International Expansion Strategies: Are Cross-Border Mergers & Acquisitions Successful?

Msc in International Economic Consulting International Expansion Strategies: Are Cross-Border Mergers & Acquisitions Successful? Submitted for the degree of Master of Science in International Economic Consulting Author: Academic supervisor: Orhan Yesilyurt Valérie Smeets Aarhus School of Business, Aarhus University January 2012

Contents 1 INTRODUCTION... 1 2 INTERNATIONAL EXPANSION STRATEGIES... 3 2.1 Entry modes... 3 2.1.1 Export entry mode... 3 2.1.2 Contractual entry mode... 4 2.1.3 Investment entry mode... 5 2.2 Factors influencing the entry mode decision... 6 2.2.1 External factors... 7 2.2.2 Internal factors... 8 2.3 Entry mode choice: Theoretical explanations... 9 2.3.1 Internal focus... 10 2.3.2 External focus... 11 2.3.3 The electic model... 11 3 CROSS-BORDER MERGERS & ACQUISITIONS... 13 3.1 Definition of Mergers and Acquisitions... 13 3.2 Typology of Mergers & Acquisitions... 14 3.3 Motives for cross-border M&A... 15 3.3.1 Competitive considerations... 15 3.3.2 Response to a changing environment... 16 3.3.3 Inefficient capital markets... 16 3.4 Success of M&A... 17 4 LITERATURE REVIEW... 18 4.1 Wealth effects in M&A... 18 4.2 Wealth effects in cross-border M&A... 19 4.3 Factors affecting shareholder wealth... 19 I

5 METHODOLOGY... 21 5.1 The event study methodology... 21 5.1.1 Estimation period and event window... 21 5.1.2 Calculating abnormal returns... 22 5.1.3 Aggregating abnormal returns... 24 5.2 Testing the significance of abnormal returns... 25 5.2.1 Parametric tests... 25 5.2.2 Non-parametric tests... 26 5.3 Identifying factors influencing wealth effects... 27 5.3.1 Analyzing abnormal returns... 27 5.3.2 Factors affecting shareholder wealth... 28 5.4 Problems with event studies... 34 6 DATA... 35 6.1 Sample selection... 35 6.2 Data sources... 36 6.3 Sample characteristics... 37 6.4 Firm and deal characteristics... 40 7 EMPIRICAL RESULTS... 42 7.1 Wealth effects... 42 7.1.1 Full sample... 42 7.1.2 Pre-crisis vs. crisis... 45 7.2 Wealth effects by region... 48 7.3 Factors influencing wealth effects... 50 7.3.1 Full sample... 50 7.3.2 Pre-crisis vs. crisis... 56 7.4 Case study... 60 8 CONCLUSION... 62 REFERENCES... 65 APPENDIX... 74 II

List of Figures Figure 1: Factors in the entry mode decision... 9 Figure 2: Entry mode choice: The electic model... 13 Figure 3: Time line of an event study... 22 Figure 4: Average abnormal returns: Full sample... 42 Figure 5: Cumulative average abnormal returns: Full sample... 43 Figure 6: Average abnormal returns: Pre-crisis vs. crisis... 45 Figure 7: Cumulative average abnormal returns: Pre-crisis vs. crisis... 46 Figure 8: An acquisition in the pre-crisis period: The case of Naturex... 60 Figure 9: An acquisition in the crisis period: The case of Koninklijke DSM... 61 III

List of Tables Table 1: Characteristics of different entry modes... 6 Table 2: Data sources... 37 Table 3: Sample size and frequency of CBM&A by years... 37 Table 4: Frequency of CBM&A by target region... 38 Table 5: Frequency of CBM&A by industry classification of the acquirer... 39 Table 6: Firm characteristics... 40 Table 7: Deal characteristics... 41 Table 8: Average abnormal returns surrounding the announcement of CBM&A: Full sample... 44 Table 9: Average abnormal returns surrounding the announcement of CBM&A: Pre-crisis vs. crisis period... 47 Table 10: Cumulative average abnormal returns by target region... 49 Table 11: Cross-sectional regression results: Full sample... 51 Table 12: Cross-sectional regression results: Pre-crisis vs. crisis... 56 IV

1 Introduction Multinational enterprises (MNE) face several possibilities when expanding overseas. The entry mode choice is influenced by a number of factors such as market, production, environmental, transaction and resource factors. Expansion strategies range from export entry modes to investment entry modes. The export entry mode is a low risk foreign market entry mode. Products (or services) are produced in the home market and transferred to the foreign market. With an increasing involvement in the foreign market, risks also increase. Cross-border Mergers & Acquisitions (CBM&A) require high investments and a high involvement in the target country. Additional risks and complexities arise; countries of the acquirer and target differ in political and economic environment, culture, national laws, tax and accounting rules. With growing globalization, CBM&A are gaining in importance. The total CBM&A value increased from 200 billion USD in 1990 to 1637 billion USD in 2007 (Sudarsanam, 2010). The increasing CBM&A activity combined with the high investments and complexity involved with CBM&A raise the question whether CBM&A are worthwhile or not. Wealth effects of domestic Mergers &Acquisitions (M&A) are discussed to a large extent in the literature, whereas there is still a need for research in CBM&A. In both domestic and cross-border M&A, scholars agree on positive wealth effects to the target. However, wealth effects to the acquirer are less clear. Where some studies report negative wealth effects (Andrade et al., 2001; Mulherin and Boone, 2000; Franks et al., 1991 and Walker, 2000), others report positive wealth effects (Asquith et al., 1983; Loderer and Martin, 1990; Schlingemann, 2004; Moeller, Schlingemann, 2005 and Antoniou et al., 2007). In CBM&A, wealth effects tend to be positive. The bulk of the literature focuses on M&A in the US and UK. This thesis examines wealth effects in the Euro zone and covers the manufacturing industry in the period 2005 to 2010. The full sample consists of 345 M&A transactions and is divided into two subsamples to analyze wealth effects prior to the crisis and in the crisis. It is expected that shareholders experience positive wealth effects in CBM&A. Wealth effects are expected to be higher in the crisis. Only a limited number of companies are able to acquire others in a crisis. It is expected that an acquisition of a company in the crisis is perceived as a sign of health and economic strength by the market, leading to higher positive abnormal returns than in the pre-crisis period. 1

Furthermore, determinants of the wealth effects are analyzed. Firm and deal factors are used to explain wealth effects to a large extent in the literature. The focus of the thesis is on macroeconomic factors. It is expected that macroeconomic factors are important in explaining wealth effects. Better economic indicators in the target country should affect shareholder wealth positively. This is especially true for the crisis. It is expected that macroeconomic factors of the target are more important in the crisis period. CBM&A are a way to capture growth in other markets when the home market is weakening. As expected, the analysis results in positive wealth effects to acquirers. Wealth effects of 1,1% are experienced around the announcement day. Wealth effects are higher in the crisis (1,47%) compared to the pre-crisis period (0,93%), however the difference is not statistically significant. Although there is not a significant difference in the size of wealth effects between the pre-crisis and crisis, the analysis leads to an interesting finding. In the pre-crisis, wealth effects are experienced in a longer time period. The market evaluates M&A announcements and market adjustments occur. Adding the results of the analysis of the determinants shows that macroeconomic factors do not seem to play an important role; firm and deal factors are more important in explaining shareholder wealth. The market evaluates deal and firm factors as they become public. This takes time and the reaction occurs slower. The opposite is true in the crisis period. Wealth effects in the crisis are captured shortly around the announcement and macroeconomic factors are important in explaining shareholder wealth while firm and deal factors do not seem to be important. The market reacts instantaneously around the announcement without evaluating deal or firm factors. M&A announcements are seen as positive per se. Wealth effects are bigger, the worse the macroeconomic factors in the acquirer country and the better the macroeconomic factors in the target country. As expected, macroeconomic factors are important in explaining shareholder wealth in the full sample and crisis period. The results presented in the thesis are limited to acquirers in the Euro zone and the manufacturing industry. Furthermore, only short-term effects are analyzed. The conducted event study is a short horizon event study. The underlying assumption is market efficiency. The market reacts to the M&A announcement when the information becomes public. Long horizon event studies (over one to five years) account for market 2

inefficiency, i.e. the market does not process the information immediately and react to M&A announcements efficiently (Kothari, Warner, 2006). The remainder of the thesis is structured as follows. Section 2 puts CBM&A in context of international expansion strategies. This is different to other, pure financial, studies. Instead of focusing on the financial literature, a more interdisciplinary approach is chosen. First, an overview of international expansion strategies is given (2.1). The choice of entry mode is influenced by a number of factors (2.2). Theories from various disciplines try to explain the choice of the entry mode (2.3). Section 3 shifts the focus to M&A. The term M&A is defined (3.1) and specified (3.2), motives are presented (3.3) and finally the success of M&A is defined (3.4). Section 4 gives an overview over the current literature. The methodology applied in the thesis is presented in section 5. Section 6 summarizes the data, before empirical results are presented in section 7. Section 8 concludes the thesis. 2 International Expansion Strategies 2.1 Entry modes When multinational enterprises (MNE) plan to expand overseas, they face several entry modes. Root (1994) defines an international market entry mode as an institutional arrangement that makes possible the entry of a company s products, technology, human skills, management, or other resources into a foreign country. Entry modes can be classified into three categories: Export entry mode, contractual entry mode and investment entry mode (Root, 1994). 2.1.1 Export entry mode Exporting is the most common form of international expansion. It describes a process where a product or a service is produced in the domestic market and transferred to a foreign market (Root, 1994). Exporting is a less risky mode of foreign market entry. Products are produced in the domestic market. Additional risks of producing in the foreign market do not arise. The foreign market can be experienced before investments are made. Potential risks of operating overseas are reduced. However, exporters are dependent of overseas agents. 3

They have no control over the marketing mix. The lack of control is the main disadvantage of exporting (Root, 1994). 2.1.2 Contractual entry mode Contractual entry modes are defined as long-term nonequity associations between an international company and an entity in a foreign target country that involve the transfer of technology or human skills from the former to the latter (Root, 1994). Contractual entry modes focus on the transfer of knowledge and skills rather than physical products. The most known contractual entry mode is licensing. In a licensing arrangement, a company transfers to a foreign entity (usually another company) for a defined period of time the right to use its industrial property (patents, know-how, or trademarks) in return for a royalty or other compensation (Root, 1994). 1 Licensing is a good way to increase income on products that are already developed; it requires only little capital investment. Foreign markets can be entered in a fast way without making high investments. This also allows for the rapid exploitation of new products worldwide. The domestic company can immediately use the advantages of the foreign company s local marketing and distribution channels and the existing customer base without establishing his own in a costly and time consuming process. Furthermore, licensing facilitates the expansion to markets that are otherwise closed because of high duties or import quotas. However, licensing also brings some disadvantages. The licensee receives sales rights for certain territories. If the licensee cannot meet expectations, renegotiations might be expensive. Also, the licensor might face a competitor instead of a licensee after the expiry of the licensing agreement. Furthermore, the licensor has no control over a licensee s operations in the foreign market. In this circumstance, quality control might be important. Because products are often sold under the licensor s brand name in the foreign market, the brand name might be harmed by quality defects of the licensee (Hollensen, 2007). In general, license fees are a small percentage of the turnover (about 5 percent). A company can always do better by performing operations itself. In this sense, license agreements are only a second best solution compared to a company s own production (Hollensen, 2007). 1 Other contractual entry modes are franchising, the transfer of services directly to the foreign country in return for monetary compensation (technical agreements, service contracts, management contracts and construction contracts) or in return for products (contract manufacture and co-production agreements). 4

2.1.3 Investment entry mode The investment entry mode is characterized by an ownership involvement in the target country. This might be an involvement in form of plants or other production units in the foreign country that perform similar activities (horizontal), upstream or downstream activities (vertical) or unrelated activities (conglomerate). Two main types of the investment entry mode exist: sole venture and joint venture. These types differ in the degree of ownership and control. In a sole venture the domestic company has full ownership and control over the foreign subsidiary. A sole venture might be in the form of acquiring an existing company (acquisition) or creating a new company (Greenfield investment). In a joint venture, ownership and control are shared with a foreign company (Root, 1994). An acquisition is a rapid way of entering a new market. The domestic company (acquirer) enters the foreign market by acquiring an already established company. This way, the acquirer can take advantage of distribution channels, a qualified labor force, management experience, local knowledge and an established brand name (reputation). However, an acquisition is an expensive option and involves high risks. The integration of the acquired company (target) might be hindered by cultural differences. In addition, cultural differences might also lead to communication and coordination problems. Furthermore, the acquisition of a company could cause resistance in the foreign market, especially when the company is regarded as part of the country s heritage (Hollensen, 2007). A Greenfield investment on the other hand is a slow entry mode and requires high investments. However, it allows forming a subsidiary shaped to meet the needs of the domestic company. As a result, operational efficiency might increase. Furthermore, there is no need to find an adequate target, which could prove to be very difficult (Hollensen, 2007). Joint ventures are a less costly entry mode because risks and benefits are shared. In this mode, the domestic company gets access to the expertise and contacts in the foreign market. The foreign partner furthermore contributes with skills and knowledge that is required to manage a business in the foreign country. A joint venture might also be a response to government policies of the foreign country. In some countries, sole ventures are prohibited or discouraged by the foreign government (e.g. China); Joint ventures are 5

then a way to overcome these restrictions. However, flexibility is low compared to sole ventures as a result of the cooperation. Cultural differences and different management styles could cause further problems. Another point is the conflict potential between the partners. Objectives may change over time and lead to conflicts (Hollensen, 2007). To summarize, the investment entry mode may lower the costs of supplying the foreign market. As a result, transportation costs and customs duties are saved. Furthermore, costs might decrease due to lower production costs (from lower input costs such as wages, materials, etc.). In addition, the investment entry mode can be used to increase the supply of products when import quotas or a full-capacity working domestic production hinders the expansion. In contrast to licensing a more uniform quality might be realized. However, the investment entry is the most costly entry mode. It requires more capital, management and other resources. Furthermore, it is a very risky mode. Investment entry modes are more exposed to political, economic, socio-cultural and market risks than other modes, since they come along with an ownership involvement in the foreign country (Root, 1994). Table 1 summarizes and compares the presented entry modes. With an increasing involvement in the foreign market the degree of control increases, returns increase (control allows companies to maximize their returns), more resources (investments) are required and risks increase (due to higher commitment) (Agarwal Ramaswami, 1992). Table 1: Characteristics of different entry modes Entry mode Control Resources Risks Returns Exporting Low low low low Licensing Low low low low Joint venture Medium medium medium medium Sole venture high high high high Source: Own illustration based on Agarwal and Ramaswami (1992) and Hill et al. (1990). 2.2 Factors influencing the entry mode decision The choice of entry modes is affected by various factors and is associated with a high degree of complexity. Influencing factors can be grouped into two categories: external factors and internal factors. 6

2.2.1 External factors The following factors are external to the company. The company cannot influence these factors; however it can consider them in the decision-making process. External factors are the target country s market factors, production factors and environmental factors. Market factors The entry mode choice might be affected to a high extent by market factors, such as the market size, growth potential, demand uncertainty, competition intensity and the existing infrastructure in the foreign market. Large markets and high growth potentials justify a high involvement in the target country (investment entry mode). Furthermore, an insufficient market infrastructure (the absence of adequate agents, distributors or partners) makes the investment entry mode necessary (Chen, Mujtaba, 2007; Root, 1994). However, demand uncertainty and competition intensity favor less involvement in the target country. A high demand uncertainty implies risks. A low investment entry mode that allows firms to leave the market without losing substantial sunk costs, in case of low demand, is favored. Competition intensity results in lower profitability, since a number of firms are competing for profits. The prospect of low profits does not justify a high investment (Kim, Hwang, 1992). Production factors Production factors such as raw materials, labor, energy, as well as economic infrastructure (e.g. transportation and communication) are important factors when choosing the entry mode. Low costs encourage production in the foreign market (Root, 1994). Environmental factors Environmental factors can be grouped into three categories: government policies and regulations, country risk and cultural distance. Countries differ in their attitude towards foreign ownership. Government policies and regulations may hinder or exclude the investment entry mode (sole ventures) (Koveos, 1997). 7

Country risk summarizes a number of risks, which have to be considered when entering foreign markets. Country risks consist of economic risk (the risk of a change of the economic condition in the target country), transfer risk (the risk of restrictions on capital movements), exchange risk (risk of unexpected adverse exchange rate movements) and political risk (risk of change of political environment; expropriation risk). Country risks discourage high investment entry modes (Meldrum, 2000; Busse, Hefeker, 2007; Jinjarak, 2007; Schmidt, Broll, 2009). The entry mode choice is also influenced by the cultural distance (Kogut, Singh, 1988). Cultural distance summarizes differences in cultural values, language, social structure and lifestyles between the home and foreign country (Root, 1994). Firms have to acclimate to foreign cultural environments. A low investment entry mode allows experiencing the market before making high investments (Kim, Hwang, 1992). 2.2.2 Internal factors In the following internal factors are presented. The choice of the entry mode is influenced by transaction factors, production factors and resource factors. Transaction factors The entry mode choice might be influenced by transaction factors, such as asset specificity, firm specific know-how or tacit know-how. Asset specificity or firm specific know-how creates dissemination risk. Partners seek for rents and may act opportunistically (Chen, Mujtaba, 2007; Hill et al., 1990). Alternatively, know-how might not be transferable. If the know-how is of tacit nature, the transfer to other companies is difficult by definition. A company can transfer a blueprint to a licensee; however, tacit know-how might hinder the blueprint to turn into a successful product. Although in theory, contracts could be used to solve the mentioned problems, in practice, contracts are incomplete and provide only partial insurance (Hill et al., 1990; Kim, Hwang, 1992). Entry modes with high control (export or investment entry mode) are preferred in the case of asset specificity, firm specific know-how or tacit know-how (Chen, Mujtaba, 2007; Hill et al., 1990). 8

Product factors The entry mode choice is further influenced by product factors. Highly differentiated products give firms more power. As a result, products do not directly compete, and high transportation costs or import duties are easier to overcome. However, if the product is weakly differentiated, sellers will have to compete on the basis of price. This causes transportation costs and import duties to become more important and products in the foreign market to become less competitive (Root, 1994). Resource factors Entry mode options increase with the number of recourses (management resources, capital, technology, production skills and marketing skills) the expanding company has. Limited resources allow only a low involvement in the foreign market (Root, 1994). The firm size is an important factor. Larger firms have more resources, are more capable of absorbing risks and are more likely to undertake high involvement entry modes (Chen, Mujtaba, 2007). Figure 1 summarizes the factors affecting the entry mode decision. Figure 1: Factors in the entry mode decision Source: Own illustration based on Chen and Mujtaba (2007), Hill et al. (1990), Kim and Hwang (1992) and Root (1994). 2.3 Entry mode choice: Theoretical explanations Numerous theories try to explain the choice of the foreign entry mode. It is a crucial decision for the success of the international expansion and forms one of the core topics in international management research (Werner, 2002). Disciplines that explain the foreign entry mode choice range from industrial economics, organizational economics, 9

competitive strategy models, finance, international economics and trade, organizational theory and transaction cost economics (Sudarsanam, 2010). The multidisciplinary character of this field results in a lack of common theoretical base and impedes a conceptual consensus (Melin, 1992). The diverse theoretical approaches are more complementary than exclusive (Chen, Mujtaba, 2007). In the following, the most important theories are presented. The entry mode decision is affected by various factors and the theories are grouped according to the factors they focus on. 2.3.1 Internal focus The most commonly used theory is the transaction cost theory. The focus is on the cost side. Transaction costs cover costs of writing and enforcing contracts, information costs and costs of monitoring actions in the foreign country. The basic rationale behind the transaction cost theory is that firms need to create governance structures that will minimize costs and inefficiencies associated with entering and operating in a foreign market (Canabal, White, 2008). A similar theory that focuses on transaction costs is the internalization theory. Both theories focus on transaction costs when deciding on the entry mode. The transaction cost theory concentrates on transaction characteristics (asset specificity), whereas the internalization theory looks at the market for know-how (Madhok, 1997). These theories can also be seen as the same theory (Chen, Mujtaba, 2007). According to these theories, the investment entry mode has to be chosen if transaction costs are high. Another theory explaining the choice of foreign entry is the organizational capability theory. This theory has a broader perspective, looking at the firm instead of transactions. Companies do not only enter markets to exploit advantages, but also to develop advantages. Competitive advantages are potential rent-generating abilities of an asset or know how (Madhok, 1997). The specific entry mode depends on the capabilities of the companies. A sole entry is preferred if the company has superior capabilities. However, if the company has capability constrains, collaborations with foreign companies are preferred (Malhotra et al., 2003). This theory goes in line with other theories that propose an increasing involvement in the foreign market. The internationalization theory suggests that with increasing knowledge about the foreign market and operations (or capabilities in terms of the organizational capability theory), the commitment in the foreign market increases (Johanson, Vahlne, 1990). 10

2.3.2 External focus Hymer s market imperfections theory focuses on external factors. The theory explains why companies enter foreign markets although they are disadvantaged to begin with. Entrants into foreign markets are limited in their knowledge about the market and are exposed to additional costs due to the new environment. Hymer (1976) argues that market imperfections and unique advantages of companies are competitive advantages that stimulate foreign direct investments (investment entry mode) (Malhotra et al., 2003). Four imperfections are discussed by Hymer: (1) imperfections in goods markets (brand names, marketing skills, product differentiation, and price collusion, which other firms do not have), (2) imperfections in factor markets (exclusive resourcing capabilities, proprietary managerial skills, and technology), (3) imperfect competition due to economies of scale (cost declines), and (4) imperfect competition due to government intervention (policies in favor of FDI) (Hymer, 1976; Malhotra et al., 2003). Another theory worth mentioning is the network theory, which focuses on the environment of companies. The international expansion is explained by interorganizational and interpersonal relationships between companies rather than company specific factors. The entry mode decision is then influenced by these network relationships 2. Relationships are characterized as dynamic, complex and less structured (Malhotra et al., 2003). 2.3.3 The electic model The electic model (or OLI-model) is the dominant and most powerful model in the foreign entry mode literature. It explains the foreign entry mode decision with three factors rather than focusing on a specific factor. According to the model, a company s entry choice depends on ownership, location and internalization advantages. The foreign involvement increases with increasing fulfillment of these advantages (Hansen, Nielsen, 1997). In the following, these factors are presented. Ownership The model first answers whether companies should expand overseas or not; ownership advantage is the crucial criteria. Ownership advantage consists of two dimensions: asset 2 The theory does not predict the choice of entry mode. 11

exploiting and asset augmenting. Asset exploiting describes the transfer of competitive advantages from the home market to the foreign market. Competitive advantages are brand name, reputation, design, production and management capabilities, engineering and technological expertise. Asset augmenting describes the augmenting of resources and capabilities of the foreign market and integrating those with the company s own capabilities. Resources might be natural resources (minerals), intermediate goods (components) or intangible assets (technology). The model suggests that companies should expand overseas if they have an ownership advantage that gives them market power or cost advantages (Sudarsanam, 2010). Internalization 3 The internalization decision deals with the decision to make or buy (Sudarsanam, 2010). Coase (1937) argues that the optimal degree of internalization depends on the balance between transaction costs and organizational costs, which determines the boundaries of the firm. Returns are usually higher when companies perform activities themselves compared to externalizing them (Hansen, Nielsen, 1997). However, externalization might be necessary due to foreign government policy, regulations or other factors (Koveos, 1997). Internalization advantages support the use of the export or investment entry mode. If there are no internalization advantages, the model suggests the contractual entry mode (Sudarsanam, 2010). Location Location advantages are all advantages that occur due to production in the foreign market (Hansen, Nielsen, 1997). Pull factors of the foreign market as well as push factors in the domestic market are important. The following pull factors might favor a production in the foreign market: the size of the market, demand for products, scale economics in local production and distribution, proximity to immobile resources, availability of complementary assets and foreign government incentives. Push factors might be: the maturity of the domestic market, intensive competition, poor infrastructure, government-, regulatory-, fiscal- and other policies, lack of access to cheap inputs, and political and economic uncertainty. The location advantage separates 3 The internal versus external decision depends on a number of factors, interested readers are referred to Holmström, Roberts (1998). 12

the export and investment entry mode. Home country advantages favor the export entry mode and foreign country advantages the investment entry mode (Sudarsanam, 2010). Figure 2 visualizes the electic model. Figure 2: Entry mode choice: The electic model Source: Sudarsanam (2010). The model suggests that firms should use the investment entry mode, if they have ownership advantages, internalization advantage and a host country advantage. 3 Cross-border Mergers & Acquisitions 3.1 Definition of Mergers and Acquisitions The term Mergers & Acquisition (M&A) is defined in many ways in the literature. There is no generally accepted definition; it is rather a collective term. The collective term character is also expressed in the definition of Copeland and Weston (1988): [ ] the traditional subject of M&A's has been expanded to include takeovers and related issues of corporate restructuring, corporate control, and changes in the ownership structure of firms, [ ]. Thus, M&A in the broader sense are all transactions that lead to changes in the ownership structure. 13

An important distinguishing character between M&A is the interdependence between the involved companies. A merger describes the complete fusion of two or more legally and economically independent companies into one company. At least one of the companies loses its independence and is subordinated to the other company (Lucks, Meckl, 2002). In an acquisition, however, the legal independence remains, where the target s economic independence is limited or completely lost (Mußhoff, 2007). In the following section, M&A are further classified with regard to other dimensions. It becomes clear why a uniform definition is difficult. 3.2 Typology of Mergers & Acquisitions M&A deals can be grouped into numerous categories according to their characteristics. The following section focuses on characteristics used in this thesis. Both public and private companies can be involved in an M&A deal. In this thesis, acquirers are public companies. However, targets are both public and private companies. M&A deals do not need to comprise the entire target. It is also possible that the deal is limited to a share of the target. The influence of the acquirer differs depending on the acquired stake. Minority stake, parity stake, majority stake or a full acquisition can be distinguished. A minority stake gives the acquirer only a limited influence. However, if the blocking minority is reached, the acquirer can veto certain decisions. The threshold of a blocking minority varies internationally between one quarter (e.g. Germany) and one third (e.g. France) of the shares of the target. A majority stake describes an acquisition of more than 50% of the shares of the target and gives the acquirer a significant influence on the target. The acquirer can even decide independently on merging with another company, if others do not obtain a blocking minority. An acquisition of 50% is interesting when another party holds the remaining 50%. Consequently, important decisions have to be decided jointly (Luck, Meckl, 2002). In this thesis, an acquisition is defined as acquiring at least 50% of the target. M&A deals can be paid by cash, shares or other financial instruments. Mixed forms are also possible. The method of payment depends on the resources of the acquirer and on tax benefits of both target and acquirer. The capital markets interpret the method of 14

payment as a signal and include it in their evaluation of the deal (Mußhoff, 2007; Wübben, 2007). Furthermore, M&A deals can be classified as related or unrelated. The relatedness reveals whether the acquirer follows a focus or diversification strategy. 3.3 Motives for cross-border M&A Cross-border M&A activity can be the result of a number of motives. A standardized systematization of the motives is non-existent in the literature. Cantwell and Santangelo (2002) suggest a categorization in competitive considerations, a response to a changing environment and inefficient capital markets. 3.3.1 Competitive considerations Increasing market power One motive behind M&A activity is to increase the market power. By acquiring other firms in the same industry, firms can increase their market share, and thus their market power (Ahammad, Glaister, 2010). Firms with market power can consistently charge higher prices than those realized in competitive markets (Alvarado, 1998). Defensive strategies M&A can also be the result of defensive strategies. Firms may acquire other firms to increase the firm size and thus avoid a takeover by other firms. However, it is also possible that a firm acquires another firm to prevent the firm from being acquired by a third-party. This might also be done to prevent a competitor from becoming too strong and building market power (Cantwell, Santangelo, 2002). Economics of scope and synergy The potential of economics of scope or synergy are another reason for M&A activity. Economics of scope arise when producing two different goods jointly costs less than producing them separately (Cantwell, Santangelo, 2002). Likewise, economics of synergy arise when combining two firms leads to a larger value than adding the values of the two individual firms (Seth et al., 2000). Synergies can appear in various ways. Two main types of synergies can be distinguished: operating and financial synergies. Operating synergies are synergies realized through combining operations or activities. This can lead to an increase in the firm s capacity and a decrease in costs through largescale production. Combining R&D and marketing activities are the most common 15

synergy sources. Financial synergies are a further motive for M&A activity. Financial synergy aims to reduce cost of internal financing rather than costs of production. Acquiring firms with a high level of cash can lower the cost of capital. Furthermore, the acquirer gains access to the financial network of the target (Ahammad, Glaister, 2010). Reduction of transaction and information costs By internalizing upstream or downstream activities, transaction and information costs can be reduced. In this case, firms are seeking for more control. This is especially important for critical activities along the value chain (Cantwell, Santangelo, 2002). 3.3.2 Response to a changing environment Regulation Regulatory reasons could be another motive for M&A activity. Changes in regulation might induce a shift from the regulated market to the unregulated market. Furthermore, M&A activity could be stimulated by tax savings (Cantwell, Santangelo, 2002). Access to resources or technologies Another motive for CBM&A is to acquire resources and technologies. CBM&A can be seen as an opportunity to acquire new skills and knowledge. Products are based on a variety of technologies, which are critical for the success. By acquiring an existing foreign company, the acquirer gains access to resources and technologies, such as patent-protected technologies, superior management and marketing skills (Desyllas, Hughes, 2008; Ahammad, Glaister, 2010). 3.3.3 Inefficient capital markets Removal of inefficient management According to the Hubris-Hypothesis, managers believe that they can lead the target better than the management of the target. The replacement of the inefficient management as result of the M&A should then increase the value of the acquired firm. This argument is particularly valid for large companies acquiring small ones. As the small company grows, management skills requirements grow too (Ahammad, Glaister, 2010). However, errors are made in the assessment of the target; especially in CBM&A. Asymmetric information is greater in CBM&A than in the domestic counterparts (Seth et al., 2000). The incorrect assessment of the target shifts profits from the shareholders of the acquirer to the shareholder of the target (Ahammad, Glaister, 2010). 16

Corporate Hedging Diversification is an important motive. It allows firms to achieve growth and reduce overall risk. Diversification means, in a general sense, spreading the business activities of the firm. This can be achieved by introducing new products, focusing on new custom segments or new geographic markets. The business activities of the firm are thus less correlated (Chan-Olmsted, Chang, 2003). However, diversification is not valuemaximizing from a financial perspective. Shareholders can generate these benefits to lower costs individually by diversifying their portfolios (Ahammad, Glaister, 2010). Managerial Ego There are also motives that do not follow an economic rationale. The Empire-Building hypothesis states that managers are aiming for growth through M&A for personal reasons (Seth et al., 2000). The interests of the company are subordinated in this case. Economically unfavorable M&A deals are made at the expense of shareholders. The company is growing beyond an optimal size, which leads to a lower corporate performance and shareholder value (Hope, Thomas, 2008). However, managers have incentives to increase the size of the company, since with increasing company size their salaries, power and prestige increases. Therefore, managers will pay higher prices for the target if governance mechanisms are weak (Ahammad, Glaister, 2010). 3.4 Success of M&A The previous section presented the motives for M&A. Whether M&A aim to increase market power, realize economics of scope or synergy, access resources or technologies, diversification or other strategies, the primary goal is to enhance value for its shareholders. This does not mean that the interests of other stakeholders are irrelevant, but instead, the widespread view of the shareholder value approach is followed. The shareholder value approach states that the interests of the shareholders are above those of all others (Rappaport, 1998). The definition of success depends on the shareholders required returns. Three outcomes are possible: 1. Value conserved: Shareholders get what they required. In this case they earn normal returns, meaning that shareholders earn the return that they would earn on a similar risky investment. 2. Value creation: If value creation is realized, shareholders earn more than what they required. abnormal returns are realized when shareholders earn more than what they would earn on similar risky investments. 3. 17

Value destroyed: Value destroyed means that shareholders earn less than what they required. In this case, shareholders could have earned more by investing in another similar risky investment. M&A deals that do not destroy value are defined as successful (Bruner, 2004). The following section reviews the literature on shareholder wealth of M&A. Prior empirical findings are presented. 4 Literature review 4 4.1 Wealth effects in M&A Wealth effects of M&A to shareholders are discussed to a large extent in the literature. Whereas scholars agree on wealth effects to the target s shareholders, wealth effects to the acquirer s shareholders are less clear. The target s shareholders receive large premiums varying between 10% and 30% on the pre-acquisition stock price. Positive wealth effects are experienced. Studies conducted for the US result in wealth effects of 29% in 1963-86 (Jarrell, Poulsen, 1989), 27% in 1971-82 (Kaplan, Weisbach, 1992), 24% in 1972-87 (Servaes, 1991), 16% in 1973-98 (Andrade et al., 2001), 28% in 1975-84 (Franks et al., 1991) and 21% in 1990-99 (Mulherin, Boone, 2000). Similarly, shareholders experienced wealth effects of 24% in 1955-85 (Franks, Harris, 1989), 19% in 1966-91 (Danbolt, 2004) and 13% in 1990-2001 (Goergen, Renneboog, 2004) in the UK and Europe. In contrast to wealth effects to the shareholders of the target, wealth effects to the shareholders of the acquirer are small. Studies result in wealth effects similar in size varying between 0% and 2% 5. However, there is no consensus about the sign of the effect. Some studies report negative wealth effects of -0,7% in 1973-98 (Andrade et al., 2001), -0,4% in 1990-99 (Mulherin, Boone, 2000), -1,0% in 1975-84 (Franks et al., 1991) and -0,8% in 1980-96 (Walker, 2000), where other studies report positive wealth effects of 0,9% in 1963-79 (Asquith et al., 1983), 0,7% in 1966-84 (Loderer, Martin, 4 An overview of the literature is given in Appendix A. 5 Looking at the time frame reveals whether wealth effects between 0 and 2% are important or not. Returns are usually realized in a short time period. A return of 0,5% [1 %] in five days (one week) would yield in a return of 30% [68 %] in a year. This would imply that a return of 0,5% is achieved every week. This is possible in theory, but unrealistic in practice. However, it shows that even small returns can have a big economic importance when considering the time frame (Bruner, 2004). 18

1990), 1,6% in 1984-98 (Schlingemann, 2004), 1,2% in 1985-95 (Moeller, Schlingemann, 2005) and 1,3% in 1987-2004 (Antoniou et al., 2007). The combined effect to targets and acquirers is unanimously positive. Studies document wealth effects of 11,3% in 1968-86 (Lang et al., 1989), 1,8% in 1973-98 (Andrade et al., 2001), 3,9% in 1975-84 (Franks et al., 1991) and 3,6% in 1990-99 (Mulherin, Boone, 2000). 4.2 Wealth effects in cross-border M&A In cross-border M&A, wealth effects to the acquirer s shareholders are mainly positive. Markides and Oyon (1998) analyzed wealth effects of 236 acquisitions made by US acquirers between 1975 and 1988 and found positive and statistically significant wealth effects of 0,4%. Similar results are found by Moeller and Schlingemann (2005). Moeller and Schlingemann (2005) examined wealth effects to 383 US acquirers in the period 1985 to 1995 and found positive, however statistically insignificant, wealth effects of 0,3%. The results for US acquirers are further supported by other studies (Doukas, Travlos, 1988; Markides, Ittner, 1994 and Biswas et al., 1997). A few studies focus on European acquirers when analyzing shareholder wealth. Campa and Hernando (2004) analyzed 80 CBM&A within the European Union in the period 1998 to 2000 and found positive wealth effects of 0,1%. Cakici et al. (1996) examined wealth effects to 195 foreign acquirers (mainly European) in the US between 1983 and 1992, and reported statistically significant wealth effects of 0,6%. Wealth effects to Dutch acquirers are analyzed by Corhay and Rad (2000). The study covered 84 acquisitions in the period 1990 to 1996 by Dutch companies in Europe and concluded in statistically significant wealth effects of 1,1%. Lowinski et al. (2004) obtained similar results for Swiss acquirers. The analysis covered 91 CBM&A between 1990 and 2001 and resulted in statistically significant wealth effects of 1,3%. 4.3 Factors affecting shareholder wealth The bulk of the shareholder wealth literature focuses on firm or deal factors, such as the relative size of the target to the acquirer, ownership structure of the acquirer, the method of payment, strategic direction and relatedness of the involved companies. These factors 19

are also transferred to cross-border M&A studies. 6 The focuses of the thesis is on macroeconomic factors. 7 Although some studies include macroeconomic factors such as foreign exchange rates (Markides, Oyon, 1998; Moeller, Schlingemann, 2005), only a few studies go further. In the following, these studies are presented. Kiymaz and Mukherjee (2000) examine wealth effects to US acquirers in 112 crossborder M&A between 1982 and 1991. The study aims to explain the effect of country diversification on shareholder wealth. The authors argue that wealth effects to shareholders increase with higher differences between the countries of the target and acquirer. Country diversification is seen as a source for wealth gains. To measure comovements between the two countries, the authors use the correlation in the growth rates of the gross national product (GNP) and the correlation in the stock market indexes. Furthermore, the study uses the strength of the foreign exchange rate, firm and deal factors to explain wealth effects. The results support the hypothesis of the authors; country diversification creates wealth in cross-border M&A. The correlation in growth rates of the GNP and the correlation in stock market indexes have a statistically significant negative effect on shareholder wealth. The strength of the foreign exchange rate, however, does not have a statistically significant effect. The impact of macroeconomic factors is furthermore examined by another study of Kiymaz. Kiymaz (2004) analyzes wealth effects to US acquirers in the financial services industry. The study covers 207 CBM&A between 1989 and 1999. Wealth effects are analyzed by using macroeconomic factors such as the foreign economic condition, level of economic development and exchange rate volatility. Firm and deal factors are included as well. The results show that macroeconomic factors are important in explaining wealth effects in CBM&A. Kiymaz (2004) found that the foreign economic condition and exchange rate volatility have a statistically significant negative effect on shareholder wealth. Furthermore, acquisitions in developing counties result in higher wealth gains than acquisitions in developed countries. Both the strength of the foreign exchange rate and the correlation in GNP growth rates between the two countries do not seem to affect shareholder wealth. 6 Most of the studies in the literature use endogenous factors in explaining shareholder wealth; these factors can be influenced directly or indirectly by the management of the involved companies (Wübben, 2007). The thesis focuses on exogenous factors, i.e. on factors that can only be observed and not be influenced by the management of the involved companies. 7 Factors used in the thesis are explained in section 5.3. 20

Markides and Ittner (2004) provide another study that examines the impact of macroeconomic factors. They analyze wealth effects to US acquirers in 276 CBM&A between 1975 and 1988. The study includes factors such as tax regulations, the strength of the US dollar, the difference in GDP growth between the foreign country and US, the correlation between the stock market returns in the US and the foreign country, the difference in inflation, and hourly wages between the foreign country and US. Furthermore, a dummy variable captures the effect of the stock market crash in 1987. The authors found a statistically significant positive effect of the strength of the US dollar. However, they could not find evidence for the importance of the other macroeconomic factors. 5 Methodology 5.1 The event study methodology An event study methodology 8 is used to examine wealth effects of cross-border M&A. The main idea of an event study is to observe stock prices around a specific event and measure the change due to the event (Petersen, 1989). In the thesis, the event is defined as the day when the acquisition becomes public, i.e. the announcement day. The underlying assumption of the event study methodology is rationality in the market. This means that the market responds to new information immediately, i.e. stock prices reflect the effects of events such as M&A (MacKinley, 1997). Although scholars refer to the term standard event study methodology, there is no completely standardized methodology existent in the literature. Studies differ with respect to crucial characteristics (Petersen, 1989). These characteristics are presented in the following. 5.1.1 Estimation period and event window The event study methodology compares realized returns around the announcement day with normal returns (also referred to as predicted returns). Normal returns are returns that would be realized if no event occurs, i.e. if the M&A is not announced. 8 An event study is a capital market based study. Capital market based studies are dominating the literature. However, there are also other methods of measuring the success of M&A such as accounting studies, surveys of managers and case studies. In contrast to capital market studies, these studies are past oriented. The capital market based view is a future-oriented view that uses market expectations to determine the success of M&A. Furthermore, wealth effects are measured directly (Bruner, 2004; Wübben, 2007). 21

The period around the announcement day is called the event window. Days around the event are considered because of the possibility of information dissemination prior to the announcement and a possible lag in the market adjustment process. Normal returns are estimated using a period prior to the event window: the so-called estimation period. (MacKinley, 1997; Petersen, 1989). The following figure illustrates the relationship. Figure 3: Time line of an event study Source: Own illustration based on MacKinley (1997). Comments: The announcement day is defined as day t=0. The period from T 0 to T 1 represents the estimation period and the period from T 1 to T 2 the event window. The two periods are not overlapping, which is required to prevent the estimation of normal returns from being influenced by the event. The impact of the event is captured by the event window (MacKinley, 1997). The length of the estimation period and the event window vary from study to study. Benefits of longer periods (an improved prediction model) and costs of longer periods (model parameter instability) must be considered (Petersen, 1989). The thesis follows dominant periods used in the literature. The length of the estimation period is set to 200 days and the length of the event window to 21 days, i.e. the event day plus and minus 10 days. 5.1.2 Calculating abnormal returns Wealth effects to shareholders are measured by abnormal returns. Abnormal returns are defined as the difference between realized returns and normal returns: AR it = R it NR it (1) where AR it is the abnormal return on stock i on day t, R it is the return on stock i on day t, NR it is the normal or predicted return on stock i on day t. 22

Returns are calculated as natural logarithmic returns. Logarithmic returns have theoretical and empirical advantages over discrete returns. They are more tractable when forming intervals over longer periods and are more likely to be normally distributed (Strong, 1992). The return on stock i on day t (R it ) is calculated as follows: R it = log P it P it 1 (2) where P it is the stock price of stock i on day t, P it 1 is the stock price of stock i on day t-1. Normal returns, however, can be obtained in various ways. In the thesis, normal returns are estimated using a market model: R it = α i + β i R mt + ε it (3) where R it is the return on stock i on day t, R mt is the return on the market index m on day t, α, β are regression coefficients, is the error term. ε it The market model is used to obtain estimates of the regression coefficients. The regression coefficients are estimated in the estimation period. The estimates of the regression coefficients are then used to predict normal returns: NR it = α i + β i R mt (4) where NR it is the normal or predicted return on stock i on day t, R mt is the return on the market index m on day t, α, β are OLS estimates of the regression coefficients. The market model has some advantages over other models, such as the mean-adjusted model or market-adjusted model. The mean-adjusted model uses the average of returns over an arbitrary period as normal returns. The main disadvantage of this model is that it does not correct for market movements. Stock price movements might be the result of overall market movements rather than the event. Results are biased in this case, since stock price movements are partially or completely caused by the market. The market- 23

adjusted model solves this problem. It uses returns of a market index as normal returns, so that market movements are considered. However, this method assumes that all stock prices follow exactly the market (β is always one). This is obviously not the case. The market model accounts for different market co-movements of stocks (a β is estimated for each stock). Thus, the portion of the returns that follow market movements is removed (De Jong, 2007; MacKinley, 1997). There are also models that include more factors than the market index. However, gains of these so-called multifactor models are limited in event studies. The marginal explanatory power of additional factors is small. In addition, there are also other types of models, e.g. economic models such as the capital asset pricing model (CAPM) or the arbitrage pricing theory (APT). Economic models are based on equivalent theories. The theoretical restrictions imposed by economic models are questionable in practice. The market model is a statistically motivated model without theoretical restrictions (MacKinley, 1997). 5.1.3 Aggregating abnormal returns Abnormal returns are aggregated to make overall conclusions regarding the announcement effect of acquisitions. The aggregation occurs in two dimensions: across stocks and time (De Jong, 2007; MacKinley, 1997). Abnormal returns are aggregated across stocks as follows: AR t = 1 N N i=1 AR it (5) where AR t are average abnormal returns on day t, AR it is the abnormal return on stock i on day t, N is the number of M&A announcements in the sample. The abnormal returns are centered on the announcement of the acquisition; other factors that influence the abnormal returns are canceled out in that way. Abnormal returns are indicated by deviations from zero (De Jong, 2007). To analyze the performance of M&A over longer periods, abnormal returns are further aggregated across time (De Jong, 2007; MacKinley, 1997). Abnormal returns are aggregated across time as follows: 24

CAAR [t 1 ; t 2 ] = t 2 AR t (6) t=t 1 where CAAR [t 1 ; t 2 ] are cumulative average abnormal returns from day t 1 to day t 2 (T 1 < t 1 t 2 T 2 ). Cumulative average abnormal returns (CAAR) are examined since the effect of the announcement might occur on a different day than the announcement day, e.g. the acquisition is announced after stock markets are closed, thus the effect of the acquisition would occur on day t=1 and not on day t=0. Furthermore, information leakages and lags in marked adjustments are reasons for looking at days surrounding the announcement. 5.2 Testing the significance of abnormal returns The significance of abnormal returns is analyzed next, i.e. whether the calculated abnormal returns are significantly different from zero at a defined significance level. The null hypothesis to test is: H o : E AR t = 0 (7) A number of tests are applied to give conclusive evidence on the stock price performance. The tests vary in assumptions and address different potential problems in the dataset. There are two groups of tests: parametric and non-parametric tests. 9 5.2.1 Parametric tests Parametric tests use the mean and the variance of abnormal returns to determine whether abnormal returns are statistically significant or not. The crucial underlying assumption is normal distribution of the abnormal returns of individual firms (Serra, 2002). Three parametric tests are applied: cross-sectional interdependence, crosssectional dependence and standardized abnormal returns. T1: Cross-sectional interdependence The cross-sectional interdependence test assumes that abnormal returns are independent across stocks and that they have the same mean and variance. These assumptions are very restrictive and relaxed in the next tests (Brown, Warner, 1980; De Jong, 2007; Serra, 2002). 9 Test statistics are presented in Appendix F. 25

T2: Cross-sectional dependence The cross-sectional dependence test accounts for dependence across stocks abnormal returns, thus correlation between the abnormal returns. A reason for cross-sectional dependence might be event clustering, meaning that several events occur in the same period (Brown, Warner, 1980; De Jong, 2007; Serra, 2002). T3: Standardized abnormal returns Standardized abnormal returns are calculated to account for differences in variances. Some stocks have a higher volatility than others. Abnormal returns are standardized to ensure that abnormal returns have the same variance. Less weight is put on stocks with a high variance (Brown, Warner, 1980; De Jong, 2007; Serra, 2002). 5.2.2 Non-parametric tests In contrast to parametric tests, non-parametric tests make no restrictive assumptions regarding the distribution of abnormal returns (MacKinley, 1997). The normal distribution assumption in parametric tests is very restrictive and cannot be held for daily data. An approximation of normal distribution can be assumed if the sample size is large enough under the central limit theorem (N>30). However, especially in small samples the approximation of normal distribution might be very poor (De Jong, 2007). The application of non-parametric tests in addition to parametric tests is a check of robustness of the conclusions. 10 Two non-parametric tests are applied in this study: the sign test and rank test. T4: Sign test The sign test focuses on the sign of abnormal returns. The null hypothesis states that the proportions of positive and negative abnormal returns are equal, meaning that there are no abnormal returns (De Jong, 2007; MacKinley, 1997). T5: Rank test The Rank test accounts for the magnitude of abnormal returns. Abnormal returns of each firm are ranked over the entire period (estimation and event period). The ranks in the event period are then compared to expected average ranks that would occur under the null hypothesis of no abnormal returns (Corrado, 1989; De Jong, 2007; Serra, 2002). 10 Campbell and Wasley (1993) showed that it is worth to check for the robustness of the parametric tests. They report that standard test statistics are misspecified when using NASDAQ securities. 26

In a similar way, tests are further applied to CAAR to analyze abnormal returns of longer periods. Moreover, the sample is divided into subsamples to analyze the significance of abnormal returns in the pre-crisis and crisis period, and different geographical regions (Europe, U.S., Far East Asia, South and Central America and Others ). 5.3 Identifying factors influencing wealth effects Abnormal returns are further analyzed to identify factors affecting wealth effects. The focus of the thesis is on macroeconomic factors. 5.3.1 Analyzing abnormal returns A cross-sectional (OLS) regression of factors affecting abnormal returns is performed on cumulative abnormal returns (CAR) to identify the determinants of wealth creation: CAR[t 1 ; t 2 ] i = β o + β 1i x 1i + β 2i x 2i + + β ki x ki + ε i, i = 1,, N. (8) where CAR[t 1 ; t 2 ] i are cumulative abnormal returns on stock i from day t 1 to day t 2 (T 1 < t 1 t 2 T 2 ), x ki are factors affecting cumulative abnormal returns, β o is the regression constant, β ki are coefficients of the factors, ε i is the error term of stock i. The dependent variable, cumulative abnormal returns of stock i from day t 1 to day t 2, is defined as follows: CAR[t 1 ; t 2 ] i = t 2 AR it (9) t=t 1 where CAR[t 1 ; t 2 ] i are cumulative abnormal returns on stock i from day t 1 to day t 2 (T 1 < t 1 t 2 T 2 ). The analysis is conducted for the full sample and for the two subsamples: pre-crisis and crisis. 27

5.3.2 Factors affecting shareholder wealth Three categories of factors affecting shareholder wealth are used: macroeconomic and cultural factors, firm factors and deal factors. Furthermore, geographical and industry variables are included to control for geography and industry fixed effects. 5.3.2.1 Macroeconomic and cultural factors The focus is on macroeconomic factors. When MNE expand overseas, the economic condition of the target country is important without doubt. As presented in section 2.3.3, the electic model favors M&A if there are location advantages in the foreign country. Acquisitions in countries with a large economic size, a good economic condition and good economic prospects are expected to create value for shareholders. The economic size is measured by the GDP. The variable GDP represents logged GDP values in the year prior to the announcement (GDP values are in constant 2000 US dollars). As described in section 2.1.3, M&A are involved with high investments. High investments go along with high risks and are only justified for big markets. It is expected that acquisitions in countries with a larger economic size have a positive effect on shareholder wealth. However, due to increasing regional economic integration this effect might vanish. The size of the economy has to be complemented with the economic condition. The economic condition is measured by the GDP per capita. GDP PER CAPITA is constructed as logged GDP per capita in the year prior to the announcement (GDP per capita values are in constant 2000 US dollars). It is important to look at the economic condition. The economy might be big due to the size of the population, whereas the purchasing power in the economy is low. 11 Furthermore, other key economic measures, such as the interest rate and the inflation rate, are introduced to account for the economic condition. Interest rates measure the cost of external capital. Capital abundant countries in good economic condition have generally low interest rates. The cost of financing an acquisition is thus low. The variable INTEREST RATE measures the difference between the interest rate in the target country and the acquirer country. The higher the difference, the higher is the cost of financing an acquisition in the target country compared to the acquirer country. It is 11 Luxembourg and Egypt are good examples. Both are big in size, however, the countries differ widely in their economic condition. 28

expected that the interest rate difference has a positive effect on shareholder wealth. First, there are potential financial synergies due to the access to external funds at lower cost in the acquirer country. Second, access to capital for potential competitors in the target country becomes more expensive, which discourages a potential acquisition (Uddin, Boateng, 2010). Furthermore, the inflation rate is introduced as a variable. The variable INFLATION measures the inflation in the target country. High inflation rates indicate a bad economic condition, since with increasing inflation capital loses value. Prices of products must be increased to capture the value destruction. However, price increases are always to be regarded with caution. Price increases might result in market share loses. It is expected that inflation has a negative effect on shareholder wealth (McKinsey, 2010). Besides the current economic situation of a country, its future prospect is important too. The economic prospect of a country is measured by the GDP growth rate. The variable GDP GROWTH represents the growth rate in the year prior to the announcement. The economic prospect in the target country is especially important for European companies. Most of the countries in the Euro zone are highly developed countries with a saturated market. Growth rates are usually low. Companies might seek growth in other markets that have higher growth rates, i.e. emerging markets. These markets might also be seen as the markets of the future. With an improving economy, the purchasing power in these markets increases, and thus creates more demand for products. For European companies, acquisitions in growing markets are very attractive and expected to have a positive effect on shareholder wealth. They can capture part of the growth and establish themselves in the markets of the future. In addition, the GDP growth rate can also be seen as a short-term economic condition measure, where high growth rates indicate a good economic condition. The same macroeconomic variables are also constructed for the acquirer country. 12 Although it is not expected that the macroeconomic environment in the acquirer country affects shareholder wealth in cross-border M&A 13, they are still included in the analysis to gain more insight. The crisis period here is especially of interest; the relationship might change. 12 The variables for the acquirer country are marked with an A in front of the variable. 13 It should be noted that general market movements are considered in the applied market model. 29

Exchange rates are analyzed in two ways. A measure is introduced to account for the strength of the foreign exchange rate compared to the Euro. EXCHANGE RATE STRENGTH is constructed following Kiymaz (2004). The difference between the foreign currency in terms of Euro in the year of announcement and the average foreign exchange rate during the study period is divided by the average exchange rate. If the value is positive, the foreign currency is stronger relative to the Euro, whereas a negative value indicates a weaker foreign currency. The effect of the exchange rate strength is not clear. Generally, a stronger home currency is favorable at the time of the acquisition; however, a weak home currency is favorable when cash flows and dividends flow back to the home country (Kiymaz, 2004). In addition to the exchange rate strength, a variable is introduced to measure the volatility of the exchange rate. EXCHANGE RATE VOLATILITY is defined as the standard deviation of daily exchange rates in the year of the acquisition. It is expected that increasing exchange rate volatility affects shareholder wealth negatively, since the value of future cash flows becomes more and more uncertain with increasing volatility (Kiymaz, 2004). MNE engaging in cross-border activities take not only the economic, but also the political situation of the target country into account. An acquisition goes along with huge investments. Especially in developing countries, regime changes and changes in the regulatory environment put investments under risks (Aguiar, Gopinath, 2007). Furthermore, political risks summarize the potential for internal and external conflicts, the threat of expropriation and changes in the legal system, in particular the tax rates (Meldrum, 2000; Brockmann, 2007). It is expected that wealth effects will be negatively affected by increasing political instability. The World Bank political stability indicator is used as a measure of political stability. The measure is rescaled so that it varies between 0 and 5, where the indicator increases with increasing instability. The POLITICAL STABILITY variable is also highly correlated with other World Bank indicators such as government effectiveness, regulatory quality, rule of law, and control of corruption. It can also be seen as a proxy for these indicators, as well. Cultural differences are measured using Hofstede s four cultural dimensions: the power distance index, individualism, masculinity and uncertainty avoidance index. 14 The CULTURE variable is constructed as the sum of the absolute values of the differences 14 The four dimensions and the culture variable are presented in Appendix G in more detail. 30

between the target and acquirer country scores of each dimension. A higher score indicates bigger cultural differences. Cultural differences can cause problems. The acquirer must be aware of diverse leadership styles, national peculiarities and other culture-related factors (Koveos, 1997). Majidi (2007) confirms the influence of cultural differences on the success of cross-border M&A. Cultural differences can be the reason for misunderstandings and conflicts between the involved companies. Thus, it is expected that an increasing cultural difference score affects shareholder wealth negatively. Furthermore, LANGUAGE is included as a dummy variable. The dummy takes the value 1 if the same language is spoken in both countries and 0 otherwise. A common language indicates lower transaction costs; companies communicate more efficiently with each other and misunderstandings are avoided (Kiymaz, 2004). It is expected that a common language has a positive effect on shareholder wealth. 5.3.2.2 Geographic variables Geographic dummy variables are included to capture the effect of geographical diversification. Regions differ in global integration and thus result in different diversification potentials (Kiymaz, 2004). Targets are grouped into the following region categories: Europe, Far East and Central Asia (FECA), North America (NA), South and Central America (SCA) and Others. 15 Dummy variables are included to control for differences among the regions that could not be captured by the other variables. The variable FECA takes the value one if the acquisition takes place in Far East and Central Asia, and 0 otherwise. The variables NA, SCA and OTHERS are constructed in a similar way. The region Europe is used as the control group and has thus no variable in the regression. 5.3.2.3 Industry variables Industry classification variables are included to control for differences between industries. Industries may vary in their structure and ability to exploit international opportunities (Kiymaz, 2004). A dummy variable is included for each two-digit industry in the sample. 16 The NACE Rev.2 category 10 (manufacturer of food products) is defined as the control group. A dummy variable (NACE 11, NACE 20,...) is created for 15 A list of countries by regions is provided in Appendix D. 16 The included two-digit NACE Rev. 2 categories are later presented in section 6.3. 31

each industry that takes the value of 1 if the acquirer is in the industry and 0 otherwise. Categories with only a few observations are grouped into one category. 27 companies are classified as other industries. The dummy variable OTHER INDUSTRIES takes the value 1 if the acquirer is in the NACE Rev. 2 category 13, 14, 17, 18, 22, 30 and 33, and 0 otherwise (Eurostat, 2008). 5.3.2.4 Firm and deal variables Firm variables are added to control for differences among firms. Besides the relative firm size, financial ratios are introduced. Furthermore, deal variables are added to account for deal characteristics. The variable RELATIVE SIZE is constructed as the logged target size divided by the logged acquirer size. The deal value (in Euro) is used as a proxy for the target size; the deal value equals the target size in a full acquisition and is used to estimate it otherwise. Although it is expected that the deal value is higher due to a premium paid by the acquirer, it serves as a good proxy for the target size. The acquirer size is defined as the total assets of the acquirer (in Euro) in the year prior to the announcement. The positive effect of the relative size of the target to the acquirer is reported in a number of studies (Asquith et al., 1993; Jarell, Poulsen, 1989; Markides, Ittner, 1994; Moeller, Schlingemann, 2005). The financial ratios that are introduced are: CURRENT RATIO 17, SOLVENCY RATIO 18, PROFIT MARGIN 19 and RETURNS ON SHAREHOLDER FUNDS 20. The current ratio measures the ability of a company to meet short-term obligations. The ability of a company to meet long-term obligations is measured by the solvency ratio. The profit margin measures the profitability of a company. Another profitability measure is the returns on shareholder funds ratio. It measures returns in relation to funds added by shareholders. It is expected that increasing values for the acquirers financial ratios, affect shareholder wealth positively (EQUIFAX, 2011). Furthermore, three deal variables are included: RELATED, CONTROL and CASH. RELATED is a dummy variable that takes the value 1 if target and acquirer are in related 17 The current ratio is defined as current assets divided by current liabilities. 18 The solvency ratio is defined as the company s net profits plus depreciation divided by total liabilities. 19 The profit margin is defined as the net income divided by the revenues. 20 The returns on shareholder funds ratio is defined as the profits (before tax) divided by shareholder funds. 32

industries and 0 otherwise. Industries are related if their first two NACE digits are the same. The variable measures the importance of economics of scale and diversification. An acquisition in a related industry indicates potential economics of scale, whereas an acquisition in an unrelated industry indicates potential gains from diversification. Furthermore, acquiring a company in the same industry can be seen as increasing market power (Sudarsanam et al., 1996). The relatedness of the companies involved in the M&A is analyzed in various studies (Markides, Ittner, 1994; Sudarsanam et al., 1996; Corhay, Rad, 2000). Markides and Ittner (1994) and Sudersanam et al. (1996) support the synergy argument, whereas Corhay and Rad (2000) found support for the diversification argument. It should be noted that conclusions may vary between industries and regions. Thus, the importance of economics of scale and diversification for the manufacturing industry in the Euro zone is an empirical question that is answered in the thesis. CONTROL is a further dummy variable; it takes the value 1 if the target is acquired completely and 0 otherwise. Acquiring the entire target allows the acquirer to transform its management expertise to the target. The acquirer has full control over the target and can reorganize it according to its needs (Kiymaz, 2004). If the entire company is not acquired, other shareholders can hinder actions by the acquirer (Butz, 1994). This is also discussed in section 3.2. Different ownership shares go along with different rights. In domestic M&A, a full acquisition is expected to have a positive effect. However, for CBM&A, the effect could be different. Having local partners who know the foreign market could prove to be important. CASH is also a dummy variable; it takes the value of 1 if the acquisition is fully cash financed and 0 otherwise. 21 The method of payment is interpreted as a signal by the market. A cash payment indicates that the shares are undervalued, meaning that the real value of the shares is higher. A payment by shares would however mean that shares are overvalued and thus are not worth the value they represent (Kiymaz, 2004). Consequently, using cash as the method of payment affects shareholder wealth positively; this is also documented by Asquith and Mullins (1986), Myers and Majluf (1984), Travlos (1987) and Wansley et al. (1983). 21 The dummy variable takes the value of 0 if the acquisition is financed by shares or the method of payment is unknown. Unknown methods of payment are treated in the same way by Markides and Ittner (1994). It is a wide known fact that cash payments have positive effects; it can be assumed that unknown methods of payments are at least not fully cash paid. 33

5.4 Problems with event studies When using event studies some potential problems must be considered. This section aims to show the problems that might occur in event studies and how these problems can be solved. Non-normality Event studies assume normal distribution of the abnormal returns. However, the empirical evidence shows that the distribution is fat-tailed relative to a normal distribution. This is not a big issue and can be solved by using a large sample size. The central limit theorem states that the distribution tends to be normal distributed with increasing sample size. However, for the central limit theorem to be valid, abnormal returns must be independent and identically distributed with a finite mean and variance (Brown, Warner, 1985). Non-synchronous trading Another potential problem is non-synchronous trading. Non-synchronous trading means that stock prices are recorded at intervals of one length although in reality they are not of one length. Event studies use closing prices, which is the last traded price during that trading day. These closing prices vary between days, however calling them daily prices implies that the prices are determined every 24 hours. This effect causes biases in estimating the market model. Movements and co-movements of stock price and market index returns are biased due to the non-synchronous trading. This bias can be reduced by applying the methodology based on Scholes and Williams (1977). However, the benefit is negligible (Brown, Warner, 1985; MacKinley, 1997). Variance estimation Further problems might occur with regard to the variance estimation. Autocorrelation in the time series of mean daily abnormal returns could bias the estimated variance. Procedures could be applied to adjust for autocorrelation. However, the improvements are negligible and only beneficial in special cases (Brown, Warner, 1985). Similarly, adjustments for dependence in the cross-section of abnormal returns are only advantageous in special cases. Adjustments could be even unfavorable compared to assuming independence, meaning that adjustments can make the detection of abnormal returns more difficult when they are present (Brown, Warner, 1985). 34

Another problem might occur due to higher variance in the event period. Methods that use time series in the estimation period for the variance estimation of abnormal returns lead to many rejections of the null hypothesis if there is a substantial increase in the variance in the event period. Cross-section procedures can overcome this problem. The variance can be estimated across stocks in the event window. However, problems with cross-section procedures occur if the increase in variance occurs in different times across stocks. The identical distribution assumption is violated and the cross-section procedure is misspecified. Furthermore, the cross-section procedure is not very powerful, if there is not an increase in variances in the event period, since the estimation period is ignored in this procedure (Brown, Warner, 1985). According to Brown and Warner (1985), non-normality and non-synchronous trading are not very important in event studies, whereas the variance estimation seems to be a source of concern. A recent methodology review by Corrado (2011) reports that eventinduced volatility remains to be a source of concern among researchers. 22 6 Data 6.1 Sample selection The sample consists of cross-border M&A made by manufacturers in the Euro zone between 01.01.2005 and 31.12.2010, as reported in the Zephyr database. Acquirers are publicly listed companies. Furthermore, only M&A deals with a known deal value that are announced in the study period are considered. All M&A announcements are currently completed. The preliminary sample contains 483 acquisition announcements. 23 In order to meet the cross-border M&A definition of the thesis, the sample size is reduced to 360 acquisition announcements. CBM&A are excluded where the main acquirer is neither in the Euro zone nor a manufacturer. CBM&A are defined as acquisitions where the acquirer is a company in the Euro zone and the target is a company outside the Euro zone. Therefore, acquisitions within the Euro zone are excluded. Furthermore, only an acquisition of minimum 50% of the target is considered 22 The thesis applies various tests in the abnormal returns analysis to overcome potential problems (see section 5.2). For the cross-sectional analysis, a robustness check is presented in Appendix K. 23 The Zephyr search criteria are presented in Appendix B and a list of the M&A announcements is provided in Appendix C. 35

as an acquisition. Acquisitions below the 50% threshold are excluded (see section 3.2). The remaining CBM&A announcements are matched with the Datastream database to obtain the final sample. Finally, the obtained stock data is controlled for missing returns. Wealth effects are analyzed using daily stock data. 24 If stocks are not traded frequently, returns are not realized. Missing returns in the estimation and event period lead to biases in the abnormal return calculation. The event study methodology is misspecified for thinly traded stocks (Maynes, Ramsey, 1992). Stocks with a trade frequency of lower than 40% in the estimation and event period are defined as thinly traded stocks (Bartholdy et al., 2007). To avoid the bias due to thin trading, thinly traded stocks are excluded from the sample. 25 The exclusion of thinly traded stocks leads to the final sample size of 345. The total deal value in the study period is 212 billion Euros. Furthermore, the study period is split into two periods, i.e. the pre-crisis period (2005 to 2007) and the crisis period (2008 to 2010). 26 The Morgan Stanley Capital International (MSCI) Europe Industrials index is chosen as the benchmark index for the market model. The MSCI Europe Industrials index comprises European industrials and represents the companies in the sample. It is used to calculate normal returns. 27 The MSCI Europe Industrials index is a value-weighted index, as suggested by Brown and Warner (1980). 6.2 Data sources The data is collected using a number of different data sources. Attention is paid to the most appropriate sources in the data source selection process. Databases of international organizations are the main sources. Table 2 summarizes the data sources that are used in the thesis, and presents the corresponding data obtained from these sources: 24 MacKinley (1997) reports that shorter sampling intervals are preferred compared to longer intervals, i.e. daily data is preferred to weekly or monthly data. 25 This might lead to a selection bias; however, the number of excluded stocks is very low (15). It is not expected that the exclusion of such a low number leads to a severe bias. There are alternative methods to handle thin trading, however, these methods lead to other problems. Due to the low number of thin traded stocks in the sample, the issue is not further discussed. Interested readers are referred to Petersen (1989). 26 The crisis period is defined in Appendix E. 27 Alternatively, the MSCI Europe or the MSCI EMU (European Monetary Union) could be used. However, these indices are diversified indices for Europe and the Euro zone respectively. Both indices have been tested. However, the MSCI Europe Industrials index represents the companies in the sample the best. 36

Table 2: Data sources Data source Datastream database European Central Bank database Itim International Orbis database Data Exchange rates, MSCI index, Stock prices Interest rates of the Euro zone Country scores for the four cultural dimensions of Hofstede Firm specific data (i.e. company size and financial ratios) The Global Market Information database Interest rates of the target country 28 The World Development Indicators & Global Development Finance database of the World Bank The World Governance Indicators database of the World Bank Zephyr database Source: Own illustration. Macroeconomic data (i.e. GDP, GDP per capita, GDP growth rates and inflation rates) Political stability indicator Deal specific information (i.e. deal type, deal value, method of payment and NACE codes of the participants) 6.3 Sample characteristics The sample contains 345 M&A announcements between 2005 and 2010. The sample size and the frequency of acquisition announcements by years are presented in Table 3. Table 3: Sample size and frequency of CBM&A by years Panel A: Sample size Sample size 345 (100%) Pre-crisis 231 (67%) Crisis 114 (33%) Panel B: Frequency by years 2005 68 (20%) 2006 78 (23%) 2007 85 (25%) 2008 56 (16%) 2009 29 (8%) 2010 29 (8%) Total 345 (100%) Source: Own illustration. 28 It would be optimal to obtain both interest rates of acquirer countries and target countries from the same source. However, this was not possible. 37

Number of deals Panel A of Table 3 presents the sample size. The sample consists of two subsamples, i.e. the pre-crisis period and crisis period. Two-thirds of the deals are announced in the precrisis period (2005-2007) and one-third in the crisis period (2008-2010). The number of deals halved in the crisis period. Panel B reports the distribution of the sample by years. The number of acquisitions dropped from relatively high levels in the pre-crisis period to relatively low levels in the crisis period. In the years 2005 to 2007, the number of M&A announcements ranged between 68 and 85. In 2008, the number dropped to 56 and in 2009 even to 29. M&A announcements remained on a low level in 2010. The distribution of the sample by location of the target is presented in Table 4. Table 4: Frequency of CBM&A by target region Panel A: Frequency by target region: full sample Europe 115 (33%) Far East and Central Asia 41 (12%) North America 118 (34%) South and Central America 31 (9%) Others 40 (12%) Total 345 (100%) Panel B: Frequency by target region: pre-crisis vs. crisis period 90 80 70 60 50 40 30 20 10 0 79 36 Europe Source: Own illustration. 31 Panel A of Table 4 reports the frequency of the M&A announcements by target region for the full sample. North America ranks first with 118 acquisitions (34%), followed by 115 (33%) acquisitions in Europe, 41 acquisitions (12%) in Far East and Central Asia (FECA) and 30 acquisitions (9%) in South and Central America (SCA). Panel B shows that especially acquisitions in FECA reduced in the crisis period. The number of total acquisitions halved in the crisis period, whereas the number of acquisitions in FECA dropped by two-thirds. Europe, North America and SCA follow the overall pattern; acquisitions dropped by 54%, 47% and 52% respectively. 10 Far East and Central Asia 77 41 North America 21 23 10 South and Central America 17 Others Pre-Crisis Crisis 38

NACE 10 NACE 11 NACE 13 NACE 14 NACE 17 NACE 18 NACE 20 NACE 21 NACE 22 NACE 23 NACE 24 NACE 25 NACE 26 NACE 27 NACE 28 NACE 29 NACE 30 NACE 32 NACE 33 Number of deals Table 5 presents the distribution of the sample by the industry classification of the acquirer. Table 5: Frequency of CBM&A by industry classification of the acquirer Panel A: Frequency by industry classification: full sample NACE 10 - Manufacture of food products 27 (8%) NACE 11 - Manufacture of beverages 25 (7%) NACE 13 - Manufacture of textiles 1 (0%) NACE 14 - Manufacture of wearing apparel 3 (1%) NACE 17 - Manufacture of paper and paper products 5 (1%) NACE 18 - Printing and reproduction of recorded media 2 (1%) NACE 20 - Manufacture of chemicals and chemical products 30 (9%) NACE 21 - Manufacture of basic pharmaceutical products and pharmaceutical preparations 34 (10%) NACE 22 - Manufacture of rubber and plastic products 8 (2%) NACE 23 - Manufacture of other non-metallic mineral products 26 (8%) NACE 24 - Manufacture of basic metals 25 (7%) NACE 25 - Manufacture of fabricated metal products, except machinery and equipment 19 (6%) NACE 26 - Manufacture of computer, electronic and optical products 56 (16%) NACE 27 - Manufacture of electrical equipment 26 (8%) NACE 28 - Manufacture of machinery and equipment n.e.c. 25 (7%) NACE 29 - Manufacture of motor vehicles, trailers and semi-trailers 15 (4%) NACE 30 - Manufacture of other transport equipment 7 (2%) NACE 32 - Other manufacturing 10 (3%) NACE 33 - Repair and installation of machinery and equipment 1 (0%) Total 345 (100%) Panel B: Frequency by industry classification: pre-crisis vs. crisis 45 40 39 35 30 25 20 15 10 5 0 21 18 19 19 18 17 15 15 9 10 9 7 7 7 3 1 2 0 0 3 11 1 2 17 17 15 11 9 10 7 4 43 3 10 Pre-Crisis Crisis Source: Own illustration. Panel A of Table 5 reports the frequency of the M&A announcements by industry classification of the acquirer for the full sample. The most acquisitions occurred in the 39

manufacturing of computer, electronic and optical products industry (NACE 26, 16%), the manufacturing of basic pharmaceutical products industry (NACE 21, 10%), the manufacturing of chemicals and chemical products industry (NACE 20, 9%), the manufacturing of food products industry (NACE 10, 8%), the manufacturing of other non-metallic mineral products industry (NACE 23, 8%) and the manufacturing of electrical equipment industry (NACE 27, 8%). Panel B shows the distribution of acquisition announcements by the industry classification of the acquirer for the precrisis and crisis period. The figure reveals that some industries are affected more by the crisis than others. The drop in acquisitions ranges between 88% in the manufacturing of fabricated metal products industry (NACE 25) and 33% in the manufacturing of beverages industry (NACE 11). The only industry that experienced an increase in acquisition activity is the manufacturing of basic pharmaceutical products industry (NACE 21). 29 Acquisitions increased from 15 to 19 by 27%. 6.4 Firm and deal characteristics Firm characteristics Firm characteristics are summarized in Table 6. The average firm size of an acquirer is 10,3 billion Euros in the sample. The sample contains both big and small acquirers ranging from a firm size of 10 million Euros to 218 billion Euros. The average firm size of a target is 700 million Euros and is ranging from 260 thousand Euros to 41 billion Euros. Both acquirers and targets are on average bigger in the crisis period than in the pre-crisis period. However, in both periods the acquirer sizes and target sizes vary highly. There is not a statistically significant difference in company sizes between the pre-crisis period and the crisis period. Table 6: Firm characteristics Firm characteristics Overall Pre-Crisis Crisis Acquirer size (in bil. Euro) 10,3 10,2 10,6 Target size (in bil. Euro) 0,7 0,6 0,8 Current ratio (in %) 1,7 1,7 1,7 Solvency ratio (in %) 41,3 39,7 44,7 Profit margin (in %) 9,3 9,6 8,8 Return on shareholder funds (in %) 19,5 21,8 15,0 N= 345 231 114 Source: Own illustration. Comments: All values are averages. Financial ratios are presented for acquirers. 29 Industries with a small number of acquisitions are neglected. 40

Furthermore, Table 6 reports financial ratios of the acquirer firms. The average current ratio of the firms in the sample is 1,7%. There is not a statistically significant difference between the pre-crisis and crisis period. The average solvency ratio of the firms in the sample is 41,3%. Companies in the crisis period have a statistically significant higher solvency ratio, i.e. a higher ability to meet long-term obligations. The profit margins of both periods are not statistically different from each other; the average profit margin of acquirers in the sample is 9,3%. Returns on shareholder funds are statistically significant higher in the pre-crisis period. This is not surprising, since returns in general are lower during the crisis. The average return on shareholder funds is 19,5%. Current ratios usually vary between 1,1% and 1,6% in the manufacturing industry and solvency ratios range on average between 30% and 50%. Regarding the profit margin, margins vary usually between 6% and 8% (EQUIFAX, 2011). The financial ratios of the firms in the sample reveal that, in general, better performing companies undertake acquisitions. In addition, returns on shareholder funds are high in both periods. Although they are significantly higher in the pre-crisis period, considering that returns are generally lower during a crisis, it can be concluded that acquirers experience high returns on shareholder funds in the both the pre-crisis and crisis period. Deal characteristics Deal characteristics are presented in Table 7. Table 7: Deal characteristics Deal characteristics Overall Pre-Crisis Crisis Deal value (in bil. Euro) Total value 212 125 87 Average 0.6 0.5 0.8 Type of Acquisition Full 282 (83%) 192 (86%) 90 (79%) Partial 63 (18%) 32 (14%) 24 (21%) Method of Payment Cash 139 (40%) 96 (42%) 43 (38%) Other 206 (60%) 135 (58%) 71 (62%) Relatedness Related 170 (49%) 116 (50%) 54 (47%) Unrelated 175 (51%) 115 (50%) 60 (53%) N= 345 (100%) 231 (100%) 114 (100%) Source: Own illustration. Comments: For Type of Acquisition, Method of Payment and Relatedness the number of acquisitions in the sample and subsamples are presented, in parentheses are the equivalent shares. 41

-10-9 -8-7 -6-5 -4-3 -2-1 0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 AAR The total deal value in the sample is 212 billion Euros, where the total deal value of the pre-crisis period and crisis period is 125 billion Euros and 87 billion Euros respectively. The average deal size in the sample is 600 million Euros. The average deal size is higher in the crisis period; however the difference is not statistically significant. The sample consists of 83% full acquisitions and 17% partial acquisitions. In 40% of the acquisitions acquirers chose cash as the method of payment and in 49% of the acquisitions acquirers acquired related targets. These shares are similar in both the precrisis and crisis period. The behavior of acquirers with regard to deal characteristics tends to be the same in both periods. To summarize, neither firm nor deal characteristics show a significant difference between the pre-crisis and the crisis period. 7 Empirical Results 7.1 Wealth effects Wealth effects to acquirers are analyzed by abnormal returns, i.e. returns generated by the M&A announcements (see section 5.1). The analysis is conducted for the full sample (2005-2010), and the subsamples: pre-crisis (2005-2007) and crisis (2008-2010). 7.1.1 Full sample Figure 4 presents daily average abnormal returns (AAR) ten days before and after the announcement day, generated by the 345 acquisition announcements in the full sample. Figure 4: Average abnormal returns: Full sample 0,70% 0,60% 0,50% 0,40% 0,30% 0,20% 0,10% 0,00% -0,10% -0,20% Days Source: Own illustration. Comments: Day 0 is the announcement day. 42

-10-9 -8-7 -6-5 -4-3 -2-1 0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 CAAR Figure 4 shows a strong market reaction to M&A announcements at the announcement day; average abnormal returns of 0,6% are experienced. The other days surrounding the announcement range mainly between -0,10% and +0,10%. Cumulative average abnormal returns (CAAR) are presented in Figure 5. Figure 5: Cumulative average abnormal returns: Full sample 1,80% 1,60% 1,40% 1,20% 1,00% 0,80% 0,60% 0,40% 0,20% 0,00% Days Source: Own illustration. Comments: Day 0 is the announcement day. There is a steady yet slow increase in returns prior to the announcement. Around the announcement day, from day -1 to day +1, average abnormal returns increase by 1,11% and continue the former path. The graphical analysis does not indicate any other significant market reactions in the pre and post announcement period. There are no signs of information leakages and insider trading prior to the announcement or market adjustments after the announcement as more information becomes public. It seems that stock prices reflect the new information as soon as it becomes public. This result supports the Efficient Market Hypothesis (EMH) in the weak and semi-strong form. In the weak form the EMH states that all past information is incorporated in stock prices. The semi-strong form adds that all information is incorporated as soon as they become public (Malkiel, 2003). 30 The graphical analysis is further supported by the numerical analysis. Panel A of Table 8 reports average abnormal returns ten days surrounding the announcement day for the 345 acquisition announcements in the sample and presents their test statistics. As documented in section 5.2, five tests are performed to provide conclusive evidence for shareholder wealth effects. 30 The strong form argues that hidden information is also incorporated in stock prices. 43

Table 8: Average abnormal returns surrounding the announcement of CBM&A: Full sample Panel A: Average daily abnormal returns (AAR) Day AAR (%) Test 1 Test 2 Test 3 Test 4 Test 5-10 0.09 0.83 0.85 0.35-0.16 0.29-9 0.00-0.01-0.01 0.10 1.13 0.39-8 0.09 0.87 0.89 1.57-0.21 1.14-7 0.05 0.50 0.51 0.08 0.38 0.57-6 -0.02-0.22-0.23-0.43 0.16-0.56-5 0.04 0.43 0.44 0.90-0.59 0.24-4 -0.04-0.34-0.35 0.77 0.38 0.51-3 0.13 1.21 1.24 1.95 ** 1.13 1.59-2 0.08 0.81 0.83 0.43 0.59 0.22-1 0.25 2.34 *** 2.42 *** 3.01 *** 1.13 1.83 * 0 0.60 5.67 *** 5.85 *** 6.31 *** 2.52 *** 3.44 *** +1 0.26 2.51 *** 2.59 *** 3.12 *** 1.34 2.07 ** +2 0.06 0.54 0.56 1.51-0.48 0.64 +3-0.11-1.00-1.03-1.11-1.93 ** -1.17 +4-0.01-0.10-0.10 0.56-0.32 0.34 +5-0.03-0.27-0.28-0.33-0.05-0.27 +6-0.03-0.26-0.27-0.23 0.54 0.27 +7 0.09 0.87 0.89 0.70 0.32 0.74 +8-0.02-0.23-0.24 0.31-0.75 0.01 +9-0.05-0.47-0.48-0.75-0.32-1.01 +10 0.11 1.04 1.07 1.15 1.66 * 1.32 Panel B: Cumulative average abnormal returns (CAAR) Windows CAAR (%) Test 1 Test 2 Test 3 Test 4 Test 5 CAAR [0;1] 0.86 5.78 *** 5.97 *** 6.66 *** 2.73 *** 3.90 *** CAAR [-1;1] 1.11 6.07 *** 6.27 *** 7.18 *** 2.88 *** 4.24 *** CAAR [-5;5] 1.24 3.56 *** 3.67 *** 5.16 *** 1.12 2.85 *** CAAR [-10;10] 1.54 3.21 *** 3.31 *** 4.36 *** 1.41 2.75 *** CAAR [-10;-1] 0.67 1.93 ** 2.09 ** 2.63 *** 1.24 1.97 ** Source: Own illustration. Comments: The full sample consists of 345 acquisition announcements. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels respectively. All tests result in statistically significant positive average abnormal returns at the announcement day (+0,60%). Four of the five tests find significant abnormal returns one day prior to (+0,25%) and after (+0,26%) the announcement. Additionally, Test 3 indicates significant average abnormal returns three days prior to the announcement day (+0,13%). However, this result is not supported by any of the other tests. Similarly, Test 4 is the only test that finds significant abnormal returns after the announcement day (- 0,11% at day +3 and +0,11% at day +10). Panel B of Table 8 reports cumulative average abnormal returns (CAAR) for different windows. All tests result in highly significant CAAR for the narrow windows [0; 1] (0,86%) and [-1; 1] (1,11%). The wider windows ([-5, 5], [-10, 10] and [-10; -1]) are 44

-10-9 -8-7 -6-5 -4-3 -2-1 0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 AAR significant in four of the five tests. The highest CAAR are realized in the window [-10; 10] (1,54%). Although higher CAAR are realized in wider windows, most of the CAAR are experienced in the short window of [-1, 1] (1,11%). 7.1.2 Pre-crisis vs. crisis The subsample pre-crisis and crisis contains 231 and 114 acquisition announcements respectively. The daily average abnormal returns (AAR) for the ten-day period surrounding the announcement day are presented in Figure 6, for both the pre- and crisis period. Figure 6: Average abnormal returns: Pre-crisis vs. crisis 1,00% 0,80% 0,60% 0,40% 0,20% 0,00% -0,20% -0,40% -0,60% Days Pre-Crisis Crisis Source: Own illustration. Comments: Day 0 is the announcement day. In the pre-crisis period, there is a clear reaction to the announcement instantly around the announcement day (from day -1 to day +1). Other effects are not as clear; however, the graphic indicates a positive reaction especially in day +7. This would mean that market adjustments occur when more information becomes public. In the crisis period, the reaction of the market is different. Average abnormal returns seem to be higher on average and the market seems to react earlier (from day -2 to day +1). Furthermore, there is a positive reaction seven days prior to the announcement. This would indicate information leakages or insider trading. Cumulative average abnormal returns (CAAR) are presented in Figure 7. 45

-10-9 -8-7 -6-5 -4-3 -2-1 0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 CAAR Figure 7: Cumulative average abnormal returns: Pre-crisis vs. crisis 2,50% 2,00% 1,50% 1,00% 0,50% 0,00% -0,50% Days Pre-Crisis Crisis Source: Own illustration. Comments: Day 0 is the announcement day. In the pre-crisis period, there is a clear positive market reaction to M&A announcements around the announcement day (from day -1 to day +1). However, the market reaction is not clear in the crisis period. There seems to be a positive reaction in the preannouncement period (around day -6) and around the announcement day (from day -2 to day +1). The numerical analysis gives more insight. Panel A of Table 9 reports average abnormal returns in the ten-day period surrounding the announcement day and their test statistics for both the pre-crisis and crisis period. In both periods, average abnormal returns (AAR) are positive on the announcement day. Although average abnormal returns are almost twice as high in the crisis period (0,87%) than in the pre-crisis period (0,46%), the difference is not statistically significant. Interestingly, days in the post-announcement period are statistically significant in the pre-crisis period, i.e. day +1 (0,30%) and day +7 (0,26%). This indicates market adjustments in the pre-crisis period; the market adjusts as more information becomes public. However, in the crisis period, days in the pre-announcement period are statistically significant, i.e. day -1 (0,41%) and day -7 (0,36%). This can be seen as a proof for information leakages or insider trading in the crisis period, which is leading to significant wealth effects to acquirers prior to the announcement. 46

Table 9: Average abnormal returns surrounding the announcem ent of CBM&A: Pre-crisis vs. crisis period Pre-crisis (2005-2007) Crisis (2008-2010) Panel A: Average daily abnormal returns (AAR) Day AAR (%) Test 1 Test 2 Test 3 Test 4 Test 5 AAR (%) Test 1 Test 2 Test 3 Test 4 Test 5-10 0.10 0.87 0.91 0.64 0.06 0.64 0.07 0.31 0.30-0.30-0.41-0.43-9 -0.02-0.20-0.21 0.09 0.97 0.26 0.04 0.18 0.18 0.05 0.61 0.33-8 0.11 1.00 1.04 1.81 * -0.97 0.56 0.05 0.24 0.23 0.17 1.12 1.23-7 -0.10-0.91-0.96-1.01-1.10-0.80 0.36 1.65 * 1.64 * 1.65 * 2.44 *** 2.26 ** -6-0.14-1.28-1.34-1.08-0.32-1.20 0.22 0.98 0.97 0.79 0.81 0.79-5 0.15 1.36 1.43 1.24 0.32 0.88-0.17-0.77-0.76-0.19-1.62 * -0.90-4 0.14 1.26 1.32 1.81 * 0.97 1.28-0.39-1.77 * -1.76 * -1.24-0.81-1.01-3 0.11 0.97 1.01 1.21 0.32 0.69 0.17 0.76 0.75 1.66 * 1.62 * 1.87 * -2 0.02 0.21 0.22 0.04 0.32 0.11 0.21 0.95 0.95 0.69 0.61 0.25-1 0.16 1.48 1.55 2.27 ** 0.97 1.36 0.41 1.87 * 1.86 * 2.01 ** 0.61 1.29 0 0.46 4.14 *** 4.33 *** 5.05 *** 1.62 * 2.33 ** 0.87 3.97 *** 3.95 *** 3.79 *** 2.23 *** 2.76 *** +1 0.30 2.76 *** 2.88 *** 2.74 *** 1.75 * 1.93 ** 0.18 0.82 0.81 1.52-0.20 0.86 +2 0.00 0.02 0.02 1.14-0.58 0.22 0.17 0.76 0.76 1.00 0.00 0.83 +3-0.02-0.21-0.22-0.39-0.97-0.28-0.27-1.23-1.23-1.37-2.13 ** -1.71 * +4 0.01 0.08 0.08 0.69 0.00 0.44-0.05-0.22-0.22-0.02-0.61-0.05 +5-0.10-0.94-0.99-0.92-0.06-0.43 0.12 0.56 0.56 0.74 0.00 0.16 +6 0.01 0.09 0.09 0.07 0.65 0.45-0.10-0.46-0.46-0.51 0.00-0.20 +7 0.26 2.32 ** 2.42 *** 1.71 * 1.30 1.56-0.24-1.10-1.10-1.21-1.42-1.03 +8 0.05 0.49 0.52 0.81 0.06 0.31-0.18-0.84-0.83-0.61-1.52-0.46 +9 0.09 0.78 0.82 0.62 0.13-0.18-0.32-1.47-1.46-2.19 ** -0.81-1.59 +10 0.12 1.11 1.16 1.18 1.36 1.20 0.08 0.37 0.37 0.32 1.02 0.59 Panel B: Cumulative average abnormal returns (CAAR) Windows CAAR (%) Test 1 Test 2 Test 3 Test 4 Test 5 CAAR (%) Test 1 Test 2 Test 3 Test 4 Test 5 CAAR [0;1] 0.76 4.88 *** 5.10 *** 5.50 *** 2.39 *** 3.02 *** 1.06 3.39 *** 3.37 *** 3.76 *** 1.44 2.56 *** CAAR [-1;1] 0.93 4.84 *** 5.06 *** 5.80 *** 2.51 *** 3.25 *** 1.47 3.85 *** 3.83 *** 4.23 *** 1.52 2.83 *** CAAR [-5;5] 1.23 3.35 *** 3.51 *** 4.48 *** 1.41 2.57 *** 1.26 1.72 * 1.71 * 2.59 *** -0.09 1.31 CAAR [-10;10] 1.70 3.36 *** 3.52 *** 4.30 *** 1.49 2.48 *** 1.23 1.21 1.21 1.46 0.33 1.27 CAAR [-10;-1] 0.53 1.44 1.58 2.11 ** 0.49 1.19 0.97 1.33 1.38 1.66 * 1.57 1.79 * Source: Own illustration. Comments: The pre-crisis period contains 231 and the crisis period 114 acquisition announcements. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels respectively. 47

Panel B of Table 9 reports cumulative average abnormal returns (CAAR) for different windows for both the pre-crisis and crisis period. In the pre-crisis period, the first four windows are significant. With a wider window CAAR are increasing significantly, from 0,76% in window [0, 1] to 1,70% in window [-10, 10]. The last window [-10,-1] is insignificant. These results support the assumption that market adjustments occur after the announcement. In the crisis period, only the first two windows are highly statistically significant. Furthermore, CAAR do not increase with a wider window. Wealth effects due to M&A announcements are fully absorbed around the announcement day, i.e. 1,06% in window [0, 1] and 1,47% in window [-1, 1]. Windows [-5, 5] and [-10, -1] are only slightly significant. The results are not strong enough to support the assumption of information leakages and insider trading. CAAR around the announcement day [-1, 1] are higher in the crisis period (1,47%), compared to the pre-crisis period (0,93%). The difference is, however, not statistically significant. With wider windows [-10, 10], returns increase in the pre-crisis period (1,70%). The difference between the two periods remains statistically insignificant. To summarize, the analysis shows that the market reacts faster to M&A announcements in the crisis period, leading to high wealth effects around the announcement. Only narrow windows are statistically significant ([0, 1] and [-1, 1]). Wealth effects in the pre-crisis period are similar in size after market adjustments. The market needs time to evaluate M&A announcements; the reaction is not as fast as in the crisis period. Wider windows are statistically significant ([-5, 5] and [-10, 10]). 7.2 Wealth effects by region Wealth effects to acquirers are further analyzed with respect to the region of the target. Cumulative average abnormal returns are presented in Table 10. Acquirers experience the highest CAAR with acquisitions in the region Others 31. In four of the five windows, the region Others outperforms the other regions. Although the first four windows are statistically significant, only the windows [-1, 1] and [-5; 5] are significant in both parametric and non-parametric tests. Since the sample size of Others is low (N=40), parametric tests lose importance. The normal distribution 31 See Appendix D for a list of countries in this group. 48

assumption is weak. Non-parametric tests do not assume normal distribution. Thus, non-parametric tests give a more reliable conclusion about the significance of abnormal returns in small samples. Acquirers experience wealth effects of 2,13% and 3,29% in acquisitions in the region Others in the windows [-1, 1] and [-5, 5] respectively. However, the difference between wealth effects in Others and Europe or North America (NA) is not statistically significant. Table 10: Cumulative average abnormal returns by target region Region N CAAR [0;1] CAAR [-1;1] CAAR [-5;5] CAAR[-10;10] CAAR [-10;-1] Europe 115 1.24% 1.61% 1.38% 0.60% 0.00% FECA 41 0.31% 0.21% 2.43% 3.92% 0.31% NA 118 0.63% 0.89% 0.18% 0.78% 0.56% SCA 31 0.18% -0.09% 0.53% 2.41% 0.88% Others 40 1.51% 2.13% 3.29% 3.42% 1.61% Source: Own illustration. Comments: indicates that both parametric and non-parametric tests are statistically significant at the 10% level. indicates that only parametric tests are statistically significant at the 10% level. FECA=Far East and Central Asia, NA=North America and SCA=South and Central America. Acquisitions in Europe result in the second highest CAAR, i.e. 1,24%, 1,61% and 1,38% in the windows [0, 1], [-1, 1] and [-5, 5] respectively. In NA, CAAR of 0,63% and 0,89% are experienced in the windows [0, 1] and [-1, 1] respectively. However, the difference between CAAR in Europe and NA is statistically not significant. Far East and Central Asia (FECA) outperforms the other regions in the window [-10, 10]. Statistically significant CAAR of 3,92% are realized, the effect is significant in both parametric and non-parametric tests (N=41). CAAR in FECA are statistically significant higher than CAAR in NA and Europe; however, there is no statistically significant difference to CAAR in Others. Wealth effects of acquisitions in South and Central America (SCA) are statistically insignificant in all five windows. Acquisitions in SCA do not seem to create shareholder wealth, although wealth effects tend to be positive. The market reaction to announcements of European and North American acquisitions is very fast. Wealth effects are realized instantaneously around the announcement; only short windows are statistically significant. The opposite is true for announcements of acquisitions in FECA. Here, the short windows are insignificant. Market adjustments occur in the post announcement period. This is supported by relatively low CAAR 49

(0,31%) in the period prior to the announcement [-10, -1]. The market reaction is not as responsive to acquisition announcement as in Europe or NA. The results for Others are mixed; the windows [-1, 1] and [-5, 5] are significant. The market tends to need more time to evaluate acquisitions in FECA and Others than acquisitions in Europe and NA. 32 7.3 Factors influencing wealth effects A cross-sectional regression analysis is performed for the full sample (2005-2010) and for the subsamples pre-crisis (2005-2007) and crisis (2008-2010) to identify factors that affect shareholder wealth. 7.3.1 Full sample Table 11 reports the results of the cross-sectional regression analysis. 33 Cumulative abnormal returns (CAR) around the announcement day [-1, 1] are examined. Five equation results are presented. The first equation contains only macroeconomic and cultural variables. It shows by how much macroeconomic variables explain wealth gains. In equation 2 to 4, geographic, industry, and firm and deal variables are added sequentially. Equation 2 adds geographic variables to account for geographic diversification. Effects of other regional differences other than macroeconomic and cultural differences are taken into account. Equation 3 adds industry variables to account for differences among industries. Equation 4 controls for firm and deal specific variables. The sequential adding of the variable groups provides a test for the robustness of the regression results. It analyzes whether the inclusion of further variables leads to changes in the significance of the macroeconomic variables. Equation 5 analyzes macroeconomic and cultural variables along with geographic, and firm and deal variables. 34 32 The analysis is conducted only for the full sample, since sample sizes of the region groups are too small in the subsamples pre-crisis and crisis. 33 The OLS regression is performed with robust standard errors to overcome heteroscedasticity. Further potential problems are analyzed in Appendix J. There is no problem of multi-collinearity and the sample is asymptotically normal distributed as stated by the central limit theorem. In addition, the robustness of the results is analyzed in Appendix K. 34 Industry variables are excluded to compare the results of the full sample to the results of the two subsamples (pre-crisis and crisis) in the next section. Due to a low number of companies in each industry group, industry variables are not used in the regression analysis of the subsamples. 50

Table 11: Cross-sectional regression results: Full sample Variables (1) (2) (3) (4) (5) Constant -17.530-29.699-29.458-29.019-29.054 -(0.96) (-1.60) (-1.46) (-1.49) (-1.59) Macroeconomic variables GDP -0.369 * 0.104 0.019 0.100 0.167 (-1.82) (0.40) (0.07) (0.36) (0.63) GDP per capita 1.493 *** 1.417 *** 1.505 *** 1.213 ** 1.008 ** (3.34) (3.16) (3.11) (2.43) (2.18) GDP growth 0.423 *** 0.481 *** 0.456 *** 0.446 *** 0.465 *** (2.99) (3.41) (3.13) (2.99) (3.24) Inflation 0.195 0.106 0.029 0.000 0.068 (1.45) (0.80) (0.21) (-0.00) (0.53) A-GDP -0.115-0.137-0.187-0.193-0.179 (-0.41) (-0.51) (-0.60) (-0.61) (-0.66) A-GDP per capita 1.499 1.619 1.840 1.607 1.619 (1.11) (1.22) (1.22) (1.12) (1.27) A-GDP growth -0.633 *** -0.726 *** -0.635 *** -0.580 *** -0.671 *** (-2.85) (-3.31) (-2.86) (-2.70) (-3.16) A-Inflation 0.557 * 0.691 ** 0.703 ** 0.768 ** 0.711 ** (1.61) (1.96) (1.96) (2.26) (2.10) Interest rate 0.035 0.091 ** 0.101 ** 0.088 ** 0.079 * (0.90) (2.01) (2.28) (2.04) (1.83) Exchange rate 2.603 3.307 3.651 4.012 3.740 strength (1.00) (1.24) (1.38) (1.60) (1.49) Exchange rate 17.261-13.186-16.981-20.428-14.313 volatility (0.63) (-0.44) (-0.55) (-0.65) (-0.48) Political -0.229-0.311-0.082-0.326-0.644 stability (-0.50) (-0.62) (-0.15) (-0.59) (-1.23) Culture 0.005 0.010 0.010 0.011 0.010 (0.68) (1.18) (1.13) (1.23) (1.17) Language 2.062 * 2.646 ** 2.224 * 2.004 2.368 ** (1.76) (2.20) (1.65) (1.59) (2.13) Geographic variables EUROPE - - - - FECA -1.924 * -1.276-1.900 * -2.617 ** (-1.79) (-1.15) (-1.65) (-2.27) NA -1.378-1.150-0.962-1.228 (-1.47) (-1.19) (-1.03) (-1.38) SCA -2.803 *** -2.803 ** -2.422 ** -2.522 ** (-2.66) (-2.41) (-2.19) (-2.51) Others 1.588 1.760 2.364 ** 2.122 ** (1.51) (1.63) (2.19) (2.02) Industry variables NACE 10 - - NACE 11 0.631 0.319 (0.41) (0.20) NACE 20 0.427 0.603 (0.28) (0.40) NACE 21 0.963 1.046 (0.59) (0.56) NACE 23-0.987-0.818 (-0.71) (-0.60) 51

NACE 24-1.016-0.577 (-0.63) (-0.33) NACE 25 0.376 0.148 (0.27) (0.11) NACE 26-0.806-1.494 (-0.62) (-1.03) NACE 27 0.033-0.070 (0.02) (-0.05) NACE 28-0.849-0.330 (-0.63) (-0.24) NACE 29-1.271-1.256 (-0.91) (-0.90) NACE 32 0.636 0.939 (0.29) (0.46) Other Industries 1.477 1.222 (1.03) (0.83) Firm and deal variables Relative size 4.978 ** 5.455 ** (2.24) (2.37) Current ratio -0.420-0.338 (-0.95) (-0.94) Solvency ratio 0.019 0.014 (0.92) (0.78) Profit margin -0.040-0.035 (-1.42) (-1.19) Return on shareholder funds -0.004-0.004 (-0.39) (-0.34) Control -1.111 * -0.966 (-1.67) (-1.45) Related 0.103 0.052 (0.18) (0.10) Cash 1.408 ** 1.163 ** (2.52) (2.21) R 2 (in %) 10.17 13.45 15.90 22.27 19.57 F-value 1.47 1.70 ** 1.86 *** 1.88 *** 1.70 ** N= 345 Source: Own illustration. Comments: The sample consists of 345 cross-border mergers and acquisition announcements in the period 2005 to 2010. The dependent variable is cumulative abnormal returns (CAR) for the period [-1,1] in %. Macroeconomic variables of the acquirer country are marked with an A in front of the variable. The geographic variables are dummy variables that take the value 1 if the target is in the region: FECA=Far East and Central Asia, NA=North America, SCA=South and Central America or Others. The industry variables are dummy variables that take the value 1 if the acquirer is in the industry classification: NACE 11, NACE 12,.... Language is a dummy variable that takes the value 1 if the acquirer and target country have the same language. Control is a dummy variable that takes the value 1 if the acquisition is a full acquisition. Related is a dummy variable that takes the value 1 if the acquirer and target are related companies. Cash is a dummy variable that takes the value 1 if cash is chosen as the method of payment. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels respectively. T-values are presented in parentheses. Furthermore, R 2 s and F-values are reported in Table 11. The R 2 s range from 10% to 22% and are comparable to R 2 s reported in other studies. Some studies report lower R 2 s (Cakici et al., 1996; Corhay, Rad, 2000; Doukas, Travlos, 1988 and Moeller, Schlingemann, 2005), whereas others report similar or higher R 2 s (Kiymaz, 2004; 52

Markides, Ittner, 2004 and Markides, Oyon, 1998). 35 The F-values of the equations (2) to (5) are statistically highly significant at min. 5%. 7.3.1.1 Macroeconomic variables The cross-sectional regression is first conducted using only macroeconomic and cultural variables. Macroeconomic variables are important in explaining wealth effects in crossborder M&A. Six statistically significant variables are identified in the first equation: GDP, GDP per capita, GDP growth, acquirer s GDP growth, acquirer s inflation and language. Five of these variables remain significant even after adding more variables. GDP per capita, GDP growth, acquirer s GDP growth, acquirer s inflation and language are significant through all equations. Furthermore, the interest rate variable becomes statistically significant after adding geographic variables and remains significant in the subsequent equations. As expected, a good economic condition in the target country measured in GDP per capita and GDP growth affects shareholder wealth positively. Furthermore, language has a positive effect on shareholder wealth. Low transaction costs and efficient communication between the acquiring and target country seem to have an important role. The effects of macroeconomic variables of the acquirer country are surprising. Although it was expected that macroeconomic variables of the acquirer country do not affect shareholder wealth in CBM&A, both GDP growth and inflation in the acquirer country are statistically significant. A bad economic condition in the acquirer country, i.e. decreasing GDP growth and increasing inflation, affects wealth gains positively. A bad economic condition in the acquirer country can also be interpreted as a push factor of the domestic market and a good economic condition in the target country as a pull factor of the foreign market as discussed in the electic model in section 2.3.3. Thus, there is a location advantage in the foreign market that suggests a CBM&A as the entry mode. The electic model is confirmed by positive wealth effects. The magnitude of the effects is analyzed by looking at the change in the cumulative abnormal returns when the independent variable increases by one standard deviation. An increase in one standard deviation in logged GDP per capita implies an increase in abnormal returns by 1,6%, which is 32% of the standard deviation of the dependent 35 R 2 s are not discussed in more detail, because studies apply various methods. The R 2 s reported in this study remain comparable, even if standardized abnormal returns are used as the dependent variables or adjusted R 2 s are obtained from OLS regressions without robust standard errors. 53

variable CAR. The magnitude of the GDP growth variables (for the target country and the acquirer country) is similar in size. An increase in one standard deviation in GDP growth implies an increase in abnormal returns by 1,5% for the GDP growth in the target country and a decrease by 1,5% for the GDP growth of the acquirer s country. If the same language is spoken in the acquirer and target country, abnormal returns are 2% higher compared to differing languages in both countries. Effects of the other significant variables are smaller in size. An increase in one standard deviation of the inflation variable of the acquirer country and of the interest rate variable implies an increase in abnormal returns by 0,9% (18% of the standard deviation of CAR) and 0,8% (16% of the standard deviation of CAR) respectively. The variables GDP (in target and acquirer country), inflation, exchange rate strength, exchange rate volatility, political stability and culture have the expected signs, however, are insignificant. Especially, the size of the economy measured by the GDP is highly insignificant and does not seem to affect the success of M&A. This could be the result of increasing regional agreements. The acquired companies remain legally independent and benefit from the regional agreements. For Example, by acquiring a Uruguayan company, access is obtained not only to the Uruguayan market, but also to markets within the Mercosur 36. Marginal effects of the political stability and inflation variables are very small, too. However, the mean of the political stability variable shows that the countries in the sample are generally politically stable. 37 This indicates that the political stability is rather important for the choice of the target country leading to acquisitions in more stable countries. Exchange rate strength tends to have a positive effect, while exchange rate volatility tends to have a negative effect. Interestingly, the effect of culture is positive, meaning with increasing cultural differences abnormal returns increase. However, as mentioned before, the culture variable is not statistically significant. 7.3.1.2 Geographic variables Geographic variables are included in equations (2), (3), (4) and (5). Through all equations, acquisitions in FECA, NA and SCA are less favorable compared to the control group Europe. The group Others tends to outperform acquisitions in Europe. 36 Mercado Común del Sur: Southern American political and economic agreement including Argentina, Brazil, Paraguay and Uruguay. 37 Summary statistics are presented in Appendix H. 54

SCA is negative and significant in all four equations. Acquisitions in SCA tend to result in 2-3% lower abnormal returns compared to acquisitions in Europe. Another highly significant geographic group is FECA. Acquisitions in FECA result in 2-3% lower abnormal returns compared to acquisitions in Europe. The group Others becomes significant after including firm and deal variables in equations (4) and (5). Acquisitions in Others result in 2% higher abnormal returns than in Europe. The geographic variable for NA is insignificant in all equations. However, the sign indicates lower abnormal returns compared to acquisitions in Europe. 7.3.1.3 Industry variables Industry variables are included in equations (3) and (4). The results show that some industries lead to lower abnormal returns, whereas others lead to higher abnormal returns compared to the control group NACE 10 (manufacturer of food products). However, the industry variables are not statistically significant, implying that there are no differences between industries within the manufacturing industry with regard to M&A wealth effects. 7.3.1.4 Firm and Deal variables The equations (4) and (5) include firm and deal variables. Especially, relative size and the method of payment seem to be important in explaining shareholder wealth as expected. An increase in one standard deviation in relative size implies an increase in abnormal returns by 0,7%, which is 15% of the standard deviation of abnormal returns. If cash is chosen as the method of payment, abnormal returns are 1,4% higher than other methods of payment. The control variable is significant on a 10% significance level in equation (4). The sign is negative, which is in contrast to domestic acquisitions. In domestic M&A, a full acquisition is perceived positively; it gives the management of the acquirer a complete influence on the target. In CBM&A, a complete influence of the acquirer is perceived negatively. This might show the importance of having a local partner who can assist the acquirer in the foreign market. If the acquisition is a full acquisition, abnormal returns are 1,1% lower than a partial acquisition. The variable related is insignificant. There is no support for the synergy or the diversification argument. Furthermore, the financial ratios (current margin, solvency margin, profit margin and returns on shareholder funds) that are included to capture firm 55

differences are insignificant, too. Financial ratios do not seem to be important in explaining shareholder wealth; this might be due to the fact that generally companies with good financial ratios are undertaking M&A (see section 6.4). 7.3.2 Pre-crisis vs. crisis The cross-sectional regression analysis is repeated for the two subsamples: pre-crisis and crisis. 38 The results are reported and compared to the full sample in Table 12. Table 12: Cross-sectional regression results: Pre-crisis vs. crisis Variables Pre-crisis Crisis Combined Constant -40.107 34.240-29.054 (-1.52) (0.93) (-1.59) Macroeconomic variables GDP 0.280-0.194 0.167 (0.90) (-0.33) (0.63) GDP per capita 0.982 0.136 1.008 ** (1.40) (0.16) (2.18) GDP growth 0.442 * 0.343 * 0.465 *** (1.81) (1.92) (3.24) Inflation 0.150-0.432 * 0.068 (0.95) (-1.78) (0.53) A-GDP 0.214-0.647-0.179 (0.55) (-1.38) (-0.66) A-GDP per capita 1.413-1.820 1.619 (0.80) (-0.76) (1.27) A-GDP growth -0.132-0.566 ** -0.671 *** (-0.34) (-2.20) (-3.16) A-Inflation -0.116 1.534 *** 0.711 ** (-0.25) (3.17) (2.10) Interest rate 0.050 0.050 0.079 * (1.02) (0.71) (1.83) Exchange rate strength 4.354 11.908 3.740 (1.41) (1.53) (1.49) Exchange rate volatility -56.392-2.297-14.313 (-1.32) (-0.04) (-0.48) Political stability -0.600-0.448-0.644 (-0.82) (-0.45) (-1.23) Culture 0.003 0.036 ** 0.010 (0.28) (2.00) (1.17) Language 0.552 8.851 *** 2.368 ** (0.48) (3.20) (2.13) Geographic variables EUROPE - - - FECA -3.301 ** -1.548-2.617 (-2.05) (-0.82) (-2.27) ** NA -0.683-1.565-1.228 (-0.68) (-0.77) (-1.38) 38 As dependent variables CAR [-1, 1] are used, as in the previous regressions. 56

SCA -0.709-3.603 * -2.522 (-0.65) (-1.82) (-2.51) ** Others 2.916 ** 1.919 2.122 (2.10) (0.88) (2.02) ** Firm and deal variables Relative size 7.653 *** 2.862 5.455 ** (2.89) (0.91) (2.37) Current ratio -0.313-0.775-0.338 (-0.76) (-0.85) (-0.94) Solvency ratio 0.007 0.065 0.014 (0.33) (1.64) (0.78) Profit margin 0.016-0.051-0.035 (0.51) (-1.12) (-1.19) Returns on shareholder funds -0.023-0.010-0.004 (-1.25) (-0.62) (-0.34) Control -1.698 * -0.701-0.966 (-1.85) (-0.63) (-1.45) Related -0.104-0.584 0.052 (-0.16) (-0.57) (0.10) Cash 1.307 ** 1.638 1.163 ** (2.00) (1.55) (2.21) R 2 (in %) 20.26 40.31 19.57 F-value 1.49 * 1.74 ** 1.70 ** N= 231 114 345 Source: Own illustration. Comments: The sample consists of 231, 114 and 345 cross-border mergers and acquisition announcements in the pre-crisis, crisis and combined period respectively. The dependent variable is cumulative abnormal returns (CAR) for the period [-1,1] in %. Macroeconomic variables of the acquirer country are marked with an A in front of the variable. The geographic variables are dummy variables that take the value 1 if the target is in the region: FECA=Far East and Central Asia, NA=North America, SCA=South and Central America or Others. The industry variables are dummy variables that take the value 1 if the acquirer is in the industry classification: NACE 11, NACE 12,.... Language is a dummy variable that takes the value 1 if the acquirer and target country have the same language. Control is a dummy variable that takes the value 1 if the acquisition is a full acquisition. Related is a dummy variable that takes the value 1 if the acquirer and target are related companies. Cash is a dummy variable that takes the value 1 if cash is chosen as the method of payment. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels respectively. T-values are presented in parentheses. The results show that macroeconomic variables are important, especially in the crisis period; six variables are statistically significant compared to one significant variable in the pre-crisis period. In the pre-crisis period, firm and deal variables are dominant in explaining shareholder wealth; three variables are statistically significant compared to no significant variable in the crisis period. The importance of macroeconomic variables in the crisis is supported by the R 2 s. The R 2 s indicate that the model explains more in the crisis period than in the pre-crisis period. The R 2 in the crisis period is 40% compared to 20% in the pre-crisis period. 57

7.3.2.1 Macroeconomic variables The only significant macroeconomic variable in the pre-crisis period is GDP growth in the target country. An increase in one standard deviation in GDP growth results in an increase of 1,2% in abnormal returns. In the crisis period, six macro economic variables are statistically significant: GDP growth, inflation, acquirer s GDP growth, acquirer s inflation, culture and language. The effects of the macroeconomic variables are high compared to both the pre-crisis and the full sample. An increase in one standard deviation of the target country s macroeconomic variables, GDP growth and inflation, lead to an increase of 1,4% and a decrease of 1,3% in abnormal returns respectively. The effects are 25,2% and 22,5% of the standard deviation of abnormal returns. GDP growth and inflation of the acquirer s country are statistically significant, too. The macroeconomic condition in the acquirer country has an even bigger effect than the macroeconomic condition in the target. An increase in one standard deviation of the acquirer country s macroeconomic variables, GDP growth and inflation, lead to a decrease of 2,1% and an increase of 2,5% in abnormal returns respectively. The effects are 36,5% and 43,6% of the standard deviation of abnormal returns. Furthermore, language and culture have a positive significant effect. An increase in one standard deviation in culture leads to an increase of 1,2% in abnormal returns. If the same language is spoken in the acquirer and target country, abnormal returns are 8,9% higher compared to differing languages in both countries. Language has the expected sign; a common language and thus lower transaction costs affect wealth effects positively. The sign of culture is interestingly positive; the more culturally different the countries are, the higher are wealth effects. This could be due to the assumption that culturally different countries are less affected by the crisis, however there is only a weak correlation between the GDP growth and the culture variable (0,3). The true reason might be that intangible assets in culturally different countries are acquired. This would support the organizational capability theory. Companies acquire others to expand their capabilities. 7.3.2.2 Geographic variables A view on the geographic variables shows that acquisitions in the pre-crisis period result in statistically significantly lower abnormal returns in FECA and significantly higher abnormal returns in Others, compared to the control group Europe. The SCA variable is negative and insignificant. 58

In the crisis period, geographic variables are mostly insignificant. The only slightly significant region is SCA with lower wealth gains compared to Europe. The signs of the variables do not change in the crisis period. FECA and NA have lower and Others higher abnormal returns compared to Europe. It seems like geographic diversification is not important. It is rather important to capture growth in other markets, than the market itself. 7.3.2.3 Firm and deal variables In the pre-crisis period, three variables are statistically significant: relative size, control and cash. An increase in one standard deviation in relative size leads to an increase in abnormal returns of 1,0%, which is 22,0% of the standard deviation in CAR. If cash is chosen as the method of payment, abnormal returns are 1,3% higher than other methods of payment. Control has a negative effect. If the acquisition is a full acquisition, abnormal returns are 1,7% lower than a partial acquisition. In the crisis period, all firm and deal variables are insignificant. Although firm financials seem to have a bigger effect, they remain insignificant. Firm and deal variables seem to be less important in explaining shareholder wealth in the crisis period. In the crisis period, macroeconomic variables play an important role in explaining shareholder wealth, whereas in the pre-crisis period firm and deal variables seem to have an important role. The fact that an acquisition is announced in the crisis leads to a positive market reaction without taking firm and deal characteristics into account. In the pre-crisis period, however, an acquisition is not perceived positive per se; firm and deal variables are important. This is also confirmed by the analysis in section 7.1. The market reacts to an M&A announcement very quickly in the crisis period, whereas market adjustments occur in the pre-crisis period. The market evaluates M&A acquisitions rather than seeing them positive in it-self. The difference between the two periods can be seen as different interpretations of M&A in the two periods. In the crisis period the market interprets the M&A announcement as a sign of the health or strength of the acquiring company, since only healthy and strong companies are able to undertake M&A in the crisis. This is true if we consider that wealth effects are higher when the economic condition in the acquirer country is bad. The worse the economic condition in the country of the acquirer, the stronger is the signal sent to the market. 59

Stock Price (in Euro) 7.4 Case study The results of the thesis are further presented on the basis of two cases. The first case is an M&A in the pre-crisis period and the second case an M&A in the crisis period. Pre-crisis Figure 8 shows the stock price development of the Naturex stock 20 days surrounding the announcement day. Naturex is a French producer of ingredients for food, beverage, flavor, nutraceutical, pharmaceutical, and cosmetic industries. On June the 6 th, 2005 Naturex acquired Pure World Inc., a US producer of plant extracts for the food, beverage, flavor, nutraceutical, pharmaceutical, and cosmetic industries for 30,5 Mil. Euros. 39 Figure 8: An acquisition in the pre-crisis period: The case of Naturex 38 36 34 32 30 28 26 24-20 -18-16 -14-12 -10-8 -6-4 -2 0 +2 +4 +6 +8 +10 +12 +14 +16 +18 +20 Days Predicted Stock Price without M&A Stock Price with M&A Source: Own illustration. Comments: Day 0 is the announcement day. Figure 8 shows a clear positive effect of the M&A announcement. On the announcement day, the stock price of Naturex increased from 26,68 Euros to 31,80 Euros. In addition, market adjustments led to a further increase. Finally, the stock price settled around 33,80 Euros (day +14 to day +20). The value creation becomes clear when comparing the stock price and the predicted stock price without the M&A. The predicted stock price shows a steady slow increase and settles around 28,00 Euros. However, the stock price increases after the M&A announcement and settles around 33,80 Euros. The development of both curves is similar after day 14, indicating that the 39 Deal number 35 in Appendix C. 60

Stock Price (in Euro) effect of the M&A is captured and the stock is following its former path on a higher level. Crisis Figure 9 shows the stock price development of the Koninklijke DSM stock 20 days surrounding the announcement day. Koninklijke DSM is a Dutch manufacturer operating in the health, nutrition and materials industry. On December the 21 st, 2010 Koninklijke DSM acquired the Martek Biosciences Corporation, a US producer of products from microbial sources that promote health and wellness through nutrition, for 790,5 Mil. Euros. 40 Figure 9: An acquisition in the crisis period: The case of Koninklijke DSM 46 44 42 40 38 36-20 -18-16 -14-12 -10-8 -6-4 -2 0 +2 +4 +6 +8 +10 +12 +14 +16 +18 +20 Days Predicted Stock Price without M&A Stock Price with M&A Source: Own illustration. Comments: Day 0 is the announcement day. Figure 9 shows a clear positive effect of the M&A. The stock price of Koninklijke DSM increased from 39,74 Euros to 40,99 Euros from day -3 to day -2. Wealth effects are captured within two days; at the announcement day the stock price reached 42,60 Euros. After the announcement day, both the stock price and the predicted stock price without the M&A show a similar development, indicating that the effect of the M&A is captured and the stock is following his former path on a higher level. 41 40 Deal number 336 in Appendix C. 41 In both Figures 8 and 9, stock prices and predicted stock prices are similar prior to the announcement, indicating a good prediction by the applied market model. Furthermore, stock prices follow a similar development after approximately 10 days after the announcement, indicating a good choice of the event window. 61

The two cases show that wealth effects in both the pre-crisis and crisis period are positive. However, there is an important difference. Wealth effects are captured within a short time period around the announcement day in the crisis period, whereas market adjustments occur in the pre-crisis period. In the pre-crisis period, the market evaluates the M&A after the announcement. However, in the crisis period the announcement is perceived positive per se, no adjustments occur. 8 Conclusion The thesis investigates the impact of cross-border Mergers & Acquisitions (CBM&A) on acquirers in the period 2005 to 2010. Acquirers are manufacturers in the Euro zone. The sample consists of 345 CBM&A transactions and is further divided into a pre-crisis period (2005-2007) and a crisis period (2008-2010). The findings indicate that acquirers experience statistically significant wealth effects of 1,1% around the M&A announcement, as expected. Wealth effects are higher in the crisis period (1,47%) compared to the pre-crisis period (0,93%). However, the difference is not statistically significant. This is in contrast to what was expected. It was expected that wealth effects are higher in the crisis period. In the crisis, only healthy and strong companies are able to undertake M&A. An M&A announcement should send a positive signal to the market. Since fewer companies are in a healthy situation and the identification of these companies is more difficult, wealth effects should be bigger in the crisis compared to the pre-crisis period. The results of the cross-sectional regression analysis indicate that macroeconomic factors are important in explaining wealth effects as expected. GDP per capita and GDP growth in the target country have a statistically significant positive wealth effect. Not only macroeconomic factors of the target country are important, but macroeconomic factors of the acquirer country also seem to play a role. GDP growth in the acquirer country has a statistically significant negative effect, while inflation has a significant positive effect on shareholder wealth. The results indicate that CBM&A create more value if the economic condition in the home country is bad. Furthermore, the interest rate difference between the two countries, the relative size of the target to the acquirer, cash as the method of payment, and having the same language in both countries have a statistically significant positive impact on shareholder wealth. 62

Looking at the pre-crisis and crisis period shows that macroeconomic factors are important, especially in the crisis period. Good macroeconomic indicators in the target country (high GDP growth and low inflation) and bad macroeconomic indicators in the home country (low GDP growth and high inflation) affect shareholder wealth positively. Firm and deal factors do not have a statistically significant effect on shareholder wealth. In the pre-crisis, firm and deal factors seem to play a more important role. The relative size of the target to the acquirer and the method of payment cash have a significant positive impact on shareholder wealth, whereas a full acquisition affects shareholder wealth negatively. The only slightly significant macroeconomic factor is GDP growth of the foreign country. Combining the results of the wealth effects analysis and the identification of determinants analysis leads to an interesting finding. Although there is not a significant difference in the size of wealth effects between the pre-crisis and crisis as expected, there is a difference in the market behavior. Wealth effects in the pre-crisis period are experienced in longer periods. Market adjustments occur. The market evaluates acquisitions rather than seeing them positive in it-self. This is confirmed by the regression results. Firm and deal variables are statistically significant in the pre-crisis period; the market evaluates the firm and deal characteristics. However, in the crisis period wealth effects are experienced shortly around the announcement day, and deal and firm variables are not statistically significant. The market reacts positively to the announcement without evaluating the transaction. The acquisition it-self sends a positive signal to the market. Acquirers in the crisis period are seen as healthy and strong companies. The signal is stronger, the worse the economic condition (GDP growth and inflation) in the home country. CBM&A analyzed in this thesis, create value for companies in the sample. However, it should be noted that this might not be the case for companies outside the sample. Companies choose themselves to acquire others. The M&A decision is affected by various factors. Only a specific type of company is in the sample i.e. only companies involved in an M&A are analyzed. Looking at their financial ratios shows that these companies tend to be better on average compared to their competitors. Furthermore, wealth effects might be caused by other corporate events around the announcement. Short event windows are used to reduce this potential problem as much 63

as possible. However, wider windows are also used, to capture wealth effects in case the announcement day is not exactly defined. Information might become public prior to the announcement or market participants might not be able to react on the day of the announcement. Moreover, the definition of the crisis period leaves room for discussion. In the thesis, all announcements starting from 2008 are classified as announcements in the crisis. It is difficult to specify the exact beginning of the crisis. The thesis provides a first evidence for different market reactions to M&A announcements during crises. Further investigations are needed to confirm the results and to give more insight. It is unanswered whether there is a change in market behavior in other industries or other geographic regions. Furthermore, a deeper analysis of the reasons behind the change in market behavior is of interest. 64

References Agarwal, S. and Ramaswami, S.N. (1992), Choice of Foreign Market Entry Mode: Impact of Ownership, Location and Internalization Factors, Journal of International Business Studies, vol. 23, no. 1, pp. 1-27. Aguiar, M. and Gopinath, G. (2007), Emerging Market Business Cycles: The Cycle Is the Trend, Journal of Political Economy, vol. 115, no. 1, pp. 69-102. Ahammad, M.F. and Glaister, K.W. (2010), Motives for Cross Border Mergers and Acquisitions: Perspective of UK Firms, Nottingham Trent University, Working Paper. Alvarado, F.L. (1998), Market Power: A Dynamic Definition, Bulk Power Systems Dynamics and Control IV: Restructuring Conference, Santorini, Greece. Andrade, G., Mitchell, M. and Stafford, E. (2001), New Evidence and Perspectives on Mergers, Journal of Economic Perspectives, vol. 15, no. 2, pp. 103-120. Antoniou, A., Petmezas, D. and Zhao, H. (2007), Bidder Gains and Losses of Firms Involved in Many Acquisitions, Journal of Business Finance & Accounting, vol. 34, no. 7, pp. 1221-1244. Asquith, P., Bruner, R.F. and Mullins, D.W. (1983), The Gains to Bidding Firms from Mergers, Journal of Financial Economics, vol. 11, no. 1-4, pp. 121-139. Asquith, P. and Mullins, D.W. (1986), Equity issues and offering dilution, Journal of Financial Economics, vol. 15, no. 1-2, pp. 61-89. Bartholdy, J., Olson, D. and Peare, P. (2007), Conducting Event Studies on a Small Stock Exchange, The European Journal of Finance, vol. 13, no. 3, pp. 227-252. Biswas, R., Fraser, D.R. and Mahajan, A. (1997), The international market for corporate control: Evidence from acquisitions of financial firms, Global Finance Journal, vol. 8, no. 1, pp. 33-54. Brockmann, M. (2007), Risks of International Activities and Corporate Financing A Review and Suggestions, European Business School (EBS) Wiesbaden, Working Paper. 65

Brown, S.J. and Warner, J.B. (1980), Measuring Security Price Performance, Journal of Financial Economics, vol. 8, no. 3, pp. 205-258. Brown, S.J. and Warner, J.B. (1985), Using daily stock returns: The case of event studies of event-induced variance, Journal of Financial Economics, vol. 14, no. 1, pp. 3-31. Bruner, R.F. (2004), Does M&A Pay? in Krug, J.A. (2008), Mergers and Acquisitions, SAGE Publications Ltd, London. Busse, M. and Hefeker, C. (2007), Political risk, institutions and foreign direct investment, European Journal of Political Economy, vol. 23, no. 2, pp. 397-415. Butz, D.A. (1994), How Do Large Minority Shareholders Wield Control?, Managerial and Decision Economics, vol. 15, no. 4, Special Issue: Aspects of Corporate Governance, pp. 291-298. Cakici, N., Hessel, C. and Tandon, K. (1996), Foreign acquisitions in the United States: Effect on shareholder wealth of foreign acquiring firms, Journal of Banking & Finance, vol. 20, no. 2, pp. 307-329. Campa, J.M. and Hernando, I. (2004), Shareholder Value Creation in European M&As, European Financial Management, vol. 10, no. 1, pp. 47-81. Campbell, C.J. and Wesley, C.E. (1993), Measuring security price performance using daily NASDAQ returns, Journal of Financial Economics, vol. 33, no. 1, pp. 73-92. Canabal, A. and White III, G.O. (2008), Entry mode research: Past and future, International Business Review, vol. 17, no. 3, pp. 267-284. Cantwell, J. and Santangelo, G.D. (2002), M&As and the Global Strategies of TNCs, The Developing Economies, vol. 40, no. 4, pp. 400-434. Chan-Olmsted, S.M. and Chang, B.-H. (2003), Diversification Strategy of Global Media Conglomerates: Examining Its Patterns and Determinants, Journal of Media Economics, vol. 16, no. 4, pp. 213-233. 66

Chen, L. Y. and Mujtaba, B. (2007), The Choice of Entry Mode Strategies and Decisions for International Market Expansion, Journal of American Academy of Business, Cambridge, vol. 10, no. 2, pp. 322-337. Coase, R.H. (1937), The Nature of the Firm, Economica, vol. 4, no. 16, pp. 386-405. Copeland, T. and Weston, F.J. (1988), Financial Theory and Corporate Policy, 3rd edition, Menlo Park (CA). Corhay, A. and Rad, A.T. (2000), International acquisitions and shareholder wealth Evidence from the Netherlands, International Review of Financial Analysis, vol. 9, no. 2, pp.163-174. Corrado, C.J. (1989), A nonparametric test for abnormal security-price performance in event studies, Journal of Financial Economics, vol. 23, no.2, pp. 385-395. Corrado, C. J. and Zivney, T. L. (1992), The specification and power of the sign test in event study hypothesis tests using daily stock returns, Journal of Financial and Quantitative Analysis, vol. 27, no. 3, pp. 465-478. Corrado, C.J. (2011), Event studies: A methodology review, Accounting and Finance, vol. 51, no. 1, pp. 207-234. Danbolt, J. (2004), Target Company Cross-border Effects in Acquisitions into the UK, European Financial Management, vol. 10, no. 1, pp. 83-108. De Jong, F. (2007), Event Studies Methodology, University of Tilburg. Desyllas, P. and Hughes, A. (2008), Sourcing technological knowledge through corporate acquisition: Evidence from an international sample of high technology firms, Journal of High Technology Management Research, vol. 18, no. 2, pp. 157-172. Doukas, J. and Travlos, N.G. (1988), The Effect of Corporate Multinationalism on Shareholders' Wealth: Evidence from International Acquisitions, Journal of Finance, vol. 43, no. 5, pp. 1161-1175. Eurostat (2008), NACE Rev. 2 - Statistical classification of economic activities in the European Community, Methodologies and Working papers. 67

EQUIFAX (2011), Financial ratios, http://www.findoutinfo.com/direct/cd001?stylesheet=use315.xsl#par1, accessed: 18.11.2011. Franks, J.R. and Harris, R.S. (1989), Shareholder Wealth Effects of Corporate Takeovers: The U.K. Experience 1955-1985, Journal of Financial Economics, vol. 23, no. 2, pp. 225-249. Franks, J., Harris, R. and Titman, S. (1991), The postmerger share-price performance of acquiring firms, Journal of Financial Economics, vol. 29, no. 1, pp. 81-96. Goergen, M. and Renneboog, L. (2004), Shareholder Wealth Effects of European Domestic and Cross-border Takeover Bids, European Financial Management, vol. 10, no. 1, pp. 9-45. Hansen, J.D. and Nielsen, J.U.M. (1997), An economic analysis of the EU, 2nd edition, McGraw-Hill, London. Hill, C.W.L., Hwang, P. and Kim, W.C. (1990), An eclectic theory of the choice of international entry mode, Strategic Management Journal, vol. 11, no. 2, pp. 117-128. Hollensen, S. (2007), Global marketing: a decision-oriented approach, 4th edition, Financial Times Prentice Hall, Harlow. Holmström, B. and Roberts, J. (1998), The Boundaries of the Firm Revisited, The Journal of Economic Perspectives, vol. 12, no. 4, pp. 73-94. Hope, O.-K. and Thomas, W.B. (2008), Managerial Empire Building and Firm Disclosure, University of Toronto/ University of Oklahoma, Working Paper. Hymer, S. H. (1976), The International Operations of National Firms: A Study of Direct Foreign Investment, MIT Press, Cambridge. Itim international (2011), National culture, http://geert-hofstede.com/national-culture.html, accessed on 01.11.2011. 68

Jarrell, G.A. and Poulsen, A.B. (1989), The Returns to Acquiring Firms in Tender Offers: Evidence from Three Decades, Financial Management, vol. 18, no. 3, pp. 12-19. Jinjarak, Y. (2007), Foreign direct investment and macroeconomic risk, Journal of Comparative Economics, vol. 35, no. 3, pp. 509-519. Johanson, J. and Vahlne, J.E. (1990), The Mechanism of Internationalisation, International Marketing Review, vol. 7, no. 4, pp. 11-24. Kaplan, S. and Weisbach, M. (1992), The success of acquisitions: evidence from divestitures, Journal of Finance, vol. 47, no. 1, pp. 107-138. Khotari, S.P. and Warner J.P. (2006), Econometrics of Event Studies, Forthcoming in Handbook of Corporate Finance: Empirical Corporate Finance by B. Espen Eckbo. Kim, W.C. and Hwang, P. (1992), Global Strategy and Multinationals' Entry Mode Choice, Journal of International Business Studies, vol. 23, no. 1, pp. 29-53. Kiymaz, H. and Mukherjee, T.K. (2000), The impact of country diversification on wealth effects in cross-border mergers, Financial Review, vol. 35, no. 2, pp. 37-58. Kiymaz, H. (2004), Cross-border acquisitions of US financial institutions: Impact of macroeconomic factors, Journal of Banking & Finance, vol. 28, no. 6, pp. 1413-1439. Kogut, B. and Singh, H. (1988), The Effect of National Culture on the Choice of Entry Mode, Journal of International Business Studies, vol. 19, no. 3, pp. 411-432. Koveos, P.E. (1997), International Mergers and Acquisitions: A Review of Some Current Issues, Managerial Finance, vol. 23, no. 3, pp. 72-88. Lang, L.H.P., Stulz, R.M. and Walkling, R.A. (1989), Managerial performance, Tobin's Q, and the gains from successful tender offers, Journal of Financial Economics, vol. 24, no. 1, pp. 137-154. 69

Loderer, M. and Martin, K. (1990), Corporate acquisitions by listed firms: the experience of a comprehensive sample, Financial Management, vol. 19, no. 4, pp. 17-33. Lowinski, F., Schiereck, D. and Thomas, T.W. (2004), The Effect of Cross-Border Acquisitions on Shareholder Wealth - Evidence from Switzerland, Review of Quantitative Finance and Accounting, vol. 22, no. 4, pp. 315-330. Lucks, K. and Meckl, R. (2002), Interntionale Mergers & Acquisitions - Der prozessorientierte Ansatz, 1st edition, Heidelberg. MacKinlay, C.A. (1997), Event Studies in Economics and Finance, Journal of Economic Literature, vol. 3, no.1, pp. 13-39. Madhok, A. (1997), Cost, Value and Foreign Market Entry Mode: The Transaction and the Firm, Strategic Management Journal, vol. 18, no. 1, pp. 39-61. Majidi, M. (2007), Cultural Factors in International Mergers and Acquisitions, International Journal of Knowledge, Culture and Change Management, vol. 6, no. 7, pp. 1-17. Malhotra, N.K., Agarwal, J. and Ulgado, F. M. (2003), Internationalization and Entry Modes: A Multitheoretical Framework and Research Propositions, Journal of International Marketing, vol. 11, no. 4, pp. 1-31. Malkiel, B.G. (2003), The Efficient Market Hypothesis and Its Critics, CEPS Working Paper, no. 91. Markides, C. and Ittner, C.D. (1994), Shareholder Benefits from Corporate International Diversification: Evidence from U.S. International Acquisitions, Journal of International Business Studies, vol. 25, no. 2, pp. 343-366. Markides, C. and Oyon, D. (1998), International acquisitions: Do they create value for shareholders?, European Management Journal, vol. 16, no. 2, pp. 125-135. Maynes, E. and Rumsey, J. (1993), Conducting event studies with thinly traded stocks, Journal of Banking & Finance, vol. 17, no. 1, pp. 145-157. 70

McKinsey (2010), Perspectives on Corporate Finance and Strategy, McKinsey on Finance, no. 34. Meldrum, D. (2000), Country Risk and Foreign Direct Investment, Business Economics, vol. 35, no. 1, pp. 33-41. Melin, L. (1992), Internationalization as a Strategy Process, Strategic Management Journal, vol. 13, no. S2, pp. 99-118. Moeller, S.B. and Schlingemann, F.P. (2005), Global diversification and bidder gains: A comparison between cross-border and domestic acquisitions, Journal of Banking & Finance, vol. 29, no. 3, pp. 533-564. Mulherin, J.H. and Boone, A.L. (2000), Comparing acquisitions and divestitures, Journal of Corporate Finance, vol. 6, no. 2, pp. 117-139. Mußhoff, J. (2007), Erfolgreiche M&A-Transaktionen in der europäischen Bankenindustrie, 1st edition, Wiesbaden. Myers, S.C and Majluf, N.S. (1984), Corporate financing and investment decisions when firms have information that investors do not have, Journal of Financial Economics, vol. 13, no. 2, pp. 187-221. Peterson, P.P. (1989), Event studies: A review of issues and methodology, Quarterly Journal of Business and Economics, vol. 28, no. 3, pp. 36-66. Rappaport, A. (1998), Creating Shareholder Value: A Guide For Managers And Investors, The Free Press, New York. Root, F.R. (1994), Entry strategies for international markets, Revised and expanded edition, Lexington Books, New York. Schlingemann, F.P. (2004), Financing decisions and bidder gains, Journal of Corporate Finance, vol. 10, no. 5, pp. 683-701. Schmidt, C.W. and Broll, U. (2009), Real exchange-rate uncertainty and US foreign direct investment: an empirical analysis, Review of World Economics, vol. 145, no. 3, pp. 513-530. 71

Scholes, M. and Williams, J. (1977), Estimating betas from nonsynchronous data, Journal of Financial Econometrics, vol. 5, no. 3, pp. 309-327. Serra, A.P. (2002), Event Study Tests: A brief survey, FEP Working Papers no. 117, Universidade do Porto. Servaes, H. (1991), Tobin's Q and the Gains from Takeovers, Journal of Finance, vol. 46, no. 1, pp. 409-419. Seth, A., Song, K.P. and Pettit, R. (2000), Synergy, Managerialism or Hubris? An Empirical Examination of Motives for Foreign Acquisitions of U.S. Firms, Journal of International Business Studies, vol. 31, no. 3, pp. 387-405. Strong, N. (1992), Modelling Abnormal Returns: A Review Article, Journal of Business Finance & Accounting, vol. 19, no. 4, pp. 533-553. Sudarsanam, S., Holl, P. and Salami, A. (1996), Shareholders wealth gains in mergers: Effect of synergy and ownership structure, Journal of Business Finance and accounting, vol. 23, no. 5-6, pp. 673-698. Sudarsanam, S. (2010), Creating value from mergers and acquisitions: the challenges, 2nd edition, Financial Times Prentice Hall, Harlow. Travlos, N.G. (1987), Corporate Takeover Bids, Methods of Payment, and Bidding Firms' Stock Returns, The Journal of Finance, vol. 42, no. 4, pp. 943-963. Uddin, M., and Boateng, A. (2010), Explaining the trends in the UK cross-border mergers & acquisitions: An analysis of macro-economic factors, International Business Review, forthcoming. Walker, M.M. (2000), Corporate Takeovers, Strategic Objectives, and Acquiring-Firm Shareholder Wealth, Financial Management, vol. 29, no. 1, pp. 53-66. Wansley, J. W., Lane W. R. and Yang H. C. (1983), Abnormal Returns To Acquired Firms By Type of Acquisition and Method of Payment, Financial Management, vol. 12, no. 3, pp. 16-22. 72

Werner, S. (2002), Recent developments in international management research: A review of 20 top management journals, Journal of Management, vol. 28, no. 3, pp. 277-305. Wübben, B. (2007), German Mergers & Acquisitions in the USA - Transaction management and success, 1st edition, Wiesbaden. Databases Datastream Thomson Reuters http://online.thomsonreuters.com/datastream/ European Central Bank database http://sdw.ecb.europa.eu/home.do Itim International http://geert-hofstede.com/index.php OECD database http://stats.oecd.org/index.aspx Orbis database Bureau van Dijk http://www.bvdinfo.com/products/company-information/international/orbis.aspx The Global Market Information database http://www.euromonitor.com/ The World Development Indicators & Global Development Finance database http://databank.worldbank.org/ddp/home.do?step=1&id=4 The World Governance Indicators database http://info.worldbank.org/governance/wgi/index.asp Zephyr database Bureau van Dijk http://www.bvdinfo.com/products/economic-and-m-a/m-a/zephyr 73