The effect of economic growth on bidder CARs in Cross-Border M&A.

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The effect of Economic Growth on Bidder CARs in Cross-Border M&A

Master thesis 2014 MSc Business Economics Specialization: Finance Abstract

In this thesis the effect of future economic growth and four other determinants of cross-border M&A on bidder CARs is investigated. Using a sample of 1092 U.S. bidders and targets from 58 different countries, the empirical results in this thesis suggest that bidder CARs are 4% higher when future economic growth in the target country is high, 3% higher when cultural distance between target and acquirer is small and 5% higher when the target is characterized by a civil law system. The effect of geographical distance and economic development of the target country have not turned out to play a significant effect on bidder CARs.

Name: Max van Wakeren

Student Number: 6145817

Date: 14-02-2014

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The volume of cross-border mergers and acquisitions (M&A) as a percentage of the total volume of worldwide mergers and acquisitions has grown from 27% in 1998 to 43% in 2007 (Erel et al., 2012). Empirical evidence suggests that the determinants of cross-border M&A differ substantially from the determinants of domestic M&A, national borders bring an additional set of frictions to cross-border M&A (Kiymaz, 2004). Empirical evidence states that geographical-, and cultural distance between target and acquirer country, the legal system implemented in the target country and the

macroeconomic environment in the target country have a significant impact on the cumulative abnormal returns of bidder firms around the announcement date of a takeover (Ahern et al., 2012; Erel et al., 2012; Kiymaz, 2004). The focus in these papers is mainly on the implications of a certain legal system (Barbopoulos et al., 2012), cultural values (Ahern et al., 2012) or a combination of these factors. The effect of macroeconomic variables such as economic development and economic growth on stock returns around the announcement date of an M&A is little under exposed. Although most of the papers that investigate the determinants of cross-border M&A include macroeconomic variables in their regression (Ahern et al., 2012; Erel et al., 2012; Kiymaz, 2004), the authors give very little explanation why macroeconomic variables are important when analyzing bidder

announcement returns and what the significance actually means. In this thesis the following

question is answered: Can firms time their cross-border M&A activities in terms of economic growth potential in the target country and do shareholders also recognize such timing ability? In addition, the effect of four other cross-country characteristics; cultural-, and geographical distance between target and acquirer, the legal system in the target country and economic development of the target country on bidder stock returns around the announcement date of a takeover is investigated.

Determinants of cross-border M&A such as legal system, cultural values and geographical distance have been investigated extensively (Ahern et al., 2012; Barbopoulos et al., 2012; Erel et al., 2012). Only little empirical evidence reports about the relationship between economic growth and stock returns around the announcement date of a takeover. Kiymaz (2004) investigates the effect of macroeconomic variables on bidder and target cumulative announcement returns (CAR). The sample in his research contains only financial institutions that made a M&A transaction between 1989-1999. The economic growth variable in the research by Kiymaz (2004) is defined as the economic growth of the target country in the year prior to the announcement compared to the average economic

growth during the sample period. The contribution of this thesis to the literature is that a broad sample is used in which all public U.S. firms are included. The usage of future economic growth rather than contemporaneous economic growth allows one to test whether firms are able to time the market in terms of economic growth potential when making a merger or acquisition. The

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4 relationship between future economic growth and bidder firm stock returns has not yet been

investigated to this extent when looking at cross-border M&A. Another contribution to the literature is that the effect of four other determinants of cross-border M&A (economic development, cultural distance, geographical distance and legal system) on announcement returns is investigated. Ahern et al. (2012), Erel et al. (2012) and Kiymaz (2004) look at the determinants of cross-border M&A. However, none of these papers include the same set of variables as in this thesis. In addition, the results are placed in a time frame (2000-2005) in which cross-border M&A have become increasingly important (Erel et al. 2012). The results in the paper are especially helpful for firms looking for cross-border investment opportunities. This thesis suggests that economic growth, legal system and cultural values in the target country are important factors that have to be taken into account when considering a cross-border takeover.

Empirical evidence (Fama, 1981, 1990; Schwert, 1990) as well as economic theory (Mankiw, 2007) hypothesize and find a positive relationship between future real activity and stock returns which suggests that stock markets are efficient with respect to future real activity. An important note is that these paper focus on normal returns rather than abnormal returns and that the main purpose is to test market efficiency rather than timing ability. Assuming efficient markets, a cross-border M&A transaction into a target country in which future economic growth prospects are higher is assumed to be seen as a positive signal by the market. If firms are able to time the market with respect to future economic growth, which means that they can predict future economic growth in the target country accurate, and investors recognize this timing ability, one would expect bidder abnormal returns to be higher around the announcement date of a takeover if future economic growth in the target country is high. In this thesis future economic growth of the target country is defined as ‘high’ if the five year economic growth from a country after the year of the transaction is higher than the 75th percentile in the sample. Kiymaz (2004) reports a negative relationship between economic growth and stock returns, the author suggests that higher premiums are paid when economic growth in the target country is higher, which leads to lower bidder stock returns around the announcement date of a takeover. Rossi and Volpin (2004) report that more transactions take place in countries where economic growth is higher, this suggests that competition in these

countries is higher which leads to higher prices and lower returns. The empirical evidence by Kiymaz (2004) and Rossi and Volpin (2004) suggests that markets are either inefficient, or firms are unable to time the market. Assuming that markets are efficient, firms have the ability to time the market, and investors recognize this, the hypothesis in this thesis is that bidder CARs are higher for firms that do takeover into countries in which future economic growth is higher. Furthermore, the expectation

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5 is that M&A transactions into less-developed, less geographical and cultural distant countries with a civil law system implemented are more successful.

The relationship between future economic growth and bidder announcement returns is investigated using several OLS regressions. The sample contains 1092 M&A transactions in which bidders are from the U.S. and targets are from 58 different countries. The period 2000-2005 is used, in this period the number of cross-border transactions has grown substantially as can be seen in Erel et al. (2012). This increase in cross-border M&A volume makes the understanding of the

determinants of cross-border M&A more important.

The results in this thesis suggest that bidder cumulative abnormal returns are 4% higher if the future economic growth in the target country is high, which supports the hypothesis. This suggests that markets are efficient and firms are able to time the market which is recognized by investors. The results suggest that M&A transactions into countries where future economic growth is high are more attractive for firms as well as investors. The findings are less significant after

correcting for year and industry fixed effects. Furthermore, the results show that bidder

announcement returns are 3% higher for transactions into countries that are little culturally distant and 5% higher for country characterized by a civil law system (rather than a common law system). These results support our hypotheses. Economic development and geographical distance of the target country have not turned out to play a significant role when analyzing bidder announcement returns. A sensitivity analysis suggests that economic growth is only significantly positive when using a 2 or 5 year dummy variable to measure economic growth. When using a continuous variable for economic growth the effect is insignificant. This suggests that firms have better timing ability, which is recognized by investors, when economic growth is ‘high’. High is here defined as the highest 25 percentile in the sample. A first robustness check in which only transactions with a deal value higher than 75 million dollars are included, shows that the effect of economic growth on bidder

announcement returns becomes less significant. These results do not confirm the expectation that the relationship between future economic growth and bidder returns would become more visible and more significant after reducing the sample size to large transactions only. A second robustness is done to check whether the effect of economic growth on bidder announcement returns is higher in developing or developed countries. Kiymaz (2004) reports higher bidder announcement returns when expanding into a less-developed country where economic growth is lower. An important note is that Kiymaz (2004) uses another measure for economic growth. GNP growth in the year prior to the transaction relative to the economic growth in the entire sample period (1989-1999). The results

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6 in this thesis do not support the results by Kiymaz (2004), higher bidder announcement returns are reported in less-developed countries where economic growth is higher, rather than lower.

The rest of this thesis is organized as follows. In section 2 the most important empirical evidence and theories with respect to the cross-country determinants of M&A, and bidder

announcement returns are discussed. In section 3 the hypotheses and methodology are discussed. Section 4 contains some descriptive statistics and describes the sample used. In section 5 the most important results are presented and discussed. In section 6 robustness checks are presented. Conclusions and other important remarks are shown in section 7.

2. Literature review

In this section relevant theories and papers are discussed. In section 2.1 determinants of cross-border M&A are discussed. Section 2.2 discusses the relationship between economic growth and (normal) stock returns. The general equilibrium theory as well as empirical evidence are discussed in this paragraph. In section 2.3 other firm- and deal characteristics that might have an effect on bidder CARs are discussed.

2.1 Determinants of cross-border M&A

Extensive research reports about domestic determinants of M&A (Asquith et al., 1983; Fuller et al., 2002; Moeller et al., 2004). Determinants of domestic M&A are not sufficient in analyzing cross-border M&A. The literature about the determinants of cross-cross-border M&A however, is rather limited. The main reasons for firms to elaborate cross-border are similar to those for domestic M&A. To give a few examples; synergies, economies of scale and empire building are important reasons for firms to do takeovers (Kiymaz, 2004). The main factors to control for in domestic M&A are firm- and deal characteristics. Firm size, payment method and target form are important determinants for

domestic as well as cross-border M&A (Travlos, 1987). Empirical evidence however, suggests that cross-border M&A bring some additional challenges for firms. Cross-country determinants such as economic development, economic growth, legal system and culture do matter in cross-border transactions (Ahern et al., 2012; Erel et al., 2012; Rossi and Volpin, 2004). In this section important cross-country determinants of M&A are discussed, the section is divided into two subsections. The first subsection contains articles describing the cross-country determinants of M&A using

announcement returns as the dependent variable in the regression analysis. The second part contains articles describing the cross-country determinants of M&A using the number of transactions as the dependent variable in the regression analysis. In table I an overview of the

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7 empirical evidence is provided. The relationships are described more detailed in the paragraphs below.

Table I: Literature table

In this table the findings of the most important papers are summarized. A '-' indicates a negative relationship, '+' indicates a positive relationship and 'No' indicates that the variable is included in the regressions but no significant relationship is found. The information in the table should be read as follows: Kiymaz (2004) finds a negative relationship between economic growth and CAR, this implies that announcement returns are higher if economic growth is lower. For the papers in which takeover volume is the dependent variable the interpretation is different, for instance: Rossi and Volpin find a positive relationship between economic growth and takeover volume. This means that significantly more transactions take place in target countries that have higher levels of economic growth. An important note is that a + for legal system indicates that higher bidder CAR is higher when a civil law system is implemented.

Paper Economic Growth Economic Development Geographical Distance Cultural Distance Legal System Effect of variables on bidder CAR

Kiymaz (2004) - - -

Doukas and Travlos (1988) - +

Barbopoulos et al. (2012) +

Ahern et al. (2008) + - - -

Bris and Cabolis (2008) No - +

Moeller and Schlingemann (2005) - +

Effect of variables on takeover volume

Rossi and Volpin (2004) + -

Erel et al. (2012) - - -

2.1.1 Cross-border M&A and announcement returns

Announcement returns in cross-border M&A transactions are influenced by several factors. In table I the most important findings from the literature are summarized. In this subsection the most

important articles reporting about determinants of cross-border M&A and the effect of these determinants on announcement returns are discussed.

Kiymaz (2004) investigates the effect of macroeconomic variables on the announcement returns of U.S. bidder and target firms involved in cross-border M&A. The paper only considers transactions in which financial institutions are involved and uses the sample period 1989-1999. Due to changed government policy and regulation in the financial services industry, cross-border M&A have become more attractive in the sample period investigated. The main reasons for firms to do cross-border M&A are the same reasons for which firms do domestic M&A: synergies, reduction in

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8 expenses, economies of scale and scope and the increase of market power. However, national borders add an important extra dimension to cross-border M&A and bring new potentials as well as new threats. Differences in the level of economic activity among countries, the potential for

diversification, and the legal system implemented in the target country, are important factors that need to be taken into account when reviewing a cross-border takeover. The author reports that U.S. bidder shareholders experience insignificant positive abnormal returns when announcing a cross-border takeover. The regression results show that development, exchange rate and economic growth of the target country are negatively correlated with bidder abnormal returns at the

announcement date of takeovers. This implies that M&A from the U.S. into less-developed countries where economic growth is lower are expected to be more successful. The author argues that the level of competition in developing countries is lower than in developed countries, and firms can use their expertise in a positive way to generate revenues. Furthermore, the author suggests that bidders with acquisitions in countries where economic conditions are better are forced to pay higher premiums, or alternatively, bidders also might be willing to pay higher premiums because they are more optimistic about the future potential of the acquisition when economic conditions are more favorable. This might lead to overpayment and negative wealth gains. A relevant note is that economic growth is measured using GNP growth which is defined as the target country’s GNP growth in the year prior to the announcement of the acquisition minus the average GNP growth rate of the target country during the study period, divided by the average GNP growth rate of the target country during the study period .Furthermore, he reports that expanding into another industry that is less geographical and cultural distant is most beneficial.

Doukas and Travlos (1988) investigate the effect of international acquisition announcements on the stock price of U.S. firms. The sample consists of bidding firms listed on the NYSE or the ASE making an acquisition between the years 1975 and 1983. Abnormal returns are insignificantly positive for the sample. Although returns are not significant, the positive abnormal returns for bidding firms support the positive-multinational-network hypothesis. This hypothesis predicts an increase in market value of the bidding firm in response to multinational expansion. Cross-border acquisitions provide bidders the opportunity to be flexible in transferring resources across borders through a globally maximizing network which enables the firm to exploit international distortions in capital markets and production. These advantages can only be exercised by multinational

corporations and are thus expected to be valued positive by the market which results in the increase of the stock price around the acquisition announcement date. The most important cross-country determinants of announcement returns found in the paper are country development, industry-, and geographic diversification. Significantly higher benefits are achieved for firms diversifying

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9 simultaneously across geographic and industry space into less-developed countries. This suggests that the opportunities of cross-border acquisitions mentioned earlier in this paragraph are largest for bidder firms when the target firm operates in a less efficient market in an industry that is less related to the acquiring firm’s industry. The suggestion that expanding into distantly further operating firms is most beneficial contradicts to the findings in Kiymaz (2004)

Barbopoulos et al. (2012) investigate the effect of cross-country M&A determinants on announcement returns of U.K. acquirers. According to the papers by Barbopoulos et al. (2012), Rossi and Volpin (2004) and Erel et al. (2012) the legal systems of acquirer and target country are

important determinants of cross-border M&A. During the discussion of this article an important role is dedicated to the legal system of the target country in explaining bidder announcement returns. The levels of investor protection, market for corporate control and other corporate governance mechanisms differ among legal systems. Common law systems are characterized by better shareholder protection for minority shareholders, and a better developed market for corporate control than civil law systems. 2372 cross-border deals announced between 1986 and 2005 are analysed. Announcement returns for cross-border takeovers into countries that have civil-law systems are significantly higher than announcement returns into countries that have common-law systems. The authors argue that bidding firms expanding into countries with common law systems pay higher premiums due to higher competition in the better developed markets for corporate control which leads to lower announcement returns for bidding firms. However, this premium paid might affect future returns in a positive way due to a better legal environment after acquisition. Rossi and Volpin (2004) suggest that premiums paid for targets in common law systems are higher due to higher competition and better developed markets for corporate control. The explanation given by Barbopoulos et al. (2012) support these results.

Using a comprehensive sample of 20.893 cross-border mergers, Ahern et al. (2012) suggest that greater cultural distance between merging firms reduces the likelihood of a successful merger. Bidder stock returns surrounding announcement date of a merger are found to be higher when target and acquirer are less culturally distant. The main argument given by the authors is that differences in cultural values between employees of target and acquirer could lead to mistrust, misunderstanding or mismatched goals. These differences might lead to higher friction costs, and make it more difficult to create synergies. The three values of national culture used in the paper are trust, hierarchy and individualism. In the paper a high amount of control variables are added to the regression. The findings are that bidder announcement returns are higher when GDP growth is

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10 higher, geographical distance is shorter, and when the target firm has the same law system rather than another law system.

2.1.2 Cross-border M&A and volume

Articles discussing the determinants of M&A use either CAR or transaction volume as the dependent variable. In this section papers in which transaction volume is used as the dependent variable in the regressions are discussed. In these regressions the number of deals (rather than CAR) is the

dependent variable, the independent variables are all possible determinants of M&A. The main purpose of this subsection is to touch some additional factors that determine the success of cross-border M&A.

Erel et al. (2012) focus on factors that might affect cross-border takeovers, but are not present to the same extent in domestic takeovers. Analysing 56.978 cross-border M&A that took place between 1990 and 2007, they find that geography, economic development, market-to-book ratio and quality of the accounting system matter. The probability that two firms merge is higher when geographic distance is shorter than when geographic distance is longer. The most common explanation is that short geographical distance between two firms often means that cultural differences are little, which makes it easier to create synergies. Firms in countries where economic development and accounting quality is better are more likely to be an acquirer rather than a target. In addition, acquiring firms often have significant higher market-to-book ratios than targets. This suggests that overvalued firms takeover potentially undervalued firms. A last important note on the paper is that in 26% of the transactions a private acquirer is involved. In this thesis only transactions in which public acquirers are involved are analysed.

Rossi and Volpin (2004) investigate cross-country determinants of M&A by doing five regressions using volume, cross-border ratio, takeover premium, hostile takeover and method of payment as dependent variable. A sample of deals announced in the 1990s and completed by the end of 2002 is used. They suggest that better investor protection is associated with more M&A. In cross-border deals acquirers on average have higher investor protection than targets which suggests that targets try to opt out of their own (bad) governance system using cross-border deals.

Accounting quality, law system, log (GNP per capita) and GDP growth are added as independent variables to describe the effect of investor protection on volume. The log (GNP per capita) and legal system variables are statistically significant which suggests that in countries with higher GNP per capita and a common law system (rather than a civil-law system) more deals are completed. The regression in which type of law system is regressed against takeover premium shows that higher premiums are paid by firms bidding on targets located in countries that have a common law system

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11 rather than a civil law system. The explanation given is that a common law system is characterized by a better market for corporate control, more investor protection and higher levels of competition than a civil law system, which forces bidding firms to pay higher premiums. Announcement returns are thus expected to be lower for firms expanding into countries with common law systems.

2.2 Economic growth and stock returns 2.2.1 The theory

At this moment, the most common measure for a country’s economic growth is GDP. According to the general equilibrium theory (Mankiw, 2007), there are two ways to view GDP. One way to view GDP is as the total income of everyone in the economy. Another way to view the GDP is as the total expenditure on the economy’s output of goods and services. The method used for calculating GDP does not influence the outcome and thus total income equals total expenditure on the economy’s output of goods and services. To explain the effect of GDP growth on stock returns, it is useful to decompose the GDP formula:

Looking at the change in the GDP number only, does not tell one in which direction and with what amount the variables have changed, which makes it difficult to predict the effect of GDP growth on individual firm performance. Although the effect of GDP growth on individual firm performance is unclear, the theory can be generalized. In the aggregate, an increase in one of the four components leads to better economic performance due to higher production which is expected to generate higher stock returns for the average firm in the economy. If the market is efficient and investor act rational, one would expect stock returns to increase now if economic growth is expected to be higher in the future. In the remainder of this thesis it is assumed that investors can predict future economic growth quite accurate.

2.2.2 The evidence

The literature proposes a number of articles describing the relationship between future economic growth and stock returns. The five articles discussed below are all useful in a sense that different sample periods, as well as different measures for economic growth are used. Four out of the five articles discussed find a positive relationship between economic growth and stock returns (Chen, 1991; Fama, 1981,1990; Schwert, 1990). The main purpose of the papers is to investigate whether markets are efficient and make rational forecasts of the real sector. The confirmation of market efficiency in these papers suggests that higher future economic growth is positively correlated with firm performance and stock returns. Even though all papers discussed in this subsection use large

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12 samples (at least 30 years of data), the main limitation is that four out of the five articles only use U.S. data, while only one article uses international data. The U.S. is a high growth and well-developed economy. The main disadvantage of using U.S. data only is that results might not represent the effects of economic growth on stock returns in low growth and less-developed economies.

Fama (1981, 1990) and Schwert (1990) empirically investigate the effect of future real activity on stock returns. Results of the three articles are similar, a positive correlation is found between real stock returns and future real activity. The higher stock returns as a result of higher future output suggests that the market is efficient and makes rational forecasts of the real sector. In all three articles at least 34 years of data are used in which a national sample of U.S. firms is tested. Fama (1981) uses capital expenditures, average real rate of return on capital and output growth as measures for future real activity. Fama (1990) and Schwert (1990) use future production growth as a measure for future real activity. According to the economic theory, one would expect real common stock returns to be positively correlated with real variables like capital expenditures, the real rate of return on capital, and output (growth) because they stimulate GDP growth.

Chen (1991) analyses the relation between changes in financial investment opportunities and changes in the macro economy. Using U.S. sample data from 1954-1986 and GNP growth as measure for economic growth, he finds that excess stock returns are negatively correlated with recent GNP growth and positively correlated with its future GNP growth. Chen (1991) does not provide any explanations regarding his results. The positive correlation between future GNP growth and stock returns suggests that investors believe in an efficient market where economic growth has a positive effect on firm value.

Ritter (2005) investigates the relationship between real stock returns and per capita GDP growth over the period 1900-2002 using international data from 16 countries. Per capita GDP is defined as a country’s total GDP divided by the number of citizens in a country. A negative correlation between stock returns and per capita GDP is hypothesized. The author argues that economic growth does not necessarily lead to an improvement in firm performance. The empirical results in the paper give little support to this hypothesis, a nonsignificant negative correlation of -0.37 is found between real stock returns and GDP per capita. The main point that the author tries to bring across is that GDP growth leads to higher living standards for consumers, but that GDP growth does not necessarily lead to higher earnings per share for existing shareholders. In addition to the previous argument, the author states that higher future economic growth does not lead to higher future equity returns. An important note to make once more is that normal equity returns rather

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13 than abnormal equity returns are used. The results thus only suggest that markets are inefficient, which does not necessarily mean that stock returns around the announcement date of a takeover are negative.

2.3 Other determinants of M&A and announcement returns

Section 2.1 of this literature review discusses important cross-country determinants of M&A. In this section more general determinants of M&A are handled. Articles are not discussed as detailed as previously done for cross-country determinants. The first subsection discusses firm characteristics that might influence bidder announcement returns. The second subsection discusses deal

characteristics that might influence bidder announcement returns.

2.3.1 Firm characteristics

Moeller et al. (2004) find that stockholders of small firms achieve significantly higher returns than stockholders of large firms when making a takeover announcement. To distinguish large firms from small firms a dummy variable is created. Small firms are defined as firms whose market capitalization falls below the 25th percentile of NYSE firms that year. Small firms gain significantly when they announce an acquisition, except for acquisitions of public firms paid for with equity. Large firms announcing the acquisition of a public firm face significant losses irrespective of the payment method. Furthermore, they find that acquirer gains increase significantly if relative size of the transaction increases.

Fuller et al. (2002) study bidder shareholders’ announcement returns of public firms acquiring five or more public, private or subsidiary firms. Only considering firms that made five or more takeovers enables the investigator to hold bidder characteristics constant and thus to control for bidder firm characteristics. Their results indicate that bidding firms’ announcement returns are positive when the target is private or subsidiary and negative when the target firm is public. These findings are in line with the results reported by Moeller and Schlingemann (2005).

Asquith et al. (1983) also investigate the abnormal returns to bidding firms’ shareholders from acquisitions. Their regression analysis indicates that the relationship between the abnormal returns of bidder shareholders and relative size of the target is positive and statistically significant. Relative size of the target is defined as the market value of the target divided by the market value of the bidder. The positive relationship between abnormal returns of the bidder and relative size of the target indicates that bidder size and abnormal returns are negatively correlated. The negative correlation supports the findings by Moeller et al. (2004) that small firms achieve higher abnormal returns than large firms at the announcement date of an acquisition. Although the paper by Asquith

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14 et al. (1983) defines relative size as bidder size divided by target size, in this thesis relative

transaction size is used. Generally, the outcomes are expected to be more or less similar due to the fact that often higher prices are paid if the target value is higher.

2.3.2 Deal characteristics

Three deal characteristics that potentially influence bidder announcement returns are discussed in this subsection. First, articles that investigate the method of payment are discussed. Second, articles investigating deal attitude are discussed. Third, articles investigating industry relatedness of bidder and target are discussed.

Travlos (1987),Chang (1988) and Asquith et al. (1983) examine the role of the method of payment in explaining shareholder returns of bidder firms surrounding the announcement date of an acquisition. Travlos (1987) reports significantly negative abnormal returns for firms financing an acquisition with stock and non-significant abnormal returns for those financing with cash. Besides, abnormal announcement returns are significantly higher for firms financing an acquisition with cash than for firms financing an acquisition with stock. Asquith et al. (1983) find similar results for firms financing acquisitions with cash or stock. The findings from Travlos (1987) and Asquith et al. (1983) support the signalling hypothesis. This hypothesis states that financing an acquisition with cash gives a better signal to investors than financing with equity. Financing with equity suggests that the bidder firm is overvalued. As a result one would expect returns to be higher when an acquisition is financed with cash rather than with equity. The findings by Chang (1988) contradict with the results of both, Travlos (1987) and Asquith et al. (1983). He finds positive abnormal returns for firms announcing an acquisition financed with common stock and non-significant abnormal returns for firms financing a takeover with cash. An important note is that Chang (1988) only take privately held targets into consideration.

Using the SDC definition of hostility, Schwert (2000) finds that bidder returns are lower for hostile takeovers than friendly takeovers. Other definitions of hostility used in the paper are not correlated with bidder’s stock returns. This suggests that the higher premiums paid for targets and lower success rates of hostile takeovers do not result in lower abnormal returns at the

announcement date of a takeover on average. Travlos (1987) associates cash as payment method more with hostile takeovers due to quicker registration, and stock as payment method with friendly offers. While premiums are often lower in friendly offers than hostile offers, he finds significantly negative returns for cash offers, and non-significant returns for hostile offers. The results are even holding after controlling for the method of payment. This suggests that returns are higher for hostile offers than for friendly offers.

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15 Doukas and Travlos (1988) find that higher abnormal returns are achieved for diversifying mergers and acquisitions. Moeller and Schlingemann (2005) however, find contradicting results and report that acquiring a firm that is active in the same industry, rather than another industry, leads to higher announcement returns for bidder firms.

3. Hypothesis development and methodology

In subsection 3.1 the hypotheses are formulated and discussed. For all five cross-country

determinants of M&A included in the thesis (i.e. economic growth, economic development, cultural difference, legal system and geographical distance) a hypothesis is formulated. The focus however, is on the first one which tests the effect of economic growth on stock returns. In subsection 3.2 the regression, the methodology to estimate the CAR and the definitions of all independent variables are discussed.

3.1 Hypothesis development

Section 2.1 discusses several important determinants of cross-border M&A. The main focus in this thesis is on the relationship between future GDP growth and bidder stock returns at the

announcement date of a merger or acquisition. Empirical evidence by Fama (1981, 1990), Schwert (1990) and Chen (1991) reports a positive relationship between future real activity and stock returns. This positive relationship suggests that markets are efficient and make rational forecasts of the real sector. Assuming that markets are efficient, a cross-border M&A transaction into a target country in which future economic growth prospects are higher is expected to be valued positively by the market. If firms are able to predict the future economic growth in a country accurate, and investors recognize this timing ability by firms, one would expect bidder abnormal returns around the announcement date of a takeover to be higher if future economic growth in the target country is high. This leads to the following hypothesis:

Hypothesis 1: Bidder firm cumulative abnormal stock returns aroumd the announcement date of a merger or acquisition are higher when announcing a takeover into a country in which future economic growth is high.

The second variable of interest is economic development of the target country. Empirical evidence by Kiymaz (2004) and Doukas and Travlos (1988) suggests that the acquisition of firms in less-developed countries is more beneficial to bidder shareholders. The explanation given by Kiymaz (2004) is that the level of competition is higher in developed countries which forces bidding firms to pay higher premiums. Alternatively, the author states that bidders might also be willing to pay higher premiums because they are more optimistic about the future potential of the acquisition

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16 when economic development is better. This might lead to overpayment and negative wealth gains around the announcement date. Doukas and Travlos (1988) find similar results but give a different explanation. Bidders might have the opportunity to exploit international inefficiencies in capital markets and production when the country is less developed, which leads to higher stock returns. These findings lead to a second hypothesis:

Hypothesis 2: Bidder firm cumulative abnormal stock returns around the announcement date of a merger or acquisition are higher when announcing a takeover to a country where economic development is lower.

The third variable of interest is cultural distance between acquirer and target. Ahern et al. (2012) suggest that it is more difficult to create synergies when expanding into a country that has different cultural values. The paper uses hierarchy, trust and individualism as measures for cultural similarity. In this thesis language, religion and legal system are used as cultural value measures. The hypothesis from the variables used in this thesis are similar to the hypothesis from Ahern et al. (2012). There might be some obstacles between acquirer and target firm such as different language, different religion, or different legal system. These factors can reduce efficiency and bring additional friction costs, which reduce the synergy gains between target and acquirer and reduce firm value. The third hypothesis is based on these cultural differences:

Hypothesis 3: Bidder firm cumulative abnormal stock returns around the announcement date of a merger or acquisition are higher when announcing a takeover to a country that is culturally less distant.

The fourth variable of interest is geography. According to Kiymaz (2004) and Erel et al. (2012) expanding into less geographical distant countries is often more beneficial to bidder shareholders. The explanation given by Erel et al. (2012), which is supported by Kiymaz (2004), is that shorter geographical distance often implies that cultural differences are smaller which makes it easier to create synergies and lowers the friction costs of the takeover. Doukas and Travlos (1988) find contradicting results, but provide no clear explanation for their results. Therefore, the

explanation given by Erel et al. (2012) seems most rational. These results lead to the following:

Hypothesis 4: Bidder firm cumulative abnormal stock returns around the announcement date of a merger or acquisition are higher when announcing a takeover to a country that is geographically less distant.

The fifth and last variable of interest is the legal system implemented in the target country. Barbopoulos et al. (2012) reports that common law systems are characterized by more minority

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17 shareholder protection and more efficient markets for corporate control than civil law systems. In addition to this finding he suggests that higher premiums are paid when expanding into countries with common law systems due to the higher competition in the better developed market for corporate control, which leads to lower bidder announcement returns. Rossi and Volpin (2004) find similar results. These empirical results lead to fifth hypothesis:

Hypothesis 5: Bidder firm cumulative abnormal stock returns around the announcement date of a merger or acquisition are higher when announcing a takeover to a country where a civil law system is implemented rather than a common law system.

3.2 Methodology 3.2.1 The regression

The regression used to estimate the effect of several independent variables on the announcement returns consists of one dependent variable, 12 independent variables and an error term:

The first five variables are of most interest, these are cross-country characteristics. The main focus is on the coefficients of these variables of interest. The other seven variables are helpful control variables. Four firm characteristics are included the target form ‘public’ is left out to prevent multicollinearity between public, private and subsidiary. Three deal characteristics are included. In the remainder of section 3.2 all variables, their methodology and their expected outcomes are discussed in detail. In section 3.2.1.1 the dependent variable CAR and the single market model used to estimate the CAR are discussed. The cross-country characteristics are discussed in 3.2.1.2, the deal characteristics are discussed in 3.2.1.3 and the last group of variables, the firm characteristics, are discussed in 3.2.1.4.

3.2.1.1 CAR and the market model

Standard event study methodology (Brown and Warner, 1985) is used to measure the effect of takeovers on abnormal returns at the announcement date. A single market model is used for the estimation of abnormal returns for bidding firms:

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18 Where:

= the rate of return on stock i on day t

= the rate of return on the market value weighted CRSP Index

= the slope of the regression line of the firm i’s returns against the returns on the market value

of the CRSP Index = the intercept term

= the residuals

Abnormal returns for stock i on day tis defined as:

̂

Where

̂ ̂ ̂

The market model parameters in the sample are estimated using the pre-estimation period

(t=-265 to -10). Which means that the parameters are estimated over a period of 255 days (i.e. 255=265-10) before the event date. Cumulative abnormal return (CAR) is the sum of the abnormal returns. Three event windows are used to estimate the CARs, 3 days (-1,+1), 11 days (-5,+5) and 21 days (-10,+10) around the announcement date of the takeover. Section 5.1 adds an univariate analysis to test for significance of the dependent as well as the independent variables. Section 5.2 discusses a cross-sectional analysis. In that section several firm-, and deal characteristics are added as control variables.

3.2.1.2 Country characteristics

5YearGDPGrowth is a dummy variable indicating whether the summed economic growth in the target country in the 5 years after the transaction is high or not. The 273 observations with the highest summed GDP growth (highest 25%) in the sample have value 1, while the 819 observations with the lowest GDP growth (lowest 75%) have value 0. In section 5.2 the dummy variable for 5 Year GDP Growth is replaced by either a dummy for 3 Year GDP Growth, or a quantitative 5 Year GDP Growth variable. The 3YearGDPGrowth variable is calculated similarly to the 5YearGDPGrowth dummy variable, now using the sum of the three year GDP growth instead of five years. The

quantitative variable uses the summed five year GDP growth after the takeover, this is a continuous variable. As the first hypothesis in section 3.1 states, higher announcement returns for bidder firms are expected if total five year economic growth after the year of announcement is higher.

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19 According to the second hypothesis, the expectation is that bidder announcement returns are higher when acquiring a firm in a developing country. The IMF year report of 2005 is used to determine whether a country is developed or developing. CulturalDistance is an index variable that measures to what extent target and acquirer firm share the same cultural values. Three dummy variables are created, one for same language, one for same religion, and one for same legal system. The cultural distance index value for a certain country is the sum of the outcomes of the three dummies. The index can have values between zero and three. The index has value three if all cultural values of the target country are the same as for the U.S. (i.e. language is English, religion is Christian and legal system is common law) and value zero if all cultural values are different. Data for language and religion of the target countries is obtained from (Stulz and Williamson, 2003). According to

hypothesis three the expectation is that bidder firms stock returns surrounding the announcement date of a merger or acquisition are higher when announcing a takeover to a country that is culturally less distant. GeographicalDistance is an index variable that measures the difference in geographical distance between the capitals of target and acquirer country. The geographical distance index can have values between one and 10. The 58 countries in the sample are divided into 10 groups sorted by geographical distance. The six countries with the smallest geographical distance to the U.S. have value one. The second six countries with the smallest geographical distance have value two and so on. The four most distant countries have thus value ten. According to hypothesis four, bidder firms stock returns surrounding the announcement date of a merger or acquisition are expected to be higher when announcing a takeover to a country that is geographically less distant. LegalSystem is a dummy variable that separates countries with a common law system from countries with a civil law system. Data is obtained from La Porta et al. (2000). According to the fifth hypothesis the

expectation is that expanding into countries with a common law system leads to lower announcement returns for bidding firms.

3.2.1.3 Firm characteristics

Log(AcquirerMarketValue) variable measures the height of the acquirer market value four weeks prior to the announcement date. The natural logarithm is taken because the distribution is

“skewed”. Skewness has been proved by doing a Jarque-Bera test which indicates whether a variable is normally distributed or not. The H0 hypothesis that the variable is normally distributed is rejected at the 1% significance level. Plotting a histogram of the variable shows that the distribution has a long right tail (it is “skewed” to the right). Taking the logarithm allows one to standardize the distribution. The Jarque-Bera test statistic of the log variable is non-significant which suggest that the variable is standardized successfully. Moeller et al. (2004) find that abnormal returns from small firms are significantly higher than abnormal returns of large firms at the announcement date of the

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20 takeover. In contrast to Moeller et al. (2004), this thesis does not distinguish between small and large firms using a dummy variable, but analyses differences in firm size using the natural logarithm the acquirer market value four weeks prior to the announcement date of the takeover.

RelativeSize(Win) is defined as transaction value divided by acquirer market value four weeks prior to the announcement date. Plotting a histogram shows that for some transactions the relative size value is extremely high which suggests that outliers play a significant role when including the

variable in the regression. As a solution the variable is winsorized which means that the values above the 99th percentile are set equal to the 99th percentile. The purpose is to reduce the effect of

potential outliers. According to Asquith et al. (1983) and Moeller et al. (2004) the expectation is that bidder firms with a higher ratio of transaction value divided bidder firm size achieve higher

announcement returns. The variables Private and Subsidiary are two dummy variables indicating whether the target firm is a private firm or a subsidiary. The third possibility, public, is left out to prevent multicollinearity between the three target forms. Articles investigating the relationship between target form and bidder abnormal returns are unambiguous. According to Moeller and Schlingemann (2005) as well as Fuller et al. (2002) the expectation is that bidders acquiring a target that is private or subsidiary achieve positive abnormal returns at the announcement date. In addition, findings in both papers suggest that public firms achieve non-significant or negative abnormal returns at the announcement date of the takeover.

3.2.1.4 Deal characteristics

PaymentMethod is a dummy variable indicating whether a deal is 100% cash financed or not. According to the signalling hypothesis one would expect announcement returns to be higher for deals financed with 100% cash, rather than deals financed with less than 100% cash. Empirical results by Travlos (1987) and Asquith et al. (1983) support the signalling hypothesis while Chang finds opposite results. Following the economic theory the expectation is that firms financing deals with 100% cash achieve higher announcement returns than firms financing with less than 100% cash. SameIndustry is a dummy variable indicating whether bidder and target are active in the same industry. Two firms are considered to be active in the same industry if there two digit SIC codes are identical. Results in existing literature about industry diversification are ambiguous. The expectation is that synergies are easier to achieve when expanding to a firm that is active in the same industry, which leads to higher announcement returns. DealAttitude is a dummy variable indicating whether the deal is friendly or hostile. According to Schwert (2000) one would expect returns for friendly offers to be higher than hostile offers when using the SDC definition for hostile. However, the results of Travlos (1987) contradict to the results found by Schwert (2000). Using common sense to

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21 hypothesize, one would expect abnormal returns to be higher for friendly offers than hostile offers due to the higher premiums paid in hostile offers.

4. Data and descriptive statistics

In this section the sample selection process and important findings regarding the final sample are discussed. The final sample is obtained by merging several datasets into one dataset. The main sources for the collection of the required data are Thomson One Reuters, Eventus (for which CRSP stock databases are used), the IMF and the Worldbank.

Table II presents summary statistics on the sample of individual deals. Panel A of Table II shows that the final sample contains 1092 M&A transactions. The master dataset, in which the Eventus dataset is merged, is obtained from the Thomson One Reuters database. Other important variables are hand collected. There are several requirements for deals to be included in the sample. First, mergers and Acquisitions must take place between public US firms and public, private or subsidiary non-US firms during the period 2000-2005 (i.e. cross-border deals only). Erel et al. (2012) report that the number of cross-border deals relative to total deals has increased substantially. Second, only completed deals in which the deal value and the acquirer’s market value are larger than 1 million USD are included. And third, the percent of shares owned by the acquirer after the transaction has to be more than 50%. After these requirements the sample consists of 1280 deals. Using these 1280 deals the event study is done as described in section 3.2.1.1. Stock return data for 188 bidder firms is missing which leads to a final usable sample of 1092 data points.

Panel B of table II distributes the sample by firm and deal characteristics. Panel B only contains descriptive statistics for dummy variables. From the 1092 deals in the sample most deals took place in 2004 and 2005, 230 (21%) and 227 (21%) respectively. Least deals took place in 2002 (122) which covers 11% of the sample. Furthermore, panel B shows that less than one third of all transactions are financed fully by cash and that the percentage of transactions financed fully by cash are highest in 2005 (86%). When looking at target form the most remarkable is that acquirers more often target private held firms (47%) or subsidiaries (38%) rather than publicly held firms (15%). The next column of panel B in table II shows that acquirers prefer firms that are active in the same industry (56%) over firms that are active in a different industry (44%). Here two firms are considered to be active in the same industry if their two digit SIC codes are equal. The most striking statistic about the deal attitude is that 1085 (99%) of all deals (1092) are friendly, while only 7 (1%) deals are hostile. In the years 2002 and 2003 there are even zero hostile takeovers in the sample. Overall, the

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22 divisions of the variables among the years are quite spread. From the data it does not seem that takeovers patterns have changed a lot due to notable changes over the years.

In panel C of table II summary statistics for cumulative announcement returns (CARs), country-, firm- and deal characteristics are presented. The CARs for three different event window are presented. For the 3 day and the 11 day windows mean CARs are positive, while for the 21 day CAR window the mean is negative. The third column shows t-values for a one-sample mean-comparison test. Only the t-value for the 3 day event window (1.76) is significantly positive at the 10% level. When looking at the last two columns in panel C of table II, one sees that the division among transactions with positive and negative values for CARs is almost equal for all event windows. In the remainder of table II summary statistics for all independent variables are shown. The average 5 year GDP growth value in the sample is 12.34% which is significantly different from zero at the 1% level (t-value of 40.71). Furthermore, it is important to discuss the 75 percentile value of the 5 Year Economic Growth variable, because this value is used to define the 5 Year Economic Growth dummy variable used in the regression. The 75th percentile value for the 5 Year Economic Growth variable is 14.82%, this means that 273 (25%) of total observations (1092) in the sample have a value for 5 year Economic Growth that is higher than 14.82%. These 273 observations have value one (high) for the 5 year Economic Growth dummy variable in the sample, the other 819 observations in the sample have value zero (non-high). The 3 Year Economic Growth dummy variable is constructed the same. The 5 year Economic Growth, as well as the other country characteristics, Economic Development, Cultural Distance, Geographical Distance and Legal System are discussed in more detail in table III. Looking at firm and deal characteristics, it is notable that six out of the eight variables are dummies (Private, Public, Subsidiary, Payment Method, Same Industry and Deal Attitude). For these dummy variables it is most interesting to look at the mean values. For a dummy variable the mean can be interpreted as the percentage of the sample that has value one. For instance, 47% of the sample are private firms, which automatically means that 53% of the firms in the sample are non-private. The most striking when looking at the dummy variables is the mean of the Deal Attitude dummy. 99% of the deals are friendly, while only 1% of the deals are hostile. Furthermore, when looking at the Relative Size variable it is notable that maximum values are extremely high relative to the mean values which suggest that outliers play an important role in the distribution of this variable. These outliers are excluded from the sample by winsorizing the variable. The last note about panel C of table II is that the natural logarithm of the Acquirer Market Value variable is used in the thesis.

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Table II: Sample size and summary statistics

This table consists of three panels. In panel A the sample size is discussed, in panel b and c summary statistics about country-, firm-, and deal characteristics are discussed. One important note about panel B is that the percentages in the column 'N obs' add up to 100%, while in the columns for payment method, target form, same industry, and deal attitude the column percentages do not add up to 100%. For these variables the percentages add up to 100% when adding up the rows for each variable. An important note for panel C is that Economic Development, Legal System, Private, Public, Subsidiary, Payment Method, Same Industry and Deal Attitude are all dummy variables. The interpretation of these variables is different, In the appendix the variable descriptions can be found. Here one can see when the value of a particular variable is zero and when it is one.

Panel A: Sample size

M&As reported 1280

No returns found 188

Final sample 1092

Panel B: Frequency by year, firm-, and deal characteristics

Year N obs. Payment method Target form Same industry Deal attitude

All cash Other Private Public Subsidiary Yes No Friendly Hostile 2000 205 (19%) 47 (23%) 158 (77%) 109 (53%) 30 (15%) 66 (32%) 117 (57%) 88 (43%) 202 (99%) 3 (1%) 2001 157 (14%) 33 (21%) 124 (79%) 60 (38%) 35 (22%) 62 (39%) 95 (61%) 62 (39%) 155 (99%) 2 (1%) 2002 122 (11%) 41 (34%) 81 (66%) 57 (47%) 18 (15%) 47 (39%) 65 (53%) 57 (47%) 122 (100%) 0 (0%) 2003 151 (14%) 39 (26%) 112 (74%) 58 (38%) 19 (13%) 74 (49%) 75 (50%) 76 (50%) 151 (100%) 0 (0%) 2004 230 (21%) 81 (35%) 149 (65%) 116 (50%) 33 (14%) 81 (35%) 128 (56%) 102 (44%) 229 (100%) 1 (0%) 2005 227 (21%) 86 (38%) 141 (62%) 118 (52%) 24 (11%) 85 (37%) 136 (60%) 91 (40%) 226 (100%) 1 (0%) Total 1092 327 (30%) 765 (70%) 518 (47%) 159 (15%) 415 (38%) 616 (56%) 476 (44%) 1085 (99%) 7 (1%)

Panel C: Summary statistics for all variables

Mean St. Dev. t-value Median 75th Percentile min. max. N Pos. N Neg. N obs. Dependent variables CAR (-1,+1) 0,49% 9,11% 1,76* 0,16% 3,10% -49,85% 160,39% 573 519 1092 CAR (-5,+5) 0,60% 13,52% 1,47 0,26% 5,70% -69,76% 140,92% 564 528 1092 CAR (-10,+10) -0,16% 18,30% 0,29 -0,05% 6,59% -93,99% 176,21% 541 551 1092 Country characteristics 5 year GDP growth 12,34% 10,02% 1,23 10,95% 14,82% -13,45% 57,90% 1074 18 1092 3 Year GDP growth 8,85% 6,07% 1,46 8,04% 9,16% -13,22% 38,20% 1084 8 1092 Economic Development 0,81 0,40 0 1 1092

Cultural Distance (index) 1,93 1,09 1 3 0 3 1092

Geographical Distance (index) 4,04 2,71 3 5 1 10 1092

Legal System 0,47 0,50 0 1 1092

Firm characteristics

Acquiror Market Value ($mil.) 14697 49265 1349 6925 1226 463885 1092

Relative Size 0,16 1,12 0,03 0,10 0,00 34,24 1092 Private 0,47 0,50 0 1 1092 Public 0,15 0,35 0 1 1092 Subsidiary 0,38 0,49 0 1 1092 Deal characteristics Payment Method 0,30 0,46 0 1 1092 Same Industry 0,56 0,50 0 1 1092 Deal attitude 0,99 0,08 0 1 1092

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Table III: Cross-country characteristics

This table provides information about the five cross-country characteristics used during the research. 'Same' in the third and fourth column means same as in the U.S. The cultural distance index for each country has a value between zero and three, dependent on language, religion and legal system. GeographicalDistance is an index value that measures the difference in geographical distance between the capitals of target and acquirer country. The geographical distance index can have values between one and 10. The 3 and 5 year average economic growth is measured by taking the average of the summed GDP growth for the periods 2001-2005, 2002-2006, 2003-2007, 2004-2008, 2005-2009 and 2006-2010. A more elaborated description of the variables can be found in paragraph 3.2.1.2. For variable definitions refer to appendix A.

Economic Developed Same Language Same Religion Legal System Cultural Distance Index Geographic Distance Index

Economic Growth N obs.

5 Year Avg. 3 Year Avg.

Country

Antigua No No No Common 1 1 24% 19% 1

Argentina No No No Civil 0 7 21% 18% 10

Aruba No No Yes Civil 1 1 -1% 3% 1

Australia Yes Yes Yes Common 3 10 16% 10% 49

Austria Yes No Yes Civil 1 5 10% 7% 1

Belgium Yes No Yes Civil 1 4 9% 6% 12

Bermuda No Yes Yes Common 3 1 10% 8% 1

Brazil No No Yes Civil 1 6 19% 11% 18

Bulgaria No No Yes Civil 1 7 26% 18% 1

Canada Yes Yes Yes Common 3 1 11% 8% 241

Chile No No Yes Civil 1 7 21% 14% 10

China No No Yes Civil 1 8 55% 33% 28

Colombia No No Yes Civil 1 2 24% 14% 2

Czech Republic No No No Civil 0 5 22% 15% 2

Denmark Yes No Yes Civil 1 4 6% 5% 14

Ecuador No No Yes Civil 1 2 22% 14% 1

Egypt No No No Civil 0 8 26% 14% 4

Finland Yes No Yes Civil 1 5 12% 10% 7

France Yes No Yes Civil 1 3 7% 5% 69

Germany Yes No Yes Civil 1 5 6% 4% 107

Ghana No Yes Yes Common 3 7 30% 17% 1

Greece Yes No No Civil 0 7 15% 12% 2

Hong Kong Yes No No Common 1 9 26% 16% 12

Hungary No No Yes Civil 1 6 12% 11% 4

Iceland Yes No Yes Civil 1 2 19% 14% 1

India No No No Common 1 9 40% 23% 12

Indonesia No No No Civil 0 10 27% 16% 2

Ireland Yes Yes Yes Common 3 2 18% 14% 12

Israel No No No Civil 0 8 19% 11% 32

Italy Yes No Yes Civil 1 6 3% 3% 25

Japan No No No Civil 0 8 5% 4% 9

Kazakhstan No No No Civil 0 8 43% 29% 1

Lithuania No No Yes Civil 1 6 30% 24% 1

Luxembourg Yes No Yes Civil 1 4 17% 12% 3

Malaysia No No No Common 1 10 26% 17% 1

Mexico No No Yes Civil 1 1 12% 8% 23

Morocco No No No Civil 0 3 24% 15% 1

Netherlands Yes No Yes Civil 1 3 9% 6% 30

New Zealand Yes Yes Yes Common 3 9 12% 9% 9

Norway Yes No Yes Civil 1 4 9% 7% 12

Peru No No Yes Civil 1 2 32% 18% 1

Philippines No No Yes Civil 1 9 25% 15% 4

Poland No No Yes Civil 1 5 23% 13% 3

Portugal Yes No Yes Civil 1 3 4% 3% 2

Puerto Rico No Yes Yes Common 3 1 -2% 1% 5

Romania No No No Civil 0 7 27% 19% 3

Russian Federation No No Yes Civil 1 6 29% 21% 4

Singapore Yes No No Common 1 10 32% 19% 6

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25 In table III data on country characteristics discussed. No numbers on individual deals are discussed in this table. The five variables included in the sample are Economic Development, Legal System, Cultural Distance, Geographic distance and Economic Growth. Target firms in the sample are from 58 different countries. Most of these countries are developing (35) rather than developed (23). Looking at deal frequency however, shows that more than four times as much deals take place in developing countries (881) than in developed countries (211). The four most frequently targeted countries in the sample are Canada (241), UK (219), Germany (107) and Australia (49). All of these countries are developed and three out of the four have common law systems implemented, only Germany is characterized by a civil law system. From all target countries in the sample the majority is characterized by a civil law system (44). The US and 15 other target countries have common law systems implemented. Firms with common law systems (575) however, are more frequently targeted than firms with civil law systems (517) in this sample. In 10 of the 58 countries the same language (English) is spoken. In 42 out of 58 countries the majority of the population has the same religion as in the U.S. Same religion is defined ‘yes’ if the majority of the target country is Christian (i.e. protestant or catholic). The Cultural Distance Index is defined as the sum of the Legal System, Same Language and Same Religion dummies. The variables used to calculate the cultural distance between target and acquirer (legal system, language and religion) are the same as in Erel et al. (2012). The main difference is that in this thesis an index is used, where Erel et al. (2012) use three separate dummy variables. Nine out of 58 countries are considered to be culturally ‘least distant’ to the U.S., these countries have a value three for Cultural Distance Index, which means that all three dummies have value one. 10 out of 58 have value zero, these countries are considered to be

culturally most distant from the U.S and have the value zero for all three dummies. 33 out of 58 have value one, notable is that none of the countries have value two for the Cultural Distance Index. The values of the Geographical Distance Index are equally divided over the sample except that only four instead of six countries have value 10. This is due to the fact that 58 different target countries are included in the sample rather than 60.

South Africa No Yes Yes Common 3 9 21% 13% 3

South Korea No No Yes Civil 1 8 21% 13% 15

Spain Yes No Yes Civil 1 3 13% 10% 11

Sweden Yes No Yes Civil 1 4 13% 9% 20

Switzerland Yes No Yes Civil 1 4 11% 6% 17

Thailand No No No Common 1 9 23% 16% 3

Ukraine No No Yes Civil 1 6 27% 22% 1

United Kingdom Yes Yes Yes Common 3 3 11% 8% 219

United States Yes Common 3 10% 7%

Venezuela No No Yes Civil 1 2 30% 17% 2

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26 The independent variable of interest is the 5 year economic growth variable. Some basic summary statistics about this variable are discussed in table II, in table III the 5 Year Economic Growth is sorted by country. China has the highest 5 year average growth rate (55.22%) followed by Kazakhstan (42.78%), India (39.85%) and Singapore (32.02%). Two countries have negative average 5 year growth rates, Puerto Rico (-1.78%) and Aruba (-1.14%). The average 5 Year growth number for all countries is 18.98%. The average 5 year growth for the developed countries is 12.41%, where the average for the developing countries is 23.49%. Worth noting is that from the developed countries only two countries (8% of all developed countries) face average 5 year economic growth rates higher than 20%, while this is the case for 27 developing countries (77% of all developing countries). An important note about the average 5 year growth numbers is that the numbers presented in the table are only used to give a general overview of the economic growth in all different countries. For an overview of the economic growth numbers used for the deals in the sample is referred to table X in appendix B. For instance, if the takeover of an Australian firm is announced in the year 2000, the future growth rate of the subsequent 5 years (2001-2005) is used, which is 16.30%.

5. Results

In this section regression findings on the effect of the cross-country characteristics on bidder

announcement returns in cross-border M&A are discussed. In subsection 5.1 an univariate analysis is discussed which is presented in table IV. In subsection 5.2 table V is discussed. The results in table V present the effect of the 5 year economic growth dummy variable and several other cross-country determinants and control variables on bidder announcement returns. In subsection 5.3 table VI is discussed. The regressions in table VI are similar to those table V, but instead of the dummy variable for 5 year economic growth two other measures for economic growth are used. The purpose of the regressions in table VI is to analyse what the effect of a shorter future economic growth period (3 years instead of 5) and a different measure for the 5 year economic growth (quantitative instead of dummy) is on bidder returns around the announcement date of a takeover.

5.1 Univariate analysis

Table IV presents an univariate analysis for the five variables of interest. In the columns ‘high’ and ‘low’ respectively, mean CAR values for the high and low end of the sample are presented. A one-sample mean-comparison test shows whether these mean CAR values differ significantly from zero or not. The column ‘t-test for means’ shows t- values for a two-group mean-comparison Welch test with unequal variances.

The effect of economic growth on bidder CARs in Cross-Border M&A.

The effect of Economic Growth on Bidder CARs in Cross-Border M&A

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