oun ry, Ind ustry, an d R sk Factor Factor o a d in in g s n P o r t o l o a n a g e m e n t Country effects declining in importance; industry effects growing.
Jean-Frangois L'Her, L'Her, Ouma Sy, and Mohamed Yassine Tnani
global portfolio built using top-dovyn a p p r o a c h m a y u s u a l l y be a l l o c a t e d on the basis c o u n t r y or i n d u s t r y d i m e n s i o n . c h o i c e o f d im i m e n s i o n w iU iU d e p e n on w h e t h e r t h e p o r t f o h o m a n a g e r b e h e v e s t h a t r e t u r n s are g o v e r n e d p r i m a r i l y by c o u n t r y or by industry effects.
JEAN-FRANCOIS
L'HER is
a research advisor de depot
Caisse
placement
Quebec in Montreal (Quebec H3A 3C7).
[email protected]
OUMAR
SY is
an analyst at
Caisse de depot et placement du Quebec in Montreal (Quebec H3A 3C7).
o$y@ cdp.c MOHAMED YASSINE TNANI
is an analyst at Caisse de depot et placement
Quebec
Montreal (Quebec H3A 3C7).
[email protected]
H e s t o n and R o u w e n h o r s t [ 1 9 9 4 , 1 9 9 5 ] , G r if i f fi fi n a n d K a r o i y i [ 1 9 9 8 ] , an R o u w e n h o r s t [ 19 19 9 9 ] s h o w t h a t c o u n t r y e f f e c t s , on a v e r a g e , d o i n a t e d i n d u s t r y e f fe fe c t d u r i n g th 1 9 7 5 - 1 9 9 8 p e r i o d . B a c a , C a r b e , an W e i s s [ 2 0 0 0 ] , C a v a g h a , B r i g h t m a n , and A k e d [ 2 0 0 0 ] , K e r n e i s a n d W i l l i a m s [ 2 0 0 0 ] , an H o p k i n s an M i l l e r [ 2 0 0 1 ] , h o w e v e r , p o i n t ou t h a t i n d u s t r y e f f e c t s h a v e g r o w n so m a r k e d l y in i m p o r t a n c e t h a t t h e y h a v e s u p e r s e d e d c o u n try effects in the v a r i a t i o n o f i n t e r n a t i o n a l s t o c k r e t u r n s . T h e s e t r e n d s in c o u n t r y and industry effects can be e x p l a i n e d l a r g e l y by o n g o i n g c a p i ta ta l m a r k e t i n t e g r a t i o n . T h e p a s t few y e a r s h a v e w i t n e s s e d i n c r e a s e d c o r r e l a t i o n s b e t w e e n c o u n t r y r e t u r n s (s (s ee ee F r e i m a n n [ 1 9 9 8 ]) ]) . T h i s p h e n o m e n o n i s a t t r i b u t a b l e to a n u m b e r o f s t r u c tu tu r a l c h a n g e s : r e d u c t i o n in i n t e r n a t i o n a l b a r r i e r s to i n v e s t m e n t ; m a j o r d e v e l o p m e n t s in i n f o r m a t i o n t e c h n o l o g i e s t h a t h a v e i m p r o v e d a c c e s s to g l o b a l i n f o r m a t i o n ; an u n p r e c e d e n t e d w a v e o f g l o b a l m e r g e r s and takeovers; m o v e t o w a r d p r i v a t i z a t i o n ; and t h e i n t e g r a t i o n o f g e o g r a p h i c z o n e s , e s p e cially in E u r o p e . C l o b a h z a t i o n o f t h e w o r l d e c o n o m y has h k e l y d i m i n i s h e d th b e n e f i t s of diversification across c o u n t r i e s in favor of diversification across industries. use a t w o - s t e p p r o c e d u r e to r e e x a m i n e the rel-
a t i v e i m p o r t a n c e of c o u n t r y and industry effects in the
variation of international stock returns. The first step, which estimation of the model, follows Heston an Rouwenhorst [1994, 1995] and Griffm an Karoiyi [1998].' In the second step, unhke previous authors, we separate the cross-sectional variance of monthly international stock returns into different effects, an then study the evolution of each component. Our work differs from other research in two key areas: the data se used, and, more important, the inclusion of global risk factor loadings in the analysis. Poor's C ompustat® data, taken fiom Standar Global Vantage, cover 20 developed countries and 11 broad industries, an span the period July 1989 through December 2000. An advantage of our data set is that it covers great number of stocks (7,348 firms), m aking it possible to obtain more cross-sectional variance in the size characteristics of firms. The more distinctive element of the research pertains to inclusion of global risk factor loadings in the model. Studies examining the relative importance of country an industry effects s a source of variation in international stock returns have assumed identical global risk exposure fo each stock. authors have demonstrated the presence of global premiums related to size (Heston, Rouwenhorst, an Wessels [1995]), book-tomarket (ArshanapaUi, Coggin, and Doukas [1998], Fama and French [1998]), an price momentum (Rouwenhorst [1998]) (see Liew and Vassalou [2000] fo evidence on these three premiums). We use a global four-factor pricing model to control fo differences in global risk factor loadings between international stocks. Using country/industry dummy variable framework, show that country effects dominated industry effects during the 1992-2000 sample period, corrobo ratin the findings of Heston an Rouwenhorst [1994, 1995] and Griffm an Karoiyi [1998]. Consequently, country diversification was on average more eflicient than industry diversification during the nineties. Like Baca, Garbe, and W eiss [2000], Cavaglia, Brightm an, and Aked [2000], Kerneis an WiUiams [2000], an Hopkins an MiUer [2001], we also note that industry effects have gained importance. ongoing trend toward integration has reduced the benefits of country diversification; consequently, industry-oriented approaches to global management could be as effective country-oriented approaches in the future. Top-down approaches to global equity portfolio aUocation should consider both the country an indus-
try dimensions. Carrieri, Errunza, an Sarkissian [2000, p. 26] conclude that: "In other words, .. investors should use both cross-country an cross-industry diversification as a way to improve portfoho performance" (emphasis in the o riginal) More important, the globalization ofthe economy has also strengthened the role of global risk factors s source of variation in international stock returns. While the main trends of the cou ntry/in dustry analysis remain the same, global risk effects became stronger during the sample period, and are currently more significant than country an industry effects. Consequently, global managers should consider exposure to these global risk factors when they construct their portfohos. DATA
Our data set, extracted from the Standard & Poor's Compustat® Global V antage database, spans the perio d July 1989—December 2000 an covers a total of 20 cou ntries and 11 industries.^ The set covers 7,348 stocks, more than oth er studies includes smaU-capitahzation stocks, which enables us to obtain more cross-sectional variance in size factor loadings.' Th e sample includes firms which information as foUows available: doUar-denominated total return, market capitaHzation, book-to-market ratio, and description ofth e industry and country affihation." Exhibit provides descriptive statistics on the returns observed from January 1992 through December 2000. Panel gives the main return characteristics by country. On average, we examine 367 firms per country, bu the number of firms an industries varies by country. The United States by the most represented, with almost one-qua rter o ft e firms covered (1,757 firms) foUowed by Japan (1,682 firms),and the United Kingdom (908 firms). With the exception of Finland, the most volatile index returns are observed in the Far East countries. Finland (4.04% per month) an Sweden (2.09% pe month) are the countries with the highest cap-weighted average return. Austria and Japan recorded the poorest average returns (respectively, -0.12% and 0.16% per month). The low tracking error bet'ween the index returns of the G-7 countries an their corresponding MSCI country index returns s guarantee of the quality of our data. Panel B provides the main return characteristics by industry. The number of firms in industries varies from 1,837 fo consum er cychcal to 62 fo communication ser-
EXHIBIT 1 Country, Industry, and Global Retums January 1992-December 2000 Panel A. Countries
Australia Austria Belgium Canada Denmark Finland France Germany Hong Kong Italy Japan Malaysia Netherlands Norway Singapore Spain Sweden Switzerland U,K, U,S,
Mean Median Panel B. Industries
Basic Materials Capital Goods Communication Services Consumer Staples Consumer Cyclicals Energy Financials Health Care Technology Transportation Utilities Mean Median
Panel C. Global Risk Factors
WML..
Number of Firms
Weight
Retum
Standard Deviation
90 1757
1.41 0.17 0.67 2.23 0.34 0.57 4.28 5.33 1.85 2.25 18.82 0.69 1.96 0.24 0.51 0.96 0.85 1.97 8.75 46.13
0.80 -0.12 0.91 1.05 0.82 4.04 1.13 0.90 1.44 0.81 0.16 0.82 1.68 0.87 0,99 1,16 2,09 1,49 1.02 1,35
5,06 5.04 3,83 5,57 4,89 11,55 4,76 4,73 9,77 7,06 6,82 12,00 5,06 6,60 10,17 6,15 7,83 4,58 4,07 3,90
36
5,00
1,17
6,47
148
1,63
1,01
5,32
41
81
14 15
1682 363 131 18 12 13
Num ber of Firms
1345
1837
28 191
668 71
Weight
Retu
Standard Deviation
6,02 8,91 9,93 9,86 12,30 4,24 19,50 6,82 15,75 2,53 4,14
0,56 0,89 0,86 0,84 0,57 1,17 1,29 1,92 0,33 0,81
4,18 4,24 6,19 3,11 3,75 4,75 4,59 4,29 6,69 3,76 2,84
9,09
0,94
4,40
8,91
0,86
4,24
t-test
Retum
Standard Deviation
2,80
1,00
3,72
1,69
0,61
3,72
0,74
0,21
2,93
-0,23
-0,08
3,60
2,20
0,76
3,58
Retums are expressed in USD . Weights, retums, and standard deviations are expressed in percentage on a monthly basis.
vices. Over the period considered, technology (1,92% per month) and health care (1,29% per month) posted the best returns; basic m aterial (0,56% per month) and consumer cyclicals (0.57% per mo nth) posted the lowest returns. The technology returns were the most volatile (6.69% standar deviation of monthly returns). At the other extreme, utilities returns registered standard deviation of only 2.84%. Panel shows the average return and standard deviation of each of the four global risk factors: the global marke {R an three global zero net investment portfolios constructed on the basis of firm market capitalization (small minus big: SMB^), firm book-to-market (high minus low: HML^), an stock price momentum (winners minus losers: WML^). Th e global market posted return of 1% pe month (t-statistic 2,80), The global market premium was 0.61% per month and significantly different firomzero only at the 10% level. SMB^, HML^, an ^ML^ posted monthly returns of 0.21%,"'-0,08%,'and 0,76%, respectively. is significant at the 1% level (t-statistic 2.20), while and HM are not significant.^ See the appendix fo details on the construction of factors. METHODOLOGY
Th e first methodology presented is based on country an industry fixed effects, and the second integrates country and industry fixed effects with global factor loadings. Country
and
wise, Ij s a dummy variable that equals when firm belongs to industry i (i = 1, ..,, 11) and 0 otherwise, an is the error term. To solve the identification problem induced by dummy variables and to facilitate interpretation of the coefficients, we im pose th e same restrictions Heston and Rou we nhorst [1994,1995 ] and Griffin and Karoiyi [1998]: 20
(2-A)
c=
II
(2-B)
an where (f)^^ an (p.^ are the weights of country industry in the world portfolio at the beginning of th onth . Given these restrictions, the parameter a^^ can be interpreted as the cap-weighted average return of the world portfolio time t, an coefficients and X.^ stand for the "pure" bet at time on country without industry bias and the "pure" bet on industry i at time without country bias. In the second step, the cross-sectional variance of the international stock returns s segmented in order to identify the proportion of the variance attributable to stockspecific {S/T ), country {C/T ), and industry (I/T effects. T his allows us to determine fo each month components that best explain the cross-sectional variance of international stock returns. The three components are calculated as follows:
Industry Fixed Effects
We use a two-step procedure to differentiate between the variance of international stock returns due to country effects and the variance attributable to industry effects. Th e first step, whic h separates country-related performance fi-om ndustry-related performance, is similar to th dummy variable regressionfi-ameworkused in Heston and Rouwenhorst [1994, 1995] and Griffin an Karoiyi [1998]:'^
C.
c=
t_
T.
where,
(3)
S^
stands for the total effects.''
(1)
Fixed Effects and Global Risk Loadings where R., is the return of firm j ' = 1, .,., N = 7,348) for period is dummy variable that equals 1 when firmj belongs to country c c = 1, ,.., 20) and other-
The analysis considers fixed country an industry effects exclusively, and assumes that all stocks have the same global risk exposure. Our main contribution is to exam-
ine the relative importance of loadings on the four global risk factors and c oun try/i nd ustry dum my variables as source of variation in international stock returns. We use a four-factor global pricing model to estimate the factor loadings for each stock j ( ) 3 _ . , , y3,,.,, ^,,.,, andi§^^.,) and then estimate this model monthly:*
20
(4)
where (^ is the world return for the period that is not explained by the four global risk factors, and the parame^^^^ ^umt' «u.«' "whr an'^ «, ^, represent the global risk pre miums associated w ith each factor loading. Equation (4) makes it possible to compare coun try and industry effects while controlling for differences in exposure of internationa l stock returns to the four global source risk.
Equation (4). As in the country/industry analysis, the second step consists of subdividing the cross-sectional variance of international stock returns into four components: the stock-specific (S/T), country (C/T), industry (I/T), and global risk factor loading (G/T) components. The first three components are calculated using the procedure above (except T) , while the fourth, w hich s the variance explained by global risk factor loadings, is equal to:
(5)
where T^-Sj + Cj I^ G, represents the total effects. To assess the relative im portan ce of each individual global factor loading, we separate the {G/T^ variable into four components related to the exposure to global market {RJT), size (SMBJT), book-to-market (HMLJT), and return momentum { W M L / T ) . ' RESULTS We present the results ofthe methodology that considers solely fixedcoun try and industry effects first. N ext,
EXHIBIT Evolution of Stock-Specific, Country, and Industry Effects Year
Stock-Specific
Country
Industry
1992
67,65
26,81
5,54
1993
68,11
25,35
6,54
1994
72,51
21,26
6,23
1995
75,80
17,99
6,22
1996
78,94
14,70
6,36
1997
73,32
19,02
7,67
1998
77,30
13,72
8,98
1999
75,35
9,75
14,90
2(X)0
74,76
7,85
17,39
Mean
73.75
17.38
8.87
Median
74.76
17.99
6.54
Each effect is measured by the average annual proportion ofthe variance that is due to the stock-specific, country, and industry components [Equation (3)]. Proportions expressed in percentages.
we analyze the results obtained when global factor loadings are introdu ced. We the n investigate the robustness of the results using a different industry classification, fewer countries, and only large market capitalization firms. Country and Industry Fixed Effects Exhibit 2 shows the contribution of each of the three effects (specific, country, and industry) to the crosssectional variance of international stock returns for each year studied. With on average 73,75% ofthe total effects, the stock-specific component largely dominates the other effects. This result confirms the relevance of investing in a portfolio rather than in a single stock, given that the stock-specific com pon ent can be significantly reduced by forming portfolio of non-perfectly correlated securities. The remainder of the international stock return cross-sectional variance is explained by country and industry effects. For the total sample period, country effects explain on average 17,38% ofth e return variance, dom inating the 8.87% explained by industry effects. T his result is consistent with the conclusions of Heston and R ou en horst [1994, 1995] and Griffin and Karolyi [1998]. There are significant portfolio management implications to be drawn from the dominance of country effects over industry effects in th e variation of internatio nal stock returns. The most important is that diversification across countries has been more effective than diversification across industries during this period. As Heston and
Rouwenhorst conclude: "There are substantial benefits to international diversification beyond the amounts attributable to industrial or currency diversification" [1994, p. 26]. This result nevertheless overshadows the evolution of country and industry effects. There has been a significant shift from country to industry infiuences in recent years. Indeed, the relative importance of country effects declined significantly durin g the period studied, dropping from 26.81% of the total effects in 1992 to 7.85% in 2000. This decline is consistent, except for 1997. Conversely, the relative importance of industry effects has continued to increase. The p ortion ofth e global return variance accounted for by industries increased from 5.54% in 1992 to 17.39% in 2000. As a result, industry effects exceeded country effects as a source of variation in crosssectional returns for 1999 and 2000. During these two years, diversification across industries would have been mo re advantageous tha n diversification across countries. This result is consistent w ith results in Baca, Garbe, and Weiss [2000], Cavaglia, Brightm an, and Aked [2000], and Hopkins and Miller [2001]. Hopkins and Miller emphasize that the increase in industry effects is indicative either of more extreme sector returns around the global average or of the rising importance of sectors as drivers (information technology, energy, and utilities). Fixed Effects and Global Factor Loadings Exhibit 3, Panel A, presents the results of estimation of Equation (4), which combines both fixedcountry/industry effects and global risk factor loadings. The informa-
tion included in this panel somewhat resembles that of Exhibit 2, except that it introduces the differences in global risk exposure as a source of variation in international stock returns. The stock-specific component remains very high at 72.75% on average a slight decline from level of 73.75% with fixed effects only). For the entire sample period, country effects on average continue to dominate industry effects (14.77% versus 7.64%). While the fixed effects model assumes that all stocks have the same global risk exposure, results here show that global factor loadings explain on average 4.83% oft he return variance. As in the analysis based solely on country and indu stry fixed effects, the relative importance of country effects in the variation of international stock returns has drasticaUy dechned (from 22.07% in 1992 to 8.20% in 2000). Industry effects have gained concomitantly in importance; they represented 4.62% ofthe cross-sectional variance in 1992 versus 10.71% in 2000. Since 1999, industry effects have even surpassed country effects. The most interesting result pertains to global factor loadings. From 1992 to 2000, the global factor loadings as a source of variation in inte rnational stock returns have grown so dramatically—increasing fiom 6.20% in 1992 to 11.51% in 2000—that global factor loadings outweighed coun try and industry effects in 20 00. As the global factor loading effects are non-diversifiable, we can conclude that benefits of international diversification have been significantly declining in m ore recent years, particularly in 200 0. Exhibit 3, Panel B, shows the breakdown of the global factor loadings into four components. The correlations betwee n the four global risk factor loadings aside.
EXHIBIT 3
Evolution of Stock-Specific, Country, Industry, and Global Risk Effects
Year 1992 1993 1994 1995 1996 1997 1998 1999 2000 Mean Median
Stock-Specific 67.11 67.19 71.92 75.11 78.23 72.00 75.51 72.47 69.59 72.75 72.23
Panel A Country Industry 4.62 22.07 21.84 6.46 5.85 19.15 5.73 15.69 13.51 6.09 16.64 7.15 7.24 13.49 9.68 11.90 10.71 8.20 14.77 7.64 6.80 14.60
Global
Market
Size
6.20 4.52 3.07 3.47 2.17 4.21 3.76 5.96 11.51 4.83 3.99
0.77 0.43 1.32 0.89 0.71 0.53 1.51 3.70 2.60 1.46 1.11
0.96 1.27 0.95 1.19 0.57 1.68 0.36 0.35 2.61 1.12 1.07
Panel B B-to-Mkt 2.62 1.07 0.45 0.44 0.25 0.69 1.23 1.64 2.72 1.06 0.88
Momentum 2.41 1.56 0.38 0.59 0.59 2.10 0.78 0.39 1.31 0.96 0.69
the most important global risk exposure is that of the global market, with 1.46% ofthe total variance followed by S M B , H M L , and WMLvnth 1.12%, 1.06%, and 0,96% of the variance, respectively. The increase of the relative importance of global factor loadings as a source of variation in internationa l stock returns is driven m ainly by the increase in the percentage of variance explained by the global market and size loadings (from 0.77% and 0,96% in 1992 to 2.60% and 2.61% in 2000). Th e percentage explained by the book-to-market loadings increased by only 0.1% for the same period, while that explained by the m om entum loadings declined from 2.41% in 1992 to 1.31% in 2 000. ROBUSTNESS OF RESULTS We also analyze the sensitivity of the results to the definition of industries, the number of countries studied, and firm size. First, we analyze whether the tests could be biased against findin g any industry effects. Following GrifFm and Karolyi [1998], we use a more refined classification of industry sectors, to include 21 subindustries rather than 11 industries. We also exam ine the results for possible bias toward finding country effects by considering only the four largest stock m arkets: Un ited States, Japan, United Kingdom , and Germany (77% of the global market capitahzation for 1992-2000). In addition, we look at how the country, industry, and global factor loadings effects behave when managers are restricted to the largest global stocks. Exhibit 4, Panel A, shows the results for a classification based on 21 subindustries. Such a classification slightly amphfies the industry effects for all the years considered. Compared with Exhibit 3, the average industry effect rises from 7.64% to 8.52%. This modest increase in industry effects is accompanied by a slight increase in country effects (14,77% to 15,59%), mostly to the detriment of stock-specific effects, which decline on average from 72,75% ofthe total effect to 70,95%. Consequently, we conclude that the industry classification has little impact on average results, consistent with Griffin and Karolyi [1998] finding. Th e declining weight of the country effects persists with the more refined industry classification, while the upward trend of global and industry effects remains the same. Exhibit 4, Panel B, shows the results when the global portfolio manager limited to the U.S., Japan, th U.K., and G ermany. Compared w ith Exhibit 3, the aver age country effects dechne from 14.77% to 10.97%. This
EXHIBIT 4
Evolution of Stock-Specific, Country, Industry, and Global Risk Effects—Sensitivity Ana lysis 1 Year Stock-Specific Country Industry Global Panel A. 20 Countries and 21 Subindustries 1992 66.00 22.12 5,71 6,18 1993 65.77 21,89 7,86 4.48 1994 71.19 18.74 6.98 3.08 1995 73,92 15.61 6.96 3.51 1996 76.75 13.84 7.35 2,07 1997 70.71 16,72 8,50 4,07 1998 74,69 13,30 8,40 3,61 1999 71,47 9,84 12,74 5.96 2000 68,08 8,27 12,18 11,47 Mean 70.95 15.59 8.52 4.94 Median 71.19 15.61 7.86 4.07 1 Panel B. Four Largest Stock Markets 1992 68.50 16.94 5,61 8,96 1993 70,11 15.09 8.38 6.43 1994 75,92 12.11 7.64 4.34 1995 74,88 13.17 7.42 4.53 1996 78.64 10,88 7,69 2,80 1997 74,16 11,44 8,77 5.63 1998 78,45 8,11 8,83 4.61 1999 74.25 5.96 12.98 6.81 2000 70.78 5.02 11,76 .u Mean 73.96 10.97 8.79 6.28 Median 74.25 11.44 8.38 5.63 Panel C. 25% Largest Com panies in the World 1992 60,95 24.40 5,92 8.73 1993 60,65 23.85 8,69 6.81 1994 66.71 21,25 7.68 4,36 1995 69.58 16,62 7.24 6,57 1996 73,70 7.87 15,54 2,88 1997 69.59 16.76 8.93 4.73 1998 73,65 13.83 8,32 4.20 1999 69.82 10.23 13.18 6,77 2000 67.49 8,65 11.60 12.26 Mean 68.02 16.79 8.83 6.37 Median 69.58 16.62 8.32 6.57
drop translates into an increase in the relative importance of industry and global risk effects. Industry effects are clearly stronger when we consider only th e four largest markets (8.79% versus 7.64%). Moreover, industry effects dominate country effects as early 1998, T he average global risk effects, mea nwh ile, increase from 4.83% to 6,28%. The reduced benefits of international diversification are thus more marked if we consider the four largest stock markets exclusively.
Panel C shows the stock-specific, country, industry, and global risk effects when only the top 25% of firms in terms of market capitalization are considered, Kerneis and Williams [2000] have shown that the large-cap stocks have a more sensitivity to global industry factors than the total universe. O ur earlier results are not materially changed by restricting the total universe to large-cap stocks. Like Kerneis and Williams [2000], we note that the stock-specific components have less impact in the largecap universe. This decline ofthe stock-specific component results in heightened industry effects (from 7,64% to 8,83%), country effects (from 14.77% to 16.79%), and global risk effects (from 4,83% to 6.37%). Even for largeindustry effects during the period of 1992-2000, Industry and global risk effects, however, are still more apparent than country effects in 1999 and 2000.
role of global risk factors in explaining co-movements in international stock returns. The extent of stock market returns explained by differences in exposure to global risk factors rose considerably during the period covered. Global risk effects dominated both country and industry effects in 2000, with 11,51% versus 10,71% for industry effects and 8.20% for country effects. Global management strategists co uld consequ ently delineate asset classes on the basis of their global risk factor loadings. The trend toward globalization is instrumental in determining the relative importance of country, industry, and global risk effects. The structural changes in global economies probably explain why in the last decade country effects have been losing ground in favor of industry and global risk effects. Given that these three effects have become equally important in the recent period , it is bes to consider all three dimensions—country, industry, and global risk factors—in constructing portfohos.
SUMMARY
We have compared the relative impo rtance of coun try, industry, and global factor loading effects in explaining the variation in international stock returns during the 1990s (from January 1992 throug h D ecem ber 2000), We factor a risk dimension into the analysis, making it possible to identify the portion ofthe variation in international returns attributable to global risk levels incurred. If we consider the country and industry dimensions exclusively, on average country effects dominated industry effects over the entire period. Consequently, diversification across countries was on average more efficient than diversification across industries. Country effects dechned significantly during the nineties, however. The portion of the return variance attributable to country effects declined from 26.81% in 1992 to only 7.85% in 2000, a decrease of 70.72%. Indu stry effects came to play greater role in explaining the variance of international stock market returns, shifting from 5.54% in 1992 to 17,39% in 2000; they dominated country effects in both 1999 and 2000. Thus, ongoing global integration has made industry-oriented approaches to global investment as effective as country-oriented approaches. By implication, global management strategies should pay greater attention to the benefits of industrial diversification. As country effects remain more than trivial, however, asset classes should be defined using both co un try and industry dimensions to maximize the benefits of diversification. Furthermore, globalization has strengthened the
APPENDIX Construction of Global Risk Factors
As we focus on bo th c oun try and indu stry effects, w e do not com pute global factors as weighted averages of country (see Fama and French [1998]) or industry factors. Instead, we com pute them regardless of countries or industries. 1 t June of year For each month from July of year y, we rank stocks based on size and boo k-to-m arket ratio ofjune y - 1 and their previous performance b etween t - 12 and 1, e perform independen t sorts beginning in July 1990 to create SMB^^, HML^, and W M L ^ . We use 50% break points for size, and 30% and 70% break points for book-to-market and prior performance. Following Fama and French [1993], we form six global value-weigh t portfoHos S/L, S/M, S/H, B/L, B/M, and B/H, s the intersection of size and boo k-to-m arket groups. We fol low the same procedure for prior performance as for b oo k-t omarket; that is we form six global value-weight portfolios, S/L, S/M, S/W, B/L, B/M, and B/W, s the intersection of size and prior performance groups, SMB^, HML^, and W M L ^ 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, and WML^ = [{S/W-S/L) + {B/W- B/L)]/2.
Consequently, ou r m ethodology can be compared directly to neither that of Liew and Vassalou [2000], who use three sequential sorts, nor to that of Arshanapalli, Coggin, and Doukas and con [1998], who use 70% and 30% break points for struct by selecting the highest book-to-price stocks until half of the capitalization of each m arket is accumu lated.
ENDNOTES The authors thank for insightful comments Stephanie Desrosiers, Richard Guay, W alid H ached, and Aurel Wisse, and for helpful translation assistance Karen Sherman. as first 'This dummy variable regression framework developed by Solnik and de Freitas [1988] and Grinold, Rudd, and Stefek [1989]. It was later used by Beckers et al. [1992], Drummen an Zimmerman [1992], Roll [1992], an Heston and Rouwenhorst [1994], among others. ^The countries are Canada, the United States, Malaysia, and ofthe 20 countries in the EAFE index. Like Cavaglia, Brightman, an Aked [2000], we focus only on developed countries that ar more economically integrated in order to avoid possible country bias effect related to emerging cou ntries. The Com pustat database defines 12 industries. There ar few firm an only four countries in the biotechnology sector, so we group it with the information technology sector, resultin in 11 indu stries To analyze the sensitivity of results to the definition of industries we use the first two digits ofthe Co pustat 103-sector additional classification, leading to classification of 21 subindustries. ^On the wh ole, 14,452 securities were extracted from the database. We dropped 3,534 securities that are not classified within an industry, and then subsequently eliminated 3,570 firms lacking sufficient data. This process resulted in a sample of 7,348 firms, of which 1.21% are inactive. Heston an Rouwenhorst [1994] use 829 firm s; G riffi and Karoiyi [1998] about 2,600. More recently, Baca, Garbe, and Weiss [2000] an Cavaglia, Brightman an Aked [2000] examine 3,212 an 2,645 firms , respectively. ''With respect to dollar retums, Heston and Rouwenhorst emphasize that "most of the variance of the country effect cannot be explained by currency movements" [1994, p. 24]. ^By comparison, Liew and Vassalou [2000] report in ten major markets over 1978-1996 both economically an statistically significant premiums. Furthermore, because growth stocks posted relatively high re turn in the 1992-2000 period, th HMLu; premium is much lower than the ones reported by Fama and French [1998] and ArshanapaUi, Cog gin, and Doukas [1998] over the 1975-1995 period. 'A in Griffin an Karoiyi [1998], in order to take into consideration the relative im pact of market capitalization, we use the weighted least squares method rather than the ordinary least squares method. 'To calculate the cross-sectional variance, each stock assigned weight equal to its capitalization weight in the world portfolio at the beginning ofthe mon th. Note that the decomposition ignores the covariance between the fixed industry and country effects. The proportion ofthe variance attributable to specific effects therefore not perfectly equal to This approximation is reasonable insofar covariance between fixed industry an country effects is not very different from zero.
loadings on the global market and the three zeronet investment portfolios ar estimated after June 1993 using 36-month moving windows (between t - 37 and t - 1) and imposing the restriction that 24 months of data be available in each security w indow For the first mon ths, how ever, we use shorter moving windows. For better comparison, we estimate both model specifications from 1992 through 2000. 'These components ar calculated using the equations Var\
3.ncl
The sum of these individual compon ents is not equal to the global factor loadings effect, G/T^ because this latter term considers the covariances between these com ponen ts.
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