 3.38 43% 6.33 0.516

% of payment in stock 34% 5.76 13% 8.11 0.038**

Transaction value (mln USD) 5 381 2 648 432 198 0.068*

This table reports the means of several deal characteristics for the motives Synergy and Hubris. F-stats are reported for the difference between the motives. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Source: own computations.

32

5.4 Sensitivity analysis

5.4.1 Effect of extreme values

To capture the effect of extreme or missing values, the multinomial logit is run again with slightly

adjusted variables. The variables MTB ratio, Tobin’s Q, CAR run-up, Return on Assets and

Acquirer-Target shareholder protection are winsorized, 5% at each tail. Furthermore, the logarithm of

Cash/transval, Collateral and Leverage replaces the original variables and normalizes the distribution of

these variables. For Acquirer CF/transval and Relative value, the logarithm also replaces the original

variables, which are subsequently winsorized.

The results, in table 10, reveal that the results remain quantitatively unchanged regarding the influence

of M&A motives. To some extent, there is an impact on the significance of the control variables. Most

importantly, no coefficients which were significant at the 1% level became insignificant, and vice versa.

Two coefficients which were previously insignificant, reached the 5% significance threshold. Most

notably, the impact of intra-industry deals, Tobin’s Q and RoA is greater, while the coefficient of MTB is

less significant. The sensitivity analysis reveals that the likelihood of stock financing increases with the Q

ratio, as identified in previous studies (Martynova & Renneboog, 2009).

33

Table 10: Multinomial logit model for sensitivity analysis

N = 400

VIFmax = 5.88

Cash

(1)

Debt

(2)

Cash &

debt

(3)

Cash &

stock

(4)

Stock &

debt

(5)

Cash, stock,

debt

(6)

Coefficient reporting probability of financing method (x) versus stock financing

Synergy -.23 .29 -.15 -.33 1.31*** .38

Synergy/hubris .50 -1.51 .39 -13.54 .91 .27

Synergy/market timing 2.63*** 1.57** 1.26 .71 .50 .79

Agency 1.05 .32 -.47 .61 -.17 -14.10

Hubris .12 .07 -.04 .08 1.42** .63

Hostile -12.19 .94 -.62 -13.37 1.01 -.49

Domestic -.93 -.86* -1.12** 1.25 .46 -.88

Same industry -1.29** -.83** -.92** -1.33** -.12 -.81*

Tender offer -.05 1.07** 1.27** .66 .45 .77

Log(Age) .22 .91** .11 -.27 -.08 .40

Market value -.08 -.14 -.20 -.35 .48** -.22

Relative value -1.28*** -.69** -.98*** -.83** -.11 -.74**

MTB ratio -.29* -.12 -.03 -.04 -.07 -.13

Tobin’s Q -.30 -.24* -.30** -.28 -.13 -.10

CAR run-up .72 -4.73*** .34 -1.00 -1.98 .42

RoA 10.58** 12.80*** 6.94* 4.43 6.42** 9.87***

CF/transval 2.34 3.54 6.73 2.37 -1.15 -7.19

Cash/transval 2.28* -.00 -1.44 .83 -4.18** -3.39

Collateral 1.34 3.09 .97 2.02 1.70 .39

Leverage 1.22 4.95 -4.01 -3.14 .50 -8.24*

Credit. rights .22 .09 .08 .20 .15 .06

Shareh. rights .00 -.00 -.02 -.02 .01 .00

Acq – tar rights -.00 .00 .00 .03 .01 .02

1997-1999 .44 .46 .96 .60 .35 1.11

2000-2001 .52 -.20 .22 -.04 -.02 .33

2002-2005 .85 .27 -.28 .73 -.96 .51

2006-2007 .03 1.08 .47 1.02 .13 1.13

This table reports the coefficients estimated by a multinomial logit model for the choice of financing method. Each coefficient represents the probability of choosing the method of financing in that column relative to the probability of choosing stock financing, which is the benchmark. Detailed p-values are no longer reported to improve the readability. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Coefficients highlighted in blue indicate that it gained or lost significance. Source: own computations.

34

5.4.2 Classification of motives

Any methodology that hinges on assumptions is greatly influenced by the characteristics of these

assumptions. In this section, the impact of the choice of event window on the results is discussed. The

impact of a different threshold for significance is also considered. The (-5,+5) window, used for the

findings presented earlier, serves as the reference category.

Graph 2: Sensitivity of motive identification

Graph 2 reports the sensitivity of the motive identification depending on the event window and significance threshold. No data are missing. Source: own computations.

Graph 2 shows that these three common windows to measure short-term wealth effects (Martynova &

Renneboog, 2008) result in significantly different distributions of the motives. It is not very surprising

that relatively more M&As create value when considering a longer period of time, but the large shifts

within the value-increasing and value-destroying categories are remarkable. For instance, when moving

from the (-2,+2) to the (-10,+10) window, the number of M&As identified as synergy/market timing

almost vanishes, whereas the opposite is true for agency-motivated M&As and synergy/hubris.

When the (-2,+2) or (-10,10) windows are considered for the cumulative abnormal returns, the findings

are quite different from the regressions reported above. This should not be surprising, since previous

studies also indicate that results are sensitive to the window specifications (Hodgkinson & Partington,

2008).

0

20

40

60

80

100

120

140

160

180

Nu

mb

er

of

take

ove

rs

Motive

Sensitivity of motive identification

(-2,+2) (-5,+5) 2% significance threshold (-10,+10)

35

Table 11: Multinomial logit model for (-2,+2) CAR window

(-2,+2) Cash

(1)

Debt

(2)

Cash & debt

(3)

Cash & stock

(4)

Stock & debt

(5)

Cash, stock,

debt

(6)

Coefficient reporting probability of financing method (x) versus stock financing

Synergy -.05 -.24 -.44 -.44 .56 .41

Synergy/hubris -13.27 2.24** .97 -15.42 .81 2.75***

Synergy/market timing 1.83*** .97 1.11* .83 -1.39 1.54

Agency .91 -15.15 .80 -15.28 -15.81 -14.83

Hubris 1.34* .17 1.19* .30 1.62*** .88

[…] The complete regression results can be found in appendix C.

This table reports the coefficients estimated by a multinomial logit model for the choice of financing method. Each coefficient represents the probability of choosing the method of financing in that column relative to the probability of choosing stock financing. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Coefficients highlighted in blue indicate that it gained or lost significance. Source: own computations.

Regarding the synergy hypothesis, the findings for ‘pure’ synergy are weakened in the (-2,+2) window

and strengthened in the (-10,+10) window. The results for synergy/hubris for the alternative windows,

provide some minor support for debt being the preferred source of financing. The short window

suggests debt-only financing, whereas the long window suggests debt used in conjuncture with stock.

The results for synergy/market timing remain more or less the same in the (-2,+2) window, but lose their

significance in the (-10,+10) window. In general, the results remain relatively stable for the synergy

motivation and therefore the conclusions drawn for hypothesis 1 are not altered.

Table 12: Multinomial logit model for (-10,+10) CAR window

(-10,+10) Cash

(1)

Debt

(2)

Cash & debt

(3)

Cash & stock

(4)

Stock & debt

(5)

Cash, stock,

debt

(6)

Coefficient reporting probability of financing method (x) versus stock financing

Synergy 1.62** 1.55** 1.56* .05 1.25* .61

Synergy/hubris 1.71* 1.38 1.43 .12 2.06*** 1.79**

Synergy/market timing .46 -.68 -15.86 -.91 -13.99 -15.34

Agency -.53 .51 .64 .96 .21 -.90

Hubris 1.35 .86 1.53 -.13 1.75** .55

[…] The complete regression results can be found in appendix D.

This table reports the coefficients estimated by a multinomial logit model for the choice of financing method. Each coefficient represents the probability of choosing the method of financing in that column relative to the probability of choosing stock financing. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Coefficients highlighted in blue indicate that it gained or lost significance. Source: own computations.

36

For the agency motive, the increased representation in the sample when considering the (-10,+10)

window still does not yield any significant results. Regarding the hubris motive, cash seems to play a

more prominent role when considering the shorter event window. However, according to the results,

debt remains the preferred source of financing in these takeovers. As a result, the conclusions drawn for

hypotheses 2, 3 and 4 remain unchanged as well.

Another assumption is the threshold for significance of gains, to distinguish between hubris and agency

motives. To account for the impact of this assumption, a different threshold is considered: total gains

exceeding 2% of total assets, either in a positive or negative sense. The graph at the beginning of this

chapter clearly shows that some M&As move from synergy/hubris to hubris, whereas some takeovers

previously considered hubris are now in the agency category. This is because positive gains below 2%

are now considered insignificant, and negative gains between 2% and 10% of total assets are now

considered to be significant.

Table 13: Multinomial logit model with 2% significance threshold

2% significance threshold Cash

(1)

Debt

(2)

Cash & debt

(3)

Cash & stock

(4)

Stock & debt

(5)

Cash, stock,

debt

(6)

Coefficient reporting probability of financing method (x) versus stock financing

Synergy -.04 .30 -.02 -.09 1.45*** .47

Synergy/hubris 3.26** 1.17 -13.48 -13.54 1.73 -14.12

Synergy/market timing 2.27*** 1.59** 1.25 -.92 .90 .72

Agency -.14 -.39 -.53 .49 1.00 .05

Hubris .34 -.62 .16 -1.04 1.14 .32

[…] The complete regression results can be found in appendix E.

This table reports the coefficients estimated by a multinomial logit model for the choice of financing method. Each coefficient represents the probability of choosing the method of financing in that column relative to the probability of choosing stock financing. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Coefficients highlighted in blue indicate that it gained or lost significance. Source: own computations.

Again, the results in table 13 are in line with the general findings. The coefficient for hubris loses its

significance, probably due to a smaller subsample. A coefficient for synergy/hubris becomes significant,

indicating that cash is preferred over stock. In the aggregate, although the results are certainly sensitive

to the assumptions, they remain generally the same. The conclusions regarding the hypotheses remain

unchanged because the differences are related to the significance of coefficients instead of the signs of

significant coefficients. Therefore, in broad terms, the contribution of the sensitivity analysis is that the

results are sensitive to the assumptions, but the conclusions are not.

37

6 Conclusion

6.1 Findings and implications

This paper attempts to answer the question whether the motive behind an acquisition influences the

source of financing used for that acquisition. Generally speaking, I find evidence that there is a link

between M&A motives and methods of financing.

The analyses confirm that M&As where the true underlying motive is value creation for both parties -

labeled synergy - have a tendency to use debt, most probably combined with equity. These results are

robust for a wide range of changes in the model’s assumptions. The support for hypothesis 1 in the strict

sense is in line with the static trade-off theory. In takeovers where synergy gains are partially impeded

by management overconfidence, labelled synergy/hubris, no specific preference can be identified.

Although some models produce significant results, the evidence is contradictory. When management

tries to create value whilst timing the market, they have a strong tendency to use cash as financing

source, followed by debt financing. Therefore, considering all value-creating takeovers, the evidence is

mixed, and hypothesis 1 is rejected in the broad sense.

When M&As are driven by agency problems, managers try to take advantage of their own shareholders.

For these takeovers, no preferred method of financing can be identified. Insignificant results do indicate

a preference for all-cash financing, so future research employing a bigger subsample of agency

takeovers may be able to find support for hypothesis 2.

In takeovers driven mainly by managerial hubris, it is most likely that a combination of stock and debt

will be used to finance the deal. Considering all takeovers initiated to create value for all parties,

acquirer management prefers a combination of stock and debt financing, whether hubris is present or

not. Management under the influence of hubris does seem to have more difficulty convincing creditors

to finance a large part of the transaction, and convincing targets to accept their stock as a payment

method. However, in contrast to the expectations, overconfident management seems to be able to

convince creditors in the end. This may be in part due to the stock financing component, which reduces

the risk for creditors and the amount for which debt financing is needed. Hypothesis 3, based on the

pecking order theory, is rejected.

Significant results are obtained for the market timing motive in M&As: the analyses reveal that

takeovers motivated primarily by market timing are most likely financed with equity. This finding is in

line with hypothesis 4 and the pecking order theory, predicting that management uses overvalued

equity for takeovers during market booms.

38

In line with other empirical research, the preferred financing source is also dependent on a range of deal

and firm characteristics. Risk, size, market valuations and firm-specific incentives to use cash have a

significant impact on the financing decision. Whether deals are cross-border, diversifying, or in the form

of a tender, also influences the preferred source of financing. Hostility of deals and firm-specific

incentives to use debt or equity financing are not significant, which is not always in line with previous

empirical research, possibly because these studies did not account for the influence of the M&A motive.

Perhaps the most interesting implication of this paper’s findings is that stock financing shouldn’t always

be perceived as a negative signal. The negative market reaction for stock offers is largely a consequence

of the assumption that acquirers are taking advantage of overvalued stock. However, some degree of

stock financing is also present in value-creating deals, motivated by synergy. Investors should carefully

assess the likelihood of overvaluation in the case of M&As financed with some stock. In those cases, the

amount of debt financing should provide valuable information, since the likelihood of value creation

generally increases with the proportion of debt financing.

6.2 Limitations

The validity of the results depends to a large extent on the assumptions used to identify motives based

on abnormal returns. Furthermore, the interpretation of these returns is not always straightforward,

because the returns result from a market reaction on all of the information available at that time. It is

possible that bids are already anticipated and price reactions identified in the event study are merely

noise, or that the real market reaction is buried in the noise (Roll, 1986).

Our sample focuses on European M&As from 1997 to 2010, so applying the results outside this setting

may reduce the validity. One should be cautious in extrapolating the findings to other geographical or

time settings. Especially the effects of differences in the regulatory framework should not be

underestimated.

In the absence of reliable ownership data, ownership effects were not concluded in the analyses. This

may have had an influence on the results. It is up to future research to determine the extent to which

this had an impact on the conclusions.

39

6.3 Further research

In this paper, I presented a new way to identify takeovers motivated by market timing. The theoretical

framework for the identification of M&A motives is far from complete, providing a fruitful avenue for

future research.

The mixed evidence for the hypothesis regarding synergetic M&As suggests that the theoretical

framework surrounding the motives is not complete. Especially for value-creating takeovers which

destroy value for the target shareholders, identified as synergy/market timing in this paper, further

research is required. A preference for cash financing is observed, which is consistent with neither the

synergy, nor the market timing motive, although we found support for the hypotheses for these

motives. This suggests that the target value destruction might not be due to market timing. The

preference for cash points towards the agency motive, although this paper’s results could not confirm

the link between agency-motivated takeovers and cash financing.

VII

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XI

8 Appendix

Appendix A: Variable definitions

Note: unless otherwise reported, accounting data were lagged 1 year, and the data was provided by Ghent University (originating from Thompson and Factiva).

Variable name Descriptions

(Financing method) Dummy variable equals 1 for the relevant method of financing; 0 otherwise. Debt financing comprises bank financing, regular bonds, convertible bonds and/or loan notes.

(Motive) Dummy variable equals 1 for the motive identified; 0 otherwise. (Year dummies) Dummy variable equals 1 if the bid was completed in the relevant period,

between 1 January and 31 December; 0 otherwise. Hostile Dummy variable equals 1 if the bid was hostile; 0 otherwise. UK deal Dummy variable equals 1 if the acquirer is UK-based; 0 otherwise. Domestic Dummy variable equals 1 if the acquirer and target are based in the same

nation; 0 otherwise. Same industry Dummy variable equals 1 if the acquirer and target have the same 4-digit SIC

code; 0 otherwise. Tender offer Dummy variable equals 1 if the bid was a tender offer; 0 otherwise. New deal Dummy variable equals 1 if the final deal was in fact a new deal; 0 otherwise. Log(Age) The natural logarithm of the acquirer firm’s age. Market value The acquirer market value in 1 000 U.S. Dollar. Own computations based on

number of shares at t-6 and share price at t-6. Source: Thomson Datastream Relative value The transaction value relative to the acquirer’s market value at t-60. MTB ratio The acquirer price-to-book value. Source: Thomson Datastream Tobin’s Q The acquirer’s ratio of market value of equity and book value of debt over the

book value of equity and debt. CAR run-up The acquirer’s CAR run-up in the (-59,-20) event window. Source: own

computations. RoA The acquirer’s return on assets, calculated as earnings before interest and

taxes divided by total assets. CF/transval The acquirer’s cash flow generation, calculated as net income plus

depreciation and amortization, over the transaction value. Cash/transval The acquirer’s cash and short term investments over the transaction value. Collateral The acquirer’s collateral, calculated as the ratio of Property, Plant and

Equipment over total assets. Leverage The acquirer’s leverage, calculated as the ratio of total debt plus the

transaction value; divided by the sum of total assets and transaction value. Credit. rights The creditor rights in the acquirer’s country multiplied by the level of

enforcement of the law. Shareh. rights The shareholder rights in the acquirer’s country multiplied by the level of

enforcement of the law. Acq-tar rights The difference between shareholder rights in the acquirer’s country (x rule of

law) and in the target’s country (x rule of law). % of debt financing Proportion of debt financing relative to the total transaction value. % of stock financing Proportion of stock financing relative to the total transaction value. % of payment in stock Proportion of stock payment relative to the total payment. Transaction value Total price paid for the acquisition in million USD.

XII

Appendix B: Correlation matrix H

ost

ile

UK

de

al

Do

mes

tic

Sam

e in

du

stry

Ten

der

off

er

Ne

w d

eal

Log(

Age

)

Mar

ket

valu

e

Re

lati

ve v

alu

e

MTB

rat

io

Tob

in’s

Q

Car

ru

n-u

p

Ro

A

CF/

tran

sval

Cas

h/t

ran

sval

Co

llate

ral

Leve

rage

Cre

dit

. Rig

hts

Shar

eh

. Rig

hts

Acq

-tar

sh

are

h. r

igh

ts

19

97

-19

99

20

00

-20

01

20

02

-20

05

20

06

-20

07

Hostile 1 UK deal -.06 1 Domestic -.00 .42 1 Same industry .00 -.01 -.10 1 Tender offer .08 .30 .09 -.00 1 New deal -.10 .13 -.00 .00 .05 1 Log(Age) .04 -.17 -.10 .01 -.02 .10 1 Market value .02 -.19 -.30 .05 -.05 .06 .23 1 Relative value .04 .06 .06 -.05 -.00 .05 -.02 -.06 1 MTB ratio .03 -.06 .01 -.01 -.01 -.03 -.00 .01 .10 1 Tobin’s Q .00 .13 .07 -.02 .10 .03 -.04 -.03 -.02 -.02 1 CAR run-up .19 .00 .02 .02 .03 -.03 -.06 .01 .02 .05 .04 1 RoA .02 -.08 -.08 .03 .02 -.02 .05 .16 -.15 -.10 -.04 -.12 1 CF/transval -.04 -.18 -.07 -.06 -.16 .08 .11 .28 -.09 .00 -.02 .00 .04 1 Cash/transval -.03 -.18 -.05 -.05 -.12 .07 .16 .27 -.08 .00 -.03 -.03 .00 .82 1 Collateral .11 .02 -.07 .09 -.01 -.01 .11 .13 .00 -.05 -.08 .02 .01 -.01 -.06 1 Leverage -.04 .06 -.10 .02 .02 .45 .02 .16 .16 -.01 .28 -.03 -.00 -.00 -.04 .28 1 Credit. rights -.04 .16 .06 .02 .14 .06 -.02 -.14 .04 .01 .03 .04 .04 -.14 -.11 .03 -.03 1 Shareh. rights -.08 .87 .34 -.03 .30 .12 -.15 -.17 .08 -.05 .11 .02 -.09 -.16 -.14 -.02 .00 .40 1 Acq-tar rights .02 .41 .19 -.00 .06 -.71 -.20 -.15 -.00 -.05 .04 .05 -.02 -.15 -.15 .00 -.38 .22 .51 1 1997-1999 .09 .08 -.00 -.02 .10 .05 .05 -.08 -.01 -.00 .04 .00 .14 -.06 -.08 .20 .09 .21 .01 -.02 1 2000-2001 -.03 .03 -.00 -.05 .08 -.02 .01 -.01 .02 .05 .01 .02 .04 -.04 -.03 .11 .07 -.03 .00 .02 -.28 1 2002-2005 -.01 -.05 .01 .05 -.10 .15 .00 -.03 -.03 .01 -.09 .03 -.21 .01 .06 -.05 -.06 -.09 -.10 -.19 -.30 -.30 1 2006-2007 -.02 -.05 -.05 .01 -.03 -.10 -.03 .10 .07 -.09 .07 -.08 .02 .08 .05 -.15 -.01 -.09 .02 .09 -.25 -.25 -.27 1

This matrix reports correlations between the independent variables to determine the appropriateness of including a certain variable in a model. Due to high correlations with the other variables, UK deal and New deal were not included in the multinomial logit models. Notwithstanding the high correlation between CF/transval and Cash/transval, I include both variables in the models for two reasons. First of all, the VIF is still at an acceptable level of 3.35 when including both variables. Second, keeping both variables in the model improves the comparability with previous research. The same argument can be made for the shareholder and creditor rights variables. Source: own computations.

XIII

Appendix C: Multinomial logit model for (-2,+2) CAR window

(-2,+2) Cash

(1)

Debt

(2)

Cash &

debt

(3)

Cash &

stock

(4)

Stock &

debt

(5)

Cash, stock,

debt

(6)

Coefficient reporting probability of (x) versus stock financing

Synergy -.05 -.24 -.44 -.44 .55 .41

Synergy/hubris -13.26 2.23** .97 -15.41 .80 2.74***

Synergy/market timing 1.83 .96 1.10* .83 -1.39 1.53**

Agency .91 -15.14 .79 -15.27 -15.80 -14.82

Hubris 1.34* .16 1.18 .29 1.61*** .87

Hostile -14.75 .29 -1.21 -15.74 .67 -1.40

Domestic -.90 -1.14** -1.21** 1.46 -.10 -1.22**

Same industry -1.02** -.86** -.90** -1.30** -.01 -.85*

Tender offer -.17 1.11** 1.39*** .69 .68 .98*

Log(Age) .41 1.17*** .43 -.22 .08 .68

Market value .23 -.13 .00 -.11 .42* -.25

Relative value -2.58** -1.45** -.91* -.05 .06 -.80*

MTB ratio -.03 -.06 -.05 -.02 .00 -.01

Tobin’s Q -.00 -.06 -.02 -.02 .00 -.03

CAR run-up .04 -3.42 .30 -.57 -1.03 .77

RoA -.75 7.20** 3.04 1.73 2.81 4.02

CF/transval .30** .12 .32** .26* -.25 -.23

Cash/transval .39*** .36** .22 .35** -.81* -.48

Collateral 1.29 1.11 .54 1.33 1.72* .62

Leverage -1.94 1.98 -.89 -2.69 -.84 -2.86*

Credit. rights .21 .12 .09 .16 .03 .05

Shareh. rights .01 .00 -.03 -.01 .01 .00

Acq – tar rights -.02 .00 .00 .02 .00 .00

1997-1999 .27 .41 .78 .59 .23 1.06

2000-2001 .34 -.22 .21 .02 -.15 .29

2002-2005 .52 .13 -.25 .83 -.60 .61

2006-2007 .21 1.01 .44 1.21 .26 1.27

This table reports the coefficients estimated by a multinomial logit model for the choice of financing method. Each coefficient represents the probability of choosing the method of financing in that column relative to the probability of choosing stock financing, which is the benchmark. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Source: own computations.

XIV

Appendix D: Multinomial logit model for (-10,+10) CAR window

(-10,+10) Cash

(1)

Debt

(2)

Cash &

debt

(3)

Cash &

stock

(4)

Stock &

debt

(5)

Cash, stock,

debt

(6)

Coefficient reporting probability of (x) versus stock financing

Synergy 1.62** 1.55** 1.55* .05 1.25* .60

Synergy/hubris 1.71* 1.37 1.42 .12 2.06*** 1.79**

Synergy/market timing .46 -.67 -15.86 -.91 -13.99 -15.33

Agency -.52 .50 .63 .95 .21 -.90

Hubris 1.34 .85 1.53 -.12 1.74** .55

Hostile -14.46 .75 -.73 -15.59 .42 -1.41

Domestic -.72 -1.01* -1.12** 1.22 .03 -1.03*

Same industry -.77 -.70 -.68 -1.32** -.18 -.77*

Tender offer -.22 .83 1.18** .35 .55 .75

Log(Age) .29 1.10*** .35 -.26 .16 .63

Market value .38 -.10 -.02 -.37 .39 -.17

Relative value -2.54** -1.33** -.81* -.09 .09 -.86*

MTB ratio -.04 -.09* -.06* -.02 -.00 -.05

Tobin’s Q .00 -.03 -.00 -.07 .01 .00

CAR run-up -.95 -4.00*** -.51 -.10 -1.09 1.04

RoA -1.22 7.24** 3.47 1.32 2.48 4.07*

CF/transval .43*** .25 .45*** .39** -.14 .07

Cash/transval .31** .29* .17 .28* -.70* -.74

Collateral .34 .99 .13 1.46 1.08 -.05

Leverage -2.04 1.79 -1.19 -2.24 -.95 -3.45**

Credit. rights .15 .11 .11 .14 .09 .05

Shareh. rights .03 -.00 -.02 -.01 .01 .00

Acq – tar rights -.02 .00 .00 .02 .00 .00

1997-1999 .25 .26 .57 .61 .19 .90

2000-2001 .59 -.07 .37 .09 -.23 .24

2002-2005 .71 .15 -.31 .65 -.61 .60

2006-2007 .10 1.02 .26 1.18 -.04 .87

This table reports the coefficients estimated by a multinomial logit model for the choice of financing method. Each coefficient represents the probability of choosing the method of financing in that column relative to the probability of choosing stock financing, which is the benchmark. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Source: own computations.

XV

Appendix E: Multinomial logit model with 2% significance threshold

(-10,+10) Cash

(1)

Debt

(2)

Cash &

debt

(3)

Cash &

stock

(4)

Stock &

debt

(5)

Cash, stock,

debt

(6)

Coefficient reporting probability of (x) versus stock financing

Synergy -.04 .29 -.01 -.08 1.45*** .46

Synergy/hubris 3.26** 1.17 -13.48 -13.53 1.72 -14.11

Synergy/market timing 2.27*** 1.58** 1.24 .91 .89 .72

Agency -.14 -.38 -.53 .49 .99 .04

Hubris .33 -.62 .15 -1.03 1.14 .31

Hostile -12.11 1.01 -.66 -13.78 .84 -.97

Domestic -.89 -1.11** -1.16** 1.13 .04 -1.06*

Same industry -.87* -.64 -.73* -1.21** -.01 -.60

Tender offer -.20 .99* 1.30** .61 .46 .70

Log(Age) .23 1.04** .38 -.15 .03 .59

Market value .20 -.19 -.04 -.26 .38 -.23

Relative value -3.13** -1.39** -.88* -.06 .05 -.66

MTB ratio -.03 -.07* -.06* -.03 .01 -.05

Tobin’s Q -.00 -.04 -.01 -.04 .00 -.00

CAR run-up .25 -3.37*** .02 -.67 -1.31 .59

RoA -.37 7.63*** 3.49 1.45 2.85* 4.35*

CF/transval .36** .18 .38** .29 -.20 -.07

Cash/transval .36** .35** .20 .35** -.81* -.64

Collateral .94 1.16 .29 1.36 1.64* .66

Leverage -1.96 2.18 -.73 -2.42 -1.07 -3.35**

Credit. rights .24 .16 .16 .20 .18 .09

Shareh. rights .02 -.00 -.03 -.00 .02 .01

Acq – tar rights -.01 .01 .01 .02 -.00 .00

1997-1999 .50 .57 .84 .78 .03 .97

2000-2001 .49 -.01 .29 .23 -.16 .38

2002-2005 .91 .40 -.21 .96 -.89 .52

2006-2007 .23 1.18 .45 1.19 .14 1.11

This table reports the coefficients estimated by a multinomial logit model for the choice of financing method. Each coefficient represents the probability of choosing the method of financing in that column relative to the probability of choosing stock financing, which is the benchmark. *, ** and *** denote statistical significance at the 10%, 5% or 1% level, respectively. Source: own computations.