Sector Effects in Developed vs. Emerging Markets

CFA Institute

Sector Effects in Developed vs. Emerging MarketsAuthor(s): Jianguo Chen, Andrea Bennett and Ting ZhengSource: Financial Analysts Journal, Vol. 62, No. 6 (Nov. - Dec., 2006), pp. 40-51Published by: CFA InstituteStable URL: http://www.jstor.org/stable/4480789 .

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Financial Analysts Journal Volume 62 . Number 6 02006, CFA Institute

Sector Effects in Developed vs. Emerging Markets

Jianguo Chen, Andrea Bennett, and Ting Zheng

This examination of developed and emerging markets suggests that toward the end of the 20th century, sector effects caught up with country effects in the developed markets of the world, as a result of rising sector effects rather than declining country effects. For emerging markets, however, country effects have remained the dominant influence relative to sector effects, although the importance of country effects has been on a steady decline. These results confirm that international equity managers should emphasize sector-based approaches when investing in the developed countries but should continue country-based allocation strategies in emerging markets.

ncreased links among capital markets, the establishment of trading blocs of countries, and the increasingly global nature of business are changing the investment scene for global

equity managers. In light of these trends, how will international investment managers-with grow- ing funds under management-continue to find diversification opportunities and excess returns in the future?

A fundamental starting point for global equity managers when applying a top-down approach to their investing has been the selection of a country. This focus arose out of numerous studies (e.g., Grinold, Rudd, and Stefek 1989; Heston and Rou- wenhorst 1994) that established country factors as the major influence on equity returns. The relative importance of the country factor has been chal- lenged, however, by Baca, Garbe, and Weiss (2000) and Cavaglia, Brightman, and Aked (2000), who concluded that the importance of industrial factors has grown to exceed that of country factors. Two questions of great interest to global portfolio managers are (1) whether this apparent shift is ongoing and (2) whether it applies to all markets. If the shift varies by country (e.g., between developed markets and emerging markets), investment managers who assume that all markets behave in a similar way could find their strategies becoming ineffec- tive when they broaden their horizons from developed to emerging markets.

Emerging market research by Bekaert, Erb, Harvey, and Viskanta (1998) found the distribu- tions of emerging market equity returns to show

considerable skewness and kurtosis, which has im- plications for asset allocation. In their study of global equity investing, Cavaglia, Diermeier, Moroz, and De Zordo (2004) suggested that cross-border mergers and acquisitions play a role in increased capital market integration, but the United Nations Center for Trade and Development reported that the growth in cross-border mergers and acquisitions over the 1995-2000 period occurred in developed markets, not emerging markets.

We investigate the part that type of market- that is, whether developed or emerging-plays in the importance of country factors and sector factors in explaining observed variations in global equity returns. To examine this issue, we begin by clearly delineating developed markets from emerging markets. Most previous studies concen- trated on developed markets and paid little atten- tion to whether emerging markets behave differently from developed markets. In two different approaches, however, Wang, Lee, and Huang (2003), studying emerging and developed Asian markets, found that industrial effects dominate whereas Serra (2000), studying only emerging markets, found that country effects dominate.

In addition to delineating country versus sector effect by type of market, we examine how the factors have changed over time, with special interest in the period of the Asian financial crisis and the period after 2000.

Data Our sample consists of all available monthly returns of market capitalization-weighted equity indices for the countries classified as developed markets and emerging markets from Thomson Financial's Datastream global indices database. The database contains information on the largest stocks in each

Jianguo Chen and Andrea Bennett are senior lecturers of finance at Massey University, Palmerston North, New Zealand. Ting Zheng is a research associate at Massey University, Palmerston North, New Zealand.

40 www.cfapubs.org ?2006, CFA Institute




market (covering approximately 80 percent of each country's total market capitalization). The sample period is January 1994 through May 2005. For ana- lyzing the influence of sectors, we used 10 sector indices (with sector classifications as defined by the Financial Times Actuaries Index).

Table 1 provides a breakdown of sample countries and sectors at the beginning of 2005 in terms of number of companies and U.S. dollar- denominated market capitalization. The 23 developed markets included in the study are shown in Panel A, and the 26 emerging markets are in Panel B. The total market value of the developed market portfolio is almost 10 times that of the emerging market portfolio. The top three developed markets are the United States (about 54 percent of developed market portfolio value), Japan (about 14 percent), and the United Kingdom (about 11 percent). The top three emerging markets are South Korea (about 14 percent), Taiwan (about 14 percent), and Brazil (about 13 percent).

Clearly, the distribution of sector components in the country indices is not uniform. The most important sectors in the United States and Japan are the financial industry and cyclical services industry, whereas Chile has no representative company in two sectors (cyclical consumer goods and information technology). The distribution of country components in sector indices is also not uniform. For example, Canada's country index is dominated by the resources sector. This lack of uniformity illustrates why country effects and sector effects need to be separated from the aggregate indices.

As an indication of diversification possibilities, we give in Table 2 a summary of correlations between country indices and between sector indices separated into the developed and emerging markets for various periods. Two features of the correlations stand out. The first is the low average correlation between emerging country indices (e.g., 0.228 for the full sample period) and the high average correlation for the developed country indices (e.g., 0.572 for the full sample period). This finding is in line with many recent studies showing that emerging markets tend to have lower equity return correlations between themselves than do the developed markets.

The second obvious feature of the correlations in Table 2 is that the country correlations versus sector correlations tend to have different ranks in the developed markets from their ranks in the emerging markets. Specifically, for developed markets, the average 1994-2005 country correlation of 0.572 is larger than the average sector correlation, 0.541; that is, the sectors are slightly less integrated than are the countries. This finding suggests that

for developed markets, using a sector-based approach for an investment portfolio has potential for profitable diversification. In contrast, for emerging markets, the sector correlation is much greater than the country correlation, which suggests that a sector approach would be much less useful for profitable diversification. These correlations are consistent with the rising trend of pure sector variance to be discussed later.

Why are the sectors so highly correlated with each other, on average, in the emerging markets? The observation is consistent in each subperiod shown in Table 2, but is this phenomenon stable, or is the period we studied an exception?

Methodology Earlier studies used raw country and raw industry return indices on which to base their discussions of the difference between diversification by country and diversification by industry. The major problem of that methodology is that the raw index difference between two countries may be caused not only by characteristics of the countries but also by differences in the composition of the countries' industry sectors. Similarly, the raw sector difference may be caused by the country composition of the sector as well as common sector effects across countries.

To adjust for the varying sector (country) struc- tures among countries (sectors) to obtain a pure country (sector) factor, we used a two-step process. First, we applied a factor model as used by Heston and Rouwenhorst (1994) and Griffin and Karolyi (1998) that decomposes an individual equity return index into three parts:

Rit =at + jt + Ykt +-its (1)

where Rit = actual return on the ith security that be-

longs to sector j in country k in period t at = a constant or base level of return

Pit = pure sector component of return for sector ]

Ykt = pure country component for country k &it = company-specific disturbance term The model specifies that all of the securities

belonging to a particular industry contain a common factor in their return rates called the "pure sector factor" and denoted ,B and all of the securities within a particular country contain a common factor in their return rates called the "pure country factor" and denoted y. Equation 1 indicates that a security return can be decomposed into four parts- the base portfolio rate of return, a pure sector factor, a pure country factor, and a unique security factor.1

November/December 2006 www.cfapubs.org 41



Financial Analysts Journal

Table 1. Number of Companies by Country and Sector, January 2005 Number of Companies Total

Market Cap (US$

Market RES BI GI CYCG NCYCG CYCS NCYCS UTL FIN IT Total millions)

A. Developed markets

Australia 24 26 5 3 16 31 4 4 46 1 160 636

Austria 2 10 5 na 2 3 1 2 22 na 47 6

Belgium na 11 6 2 14 8 4 3 29 7 84 7

Canada 68 30 11 7 17 31 10 12 52 7 245 952

Denmark na 4 4 2 12 7 1 1 16 2 49 138

Finland na 11 8 3 3 7 3 2 5 4 46 29

France 7 17 26 18 39 57 12 3 40 26 245 219

Germany na 29 36 23 34 32 4 8 59 18 243 570

Greece 2 11 1 2 3 11 2 2 14 1 49 0.3

Hong Kong 2 9 12 8 8 31 7 7 36 8 128 707

Ireland 8 6 1 3 12 9 1 na 6 4 50 133

Italy 4 18 14 17 8 18 2 13 53 6 153 0.4

Japan 9 138 155 104 105 164 20 18 188 83 984 3,473

Netherlands 1 13 20 9 12 26 7 na 28 13 129 278

New Zealand 2 4 2 3 5 18 1 2 12 na 49 39

Norway 13 4 6 2 2 8 1 2 7 4 49 136

Portugal na 13 4 3 3 12 5 1 5 3 49 0.4

Singapore 2 3 17 3 12 21 5 na 26 6 95 154

Spain 3 21 10 7 18 20 3 8 28 2 120 4

Sweden 2 9 16 3 9 7 3 na 17 3 69 344

Switzerland na 18 27 6 24 14 2 9 41 6 147 814

United Kingdom 28 40 38 9 45 155 15 12 167 26 535 2,709

United States 67 70 76 39 155 173 26 67 215 102 990 13,225

Total no. of companies 244 515 500 276 558 863 139 176 1,112 332 4,715

Total market cap (US$ millions) 1,899 1,256 2,016 932 4,035 3,430 1,530 860 2,433 6,185 24,574.45

B. Emerging markets

Argentina 2 13 2 3 4 2 5 9 10 na 50 20.5

Brazil 5 19 7 3 7 6 18 17 13 na 95 317.8

Chile 1 10 5 na 6 3 4 10 10 na 49 92.5

China 6 8 4 1 1 12 1 5 5 2 45 7.1

Colombia 3 10 2 6 3 3 3 18 1 49 20.3

Czech Republic 3 5 3 1 3 4 1 7 1 1 29 30.3

Egypta - - - 8.1

Hungary 1 5 7 3 8 3 1 4 9 3 44 28.0

India 8 18 10 5 14 7 4 5 19 9 99 306.7

Indonesia 6 6 1 3 9 4 4 1 16 na 50 64.0

Israel 1 6 9 1 4 na 5 na 17 5 48 59.8

Korea 4 16 17 13 10 9 5 2 18 5 99 353.4

Malaysia 2 7 6 5 17 17 3 7 20 2 86 137.0

Mexico 1 15 5 1 21 23 9 13 na 88 184.4

Morocco' a 2.0

Pakistan 8 10 2 5 5 2 1 4 12 na 49 21.4

Peru 5 11 1 2 12 1 2 3 13 na 50 14.9

Philippines 2 2 2 na 6 3 5 3 24 na 47 26.1

Poland 1 9 na 4 3 3 2 1 16 3 42 62.7

Russia 20 10 na na 1 2 6 5 2 1 47 8.0

(continued)





We estimated a cross-sectional regression (details are in Appendix A) monthly for 12 years, with each month weighted by the market capitalization at the beginning of the month. As discussed in the literature, this process generates a time series of coefficients, Pit and ykt, that represent the pure return elements of each sector or country-whether developed or emerging market.

Second, with the time series of the intercept, country coefficients, and sector coefficients from the first step, we reconstructed individual country or sector return estimates as expressed in the following equation:

N Rk = a+ Yk + Xj,k0ji (2)

j=1

where j=1 Xj,kfj denotes the cumulative sector effect. Equation 2 means that an estimated country return is determined by Et (the global effect, to

which all equity returns are subject), -

(the pure country effect unique to country k), and the final component, which is the cumulative sector effect determined by the sector structure in the country and the level of sector effect in the country's N sectors. For instance, the return for Canada may differ from the return for other countries because of its pure country factor (the common element appearing in all of its securities) and because of a difference between its sector composition and the sector compositions of other countries.

Similarly, the role of the common sector return can be expressed as

M Rj = ai+ j+XpkjYk,f (3)

k=1

where Yk IkfJk represents the cumulative country effect.

Equations 2 and 3 show that a pure country/ sector factor is an estimate of aggregated country/ sector index return net of the factor's country/sector composition return; alternatively, as described by Heston and Rouwenhorst (1994), it is an estimate of the return on a globally diversified portfolio of companies in the country/sector.

Index Variance Methodology. With the estimates of the returns of the pure country and sector effects, we can use Equations 2 and 3 to measure the variation of country and sector factors. If a country's excess return (R - a) can be expressed as

Table 1. Number of Companies by Country and Sector, January 2005 (continued) Number of Companies Total

Market Cap (US$

Market RES BI GI CYCG NCYCG CYCS NCYCS UTL FIN IT Total millions)

South Africa 10 8 4 1 7 15 6 na 19 na 70 226.6 Sri Lanka 2 2 6 4 12 8 2 na 13 na 49 2.5 Taiwan na 7 4 4 1 6 3 na 16 29 70 343.1 Thailand 4 10 1 na 4 10 5 2 11 2 49 85.4 Turkey 3 5 5 8 4 4 2 1 15 1 48 84.6 Venezuela 3 12 6 2 na 1 1 1 23 na 49 7.5

Total no. of companies 101 224 109 69 165 148 98 90 333 64 1,401

Total market cap (US$ millions) 298.6 305.0 171.7 99.3 191.1 169.5 311.3 145.1 265.4 557.9 2,514.9

Notes: Sectors: RES = resources; BI = basic industries (building materials, chemicals, etc.); GI = general industrials (aerospace and defense, electronic and electrical equipment, etc.); CYCG = cyclical consumer goods (automobiles, household goods, etc.); NCYCG = noncyclical consumer goods (food, beverage, etc.); CYCS = cyclical services; NCYCS = noncyclical services; UTL = utilities; FIN = financials; and IT = information technology. aNot available in the Datastream database; only index series are provided. na = not applicable; that is, either no stock existed in the specified market or the equity index did not cover any stocks traded in the market.

Table 2. Average Correlation Coefficients for Raw Equity Return Indices, January 1994-May 2005

Developed Markets Emerging Markets

Period Sector Country Sector Country

1994-1996 0.670 0.453 0.539 0.176 1997-1999 0.531 0.615 0.767 0.299 2000-2002 0.505 0.568 0.683 0.220 2003-2005 0.541 0.542 0.689 0.252 1994-2005 0.541 0.572 0.655 0.228





a country return plus the cumulative sector return plus a country residual disturbance, we can view the total variance of the excess return as coming from three basic variance components-pure country variance, cumulative sector variance, and country residual variance.2 Similarly, we can view the excess-return sector variance as being composed of pure sector variance, cumulative country variance, and sector residual variance.

Pure Factor Variance and Mean Absolute Deviation Methodology. Variance is the most popular measure of risk of the financial pricing factors because of its simple diversification expres- sion in Markowitz portfolio theory. It also indicates the level of dispersion for a factor. The higher the factor variance, the greater the potential benefit for an investment strategy based on the factor.

The magnitude of a factor's importance can be represented by its mean absolute deviation (MAD). This measure has the advantage of being a unit of return (rather than the squared unit of the variance); therefore, it indicates the potential benefit of having "perfect foresight" (Cavaglia et al. 2000). In this sense, we can use MAD to measure the degree of sector and country factor effects.3

Results and Analysis This discussion of our results focuses on how much return variation the country and sector factors explain for equity returns. We performed a detailed analysis of the variance and MAD for the country and sector effects.

Countries vs. Sectors for Developed and Emerging Markets. Table 3 reports for each country and each sector the breakdown of the index into the variance components; developed markets are in Panel A, and emerging markets are in Panel B. For the developed markets, the country-effect data reveal that Finland's excess return (last column) had the highest total risk (64.70) whereas the United States had the lowest total risk in the period.4 The low U.S. country return variance could be a result of the portfolio being heavily weighted by the U.S. market. For the sectors in the developed markets, the information technology (IT) industry had the highest excess-return risk and the cyclical services (CYCS) industry and general industrials (GI) had the lowest risk.

The level of variance for the pure country effect shown for the developed countries in Panel A of Table 3 is comparable to the findings of Cavaglia et al. (2000). For both country effect and sector effect, the factor-risk portion explains a large part of total

excess-return risk. The pure country risk is about 87 percent of the total excess-return risk, and the pure sector risk is about 66 percent of the total. The cumulative sector risk explains about 7 percent of the total risk, and the cumulative country risk explains only 6 percent of the total risk. Unex- plained risk is about 26 percent for the country indices and 46 percent for the sector indices for these developed markets.5

For the emerging market indices shown in Panel B of Table 3, the "Country average" total variance for the excess return, at about 80.65, is far in excess of that figure for the developed markets (18.68). The highest excess-return total variance in this period is found for the Turkey index; the variance is more than 10 times that of the lowest, for South Africa. The highest industry risk in the period is, again, that of the IT sector; at 211.67, it is much greater than the total variance of the second- highest sector, noncyclical services (NCYCS). The lowest total variance in the emerging markets is that of basic industries (BI).

The pure country risk for these emerging markets is about 61 percent of the total risk, and the pure sector risk is about 14 percent of the total (mainly because of the IT sector, in which the sector variance is only 4 percent of the total variance). For the emerging markets, the cumulative sector portion in the country analysis, with a relative ratio of 1 percent, is unimportant, but for these markets, we cannot ignore the cumulative country portion in the sector analysis because the cumulative country risk makes a contribution to the total risk of 11 percent of the relative ratio; we attribute this result to the dominance of the country factor in emerging markets.

In contrast, we found for the developed markets (consistent with Baca et al. 2000) that both the average cumulative sector-effect and the average cumulative country-effect variances are too low to be worthy of further analysis.

Variance and MAD Trends. To examine the trends in sector and country returns over time, we graphed the rolling 36-month average variance of the pure country return and pure sector return for January 1994 through May 2005.6 Figure 1 reveals a pattern for the developed markets (Panel A) that is distinctly different from the pattern for emerging markets (Panel B). For the developed markets, the country variance steadily rose from about 10 at the beginning of 1997 to above 29 in January 2001 (keep in mind that the Asian financial crisis occurred in 1997). Then, it declined steadily back to 10 by the end of the period.

The sector variance in Panel A also steadily increased in the early part of the sample period. And





Table 3. Return Variance of Index Components, January 1994-May 2005 A. Developed markets

Pure Country Effect Cumulative Sector Effect Residual Excess-Return

Country Variance Relative Ratio Variance Relative Ratio Variance Relative Ratio Total Variance

Australia 8.85 1.17 0.74 0.10 2.76 0.36 7.58 Austria 13.47 0.92 2.24 0.15 3.15 0.22 14.61 Belgium 11.28 0.87 2.08 0.16 3.69 0.28 12.99 Canada 5.41 0.85 0.28 0.04 1.40 0.22 6.36 Denmark 10.28 0.77 0.53 0.04 7.58 0.57 13.37 Finland 13.09 0.20 9.55 0.15 28.54 0.44 64.70 France 12.19 1.05 0.16 0.01 0.50 0.04 11.58 Germany 10.25 0.90 0.34 0.03 1.79 0.16 11.38 Greece 65.70 1.11 0.62 0.01 10.56 0.18 59.39 Hong Kong 40.50 1.12 0.64 0.02 8.03 0.22 36.14 Ireland 11.41 0.81 2.84 0.20 7.30 0.52 14.07 Italy 26.16 1.02 0.76 0.03 2.35 0.09 25.75 Japan 13.74 0.85 0.12 0.01 0.70 0.04 16.21 Netherlands 9.59 1.13 1.05 0.12 1.29 0.15 8.50 New Zealand 12.86 0.87 0.69 0.05 2.37 0.16 14.76 Norway 14.39 0.95 1.66 0.11 3.41 0.23 15.09 Portugal 18.03 0.80 0.85 0.04 7.42 0.33 22.62 Singapore 33.39 1.33 0.38 0.01 7.55 0.30 25.18 Spain 13.54 0.92 0.76 0.05 3.61 0.25 14.66 Sweden 11.51 0.63 1.08 0.06 3.61 0.20 18.41 Switzerland 10.92 1.26 2.79 0.32 3.97 0.46 8.68 United Kingdom 6.06 1.22 0.68 0.14 1.04 0.21 4.95 United States 1.84 0.71 0.10 0.04 0.82 0.32 2.58

Country average 16.28 0.87 1.35 0.07 4.93 0.26 18.68

Pure Sector Effect Cumulative Country Effect Residual Excess-Return Sector Variance Relative Ratio Variance Relative Ratio Variance Relative Ratio Total Variance

RES 9.35 0.57 0.74 0.04 5.80 0.35 16.44 BI 5.92 0.70 0.47 0.05 2.35 0.28 8.50 GI 2.35 0.83 0.14 0.05 2.25 0.80 2.82 CYCG 5.54 0.65 2.41 0.28 6.10 0.72 8.49 NCYCG 6.45 0.60 2.41 0.22 6.94 0.64 10.80 CYCS 1.29 0.55 0.03 0.01 2.56 1.10 2.32 NCYCS 7.50 0.67 0.15 0.01 4.12 0.37 11.13 UTL 10.28 0.62 0.06 0.00 7.06 0.43 16.49 IT 25.72 0.68 0.35 0.01 15.86 0.42 37.58 FIN 5.02 0.84 0.15 0.02 2.42 0.41 5.96

Sector average 7.94 0.66 0.69 0.06 5.55 0.46 12.05

B. Emerging markets

Pure Country Effect Cumulative Sector Effect Residual Excess-Return


Argentina 64.32 0.82 0.48 0.01 8.77 0.11 78.38 Brazil 21.51 0.11 0.50 0.00 144.69 0.74 195.06 Chile 17.49 0.85 0.47 0.02 2.96 0.14 20.65 China 83.87 0.84 0.98 0.01 11.13 0.11 99.26 Colombia 35.59 0.68 0.95 0.02 10.75 0.21 52.00 Czech Republic 35.20 0.48 0.66 0.01 27.79 0.38 73.03 Egypt 27.66 0.39 1.12 0.02 26.29 0.37 70.95 Hungary 55.36 0.49 0.56 0.00 25.31 0.22 113.93 India 44.88 0.94 0.33 0.01 4.00 0.08 47.72

(continued)





in a pattern similar to the trend of the country variance, the sector variance declined continuously after the peak, but at a little faster rate than the decline of the country variance. This graph supports the finding that the country effect was more important than the sector effect for developed markets up to recent times. Since about 2000, the difference between the two effects has not been so marked.

The graph for the country variance in emerging markets shown in Panel B has a pattern very similar to that for the developed markets over the period. The country variance reached its highest level, 78, in July 2000 and, after that time, steadily declined to 27 in May 2005. The graph for the pure sector effect, unlike the pattern for the developed markets, shows little upward movement. Sector variance never rose above 6.9 (for April 2001), and

throughout the period, the sector effect was strongly dominated by the country effect.

Thus, we conclude that the importance of the sector effect has increased for portfolio strategies involving developed markets but not for portfolio strategies involving emerging markets. In the emerging markets, diversification across countries still offers greater risk reduction than diversification across sectors.

Beginning in 2002, Panel A of Figure 1 shows that sector variance and country variance in the developed markets began to trend downward. This evidence suggests that integration in the global equity market is increasing. The downward trend is also observed in the emerging markets, but it is important only for the country effect.

Table 3. Return Variance of Index Components, January 1994-May 2005 (continued) B. Emerging markets

Pure Country Effect Cumulative Sector Effect Residual Excess-Retumn


Indonesia 33.95 0.41 0.44 0.01 19.79 0.24 82.96

Israel 24.69 0.53 0.77 0.02 13.90 0.30 46.30

Korea 73.58 1.01 0.28 0.00 5.61 0.08 72.77

Malaysia 50.00 0.90 0.23 0.00 1.68 0.03 55.86

Mexico 17.20 0.74 0.37 0.02 4.63 0.20 23.15

Morocco 23.09 0.52 1.78 0.04 11.46 0.26 44.54

Pakistan 73.65 0.66 0.41 0.00 10.35 0.09 110.96

Peru 36.62 0.90 0.56 0.01 13.23 0.32 40.87

Philippines 40.85 1.02 0.34 0.01 8.10 0.20 40.04

Poland 37.98 0.41 0.62 0.01 24.24 0.26 92.88

Russia 76.89 0.45 1.74 0.01 43.09 0.25 169.88

South Africa 16.95 0.90 0.54 0.03 5.07 0.27 18.92

Sri Lanka 37.54 0.56 0.67 0.01 8.05 0.12 66.54

Taiwan 23.57 0.60 0.72 0.02 11.19 0.29 39.04

Thailand 65.07 0.66 0.32 0.00 13.82 0.14 99.29

Turkey 214.36 0.97 0.30 0.00 9.54 0.04 221.74

Venezuela 42.90 0.36 0.40 0.00 31.71 0.26 120.33

Country average 49.03 0.61 0.64 0.01 19.12 0.24 80.65

Pure Sector Effect Cumulative Country Effect Residual Excess-Return

Sector Variance Relative Ratio Variance Relative Ratio Variance Relative Ratio Total Variance

RES 4.06 0.27 3.31 0.22 8.62 0.58 14.90

BI 3.68 0.67 1.13 0.21 2.52 0.46 5.48

GI 3.56 0.20 2.83 0.16 11.31 0.63 18.06

CYCG 4.70 0.48 3.18 0.33 6.70 0.69 9.77

NCYCG 3.85 0.50 3.18 0.41 8.66 1.12 7.75

CYCS 2.98 0.77 2.74 0.71 2.95 0.76 3.89

NCYCS 4.89 0.33 2.26 0.15 7.04 0.47 14.96

UTL 4.36 0.38 2.47 0.22 4.67 0.41 11.34

IT 7.60 0.04 10.60 0.05 163.07 0.77 211.67

FIN 2.69 0.47 1.13 0.20 2.93 0.52 5.66

Sector average 4.24 0.14 3.28 0.11 21.85 0.72 30.35

Note: Variances are in percent squared per month, in multiples of 10,000.

46 www.cfapubs.org 02006, CFA Institute




Our second method for measuring the importance of country and sector effects was MAD, also used by Rouwenhorst (1999) and Cavaglia et al. (2000), which measures the cap-weighted absolute value of country and sector effects. MAD allows portfolio managers to track how much an inves- tor's return differs from a benchmark portfolio when the manager tilts the portfolio toward a particular sector or country.

Figure 2 depicts detailed movement of the MADs based on a 12-month lagged moving average

over the sample period. For the developed markets (Panel A), the calculated 12-month average sector MAD was at 0.1 percent at the beginning of 1995 (monthly average of January 1994 through Decem- ber 1994), shot up to a maximum of about 0.54 percent in November 2000, and ended at 0.13 percent in May 2005. The country-effect MAD moved mostly in a stable range of 0.2-0.3 percent and declined after 2004 to about 0.13 percent in May 2005.

For the emerging markets (Panel B), the MAD patterns are generally similar to the variance

Figure 1. Pure Country and Sector Variances: Developed vs. Emerging Markets, January 1994-May 2005 (rolling 36-month average)

A. Developed Markets Variance (% squared)

35

30 f'Xu~re Country

25

20 -

15

1 Pure Sector

97 98 99 00 01 02 03 04 05

B. Emerging Markets Variance (% squared)

90

80

70 - Pure Country

60 -

50-

40-

30-

20

10 - Pure Sector .......... ..... . - .. . . - .. . . . .

... . . . ............... -----*-

O .... . .. . ..

......... 97 98 99 00 01 02 03 04 05

Note: Variances are in percent squared per month, in multiples of 10,000.





analysis patterns in Panel B of Figure 1. The country MAD stayed in a range of 0.4-0.8 percent, and the sector MAD ranged between 0.15 percent and 0.25 percent; both moved in the same direction, with the country factor always dominating the sector factor. By May 2005, both had declined.

The results of both variance analysis and MAD analysis support our findings that in the developed markets, country effects dominated sector effects up to the turn of the century but sector effects rose rapidly toward the end of the century-in both relative and absolute terms. Our findings suggest that sector effects approach the same level as country effects (by the variance method) or exceed country effects (by the MAD method) in the developed markets. Note that both effects as measured by both

methods declined steadily in the new century, with a markedly smaller difference between them. The reason could be increased financial integration across countries.

Because the MAD method clearly showed a rapid rise and sharp decline in the sector effects (particularly for the developed markets), which characterizes a typical market bubble, we explored this possibility. Brooks and Del Negro (2004) reported a strong TMT (telecommunications/ media/technology) sector effect; therefore, we investigated the possible influence of the IT sector on our results. We recalculated our metrics with the IT sector index removed from the market portfolio.

In general, we did not observe any difference for the emerging market results (the MAD graph

Figure 2. Cap-Weighted Pure Factor MADs, January 1994-May 2005 (rolling 12-month average)

A. Developed Markets MAD (%o)

0.60

Pure Sector 0.50

0.40

0.30 FX Pure Country

0.20

0.10k-"

95 96 97 98 99 00 01 02 03 04 05

B. Emerging Markets MAD(%)

0.80

0.70 Pure Country

0.60

0.50

0.40

0.30

0.20 ..--*----- *- *--.

0.10 * ----------------- Pure Sector

95 96 97 98 99 00 01 02 03 04 05





was the same as that shown in Panel B of Figure 2). For the developed markets, the pure country effects measured by MAD were basically the same, but as Figure 3 shows, the pure sector effects differ substantially from the results shown in Panel A of Figure 2. Figure 4 provides a direct comparison.7 Figure 4 clearly shows that the strongest IT influence happened in a two-year period at the beginning of the 21st century. The maximum difference is 20.6 percentage points near the end of 2000; the difference declined steadily afterward-to only 1.5 percentage points by the end of the sample period.

Our evidence shows that the steady increase in the sector effect up to the 21st century was a general trend in the world market. It was not purely the result of an IT bubble, which is a finding consistent with that of Ferreira and Gama (2005). The sharp increase of the sector effect near the end of 2000, however, was primarily a result of the IT bubble. Previous literature (Brooks and Del Negro 2004; Ferreira and Gama 2005), given the time period of their data, could observe only the sharp increase in sector effects and concluded that the rise of sector effects was a temporary result of a TMT bubble. As far as we know, our study presents the first com- plete picture of the bubble's effect on the sector factor as of this writing.

Country vs. Sector Strategy To investigate possible differences in the results of using a strategy based on the country factor and a strategy based on the sector factor, we simulated

portfolios by using the active country (and industry) group rotation portfolio strategy discussed and used by Sorensen and Burke (1986). For the 1994- 2005 period, we ran separate simulations for the developed markets and emerging markets. This strategy was based on country- or sector-factor rankings. We then used a momentum-type strategy to work out the next period's portfolio.8 We hypothesized that the higher the factor influence was, the higher the return would be for the corresponding strategy.

In the emerging markets, the country-based portfolios produced more than 50 percent higher returns than the sector-based portfolios, on average, whereas we found no substantial difference between country-based and sector-based strategies in the developed markets. These results are consistent with the previous pure factor analysis.

Conclusion We provided empirical evidence on the relative importance of country effects and sector effects in the developed versus emerging markets. Our tests covered the period from January 1994 through May 2005. We found that in the early part of the sample period, country effects dominated sector e#fects in the developed markets. Then, the importance of sector effects grew to rival that of country effects. In the 21st century, the effects declined in a similar fashion. The ascendancy of sector effects is a conse- quence of a rise in sector effects rather than a fall in country effects. Despite the 1997-99 period, when

Figure 3. Cap-Weighted Pure Factor MADs for the Developed Markets: IT Index Excluded, January 1 994-May 2005 (rolling 12-month average; annualized rates)

MAD(%)

0.40

0.30 Pure Country

0.20

0.10 ...... Pure Sector

0 I I I I I I I l 95 96 97 98 99 00 01 02 03 04 05





the Asian financial crisis created a huge increase in average risk in the world equity market, sector effects grew in importance.

The rise in globalization has been accompanied by a general decline in the importance of the country factor. This trend may have already changed what kinds of strategies will be profitable for a developed market portfolio from a country-dominant strategy to one that incorporates both country and sector factors, as described by Cavaglia and Moroz (2002).

For the emerging markets, we observed a much greater effect of the pure country factors than of the sector factors for the whole period. Country effects remained a dominant influence relative to sector effects throughout, although the influence of sector effects has been on a slow rise. For emerging markets, therefore, we affirmed that portfolio managers should continue to base their diversified portfolios on the dominating country effects.

Nevertheless, we warn that the dominance of country factors in the emerging markets may diminish in the future. Will investment managers increasingly find it difficult to add value through pure country-based allocations?

Appendix A. Cross-Sectional Regression Model As with previous research (Heston and Rouwen- horst 1994), we define a dummy variable I,, that equals 1 if a security i belongs to sector j and 0 otherwise and a dummy variable Cik that equals 1 if security i belongs to country k and 0 otherwise. So, for our 23 countries and 10 sectors for developed markets, we can rewrite Equation 1 as

Ri= ax + fIil + I32I2 + .+P+ 1IoIio

+'YICil + y2Ci2 + +723CM23 + i (Al)

Because each company is in one country and one sector, country or sector effects must be measured relative to some benchmark; therefore, we follow previous research and make the market cap- weighted average of both country and sector coefficients zero. That is, we add two restrictions to Equation Al:

10

I WjEj=o (A2) j=l

and

23

X VkYk =0, (A3) k=1

where Wj and vk are the weights of, respectively, sectors and countries in the corresponding sample market portfolio. So, constant term at becomes the return of the cap-weighted global index.

Figure 4. Cap-Weighted Pure Factor MADs for the Developed Markets: All Sample Indices vs. IT Index Excluded, January 1994-May 2005 (rolling 12-month average; annualized rates)

MAD(%)

0.60

0.50 All Sample Indices

0.40

0.30

0.20

IT Index Excluded .

95 96 97 98 99 00 01 02 03 04 05

We are grateful to Ben Marshall for extremely helpful suggestions and comments.

This article qualifies for 1 PD credit.

50 www.cfapubs.org 02006, CFA Institute




Notes 1. The common factor specification for each security in the

world is debatable. We believe it is especially helpful in estimating the aggregated effect, such as the global sector and country effects examined in the study.

2. An additional component is the covariance between the pure country return and pure cumulative sector return, which is close to zero because of the model definition.

3. The pure factor variance formulas are var (Pt) -

E N (j,) and var()t)XE M (7k,( )2. And the pure

factor MAD formulas are MAD (It) = " |ij and

MAD(yJ)=J X?hk,tj. If we can perfectly forecast the

next-period factor value, the absolute value of the factor is the suitable measure of additional return we can obtain by appropriately adjusting the investment portfolio. We can sell the factor short if the forecast for the next-period factor is negative or buy the factor if the forecast for the next period is positive.

4. Variance in this discussion is in percent squared.

5. In the study, we focused on the pure country and sector risks, but the level of the residual suggests a need for future research on global risk factors-along the lines of L'Her, Sy, and Tnani (2002).

6. The variance of January 1997 was calculated from the coefficients of January 1994 through December 1996. Because the study sample period is 11 years, we followed the assumption that the degree of market integration may change over time. For this reason, following Baca et al. (2000), we calculated the variances over both a rolling 36- month and a rolling 48-month time period. The results of using rolling 48 months were not substantially different from the reported results for rolling 36 months.

7. The coefficients for the market portfolio with IT excluded were estimated from all equity indices for the sample countries with the IT indices removed.

8. Similar to Sorensen and Burke (1986), we measured and ranked the country (or sector) index in terms of its recent quarterly return rate. The top n portfolio was simulated on the basis of the prior period's estimation (n = 3, 5, and 10) by selecting these indices in equal weights. The portfolio was maintained for the following quarter, and at the end of the quarter, the selection process was repeated anew.

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Sector Effects in Developed vs. Emerging Markets