Liquidity premium in stock returns, the case of vietnam

From this gap in literature, this empirical research is conducted to examine the influence of liquidity on stock returns in Vietnam stock market, a frontier market.. LIST OF ABBREVIATION

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

VIETNAM – NETHERLANDS PROGRAM FOR MA IN

DEVELOPMENT ECONOMICS

LIQUIDITY PREMIUM IN STOCK RETURNS,

THE CASE OF VIETNAM

A thesis submitted in partial fulfilment of the requirement for the degree of

MASTER OF ART IN DEVELOPMENT ECONOMICS

DECLARATION

It is to certify that this thesis entitled “Liquidity premium in stock returns, the case of Vietnam” meet all requirements for the Master Degree of Art in Development Economics This thesis and all contents presented in it are developed by me as my own original research It is neither

in part nor in whole been presented for another degree elsewhere

ACKNOWLEDGEMENT

Firstly, I would like to express my profound appreciation to my supervisor, Dr Phạm Phú Quốc He has kindly guided and shared with me his experience as well as knowledge about conducting a research He always reminded me and assist me in selecting the right path for my thesis

I also would like to say thank to Dr Võ Hồng Đức, who initially shared with me the idea about research in stock returns

My special thanks to Nguyễn Duy Tân and Võ Thế Anh, who dedicated their time and effort in helping me attain the huge data set for this thesis as well as overcame number of obstacles during my thesis

I would like to express my sincere gratitude toward all lectures and staffs in Vietnam Netherlands program for their kindness and dedication in teaching and providing the best study environment

Furthermore, I would like to say thank to all my friends in the course of my study at this program We have studied and been through many subjects, assignments together They always beside and remind me whenever I feel discouragement so that I can finally finish this thesis

Last but not least, I am indebted to my parents who gave me all the love and support for every steps of my life Their contribution is enormous and I can never pay back this

ABSTRACT

Research about the role of liquidity in explaining stock returns has mainly been conducted

in developed market and yielded ambiguous conclusion about its explanatory power From this gap in literature, this empirical research is conducted to examine the influence of liquidity on stock returns in Vietnam stock market, a frontier market From literature of liquidity and stock returns, there are number of available proxies for liquidity In this research, Turnover and Amihud illiquidity ratio are selected as two main liquidity proxies These two proxies were selected because they showed a great consistency and reliability among available liquidity proxies for empirical research This study also includes some common explanatory variables in stock return literature as control variables in empirical regressions These variables are five premium factors of Fama and French as well as cumulative returns factor All of these factors are constructed by using portfolio formation method of Fama and French The sample for this research includes all non- financial firms in Ho Chi Minh stock exchange (HOSE) for period 2007 to 2013 The regression method is Fama MacBeth which is often employed in finding stock returns This research reveals some noticeable findings Firstly, liquidity negatively related to stock returns This finding was reliable

as two liquidity proxies point to the same conclusion Secondly, all Fama and French factors showed that they are very effective in explain stock returns as all these variables present very convincing results Thirdly, the empirical result from this study fail to support the role cumulative return factor in explaining stock returns in Vietnam

Key words: Fama and French factors, turnover measure, Amihud illiquidity ratio, stock returns, Fama McBeth regression, listed companies, SMB, HML, CMA, RMW

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION 1

1.1 PROBLEM STATEMENT: 1

1.2 RESEARCH OBJECTIVE: 4

1.3 RESEARCH QUESTION: 4

1.4 RESEARCH SCOPE: 4

1.5 RESEARCH METHODOLOGY: 4

1.6 THE STRUCTURE OF THIS THESIS: 5

CHAPTER 2: LITERATURE REVIEW 7

2.1 FUNDAMENTAL THEORIES: 7

2.1.1 The Efficient Market Hypothesis: 7

2.1.2 Modern Portfolio Theory: 8

2.1.3 The Capital Asset Pricing Model: 11

2.2 ASSET RETURNS LITERATURE: 13

2.2.1 Theoretical vs Data Mining Research: 13

2.2.2 Fama – French Three Factors Model: 14

2.2.3 Carhart Four Factors Model: 17

2.2.4 Fama – French Five Factors Model: 18

2.3 LIQUIDITY LITERATURE: 21

2.3.1 Transaction Cost Theory: 22

2.3.2 Subsequent Development in Liquidity research: 24

2.4 MAIN HYPOTHESIS: 27

CHAPTER 3: DATA AND METHODOLOGY 29

3.1 REGRESSION MODEL AND RESEARCH FRAMEWORK: 29

3.1.1 Regression Models: 29

3.1.2 Research Framework: 31

3.2 DATA 31

3.3 REGRESSION METHOD: 31

3.4 VARIABLES 32

3.4.1 Dependent Variables: 32

3.4.2 Explanatory Variables: 32

3.4.3 Control Variables: 37

3.5 DATA PROCESSING: 38

3.5.1 Primary Data Calculation: 38

3.5.2 Factor Construction: 40

3.6 SOLVING POTENTIAL ECONOMETRIC ISSUES: 46

3.6.1 Dealing with Heteroscedasticity and Autocorrelation: 46

3.6.2 Dealing with Multicollinearity: 47

CHAPTER 4: DATA ANALYSIS AND RESULTS 53

4.1 DESCRIPTIVE STATISTIC: 53

4.1.1 Turnover Measure: 53

4.1.2 Amihud Illiquidity Ratio: 54

4.2 ECONOMETRIC RESULTS: 55

4.2.1 Empirical Results for Turnover Measure: 55

4.2.2 Empirical Results for Amihud’s Illiquidity Measure: 57

4.3 DISCUSSIONS: 58

CHAPTER 5: CONCLUSION 61

5.1 OVERVIEW: 61

5.2 EMPIRICAL RESULTS: 61

5.3 CONTRIBUTIONS: 62

5.4 IMPLICATIONS: 62

5.5 LIMITATIONS AND FUTURE RESEARCH SUGGESTION: 63

REFERENCE 64

APPENDIX 67

APPENDIX 1: TWO STAGE FAMA MACBETH REGRESSION 67

LIST OF ABBREVIATIONS

HOSE : Ho Chi Minh City Stock Exchange

NYSE : New York Stock Exchange

EMH : Efficient Market Hypothesis

MPT : Modern Portfolio Theory

CAPM : Capital Asset Pricing Model

SMB : Small minus Big

CMA : Conservative minus Aggressive

RMW : Robust minus Weak

CHAPTER 1: INTRODUCTION

1.1 PROBLEM STATEMENT:

This problem statement will firstly discuss about the essential role of stock market in each country as the stock market provides multiple benefits for investors, corporations and country‘s economy Drawing from these benefits, the second paragraph will mention about the establishment

of stock market in most countries around the world With the establishment of stock market and involvement of many social parties, stock returns become a special interest for many participants, especially for participants who directly engaged in the market From this realistic need of many social parties about stock returns, the third, fourth and fifth paragraphs are dedicated for discussion about the quest of researchers in studying determinants of stock returns and the emergence of liquidity as one determinant of stock returns The rest of this problem statement will demonstrate that current trend of liquidity research was excessively concentrated on developed market and there

is an obvious need to have a thorough research about the influence of liquidity on stock returns in

a frontier market, such as Vietnam

Stock market is undeniably an integral part of each country economy Stock market provide number of benefits for individual investors, corporations and economy For corporation, stock market allow company to gain access to huge capital market Once the company is listed, it can expand its capital through share issuance In addition, merge and acquisition can be facilitated by share purchase in the stock market For investors, stock market provide investors a channel for investing their money There are many different types of companies which should suit the taste of different investors About economy, the key benefit of stock market is that it promotes economic growth by encouraging investors to put their saving into listed companies As a result, it encourages the companies’ development and promotes economic growth

With all of these advantages, stock market has been developed in many countries Developed countries have their stock markets established for over centuries ago With the establishment of stock market and involvement of many social parties, the stock returns become a special interest for many participants, especially for participants who directly involved in the market Specifically, for investors, stock returns is a major factor in deciding where their fund will

be invested For corporations, stock return should be a reliable gauge of companies’ performance

as investors purchase and sell companies’ stock based on its fundamental and prospect For economy and government, stock return is a dependable barometer for gauging the economic

condition of a country The stock price reflects the macroeconomic condition and major changes

in country economy The rise and fall of stock price often coincides with economic cycle of a country (Pujari, 2010) As a result, government can observes the health of their stock market and promulgates suitable policies to regulate and develop their own economy

From this realistic need, many researchers have been devoting their efforts in studying about determinants of stock returns and its mechanism The literature on stock returns nowadays

is incredibly enormous and many scholars still keep searching for unknown determinants and new methodologies

In reality, one factor that attracted attention of stock market participants and it has been noticed for a long time, this is liquidity Liquidity is commonly defined as the ability to purchase

or sell a large quantity of stocks quickly at low cost without affecting the price significantly (Choe

& Yang, 2008) Through many years, investors observed that liquid stock can be easily converted into cash without much difficulties In contrast, illiquid stock caused some level of difficulties for investors when they want to convert the stocks into cash It often requires investors to sell at a lower price or endure a greater transaction cost for the sale With this in mind, investors well aware

of the liquidity premium (the price spread between liquid and illiquid stocks) However, there wasn’t any remarkable study about liquidity until the well-known paper of Amihud and Mendelson (1986)

Amihud and Mendelson (1986) were the pioneers in liquidity research when they proposed the transaction cost theory In their landmark paper, they found out evidence that there is a significant liquidity premium in asset return Since the publication of this paper, many scholars started to develop this new field of literature Many new proxies for liquidity have been formulated due to the need for measuring liquidity in different stock market After years of liquidity research, some scholars agreed on the view that investors often require a higher rate of stock returns in compensating for illiquidity (Amihud, 2002), (Brennan & Subrahmanyam, 1996), (Brennan, Chordia, & Subrahmanyam, 1998), (Chordia, Roll, & Subrahmanyam, 2000) This basically means liquidity affects negatively on stock returns Nonetheless, there is divergence from the above viewpoint when other researchers found an opposite relationship They claimed that the relationship is actually positive To support this viewpoint, they also had their arguments and empirical researches (Bali, Peng, Shen, & Tang, 2014), (Abzari, Fathi, & Kabiripour, 2013)

An important fact is that the majority of researches about liquidity has been conducted in developed financial market, especially in United States (where most of data are available and it is

one of the most developed financial market in the world) However, Bekaert, Harvey, and Lundblad (2007) stated that the liquidity effect should be stronger in emerging market than in developed one They stated some rationales for their conclusion The first reason is that poor liquidity was the main factor that prevented foreign institutional investors from invest their money into different types of stock in emerging market As a result, this would intensify the liquidity premium (frontier market even suffers a greater liquidity gap than emerging market) Secondly, many emerging market went through market liberalization during their research period And this incident should assist researchers in study the importance of liquidity on expected return The reason is that, after liberalization, the liquidity often increases noticeably in comparison with prior liberalization period Thirdly, developed markets often have diversified ownership structure with both long term and short term investors Thus, Bekaert et al (2007) suggest the clientele effects in selecting investment portfolio should reduce the pricing of liquidity In case of emerging markets, the diversification in number of securities and ownership is lacked of which often intensify the liquidity effects

From above discussion, there is an obvious need for conducting a research about liquidity premium in frontier market, which is considered as a pre-emerging market The first reasons is that there is a divergence in viewpoint among researchers about the influence of liquidity on stock returns In addition, the liquidity gap between liquid and illiquid stocks in frontier market, such as Vietnam, is very significant Last but not least, the number of researches about liquidity in frontier market, especially in Vietnam, are very limited This cannot provide a thorough understanding about the role of liquidity in Vietnam stock market The only paper that I found, which studied the relationship between liquidity and stock returns in Vietnam, is from Xuân Vinh and Hồng Thu (2013) This paper, however, published a positive relationship between liquidity measures and stock returns (a negative relationship between illiquidity measures and stock returns) They provide two rationales for their findings The first rationale is that investors in Vietnam are small and trade frequently They believed this will boost the demand for large and liquid stocks and will create a higher return on these stocks The second rationale is that Vietnamese stock market is a newborn market and there are many newly listed stocks every year These new stocks will create

a huge uptrend in price and volume at their Initial Public Offer which cause an increase in both liquidity and stock price

I believed their rationale is quite vague and their method for constructing the liquidity measures is different from the well – known method of Fama and French (2013)

All above reasons have motivated me to conduct a thorough research about liquidity premium in Vietnam financial market using a well-known method from Fama and French (2013) This research hopefully will provide an additional piece in literature about liquidity premium in Vietnam stock market, a frontier market

1.2 RESEARCH OBJECTIVE:

The specific objectives of this study are:

 To empirically examine the influence of liquidity on stock return in Vietnam (a frontier market) by using renowned Fama and MacBeth (1973) method

1.3 RESEARCH QUESTION:

Does liquidity influence on stock returns?

1.4 RESEARCH SCOPE:

This thesis will focus on study the effect of liquidity on stock returns of listed companies

in Ho Chi Minh Stock Exchange (HOSE) from January 2008 to December 2013

The reason for choosing HOSE as the primary stock exchange for this study is because of following rationales: (1) HOSE require a more stringent process for audited financial information and public exposure than Ha Noi Stock Exchange (HNX), (2) HOSE comprises of bigger market cap and more prestigious companies than HNX, (3) the total market capitalization of HOSE is significantly larger than HNX (as of 16 April 2015, the market capitalization of HOSE is 1.051.445 billion VND, while HNX market capitalization is 139.980 billion VND), and (4) the market liquidity of HOSE is also remarkably greater than HNX (as of 16 April 2015, the daily traded amount in HOSE is 1.825 billion VND, while HNX’s daily traded amount is 729 billion VND) With all of these reasons, selecting HOSE as primary trading exchange for collecting data should ensure the quality and prevent possible biases for this research

The basic procedure for constructing premium factor includes three main phases for each variable All variables are derived from this procedure, except for SIZE, HML, RMW and CMA variables where their daily values are not available The first phase is primary data calculation in which required daily data for each variable is collected from Vietstock and financial audited statement The daily data for each variable will contain all non-financial firms traded in HOSE in

a specific year These individual firm data is then calculated based on a distinct formula of each variable to derive individual firm value for each variable These daily firm value of each variable

is then averaged out to obtain the average daily value of each firm during a year

In the second phase, all yearly average firm value of each variable will used to calculated breakpoint for premium portfolios These portfolios are constructed on annual basis in order to update about fundamental change in each variable for each year The breakpoints are decided in corresponding to each variable which are often selected at 30 and 70 percentile of yearly average value of all firms Subsequently, each firm is allocated into one appropriate portfolio corresponding to its own yearly average value

In the third phase, after each firm is allocated into one distinct portfolio based on its individual variable value, average monthly stock return of all firms within a portfolio is combined

to calculate the average monthly return of a whole portfolio The premium factor is finally derived

by subtracting the average monthly return of first and the last portfolio within each variable The underlying idea for this whole lengthy procedure is to compute the excess stock return by simply employing a passive investment strategy with each premium factor For example, with Size factor, the size premium is the excess stock return an investor can earn by simply buying stock of small companies and selling their current stock of large companies

1.6 THE STRUCTURE OF THIS THESIS:

This thesis contains six chapters with the following arrangement:

Chapter 1 provides an overview of thesis with problem statements for selecting this subject, research question, research scope, basic information about data and methodology Chapter 2 focuses literature review In this chapter, some fundamental theories in finance is first reviewed Then, related literature about dependent and independent variables will also be explained Notably, the theoretical and empirical studies about liquidity is carefully presented in a separate section of chapter 2 The final section of this chapter will be dedicated for the main hypothesis and its rationales

Chapter 3 concentrates on data and methodology Firstly, two regression models and research framework will be discussed to provide an overview to readers Following this section, data and regression method section will be presented After that, variables section will concisely introduce about dependent and independent variables in this thesis The subsequent section in chapter 3 will concentrate on data processing and construction of each variables The last section

of this chapter will be dedicated to potential econometric issues and how they can be solved in this thesis

Chapter 4 will focus on statistical and empirical result of this study At the end of the chapter, some discussions about the final result are also put forward which show noticeable observations as well as comments about this research Chapter 5 will be devoted for the conclusion, possible implications and some drawbacks of this thesis It also suggests a direction for further research about stock returns in frontier market, especially in Vietnam

CHAPTER 2 : LITERATURE REVIEW

The capital market theories laid the foundation for the subsequent formation of financial asset pricing models The current and prominent view of finance scholars support the view of perfectly efficient capital market in which financial asset prices is quickly and accurately adjust to new information as it is available to the market In a perfect efficient capital markets, investors are considered to be risk – averse and rational in their decision making

Under the assumption of perfect efficient capital market, Efficient Market Hypothesis (EMH) was developed and published by Fama (1970) which is considered as a fundamental theory

of modern finance However, the efficient market hypothesis didn’t give investors any guidance

in assigning their assets As a result, other theories are introduced to assist risk – averse investors

in allocating their assets in an efficient capital market The most renowned theory among them are Modern Portfolio Theory (MPT) (Markowitz, 1952)

Within the stream of market portfolio framework, risk – averse investors are believed to have homogenous expectations about the covariance, variance and mean of the asset returns, and they always strive to obtain maximum expected utility in their investment decisions In essence, MPT theory suggests a method to diversify and achieve optimal risk portfolio for investment The only source of risk to the portfolio is derived from its co-movement corresponding with the market portfolio Capital Asset Pricing Model (CAPM) is subsequently developed as an extension of the MPT theory, this theory provides investors with a tool to price assets in an efficient market

This literature will first review some fundamental theories of contemporary finance which are efficient market hypothesis, efficient capital market, and capital asset pricing theory These theories form the foundation for the current research in asset returns After reviewing these theories, the literature and empirical research of related variables in this study will be discussed Base on the literature and empirical review of all variables, main hypothesis for this study will be constructed

2.1 FUNDAMENTAL THEORIES:

2.1.1 The Efficient Market Hypothesis:

The concept of efficient market is used to define a market where investors cannot consistently outperform other investors by making abnormal return after adjusting for risk Fama

(1970) defined perfect efficient capital market as: “a market in which firm can make production

investment decisions and investors can choose among the securities that represents ownership of

firms’ activities under the assumption that the security prices at any time fully reflect all available information”

In trading activities, investors employ all trading information that available to them as tools

in order to make profits in the market There are numbers of information which available to investors The most basic instruments are the historical price patterns, traded volume data Other information is company related public announcement Besides, there is an existence of inside information which usually did not completely comprehended or accessible to all investors

Fama (1970) reviewed the work of previous researchers about asset prices in capital market and put forward his theory about different form of efficient capital market based on the types of information and how these information are incorporated into asset prices Fama (1965) asserted that there are three basic level of market efficiencies: strong form, semi – strong form and weak form Each type of market efficiency provide an increase level of market efficiency which excludes certain group of investors to continuously outperform the market by using specific type of mentioned information as their trading tools With an increasing level of market efficiency, specific types of information that investors utilize to outperform the market will become less effective because other investors would also study the use of these information and act correspondingly to it In weak form efficient market, all historical price patterns are fully incorporated into asset prices Therefore, technical analysts who employ price and volume data cannot generate a positive abnormal return on a regular basis in this form of market A semi –strong form level, EMH hypothesis asserted that asset prices incorporated all current and publicly available information into it, hence, fundamental analysts who utilize economic and company performance information cannot outperform the market in this form of efficient market In the strong form efficient market, the inside information do not only accessible to company insiders but to outsiders Hence, investors, who use inside information for making investment decision, do not outperform the market of this form

2.1.2 Modern Portfolio Theory:

On the ground of the efficient market hypothesis and principle of diversification, Markowitz (1952) developed a first theory of portfolio management using the concept of risk All investors are assumed to be risk – averse base on the expected utility theory (a conventional expected utility curve is plotted in Figure 1 below)

Figure 1: Marginal Utility and Risk Aversion

Figure 1 presents that the wealth is positive related to its utility as a greater wealth or asset position provide a higher utility to investors However, the curve also exhibits diminishing property which indicates the marginal utility derived from the increase in asset position will rise

at a slower pace than the increase in asset position The interpretation is that investors will not accept for risky investment without adequate compensation for its risk

The investment decision based on the utility function above is rational and do not expose

to any psychological biases as the decision is solely depend on the calculation different possible combination of asset portfolio in Equation 1:

E (U) = p 1 u(x 1 ) + p 2 u(x 2 ) +……+ p n u(x n ) (1)

With x 1, x 2 ,… x n are the possible asset position in a combination

P 1, p 2, …, p n are the probabilities associated with each asset position

Employing the concept of risk aversion into portfolio selection process, rational investors would prefer to select investment that offer higher expected level of return for the same level of risk or lower risk for the same level of expected return into their portfolio The expected value and risk for a portfolio is calculated in a manner of Equation 2 and 3 below which is a mathematical equation for mean and variance of 2 two assets:

E (Rp) = wi E(Ri) + wj E(Rj) (2)

 P 2 = wi2i2 + wj2j2 + 2 wi wj I j ij (3)

With wi and wj are the weight of each component in the portfolio

i and j are the standard deviation of each component in the portfolio

ij is the correlation between for every two components in the portfolio

From the above equation, portfolio return is calculated as weighted average of the return

of its components, whereas the risk of the portfolio is computed from the standard deviation of historical return of each component which is less than weighted average of the standard deviation

of its components It is due to the return of each component in the portfolio is rarely perfectly correlated, therefore, the specific risk of the portfolio is successfully diversified As Equation 3 shown, the lower the correlation efficient between each pair of component in the portfolio, the lower is the standard deviation and variance of the portfolio Therefore, the portfolio risk does not rise as the same ratio as the increase in expected return of portfolio when new assets is combined into the portfolio

Incorporating the discussed theory, Markowitz (1952) derived the efficient frontier of risky asset from optimization of mean variance (the objective is to maximize the expected return at every level of variance from possible combination of assets) The asset combination laid on the efficient frontier represents the efficient mean – variance combination of assets, which means the combination provide highest expected return for a specific level of risk and is preferred by risk – averse investors

Figure 2: Markowitz Efficient Frontier

However, the Markowitz efficient frontier only comprises of risky assets In order to manage the risk more effective, investors can allocate a fraction of their capital into assets that provide certainty return (risk free assets) Common risk free assets are Treasury bill and bond with virtually zero probability of default

Consider the efficient mean – variance portfolio A and B in Figure 2, all the combination between risk free asset and portfolio A can be depicted by Capital Allocation Line for A (CALA) which is connected from Risk Free point to A on the Efficient Frontier Curve Similarly, the Capital Allocation Line for B (CALB) is created in the same manner CALA outperforms CALB since any combination between portfolio A and risk free assets provide a higher expected return than any combination between portfolio B and risk free assets given the same level of risk Therefore, CALA

would be preferred by risk averse investors than CALB Using the same approach and keep moving upward along the Efficient Frontier, the optimum Capital Allocation Line can be reached by tangent line between CAL of M and Efficient Frontier Curve This optimum CAL is called the Capital Market Line (CML) which delivers the highest expected return for a given level of risk and lowest risk for any given level of expected return

Equation 4 shows the mathematical calculation of Capital Market Line that expected return

on an Efficient Frontier Portfolio is equal the return on risk free asset (Rf) plus the Market Risk Premium (E(RM) – Rf) time the ratio between total risk of the portfolio and total risk of Market Portfolio

(4) With E (RP) expected return of Portfolio P;

E (RM) expected return of Portfolio M;

Rf return of risk free asset;

P the variance of portfolio P;

M the variance of portfolio M;

2.1.3 The Capital Asset Pricing Model:

Market Portfolio Theory provide risk averse investors a foundation for understanding about asset allocation in an Efficient Market However, these theory did not provide a mechanism for pricing assets or portfolio in an efficient market Therefore, Capital Asset Pricing Model (CAPM),

as an extension of MPT, provides risk averse investors a single factor linear model for computing the equilibrium rate of assets return in an efficient market The model is developed by three researchers: Sharpe (1964), Lintner (1965), Mossin (1966), each researcher contribute independently to CAPM model

The basic assumption of CAPM is that unsystematic risk (firm specific risk) can be diversified away, therefore, the only systematic risk (market risk) is required to endure by investors Furthermore, the systematic risk for a given asset can be measured by its covariance with market portfolio (i,M)

Substituting (i,M) into Equation 4, a relationship between risk and expected return can be derived as Equation 5 below:

As a result, it can be interpreted that assets with higher βi (higher systematic risk) must provide a higher return to investors for enduring a higher risk investment A systematic risk – expected return relationship of SML is described in Figure 3:

Figure 3: Security Market Line (SML)

When the market is in equilibrium, an efficient portfolio should be graphed on SML line which delivers a return appropriate to their systematic risk An undervalued asset is drawn above the SML line as it delivers a higher return than expected from the market in corresponding to its systematic risk On contrary, an overvalued asset is sketched below the SML line as it delivers a lower return than expected in corresponding to its systematic risk

Although the CAPM model was built on a solid foundation with its fundamental economic theory, there are number of drawbacks from CAPM model Firstly, Roll (1977) critiqued that market in CAPM theory does not only consist on Equity Market, where most of Empirical Test on CAPM is carried out, but a Portfolio of all wealth A market Portfolio, according to Roll (1977), should comprises bonds, stocks, properties, human capital and any other assets that contribute to the wealth of mankind From this view point, he further asserted that the CAPM can only be tested when the true market portfolio is known with certainty And tests of CAPM is only a test of mean – variance efficiency of the Portfolio that is considered as market proxy Due to this reason, a finding of a mean – variance efficiency Portfolio within any sample cannot inform researchers whether CAPM model is correct or not

Moreover, apart from its weakness, there are increasing evidences that Beta is not the only risk factor affecting stock returns There are many other factors which can be named, such as book

to market ratio (Fama & French, 1992), company size (Banz, 1981), price / earnings ratio (Basu, 1977) and a number of other systematic factors These factors will be discussed in details in following sections

2.2 ASSET RETURNS LITERATURE:

2.2.1 Theoretical vs Data Mining Research:

After recognized the possible flaws of CAPM, many researchers had embarked on finding additional factors which can explain the assets return Generally, these models act as a refinement

in spirit of CAPM by adding other explanatory factors to capture more the movement of asset prices The influential factors on stock return, but not Beta factor of CAPM, are now conventionally called anomalous variables or anomalies Although, some of the models have been proven to add more explanatory power to observed asset return, for example Fama and French (1993) Three factor models and Carhart (1997) model, these models were considered a result of fitting data stream rather than establish from theoretical foundation Black (1998) considered the famous Three Factors Model of Fama and French (1993) as “data mining” because conducting a research with sizable of potential explanatories variables should produce some positive result

Furthermore, Subrahmanyam (2010) expressed that: “the tendency of scholars to use one methodology or the others raise the question of whether the results are robust to different methodology”

As a result, a question that many people might concern is why academic in Finance were

so committed to “data mining” There are some convincing explanations for this question Firstly, Dempsey (2013) revealed that: “a good deal of finance is now an econometric exercise in mining data either for confirmation of a particular factor model or for the confirmation of deviation from the model’s prediction as anomalies” From the argument of Dempsey (2013), the persistent use

of “data mining” originated from the desire to support the scholar’s prior belief

Secondly, the Economist magazine (20 -26 November 2010) gave us another persuasive reason for high level data mining that stem from the establishment of CRSP data base It is estimated that more than one – third of published researches have been conducted by using data from CRSP data base The data base has given financial scholars an enormous opportunity to perform their econometric and “data mining” research

2.2.2 Fama – French Three Factors Model:

From above literature, we would accepted explanatory factors are not always derived from theoretical background, but it might be derived from different researches on a particular data base

or “data mining” activities Therefore, it is hard to claim what Asset Pricing Model is the best model A more suitable question would be to ask what Model and Explanatory Variables should

be the most appropriate for a particular research Three renowned Asset Pricing Model will be discussed below which were constructed during a search by many scholars to explore other significant explanatory factors for asset return

Two famous researchers in Finance, Eugene Fama and Ken French have performed extensive researches about Asset Pricing In 1993, they came up with a conclusion that apart from the market risk premium (Beta), “value” factor and “size” factors can explain a significant portion

of asset returns In order to account for these factors, SMB is constructed to represent “size” factor and HML is constructed to address “value” factor

SMB variable is an abbreviation of Small Minus Big and designed to capture the excess return which investors can earn by investing their money into a relative small capitalization company instead of putting their money into a big market capitalization company Similarly, HML variable is an abbreviation of High Minus Low and designed to capture the excess return which investors can attain by investing their money into high book to market ratio (B/M) companies

instead of putting their money into low B/M ratio companies (Book to Market ratio is a ratio where company value calculated by accountants is divided by company value in stock exchange market)

Although these factors were not built upon a strong theoretical foundation, these factors do provide a meaningful interpretation about their significance Firstly, for SMB factor, the intuitive behind is that small companies should be more vulnerable to many risk factors due to their relative small size and undiversified nature Thus, a substantial reduction in their ability to confront negative economic and financial events Therefore, investors require a higher return as a premium for taking extra risk in this investment Secondly, for HML factor, the intuitive behind this factor

is that a company with high B/M ratio is an indication that the value of the company in public market has decreased due to company current situation or low expected future earnings As a result,

we expect that these companies should be more vulnerable to various risks and have lower returns

in comparison to those with low B/M ratio (which means they are valued higher by the public market) So that, investors also require a higher return as a premium for taking extra risk in buying high B/M ratio stocks

In summary, the formation of Fama – French Three factor model is as below:

R it – R Ft = a i + b i (R Mt – R Ft ) + s i SMB t + h i HML t + e it (7)

b i is a coefficient of relationship between asset return and market risk premium

s A is a coefficient of relationship between asset return and size factor

h A is a coefficient of relationship between asset return and value factor

The Model can be considered as an expansion of CAPM model since it combines the traditional market risk factor with two new factors This model explained the return of assets in correspondence with three risk factors: market risk premium, size risk and value risk

2.2.2.1 Empirical researches of Three Factors Model:

The effectiveness of Fama and French Three factors model has been tested by many empirical studies in different market Most of these studies provide positive feedbacks toward the model

K S Lam (2002) carried out a research on 100 companies over the period 1980 – 1997 and traded on Hong Kong stock exchange He investigated the relationship between stock returns and β, size, leverage, book to market equity ratio, earning/ price ratio in Hong Kong follows Fama and French approach The result of this research failed the role of β in capturing the average monthly return of stocks listed in the period July 1984 – June 1997 However, the result was very supportive for size, book to market ratio and earning/ price ratio since they can explained a large

portion of cross sectional variation in average monthly return They also concluded that book leverage and market factors do have explanatory power over the variation of cross sectional return Nevertheless, these factors seem to be redundant when above three factors are included in the model The result is also consistent across different size groups and months So that, the result is not effected by extreme observations and abnormal return behavior in different size groups or months

Ajili (2003) conducted a test between Fama and French three factors and the Characteristic model of Daniel and Titman (1997) on French stock market in 1976 – 2002 period Stock are sorted into different portfolios of size and book/ market ratio following Fama and French methodology Some noticeable inferences from this research are that 90.5% on average of the expected return is explained by the power of 6 size and B/M ratio portfolios Moreover, the prediction, that the intercepts in the regression of characteristic balanced portfolio of FF Three factors model should be zero, is support by the study over the Characteristic model of Daniel and Titman (1997) which predicted that these intercepts should be negative

In a study by Drew (2003), he conducted a test between CAPM model and FF Three factor model for Hong Kong, Korea, Malaysia and Philippine during the 1990 – 1999 period He found that CAPM model alone were not capable of explaining cross sectional of stock returns in these countries and the absolute pricing errors of the CAPM is larger in comparison with FF Three factors model Overall, the research concluded that size and book to market ratio factors did play

a significant role in capturing the movement of cross sectional stock returns

O'Brien, Brailsford, and Gaunt (2008) provide a thorough study about value premium in Australian stock market when they conducted a test covered 98% of Australian listed firm over a period of 25 years Before their research, “size effect” is recognized in Australia but the lack of data have limited the ability of researchers to analyze the “value effect” in Australian market Brailsford, Gaunt, and O'Brien (2012) also emphasized that: “for the first time in Australia, with both time series and cross sectional test, the constructed factors reveal a significantly positive priced premium” The authors also suggested that Fama Three factors model had a better explanatory power than CAPM in explaining asset returns when it explained nearly 70% of the movement in return

2.2.3 Carhart Four Factors Model:

The four factors model was put forward by Carhart (1997) as a result from momentum effect presented by Jegadeesh and Titman (1993) The momentum effect could not be explained

by the famous Three Factor Model of Fama and French

In a study by Jegadeesh and Titman (1993), portfolios were created by using an assumption that the stocks has made gain (or loss) in the past will continue to make gain (or loss) in the future The time frame for momentum effect fluctuate from 3 – 12 months The four factor model was then tested by Carhart in 1997 This model was created by adding Momentum factor of Jegadeesh and Titman (1993) to the Three factors Model of Fama – French The formation of Model is as below:

(8)

The only factor which was added is PR1YR (represent Momentum) calculated by taking the difference between highest 30% return of prior 12 months and lowest 30% return of prior 12 months The test was carried out by Carhart and successfully confirmed the validity of Momentum factor in explaining stock returns

2.2.3.1 Empirical researches of Carhart Four Factors Model:

Four factors model of Carhart was tested in many countries and also yielded positive results toward the model L’Her, Masmoudi, and Suret (2004) conducted a Four factor model test on Canadian stock market over 1960 – 2001 period FF methodology is used to construct the risk factors The result showed that, in Canadian market, the “size” factor is remarkably higher in January in comparison with other months In addition, the “momentum” factor is always meaningful during a year, with the only exception of January Book to market ratio returns has a highly significant value in down trend market Whereas, in the uptrend, the book to market factor just barely have explanatory power on the stock return Macro environment also considered in this research, particularly the monetary policy environment In the expansive monetary policy environment, the “size” and “value” premium were founded to be highly significant

Bennaceur and Chaibi (2007) performed a test on Tunisian stock exchange with different model from CAPM to FF Three factor model and Carhart 4 factors model in order to test explanatory power of these asset pricing models The performance of each model is evaluated by using Schwartz and Akaike indicators The result from the model suggested that the momentum premium is the highest premium (8.8%) in Tunisian Stock Exchange (TSE) The value premium

is 2.88% and relatively smaller than other premium Surprisingly, the size premium in this paper has a negative value (- 3.4%) which disagrees with other literatures where the small firm commonly deliver higher return Overall, the paper claimed that the best Asset Pricing Model for Tunisian Stock Market is the Carhart four factor model when all the selecting criteria guided to the same conclusion

K S Lam, Li, and So (2010) provided another study supporting the 4 factors model The study was conducted in Hong Kong stock market during 1981 – 2001 period After testing the four factors model with data from Hong Kong stock market, all four factors were founded to have significant explanatory power Moreover, the intercept also was unveiled to be insignificant different from zero A further supportive evidence for the Carhart four factor model when value of adjusted R2 is relatively high and the residual standard deviation of additional explanatory variables is insignificant which mean a large portion in stock returns variation had been successfully explained by Carhart model

2.2.4 Fama – French Five Factors Model:

Many researchers, who completed their empirical researches in different market, claimed that Fama French Three Factors Model (TFM) indeed provide explanatory power of average stock returns However, the explanatory power of the TFM is still weak since a portion of average returns left unexplained As a result, Fama and French persistently put their effort in developing a better asset pricing model During their research, profitability and investment factors arose as new explanatory factors with supporting theoretical framework from dividend discount model Additionally, there are number of empirical studies which have been conducted and gave positive result of these two factors

Initially, the Dividend Discount Model claimed that the market value of a stock is a sum

of discounted value stream of expected dividends for each share

M t denoted share price at time t

r is the long term expected stock return or internal rate of return of expected dividends

E(dt+ ) is the expected dividend per share for time (t+)

From equation (9), we can infer that if two different stocks which offer investors the same expected dividends but their share prices are different, the lower price’s stock would be required

a higher expected return by investors Or we can infer that the stock with lower price would have higher risk or uncertainty in paying out their expected dividend than the other stock despite having

a same amount of expected dividend

Miller and Modigliani (1961) further developed equation (9) and presented equation (10) which shows the total market value of a firm’s stock at a particular time t:

With Yt+  is total equity earnings for period t+

dBt+  = Bt+  - Bt+  -1 is the change in total book value between period t+ and t+-1

Dividing equation (10) by book value at time t bring us equation (11):

Secondly, if each term in equation (11) is kept unchanged except for expected future earnings (Yt+ ) and expected stock return (r) We would interpret that a higher expected earnings

(profitability) implies a higher expected return

Thirdly, fixing other terms in equation (11) (Bt,Mt and Yt+ ) except for expected growth in book equity (dBt+ ) and expected return (r) This will provide us with an interpretation that a higher

growth in expected book equity (investment) implies a lower expected return

Through above conclusions from equation (11), we can observe there are theoretical relationships between books to market equity ratio, expected earnings (profitability), growth in book equity (investment) and stocks’ expected return As a result, these conclusions are the premises from which the Fama and French (2013) Five Factors Model (FFM) is developed

The basic formation of FFM is as below:

R it – R Ft = a i + b i (R Mt – R Ft ) + s i SMB t + h i HML t + r i RMW t + c i CMA + e it (12)

Rit is the return on stock or portfolio i for period t

RFt is the riskfree return

RMt is the return on value weight market portfolio

s i is a coefficient of relationship between asset return and size factor

h i is a coefficient of relationship between asset return and value factor

r i is a coefficient of relationship between asset return and profitability factor

c i is a coefficient of relationship between asset return and investment

2.2.4.1 Empirical researches of Five Factors Model:

From the interpretation of Miller Modigliani valuation equation, Fama and French explained theoretically there are relationships between future stock returns, current B/M, firm level, expected profitability and firm level investment They also performed an empirical test on these relationships Aharoni, Grundy, and Zeng (2013) studied an empirical test for the data drawn from Center for Research in Security Prices (CRSP) for period of 1963 - 2009 Aharoni et al (2013) claimed that, when variables are measured at the firm level, the expected relationship between these variables hold true The rationale was that share level measurement will be affected

by issuance and repurchase which will alter the calculation of share growth As a result, the firm – level growth and per share growth can be significantly different At the firm level, Aharoni et al (2013) confirmed following relationships between variables: a positive relationship between B/M and stock returns, a positive relationship between expected profitability and returns, and a notably negative relationship between expected investment and return

In another paper by Titman, Wei, and Xie (2004), they also concluded that there is a negative relationship between abnormal capital investment and stock returns Their argument is that, in theory, an increase in investment can convey both favorable and unfavorable information The positive information is that firm might have better investment opportunities so that they invest more than other companies The negative information is that firm might be controlled by managers who have a tendency of overinvestment They further claimed that the unfavorable information was underestimated by investors when investing their money into the high level of investment stocks Accordingly, they concluded there is a negative relationship between capital investment and stock returns in their research Additionally, firms with high cash flows or low debt level have more serious problem of high investment and low return This is due to tendency to overinvest in these firms

In a study by (Novy-Marx (2013)), he also confirmed there is a positive relationship between profitability and cross section of average returns He concluded that profitability, calculated by gross profits to its assets, has similar explanatory power to B/M ratio in predicting the abnormal average returns He also realized that high profitable firms had much higher stock

returns than unprofitable firms despite these stocks have a remarkably higher B/M ratio compare with similar stocks Knowing the negative relationship between B/M and profitability, investors can gain advantages of capturing the profitability premium without withstanding any additional risk The mechanism, through which profitability influence average returns, represents consideration of investors about firm value While “value strategy” investors gain their abnormal returns through purchasing inexpensive assets (high B/M ratio stocks) and selling expensive assets (low B/M ratio stocks), “profitability strategy” investors receive their abnormal returns by buying productive asset (high profitability stocks) and selling unproductive assets (low profitability stocks)

2.3 LIQUIDITY LITERATURE:

Liquidity is generally understood as a measure for convertibility of securities into cash without lowering the market price and incur significant transaction costs Therefore, a highly liquid stock could be convert into cash without losing its value and incur large amount of transaction costs The opposite is true for illiquid stocks, investors who want to immediately liquidate the stock will have to lower its price and incur more transaction costs than liquid stocks There is no real measure for liquidity, however, researchers often utilize number of proxies for measuring liquidity These proxies will be introduced in data and methodology section

The liquidity premium is defined as a premium for keeping the illiquid stocks over the liquid stocks The existence of liquidity premiums is due to number of reasons The liquidity premium can be considered as premium for trading cost of securities Another explanation for liquidity premium could be due to the difference in trading horizon of investors in which illiquid stocks are often kept by investors with long investment horizons In addition, the risk from illiquid stock are shared by less investors than the liquid ones So that, illiquid stocks command a premium over the liquid stocks

The relationship between liquidity and expected stock return has come into attention since the introduction of transaction cost theory (Amihud & Mendelson, 1986) In this theory, the link between liquidity and stock returns is created base on the notion that investors demand a higher expected assets return when these assets incur a higher transaction cost

2.3.1 Transaction Cost Theory:

Amihud and Mendelson (1986) were the first to introduce this model in which trading different assets include numbers of elements, such as transaction costs and trading frequencies The model later was further developed by Kane (1994)

Assume there was a market with three risk-free assets and each asset only has two outstanding shares Each asset has its own expected return Ri, i=1,2,3 The transaction costs for trading these assets are Si, i=1,2,3 (assuming S3-S2 =S2-S1 =S>0 and S1 =0) The market has two types of investors, each type of investors have three people and each type of investors has different trading interval or frequency

In this framework, liquidity premium (ri) is defined as the difference between returns of assets with positive trading expenses and the return of the asset with zero trading cost, which is, ri

=Rr-R1 ,i=1,2,3 (as we assumed that the trading expense of asset 1 = 0) Hence, for type 1 investors,

by holding asset I, investors will expect to receive (R1+ri)-(TjSi) returns, where TjSi is the total trading expenses paid during given investment period

Another assumption is that investors are risk-neutral and select to retain the asset with the highest net expected return The equilibrium in this economy will display a clientele effect, which mean, investors will not select to keep all assets, but they will only retain the assets which match their preferences If two assets 1 and 2 are kept by type 1 investors, it must signify that the net returns on asset 1 and 2 are equal and their expected returns are higher than net returns on other assets Hence,

For type-1 investors whose trading frequency is T1:

Reorganizing inequality (13.3), we have r2-r 1 > T2 (S2-S1) > 0 Therefore, higher liquidity premium is required by investors for lower trading volume or higher trading cost assets

Reordering inequality (13.3) and (13.4), we have r2-r1 > T2 (S2-S1) and T2 (S3-S2) = r3-r2 Dividing the inequalities by the difference in trading volume or cost, inequalities in terms of the first derivative are derive as below:

Figure 4: The relationship between Liquidity vs Turnover and Trading Cost

Therefore, under the described economy, (1) lower trading frequency investors will elect assets with higher transaction costs, (2) the expected liquidity premium ri is a concave and increasing function of its trading expenses, and (3) the expected liquidity premium is a concave and decreasing function of its trading turnover

Some researchers have been criticized the trading frequency hypothesis in a circumstance where an economy has only one type of investor with trading frequency T, investors will keep all assets with the same net returns Hence,

r1 – TS1 = r2 – TS2 = r3 – TS3 = 0 (16);

and, ri = TSi, for all I = 1,2,3

With this assumption, the trading frequency is the same for all assets, there will be no cross sectional relationship between turnover and trading frequency The cross sectional liquidity premium will increase linearly with the transaction cost

Nonetheless, there are two other theories which can explain the relationship between turnover and liquidity premium through transaction cost The first theory is information-based trading hypothesis (Easley, Kiefer, O'hara, & Paperman, 1996) which claimed that stocks with higher turnover has lower probability of information-based trading and it also decrease the transaction cost The second theory, the order-processing cost hypothesis, stated that, in a fixed cost environment, stocks with higher turnover will decrease dealer’s average cost and hence, it will subsequently reduce investor’s cost As a result, a lower cost will reduce required liquidity premium from investors for their investment

2.3.2 Subsequent Development in Liquidity research:

a) Divergence between the effect of liquidity on stock returns:

After the theory developed by Amihud and Mendelson (1986), a new era for research in liquidity and asset return has begun Some researches subsequently found to be in agreement with result from Amihud and Mendelson (1986)’s paper that liquidity negatively related to asset returns

Below are some prominent researchers that pose a negative (positive) relationship between liquidity (illiquidity) measures and excess stock returns:

Brennan and Subrahmanyam (1996), using price changes and order flows for measuring illiquidity, claimed that their illiquidity measurements provide a positive relationship between illiquidity and stock return Their result has been verified and proved significant, even after controlling for Fama and French (1993)’s three risk factors In another paper by Brennan et al (1998), share turnover is utilized as a proxy for liquidity and this measure suggested that liquidity and asset returns has a negative relationship

Baker and Stein (2004) built a model that predicted an increase liquidity will subsequently lead to a lower stock returns at both firm level and aggregate data In this model, number of liquidity proxies were used such as bid-ask spread, price impact of trade or turnover They all pointed to the same conclusion of a negative relationship between liquidity and stock returns An innovative feature of this model is that it includes a class of irrational investors, who under-react

to information in order-flow And high liquidity is an indication that the market is overwhelmed

by irrational investors, thus it is over-valued

In a paper of Amihud (2002), he employed an illiquidity measure (the daily ratio of absolute stock return to dollar volume) This paper revealed that, over a long timeframe, the expected market illiquidity positively influences the stock excess return This suggests that there is a negative influence of liquidity on stock excess return over a long timeframe However, he further stressed that, stock returns are negatively correlated with temporary unexpected illiquidity (liquidity shock)

In contrary to above research paper, there are existing empirical studies in stock markets which showed a contrary relationship In a recent study by Bali et al (2014) for all common

stocks traded on NYSE, Nasdaq from 1963 to 2010, they concluded there is a negative relation between firm level illiquidity and stock returns Their decile portfolios that buy negative illiquidity stocks and sell positive illiquidity stocks provide a risk-adjusted return larger than 1% per month The result is significant even after testing with different measures of liquidity and controlling for different risk factors as well as firm characteristics Their explanation for the results is that investors underreact to illiquidity level of firms This under-reaction tends to be stronger for stocks having less attention from general public

In addition, in a paper by Abzari et al (2013) conducted in an emerging market, particularly Iran stock market The sample for this study was all common stocks at Tehran Stock Exchange at monthly level from March 2002 to November 2011 They showed that illiquidity is an important factor in explaining the asset returns and it negatively impacts on asset returns The authors also suggested that illiquidity measures can partly cover the market factors effect In addition, they stated that four factor models (market, illiquidity, size and value factors) is the best models to explain asset returns and the momentum factor is not priced in Iran stock market

With both positive and negative evidences on the role of liquidity in explaining stock

returns, there seem to be an ambiguous conclusion about the effect of liquidity on stock excess

returns

b) Liquidity effect in emerging market:

A research by Harvey (1995), he revealed that the correlation between emerging market equities and other markets is low As a result, it significantly reduces unconditional portfolio risk

of a world investors Additionally, the standard global asset pricing model, which adopt a full integration of global capital market, cannot explain the average returns in emerging countries He also concluded that the equity returns in emerging market tend to be control by local information

With the data from 27 emerging markets from January 1992 to December 1999, Jun, Marathe, and Shawky (2003) indicated aggregate market liquidity in emerging markets positively affected on stock returns Turnover ratio, trading value and turnover – volatility multiple are used

as liquidity measures in their study Importantly, they also concluded that emerging markets have lower integration level with global capital market

In paper by Bekaert et al (2007), they believed emerging markets are ideal environments

to exam the influence of liquidity on stock excess returns They provided some explanations for this conclusion Firstly, the emerging markets have relatively poor liquidity in comparison to developed markets This poor liquidity has prevented many institutional investors from putting their money into different types of stock As a result, this even intensifies the gap between liquid and illiquid stocks Secondly, many emerging market went through market liberalization during their research period And this incident should assist researchers in study the importance of liquidity on expected return The reason is that, after liberalization, the liquidity often increases noticeably in comparison with prior liberalization period Thirdly, developed markets often have diversified ownership structure with both long term and short term investors Thus, Bekaert et al (2007) suggest the clientele effects in selecting investment portfolio should reduce the pricing of liquidity In respect of emerging markets, the diversification in number of securities and ownership

is lacked of which often intensify the liquidity effects

With all above researches and arguments, we can expect that the liquidity research in frontier market should yield a very convincing result The fact that liquidity gap between liquid and illiquid stocks, which is very significant in emerging market (even more severe in frontier market), gives researchers a real opportunity to discover the effect of liquidity on stock returns In addition, there are very limited number of liquidity researches conducted in frontier markets (pre-emerging markets) Most liquidity researches were conducted in developed market (Bekaert

et al., 2007) Therefore, there is a need for further liquidity research in frontier market environment

in order to clearly understand about the liquidity effect in different environments and economic conditions

Vietnam is an interesting case for liquidity research for some reasons Firstly, liquidity in Vietnam market has increase gradually year after year due to more liberalizing policies from the government This provides opportunity to study the effect of improved liquidity on stock returns Moreover, the liquidity gap between liquid and illiquid stock in Vietnam is quite large due to foreign investors often invest in large and reputable stocks Thus, observed liquidity in other stocks

are often noticeably less than those liquid stocks Last but not least, Vietnam equity market adopted matching order system, which do not allow common liquidity measures such as bid ask spread, zero daily return, etc… to be employed in Vietnam equity market Therefore, liquidity research in Vietnam equity market will involve the use of different liquidity measures to yield final conclusion This should enable researchers to test whether different liquidity measures give a convincing conclusion about liquidity effect in the case of Vietnam

From above arguments, there is an obvious need to study about the liquidity effect in Vietnam market for some reasons The first reason is that there is a lack of literature about liquidity effect in Vietnam, a frontier market Moreover, there is a real need from various social parties (investors, policy makers, companies, etc…) to thoroughly understand how liquidity influence asset returns So that, each party can have the right decisions for their particular interests which will benefit the society at a whole

2.4 MAIN HYPOTHESIS:

From above literature about liquidity and stock return, most researchers agreed that liquidity plays a role in explaining stock return However, there is still a debate about whether the effect is positive or negative sign

In the case of this research, the research was carried out in a frontier market, particularly

in Ho Chi Minh City stock exchange Some considerations was put forward in order to establish the hypothesis for this study The first consideration was Ho Chi Minh City stock exchange was a new and relative small capital market Thus, it prevented large foreign institutional investors to invest their fund in most of company stocks (Bekaert et al., 2007) As a result, high liquidity stocks, which often selected by institutional investors for investment, commonly have good fundamental and represent low risk investment

Secondly, illiquid stocks usually hold by only a limited investors Hence, they are risker than liquid stocks as the risk from illiquid stocks are not shared by many investors

The third consideration was a majority of investors in Vietnam is small and inexperienced investors And inexperienced investors are more inclined to participate into over-value stocks and trend-chasing behavior (Greenwood & Nagel, 2009) This means they tend to invest into stocks which quickly increase in price and may create bubble And an important condition for stocks to quickly rise is that its liquidity should be limited and add up only small portions of total outstanding stocks In case of high liquidity stocks, they rarely can soar rapidly in price or create bubble due

to voluminous awaiting sellers in the market (include big investment firms) when these sellers observe a bubble in asset price

From these considerations, we can infer that illiquid stocks often have higher risk than liquid stocks And investors will demand a premium for buying illiquid stocks as a compensation for taking extra risk Additionally, illiquid stocks’ prices can shift up and down quickly due to their illiquidity nature This means illiquid stock can remain a significant rise in price which hardly occur in liquid stocks

As a result of above arguments, the main hypothesis for this thesis is:

Liquidity negatively influences on stock returns

CHAPTER 3: DATA AND METHODOLOGY

This chapter will be dedicated to data and research methodology of this thesis Regression models and research framework of this thesis are firstly presented to give readers an overview about variables and their linkage Subsequently, Data section is presented to show data source and its reliability The upcoming section will discuss about the selection of Fama and MacBeth regression After regression method is discussed, all variables and their calculation will be mentioned to provide a clear picture about the procedure of constructing premium factors This chapter will end with the presentation of potential econometric issues and how they are solved in the thesis

3.1 REGRESSION MODEL AND RESEARCH FRAMEWORK:

3.1.1 Regression Models:

Although Fama and MacBeth regression is a complex method, this thesis will employ Fama and MacBeth (1973) regression to test the empirical relationship between liquidity and stock returns This regression method is considered as one of the most effective approach for empirical research about stock returns The detail about this approach is presented in Appendix section for interested reader

In literature review part, some of the most prominent premium factors in the asset returns have been introduced (Fama French five factors, momentum factor) These factors frequently used

by governmental agencies, institutional investors to calculate the required return for their investment They often proved effective and applied world-wide for asset valuation purpose

The main objective of this study is to verify the role of liquidity in explaining asset returns Therefore, all well-known premium factors in asset returns’ literature are combined into regression models These factors will represent control variables in this research So that, any liquidity effect that can be measured is the true effect of liquidity on asset returns

This thesis will involve the use of two proxies of liquidity (turnover ratio measure and Amihud illiquidity measure) for two separated models There are many available liquidity measures in the literature Nonetheless, these two measures showed they are the best candidates for liquidity research in the case of Vietnam market All the control variables will be kept unchanged between the two models In summary, two models are defined as follow:

Model (1):

RETURNi,t = α0 + α1TURNOVERi,t + α2RISKi,t + α3SMBi,t + α4 HMLi,t + α5CMAi,t +

α6 RMWi,t + α7 RET23i,t + α8 RET46i,t + α9 RET712i,t + εi,t

Model (2):

RETURNi,t = α0 + α1AMIHUDi,t + α2RISKi,t + α3SMBi,t + α4 HMLi,t + α5CMAi,t +

α6 RMWi,t + α7 RET23i,t + α8 RET46i,t + α9 RET712i,t + εi,t

i: cross – listed firms, t: time period from 2008 to 2013

Where:

RETURNi,t : Excess stock returns on portfolio i for month t, it is calculated by using

average daily return on portfolio i minus risk free rate return for the

same period ( Rit – R Ft)

TURNOVER : Turnover measure, it is calculated as the difference between average

stock return of high and low Turnover portfolios

AMIHUD : Amihud illiquidity ratio measure, it is calculated as the difference

between average return of high and low Amihud illiquidity portfolios RISK : Market risk premium, it is calculated by using average daily return on

market portfolio (VNindex) minus risk free rate return for the same

period (RMt – RFt)

SMB : Size Premium factor which is the difference in average return between

small and big capitalization companies HML : Value Premium factor which is the difference in average return

between high and low Book to Market ratio (B/M ratio) companies CMA : Investment Premium factor which is the difference in average return

between low and high growth in book equity companies

RMW : Profitability Premium factor which is the difference in average return

between high and low earnings companies

RET23 : Cumulative return of month 2 and 3, previously It is calculated as the

difference between high and low cumulative return portfolios

RET46 : Cumulative return of month 4 to 6, previously It is calculated as the

difference between high and low cumulative return portfolios

RET712 : Cumulative return of month 7 and 12, previously It is calculated as

the difference between high and low cumulative return portfolios