This article was downloaded by: [Northeastern University]On: 25 November 2014, At: 23:40Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Journal of Asia-Pacific BusinessPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/wapb20

Emerging Market Multinationals: An Analysis ofPerformance and Risk CharacteristicsBülent Aybar a & Arul Thirunavukkarasu DBA ba International Business Department , Southern New Hampshire University , 2500 North RiverRoad, Manchester, NH, 03102, USA E-mail:b International Business Department , Southern New Hampshire University , 2500 North RiverRoad, Manchester, NH, 03102, USA E-mail:Published online: 24 Sep 2008.

To cite this article: Bülent Aybar & Arul Thirunavukkarasu DBA (2005) Emerging Market Multinationals: An Analysis ofPerformance and Risk Characteristics, Journal of Asia-Pacific Business, 6:2, 5-39, DOI: 10.1300/J098v06n02_02

To link to this article: http://dx.doi.org/10.1300/J098v06n02_02

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Emerging Market Multinationals:An Analysis of Performance

and Risk CharacteristicsBülent Aybar

Arul Thirunavukkarasu

ABSTRACT. This study explores the risk and performance characteris-tics of emerging market multinationals (EMNCs). We use a samplecomposed of 79 EMNCs from 15 countries located in Africa, Asia, East-ern Europe-Russia, and Latin America. Our risk and performance analy-ses are based on monthly share price returns collected over 1996-2003period and annual accounting data. We find that EMNCs on average per-form better than their respective country market indices, a widely usedEM benchmark, S&P500 and, global market index (MSCI-World) dur-ing the period of analysis. Our sample firms on average earn 13.21% re-turn on assets, 8.97% return on equity, and 11.96% return on investedcapital. We also find that EMNC returns are highly volatile, and despitesome level of diversification achieved by EMNCs, their returns remainhighly sensitive to local market shocks. The cross-sectional analysis ofthe determinants of the performance of the EMNCs reveals that leverageand systematic risk are the most important factors, followed by size. Ouranalysis indicates that performance is not affected by the degree of inter-nationalization and EMNC investments in developed markets have apositive impact on the value. Finally, our results indicate that EMNCs inless risky emerging markets enjoy higher firm value. [Article copies avail-able for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH.E-mail address: <docdelivery@haworthpress.com> Website: <http://www.HaworthPress.com> © 2005 by The Haworth Press, Inc. All rights reserved.]

Bülent Aybar is Professor of International Finance (E-mail: b.aybar@snhu.edu) andArul Thirunavukkarasu is a DBA Candidate (E-mail: arul.thirunavukkarasu@snhu.edu),both at the International Business Department, Southern New Hampshire University, 2500North River Road, Manchester, NH 03102 USA.

Journal of Asia-Pacific Business, Vol. 6(2) 2005Available online at http://www.haworthpress.com/web/JAPB

© 2005 by The Haworth Press, Inc. All rights reserved.doi:10.1300/J098v06n02_02 5

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

KEYWORDS. Emerging markets, multinational corporations, devel-oping nation multinationals, EMNCs, EMNEs, risk and return, perfor-mance characteristics

INTRODUCTION

Frequently portrayed symbols of the remarkable pace of change andprogress in emerging market economies are the stabilization of the mac-roeconomic landscape and development of market institutions.1 It isplausible to argue that a far less noticed but potentially more significantimprovement is the transformation of corporations in these markets.Under the internal and external pressures owed to the massive restruc-turing of their environment, a group of emerging market firms turnedfrom predominantly inward orientation to increasingly outward lookingpostures. These rather drastic strategic shifts are either motivated to takeadvantage of regional or global business opportunities or to respond toincreasing competition from new domestic entrants and/or from foreigncompanies. Consequently, emergence of a group of world class multi-national companies from these countries, who made their marks in in-ternational commerce by competing successfully in global markets,challenges the notion that only advanced economies routinely producesuch companies. These new players–referred to as Emerging MarketMultinational Companies (EMNCs)2–with regional and global focusare becoming a significant mechanism for the transfer of capital, tech-nology, management and other assets within and between developingand developed countries, and creating new engines of growth inemerging markets.

Despite their growing regional and global importance, our knowl-edge of various characteristics of these firms is limited. Deeply en-trenched perceptions that emerging market companies are plagued bytheir unstable environment, lack the resources, capabilities, and sophis-tication to compete against industrialized country giants neglect theEMNCs as infant economic endeavors. Associated sentiment automati-cally and without much question is projected to the performance of thegroup and the popular opinion discounts EMNCs value due to theirregional character, size, high leverage, and systematic risk.

Such oversimplification is unjustified. Actually these companies onthe average outperform many widely used performance benchmarks.Some of these companies, such as Ranbaxy of India, Cemex of Mexico,and South African Breweries of South Africa, have successfully built

6 JOURNAL OF ASIA-PACIFIC BUSINESS

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

enduring and profitable international businesses from their emergingmarket home bases. Dismissing them as poor copies of their counter-parts in advance economies is a short sighted view. We argue that thestrategic paths taken by the EMNCs in response to their changing envi-ronment create rather complex organizational structures with possiblydistinct performance patterns and risk exposures, which may not neces-sarily be similar to their more domestically oriented peers or industrial-ized country MNCs. Therefore, analyzing the risk and performancecharacteristics of EMNCs as a distinct group is an overdue task with anumber of merits. Also, in view of their overall surprising viability,what makes successful EMNCs exceed the typical standards is a validquestion. Accordingly, we explore the role of firm specific characteris-tics such as scale, use of leverage, access to capital and exposure to eco-nomic shocks on the EMNC performance. Additionally, we investigatethe role of industry characteristics and home market conditions on theperformance. Finally, we consider the impact of target market geo-graphies on the EMNC performance.

We contend that the analysis of risk-performance characteristics ofEMNCs and determinants of EMNC performance may provide valu-able insights to individual and institutional investors who are in searchfor more refined and complex investments strategies in emerging mar-kets as well as business analysts.

Our study is organized in the following order. In section two, we ex-pand on the EMNC phenomena and briefly review the extant literatureon EMNCs. In section three, we discuss our data and methodology. Insection four, we present our descriptive and empirical findings. Finally,we conclude with remarks in section five.

EMERGING MARKETSAND EMERGING MARKET MULTINATIONALS (EMNCs)

Despite its widespread use, there is no commonly accepted definitionof an emerging market. However, there are three underlying character-istics that are consistently relevant to the designated countries. The firstone is the absolute level of economic development usually indicated bythe average GDP per capita, the second one is the relative pace of eco-nomic development that is indicated by the GDP growth rate, and thethird one is the extent of free market system (Arnold and Quelch, 1998).Most of the countries designated as “emerging markets” fall into thelower and upper middle income categories.3 Although on average they

Bülent Aybar and Arul Thirunavukkarasu 7

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

experience higher annual growth rates than industrialized economies,we should note that growth rates exhibit a significant cross-sectionaland longitudinal variation. Unsurprisingly, higher rates of growth implymassive changes in sectoral balances in a short span of time, and signifi-cant business opportunities that can be translated into higher profits andrates of return for investors.4

The third characteristic is the most critical but less easily defined cri-terion for the designation. All countries in the list suffer from varyingdegrees of institutional flaws that lead to high transaction costs (highercost of capital, limited labor mobility and increased cost of trading),which undermine the effectiveness of the market mechanisms and ren-der these economies inefficient.5 Additionally, an underdeveloped legalinfrastructure leading to rampant property right violations, lack of ad-herence to laws, and discretionary and unfair enforcement of laws fur-ther increase the transaction costs and undermine sound commercialdevelopment. Across the emerging markets these institutional voidspose significant challenges for the governments. A differentiating char-acteristic of the emerging markets is the implementation of reforms ad-dressing these gaps towards building a functioning market economy.However, it is important to note that there is a great deal of variation inthe extent and effectiveness of these efforts. While some countries are atfairly advanced stages of this process such as Taiwan, Hong Kong, andPortugal, others are either cautiously pursuing reform as China or at thevery initial stages as Vietnam.

EMNCs operate in a multifaceted environment offering a complexmix of opportunities and shortcomings as described above. Because oftheir home country characteristics, EMNCs are exposed to additionalrisks including accelerated inflation, wild exchange rate fluctuations,adverse reparation laws and fiscal measures and macroeconomic andpolitical distress.6 But their home markets also offer higher growthrates, which create opportunities for rapid and profitable value addingexpansions to the extent that they can overcome the hurdles resultingfrom institutional voids. In a meticulous analysis, Bartlett and Ghoshal(2000) identified considerable variations in strategic, organizational,and managerial orientations adopted by EMNCs. They found that suc-cessful EMNCs develop internal markets for labor, capital, and technol-ogy compensating for the environmental shortcomings and use foreignventures to build their capabilities to compete in more profitable seg-ments of their industry. On the lower end of the spectrum they findEMNCs who enter the global markets in the low value added segmentof the market and stay there. Obviously, this group of EMNCs is far

8 JOURNAL OF ASIA-PACIFIC BUSINESS

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

more vulnerable to internal and external shocks and has limited profit-ability and value creation capacity.

Arguments developed in the early literature on EMNCs (Wells,1977, 1981, and 1983) suggest that the competencies of these compa-nies center on the development of small scale, labor intensive, and flexi-ble processes and products, which are appropriate to the emerging andless developed markets, and their ability to substitute locally availableinputs. Accordingly, EMNCs are particularly motivated to invest in otheremerging markets when increasing domestic labor costs underminetheir price competitiveness in home markets. Those that have superiorcapabilities in labor intensive production relative to firms in the peeremerging markets maintain their advantages by expanding into lowerlabor cost environments. These distinct characteristics have also beenobserved in EMNCs originating from South East Asia (Lecraw, 1977;Kumar and Maxwell, 1981; Ting and Chi, 1981). Despite notable dif-ferences identified in types and degree of firm specific assets and skillsof South Asian EMNCs, the main characteristics listed above have beenbroadly pertinent to this group as well (Lall, 1983; Makino et al., 2002).

Grosse (2003) claims that EMNCs’ ability to deal with other emerg-ing market governments offers a unique advantage for these companiesto tap markets perceived as too risky or neglected by developed countryMNCs. The case in point is the successful expansion of Chilean firmsoperating in regulated industries such as electric utilities, airline, andbanking industries to Argentina, Peru, Brazil, and other Latin Americancountries. These explanations are relevant primarily to emerging mar-ket firms investing in peer emerging markets or much less developedcountries than their home countries to leverage their physical andintangible assets.

More recent literature have emphasized that a growing number ofEMNCs invest in foreign countries not only to exploit but also to enhancetheir firm-specific advantages or acquire necessary strategic assets in ahost country.7 The EMNCs are motivated to invest in developed coun-tries (DCs) when they lacked some technology or know-how that is nec-essary to compete in domestic, regional, and developed country markets.Those that have the capability to absorb the relevant technology invest indeveloped markets where these technologies are available. Lecraw(1993) suggested that EM firms seeking strategic assets primarily acquiremanagement technology, and marketing expertise, to enhance their rathertraditional competitive advantages such as access to low-wage labor andlow cost physical inputs. Kumar (1998) presents evidence that EMNCsgain access to established brand names, novel product technologies, and

Bülent Aybar and Arul Thirunavukkarasu 9

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

extensive networks of distributors, typically via aggressive acquisitionsof developed country firms in the host countries.

International expansion patterns revealed in the literature suggestthat EMNCs tend to invest in neighboring countries to leverage theirassets and invest in developed industrialized countries to enhancetheir existing capabilities or acquire new ones. Intra-regional invest-ment is a key part of the expansion strategies for these corporations.South African EMNCs for example have 36% of their investmentswithin the African continent (Broaden, 2003). Similarly, Asian andLatin American EMNCs both have over 43% of investments withintheir respective regions. In particular, firms such as Acer (Taiwan), LGElectronics (S. Korea) and YPF (Argentina) have over 20 foreign sub-sidiaries in their respective regions. Broaden (2003) indicates thatover one-third of the investments of the EMNCs are in the developedeconomies, primarily the Triad represented by North America (pri-marily the USA), Europe, and Japan.

With some qualified exceptions EMNCs originate from inherently un-stable economic and political environments. Their home turf also oftenlack sophisticated financial markets. Additionally limited savings in the lo-cal economy further places constraints on their reach to capital. Limitedavailability of external capital and narrow range of financial instrumentsclustered on the short term end of maturity structure may handicap theEMNCs’ efforts to build and expand their operations. These constraintsimposed on EMNCs can be overcome to some extent by internal capital al-location. This is more likely to be the case for diversified conglomerates.Assuming that EMNCs maintain dominant domestic market positions andmaintain high levels of profitability across the business segments, theyhave the capability to cross-subsidize and position critical components oftheir business network for growth. However, these efforts are likely to behampered by macroeconomic gyrations experienced in home market fol-lowed by demand and supply shocks, as well as deep financial crises. An-other way out for EMNCs to overcome the limitations of the domesticmarkets is access to international capital markets. EMNCs with strong do-mestic and regional business networks can access to different segments ofthe international debt markets, issue equity through depositary receipts orcross-listings, and use subsidiaries to take advantage of subsidiary’s na-tional financial markets.8 We argue that easy access to international capitalmarkets broadens strategic choices available to EMNCs and enhance per-formance. Additionally, since the access to international capital marketssuch as through ADR issues, require commitment to increased levels ofdisclosure, we expect a reduction in potential informational asymmetries

10 JOURNAL OF ASIA-PACIFIC BUSINESS

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

between the EMNC management and its shareholders or among buyersand sellers of the EMNC shares.9 The voluntary disclosure reduces the riskborne by the EMNC investors and creditors. As a result, EM firms commit-ted to disclosure reduce their external cost of capital, and capitalize on thegrowth prospects in their respective market.

We explore our sample data to identify EMNC’s risk and performancecharacteristics and to evaluate the validity of the claims reviewed above.

DATA

The sample used in the study was compiled from multiple sources.Our primary company selection tool is the Top-50 Emerging MarketTransnational list published in the annual World Investment Report byUNCTAD. All the lists of Emerging Market MNCs published since1996 was used to compile the EMNC list. If a company appeared in thelist at least once, it was included in the sample. Additionally, we alsoused Top 25 Transitional Multinationals list published in the World In-vestment Report. The combination of these sources created a sample of110 companies with certified multinationality. Once the company ros-ters were created, data for analysis were retrieved from DataStream andThomson Research databases. Our database screening of the rostercompanies revealed that some companies either did not have relevantdata or consistent time series in the databases. This has reduced our totalsample of companies to 79. Our final roster included 79 companiesfrom 15 countries located in four regions of the world (Africa, Asia,Eastern Europe-Russia, and Latin America). The large number of thecompanies from Asia and Latin America provided us with reasonablediversity to draw meaningful conclusions. As expected, two-thirds ofthe companies come from middle income emerging market countries. Arelatively diverse set of 34 industries ranging from high value addedmanufacturing to natural resources are represented in the sample.10 Riskand performance analyses are based on monthly share price returns col-lected over 1996-2003 periods and annual accounting data. The choiceof period was driven by the desire to optimize the sample size.

METHODOLOGY

We use a range of indicators to analyze performance and risk characteristicsof EMNCs. A list of the variables and proxies used in this study is providedin Table 4 (see Appendix 2). The first set of performance indicators are the

Bülent Aybar and Arul Thirunavukkarasu 11

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

monthly raw returns, which are calculated from monthly return indices.These monthly raw returns are used to calculate annual holding periodreturns for each year. We use annual HPRs as an alternative perfor-mance proxy. In addition to raw monthly returns and annual HPRs, wecalculate excess returns over certain benchmark indices. The excess re-turns are calibrated returns with respect to several benchmarks includ-ing MSCI-Local Market Index, MSCI-Emerging Market Index,MSCI-World Market Index and S&P500. These calibrations allow thebenchmarking of the sample firm performances with respect to homemarket peers, a larger group of emerging market firms, global marketportfolio, and the US market. These calibrations are particularly inter-esting and valuable from global portfolio manager’s perspectives toevaluate the relative performance of the sample firms. In order to iden-tify regional, sectoral, and country related patterns, we tabulate returndata by region, country and industry. We also test the cross-sectionalrisk and performance differences across regional and industry classifi-cations by using ANOVA.

In addition to the returns calculated from share price data, we useseveral performance proxies based on accounting data. These perfor-mance proxies are Return on Assets (ROA), Return on Equity (ROE),and Return on Capital Employed (ROCE). All return indicators arebased on the earnings before interest taxes and depreciation (EBITD)due to its neutrality to depreciation methods, leverage and tax treatment.While all three indicators measure management’s operating efficiency,they represent returns on different sources of capital.

Finally, we use Tobin’s-Q which is an important and widely acceptedmeasure of corporate performance. Tobin’s-Q is defined as the ratio ofmarket value of the firm to the replacement cost of its assets. We usedChung-Pruitt (1994) approximation to calculate the Q ratio:

Tobin s QMVE PS DEBT

TotalAssetstt

t

' - =+ +

where MVE is the product of a firm’s share price and the number of out-standing common shares, PS is the liquidating value of the firm’s out-standing preferred stock, DEBT is the value of the firm’s short termliabilities net of its short term assets, plus the book value of the firm’slong term debt, and TA is the book value of the total assets of the firm.

This indicator is employed to explain a number of diverse corporatephenomena such as cross-sectional differences in investment and diver-

12 JOURNAL OF ASIA-PACIFIC BUSINESS

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

sification decisions, the relationship between managerial equity owner-ship and firm value. We are particularly interested in using Tobin’s-Q togain comparative insights on the effectiveness of the multinationalstructure which may be associated with overinvestment or investmentsin low benefit or value destroying projects as reported in recentliterature (e.g., Click and Harrison, 2000).

We use a range of firm specific risk measures to analyze the risks asso-ciated with EMNCs. First set of risk indicators are based on raw monthlyreturns. We simply calculate monthly return volatilities and comparethem with benchmark volatilities. We report these comparative risk indi-cators as volatility multiples. These multiples provide us with compara-tive perspectives about the relative riskiness of the EMNCs. We alsocalculate and report annualized holding period return volatilities. Alter-natively, we calculate systematic risk indicators, namely company betas,based on alternative market portfolios including local market, emergingmarkets, global market and S&P500. These alternative benchmarks arerelevant under varying assumptions of market segmentation and integra-tion. While under the market segmentation assumption, the relevantbenchmark is the local market portfolio, under the integrated global mar-kets assumption, the relevant benchmark is the global market portfolio.Finally, we used the S&P500, to measure the perceived systematic riskfrom US bound investors’ perspective. We used 60-month running re-gressions to estimate betas.

In order to explore the determinants of performance and risk we useseveral alternative specifications. Our general model suggests that perfor-mance is determined by a combination of firm, industry and country spe-cific factors. At the firm level, we are particularly interested in the impactof degree of multinationality or internationalization on the firm perfor-mance in the context of EMNCs. Some recent empirical studies focusingon US multinationals raise doubt about the value of certain forms of inter-national diversification and establish a linkage between multinationalityand value destruction (Click and Harrison, 2000; Denis, Denis, and Yost,2002). Several measures have been used in the empirical literature to cap-ture the multinational involvement of a firm but foreign to total sales(FSTS) ratio is the most widely used and accepted measure of the extentof internationalization.11 Sullivan (1994) shows that the ratio of foreignsales to total sales is an unambiguous measure of international involve-ment of a firm. In order to capture the degree of international experienceand the involvement of the EMNCs we used this ratio.

For a group of companies in the sample we were able to identify thesubsidiary locations. We classified these into developed and developing

Bülent Aybar and Arul Thirunavukkarasu 13

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

country groups and we compiled an upstream downstream dummy. Ifan EMNC has subsidiaries in developed countries, the dummy variabletakes the value of 1, and 0 otherwise. Recent literature reviewed abovesuggests that EMNC investments in developed countries generallygeared towards acquisition of strategic assets and have the potential toenhance competitive advantage of the EMNCs at home and foreignmarkets. Also, Kwok and Reeb (2000) suggest that MNC diversifica-tion to downstream (emerging economies) markets is associated withhigher risks. Hence, this variable allows us to determine the impact ofupstream diversification or strategic asset seeking expansion on thefirm performance. A priori, we expect a positive association betweengeographic expansion into more stable developed markets and the firmperformance.

The relationship between firm performance and risk is well estab-lished. We use local and global beta, and annualized return volatility asalternative risk indicators to explore their impact on the firm perfor-mance.

We used ADR issues by EMNCs as a proxy for ease of access to in-ternational capital. We interpret EMNCs capability to issue ADRs as asignal of commitment to voluntary disclosure and an informationalasymmetry reducing factor which opens up opportunities to raise equityand debt capital. Reductions in informational asymmetry also reducethe perceived risk borne by the investors and decrease the cost of capitalfor the firm. A lower required rate of return demanded by the investorsleads to higher valuations. A dummy variable was used to capture theimpact of this factor on the performance. We used two different ADRdummies. The first ADR dummy is coded 1 if EMNC has an outstand-ing ADR issue regardless of the level of ADR in the current year oryears prior, 0 otherwise. The second ADR dummy takes the value 1only if the company has an outstanding Level-III ADR, 0 otherwise.12

The empirical evidence on the impact of product/segment diversifi-cation on firm performance and value in developed markets suggeststhat diversified firm performance is inferior to single industry focusedfirm performance. However, the impact of diversification on firm per-formance is not well established in emerging markets, and it is arguedthat internal capital and labor markets within diversified conglomeratesmay compensate for the endemic weak institutional infrastructure inemerging markets (Khanna and Palepu, 1997). In order to capture theimpact of company’s utilization of internal labor and capital markets weuse a structure dummy, which differentiate diversified versus singleindustry firms.13

14 JOURNAL OF ASIA-PACIFIC BUSINESS

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

In order to capture the impact of the industry the company operateswe used industry dummies. It is conceivable to think that companies op-erating in traditional industries would exhibit different risk and perfor-mance patterns than the companies operating in high tech industries.

It is almost axiomatic that economic and political stability is a signifi-cant determinant of firm performance in emerging markets. In order tocapture this country level effect, we use Euromoney Country Risk Indi-cator (ECRI) as a proxy for economic and political stability.14 Addition-ally we use a region dummy in order to explore possible linkagesbetween performance and geographic location. Although our initialanalysis did not reveal any regional patterns, we have the opportunity toverify ANOVA results in our multivariate analysis.

Finally, we use two control variables: Size and Leverage. It is estab-lished in theory that MNCs, by internalizing market imperfections, areable to extract above market returns on their specific assets which, in ef-ficient financial markets, are capitalized into a higher value of the firm.The specific source of these gains to firm value from growing geograph-ically comes from expanding firm-specific assets and potential econo-mies of scale for the use of these assets. Economies of scale of specificassets such as marketing and research and development suggest thattheir value to the firm increase with the size of the firm’s activities thatuse these specific assets. We control for leverage as a proxy for anyfinancing benefits of being a multinational.

BASIC MODEL

PERFORMANCE = + (LASALES) + (LEVERAGE)

+ (F1 2

3

α ß ß

ß STS) + (FRISK) + (CRISK) + (UPSTREAM)

+ (REG4 5 6

7

ß ß ß

ß ION) + (INDUSTRY)8ß

The impact of company, industry, and country level variables on theperformance was estimated by using pooled time series regressionmethod.15 Consequently, ROA, Tobin’s-Q and HPR were used as per-formance indicators and as independent variables in the regressionequation. Dummy variables were included to separate the industry andregional effects. It is possible that the size and leverage of the firm canimpact the performance of the firm and it is therefore necessary to con-trol for these effects. Total Assets and Total Sales were added to control

Bülent Aybar and Arul Thirunavukkarasu 15

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

for the firm size and the debt to total assets ratio was employed to con-trol for the leverage effect. The degree of internationalization of the firmcould also impact the performance, consequently the ratio of foreign as-sets to total assets and foreign sales to total sales were added to themodel to control for this effect.

EMPIRICAL RESULTSSAMPLE CHARACTERISTICS

Despite the fact that our sample firms represent larger EMNCs, thereis a considerable variation in sales, asset values, and market capitaliza-tions. Table 1 (see Appendix 2) shows the overall sample characteristicsof the data used. The country and industry breakdown sample character-istics are provided in Tables 2 and 3 (see Appendix 2). The total salesamong the sample firms range between $26 billion and $50 million,with a mean of $3 billion. Similarly, the asset size of the sample firmsfall between $34 billion and $93 million with a mean value of $5 billion.Although the mean market capitalization is a moderate $3 billion, thesample included firms as large as $36bn and as small as $34 million.While the sample firms have an average leverage of 29 percent, meanROA, ROE, and ROCE are 13.21 percent, 8.97 percent and 11.36 per-cent, respectively. Sample mean of Tobin’s-Q is 0.92. The foreign salesto total sales ratio ranged between 97 percent and 1 percent with a sam-ple mean of 38 percent. Similarly, mean foreign assets to total assets ra-tio is 31 percent with a maximum of 97 percent and a minimum of 3percent. The largest firms in the sample measured by sales and asset sizecome from diversified industries. In contrast, smallest firms by the samecategory come from basic industries. Firms in diversified industriesalso have the highest market value. While highest ROA, ROCE, andROE are observed in non-cyclical consumer goods, the lowest ROA isobserved in diversified industries.

PERFORMANCE AND RISK

The average monthly returns of 79 emerging market multinationalsfrom 15 countries and 9 distinct industries were analyzed in this study.The average monthly returns ranged from 3.55 percent to 5.84 per-cent, indicating a large cross-sectional variation. While the annualizedequivalent of highest average monthly return turned out to be 70.14 per-cent, annualized equivalent of the lowest average monthly return is

16 JOURNAL OF ASIA-PACIFIC BUSINESS

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

42.62 percent. Highest monthly volatility in the sample is 31.90 per-cent or 110.52 percent in annualized terms. The lowest monthly volatil-ity proved to be only 5.44 percent or 18.83 percent in annualized terms.Sample average of monthly returns for emerging market multinationalsis 0.85 percent (annualized 10.24 percent). Average monthly Volatilityof the sample is 15.85 percent (or 54.9 percent).

A brief analysis of regional averages suggests that multinationalsfrom Eastern Europe yield highest monthly returns (see Table 5 in Ap-pendix 2). This was followed by Asia and Latin America. African MNCsrepresented by only South Africa on average yield lowest returns. TheEast European MNCs also proved to be most volatile. This is consistentwith the high returns provided by the same group of MNCs and is a con-firmation of the high reward-high risk trade-off. Second largest volatil-ity was observed in the Asian cluster.

A parallel analysis conducted across the countries indicates that averagereturns are high for Korean and Colombian MNCs. Highest volatilities areobserved in the Korean cluster.

An analysis of monthly return averages by industry reveals that Cy-clical Consumer goods, Resources, Information Technology industriesgenerated the highest average monthly returns. In contrast, lowestmonthly returns were generated by Utilities, Non-Cyclical Service, andCyclical Service (see Table 6 in Appendix 2). Volatility varied signifi-cantly across industries. Industry segments such as Cyclical Consumergoods, Information Technology and Diversified Industries displayedhigher volatility as compared to other industries (see Table 6).

Although average monthly returns are instructive indicators of per-formance, they offer limited analytical insights regarding the relativeperformance of the companies in the sample. In order to gain further in-sight about the performance of the sample firms, several benchmarkswere used. First benchmark employed to explore the relative perfor-mance of sample firms is their respective home market index, whichrepresents the collective performance of the home market firms. Mor-gan Stanley Capital International (MSCI) local index was used for thispurpose. The second benchmarks, S&P500, were used to measure therelative performance of the sample firms with respect to the US marketwhich is a primary concern for individual and institutional investors. Fi-nally, we used MSCI-World index to gauge the performance of the sam-ple firms against global market performance. The calibration of returnsagainst this benchmark provides valuable insights for global investors.Overall, emerging market multinationals in the sample earned average0.57 percent monthly excess return over their home market returns. This

Bülent Aybar and Arul Thirunavukkarasu 17

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

translates into approximately 6.84 percent annualized premium. Theexcess returns over the S&P500 and MSCI World Index were moremoderate at 0.176 percent (or 2.11 percent annualized premium) and0.567 percent (annualized 6.80 percent premium) respectively. Theseresults suggest that on average EMNCs performed considerably betterthan their home market, US and global benchmarks.

A closer look at the regional patterns indicate that while on averageAsian EMNCs earn the highest monthly premiums over their local mar-ket benchmark (0.95 percent), Eastern European firms earn the highestpremiums over the US and global market benchmarks (see Table 4).The average monthly excess returns earned by Korean EMNCs exceedall other countries in all three benchmarks. In 73 percent of the cases,average monthly country returns exceeded local market benchmark.The same ratios are 53 percent and 87 percent for the US and globalbenchmarks respectively (see Table 5).

Analysis of average excess returns by industry suggests that highestpremiums over the local market index were earned in Cyclical Con-sumer goods, Resources and Information Technology. While in 89 per-cent of the industries, sample firms generated higher returns than thelocal benchmark, the corresponding figure for the US and globalbenchmarks was more 44 and 89 percent (see Table 6). Overall, thesepatterns suggest that emerging market multinationals consistentlyperform better than the selected benchmarks.

As in the case of monthly returns, to gain comparative insight into thevolatility of the sample firms a range of comparative metrics were cal-culated. In order to maintain consistency with the performance mea-sures, three indicators were calculated. These are relative volatility ofthe sample firms to three benchmarks used in the study, namely volatil-ity of the local market benchmark, US market benchmark and globalbenchmark. In each case we calculated the ratio of sample firm volatil-ity to the volatility of the selected benchmark. Since these ratios arecommonly called “volatility multiples” in finance literature, the sameterm was used in this study to refer to these ratios.

An overall analysis of these ratios suggests that emerging marketmultinationals are significantly riskier than the market benchmarks.The average sample firm volatility is 1.47 times the local market bench-mark. The corresponding figures for the US market and the global mar-ket benchmarks are 3.19 and 3 times, respectively. In order to identifypossible patterns in the sample, we analyzed the volatility multiples byregion, country, and industry. Our regional analysis indicates that Afri-

18 JOURNAL OF ASIA-PACIFIC BUSINESS

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

can firms have the highest multiple with respect to the local market fol-lowed by Asian firms. On the other hand, East European firms have thehighest volatility multiple with respect to the US market. Finally, Asianfirms have the highest volatility multiple with respect to global marketbenchmark (see Table 7 in Appendix 2).

Review of multiples by the country reveals that South Korea has thehighest average multiple with respect to the local, US and global marketbenchmarks (see Table 5). A glance at the industry volatility multiplessuggest that Cyclical Consumer goods has the highest volatility multi-ple followed by Information Technology sector. On the other hand,household appliances have the highest multiples with respect to US andglobal benchmarks (see Table 6).

Firm beta is a widely used risk metric in finance literature. Firm beta es-sentially measures sensitivity of firm returns to market shocks, and there-fore is used as a measure of vulnerability of the firm to market movements.In Asset pricing models such as CAPM, beta is defined as a measure ofundiversifiable systematic risk that deserves compensation. In the contextof this study, beta was used both as a broad indicator of firm returns’ sensi-tivity to selected market benchmarks and as a measure systematic risk.

The following section addresses the analysis of the firm betas in gen-eral sample, by region, country, and industry.

Overall results indicate that sample firms have an average local mar-ket beta of 1.035 when the local market index was used as the marketportfolio proxy. In other words, for every 1 percent movement in themarket index, on average sample firm returns respond with a change of1.035 percent. Since the cross-sectional variation in calculated betas issignificant we should be cautious in carrying this generalization further.While the maximum local market beta observed in the sample is 1.74, inthe other extreme smallest beta is 0.31. An interesting result is thehigher US market and global market betas observed in the sample on av-erage. For 33 percent of the firms, sensitivity to US market shocks ishigher than the sensitivity to local market shocks. Similarly, 67 percentof the firms are more sensitive to global market shocks than the localmarket shocks. This result suggests the level of integration achieved bythe emerging market multinational firms. On the other hand, it also sug-gests that global diversification of these firms both in terms of their cashflows and investor base did not help to reduce their perceived risk. Onthe contrary, in most cases we have higher risks associated with thesefirms as a result of internationalization of their operations.

A further look at the betas by region, country, and industry reveal thefollowing results. While Asia has the highest average local, US and

Bülent Aybar and Arul Thirunavukkarasu 19

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

global market betas, Latin America has the largest emerging market beta.Interestingly, Latin American average US and global betas are lower thanthe corresponding Asian betas. Companies located in Colombia, India,and Hungary have on average significantly lower sensitivity to US mar-ket shocks. This can probably be attributed to capital controls in place inChile and Malaysia, and segmented nature of Indian market. A review ofindustry patterns indicates that US and global market sensitivities are re-markably higher in Cyclical Consumer goods and diversified industries.In contrast, some locally bounded service industries such as utilities havesignificantly lower exposure to US and global market shocks.

CROSS-SECTIONAL ANALYSIS OF DETERMINANTSOF EMNC PERFORMANCE

Our multivariate pooled time series regressions suggest that firmsize, degree of leverage, systematic risk, industry focus, and presence inindustrialized country markets significantly affect the EMNC perfor-mance (see Table 8 in Appendix 2). Our findings indicate that size is animportant determinant of performance as measured by return on assetsand it affects the performance positively. This is consistent with the em-pirical evidence associated with developed market MNCs, as largerMNC networks can exploit market imperfections more effectively byleveraging their specific assets and capabilities and gaining economiesof scale. The results lead us to conclude that despite their distinct char-acteristics, size also matters for EMNCs and provide them with theopportunity to leverage their assets and take advantage of economies ofscale.

Our cross-sectional analysis indicates that leverage affects the EMNCperformance adversely. In other words, higher leverage is associated withlower ROA. While the extent of leverage does signify EMNCs’ capabil-ity to tap external fund sources effectively, it also means increased expo-sure to domestic and international market shocks. This finding isconsistent with the fact that EMNCs originate from moderate to high riskeconomic environments, and their home markets are subject to frequentfinancial and economic shocks. It is also important to note that this aspectof leverage may be particularly pronounced because our sample period1996-2002 includes a number of crises experienced in emerging markets,such as Asian crisis of 1997 and Argentinean crisis of 2001-2002. Even ifan emerging market country may not be hit directly, contagion may causesudden disruption in access to capital through financial sector troubles

20 JOURNAL OF ASIA-PACIFIC BUSINESS

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

and interest rate hikes, which create liquidity problems and contraction inreal sectors. Highly leveraged firms caught off guard are most likely toexperience declining revenues, increasing debt service costs and signifi-cantly higher rollover rates which eventually undermine the bottom-line.

Another important determinant of the performance is the systematicrisk of the firm. Regression results indicate that higher systematic risksare associated with poor performance. Our sample average of local betasis 1.04. Country and Industry averages are also not very far from 1 (seeTables 5 and 6).16 Our data analysis reveals that EMNCs are vulnerable tolocal market shocks, and higher sensitivities to the market shocks lead tolower performance measured in term of return on assets (ROA). Simi-larly, higher systematic risks are associated with lower firm valuations,which is consistent with higher expected risk premiums.

Our regression results suggest that although degree of internationaliza-tion as measured by foreign sales to total sales ratio has a negative associ-ation with performance, it is not statistically significant. The sign of thedegree of internationalization or multinationality is consistent with the re-cent empirical evidence which suggests a multinationality discount on thefirm value. However, we cannot verify the significance of this factor. Useof alternative proxies such as foreign assets to total assets ratio andUNCTAD’s Transnationality Index did not change the results qualita-tively. In all cases coefficient sign was negative, but insignificant at 1, 5and 10% significance levels.

Our alternative models produce conflicting results regarding theEMNC performance and home country risk relationship. The countryrisk indicator we used in our analysis is a composite of nine factors thatreflect home country conditions and international perception of homecountry conditions. Since lower country risk score implies higher politi-cal instability, poor economic performance, deteriorating domestic andinternational debt indicators, poor credit ratings, limited access to moneyand capital markets and high discount rates, it is expected to be associatedwith poor firm performance. However, a unit increase in the country riskindicator implies 5.5 percent decline in the ROA. Although the impactseems to be pronounced in the first model where we use ROA as perfor-mance indicator, the coefficient of the country risk indicator is statisti-cally insignificantly different from zero. Interestingly, the model-2 wherewe use Tobin’s-Q as the proxy for firm value produces the correct sign inthe coefficient, as it is positive and statistically significant. Although intu-itively country risk should be a significant factor in explaining firm per-formance, and the direction of this association is expected to be positive,

Bülent Aybar and Arul Thirunavukkarasu 21

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

our regression results provide mixed evidence depending on our choiceof performance indicator.

Our investigation of industry effects on EMNC performance re-vealed that diversified industry EMNCs earn lower return on assets.Our model suggests that diversified firms’ ROA is on average 2.12 per-cent lower than single industry firms.17 On the other hand, diversifiedfirms proved to have higher Tobin’s-Q as compared to single industryfirms. This finding is consistent with the recent empirical evidence andthe arguments that internal capital and labor markets within diversifiedfirms compensate weak institutional infrastructure in emerging marketsas suggested by Khanna and Palepu (1999).

Finally, our regression results indicate that while the presence in thedeveloped country markets have a negative impact on the ROA earnedby EMNCs, it has an unambiguous positive impact on the firm value asmeasured by Tobin’s-Q. This discrepancy can be explained by the highcosts associated with expansion into the developed country marketswhich are incurred in the short run. While such costs are reflected onROA through the reported earnings, rewards accrue in the longer runand may be reflected on the market value of the firm.

CONCLUDING REMARKS

EMNCs on average perform better than their respective country mar-ket indices, a widely used benchmark to measure emerging market re-turns, S&P500 and, global market index (MSCI-World) during theperiod of analysis. We observe considerable cross-sectional variation infirm performances. A closer look at the firm performances with respectto selected benchmarks suggest that 65 percent of the sample firms per-form better than their local markets. While a slightly smaller group (62percent) outperforms the global market index, only 48 percent of theEMNCs in our sample earn higher returns than S&P500. South Koreanfirms in particular and Asian firms in general perform better than theircounterparts in other countries and regions with respect to their localmarket indices. In contrast, while Eastern European EMNCs fail to out-perform their local market index, they outperform S&P500 and GlobalMarket indices by a larger margin than their counterparts in other coun-tries. Although EMNCs operating in cyclical goods industries generatehigher excess returns adjusted for all benchmarks, we cannot verify thatthe differences are statistically significant.

22 JOURNAL OF ASIA-PACIFIC BUSINESS

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

In addition to the performance measures based on monthly returns, wealso looked at several performance indicators based on accounting data.Our sample firms on average earn 13.21 percent return on assets, 8.97percent return on equity and 11.36 percent return on invested capital. TheArgentinean and Brazilian EMNCs earn highest return on assets. Ananalysis of the industry cross-sections reveals that EMNCs operating innon-cyclical services and resource industries earn highest return on as-sets. Due to significant variation in our sample, cross-sectional differ-ences in EMNC performances are not statistically significant.

Monthly return volatilities of EMNCs reveal the extent of risks associ-ated with these companies. On average our sample company volatilitiesare 1.47 times their local market index volatility. The extent of risks asso-ciated with EMNCs become clearer, when we compare their averagevolatilities with the volatilities of S&P500 and Global Market index,which are 3.19 times and 3.02 times, respectively. On average EMNCsare also highly sensitive to local and global market shocks as revealed bylocal market and global market betas. EMNCs’ sensitivity to global mar-ket shocks is an indication of their growing participation in global goodsand capital markets.

The cross-sectional analysis of the determinants of the performanceof the EMNCs reveals that leverage and systematic risk are the most im-portant factors, followed by size. While larger EMNCs earn higher re-turns, increasing use of debt seems to have a negative impact onperformance. Our analysis indicates that performance is not affected bythe degree of internationalization. We find that investments in devel-oped markets have a positive impact on the value of EMNCs. Finally,our results indicate that EMNCs in less risky emerging markets enjoyhigher firm value.

In this study we attempted to explore some preliminary patterns inEMNC performance and risk. Due to the limited sample size, and par-ticularly small number of observations in country and industry crosssections, our results should be interpreted cautiously.

NOTES

1. The initial use of the phrase “Emerging Market” is attributed to International Fi-nance Corporation which began using the phrase to describe nine newly developingstock markets. This small list later was expanded to 25 countries based on informal cri-terion of 30 to 50 listed companies with a combined market capitalization of $1bn ormore and annual trading volume of $100m (Beim and Calomiris, 2003). The phrase

Bülent Aybar and Arul Thirunavukkarasu 23

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

caught on in the 1990s and is now widely used to describe a large group of developingcountries. DataStream International list of Emerging Markets consists of 9 LatinAmerican, 16 European and Middle Eastern, 13 Asian, and 8 African countries.

2. UNCTAD uses the term Emerging Market Transnational and the term we use istechnically identical to UNCTAD definition. UNCTAD tracks and publishes informa-tion on top 50 Emerging Market Transnational and Top 25 Transition Economy Trans-national since 1996 and 2000, respectively.

3. The lower-upper middle income range is between $765 and $9,385 and there are95 countries in this group according to World Bank classification. The exceptions suchas Hong Kong, Singapore, South Korea, and Taiwan which technically fall into thehigh income economies and countries such as Vietnam, India, China, and Zimbabwewhich fall into the low income category.

4. Although the pace of growth is an important criterion for emerging market desig-nation, many countries included in the list fail to fulfill this criteria at least on a regularbasis and exhibit high but erratic growth rates.

5. In a survey conducted on ASEAN countries, investors expressed frustration overthe way certain policies were implemented. For instance, an executive at a consumergoods company, making a common complaint explained that ASEAN’s tariffs ratewere determined more by the whim of customs officials than by government policy,(Schwartz and Villinger, 2004).

6. Cases in point are high profile setbacks evidenced in Mexico, South East Asia,Russia, Brazil, Turkey, and Argentina and structural difficulties in managing opera-tions in China.

7. See for example FDI literature addressing primarily developed country MNCs’expansion motivations (Almeida, 1996; Chang, 1995; Dunning, 1993, 1995; Frost,2001; Shan and Song, 1997; Teece, 1992).

8. However, these options are also to a large extent constrained by the EMNCshome market conditions and risks associated with the home market. For instance, mac-roeconomic and/or financial shocks felt at home may immediately limit EMNCs accessto international capital markets, depress its valuation through declining share prices inforeign listed markets, and trigger covenants embedded in debt instruments and loanarrangements.

9. Voluntary disclosure also increases liquidity of firm’s stock by attracting largergroup investors, who are more confident that the stock transactions occur at fair prices.Voluntary disclosure can also lower the cost of information acquisition for analysts,and hence, increase the supply of information about the company, reinforcing furtherreductions in informational asymmetry.

10. Our original industry classifications were based on level-6 classifications byDataStream International. In our analysis of industry patterns we reduced the industryclassifications to level-3 (roughly corresponds to two-digit SIC codes) to allow suffi-cient sample size in each subcategory.

11. Alternatively, we also used Foreign Assets/Total Assets ratio and TransnationalityIndex to test the robustness of the measure. All three measures lacked explanatory powerin our pooled times series regressions.

12. Level III ADRs are exchange listed and allows capital raising public issues inUS market.

13. We are aware of the fact that this proxy has flaws and it may not appropriatelycapture company’s ability to compensate the institutional void in its environment.

24 JOURNAL OF ASIA-PACIFIC BUSINESS

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

14. ECRI is a relatively sophisticated indicator composed of nine sub-factors: Polit-ical Risk (weight 25%), Economic Performance (weight 25%), Debt Indicators(weight 10%), Debt in Default or Rescheduled (weight 10%), Credit Ratings (weight10%), Access to Bank Finance (weight 5%), Access to Short-Term Finance (weight5%), Access to Capital Markets (weight 5%), and Discount on Forfeiting (weight 5%).

15. See the Appendix for a detailed discussion of the estimation technique.16. The sample average local betas around 1 is plausible since in most cases

EMNCs make up a large portion of the local market capitalization and trading volumeand invariably they are a significant component of the market index.

17. We also checked the impact of belonging to a specific industry such as high techmanufacturing, utilities and resource industries on the performance; however, perfor-mance differences proved to be statistically insignificant. These results were not re-ported in our regression tables.

REFERENCES

Arnold, D. J. & Quelch, J. A. (1998), New Strategies in Emerging Markets, Sloan Man-agement Review, (Fall), 40(1): 7-20.

Beim, D. & Calomiris, C. (2003), Emerging Financial Markets, McGrawHill-Irwin,Boston.

Bartlett, C. A. & Ghoshal, S. (2000), Going Global: Lessons from Late Movers, Har-vard Business Review, (March-April), 78(2): 132-142.

Broaden, C. (2003), Reverse Investment: FDI Flows from Large Developing CountryFirms to Developing and Developed Economies, Unpublished Doctoral Disserta-tion, Southern New Hampshire University, NH.

Chung, K. H. & Pruitt, S. (1994), A simple approximation of Tobin’s Q, FinancialManagement, (Autumn) 23(3): 70-74.

Click, R. & Harrison, J. (2000), Does multinationality matter? Evidence of value de-struction in U.S. multinational Corporations, Unpublished Manuscript, GeorgeWashington University.

Denis, D. J., Denis, D. K. & Yost, K. (2002), Global diversification, industrial diversi-fication and firm value, Journal of Finance, (October), 57(5): 1951-1979.

Grosse, R. (2003), The Challenges of Globalization for Emerging Market Firms, Acad-emy of International Business Annual Meeting, Monterey, California. (July 5-8).

Khanna, T. & Palepu, K. (1997), Why Focused Strategies May Be Wrong for Emerg-ing Markets, Harvard Business Review, (July-August), 75(4): 41-49.

Khanna, T. & Palepu, K. (1999), The Right Way to Restructure Conglomerates inEmerging Markets, Harvard Business Review, (July-August), 77(4): 125-135.

Kumar, K. & Maxwell, G. M. (1981), Multinationals from Developing Countries,Massachusetts: Lexington.

Kumar, N. (1998), Globalization, Foreign Direct Investment and Technology Trans-fers: Impacts on and Prospects for Developing Countries, New York: Routledge.

Kwok, C. & Reeb, D. M. (2000), Internationalization and Firm Risk: An Upstream-Downstream Hypothesis, Journal of International Business Studies, (Fourth Quarter)31(4): 611-629.

Bülent Aybar and Arul Thirunavukkarasu 25

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

Lall, S. (1983), The New Multinationals: The Spread of Third World Enterprises, NewYork: Wiley.

Lecraw, D. J. (1977), Direct Investment by Firms from Less Developed Countries, Ox-ford Economic Papers, 29: 442-457.

______ (1993), Outward Direct Investment by Indonesian Firms: Motivation and Ef-fects, Journal of International Business Studies, (Third Quarter), 24(3): 589-600.

Makino, S., Chung, L., & Rhy, Y. (2002), Asset-Exploitation versus Asset Seeking:Implication for location choice of foreign direct investment from newly industrial-ized economics, Journal of International Business Studies, 33(3): 403-421.

Schwartz, A. & Villinger, R. (2004), Integrating Southeast Asia’s economies, TheMckinsey Quarterly, Number 1.

Sullivan, D. (1994), Measuring Degree of Internationalization of the Firm, Journal ofInternational Business Studies, (Second Quarter), 25(2): 325-42.

Ting, W. & Chi, S. (1981), “Direct Investment and Technology Transfer,” In KrishnaKumar and Maxwell G. McLeod (Eds), Multinationals from Developing Countries,(pp. 101-114), Massachusetts: Lexington.

Wells, L. T. (1977), “The Internationalization of Firms from Developing Countries,”In Tamir Agmon and Charles P. Kindleberger (Eds), Multinationals from SmallCountries (pp. 133-156), Cambridge: MIT Press.

______ (1981), “Foreign Investors from the Third World,” In Krishna Kumar andMaxwell G. McLeod (Eds), Multinationals from Developing Countries, (pp. 23-36),Massachusetts: Lexington.

______ (1983), Third World Multinationals: The Rise of Foreign Investment from De-veloping Countries, Cambridge: MIT Press.

26 JOURNAL OF ASIA-PACIFIC BUSINESS

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

Bülent Aybar and Arul Thirunavukkarasu 27

APPENDIX 1

ESTIMATION TECHNIQUE:POOLED TIME SERIES REGRESSION

The coefficients in each specification were estimated by using Pooled TimeSeries Regressions. Pooled time series regression allows us to estimate equationsof the form:

y xit it it iti= + +α β ε'

'

Where yit is the dependent variable, and xit and β i are k-vectors of non-constantregressors and parameters for i N= 1 2, , . . . cross-sectional units. Each cross-sectionunit is observed for dated periods t T= 1 2, , . . . .

The data can be viewed as a set of cross-section specific regressions so that there areN cross-sectional equations:

y xi i i i i= + +α β ε'

each with observations, stacked on top of one another. The stacked representation arepresented as follows:

Y X= + +α β ε

Where, a, b and X are set up to include any restrictions on the parameters betweencross-sectional units. The residual covariance matrix for this set of equations isgiven by:

Ω = =E E

N

( ')

...

...

. ... ...

' ' '

' '

εε

ε ε ε ε ε εε ε ε2ε

ε

2 11 1 1

21 1 2

Nε ε ε1' '... ... Ν Ν

The pool specification is treated as a system of equations and the model is esti-mated by using system OLS. This specification is appropriate when the residuals arecontemporaneously uncorrelated, and time-period and cross-section homoskedastic:

Ω = ⊗σ 2I IN T

The coefficients and their covariances are estimated using the usual OLS techniquesapplied to the stacked model.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

28 JOURNAL OF ASIA-PACIFIC BUSINESS

APPENDIX 1 (continued)

CROSS-SECTION WEIGHTING

We use cross-section weighted regression to account for cross-sectional heteroskedasticand contemporaneously uncorrelated residuals:

Ω = =

E E

I

I

I

T

T

N TN

( ')

...

. ... .

. . ... .

. . ...

εε

σσ

σ

12

22

2

1

2

0 0

The FGLS (Feasible Generalized Least Square) withσ i2 estimated from a first-stage

pooled OLS regression. The estimated variances are computed as:

σ i it it it

T

y y Ti

2 2

1

==∑( – ) /

Where yit the OLS are fitted values. The estimated coefficient values and covariancematrix are given by the standard GLS estimator.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

AP

PE

ND

IX2

TA

BLE

1.O

vera

llS

ampl

eP

rofil

e(T

hous

ands

US

Dan

dP

erce

nt)

Tab

le1

repo

rts

the

mea

n,m

axim

um,a

ndm

inim

umva

lues

ofth

eva

rious

finan

cial

indi

cato

rsof

emer

ging

mar

ketm

ultin

atio

nalf

irms.

Tot

alS

ales

isca

lcul

ated

asth

esu

mof

gros

ssa

les

and

othe

rope

ratin

gre

venu

esle

ssdi

scou

nts,

retu

rns

and

allo

wan

ces

inth

ousa

nds

ofdo

llars

.Tot

alA

sset

sis

the

sum

ofta

ngib

lefix

edas

sets

,int

angi

ble

asse

ts,i

nves

tmen

ts,o

ther

asse

ts,t

otal

stoc

ksan

dw

ork

inpr

ogre

ss,t

otal

debt

ors

and

equi

vale

nt,a

ndca

shan

dca

sheq

uiva

lent

sof

the

firm

inth

ousa

nds

ofdo

llars

.Tot

alD

ebti

sth

eto

talo

fall

inte

rest

bear

ing

and

capi

talis

edle

ase

oblig

atio

nsal

sore

port

edin

thou

sand

sof

dolla

rs.M

arke

tVal

ueis

com

pute

das

the

num

bero

fsha

res

mul

tiplie

dby

the

aver

age

stoc

kpr

ice

ofth

efir

min

thou

sand

sof

dolla

rs.R

etur

non

Ass

ets

(RO

A)i

sca

lcul

ated

asa

ratio

ofth

eE

BIT

DA

and

tota

lass

ets

and

isre

port

edas

ape

rcen

tage

.Ret

urn

onE

quity

(RO

E)i

sth

era

tion

of“e

arne

dfo

rord

inar

y”an

d“e

quity

capi

tala

ndre

serv

es”

and

isal

sost

ated

asa

perc

enta

ge.R

etur

non

Cap

italE

mpl

oyed

(RO

CE

)is

calc

ulat

edas

the

EB

ITdi

vide

dby

the

sum

ofto

talc

apita

lem

ploy

edan

dsh

ort-

term

borr

owin

g.T

obin

’s-Q

isco

m-

pute

das

the

mar

ketv

alue

ofou

tsta

ndin

gsh

ares

plus

the

liqui

datio

nva

lue

ofpr

efer

red

stoc

kpl

usth

esh

ortt

erm

liabi

lity

neto

fits

shor

tter

mas

sets

plus

the

long

term

debt

divi

ded

byto

tala

s-se

tsof

the

firm

.For

eign

Sal

esto

Tot

alS

ales

(FS

TS

)is

the

perc

enta

geof

fore

ign

sale

sof

the

firm

divi

ded

byto

tals

ales

.For

eign

Ass

ets

toT

otal

Ass

ets

(FA

TA

)is

the

perc

enta

geof

fore

ign

asse

tsof

the

firm

divi

ded

byits

tota

lass

et.

NM

ean

Std

.Dev

iati

on

Ske

wn

ess

Ku

rto

sis

Sta

tistic

Std

.Err

orS

tatis

ticS

td.E

rror

TO

TA

LS

ALE

S77

$3,0

55,4

80.3

7$4

,953

,917

.73

3.63

0.27

413

.05

0.54

TO

TA

LA

SS

ET

S79

$5,1

63,1

97.1

2$6

,824

,874

.53

2.75

0.27

18.

270.

54

TO

TA

LD

EB

T79

$1,5

07,3

56.8

1$2

,105

,642

.70

2.86

0.27

18.

950.

54

MA

RK

ET

VA

LUE

79$3

,554

,520

.00

$5,8

66,3

20.0

03.

500.

271

15.2

00.

54

RO

A79

13.2

16.

060.

550.

271

0.06

0.54

RO

E79

8.97

13.1

5–1

.20

0.27

14.

070.

54

RO

CE

7911

.36

7.50

1.33

0.27

12.

710.

54

TO

BIN

’S-Q

770.

920.

193.

310.

274

13.7

90.

54

FS

TS

580.

380.

250.

650.

314

–0.1

10.

62

FA

TA

580.

310.

231.

300.

314

1.34

0.62

29

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

TA

BLE

2.S

ampl

eP

rofil

eby

Cou

ntry

Tab

le2

repo

rts

the

mea

n,m

axim

um,a

ndm

inim

umva

lues

ofth

eva

rious

finan

cial

indi

cato

rsof

emer

ging

mar

ketm

ultin

atio

nalf

irms

sort

edby

each

coun

try.

Tot

alS

ales

isca

lcul

ated

asth

esu

mof

gros

ssa

les

and

othe

rop

erat

ing

reve

nues

less

disc

ount

s,re

turn

san

dal

low

ance

sin

thou

sand

sof

dolla

rs.T

otal

Ass

ets

isth

esu

mof

tang

ible

fixed

asse

ts,i

ntan

gibl

eas

sets

,inv

est-

men

ts,o

ther

asse

ts,t

otal

stoc

ksan

dw

ork

inpr

ogre

ss,t

otal

debt

ors

and

equi

vale

nt,a

ndca

shan

dca

sheq

uiva

lent

sof

the

firm

inth

ousa

nds

ofdo

llars

.Tot

alD

ebti

sth

eto

talo

fall

inte

rest

bear

ing

and

capi

talis

edle

ase

oblig

atio

nsal

sore

port

edin

thou

sand

sof

dolla

rs.M

arke

tVal

ueis

com

pute

das

the

num

bero

fsha

res

mul

tiplie

dby

the

aver

age

stoc

kpr

ice

ofth

efir

min

mill

ions

ofdo

llars

.Ret

urn

onA

sset

s(R

OA

)is

calc

ulat

edas

ara

tioof

the

EB

ITD

Aan

dto

tala

sset

san

dis

repo

rted

asa

perc

enta

ge.R

etur

non

Equ

ity(R

OE

)is

the

ratio

of“e

arne

dfo

rord

inar

y”an

d“e

q-ui

tyca

pita

land

rese

rves

”and

isal

sost

ated

asa

perc

enta

ge.R

etur

non

Cap

italE

mpl

oyed

(RO

CE

)is

calc

ulat

edas

the

EB

ITdi

vide

dby

the

sum

ofto

talc

apita

lem

ploy

edan

dsh

ort-

term

bor-

row

ing.

Tob

in’s

-Qis

com

pute

das

the

mar

ketv

alue

ofou

tsta

ndin

gsh

ares

plus

the

liqui

datio

nva

lue

ofpr

efer

red

stoc

kpl

usth

esh

ortt

erm

liabi

lity

neto

fits

shor

tter

mas

sets

plus

the

long

term

debt

divi

ded

byto

tala

sset

sof

the

firm

.For

eign

Sal

esto

Tot

alS

ales

(FS

TS

)is

the

perc

enta

geof

fore

ign

sale

sof

the

firm

divi

ded

byto

tals

ales

.For

eign

Ass

ets

toT

otal

Ass

ets

(FA

TA

)is

the

perc

enta

geof

fore

ign

asse

tsof

the

firm

divi

ded

byits

tota

lass

et.

Co

un

try

TS

TA

TD

MV

RO

AR

OE

RO

CE

TQ

FS

TS

FA

TA

Arg

entin

aM

ean

$3,0

29,0

44$6

,239

,014

$1,8

30,4

77$5

,081

16.5

49.

708.

781.

000.

210.

23

Max

$6,5

70,6

04$1

2,70

6,88

9$3

,169

,734

$10,

130

20.2

712

.20

10.7

91.

000.

210.

23

Min

$1,0

33,5

01$1

,352

,078

$543

,647

$1,0

0913

.37

5.74

5.14

1.00

0.21

0.23

N3

33

33

33

31

1

Bra

zil

Mea

n$5

,923

,402

$7,8

25,4

03$2

,472

,105

$3,4

0716

.27

12.3

014

.20

0.98

0.24

0.28

Max

$22,

294,

637

$31,

238,

352

$9,2

69,1

16$1

2,43

826

.86

36.4

035

.31

1.00

0.40

0.46

Min

$488

,895

$473

,051

$244

,374

$34

6.55

–25.

261.

160.

800.

070.

09

N10

1010

1010

1010

106

6

Chi

leM

ean

$1,3

55,6

62$3

,314

,961

$1,0

96,9

26$1

,596

12.6

911

.89

12.5

40.

740.

140.

13

Max

$3,4

52,9

52$1

4,83

4,40

4$6

,145

,820

$4,6

2730

.60

43.9

140

.39

0.93

0.23

0.15

Min

$183

,448

$219

,494

$6,6

61$3

146.

06–6

.91

3.02

0.63

0.09

0.11

N9

99

99

99

93

3

Col

ombi

aM

ean

$1,5

10,4

81$4

,015

,273

$688

,480

$1,3

2210

.80

7.77

13.2

80.

130.

240.

07

Max

$1,5

10,4

81$4

,015

,273

$688

,480

$1,3

2210

.80

7.77

13.2

80.

130.

240.

07

Min

$1,5

10,4

81$4

,015

,273

$688

,480

$1,3

2210

.80

7.77

13.2

80.

130.

240.

07

N1

11

11

11

11

1

Hon

gK

ong

Mea

n$2

,343

,261

$9,6

85,7

52$2

,633

,761

$7,9

9710

.25

11.0

89.

751.

000.

470.

34

Max

$5,6

98,4

19$3

4,79

6,69

2$1

1,14

7,70

0$3

6,35

620

.10

22.6

820

.09

1.00

0.97

0.91

30

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

31

Min

$351

,776

$498

,702

$26,

612

$298

4.52

4.20

4.66

0.99

0.02

0.07

N10

1010

1010

1010

109

10

Hun

gary

Mea

n$1

,293

,431

$1,1

77,1

11$3

06,9

23$7

4113

.32

6.44

8.48

0.85

0.25

0.15

Max

$3,2

22,3

91$2

,892

,533

$760

,349

$1,8

0514

.06

7.79

11.3

20.

880.

390.

28

Min

$50,

669

$93,

699

$26,

788

$60

12.2

75.

385.

000.

830.

110.

03

N3

33

33

33

33

3

Indi

aM

ean

$2,0

76,1

35$5

,308

,358

$1,8

66,5

94$4

,547

13.8

515

.46

13.1

20.

980.

050.

15

Max

$2,0

76,1

35$5

,308

,358

$1,8

66,5

94$4

,547

13.8

515

.46

13.1

20.

980.

050.

15

Min

$2,0

76,1

35$5

,308

,358

$1,8

66,5

94$4

,547

13.8

515

.46

13.1

20.

980.

050.

15

N1

11

11

11

11

1

Mal

aysi

aM

ean

$1,4

43,8

56$3

,061

,697

$1,0

37,7

08$3

,024

11.4

52.

9410

.59

1.00

0.37

0.27

Max

$2,8

57,0

64$6

,737

,510

$1,8

58,6

86$1

1,01

717

.30

19.2

219

.61

1.00

0.69

0.50

Min

$408

,795

$615

,708

$60,

212

$300

3.74

–43.

862.

561.

000.

040.

09

N8

88

88

88

87

7

Mex

ico

Mea

n$2

,029

,820

$3,1

87,4

63$1

,008

,806

$857

15.1

26.

849.

871.

000.

480.

37

Max

$4,1

90,9

05$1

1,23

6,81

3$3

,392

,887

$2,0

2826

.08

19.7

222

.75

1.00

0.59

0.56

Min

$591

,649

$526

,054

$41,

966

$282

3.71

–11.

64–0

.82

1.00

0.20

0.14

N10

1010

1010

1010

84

4

Phi

lippi

nes

Mea

n$2

,281

,101

$3,3

36,4

35$1

,114

,618

$2,1

9313

.86

14.4

512

.61

0.99

0.13

0.38

Max

$2,2

81,1

01$3

,336

,435

$1,1

14,6

18$2

,193

13.8

614

.45

12.6

10.

990.

130.

38

Min

$2,2

81,1

01$3

,336

,435

$1,1

14,6

18$2

,193

13.8

614

.45

12.6

10.

990.

130.

38

N1

11

11

11

11

1

Pol

and

Mea

n$7

70,0

72$1

,123

,067

$251

,026

$547

13.4

7–6

.66

14.9

61.

000.

310.

07

Max

$1,2

11,4

80$1

,638

,197

$606

,597

$936

19.2

9–1

0.10

26.6

81.

000.

400.

12

Min

$338

,384

$198

,998

$10,

960

$95

4.69

–30.

934.

991.

000.

220.

03

N$3

$3$3

$33

33

32

2

Sin

gapo

reM

ean

$1,8

15,3

22$4

,778

,907

$783

,026

$4,8

8412

.98

9.40

11.3

91.

000.

570.

51

Max

$4,8

12,5

20$1

4,85

4,54

8$2

,284

,504

$28,

014

25.0

326

.52

26.9

81.

000.

970.

97

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

TA

BLE

2(c

ontin

ued)

Co

un

try

TS

TA

TD

MV

RO

AR

OE

RO

CE

TQ

FS

TS

FA

TA

Min

$457

,423

$719

,428

$104

,251

$200

2.77

–14.

143.

911.

000.

010.

11

N$9

$9$9

$99

99

99

9

S.A

fric

aM

ean

$3,1

29,8

97$2

,779

,759

$880

,564

$1,3

4711

.24

12.3

811

.01

1.00

0.47

0.54

Max

$3,2

61,1

51$4

,851

,550

$1,9

03,5

74$1

,698

13.2

316

.51

12.8

81.

000.

730.

72

Min

$2,9

00,7

79$1

,162

,069

$162

,597

$740

9.85

8.66

7.39

1.00

0.25

0.45

N3

33

33

33

33

3

S.K

orea

Ave

rage

$14,

876,

405

$11,

026,

244

$3,9

12,6

45$6

,097

14.3

910

.21

10.4

30.

510.

330.

21

Max

$26,

818,

363

$18,

790,

542

$6,2

88,0

27$1

6,29

323

.81

18.7

017

.36

0.63

0.46

0.31

Min

$3,4

14,4

42$4

,529

,625

$2,3

22,4

74$8

563.

562.

313.

740.

370.

190.

10

N4

44

44

44

44

4

Tai

wan

Ave

rage

$1,7

15,8

25$3

,211

,530

$896

,499

$5,0

2111

.26

11.7

912

.28

0.98

0.37

0.24

Max

$3,2

86,4

84$4

,690

,276

$1,4

41,6

01$1

1,63

318

.01

15.7

120

.34

0.99

0.50

0.34

Min

$705

,761

$279

,378

$45,

375

$390

8.12

5.67

3.86

0.96

0.15

0.11

N4

44

44

44

44

3

Ove

rall

Ave

rage

$3,0

55,4

80$5

,163

,197

$1,5

07,3

57$3

,555

13.2

18.

9711

.36

0.92

0.38

0.31

Max

$26,

818,

363

$34,

796,

692

$11,

147,

700

$36,

356

30.6

043

.91

40.3

91.

000.

970.

97

Min

$50,

669

$93,

699

$6,6

61$3

42.

77–4

3.86

–0.8

2–0

.13

0.01

0.03

N77

7979

7979

7979

7758

58

32

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

33

TA

BLE

3.S

ampl

eP

rofil

eby

Indu

stry

Tab

le3

repo

rts

the

mea

n,m

axim

um,a

ndm

inim

umva

lues

ofth

eva

rious

finan

cial

indi

cato

rsof

emer

ging

mar

ketm

ultin

atio

nalf

irms

sort

edby

each

indu

stry

.Tot

alS

ales

isca

lcul

ated

asth

esu

mof

gros

ssa

les

and

othe

rop

erat

ing

reve

nues

less

disc

ount

s,re

turn

san

dal

low

ance

sin

thou

sand

sof

dolla

rs.T

otal

Ass

ets

isth

esu

mof

tang

ible

fixed

asse

ts,i

ntan

gibl

eas

sets

,inv

est-

men

ts,o

ther

asse

ts,t

otal

stoc

ksan

dw

ork

inpr

ogre

ss,t

otal

debt

ors

and

equi

vale

nt,a

ndca

shan

dca

sheq

uiva

lent

sof

the

firm

inth

ousa

nds

ofdo

llars

.Tot

alD

ebti

sth

eto

talo

fall

inte

rest

bear

ing

and

capi

talis

edle

ase

oblig

atio

nsal

sore

port

edin

thou

sand

sof

dolla

rs.M

arke

tVal

ueis

com

pute

das

the

num

bero

fsha

res

mul

tiplie

dby

the

aver

age

stoc

kpr

ice

ofth

efir

min

mill

ions

ofdo

llars

.Ret

urn

onA

sset

s(R

OA

)is

calc

ulat

edas

ara

tioof

the

EB

ITD

Aan

dto

tala

sset

san

dis

repo

rted

asa

perc

enta

ge.R

etur

non

Equ

ity(R

OE

)is

the

ratio

of“e

arne

dfo

rord

inar

y”an

d“e

q-ui

tyca

pita

land

rese

rves

”and

isal

sost

ated

asa

perc

enta

ge.R

etur

non

Cap

italE

mpl

oyed

(RO

CE

)is

calc

ulat

edas

the

EB

ITdi

vide

dby

the

sum

ofto

talc

apita

lem

ploy

edan

dsh

ort-

term

bor-

row

ing.

Tob

in’s

-Qis

com

pute

das

the

mar

ketv

alue

ofou

tsta

ndin

gsh

ares

plus

the

liqui

datio

nva

lue

ofpr

efer

red

stoc

kpl

usth

esh

ortt

erm

liabi

lity

neto

fits

shor

tter

mas

sets

plus

the

long

term

debt

divi

ded

byto

tala

sset

sof

the

firm

.For

eign

Sal

esto

Tot

alS

ales

(FS

TS

)is

the

perc

enta

geof

fore

ign

sale

sof

the

firm

divi

ded

byto

tals

ales

.For

eign

Ass

ets

toT

otal

Ass

ets

(FA

TA

)is

the

perc

enta

geof

fore

ign

asse

tsof

the

firm

divi

ded

byits

tota

lass

et.

Ind

ust

ryT

ST

AT

DM

VR

OA

RO

ER

OC

ET

QF

ST

SF

AT

A

Bas

icin

dust

ry

Mea

n$1

,941

,359

.33

$3,4

74,6

59.4

9$1

,161

,024

.72

$1,4

39.0

213

.16

6.33

10.2

30.

900.

350.

30

Max

imum

$8,9

64,1

35.7

0$1

4,57

5,17

3.00

$4,6

56,3

82.0

0$6

,160

.20

19.3

019

.70

26.7

01.

000.

730.

72

Min

imum

$50,

669.

30$9

3,69

8.60

$10,

960.

10$6

0.00

3.70

–43.

902.

600.

370.

050.

03

N18

1818

1818

1818

1814

13

Cyc

lical

cons

umer

good

s

Mea

n$1

,772

,447

.28

$2,2

37,6

75.0

0$1

,075

,374

.08

$508

.00

14.8

3–2

.38

8.03

0.90

0.52

0.44

Max

imum

$3,4

14,4

41.9

0$4

,529

,625

.10

$2,3

22,4

74.1

0$1

,079

.20

20.0

09.

4011

.50

1.00

0.97

0.91

Min

imum

$488

,895

.30

$473

,050

.60

$26,

612.

00$3

4.00

11.1

0–2

5.30

2.60

0.61

0.20

0.14

N4

44

44

44

43

3

Cyc

lical

serv

ices

Mea

n$2

,163

,506

.40

$2,8

94,6

72.4

3$8

22,4

90.4

3$1

,756

.40

11.1

65.

568.

780.

960.

490.

43

Max

imum

$4,8

12,5

19.6

0$9

,388

,407

.30

$3,0

99,3

03.0

08,

622.

7017

.20

17.4

021

.00

1.00

0.96

0.90

Min

imum

$591

,649

.20

$1,0

06,7

03.8

0$8

8,05

2.10

$46.

806.

50–1

5.50

–0.8

00.

670.

060.

15

N12

1212

1212

1212

126

6

Div

ersi

fied

indu

stry

Mea

n$5

,632

,170

.94

$9,6

67,4

08.7

6$2

,750

,567

.69

$6,3

77.4

88.

575.

008.

780.

930.

430.

32

Max

imum

$26,

818,

363.

40$3

4,79

6,69

2.00

$11,

147,

700.

30$3

6,35

6.10

23.8

018

.70

17.4

01.

000.

850.

79

Min

imum

$351

,776

.00

$719

,428

.30

$23

5,52

3.70

$200

.20

2.80

–30.

903.

700.

420.

140.

07

N14

1414

1414

1414

1413

13

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

34

TA

BLE

3(c

ontin

ued)

Ind

ust

ryT

ST

AT

DM

VR

OA

RO

ER

OC

ET

QF

ST

SF

AT

A

Info

rmat

ion

tech

nolo

gy

Mea

n$1

,782

,349

.83

$3,1

14,9

74.2

0$7

85,2

17.4

0$5

,078

.63

11.4

311

.70

12.4

70.

970.

440.

24

Max

imum

$3,2

86,4

83.6

0$4

,690

,276

.00

$1,4

41,6

00.7

0$1

1,63

2.80

18.0

015

.70

20.3

00.

990.

500.

34

Min

imum

$705

,760

.80

$279

,378

.30

$45,

374.

80$3

90.0

08.

105.

703.

900.

960.

350.

11

N3

33

33

33

33

3

Non

-cyc

lical

cons

umer

good

s

Mea

n$1

,298

,427

.60

$2,0

63,4

98.7

2$5

38,5

54.4

9$2

,004

.64

15.8

413

.93

14.6

70.

830.

490.

41

Max

imum

$3,5

98,8

20.1

0$4

,015

,273

.30

$1,2

17,5

66.7

0$1

2,43

7.60

30.6

043

.90

40.4

01.

000.

970.

97

Min

imum

$183

,448

.10

$219

,493

.60

$6,6

60.6

0$2

82.0

03.

70–1

1.60

1.00

0.13

0.13

0.07

N14

1414

1414

1414

129

9

Non

-cyc

lical

serv

ices

Mea

n$2

,709

,921

.37

$5,3

94,0

29.9

3$8

90,5

59.2

7$1

3,26

7.67

19.1

016

.63

18.9

01.

000.

040.

21

Max

imum

$3,3

26,5

59.0

0$6

,940

,944

.40

$1,7

83,5

89.9

0$2

8,01

3.70

25.0

026

.50

27.0

01.

000.

080.

29

Min

imum

$2,1

20,9

83.7

0$2

,503

,635

.50

$205

,054

.60

$772

.00

15.0

011

.20

10.8

01.

000.

010.

09

N3

33

33

33

33

3

Res

ourc

es

Mea

n$8

,595

,300

.67

$12,

801,

148.

21$3

,694

,607

.16

$5,6

35.4

317

.10

16.5

314

.43

0.93

0.18

0.13

Max

imum

$22,

294,

637.

00$3

1,23

8,35

2.00

$9,2

69,1

16.3

0$1

0,12

9.90

23.2

028

.30

21.3

01.

000.

410.

23

Min

imum

$1,1

30,5

98.3

0$6

15,7

08.3

0$6

0,21

1.70

$572

.20

10.2

07.

808.

500.

630.

070.

03

N7

77

77

77

75

5

Util

ities

Mea

n$1

,966

,924

.73

$7,0

36,8

13.4

5$2

,165

,936

.18

$6,3

32.2

316

.18

18.0

014

.20

0.98

0.05

0.13

Max

imum

$3,4

52,9

51.7

0$1

4,83

4,40

3.90

$6,1

45,8

19.9

0$1

0,44

9.50

20.1

022

.70

20.1

01.

000.

090.

17

Min

imum

$408

,794

.80

$2,1

64,9

93.3

0$4

14,9

53.3

0$3

,239

.00

11.1

08.

907.

500.

930.

020.

11

N4

44

44

44

42

3

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

TA

BLE

4.S

umm

ary

ofth

eV

aria

bles

and

Pro

xies

Ope

ratin

gR

etur

non

Ass

ets

(RO

A)

isde

fined

asth

eco

mpa

ny’s

oper

atin

gea

rnin

gsbe

fore

inte

rest

and

taxe

sas

perc

enta

geof

tota

lass

ets.

Tob

in’s

-Qis

com

pute

das

mar

ketv

alue

ofou

t-st

andi

ngsh

ares

plus

liqui

datio

nva

lue

ofpr

efer

red

stoc

kpl

usne

tcur

rent

asse

tspl

uslo

ngte

rmde

btdi

vide

dby

tota

lass

ets

ofth

ebi

dder

.HP

Ris

the

hold

ing

perio

dre

turn

.Firm

size

ispr

oxie

dby

the

loga

rithm

ofto

tals

ales

.Lev

erag

eis

the

tota

ldeb

tto

tota

lass

ets

ratio

.For

eign

toT

otal

Ass

ets

(FA

TA

)is

the

perc

enta

geof

fore

ign

asse

tsof

the

firm

divi

ded

byto

tala

sset

s.F

orei

gnto

Tot

alS

ales

(FS

TS

)is

the

perc

enta

geof

fore

ign

sale

sof

the

firm

divi

ded

byne

tsal

es.B

etas

are

calc

ulat

edw

ithre

spec

tto

sele

cted

benc

hmar

ks,n

amel

ylo

cali

ndex

,em

ergi

ngm

arke

tind

ex,

US

inde

x,an

dth

ew

orld

inde

x.T

hepr

oxy

fort

hefir

mris

kis

the

syst

emat

icris

k.C

ount

ryR

isk

prox

yis

the

Eur

omon

eyC

ount

ryR

isk

Indi

cato

r.F

XV

OL

isth

eF

orei

gnE

xcha

nge

Ris

kin

dica

tor.

AD

Rdu

mm

y-1

isco

ded

1if

EM

NC

has

anou

tsta

ndin

gA

DR

issu

ere

gard

less

ofth

ele

velo

fAD

Rin

the

curr

enty

ear

orye

ars

prio

r,0

othe

rwis

e.A

DR

dum

my-

2ta

kes

the

valu

e1

only

ifth

eco

mpa

nyha

san

outs

tand

ing

Leve

l-III

AD

R,0

othe

rwis

e.U

pstr

eam

dum

my

isco

ded,

ifan

EM

NC

has

subs

idia

ries

inde

velo

ped

coun

trie

s,th

edu

mm

yva

riabl

eta

kes

the

valu

eof

1,an

d0

oth-

erw

ise.

Dow

nstr

eam

dum

my

isco

ded,

ifan

EM

NC

has

subs

idia

ries

inde

velo

ping

coun

trie

s,th

edu

mm

yva

riabl

eta

kes

the

valu

eof

1,an

d0

othe

rwis

e.D

IV_D

umm

yis

code

d,if

anE

MN

Cis

adi

vers

ified

indu

stry

firm

,the

dum

my

varia

ble

take

sth

eva

lue

of1,

and

0ot

herw

ise.

Indu

stry

Dum

my

indi

cate

sth

ety

peof

the

indu

stry

and

Reg

ion

Dum

my

indi

cate

sre

gion

from

whi

chE

MN

Cor

igin

ates

.

VA

RIA

BL

ES

PR

OX

IES

Per

form

ance

RO

AT

obin

’s-Q

HP

R

Con

trol

Tot

alS

ales

Tot

alA

sset

sLe

vera

ge

Deg

ree

ofIn

tern

atio

naliz

atio

nF

AT

AF

ST

S

Com

pany

-Ris

k(F

RIS

K)

LBE

TA

EM

BE

TA

GB

ET

AU

SB

ET

AT

OT

AL

RIS

K

Cou

ntry

-Ris

k(C

RIS

K)

CR

FX

-Ris

kF

XV

OL

Acc

ess

toIn

tern

atio

nal

Cap

italM

arke

tsA

DR

-D

umm

y-1

AD

R-

Dum

my-

2

Exp

ansi

onG

eogr

aphy

UP

ST

RE

AM

DO

WN

ST

RE

AM

Str

uctu

re-D

umm

yD

IV_D

UM

MY

Indu

stry

Dum

my

IND

_XX

Reg

ion

Dum

my

AS

IAE

ELA

35

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

36

TA

BLE

5.R

isk

and

Ret

urn

Cha

ract

eris

tics

ofS

ampl

eF

irms

byC

ount

ry

Mon

thly

retu

rns

and

vola

tiliti

esar

eth

eav

erag

esfo

rthe

EM

NC

sfr

omth

eco

rres

pond

ing

coun

try.

Exc

ess

retu

rns

fori

ndiv

idua

lfirm

sar

eca

lcul

ated

bysu

btra

ctin

gm

onth

lyre

turn

sfr

omth

eco

rre-

spon

ding

loca

lind

ex,S

&P

500

and

the

wor

ldin

dice

s.T

heva

lues

inth

eta

ble

are

the

aver

ages

fort

heE

MN

Cs

from

the

resp

ectiv

eco

untr

ies.

Vol

atili

tym

ultip

les

are

the

ratio

ofco

mpa

nyvo

latil

ities

tose

lect

edbe

nchm

ark

vola

tiliti

es.

The

valu

esin

the

tabl

ere

pres

ent

the

aver

ages

for

the

resp

ectiv

eco

untr

y.B

etas

are

calc

ulat

edw

ithre

spec

tto

sele

cted

benc

hmar

ks,

nam

ely

loca

lind

ex,

emer

ging

mar

keti

ndex

,US

inde

x,an

dth

ew

orld

inde

x.A

NO

VA

test

sin

dica

teth

atgr

oup

diffe

renc

esac

ross

coun

trie

sar

esi

gnifi

cant

form

onth

lyre

turn

s,vo

latil

ity,v

ola

tility

mul

tiple

s,an

dem

erg-

ing

mar

ket

beta

atth

e5%

sign

ifica

nce

leve

l.

Cou

ntri

es#

ofFi

rms

Mon

thly

Ret

urn

Mon

thly

Vo

lati

lity

An

nu

aliz

edR

etu

rnA

nn

ual

ized

Vo

lati

lity

Lo

cal

Mo

nth

lyE

xces

sR

etu

rn

US

Mo

nth

lyE

xces

sR

etu

rn

Glo

bal

Mo

nth

lyE

xces

sR

etu

rn

Lo

cal

Vo

lati

lity

Mu

ltip

le

US

Vo

lati

lity

Mu

ltip

le

Glo

bal

Vo

lati

lity

Mu

ltip

le

CV

Lo

cal

Bet

aE

MB

eta

US

Bet

aG

loba

lB

eta

Arg

entin

a3

0.56

%14

.69%

6.75

%50

.89%

0.73

%–0

.11%

0.28

%1.

202.

962.

80–6

9.08

1.00

0.12

1.02

1.32

Bra

zil

101.

63%

17.4

6%19

.51%

60.4

7%0.

89%

0.95

%1.

34%

1.38

3.51

3.32

7.44

0.83

0.53

0.64

0.95

Chi

le9

–0.1

1%13

.54%

–1.2

9%46

.89%

0.24

%–0

.78%

–0.3

9%1.

282.

722.

580.

521.

200.

430.

810.

98

Col

ombi

a1

2.64

%15

.78%

31.6

3%54

.67%

2.25

%1.

96%

2.35

%1.

503.

183.

005.

991.

310.

360.

200.

51

Hon

gK

ong

100.

82%

13.6

5%9.

87%

47.2

8%0.

54%

0.14

%0.

54%

1.50

2.75

2.60

37.5

61.

07–0

.08

1.31

1.38

Hun

gary

31.

53%

16.0

3%18

.37%

55.5

2%–0

.67%

0.85

%1.

24%

1.38

3.23

3.05

–40.

201.

070.

150.

170.

68

Indi

a1

1.71

%11

.76%

20.5

2%40

.72%

1.19

%1.

03%

1.42

%1.

292.

372.

246.

871.

060.

27–0

.26

0.00

Mal

aysi

a8

–0.1

0%16

.15%

–1.2

0%55

.95%

–0.1

5%–0

.78%

–0.3

9%1.

273.

253.

07–6

6.14

1.01

0.30

0.89

1.26

Mex

ico

100.

68%

14.9

2%8.

18%

51.6

7%–0

.47%

0.00

%0.

39%

1.59

3.00

2.84

47.4

10.

070.

931.

22

Phi

lippi

nes

10.

38%

9.37

%4.

53%

32.4

6%1.

94%

–0.3

0%0.

09%

0.86

1.89

1.78

24.8

20.

57–0

.24

0.52

0.63

Pol

and

30.

60%

17.6

5%7.

19%

61.1

5%–0

.11%

–0.0

8%0.

31%

1.56

3.55

3.36

–181

.33

0.88

0.27

1.11

1.38

Sin

gapo

re9

0.58

%14

.61%

7.02

%50

.61%

1.49

%–0

.09%

0.30

%1.

632.

942.

78–1

.25

1.08

0.02

1.06

1.35

Sou

thA

frica

30.

51%

15.0

6%6.

17%

52.1

8%0.

47%

–0.1

6%0.

23%

1.75

3.03

2.87

50.0

61.

010.

170.

590.

97

Sou

thK

orea

43.

19%

28.5

6%38

.32%

98.9

3%2.

38%

2.52

%2.

91%

1.83

5.75

5.44

9.92

1.13

0.86

1.13

1.72

Tai

wan

41.

37%

16.9

9%16

.45%

58.8

6%1.

24%

0.69

%1.

08%

1.67

3.42

3.23

–0.2

31.

110.

550.

861.

32

Ave

rage

790.

85%

15.8

5%10

.24%

54.9

1%0.

57%

0.18

%0.

57%

1.47

3.19

3.02

–3.2

51.

040.

250.

881.

18

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

37

TA

BLE

6.R

isk

and

Ret

urn

Cha

ract

eris

tics

ofS

ampl

eF

irms

byIn

dust

ry

Mon

thly

retu

rns

and

vola

tiliti

esar

eth

eav

erag

esfo

rth

eE

MN

Cs

from

the

corr

espo

ndin

gin

dust

ry.

Exc

ess

retu

rns

for

indi

vidu

alfir

ms

are

calc

ulat

edby

subt

ract

ing

mon

thly

retu

rns

from

the

corr

espo

ndin

glo

cali

ndex

,S&

P50

0an

dth

ew

orld

indi

ces.

The

valu

esin

the

tabl

ear

eth

eav

erag

esfo

rthe

EM

NC

sfr

omth

ere

spec

tive

indu

strie

s.V

olat

ility

mul

tiple

sar

eth

era

tioof

com

pany

vola

tiliti

esto

sele

cted

benc

hmar

kvo

latil

ities

.The

valu

esin

the

tabl

ere

pres

entt

heav

erag

esfo

rthe

resp

ectiv

ein

dust

ry.B

etas

are

calc

ulat

edw

ithre

spec

tto

sele

cted

benc

hmar

ks,n

amel

ylo

-ca

lind

ex,e

mer

ging

mar

keti

ndex

,US

inde

x,an

dth

ew

orld

inde

x.A

NO

VA

test

sin

dica

teth

atgr

oup

diffe

renc

esac

ross

indu

strie

sar

esi

gnifi

cant

for

vola

tility

,vol

atili

tym

ultip

les,

and

allf

our

beta

sat

the

5%si

gnifi

canc

ele

vel.

Ind

ust

ry#

Fir

ms

Mon

thly

Ret

urn

Mo

nth

lyV

ola

tilit

yA

nn

ual

ized

Ret

urn

An

nu

aliz

edV

ola

tilit

yL

oca

lM

on

thly

Exc

ess

Ret

urn

US

Mo

nth

lyE

xces

sR

etu

rn

Glo

bal

Mo

nth

lyE

xces

sR

etu

rn

Lo

cal

Vo

lati

lity

Mu

ltip

le

US

Vo

lati

lity

Mu

ltip

le

Glo

bal

Vo

lati

lity

Mu

ltip

le

CV

Lo

cal

Bet

aE

MB

eta

US

Bet

aG

lob

alB

eta

Info

tech

31.

33%

18.4

2%15

.92%

63.8

2%1.

20%

0.65

%1.

04%

1.81

3.71

3.51

–3.1

21.

230.

690.

981.

46

Bas

ic18

1.10

%16

.74%

13.1

7%57

.98%

0.57

%0.

42%

0.81

%1.

493.

373.

194.

091.

060.

360.

811.

14

Non

-C

yc.C

ongo

ods

140.

67%

13.1

4%8.

04%

45.5

2%0.

59%

–0.0

1%0.

38%

1.31

2.65

2.50

16.3

80.

860.

090.

560.

82

Div

ersi

fied

140.

61%

17.7

9%7.

26%

61.6

1%0.

44%

–0.0

7%0.

32%

1.66

3.58

3.39

–47.

161.

260.

271.

261.

61

Cyc

lical

serv

ices

120.

57%

16.1

5%6.

89%

55.9

5%0.

34%

–0.1

0%0.

29%

1.60

3.25

3.07

–1.6

40.

910.

390.

871.

11

Res

ourc

es7

1.44

%14

.77%

17.2

9%51

.16%

1.00

%0.

76%

1.15

%1.

212.

972.

81–2

3.39

1.10

0.22

0.74

1.21

Util

ities

40.

02%

8.72

%0.

24%

30.2

2%–0

.05%

–0.6

6%–0

.27%

0.82

1.76

1.66

15.8

20.

67–0

.14

0.60

0.67

Non

-cyc

serv

ices

30.

49%

13.8

1%5.

91%

47.8

5%0.

53%

–0.1

8%0.

21%

1.20

2.78

2.63

66.4

60.

790.

200.

911.

02

Cyc

lical

con.

good

s4

2.09

%23

.09%

25.1

2%79

.97%

1.35

%1.

42%

1.81

%2.

014.

654.

393.

401.

210.

151.

481.

67

Ave

rage

790.

85%

15.8

5%10

.24%

54.9

1%0.

57%

0.18

%0.

57%

1.47

3.19

3.02

–3.2

51.

040.

250.

881.

18

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

TA

BLE

7.R

isk

and

Ret

urn

Cha

ract

eris

tics

ofS

ampl

eF

irms

byR

egio

n

Mon

thly

retu

rns

and

vola

tiliti

esar

eth

eav

erag

esfo

rthe

EM

NC

sfr

omth

eco

rres

pond

ing

regi

on.

Exc

ess

retu

rns

fori

ndiv

idua

lfirm

sar

eca

lcul

ated

bysu

btra

ctin

gm

onth

lyre

turn

sfr

omth

eco

r-re

spon

ding

loca

lind

ex,

S&

P50

0an

dth

ew

orld

indi

ces.

The

valu

esin

the

tabl

ear

eth

eav

erag

esfo

rth

eE

MN

Cs

from

the

resp

ectiv

ere

gion

s.V

olat

ility

mul

tiple

sar

eth

era

tioof

com

pany

vola

tiliti

esto

sele

cted

benc

hmar

kvo

latil

ities

.The

valu

esin

the

tabl

ere

pres

entt

heav

erag

esfo

rthe

resp

ectiv

ere

gion

.Bet

asar

eca

lcul

ated

with

resp

ectt

ose

lect

edbe

nchm

arks

,nam

ely

loca

lin

dex,

emer

ging

mar

keti

ndex

,US

inde

x,an

dth

ew

orld

inde

x.A

NO

VA

test

sin

dica

teth

atgr

oup

diffe

renc

esac

ross

the

regi

ons

are

stat

istic

ally

nots

igni

fican

t.

Co

un

trie

s#

Firm

sM

onth

lyR

etu

rnM

on

thly

Vo

lati

lity

An

nu

aliz

edR

etu

rnA

nn

ual

ized

Vo

lati

lity

Lo

cal

Mon

thly

Exc

ess

Ret

urn

US

Mo

nth

lyE

xces

sR

etu

rn

Glo

bal

Mo

nth

lyE

xces

sR

etu

rn

Lo

cal

Vo

lati

lity

Mu

ltip

le

US

Vo

lati

lity

Mu

ltip

le

Glo

bal

Vo

lati

lity

Mu

ltip

le

CV

Lo

cal

Bet

aE

MB

eta

US

Bet

aG

loba

lB

eta

Afr

ica

30.

51%

15.0

6%6.

17%

52.1

8%0.

47%

–0.1

6%0.

23%

1.75

3.03

2.87

50.0

61.

010.

170.

590.

97

Asi

a37

0.89

%16

.23%

10.7

1%56

.22%

0.95

%0.

22%

0.61

%1.

513.

273.

09–2

.55

1.06

0.20

1.03

1.32

Eas

tern

Eur

ope

61.

07%

16.8

4%12

.78%

58.3

3%–0

.39%

0.39

%0.

78%

1.47

3.39

3.21

–110

.70.

980.

210.

641.

03

Latin

Am

eric

a33

0.80

%15

.32%

9.61

%53

.06%

0.33

%0.

12%

0.51

%1.

403.

082.

9210

.66

1.02

0.32

0.79

1.06

Ave

rage

790.

85%

15.8

5%10

.24%

54.9

1%0.

57%

0.18

%0.

57%

1.47

3.19

3.02

–3.2

51.

040.

250.

881.

18

38

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014

TA

BLE

8.M

ultiv

aria

teA

naly

sis-

Poo

led

Tim

esS

erie

sR

egre

ssio

ns

PER

FOR

MA

NC

E=

a+

b 1(L

SAL

ES)

+b 2

(LE

VE

RA

GE

)+

b 3(F

STS)

+b 4

(FR

ISK

)+

b 5(C

RIS

K)

+b 6

(AD

R3)

+b 7

(UPS

TR

)+

b 8(R

EG

ION

)+

b 9(I

ND

UST

RY

)

We

used

two

alte

rnat

ive

mea

sure

sof

perf

orm

ance

.In

mod

el-1

we

used

RO

A,i

nm

odel

-2w

eem

ploy

edT

obin

’s-Q

.Ope

ratin

gR

etur

non

Ass

ets

(RO

A)d

efin

edas

com

pany

’sop

erat

ing

earn

-in

gsbe

fore

inte

rest

and

taxe

sas

perc

enta

geof

tota

lass

ets.

Tob

in’s

-Qis

com

pute

das

mar

ketv

alue

ofou

tsta

ndin

gsh

ares

plus

liqui

datio

nva

lue

ofpr

efer

red

stoc

kpl

usne

tcur

rent

asse

tspl

uslo

ngte

rmde

btdi

vide

dby

tota

lass

ets

ofth

ebi

dder

.For

eign

toT

otal

Sal

es(F

ST

S)i

sth

epe

rcen

tage

offo

reig

nsa

les

ofth

efir

mdi

vide

dby

nets

ales

.Firm

size

ispr

oxie

dby

the

loga

rithm

ofto

-ta

lsal

es.L

ever

age

isth

eto

tald

ebtt

oto

tala

sset

sra

tio.

The

prox

yfo

rthe

firm

risk

isth

esy

stem

atic

risk.

Cou

ntry

Ris

kpr

oxy

isth

eE

urom

oney

Cou

ntry

Ris

kIn

dica

tor.

T-v

alue

sar

ere

port

edin

pare

nthe

ses.

Not

eth

at**

*,**

,and

*de

note

stat

istic

alsi

gnifi

canc

eat

1%,5

%,1

0%le

vels

,res

pect

ivel

y.

Mo

del

-1M

od

el-2

Dep

end

ent

Var

iab

leR

OA

To

bin

's-Q

Per

form

ance

Ind

epen

den

tV

aria

ble

sC

oef

fici

ent

t-st

atC

oef

fici

ent

t-st

at

Con

stan

t0.

052

0.01

1.35

5.28

Log

(Sal

es)

(LS

ALE

S)

1.88

65.

11**

*–0

.04

–2.8

2***

Leve

rage

(LE

V)

–18.

688

–7.0

6***

0.30

2.76

***

Firm

Ris

k(F

RIS

K)

–4.7

58–4

.02*

**–0

.23

–4.4

1***

Deg

ree

ofIn

tern

atio

naliz

atio

n(F

ST

S)

–0.0

47–0

.03

0.04

0.58

Cou

ntry

Ris

k(C

RIS

K)

–0.0

55–1

.61

–0.0

039

2.81

***

Acc

ess

toIn

t'lC

apita

l(A

DR

3)0.

035

2.55

**0.

041

1.19

2

Ups

trea

m/D

owns

trea

mD

umm

y(U

PS

TR

)–1

.849

–1.9

3*0.

051.

23

Indu

stry

Dum

my

(IN

DU

ST

RY

)–2

.120

–1.9

9**

0.04

0.96

Reg

ion

Dum

my

(RE

GIO

N)

–0.1

44–0

.14

0.01

0.26

R-s

quar

ed0.

330.

17

Adj

uste

dR

-squ

ared

0.31

0.14

F-s

tatis

tic17

.16

5.92

Pro

b(F

-sta

tistic

)0.

0000

0.00

00

39

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

23:

40 2

5 N

ovem

ber

2014