An Analysis of Emerging Market Diversification for an Irish Investor

539
-1

Published on

Published in: Economy & Finance, Business
1 Comment
0 Likes
Statistics
Notes
  • Be the first to like this

No Downloads
Views
Total Views
539
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
3
Comments
1
Likes
0
Embeds 0
No embeds

No notes for slide

An Analysis of Emerging Market Diversification for an Irish Investor

  1. 1. An Analysis of Emerging Market Diversification for an Irish Investor Shane O’DohertyMSc Finance & Capital Markets 2011
  2. 2. An Analysis of Emerging Market Diversification for an Irish investor Shane O’Doherty (BBS in Business and Finance) Dublin City University Business School Dublin City University Supervisor: Dr Alex Eastman Course Director: Dr Valerio PotiMSc Finance & Capital Markets July 2011
  3. 3. DeclarationI hereby certify that this material, which I now submit for assessment on the programme ofstudy leading to the award to Master of Science in Finance and Capital Markets, is entirelymy own work, and has not been taken from the work of others, save and to the extent thatsuch work has been cited and acknowledged within the text of my work.Signature:Date:
  4. 4. AcknowledgementsFirst and foremost I would like to dedicate this research article to my mother and thank herfor her help and support during the last year.I would also like to thank my fellow students and classmates whose assistance and moralsupport throughout this difficult year was invaluable.Finally I would like to thank Dr. Alex Eastman and Prof. Liam Gallagher who work in the areaof Economics, Finance and Entrepreneurship in Dublin City University for their guidance andsupport during this research paper.
  5. 5. AbstractThe benefits of International Diversification have been recognized for decades. Since 1981when the IFC made accurate information pertaining to Emerging Markets their popularityhas increased dramatically.In this paper I investigate contemporary risk, return characteristics of Developed andEmerging Markets. I also examine whether favourable correlations still exist betweenDeveloped and Emerging Markets taken from the perspective of an Irish investor. Finally, Iconstruct two portfolios denominated in Ireland. One consisting of only Developed MarketsIndexes, and the other composed of Developed and Emerging Market indexes. I thencompare the portfolios in terms of the return and risk they offer the Irish investor.All calculations were based on markets price indexes taken from 11 Developed Marketcountries and 22 Emerging Market countries from Bloomberg. The data set chosen was a 15year time horizon from 1995 – 2010. Three sub-periods were also tested in order to identifytrends. These were from 1995 – 1999, 2000 – 2006 and from 2007 – 2010.
  6. 6. Table of Contents PageChapter 1 Introduction 1Chapter 2 Literature Review 5 2.1 Diversification 6 2.2 International Diversification 12 2.3 Emerging Markets Diversification 18Chapter 3 Research Methodology 35 3.1 Hypothesis 36 3.2 Data Description 37 3.3 Relevant Formulae 40Chapter 4 Data Analysis 44 4.1 Risk, Return, Periodic Growth 45 4.2 ISEQ Correlations 54 4.3 Irish Portfolios 67Chapter 5 Empirical Findings 74Conclusions 84Appendices 88Bibliography 111
  7. 7. Chapter 1Introduction 1
  8. 8. The most important issue for any investor is the risk and return that an investment presents.This is whether the investor is investing in a small number or a very large number of assets.An intrinsic concept in portfolio construction is diversification. The investor can diversifydomestically among different assets and between different industries. InternationalDiversification is favourable in that it allows investors and portfolio managers to improveportfolio returns while at the same time, reduces risk levels. The investor can furthermaximise returns and minimize risk by diversifying investments into Emerging Markets aswell as Developed Markets.The World Bank’s current definition of an Emerging Market is a country that has a grossnational income (GNI) of $11,456 or less per capita. An Emerging Market country can bedefined as a society transitioning from a dictatorship to a free market-oriented economy,with increasing economic freedom, gradual integration within the global marketplace, anexpanding middle class, improving standards of living and social stability and tolerance, aswell as an increase in cooperation with multilateral institutions. According to Forbes, by thisdefinition, an analysis of all 192 country-members of the U.N. leads to the selection of 81countries that can be categorized as Emerging Markets. The role of emerging marketcountries in the world is now difficult to overestimate. The territory of these countriesoccupies 46% of the earths surface, with 68% of the global population. These economiesaccount for nearly half of the gross world product.The term Emerging Markets was coined by economists at the International FinanceCorporation (IFC) in 1981, when the group was promoting the first mutual fund investmentsin developing countries and formulated the Emerging Markets Database (EMDB). Since then,references to Emerging Markets have become ubiquitous in the media, foreign policy and 2
  9. 9. trade debates, investment fund prospectuses and multinationals annual reports. Up untilthe formulation of this database, investment in Emerging Markets had been consideredunfavourable. This is most likely due to the fact that the information that existed prior tothe EMDB was thought to be very unreliable and distorted.The last 15 years has seen considerable instability in the world economy. In terms ofEmerging Economies there was the Mexican Peso Crisis in 1994 and the Asian Crisis whichbegan with the devaluation of the Thai Baht in 1996. Following closely to this Russia wentthrough its own financial crisis in 1998. Pertaining to developed economies then the periodduring the 1990’s was a strong bull market which came about due to sudden and dramaticimprovements and innovations in technology. The period at the turn of the millennium sawthis bull market come to something of a climax with the “Dot Com Bubble”. Following thisthe world economy slowed down exhibiting a period of a more bearish nature. With theworld economy emerging from a serious financial crisis that began in 2007 the outlook forthe majority of developed economies is bearish. Investors will look to minimize risk levels ofportfolios in any way they can and many will look to investment in Emerging Markets as anopportunity to reduce risk.The opening of these large economies to global capital, technology, and talent over the pasttwo decades has fundamentally changed their economic and business environments. As aresult, the GDP growth rates of these countries have dramatically outpaced those of moredeveloped economies, lifting millions out of poverty and creating new middle classes andvast new markets for consumer products and services. Large, low-cost and increasinglyeducated labour pools, meanwhile, give these markets tremendous competitive advantage 3
  10. 10. in production, and information technology is enabling companies to exploit labour in thesemarkets in unique ways.For my research article I will look at the risk return characteristics for the Emerging Marketsand compare them with those characteristics shown by Developed Markets. I will alsoexamine the correlations between 11 Developed Market indexes and 22 Emerging Marketindexes. In this section I will look primarily at correlations from the perspective of the Irishinvestor. I will also closely examine correlations for the S&P 100 with the other 32 testindexes for comparison and to increase the validity of my findings. For the final section ofmy investigation I look at two different portfolio types for an Irish investor. The firstportfolio consists of only DM indexes, while the second includes both DM and EM indexes. Icompare the two portfolios based on the risk and returns they present. For each of thethree sections of my analysis I look at data from the 15 year period 1995 – 2010. I alsocalculate results for three sub-periods from 1995 – 1999, 2000 – 2006 and from 2007 –2010. This was done to see whether there any trends evident over the time horizon.In my literature review I look in considerable detail into the history and theory behind theidea of diversification, international diversification and diversification into EmergingMarkets. In my research methodology chapter I will outline my hypothesis and give adescription of the data and formulae I used. For my data analysis I will outline the importantresults that I found in my research. In Chapter 5 I will discuss my empirical findings. In thischapter I will link the results obtained from my research with previous findings from myliterature review. The empirical findings shall also include minor limitations that myresearch may have been subject to and I will recommend areas where I believe futureresearch should be beneficial for the Irish investor. 4
  11. 11. Chapter 2Literature Review 5
  12. 12. 2.1 – Diversification “Diversification is both observed and sensible; a rule of behaviour which does not imply the superiority of diversification must be rejected both as a hypothesis and as a maxim.” (Markowitz 1952)It was not until 1952 that Harry Markowitz published a formal model of portfolio selectionembodying diversification principles. In his work Markowitz drew attention to the commonpractice of portfolio diversification and showed exactly how an investor can reduce thestandard deviation of portfolio returns by choosing stocks that do not move exactlytogether. Markowitz proposed that investors should focus on portfolios based on theiroverall risk return characteristics. Markowitz was by no means the first to consider thepotential benefits from diversification. He refers to Bernoulli’s article in 1738 as one of theinfluences of his work. Markowitz had the brilliant insight that, while diversification wouldreduce risk, it would not generally eliminate it. Markowitzs paper is the first mathematicalformalization of the idea of diversification of investments.Probably the most important aspect of Markowitzs work was to show that it is not asecuritys own risk that is important to an investor, but rather the contribution the securitymakes to the variance of his entire portfolio (Rubenstein 2002). This was primarily aquestion of its covariance with all the other securities in his portfolio. Where previoustheory concentrated more on an individual security analysis, and did not account forcorrelations of risk between assets. 6
  13. 13. “What was lacking prior to 1952 was an adequate theory of investment that covered the effects of diversification when risks are correlated” (Markowitz 1999)Markowitz also added the brilliant insight that, while diversification would reduce risk, itwould not generally eliminate it. The risk that remains even after extensive diversification iscalled market risk. This type of risk is also called systematic or non-diversifiable. The riskthat can be eliminated through diversification is called firm-specific or non-systematic risk.Markowitz assumed that the investor would be a mean-variance optimizer in looking for theoptimum “efficient” portfolio. The portfolio is considered as efficient if and only if it offers ahigher overall expected return than any other portfolio with comparable risk (Sharpe 1967).According to Markowitz’s studies the highest risk return combination is found by finding theoptimal portfolio on the efficient frontier / investment opportunity set of assets. If we treatsingle period returns for various securities as random variables, we can assign themexpected values, standard deviations and correlations. In his work in 1952 Markowitzshowed that based on these we can calculate the expected return and volatility of anyportfolio constructed with those securities. Essentially this means that we are takingexpected returns and volatility as proxies for risk and reward. If the returns are notcorrelated, diversification could reduce risk. On the other hand, if security returns areperfectly correlated, no amount of diversification can affect risk.In order to simply convey how the expected return on a portfolio might be attained underMarkowitz’s model we will take an example where an individual’s wealth is invested in 2 7
  14. 14. assets. A proportion denoted W1 is invested in the first asset, and the remainder of 1 – W1,denoted W2 is invested in the second asset. The expected return on the portfolio denotedERP can then be found by getting the weighted average of the expected return on theindividual securities (ER1) and (ER2). Such that: 1) ERP = W1 (ER1) + W2 (ER2)The next central factor to Markowitz’s optimal portfolio selection is to find the standarddeviation of the portfolio. However to do this the co-variances between the individualassets must be found as has been mentioned. In continuity with our basic case thecovariance between asset 1 and asset 2 is found by: 2) Cov12 = P (r1 – ER1) (r2 – ER2)The new factors r1 and r2 that have been introduced here represent the actual returns onthe assets. The probability of the scenario is included by the factor “P”. The result from thecovariance equation conveys the degree to which the assets’ returns move in tandem witheach other. For diversification benefits we would here be looking for the assets that give thelowest covariance readings to minimize the risk level of the portfolio.The benefits of a low covariance of returns of the individual securities can be besthighlighted by Markowitz’s formula for attaining the variance of a portfolio: 3) σP2 = W12 σ12 + W22 σ22 + 2 W1W2 Cov12 8
  15. 15. From this formula where σ12 and σ22 are the variances of the individual securities, we canclearly see that a low covariance between securities 1 and 2 will directly result in a lowerportfolio variance and therefore standard deviation i.e. the portfolio benefits fromdiversifying into different securities. It should also be noted that another method by whichthe covariance between securities can be attained is by using the correlation coefficientsuch that: 4) Cov12 = p12 σ1 σ2In the above equation p12 represents the correlation coefficient. Markowitz’s 1952 paperseems to contain the first occurrence of this equation in a published paper on financialeconomics (Rubenstein 2002). In this model the correlation can be anywhere from -1 to +1.Where the more the correlation is negative the smaller the co-variance will be andtherefore the smaller the level of risk there is in the overall portfolio. The combination ofrisk and return on a portfolio is subject to the preferences of the individual investor.As is realistically the case investors will generally have large numbers of assets to bemeasuring. In this case a variance-covariance matrix would be used to generate a standarddeviation for the portfolio. As has been mentioned the variance of the portfolio is theweighted sum if co-variances, ad each weight is the product of the portfolio proportions ofthe pair of assets in the covariance term.The bordered variance-covariance matrix has the portfolio weights for each asset placed onthe borders. To find portfolio variance, multiply each element in the covariance matrix by 9
  16. 16. the pair of portfolio weights in its row and column borders and add up the resultant terms.If there were only two assets we would get equation 2 as the result. If there are a number ofassets the matrix would look as follows: (where σ12 denotes covariance for clarificationpurposes) Weights w1 w2 w3 w4 wn w1 σ11 σ12 σ13 σ14 σ1n w2 σ21 σ22 σ23 σ24 σ2n w3 σ31 σ32 σ33 σ34 σ3n w4 σ41 σ42 σ43 σ44 σ4n wn σn1 σn2 σn3 σn4 σnn12 years on from Markowitz’s portfolio selection breakthrough, Sharpe, Lintner and Mossindeveloped a model that conveyed individual asset risk premiums as a function of asset risk(Sharpe 1964). Under this new model, the relevant measure of risk for individual assets heldas part of well diversified portfolios is not the assets standard deviation or variance; it isinstead the contribution of the asset to the portfolio’s variance which is measured by thebeta of the asset. B1 = Cov (R1, RM) / σM2In this case the assumption is taken that the mean variance optimal portfolio is consideredas being the relevant market portfolio where RM is the return on the market portfolio andσM2 is the variance of the market portfolio. The Beta co-efficient “B1” of a security is definedas the extent to which return on the stock and returns on the market move together (Bodie, 10
  17. 17. Kane, Marcus 2010). The expected return beta relationship is the most recognizedexpression of the CAPM: ER1 = rf + B1 (ERM – Rf)An important factor in the above equation is the introduction of the option to invest in ariskless asset “rf”. The option for the investor to lend or borrow at the risk free rate meansthat there will be no covariance element as was seen in Markowitz’s model. In the aboveequation the factor “ERM – Rf” represents the risk premium or the market price of risk. Thatis that it quantifies the extra return that investors demand to bear portfolio risk.The single index model, CAPM predicts that only one type of non-diversifiable risk influencesexpected security returns. That single type of risk is the “market risk”. Stephen Rossdeveloped a new theory only about a decade after the CAPM was founded. This was themulti-index model, the APT, which is more general in that it accounts for a variety ofdifferent economic risk sources. The APT provides a portfolio manager with a variety of newand easily implemented tools to control risks and to enhance portfolio performance(Burmeister, Roll, Ross 1994).Several of these economic variables were found to be significant in explaining expectedstock returns, most notably, industrial production, changes in the risk premium, twists in theyield curve, and, somewhat more weakly, measures of un-anticipated inflation and changesin expected inflation during periods when these variables were highly volatile (Chen, Roll,Ross 1986). These modern studies have found that the multifactor APT approach has fargreater explanatory power than the CAPM. 11
  18. 18. 2.2 - International DiversificationFrom the principles learned from the development of Markowiz’z portfolio theory, in theearly 1970s experts began to highlight the potential advantages from internationallydiversifying a portfolio. “The international diversification of portfolios is the source of an entirely new kind of world welfare gains from international economic relation” – (Grubel 1968)The first empirical literature on the benefits of international diversification was developedby Grubel where he looked at ex post realized rates of return from investment in 11 majorstock markets of the world. In 1970, Levy and Sarnat underwent a more comprehensivestudy dedicated primarily to looking at international diversification of investment portfolios.In order to convey the potential gains from diversification they looked at data from 1951 –1967 using rates of return from 28 different countries.Levy and Sarnat also highlighted the optimum portfolio by using the market equilibriummodel (Lintner 1965). What was perhaps the most striking feature of Levy and Sarnat’spaper was the fact that there were considerable benefits to be gained from usingdeveloping countries as part of the optimal portfolio. Their results showed that the higherthe number of countries that were invested in and the more regions that were taken intoconsideration, meant the more favourable the risk return combination of the portfolio. Theempirical results from this test were highly significant. The best combination that can becreated out of equities in the developing countries is a portfolio with a 5% return and a 12
  19. 19. 26.5% standard deviation as compared with a return of 12% and standard deviation of 8%for the optimum portfolio which included all countries. Levy and Sarnat estimated that thebenefits of diversification could be further improved by removing barriers to internationalflows of capital. This theory was empirically proved by Lessard in 1973 by using hisInvestment Union concept. “Complete freedom of international capital movements would provide investors with a maximum opportunity for diversification” – (Lessard 1973)In his work in 1974 Bruno Solnik focused on highlighting the benefits from risk reductionwith differing amounts of stocks in portfolios. He also compared risk levels of solelydomestic portfolios with internationally diversified ones. Solnik’s empirical results alsoshowed that the marginal reduction in standard deviation achieved from additional stocks inthe portfolio decreased quite rapidly. He showed that an American investor holding 20securities reduces his total risk by only another 3% if he added another 50 differentsecurities to his domestic portfolio. Solnik highlighted the fact that despite how manysecurities that are added to the portfolio, there will always be an element of risk remaining.This is the systematic/market risk when investing in domestic securities alone, which wasmentioned earlier, was shown by Solnik to have considerable reduction potential if theinvestor was to diversify internationally. It was found that in the case of the US thevariability in return of an international well diversified portfolio would be only one tenth asrisky as a typical security and half as risky as a portfolio of well diversified purely US stocks. 13
  20. 20. These results could have been even more exaggerated if developing market securities hadbeen included.In 1976 Lessard explored the effects that taxation, transaction costs, currency controls andfluctuations in exchange rates may have on international investment. In his findings Lessardsought to explain the covariance structure of equity returns in international markets. Helooked at whether it was country or industry factors that dominated and he aimed toconvey if the gains from international diversification of markets are assumed to beintegrated or segmented using two sets of data. The first is monthly percentage changes inmarket-value weighted price indexes for 16 countries and for 30 industries covering theperiod January 1959 to October 1973. The second is monthly price changes for 205individual securities from 14 countries and 14 industries for the period January 1969 toOctober 1973. Lessard’s finding supported previous work by Grubel, Levy, Sarnat and Solnik. “Country factors are the most important elements in the covariance structure, reinforcing the view that the international dimension is particularly critical in reducing risk through diversification.” – (Lessard 1976)After finding that it was indeed country factors that dominated the nature of the covariancestructure Lessard found that the magnitude of these gains will depend, however, onwhether markets are segmented or integrated internationally. Lessard found that if marketsare integrated, the benefits of international diversification may be overstated. This is partlybecause a few large countries represent the bulk of the market value and the risk elementsof these countries will contribute prominently to the world market portfolio. If markets are 14
  21. 21. segmented, on the other hand, then a more complete diversification of country effectsshould be beneficial. It was concluded that the new risks introduced by Lessard wereoutweighed by the benefits attributable to international diversification for the investor. Inhis work Lessard also points to the fact that investors tend to not diversify internationally toa theoretically efficient extent. This is idea was to be examined further in years to come. It was pointed out by Lessard in his work in 1976 that although there are inherentgains available to investors who diversify internationally; the evidence is showing that themajority of investors are not efficiently using this opportunity. In contrast to the previouswork on the subject of international diversification by Levy, Sarnat and Lessard, in the early1990’s experts began to look at investor choices rather than institutional constraints as thereason that international diversification not occurring to its efficient level. In 1991 Frenchand Porterba found that over 98% of Japanese equity portfolios were held domestically byinvestors. Analogous figures of 94% and 82% were found for the US and the UK respectively.In order to measure the costs associated with incomplete diversification, French andPorterba calculate the expected returns implied by the actual portfolio holdings of US,Japanese and UK investors. They then compute the expected returns implied by aninternational value weighted portfolio strategy for investors in each nation and compare theresults. In their empirical findings it was discovered that UK investors must expect annualreturns in the UK market more than 500 basis points above those in the US markets toexplain their 82% investment in domestic shares. Analogous figures for the US, Japanrelationship and the Japan US relationship were 250 and 350 basis points respectively.These results show that investors expect domestic returns that are systematically higher 15
  22. 22. than those implied by a diversified portfolio. French and Porterba then sought to empiricallyinvestigate whether it was institutional factors or investor choices that were to beattributed as being the primary reason for imperfect international diversification.The institutional reasons for the existence of this concept of imperfect internationaldiversification that were tested were the effect of taxes, transaction costs, market liquidity,cross border equity flows and government limitations to cross border investment. Empiricaltests on the above factors were found to be insignificant in negatively affecting the degreeof international diversification. French and Porterba therefore suggested a differentreasoning. “Because constraints on foreign holdings are not binding, this implies that incomplete diversification is the result of investor choices” - (French & Porterba 1991)The second potential factor tested that might cause imperfect international diversificationfocuses on investor behaviour. With one important possibility being that returnexpectations may vary systematically across groups of investors. In a study between the USand Japanese investors, empirical evidence showed that while Japanese investors weremore optimistic than their US counterparts with respect to both markets, they wererelatively more optimistic about the Tokyo market. In terms of risk it was found thatinvestors tend to attribute extra risk to foreign investments because they know less aboutforeign markets, institutions and firms. 16
  23. 23. As was pointed out previously it was found by Lessard (1976) that there are higherdiversification benefits to be gained when international markets are segmented than whenthey are integrated. The early 1990’s saw growth in international investment whichparalleled growth in international financial market integration. National economies alsoappeared to be becoming more dependent on the world business cycle (Odier, Solnik 1993).This prompted Odier and Solnik to test whether the international diversification was stillbeneficial from a risk return viewpoint. They look at what has changed over a 20 year periodand the implications of the changes for international investment.In their findings it was discovered that asset allocation between equities, fixed incomesecurities and cash and cash equivalents were the major factor to the performance and riskof a portfolio. They found that 90% of the monthly variation on returns on a large sample ofmutual funds was explained by asset allocation while only 10% was determined by securityselection. It was found that correlations between major nations increased as global marketvolatility increased, which is exactly when one would hope that the benefits of lowcorrelations from diversification would be recognized. However, even if the correlationbetween markets is increasing slightly, it remains quite low because of the relativeindependence of national economies and monetary policies. Odier and Solnik concludedthat even though the international environment changes over time, efficient internationalasset allocation strategy opportunities can be identified using careful research. 17
  24. 24. 2.3 – Diversification in Emerging Markets It was pointed out by Solnik in reflection of his work in 1974 that a study of theinclusion of developing markets into portfolios could further add to the potentialopportunities and efficiency for internationally invested portfolios. The term EmergingMarkets (EM) was coined by economists such as Antoine W. Van Agtmael at theInternational Finance Corporation (IFC) of the World Bank in 1981, when the group waspromoting the first mutual fund investments in developing countries (Forbes). It waspointed out by Errunza (1983) that the research on international diversification carried outup until that point was stopping short of a truly efficient global portfolio. He said that thiswas the case because of the fact that previous research had been limited to the securitiesmarkets of developed countries. Up until 1983 there was very little investment to be seen inEMs. Errunza attributed this to the fact that there was very little information available aboutthe markets and where there was information it would likely have been unreliable.In order to address this lack of information pertaining to EMs, the IFC created a new databank consisting of broad market-wide statistics on 15 EMs and security-specific return datafor the period 1976-80 from nine EMs. This new data base provided investors with their firstreal opportunity to compare EMs with developed markets (DMs) using reliable data onheavily traded individual securities. Using this data from the IFC databank Errunza foundthat the returns on EMs were generally high relative to industrialized countries. It waspointed out in his paper also that the benefits of internationally diversifying a portfolioamong purely DMs were eroding somewhat in the years approaching 1983. Errunza alsoreported on the correlations between EMs and DMs for the period 1976 – 1980. The firstempirical finding was that there were relatively high correlations amongst the DMs. The 18
  25. 25. results also showed that portfolio risk could be reduced substantially by including EMs indiversified portfolios. Furthermore, the correlations between EMs are also low incomparison to the correlations displayed by members of the European or North Americanblocs.As was highlighted by Lessard and Solnik in their research earlier, there is a significantnational factor in security returns, implying limits to risk reduction through domesticdiversification. Since security returns across countries are less than perfectly positivelycorrelated, however, a large part of the national systematic risk is diversifiable in the globalcontext. In a sample including 15 DMs and 12 EMs, Errunza also sought to explain theproportion of domestic market return variance that could be explained by alternate worldindexes. The empirical findings showed similar results as previous research for DMs. Errunzadiscovered that the proportion of variance explained by the world factor is extremely smallfor EMs suggesting definitive potential benefits from holding a truly global portfolio.As has been outlined already there are a number of barriers pertaining to internationalinvestment. Errunza discussed the relative importance of each of the different types toinvesting in EMs. Firstly he looked at currency risk, whether fluctuations in exchange ratescould unfavourably affect the real returns to investors in EMs. It was reported that therealized returns reported here did not increase volatility or reduce security returns tounacceptable levels. Therefore regarding investment in EMs currency risk should not be anissue for investors with well diversified portfolios who are looking to invest in EMs. Theimportance or political risk associated with EMs such as expropriations, nationalizations orcapital controls was found to depend on the risk aversion /opportunity set of the investorand how well the EM markets in question were functioning. Tests performed on the IFC 19
  26. 26. sample securities suggest that EMs are almost as efficient as European markets. Somecountries can have restrictions on capital flows across their borders however the majority ofEMs had little or no restrictions on capital flows, and the ones that did were eitherloosening legislation or might remove barriers in the future. In most cases it was found thatthe tax treatment of repatriated dividends and capital gains was similar to that of policies inDMs. Errunza concluded that the typical barriers to international investment did not have asignificant effect on the benefits of internationally diversifying using EM securities. Errunzadid point to the potential danger to the investor of differing policies in EMs regardingfinancial reporting that might require special knowledge and interpretation skills for crosscountry comparisons. In a later work Errunza (1988) like previous experts pointed to the fact that theaverage international portfolio manager remains very hesitant about investing in emergingmarkets. A major concern may be the impact of global recession and the debt problems thatplagued many emerging markets during the early 1980s. Errunza sought to investigatewhether these major shocks have an effect on the performance of emerging markets. Thedata for his research in this journal article covered the period from 1976 – 1984 andincluded more EM countries due to ever increasing data transparency. There was also theeffect of currency fluctuations between EMs and DMs on diversified portfolios to consider,associated with the period of financial distress in the early 80’s.The empirical findings showed that despite the global recession, the performance ofemerging markets over the 1976 - 1984 period was consistent with that reported for theearlier period between 1976 - 1980. Furthermore with respect to the benefits ofdiversification, the emerging markets actually displayed a lower correlation with developed 20
  27. 27. markets over the 1981-84 periods than over the 1976 - 1980 period. As was the case withprevious studies as well, given the long-term nature of investments in emerging markets,and the fact that any global portfolio would invest reasonably small amounts in emergingmarkets, the currency fluctuation problem is not critical in terms of its effect on overallportfolio diversification. An in depth quantitative analysis of EMs was developed by Divecha, Drach andStefek in 1992. In their research they aimed to develop a model that would shed light on theforces that drive EMs. This would help investors make better informed decisions to avail ofinternational diversification using EMs. In tandem with the previous empirical research(Errunza 1983, 1988), they found that EMs are more volatile than DMs. It was also conveyedthat EMs have relatively low correlations amongst each other, and that there were lowcorrelations between EMs and DMs. These low correlations highlight the opportunity fordiversification associated with the addition of EM securities to an international portfolio.The data that was selected for this analysis was taken from the IFC and consisted of 23 EMcountries, as well as the US, UK and Japan. The sample period covered from February 1986 –July 1991.In their analysis it was seen that homogeneity amongst securities within a given EM wasevident. That is to say that all stocks within a given EM are very sensitive to changes in thegiven country’s market index. One could say that individual stocks in EMs have high Betaswith the market portfolio, more so anyway than in DMs. In the second part of their researchthey looked at correlations across EMs and discovered a significant degree of heterogeneity.EMs were seen to be considerably less correlated with each other than the DMs were. The 21
  28. 28. analysis highlighted an average correlation amongst EMs as low as 0.07, meaning they arealmost uncorrelated.The implications from the point of view of an investor from this study are that there areconsiderable diversification benefits to be gained from investing in EM indexes. In theiranalysis they conveyed that over the sample horizon, a global investor who allocated 20% oftheir wealth in an EM composite index fund and 80% in DMs would have reduced theiroverall annual portfolio risk by 0.81%, while simultaneously increasing annual return by2.1%. This is in comparison to a portfolio with a 100% allocation in DMs. An analysis of risk and returns and their predictability in emerging markets wasresearched by Campbell R. Harvey (1994). Using data from the Emerging Markets Database(EMDB) and the IFC he provided the first comprehensive analysis of risk and return in EMsand the effect of their inclusion in a diversified portfolio. The data included 20 EM nationsfrom Europe, Latin America, Asia, Africa and the Middle East, as well as over 800 equities.The paper had three primary goals. Firstly Harvey sought to study the unconditional risk ofreturns of EM securities. Second, he researched why EMs have such high expected returnsand finally the time variation in EM returns was studied.Where previous authors documented low correlations of the emerging market returns withdeveloped country returns, Harvey differentiated his study to test whether adding EMassets to the portfolio problem significantly shifts the investment opportunity set and theefficient frontier. In his findings it was seen that the addition of EM securities did indeedenhance the risk return relationship of portfolios. That is that it moved the investmentopportunity set up and to the left. 22
  29. 29. In the second part of Harvey’s study he seeks to explain why the emerging market equitieshave high expected returns, when under the framework of asset pricing theory it was foundthat exposures to the commonly used risk factors are low for EMs. Applying standard oneand two-factor global asset pricing paradigms leads to large pricing errors. Harvey indicatesthis failure may be caused by the fact that under the asset pricing model the assumptionthat complete integration of world markets exist might be inaccurate.Lastly, by studying the time-variation in EM returns, Harvey conveyed that EMs contrastwith DMs in at least two respects. It was shown that EM returns are actually morepredictable than in DMs. Also, unlike in DMs, EM returns are more determined by localinformation than by global information. One interpretation derived of the influence of localinformation is that the emerging markets are segmented from world capital markets. Asecond interpretation is that there is important time-variation in the risk exposures of theemerging markets.“For countries with stable, developed industrial structures, many researchers studying time- varying asset returns have assumed that risk loadings are constant”- (Harvey 1994)This is a far less reasonable assumption for developing countries. The country risk exposurereflects the weighted average of the risk exposures of the companies that are included inthe country index. As the industrial structure develops, both the weights and the riskexposures of the individual companies could change. This may induce time-variation in riskexposure within the EMs. Harvey concluded that future research should investigate an assetpricing framework that allows for the possibility of incomplete integration and for thedegree of integration to change through time. 23
  30. 30. Pursuant to previous research discussed, EMs have considerably different featuresfrom DMs. There are four distinguishing features that separate the two. EMs have higheraverage returns, correlations with developed market returns are low, returns are morepredictable and volatility is higher (Bekaert, Harvey 1995). In a later study by Bekaert andHarvey in 1997, they sought to explore cross sectional determinants of investmentstrategies in EMs. Following that they examined some of the issues in using EM equity datasuch as investability, survivorship and non-normality.In the research they looked at data from the IFC, Morgan Stanley Capital International(MSCI) and the ING Barings Emerging Markets Indices (BEMI). The IFC and MSCI bothpresent two types of indexes, global and investable. While the BEMI only focuses oninvestable indexes. It was important to study markets before and after they were madeaccessible to international investors. This is because as has been discussed, an intrinsic partof studying EMs is the impact that capital market liberalizations have on the returns. The IFCand the MSCI were found to be very similar in the data they present. However the IFC datawas determined as the most favourable due to the fact that it covered the longest history ofdata as was therefore the least subject to omitted variable bias.Following on from their previous work in 1994, in this research they looked at the degree towhich time-varying world market integration impacts on the distribution of returns for EMs.To convey this they looked at summary annualized EM volatilities and mean returns fromthe 1980’s and the 1990’s and compared results from the two periods. Most of the capitalmarket liberalizations had taken place before 1991. Their results showed that the meanresults in most countries are much lower in the 1990’s than in the 1980’s. An example 24
  31. 31. would be that the four countries that had returns greater than 65% in the 1980’s, all hadreturns less than 25% in the 1990’s.The importance that global integration of capital markets had was also further evidenced, asthe influence of global and local information changes. The results showed the EMcorrelations were increasing over time in tandem with the ever increasing integration ofcapital markets. However it was also noted there is still more than sufficient diversificationbenefit for the investor to avail of. With respect to the Beta measurement of risk, the Betacoefficients measured in this study conveyed significantly higher readings in the 1990’s thanin the 1980’s. This reflects the fact that EM country returns are becoming more sensitive toworld market returns, further reinforcing the importance of the impact of global marketintegration on the benefits that can be gained from international diversification.The limitations of the CAPM single factor model are also further highlighted in Harvey’sresearch in 1997. This is particularly clear with regards to the results from the 1990’sdataset. In the findings Harvey used the t-statistic to measure the statistical significance ofthe results. The higher the t-value, the greater the confidence we have in the coefficient,Beta as a predictor. Low t-values are indications of low reliability of the predictive power ofthat coefficient. The Beta average return appears to be stronger in the 1990’s from firstglance. However there is one factor that is subjecting these findings to considerable error.Poland was found to have a high return and an extremely high Beta. It was discovered that ifthe average return were regressed on the Betas, the t-stat was 3.2. When Poland wasremoved from the analysis the t-stat dropped to 0.4. Coinciding with research previouslydiscussed the failure of the CAPM to explain EM returns could be interpreted in a number ofways. The benchmark world portfolio may not be mean-variance efficient and perhaps a 25
  32. 32. multifactor representation is more appropriate for EMs. The CAPM therefore based onthese findings is not useful in explaining the cross section of average returns. Instead themost suitable risk measurement in completely segmented capital markets is the volatility.Finally Harvey and Bekaert explore a group of risk attributes to EMs. These attributesincluded a wide range of country characteristics, some of which included political risk,inflation, demographics, market integration which were found to be important factors ininvestment strategies for EMs. They also found that a number of fundamental attributesincluding the International Country Risk Guide’s Composite Risk, trade to GDP and earningsto price are useful in identifying high and low expected return environments. Contrary to previous result found pertaining to EM returns, it was found that EMsdid not produce high compound returns relative to US stock markets when a 20 year timehorizon ending in June 1995 was used (Barry, Peavy, Rodriguez 1998). Pursuant to theempirical research studied here we know that EMs have experienced high levels of volatility,but they have also provided significant diversification benefits to investors when combinedwith DM portfolios. They used data from the IFCs Emerging Markets Data Base (EMDB) toexamine the risk and return characteristics of emerging markets and their diversificationbenefits for portfolios based on U.S. stocks.It was found as expected that EMs as a group portrayed monthly standard deviations ofreturns of 5.61%. This was compared to the US equivalents of 4.25% and 5.26% for the S&P500 and the NASDAQ respectively. These standard deviations were for the period 1975 –1995 and similar results were derived for the period from 1985 – 1995. Also in tandem withprevious empirical research, the correlation between EM markets and the US market overthe 20 years was 0.34 which conveys the potential gains from diversification for the investor 26
  33. 33. by including EM market indexes in their international portfolio. However there was onestatistical finding that was contrary to previous analyses. That is to say that over the 20 yearperiod, the EM composite index gave a monthly mean return of 0.99%. Lower than thereturns measured for the S&P 500 and the NASDAQ of 1.11% and 1.07% respectively.The optimal asset allocations and minimum variance portfolios to these markets wereshown to change from period to period. The minimum variance portfolio for the periodfrom 1985 – 1995 was shown to involve an allocation of 20% of funds in the EM compositeindex and 80% in the US index. Analogous percentages of 50% in both the EM compositeindex and the US index for the period from 1976 – 1985 were calculated. Some individualemerging markets provide especially powerful diversification opportunities for U.S.domestic investors. For example, allocating 20% of a portfolio to Thai stocks and theremainder to the S&P 500 would have allowed U.S. domestic investors to earn a higher rateof return at substantially lower variability than the S&P 500 alone would have given themduring the 1975 - 1995 period. It was also noted that care should be taken before investingin some of the smaller EMs where there might be less detailed information available. The ex-post framework utilized in past analyses do not reveal the whole picture forconstructing useful and profitable investment strategies and they potentially overstate thetrue level of gains which can be obtained from an emerging market diversification strategy.They are computed where past averages are substituted for portfolio inputs such as means,standard deviations and correlations, and on the assumption that, with respect to the inputsto the portfolio decision, investors are blessed with perfect foresight (Fififield, Power,Sinclair 2002). In their paper Fififield, Power and Sinclair (FPS) attempt to overcome thislimitation by estimating the ex-ante gains available from investing in EMs. The ex-ante 27
  34. 34. measurement, unlike the ex-post, generates optimal portfolios based on forecasted means,standard deviations and correlations.Firstly, FPS show ex-post risk-return advantages of a portfolio which combines UK and EMsecurities for the period 1991 – 1996. The findings from this test show that there wasindeed considerable scope for potential, or theoretical, benefits from this particular form ofdiversification as previous empirical research had shown. Furthermore, the empirical resultsobtained in their analysis suggest that EMs do indeed provide diversification benefits evenduring times of crisis, when diversification is most valuable. This was conveyed by the factthat the Mexican Peso Crisis occurred during the sample period in December 1994 whichcaused EMs throughout Latin America to move significantly in a negative direction, and to alesser extent EMs worldwide also experiences the effect of the financial crisis.However, to investigate whether the theoretical gains available from EM diversification canbe achieved in practice FPS applied a simple model to forecast the portfolio inputs ofmeans, standard deviations and correlations for the period from 1994 - 1996. Ex-anteMRPUR optimal portfolios were then generated, which is the ratio of its mean return to itsstandard deviation following Markowitz (1959). One assumption that was taken was thatinvestors place greater emphasis on the more recent past when estimating future portfolioinputs. A key result from the analysis indicated that a strategy based on forecasted means,standard deviations and correlations, achieved very few of the gains attained in ex-postanalyses of emerging market diversification. 28
  35. 35. “The poor performance of the ex-ante strategies examined pointed overwhelmingly to the inadvisability of relying on historical data to identify ex-ante, a portfolio that combines the virtues of a high expected return with a low return volatility” – (Fififield, Power, Sinclair 2002)It was also noted in their conclusion however that there is recent promising evidence thatindicates a predictable time-varying component in the returns of emerging market shareswhich can be exploited for successful investment strategies. Fififield, Power and Sinclairreflect in their conclusion that there are three future challenges to be addressed. Firstlythere should be further study dedicated to this predictable component for EMs. Second,forecasting models using longer time horizons should be used. They also point to the factthat further persistence or predictability in the EM risk-return relationship for investors’diversified portfolios should be studied. The following year from this, an analysis was undertaken to assess the effect that theglobal scale market liberalization that was taking place had on the volatility of capital flowsin EMs and the performance of investment portfolios. The pioneering studies of Errunzawere largely ignored by the practitioner communities. Nevertheless, interest in emergingmarket investments re-surfaced in the early 1990s in tandem with global capital marketliberalizations. Previous empirical research shows very significant diversification benefits foremerging market investments. These studies as has been mentioned used market indexescompiled by the IFC. However results generated from IFC data generally ignore the hightransaction costs, low liquidity, and investment constraints associated with EM investments.Bekaert and Harvey (2003) discuss the measure the diversification benefits from emergingequity markets using data on closed-end funds (country and regional funds), and American 29
  36. 36. Depository Receipts (ADRs). Unlike the IFC indexes, these assets are easily accessible toretail investors, and transaction costs are comparable to those for US traded stocks. It wasfound that investors generally have to sacrifice a substantial amount of diversificationbenefits of investing in foreign markets when they do so by holding closed-end funds. ADRsand open-end funds on the other hand track the underlying IFC indices much better thanother investment vehicles and prove to be the best diversification instrument. Pursuant tothe empirical research they also found that market liberalizations increased correlationsbetween EMs and DMs. Furthermore it was noted that; “Diversification benefits of investing in emerging markets are reduced when transactions costs and, in particular, short-sale constraints are introduced” – (Bekaert, Harvey 2003) Both the long-term risks and rewards of investing in EMs are strongly linked to theability of these markets to develop economically. Empirical analysis of EM investments ishindered by both the short history and the selection bias of the data as has been describedearlier. Furthermore, major economic, social, and political changes in EMs limit theapplicability of historical data. In their analysis in 2004, Tokat and Wikas sought to blendtheoretical and empirical approaches in determining an investor’s efficient allocation ofwealth including EM indexes to an internationally diversified portfolio. Pursuant to previousresearch they point to the fact that over the long run EMs have been shown to enhanceportfolios’ risk adjusted returns. In some shorter periods, however, the empirical case hasbroken down. Three short term phenomena that raise the most troubling questions are thecycle of bull and bear markets, financial crises, and stock market booms and bubbles.Investors might find it hard to realize the opportunities that EMs present over the longterm. This is due to the fact that EMs often experience significant negative short term 30
  37. 37. deviations away from their long-run averages. Tokat and Wicas highlight this point byreferring to the fact that when the US was in a bear market US investors’ benefit from theirEM exposure was on average, less than the benefit from their exposure to other DMs. Intheir findings it was seen that from 1985 – 2003 portfolios that included EMs providedhigher returns and diversification benefits than purely developed market portfolios.Nevertheless, there have been significant short-term deviations away from this long-termperformance. This point is conveyed in their finding as they show that from 1998 – 2000, forexample, even a modest 3% allocation to EM equities reduced a portfolios return andincreased its volatility despite the presence of imperfect correlation.It was found that when the US was taken as the relevant developed market, the long termbenefits of EM investment was obscured when there were bull and bear markets. In theirfindings the evidence suggests that the performances of equity markets in large economieshave a significant impact on the performances of equity markets in smaller economies. Theresults showed that more than 70% of developed international stock markets experiencedbear markets when the US was in a bear market. A smaller amount, 30% of EMsexperienced bear markets in tandem with declines in the US; however this is still significantin that it reduces the benefits gained from international diversification using EMs.Furthermore it was found that during bear markets such as after the September 11 th attackon the World Trade Centre, the correlations between the US and emerging markets rose,precisely at the time when the benefits from diversification were needed the most. This wasconveyed in their statistical findings where they showed that in the most recent US bearmarket the correlation between the returns of U.S. stocks and those of EMs increased to 31
  38. 38. 70%. It should also be noted however that during bull markets, EMs were found tooutperform the US market which backs up prior research about the high volatility of EMs.Furthermore, during the EM financial crises such as the Mexican Peso crisis (1994) and theAsian Currency Crisis (1997) a contagion effect was found. This means that during financialcrises correlations between EMs were seen to spike. US investors’ EM exposure during theseperiods reduced portfolio return and increased portfolio volatility. Results showed thatmore than 90% of EMs experienced bear markets during the Latin American crisis of 1994 –1995 and the Asian crisis of 1996 – 1998. These increased correlations would clearly havenegative implications for an investor looking to diversify using EMs.The final transitory factor included in this research was the effect of stock market’s bubblesand booms on returns and volatility between EMs and DMs. Investor optimism regardingthe impact of new innovations and profitability in the global economy resulted in a bullmarket in the 1990s. Analogous to the previous transitory factors, these boom periodsresulted in correlations between EMs and DMs increasing. Following this the bust periodsthen correlations started decreasing again.However there are still gains to be made from investing in EMs. Financial theory suggeststhat higher returns should compensate for the higher volatility of emerging equity markets.Emerging markets are expected to enjoy faster economic growth than developed markets.Faster economic growth should translate into faster growth in corporate earnings and thus,into higher equity market returns. Essentially the long term case for investing in EMs restson the idea of enhancing a portfolios return while reducing its risk level throughdiversification. 32
  39. 39. “These shorter-term departures from long-term expectations don’t invalidate the long-term case for investing in emerging markets for risk-tolerant investor” – (Tokat, Wicas 2004)The application of efficient market theory and historical mean variance analysisrecommends a substantial portfolio allocation to EM equities. This article howeverrecommends more behavioural and practical considerations which imply that a smallerallocation to EMs would be more beneficial to the investor. Tokat and Wicas conclude theirarticle by conveying that investors should consider long term and short term information aswell the fundamental portfolio construction factors in order to determine their ownpreferential wealth allocation. One of the most recent studies on diversification in EMs was performed byAbumustafa (2007). In his paper he test whether diversification benefits for the investor canbe gained from investing in EMs in the Arab Stock Markets. His paper examines therelationship between stock prices and economic activity and how this relationship isrelevant to diversification. In their investigation they studied data from the IFC and theStandard & Poor’s database from 1986 – 2002 6 Arab countries and 3 DMs. In order toassess the relationship between stock prices and economic activity, Abumustafa used a timeseries analysis to see whether increases in the stock market of a country Granger causesincreases in GDP. The results showed that increases in stock prices did indeed causeincreases in GDP. This was also conveyed as having a positive influence for the internationalportfolio of the investor looking to allocate wealth to the Arab stock markets. “We show that the higher the causality between stock market capital and GDP in any economy, the lower the risk for investors in stock markets” – (Abumustafa – 2007) 33
  40. 40. EM investment management may require extensive, and expensive, on-site companyresearch, annual fund management expenses among other costs. These can make investorsreluctant to us EMs in their portfolios. A good way for an individual to efficiently invest inEMs and avoid some unnecessary costs and risks is through a mutual fund. EM fundsconcentrate on investments in these markets around the world or in a specific country orregion. Mutual funds offer the advantage of diversification and professional management ofthe investors’ wealth. 34
  41. 41. Chapter 3Research Methodology 35
  42. 42. 3.1 – Hypothesis to be TestedAs has been mentioned the aim of this research paper is to analyse EM diversification.Furthermore to convey whether there is still benefits to be gained for an Irish investor fromholding an international portfolio which consists of both Developed Markets (DM) andEmerging Markets (EMs), as opposed to a portfolio consisting of only DM indexes. Theinvestor benefits from investing in both market types through diversification. That is to saythat low correlations exist between EMs and DMs, thereby reducing the risk of theinvestor’s portfolio that has a position in both.The majority if the previous work done on the topic of EM diversification has taken placebefore the global financial crisis which started in 2007 therefore this paper includes resultsfrom the years of the credit crunch and investigates the effect that it might have had ondiversification. I will examine whether this particular financial crisis had an impact on thecorrelations of EMs and DMs. As well as this, with the ever increasing harmonization ofcapital markets and globalization in general, the correlations between markets could verywell be changing. This paper will investigate as to whether there are still diversificationbenefits to be gained from investing in EMs and if so how does it compare with the earlierperiods. The primary focus will be from the perspective of the Irish investor. Howevercorrelations for the US with EMs will also be looked at in some detail to make the resultsmore viable and for comparison.In order to test the hypothesis this paper will convey the risk return relationship betweenEMs and DMs. Secondly I will examine the correlations between DMs and EMs, primarilyfrom the perspective of an Irish / US investor. Finally I will compare the returns and risks ofportfolios consisting of purely DMs, with portfolios consisting of both EMs and DMs. 36
  43. 43. 3.2 - Data DescriptionMonthly Stock prices indexes for 33 countries from a number of regions around the globeincluding Eastern and Western Europe, Asia, North and South America and Africa weretaken from Bloomberg for this research paper. Multiple regions were included to give atruly global portfolio. It was decided that monthly data should be used as it gives a moredetailed and accurate portrayal as to the behaviour of the given stock market than youwould get from quarterly or yearly data. The time horizon that is included in the data rangesfrom the 1st January 1995 to 1st January 2011. The length of the period of 15 years and thenumber of test countries chosen are relatively large in comparison to test periods inprevious research in order to minimize the risk of omitted variable bias. The majority ofprevious papers as seen in the literature section of the paper include 5 – 10 years of data fortheir tests, and for the most part about 10 – 20 countries had only previously beenexamined at a time.As well as investigating the results from 1995 – 2010 there were 3 sub-periods that wereexamined also. The sub-period was from January 1995 to September 1999, just before theintroduction of the Euro currency. The penultimate sub-period examined included themarket index prices up until the financial crisis, covering the time horizon from January 2000to December 2006. The final sub period covered mainly the period of the global recessionfrom January 2007 to December 2010. The EM and DM relationship and portfolio risk andreturns will also therefore be checked across these different time horizons. 37
  44. 44. Developed Market IndicesISEQ Irish Stock ExchangeFTSE 100 Financial Times Stock ExchangeS&P 100 Standard & PoorsDJ Ind. 30 Dow Jones Industrial AverageCAC 40 Paris Bourse IndexDAX 30 German Stock IndexNIKKEI 225 Japanese Stock ExchangeHSI Hang Seng IndexSGX Singaporean Stock ExchangeASX Australian Stock ExchangeSMI Swiss Market IndexEmerging Market IndexesIBOV Brazilian Stock ExchangeMICEX Russian Stock ExchangeSENSEX 30 Indian Stock ExchangeSHCOMP Shanghai Composite IndexWIG Warsaw IndexPX 50 Prague Stock ExchangeBUX Budapest Stock ExchangeSAX Slovakian Stock ExchangeMERVAL Buenos Aires IndexIPSA Santiago Stock ExchangeJCI Jakarta Stock IndexPSE 30 Philippines Stock ExchangeBURSA Malaysian Stock ExchangeSET Stock Exchange of ThailandTWSE 50 Taiwan Stock ExchangeMEXBOL Mexican Stock Exchange 38
  45. 45. SASEIDX Saudi Arabian Stock ExchangeXU 100 Turkish Stock ExchangeMADX Moroccan Stock ExchangeTUSISE Tunisian Stock ExchangeKSE Kuwait Stock ExchangeJALSH Johannesburg All Share IndexAs is highlighted by the above tables stock market index prices were taken from 11 DMcountries and from 22 EM countries. The market index prices from the above countrieswere then used to determine monthly returns for each index.For the first part of my research I wanted to examine and the risk return relationshipbetween EMs and DMs and compare them across the different sub-periods that wereoutlined previously. To do this I used Microsoft Excel to determine the mean returns andstandard deviations of all 33 countries over the 4 different test horizons. The second part ofmy research is based around looking at the correlations between Irish Stock Exchange (ISEQ)and other DMs. Then I will look at the correlations between the ISEQ and EM indexes andcompare the two results over the whole sample horizon as well as across the different sub-periods. Analogous calculations were also done from a US perspective. Finally in order toconvey the benefits that arise from diversifying a portfolio using EM, I generated a borderedcovariance matrix from the mean returns, standard deviations and correlations foundpreviously. There will be two portfolios generated for each time period. The first portfoliowill have 50% of funds invested in the ISEQ as the home market and the remaining 50%equally weighted amongst the rest of the DMs. The second portfolio will consist of 50%invested in the ISEQ and the remaining wealth equally weighted amongst both the EMs andthe DMs. 39
  46. 46. 3.3 – Relevant FormulaeAfter getting the stock market index prices for the 33 countries from Bloomberg I was thenable to determine the monthly returns for each monthly observation of the indexes for eachtime period. This and the rest of the calculations were done through Microsoft Excel.Monthly returns were determined as follows:Monthly Returns:Where:Rit = the monthly return of index (i) at time (t).Pit = the value of the stock index (i) at time (t).Pit-1 = the value of stock index (i) at the previous time period (t-1).As has been outlined it is generally considered that mean returns and standard deviationsare acceptable proxies for clarifying levels of risk and return. For the first part of myresearch I identify the degree of risk and returns associated with each of the 11 DMs and 22EMs and compare the relationship between the two in each time period and across thedifferent sub-periods. The calculations for risk and return were done using these formulaefor mean monthly returns, variance and standard deviation: 40
  47. 47. Mean Monthly Returns: ̅ ∑Where:̅ = Mean return of the monthly returns for the stock index (i).n = Number of monthly observations.Variance & Standard Deviation: ∑ ̅ σi = √Where:σi2 = the variance of monthly returns of stock index (i)Rit = the value of the return of stock index (i) at time (t)̅ = the mean monthly return of stock index (i)Pursuant to previous research I thought it would be beneficial to calculate the absolutegrowth of each of the DMs and EMs per period. This was done to highlight the differencesbetween the two and to convey the relatively high growth opportunities that diversifyinginto EMs can have for the investor. This simple formula for periodic growth was used: 41
  48. 48. Periodic Growth:Where:GiT = the absolute growth of index (i) for the time horizon (T).PiEND = the market index price at the end of the sample horizon.PiBEG = the market index price at the beginning of the sample horizon.Prior to calculating the covariance matrix the correlation matrix between indexes wasgenerated using the correlation function on Microsoft Excel. Now that the inputs ofstandard deviations and correlations have been found this leads to the next step which iscalculating the co-variance matrix:Co-Variance:Where:Covij = the covariance between index (i) and index (j).σi = the mean standard deviation of index (i) for that time period.σj = the mean standard deviation of index (j) for that time period. = the correlation co-efficient between index (i) and index (j)Between the inputs that have been calculated using the above formulae and the correlationand covariance matrices generated using Excel there are now sufficient inputs to determinethe portfolio returns and standard deviations. The portfolio return was found simply bygetting the weighted average of the index mean returns. The portfolio variance was foundusing the below formula through generating a bordered covariance matrix based on the 42
  49. 49. weights invested the relevant indexes and the co-variances calculated from the aboveformula. The portfolio standard deviation is simply the square root of the portfolio variance.Portfolio Return: ∑ ̅Where:RP = the return on the portfolio.Wi = the weight of the portfolio invested in index (i).̅ = the mean monthly return of stock index (i).Portfolio Variance: ∑∑Where:σP2 = the variance of the portfolio.Wi = the weight invested in index (i).Wj = the weight invested in index (j).Cov (ri, rj) = the covariance of returns between index (i) and index (j). 43
  50. 50. Chapter 4Data Analysis 44
  51. 51. My data analysis will be split up into three different sections. The first section will outlinethe risk, return and growth characteristics of Emerging Markets (EMs) and DevelopedMarkets (DMs). As has been stated the mean monthly returns I have calculated willrepresent the returns, and standard deviations will be used as the measure of risk for theindexes. This part of the analysis was important in identifying the trends and characteristicsthat might be present between EMs and DMs, as well as between the different samplehorizons. In the penultimate section I will portray the diversification opportunitiespresented by EMs by looking at correlations between EMs and DMs from the perspective ofan Irish investor and from a US investor. The perspective of the US investor was also takento for comparison purposes with the S&P 500 as the benchmark DM. In my final section Iwill look to compare an Irish denominated portfolio that is solely invested in DM securities,with an Irish portfolio that is equally invested in EMs and DMs. The data analysis in eachsection will be split into an analysis of the four different time periods from 1995 – 2010,1995 – 1999, 2000 – 2006 and from 2007 – 2010.4.1 – Risk, Return and Periodic GrowthIn order to get a true understanding of the potential benefits from diversifying using EMsecurities it was vital to look at the market risks and returns that would be associated withthe different DM and EMs indexes. In the data I sought to identify trends and characteristicsbetween EMs and DMs. Periodic growth is also used to further convey the potentialopportunities that can be harnessed by investing in EM securities. 45
  52. 52. 1995 - 2010The first time horizon that I looked for risk and return was from January 1995 – December2010. It could be expected that returns and risk over this time period covering 15 yearsshould have relatively less extreme results for risk and return than the sub-periods due tothe fact that it is generally accepted by economists that index returns generally possessmean reverting tendencies. Table 1.1 shows the monthly returns and risk in the form ofstandard deviation for DMs and EMs in the first test period. The results were calculatedfrom the market price indexes outlined in the previous chapter. Table 1.1: Index Risk and Returns 1995 - 2010 Developed Markets Emerging Markets Return Std.Dev Return Std.Dev UK 0.004432 0.041774 BRAZIL 0.020682 0.093155 US(S&P) 0.006088 0.046283 RUSSIA 0.02811 0.134543 US(DJ) 0.00664 0.045494 INDIA 0.012474 0.077666 FRA 0.005641 0.056761 CHINA 0.012488 0.088705 GER 0.008524 0.066187 POLAND 0.014104 0.085656 JAP -0.00092 0.058875 CZECH 0.007662 0.072801 IRE 0.004075 0.059954 HUNG 0.019159 0.088301 HK 0.00825 0.075829 SLOVAK 0.009457 0.060262 SING 0.006597 0.048484 ARG 0.018538 0.108887 AUS 0.00469 0.041654 CHILE 0.010355 0.05446 SWISS 0.005965 0.048261 INDO 0.015044 0.087308 PHILLI 0.013649 0.077403 MALAY 0.006006 0.069955 THAI 0.003241 0.094063 TAIWAN 0.0047 0.077981 MEXICO 0.019759 0.072567 SAUDI 0.011611 0.071319 TURKEY 0.039111 0.149926 MORROC 0.008502 0.058706 TUNISIA 0.008752 0.032731 KUWAIT 0.014937 0.082605 SAFRICA 0.011724 0.059301 46
  53. 53. In order to get an idea of aggregate differences between EMs and DMs pertaining to riskand returns Table 1.2 shows the average returns and standard deviations for DMs and EMsin the first time horizon that was examined. From the data below we can see that EMsdisplay higher returns by 0.008641, and higher risk level for the investor by 2.81% over the15 year horizon. The maximum return found among the EM indexes was for Turkey at0.0319. This is significantly higher than the highest return found among DMs which was theGerman DAX at 0.0085. Similarly results were found in relation to standard deviation whereTurkey, the EM country with the highest risk, had a standard deviation of 14.99%. This isrelatively very high in comparison to Hong Kong which was the riskiest DM index with a7.58% standard deviation. Table 1.2: Average DM, EM Risk, Returns 1995 - 2010 DMs EMs Return 0.005453 0.014094 Std.Dev 0.053596 0.081741Data for periodic growth was also calculated for each of the 11 DMs and 22 EMs. The resultsfor which are shown in Appendix 1. Table 1.3 here shows the average, maximum andminimum growth for EMs and DMs for the first time horizon from 1995 – 2010. From thetable below one can clearly see that growth opportunities for investment in EMs areconsiderably larger on average than those available in DMs. Over the 15 year period DMindexes saw an average growth of 127%. EMs on the other hand experienced growth, onaverage of 1,720%. That’s an average growth rate of over 17 times the original market pricefrom the beginning of 1995 to the end of 2010 for EMs. 47
  54. 54. Table 1.3: Average, Max. and Min. Growth levels 1995 - 2000 DMs EMs Average 1.279926 17.20943 Max 2.295846 225.6404 Min -0.40018 -0.19846 1995 – 1999The risk and returns and the periodic growth for the 33 countries were then calculated forthe 5 year period from January 1995 to December 1999. The returns and standard deviationfor this first sub-period can be seen in Table 1.4. During the analysis of these results it wasnoted that Mexico was emerging from the Peso crisis of 1994 and also during this timehorizon the Asian Currency Crisis which began in Thailand in 1997 had occurred. 48
  55. 55. Table 1.4: Index Risk and Returns 1995 - 1999 Developed Markets Emerging Markets Mean Std.Dev Mean Std.Dev UK 0.013318 0.034944 BRAZIL 0.029934 0.118341 US(S&P) 0.0186 0.041228 RUSSIA 0.043339 0.192604 US(DJ) 0.018334 0.044141 INDIA 0.009114 0.079734 FRA 0.018976 0.056303 CHINA 0.023782 0.098782 GER 0.018294 0.060559 POLAND 0.020671 0.115141 JAP 0.002332 0.059716 CZECH 0.003569 0.073345 IRE 0.017824 0.045465 HUNG 0.038523 0.116082 HK 0.01222 0.09537 SLOVAK 0.019213 0.048857 SING 0.021953 0.04368 ARG 0.015681 0.111316 AUS 0.012291 0.033248 CHILE 0.003689 0.070158 SWISS 0.019613 0.057723 INDO 0.009952 0.112705 PHILLI 0.029758 0.073166 MALAY 0.020898 0.126218 THAI -0.01389 0.127145 TAIWAN 0.005904 0.079524 MEXICO 0.026032 0.090757 SAUDI 0.007111 0.045311 TURKEY 0.06944 0.167993 MORROC 0.022419 0.059905 TUNISIA 0.003413 0.038253 KUWAIT 0.012852 0.106511 SAFRICA 0.010906 0.070009The average return and standard deviation for the second test period can be seen in Table1.5. As was the case in the 15 year time horizon I examined the EM indexes and theyshowed higher levels of both return and risk than the DM indexes. From the results we cansee that growth is also noticeably higher in EMs than in DMs thou to a far smaller extentthan in the first test period. This is largely likely to be because this test period is 10 yearsshorter in its horizon than the first giving far less scope for growth. It should also be notedthat the difference between EMs and DMs in standard deviation is considerably larger inthis second time period than in the first this is likely due to swings in returns that wouldhave been caused by the Asian Currency Crisis. This information is re-enforced by the factthat the lowest mean monthly returns among the EM indexes from 1995 – 1999 were seen 49
  56. 56. in the South East Asian countries. Furthermore Thailand actually experienced the lowestmean monthly return of all the indexes at -0.014 for the period. The data in Table 1.4 couldalso be said to portray the contagion effect that the Asian currency crisis had. That is to saythat Russia went through its own financial crisis as a direct result of the Asian currency crisis.This is conveyed by the fact that as seen in Table 1.4 Russia experienced the highest risklevel from 1995 – 1999 with a standard deviation of 19.26%. Table 1.5: Average DM, EM Risk, Returns and Growth 1995 – 1999 DMs EMs Mean 0.015796 0.018742 Std.Dev 0.052034 0.096448 Growth 1.273372 2.078162 2000 – 2006The penultimate sub-period under which the risk, return and absolute growth are to beexamined is from January 2000 to September 2006. This sub-period spans a horizon fromthe “Dot Com” bubble up until just before the beginning of the global financial crisis whichbegan in 2007. Table 1.6 depicts the monthly returns and standard deviations for the thirdtest period which was calculated from the monthly stock index prices from Bloomberg. 50
  57. 57. Table 1.6: Index Risk and Returns 2000 - 2006 Developed Markets Emerging Markets Mean Std.Dev Mean Std.Dev UK 0.000631 0.037858 BRAZIL 0.015362 0.081237 US(S&P) 0.001046 0.04109 RUSSIA 0.020834 0.096092 US(DJ) 0.002416 0.041179 INDIA 0.014215 0.069473 FRA 0.001155 0.052774 CHINA 0.008802 0.066152 GER 0.002019 0.069184 POLAND 0.013627 0.064985 JAP -8.3E-05 0.053597 CZECH 0.014726 0.062837 IRE 0.009186 0.050883 HUNG 0.013664 0.064752 HK 0.004555 0.055235 SLOVAK 0.003345 0.062934 SING -0.00184 0.04522 ARG 0.022412 0.119562 AUS 0.001703 0.038456 CHILE 0.011277 0.044769 SWISS 0.003794 0.041521 INDO 0.014959 0.06856 PHILLI 0.00232 0.081978 MALAY 0.001157 0.06782 THAI 0.00711 0.075627 TAIWAN 0.000253 0.07709 MEXICO 0.018778 0.061757 SAUDI 0.019555 0.073582 TURKEY 0.019171 0.136286 MORROC 0.006947 0.051264 TUNISIA 0.017748 0.037462 KUWAIT 0.020814 0.084561 SAFRICA 0.017524 0.053284Table 1.7 below summarises the data presented previously by conveying the average risk,return and periodic growth that was displayed by the 11 DM indexes and the 22 EM indexes.Pursuant to the previous two periods examined it can be seen from this that EM indexesagain displayed higher volatility and return levels than the DM indexes. Average monthlyreturn among the EM indexes was 1.07% higher than in the DM indexes. Also in tandemwith previous calculations, the average monthly standard deviation of EMs was 2.49%higher than in DMs. Furthermore, the below table depicts considerably higher growth forthe EM indexes of over 165%, in comparison to only an average 12% growth for DM indexes. 51
  58. 58. Table 1.7: Average DM, EM Risk, Returns and Growth 2000 - 2006 DMs EMs Mean 0.002235 0.012936 Std.Dev 0.047909 0.072821 Growth 0.120341 1.654209 2007 – 2010The final time sub-period covers the horizon from the beginning of the global recession in2007 up until the end of 2010. As in the previous sub periods standard deviations and meanmonthly returns were calculated as indicators for risk and return. Table 1.8 shows theresults generated from the Bloomberg market price indexes for the final sub-period. Table 1.8: Index Risk and Returns 2007 - 2010 Developed Emerging Markets Markets Mean Std.Dev Mean Std.Dev UK 0.000279 0.051968 BRAZIL 0.012211 0.074336 US(S&P) -0.00119 0.057498 RUSSIA 0.024311 0.049543 US(DJ) -0.00043 0.052941 INDIA 0.012153 0.091743 FRA -0.00644 0.059851 CHINA 0.0065 0.11116 GER 0.00243 0.064064 POLAND 0.000133 0.078421 JAP -0.00876 0.069186 CZECH -0.00249 0.088168 IRE -0.02104 0.081246 HUNG 0.001071 0.084287 HK 0.006178 0.08097 SLOVAK 0.000931 0.062432 SING 0.001473 0.054861 ARG 0.015962 0.09277 AUS 0.000383 0.052872 CHILE 0.012597 0.050374 SWISS -0.00646 0.043942 INDO 0.019781 0.085065 PHILLI 0.006078 0.061514 THAI 0.013026 0.078459 TAIWAN 0.006357 0.078959 MEXICO 0.009172 0.063504 SAUDI 0.002654 0.089405 TURKEY 0.014909 0.098446 MORROC -0.00779 0.066871 SAFRICA 0.004817 0.061001 52
  59. 59. The next piece of data as seen in Table 1.9 summarises the data gathered on risk, return andgrowth for the horizon that covers the epoch of financial turmoil. The results from thisperiod are considerably different within each market type. Six out of the 11 DM indexesactually experienced negative mean monthly returns in tandem with the recession. In theperiods from 1995 – 2010, 1995 – 1999 and 2000 – 2006 there were only 1, 0 and 2 indexesrespectively that displayed negative mean returns. From the data we can see that averagereturns in EMs were again higher than in DMs. The difference in average monthly returns inthis case is 1.1% which is the highest out of the 4 test periods which should be noted. Aswell as this the recession period EMs displayed higher risk than in DMs. Standard deviationin EMs was 7.71% and in DMs the figure was 6.09%. The difference of 1.63% between thetwo is the smallest difference amongst the time periods. Table 1.9: Average DM, EM Risk, Returns and Growth 2007 – 2010 DMs EMs Mean -0.00305 0.00802 Std.Dev 0.060854 0.077182 Growth -0.16878 0.661133 53

×