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What are the announcement effects on acquiring

firms when the target is in emerging markets?

A country-specific analysis of Brazil.

Frederick Althaus

Faculty of Economics and Business, University of Amsterdam

BSc Business Administration – Specialisation: Finance

6013B0520Y: Bachelor´s Thesis and Thesis Seminar Finance for Business

Supervisor: Dr. Solomon George Zori

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Statement of Originality

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

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

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

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Abstract

This paper analyses the announcement effects on the share price of acquiring firms in 261 takeovers of Brazilian target firms between 2010 and 2019, of which 124 were cross-border acquisitions. To establish the abnormal returns, MacKinlay´s (1997) event study methodology is used. Significant positive cumulative abnormal returns are found for the three-, five- and seven-day event windows around the announcement date. Next, the payment method is analysed and, contrary to empirical consent, no statistical evidence for an

outperformance of cash- paid transactions over equity-paid transactions could be established. In the seven-day event period, cash-paid takeovers even showed a negative coefficient, signalling an outperformance of equity-paid transactions. The coefficient, however, was insignificant. Lastly, cross-border acquisitions were compared with domestic takeovers and a significant positive impact of cross-border acquisitions was found in the three-day event window. The effect was insignificant, however, in the other two periods. This is consistent with previous literature and the semi-strong market efficiency hypothesis.

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Table of Contents

1. Introduction ... 6

2. Literature Review ... 8

2.1 Announcement effects for acquirers ... 8

2.2 Announcement effects for acquirers of emerging market targets ... 9

2.3 Announcement effects for acquirers of Brazilian targets ... 9

2.4 Determinants ... 10

2.5 Mode of Payment ... 10

2.6 Type of acquisition: cross border versus domestic ... 11

3. Method and Hypotheses ... 12

3.1 Data Collection ... 12

3.2 Event study methodology ... 12

3.3 Model and variables description... 14

3.4. Regression Model ... 16

4. Results ... 18

4.1 Descriptive Statistics & Event-Study Analysis ... 18

4.2 Assumptions for Regression ... 20

4.3 Regression Results ... 21

4.4 Limitations & Implications for further research ... 25

5. Conclusion ... 26

Bibliography ... 28

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List of Tables

Table 1. Descriptive Statistics Predictor Variables ... 15

Table 2. Positive CAR in Sample ... 18

Table 3. Pairwise correlations ... 19

Table 4. Cumulative Abnormal Returns in Different Event Windows ... 19

Table 5. Regression 1 Results ... 21

Table 6. Regression 2 Results ... 23

List of Figures

Figure 1. Event-Study Graphic ... 14

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1. Introduction

Global merger and acquisition transactions have increased over the past three decades as companies have sought to boost their competitive advantage through synergies, diversification and greater market share. Worldwide M&A activity totalled 2.8 trillion USD in 2019, close to the average for the previous six years (Massoudi et al., 2020). This indicates a continued interest in deal-making as firms take advantage of increasingly globalised and integrated financial and product markets. With more than 790,000 deals valued at more than 57 trillion USD since 2000 (IMAA, 2019), emerging markets have enjoyed a sizeable slice of global M&A activity. The share of developing countries in total global foreign direct investment rose a record 52 per cent in 2018, while flows to developed countries fell to their lowest level since 2004 (UNCTAD, 2019).

Although China received the largest share of FDI in recent decades, “true” FDI, minus reinvested earnings, shows that, relative to the size of their economies, resource-rich countries like Brazil, Chile and Columbia were the biggest FDI receivers (IIF, 2020). Between January 2018 and summer 2019, M&A activity in Latin America led to an inflow of 152.3 billion USD, with the country’s rich natural resources, low-cost labour markets and growing consumer sector continuing to attract global companies (Deloitte, 2019).

This paper will focus on Brazil, which is Latin America’s largest economy and had the strongest M&A activity in the region (785 transactions) between August 2018 and August 2019 (Deloitte, 2019). In 2019, the country caught the attention of foreign investors with deals such as the sale of the state-owned oil company Petroleo Brasileiro SA’s gas pipeline system TAG to a group led by France’s Engie SA and CDPQ of Canada for 8.7 billion USD and in 2018 with the 10.3 billion USD takeover of Fibria Celulose by Suzano, a fellow Brazilian pulpmaker.

Following a boom at the beginning of the decade, M&A activity in Brazil has declined since 2012 due to lower commodity prices, as well as the country’s economic downturn and growing political crisis. However, M&A transactions are expected to increase from 2019 onwards because of a series of privatisations of public firms in the country (Patterson, 2018), forecasts for economic growth and market-friendly fiscal reforms. Brazil, a member of the BRICS group (Brazil, Russia, India, China and South Africa) of leading emerging market

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7 economies, is likely to remain a magnet for investors, with M&A activity in the country expected to rise above 40 billion USD in 2021/2022 (Baker McKenzie, 2020). The country also has a large and efficient capital market, making it less volatile than other emerging markets. (Ejnisman et al., 2015). How the current global Covid-19 pandemic will affect these forecasts is difficult to predict as per July 2020.

Previous studies into the short-term value effects of M&A announcements for acquirers in emerging markets reached mixed conclusions. Studies have largely focused on China, where, for example, Ma et al. (2009) Chi et al. (2011) and Wong et al. (2009) find positive short-term abnormal returns for acquirers. Kinareder et al. (2017), however, conclude that domestic acquirers in BRICS countries lose slightly following acquisitions. In Africa, Amewu & Alagidede (2018) find that public acquisition announcements create value for domestic bidders, while Brito et al. (2005) find insignificant abnormal returns for acquirers in the Brazilian financial sector.

This paper is therefore motivated by the lack of agreement on the short-term value creation of announcement effects in emerging market targets, as well as by the shortage of research on the subject in Brazil, which, for the reasons mentioned above, is likely to remain a popular emerging market for bidder firms in coming years. In doing so, this paper aims to answer the research question: do acquisition announcements create value for acquirer shareholders of Brazilian target firms?

This study will furthermore contribute to existing literature by analysing several drivers of bidder returns following acquisition announcements. These drivers, which previous studies have found to be highly relevant (Alexandridis et al., 2010), (Travlos, 1987), (Colombage et al., 2014), (Amewu & Alagidede, 2018), include: the acquirer’s method of payment (equity, cash or other) and the acquisition form (domestic or cross border, private or listed).

The results of this study will be useful for bidder shareholders of emerging market targets, as well as for strategic planning and acquisition proposals by senior managers. It will also be useful for global fund managers and equity analysts planning investment strategies in Brazil or other emerging markets.

The study will be structured as follows: Section 2 will include a review of relevant literature, while Section 3 will contain details on the event study methodology and data, Section 4 will present the results and discussion, and Section 5 will conclude.

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2. Literature Review

Mergers and acquisitions occur when two companies join up, with the bidder, depending on the synergies involved, usually paying a premium for the target (Gersdorf & Bacon, 2009). Sherman & Hart (2006) argue that the terms “merger” and “acquisition” are interchangeable as in both cases, separately-owned firms operate as one following the M&A transactions and have the same strategic goals. There is empirical evidence that mergers and acquisitions are one of the best ways to achieve international brand exposure, as well as cost savings, improved market share and corporate growth (Francis et al., 2008), (UNCTAD, 2000). Opinion is divided, however, on whether M&A activity creates value for both bidder and target shareholders. Franks et al. (1991) find that acquirer firms gain in the long run, while Campa & Hernando (2004) conclude that the returns of acquiring firms are insignificantly different to zero. Berk & DeMarzo (2017) mention that while bidders usually have close to zero returns around the announcement of a takeover, target shareholder gain up to 15% in value.

Existing research shows that M&A investors in emerging markets may have to overcome certain hurdles, such as cultural differences (Ma et al., 2009) or weak enforcement of existing laws (La Porta et al., 1999), overpayment for targets (Eckbo & Thorburn, 2000) or

information leakage in target markets where there are low standards of corporate governance (Duppati et al., 2015), (Ma et al., 2009).

2.1 Announcement effects for acquirers

There has been broad research into how acquisition announcements influence the short-term returns of bidder shareholders. The conclusions are mixed. In a study of the bidder returns of 554 US M&A transactions between 1980 and 1997, Fee & Thomas (2004) find significant positive abnormal returns for merging companies post acquisition announcements, consistent with a study by Healy et al. (1992) of 50 big US industrial companies between 1979 and 1984. Doukas et al. (2002) find that shareholders of multinational companies show significant positive abnormal returns when acquiring in a target country for the first time, while Byrd & Hickman (1992) find cross-border acquisition announcements lead to negative returns for bidders. Conn & Connell (1990) find acquiring firms’ short-term performance around the announcement of cross-border M&A deals in US and the UK are not rewarded with positive abnormal returns.

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9 2.2 Announcement effects for acquirers of emerging market targets

Until now, most research on the impact of acquisition announcements on bidder returns has focused on the North American and European markets. A number of studies have investigated the short-term announcement effects for acquirers of emerging market targets but the majority of these have concentrated on Asia and in particular, China. China is an attractive M&A target as it has seen steady growth in the past two decades and is by far the leading global emerging market economy, with current GDP of about 14 trillion USD. Researchers largely find that announcement effects of acquisitions of emerging market targets lead to positive returns. Ma et al. (2009) note that acquisition announcements in 10 emerging Asian markets (China, India, Hong Kong, Indonesia, Malaysia, the Philippines, Singapore, South Korea, Taiwan, and Thailand) led to positive cumulative abnormal returns for acquirers in three different short-term event windows. Duppati et al. (2015) find that mergers and acquisitions announcements in China create value for bidders and agree with Ma et al.

(2009) that pre-announcement information leakage is a problem in Chinese stock markets and has a significant impact on returns prior to acquisition announcements. Reddy et al. (2019), however, find acquisition announcements do not create value for acquirers in China and India. Kinareder et al. (2017) conclude that domestic acquirers in BRICS countries lose slightly following acquisitions. Shah & Arora (2014) find that, unlike target firms, acquirers fail to make statistically significant abnormal returns across all the event windows.

In a study of 842 acquirers in nine Latin American and East Asian markets (including Argentina, Indonesia, Malaysia, Mexico, Philippines, South Korea, and Thailand),

between 1988-2002, Chari et al. (2004) find that developed market bidders can expect significantly positive returns when buying emerging market targets, with characteristics including method of payment, the size, the target country as well as the liquidity of the

bidders and the targets influencing the size of the returns. A further important determinant that they point to is the acquisition of majority control by developed market acquirers.

2.3 Announcement effects for acquirers of Brazilian targets

There are only a handful of studies that investigate abnormal returns for bidders following acquisition announcements in Latin America and, to the best of my knowledge, this paper is one of first to focus solely on Brazil, despite the fact that country has the largest economy in the region, receives billions of dollars of foreign direct investment each year and, like China, belongs to the BRICS group of leading developing countries. Other research on

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10 abnormal returns in Brazil has concentrated mainly on the financial sector. For example, in a study of M&A banking transactions worth 100 million USD in BRICS-T countries before and after the 2008 Lehman crisis, Sevindik & Gökgöz (2018) find that acquirers of Brazilian banking targets recorded small negative abnormal returns in the period from September 2003 to November 2008, as well as after the crisis, from November 2008 to December 2013, with an increased negative value in the post-crisis period versus the pre-crisis period. On the other hand, Brito et al. (2004) observe insignificant returns for acquirers in all the event windows employed in a study of 17 acquisitions in the Brazilian financial sector between 1997 and 2003.

Likewise, Simões et al. (2012), in a study of 14 Argentinian, 28 Brazilian and 11 Chilean companies, find a lack of significant returns for investors in Brazilian targets in all the event windows employed. Significantly abnormal returns were only recorded on the event day, which the study argues is evidence that the Brazilian market is efficient in keeping with the semi-strong market efficiency hypothesis (EMH). The EMH is based on the assumption that asset prices in efficient market full reflect all available information. In its semi-strong form, historical and recent public information is available.

In light of the largely significant, if mixed, findings of previous studies of abnormal bidder returns following acquisition announcements, my first hypothesis is therefore:

H1: There will be significant abnormal returns for bidders across the event windows following acquisitions announcements that target Brazil

2.4 Determinants

Prior research shows that several deal and company characteristics trigger important reactions in abnormal returns around acquisition announcements, (Shleifer & Vishny, 2003), (Chang & Tsai, 2013). These characteristics largely include mode of payment (equity, cash or other) and type of deal (cross border versus domestic, private versus domestic).

2.5 Mode of Payment

Mushidzhi & Ward (2004), Brown & Ryngaert (1991) and Shams et al. (2013) observe significant differences in the abnormal returns of equity- and cash-financed

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11 from US, Canadian and UK companies finds that shareholders of listed target firms register deep losses when deals are financed with equity. They attribute the higher returns for cash offers largely to signalling effects. Loughran & Vijh (1997), using a sample of 947 firms listed on the New York Stock Exchange (NYSE) between 1970 and 1989, observe that cash offers generate significantly positive abnormal returns in the long term, while equity

transactions generate negative returns.

These results are consistent with findings over different time periods that cash-financed transactions generate higher returns than those paid with a mix of cash, equity and loan notes (Goergen & Renneboog, 2003) (Amewu & Alagidede, 2018).

In an earlier study, Travlos (1987) goes further to argue that inconsistent evidence in earlier studies on abnormal returns around acquisitions announcements may be due to researchers’ failure to analyse the method of payment.

Farias & Gutierrez (2017), however, find in a study of 709 transactions by bidders traded on Brazil’s SBM&FBOVESPA stock exchange that transactions paid in shares generate the best short-term returns in a bull market, while in bear markets cash acquisitions are the best choice in the long term.

Taking previous literature into account, my second hypothesis is:

H2: There will be significantly higher abnormal returns in the event window following acquisition announcements where the method of payment is cash as opposed to equity.

2.6 Type of acquisition: cross border versus domestic

Numerous prior studies that analysed the effect on bidder returns of cross-border versus domestic acquisitions find that cross-border acquisitions create greater value (Colombage et al., 2014) (Amewu & Alagidede, 2018).

In a study of Indian acquisitions of 268 domestic and 255 cross-border target firms between 2003 and 2008, Rani et al. (2013) find further evidence that cross-border acquisitions generate higher bidder returns (significant at 5 per cent in that study) than domestic

acquisitions. They note that domestic abnormal returns are positive (though statistically insignificant) for partial/majority control domestic acquisitions.

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H3: There will be significant abnormal returns in the event window following acquisition announcements for cross-border bidders targeting Brazil.

3. Method and Hypotheses

In order to test the effect of merger and acquisition announcements on acquiring firms’ share price, the event study methodology is used with several ordinary least squares regressions accounting for different cumulative abnormal return periods around the announcement date.

3.1 Data Collection

The data for this study was collected from two databases. All M&A-related data was retrieved from the Zephyr Database. This provided 286 takeovers in Brazil between January 1, 2010 and December 31, 2019. The transactions fulfilled the additional criteria that they were successfully completed and contained information regarding the method of payment and the industry of the target and acquirer as these were seen to be relevant control variables (as to be detailed in section 3.3). Lastly, it was necessary that the acquirer be listed given that the stock prices and returns are needed for the abnormal return calculations. This stock data was retrieved from the DataStream database. After scanning the data for missing or unavailable data either for the variables (for example missing ISIN code, payment information or targets outside Brazil) and the stock returns, the sample was reduced to 261 takeovers.

3.2 Event study methodology

Event study methodology has been used for many years for research on abnormal returns and is commonly applied in Finance and Economics (De Jong, 2016). The method aims to measure effects that occur in relation to a specific event, which in this research are the effects on the bidder markets upon public announcement of a takeover where new information will be incorporated into the stock price. The goal of this research is to determine whether cumulative abnormal returns (CARs) are present in Brazilian target takeovers. The Null hypothesis states that expected CARs are non-existent, whereas the counterhypothesis states

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13 that CARs are non-zero. Significant abnormal returns mean that the Null hypothesis can be rejected.

Abnormal returns depend on the definition of normal returns which in this case are defined as the average returns in the estimation window. In this paper, the estimation window of [Day -90, Day -30] is used, as well as the mean-adjusted model of MacKinlay (1997) and Peterson (1989). This event study therefore adopts MacKinlay´s assumption that the average return is stable across time. In the process of computing CARs, we first need to determine the daily returns of the acquirer stocks:

𝑹

𝒊𝒕

=

𝑷

𝒕

− 𝑷

𝒕−𝟏

𝑷

𝒕−𝟏

The normal returns are computed as follows:

𝑵𝑹𝒊𝒕

=

𝟏

𝑻

𝑹

𝒊𝒔 𝑻𝟐

𝑻𝟏

where T=T2-T1+1 is the length of the estimation window.

From here, the daily abnormal returns are computed as follows:

𝑨𝑹

𝒊𝒕

= 𝑹

𝒊𝒕

− 𝑵𝑹

𝒊𝐭

Lastly, we arrive at the cumulative announcement effect by estimating cumulative abnormal returns:

𝑪𝑨𝑹

𝒊

= 𝑨𝑹

𝒊,𝒕𝟏

+. . . +𝑨𝑹

𝒊,𝒕𝟐

= ∑ 𝑨𝑹

𝒊𝒕 𝒕𝟐 𝒕𝟏

According to De Jong (2016), the best way to estimate announcement effects is to have a short event window around the announcement. Therefore this research will use returns three days, five days, and seven days around the announcement days. This is equivalent to an event period of [-1, 1], [-2, 2] and [-3, 3], respectively. Thus, for each hypothesis three OLS regressions will be run. Figure 1 shows the event methodology process graphically.

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Figure 1. Event-Study Graphic

3.3 Model and variables description

To ascertain whether there is an announcement effect of cross-border takeovers targeting Brazil, ordinary least squares regressions are needed. The research aims to measure whether our predictor variables Crossborder and Cash have a significant effect on the cumulative abnormal returns of the bidder companies´ stock price.

Further, in order to include important predictors of cumulative abnormal return, control variables are added. These also reduce the bias of the model. The following variables are the outcome variables in this paper:

Cum_ab_return_3_3 which is the cumulative abnormal return between three days before and

three days after the announcement event.

Cum_ab_return_2_2 which is the cumulative abnormal return between two days before and

after the announcement event.

Cum_ab_return_1_1 which is the cumulative abnormal return between one day before and

one day after the announcement event.

The predictor variable for the first regression is “Cash”. It is coded as a dummy variable where it has the value 1 if the acquisition was paid with cash only.

The second predictor variable is “Cross-border. The variable is coded as a dummy variable with the value 1 if the acquisition was cross-border and 0 if it was domestic. Cross-border acquisitions totalled 124 deals, while the remaining 137 were domestic bidders.

The following control variables were used in both regressions:

SameSector is a dummy variable that equals 1 if the acquiring firm and target firm are in the

same sector and 0 if they are in different sectors. Of the transactions, 162 out of 261 were mergers within the same sector.

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TargetLegalForm is a dummy variable that scores 1 when the target is unlisted and 0 when

the target is listed.

LogDealValue measures the logarithmic Deal value in millions of US dollars. It was

transformed to improve the normality assumption for the OLS regressions. This will be discussed further in section 3.

Controlling for method of payment:

In the first regression where hypothesis 2 is tested, the mode of payment was split across three different variables:

Cash is the predictor variable as mentioned above and identifies transactions that were paid

solely in cash. Of the transactions, 69 were paid solely in cash.

Shares (excluded to avoid dummy variable trap) is a dummy variable that equals 1 if the

transaction was paid solely with equity. Of the transactions, 78 were paid with equity only.

Mix is a dummy variable that equals 1 if the transaction was paid with mixed methods. These

methods include additional payment modes, such as deferred payment, earnout and liability paid transactions (included in regression 2). Of the transactions, 114 were paid with mixed payment methods.

Table 1. Descriptive Statistics Predictor Variables

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VARIABLES N mean sd min max

crossborder 261 0.475 0.500 0 1 targetlegalform 261 0.885 0.320 0 1 samesector 261 0.621 0.486 0 1 logdealvalue 246 3.835 2.519 -4.605 10.68 cash 261 0.264 0.442 0 1 shares 261 0.299 0.459 0 1 mix 261 0.437 0.497 0 1

As regression 1 led to low R2 values, a second regression was run to test hypothesis 3. Here, various methods of payment were used as control variables and only the main method of payment was used (as classified as the first entry in the Zephyr database if there were several modes of payments for the same transaction). This increased the explained variance of every model and the different modes of payment are listed below:

MpCash is a dummy variable that equals 1 if cash was used as the main method of payment

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MpShares is a dummy variable that equals 1 if the main method of payment was shares and 0

if other.

MpLiab is a dummy variable that equals 1 if the main method of payment was paid with debt. MpDefPay is a dummy variable that equals 1 if the transaction was paid mainly with deferred

payment.

MpEarnout is a dummy variable that equals 1 if the transaction was paid mainly by means of

earnout.

3.4. Regression Model

As mentioned earlier, ordinary least squares regressions are run to determine the effect of Cash and Crossborder on the study’s cumulative abnormal returns. The control variables mentioned above are included in the regression equations.

For the first regression:

𝐶𝐴𝑅𝑖 = 𝛼 + 𝛽1× 𝐶𝑟𝑜𝑠𝑠𝑏𝑜𝑟𝑑𝑒𝑟 + 𝛽2× 𝑆𝑎𝑚𝑒𝑆𝑒𝑐𝑡𝑜𝑟 + 𝛽3× 𝑇𝑎𝑟𝑔𝑒𝑡𝐿𝑒𝑔𝑎𝑙𝐹𝑜𝑟𝑚

+ 𝛽4𝐷𝑒𝑎𝑙𝑉𝑎𝑙𝑢𝑒 + 𝛽5× 𝐶𝑎𝑠ℎ + 𝛽6× 𝑀𝑖𝑥 + 𝜀

For the second regression:

𝐶𝐴𝑅_𝑖 = 𝛼 + 𝛽1× 𝐶𝑟𝑜𝑠𝑠𝑏𝑜𝑟𝑑𝑒𝑟 + 𝛽2× 𝑆𝑎𝑚𝑒𝑆𝑒𝑐𝑡𝑜𝑟 + 𝛽3× 𝑇𝑎𝑟𝑔𝑒𝑡𝐿𝑒𝑔𝑎𝑙𝐹𝑜𝑟𝑚 + 𝛽4𝐷𝑒𝑎𝑙𝑉𝑎𝑙𝑢𝑒 + 𝛽5× 𝑀𝑝𝐶𝑎𝑠ℎ + 𝛽6× 𝑀𝑝𝑆ℎ𝑎𝑟𝑒𝑠 + 𝛽7× 𝑀𝑝𝐿𝑖𝑎𝑏

+ 𝛽8× 𝑀𝑝𝐷𝑒𝑓𝑃𝑎𝑦 + 𝛽9× 𝑀𝑝𝐸𝑎𝑟𝑛𝑜𝑢𝑡 + 𝜀

Three previously-mentioned return windows are used to measure whether longer-event windows lead to different returns and whether the effects found are only short lived or persist over longer periods. The results for each time window will be presented in the next section. The market value of the acquirer was found to be an important determinant of abnormal returns in previous papers by Moeller et al. (2003), who sampled 12,083 acquisitions by public firms and observe that smaller firms obtain higher announcement returns than larger firms regardless of the form of financing. This effect has been proven to be consistent over time. However, the Zephyr database provided several missing data for this variable and after further research, the values for the market value were seemingly unrealistic. To avoid distorting the assumptions for regression, I decided to omit this variable.

Chang (1998) and Fuller et al. (2002) find that the type of acquisition (private versus listed) has a significant effect on abnormal returns for bidder firms around acquisition

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17 announcements. In a study of 3,135 US bidders between 1990 to 2000, Fuller et al. (2002) find that acquirers record significant negative returns when buying listed firms and

significantly positive returns when acquiring private firms. They argue that private firms are less liquid than and not as easily sold as listed firms and that they therefore trade at a discount to listed companies. Faccio et al. (2006) find that the target’s legal form had a significant impact on abnormal returns for Western European countries between 1996 and 2001. They note that when the acquirer was listed, the return was on average negative for the bidder shareholders. Therefore, I decided to include the variable targetlegalform in my model. From table 1, one can see that 88,5% of the sample is acquisitions of unlisted targets. This is expected, however, given that, as mentioned previously, most Brazilian companies are privatised.

SameSector was used to control for effects caused by vertical and horizontal mergers,

where horizontal mergers are mergers within the same industry and vertical mergers include acquisition of companies at a different point in the supply chain. Fan & Goyal (2006) find significant positive returns when the acquisition was vertical and before 1998, and that from that point on, these returns become negative and significant. Similarly, Kedia et al. (2011) find significant small positive returns to horizontal mergers. In that paper, 62.1% of the transactions were within the same sector, and the sector classification was based on Zephyr´s classification of `major sector`. This classification could have an impact on the results.

Further, Amewu & Alagidede (2018) find that the relative deal size, industry components and the mode of payment have a significant effect on the acquirer´s returns in a study of acquisitions targeting African companies. Therefore, these factors are included as additional control variables in my study.

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4. Results

In this section, descriptive statistics as well as regression results of this study will be presented which will be used later in the analysis and discussion of the announcement effects in Brazil.

4.1 Descriptive Statistics & Event-Study Analysis

In analysing the transactions, we find that the majority of acquirers are based in Brazil, USA and Germany with 124 being cross-border takeovers and 137 being domestic (i.e. a Brazilian acquirer purchasing a Brazilian target).

Table 2. Positive CAR in Sample

(1) (2) (3)

CAR N % sd

3 Day [-1, 1] 261 57.9 0.495 5 Day [-2, 2] 261 55.2 0.498 7 Day [-3, 3] 261 55.6 0.498

The results in Table 2 show that 57.9, 55.2 and 55.6 per cent of the cumulative

abnormal returns are positive in the event windows 1, 2 and 3, respectively. This is consistent with previous studies by Eckbo & Thorburn (2000), who find that approximately half their sample had positive CARs. Table 2 shows the correlation matrix. From here, a significant positive relationship between the Crossborder variable and cumulative abnormal returns can be observed. A surprising observation is made regarding the payment method of cash that seemingly has a very small, negative correlation with my outcome variable. However, this relation appears to be negative. Lastly, multicollinearity is present in the model, which will be addressed in the Assumptions section.

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Table 3. Pairwise correlations

Variables (1) (2) (3) (4) (5) (6) (7) (8) (1) CAR 1.000 (2) crossborder 0.111* 1.000 (3) samesector -0.006 0.016 1.000 (4) logdealvalue -0.041 0.011 0.080 1.000 (5) targetlegalform -0.018 0.150** -0.059 -0.300*** 1.000 (6) cash -0.002 -0.344*** -0.140** -0.101 -0.220*** 1.000 (7) shares -0.049 0.452*** 0.027 0.107* 0.052 -0.391*** 1.000 (8) mix 0.047 -0.111* 0.099* -0.009 0.148** -0.528*** -0.575*** 1.000 *** p<0.01, ** p<0.05, * p<0.1

It is apparent from Table 4 that for the 261 observations there are significant positive cumulative abnormal returns in each of the three event windows: the highest CAR[-1, 1] has a mean of 2.14% and the highest t-value of 4.07. All CARs are significant which means that the null hypothesis that there are no cumulative abnormal returns within the event windows can be rejected. Therefore, there is statistical evidence supporting hypothesis 1 that acquisitions targeting Brazil create positive returns for acquiring shareholders.

Table 4. Cumulative Abnormal Returns in Different Event Windows

(1) (2) (3)

CAR[-1, 1] CAR[-2, 2] CAR[-3, 3]

Mean 0.0214*** 0.0179** 0.0206**

(4.07) (2.92) (2.95)

N 261 261 261

t statistics in parentheses

* p < 0.05, ** p < 0.01, ***p < 0.001

The fact that the returns in all three event windows around the event date [date 0] are small is also supported by previous literature of Goergen & Renneboog in 2002. The highest t-value is found in the shortest event window (model 1). This is supported by economic theory that market rumours and potential information leakage are most likely to occur closest to the event date which in turn lead to a higher likelihood of abnormal returns (Amewu &

Alagidede, 2018). These returns diminish the larger the event window (Berk & DeMarzo, 2017).

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20 4.2 Assumptions for Regression

Before running the regressions, it must first be established whether the ordinary least squares assumptions for all three models hold since OLS regressions are run (see Appendix for more detail). For simplicity’s sake, this paper refers to model 1 for our CAR[-1, 1] prediction, model 2 for CAR[-2, 2] and model 3 for CAR [-3, 3].

First, the normality of the model is checked with the Shapiro-Wilk test. For all three models, the Null hypothesis assuming a normal distribution is rejected. However, research typically follows the Central Limit Theorem which states that samples with a size larger than N=30 are to be assumed normally distributed. This criterion holds in this research given the sample size of 261 observations. Further, when looking at the pplots and residual versus fitted values plots, it is apparent that the data for all three models looks sufficiently normal.

Second, the Durbin-Watson test is used to look for autocorrelation. The statistical program STATA that is used in this paper has the added functionality of “robust standard errors” that accounts for autocorrelation. Therefore, all autocorrelation effects are assumed to be

eliminated.

After observing the residual plots, we can see that there appear to be potential outliers of our sample. Therefore, I test for outliers by computing Cook´s Distance which accounts for the influence and leverage a single observation has on the outcome. The general rule of thumb is that Cook´s Distance values larger than 1 are to be considered influential outliers. In all three models, the computed values are below 1. Therefore, I did not remove any further observations.

Fourth, I test for heteroscedasticity with the Breusch-Pagan test. In all three models, the Null hypothesis that the data is homoscedastic is rejected and heteroscedasticity is assumed. However, STATA´s previously mentioned robust error option accounts for heteroscedastic errors and therefore the estimated errors are reliable.

Lastly, the variance inflation factor (VIF) is measured to check for multicollinearity of the predictor variables. As the predictor variables are the same in each model, they have the same VIF scores across models. The predictor variable mpshares has the highest score (2.72) which is significantly below the score 10. The consensus in literature is that VIF-scores above 10 indicate multicollinearity.

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21 4.3 Regression Results

In order to answer our main research question whether acquisitions targeting Brazil create value for bidder shareholders, the regression results are analysed, and the hypotheses are tested.

The regression results are depicted in tables 5 and 6. The explained variance of each model as expressed by the R2 coefficient is 2.9%, 1.08% and 1.35% for models 1, 2 and 3, respectively. These results might indicate omitted variable bias. However, literature on

cumulative abnormal returns finds similarly low explained variance in their models. However, for regression 2, I decided to include further methods of payment as discussed in the method section in order to improve the model’s predictive power.

Table 5. Regression 1 Results

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CAR[-1, 1] CAR[-2, 2] CAR[-3, 3]

crossborder 0.0310* 0.0180 0.00499 (1.94) (0.93) (0.20) samesector 0.00000935 0.00298 0.00334 (0.00) (0.21) (0.23) logdealvalue -0.00150 -0.00106 0.000325 (-0.68) (-0.43) (0.12) targetlegalform -0.0119 -0.0141 -0.0224 (-0.82) (-0.78) (-1.01) cash 0.0223 0.00444 -0.0238 (1.46) (0.22) (-0.83) mix 0.0241 0.0166 0.00379 (1.42) (0.79) (0.13) _cons 0.00552 0.0148 0.0384 (0.29) (0.60) (1.28) N 246 246 246 t statistics in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

In this study, different modes of payment for the transaction are examined: regression 1 comprises three different forms of payment (cash, equity, mix) and regression 2 consists of five different payment methods that are compared with the sixth ‘other’ payment method

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22 (which is not further classified by the Zephyr database). From the regression output tables we can see that none of the payment methods had a significant effect on the cumulative abnormal returns, even across the different event windows. Therefore, there is no evidence found for the hypothesis that the method of payment has an effect on merger and acquisitions targeting Brazil.

Regarding Hypothesis 2, I decided to run a regression where all payment methods were divided into either “shares only”, “cash only” or “mixed” (again, I excluded the variable

Shares, to avoid the dummy variable trap). The results are depicted in table 5 and show that

no support is found for the hypothesis at the 95% confidence level. Both cash and mixed methods of payments are insignificant. However, in the first regression the coefficients are in relation to equity payments of transactions. Here we can see that cash payments and mixed have a more positive effect than share payments: the coefficient of 0.0223 in the first event window indicates that transactions paid with cash result in 2.23% higher cumulative abnormal returns as compared to equity payments. In the second event window, cash outperforms equity transactions by only 0.44% higher CAR. This is consistent with Linn & Switzer (2001) who find that cash-only payments outperform equity payments when looking at post-merger operational and stock performance.

Further, a positive effect of cash versus share-funded transactions supported by the signaling and agency theory regarding payment methods as touched upon by Berk &

DeMarzo (2017). They mention that cash-funded acquisitions signal confidence and the belief of the management that their stock will rise as a result of the created synergy effects of the acquisition. In the case of a successful merger, the synergy gains would flow to existing bidder shareholders. By contrast, if paid for with equity, the risks and gains of the merger would be shared with the target shareholders. To summarise, Berk & DeMarzo argue that cash payments demonstrate confidence in the company´s future value whereas equity conveys bearish, skeptical behavior. Therefore, investors may perceive an acquisition paid with shares as a sign of management’s belief that their shares are overvalued.

Interestingly, the effect of cash in the third event period appears to be negative and therefore contradicts the signaling theory mentioned earlier. Specifically, the coefficient indicates that cash-only transactions in the third event window lead to 2.38% lower returns than ones paid in equity. This appears to be noteworthy. However, all coefficients are

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23 insignificant and there is no statistical evidence that this effect holds. This result is more consistent with Conn et al. 2005, who find that cross-border, cash-paid acquisitions are not significant. The negative coefficient leads back to the research mentioned earlier by Farias & Gutierrez (2017), who find that for bidders taking part in a bullish market, transactions paid in shares generate the best short-term returns. They argue that in a bull market, cash acquisitions have a negative effect on returns as they reduce the bidder’s cash reserves. There is therefore room here for a future research possibility investigating and including the effects of bullish and bearish market scenarios and their impact on announcement effects.

Table 6. Regression 2 Results

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CAR[-1, 1] CAR[-2, 2] CAR[-3, 3]

crossborder 0.0311* 0.0186 0.0109 (2.30) (1.18) (0.51) samesector 0.00142 0.00313 0.00579 (0.11) (0.22) (0.39) logdealvalue -0.000897 -0.000118 0.00166 (-0.51) (-0.05) (0.68) targetlegalfom -0.00807 -0.0108 -0.0156 (-0.57) (-0.61) (-0.71) mpcash -0.0369 -0.0296 -0.0279 (-1.13) (-0.91) (-0.75) mpdefpay 0.0619 0.0286 0.0146 (1.63) (1.30) (0.50) mpearnout 0.0462 0.0577 0.0438 (1.13) (1.48) (0.96) mpliab 0.0162 0.0158 0.00946 (1.47) (1.04) (0.42) mpshares -0.0115 -0.0195 -0.0273 (-0.46) (-0.73) (-0.85) _cons 0.0415 0.0402 0.0452 (1.30) (1.15) (1.19) N 246 246 246 t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

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24 The results for Hypothesis 3 can be seen in table 6 and the R2 was increased to 5.62%, 3.4% and 2.2% for the three models, respectively. Here, the variable crossborder is examined across all three event windows. Figure 2 depicts the mean cumulative abnormal returns of the three event windows. From here, it seems that cross-border acquisitions earn higher returns in each event window.

Figure 2. Mean CAR for domestic and cross-border acquisitions

The regression results, however, show that Hypothesis 3 is only significant in the event window CAR[-1, 1] and insignificant in the remaining windows. Therefore, I conclude that cross-border acquisitions targeting Brazil do have a significant effect on bidder returns, however, only in the three-day period around the announcement date. In the five and seven-day event windows, there is no statistical evidence for this to be the case. This is consistent with results of Otto et al. (2020) who find that cross-border acquisitions targeting emerging markets lead to higher abnormal returns than domestic acquisitions.

The fact that the other event windows are not significant is supported by the efficient market hypotheses as indicated by Simoes et al. (2012). They observe that Brazil, in contrast to Argentina and Chile, can be classified as an efficient market according to the semi-efficient market hypothesis as explained in the literature review of this paper. In their research, Simoes

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25 et al. also find that Brazil only demonstrates abnormal returns for cross-border acquisitions on the event date. However, they fail to find any significant returns beyond the announcement or for larger event windows. The fact that Brazil can be considered an efficient market is also supported in my research as although positive CARs are to be found, with larger event windows these become insignificant as their t-values decrease. This can be seen in both table 5 and table 6. This indicates that, with time, potential value creation effects of the

announcement date are absorbed in the longer event periods.

The synergetic benefits of mergers such as economies of scale and scope, as well as tax benefits (Lambrecht, 2004) are included in the significant three-day event window returns. Seemingly these benefits are mitigated in later, longer event windows by the downsides of mergers. Berk & DeMarzo (2017) mention that when the interests of stakeholders and managers are not aligned, managers tend to engage in ‘empire building’ where they

themselves benefit from destructive investments at the cost of other shareholders. Further, a lack of understanding foreign markets (Ma et al., 2009) can lead to acquirer´s often

overpaying cross border targets (Eckbo & Thorburn, 2000) which eliminates the gains of acquisitions or possibly even leads to a negative investment scenario. La Porta et al. (1999) add that weak enforcement of property rights can reduce cross border takeover performance.

4.4 Limitations & Implications for further research

Although this study led to valuable results, limitations of the research need to be addressed. As previously established, there are potential differences in cumulative abnormal returns caused by market factors such as bullish or bearish markets. These market factors are not included in the calculation of normal returns as described in the method section: the MacKinlay model assumes that the mean distribution of returns is normal over time and does not account for market and size effects. In future research of this topic, one could therefore adopt additional return models such as the 3 Factor model by Fama & French (1993), which accounts for the overall market return, book-to-market ratios and size of firms. Including these in future research could improve the predictive power of the model as it would provide room to further investigate market types (bullish versus bearish) or industrial differences in acquisition effects. Additionally, the residual distribution appeared to be non-normal which can affect the prediction. Although the criterion for the central limit theorem was met, increasing the sample size for example for the relation between listed and unlisted acquirers

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26 (as 88,5% of the sample was unlisted) would also improve the explained variance of our model, which was found to be low.

Lastly, this research found that cross-border acquisitions significantly outperformed domestic transactions in only one event period. This opens the debate as to whether

familiarity of markets influence the abnormal returns or not. Therefore, future research could measure whether experience (i.e. when a company has previously made acquisitions in Brazil) influences cross-border bidder returns. This would expand on previous research by Doukas et al. (2002) who show that multinational companies earn positive returns when acquiring in a target country for the first time.

5. Conclusion

This paper examined the short-term takeover announcement effects on bidder

shareholders that target a Brazilian company. Following the event study methodology around the mean adjusted model by MacKinlay, cumulative abnormal returns were measured and analysed. After finding that positive CARs exist in all three event windows, it is to be

mentioned that for larger event windows the significance of the returns decreased as measured by the t-statistic. This resulted in CARs in window [-1, 1] having the largest and most

significant values. This also was the case for cross-border acquisitions targeting Brazil, where only the smallest event window indicated significant returns. The other event windows

showed positive, but insignificant returns for cross-border acquisitions which, while it is consistent with previous literature on M&A in Brazil, goes against empirical consent that cross-border acquisitions generate significantly more positive returns than domestic takeovers.

Furthermore, different methods of payment were examined, and it was tested whether cash acquisitions targeting Brazil generate higher returns than equity-paid transactions. This research found no support for the latter assumption and the hypothesis was rejected.

Interestingly, in two of three event periods cash had a positive regression coefficient, hinting an outperformance relative to equity-paid takeovers. However, the longest event window indicated that share payments outperformed cash acquisitions as the coefficient was negative.

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27 Nonetheless, the effects were insignificant, so these observations do not find statistical

support, but they opened the possibility for further research to investigate whether different mode of payments are more suitable depending on whether the market is bearish or bullish. In doing so, subsequent research should control for market- and firm-size factors, as well as increase the sample size as these points represent a limitation of this study.

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28

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34

Appendix For Car [-1, 1]:

Pplot, Density Plot, Residuals-versus fitted values, Variance-Inflation-Factor (same for all three CAR)

For Car [-2, 2]: Mean VIF 1.47 6 2.72 0.366980 4 1.11 0.902568 3 1.13 0.882643 2 1.06 0.946325 mainmp_num mpcash 2.49 0.402210 targetlega~m 1.18 0.850000 logdealvalue 1.16 0.859227 sameind 1.09 0.914775 crossborder 1.25 0.798570 Variable VIF 1/VIF

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