The Truth about Investing in Emerging Markets?

Transcription

1 The Truth about Investing in Emerging Markets? Authors: Edward Hili, Rukshila Gooneratne, Blaise Laupretre, Wee Ling Phua and Laurent Zarruk This work has been undertaken as part of a student educational project and the material should be viewed in this context. The work does not constitute professional advice and no warranties are made regarding the information presented. The Authors, Judge Business School and its Faculty do not accept any liability for the consequences of any action taken as a result of the work or any recommendations made or inferred. Abstract We seek to evaluate the widely held belief that emerging markets offer superior long-run investment returns due to higher economic growth when compared to developed markets. We find that the correlation between growth in GDP per capita and stock returns is insignificant for both developed and emerging economies. We conclude that emerging markets do not consistently outperform developed markets either on a total return basis or a risk-adjusted basis. However not only do emerging markets provide significant diversification benefits both when added to a portfolio of developed markets assets and to a portfolio of emerging markets ones, but they can also improve returns for investors under certain circumstances; we identify factors and circumstances that are relevant for maximising diversification benefits and enhancing returns. Overview This paper is organised into 4 main sections. The first seeks to address whether superior returns are driven by high GDP growth while the second performs an analysis of emerging market investment performance and shows how performance varies over time and with different microeconomic and macroeconomic factors. The third section provides evidence on how emerging market investments could add value to the investor and the final section distils implications of the various findings for investment strategies. SECTION I: ARE RETURNS DRIVEN BY GDP GROWTH? Emerging markets equities outperformed developed markets by an impressive 10.49% (annualised) from Given the strong performance of emerging markets in the recent decade, emerging markets have grown in importance as an asset class and have attracted substantial investor interest. What is an emerging market? There is no single definition of an emerging market. In practice, index providers, in consultation with users, classify markets. MSCI, for example, uses 3 criteria to classify markets: (i) economic development, (ii) size and liquidity and (iii) market accessibility while S&P considers (i) economic and political factors, (ii) relative market size, (iii) market size and structure, and (iv) investment conditions. Dimson et al. (2010) observed that regardless of the differing criteria, the resultant classifications are often similar. They further find that a cut-off of US$25,000 GDP per capita serves as an effective boundary between developed and emerging markets. 1 Computed using MSCI Emerging Market Total Return Gross Index (USD) and MSCI World Total Return Gross Index (USD) 1

2 The widely held belief is that emerging markets offer superior long-run investment returns because underlying economic growth in emerging markets is higher than that in developed markets. The following section searches for evidence to confirm whether GDP growth drives returns. Does Growth in GDP Per Capita Drive Returns: A Review of the Empirical Evidence Given that GDP could grow as a result of population increase and/or growth in GDP per capita, growth in GDP per capita is considered a more appropriate measure of underlying economic growth as it controls for growth driven purely by population increases. Empirically, the correlation between growth in GDP per capita and stock returns has either been found to be insignificant or negative 2. Several studies confirm this relationship (or the lack thereof). Ritter (2005) found the cross-sectional correlation for the compounded real return on equities and compounded growth rate of real GDP per capita for 16 countries from 1900 to 2002 to be (pvalue = 0.16). Countries included in his study were Belgium, Italy, Germany, France, Spain, Japan, Switzerland, Ireland, Denmark, Netherlands, UK, Canada, US, South Africa, Sweden and Australia. A similar relationship was obtained when Dimson et al. (2010) updated the data to 2009 and expanded the number of countries to 19 (countries added to the 16 above were Finland, Norway and New Zealand). The correlation between real equity returns and long run real growth in GDP per capita was found to be The negative correlation becomes more apparent when one considers the post-1972 period: from 1972 to 2009, countries that have the weakest economic growth in the prior 5 years delivered the strongest stock returns (Dimson et al., 2010). Studies on emerging countries gave similar conclusions. For example, the correlation between stock returns and GDP growth was found to be for 18 emerging markets for the period from 1970 to 1997 (Siegel, 1998 cited in Ritter, 2005). Ritter (2005) found the simple correlation between geometric mean annual real stock returns and arithmetic mean real per capita annual GDP growth to be 0.02 (p value = 0.94) for 13 countries, mainly emerging markets 3, over Other measurements of economic activity similarly do not suggest a direct conclusive relationship between economic growth and stock returns. Although Tsouma (2008) found that industrial production growth was related to future stock returns in 11 out of 19 emerging markets, the effects of industrial productions on returns were mixed. In 3 emerging countries, relationships were found in multiple lags of industrial production growth whereas in the other countries, a single significant coefficient was observed. More importantly, some lags of industrial production growth show a positive relationship while other lags show a negative relationship. The relationships also differ for different countries. In addition, when trying to establish causality, the hypothesis that industrial production growth does not cause stock returns is rejected in only 6 out of 19 emerging markets. 2 It is common for researchers to focus on changes in GDP per capita when analysing long run economic growth (Dimson et al, 2010). However, in a few papers that focused on emerging markets and used overall GDP, correlations between overall GDP and equity returns, have been found to be positive (albeit not overwhelmingly high). Smimou and Karabegovic (2010) found correlation of between overall GDP growth and equity returns over for 10 emerging markets in the Middle East and North Africa plus Turkey while Saldanha (2010) found the average correlation between overall GDP growth rates and equity returns for 8 emerging countries (Taiwan, Mexico, Turkey, India, Indonesia, Brazil, China, Russia) from 1980 to 2009 to be Countries included in the study are Argentina, Chile, Finland, Jordan, Malaysia, Mexico, New Zealand, Philippines, Portugal, South Korea, Taiwan, Thailand and Turkey. 2

3 Hence, generally, industrial production growth was not found to be useful in predicting stock returns. In line with Tsouma (2008), Bilson et al. (1999) found real economic activity (measured using industrial production index or manufacturing production index when the former was unavailable) to be significant in only 2 out of 20 emerging markets while lags of economic activity were found to be insignificant in all countries. Similarly, Fifield et al. (2002) did not find consistent, statistically significant relationships between economic variables and stock returns. They performed regression analyses using global (world industrial production and world inflation) and local (GDP, inflation, money supply and short term interest rates) economic variables to explain stock returns for 13 emerging stock markets 4 over and found that local factors were relevant in explaining returns for only 6 out of 13 emerging markets. In short, the empirical evidence suggests that growth in GDP per capita does not explain returns. But why doesn t growth in GDP per capita explain returns? A number of reasons have been offered. These could be categorised into 4 main areas: 1. Expectation of high growth has already been priced in Negative correlation may be due to high growth being priced in at the start of the investment period (Siegel, 1998 cited in Ritter, 2005). Ritter (2005) believes that markets tend to assign high Price-to- Earnings and Price-to-Dividend multiples when economic growth is expected to be high. Hence, to receive the same dividend, more capital has to be committed, leading to lower realised returns. Dimson et al. (2010) share the same view that high growth is already priced in. Japan provides a good example of this effect. Growth expectations of Japan were overly optimistic during the 1980s when the markets priced in 20 years of future growth. Consequently, in the last two decades, equity returns were negative even though GDP continued to grow (MSCI, 2010a). 2. Businesses operate internationally Businesses operate globally in both strong and weak economies, thus returns cannot be clearly attributed to high versus low GDP growth economies (Dimson et al., 2010; MSCI, 2010a). For example, companies in developed countries may well be the ones benefiting from growth in emerging markets. Nearly 25% of total US profits and about 30% of sales of S&P500 companies are generated abroad (Wilmot, 2010). One case in point is the US chip industry, which recently cited strength of demand in emerging markets as a key sales booster (Nuttall, 2011): emerging markets account for more than 50% of unit volume in Intel and demand is not only isolated to lower-priced items but higher-priced PCs, smart-phones and tablets too. 3. High GDP growth is not driven by higher growth of existing firms Bernstein and Arnott (2003) believe that more than half of aggregate economic growth comes from the creation of new enterprises, as opposed to growth of established enterprises. For instance, much of the market capitalisation of U.S. companies today has come from industries (e.g. 4 Countries included in the study were Chile, Greece, Hong Kong, India, Korea, Malaysia, Mexico, Philippines, Portugal, South Africa, Singapore, Thailand and Turkey. 3

4 Information Technology and Pharmaceuticals) that barely existed 100 years ago. The beneficiary of new businesses is often venture capital rather than stock investments which participate in the growth of established businesses. 4. Investors may not be able to participate in the emerging market growth story because: a) Investment opportunities are not opened to international investors Dimson et al. (2010) observed that emerging market companies may be non-investable or have limited free-float as large holdings may be held by the government, family owners, cross-holding or domestic investors, leaving global investors unable to share in growth. b) Returns are diverted away from existing investors i) To new shareholders Bernstein and Arnott (2003) found that from 1926 to 2002, US market capitalisation grew at an annual rate of 2.3% faster than the price index suggesting that new issuance dilutes returns to investors. Emerging economies with high growth rates often attract investor interest which enables companies to raise funds through new issuance. However, this also means that existing shareholdings tend to be diluted as companies issue new shares to finance their growing capital needs. For example, while China s economy grew by 11.5% per annum from 1992 to 2002, the S&P/IFC price index of Chinese equities appreciated by only 3.5% per year. At the same time, market capitalisation grew at an annualised growth rate of 39%, suggesting share dilution from new issues. ii) To managers Returns may also be diverted away from investors to managers due to weak corporate governance and employee rent extraction in the form of sweetheart deals and additional share issuances to employees (Ritter, 2005; Arnott and Bernstein, 2002). iii) To consumers/users of new technology Siegel (2000) and Asness (2001) highlighted that in competitive economies, technological change largely benefits consumers and employees rather than shareholders. Faber (1997) illustrated this line of reasoning using the US railroad industry as an example. While the railroads industry grew significantly in the 19 th century, by 1893, 85% of all railroads in the US were either bankrupt, in reorganisation or had to be refinanced. Railroad stocks continued to perform poorly even between 1906 and 1921 when other industrial stocks were rising as regulation prevented railroad companies from raising rates. Hence, investors did not benefit from the growth in the railroad industry. Instead, railroads benefited users such as farmers in the Midwest and enabled industrialisation in different regions. c) GDP growth is not financed by the stock market Dimson et al. (2010) further cited Germany and Japan as examples of how GDP growth may be financed by banks rather than by the stock market leaving investors unable to share in the underlying economic growth. 4

5 Given that investment performance is not explained by growth in GDP per capita, the next section investigates what may possibly drive investment returns in emerging markets and analyses emerging markets performance through time. SECTION II: EMERGING MARKETS PERFORMANCE & THEIR DRIVERS How have Emerging Markets Really Performed? Long-Term Historical Performance vs. Developed Markets Dimson et al. (2010) compared emerging markets and developed markets performance from and found that contrary to common belief, emerging markets (9.5% annualised) actually underperformed developed markets (10.6% annualised). Further analysis reveals that whether emerging markets deliver superior returns depends on when emerging market investments were made. For example, while Dimson et al. (2010) found that emerging markets underperformed developed markets from , an emerging market portfolio held from would have outperformed. Similarly, Barry et al. (1997) found that emerging markets outperformed US equities during but underperformed from (Exhibit 1) and rationalised that the underperformance could be due to the Latin America debt crisis and the associated lost decade (roughly from 1979 to 1988) being included in the longer period. Exhibit 1 Historical Monthly Compounded Returns and Standard Deviation Index Arithmetic Average Standard Deviation Compound Average Sharpe Index Values Return (%) (%) Return (%) (%) December 1975 June 1995 Emerging Composite S&P June 1985 June 1995 Emerging Composite S&P June 1990 June 1995 Emerging Composite S&P Source: Barry et al. (1997) Table 4. Emerging market returns are based on a composite Barry et al. (1997) constructed. Performance on a rolling-period basis However, as market timing is extremely difficult in practice, we instead considered what investors are more likely able to control, i.e. the length of their investment horizons, and examined if relative performance varies with different lengths of holding periods. Using different monthly entry points from Jan 1988, we computed the rolling 3-year, 5-year and 10- year holding period returns (compounded, annualised) for developed markets and emerging markets 5. For example, for 3-year rolling returns, we computed returns from Jan 1988 to Dec 1990, from Feb 1988 to Jan 1991 and so on, with the final 3-year period being Apr 2008 to Mar Developed market returns are computed using the MSCI World Total Return Gross Index in USD while emerging market returns are computed using the MSCI Emerging Total Return Gross Index in USD. 5

6 Exhibit 2 shows that in none of the holding periods did emerging markets consistently outperform developed markets. However, an investor who is invested in emerging markets for a shorter period of time (3-years or 5-years) seems to stand a higher chance of outperforming developed markets than one who is invested in emerging markets for a 10-year period (Exhibit 3). One possible hypothesis is that this could be because long holding periods are more likely to include the negative effects of crises. Incidentally, such negative effects are more pronounced for emerging markets than developed markets as emerging markets crises are characterised by larger price declines and longer recovery periods (Patel and Sarkar, 1998). On average 6 though, emerging markets have outperformed developed markets. Exhibit 2: Rolling 3-year, 5-year and 10-year holding period returns of Developed and Emerging Markets Source: Bloomberg; Authors calculations 6 For each investment period (with the same start and end date), the arithmetic difference between emerging market returns and developed market return is computed (i.e. emerging market return (less) developed market return). The average of all such differences is then computed for each holding period and presented in Exhibit 3. 6

7 Exhibit 3: Proportion of times Emerging markets Outperform Developed markets and Average Out (Under) Performance % of times Emerging Markets 3 Years 5 Years 10 Years Outperform Developed Markets 74% 65% 42% Underperform Developed Markets 26% 35% 58% Total no. of data points Average Returns Difference of Emerging Over Developed 5.5% 4.3% 0.7% Source: Bloomberg; Authors calculations A similar pattern emerges when computing risk-adjusted returns (defined here as returns per unit of risk 7 ). Again, emerging markets did not consistently outperform developed markets (Exhibit 4) and an investor stood a higher chance of outperforming for shorter holding periods (Exhibit 5) on a riskadjusted basis. However, when compared to non-risk adjusted returns, the number of times when emerging markets portfolios outperformed developed markets fell across all holding periods (Exhibit 5). In addition, on average 8, emerging markets underperformed developed markets on a riskadjusted basis. Exhibit 4: Rolling 3-year, 5-year and 10-year risk-adjusted returns of Developed and Emerging Indices Source: Bloomberg; Authors calculations 7 Risk is defined as the annualised standard deviation of monthly returns during the relevant holding period. 8 For each investment period (with the same start and end date), the arithmetic difference between the risk-adjusted return of emerging market and developed market return is computed (i.e. emerging market return (less) developed market return). The average of all such differences is then computed for each holding period and presented in Exhibit 5. 7

8 Exhibit 5: Proportion of times Emerging markets outperform Developed markets on a risk-adjusted basis % of times Emerging Markets 3 Years 5 Years 10 Years Outperform Developed Markets 62% 63% 27% Underperform Developed Markets 38% 37% 73% Difference in % of times emerging markets outperform developed -11% -3% -15% markets compared to Exhibit 3 Average Risk-adjusted Returns Difference of Emerging Over -4.1% -4.6% -18.1% Developed Source: Bloomberg; Authors calculations Our observations largely tie in with Barry et al. (1997) findings and comments, in which they noted that in the first 5-year period from Dec 1975 June 1980, the emerging market composite gained 227% but lost 49% in the next subsequent 5-year period, before gaining 66% in the final 5 year period. Barry et al. (1997) also cautioned that investors should be aware that the risk [of investing in emerging markets] is not necessarily removed by commitment to a long holding period. Why then is there a common misconception that emerging markets outperform developed markets? Given the rather surprising results that emerging markets returns are not consistently higher than developed market returns, a natural question is what gave rise to the common belief that emerging markets outperform developed markets. Two possible reasons are offered: 1. Most studies on emerging market performance tended to focus on the post-1984 period which would have thus excluded the Latin America debt crisis in the 1980s because 1984 was the base year for the International Finance Corporation (IFC) value-weighted indexes (Barry et al., 1997), leading emerging market performance to appear better than they in fact are. This biased view might have been further exacerbated by the fact that crises tend to have a larger impact on emerging markets than developed markets. Patel and Sarkar (1998) studied 9 stock market crises in and found that emerging markets crises tend to experience larger price declines than developed markets (Exhibit 6). Exhibit 6: Impact of Crises in Different Markets Beginning of Crisis 9 Beginning of Crash 10 Date of Trough 11 Price Decline to Trough Developed Latin America Emerging Asia Jun-73 May-74 Sep % Oct-80 Jun-82 Jun Aug-87 Nov-87 Jan Jun-80 Jul-81 Dec Sep-87 Nov-87 Nov Sep-94 Feb-95 Feb Dec-78 Aug-80 Oct Mar-90 Sep-90 Sep Apr-96 Aug-97 Dec Source: Patel and Sarkar (1998) Table 1. 9 The beginning of the crisis is defined as the month when the index reached its historical maximum prior to the month when the crash was triggered. 10 The beginning of the crash is defined as the month when the regional price index fell by more than 20% for developed markets and more than 35% for emerging markets. 11 The trough is the month when the price index reached its minimum level during the crisis. 8

9 2. Many studies are focused on recently emerged markets. Goetzmann and Jorian (1999) found that returns soon after emergence are greater than both before emergence 12 and the period after. Empirically the difference 13 in returns was found to be the greatest for the 4-year period after emergence, followed by 3-year and then 5-year. Thus, given that studies are often focused on recently emerged markets, there tends to be a bias in favour of emerging markets. A comparison of BRIC versus other emerging markets Given that the phase of emergence seems to have a significant bearing on performance of emerging markets, we analysed if this line of reasoning might be able to explain the relative performance of BRIC (Brazil, Russia, India and China) versus other emerging markets. It might be argued that BRIC are now in the after emergence phase compared to other emerging markets. Goetzmann and Jorian (1999) identification of the point of emergence is related to when investors attention is drawn to a market. We believe that global investors attention was drawn to BRIC when Goldman issued its Oct 2003 report 14 where it projected that by 2050, BRIC s GDP would surpass that of the G6. Hence, we define Oct 2003 as the point of emergence. We then compared the performance of BRIC versus that of other emerging countries in the 3 years, 4 years and 5 years after the point of emergence. These time periods were picked as Goetzmann and Jorian (1999) found that 4-year window gave the largest difference in returns followed by 3-year and then 5-year. Indeed, the out-performance of BRIC versus other emerging countries was the greatest for the 4- year period (Exhibit 7) after Goldman s report followed by the 3-year and then the 5-year periods. Exhibit 7: Annual Returns of BRIC vs. Other Emerging Countries, following Goldman s Report Date of Goldman Report Duration Period Brazil Russia India China Average Excess over EM Index Oct-03 3 years Nov 03 - Oct 06 50% 42% 39% 27% 40% 10% Oct-03 4 years Nov 03 - Oct 07 64% 40% 48% 51% 51% 12% Oct-03 5 years Nov 03 - Oct 08 27% 7% 13% 13% 15% 5% Source: Bloomberg; Authors calculations 12 In a simulation study, Goetmann and Jorian (1999) defined emergence to have occurred when market capitalisation has grown large enough indicating that the markets have attracted the attention of global investors. In their empirical analysis, they used the first date at which IFC compiles a market index as the date of emergence as introduction of new markets into performance benchmarks is closely watched by portfolio managers. 13 Goetzmann and Jorian (1999) compared the performance during the period immediately after emergence and that of a subsequent period of the same length for 3-year, 4 year and 5-year windows. 14 Earlier in November 2001, Goldman already issued a report on BRIC but arguably the earlier report gave less new news as it re-affirmed the common belief that emerging countries (e.g. BRIC) will grow faster than developed countries. Hence, in our view the Oct 2003 report was the more significant report. 9

10 Given that the above analysis shows that emerging markets performance varies through time, we next explore what could explain the varying investment performance. What Drives Emerging Markets Performance? Local Macroeconomic Factors If we assume that emerging markets are not perfectly integrated, it then follows that local macroeconomic factors may be relevant proxies for sources of risk in emerging market returns. Hence, we now turn to local macroeconomic factors that may be useful in identifying high-and lowexpected return environments in emerging markets. 1. Trade as a proportion of GDP Bekaert and Harvey (1997) argue that the size of the trade sector as a proportion of the total economy is a good proxy for openness of both the economy and the investment sector and thereby market integration. Bekaert et al. (1997) found that trade as a proportion of GDP has the ability to distinguish between high and low expected returns in emerging markets 15. In fact, they concluded that countries with small trade sectors as proportion of GDP had higher expected returns than countries with larger trade sectors, as the former were more likely to be segmented from the world economy. Bekaert et al. (1997) believe that the size of the trade sector produced a total alpha 16 of 18.9% over the MSCI AC World Index from 1991 to using an equally-weighted portfolio of emerging markets stock indexes with quarterly rebalancing, in which lagged values of the trade as proportion of GDP variable is used to determine which countries were included in the portfolio. 2. Size of the national stock market Bekaert and Harvey (1997) also argue that the size of the national stock market is an adequate proxy for the degree of a country s financial integration. They assert the view that a larger stock market suggests that a country is more likely to be integrated into world capital markets. They use market capitalization divided by last year s GDP as a measure of market integration. Bekaert et al. (1997) study further supports the arguments of Bekaert and Harvey (1997) by concluding that the size of the equity market relative to economic activity has significant predictive power regarding future expected returns. In fact, they concluded that countries with a small market capitalization as a proportion of the previous year s GDP had higher expected returns than countries with larger market capitalization as the former were more likely to be segmented from the world economy. In fact, Bekaert et al. (1997) found an alpha of 22.3% over the MSCI AC World Index from 1991 to using an equally weighted portfolio of emerging markets stock indexes with quarterly 15 The study included the following countries: Argentina, Brazil, Chile, China, Colombia, Czech Rep., Greece, Hungary, India, Indonesia, Jordan, Malaysia, Mexico, Nigeria, Pakistan, Peru, Philippines, Poland, Portugal, South Africa, South Korea, Sri Lanka, Taiwan, Thailand, Turkey, Venezuela and Zimbabwe. 16 Alpha is a risk-adjusted measure used to calculate the return in excess of the compensation for the risk borne by the investor. 17 The 1991 to 1996 sample period is chosen so as to remove the impacts that emerging market integration have on returns. We advise the reader to consider these results with care as the regression results are likely to suffer from a small sample bias. Moreover, when future returns are regressed on lagged variables, the error terms of the regression are likely to be correlated with the regressor s innovations. Consequently, both the coefficient estimates and the significance levels are likely to be biases. 18 The 1991 to 1996 sample period is chosen so as to remove the impacts that emerging market liberalizations have on returns. Please also refer to footnotes 15 and

11 rebalancing, in which lagged values of the market capitalization as a proportion of the previous year s GDP variable was used to determine which countries were included in the portfolio. 3. Political and Economic Risk Using the International Country Risk Guide (ICRG) 19 published by Political Risk Services, which includes a political risk index (ICRGP), an economic risk index (ICRGE), a financial risk index (ICRGF), and a composite risk index (ICRGC), Bekaert et al. (1997), subsequent to running regressions on each ICRG index, found that considerable information is contained in the economic and political ICRG ratings. Bekaert et al. (1997) found that portfolios comprised of emerging markets with low economic and political IRCG ratings (higher risk) produced an alpha of 37.3% and 29.3% respectively over the MSCI AC World Index from 1991 to 1996 using capitalization weights and quarterly rebalancing. These results are consistent with those presented in the Bekaert et al. (1996) study, which finds that countries which begin with very low country credit ratings (high risk) tend to improve (deteriorate), producing abnormal returns 20. However, a limitation of such studies is that whenever a survey is used to rate credit, political, or financial/economic risk, it is hard to define exactly the parameters taken into account as, at any point in time, an expert s recommendation will be based upon factors he or she feels are most relevant. 4. Inflation Empirical tests provided mixed results. While Fama and Schwert (1977) and Gultekin (1983) found negative relationships between inflation and nominal stock returns, Ritter and Warr (1997) presented evidence that valuation ratios tend to rise as inflation drops. 5. Country Credit Rating Country credit ratings were found to have substantial predictive power in differentiating between high and low expected returns in emerging stock markets (Erb et al., 1995). To validate their hypothesis, Erb et al. (1995) constructed equally weighted quartile portfolios of 14 emerging countries from highest country credit rating (1 st quartile) to lowest country credit rating (4 th quartile) over a period beginning in 1980 and ending in As can be seen in Exhibit 8, the credit risk variable is able to distinguish between high and low expected returns. The highest risk quartile has an average annual performance of 34.3% per year, while the lowest risk quartile has an average annual performance of 7.9%. Surprisingly, the volatility of the returns in all portfolios is fairly similar. 19 For more information, please visit 20 The Bekaert et al (1996) study, which has different sample period ( ), finds that the composite, financial and economic ratings are able to distinguish between high and low expected returns in emerging markets. 11

12 Exhibit 8: Country Selection Strategies Based on Country Credit Rating Rating Portfolio Characteristics-March 1980 to December 1993-In U.S. Dollars Annual Return (%) Annual Volatility (%) Average Dividend Yield (%) Highest Credit Risk High Credit Risk Low Credit Risk Lowest Credit Risk Source: Erb et al. (1995) Average Credit Rating 6. Currency Risk Barry et al. (1998) found that over a 20-year period from December 1975 June 1995, approximately 50% of returns (in local currency terms) were erased by depreciating currencies for a composite index, measured in US dollars, consisting of 17 emerging markets 21. Similar declines were found for 10-year and 5-year periods ending June However, this is not to be interpreted simplistically as emerging markets mean falling currency values. For example, in the 10-year period ending in June 1995, Taiwan, Malaysia and Thailand experienced currency appreciation. These findings highlight the importance of considering currency risks for global investors and the value of predicting currency movements. Research from Kumar et al. (2003) suggests that useful variables in predicting currency crashes include 12-month percentage changes in foreign exchange reserves, real GDP expressed as a deviation from trend and regional contagion. In non-crisis periods, a combination of fundamental analysis (based on relative real interest rate and relative GDP growth rates for directional trades) and technical trading rules (based on moving average, support and resistance rules) has been found to improve risk-adjusted returns (de Zwart et al., 2009). In addition to considering the direction of currency movements, it is also important to consider correlation of exchange rates movement with other asset classes. Analysing real exchange rates behaviour over , Dimson et al. (2002) observed that in spite of the high currency volatility during the 20 th century, currency risk did not add greatly to the risk of a US investor invested in international equities because the correlation between equity and currency returns were typically low and slightly negative, averaging across 15 non-us developed equity markets. In fact, exchange rate risk could offset local equity market risk so much so that a US investor would have experienced less volatility than domestic investors in Netherlands, Denmark and Sweden between 1950 and The negative correlation in developed countries could be the result of depreciating currencies increasing the attractiveness of the countries exports, leading to increased earnings and stock prices of exporters (Solnik and McLeavey, 2009 cited in Conover, 2011). Hence, in considering the impact of currency risk, it is important to consider correlation between exchange rate fluctuations and equities. In emerging markets, stock returns and currency changes are traditionally positively correlated as investors tend to lose confidence in both emerging market currencies and stocks during times of crisis (Conover, 2011). For example, using data from 1996 through 2005 for 17 emerging markets 22, 21 Countries covered in the study were Greece, Jordan, Nigeria, Zimbabwe, Argentina, Brazil, Chile, Colombia, Mexico, Venezuela, Korea, Philippines, Taiwan, India, Malaysia, Pakistan and Thailand. 22 Emerging markets covered in the study were Argentina, Brazil, Chile, Mexico, Peru, India, Indonesia, Korea, Malaysia, Philippines, Taiwan, Thailand, Czech Republic, Hungary, Poland, Russia and Turkey. 12

13 Cumperayot et al. (2006) found that extreme stock market declines are associated with currency declines in 13 out of 17 emerging countries. In a number of emerging markets undergoing a severe crisis 23, an extreme stock market decline significantly increased the probability of an extreme currency depreciation occurring on the same day. The observed effect of stock markets on currency markets might be due to large equity outflows depressing the currency or the fact that stock market anticipates a devaluation of a fixed or managed currency just before it occurs. Interestingly, more recent empirical research suggests that the relationship between stock market and currency returns might be changing in emerging markets. Chue and Cook (2008) examined two periods: and and found that in the earlier period, stock market and currency returns are positively related whereas in the later period, they found that this relationship is no longer significant and has even reversed sign. The positive relationship in the earlier period is believed to be the result of emerging markets having taken on excessive foreign-currency debt which implies that a depreciation of the local currency would increase firms debt burden, reducing returns. In the later period, the lack of relationship is consistent with the observations that domestic bond markets have grown in importance relative to international bond markets and that growing derivatives markets have enabled firms to hedge currency risks. In our view, the reversal in sign (albeit still statistically insignificant) may suggest that the correlation between currency and stock returns of emerging markets are gradually approaching that of developed markets where negative correlations are observed, in which case it may mean that hedging of currency risks for investors may become less important over time. Cumperayot et al. (2006) further found that spill-over of currency declines are usually contained within a region and that in some cases negative links were found across regions indicating flight of capital from one region to another during crisis periods. This suggests that investors could diversify currency risks by investing in different regions. Microeconomic Factors 1. Valuation Multiples a) Dividend-to-price ratio 24 Campbell and Shiller (2001) premised that the stability of a valuation ratio such as D/P and P/E implies that when a valuation ratio is at an extreme level, either the numerator (price) or the denominator (earnings or dividend) must move in a direction to restore the ratio to its normal level. Using aggregate annual US data from 1871 to 2000, Campbell and Shiller (2001) found that it is the denominator (price) and not the numerator (dividend) that brings the ratio back to its mean. They further showed that the D/P ratio had no forecasting power for stock price changes over the shortterm horizon (1 to 5 years), as the initial D/P ratio explains less than 1% of the annual variance of stock prices. However, when a long-term horizon of 10 years is considered, Campbell and Shiller (2001) noted a very significant positive relationship between the initial D/P ratio and the subsequent 10 years price growth. However, Campbell and Shiller (2001) study may be limited in that their study 23 The conditional probability of an extreme currency depreciation given a stock market decline was more than 20% for countries directly affected by the Asian crisis i.e. Indonesia (26%), Korea (23%), Malaysia (34%), Philippines (22%) and Thailand (22%). This is significantly higher than the 5% unconditional probability. 24 The D/P ratio is a widely used valuation ratio, but it can be affected by shifts in corporate financial policy. For instance, if stock repurchases replace dividends, then the past history of the dividend yield will be a misleading guide to future stock returns. 13

14 focused on the US, a developed market and thus it could be argued that their findings may not be applicable to emerging markets. b) Earnings Yield 25 Contrary to studies conducted on the US market (Bali et al. (2008); Lamont (1998) 26 ), Demirtas and Zirek (2010), who examined the time-series predictability of aggregate stock returns in 20 emerging countries 27 from 1977 to 2009, found that the earnings yield can predict one-quarter ahead market returns. In fact, they found the earnings yield coefficient was positive and highly significant in predicting expected returns 28. Moreover, they found that aggregate earnings co-vary positively with market returns, hence it is not just the mean reversion of stock prices (which was found to be negative and significant) that is responsible for the forecasting power of the earnings yield. There exists a positive and significant relationship between aggregate earnings and expected returns in emerging markets 29. A study by Demirtas and Zirek (2010), which examined the time-series predictability of aggregate stock returns in 20 emerging countries 30 from 1977 to 2009, found that, contrary to the US market (Shiller (1984); Fama and French (1988a and 1988b)), aggregate earnings may still contain information about future expected cash-flows. As opposed to the US, emerging markets have a lower number of industries and stocks are more highly correlated, meaning that idiosyncratic volatility is lower. Demirtas and Zirek (2010) were able to show that aggregate earnings co-vary positively with market level returns. c) Price-to-book ratio Using data from 1991 to 2001, Kouwenberg and Salomons (2003) found that emerging markets portfolio invested in countries with low price-to-book values significantly outperformed a portfolio of high price-to-book countries. The study covered 23 emerging markets from Asia, Latin America and Europe, Middle East and Africa 31 which investment professionals tended to focus on, thus excluding smaller and less liquid markets where investment strategies would be hard to implement due to transaction costs or other restrictions. The authors also set the beginning of the sample 25 In addition, while smoothed earnings used in the Campbell and Shiller (2001) study removes the occasional spikes in the P/E ratio and is in line with Graham and Dodd (1934) belief of using average earnings spanning across 7 or 10 years for valuation ratios, smoothed earnings also suffer from changes in accounting practices and inflation which can make it an unreliable indicator. 26 In contrast to Lamont (1998) and Bali et al (2008), Campbell and Shiller (2001) found that price-to-smoothed earnings (10 yr. moving average of earnings) ratio is good forecaster of 10 year growth in stock prices. These conflicting studies suggest to us that P/E or earnings yield may not be good predictors of returns in the US and possibly other developed markets. 27 Emerging countries covered in the study include Argentina, Brazil, Chile, China, Colombia, Czech Republic, Hungary, India, Indonesia, Malaysia, Mexico, Pakistan, Peru, Philippines, Poland, Russia, South Africa, Taiwan, Thailand and Turkey. 28 We note that these results are robust across different estimation methods and after Demirtas and Zirek (2010) control for a small sample bias in predictive regressions and for different macroeconomic variables. 29 A brief explanation about the predictive power of aggregate earnings follows: as opposed to the US market, the information content of firm-level earnings (unsystematic earnings) about future cash flows is not fully diversified away at the market level in emerging market, and hence, aggregate earnings covary positively with market level expected returns. 30 Emerging countries covered in the study include Argentina, Brazil, Chile, China, Colombia, Czech Republic, Hungary, India, Indonesia, Malaysia, Mexico, Pakistan, Peru, Philippines, Poland, Russia, South Africa, Taiwan, Thailand, and Turkey 31 Countries included in the study are China, India, Indonesia, Korea, Malaysia, Philippines, Taiwan, Thailand, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Venezuela, Czech Republic, Egypt, Hungary, Israel, Poland, Russia, South Africa and Turkey. 14

15 period to 1991, which is arguably the period when most emerging markets liberalization had already occurred by, in order to minimise the impact of a structural break in the data. Kouwenberg and Salomons (2003) ranked countries according to their P/B ratio at the beginning of each month and constructed four equally weighted portfolios, consisting of the emerging countries in each of the four P/B quartiles and recorded the returns of each portfolio during the month using Standard & Poor s and International Financial Corporation (S&P/IFC) indices for emerging equity markets. 32 The results of their study (Exhibit 9) showed that investing in countries with low P/B ratios (first quartile) led to outperformance in the sample period between January 1991 and December The average monthly return of 1.75% for the long strategy (first quartile) and 1.77% 33 for the long/short strategy (i.e. long position in the first quartile countries (low P/B) and short position in the fourth quartile countries (high P/B)) exceeded the average return of 0.73% for the equally weighted index portfolio of all emerging markets. Exhibit 9: Return and risk of the P/B strategy EW Index Q1 Q2 Q3 Q4 Long/Short Average Return 0.73% 1.75% 0.68% 0.48% -0.03% 1.77% (t-value) (1.35) (2.40) (0.96) (0.77) (-0.05) (2.84) Standard Deviation 6.2% 8.4% 8.1% 7.1% 6.2% 7.2% Downside Deviation 4.3% 5.2% 4.7% 4.9% 4.7% 4.1% Downside Correlation 61% 51% 39% 61% 59% 2% Worst returns -27.4% -31.8% -27.1% -25.3% -24.9% -15.6% Worst month Aug 98 Aug 98 Aug 98 Aug 98 Aug 98 Jan 99 Kouwenberg and Salomons (2003) Study From the above findings, one may interpret that higher returns are compensation for taking on additional risk (measured by higher standard deviation). However, such reasoning would not hold true for the long/short value strategy as it has lower downside deviation, downside correlation, and worst return but higher average return than the equally weighted index portfolio of all emerging markets. Kouwenberg and Salomons (2003) also found that using market beta as an alternative form of risk measurement similarly does not explain the positive performance of the long/short value strategy. Linkage between local macroeconomic variables and microeconomic factors Kouwenberg and Salomons (2003) provided a possible linkage between local macroeconomic variables and microeconomic factors by measuring the macroeconomic variables for the countries in each P/B quartile at portfolio formation 34 (Exhibit 10). Countries in the low P/B portfolio (Q1) had low GDP growth and higher GDP growth volatility compared to countries in the high P/B portfolio (Q4). Results also suggest that low P/B countries also had significantly higher inflation rates, and a more volatile and overvalued exchange rate on average 32 The quartile portfolios are rebalanced based on the new price-to-book ratios on a monthly basis 33 Transaction costs are believed to be limited, as even for the long/short portfolio, there are only two changes to the portfolio per month. 34 As the portfolios are equally weighted, Kouwenberg and Salomons (2003) also weight the macroeconomic variables of all countries in the portfolio equally each month, in order to get a time series of economic measures at the portfolio level. 15

16 than countries in the high P/B portfolio. Seen in this light, the price-to-book strategy is buying recent macroeconomic losers and selling recent macroeconomic winners. 16

17 Exhibit 10: Average of the five macroeconomic variables for the price-to-book quartile portfolios Variable Q1 (long) Q4 (short) Q1 Q4 (long/short) T-Value RG 3.1% 5.4% -2.3% -9.7 RGV 5.3% 1.6% 3.7% 14.2 IF 142.8% 17.3% % 6.8 PPPFX 9.1% 3.1% 6.0% 9.8 FXV Source: Kouwenberg and Salomons (2003) Study. RG refers to real GDP growth (YoY change); RGV refers to volatility of real GDP growth based on 3 years of monthly data; IF refers to inflation (YoY change); PPPFX refers to percentage deviation of spot rate from PPP and FXV refers to coefficient of variation based on 3 years of monthly data. The results from the Kouwenberg and Salomons (2003) study offer an interesting analogy with earlier research by Lakonishok et al. (1994), which provides a behavioural explanation for the superior returns of value strategies: naive investors extrapolate past experience too far into the future. Applied in this context, countries in the low P/B portfolio (Q1) might be perceived as losers with a bad economic outlook based on current conditions (lower and more volatile growth, higher inflation, currency turmoil, etc) and investors do not expect a macroeconomic reversal for these countries, leading to depressed equity prices and low P/B multiples. However, after portfolio formation, these countries often beat the low expectations and show some economic improvement, leading to superior stock market performance relative to countries in the high P/B portfolio. On the other hand, countries with high P/B multiples are expected to perform well in the future based on past and current favourable macroeconomic conditions, but often fail to beat expectations, leading to relatively subdued equity market performance. Exhibit 11 provides additional statistical evidence for the extrapolation hypothesis. It demonstrates that 3 years after portfolio formation, the macroeconomic gap between low P/B countries ( macroeconomic losers ) and high P/B countries ( macroeconomic winners ) had reduced significantly for all variables, except for currency overvaluation (PPPFX). Exhibit 11: Average of the five macroeconomic variables for the long-short P/B portfolio 3 years after formation Variable Q1 Q4 at portfolio formation Q1 Q4 36 month after formation Change in exposure from formation to 36m after RG (t-value) -2.3% (-9.6) -0.7% (-2.8) 1.5% (3.4) RGV (t-value) 3.7% (14.2) 1.3% (7.8) -3.2% (-12.1) IF (t-value) 125.4% (6.8) 97.8% (3.4) -74.6% (-2.0) PPPFX (t-value) 6.0% (9.8) 8.9% (7.8) 2.8% (1.7) FXV 0.18 (t-value) (10.1) Source: Kouwenberg and Salomons (2003) Study 0.09 (4.8) (-9.9) Having obtained an understanding of emerging markets investment performance and factors that affect emerging market returns, the next section looks at why an investor might want to invest in emerging markets particularly in a portfolio context. 17

18 SECTION III: WHAT IS THE POTENTIAL VALUE TO BE GAINED FROM INVESTING IN EMERGING MARKETS? Diversification In the following paragraphs, we analyse diversification benefits of investing in emerging markets at multiple levels: from the broader perspective of diversifying between emerging markets and developed markets to diversifying amongst emerging markets and finally diversifying within a single emerging market. Adding Emerging Markets to a Developed Markets Portfolio The long-term case for investing in emerging markets rests upon the prospect of taking advantage of diversification opportunities, thereby enhancing a portfolio s risk-return profile. The benefits available are a function of the level of correlation between emerging and developed market returns, as well as their relative risks and returns. Various empirical studies have been conducted that support the diversification potential of emerging markets. Errunza et al. (1999) found that the average correlation of the S&P 500 Index with the authors sample of nine emerging markets was 0.09, while the comparable average correlation with their sample of seven developed markets was considerably higher, at 0.40 for the period The findings of Harvey s (1995) study similarly support the diversification potential of emerging markets, with his study reporting correlation between developed and emerging markets of only 0.14 for the period March 1986 to June Our own research also supports the case for emerging market assets as diversification tools. We find that for the period January 1999 to March 2011, the average correlation between the MSCI World Index (which is a developed markets index) and twenty emerging markets was 0.56 compared to the average correlation between twenty-four developed countries, which was found to be This indicates the possibility for investors to improve the risk-return profile of their portfolio by including emerging market assets. As a result, although emerging markets experienced high volatility relative to developed markets during the period January 1999 to March 2011, Exhibit 12 illustrates that a portfolio mix of approximately 20% invested in the emerging markets index and 80% in the developed markets index produced the minimum variance portfolio for these two assets. Therefore, the addition of riskier emerging market stocks created a less risky portfolio than one composed entirely of the developed markets composite index (MSCI World Index), as well as resulting in higher return. In fact, a portfolio comprising only developed markets securities (as represented by the MSCI World Index) is not on the efficient frontier for this period. 35 Notably, correlation coefficients in our research are higher than earlier researches cited in the paragraph above. Several reasons could account for the difference: (i) Enhanced integration between financial markets increases correlation (ii) correlations tend to increase during crisis periods and the subprime crisis in was included in our study and (iii) our research uses different variables from the earlier study. 18

19 Exhibit 12: Portfolio Combinations of emerging markets and developed indices (Jan 1999-Mar 2011) Arithmetic Average Annualised Monthly Returns 14.0% 13.8% 13.6% 13.4% 13.2% 13.0% 12.8% 12.6% 100% emerging index 12.4% 100% 12.2% developed index 22.5% 23.0% 23.5% 24.0% 24.5% 25.0% Source: Bloomberg; Authors calculations Standard Deviation of Annaulised Monthly Returns If one examines the longer period January 1988 to March 2011, we can see that similar diversification opportunities existed (Exhibit 13). Over that period, the correlation between the indices was 0.76, as compared with 0.86 over the shorter period. The minimum variance portfolio required approximately a 75% investment in the emerging market index. Exhibit 13: Portfolio Combinations of emerging markets and developed indices (Jan 1988-Mar 2011) 12.90% 12.80% 12.70% 100% emerging index Arithmetic Average Annualised Monthly Returns 12.60% 12.50% 12.40% 12.30% 12.20% 100% developed index 19.00% 20.00% 21.00% 22.00% 23.00% 24.00% Standard Deviation of Annaulised Monthly Returns Source: Bloomberg; Authors calculations These results are indicative of the fact that relative performance varies over time (a topic which will be dealt with in more detail in a later section). Consequently, asset allocation must be dynamic in order to optimise portfolio performance. Asset allocations based on historical returns tend not to produce optimal performance in future portfolios. It is important to note that the results are limited by the fact that here we are considering a composite index of emerging countries. By doing so, one loses some of the idiosyncrasies of 19

20 individual emerging markets. In fact, we find that during the period January 1999 to March 2011, the average correlation between the MSCI World Index and twenty emerging markets 36 was 0.56, significantly lower than that for the emerging markets index (which was 0.86). Furthermore, it is important to note that this is merely an average and that the correlation between individual emerging countries and developed market index varies greatly. In fact, over the period tested, correlation was found to be as low as 0.19 in the case of Poland, but as high as 0.81 in the case of Mexico (see Appendix). This highlights the importance of looking at individual countries rather than simply considering emerging markets as a group in order to maximise the benefit that is gained by adding emerging market assets to a developed markets portfolio. This is supported by Barry et al. (1997) who concluded that some individual emerging markets provide powerful diversification opportunities for U.S. domestic investors. They found that some of the smaller and, at the time, newer emerging markets did not provide meaningful diversification benefits for U.S. stock portfolios. The reason for this was that correlations between those markets and the U.S. market were not low enough to offset the effects of high variability within individual emerging markets. Similarly, Barba and Ceretta (2010) found that for international diversification, each country should be analysed separately. Investigating four Latin American emerging markets (Argentina, Brazil, Chile and Mexico) and the United States before, during and after the financial crisis of 2007/2008, they concluded that the Latin American stock markets do not react homogenously to shocks in the U.S. stock market. Correlation over time Although the advantages of diversifying a portfolio through international investing are widely accepted, the benefits of doing so are not constant over time. Exhibit 14 shows that average correlation between emerging markets and the developed markets index has strengthened tremendously over the past ten years, with a spike in correlation when the financial crisis hit. Once the initial effect of the crisis wore off, correlation weakened, however, the general upward trend remains clearly evident. Exhibit 14: 24-month rolling average correlation between individual emerging markets and the developed markets index M M M M M M M M M M M M M M M M M M M M M M M M M01 36 Emerging markets are taken to be twenty of the twenty-one markets forming the MSCI Emerging Markets Index as at 27th May, 2010: Brazil, Chile, China, Colombia, Czech Republic, Egypt, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Morocco, Peru, Philippines, Poland, Russia, South Africa, Taiwan, and Thailand. Data for Turkey was not included due to limitations in data availability. 20

21 Source: Bloomberg; Authors calculations This implies that over time the diversification benefit to be had from investing in emerging markets has diminished. It is important to understand why correlation has behaved in the manner described so as to build one s expectations for future trends. Bekaert and Harvey (2000) found that as the financial and economic integration of different regions increases through trade and financial flows, the correlation among these regions financial markets also increases. In this respect, since the 1990s through the last decade, the pace of economic integration, both among emerging markets, as well as between emerging and developed markets has accelerated rapidly. This may be partly attributed to direct efforts at promoting economic integration, such as through treaties, including the European Union s Maastricht Treaty, the North American Free Trade Agreement (NAFTA) and the Asia Free Trade Agreement (AFTA). Emerging markets have actively participated in promoting integration by reducing barriers to trade, encouraging foreign investment in their economies and restructuring economic, political and social systems. Bekaert (1995) suggests that historically three categories of barriers have discouraged investment in emerging economies: legal barriers, indirect barriers, such as inadequate accounting standards and investor protection, largely unpredictable risks, such as liquidity risk, economic policy risk, political risk and currency risk. These barriers have gradually been reduced in recent years. Another important factor to bear in mind when considering emerging market assets as tools for diversification is the observation by Erb et al. (1994), Longin and Solnik (1995), and Strongin et al. (1997) that correlations in international equity returns increase during unfavourable market conditions. Specifically, during periods of high return volatility and global economic contractions, correlations tend to increase; during economic expansions and periods of low market volatility, correlations fall. For example, Bertero and Mayer (1990), King and Wadhwani (1990) and King et al. (1994) found greater integration of world stock markets in the period surrounding the crash of This finding suggests that international diversification is least effective during periods when it is most needed. The finding of Conover et al. (2002) that emerging market correlations are lower during restrictive monetary periods, which are periods of generally poor performance for U.S. stocks, is also important to consider. This finding has favourable implications for the diversification potential of emerging markets specifically, the prospects that emerging markets can serve as a hedge against adverse U.S. monetary conditions. Following our study of the potential diversification benefits of adding emerging market indices to a diversified portfolio of developed market indices, we will now focus on the efficient frontier taking individual market indices as opposed to an index for developed markets and one for emerging markets into consideration. Taking individual market indices present a finer analysis, although not the finest possible as this would be at the individual companies stocks level. We will summarise quickly the main steps of the derivation, and will focus on the results of the different frontiers construction. The Markowitz-Sharpe Model Before implementing the model in Excel, it might be useful to summarise the model in a more formal way. Markowitz portfolio can have different combinations, where short sales and lending or borrowing might be allowed, or not. We will focus on the construction of a portfolio where short 21

22 sales are allowed and riskless borrowing and lending is possible, as we consider this configuration closer to the real life situation although short selling in some emerging countries may happen to be difficult. The aim is to find a single portfolio the optimal portfolio of risky assets with the least possible standard deviation that is preferred to all other portfolios with the same level of return, and to draw the set of minimum variance portfolios. One way of achieving this is by maximising the slope of the straight line ( M), where is the riskfree rate and M is the optimal portfolio. The slope (often referred to as Sharpe ratio) is: under the constraint: where is the return of the portfolio (approximated as the weighted average of the different assets composing the portfolio), is the risk-free rate, represents the standard deviation of the portfolio and the weight of the asset i in the portfolio. We can simplify this constrained equation by writing as: The optimisation problem under constraint has now become an unconstrained one, and knowing that and, we have: The maximisation problem can be solved by getting the partial derivatives and equating them to zero. We end up with a system of N equations: with 22

23 (1) Let s define the Lagrange multiplier λ as: Then (1) becomes: with (2) Solving this equation is similar to solving the following N equations with N unknown: And once we solve for all, we can get all the by normalising the vector Z: In order to implement this algorithm, we will use the matrix notation, and this gives: (2) (2 ) 23

24 where R is the vector of all the individual returns, 1 is a N-vector of 1, V the matrix of variance-covariance of returns, and is its transposed matrix. Recall Z is. The solution of (2 ) is then: And ultimately,. And by varying the choice of we can compute the efficient frontier of optimum portfolios. Tests and results We first started by forming two baskets. One composed of MSCI indices from developed economies 37 (basically the countries that form the MSCI World Index, designed to measure the equity market performance of developed markets, 24 in total). And the second one composed of the MSCI indices from the first basket plus 20 MSCI indices from emerging economies 38 (countries that are included in the MSCI Emerging Markets Index, designed to measure equity market performance of emerging markets, without Turkey as the individual country index did not have enough data points, i.e. 20 emerging market indices in total, that is overall 44 indices). The idea is to test whether emerging economies equities can help improving the risk/return profile of our initial basket composed of developed economies indices: do we find a better efficient frontier with the basket that includes the emerging economies or not? In carrying out the test, we adopted the following definitions and notations: - Returns, standard deviations and interest rates are not annualised - Returns are normal simple returns, i.e. - On the following next charts, the x-axis is the standard deviation of the portfolio σ p, and the y- axis is the expected return of the portfolio E(R p ) - DV stands for Developed economies, and EM for Emerging economies. E(R p ) DV therefore is the frontier for the basket composed of indices from developed economies only, and E(R p ) DV + EM is the one for the basket composed of indices from developed and emerging economies - The timeframe of our study ranges from 30-May-2002 to 13-April-2011 (all period returns are computed daily) 37 MSCI indices from the following countries: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong, Ireland, Israel, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the United Kingdom, and the United States. Bloomberg tickers are respectively: GDDUAS, GDDUAT, GDDUBE, GDDUCA, GDDUDE, GDDUFI, GDDUFR, GDDUGR, GDUESGE, GDDUHK, GDDUIE, GDUESIS, GDDUIT, GDDUJN, GDDUNE, GDDUNZ, GDDUNO, GDDUPT, GDDUSG, GDDUSP, GDDUSW, GDDUSZ, GDDUUK, GDDUUS Index. 38 Brazil, Chile, China, Colombia, Czech Republic, Egypt, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Morocco, Peru, Philippines, Poland, Russia, South Africa, Taiwan, Thailand. Bloomberg tickers are respectively: GDUEBRAF, GDUESCH, GDUETCF, GDUESCO, GDUESCZ, GDUESEG, GDUESHG, GDUESIA, GDUESINF, GDUESKO, GDDUMAF, GDUETMXF, GDUESMO, GDUESPR, GDUESPHF, GDUESPO, GDUESRUS, GDUESSA, GDUESTW, GDUESTHF Index. 24

25 We then computed different efficient frontiers for 1 month returns, 3 months, 6 months, 1 year, 2- years, 3 years and 5 years returns for both baskets. The results are quite consistent across all holding periods, and are summarised in the following pages. 25

26 Exhibit 15a: 1-months returns E(Rp) DV + EM E(Rp) DV Source: Bloomberg; Authors calculations Exhibit 15b: 3-month returns E(Rp) DV + EM E(Rp) DV Source: Bloomberg; Authors calculations Exhibit 15c: 6-month returns E(Rp) DV + EM E(Rp) DV Source: Bloomberg; Authors calculations 26

27 Exhibit 15d: 1-year returns E(Rp) DV + EM E(Rp) DV Source: Bloomberg; Authors calculations Exhibit 15e: 2-year returns E(Rp) DV + EM E(Rp) DV Source: Bloomberg; Authors calculations Exhibit 15f: 3-year returns E(Rp) DV + EM E(Rp) DV

28 Source: Bloomberg; Authors calculations Exhibit 15g: 5-year returns E(Rp) DV + EM E(Rp) DV Source: Bloomberg; Authors calculations Exhibit 15h: 5-year returns before the crisis (i.e. last 5-year return ends on 31-Dec-2007) E(Rp) DV + EM E(Rp) DV Source: Bloomberg; Authors calculations On a 1-month to 3-year returns basis, adding emerging economies indices to our initial portfolio of developed economies indices, consistently improved the efficient frontier. The difference is quite significant in most cases. These results are only valid though if we can select all the individual country indices separately as opposed to just adding a global emerging market index and if we can sell them short as well. We can notice with the 5-year returns, that the phenomenon is actually the opposite compared to the other returns, although if we look at the period excluding the financial crisis, we can see that the impact is in fact more neutral. This phenomenon might arise be because the emerging markets suffered more on a long-term basis than developed ones during past financial crises. One bias in the 5-year results is that most of our sampling data fall into the crisis period: our first data point for instance has a start date in May 2002 and an end date in May 2007, which is only a couple of months before the financial crisis. This means we end up with only a couple of months of crisis-free data. 28

29 The other main limitations to these results are: - Returns are considered normally distributed in Markowitz portfolio theory. However, returns in our sample are not quite normally distributed (as elaborated upon below) - The time period of our data is relatively limited (9 years), especially for longer-term returns; this is mainly due to the limited data availability of some of the individual country indices - Our individual investment is a country index, as opposed to individual companies. We could obtain finer results using individual companies from all the countries listed previously, but which would require much larger amount of data and time to synthesise the data as well as to compute the results. - Short selling is possible. This should be possible for the largest countries indices through ETFs, but still challenging, if not impossible, for some smaller countries. - One could argue that the positive benefit of diversification from adding emerging markets to developed markets simply comes from the fact that we are introducing new assets, and therefore increasing the total number of available assets. This is true to some extent, but equally the number of developed market countries is limited and hence our sample is actually very close to the population of available developed market countries. Thus practically the only new assets available are the emerging market ones. As mentioned above, returns are supposed to be normally distributed in Markowitz s Modern Portfolio Theory. We draw the distributions of all returns (1 month to 5 years), and compare to the equivalent normal distribution. The results are as follows: Exhibit 16: Distribution of Returns M returns Normal M returns Normal % -58% -51% -44% -37% -30% -23% -16% -9% -2% 5% 12% 19% 26% 33% 40% 47% 54% -70% -61% -52% -43% -34% -25% -16% -7% 2% 11% 20% 29% 38% 47% 56% 65% 74% 83% Source: Bloomberg; Authors calculations Source: Bloomberg; Authors calculations M returns Normal Y returns Normal % -93% -76% -59% -42% -25% -8% 9% 26% 43% 60% 77% 94% 111% 128% 145% 162% 179% 196% -110% -89% -68% -47% -26% -5% 16% 37% 58% 79% 100% 121% 142% 163% 184% 205% 226% 247% 268% 289% 29

30 Source: Bloomberg; Authors calculations Source: Bloomberg; Authors calculations Y returns Normal Y returns Normal % -304% -248% -192% -136% -80% -24% 32% 88% 144% 200% 256% 312% 368% 424% 480% 536% 592% 648% 704% -180% -154% -128% -102% -76% -50% -24% 2% 28% 54% 80% 106% 132% 158% 184% 210% 236% 262% 288% 314% Source: Bloomberg; Authors calculations Source: Bloomberg; Authors calculations Y returns 100 Normal 50 Source: Bloomberg; Authors calculations As we can see, the 1 month and 3 months returns distribution seem to be close to normal distribution but are leptokurtic, while from 6 months returns to 5 years, the distribution of returns is clearly not normal: 6 months and 1 year distributions exhibit fat tails on the lower end of the distribution, while the 2 years and 3 years seem to be bimodal. As for the 5 years returns distribution, it shows some similarities with a log-normal distribution, although not quite entirely. Despite some limitations due to the necessary initial assumptions and/or practical reasons, the results described above show some positive diversification effect of adding individual emerging economies indices to a basket of developed economies indices, by enhancing the efficient frontier, i.e. allowing to reach higher returns for the same standard deviations or the same returns for lower standard deviations, or both higher returns for lower standard deviations. And although the 5 years returns is overshadowed by the financial crisis in our data set, this result stands for various maturities of returns, from short terms (1 month) to longer terms (3 years). Diversification among emerging markets % -849% -698% -547% -396% -245% -94% 57% 208% 359% 510% 661% 812% 963% 1114% 1265% 1416% 1567% 1718% 1869% Emerging market assets may not always be attractive as stand-alone investments due to high volatility, however, they entail a much more desirable proposition when combined together. In particular, the volatility decreases dramatically when the emerging markets are considered jointly in a portfolio. 30

31 The reason for this is that correlations among emerging markets are notably lower than those among developed markets as a result of them being less integrated as a group than developed countries. Over the period January 1999 to March 2011, correlation among emerging markets averaged 0.42 as compared with an average of 0.67 for developed markets. This implies that broad diversification among emerging markets significantly reduces the volatility of an emerging markets portfolio, as suggested by Tokat and Wicas (2004). In fact, the volatility of the emerging markets index over the period in question stood at 24.8%, which compares favourably with the average volatility of 32.02% in emerging markets. However, over this period, the average level of correlation in emerging markets has increased, thereby narrowing the gap between the emerging markets index volatility (which may be seen as a proxy for a diversified emerging markets portfolio) and the average volatility in emerging markets. This trend is illustrated in Exhibits 17a and 17b. The level of correlation may be viewed as the link between the average volatility in emerging countries and the volatility of the emerging markets index. As the level of correlation between emerging markets rises, the gap between the average volatility and the index volatility narrows. This means that over time, the extent to which diversifying an emerging markets portfolio among emerging markets reduces volatility declined. Figure 17a: 24-month rolling volatility (emerging markets index vs. emerging markets average) 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% EM Index EM Average 2001M M M M M M M M M M M M M M M M M M M M M M M M M01 Source: Bloomberg; Authors calculations 31

32 Figure 17b 24-month rolling average correlation between emerging markets Source: Bloomberg; Authors calculations Diversifying across regions: Regional Commonality It may also be more beneficial to diversify emerging market portfolios across regions rather than across markets within the same region. Using a Principal Components Analysis approach, Bilson et al. (1999) investigated the degree of commonality between 20 emerging market equity returns 39. They ran a regression of emerging market returns from 1985 to 1997 against the following four principal components: level of economic activity, consumption, political risk, and global effects 40 and found that although there is no commonality (e.g. similar coefficients) when all 20 emerging markets are considered, they found common exposure when regions are considered 41. Latin American and European markets displayed the strongest regional commonality, with similar coefficients for each of the 3 and 4 components respectively 42. This suggests that it may be more effective to diversify across regions than within regions. Diversification within a single emerging market When selecting securities within an emerging market, investors should also be aware that stocks in emerging markets tend to move together more so than in developed markets suggesting that there may be limited diversification opportunities from investing in different securities within an emerging 39 Bilson et al (1999) included the following countries in their study: Argentina, Brazil, Chile, Colombia, Greece, India, Indonesia, Jordan, South Korea, Malaysia, Mexico, Nigeria, Pakistan, Philippines, Portugal, Taiwan, Thailand, Turkey, Venezuela, Zimbabwe. 40 The first component (economic activity) reflects real activity (an industrial production index or manufacturing production index) and the size of the trade sector (e.g. export plus import as a proportion of GDP). The second component (consumption) refers to money supply and the trade sector. The third component (political risk) relates to country risk (e.g. International Country Risk Guide). The fourth component (global effect) represents global effects (e.g. world market return such as MSCI World Index) of which P/E and D/P also load into this factor. 41 Please note that the commonality in the determinants of the returns does not suggest that the factors are the same for every market. Returns will differ between emerging markets so long as they exhibit different exposures to each components. 42 Commonality was also found for the Asian counties and Africa and the Middle East, which exhibit commonality for 2 components. 32

33 market. For example, over an average week in 1995, 58% of stocks move in the same direction in US while in Malaysia, China and Poland, 75%, 80% and 83% of stocks move in the same direction respectively (Morck et al., 2000). SECTION IV: IMPLICATIONS FOR INVESTMENT STRATEGY & PRACTICAL CONSIDERATIONS How to Maximise Diversification Benefits Emerging markets provide diversification benefits when added to a portfolio of developed markets assets. However, in order to maximise diversification benefits, an investor could consider 1. Selecting Time Periods when Divergence in National Policies is the Greatest Diversification benefits are more likely to accrue when policies and circumstances in emerging markets differ from those in developed markets. For example, for a US investor, benefits from investing in emerging markets were substantially derived during periods of restrictive US monetary policy when returns from US investments tend to be low (Conover et al., 2002). Investing in other developed markets offers minimal benefit as developed countries tend to align their monetary policies with the US while emerging countries are less likely to. It is important to note though that when crises strike, correlations generally increase, limiting diversification benefits. 2. Investing in Individual Emerging Countries rather than Composite Indices Correlation between individual emerging countries and developed markets varies greatly, highlighting the importance of selecting individual countries rather than investing in an emerging market index. When selecting individual countries, research suggests that emerging countries with the following characteristics are more likely to improve risk return profile of a portfolio: a) Small trade sectors It could be interpreted that countries with smaller trade sectors are less dependent on the rest of the world and hence are probably less correlated, offering greater diversification opportunities. b) Smaller national stock market As countries with smaller national stock markets can be argued to be less integrated with world capital markets, securities are less likely to move in the same direction, providing diversification benefits. c) Low Price-to-Book (P/B) ratios Countries with low P/B are a proxy for recent macroeconomic losers. Given that investors tend to over-extrapolate negative performance in the past to the future, such macroeconomic losers are more likely to outperform. 33

34 d) High Earnings Yield (E/P) As aggregate earnings are positively related to expected returns and prices are negatively related to expected returns, countries with high earnings yield or low P/E are expected to reap higher future returns Diversifying across regions rather than within a region Given that stock markets in the same region are likely to be exposed to common factors (Bilson et al., 1999), in our view, these results suggest that investors should diversify across regions, rather than across markets within the same region as diversification benefits are greatest when the factors driving returns in each country are uncorrelated. Cumperayot et al. (2006) study on currency declines also suggests that currency contagions are usually contained within a region and hence investing across regions would be more effective in diversifying currency risks. 4. Holding Period If the key motivation of investing in emerging markets is to enhance returns rather than to take advantage of the lower correlation between emerging markets and developed markets from a portfolio perspective, a possible way, to increase returns is to invest in emerging markets in the immediate 3-4 years after a country s emergence point i.e. the point in time when global investors attention is drawn to the country. It is also possible that holding emerging market investments for too long (e.g. 10 years) increases the chance of being negatively affected by crises and emerging market crises tend to take a longer time to recover. Practical Considerations when Investing in Emerging Markets Investors in emerging countries should also consider the following issues which are more particular to emerging markets. 1. Higher Agency Costs Agency costs are likely to be higher due to weaker corporate governance and limited information disclosure, which diverts returns away from shareholders. 2. Illiquidity Smaller market capitalisation size and presence of block holdings may limit volume of trades and liquidity in the market such that it could be more difficult to exit positions. 3. Inconsistent National Policies 43 We warn the reader that care should be taken when utilizing the earnings yield to identify potential investments in emerging markets as few studies have investigated the predictive power of the ratio in an emerging market context. Moreover, the Demirtas and Zirek (2010) study only investigated whether the earnings yield could predict one-quarter ahead market returns. Also, most of the research on the topic is focussed on the US market, and contrary evidence is easily found. 34

35 Barry et al. (1997) noted that emerging markets typically implement sweeping reforms. However, it was also common for reforms to be replaced after their introduction by reforms in the opposite direction. Practically, this makes investing in emerging markets less predictable. 4. Lack of Access for International Investors It is not uncommon for countries to put in place foreign ownership restrictions. However, these should not act as a deterrent to entering a market. Barry et al. (1997) found that among emerging countries, investable securities consistently outperformed all securities from late 1988 to mid- 1995, possibly because it is investable securities that experienced substantial capital flows and price increase. Moreover, international investors can also gain access by investing in American Depositary Receipts (ADRs). 5. Implementability Although this report might suggest possible outperformance strategies, we strongly recommend a potential investor in emerging markets to consider some of the limitations with regards to the investability of the different portfolios with respect to local or price-to-cash flow ratios that are found to have predictive power. For instance, it is unclear as to whether some of the data would have been accessible to a foreign investor in a form which would have allowed him or her to form the same type of emerging portfolio as academics formed using historical information. Although some studies allowed for a lag (Bekaert et al., 1997) with respect to the variables (e.g. size of the trade sector as a proportion of GDP, size of the national stock market, political and economic risk surveys) used to determine which countries to include in the portfolios, it is unclear as to whether the data had been revised after issuance. Therefore, before deploying capital to any one of these strategies, we recommend purchasing reliable emerging market data and conducting in-house analysis to determine not only if these variables actually do have significant predictive power, but also, if they would be investable/implementable for a foreign investor. Potential Areas for Future Research 1. Frontier Markets One potential area for future research is frontier markets, a term used to describe pre-emerging developing countries. Many of these countries have been overlooked or until now were inaccessible to foreigners. We believe that, as they evolve into more stable emerging markets, investing in these countries, particularly in the immediate years after the point of emergence, could be advantageous, providing investors with high returns and another source of diversifying returns from the broader market. However, we note that additional research is needed on the topic, to determine if the same characteristics that are found to be significant in identifying successful emerging markets stock markets, are also applicable to frontier markets. We also note that liquidity, investability and transparency might be lower and volatility might be higher in the case of frontier markets. Exhibit 18: The location of frontier markets according to FTSE 35

36 2. Emerging Market Debt Our research above has focused on emerging market equities. It might be interesting to research how the findings above may apply or differ for other asset classes. For instance, our preliminary literature review has revealed the following similarities and differences between emerging market equities and debt: a) Expectations of GDP growth are similarly priced in Beck (2001) suggests that month-on-month changes in credit spreads are driven by consensus forecasts of real GDP growth whereby higher real GDP growth forecasts would lead to lower bond spreads as a country s ability to pay is deemed to be increased, suggesting that expectations of GDP growth are priced in. b) Certain fixed income instruments allow direct participation in GDP growth, unlike equities GDP-indexed bonds allow investors to directly participate in GDP growth, thereby possibly circumventing some of the problems associated with equity investment where part of the growth is diverted away from shareholders to other parties (see p. 4) or driven by new enterprises instead of existing ones (see p. 3). Such an investment may also present diversification benefits for an investor who is predominantly exposed to his home country s GDP to the extent that the GDP growth of the emerging market is different from that of the investor s home country. c) Global factors explain Latin America debt unlike equity Unlike Latin American equities which do not react homogenously to shocks in the US (Barba and Ceretta, 2010), Diaz and Gemmill (2004) found that 25% of the variance in distance-to-default bond spreads of 4 Latin American countries (Argentina, Brazil, Mexico and Venezuela) from 1994 to 2001 is attributable to global conditions which are mainly affected by US stock market returns. Since distance-to-default spread is affected predominantly by global factors, the implication is that the credit risk of these Latin American bonds is less diversifiable. Conclusion Although the conventional belief among many investors is that emerging markets offer superior returns given their higher economic growth, empirical evidence suggests that GDP per capita growth fails to explain equity returns. Perhaps more surprisingly, emerging markets returns also do not consistently outperform developed markets even over long holding periods. Ironically, holding 36

37 emerging market investments for longer periods reduces the chances of outperforming developed markets as long holding periods are more likely to expose the investor to the negative effects of crises which typically persist for longer for emerging markets. However, these findings do not mean that investors should not invest in emerging markets. Given their lower correlation with developed markets, emerging markets provide good diversification benefits which are proven empirically in this paper. As emerging markets are far from homogenous, greater benefits accrue to the discerning investor who selects particular emerging markets to invest in: emerging countries with small trade sectors, which are less financially integrated and with low valuation ratios (such as P/B and P/E) are more likely to enhance risk-return profile. As correlation varies over time, an investor could also select time periods where correlations are lower such as when monetary policies diverge between developed and emerging countries, to invest in emerging markets. As returns also vary over time, an investor who is able to correctly identify the point of emergence, is likely to reap the highest return by being invested in the market within 3 4 years after the point of emergence. Given the rather surprising findings on emerging markets performance and the lack of relationship between economic growth and returns, one valuable lesson may be that investing in emerging markets is still not well-understood amongst most investors and potential benefits could be reaped by those who question conventional wisdom and search for the real drivers and value behind emerging markets investments. 37

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44 Appendix BRAZIL CHILE CHINA COLOMBIA Correlation Matrix - Emerging Markets CZECH REPUBLIC EGYPT HUNGARY INDIA INDONESIA KOREA MALAYSIA MEXICO MOROCCO PERU PHILIPPINES POLAND RUSSIA SOUTH AFRICA TAIWAN THAILAND EMERGING MARKETS INDEX BRAZIL CHILE CHINA COLOMBIA CZECH REPUBLIC EGYPT HUNGARY INDIA INDONESIA KOREA MALAYSIA MEXICO MOROCCO PERU PHILIPPINES POLAND RUSSIA SOUTH AFRICA TAIWAN THAILAND AVERAGE CROSS- COUNTRY CORRELATIONS EMERGING MARKETS INDEX DEVELOPED MARKETS INDEX A-1

45 Appendix Correlation Matrix - Developed Markets AUSTRALIA AUSTRIA BELGIUM CANADA DENMARK FINLAND FRANCE GERMANY GREECE HONG KONG IRELAND ISRAEL ITALY JAPAN NETHERLANDS NEW ZEALAND NORWAY PORTUGAL SINGAPORE SPAIN SWEDEN SWITZERLAND AUSTRALIA AUSTRIA BELGIUM CANADA DENMARK FINLAND FRANCE GERMANY GREECE HONG KONG IRELAND ISRAEL ITALY JAPAN NETHERLANDS NEW ZEALAND NORWAY PORTUGAL SINGAPORE SPAIN SWEDEN SWITZERLAND UK US AVERAGE CROSS- COUNTRY CORRELATIONS DEVELOPED MARKETS INDEX A-2