Country, Industry, and Risk FactorLoadings in Portfolio ManagementCountry effects declining in importance; industry effects growing.

Jean-Frangois L'Her, Oumar Sy, and Mohamed Yassine Tnani

JEAN-FRANCOIS L ' H E R is

a research advisor at Caissede depot et placement duQuebec in Montreal (QuebecH3A 3C7).jlher@cdp.ca

OUMAR SY is an analyst at

Caisse de depot et placementdu Quebec in Montreal (Que-bec H3A 3C7).o$y@cdp.ca

MOHAMED YASSINE TNANI

is an analyst at Caisse de depotet placement du Quebec inMontreal (Quebec H3A 3C7).mtnani@cdp.ca

Aglobal portfolio built using a top-dovynapproach may usually be allocated on the basisof a country or industry dimension. Thechoice of dimension wiU depend on whether

the portfoho manager beheves that returns are governedprimarily by country or by industry effects.

Heston and Rouwenhorst [1994, 1995], Griffinand Karoiyi [1998], and Rouwenhorst [1999] show thatcountry effects, on average, dominated industry effectsduring the 1975-1998 period. Baca, Carbe, and Weiss[2000], Cavagha, Brightman, and Aked [2000], Kerneisand Williams [2000], and Hopkins and Miller [2001],however, point out that industry effects have grown somarkedly in importance that they have superseded coun-try effects in the variation of international stock returns.

These trends in country and industry effects can beexplained largely by ongoing capital market integration.The past few years have witnessed increased correlationsbetween country returns (see Freimann [1998]). This phe-nomenon is attributable to a number of structural changes:reduction in international barriers to investment; majordevelopments in information technologies that haveimproved access to global information; an unprecedentedwave of global mergers and takeovers; a move toward pri-vatization; and the integration of geographic zones, espe-cially in Europe. Clobahzation ofthe world economy hashkely diminished the benefits of diversification acrosscountries in favor of diversification across industries.

We use a two-step procedure to reexamine the rel-ative importance of country and industry effects in the

7 0 COUNTRY, INDUSTRY, AND RISK FACTOR LOADINGS IN PORTFOLIO MANAGEMENT SUMMER 2002

variation of international stock returns. The first step,which is estimation of the model, follows Heston andRouwenhorst [1994, 1995] and Griffm and Karoiyi[1998].'

In the second step, unhke previous authors, we sep-arate the cross-sectional variance of monthly interna-tional stock returns into different effects, and then studythe evolution of each component. Our work differs fromother research in two key areas: the data set used, and,more important, the inclusion of global risk factor load-ings in the analysis.

Our data, taken fiom Standard & Poor's Compustat®Global Vantage, cover 20 developed countries and 11broad industries, and span the period July 1989 throughDecember 2000. An advantage of our data set is that itcovers a great number of stocks (7,348 firms), making itpossible to obtain more cross-sectional variance in the sizecharacteristics of firms.

The more distinctive element of the research per-tains to inclusion of global risk factor loadings in themodel. Studies examining the relative importance ofcountry and industry effects as a source of variation ininternational stock returns have assumed identical globalrisk exposure for each stock. Yet authors have demon-strated the presence of global premiums related to size(Heston, Rouwenhorst, and Wessels [1995]), book-to-market (ArshanapaUi, Coggin, and Doukas [1998], Famaand French [1998]), and price momentum (Rouwen-horst [1998]) (see Liew and Vassalou [2000] for evidenceon these three premiums). We use a global four-factorpricing model to control for differences in global risk fac-tor loadings between international stocks.

Using a country/industry dummy variable frame-work, we show that country effects dominated industryeffects during the 1992-2000 sample period, corroborat-ing the findings of Heston and Rouwenhorst [1994,1995] and Griffm and Karoiyi [1998]. Consequently,country diversification was on average more eflicient thanindustry diversification during the nineties. Like Baca,Garbe, and Weiss [2000], Cavaglia, Brightman, and Aked[2000], Kerneis and WiUiams [2000], and Hopkins andMiUer [2001], we also note that industry effects havegained in importance. The ongoing trend toward inte-gration has reduced the benefits of country diversification;consequently, industry-oriented approaches to globalmanagement could be as effective as country-orientedapproaches in the future.

Top-down approaches to global equity portfolioaUocation should consider both the country and indus-

try dimensions. Carrieri, Errunza, and Sarkissian [2000,p. 26] conclude that: "In other words, ... investors shoulduse both cross-country and cross-industry diversificationas a way to improve portfoho performance" (emphasis inthe original).

More important, the globalization ofthe economyhas also strengthened the role of global risk factors as asource of variation in international stock returns. Whilethe main trends of the country/industry analysis remainthe same, global risk effects became stronger during thesample period, and are currently more significant thancountry and industry effects. Consequently, global man-agers should consider exposure to these global risk fac-tors when they construct their portfohos.

DATA

Our data set, extracted from the Standard & Poor'sCompustat® Global Vantage database, spans the period July1989—December 2000, and covers a total of 20 countriesand 11 industries.^ The set covers 7,348 stocks, morethan other studies, and includes smaU-capitahzation stocks,which enables us to obtain more cross-sectional variancein size factor loadings.'

The sample includes aU firms for which informationas foUows is available: doUar-denominated total return,market capitaHzation, book-to-market ratio, and a descrip-tion ofthe industry and country affihation." Exhibit 1 pro-vides descriptive statistics on the returns observed fromJanuary 1992 through December 2000.

Panel A gives the main return characteristics bycountry. On average, we examine 367 firms per country,but the number of firms and industries varies by coun-try. The United States is by far the most represented, withalmost one-quarter ofthe firms covered (1,757 firms)foUowed by Japan (1,682 firms), and the United Kingdom(908 firms).

With the exception of Finland, the most volatileindex returns are observed in the Far East countries. Fin-land (4.04% per month) and Sweden (2.09% per month)are the countries with the highest cap-weighted averagereturn. Austria and Japan recorded the poorest averagereturns (respectively, -0.12% and 0.16% per month). Thelow tracking error bet'ween the index returns of theG-7 countries and their corresponding MSCI countryindex returns is a guarantee of the quality of our data.

Panel B provides the main return characteristics byindustry. The number of firms in industries varies from1,837 for consumer cychcals to 62 for communication ser-

SUMMER 2002 THE JOURNAL OF PORTFOLIO MANAGEMENT 7 1

EXHIBIT 1Country, Industry, and Global Retums January 1992-December 2000

Panel A. Countries Number of Firms

241

61

64

363

81

52

382

370

144

152

1682363131

66

18093

121

137

9081757

Weight

1.410.170.672.230.340.574.285.331.852.25

18.820.691.960.240.510.960.851.978.75

46.13

Retum0.80

-0.120.911.050.824.041.130.901.440.810.160.821.680.870,991,162,091,491.021,35

StandardDeviation

5,065.043,835,574,89

11,554,764,739,777,066,82

12,005,066,60

10,176,157,834,584,073,90

AustraliaAustriaBelgiumCanadaDenmarkFinlandFranceGermanyHong KongItalyJapanMalaysiaNetherlandsNorwaySingaporeSpainSwedenSwitzerlandU,K,U,S,

MeanMedian

367

1485,00

1,63

1,171,01

6,475,32

Panel B. Industries Number of Firms Weight RetumStandardDeviation

Basic MaterialsCapital GoodsCommunication ServicesConsumer StaplesConsumer CyclicalsEnergyFinancialsHealth CareTechnologyTransportationUtilities

7121345

62

786

1837206

909

290722

288191

6,028,919,939,86

12,304,24

19,506,82

15,752,534,14

0,560,890,860,840,571,151,171,291,920,330,81

4,184,246,193,113,754,754,594,296,693,762,84

MeanMedian

668712

9,09

8,91

0,94

0,86

4,40

4,24

Panel C. Global Risk Factors t-test RetumStandardDeviation

WML..

2,80

1,69

0,74

-0,23

2,20

1,000,610,21

-0,080,76

3,72

3,72

2,93

3,60

3,58

Retums are expressed in USD. Weights, retums, and standard deviations are expressed in percentage on a monthly basis.

72 COUNTRY, INDUSTRY, AND RISK FACTOR LOADINGS IN PORTFOLIO MANAGEMENT SUMMER 2002

vices. Over the period considered, technology (1,92% permonth) and health care (1,29% per month) posted the bestreturns; basic materials (0,56% per month) and consumercyclicals (0.57% per month) posted the lowest returns. Thetechnology returns were the most volatile (6.69% standarddeviation of monthly returns). At the other extreme, util-ities returns registered a standard deviation of only 2.84%.

Panel C shows the average return and standard devi-ation of each of the four global risk factors: the global mar-ket {R ) , and three global zero net investment portfoliosconstructed on the basis of firm market capitalization(small minus big: SMB^), firm book-to-market (highminus low: HML^), and stock price momentum (win-ners minus losers: WML^).

The global market posted a return of 1% per month(t-statistic 2,80), The global market premium was 0.61%per month and significantly different firom zero only at the10% level. SMB^, HML^, and ^ML^ posted monthlyreturns of 0.21%,"'-0,08%,'and 0,76%, respectively.is significant at the 1% level (t-statistic 2.20), whileand HML ^ are not significant.

See the appendix for details on the construction offactors.

METHODOLOGY

The first methodology presented is based on coun-try and industry fixed effects, and the second integratescountry and industry fixed effects with global factorloadings.

Country and Industry Fixed Effects

We use a two-step procedure to differentiate betweenthe variance of international stock returns due to coun-try effects and the variance attributable to industry effects.The first step, which separates country-related perfor-mance fi-om industry-related performance, is similar to thedummy variable regression fi-amework used in Heston andRouwenhorst [1994, 1995] and Griffin and Karoiyi[1998]:'

(1)

where R., is the return of firm j (;' = 1, .,., N = 7,348)for period (, C . is a dummy variable that equals 1 whenfirmj belongs to country c (c = 1, ,.., 20) and 0 other-

wise, Ij is a dummy variable that equals 1 when firm jbelongs to industry i (i = 1, ..,, 11) and 0 otherwise, ande.j is the error term.

To solve the identification problem induced bydummy variables and to facilitate interpretation of thecoefficients, we impose the same restrictions as Heston andRouwenhorst [1994,1995] and Griffin and Karoiyi [1998]:

20

c=l

II

(2-A)

(2-B)

where (f)^^ ^ and (p.^ ^ are the weights of country c andindustry i in the world portfolio at the beginning of themonth. Given these restrictions, the parameter a^^ can beinterpreted as the cap-weighted average return of theworld portfolio at time t, and coefficients / ^ and X.^ standfor the "pure" bet at time t on country c without indus-try bias and the "pure" bet on industry i at time t with-out country bias.

In the second step, the cross-sectional variance of theinternational stock returns is segmented in order to iden-tify the proportion of the variance attributable to stock-specific {S/T ), country {C/T ), and industry (I/T )effects. This allows us to determine for each month com-ponents that best explain the cross-sectional variance ofinternational stock returns. The three components are cal-culated as follows:

C. c=l

t_

T. ~ (3)

where, T^ = S^ stands for the total effects.''

Fixed Effects and Global Risk Loadings

The analysis considers fixed country and industryeffects exclusively, and assumes that all stocks have the sameglobal risk exposure. Our main contribution is to exam-

SUMMER. 2002 THEJOURNAL OF PORTFOLIO MANAGEMENT 7 3

ine the relative importance of loadings on the four globalrisk factors and country/industry dummy variables as asource of variation in international stock returns. We usea four-factor global pricing model to estimate the factorloadings for each stock j()3_.,, y3,,.,, ^,,.,, andi§^^.,) andthen estimate this model monthly:*

EXHIBIT 2Evolution of Stock-Specific, Country,and Industry Effects

20

c=l

(4)

where C(^ is the world return for the period t that is notexplained by the four global risk factors, and the parame-^^^^ ^umt' «u.«' "whr an' «,^, represent the global risk pre-miums associated with each factor loading.

Equation (4) makes it possible to compare country andindustry effects while controlling for differences in expo-sure of international stock returns to the four global sourcesof risk. Restrictions (2-A) and (2-B) are also imposed inEquation (4).

As in the country/industry analysis, the second stepconsists of subdividing the cross-sectional variance ofinternational stock returns into four components: thestock-specific (S/T), country (C/T), industry (I/T), andglobal risk factor loading (G/T) components. The firstthree components are calculated using the procedureabove (except T), while the fourth, which is the varianceexplained by global risk factor loadings, is equal to:

(5)

where T^-Sj + Cj + I^ + G, represents the total effects.To assess the relative importance of each individual

global factor loading, we separate the {G/T^ variable intofour components related to the exposure to global mar-ket {RJT), size (SMBJT), book-to-market (HMLJT),and return momentum {WML /T). '

RESULTS

We present the results ofthe methodology that con-siders solely fixed country and industry effects first. Next,

Year1992

1993

1994

1995

1996

1997

1998

1999

2(X)0

Mean

Median

Stock-Specific67,65

68,11

72,51

75,80

78,94

73,32

77,30

75,35

74,76

73.75

74.76

Country26,81

25,35

21,26

17,99

14,70

19,02

13,72

9,75

7,85

17.38

17.99

Industry5,54

6,54

6,23

6,22

6,36

7,67

8,98

14,90

17,39

8.87

6.54

Each effect is measured by the average annual proportion ofthe variance thatis due to the stock-specific, country, and industry components [Equation (3)].Proportions expressed in percentages.

we analyze the results obtained when global factor load-ings are introduced. We then investigate the robustness ofthe results using a different industry classification, fewercountries, and only large market capitalization firms.

Country and Industry Fixed Effects

Exhibit 2 shows the contribution of each of thethree effects (specific, country, and industry) to the cross-sectional variance of international stock returns for eachyear studied. With on average 73,75% ofthe total effects,the stock-specific component largely dominates the othereffects. This result confirms the relevance of investing ina portfolio rather than in a single stock, given that thestock-specific component can be significantly reduced byforming a portfolio of non-perfectly correlated securities.

The remainder of the international stock returncross-sectional variance is explained by country and indus-try effects. For the total sample period, country effectsexplain on average 17,38% ofthe return variance, domi-nating the 8.87% explained by industry effects. This resultis consistent with the conclusions of Heston and Rouwen-horst [1994, 1995] and Griffin and Karolyi [1998].

There are significant portfolio management impli-cations to be drawn from the dominance of countryeffects over industry effects in the variation of internationalstock returns. The most important is that diversificationacross countries has been more effective than diversifica-tion across industries during this period. As Heston and

74 COUNTRY, INDUSTRY, AND RISK FACTOR LOADINGS IN PORTFOLIO MANAGEMENT SUMMER 2002

Rouwenhorst conclude: "There are substantial benefits tointernational diversification beyond the amountsattributable to industrial or currency diversification"[1994, p. 26].

This result nevertheless overshadows the evolutionof country and industry effects. There has been a signif-icant shift from country to industry infiuences in recentyears. Indeed, the relative importance of country effectsdeclined significantly during the period studied, droppingfrom 26.81% of the total effects in 1992 to 7.85% in2000. This decline is consistent, except for 1997.

Conversely, the relative importance of industry effectshas continued to increase. The portion ofthe global returnvariance accounted for by industries increased from 5.54%in 1992 to 17.39% in 2000. As a result, industry effectsexceeded country effects as a source of variation in cross-sectional returns for 1999 and 2000. During these twoyears, diversification across industries would have beenmore advantageous than diversification across countries.

This result is consistent with results in Baca, Garbe,and Weiss [2000], Cavaglia, Brightman, and Aked [2000],and Hopkins and Miller [2001]. Hopkins and Milleremphasize that the increase in industry effects is indica-tive either of more extreme sector returns around theglobal average or of the rising importance of sectors asdrivers (information technology, energy, and utilities).

Fixed Effects and Global Factor Loadings

Exhibit 3, Panel A, presents the results of estimationof Equation (4), which combines both fixed country/indus-try effects and global risk factor loadings. The informa-

tion included in this panel somewhat resembles that ofExhibit 2, except that it introduces the differences inglobal risk exposure as a source of variation in interna-tional stock returns.

The stock-specific component remains very high at72.75% on average (a slight decline from a level of 73.75%with fixed effects only). For the entire sample period,country effects on average continue to dominate indus-try effects (14.77% versus 7.64%). While the fixed effectsmodel assumes that all stocks have the same global riskexposure, results here show that global factor loadingsexplain on average 4.83% ofthe return variance.

As in the analysis based solely on country and indus-try fixed effects, the relative importance of country effectsin the variation of international stock returns has drasti-caUy dechned (from 22.07% in 1992 to 8.20% in 2000).Industry effects have gained concomitantly in impor-tance; they represented 4.62% ofthe cross-sectional vari-ance in 1992 versus 10.71% in 2000. Since 1999, industryeffects have even surpassed country effects.

The most interesting result pertains to global factorloadings. From 1992 to 2000, the global factor loadingsas a source of variation in international stock returns havegrown so dramatically—increasing fiom 6.20% in 1992 to11.51% in 2000—that global factor loadings outweighedcountry and industry effects in 2000. As the global factorloading effects are non-diversifiable, we can conclude thatbenefits of international diversification have been signifi-cantly declining in more recent years, particularly in 2000.

Exhibit 3, Panel B, shows the breakdown of theglobal factor loadings into four components. The corre-lations between the four global risk factor loadings aside.

EXHIBIT 3Evolution of Stock-Specific, Country, Industry, and Global Risk Effects

Year199219931994199519961997199819992000MeanMedian

Stock-Specific67.1167.1971.9275.1178.2372.0075.5172.4769.5972.7572.23

Panel ACountry

22.0721.8419.1515.6913.5116.6413.499.688.20

14.7714.60

Industry4.626.465.855.736.097.157.24

11.9010.717.646.80

Global6.204.523.073.472.174.213.765.96

11.514.833.99

Market0.770.431.320.890.710.531.513.702.601.461.11

Size0.961.270.951.190.571.680.360.352.611.121.07

Panel BB-to-Mkt

2.621.070.450.440.250.691.231.642.721.060.88

Momentum2.411.560.380.590.592.100.780.391.310.960.69

SUMMER 2002 THE JOURNAL OF PORTFOLIO MANAGEMENT 7 5

the most important global risk exposure is that of theglobal market, with 1.46% ofthe total variance followedby SMB, HML, and WMLvnth 1.12%, 1.06%, and 0,96%of the variance, respectively. The increase of the relativeimportance of global factor loadings as a source of vari-ation in international stock returns is driven mainly by theincrease in the percentage of variance explained by theglobal market and size loadings (from 0.77% and 0,96%in 1992 to 2.60% and 2.61% in 2000). The percentageexplained by the book-to-market loadings increased byonly 0.1% for the same period, while that explained bythe momentum loadings declined from 2.41% in 1992 to1.31% in 2000.

ROBUSTNESS OF RESULTS

We also analyze the sensitivity of the results to thedefinition of industries, the number of countries studied,and firm size. First, we analyze whether the tests couldbe biased against finding any industry effects. FollowingGrifFm and Karolyi [1998], we use a more refined classi-fication of industry sectors, to include 21 subindustriesrather than 11 industries.

We also examine the results for a possible bias towardfinding country effects by considering only the four largeststock markets: United States, Japan, United Kingdom, andGermany (77% of the global market capitahzation for1992-2000). In addition, we look at how the country,industry, and global factor loadings effects behave whenmanagers are restricted to the largest global stocks.

Exhibit 4, Panel A, shows the results for a classifi-cation based on 21 subindustries. Such a classificationslightly amphfies the industry effects for all the years con-sidered. Compared with Exhibit 3, the average industryeffect rises from 7.64% to 8.52%. This modest increase inindustry effects is accompanied by a slight increase incountry effects (14,77% to 15,59%), mostly to the detri-ment of stock-specific effects, which decline on averagefrom 72,75% ofthe total effect to 70,95%.

Consequently, we conclude that the industry clas-sification has little impact on average results, consistentwith Griffin and Karolyi s [1998] finding. The decliningweight of the country effects persists with the morerefined industry classification, while the upward trend ofglobal and industry effects remains the same.

Exhibit 4, Panel B, shows the results when theglobal portfolio manager is limited to the U.S., Japan, theU.K., and Germany. Compared with Exhibit 3, the aver-age country effects dechne from 14.77% to 10.97%. This

EXHIBIT 4Evolution of Stock-Specific, Country, Industry,and Global Risk Effects—Sensitivity Analysis

1 Year | Stock-Specific Country IndustryPanel A. 20 Countries and 21 Subindustries

199219931994199519961997199819992000MeanMedian

66.0065.7771.1973,9276.7570.7174,6971,4768,0870.9571.19

22.1221,8918.7415.6113.8416,7213,309,848,27

15.5915.61

1 Panel B. Four Largest Stock Markets199219931994199519961997199819992000MeanMedian

68.5070,1175,9274,8878.6474,1678,4574.2570.7873.9674.25

16.9415.0912.1113.1710,8811,448,115.965.02

10.9711.44

5,717,866.986.967.358,508,40

12,7412,188.527.86

5,618.387.647.427,698,778,83

12.9811,768.798.38

Panel C. 25% Largest Companies in the World199219931994199519961997

60,9560,6566.7169.5873,7069.59

1998 73,6519992000MeanMedian

69.8267.4968.0269.58

24.4023.8521,2516,6215,5416.7613.8310.238,65

16.7916.62

5,928,697.687.247.878.938,32

13.1811.608.838.32

Global

6,184.483.083.512,074,073,615.96

11,474.944.07

8,966.434.344.532,805.634.616.81

n.u6.285.63

8.736.814,366,572,884.734.206,77

12.266.376.57

drop translates into an increase in the relative importanceof industry and global risk effects.

Industry effects are clearly stronger when we con-sider only the four largest markets (8.79% versus 7.64%).Moreover, industry effects dominate country effects asearly as 1998, The average global risk effects, meanwhile,increase from 4.83% to 6,28%. The reduced benefits ofinternational diversification are thus more marked if weconsider the four largest stock markets exclusively.

7 6 COUNTRY, INDUSTRY, AND RISK FACTOR LOADINGS IN PORTFOLIO MANAGEMENT SUMMER 2002

Panel C shows the stock-specific, country, industry,and global risk effects when only the top 25% of firms interms of market capitalization are considered, Kerneis andWilliams [2000] have shown that the large-cap stocks havea more sensitivity to global industry factors than the totaluniverse. Our earlier results are not materially changed byrestricting the total universe to large-cap stocks.

Like Kerneis and Williams [2000], we note that thestock-specific components have less impact in the large-cap universe. This decline ofthe stock-specific componentresults in heightened industry effects (from 7,64% to8,83%), country effects (from 14.77% to 16.79%), andglobal risk effects (from 4,83% to 6.37%). Even for large-cap companies, country effects have on average dominatedindustry effects during the period of 1992-2000, Indus-try and global risk effects, however, are still more appar-ent than country effects in 1999 and 2000.

SUMMARY

We have compared the relative importance of coun-try, industry, and global factor loading effects in explain-ing the variation in international stock returns during the1990s (from January 1992 through December 2000), Wefactor a risk dimension into the analysis, making it pos-sible to identify the portion ofthe variation in interna-tional returns attributable to global risk levels incurred.

If we consider the country and industry dimen-sions exclusively, on average country effects dominatedindustry effects over the entire period. Consequently,diversification across countries was on average more effi-cient than diversification across industries.

Country effects dechned significantly during thenineties, however. The portion of the return varianceattributable to country effects declined from 26.81% in1992 to only 7.85% in 2000, a decrease of 70.72%. Indus-try effects came to play a greater role in explaining the vari-ance of international stock market returns, shifting from5.54% in 1992 to 17,39% in 2000; they dominated coun-try effects in both 1999 and 2000. Thus, ongoing globalintegration has made industry-oriented approaches to globalinvestment as effective as country-oriented approaches.

By implication, global management strategies shouldpay greater attention to the benefits of industrial diversi-fication. As country effects remain more than trivial,however, asset classes should be defined using both coun-try and industry dimensions to maximize the benefits ofdiversification.

Furthermore, globalization has strengthened the

role of global risk factors in explaining co-movements ininternational stock returns. The extent of stock marketreturns explained by differences in exposure to globalrisk factors rose considerably during the period covered.Global risk effects dominated both country and industryeffects in 2000, with 11,51% versus 10,71% for industryeffects and 8.20% for country effects. Global managementstrategists could consequently delineate asset classes on thebasis of their global risk factor loadings.

The trend toward globalization is instrumental indetermining the relative importance of country, industry,and global risk effects. The structural changes in globaleconomies probably explain why in the last decade coun-try effects have been losing ground in favor of industryand global risk effects. Given that these three effects havebecome equally important in the recent period, it is bestto consider all three dimensions—country, industry, andglobal risk factors—in constructing portfohos.

APPENDIX

Construction of Global Risk Factors

As we focus on both country and industry effects, we donot compute global factors as weighted averages of country (seeFama and French [1998]) or industry factors. Instead, we com-pute them regardless of countries or industries.

For each month t from July of year y - 1 to June of yeary, we rank stocks based on size and book-to-market ratio ofjuney - 1 and their previous performance between t - 12 and t —1, We perform independent sorts beginning in July 1990 to cre-ate SMB^^, HML^, and WML^. We use 50% break points forsize, and 30% and 70% break points for book-to-market andprior performance.

Following Fama and French [1993], we form six globalvalue-weight portfoHos, S/L, S/M, S/H, B/L, B/M, and B/H,as the intersection of size and book-to-market groups. We fol-low the same procedure for prior performance as for book-to-market; that is we form six global value-weight portfolios,S/L, S/M, S/W, B/L, B/M, and B/W, as the intersection ofsize and prior performance groups, SMB^, HML^, and WML^are as foUows: SMB^, = {{S/L- B/L) + {S/M- B/M) + {S/H- B/H)]/3, HMLJ= [{S/H-S/L) + (B/H- B/L)]/2, andWML^ = [{S/W-S/L) + {B/W- B/L)]/2.

Consequently, our methodology can be compared directlyto neither that of Liew and Vassalou [2000], who use threesequential sorts, nor to that of Arshanapalli, Coggin, and Doukas[1998], who use 70% and 30% break points for SMB and con-struct HML by selecting the highest book-to-price stocks untilhalf of the capitalization of each market is accumulated.

SUMMER 2002 THEJOURNAL OF PORTFOLIO MANAGEMENT 7 7

ENDNOTES

The authors thank for insightful comments StephanieDesrosiers, Richard Guay, Walid Hached, and Aurel Wisse, andfor helpful translation assistance Karen Sherman.

'This dummy variable regression framework was firstdeveloped by Solnik and de Freitas [1988] and Grinold, Rudd,and Stefek [1989]. It was later used by Beckers et al. [1992],Drummen and Zimmerman [1992], Roll [1992], and Hestonand Rouwenhorst [1994], among others.

^The countries are Canada, the United States, Malaysia,and 17 ofthe 20 countries in the EAFE index. Like Cavaglia,Brightman, and Aked [2000], we focus only on developedcountries that are more economically integrated in order toavoid a possible country bias effect related to emerging coun-tries. The Compustat database defines 12 industries. There arefew firms and only four countries in the biotechnology sector,so we group it with the information technology sector, result-ing in 11 industries. To analyze the sensitivity of results to thedefinition of industries, we use the first two digits ofthe Com-pustat 103-sector additional classification, leading to a classifi-cation of 21 subindustries.

^On the whole, 14,452 securities were extracted from thedatabase. We dropped 3,534 securities that are not classifiedwithin an industry, and then subsequently eliminated 3,570 firmslacking sufficient data. This process resulted in a sample of7,348 firms, of which 1.21% are inactive.

Heston and Rouwenhorst [1994] use 829 firms; Griffinand Karoiyi [1998] about 2,600. More recently, Baca, Garbe,and Weiss [2000] and Cavaglia, Brightman, and Aked [2000]examine 3,212 and 2,645 firms, respectively.

''With respect to dollar retums, Heston and Rouwenhorstemphasize that "most of the variance of the country effectcannot be explained by currency movements" [1994, p. 24].

^By comparison, Liew and Vassalou [2000] report in tenmajor markets over 1978-1996 both economically and statis-tically significant premiums. Furthermore, because growthstocks posted a relatively high return in the 1992-2000 period,the HMLu; premium is much lower than the ones reported byFama and French [1998] and ArshanapaUi, Coggin, and Doukas[1998] over the 1975-1995 period.

'As in Griffin and Karoiyi [1998], in order to take intoconsideration the relative impact of market capitalization, weuse the weighted least squares method rather than the ordinaryleast squares method.

'To calculate the cross-sectional variance, each stock isassigned a weight equal to its capitalization weight in the worldportfolio at the beginning ofthe month. Note that the decom-position ignores the covariance between the fixed industry andcountry effects. The proportion ofthe variance attributable tospecific effects is therefore not perfectly equal to 1 - R l Thisapproximation is reasonable insofar as covariance between fixedindustry and country effects is not very different from zero.

loadings on the global market and the three zero-net investment portfolios are estimated after June 1993 using36-month moving windows (between t - 37 and t - 1) andimposing the restriction that 24 months of data be available ineach security window. For the first months, however, we useshorter moving windows. For better comparison, we estimateboth model specifications from 1992 through 2000.

'These components are calculated using the equations

Var\

, 3.ncl

The sum of these individual components is not equal tothe global factor loadings effect, G/T^ because this latter termconsiders the covariances between these components.

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To order reprints of this article please contact Ajani Malik atamalik@iijournals.com or 212-224-3205.

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