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Determinants of stock arket development in southeast asian countries

Determinants of stock arket development in southeast asian countries. This p ap er examines the determinants of stock market develop ment in Southeast Asian countries. Our findings show that income growth rate, saving rate, financial develop ment, stock market liquidity, and macroeconomic stability are the main determinants of market cap italization.

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Journal of Economics and Development Vol. 44, No.1, April 2012, pp. 101-112

Determinants of Stock Market

ISSN 1859 0020

Development in Southeast Asian Countries
Phan Dinh Nguyen
Hochiminh City University of Technology and University of Adelaide, Australia
Email: nguyenpdinh@yahoo.com
Vo Thi Ha Hanh
Deutsche Bank Hochiminh City Branch
Email: nguyenpdinh@yahoo.com


This paper examines the determinants of stock market development in Southeast
Asian countries. Our findings show that income growth rate, saving rate, financial
development, stock market liquidity, and macroeconomic stability are the main
determinants of market capitalization. Meanwhile macroeconomic stability measured by the change in inflation and the financial crisis have had a negative effect on
market capitalization, other variales have a potivive effect.
Keywords: Stock market development, ASEAN stock markets, panel data

Journal of Economics and Development


Vol. 14, No.1, April 2012

1. Introduction

Since the 1990s, there have been numerous
debates as to whether stock market development has led to economic growth. The question is an important one in order to clear policy implications for countries that have financial sectors that are comparatively underdeveloped. As Levine and Zervos (1998) argued that
a well-established stock market not only can
mobilize capital and diversify risks between
market agents but it is also able to provide different types of financial services than banking
sector to stimulate economic growth. The role
of financial markets has been more and more
affirmed as one of the main indicators of economic stability, in which stock market indexes
provides indications on the economic health of
a country. However, the concern on whether
developing countries themselves reap any benefits from their stock market development with
the boom over the past decades still exists.
New theoretical work shows that stock market
development may give a big boost to the longrun economic growth in emerging markets,
and new empirical evidence supports this
view. For example, Demirguc and Levine
(1996a), Singh (1997), and Levine and Zervos
(1998) found that stock market development
plays an important role in predicting future
economic growth. On the contrary, some analysts view stock markets in developing countries as “casinos” that have little positive
impact on economic growth.

Examine the stock market performance and
economic growth of Southeast Asia countries
in the last two decades, we see that there is a
huge booming in financial market of those
countries. The growth of these emerging marJournal of Economics and Development

kets had been so dramatic and has developed
faster than ever before with the explosion of
the diversified investment channels. World
investors have paid more attention to
Southeast Asian markets such as Singapore
and Malaysia, followed closely behind are
Thailand, Indonesia, the Philippines and
Vietnam. This is due to their large populations
which provide the potential to grow their
labour intensive exports, capitalize on the
process of low-cost production and most
importantly is a market for more goods and
services when their income grow. While the
gross income of region increased by 279%
from $331 billion USD to $1,257 (1990-2007),
the stock market capitalization mobilized in
2007 hit an unexpected number $1,210 billion,
compared with $121 billion in 1990 is 901%
increase. That shows a positive relationship
among two indicators. However, besides those
remarkable developments, these decades have
also witnessed many negative fluctuations for
the ASEAN region as well as the world economy. The first noticeable was the Asian financial crisis of 1997 that caused turmoil. The
composite stock index in Malaysia dropped
from a high of 1237.96 to 594.44 points in
1997, the Thailand Stock Index dropped to
372.69 points in 1997 from a peak of 831.57 in
1996, etc. After ten years of recovery and
enhancement of financial system, the global
sub-prime crisis 2008 once more pushed the
ASEAN economies turndown, plunging GDP
growth from 5.75% in 2006-2008 to 1.5% in
2009. A major financial channel, the stock
markets in those countries underwent difficulty as well. News of problems and failures of
banking institutions, insurance giants and

Vol. 14, No.1, April 2012

eventually multinational manufacturing concerns dampened investor confidence and the
outlook for the next year remains gloomy.

The above vacillations of the financial market and the stock-economic relationship have
long been of interest to many researchers.
However, few researchers have paid attention
to the whole ASEAN stock markets and measured the impact of the world crisis 2008 so far.
Therefore, the purpose of this research is to
identify the main determinants that affect
financial market performances of six
Southeast Asian countries namely Indonesia,
Malaysia, Philippines, Singapore, Thailand
and Vietnam in the long run. The research also
examines the impacts of the two crises 1997
and 2008 to ASEAN stock market volatility.
Finally, the paper will further investigate the
relationship between financial intermediary
development and stock market development.
As Demirguc and Levine (1996b) have shown
that countries with well-developed financial
intermediaries tend to have well-developed
stock markets. Hence, we intend to examine if
this complementary relationship exists in our
2. Model for estimation

The research employs the Constant
Coefficients Model using Pooled OLS Method
to examine determinants of ASEAN stock
market development. This method can be
applied here rather than estimating the equation in one cross section, which would be
wasteful as it would leave out information in
the data set. Besides, the research do not
employ the more popular models whinch are
Fixed Effect Model and Random Effect Model
due to their limitation to test panel unit root
Journal of Economics and Development

and cointegration in unbalance panel data.
Moreover, the Random Effect Model also
requires number of cross section identifiers
higher than number of variables that our
research can not meet. Therefore, the Pooled
OLS method is used as the most appropriate
one; also it can extend the number of observations. It is simpler to conduct and is defined
according to the following regression model:
yit = α + βXit + µit
i = 1,......,N ; t = 1,…….,Ti

yit indicates the dependent variable
 Xit determines the vector of k explanatory variables.

α: constant coefficients

β: the vector of coefficients.

Variables are presented in table 1 and
explained as follows:
Dependent variable

Stock market development (CAP): is measured by Market capitalization (also known as
market value). This measure equals the value
of listed shares divided by GDP. The assumption behind this measure is that overall market
size is positively correlated with the ability to
mobilize capital and diversify risk on an economy-wide basis.
Independent variables

1. Income growth rate (GDP_RATE): is
annual percentage growth rate of GDP at market prices based on constant local currency.
GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included

Vol. 14, No.1, April 2012

Table 1: Variables summary



Dependent Variable

Independent Variables


Market capitalization to GDP


Income growth rate



Turn over ratio




Value traded per GDP


Gross domestic saving per GDP

Credit to private sector rate % of GDP

Liquid liabilities of financial system (M2/GDP)
Inflation change

Crisis - dummy variable

in the value of the products. Because higher
income usually goes hand in hand with better
defined property rights, better education, and a
better general environment for business, we
expect it to have a positive effect on the stock
market capitalization.
The research measures the stock market liquidity in two ways.

2. First, value traded (TRADE): % of stock
value traded per GDP, measures the value of
stock transactions relative to the size of the

3. Second, turnover ratio (TURN): calculated as the ratio of the total value traded by stock
market capitalization measures the value of
equity transactions relative to the size of the
equity market.
The above two liquidity indicators do not
directly measure how easily investors can buy
and sell securities at posted prices. However,

Journal of Economics and Development


they do measure the degree of trading in comparison to the size of both the economy and the
market. Therefore they positively reflect stock
market liquidity on an economy wide and market wide basis. Moreover, these two measures
complement each other. For example, in
Indonesia the ratio of value traded to GDP is
1.8%, but the turnover ratio is 219%, which
means that Indonesia is a small but active market. In contrast, in Taiwan the value traded to
GDP ratio is 151%, but turnover ratio is 24%,
which means that Taiwan is a big but relatively inactive market.

4. Gross domestic savings (% of GDP)
(SAVE): Gross savings are calculated as gross
national income less total consumption, plus
net transfers. The saving rate is calculated as
the ratio of gross saving to GDP. Like financial
intermediaries, stock market intermediate savings to investment projects. Usually the larger
the savings, the higher the amount of capital

Vol. 14, No.1, April 2012

flows through the stock market. Thus, we
expect savings to be an important determinant
of stock market capitalization.
Financial intermediaries are measured by
two following indicators:

5. Financial resources provided to the private sector (CRE): credit to private sector rate
% of GDP. Private credit is the most comprehensive indicator of the activity of commercial
banks. It captures the amount of external
resources channelled through the banking sector to private firms. In addition, it measures the
activity of the banking system in one of its
main function: channelling savings to

6. Liquid liabilities of the financial system
M2/GDP (M2) includes all loans, purchases of
non-equity securities, trade credits and other
accounts receivable for the repayment provided for the private sector to GDP. We use liquid
liabilities of the financial system to proxy M2.
Liquid liabilities consists of currency held outside the banking system plus demand and
interest-bearing liabilities of banks and nonbank financial intermediaries. The M2 to GDP
ratio is an indicator of the size of the banking
sector in relation to the economy as a measure
of financial depth, therefore we expect a positive relationship with the market capitalization.
Macroeconomic stability

7. Economy stabilization: inflation change
(INF_CHANGE): we use the difference of
inflation rates to measure macroeconomic stability. We calculate the change of this year’s
Journal of Economics and Development

inflation rate from last year. Inflation change is
used because we believe that high, stable inflation may not represent much instability, but
inflation rates that bounce around a lot probably do represent macroeconomic instability.
Hence, the expected relation is negative.

8. Financial crisis (CRI): is measured by a
dummy variable (0,1) and value at 1 when crisis occurs - Asia crisis (1997,1998) and word
crisis (2008). As Dirk (2006), crisis causes
some negative impacts to the development of
financial as well as stock market.

From the discussion above, the suggested
model employed in this paper is as follow:

3. Data collection and research methodology

For the data with two dimensions times
series and cross sections, the paper uses the
panel data analysis via Constant Coefficients
Model, Pooled OLS Method, panel unit root
test as well as panel cointegration test to examine the main explanatory variables of stock
market developments in selected ASEAN
countries in the long run. Cointegration analysis allows using non-stationary (series) data so
that avoid the spurious regression results as
Utkulu (1997). The “cointegration” concepts
were first introduced by Granger (1981) then
developed by Engle and Granger (1987). The
non-stationary and stationary states are the
crucial concepts of this method. It means that
econometric analysis, both non-stationary and
stationary processes might be linked by equi105

Vol. 14, No.1, April 2012

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