1
Value creation in M&A in Europe and Latin America
Faculty of Economics and Business MSc International Financial Management
10-02-2020
By
Laurent Hooimeijer Student number: S3853837
2 Abstract
This study examines post-announcement shareholder returns of acquiring companies. The study contributes to the empirical literature on mergers and acquisitions in emerging markets by comparing short- and long-term abnormal returns in Latin America and Europe in cross-border and domestic deals. The sample consists of 190 Latin American acquirers that are matched with 190 European counterparts based on deal volume and industry and the deal period is from 2000 – 2018. The results for short-term cumulative abnormal returns show that Latin American acquirers destroy shareholder value whereas European acquirers generate value in cross-border and domestic deals.
3 Table of Contents
1. Introduction ... 4
2. Literature review and hypotheses ... 7
2.1 Literature review ... 7
2.2 Hypothesis ... 11
3. Data and methodology ... 12
3.1 Data ... 12 3.2 Descriptive statistics ... 12 3.3 Variables... 15 3.3.1 Dependent variables ... 15 3.3.2 Independent variables ... 15 3.4 Methodology... 17 3.4.1 Event study ... 17
3.4.2 Cumulative Abnormal Returns ... 17
3.4.3 Non-parametric tests ... 18
3.4.4 Buy-and-Hold-Abnormal Returns ... 20
3.3.5 Multivariate Regression Analysis ... 21
4. Results and discussion ... 21
4.2 Multivariate analysis ... 27
5. Conclusion ... 31
4 1. Introduction
Globally, mergers and acquisitions (M&As) have grown substantially in size, frequency and strategic importance from below USD 1.3 trillion in 2002 to 4.1 trillion in 2018 (J.P. Morgan, 2019). After the 2009 financial crisis, M&A activity was upbeat which gave rise to the 7th M&A wave, which peaked in 2015 with a volume of USD 4.5 billion. According to Alexandridis, Antypas, & Travlos (2017), among the drivers of the increased activity has been the combination of difficult operating conditions with many businesses grappling to generate sales on the one side, and an ultra-low interest rate environment on the other, making acquisitions an attractive way to enhance top line growth. Acquisitions often entail paying a significant premium for the target company without guarantees that the transaction will improve the firm performance of the acquirer.
There is no shortage of examples to illustrate the benefits and risks of acquiring a company. In 2013, Microsoft CEO Steve Ballmer announced that Microsoft would acquire Nokia’s mobile division for USD 7.2 billion as the former sought to compete directly with Apple and Samsung in the mobile phone industry. Expectations were high given the potential synergies between Microsoft’s software and Nokia’s hardware. Nevertheless, the merger significantly destroyed shareholders value. Three years after the announcement, Microsoft stated it would write off most of the USD 7.2 billion Nokia deal and sell the mobile devices unit to HMD Global and Foxconn Technology for just USD 350 million (Deutsche Welle, 2016). Despite potential downside risks, acquisitions can also significantly improve firm performance. In 2017, Groupe PSA bought the lossmaking Opel brand for USD 2.3 billion from General Motors. The post-acquisition strategy entailed strengthening brands, concentrating sales by reducing the variety models available, offering more leases and ensuring strict cost efficiency (Löfgrén, Dawson, Fæste, Seppä & Cunningham, 2018). The turnaround proved highly successful for Groupe PSA as the company rebounded from losing money to an earnings before interest and tax (EBIT) margin of 6% in 2018.
5 addition, the acquirers stock gained 22% in value six months after the deal announcement. This example highlights that successful M&A’s for acquirers occur despite the limited attention in media and academia.
The effect of M&A announcements on the share price of the acquirer has often been studied in the empirical literature by using the event study methodology. Brown and Warner (1985) proposed using daily returns to compute cumulative abnormal returns (CARs). This methodology is widely used to calculate the abnormal returns surrounding the announcement date. To examine the announcement effect for a longer time horizon, Barber and Lyon (1997) proposed the buy-and-hold-abnormal return (BHAR) methodology, which allows for time horizons of several years. However, given the longer time horizon, there is significantly more noise, which might not be caused by the merger announcement and can thus distort the abnormal returns attributable to the acquisition. An additional problem with long-term event studies is the survivorship bias as only companies that continue to exist in the event window are analyzed.
This study uses the CAR and BHAR methodology to examine if the shareholders of an acquirer experience value creation through abnormal returns following a take-over announcement. Specifically, the short-term and long-term abnormal returns from acquirers in Latin America and Europe are examined. These returns also examined by comparing domestic and cross-border deals. Afterwards, this study examines several determinants of abnormal returns. A substantial body of research has investigated the cross-country determinants of mergers and acquisitions. Erel, Liao and Weisbach (2012) identified several international factors that affect the cross-sectional pattern of mergers. The authors state that cross-border mergers have a higher probability to occur between firms of countries that trade more commonly with each other. Given the close proximity of European countries, their free-trade agreements and customs union, the literature predicts a higher number of European deals relative to Latin America.
6 focusing on two geographical areas, a comparison can be made between both markets to highlight similarities and differences. Lastly, the focus of this research is on the period 2010 – 2018 as this period represents the 7th merger wave and will thus contribute to the current research. To examine the abnormal returns, this research uses the Zephyr database to find M&A deals. Next, Latin American and European deals are matched based on deal size, date and industry, which resulted in 380 “matched” deals. The deals are matched in order to make a meaningful comparison and to avoid large differences in the sample composition. Next, Eikon Reuters was used to obtain financial data on stock prices and companies.
This research investigates two main issues. Firstly, it examines differences in the short-term and long-short-term stock performance of European and Latin American domestic and cross-border acquirers following a takeover announcement. Secondly, several determinants of the M&A performance are analyzed using an OLS regression. Therefore, an attempt will be made to answer the following research question:
“To what extent do Latin American acquirers generate higher shareholder returns compared to European acquirers in the announcement period of 2010 – 2018?”
This paper finds evidence that European acquirers have higher short-term abnormal returns compared to their Latin American counterparts. Therefore, value is created for shareholders through abnormal returns. For long-term abnormal returns, this study finds evidence that 6 months after the announcement, value is destroyed for Latin American acquirers. In contrast, for European acquirers, value is created. However, no conclusive evidence was found with longer time horizons of 12 and 18 months.
7 2. Literature review and hypotheses
2.1 Literature review
A number of scholars have identified several motivations for companies to conduct acquisitions. Bower (2001) outlines five reasons why acquisitions occur: (a) to deal with overcapacity through consolidation in mature industries, (b) to roll-up competitors in geographically fragmented industries, (c) to extend into new products or markets, (d) as a substitute for R&D and (e) to exploit eroding industry boundaries by inventing an industry. Additionally, scholars have proposed a number of other reasons for engaging in M&A activity. These include growth opportunities (Harrison, Hitt & Ireland, 2001), gaining value in response to regime shifts in an industry (Mitchell & Mulherin, 1996) and managerial hubris (Roll, 1986).
Regardless of the motivations for acquiring another company, the transaction often affects the acquirers and targets share price following the announcement. As the stock prices of companies reflects all available information (Malkiel & Fama, 1970), an announcement will thus represent new information, which results in a new value of the company. This ‘‘announcement effect’’ (McNichols & Manegold, 1983) is widely studied in financial literature and occurs after an M&A announcement or earnings announcements. While most studies find that abnormal returns are generated following an earnings announcement, there is less consensus on the value creating or destroying effect of M&A announcements for acquirers. Andrade, Mitchell and Stafford (2001) examined 200 international deals in the period 1973-1998 and applied a 3-day event window surrounding the announcement date. The authors found statistically insignificant negative CARs of 0.7% for bidding companies and conclude that there is no clear evidence that bidders engage in value destruction per se. In contrast, Moeller, Schlingemann and Stulz (2004) and Masulis, Wang and Xie (2007) find positive abnormal returns when studying transactions occurring in the US market; acquirers realized CARs of 1.10% at a significance level of 1%. Kiymaz and Baker (2008), who conducted their research on the 100 largest M&A deals in the US during 1989 – 2003, found a negative CARs of 0.82% and conclude that bidders often loose in transactions. Lastly, Ishii and Xuan (2013) examined US acquirers during 1997 – 2007, reported CARs over several event windows. They found for all event windows negative CARs. For the five-day event window, this resulted in a CAR of -1.92% and -2.20% for the seven-day event window.
8 value of cross-border M&A activity in emerging markets reached USD 129 billion which is 37% of the world’s total value of cross-border M&A. Lebedev, Peng, Zie and Stevens (2014) report that many multinationals in emerging economies engage in cross-border acquisitions in order to enter new markets due a latecomer disadvantage or gain more scale-based advantages. Nevertheless, the literature on shareholder wealth creation for acquirer companies in emerging shows no consistent pattern. Aybar and Ficici (2009) find that cross-border expansions for emerging-market multinationals through acquisitions do not create value, but point to value destruction for more than half of the transactions analyzed. Specifically, they find that the average abnormal return on the day of the announcement is -1.38% while the cumulative abnormal returns surrounding the event are also negative. On the other hand, Bhagat, Malhorta and Zhu (2011) find that emerging country acquirers experience a positive and significant market response of 1.09% on the announcement day. Lebedev et al. (2014) summarize the existing literature and find that there is no established trend for the acquirer’s gain in emerging economies.
9 A substantial body of research has investigated the cross-country determinants of mergers and acquisitions. Erel, Liao and Weisbach (2012) identified several international factors that affect the cross-sectional pattern of mergers. The authors state that geography plays an important role as the shorter the distance between two countries, the higher the probability to observe an acquisition between the countries. Moreover, as pointed out by Li, Duan, He and Chan (2018), language and culture play an important role in M&A. As the authors point out, “language is the carrier of culture, and it can be viewed as one the important familiarity attributes” (Li et al., 2018, p. 82). Therefore, differences in language can create significant cultural frictions, which can result in misunderstandings or misevaluations in a corporate setting. This, in turn, may lead to value-destroying acquisitions or overpaying the targets, thereby destroying shareholder value. Li et al. (2018) conclude that acquirers will thus favor less the targets that have greater cultural distance due to uncertainties in the valuation and possible difficulties in post-merger acquisition.
10 frequent acquirers possess the skill to select undervalued companies and identify potential synergies. Given this superior skill, the announcement to acquire another company would generate positive CARs for the acquirers. The authors empirically tested this theory by examining 600 acquirers in the United States who conducted more than 4000 acquisitions between 1990-2000 and found evidence to support the hypothesis. Jaffe, Pedersen and Voetmann (2013) found similar results when examining a sample of 18,876 U.S. based acquisitions over the period 1980-2007 and conclude that the differences in acquisition skills are economically meaningful. Interestingly, acquisition experience also translates in a higher probability of successfully conducting cross-border acquisitions. Dikova and Sahib (2013) point out that “only experienced acquirers are able to successfully deal with acquisition challenges and subsequently benefit fully from cultural differences in cross-border acquisitions” (p.78). Therefore, cross-border acquisition experience results in developing pre-deal awareness of cultural differences leading to stricter target-selection criteria, better diligence and improved integration skills between culturally different partners.
11 economic restrictiveness of the country where the target company is located is negatively related to bidder returns. Furthermore, Healy, Palepu, and Ruback (1992)’s cash flow based approach shows that cross-border acquirers’ operating performance improves significantly less than that of domestic buyers. Lastly, Huang, Officer and Powell (2016) investigated differences in the method of payment between cross-border and domestic acquisitions. The authors find that greater governance risk in the country where the target is located increases the probability of stock acquisition. As a result, stock is used more in cross-border deals while cash remains the preferred method of payment across all deals (domestic and cross-border).
2.2 Hypothesis
As European acquirers engage more frequently in acquisitions and are more economically integrated via a common market, the first hypothesis states the Latin American will generate lower short-term returns (cross-border and domestic deals). Thus, the first hypothesis is: 𝐻1: The cumulative abnormal returns for Latin American acquirers are lower than the returns
of European acquirers after the acquisition announcement
The empirical results for long-term abnormal returns also remain ambiguous. However, based on the literature on cultural integration and earlier empirical findings on domestic and cross-border acquisitions, it is hypothesized that Latin American acquirers generate lower long-term returns for cross-border and domestic deals. Therefore, the second hypothesis is:
𝐻2: The buy-and-hold abnormal returns for Latin American acquirers are lower than the
12 3. Data and methodology
The following section outlines the data sampling and presents the descriptive statistics. The definitions of the dependent and independent variables are also included in this section. 3.1 Data
This thesis seeks to examine the post-announcement effect on share prices for Latin American and European firms during the period 2010 - 2018. The data on M&A deals was retrieved form Zephyr and later complemented with financial data from Eikon Datastream.
Companies and deal characteristics needed to meet the following conditions in order to be included in the sample:
For Latin American deals, the acquirers come from the following countries: Brazil, Peru, Mexico, Chile, Colombia and Argentina
For European deals, the acquirers come from the following countries: United Kingdom, the Netherlands, France, Italy, Belgium and Germany
Acquirers are publicly traded companies and are listed on exchanges in either Latin America or Europe
All deals must either be announced or completed All deals must have a deal value of at least EUR 20m
Deals from the financial sector (e.g.: banks and insurers) were excluded
In order to compute the expected returns for firms based on the market model (explained in methodology), several market indices were required. As to increase the predictive power of the model, each country was assigned its own market index as opposed to using a regional one. These indices were also used in the subsequent BHAR analysis. For all Latin American countries except Argentina, the MSCI country index of the respective country was selected. The market index for Argentina is the Merval index as there was no available MSCI index. All European countries were assigned the countries respective MSCI index.
3.2 Descriptive statistics
13 is that Latin American deals are more prevalent than European deals during 2010-2013. This may be caused by the economic conditions in Latin America, which were not as affected by the aftermath of the 2007-2008 financial crisis. During that period, Europe was struggling with the debt crisis and the financial situation in Greece. In 2015-2017, relatively more deals occurred in Europe as the economy showed strong growth resulting in significant M&A activity.
Figure 1
Development of M&A deals in Latin America and Europe in the period 2010 – 2018_______
Table 1 presents an overview containing the number of deals and the mean deal value per industry. In terms of number of deals, the most active industry in this sample is the Food, beverages and tobacco industry (72 deals) followed by chemicals, rubber, plastics and non-metallic products (66 deals).In part, this can be explained by Latin American economies which rely heavily on agricultural exports and heavy industry. The industry with the largest mean deal value is post and telecommunications. This can be explained by the consolidation of the telecommunications market, which occurred both in Latin America as in Europe. The total deal volume for the period is USD 4.2 trillion and the mean deal value for the entire sample is
63% 57% 60% 51% 50% 25% 38% 47% 49% 37% 43% 40% 49% 50% 75% 62% 53% 51% 0 5 10 15 20 25 30 35 2010 2011 2012 2013 2014 2015 2016 2017 2018
14
Table 1
Composition of M&A deals in sample This table shows in which industry the deals took place and what the mean deal value was. The presented numbers are based on the full sample consisting of 190 Latin American acquirers and 190 European acquirers. The deals took place between 2010 - 2018. Deals are adjusted for inflation.
Industry Number of
deals
Mean deal value in USD thousands Post and telecommunications
14
771,951 Metals & metal products
36 670,122 Wholesale 34 450,760 Transport 16 419,592 Gas, Water, Electricity
44
413,362 Food, beverages, tobacco
72 355,701 Construction 24 235,749 Chemicals, rubber, plastics, non-metallic
products 66 202,499 Education, Health 16 165,599 Wood, cork, paper
20
130,682 Primary Sector (agriculture, mining, etc.)
14
122,799 Machinery, equipment, furniture, recycling
12
119,599 Hotels & restaurants
15 3.3 Variables
This section elaborates which variables are used and how they relate to the existing literature. As this study examines the short- and long-term abnormal returns, the dependent variables are the cumulative abnormal returns and the buy-and-hold abnormal returns. The independent variables are Latin American acquirer, deal size, cash payment, company size and book-to-market ratio. The independent variables serve as determinants of the abnormal returns. An overview of the definition is shown in table 3.
3.3.1 Dependent variables
To obtain the short-term abnormal returns for acquires, an estimation period of (-100, -5) is used and market indexes are used depending on the geographic location of acquirer. As pointed out by Brooks (2018), the estimation period can comprise anything from 100 to 300 days. Since the event window in this study are short, an estimation period of 105 days is selected. After setting the estimation period, the CARs can be calculated (see methodology section). The short-term multivariate analysis uses a CAR window of (-2, 2) following earlier research by Bradley and Sundaram (2004) and Goergen and Renneboog (2004).
3.3.2 Independent variables 1. Latin American acquirer
By including a dummy variable for the location of the acquirer, this study examines whether the region of the acquirer acts as determinant of abnormal returns. This particular topic has not been researched so far. However, based on the findings of Erel et al. (2012) and Croci and Petmezas (2009), it is expected that acquirers from Latin America negatively influence the abnormal returns.
2. Deal size
16 3. Cash payment
M&A deals can be financed via cash, equity or a hybrid financing which is a combination of equity and cash. This research will not include hybrid financing and focusses on cash and equity offers. Goergen and Renneboog (2004) find that the shareholders of European acquirer firms prefer stock offers as opposed to cash offers. Based on these findings, it is expected that 100% cash offers positively influence abnormal returns.
4. Company size
Following Barber and Lyon (1997), the company size is calculated by multiplying the firms number of outstanding shares in June with the closing price of the shares in June. Moeller at al. (2004) examined the relationship between company size and abnormal returns and found it to be negative. Thus, it is expected that company size negatively influences abnormal returns. 5. Book-to-market ratio
Following Barber and Lyon (1997), the book-to-market ratio is computed by dividing the book value of common equity with the market capitalization of the company. High book-to-market rations arise when company’s equity is valued cheaply by the market relative to its book value. Thus, if the ratio is above one then this is indicative of undervaluation while any ratio below one shows overvaluation. It is expected that undervalued firms perform better overvalued firms and that there is a positive relationship between book to market ratio and abnormal returns.
Table 2
Variable definitions
This table defines the dependent variables (cumulative abnormal returns (CAR) and buy-and-hold abnormal returns(BHAR)) and the independent variables. The independent variables are: Latin American acquirer (Latam), natural log of deal size (Ln(Dealsize)), cash payment (Cash payment), natural log of company size (Ln(Companysize)) and natural log of acquirer's book to market ratio (Ln(BTM)).
Dependent variables
CAR (-2, 2) Five-day cumulative abnormal return of the acquirer. The estimation period used in the market is (-100, -5).
BHAR Six-, twelve and eightteen-month buy-and-hold abnormal return of the acquirer.
Independent variables
Latam Dummy variable that takes a value of 1 if the acquirer is Latin American and 0 is the acquirer is European.
Ln(Dealsize) Deal size is the price paid by the acquirer at the announcement date.
17 Ln(Companysize) Company size is calculated by multiplying the shares outstanding with the
current shareprice.
Ln(BTM) Book to market ratio is calculaed by dividing the book value of equity with the market capitalization.
3.4 Methodology
The methodology section is comprised of two main parts. The first part outlines how the event study methodology is used to compute cumulative abnormal returns (CARs) and Buy and Hold Abnormal Returns (BHAR). The second part describes the ordinary least squares (OLS) regression used to examine the relationship between the abnormal returns and the geographical location.
3.4.1 Event study
The event study methodology is widely used to measure the stock performance of companies after an event such as a merger announcement or dividend payout. Primarily, the goal of an event study is to examine whether the stock price reaction to a specific event results in value creation for the shareholders. As to such, event studies operate on the assumption that financial markets are informationally efficient as the reaction to the event should be immediate after the announcement (Brooks, 2014). This thesis uses the event study methodology to calculate the cumulative abnormal returns (CARs) as proposed by Brown and Warner (1985) for short-term events and the Buy-and-Hold-Abnormal Returns (BHAR) methodology for long-term events (Barber & Lyon, 1997). The difference between both approaches is that BHARs aggregates abnormal returns geometrically (instead of arithmetically) and it allows for compounding (Renneboog & Vansteenkiste, 2019).
3.4.2 Cumulative Abnormal Returns
To calculate the expected returns, the market model is used by constructing the expected return using a regression on the return to stock i on a constant and the return to the market portfolio. This yields:
-18 5). Next, the abnormal returns are calculated by taking the daily stock returns of the firms in the sample and subsequently subtracting the expected return:
𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡− 𝐸(𝑅𝑖𝑡) (2)
Next, in order to obtain the average abnormal return (AAR) across all stocks for any individual time t:
𝐴𝐴𝑅𝑡 = 1
𝑁 ∑ 𝐴𝑅𝑖𝑡 𝑁
𝑖=1 (3)
Following MacKinlay (1997), its variance is 𝑣𝑎𝑟 (𝐴𝐴𝑅𝑡) = 1
𝑁2∑ 𝜎𝜖𝑖
2 𝑁
𝑖=1 (4)
Finally, the cumulative abnormal return and cumulative average abnormal return is calculated as:
𝐶𝐴𝑅𝑖,(𝑡1,𝑡2)= ∑𝑡2𝑡=𝑡1𝐴𝑅𝑖𝑡 (5)
According to MacKinlay (1997), because of the variance of 𝜎𝜖𝑖
2, the null hypothesis in event studies for the 𝐴𝐴𝑅𝑡 can be tested using:
Θ = 𝐴𝐴𝑅𝑡
𝑣𝑎𝑟 √𝐴𝐴𝑅𝑡 (6)
The 𝐶𝐴𝑅𝑖,(𝑡1,𝑡2) can be tested using: Θ = 𝐶𝐴𝑅𝑖,(𝑡1,𝑡2)
𝑣𝑎𝑟 √𝐶𝐴𝑅𝑖,(𝑡1,𝑡2) (7)
3.4.3 Non-parametric tests
According to Corrado (1989), for the t-test to be optimal, the underlying distribution must be normal. The non-parametric Corrado rank test offers improved specification under the null hypothesis and more power under the alternative hypothesis of the performance of abnormal returns. The Corrado rank test is specified by:
𝑡 =
1 𝑁∑
𝐾𝑖0−𝐿+12 𝑠(𝐾) 𝑁 𝑡=1(8)
19 𝑠(𝐾) = √1 𝐿2 ∑ ( 1 𝑁∑ (𝐾𝑖𝑡− 𝐿+1 2 )) 2 𝑁 𝑖=1 𝑡2 𝑡=𝑡1 (9)
where L is the event window, N is the number of events and 𝐾𝑖𝑡 is the rank of the abnormal returns. The ranking procedure transforms a distribution of abnormal returns in a uniform distribution of rank value that is independent of the asymmetry of the original distribution (Corrado, 1989). Thus, we can assume that the distribution is normal and we can test the null hypothesis that there are no abnormal returns by a one sided t-test. Corrado (1989) uses the rank test for a one-day event window. Cowan (1992) uses the same approach and extends the rank test to more event windows by assuming that the daily return ranks within the window are independent. Accordingly, the rank test procedure treats the estimation period and the event period as a single time series and assign a rank to each daily return (Cowan, 1992).
Lastly, this study compares the CARs and BHARs of Latin American and European acquirers. To do this, the CARs and BHARs of both samples are compared using the Mann-Whitney test. In contrast to the Wicoxon test, the Mann-Whitney test allows for comparisons of unequal sample sizes (Daniel, 1978). First, the two samples 𝑛1and 𝑛2 are combined and their observations are ranked form smallest to largest. Next, the ranks of 𝑛1 are summed. Depending on the hypothesis, the rationale of the test statistic is based on the notion that either a sufficiently small or large sum of ranks assigned to sample observations from 𝑛1 causes us to reject the null hypothesis (Daniel, 1978). When 𝑛1 or 𝑛2 are larger than 20, the central limit theorem applies. Thus, the test statistic is
𝑧 = 𝑇−𝑛1𝑛2/2
√𝑛1𝑛2(𝑛1+𝑛2+1)/12 (10)
where T is 𝑇 = 𝑆 − 𝑛1(𝑛1+1)
20 3.4.4 Buy-and-Hold-Abnormal Returns
Barber and Lyon (1997) and Jegadeesh and Karceski (2009) have proposed long-run estimation windows ranging from 1 to 5 years after the event date. As a significant number of deals in the sample occur in the period 2015-2018, there is no benefit in using long event windows of multiple years. Instead, an event window of 6 and12 months is used. In contrast to using daily returns as with CARs, the data used for BHARs relies on monthly data given the long horizon of the analysis (Kothari & Warner, 2007). It should be noted that in calculating BHARs, it is assumed that all returns are equally weighted and not according to market capitalization. To calculate the BHARs for company i:
𝐵𝐻𝐴𝑅𝑖,𝑡 = ∏𝑇𝑡=1[(1 + 𝑟𝑖;𝑡)] − ∏𝑇𝑡=1[(1 + 𝐸(𝑅𝑖,𝑡)] (12) Next, the average BHAR can be calculated as:
𝐴𝐵𝐻𝐴𝑅𝑡,𝑇 = 1
𝑁∑ 𝐵𝐻𝐴𝑅𝑖;(𝑡,𝑇) 𝑁
𝑖=1 (13)
Under the null hypothesis of no event effect, the expected value of BHAR is zero. Following Lyon, Barber and Tsai (1999), this hypothesis is tested in the literature by a conventional t-statistic:
𝑡 = 𝑡=𝐴𝐵𝐻𝐴𝑅𝑡.𝑇
𝜎(𝐴𝐵𝐻𝐴𝑅𝑡,𝑇)/√𝑛 (14)
In their paper, Lyon, Barber and Tsai (1999) advocate the use of a bootstrapped skewness-adjusted t-statistic in order to eliminate the skewness bias in long-run abnormal returns. The skewness-adjusted t-statistic is calculated is
21 3.3.5 Multivariate Regression Analysis
An OLS regression is used to determine which of the independent variables can account for the and long-term acquirer returns after the announcement. Therefore, to analyze the short-term post-acquisition returns, the 7-day CAR is regressed on Latin American acquirer, deal size, cash payment, company size and book to market (see table 3 for definitions). Equivalently, the independent variables are regressed on long-term post-acquisition returns, which are measured by BHAR (0, +6), BHAR (0, +12) and BHAR (0, +18).
𝐶𝐴𝑅𝑖,𝑡 = 𝛽0+ 𝛽1∗ 𝐿𝑎𝑡𝑎𝑚 + 𝛽2∗ 𝐿𝑛(𝐷𝑒𝑎𝑙𝑠𝑖𝑧𝑒) + 𝛽3∗ 𝐶𝑎𝑠ℎ 𝑝𝑎𝑦𝑚𝑒𝑛𝑡 + 𝛽4∗
𝐿𝑛(𝐶𝑜𝑚𝑝𝑎𝑛𝑦𝑠𝑖𝑧𝑒) + 𝛽5∗ 𝐿𝑛(𝐵𝑇𝑀) (17)
𝐵𝐻𝐴𝑅𝑖,𝑡 = 𝛽0+ 𝛽1∗ 𝐿𝑎𝑡𝑎𝑚 + 𝛽2∗ 𝐿𝑛(𝐷𝑒𝑎𝑙𝑠𝑖𝑧𝑒) + 𝛽3∗ 𝐶𝑎𝑠ℎ 𝑝𝑎𝑦𝑚𝑒𝑛𝑡 + 𝛽4∗
𝐿𝑛(𝐶𝑜𝑚𝑝𝑎𝑛𝑦𝑠𝑖𝑧𝑒) + 𝛽5∗ 𝐿𝑛(𝐵𝑇𝑀) (18) 4. Results and discussion
22 4.1 Univariate analysis
Table 3
Average abnormal returns acquirer firms in Latin America and Europe
The table shows the average abnormal returns (AARs) around the acquisition announcement for acquirers. The presented numbers are for the full sample consisting of 190 firms from Latin America and 190 firms from Europe. In Latin America, 51 deals are cross-border and 139 are domestic. In Europe, 73 deals are cross border and 117 deals are domestic. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.
Europe
Cross-border Domestic
Event day AAR t-Value Corrado
rank AAR t-Value
Corrado rank -2 -0.0008 -0.42 1.07 -0.0033 -0.74 0.54 -1 0.0017 0.89 0.01 0.0056 1.19 0.28 0 0.0065** 2.26 3.12 0.0155** 2.15 4.64 1 0.0048 0.67 0.53 0.005** 2.25 2.93 2 0.006* 1.89 1.22 0.0030 1.33 -0.02 3 0.0013 0.41 0.21 0.0011* 1.95 0.93 4 -0.0010 -0.37 0.19 -0.0052 -0.82 -0.41 Latin America Cross-border Domestic
Event day AAR t-Value Corrado
rank AAR t-Value
Corrado rank -2 0.0009 0.31 0.07 -0.0017 -1.59 1.51 -1 0.0005 0.61 0.05 0.0022 1.00 0.98 0 -0.0053* -1.69 3.01 0.0085*** 2.61 3.72 1 0.0028* 1.66 2.93 -0.0034 -0.58 0.92 2 0.0017 1.44 0.89 0.0036 1.21 0.65 3 0.0003 0.08 0.16 0.0027 0.96 0.21 4 0.0001 1.47 0.07 0.0025 0.90 1.32
23 abnormal return of 0.65% while the domestic acquisitions generates 1.55%. These results are statistically significant.
Table 4
Cumulative abnormal returns of acquirer firms in Latin America and Europe
The table shows the average abnormal returns (AARs) around the acquisition announcement for acquirers. The presented numbers are for the full sample consisting of 190 firms from Latin America and 190 firms from Europe. In Latin America, 51 deals are cross-border and 139 are domestic. In Europe, 73 deals are cross border and 117 deals are domestic. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.
Europe
Cross-border Domestic
Event window CAR t-Value Corrado
rank CAR t-Value
Corrado rank (-1,+1) 0.0130* 1.68 1.87 0.0261** 2.01 2.22 (-2,+2) 0.0182* 2.11 2.18 0.0268* 1.78 1.84 (-2,+4) 0.0185** 2.07 2.11 0.0217* 1.69 1.83 Latin America Cross-border Domestic
Event window CAR t-Value Corrado
rank CAR t-Value
Corrado rank
(-1,+1) -0.002* -1.77 -2.22 0.0073*** 3.52 3.62
(-2,+2) 0.0006 1.31 1.43 0.0092* 1.84 2.01
(-2,+4) 0.0010 1.28 1.59 0.0144* 1.65 1.78
Cross-border difference Domestic difference
Event window Difference z-Value Difference z-Value
(-1,+1) 0.0150* 1.66 0.0188* -1.73
(-2,+2) 0.0176* 1.75 0.0176* -1.77
24 When observing the CARs in table 4, we see that cross-border deals in Latin America generate -0.20% and 0.73% for domestic deals when using an event window of (-1, 1). Domestic deals that occur in Latin American countries also have positive CARs of 0.92% and 1.44% for 5-day and 7-day event windows, respectively. Consequently, Latin American domestic deals generate significantly higher returns compared to cross-border deals. These findings are in line with earlier empirical findings on cross-border acquisitions from Datta and Puia (1995). For a 3-day event window, Latin American cross-border generate a statistically significant negative CAR of 0.20%. This is in line with earlier findings by Aybar and Ficici (2009) who found that emerging-market acquirers destroy value. For longer event windows, the CARs are positive but not statistically significant. A similar but more pronounced pattern is observable in Europe. Cross-border European deals generate positive CARs for all three event windows. For a 3-day event window, the CAR is 1.30% whereas 5-day and 7-day event windows yield 1.82% and 1.85%, respectively. In addition, positive returns are generated for domestic deals; 2.61%, 2.68% and 2.17% for 3-, 5-, and 7-day event windows, respectively. These findings are in line with the results of Moeller and Schlingemann (2005) who found that cross-border deals incur announcement returns of about 1% less than domestic acquirers.
By comparing the European and Latin American cross-border deals, we can observe that European cross-border outperform Latin American acquirers by a significant margin. The 3-, 5- and 7-day difference is 1.5%, 1.76% and 1.75%, respectively. Moeller and Schlingemann (2005) stated that the economic restrictiveness of the country where the target company is located negatively influences bidder returns. This insight explains the difference between European and Latin American cross-border differences since economic restrictiveness is more prevalent in Latin America than in Europe with its high level of economic integration and the European Single Market. Moreover, in both regions, domestic deals outperform cross-border deals. These findings are in line with previous empirical findings on short-term European acquirer returns. Martynova and Renneboog (2008) found that European acquirers’ CAR is 0.53% while Goergen and Renneboog (2004) found acquirer CARs of 0.70%.
25 bidders. This is an additional explanation for the significant differences in cross-border CARs and confirms earlier empirical findings by Jaffe, Pedersen and Voetmann (2013) who conclude that the differences in acquisition skill are economically meaningful.
Table 4
Buy-and-hold abnormal returns of acquirer firms in Latin America and Europe
This table shows the BHAR during specified time window. BHAR is reported after an event window of 6 months (0, +6), 12 months (0, +12) and 18 months (0, +18). *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.
Europe
Cross-border Domestic
Event window BHAR t-Value
Skewness-adj. BHAR t-Value
Skewness-adj. (0, +6) 0.0164* 1.69 1.75 0.0195* 1.77 1.82 (0, +12) -0.0191 1.23 1.54 -0.0157 0.89 1.31 (0, +18) -0.0210 1.11 1.39 -0.0199 0.54 0.94 Latin America Cross-border Domestic
Event window BHAR t-Value
Skewness-adj. BHAR t-Value
Skewness-adj.
(0, +6) -0.0160** 2.02 2.21 -0.0112* 1.78 1.84
(0, +12) -0.0242 1.34 1.47 -0.0191 1.54 1.63
(0, +18) -0.0287 0.68 1.01 -0.0210 0.87 1.04
Cross-border diffference Domestic difference
Event window Difference z-Value Difference z-Value
(0, +6) 0.0324* 1.73 0.0307* 1.66
(0, +12) 0.0051 1.30 0.0034 -1.00
(0, +18) 0.0077 1.49 0.0011 -0.61
vis-26 à-vis cross-border deals. These findings can be explained using Healy et al. (1992) who found that cross-border acquirers’ operating performance improves significantly less than that of domestic buyers.
The BHARs for time horizons of 12 and 18 months are negative for Latin American and European acquirers and point towards long-term value destruction. However, this is not conclusive due to statistical insignificance. The statistical insignificance can be explained, in part, by assigning equally weighting to each company in the BHAR analysis, which is a substantial simplification. When comparing 6 months cross-border acquirer returns, European bidders outperform Latin American firms with a statistically significant margin of 3.24%. As mentioned before, this difference is attributable to the tighter economic integration in Europe and that acquisitions occur with greater frequency because of geographic proximity and trade. Additionally, this substantial difference in cross-border returns is indicative of the difficulties arising in post-acquisitions integration due to cultural differences.
Thus, this finding supports earlier research by Datta and Puia (1995) and Chakrabarti et al. (2009) whose research concludes that cultural fit has a significant impact on wealth creation and that cross-border acquisition perform better in the long-term if the bidder and target come from culturally similar countries. In addition, this confirms the hypothesis that acquisition experience translates into a higher probability of successfully managing cross-border transactions. The increased cross-border acquisition frequency results in the development of pre-deal awareness of cultural differences, which results in better, and stricter target-selection criteria, better diligence and improved integration skills between two culturally different entities (Dikova & Sahib, 2013).
27 4.2 Multivariate analysis
Table 5
Regression results of acquirer cumulative average abnormal returns
This table shows the OLS regressions for acquirer's CAR around the acquisition announcement. The dependent variable is the acquirer cumulative average abnormal return over the 5-day event window. Variable definitions are presented in table 3. Model 1 reports the results for all deals. Model 2 reports the results for cross-border deals and model 3 reports the results for domestic deals. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.
Model 1 2 3 Deal characteristics Latam -0.005** -0.011* -0.003** (0.00) (0.00) (0.00) Ln(Dealsize) -0.043* -0.073* 0.018* (0.00) 0.00 (0.00) Cash Payment 0.013** -0.018** 0.008* (0.00) (0.00) (0.00) Firm specific Ln(Size) -0.007* -0.005* -0.003* (0.00) (0.00) (0.00) Ln (BTM) 0.009 0.009 0.012 (0.01) (0.01) (0.01) Observations 380 124 256 R-squared 0.033 0.028 0.041
Table 5 shows the results for the 5-day interval surrounding the announcement day for three different samples. All models show that the Latam dummy has a negative and statistically significant effect on dependent variable. For the cross-border sample, this effect is the largest, which is in line with earlier findings in the univariate analysis. The deal size coefficient is negative in model 1 and 2, which is in line with the findings of Alexandridis et al. (2013) whose research shows that larger deal sizes result in lower returns for the acquirer. Surprisingly, the domestic deal sample is positive and statistically significant, which can be explained by the fact that domestic acquisitions are easier to conduct due to the absence of cultural differences.
28 acquirer (Huang et al., 2016). Accordingly, when the target is located in a country with lower corporate governance practices, weaker shareholder protection or less transparency, the acquirer is restricted in what it can know about the institutional environment in which the target resides. This governance risk increases the probability that the acquirer overpays for the target firm (Huang et al., 2016). Consequently, bidder firms use stock as method of payment more often in cross-border deals in order to internalize the overpayment risk since both acquirer and target shareholders share any post-takeover losses arising from overpayment.
29
Table 6
Regression results of 6 months buy-and-hold abnormal returns This table shows the OLS regression for acquirer's 6 months BHAR around the acquisition announcement. The dependent variable is the acquirer 6 months BHAR. Variable definitions are presented in table 3. Model 1 reports the results for all deals. Model 2 reports the results for cross-border deals and model 3 reports the results for domestic deals. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.
Model 1 2 3 Deal characteristics Latam -0.201* -0.233** -0.179* (0.00) (0.00) (0.00) Ln(Dealsize) -0.015 -0.024 -0.013 (0.07) (0.05) (0.09) Cash Payment 0.082* -0.061** 0.117* (0.05) (0.03) (0.06) Firm specific Ln(Size) -0.008 0.003 0.004 (0.04) (0.03) (0.05) Ln (BTM) 0.104 0.105 0.101 (0.12) (0.15) (0.13) Observations 380 124 256 R-squared 0.027 0.023 0.038
30 Table 7
Regression results of 12 months buy-and-hold abnormal returns
This table shows the OLS regression for acquirer's 12 months BHAR around the acquisition announcement. The dependent variable is the acquirer 12 months BHAR. Variable definitions are presented in table 3. Model 1 reports the results for all deals. Model 2 reports the results for cross-border deals and model 3 reports the results for domestic deals. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.
Model 1 2 3 Deal characteristics Latam -0.210 -0.243 -0.190 (0.07) (0.12) (0.05) Ln(Dealsize) -0.039 -0.041 -0.032 (0.05) (0.09) (0.14) Cash Payment 0.098 -0.021 0.147 (0.06) (0.05) (0.14) Firm specific Ln(Size) -0.028 0.030 0.024 (0.09) (0.05) (0.07) Ln (BTM) 0.132 0.109 0.143 (0.13) (0.09) (0.15) Observations 380 124 256 R-squared 0.019 0.020 0.031
31 5. Conclusion
The purpose of this paper is to examine the differences in post-announcement shareholders returns between Latin American and European acquirers. To that end, a sample of 190 Latin American and 190 European deals that were similar in deal volume and industry were selected. The first hypotheses tested whether the short-term abnormal returns for shareholders were higher for Latin American acquirers compared to their European counterparts. The results show that Latin American acquirers destroy value in the days surrounding the announcement for cross-border and domestic deals. Specifically, European cross-border deals outperform Latin American deals by 1.5%, 1.76% and 1.75% for 3-, 5- and 7-day event windows, respectively. European domestic deals also outperformed Latin American cross-border deals by 1.88% and 1.76% for a 3- and 5-day event window. These findings are in line with earlier empirical findings that found negative returns for acquirers in developing economies. Additionally, they confirm that Europe’s economic integration, geographic proximity and acquisition experience positively influence shareholder returns. The long-term analysis shows that European cross-border deals outperform Latin American counterparts by 3.24% 6 months after the announcement. For domestic deals, this difference amounts to 3.07%. The multivariate analysis shows that Latin American acquirers negatively influence abnormal returns. Moreover, cash payment positively influences returns whereas stock financed deals in cross-border transaction are negatively related.
Based on the findings of this study, it can be concluded that shareholder value for Latin American acquirers is destroyed following the announcement of a take-over. However, due to the lack of statistical significance no definitive conclusions can be made for long-term returns beyond 6 months. The reason for this diverging performance can be found in the regional differences in which acquirers operate. European acquirers are located in closer proximity to each other and conduct more trade due to common market and customs union. This allows for more frequent M&As which enables acquirers to obtain a superior skill in selecting undervalued targets and completing acquisitions successfully. Therefore, geographic proximity and obtaining a superior skill by frequently acquiring companies appears to be more conducive to higher shareholders returns than cultural similarity as proposed by Li (2018).
32 empirical findings that acquirers in emerging markets destroy value for shareholders. However, this research is subject to several limitations. Firstly, the limited size of the data sample contributed to the absence of statistical significance for long-term returns. A larger data sample might address this issue and yield more significant results for longer time horizons. Additionally, there is the problem of selection bias as European deals had to be matched with Latin American. Therefore, the European deals examined in this study are not representative for all deals which occurred in the period 2010 – 2018. In terms of computing the BHARs, a substantial simplification was made by assigning equal weights, which contributed to inconclusive results concerning long-term performance. This assumption is a significant simplification of reality as it equalizes industry differences.
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