Cross-border acquisitions and firm value: An analysis of emerging-market multinationals · 2017-09-09 · The mergers and acquisitions announcements data come from the Thomson SDC

Cross-border acquisitions and firm value:

An analysis of emerging-market multinationals

Bulent Aybar andAysun Ficici

School of Business, Southern New HampshireUniversity, Manchester, NH, USA

Correspondence:B Aybar, School of Business,Southern New Hampshire University,Manchester, NH 03106, USA.Tel: þ1 603 644 3116;Fax: þ1 603 645 9737

Received: 17 January 2006Revised: 2 November 2008Accepted: 21 November 2008Online publication date: 30 April 2009

AbstractThe primary objective of this study is to examine the value implications of cross-border acquisitions of emerging-market multinationals (EMMs). We examine

433 mergers and acquisitions announcements associated with 58 EMMs during

the sample period 1991–2004. The mergers and acquisitions announcementsdata come from the Thomson SDC Platinum database. We employ event study

methodology to explore the impact of the announcements on the value of

acquiring firms. The results show that, on average, cross-border expansionsof EMMs through acquisitions do not create value, but point to value

destruction for more than half of the transactions analyzed. To explore the

factors influencing the direction and magnitude of market reaction, we analyze

a cross-sectional sample of firms. While we find that target size, ownershipstructure of the target (private vs public), and structure of the bidder

(diversified vs non-diversified) positively affect the bidder value, high-tech

nature of the bidder and pursuit of targets in related industries negatively affectthe bidder value. Our empirical findings provide some support for the positive

impact of the stake pursued in the target firm and cultural distance, but not for

the international experience and enhanced corporate governance.Journal of International Business Studies (2009) 40, 1317–1338.

doi:10.1057/jibs.2009.15

Keywords: internationalization; emerging-market multinationals; foreign direct invest-ment; cross-border mergers and acquisitions; international investments; firm value

INTRODUCTIONThe internationalization of companies originating from theemerging economies is not a new phenomenon. Increasinglyoutward-oriented postures by emerging-market companies paralleltheir home countries’ integration into the world economy. Such apattern has intensified during the early 1990s, and a group ofemerging-economy companies embarked on myriad internationalfeats to take advantage of regional and global business opportu-nities. Broadly referred to as emerging-market multinationals(EMMs), this new breed of companies faced increasing competitionfrom their domestic rivals, and aggressive outward expansionby foreign internationals into their markets. In response, theEMMs sought value by adopting an outward strategic orientation.In this study we explore whether such value manifests itself incross-border company acquisitions by EMMs.

Despite EMMs’ growing regional and global importance, ourknowledge of their various attributes is limited. For this reason, aswe study the impact of cross-border expansion on shareholder

Journal of International Business Studies (2009) 40, 1317–1338& 2009 Academy of International Business All rights reserved 0047-2506

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wealth, we focus not only on the players but also onthe process of outward posturing by EMMs. Theanalysis is based on 433 cross-border M&A expan-sion announcements associated with 58 biddingfirms between 1991 and 2004. We use standardevent study methodology to capture the impact ofeach announcement on firm value around theannouncement date.

Our findings indicate that, on average, cross-border expansions of EMMs through acquisitionsdo not create value; rather, they point to valuedestruction for more than half of the transactionsanalyzed. To explore the factors influencing thedirection and magnitude of market reaction, weanalyze a cross-sectional sample of firms. Whilewe find that target size, ownership structure ofthe target (private vs public), and structure of thebidder (diversified vs non-diversified) positivelyaffect bidder value, the high-tech nature of thebidder and the pursuit of targets in related indus-tries have negative effects on bidder value. Ourempirical findings provide support for the positiveimpact of the extent of the stake pursued in thetarget firm and cultural distance, but not forinternational experience or enhanced corporategovernance.

The paper continues as follows. In the nextsection we provide a brief theoretical and concep-tual background for our inquiry, and review theevidence in the literature. This is followed by ourproposed research hypotheses to be tested. We thendiscuss our data and methodology, and follow thiswith a presentation of our findings. The finalsection concludes the study.

INTERNATIONAL EXPANSION AND FIRMVALUE: THEORETICAL ISSUES

The internalization framework in the literaturefavors the contention that firms extract above-normal returns from cross-border investments byinternalizing host-country market imperfectionswhen their firm-specific assets cannot find compar-able value elsewhere (e.g., Buckley & Casson, 1976;Caves, 1971, 1998; Hymer, 1976; Morck & Yeung,1991, 1992; Williamson, 1979). The resulting rentsderived from internalization are expected to becapitalized into a higher value of the firm. Such aneffect, under the multinational network hypoth-esis, is multiplied by positive network externalitiesso that investment decisions improve the expand-ing firm’s ability to benefit from the systemicadvantages inherent in a multinational network.This finding is true because, as options increase, the

value of the firm should increase to reflect theincremental value of these options, as long as theyremain non-imitable (Doukas & Travlos, 1988;Errunza & Senbet, 1981, 1984).

The valuation effects of strategic actions leadingto the creation of a multinational network stemfrom the firm’s ability to arbitrage across institu-tional environments, the informational external-ities captured by the firm, and the cost savingsgained by economies of scale in production,marketing and finance. Cross-border acquisitionsmay also increase the operational flexibility of thefirm by giving it the opportunity to exploit marketconditions (Kogut, 1983). A similar argument canbe made for average output prices in internationalmarkets when demand shocks are not perfectlycorrelated. As long as the costs of creating andmaintaining a diversified corporate network are notexcessive, presence in multiple markets can yieldadditional value to the firm because of its ability toexploit more diverse conditions.

So international expansion through acquisitionsoffers significant value-creation opportunities forfirms; but it also presents significant challenges thatjeopardize the potential hypothesized gains. Forexample, an often-cited complexity in cross-borderacquisitions is the difficulties associated with post-acquisition integration of the acquired company. Inthis context various researchers highlight risks suchas ‘‘liability of foreignness’’ and ‘‘double-layeredacculturation’’ (Barkema, Bell, & Pennings, 1996;Eden & Miller, 2004). Such risks pertain to thedifferences in natural culture, customer prefer-ences, business practices, and institutional forces;and they are exacerbated impediments to thecomplete realization of strategic objectives.1 Lackof experience in the acquiring firm of executingacquisitions, organizational inertia in absorbing thetarget, and prior absence in the country of thetarget company may inhibit the benefits of acqui-sition for firm value. Additionally, complicationsin target assessment, misidentification of asset com-plementarities, informational asymmetries, andhigh premiums paid for the targets may also haveadverse effects on the value of acquiring firms (Hitt,Hoskisson, & Ireland, 2001a; Hitt, Ireland, Camp, &Sexton, 2001b; Kissin & Herrera, 1990).

Although it is reasonable to expect value creationfrom EMMs’ foreign acquisitions, there are oppos-ing arguments about the impact of such expan-sions. For example, evidence from the recentliterature on industrial diversification providesinsights into the potential value-destructive effects

Cross-border acquisitions and firm value Bulent Aybar and Aysun Ficici

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Journal of International Business Studies

of cross-border horizontal expansions. Denis,Denis, and Yost (2001) propose that global diversi-fication can lead to the inefficient cross-subsidiza-tion of less profitable business units.2 Similarly,another group of scholars, adopting the agency costframework, suggest that managers’ self-servinggoals and incentives in value-reducing diversifica-tion strategies may not be entirely consistent withshareholder wealth creation (Denis et al. 2001).Roll’s hubris hypothesis and Stulz’s empire-buildingmotives (e.g., Roll, 1988; Stulz, 1990) are alongthese lines. This view is justified on the groundsthat the cash flows of global segments are imper-fectly correlated, while global diversificationreduces the risk of the manager’s relatively undi-versified personal portfolio (Ahimud & Lev, 1981).

In summary, the literature offers conflictingevidence about the effects of international expan-sion on firm value. The opposing views, and thevariation in empirical results, are not surprising,given that the M&A literature supports a complexinterplay of firm-specific, industry-specific, andcountry-specific factors in the process of cross-border acquisition. Our study applies a lens to theimpact and significance of these factors as itanalyzes possible value creation by internationallyexpanding EMMs.

HYPOTHESES DEVELOPMENTIn this paper we consider an array of firm-specific,industry-specific, and target-country-specific fac-tors in international acquisitions of EMMs.

Firm Characteristics and Value ImplicationsHere we set out to discern the target and acquirercharacteristics that are likely to have an impact onthe value of the expanding firm. The internationalbusiness and finance literature provides ampleevidence for a large array of factors that have animpact on the value captured by the acquirers ininternational expansions. The list includes firmsize, leverage, acquirer’s international experience,prior presence in the host country, relative size ofthe target, the stake acquired in the target (con-trolling or non-controlling), and acquirer’s corpo-rate governance structure.

In addition to these factors, we also explore thesignificance of the regional domicile of EMMs, asthe regional characteristics of Asian, Latin American,and Eastern European EMMs may lead to discern-ible patterns.3 Below we develop our first set ofhypotheses involving the six factors:

The regional domicile hypothesis in this study followsprevious studies that consider geographic influenceon the performance of acquiring firms, and the wayin which markets react to their strategic activities(Brouthers & Brouthers, 2000; Krugman, 1991;Penrose, 1959; Shrivastava, 1986).

The investment size hypothesis is based on theclassical argument that firms can achieve operatingeconomies, leading to economies of scale inmanagement, marketing, production, or distribu-tion. Like their counterparts in developed coun-tries, EMMs may accrue significant benefits frommore efficient use of fixed capital, and extendedglobal market presence: hence ultimately higherprofitability for EMMs. The increase in size throughsuccessful cross-border acquisitions might lead to acombined value of the two companies that ishigher than their standalone value (Lamacchia,1997). In contrast, studies focusing on thevalue-destructive aspects of acquisitions pointto misidentified complementarities, asymmetricinformation in target assessment and valuation,and challenges in post-acquisition integration ofthe target, particularly in cross-border transactions.Additionally, if the transaction process takes longerthan anticipated, negative market reaction could beobserved (Mulherin & Boone, 2000).

Level of control in target is another factor. Earlierstudies consider this factor as being related toopportunistic behavior of the venture partners(Beamish & Banks, 1987; Geringer & Hebert,1989; Hanvanich & Cavusgil, 2001). Indeed, Chari,Ouimet, and Tesar (2004) find evidence on thesignificance for acquirer value of gaining a control-ling stake in the acquisition of emerging-markettargets by developed-country multinationals.

The target status hypothesis is based on the notionthat acquirers earn significantly negative returnswhen buying public targets, and earn signifi-cantly positive returns when buying private orsubsidiary targets (Fuller, Netter, & Stegemoller,2002). This finding is largely attributable to thecomplexity of the ownership structure in a publiccompany, which increases the possibility thatthe transaction price will be increased to satisfythe interests of a diverse group of shareholdersas well as stakeholders of a target firm (Choi &Russell, 2004). However, EMMs’ inexperience insophisticated cross-border acquisitions may alsolead to significantly higher premiums paid for


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public targets to reduce the resistance by currentshareholders.

The level of international experience, both general andtarget-country specific, is a salient factor in cross-border expansions, and it is widely discussed in theliterature (Barkema & Vermeulen, 1998; Brouthers &Brouthers, 2000; Kogut & Singh, 1988; Markides &Ittner, 1994). Previous studies suggest that experi-ence in the international market is a source ofsustainable advantage for investing firms, and isassociated with positive abnormal returns generatedaround acquisitions (Harzing, 2002). Doukas andTravlos (1988) show that the acquisition announce-ments of firms with an established presence in thetarget country generate positive and statisticallysignificant abnormal returns. It is plausible to arguethat firms with a local presence are better positionedto identify investment opportunities in the hostmarket, and are less likely to incur high premiums,than firms with no prior local presence. In addition,EMMs’ familiarity with the local environment mayreduce the post-acquisition integration costs, sincesuch acquisitions are less risky than acquisitions inenvironments with no prior presence. In otherwords, information asymmetries and the liabilityof foreignness are reduced in these cases (Martin,Swaminathan, & Mitchell, 1998).

Good corporate governance on the acquirer side isexpected to contribute to acquirer value. This factoris particularly important, because poor corporategovernance practices in emerging markets, andtheir implications, have been well documented inthe literature. Lax disclosure requirements, the lackof effective monitoring systems, and the under-developed nature of local equity markets increasemanagerial discretion, and create incentives forvalue appropriation at the expense of minorityshareholders. In light of these emerging-marketregularities, shareholders might approach foreignacquisitions of EMMs with suspicion, and perceivesuch strategies as an integral part of empirebuilding or value appropriation efforts. To capturethis effect, we use Level II and Level III ADRissuance by the EMMs as governance proxy. Whilewe contend that these two types of ADR issuance byEMMs are important steps towards good corporategovernance, a proxy based on these initiatives haslimitations.4

In the light of the above discussion we offer thefollowing two hypotheses on firm-specific effects.

Hypothesis 1a: Regional domicile, effectivenessof corporate governance, investment size, level ofcontrol, experience, and target charter status aresignificant factors in affecting bidder value incross-border expansions.

Hypothesis 1b: The lower level of control andpublic status of the target company has a negativeimpact on acquirer/bidder value.

Industry-Specific Factors and Firm Value inCross-Border Acquisitions of EMMsEvidence reported in the extant literature suggeststhat both the type of industry and the structureof the firm affect the expansion decisions, the typeof expansion activity, and the value implications ofexpansions (Brouthers & Brouthers, 2000; Markides& Ittner, 1994; Shimizu, Hitt, Vaidyanath, &Pisano, 2004). In this study, we focus on the high-tech/non-high-tech dichotomy by industry type,and therefore evaluate the impact of the firm beingembedded in a high-tech industry. While acquisi-tions in high-tech industries may bring significantproduct and process technologies to EMMs, andpropel their product development and efficiencyenhancement efforts, the informational asymme-tries associated with the assets acquired and theircompatibility, as well as the ensuing premiumsassociated with them, may altogether lead to valuedestruction.

On the other hand, theoretical arguments suggestthat diversification into related and unrelatedbusinesses affects firm value. For example, theinternal capital markets show a higher degree ofindependence from specific industry segments thanexternal capital markets: therefore we expectresource allocation to be more efficient in diversi-fied firms than in non-diversified firms (Matsusaka& Nanda, 1996; Rieck, 2002; Stein, 1997). Diversi-fied firms have additional advantages from whatis referred to as the co-insurance effect, as theircombined cash flow will be less unstable than thatof non-diversified firms of similar size. In thisrespect, diversified cross-border expansions maylead firms to balance gains and losses from differentsegments (Stulz, 1990). Diversified firms may alsohave a higher degree of conglomerate power byengaging in cross-subsidization. In contrast,however, diversification can decrease firm value,since it can cause an increase in the cross-subsidiza-tion of failing business segments and an increasein the agency costs of firms (Denis et al. 2001;Jensen, 1986; Rieck, 2002; Stein, 1997; Stulz, 1990).


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Accordingly, the following hypothesis addresses theimpact of the acquiring company’s structure on thevalue of the acquirer:

Hypothesis 2: EMMs’ industry characteristics(high-tech or non-high-tech) strategic focus ofEMMs (diversified vs non-diversified), and thetype of expansion (through acquisition of arelated or an unrelated company) are associatedwith bidder value in cross-border acquisitionannouncements of EMMs.

Target Country Characteristics and Firm Valuein Cross-Border AcquisitionsWe consider two sets of target-country character-istics to evaluate their impact on the value capturedby the acquirer: geographic and cultural proximityto the acquirer’s home country, and the level ofeconomic development (developed vs developing)and the institutional infrastructure.

Geographic and cultural proximity is a long-studiedeffect. While earlier studies provide evidence thatexpansion into new geographical and economicallydissimilar areas increase shareholders’ wealth (e.g.,Doukas & Travlos, 1988), more recent studiesdemonstrate the benefits of proximity. Barkemaet al. (1996) argue that foreign acquirers are morelikely to fail in the cultural adjustment process thanlocal acquirers ‘‘whenever acculturation involved ismore demanding.’’ Brock (2005) emphasizes theimpact of cultural distance in the context ofmergers and acquisitions, and how it hinders therealization of intended synergies through its impacton the integration process, managerial commit-ment, and ease of resource sharing. Coval andMoskowitz (2001) propose that geographic andcultural proximity sharply reduces informationacquisition costs. Ghemawat (2001) identifies fourdimensions of distance – cultural, administrative,geographic, and economic – and argues that tech-nological innovations have not eliminated the veryhigh costs of distance. In addition to the taxingeffect of cultural distance, there is ample evidencesuggesting that geographic distance raises thecost of transferring knowledge and technology,and dramatically reduces the effectiveness ofknowledge-sharing (e.g., Almeida & Kogut, 1999;Branstetter, 2001; Keller, 2002; Storper & Venables,2004).

Institutional infrastructure is another substantialfactor in the development of an outward posture

in an underdeveloped market. These are marketsbearing higher levels of operational and investmentrisk due to inefficient and corrupt legal infrastruc-tures, insufficient property rights protection, dys-functional financial systems, restrictive and volatileregulatory regimes, and external investment andtrade barriers (Brouthers, 2002). While investingfirms may be able to take advantage of marketimperfections, they may also have to deal with theexcessive costs of uncertainty and governmentdiscretion. La Porta, Lopez de Silanes, Shleifer, andVishny (1998) argue that in countries with lesspolitical and economic freedom, business opportu-nities are also undermined. In an effort to evaluatethe impact of institutional infrastructure on thevalue captured by the acquirers, we use an econom-ic freedom index for each target country. Ourhypotheses are formulated as follows:

Hypothesis 3a: Geographic and cultural proxi-mity of the target country may be an influentialfactor affecting bidder value in cross-borderacquisition announcements of EMMs.

Hypothesis 3b: The poor institutional infrastruc-ture of the target country may have a negativeimpact on bidder value in cross-border acquisi-tion announcements.

DATA AND METHODOLOGY

DataThe cross-border announcements we analyze in thisstudy are associated with 58 EMMs compiled fromvarious issues of the World Investment Report (UnitedNations, 1999–2002). This is published by UNCTADannually: it provides a list of the top emerging-market and transition-economy transnational cor-porations. Mergers and acquisitions announce-ments data for the period 1991–2004 are extractedfrom the Thomson SDC Platinum database. Theinformation on transaction value, shares acquiredby the bidder, and shares owned by the bidder afterthe transaction also come from the Thomson SDCPlatinum database.5 Equity prices and companyaccounts data are compiled from DataStream Inter-national. Information on company foreign salesand foreign employees comes from UNCTAD’sWorld Investment Report, individual companyannual reports, and mandatory filings.

Our initial roster of announcements was filteredfor other major corporate events associated withthe acquiring firms to control for confounding


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events that could otherwise have had an impact onfirm value. We also screened the initial roster fordata availability to conduct event study and cross-sectional analysis. After controlling for confound-ing events, we compiled a workable sample of 433cross-border merger and acquisition announce-ments associated with 58 firms. Our sample periodcovered the time between 1991 and 2004.

The sample firms operate in a range of industries,and originate mainly from Latin America6 (Argentina,Brazil, Colombia, Chile, and Mexico) and Asia(Hong Kong, India, Malaysia, Philippines, SouthKorea, and Singapore). Two firms originate fromHungary and three from South Africa. A largepercentage of the transactions (78.9%) covered inour analysis were initiated by Asian EMMs. TheEMMs from Latin America account for 15% of thetransactions.

Approximately 30% of the transactions areaccounted for by 11 high-tech firms in our sample.About 38% of transactions are attributed to 13diversified conglomerates. While, on average, 52%of the targets pursued by EMMs are located in

culturally proximate environments, 39% of theacquisition announcements involved targetslocated in developed markets. We provide detailson sample characteristics in Tables 1, 2 and 3.

MethodologyEvent study methodology in the finance literaturehas become a standard in evaluating the stock pricereaction to a specific event. This approach has alsobeen used to identify the organizational and publicpolicy implications of both endogenous and exo-genous corporate events (McWilliams & Siegel,1997). Event study allows researchers to concludewhether an event had a positive or negative effecton shareholder wealth. Traditionally, the ‘‘marketmodel’’ is assumed to be the underlying returnprocess.7 The market model assumes a linearrelationship between the return of a security andthe return of the market portfolio. For each securityi, the market model assumes that the returnsgenerated are given by

Rit ¼ ai þ biRmt þ eit ð1Þ

Table 1 Sample firms by industry, country, region, and listing market

Company name Country Region Industry Listing market

Acer Taiwan Asia Computer hardware LSE/Taiwan

Amsteel Malaysia Asia Steel Kuala Lumpur/Singapore

Asia Pacific Brews Ltd Singapore Asia Brewers Singapore

Barloworld South Africa Africa Diversified LSE/JSE

Bavaria SA Colombia Asia Brewers Colombia

Berjaya Malaysia Asia Diversified HK

Cathay Pacific Airways HK Asia Airlines and airports HK

Cemex Mexico LA Building materials NYSE/Mexico

CITIC Pacific HK Asia Diversified LSE/HK

CLP Holdings Ltd HK Asia Electricity HK/Shenzen

Creative Technology Ltd Singapore Asia Computer hardware Singapore

Empresas ICA Sociedad Control Mexico LA Other construction NYSE/Mexico

Enersis SA Chile LA Electricity NYSE/Santiago

Evergreen Marine Taiwan Asia Shipping and ports LSE/Taiwan

First Pacific HK Asia Diversified LSE/HK

Formosa Taiwan Asia Chemicals and

advanced materials

LSE/Taiwan

Fraser & Neave Ltd Singapore Asia Soft drinks Singapore

Genting Malaysia Asia Hotels Kuala Lumpur/Singapore

Gerdau Brazil LA Steel NYSE/Brazil

Gruma Mexico LA Food processing NYSE/Mexico

Guangdong Investment HK Asia Water HK

Guangzou Investment HK Asia Diversified HK/Shanghai

Hong Leong Malaysia Asia Diversified Singapore

Hutchison Whampoa HK Asia Diversified HK

Hyundai South Korea Asia Diversified KSE

Keppel Corporation Ltd Singapore Asia Engineering/general LSE/Singapore/HK


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where Rit is the return on security i at time t.The subscript t indicates the time, the subscripti indicates a specific security, and the subscriptm indicates the market. Rmt is the return onthe market portfolio during period t. Under theassumption of linearity and normality of returns,et is a random error term for security i at time t,and bi is a firm-specific coefficient, to be estimated

from the market model regressions. The marketmodel expressed in Eq. (1) is used to computethe return on the stock that would have beenexpected on the day of the event, or during aselected event window if the event had notoccurred. Equation (1) is estimated by using a255-day estimation period from t¼�11 to t¼�265,where t¼0 is the event day.

Table 1 Continued

Company name Country Region Industry Listing market

LG Corporation South Korea Asia Diversified KSE

Magyar Olaj Gazi (MOL) Hungary E. Europe Oil Integrated Frankfurt/Budapest

Malaysian International Shipping Malaysia Asia Shipping and ports Kuala Lumpur

Mosel Vitalic Taiwan Asia Semiconductors Taiwan

Natsteel Ltd Singapore Asia Steel Singapore

Neptune Orient Airlines Ltd Singapore Asia Shipping and ports Singapore

New World Development HK Asia Diversified HK

Petronas Malaysia Asia Oil integrated Kuala Lumpur/HK

Posco South Korea Asia Steel NYSE/KSE

Ranbaxy India Asia Pharmaceuticals Bombay

Reliance Industries Ltd India Asia Oil integrated Bombay

Samsung Electronics South Korea Asia Semiconductors LSE/KSE

San Miguel Corporation Philippines Asia Brewers Philippines

Sappi Ltd South Africa Asia Paper NYSE/JSE

Savia SA de CV Mexico LA Farming and fishing Mexico

Sime Darby Malaysia Asia Diversified Kuala Lumpur/HK

Singapore Airlines Singapore Asia Airlines and airports Singapore

Singapore Telecom Singapore Asia Telecom Singapore

Ssyangyong Cement South Korea Asia Building Materials KSE

Swire Pacific HK Asia Diversified HK

Taiwan Semiconductor Taiwan Asia Semiconductors NYSE

Tata India Asia Diversified Bombay

Telekom Malaysia Malaysia Asia Telecom Kuala Lumpur

Tiszai Vegyi Kombinat Rt Hungary Eastern Europe Chemicals/commodity Budapest

Tong Yang South Korea Asia Building Materials KSE

United Microelectronics Taiwan Asia Semiconductors NYSE

Varig Brazil LA Airlines and airports Brazil

Vitro Mexico LA Glass Mexico

Want Want Holdings Singapore Asia Food processing Singapore

Wing On HK Asia Retailers HK

YPF Argentina LA Oil and gas exploration

and production

NYSE/Buenos Aires

Table 2 Average assets and sales of the sample firms (Millions of USD)

Mean Median St. dev. Kurtosis Skewness

Total assets ($) 7,803.75 5,677.72 7,342.8901 0.9382808 1.2976253

Foreign assets ($) 2,370.38 1,362.40 2,955.6298 2.4534347 1.8717439

FA/TA (%) 19.54 18.55 0.1649114 2.9922848 1.5891776

Total sales ($) 4,221.39 2,426.50 4,738.7111 5.3600439 2.3332731

Foreign sales ($) 1,526.47 1,242.19 1,588.0905 3.4174761 1.8153563

FS/TS (%) 23.84 21.89 0.1828255 0.8710406 0.9570968


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The abnormal return (AR) due to the announce-ment on any given day therefore equals the actualreturn minus the predicted normal return:

ARit ¼ Rit � ðai þ biRmtÞ ð2Þ

Daily abnormal returns are then computed for eachday t for each firm i. To obtain a general insight intothe abnormal return observations for a sample of Nfirms, abnormal returns (AR) for each day t areaveraged as follows:

ARt ¼1

N

XNi¼1

ARit ð3Þ

Since the full impact of an event on firm value maynot be felt on a single day, event studies oftenexamine the returns for periods around an event,called the event window.

In our study, we define the event window asthe period between 10 days prior to the eventand 10 days after the event. The expected returnson the stock calculated from model (1) for thesecurity during the event window (�10, þ10) are

compared with the actual returns observed on eachday within the event window. The differencebetween the predicted return and the actual returnfor a period such as event window is called thecumulative abnormal return and is calculated asfollows:

CARi ¼XT

t¼1

ARit ð4Þ

More specifically, the cumulative abnormalreturn during the event window (T1, T2), CARi(T1, T2)¼CARi�EW, is given as

CARi�EW ¼XT2

t¼T1

ARit ð5Þ

When CARs differ from zero, parametric tests can beperformed to see whether this deviation is statisti-cally significant. Coutts, Mills, and Roberts (1995)suggest using standardized cumulative abnormalreturns (SCARs) for longer event windows to correct

Table 3 Average assets and sales by industry (Millions of USD)

Industry No. of

transactions

Total

assets ($)

Foreign

assets ($)

FA/TA

(%)

Total

sales ($)

Foreign

sales ($)

FS/TS

(%)

Airlines and airports 21 2,504.08 484.78 7.16 1,397.54 775.77 18.13

Brewers 18 3,053.03 524.03 30.51 1,314.53 410.69 32.33

Building materials 42 7,623.25 4,597.78 37.26 3,022.06 1,649.69 33.33

Chemicals, commodity 11 596.47 17.40 3.78 398.96 138.97 28.57

Chemicals and advanced materials 1 476.50 81.75 4.29 372.75 58.25 15.63

Computer hardware 8 1,657.12 431.73 19.43 1,657.95 1,004.18 35.32

Diversified industry 168 11,592.95 3,595.93 18.38 6,803.46 2,369.73 26.13

Electricity 3 8,058.50 848.50 5.26 1,703.00 153.00 4.49

Electronic equipment 3 NA NA NA NA NA NA

Engineering, general 18 12,161.56 1,423.49 9.20 1,766.96 235.73 8.89

Farming and fishing 2 4,121.50 1,129.00 40.06 1,763.00 729.00 60.96

Food processors 2 1,019.38 733.13 81.76 767.25 506.93 76.76

Hotels 3 2,486.45 715.75 28.90 905.65 183.75 21.59

Oil and gas exploration and production 5 9,349.47 2,129.88 17.40 4,430.29 867.94 15.60

Oil integrated 17 11,677.63 1,851.13 6.10 5,870.34 2,461.81 21.15

Other construction 2 816.00 80.25 2.46 346.50 23.75 0.00

Paper 6 2,953.03 2,222.51 43.25 2,195.49 1,566.60 40.45

Pharmaceuticals 1 1,247.40 – 0.00 781.40 40.00 0.86

Retailers, multi-department 1 1,460.13 583.00 39.73 380.15 53.65 14.11

Security and alarms 2 12,684.00 2,114.00 16.68 5,959.00 3,440.00 57.73

Semiconductors 30 6,665.99 556.97 2.73 6,419.30 1,925.45 9.51

Shipping and ports 7 1,076.45 643.00 20.87 1,055.29 650.98 24.08

Soft drinks 6 1,774.13 574.75 15.67 839.88 525.50 24.27

Steel 27 1,220.52 348.07 17.06 898.00 197.96 10.51

Telecom fixed line 22 5,779.02 1,861.02 17.91 2,030.31 102.45 2.56

Water 7 2,589.73 1,844.36 68.64 804.23 614.59 77.58


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for serial correlation of daily event period abnormalreturns for the same firm.8

Each firm’s cumulative abnormal return is stan-dardized according to

SCARiðT1;T2Þ ¼CARiðT1;T2Þ

SDi¼ CARi�EW

SDið6aÞ

where SDi is given as

SDi ¼ Si

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

kþ k

Tþ

Pkt¼1

Rmt � kð �RmÞ2

PTt¼1

ðRmt � �RmÞ2

vuuuuuut ð6bÞ

where Si is the standard error of the market modelregression, T is the number of observations in theestimation period, Rmt is the return on the marketportfolio for day t, Rm is the average return of themarket portfolio for the estimation period, and k isthe number of days in the event window.

A Z statistic is calculated according to

Z ¼ 1ffiffiffiffiNp

XNi¼1

SCARi ð7Þ

Under the null hypothesis of no stock price effect,this statistic will have approximately a standardnormal distribution. As briefly discussed above, thismethod is preferred because it accounts for possibleserial correlation among the abnormal returnswithin the event window.

We report the SCARs for the following eventwindows: SCAR(�10, þ10), SCAR(�5, þ5), SCAR(�10, þ5), SCAR(�5, þ1), SCAR(�2, þ1), SCAR(�1,þ1), and SCAR(�1, 0). The SCARs calculated in theevent study are utilized as dependent variables in themultivariate and logistics regression analyses.

Cross-Sectional Analysis of CumulativeAbnormal ReturnsAs discussed earlier, value captured in an acquisi-tion depends on a range of firm-, industry-, andcountry-specific factors. To explain the cross-sectional variation in the cumulative abnormalreturns, we use the following multivariate model:

SCARðT1;T2Þ ¼ b0 þ b1ðSIZEÞ þ b2ðTYPEÞþ b3ðTSTATUSÞ þ b4ðCONTROLÞþ b5ðINVSTSIZEÞ þ b6ðINSTITUTIONÞþ b7ðPROXIMITYÞ þ b8ðINTEXPRÞþ b9ðPRIORPRESÞ þ b10ðHITECHÞþ b11ðGOVERNÞ þ b12ðSTRUCTUREÞþ b13ðREGION 1Þ þ b14ðREGION 2Þ þ e

ð8Þ

where:

SIZE¼ log of bidder firm total assets.TYPE¼ a dummy variable, taking the

value 1 if the target firm is in arelated industry, and 0 otherwise.The dummy variable is assignedafter comparing the four-digit SICcodes for the acquirer and the tar-get: it takes the value 1 for match-ing SIC codes, and 0 otherwise.

TSTATUS¼ target status: a dummy variabletaking the value 1 if the target isprivately owned, and 0 otherwise.Only 23 of 433 transactions in oursample involved publicly ownedtargets.

CONTROL¼ level of control (percentage stakepursued by the bidder). Weuse the percentage of sharesacquired, as reported by theThomson SDC Platinum database.

INVSTSIZE¼ ratio of the dollar value of thetransaction to the bidder’s marketvalue. The dollar value of thetransaction was gathered fromthe Thomson SDC Platinumdatabase, and bidder value wascompiled from DataStream Inter-national.9

INSTITUTION¼ level of development of the insti-tutional infrastructure. We use twoproxies to measure this. The first isbased on the Fraser Institute’sWorld Economic Freedom Index:the index takes values between 1(non-market economy) and 10(fully functional market econo-my).10 The second is based onincome level: a country is consid-ered developed if it is a high-income OECD economy (weexclude the Czech Republic andSouth Korea because of someinstitutional weaknesses in thesecountries). By this measure39.4% of our cross-borderM&A expansion announcementsinvolve targets located in a devel-oped economy over the period1991–2004.

PROXIMITY¼ geographic and/or cultural proxi-mity. We use two different proxies


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to measure cultural distance. Ourfirst proxy combines cultural andgeographical distance (Geoculprox).By this measure, 52.4% of ourcross-border M&A expansionannouncements involve cultu-rally proximate targets over theperiod 1991–2004. Our secondcultural distance measure is acultural distance index based onHofstede’s cultural dimensions.The CDI index takes valuesbetween 1 and 0: a score closeto 1 implies significant culturaldistance; a score close to 0implies significant cultural proxi-mity. Our median sample CDIscore is 0.41 (see the Appendixfor a detailed discussion of theproxies used for proximity).

INTEXPR¼ extent of international experi-ence of the bidder. We use tworatios (foreign sales/total salesand foreign assets/total assets) asa proxy for international experi-ence. These variables were com-piled from data provided inUNCTAD’s World InvestmentReport, company annual reports,and DataStream International.

PRIORPRES¼ prior presence in target market:a dummy variable, taking thevalue 1 if the acquiring firm hasprior presence in the targetmarket, and 0 otherwise. In orderto identify companies with priorpresence in the target market wescreened a variety of sources,including the Thomson SDC Pla-tinum database, company annualreports, company websites, andnews articles compiled from data-base searches with key words.In 58% of the transactions, theacquiring firm had a prior pre-sence in the target country.

HITECH¼ acquirer industry: a dummyvariable, taking the value 1 if theacquirer is in a high-tech indus-try, and 0 otherwise.

GOVERN¼ a dummy variable, taking thevalue 1 if the EMM has issuedLevel II or LEVEL III ADRs, and

otherwise; As it was discussedearlier, companies with Level IIand Level III ADRs are consideredto have enhanced corporategovernance structures. In about13.8% of the transactions theacquirer has an outstanding LevelII or Level III ADR.

STRUCTURE¼ Strategic Orientation and Struc-ture of the Bidder (Dummy vari-able takes value of 1, if the bidderis a diversified conglomerate,0 otherwise).

REGION 1¼ regional domicile: a dummyvariable, taking the value 1 if theacquirer comes from Asia, and 0otherwise.

REGION 2¼ regional domicile: a dummyvariable, taking the value 1 ifthe acquirer comes from LatinAmerica, and 0 otherwise.

To check the robustness of our multivariate regres-sion results, we also employ binary logistic regres-sion analysis.11 In our logistic regression model,we define the dependent variable DSCAR as adichotomous variable, designated to a value of 1 ifit is positive and 0 otherwise. We use the same set ofindependent variables as in the multivariateregression model (Eq. (8)). The resulting logisticregression model is specified as follows:

DSCARðT1;T2Þ ¼ b0 þ b1ðSIZEÞ þ b2ðTYPEÞþ b3ðTSTATUSÞ þ b4ðCONTROLÞþ b5ðINVSTSIZEÞ þ b6ðINSTITUTIONÞþ b7ðPROXIMITYÞ þ b8ðINTEXPRÞþ b9ðPRIORPRESÞ þ b10ðHITECHÞþ b11ðGOVERNÞ þ b12ðSTRUCTUREÞþ b13ðREGION � 1Þ þ b14ðREGION� 2Þ þ e

ð9Þ

ANALYSIS AND RESULTSWe analyze SCARs for varying event windows. Ourresults, reported in Table 4, show that announce-ments of international acquisitions of EMMs are,on average, associated with negative abnormalreturns. While the mean (median) cumulativeabnormal returns immediately prior to and afterthe announcement (two- and three-day eventwindows) are negative and statistically significantat the 10% level, mean (median) cumulativeabnormal returns in wider event windows arestatistically insignificant. Positive market reactions


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with varying event windows range from 45.03 to47.81%. Doukas’ positives/negatives test showssignificant Z values at (�5, þ1), (�2, þ1),(�1, þ1), and (�1, 0) at the 5, 10, 5, and 5% levels,respectively, and confirm the dominance of thenegative reactions.

The negative two- and three-day cumulativeabnormal returns suggest that the potential bene-fits expected from cross-border expansion are offsetby various costs associated with the acquisition ofthe targets. Our results indicate that overall inves-tor sentiment with reference to the EMMs’ inter-national expansions through acquisitions is notpositive. In other words, investors do not perceiveEMMs’ cross-border acquisitions as value-creatingstrategic initiatives. These findings contradict thepositive returns reported for similar event windowsin earlier studies focused on international acquisi-tions (Doukas & Travlos, 1988; Morck & Yeung,1992), but are in line with the hypothesized valuedestruction elaborated in studies by Click andHarrison (2000), Hitt et al. (2001a, 2001b) andKissin and Herrera (1990). The negative valuationeffect of international acquisitions of EMMs docu-mented here also appears to be consistent with thevalue-reducing diversification of US multinationalsreported by Denis et al. (2001), Jensen’s hubrishypothesis and Stulz’s empire-building motives (seeJensen, 1986, as well as Stulz, 1990). However, it isplausible to suggest that firm characteristics, thenature of the investment, strategic fit, and targetmarket conditions might be influential factors ininvestor reactions. In the following section wediscuss these factors.

Firm-Specific FactorsWe consider several firm-specific factors, rangingfrom regional domicile to prior presence of theacquiring firm in the target firm’s country, toexplain the cross-sectional variations in SCARs.

The classification of transactions based on theregional origin of the acquirer indicates that failureto create value is a commonly observed outcomeof cross-border EMM acquisitions, regardless ofthe regional domicile of the acquirer. We reportSCARs classified by region in Table 5. An initialreview of the SCARs points to some differences inSCARs across different event windows. Widerevent windows of (�10, þ10) and (�10, þ5)suggest that EMMs from South Africa and Hungaryexperience more acute value destruction than theirAsian and Latin American counterparts. In contrast,for narrower windows, mean SCARs suggesthigher-level value destruction for Asian EMMs.However, mean SCAR differences are not statisti-cally significant at any of the event windowsthat we analyze. Our parametric and non-para-metric tests reported in Panels D and E ofTable 5 indicate that differences of mean andmedian SCARs are not statistically significant.Although they are not reported here, pairwisedifferences of means tests also confirm thesefindings. The insignificance of regional origin isalso confirmed in our cross-sectional analysisreported in Panels A and B of Table 6. Althoughthe coefficient signs are consistent with theunivariate findings, the region dummies are insig-nificant in both linear and binary logistic regressionmodels.12

Table 4 Daily and standardized cumulative abnormal returns from cross-border expansion announcements

Interval Mean Z-value WSR Z-value Positive:

negative

Doukas’ Z for

positive:negative

Total no. of

transactions

Positive market

reaction (%)

Mean Median

(�10, +10) �0.04843 �1.11716 �0.06398 �1.23121 207:226 �0.91308 433 47.81

(�10, +5) �0.05759 �1.33182 �0.0626 ** �1.71485 206:227 �1.0092 433 47.58

(�5, +5) �0.05384 �1.28754 �0.05582* �1.61235 207:226 �0.91308 433 47.81

(�5, +1) �0.04962 �1.13237 �0.06612* �1.59322 198:235** �1.77811 433 45.73

(�2, +1) �0.05442 �1.22589 �0.05618** �1.66743 198:236** �1.77811 433 45.73

(�1, +1) �0.09133 ** �1.95378 �0.06023** �2.25568 195:238** �2.06645 433 45.03

(�1, +0) �0.12158*** �2.76804 �0.06228*** �2.73531 195:239** �2.06645 433 45.03

***, **, and * denote statistical significance at the 1, 5, 10% levels, respectively. The table presents the daily standardized abnormal returns (SCARs) of433 cross-border M&A expansion announcements by emerging-market multinationals (EMMs) over the period 1991–2004. Daily standardizedabnormal returns (SCARs) are computed from the market model as prediction errors. Day 0 refers to the announcement day of acquisitions as reportedin the SDC Database. Z-statistics (Wilcoxon signed-rank test) are used to test for the statistical significance of mean SCARs. The statistical significance ofthe mean (median) difference between groups is computed by the Mann–Whitney test for unmatched pairs. Z statistics (Doukas’ test) are used to testfor the statistical significance of positives/negatives.


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Table 5 Daily and standardized cumulative abnormal returns of cross-border expansion announcements

Interval Mean Z-value WSRT Zfor median

Positive:negative

Doukas Z forpositive:negative

Total no.of events

Positive marketreaction (%)

Mean Median

Panel A EMMs from Asia(�10, 10) �0.0124 �0.25883 �0.01577 �0.45415 170:172 �0.10815 342 49.71(�10, +5) �0.02443 �0.50945 0.019907 �0.58231 175:167 0.43259 342 51.17(�5, +5) �0.02771 �0.60405 0.01056 �0.58777 173:169 0.216295 342 50.58(�5, +1) �0.0385 �0.78602 �0.03909 �0.84955 165:177 �0.64889 342 48.25(�2, +1) �0.06024 �1.17211 �0.05249* �1.46821 159:183* �1.29777 342 46.49(�1, +1) �0.10273** �1.8883 �0.07362*** �2.46313 149:193*** �2.37925 342 43.57(�1, +0) �0.1345*** �2.64059 �0.08403*** �2.73284 152:190** �2.0548 342 44.44

Panel B EMMs from Latin America(�10, +10) �0.15316* �1.34307 �0.16887 �1.0732 29:37 �0.98473 66 48.00(�10, +5) �0.17695* �1.59262 �0.3164** �1.94836 24:42** �2.21565 66 40.00(�5, +5) �0.14748 �1.21587 �0.23063* �1.62257 26:40** 1.72328 66 48.00(�5, +1) �0.10433 �0.8692 �0.13587** �1.69923 24:42** �2.21565 66 40.00(�2, +1) �0.01311 �0.13056 �0.07726 �0.6963 28:38 �1.23091 66 48.00(�1, +1) �0.05229 �0.51824 �0.03514 �0.37051 31:35 �0.49237 66 56.00(�1, +0) �0.05137 �0.52941 �0.00744 �0.35454 32:34 �0.24618 66 44.00

Panel C Other EMMs (South Africa and Hungary)(�10, +10) �0.26489 �1.30436 �0.24463 �1.20464 8:17* �1.80 25 32.00(�10, +5) �0.19602 �0.94716 �0.14942 �0.72202 7:18** �2.20 25 28.00(�5, +5) �0.16411 �0.98196 �0.23025 �1.37776 8:17* �1.80 25 32.00(�5, +1) �0.05722 �0.34399 �0.14342 �0.8622 9:16 �1.40 25 36.00(�2, +1) �0.08383 �0.50535 �0.05532 �0.33347 11:14 �0.60 25 44.00(�1, +1) �0.0385 �0.21664 0.084645 0.476275 15:10 1.00 25 60.00(�1, +0) �0.1304 �0.77655 �0.09589 �0.57108 15:10 1.00 25 44.00

Event window Regions Other F-statistic p-value

Asia LA

Panel D Testing regional differences: one-way ANOVA

(�10, +10) �0.00012 �0.00153 �0.00265 1.440 0.238

(�10, +5) �0.00024 �0.00177 �0.00196 1.109 0.331

(�5, +5) �0.0003 �0.0015 �0.0016 0.736 0.480

(�5, +1) �0.0004 �0.0010 �0.0006 0.145 0.865

(�2, +1) �0.0006 �0.0001 �0.0008 0.085 0.918

(�1, +1) �0.0010 �0.0005 �0.0004 0.113 0.893

(�1, +0) �0.0013 �0.0005 �0.0013 0.229 0.795

Event window Chi-square statistic p-value

Panel E Testing regional differences: Kruskal–Wallis test

(�10, +10) 2.618 0.270

(�10, +5) 4.160 0.125

(�5, +5) 3.408 0.182

(�5, +1) 1.470 0.479

(�2, +1) 0.003 0.998

(�1, +1) 1.363 0.506

(�1, +0) 0.553 0.758

***, **, and * in Panel A denote statistical significance at the 1, 5 and 10% levels, respectively.** and * in Panel B denote statistical significance at the 5 and 10% levels, respectively.** and * in Panel C denote statistical significance at the 5 and 10% levels, respectively.The table presents the daily and standardized cumulative abnormal returns (SCARs) of 342 cross-border MA expansion announcements by emerging-market multinationals (EMMs) originating from Asia over the period 1991–2004. Daily standardized cumulative abnormal returns (SCARs) arecomputed from the market model as prediction errors. Day 0 refers to the announcement day of acquisitions as reported in the SDC Platinum database.Z-statistics (Wilcoxon signed-rank test) are used to test for the statistical significance of mean SCARs. The statistical significance of the mean (median)difference between groups is computed by the Mann–Whitney test for unmatched pairs. Z statistics (Doukas’ test) are used to test for the statisticalsignificance of positives/negatives.


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A comparative analysis of EMMs’ acquisition ofsmall and large foreign targets indicates thatrelative size (measured as the ratio of the dollarvalue of the acquired stake to the bidder’s marketvalue) is a significant factor for discerning investorreaction to cross-border acquisition announce-ments. Whereas mean SCAR differences are nega-tive for wider event windows, they are positive fornarrow windows. The significance of the positivedifferences of mean SCARs can be confirmed atthe 10% level for (�1, þ1) and (�1, 0) eventwindows.13 In our cross-sectional linear regressionmodel we find that relative target size is significantin three event windows – (�5, þ1), (�2, þ1) and(�1, þ1) – at the 1% significance level. In ourbinary logistic regression model, relative invest-ment size is significant only in the (�1, þ1) eventwindow at the 10% significance level. A surprisingfinding in the univariate and multivariate models isthe positive sign of the coefficient in the significantevent windows, which is contrary to what weexpected to find. While the negative impact ofthe large acquisitions on the bidder value is widelyreported in the literature, our findings suggest that,for EMMs, investors perceive better prospectswhen the acquirer bids for larger targets relativeto its assets.

In our univariate analysis we compare the marketreactions to transactions involving bids for 50% orhigher percentage of the target shares against bidsinvolving less than 50% of the target shares. In 134transactions bidders contemplated 50% or more ofthe target shares. The results suggest that theimpact on acquirer value of the level of controlgained by the acquirer is positive for five out of theseven event windows, and significant for at leastone event window. In contrast, we fail to confirmthe significance of the level of control in ourmultivariate analysis.14 In our linear regressionmodel, the sign varies across the event windows,but the coefficient remains insignificant. In ourbinary logistic regression analysis, the sign isconsistently negative across the event windows.

Our analysis of the effect of target status on theacquiring firm value indicates that, on average,EMMs experience a higher percentage of positivereactions for all event windows when announce-ments involve private targets. Regardless of thetarget status, SCARs reported for narrow windowsare negative and statistically significant for bothprivate and public targets.15 The parametric andnon-parametric tests of the mean SCAR differencesshow that the differences are statistically insignif-

icant for all event windows. Here, our multivariateanalysis results contradict the results of theunivariate analysis. In our linear regression analy-sis, target status proves to be a significant factor,explaining variation in SCARs at the 10% signifi-cance level for all event windows. In contrast, inour binary logistic regression analysis, significancecan be confirmed only for event window (�5, þ5)at the 5% significance level. The sign of thecoefficient is consistent with our expectations infive out of seven event windows, indicating thatthe acquisition of private targets is not as valuedestructive as the acquisition of public targets.A caveat to these findings is the existence of arelatively small number of ‘‘publicly’’ owned targetsin the sample. We have only 23 transactionsinvolving publicly owned targets vs 410 transac-tions involving privately owned targets.

We expected international experience in generaland knowledge of the target market in particular toimprove EMMs’ ability to capture value frominternational acquisitions on familiar turf. Yet ourunivariate analysis results indicate that prior pre-sence in the target market does not have asignificant or consistent impact on bidder value.The mean differences of SCARs are between twogroups of acquirers: those that have prior presencein the target market, and those that do not revealvarying signs depending on the event window. Wefind weak support for the significance of priorpresence in one of two multivariate models at theevent window (�5, þ5). Overall, in light of ourempirical findings, we fail to find strong support forthe significance of this factor.

Consideration of two commonly used proxies forinternational experience (or the extent of inter-nationalization of the firm) leads us to a similarconclusion. In other words, we cannot verify withconfidence the proposition that more experiencedfirms are more likely to capture value from cross-border acquisitions. In contrast, we obtain anopposite sign for the mean differences and thecoefficient of the international experience proxy inunivariate and multivariate analyses, respectively.In our univariate analysis, mean SCAR differencesfor event window (�2, þ1) are negative andsignificant, suggesting deeper value destructionfor experienced bidders. We find a similar resultfor the event window (�10, þ10) in our linearregression analysis (see Table 6, Panel A). For allother windows, mean SCAR differences and coeffi-cients prove to be insignificant. Although we donot report the multivariate specifications with


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Table 6 Cross-sectional regressions: standardized cumulative abnormal returns of emerging-market multinationals (EMMs)

Dependent variable SCAR(�10, +10)

SCAR(�10, +5)

SCAR(�5, +5)

SCAR(�5, +1)

SCAR(�2, +1)

SCAR(�1, +1)

SCAR(�1, 0)

Independent variable

Panel A Linear regressionIntercept 0.0251 0.0275 0.0209 0.0198 0.0053 0.0050 0.0018

0.0068 0.0100 0.0247 0.0279 0.5101 0.5576 0.8173SIZE �0.000036 0.000699 0.000555 0.000623 0.000510 0.000257 0.000190

0.9722 0.4959 0.5628 0.5375 0.6136 0.7986 0.8511TYPE 0.0014 0.0023 0.0017 0.0014 0.0020 0.0010 0.0016

0.4076 0.1452 0.2180 0.4024 0.2349 0.5562 0.3206TSTATUS �0.0141*** 0.0097*** 0.0135*** 0.0160*** �0.0062** 0.0109*** 0.0078***

0.0000 0.0003 0.0000 0.0000 0.0293 0.0004 0.0089CONTROL �0.0024 �0.0012 �0.0004 0.0014 0.0013 �0.0007 0.0027

0.3151 0.5797 0.8357 0.5060 0.6061 0.7927 0.2725INVSTSIZEa 0.0041 0.0015 0.0128 0.0303*** 0.0285*** 0.0302*** 0.0035

0.6722 0.8678 0.1941 0.0001 0.0094 0.0207 0.6694INSTITUTIONb �0.0019** �0.0019** �0.0011 �0.0003 0.0003 0.0008 0.0004

0.0482 0.0280 0.1396 0.6961 0.6503 0.2752 0.5491PROXIMITYc 0.0072 0.0028 0.0022 �0.0008 0.0017 0.0038 0.0061

0.2498 0.6306 0.6669 0.8651 0.7281 0.4992 0.2243INTEXPRd �0.0166** �0.0181* �0.0131 �0.0114 �0.0021 �0.0015 0.0025

0.0175 0.0853 0.1837 0.1738 0.5313 0.7710 0.5068PRIORPRES 0.0015 0.0027 0.0036** 0.0021 0.0001 �0.0013 0.0003

0.4293 0.1609 0.0380 0.1982 0.9308 0.4778 0.8377HITECH �0.0042* �0.0025 �0.0026 �0.0002 �0.0014 �0.0034 0.0002

0.0581 0.2269 0.1936 0.9150 0.4731 0.1096 0.9027GOVERN 0.0022 0.0016 0.0006 0.0014 �0.0030 �0.0065* �0.0026

0.5524 0.6982 0.8525 0.6804 0.2833 0.0687 0.3982STRUCTURE 0.0021 0.0032 0.0021 0.0040* 0.0029 0.0020 0.0015

0.3380 0.1341 0.2468 0.0542 0.1384 0.3175 0.4251REGION 1 (Asia) 0.0023 0.0006 0.0026 �0.0011 �0.0004 �0.0038 �0.0030

0.4487 0.8206 0.2092 0.6673 0.8511 0.1681 0.2570

Panel B Binary logistic regressionIntercept 1.4853 0.3197 0.0000 �1.2020 �0.7036 0.9082 �0.8830

0.3249 0.8348 0.0000 0.4410 0.6550 0.5984 0.5719TYPE �0.4924 �0.2034 �0.0937 �0.2118 �0.5503* �0.2258 0.0719

0.1363 0.5262 0.7687 0.5165 0.0805 0.4976 0.8232TSTATUS �0.9223 �1.1269 �1.8638** �1.3939 �0.4160 �0.3258 �0.6943

0.4137 0.3406 0.0195 0.1811 0.7033 0.7375 0.5160CONTROL �0.5448 �0.3833 �0.5314 �0.0102 �0.0770 �0.2623 0.0067

0.2651 0.4089 0.2508 0.9821 0.8655 0.5569 0.9881INVSTSIZEa 0.3998 0.4697 1.3451 4.1754 2.2106 5.6872* 3.0402

0.8445 0.8223 0.5300 0.1406 0.3664 0.0981 0.1844INSTITUTIONb �0.1735 �0.0383 0.0814 0.2523 0.1022 �0.1421 �0.0437

0.2809 0.8063 0.5216 0.1036 0.5037 0.3776 0.7773PROXIMITYc 2.3239** 1.9377* 1.7528 0.7211 1.4907 2.1759* 2.6807**

0.0429 0.0783 0.1121 0.5174 0.1522 0.0540 0.0191INTEXPRd �0.5437 0.7671 �0.0031 1.6610 0.6410 0.6924 0.8279

0.5693 0.4580 0.9973 0.1048 0.4934 0.5258 0.3938PRIORPRES �0.2229 �0.3229 �0.1348 0.0713 0.0581 0.0316 0.3577

0.4875 0.7010 0.6752 0.8241 0.8545 0.9224 0.2816HITECH �0.7909** �0.2112 �0.3500 �0.2385 �0.1548 �0.1761 0.0656

0.0355 0.5617 0.3376 0.5347 0.6742 0.6391 0.8583GOVERN �0.0648 �0.1795 0.0924 �0.7903 �0.0749 �0.6196 0.3243

0.9114 0.7492 0.8699 0.1739 0.8969 0.3668 0.5775STRUCTURE 0.1128 0.1369 0.2331 0.4102 0.0811 0.1536 0.3497

0.7630 0.7047 0.5214 0.2594 0.8226 0.6728 0.3346REGION 1 (Asia) 0.6956 0.7010 0.8202 �0.0623 �0.3720 �0.7620 �0.2433

0.2014 0.2014 0.1122 0.9108 0.4992 0.2530 0.6632

aThe variable used in the model is relative investment size (ratio of acquired stake to bidder’s market value).bThe variable used in the model is the economic freedom index.cThe variable used in the model is the CDI index, based on Hofstede’s cultural dimensions.dThe variable used is foreign assets/total assets ratio.p-values are reported in italics below the coefficient estimates.***, **, and * denote statistical significance at the 1, 5, and 10% levels, respectively.The dependent variable in the regressions is the standardized cumulative abnormal return (SCAR) of EMMs engaged in cross-border acquisitions overthe period 1991–2004. SCARs are defined over various event windows around the acquisition announcement.


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foreign sales ratio, it is crucial to mention thatcoefficients are insignificant for all event windows,and that coefficient signs do not deviate from thefindings reported for foreign asset ratio.

We also evaluate the impact of good corporategovernance on acquirer value in cross-borderacquisitions. This consideration could be a signifi-cant issue, as investors of EMMs may perceiveforeign acquisitions as efforts to appropriate valuefrom current shareholders. Earlier, in the theorysection of this paper, we conjectured that ADRissuance by EMMs can be interpreted as a signal forcommitment to good corporate governance, asEMMs often choose voluntarily to meet higherdisclosure and reporting requirements than homemarkets. Although voluntary adherence to higherregulatory standards does not necessarily guaranteegood governance, one might argue that, from theEMMs’ perspective, this practice can be an impor-tant step towards moderation of informationalasymmetry. However, our empirical results do notprovide strong support for this argument: thereforewe are unable to verify the impact of goodcorporate governance on bidder value. Yet ourunivariate analysis displays negative mean SCARdifferences, which suggests value destruction forbidders with enhanced governance. The statisticalsignificance of the negative impact can be verifiedonly for the event window (�5, þ5). In our multi-variate analysis we report a similar finding for theevent window (�1, þ1), explicitly a negative andsignificant coefficient, thereby suggesting valuedestruction for bidders with outstanding Level IIand Level III ADR issues. The logistic regressionresults, on the other hand, indicate positivecoefficients for larger event windows (up to(�5, þ1)) and negative coefficients for narrowerevent windows.

Industry-Specific FactorsWe find generally negative market reactions tohigh-tech EMMs’ cross-border acquisitions. Themean SCAR differences are significant, with theexception of the (�5, þ1) and (�2, þ1) eventwindows. In our cross-sectional analysis we canconfirm the negative impact of being a high-techbidder; however, the coefficient is statisticallysignificant only for the (�10, þ10) window.Cross-sectional linear and binary logistic regressionresults appear in Table 6, Panels A and B. Generally,the sign of the coefficient is consistent with ourexpectations, namely that the acquisition of high-tech targets may have some value-reducing attri-

butes, such as incompatibility of the acquired assetsdue to informational asymmetries, and high pre-miums paid for the targets.

Our analysis of the impact of the structure ofEMMs on market reaction reveals that mean SCARdifferences are negative, with the exception ofevent window (�10, þ5), which implies thatdiversified conglomerate-type EMMs experienceless value destruction than non-diversified EMMs.Our univariate analyses also concur with thepositive to negative market reaction ratios. While,on average, non-diversified EMMs experience a39.5–46.5% positive market reaction across variousevent windows, the corresponding range for thediversified EMMs is 44.6–54.89%. The multivariatecross-sectional analyses do not provide strongsupport for our findings. Although the binarylogistic regression results reported in Table 6, PanelB, produce the expected sign for the diversificationdummy (i.e., positive), coefficients are not signifi-cant across the event windows. We can verifystatistical significance only for event window(�5, þ1) in our linear regression analysis (seeTable 6, Panel A). Overall, our empirical findingsprovide some support for the hypothesized sub-stitution of institutional deficiencies through thecreation of internal markets.

Finally, we find some weak evidence that bidderspursuing related targets experience deeper valuedestruction than those attempting diversificationthrough unrelated targets. Although our univariateanalysis results point to significant value destruc-tion in the pursuit of related targets in three out ofseven event windows, we can verify this finding foronly one event window in our binary logisticregression analysis. This finding evidently contra-dicts the widely reported results in favor of focusedstrategies in developed country settings, yet it isconsistent with the benefits attributed to diversifi-cation in emerging markets.16

Target Country CharacteristicsIn this section, we discuss our findings on twospecific country characteristics: geographical-cul-tural proximity between the target and biddercountries, and the development level of the marketinstitutions in a given target country.17 Themeasurement of cultural distance is a challengingprocess, and inevitably methods used to capturecultural distance are open to criticism. While themeasure suggested by Kogut and Singh (1988) hasbeen widely embraced in the IB literature, it hasalso been criticized for conceptual and theoretical


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weaknesses.18 In our study, we adopt two alter-native measures to capture cultural distance. Aspresented in the methodology section, our firstmeasure combines geographic and cultural distancemeasures into a single indicator based on thedistance scores of the bidder and target countries.Our analysis based on this measure suggests thatcultural proximity does not enhance a bidder’sprospect of success, as hypothesized in our theore-tical discussion and in the related literature. On thecontrary, we find that average SCARs for geogra-phically and culturally proximate targets are dom-inantly negative and statistically significant. Forvarious event windows we find that only 38.8–48.6% of transactions between proximate biddersand targets produce a positive investor reaction.The positive reaction percentage is higher for trans-actions between distant bidders and targets (37.8–58.44% for various event windows). The univariateanalysis results show that the mean SCAR differ-ences between these two groups (i.e., the differencebetween the mean SCARs of proximate targets anddistant targets) are not statistically significant.Although consideration of this particular measureof cultural distance suggests that proximity leads toincreased value destruction, as suggested by thenegative mean SCAR differences on all eventwindows, we cannot verify this finding statistically.

We find a similar pattern when we considerour alternative cultural distance measure based onHofstede’s cultural dimensions (see Hofstede, 1980,1984). Although we cannot verify statistical signi-ficance, we find that the impact of cultural distanceis positive for narrow event windows. In otherwords, the higher the cultural distance, the lower isthe value-destructive impact of the announcement.Our binary logistic regression analysis providessome support for the significance of the positiveimpact of cultural distance on bidder value. Thecoefficient estimate for the cultural distance vari-able is positive for all event windows, and statisti-cally significant for the intervals (�10, þ10),(�1, þ1), and (�1, 0). Contrary to our expecta-tions, multivariate cross-sectional regre-ssion analysis shows that higher cultural distanceis associated with higher cumulative abnormalreturns for a number of event windows. Our linearregression analysis produces similar results, butdoes not portray significant estimated coefficients.Based on our empirical findings, we conclude thatinvestors do not perceive value-creative strategicbenefits in the acquisition of culturally proximatetargets.

In this study, we also propose that the economicdevelopment level of the target country is animportant determinant of the benefits that can begained from a strategic cross-border expansion. Aswe show, in an institutionally underdevelopedenvironment acquirers face tradeoffs between theability to take advantage of market imperfectionsand the excessive costs of uncertainty and govern-ment discretion. However, in institutionally devel-oped environments EMMs are likely to encounterreduced uncertainty and limited governmentdiscretion in exchange for a highly complexand fiercely competitive marketplace. While theperceived benefits might depend on particularcharacteristics of the target, we have the opportu-nity to evaluate the impact of the institutionalcharacteristics of the target domain on the acquirervalue as perceived by investors. In our sample wehave 171 transactions where targets are located indeveloped economies as defined by the WorldBank. We should point out here that, usually, ahigher economic development level also implies amore advanced institutional infrastructure. We finda higher percentage of positive reactions to acquisi-tion announcements when the target is located in adeveloped economy than when it is located in anemerging economy. Furthermore, we discover thattransactions involving targets in developed econo-mies tend to produce higher cumulative abnormalreturns for all event windows. Additionally, ourunivariate analysis of mean SCAR differencessuggests that mean SCARs for targets located indeveloped economies are higher, and hence weare able to confirm the statistical significance ofthe mean differences in four out of seven eventwindows.

In our cross-sectional multivariate analysis we usean alternative measure of the level of institutionaldevelopment – the economic freedom index score.When we apply the economic freedom index as aproxy for the level of development, we obtainmixed results, because the coefficient for thisvariable does not have a consistent sign across theevent windows. In our linear regression analysis,however, the sign of the coefficient is negative andsignificant in wider event windows, suggestingvalue destruction for targets located in moredeveloped institutional settings, but it is positiveand insignificant for narrow event windows. In ourbinary logistic regression analysis we encounter asimilar inconsistency in the sign of the coefficient,but coefficients remain insignificant in all eventwindows. Our cross-sectional linear and logistics


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regression results appear in Panels A and B ofTable 6.

Overall, we cannot verify the impact of level ofeconomic development on acquirer value byusing the economic freedom index, but a simpledichotomous separation of the sample trans-actions, depending on the target location, suggeststhat investors perceive value creation opportu-nities when EMMs pursue targets in developedeconomies.

CONCLUDING REMARKSIn this study, we examine the value implications ofcross-border acquisitions of EMMs during the 1991–2004 time frame. Our sample of 433 acquisitionannouncements originates from a variety of coun-tries across Latin America, Eastern Europe, Asia, andAfrica. An initial screening of the sample revealsthat while 60.51% of EMMs’ acquisition targets arelocated in emerging-market economies, the remin-der of the targets come from developed economies.

Our findings indicate that the equity marketsreact negatively to the cross-border acquisitionannouncements of EMMs. The average abnormalreturn on the day of the announcement is �1.38%(see Table 7), which is significant in both economic

and statistical terms. Furthermore, cumulativeabnormal returns surrounding the announcementday also suggest that acquisition announcements ofEMMs, on average, are perceived by investors asvalue destructive.

In this study, cross-sectional analysis resultsindicate that the relative size of the target, bidsfor privately owned targets, and a diversifiedcorporate structure positively influence the abnor-mal returns around the expansion announcement.In contrast, we find that acquisition announce-ments of high-tech EMMs and acquisitionannouncements of related targets are associatedwith value destruction. We find some support forthe positive influence of the extent of controlpursued by the bidder and the negative impactof cultural proximity. Despite the expectation ofpositive influence of cultural proximity on cumu-lative abnormal returns, the findings show theopposite. Although it is sensitive to the selection ofthe proxy for level of institutional development,our results point to the influence of the level ofinstitutional development of the target country.The use of a simple dummy variable separatingdeveloped and emerging markets proved to bemore effective in differentiating value-creative and

Table 7 Bidders’ daily abnormal returns (SARs) M&As

Day Mean Z-value WSRT-Z formedian

Positive:negative

Doukas’ Z forpositive:negative

Total no. oftransactions

Positive marketreaction(%)

Mean Median

SAR �10 0.0453899 1.001728 0.015553 0.993685 226:206 0.96225 432 52.31SAR �9 �0.0292348 �0.61631 �0.03562 �1.34442 197:236** �1.87422 433 45.50SAR �8 �0.0386052 �0.82345 �0.01913 �1.10365 208:225 �0.81697 433 48.04SAR �7 �0.007518 �0.15965 �0.02268 �0.56378 203:230 �1.29754 433 46.88SAR �6 �0.0220278 �0.43639 �0.00597 �0.60432 215:218 �0.14417 433 49.65SAR �5 0.014045 0.320164 �0.03242 �0.2555 200:232* �1.5396 432 46.30SAR �4 �0.0376143 �0.70562 �0.00569 �0.36546 213:220 �0.3364 433 49.19SAR �3 0.0013026 0.028064 �0.00267 �0.14305 213:220 �0.3364 433 49.19SAR �2 0.0491559 1.059238 �0.01601 �0.12813 207:226 �0.91308 433 47.81SAR �1 �0.1059454*** �2.38393 �0.03562** �2.29967 199:234* �1.68199 433 45.96SAR 0 �0.0659145* �1.38357 �0.02989** �1.86861 192:241*** �2.35479 433 44.34SAR 1 0.0135414 0.241329 �0.0317 �0.60948 206:227 �1.0092 433 47.58SAR 2 �0.0518635 �1.05499 �0.04424** �1.93994 193:240** �2.25868 433 44.57SAR 3 0.035915 0.746715 �0.02516 �0.28361 203:230 �1.29754 433 46.88SAR 4 0.0003904 0.008847 �0.01185 �0.71314 209:224 �0.72085 433 48.27SAR 5 �0.0317581 �0.74269 �0.04421* �1.37578 190:243*** �2.54702 433 43.88SAR 6 0.0135948 0.249657 �0.00655 �0.1375 210:223 �0.62474 433 48.50SAR 7 �0.0479753 �0.88633 �0.04424* �1.52935 197:236** �1.87422 433 45.50SAR 8 0.0387834 0.819585 �0.02176 �0.35647 209:224 �0.72085 433 48.27SAR 9 �0.0219282 �0.47266 �0.02803 �0.91604 202:231* �1.39365 433 46.65SAR 10 0.0263181 0.546626 �0.0085 0.052782 209:224 �0.72085 433 48.27

***, **, and * denote statistical significance at the 1, 5, and 10% levels, respectively. The table presents the daily standardized abnormal returns (SARs) of433 cross-border M&A expansion announcements by emerging-market multinationals (EMMs) over the period 1991–2004. Daily standardizedabnormal returns (SARs) are computed from the market model as prediction errors. Day 0 refers to the announcement day of acquisitions as reported inthe SDC database. Z-statistics (Wilcoxon signed-rank test) are used to test for the statistical significance of mean SARs. The statistical significance of themean (median) difference between groups is computed by the Mann–Whitney test for unmatched pairs. Z-statistics (Doukas’ test) are used to test forthe statistical significance of positives and negatives.


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value-destructive transactions than the use of theeconomic freedom index. By using this simplemeasure we could verify the significance of theinstitutional infrastructure factor more decisively.Other factors, such as the bidder’s prior presence inthe target market, international experience andenhanced governance, proved to be insignificantin determining cumulative abnormal returns. Acaveat is the imperfection of our ‘‘good govern-ance’’ proxy. As we discussed in our hypothesesdevelopment section, various levels of ADRissuance might not fully capture the nature ofgovernance. Hence we cautiously interpret theinsignificance of the governance factor.

The recent surge in outward foreign directinvestment from emerging-market economies, andprojected trends, suggest that EMMs will continuein their efforts to catch up with established playersby accessing strategic assets, new technologies, andmarkets (e.g., Sauvant, 2008). These trends confirmthe relevance of our empirical findings, and provideinsights for investors and managers of EMMs.While our research presents a rigorous attempt toexplore the international expansion–firm valuenexus, its limitations should be noted. First, despiteits statistically desirable properties, our event studymethodology is based on the assumption that themarket response to public information about thestrategic event is instantaneous, complete, andunbiased, based on the semi-strong form of theefficient market hypothesis. Hence the value cre-ated or destroyed by the international acquisitionannouncements should be interpreted cautiously,as it reflects the market’s evaluation of complex,and in some cases infrequent, strategic initiatives. Itis plausible that the performance implications ofsuch complex strategic ventures are not fullyunderstood by market participants, and may beprone to heuristic biases. In light of this metho-dological constraint, the use of long-term perfor-mance measures to supplement future event studiesmay provide a robustness check on the event studyresults.

The second key limitation of the study is theregional concentration of the parent companiesin Asia and Latin America, which warrants cautionin generalizing the results across the emerging-markets universe. However, the recent surgesin outward investment from Russia, India, andChina, as well as smaller emerging-market econo-mies in eastern and south-eastern Europe, offer thepotential to design studies with more diversesamples.

Finally, it is important to emphasize that ouranalyses focus on value creation or destructionfrom the bidder’s perspective. While we reportvalue destruction from bidder’s standpoint, thecombined value for the bidder and target mightbe positive.

To the best of our knowledge this study constitu-tes one of the rare multi-country studies of EMMsfocusing on the value implications of internationa-lization through acquisitions. Our conclusion thatEMMs’ cross-border acquisitions are, on average,value destructive agrees with various findingsreported in the contemporary literature focusingon the value of multinational firms.

Our research supports the view that EMMs willcontinue to seek value in cross-border acquisitionsas their domestic environment pressures them toexpand. While empirical studies with a largersample size and a more balanced geographicdiversification are likely to expand our understand-ing, qualitative studies based on primary data willbe particularly revealing in arriving at a morereliable picture with robust explanations. Alongthis direction our study paves the way for futureresearch to untangle the further specifics of thecircumstances that surround this new breed of MNEand their international expansion.

ACKNOWLEDGEMENTSWe would like to thank two anonymous reviewers andJIBS Departmental Editor Lemma Senbet for theirhelpful editorial guidance and constructive feedback.We are particularly grateful for outstanding commentsand feedback on various versions of the paper receivedfrom Seyda Deligonul. Seyda’s suggestions, along withthose of the reviewers, helped us sharpen our thinkingand articulate our arguments in a clearer manner.Finally, we sincerely thank John Fleming for hisassistance in editing the final version of the document.

NOTES1For a detailed discussion of the risks associated with

M&As, see Shimizu et al. (2004).2Meyer, Milgrom, and Roberts (1992), Rajan and

Zingales (1995), Rajan, Servaes, and Zingales (2000),and Scharfstein and Stein (2000) present models inwhich divisional managers exert influence to increasethe assets under their control. This influence leads, insome cases, to less profitable divisions being sub-sidized by, and at the expense of, more profitabledivisions.

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


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4We are fully aware of the limitations of this proxy.As recent corporate scandals in the US unambiguouslydemonstrate, being subject to a stringent ‘‘corporategovernance regime’’ does not necessarily lead to goodcorporate governance practices. However, despiterecent convergence in corporate governance regimesacross the world, voluntary subjection to morestringent governance standards rather than their beingimposed by home markets can be construed as asignal for improved corporate governance.

5To best of our knowledge, the information pro-vided by the SDC Platinum database pertaining totransaction value is based on the announcement date.

6Although Mexico is often classified as a NorthAmerican country (particularly after being part ofNAFTA), we thought that regional designation in LatinAmerica would accurately capture the countries in oursample.

7There are various other alternative return-generat-ing models used in this context; however, theconsensus in the finance literature is that the choiceof RGM does not have any significant impact on theevent study results.

8Coutts, Mills, and Roberts (1995) concluded thattreating the abnormal returns as being independentcan make a substantive difference for the longer eventperiods.

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

10The index is accessible at http://www.fraserinstitute.org/researchandpublications/researchtopics/economicfreedom.htm The index is based on scores assignedin five categories: the size of the government; legalstructure and property rights; freedom to trade inter-nationally; access to sound money; and regulation ofcredit, labor and business.

11We use the maximum likelihood method in ourlogistic regression estimations.

12In the multivariate models, the Region 2 dummyproved to be insignificant in all event windows. In thereported model, the Region 2 model was excluded toattain a more parsimonious model.

13We do not report the univariate analysis tables inthe paper, but the results are available upon requestfrom the authors.

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

15The analyses of the SCARs for each group,including positive/negative reaction ratios, are notreported here because of space constraints.

16For instance, see Khanna and Palepu (1997, 1999).17Originally we considered the development level of

institutional infrastructure and the overall level of

economic development as two separate variables.However, because of the high correlation bet-ween these two variables in our multivariate model,we focused on the degree of institutional infra-structure development. We use the economic freedomindex as a proxy for the development level ofinstitutional infrastructure. As we discuss in themethodology section, the economic freedom index isa scale variable taking values between 1 and 10, where10 indicates a high level of institutional infrastructuredevelopment.

18For instance, see Yeganeh and Su (2006) for anextensive review of cultural distance measures and acriticism of the Kogut and Singh measure.

19See Robusto (1957). The haversine formula useslatitudes to measure geographic distance d. GivenR¼the earth’s radius (mean radius¼ 6371 km), and

Dlat ¼ lat2 � lat1

Dlong ¼ long2 � long1

a ¼ sin2ðDlat=2Þ þ cosðlat1Þ:cosðlat2Þ:sin2ðDlong=2Þ

c ¼ 2a tan2½ffiffiffiap

;ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1� að Þ

p�

then

d ¼ Rc:

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

21If the distance obtained from the latitude andlongitude calculation is 3835.914 km or more (thehighest being 18,528.5367 km), this shows no geo-graphical proximity: therefore a value of 0 (dummyvariable) is assigned to the distance between theacquirer and the target. If the distance is 3835.914 kmor less (the least being 9.4935 km), this showsgeographical proximity: therefore a value of 1 isassigned. The distance of 3835.914 km was chosenbecause, according to our data, this is the closestdistance between the continental divisions that EMMsexpanded (or the expansion announcement wasexecuted) into a particular location. Our latitude andlongitude calculations were based on the followingfigures: the earth’s circumference at the equator is40,075.16 km and between the North and South Polesis 40,008 km. In addition, we also considered thedistances between the continents. Hence distancesbetween continents are regarded as non-proximate,and distances within continents as proximate. Theliberty of choosing such a method may easily bejustified by Alfred Wegener’s theory of continental driftand/or the drifting of continental shelves, which,


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among other things, states that most continents (otherthan Eurasia) do not share the same tectonic platesthat make up the Earth’s surface. This theory is

supported more today than in the past, owing totechnological and sophisticated research findings(Kearey & Vine, 1996).

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APPENDIX

PROXIMITY (Geographic and/orCultural Proximity)In order to measure geographic/cultural proximity,we use two alternative measures. Our first measureis a composite geographic and cultural distancemeasure. Our geographic distance measure is basedon the haversine formula.19 Our cultural proximitymeasure is based on the KOF index compiled byKeohane and Nye (2000).20 Although the KOFindex measures a country’s globalization level, itconsists of a sub-index measuring the culturalattributes of each country. Cultural distances areestimated based on the cultural dimension scores ofthe acquirer and target countries. Cultural andgeographic distance measures are then convertedinto a dummy variable that takes the value 1 if thetarget country is geographically and/or culturallyproximate to the acquirer country, and 0 otherwise.21

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

For each transaction i (i¼1,y, N, where N¼433 inour case) we compute four cultural distance (CD)measures, one for each of Hofstede’s culturaldimensions j ( j takes values from 1 to 4, eachexpressing a specific dimension: PDI, IDV, MAS orUAI). CDij is the absolute difference between theacquirer country and target country dimensionscore for the cultural dimension j, and is given by

CDij ¼ Dj;acquirer �Dj;target

�� where Dj is the score of one of the culturaldimensions for transaction i. We create a compositeCD index (CDI) from four different CD measures.


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The CDI is essentially a composite cultural dimen-sion difference measure. It is computed as follows:

CDIi ¼1

N

1

J

XJ

j¼1

RankjðCDijÞ

where Rankj(CDij) is the rank function, whichassigns a rank for each observation in our sample,from the least different (rank of 1) to the mostdifferent (rank of N). CDij is the jth measure ofcultural difference for transaction i in our sample,and J represents the number of CD measures. Thedenominator, J, averages the ranks by the numberof different CD variables available for each firm inthe sample. Because our sample firms are requiredto have data on all four CD measures, J is equal to 4.Finally, by dividing by N, we scale the CDI from 0(least different) to 1 (most different). In otherwords, a low CDI score (close to 0) implies lowcultural distance, and a high CDI score (close to 1)implies high cultural distance.

ABOUT THE AUTHORSC Bulent Aybar ([email protected]) is a Professorof International Finance in the School of Businessat Southern New Hampshire University. He earnedhis PhD from Fisher College of Business at OhioState University. His teaching and research interestsspan a range of areas from international corporatefinance to risk management. His current research isfocused on cross-border mergers and acquisitions,and foreign exchange exposure.

Aysun Ficici ([email protected]) earned herdoctoral degree from Southern New HampshireUniversity, where she is an Assistant Professor ofInternational Business. Her current research isfocused on emerging-market multinationals, globalgovernance, economic regionalism and the EU.She is also a research fellow at the University ofMaastricht.

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


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Cross-border acquisitions and firm value: An analysis of emerging-market multinationals · 2017-09-09 · The mergers and acquisitions announcements data come from the Thomson SDC