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An empirical investigation of factors affecting stock prices in Vietnam

An empirical investigation of factors affecting stock prices in Vietnam. This paper investigates factors affecting Vietnam’s stock prices including US stock prices, foreign exchange rates, gold prices and crude oil prices. Using the daily data from 2005 to 2012, the results indicate that Vietnam’s stock prices are influenced by crude oil prices.



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Journal of Economics and Development, Vol.16, No.1, April 2014, pp. 74-89

ISSN 1859 0020

An Empirical Investigation of Factors
Affecting Stock Prices in Vietnam
Vo Xuan Vinh
University of Economics, Ho Chi Minh City, Vietnam
Email: vinhvx@ueh.edu.vn

Abstract
This paper investigates factors affecting Vietnam’s stock prices including US stock prices,
foreign exchange rates, gold prices and crude oil prices. Using the daily data from 2005 to 2012,
the results indicate that Vietnam’s stock prices are influenced by crude oil prices. In addition,
Vietnam’s stock prices are also affected significantly by US stock prices, and foreign exchange
rates over the period before the 2008 Global Financial Crisis. There is evidence that Vietnam’s
stock prices are highly correlated with US stock prices, foreign exchange rates and gold prices
for the same period. Furthermore, Vietnam’s stock prices were cointegrated with US stock prices
both before and after the crisis, and with foreign exchange rates, gold prices and crude oil prices
only during and after the crisis.
Keywords: Cointegration, Granger causality, stock prices, oil prices, foreign exchange rate.

Journal of Economics and Development

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Vol. 16, No.1, April 2014

1. Introduction

but the preponderance of the literature indicates that there is instability in the relationship
(Fischer and Palasvirta, 1990; Kearney and
Lucey, 2004; Longin and Solnik, 1995; Madura and Soenen, 1992; Makridakis and Wheelwright, 1974; Maldonado and Sounders, 1981;
Meric and Meric, 1989; Wahab and Lashgari,
1993) and that this is determined primarily by real economic linkages between countries (Arshanapalli and Doukas, 1993; Bachman et al. 1996; Bodurtha, Cho and Senbet,
1989; Bracker and Koch, 1999; Campbell and
Hamao, 1992; Roll, 1992). Employing the Engle–Granger cointegration methodology, Kasa
(1992) examines the major equity markets over
the 1974 -1990 period and finds a single cointegrating vector indicating a very low level of
integration. Other studies also find similar results of low integration (Allen and MacDonald,
1995; Arshanapalli and Doukas, 1993; Chan,
Gup and Pan, 1992; Chan, Gup and Pan, 1997;
Gallagher, 1995). Kanas (1998) employs multivariate trace statistics, the Johansen method,
and the Bierens nonparametric approach to test
for pairwise cointegration between the US and
each of the six largest European equity markets
covering the period from 1983 to 1996, and
finds that the US market is not pairwise cointegrated with any of the European markets. Vo
and Daly (2005a, 2005b) analyse and test the
10-year period daily return data from 1994 to
2003 of Asian equity market indices and selected advanced nation’s equity market indices
by employing correlation, cointegration and
the Granger causality test. They find a weak
causal relationship between Asian equity markets and developed countries’ equity markets.
In addition, employing the same methodology,

Stock prices are one of the economic indicators employed to proxy for the health as well
as the growth of the economy. An examination
of the impact of the main economic factors on
stock indices has an important implication for
both the government and investors. This paper
investigates the long and short-run relationship
between Vietnam’s stock prices (VN-Index)
and US stock prices (S&P 500 Index), the US
Dollar - VN Dong exchange rates, gold prices,
and crude oil prices covering a nearly 5-year
time frame before and after the 2008 Global Financial Crisis.
The purpose of this study is to suggest answers to the following critical issues. Firstly,
are there relationships between the pairs of the
VN-Index and the other variables? Secondly,
are there lead-lag relationships between the
VN-Index and the other variables in pairwise
analyses? To explore the short-run relationships
among the variables, the techniques of correlations are utilized. The technique of Granger
causality is applied to test whether movements
in one variable appear to lead those of another.
The technique of cointegration is employed to
investigate the long-run relationships. The remainder of this study is structured as follows.
Section 2 reviews the literature. Section 3 describes the methodology employed in the study.
Section 4 presents the data descriptive statistics. Section 5 reports the empirical results.
Section 6 concludes the study.
2. Literature review
Firstly, in terms of the integration of international equity markets, many studies have found
stability of the correlation structure over time
(Panton, Lessig and Joy, 1976; Watson, 1980),
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skewness. Baur and Lucey (2010) carry out an
investigation of the relationship between U.S.,
U.K., and German stock and bond returns, and
gold returns, and find that gold just acts as a
hedge against stocks, and as a safe haven for
stocks, but not for bonds, in any market. They
further suggest that investors should not keep
gold too long because the safe haven just exist
for a limited period of time. In addition, analyzing whether gold and stocks having a negative correlation, in other words, the absence of
co-movement between gold and stocks, many
studies suggest the role and the proportion of
gold in a portfolio in order to reduce risks and
increase returns (Chua, Sick and Woodward,
1990; Klement and Longchamp, 2010; Scherer,
2009; Sherman, 1982).

Vo and Daly (2005a) also suggest that there
are very low linkages among European equity
markets. On the other hand, other authors state
that the long-run covariances between markets
are higher than in the short-run, and hence the
benefits of international diversification are lower (Grubel and Fadner, 1971; Panton, Lessig
and Joy, 1976; Taylor and Tonks, 1989). Employing the more sophisticated Johansen multivariate approach, other studies yield contrary
results of strong integration (Chou, Ng and Pi,
1994; Gilmore and McManus, 2002; Hung and
Cheung, 1995; Kearney, 1998; Manning, 2002;
Ratanapakorn and Sharma, 2002).
Secondly, in terms of the relationship between stock prices and exchange rates, most
studies show that a falling domestic currency
value has a negative short-run and long-run
effect on the aggregate domestic stock price.
Domestic currency appreciations, on the contrary, often lead to higher stock prices. On the
other hand, when the aggregate domestic stock
price increases, domestic currency value drops
in the short-term but goes up in the long-term
(Ajayi and Mougoue, 1996; Dimitrova, 2005;
Wang, Wang and Huang, 2010). Conflicting
with the above authors, some researchers state
that stock price reactions to changes in currency value are ambiguous (Granger, Huangb and
Yangc, 2000).

Finally, referring to the impact of oil price
changes on stock prices, there are conflicting
views among researchers. Mussa (2000) indicates that when oil prices increase, although
consumer and business confidence fall fairly
strongly, stock prices drop. The decrease is
much more caused by non-oil related factors.
Some authors, on the contrary, indicate that
there is a relationship between crude oil prices and equity values (Arouri, 2011; El-Sharifa et al., 2005; Filis, Degiannakisa and Floros,
2011) but they have different conclusions as
to whether this relationship is positive or negative. Especially, the most notable study is
the one carried out by Narayan and Narayan
(2009). By adding the US Dollar - VN Dong
exchange rate as an additional determinant
of stock prices, and exploring daily data for
the period 2000–2008, Narayan and Narayan
(2009) find that there are relationships between
Vietnam’s stock prices and oil prices, nominal

Thirdly, there has been a great deal of research on gold in the last number of years and
most of the academic studies concentrate on the
areas which are: gold as a diversifier, gold as a
hedge against inflation or other assets, and the
operation’s efficiency of the gold market. This
attractiveness comes from gold’s characteristic low/negative correlation and high positive
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ly examined to test if investors have potential
gains by diversifying those products. However,
the correlation coefficient only shows the shortrun relationship, and using this parameter may
yield false results because economic variables
tend to diverge in the short terms but converge
over longer terms. To avoid this major disadvantage, cointegration tests are utilized to spot
any long-run combinations between couples of
these economic variables.

exchange rates, and a rise in the oil price and
exchange rate (Vietnamese currency’s depreciation) which makes Vietnam’s stock price increase significantly. In addition to investigating
the relationship between the oil price and stock
prices, there are some papers that explore the
impact of oil price risk on stock returns (Basher
and Sadorsky, 2006; Fayyad and Daly, 2011;
Nandha and Faffa, 2008; Sadorsky, 1999) and
find that oil price risk has a significant effect
on stock returns. Nandha and Faffa (2008)
performed an empirical investigation with 35
DataStream global industry indices for the period from April 1983 to September 2005 and
find that a rise in oil price affects negatively on
equity returns for all sectors except mining and
the oil and gas industries, and suggest that the
international portfolio investors should hedge
oil price risk. By employing Vector Auto Regression (VAR) analysis with daily data from
September 2005 to February 2010 relating to
seven countries (Kuwait, Oman, UAE, Bahrain, Qatar, UK and USA), Fayyad and Daly
(2011) indicate that a rise in oil prices brought
about an increase in the predictive power of
oil price on stock returns and the impulsive
response of a shock to oil price raised during
Global Financial Crisis periods.

3.2. Cointegration
Cointegration has been showing as an important technique to examine whether the economic and financial time series are cointegrated. Besides, there are many areas of finance
where cointegration might be expected to hold.
Therefore, the methodology of cointegration
has been more and more widely used in empirical studies. The current article will employ
the cointegration technique to investigate the
linkages between the VN-Index and the S&P
500 Index, the US Dollar - VN Dong exchange
rates, gold prices and crude oil prices pre- and
post- the 2008 Global financial crisis. In addition, the analysis of these links has strong implication for diversification; especially investment with long-term horizons. Furthermore,
knowledge about these links will help investors
to forecast the movement of the VN-Index,
basing themselves on information about the
given changes of the S&P 500 Index, the US
Dollar - VN Dong exchange rates, gold prices
and crude oil prices. In order to test for cointegration, the first step is to check if each series
(in levels) is integrated of the same order. It is
common in financial market data that most of
the macroeconomic and financial time series
are integrated of order one, in other words, they

3. Methodology
3.1. Correlation
The correlation coefficient is traditionally
employed to measure the degree of integration between any two variables using historical
data. Therefore, the correlations between the
pairs of the VN-Index and the S&P 500 Index,
the VN-Index and gold prices, the VN-Index
and the US Dollar - VN Dong exchange rates,
and the VN-Index and crude oil prices, are firstJournal of Economics and Development

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Vol. 16, No.1, April 2014

the lagged x’s to see if they are statistically significant based on an F-test.

are following an I(1) process.
3.3. Unit root

This method runs the bivariate regression of
the form:

A time series must be examined to determine
if it is stationary because the use of non-stationary data can lead to spurious regressions. A
time series is non-stationary when it contains a
unit root (integrated of order one) and its first
difference is stationary (integrated of order
zero). For this reason, this article uses the Dickey–Fuller (DF) and Augmented Dickey-Fuller
(ADF) methodologies to test for a unit root. An
important factor which influences the results
of these tests is choosing the appropriate lag
length. It is normally a problem in determining the optimal number of lags of the dependent variable. As suggested by Brooks (Brooks,
2002), there are two ways to do this. Firstly,
it could be decided based on the frequency of
the data. However, as high frequency data (daily) are used, it is not an obvious choice in this
case. Secondly, another option, which is more
appropriate in this case, is to base the decision
on the information criterion. There are three
popular information criteria, Akaike’s (1974)
information criterion (AIC), Schwarz’s (1978)
Bayesian information criterion (SBIC) and the
Hannan-Quinn criterion (HQIC). In this paper,
we use SBIC to identify the optimal lag length
as SBIC embodies a much stiffer penalty than
AIC, while HQIC is somewhere in between.

yt= α0 + α1yt-1 + …+ α1yt-1 + β1xt-1 + …+ β1xt-1 + εt
xt­ = α0 + α1xt-1 + …+ α1xt-1 + β1yt-1 + …+ β1yt-1 + ut
for all possible pairs of (x, y) series in the
group. The reported F-statistics are the Wald
statistics for the joint hypothesis:
β1 = β2 = …= β1 = 0
for each equation. The null hypothesis is that x
does not Granger-cause y in the first regression
and that y does not Granger-cause x in the second regression.
4. Data
4.1. Data descriptive statistics
The VN-Index data are downloaded from Ho
Chi Minh City stock exchange websites and the
S&P 500 Index is extracted from Bloomberg.
The US Dollar - VN Dong exchange rates are
the inter-bank average rate of VN Dong versus US Dollar supplied by the State Bank of
Vietnam. The gold prices are London Afternoon (PM) Gold Prices, extracted from the
USA Gold website. The oil price data are the
WTI crude oil spot prices and extracted from
the United States Department of Energy via
Wikiposit website. All data are daily closing
prices over the period from 4 January 2005 to
31 December 2012 and are divided into two
sub-periods. The first sub-period runs from the
beginning of the data set to 28 December 2007.
The second sub-period is from 2 January 2008
to the end of the data set. The rationale for this
division is to avoid the excessive fluctuations
during the financial crisis and to uncover the
differences in linkages before and during the

3.4. Granger causality
The Granger causality method seeks to determine how much of a current variable, y, can
be explained by past values of y and whether
adding lagged values of another variable, x, can
improve the explanation. Then, y is said to be
“Granger-caused” by x if x helps to predict y.
In other words, one looks at the coefficients on
Journal of Economics and Development

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Vol. 16, No.1, April 2014

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