Cross-border acquisitions in emerging and developed countries

Cross-border acquisitions in emerging and

developed countries

Written under the academic supervision of Shivesh Changoer Abstract

In this paper, I study the market reaction to cross-border acquisition announcements by acquirers, based on a sample of 305 acquisitions by European listed companies in the period 2013 to 2017. Special attention is given to the impact of targets located in an emerging or developed country on the market reaction. My results could suggest that cross-border acquisitions have a positive effect on the market reaction. In these cases where the target is located in an emerging country, I find a stronger positive market reaction than in cases where the target is located in a developed country given that the acquirer is located in a developed country. These findings might indicate, although statistically insignificant, that investors assume that cross-border mergers with an acquirer located in a developed country and a target located in an emerging country create more value than cross-border acquisitions in which both firms are located in a developed country.

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Statement of Originality

This document is written by Imme Pruijt who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in the 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.

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1. Introduction

Each year, trillions of US Dollars are spent on Mergers and Acquisitions (M&A), according to the Institute for Mergers, Acquisitions and Alliances (IMAA, 2017). M&A strongly increased in the period 1985 till 1999 from 347 billion to 4.1 trillion US Dollars. The number of deals and total value fluctuated from 1999 till 2017. In 2007, M&A reached an all-time high with a total transaction value of almost 5 trillion US Dollars. After a dip during the crisis, the M&A market is recovering from 2.2 trillion US Dollars in 2009 to 3.7 trillion US Dollars in 2017. In 2017, companies announced over 50,600 (IMAA, 2017).

Previous studies suggest that the rate of cross-border M&A is growing rapidly (Hitt, Harrison & Ireland, 2001). Industry consolidation, privatization and market liberalization are, among others, factors for the growth of cross-border acquisitions (Shimizu, Hitt, Vaidyanath & Pisano, 2004). Cross-border deals nowadays account for roughly one-third of the value of total M&A deals (OECD, 2017). As a result of this increase in cross-border M&A, researchers focus more on this type of M&A. These studies show a significant positive abnormal return for the target firm after the announcement of the acquisition, but there is little evidence for a significant abnormal return different from zero for the acquirer (Fuller, Netter & Stegemoller, 2002).

To extend the previous M&A literature, I study the effect of cross-border acquisitions in developed and emerging countries. Since there is not a real definition for emerging and developed countries, I will use the market classification framework from MSCI (2014). This framework defines markets as developed, emerging or frontier based on multiple requirements. If a market does not meet all the requirements that belong to developed, emerging or frontier markets it will not be classified as such. Consequently, underdeveloped countries will not be specified as developed, emerging or frontier markets. The requirements for a developed market are divided into economic development, size and liquidity, and market accessibility criteria. The economic development criteria are not used to determine the classification of emerging countries given the very wide variety of development levels in emerging countries.

To investigate the cross-border effect for developed and emerging countries I formulate the following research question: what is the effect of cross-border acquisitions between firms located in developed and emerging countries on the cumulative abnormal return? To answer this question I test the following hypothesis: the market reaction of an acquisition announcement is on average more positive for an acquisition in which the acquirer is located in a developed country and the target is located in an emerging country than for an acquisition in

4 To test my hypothesis, I examine the market reaction on the acquisition announcements by bidding firms. My analysis is based on a sample of 305 acquisitions performed by listed firms located in countries that are a member of the European Union over the period 2013-2017. In this sample, 187 acquisitions are specified as cross-border acquisitions. To conduct this research, I calculate the cumulative abnormal returns (CAR) for the acquisitions. The CARs are then used as the dependent variables in an OLS-regression.

I find a more positive market reaction for cross-border acquisitions with an acquirer located in a developed country and a target located in an emerging country than for cross-border acquisitions in which both firms are located in a developed country. These results are in line with my hypothesis. However, these results are statistically insignificant.

I contribute to the M&A literature in two ways. First, my thesis contributes to the research on M&A since most M&A studies do not differentiate between cross-border and domestic acquisitions. The few studies that do differentiate between cross-border and domestic acquisitions find evidence for a positive abnormal return for the target but do not find clear evidence that cross-border acquisitions create more value than domestic acquisitions (Fuller et al., 2002). Francis, Hasan & Sun (2007) extend this literature by also examining the financial market integration. Segmented financial markets are characteristic for emerging countries and integrated financial markets are characteristic for developed countries (Francis et al., 2007). Using a sample of US acquirers during late 1990s and early 2000s they find significant evidence for a cross-border effect. They find particular evidence for a significant higher abnormal return for those that acquire targets from segmented financial markets than for those that acquire from integrated financial markets. Second, I contribute by focussing on a more recent time period. Due to the increased share of cross-border acquisitions and the shrinkage of differences between emerging and developed countries it is possible that conclusions from previous literature are no longer valid for this period.

This paper consists of seven sections. The second section contains the literature review and background information about acquisitions in general, information about the market reaction to M&A announcements and information about domestic and cross-border acquisitions. In the third section, I motivate my hypothesis. The fourth section describes the method I use to test my hypothesis and the fifth describes the data selection and characteristics of the sample. The sixth section contains the results. The seventh section concludes the findings and the discussion.

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2. Literature review

2.1 M&A background information

Acquisitions are one of the important events in corporate finance, for both the firms involved and the economy around the firms (Fuller et al., 2002). A merger is a voluntary amalgamation of two firms into a new legal entity. The owners of both firms become owners of the new legal entity. An acquisition is a process in which an acquirer takes over a target whereby the target transfers the shares to the acquirer. The market for M&A is highly cyclical causing high M&A activity during economic expansion and periods with low M&A activity during times of economic contraction (Berk & Demarzo, 2007).

Acquisitions can be done for different purposes. One reason for acquisitions is to add economic value. This can be done by creating synergies. Synergies can be divided into operating and financial synergies (Devos, Kadapakkam & Krishnamurthy, 2008). Operating synergies arise from economies of scope and scale (Berk & Demarzo, 2007). Economies of scope apply to cost reduction by combining locations and departments of the acquirer and target. Economies of scale apply to cost reduction due to operating on a larger scale, which reduces the average fixed costs per product. Financial synergies arise from the increase of interest tax shields. Devos et al. (2008) find that financial synergies only compromise less than 17% of the total synergies and that synergies primarily arise from operating synergies.

Berk & Demarzo (2007) specify multiple other reasons for acquisitions aside from synergies. The first reason is expertise. Skilled workers can be hard to attract. Therefore acquiring a company can be a solution to bring in skilled workers. The second reason Berk & Demarzo mention is the possibility of monopoly gains, because taking over a company in the same industry reduces competition in that industry, which should increase profit. However, there is a lack of convincing evidence for monopoly gains (Berk & Demarzo, 2007). The third reason they mention for an acquisition is diversification. Two justifications for the benefits of diversification are risk reduction due to lower idiosyncratic risk, and larger debt capacity and lower borrowing costs. Berk & Demarzo (2007) mention that the idiosyncratic risk decreases since the combined firm is larger, but they argue that lower risk is not important for shareholders since they could easily achieve this themselves by diversifying their own portfolio. The second justification for diversification that Berk & Demarzo provide is that the debt capacity is larger and the borrowing costs are lower. They argue that more diversified firms have a lower chance of bankruptcy and can therefore enjoy increased leverage and tax savings.

6 example, do an acquisition to achieve personal objectives which are not in line with the maximization of shareholder value. If acquisitions serve these personal objectives of the manager, value-destroying acquisitions can be done (Morck, Shleifer & Vishny, 1990).

2.2 Market reaction towards M&A announcements

Researchers try to investigate how investors think about acquisitions. They do so by examining the market reaction around the day of the acquisition announcement. Researchers investigate the announcement effect and not the completion date of the acquisition. The reason is that the stock prices around the announcement date can give insight in changes due to new information about the acquisition while the stock prices around the completion date do not change that much since most of the information about the acquisition is already known at the announcement date. The objective of the researchers is to understand the market reaction immediately around the announcement date and the drivers behind this reaction.

Datta, Pinches & Narayan (1992), who performed a meta-analysis to analyze the market reaction after an acquisition announcement, find a positive market reaction for the target and a weak or insignificant market reaction for the acquirer. This is also supported by Fuller et al. (2002). In line with Datta et al. (1992) they find a significant positive market reaction for the target and do not find a significant market reaction for the acquirer.

Firm-specific factors of the acquirer and the target can be an influence on the market reaction. Datta et al. (1992) describe multiple factors that affect the wealth creation for acquirers and targets. The acquisition approach of the bidder can be influential for the market reaction. Deals that are directly negotiated with the target firm’s management create more value for the acquirer than tender offers in which the offer is made directly to the shareholders of the target firm. The announcement of a tender offer can attract other bidders, which causes increased competition and as a result higher acquisition premiums (Datta et al., 1992). This could affect the market reaction negatively. The mode of payment could also have an effect on the market reaction. Stock payments tend to increase the duration of the acquisition due to the needed approval of financial authorities. This results in higher transaction costs. Myers & Majluf (1984) further suggest that the issuance of stock is viewed negatively by the capital markets. Stock payments can therefore negatively impact the market reaction. Another factor that could affect the market reaction is the type of acquisition. Salter & Weinhold (1979) argue that possible synergies due to a transfer in specific skills between bidding and target firm in a related acquisition result in a more positive market reaction than conglomerate acquisitions.

7 Country-specific factors could also influence the market reaction of a cross-border acquisition announcement. Ahern, Daminelli & Fracassi (2012) find strong evidence that distance between trust, hierarchy and individualism, which are key dimensions of national culture, affect merger volume and synergy gains. They also find that the combined distance of trust and individualism has a negative impact on the market reaction. Salter (1998) suggests that the quality of corporate financial disclosure can have a positive relationship with the market reaction. A low quality of corporate financial disclosure increases risk, which can negatively influence the market

reaction.

Francis et al. (2008) examine the influence of financial market integration in cross-border deals on the market reaction for M&A announcements. Financial market integration is closely related with emerging and developed countries. Segmented financial markets are characteristic for emerging countries and integrated financial markets are characteristic for developed countries (Francis et al., 2007). They find that the market reaction is more positive in cases where the target is located in a country with a segmented financial market. These findings suggest that investors expect acquisitions between emerging and developed countries to create more value than acquisitions between two developed countries. Francis et al. (2007) argue that this is because when a firm in a developed country acquires a firm in an emerging country than the bidder firm is able to provide the target firm with capital at a lower cost than the target firm would get otherwise. This is possible due to the difference in financial market integration, which results in a relatively lower cost of capital for developed countries. This allows the target firm after the acquisition to invest in profitable projects which would not have been profitable with the higher cost of capital.

Factors that could have a negative impact on the market reaction are high volatility, shocks induced by regulatory changes, exchange rate devaluations and political crises since these factors are more likely to occur in emerging countries (Bekaert, Erb, Harvey & Viskanta, 1998). Furthermore, corporate financial disclosure is also lower in emerging countries than in developed countries (Salter, 1998). These factors result in higher risk, which could result in a lower cumulative abnormal return after an acquisition announcement if the acquisition price is not adjusted for these risks.

2.3 Cross-border acquisitions

Multiple arguments for domestic and cross-border acquisitions are the same. The main goal of the acquisitions is still value creation and synergies are still one of the most common reasons

8 for the value creation (Erel, Liao & Weisbach, 2012). Access to new markets to create value is strategically seen the most discussed and influential factor to perform cross-border M&A’s. Other factors are access to resources, opportunities for production efficiencies and reducing political risk (Datta & Puia, 1995). These factors give firms the opportunity to develop their products and services and reduce costs and risk. Datta & Puia (1995) argue that the access to new markets can eliminate barriers and higher tariffs. For example, the European Union (EU) has certain restrictions for companies outside the EU. These restrictions can be partly eliminated by acquiring a target within the EU. Furthermore, they argue that cross-border acquisitions can reduce market risk. International market diversification can stabilize overall firm returns because market return and economic conditions tend to be uncorrelated across different international market areas (Datta & Puia, 1995). However, cultural and geographical differences could increase the cost to combine the acquirer and target. Governance-related differences could increase the integration costs and imperfect integration of capital markets could lead to more risk due to changes in exchange rates but enables the targets also to obtain capital at a lower cost.

Results from previous studies are mixed. Moeller & Schlingemann (2005) investigate the effect of cross-border acquisitions on the abnormal return over the period 1984 to 1998. The results are insignificant, but they find a larger positive effect on the abnormal return in domestic acquisitions than for cross-border acquisitions. In line with Moeller & Schlingemann (2005), Conn, Cosh, Guest & Hughes (2005) find significant evidence for higher abnormal returns for domestic acquisitions than for cross-border acquisitions. Goergen & Rennboog (2004) find evidence for a significant positive effect on the abnormal return for cross-border acquisitions and an insignificant negative effect on the abnormal return for domestic acquisitions.

3. Hypothesis

In this paper, I investigate the effect of cross-border acquisitions, in which acquirer and target are located in an emerging or developed country, on the market reaction of the acquirer. Evidence for an acquirer’s abnormal return in domestic or cross-border acquisitions is mixed, still scarce for acquisitions in developed and emerging countries, and especially inconclusive about the combined effect of cross-border acquisitions and targets from developed and emerging countries (Fuller et al., 2002)

Globalization, liberalization, industry consolidation and privatization have led to an increase in cross-border acquisitions (Shimizu et al., 2004). Liberalization resulted in an

9 increasing amount of M&A activities in emerging countries (Errunza & Miller, 2000). The financial markets of emerging countries are often not fully integrated which results in market imperfections. These market imperfections can be diminished by the M&A mechanism. Acquirers located in a developed country can benefit by providing capital to targets in emerging countries at a lower cost than the target otherwise could get in the local financial market (Francis et al, 2007). This gives the target the opportunity to participate in projects that are profitable, but which would not have been profitable with the higher cost of capital. The opportunity to benefit from these market imperfections result in a higher abnormal return for the acquirer. On the other hand are high volatility, shocks induced by regulatory changes, exchange rate devaluations and political crises more likely to occur in emerging countries (Bekaert et al., 1998). These factors could negatively influence the market reaction.

Datta & Puia (1995) argue that access to new markets is the most influential factor to perform cross-border acquisitions. This can eliminate trade barriers and higher tariffs. Other factors why cross-border acquisitions are value enhancing are access to resources, opportunities for production efficiencies and the reduction of political and market risk. However, cultural distance, geographical distance and language barriers could be value destroying.

Taking all these factors into consideration I formulate the following hypothesis for this thesis:

Hypothesis: the market reaction of an acquisition announcement is on average more positive for an acquisition in which the acquirer is located in a developed country and the target is located in an emerging country than for an acquisition in which both firms are located in a developed country.

4. Methodology

4.1 Cumulative abnormal return

To test the hypotheses, I will estimate abnormal returns in the days immediately surrounding M&A announcements. I calculate the abnormal return on each day by subtracting the expected return from the realized return.

I calculate the expected return using the Market Model since it is the most widely-used methodology in event studies (Moeller & Zhu, 2016). This model can be expressed as follows: Model (1) 𝐸(𝑅)_𝑖𝑡 = 𝛼_𝑖 + 𝛽_𝑖 ∗ 𝑅_𝑚𝑘𝑡 + 𝜀_𝑖𝑡

10 E(𝑅)it = expected return of security i, on event date t

𝑅𝑚𝑘𝑡 = daily return of market, on event date t

𝛼𝑖 = intercept security i

𝛽𝑖 = parameter security i

𝜀𝑖𝑡 = residual

The intercept alpha and the parameter beta can be estimated by using an estimation window of the market return from the market that is the best representation of the acquirer according to DataStream. I use an estimation window of 110 trading days to 20 trading days before the announcement date. This is close to MacKinlay (1997) who stated that an estimation window could be estimated over 120 calendar days, which is around the 90 trading days used in this study. A list with the market indices I use for the market return is included in Appendix 1.

After estimating model (1), I calculate the abnormal returns as follows: 𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡 − (𝛼̂ + 𝛽𝑖 ̂ ∗ 𝑅𝑖 𝑚𝑘𝑡 )

All variables are the same as in model (1), 𝛼̂ and 𝛽_𝑖 ̂ are the estimated values for the _𝑖 values of 𝛼𝑖 and 𝛽𝑖 in model (1).

Then, I compute the cumulative abnormal return using the following formula:

𝐶𝐴𝑅𝑖 = 𝐴𝑅𝑖;𝑡1 + ⋯ + 𝐴𝑅𝑖;𝑡2 = ∑ 𝐴𝑅𝑖𝑡 𝑡2

𝑡=𝑡1

𝐴𝑅_𝑖𝑡 is defined as above and is defined relative to the announcement date. I use a different t1 and t2 since I use two event windows, namely a three-day event window of one day before till one day after the announcement [-1,1] and an eleven-day event window of five days before till five days after the announcement [-5,5].

4.2 OLS-regression

After computing the CAR, I follow Moeller & Schlingeman (2004) and estimate the following model:

11 𝐶𝐴𝑅_𝑖𝑡 = 𝛽₀+ 𝛽₁(𝐶𝑅𝑂𝑆𝑆) + 𝛽2(𝐷𝐸𝑉𝐸𝐿𝑂𝑃𝐸𝐷) + 𝛽3(𝐶𝑅𝑂𝑆𝑆 ∗ 𝐷𝐸𝑉𝐸𝐿𝑂𝑃𝐸𝐷)

+ 𝛽₄𝐿𝑁(𝐷𝑉𝐴𝐿𝑈𝐸) + 𝛽₅(𝐿𝐼𝑆𝑇𝐸𝐷) + 𝛽₆(𝐶𝐴𝑆𝐻) + 𝛽₇(𝑀𝐼𝑋𝐸𝐷) + 𝛽8(𝑅𝐸𝐿𝐴𝑇𝐸𝐷)

The dependent variable is the cumulative abnormal return (CAR), the research specific variables are a dummy variable for if the acquisition is cross-border or not (CROSS), a dummy variable for if the target is located in a developed country (DEVELOPED) and an interaction variable between CROSS and DEVELOPED. The first control variable is the natural logarithm of the deal value LN(DVALUE). The second control variable is a dummy for if the target is listed or not on a stock exchange (LISTED). Other control variables are a dummy variable for if the acquisition is fully paid in cash or not (CASH), a dummy variable for if the acquisition is partially paid in cash and partially in stock (MIXED) and a dummy variable for if the acquirers and targets industry is related (RELATED). The variable LN(DVALUE) is included because some studies (Chari, Ouimet & Tesar, 2010; Sudarsanam, Holl & Salami, 1996) find a positive relation between deal size and the market reaction to M&A announcements. A possible explanation for this finding is that smaller deal size is related to a smaller company which makes the integration process less complicated (Sudarsanam et al., 1996).

LISTED is a control variable because Faccio, McConnell & Stollin (2006) find that acquirers of listed targets earn a negative insignificant average abnormal return of 0.38 percent and that acquirers of unlisted targets earn a positive significant average abnormal return of 1.48 percent. They do not find fundamental significant factors why this is the case. I include CASH and MIXED for two reasons. The first reason is that Loughran & Vijh (1997) find a positive relation between cash payments and market reaction to M&A announcements. Datta et al. (1992) argue that cash payments increase the speed of the acquisition, and therefore decreases the transaction costs. The duration of the acquisition process with stock options is generally longer due to the need of approval of financial authorities. The second reason is that Rossi & Volpin (2004) suggest that stock bids are less popular in countries with low shareholder protection which is characteristic for emerging countries due to the low level of corporate governance and weak legal environment (Klapper & Love, 2002).

I include RELATED because previous research generally shows a negative relationship between related industry and abnormal return. This is in line with the popular belief that controlled diversity is associated with the highest performance (Lubatkin & Rogers, 1989).

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5. Sample selection

I collect the data of the acquisition deals via the online database Zephyr, which uses data from Bureau van Dijk, and DataStream. The sample exists of acquisitions done by firms located in the European Union over the period 2013-2017. I then filter out deals that are not interesting for this study:

- Acquirer is located in a country that is a member of the European Union - Time period: 01/01/2013 – 01/01/2018

- Deal type: acquisition

- Deal is completed confirmed or completed assumed - Minimum stake of 99%

- Acquirer is listed

- Deal value with a minimum of 50 million Euro

I only use acquisitions with a European based acquirer because Europe’s percentage of cross-border acquisitions is 53.2%, which is above the worldwide average of 43.3% and above the US average of 43.7% (Mergermarket, 2014). Data from the Institute for Mergers, Acquisitions and Alliances (IMAA) indicate that the number of deals and the deal volume in Europe started to recover from the financial crisis in 2012. Previous research indicates (Arguiar & Ginopath, 2005; Ravinchdran, 2009) that the financial crisis could influence the abnormal return after an acquisition announcement since the M&A market is highly cyclical. For this reason, I choose to study the period starting in 2013 to be sure that the effects of the crisis are excluded.

The minimum stake is set at 99% to exclude deals in which other shareholders could influence the return. The acquirer must be listed to determine the return. Following Lowinski, Schiereck and Thomas (2004) the deal value must have a minimum of 50 million Euro since small deals could have none or just a little influence on the stock price.

These restrictions resulted in an initial sample of 519 firms. Cleaning for missing data, including missing deal value, announcement date or information about deal completion, resulted in a sample of 323 deals.

Hereafter, I filter the data through the removal of deals where the acquirer or target is located in neither an emerging or developed country. I do this by checking for each firm of my sample if they are located in a developed or emerging country. To do so, I use the market classification from Morgan Stanley Capital International Inc. (MSCI). There are multiple indices which measure and classify countries as underdeveloped, emerging or developed

13 countries. MSCI is one of the leading providers of equity indices and offers the most widely used international equity benchmarks by international investors (Jung, Chae, Yang & Moon, 2006). Therefore I use the classifications from MSCI for the emerging and developed countries. The MSCI World Index includes 24 developed countries and the MSCI Emerging Markets Index includes 25 emerging markets. I exclude 12 deals in which the acquirer or target are neither located in an emerging nor developed market. Next, I obtain stock prices and market index prices for my sample from DataStream. Cleaning for missing data resulted in a final sample of 305 deals.

Table 1 shows the distribution of the acquisitions included in the dataset. The most deals are in or between developed countries (95.4%). Deals that include emerging countries only account for 4.6% of the total sample.

Table 2 shows the distribution for cross-border acquisitions. Deals between firms located in two developed countries account for 94,1% and deals between a firm located in a developed country and a firm located in an emerging country account for 5,9% of the total cross-border sample. Using the above-described restrictions for the sample, I do not find any deals in which a firm located in an emerging country performed a cross-border acquisition.

Table 1 Distribution of M&A deals

Country of target firm

(n=305) Developed Emerging

Country of acquired firm

Developed 291 (95.4%) 11 (3.6%)

Emerging 0 (0.0%) 3 (1.0%)

Table 2 Distribution of cross-border M&A deals

Country of target firm

(n=187) Developed Emerging

Country of acquired firm

Developed 176 (94.1%) 11 (5.9%)

14 Table 3 shows an overview of the countries included in the final sample and if the country is classified as developed or classified as emerging by NSCI indices mentioned before. It furthermore shows the distribution between domestic and cross-border deals per country. Most targets are located in the United States and the United Kingdom with a joint stake of 52.8% of the total sample. This is not surprising since the United States are Europe’s largest target based on data from Thomson Reuters (Financial Times, 2017) and since the UK has the largest M&A market in Europe.

Table 3 Sample description

Total target country

(n=305) Cross-border (n=187) n % n % Emerging Poland 4 1.3 1 0.5 Mexico 2 0.7 2 1.1 Russia 2 0.3 2 1.1 United Arab Emirates 2 0.3 2 1.1 Brazil 1 0.7 1 0.5 China 1 0.3 1 0.5 Czech Republic 1 0.7 1 0.5 Chile 1 0.3 1 0.5 Developed United States 83 27.2 83 44.4 United Kingdom 78 25.6 11 5.9 Netherlands 16 5.2 15 8.0 France 16 5.2 4 2.1 Italy 13 4.3 7 3.7 Germany 13 4.3 7 3.7 Spain 12 3.9 5 2.7 Sweden 9 3.0 3 1.6 Finland 9 3.0 6 3.2 Norway 8 2.6 8 4.3 Ireland 6 2.0 3 1.6 Belgium 6 2.0 4 2.1 Switzerland 5 1.6 5 2.7 Austria 5 1.6 5 2.7

15 Canada 5 1.6 5 2.7 Denmark 4 1.3 2 1.1 Luxembourg 2 0.7 2 1.1 Australia 1 0.3 1 0.5 Sample description

Table 4 presents the sample statistics for the sample including 305 acquisitions. CAR[ -1,1] and CAR[-5,5] are both statistically significant from zero at a 1% significance level, but they are not significantly different from each other (t(305)=-0.37, p>.05). The minimum values in combination with the standard errors indicate slightly negative skewness. The high minimum and maximum values also indicate outliers, which will be dropped before the regressions using the winsorizing function in Stata. The variable CROSS has a mean of .613 which means that 61.3% of the acquisitions are cross-border. The same goes for DEVELOPED which means that 95.4% of the deals are between developed countries. This is high, but apparently, there are not that many deals involving emerging countries when the acquirer is located in Europe. LN(DVALUE) with a mean of 12.43 and a standard deviation of 1.277 indicates that the maximum value of 16.61 could be an outlier. This problem will be solved by winsorizing the data of LN(DVALUE) on a 1% and 99% level.

Table 4 Sample statistics

(1) (2) (3) (4) (5)

Variables N mean sd min max

CAR [-5,5] 305 -0.021 0.089 -0.667 0.486 CAR [-1,1] 305 -0.018 0.064 -0.390 0.222 CROSS 305 0.613 0.488 0 1 DEVELOPED 305 0.954 0.210 0 1 CROSS*DEVELOPED 305 0.577 0.495 0 1 LN(DVALUE) 305 12.43 1.277 10.83 16.61 LISTED 305 0.138 0.345 0 1 MIXED 305 0.374 0.485 0 1 CASH 305 0.449 0.498 0 1 RELATED 305 0.610 0.489 0 1 6. Results 6.1 CARs analysis

16 The CARs for different event windows are displayed in Table 5. I analyzed CARs of the 305 acquisitions in my sample. For the three-day event window, I find a negative return of 1.8%. This result is significant at a 1% significance level. The abnormal returns for the five-day and eleven-day event window are respectively -2.0% and -2.1%. Both are significant at a 1% level. The means between the three-day event window and the other two are significantly different with (t(305)=6.202, p<.01) for the fives day event window and (t(305)=6.535, p<.01) for the eleven-day event window. The five-day and eleven-day event windows are not statistically significantly different from each other (t(305)=-0.898 p>.05). I will therefore do a regression analysis for the three-day event window as well as for the eleven-day event window since the difference between these CARs is the largest.

These results are in line with previous studies. Mulherin & Boone (2000) find a small insignificant negative abnormal return for bidders. This is also consistent with results from other studies who use data from earlier time periods (Jensen & Ruback (2001), Jarrel, Brickley & Netter (1988), Bradley, Desai & Kim (1988)).

Table 5 Cumulative abnormal return

Event window Coef. Robust std. Error t-test p>[t] 95% conf interval CAR[-1,1] -0.018 0.004 -5.01 0.000*** -0.025 -0.011 CAR[-2,2] -0.020 0.004 -4.73 0.000*** -0.029 -0.012 CAR[-5,5] -0.021 0.005 -4.06 0.000*** -0.031 -0.011

***, **, * Statistical significance level of 1, 5 and 10% respectively

6.2 OLS regression

The CARs for the three-day [-1,1] and eleven-day [-5,5] event windows are used as the dependent variable in the OLS regression. The research variables are CROSS, DEVELOPED and the interaction variable CROSS*DEVELOPED. The control variables are LN(DVALUE), LISTED, CASH, MIXED and RELATED.

Table 6 displays the correlation matrix for dependent and independent variables. The bold text indicates that the correlation is significant. To prevent multicollinearity, I also perform a regression with and without the variable MIXED. The correlation between MIXED and CASH is high. Since there is no universal guideline for which value multicollinearity is assumed I will check if dropping MIXED has an impact on the regression.

17 CA R [-1, 1] CA R [-2, 2] CA R [-5, 5] CROSS DE V EL OP ED CROSS*DEV ELOP ED LN (DV A LU E) LIS T ED MI XE D CA SH RE LA T ED CA R [-1, 1] 1. 000 0 CA R [-2, 2] 0. 798 2 1. 000 0 CA R [-5, 5] 0. 758 1 0. 828 5 1. 000 0 CROSS 0. 032 5 -0, 012 5 -0. 003 9 1. 000 0 DE V EL OP ED -0. 012 8 -0. 015 5 -0. 052 4 -0. 077 7 1. 000 0 CROSS*DEV ELOP ED 0. 012 0 -0. 030 7 -0. 034 1 0. 927 9 0. 256 2 1. 000 0 LN (DV A LU E) 0. 063 7 0. 065 2 0. 032 6 0. 097 5 0. 075 6 0. 124 8 1. 000 0 LIS T ED 0. 164 8 0. 140 7 0. 086 3 -0. 014 7 0. 087 7 0. 014 7 0. 410 5 1. 000 0 MI XE D -0. 000 9 -0. 023 7 -0. 004 2 -0. 109 9 0. 039 9 -0. 093 1 0. 155 6 0. 241 9 1. 000 0 CA SH 0. 053 3 0. 099 2 0. 066 4 0. 135 4 0. 009 1 0. 119 3 -0. 140 1 -0. 112 2 -0. 697 7 1. 000 0 RE LA T ED -0. 028 2 -0. 028 1 0. 002 4 -0. 014 3 -0. 047 0 -0. 045 3 -0. 016 7 0. 105 1 0. 048 3 0. 006 1 1. 000 0 T ab le 6 corr el ati on ma tri x for dep end ent and i nde pen den t vari abl es Bo ld tex t indi cate s tha t corr el ati ons are stat isti cal ly si gni fi cant at a 5% si gni fi canc e level

18 Table 7 displays the Variance Inflation Factor (VIF). The VIF value indicates whether there is multicollinearity, which could be the case if the VIF value is larger than 10. The high values for CROSS and CROSS*DEVELOPED can be ignored since this is due to the interaction term. Most of the cross-border deals in my sample are between developed countries. Therefore, both the VIF value and the correlation between CROSS and the interaction term in Table 6 are high, which is not a problem using an interaction term. I also perform an inter-quartile range test to be sure that the outliers are removed after winsorizing the CARs. Furthermore, I use a Ramsey RESET Test to check if there is an omitted variable bias, which is not the case.

Table 7 Variance Inflation Factor

Variable VIF 1/VIF

CROSS*DEVELOPED 33.34 0.029991 CROSS 31.39 0.031853 DEVELOPED 4.66 0.214690 MIXED 2.08 0.480603 CASH 2.02 0.493928 LISTED 1.29 0.776295 LN(DVALUE) 1.25 0.802463 RELATED 1.03 0.971631

The results of the OLS-regression for the three-day event window are displayed in Table 8 and the results for the eleven-day event window are displayed in Table 9. Results do not show any significant outcomes, except for the control variable LISTED.

The results from the regressions with the three-day event window are displayed in Table 8. I did the first regression to show the effect of an interaction variable used in the second, third and fourth regression. The results for CROSS and DEVELOPED change when using an interaction variable. The coefficient for CROSS is insignificant but slightly positive and the coefficient for DEVELOPED is insignificant but slightly negative. A cross-border acquisition between two firms located in developed countries has a combined insignificant negative effect on the abnormal return.

The second regression involves the research variables, the interaction variable and all control variables. Results indicate a small insignificant positive cross-border effect. This is in line with Moeller & Schlingemann (2008) who also find an insignificant positive cross-border effect. The variable DEVELOPED also indicates an insignificant but small positive effect. Francis et al. (2007) also find a positive effect. The interaction term between CROSS and DEVELOPED is negative but insignificant. A cross-border acquisition between two firms located in developed countries has now a combined insignificant positive effect on the abnormal

19 return since the combined positive coefficients for CROSS and DEVELOPED are larger than the interaction variable. This outcome is different than in regression 1, which indicates the influence of the interaction variable.

I removed MIXED from the third and fourth regression since the correlation with CASH is high. CASH becomes more significant without MIXED. The R-squared remains the same. I also remove LN(DVALUE) in the fourth regression due to the small influence as a control variable. Removing LN(DVALUE) does not significantly change the other independent variables, but it decreases the R-squared. Therefore, I will use regression three to test my hypothesis.

Table 8 Regression results

Dependent: CAR [-1,1] (1) (2) (3) (4) CONSTANT -0.003 -0.027 -0.028 -0.044 (0.044) (0.051) (0.050) (0.040) CROSS 0.003 0.046 0.045 0.045 (0.008) (0.043) (0.043) (0.042) DEVELOPED -0.009 0.026 0.025 0.025 (0.019) (0.041) (0.040) (0.040) CROSS*DEVELOPED - -0.047 -0.046 -0.047 - (0.044) (0.043) (0.043) LN(DVALUE) -0.000 0.001 0.001 - (0.003) (0.002) (0.002) - LISTED .033*** 0.025*** 0.024*** 0.022*** (0.012) (0.009) (0.009) (0.009) CASH .011 0.003 0.005 0.005 (0.012) (0.008) (0.006) (0.006) MIXED .003 -0.003 - - (0.013) (0.009) - - RELATED -.006 -0.007 -0.007 -0.007 (0.007) (0.006) (0.006) (0.006) n 305 305 305 305 R-squared 0.036 0.049 0.049 0.035 Robust standard errors are in parenthesis

***, **, * Statistical significance level of 1, 5 and 10% respectively

Table 9 shows the results of the regression with the eleven-day event window. In the first regression, the variables CROSS and DEVELOPED are both insignificant and slightly negative. The explanatory variables do not significantly change with regard to the regression for the three-day event window. However, the control variable LISTED is now only significant for regression three and four with a 10% significance level.

20 Table 9 Regression results

Dependent: CAR [-5,5] (1) (2) (3) (4) CONSTANT -0.017 -0.070 -0.067 -0.056 (0.055) (0.070) (0.070) (0.051) CROSS -0.003 0.065 0.067 0.067 (0.011) (0.052) (0.053) (0.053) DEVELOPED -0.028 0.027 0.030 0.030 (0.020) (0.050) (0.051) (0.051) CROSS*DEVELOPED - -0.070 -0.073 -0.073 - (0.053) (0.055) (0.054) LN(DVALUE) 0.001 0.001 0.001 - (0.004) (0.004) (0.004) - LISTED 0.023 0.023 0.025* 0.026* (0.015) (0.015) (0.015) (0.014) CASH 0.022 0.020 0.014 0.014 (0.018) (0.018) (0.011) (0.011) MIXED 0.109 0.010 - - (0.018) (0.018) - - RELATED - 0.002 -0.003 -0.003 -0.003 (0.010) (0.010) (0.010) (0.010) n 305 305 305 305 R-squared 0.019 0.024 0.023 0.023 Robust standard errors are in parenthesis

***, **, * Statistical significance level of 1, 5 and 10% respectively

6.2 Hypothesis testing

My hypothesis is that the market reaction of an acquisition announcement is on average more positive for an acquisition in which the acquirer is located in a developed country and the target is located in an emerging country than for an acquisition in which both firms are located in a developed country.

To test this hypothesis, I will first look at the results of regression 3 with the three-day event window [-1,1] displayed in Table 8. These results display a positive coefficient for CROSS of 0.045 and a positive coefficient for DEVELOPED of 0.025. The interaction variable CROSS*DEVELOPED has a negative coefficient of -0.046. This indicates that a cross-border acquisition with both firms located in a developed country has a combined positive effect of 0.024 on the cumulative abnormal return of the acquirer. A cross-border acquisition with an acquirer located in a developed country and a target located in an emerging country has a combined positive effect of 0.045 on the cumulative abnormal return of the acquirer.

These results imply that an acquisition announcement has on average a stronger positive effect on the market reaction for an acquisition in which the acquirer is located in a developed

21 country and that the target is located in an emerging country than for an acquisition in which both firms are located in a developed country. These results indicate that the hypothesis is correct, but since all my results are insignificant I cannot confirm my hypothesis. The results of the regression with the eleven-day event window are slightly different, but imply the same effect on the CAR and are also insignificant.

7. Conclusion and discussion

In this study, I investigate the market reaction to acquisition announcements for cross-border deals with both acquirer and target from either emerging or developed countries using a sample of EU acquirers over the period 2013 to 2017. The announcement effect of cross-border acquisitions has been studied intensively. Most studies find a positive market reaction for the target and a weak or insignificant market reaction for the acquirer (Fuller et al., 2002). However, the number of studies that focus only on emerging and developed countries is scarce. M&A is relatively new for emerging countries because restrictions made it hard for companies in these countries to participate in the international market. Due to globalization and the liberalization of financial markets in emerging countries, companies can now seek growth possibilities internationally.

The research question of this study is: what is the effect of cross-border acquisitions between firms located in developed and emerging countries on the cumulative abnormal return? The hypothesis of this study is:

Hypothesis: the market reaction of an acquisition announcement is on average more positive for an acquisition in which the acquirer is located in a developed country and the target is located in an emerging country than for an acquisition in which both firms are located in a developed country.

To test my hypothesis, I examined the market reaction to the acquisition announcements of bidding firms. Using an event study, I found on average a significant negative market reaction of 1.8% for the three-day event window [-1,1] and a significant negative market reaction of 2.1% for the eleven-day event window [-5,5]. The results of the OLS-regressions imply a more positive market reaction for cross-border acquisitions with an acquirer located in a developed country and a target located in an emerging country than for cross-border acquisitions with both firms located in a developed country. This is in line with my hypothesis, but since the results are insignificant the hypothesis cannot be confirmed.

22 The results of the two research variables separately are in line with previous research. The small positive cross-border effect is in line with Moeller & Schlingemann (2008) who also find an insignificant positive cross-border effect. A more positive effect on the abnormal return for acquisitions between a developed and emerging country is in line with Francis et al. (2007).

The findings contribute to the M&A literature for two reasons. First, this study contributes by examining the difference between the market reaction for the acquirer of an acquisition announcement between developed and emerged countries. Second, this study examines a recent period, which is interesting due to the changes in the M&A landscape with regard to cross-border acquisitions and the rapid growth of acquisitions involving emerging countries.

The main limitation of this study is the low share of deals that include emerging targets. The global M&A market is still dominated by firms located in developed countries. Further limitations, due to the limited time that is available for a bachelor thesis, are the relatively small sample and that I did not use other models to perform an event study to check if the findings are the same.

I have several recommendations for future research. First, it would be interesting to use a different sample in which the share of emerging targets is larger. Since the sample selection is limited to the EU, it is possible to solve this problem by using another geographic restriction. Second, replication of this study using another time period could be interesting when the M&A landscape further changes with regard to emerging countries, since the share of emerging countries is increasing fast. Financial markets in emerging countries could also become more integrated over time, which can affect the results. My last recommendation is to perform a study that focuses on the abnormal return of the target instead of the acquirer for cross-border acquisitions in emerging and developed countries. There are, to my knowledge, no studies which investigate the abnormal return of the target when focussing on cross-border acquisitions between developed and emerging countries.

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9. Appendix

Appendix 1: acquirers by country and used market index for the CARs calculation

Company Country Market index Company Country Market index

ABERTIS INFRAESTRUCTURAS SA ES IBEX35I AMADEUS IT HOLDING SA ES IBEX35I

ACADEMEDIA AB SE OMXAFGX AMDOCS LTD GB S&PCOMP

ACAL PLC GB FTALLSH AMINO TECHNOLOGIES PLC GB FTALLSH

ACCENTURE PLC IE S&PCOMP AMUNDI SA FR FSBF120

ACTAVIS LTD IE S&PCOMP ARKEMA SA FR FSBF120

ADIDAS AG DE DAXINDX ARRIS INTERNATIONAL LTD GB S&PCOMP

AIR LIQUIDE SA FR FSBF120 ARROW GLOBAL GROUP PLC GB FTALLSH

ALFA LAVAL AB SE OMXAFGX ASR NEDERLAND NV NL AMSTEOE

ALIMAK GROUP AB SE OMXAFGX ATLASSIAN CORPORATION PLC GB S&PCOMP

ALLEGION PLC IE S&PCOMP ATOS SE FR FSBF120

ALLIANCE PHARMA PLC GB FTALLSH ATTENDO AB SE OMXAFGX

25

Company Country Market index Company Country Market index

AXA SA FR FSBF120

DEUTSCHE ANNINGTON

IMMOBILIEN SE DE DAXINDX

BABCOCK INTERNATIONAL GROUP PLC GB FTALLSH DEUTSCHE POST AG DE DAXINDX

BAE SYSTEMS PLC GB FTALLSH DIASORIN SPA IT FITASHE

BANCA SISTEMA SPA IT FITASHE DIGNITY PLC GB FTALLSH

BANCO DE SABADELL SA ES IBEX35I DIRECT ENERGIE SA FR FSBF120

BANKIA SA ES IBEX35I DOM DEVELOPMENT SA PL POLWG20

BCA MARKETPLACE PLC GB FTALLSH DOMETIC GROUP AB SE OMXAFGX

BEKAERT SA/NV BE BGBEL20 DRAX GROUP PLC GB FTALLSH

BENCHMARK HOLDINGS PLC GB FTALLSH ECO CITY VEHICLES PLC GB FTALLSH

BPOST NV BE BGBEL20 ELECTRICITE DE FRANCE SA FR FSBF120

BREEDON AGGREGATES LTD GB FTALLSH ELEMENTIS PLC GB FTALLSH

BRITISH SKY BROADCASTING GROUP

PLC GB FTALLSH ELIS SA FR FSBF120

BRITVIC PLC GB FTALLSH ELISA OYJ FI HEXINDX

BT GROUP PLC GB FTALLSH ENDO INTERNATIONAL PLC IE S&PCOMP

BTG PLC GB FTALLSH ESPRINET SPA IT FITASHE

CALEDONIA INVESTMENTS PLC GB FTALLSH

EUROMONEY INSTITUTIONAL

INVESTOR PLC GB FTALLSH

CAMBIAN GROUP PLC GB FTALLSH EURONEXT NV NL FSBF120

CAPITA PLC GB FTALLSH EUTELSAT COMMUNICATIONS SA FR FSBF120

CAPITAL STAGE AG DE DAXINDX FONCIERE DES MURS SA FR FSBF120

CARILLION PLC GB FTALLSH GAS NATURAL SDG SA ES IBEX35I

CARLSBERG A/S DK COSEBMI GEMALTO NV NL AMSTEOE

CARREFOUR SA FR FSBF120 GENERALE DE SANTE SA FR FSBF120

CASTELLUM AB SE OMXAFGX GERRESHEIMER AG DE DAXINDX

CELLNEX TELECOM SA ES IBEX35I

GERRY WEBER INTERNATIONAL

AG DE DAXINDX

CELSUS THERAPEUTICS PLC GB S&PCOMP GKN PLC GB FTALLSH

CENTRICA PLC GB FTALLSH GLANBIA PLC IE ISEQUIT

CHESNARA PLC GB FTALLSH GLAXOSMITHKLINE PLC GB FTALLSH

CHR HANSEN HOLDING A/S DK COSEBMI GLOBAL DOMINION ACCESS SA ES IBEX35I

CINEWORLD GROUP PLC GB FTALLSH GRAFTON GROUP PLC IE FTALLSH

CITYCON OYJ FI HEXINDX GREENCOAT UK WIND PLC GB FTALLSH

CLINIGEN GROUP PLC GB FTALLSH GRIFOLS SA ES IBEX35I

CLX COMMUNICATIONS AB SE OMXAFGX GSG GROUP LU DAXINDX

COBHAM PLC GB FTALLSH HALMA PLC GB FTALLSH

COLOPLAST A/S DK COSEBMI HANGAR 8 PLC GB FTALLSH

COMPAGNIE IMMOBILIERE DE

BELGIQUE SA BE BGBEL20 HENKEL AG & CO. KGAA DE DAXINDX

COMPAGNIE INDUSTRIELLE SA FR FSBF120 HEXAGON AB SE OMXAFGX

CONSORT MEDICAL PLC GB FTALLSH HEXPOL AB SE OMXAFGX

CONSTELLIUM NV NL S&PCOMP

HISPANIA ACTIVOS

INMOBILIARIOS SOCIMI SA ES IBEX35I

CONTINENTAL AG DE DAXINDX HOCHSCHILD MINING PLC GB FTALLSH

COSTAIN GROUP PLC GB FTALLSH HOMESERVE PLC GB FTALLSH

CRH PLC IE FTALLSH HORIZON PHARMA PLC IE S&PCOMP

CRODA INTERNATIONAL PLC GB FTALLSH ICA GRUPPEN AB SE OMXAFGX

DALATA HOTEL GROUP PLC IE ISEQUIT ICON PLC IE S&PCOMP

DANSKE BANK A/S DK COSEBMI ID LOGISTICS SAS FR FSBF120

DAVIDE CAMPARI-MILANO SPA IT FITASHE IMI PLC GB FTALLSH

26

Company Country Market index Company Country Market index

DELPHI AUTOMOTIVE PLC GB S&PCOMP INDRA SISTEMAS SA ES IBEX35I

INFINEON TECHNOLOGIES AG DE DAXINDX MERLIN PROPERTIES SOCIMI SA ES IBEX35I

INGERSOLL RAND PLC IE S&PCOMP MYLAN NV NL S&PCOMP

INMOBILIARIA COLONIAL SA ES IBEX35I NEW STERIS LTD GB S&PCOMP

INTERCONTINENTAL HOTELS GROUP

PLC GB FTALLSH NIBE INDUSTRIER AB SE OMXAFGX

INTERPUMP GROUP SPA IT FITASHE NMC HEALTH PLC GB FTALLSH

INTERSERVE PLC GB FTALLSH NOKIA OYJ FI HEXINDX

INTERTEK GROUP PLC GB FTALLSH NON-STANDARD FINANCE PLC GB FTALLSH

INTERTRUST NV NL AMSTEOE NORCROS PLC GB FTALLSH

INTRUM JUSTITIA AB SE OMXAFGX OPERA INVESTMENTS PLC GB FTALLSH

IQE PLC GB FTALLSH PENNON GROUP PLC GB FTALLSH

ITALGAS SPA IT FITASHE PENTAIR PLC IE S&PCOMP

JACQUET METAL SERVICE SA FR FSBF120 PERRIGO COMPANY PLC IE S&PCOMP

JAMES HARDIE INDUSTRIES PLC IE ASXAORD PEUGEOT SA FR FSBF120

JARDINE LLOYD THOMPSON GROUP

PLC GB FTALLSH

PGE POLSKA GRUPA

ENERGETYCZNA SA PL POLWG20

JOHN WOOD GROUP PLC GB FTALLSH PLASTIC OMNIUM SA FR FSBF120

JOHNSON MATTHEY PLC GB FTALLSH POLISH ENERGY PARTNERS SA PL POLWG20

JOHNSON SERVICE GROUP PLC GB FTALLSH POLYPIPE GROUP PLC GB FTALLSH

JUST EAT PLC GB FTALLSH PREMIER OIL PLC GB FTALLSH

JYSKE BANK A/S DK COSEBMI PROVIDENT FINANCIAL PLC GB FTALLSH

KARO PHARMA AB SE OMXAFGX PUBLICIS GROUPE SA FR FSBF120

KEMIRA OYJ FI HEXINDX QIAGEN NV NL DAXINDX

KESKO OYJ FI HEXINDX QINETIQ GROUP PLC GB FTALLSH

KIER GROUP PLC GB FTALLSH QIWI PLC CY S&PCOMP

KINEPOLIS GROUP NV BE BGBEL20 RANDSTAD HOLDING NV NL AMSTEOE

KINGSPAN GROUP PLC IE ISEQUIT REDCENTRIC PLC GB FTALLSH

KLEPIERRE SA FR FSBF120 RENTOKIL INITIAL PLC GB FTALLSH

KONECRANES OYJ FI HEXINDX RESTORE PLC GB FTALLSH

KONINKLIJKE AHOLD NV NL AMSTEOE RM PLC GB FTALLSH

KONINKLIJKE PHILIPS NV NL AMSTEOE ROYAL UNIBREW A/S DK COSEBMI

LAIRD PLC GB FTALLSH RWS HOLDINGS PLC GB FTALLSH

LASSILA & TIKANOJA OYJ FI HEXINDX SAFESTORE HOLDINGS PLC GB FTALLSH

LLOYDS BANKING GROUP PLC GB FTALLSH SAFRAN SA FR FSBF120

LONDON STOCK EXCHANGE GROUP

PLC GB FTALLSH SAGE GROUP PLC, THE GB FTALLSH

LOOKERS PLC GB FTALLSH SALINI IMPREGILO SPA IT FITASHE

LOOMIS AB SE OMXAFGX SAMSONITE INTERNATIONAL SA LU HNGKNGI

MAINTEL HOLDINGS PLC GB FTALLSH SANNE GROUP PLC GB FTALLSH

MAPFRE SA ES IBEX35I SARTORIUS AG DE DAXINDX

MARSHALL MOTOR HOLDINGS PLC GB FTALLSH SCANDIC HOTELS GROUP AB SE OMXAFGX

MASSOLIT MEDIA AB SE OMXAFGX SCHNEIDER ELECTRIC SE FR FSBF120

MATCHTECH GROUP PLC GB FTALLSH SEAGATE TECHNOLOGY PLC IE S&PCOMP

MEDEA SA FR FSBF120 SHIRE PLC GB FTALLSH

MEDIAWAN SA FR FSBF120 SIEMENS AG DE DAXINDX

MEDTRONIC PLC IE S&PCOMP SIGMAROC PLC GB FTALLSH

MEGGITT PLC GB FTALLSH SMURFIT KAPPA GROUP PLC IE ISEQUIT

27

Company Country Market index Company Country Market index

SOLVAY SA BE BGBEL20

TERNA - RETE ELETTRICA

NAZIONALE SPA IT FITASHE

SPECTRIS PLC GB FTALLSH TERNIUM SA LU S&PCOMP

SSAB AB SE OMXAFGX TESCO PLC GB FTALLSH

STADA ARZNEIMITTEL AG DE DAXINDX THALES SA FR FSBF120

STAFFLINE GROUP PLC GB FTALLSH TIETO OYJ FI HEXINDX

STANLEY GIBBONS GROUP PLC GB FTALLSH TOTAL SA FR FSBF120

STILLFRONT GROUP AB SE OMXAFGX TT ELECTRONICS PLC GB FTALLSH

STROER MEDIA SE DE DAXINDX TYCO INTERNATIONAL LTD IE S&PCOMP

SVENSKA CELLULOSA AB SE OMXAFGX TYMAN PLC GB FTALLSH

SWECO AB SE OMXAFGX UBM PLC GB FTALLSH

SYDBANK A/S DK COSEBMI UDG HEALTHCARE PLC IE FTALLSH

SYMRISE AG DE DAXINDX VALMET OYJ FI HEXINDX

SYNTHOMER PLC GB FTALLSH VESTAS WIND SYSTEMS A/S DK COSEBMI

TECHNICOLOR SA FR FSBF120 VIANINI INDUSTRIA SPA IT FITASHE

TELE COLUMBUS AG DE DAXINDX VICTORIA PLC GB FTALLSH

TELE2 AB SE OMXAFGX VINCI SA FR FSBF120

TELECOM PLUS PLC GB FTALLSH VIVENDI SA FR FSBF120

TELENET GROUP HOLDING NV BE BGBEL20 WAREHOUSES DE PAUW SCA BE BGBEL20