An analysis of the drivers of value creation in public cross-border mergers and acquisitions

An analysis of the drivers of value creation in

public cross-border mergers and acquisitions

Thomas Jansz

Abstract

In this research paper I examine the market reaction to cross-border mergers and acquisitions (M&As). I find that the market reaction is positive for both acquiring and targeted firms and that social development and governance are positively correlated with the market reaction to cross-border M&A announcements for target companies. I also find that social development and governance are negatively correlated with the market reaction to cross-border M&A announcements for acquiring companies. These findings suggests that the economic and political landscape in which a target company is in are correlated with value creation in cross-border M&As.

Author: Supervisor:

Thomas Jansz Shivesh Changoer

10773541

Economics & Business Economics & Finance 12 ECTS

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Statement of Originality

This document is written by Thomas Jansz who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

3 1. Introduction

M&As is a term that refers to the combining of two companies. This can be done through a merger, an acquisition or another type of transaction. M&As are popular, the last four years it’s deal value has exceeded three trillion U.S. dollars1_{. A new record has already}

been broken this year, with two trillion U.S. dollars in deal value hit on the 22nd of May 2018. This is not a new trend though, M&As have risen in popularity for over forty years already.

An increasingly globalized economy is also causing cross-border M&As to increase. The cross-border deal value of the first half of 2017 went up by 27.7% compared to the same period in 20162_{. Especially China has driven this trend with their investments in Africa}

because their domestic growth is slowing down3.

Various researchers have examined whether the market reaction to cross-border M&As differs from that of domestic M&As. However, the results are mixed. Some researchers find a positive market reaction while others find either a negative or an

insignificant market reaction. Ignoring these mixed results, I examine the effect of various country indices of the target country on the market reaction to cross-border M&As.

For the targeted firms I find that market reaction is positive and that the level of social development of the target nation and the governance of the target firm’s nation are positively correlated with the market reaction to cross-border M&A announcements. These findings suggest that investors think that cross-border M&As in which the target company’s has more social development and better governance will create more value for the target’s shareholders than cross-border M&As in which the target’s country is socially less developed or ill

governed. For the acquiring firms, I find that the market reaction is also positive and that social development and the level of governance in the target firm’s country are negatively correlated with the market reaction to cross-border M&A announcements. These findings suggest that investors believe that M&As in which the target company’s country has more social development or is better governed create less value for the acquiring firm’s

shareholders than cross-border M&As in which the target firm’s country is less developed

socially or ill governed.

My thesis contributes to prior research on cross-border M&As because most other studies only examine if there is a difference between domestic- and cross-border M&As, while I try to explain cross-sectional variation in the market reaction to cross-border M&A announcements. As far as I am aware, I am the first to show that distance, social development, economic fitness and governance have an effect on the market reaction to cross-border M&As announcements. My findings are important because it gives an insight into how investors think about different types of cross-border M&As.

The structure of the thesis is as follows. In section 2, I review the literature, discuss the announcement effect and provide background information about value creation in M&As. In section 3, I formulate the hypothesis with for each hypothesis an argumentation and

1 https://www.reuters.com/article/us-markets-m-a/global-ma-hits-record-2-trillion-in-year-to-date-idUSKCN1IN2C0 2 _{http://middlemarketgrowth.org/cross-border-deals-global-mergers-and-acquisitions/} 3 https://www.bcg.com/publications/2017/corporate-development-finance-technology-digital-2017-m-and-a-report-technology-takeover.aspx

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expectation of the sign of the coefficient. In section 4, I describe the design of the research. In section five, I present the results. In section 6, I perform an additional analysis to confirm my results. In section 7, I describe the importance of my findings. I also discuss the limitations of my research and suggest topics for further research.

2. Background information & Literature review 2.1 Value creation in mergers

There are many reasons why firms engage in M&As. The ambition to expand globally, increased competition, strategic reasons or a path for rapid growth are some of them. Another reason that companies engage in M&A activity is to realize synergy. There is synergy when the value of a combination of entities is greater than the combined value of the entities on their own.

Firms can realize different types of synergy: operational, managerial and financial synergies (Sudersanam et al., 1996). Operational synergies can be realized when the two firms have related business activities and when combining those activities results in lower costs than if the activities were run separately (Berk & DeMarzo, 2007). A company can benefit from economies of scale simply by the increased size of the newly formed firm. For instance, it can use non-primary business departments such as marketing or distribution for the sale of multiple related products, also known as economies of scope (Berk & DeMarzo, 2007). The market power also increases significantly because the size of the business increase and a competitor also disappears. All of these help create value in M&A’s (Singh & Montgomery, 1987).

Managerial synergies arise if a combination of the firms’ managements leads to an improvement of the firm’s performance. This can be caused by one management

complementing the other or if the management of the targeted company runs their business inefficiently and it is replaced by new a management that is more efficient or has better knowledge (Berk & DeMarzo, 2007).

Financial synergies can arise if the combined entity result in lower financing cost or better resource allocation. For example, holding debt from a combination of firms is less risky than of those firms separately (Tong, 2012). Combined entities therefore are less risky which decreases their cost of capital, considering all else to be equal (Berk & DeMarzo, 2007). Because they are less risky they can also increase their leverage which creates a larger tax shield (Berk & DeMarzo, 2007). Other occasional financial benefits include unused debt capacity, complementary growth opportunities and financial resources obtained (Sudersanam et al., 1996).

There are also reasons why companies engage in M&A activity that are not backed by economic reasoning. Managers often seek M&A for personal reasons, their reputation and compensation increase as firm size increases (Berk & DeMarzo, 2007). CEO’s of larger firms are paid more on average, making it attractive to increase size by M&A’s. They are also usually rewarded for growth, making it even more attractive to increase in size (Murphy, 1985).

Most researchers find that mergers that are done out of the management’s own interest instead of economic backed reasoning are value destroying (Sudersanam et al., 1996).

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M&A is done out of economic reasoning or out of management’s interest. Thus, it is also unclear whether the M&A will create or destroy value. The market reaction to a M&A announcement is usually used to answer this question. Since prices change according to new relevant information and the expectation of future profitability according to the Efficient Market Hypothesis, the stock returns around the announcement can be used to determine whether the M&A will be beneficial or not (Fama, 1970). This technique will be used in this research as a proxy for the value creation.

2.2 The market reaction to M&A announcements

Many researchers examine the market reaction to domestic M&A announcements for the targeted companies. Most of these studies find a positive market reaction (see, e.g. Jarell & Poulsen, (1989), Sudarsanam et al., (1996), Goergen & Renneboog, (2004)).

A large number of studies have also been conducted on the market reaction to domestic M&A announcements for the acquiring companies. Some of these studies find a positive market reaction (see, e.g. Goergen & Renneboog, (2004)). Other studies find no significant market reaction (see, e.g. Datta et al., (1992) or Jarell & Poulsen, (1989)). Yet other studies find a negative market reaction (see, e.g. Walker, (2000)).

2.3 Factors influencing the market reaction to M&As

Previous researches have already examined what factors influence value creation in domestic M&As. Mantravadi and Reddy find that the relative sizes of the firm play a role in the market reaction to M&A announcements (2007). Relatively small target firms have decreased profit margins and returns and targets that are bigger than their acquirors have the same problems but to a greater extent. Thus, this worsens the market reaction for the

acquiring firms.

The absolute size of the acquiring firm also influences the market reaction to M&A announcements. Moeller et al. find that large acquirors receive less positive market reactions on average because of agency problems and overconfidence (2005).

Industry relatedness also influences the market reaction to M&A announcements, indicating whether economies of scale, economies of scope and increased market power can be realized (Seth, 1990).

The method of payment is also an important factor in the market reaction to M&A announcements. Bid that are paid with cash have an higher average market reaction than those that are paid with shares (Sudarsanam et al., 1996).

Bargaining power has also been proven to be correlated with the market reaction to M&A announcements. It can arise due to the presence of multiple bidders or if the takeover is hostile in nature (see, e.g. Frank & Harris, (1989) on multiple bidders and Goergen &

Renneboog, (2004) on hostile takeovers). Both result in an improved market reaction for the target.

The corporate governance also influences the market reaction to M&As. Adopting the better corporate governance of the acquiror is positively correlated with the market reaction to M&A announcements (Martynova & Renneboog, 2008). Apart from the better market

reaction, it is also proven to be pareto efficient (Bris et al., 2008).

Researchers also think that there is a difference in the market reaction to cross-border M&A announcements compared to domestic M&A announcements, although a consensus has

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not yet been reached. Conn et al. (2005) find that cross-border M&As receive a less positive market reaction than the domestic M&As, but Kohli and Mann find the opposite (2012).

2.4 Domestic versus cross-border M&As

Kohli and Mann give three possible explanations for the improved market reaction to cross-border M&As being international diversification of risk, internalization of tangible assets and the exchange rate. By engaging in cross-border M&As firms can diversify risk in a way that an individual investor cannot, due to governmental restrictions and information asymmetry (Hymer, 1976). Cross-border M&As give an opportunity to reach new markets in which a firm can utilize their intangible assets. The value of these intangible assets depend on the use it gives, which increases as the scale of its use increases (Buckley & Casson, 1998). The exchange rate gives the acquiror an opportunity to increase its gains. It is proven that gains of cross-border M&As are positively correlated with the value of the acquiror’s currency (Markides & Ittner, 1994).

In their research Conn et al. state that the difference in gains from cross-border M&As compared to domestic M&As should arise from geographical diversification (2005). However they find that it falls short of the problems that arise post takeover due to cultural differences, resulting in a worse market reaction for cross-border M&As compared to domestic M&As. They find that high-tech M&As do receive better market reaction to M&A announcements, suggesting that the internalizations theory is true (Conn et al., 2005). They fail to find support for differences in legal systems, accounting standards, taxes and exchange rates.

3. Hypothesis

I predict that cross-border M&As will create less value if the target’s country is further away from the acquiror’s country. This prediction is based on the idea that as the distance (DIST) is greater, it becomes increasingly hard to manage the firm. I presume that both firms will be harder to manage if the distance is large, because there will be time difference after a certain point and management will also be less familiar with the desires of their new

customers. If investors also expect this to be true then the market reaction to cross-border M&A announcements will be negatively correlated with the distance between the two firms. Therefore my first hypothesis will be:

The market reaction to cross-border M&A announcements will be negatively correlated with the distance between acquiror and target.

My second prediction is that cross-border M&As will create less value if the country in which the target is located is more socially developed (SOCDEV). This prediction is based on the managerial synergies. If a country is more socially developed it will produce more talented managers, leaving less gains to be made if they are replaced with a new management. If investors follow the same reasoning as I do, then the market reaction to cross-border M&A announcements will be negatively correlated with the social development of the target’s home country. Thus my second hypothesis will be:

The market reaction to cross-border M&A announcements will be negatively correlated with the social development of the target’s home country.

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My last hypothesis is that cross-border M&As will create less value if the target is located in a country with a stable government (GOV). This prediction is based on the idea that cross-border M&As will create less value if there is political instability or corruption. Political risk increases the cost of capital, lowering the financial synergies. If investors think alike, then the market reaction to cross-border M&As will be positively correlated with the level of governance in the target’s home country. So my last hypothesis will be:

The market reaction to cross-border M&As announcements will be positively correlated with the level of governance of the target’s home country.

4. Research design

4.1 Method, model and variables

To test my hypotheses I run the following model

𝑌𝑖 = 𝛽0+ 𝛽1∗ 𝐷𝐼𝑆𝑇 + 𝛽2∗ 𝑆𝑂𝐶𝐷𝐸𝑉 + 𝛽3∗ 𝐺𝑂𝑉 + ⋯ + 𝜀 (1)

where 𝑌_𝑖 is stand for the three days’ abnormal return around the announcement (ARA for acquirors ART for targets). So if 𝑡 is the day of the announcement, a return will be calculated on the end of day adjusted stock prices from 𝑡 − 1 until 𝑡 + 1. The three day market return for the same period is then subtracted to obtain the three day abnormal stock return. In formula:

𝐴𝑅 = (𝑆𝑡+1−𝑆𝑡−1

𝑆𝑡−1 − (1 + 𝑟𝑀𝑡−1)(1 + 𝑟𝑀𝑡)(1 + 𝑟𝑀𝑡+1)) ∗ 100% (2) where 𝑆 stands for the end of day adjusted closing price and 𝑡 stands for the day of the announcement. 𝑟_𝑀 stands for the daily market return If there is no closing price because the exchanges are closed, the next closest closing price will be used.

DIST is the natural logarithm of the shortest linear distance between the two nations in which the companies are located in kilometers plus one. All water in seas are defined as international water. Distance is measured in kilometers. If two countries share a land border, the minimum distance equals zero.

SOCDEV is measured by the Human Development Index4. It is calculated yearly on a country level basis and is widely used in sociology. It is an index which consist of three parts: Life expectancy, Education and Income. Life expectancy is how old one is expected to

become in their life, measured at birth. Education consists of two subparts, the expected years of schooling for children who become school age and the average years of schooling of people above 25 years. Income consist of the logarithm of gross national income per capita, adjusted with the purchasing power parity in US dollars. The index ranges from values of zero to infinity theoretically, but in practice it ranges from zero to one. It is defined as:

𝐻𝐷𝐼 = √𝐿𝐸𝐼 ∗ 𝐸𝐼 ∗ 𝐼𝐼3 (3)

where 𝐿𝐸𝐼 stands for Life Expectancy Index, 𝐸𝐼 stand for Education Index and 𝐼𝐼 stands for Income Index. They are calculated as follows:

8 𝐿𝐸𝐼 =𝐿𝑖𝑓𝑒 𝐸𝑥𝑝𝑒𝑐𝑡𝑎𝑛𝑐𝑦−20 85−20 (4) 𝐸𝐼 = 𝑀𝑒𝑎𝑛 𝑦𝑒𝑎𝑟𝑠 𝑜𝑓 𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑛𝑔 15 + 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑦𝑒𝑎𝑟𝑠 𝑜𝑓 𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑛𝑔 18 2 (5) 𝐼𝐼 =𝐿𝑛(𝐺𝑁𝐼 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 𝑎𝑡 𝑃𝑃𝑃)−𝐿𝑛(100) 𝐿𝑛(75000)−𝐿𝑛(100) (6)

where GNI stands for Gross National Product and PPP stands for Purchasing Power Parity. GOV is the Worldwide Governance Indicator5. Again a country-based index that is calculated yearly and it is based on six categories: control of corruption, government

effectiveness, political stability and absence of violence/terrorism, regulatory quality, rule of law and voice and accountability. Control of corruption is a measure of the misuse of public power for private gain and the influence of private interests on the government. Government effectiveness is about the political independency of the government, policy making and realization and its credibility. Political stability measures the probability of terrorism or political instability. Regulatory quality measures to which extent the government is able to produce and apply policies that will help the private sector develop. Rule of law maps crime, effectiveness of jurisdiction and the quality of contract enforcement and property rights. Voice and accountability is about the level of democracy, free speech and free media. Each country is ranked on the aggregate indicator for each subject. The aggregate indicator ranks on a standard normal distribution. In this research, each country is assigned a value that is the average of these six categories.

A set of control variables is also included. The three dots in the first formula represent all the control variables and their betas. The relative firm size (RELSIZE) is the first control variable. It is measured in book value and is obtained from the latest financial report

available. I include this variable because Mantravadi and Reddy (2007) show that the market reaction to M&A announcements is less positive for relatively small targets, and because relatively small mergers have decreased profit margin and returns post-merger (Mantravadi & Reddy, 2007). A binary control variable (HOSTILE) is also added to indicate whether the offer was friendly or hostile. 1 for a hostile takeover, 0 for a friendly takeover. I include this variable because Goergen and Renneboog show that hostile takeovers have a more positive market reaction to M&A announcements for the target firm (2004). Another binary control variable is included (MULTI) for the presence of multiple bidders. This variable is 1 if there are more than one bidder, 0 if there is only one bidder. This variable is included because Frank and Harris find that the market reaction to M&A announce for the target firm are more positive if there are multiple bidders (1989). The price-earnings ratio (PE) is also a control variable. I included this variable because it is the most common valuation multiple. It is relevant since a stock is in essence the right to a firm’s future earnings (Berk & DeMarzo, 2007). I included a binary variable (RELATE) to account for the known benefits of horizontal M&As. They create value due to economies of scale, economies of scope and market power and therefore receive more positive market reactions to M&A announcements (Seth, 1990). The size of the acquiror (SIZE) is also considered a control variable. I include this variable because large acquirors tend to receive a less positive public reaction to the announcement on average. This is likely caused by agency problems or overconfidence (Moeller et al., 2005). I

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also added a year dummy (YEAR) to account for yearly differences in macroeconomic trends and investors’ optimism for the future.

4.2 Databases, sample and filtering.

To run my analysis, I collect M&A data from the Thomson One database and I use it for the financial data too. Then I obtain closing prices from DataStream and collect The HDI-index data is from the World Bank’s Databank. I used Ken French data to obtain the (excess)

market returns6.

Before I merge the data, I exclude all cross-border M&As in which one of the countries is not recognized by the United Nations. I delete M&As between China and either Hong Kong or Taiwan because Thomson One defines Taiwan and Hong Kong as independent nations, whereas this research defines them as a part of China. M&As between any other country than China and either Hong Kong and Taiwan are viewed as M&As between that country and China. Also, I delete M&As in which either target or acquiror is from Isle of man, Bermuda, Gibraltar, Guernsey, Jersey and the Cayman islands since there are no indices available because they are calculated at the country level. Viewing them as a part of the United Kingdom is unreasonable since some of these countries are thousands of kilometers away from it. I also drop observations containing the Netherlands’ Antilles by the same rules as those of Great Britain/The United Kingdom. I drop observations from Luxembourg,

Jamaica, Sri Lanka, Barbados, Papua New Guinea and Namibia because they are not included in the HDI index. Luckily, these countries only amount to about ten observations. I dropped some observations in their respective regression because their closing prices could not be found in DataStream. Lastly, I drop all observations in which the acquiring firm’s abnormal return is below -50% or above 100% in the acquiring firm’s regression. This is also done for the targeted firm’s abnormal returns in their regression. These are considered outliers and are removed to in order to improve the accuracy of the estimators.

After the filtering I have 986 and 956 observations left for the acquiring and target firms respectively. The descriptive statistics of the abnormal returns are shown in table 1 and 2. Both the abnormal returns of the acquiring companies as of the targeted companies are significant at the 𝛼 = 0.01 level with 𝑡 = 3.05 and 𝑡 = 24.56 respectively. The abnormal returns of the acquiring companies are small but positive and the returns of the targeted companies are positive. These findings support the findings of Goergen and Renneboog (2004).

Table 1: Descriptive statistics of ARA.

Dependent variable = ARA

ARA Mean 0.60385% ***

Std. dev 0.19807%

No. of obs. 986

𝑅2 _0.0000

This table presents the descriptive statistics of ARA. *,**,*** are significant at the 10%, 5% and 1% level respectively.

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Table 2: Descriptive statistics of ART.

Dependent variable = ART

ART Mean 17.45764% ***

Std. dev 0.71055%

No. of obs. 956

𝑅2 0.0000

This table presents the descriptive statistics of ART. *,**,*** are significant at the 10%, 5% and 1% level respectively.

5. Results

5.1 Correlations

Table 3 and 4 contain the correlation matrices for the regression of ARA and ART respectively. The correlation between the two explanatory variables SOCDEV and GOV is 0.85, which is very high. That is why I drop GOV from the model because it would cause multicollinearity problems. In the additional analysis, I check whether I obtain the same results when SOCDEV is excluded from the model instead of GOV. Both correlation matrices lack significant correlations, indicating that our model will probably not be good in terms of variation explained. The correlations of the target abnormal returns are more interesting than those of the acquiring abnormal returns, but since none of them are high enough they will not be discussed.

Table 3: Correlation matrix of the acquiring regression

ARA DIST SOCDEV GOV PE MULTI SIZE HOSTILE RELATE RELSIZE

ARA 1 DIST -0.0037 1 SOCDEV -0.0966 -0.0878 1 GOV -0.1048 -0.0574 0.8500 1 PE -0.0170 -0.0193 -0.0455 -0.0282 1 MULTI -0.0497 -0.0590 0.0897 0.0996 -0.0188 1 SIZE -0.0283 0.0254 -0.1400 -0.1831 0.0087 -0.0353 1 HOSTILE 0.0156 0.0130 -0.0818 -0.0911 0.0058 0.0146 0.0413 1 RELATE -0.0130 -0.0386 -0.0360 -0.0464 0.0358 -0.0058 0.0962 0.0468 1 RELSIZE 0.2759 0.0094 -0.0267 -0.0018 -0.0051 -0.0116 -0.0365 -0.0126 -0.0015 1

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Table 4: Correlation matrix of the target regression

ART DIST SOCDEV GOV PE MULTI SIZE HOSTILE RELATE RELSIZE

ART 1 DIST 0.0020 1 SOCDEV 0.2273 -0.0896 1 GOV 0.1902 -0.0550 0.8501 1 PE 0.0072 -0.0197 -0.0459 -0.0286 1 MULTI -0.0366 -0.0631 0.0913 0.1035 -0.0189 1 SIZE -0.0668 0.0275 -0.1439 -0.1872 0.0088 -0.0354 1 HOSTILE -0.0970 0.0146 -0.0794 -0.0895 0.0060 0.0143 0.0422 1 RELATE 0.0232 -0.0413 -0.0384 -0.0511 0.0364 -0.0127 0.1029 0.0464 1 RELSIZE -0.0645 0.0103 -0.0255 -0.0008 -0.0050 -0.0118 -0.0364 -0.0137 -0.0022 1

This table represents the correlations between the variables for the regression of the targeted firms. 5.2 Regressive results

The results for the regression of the acquiring firms is shown in table 5. Hardly any of the variables are significant, unfortunately. DIST is not significant, suggesting that there might not be a relation between distance and the market reaction to cross-border M&As. I find a coefficient of -0.069 for SOCDEV. Because the coefficient on this variable is negative, it suggests that social development of the target nation worsens the market reaction to cross-border M&As. The only other significant variable is RELSIZE with a coefficient of 0.0063, suggesting that cross-border M&As of relatively small firms receive less positive market reaction than more equally sized firms. This finding is consistent with the findings of Mantravadi and Reddy (2004).

The regressive results for the target returns are shown in table 6. The coefficient of DIST is not significant for the targets either. This finding suggest once more that there might not be a relation between the market reaction to cross-border M&As and the distance between firms. I find a positive coefficient on SOCDEV, suggesting that the market reaction to cross-border M&As for target firms is better if the social development is high in the home country of the target. This finding is inconsistent with my predictions. A possible explanation for this surprising finding is that old management serves a complimentary role instead of being replaced. If it is indeed a complimentary role, then it is better to have a talented management. I find a negative coefficient for HOSTILE, which does not correspond with my expectation. A possible explanation is that this research divided the M&As in only two groups, being friendly and hostile. This was done because there were only four M&A’s in which the mood was hostile, but it turns out that the division that was made was oversimplified. A less assumable cause is that the research of Goergen and Renneboog turns out to be untrue (2004).

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Table 5: Regressive results of the acquirors

Dependent Variable = ARA

DIST -0.0003643 (0.0005516) SOCDEV -0.0692409*** (0.0224419) PE -0.00000132 (0.00000215) MULTI -0.0121449 (0.0094006) SIZE -0.00000001 (0.00000001) HOSTILE 0.0029202 (0.0073002) RELATE -0.0020313 (0.0038896) RELSIZE 0.0062513*** (0.0007057) CONSTANT 0.0636936*** (0.0207883)

Year dummy Included

No. of obs. 986

Adj. 𝑅2 0.0800

This table presents the regression that test the value creation for acquiring firms in cross-border M&As. *,**,*** are significant at the 10%, 5% and 1% level respectively.

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Table 6: Regressive results of the targets

Dependent variable = ART

DIST 0.0012771 (0.0019799) SOCDEV 0.5497379*** (0.0800733) PE 0.00000388 (0.00000760) MULTI -0.0525479 (0.0336799) SIZE -0.00000002 (0.00000002) HOSTILE -0.0644829** (0.0259033) RELATE 0.0178667 (0.0140093) RELSIZE -0.0048445* (0.0025003) CONSTANT -0.3459031*** (0.0741784)

Year dummy Included

No. of obs. 956

Adj. 𝑅2 0.0752

This table presents the regression that test the value creation for targeted firms in cross-border M&As. *,**,*** are significant at the 10%, 5% and 1% level respectively.

6. Additional analysis

Table 7 and 8 contain the regressions in which GOV was left out instead of SOCDEV. The results are more or less the same. I find a negative coefficient for GOV in the regression of the acquirors which suggest that the market reaction for cross-border M&As is worse for the acquirors if the target is properly governed. This is not consistent with what I expected. This could be because investors expect the takeover to be less likely to succeed due to better competition law in well-governed countries.

I find a positive coefficient for GOV in the regression for the targets. This finding is consistent with my expectations and suggests that political instability and corruption in the target nation create a less positive market reaction to cross-border M&As. I find a negative coefficient for HOSTILE just like in the other regression, but here I also find a negative coefficient for RELSIZE.

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Table 7: Alternative regressive results of the acquirors

Dependent Variable = ARA

DIST -0.0003133 (0.0005494) GOV -0.0081000*** (0.0022854) PE -0.00000125 (0.00000214) MULTI -0.0115478 (0.0093919) SIZE -0.00000001 (0.00000001) HOSTILE 0.0022553 (0.0072984) RELATE -0.0020980 (0.0038838) RELSIZE 0.0062513*** (0.0007043) CONSTANT 0.0149178** (0.0068027)

Year dummy Included

No. of observartions 986

Adj. 𝑅2 0.0829

This table presents the alternative regression that test the value creation for acquiring firms in cross-border M&As. *,**,*** are significant at the 10%, 5% and 1% level respectively.

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Table 8: Alternative regressive results of the targets

Dependent Variable = ART

DIST 0.0006400 (0.0019863) GOV 0.0082335*** (0.0800733) PE 0.00000302 (0.00000764) MULTI -0.0520722 (0.0339093) SIZE -0.00000002 (0.00000002) HOSTILE -0.0632107** (0.0260890) RELATE 0.0180097 (0.0140928) RELSIZE -0.0052744** (0.0025142) CONSTANT 0.0657466*** (0.0244227)

Year dummy Included

No. of obs. 956

Adj. 𝑅2 0.0643

This table presents the alternative regression that test the value creation for target firms in cross-border M&As. *,**,*** are significant at the 10%, 5% and 1% level respectively.

Table 9 and 10 present the results of an alternative regression that uses the excess market return instead of the market return to calculate the abnormal returns. These new variables are called ARA* and ART* for acquiror and target respectively. For the acquirors there are no differences in which variables are significant and for the target only some control variables changed.

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Table 9: Alternative regressive results for the acquirors

Dependent Variable = ARA*

DIST -0.0002923 (0.0005632) SOCDEV -0.0737244*** (0.0231057) PE -0.00000110 (0.00000217) MULTI -0.0156804 (0.0096997) SIZE -0.00000001 (0.00000001) HOSTILE 0.0020769 (0.0074190) RELATE -0.0021914 (0.0039853) RELSIZE 0.0062509*** (0.0007118) CONSTANT 0.0691535*** (0.0214151)

Year dummy Included

No. of obs. 954

Adj. 𝑅2 0.0818

This table presents the alternative regression that test the value creation for target firms in cross-border M&As. *,**,*** are significant at the 10%, 5% and 1% level respectively.

17

Table 10: Alternative regressive results for the targets

Dependent Variable = ART*

DIST -0.0003015 (0.0005687) SOCDEV -0.0744202*** (0.0232293) PE -0.00000110 (0.00000217) MULTI -0.0158602 (0.00979560 SIZE -0.00000001 (0.00000001) HOSTILE 0.0016147 (0.0074176) RELATE -0.0018922 (0.0040447) RELSIZE 0.0062137*** (0.0007106) CONSTANT 0.0698613*** (0.0215318)

Year dummy Included

No. of obs. 925

Adj. 𝑅2 0.0846

This table presents the alternative regression that test the value creation for target firms in cross-border M&As. *,**,*** are significant at the 10%, 5% and 1% level respectively.

7. Conclusion

In this research I examine the market reaction to cross-border M&A announcements. My analysis is based on a sample of a sample of M&A’s between 2007 and 2015.

For the targeted firms I find that market reaction is positive and that the level of social development of the target nation and the governance of the target nation are positively

correlated with the market reaction to cross-border M&A announcements. These findings suggest that investors think that cross-border M&As in which the target company’s has more social development and better governance will create more value for the target’s shareholders than cross-border M&As in which the target’s country is socially less developed or ill

governed. For the acquiring firms, I find that the market reaction is also positive and that social development and the level of governance in the target firm’s country are negatively correlated with the market reaction to cross-border M&A announcements. These findings suggest that investors believe that M&As in which the target company’s country has more social development or is better governed, create less value for the acquiring firm’s

shareholders than cross-border M&As in which the target firm’s country is less developed

socially or ill governed.

These findings are the first of its kind and lay a groundwork for further research. We now have some knowledge about what possibly influences value creation in cross-border public M&A’s for both acquiring and target firms, but this research is rather limited. Given

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the data and time available, every explanatory variable had to be calculated at the country level. In reality however, a firm can be quite different from a countries’ average. If this research would be done with company level indicators instead of country level indicators, better results can be found. This research merely sought for correlations and did not try to seek for causality. Also, the indicators used in this research consisted of indicators that consisted of various smaller indicators. I also used the market return to determine the abnormal returns due to the amount of work it would cost to estimates each companies expected return. Further research could test the basal variables that make up our tested indicators. It can also be tested for different timeframes to see if there are any changes over time.

Literature list

Asquith, P., Bruner, R. F., & Mullins Jr, D. W. (1983). The gains to bidding firms from mergers. Journal of Financial Economics, 11(1-4), 121-139.

Berk, J. B., & DeMarzo, P. M. (2007). Corporate finance. Pearson Education.

Bradley, M., Desai, A., & Kim, E. H. (1988). Synergistic gains from corporate acquisitions and their division between the stockholders of target and acquiring firms. Journal of financial Economics, 21(1), 3-40.

Bris, A., Brisley, N., & Cabolis, C. (2008). Adopting better corporate governance: Evidence from cross-border mergers. Journal of Corporate Finance, 14(3), 224-240.

Buckley, P. J., & Casson, M. C. (1998). Analyzing foreign market entry strategies: Extending the internalization approach. Journal of International Business Studies, 29(3), 539–561

Chatterjee, S. (1986). Types of synergy and economic value: The impact of acquisitions on merging and rival firms. Strategic management journal, 7(2), 119-139.

Conn, R. L., Cosh, A., Guest, P. M., & Hughes, A. (2005). The impact on UK acquirers of domestic, cross‐border, public and private acquisitions. Journal of Business Finance & Accounting, 32(5‐6), 815-870.

Datta, D. K., Pinches, G. E., & Narayanan, V. K. (1992). Factors influencing wealth creation from mergers and acquisitions: A meta‐analysis. Strategic management journal, 13(1), 67-84. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.

Franks, J. R., Broyles, J. E., & Hecht, M. J. (1977). An industry study of the profitability of mergers in the United Kingdom. The journal of Finance, 32(5), 1513-1525.

Franks, J. R., & Harris, R. S. (1989). Shareholder wealth effects of corporate takeovers: the UK experience 1955–1985. Journal of financial Economics, 23(2), 225-249.

Goergen, M., & Renneboog, L. (2004). Shareholder wealth effects of European domestic and cross‐border takeover bids. European Financial Management, 10(1), 9-45.

Hymer, S. H. (1976). The international operations of national firms: A study of direct foreign investment. Cambridge, MA: MIT Press.

19

Jarrell, G. A., & Poulsen, A. B. (1989). The returns to acquiring firms in tender offers: Evidence from three decades. Financial management, 12-19.

Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. The American economic review, 76(2), 323-329.

Jit Singh Mann, B., & Kohli, R. (2011). Target shareholders' wealth creation in domestic and cross-border acquisitions in India. International Journal of Commerce and

Management, 21(1), 63-81.

Kohli, R., & Mann, B. J. S. (2012). Analyzing determinants of value creation in domestic and cross border acquisitions in India. International Business Review, 21(6), 998-1016.

Mantravadi, P., & Reddy, A. V. (2007). Relative size in mergers and operating performance: Indian experience. Economic and Political weekly, 3936-3942.

Markides, C. C., & Ittner, C. D. (1994). Shareholder benefits from corporate international diversification: Evidence from U.S. international acquisitions. Journal of International Business Studies, 25(2), 343–366

Martynova, M., & Renneboog, L. (2008). Spillover of corporate governance standards in cross-border mergers and acquisitions. Journal of Corporate Finance, 14(3), 200-223. Moeller, S. B., Schlingemann, F. P., & Stulz, R. M. (2004). Firm size and the gains from acquisitions. Journal of financial economics, 73(2), 201-228.

Moeller, S. B., Schlingemann, F. P., & Stulz, R. M. (2005). Wealth destruction on a massive scale? A study of acquiring‐firm returns in the recent merger wave. The journal of

finance, 60(2), 757-782.

Murphy, K. J. (1985). Corporate performance and managerial remuneration: An empirical analysis. Journal of accounting and economics, 7(1-3), 11-42.

Nielsen, J. F., & Melicher, R. W. (1973). A financial analysis of acquisition and merger premiums. Journal of Financial and Quantitative Analysis, 8(2), 139-148.

Seth, A. (1990). Sources of value creation in acquisitions: an empirical investigation. Strategic Management Journal, 11(6), 431-446.

Sudarsanam, S., Holl, P., & Salami, A. (1996). Shareholder wealth gains in mergers: effect of synergy and ownership structure. Journal of Business Finance & Accounting, 23(5‐6), 673-698.

Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A., & Pietronero, L. (2012). A new metrics for countries' fitness and products' complexity. Scientific reports, 2, 723.

Tong, Z. (2012). Coinsurance effect and bank lines of credit. Journal of Banking & Finance, 36(6), 1592-1603.

Walker, M. M. (2000). Corporate takeovers, strategic objectives, and acquiring-firm shareholder wealth. Financial management, 53-66.

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Waud, R. N. (1970). Public Interpretation of Federal Reserve Discount Rate Changes:

Evidence on the" Announcement Effect". Econometrica: Journal of the Econometric Society, 231-250.

8. Appendix

Abnormal Returns: 𝐴𝑅 = (𝑆𝑡+1−𝑆𝑡−1

𝑆𝑡−1 − (1 + 𝑟𝑀𝑡−1)(1 + 𝑟𝑀𝑡)(1 + 𝑟𝑀𝑡+1)) ∗ 100%

DIST: Minimum distance between the two countries the firms are in. SOCDEV: 𝐻𝐷𝐼 = √𝐿𝐸𝐼 ∗ 𝐸𝐼 ∗ 𝐼𝐼3 in which 𝐿𝐸𝐼 =𝐿𝑖𝑓𝑒 𝐸𝑥𝑝𝑒𝑐𝑡𝑎𝑛𝑐𝑦−20₈₅₋₂₀ , 𝐸𝐼 = 𝑀𝑒𝑎𝑛 𝑦𝑒𝑎𝑟𝑠 𝑜𝑓 𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑛𝑔 15 + 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑦𝑒𝑎𝑟𝑠 𝑜𝑓 𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑛𝑔 18 2 and 𝐼𝐼 =𝐿𝑛(𝐺𝑁𝐼 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 𝑎𝑡 𝑃𝑃𝑃)−𝐿𝑛(100) 𝐿𝑛(75000)−𝐿𝑛(100) .

GOV: defined on https://datacatalog.worldbank.org/dataset/worldwide- governance-indicators

SIZE: Acquiror total assets in book value in US$.

MULTI: Binary variable, 1 for multiple bidders, 0 for one bidder.

PE: Price-earnings ratio of the target company on the day of the announcement. HOSTILE: Binary variable, 1 for non-friendly mood, 0 for friendly mood.

RELATE: Binary variable, 1 for same SEIC-code, 0 for different SEIC-codes.

RELSIZE: The relative size of the target and acquiror, measured by total book value of assets.

YEAR: A binary variable for each year is also included.

This is the code that was used to determine the minimum distance between two countries. It is written in the programming language called R. The data is collected from

www.naturalearthdata.com. The code is as follows: library(maps)

library(geosphere) library(dplyr) library(sp)

world.map <- map("world", fill = TRUE)

indicePays <- seq(1,length(world.map$names)) # https://stat.ethz.ch/pipermail/r-help/2010-April/237031.html splitNA <- function(x){ idx <- 1 + cumsum(is.na(x)) not.na <- !is.na(x) split(x[not.na], idx[not.na]) }

21 # Coordinates of every country

lesCoordsX <- splitNA(world.map$x) lesCoordsY <- splitNA(world.map$y)

lesDistancesUnPays <- function(unIndicePays){ # Borders coordinates for current country

coordsPays <- data.frame(long = lesCoordsX[[unIndicePays]], lat = lesCoordsY[[unIndicePays]]) # Indexes of countries except the current one

# and the one for which the computation has already been done lesIndicesAutresPays <- indicePays[indicePays > unIndicePays]

distancePoint <- function(unPoint){ unPoint.m <- matrix(unPoint, ncol = 2)

distancePointPays <- function(unIndicePays2){

coordsPays2 <- matrix(cbind(long = lesCoordsX[[unIndicePays2]], lat = lesCoordsY[[unIndicePays2]]), ncol = 2)

lesDistPointPays2 <- spDists(x=coordsPays2, y=matrix(unPoint, ncol=2), longlat=TRUE)

return(min(lesDistPointPays2)) # shortest distance between unPoint and country which index is unIndicePays2

}

lesDistPointPays2 <- lapply(lesIndicesAutresPays, distancePointPays) res <- unlist(lesDistPointPays2)

return(res) }

distancesPays <- apply(coordsPays, 1, distancePoint)

# Shortest distances between unPoint and every other country if(!is.matrix(distancesPays)){

# For the last country on the list

plusCourtesDistances <- min(distancesPays) }else{

plusCourtesDistances <- apply(distancesPays, 1, min) }

resul <- cbind(pays1 = rep(unIndicePays, length(plusCourtesDistances)),pays2 = lesIndicesAutresPays, dist = plusCourtesDistances)

return(resul) }

lesDist <- lapply(indicePays[-length(indicePays)], lesDistancesUnPays) # Convert to data frame

distMat <- do.call(rbind, lesDist) lesDist <- as.data.frame(distMat)

22 lesDist2 <- data.frame(cbind(pays1 = rep(indicePays, each = length(indicePays)),

pays2 = rep(indicePays, length(indicePays)))) lesDist2 <- lesDist2[-which(lesDist2$pays1 == lesDist2$pays2),]

lesDist2$ID <- paste(sprintf("%04d", lesDist2$pays1), sprintf("%04d", lesDist2$pays2), sep = "") lesDist2$ID2 <- paste(sprintf("%04d", lesDist2$pays2), sprintf("%04d", lesDist2$pays1), sep = "") lesDist2$match <- match(lesDist2$ID, lesDist$ID)

lesDist2[is.na(lesDist2$match),"match"] <- match(lesDist2$ID2[is.na(lesDist2$match)], lesDist$ID) lesDist2$dist <- lesDist[lesDist2$match, "dist"]

lesDist2 <- lesDist2[,c("pays1", "pays2", "dist")] lesDist <- lesDist2

rm(lesDist2)

lesDist$pays1 <- world.map$names[lesDist$pays1] lesDist$pays2 <- world.map$names[lesDist$pays2]

allCountries <- sort(unique(regmatches(world.map$names, regexpr('^([A-Za-z]*[^:])*', world.map$names, ignore.case = TRUE))))

newLesDist <- data.frame(pays1=character(),pays2=character(),dist=double()) for (country1 in allCountries) {

print(paste("Processing country: ",country1)) for (country2 in allCountries) {

if (country1 == country2) { next

}

countryDists1 <- lesDist[which( grepl(paste('^',country1,sep=''), lesDist$pays1) & grepl(paste('^',country2,sep=''), lesDist$pays2)), ]

countryDists2 <- lesDist[which( grepl(paste('^',country2,sep=''), lesDist$pays1) & grepl(paste('^',country1,sep=''), lesDist$pays2)), ] minDist1 <- min(countryDists1$dist) minDist2 <- min(countryDists2$dist) trueMinDist <- NA if (minDist1) { trueMinDist <- minDist1 } else { trueMinDist <- minDist2 }

result <- cbind(pays1 = country1, pays2 = country2, dist = trueMinDist) newLesDist <- rbind(newLesDist, result)

} }

write.csv(newLesDist, file="countries_distances.csv")