Faculty of Economics and Business Master’s Thesis Strategy & Innovation

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Faculty of Economics and Business

Master’s Thesis Strategy & Innovation

GOING AGAINST THE MAINSTREAM

METHOD OF PAYMENT IN THE FIFTH

AND SIXTH MERGER WAVE

Luciënne Schobben

Scheldestraat 14-3 R

1078 GK Amsterdam

06-12813885

lucienneschobben@hotmail.com

S1777130

First supervisor: K. McCarthy

Second supervisor: R. Van Der Eijk

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TABLE OF CONTENTS

INDEX TABLES AND FIGURES 4

PREFACE 6

1. ABSTRACT 7

2. INTRODUCTION 8

3. LITERATURE REVIEW 11

3.1 FACTORS INFLUENCING THE PERFORMANCE OF A MERGER OR 11 ACQUISTIONS

3.1.1 METHOD OF PAYMENT 12

3.2 MERGER WAVES 18

3.2.1 THE FIFTH MERGER WAVE 18

3.2.2 THE SIXTH MERGER WAVE 19

4. METHODOLOGY 21 4.1 SAMPLE 21 4.2 VARIABLES 23 4.3 METHODOLOGY 24 5. RESULTS 27 5.1 DESCRIPTIVE STATISTICS 27 5.2 REGRESSION ANALYSIS 31

5.2.1 THE FIFTH MERGER WAVE 31

5.2.1.1 STOCK 33 5.2.1.1.1 SUCCESSFUL STOCK 35 5.2.1.1.2 UNSUCCESSFUL STOCK 37 5.2.1.2 CASH 38 5.2.1.2.1 SUCCESSFUL CASH 40 5.2.1.2.2 UNSUCCESSFUL CASH 41

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INDEX TABLES AND FIGURES

- Table 1: Definitions of the independent variables 23

- Table 2: Summary statistics abnormal and cumulative abnormal return 27

- Table 3: Descriptive statistics 27

- Table 4: Mean cumulative abnormal return three years after the event of the

mergers and acquisitions financed in the fifth merger wave 33 - Table 5: Univariate logit regressions stock in the fifth merger wave 34 - Table 6: Multivariate logit regression stock in the fifth merger wave 34 - Table 7: Mean cumulative abnormal return three years after the event of the

mergers and acquisitions financed with stock in the fifth merger wave 35 - Table 8: Univariate logit regressions successful stock in the fifth merger wave 36 - Table 9: Multivariate logit regression successful stock in the fifth merger wave 36 - Table 10: Univariate logit regressions unsuccessful stock in the fifth merger wave 37 - Table 11: Multivariate logit regression unsuccessful stock in the fifth merger wave 38 - Table 12: Univariate logit regressions cash in the fifth merger wave 39 - Table 13: Multivariate logit regression cash in the fifth merger wave 39 - Table 14: Mean cumulative abnormal returns three years after the event of the

mergers and acquisitions financed with cash in the fifth merger wave 40 - Table 15: Univariate logit regressions successful cash in the fifth merger wave 41 - Table 16: Multivariate logit regression successful cash in the fifth merger wave 41 - Table 17: Univariate logit regressions unsuccessful cash in the fifth merger wave 42 - Table 18: Multivariate logit regression unsuccessful cash in the fifth merger wave 43 - Table 19: Univariate logit regressions cash in the sixth merger wave 45 - Table 20: Multivariate logit regression cash in the sixth merger wave 46 - Table 21: Mean cumulative abnormal return three years after the event of the

mergers and acquisitions financed in the sixth merger wave 46 - Table 22: Mean cumulative abnormal return three years after the event of the

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- Table 28: Multivariate logit regression stock in the sixth merger wave 51 - Table 29: Mean cumulative abnormal returns three years after the event of the

mergers and acquisitions financed with stock in the sixth merger wave 52 - Table 30: Univariate logit regressions successful stock in the sixth merger wave 53 - Table 31: Multivariate logit regression successful stock in the sixth merger wave 53 - Table 32: Univariate logit regressions unsuccessful stock in the sixth merger wave 54 - Table 33: Multivariate logit regression unsuccessful stock in the sixth merger wave 55

- Figure 1: Conceptual framework 17

- Figure 2: Merger waves 1895 - 2010 18

- Figure 3: Method of payment in the fifth merger wave 32 - Figure 4: Method of payment in the sixth merger wave 44

- Appendix I: Firms divided by country of origin 66

- Appendix II: Firms divided by industry SIC code 67

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ACKNOWLEDGEMENTS

I am proud to present to you my master’s thesis on why firms used cash in the fifth merger wave and stock in the sixth merger wave and as such went against the mainstream method of payment to finance their mergers and acquisitions in those periods. I have written this thesis to conclude my master studies Business Administration – Strategy and Innovation at the University of Groningen. I have learned so much during the process of writing this thesis, and I hope the experience I gained and the skills I developed in conducting this research will help me in writing my thesis for my second master study Business Administration – Financial Management at the Free University of Amsterdam.

I would like to thank a number of people for their contribution to this thesis. First of all, I would like to thank my supervisor, Killian McCarthy, for his support, guidance and feedback during the process of writing my thesis. Next, I would like to thank my family and friends for their moral support and unconditional confidence in me. A special thanks goes out to my sister, Vivian Schobben, for being my personal STATA help desk. I could not have done it without you.

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1. ABSTRACT

This study analyzed the method of payment for 624 mergers and acquisitions that occurred in the pharmaceutical industry during the fifth and the sixth merger wave. Findings show that

though there was not a clear preference for a particular method of payment to finance a merger or acquisitions in the fifth merger wave, there was a clear preference for cash transactions in the sixth merger wave. Since prior research suggests that there does exist a preference for stock transactions in the fifth merger wave, the first finding is not in

accordance with previous literature. The second finding is however in accordance with existing literature. This study also found that those firms that went against the trend, and used cash in the fifth merger wave and stock in the sixth merger wave, are performing better than those firms that did follow the trend. In addition, the value of the deal, the level of total debt of the acquiring firm, whether the acquirer and target operate in the same industry, and the acquirer’s firm size influence the choice to use stock in the fifth merger wave; no significant results are found on what influences the choice to go against the mainstream and use cash to finance a merger or acquisition. Moreover, the earnings before interest and taxes of the

acquiring firm seem to influence the decision to use stock in the sixth merger wave and is thus considered a reason why firms went against the trend of using cash to finance M&A activities in the sixth merger wave.

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2. INTRODUCTION

The pharmaceutical industry is an industry that engages heavily in mergers and acquisitions since pharmaceutical firms can overcome the large costs and high risks associated with bringing a drug to market by the creation of economies of scale which can be achieved by a merger or acquisition. Moreover, the oligopolistic nature of the pharmaceutical industry and the patent protection that drugs enjoy support the expectation of abnormal returns from M&A. In addition to the struggle to cope with the increased competitive forces that arise after the expiration of patents, the pharmaceutical industry is currently facing a productivity crisis characterized by decreasing numbers of new drug approvals despite ever increasing investment in R&D (Cockburn, 2004; Munos, 2009). Increasing R&D costs make it even more difficult to overcome this R&D productivity challenge (Cockburn, 2004; Pamolli, Magazzini, Riccaboni, 2011). In response to these changing dynamics, firms in the

pharmaceutical industry try to save R&D costs and improve efficiency by outsourcing R&D to external research organizations (Cockburn, 2004; Rafols, Hopkins, Hoekman, Siepel, O’Hare, Perianes-Rodríguez, Nigtingale, 2012). Additionally, they engage extensively in mergers with each other, acquire smaller drug discovery firms and close R&D sites in Europe and the United States and relocate them to emerging countries with large markets.

When engaging in M&A activity, firms need to decide how to finance the merger or acquisition; they either use cash or stock or a mix of both. Alexandridis, Mavrovitis, and Travlos (2011) found that the mergers and acquisitions made during the fifth merger wave were predominantly paid with stock whereas in the sixth merger wave cash was the preferred method of payment. This finding thus suggests that the preference for a method of payment did change over time.

Prior research suggests that mergers and acquisitions are occurring at an ever increasing rate (Evenett, 2004; Faccio and Maslusis, 2004). They are however destroying huge amounts of value. Moeller, Schlingemann and Stulz (2005) argue that annual losses to mergers and acquisitions are in the range of $60 billion and that shareholders of the acquiring firm lost $240 billion during the period 1998-2001. It is thus not surprising that between 65 and 85% of all mergers eventually fail (Puranam & Singh, 1999) and that the gains that some mergers do create for shareholders are small and perishable (Gaughan, 2011). The challenge is to

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understood what factors influence the success or failure of a merger or acquisition. It is however established that the method of payment for a merger or acquisition is influencing the long-term performance of the acquiring firm; Travlos (1987), Asquith, Bruner and Mullins (1990), Loughran and Vijh (1997), and André et al. (2004) argue that stock transactions result in negative abnormal returns for acquirers while positive abnormal returns are generated by financing the deal with cash. Given this influence on the long-term outcome of the merger or acquisition, and the high number and often large size of M&A transactions, the financing decision can have a significant impact on the acquiring firm’s ownership structure, financial leverage, subsequent financing decisions, corporate control, risk bearing, tax payments, and future cash flows (Faccio et al., 2004). Thus, the choice for a particular method of payment to finance a merger or acquisition can have a considerable influence on the long-term

performance of not only the merger or acquisition but also the firm itself.

This research contributes to this field of study by focusing on the method of payment for a merger or acquisition. There will be looked at 624 merger cases in the pharmaceutical

industry to determine (1) what firm and deal characteristics influence the decision of the firm to use either stock or cash to finance a merger or acquisition, (2) if the preference for a

method of payment has changed comparing the fifth and the sixth merger wave, and if yes, (3) why some firms did go against the trend of using stock and cash in the fifth and sixth merger wave respectively. Moreover, most empirical research has examined the outcome of mergers and acquisitions on a short-term basis; few long-term event studies have been done on mergers and acquisitions so the long-term event study performed in this thesis will be a contribution to this field of study as well.

The objective of this research is to give an insight in the current transformation of the pharmaceutical industry and its influence on the method of payment used to finance M&A activities in the fifth and sixth merger wave. This study focuses on the firms that went against the trend of using stock or cash to finance a merger or acquisition in the fifth and sixth merger wave respectively. The main research question of this thesis is formulated as follows:

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The following specific sub-questions will be answered in the literature review:

- “What factors influence the performance of a merger or acquisition?” - “Which methods of payment do exist?”

- “What factors influence the method of payment chosen?”

Evidence to answer the main research question is sought in 624 merger cases that occurred in the pharmaceutical industry of Northern America, Western Europe and Japan in the period 1995-2008. This time period comprises both the fifth and sixth merger wave which took place during 1991 – 2001 and 2003 – 2008 respectively.

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3. LITERATURE REVIEW

The available literature on the topic of this research is presented in this section. First, firm and deal characteristics and their influence on the long-term performance of the acquiring firm are discussed. Next, special attention will be paid to the method of payment and hypotheses on the factors that influence the decision of which method of payment to use to finance a merger or acquisition will be formulated. These hypotheses will be based on prior research. Additionally, the free cash flow theory of Jensen and the pecking order theory of Myers and Majluf are discussed thoroughly in this section. Lastly, the characteristics of the fifth and the sixth merger wave will be discussed.

3.1 FACTORS INFLUENCING THE PERFORMANCE OF A MERGER OR ACQUISTIONS

Firm and deal characteristic can influence the long-term outcome of a merger or acquisition. Firm characteristics include the country of origin of the firm, the size of the firm, the industry in which the firm operates, a firm’s earnings before interest and taxes, the market-to-book value of the firm and the amount of debt accumulated by the firm. Deal characteristics include whether the merger was a national one or cross-border deal, the value of the deal, the country of origin of the target firm, the industry in which the target is operating, and method of payment. These are among the firm and deal characteristics that will be discussed in the remained of this literature review.

Prior research suggest that some of these characteristics indeed influence the long-term

performance of the acquiring firm. For example, cross-border deals perform poorly in the long run (Black et al., 2001; André et al., 2004). Moeller, Schlingemann, and Stulz (2003),

Hawawinia and Swary (1990) and Zollo and Leshchinkskii (2000) found that small acquirers earn higher abnormal returns than their large counterparts. In addition, mergers and

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acquiring firm; Travlos (1987), Asquith, Bruner and Mullins (1990), Loughran and Vijh (1997), and André et al. (2004) argue that stock transactions result in negative abnormal returns for acquirers while positive abnormal returns are generated by financing the deal with cash.

3.1.1 METHOD OF PAYMENT

In making the decision how to finance a merger or acquisition, the acquiring firm is faced with a choice between using cash or stock. In addition, a mix of methods of payments can be used. Financing with cash often involves the issuing of debt since many firms have only limited cash reserves. Thus, the acquiring firm actually faces the choice between debt and stock which involves a tradeoff between rising financial distress costs of issuing debt and corporate control concerns of issuing equity (Faccio et al., 2004). When issuing debt, the firm needs to make principal and interest payments and if it is not able to do so, it might go

bankrupt; financial distress costs rise. When issuing equity, some corporate control is transferred to the shareholders which might not be in the best interest of the firm.

Contrastingly, a target firm has to choose between the tax advantages that stock transactions bring and the benefits of liquidity and risk-minimization of cash transactions; targets might be able to defer their tax liabilities by accepting acquirer’s stock as payment or to circumvent the risk of becoming a minority shareholder of the acquiring firm with concentrated ownership by accepting cash as payment (Faccio et al., 2004).

Given the high number and often large size of M&A transactions, the financing decision can have a significant impact on the acquiring firm’s ownership structure, financial leverage, and subsequent financing decisions. The method of payment can also have serious corporate control, risk bearing, tax, and cash flow implications for the acquiring and target firms and their shareholders (Faccio et al., 2004).

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Hypothesis 1: Cash is used for small deals; stock is used for large deals.

In addition, Faccio et al. (2004) found that the probability of using cash as the method of payment for a merger or acquisition decreases when target size increases. This finding supports Hansen’s (1987) reasoning; the problem of asymmetric information between the acquirer and the target should be larger as the size of the target firm increases. If target size increases, stock financing should be more likely since the acquirer seeks to force the target to share in any post-acquisition revaluation effects. In contrast, Travlos (1987) and Martin (1996) found that the relative size of the target firm does not influence the method of payment for a merger or acquisition.

Hypothesis 2: Cash is used when the target’s firm size is small; stock is used when the target’s firm size is large.

Moreover, Moeller et al. (2003) found that small firms are more likely to pay for acquisitions with cash than with equity. In contrast, Faccio et al. (2004) argue that cash transactions are more feasible in larger firms due to them being more diversified and as such, having lower expected bankruptcy costs than smaller firms. Large firms also have better access to debt markets which makes the issuing of debt easier. This is supported by Weitzel and McCarthy (2009) who found that SME M&As are more likely to be financed with equity over cash and debt.

Hypothesis 3a: Cash is used when the acquirer’s firm size is small; stock is used when the acquirer’s firm size is large.

Hypothesis 3b: Cash is used when the acquirer’s firm size is large; stock is used when the acquirer’s firm size is small.

Furthermore, Gaughan (2002) argues that in cross-border deals, targets are frequently

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Hypothesis 4: Cash is used in cross-border deals; stock is used in national deals.

Faccio et al. (2004) argue that stock is used to finance deals that occur in the same industry since targets are less risk averse to accept acquirer’s stock when they operate in the same industry. The same was found by García-Feijóo, Madura, and Ngo (2012).

Hypothesis 5: Cash is used in deals in which the acquirer and target operate in different industries; stock is used in deals between parties operating in the same industry.

Jensen argues that the conflict of interests between management and shareholders of the firm is especially severe when large cash flows are generated. Debt can be used to reduce the agency costs this conflict creates; the creation of debt enables management to effectively keep its promise to pay out future cash flows to shareholders resulting in a reduction in agency costs of free cash flow by diminishing the cash flow that is available for management to spend on (low-return) investment projects. Moreover, debt creation increases efficiency by forcing management of firms that have large cash flows but few high-return projects to invest in and thus have low growth opportunities to pay cash to its shareholders. Debt thus increases

efficiency by preventing firms from investing in projects that generate low returns. Therefore, free cash flow theory predicts that firms with unused debt capacity and substantial free cash flows are more likely to undertake mergers and acquisitions that generate low returns or are even value-destroying.

In conclusion, Jensen (1986) argues that if firms have large free cash flows and thus high cash availability they are financing their mergers and acquisitions with cash; if a firm has low free cash flow, there is little cash available and therefore, mergers and acquisitions are paid with stock. Moreover, Jensen’s free cash flow theory implies that mergers and acquisitions

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Another theory of the method of payment is the pecking order theory of Myers and Majluf (1984). The pecking order theory states the following four assumptions:

1. Firms prefer internal finance (cash and short-term investments) over external finance (debt and equity).

2. They adapt their target dividend payout ratios to their investment opportunities, although dividends are sticky and target payout ratios are only gradually adjusted to shifts in the extent of valuable investment opportunities.

3. Sticky dividend policies, plus unpredictable fluctuations in profitability and

investment opportunities, mean that internally-generated cash flow may be more or less than investment outlays. If it is less, the firm first draws down its cash balance or marketable securities portfolio.

4. If external finance is required, firms issue the safest security first. That is, they start with debt, then possibly hybrid securities such as convertible bonds, then perhaps equity as a last resort. In this story, there is no well-defined target debt-equity mix, because there are two kinds of equity, internal and external, one at the top of the pecking order and one at the bottom. Each firm's observed debt ratio reflects its cumulative requirements for external finance. (Myers, 1984: 585)

The pecking order theory thus revolves around two central ideas: the preference for internal finance, and the preference for debt over equity in the case of external financing. The pecking order theory modified by Myers and Majluf recognizes asymmetric information between management and the shareholders of the firm which causes external capital to be subject to adverse selection, transaction costs and costs of financial distress and argue that as such a firm faces increasing costs as it climbs up the pecking order. In addition, asymmetries in

information encourage opportunistic behavior which increases agency costs (Franks, Harris, Mayer, 1988). The preference for internal finance over external finance is as such not

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EBIT (Earnings Before Interest and Taxes) are believed to have an influence on the method of payment; Myers and Majluf (1984) argue that firms prefer internal finance (cash and short-term investments) over external finance (debt and equity). As such, if firms have large free cash flows and thus high cash availability they are financing their mergers and acquisitions with cash (Jensen, 1986).

Hypothesis 6: Cash is used when EBIT are high; stock is used when EBIT are low.

Building on this theories and looking at the debt ratio of a firm, it is expected that if the debt level of a firm is low the firm will finance its M&A activities with cash or additional debt but if a firm is highly leveraged and cannot take on additional debt it will finance its M&A activities with stock.

Hypothesis 7: Cash is used when the total debt level is low; stock is used when the debt level is high.

Raghavendra Rau and Vermaelen (1998) and Andrade et al. (2001) found that firms with low book-to-market ratio, the so-called “glamour” firms, use stock to finance their mergers and acquisitions while firms with a high book-to-market ratio, the so-called “value” firms, finance their M&A activities with cash.

Hypothesis 8: Cash is used when a firm’s market-to-book value is high; stock is used when a firm’s market-to-book value is low.

Lastly, Myers and Majluf (1984) argue that due to information asymmetry between

management and the shareholders of the firm, management is induced to issue stock when it perceives it to be overvalued. In the context of M&A, this theory entails that acquiring firms prefer to finance mergers and acquisitions with stock when their stock is overvalued and to finance mergers and acquisitions with cash when their stock is undervalued. Debt-to-equity can be used as a proxy for stock under- or overvaluation1; high debt-to-equity ratios are associated with low PE ratios and stock with low PE ratios are likely to be undervalued. As

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such, if the debt-to-equity ratio is low the acquiring firms’ stock is overvalued and therefore prefer to finance their M&A activities with cash; if the debt-to-equity ratio is high the acquiring firms prefer to finance their M&A activities with stock.

Hypothesis 9: Cash is used when stock is overvalued; stock is used when stock is undervalued.

Figure 1 presents the conceptual framework that can be derived from these hypotheses. This thesis will test the hypotheses in order to assess whether they should be accepted or rejected in the context of the pharmaceutical industry and the fifth and sixth merger wave.

Figure 1: Conceptual Framework

Additionally, prior research suggests that the preference for the method of payment for a merger or acquisition changes over time. Andrade, Mitchell and Stafford (2001) argue that about 58% of all mergers and acquisitions made during the 1990s are financed with stock. This is approximately a 50% increase in the number of stock payments made during the 1980s. Thus, whereas the mergers and acquisitions made during the fifth merger wave were predominantly paid with stock, in the sixth merger wave cash was the preferred method of payment (Alexandridis et al., 2011). This study will specifically look at those firms that went against this trend; in other words, those firms that financed their M&A activity with cash in

Method

of

payment

Same

industry

Acquirer's

firm size

Target's

firm size

EBIT

Deal value

National/

international deal

MTBV

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the fifth merger wave and with stock in the sixth merger wave.

3.2 THE MERGER WAVES

Figure 1 shows aggregate M&A activity over the past 120 years. The graph shows that mergers and acquisitions typically occur in cyclical patterns, waves in which periods of intense M&A activity are followed by periods of fewer mergers and acquisitions. Six waves can be identified. This section describes the characteristics of the two merger waves that are of interest in this thesis, notably the fifth and the sixth merger wave.

Figure 2: Merger waves 1895- 2010

Source: McCarthy, September 20112

3.2.1 THE FIFTH MERGER WAVE

The fifth merger wave took place during 1991 – 2001. This merger wave was a response to the economic recovery, the fall of the Berlin Wall in 1989, and globalization and deregulation forces. Andrade et al. (2001) state that the 1990s were the “decade of deregulation” since they found that deregulation is a dominant factor in M&A activity after the late 1980s and that its significance continues today; deregulation accounts for almost half of all M&A activity since then. The wave was characterized by mega-deals, extensive overpayment, overvaluation of acquiring firms, greater use of equity method of payment, and massive losses to the acquirer’s shareholders (Andrade et al., 2001; Moeller et al., 2005). Moreover, the 1990s continue the trend of an ever-increasing proportion of mergers and acquisitions where both the acquiring

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and the target firm operate in the same industry. Furthermore, the percentage of hostile takeovers decreased from 14% in de 1980s to 4% in de 1990s. Additionally, the prevailing method of payment in this period is stock; about 70% of all deals during the 1990s involved some sort of stock compensation, with 58% of all deals being financed entirely with stock. This is approximately a 50% increase in the number of stock payments made during the 1980s. Thus, M&A activity in the fifth merger wave is characterized by the acquirer and target, often operating in closely related industries, negotiating a friendly stock exchange. Metal mining, media and telecommunications, banking, real estate and hotels are the industries in which the highest annual average M&A activity can be seen.

The 500 percent increase in cross-border deals and the bigger role developing countries began to play in the 1990s resulted in the wave spreading globally. Additionally, firms pursued focus strategies which can be seen in the industry clustering of M&A activity.

The extensiveness of the fifth merger wave can be illustrated with the following figures; US and European firms spent $9000 billion on mergers and acquisitions between 1995 and 1999. As a percentage of US GDP, M&A activity increased from 3.6% in the 1980s to 15.4% in 1999. Additionally, more mergers deals took place between 1998 and 2000 than in the preceding 30 years (McCarthy, 2011)3.

The wave was brought to an end by globalization and a stock market crash.

3.2.2 THE SIXTH MERGER WAVE

The sixth merger wave started in 2003 and came to an end in 2008. This wave occurred in response to economic recovery, and cheap money. Just as the fifth merger wave, the sixth merger wave was global in nature and industry clustered. The wave is characterized by the availability of abundant liquidity due to the lower U.S. corporate loan prime rates and stronger cash balances for acquirers resulting in more debt and free cash financing of M&A activity respectively. M&A activity increased by 50% in Europe in 2004 - 2005 and peaked in 2006 with more than $1 trillion spent on deals within the U.S. (Alexandradis et al, 2011). Moreover, the proportion of stock transactions dropped by more than 57% while the overall amount of equity in the financing decreased by about 32% (Alexandradis et al, 2011). Easy

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access to cash and firm valuations might explain why much fewer mergers and acquisitions were financed with stock compared to the previous merger wave.

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4. METHODOLOGY

In order to answer the main research question, extensive research needs be conducted. Data should be gathered and analyzed. The data and the methodology used to collect and analyze these data are discussed in this section.

4.1 SAMPLE

The database from which primary data will be collected in this research is the Thomson SDC / Platinum / One / Banker database. This database is the world's largest financial statistical database. It includes extensive data on asset classes, estimates, fundamentals, indices and economic data (online.thomsonreuters.com). An advantage of this database is that it can be easily linked to other databases which makes the exploration of relationships between different datasets possible. In this thesis, the Thomson SDC database is linked to the Datastream database to complement the data and to make exploration of relationships possible.

Several refinements are needed in order to deduce the data to a manageable size. First, this thesis will focus specifically on acquiring firms in the pharmaceutical industry. This industry is chosen for several reasons already discussed in the introduction. First, the pharmaceutical industry is a global industry. Second, this industry engages extensively in mergers and acquisitions. This extensive engagement in M&A activity results from the high costs of bringing a drug to market and the relative low success rate for those drugs that eventually reach the market. Companies can overcome these large costs and high risks by economies of scale which can be achieved by a merger or acquisition. Finally, the oligopolistic nature of the pharmaceutical industry and the patent protection that drugs enjoy support the expectation of abnormal returns from M&A. When these patents do however expire the conditions of

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To summarize, there will be looked at Northern American, Western European and Japanese publicly-listed firms in the pharmaceutical industry that merged with or acquired either domestic or international targets in either in their own or another industry during the period 1995 – 2008.

discovery firms and close R&D sites in Europe and the United States and relocate them to emerging countries with large markets. Moreover, they try to save R&D costs and improve efficiency by outsourcing R&D to external research organizations (Rafols, Hopkins, Hoekman, Siepel, O’Hare, Perianes-Rodríguez, Nigtingale, 2012; Cockburn, 2004).

This thesis will focus on Northern American, Western European and Japanese companies that merged with or acquired international targets either in the same or another industry. North America includes the United States and Canada; Western Europe includes Finland, France, Switzerland, Germany, Ireland-Republic, United Kingdom, Netherlands, Spain and Denmark. Appendix I shows an overview of the countries of origin of the firms included in this research.

The sample period will comprise the period 1995 – 2008. This time period is chosen for several reasons. First, though mergers and acquisitions in general are occurring at an ever increasing rate, a significant portion of this explosive growth in mergers and acquisitions has been in cross-border deals, which increased 500 percent during the 1990s (Black, Carnes, Jandik, 2001: 2). Evenett (2004) argues that the growth in cross-border M&As accelerated after 1996 and reached a peak of $828 billion in 2000. Second, developing countries played a much larger role in this merger wave than in the 1980s wave (Evenett, 2004). Moreover, this time period comprises both the fifth and sixth merger wave which took place during 1991 – 2001 and 2003 – 2008 respectively.Many data are available on this waves. Additionally, it might be interesting to compare these two periods and see if there are differences between the sample cases of these periods. Thus, this sample period is selected for its availability of data, its focus on rather recent mergers and acquisitions and the ability to collect sufficient post-merger performance data.

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4.2 VARIABLES

Several variables will be included in the analysis. The variable of interest in this thesis is method of payment. This variable shows if the acquiring firm financed the deal with cash, stock or a mix of methods of payments. Method of payment is a dummy variable; this means that it is 0 if the firm did not use cash, stock or a mix of methods of payment respectively but turns 1 if the firms did use cash, stock, or a mix of methods of payments respectively.

The independent variables include the international, same industry, acquirer’s firm size, target firm size, deal value, EBIT, MTBV, total debt and debt-to-equity. The variable international is a dummy variable which is 0 if the deal is national but turns 1 if the deal is international. The dummy variable same industry is created to indentify whether the acquiring firm merged with or acquired a target firm in the same industry or not. As mentioned

previously, the same industry means the industry with the same SIC code. Acquirer’s firm size is based on the enterprise value a year before the merger or acquisition became effective; Earnings Before Interest and Taxes (EBIT) and total debt are also measured a year before the merger or acquisition became effective. Size distinctions are made in order to identify

possible size effects in the returns from the merger or acquisition. For example, small

acquirers are defined as firms with a size below the mean enterprise value of the sample; large acquirers are defined as firms with a size above the mean enterprise value of the sample. The same size distinction is made for the target’s firm size, deal value, EBIT, MTB, total debt and debt-to-equity. Lastly, Table 1 presents the definitions, units of measurements and the

databases from which this information is gathered of the independent variables.

Table 1: Definitions of the independent variables

Variable Definition Unit Thomson SDC Datastream

Deal value Total amount of consideration paid by the acquirer in the related deal.

$ million X

Enterprise value

Market Capitalization at the end of the fiscal year + Preferred Stock + Minority Interest + (Total Debt – Cash).

$ thousand X

EBIT (Earnings Before Interest and Taxes)

The earnings of a company before interest expense and income taxes. It is calculated by taking the pre-tax income

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and adding back interest expense on debt and

subtracting interest capitalized. MTBV

(Market To Book Value)

The market value of the ordinary common equity divided by the balance sheet value of the ordinary common equity in the company.

% X

Total Debt All interest bearing and

capitalized lease obligations. It is the sum of long and short term debt.

$ thousand X

Debt-to-equity Total debt divided by common equity.

% X

This thesis will investigate which of these firm and deal characteristics influence which method of payment is used to finance a merger or acquisition.

4.3 METHODOLOGY

Quantitative research methods are used to analyze the data statistically. Quantitative research entails the empirical investigation of phenomena via statistical techniques. Computer

programs such as Microsoft Excel and STATA will be used to analyze the data statistically. The statistical program STATA 11.0 is used to perform regression analyses in order to assess the influence of the various independent variables on the dependent variable, method of payment. These variables will be thoroughly discussed in a later section. Logit regression models are used. This type of regression is used whenever the dependent variable is a binary one which takes only values 0 or 1. In this study, the dependent variable is method of payment which is a dummy variable; this means that it is 0 if the firm did not use cash or stock or a mix of methods of payment respectively but turns 1 if the firms did use cash or stock or a mix of methods of payments respectively. Logit regression is a nonlinear regression model that forces the output or predicted value to be either 0 or 1. It estimates the probability of the dependent variable to be 1 if certain independent variables are present. An example for this study is the following; given the deal is international, what is the probability that the deal is financed with cash?

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long-term outcome. Duso, Gugler and Yurtoglu (2006) argue that an event study is useful for the analysis of mergers and acquisitions. An event study is an analysis of whether there is a reaction in the financial markets to the occurrence of a given type of event that is

hypothesized to affect the firm’s market value (Grinblatt and Titman, 2002). In this thesis, the type of event is a merger or acquisition and since it is a long-term study, the event window will be three years.

Furthermore, the long-term outcome of the merger or acquisition will be assessed by performing a long-term event study as previously discussed. The dependent variable in the event study is firm performance. Firm performance will be measured by abnormal returns. Abnormal returns are actual returns in excess or shortage of the expected returns. From the analysis of abnormal returns, or cumulative abnormal returns, can be derived if the merger or acquisition created or destroyed value.

Firm performance will be measured using a long-term approach. Since the event window is three years, firm performance will be measured during the three-year post-event period. In this case, the event is the merger of acquisition. As in an short-term event study, there will be looked at the stock performance of the firm which is measured by abnormal returns.

Abnormal returns are actual returns in excess or shortage of the expected returns. This definition implies that there should be a benchmark, or “normal”, return against which actual returns can be compared in order to identify “abnormal” returns. The normal return is defined as the expected return without conditioning on the event taking place (MacKinlay, 1997). The benchmark can be assessed by using different models. In this thesis, normal returns are calculated according to the Capital Asset Pricing Model (CAPM) of Sharpe (1964) and

Lintner (1965). This model is an equilibrium theory where the expected return of a given asset is determined by its covariance with the market portfolio (MacKinlay, 1997: 19). The use of the CAPM is common in event studies. The assumptions underlying the CAPM are defined by Elton and Gruber (1995) as:

1. There are no transaction costs 2. Assets are infinitely divisible 3. Personal income tax is absent

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5. Investors make decisions solely in terms of expected values and standard deviations of the return on their portfolios

6. Unlimited short sales are allowed

7. Unlimited lending and borrowing at the riskless rate is possible

8. Investors are concerned with the mean and variance of returns and define the relevant period in exactly the same manner

9. All investors are having the identical expectations with respect to the necessary inputs to the portfolio decision

10. All assets are marketable

The expected return of a security ( is a function of the risk-free rate ( and the

systematic risk of the security ( times the risk premium which is the market return ( minus the risk-free return ( . This brings the CAPM equation to be

Abnormal returns are the actual ex post returns of the security over the event window minus the normal returns of the firm over the event window (MacKinlay, 1997). For firm and event the abnormal return is

where , , and are the abnormal, actual, and normal returns respectively for time period . is the condition information for the normal return model (MacKinlay, 1997).

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5. RESULTS

This section will present the findings of the research. First, the descriptive statistics of the variables will be discussed. Next, the results of the regression analyses will be presented.

After excluding all events that had any missing data on company return index or market return index during the estimation window, which was set at the period five till two years before the event, 624 events were left. Abnormal returns are calculated one year before and three years after the event. In this research, the focus is on the three-year post-event period so the abnormal and cumulative abnormal returns listed in this section are those measured three years after the event. A summary of the statistics of abnormal returns and cumulative

abnormal returns during the three year post-event period for these cases is shown in table 2.

Table 2: Summary statistics abnormal and cumulative abnormal return

Variable Mean Minimum Maximum

Abnormal return -0.2287903 -48.18224 5.24173

Cumulative abnormal return

-1.067104 -105.5657 18.65803

Table 2 shows that the mean of both abnormal return and cumulative abnormal return is slightly negative which indicates that in general the return three years after the event is negative. From the analysis of the cumulative abnormal returns three years after the event can be concluded that 264 firms experienced positive cumulative abnormal returns while the majority, notably 360 firms, experienced negative cumulative returns. The largest positive cumulative abnormal return included in this sample is 8.4%, while the largest negative cumulative abnormal return is -9.8%.

5.1 DESCRIPTIVE STATISTICS

This section discusses the descriptive statistics of the firm and deal characteristics discussed in the introduction. Table 3 shows an overview of these descriptive statistics.

Table 3: Descriptive statistics

Variable N Mean Minimum Maximum CAAR

International deal

Yes 228 -1.45

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29 Small 460 -1.28 Large 164 -0.46 Total debt 611 2,087,074 0 7,040,000,000 Small 471 -1.20 Large 135 -0.66 Debt-to-equity ratio 613 -1.31 -697.48 35.16 Small 16 2.47 Large 608 -1.16

Size distinctions are based on the mean of the variable; small firms are those that have a value below the mean, large firms are those that have a value above the mean. Deal value is measured in million $. Acquirer’s firm size, target’s firm size, EBIT and total debt are measured in thousand $. All figures are rounded to 2 decimals.

From the analysis of cumulative average abnormal returns (CAAR) presented in the table above the following can be concluded:

 Acquiring firms that made a national deal are performing better than those firms that acquired an international target. This is consistent previous studies discussed in the literature review.

 Acquiring firms from Northern America are performing the best.

 Acquiring firms operating in the industry with SIC code 2833 are performing the best. There are however just 11 mergers in this industry included in this sample so this conclusion might not be decisive.

 The CAAR for the years 1997 and 2009 and the period 2000 – 2001 is positive while for the other years the CAAR is negative. It is remarkable that the CAAR for the mergers and acquisition that occurred in the sixth merger wave, which comprises the period 2003 – 2008, are all negative.

 Large acquiring firms are performing better than the small acquiring firms and that the firms that acquired a small target are performing better than those firms that acquired a large one. While the first conclusion is not consistent with prior research, the second does comply with previous studies.

 Acquiring firms that undertook a large deal are better off regarding their cumulative abnormal returns than the acquiring firms that made a deal with a small deal value. This is not in line with prior research as discussed in the literature review.

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With regard to cash and stock, table 2 shows that mergers and acquisition that are financed with cash are performing better than those financed with stock. This is in line with the findings of previous studies.

 Though both figures on CAAR are negative, it can be concluded that acquiring firms that earn high EBIT are performing better than those firms that earn low EBIT.

 Although both figures on CAAR are negative, table 2 presents that acquiring firms that have a high MTBV, the so-called “value firms”, are performing better than acquiring firms that have a low MTBV, the so-called “glamour firms”.

 Both CAARs have a negative value but acquiring firms that have a high total debt level are better off regarding their cumulative abnormal returns than those firms that have a low total debt level.

 Acquiring firms with a low debt-to-equity ratio are earning substantially higher CAARs than those acquiring firms that have a high debt-to-equity ratio. There are however just 16 acquirers with a low debt-to-equity ratio in this sample so this conclusion might not be decisive.

Some additional information on the variables country of origin and industry is presented in the Appendix. Appendix I shows an overview of the acquirers’ and targets’ country of origin. From this table can be concluded that approximately 2/3 of both the acquiring and target firms are from the United States. Other countries that are engaging fairly heavily in M&A activity, though at a fair distance from the United States, are the United Kingdom and Canada. Appendix I also shows that the target firms are from a wider variety of countries than the acquiring firms. This is not surprising given the various reasons behind mergers and

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5122 (23 firms), and 8731 (42 firms). A complete list of the SIC codes with corresponding industry can be found in Appendix III. This variety in target’s industries can also be explained by the variety of reasons behind mergers and acquisitions; the creation of synergies, product diversification and supply chain integration might be the explanation here.

5.2 REGRESSION ANALYSIS

This section present the results of the regression analyses. Logit regressions are used to analyze the M&A activity in the fifth and the sixth merger waves as discussed in the

methodology section. First, all mergers and acquisitions undertaken in the fifth and the sixth merger wave will be compared. There will be looked at the methods of payment that are used most often during these periods and if the preference for a particular method of payment has changed over time. Second, the sample is subdivided into those mergers and acquisition that are financed with cash or stock. There will be determined which independent variables influence the decision to use a particular method of payment to finance a merger of

acquisition. Special attention will be paid to the firm and deal characteristics of those firms that went against the trend of using cash or stock in the fifth and sixth merger wave

respectively. Next, this sample will again be subdivided; those mergers and acquisitions that are paid with cash are subdivided into those that have a positive long-term outcome, e.g. positive cumulative abnormal returns three years after the merger or acquisition has taken place, and those that that have a negative long-term outcome, e.g. negative cumulative

abnormal returns three years after the merger or acquisition has taken place. The same is done for the mergers and acquisitions that are paid with stock. Mergers and acquisitions that are financed with mixed of methods of payment are excluded in the regression analysis. Note however that a mixed method of payment does not necessarily mean a mix of only cash and stock; other methods might be included as well.

5.2.1 THE FIFTH MERGER WAVE

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Figure 3 shows that all three methods of payment are used in approximately the same number of cases; cash is used to finance 52 merger cases whereas stock and a mix of methods of payment are used to finance 53 merger cases. Thus, there does not seem to be a clear preference for a particular method of payment. These findings are inconsistent with prior research which suggests that stock was the preferred method of payment during the fifth merger wave (Alexandridis, Mavrovitis, Travlos, 2011).

43 merger cases involve an international deal while 115 merger cases occurred domestically. The acquiring and target firm operate in the same industry in 64 of the merger cases, the parties to the other 94 merger cases operate in different industries. Mean deal value is $317.1 million. The smallest deal has a value of $160,000 while the largest deal has a value of over $11 billion. Lastly, the mean enterprise value of the acquiring firms in this sample is $1790 billion. The smallest acquirer has a negative value of -$592,000, the enterprise value of the largest acquiring firm is $22,400 billion. Information on acquirer’s enterprise value is only available for 154 cases. The average EBIT the acquiring firms are earning is $1,329,441,000. The lowest EBIT are negative and have a value of -$235,700,000 whereas the highest EBIT

0 10 20 30 40 50 F re q u e n cy

Fifth merger wave 1996-2001

Method of Payment

cash stock mixed

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are $1,940 billion. Moreover, the mean MTBV is 4.15. The lowest MTBV is 1.41 and the highest MTBV is 14.07. Information on EBIT and MTBV is however only available for 153 out of 158 cases. The mean amount of total debt the acquiring firms issued is $3,155,084,000. The lowest total debt level is $106,000, the highest total debt level has a value of $5030 billion. Lastly, the average debt-to-equity ratio is 29.02. The lowest debt-to-equity ratio is 0.03 and the highest debt-to-equity ratio is 90.38. Data on total debt and debt-to-equity are missing in three cases so information is gathered from 155 cases.

The table below shows that the cumulative average abnormal return (CAAR) of all 158 mergers and acquisitions that were undertaken in the fifth merger wave is 0.1300953. Thus, M&A activity in the fifth merger wave have a positive long-term return and can as such be labelled “successful” for the acquiring firms.

Table 4: Mean cumulative abnormal return three years after the event of the mergers and acquisitions financed in the fifth merger wave

N Mean Std. Err. 95% Conf. Interval

Cumulative abnormal return

158 0.1300953 0.751289 -1.353843 1.614033

5.2.1.1 STOCK

In the fifth merger wave, 53 mergers cases were financed with stock and as such followed the trend of using stock to finance mergers and acquisitions. Of those 53 cases, 12 are

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whereas the highest debt level has a value of $6,097,149,000. Lastly, the average debt-to-equity ratio is negative and has value of -11.84. The lowest debt-to-debt-to-equity ratio is -1853.12, the highest debt-to-equity ratio is 177.89.

Table 5 shows the results of the univariate logit regressions of the mergers and acquisitions financed with stock in the fifth merger wave. No conclusions on the influence of the independent variables on the dependent variable stock can be derived since none of the findings are statistically significant at either the 5%- or 10%-significance level.

Table 5: Univariate logit regressions stock in the fifth merger wave

Coefficient Std. Err. Z P > |z| International -0.3585875 0.3917846 -0.92 0.360 Same industry -0.05527992 0.3438806 -0.16 0.872 Deal value 0.0002369 0.0001929 1.23 0.219 Acquirer’s firm size 7.02e-10 4.20e-09 0.17 0.867

EBIT -5.63e-08 6.26e-08 -0.90 0.369

MTBV 0.0089131 0.013236 0.67 0.501

Total debt -9.01e-08 7.74e-08 -1.16 0.245

Debt-to-equity -0.00286 0.0021798 -1.31 0.190

Stock is the dependent variable in this regressions. The independent variables include international, same industry, deal value, acquirer’s firm size, EBIT, MTBV, total debt and debt-to-equity.

There are also no statistically significant results in the multivariate logit regressions since the p-values of the independent variables are all above 0.05 and even above 0.10.

Table 6: Multivariate logit regression stock in the fifth merger wave

Coefficient Std. Err. Z P > |z| International 0.0537741 0.4233144 0.13 0.899 Same industry 0.0493599 0.37711754 0.13 0.894 Deal value 0.0003154 0.0002538 1.24 0.214 Acquirer’s firm size 2.06e-09 7.58e-09 0.27 0.786

EBIT 4.97e-08 2.11e-07 0.24 0.814

MTBV 0.0124503 0.0153617 0.81 0.418

Total debt -1.96e-07 2.15e-07 -0.91 0.362

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Stock is the dependent variable in this regressions. The independent variables include international, same industry, deal value, acquirer’s firm size, EBIT, MTBV, total debt and debt-to-equity. Only cases that earned negative cumulative abnormal returns three years after the event are included.

The table below shows that the acquiring firms that financed their M&A activity with stock in the fifth merger wave earn negative cumulative abnormal returns. These mergers and

acquisitions can thus be labelled “unsuccessful”.

Table 7: Mean cumulative abnormal return three years after the event of the mergers and acquisitions financed with stock in the fifth merger wave

53 -1.078786 2.109525 -5.311857 3.15428

5.2.1.1.1 SUCCESSFUL STOCK

Approximately half of the mergers and acquisitions that are financed with stock earn positive abnormal returns three years after the event as such 26 cases can be considered “successful”. Only five cases are international deals; 21 cases are domestic deals. Moreover, 14 cases occurred within one industry; in 12 cases the acquirer and the target are not operating in the same industry. The mean deal value of this subsample is $87.6 million. The smallest deal has a value of almost $3 million, the largest deal has a value of $916.3 million. Additionally, the mean acquirers’ firm size is $5,720,322,000. The smallest acquirer has a negative enterprise value of -$330,000 whereas the largest acquirer has an enterprise value of $7,530 billion. The average EBIT the 26 acquiring firms earn are $228,911,000. The lowest EBIT have a negative value of -$40,853,000 while the highest EBIT are $3,449,005,000. Furthermore, the average MTBV in this subsample is 7.87. The lowest MTBV has a negative value of 27.97 and the highest MTBV has a value of 149.15. The mean total debt level is $118,534,300. The lowest total debt level is $0 while the highest total debt level is $1,555,822,000. Lastly, the mean debt-to-equity ratio is 29.77. The lowest debt-to-equity ratio is 0, the highest debt-to-equity ratio is 177.89.

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deal value has a slight negative influence on stock; for every unit increase in deal value, the log odds of stock (versus cash) decreases by 0.023.

Table 8: Univariate logit regressions successful stock in the fifth merger wave

Coefficient Std. Err. Z P > |z| International -0.3819346 0.5089832 -0.66 0.511 Same industry 0.5740045 0.4762474 1.21 0.228 Deal value -0.0023052 0.001173 -1.97 0.419 Acquirer’s firm size -1.50e-08 1.12e-08 -1.34 0.180

EBIT -3.84e-07 2.67e-07 -1.44 0.150

MTBV 0.0096759 0.0135955 0.71 0.477

Total debt -1.29e-06 7.88e-07 -1.64 0.102

Debt-to-equity -0.0073111 0.0058403 -1.25 0.211

Stock is the dependent variable in this regressions. The independent variables include international, same industry, deal value, acquirer’s firm size, EBIT, MTBV, total debt and debt-to-equity. Only cases that earned positive cumulative abnormal returns three years after the event are included.

Table 9 shows the outcome of the multivariate regressions. Same industry is statistically significant at the 5%-significance level given its p-value of 0.027. Additionally, deal value and total debt are statistically significant at the 10%-significance level given their p-value of 0.061 and 0.080. The coefficients shows that for every unit increase in same industry, the log of odds using stock increases with 0.9658102; that for every unit increase in deal value, the log odds of using stock decreases with 0.0029 and that for every unit increase in total debt, the log odds of using stock decreases with 3.98e-06.

Table 9: Multivariate regression successful stock in the fifth merger wave

Coefficient Std. Err. Z P > |z| International -0.1626961 0.7147364 -0.23 0.820 Same industry 1.36883 0.6175907 2.22 0.027 Deal value -0.0024155 0.0012911 -1.87 0.061 Acquirer’s firm size 3.13e-08 5.40e-08 0.58 0.562

EBIT 1.20e-06 1.34e-06 0.90 0.370

MTBV 0.0157057 0.0189284 0.83 0.407

Total debt -3.98e-06 2.27e-06 -1.75 0.080

Debt-to-equity -0.0012743 0.0017825 -1.71 0.475

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5.2.1.1.2 UNSUCCESSFUL STOCK

27 of the mergers and acquisitions that are financed with stock earn negative cumulative abnormal returns three years after the event. Seven cases are international deals, 20 cases are national ones. In addition, seven cases occurred between parties that operate in the same industry, in 20 cases the acquirer and the target operate in different industries. The average value of the deals is $866.7 million. The smallest deal has a value of $1.66 million whereas the largest deal has a value of over $11 billion. The mean acquirer’s firm size is $3,120 billion. The smallest acquirer has an enterprise value of $21,531,000, the largest acquirer has an enterprise value of $14,400 billion. The average EBIT the acquiring firms earn are

$178,652,000. The lowest EBIT are negative and have a value of - $29,123,000. The highest EBIT have a positive value of $6.7 billion. This information is gathered from 26 out of the 27 cases. The average MTBV is 4.23. The lowest MTBV has a negative value of 11.16, the highest debt-to-equity ratio has a value of 11.34. The mean total debt has a value of $323,206,000. The lowest total debt is $0 while the highest amount of total debt in this sample is $6,097,149,000. Lastly, the average debt-to-equity ratio is negative and has a value of -51.91. The lowest debt-to-equity ratio is negative as well and has a value of -1853.12 while the highest debt-to-equity ratio has value of 97.75.

Table 10 shows the outcome of the univariate logit regressions of the unsuccessful mergers and acquisitions in the fifth merger wave. Only acquirer’s firm size is statistically significant at the 10%-significance level given its p-value of 0.093. The coefficient shows that for every unit increase in the acquirer’s firm size, the log odds of using stock increase with 1.03e-8.

Table 10: Univariate logit regressions unsuccessful stock in the fifth merger wave

EBIT 2.87e-08 7.41e-08 0.39 0.699

MTBV 0.0010726 0.6724 0.02 0.987

Total debt -2.91e-08 5.00e-08 -0.58 0.560

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Table 11 shows the results of the multivariate regression. None of the independent variables are statistically significant.

Table 11: Multivariate logit regression unsuccessful stock in the fifth merger wave

Coefficient Std. Err. Z P > |z| International 0.3032908 0.6203283 0.49 0.625 Same industry -0.7170591 0.6026286 -1.19 0.234 Deal value 0.0016005 0.0013739 1.16 0.244 Acquirer’s firm size 6.97e-09 1.10e-08 0.63 0.527

EBIT 7.46e-09 1.85e-07 0.04 0.968

MTBV -0.0020123 0.0979461 -0.02 0.984

Total debt -1.14e-07 1.60e-07 -0.71 0.476

Debt-to-equity -0.0025579 0.0023393 -1.09 0.274

5.2.1.2 CASH

Though it is not clear from this analysis but prior research does suggest there was a

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value of $0 whereas the highest total debt has a value of $6,530 billion. Lastly, the average equity ratio is 31.85. The lowest equity ratio is 0 and the highest debt-to-equity ratio is 170.66. Information on acquiring firm size, EBIT, total debt and debt-to-debt-to-equity are gathered from 50 out of the 52 merger cases in this subsample.

Table 12 shows the results of the univariate logit regressions. None of the independent variables is statistically significant since all p-value are above 0.05 and even above 0.10.

Table 12: Univariate logit regressions cash in the fifth merger wave

Coefficient Std. Err. Z P > |z| International -0.1690763 0.3860768 -0.44 0.661 Same industry -0.1270587 0.3465636 -0.37 0.714 Deal value -0.0001516 0.0002266 -0.67 0.504 Acquirer’s firm size 8.08e-10 4.25e-09 0.19 0.849

EBIT 7.42e-08 5.33e-08 1.39 0.164

MTBV -0.0100495 0.0187342 -0.54 0.592

Total debt 4.22e-08 2.99e-08 1.41 0.158

Debt-to-equity -0.0001819 0.0005951 -0.31 0.760

Cash is the dependent variable in this regressions. The independent variables include international, same industry, deal value, acquirer’s firm size, EBIT, MTBV, total debt and debt-to-equity.

Table 13 shows the outcome of the multivariate logit regressions. Again, no statistically significant findings can be identified.

Table 13: Multivariate logit regression cash in the fifth merger wave

Coefficient Std. Err. Z P > |z| International -0.4485403 0.4311759 -1.04 0.298 Same industry 0.0608876 0.3771816 0.16 0.872 Deal value -0.0003717 0.0003571 -1.04 0.298 Acquirer’s firm size -1.00e-09 6.06e-09 -0.17 0.868

EBIT 7.62e-08 1.08e-07 0.70 0.482

MTBV -0.0083178 0.1831 -0.45 0.650

Total debt 2.84e-08 4.69e-08 0.6169 0.544

Debt-to-equity -0.0000886 0.0006081 -0.15 0.884

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The table below shows that the acquiring firms that financed their M&A activity with cash earn positive cumulative average abnormal return of 0.9367668. These mergers and

acquisitions can thus be labelled “successful”.

Table 14: Mean cumulative abnormal returns three years after the event of the mergers and acquisitions financed with cash in the fifth merger wave

52 0.9367668 0.5845021 -0.2366701 2.110204

5.2.1.2.1 SUCCESSFUL CASH

The majority of the mergers and acquisitions that are financed with cash can be labelled “successful” since 29 out of the 52 cases earn positive cumulative abnormal returns three years after the event. Six cases are international deals, 23 are domestic ones. Moreover, 11 mergers are undertaken by acquiring firms that operated in the same industry as the target firm. This was not the case in the other 18 cases; here the deals are cross-industry. The average value of the deals in this subsample is $349.79 million. The smallest deal has a value of $180,000, the largest deal value is $2,196.18 million. Furthermore, the mean firm size of the acquirer is $2240 billion. The smallest acquirer has a negative enterprise value -$592,000, the largest acquiring firm has an enterprise value of $22,400 billion. The average EBIT are $1,704,877,000. The smallest EBIT earned have a negative value of over $55.8 million, the largest EBIT have a value of $1810 billion. Moreover, the mean MTBV of the acquiring firms is 4.14. The lowest MTBV is 0.51 while the highest MTBV is 16.53. Data on MTBV are however missing in three cases. The mean level of total debt has a value of $3,213,131,000. The lowest amount of total debt is $0 and the highest amount of total debt is $6,530 billion. Lastly, the average debt-to-equity ratio is 34.26. The lowest debt-to-equity ratio in this sample is 0, the highest debt-to-equity ratio is 170.66. The information in acquirer’s firm size, EBIT, total debt and debt-to-equity is not available for two cases which leads to this information being gathered from only 27 cases.

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Table 15: Univariate logit regressions successful cash in the fifth merger wave

EBIT 9.41e-08 7.76e-08 1.21 0.226

MTBV -0.0102714 0.0193542 -0.53 0.596

Total debt 4.79e-08 4.88e-08 0.98 0.327

Debt-to-equity -0.0037624 0.0052602 -0.72 0.474

Cash is the dependent variable in this regressions. The independent variables include international, same industry, deal value, acquirer’s firm size, EBIT, MTBV, total debt and debt-to-equity. Only cases that earned positive cumulative abnormal returns three years after the event are included.

The multivariate logit regression model shows also no statistically significant result; the p-values of all independent variables are well above 0.05 and even above 0.10. Consequently, no conclusions can be drawn on the influence of the independent variables on the dependent variable cash.

Table 16: Multivariate logit regression successful cash in the fifth merger wave

EBIT -1.11e-07 2.11e-07 -0.53 0.600

MTBV -0.0138142 0.0238973 -0.58 0.563

Total debt 7.52e-08 8.79e-08 0.86 0.392

Debt-to-equity -0.0027421 0.0057811 -0.47 0.635

Cash is the dependent variable in this regressions. The independent variables include international, same industry, deal value, acquirer’s firm size, EBIT, MTBV, total debt and debt-to-equity. Only cases that earned positive cumulative abnormal returns three years after the event are included.

5.2.1.2.2 UNSUCCESSFUL CASH