Cross-border acquisitions, risk and the global financial crisis

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Cross-border acquisitions, risk and the global

financial crisis

Abstract

This thesis examines the influence of cross-border acquisitions on acquirer risk as resembled by the acquirer’s bonds yield spreads. This study also investigates the impact of the global financial crisis (GFC), foreign experience and target country characteristic on risk changes following the announcement of a cross-border acquisition. Results show that acquirer foreign experience and target country characteristics do influence yield spread changes. However, my results do not support a lasting effect of the GFC and identifies no overall abnormal effect of cross-border acquisitions on risk.

JEL classification: G14, G32, G34 Keywords:

Cross-border acquisitions, Risk, Yield spreads, Cost of debt, Global financial crisis

Supervisor: Dr. Robinson Kruse

Author: Wessel Visschedijk

Student number: s2184826

MSc Business and Economics Department of Business Studies Uppsala University

MSc International Financial Management Faculty of Economics and Business

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1. Introduction

Mergers and acquisitions have been a popular strategy for companies to realize growth and strategic expansion for an extensive period of time. Throughout the years multinational corporations have utilized mergers and acquisitions (M&A) as a major strategic tool for growth (Hitt, Harrison and Ireland, 2001) and foreign direct investment (Stiebale and Trax, 2011). During the past decades M&A activity has increased dramatically from a global 1,1 trillion dollars in 1996 to 4,6 trillion dollars in 2007, equal to 8% of global gross domestic product (GDP) (JP Morgan, 2016). Concurrently with the growth in M&A activity, the importance of cross-border acquisitions has increased even more rapidly. While the majority of deals have continued to be domestic, Erel, Liao and Weisbach (2012) find that the share of cross-border acquisitions in total acquisition volume has increased from 23% in 1998 to 45% in 2007. Moreover, Stiebale and Trax (2011) find that 80% of global foreign direct investments in 2007 consisted of cross-border acquisitions.

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4 This growing importance of cross-border acquisitions has fuelled more extensive research in recent years (Lebedev, Peng, Xie and Stevens, 2015; Erel et al., 2012; Stiebale and Trax, 2011; Du and Boateng, 2015; Buckley, Elia and Kafouros, 2014). However, the vast majority of research focuses on how cross-border acquisitions affect firm performance (Stiebale and Trax, 2011; Du and Boateng, 2015; Buckley, Forsans and Munjal, 2012). A field of research that has received less attention is how cross-border acquisitions influence the riskiness of the acquiring firm and more specifically how it affects the perceived risk by bondholders of the acquiring firm. While this relationship has been researched specifically for the banking sector (Choi, Francis and Hasan, 2009; Amihud, DeLong and Saunders, 2002) and in the broader context of the degree of internationalisation and risk (Reeb, Mansi and Allee, 2001; Reeb and Kwok, 1998), the direct relationship between cross-border acquisitions by firms and riskiness presents an interesting gap in the existing literature since theory does suggest that such a relationship exists.

Previous research has argued that cross-border acquisitions or a larger degree of internationalisation can either positively or negatively influence a firm’s risk. Through diversification advantages a firm is able to enjoy a lower perceived risk than domestic companies (Hughes, Logue and Sweeney, 1975; Rugman, 1976). On the contrary, cross-border acquisitions also increase agency (Singh and Nejadmalayeri, 2004) and monitoring costs. Moreover, entering foreign markets bears inherent risks (Johanson and Vahlne, 1977; Zaheer, 1995; Reeb and Kwok, 1998). However, the net effect of cross-border acquisitions on acquirer risk is ambiguous and poses an interesting topic for research.

Furthermore, focusing on cross-border acquisitions specifically is intriguing since this equity entry mode is associated with higher risks, costs (Shimizu, Hitt, Vaidyanath and Pisano, 2004) and possible benefits compared to non-equity entry modes. Additionally, investigating the effect of cross-border acquisitions on acquirer risk in the context of the GFC is highly interesting since its economic consequences have been both evident and severe. Therefore, the main research question of this thesis is whether cross-border acquisitions significantly impact the risk of the acquirer and if the GFC has influenced this relationship.

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5 yield spreads are used since they directly measure the risk that is perceived by the firm’s bondholders, an important group of stakeholders. Moreover, it aims at investigating potential differences in bondholder reactions to cross-border acquisitions before and after the financial crisis. Finally, through applying cross-sectional analysis this thesis examines the importance of country and firm characteristics as determinants of the perceived risk changes due to cross-border acquisitions.

This thesis does not find evidence that cross-border acquisitions are necessarily risk increasing or decreasing. However, the thesis does find evidence that foreign experience of the acquirer is important in determining the influence of cross-border acquisitions on risk. Moreover, the findings support the arguments that target country characteristics such as the level of economic development and strength of legal protection influence changes in risk.

The remainder of this thesis is structured as follows. The next section presents a review of existing literature on cross-border acquisitions, its relation to firm risk and the potential implications of the financial crisis. Additionally, the hypotheses are formulated and discussed. Section 3 describes the data collection and methodology. Section 4 presents the descriptives, results of the event study analysis and cross-sectional analysis. Finally, Section 5 discusses limitations and implications of the study, highlights avenues of future research and concludes the thesis.

2. Literature review & hypotheses

In this section the existing literature on cross-border acquisitions and risk will be reviewed, an overview of the potential effects of the financial crisis will be provided and testable hypotheses will be developed.

2.1. Risk and cross-border acquisitions

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6 The risk reducing potential that cross-border acquisitions offer has been thoroughly investigated. The main argument for potential risk reductions through cross-border acquisitions concerns the gained diversification benefits which are obtained through internationalisation. As firms internationalize and enter various markets, their assets and revenues become more diversified (Reeb et al., 2001). This results in a decrease in earnings volatility since cash flows are generated in markets which are imperfectly correlated (Hughes et al., 1975). These imperfect correlations imply a reduction of risk and bankruptcy costs of the firm. Moreover, through diversification, the firm simultaneously fulfils an important mechanisms for investors to spread their risk across markets as investors themselves experience financial market barriers to diversify internationally. The paper by Hughes et al. (1975) presented some of the first evidence regarding internationalisation and diversification. Comparing multinational and domestic firms, they found that multinational firms enjoyed lower risk due to perceived diversification benefits. Rugman (1976), Agmon and Lessard (1977) and Shapiro (1978) find similar results, suggesting that investors value the diversification benefits, leading to greater stability against volatile markets, provided by international firms. Galai and Masulis (1976) argue that, since debtholders do not have a residual claim and prefer lower risk operations, they benefitted from the risk reduction through lower cash flow variability. These findings are confirmed by Reeb et al. (2001) and Singh and Nejadmalayeri (2004) which find that a higher degree of internationalisation leads to a lower yield spread and improved credit ratings. These findings suggest that internationalisation yields significant diversification benefits which result in a reduction of risk.

However, Reeb and Kwok (1998) find that internationalisation has a strong positive relationship with the firm’s systematic risk. This finding is conflicting with papers who identify significant diversification benefits through internationalisation. Reeb and Kwok (1998) argue that while diversification benefits may be present, these are outweighed by factors such as agency problems, political risk and pervasive economic factors.

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7 (1996) argues that monitoring costs increase with higher internationalisation due to geographical constraints, language barriers, legal differences and cultural mismatches. These higher monitoring costs, originating from more serious agency problems translate into a higher risk. Additionally, asset substitution, resulting from increasing monitoring difficulties, can also be a source of stronger agency problems (Lee and Kwok, 1988) because shareholders have an incentive to expropriate debtholders (Jensen and Meckling, 1976). Shareholders prefer high risk, high return projects since these create the most value for them. On the contrary, debtholders have a preference for safer returns to ensure future debt payments. To tackle this conflict detailed debt contracts are employed. However, as firms become more internationalized, these debt contracts are more difficult to create and monitor due to the increasingly higher complexity in the international environment. Moreover, Kim and Lyn (1986) find that multinational firms outperform domestic companies since they enjoy monopolistic advantages which are reflected in the value of real options. Consequently, multinational firms enjoy more real options for investments compared to domestic firms (Lee and Kwok, 1988). The higher availability of these options indicates greater potential underinvestment problems (Myers, 1977). This problem manifests when debt reaches maturity after a real option has expired and shareholders reject positive net value projects since the benefits accrue to debtholders mostly. Subsequently, this translates into a higher perceived risk of the firm since creditors fear missing returns unjustly.

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8 increases the volatility in domestic currency denominated earnings (Reeb et al., 1998, He and Ng, 1998). Moreover, firms conducting cross-border acquisitions might face additional new risks compared to domestic firms such as the liability of foreignness (Zaheer, 1995). He and Ng (1998) and Reeb and Kwok (1998) argue that international firms face higher risk than domestic firms because of increasing political risks. Moreover, they posit that due to their international nature, their foreign exposure is significantly higher than domestic firms and thus face greater variance in returns. These arguments suggest that bankruptcy of multinational firms is more likely and thus are perceived as more risky. Armstrong and Riddick (1998) suggest that stakeholder heterogeneity and information asymmetry are considerably higher in international firms. Various groups of creditors within the multinational firm receive information which is different. These information differences, combined with the added complexity of operating within different legal jurisdictions increases the costs of bankruptcy and translates into a higher risk.

Literature argues that cross-border acquisitions potentially influence important determinants of firm risk. However, the findings are conflicting. Several papers present evidence of the diversification benefits that can be enjoyed through conducting a cross-border acquisition. Alternatively, literature also argues that cross-border acquisitions aggravate agency problems and increases bankruptcy costs since the acquirer is faced by additional risks in the international environment. Therefore, I formulate the following hypothesis:

H1: Conducting a cross-border acquisition affects the acquirer’s firm risk.

2.2. The global financial crisis

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9 capital, firms perceive higher levels of environmental uncertainty during crisis times (Chen and Miller, 2007) and see environmental munificence reduce (Wan and Yiu, 2009), which results in constrained and more conservative firm behaviour. Moreover, Fama (1986) and Duffee and Singleton (2003) present evidence that macroeconomic conditions, which were relatively unfavourable during the GFC, significantly impact the probability of default and thus experience increasing bankruptcy costs. Furthermore, the turmoil in financial markets spurred a substantial panic among investors, who shifted away from risky assets towards safer investments and lowered their risk appetite (Rizzi, 2014). In general, the GFC created an extremely harsh environment for firms and investors alike. The lack of available and affordable capital, high uncertainty and poor investor confidence diminished M&A activity. While the value of post-crisis M&A activity has regrown to its pre-crisis level, suggesting similar economic conditions and renewed investor confidence and appetite for risk, this study seeks to explore whether the net effect of cross-border acquisitions on risk differs between the pre-crisis and post-crisis period.

Investors may place more value on potential risks involved with cross-border acquisitions, having just experienced the GFC. Major detrimental events, such as the GFC, influences investors’ behaviour (Rizzi, 2014). Nagel (2011) suggests that while the impact of the GFC on investors’ risk appetite is not permanent, it gradually decreases over the long term. Moreover, Rizzi (2014), utilizing a behavioural finance framework, argues that a persistent effect of the financial crisis on risk perception, risk tolerance and market price of risk is likely. These arguments suggest that risk perception in the post-crisis period differs from the pre-crisis period and consequently affects the net effect of cross-border acquisitions on risk.

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10 Literature and economic conditions provide arguments for a different effect of cross-border acquisitions on risk in the post-crisis period compared to the pre-crisis period. A different perception of risk by debtholders may lead to more negative effects of cross-border acquisitions on risk. However, a potential higher value of diversification benefits and more affordable capital points towards an opposite change. Therefore I formulate the following hypothesis:

H2: Conducting a cross-border acquisition post-crisis leads to a different change in risk compared to conducting a cross-border acquisition pre-crisis.

2.3. Foreign experience

Literature has examined the importance of firm foreign experience when expanding abroad and operating in an international environment. Johanson and Vahlne (1977) argue that foreign experience is essential to utilize competitive advantages, obtain foreign resources and mitigate risks that the acquiring firm is exposed to. Additionally, they argue that firms with more experience are more capable of estimating risks and rewards of foreign operations. This implies that when acquiring firms possess foreign experience they should be better at assessing and managing the potential risks involved in a cross-border acquisition. Moreover, Conn, Cosh, Guest and Hughes (2005) argue that the success of cross-border acquisitions heavily depend on the ability to effectively manage and integrate the target firm into the MNCs operations. Furthermore, firms with international experience likely will be more capable of effective monitoring and bonding in an international organization. Thus, firms which possess foreign experience are generally more competent at managing the post-acquisition process of integration, extracting knowledge and utilizing target firm synergies. Consequently, it is likely that cross-border acquisitions by foreign experienced firms are perceived as less risky compared to firms without foreign experience.

However, foreign experience can also potentially reduce diversification benefits derived from a cross-border acquisition. Mansi and Reeb (2002) argue that during initial international expansion, diversification benefits can be substantial but diminish marginally with an increasing degree of internationalisation since operating in a few low correlated countries provides a relative substantial part of total diversification benefits. Thus, diversification benefits are possibly more pronounced during initial cross-border acquisitions.

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11 identifying and mitigating risks and is beneficial in the post-acquisition process. This should translate into lower incurred risks as a consequence of a cross-border acquisition. However, arguments for decreasing marginal diversification benefits suggest that foreign experience increasingly inhibits the potential diversification benefits. Therefore I formulate the following hypothesis:

H3: A cross-border acquisition conducted by a foreign experienced firm leads to a different change in the acquirer’s risk compared to a cross-border acquisition conducted by a firm without foreign experience.

2.4. Target country economic development

Literature has argued that the perceived risk change as a consequence of cross-border acquisitions is dependent on the diversification benefits that are realized and the costs and risks incurred because of the acquisition. The risk and costs involved in the acquisition are to some degree dependent on host country characteristics such as political risk and tax uncertainty (Reeb and Kwok, 1998; Burgman, 1996). Lebedev et al. (2015) find that countries which feature low economic development are characterised by less developed financial markets, especially the market for corporate control. As such, acquisitions targeted at these countries involve a higher degree of uncertainty, lower transparency and higher transaction costs. Moreover, Agyei-Boapeah (2015) argues that the target country state of development is pivotal in determining the impact of cross-border acquisitions on the agency and bankruptcy costs. Consequently, investors will perceive these acquisitions as more risky. Kwok and Reeb (2000) and La Porta, Lopez-De-Silanes, Shleifer and Vishny (1998) find that economically high developed countries feature superior legal protection, higher technological advancements, lower political risk and higher financial stability and information disclosure. Because of this, the risks involved with an acquisition are deemed less significant. On the contrary, economically low developed countries are associated with higher political and financial risks, inferior legal protection and more significant exchange rate exposure. Berry (2006) confirms these findings, stating that countries which are more institutionally developed are a less risky environment in which knowledge can be acquired and investments protected.

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12 countries which feature lower economic development possess worse institutions and pose a riskier economic environment. Therefore I formulate the following hypothesis:

H4: The degree of economic development of the target country is negatively related to risk.

2.5. Legal protection

Focusing more specifically on the importance of legal protection, La Porta et al. (1997, 1998) show that the strength of legal protection for investors is an important determinant of the degree of financial market development in a country. Additionally, La Porta et al. (2002) find that a higher degree of legal protection is correlated with a higher valuation of firm assets. They argue that as the protection of investors and creditors increases, they are willing to pay more for financial assets since they receive a larger portion of the firm’s profits in the form of dividends or interest. Moreover, due to strong legal protection, insiders are less able to expropriate investors and creditors. For creditors specifically, stronger legal protection entails laws and procedures that deal with bankruptcy and reorganization which facilitate the repossession of collateral by creditors and protect their seniority (La Porta et al., 2000), which in turn reduce bankruptcy costs. These arguments suggest that higher legal protection is indicative of a lower risk to investors. Thus, when legal protection is relatively weaker in the target country, acquirer risk is increased. Similarly, when legal protection is relatively strong in the target country, acquirer risk is not increased and could potentially be reduced. Therefore I formulate the following hypothesis:

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3. Data and methodology

3.1. Data

In this study we examine cross-border acquisitions that are announced in the pre-crisis period from 2004 till end 2006 and post-crisis period from 2011 till end 2013. To be considered the acquirer needs to be based and listed in the US and be active in non-financial sectors. Additionally, the acquirer is required to have acquired at least a stake of 50% after the transaction. Acquisitions which are built over a period of time utilizing footholds are eliminated. Moreover, leveraged buyouts are excluded because of the debt intensive nature of these transactions. The announced acquisition is required to have been completed as well. Finally, acquisitions that were announced within 30 weeks of a previous acquisition by the same acquirer are removed from the sample to ensure that no other acquisition announcements take place in the estimation window. The data on conducted cross-border acquisitions is collected from the Bureau van Dijk’s Zephyr database. An initial screening generates 1,091 pre-crisis and 1,286 post-crisis cross-border acquisitions that meet the criteria. Data on bond and firm characteristics is obtained via Datastream. Cross-border acquisitions of which Datastream does not offer acquirer bond or firm information on are excluded. Furthermore, the acquiring firm should have at least one bond outstanding with a minimum remaining maturity of two years and maximum maturity of thirty years to allow for the interpolated yield spread computation. Callable, puttable, convertible, subordinated are ignored. Since junk bonds are excluded as well, outstanding bonds which featured no rating or a withdrawn rating are removed from the sample. A sample of 73 pre-crisis and 148 post-crisis cross-border acquisitions is derived which adhere to the listed criteria. Country specific data such as GDP levels, growth and legal protection is obtained from the World bank and Doing business databases.

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14 TABLE 1

Overview of acquisitions included in the analyses, based upon target country and announcement year Number of acquisitions: country and year

Pre-crisis Post-crisis

Target country 2004 2005 2006 2011 2012 2013 Total

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3.2. Event study methodology

The yield spread is defined as the difference between the corporate bond yield of a particular acquirer and the yield of a government bond with comparable maturity from the acquirer’s home country. This spread resembles the perceived risk of the firm by bondholders and is thus a major determinant of the firm’s cost of public debt. Since bond markets are characterized by thin trading, weekly yield data is used to control for this issue (Choi et al., 2009). In this study the yield spread of individual bonds is calculated using the interpolated yield spread method (O’Kane and Sen, 2004). Using the interpolated yield calculation, the existing maturity mismatch between a corporate and government bond is largely avoided. The interpolated yield spread is calculated using two United States treasury constant maturity benchmark yields which straddle the corporate bond in terms of maturity in

𝐼𝑠𝑝𝑟𝑒𝑎𝑑 = 𝑦 _𝐷− [𝑌_𝐺1((𝑌𝐺2−𝑌𝐺1)

(𝑇𝐺2−𝑇𝐺1)) (𝑇𝐷− 𝑇𝐺1)], (1)

Where 𝑦 _𝐷 and 𝑇_𝐷 are the yield and term to maturity of the corporate bond and 𝑌_𝐺1, 𝑌_𝐺2, 𝑇_𝐺1 and 𝑇_𝐺2 are the yield and term to maturity of the United States treasury constant maturity benchmark yields. After calculation of individual bond yield spreads the issue is addressed of firms in the sample which have multiple bonds outstanding.

According to Bessembinder, Kahle, Maxwell and Xu (2009) three approaches exist to address this issue, namely the bond level approach, which treats each bond as a separate observation, the representative bond approach, which selects a single representative bond for each firm and the firm level approach which creates a portfolio of bonds for each firm. In this study the firm level approach is used through creating a firm portfolio of the market value weighted yield spreads of all outstanding bonds. The weights are based upon the market value of the individual issues at τ - 30. Utilizing the firm level approach mitigates two problems associated with the bond level and representative level approaches (Bessembinder et al., 2009). Firstly, cross-correlation is avoided. Moreover, the results should be more stable since multiple observations are taken into account per firm. Secondly, it represents more accurately the total change in yield spread due to the event.

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16 acquisition. Furthermore, the event windows over which the yield spreads are to be examined are established, in weeks, as follows, (-4,+4), (-1,+1), (-4,0) and (0,+4) with τ = 0 as the announcement of the acquisition. Utilizing multiple different event windows can potentially convey interesting information. The (-4,0) window examines whether a market reaction in the weeks prior to the event is observed. The (0,+4) window solely analyses whether a market reaction is detected directly after the event. The (-4,+4) and (-1,+1) windows seek to capture both the period prior and after the event.

The estimation windows are defined, in weeks, as (-30,-5) and (-17,-5). Two estimation windows are utilized to check the robustness of the computation of the normal changes in yield spread. After establishing the event of interest, event windows and estimation windows, the data is selected according to the criteria as described in Section 3.1. We will refer to abnormal changes in yield spreads as abnormal returns in the methodology to maintain consistency with the formulas MacKinlay (1997) and Campbell et al. (1997) utilize.

To measure the impact of the announcement of the cross-border acquisition, the abnormal returns have to be calculated. The abnormal return is the actual return during the event period minus the normal return during the event window. The normal return is the return that would have been realized if the event had not occurred (MacKinlay, 1997; Campbell et al. 1997). The abnormal returns are calculated as

𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙 𝑟𝑒𝑡𝑢𝑟𝑛 = 𝐴𝑅𝑖𝜏 = 𝑅𝑖𝜏− 𝐸(𝑅𝑖𝜏|𝑋𝜏). (2)

Where 𝐴𝑅_𝑖𝜏, 𝑅_𝑖𝜏 and 𝐸(𝑅_𝑖𝜏|𝑋_𝜏) represent the abnormal, actual and expected normal return for bond i and time period 𝜏. 𝑋_𝜏 resembles the conditioning information concerning the normal return model. In this study the normal return is determined using the constant mean return model. While it is considered to be the most basic model, it has been found to yield results largely similar to more complex and sophisticated models (Brown and Warner, 1980; 1985; Mackinlay, 1997; Campbell et al. 1997). Moreover, the constant mean return model is preferred over the market model in this paper since constructing matching bond portfolios for each individual firm observation would have been immensely time-consuming. The constant mean return model is defined as

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17 Where 𝑅𝑖𝜏 is the return for bond i in time period 𝜏. 𝜉𝑖𝜏 is the disturbance term for bond i during

time period 𝜏 which is expected to be zero with a variance of 𝜎_𝜉𝑖2. To facilitate the measurement and analysis of abnormal returns notations are introduced. Using 𝜏, the returns will be indexed into event time with 𝜏 = 0 as the event date. The event window is defined as 𝜏 = 𝑇1+ 1 to

𝜏 = 𝑇₂ and the estimation window as 𝜏 = 𝑇₀+ 1 to 𝜏 = 𝑇₁. The post-event window is not applicable in this study. The length of the estimation window is constituted as 𝐿1 = 𝑇₁− 𝑇₀ and of the event window as 𝐿2 = 𝑇2− 𝑇1. These notations are graphically depicted below. 𝐿3

is not applicable in this study.

To draw general inferences for the event of interest the abnormal return observations need to be aggregated, both through time and across events to form the Cumulative average abnormal return (CAAR). Firstly, the abnormal returns are aggregated through time for individual events. The cumulative abnormal returns (CAR) are defined as

𝐶𝐴𝑅_𝑖(𝜏1, 𝜏2) = ∑𝜏𝜏=𝜏12 𝐴𝑅𝑖𝜏. (4)

The corresponding variance of 𝐶𝐴𝑅_𝑖 is

𝜎_𝑖2(𝜏1, 𝜏2) = (𝜏2− 𝜏1+ 1)𝜎𝜀𝑖

2_. ₍₅₎

With 𝜎_𝜀2_𝑖 being the variance of the disturbance term. Since the CARs show several extreme values we winsorize the data at a 1% level. However, we do present the analysis of abnormal effects on both the winsorized and unwinsorized dataset in Section 5. Next, we aggregate the CARs across events to obtain the CAAR. The CAAR is defined as

𝐶𝐴𝑅 ̅̅̅̅̅̅(𝜏1, 𝜏2) = 1 𝑁∑ 𝐶𝐴𝑅𝑖( 𝑁 𝑖=1 𝜏1, 𝜏2), (6)

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18 𝑣𝑎𝑟(𝐶𝐴𝑅̅̅̅̅̅̅(𝜏₁, 𝜏₂)) = 1 𝑁2∑ 𝜎𝑖 2 𝑁 𝑖=1 (𝜏₁, 𝜏₂). (7)

After obtaining the CAAR and corresponding variance we test for significance of the CAAR to test the null hypothesis which states that abnormal returns are zero. The significance of the CAAR is determined using

Θ₁ = 𝐶𝐴𝑅̅̅̅̅̅̅(𝜏1, 𝜏2)

𝑣𝑎𝑟(𝐶𝐴𝑅̅̅̅̅̅̅(𝜏₁, 𝜏₂))1 2⁄ ~𝑁(0,1), (8)

which follows a standard normal distribution, resembled by ~𝑁(0,1). The test statistic resulting from equation (8) determines whether the null hypothesis is rejected or accepted. The CARs which are obtained using equation (4) are utilized as the dependent variable in the cross-sectional analysis which is further explained below.

3.3. Regression analysis methodology

After computing the abnormal changes in yield spreads, the relative importance of the variables mentioned in the literature and hypothesis development section are explored. This cross-sectional analysis is conducted utilizing regression analysis where the obtained CARs are modelled as the dependent variable. Six variables are included in the regression which seek to test the presented hypotheses and explain the observed differences in the computed CARs. The following equation is formed to test the hypotheses:

𝐶𝐴𝑅 = 𝛼 + 𝛽₁𝐶𝑅𝐼𝑆𝐼𝑆 + 𝛽₂𝐹𝑂𝑅𝐸𝑋𝑃 + 𝛽₃𝐿𝑜𝑔𝐺𝐷𝑃 𝑇𝐴𝑅𝐺𝐸𝑇

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19 acquirer’s Tobin’s Q value and is added as a control variable since firms with high market to book values are more likely to have more valuable investment opportunities (Lang et al., 1989).

4. Results

This section will first provide descriptive statistics of the collected data as discussed in section 3.1. Next the results of the event study analysis of the full sample, pre-crisis sample and post-crisis sample are presented. Finally, the results of the multivariate analysis are provided.

4.1. Descriptive statistics and correlations

Panel A in table 2 presents the descriptive statistics of the full sample as employed in this study’s analysis. The number of observations for all variables is 221. The computed CARs show a mean close to zero basis points. However, the maximum and minimum cumulative average abnormal return observed deviate considerably from zero regardless of event window specification. The first independent variable, the CRISIS dummy, has a mean value of 0.670. This means that 67% of the cross-border acquisition announcements have taken place after the crisis. The second independent variable, the FOREXP dummy, has a mean value of 0.955, showing that 95.5% of the sample has foreign experience when announcing the acquisition. Since the sample consists of listed firms it seems logical that the vast majority already controlled foreign assets at the time of announcement. GDPGROWTH resembles the economic growth in percentages of the target country. The mean growth across the sample is 2% while it varies from 12.1% to an economic decline of 3.2%. LEGAL can vary from 0 to 12. The minimum legal strength index in this sample is 2. The mean legal strength amounts to 5.8. GDP shows a negative mean which indicates that on average, the target countries have a lower GDP level compared to the US. This is expected since the GDP of the US is relatively high. TOBIN shows a mean of 0.091 and a median of 0.131. This shows that the sample consists of more overvalued rather than undervalued companies.

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20 TABLE 2

Panel A. Descriptive statistics

Mean Median Maximum Minimum Std. Dev. N

CAR04 0.006 0.000 0.775 -0.599 0.188 221 CAR11 0.000 0.002 0.347 -0.435 0.110 221 CAR40 -0.001 0.010 0.477 -0.488 0.157 221 CAR44 0.005 0.020 0.907 -0.925 0.269 221 CRISIS 0.670 1.000 1.000 0.000 0.471 221 FOREXP 0.955 1.000 1.000 0.000 0.208 221 GDPGROWTH 2.015 1.677 12.061 -3.200 2.346 221 LEGAL 5.760 6.000 12.000 2.000 2.318 221 GDP -0.582 -0.184 1.066 -4.107 0.950 221 TOBIN 0.091 0.131 1.427 -1.379 0.571 221 Panel B. Correlations

CAR04 CAR11 CAR40 CAR44 CRISIS FOREXP GDPGROWTH LEGAL GDP TOBIN

CAR04 _1.000 CAR11 _0.592 _1.000 CAR40 _0.354 _0.487 _1.000 CAR44 _0.798 _0.613 _0.783 _1.000 CRISIS _-0.023 _0.040 _-0.053 _-0.037 _1.000 FOREXP _0.193 _0.121 _0.149 _0.172 _-0.060 _1.000 GDPGROWTH _0.105 _0.103 _0.062 _0.091 _-0.351 _0.125 _1.000 LEGAL _-0.137 _-0.095 _-0.054 _-0.134 _0.077 _-0.023 _-0.141 _1.000 GDP _-0.081 _-0.069 _0.032 _-0.001 _0.101 _-0.111 _-0.633 _0.420 _1.000 TOBIN _-0.082 _-0.095 _0.062 _-0.015 _-0.126 _0.090 _0.025 _0.054 _0.035 _1.000

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4.2. Cross-border acquisitions and yield spreads

This section presents the results of the event study analysis. This analysis examines the effect of cross-border acquisition announcements on the yield spreads, a proxy for acquiring firm riskiness, of the acquirers. The literature suggests that cross-border acquisition announcements may either increase or decrease the acquirer’s riskiness. The acquirer’s riskiness could increase due to increased agency costs (Lee and Kwok, 1988) and exposure to additional risks in the international environment (Aliber, 1984; Reeb and Kwok, 1998). On the other hand, the acquirer’s riskiness could decrease due to potential diversification benefits gained through a cross-border acquisition (Reeb et al., 2001; Hughes et al., 1975). Moreover, the recent GFC may influence the changes in risk due to cross-border acquisitions. Furthermore, the foreign experience of the acquirer, economic development of the target country and strength of the target country’s legal system may influence the risk. The event study methodology as described in Section 3.2 is utilized to determine the abnormal announcement effect on the acquirer’s yield spread.

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22 TABLE 3

Panel A. Overall abnormal effects on yield spreads

Announcement effect on yield spreads Winsorized announcement effect on yield spreads

Full sample Winsorized full sample

Windows 26 weeks 13 weeks Windows 26 weeks 13 weeks

(-4,0) -0.008 -0.005 (-4,0) -0.001 0.002 (-0.608) (-0.385) (-0.053) (0.145) (0,+4) 0.009 0.012 (0,+4) 0.006 0.008 (0.688) (0.889) (0.420) (0.626) (-1,+1) 0.001 0.002 (-1,+1) -0.000 0.002 0.048 (0.203) (-0.035) (0.167) (-4,+4) 0.007 0.013 (-4,+4) 0.005 0.013 (0.433) (0.721) (0.280) (0.754)

Panel B. Abnormal effects on yield spreads of pre-crisis vs. post-crisis

CAR CAR CAR CAR

(-4,0) 0.011 0.004 (-4,0) -0.007 0.001 (0.606) (0.209) (-0.367) (0.054) (0,+4) 0.012 0.004 (0,+4) 0.003 0.011 (0.644) (0.183) (0.145) (0.611) (-1,+1) -0.007 -0.010 (-1,+1) 0.003 0.008 (-0.442) (-0.626) (0.184) (0.521) (-4,+4) 0.019 0.009 (-4,+4) -0.002 0.015 (0.811) (0.347) (-0.097) (0.669)

This table represents the overall announcement effect on acquirer yield spreads. The sample consists of 221 US based acquirers from the pre-crisis period (2004-2006) and post-crisis period (2011-2013). The abnormal changes are averaged across firms with equal weights. 26 weeks and 13 weeks indicate the utilized estimation window while (-4,0), (0,+4), (-1,+1), and (-4,+4) represent the event window. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively. The t-statistics are presented in parentheses.

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23 that there is no significant difference between pre-crisis and post-crisis concerning abnormal effects on yield spreads due to a cross-border acquisition announcement.

The results in Table 3 show that on average, the bond market does not react significantly to the announcement of a cross-border acquisition. This could imply that the bond market does not show any reaction at all when a cross-border acquisition is announced. However, since literature presents arguments for both a positive reaction and a negative reaction, which are not mutually exclusive, it could be that the net effect of positive factors and negative factors amount to zero. To examine whether this is accurate the thesis continues to further analyse observations which show either a positive or a negative reaction.

Table 4 contains the results related to separating the sample into two groups based upon the sign (positive or negative) of the abnormal changes in yield spreads. More specifically, Panel A presents these results for the full sample, Panel B depicts the results for the pre-crisis sample and Panel C displays the results for the post-crisis sample. The results for the full sample show that all the abnormal effects for both the positive and the negative group are statistically significant at the 1% level, regardless of estimation and event windows. Furthermore, the abnormal effects of both positive and negative group maintain their statistical significance when analysing the pre-crisis and post-crisis samples separately, indicating that the obtained results are robust. These results suggests that the announcement of a cross-border acquisition has an asymmetric effect on yield spreads.

TABLE 4

Panel A. Full sample: positive group vs. negative group

Positive group Negative group

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24

Panel B. Pre-crisis: positive group vs. negative group

(-4,0) 0.073*** 0.081*** (-4,0) -0.083** -0.100*** (3.603) (4.535) (-2.447) (-2.624) (0,+4) 0.093*** 0.102*** (0,+4) -0.071*** -0.097*** (3.715) (4.938) (2.673) (-2.977) (-1,+1) 0.047** 0.053*** (-1,+1) -0.059*** -0.059** (2.170) (2.731) (-2.849) (-2.516) (-4,+4) 0.120*** 0.142*** (-4,+4) -0.143*** -0.163*** (4.522) (5.885) (-3.222) (-3.444)

Panel C. Post-crisis: positive group vs. negative group

(-4,0) 0.102*** 0.121*** (-4,0) -0.131*** -0.141*** (4.554) (5.722) (-4.636) (-4.699) (0,+4) 0.135*** 0.157*** (0,+4) -0.127*** -0.143*** (5.927) (6.713) (-4.676) (-5.233) (-1,+1) 0.078*** 0.089*** (-1,+1) -0.086*** -0.081*** (4.091) (4.395) (-3.914) (-3.830) (-4,+4) 0.166*** 0.212*** (-4,+4) -0.229*** -0.223*** (6.018) (8.255) (-5.859) (-5.507)

This table represents the announcement effect on acquirer yield spreads based upon the sign (positive vs. negative). The sample consists of 221 US based acquirers. The abnormal changes are averaged across firms with equal weights. 26 weeks and 13 weeks indicate the utilized estimation window while (-4,0), (0,+4), (-1,+1), and (-4,+4) represent the event window. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively. The t-statistics are presented in parentheses.

Overall, the event study results show that no average abnormal effect on yield spreads from cross-border acquisitions can be identified. Moreover, the results present no evidence for the hypothesis that the reaction to cross-border acquisition differs prior and after the crisis. However, since both the positive group and negative group show significant abnormal effects it is expected that no average abnormal effect can be observed since the potential diversification benefits are offset by the risks and costs incurred inherent to a cross-border acquisition. Next, this study will employ multivariate analysis to examine potential factors which either stimulate a risk increase or decrease as a reaction to a cross-border acquisition.

4.3. Multivariate analysis

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25 Table 5 displays the full sample results of the multivariate regression analysis utilizing the cumulative average abnormal returns obtained using the 26 weeks estimation window as the dependent variable. The results are based upon the estimates from regression equation (9). The coefficients of the variables are displayed. Values in parentheses indicate the value of the t-statistic and asterisks represent the t-statistical significance at the 10%, 5% and 1% level. The CRISIS variable tests hypothesis 2 to examine whether a different reaction to cross-border acquisitions is observed before and after the GFC. However, the results show that while the coefficients are positive, these are insignificant. This finding is in line with the results from the event study, stating that the GFC has not changed the reaction of bondholders on the announcement of a cross-border acquisition. Therefore the null hypothesis, which states that the GFC does not lead to a different change in risk following a cross-border acquisition announcement, cannot be rejected.

TABLE 5

Regression results

Dependent variable CAR (-4,0) CAR (0,+4) CAR (-1,+1) CAR (-4,+4)

INTERCEPT -0.066 -0.106 -0.054 -0.095 (-1.088) (-1.490) (-1.285) (-0.931) CRISIS 0.000 0.009 0.021 0.017 (0.006) (0.314) (1.256) (0.406) FOREXP 0.109** 0.174*** 0.065** 0.223*** (2.144) (2.902) (1.815) (2.591) GDP 0.030* 0.016 0.010 0.063** (1.854) (0.836) (0.914) (2.295) GDPGROWTH 0.009 0.010 0.008** 0.022** (1.482) (1.323) (1.748) (2.070) LEGAL -0.007 -0.012** -0.005 -0.023*** (-1.432) (-1.981) (-1.465) (-2.646) TOBIN 0.011 -0.033 -0.020 -0.017 (0.603) (-1.505) (-1.508) (-0.548) Adj R 0.018 0.046 0.023 0.047 Obs 221 221 221 221

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26 Foreign experience shows a strong positive coefficient regardless of event window specification. Moreover, it is significant at the 5% level in the CAR (-4,0) and (-1,+1) models while expressing significance at the 1% level in the (0,+4) and (-4,+4) models. This allows for the null hypothesis to be rejected and to accept the alternative hypothesis, finding that when the acquirer has foreign experience, the increase in perceived risk is higher when the firm announces a cross-border acquisition. This interesting finding is in line with the argument by Mansi and Reeb (2002), which states that potential diversification benefits are more pronounced during initial internationalisation.

GDP and GDPGROWTH test hypothesis 4, which argues that the reaction to the announcement of a cross-border acquisition depends on the economic development of the target country. Interestingly, the coefficient of GDP is positive, indicating that when the economic development is higher, the perceived risk increase is more substantial. A possible explanation for this is that, since the US is an economically strong developed country, the diversification benefits of a cross-border acquisition in another developed country are less extensive than in economically less developed countries. The coefficient of GDPGROWTH is positive as well, indicating that when the economy of the target country is growing more rapidly, it is perceived as more risky by the bond market. Possibly, a higher economic growth can be perceived as being more uncertain and is therefore considered more risky. GDP shows significance in the CAR 4,0) and 4,+4) models while GDPGROWTH is significant in the CAR 1,+1) and (-4,+4) models. This offers partial evidence that economic development does seem to have an effect on the perceived risk. However, these results are not in line with the formulated hypothesis 3, stating that a higher economically developed target country leads to lower risk. The results show that the strength of legal protection is negatively related to risk since the coefficient of LEGAL is negative across the different event window specifications. This finding is consistent with the expectations of La Porta et al. (1997; 1998; 2000; 2002). Moreover, it is statistically significant in the CAR (0,+4) and (-4,+4) models and approaching significance in the CAR (-4,0) and (-1,+1) models. Therefore, concerning hypothesis 5, the null hypothesis is rejected and the alternative hypothesis is accepted, arguing that the strength of legal protection is negatively related to the change in risk.

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27 book value of the acquirer when announcing a cross-border acquisition is perceived as less risky by the bond market. However, the coefficients are insignificant across all models. Table 6 presents the results of the multivariate regression analysis on the pre-crisis and post-crisis sample. The results of the post-post-crisis sample are comparable to the results of the full sample. FOREXP is significant in the CAR (-1,+1), (0,+4) and (-4,+4) models and approaches significance in the (-4,0) model, providing further evidence that initial internationalisation offers more pronounced potential diversification benefits. Similar to the full sample results, GDP shows significance in the CAR (-4,0) and (-4,+4) models while GDPGROWTH is significant in all model specifications. This strongly confirms the findings that the level of economic development and economic growth in the target country are important factors influencing the relationship between cross-border acquisitions and firm risk. Furthermore, LEGAL is significant at various levels across all models, supporting the findings of the full sample multivariate regressions which show that the level of legal protection is negatively related to the change in firm risk.

However, the multivariate regression results of the pre-crisis sample do not show any significant coefficients, save the significance of the FOREXP coefficient at the 10% level in CAR (-4,0) model. These results can suggest that the FOREXP, GDP, GDPGROWTH and LEGAL do not significantly influence the relationship between cross-border acquisitions and firm risk prior to the crisis. Nevertheless, it seems more likely that the characteristics of the pre-crisis sample are different compared to the post-crisis sample due to a potential selection bias. A possible explanation could be a survivor bias since data could exclusively be collected on acquirers which are currently still active. Since these acquirers have survived the harsh economic conditions of the GFC, the characteristics of the firms and announced acquisitions directly prior to the GFC can be different to the characteristics of firms and announced acquisitions in the post-crisis sample.

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28 TABLE 6

Regression results

Dependent variable CAR (-4,0) CAR (0,+4) CAR (-1,+1) CAR (-4,+4) CAR (-4,0) CAR (0,+4) CAR (-1,+1) CAR (-4,+4)

INTERCEPT -0.143 0.045 -0.004 -0.104 -0.015 -0.116 -0.030 -0.016 (-1.367) (0.371) (-0.058) (-0.606) (1.527) (-1.384) (-0.599) (-0.137) CRISIS FOREXP 0.170* 0.058 0.006 0.211 0.091 0.195*** 0.075* 0.219** (1.755) (0.521) (0.090) (1.318) (1.527) (2.726) (1.764) (2.154) GDP -0.005 0.002 0.003 -0.009 0.054** 0.018 0.012 0.106*** (-0.217) (0.077) (0.179) (-0.257) (2.459) (0.696) (0.787) (2.840) GDPGROWTH -0.008 -0.009 -0.001 -0.015 0.022** 0.024** 0.015** 0.048*** (-0.892) -(0.887) (-0.088) (-1.018) (2.535) (2.319) (2.405) (3.302) LEGAL 0.003 -0.010 -0.000 -0.007 -0.014** -0.015* -0.009** -0.035*** (0.383) (-1.201) (-0.095) (-0.540) (-2.094) (-1.880) (-1.861) (-3.099) TOBIN -0.028 -0.014 -0.010 -0.022 0.024 -0.045 -0.027* -0.019 (-0.909) (-0.386) (-0.451) (-0.432) (1.063) (-1.640) (-1.656) (-0.476) Adj R -0.005 -0.028 -0.070 -0.025 0.052 0.093 0.066 0.100 Obs 73 73 73 73 148 148 148 148

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29

5. Conclusions

Cross-border acquisitions are important mechanisms for external growth and foreign direct investment (Hitt et al., 2001; Stiebale and Trax, 2011). One school of thought argues that cross-border acquisitions potentially reduce a firm’s risk through obtained diversification benefits. Another contends that these transactions are rather risk increasing through increased agency problems, bankruptcy costs and risks inherent to cross-border acquisitions. Empirical analysis on this subject is surprisingly limited although the number of these acquisitions and their value have rapidly increased throughout recent years. Moreover, while research has examined the effect of cross-border acquisitions on firm performance and its relation to shareholder wealth, it has neglected its impact on bondholders, an essential group of stakeholders for most firms. This study seeks to close this gap to some extent through examining the effects of cross-border acquisitions on the riskiness of the acquiring firms. Furthermore, the analysis presents evidence on the role of foreign experience and the relative importance of several target country characteristics. Finally, the study investigates the potential impact of the GFC on the relationship between cross-border acquisitions and acquirer risk.

Utilizing weekly data on yield spread changes this study finds no overall effect on the acquirer’s risk following the announcement of a cross-border acquisition, indicating that bondholders do not require higher nor lower compensation for perceived risk changes due to cross-border acquisitions. These results are similar regardless of estimation and event window specification. Interestingly however, the thesis does find that acquirer foreign experience influences the changes in yield spreads following cross-border acquisition announcements. Moreover, the results show that differences in the level of economic development and strength of legal protection between the countries are important in explaining yield spread changes. Overall, the results show that institutional factors and firm characteristics are important in determining risk changes following cross-border acquisition announcements.

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30 concerns the availability of target firm data. Due to data limitations target data was scarcely available. For example, Choi et al. (2009) find that, within the banking industry, target characteristics are important in understanding yield spread changes. The forced omission of this data has likely limited the explanatory capacity of this thesis. Moreover, due to data restrictions, observations incorporated exclusively acquirers which are still active, thereby creating a potential selection bias. Future research should focus on incorporating target data to gain a better comprehension of the factors influencing yield spread changes following the announcements of cross-border acquisitions. This study has demonstrated the importance of target country characteristics such as the difference in economic development and strength of legal protection. However, the influence of cultural distance on perceived risk changes should provide another interesting topic since literature does suggest a potential effect (Shimizu et al., 2004).

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Cross-border acquisitions, risk and the global financial crisis