Empirical investigation of market value change in Vietnam stock market.pdf

MINISTRY OF EDUCATION AND TRAINING
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

MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF ECONOMICS HOCHIMINH CITY
--- 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
SUPERVISOR: Assoc. Prof, Ph.D. TRAN HUY HOANG
Ho Chi Minh City – 2011

i
ACKNOWLEDGEMENT
I am grateful to my master thesis supervisor, Assoc. Prof, Ph.D. Tran Huy Hoang,
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

ii
ABSTRACT
This paper attempts to determine stock price volatility in Vietnam stock market
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.

iii
CONTENTS
ACKNOWLEDGEMENT...................................................................................... i
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

iv
4.3.5 Hausman test....................................................................................29
5 RESULTS AND DISCUSSION OF RESULTS .......................................... 31
5.1 Correlation matrix...................................................................................31
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
5.2.4 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

v
LIST OF ABBREVIATION
HOSE Ho Chi Minh City Stock Exchange
HNX Hanoi Stock Exchange
PV Price volatility
EV Earning volatility
ROA Return on assets
ROE Return on equity
ASGR Asset growth rate
LEVR Firm leverage
CURR Current ratio
DY Dividend yield
POR Payout ratio
SIZE Firm size
AGE Firm age
TOVR Liquidity
EBIT Earning Before Interest and Tax
IPO Initial Public Offer
OLS Ordinary Least Square

vi
LIST OF TABLES
Table 3.1 Summary of industry structure...............................................................14
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 5.10 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

Empirical investigation of stock price volatility in Vietnam stock market
Huynh Thi Bich Thao 1
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?
1.3 Methodology
Our study engages in the analysis of a panel of 110 companies listed on Ho Chi
Minh City Stock Exchange between 2007 and 2009.
Our empirical analysis is conducted as follows.
- Descriptive statistics.
- 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:
(1) the companies must be listed on HOSE by the end of 2007 and 3 full-year
audited financial statements and annual reports are available;
(2) the companies are non-financial companies;
(3) the companies were not de-listed from HOSE over the period from 2007 to
2009 ;
(4) the firm’s fiscal year-end is December;
(5) the firm’s stock is consistently traded over the period from 2007 to 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.
(ii) Return on assets (ROA): is measured as net income divided by the book
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
Industry Frequency Percentage
Basic materials 10 9.09%
Consumer goods and services 16 14.55%
Foods and beverages 22 20.00%
Industrials 22 20.00%
Real estates, Construction and Materials 29 26.36%
Others 11 10.00%
Total 110 100.00%

2Table 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

3Table 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

4 Table 3.4 Data descriptive statistics for firm attributes by year
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

5 Table 3.5 Data descriptive statistics for firm attributes by industry
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
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
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
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
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
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
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
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.

6Table 3.6 Data description and expected relation to stock price volatility
No. Variables Description Formula Expected relation to stock price volatility
1 ROA Return on assets Net profit
Total assets
+
2 ASGR Assets growth rate Total assets (t) – total assets (t-1)
ln(
Total assets (t-1)
)
+
3 LEVR Leverage ratio Total liabilities
Total assets
+
4 CURR Current ratio Current assets
Current liabilities
-
5 DY Dividend yield Total cash dividend
Market value
-
6 POR Payout ratio Total cash dividend
Total earnings
-
7 SIZE Firm size ln(total assets) -
8 AGE Firm age ln(1 + year(t) - year(IPO)) -
9 TOVR Liquidity Trading turnover
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
i
t
i
t
i X
y ,
,
, ε
β
α +
+
=

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
i
t
i
t
i ROA
y ,
,
, )
( ε
β
α +
+
=
t
i
t
i
t
i ASGR
y ,
,
, )
( ε
β
α +
+
=
t
i
t
i
t
i LEVR
y ,
,
, )
( ε
β
α +
+
=
t
i
t
i
t
i CURR
y ,
,
, ε
β
α +
+
=
t
i
t
i
t
i DY
y ,
,
, )
( ε
β
α +
+
=
t
i
t
i
t
i POR
y ,
,
, )
( ε
β
α +
+
=
t
i
t
i
t
i SIZE
y ,
,
, )
( ε
β
α +
+
=
t
i
t
i
t
i AGE
y ,
,
, )
( ε
β
α +
+
=
t
i
t
i
t
i TOVR
y ,
,
, )
( ε
β
α +
+
=
4.3 Multivariate analysis
After running bivariate tests, we perform a multivariate analysis consisting
regression estimations on stock price volatility. This method attempts to find the
collective impact of all factors on price volatility by regressing all the independent
factors against the dependent variable.
We begin with the single equations which treat all variables as exogenous. The
model is specified as follows:
t
i
t
i
t
i X
y ,
,
, ε
β
α +
+
=
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,
X1 is firm performance (ROA or ROE or EV).
X2 is asset growth (ASGR).
X3 is firm leverage (LEVR and CURR).
X4 is dividend policy (DY and POR).
X5 is firm size (SIZE).
X6 is firm age (AGE).
X7 is liquidity (TOVR).
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
coefficients. The random effects model, including ui correlated with other
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.
5.1 Correlation matrix
Table 5.1 shows the correlation coefficient matrix of variables for the whole data
set.
At the first glance, it can be seen that price volatility positively correlates with
leverage, current ratio, dividend yield, pay-out ratio, firm age and liquidity.
However, price volatility negatively correlates with return on assets, asset growth
and firm size. The low inter-correlations among the explanatory variables used in
the regressions indicate no reason to suspect serious.
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.

7Table 5.1 Correlation matrix among variables
PV ROA ROE EV ASGR LEVR CURR DY POR SIZE AGE TOVR
PV 1
ROA -0.0138 1
ROE 0.0029 0.8001 1
EV -0.0040 0.9686 0.7933 1
ASGR -0.1987 0.0374 0.1053 0.0121 1
LEVR 0.0121 -0.1898 -0.0968 -0.1814 0.0786 1
CURR 0.0229 0.1722 -0.0010 0.1165 0.0547 -0.1345 1
DY 0.2763 0.1248 0.0232 0.1530 -0.1959 -0.1343 0.0423 1
POR 0.0831 -0.0314 -0.0654 -0.0300 -0.0008 -0.0485 -0.0185 0.1924 1
SIZE -0.0155 -0.0095 0.0862 -0.0355 0.0855 0.4259 -0.0457 -0.1959 -0.0468 1
AGE 0.2708 -0.0446 0.0284 -0.0329 -0.0559 -0.2305 0.1127 0.0720 0.0376 -0.1734 1
TOVR 0.4006 0.0127 0.0100 -0.0184 -0.1498 0.2691 0.1043 0.0521 -0.0139 0.5561 0.1934 1

To further test whether multicollinearity exists, the Variance Inflation Factor (VIF)
is employed. The results of this test are presented in Table 5.2. The mean of VIF is
1.3300, which is much lower than the threshold of 10. The VIF for individual
variables are also very low. This indicates that the explanatory variables used in the
regression model are not substantially correlated with each other.
8 Table 5.2 Variance Inflation Factor
Variables Variance Inflation Factor
ROA 1.1033
ASGR 1.1160
LEVR 1.3709
CURR 1.0831
DY 1.1715
POR 1.0463
SIZE 1.9383
AGE 1.2529
TOVR 1.8880
Mean 1.3300
5.2 Bivariate analysis
Table 5.3 reports the test results of regression between stock price volatility (PV)
and each of the independent variables. This investigates and generates simple
relationship between dependent variable and each of independent variable
separately. The results of this regression shows that returns on assets (ROA),
leverage ratio (LEVR), current ratio (CURR), payout ratio (POR) and firm size
(SIZE) are not significantly explaining the stock price volatility (PV). The other
four variables are significant but not substantial enough to explain a large portion of
price variation except for dividend yield (DY). Similarly to results from previous
studies (Baskin, 1989, Allen and Rachim, 1996), the highest the coefficient of
dividend yield is stood at highest of 4.64 as compared to the coefficients of other
variables. Contrary to the theories which are presented in hypotheses, asset growth
(ASGR), dividend yield (DY) and firm age (AGE) have signs that are opposite to
those predicted by theories in simple regression test. Among the set of significant
variables, only liquidity (TOVR) shows the similar sign theoretically.

9Table 5.3 Bivariate regression results
Variables Coef. Prob. Coef. Prob. Coef. Prob. Coef. Prob. Coef. Prob. Coef. Prob. Coef. Prob. Coef. Prob. Coef. Prob.
C 1.10 1.17 1.09 1.08 0.89 1.07 1.35 0.50 -2.40
ROA -0.10 0.80
ASGR -0.20***
0.00
LEVR 0.06 0.83
CURR 0.01 0.68
DY 4.64 0.00***
POR 0.05 0.13
SIZE -0.02 0.78
AGE 0.79***
0.00
TOVR 0.48***
0.00
Note: The dependent variable is PV, *, **, *** indicates significance at the 10%, 5% and 1% respectively.

5.3 Multivariate analysis
5.2.1 Overall regression results
Firstly, an Ordinary Least Square (OLS) model regressing stock price volatility on
the nine independent variables shows that the explanatory power of the model is
0.30 which means that 30% of the variation in the dependent variable is explained
by the used set of independent variables (Table 5.4, model 1A).
Next, due to the common characteristics of panel data, panel data may have
heterokadasticity in data. These effects are either fixed effect or random effect. A
fixed effect model assumes differences in intercepts across firms or time periods,
whereas a random effect model explores differences in error variances. A one-way
model includes only one set of dummy variables (e.g., firms or times), while a two-
way model considers two sets of dummy variables (e.g., firms and years).
Our sample is balanced panel with 330 observations. The fixed effect models are
estimated for cross-section and period effects respectively (Model 2A and 3A in
Table 5.4) and together (Table 5.4, model 4A).
The data in this study is quite short panel data with only three years data is
available. To perform between estimates it requires that the number of cross-section
to be greater than the numbers of coefficients in the model. Due to this data
limitation, only cross-section random effect model is estimated (Table 5.4, model
5A).

10 Table 5.4 Overall regression results
Model 1A Model 2A Model 3A Model 4A Model 5A
Ordinary Least Square Cross-section fixed
(dummy variables)
Period fixed (dummy variables) Cross-section fixed (dummy variables)
Period fixed (dummy variables)
Cross-section
random
Variable Coefficient Prob. Coefficient Prob. Coefficient Prob. Coefficient Prob. Coefficient Prob.
C 0.418900 0.6411 -12.01505 0.0119 2.861981 0.0003 -6.461369 0.1444 0.161965 0.8540
ROA -0.155525 0.6647 -0.969101*
0.0682 0.687350**
0.0279 0.431747 0.3624 -0.214630 0.5289
ASGR -0.060420 0.2147 -0.088417 0.1159 0.094696**
0.0279 0.035228 0.4889 -0.066779 0.1381
LEVR 0.151068 0.5464 -0.264332 0.6761 0.127141 0.5438 0.100489 0.8535 0.156481 0.5212
CURR -0.013482 0.3180 0.033770 0.1710 -0.011732 0.3019 0.021174 0.3101 -0.012329 0.3427
DY 3.110236***
0.0003 4.004380***
0.0001 -1.703271**
0.0392 -1.627933 0.1212 3.370899***
0.0000
POR 0.021446 0.4258 0.008552 0.7776 0.023339 0.3003 0.011293 0.6584 0.020530 0.4098
SIZE -0.331465***
0.0003 0.720966*
0.0932 -0.450610***
0.0000 0.348715 0.3618 -0.314678***
0.0005
AGE 0.379646**
0.0135 1.523781***
0.0038 0.073128 0.5825 0.119541 0.8525 0.422145***
0.0049
TOVR 0.585396***
0.0000 0.458125***
0.0000 0.484368***
0.0000 0.455669***
0.0000 0.588079***
0.0000
No. of Observations 330 330 330 330 330
R-squared 0.299216 0.629032 0.513417 0.738612 0.312619
Adjusted R-squared 0.279506 0.421572 0.496586 0.588533 0.293286
F-statistic 15.18123 3.032055 30.50337 4.921486 16.17055
Prob (F-statistic) 0.000000 0.000000 0.000000 0.000000 0.000000
Note: The dependent variable is PV, *, **, *** indicates significance at the 10%, 5% and 1% respectively.

Overall, return on assets measure is negative and statistically significant at 10%
level in cross-section fixed effect model. It implies that stock price volatility of
listed firms on HOSE is explained by firm performance and stock price behavior is
negatively affected by return of assets. This finding does not support hypothesis 1
of our study. However, the signs of coefficients of return on assets in other models
are positive but not statistically significant.
Dividend yield is positive and statistically significant at 1% level in models 1A, 2A
and 5A. This confirms that stock price behavior is positively affected by firm
dividend yield. This finding is inconsistent with the hypothesis of Baskin (1989)
that there is a negative relationship between dividend yield and stock price
volatility. This finding is also inconsistent with the hypothesis of Irfan and Nishat
(2003) which reports that dividend yield has strong negative association with stock
price volatility. However, this result supports the previous study of Asghar et al.,
(2010) which states that price volatility and dividend yield have strong positive
correlation. Similar to the findings of most of previous studies, dividend yield has
the largest effect on stock price volatility among the set of variables.
In addition, firm size is positively associated with stock price volatility and
significant at level of 10% in model 2A but negatively affects stock price volatility
in models 1A, 3A and 5A. The positive sign is similar to those of Baskin (1989) and
Irfan and Nishat (2003). Meanwhile, the negative sign supports the hypothesis of
Allen and Rachim (1996). Our hypothesis 7 is also supported by the negative
coefficient of return on assets. According to theories, the stock price of small firms
may be more unstable as compared to large forms as small firms are less diversified
than large firms. However, these reserved results may be due to the special
characteristics of developing markets such as Pakistan and Vietnam.
The coefficient of firm age is positive and statistically significant at 1% level in
models 2A and 5A and at 10% significance level in model 1A. This indicates that
the older the firm is, the larger its stock price volatility is. This finding contrasts

with the result of P´astor and Veronesi (2003) which declares a negative relation
between volatility and firm age. Therefore, hypothesis 8 is not supported by this
findings.
Moreover, liquidity is positive and significant at 1% level in all models. Our result
is similar to the finding of Gallant et. al. (1992), finding a positive relationship
between stock volatility and volume. This finding indicates that the larger the
trading value of stock is, the more the stock price volatiles.
Asset growth is found to be negative correlated with stock price volatility but not
significant in most of models. This negative association is different from the result
of previous studies (Baskin, 1989, Irfan and Nishat, 2003). By contrast, this result is
similar to investigation of (Asghar et al., 2010, Nazir et al., 2010).
The coefficients of firm leverage and current ratio are not significant in all models.
This finding does not support the results of most previous researches.
We also do not find any evidence to support the idea of Allen and Rachim (1996)
that there is a significant negative relationship between price volatility and payout
ratio as the coefficients of payout ratio are positive but not significant in our
regression results. It means that stock price volatility is not explained by payout
ratio of the firm.
A redundant fixed effect test is used to test between OLS model and fixed effect
models. The hypothesis testing OLS regression model against cross-sectional
heterogeneity has F-statistic F(109,211) = 1.72 (p-value: 0.0004), so the null
hypothesis of no cross-sectional heterogeneity is rejected. The hypothesis testing
OLS against temporal heterogeneity has F-statistics F(2,318)= 69.99 (p-value:
0.0000), so the null hypothesis of no time-dimension heterogeneity is also rejected
(Table 5.5, section 1). In addition, the explanatory power of model 2 is better than
model 3A. These test results conclude that a cross-sectional fixed effect model is
preferred to the simple OLS model.

We need further test to choose between fixed effect model and random effect
model. The Hausman specification test is based on the contrast between the fixed
effects and random effects estimators. The null hypothesis of Hausman test is the
difference in the coefficients estimated by the two methods is not systematic, and
then random effect can be used. The p-value for the test is less than 1%, indicating
that the random effects model is not appropriate and that the fixed effects
specification is preferred. In our case, H0 is rejected and we choose to use fixed
effect (Table 5.5, section 2). Our discussion on the results is therefore based on the
outcome of cross-section fixed effect model. However, findings obtained from OLS,
period fixed effect and random effect models are also discussed for comparison
purpose.
11Table 5.5 OLS, fixed effect and random effect tests
Test items Statistic d.f. Prob.
1. Redundant Fixed Effects Test
Cross-section F-statistic 1.721046 (109,211) 0.0004
Cross-section Chi-square 209.908094 109 0.0000
Period F-statistic 69.994334 (2,318) 0.0000
Period Chi-square 120.381709 2 0.0000
2. Hausman test for fixed versus random effects
Chi-Sq. Sta 59.249529 9 0.0000
5.2.2 Overall regression with industry dummies
In order to validate our regression results, industry dummies variables are further
concluded to examine whether stock price volatility favors a specific industry. In
other words, these tests verify if there is any difference of stock price behavior
among classified industries.
Table 5.6 reports the regression results of regressions when we include dummy
variables to control for industry effect. Overall, the R2
of the models with industry
dummy is a slightly improved as compared to the R2
of the models without industry
dummy. It means that the explanatory power of the estimation model with industry

dummy is better than that of the estimation model without industry dummy. The
results are the same in terms of the number of significant variables and the
directions of relationship. Of which, the results from OLS and random effect
models show that foods and beverages, industrials and real estates, construction &
materials sectors are positively associated with stock price volatility. Meanwhile,
the findings from period-fixed effect model indicate that all the six industries have
affects on stock price volatility.
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12Table 5.6 Overall regression results with industry dummies
Model 1A Model 1B Model 3A Model 3B Model 5A Model 5B
OLS w/o dummy OLS with dummy Period fixed w/o
dummy
Period fixed with
dummy
Cross-section
random w/o dummy
Cross-section
random with dummy
Variable Coefficient Prob. Coefficient Prob. Coefficient Prob. Coefficient Prob. Coefficient Prob. Coefficient Prob.
C 0.418900 0.6411 -0.082015 0.9308 2.861981 0.0003 2.430433 0.0031 0.161965 0.8540 -0.283434 0.7560
ROA -0.155525 0.6647 -0.013937 0.9702 0.687350**
0.0279 0.813323**
0.0112 -0.214630 0.5289 -0.071598 0.8381
ASGR -0.060420 0.2147 -0.072204 0.1410 0.094696**
0.0279 0.085071**
0.0475 -0.066779 0.1381 -0.076208*
0.0922
LEVR 0.151068 0.5464 0.186789 0.4711 0.127141 0.5438 0.219821 0.3060 0.156481 0.5212 0.189213 0.4472
CURR -0.013482 0.3180 -0.010490 0.4488 -0.011732 0.3019 -0.005643 0.6250 -0.012329 0.3427 -0.009726 0.4605
DY 3.110236***
0.0003 3.028749***
0.0005 -1.703271**
0.0392 -2.001396**
0.0161 3.370899***
0.0000 3.242475***
0.0001
POR 0.021446 0.4258 0.019370 0.4724 0.023339 0.3003 0.019648 0.3790 0.020530 0.4098 0.019000 0.4450
SIZE -0.331465***
0.0003 -0.307542***
0.0012 -0.450610***
0.0000 -0.434622***
0.0000 -0.314678***
0.0005 -0.294410***
0.0012
AGE 0.379646**
0.0135 0.367272**
0.0179 0.073128 0.5825 0.069129 0.6024 0.422145***
0.0049 0.400114***
0.0074
TOVR 0.585396***
0.0000 0.583611***
0.0000 0.484368***
0.0000 0.482249***
0.0000 0.588079***
0.0000 0.585788***
0.0000
DUM1 0.187657 0.2054 0.223653*
0.0688 0.191533 0.1838
DUM2 0.156928 0.2485 0.201517*
0.0741 0.157464 0.2336
DUM3 0.306377**
0.0158 0.361902***
0.0006 0.306174**
0.0131
DUM4 0.251085**
0.0461 0.211388**
0.0428 0.252400**
0.0393
DUM5 0.285519**
0.0164 0.303076***
0.0022 0.285482**
0.0136
No. of Observations 330 330 330 330 330 330
R-squared 0.299216 0.317021 0.513417 0.534857 0.312619 0.325564
Adjusted R-squared 0.279506 0.286666 0.496586 0.511079 0.293286 0.295589
F-statistic 15.18123 10.44391 30.50337 22.49442 16.17055 10.86122
Prob (F-statistic) 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Note: - The dependent variable is PV, *, **, *** indicates significance at the 10%, 5% and 1% respectively.
- Estimations of cross-section fixed effect and both side fixed effect models cannot be conducted due to near singular matrix.
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5.2.3 Regression results in each year
This sub-section represents the results of regressions when we run the model for
each year from 2007 to 2009. The findings are as follows.
The detailed regression results for each year are as follows.
5.2.3.1 Regression results of 2007
13Table 5.7 Regression results of 2007 without and with industry dummies
Model 6A Model 6B
Without industry dummies With industry dummies
Variable
Coefficient t-Statistic Prob. Coefficient t-Statistic Prob.
C 5.140875 4.833345 0.0000 5.718594 4.944327 0.0000
ROA 2.641118
***
4.453143 0.0000 2.190460
***
3.278527 0.0015
ASGR 0.074085
*
1.836315 0.0693 0.084424
**
2.046808 0.0434
LEVR 0.357676 1.163684 0.2473 0.433522 1.347335 0.1811
CURR -0.010206 -0.594969 0.5532 -0.006977 -0.394851 0.6938
DY -15.84090***
-3.759099 0.0003 -16.59726 -3.690975***
0.0004
POR 0.528831
**
2.477176 0.0149 0.506641 2.213340
**
0.0293
SIZE -0.638786
***
-6.549048 0.0000 -0.676067 -6.637616
***
0.0000
AGE 0.150677 0.952995 0.3429 0.177576 1.098953 0.2746
TOVR 0.371015
***
4.696895 0.0000 0.363082 4.340706
***
0.0000
DUM1 0.123535 0.701381 0.4848
DUM2 -0.097759 -0.611076 0.5426
DUM3 -0.032857 -0.219831 0.8265
DUM4 -0.148917 -0.990995 0.3242
DUM5 -0.079970 -0.567279 0.5719
No. of
Observations
110 110
R-squared 0.464041 0.480398
Adjusted
R-squared
0.415804
0.403825
F-statistic 9.620141 6.273726
Prob (F-statistic) 0.000000 0.000000
Note: The dependent variable is PV, *, **, *** indicates significance at the 10%, 5% and 1%
respectively.
In 2007, stock price volatility is significantly positively affected by return on assets,
asset growth, payout ratio and liquidity. By contrast, stock price volatility is
significantly negatively affected by dividend yield and firm size. Regarding to the
model with industry dummies, the results are also the same with the exception that
stock price volatility favors others industry.
6674091

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