Journal of Financial Services Research

, 36:169

Cross-Border Bank Acquisitions: Is there a Performance Effect?

Authors

    • Board of Governors of the Federal Reserve System
Article

DOI: 10.1007/s10693-008-0043-6

Cite this article as:
Correa, R. J Financ Serv Res (2009) 36: 169. doi:10.1007/s10693-008-0043-6

Abstract

This paper uses a unique database that includes deal and bank balance sheet information for 220 cross-border acquisitions between 1996 and 2003 to analyze the characteristics and performance effects of international takeovers on target banks. A discrete choice estimation shows that banks are more likely to get acquired in a cross-border deal if they are large, bad performers, in a small country, and when the banking sector is concentrated. Post-acquisition performance for target banks does not improve in the first 2 years relative to domestically-owned financial institutions. This result is explained by a decrease in the banks’ net interest margin in developed countries and an increase in overhead costs in emerging economies.

Keywords

Mergers and acquisitionsPerformanceInternational banking

For the past 15 years the international financial system has experienced significant changes that have reshaped its structure and exposure to global shocks. An important issue in this trend has been the increasing presence of foreign banks in developed and emerging countries. The existing literature has associated financial liberalization with an increase in growth (Levine 2005), stability (Crystal et al. 2001), and better credit allocation (Giannetti and Ongena 2005) in emerging economies. It has also become one of the main policy recommendations from multilateral organizations.1

This paper uses a unique cross-border Mergers and Acquisitions (M&As) database to answer four questions: Which factors influence cross-border acquisitions? Does this type of acquisitions improve the target’s performance? Is there any post-acquisition difference in performance for targets in developed and emerging economies? Is it influenced by host-country or home-country characteristics?

The cross-border banking M&A literature has analyzed different motives that banks have to expand abroad (Amel et al. 2004). The first question addressed in this study reformulates the object of analysis by focusing on the targets instead of the acquirers. Although the approach to this question is mainly descriptive, it expands the understanding of cross-border deals in two fronts. The first contribution is to compare the characteristics of targets in cross-border deals to potential targets—in the same country or other countries—that did not get acquired. This type of analysis moves from the widely used target-acquirer comparison to a broader study of the target’s attributes relative to its peers. The second contribution is to expand the analysis to deals in emerging economies. Most of the evidence on cross-border deals has come from studying deals within the US or in Europe (Calomiris and Karceski 2000; Vander-Vennet 2002). By including targets in emerging countries, I take into account other dimensions, like the level of development that cannot be addressed using deals between banks in developed economies.

I evaluate the characteristics of banks involved in cross-border acquisitions using 220 deals that took place between 1996 and 2003. I estimate a discrete choice model to test the factors that increase the probability of an international takeover. This study finds that larger and less profitable banks doing business in highly concentrated banking sectors with lower levels of financial intermediation are more likely to get acquired in a cross-border deal.

There are two differences between these results and the previous evidence on cross-border deals. First, I find that larger banks, relative to other potential targets, are more likely to get acquired in a cross-border transaction. This result is explained by the size of target banks in emerging economies. In the wave of cross-border acquisitions that took place in the 1990s, foreign banks acquired some of the largest banks in these countries. This evidence differs from the one obtained using deals observed in developed economies where smaller targets are usually acquired (Vander Vennet 2002). The second result that diverges from the findings in the previous literature is the positive relation between market concentration and the likelihood of a cross-border acquisition. In this case the explanation is technical. Banking sector concentration increases the likelihood of a cross-border deal conditional on foreign bank entry being allowed. Previous studies used bank concentration as a measure of bank entry restrictions (Focarelli and Pozzolo 2005).

The second and third questions focus on the post-acquisition performance of target banks after a cross-border deal. As described in Berger et al. (2000), the international banking literature has outlined two hypotheses to describe the effects of global financial integration: the home field advantage hypothesis and the global advantage hypothesis. Under the home field advantage hypothesis, domestically-owned banks are on average more efficient than their foreign-owned counterparts, all else equal. This is explained by the diseconomies of managing subsidiaries from a distance, and the inherent cultural and institutional differences between countries. Under the global advantage hypothesis, well managed Multinational banks (MNBs) are able to overcome these cross-border disadvantages and with better technology, management skills, and the diversification of geographical risks, perform better than domestically-owned banks. These hypotheses have been tested using information for individual developed countries or by studying cross-border deals in Europe. The evidence shows that foreign banks do not perform better, on average, than domestically owned banks (Berger et al. 2000). In addition, the evidence on post-acquisition performance after a cross-border deal is mixed (Campa and Hernando 2006; Vander Vennet 2002). However, foreign banks in emerging economies are found to be better performers than their domestic counterparts.2

This paper tests a version of the global advantage hypothesis using a dynamic cross-country approach.3 First, I evaluate if foreign acquirers increase the target’s performance in the short-run relative to the performance of domestically-owned banks. In the second step, I compare if there is a significant difference, in terms of post-acquisition performance, between targets located in emerging and developed economies after a cross-border acquisitions. If the global advantage hypothesis is true, the acquisitions conducted by more efficient foreign banks should have a noticeable effect on the performance of targets in emerging economies, as the differences in terms of geographical diversification and technological advantage are maximized.

Post-acquisition changes in performance are tested using a sub-sample of 102 deals with information for at least 2 years before and after a cross-border deal. A difference-in-difference analysis is used to control for economy-wide and country-specific effects. As the counterfactual to the targets’ profitability measures, I construct a country-specific index that reflects the aggregate performance of local non-acquired banks. I find that acquired banks perform at the same level—and sometimes worse—relative to the country-specific indices after a takeover. This negative change in profitability is mostly explained by a decline in Net Interest Margins. Loan Loss Provisions decrease after acquisitions, partially compensating the negative effect of the cross-border deal on income.

The next step is to compare deals involving targets located in emerging economies to those associated with targets in developed countries. The targets overall performance is not significantly different for the two groups of banks after cross-border deals. A detailed look at the change in individual components of the banks’ income statements shows little differences between banks in emerging and developed countries after an international deal. Nevertheless, there are some contrasts that have to be noted. In particular, median Net Interest Margins and expenditures in non-interest and personnel costs decline in developed countries while the opposite is the case in emerging economies.

These results show weak support for the global advantage hypothesis, at least in the short run. MNBs are not able to increase the performance of the acquired targets relative to that of domestically-owned banks, but they do not perform significantly worse than their domestic counterparts. These findings demonstrate the difficulties in improving efficiency in different institutional, economic, and cultural environments.4

Given the weak performance of foreign-owned banks after a cross-border deal, I test the significance of diseconomies in managing foreign subsidiaries. In particular, I assess the effect of differences in language, legal origin, and geographical distance on performance. I find that these diseconomies are important, in particular, in post-acquisition cost reductions. Targets perform better if the home country of the acquirer and the host country share the same language. In contrast, differences in neither legal origin nor distance appear to affect performance negatively in the post-acquisition period.

The rest of the paper is organized as follows. “Section 1” reviews the literature on cross-border acquisitions and their impact on bank performance. “Section 2” describes the empirical methodology used to answer the questions posed in this study. “Section 3” describes the data and sample selection criteria. “Section 4” presents the main results. Finally, “Section 5” concludes.

1 Motivation and related literature

The literature on cross-border acquisitions has studied the motivation and consequences of this type of deals from different perspectives. A first set of studies analyzes the determinants of cross-border bank acquisitions. The motivation for cross-border consolidation ranges from the “follow-your-customer” hypothesis (Miller and Parkhe 1998; Esperanca and Gulamhussen 2001) to differences in efficiency between acquirers and target banks (Berger et al. 2000). Some studies have explained these deals using arguments from the Foreign Direct Investment (FDI) literature (Goldberg 2004) and New Trade Theory (Berger et al. 2004) literature. Using a sample of OECD countries, Focarelli and Pozzolo (2005) find that it is more likely for MNBs to enter countries “where the expected economic growth is higher”, banking sector concentration is lower, and the regulatory environment is less stringent.5 In a related study, Claessens and Van Horen (2007) argue that institutional competitive advantages are an important determinant of locational decisions in international banking. MNBs expand to countries with institutions that are similar to those that they have in their home country—relative to the institutional environment of competing MNBs in other countries. Lastly, cross-border acquisitions have been relatively scarce compared to their domestic counterpart. Buch and DeLong (2004) argue that information costs and regulatory restrictions significantly reduce the number of cross-border deals.

This paper expands the literature reviewed above by analyzing both the determinants of financial FDI at the country level, and also focusing on the target-specific characteristics that motivate cross-border acquisitions. The framework used in this study is similar to the approaches followed in Focarelli et al. (2002) for Italian banks and Hannan and Rhoades (1987) for US institutions.

A second strand of the literature focuses on the effect of M&As on stock prices and accounting measures of performance. Piloff and Santomero (1998) and Calomiris and Karceski (2000) review the main findings in this literature for US financial institutions.6 The typical analysis of M&As using stock price data compares the change in returns after a deal is announced. These studies find a negligible effect of M&As deals on stock market value. There is a transfer of wealth from the acquirer to the target’s shareholders mostly explained by high premiums paid on these transactions. The lack of comparable stock price information across countries—outside of Europe—has limited the amount of studies using the event methodology to analyze performance effects after cross-border M&As.7 In one of the few studies that uses the link between cross-border deal information and stock prices, Amihud et al. (2002) find that there is no reduction in risk for those banks that diversify geographically by acquiring financial institutions abroad. Moreover, the cumulative abnormal returns for acquirers in these transactions are negative and significant.

Another group of studies uses accounting data to asses the effect of M&As on operating performance. Chamberlain (1998) analyzes a sample of deals that took place in the US in the 1980s and finds that these transactions did not yield any operating efficiencies. This result is consistent with similar evidence that shows no improvements in Return on Assets (ROA) or growth in operating income in the same time period (Linder and Crane 1992). A limited number of studies show positive changes in performance after M&A deals in 1980s, for instance, Cornett and Tehranian (1992) find an increase in the post-acquisition Return on Equity (ROE) and operating cash flow, but the authors focus only on 30 mergers between 1982 and 1987. In the 1990s, the observed post-acquisition performance of institutions involved in M&A deals improved on average. Technological changes and the deregulation of national branching by financial institutions are suggested as possible explanations for this difference in the post-acquisition performance of merged institutions (Cornett et al. 2006; Berger et al. 1999).

On the international side, Vander Vennet (2002) studies a sample of European cross-border deals and finds an increase in profit efficiency for target banks on the first year after an acquisition. Nevertheless, the author does not find similar improvements in the cost efficiency and ROA measures. Using a larger sample of cross-border deals, Becalli and Frantz (2007, unpublished manuscript) find the opposite result: a decrease in profit efficiency and an increase in cost efficiency after cross-border deals. The difference in these findings could be explained by the laxer sample selection criteria used in the latter study. The authors do not restrict the sample of deals to those acquisitions were the target bank’s control is transferred to the acquiring institution. Therefore, the results might be driven by the effect of minority share acquisitions. As summarized in these two studies, the effect of cross-border M&As on the targets’ post-acquisition performance is inconclusive, and might depend on the location of the target and the level of control of the acquirer over its new subsidiary.

The literature reviewed in this section finds mixed effects in terms of the impact of M&As on banks in developed economies. Alternatively, some empirical studies suggest that foreign bank presence benefits emerging economies in different dimensions. In countries with a larger presence of MNBs, the domestic banking sector is more efficient (Claessens et al. 2001; Bayraktar and Wang 2004), stable (Crystal et al. 2001), capital allocation improves (Giannetti and Ongena 2005), and economic growth is enhanced (Levine 2001).

The current paper expands these last two strands of the literature by using accounting data to assess the effect of cross-border acquisitions on the targets’ operating performance. To analyze this effect, I construct a large sample of deals that includes targets in developed and emerging economies and focus on acquisitions where control of the target institution is passed to the foreign acquirer.

2 Empirical methodology

2.1 Determinants of cross-border acquisitions

This section describes the methodology used to test the first question addressed by this study. Following Vander-Vennet (2002) and Focarelli et al. (2002), I use a probit-model to estimate the characteristics of banks that are involved in cross-border acquisitions in comparison to those that are not part of any deal during the sample period. The dependent variable is a binary choice variable equaling one, the year a bank is the target in a takeover where the acquirer is a foreign financial institution. The model to estimate is given by:
$$\Pr \left( {Y_{ijt} = 1} \right) = \Phi \left( {X_{it - 1} ,Z_{jt - 1} ,M_{jt - 1} } \right)$$
(1)
where Yijt equals one when bank i in country j gets acquired in year t by a foreign bank and zero otherwise; Φ is the standard cumulative normal probability distribution; Xit − 1 is a vector of bank-specific variables; Zjt − 1 represents a vector of country characteristics, including macroeconomic aggregates and financial indicators; Mjt − 1 is a vector of variables that describe the regulatory environment and concentration level in the banking sector by country. Estimations include year fixed effects and standard errors are clustered by country.

All explanatory variables enter in the regression with one lag. This specification assumes that buyers take the decision to acquire a target using information available to them at the end of the year before the acquisition takes place. The regressors’ coefficients in this model indicate the change in the probit score in terms of standard deviations, following a one-unit increase in the predictors. To establish the relevant characteristics determining cross-border deals, I test the significance and magnitude of these coefficients.

Following Focarelli and Pozzolo (2000), four sets of variables are included in these estimations. The first group of variables consists of bank-specific measures of the ex ante levels of bank profitability, size, capital, and lending activity.8 The main focus of this analysis is to compare the characteristics of banks that were targets in cross-border deals to those that did not get acquired. The previous literature on cross-border acquisitions compares acquirers to targets and finds that larger and more profitable banks acquire smaller and less profitable targets (Vander-Vennet 2002; Focarelli and Pozzolo 2005). This evidence implies that the coefficient on log assets and profitability should be negative. The ex ante expectations on capital and lending activity are ambiguous. Previous studies have not found a significant difference between targets and non-acquired banks with regard to these measures.

The second set of variables is taken from the literature on the determinants of economic growth, and includes real GDP, inflation, GDP per capita growth, and Private Credit to GDP—a measure of financial intermediation (Levine 2005). I expect the signs on the coefficients of the first three variables to be negative, except for inflation, as the potential growth of countries that have lower levels of output and consumer prices is higher. Banks will be more likely to enter countries were potential growth is expected to be higher.

The third group includes variables that proxy for regulatory restrictions and bank concentration.9 These proxies measure a host country’s banking structure and its limitations to bank entry. Although higher concentration may imply significant barriers to entry, it also means that a foreign entrant will likely face lower threats of competition from other MNBs in the future (Focarelli and Pozzolo 2005). Therefore, the sign on the concentration proxy is ambiguous. Regulatory restrictions are expected to have a negative effect on the likelihood of foreign bank entry.

Finally, the last set of variables measure the financial structure of market-based financial intermediation in the host country. I use three ratios to capture the size of financial markets: the stock market capitalization to GDP, and the private and public bond market capitalization to GDP. The expected sign on the coefficients of these variables is ambiguous. On the one hand, a higher level of financial market capitalization implies larger expected levels of economic growth (Levine and Zevros 1998). Banks will have an incentive to acquire targets in countries with developed financial markets to gain the benefits of higher potential growth. On the other hand, markets may compete with banks as a source of financial intermediation. This substitution effect will potentially discourage them from acquiring targets in countries where market based financing is well developed.10

2.2 Performance effect

The second question outlined in this paper analyzes the change in performance for target banks after a cross-border acquisition. In order to measure this change, I have to determine what the target’s performance would have been if the acquisition had not taken place. This study draws on Cornett et al. (2005, unpublished manuscript) and measures the counterfactual of the target’s performance with a country-specific bank index. The effect of the deal is calculated by subtracting this benchmark from the acquired-bank’s performance indicators, and comparing this measure between the pre- and post-acquisition period. This estimation technique controls for possible differences in accounting methods across countries, regulatory environments, and country specific-economic activity.

The empirical methodology in this section follows Chamberlain (1998). The target’s performance is assumed to be given by:
$$r_{\tau i} = \mu _z + c_{\tau i} + \eta _{\tau i} $$
(2)
where \(r_{\tau i} \) represents the performance proxy for target i at event time τ; μz is a constant treatment effect; \(c_{\tau i} \) is an unobserved target control effect; and \(\eta _{\tau i} \) represents a target specific error term.
The control effect (\(c_{\tau i} \)) is measured with error using the country (j) specific industry index. This measure is defined as:
$$c_{\tau {\text{j}}} = c_{\tau i} + \varepsilon _{\tau j} $$
(3)
It is assumed that \(\eta _{\tau i} \) and \(\varepsilon _{\tau j} \) are mutually and cross-sectionally independent, but could be correlated over time. Then, by subtracting Eq. 3 from Eq. 2 I obtain:
$$r_{\tau i} - {\text{c}}_{\tau {\text{j}}} = \mu _z + \eta _{\tau i} - {\text{ $ \varepsilon $ }}_{\tau i} = \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\mu } _{\tau i} $$
(4)
With this expression I can compute the pre-acquisition (\(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\mu } _{bi} \)) and post-acquisition (\(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\mu } _{ai} \)) relative performance measures by averaging all \(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\mu } _{\tau i} \) in each period. These measures will proxy for the treatment effect μz with an error that is independent across observations. Using the sample distributions of \(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\mu } _{bi} \) and \(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\mu } _{ai} \), I test for changes in the target’s relative performance (ρ) after an acquisition. By subtracting \(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\mu } _{bi} \) from \(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\mu } _{ai} \), ρ plus an error term (νi) are obtained:
$$\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\mu } _{ai} - \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\mu } _{bi} = \rho + \nu _i = \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\rho } _i $$
(5)

The Sign Test and \(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\rho } _i \) are used to examine the null hypothesis that the number of positive and negative relative differences are equal.11 In other words, this method tests if cross-border acquisitions had an effect on the target banks’ performance. The only requirement for the Sign Test is that each νi has to come from a continuous median zero distribution.

Bank performance is measured using three accounting ratios: Return on Average Assets (ROA), Return on Average Equity (ROE) and the Cost to Income Ratio.12 In addition, I analyze the post-acquisition change in four revenue and cost components: Net Interest Margin, Non-Interest Income, Overhead, and Loan Loss Provision.13

Finally, to answer the question about the differences in post-acquisition performance by targets depending on the level of the development of the host country, I divide the sample between the targets located in emerging countries and those in developed economies. Following Barth et al. (2000), a bank is defined as being located in a developed country, if GDP per capita in the host-country is above 10,000 dollars (2000 US dollars). Then, performance and other income indicators are compared using the Sign Test, Wilcoxon Test, and the Median Test.

2.3 Performance, economic integration, and information costs

The third set of tests assesses the effect of economic integration and information costs on the target’s performance after a cross-border acquisition takes place. Buch and DeLong (2004) find that information costs and regulation decrease the amount of cross-border M&A activity.14 I evaluate whether similar factors also have an effect on the post-acquisition performance of the targets. The following empirical specification includes measures of information costs and economic integration to test their effect on post-acquisition bank profitability:
$$y_{{ijt}} = \alpha _{0} + \alpha _{1} Yr0 + \alpha _{2} Yr12 + \alpha _{3} Yr3^{ + } + X_{{jh}} \beta \prime + Z_{{jt}} \gamma \prime + \upsilon _{i} + \eta _{j} + \varepsilon _{{ijt}}$$
(6)
where yijt is the performance proxy for year t, country j, and deal i. This variable is a transformation of the original balance sheet ratios into percentile ranks in the distribution of all non-acquired banks by country.15 This method makes it possible to control for changes in the distribution of the relevant variables over time, as well as comparing the target banks to their relevant peer group. Yr0, Yr12, and Yr3+ are indicator variables equal to one for the year the deal takes place, for the first and second year after the deal, and for the third year and after, respectively; Xjh is a vector of bilateral variables representing information costs and the level of integration between the host country j and the home country h; Zjt is a vector of macroeconomic aggregates and bank competition variables; ηj and υi are host-country and target fixed effects, respectively.

As discussed in Berger and DeYoung (2001), there are diseconomies in managing subsidiaries that are located at longer distances relative to their parent bank’s location. The same argument applies to other dimensions of distance like the difference in language and legal systems across countries. Vector X controls for these factors as it includes a dummy indicating if the country of the acquirer and target share the same principal language (Same Language); another indicator variable equals one if both countries have similar legal systems (Same Legal).16Log distance measures the geographical distance between the host country and home country of the acquirer; Same Region is a dummy variable equaling one if the target and acquirer are located in the same region. Consistent with the previous literature, I expect that subsidiaries that are managed at longer distances and where the legal system and language of the acquirer’s and target’s countries are different will have post-deal performance that is worse than its peers.

Following Berger et al. (2004) I also include an index of comparative size (Similar GDP) and another index measuring comparative economic development (Similar GDP PC) between the home and host countries.17 These indices range from 0 to 1, with a value of 1 indicating that both countries have the same size or the same GDP per capita. The expected sign on the coefficients of these variables is ambiguous. An acquirer from a developed country entering an emerging economy has a potential advantage in terms of its technology, financial diversification, and scale. These characteristics will make it more likely for the acquirer to extract the benefits of the higher potential growth opportunities in the host country. Therefore, larger disparity between the host and home countries in terms of size and economic development will increase the post-deal performance of the target relative to its peers. On the other hand, significant differences in these dimensions, will force the acquirer to assume the costs of learning to operate in a new environment. Depending on the magnitude of these counterbalancing effects, the variables will have positive or negative signs.

3 Data description

To estimate the models defined in the previous section, I compile a sample of banks involved in cross-border deals between 1994 and 2003. I search all cross-border deals included in the Zephyr database from Bureau van Dijk, the SDC Platinum database from Thomson Financial Securities Data, and individual bank webpages in the sample period. The financial statements of the target banks are collected from the Bankscope database maintained by Bureau van Dijk. This dataset contains annual statements for listed and unlisted banks in 179 countries starting at the beginning of the 1990s. One of the motivations of this paper is to assess the differences between targets involved in cross-border deals and banks that have not been part of this type of transactions but compete in the same markets. For this purpose, I use Bankscope to compile a sample of banks located in the targets’ countries that are not part of any domestic or cross-border deal. This group of banks represents the control sample against which targets will be compared.

The next sub-section describes the sample selection criteria for banks included in the cross-border deal and control samples. Then, I define the methodology to construct control indices that are used as benchmarks in the performance tests. Finally, I describe a set of additional controls used in the main estimations.

3.1 Sample selection

In this section I describe the criteria used to select the sample of banks included in the empirical estimations. The end result is three different samples: a sample of banks not involved in any cross-border deals, a sample of targets acquired in cross-border deals, and a restricted sample of targets with balance sheet information for at least 2 years before and after a cross-border deal.

The starting point to select the sample of banks used in the empirical tests involves extracting information for all financial institutions classified as Commercial Banks in Bankscope between 1994 and 2003.18 From this sample, I exclude banks from countries that did not have any cross-border deals in this period, or banks with financial information that is considered to be extreme.19 After applying these criteria, the complete sample includes 3,295 banks in 71 countries. Table 1 shows the distribution of banks across countries. A large fraction of the sample is represented by financial institutions from the US (27.3%), Germany (5.5%), and France (5.3%). Amongst emerging economies, Brazil (2.9%), Argentina (2%), and Panama (1.8%) have the largest shares.20
Table 1

Banks and deals by country. Deal data is from Zephyr, SDC and the banks’ webpages. Bank data is from Bankscope. The deals’ sample period ranges between 1996 and 2003. Bank balance sheet and income statement information covers the period between 1994 and 2004

 

Total banks

Total deals

Performance deals

Banks Percentage

Deals Percentage

Deals Percentage

Albania

5

0.2%

0

0.0%

0

0.0%

Argentina

66

2.0%

11

5.0%

1

1.0%

Australia

25

0.8%

1

0.5%

1

1.0%

Austria

47

1.4%

3

1.4%

2

2.0%

Belarus

9

0.3%

1

0.5%

0

0.0%

Belgium

35

1.1%

7

3.2%

6

5.9%

Bolivia

11

0.3%

2

0.9%

1

1.0%

Bosnia-Herzegovina

15

0.5%

2

0.9%

1

1.0%

Brazil

94

2.9%

12

5.5%

6

5.9%

Bulgaria

22

0.7%

5

2.3%

3

2.9%

Cameroon

4

0.1%

1

0.5%

0

0.0%

Canada

47

1.4%

2

0.9%

0

0.0%

Chad

3

0.1%

0

0.0%

0

0.0%

Chile

24

0.7%

4

1.8%

2

2.0%

Colombia

23

0.7%

2

0.9%

2

2.0%

Croatia

32

1.0%

4

1.8%

2

2.0%

Czech Republic

17

0.5%

7

3.2%

2

2.0%

Denmark

53

1.6%

3

1.4%

2

2.0%

Dominican Republic

24

0.7%

1

0.5%

0

0.0%

Egypt

28

0.8%

4

1.8%

2

2.0%

El Salvador

7

0.2%

1

0.5%

0

0.0%

Estonia

5

0.2%

3

1.4%

0

0.0%

Finland

5

0.2%

1

0.5%

0

0.0%

France

173

5.3%

12

5.5%

6

5.9%

Germany

182

5.5%

12

5.5%

8

7.8%

Ghana

10

0.3%

1

0.5%

0

0.0%

Hong Kong

14

0.4%

0

0.0%

0

0.0%

Hungary

27

0.8%

4

1.8%

1

1.0%

Indonesia

49

1.5%

4

1.8%

2

2.0%

Ireland

15

0.5%

0

0.0%

0

0.0%

Italy

110

3.3%

1

0.5%

1

1.0%

Jamaica

6

0.2%

1

0.5%

0

0.0%

Japan

133

4.0%

0

0.0%

0

0.0%

Kenya

23

0.7%

0

0.0%

0

0.0%

Republic of Korea

13

0.4%

0

0.0%

0

0.0%

Latvia

19

0.6%

7

3.2%

1

1.0%

Lebanon

43

1.3%

1

0.5%

0

0.0%

Lithuania

10

0.3%

6

2.7%

0

0.0%

Luxembourg

102

3.1%

4

1.8%

2

2.0%

Macau

5

0.2%

1

0.5%

1

1.0%

Macedonia (Fyrom)

10

0.3%

2

0.9%

1

1.0%

Mexico

36

1.1%

6

2.7%

3

2.9%

Mongolia

3

0.1%

0

0.0%

0

0.0%

Morocco

7

0.2%

1

0.5%

1

1.0%

Netherlands

21

0.6%

2

0.9%

2

2.0%

New Zealand

8

0.2%

0

0.0%

0

0.0%

Nicaragua

8

0.2%

1

0.5%

1

1.0%

Norway

12

0.4%

3

1.4%

2

2.0%

Pakistan

19

0.6%

0

0.0%

1

1.0%

Panama

59

1.8%

3

1.4%

0

0.0%

Paraguay

18

0.5%

1

0.5%

0

0.0%

Peru

16

0.5%

3

1.4%

1

1.0%

Philippines

22

0.7%

1

0.5%

1

1.0%

Poland

39

1.2%

11

5.0%

7

6.9%

Portugal

21

0.6%

1

0.5%

0

0.0%

Romania

14

0.4%

4

1.8%

2

2.0%

Russian Federation

80

2.4%

0

0.0%

0

0.0%

Slovakia

12

0.4%

7

3.2%

4

3.9%

Slovenia

17

0.5%

3

1.4%

3

2.9%

Spain

74

2.2%

7

3.2%

3

2.9%

Switzerland

157

4.8%

8

3.6%

3

2.9%

Thailand

7

0.2%

1

0.5%

1

1.0%

Tunisia

15

0.5%

1

0.5%

1

1.0%

Turkey

10

0.3%

0

0.0%

0

0.0%

Uganda

12

0.4%

1

0.5%

0

0.0%

Ukraine

29

0.9%

0

0.0%

0

0.0%

UK

63

1.9%

2

0.9%

1

1.0%

Uruguay

31

0.9%

2

0.9%

1

1.0%

US

900

27.3%

12

5.5%

6

5.9%

Venezuela

37

1.1%

5

2.3%

2

2.0%

Western Samoa

3

0.1%

1

0.5%

0

0.0%

Total

3,295

 

220

 

102

 
The second sample includes all banks—selected from the total group of banks defined above—that were acquired in cross-border transactions between 1996 and 2003. This paper requires two conditions for a deal to be defined as a cross-border acquisition: first, the transaction has to give the acquiring bank a majority stake (more than 50%) in the target bank, provided that it previously held either no shares or a minority stockholding in the target. Additionally, the headquarters of the target bank has to be located in a country different from the home-country of the ultimate parent of the acquirer. The result is 220 deals matched to Bankscope. As shown in Table 1, one-third of the deals involve targets in the US, France, Germany, Brazil, Argentina, and Poland.21 Panel A in Table 2 shows that 174 of these targets were acquired by Western European institutions. The preferred destinations of these acquirers are Western and Eastern European countries (56 and 55 targets, respectively), closely followed by Latin American countries (40).
Table 2

Deals by region. Deal data is from Zephyr, SDC and the banks’ webpages. The deals’ sample period ranges between 1996 and 2003

 

Acquirer

Latin America

Eastern Europe

East Asia

Western Europe

US and Canada

Oceania

Africa

Middle East

Total

Panel A: All Deals

 Target

Latin America

7

0

0

40

7

0

0

1

55

Eastern Europe

0

8

1

55

2

0

0

0

66

East Asia

0

0

3

3

1

0

0

0

7

Western Europe

1

3

0

56

5

0

0

1

66

US and Canada

1

0

1

10

2

0

0

0

14

Oceania

0

0

0

1

0

1

0

0

2

Africa

0

0

0

9

0

0

0

0

9

Middle East

0

0

0

0

0

0

0

1

1

 

Total

9

11

5

174

17

1

0

3

220

 

Panel B: Performance Deals

 Target

Latin America

0

0

0

17

2

0

0

1

20

Eastern Europe

0

1

0

25

1

0

0

0

27

East Asia

0

0

2

2

1

1

0

0

6

Western Europe

1

3

0

33

0

0

0

1

38

US and Canada

1

0

1

2

2

0

0

0

6

Oceania

0

0

0

1

0

0

0

0

1

Africa

0

0

0

4

0

0

0

0

4

Middle East

0

0

0

0

0

0

0

0

0

 

Total

2

4

3

84

6

1

0

2

102

Table 3 displays summary statistics for this sample of cross-border deals. Acquired and non-acquired banks are similar in terms of their level of equity as shown in Panels A and B, but the median size, defined as Real Assets, is larger for the former group. The three performance measures for non-acquired banks, ROA, ROE, and the Cost to Income Ratio, have larger medians in the first two cases and lower in the last case, relative to the target banks. These statistics show that the median acquired bank performed less efficiently than its non-acquired counterpart during the sample period.
Table 3

Summary statistics. Bank Balance Sheet and Income Statement data is from Bankscope. The sample period is 1994 to 2003. The variable Real Assets is defined in terms of millions of 2000 US dollars. The rest of the variables are defined in terms of percentage points

 

Obs.

Mean

Median

SD

Min.

Max.

Panel A: Acquired Banks

 Real assets

1,576

6,357

1,075

15,618

5

150,292

 Equity to avg. assets

1,578

12.22

9.28

10.8

1.0

95.2

 ROA

1,578

1.02

0.84

2.0

−8.8

11.8

 ROE

1,577

9.09

9.34

18.5

−96.9

135.4

 Cost to Income Ratio

1,578

71.80

67.55

27.6

3.4

232.4

 Net loans to avg. assets

1,577

48.37

49.56

20.7

0.0

98.8

 Net interest margins

1,578

4.82

3.80

3.9

−1.8

27.8

 Non-interest inc. to avg. ass.

1,578

2.73

1.86

3.3

0.0

54.6

 

Panel B: Non-acquired Banks

 Real Assets

30,096

11,244

854

54,661

0

1,352,996

 Equity to Avg. Assets

30,393

12.66

8.79

13.6

0.0

100.0

 ROA

30,404

1.09

0.92

1.7

−10.0

12.0

 ROE

30,367

10.55

10.07

19.2

−100.0

928.0

 Cost to Income Ratio

30,404

65.22

63.45

24.3

0.0

244.0

 Net loans to avg. assets

30,106

51.68

55.99

23.6

0.0

100.0

 Net interest margins

30,404

4.08

3.53

3.4

−2.3

28.0

 Non-interest inc. to avg. ass.

30,404

2.50

1.28

4.3

0.0

92.5

The third sample of cross-border deals is used in the performance estimations described in “Section 2.2.” From the total group of targets involved in cross-border deals, I select those banks with at least 2 years of non-missing information before and after a deal.22 This restriction reduces the sample to 102 deals, shown in the last two columns of Table 1. A significant share of targets is located in Germany (7.8%), Belgium (5.9%), Brazil (5.9%), Poland (6.9%), and the US (5.9%). The share of Argentinean (1%) banks in this sample decreases relative to the full set of deals in this country, due to missing and outlier observations attributed to the banking crisis in 2001. Panel B in Table 2 shows that most of the acquirers are based in Western European countries (84). These financial institutions are mostly involved in deals within the region (33) or in Eastern European (25) and Latin American (17) countries.

Figure 1 shows the number of total deals and the sub-sample of deals used in the performance estimations by year. Most of the deals are clustered around the last years of the 1990s. Data restrictions for the performance estimations reduce the sample of deals considerably.
https://static-content.springer.com/image/art%3A10.1007%2Fs10693-008-0043-6/MediaObjects/10693_2008_43_Fig1_HTML.gif
Fig. 1

Number of cross-border deals by year

To estimate the regressions in “Section 2.3”, I relax the restriction to include deals with non-missing information from two to at least 1 year before and after the acquisition. This change in the selection criteria increases the sample to 132 cross-border deals for the period between 1996 and 2003.

3.2 Control indices

As it was described in “Section 2.2”, to accurately assess the change in performance before and after a cross-border acquisition, I have to control for the overall changes in banking activity at the country level. This study uses the methodology described in Cornett and Tehranian (1992) and Linder and Crane (1992) to construct a benchmark against which the performance of target banks is compared. For this purpose, I estimate banking industry indices for each country where targets in cross-border deals were acquired.

The selection of banks included in these indices starts with the sample of non-acquired banks described in the previous sub-section. Countries with less than five banks with non-missing information in any year between 1994 and 2004 are excluded. I use this sample of banks and estimate averages for the relevant performance and income statement variables. I call these aggregates country indices and use them as the counterfactual to the target banks’ profitability measures.

In “Section 2.3”, yijt was defined as a percentile rank transformation of the performance ratios. I use the same peer group of banks, included in the country indices, to calculate these percentile ranks. A value of 0.50 in the percentile rank scale indicates that a bank has better performance than 50% of its peers in the variable being evaluated.

3.3 Additional controls

In addition to bank information, controls at the country level are also included in the estimations. Macroeconomic and financial aggregates are from the World Development Indicators (WDI) database and the Financial Structure and Development database published by the World Bank. The Banking Freedom index is constructed by the Heritage Foundation.23 It has values between 0 and 100 and measures the stringency of financial regulation in a country. Higher values for this index imply a more liberalized banking sector. Institutional variables are taken from La Porta et al. (2002), and bilateral data were compiled by Rose and Spiegel (2004).

4 Results

4.1 Determinants of cross-border acquisitions

Table 4 shows the results of the probit estimation described in Eq. 1. Columns (1) through (3) include bank, country, and banking market characteristics as regressors. These columns differ in the performance proxy used in the estimations. The coefficients for ROA and ROE are negative, and positive and significant for the Cost to Income Ratio. All this coefficients are significant at the 1% level. This suggests that there is a higher probability for ex ante poorly performing banks of being acquired in a cross-border deal. In addition, larger banks are more likely to be targets, especially if they are located in smaller countries with low levels of financial intermediation. This is supported by the coefficients on Log Assets, Log GDP, and Private Credit to GDP, respectively. Finally, Concentration has a positive and significant coefficient, with a similar level across the three columns.
Table 4

Determinants of cross-border acquisitions. The empirical model in Eq. 1 has been estimated using a probit specification. The dependent variable equals one if a bank is acquired by a foreign institution in year t and zero otherwise. The model is explained in “Section 2.1”; the sample is defined in “Section 3.1”. The model is estimated for the 1994–2003 period. Columns (1) through (6) differ in the performance proxy included. In Columns (1) and (3) profitability is measured by the Return on Average Assets (ROA). Columns (2) and (5) include the Return on Average Equity (ROE). In Columns (3) and (6) performance is defined as the Cost to Income Ratio. Columns (4) to (6) include Financial Development proxies in addition to the variables included in the first three columns. All estimations include time fixed effects. Robust standard errors clustered by country in brackets

 

ROA

ROE

Cost to income ratio

ROA

ROE

Cost to income ratio

(1)

(2)

(3)

(4)

(5)

(6)

Performance

−0.0511a

−0.0060a

0.0052a

−0.0430b

−0.0053a

0.0054a

[0.0160]

[0.0018]

[0.0011]

[0.0180]

[0.0019]

[0.0013]

Log assets

0.0815a

0.0821a

0.0909a

0.0609b

0.0616b

0.0723a

[0.0212]

[0.0211]

[0.0209]

[0.0248]

[0.0249]

[0.0248]

Equity to assets

0.0015

−0.0005

0.0009

0.0003

−0.0012

0.0000

[0.0021]

[0.0021]

[0.0021]

[0.0026]

[0.0024]

[0.0026]

Net loans to assets

−0.0005

−0.0006

0.0002

0.0000

−0.0001

0.0007

[0.0016]

[0.0016]

[0.0016]

[0.0021]

[0.0021]

[0.0020]

Non-interest income to total income

0.0733

0.0714

0.0066

0.1522

0.147

0.0475

[0.1161]

[0.1176]

[0.1132]

[0.1153]

[0.1182]

[0.1177]

 

Log GDP

−0.0830a

−0.0838a

−0.0909a

0.0018

0.0073

−0.0073

[0.0202]

[0.0204]

[0.0197]

[0.0510]

[0.0518]

[0.0512]

GDP per capita growth

−0.0073

−0.0071

−0.0068

−0.0090b

−0.0092b

−0.0092a

[0.0056]

[0.0059]

[0.0063]

[0.0037]

[0.0038]

[0.0035]

Inflation

−0.0033

−0.0035

−0.0034

−0.0022

−0.0028

−0.0029

[0.0049]

[0.0051]

[0.0048]

[0.0069]

[0.0072]

[0.0068]

Private credit to GDP

−0.4325a

−0.4388a

−0.3890a

−0.4480a

−0.4748a

−0.4069a

[0.1062]

[0.1081]

[0.1059]

[0.1209]

[0.1242]

[0.1190]

 

Banking freedom index

−0.0029

−0.0026

−0.0026

0.0006

0.0006

0.0011

[0.0024]

[0.0024]

[0.0024]

[0.0030]

[0.0030]

[0.0033]

Concentration

0.9003a

0.8970a

0.8453a

1.2550a

1.3021a

1.2055a

[0.2034]

[0.2064]

[0.2086]

[0.3203]

[0.3246]

[0.3212]

 

Market cap. to GDP

   

−0.0011

−0.001

−0.0011

   

[0.0007]

[0.0007]

[0.0007]

Priv. bond mkt. cap. to GDP

   

−0.2482c

−0.2553c

−0.2237

   

[0.1314]

[0.1316]

[0.1374]

Pub. bond mkt. cap. to GDP

   

−0.2268

−0.2267

−0.2433

   

[0.2930]

[0.2922]

[0.2923]

 

Observations

20,575

20,554

20,575

16,776

16,762

16,776

Countries

66

66

66

33

33

33

LR chi2

228.5

227.4

280.6

758.3

816.9

933.6

Pseudo R2

0.09

0.09

0.10

0.09

0.09

0.10

aSignificant at 1%

bSignificant at 5%

cSignificant at 10%

The results on the performance variables could be explained, as in Vander Vennet (2002), by efficiency motivations. Better technology, geographical diversification, and management skills are factors that induce MNBs to acquire targets of considerable importance in local market where they are able to exert some market power and turn around the banks’ profitability ratios.

The positive coefficient on bank size differs from what has been found in the previous literature. This is explained by the inclusion of targets located in emerging economies. Cross-border acquisitions in these countries have involved some of their largest banks (e.g. BBVA’s acquisition of Bancomer and Citigroup’s acquisition of Banamex, the first and second largest banks in Mexico in terms of assets, respectively; Unicredito’s acquisition of Bank Pekao, the second largest bank in Poland). This is an important difference compared to the evidence from developed economies.

The concentration result differs from the evidence found in Focarelli and Pozzolo (2005), who find that this variable has a negative effect on cross-border bank entry using a sample of OECD countries. In their analysis, bank concentration is a proxy for bank entry restrictions. The estimations shown in Table 1 include only countries where at least one cross-border acquisitions is observed. Therefore, I am conditioning on foreign bank entry through acquisitions being allowed. This sample selection criterion changes the interpretation of this variable. Concentration in this setting measures market structure and the degree of future potential entry by rival MNBs. If a few banks have a large share of the market, potential entrants will have limited opportunities to enter the market through the acquisition of a big domestic bank. For this reason, MNBs will likely enter host countries with concentrated banking sectors. This is particularly relevant in emerging economies.

Columns (3) through (6) include three additional proxies for financial development. Missing observations reduce the number of countries and deals covered from 66 to 33 and from 214 to 125, respectively. The coefficients on the performance measures are still significant, and with the same sign as in previous estimations. The coefficient on Priv. Bond Mkt. Cap. to GDP enters with a negative and significant sign in two out of the three estimations. More developed capital markets compete with the banking sector in the allocation of resources. This reduces market power and makes entry less attractive for international banks.24

In Table 5, I estimate the model described in “Section 2.1” dividing the sample between potential targets located in emerging and developed economies. Columns (1) through (3) show the results for the former group. As in Table 4, the coefficients for the three performance proxies, bank size, Private Credit to GDP, and concentration are significant. These results suggest that MNBs are attracted to poor performing large banks in concentrated banking markets with low levels of financial intermediation. Columns (4) through (6) display the same estimations, restricting the sample to developed economies. In this case, performance and concentration have significant coefficients. As opposed to the estimations including target banks in emerging countries, GDP per capita growth has a negative and significant coefficient. This result shows that acquisitions primarily take place in countries with lower growth rates. Of particular interest is the coefficient on Non-Interest Income to Total Income. It is positive in the three estimations and significant in two, and differs from the values observed in emerging economies. This result implies that acquirers target banks with a significant revenue stream not tied to interests in developed countries. It is consistent with a larger emphasis on economies of scope in these countries (Amel et al. 2004).
Table 5

Determinants of cross-border acquisitions—emerging vs. developed economies. The empirical model in Eq. 1 has been estimated using a probit specification. The dependent variable equals one if a bank is acquired by a foreign institution in year t and zero otherwise. The model is explained in “Section 2.1”; the sample is defined in “Section 3.1”. The model is estimated for the 1994–2003 period. Columns (1) through (6) differ in the performance proxy included. In Columns (1) and (3) profitability is measured by the Return on Average Assets (ROA). Columns (2) and (5) include the Return on Average Equity (ROE). In Columns (3) and (6) performance is defined as the Cost to Income Ratio. Columns (4) to (6) include Financial Development proxies in addition to the variables included in the first three columns. A country is defined as an Emerging Economy if its real GDP per capita is below US$10,000 in 2000 prices. Developed Economies are defined as the complement to this group. All estimations include time fixed effects. Robust standard errors clustered by country in brackets

 

Emerging economies

Developed economies

ROA

ROE

Cost to income ratio

ROA

ROE

Cost to income ratio

(1)

(2)

(3)

(4)

(5)

(6)

Performance

−0.0438b

−0.0050b

0.0050a

−0.0786a

−0.0090a

0.0052a

[0.0197]

[0.0022]

[0.0015]

[0.0239]

[0.0021]

[0.0015]

Log assets

0.1675a

0.1662a

0.1748a

0.0169

0.0195

0.0267

[0.0278]

[0.0278]

[0.0279]

[0.0294]

[0.0292]

[0.0275]

Equity to assets

0.0034

0.0014

0.003

0.0004

−0.002

−0.0005

[0.0027]

[0.0027]

[0.0026]

[0.0033]

[0.0032]

[0.0034]

Net loans to assets

−0.0005

−0.0007

0.0005

0.0001

0.0001

0.0006

[0.0019]

[0.0019]

[0.0018]

[0.0027]

[0.0028]

[0.0026]

Non-interest income to total income

−0.1621

−0.1775

−0.1903

0.2514b

0.2652b

0.1456

[0.2120]

[0.2111]

[0.1826]

[0.1150]

[0.1147]

[0.1255]

−0.0784b

−0.0810b

−0.0837b

−0.0521b

−0.0579b

−0.0649b

 

Log GDP

[0.0382]

[0.0380]

[0.0378]

[0.0238]

[0.0245]

[0.0260]

GDP per capita growth

0.0183

0.0185

0.021

−0.0124a

−0.0126a

−0.0123a

[0.0151]

[0.0151]

[0.0148]

[0.0020]

[0.0020]

[0.0020]

Inflation

−0.0051

−0.0052

−0.0051

0.0204

0.0214

0.0224

[0.0057]

[0.0058]

[0.0058]

[0.0387]

[0.0384]

[0.0391]

Private credit to GDP

−0.6998a

−0.7068a

−0.6214a

−0.3034c

−0.3313b

−0.2559

[0.2355]

[0.2311]

[0.2211]

[0.1586]

[0.1634]

[0.1634]

 

Banking freedom index

0.0012

0.0011

0.0014

−0.0044

−0.004

−0.0046

[0.0029]

[0.0030]

[0.0030]

[0.0029]

[0.0031]

[0.0031]

Concentration

0.8089b

0.7842b

0.8174b

0.9287a

0.9190a

0.8406a

[0.3545]

[0.3581]

[0.3638]

[0.2211]

[0.2314]

[0.2419]

 

Observations

6,192

6,173

6,192

14,383

14,381

14,383

Countries

45

45

45

22

22

22

LR chi2

113.5

127.7

150.9

4,448

6,153

1,830

Pseudo R2

0.06

0.07

0.07

0.08

0.08

0.08

aSignificant at 1%

bSignificant at 5%

cSignificant at 10%

4.2 Performance effect

This section reports the results for the difference-in-difference estimations described in “Section 2.2”. Tables 6 through 8 provide distributional characteristics on the acquired banks (Targets), country indices (Industry), and on the differences between these two measures (Targ-Ind). The columns’ headings in Tables 7 and 8 indicate pre-acquisition (Before), acquisition-year (Yr0), post-acquisition (After), and changes (Change) in the performance and income statement items of target banks. The Sign Test statistically evaluates the null hypothesis of a median equal to zero for Targ-Ind in each one of this event stages.25
Table 6

Ex-ante target and bank indices characteristics. Bank Balance Sheet and Income Statement data is from Bankscope. The sample period is between 1994 and 2004. The variable Real Assets is defined in terms of millions of 2000 US dollars. The rest of the variables are defined in terms of percentage points. Frac > 0 is the fraction of deals with positive Targ-Ind values. The Sign Test statistically evaluates the null hypothesis of a median equal to zero for Targ-Ind in each event stage. t(mean) tests the null hypothesis that mean Targ-Ind is equal to zero

 

Total assets (Millions 2000 $US)

Equity to total assets

Net loans to average assets

Net loans to customer funds

Targets

Mean

7,956.9

11.33

48.17

62.53

SD

20,232.2

8.86

21.52

31.85

Median

1,121.9

8.86

50.11

62.42

Industry

Mean

5,050.7

13.40

47.25

65.69

SD

5,232.5

5.10

12.99

18.48

Median

2,785.4

11.83

47.61

64.42

Targ-Ind

Mean

2,906.2

2.08

0.93

−3.17

SD

19,630.9

8.18

18.80

29.99

Q1

−4,147.0

−6.80

−11.91

−26.79

Median

−450.5

−2.98

2.45

−2.69

Q3

2,873.6

0.10

13.13

12.69

Frac > 0

0.44

0.25

0.56

0.44

 

 

Sign test (P value)

0.28

0.00

0.28

0.28

 

t(mean)

1.50

−2.57

0.50

−1.07

Table 7

Difference-in-difference analysis—performance. The variables of interest are Return on Assets, Return on Equity and the Cost to Income Ratio. The difference-in-difference methodology is explained in “Section 2.2”; variables are defined in “Section 3”. The sample includes 102 deals with at least two pre- and post-acquisition years. Rows display summary statistics for acquired banks (Targets), country indices (Industry), and differences between these two measures (Targ-Ind). The column headings indicate pre-acquisition (Before), acquisition-year (Yr0), post-acquisition (After), and changes (Change) in the dependent variable. Construction of the country indices is explained in “Section 3.2”. Frac > 0 is the fraction of deals with positive Targ-Ind values. The Sign Test statistically evaluates the null hypothesis of a median equal to zero for Targ-Ind in each event stage. t(mean) tests the null hypothesis that mean Targ-Ind is equal to zero

 

Return on assets (%)

Return on equity (%)

Cost to income ratio (%)

Before

Yr0

After

Change

Before

Yr0

After

Change

Before

Yr0

After

Change

Targets

Mean

1.03

0.48

0.73

−0.31

6.67

3.44

6.12

−0.54

67.87

76.51

77.53

9.65

SD

1.71

2.35

2.10

2.26

22.33

24.31

21.24

30.85

24.11

36.33

30.26

30.56

Median

0.99

0.61

0.67

−0.35

9.15

8.52

7.91

−1.53

63.54

68.74

71.63

8.07

Industry

Mean

1.12

1.07

0.99

−0.13

8.96

9.38

9.38

0.43

66.52

65.83

67.15

0.64

SD

0.83

0.95

0.79

0.78

8.94

17.51

9.43

10.47

9.51

9.27

8.76

9.71

Median

1.05

1.02

0.95

−0.03

8.62

9.81

9.97

0.09

67.39

65.20

67.60

−0.15

Targ-Ind

Mean

−0.09

−0.59

−0.26

−0.17

−2.29

−5.95

−3.26

−0.97

1.36

10.67

10.37

9.02

SD

1.51

2.29

1.86

2.08

21.87

23.21

19.35

28.29

24.08

35.23

29.08

29.73

Median

−0.10

−0.26

−0.18

−0.11

0.57

−1.47

−2.07

−1.18

−2.35

3.35

4.27

9.08

Frac > 0

0.43

0.52

0.44

0.46

0.53

0.54

0.46

0.44

0.44

0.74

0.59

0.64

 

 

Sign test (P value)

0.20

0.01

0.28

0.49

0.62

0.03

0.49

0.28

0.28

0.26

0.09

0.01

 

t(mean)

−0.60

−2.52

−1.43

−0.84

−1.06

−2.90

−1.70

−0.35

0.57

3.20

3.60

3.06

Table 8

Difference-in-difference analysis–income statement components. The variables of interest are Net Interest Margin to Average Assets, Non-Interest Income to Average Assets, Overhead costs to Average Assets and Loan Loss Provisions to Average Assets. The difference-in-difference methodology is explained in “Section 2.2”; variables are defined in “Section 3”. The sample includes 102 deals with at least two pre- and post-acquisition years. Rows display summary statistics for acquired banks (Targets), country indices (Industry), and differences between these two measures (Targ-Ind). The column headings indicate pre-acquisition (Before), acquisition-year (Yr0), post-acquisition (After), and changes (Change) in the dependent variable. Construction of the country indices is explained in “Section 3.2.” Frac > 0 is the fraction of deals with positive Targ-Ind values. The Sign Test statistically evaluates the null hypothesis of a median equal to zero for Targ-Ind in each event stage. t(mean) tests the null hypothesis that mean Targ-Ind is equal to zero

 

 

Net interest margin to avg. assets (%)

Non-interest income to avg. assets (%)

Overhead to avg. assets (%)

Loan loss prov. to avg. assets (%)

Before

Yr0

After

Change

Before

Yr0

After

Change

Before

Yr0

After

Change

Before

Yr0

After

Change

Targets

Mean

4.05

3.74

3.38

−0.67

2.50

2.28

2.25

−0.25

4.12

4.26

4.10

−0.02

1.11

1.01

0.61

−0.50

SD

3.04

2.78

2.32

2.01

2.75

2.11

1.83

2.26

2.70

2.55

2.37

2.07

1.86

1.66

1.25

2.20

Median

3.34

3.13

3.00

−0.37

1.83

1.59

1.57

−0.03

3.54

3.64

3.52

0.07

0.52

0.37

0.27

−0.11

Industry

Mean

4.06

3.92

3.75

−0.31

2.54

2.53

2.46

−0.07

4.30

4.17

4.14

−0.16

0.74

0.80

0.75

0.01

SD

2.23

2.33

2.17

0.91

1.85

1.55

1.56

1.36

2.18

2.12

2.16

1.18

0.57

0.68

0.61

0.51

Median

3.60

3.22

3.30

−0.15

2.09

2.00

2.03

−0.03

3.81

3.68

3.41

−0.09

0.63

0.63

0.62

0.00

Targ-Ind

Mean

−0.02

−0.19

−0.38

−0.36

−0.04

−0.25

−0.21

−0.17

−0.18

0.09

−0.03

0.14

0.38

0.20

−0.13

−0.51

SD

2.02

1.86

1.54

1.76

1.92

1.86

1.81

1.89

2.47

1.96

1.99

2.22

1.63

1.44

1.00

1.97

Median

−0.17

−0.32

−0.48

−0.10

−0.31

−0.54

−0.36

−0.09

−0.57

−0.09

−0.04

0.06

−0.01

−0.06

−0.24

−0.11

Frac > 0

0.46

0.58

0.33

0.44

0.41

0.56

0.38

0.49

0.36

0.65

0.48

0.52

0.49

0.63

0.29

0.36

 

 

Sign test (P value)

0.49

0.14

0.00

0.28

0.09

0.08

0.02

0.92

0.01

0.80

0.77

0.77

0.92

0.55

0.00

0.01

 

t(mean)

−0.08

−1.03

−2.46

−2.07

−0.20

0.27

−1.18

−0.93

−0.72

1.26

−0.16

0.66

2.33

2.43

−1.35

−2.61

Table 6 shows summary statistics for the sample of 102 deals in the two pre-acquisition years and compares them to the country indices. Targets in this sample are smaller than their non-acquired counterparts, as measured by median real assets, and have a lower Equity to Total Assets ratio. Only the latter difference is significant (at the 1% level) as shown by the Sign Test. In terms of the level of net loans in the balance sheet, the null hypothesis of a zero median for the differences in ratios between target and country indices cannot be rejected.

Table 7 compares the three performance proxies, ROA, ROE and Cost to Income Ratio, for targets and country indices before and after the acquisitions. In particular, the null hypothesis of no changes in performance is evaluated by testing the Targ-Ind median in the Change column.26 Although the ROA and ROE are lower for acquired banks after a cross-border deal, I cannot reject the null hypothesis of a zero median relative change. In contrast, the median Cost to Income Ratio is 8.07 percentage points higher in the post-acquisition period for targets while the industry index decreases by 0.15. The median adjusted change in the Cost to Income Ratio is 9.1 percentage points higher, and the Sign Test rejects the null hypothesis of an equal share in positive and negative values for this measure. In total, 64% of targets experience an increase in their costs relative to interest and non-interest income.

Table 8 reports the main earning components in the banks’ income statement. Excluding Overhead costs and Non-Interest Income, in the pre-acquisition period target banks have similar indicators compared to the country indices. After the deal takes place, Net Interest Margins are lower for targets, but the median net change is not significantly different from zero. These results are consistent with more competition in the local banking sector after MNB acquisitions, or a reduction in prices and fees implemented to gain market share.27

The items representing bank costs, like median Overhead expenditures, have a slight increase for targets in the post-acquisition period, but its median relative change is not different from zero. These findings show that in the short run, there are few gains in terms of cost efficiency for this sample of cross-border deals. In contrast, the result on Loan Loss Provisions shows that there is a significant decline in this accounting measure for the target banks. The fraction of negative net changes is 36%, which in turn implies that the median is significantly different from zero. This is caused by a decrease in lending in the post-acquisition period.

These tests confirm the findings in Vander Vennet (2002) for a sample of European M&As. The author finds that there is no positive performance effect in the short term after a cross-border acquisition. Profitability is affected by a reduction in interest income, and by a lack of cost-efficiency gains. This pattern is also found in Chamberlain (1998) for US mergers during the 1980s, but it contrasts with the positive performance results described in Cornett, McNutt, and Tehranian (2006) for US banks’ M&As in the 1990s. These results also confirm that the global advantage hypothesis is rejected using the sample of deals in developed and emerging economies. Operational diseconomies to managing foreign subsidiaries are greater than the advantages possessed by MNBs. Next, I will proceed to analyze whether this hypothesis is also rejected for the group of emerging economies in the sample.

Table 9 divides the sample between targets located in developed and emerging economies. Column (1) shows that the number of deals is evenly divided across these two groups. The three performance measures deteriorate in the post-acquisition period, but only the change in the Cost to Income Ratio is significant. The proxies for revenues decrease for developed countries, but these figures are not significantly different from the median observed for target banks located in emerging countries. In contrast, the Median test shows that changes in Overhead costs are significantly different at the 11% level amongst the targets in the two sets of countries. For emerging economies there is a median relative increase of 0.59, while for targets in developed countries this ratio decreases by 0.10. This result shows that cost efficiencies are harder to realize in emerging countries in the short run. Finally, like in the full sample case, there is a decrease in Loan Loss Provisions. This is explained by a reduction in the loan portfolio for targets located in emerging economies, but the change in lending activity is not observed in the data for banks in developed countries. The decrease in loan loss provisions in targets located in these countries could be attributed to earnings management (Scholes et al. 1990) or the use of better techniques in loan monitoring and screening.
Table 9

Difference-in-difference analysis—Emerging vs. Developed economies. The variables of interest are defined as difference-in-difference using the country indices as controls. The methodology is explained in “Section 2.2”; variables are defined in “Section 3”. The sample includes 102 deals with at least two pre- and post-acquisition years. A country is defined as being developed if GDP per capita is above US$10,000 in 2000 prices. The Sign Test statistically evaluates the null hypothesis of a median equal to zero for the difference-in-difference measure. Frac > 0 is the fraction of deals with positive Targ-Ind values. The Wilcoxon Test evaluates the hypothesis that two independent samples (i.e., unmatched data) are from populations with the same distribution. The Median Test evaluates the null hypothesis that the samples of developed and emerging country deals were drawn from populations with the same median

 

Change in relative performance

Deals

Mean

SD

Median

Frac > 0

Sign Test (P value)

Wilcoxon

Median

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Return on assets (%)

Developed

48

−0.17

1.24

−0.08

0.46

0.67

−0.27

0.00

Emerging

54

−0.18

2.62

−0.11

0.46

0.68

Return on equity (%)

Developed

48

−1.83

19.61

−0.95

0.46

0.67

−0.31

0.00

Emerging

54

−0.21

34.39

−1.18

0.43

0.34

Cost to income ratio (%)

Developed

48

12.32

26.81

7.06

0.65

0.06

−0.44

0.16

Emerging

54

6.08

32.08

9.74

0.63

0.08

Profits before taxes and provisions (%)

Developed

48

−0.54

1.38

−0.44

0.40

0.19

0.07

0.63

Emerging

54

−0.79

3.41

−0.30

0.43

0.34

Net interest margin (%)

Developed

48

−0.07

0.87

−0.13

0.35

0.06

−0.17

0.63

Emerging

54

−0.62

2.25

0.14

0.52

0.89

Non-interest income (%)

Developed

48

−0.23

1.32

−0.13

0.44

0.47

0.72

0.63

Emerging

54

−0.13

2.30

0.19

0.54

0.68

Overhead costs (%)

Developed

48

0.27

1.45

−0.10

0.44

0.47

0.72

2.52

Emerging

54

0.04

2.75

0.59

0.59

0.22

Loan loss provisions (%)

Developed

48

−0.38

1.06

−0.11

0.29

0.01

0.13

0.00

Emerging

54

−0.63

2.53

−0.10

0.43

0.34

To summarize, dividing the target banks by the host country’s level of development provides similar aggregate results compared to the ones observed for the full sample. The only noticeable difference is in the change in Overhead expenditures. It appears that cost reductions are more difficult to implement in emerging markets. These findings are consistent with the rejection of the global advantage hypothesis for the overall sample. In the next section, I analyze some of the potential diseconomies that make it difficult for MNBs to manage subsidiaries abroad.

4.3 Performance, economic integration and information costs

Tables 10, 11, 12 and 13 show the results for the regression outlined in Eq. 6. This section tests the presence of diseconomies associated with operating subsidiaries after being acquired in a cross-border deal. The dependent variable is measured in terms of percentile ranks relative to the relevant peer group defined in “Section 3.2”. An x percentile rank indicates that the target bank ranks above x percent of the peer group of banks in terms of performance, revenue or income for a particular year. The sample used in these estimations includes deals with at least one pre-acquisition and post-acquisition year of non-missing information.
Table 10

Performance, economic integration and information costs. The dependent variable is a percentile rank transformation of the performance measure. The models are explained in Section 2.3; variables are defined in Section 3. The models are estimated for the 1994–2004 period. Three sets of variables are included as regressors: event dummies for the year of the deal (Yr0), 1 and 2 years after (Yr12), and three or more years after (Yr3+); country pair characteristics reflecting similarities between the host and home countries; and host country market and macroeconomic characteristics. The regressions include deal and country fixed effects. Robust standard errors in brackets

 

ROA

ROE

Cost to income ratio

(1)

(2)

(3)

Yr0

−0.132

−0.007

−0.152

[0.106]

[0.111]

[0.104]

Yr12

−0.073

0.048

−0.144

[0.106]

[0.111]

[0.104]

Yr3+

−0.066

0.042

−0.13

[0.106]

[0.111]

[0.103]

Same language

0.059

0.109b

0.148a

[0.041]

[0.043]

[0.042]

Same legal

−0.073c

−0.117a

−0.152a

[0.040]

[0.041]

[0.039]

Similar GDP PC

−0.128

−0.126

−0.129

[0.092]

[0.094]

[0.092]

Similar GDP

0.075

0.124c

0.115b

[0.064]

[0.068]

[0.055]

Log distance

0.013

−0.001

0.022

[0.016]

[0.017]

[0.017]

Same region

0.035

0.022

0.07

[0.067]

[0.072]

[0.069]

Concentration

−0.029

0.016

−0.076

[0.111]

[0.109]

[0.098]

GDP growth

0.004

0.001

0.004

[0.003]

[0.003]

[0.003]

Inflation

0.01

0.017a

0.005

[0.006]

[0.006]

[0.008]

 

Observations

1,196

1,178

1,191

R-squared

0.45

0.46

0.51

aSignificant at 1%

bSignificant at 5%

cSignificant at 10%

Table 11

Performance, economic integration and information costs—Emerging vs. Developed economies. The dependent variable is a percentile rank transformation of the performance measure. The models are explained in “Section 2.3”; variables are defined in “Section 3.” The models are estimated for the 1994–2004 period. A country is defined as being developed if GDP per capita is above US$10,000 in 2000 prices. Three sets of variables are included as regressors: event dummies for the year of the deal (Yr0), 1 and 2 years after (Yr12), and three or more years after (Yr3+); country pair characteristics reflecting similarities between the host and home countries; and host country market and macroeconomic characteristics. The regressions include deal and country fixed effects. Robust standard errors in brackets

 

Developed economies

Emerging economies

ROA

ROE

Cost to income ratio

ROA

ROE

Cost to income ratio

(1)

(2)

(3)

(4)

(5)

(6)

Yr0

0.262

0.279

0.018

−0.235

−0.119

−0.344b

[0.187]

[0.182]

[0.150]

[0.188]

[0.200]

[0.167]

Yr12

0.269

0.277

0.005

−0.146

−0.027

−0.317c

[0.188]

[0.185]

[0.150]

[0.189]

[0.200]

[0.166]

Yr3+

0.28

0.303c

0.036

−0.164

−0.077

−0.320c

[0.186]

[0.184]

[0.149]

[0.192]

[0.202]

[0.168]

Same language

0.082

0.101c

0.173a

0.02

0.104

0.119

[0.056]

[0.052]

[0.044]

[0.063]

[0.071]

[0.074]

Same legal

−0.185a

−0.252a

−0.177a

0.072

0.041

−0.102

[0.051]

[0.049]

[0.042]

[0.061]

[0.069]

[0.069]

Similar GDP PC

−0.348a

−0.314b

−0.340a

−0.059

−0.06

−0.116

[0.133]

[0.123]

[0.103]

[0.167]

[0.176]

[0.171]

Similar GDP

0.254a

0.219b

0.273a

−0.073

0.035

0.038

[0.092]

[0.088]

[0.066]

[0.087]

[0.101]

[0.088]

Log distance

−0.031

−0.027

0.009

0.008

−0.017

0.014

[0.030]

[0.029]

[0.025]

[0.021]

[0.023]

[0.024]

Same region

−0.054

0.034

0.024

0.012

−0.131

−0.105

[0.096]

[0.094]

[0.078]

[0.141]

[0.176]

[0.169]

Concentration

0.202

0.141

−0.086

−0.089

−0.015

−0.115

[0.178]

[0.189]

[0.174]

[0.138]

[0.135]

[0.122]

GDP growth

−0.002

0

0.012

0.005c

0.001

0.003

[0.010]

[0.009]

[0.009]

[0.003]

[0.003]

[0.003]

Inflation

0.54

0.564

−0.501

0.010c

0.016a

0.005

[1.760]

[1.645]

[1.421]

[0.006]

[0.006]

[0.008]

 

Observations

495

495

495

701

683

696

R-squared

0.54

0.56

0.64

0.41

0.41

0.43

aSignificant at 1%

bSignificant at 5%

cSignificant at 10%

Table 12

Costs, revenue, economic integration and information costs. The dependent variable is a percentile rank transformation of the income statement ratios. The models are explained in “Section 2.3”; variables are defined in “Section 3.” The models are estimated for the 1994–2004 period. Three sets of variables are included as regressors: event dummies for the year of the deal (Yr0), 1 and 2 years after (Yr12), and three or more years after (Yr3+); country pair characteristics reflecting similarities between the host and home countries; and host-country market and macroeconomic characteristics. The regressions include deal and country fixed effects. Robust standard errors in brackets

 

Net interest margins

Overhead costs

Non interest income

(1)

(2)

(3)

Yr0

−0.142c

−0.133

−0.079

[0.085]

[0.082]

[0.093]

Yr12

−0.149c

−0.143c

−0.055

[0.085]

[0.083]

[0.091]

Yr3+

−0.123

−0.127

−0.112

[0.087]

[0.082]

[0.092]

Same language

−0.053

0.108a

−0.011

[0.033]

[0.031]

[0.030]

Same legal

0.01

−0.059b

0.021

[0.032]

[0.030]

[0.031]

Similar GDP PC

0.099

−0.155b

−0.068

[0.067]

[0.069]

[0.065]

Similar GDP

−0.002

0.031

0.017

[0.047]

[0.044]

[0.045]

Log distance

0.029b

0.016

0.002

[0.013]

[0.013]

[0.015]

Same region

−0.032

0.151a

−0.035

[0.050]

[0.050]

[0.046]

Concentration

0.147

−0.141c

0.098

[0.096]

[0.082]

[0.093]

GDP growth

0.006b

0.004c

0.002

[0.002]

[0.002]

[0.003]

Inflation

0.01

−0.008

−0.011c

[0.006]

[0.008]

[0.006]

 

Observations

1,196

1,189

1,195

R-squared

0.64

0.63

0.52

aSignificant at 1%

bSignificant at 5%

cSignificant at 10%

Table 13

Costs, revenue, economic integration and information costs—Emerging vs. Developed economies. The dependent variable is a percentile rank transformation of the income statement ratios. The models are explained in “Section 2.3”; variables are defined in “Section 3.” The models are estimated for the 1994–2004 period. A country is defined as being developed if GDP per capita is above US$10,000 in 2000 prices. Three sets of variables are included as regressors: event dummies for the year of the deal (Yr0), 1 and 2 years after (Yr12), and three or more years after (Yr3+); country pair characteristics reflecting similarities between the host and home countries; and host country market and macroeconomic characteristics. The regressions include deal and country fixed effects. Robust standard errors in brackets

 

Developed economies

Emerging economies

Net interest margins

Overhead costs

Non interest income

Net interest margins

Overhead costs

Non-interest income

(1)

(2)

(3)

(4)

(5)

(6)

Yr0

0.148

−0.14

0.167

−0.135

−0.141

−0.005

[0.105]

[0.118]

[0.106]

[0.176]

[0.147]

[0.150]

Yr12

0.139

−0.168

0.191c

−0.139

−0.134

0.015

[0.108]

[0.119]

[0.109]

[0.177]

[0.147]

[0.147]

Yr3+

0.155

−0.155

0.115

−0.111

−0.126

−0.017

[0.115]

[0.117]

[0.110]

[0.178]

[0.148]

[0.150]

Same language

−0.061

0.105a

−0.090a

−0.051

0.121b

0.08

[0.037]

[0.029]

[0.033]

[0.054]

[0.061]

[0.054]

Same legal

0.01

−0.047c

0.046

−0.013

−0.068

−0.018

[0.034]

[0.028]

[0.035]

[0.056]

[0.058]

[0.057]

Similar GDP PC

0.097

−0.286a

0.012

0.325b

−0.153

−0.107

[0.070]

[0.067]

[0.081]

[0.158]

[0.134]

[0.143]

Similar GDP

0.068

0.027

−0.042

−0.114

0.069

0.045

[0.063]

[0.042]

[0.051]

[0.071]

[0.075]

[0.074]

Log distance

−0.014

0.014

−0.039b

0.046b

0.005

0.007

[0.016]

[0.019]

[0.017]

[0.018]

[0.019]

[0.022]

Same region

−0.082

0.103c

−0.140a

−0.011

0.048

0.106

[0.055]

[0.058]

[0.052]

[0.118]

[0.128]

[0.087]

Concentration

0.199

−0.053

−0.043

0.149

−0.194c

0.189

[0.140]

[0.108]

[0.140]

[0.124]

[0.108]

[0.123]

GDP growth

0.004

−0.002

−0.003

0.006b

0.004c

0.003

[0.006]

[0.006]

[0.008]

[0.002]

[0.002]

[0.003]

Inflation

2.680a

−0.759

2.502b

0.01

−0.008

−0.013b

[0.966]

[0.861]

[1.186]

[0.006]

[0.008]

[0.006]

 

Observations

495

495

495

701

694

700

R-squared

0.83

0.79

0.72

0.49

0.53

0.41

aSignificant at 1%

bSignificant at 5%

cSignificant at 10%

In Table 10 the dependent variables are the ROA, ROE and the Cost to Income Ratio. Three sets of variables are included as regressors: event dummies for the year of the deal (Yr0), 1 and 2 years after (Yr12), and three or more years after (Yr3+); country pair characteristics reflecting similarities between the host and home countries; and host country market and macroeconomic characteristics. The coefficients on the event time indicator variables are negative in almost all cases. These results confirm the findings in the last sub-section, namely, that there is a negative effect on the target’s performance in the short run triggered by a cross-border acquisition.

In Table 11 the deals are divided by the host country’s level of development. Columns (1) through (3) estimate the model using deals where the acquired bank is located in a developed economy. In contrast to the estimations including all deals, performance increases in the post-acquisition period for this sub-sample of targets. This result is significant for the ROE after the second post-acquisition year. As expected, the coefficients for Same Language and Similar GDP are positive. Alternatively, the coefficients for Same Legal and Similar GDP PC are negative and significant. This result implies that differences in legal systems and GDP per capita do not act as barriers when managing subsidiaries abroad.

The results for emerging economies shown in Columns (4) through (6) are in line with the aggregate estimations displayed in Table 10. The coefficients on the event time indicators are all negative but only significant in the Cost to Income Ratio estimation. Country pair characteristics do not enter the regressions with significant coefficients, although language, legal, and comparative economic size have the right signs in most of the cases.

Lastly, Tables 12 and 13 use the same estimating equation to determine the factors that influence revenue and cost items for targets. For all estimations, except for Non-interest Income and Net Interest Margins in developed countries, the coefficient on the time-event dummies are negative. Acquired banks have higher Net Interest Margins if the host and home countries are similar in terms of GDP per capita, especially when the host is located in an emerging country (Column (4), Table 13). Overhead costs are lower in the post-acquisitions period if the countries share the same language or are located in the same region. The opposite result is true if they share the same legal origin. These results are influenced by deals within the EU. In emerging economies, bank concentration reduces the incentive for target to decrease these costs as shown in Table 13, Column (5). Finally, the results on Non-interest Income are very different for emerging and developed economies. For the former group, having the same language increases the percentile rank of targets after an acquisition, while the opposite applies to the latter set of countries.

These results show significant information costs associated with the language used in the host and home countries, especially when measuring Overhead costs and Non-interest Income after an acquisition. On the other hand, difference in legal origin and geographical distance do not have an effect on the post-acquisition performance of the target banks.

5 Conclusions

This paper uses a unique database on cross-border acquisitions to examine the determinants of international takeovers and their impact on the performance of target banks. The results show that banks are more likely to get acquired in a cross-border deal if they are large, bad performers, in countries with less financial intermediation, and when the banking sector concentration is high. Nevertheless, post-acquisitions performance does not improve in the first two years after a cross-border acquisition. This is caused by a decrease in Net Interest Margins and an increase in Overhead costs in targets located in emerging economies. The absence of net performance gains is linked to diseconomies in managing international subsidiaries, in particular differences in language between the host- and home-country.

The effect of M&As has been studied in developed economies or using cross-border deals in Europe. Evidence from emerging economies is mostly limited to acquisitions in Eastern European countries or to static analysis of efficiency. The current paper shows dynamic evidence on performance and expands the sample of transactions to 220 in 58 different countries. Moreover, using the same database, I analyze both the determinants of cross-border deals, as well as its impact on post-acquisition efficiency.

Foreign bank entry liberalization has been recommended as a policy designed to increase stability in the domestic banking sector and prevent financial crises. In addition, foreign bank presence has been linked to growth and better allocation of resources in emerging markets. The results shown in this paper do not challenge these findings, but indicate that benefits, in terms of bank performance, are not observed in the short run.

So, why do MNBs decide to expand abroad, and in particular, through the acquisition of local banks? This is a question that has been partially addressed by the previous literature, but still needs to be studied further. There are two possible answers that depend on the assumptions about the behavior of the managers. First, if it is assumed that managers’ objective is to maximize the value of the firm, there are three possible explanations for a bank’s international expansion: cross-border acquisitions are valuable for the acquirer in the long run, so any short run analysis will underestimate their benefits (Amel et al. 2004); when MNBs decide to expand abroad, they do not internalize the potential entry of other MNBs—reducing the profit margins in the host country—or undervalue the transaction cost of operating a subsidiary abroad; finally, MNBs may expand internationally to diversify their portfolio geographically, then, the decision to enter a particular country will depend on the global diversification of the bank and not the profitability obtained in any particular market (Amihud et al. 2002). In contrast, if we assume that there are agency problems between managers and shareholders, cross-border acquisitions could be motivated by non-profit maximizing decisions (Cornett et al. 2003). In the literature, this behavior is called managerial hubris, and given the findings of the paper, may explain the relatively poor performance of acquired banks.

Footnotes
1

See Mishkin (2001) and Tschoegl (2004) for a discussion on the benefits and costs of foreign bank entry as a policy to prevent financial crises.

 
2

Micco et al. (2007) show evidence on performance indicators divided by type of ownership. Berger et al. (2005) use a sample of Argentinean banks to determine the static and dynamic effects of corporate governance. The authors find that foreign-owned banks perform worse than domestically-owned banks, but their performance is significantly better than that of state-owned banks.

 
3

The formal version of the global advantage hypothesis states that some banks perform better both at home and abroad. In this paper, I assume that banks that acquire targets abroad perform better than all other banks in their home-country (see Focarelli and Pozzolo 2001). Hence, I test a restricted version of this hypothesis and focus only on the performance of the targets after being acquired by the foreign banks.

 
4

Demirgüc-Kunt et al. (2004) do a cross-country comparison of the link between regulation and national institutions and bank overhead costs and interest margins.

 
5

For a theoretical explanation of banking M&A’s, see Milbourn et al. (1999).

 
6

These authors argue that there are several shortcomings in the empirical methods used in these performance studies, and recommend more M&A case-study analyses.

 
7

See Altunbas and Marqués-Ibáñez (2008), Beitel and Schierek (2001), Beitel et al. (2004), and Cybo-Ottone and Murgia (2000) for evidence on the performance effect in European M&As.

 
8

In robustness tests, I include a measure of risk proxied by the volatility of the ROAs. I exclude this measure from the final estimation because it is time invariant. The main results are robust to the inclusion of this variable.

 
9

Bank concentration is measured as the share of the three largest banks by country and year. We use this measure because of data constraints. One popular measure of bank concentration is the Herfindahl-Hirshman index. To estimate it I would need data for all banks in all countries in the sample. Another widely used measure of bank competition is the Panzar and Rosse H-statistic. Apart from the strong assumptions needed to estimate it (see Goddard and Wilson 2008, unpublished manuscript), the small number of banks with information in some emerging economies restricts the number of deals and countries that could be included in the sample.

 
10

Although these three measures of financial market size are somewhat correlated, the cross-country differences might be important in the bank’s decision-making process to enter a particular country through a cross-border acquisition. Equity market capitalization is not correlated with public bond market capitalization, while the correlation with private bond market capitalization is under 0.2.

 
11

The Sign Test is used instead of the t-test because the sample distributions of the relative—differenced with respect to the country index—accounting ratios are skewed. This would make the use of parametric techniques inappropriate. See “Section 4.”

 
12

The Cost to Income Ratio is defined as Overhead costs divided by Net Interest Revenue and Non-interest Income.

 
13

These variables are all divided by Average Assets. This measure is calculated by averaging Assets using t and t − 1 information.

 
14

Berger et al. (2004) use similar variables to analyze exports and imports of financial Foreign Direct Investment (FDI) across countries.

 
15

Berger (1998) and Focarelli et al. (2002) use the same transformation.

 
16

There are five legal origin categories: British, French, Socialist, German and Scandinavian.

 
17

Similar GDP and Similar GDP PC are equal to \(1 - {{\left[ {abs\left( {X_j - X_h } \right)} \right]} \mathord{\left/ {\vphantom {{\left[ {abs\left( {X_j - X_h } \right)} \right]} {\max \left( {X_j ,X_h } \right)}}} \right. \kern-\nulldelimiterspace} {\max \left( {X_j ,X_h } \right)}}\), where X is defined as GDP in the former case and GDP per capita in the latter.

 
18

This paper focuses on Commercial Banks due to their central role in retail banking in emerging economies. In addition, some Bank Holding Companies are included due to their similarities to Commercial Banks, especially in countries different from the US. I use unconsolidated financial statements when available (codes U1 and U2 in Bankscope).

 
19

A bank is considered to have extreme financial information if Equity to Total Assets, Non-interest Income or Net Loans to Total Assets are less than 0. I also exclude observations with Net Interest Margins below −2.5 or above 28; ROA less than −10 or more than 12; ROE less than -100; Cost to Income Ratios below 0 or above 244; Non-interest Expenses to Average Assets above 100.

 
20

Panama is an international financial center.

 
21

Some countries are listed with zero cross-border deals. Cross-border acquisitions did take place in these countries, but due to extreme values in the financial data of the targets these observations, and the deals, were excluded.

 
22

For these estimations I use bank data from 1994 to 2004.

 
23

As a robustness check, I use the Investment Profile measure from the International Country Risk Guide (ICRG). Although it is a more general measure of the overall restriction on cross-border investments in a country, its inclusion does not change the main results.

 
24

For a discussion on market-based and bank-based economies see Demirgüç-Kunt and Levine (2001).

 
25

Estimations in this section include privatizations. They represent about 22% of the sample (23 deals). Excluding these deals does not change the main findings.

 
26

Estimations using matched pair controls instead of industry indices yield similar results.

 
27

Bayraktar and Wang (2004) show that there is a decrease in Net Interest Margins, Non-interest Income and profitability as foreign banks increase their share in the local banking sector. This is true for countries that liberalized the stock market first. See also Demirgüç-Kunt and Huizinga (1999) for cross-country evidence on net interest margins and profitability.

 

Acknowledgements

I would like to thank the editors of the special issue Robert DeYoung, Douglas Evanoff, Phil Molyneux, and two anonymous referees. I am also grateful to Charlie Calomiris, Elijah Brewer III, Juan J. Cruces, Dale Henderson, seminar participants at the International Finance Workshop at the Federal Reserve Board, the LACEA-LAMES annual meeting in Bogota, Colombia, the conference on “Mergers and Acquisitions of Financial Institutions” at the FDIC, and the annual meeting of the Midwest Finance Association in San Antonio, Texas. The usual disclaimer applies. The views in this paper are solely the responsibility of the author and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System.

Copyright information

© Springer Science+Business Media, LLC 2008