Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Trade Network and Cultural Distance in Cross-border
Acquisitions: An Empirical Analysis with U.S. Acquiring
Firms
Seung Hun Han* and Eun Jin Jo**
We examine trade networks and cultural distance in cross-border acquisitions.
Using a sample of 938 cross-border acquisitions during 2000-2007, we found
that cultural distance between acquiring nation and target nation negatively
affects the post-performance of cross-border acquisitions. We mainly used the
Greet Hofstede cultural distance score as the cultural distance proxy. We also
measured the cross-national relationship with eigenvector centrality by using
trade volume data. We found that acquisitions with stronger centrality give the
higher abnormal return to acquiring firms. We conclude that the trade network
measured by eigenvector centrality explains better the performance of
acquiring firms than cultural distance measurement.
JEL Codes: G34, G15
1. Introduction
In recent decades, the trend in world economy is characterized by liberalization
through deregulation and consequently the financial markets are globalized and
opened to other countries(Helleiner 1995). Thus, not only financial markets but also
product markets are integrated. In other words, through the world market
globalization both the real economy (i.e., trade) and the financial economy (i.e.,
financial transactions) are integrated(Vo and Daly 2007). Accordingly, cross-border
M&A’s have been increasing significantly and many previous literatures document
the increasing trend of the cross-border acquisitions in terms of volume and
frequency. Even though the global markets are integrated and cross-border
acquisitions are prevalent in global markets, still there exist various variations in
terms of both country-specific factors (i.e., culture, religion, language) and dealspecific components (i.e., payment methods) between the target nation and
acquiring nations(Ahern, Daminelli et al. , Morosini, Shane et al. 1998, Rossi and
Volpin 2004, Chakrabarti, Gupta-Mukherjee et al. 2008, Aybar and Ficici 2009).
Therefore, in this study, we examine the impact of national trade connectivity using
complex network analysis on the performance of cross-border acquisitions of the US
firms.
Studies in cross-border mergers and acquisitions, bilateral relationship such as
cultural distance or trade openness has been the focus of successful transactions
between target and acquiring nations. Because acquisition with target nations having
stronger power in international connectivity or trade network may allow managers of
acquiring firms to reinforce the efficiency of the post-merger integration process
(Chakrabarti, Gupta-Mukherjee et al. 2008), investigating relationship between target
and acquiring nations in international trade network contexts can provide valuable
* Seung Hun Han, Business and Technology Management, Korea Advanced Institute of Science and
Technology, Republic of Korea, Email: synosia@kaist.ac.kr
** Eun Jin Jo, Business and Technology Management, Korea Advanced Institute of Science and
Technology, Republic of Korea, Email: eunjin.jo@kaist.ac.kr
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
insight in understanding the determinants of cross-border acquisitions returns. In
addition to that, acquisitions with target nation which is more open to world trade can
give a chance in global market expansions for acquiring companies. A few studies
introduce the bilateral trade concepts in cross-border mergers and acquisitions
research by using export and import trade volume data, and they empirically show
positive and significant relationship between M&As performance and bilateral trade
between target and acquiring nations (Rossi and Volpin 2004, Chakrabarti, GuptaMukherjee et al. 2008).
However, the bilateral trade openness has a few limitations since they do not
consider the complexity in world trade contexts. In other words, the trade openness
measurement should be more than bilateral, because world economy is getting more
integrated and more interconnected through globalizations. Long before financial
research begins to focus on bilateral trade, studies regarding the international trade
have been investigated in economics and social networks. De Benedictis and Tajoli
(2011) argue that world trade should be considered as network and analyzed with
various network analysis measurements such as closeness, betweenness
centralities. Likewise, adopting a network analysis can complement bilateral
relationship simply measured by trade volume from target to acquiring nations.
Therefore, we use eigenvector centrality which is one of the most popular methods in
social network filed instead of bilateral trade variables.
In addition to trade network, we also focus on cultural distance between target and
acquiring nations, because the cultural disparity has been the most popular variable
in recent cross-border M&A studies(Ahammad, Tarba et al. , Chakrabarti, GuptaMukherjee et al. 2008, Reus and Lamont 2009, Erel, Liao et al. 2012). Studies in
cultural distance empirically show the significant relationship with M&A performance.
However, there are also a few controversies whether cultural factors positively or
negatively affect the M&A returns. Some researches show the positive relationship
between cultural difference and post-performance returns since cultural disparity can
provide a variety of routines and repertories (Morosini, Shane et al. 1998,
Chakrabarti, Gupta-Mukherjee et al. 2008). However, there are also contradicting
results. Since cultural distance can be a cost for acquiring companies in post
integration process and cause cultural clashes, cultural distance is negatively related
with short-term performance of cross-border M&A (Datta and Puia 1995, Stahl and
Voigt 2008). In this research, we focus on short-term performance with two days
period window and
Pragmatic versus Normative(PRA) and Indulgence versus
Restraint (IND) which have not studied in previous studies). In addition, Stulz and
Williamson (2003) argue that countries can stand to benefit from the bilateral trade,
and it is important to consider not only cultural effect but impact from other countries.
So this examine the moderating effect of openness measured by trade network with
cultural distance. Therefore, this study hypothesizes the negative relationships
between cultural distance and acquisition performance and moderating effect of
trade network.
In this research, we use the 909 acquisitions from 43 different target nations and U.S.
acquiring firms during 2000-2007. With the event study methodology and network
analysis measurements, we investigate the impact of centrality in world trade
network and cultural distance measured by Greet Hofstede on the performance of
M&As and control for firm specific factors such as firm size, profitability, and financial
leverage. We find that acquisitions with target nations having more centrality in
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
cross-border trade network give higher returns for acquiring companies. This result
strongly supports our hypothesis and consistently significant after adding the cultural
distance variables and controlling firm specific variables and deal specific variables.
Also, our empirical evidence shows that cultural distance variables are negatively
related with performance of M&As. These results are consistent with previous
literatures which short-term performance of cross-border acquisitions is negatively
related with cultural distance due to cultural clashes (Datta and Puia 1995).
We contribute to the previous literatures with introduction of network analysis in
financial studies, which are more elaborated measurements for bilateral trade. Also
we capture trade relationship between target and acquiring nation in the contexts of
world trade network rather than simple bilateral transactions. In addition, we confirm
the negative effect of cultural distance in short-term returns of cross-border
acquisitions with Hofstede measurements. In cultural distance measurements, we
use six-dimensional distances rather than four-dimensional measurements which
widely-adopted in previous literatures.
The rest of the paper is organized as follows. The next section describes the
previous literatures. Next section presents the sample selection and applied
methodologies. This is followed by a discussion of the effect of trade network and
cultural distance to post performance of cross-border acquisitions. This paper
concludes with summarized results and several suggestions for the future research
in this area.
2. Literature Review and Hypothesis Development
Since early 1990, the international mergers and acquisitions (M&As) begin to
prosper along with the increase in economic globalization (Martynova and
Renneboog 2008). Since domestic transactions are dominated in M&As market up
till then, the globalization of M&A is the most striking features in recent M&As studies.
Unlike domestic deals, international deals give an important way for multinational
companies to achieve global market power. Yet, there are some studies examining
the determinants of cross-border takeover gains in bidder and target firms. In
addition to the various factors which affecting the M&As performance such as firm
specific or deal specific factors, cultural distance between acquirer and target firms
come to the fore in recent literature(Ahern, Daminelli et al. , Morosini, Shane et al.
1998, Rossi and Volpin 2004, Chakrabarti, Gupta-Mukherjee et al. 2008, Aybar and
Ficici 2009).
A substantive theory and research examining the determinants of success in crossborder mergers and acquisitions have done for the past few decades, however, there
does not exist deterministic conclusions in this area. Also, cultural difference
between two parties in M&As is the focus of international studies. Although many
recent studies already discuss the effects of cultural disparity on the cross-border
M&As returns, there are some controversial or contentious about this issue. Morosini,
Shane et al. (1998) posit both positive and negative effect of cultural distance on
cross-border acquisitions since cultural distance has both synergy and disruption to
the acquirers And they empirically prove that cultural distance affects positively to the
percentage of sales growth of acquiring firms since cultural distance provides variety
of patterns and methods. However, this literature has limitations in using percentage
of sales growth as dependent variable to show performance of the acquiring
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
company (Morosini, Shane et al. 1998). Also, more recent literature show that
acquisitions with more culturally dispersed give higher returns in the long-term
perspectives(Chakrabarti, Gupta-Mukherjee et al. 2008).
On the other hand, some studies argue that cultural distance can affect negatively to
the shareholders’ return of acquiring firms since cultural distance can be cost to the
acquiring firms and there is also cultural risk in post-acquisition process(Ahern,
Daminelli et al. ,Stahl and Voigt 2008). Also, Datta and Puia (1995) prove that
negative side of cultural fitness on shareholders in cross-border acquisitions with the
acquisitions undertaken by the U.S. acquirers. Since this study focus on
announcement effect rather than long-term performance, they have a contribution to
the previous literature.
𝐻1 : Cultural distance negatively affects returns of acquiring firms in cross-border
acquisitions.
As well as cultural difference measured by Hofstede (1984), other cultural factors
such as language and religion are viewed as important variables in international
finance studies (Ahern, Daminelli et al. , Stulz and Williamson 2003, Rossi and
Volpin 2004, Chakrabarti, Gupta-Mukherjee et al. 2008). Stulz and Williamson (2003)
argue that language can be a tool for communication and promote transferring the
ideas among employees, so language plays a role in international acquisitions.
Likewise, religion can be a route for innovation and plays a key role in finance (Stulz
and Williamson 2003). Also other literature empirically shows that same language
between two acquisition partners positively affects the propensity for cross-border
acquisitions rather than domestic deals (Rossi and Volpin 2004). Therefore,
language and religion can play a crucial role in cross-border mergers and
acquisitions since these two factors affect values, resources allocation, institutions in
a country (Stulz and Williamson 2003).
𝐻2 : Same language and religion are positively related with takeover returns in crossborder acquisitions.
In addition to the cultural indexes, national economic factors can be critical factor for
cross-border acquisitions since cross-border acquisitions are an important way for
gaining international market power. Therefore, recent studies start to focus on
economic difference between acquirer nation and target nation and openness of
target nation to international trade market (Rossi and Volpin 2004, Chakrabarti,
Gupta-Mukherjee et al. 2008). Rossi and Volpin (2004) show that the bilateral trade
between two acquisition parties positively and significantly affects the propensity for
cross-border deals. Although it gives reverse results for bilateral trade variables,
Chakrabarti, Gupta-Mukherjee et al. (2008) prove that openness of target nation to
international trade is also crucial part in cross-border acquisitions studies.
Researchers in international economic studies consider international trade as a
network structure for a long time (De Benedictis and Tajoli 2011). However, the
methodology which they adopted does not consider international trade as network
and does not capture the complexity of whole international trade network.
Rather than adopting previous measurement, social network analysis can be a
proper measurement for examining the international trade network. Since centrality,
one of well-kwon methodology in network analysis, can capture the positions of a
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
node in the network structure. Social network methodology rapidly rises as tool for
measuring complex relationships in broad areas in research such as marketing,
economics (De Benedictis and Tajoli 2011, Yoganarasimhan 2012). Yoganarasimhan
(2012) examines the positions of the writers in local network with connectivity,
clustering, and centrality which are well-adopted tools in social network analysis. De
Benedictis and Tajoli (2011) explore the world trade network with centrality
measurement. They argue that utilization of centrality measurements can
appropriately make up for other empirical tools in measuring the world trade network
(De Benedictis and Tajoli 2011).
𝐻3 : Acquisitions with target nations having stronger power in international bilateral
network give higher returns for acquiring companies.
3. Data and Methodology
Our empirical analysis of cross-border acquisitions is based on the sample occurred
during 7-year period, 2001-2007. The acquisitions data is obtained from the SDC
Platinum Mergers & Acquisitions database. Firstly, we get 8,767 observations from
the U.S. acquirers excluding the targets or acquirers which industry sector is
financial industry. From that, we select events with several steps. The sample
selection criteria used in this paper are 1) acquirer is publicly traded; 2) the deal is
completed; 3) target and acquiring nations have Hofstede cultural distance score; 4)
target or acquirer industry is not utility industry, SIC codes from 4900-4999; 5) the
acquirer owns 100% shares of target firms after transaction; and 6) non-U.S. target
nations; 7) target nations have trade volume data. Finally, we have 899 samples with
the U.S. acquirer nation from 42 different target nations. Table 1 shows the number
of transactions for each target nations during 2001-2007. The majority of acquisitions
with the United States as the acquirer are made by the top two target nations, United
Kingdom and Canada.
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Table 1 Breakdown of sample of target nations which conducted cross-border
acquisitions during 2000-2007
Target nations
No. of acquisitions
United Kingdom
199
Canada
182
Germany
96
France
66
Australia
50
China
41
Netherlands
32
Italy
21
Switzerland
21
Mexico
18
Sweden
16
Brazil
15
Norway
13
Belgium
12
Spain
12
India
11
South Korea
10
Others
84
Total
899
We mainly use the ‘Eventus’ methodology for the event study to observe the stock
price reaction to the announcement of acquisitions. This approach is widely used in
finance research when seeing the returns of certain events, such as mergers and
acquisitions, stock repurchase, stock split, and so on. Under the market-adjusted
returns (MAR) model, we get the cumulative abnormal returns (CARs) of acquirer
firms. Abnormal returns (AR) are computed by the subtracting the market index
return for day t from the rate of return of j company at time t. As a result, the
abnormal return of company i at time t is given by
AR i,t = R i,t − R m,t (1)
Cumulative abnormal returns (CARs) for window (𝑡1 , 𝑡2 ) is given by
t2
CARsi (t1, t 2 ) = ∑T=t
AR i,t
1
(2)
We construct five windows, (-5, +5), (-3, +3), (-1, +1), (-1, 0), (0, +1) for cumulative
abnormal returns (CARs) where t=0 is announcement date. We mainly use the CARs
(-1, 0) as the dependent variable for multivariate regression models in this study.
Deal specific characteristics are likely to affect the performance of acquiring firms.
Therefore, deal specific variables are added in our regression models as
independent dummy variables. From the SDC Platinum database, we also obtain
deal specific variables regarding the deal type. With this information, we construct
the dummy variable as friendly, hostile, and neutral. In our regression models, hostile
equals one if the transaction is the hostile deal.
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
In addition to that, firm-level variables also have an impact on the acquisition returns
and should be controlled to illuminate the impact of other major variables, such as
network centrality and cultural factors. Therefore, we use three variables to control
the firm specific characteristics such as firm size, financial leverage, and profitability.
Required data are obtained from COMPUSTAT database. LN(Total asset) is the
natural logarithm of total asset as proxy for firm size. Leverage is the debt ratio
calculated as total debt divided by total asset as proxy for financial leverage. ROA is
the return on asset (ROA) calculated as net income divided by total assets.
In recent studies, cultural factors have been viewed as key factors determining the
success of cross-border mergers and acquisitions. Therefore, we examine the
cultural factors with several variables. Firstly, we use the cultural distance developed
by Greet Hofstede (1980, 2001, 2010). They observe cultural disparity with sixdimensional factors, power distance index (PDI), individualism versus collectivism
(IDV), masculinity versus femininity (MAS), and uncertainty avoidance (UAI),
pragmatic versus normative (PRA), and indulgence versus restraint (IND). Each
variable is calculated as the absolute difference between target and acquiring
nations. To capture the impact of each variable, we run the regression with six
variables separately. Also, we investigate the overall influence of cultural disparity
with formula (3) (Morosini, Shane et al. 1998). In our regression model, we use the
natural logarithm of aggregate cultural distance as ‘LN(Aggregate Cultural distance)’
to see the combined influence of Hofstede’s cultural distance.
6
Aggregate CD (T, U) = √∑(𝐻𝑖,𝑡 − 𝐻𝑖,𝑢 )
2
(3)
𝑖=1
On the other hand, we also investigate the impact of language and religion on the
acquisitions gains. We construct the language dummy variable which given score 1 if
the target and acquiring nations share same the first language, and 0 otherwise. Also,
we construct the religion dummy variable which given score 1 if the target and
acquiring nations share the same primary religion, and 0 otherwise. All the data
required is obtained from CIA World Fact Book.
In recent international M&A studies, trade relationship between two acquisition
parties measured by bilateral trade emerges as important factor (Rossi and Volpin
2004, Chakrabarti, Gupta-Mukherjee et al. 2008). With this trend, we also observe
the international trade network. Besides countries’ own properties, network analysis
investigates the relationship between countries and also gives emphasis on the
power of flow in the bilateral trade (De Benedictis and Tajoli 2011). De Benedictis
and Tajoli (2011) argue that the bilateral trade should be investigated as network and
gives more priority on the interconnectedness rather than on countries’ attributes.
Therefore, they analyze world trade network with network analysis such as degree,
closeness, and betweenness centralities.
In previous literature, degree, closeness, and betweenness centralities are widelyused in network analysis (De Benedictis and Tajoli 2011, Yoganarasimhan 2012).
Centrality measurements used in the existing literature also have limitations that they
are only useful in rigorously binary connections between nodes (Bonacich 2007).
Different from those measurements, eigenvector centrality gives weights differently
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
depending on their centralities (Bonacich 1972). Eigenvectors, and Bonacich’s c(β)
gives advanced measures with the power measurements in complex networks
(Bonacich 2007). Therefore, we use eigenvectors, and Bonacich’s c(β) to
investigate the world trade network.
The centrality power of target nation in bilateral trade network can affect the
acquisitions’ returns, we therefore measure centrality of target nations with
eigenvectors, and Bonacich’s c(β). In this study, we only use annual bilateral ‘export’
volume data of 42 nations measured in US dollars from World Bank database.
Although target nations are only 42 nations in this study, we collect all the data
provided to capture the target nation’s centrality in entire world trade network.
According to Bonacich (2007), eigenvector centrality, ‘x’ can be shown as two ways.
One is defined as matrix form and the other is calculated as sum of path. In matrix
form, λ is defined as the largest eigenvalue of A and ‘n’ is defined as the number of
nodes or the number of nations in this research. And in the right equation, 𝑎𝑖𝑗
equals 1 if node i is connected to the node j, and 0 otherwise.
Ax = λx,
λ𝑥𝑖 = ∑𝑛𝑗=1 𝑎𝑖𝑗 ∙ 𝑥𝑗 ,
𝑖 = 1, ⋯ , 𝑛
(4)
According to the formula (4), we get the value of centralities for each year. In
regression model, the eigenvector centrality of target nation is represented as
‘Centrality’ and used as major determinants to explain the determinants of takeover
returns. Table 3 shows target nations’ eigenvector centralities in the world trade
network, ranked in descending and ascending order for each year. In table 3, the
order is fluctuating from year to year.
And also, we add other economic variables such as gross domestic product (GDP)
and growth rate of gross domestic product (GDP Growth). These variables are
obtained from the World Bank database, and we use natural logarithm of GDP for
this study, LN(GDP).
In Table 3, we construct the descriptive statistics for dependent variables and control
variables. First part of the table 3 shows the explanatory variables regarding the
Hofstede’s cultural distance, and second part shows the explanatory variables such
as openness using trade volume data, economic data, and last part shows the firm
specific, control variables.
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Table 2 Target Countries' Bonachici Eigenvector Centrality in the World Trade
Network
Rank
Country
Descending Order
2001
1
Canada
2
Japan
3
Mexico
4
Germany
5
China
2002
1
Canada
2
Japan
3
Mexico
4
Hong Kong
5
Germany
2003
1
Canada
2
Germany
3
Mexico
4
China
United
5
Kingdom
2004
1
Canada
2
Japan
3
China
4
Germany
5
Mexico
2005
1
Canada
2
China
3
Hong Kong
4
Mexico
5
Germany
2006
1
Canada
2
China
3
Hong Kong
4
Germany
5
Mexico
2007
1
China
2
Canada
3
Germany
4
Japan
5
Mexico
Index
Rank
Country
Ascending Order
Index
0.4298
0.2823
0.257
0.1893
0.1419
1
2
3
4
5
Egypt
Argentina
Finland
Denmark
Norway
0.0027
0.0091
0.014
0.014
0.0202
0.4031
0.2807
0.2516
0.2064
0.2036
1
2
3
4
5
Chile
Argentina
Finland
Norway
Australia
0.0091
0.0095
0.014
0.0215
0.0229
0.3727
0.2308
0.2294
0.2145
1
2
3
4
New Zealand
Czech Republic
South Africa
Finland
0.0067
0.0137
0.0143
0.0155
0.1358
5
Denmark
0.0173
0.3579
0.259
0.2475
0.2355
0.2196
1
2
3
4
5
New Zealand
Peru
Argentina
Finland
Hungary
0.007
0.0073
0.0104
0.0139
0.014
0.362
0.2866
0.2273
0.2181
0.2161
1
2
3
4
5
Bulgaria
Morocco
Romania
Luxembourg
New Zealand
0.0019
0.0021
0.0049
0.0057
0.0065
0.3288
0.3187
0.2317
0.221
0.2176
1
2
3
4
5
Romania
New Zealand
Luxembourg
Peru
Argentina
0.0048
0.0058
0.0067
0.0095
0.0106
0.343
0.2999
0.2354
0.221
0.199
1
2
3
4
5
Romania
New Zealand
Finland
Hungary
Denmark
0.0057
0.0059
0.0156
0.0165
0.0169
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Table 3 Descriptive statistics
Variable
LN(Aggregate Cultural distance)
Power Distance (PDI)
Individualism vs. Collectivism
(IDV)
Masculinity vs. Femininity (MAS)
Uncertainty Avoidance (UAI)
Pragmatic vs. Normative (PRA)
Indulgence vs. Restraint (IND)
Centrality
Openness
LN(GDP)
GDP Growth
LN(Total asset)
ROA
Leverage
N
899
899
Mean
3.7168
11.8209
Median
4.0414
5
Std dev.
0.7494
13.8619
899
23.257
13
23.5001
899
899
899
899
899
899
899
899
899
899
899
13.3037
15.2114
28.1691
12.4883
0.1674
0.1359
27.7027
3.0672
7.1213
0.0061
0.1599
9
11
31
3
0.1293
0.0492
27.9224
2.823
7.0373
0.0483
0.1238
14.4153
13.1619
19.9434
15.2395
0.1237
0.1641
0.8944
2.4491
1.9787
0.2344
0.169
4. Empirical Results
In this study, we use the CARs to measure the short-term performance of acquiring
firms. Since we focus on announcement effect, we collect five windows for this event
study on the basis of the announcement date. Five windows are CARs (-1, 0), CARs
(0, +1), CARs (-1, +1), CARs (-3, +3), and CARs (-5, +5). Among them, we mainly
use the one-day window from a day before and the event day, CARs (-1, 0). Since
there can be information linkage in equity market, we can observe the abnormal
returns of acquiring firms with observation period from one day before event day to
event day(Chen and Young 2010). Table 4 shows the descriptive statistics of CARs (1,0) of acquiring firms. Although literature focusing on long-term performance of
acquiring firms shows negative returns (Chakrabarti, Gupta-Mukherjee et al. 2008),
we observe the positive abnormal returns.
In this study, we use multiple linear regression models to test the impact of crossnational trade network and cultural distance to the performance of acquirers. Since
we capture the heteroskedasticity in our models, we perform OLS regression with
White’s MacKinnon and White (1985)’s heteroscedasticity consistent standard errors
for both Table 5 and Table 6. In Table 4, we show the results of our regression
models with independent variables and control variables. The dependent variable is
CARs obtained from event study methodologies explained in above section. The
main independent variables in this study are trade network centrality and several
cultural factors. Also we use several variables to control the firm specific
characteristics. The control variables are firm size (Natural logarithm of total asset),
financial leverage (Debt ratio), and profitability (ROA).
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Table4 Descriptive Statistics (CAR10)
Total
Number of
sample
N = 899
Mean
Median
t-Test
Wilcoxon
Test
3.4497***
(0.001)
0.7177
(0.473)
2.8815 ***
(0.004)
1.5192
(0.129)
2.564**
(0.010)
-0.097
(0.923)
2.197**
(0.028)
0.449
(0.654)
Same Language
N = 440
0.0086
0.0028
Different
Language
N = 459
0.0019
-0.0003
Same Religion
N = 363
0.0039
0.0025
Different Religion
N = 536
0.0071
0.0003
N = 364
0.0000
-0.0016
-0.0103
(0.992)
-0.799
(0.424)
N = 535
0.0087
0.0031
3.8813***
(0.000)
2.879***
(0.004)
N = 430
0.0101
0.0039
N = 469
0.0007
0.0009
3.2941***
(0.001)
0.3319
(0.740)
2.040**
(0.041)
0.399
(0.690)
LN(Aggregate
Cultural distance)
above median
LN(Aggregate
Cultural distance)
below median
Centrality above
median
Centrality below
median
We investigate eleven models in Table 5. The dependent variable in these models is
CARs (-1, 0). The first model contains only centrality measurement as explanatory
variable and do not include control variables. Although we do not control any firm
specific variables, first model shows that trade network is positive and statistically
significant at 1 % level. This result does not change even after controlling the firm
specific variables. In Table 5, model (3), we include six-dimensional Hofstede’s
cultural measurements. In this model, only one cultural distance factor, MAS, shows
significant results and centrality is significant at 5% level. The trade network
centrality shows consistent results in three models with cultural factors and firm
specific characteristics considered. This result indicates that acquisitions with target
nations having stronger power in trade network give higher returns in the short-term
period.
Since cultural distance might be related with each other, we run the regression
separately with six different cultural dimensions in model (4) to (9) in Table 5. Among
six different cultrual distnace measures, only one variale show statistical significance.
The coefficient of MAS (Masculinity versus Femininity) is negative and significant at
5% level. Throughout the six models from (4) to (9), the Hofstede measurements are
consistently negative to the acquring firm’s performance. Although other cultrual
distance variables do not show significant results, results are consistent with
previous literatures supporting that cultural distance can be a cost for an acquiring
company and it gives negative effect on the returns of acquiring firms in the short run
period (Ahern, Daminelli et al. , Chakrabarti, Gupta-Mukherjee et al. 2008).
Table 5 Regressions for announcement period abnormal returns associated with acquiring companies which announce crossborder acquisitions during 2001-2007
DEP: CAR(-1, 0)
INDEP:
Centrality
(1)
0.0421**
(0.017)
(2)
0.0422**
(0.017)
PDI
IDV
MAS
UAI
PRA
IND
(3)
0.0380**
(0.048)
0.0002
(0.512)
-0.0001
(0.414)
-0.0002*
(0.057)
-0.0002
(0.503)
0.0002
(0.238)
-0.0001
(0.728)
(4)
0.0412**
(0.043)
-0.00003
(0.859)
(5)
0.0416**
(0.021)
(6)
0.0350*
(0.051)
(7)
0.0378**
(0.046)
(8)
0.0455**
(0.020)
(9)
0.0412**
(0.025)
(10)
0.0369*
(0.063)
-0.0001
(0.480)
-0.0002**
(0.020)
-0.0001
(0.488)
0.0001
(0.520)
-0.0001
(0.718)
Language
0.0036
(0.383)
Religion
LN(TA)
TD/TA
NI/TA
Intercept
F-Value
R-squared
No. of obs
(11)
0.0470***
(0.007)
-0.0019
(0.511)
5.72
0.0092
899
0.0003
(0.750)
-0.004
(0.737)
-0.0024
(0.618)
-0.0035
(0.660)
1.63
0.0094
899
0.0003
(0.777)
-0.0046
(0.687)
-0.0026
(0.614)
-0.0004
(0.965)
1.8
0.0165
899
0.0003
(0.746)
-0.0041
(0.729)
-0.0024
(0.625)
-0.0030
(0.759)
1.55
0.0095
899
0.0032
(0.748)
-0.0047
(0.690)
-0.0022
(0.653)
-0.0016
(0.653)
1.99
0.0104
899
0.0003
(0.767)
-0.0040
(0.739)
-0.0027
(0.590)
0.0010
(0.898)
2.45
0.0131
899
0.0003
(0.750)
-0.0043
(0.720)
-0.0026
(0.606)
-0.0013
(0.884)
1.40
0.0099
899
0.0003
(0.752)
-0.0036
(0.759)
-0.0024
(0.630)
-0.0065
(0.529)
1.90
0.0049
899
0.0003
(0.748)
-0.0044
(0.706)
-0.0025
(0.619)
-0.0026
(0.765)
1.49
0.0096
899
0.0003
(0.748)
-0.0048
(0.683)
-0.0026
(0.606)
-0.0042
(0.583)
1.71
0.0103
899
0.0058*
(0.089)
0.0003
(0.738)
-0.0044
(0.715)
-0.0027
(0.590)
-0.0067
(0.407)
2.22
0.0121
899
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Table 6 Regressions for announcement period abnormal returns associated with acquiring companies which announce crossborder acquisitions during 2001-2007
DEP: CAR( -1, 0)
INDEP:
Centrality
(1)
0.0422**
(0.017)
Hostile
(2)
0.0482***
(0.006)
-0.0367**
(0.026)
LN(GDP)
(3)
0.0324877*
(0.084)
-0.0348**
(0.030)
0.0044*
(0.056)
GDP Growth
(4)
0.0325*
(0.084)
-0.0339**
(0.032)
0.0044*
(0.056)
0.0010
(0.414)
LN(Agg CD)
(5)
0.0235
(0.237)
-0.0352**
(0.024)
0.0048**
(0.038)
0.0014
(0.267)
-0.0044*
(0.076)
(6)
(7)
-0.0326**
(0.019)
0.0059***
(0.008)
0.0015
(0.247)
-0.0056**
(0.016)
-0.0331**
(0.018)
0.0060**
(0.016)
0.0011
(0.391)
Language
Religion
LN(TA)
TD/TA
NI/TA
Intercept
F-Value
R-squared
No. of obs
0.0003
(0.75)
-0.0040
(0.737)
-0.0024
(0.618)
-0.0035
(0.60)
1.63
0.0094
899
0.0058*
(0.088)
0.0003
(0.743)
-0.0047
(0.694)
-0.0026
(0.607)
-0.0067
(0.409)
2.45
0.0136
899
0.0003
(0.737)
-0.0032
(0.786)
-0.0027
(0.605)
-0.1249*
(0.059)
2.61
0.0156
899
0.0003
(0.741)
-0.0025
(0.828)
-0.0034
(0.516)
-0.1285*
(0.057)
2.24
0.0178
899
0.0003
(0.764)
-0.0031
(0.791)
-0.0035
(0.504)
-0.1212*
(0.076)
2.59
0.0209
899
0.0003
(0.785)
-0.0031
(0.787)
-0.0034
(0.529)
-0.1423**
(0.035)
3.04
0.0186
899
0.0067*
(0.084)
-0.0029
(0.477)
0.0003
(0.754)
-0.0036
(0.757)
-0.0034
(0.531)
-0.1673**
(0.021)
2.24
0.0163
899
(8)
(9)
(10)
0.0063***
(0.007)
0.0016
(0.222)
-0.0082**
(0.039)
-0.0046
(0.446)
0.0067***
(0.007)
0.0014
(0.258)
-0.0061**
(0.015)
0.0059***
(0.008)
0.0015
(0.245)
-0.0055**
(0.019)
-0.0034
(0.408)
0.0003
(0.779)
-0.0025
(0.832)
-0.0034
(0.519)
-0.1612**
(0.026)
2.37
0.0181
899
0.0003
(0.778)
-0.0028
(0.809)
-0.0035
(0.514)
-0.1432**
(0.034)
2.73
0.0174
899
0.0003
(0.793)
-0.0021
(0.855)
-0.0037
(0.525)
-0.1430**
(0.035)
2.44
0.0179
899
Stulz and Williamson (2003) argue that language and religion should not be ignored
when exploring the cross-national studies in finance, since it can affect how to
distribute resources in an economy, also systems of organizations, and the inherent
values in a society. Therefore, language and religion should be examined when
exploring the cross-border researches. We include language and religion dummy
variables in model (10) and (11) respectively. Language dummy variable does not
show significant results, in model (10). However, Religion dummy variable is positive
and significant at 10% level. This result indicates that acquisitions with target nations
which the majority of people believe same religion with acquiring nation perform
better in the short-run. In this case, target nations whose first religion is the Christian
give higher returns. These results are consistent with the previous researches (Rossi
and Volpin 2004, Chakrabarti, Gupta-Mukherjee et al. 2008).
We analyze the effect of trade network, economic factors, deal-specific variable and
cultural factors with ten models. Because of heteroskedasticity, we use same
regression model with Table 5. In Table 6, we also mainly use CAR (-1, 0) to capture
the meaningful factors affecting acquisitions’ returns. In model (1), we only run the
regression with centrality variable and control variables. It shows that centrality
significantly affects the acquisitions’ returns at 5% significance level. From the model
(2) to (8), we add the hostile dummy variable and religion dummy variable. Hostile
variable is negative and significant at 5% level, which means that hostile takeover
negatively affects the returns after acquisitions. Therefore, takeovers which
accomplished without agreement from the target company do not positively affect the
post-acquisition performance. Also, religion dummy variable is positive and
significant at 10% level in model (2) and (3), which means acquisitions which sharing
same primary religion positively affects the abnormal returns of the acquirers.
LN(GDP) variable is positive and significant throughout the eight models from model
(4) to (11), which means acquisitions with target nations having larger GDP, or
economic power gives positive affects to the acquisitions. And also, LN(Aggregate
Cultural distance) variable is negative and significant in four models from (6) to (11)
except model (8), this results are consistent with the previous studies (Ahern,
Daminelli et al. , Chakrabarti, Gupta-Mukherjee et al. 2008).
5. Conclusion
Previous studies argue that cultural distance can affect negatively to the acquiring
firms since cultural distance is a cost for acquirers to integrate with target companies.
Therefore, we hypothesize that cultural distance can affect negatively to the postacquisitions returns of acquiring companies. To test this hypothesis, we use a
multiple-regression models and event study methodology with cultural distance
variables developed by Greet Hofstede. In addition to that, economic variables which
measure the openness of target nation with trade volume data and gross domestic
nation (GDP) are focused on previously. Instead of using openness measurement
suggested by previous literature, we apply the network analysis methodology with
trade volume data. We expect that cross-national relationships measured by network
measurement, eigenvector-centrality can explain better in cross-border acquisitions.
Therefore, we add a centrality variable as main explanatory variable for this paper.
And, we confirm that acquisitions with target nation which is more open to
international trade network show positive and higher returns to the acquiring firms.
14
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
We extend the cross-border merger and acquisitions literature on the role of crossnational trade network. We focus on the cross-relationships between acquiring and
target nations whereas previous literature only focus on the openness to the
international trade market of acquiring firms. Also, we confirm the cultural distance
negatively affects the short-term returns of acquiring firms.
However, further study is needed to explore the determinants of cross-border
mergers and acquisitions by adopting other network methodologies. And also, we
only gather the export volume data only. So, analyzing trade network with both
export and import data is needed in future research.
References
Ahammad, M. F., et al. 2014. "Knowledge transfer and cross-border acquisition
performance: The impact of cultural distance and employee retention." International
Business Review
Ahern, K. R., et al. 2012. "Lost in translation? The effect of cultural values on
mergers around the world.", Journal of Financial Economics
Aybar, B. and A. Ficici. 2009. "Cross-border acquisitions and firm value: An
analysis of emerging-market multinationals." Journal of International Business
Studies, vol.40, no.8, pp.1317-1338.
Bonacich, P.1972. "Factoring and weighting approaches to status scores and clique
identification." The Journal of Mathematical Sociology, vol.2, no.1, pp. 113-120.
Bonacich, P. 2007. "Some unique properties of eigenvector centrality." Social
Networks, vol.29, no.4, pp.555-564.
Chakrabarti, R., et al., 2008, "Mars–Venus marriages: Culture and cross-border
M&A." Journal of International Business Studies, vol.40, no.2, pp.216-236.
Chen, Y. Y. and M. N. Young. 2010. "Cross-border mergers and acquisitions by
Chinese listed companies: A principal–principal perspective." Asia Pacific Journal of
Management, vol.27, no.3, pp.523-539.
Datta, D. K. and G. Puia. 1995. "Cross-border acquisitions: An examination of the
influence of relatedness and cultural fit on shareholder value creation in US acquiring
firms." MIR: Management International Review, pp.337-359.
De Benedictis, L. and L. Tajoli, 2011, "The world trade network." The World
Economy, vol34, no.8, pp.1417-1454.
Erel, I., et al., 2012, "Determinants of Cross-Border Mergers and Acquisitions." The
Journal of Finance, vol67, no.3, pp.1045-1082.
Helleiner, E., 1995, "Explaining the Globalization of Financial Markets: Bringing
States Back In." Review of International Political Economy, vol.2, no.2, pp.315-341.
Hofstede, G., 1984, Culture's consequences: International differences in workrelated values, sage.
MacKinnon, J. G. and H. White, 1985, "Some heteroskedasticity-consistent
covariance matrix estimators with improved finite sample properties." Journal of
Econometrics, vol.29, no.3, pp.305-325.
Martynova, M. and L. Renneboog, 2008, "A century of corporate takeovers: What
have we learned and where do we stand?" Journal of Banking & Finance vol.32,
no.10, pp.2148-2177.
Morosini, P., et al., 1998, "National Cultural Distance and Cross-Border Acquisition
Performance." Journal of International Business Studies, vol.29, no.1, pp.137-158.
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Reus, T. H. and B. T. Lamont, 2009, "The double-edged sword of cultural distance in
international acquisitions." Journal of International Business Studies, vol.40, no.8,
pp.1298-1316.
Rossi, S. and P. F. Volpin, 2004, "Cross-country determinants of mergers and
acquisitions." Journal of Financial Economics, vol.74, no.2, pp.277-304.
Stahl, G. K. and A. Voigt, 2008, "Do cultural differences matter in mergers and
acquisitions? A tentative model and examination." Organization Science, vol19, no.1,
pp.160-176.
Stulz, R. M. and R. Williamson, 2003, "Culture, openness, and finance." Journal of
Financial Economics, vol.70, no.3, pp.313-349.
Vo, X. V. and K. J. Daly, 2007, "The determinants of international financial
integration." Global Finance Journal, vol18, no.2, pp.228-250.
Yoganarasimhan, H., 2012, "Impact of social network structure on content
propagation: A study using YouTube data." Quantitative Marketing and Economics,
vol.10, no.1, pp.111-150.