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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
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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: <[email protected]> Website: <http://www.HaworthPress.com> © 2005 by The Haworth Press, Inc. All rights reserved.]
Bülent Aybar is Professor of International Finance (E-mail: [email protected]) andArul Thirunavukkarasu is a DBA Candidate (E-mail: [email protected]),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
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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
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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
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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
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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
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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
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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
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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-
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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
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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
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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
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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
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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
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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-
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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
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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
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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,
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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.
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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
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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.
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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.
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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.
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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.
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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.
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AP
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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