Market valuation and cross-

border M&A quality.

Master thesis finance

Tilburg School of Economics and Management

Department Finance

Tilburg University

Name: Sven Rustenhoven

ANR: 501168

Student number: U1255546

Supervisor: prof. dr. J.J.A.G. Driessen

Date: 26 September 2014

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Table of contents

Introduction 3

Section 1. Mergers and Acquisitions 5

1.1 M&A history 5

1.2 Rationale for mergers and acquisitions 6

Section 2. Cross-border M&A and Market Valuation 9

2.1 Cross-border M&A 9

2.1.1 Cross-border versus domestic M&A 10

2.1.2 Cross-border M&A and method of payment 11

2.1.3 Empirical evidence on cross-border M&A returns 11

2.2 Market valuation 12

Section 3. Hypotheses 14

3.1 Hypotheses 14

Section 4. Data 18

4.1 Data 18

4.2 High-, neutral-, and low-valuation markets. 18

4.3 Summary statistics 20

Section 5. Methodology 22

5.1 Announcement returns 22

5.2 Multivariate regression framework 23

5.3 Control Variables 25

5.3.1 Method of payment 25

5.3.2 Diversifying and focused M&A 26

5.3.3 Tender offer 27

5.3.4 Private and public targets 27

Section 6. Results 29

6.1 Univariate announcement results 29

6.2 Multivariate regression results 31

Section 7. Conclusion 39

References 41

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Introduction

During the last few decades cross-border mergers and acquisitions (M&A) became more

important as a tool for firms to achieve their strategic and business objectives. Since the

nineteen-eighties cross-border mergers and acquisitions share of worldwide foreign direct

investment (FDI) increased by around 50 percent. In 1987 M&A made up around 52 % of

global FDI. This increased to 83% in 1999 and after 1999 this fluctuated between 80 and

85% of global FDI (UNCTAD, 2008). Cross-border mergers and acquisitions have a sizeable

impact on the global economy because FDI makes up between 2 to 4% of the world economy

in the last decade (World Bank Group, 2014).

Research about the wealth effects of mergers and acquisitions and the influence of

certain deal characteristics on the activity and quality of mergers and acquisitions is plentiful

over the last few decades. According to Martynova and Renneboog (2005) evidence on the

returns for targets is conclusive. Returns are often significant and positive for the

shareholders of the target firms, whether it is a European, UK, or US firm. For shareholders

of the acquiring firm the abnormal returns are mixed (Martynova and Renneboog, 2005).

Research shows that several deal characteristics influence the returns of acquirers. These

characteristics are for example: method of payment, type of the target, and the mode of the

M&A (Bouwman, Fuller, and Nain, 2009). Acquirers’ stock returns of domestic deals

outperform acquirers’ returns of cross-border deals (Conn et al, 2005). Target firms involved

in domestic M&A have generally lower abnormal returns than target firm involved in cross-

border M&A (Conn et al, 2005).

A distinctive mark of M&A is that it occurs in waves and has a tendency to cluster by

industry within each wave (Andrade, Mitchell, and Stafford, 2001). A lot of research has

been conducted on M&A activity and waves. For example: the relation between stock prices

and M&A waves. Jovanovic and Russeau (2001) develop a model that explains the positive

correlation between M&A waves and market valuation. Bouwman, Fuller, and Nain (2009)

take a look at the relationship between market valuation and the quality of M&A deals. They

find that in high-valuation markets U.S. acquirers have higher returns at announcement than

acquirers in low-valuation markets. They also find that acquirers have higher long-term

abnormal stock return in low-valuation markets than those buying in high-valuation markets.

It is interesting to see what happens to the, previously mentioned, results from

Bouwman et al. (2009) when the sample is split between cross-border and domestic M&A.

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This paper investigates if cross-border mergers and acquisitions undertaken in booming stock

markets differ in quality from those undertaken when stock markets are depressed. Also this

paper tries looks at the influence of cross-border characteristic on the quality of mergers and

acquisitions. This research investigates if a specific valuation state has influence on cross-

border merger and acquisition quality.

The rest of this paper is outlined as follows. Section 1 contains a brief overview on the

history of mergers and acquisitions. It also covers the rationale for mergers and acquisitions.

Section 2 covers the primary characteristics of this research, cross-border and market

valuation. Here theory and empirical evidence will be covered. The content of section 2

forms the basis for the hypotheses, which is outlined in section 3. Section 4 comprises the

data and summary statistics, and how to distinguish different market-valuation states. The

methodology used is covered in section 5. Also, section 5 provides some theory on the

control variables. Section 6 comprises the results and a discussion of the results. Section 7

holds the summary and conclusions.

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Section 1. Mergers and Acquisitions

1.1 M&A history

Golbe and White (1993) find evidence that US M&A activity comes in waves and follow a

cyclical pattern. Martynova and Renneboog (2005) distinguish five M&A waves: those of the

early 1900s, 1920s, 1960s, 1980s, and 1990s. Most of the scientific research on M&A makes

use of US and UK transactions. This is due to the fact that in the last era most of the M&A

activity took place in the US and UK and data is easily accessible. Only in the last wave, the

1990s wave, Europe could match the quantity and value of the UK and US wave.

The first two waves, those of the 1900s and the 1920s, solely occurred on US soil.

The first wave occurred after a period marked by great economic expansion, which was

followed by a period of stagnation. This wave led to an economic environment dominated by

a few firms, also known as a monopoly. This horizontal consolidation led to the

disappearance of more than 1800 firms. The second wave was a reaction of the competitors

on the few firms having monopoly powers. The competitors merged or were forced to merge

together, creating an oligopoly (Sudarsanam, 2010, chapter 2).

The third wave, during the 1960s, in the US was focused on growth through

diversification. This could be due to the stronger antitrust rules that did not allow market

power increasing horizontal or vertical mergers. At the same time the first UK merger wave

took place. Horizontal mergers characterized this wave. The reason for the dominance of

horizontal mergers could be due to the fact that the UK government adopted policies to make

UK firms big national firms so they could compete with the rest of the world (Sudarsanam,

2010, chapter 2).

The fourth US wave reversed the mergers undertaken in the third wave. This wave is

characterized by acquisitions and divestures. Firms focused more on their core business and

tried to exploit their competitive advantage. This was achieved by divesting in non-core

activities. Further, firms acquired firms and activities in which they already had a competitive

advantage. This wave introduced the hostile tender offer and the acquisition of diversified

firms. These diversified firms were dismantled and individual parts were sold. The size of

acquisitions increased tremendously compared to earlier waves. This was partially caused by

the emergence of private equity firms and thus an increase in takeover capital. At the same

time in the UK the third wave was going on. The deregulation of the financial sector in the

UK led to a huge inflow of US (investment) banks. They had more sophisticated and

developed techniques than the UK banks. US and European financials swallowed up most of

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the UK financials. These financials transformed the M&A landscape in the UK. They

employed more aggressive hostile deals and predatory tactics. The fourth wave was the first

global merger wave. (Sudarsanam, 2010, chapter 2).

The fifth wave in the US occurred at the end of the 1990s and the beginning of 2000s.

It is the biggest wave, measured in value, so far. This had several reasons including: new

technologies such as the internet, the globalization of product, services, and capital markets,

lower trade barriers through the foundation of the WTO and big trading blocs, deregulation of

industries, and the objective of maximizing shareholder value. This wave is characterized by

the huge individual deals that were made in for example the telecom sector. The view that

focussing on core competences is the source of competitive advantage continued from earlier

waves through to the fifth wave. The 1990s wave in the UK and the wave in Europe have

equal characteristics. Many state-owned enterprises were privatized and deregulation took

place in many sectors (Sudarsanam, 2010, chapter 2).

The sixth and last wave occurred in the new millennium. In the US, UK, and Europe

the wave was smaller than the previous wave. It was characterized by the increased

importance of private equity acquirers, the emergence of hedge funds, and increased

shareholders activism.

1.2 Rationale for mergers and acquisitions

Research argues that M&A activity is caused by industry-level shocks (Jensen, 1986).

Mitchell and Mulherin (1996) found evidence that deregulation, oil price shocks, foreign

competition, and financial innovations explain a significant piece of M&A activity in the

1980s (Andrade, Mitchell, and Stafford, 2001). The rational view within economics sees

optimizing shareholder value of public firms as reason for individual mergers and

acquisitions. This model sees mergers and acquisitions as a tool for managers to increase

shareholder value.

The rationale behind this model can be found in four different motives. The first

motive is synergy. Managers of the acquiring firm believe that the stock price of the target is

accurate but think they can add value through synergies. According to Martynova and

Renneboog (2006) takeovers are expected to create financial synergies and operating

synergies. Financial synergies are argued to benefit a diversifying takeover. A diversifying

takeover presumable ad stability to the cash flows of the acquirer. These more stable cash

flows and the increase in firm size after the takeover are associated with easier access to,

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more and different, capital markets and a lower cost of capital. Also, more stable cash flows

leads in turn to a lower probability of the acquirer going bankrupt (Martynova and

Renneboog, 2006). When a firm acquires a target with high levels of cash this can result in

lower internal financing cost. External financing cost can be lowered by acquiring a target

that has lower financial leverage and unused debt capacity (Ghosh and Jain, 2000).

A takeover results often in a bigger firm that can make more and better use of

economies of scale en scope, vertical integration, the elimination of duplicate activities, and a

reduction in agency costs by bringing organization-specific assets under common ownership

(Martynova and Renneboog, 2006). Comment and Jarrell (1995) state that operating

synergies primarily effect same- or related-industry mergers and acquisitions. Operational

synergies include acquisition of new technology, technology, or intangible assets (Martynova

and Renneboog, 2006). The acquirer could also profit from the economy of learning by

taking over effective and already tested and employed practices from the target (Sudarsanam,

2010, chapter 3).

The second motive for an acquisition is to force discipline on the target. These

acquisitions are considered to often have a hostile nature (Martynova and Renneboog, 2006).

According to this motive the deal happens because the acquirer believes that the target firm’s

management underperforms and therefore the stock price and profits are below the potential

maximum. The bidder aims to increase profitability of the target by replacing the target’s

management. According to Slusky and Caves (1991) profits can be increased by

implementing the following changes after the takeover and subsequent board removal: by

optimizing or stopping the suboptimal use of debt, by matching the opportunities the targets

has on the market and its policies, and by exploiting opportunities regarding sales and assets

that the former target’s management refused to do. There is evidence that hostility may be a

result of the bidding process to attain the maximum outcome for the shareholders of the target

(Schwert, 2000).

The rationale for mergers and acquisitions is not always the maximization of

shareholder value. The principal-agent theory by Jensen and Meckling (1976) sees managers

as agents of the shareholders. Managers do not have the same objectives as shareholders.

They could pursue their own objectives and increase their own wealth at the expense of the

wealth of the shareholders. Shareholders can align the objective of the mangers with their

own objectives through writing, monitoring, and enforcing contracts with the managers or

hire someone to this for them (Sudarsanam, 2010, chapter 3). But the costs of monitoring and

enforcing the contracts could outweigh the profits of the alignment of incentives.

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Behavioral economists have come up with a different model. This behavioral agency

model takes into account the behavioral biases of managers. In this model managers could

suffer from hubris, overconfidence, and overoptimism. Hubris is arrogance; overestimation of

one’s skill and capabilities. Also if a manager in severe degree arrogates accomplishments to

himself and blames failure on others or external factors. In case of Hubris managers genuine

believe that they have superior skills compared to other people and managers. Overoptimistic

manager underestimate the chance that danger of hazards affecting them or their decisions.

This all could lead them to overpay for the target or misjudge the amount of improvement

they are able to make compared to the previous, in their view underperforming, management

of the target (Sudarsanam, 2010, chapter 3; Roll, 1986).

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Section 2. Cross-border M&A and Market Valuation

A lot of research in finance on mergers and acquisitions tries to identify deal characteristics

that influence abnormal returns. Several influential characteristics that have been described

are: the method of payment, the public state of the target firm, friendly versus hostile deals,

relative size of the deal, and industry-relatedness in M&A. The theory and empirical evidence

on the aforementioned variables will be covered in section 5, because in this research they are

solely used as control variables. This section covers the theory and empirical evidence on the

influence that cross-border mergers and acquisitions and market valuation have on abnormal

returns and why. First the cross-border characteristic is covered. Second market valuation

will be explained.

2.1 Cross-border M&A

During the last few decades cross-border mergers and acquisitions (M&A) became more

important as a tool for firms to achieve their strategic and business objectives. A cross-border

M&A entails: “the control of assets and operations is transferred from a local to a foreign

company, the former becoming an affiliate of the latter” (UNCTAD, 2000, p. 99). Since the

nineteen-eighties cross-border mergers and acquisitions share of worldwide foreign direct

investment (FDI) increased by around 50 percent. In 1987 M&A made up around 52 % of

global FDI. This increased to 83% in 1999 and after 1999 this fluctuated between 80 and

85% of global FDI (UNCTAD, 2008). Cross-border mergers and acquisitions have a sizeable

impact on the global economy because FDI makes up between 2 to 4% of the world economy

in the last decade (World Bank Group, 2014).

Cross-border M&A gains terrain on other sorts of FDI like Greenfield investments,

joint ventures or other strategic alliances. The reason for the increasing popularity of cross-

border M&A when a firm engages in FDI can be found in several factors. The availability of

capital to finance acquisitions has increased tremendously. Also the increased sophistication

and growth of capital markets leads to easier and cheaper access to capital. Access to good

and cheap information increased with the explosion in technology. For example: the

invention of Internet, satellites, and more and cheaper travel opportunities. Establishment of

the European Monetary Union (EMU); 19 countries adopted the euro as common currency.

The absence of conflict between countries that have the world’s biggest economies. All the

aforementioned reasons, and a lot more, resulted in less costs for firms to undertake,

establish, and maintain a cross-border M&A. So cross-border M&A got relatively more

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attractive compared to the less drastic Greenfield investments or strategic alliances, which

have as advantage that if things go wrong the loss would be relatively limited.

2.1.1 Cross-border versus domestic M&A

There are a few reasons why the returns from cross-border M&A could differ from domestic

M&A. The first reason is diversification. If the domestic market crashes, then the foreign

market might not plummet as deep or not crash at al, and vice versa. The producing costs

could be lower due to lower wages, superior technology, or better access to (scarce)

recourses. Cross-border deals give a firm the opportunity to capture rents from foreign market

inefficiencies or due to a more beneficial tax environment (Scholes and Wolfson, 1990 as

cited by Martynova and Renneboog, 2006). Also, in the case of imperfect capital markets

firms could profit from exchange rate movements by moving operations to other countries

(Froot and Stein, 1991). More directly, (Markides and Ittner, 1994) argue that the model

developed by Froot and Stein (1991) can explain the link between cross-border acquisitions

and exchange rates. “They, Froot and Stein, argue that given information asymmetries about

an asset's payoffs, entrepreneurs find it impossible or very costly to purchase the asset solely

with externally obtained funds. As a result, "information intensive" investments, such as

buying a company, will be partially financed by the net wealth of the entrepreneur”. So when

the acquirer’s currency is strong compared to that of the target, then the acquirer has an

advantage due to increased purchasing power. This in turn should lead to higher returns for

the acquirer, because they pay less in their currency compared to what the value of the target

is in their opinion. Markides and Ittner (1994) find evidence that a relative strong currency

leads to higher returns of the acquirer.

However, cross-border M&A could encounter some disadvantages. Often regulations

and laws are very different in foreign countries. To adapt to these regulations and laws takes

time and money, whereby additional lawsuits and fines are also possible expenses. The

acquirer is dependent on the foreign and local government and institutions. Countries are far

from all stable democracies, sudden instability could lead to additional costs or even

complete loss of the foreign component of the firm. Correct valuation of the foreign target is

more difficult than a domestic target Conn et al. (2005). Targets located in overseas countries

or located in less developed countries are harder to valuate. Information is often not perfect

and hard and expensive to obtain. This all contributes to the chance that the bidder overpays

and destroys shareholder value.

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The factor that could influence the deal negatively is culture. When companies are

culturally dissimilar the returns could suffer from communication and information problems.

This leads to information and valuation problems before the acquisition. It could also give

problems after the acquisition in the form of slow or bad firm-integration. Research has been

conducted on this factor regarding cross-border mergers and acquisitions. On the

announcement-day there is a significant negative effect on the abnormal returns of the bidder

if the companies are culturally dissimilar (Chakrabarti, Gupta-Mukherjee, and Jayaraman,

2009 and Datta and Puia, 1995). However, in the long-run Chakrabarti et al. (2009) find a

positive effect on abnormal returns of the bidder if the companies are culturally dissimilar.

Conn et al. (2005) find that cross-border acquisitions with low cultural differences perform

relatively well.

2.1.2 Cross-border M&A and method of payment

Most of the empirical evidence suggests that cash deals outperform equity deals (see

paragraph 2.1). For cross-border deals this could not be the case because other factors

influence the method of payment. As Conn et al. (2005) state:” For example, the use of equity

by cross-border acquirers may be due to the greater uncertainty connected with the

information problems associated with acquiring abroad. This may be especially true for

private overseas deals where the information may be even more imperfect. If bidder

shareholders recognize this reasoning then the usual positive impact of cash bids compared to

equity bids may be nullified.” Also, refusal by the target’s management of foreign equity, due

to information asymmetry, can lead to a forced cash bid (Gaughan, 2002 as cited by Conn et

al., 2005).

2.1.3 Empirical evidence on cross-border M&A returns

Empirical evidence on cross-border M&A returns can be split in two parts. First there are the

short-run announcement effects. Second, there are the long-term abnormal returns.

Conn et al. (2005) find that announcement returns of domestic acquisitions are higher

than those of cross-border acquisitions. Both domestic and cross-border acquisitions have

positive abnormal return. However, Conn et al. (2005) show that the former results depend on

the nature, is it a private or public firm, of the acquirer. When looking at public firms cross-

border deals have zero abnormal return and outperform domestic deals, which have negative

abnormal returns. When looking at private firms it is the other way around. Domestic deals

outperform cross-border deals and both have positive abnormal returns.

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According to Conn et al. (2005) there is limited empirical evidence on long-term

abnormal return in cross-border acquisitions. They mention four studies, Conn and Conell

(1990), Danbolt (1995), Black et al. (2003), and Aw and Chatterjee (2004), who look at the

abnormal returns on cross-border acquisitions of public firms. All these studies find negative

and significant long-term abnormal returns for the acquirers. Two other studies, Eckbo and

Thorburn (2000) and Gregory and McCorriston (2004), look at cross-border acquisition of

both public and private firms. They find no evidence of negative long-term abnormal returns.

Conn et al. (2005) themselves find that domestic deals have higher long-term abnormal

returns than cross-border deals.

2.2 Market valuation

Recent research investigates the possible link between stock price and M&A activity

and quality. First, this paragraph looks into theory and research of the correlation between

stock price and M&A activity. Secondly the theory of possible correlation between stock

price and M&A quality is treated.

Rhodes-Kropf and Viswanathan (2004) developed a model to describe stock M&A activity

and stock price. Through private information this models shows that there is a correlation

between M&A activity and stock price. They suggest that targets make mistakes when

valuing synergies in non-normal market conditions. The model involves rational managers,

who have information at firm- and market-level, and inefficient markets. This information

tells bidders and targets about their own misvaluation, but they have no information if this is

due to firm-specific reasons and/or due to market reasons. So bidders rationally adjust the

stock offer for potential misvaluation of the bidder. However, the target behaves as a

Bayesian and therefore assigns some probability to synergies too. So, the greater

overvaluation of the market, the greater is the estimation error of the synergy. This increases

the chance that the target accepts bids. So overvaluation at the market level increases the

chance that the target overestimates the potential synergies due to underestimation of the

misvaluation (Rhodes-Kropf, Robinson, and Viswanathan, 2005). Empirical evidence that

market valuation and in particular market misvaluation drives M&A activity was found by

Rhodes-Kropf, Robinson, and Viswanathan (2005). They also find that firms with a high

market-to-book ratio pay with stock more often than firms with a lower market-to-book ratio.

Theory and empirical evidence about the correlation between stock price and M&A

quality is covered below. Jovanovic and Rousseau (2001) develop a model that explains the

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positive correlation between M&A waves and market valuation. Bouwman et al. (2009)

investigate the quality of mergers and acquisitions that are made during different market-

valuation states. They find that in high-valuation markets U.S. acquirers have higher returns

at announcement compared to acquirers in low-valuation markets. They find opposite results

when examining the long-term abnormal returns. In this case acquirers have higher long-term

abnormal return in low-valuation markets than those in high-valuation markets. Bouwman et

al. explain the lower long-run abnormal returns for acquirers in high-valuation markets

through managerial herding. Managerial herding suggests that at the moment that mergers or

acquisition by early acquirers are shown to be successful, other firm want to make a similar

move. This puts more pressure on the possible synergies in takeovers made by these late-

movers. Also, the late movers could be affected by the possibility that the premium-quality

deals/targets are already picked up by the early-movers and the remaining deals/targets are of

less quality. Managerial herding suggests that merger waves tend to end at the moment firms

observe the long-term bad results from (late) acquirers. By this time many value-destroying

acquisitions are already made. Thus, managerial herding suggests that acquirers who move

late perform relatively worse compared to acquirers who move earlier. Bouwman et al.

predict and find evidence that managerial herding is primarily present during merger waves

that accompany booming stock markets.

Goel and Thakor (2010) have a different approach to explain the link between M&A

activity and M&A quality. They developed a model based on CEO envy. Their model

suggests that after a shock, which causes the first deals, the envy of CEO’s kicks in and

makes them want a bigger firm, higher pay, and be more prestigious than their competitors.

This also leads to value-destroying acquisitions, which are executed mostly at the end of a

takeover wave. They predict that this behaviour leads to smaller synergies for deals made in

bull markets compared to those made during bear markets.

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Section 3. Hypotheses

In this section the hypotheses will be formulated and substantiated with arguments. This

research only looks at acquirers’ abnormal returns around the announcement date, so all

references to returns are from the acquirers’ perspective.

3.1 Hypotheses

1. Are the average announcement period abnormal returns for acquirers positive or

negative?

Neoclassical economics sees mergers and acquisitions as a way to improve efficiency and

ultimately increase shareholder value. This basic assumption of this theory is that the

combination of bidder and target is worth more than the sum of their standalone value. This

can be due to: synergies, both financial and operational, economies of scale and scope, or by

replacing poor management. According to these arguments abnormal returns for acquirers

should be positive.

According to other theories mergers and acquisitions can also be value destroying. Rational

managers could pursue their own objectives and increase their own wealth at the expense of

the shareholders. Behavioral economics suggests that managers could be biased. They could

suffer from hubris, overconfidence, and/or overoptimism. These biases could lead to

overpayment or misjudge the amount of improvement they can establish when the firms are

combined.

2. What is the difference in quality between domestic and cross-border deals?

There are several arguments why cross-border deals should or should not outperform

domestic deals. Cross-border deals could outperform domestic deals because for example:

potential value added through diversification by entering a new country, the producing costs

could be lower due to lower wages, superior technology, better access to (scarce) recourses,

favourable tax conditions, and the opportunity to capture rents from foreign market

inefficiencies. There are also reasons why cross-border deals could underperform, for

example: different laws and regulations, more difficult to value a target correctly, and the

difference in culture can make it costlier to obtain value through synergies.

Second, the method of payment could influence the difference in quality. There is consensus

on the fact that cash deals outperform equity deals. Conn et al. (2005) find that cross-border

deals are more often paid with cash than domestic deals. This could be due to the fact that

cross-border targets do not have perfect information on the acquirer’s stock price and

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therefore demand cash payment. So cross-border deals could outperform domestic deals

because of the larger stake of cash transactions.

3. Deals made during low-valuation markets are expected to have higher announcement

returns than those made during high-valuation markets.

As mentioned in paragraph 2.2 managerial herding could cause returns from deals

announced in high-valuation markets to be different from those made during low-valuation

markets. Managerial herding is associated with M&A waves and even to influence and end

waves (Bouwman et al., 2009). Managerial herding suggests that at the moment that mergers

or acquisition by early acquirers are shown to be successful, other firm want to make a

similar move. This puts more pressure on the possible synergies in takeovers made by these

late-movers. Also, the late movers could be affected by the possibility that the premium-

quality deals/targets are already picked up by the early-movers and the remaining

deals/targets are of less quality. Managerial herding suggests that merger waves tend to end at

the moment firms observe the long-term bad results from (late) acquirers. By this time many

value-destroying acquisitions are already made. Thus, managerial herding suggests that

acquirers who move late perform relatively worse compared to acquirers who move earlier.

So, according to this theory deals made during low-valuation markets are expected to have

higher announcement return than those made during high-valuation markets due to smaller

synergies and lower-quality deals during high-valuation markets relative to those made in

low-valuation markets.

4. What is the difference in quality between cross-border deals made during high-

valuation markets and those made during low-valuation markets?

There are two theories concerning the possible difference between the returns from cross-

border deals made during high-valuation markets and those made during low-valuation

markets. The first theory suggests that differences in returns are caused by the exchange rate.

The second theory suggests that differences in returns are due to managerial herding.

The first theory, which suggest that the difference between the returns from cross-

border deals made during high-valuation markets and those made during low-valuation

markets is caused by the exchange rate, is explained in two stages: first the exchange rate will

be linked to cross-border deal quality and in the second stage the exchange rate will be linked

to market valuation.

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To link exchange rate levels to cross-border M&A performance a model developed by

Froot and Stein (1991) is used, see paragraph 2.1.1. They argue that the bidder prefers to pay

with cash because they find it costly to pay with external funds due to imperfect information

about the true value of the cross-border target. In addition Conn et al. (2005) and Gaughan

(2002) suggest that due to information asymmetry the target’s management could demand or

force a cash bid. Next, when a bid for a foreign target is in cash, a relative strong acquirer’s

currency compared to that of the target gives the acquirer the advantage of increased

purchasing power (Markides and Ittner, 1994). This means that the acquirer can buy the

target, against a lower price, stated in his home currency. This should result in higher returns

for the bidder.

In the second stage the exchange rate will be linked to market valuation. To do this

the portfolio balance approach by Bahmani-Oskooee and Sohrabian (1992) is used. In this

model individuals allocate their wealth among domestic money and securities and foreign

money and securities. The relationship between the interest rates and the demand for money

and securities in this model is as follows: the demand for domestic (foreign) money is

inversely related to the domestic and foreign interest rate. The demand for domestic (foreign)

securities is positively (negatively) related to the domestic interest rate and negatively

(positively) to the foreign interest rate. In this model the exchange rate has to balance the

demand and supply of assets, this results in the equilibrium exchange rate.

When stock prices rise, as is the case in high-valuation states, this results in an

increase in domestic wealth. According to portfolio approach, the increased wealth will lead

to in an increase in demand for domestic money and so in an increase in domestic interest

rates. According to the model a higher domestic interest rate attracts foreign capital, which in

turn results in an appreciation of the domestic currency. This clearly shows that according to

this model there is a positive correlation between stock market levels and exchange rate

levels. (Bahmani-Oskooee and Sohrabian, 1992).

The combination of the two stages leads to the prediction that a cross-border deal

made in high-valuation markets experiences higher returns caused by a stronger currency,

due to the argument that stock market levels and exchange rate levels are positively

correlated.

The second theory suggests that differences in returns are due to managerial herding.

Managerial herding is associated with M&A waves and even to influence and end waves

(Bouwman et al., 2009). It suggests that at the moment that mergers or acquisition by early

acquirers are shown to be successful, other firm want to make a similar move. This puts more

17

pressure on the possible synergies in takeovers made by these late-movers. Also, the late

movers could be affected by the possibility that the premium-quality deals/targets are already

picked up by the early-movers and the remaining deals/targets are of less quality. Managerial

herding suggests that merger waves tend to end at the moment firms observe the long-term

bad results from (late) acquirers. By this time many value-destroying acquisitions are already

made. According to this theory deals made during low-valuation markets are expected to

have higher announcement return than those made during high-valuation markets due to

smaller synergies and lower-quality deals during high-valuation markets relative to those

made in low-valuation markets. There are no reasons or evidence to assume that this

prediction changes when the sample is split between domestic and cross-border deals.

The two theories predict two different outcomes. The exchange rate theory predicts

that the cross-border deals made during high-valuation market outperform those made during

low-valuation markets. Managerial herding theory predicts the exact opposite.

18

Section 4. Data

In this section the used data sample is described and which conditions it has to meet. Further,

this section will explain how the state of the market is split into high-, neutral-, and low-

periods. Last the summary statistics of the sample are covered.

4.1 Data

Data of US mergers and acquisitions is obtained from the Thomson Reuters Security Data

Corporations (SDC) Platinum US Mergers & Acquisitions Database. The obtained data has to

meet the following conditions (these follow the conditions imposed by Bouwman et al (2009)

very closely):

1) M&A was announced between 1st of January 1990 and 31st of December 2009.

2) The acquirer is a US firm listed on either the NYSE, NASDAQ or AMEX

3) The target is not a subsidiary1

4) The transaction value is at least $100 million

5) The acquirer obtains at least 50% of the shares of the target and owns less than 50%

of the target’s shares before the acquisition.

6) The closing share price of the acquirer for the month before the announcement is at

least $3 (see Loughran and Vijh, 1997). This eliminates firms that are very small or in

distress.

7) Daily acquirer return data are available for three days around the announcement date.

After running the query, deleting outliers, and excluding observations with missing variables

the sample contains 3289 transactions.

4.2 High-, neutral-, and low-valuation markets.

This research examines the quality of M&A deals undertaken in several different market

conditions. It is therefore important to make a clear distinction between various states of the

market. This distinction can be made through P/E ratio (price to earnings ratio) from for

example the S&P 500 as used by Bouwman et al. (2009). Who, in this way, makes a

distinction between high-valuation markets and low-valuation markets. Bouwman et al. test if

the outcomes of their research differ when another method is used. Their conclusion is that is

does not matter which of their 7 suggested and tested methods is used.

1 “Hansen and Lott (1996) and Fuller, Netter, and Stegemoller (2002) justify the exclusion of subsidiary

acquisitions” Bouwman et al (2009)

19

To make this distinction in the aforementioned way the P/E ratio had to be detrended.

This is due to the fact that the P/E ratio from the S&P 500 is increasing over time. First, the

market P/E ratio is detrended by subtracting the best straight-line fit from the market P/E

ratio of the concerning month and the five years preceding this month. Next, the month is

categorized as above average if its detrended market P/E ratio is above the past five year

dentrended P/E ratio average and below if its detrended market P/E ratio is below its past five

year average. This process is repeated for every month in the sample. Last, the bottom half of

the below-average months are noted as low-valuation markets and the top half as neutral-

valuation markets. For the above-average months the top half is noted as high-valuation

markets and the bottom half as neutral-valuation markets. This results in half of the months

being neutral-valuation markets and the other half being high- and low-valuation markets

(Bouman et al., 2009).

For the sample2, January 1990 to December 2009, in this research it results in 70 high-

valuation periods, 50 low-valuation periods, and 120 neutral-valuation periods. Figure 1

shows graphically for every month the detrended P/E ratio. Figure 2 shows whether the

market-valuation is high, neutral, or low. Figure 2 shows that the low-valuation periods are

mainly occurring in the second half of the time period.

Figure 1

Shows graphically for every month the detrended P/E ratio.

2 Data of P/E ratios is obtained from http://www.irrationalexuberance.com/index.htm from Robert J. Schiller.

-14

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Detrended P/E ratio

20

Figure 2

Shows graphically for every month whether the market is in a high (3), neutral (2), or low (1) state.

4.3 Summary statistics

The summary statistics of the sample are presented in table 1 and table 2. Table 1 shows that

the number of acquisitions during high- and neutral-market valuations are double of those

during low-market conditions. This could well be due to the fact that there are often less

acquisitions in depressed-markets. Further, during high- and neutral-valuation markets there

is a slight preference for stock deals, which vanishes when looking at low-market valuation

deals. Most of the deals, around 90%, are domestic. Only when markets are depressed the

number of cross-border deals slightly increases. Around three-quarter of the deal involves a

public target and slightly more than half of the deals is same industry deal. Noteworthy is the

substantial increase in the amount of tender offers during low-market valuations compared to

other states of the market.

Table 1

Summary Statistics

Number of

acquistions Number of

acq. during

high-market

Number of acq.

during neutral-

market

Number of

acq. during

low-market

All acquisitions 3289 (100%) 1319 (40.1%) 1349 (41.0%) 621 (18.9%)

Cash 1042 (31.7%) 428 (32.4%) 408 (30.2%) 206 (33.2%)

Stock 1262 (38.4%) 488 (37.0%) 572 (42.4%) 202 (32.5%)

Mixed 985 (29.9%) 403 (30.6%) 396 (29.4%) 213 (34.3%)

Cross-border 357 (10.9%) 140 (10.6%) 135 (10.0%) 82 (13.2%)

Domestic 2932 (89.1%) 1179 (89.4%) 1214 (90.0%) 539 (86.8%)

0

1

2

3

4

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09

.01

Market-valuation state

3= High-market

2=Neutral-market

1=Low-market

21

Target is public firm 2411 (73. 3%) 976 (74.0%) 1001 (74.2%) 434 (69.9%)

Target is private firm 878 (26.7%) 343 (26.0%) 348 (25.8%) 187 (30.1%)

Deal is tender offer 449 (13.7%) 175 (13.3%) 153 (11.3%) 122 (19.6%)

Deal is no tender offer 2840 (86.3%) 1144 (86.7%) 1196 (88.7%) 499 (80.4%)

Same industry deals 1768 (53.8%) 693 (52.5%) 728 (54.0%) 347 (55.9%)

Diversifying deals 1521 (46.2%) 626 (47.5%) 621 (46.0%) 274 (44.1%)

Table 2 shows the total deal value of the 3289 acquisitions is $5,304 billion with a mean

transaction value of $1.612 billion. Whereby acquisitions made during high- and low-

valuation markets have the highest average transaction value. Most of the total deal value,

43%, comes from acquisitions made during high-valuation markets. Only a small amount,

7,1%, of the total deal value comes from cross-border acquisitions compared to the

percentage, 10,9%, of the total number of acquisitions. In every market state cross-border

total deal value is below its percentage in number of acquisitions. This suggests that cross-

border deals involve relatively small transactions compared to the rest of the sample. In high-

market state deal value of cross-border deals is 2,5 times smaller than domestic deal value.

Table 2

Summary Statistics

Number of

acquisitions Mean transaction

value ($ million)

Total deal

value ($

million) % of total

deal value

% of total

number of

acquisitions

All acquisitions 3289 1,612 5,304,307 100% 100%

High-market acq 1319 1,732 2,286,120 43.1% 40.1%

Neutral-market acq 1349 1,424 1,922,268 36.2% 41.0%

Low-market acq 621 1,762 1,096,168 20.7% 18.9%

Cross-border acq 357 1,062 379,202 7.1% 10.9%

Domestic acq 2932 1,680 4,925,105 92.9% 89.1%

High-market Cross-B 140 759 106,193 4.6% 10.6%

High-market Domestic 1179 1,849 2,179,802 95.4% 89.4%

Neutral-market Cross-B 135 1,159 156,497 8.1% 10.0%

Neutral-market Domest. 1214 1,455 1,765,772 91.9% 90.0%

Low-market Cross-B 82 1,421 116,513 10.6% 13.2%

Low-market Domestic 539 1,817 979,531 89.4% 86.8%

22

Section 5. Methodology

In this section the methodology, which will be followed to obtain the answers for the

hypotheses, is explained. First announcement returns methodology is covered. This is used to

test if the announcement of the deal really impacts the stock return of the acquirer. Further

the multivariate regression and the used variables are explained. Last, some theory about the

control variables is given to get an idea about how they are predicted to impact the abnormal

returns.

5.1 Announcement returns

Brown and Warner’s (1985) standard event study methodology is followed to calculate

cumulative abnormal returns (CAR) for the event period, which is here a three-day period

ranging from 1 day before the announcement date of the deal to 1 day after the announcement

of the deal ( t1,t2) = (-1,1). Abnormal returns are the returns solely caused by the event and

not by for example market-wide movements. In this research investors/the market are

presumed to judge the M&A correctly and deviations in the abnormal returns are caused by

actions from the managers/firms. However this could also be the other way around. In this

case investors/the market misjudge the M&A and managers/firms act correctly.

First the abnormal returns for every firm at time t (𝐴𝑅𝑖𝑡) have to be calculated. This is done

by subtracting the benchmark return of time t (𝑁𝑅𝑖𝑡) from the return of the firm at time t

(𝑅𝑖𝑡):

𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡 − 𝑁𝑅𝑖𝑡 (1)

The subtraction of the benchmark is to isolate movements in stock price of the acquirer

caused by the event from movements that are caused by movement of the entire market. To

calculate benchmark returns three different approaches are common: mean-adjusted return,

market-adjusted returns, the market model, and the multifactor model by Fama and French

(1996). Brown and Warner (1980) show that weighting the market return by the firm its beta

does not significantly improve estimation in case of short-window event studies. Therefore

the market-adjust returns are used. So the return on the market index (𝑅𝑚𝑡) is chosen as

benchmark:

𝑁𝑅𝑖𝑡 = 𝑅𝑚𝑡 (2)

23

Here the S&P 500 is used as benchmark. The S&P 500 resembles the used sample of

acquirers because the bigger firms mostly do deals of over $100 million. According to Brown

and Warner (1980) and Bouwman et al. (2009) results are similar if either a value-weighted

index or an equally-weighted index is used.

Next, all abnormal returns around each event are summed up to get the cumulative abnormal

returns (CARs):

𝐶𝐴𝑅𝑖 = 𝐴𝑅𝑖,𝑡1+. . . +𝐴𝑅𝑖,𝑡2

= ∑ 𝐴𝑅𝑖𝑡𝑡2𝑡=𝑡1

(3)

After obtaining al the 𝐶𝐴𝑅𝑖, they have to be aggregated over all events:

𝐶𝐴𝐴𝑅 =1

𝑁∏ 𝐶𝐴𝑅𝑖

𝑁𝑖=1 (4)

Last, the t-statistics are estimated using the cross-sectional variation of the abnormal returns.

Therefore first the standard deviation has to be calculated:

𝑠 = √1

𝑁−1∑ (𝐶𝐴𝑅𝑖 − 𝐶𝐴𝐴𝑅)2𝑁

𝑖=1 (5)

Next the test-statistic can be composed:

𝑇𝑆 = √𝑁𝐶𝐴𝐴𝑅

𝑠≈ 𝑁(0,1) (6)

5.2 Multivariate regression framework

The multivariate regression framework has as purpose to examine if several other important

factors influence the abnormal returns of the acquirers. Further, to check if the results of the

univariate analysis hold. Therefore the following models are estimated:

𝐶𝐴𝑅 = 𝑎0 + 𝑎1HighValMktDummy + a2NeutralValMktDummy

+ a3CrossborderDummy

+ a4CashDummy + a5MixedPaymentDummy + a6TenderDummy

+ a7TarPubStatDummy + a8LogRelSize + a9IndustryDummy

24

𝐶𝐴𝑅 = 𝑎0 + 𝑎1DetrendedPEratio + a2CrossborderDummy +

a3CashDummy + a4MixedPaymentDummy + a5TenderDummy +

a6TarPubStatDummy + a7LogRelSize + a8IndustryDummy

In the first model market-valuation is measured through two dummy variables and in the

second model through one continues variable. The dependable variable is CAR, which is the

three-day CAR around the announcement date.

HighMktDummy: takes a value of 1 if the deal was announced when the market was in a

high-valuation state, and zero otherwise.

NeutralValMktDummy: takes values of 1 if the deal was announced when the market was in a

neutral-valuation state, and zero otherwise.

DetrendedPEratio: continues monthly variable where the market P/E ratio is detrended by

subtracting the best straight-line fit of the past 5 years from the market P/E.

CrossborderDummy: equals one if the target is located outside the United States and zero if

the target is located on US soil.

CashDummy: takes the value 1 if the transaction was paid in cash. A transaction is a cash

transaction if it includes cash, earnouts, and assumptions of liabilities or any combination of

these. CashDummy equals 0 if the deal is paid with stock or any mix of cash and stock. Stock

transactions include stock and considerations made with a form of stock.

MixedpaymentDummy: Takes the value 1 if the offer is made with any mix between cash and

stock, excluding full cash or full stock offers. It equals zero when the offer is cash or stock.

TenderDummy: takes the value 1 if the acquisition was a tender offer and zero otherwise.

TarPubStatDummy: equals 1 if the target is a private firm and zero if the target is a public

firm.

25

LogRelSize: is defined as the logarithm of the transaction value divided by the acquirer’s

market value of equity 30 days prior to the announcement date (Bouwman et al., 2009)

IndustryDummy: equals 1 if the target and acquirer are in the same industry. The same

industry means the first 3 digits from the SIC codes have to match. This dummy equals 0

otherwise.

5.3 Control Variables

In this paragraph the theory on the control variables, included in multivariate regression

framework above, is covered to get an indication on how and why they would likely

influence the acquirer’s returns. First, the method of payment is covered. Second,

diversifying or focused acquisitions on industry level. Last, the implications for abnormal

returns when the acquisition is a tender offer.

5.3.1 Method of payment

The method of payment for acquisitions ranges from a full cash payment to a full equity

payment, and any conceivable mix of the two. A deal is perceived as a cash deal when the

payment is made with 100% cash. A deal is perceived as an equity or stock deal when the

payment is made with 100% equity or stock of the bidding firm.

The choice from the bidder on how to finance the acquisition can have a major impact

on its management. According to Faccio and Masulis (2005) this choice affects the ownership

structure of the firm, future financial possibilities and decisions, cash flows, taxes, and

corporate control of the firm. Much research has been conducted on the influence that the

method of payment has on abnormal stock returns. Most of the empirical evidence suggests

that cash deals outperform equity deals (Andrade et al. 2001).

There are several arguments that explain the better performance of cash deals. The

plainest argument is that cash deals give the market the idea that the bidder’s expectations on

future returns are positive (Loughran and Vijh, 1997). Myers and Majluf (1984) argue that

acquirers send out a signal that their shares are overvalued to their shareholders and the

market when they pay or want to pay with equity. The assumption is that managers of the

bidding firm have better information about the true stock price of their firm than their

shareholders and the market have. If managers know the stock price is overvalued they will

gladly want to pay with equity. Shareholders and investors are assumed to know about the

reasoning of the managers and use this information to adjust their expectations. Thus the

stock price of the bidder will decline through the “signalling” of this bad news. Hansen

26

(1987) argues that the lager the information asymmetry between the bidders’ managers and

the targets’ managers is, the bigger the chance is that the bidders’ managers want to pay the

acquisition with equity. Bidder firms could take this knowledge in consideration as well as

target firms. This could lead to an interesting game of chess before even the first offer is

made. Fishman (1989) explains the better performance of cash deals through the fact that

acquirers offer equity when they have a low valuation of the target.

The method of payment also influences the long-term post-acquisition performance

Cash deals lead to stronger improvement of performance than any other form of payment

(Ghosh, 2001). There are two possible explanations for this: first, cash deals often require

large amounts of cash, which are often financed with debt. This makes the chance that

managers make value destroying deals bigger. Second, cash payments lead more often to

replacement of the management of the target firm. This is being associated with improved

performance of the target firm (Martynova and Renneboog, 2006).

5.3.2 Diversifying and focused M&A

Diversifying and focused M&A have both some favorable characteristics according to

economic literature. As earlier mentioned in paragraph 1.2, diversifying deals are expected to

benefit the most from financial synergies according to Martynova and Renneboog (2006).

There are also several downsides to diversifying M&A. Martynova and Renneboog (2006)

mention bureaucratic rigidity, bargaining problems within the firm and rent-seeking behavior

by divisional managers. These downsides of diversifying M&A could dominate the benefits

from financial and operational synergies.

Focused M&A presumable gains from operational synergies, because the acquirer focuses on

the business in which it has a competitive advantage. However, this strategy has also a

downside. Due to the focus on one industry the firm exposes itself more to specific market

crashes. Increasing it cash flow variance and thus a higher chance on bankruptcy and a higher

chance on incurring bankruptcy costs.

The empirical evidence on this topic is mixed. According to Renneboog et al. (2006):”

While earlier studies confirm these conjectures (Healy, Palepu, and Ruback, 1992; Heron and

Lie, 2002), later studies find the relationship between diversifying takeovers and poor post-

merger performance insignificant (Powell and Stark, 2005; Linn and Switzer, 2001; Switzer,

1996; Sharma and Ho, 2002). Furthermore, Kruse, Park, Park, and Suzuki (2002) and Ghosh

(2001) document that diversifying acquisitions significantly outperform their industry-related

peers.”

27

5.3.3 Tender offer

A deal is considered to be a tender offer if the bidder bypasses the target’s management and

directly posts its bid by the shareholders. Friendly takeovers are often mergers.

A good reason for a hostile takeover could be to replace underperforming

management. Hostile takeovers are considered to be less favorable than friendly takeovers

because of the high costs. These costs include: raised barriers through activated takeover

defenses by the target’s management, high lawyer costs, and increased costs because dealing

with shareholders is more time-consuming than with management (Schnitzer, 1996). It could

also be the case that the target’s management initially rejects the offer because they want to

maximize its shareholders’ gains.

According to Martynova and Renneboog (2006) hostile acquisitions generate higher

returns for the target than friendly acquisitions do. For the bidder it is better to engage in

friendly M&A because bidder returns for hostile M&A are lower than return for friendly

M&A (Goergen and Renneboog 2004).

5.3.4 Private and public targets

The status of a target often is public or private, however the target could also be joint venture

or a government owned firm. This research is primarily interested in the difference between

private and public firms. There are a few arguments why returns for acquirers should differ

dependent on the target’s status. Firstly, bidding on private firms can be kept quiet until the

deal is completed. This is not the case when negotiations are opened for a public target. This

quiet nature of private target acquisitions increases the chance that ending the negotiations

results in the loss of face for the acquirer. This could well be the case with public targets,

which puts acquirers under pressure to make bad deals to save their face. So, private bids

should result in higher returns for acquirers (Conn et al 2005).

Secondly, the competition for private targets is often far less than for public targets.

This is due to the illiquid nature of the market for private targets. So overpayment due to

severe competition for a private target is less likely to occur than for a public target. This

should lead to higher returns for acquirers when they buy a private target. (Conn et al 2005).

Thirdly, when a private target is relatively large compared to the acquirer, the bid

consists primarily out of shares, and the target its management own a majority of the shares,

then the target management is more likely to perform a good due diligence investigation

28

before the takeover. The incentive for this action is that they will end up as big shareholders

of the combined firm. This should lead to higher returns for acquirers when they buy a

private target (Conn et al 2005).

29

Section 6. Results

In this section the results of the univariate analysis and multivariate regression will be

discussed and also the hypotheses will be answered.

6.1 Univariate announcement results

Table 3 shows the results from the univariate analysis. First of all, when looking at the first

column with all acquisitions, acquirers in this sample experience significant negative

abnormal returns of -0.59%. This negative return is in line with theories of managers who

pursue their own objectives, for example: empire building, and increase their own wealth at

the expense of the shareholder or that managers are biased and suffer from hubris,

overconfidence, and/or overoptimism.

The first column also shows the difference between domestic and cross-border deals.

Domestic deals experience significant negative abnormal announcement returns, -0.71%,

whereas cross-border deals experience insignificant positive abnormal returns. It seems that

cross-border deals outperform domestic deals. This is reinforced by the results of table 4. It

shows that the difference between the three-day CARs for cross-border and domestic deals is

positive (1.16%) and significant. The better performance of cross-border deals compared to

domestic deals could be due to: potential value added through diversification by entering a

new country, the producing costs could be lower due to lower wages, superior technology,

better access to (scarce) recourses, favourable tax conditions, and the opportunity to capture

rents from foreign market inefficiencies. However, the outperformance of domestic deals by

cross-border deals could be caused by the percentage of cash deals. Foreign targets could

prefer cash to stock to reduce uncertainty about the true stock value. Cash deals are known to

outperform stock deals. Therefore this could also lead cross-border deals to outperform

domestic ones.

30

Table 3

Univariate analysis

All acquisitions High-market

acquisitions

Neutral-market

acquisitions

Low-market

acquisitions N CAR N CAR N CAR N CAR

All 3289 -0.59% 1319 -0.40% 1349 -0.29% 621 -1.65%

(-3.74)*** (-1.78)* (-1.15) (-4.37)***

Cross-

border

357 0.44%

(0.99)

140 0.95%

(1.72)*

135 0.75%

(0.85)

82 -0.93%

(-1.06)

Domestic 2932 -0.71% 1179 -0.56% 1214 -0.40 539 -1.76%

(-4.46)*** (-2.45)** (-1.56) (-4.25)***

* significant at 10% level; ** significant at 5% level; *** significant at 1% level

Table 4

Differences in mean three day CARs

High-market minus Low-market acquisitions 1.25%

(2.89)***

Cross-border minus Domestic acquisitions 1.16%

(2.43)**

High-market Cross-border minus Low-market Cross-border 1,88%

(1.81)*

High-market Domestic minus Low-market Domestic 1.20%

(2.54)***

High-market Cross-border minus High-market Domestic 1.51%

(2.53)**

Low-market Cross-border minus Low-market Domestic 0.83%

(0.85) * significant at 10% level; ** significant at 5% level; *** significant at 1% level

The second, third, and fourth column of table 3 show that acquisitions made during

low-valuation markets underperform those made in neutral- and high-valuation markets. Both

acquisitions made in low- and high-valuation markets are negative and significant. The

aforementioned results are reinforced by the results from table 4. Table 4 shows that the

difference between the three-day CARs for deals made during high-valuation markets and

those made during low-valuation markets is positive (1.25%) and significant. This result is

not as predicted by the third hypothesis. Deals made during low-valuation markets were

expected to outperform those made during high-valuation markets due to the fact that targets

filter out misvaluation in stock prices better during low-valuation markets. The obtained

result is similar to the result found by Bouwman et al. (2009). They find that the results are

31

reversed if one looks at the long-run. Their abnormal returns from acquisitions made during

low-valuation markets stay negative but those made during high-valuation markets decrease

heavily. They investigate this phenomenon further and conclude that managerial herding

causes it. This means that the underperformance of deals made during high-valuation markets

suffer from late movers during a M&A wave. Managerial herding suggests that firms who

move later in a wave perform poorly relative to firms that move earlier (Bouwman et al.,

2009). However, this does not explain their findings and the finding of this research about the

announcement effects. The short-run effects might occur because the investors/markets are

wrong in their judgment. This can be due to two reasons: first they do not have the

information available or they act irrational. Further research should be conducted to find out

what exactly is the reason behind these results.

When looking at the second row of table three cross-border deals seem to be value

creating on average for bidders. When the sample is split into high-, neutral-, and low-

valuation, some differences between cross-border deals appear. Cross-border deals made

during high- and neutral-valuation markets outperform those made in low-valuation markets

by almost 2%. Cross-border deals made during high- and neutral-valuation markets have

similar economic coefficients, 0.95% and 0,75%, but only those made during high-valuation

markets are statistically significant. Table 4 shows that the difference between the three-day

CARs for cross-border deals made during high-valuation markets and those made during low-

valuation markets is positive (1.88%) and only significant at the 10% level. The result

predicted by the theory concerning the exchange rate, which predicts that a cross-border deal

made in high-valuation markets experiences higher returns caused by a stronger currency,

due to the argument that stock market levels and exchange rate levels are positively

correlated, seems right at first sight. However, a similar result is found for all deals made

during different market-valuations, only the economic magnitude is larger for cross-border

deals.

6.2 Multivariate regression results

The multivariate regression consists out of three different regressions which all have the

three-day car as dependent variable. The first and second regression, table 5 and 6, use the

model below. The first regression consists out of three runs. Whereas, the second regression

consists out of 4 runs and also includes interaction terms.

32

𝐶𝐴𝑅 = 𝑎0 + 𝑎1HighValMktDummy + a2NeutralValMktDummy

+ a3CrossborderDummy

+ a4CashDummy + a5MixedPaymentDummy + a6TenderDummy

+ a7TarPubStatDummy + a8LogRelSize + a9IndustryDummy

The third regression, table 6, uses the model below and includes the independent variable

DetrendedPEratio. This continuous variable is used to test whether it makes a difference if

market valuation is measured through dummy variables or a continuous variable. Also, the

relevant interactions terms are replaced by this continuous variable. In table 7 the results of

this model are compared to the first and second regression.

𝐶𝐴𝑅 = 𝑎0 + 𝑎1DetrendedPEratio + a2CrossborderDummy +

a3CashDummy + a4MixedPaymentDummy + a5TenderDummy +

a6TarPubStatDummy + a7LogRelSize + a8IndustryDummy

The first run of the first regression uses HighValMktDummy and NeutralValMktDummy as

independent variables. The results are in table 5. This table shows that if an acquisition is

announced in a high- or neutral-valuation market it has a significant, at the 1% level, positive

effect on the abnormal returns of the acquirer, which will be respectively 1.3% and 1.4%

higher. Acquirers who buy in low-valuation markets experience significant lower returns (-

1.7%). This all is in line with the results from the univariate analysis.

The CrossborderDummy is added to the independent variables in second run. The

results of the first run hold. If an acquisition involves a foreign target the CARs are

significant, at the 5% confidence interval, and higher, 1.2%, whereas the target would be

domestic the CARs would be significantly lower.

In the third run the independent variables CashDummy, MixedPaymentDummy,

TenderDummy, TarPubStatDummy, LogRelSize, and IndustryDummy are added. The results

of runs 1 and 2 hold except for the CrossborderDummy which loses its significance and a

great part of its economic meaning, only 0.3% against 1.2% earlier. This result does not

support the results found in the univariate analysis and make clear that a deal being cross-

border does not influence bidder returns.

If a deal is paid with cash the acquirer experiences significant higher CARs (1.8%) as

found and predicted in the literature. This is also the case for stock acquisitions, which face

33

significant lower CARs. If the target is a private firm or the acquisition involves a tender

offer, CARs for acquirers will be significantly higher (1,1% ; 3,8%). This is in line with

predictions made by the literature. Evidence is found that supports research from Powell and

Stark, 2005, Linn and Switzer, 2001, Switzer, 1996, Sharma and Ho, 2002, Kruse, Park, Park,

and Suzuki, 2002, and Ghosh (2001) who all find that that diversifying acquisitions

significantly outperform their industry-related peers. In this sample significant evidence is

found that focused acquisitions face lower CARs, -0.5%, than their diversifying peers.

Further, tender offer acquisitions experience significantly higher CARs, 1.1% which is

significant at the 5% level, for bidders. Last, a strong, positive, and significant effect is found

when the target is a private firm. If the target is a private firm the abnormal returns of the

bidder are 3.8% higher and significant at the 1% level. This is in line with theory from Conn

et al (2005) that bidders experience higher returns from private targets than from public

targets. This is presumably caused due to lower bid-competition, less chance of the loss of

face for the bidder when they end the negotiations, and more involvement of the target’s

management.

Table 5

Multivariate regression

Dependent variable = three-day CAR

Regression 1 2 3

Constant -0.017 -0.018 -0.034

(4.76)*** (5.14)*** (6.87)***

HighValMktDummy 0.013 0.013 0.015

(2.98)*** (3.05)*** (3.58)***

NeutralValMktDummy 0.014 0.014 0.017

(3.25)*** (3.35)*** (4.07)***

CrossborderDummy 0.012 0.003

(2.51)** (0.56)

CashDummy 0.018

(4.51)***

MixedPaymentDummy 0.004

(1.14)

TenderDummy 0.011

(2.23)**

TarPubStatDummy 0.038

(10.91)***

LogRelSize 0.001

(0.46)

IndustryDummy -0.005

(-1.84)*

Observations:3289

R-squared 0.05 0.05 0.05

Absolute value of t-statistics in parentheses

*significant at 10% level; ** significant at 5% level;

34

*** significant at 1% level

The dependable variable is CAR, which is the three-day CAR around the announcement date. HighMktDummy: takes a value

of 1 if the deal was announced when the market was in a high-valuation state, and zero otherwise. NeutralValMktDummy:

takes a value of 1 if the deal was announced when the market was in a neutral-valuation state, and zero otherwise.

CrossborderDummy: equals one if the target is located outside the United States and zero if the target is located on US soil.

CashDummy: takes the value 1 if the transaction was paid in cash. A transaction is a cash transaction if it includes cash,

earnouts, assumptions of liabilities or any combination of these. CashDummy equals 0 if the deal is paid with stock or any

mix of cash and stock. Stock transactions include stock and considerations made with a form of stock.

MixedpaymentDummy: Takes the value 1 if the offer is made with any mix between cash and stock, excluding full cash or

full stock offers. It equals zero when the offer is cash or stock. TenderDummy: takes the value 1 if the acquisition was a

tender offer and zero otherwise. TarPubStatDummy: equals 1 if the target is a private firm and zero if the target is a public

firm. LogRelSize: is defined as the logarithm of the transaction value divided by the acquirer’s market value of equity 30

days prior to the announcement date (Bouwman et al., 2009) IndustryDummy: equals 1 if the target and acquirer are in the

same industry. The same industry means the first 3 digits from the SIC codes have to match. This dummy equals 0

otherwise.

Table 6 contains the results of the second regression with interaction terms. The economic

meaning of the variables does not change much when the interaction terms are included. The

economic significance doubles for cash deals and cross-border deals, whereas the publicstate

dummy loses some of its economic meaning. The statistical significance of the variables only

slightly diminishes after including 11 interaction terms.

Of the interaction term only two terms are statistically significant. Cash x NeutralMkt

is negative, -2.1%, and significant at the 5% level. This means that the positive partial

derivative of CAR with respect to Cash (NeutralValMkt) becomes less positive (less positive)

if the deal has the characteristic NeutralValMkt (Cash). So when the deal is made during

neutral-valuation markets and paid for with cash the total effect of those variables combined

comes to 3.2%. The second term which is significant is TarPubState x NeutralValMkt. It is

positive, 2.1%, and significant at the 5% level. This result means that the positive partial

derivative of CAR with respect to TarPubState (NeutralValMkt) becomes more positive

(more positive) if the deal has the characteristic NeutralValMkt (TarPubState). So when the

deal is made during neutral-valuation markets the target is a private firm the total effect of

those variables combined comes to 6.6%.

Another interesting interaction is the one between cross-border and targetpublicstate.

When a target is cross-border and private the respective partial derivatives of 0.6% and 2.7%

are reduced by 1.2% through the interaction effect. Further, the market does not seem to

appreciate if acquirers pay with cash during high-valuation markets, this leads to a negative

effect on returns of 1.6%.

35

Table 6

Multivariate regression with interaction terms

Dependent variable = three-day CAR

Regression 1 2 3 4

Constant -0.034 -0.034 -0.036 -0.036

(6.87)*** (6.69)*** (5.17)*** (5.13)****

HighValMktDummy 0.015 0.014 0.018 0.017

(3.58)*** (3.27)*** (2.38)** (2.29)**

NeutralValMktDummy 0.017 0.017 0.018 0.018

(4.07)*** (3.83)** (2.49)** (2.43)**

CrossborderDummy 0.003 0.007 0.003 0.006

(0.56) (0.51) (0.60) (0.43)

CashDummy 0.018 0.019 0.033 0.035

(4.51)*** (4.56)*** (3.85)*** (3.95)***

MixedPaymentDummy 0.004 0.004 0.007 0.007

(1.14) (1.00) (0.89) (0.81)

TenderDummy 0.011 0.010 0.010 0.009

(2.23)** (1.97)** (2.00)** (1.76)*

TarPubStatDummy 0.038 0.039 0.025 0.027

(10.91)*** (10.74)*** (3.37)*** (3.49)***

LogRelSize 0.001 0.001 0.001 0.001

(0.46) (0.38) (0.51) (0.43)

IndustryDummy -0.005 -0.005 -0.006 -0.006

(-1.84)* (1.84)* (1.91)* (1.91)*

Interaction terms:

Crossb x HighMkt 0.004 0.007

(0.33) (0.52)

Crossb x NeutralMkt 0.000 0.003

(0.03) (0.27)

Crossb x Cash -0.006 -0.007

(0.51) (0.61)

Crossbo x MixedPay 0.005 0.004

(0.38) (0.30)

Crossb x TarPubState -0.013 -0.012

(1.23) (1.20)

Cash x HighMkt -0.015 -0.016

(1.54 (1.58)

Cash x NeutralMkt -0.021 -0.021

(2.09)** (2.10)**

MixedPay x HighMkt -0.005 -0.004

(0.45) (0.41)

MixedPay x NeutralMkt -0.002 -0.002

(0.24) (0.22)

TarPubState x HighMkt 0.011 0.011

(1.25) (1.22)

TarPubState x NeutrMkt 0.021 0.021

(2.32)** (2.30)**

Observations: 3289

R-squared 0.05 0.06 0.06 0.06

Absolute value of t-statistics in parentheses

*significant at 10% level; ** significant at 5% level;

*** significant at 1% level

The dependable variable is CAR, which is the three-day CAR around the announcement date. HighMktDummy: takes a value

of 1 if the deal was announced when the market was in a high-valuation state, and zero otherwise. NeutralValMktDummy:

36

takes a value of 1 if the deal was announced when the market was in a neutral-valuation state, and zero otherwise.

CrossborderDummy: equals one if the target is located outside the United States and zero if the target is located on US soil.

CashDummy: takes the value 1 if the transaction was paid in cash. A transaction is a cash transaction if it includes cash,

earnouts, assumptions of liabilities or any combination of these. CashDummy equals 0 if the deal is paid with stock or any

mix of cash and stock. Stock transactions include stock and considerations made with a form of stock.

MixedpaymentDummy: Takes the value 1 if the offer is made with any mix between cash and stock, excluding full cash or

full stock offers. It equals zero when the offer is cash or stock. TenderDummy: takes the value 1 if the acquisition was a

tender offer and zero otherwise. TarPubStatDummy: equals 1 if the target is a private firm and zero if the target is a public

firm. LogRelSize: is defined as the logarithm of the transaction value divided by the acquirer’s market value of equity 30

days prior to the announcement date (Bouwman et al., 2009) IndustryDummy: equals 1 if the target and acquirer are in the

same industry. The same industry means the first 3 digits from the SIC codes have to match. This dummy equals 0

otherwise.

Table 7 contains the results of the third regression with a continuous variable for market

valuation and with interaction terms. Runs 1 and 2 compare the results when either a dummy

variable or a continuous variable is used for market valuation. It stands out that when the

continuous variable is used the economic significance is small and positive, 0.1%, and is

statistical significant at the 5% level. This result reinforces the earlier univariate and

multivariate regressions that a higher market valuation leads to higher bidder abnormal

returns in the short run. The other variables remain equal in both economical and statistical

significance.

When looking at runs 3 and 4, where the different interaction terms are included, the table

shows that the economic and statistical significance remains fairly equal. Only cash and

PubState do not increase their economic significance in the regression with the continuous

measurement of market-valuation. While they do when market-valuation is measured with a

dummy. Further, the CrossborderDummy’s economic significance gets three times bigger,

from 0.3% to 0.9%, but it remains statistically insignificant.

When looking at the interaction terms, which are present in both run 3 and 4, it shows

that they are almost equal in terms of economical and statistical significance. However, the

continuous interaction terms which replace the dummy interaction terms are all economically

less significant. The signs of both runs are the same but for example: Cash x Detrended P/E

ratio its economic significance -0.2% is ten times smaller than Cash x NeutralMkt, -2.1%.

37

Table 7

Multivariate regression with the Detrended P/E ratio and interaction terms

Dependent variable = three-day CAR

Regression 1 2 3 4

Constant -0.021 -0.034 -0.021 -0.036

(5.81)** (6.87)*** (5.85)*** (5.13)***

HighValMktDummy 0.015 0.017

(3.58)*** (2.29)**

NeutralValMktDummy 0.017 0.018

(4.07)*** (2.43)**

Detrended P/E ratio 0.001 0.001

(3.60)*** (2.12)**

CrossborderDummy 0.003 0.003 0.009 0.006

(0.55) (0.56) (0.88) (0.43)

CashDummy 0.017 0.018 0.019 0.035

(4.33)*** (4.51)*** (4.43)*** (3.95)***

MixedPaymentDummy 0.003 0.004 0.003 0.007

(0.95) (1.14) (0.81) (0.81)

TenderDummy 0.011 0.011 0.009 0.009

(2.15)** (2.23)** (1.83)* (1.76)*

TarPubStatDummy 0.038 0.038 0.039 0.027

(10.78)*** (10.91)*** (10.60)*** (3.49)***

LogRelSize 0.001 0.001 0.001 0.001

(0.56) (0.46) (0.45) (0.43)

IndustryDummy -0.005 -0.005 -0.006 -0.006

(1.84)* (-1.84)* (1.88)* (1.91)*

Interaction terms:

Crossb x HighMkt 0.007

(0.52)

Crossb x NeutralMkt 0.003

(0.27)

Crossb x Detrended P/E ratio 0.001

(0.97)

Crossb x Cash -0.007 -0.007

(0.59) (0.61)

Crossbo x MixedPay 0.006 0.004

(0.43) (0.30)

Crossb x TarPubState -0.013 -0.012

(1.21) (1.20)

Cash x HighMkt -0.016

(1.58)

Cash x NeutralMkt -0.021

(2.10)**

Cash x Detrended P/E ratio -0.002

(1.76)*

MixedPay x HighMkt -0.004

(0.41)

MixedPay x NeutralMkt -0.002

(0.22)

MixedPay x Detrended P/E ratio -0.000

(0.28)

TarPubState x HighMkt 0.011

(1.22)

38

TarPubState x NeutrMkt 0.021

(2.30)**

TarPubState x Detrended P/E ratio 0.002

(1.78)*

Observations: 3289

R-squared 0.05 0.05 0.06 0.06

Absolute value of t-statistics in parentheses

*significant at 10% level; ** significant at 5% level;

*** significant at 1% level

The dependable variable is CAR, which is the three-day CAR around the announcement date. HighMktDummy: takes a value

of 1 if the deal was announced when the market was in a high-valuation state, and zero otherwise. NeutralValMktDummy:

takes a value of 1 if the deal was announced when the market was in a neutral-valuation state, and zero otherwise.

DetrendedPEratio: continues monthly variable where the market P/E ratio is detrended by subtracting the best straight-line

fit of the past 5 years from the market P/E. CrossborderDummy: equals one if the target is located outside the United States

and zero if the target is located on US soil. CashDummy: takes the value 1 if the transaction was paid in cash. A transaction

is a cash transaction if it includes cash, earnouts, assumptions of liabilities or any combination of these. CashDummy equals

0 if the deal is paid with stock or any mix of cash and stock. Stock transactions include stock and considerations made with a

form of stock. MixedpaymentDummy: Takes the value 1 if the offer is made with any mix between cash and stock, excluding

full cash or full stock offers. It equals zero when the offer is cash or stock. TenderDummy: takes the value 1 if the acquisition

was a tender offer and zero otherwise. TarPubStatDummy: equals 1 if the target is a private firm and zero if the target is a

public firm. LogRelSize: is defined as the logarithm of the transaction value divided by the acquirer’s market value of equity

30 days prior to the announcement date (Bouwman et al., 2009) IndustryDummy: equals 1 if the target and acquirer are in the

same industry. The same industry means the first 3 digits from the SIC codes have to match. This dummy equals 0

otherwise.

39

Section 7. Conclusion

Following the study by Bouwman, Fuller, and Nain (2009), who took a look at the

relationship between market valuation and the quality of M&A deals, this study investigates

the bidder returns of cross-border mergers and acquisition and of different market-valuation

states. The main question was: if a specific valuation state has influence on cross-border

merger and acquisition quality.

The acquisitions in this study are on average negative and significant for bidders. The

univariate analysis suggests that cross-border deals outperform domestic deals. Whereby

cross-border (domestic) deals resulted in positive (negative) abnormal returns except for

cross-border deals made during low-valuation markets. However, the multivariate analysis

showed that the effects from cross-border deals found in the univariate analysis are caused by

other variables such as valuation state, method of payment, and the target’s public state.

Multivariate analysis shows that cross-border deals only have a slight positive economic

impact on deal quality and this impact is statistically insignificant. Deals made during high-

valuation markets outperform those made during low-valuation markets, whereby all

valuation states encountered negative returns. These results are found in both the univariate

and multivariate analysis. The obtained result is similar to the result found by Bouwman et al.

(2009). In addition, Bouwman et al. find that the results are reversed if one looks at the long-

run. Abnormal returns from acquisitions made during low-valuation markets stay negative

but those made during high-valuation markets decrease heavily. They investigate this

phenomenon further and conclude that managerial herding causes it (Bouwman et al, 2009).

However, this does not explain their findings and the finding of this research about the

announcement effects. The short-run effects might occur because the investors/markets are

wrong in their judgment. This can be due to two reasons: first they do not have the

information available or they act irrational. Further research should be conducted to find out

what exactly is the reason behind these results.

The multivariate analysis shows that when the deal is paid with cash or involves a

private target it has a statistically and positive economic significant impact on bidder returns.

Further, same-industry deals have negative impact on bidder returns. Also, bidder returns are

higher when deals involve a tender offer.

Turning to the main question of this study: if a specific valuation state has influence

on cross-border merger and acquisition quality. Results from the univariate analysis show

40

that cross-border deals made during high- and neutral-valuation markets outperform those

made in low-valuation markets by almost 2%. The return in high-valuation state is 0.95% and

in low-valuation state -0.93%. The difference between the three-day CARs for cross-border

deals made during high-valuation markets and those made during low-valuation markets is

positive (1.88%) and only significant at the 10% level. The result predicted by the theory

concerning the exchange rate, which predicts that a cross-border deal made in a high-

valuation market experiences higher returns caused by a stronger currency, due to the

argument that stock market levels and exchange rate levels are positively correlated, seems

right at first sight. However, the multivariate analysis finds only weak, statistically

insignificant, economic effects for the influence of the cross-border variable on bidder

returns. Also the interaction between high-valuation markets and cross-border is statistically

insignificant and economically weak. So, the conclusion of this research is that it cannot find

evidence that the cross-border characteristic does influence bidder returns. Therefore, there is

also no evidence that market valuation does influence bidder returns of cross-border deals.

For further research it would be interesting to take the dataset, for example from Conn

et al. (2005), from a study which already found a link between the cross-border characteristic

and deal quality or that is based on a different country, for example the UK, and test whether

market valuation influences bidder returns of cross-border deals. Further, this research only

looks at relatively large transactions, while the majority of all transactions are rather small

and involve more private companies. Therefore, further research could look at deals with

smaller transaction value.

41

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