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An empirical study on stock returns, volume, and volatility listed companies on the ho chi minh city stock exchange stock exchange

UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUES
THE NETHERLANDS

VIETNAM – NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

AN EMPIRICAL STUDY ON STOCK RETURNS, VOLUME, AND VOLATILITY:
LISTED COMPANIES ON THE HO CHI MINH CITY STOCK EXCHANGE
By
Nguyen Dinh Tu Nhi

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

July 2012



UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUES
THE NETHERLANDS

VIETNAM – NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

AN EMPIRICAL STUDY ON STOCK RETURNS, VOLUME, AND VOLATILITY:
LISTED COMPANIES ON THE HO CHI MINH CITY STOCK EXCHANGE

A thesis submitted to Vietnam – Netherlands Programme in partial fulfillment
of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By
Nguyen Dinh Tu Nhi

Supervisor
Dr. Truong Tan Thanh

July 2012


ACKNOWLEDGEMENTS

I am not able to finish this thesis without the guidance of my supervisors and committee
members, supports from classmates, and aids from my family.
I would like to express my very great appreciation to my supervisors, Dr. Le Van Chon and Dr.
Truong Tan Thanh, for their patient guidance, enthusiastic assistance, and useful critiques, valuable and
constructive suggestions during my research. I am also particularly grateful for the assistance given by
Dr. Nguyen Trong Hoai and Dr. Pham Khanh Nam for motivating and supporting me to complete the
thesis. I would like to offer another thank to Dr. Duong Nhu Hung, who inspires me to choose this topic
for my thesis. My grateful thanks are also extended to staffs of the Administration Department and
Library of Vietnam-Netherlands Programme in providing me good environment and facilities to complete
the thesis.
Finally, I would like to express my love and gratitude to my family and friends for their


understanding, supports, and encouragements throughout the research.


TABLE OF CONTENTS

CHAPTER 1:

INTRODUCTION ............................................................................................ 1

1.

Problem statement ................................................................................................................... 1

2.

Research questions .................................................................................................................. 3

3.

Research objectives ................................................................................................................. 4

CHAPTER 2:

LITERATURE REVIEW ................................................................................. 5

1.

The Efficient Market Hypothesis ............................................................................................ 5

2.

The Mixture of Distributions Hypothesis ................................................................................ 8

3.

The Sequential Information Arrival Hypothesis ................................................................... 10

4.

The Generalized Autoregressive Conditional Heteroskedasticity ......................................... 10

CHAPTER 3:

VIETNAMESE STOCK MARKET AND LISTED COMPANIES ON ........ 15

THE HO CHI MINH CITY STOCK EXCHANGE ..................................................................... 15
1.

Vietnamese stock market ....................................................................................................... 15

2.

Data........................................................................................................................................ 17

3.

Summary statistics ................................................................................................................. 22

4.

Graphical analysis ................................................................................................................. 25

CHAPTER 4:

ECONOMETRIC MODELS AND DISCUSSION........................................ 27

1.

Test for stationarity in stock return and trading volume: ...................................................... 27

2.

Trading volume and return volatility ..................................................................................... 33

CHAPTER 5:

CONCLUSIONS ............................................................................................ 48

REFERENCE................................................................................................................................ 51


LIST OF TABLES

TABLE

PAGE

1. Table 1 Description of stocks………………………………………………………….18
2. Table 2 Descriptive statistics ………………………………………………………….22
3. Table 3 ADF test……………………………………………………………………….28
4. Table 4 PP test ………………………………………………………………………....31
5. Table 5 GARCH (1,1) model without LnVol………………………………………….36
6. Table 6 Likelihood ratios of stocks…………………….. ……………………………..39
7. Table 7 GARCH (1,1) model with LnVol……………………………………………...40


ABSTRACT
AN EMPIRICAL STUDY ON STOCK RETURNS, VOLUME, AND VOLATILITY LISTED COMPANIES ON THE HO CHI MINH CITY STOCK EXCHANGE
By
Nguyen Dinh Tu Nhi

The thesis examines the relationship between stock returns, trading volume and return volatility.
With the focus on listed companies on the Ho Chi Minh City Stock Exchange over the period
between 01 Jan 2007 and 31 Dec 2011, the study conducts GARCH (1,1) to model the relationship
between stock return, trading volume, and volatility. We also include a dummy to capture possible
effect of pre and post-crisis on stock return volatility. The analysis results show that there exists an
influence of trading volume on stock return, even after controlling effects of foreign trading
volume. It is also evident that trading volume has some predictive power to return volatility. We
also find our results consistent with previous studies such as Clark (1973) and Copeland (1976).
The result also implies that Vietnamese stock market is efficiently weak at least for listed
companies on the Ho Chi Minh City Stock Exchange.
Key words: Trading volume, stock returns, return volatility, foreign trading, GARCH.


CHAPTER 1:
1.

INTRODUCTION

Problem statement
In each country, the stock market reflects the health of its economy. It does not only affect
foreign exchange and gold markets but also credit market and option market. Actually,
when a stock market is strong, the investors often tend to convert foreign currency and gold
into cash to invest in stocks. It will depreciate foreign currency and gold consistently and
vice versa. In other hands, in term of weak stock market, the Government will tighten cash
flow for stocks as well as the banks will reduce disbursement for stocks and vice versa. For
option market, the strong stock market will lead to growth of different kinds of options
because investors expect to earn more profits.
In Vietnam, the stock market also plays an important role to mirror the changing economy.
For instance, when the information of bad debts or higher inflation is proclaimed, the VNindex will decrease sharply. Likewise, when the Government introduces some supporting
policies to the economy, the VN-index has a chance to increase. The fluctuation of VNindex also indicates a development or recession of economy. That is called bi-directional
effect of information and stock market.
In the mean-variance analysis, the expected stock returns and return volatility are important
factors that investors concentrate on because returns and volatility imply risks for
investors’ portfolio. Moreover, the volume of trade is also supposed to be an authoritative
component of absorbing information in the stock market. In case investors believe in higher
return on stocks, they tend to deal more and lead to higher trading volume in the stock
market. In contrast, when they forecast lower return on stocks, they will trade less or the
trading volume will decrease. Hence, the higher or lower trading volume may be a signal of
1


the fluctuations of stock returns.
As a result, the relationships among stock returns, trading volume and return volatility have
become vital topics in empirical researches. There are many papers on return-volume and
volume-volatility relationships. For the return-volume relationship, Karpoff (1987) finds
the positive asymmetric relationship between volume and price change in the equity
market. Another model which also predicts the asymmetric relationship between trading
volume and price changes is initiated by Epps (1975) and complemented by Jennings,
Starks, and Fellingham (1981). Two above models relates to flow of information.
Furthermore, Granger, Morgenstern, and Godfrey (1964) and Granger (1968) use data of
indices and individual stocks on the New York Stock Exchange to test the relationship
between price changes and trading volume. They find that price changes follow a random
walk in which the past trend of stock price cannot predict its future trend. Mohammadreza
Mehrabanpoor, Babak Valizadeh Bahador, and Gholamreza Jandaghi (2005) also get the
positive relationship between market turnover and indices on the Tehran Stock Exchange.
In addition, Michael Long (2007) finds a significantly positive interaction between absolute
value of call price changes and trading volume in the option markets.
For the volume-return volatility relationship, Engle (1982) originates the Autoregressive
Conditional Heteroskedasticity (ARCH) Model, which enumerates that stock returns follow
a mixture of distribution. Later, Bollerslev (1986) starts the Generalized Autoregressive
Conditional Heteroskedasticity (GARCH) Model and consider trading volume as a proxy
of information flow. Thus, the model is developed by Lamoureux and Lastrapes (1990),
Brailsford (1996), Mestel, and Gurgul and Majdosz (2003). Moreover, Timothy J.
Brailsford (1996) contends that the relationship between stock return volatility and volume
2


is positive through GARCH model. However, some present opposite views. A. Fujihara
and Mbodja Mougoue (1997) find that there is no causal relationship between return and
volume. According to Roland Mestel, Henryk Gurgul, and Pawel Majdosz (2003), the
relationship between stock return and trading volume is too weak to forecast each other.
Berna Okan, Onur Olgun, and Sefa Takmaz (2009) conclude that trading volume has
negative effect on return volatility by applying GARCH, EGARCH, and VAR models. For
the Vietnamese market, Truong Dong Loc (2009) investigates the unilateral causality effect
of HNX-index to trading volume. Furthermore, Truong Dong Loc and Dang Thi Thuy
Duong (2011) replicate the study with the data of foreign trading volume, and find that the
index influences net foreign volume, but the reverse is not true.
There are only few researches that has examined relationship between stock returns and
trading volume during the recent crisis, and accounted for effect of foreign trading volume
across different industries using GARCH model. This thesis attempts to fill this gap by
examining the relationship between trading volume and stock return and between return
and volatility for listed companies on the Ho Chi Minh City Stock Exchange. Particularly,
we test the effect of trading volume on stock return and return volatility by applying
GARCH (1, 1) model.
2.

Research questions
To clarify the relationships among trading volume, stock returns and return volatility, I
collect data series of intra-day stock prices to test the appropriate model. The final purpose
is that, in this paper, I am going to answer the following research questions:
(i)

Is there the relationship between trading volume and return volatility? and

(ii)

Does trading volume cause stock returns?
3


3.

Research objectives
To reach above aims, my objectives of the paper are:
(1) To examine the relationship between trading volume and return volatility and,
(2) To understand the impact of trading volume on stock return through GARCH (1,1)
model.
To that end, I use data of eight listed companies on the Ho Chi Minh City Stock Exchange
(HOSE) before and after the recession triggered by the US sub-prime mortgage crisis.
The remainder of the paper is arranged as follows. Section Two gives a brief literature
review of empirical studies. Section three presents description of Vietnamese stock market
and explains specific characteristics of data. This section also exposes statistics of selected
stocks in the HOSE. The methodology and discussion of empirical results are in section
four which is followed by Conclusion.

4


CHAPTER 2:

LITERATURE REVIEW

There are many empirical studies on relationships among trading volume, stock returns and
return volatility. At the outset, the Efficient Market Hypothesis is important for stock
markets; however, it also contains some controversies. Hence, the Mixture of Distribution
and the Sequential Information Arrival Hypotheses are introduced to supplement the
Efficient Market Hypothesis.
1.

The Efficient Market Hypothesis
The Efficient Market Hypothesis (EMH) is one of important theories for financial series
data. The EMH is introduced by Fama (1970) and widely admitted by modern economists.
They think that the financial market is extremely efficient to reflect information about
individual stocks and the stock market. The EMH hypothesizes that stock prices are able to
reflect all available information so that they mirror all credence of investors about the
future. Under the EMH, the flow of information integrates with prices of stock promptly
and efficiently at any point in time, therefore, the current stock price is not used to forecast
movements of price hereafter.
Based on the availability of information, Keith and Dirk (2005) recommend three versions
of the EMH, including weak, semi-strong, and strong forms. In the weak-form efficiency,
stock prices merely reflect all public available information in the past. The stock prices are
the most easily public accessible information in the stock markets.
In the semi-strong form efficiency, the stocks prices reflect not only all available public
information but also new public information immediately. The available public information
compromises both past prices and relevant information, such as: claimed financial
statements, announcements of dividends and profits, share splits, merge and acquisitions,
5


influence of macroeconomics (inflation, interest rate, exchange rate, so on), and vice versa.
Under the semi-strong form, the current market price is the best predictor of a fair stock
price. Thus, both above hypotheses claim that no one can earn by trading on the
information that other investors have comprehended. However, the semi-strong form is
stronger than weak form.
Increasingly, in the strong form efficiency, the current stock prices reflect both private and
public information for companies which is incorporated with market prices quickly.
Hence, no investors can find under-valued stocks consistently or inside traders are not able
to take any advantages. In this form, the current market price is the best forecaster of a fair
price.
There are many researches supporting the market efficiency broadly. Thomas E. Copeland
and Daniel Friedman (1987) observe variables of price behavior, trading volume, and
portfolio composition in response to information arrival on the National Association of
Securities Dealers Automated Quotations (NASDAQ). They compare the price, volume,
and allocations of shares in three market equilibrium models: telepathic rational
expectations, ordinary rational expectations and private information. Finally, they find that
stronger form of market forecasts equilibrium prices better than weaker one.
Maloney and Mulherin (2003) study velocity and precision of price through relationship
between stock returns and trading volume in the crash of space shuttle challenger for four
firms. They find that price movement happens even in a trading hitch to resolve faults or
with low trading profits. They are not capable to discover real manner so that the informed
traders can provoke price movement.
While Fama (1970) and above researchers dominates over the EMH, there are some
6


controversial researches in the market efficiency. For instance, Sanford J. Grossman and
Joseph E. Stiglitz (1980) attribute that an informational efficiency is impossible. Stock
prices cannot mirror the available information absolutely since the information is
expensive. In case of perfectly informational market, the investors will not profit to get
information and make any analysis on it. LeRoy and Porter (1981) fail to accept the market
efficiency because of excess volatility of stock markets. Marsh and Merton (1986) also
reject the EMH. Furthermore, Lawrence H. Summers (1986) examines the statistical tests
to valuate efficiency of speculative stock market. He observes that the speculation seems to
plague the rational valuation as well as efficiency of the market. In 1990, Laffont and
Maskin fail to accept the EMH in case of imperfect competition and Jegadeesh rejects the
random walk hypothesis1 with strong evidence of predictability of stock returns. Recently,
Lee et al. (2010) have purported that stock market is not efficient in term of stationarity of
stock prices.
For these reasons, some more rational theories are ascribed. Karpoff (1987) brings the most
important study from results of many previous studies in various markets. He makes a
survey to examine the relationship between price changes and trading volume. He assorts
his studies into two parts: testing the relationship between absolute price changes and
trading volume as well as examining the relationship between price changes and trading
volume. He revises empirical studies on those relationships and finds out the positive
relationship between volume and price change in equity market. Lastly, he also debates that
there may exist asymmetric relationship between price change and volume although a
number of researchers use linear models to investigate this relationship. His effort is
1

Random Walk Hypothesis: is consistent with the Efficient Market Hypothesis

7


necessary to explain the Mixture of Distribution Hypothesis (Clark, 1973) and the
Sequential Information Arrival Hypothesis (Copeland, 1976).
2.

The Mixture of Distributions Hypothesis
The Mixture of distributions hypothesis is initially proposed by Clark (1973). According to
Clark (1973), the price changes and trading volume are influenced by same information
flow so that volume and volatility are correlated. Meanwhile, the variable of return
volatility is the total of random number of intra-day return volatility. In the MDH model,
the trading volume is considered a proxy for the rate of information flow and is used to
valuate stock returns by new information flow. It explains that returns and volume will alter
simultaneously so as to response to arrival of new information. It also states that stock
prices and trading volume are positively correlated since variance of stock price on a
transaction depends on its trading volume.
The MDH is developed by Epps and Epps (1976), Tauchen and Pitts (1983), Lamoureux
and Lastrapes (1990), and Andersen (1996). Epps and Epps (1976) argue that the change of
logarithm of price can follow a mixture of distribution and trading volume can be a mixing
variable. Andersen (1996) supplements trading volume into the hypothesis which is
uncorrelated to flow of information. Volume is a result of trading on noise or liquidity
which is opposite to trading on the arrival of information. Unlike Tauchen and Pitts (1983)
suppose an i.i.d.2 arrival process of information, Andersen (1996) implies that the rate of
information arrival has been correlated serially. That is, the lagged volumes and volatilities
are positively correlated with current volatilities and volumes.

2

i.i.d. means independent and identically distributed

8


Chen and Tse (1993), Omran and McKenzie (2000), Zarraga (2003), Pyun et al. (2000),
and Bohl and Henke (2003) find supportive evidence from Japanese, UK, Spanish, Korean,
and Polish stock markets, respectively.
Huson Joher Ali Ahmed, Ali Hassan, and Annuar M.D Nasir (2005) test the volatility of
Kuala Lumpur Stock Exchanges by the Mixture of Distribution Hypothesis. The paper
implements the GARCH model to imply that the return volatility is analyzed best by
GARCH (1,1) model. They also add trading volume as explanatory variable in the model to
find that the volatility persistence unchanged in case of inclusion of volume.
However, the MDH is criticized by some evidences. Firstly, it does not stipulate volatility
on volume. Hence, it fails to show volatility persistence after adding trading volume as
explanatory variable. Secondly, Fong (2003) and Xu et al. (2006) prove that the MDH does
not condition serial dependence on return volatility and volume. Furthermore, B.M.
Nowbutsing and S. Naregadu (2009) employ thirty six stocks, six constructed indices and
the Stock Exchange of Mauritius (SEM) Index to test the relationships between trading
volume and return and between volume and volatility. The paper uses ARCH-in-mean
model and finds that the relationship between trading volume and volatility is weakly
positive. Therefore, the study has not supported the MDH and SIAH on the SEM.
Brajesh Kumar, Priyanka Singh, and Ajay Pandey (2009) use data of 50 Indian stocks to
investigate the relationship between price and trading volume. They apply the GARCH
model to test the relationship between the conditional volatility and trading volume. Their
findings indicate that there is a positive asymmetric relationship between volume and
unconditional volatility exists. However, under the MDH, the results are mixed because
they do not decline the MDH perfectly but do not support it unconditionally.
9


3.

The Sequential Information Arrival Hypothesis
On the contrary, the Sequential Information Arrival Hypothesis (SIAH) induces different
argument from the MDH. The SIAH is originally introduced by Copeland (1976) and
argued more by Jennings et al. (1981). It supposes that the arrival of new information is
diffused to informed and uninformed traders sequentially. In the SIAH, the market
participants do not response to new information contemporarily, but sequentially. Hence,
the informed investors who absorb the information flow first could take advantages and
control their portfolios accordingly. The sequential information flow has driven a positive
bi-directional causality relationship between absolute value of returns and trading volume
since the lagged absolute returns are able to predict current trading volume and vice versa.
Later, Jennings et al. (1981) develops the theory. Moreover, by using stock prices and
trading volume in the NYSE, Harris (1987) and Smirlock and Starks (1988) report positive
interaction between volume changes and prices. To support the SIAH, Hiemstra and Jones
(1994) contend that a flow of sequential information enables lagged trading volume to
predict current absolute returns (price changes) and enables lagged absolute returns to
predict current trading volume.
Both the MDH and SIAH attempt to explain the positive relationship between volume and
price changes. However, whereas the MDH implies a contemporaneous relationship
between price changes and trading volume, the SIAH proposes a sequential dynamic
relationship between returns and volume in which lagged price change can forecast current
trading volume and vice versa (Darrat et al. , 2003).

4.

The Generalized Autoregressive Conditional Heteroskedasticity
Under the MDH theory, many recent studies propose several models in the relationship
10


between price and volatility accordingly. Engle (1982) introduces the Autoregressive
Conditional Heteroskedasticity (ARCH) Model depending on the theory that the time series
of stock return are not derived from single distribution but mixture of distribution to create
expected return. The ARCH model is appropriate for financial data, especially time series
data. In the ARCH model, the arrival of information is the stochastic mixing variable
whereas daily return is a mixture of distribution.
Afterward, Bollerslev (1986) presents the Generalized Autoregressive Conditional
Heteroskedasticity (GARCH) model. Both ARCH and GARCH models are able to deal
with heteroskedasticity of time series because they can measure the volatility of portfolio,
analyze portfolio and price assets. The GARCH model uses trading volume as a proxy of
flow of information in the market to analyze market volatility.
The model is developed by Lamoureux and Lastrapes (1990), Brailsford (1996) and
Mestel, Gurgul and Majdosz (2003). The important implication of this model is to limit the
conditional variance of time series and to rely on past squared residuals on the process. For
example, Lamoureux and Lastrapes (1990) use daily trading volume as an explanatory
variable to explain the volume-return volatility relationship for active stocks in the US. The
results of mixed model connote that the variance of daily price is heteroskedastic and
relates to arrival of information positively. Moreover, they also find that ARCH effect or
persistence of volatility will diminish when the trading volume is added into the conditional
variance equation. In addition, they find that the inclusion of lagged volume in the variance
equation is insignificant for almost cases.
Najand and Yung (1991) use treasure bond futures to analyze as Lamoureux and Lastrapes
(1990) conduct. They report that the lagged volume explains volatility better than
11


contemporaneous volume.
Furthermore, Timothy J. Brailsford (1996) advocates that the relationship between price
changes and volume is positive through three measures of trading volume. He selects top
eight stocks in the Australian Stock Market ranked by market capitalization. He applies the
conditional variance equation of GARCH model to examine the relationship between
volume and stock market volatility. The result proves that the slope of volume and
volatility for negative returns is less than slope for positive returns. Therefore, it
supplements asymmetric relationship. When trading volume is considered as an exogenous
variable, the GARCH coefficient reduces their significance and magnitude, respectively.
The paper is an initial analysis of volume and price change relationship which will
accelerate more works in the future.
According to Roland Mestel, Henryk Gurgul, and Pawel Majdosz (2003), the
contemporaneous and dynamic relationship between stock return and trading volume is
weak, meaning they cannot forecast each other. Meanwhile, the relationship between return
volatility and trading volume is strong, meaning the volatility predicts volume in some
cases.
Safi Ullah Khan and Faisal Rizwan (2008) examine the relationship between volumes and
return volatility by the GARCH (1,1) model. They use data series of the Karachi Stock
Exchange (KSE-100 index) to indicate that a positive contemporaneous relationship
between volume and volatility exists after controlling heteroskesaticity.
Sarika Mahajan and Balwinder Singh (2009) use daily data of the Sensitive Index
(SENDEX) of Bombay Stock Exchange – India’s stock exchange - over the period of
October 1996 to March 2006 to test the relationship between return, volume and volatility.
12


The empirical results conduct positively significant interaction between volume and return
volatility based on both MDH and SIAH. Applying GARCH (1,1), the paper declines the
persistence of variance when including trading volume as a proxy of information flow in
the conditional volatility equation.
Tarika Singh and Seema Mehta (2010) attempt to investigate the relationship between
trading volume and stock return volatility in Asian stock. They also apply the GARCH
(1,1) model to analyze volatility and predict returns of individual stocks. They verify that
the parameters for all series in the return volatility equation are fully significant and the
impact of recession is obvious through figures.
Pratap Chandra Pati and Prabina Rajib (2010) use National Stock Exchange S&P CRISIL
NSE Index Nifty Index futures, and GARCH and ARMA-EGARCH models to analyze the
persistence of volatility. The findings indicate that the addition of trading volume into
GARCH model dampens the persistence of volatility. Yet, the GARCH effect entirely does
not disappear.
On the other hands, Chen et al. (2001) report the opposite findings to Lamoureux and
Lastrapes (1990). They find that the volatility persistence is not eliminated in case of
including the contemporaneous trading volume in GARCH model.
All in all, there are many and many empirical studies on relationship among trading
volume, stock returns and price changes. It can be positive or negative. It always happens
in both the developed and emerging markets day by day. However, there are a few papers
researching on impacts of foreign buy volume, foreign sell volume, and pre and postrecession on GARCH (1,1) model on the Ho Chi Minh Stock Exchange. As a result, I
continue to study more on the relationship between volume and returns and between
13


volatility and volume on the data series of eight listed companies on the Ho Chi Minh stock
market.

14


CHAPTER 3:

VIETNAMESE STOCK MARKET AND LISTED COMPANIES ON

THE HO CHI MINH CITY STOCK EXCHANGE
1.

Vietnamese stock market
There are many researches on Vietnamese stock market, both domestic and foreign
researchers. Vietnam opens and develops the economy from well-known “Doi Moi” policy.
Vietnam adjusts many things on policies to integrate with the globe and gain high
economic growth. Therefore, the growth rate of GDP in Vietnam is about 7 percent yearly
over the past decade. The economic growth under the development of finance is a certainty.
As a result, Vietnamese stock market is born to meet requirement of the economy (Vuong
Thanh Long, 2008).
The stock market is born firstly in Ho Chi Minh City on July 2000 with merely two listed
stocks. In the first two years, stock trading occurs only in a few alternative days. On July
2010, HOSE has 247 listed companies with market capitalization of about 28.28 billion
dollars. Meanwhile, Hanoi Securities Trading Center (HASTC), which is established by the
Decision No. 127/1998/QĐ-TTg dated July 11, 1998, is smaller than HOSE. In 2009,
Hanoi stock exchange (HNX) is established by the Decision No. 01/2009/QĐ-TTg dated
January 02, 2009 by the Prime Minister of Vietnam when restructuring Hanoi Securities
Trading Center.
According to statistical data, over the period of 2000 – 2003, the stock market grows up
strongly from 571 points on June 2001 and down to 139 points on April 2003. Meanwhile,
in the period 2004 – 2005, VN index moves up from 213 to 307 points. In the period of
2006 – 2009, VN index reaches remarkable point to the top of 1,167 points on Feb 2007. It
is used to be unforgettable point of time for all investors. The index falls sharply on Feb
15


2009 at 235 points and moves up to 480 on December 31, 2010 (Nguyen Thi Kim Yen,
2011).
The stock market has become larger and larger with new listed companies. In 2008, there
are 164 companies listed on HOSE and 154 ones on HNX. The number of listed companies
increases up to 627 ones in the year 2010 including companies on HOSE, on HNX, and
fund management companies (Nguyen Thi Kim Yen, 2011).
Some summarized numbers of the year 2010 as follow:
Market capitalization (USD billion)

35

Average daily trading value (USD million)

80

Listed securities

627

Dividend yield

2.5%
Source: Bloomberg, 2010

For the Ho Chi Minh City Stock Exchange (HOSE), the Prime Minister signs Decision
No.559/2007/QD-TTg on May 11th, 2007 to alternate Ho Chi Minh Securities Trading
Center into Ho Chi Minh Stock Exchange. The stocks listing on Ho Chi Minh Stock
Exchange (HOSE) have to meet criteria as follows (on 31st December 2011):
-

The listing companies are joint stock companies with paid-up capital at the time of

registration for listing at least VND80 billion at book value.
-

Business operation in two years before the year of listing has to be profitable and

has no accumulated losses up to the year of registration for listing.

16


-

There are no overdue debts which has not been reserved compliance with

regulations; public all debts toward the company by members of the Board of Directors,
Board of Supervisors, Manager or General Manager, Deputy Manager or Deputy General
Manager, Chief Accountant and Major shareholders.
-

At least 20% of voting shares must be owned by at least 100 shareholders who are

not professional investors and major shareholders, except for the case that the State-owned
enterprises transform into joint stock companies.
-

Officer-shareholders have to commit to hold 100 percent of their shares in 6 months

from the date of listing and 50 percent of them for the following 6 months.
In this paper, I use the data of listed companies on the Ho Chi Minh City Stock Exchange
to examine the relationships since HOSE has more requirements than other exchanges. I
also study the period of 2007 to 2011 to evaluate the impact of economy on stock market.
2.

Data
Database includes daily price changes and trading volume of eight stocks listed on the Ho
Chi Minh City Stock Exchange.
Table 1: Description of stocks (on 17th Jul 2012)

No

Stock

Name of Company

1

DPM

2

PVD

3

SSI

Petro Vietnam
Fertilizer and
Chemicals
Corporation
Petro Vietnam
Drilling and Well
Services J.S.C
Sai Gon Securities
Inc.

Sector

Chartered
capital
(mil)

Fertilizer

3,800,000

29 Oct 2007

Market
capitalization
(17 Jul 12)
(mil)
13,214,401

Petroleum
exploiting service

2,105,082

15 Nov 2006

7,289,543

4.9%

2.1%

Securities

3,526,117

18 Oct 2007

7,235,371

4.9%

2.5%

17

Date of
listing

Portfolio
weight

VN30index
weight

8.9%

3.1%


No

Stock

Name of Company

Sector

Chartered
capital
(mil)

4

VNM

Viet Nam Dairy
Products J.S.C

Milk, beverage

5,561,147

5

VIC

Vingroup J.S.C

Real estates

6

FPT

FPT Corporation

7

GMD

8

VSH

General Forwarding
& Agency
Corporation
Vinh Son – Song
Hinh Hydropower
Joint Stock
Company

Date of
listing

Portfolio
weight

VN30index
weight

28 Dec 2005

Market
capitalization
(17 Jul 12)
(mil)
48,068,368

32.4%

11.2%

7,004,621

07 Sep 2007

55,336,633

37.3%

16.2%

IT and
Telecommunication

2,699,601

21 Nov 2006

12,775,186

8.6%

6.1%

Logistics

1,090,071

08 Mar 2002

2,300,000

1.5%

1.3%

Hydropower

2,062,412

28 Jun 2006

2,184,205

1.5%

0.6%

Source: HOSE

I choose the information from listing companies because they provide investors transparent
and reliable information. I select only eight stocks above which represent for particular
industries in the economy according to high market capitalization, listing years, scale of
firm, liquidity, especially its impacts on VN-index for analysis.
There is VN30-index which Ho Chi Minh City Stock Exchange selects 30 top stocks which
have largest impacts on VN-index based on high market capitalization (about 80 percent of
total market capitalization), free float, total traded value (about 60% total value), and high
liquidity. Then, I select my own stocks which meet criteria among the 30 stocks,
comprising 17 ones (on 17th Jul 2012). I classify the 17 stocks into eight different
industries, such as: processing and manufacturing, mining, finance, consuming goods, real
estate and construction, telecommunication and information, transport and warehouse, and
electricity. Finally, I choose eight characterized stocks presenting for eight industries based
on highest market capitalization and biggest impacts on VN-index. Still, in term of SSI, I
select it because it reflects trend of whole stock market in spite of lower market

18


capitalization than STB (Saigon Thuong Tin Commercial J.S.C). Details of corporations
are below.
DPM (Petro Vietnam Fertilizer and Chemicals Company) is established in 28 March
2003 with chartered capital of 3,800 billion up to now. The major business activities are to
produce fertilizers with capacity of 740 thousand tons per year; ammonia with capacity of
1,350 tons per day; Urea with capacity of 2,200 tons per day; and trade liquid ammonia
with capacity of 96 thousand tons per year. DPM’s outputs fulfill 40% of requirement on
fertilizer in our country and occupy 50% of fertilizer share market of South and middle
South areas.
PVD (Petro Vietnam Drilling and Well Services Corporation) is established in 1994.
The major business activities are to offer drilling on contract basis and well maintenance
and to condition services to petroleum production companies and energy services of
geotechnical and logging, to map oil fields and oil spills control, and to lease drilling
equipments and oil rigs. The company is a subsidiary of Vietnam Nation Oil and Gas
Group (Petro Vietnam). It has three oil exploration and production joint ventures and six
subsidiaries managing operations.
SSI (Saigon Securities Inc.) is established in 1999. The major business activities are to
provide brokerage services for investors, minor shareholders services, manage portfolio,
provide advisory and underwriting services for corporate customers, and research reports
for investment. SSI is one of the largest securities company as well as leader broker with
17% market share in Vietnam. SSI has the largest foreign customer source in over 100
institutions with 2,500 individual accounts accounting for 30% of the market.
VNM (Vietnam Dairy Products J.S.C) was established in 1976. Its products include:
19


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