Cross-border mergers and acquisitions: the European–US experience

Journal of Multinational Financial Management 8 (1998) 431–450

Cross-border mergers and acquisitions: the European–US experience G.M. Vasconcellos *, R.J. Kish Lehigh University, Department of Business, Bethlehem, 18015-3117 PA, USA Received 1 February 1997; accepted 1 May 1998

Abstract Our study, utilizing logit and multiple regression models, tests the hypothesis that macroeconomic variables, in particular bond yields, exchange rates, and stock prices, influenced the number and direction of cross-border acquisitions between firms in the United States and each of four European countries: Germany, Italy, the United Kingdom, and France. While the logit model results suggest that bond yields explain the trends in cross-border acquisitions, the regression results show the US stock prices to be a good explanatory variable. In general, the results suggest that foreign acquisitions occur more frequently when bond yields in the acquirer’s country are higher than those from the country of the firm being acquired. In addition, a depressed US stock market relative to foreign stock markets encourages foreign acquisition of US companies. © 1998 Elsevier Science B.V. All rights reserved. JEL classification: G11; G15 Keywords: Mergers and acquisitions; European; Cross-border

1. Introduction Foreign direct investment ( FDI ), which includes cross-border mergers and acquisitions, is an integral part of the activities of major US and foreign firms. The flow of FDI is significantly influenced by key variables found in the US and foreign capital markets. The significant rise in the number of cross-border mergers and acquisitions involving US companies warrants a better understanding of the factors affecting these activities. In particular, the publicity in the 1980s surrounding foreign acquisition activity in the US created public concern over American firms being acquired by foreign entities. Subsequently, several studies have examined the wealth effects of foreign acquisitions and capital markets factors that affect acquisition activity. * Corresponding author. Tel: +1 610 758-5347; Fax: +1 610 758-6429; e-mail: [email protected] 1042-444X/98/$ – see front matter © 1998 Elsevier Science B.V. All rights reserved. PII S 10 4 2 -4 4 4 X ( 9 8 ) 0 0 04 1 - 3

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Since the early 1980s, the flow of cross-border acquisitions has shifted. During most of the last decade, foreign firms acted predominately as acquirers of US firms. Since the late 1980s, US firms have acquired more foreign firms than foreign firms have acquired US firms.1 Studies done on acquisition activity between the US and Great Britain (e.g. Vasconcellos et al., 1990) and between the US and Japan (e.g. Kish and Vasconcellos, 1993) explore macroeconomic variables that contribute to this phenomenon. Our paper builds on these prior studies by testing the hypothesis that movements among key macroeconomic variables explain trends in acquisitions between the US and the four largest economies of the European Union ( EU ). Another objective of this paper is to examine the similarities and differences between the factors explaining acquisition activity between US and UK firms (e.g. see Vasconcellos et al., 1990) and those affecting cross-border mergers between US firms and the major economies in continental Europe. In addition, the period of study is longer than previous published research. The EU is one of the three largest trading and investment partners of the US (the other two being Canada and Japan). Foreign direct investment represents a large portion of the cross-border investment flows occurring between these regions. Following from this point, and the fact that FDI often occurs in the form of a merger or acquisition, we have looked at the empirical evidence of cross-border acquisitions between the US and the EU from 1982 to 1994. Our study explores the hypothesis that the following four factors affect the number and direction of crossborder acquisitions: exchange rates, bond yields, and the level of the equity markets both home and abroad. Specifically, we examine the correlation of these four variables with the direction of acquisition activity. In measuring acquisition activity for the EU, we focused on four countries: Germany, Italy, the United Kingdom and France. Our rationale for selecting these countries is that they are the largest and most diversified economies in the EU. The acquisitions’ statistics for these four countries also represent the bulk of the acquisitions from the EU. In Section 2, we examine developments in the EU that may have had an effect on cross-border acquisitions during the 1982–94 period. We then discuss factors affecting cross-border acquisitions (Section 3) as well as variables hypothesized as predictors of such activity. Subsequently, we present the statistical models used in our empirical work and our findings (Section 4). Finally, we present our conclusions and suggestions for similar efforts in the future (Section 5).

2. Developments in the EU The acquisition of a foreign firm is one of the fastest methods of entering into a foreign market. In the early and mid-1980s, this method seemed especially attractive to businesses wanting to become involved in the single European market as it evolved. As a result, there was a surge of foreign takeovers in the EU during this period. This surge demonstrated that businesses had confidence in the EU forming 1 See Table 1 for a summary of the merger activity in the sample period, 1982 through 1994.

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a single internal market. In fact, many of these acquisitions took place before remaining national barriers came down. The rationale for this may be attributed in part to a growing concern that a unified Europe could translate into a protectionist ‘Fortress Europe’. Many foreign companies believed that the only way to participate in a unified Europe was to quickly become an insider. For example, US firms invested approximately $100 billion (book value) via FDI in Europe during the early 1980s. Over $30 billion of this went toward acquisitions in the UK, and almost $16 billion was used to acquire firms in Germany.2 Acquisitions subsided in the late 1980s. According to Rugman and Verbeke (1991), the very steps that were being taken to bring Europe together were creating a natural barrier to entry for outsiders. Many mergers and acquisitions were taking place within the EU, creating larger, more efficient European businesses. This left fewer opportunities for foreign companies. By the early 1990s, however, acquisitions of European firms were on the rise again primarily for two reasons. First, there was a need to complete the restructuring that had begun in the 1980s that could not be done by European firms alone. And secondly, regulatory changes enabled hostile takeovers to occur more easily.3 For instance, with the Single European Act (Project 1992), the stage was set for the removal of restrictions of movement of goods, services and capital. The signing of the Maastrich Treaty in the early 1990s reinforced this new direction for European commerce by setting the conditions for an economic and monetary union. This set of events by the EU sent a strong signal to other industrial economies, especially to the US and Japan, that full economic and monetary union ( EMU ) was going to happen, because the political will was there. This was reinforced by the selection of the eleven countries to partake in the first phase of the EMU and the scheduled launch of the euro, the single European currency, in 1999. Moreover, earlier removal of barriers to capital flows and attempts by France and Germany to support joint financial market initiatives as a counterweight to London’s dominance created more favorable conditions for the financing of mergers and acquisitions. By 1994, the book value of total FDI by the US in the EU exceeded $300 billion, an increase of 200% since 1982. Of this, $102 billion represented acquisitions in the UK (up 240% from 1982) and $40 billion was invested in acquisitions of German firms (up 150% from 1982).4 In fact, one third of all EU acquisitions occurred within the UK. In 1993, an international survey done by PLI, a Brussels-based consulting group ranked northern England first, as the most accommodating region in the world to foreign investors. This was due in part to its low labor costs, sound infrastructure, and access to the EU.5

2 US Department of Commerce. Survey of Current Business and Statistical Abstract of the US, 1983, August pp. 24. 3 See The Economist, 1992 (July 4, 1992, pp. 57–58) and Business Week (April 27, 1998, pp. 25–51) for examples of market restrictions that are being relaxed such as freer flow of goods and the ability of professionals to practice across borders. 4 US Department of Commerce, Survey of Current Business and Statistical Abstract of the US (various issues). 5 Investment in the UK: an open invitation. Far Eastern Economic Review, 1994, 5, May pp. 58.

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3. Factors motivating cross-border acquisitions In her extensive discussion of the merger and acquisition process, McDonagh Bengtsson (1990) (pp. 75–107) proposes that the following factors motivate many companies to acquire foreign firms: the desire to spread products and diversify risks geographically; to gain back-up products; to exploit synergies; and to attain economies of scale. However, she cautions that workforce problems, poor facilities, as well as social and technological differences may expose the acquiring company to new risks. Other studies in the area of cross-border acquisitions attribute the pattern of acquisitions to several competing factors, both favorable and unfavorable. The discussion that follows surveys a sampling of these factors, examining first the favorable acquisition variables (i.e. variables that appear to influence the firm’s concerned with cross-border deals), then the unfavorable ones. We pay particular attention to those factors more directly related to the countries under study.6 3.1. Favorable acquisition factors Although there are a number of factors that favor acquisition activity, we focus on those that seem to affect cross-border acquisitions between the US and the EU. These factors include exchange rates, diversification, and economic conditions in the home country, as well as technology and human resources. 3.1.1. Exchange rates Current and forecasted future exchange rates affect the home currency equivalent of acquisition prices, as well as the present value of future cash flows accruing to the acquired firm; therefore, the dominant effect in any particular case is ultimately an empirical question. Existing studies, predictably, arrive at different conclusions concerning the role of exchange rates. For example, Froot and Stein (1991) propose that, while there is a relationship between the exchange rates and acquisition activity, there is no evidence that a change in the exchange rate improves the position of foreign acquirers relative to their US counterparts. They contend that when the dollar depreciates, the US becomes a cheaper place for any firm to do business — foreign or domestic. In addition, they downplay the relationship between foreign acquisitions and exchange rates, arguing that improved capital mobility leads to equalized, risk-adjusted returns on international investments. Goldberg (1993) reaches different conclusions. She finds that a depreciated US dollar reduces FDI in American businesses. She also contends that the reverse holds true, that is, if the dollar is strong, one observes an increase in foreign acquisition of US firms and a downward trend in US acquisitions of foreign firms. However, Harris and Ravenscraft (1991) present empirical evidence that is in contrast to 6 Other studies used in justifying the variables include Almor and Hirsch (1995) and Jacquemin and Sapir (1991) from the European sector, and Dewenter (1995b), Kang (1993), Markusen et al., (1995), Metzger and Ginsberg (1989), and Vasconcellos and Kish (1996).

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Goldberg’s findings. In particular, they contend that a depreciated dollar increases the number of foreign acquisitions of US firms. 3.1.2. Diversification This argument is based on the empirical observation that the covariance of returns across different economies, even within the same industries, is likely to be smaller than within a single economy. It follows that the prospective acquiring company must first decide on its desired levels of risk and return. Only then should it attempt to identify countries, industries, and specific firms that fall within its risk class. In addition, by acquiring ongoing foreign concerns, companies may be able to circumvent tariff and non-tariff barriers, thereby improving their risk–return tradeoff by lowering the level of unsystematic risk.7 3.1.3. Economic conditions in the home country Favorable cyclical conditions in the acquiring firm’s home country should facilitate cross-border acquisitions as a means for increasing demand and levels of diversification. On the other hand, adverse economic conditions, such as a slump, recession, or capital market constraints, may cause prospective acquiring firms to concentrate on their domestic business while postponing any international strategic moves. 3.1.4. Acquisition of technological and human resources If a firm falls behind in the level of technological knowledge necessary to compete efficiently in its industry, and it is unable or unwilling to obtain the required technology through research and development, then it may attempt to acquire a foreign firm which is technologically more advanced. In their study, Cebenoyan et al. (1992) support this point, showing that the expansion into new markets through acquisitions allows firms to gain competitive advantage from the possession of specialized resources. 3.2. Unfavorable acquisition factors The factors discussed thus far generally tend to encourage firms to make crossborder acquisitions. In contrast, there are other variables that often appear to restrain cross-border combinations. These include information asymmetry, monopolistic power, as well as government restrictions and regulations. 3.2.1. Information asymmetry. Roll (1986) contends that information about a prospective target firm (e.g. market share, sales, cash flow forecasts) is crucial in the decision-making process of an acquiring firm. If the necessary information is not available, Roll (1986) argues that the prospective acquiring firm may be forced to delay or discontinue its plans, even 7 Since the four economies chosen are highly related to the US economy, the benefits of diversification are weakened. Furthermore, John and Ofek (1995) point out that specialization or focusing on an expertise shows benefits, which contrasts to the perceived benefits of diversification.

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though the foreign firm appears to be an attractive target. In contrast, Stoughton (1988) argues that information effects are not always harmful. He points out that the prospective acquirer may be able to obtain information about the target firm that is not available to other market participants. 3.2.2. Monopolistic power If a firm enjoys monopolistic power (a difficult prospect in the US, due to antitrust laws), then entry into the industry becomes more difficult for potential competitors, domestic or foreign. Moreover, a monopolist is much more likely to resist a takeover attempt. Other barriers to entry that make cross-border acquisitions especially difficult within a monopolistic environment include extensive outlays for research and development, capital expenditures necessary to establish greenfield production facilities, and/or product differentiation through a massive advertising campaign. 3.2.3. Government restrictions and regulations Most governments have some form of takeover regulations in place. In many instances, government approval is mandatory before an acquisition by a foreign firm can occur. In addition, there may exist government restrictions on capital repatriations, dividend payouts, intracompany interest payments, and other remittances. Scholes and Wolfson (1990) for example, discuss periods in the US where regulatory events discouraged acquisition activity; they cite the William’s Amendments and the Tax Reform Act of 1969 as significant legal and regulatory changes that contributed to a significant showdown in merger activity in the 1960s. In addition, Scholes and Wolfson (1990) argue that there was a similar impact resulting from changes in US tax laws in the 1980s, because those changes increased transaction costs in acquisitions involving US sellers and foreign buyers. On the other hand, Dewenter (1995a) contends that her empirical findings in the chemical and retail industry contradict Scholes and Wolfson (1990). She concludes from her findings that the US tax regime changes in the 1980s provide no explanation for the level of foreign acquisition activity. However, one must note that while Dewenter (1995a) only examined two industries, representing approximately 16% of foreign acquisition activity during 1978–89, Scholes and Wolfson (1990) examined activity from 1968 through 1987 and included a large number of industries in their study. This discussion of the variables that influence cross-border acquisitions, both positively and negatively, suggests that whereas there exists a substantial measure of added complexity in mergers involving firms in different countries, some fundamental aspects of merger analysis remain unchanged. That is, a merger or acquisition, cross-border or domestic, can be treated in the framework of a capital budgeting decision, where the main variables to be estimated are the future cash flows, the acquisition price, and the costs of financing the transaction. Therefore, it stands to reason that, at a macroeconomic level, one should include both the exchange rates and the cost of financing the acquisition. Exchange rates affect both the current price of the target as well as the future cash flows. The cost of financing the acquisition with a mix of debt and equity (i.e. the yields on long-term debt and

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stock prices) should also play an important role. This is true even though most multi-national corporations have access to global financial markets and will, ceteris paribus, raise funds where the cost of capital is the lowest. We now turn to the empirical evidence on cross-border acquisitions between the US and the EU in the period 1982–94.

4. Empirical evidence This section of our paper examines the statistical models used to evaluate selected variables affecting the number of cross-border mergers and acquisitions. We first discuss the models; later we examine the data and present our empirical findings. The models are applied to the analysis of cross-border mergers and acquisitions involving firms from the US and a representative sample of the European Union over the period 1982–94. Specifically, we will examine the mergers involving the US and each of the following four European countries: Germany, Italy, UK and France. Table 1 shows the number of US acquisitions in each country and, conversely, the number of acquisitions of US companies by firms located in each of those nations. While this table depicts varying trends among the countries, there appears to be a significant increase in the number of cross-border acquisitions from 1982 through the early 1990s. Unfortunately the values of a majority of the acquisitions are not available. Few companies involved in mergers disclose the dollar amounts involved. This lack of data is attributed to the practice of not disclosing the market value of mergers for strategic reasons. 4.1. The model The logit model and ordinary least squares (OLS) regression are used to analyze select factors affecting cross-border mergers. Both models examine the correlation between the explanatory variables and the level of cross-border activity. The logit model provides a maximum likelihood analysis and assumes that the mean of the response variable is linearly related to the explanatory variables. In short, it focuses on the trend of the acquisition activity. When the number of acquisitions of US firms by foreign firms is greater than the number of US acquisitions of a given country, 1 is used to represent the acquisition activity; 0 is used in the opposite instance. The logit model uses the logistic distribution as a probability function. One of the basic benefits of this distribution is that it constrains the dependent variable to lie between 0 and 1. The model coefficients are estimated using the maximum likelihood function. Logistic regression is utilized rather than discriminant analysis since it does not require the assumption of multivariate normality. The only assumption necessary for a logit regression is that the probability ( p) of a cross-border acquisition taking place with a foreign company as acquirer equals: p=1/[1+exp(−BX )],

(1)

438 G.M. Vasconcellos, R.J. Kish / Journal of Multinational Financial Management 8 (1998) 431–450 Table 1 European–US cross-border acquisitions, 1982–94 (number of transactions) Germany

Italy

United Kingdom

Year Qtr G–US US–G DIF

I–US

US–I

DIF

UK–US

France

US–UK DIF F–US US–F DIF

1982

I II III IV

0 2 1 0

2 2 3 0

−2 0 −2 0

0 0 1 1

1 0 1 0

−1 0 0 1

10 8 13 14

6 4 3 0

4 4 10 14

3 3 1 2

1 2 0 0

2 1 1 2

1983

I II III IV

0 0 0 0

2 3 1 1

−2 −3 −1 −1

0 0 0 0

0 0 0 3

0 0 0 −3

4 8 8 3

6 5 4 6

−2 3 4 −3

0 1 1 0

0 0 2 2

0 1 −1 −2

1984

I II III IV

1 0 2 0

4 0 5 1

−3 0 −3 −1

0 0 0 0

0 0 0 0

0 0 0 0

8 0 6 13

3 0 12 9

5 0 −6 4

0 0 2 1

1 0 3 2

−1 0 −1 −1

1985

I II III IV

2 6 2 2

4 3 1 2

−2 3 1 0

0 0 1 1

0 1 2 3

0 −1 −1 −2

10 13 12 12

6 10 8 2

4 3 4 10

5 1 0 3

4 3 2 0

1 −2 −2 3

1986

I II III IV

1 2 4 7

1 3 2 6

0 −1 2 1

0 1 0 2

0 0 0 1

0 1 0 1

12 17 16 33

3 4 12 12

9 13 4 21

0 0 0 5

0 3 2 3

0 −3 −2 2

1987

I II III IV

3 0 0 2

4 0 0 6

−1 0 0 −4

2 0 0 1

3 0 0 1

−1 0 0 0

20 0 0 30

9 0 0 4

11 0 0 26

3 0 0 4

4 0 0 7

−1 0 0 −3

1988

I II III IV

5 2 5 6

1 2 2 4

4 0 3 2

1 1 0 2

0 1 3 2

1 0 −3 0

45 36 33 38

7 6 3 9

38 30 30 29

5 7 4 6

2 1 2 3

3 6 2 3

1989

I II III IV

5 7 6 3

2 4 1 5

3 3 5 −2

0 1 2 2

2 3 3 4

−2 −2 −1 −2

31 33 17 20

11 8 6 8

20 25 11 12

3 2 4 5

2 1 5 2

1 1 −1 3

1990

I II III IV

7 4 5 5

5 5 4 5

2 −1 1 0

1 2 3 0

0 1 3 0

1 1 0 0

28 23 24 19

8 14 8 14

20 9 16 5

10 8 9 8

2 8 2 1

8 0 7 7

G.M. Vasconcellos, R.J. Kish / Journal of Multinational Financial Management 8 (1998) 431–450 439 Table 1 (continued ) European–US cross-border acquisitions, 1982–94 (number of transactions) Germany

Italy

United Kingdom

Year Qtr G–US US–G DIF

I–US

US–I

DIF

UK–US

France

US–UK DIF F–US US–F DIF

1991

I II III IV

4 1 4 5

9 5 4 5

−5 −4 0 0

1 1 0 2

1 1 3 0

0 0 −3 2

9 13 18 8

18 9 15 5

−9 4 3 3

0 8 2 0

5 5 4 2

−5 3 −2 −2

1992

I II III IV

0 0 3 1

0 0 8 9

0 0 −5 −8

0 0 1 0

0 0 1 1

0 0 0 −1

0 0 10 11

0 0 12 16

0 0 −2 −5

0 0 1 1

0 0 3 5

0 0 −2 −4

1993

I II III IV

4 4 4 2

11 −7 5 −1 15 −11 11 −9

2 1 0 1

1 4 11 3

1 −3 −11 −2

10 26 19 27

15 12 41 28

−5 14 −22 −1

4 2 2 3

10 4 8 8

−6 −2 −6 −5

1994

I II III IV

2 3 1 8

9 −7 13 −10 11 −10 13 −5

0 0 0 1

9 13 5 8

−9 −13 −5 −7

21 18 19 23

21 25 25 28

0 −7 −6 −5

1 3 2 4

8 4 15 5

−7 −1 −13 −1

where B denotes the vector of regression parameters and X is the vector of explanatory variables. Both the Logit and OLS regression models were specified as: ACQG t ACQI t ACQUK t ACQF t

? =f( EXRATE , t−n = f( EXRATE , t−n = f( EXRATE , t−n = f( EXRATE , t−n

− BYDIFG , t−n BYDIFI , t−n BYDIFUK , t−n BYDIFF , t−n

− STKUS , t−n STKUS , t−n STKUS , t−n STKUS , t−n

+ STKG )+e t−n t STKI )+e t−n t STKUK )+e t−n t STKF )+e t−n t

(2a)0 (2b) (2c) (2d )

where ACQ = acquisition differences (Logit regression — binary variable where ACQ= 0 when the number of foreign acquisitions of American firms is less than or equal to the number of American acquisitions of that country’s firms and ACQ=1 otherwise; OLS regression — actual cardinal difference between the number of foreign acquisitions of American firms and the number of American acquisitions of foreign firms); EXRATE=the exchange rate, measured in FX/$; BYDIF=bond yield differential (Foreign–US); STKUS=stock market index in US; STKG=stock market index in Germany; STKI=stock market index in Italy; STKUK=stock market index in the UK; STKF=stock market index in France;

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The signs above the independent variables reflect the hypothesized direction of their influence on the dependent variable. The question mark above the exchange rate variable denotes the ambiguity associated with determining the impact that a change in the exchange rate has on the dependent variable. For example, a strong US dollar may persuade a US firm to acquire a foreign firm because the initial acquisition price is favorable when measured in dollar terms in a net present value calculation. Conversely, if the US currency subsequently depreciates, the value of repatriated cash flows from a foreign subsidiary will increase. The sign of the coefficient is negative when the acquisition price is a dominant factor in the shortrun, whereas the sign is positive when higher discounted cash flows are dominant in the long run. Whether foreign bond yields are already higher than US yields (represented by a + sign on average in Table 2) or foreign bond yields are below US yields (represented by a − sign on average in Table 2) increases in foreign bond yields relative to US yields will decrease the number of foreign acquisitions of US firms. Similarly, high stock prices in the US promote more US acquisitions of foreign firms. On the other hand, high foreign stock prices encourage more foreign acquisitions of US firms. The corresponding statistical model was hypothesized for both the logit and OLS regressions as follows: ACQ =b +b EXRATE +b BYDIF +b STKUS +b STKX +e , t 0 1 t−n 2 t−n 3 t−n 4 t−n t (3) where X=G (Germany), I (Italy), UK ( United Kingdom), or F (France) for the given foreign country, n=0 for the contemporaneous model and n=1,2,3 or 4 for the number of quarters used in the lagged models. 4.2. The data The data for the dependent variable and the explanatory variables were obtained on a quarterly basis from 1982 through 1994. In this thirteen year period, the economies of the US and the four European countries experienced a record period of expansion in the 1980s and subsequently a recession in the early 1990s. In addition, the dollar has declined steadily since 1985 relative to the German mark and French franc. On the other hand, while the dollar experienced a similar decline since 1985 relative to the British pound and the Italian lira, the dollar rebounded somewhat beginning in the Fall of 1992 compared with these two currencies. The number of completed acquisitions, the dependent variable (ACQ), was obtained on a quarterly basis from Mergers and Acquisitions (several issues). Foreign acquisitions of US firms were recorded separately from US acquisitions of foreign firms. Then the number of US acquisitions was subtracted from the number of foreign acquisitions. The data for exchange rates ( EXRATE) and bond yields were downloaded from the Datastream database. For each month, the bond yield difference (BYDIF ) was calculated by subtracting the US bond yield from each European country’s bond yield.

G.M. Vasconcellos, R.J. Kish / Journal of Multinational Financial Management 8 (1998) 431–450 441 Table 2 Descriptive statistics Variable

A. US US Stocks

Mean

Std Dev.

Minimum

Maximum

290.576

111.983

113.820

469.460

B. Germany German acquisitions of US firms US acquisition of German firms Difference: German−US acquisitions German bond yields US bond yields Difference: German−US bond yields German exchange rates German stocks

2.750 4.308 −1.558 7.430 9.176 −1.744 2.032 1,631.000

2.300 3.776 3.680 1.020 1.985 1.702 0.489 491.461

0 0 −11 5.700 5.940 −4.970 1.430 682.330

8 15 5 9.800 14.220 0.660 3.190 2,347.000

C. Italy Italy acquisitions of US firms US acquisition of Italy firms Difference: Italy−US acquisitions. Italy bond yields US bond yields Difference: Italy−US bond yields Italy exchange rates Italy stocks

0.673 1.904 −1.231 13.524 9.176 4.348 1,450.000 484.534

0.810 2.830 2.935 2.988 1.985 1.889 219.993 192.853

0 0 −13 8.810 5.940 1.680 1,133.000 161.070

3 13 2 20.940 14.220 8.840 2,001.000 757.100

D. UK UK acquisitions of US firms US acquisition of UK firms Difference: UK−US acquisitions. UK bond yields US bond yields Difference: UK−US bond yields UK exchange rates UK stocks

16.327 9.615 6.712 9.947 9.176 0.771 0.633 1,913.000

10.799 8.353 11.583 1.420 1.985 1.192 0.076 737.349

0 0 −22 6.410 5.940 −2.340 0.515 699.700

45 41 38 15.580 14.220 3.170 0.870 3,263.000

2.673 3.038 −0.365 10.336 9.176 1.161 6.508 360.680

2.677 3.023 3.630 2.850 1.985 1.237 1.236 192.397

0 0 −13 5.780 5.940 −0.320 4.860 82.100

10 15 8 16.970 14.220 5.400 9.760 648.230

E. France France acquisitions of US firms US acquisition of France firms Difference: France−US acquisitions France bond yields US bond yields Difference: France−US bond yields France exchange rates France stocks

The monthly values for stock and bond prices were combined to produce a quarterly average for the respective variables. The US stock prices (STKUS ) were drawn from the S & P 500 Composite from S & P’s Security Price Index Record.

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Stock prices for three of the European countries were gathered from Moody’s International Manual as follows: West Germany (Commerzbank — Commerzbank Index), United Kingdom (London Stock Exchange — FT/SE 100 Index), and Italy (Banca Commerciale Italiana Strategic Research Inc. — Banca Commerciale Index). Furthermore, the stock prices for France (CAC index) were provided by Lazard Freres and Co., a brokerage firm. 4.3. Results Table 2 provides some descriptive statistics: the mean, standard deviation, and minimum and maximum values for the dependent and independent variables. Using the UK as an example, a positive integer value for UK acquisitions of 6.712 means that the number of UK acquisitions of US firms exceeded the number of US acquisitions of UK acquisitions, on average. For comparable long-term debt securities, UK annualized yields exceeded US yields by 0.771% (77.1 basis points) on average during the period 1982–94. The remaining information in the table may be interpreted in a similar manner. The results of our empirical analysis are presented in Table 3 (OLS ), Table 4 ( logit), and Table 5, which provides a summary for our results for the OLS and logit models. The statistical model ( Eq. (3)) was estimated with contemporaneous variables and with four quarters of lagged variables for both the OLS and logit regressions. 4.3.1. OLS model Table 3 displays the goodness-of-fit tests for the OLS model, along with the parameter estimates and the tests of significance for the independent variables. For each European country, we selected the contemporaneous or lagged model that demonstrated the best performance of the estimating equation.8 Table 5 provides a summary so that comparisons can be made among the results for the four countries. The model performs well in terms of the joint significance of the variables for all of the countries. We observe high F-values and low p-values ( less than 0.1%). For two countries, Germany and Italy, the adjusted R2 values imply that the independent variables explain more than 40% of the variability of the sample. In comparison, for the UK and France, we observed adjusted R2 values below 30%. This indicates that the macroeconomic variables explain only a relatively small portion of the sample variability of the dependent variable. Industry and/or firm-specific variables should explain the remaining variation. The signs of the coefficients are as expected for all significant variables for the OLS model. In contrast, for a few insignificant variables, we observed signs of the coefficients that were opposite of our expectations, particularly the BYDIF for the UK. We cannot reject the null hypothesis for these coefficients at the 5% level of significance. These insignificant variables are not statistically different from zero. 8 All the contemporaneous and lagged results (one, two, three, and four quarters) from both the OLS and logit models are available from the authors upon request.

G.M. Vasconcellos, R.J. Kish / Journal of Multinational Financial Management 8 (1998) 431–450 443 Table 3 OLS regression results A. Germany — lagged four quarters Model: ACQG =b +b EXRATEG +b BYDIFG +$ STKUS +$ STKG +e t 0 1 t−4 2 t−4 3 t−4 4 t−4 t Parameter estimates and test of significance: Variable Intercept EXRATE BYDIF STKUS STKG Coefficient b b b b b 0 1 2 3 4 Estimated value 4.1284 −4.2556 −1.6865 −0.0296 0.0051 (t-value) 0.7840 −2.5990 −2.7950 −2.9880 3.3270 Prob.>t2 0.4371 0.0128 0.0077 0.0046 0.0018 R2 0.4921 F-value 10.4160 Adjusted R2 0.4449 Prob.>F 0.0001 B. Italy — lagged one quarter Model: ACQI =b +b EXRATEI +b BYDIFI +b STKUS +b STKI +e t 0 1 t−1 2 t−1 3 t−1 4 t−1 t Parameter estimates and test of significance: Variable Intercept EXRATE BYDIF STKUS STKI Coefficient b bbbb 0 1 2 3 4 Estimated value 10.3940 −0.0054 0.0715 −0.0215 0.0040 (t-value) 2.5240 −2.8630 0.3130 −5.0700 1.3100 Prob.>t2 0.0152 0.0064 0.7560 0.0001 0.1968 R2 0.4555 F-value 9.4110 Adjusted R2 0.4071 Prob.>F 0.0001 C. UK — contemporaneous variables Model: ACQUK =b +b EXRATEUK +b BYDIFUK +b STKUS +b STKUK +e t 0 1 t t t t t t t t Parameter estimates and test of significance: Variable Intercept EXRATE BYDIF STKUS STKE Coefficient b b b b b 0 1 2 3 4 Estimated value 58.0874 −61.1998 2.3190 −0.2312 0.0276 (t-value) 3.9730 −2.9010 1.5210 −3.1230 2.5170 Prob.>t2 0.0002 0.0056 0.1348 0.0031 0.0153 R2 0.3259 F-value 5.6820 Adjusted R2 0.2686 Prob.>F 0.0008 D. France — contemporaneous variables Model: ACQF =b +b EXRATEF +b BYDIFF +b STKUS +b STKFt+e t 0 1 t 2 t 3 t t t Parameter estimates and test of significance Variable Intercept EXRATE BYDIF STKUS STKF Coefficient b b b b b 0 1 2 3 4 Estimated Value 14.4730 −0.8146 −0.2017 −0.0672 0.0283 (t-value) 2.1630 −1.0980 −0.3820 −3.4440 2.0430 Prob.>t2 0.0357 0.2776 0.70440.0012 0.0467 R2 0.3363 F-value 5.9540 Adjusted R2 0.2798 Prob.>F 0.0006

Table 3, Section A for Germany reflects the best results for the OLS model. All four of the explanatory variables are significant at the 5% level or better. For the exchange rate, the negative sign implies that the acquisition price is dominant in determining the effect that the exchange rate variable has on the dependent variable. In addition, the bond yield difference and both US and German stock prices are

444 G.M. Vasconcellos, R.J. Kish / Journal of Multinational Financial Management 8 (1998) 431–450 Table 4 LOGIT model results A. Germany — lagged four quarters ACQ=0 when AGAU Analysis of maximum likelihood estimates Variable Intercept ExRate Parameter estimate −9.1389 −3.7481 Standard error 6.4983 1.6113 Wald x2 1.9778 5.4107 Prob.>x2 0.1596 0.0200 Criteria for assessing model fit Intercept and covariates x2 AIC 42.273 — SC 51.629 — −2LogL 32.273 21.711 Score — 18.711 Classification table Event Observed Event 7 No event 4 Total 11 B. Italy — contemporaneous variables ACQ=0 when AIAU Analysis of maximum likelihood estimates Variable Intercept ExRate Parameter estimate −4.5101 −0.0015 Standard error 5.7664 0.0030 Wald x2 0.6117 0.2671 Prob.>x2 0.4341 0.6053 Criteria for assessing model fit Intercept and covariates x2 AIC 47.477 — SC 57.233 — −2Log L 37.477 7.173 Score — 6.294 Classification table Predicted Event Observed Event 0 No event 2 Total 2

Bydif −3.0915 1.0358 8.9089 0.0028

STKUS 0.0121 0.0126 0.9236 0.3365

with 4 DF with 4 DF Predicted No event 5 32 37

p=0.0002 p=0.0009

Bydif

STKUS −0.0064 0.0053 1.4523 0.2282

0.5788 0.3478 2.7694 0.0961

STKG 0.0037 0.0020 3.4725 0.0624

Total 12 36 48

with 4 DF with 4 DF

p=0.1270 p=0.1782

No event 8 42 50

Total 8 44 52

STKI 0.0080 0.0038 4.3924 0.0361

significant at the 1% level or better. Since German bond rates are below US bond rates, a higher bond yield differential would encourage US firms to acquire more German firms than German firms to acquire US firms. In addition, the German companies would tend to invest locally where the cost of debt is lower. Furthermore, higher stock prices in the US and lower stock prices in Germany provide more favorable conditions for US firms to acquire German firms. By the same token, higher stock prices in Germany and lower stock prices in the US favor more German acquisitions of US firms. In Table 3, Section B for Italy, the exchange rate and US stock prices are significant

G.M. Vasconcellos, R.J. Kish / Journal of Multinational Financial Management 8 (1998) 431–450 445 Table 4 (continued ) LOGIT model results C. UK — contemporaneous variables ACQ=0 when AUKAU Analysis of maximum likelihood estimates Variable Intercept EXRATE Parameter estimate 6.6870 0.5652 Standard error 4.4699 6.2699 Wald x2 2.2380 0.0081 Prob > x2 0.1347 0.9282 Criteria for assessing model fit Intercept and covariates x2 AIC 49.653 — SC 59.409 — −2Log L 39.653 27.431 Score — 21.458 Classification table Event Observed Event 31 No event 6 Total 37 D. France — Lagged one quarter ACQ=0 when AFAU Analysis of maximum likelihood estimates Variable Intercept ExRate Parameter estimate 18.5853 −1.4803 Standard error 6.4116 0.6813 Wald x2 8.4024 4.7215 Prob.>x2 0.0037 0.0298 Criteria for assessing model fit Intercept and covariates x2 AIC 55.404 — SC 65.063 — −2LogL 45.404 20.820 Score — 18.297 Classification table Event Observed Event 10 No event 6 Total 16

BYDIF 1.9892 0.7180 7.6751 0.0056

STKUS −0.0384 0.0188 4.1712 0.0411

with 4 DF with 4 DF Predicted No event 3 12 15

p=0.0001 p=0.0003

Bydif −0.8809 0.4325 4.1478 0.0417

STKUS −0.0412 0.0186 4.9146 0.0266

with 4 DF with 4 DF Predicted No event 8 27 35

p=0.0003 p=0.0011

STKE 0.0020 0.0026 0.5785 0.4469

Total 34 18 52

STKF 0.0086 0.0115 0.5560 0.4559

Total 18 33 51

at the 1% level or better. Similar to Germany, the sign of the coefficient for the exchange rate is negative which conveys that the acquisition price is the dominant factor. Also similar to Germany, low US stock prices provide favorable conditions for more Italian firms to acquire US firms as compared with US firms acquiring Italian firms and vice-versa. Our analyses for Sections C and D include similar observations as compared with Germany and Italy. In Section C for the UK, exchange rates and US stock prices are significant at the 1% level or better. In addition, the UK stock prices are significant at the 5% level or better. In Section D

446 G.M. Vasconcellos, R.J. Kish / Journal of Multinational Financial Management 8 (1998) 431–450 Table 5 OLS regression and LOGIT model results Lags (best fit)

Exchange rates

Bond yields

US stock prices Foreign stock prices

OLS regression results Rank of variable defined by prob.>|T| Germany Lagged four quarters 4** Italy Lagged two quarters 2* UK Contemporaneous 2* France Contemporaneous 3

3* 4 4 4

2* 1* 1* 1*

1* 3 3** 2**

LOGIT model results Rank of variable defined by prob.>x2 Germany Lagged four quarters 2** Italy Contemporaneous 4 UK Contemporaneous 4 France Lagged one quarter 2**

1* 2*** 1* 3**

4 3 2** 1**

3*** 1** 3 4

Scale: 1–4 (most significant=1). * Significant at 1% level or better. ** Significant at 5% level or better. *** Significant at 10% level or better.

for France, US and French stock prices are significant at the 1% and 5% levels or better, respectively. Unlike the other three countries, the exchange rate is not a significant variable for France. As mentioned in prior sections, exchange rates influence acquisitions in several ways, depending on whether the acquisition price or the values of future cash flows dominate the investor’s decisions. In addition, in a previous study, Kish and Vasconcellos (1993) also noted that the exchange rate was not a significant variable and could have been affected by the presence of multicollinearity. They believe that exchange rates perform the role of predictor of trends of cross-border acquisitions. Our review of the correlation matrix shows some large correlations between the independent variables.9 While we observed that, in some cases, the exchange rate may not have explanatory power, we would like to reiterate that our focus is to review the combined impact of all of the independent variables to explain the trends of cross-border acquisitions. 4.3.2. Logit Table 4 presents results for the logit model ( Eq. (3)). This table shows parameter estimates of the model, as well as tests of significance for the explanatory variables, the Wald chi-square test for goodness-of-fit, selected criteria for assessing model fit, and a classification table. As with the case of the OLS results, the logit results reported in Table 4 represent the best performance of the estimating equation.10 9 Correlation tables are available upon request from the authors. 10 See footnote 7.

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Once again, Table 5 provides a summary that illustrates that our results in these tables support the findings of the OLS model. The logit model performs well for all of the countries, as indicated by the significance levels of the −2LogL and score criteria in the sections of the tables for assessing model fit. Furthermore, the best measure of the adequacy of the logit model is the percentage of correct predictions. In our classification tables, the percentage of correct predictions exceeds 70% for all four of the countries and is higher than 80% for Germany, Italy, and the UK.11 The signs of the coefficients are as expected for all significant variables, except for the BYDIF for the UK (panel C ). Similar to the OLS model, we observed signs of non-significant coefficients that were opposite of our expectations, particularly US stock prices for Germany (panel A) and BYDIF for Italy (panel B). We cannot reject the null hypothesis for these coefficients at the 5% level of significance. These insignificant variables are not statistically different from zero. In Table 4, Section A for Germany, two explanatory variables, exchange rates ( EXRATE ) and bond yield difference (BYDIF ) are significant at the 5% and 1% levels or better, respectively. These results are consistent with the OLS model. However, the US and German stock price variables are also significant in the OLS model. Within Section B for Italy, the Italian stock prices (STKI ) are significant at the 5% level or better, whereas the exchange rate variable is not, possibly for the reasons discussed under the OLS model. Based on the logit model, one would expect that higher Italian stock prices would lead to more Italian acquisitions of US firms than US acquisitions of Italian firms. In Section C for the United Kingdom, US stock prices and the bond yield difference are significant at the 5% and 1% levels or better, respectively. UK bond yields were not much higher, on average, than US bond yields. Still, the positive and highly significant coefficient is contrary to expectations. It is possible that UK firms have more access to the US bond market compared with their continental counterparts. Finally, in Section D for France, EXRATE, BYDIF, and STKUS are all significant at the 5% level or better. Table 5 provides a summary of our results for the OLS and the logit models. It is difficult to rationalize why both the OLS and logit models reflecting the ‘best fit’ are lagged four quarters in the case of Germany and both contemporaneous in the case of the UK, whereas in the case of France and Italy one is contemporaneous and the other lagged (but different for OLS and Logit models). We believe that the lags reflect the timing of the acquisition decision, i.e. the longer the lag, the longer the period of time between the decision and its implementation. What seems reasonably clear is that the UK and Germany are at the opposite ends of the decision lag, with France and Italy in between. While both the logit and OLS models performed well, these models identified different significant explanatory variables. Most noticeably, the logit model identified the bond yields as most significant, except for Italy. In contrast, only for Germany did the OLS model identify bond yields as significant, whereas the US stock price 11 The percentages correct were 81.3% (Germany), 80.8% (Italy), 82.7% ( UK ), and 72.5% (France).

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is the explanatory variable that performs best. These differences in results may be due to the presence of multicollinearity. Once again, our review of the correlation matrix highlighted strong correlations among many of the independent variables. As Kennedy (1985) explains, although the OLS estimator retains its desirable properties in the presence of multicollinearity, the variances of the OLS estimates may turn out to be quite large. In a previous study, Kish and Vasconcellos (1993) noted that the exchange rate performs the role of predictor of trends in cross-border acquisitions, whereas the costs of debt and, in particular, the stock prices are the deciding factors on a contemporaneous basis. In summary, the combined impact of the explanatory variables appears to explain well the trends of cross-border acquisitions between the US and these four individual European countries.

5. Summary and conclusions The quickly evolving single European market in the late 1980s and early 1990s encouraged many non-European firms to establish a presence in Europe before the barriers to entry intensified. Consequently, by 1994 US foreign direct investment in the EU increased by approximately 200% from the early 1980s. During the 13-year period examined (1982–94), the US and the four largest European economies experienced a period of expansion in the 1980s and subsequently a recession in the early 1990s. The increasing globalization of trade and capital mobility encouraged many firms to look outside their home country borders to find factors of production that could provide competitive advantages. Our study examines how movements among bond yields, exchange rates, and stock prices influence the number of cross-border acquisitions. We utilize the logit and OLS regression models to analyze the impact these macroeconomic variables have on the trends of cross-border acquisitions between the US and Germany, Italy, the UK, and France. While we have hypothesized the expected direction (i.e. signs) of each explanatory variable, our focus remains on the joint effect of the macroeconomic variables on the level of cross-border mergers and acquisitions, as opposed to firm- and industry-specific variables. In our empirical analysis using the OLS model, we find that US stock prices are major factors that influence cross-border mergers and acquisitions for all of the countries examined. Similarly, foreign stock prices are significant explanatory variables for three of the four countries, excluding Italy. In contrast, in the logit model, our results suggest that bond yields are major causal factors. This implies that bond yields may be one of the final negotiating points in the decision to consummate an acquisition. Finally, supporting previous research findings, the exchange rate does not consistently acquire significance for all countries. We continue to think that the exchange rate serves mainly as a predictor of trends in acquisitions. As the European integration continues, it will be interesting to monitor whether or not the joint significance of the explanatory variables increases or decreases. A study to explore why US stock prices were significant explanatory variables for the OLS model, yet not for the logit model, could also be undertaken. Similarly, we

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would like to ascertain why bond yields are significant for the logit model, yet not for the OLS model. In summary, the factors that motivate merger and acquisition activity will continue to evolve as all companies feel the ongoing effects of globalization.

Acknowledgements The authors would like to acknowledge the outstanding research assistance of William Brassington, Colleen Clark, and Marvin Metzger in gathering the data and background studies for this manuscript. We thank Jim Mylett of Lazard Freres for providing data used in this study. We are also grateful for the input provided to us during the 1997 Meetings of the Eastern Finance Association, particularly by Ronnie Clayton, our discussant. Finally, we thank an anonymous referee and Ike Mathur, the editor, for comments that helped to improve the paper substantially. All remaining errors are our own.

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