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Cross-border acquisitions and firm value: An analysis of emerging-market multinationals

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Abstract

The primary objective of this study is to examine the value implications of cross-border acquisitions of emerging-market multinationals (EMMs). We examine 433 mergers and acquisitions announcements associated with 58 EMMs during the sample period 1991–2004. The mergers and acquisitions announcements data come from the Thomson SDC Platinum database. We employ event study methodology to explore the impact of the announcements on the value of acquiring firms. The results show that, on average, cross-border expansions of EMMs through acquisitions do not create value, but point to value destruction for more than half of the transactions analyzed. To explore the factors influencing the direction and magnitude of market reaction, we analyze a cross-sectional sample of firms. While we find that target size, ownership structure of the target (private vs public), and structure of the bidder (diversified vs non-diversified) positively affect the bidder value, high-tech nature of the bidder and pursuit of targets in related industries negatively affect the bidder value. Our empirical findings provide some support for the positive impact of the stake pursued in the target firm and cultural distance, but not for the international experience and enhanced corporate governance.

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Notes

  1. For a detailed discussion of the risks associated with M&As, see Shimizu et al. (2004).

  2. Meyer, Milgrom, and Roberts (1992), Rajan and Zingales (1995), Rajan, Servaes, and Zingales (2000), and Scharfstein and Stein (2000) present models in which divisional managers exert influence to increase the assets under their control. This influence leads, in some cases, to less profitable divisions being subsidized by, and at the expense of, more profitable divisions.

  3. Brouthers and Brouthers (2000) provide some support for strategic coherence in regional clusters.

  4. We are fully aware of the limitations of this proxy. As recent corporate scandals in the US unambiguously demonstrate, being subject to a stringent “corporate governance regime” does not necessarily lead to good corporate governance practices. However, despite recent convergence in corporate governance regimes across the world, voluntary subjection to more stringent governance standards rather than their being imposed by home markets can be construed as a signal for improved corporate governance.

  5. To best of our knowledge, the information provided by the SDC Platinum database pertaining to transaction value is based on the announcement date.

  6. Although Mexico is often classified as a North American country (particularly after being part of NAFTA), we thought that regional designation in Latin America would accurately capture the countries in our sample.

  7. There are various other alternative return-generating models used in this context; however, the consensus in the finance literature is that the choice of RGM does not have any significant impact on the event study results.

  8. Coutts, Mills, and Roberts (1995) concluded that treating the abnormal returns as being independent can make a substantive difference for the longer event periods.

  9. We thank an anonymous referee for the suggestion of using relative size as an alternative.

  10. The index is accessible at http://www.fraserinstitute.org/researchandpublications/researchtopics/economicfreedom.htm The index is based on scores assigned in five categories: the size of the government; legal structure and property rights; freedom to trade internationally; access to sound money; and regulation of credit, labor and business.

  11. We use the maximum likelihood method in our logistic regression estimations.

  12. In the multivariate models, the Region 2 dummy proved to be insignificant in all event windows. In the reported model, the Region 2 model was excluded to attain a more parsimonious model.

  13. We do not report the univariate analysis tables in the paper, but the results are available upon request from the authors.

  14. In our multivariate analysis, level of control enters as an interval variable ranging from 5 to 100%.

  15. The analyses of the SCARs for each group, including positive/negative reaction ratios, are not reported here because of space constraints.

  16. For instance, see Khanna and Palepu (1997, 1999).

  17. Originally we considered the development level of institutional infrastructure and the overall level of economic development as two separate variables. However, because of the high correlation between these two variables in our multivariate model, we focused on the degree of institutional infrastructure development. We use the economic freedom index as a proxy for the development level of institutional infrastructure. As we discuss in the methodology section, the economic freedom index is a scale variable taking values between 1 and 10, where 10 indicates a high level of institutional infrastructure development.

  18. For instance, see Yeganeh and Su (2006) for an extensive review of cultural distance measures and a criticism of the Kogut and Singh measure.

  19. See Robusto (1957). The haversine formula uses latitudes to measure geographic distance d. Given R=the earth's radius (mean radius= 6371 km), and

  20. Details of the index and the scores can be found in an extensive website dedicated to the measurement of globalization: http://globalization.kof.ethz.ch/.

  21. If the distance obtained from the latitude and longitude calculation is 3835.914 km or more (the highest being 18,528.5367 km), this shows no geographical proximity: therefore a value of 0 (dummy variable) is assigned to the distance between the acquirer and the target. If the distance is 3835.914 km or less (the least being 9.4935 km), this shows geographical proximity: therefore a value of 1 is assigned. The distance of 3835.914 km was chosen because, according to our data, this is the closest distance between the continental divisions that EMMs expanded (or the expansion announcement was executed) into a particular location. Our latitude and longitude calculations were based on the following figures: the earth's circumference at the equator is 40,075.16 km and between the North and South Poles is 40,008 km. In addition, we also considered the distances between the continents. Hence distances between continents are regarded as non-proximate, and distances within continents as proximate. The liberty of choosing such a method may easily be justified by Alfred Wegener's theory of continental drift and/or the drifting of continental shelves, which, among other things, states that most continents (other than Eurasia) do not share the same tectonic plates that make up the Earth's surface. This theory is supported more today than in the past, owing to technological and sophisticated research findings (Kearey & Vine, 1996).

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Acknowledgements

We would like to thank two anonymous reviewers and JIBS Departmental Editor Lemma Senbet for their helpful editorial guidance and constructive feedback. We are particularly grateful for outstanding comments and feedback on various versions of the paper received from Şeyda Deligönül. Seyda's suggestions, along with those of the reviewers, helped us sharpen our thinking and articulate our arguments in a clearer manner. Finally, we sincerely thank John Fleming for his assistance in editing the final version of the document.

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  1. School of Business, Southern New Hampshire University, Manchester, NH, USA

    Bülent Aybar & Aysun Ficici

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Accepted by Lemma Senbet, Area Editor. This paper has been with the authors for three revisions.

Appendix

Appendix

PROXIMITY (Geographic and/or Cultural Proximity)

In order to measure geographic/cultural proximity, we use two alternative measures. Our first measure is a composite geographic and cultural distance measure. Our geographic distance measure is based on the haversine formula.Footnote 19 Our cultural proximity measure is based on the KOF index compiled by Keohane and Nye (2000).Footnote 20 Although the KOF index measures a country's globalization level, it consists of a sub-index measuring the cultural attributes of each country. Cultural distances are estimated based on the cultural dimension scores of the acquirer and target countries. Cultural and geographic distance measures are then converted into a dummy variable that takes the value 1 if the target country is geographically and/or culturally proximate to the acquirer country, and 0 otherwise.Footnote 21

Our second measure focuses on cultural distance and is based on Hofstede's (1980) widely used five cultural dimensions: power distance, individuality, masculinity, uncertainty avoidance, and long-term orientation. Since data for the fifth dimension (long-term orientation) were not available for a large number of countries in our sample, we excluded this measure in our calculation of CDI. The composite cultural distance score is based on the method suggested in Antia, Lin, and Pantzalis (2007). We briefly describe the calculation process for the CDI below.

For each transaction i (i=1, …, N, where N=433 in our case) we compute four cultural distance (CD) measures, one for each of Hofstede's cultural dimensions j (j takes values from 1 to 4, each expressing a specific dimension: PDI, IDV, MAS or UAI). CD ij is the absolute difference between the acquirer country and target country dimension score for the cultural dimension j, and is given by

where D j is the score of one of the cultural dimensions for transaction i. We create a composite CD index (CDI) from four different CD measures. The CDI is essentially a composite cultural dimension difference measure. It is computed as follows:

where Rank j (CD ij ) is the rank function, which assigns a rank for each observation in our sample, from the least different (rank of 1) to the most different (rank of N). CD ij is the jth measure of cultural difference for transaction i in our sample, and J represents the number of CD measures. The denominator, J, averages the ranks by the number of different CD variables available for each firm in the sample. Because our sample firms are required to have data on all four CD measures, J is equal to 4. Finally, by dividing by N, we scale the CDI from 0 (least different) to 1 (most different). In other words, a low CDI score (close to 0) implies low cultural distance, and a high CDI score (close to 1) implies high cultural distance.

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Aybar, B., Ficici, A. Cross-border acquisitions and firm value: An analysis of emerging-market multinationals. J Int Bus Stud 40, 1317–1338 (2009). https://doi.org/10.1057/jibs.2009.15

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