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ERP Comparison of Developed and Emerging Markets

Info: 13401 words (54 pages) Dissertation
Published: 6th Oct 2021

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Tagged: Finance

Chapter 1: Introduction

1.1 Research Topic

The investment dilemma hits when individuals earn more than their consumption needs. Considering the fast rising inflation globally, saving the surplus earnings for future consumption is not sufficient anymore. Hence, making an investment such that the surplus earnings grow or even multiply over time is almost imperative. Such an investment can be made in many ways for instance commodities, stocks, bonds, pension funds, real estate etc. This study is concerned with individuals’ investment in stocks.

When an individual invests, he/she expects a certain rate of return in the future from the investment which should ideally compensate future consumption needs, future increase in inflation and uncertainty of return if any. Therefore, investments with higher returns are preferred. A number of studies find evidence of stocks giving higher return than government bonds, although the relative uncertainty of return from stocks being much higher than from bonds (Dimson et al, 2002; Ibbotson & Senquefield, 1976). Consequently, the more uncertain the future return gets, the riskier it is to invest. Hence, when an individual invests in stocks, he/she expects added compensation for added risk which leads to the concept of Equity Risk Premium (ERP). ERP is the surplus return from stocks/equities over the return from nearly risk-free (here on mentioned as risk-free) asset such as government bonds.It is the premium that individuals demand for bearing the additional risk in equity investments (Reilly & Brown, 1999).

ERP is calculated using equation-1. Stock returns can be the returns from a benchmark index (market returns) such as FTSE 100 and the returns from risk-free asset (risk-free returns) can be those from UK gilts.

(Reilly & Brown, 1999)

ERP is an important consideration from an investor’s point of view for building and analysing a domestic equity portfolio or an entire equity market especially for an investor looking to diversify globally (here on mentioned as global investor). Therefore, it is a widely researched topic, however yet the existing literature is inadequate, considering there are numerous debates and puzzles pertaining to various aspects of ERP. Hence, looking at its significance in theoretical & practical finance, ERP is chosen as the central topic to be researched in this study.

1.2 Research Background

Individuals (retail investors) use ERP to forecast the expected growth of their equity portfolios over long-term and for portfolio allocation decisions. Corporations (here on mentioned as organisations) need ERP as an input to determine the cost of equity i.e. the annual expected rate of return from investment in stocks and for capital budgeting decisions. Overall, ERP is a significant factor in most risk-return models of corporate finance and investment management. Hence, estimating future ERP and identifying possible reasons for the results found, is an important financial and economic research topic for academia and practitioners alike. Although historical data is most popularly used to estimate future ERP, there exist financial, economic & asset pricing models developed over the years which predict an implied ERP based on companies’, macroeconomic & equity market data. Evidence from the relevant literature suggests that every ERP estimation method has a distinct set of assumptions and underlying ideas therefore exuding both merits and demerits when compared to another estimation method.

Rapid economic growth of emerging countries has been apparent especially because of industrialisation. Consequently the performance of emerging equity markets has been remarkable in the past decade. The big 4 i.e. Brazil, Russia, India & China (BRIC) alone, accounted for more than 50% of the world GDP in 2006 (RICS, 2008). Due to saturation in developed countries and growing avenues for investment in those emerging, the ERP of emerging markets has risen due to growing investor confidence. Although perceived social, economic & political risks are equally high, financial systems have strengthened and macro-economic conditions have improved drastically for most emerging countries. Barry et al (1997) argues that investing in emerging markets is more than just profitable, considering the risk-return trade-off. Hence, gauging the future of emerging equity markets has become a vital research topic for economists, finance professionals and global investors alike.

In a discussion of emerging markets, India cannot be left out. Post liberalisation (i.e. post 1991) India is definitely the secondmost preferred emerging economy by global investors after China. Although Foreign Direct Investment (FDI) flows have been average compared to other emerging countries, Foreign Institutional Investment (FII) flows increased almost 10 times, from United States Dollar (USD) 739million in 2002 to a record USD 7.59billion in 2003. CALPERS, the world’s biggest pension fund with a base of USD 165billion has recently included India in their list of countries for investment (BSE India, 2008). The noteworthy rise to the position of the sixth largest emerging equity market with a total market capitalisation of USD 818billion and 8% p.a. average economic growth (CIA Fact-book, 2008) over past decade accentuates the importance of India’s ERP estimation and analysis.

1.3 Research Gap, Objective & Questions

Most of the research on ERP has focussed on developed markets clearly because of their sound history and stable fundamentals. Within limited research conducted on ERP in emerging markets, Salomons & Grootveld (2003) demonstrate the evident differences in ERPs of developed and emerging markets and claim that global business cycle influences these differences. Claessens (1995) argues through his empirical research that investment in emerging markets can be fruitful in long-term considering that high ERP compensates for high risk. Although these and similar related researches vaguely guide investors wanting to explore emerging markets, there lacks a clear evidence of the possible risks attached and whether those risks can be tackled to earn complete benefit of the high ERP. Bernartzi & Thaler (1995) and Campbell & Cochrane (1999) claim that the reason for increase in investors’ interest in U.S. markets was the high ERP it offered. Hence if the same rule is applied to emerging markets then investments should be made without any prior estimation of possible risks, especially considering the success of U.S. markets. However it is not the case, as investors are still sceptical about getting confirmed high returns from emerging markets. Therefore, the precise reasons for the difference in ERPs of developed and emerging markets have not been clearly identified as yet, hence constituting the first research gap.

There exists considerable evidence on how political, social and especially macroeconomic factors affect the equity market returns of developed countries, especially U.S. (Chen et al, 1986). Considering the limited work done on ERP of emerging markets on the whole, negligible contribution has been made to analysing ERP in India with respect to its growing economy, Mehra (2006) being the most notable, hence constituting the second research gap.

Considering the importance of ERP it is interesting to note that in-spite of there being many ways to calculate ERP; there exists no consensus on the best approach. Financial market analysis is performed based on historical data and the ERP measured from past performance of equity markets is most commonly used as an estimate of future ERP. For instance Ibbotson & Sinquefield (1976) exemplified first accurate calculations of annual rate of return on equity investments in U.S. and ERP. Since then, Siegel (1992) & Dimson et al (2002) are two of the most notable researches on ERP estimations using the historical method. However, there exist models developed for instance by Fama & French (2002) and Arnott & Bernstein (2002) that determine future ERP entirely based on forward-looking information through estimation of future investors’ & markets’ expectations. This variation of approaches to ERP estimation has only widened the range of results and complicated the unresolved debate, hence constituting the third research gap.

The 3 research gaps identified above lead to the overall Research Objective of this study, which is:

Comparative analysis of ERP in the leading developed & emerging markets; determine the macroeconomic influence on ERP and examine the ERP estimation methods; all from a global investor’s point of view.

It is not realistically possible to fill the research gaps entirely through this study considering time, knowledge and relevant experience constraints. However, this study aims to fulfil the above objective through the accomplishment of satisfying solutions to the following 3 Research Questions:

  1. After estimating future ex-post ERP in the chosen sample index of developed and emerging markets, what is the impact of risk responsible for the differences found through the comparison of their risk-return trade-off?
  2. What effect do the country specific macroeconomic factors have on the ERP in India, if any?
  3. After estimating future ex-ante ERP in India using a supply-side method and comparing it with the estimated ex-post ERP, what is the most suitable method for global investors, if at all, and why?

Research Contribution

As this study is predominantly aimed at analysing the ERP of leading emerging markets and particularly India, it is hoped that this study contributes to simplify the decision making of global investors regarding their equity investments in emerging markets & India. Furthermore, it is hoped that this study provides guidance to the global investors regarding the macroeconomic situation in India and its influence on the ERP, for sound portfolio management. Moreover, it is hoped that this study adds a small brick to the large edifice of ERP analysis/measurement/estimation on the whole. Finally, if this study motivates the eminent researchers and consequently triggers some ground breaking academic scholarship regarding the ERP of emerging markets, then the worthiness of this study will be truly identified.

1.4 Research Structure

The following is the chronology and brief content of the chapters in this study here on:

Chapter 2: Literature Review: This chapter aims to explain the historical development of ERP through empirical researches and relevant theoretical background. Furthermore, it examines important research literature on ERP estimation methods and emerging equity markets.

Chapter 3: Overview of Research Methodology: This chapter aims to briefly explain the chosen research methodology for this study and justify its appropriateness. It also describes the chosen data collection method and clarifies how the data will be collected & used for achieving the research objective.

Chapter 4: Data Analysis, Findings & Interpretative Analysis: This chapter aims to identify the collected data, explain the data analysis technique/model/method in detail, analyse the data that is collected by using the chosen methods & models; and finally, interpret, examine & evaluate the results/findings from the analysis to identify justifiable solutions to the research questions. The chapter is divided into 3 parts, each part pertaining to each research question and the procedure is conducted separately for each.

Chapter 5: Discussion & Conclusion: This chapter aims to summarise the results from chapter 4, recapitulate the entire paper and testifies the level of fulfilment of the research objective. Also, it plausibly links the past literature & results from this study to check the level of accomplishment in filling the research gap and to identify the need for future study.

Chapter 2: Literature Review

2.1 Chapter Introduction

ERP is a vital numerical figure in practical modern finance as it is considered by financial analysts, business managers and economists for the purpose of decision-making; perhaps best testified by Welch (2000, p.501) wherein he calls ERP “the single most important number in financial economics…”. Consequently, it is and has been one of the most fascinating topics for academic scholarship leading to vast amount of literature.

This Chapter discusses the various significant perspectives about ERP generated from the literature. The literature reviewed in this chapter is primarily related to the research questions that this paper aims to answer; having said that, other theoretical developments and empirical researches in the field of portfolio management and corporate finance that are significantly relevant to the research topic, are also discussed. Broadly speaking, the content matter in this chapter is organised in chronological order beginning from the earliest.

Here on this chapter is divided into 5 sections. The historical advancements in productive assessment of the relationship between equity risk and return resulting from empirical researches which lead to the conceptualisation of ERP is discussed in section-2. The next section-3 highlights the important theoretical developments which laid the foundation for the large edifice of researches on investment management. Section-4 focuses on the models/methods that were formulated based on the theories, with an aim to calculate expected returns and measure & estimate ERP. It also looks at the important contemporary researches in the field of ERP with a brief backdrop of macroeconomic factors. The following section-5 highlights the important literature with respect to the ‘ERP Puzzle’. It discusses the significant attempts by researchers to solve the puzzle. The next section-6 follows which briefly looks at the important literature on emerging equity markets overall. Finally, section-7 summarises the entire discussion.

2.2 Historical Conceptualisation of ERP

The apt risk-return trade-off sought by investors worldwide augmented the importance of ERP evaluation and forecasting. Consequently, vast theoretical & empirical research under various objectives has been conducted till date since the early 20thcentury on measuring, estimating and analysing ERP, most of which has concentrated on the developed markets, especially U.S. Furthermore, eminent financial economists have been engaged in empirical analysis of past investment results to gauge future investment strategies.

In the late 19th and early 20thcenturies, most economists did not endorse the importance of risk in evaluating and justifying excess returns. The conception of the fact that incremental profit on equity investments is a result of the higher risk attached, was a gradual process. For instance, Clark (1892), professor at university of Columbia, claims that investments in some organisations give higher returns than risk-free rate & some other organisations because those organisations have an advantage of monopoly in the market. Furthermore, modernisation and development in technology lead to comparatively higher competitive advantage which in turn gives excess returns.

However, renowned author of the book Risk, Uncertainty and Profit, Knight (1921), does not endorse Clark’s view but instead criticises him for inadequately exploring the association of risk and return in the models used in his economic research. Knight analyses the importance of risk in equity investments through past performance of U.S. markets and aimed at relating it to the concept of profit in the basic economic theory. He argues that any kind of risk deserves a premium (i.e. excess returns), even if the risk is unquantifiable (which he later termed as uncertainty), although, he could not suggest any solid and foolproof way of measuring the premium that he justified.

As a cumulative result, the debate on equity risk and the attached premium flared up which necessitated ground breaking empirical researches based on historical data of past performance. Hence, many scholars developed stock price indices in early 20thcentury in order to measure long-term investment performance and estimate future returns; For instance, Mitchell (1910, 1916), Persons (1916, 1919), Cole & Frickey (1928) in the U.S. and Smith & Horne (1934) and Bowley et al (1931) in the U.K. However, Hautcoeur et al (2005) in their analyses of early stock market indices; argue that the main motive in development of these indices was forgotten in no time and instead they were used to gauge the influence of macroeconomic cycles on equity markets and as an easier way to estimate macroeconomic fluctuations. The popular index of 30 stocks developed by Charles Dow was never aimed at estimating future long-term returns but instead to measure daily returns on the market.

Consequently, the relevance of the returns from risk-free assets like government bonds to comparatively risky equity returns was tested. The difference in their rate & magnitude of returns solidified the so far debated idea of returns being a compensation of the risk attached to the investments made. Smith (1924) advocates through empirical research and later through his book that; equities give higher returns than bonds because they carry higher risk. He collected historical data on stock prices, dividends and corporate bonds from the stock exchanges at Boston and New York spanning 1866-1923. Furthermore, he divided this period into 4 sub-periods to recognise the economic development. After creating separate portfolios for each asset class (10 securities in each portfolio), he measured cash income and capital gains from both. Equity investments give higher appreciation and returns than bonds in the long-term in-spite of economic changes in the sub-periods, was his conclusion. Further in his book, he suggested a mechanical way of calculating ERP by paying out the equivalent amount of bond returns from the total equity returns and re-investing the remaining in the same equity portfolio. In this way, the relative growth rate of the equity portfolio is the ERP over the bond portfolio.

Smith’s estimation and method of ERP calculation attracted many retail investors towards the equity markets in 1920s. Later, Smith’s attempt to assess equity investment returns over bonds; was improvised by Cowles (1938). He collected historical data on most of the stocks of NYSE instead of only 10 for the period 1872-1937 and notably created the first nearly-accurate index of total returns from common stock investments. Furthermore, he suggested of re-investing the dividend yields into the equity portfolio to save from measuring cash returns and value appreciation separately, the way Smith did. However, he made no concluding remarks such as equity investments can be more profitable than bonds, unlike Smith.

By then, although the idea of an ERP was making financial & economic sense, a solid way of estimating future ERP could not be developed yet; the two main reasons being the unavailability of adequate historical equity market data and the ignorance about the possibility of a forward looking method. However later, John Williams (1938) wrote the first book that defined; modelled and estimated forward looking ERP. Although he estimated future ERP in U.S. using Dividend Discount Model (DDM), he argued that ERP estimates based on Historical Method are equally precise. He believed that the most suitable way to calculate the riskiness of a security is by appending a premium to the risk. Later, he also became the first researcher to numerically estimate a forward looking ERP for U.S. By then, the concept of ERP had been clearly understood and its importance had been recognised.

Nearing late 1940s economists and researchers had realised the importance of risk and conceptualised ERP as an essential ingredient to calculate future returns on equity investments. Moreover, enough historical data of U.S. equity markets was also available for past performance analyses and empirical researches. Even so, there was no method/measure that could quantify future risk and returns for any given portfolio of investments, as most experts and investors believed in calculating risk-return trade-off individually for equities and other securities. However, that did not serve the purpose of optimal risk-return trade-off as far as entire portfolio of investments was concerned, until 1952 when crucial theoretical developments began.

2.3 Theoretical Developments

This section summarises the important theoretical developments which built models to quantify future risk and returns of equities and related vital researches in portfolio & investment management and corporate finance, with a backdrop of their implications on ERP. The 4 most important theories/models reviewed in this section are Portfolio Theory, Capital Market Theory, Capital Asset Pricing Model and Arbitrage Pricing Theory.

2.3.1 Markowitz’s Portfolio Theory

Harry Markowitz (1952) introduced the Portfolio Theory or now what is called the Modern Portfolio Theory (MPT). It provides a formalised method to diversify the portfolio of all investments (not just equity) with an aim to achieve highest possible returns for lowest possible risk. MPT records expected returns, volatility or risk (standard deviation) for each investment and correlation of one investment to another to create the best combination. Therefore, risk is minimised while maintaining the expected returns, if investments are diversified based on the risk of each individual investment.

However, Markowitz (1952) assumed that investors are naturally risk averse, i.e. they tend to choose the investment with highest returns for a given level of risk and refrain from investing if risk is higher than acceptable/favourable levels. Hence, by applying MPT, investors can choose less risky and highly risky investments at the same time in such a way that cumulative expected returns are unharmed and optimised.

The risk appetite, although, of each investor differs from the other. Therefore, based on the above assumption, Markowitz (1952) believed that depending on the risk appetite, every investor aims at attaining highest possible returns for the level of risk that he/she is ready to bear. In other words, aims to build an Efficient Portfolio. Consequently, all the portfolios, ranging from high-risk to low-risk, which give optimal returns lie on the Efficient Frontier, as termed by Markowitz.

Although Markowitz’s MPT is still followed by many experts and investors, it also faces criticism on its unreal assumptions.

MPT’s assumption of volatility with figures of standard deviation or variance of an investment as its risk measurement may not always be true, especially for equities. It speaks about only a single period when actually volatility changes over time. Therefore, even if a portfolio is efficient today, it may be not be the same tomorrow. For instance, in an economic crisis or equity market crash, there is a high possibility of correlation of two assets in an efficient portfolio increasing than average. Malkiel & Xu (1997) empirically prove that volatility of stocks increases with an increase in institutional ownership in the organisations. Similarly Campbell (2000) shows results of increased volatility with reduction in number of conglomerates as organisations started to narrow their focus.

Lofthouse (2001) criticises MPT on the fact that it bases its calculation of expected returns, volatility and correlation on past historical figures which is inadequate especially when the aim is to build the most efficient portfolio possible. Furthermore, Bernstein (2002) notes that; MPT assumes that there is a possibility that some investments absolutely do not correlate with any of the other investments which is untrue, as each investment at some point in time correlates with one or the other investment in the portfolio.

Hence, although MPT model enables investors to optimally gauge the future risk to gain highest possible returns, it is based on idealistic, theoretically decorative and practically unreal assumptions.

2.3.2 Capital Market Theory

After MPT was developed, many researchers worked on the most important missing link in MPT, the inclusion of risk-free asset with zero volatility, zero correlation with risky assets and certain future returns. Tobin (1958) was the first to extend Markowitz’s Portfolio Theory by introducing risk-free asset to the Efficient Portfolio. Later, Sharpe (1964), Lintner (1965) and Mossin (1966) contributed to his idea as they independently worked on similar theories. The final development is known as Capital Market Theory (CMT).

It is important to note that CMT shares 3 assumptions with those made by Markowitz (1952) for MPT, as follows:

  • Investors are always risk averse

  • Investors’ decisions are solely based on expected returns and their volatility

  • There exist no transaction costs and taxes

However following are the new assumptions that CMT makes as extracted from Lofthouse (2001):

  • All the investors have the exact same time-horizon for their investments

  • Borrowing and lending at the risk-free rate is not restricted

  • All the investors have the exact same expectations for correlation, risk and returns

CMT states that the volatility for Efficient Portfolios that include risk-free asset; is actually the linear equivalent of volatility (risk) for the portfolios before risk-free asset inclusion. Hence these combined Efficient Portfolios lie on the straight line graph of risk and return, joining the risky and risk-free assets. This way, the optimal combined portfolio i.e. point-M in Figure.2.2, is identified at the tangency point formed by the ray starting from point-F in Figure.2.2 i.e. expected return of risk-free asset and the Efficient Frontier. It is optimal because it gives the highest possible returns for any level of risk. Therefore, it is known as Market Portfolio as it has all risky assets and the ray is known as Capital Market Line (CML). CMT advocates that all the investors should aim to build their portfolios on CML depending on their risk appetite. They could invest in risk-free asset by lending or borrow at risk-free rate to invest in Market Portfolio. Either way their portfolios will earn more returns than other portfolios (blue spots in Figure.2.2) on or off the Efficient Frontier, for any given risk (Brealey et al, 2007).

Therefore, under the CMT the expected returns of the equity portfolio are calculated by determining the slope of CML which is the change in return for a given change in risk and intercept which is return of risk-free asset (See Equation-2). The risk is measured by the standard deviation (Lofthouse, 2001).

The development of CMT was ground-breaking in the field of investment management. It clarified the effect of including risk-free asset in an equity portfolio. It formed the first equation made of ERP, risk and returns, all together. In Equation-2, change in return is market return less the risk-free return which is actually the ERP. However, this estimation of ERP is an empirical deduction (calculated from slope of CML), as early development of CMT by Tobin (1958) was just an extension of MPT. Until it was theoretically formalised by Sharpe (1964), Lintner (1965) and Mossin (1966) independently, which then led to the gradual development of the Capital Asset Pricing Model (CAPM). Hence, the CAPM is usually referenced as SLM’s CAPM for Sharpe’s, Lintner’s and Mossin’s equal and vital contributions.

2.3.3 Capital Asset Pricing Model

The CAPM is undoubtedly the most widely known model to calculate expected returns. It is a sophisticated improvisation of CMT, which in-turn is an extension of MPT and therefore builds on the relationship/trade-off between risk and returns. It is primarily based on the universal classification of risk into 2 broad categories namely:

  • Systematic: Risk that affects almost all assets equally

  • Unsystematic or Specific: Risk that affects only individual asset or asset class

(Sharpe, 1964)

The CAPM is developed through the conception of Security Market Line (SML) (See Figure.2.3) which is a ray similar to CML originating from the return of risk-free asset. However, the big difference being that SML represents the linear relationship between risk and return from individual assets and/or inefficient portfolios in respect to market portfolio, unlike CML which only represents efficient portfolios. The risk that is measured is only systematic as it is un-diversifiable and hence rewarded, unlike unsystematic risk. The standardised measure of this systematic risk is called Beta which is covariance of an asset or portfolio with market portfolio divided by variance of market portfolio. Market portfolio has Beta equal to 1. Asset with Beta higher than 1, is riskier than market portfolio and hence higher return is expected. Assets with Beta lower than 1, are less risky with lower return. The expected returns are calculated by adding return on risk-free asset to the product of ERP and systematic market risk borne by the stock (See Equation-3) (Sharpe, 1964), (Lofthouse, 2001).

However, the value of Beta for individual stocks of portfolios is not known. It needs to be estimated and is hence subject to errors.

Understanding the mechanics and application of the CAPM is imperative to the study of ERP, as the slope of SML i.e. linear relationship between risk (Beta) and return, equals the difference between market returns and risk-free returns which is ERP. The application of the CAPM is extremely vital in the context of ERP measurement methods as it uses ERP as an input to calculate the expected returns on a stock. The empirical studies and relevant literature related to the CAPM and its applicability in ERP estimation methods are discussed in section 2.4.3.

2.3.4 Arbitrage Pricing Theory

As seen before, MPT and CMT both assess only the cumulative risk of individual assets and market risk respectively, while calculating expected future returns. Ross (1976) proposed the Arbitrage pricing Theory (APT) based on the perception that the risk of assets and their future returns vary in accordance with the risks affecting the overall economic situation.

Ross believed that unsystematic risks can be curbed/nullified through diversification as suggested by MPT & CAPM and hence will not affect expected returns. But systematic risks having influence on all assets cannot be diversified and hence can cause fluctuation in the expected returns. Although he did not suggest any particular factors that can trigger the systematic risk, empirical results of Burmeister et al (1997) implied the following 5 factors:

  • Inflation

  • Business cycle

  • Investor confidence

  • Time horizon

  • Market timing

APT states that; the sensitivity of assets to the unanticipated instability in the above factors varies due to which one of them can get mispriced therefore creating an arbitrage opportunity. Consequently, by selling the highly-priced asset to buy the low-priced asset, the investor can ensure profit and nearly-perfect pricing of both assets. This arbitrage can be termed as the Risk Premium for that particular factor. However, this profit is expected and not guaranteed unlike usual arbitrage gains. Like MPT and CMT, APT also has some underlying assumptions as follows:

  • No transaction costs

  • Short selling i.e. selling assets that are not owned, is allowed

  • Enough assets to diversify unsystematic risks

(Ross, 1976)

APT has faced many criticisms on its applicability in calculating expected future returns. Diacogiannis (1986) showed evidence of changing factors for a given group of assets at different times in the period 1956-81. He also noted that as the group's constitution changed so did the factors. APT assumes that unsystematic risk is uncorrelated; however Garrett & Priestly (1997) exemplified correlation between the unsystematic risks of UK stocks. Furthermore, by applying a pre-specified factor structure, no arbitrage opportunities were identified.

In the UK alone, relatively recent researches like Clare et al (1997), Baron et al (1995), Antonio et al (1995) and Poon & Taylor (1991) amongst others, have all present varying number of factors in the range of 0 to 20. Hence, suggesting that although APT is theoretically true and is more flexible than CMT in forecasting profitable risk-return trade-off, it is extremely complicated and unreliable in practical scenarios.

2.4 Estimation of ERP

Considering the significant empirical research and theoretical progression in predicting future returns on equity investments, development of ways/methods to estimate and justify ERP over risk-free investments followed. Practitioners, industry experts, economists and finance authors adopted/suggested diverse approaches of ERP estimation. Each was justified in terms of the assumptions made, however, the practicality varied.

Before introducing the important methods, it is imperative to identify the two broad types/interpretations of ERP estimates:

  • Ex-post: These ERP estimates are based on analyses of historical/past figures of equity & bonds/bills returns, stock-specific factors & overall macro-economic indicators

  • Ex-ante: These ERP estimates are based on forward-looking figures derived from assumptions/forecasts (or even educated guesses) of future fundamentals such as earnings, dividends, price targets, terminal values or overall economic output

(Salomons & Grootveld, 2003)

The difference between the average ex-post and ex-ante estimates is notably large and cannot be ignored. For, instance Ibbotson and Sinquefield (1976) estimated future U.S. ERP to be about 6% p.a. based on average historical returns of the period (1926-1974), whereas the realised average ERP since then has exceeded 9% p.a. (Goetzmann & Ibbotson, 2005). On the other hand, Fama & French (2002) estimated future U.S. ERP to be 2.5% p.a. based on a variation in DDM, whereas the realised average ERP during that period was 7.43% p.a. Therefore, not only are there discrepancies between ex-post and ex-ante ERP estimates, but also between two ex-post or two ex-ante estimates; which suggests that, there exists no solid justification for a particular method being superior to others.

Ibbotson & Chen (2001) most definitively classified the methods of ERP estimations into 4 categories, namely historical method, demand-side methods, supply-side methods and survey method.

2.4.1 Historical Method

It is the most popular method that gathers historical figures over a substantial period of time of actual bond (or any risk-free asset) returns and subtracts them from the actual equity returns to arrive at an average (arithmetic or geometric) ERP. This ex-post ERP is then suggested as an estimate of the future.

Ibbotson & Sinquefield (1976) exemplified first estimation of ERP using historical method. Since then, Ibbotson along with other researchers has produced ample versions of his estimations, each evaluating various influences on ERP in U.S over 200 years. His findings are updated annually featuring on the official website of Ibbotson Associates (now known as Morningstar) and most preferred ERP estimations in academic research, corporate finance and investment management decision-making by practitioners. Later, Siegel (1992) found an average ERP of 5.3% p.a. in U.S. for the period from 1802 to 1990. Dimson et al (2002) estimated the ERP for 12 countries. They gathered historical returns for 12 international equity markets over a period of 100 years and found that Germany had the highest ERP of 10% p.a., while Denmark had the lowest of 3.5% p.a. with U.S. and U.K. being at 5.6% p.a. & 4.5% p.a. respectively.

This approach has evidently been the most widely accepted method so far; primarily because of its simplicity in calculation and then for the usually preferred long time periods. The longer the period considered, the lesser is the standard error as they include entire business cycles with a range of diverse events that influence asset prices. This is aptly justified by Damodaran (2000) who observes that shorter time periods lead to extravagantly high ERP. Although shorter time periods identify changing risk aversion of investors and hence provide updated estimates, they fail to identify the appropriate standard deviation of equities leading to inadequately large standard errors. He provides a table of possible standard errors against most preferred time periods for ERP estimation with an annual average standard deviation of 20% of U.S. stocks for 1926-1997

Hence, it is debatable as to whether updated ERP from highly error-prone shorter time period is more advantageous than more reliable but relatively older ERP from longer time period or vice versa.

The choice of risk-free asset for the purpose of ERP calculation is also a debate (Ibbotson & Chen, 2001). U.S. treasury bills are relatively short-term as compared to U.S. bonds. Therefore, shorter the period, shorter is the yield and therefore higher is the ERP calculated, as yield of the risk-free asset is considered as the return and is subtracted from equity returns. Hence, ERP estimates using U.S. treasury bills are inadequately higher than those using bonds. However, bills facilitate easy calculation as they mature quicker and their yields can be readily used as returns, unlike those of bonds. Freeman & Davidson (1999) and Damodaran (1998) argue that the rate of the risk-free asset should be the rate used while discounting future cash-flows for valuation or other similar corporate finance calculations. As a longer-term rate is preferred for such purposes, bonds should be preferred over bills for ERP estimation.

The final debate that engulfs the historical method is whether arithmetic mean or geometric mean should be considered. Fama & French (1988) empirically suggest that there exists a negative correlation between equity returns as the time period goes on increasing. Therefore, compounded geometric mean is more reliable considering that the straight forward arithmetic mean can get overstated. However, if the returns are uncorrelated then arithmetic mean is more appropriate for ERP estimation of immediate future. Hence, ERP estimates from historical method can vary for different time periods used, the choice of risk-free asset or the average considered. Following table

2.4.2 Supply-Side Methods

These methods are based on the conception that future long-term equity returns, called as 'supply of equity returns' by Ibbotson & Chen (2001), hence the name; depend on the overall productivity of the organisations listed on the stock market. Direct cash income in the form of dividends and long-term capital gains together form these returns. The past figures of fundamental indicators of organisations, for instance factors like Earnings per Share (EPS), EBITDA margins, Net Asset Value (NAV), dividend yield etc. are used to estimate future ERP.

Most of the forward-looking models which calculate expected returns are derived from the DDM. It is well known and researched that an asset's value can be best determined by calculating its present value. DDM facilitates the calculation of present value of equities. Gordon & Shapiro (1956) exemplified that the DDM can be used to calculate the expected future returns, for instance on an investment project of an organisation. This model was later extensively used to estimate expected returns on equity investments also. The mechanics of this model say that expected future returns on stocks or market index are equal to the sum of dividend yield and expected growth rate in future dividends (Equation-4).

Here, the dividend yield is the dividend expected next year (or any next period) on the stock market index divided by current value of the stock market index. Dividend growth rate and dividend yield are both an estimate of the future based on historical figures of dividends and rate of increase in dividends over a period of time. ERP can then be estimated by subtracting the average returns on the risk-free asset (for the same period) from the average expected future returns of the stock market. Gordon (1962) believed that over a long period of time, the rate of increase in dividend is in accordance with the rate of increase in stock market value. Consequently, the dividend to market value ratio is actually a constant and hence dividends are an appropriate indicator for estimating future ERP. Later, this model was known as the Gordon Growth Model (GGM) or constant-growth DDM.

Although, the DDM-based approach makes theoretical sense, its practicality can be questioned. Forecasting dividend growth rate and future dividend yields can be error-prone, especially considering that there exists no pattern to dividend payout policies of organisations. Furthermore, even if a pattern is identified there is a high possibility that it is a result of data-mining. However, having said that, the supply-side methods for ERP estimation are improvisations and/or derivations of the GGM. Fama & French (2002) modified the GGM. They suggested that expected future returns can be calculated by adding dividend yield and rate of capital gains. Here the dividend yield is same as in GGM, but rate of capital gains is a year on year compounded increase in market value for a particular period. Furthermore, they developed the Earnings Growth Model (EGM) by replacing rate of capital gains by earnings growth rate. They justified it by arguing that if dividends can be used to calculate future returns then any factor integrated in the stock price can be used.

Because Fama & French (2002) used a long time period from 1951-2000, based on the constant growth rate argument of Gordon (1962) they used the GGM and not their modified version (using capital gains rate) to calculate ERP. They calculated ERP using EGM as well and surprisingly enough the difference between the two estimates and the average return using historical method was quite large ranging from 40% to 65%. As mentioned earlier, ERP estimates of Fama & French (2002) using GGM and EGM were 2.55% p.a. and 4.32% p.a. respectively, whereas ERP using historical method was 7.43% p.a. as mentioned earlier. However, they argued that the lower ERP based on fundamentals i.e. earnings and dividend is more realistic and justified their argument as follows:

  • The standard error of ERP using GGM is less than half of the standard error of ERP using historical method

  • The Sharpe Ratio (i.e. ERP divided by standard deviation of returns assuming constant risk-free rate) for ERP using historical method has doubled from 1872-1950 to 1951-2000, whereas it has remained almost unchanged for ERP using GGM

  • Estimate of expected returns derived using the fundamentals of Valuation Theory like book-to-market value (i.e. book value of organisations divided by market value) are more in line with the estimate using GGM as compared to those found using historical method (Fama & French, 2002)

The comparison of historical method and supply-side methods to identify the superior of the two in terms of applicability for ERP estimation has been an issue that has triggered an unresolved debate and consequently ample academic scholarship. Arnott & Ryan (2001) and Arnott & Bernstein (2002) provide evidence of possibly zero or even negative ERP in the future for U.S. by using supply-side methods. They argue that U.S. ERP figures of 5% p.a. to 8% p.a. estimated using historical method are improbable because the reason for such high ERP is extravagant valuations of equities and high dividends especially during the Bull-Run from 1982 to 1999 which is completely irrelevant and unfeasible in the future. Similarly, Shiller (2000) also challenges the estimated U.S. ERP using historical method entirely based on supply-side methods with past fundamental information. He argues that there is no correlation between the current lofty valuations and the fundamentals, hence there is a high possibility that future equity returns will fall drastically. Another possible reason suggested by Siegel (2000) is underestimation of returns from risk-free asset considering his own estimation of U.S. ERP which is from 1% p.a. to 2% p.a.

Overall, historical method is more preferred, although, there exist many supply-side methods for ERP estimation. The possible inference could be equity pricing is done based on investor confidence, sentiment and past technical trends more than fundamental analysis.

2.4.3 Demand-side methods

These methods estimate ERP directly based on investors' behaviour towards risk and the future returns demanded by investors in exchange for the risk they bear from investing in stock market, instead of estimating future market returns the supply-side methods. The CAPM developed by Sharpe (1964), Lintner (1965) and Mossin (1966) is the most widely used demand-side method which is an extension of MPT and is very closely based on CMT's idea of general equilibrium. APT model discussed earlier is also an example of Demand-side method for estimation of future returns.

The CAPM was most rightly used by Mehra & Prescott (1985) to derive a consumption based, macroeconomic asset pricing method for ERP estimation called the C-CAPM. Their estimates were based on assumption of frictionless markets and varying market consumption in relation to the marginal utility sought by investors. After applying the standard asset pricing theory of CMT through the C-CAPM, their quantitative predictions stated that ideally U.S. ERP should be 1% p.a. They dismiss the fact the investors are highly risk-averse, at least not as much as the historical ERP figures denote. Furthermore, they also bring in the other reason for it as the very low risk-free return. Hence, they term this unexplainable difference in historical ERP and their estimate based on the C-CAPM method as 'Equity Risk Premium Puzzle'. Numerous variations of the C-CAPM have developed over time, each with the primary aim to explain the 'ERP Puzzle' (Hansen, 1982; Kocherlakota, 1996, 1997; Hansen & Jagannathan, 1991, 1997). However, most models developed so far have estimated the ERP based on short-term returns unlike the Historical Method wherein long time periods are preferred for a more confirmed estimation. Having said that, Cochrane & Hansen (1992) and Daniel & Marshall (1996) also attempt to solve the puzzle, however they argue that the error in measuring the total consumption reduces as the period considered for estimation is increased. The important implications of the 'ERP Puzzle' are discussed in greater detail in section 2.5.

The original CAPM's applicability in valuing assets has also been tested extensively over the years and the evidence in UK regarding the same, has been diverse. Strong & Xu (1997) did actually exemplify a relationship between Beta and return. They studied the period from 1971 to 1992 and found that Beta is a valid indicator to estimate required future premium. However, they also argue that book-to market value, size of an organisation and liquidity are more robust indicators than Beta to explain a the relationship between risk and premium demanded/expected. Similarly, Miles & Timmerman (1996) and Chan & Chui (1996) also find similar results from their research on periods 1971-1990 and 1979-1991 respectively. Clare et al (1998) applied a modified method while testing the CAPM over the period 1980-1993. They assumed that unsystematic returns are uncorrelated as opposed to the underlying assumption of the CAPM. After which they show evidence of Beta having an insignificant influence over expected returns. However when unsystematic correlation was incorporated in the model, there existed a strong effect of Beta over the returns. Furthermore, they also found variables such as book-to-market value, size and liquidity to be completely unrelated.

Like every theory or model faces criticism regarding its applicability, so does the original the CAPM. The theory behind the CAPM is based on estimating expected returns and future Beta. However Burton (1998) argues that figures of past (realised) returns and Beta are used in the model to do so. As these are ex-post figures, the estimates of future expected returns can face the exact same criticism as faced by the Historical Method which is 'How reliable are the past returns to estimate the future/expected returns'. Lofthouse (2001) disapproves of the CAPM's assumption that every investor portfolio has the exact same investment horizon without specifying what exactly it is. Therefore, although the CAPM may hold true for a particular period, say 'x', it may be rejected for another period 'y' or vice versa.

Other constructive criticism on the CAPM came from Fama & French (1992) as they out rightly reject the CAPM based on their empirical findings. They find that in the U.S. for the period 1963-1990, Beta and returns are completely unrelated and only weakly related for the period 1941-1990. Furthermore, they argue that small market capitalisation organisations and organisations with higher NAV tend to have higher returns and vice versa. Basu (1977), Cohray et al (1987) and Roll & Ross (1994) find similar results that question the CAPM's applicability. However, Black (1993) and Grundy & Malkiel (1996) exemplify the significance of Beta that is used by the CAPM as a measure of risk. They argue that investors and fund managers can at least partly rely on Beta for an indication of a stock's riskiness, if not completely base their decisions on it. The latter back their argument with empirical findings from an analysis of the period 1968-1992. They note that high Beta stocks face maximum downside in a bear market but they also give maximum returns in an upside trend i.e. a bull market. Chan & Lakonishok (1993) find similar results for the period 1926-1991. Hence it is safe to conclude that although most recent researches show little evidence that support the CAPM's applicability in the practical world, many argue that Beta is a worthy and reliable measure of systematic risk or risk overall if unsystematic risk is diversified.

2.4.4 Survey Method

This method estimates ERP based on the opinions of experts gathered through a broad survey. Welch (2001) presents an estimate of the future expected ERP of 5% p.a. to 5.5% p.a. for the U.S. markets based on a survey of 510 academic professionals from the field of finance and economics. These estimates are in between the high historical ERP of 6% p.a. to 9% p.a. (Goetzmann & Ibbotson, 2005) and 1% p.a. to 3% p.a. as estimated by Mehra & Prescott (1985) and Fama & French (2002). However, the survey results of Welch (1998) are much higher with ERP estimates of around 7% p.a. This suggests that although survey estimate is ex-ante, most experts base their opinion on past realised returns considering that the U.S. markets faced a notable downside between 1998 and 2001.

The empirical application of Survey Method for ERP estimation has negligible as compared to other methods. Best & Byrne (2000) estimate a low future ERP of 2% p.a. for U.S. and 2.1% p.a. for U.K. based on a survey of economic forecasts. They calculate the ERP using GGM and take the estimates of immediate future dividends & the growth in dividends gathered through their survey. They conclude that ex-ante ERP estimate is much lower than past ERP by conforming to the low estimates of Fama & French (1999), and in turn the reliability of Historical Method is lessening. The survey of estimates from Chief financial Officers (CFOs) conducted by Graham & Harvey (2002) has been most notable research after Welch (1998, 2001). It generated an average ex-ante 10-year ERP estimate ranging between 3% p.a. to 4.7% p.a. for U.S. Furthermore, they claim that majority of CFOs use the CAPM to estimate the hurdle rate. They concluded that 10-year ERP estimates are more reliable than 1-year estimates as they are less volatile.

It is possible, however, that opinions of academic experts or practitioners like CFOs can be based on biases towards a particular method of ERP estimation. For instance, Welch (2001) results suggest that Historical Approach is preferred whereas Graham & Harvey (2002) claim that CAPM is most widely used. Hence, due to such biases, reliability of survey estimates can also be questioned.

The above discussion on ERP estimation methods was confined to 4 most practised methods from 4 most significant research studies i.e. Goetzmann & Ibbotson (2005) Historical Approach based on past returns, Fama & French (2002) supply-side Approach based on variation in DDM, Mehra & Prescott (1985) demand-side approach based on Consumption CAPM and Welch (2001) survey approach; because discussing all the methods developed till date is outside the scope of this study. However, following Table.2.3 summarises various ERP estimates from other important researches using diverse methods of estimation. This table is an evidence of there being no consensus on the best ERP estimation method and hence accentuates the importance of the third research question addressed in this study. The ERP findings mentioned in Table.2.3 are in a wide range with the highest being ex-post 8.4% p.a. from historical approach and lowest being ex-ante -0.9% from GGM.

2.5 Equity Risk Premium Puzzle

Mehra & Prescott (1985) exemplified highly risk-aversive nature of investors based on extraordinarily high average ERP of 6% per annum in U.S. from 1889-1978. As mentioned earlier, real risk when measured by them, is much lower than the compensation demanded by investors and found that the two cannot be associated theoretically, hence terming this phenomenon as 'ERP Puzzle'. Ever since, the already augmented importance of estimating future ERP of developed markets, especially U.S. and identifying possible reasons for the results found; has been enhanced further by academia and practitioners from financial and economic background.

The high realised ERP in U.S. and most developed markets is unexplainable by any macroeconomic asset-pricing demand-side model developed till date. Although all attempts made to solve the puzzle are through the demand-side models derived from the popular & widely tested CAPM or the APT model, yet they are futile because the original models use ERP as an input to calculate expected returns on portfolios or specific equities, whereas their derivations use past returns as an input to estimate the ERP. Therefore, two reasons emerge as to why the 'ERP Puzzle' is unsolved until now, EITHER the models assessing the risk-aversive behaviour of investors are incorrectly developed and that investors have always been rewarded higher for their deeply risk-aversive nature OR the models are right but the estimation through Historical method is incorrect due to discrepancies.

Consequently, the literature from the research conducted on 'ERP Puzzle' so far can be categorised as follows:

  1. Researches exemplifying inadequacy and limitations of historical data

  2. Researches improvising the theoretical macroeconomic asset-pricing models to solve the puzzle.

2.5.1 Historical Data

Following are the discrepancies identified by researchers in the Historical method of calculating ERP:

1. Unexpected higher returns: The remarkable development in the West especially after World War Second has contributed to soaring equity valuations over the years. A large part of this sudden growth of valuations in the beginning was not backed by fundamental improvements. Bernstein (1977) observed that prices of most stocks soared considerably and multiplied over the length of the sample period (chosen by Mehra & Prescott, 1985). The stocks with price-to-earnings ratio (P/E) of 10 in early 1930's; had P/E of 20 or more in early 1970's. Consequently, unrealistic historical returns from exaggerated PE multiples lead to miscalculation and hence overstatement of future ERP. Fama & French (2002) observed the same with sample period of 1951-2000. They calculated ERP of 2.55% using the DDM which was much lower than ERP of 7.43% calculated from average historical stock returns.

2. Taxes and transaction costs: The above mentioned unrealistic rise in stock prices was primarily because of sizable reduction in dividend tax-rates. Furthermore, the real ERP is below 1% if taxes and transaction costs are accounted for as claimed by McGrattan & Prescott (2001 & 2003).

3. Survivorship bias: When an investor chooses a profitable investment option for example a mutual fund, more often than not, the decision is triggered by a successful/positive historical track record of that fund. However, most mutual fund companies always include/run only the out-performing funds and exclude/drop the underperformers. Hence, there is a high possibility that the investor could get deceived by the past record of the company and overestimate the valuation of its funds. Therefore, the surviving funds skew (generate a bias) towards higher prices, known as the survivorship bias (NYU Stern, 1997). Goetzmann & Jorian (1999) argued that ERP estimated from U.S. financial-data alone is overstated because it is subject to survivorship-bias. It is an analysis of only the global economic winner for over a century and not other markets which have faced more turbulent times. This argument lead to a series of researches conducted by Blanchard (1993), Fase (1997) and Dimson et al (2001) studying ERP of other developed markets. However, they evaluated ERPs of 17 European markets including Belgium, France, Germany, Netherlands and UK in post World-War Second period and found them comparable to that in U.S.

4. Short-term bills v/s long-term bonds: Short-term treasury bills provide more options to liquidate and do not constitute a notable part of long-term holdings of investors. Therefore, McGrattan & Prescott (2003) argue that, instead, long-term bonds should be used as risk-free assets to calculate ERP. Furthermore, Siegel (2005) claimed that the most relevant risk-free asset for long-term investors is an idealistic annuity that offers stable real returns for an extended time-period. Hence, long-term inflation-indexed government bonds should be chosen over short-term bills for ERP calculations, as their returns are closest to those of the most ideally preferred annuity.

2.5.2 Theoretical Models

Following are the improvisations suggested by researchers for the theoretical macroeconomic model of Mehra & Prescott (1985):

1. Behavioural explanation: 'Prospect Theory' states that investors under-weigh probable outcomes against those certain. They are more sensitive to shrinkage of wealth than its increment, termed as the 'Loss Aversion feature of Prospect Utility'. Kahneman & Tversky (1979) developed the 'Prospect Theory' based on inadequacies and underlying ideas of 'Expected Utility Theory' with an aim to analyse the decision-making process of investors pertaining to financial risk. Based on the 'Prospect Theory', Bernartzi & Thaler (1995) explain high ERP by assuming that even long-term investors are loss-averse and practise annual portfolio evaluation; hence terming it as 'Myopic Loss Aversion'. Recent research by Barberis & Huang (2006) supports this argument with similar results.

2. Unfavourable and unusual events: The unexpected and unfavourable equity market disasters can lead to large decrease in consumption. Although their probability is negligible; mere existence of this negligible probability creates need for high ERP to compensate investors for possible losses, as claimed by Reitz (1988). However, Mehra & Prescott (1988) argued that consumption had to decrease by 25% in order to justify the estimated ERP of 6%, whereas the maximum decrease in consumption for 100 years was only 8.8%. Even so, recent research covering 35 countries by Barro (2006) supports this argument by calculating 2% annual probability of rare market crashes causing a 15% to 60% decline in consumption.

3. Life cycle and borrowing constraint: Young investors between the ages of 20 and 40 seek future income from their stream of wages in middle ages i.e. 40-60. Ideally they would prefer borrowing in order to invest in the equity markets for higher income but due to constraints such as government regulations, young investors are unable to do so. Whereas, middle-aged investors seek future income from their savings derived from equity investments and risk-free fixed income from bond investments. Usually, with an aim to secure their future after retirement the middle-aged investors tend to prefer bonds over equities. Mehra et al (2002) build on this economic theory and argue that because of the varying relationship between equity investments and the earnings level of an investor through that investor's life span, the level at which the investor is willing to price them also varies. Considering young investors cannot price the equities and middle-aged investors prefer the bonds, the prices of bonds rise and those of equity fall, leading to high ERP as falling equity valuations make them risky and the premium demanded on them rises.

From the above discussion on arguments made by various authors, it is safe to conclude that ERP Puzzle exists because of a bias that investors have in favour of the high historical returns. For instance, although the U.S. equity markets have faced downside since year 2000, the historical ERP estimates have not corrected by that margin. It is either because the investors are completely blind towards the deteriorating fundamentals of the organisations and only aim on riding the momentum of stock prices or the bond market has performed terribly below expectations. Another way of looking at it would be, the bonds markets have outperformed the equities by a huge margin leading to investors demanding higher premium on equities and settling for lower yields from bonds. Hence, Mehra (2002) who is one the pioneers of this puzzle, mentions that the ERP Puzzle will hold in future as future ERP will conform to the past figures, especially due to investors with long-term horizon.

2.6 Emerging Equity Markets

The emerging equity markets have truly made their presence felt on the global front in the last decade, especially Korea, Taiwan and the BRIC nations. They are considered as new and promising investment avenues by most global investors; therefore comparing them with the established developed markets is not just imperative and but also inevitable. It has been observed and documented over time that emerging capital markets are largely diverse from those developed. Bakaert & Harvey (1997) highlight 4 characteristics of emerging capital markets that differentiate them from developed capital markets as follows:

  • Higher volatility

  • Low correlation with developed markets

  • Higher returns

  • Predictable returns

Through their empirical research on 20 emerging markets, Bakaert & Harvey (1997) conclude that estimated ERP in emerging markets is very high, considering it is directly correlated with volatility in expected equity returns. Furthermore, higher volatility can lead to higher cost of capital. Harvey (1994) argues that emerging markets' low correlation with developed markets is advantageous for a global investor i.e. for an investor who is willing to diversify internationally, as that can reduce the risk considerably. However, he also notes that because the returns from emerging markets are comparatively more predictable, they are highly influenced by domestic news and events more that developed markets, which can be disastrous; as such a risk cannot be diversified. Hence, it is safe to say that all 4 distinct characteristics of emerging markets mentioned above have almost equivalent advantages and disadvantages for equity investments.

The entire economic constitution of emerging countries varies from those developed in several areas. As equity market returns are affected by factors other than just investors' perception or market fundamentals, the ERP in emerging markets also varies in many aspects from the ERP in developed markets. Bailey & Chung (1995) observe significant influence of political and exchange-rate risk on the ERP of Mexican market. They generalise their findings by adding that investors in emerging markets tend to demand high ERP as a compensation for those risks. Perotti & Oijen (2001) also note effects of political risk on ERP in emerging markets. However, their analysis of 31 emerging countries suggests that privatisation is a robust solution which guarantees to reduce the political risk and consequently strengthen their equity markets. They also note that relatively privatised emerging countries with evident long-term economic reforms have lesser ERP and higher market capitalisation than others as a consequence of increased investors' confidence. Therefore, it can be deduced that changing macroeconomic factors in emerging countries have a more significant impact on their equity markets and ERP as compared to those in developed countries.

Having noted that emerging and developed equity markets exude diverse characteristics, yet certain correlation between their performances tends to exist, as most emerging markets are well integrated with the leading developed markets, especially with U.S. After conducting an empirical research on 19 emerging equity markets, Bekaert (1995) argues that most of them exhibit high market integration with U.S. and that their level of integration is one of the indicators of the magnitude of their possible investment barriers. Furthermore, he claims that higher the market integration with developed markets, lesser the investment barriers. These investment barriers can comprise of exchange rate controls, small sized stock markets, higher inflation, and lesser accountability. Therefore, emerging markets can pose issues for global investors other than just the inexplicable volatility.

Most emerging equity markets have undergone liberalisation i.e. demolish the existing barriers for foreign investors/investments, in the past 2 decades. The drastic increment in their total returns has also been seen in the past 10-15 years. Hence, it seems sensible to believe that stock market liberalisation can lead to an increase in ERP. This notion is supported by Lundblad et al (2003) and Cha & Oh (2000) who argue that stock market liberalisation in emerging countries has led to an increase in their integration with developed markets, which has in turn benefitted the domestic companies as the foreign capital flow have increased. Furthermore, the increase in capital flows triggers higher investment interest leading to higher returns which finally benefits the domestic & global investors alike.

The transparency in the available or provided information in emerging equity markets is questioned time and again. Most organisations tend to delay or completely conceal the disclosure of sensitive information with respect to their stockholders. Furthermore, regulatory bodies have been seen to have little control over this practice. Feldman & Kumar (1995) observe the above occurrence in their exploratory research and suggest that an increase in market efficiency and global investors' interest through integration with developed markets can be brought about by serious enforcement from the regulatory bodies to ensure absolute lucidity in trading activities, information disclosure and overall corporate governance. Hence, if the government authorities take such an initiative then it can possibly reduce the speculation & volatility in returns and increase the global investors' confidence to invest in emerging equity markets.

To sum up, although emerging equity markets promise higher equity returns than those developed, the uncertainty & volatility of those returns is also definite. Measuring the risk alone is not sufficient to make an investment decision, but a transparency check on other requisite fundamentals with respect to the organisations, regulatory bodies and overall working of the markets is also essential.

Chapter 3: Overview of Research Methodology

3.1 Chapter Introduction

Saunders et al (2007) describe the term 'Research Methodology' as choosing/building an apt theoretical foundation so that it facilitates the research to be conducted smoothly; which is diverse from the term 'Research Method' that they describe as the technique/procedure applied for the purpose of data collection & analysis. This chapter aims to explain the chosen research methodology for this study by presenting justifiable motives for the choices made. It explains the chosen research role, approach and strategy for this study. Furthermore it presents an overview of the data collection and data analysis methods. This chapter also presents the chosen sampling technique and population for this study. Lastly, possible limitations/issues/constraints that can arise during the course of this study are mentioned.

3.2 Research Role

The role of this study is exploratory. An exploratory study as described by Robson (2002, p 59) is to enquire what is happening, to ask questions and to assess a phenomenon in a new light. Hence, conforming to his statement, an exploratory role is identified for this study because, research question-1 'enquires'about the ERP in developed and emerging markets and attempts to identify the better risk-return trade-off between them, research question-2 'assesses' the influence of macroeconomic factors on ERP in India, and finally research question-3 'asks' a question about the most suitable ERP estimation method and attempts to answer it. Finally, an exploratory study can facilitate adaptability and flexibility considering the high-variability possible in required-data leading to unlikely results.

3.3 Research Approach

The approach to this study is deductive. The principal reason to identify a deductive approach for this study is because the analysis moves from theory/hypothesis to data in all 3 research questions (Saunders et al, 2007). Research question-1 is based on the theory that emerging markets may exude high risk due to a possibly high ERP, research question-2 is based on the theory that macroeconomic factors may influence the ERP as they tend to affect the capital flows to equity markets and research question-3 is based on the theory that there exist vast differences between ERP estimation methods. Secondly, considering this study is entirely based on quantitative data, a deductive approach is most appropriate. Finally, considering that the overall approach is structured and that the researcher of this study has the required independence to choose the research topic & methodology; a deductive approach is comparatively more fitting (Robson, 2002).

3.4 Research Strategy

The strategy chosen for this study is Archival. As the data required to conduct this study constitutes documented historical figures & records, an archival strategy is adopted. The figures for equity & bond returns from emerging & developed markets, the chosen macroeconomic factors and vital inputs for the ex-ante estimation of ERP for research questions 1, 2 & 3 respectively, are all historical and obtained from archives on official databases & websites. Furthermore, an archival research strategy is suitable for an exploratory research that focuses on or that is based on assessing/analysing longer continuous time periods (Hakim, 2000).

3.5 Sampling

Sampling is required for research question-1 only, as the choices made for research question 2 & 3 are loosely based on past literature and are explained in detail in their respective sections of the following chapter 4.

Sampling Population for research question-1 will include the world's developed and emerging equity markets. They are classified based on an assessment of GDP per capita, factors affecting market and regulatory environment, custody and settlement, liquidity for trading, development of derivatives market and size of stock markets (Calpers, 2007). Although developed markets belong to high income and information efficient countries, potential growth in ERP and profitability is low whereas emerging markets offer higher desirable ERP reflecting extraordinary GDP growth rate but relatively low GDP per capita. This population is relevant to chosen research area as global investors' interests are in a transitional process. Frontier markets belonging to very low income countries with under developed capital market structure and regulatory mechanisms; and Uninvestable markets that lack convertible currencies and restrict investment by non residents are excluded from the population considering unavailability of data and irrelevance of available information to chosen research area (Calpers, 2007).

Considering the large population size, accessing relevant data of all developed and emerging markets will be time consuming and problematical. Furthermore, data analysis will be complicated. Hence, choosing an appropriate sample representing the population is inevitable.

Sampling Size: Financial Times Stock Exchange i.e. FTSE (2008), classifies the most sought after equity markets of the world into developed, advanced-emerging & secondary-emerging; with 24, 7 and 16 countries in each category respectively. Hence, in relevance to chosen research area, there exist 24 developed and 23 emerging markets.

Entirely based on the access issues mentioned earlier, 20% representation of the population will be attempted. The selection of countries will be conducted through using the systematic sampling technique based on GDP figures obtained from World-Bank (2007), as it is the most popular and comprehensive way of estimating the size of a country's economy, which is in turn correlated to equity market returns and consequently to the global investors' interest in equity markets. However, FTSE's sub-classification of emerging-markets cannot be neglected. Hence, 5, 3 & 2; developed, secondary-emerging and advanced-emerging markets are chosen respectively in order of GDP (See Table.3.1).

3.6 Data Collection

All the data required to conduct this study will be collected using multiple secondary sources. Archival secondary data, for instance country specific financial reports, industry statistics, government publications, surveys and processed historical information from databases will be used. Documentary secondary data will mainly be collected through a comprehensive search of literature from various sources, for instance journals, books, working papers, magazines and newspaper articles (See Table.3.2). Predominantly databases will be used to access past equity markets' and economic data of selected countries. Furthermore, journals will be mainly used for critically evaluating the relevant researches conducted, to gain better understanding of the research area and for presenting solutions to the questions addressed.

3.7 Data Analysis

Considering this is a multi-method quantitative study, 3 diverse research methods are used for data analysis of each of the 3 research questions. Hence, this section presents an overview of the chosen research methods for each, however the identification of the data collected and a detailed explanation of the data analysis technique/procedure chosen for each research question are included in their respective sections in the following Chapter 4. Such a procedure is followed to present a more coherent structure for each research question. A longitudinal time horizon of precisely 10 years is considered in data collection and analysis for all 3 research questions.

Research Question-1: Ex-post ERP of emerging and developed markets will be estimated using the historical data on risk-free rate and equity returns. Important descriptive statistical indicators for instance standard deviation, mean, skewness and so on, will be calculated using the Statistical Software for Social Sciences (SPSS). Important inferences from a will be drawn findings through conducting a comparative analysis. Furthermore, important financial ratios and tests for instance Sharpe Ratio and so on will be conducted and the results will be analysed through an interpretative analysis to determine the risk-return trade-off of both sample groups.

Research Question-2: Hypothesis will be developed to test the influence of the chosen macroeconomic variables on the ERP in India. A detailed explanation of the foundation of hypothesis development will be presented. An analysis of the past trend of the macroeconomic variables and their inter-relationships will be conducted with an aim to present a background of the macroeconomic situation in India. Finally, the significance & solidarity of the hypothesis will be examined through multiple regression method. Inferences will be drawn through an interpretative analysis of the empirical results found.

Research Question-3: Ex-ante ERP of India will be estimated using the Gordon Growth Model (GGM). A comparative analysis of the result found and the ex-post ERP from question-1 will be conducted. Inferences will be drawn as to which ERP estimation method is more suited or apt from the global investor's point of view, if at all. The analysis will be partly subjective in nature based on results & views of similar past researches and partly objective based on interpretation the trend of significant variables and/or financial ratios.

3.8 Possible Research Limitations

  • The availability of historical macroeconomic and equity market figures can be an issue

  • Access constraints to available data due to limited funds & time, may arise

  • Generalisation of results from the analysis of the chosen samples could be questioned

  • Secondary data bias for instance contrasting views of different government or country-specific reports, could exist

  • As the sample chosen for research question-1 comprises of leading developed and emerging countries, survivorship bias is a possibility

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Finance is a field of study involving matters of the management, and creation, of money and investments including the dynamics of assets and liabilities, under conditions of uncertainty and risk.

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