Master Thesis
Diversification Benefits for
European Investors:
Is it essential to invest abroad?
February 2009
University of Groningen Faculty of Economics and Business
University of Groningen Address: Nettelbosje 2
9747 AD Groningen, the Netherlands
Student: Timo Hülsmann
Student number: 1407198
E-mail: t.hulsmann@student.rug.nl
Table of Contents
Chapter Page 1. Abstract 3 2. Introduction 4 3. Literature review 6 4. Research questions 15 5. Sample selection 17 5-1 Data source 16 5-2 Country selection 17 5-3 Data type 17 6. Theoretical concepts 18 6-1 Mean-variance analysis 186-2 Testing for significance 21
7. Method of analysis 22
8. Results 28
8-1 National diversification 28
8-2 International diversification 35
8-3 Robustness tests 41
8-3-1 Varying levels of risk 41
8-3-2 Diversification at the lowest, the average as well as the highest
1. Abstract
This paper studies the potential diversification benefits of investors from developed and emerging economies in Europe during the last 9 years. I construct optimized portfolios on a national as well as an international level by applying Markowitz’s mean-variance analysis. The returns of nationally and internationally optimized portfolios are tested for significant differences.
I conclude that investors (apart from the Russian ones) are able to gain significantly larger returns when going from a nationally optimized industry portfolio towards an international one. This applies to developed as well as emerging markets’ investors. The optimal weights of industries within a portfolio differ among the sample countries, so that investors are able to gain from industries that have high returns in foreign countries but do not so in the home market. These results also indicate that a focus on diversification across European wide industries is not recommendable. My main conclusion is contrary to other U.S. research. In Europe it seems advisable for European investors to diversify outside the home country.
Thanks: I would like to thank my parents for their continuous (moral and, quite
important, financial) support during my study. My friends and fellow students deserve to be mentioned, too, for the time they took to discuss my ideas on this paper. Last but not least, I would like to mention my supervisor dr. Henk von Eije for his smooth guidance and the time he invested to offer interesting discussions.
Key words: Portfolio diversification, home bias of investors, emerging markets, Europe,
mean-variance analysis
2. Introduction
Asset allocation and optimal diversification are the core of portfolio theory. Benefits of diversification originate from holding less than perfectly correlated assets. Hence, holding diverse assets is the key to return maximization and/or risk minimization. In theory, asset allocations across developed as well as emerging markets have been proven beneficial from a theoretical perspective. However, in practice, individuals do not diversify optimally. Internationally diversified portfolios are still heavily biased towards domestic assets (French and Poterba, 1991; Lewis, 1999; Oehler, Rummer, Walker and Wendt, 2006; Karlsson and Nordén, 2007; Driessen and Laeven, 2007). This phenomenon is known as ‘home bias’ of investors. In other words, individuals hold too little of their wealth in foreign assets.
the home bias of investors might not be as negatively as past studies suggest with respect to developed markets.
This paper is structured as follows. The third chapter reviews the existing literature. Next, the main as well as the sub-research questions are discussed. The fifth section presents the sample and data selection. This is followed by an overview of the theoretical concepts underlying this research. The seventh chapter describes the steps of the analysis including the hypotheses which are tested. I present the results of the analysis and the robustness tests in the eighth section. Thereafter, I draw conclusions on the obtained results with respect to the stated hypotheses as well as the main and sub-research questions. The last section contains a discussion of the results.
3. Literature Review
In 1952 Markowitz developed a model that stresses the importance of diversification of investments. He considers risk, which is measured by the variance of the rate of return of a stock, to be negative and the expected rate of return of a stock as positive for investors. A prerequisite of the benefits of diversification is a less than perfect correlation between the acquired stocks. For example, an investor invests equally in two different assets with equal variances of their expected rates of return. The variance of the resulting portfolio is lower than the variance of either of the two original assets, if their correlation is less than one. He already points out that a portfolio consisting of, for instance, 20 different stocks of companies within one sector is less well diversified than a portfolio with the same amount of stocks of companies from different industries. An investor prefers a portfolio consisting of stocks from companies in different industries, because these stocks correlate less with each other than stocks of companies within the same industry. This is certainly true according to Markowitz, if the industries have different economic characteristics. Markowitz's analysis of the risk and expected return of stocks is referred to as mean-variance analysis.
selling is ruled out, so-called “corner” solutions are likely to be obtained. This means that a weight of zero percent is assigned to some assets, whereas very large weights are assigned to others. On an international level, the obtained portfolio weights do not depend on the market capitalization of the respective country, but large weights could also be assigned to countries with a small market capitalization of their stock market indices. According to Black and Litterman (1992) these inadequate results are obtained due to two issues. First, the expected returns are difficult to estimate. Using historical returns to estimate expected returns is inappropriate. This leads to the second point of criticism of Black and Litterman. The determination of expected returns is very sensitive to their measurement and the predetermined assumptions of the respective method.
Fender (2002), Syriopoulos (2004) and Beach (2006) analyze the diversification benefits of investors from different countries based on correlations as proposed by Markowitz's mean-variance framework.
followed by a rise. Diversifying across international equities, an investor could gain from counter cyclical effects outside the U.S.
Syriopoulos (2004) emphasizes potential gains from international diversification if the correlation between different national stock markets is less than perfect as well as stable. Theoretically, the return of emerging markets' stock market indices correlate on a low level with the returns of stock market indices in developed markets. Syriopoulos (2004) argues that the empirical evidence of diversification benefits in Eastern European markets is ambiguous and sometimes contradictory depending on the applied statistical tool and the sample countries. Based on the market capitalization of stock markets, their turnover rates and the number of traded securities, he investigates diversification benefits in Poland, the Czech Republic, Hungary and Slovakia. Developed markets are represented by Germany and the U.S. The results indicate that the Eastern European markets are not very integrated within each other. The developed markets have an influence on the Eastern European ones, whereas the effect is not vice versa. Also the two developed markets are very much integrated. These findings indicate that diversification benefits in different Eastern European countries might be limited for investors in developed markets. The low integration between Eastern European countries, on the other hand, indicates that diversification benefits exists for investors from these countries.
Beach (2006) finds out that the correlation between developed markets are larger than the correlation between emerging ones. Hence, even from a theoretical point of view of the diversification effect, adding emerging markets to an internationally diversified portfolio makes sense. By applying the Capital Asset Pricing Model (CAPM), the author proves that the return of emerging markets' stock indices compensates for the risk to which investors are exposed to.
The above discussed articles focus on diversification benefits between countries.
Rouwenhorst (1999), Morck, Yeung and Yu (2000), Taing and Worthington (2004), Estrada, Kritzman and Page (2006) as well as Moerman (2008) compare diversification benefits across industries within countries.
larger returns when it is diversified across Western European wide industry indices instead of country indices. The results of Moerman (2008) are in contrast to the ones of Rouwenhorst (1999) who finds evidence that a portfolio diversified across countries generates larger returns compared to a portfolio diversified across industries in earlier years, namely from 1993 until 1998. More recent research by Cavaglia, Brightman and Aked (2000), Flavin (2004) and Moerman (2008) prove that international diversification across industries achieves larger returns than diversification across international stock market indices.
Taing and Worthington (2004) analyze five different industries within six large and small European countries between 1999 and 2002. Their findings suggest that stocks of companies within one industry could develop differently in various countries. For example, industry A could be the most profitable in country one, whereas in country two industry B is the one with the largest future return. This result implies that the decision about an internationally diversified industry portfolio should be based on country specific analyses.
Morck, Yeung and Yu (2000) analyze correlations within national stock markets. They find that stock prices move more simultaneous in countries with a low gross domestic product (GDP) than in countries with a high GDP per capita. In other words, the correlation between stock returns within emerging markets is higher than within developed markets. These findings imply that diversification benefits across industries are larger within developed countries than in emerging countries. Estrada, Kritzman and Page (2006) focus on emerging markets applying the bootstrapping analysis, which is based on the selection of a random sample from the data set. The data set consists of world wide emerging markets. They conclude that portfolio managers in emerging markets should focus on diversification across countries and not across industries.1 This result for emerging markets from Estrada, Kritzman and Page (2006) contradicts previous findings for developed markets (Cavigli, Brightman and Aked, 2000; Flavin, 2004; Moerman, 2008).
1
A large number of countries and industries are analyzed by Phylaktis and Xia (2006). They investigate the effect of country and industry effects on equity returns of 34 countries around the world and 50 industries between 1992 and 2001. The implication for portfolio diversification is that if country effects dominate industry effects, diversification across national stock market indices is more beneficial for investors than diversifying across industries, and vice versa. The results suggest that country effects dominate over the full time period. Broken down into three time periods of each three years, the authors discover that industry effects dominate country effects in North America as well as in Europe. They conclude that diversification across industries has become more beneficial for European investors over time.
Despite some empirical evidence of international diversification benefits, researchers have also shown that individuals are far from holding optimal portfolios. Lewis (1999) determines that investors do not hedge their risk efficiently on an international level by holding too many stocks within their home country. Academic literature refers to this phenomenon as 'home bias' of investors.
Cooper and Kaplanis (1994), Tesar and Werner (1995) and Zweig (2007) analyzed the home bias of investors by applying the International Capital Asset Pricing Model (ICAPM). According to this model, an investor holds an efficient, globally diversified portfolio, if the market capitalization of a country's stock market relative to the global market capitalization equals its participation within this portfolio. They find that investments within the home market are very large, although the relative stock market capitalization is very low implying a high rate of home bias. According to Zweig (2007), U.S. investors keep 87%2 of their money within their home country, although the U.S. stock market only represents about 50% of the global market in terms of market capitalization. Greek investors allocate 75% of their investments in Greek stocks, although their stock market only makes up 1% of global market capitalization. Oehler, Rummer, Walker and Wendt (2006) detected that German investors are very much home biased. Based on the Morgan Stanley Country Index (MSCI) for all countries as a
2
benchmark, their findings suggest that German investors should invest much more in foreign countries. However, the home bias of investors became less during the 1990's. Considering international investments, the authors also find a Europe bias. Much of the cross border investments stay within Europe and non-European countries are very much underrepresented in international portfolios. Karlsson and Nordén (2007) analyze the investment behaviour of Swedish investors with respect to a newly introduced pension fund plan. Although barriers to trade internationally and information asymmetries are virtually not present, the Swedish investors focus on their home market much more than expected by theory.
As for the reasons to invest a large proportion within the home market, four issues have been considered. Each one is discussed separately. First, it is believed that investors hedge domestic inflation with domestic equity (Cooper and Kaplanis, 1994; Lewis, 1999). Based on international diversification gains and the resulting necessity to include foreign assets in a portfolio, Cooper and Kaplanis (1994) as well as Lewis (1999) find this reason to be invalid. The correlation coefficient between the equity returns and inflation is close to zero. If equity returns reflected inflation, the coefficient would be close to one.
that these costs can hardly explain the home bias of investors. Brealey, Cooper and Kaplanis (1999) summarize the costs of diversifying across borders as 'access costs'. The more segmented the markets are, the larger are these 'access costs'. The authors take these costs as given and rather than considering the effects of these costs, they argue that firms themselves are able to reduce these costs by either cross-listing or engaging in cross border mergers and acquisitions. Cross border mergers and acquisitions internationalize a company’s operations and substitute for international diversification of investors. Cross-listing lowers the costs for investors, that are located in the country in which the company cross-lists, to gather information about the respective company. Thereby, these investors are able to obtain shares of a company that operates abroad. Kang and Stulz (1997) analyze the Japanese market and find that foreign investors disregard small firms and over-invest in large ones, although their volatility is larger than the Japanese stock market index at equal rates of return. According to the authors, investors seem to have more knowledge about large firms than small ones. One can conclude that the cost of acquiring information about small companies is too high and/or that investors assume that they take a lower risk by investing in larger firms due to the firm's risk diversification in operations. Oehler, Rummer, Walker and Wendt (2006) find evidence that the home bias has become less for German investors during the 1990's. They attribute the cheaper access to information sources to this development.3 Although there is not much empirical evidence on the effect of information asymmetries, Coval and Moskowitz (2001) discover that the returns on investments for U.S. investment managers are larger, the more geographically proximate the investment manager is located to the headquarter of the company that is invested in. They conclude that investment managers are able to utilize the proximity by improved monitoring capabilities, access to private information and the psychological ease to act within familiar boundaries have positive implications in selecting stocks with large returns.
These psychological reasons belong to the third possible explanation. French and Poterba (1991) arrive at the conclusion that transaction costs are not high enough to explain the home bias among investors. They find a behavioural reason for this effect with respect to
the expected rate of returns. Investors seem to have too positive expectations about future returns of assets within their home market, while the expectations of future returns of assets within foreign markets are too negative compared to rational expectations according to finance theory. Karlsson and Nordén (2007) find out that for Swedish investors sophistication plays a role in an investor's tendency for home bias. The higher the education of the investor is, the less is the investor’s home bias. Further, they find that men are more home biased than women and that home bias, in general, is caused by overconfidence about the home market. Based on their results, a typical home-biased Swedish investor is an older, not very well educated male person having a secured job and investing only relatively small amounts of money. Zweig (2007) refers to a research of a neuroscientist called Peter Kenning from the University of Münster. Kenning finds out that as soon as investors consider investing in a mutual fund that allocates part of the investment in foreign stocks, an alarm in people's fear center within the brain goes off. Zweig (2007) concludes that people feel more comfortable in a familiar environment, even if people do not know as much as they think they do. Hatchondo (2008) applies the conclusions of Zweig (2007) as one of his prerequisites assuming that local investors outperform non-locals due to their beneficial access to private information and less costly monitoring activities. Local investors do not necessarily outperform non-locals in predicting future returns of the complete local stock market index, but local investors outperform non-locals due to their ability to pick single stocks which generate a larger return in the future. Another assumption, which is proven by Hatchondo's research is that short-selling is expensive due to taxes and transaction costs. Hatchondo argues that usually, investors buy shares about which they have positive expectations and sell short the stocks about which they have negative expectations. Under the assumption that short-selling is costly, investors pass on short-short-selling and also cut down on investing in international equities sacrificing diversification benefits. Overall, investors obtain mostly local stocks which are expected to increase in value.
the existence of the home bias itself might be overvalued. Errunza, Hogan and Hung (1999) dedicated a research about the existence of the home bias of investors and test whether international diversification benefits could be mimiced at the home market. They study the possibility to mimic foreign stock market indices with domestically traded stocks. Further, they investigate whether the returns of the foreign indices and the home market portfolio are significantly different from each other. The paper considers the U.S. market to be the home country. The authors find out that a U.S. investor is able to mimic the return of foreign stock market indices by investing in assets traded within the home market. The additional rates of return which could be achieved beyond the ones of home-made diversification are insignificant. The authors conclude that a U.S. investor does not need to diversify across any border to achieve an internationally mean-variance efficient portfolio. Errunza, Hogan and Hung (1999) include seven developed and nine emerging markets in their study. Within these countries they find out that the correlation between the stock market index and the most augmented portfolio4 within one country is lower within emerging markets than in developed ones. Based on these findings they conclude that diversification benefits are larger within emerging markets than within developed ones.
Although Errunza, Hogan and Hung (1999) find that the home bias does not exist for U.S. investors, this issue needs further investigation. This is due to the fact that the authors only investigated the U.S., which is a developed country with a large and diverse equity market. In this regard, I research whether investors forego significantly larger returns by investing only in the home market instead of diversifying across borders from the perspective of European investors in emerging as well as developed European countries.
4. Research Questions
The main research question that will be answered is the following:
What are the possibilities to achieve similar returns by diversifying across industries within the home market, on the one hand, and by diversifying across international stock market indices and optimized industry portfolios in developed and emerging countries, on the other hand?
This general research question will be answered by analyzing the risk and return behaviour of portfolios constructed on a national and international level in four emerging and four developed markets. In order to achieve similar returns on a national and international level, the generated return of the equity within the home market should not differ significantly from the return of an optimally diversified international portfolio, at equal risk (Kan and Zhou, 2001).
The analysis is done in two steps. First, I determine the most efficient portfolios on a national level for all eight sample countries and study whether their returns differ significantly. Second, I identify internationally optimized portfolios across stock market indices as well as across nationally optimized portfolios from each country’s perspective and study whether the returns of national and international portfolios differ significantly. These two steps are described further with the following two sub-questions.
Research Question 1: Can an investor improve his/her risk-return position significantly by diversifying across industries instead of investing in the stock market index of the home market?
emerging markets, but companies from diverse industries are listed. I study whether an investor in an emerging as well as developed market could utilize the implied less than perfect correlation between industries and the forthcoming diversification possibilities within the respective home market in order to outperform the index. This analysis is based on whether the rate of return of a nationally diversified portfolio across industries is larger than the rate of return of the stock market index within each country at an equal level of risk. The difference is then tested for significance.
Research Question 2: Are there additional gains from diversification across foreign stock market indices as well as nationally optimized industry portfolios in foreign markets compared to a national industry portfolio?
5. Sample selection
5-1 Data source
Stock market as well as industry data are gathered from a database jointly developed by the FTSE and the Dow Jones. It is referred to as the Industry Classification Benchmark (from hereon: ICB). National stock markets are divided into ten industries.5
5-2 Country selection
The ICB database includes 67 countries worldwide6. This paper focuses on European countries only. The selection of the developed markets is based on the market capitalization of their respective stock markets. According to the statistics institution of the European Union (Eurostat), the United Kingdom, France, Germany and Spain exhibit the largest market capitalization in 2005, 2006 and 2007 (except France, for which data for the year 2007 are not available yet in the Eurostat statistics).7
The country selection for Eastern Europe has a stronger focus on data availability. The stock market and industry data for many Eastern European markets are available for the years after 2005. The time period until today is too short to generate representative results. So, most Eastern European countries have to be excluded due to the non-availability of appropriate data, except Poland, the Czech Republic, Hungary and Russia. Overall, I study the diversification possibilities from eight perspectives corresponding the four developed and four emerging markets.
5-3 Data type
The ICB stock market and industry data are index values with a base date of December 31, 1993. The analysis is based on monthly return data from January 1999 until December 2007. The stock market data is analyzed from the perspective of each country and, thereby, in each country’s home currency. Since France, Germany and Spain adopted the Euro in the beginning of 1999, I use that date as the beginning of the time period of this research.
5
See Table A1 in the Appendix for a list and subdivisions of these industries. 6 See Table A2 in the Appendix for a complete list.
The analysis from each country’s perspective and, hence, each country’s currency implies that the return on foreign investments depends on the return of foreign stocks as well as any changes in currencies. I assume that investors are fully exposed to changes in the exchange rates meaning that they do not hedge. In order to hedge effectively, an investor should be able to forecast changes in any currency perfectly. Due to this unlikely situation, I allow currency changes to be part of cross-country investments.
6. Theoretical concepts
I apply the mean-variance analysis for the portfolio construction developed by Markowitz (1952 and 1957) and apply the independent t-test as well as the matched-pairs t-test (Newbold, Carlson and Thorne, 2003; Kanji, 2006) to determination of possible significant differences in portfolios.
6-1. Mean-variance analysis
Markowitz (1952 and 1957) developed the mean-variance model for portfolio selection. He assumes that investors are risk averse meaning that they choose for the investment with the lowest risk or correspondingly they would demand larger returns, if they bear larger risk.
From the point of view of a risk-averse investors, risk is defined as the total variability of a security measured by the standard deviation (SD) of an asset’s return (Ross, Westerfield and Jaffe, 2005). The SD is determined by the square root of the variance.
Var
SD = (1)
Markowtiz’s model of portfolio selection is based on the “diversification effect”. It says that risk is reduced by investing in less than perfectly correlated assets (correlation ρ<1). The correlation ρ between two assets A and B is calculated as follows:
where σAB is the covariance between assets A and B and σAand σB are the standard
deviations of assets A and B respectively. The correlation between two assets, in general, can lie between plus and minus one. A correlation of plus one implies a perfect correlation. The diversification benefits are larger the less perfectly correlated assets A and B are. If two assets are less than perfectly correlated, the portfolio variance is less than the weighted average of the variance of each asset (Speidell and Sappenfield, 1992; Ross, Westerfield and Jaffe, 2005). In other words, if the correlation between assets is less than one, the risk of their combined portfolio, is less than a weighted average risk of these assets. The return, on the other hand, is still determined by a weighted average of the return of each asset that is invested in. Consequently, the risk-return relation is improved by diversifying across less than perfectly correlated assets.
The variance of a portfolio P consisting of two assets A and B is as following:
2 2 2 2 2 ) (portfolio XA A XAXB A B AB XB B VAR = σ + σ σ ρ + σ (3)
where X and A X are the proportions of an asset in the portfolio P, B 2 A
σ and σB2 are the
variance of assets A and B respectively. The term σA*σB*ρAB equals the covariance AB
σ between assets A and B (see formula 2) and contains the correlation coefficient.
In a portfolio P of N assets, each asset is related to the other one:
2 2 2 2 2 2 2 2 2 2 2 2 2 ... 2 2 ) ( N N MN N M N M M M C C AC C A C A A A B B AB B A B A A A X X X X X X X X X X X X portfolio VAR σ ρ σ σ σ σ ρ σ σ σ σ ρ σ σ σ + + + + + + + + + = (4)
Figure 1: Relation between expected return and standard deviation
Theoretically, no portfolio can be achieved lying above the Efficient Frontier because the expected return would be too high with respect to the taken risk. Portfolios lying inside the Efficient Frontier are inefficient because a higher return could be achieved at the same risk level with a more efficient set of assets.8
This analysis by Markowitz is based on some key assumptions. First, there are no taxes and transaction costs and all investors have the same information implying that markets work efficiently. Practically, this assumption does not apply because there are transaction costs, taxes and not every investor has the same amount of information. Even if they had the same information it might be interpreted differently. Second, all investors are risk averse meaning that they require a larger rate of return for taking more risk. Third, there is unlimited short selling at the risk free rate and all proceeds are reinvested. Black and Litterman (1992) criticize the issue of unlimited short-selling because it can result in very large short positions of certain assets. Short positions are indicated by negatively assigned weights of the respective assets within a portfolio. I do not allow for short-selling, which might result in so-called “corner solutions” with weights of zero percent. However, as Hatchondo (2008) finds out, short-selling is costly for investors due to several (transaction) costs. Further, Black and Litterman (1992) as well as Lewis (1999) criticize that Markowitz’s mean-variance analysis is based on expected returns because
8
expected values depend on the method by which they are calculated. I use historical data in order to evaluate optimal diversification gains. Expected returns should have a greater emphasis when advice on future investments is given. Hence, the issue of expected returns is not a considerable problem.
The mean-variance analysis does not assume normally distributed returns. However, due to the fact that most stock market indices do not have normally distributed returns9, I assume that investors have a quadratic utility function (Samuelson, 1967). This assumption implies that only the mean and standard deviation of stocks matter to investors, which is in line with the implied efficient market hypothesis, discussed above. Acknowledging the theoretical assumptions of this model, it should be said that the obtained results represent theoretically optimal portfolios. Overall, the evaluation of the home bias of investors is an empirical analysis of national as well as international diversification gains under these assumptions.
6-2. Testing for significance
Significant differences between returns are analyzed in two ways. First, I test whether returns for each country differ significantly on varying levels of national as well as international diversification (these levels are discussed below). Second, I test whether the change in returns at varying steps of diversification differ significantly between the two groups of developed and emerging markets. For both measures, I apply a matched-pairs t-test (Newbold, Carlson and Thorne, 2003; Kanji, 2006). It compares the means of two populations, which are fully related meaning that the numbers of observations come from the same source (here: month)10. As discussed in section 6-1, the returns of 50% of the stock market indices are not normally distributed. According to Kanji (2006), the matched-pairs t-test is still applicable, if the assumption of the normal distribution is not
9 I test the normality of the monthly returns of the eight stock market indices with the Kolmogorov-Smirnov as well as the Shapiro-Wilks test. Both their null hypotheses assume a normal distribution of returns. At a significance level of 5% the return distributions of the Spanish stock market are not normal using the Kolmogorov-Smirnov test. According to the Shapiro-Wilks test, the returns of the British, German, Spanish and Russian stock markets are not normally distributed at a 5% significance level. According to both tests, only the French, Polish, Czech and Hungarian stock markets have normally distributed returns.
met. However, the test should be regarded as an approximate measure. The null-hypothesis states that the means of both populations do not differ significantly. Hence, if the calculated p-value is below a certain threshold of 1%, 5% or 10%, the null hypothesis is rejected at the level of the respected threshold and one can conclude that the means of both populations differ significantly. Assumptions about the risk (or variance) of both populations are not made. The matched-pairs t-test is described in section 7.
7. Method of analysis
Table 1 summarizes the analytical steps and assumptions of this paper for each country within and the analysis between the two groups of developed and emerging economies.
Table 1: Flow of analysis
Steps of Analysis Developed Country Emerging Country 1. National: Index
DNS
σ , rDNS σENS., rENS
2. National:
Industry σDNS, rDNP σENS, rENP
3. International:
Indices σDNS, rDIS σENS, rEIS
4. International:
Industries σDNS, rDIP σENS, rEIP
σ and r represent the standard deviation and the rate of return, respectively, in each
step. The mnemonics stand for the following:
Table 2: Explanation of mnemonics
D: Developed countries E: Emerging countries S: Stock market index P: Optimized portfolio N: National I: International
optimized portfolio (P) as well as to a national (N) or international (I) level of analysis. Hence, each standard deviation and return is specified by three letters, one from each row of table 2.
The steps of the analysis as well as the resulting hypothesis are explained in the following section.
First, I determine the average monthly rate of return as well as the risk measured by the standard deviation of the stock market index of each country in their corresponding national currency. σDNS and rDNS represent the standard deviation and return of the
national (N) stock markets (S) in developed countries, whereas σENS. and rENS represent
the standard deviation and return of the national (N) stock markets (S) of emerging markets, respectively.
to or larger than the return of the respective stock market index.11 As discussed in section 6-2, I test whether the rates of return between varying stages of diversification differ as well as whether the change in returns between developed and emerging markets as groups differ significantly by applying the matched-pairs t-test. For the former step, I compare the rates of return of the nationally optimized industry portfolio of country i from time t to time t+10612 with the rates of return of the stock market index of country i from time t to time t+106. The following formula shows the two time series, which are tested for significant differences with the matched-pairs t-test:
106 , , , ,
.
.
.
+ t i DNS t i DNSr
r
106 , , , ,.
.
.
+ t i DNP t i DNPr
r
(5)The hypotheses for this first step are:
Hypothesis 1: The return of the nationally optimized industry portfolio in developed and emerging countries is significantly larger than their respective index return at equal risk. Hypothesis 1 Alternative: The return of the nationally optimized industry portfolio in developed and emerging countries is not significantly larger than their respective index return at equal risk.
I assume that the return of the industry portfolios in developed and emerging markets (rDNP and rENP, respectively) is significantly larger than the index returns (rDNSand rENS,
respectively) at equal risk levels of their corresponding national stock market index . That is due to the fact that national stock markets are diversified to a certain extend, however, they do not represent a nationally optimized portfolio across all industries.
11
This relation of equal or larger returns of an improved portfolio is also valid for further diversification steps, which are the international diversification across stock market indices as well as across nationally optimized portfolios.
For the comparison of changes in the level of returns between the two groups of developed and emerging markets, I determine the average absolute increase in returns for each group when going from the national stock market to nationally optimized industry portfolios for each period from time t to time t+106. Then, I test whether the average, absolute increase in returns of one group differs from the other group.13 The following formula summarizes the matched-pairs t-test for comparing the average absolute increase in returns between the two groups of developed and emerging markets:
4 ) ( . . . 4 ) ( 106 4 1 4 1 + = =
∑
∑
− − t i DNS DNP i t DNS DNP r r r r 4 ) ( . . . 4 ) ( 106 4 1 4 1 + = =∑
∑
− − t i ENS ENP i t ENS ENP r r r r (6)The hypotheses for this step are as follows:
Hypothesis 2: The increase in portfolio returns compared to national stock market indices is significantly larger in developed than in developing markets.
Hypothesis 2 Alternative: The increase in portfolio returns compared to national stock market indices is not significantly larger in developed than in emerging economies.
Since the stock markets of developed countries are supposed to be more diverse than the ones from emerging markets (Cavigli, Brightman and Aked, 2000; Estrada, Kritzman and
Page; 2006; Moerman, 2008), I expect to find larger diversification benefits between industries within developed markets. Hence, I expect a significantly larger increase in the rate of return of national industry portfolios in developed (rDNP) than emerging markets
(rENP) compared to their respective index returns at the same level of risk.
Third, I allow for international diversification and adopt different country specific views. The country from which the view is currently from is the ‘home country’. Here, I always assume that investors diversify across industries in order to receive an optimal return for their investments. Further, I add the seven remaining foreign indices to the industry based portfolio within the home market. Foreign index returns are added in the currency of the respective home country. Thereby, exchange rate gains and/or losses are included in the international portfolio return. This international portfolio will be optimized applying the same method as was used on the national level. The variance of the international portfolio is equalized to the variance of the national portfolio, which is ultimately the variance of the home country’s market index.
Hypothesis 3: Adding foreign indices to an optimized industry portfolio of the home market and then optimizing the portfolio leads to significantly larger returns.
Hypothesis 3 Alternative: Adding foreign indices to an optimized industry portfolio of the home market and then optimizing the portfolio does not lead to significantly larger returns.
After national portfolios have been optimized and foreign indices are added, I assume that there is an increase in returns (rDIS > rDNP and rEIS> rENP), since some differences in
Hypothesis 4: Internationally optimized diversification across foreign indices leads to a significantly larger increase in returns for investors in emerging markets than for the ones in developed markets.
Hypothesis 4 Alternative: Internationally optimized diversification across foreign indices does not lead to a significantly larger increase in returns for investors in emerging markets than for the ones in developed markets.
I expect the increase in returns for emerging markets (rEIS-rENP) to be larger than the
increase for developed markets (rDIS-rDNP) on an international level. This is due to the
fact that diversification benefits were present within developed markets already on an international level so that benefits for index diversification across national boundaries are larger for emerging markets. I apply the matched-pairs t-test to test for significance as in formula 6.
Fourth, I construct international portfolios by adopting the same ‘home country’ view, whereas the optimal industry portfolio of the seven remaining markets are added to the home market industry portfolio. Foreign, national industry portfolios were construct in the currency of the respective home country and added to the international portfolio. Again, the variance of the portfolio is equalized to the variance of the respective home market index.
Hypothesis 5: Internationally optimized diversification across nationally optimized portfolios leads to a significantly larger return than internationally optimized diversification across foreign indices.
Hypothesis 5 Alternative: Internationally optimized diversification across nationally optimized portfolios does not lead to a larger return than internationally optimized diversification across foreign indices.
diversification across optimized national portfolios should outperform international diversification across indices. Again, the matched-pairs t-test is applied to test for significant differences in returns (see formula 5).
Hypothesis 6: Investors from emerging markets can gain significantly more from international diversification across nationally optimized portfolios than investors from developed markets in terms of increasing returns.
Hypothesis 6 Alternative: Investors from emerging markets cannot gain significantly more from international diversification across nationally optimized portfolios than investors from developed markets in terms of increasing returns.
As in the third step (see hypothesis 4), the return of emerging home markets (rEIP) should
increase significantly more compared to the return of developed home markets (rDIP).
This is due to the fact that diversification benefits in terms of industrial diversification are supposed to be larger for developed markets. So, now also emerging markets obtain the benefits of diversification within developed markets. I apply the matched pairs t-test to test (see formula 6) for significant differences between the two groups of developed and emerging markets.
Note that hypotheses 1, 3 and 5 deal with increases in returns per country, whereas hypotheses 2, 4 and 6 are about the increase in returns between groups of countries. After analyzing the rates of return at different levels of diversification, the results regarding the hypothesis are summarized in table 12.
8. Results
8-1 National diversification
industries for each country. Table 3 summarizes the weights of each industry within an optimal portfolio across the selected countries.
Table 3: Industry weights of industry portfolios at the risk level of the national stock market index in national currencies from January 1999 to December 2007
Oil and Gas Basic Materials In-dustrial Consumer goods Health care Consumer
services Telecom Utilities Financial Tech-nology UK 6.64% 33.82% 0.00% 0.00% 0.46% 0.00% 0.00% 59.08% 0.00% 0.00% France 9.11% 76.17% 0.00% 0.00% 14.72% 0.00% 0.00% 0.00% 0.00% 0.00% Germany n.a. 0.00% 0.00% 0.00% 74.63% 0.00% 0.00% 0.00% 0.00% 25.37% Spain 0.00% 69.65% 0.00% 3.06% 0.00% 0.00% 27.29% 0.00% 0.00% 0.00% Poland 0.00% 41.53% 0.00% 1.92% 0.00% 0.00% 0.00% n.a. 49.11% 7.43% Czech Republic 0.00% 4.32% 0.00% 3.30% 5.84% 0.00% 0.00% 52.71% 33.84% n.a. Hungary 8.39% 0.00% 0.00% 9.94% 3.45% 0.00% 0.00% 5.97% 72.26% n.a.
Russia 0.00% 76.56% n.a. 0.00% 0.00% 0.00% 0.00% 0.00% 23.44% n.a.
The weights of each industry within the countries are very different.14 As argued above, one should keep in mind that an optimal portfolio within each country is determined by two factors, which are the risk-return data of one industry compared to the other as well as the correlations between these industries.
The UK’s portfolio is characterized by large weights in the utilities (59.08%) and the basic materials (33.82%) sector. Both sectors have an excellent risk-return position, whereas the utility sector also correlates on a low level with all other industries. The health care and technology sectors also correlate very low with other industries but their risk-return positions are relatively unfavorable. Especially the technology sector has a high risk (SD of 13.13%) with a negative average monthly rate of return (-0.91%).
As in the UK, also the basic materials’ sector (76.17%) in France has an excellent risk-return position. The percentages of the oil and gas (9.11%) as well as the health care (14.72%) sector stem from a combination of a relatively good risk-return position and low correlations with other industries. The technological sector, also as in the UK, is characterized by a very high standard deviation (14.65%), whereas its return is only slightly positive (0.43%).
In Germany the health care sector (74.63%) has an excellent risk-return position and correlates on a low level with other industries. The technological sector (25.37%) has both, a large risk (SD of 15.54%) and a large rate of return (1.17%), combined with low correlations with other industries. Especially the financial sector seems to correlate highly with other sectors.
In Spain, like in the UK and in France, the basic materials sector (69.65%) has a good risk-return position. The health care and technology sector have very low and sometimes negative correlations with other industries, however, these other industries outperform those two sectors in terms of risk and return. Particularly the technology sector is very risky (SD of 11.28%) with a large negative rate of return (-1.31%). The risk and return of the consumer goods industry (3.06%) is not exceptional, but its small percentage is due to the diversification benefits coming from its low correlation with other industries.
There is an emphasis of the financial sector (49.11%) in the Polish portfolio. Although its correlation with other sectors is one of the highest compared to other industries’ correlations, the financial sector has high rates of return (2.27%) accompanied by high risk (SD of 8.77%). The second strongest sector, in terms of portfolio participation, is the basic materials one (41.53%). Its risk is slightly larger than the one of the financial sector (SD of 10.83%), but also its average monthly rate of return is a bit larger with 2.72%. The technology (7.43%) and the consumer goods sector (1.92%) participate in the overall portfolio. The technology sector, which has a large standard deviation of 37.28% and a high average monthly rate of return of 4.77%, is balanced by the consumer goods sector and its low level of risk (SD of 5.98%) and return (1.09%). Additionally, both industries have low correlations with other industries. Other sectors do not participate in the portfolio due to either high risk and negative returns, such as the industrial sector, or a relatively low level of return compared to their level of risk.
small extent within the portfolio (participation of 4.32%, 3.30% and 5.84%, respectively). All three sectors are characterized by large risk and comparably medium rates of return (SD of 13.33%, 40.26% and 14.21% and a return of 2.86%, 3.11% and 1.12%, respectively). However, their correlations with other sectors are very low. The correlation of the consumer goods sectors even correlates negatively with some others. Hence, these sectors add value to the portfolio in terms of lowering the overall portfolio risk.
In the Hungarian portfolio, the financial sector has the largest weight (72.26%) due to its excellent risk-return position (SD of 9.61%, return of 2.37%). The consumer goods sector is the second largest with a weight of 9.94%. It has not only a good risk-return position (SD of 3.78%, return of 0.70%), but its correlation with almost all other sectors low. The oil and gas sector is the third largest within the portfolio (weight of 8.39%) due to its relatively good risk-return behaviour (SD of 9.64%, return of 1.81%). It is to point out that the second and third largest sectors within the portfolio slightly correlate negatively to each other (correlation of -0,002). Thereby, they are very favorable for the overall portfolio risk. Probably because of this effect, the utilities sector has a portfolio weight of 5.97%. The standard deviation of 23.36% is very large, but its large average monthly rate of return of 2.13% is slightly above the return of the overall portfolio.
The Russian portfolio is composed of only two sectors, basic materials (76.56%) and financials (23.44%). The basic materials sector has a high risk (SD of 16.30%) compensated by a large average monthly rate of return of 6.85%. In order to balance the overall portfolio, the financial sector is included because it has a relatively low risk (SD of 7.60%) with a proportionately high return of 2.57%. Due to their low correlation of 0,053 with each other, diversification benefits are large. Other sectors, such as the oil and gas, telecom and utilities sectors are characterized by good risk-return positions (SD of 12.98%, 17.27% and 16.30% as well as an average monthly rate of return of 3.50%, 4.30% and 4.47%, respectively) but these are not sufficiently favorable in order to be included in a portfolio.
(9.11%), Hungary (8.39%) and the UK (6.64%). This seems to be surprising, since the oil and gas resources have decreased leading to higher prices, which would turn this industry into a lucrative investment due to increasing profits within this industry. Especially the Russian market relies heavily on this industry, but this sector did not qualify for any portfolio participation in this market at all. Its correlation with other industries is relatively low. It is mostly below 0.5, so that diversification benefits are present. The low participation within portfolios can be explained by low returns relative to their risk correspondingly, the risk is too high at the given level of returns.
The basic materials industry is relatively important within Russia, France and Spain, where its portfolio participation is 76.56%, 76.17% and 69.65%, respectively. Only in Germany and Hungary it is not part of the portfolio. The health care sector reaches a maximum participation of 71.66% in Germany, whereas it is only included in the French industry portfolio by 14.72% and it does not reach a value above 6% in all other countries. The telecom industry is only included in the Spanish portfolio with a weight of 27.29%, but its weight in other markets is zero. The utilities sector has a weight of 59.08% in the UK and 52.71% in the Czech Republic (with an exception of only 5.97% in Hungary), whereas it has a weight of zero in all other countries. A remarkable sector is the financial one. It does not play a role in the developed market, although the weight within emerging markets varies from a minimum of 23.44% in Russia up to a maximum of 72.26% in Hungary. This might have to do with the fact that the development of the financial sector is connected to the growth in general. The technology sector is included in only two portfolios. It obtains a weight of 28.34% in Germany and only 7.43% in Poland. It should be noticed that data of the technological sector is not available for the other three emerging markets. Hence, a conclusion cannot be drawn in this respect.
Table 4: Risk and average monthly rates of return of market indices and optimized industry portfolios at equal risk levels between January 1999 and December 2007 in national currencies
Standard Deviation
Return of Stock Market Index
Return of Optimized Industry Portfolio UK 3.97% 0.18% 0.83%** France 5.10% 0.47% 1.19%** Germany 6.34% 0.43% 0.90% Spain 5.33% 0.71% 1.20% Poland 9.00% 1.80% 2.62%** Czech Republic 8.22% 2.16% 3.09%** Hungary 8.05% 1.45% 2.11%* Russia 12.70% 3.75% 5.85%**
Note: The stars indicate whether and at what level the average monthly rate of return of the nationally optimized industry portfolio is significantly larger than the one of the national stock market index of the respective country. Three stars (***) indicate significant differenences in returns at a 1% confidence level, two stars (**) indicate significant differeneces in returns at a 5% confidence level and one star (*) indicates significant differences at a 10% confidence level.
By theory, the larger the risk, which an investor is willing to take, the larger should the expected return be.15 An investor is exposed to the lowest level of risk in the UK, whereas the return is also the lowest. The opposite holds for the Russian market. Small outlyers are the German and Polish markets, which show a return that is too low considering its risk. The Czech Republic has a large return considering the level of risk relative to the other markets. Generally, the risk of stock markets in emerging European economies are larger than in developed ones. Measured by the standard deviation, the risk of stock markets in developed countries range from 3.97% in the UK to 6.34% in Germany, whereas the standard deviation in emerging European markets lies between 8.05% in Hungary and 12.70% in Russia.
Allowing for industry diversification within each country, one observes that the return of an industry portfolio exceeds the one of the market index at equal risk. In other words, an investor would have been able to outperform the index in terms of the rate of return by diversifying across industries.16 In relative terms, the largest increase in returns is in the
15
Note: The correlation between the standard deviation and the index returns of 0,97 as well as the correlation between the standard deviation and the national industry portfolio returns of 0,98 between January 1999 and December 2007 are a proof of this theoretical relation.
UK market, where the return from 0.18% to 0.83% more than tripled. Absolutely, the largest increase in the rate of return is in Russia, where an increase from 3.75% to 5.85% can be observed.
In order to draw conclusions regarding the stated hypotheses, I test for significant differences in returns. As seen in table 4, the average monthly rate of return of the nationally optimized industry portfolio is significantly larger than the index return for two out of four developed economies, namely the UK17 and France, as well as for all emerging economies.
Concluding, only half of the developed countries are able to achieve a significantly larger rate of return when diversifying optimally across national industries. So, we reject the first hypothesis (H1) and do not reject the alternative (H1A) implying that not all investors from developed markets18 are able to outperform the index by diversifying optimally across national industries. The return of nationally optimized industry portfolios is significantly larger than the return of the index in each emerging market. Here, we reject the alternative hypothesis (H1A) saying that the rate of return increases significantly when diversifying nationally across industries.
In order to evaluate the changes in the levels of returns between developed and emerging markets and, therefore, to answer the second hypothesis, I consider absolute changes. The average increase in the monthly rate of return in developing countries is 1.12%, whereas the average increase in returns for developed countries is 0.58%. The absolute differences in the average monthly rates of return between developed and emerging markets do not differ significantly. Hence, we reject the second hypothesis (H2) and accept the alternative second hypothesis (H2A).
17
Remarkably, the average monthly rate of return of Germany’s national industry portfolio is 107% larger than the return of the stock market index. Yet, the returns do not differ significantly.
8-2 International Diversification
The next step is the international diversification across industries. Table 5 summarizes the percentages of international diversification across indices. The home market perspective considers the optimal industry portfolio at home and the use of international indices abroad. The weights of each market (and later on the return data) is based on equal risk of the portfolio and the stock market of the home country.
Table 5: Market weights of internationally optimized portfolios across foreign stock market indices between January 1999 and December 2007 in national currencies and local risk levels
Perspective
from … UK France Germany Spain Poland
Czech
Republic Hungary Russia
… UK 72.13% 0.00% 0.00% 9.43% 0.00% 15.75% 0.00% 2.69% … France 0.00% 60.34% 0.00% 0.00% 0.00% 36.18% 0.00% 3.48% … Germany 0.00% 0.00% 39.55% 0.00% 0.00% 51.55% 0.00% 8.90% … Spain 0.00% 0.00% 0.00% 59.01% 0.00% 30.84% 0.00% 10.15% … Poland 0.00% 0.00% 0.00% 0.00% 28.53% 17.74% 0.00% 53.73% … Czech Republic 0.00% 0.00% 0.00% 0.00% 0.00% 58.16% 0.00% 41.84% … Hungary 0.00% 0.00% 0.00% 0.00% 0.00% 25.55% 27.79% 46.67% … Russia 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00%
also from the emerging markets’ perspective, diversification benefits are only present within other emerging markets. Investments should not have been allocated in stock markets of developed markets. This might be due to the fact that Eastern European stock markets are subject to high risk but also high return. Therefore, diversification benefits at these high risk levels are not realizable in stock markets of developed countries due to their relatively low risk and low return.
Most investments from developed markets should have been allocated in the Czech Republic, whereas developing markets could have gained most by diversifying into the Russian stock market. Again, the reasons are the high rate of the Russian return and the high level of local risk. The Russian market has the largest average monthly rate of return, but it comes along with a high level of risk, which is too high for investors from developed markets. The Czech Republic’s level of risk is also larger than the one of the developed markets, but the average monthly rate of return of the Czech stock market is relatively high, which makes this market attractive for investors from developed markets.
Table 6 shows the increase in returns from nationally optimized industry portfolios to internationally optimized portfolios across stock market indices.
Table 6: Risk and average monthly rates of return of nationally optimized industry portfolios and internationally optimized portfolios across foreign stock market indices between January 1999 and December 2007 in national currencies
Perspective from … Standard Deviation Return of Optimized Industry Portfolio Return of Internationally Optimized Portfolios across Foreign Stock Market Indices
… UK 3.97% 0.83% 1.10%* … France 5.70% 1.19% 1.60% … Germany 6.34% 0.90% 1.77% … Spain 5.33% 1.20% 1.76%* … Poland 9.00% 2.62% 3.15% … Czech Republic 8.22% 3.09% 3.31% … Hungary 8.05% 2.11% 2.89% … Russia 12.7% 5.85% 5.85%
A British investor should put 72.13% of his investments in his home market (see table 5), which is the highest rate among developed markets. Comparing the average monthly rate of return of the national industry portfolio and the internationally diversified one, there is an absolute increase of 0.27% in returns in the international portfolio, which is the lowest absolute increase among developed markets. The largest absolute increase in average monthly returns of 0.87% could have been achieved by German investors. Their weight of 39.6% of the national industry portfolio is the lowest among developed markets. This means that 60.5% of investments should have gone into foreign market indices. The Spanish investors should have kept 59% within their own country, which is almost the same weight of 60.3% for the French investors. However, the average monthly rate of return for the Spanish investor increases absolutely by 0.56% which is the second largest relative increase among developed markets, whereas the French investor gains an additional 0.41%.
Among the emerging markets, only Russian investors could not have gained by diversifying across international indices. However, their average rate of return is still the largest one among all sample countries. Polish and Hungarian internationally optimized portfolios across foreign stock market indices have the lowest rates of participation of their home country portfolio, which is 28.53% and 27.79%, respectively. However, these investors could achieve an absolute increase in average monthly returns of 0.53% and 0.78%. In general, one observes, first, that the lower the absolute average monthly rate of return of a nationally diversified portfolio is, the more investments should have been allocated abroad. Second, the larger the share of investments into foreign stock market indices is, the larger is the absolute increase in the rate of return.19
The results indicate that each market achieves higher returns when diversifying across international stock market indices. However, these increases are not significant for all countries except for the UK and Spain.20 Hence, we reject the third hypothesis (H3) for
19 An exception is the UK, which allocates only 28% of their investments into foreign indices, whereas the return of the British industry portfolio is among all countries the lowest. The reason is that the British stock market index is characterized by a very low standard deviation or (correspondingly) risk.
developed as well as emerging markets. In other words, investors from emerging markets are not able to achieve significantly larger returns by going from nationally optimized portfolios to an internationally optimized portfolio across stock market indices, whereas investors from half of the developed markets are able to do so.
Considering the absolute changes in the average monthly rate of return of nationally optimized industry portfolios and internationally optimized portfolios across foreign stock market indices, investors from developed markets could increase their average monthly rate of return by 0.53% compared to 0.38% for investors in emerging markets. This difference is not significant. So, we reject the fourth hypothesis (H4) because the improvement in absolute returns do not differ for investors from emerging and developed markets.
Table 7 exhibits the market weights of an optimized international portfolio across nationally optimized industry portfolios in each country from each country’s perspective. That means that a portfolio is determined in the currency as well as the level of risk of the stock market index of the respective home country.
Table 7: Market weights of international diversification across optimal stock market portfolios between January 1999 and December 2007 in national currencies
Perspective
from … UK France Germany Spain Poland
Czech
Republic Hungary Russia
… UK 33.80% 3.16% 14.74% 14.42% 0.00% 21.00% 0.00% 12.88% … France 0.00% 8.68% 13.04% 26.55% 0.00% 40.29% 4.52% 6.92% … Germany 0.00% 0.00% 9.96% 21.28% 0.00% 38.63% 4.90% 25.23% … Spain 0.00% 0.00% 10.68% 21.54% 0.00% 38.33% 4.57% 24.89% … Poland 0.00% 0.00% 0.00% 0.00% 0.00% 32.33% 0.00% 67.67% … Czech Republic 0.00% 0.00% 0.00% 0.00% 0.00% 41.24% 0.00% 58.76% … Hungary 0.00% 0.00% 0.00% 0.00% 0.00% 46.49% 0.00% 53.51% … Russia 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00%
International diversification across nationally optimized portfolios leads to more diverse portfolios meaning that the portfolio weights are more dispersed compared to international diversification across foreign stock market indices (compare table 7 to table
5). Compared to the former diversification strategy, each country reduced the weight of its home market within the portfolio resulting in larger investments abroad. An exception is Russia, which still has a weight of 100% of its own industry portfolio. Russia’s risk and average monthly rate of return is so large that adding any country’s optimal industry portfolio decrease the return of an internationally diversified portfolio from the Russian perspective. While developed markets allocate only up to 25.23% in optimized the Russian market, the weights from an Eastern European perspective allocated to the optimized Russian industry portfolio are roughly between 53% and 68%. Developed markets should also allocate a large portion of investments in the Czech industry portfolio. According to table 7 the French participation is above 40% and the weight from a Spanish and German perspective should reach almost 40%.
Also remarkable is the fact that diversification for Western investors in optimized Western and Eastern European industries adds value to their respective portfolio, whereas Eastern European investors should not allocate any investments in optimized Western European industries. This might be a consequence of how the portfolios where constructed. As it was the case for the Russian market before, the Western European combination of low risk with low returns (relative to their Eastern European counterpart) does apparently not add sufficient value for optimized portfolios.
