Empirical investigation of market value change in vietnam stock market

luận văn, khóa luận, chuyên đề, đề tài

UNIVERSITY OF ECONOMICS HOCHIMINH CITY

- oOo -

HUỲNH THỊ BÍCH THẢO

EMPIRICAL INVESTIGATION

OF MARKET VALUE CHANGE

IN VIETNAM STOCK MARKET

MASTER THESIS

Ho Chi Minh City – 2011

- oOo -

HUỲNH THỊ BÍCH THẢO

EMPIRICAL INVESTIGATION

OF MARKET VALUE CHANGE

IN VIETNAM STOCK MARKET

MAJOR: FINANCE - BANKING MAJOR CODE: 60.31.12

MASTER THESIS

Ho Chi Minh City – 2011

for his guidance, time and insightful comments on my work It is my honor indeed

to have the opportunity to work with him and I appreciate on the things we have shared during this time

I would like to express profound gratitude to Dr Vo Xuan Vinh for his invaluable supports and useful suggestions as well as his excellent advising from the very first

to the final steps of mine in conducting the work leading to this thesis

I would like to express my sincere gratitude to all of my lecturers for their teaching and guidance during my maser course at the University of Economics, Ho Chi Minh City

Finally, the people I would like to thank the most are my parents and my fiancé Without their continual encouragement and understanding, I would not have been able to complete this journey In addition to immediate family members, there are many closed friends who have supported me through this rough journey I feel very fortunate to have such wonderful friends and supporters in my life

With my appreciation

Huynh Thi Bich Thao

using a rich and detailed data set, including both market data and firm attributes In particular, we aim to investigate which firm characteristics affect stock price volatility From the perspective of informational asymmetry, the paper examines the relationship between stock price volatility and firm characteristics of Vietnamese listed firms on Ho Chi Minh City Stock Exchange A sample of 110 listed companies in Vietnam stock market is examined for a period from 2007 to 2009 The empirical estimation is based on panel data modeling technique The findings

of the paper indicate that stock price volatility is positively affected by dividend yield, firm age and liquidity Meanwhile, it is negatively correlated with firm size

In addition, the results of this study also report that stock price volatility favors foods and beverages, industrials and real estates, construction & materials industries

Keywords: Stock price volatility, firm attributes, Vietnam stock market, panel data

ABSTRACT ii

CONTENTS iii

LIST OF ABBREVIATION v

LIST OF TABLES vi

1 INTRODUCTION 1

1.1 Background, research motivation and rationale 1

1.2 Research objectives and research questions 2

1.3 Methodology 3

1.4 Contribution 3

1.5 Structure of the thesis 4

2 LITERATURE REVIEW 5

2.1 Stock price volatility and dividend policy 6

2.2 Stock price volatility and firm age 8

2.3 Sstock price volatility and trading liquidity 8

2.4 Other firm attributes and stock price volatility 9

3 DATA DESCRIPTION AND DEVELOPING EMPIRICAL RESEARCH HYPOTHESES 11

3.1 Data description 11

3.2 Developing empirical research hypotheses 20

4 METHODOLOGY 25

4.1 Descriptive statistics and correlation matrix 25

4.2 Bivariate analysis 25

4.3 Multivariate analysis 26

4.3.1 Ordinary Least Square (OLS) regression 27

4.3.2 Fixed effects regression 28

4.3.3 Random effect regression 29

4.3.4 F-statistic test 29

5.2 Bivariate analysis 33

5.3 Multivariate analysis 35

5.2.1 Overall regression results 35

5.2.2 Overall regression with industry dummies 39

5.2.3 Regression results in each year 42

5.2.3.1 Regression results of 2007 42

5.2.3.2 Regression results of 2008 43

5.2.3.3 Regression results of 2009 44

5.2.4 Regression results for each industry 45

5.2.4.1 Basic materials industry 45

5.2.4.2 Consumer goods and services industry 46

5.2.4.3 Food and beverage industry 47

5.2.4.4 Industrials industry 48

5.2.4.5 Real estate, construction & materials industry 49

5.2.4.6 Others industry 50

6 CONCLUSIONS 54

6.1 Reviews of findings 54

6.2 Contribution 55

6.3 Limitations and recommendations for future researches 55

REFERENCES 57

APPENDICES 60

Appendix A: Regression results 60

Appendix B: List of 110 companies 88

HNX Hanoi Stock Exchange

Table 3.2 Description of stock price volatility in Vietnam 15

Table 3.3 Data descriptive statistics for firm attributes 16

Table 3.4 Data descriptive statistics for firm attributes by year 17

Table 3.5 Data descriptive statistics for firm attributes by industry 18

Table 3.6 Data description and expected relation to stock price volatility 24

Table 5.1 Correlation matrix among variables 32

Table 5.2 Variance Inflation Factor 33

Table 5.3 Bivariate regression results 34

Table 5.4 Overall regression results 36

Table 5.5 OLS, fixed effect and random effect tests 39

Table 5.6 Overall regression results with industry dummies 41

Table 5.7 Regression results of 2007 without and with industry dummies 42

Table 5.8 Regression results of 2008 without and with industry dummies 43

Table 5.9 Regression results of 2009 without and with industry dummies 44

Table 5.10 Regression results for basic materials industry 45

Table 5.11 Regression results for consumer goods and services industry 46

Table 5.12 Regression results of food and beverage industry 47

Table 5.13 Regression results of industrials 48

Table 5.14 Regression results of real estates, construction & materials industry 49

Table 5.15 Regression results of others industry 50

Table 5.16 OLS Regression results with ROA, ROE and EV 52 Table 5.17 Cross-section fixed effect regression results with ROA, ROE and EV 53

1 INTRODUCTION

This section starts with reviewing background and figuring out motivation and rationale of this research This section is then followed by discussing research objectives and research questions After that, a brief of methodology, contribution and structure of this study is presented

1.1 Background, research motivation and rationale

Stock price volatility and its determinants remain a source of controversy despite years of theoretical and empirical research Investigations of share price changes appear to yield evidence that changes in fundamental variables should jointly bring about changes in share prices both in developed and emerging markets However, the actual fundamental factors found to be relevant may vary from market to market It is widely agreed that a set of fundamental variables as suggested by individual theories is no doubt relevant as possible factors affecting share price changes in the short and the long-run

A substantial a mount of research has been directed toward analyzing the relationship between stock price volatility and firm attributes Among those substantial research in developed market, it can be listed out some outstanding findings such as Baskin (1989) and Fama and French (1992) in the United States context and Allen and Rachim (1996) in Australian context While Baskin (1989) reports a strongly significant relationship between dividend yield and stock price volatility, Allen and Rachim (1996) cannot find any evidence to support this hypothesis but finds another interesting results related to payout ratio

Even though Vietnam initiates the stock market later than many other developed countries, there has been a substantial growth The first stock exchange in Ho Chi Minh city was established in 2000 with four listed companies Increased foreign interest and the privatization of state-owned enterprises lead to a rapid increase in listings At the end of 2009, there are about 250 firms listed on the Ho Chi Minh Stock Exchange and the smaller exchange in Hanoi

Most of the previous studies on determinants of stock return volatility focus on well-developed markets with less attention given to the developing markets To the best of the author knowledge, there are very few studies that address the issue of stock price volatility and fundamental factors in the Vietnamese context This motivates the present study to examine whether firm characteristics can affect the stock price volatility of the Vietnamese companies This study focuses on the same issue for Vietnam Stock market, a developing market Apart from using the latest data, we develop this study by incorporating selected variables for selected purposes

to examine the determinants of stock price volatility In addition, industry effects are also taken into consideration of this research

As general thinking, the stock prices response to market news everyday Therefore, many researches are conducted on investigation over a short horizon with event study on information announcement effects By contrast, this study attempts to examine the relationship between stock price volatility and firm attributes in a long run basis In order to facility our primary aim, the stock price volatility is calculated using Parkinson (1980) method which reduces the mismatch between relevant time for the share prices and the fundamental ratios Under this method, this study in ideal situation should employ quarterly data for analysis However, there is a large difference between the internal financial statements of firms and the audited reports

In addition, according to the regulation, only annual audited reports are required to submit This study, therefore, use annual data from audited reports for more accurate firm attributes

1.2 Research objectives and research questions

This study is conducted to analyze the behavior of stock price from a broad perspective The main purpose of this paper is to determine the relationship between stock price volatility and firm attributes We also look at the influence of industry effect on stock price volatility In addition, stock price behavior in each year and each industry is also discussed in this study in order to identify whether there is any

difference in stock price movement from one year to another year, from one industry to another industry

In other words, this research will provide answers to the following questions:

1 Which firm attributes affect firm stock price volatility in Vietnam stock market?

2 Do firms in different specific industries have different behaviors in stock price?

- Correlations matrix between the dependent and independent variables

- Bivariate analysis involving regressing the dependent variable PV against each independent variable separately

- Multivariate analysis including ordinary least square regression, fixed effect regression and random effect regression

We also take into accounts some robustness test to validate our results

Eviews software version 6 is used as a data analysis tool to implement this research

1.4 Contribution

This study applies new method which implements econometric testing and econometric package to test for empirical results Firstly, to the best of the author knowledge, this paper is the very first research carefully investigating the characteristics of stock price volatility in Vietnam stock market Our main contribution to the financial literature is to provide an extensive empirical analysis

on the stock price movements and firm attributes relation over an extended time

period The construction of stock price data, together with detailed attributes of listed firms in Ho Chi Minh City Stock Exchange, allows us to achieve this task In addition, this study also takes into account industry effect by allowing industry dummies in order to consider whether stock price volatility favors a specific industry in Vietnam stock market

Secondly, this research provides a useful caution for the investors in terms of real relationship between stock price volatility and firms attributes

Last but not least, the limitation of data constraints in this study may offer signals for policy makers to more strictly regulate on accounting standards and publication rules

1.5 Structure of the thesis

This thesis does not follow conventional method which divides into chapters We consider each chapter covers a separate matter so that we structure the thesis into parts which is a better representation

Our paper is divided into 6 main sections Section1 briefly introduces major concerns of this thesis Section 2 presents theoretical aspects of stock price volatility focusing on impacts from fundamental factors Section 3 introduces data description and hypothesis development Section 4 describes methodology The results of the empirical analysis and their discussions are then presented in Section

5 Finally, Section 6 draws the conclusions of our study, follows by discussions on the contributions, limitations, and implications for future research

The structure and methodology of this thesis are guided by Brooks (2008) with econometric approach to an empirical investigation

2 LITERATURE REVIEW

This section provides the necessary theoretical background for the models to be developed in the next part In this section, we reviews the literature related to stock price volatility In the limited scope of this study, the majority of this section is focused on reviewing fundamental analyses of stock price volatility which study relationship between stock price movement and firm attributes in the long run For information purpose, reviews of value relevance study for short term relationship between stock price behaviors with event announcements are also briefed in this section In addition, since most of previous studies execute investigation on relationship between stock price volatility and dividend policy with adding other factors as controlling variables, this section first reviews those literature strands and summarizes key fundamental factors in the later part

Share prices are the most important indicators readily available to the investors for their decision to invest or not in a particular share Factors affecting stock prices are studied from different points of view Several researchers examine the relationship between stock prices and selected factors which could be either internal or external Theories suggest that share price changes are associated with changes in fundamental variables which are relevant for share valuation such as payout ratio, dividend yield, capital structure, earnings, size of the firm and its growth (Rappoport, 1986, Downs, 1991)

Ball and Brown (1968) are the first to highlight the relationship between stock prices and information disclosed in the financial statements Empirical research on the value relevance has its roots in the theoretical framework on equity valuation models Ohlson (1995) depicts in his work that the value of a firm can be expressed

as a linear function of book value, earnings and other value relevant information The link between fundamental factors and share price changes is extensively investigated over short horizons but only few studies attempt to model it over lengthy periods of time Studies over short windows commonly apply cross-

sectional tests using event-based research methodology The cases of studies examining this relation cross-sectional or inter-temporally are few, and these have one common feature i.e., the fundamental factors used in a specific study are either one or two although there is a long list of fundamental factors Furthermore, while price revisions at the time of announcements of price relevant disclosures are valid

as announcement effects shown over short horizons, it is equally important to test the effect over a lengthier period of time using data over several years as measure of the variables

2.1 Stock price volatility and dividend policy

It is well known that the most important internal factors are related to dividend policy which includes dividend yield and payout ratio Different researchers have different views about the relationship among dividend policy and stock prices The relationship between dividend payouts and stock price volatility, which is firstly initiated by Modigliani and Miller (1958) , is still open for discussion and investigation According to Modigliani and Miller (1958), firm value is irrelevant to dividend policy and firm stock price volatility is solely based upon its earning ability Miller and Rock (1985), John and Williams (1987) report that the above statement could be only true if shareholders have symmetric information about the company’s financial position However, managers normally pass positive information to the shareholders by retaining any negative information until any regulation or financial constraint to force them to disclose that information

Gordon (1963) argues that stock prices are influenced by dividend payouts He reports that firm with large dividends faces less risk in terms of stock price volatility

Friend and Puckett (1964) initiate the work on relationship between dividend and stock price volatility They find a positive relationship among dividend and stock prices

Jenson (1986) states that there is a positive relationship between dividend and stock price reaction He argues that dividend payouts reduce the cost of funds and increase the cash flows of the firm The company after paying cash dividends to stockholders would have less idle funds in the hands of managers to invest in less or negative NPV projects

In the context of the United States, Baskin (1989) argues that there is significant, dominating negative relationship between dividend and stock price volatility He advances four basic models which relate dividends to stock price risk: duration effect, rate of return effect, arbitrage effect and informational effect He suggests the use of the following control variables in testing the significance of the relationship between dividend yield and price volatility: operating earnings, firm size, level of debt financing, payout ratio and level of growth According to his findings, dividend yield and payout ratio are negatively correlated with stock price volatility Whereas, firm size, asset growth and firm leverage positively affect stock price volatility

With a slight different approach from stock returns not stock prices, Fama and French (1992) infer that dividend and cash flow variables such as earning, investment and industrial production may serve as indicator of stock returns

Allen and Rachim (1996) fail to find any evidence that dividend yield influence the stock price volatility in Australia However, they find a significant positive correlation among stock price volatility and earning volatility and leverage, and a significant negative relationship between price volatility and payout ratio According to their results, there is a negative correlation between size and stock price volatility, as large companies incur more liabilities

Regarding to emerging markets, Irfan and Nishat (2003) in a study in Pakistan argue that both dividend payout ratio and dividend yield have significantly negative effect on stock price volatility Most of their findings are similar to those of Baskin

(1989) They observe a positive correlation between debt and price volatility but its influence is less than that of dividend yield

Following Irfan and Nishat (2003), a number of studies are conducted in Pakistan regarding to dividend policy and stock price volatility Asghar et al., (2010) states that price volatility and dividend yield have strong positive correlation but price volatility is highly negatively correlated with growth in assets Nazir et al., (2010) finds that dividend yield and payout ratio have significant impact on the share price volatility The effect of dividend yield on stock price volatility increase during the studying period whereas payout ratio has only a significant impact at lower level of significance

Rashid and Rehman (2008) find a positive but insignificant relationship among stock price volatility and dividend yield in the stock market of Dhaka

2.2 Stock price volatility and firm age

P´astor and Veronesi (2003) find a negative cross-sectional relation between volatility and firm age The median return volatility of the United States stocks falls monotonically from 14% per month for 1-year-old firms to 11% per month for 10-year-old firms The authors’ model predicts higher stock volatility for firms with more volatile profitability, firms with more uncertain average profitability, and firms that pay no dividends

2.3 Sstock price volatility and trading liquidity

Various studies report that there are significant relationships between volume and stock price movement and liquidity, due to the fact that trading volume is a source

of risk because of the flow of information For example, Saatccioglu and Starks (1998) find that volume lead stock prices changes in four out of the six emerging markets Jones et al., (1994) found that the positive volatility-volume relation documented by numerous researchers reflected a positive relationship between

volatility and the number of transactions Gallant, et al., (1992) investigate the price and volume co-movement using daily data from 1928 to 1987 for New York Stock Exchange and find positive correlation between conditional volatility and volume Song, et al., (2005) examine the roles of the number of trades, size of trades, and share volume in the volatility-volume relation in the Shanghai Stock Exchange and confirm that mainly the number of trades drives the volatility volume relation In addition, other studies report that stock trading volume represents the highest positive correlation to the emerging stock price changes; thus represent the most predicted variables in increasing price volatility in both emerging and developing stock markets (Sabri, 2004)

2.4 Other firm attributes and stock price volatility

Ariff et al., (1994) establish the joint linear effect of these six variables for the three markets using data relating to samples of firms over 16 or more years in Japan, Malaysia and Singapore In general, the six variables are significantly related to share price volatility in the three markets although some were not significant in particular markets In the case of more analytically intensive Japanese market, changes in the fundamental factors account for two fifth of the variation in share price volatility The same is not the case in the less analytically intensive developing markets of Malaysia and Singapore Obviously, larger portions of price variation appear not to be explained by the variation in the six firm-specific fundamental variables in the less developing markets

In another study, Ariff and Khan (2000) on a sample of hundred homogenous industrial firms, four out of these six factors are found significant and explained two-third of share price volatility over a window of twenty years for US market Irfan and Nishat (2003) identify the joint-effect multiple factors exert on share prices on Karachi Stock Exchange in the long run The significant joint factors observed are payout ratio, size, leverage and dividend yield This study undertakes investigation for pre-reform, post-reform and overall period

After reviewing some distinguished works in the field, it can be seen that many works are done so far on this topic However, to the best knowledge of the author, there are very few studies about stock price fluctuations and firm attributes in developing countries, especially in Vietnam The empirical evidence of stock price volatility in Vietnam stock exchange is lack in the literature This gives the current study great relevance and is the impetus for the researcher to begin investigation In lieu of the current literature, this research enriches the literature by examining whether stock price volatility is affected by firm attributes as in previous related studies

This study may contribute to the literature by reducing the death of studies on relationship between stock price volatility and firm characteristics for firms listed in Vietnam stock market

3 DATA DESCRIPTION AND DEVELOPING EMPIRICAL RESEARCH HYPOTHESES

This section begins by presenting a detailed description of the main data sources and an explanation of selected variables The remained part of section 3 follows with development of empirical research hypothesis

3.1 Data description

The data employed in this study include 110 listed companies in the Ho Chi Minh City Stock Exchange (HOSE) over the period from 2007 – 2009 This research uses secondary data collected from audited consolidated financial statements

Data of stock prices for the purpose of this study comprise daily closing share prices

of 110 companies from the Ho Chi Minh City Stock Exchange over the period from

01 Jan 2007 to 31 Dec 2009 The share prices are adjusted for dividends and stock splits in order to reflect more accurate returns

The sample companies are subjected to the following selection criteria:

audited financial statements and annual reports are available;

2009 ;

The initial data of this study consists of 116 companies which are listed on HOSE

by 31 Dec 2007 However, since several observations are not similar to the whole sample, they are taken out of the final sample First, 5 financial firms such as bank (STB), security company (SSI), investment funds (MAFPF1, PRUBF1, VFMVF1) are excluded from the purview of this study since they are subjected to a different

regulatory framework that does not apply on other listed companies, given either different financial statement formats or specific characteristics of the financial sectors Second, a manufacturing firm, Bach Tuyet Cotton Corporation (BBT), is also eliminated from this study since they had failed to deliver its statement for

2008 and was de-listed from HOSE Therefore, the final sample contains 110 companies matching all selection criteria, which together creates 330 observations

1 Dependent variables:

Price volatility (PV): This is derived from Parkinson (1980) extreme value

estimation of the variance of returns In this case, for each year, the annual range of stock prices, which is the difference between maximum and minimum value, is divided by the average of the high and low stock prices and then raised to the second power Parkinson (1980) method is known to be far superior to the traditional method of estimation, which uses closing and opening prices only This measure is appropriate to capture the changes in share prices on an annual basis This variable is collected from the price timeline of each firm from HOSE which are adjusted for dividends and stock splits

Firm attributes:

In this subsection, we briefly introduce a number of firm-specific attributes used in the empirical analysis To enable easy comparison, we first choose essentially the selected attributes as previous researches These are:

(i) Earning volatility (EV): is defined as the ratio of the company’s earnings

before interest and tax (EBIT) to total assets This is calculated from the consolidated audited financial statements

value of assets at year-end This is calculated from the consolidated audited financial statements

(iii) Return on equity (ROE): is measured as net income divided by the book

value of equity at year-end This is calculated from the consolidated audited financial statements

(iv) Asset growth (ASGR): is calculated through the natural logarithm of the

ratio between the total assets at the end of the financial year and total assets at the beginning of the same financial years This is calculated from the consolidated audited financial statements

(v) Current ratio (CURR): is used as a proxy for short-term financial distress It

is calculated as current assets divided by current liabilities at year-end, and measures the ability of the firm to meet its short-term payment requirements This is calculated from the consolidated audited financial statements

(vi) Leverage ratio (LEVR): is a measure of long-term financial distress It is

defined as the ratio of total liabilities to total assets at year-end This is calculated from the consolidated audited financial statements

(vii) Dividend yield (DY): is the value of all cash dividends paid to common

stockholders divided by the market value of the firm at year-end This is derived from the dividend timeline on HOSE

(viii) Payout ratio (POR): is the value of all cash dividends paid divided by total

earnings This ratio is calculated for each year and is derived from the dividend timeline on HOSE

(ix) Firm Size (SIZE): is the book value of total assets at the year-end In the

regressions, we consider the natural logarithm of total assets This is calculated from the consolidated audited financial statements

(x) Firm Age (AGE): is the number of year plus one elapsed since the year of

the company’s IPO We refer to this variable as the firm’s listing age We add one year to avoid ages of zero Then, natural logarithm is calculated The

variation of the transformed variable is smaller and leads to less biased results This is collected from the company profile

(xi) Liquidity (TOVR): We employ the trading turnover rate to proxy for

liquidity of the firm's shares It is defined as the total value of stocks traded over a year divided by the market value of the firm at the year-end This is a proxy of liquidity employed by many papers (Brennan et al., 1998, Chordia et al., 2001, Datar et al., 1998, Rouwenhorst, 1999) This is colleted from the trading timeline on HOSE

In addition, we also group firms in our data into different industries There are 6 industries in our dataset including basic materials, consumer goods & services, foods & beverages, industrials, real estates, construction & materials and others Table 3.1 represents the component of industry Among the 110 selected companies

in the sample, there are 10 firms in basic materials industry, 16 firms in consumer goods and services industry, 22 firms in foods and beverages industry, 22 firms in industrials industry, 29 firms in real estates, construction & materials industry and

11 firms in others industry

1 Table 3.1 Summary of industry structure

Real estates, Construction and Materials 29 26.36%

2 Table 3.2 Description of stock price volatility in Vietnam

Year 2007 2008 2009

Basic materials

Consumer goods and services

Foods and beverages Industrials

Real estates, Construction

&Materials

Others Whole

sample

Mean 0.50847 1.47370 1.29569 1.08802 1.06799 1.16968 1.09310 1.14899 0.82891 1.09262 Median 0.40437 1.48880 1.28231 1.11287 0.95265 1.24868 1.16228 1.12991 0.68927 1.11088 Maximum 3.56338 2.51447 2.78530 3.56338 2.78530 2.62376 2.41704 2.51447 1.79953 3.56338 Minimum 0.00057 0.29016 0.31864 0.00866 0.07867 0.00057 0.00756 0.00907 0.30777 0.00057 Std Dev 0.48457 0.53785 0.52305 0.79720 0.64190 0.67381 0.64320 0.69850 0.42724 0.66386 Skewness 2.80366 -0.08753 0.37798 0.88375 0.52838 -0.06492 0.03712 0.21032 0.65387 0.34804 Kurtosis 16.2166 2.08322 2.82955 4.26845 2.63047 2.06493 2.02068 1.92483 2.24081 2.55052 Jarque-Bera 944.724 3.99268 2.75243 5.91630 2.50661 2.45082 2.65259 4.83192 3.14400 9.44002 Probability 0 0.13583 0.25253 0.05192 0.28556 0.29364 0.26546 0.08928 0.20763 0.00892 Sum 55.9314 162.107 142.526 32.6406 51.2634 77.1992 72.1447 99.9622 27.3541 360.564 Sum Sq Dev 25.5941 31.5321 29.8201 18.4303 19.3659 29.5115 26.8911 41.9597 5.84111 144.993 Observations 110 110 110 30 48 66 66 87 33 330

3 Table 3.3 Data descriptive statistics for firm attributes

ROA ROE EV ASGR LEVR CURR DY POR SIZE AGE TOVR

Mean 0.08029 0.32686 0.11011 0.402663 0.10401 2.45604 0.04263 0.48296 11.8405 0.75401 7.21470 Median 0.06922 0.26711 0.09825 0.224945 0.03966 1.71989 0.03010 0.42684 11.7790 0.77815 7.18851 Maximum 0.50096 3.23416 0.60901 6.059177 0.65476 19.4826 0.18519 20.6897 13.1557 1.25527 8.55964 Minimum -0.44500 -1.92080 -0.36580 -0.51451 0 0.11378 0 -1.66667 10.9475 0.00000 4.50687 Std Dev 0.09103 0.39074 0.09717 0.675254 0.14537 2.39844 0.03957 1.18157 0.47185 0.22760 0.54953 Skewness -0.18212 1.79379 0.25369 4.092851 1.86453 3.63698 1.18981 15.2902 0.62079 -0.58345 -0.50193 Kurtosis 10.4425 20.0248 9.31687 27.71651 5.93011 20.3654 4.17736 261.047 2.84984 3.44450 5.13536 Jarque-Bera 763.443 4162.34 552.204 9321.286 309.258 4873.92 96.9211 928444 21.5058 21.4391 76.5529 Probability 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00002 0.00002 0.00000 Sum 26.4955 107.863 36.3349 132.879 34.3235 810.495 14.0680 159.377 3907.38 248.822 2380.85 Sum Sq Dev 2.72644 50.2302 3.10646 150.014 6.95257 1892.58 0.51506 459.321 73.2480 17.0421 99.3522 Observations 330 330 330 330 330 330 330 330 330 330 330

4Table 3.4 Data descriptive statistics for firm attributes by year

ROA ROE EV ASGR LEVR CURR DY POR SIZE AGE TOVR

2007

Mean 0.09434 0.37820 0.12195 0.76762 0.09875 2.54244 0.01870 0.37352 11.7719 0.66382 6.92857 Median 0.07515 0.31913 0.10322 0.54224 0.04349 1.80817 0.01852 0.37493 11.7140 0.69897 6.97598 Maximum 0.45786 2.33766 0.52608 6.05918 0.59652 14.3222 0.05455 1.21852 12.9860 1.20412 8.00721 Minimum 0.00839 0.03161 0.01589 -0.43878 0.00000 0.18280 0.00000 0.00000 10.9475 0.00000 4.50687 Std Dev 0.06477 0.30964 0.07176 0.96777 0.13148 2.22471 0.01252 0.24138 0.45587 0.26191 0.55289 Observations 110 110 110 110 110 110 110 110 110 110 110

2008

Mean 0.05682 0.21896 0.08959 0.20019 0.10338 2.54223 0.06627 0.65840 11.8338 0.76125 7.25934 Median 0.05204 0.21002 0.08481 0.10875 0.03356 1.73025 0.06534 0.50103 11.7763 0.77815 7.21287 Maximum 0.35415 1.35799 0.38924 2.33587 0.61648 19.4826 0.18519 20.6897 13.0333 1.23045 8.54394 Minimum -0.44500 -1.92080 -0.36580 -0.51451 0.00000 0.11378 0.00000 -1.66667 11.0224 0.30103 6.19913 Std Dev 0.10038 0.36279 0.10423 0.38097 0.15039 2.78868 0.04987 1.98459 0.46732 0.20632 0.45180 Observations 110 110 110 110 110 110 110 110 110 110 110

2009

Mean 0.08971 0.38341 0.11878 0.24019 0.10990 2.28346 0.04293 0.41697 11.9159 0.83695 7.45618 Median 0.07499 0.31697 0.09806 0.17284 0.04647 1.59178 0.03984 0.40094 11.8544 0.84510 7.44836 Maximum 0.50096 3.23416 0.60901 1.37702 0.65476 17.2812 0.13986 3.57143 13.1557 1.25527 8.55964 Minimum -0.32924 -0.88891 -0.31476 -0.35755 0.00000 0.14683 0.00000 -0.25685 11.0977 0.47712 6.13447 Std Dev 0.09948 0.46444 0.10883 0.30354 0.15433 2.14288 0.03065 0.41177 0.48504 0.17316 0.50814 Observations 110 110 110 110 110 110 110 110 110 110 110

5Table 3.5 Data descriptive statistics for firm attributes by industry

ROA ROE EV ASGR LEVR CURR DY POR SIZE AGE TOVR Basic materials

Mean 0.15988 0.45281 0.19656 0.18705 0.05191 2.55180 0.05380 0.51325 11.93652 0.65275 7.32259 Median 0.14901 0.40844 0.18936 0.13105 0.03074 1.75642 0.04052 0.52279 11.90438 0.65052 7.23303 Maximum 0.45786 1.50283 0.52608 1.04417 0.36250 7.98875 0.13333 1.14635 13.01044 0.95424 8.54394 Minimum 0.02110 0.04360 0.03245 -0.43878 0.00002 0.86331 0.00753 0.07985 10.97542 0.30103 5.78740 Std Dev 0.10725 0.31926 0.10561 0.35548 0.08519 2.03404 0.03926 0.22584 0.52077 0.18748 0.58533 Observations 30 30 30 30 30 30 30 30 30 30 30

Consumer goods & services

Mean 0.08137 0.32793 0.11741 0.40862 0.04730 2.12809 0.05197 0.43432 11.64331 0.79496 7.12841 Median 0.06976 0.30187 0.10341 0.26163 0.01307 1.69605 0.03639 0.42962 11.63898 0.84510 7.09157 Maximum 0.50096 2.55628 0.52000 1.77567 0.25880 7.03273 0.17483 0.87767 12.49048 1.04139 8.12130 Minimum -0.10002 -0.39719 -0.07744 -0.21228 0.00000 0.81417 0.00000 0.00000 10.94747 0.30103 6.13447 Std Dev 0.07870 0.38110 0.08171 0.44704 0.06775 1.38631 0.04419 0.25722 0.31019 0.19581 0.46934 Observations 48 48 48 48 48 48 48 48 48 48 48

Foods & beverages

Mean 0.07277 0.27554 0.10450 0.36986 0.06827 1.94045 0.04958 0.77494 11.80119 0.75596 7.14037 Median 0.07735 0.30187 0.10444 0.24645 0.03036 1.54909 0.03984 0.43834 11.78003 0.77815 7.09979 Maximum 0.28013 0.86095 0.32778 2.48598 0.38879 4.51198 0.17157 20.68966 12.92850 1.17609 8.00449 Minimum -0.44500 -1.92080 -0.36580 -0.51451 0.00000 0.17604 0.00000 -1.66667 11.07587 0.00000 5.50977 Std Dev 0.10973 0.40638 0.11105 0.49706 0.08628 1.02617 0.03826 2.54227 0.43577 0.24501 0.49879 Observations 66 66 66 66 66 66 66 66 66 66 66

Table 3.5 (cont.) Data descriptive statistics for firm attributes by industry

ROA ROE EV ASGR LEVR CURR DY POR SIZE AGE TOVR Industrials

Mean 0.05388 0.24931 0.08110 0.48540 0.12798 3.24848 0.03130 0.35334 11.75152 0.78685 7.18536 Median 0.05679 0.17397 0.08561 0.20010 0.03979 1.76138 0.02123 0.34272 11.57991 0.84510 7.20304 Maximum 0.21587 2.33766 0.29126 4.94300 0.61648 19.48263 0.17544 1.41509 12.80534 1.23045 8.55964 Minimum -0.18740 -0.38710 -0.17532 -0.34047 0.00000 0.28136 0.00000 -0.11799 11.02244 0.00000 4.50687 Std Dev 0.07427 0.37554 0.07974 0.91603 0.16682 3.81021 0.03581 0.34262 0.49577 0.26602 0.72303 Observations 66 66 66 66 66 66 66 66 66 66 66

Real estates, Constructions & Materials

Mean 0.07056 0.34251 0.09658 0.46391 0.13284 2.19502 0.04095 0.40971 11.88837 0.75854 7.23738 Median 0.05549 0.22685 0.08044 0.26180 0.07964 1.72576 0.02747 0.40554 11.85752 0.77815 7.27413 Maximum 0.45278 3.23416 0.60901 6.05918 0.65476 12.68903 0.18519 3.57143 13.15574 1.25527 8.37340 Minimum -0.32924 -0.88891 -0.31476 -0.26962 0.00000 0.46412 0.00000 0.00000 11.05594 0.00000 5.81903 Std Dev 0.08460 0.43744 0.09858 0.83158 0.16005 1.69627 0.04259 0.44540 0.43583 0.22103 0.44535 Observations 87 87 87 87 87 87 87 87 87 87 87

Others

Mean 0.09986 0.42728 0.12579 0.32868 0.18142 2.98050 0.03210 0.39455 12.17070 0.70496 7.38963 Median 0.10247 0.38842 0.11670 0.22130 0.09658 2.09758 0.02632 0.43413 12.02736 0.69897 7.31729 Maximum 0.23807 1.35897 0.27135 1.11749 0.59652 17.28121 0.09524 1.00056 13.09231 1.00000 8.41904 Minimum -0.01971 -0.06522 -0.02752 -0.11214 0.00024 0.11378 0.00000 -0.45872 11.13065 0.30103 6.40217 Std Dev 0.05138 0.28994 0.05868 0.37324 0.20938 3.27532 0.02536 0.27025 0.54891 0.17469 0.54978 Observations 33 33 33 33 33 33 33 33 33 33 33

Table 3.2 shows description of stock price volatility in Vietnam in each year, each industry and in general as a whole

The mean value and standard deviation of price volatility in 2007 are 0.51 and 0.48 respectively The mean value and standard deviation of price volatility in 2008 is largest at 1.48 and 0.54 respectively The values of price volatility in 2009 are minimized at 0.32 and maximized at 2.79

The mean values of price volatility are highest in foods and beverages industry at 1.17 and lowest in others industry at 0.83

In general, the results from table 3.2 indicate that the mean value of price volatility

is 1.09 with a standard deviation of 0.66, which means that it remains highly volatile during the investigation period

Table 3.3 presents a description of firm attributes of listed firms in Vietnam Among the independent variables, the mean of ROA remains 0.08 with standard deviation of 0.09, which indicate very little volatility On one hand, the leverage ratios of firms spread from 0 to 0.65 in the investigation period On the other hand, the minimum and maximum values of current ratio are 0.11 and 19.5 respectively The dividend yield has mean value of 0.04 and standard deviation of 0.04, which imply less volatility Meanwhile, mean value and standard deviation of payout ratio stay at 0.48 and 1.18 respectively

Table 3.4 and table 3.5 illustrate the descriptive statistics for all independent variables i.e firm attributes These tables reveal that behavior of each firm attribute varies from on year to another year, from one industry to another industry

3.2 Developing empirical research hypotheses

This section proposes several empirical hypotheses which are consistent with the literature These hypotheses also allow us to make comparisons between the characteristics of stock price volatility in Vietnam and other markets including

developed market such as the United States and Australia as well as emerging market such as Pakistan and Bangladesh

H1: Stock price volatility is positively influenced by return on asset, with all other factors remaining constant

The relation between dividend and earnings follows that greater the volatility of earnings of a firm, the less is the likelihood of dividend yield being changed by the firm’s management Hence return on assets is directly related to share price volatility

H2: Stock price volatility is positively influenced by asset growth rate, with all other factors remaining constant

We include a variable to see the growth in assets because it is quite possible that any other relation between dividend policy and stock price volatility could be occurred Dividend payout policy could be inversely linked to growth and investment opportunities Therefore, we add Assets Growth as a control variable to reflect firm growth

H3: Stock price volatility is positively influenced by firm leverage with all other factors remaining constant

The level of debt financing by the firm has impact in the value of the firm’s assets Hamada (1972) and Sharpe (1964) specify their theories regarding the capital structure A high-risk firm (a firm with debt) must generate high return consistent with the investor’s expected return It follows that with higher debt firm should have greater rate of change in its share price Hence capital structure changes must

be directly related to the share price volatility Modigliani and Miller (1958) emphasize that in competitive capital markets the value of a firm is independent of its financial structure But if markets are imperfect due to transaction cost, taxes, informational asymmetry, agency cost etc then capital structure matters and influences the share prices As due to operation risk, there is a possibility of direct link between and leverage Small firms that are not supposed to be highly

diversified in their operations, so financial institutions and investors are also less interested in these types of firms and they are less interested in the analysis of stocks of these small firms This could cause stocks of small firms less informed in the market and more illiquid It leads to greater price volatility of their stocks

H4: Stock price volatility is negatively influenced by current ratio, with all other factors remaining constant

Witkowska (2005) in a working paper employs current ratio as a measure for stock volatility Current ratio measures the ability of the firm to meet its short-term payment requirement If a firm has lower current ratio, the probability to engage in short-term distress is higher and thus its risk is larger than firm with higher current ratio As a result, stock price volatility is higher as well

H5: Stock price volatility is negatively influenced by dividend yield, with all other factors remaining constant

According to literature review, dividend yield is the most important factors affecting stock price volatility (Baskin, 1989) He argues that there is a significant negative relationship between dividend yield and stock price volatility

H6: Stock price volatility is negatively influenced by dividend payout ratio, with all other factors remaining constant

This hypothesis is derived from the hypothesis of Allen and Rachim (1996) which indicates a significant negative relationship price volatility and payout ratio The dividend payout policy also expected to be negatively related to investment opportunities

H7: Stock price volatility is negatively influenced by firm size, with all other factors remaining constant

Size of a firm does have effect on the valuation of the firm assets Smaller stocks have higher average returns The size of the firm is expected to influence the share prices positively as large firms are better diversified than small ones and thus are

less risky Benishay (1961) and Atiase (1985) show that as the size of the firm increases, their share price volatility declines Size of firm is important variable that affect the stock volatility This stock price of small firms may be more unstable compared to large firms, as small firms as less diversified than large firms Moreover, investor of small firms acts more irrationally to new events Hence size

of firm nay affect choice of dividend policy as well

H8: Stock price volatility is negatively influenced by firm age, with all other factors remaining constant

It is widely accepted that firm with longer history is more experienced in running its business Therefore, its default probability is lower which leads to lower operation risk Stock price of such firm with lower risk is less volatile as a result The reason

is that stock price may be less volatile for mature company which appears to be less risky and more stable Therefore, we anticipate firm age to have negative impact on stock price volatility

H9: Stock price volatility is positively influenced by liquidity, with all other factors remaining constant

The concept of the volume impact is built on the fact that prices need volume to move, thus, the high volatility of stock prices may be produced as a consequence of volume volatility and trading activity Previous researches also imply a significant relationship between trading volume and stock prices (Song et al., 2005, Saatccioglu and Starks, 1998, Jones et al., 1994)

Table 3.6 summaries all hypotheses as follows

6 Table 3.6 Data description and expected relation to stock price volatility

Market value of firm

+

4 METHODOLOGY

This section explain the econometric and empirical techniques used in this research

We choose to utilize panel data in agreement with the literature which recommends

it as the most appropriate method for the focus of our study Hsiao (2006) mentions some of the advantages of panel data in studying historical series of a set of companies: it estimates model parameters accurately; it offers tools to counteract model misspecifications and omitted variables; last but not least, it eases computation and interpretation of results

Taking into account some problems which affect results of regression, we perform a gradual breakdown and make additional analysis as follows

4.1 Descriptive statistics and correlation matrix

Firstly, we implement a descriptive statistics analysis to examine differences stock price movements of firms in different industries, different years and in general In addition, overall description of independent variables and description in each year and each industry are also presented The results are described in Chapter 3 The correlation matrix which follows helps us to identify whether there is any perfect or near multicollinearity among independent variables that would affect our final results

4.2 Bivariate analysis

This provides a crude test of the single relationship between common stock price volatility and the theory suggested fundamental variables individually, thus providing the impact of each variable on stock price change if no other factor is considered

The single linear regression model is specified as follows:

t t

where yi,t denotes the stock price volatility of firm i at time t; Xi,t is a vector that represents the firm characteristic variable of firm i at time t; and εi,t is the error term

Of which, X represents for each single independent variable at one time In details,

9 single regression models are conducted as follows

t t

t t

t t

t t

t t

t t

t t

t t

t t

We begin with the single equations which treat all variables as exogenous The model is specified as follows:

t t

where yi,t denotes the stock price volatility of firm i at time t; Xi,t is a vector that represents the firm characteristic variable of firm i at time t; and εi,t is the error term

In which,

X5 is firm size (SIZE)

The variables used in this model are almost identical to the previous utilized The difference is that we do not include Earning Volatility (EV) in the final model since they are not significant in any of the basic estimations and they generate a low fit of the model Instead, we employ another variable of return on assets representing for firm performance However, in the later part of this study, we also present regression results with earning volatility for comparison purpose

Since the results are highly sensitive to the estimation method, we employ several specifications so as to counteract the problems implied by panel data In the next lines, we will describe the steps followed in the analysis

4.3.1 Ordinary Least Square (OLS) regression

This is the basic specification of the model and it does not take into account the special structure of the panel data, with the double cross-sectional and time dimension The simplest method is to assume that the intercept and all coefficients are constant across time and individuals This approach ignores the characteristics

of panel data and estimates coefficients by OLS regression

Now some of the shortcomings of the data have been unraveled, but we still have to control for possibly autocorrelations of the error terms At this stage, there are two

options of using either random or fixed effects If the dependent variables are determined by individual time invariant characteristics which have not been, and if these individual characteristics are not correlated among themselves, the fixed effects model is indicated; it removes the effect of the time-invariant characteristics and assesses the net effect of the predictor Otherwise, if the differences among individuals are random, and the individual characteristics are correlated between one another, the random effects model is more suitable This is a sensitive matter in our case Therefore, we pursue the standard procedure and estimate the fixed model

as well and perform Hausman test to compare fixed versus Random Effects

4.3.2 Fixed effects regression

One of commonly used methods for panel data regression is to relax the assumption

of the constant intercept across cross-sectional units while continuing to hold the assumption of the constant coefficients for independent variables This method is called the Fixed Effects Model because the intercept of each cross-sectional unit is

assumed to not change over time The differences of intercept across i may be due

to the specific characteristics of cross-sectional units The simplest types of fixed effects models allow the intercept in the regression model to differ cross-sectionally but not over time, while all of the slope estimates are fixed both cross-sectionally and over time

One of the advantages in the fixed effects model is that it is easy to use, but this method is costly in terms of the degrees of freedom due to the inclusion of numerous dummy variables in the model If the number of cross-sectional units is in the thousands, estimations using the fixed effects model may be time-consuming and exceed the capabilities of any computer (Greene, 2002) In addition, the fixed effects model is not proper for measuring the effect of time-invariant variables (Gujarati, 2002) The fixed effect model includes the dummy variables which could have a linear relationship with the time-invariant variables, resulting in multicollinearity

4.3.3 Random effect regression

This is sometimes also known as the error components model The random effects approach proposes different intercept terms for each entity and again these intercepts are constant over time, with the relationships between the explanatory and explained variables assumed to be the same both cross-sectionally and temporally Under the random effects model, the intercepts for each cross-sectional unit are assumed to arise from a common intercept (which is the same for all cross-

sectional units and over time), plus a random variable that varies cross-sectionally but is constant overtime measures the random deviation of each entity’s intercept

term from the ‘global’ intercept term

The selection of the panel data regression model depends on the correlation between

a random disturbance, ui, and other independent variables If ui is correlated with other independent variables, the fixed effects model is appropriate for estimating

regressors, may cause an inconsistency problem due to the omitted variables (Greene 2002) If the number of independent variables is sufficiently large and the data are randomly drawn from a large sample, the random effects model is more appropriate than the fixed effects model

4.3.4 F-statistic test

This is a redundant fixed affect test used to test between OLS regression model and fixed effect models The results of this test help us to identify which model is the most suitable

4.3.5 Hausman test

This is conducted to decide whether choosing fixed effect model or random effect

model is more appropriate Hausman (1978) proposes a method to test model specification by comparing two sets of estimates The basic idea of the Hausman

test is to compare estimates from the random effects model with those from the fixed effects model under the null hypothesis that both models’ estimates are consistent If the difference between the two set of estimates are large, the null hypothesis is rejected and the conclusion is in favor of the fixed effects model These panel data techniques and related tests are guided by Baltagi (2008) and Hsiao (Hsiao, 2006)

To further ensure the validity of the results, apart from F-statistic test and Hausman test, we also conduct several more robustness checks Firstly, it is assumed that the relationship between stock volatility and firm characteristics is due to broad industry patterns rather than individual differences among firms In order to represent the industry characteristics on stock volatility, we also consider whether stock volatility differentiates a specific industry in HOSE by allowing six dummy variables to proxy for industry For simplicity the industry is classified into the six categories such as basic materials (DUM1), consumer goods and services (DUM2), foods and beverages (DUM3), industrials (DUM4), real estates, construction & materials (DUM5) and others (DUM6)

In addition, we also run the regressions with different year and different industry

5 RESULTS AND DISCUSSION OF RESULTS

In this section, we present the empirical findings and an in-depth analysis of the results This section begins with necessary steps for testing multi-collinearity including correlation matrix and calculation of variance inflation factor Overall estimations are implemented using OLS regression, fixed effect and random effect regressions After that, this section continues with an empirical test to explain why panel data analysis is used in this dissertation Firstly, the F-statistics test will be presented to compare the fixed effects model and the OLS regression The Hausman test (1978) will then be utilized to compare the two different panel data regression methods - the fixed effect model and the random effects model - followed by the empirical regression results Regressions on year-by-year basis and for each industry are then conducted Lastly, we will present in-depth analyses of the hypotheses test and assess the robustness of the results

It also can be seen that the correlation coefficients between explanatory variables are lower than 0.56, suggesting that there is no multicollinearity problem among these independent variables Another important note drawn from this correlation matrix test is that it figures out low correlated relationship between dividend yield and payout ratio of only 0.19 This result is not similar to most of previous studies

7 Table 5.1 Correlation matrix among variables

PV ROA ROE EV ASGR LEVR CURR DY POR SIZE AGE TOVR