The capital asset pricing model beta and what else

ABBREVIATIONS APT: Arbitrage Pricing Theory ASEAN: Association of Southeast Asian Nations C4F: Carhart four-factor CAL: Capital Allocation Line CAPM: Capital Asset Pricing Model CML

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

THE CAPITAL ASSET PRICING MODELS:

BETA AND WHAT ELSE

BY PHAM NGOC THACH

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, NOVEMBER 2015

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

THE CAPITAL ASSET PRICING MODELS:

BETA AND WHAT ELSE

A thesis submitted in partial fulfillment of the requirements for the degree of

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

DECLARATION

I hereby declare, that the thesis report entitled, “The Capital Asset Pricing Models:

Beta and what else” written and submitted by me in fulfillment of the requirements for the

degree of Master of Art in Development Economics to the Vietnam – Netherlands

Programme This is my original work and conclusions drawn are based on the material

collected by me

I further declare that this work has not been submitted to this or any other university for

the award of any other degree, diploma or equivalent course

HCMC, November 2015

Phạm Ngọc Thạch

ACKNOWLEDGEMENTS

Immeasurable appreciation and deepest gratitude for the help and support are extended

to the following persons who in one way or another have contributed in making this study

possible

Above all, I would like to express my special appreciation to my supervisor - Dr Võ

Hồng Đức, for his supports, advices, guidance, valuable comments and suggestions It is an

honor to work with him

I would like to acknowledge all the lecturers and staffs at the Vietnam – Netherlands

Programme for their useful knowledge and support during the time I studied at the program

In specific, I am grateful to Prof Nguyễn Trọng Hoài, Dr Phạm Khánh Nam and Dr Trương

Đăng Thụy, who guided me the first steps in the courses as well as in the thesis writing

process

I would like to thank my friends at Class 20 for their helps

Last, but not least, I would like to thank family, my parents and my sister, who always

love, take care of and support me unconditionally on the way I have chosen

HCMC, November 2015

Phạm Ngọc Thạch

ABBREVIATIONS

APT: Arbitrage Pricing Theory

ASEAN: Association of Southeast Asian Nations

C4F: Carhart four-factor

CAL: Capital Allocation Line

CAPM: Capital Asset Pricing Model

CML: Capital Market Line

FF3F: Fama-French three-factor

FF5F: Fama-French five-factor

FGLS: Feasible Generalized Least Squares

LAD: Least Absolute Deviations

MPT: Modern Portfolio Theory

OLS: Ordinary Least Squares

QR: Quantile regression

RIV: Residual Income Valuation

SML: Security Market Line

ABSTRACT

It has been 50 years since the first Capital Asset Pricing Model (CAPM) was developed

by Sharpe (1964) and Lintner (1965) Similar to any other theory, CAPM has been facing with hundreds of critiques from theoreticians and empiricists Recent evidences suggest that CAPM is still applied widely in the practice by regulators and practitioners While the question whether CAPM is valid in relation to the estimate of stock expected return is far from completeness, the so-called alternative models have also been developed Typical competing and substitutable models for the Sharpe-Lintner CAPM include the Fama-French three-factor model, which was recently revised to be the five-factor model; and the Carhart four-factor model The introduction of Fama-French three-factor model has attracted scholars’ attention However, the empirical studies related to multi factor asset pricing model

in general and Fama-French three-factor model in particular present a completely mixed results To date, in relation to the multi factor model of estimating the expected return, more than 300 explanatory factors have been attempted in empirical studies and the long list does not appear to end there In the Vietnamese context, empirical evidences provided by Vietnamese scholars have presented the similarly ambiguous outcome

Vietnam, together with other ASEAN economies, is on the way to achieve the dream of being a next young Tiger in ASEAN In achieving this dream, a sale of government owned assets to the private investors, particularly in the capital-intensive energy industry, is unavoidable The question is that how the Government of Vietnam can determine a reasonable price for the assets Equally important, it is essential for new investors to determine how much they can earn or how risky they have to face across various industries,

to make the appropriate investment decisions

This study is conducted to achieve the following three objectives First, an estimate of

equity beta, a key input of the CAPM, is required in determining a reasonable price for Vietnamese Government’s assets in the utilities industry and the others in the process of

privatization and equitization Second, the first Risk-Return framework is developed in order

to provide guidance to investors in making their investment decisions, for various industries

in Vietnam Third, as the first and preliminary attempt, this study is to test and provide a

group of factors which can be used to explain the stock returns in Vietnam This chosen factor must be supported by theory and empirical evidence

The findings seem to be attractive to note First, utilities businesses face a relatively

lower risk in comparison with the market as the whole Moreover, there is a divergence of the equity beta estimates for the five countries in the ASEAN including Vietnam, Singapore,

Thailand, Malaysia and the Philippines Second, the Construction and Real Estate is ranked

highest in terms of risk (as a result, highest expected return), followed by Agriculture Production, Transportation and Warehousing, Manufacturing and Wholesale Trade and Retail Trade industries The lower ranks belong to the Utilities, Accommodation and Food services, and Arts, Entertainment, and Recreation whereas the industry with lowest level of risk is Information and technology industry These empirical findings are somewhat

consistent with expectation from a leading practitioner in the area, the UBS Third, using a

combination of DuPont analysis and the Residual Income Valuation, this study provides

evidence to confirm that return on equity ratio and its change are informative about stock returns Moreover, the level of capital turnover and financial cost ratio, together with the change in capital and the change in financial cost ratio contain incremental explanatory

powers in explaining returns within the capital asset pricing model framework

Keywords: CAPM, multi factor asset pricing models, utilities, ASEAN 5, quantile

regression, Risk-Return framework

“Where we cannot invent, we may at least improve; we may give somewhat of novelty to that which was old, condensation

to that which was diffuse, perspicuity to that which was obscure, and currency to that which was recondite.”

Charles Caleb Colton

TABLE OF CONTENTS

DECLARATION I ACKNOWLEDGEMENTS II ABBREVIATIONS III ABSTRACT IV TABLE OF CONTENTS VII LIST OF TABLES X LIST OF FIGURES XI

CHAPTER 1 INTRODUCTION 1

1.1 Problem statement 1

1.2 Research objectives 4

1.3 Research questions 4

1.4 Contributions of the thesis 4

1.5 Structure of the thesis 5

CHAPTER 2 LITERATURE REVIEW 6

2.1 Theoretical literature 6

2.1.1 Modern Portfolio Theory 7

2.1.2 The Capital Asset Pricing Model 10

2.1.2.1 The Capital Market Line 11

2.1.2.2 The Security Market Line 11

2.1.3 The Arbitrage Pricing Theory 13

2.1.4 Fama-French three-factor model 14

2.1.5 The Carhart four-factor model 15

2.1.6 The Fama-French five-factor model 16

2.1.7 The DuPont analysis 17

2.2 Empirical literature 20

2.2.1 Empirical evidences on the asset pricing models 20

2.2.2 Current approaches to estimate β 24

2.2.2.1 Ordinary Least Squares 25

2.2.2.2 Least Absolute Deviations 25

2.3 The use of DuPont analysis on asset pricing model 26

CHAPTER 3 METHODOLOGY AND DATA 28

3.1 Data 28

3.1.1 Utilities industry in ASEAN 5 28

3.1.2 Beta ranking for all industries and asset pricing factors in Vietnam market 29

3.2 Research methodology 30

3.2.1 Estimating beta in Capital Asset Pricing Model 30

3.2.1.1 Return and return period 30

3.2.1.2 A new approach – Quantile regression 31

3.2.1.3 Portfolio construction 33

3.2.1.4 De-levered/Re-levered estimates of β 33

3.2.2 Beta ranking construction 34

3.2.3 The use of DuPont on asset pricing model 34

3.2.3.1 Model specification and estimation method 35

3.2.3.2 Variables measurements 36

CHAPTER 4 RESULTS AND DISCUSSIONS 38

4.1 Objective 1: Estimating the beta coefficients for the utilities industry in the ASEAN 5 38

4.1.1 Individual companies’ beta estimates 38

4.1.2 Beta estimates of various portfolios 40

4.1.3 De-levered/Re-levered estimates of β 45

4.2 Objective 2: The Risk-Return framework for various industries in Vietnam 46

4.3 Objective 3: New explanatory factors of expected stock returns in the Vietnam context 50

4.2.1 Descriptive statistics 50

4.2.2 Diagnostics tests 51

4.2.3 Estimation results 53

CHAPTER 5 CONCLUSIONS AND POLICY IMPLICATIONS 56

5.1 Concluding remarks 56

5.2 Policy implications 58

5.2.1 For the Vietnamese government 58

5.2.2 For investors 59

5.2.3 For practitioners 59

5.2.4 For academics 59

5.3 Limitations and further study 60

REFERENCES 61

APPENDIX 67

LIST OF TABLES

Table 2.1 Factor classification

Table 3.1 Listed utilities companies in the sample Table 3.2 Summary of non-financial listed companies in HOSE Table 3.3 Variables definitions and measurements

Table 4.1 Estimates of equity beta for individual companies, using the weekly return

from Friday-to-Friday week closing prices Table 4.2 Estimates of equally-weighted portfolios equity beta Table 4.3 Estimates of value-weighted portfolios equity beta Table 4.4 Differences in the estimates of equity beta for Portfolio 1: A longest period: 09

February 2007 to 31 July 2015 and the 13 April 2012 – 31 July 2015 period Table 4.5 De-levered/Re-levered estimates of β for weekly frequency:

Individual companies Table 4.6 De-levered/Re-levered estimates of β for weekly frequency: Portfolios Table 4.7 List of industry and related information in Vietnam Table 4.8 Risk-Return framework for the Vietnam market Table 4.9 Descriptive statistics Table 4.10 The correlation matrix and Variance Inflating factor among variables

Table 4.11 Heteroskedasticity and Autocorrelation test Table 4.12 Regression results

LIST OF FIGURES

Figure 2.1 The Efficient Frontier Curve 8

Figure 2.2 The Capital Allocation Line 10

Figure 2.3 The Security Market Line 12

Figure 3.1 Conceptual framework 35

Figure 4.1 The scatter plot of Portfolio 1’s returns and market returns Figure 4.2 Risk-Return framework

Chapter 1 INTRODUCTION

1.1 Problem statement

Estimating a return on equity is an extremely complicated task Although researchers and practitioners have been looking for factors to contribute in explaining the relationship between risk and return for decades, there is no consensus so far On the basis of the theories from Markowitz (1952) and Tobin (1958), the first ever capital asset pricing model (CAPM), Sharpe-Lintner CAPM proposed by Sharpe (1964) and Lintner (1965), plays a key role in finance literature in which a capital asset can be priced The CAPM theory gains a lot of researcher’s attention worldwide This leads to another well-known name for CAPM, the single factor Asset Pricing model Almost immediately since the introduction of the model in

1964 - 1965, this theory has been testing for its implications by empiricists While some of the typical results advocate the validity of CAPM (Fama & MacBeth, 1973; Jensen, Black, & Scholes, 1972), others offer their critiques (Basu, 1977, 1983; Bhandari, 1988)

The studies of Fama and French (1992) and Fama and French (1993) later suggest an alternative model for CAPM, called the Fama-French three-factor model (FF3F) by adding size and book-to-market ratio to the original model of CAPM The works of Fama and French lead to one common view that is one of the most intense debates in the finance history and have been attracting a lot of scholar’s attention within two recent decades There are hundreds of quantitative studies conducted worldwide in various time-periods and contexts in order to criticize or to improve the model Nevertheless, the jury is still out on that question (Gaunt, 2004; O’Brien, Brailsford, & Gaunt, 2010) Many studies concluded that the new added factors in the FF3F are insignificant or do not have the expected sign Moreover, the quantitative results from the Fama-French three-factor model are usually considered as “data mining” since there is no robust theoretical framework relating to this model (Kogan & Tian, 2013; Wang & Wu, 2011)

In addition, in October 2013, the Nobel Prize in Economics Science had been awarded

to Professor Eugene Fama for his works on market efficiency and out-performance Recently, Professor Fama and his companion, Professor French, have introduced a new model, named the Fama-French five-factor, with the view to better explain the return on equity in the US Stock Market (Fama & French, 2015) This five-factor model is an augmented version of the

three-factor model by adding profitability factor (Novy-Marx, 2013) and investment tendency factor (Aharoni, Grundy, & Zeng, 2013)

A fundamental question challenged academia, policymakers, and practitioners is that whether the single factor CAPM is still alive for the purpose of estimating the expected equity return An answer to this question is far from completeness It is noted that more and more explanatory factors (more than 300 factors as at December 2014) have been found in the rapid development of literature For example, Harvey, Liu, and Zhu (2014) reported that

315 factors have been identified in the top ranked journals and high quality working papers

In a similar manner, Green, Hand, and Zhang (2013) identify 330 factors which have been utilized for the same purpose of explaining an expected return on equity

In Vietnam, the mixed results of the applications of the Fama-French three-factor model are achieved (Phan & Ha, 2012; Phong & Hoang, 2012; Truong & Duong, 2014; Vuong & Ho, 2008) The recent studies of Vo and Mai (2014a) and Vo and Mai (2014b) conclude that the findings from multi factor asset pricing model are not consistent in the context of Vietnam and these authors called for a great caution in relation to the applications

of the model in the policy setting environment in Vietnam Even though various studies have been attempted in the context of Vietnam, no study has been conducted to provide an answer

to the question, if new factors added by Fama and French are unable to explain an expected return in Vietnam, which factor, supported from both theories and empirical studies, is likely

to do so One of the contributions from this study is to provide an answer to fill in the gap

On the other hand, many evidences indicate that the CAPM has been and is being applied by the company’s CEO in reality worldwide (Brounen, De Jong, & Koedijk, 2004; Graham & Harvey, 2001) In terms of policy makers, according to Mckenzie and Partington (2014), regulators in Australia, Germany, New Zealand, USA, Canada and UK are currently basing their decisions primarily on the CAPM framework (see Appendix 1 for details) A key component of this single factor CAPM is the equity beta In order to estimate the equity beta, most of the previous empirical studies have adopted the standard Ordinary Least Square (OLS) and Least Absolute Deviation (LAD) (Henry & Street, 2014; Vo, Mero, & Gellard, 2014) to estimate the beta coefficient in the CAPM model It has been argued for a long time that estimating beta suffers instability of the estimates In addition, extreme outliers in the sample have been considered as a key issue for any empirical estimate of beta In this context, this study argues that quantile regression approach may be useful in minimizing the effects of outliers in the sample

Vietnam, together with other ASEAN economies, is going to be the next young Tigers, except Singapore who currently is, in the Asian region for the next decade or so In achieving this sense of purpose, privatization and/or equitization of the government-owned businesses, particular in the capital-intensive utilities industry including energy, water, electricity, and public transport, is considered unavoidable to ensure that scarce resources are best utilized The presence of the government ownership in these state-owned companies is very significant This presence will limit the government’s ability to meet its other socio-economical objectives and/or to under-invest in these public utilities companies However, selling these assets to the private sector requires an appropriate pricing to ensure that these assets will raise fair amount of money for the public In a financial term, the government is required to determine its appropriate expected return on equity for being present in these public utilities businesses in any offer of selling their assets This expected return on equity can be used to confirm whether or not current government owned businesses in the same or similar industry are efficient in running their businesses and/or to determine a fair price of the assets (the companies) should the Government decide to privatize and/or equitise these assets

In addition, as a complement rather than a substitute with the above, some of the ASEAN countries with government ownership are operating in a monopoly environment in which the market power can be easily exercised by these monopolists to raise the price of goods and services provided at the expense of the local people In such an environment, independent regulators such as those in Australia including the Economic Regulation Authority and the Australian Energy Regulatory are required to allow the monopolists to earn

a reasonable rate of return Therefore, another concern of this study is providing the estimation of beta for individual company as well as the portfolio in the utilities industry in the ASEAN 5 including Vietnam, Malaysia, Thailand, the Philippines, and Singapore using a new approach, the quantile regression approach

Moreover, the participation of both foreign individual investors and corporations recently in the Vietnam stock market has significantly been increasing over the last 10 years The increase in foreign capital inflows indicates a good signal in relation to the attractiveness

of the Vietnam economy in general, and in the Vietnamese final market in particular As always, new investors in general and foreign investors in particular need information to determine a reasonable rate of return for their investment decisions Needless to say, assessing a level of risk given a level of expected return is no doubt essential, particular when all industries are relevant, and as such, considered for their investment As a result, a Risk-

Return framework to provide guidance to investors is urgently required in the context of the Vietnamese market

1.2 Research objectives

The objectives of this study are threefold:

 First, estimating the beta coefficients for the listed companies belonging to the

utilities industry for selected ASEAN countries where data are available using the quantile regression approach

 Second, establishing the Risk-Return framework for various industries in the

Vietnamese financial market

 Third, figuring out the new explanatory factors of expected return, which

contributes to currently mixed literature in the capital asset pricing models, in the Vietnam context

Acknowledging these problems is crucially essential for not only investors but also the policy makers and regulators

- What is the Risk-Return framework for various industries in Vietnam?

- What are the explanatory factors of expected return in the Vietnam context?

1.4 Contributions of the thesis

This study greatly contributes into the wide ranging literature of asset pricing in three ways

 First, acknowledging weaknesses from current approaches in estimating the equity

beta, quantile regression is used to estimate the reliable equity beta for the utilities industry in the five countries in the ASEAN including Vietnam This contribution

is presented at two separate levels: (i) estimates of equity beta for each individual company operating in the utilities industry in the above ASEAN economies; and

(ii) estimates of equity beta for different types of portfolios formation These two groups of results will contribute empirical evidence for the Vietnamese Government to determine a reasonable price of their assets in the utilities industry and the others in the process of privatization and equitization In addition, the second group of results provides investors with evidence to form their expectation

in relation to the expected return in Vietnam when the investment decision is made

 Second, this study develops a Risk-Return framework in which beta plays a critical

role This newly established framework for the Vietnam market is then tested against a common perception of risk and return from a highly regarded investment bank This Risk-Return framework will provide guidance to investors in making their investment decisions, for various industries in Vietnam This contribution is arguably considered as the first industry ranking in Vietnam

 Third, among more than 300 different factors that have been attempted worldwide

to explain an expected return, given time constraint, this study is an attempt in finding a group of factors that can explain the stock returns in Vietnam using the DuPont analysis This third contribution will shed lights into further research in this area in Vietnam

1.5 Structure of the thesis

The structure of the thesis is as follows Following this Introduction, Chapter 2 presents

a summary of literature relating to the asset pricing models, particularly the single factor model (CAPM), multi factor models (the Fama-French three-factor and five-factor models; the Carhart four-factor model) and some related empirical studies Data description, research methodology and empirical models are presented in Chapter 3 Chapter 4 presents results and discussions This study is concluded by some main conclusions and policy implications in Chapter 5

Chapter 2 LITERATURE REVIEW

This chapter is to re-examine theoretical and empirical literature on both single factor and multi factor capital asset pricing model There are three sections in the chapter The first section reviews some basis theories in the literature of the asset pricing models The second section presents empirical evidences on the current debate between the single factor and multi factor model The last section provides the small but growing stream of research in which the DuPont analysis is applied in the Residual Income Valuation

2.1 Theoretical literature

In finance, one of the basic questions for both investors and policy makers is how the expected return could be affected by the risk in investment The Capital Asset Pricing Model (hereafter CAPM) is a way to demonstrate the relationship between the risk of an asset or stock portfolio and its expected return to the investors in a reasonable equilibrium market More specifically, CAPM determines a theoretically appropriate required rate of return of an asset, if investors are going to add this asset into an already well-diversified portfolio, given that asset’ non-diversifiable risk Indeed, the capital asset pricing model represents for a historically great achievement to understand and quantify that risk

Undoubtedly, Markowitz (1952) is the founder of the origin of CAPM In a basis and

famous theory named Modern portfolio theory, Markowitz (1952) demonstrated a reasonable

mechanism in which the investors are able to select an optimal collection of investment assets that offers relatively lower risk than any single asset Due to the fact that different types of assets may change in value in different directions, the above idea was possible Moreover, the portfolios are modeled as a combination of assets and the concept of portfolio risk is first quantified in his theory As such, rate of return of the portfolio can be calculated as the weighted combination of each stock return in the portfolio According to the assumption of this theory which is the investors are risk averse, investors will select the portfolio with the least standard deviation if the expected rate of return is given equal In other words, given the fixed risk, the portfolio with the highest expected return should be selected by the investors Tobin (1958) studied the Markowitz’ theory and extends the investors’ decisions in risky asset by adding risk free assets to the optimal portfolio He discussed the optimal weights that an investor should decide to invest in risky assets and risk free assets Followed

simultaneously and independently by Treynor (1961), Sharpe (1964), Lintner (1965) and Mossin (1966), the CAPM was developed For this contribution, Markowitz, Sharpe and Merton Miller jointly received the 1990 Nobel Memorial Prize in Economics in the Financial Economics field

Later, Ross (1976) created the Arbitrage Pricing Theory as an alternative model for CAPM This model is developed later into many forms such as Fama-French three-factor model, Carhart four-factor model and most recently the Fama-French five-factor model

2.1.1 Modern Portfolio Theory

In 1952, Harry Markowitz introduced his Modern Portfolio Theory (MPT) as the foundation for the CAPM Previous theories indicated that the risk of an individual stock was

measured as its return volatility, the standard deviation Therefore, a stock with larger standard deviation is a riskier one According to this theory, investors are able to construct a collection of various stocks, or a portfolio, in order to maximize its expected return given amount of risk In other words, given level of expected return, investors can minimize the risk

of portfolio by combining the proportions of stocks carefully With these important findings, this theory is considered as one of the most influential finance theories in terms of investment

The MPT theory can be described in the expected return and risk space as below Suppose each black dot represents one collection of stocks, or one portfolio The connection

of the highest dot leads to a curve known as the “Efficient Frontier” As such, this curve

provide a available set of best portfolios in terms of expected return and risk, or most optimal portfolios The portfolios that are above the curve are not feasible Conversely, portfolios that are below the curve are not efficient since investors are able to earn a higher expected return

for the same amount of risk by choosing its vertical point on the Efficient Frontier curve

Investors in the market are expected to select their most appropriate portfolio from this available set (Markowitz, 1952)

Figure 2.1 The Efficient Frontier Curve

In addition, the concept of stock diversification is also conducted in MPT theory Consider a portfolio includes two assets A and B, the expected return and risk of the portfolio

is calculated as follows:

(1)

in which:

 P, A, B denote for the portfolio, asset A and asset B

 is the expected return

 is the standard deviation

 is the weight of the asset in the portfolio

 is the correlation and is the covariance between two assets A and B The above equations, (1) and (2), indicate that the return of portfolio of two asset is calculated as weighted average of the return of its components A and B However, the standard deviation of the portfolio will not be simply the weighted average of the standard deviation of the two assets It is essential to consider the covariance or correlation between the two assets The covariance between two assets will indicate the movement tendencies of the returns of the two assets If the two assets are not perfectly correlated ( , the

the correlation between two assets in the portfolio is, the lower the standard deviation and variance of the portfolio are In the case of three assets, the result can be implied similarly In conclusion, diversifying reduces the risk of portfolio Under the assumption of risk aversion, rational investors would prefer to invest into the portfolio that offer higher expected level of return for the same level of risk or lower risk for the same level of expected return

In 1958, James Tobin expanded the Markowitz’s work and introduced the risk-free asset concept Risk-free asset is defined as an asset which generates a certain future return

As such, investors in the market have to decide the appropriate investment proportions in the risk-free asset and risky assets The connection of various collections of these assets is known

as the Capital Allocation Line Depends on the risk behavior in general or the risk aversion

level in particular, the investors will determine risk/return tradeoff

According to the definition of risk free asset that generates a future return certainly, it can be inferred that the standard deviation of risk free asset ( is zero since standard deviation of an asset represents for its risk Moreover, the correlation between risk free asset and any other risky assets or portfolio ( is zero Assume the expected return of the risky asset (or portfolio) A is is the weight of A and the standard deviation of the return of the risky asset is , then the expected return and the risk of the portfolio includes a risk free asset and A are as follows:

(3) √ (4) Solve for in the equation (3) and replace into the equation (4):

Graph the equation (5) into the mean – standard deviation of the portfolio space, the

Capital Allocation Line (CAL) is defined:

Figure 2.2 The Capital Allocation Line

2.1.2 The Capital Asset Pricing Model

The Capital Asset Pricing Model (CAPM), introduced by Sharpe (1964) and Lintner (1965), describes the relationship between the expected return on an asset (a portfolio) in a market and risk According to Sharpe-Lintner CAPM, two types of risk are identified:

systematic risk and unsystematic risk Unsystematic risk (also known as “specific risk” or

“idiosyncratic risk”) is specific to individual stocks The announcement of a small oil strike

by a company may affect that company alone or a few other companies Certainly, it is unlikely to have an effect on the world oil market or companies in other industries Under MPT theory, specific risk can be diversified away as investors increase the number of stocks

in their portfolio It represents the component of a stock’s return that is not correlated with general market moves The same is not possible for systematic risk within one market Diversification still cannot solve the problem of systematic risk; even a portfolio of all the shares in the stock market cannot eliminate that risk A systematic risk is any risk that affects

a large number of assets, each to a greater or lesser degree Uncertainty about general economic conditions, such as GDP, interest rates or inflation, is an example of systematic risk These conditions affect nearly all stocks to some degrees

There are two primary relationships derived from CAPM theory: (1) the capital market line and (2) the security market line

2.1.2.1 The Capital Market Line

The Capital Market Line (CML) expresses the return that a single investor expects to

obtain for holding a stock portfolio Moreover, the CML is the CAL for market portfolio This can be described as a linear function of the expected return on the risk of portfolio and can be written as follows:

( ) [

]

where:

 is portfolio return,

 is the risk free rate return,

 is the market portfolio return,

 is the standard deviation of portfolio returns,

 is the standard deviation of market portfolio returns

According to (1), the expected return of a portfolio equals to the sum of a risk free rate

of return, which is a reward for delaying current consumption, and a compensation for bearing risk in the portfolio

2.1.2.2 The Security Market Line

In the Security Market Line (SML) model, the expected return of an asset or portfolio

is given by the following linear function of risk free rate and relative risk of an asset or portfolio:

[ ] (7) where:

 is the expected return of security i,

 is the risk free rate,

 is the expected return of the market portfolio,

 [ ] is market risk premium,

 is the stock i’s sensitivity to the overall market movement,

 is the correlation between stock return and market portfolio return

Consequently, the expected return of an asset will be equal to the risk free rate plus compensation for the exposure of market risk The following SML can graph the relationship between β and expected return of an asset:

Figure 2.3 The Security Market Line

SML is a useful tool in determining if an asset being considered for a portfolio offers a reasonable expected return for risk If the security's expected return versus risk is plotted above the SML, it is undervalued since the investor can obviously expect a greater return for these levels of risk Conversely, a security plotted below the SML is overvalued since the investor would be accepting less return for the given amount of risk

As a result, CAPM theory indicates that every asset should be priced appropriately If

an asset is priced in the way that it offers a different return from the CAPM’ result, the price

of stock will be adjusted by the demand and supply force in the market For example, if the stock price proposes a higher return than what CAPM predicts, rational investors will rush to buy this stock, leading to an increase in demand, reflecting in higher price and lower return subsequently Similar mechanism happens on the market in the case stock price offers a lower return than it is implied by CAPM

In summary, CAPM theory concludes that the difference in expected return across stocks is only due to their systematic risk, or market beta This simplicity in expressing the relationship between expected return and risk makes CAPM attractive and play as a dominant role in the finance literature for two next decades The CAPM is also known as single factor asset pricing model

However, similar to any theory, CAPM has received numerous criticisms from scholars over the years, mostly from who advocate the multi factor asset-pricing model and imply that the expected return cannot be explained by the market beta alone

2.1.3 The Arbitrage Pricing Theory

Perhaps, the earliest and most “severe” competitor of CAPM theory is the Arbitrage Pricing Theory (APT) The APT was developed by the economist Stephen Ross in 1976 Arbitrage can be explained simply that if there are two identical products but different prices, people may earn some profits by purchasing one at a lower price and sell at a higher one without taking any incremental risk In the APT, Ross (1976) claimed that the expected return

of an asset depends not only on systematic risk, which is the central of CAPM, but also on a number of factors such as various macroeconomic or firm specific factors Indeed, the number of factors is unspecified and each of factors’ sensitivity to change in stock return is represented by a beta coefficient Thus, the return of a risky asset can be written as:

(8) where:

 is expected return on an asset,

 is a constant return for asset j Moreover, is equal to return of asset j

when the other factors are 0 can be also understood as risk free asset,

 is the sensitivity of the asset j to factor k, also called factor loading,

 is factor that affect the return

In the special case when market portfolio is the unique factor that proportion to expected return, APT is identical with CAPM Obviously, there are two main differences between APT and CAPM The first is regarding to theoretical aspect While the CAPM indicates there is only one non-company factor corresponded with one beta, the APT points out various macroeconomic and firm specific factors and separate beta coefficients are required for each of these factors On the other hand, the second difference is about the practical Unlike CAPM in which beta is found through a linear regression of historical stock return on the related factor, the APT seems not to reveals the factors itself The factors need

to be determined empirically Chen, Roll, and Ross (1986) were the first in examining the significant macroeconomic factors that explain stock returns In specific, the factors that were already found so far in the literature are different from contexts and times

2.1.4 Fama-French three-factor model

After 25 years since the Sharpe-Lintner CAPM was introduced and applied in examining the return on equity in the stock market, Eugene Fama and Kenneth French realized that Sharpe-Lintner CAPM was not able to explain the average return on equity of the USA Stock Market during the 1963-1990 period Therefore, Fama and French (1992) started observing stocks that perform better than the whole market and found that most of the variation in the expected return of these stocks can be described by the size and book-to-market ratio In their later work, Fama and French (1993) re-examined this issue and stated that the excess market return, the size factor and the book-to-market factor could explain US average cross section stocks return On that basis, Fama and French (1993) suggested that

there were three risk premium sources need to be considered as follows: (1) an excess return

of the market portfolio return compared to the risk free rate, or the market risk premium This

is the risk investors have to face in general for owning stocks; (2) an excess return between a

high (called Value stock) and a low book-to-market ratio portfolio (called Growth stock)

Since the value stocks tend to generate higher earnings growth rate in the long run compared

to the growth stock, investors may need more premium to compensate for holding these value

stocks This difference is called HML (High Minus Low); and (3) an excess return between a

small and a big market capitalization portfolio Again, the small market capitalization stocks may create higher return than high ones in the long run Thus, a premium is required This difference is called SMB (Small Minus Big)

An expected rate of return of a risky asset is determined by Fama-French three-factor (FF3F) model by following equation:

) (9) where:

 is the expected return of a stock or a portfolio,

 is the risk-free asset,

 is the expected return of market portfolio,

 is the expected difference of the returns between small minus big capitalization portfolios,

 ) is the expected difference of the returns between high minus low book-to-market portfolios

, are the coefficients of the market risk premium, SMB and HML factors, respectively in following times series regression:

( ) (10)

In short, Fama and French (1992) argued in the case the asset pricing is rational, two additional factors - size and book-to-market – should stand for risk While SMB represents for the risk in return related to the size, HML catches the risk from the book-to-market ratio

It is reasonable to debate that the root of explanatory powers of size and book-to-market factors is the correlation of these variables and the traditional one, market beta Fama and French (1992) targeted to this point and showed that, besides being not able to explain average returns alone, the correlation between market beta and these two variables are within 0.15

2.1.5 The Carhart four-factor model

The work of Fama and French (1992), after published, acted an especially important role since it covered all the research had been done during two decades related to asset pricing models and put them together into one formula, the FF3F model Jegadeesh and Titman (1993) and Fama and French (1996) found that the strategy in which buying stocks performed well in last 1-6 months (“past winner”) and selling stocks performed poorly in last 1-6 months (“past loser”) might create an increase in earnings This phenomenon is known as momentum in stock, which states that the stock price will keep its tendency to continue going

up if it is rising and going down if it is declining Unfortunately, the FF3F model cannot explain this fact By adding momentum factor to FF3F model, Carhart (1997) extended the original model and proposed a new model with four factors, called Carhart four-factor (C4F) model, as follows:

[ ] (11)

in which:

 is the expected difference between returns on diversified portfolios

of the winner and the loser stocks,

 Other variables are defined similarly in the FF3F model

The Carhart four-factor model is not as common as the FF3F model or CAPM In addition, it is applied widely in mutual fund evaluation model

2.1.6 The Fama-French five-factor model

In recent work, Fama and French (2015) concluded that the Asset Pricing Model can

be divided into two types: (i) a theoretical model (ii) an empirical model The theoretical Asset Pricing Model is the model, which begins from the investors’ interest assumptions and opportunities in investment portfolios, then ends by forecasting the expected return and risk based on their relationship Meanwhile, the empirical Asset Pricing Model is based on historical data sample on average return and look at how stocks behaved From these results,

a research model is suggested to explain backwards the return of assets According to this division, the FF3F model is considered as an empirical model as this model is constructed from the relationship between the realistic asset’ return and its size, book-to-market ratio and general market factors Fama and French (2015) asserted that the added factors at the time of

1993 were the well-known components in stock returns for what Sharpe-Lintner CAPM was not able to explain

However, the studies of Titman, Wei, and Xie (2004), Novy-Marx (2013) and others suggested that FF3F model is an incomplete model for expected returns because much of the variation in average returns cannot be explain by those factors Driven by these evidences, Fama and French (2015) started with familiar discounted dividend model as follows:

∑

where:

 is stock price at time t,

 is the expected dividend per share for time (t+),

 is the long-term average expected stock return or the internal rate of return

on expected dividends

Considering stocks of two firms at time t, Equation (12) implies that the stock with lower price generate a higher expected return in the case these stocks offer equal expected dividends but different prices As such, investors will have to face with higher risk for the future dividends of lower price stocks To be more specific, according to Miller and Modigliani (1961), Equation (12) could be expanded to show the relations between expected return and expected profitability, expected investment, and B/M as follow:

∑

where:

 is the total equity earnings for period t+,

 , is the change in book equity

Dividing two sides of Equation (13) by the book equity at time t yields Equation (14):

∑

From Equation (14), there are three possible scenarios for the expected stock returns First, lower value of stock price or equivalently higher value of book-to-market B/M suggest a higher expected return, if other components are kept unchanged Second, keeping the stock price and other components fixed, Equation (14) tells that a higher expected future earning, a profitability indicator, yields a higher expected return Finally, lower expected growth in book equity – an investment tendency - implies a higher expected return, ceteris paribus

By analyzing the Equation (14), it is shown that the expected stock return is not only affected by the book-to-market ratio, but also the profitability and investment tendency of stocks As a result, Fama and French (2015) added these new factors into the traditional FF3F model to complete the Fama-French five-factor model:

2.1.7 The DuPont analysis

The DuPont analysis model was built up by engineer Donaldson Brown in 1918, when

he was working for DuPont Co-operation At that time, Donaldson Brown was assigned to consider and understand the financial performance of General Motors, a car manufacturer

company that DuPont was going to acquire He found that when multiplying the two common ratios, the total asset turnover and net profit margin, yields a new ratio that is the return on total asset (ROA) The interesting finding that ROA was affected by a profitability indicator

(net profit margin) and a efficiency indicator (total asset turnover) made DuPont method

become widely used in financial analysis in large cooperation in America

Generally, DuPont model decomposes the profitability ratio into traditionally operational management ratios to explain and analyze the firm’ return improvement ability

In the origin version, DuPont model uses the ROA as follows:

As a result, maximizing ROA is a common objective of companies By realizing both profitability and efficiency measure influence ROA, better strategies on plan development and decision control are applied This origin DuPont model kept an important role in financial analysis until 1970s

According to Gitman (2000), the widely accepted objective of financial management changed time to time Maximizing the wealth of equity owners becomes the most important goal of a company Therefore, a more appropriate return ratio, the return on equity (ROE), replaced ROA in DuPont model This leaded to the first adjustment from the origin DuPont model In specific, ROE is decomposed as:

As such, the leverage is the third concern of financial managers besides the two-used ratio in the origin version, the profit margin and asset turnover Thus, to improve the

operation efficiency, or to improve ROE, firms have various choices on the basis of combining these three components

Over time, there are some other adjustments to the origin DuPont model to achieve the most appropriate model with financial analysis needs Typically, Nissim and Penman (2001) developed an adjusted version of the DuPont model to eliminate the effect of financial leverage and other factors that firm’s manager cannot control In specific, these authors re-

arranged ROE algebraically and converted it into the return on net operating assets ratio (RNOA) as follows formula:

where:

 RNOA is the return on net operating assets,

 FLEV is the financial leverage,

 SPREAD is the difference between return of the firm’s operations and borrowing costs

Recently, Hawawini and Viallet (2010) introduced another adjusted DuPont model in which the ROE is decomposed into five different components as follows:

where:

 ROE is the return on equity,

 EAT is the earnings after tax,

 EBT is the earnings before tax,

 EBIT is the earnings before interest and tax, and

 IC is the invested capital

From this formula, ROE is affected by five components: (1) operating profit margin (EBIT/Sales); (2) capital turnover (Sales/Invested capital); (3) financial cost ratio (EBT/EBIT); (4) financial structure ratio (Invested capital/Equity); and tax-effect ratio

(EAT/EBT) Each ratio captures different effects on firm’s profitability in general and ROE

in particular The first two ratios, operating profit margin and capital turnover, reflect the

influence of the investing and operating decisions of the firm The effect of the firm’s

financing policy is captured by the third, financial cost ratio, and the fourth ratios, financial structure ratio The last ratio explains the effect of corporate taxation

2.2 Empirical literature

2.2.1 Empirical evidences on the asset pricing models

Almost immediately since introduction in 1964 - 1965, CAPM theory has been testing for its implication by empiricists Among number of empirical tests, the works of Fama and MacBeth (1973) and Jensen et al (1972) were the first studies which support the validity of CAPM theory According to what is indicated from CAPM, the market beta is the unique factor that matters the variations in expected return across stocks, other variables should add nothing into explanatory power Moreover, there should be a linear relationship between these two variables, expected stock return and market beta By investigating both the cross sectional and times series approaches, Jensen et al (1972) proved that the intercepts from those regression of expected return on market return are equal to zero Starting from another aspect, Fama and MacBeth (1973) added two new explanatory variables into the regression equation The first variable was the square of market beta with the objective to consider whether the related relationship is linear or not The second variable was the variance of residual resulted from regressing return on market The reason behinds the residual variance

is that: if there are any other stock characteristics that could explain the return, they appear in the residual However, none of these two variables is useful

The earliest study gives a different signal from CAPM was conducted by Basu (1977)

In this work, he discovered that there was a difference in return related to earning/price ratio (E/P) Those stocks with high E/P could generate a significant higher return than stocks with low E/P The critiques continue to investigate deeply the CAPM In a later study, Basu (1983) found stocks with low market capitalization, on average, have higher return compared to the high market capitalization stocks Another problem with CAPM was found by Rosenberg, Reid, and Lanstein (1985) They provided evidence that the stocks with high book-to-market ratio created a notably higher return than stocks with low book-to-market ratio Bhandari (1988) was against CAPM theory by proving that stocks with higher leverage tent to have average returns

The work of Fama and French (1992), after publishing, acted an especially important role since it covered all the studies had been done during two decades related to asset pricing models and put them together into one formula In specific, they examined size, book-to-market, leverage, E/P and market beta again These two authors came to the conclusion that: (1) the effect of E/P on average return is reflected completely by Size and book-to-market

factors; (2) the book-to-market ratio captures the role of leverage With these interesting findings, Fama and French (1992) completed their model, the FF3F model Moreover, Fama and French (1993) expanded their work with more testing variables and different method and found an even more supported information for their previous findings in 1992 Although it gave some interesting results, a large number of scholars disagreed with this theory because it was not built upon any theoretical basis The SMB and HML factor are considered as self-financing portfolios due to its formation Black (1993) believed that the Fama-French’ findings were a lucky result because there were hundreds of people search for stock returns explanatory variables every day In addition, more important, the FF3F model had to deal with the view that the data mining or data snooping may be a core cause of what Fama French found (MacKinlay, 1995)

The work of Fama and French in 1992 leaded to one of the most intense argument in the finance history In October 2013, the Nobel Prize in Economics Science has awarded to Professor Eugene Fama for his contribution in term of Asset pricing model The FF3F has been attracting a lot of scholar’s attention within two recent decades There have been hundreds of quantitative studies conducted worldwide in various time periods and contexts in order to criticize or to improve the model Nevertheless, the jury is still out on that question (Gaunt, 2004; O’Brien et al., 2010) There are many studies conclude that the new added factors in the FF3F are insignificant or do not have the expected sign Moreover, the quantitative results from the FF3F are usually considered as “data mining” and there is no robust theoretical framework relating to this model (Kogan & Tian, 2013; Wang & Wu, 2011) Although the later model is not as common as the FF3F, the C4F model receives the same critiques for the added 12-month momentum factor

In the rapid development of literature that attempts to identify new return predictive signals, more and more factors have been found For example, Subrahmanyam (2010) found

50 factors; McLean and Pontiff (2014) identified 82 signals; Green et al (2013) established

330 firm-specific signals In particular, Harvey et al (2014) reported 315 factors and classify them into common and individual risk type as follows:

Table 2.1 Factor classification

factors found Common Financial Proxy for aggregate financial market movement,

including market portfolio returns, volatility, squared market returns, etc

46

fundamentals, including consumption, investment, inflation, etc

40

Microstructure Proxy for aggregate movements in market

microstructure or financial market frictions, including liquidity, transaction costs, etc

11

Behavioral Proxy for aggregate movements in investor

behavior, sentiment or behavior-driven systematic mispricing

3

Accounting Proxy for aggregate movement in firm-level

accounting variables, including payout yield, cash flow, etc

8

Other Proxy for aggregate movements that do not fall into

the above categories, including momentum, investors’ beliefs, etc

5

Individual Financial Proxy for firm-level idiosyncratic financial risks,

including volatility, extreme returns, etc

61

Microstructure Proxy for firm-level financial market frictions,

including short sale restrictions, transaction costs, etc

28

Behavioral Proxy for firm-level behavioral biases, including

analyst dispersion, media coverage, etc

3

Accounting Proxy for firm-level accounting variables, including

PE ratio, debt to equity ratio, etc

87

Other Proxy for firm-level variables that do not fall into

the above categories, including political campaign contributions, ranking-related firm intangibles, etc

24

Source: Harvey et al (2014)

In the context of mixed and ambiguous FF3F and C4F results, Graham and Harvey (2001) conducted an very interesting survey on 392 United State CFO (Chief Financial Officer) about how their firm calculate the cost of equity capital The result showed that 73.5% of them use the original CAPM Brounen et al (2004) carried out a similar study with

313 European’s CFO and 43% claimed that they rely on CAPM In term of practical applications, according to Mckenzie and Partington (2014), regulators in Australia, Germany,

New Zealand, USA, Canada and UK are still currently basing their decisions primarily on the CAPM framework (see Appendix 1 for details) Recently, Professor Fama and his companion, Professor French, have just introduced a new model named Fama-French five-factor with the objective to explain the return on equity on USA Stock Market (Fama & French, 2015) This FF5F model is an augmented version of FF3F model by adding profitability factor (Novy-Marx, 2013) and investment tendency factor (Aharoni et al., 2013)

In Vietnam, there are also some studies with the objective to test the validity of multi factor model in general However, the results are still mixing Typically, Vuong and Ho (2008) were the first to investigate the issue At that time, the market was not developed well Aiming at the stocks that listed over 3 years in the Ho Chi Minh Stock Exchange (HOSE), the sample included just 28 stocks from 01/2005 to 26/03/2008 By using OLS, these authors concluded that besides the objective impact of the market, the expected rate of return of stocks was affected by firm-specific characteristics such as SMB factor with positive effect and HML factor with negative effect The results also indicated that among three factors, the market risk premium played an important role They suggested one possible explanation for this fact was that investors in HOSE were interested in firm-specific factors at a lesser extent that the market tendency

Phan and Ha (2012) examined the stocks in HOSE during 2009 – 2011 period With

749 firm-year observations, they suggested that the FF3F model was more explanatory than CAPM Moreover, based on the adjusted R2 indicator from OLS regression, the C4F model explained the variation in stock returns better than FF3F model In addition, they found positive effects of both SMB and HML factor on expected return

Phong and Hoang (2012) explained it is essential to discover more risk factors related

to expected return in Vietnam stock market In their study, stocks listed over 2 years and stop trading in both Ho Chi Minh Stock Exchange (HOSE) and Ha Noi Stock Exchange (HNX) from 2007 to 2011 were utilized Then, these stocks were divided into 6 groups: 2 groups based on size factor and 3 groups based on book-to-market factor The OLS regression results, however, were not expected While the SMB factor kept the positive effect

non-on expected return, the effect of HML was not significant at some portfolios, i.e the B/M and B/H

As the mentioned objective to test the validity of FF3F model in HOSE, Truong and Duong (2014) used the non-financial stocks in 2006-2012 period In this study, a GARCH (1,

1) were applied besides the OLS method The results showed that all the three factors had the significantly positive effect on expected return Nonetheless, when considering at 6 portfolios, the outcomes were quite complex The SMB factor kept its expected sign at most portfolios except one with big size and high book-to-market ratio The HML factor was significant at all portfolios More interesting, the effect of HML variable was larger with higher book-to-market ratio

Vo and Mai (2014a) studied the issue with the sample of 281 listed companies in HOSE

in the year 2007-2013 Employing the two-stage cross-sectional regression and five different portfolio construction methods, these authors reached following conclusions: (1) different ways to construct portfolio leaded to different results, both in value and in the significance of the coefficients; (2) the market beta was the best pricing factor of FF3F model; (3) the HML factor seemed to explain the average stock return better than SMB In addition, Vo and Mai (2014a) recommended researchers, companies, and investors to be more cautious about confirming values obtained with the FF3F model

The presence of FF5F model attracts the special attention of scholars and policy makers In this context, Vo and Mai (2014b) did as pioneers in applying this model into Vietnam market Using the sample of 281 listed companies in the 2007-2013 periods, the results suggested that beta has the correct expected sign and statistically significance Moreover, for the two traditional factors in the three-factor model, while the Value factor had

a strong explanatory ability for the stock returns, the Size factor did not For the two new added factors, the Profitability factor could explain the stock return; however, the Investment factor showed an unexpected signs

2.2.2 Current approaches to estimate β

The systematic risk, which cannot be managed through portfolio diversification, can be obtained from the following regression:

(16)

in which, the residual is

In 2009, Associate Professor Henry from the University of Melbourne, Australia established his work in estimating equity beta for the Australian Utilities regulation as an advice to the Australian Competition and Consumer Commission (Henry, 2009) Five years

later, Henry and Street (2014) updated the estimates In these two studies, the Ordinary Least

Squares (OLS) and Least Absolute Deviations (LAD) approaches are utilized

Vo et al (2014) re-examined the estimates of beta in the Australian regulatory context

In their study, a data set was updated in comparison with Henry’s study in 2009 In addition, another key contribution from Vo et al (2014) study was that two new approaches were added in their study: (i) the Maximum Likelihood robust theory (MM) and (ii) the Theil Sen methodology For each of these new approaches, the authors argued that among the robust regression estimators currently available, the MM regression had the highest breakdown point (50 percent) and high statistical efficiency (95 percent) while the Theil Sen estimator was proposed by Fabozzi (2013) in response to the OLS estimator being acutely sensitive to outliers

The two new approaches adopted in Vo et al (2014) study were a choice of different, arguably more advanced, econometric techniques However, for the purpose of this study, these two new approaches are not considered Instead, this study considers that it is even more appropriate to introduce a new approach, a quantile regression, which is best known for its capacity to limit the effects of outliers on the estimates In addition, other two traditional approaches, the OLS and the LAD, are also in use in this study

2.2.2.1 Ordinary Least Squares

The OLS method estimates the and in the equation (16) by minimizing the sum of squared residuals:

∑ ∑ ̂

∑ ̂ ̂

The coefficient from OLS indicates the average relationship between the regressor and the outcome variable based on the conditional mean function

2.2.2.2 Least Absolute Deviations

In the LAD approach, the absolute value of residuals is minimized to achieve the estimates from equation (16) as follows:

∑| | ∑| ̂ |

∑| ̂ ̂ |

Since the sum of the absolute value of residuals is minimized rather than minimizing the sum of squares, the estimators obtained from the LAD method may alleviate the effect of outliers

2.3 The use of DuPont analysis on asset pricing model

The previous empirical studies related to DuPont analysis stopped at finding the predictive factors of future changes in profitability (Fairfield & Yohn, 2001; Nissim & Penman, 2001; Penman & Zhang, 2006)

On the other hand, in order to valuing a stock or a company, there are many different

approaches The familiar Dividend Discount Model (DDM), proposed by Gordon (1959),

expresses the stock price as a function of net present value of expected future dividend

Stemming from the assumption of clean-surplus accounting, the Residual Income Valuation (RIV) describes the stock prices in terms of accounting numbers in an algebraically

equivalent model with DDM This model is sometimes known as Edwards-Bell-Ohlson (EBO) valuation equation due to its origins in Edwards and Bell (1965) and Ohlson (1995) Accordingly, the stock price can be expressed as following accounting information:

∑

where:

 is the current stock price,

 is the book value at time t,

 is expectation based on information available at time t,

 is the return on book equity for period t+1,

 is the cost of equity capital

The important role of ROE in the performance of valuation models and residual income model was emphasized by Ohlson (1995) Combining this residual income valuation approach and DuPont analysis, Soliman (2008) pointed out that DuPont components are used

in evaluating the prospects of the firm by market participants More specific, he concluded

that the there was a positive relationship between stock returns and changes in asset turnover This finding leaded to a fact that changes in asset turnover is considered as one of return

predictive signal in 315 signals reported by Harvey et al (2014) A similar research framework was conducted by Chang, Chichernea, and HassabElnaby (2014) in the US health

care industry However, in these studies, the authors used the adjusted version of DuPont

model of Nissim and Penman (2001) in which RNOA is utilized and decomposed into profit margin and asset turnover Then, these components are added into the regression model to

examine the effect on stock returns

To sum up, the traditional Sharpe-Lintner CAPM will be applied with various econometric techniques to estimate the equity beta coefficients for stocks, which is related to the first and second objectives of this study The third objective is solved by the combination

of the Residual Income Valuation model and DuPont analysis to find out the explanatory factors of expected returns