UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS 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 TIEU LUAN MOI download : skknchat@gmail.com UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS 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 By PHAM NGOC THACH Academic Supervisor Dr. VO HONG DUC Ho Chi Minh City, November 2015 TIEU LUAN MOI download : skknchat@gmail.com 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 i TIEU LUAN MOI download : skknchat@gmail.com 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 ii TIEU LUAN MOI download : skknchat@gmail.com 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 iii TIEU LUAN MOI download : skknchat@gmail.com 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. iv TIEU LUAN MOI download : skknchat@gmail.com 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. v TIEU LUAN MOI download : skknchat@gmail.com “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 vi TIEU LUAN MOI download : skknchat@gmail.com TABLE OF CONTENTS DECLARATION. IV TABLE OF CONTENTS.
VII LIST OF TABLES. X LIST OF FIGURES. XI CHAPTER 1 INTRODUCTION .4 Contributions of the thesis .5 Structure of the thesis. 5 CHAPTER 2 LITERATURE REVIEW .1 Modern Portfolio Theory.2 The Capital Asset Pricing Model .1 The Capital Market Line .2 The Security Market Line .3 The Arbitrage Pricing Theory.4 Fama-French three-factor model .5 The Carhart four-factor model.6 The Fama-French five-factor model .7 The DuPont analysis.
17 vii TIEU LUAN MOI download : skknchat@gmail.1 Empirical evidences on the asset pricing models .2 Current approaches to estimate β .1 Ordinary Least Squares .2 Least Absolute Deviations.3 The use of DuPont analysis on asset pricing model. 26 CHAPTER 3 METHODOLOGY AND DATA .1 Utilities industry in ASEAN 5 .2 Beta ranking for all industries and asset pricing factors in Vietnam market .1 Estimating beta in Capital Asset Pricing Model .1 Return and return period .2 A new approach – Quantile regression .4 De-levered/Re-levered estimates of β .2 Beta ranking construction .3 The use of DuPont on asset pricing model .1 Model specification and estimation method. 36 CHAPTER 4 RESULTS AND DISCUSSIONS .1 Objective 1: Estimating the beta coefficients for the utilities industry in the ASEAN 5 .1 Individual companies’ beta estimates .2 Beta estimates of various portfolios. 40 viii TIEU LUAN MOI download : skknchat@gmail.3 De-levered/Re-levered estimates of β .2 Objective 2: The Risk-Return framework for various industries in Vietnam .3 Objective 3: New explanatory factors of expected stock returns in the Vietnam context.
53 CHAPTER 5 CONCLUSIONS AND POLICY IMPLICATIONS .1 For the Vietnamese government .3 Limitations and further study. 67 ix TIEU LUAN MOI download : skknchat@gmail.com LIST OF TABLES Table 2. Factor classification Table 3. Listed utilities companies in the sample Table 3.
Summary of non-financial listed companies in HOSE Table 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. Estimates of equally-weighted portfolios equity beta Table 4. 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.
De-levered/Re-levered estimates of β for weekly frequency: Portfolios Table 4. List of industry and related information in Vietnam Table 4. Risk-Return framework for the Vietnam market Table 4. Descriptive statistics Table 4.10 The correlation matrix and Variance Inflating factor among variables Table 4.
Heteroskedasticity and Autocorrelation test Table 4. Regression results x TIEU LUAN MOI download : skknchat@gmail.com LIST OF FIGURES Figure 2.1 The Efficient Frontier Curve .2 The Capital Allocation Line.3 The Security Market Line. The scatter plot of Portfolio 1’s returns and market returns Figure 4. Risk-Return framework xi TIEU LUAN MOI download : skknchat@gmail.com 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).