2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
A Disaggregate Look at Stock Price Behavior
in Malaysia and Thailand
Shamila Jayasuriya*
________________________________________________________________________
* Assistant Professor, Ohio University, Department of Economics, Athens, OH 45701.
Contact information: phone: 740-593-2094, fax: 740-593-0181, email:
jayasuri@ohio.edu.
June 24-26, 2007
Oxford University, UK
1
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
A Disaggregate Look at Stock Price Behavior in Malaysia and Thailand
Abstract
In this paper, we first construct MCAP-weighted closing stock prices for four sectors in
Malaysia and Thailand for the period December 1990 to November 2004. Using a VAR
and an impulse response function analysis, we then examine interlinkages in stock return
behavior among the different sectors and between the two countries. We find that the
lagged behavior of consumer discretionary and financials sectors affect all four sectors in
Malaysia whereas the different sectors are mainly independent of each other in Thailand.
There is also not much relation between the same sectors of the two countries except for
financials. Interestingly, the two financials sectors respond concurrently to each other’s
shocks and the impact of such a shock dies down after about two months.
JEL Classification: G14; G15
Keywords: Stock prices; Sectors; Vector Autoregressiv Analysis (VAR); Impulse
response functions; Emerging market economies
June 24-26, 2007
Oxford University, UK
2
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
1. Introduction
Malaysia and Thailand are two leading emerging economies that many foreign
investors searching for diversification benefits are attracted to. Both countries have had
equity market liberalization policies in effect since the late 1980s. The domestic stock
markets in each, therefore, have been subject to a variety of internal and external shocks
in the past couple of decades. Existing work examine mainly the aggregate stock market
behavior and not so much the sector level behavior for both markets. In this paper, we
intend to fill that gap in existing literature by providing a closer look at stock price
behavior at the sector level. In particular, we analyze the stock price behavior for the
following four sectors in each market - consumer discretionary, consumer staples,
financials, and industrials – using monthly data from December 1990 to November 2004.
One of the main research questions that we ask is whether stock prices in a given sector
are affected by stock price behavior in another sector of that economy. We also examine
whether stock price behavior in a given sector of Malaysia is affected by behavior in the
same sector of Thailand and vice versa.
Our first task is to construct closing stock prices at the sector level. In particular,
we use market capitalization (MCAP) and closing share price data for each individual
stock to construct MCAP-weighted closing prices for the different sectors. We then
compute stock returns based on the closing prices for the four sectors. Informally, we
observe that the stock prices as constructed indicate a close link among the four sectors.
In addition, stock prices in a given sector in Malaysia appear to be closely related to those
of the respective sector in Thailand. We then conduct a Vector Autoregressive (VAR)
analysis on the stock returns to formally examine these relations. We also use an impulse
June 24-26, 2007
Oxford University, UK
3
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
response function analysis to examine how long a shock from one sector to another
typically lasts in these two countries.
Our VAR results indicate that the lag behavior of consumer discretionary and
financials sectors affect all four sectors in Malaysia, while none of the sectors generally
affect the others in Thailand. In addition, macroeconomic factors such as interest rates
and inflation are not important determinants of stock return behavior at the sector level in
both countries. Our results also indicate that the lag performance of the financials sector
in Malaysia significantly affects the financials sector in Thailand and vice versa. Based
on the impulse response function analysis, we find that a shock originating in any given
sector lasts for not more than two months in that same sector in each country. Also, the
financials sectors of the two countries respond concurrently to each other’s shocks and
the impact of such a shock dies down after about two months.
The remainder of the paper is organized as follows.
Section 2 provides a
literature review of related work for Malaysia and Thailand. Section 3 discusses the
methodology. Section 4 describes the data and presents some preliminary statistics. We
document our estimation results in section 5. Finally, section 6 concludes.
2. Literature Review
In existing literature, to our knowledge, there are very few sector-level studies of
stock price behavior for both Malaysia and Thailand.
A recent study by Rim and
Mohidin (2005) examine the dynamic relationship between exchange rates and stock
prices at the industry level for Malaysia in the late 1990s. These authors find that a
strong link exists between the two during the period of the Asian financial crisis. They
June 24-26, 2007
Oxford University, UK
4
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
also find that the effects of exchange rate changes are industry specific and not common
to all. An earlier study by Habibullah and Baharumshah (1996) look at informational
efficiency in the Malaysian stock market.
These authors determine whether key
macroeconomic variables are able to predict stock prices both at the aggregate and sector
levels. Their results suggest that the Malaysian stock market is informationally efficient
with respect to money supply and output.
Recent literature at the aggregate level study the linkages between stock prices
and various macroeconomic variables such as exchange rates, interest rates, money, and
output for both Malaysia and Thailand. See Baharumshah (2004), Chong et al (2001),
Chong and Goh (2005), Ibrahim (2001), Ibrahim and Aziz (2003), Phylaktis and
Ravazzolo (2005), Ramasamy and Yeung (2002), and Wongbangpo and Sharma (2002).
General results are that long run relationships and even short run interactions do exist
between stock prices and macroeconomic variables. However, irregularities are observed
when the Asian financial crisis is taken into account. For example, Hatemi-J and Roca
(2005) look at the link between stock prices and exchange rates in relation to the Asian
financial crisis for the four ASEAN countries including Malaysia and Thailand. These
authors find that the two series are significantly linked but only in the non-crisis period.
Several studies examine a host of other issues at the aggregate level including
contagion effects, market segmentation, and market efficiency. See for example Ahmed
et al (2003), and Wu and Sarkar (1998). In general, there is empirical evidence of
contagion as measured by the co-movement of national incomes among several Asian
economies following the financial crisis. Also, evidence suggests that the impact of
external shocks is greater in Asian markets as the degree of openness to foreign investors
June 24-26, 2007
Oxford University, UK
5
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
increases. Bailey and Jagtiani (1994), and Khianarong and Vos (2004) also document a
direct correlation between price premiums (difference in foreign and local price of
stocks) and foreign equity ownership restrictions for the Thai market. On a different
note, Sadique and Silvapulle (2001) examine the presence of long memory in the stock
returns for seven countries including Malaysia. They find evidence that the Malaysian
stock returns are long-term dependent, which indicates market inefficiency.
Also,
Ibrahim (1999) provides strong evidence that suggest informational inefficiency in the
Malaysian market. This study is based on a bivariate analysis of the dynamic interactions
between stock prices and several macroeconomic variables at the aggregate level.
3. Methodology
First, we construct sector level closing stock prices using data at the individual
stock level for both Malaysia and Thailand. Next, we conduct a Vector Autoregressive
(VAR) and an impulse response function analysis to examine interactions among sectors
and between the two countries.
Sector level closing stock prices
We obtain stock level data for Malaysian and Thai companies that are listed on
the respective stock exchanges. Based on the data that we gather, each stock belongs to
one of four sectors – consumer discretionary, consumer staples, financials, and
industrials.1 We are given the date on which each stock was listed and the date, if
applicable, on which the stock was no longer listed on the stock exchange. In addition,
1
The individual stock data are in fact available for ten sectors. We select the four based on the most
availability of data starting as early as December 1990.
June 24-26, 2007
Oxford University, UK
6
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
we are given the market capitalization and the closing price in local currency for each
stock.2 We sort the company level data by sector and then construct a time series of
aggregate closing prices for each sector as follows:
 MCAP
n
(Clo sin g. Pr ice _ MCAP.Weighted ) t , j 
it , j
i 1
* Clo sin g. Pr ice it , j 
n
 MCAP
i 1
(1)
it , j
That is, at time t for sector j and stock i we multiply the market capitalization of that
stock with its closing share price. We repeat this exercise for the n different stocks in
sector j at time t and sum up the numbers as indicated by the numerator in equation (1).
Next, we divide by the total market capitalization of the n stocks in sector j at time t.
Essentially, we are constructing a series of MCAP-weighted closing stock prices
denominated in local currency that accounts for all stocks listed on the stock exchange for
a particular sector. In addition, we compute MCAP-weighted stock prices denominated
in U.S. dollars by converting local currency values to U.S. dollar values by using the
appropriate exchange rate data. The U.S. dollar closing prices are essential for crosscountry comparisons and we focus on these rather than the local currency denominated
prices in our discussions. For all estimations that follow, we compute stock returns based
on the MCAP-weighted closing prices (U.S. dollar) that we construct.
VAR estimations
Aggregate stock prices especially of regional countries are often correlated with
one another and we may observe similar trending patterns or even contagion and
2
Market capitalization is also known as the market value. The market value of a company typically is the
share price times the number of shares outstanding.
June 24-26, 2007
Oxford University, UK
7
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
spillover effects across markets. It is not unlikely that stock price behavior in different
sectors of an economy also indicate close relations. These sectors, after all, face the same
set of macroeconomic conditions and disturbances. We therefore employ a non-structural
VAR approach to estimate relationships among the four sectors. In particular, we treat
the sector returns of each country as a system of interrelated time series that we in turn
use to study the dynamic impact of a random shock on the entire system of variables. We
also employ a VAR approach to estimate relationships for a given sector between the two
countries. In other words, we repeat the earlier estimation by treating the returns for a
given sector between Malaysia and Thailand as a system of related variables. Given that
the two countries are prominent emerging markets located in close proximity in the South
East Asia region, we may observe close relations between the same sectors in the two
countries.
Given that we have four sectors, we construct a four factor VAR model as
follows:
r1,t  c1  A11 ( L).r1,t 1  A12 ( L).r2,t 1  A13 ( L).r3,t 1  A14 ( L).r4,t 1  B1 xt   1,t
r2,t  c 2  A21 ( L).r1,t 1  A22 ( L).r2,t 1  A23 ( L).r3,t 1  A24 ( L).r4,t 1  B2 xt   2,t
r3,t  c3  A31 ( L).r1,t 1  A32 ( L).r2,t 1  A33 ( L).r3,t 1  A34 ( L).r4,t 1  B3 xt   3,t
(2)
r4,t  c 4  A41 ( L).r1,t 1  A42 ( L).r2,t 1  A43 ( L).r3,t 1  A44 ( L).r4,t 1  B4 xt   4,t
We estimate the above model for Malaysia first and Thailand next. The aggregate stock
return for sector j at time t is indicated by rj,t. The four sectors that are of interest to us,
consumer discretionary, consumer staples, financials, and industrials are set equal to j =
1,2,3, and 4 respectively. In this set up, the presumption is that the sector returns are
interrelated or, in other words, endogenous. However, simultaneity is not an issue since
only the lagged values of the returns appear on the right hand side of the equations. The
June 24-26, 2007
Oxford University, UK
8
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
c and xt each denotes a vector of constant terms and a vector of exogenous variables
respectively. A and B are matrices of coefficient estimates and  t is a vector of error
terms that may be contemporaneously correlated but are uncorrelated with their own
lagged values and the explanatory variables of the model.
Based on equation (2), stock returns for the four sectors in each country are
determined jointly as a function of their one-period lagged returns and a host of
exogenous variables that are potentially good determinants of stock return behavior.3
The exogenous variables are foreign stock returns and a set of macroeconomic variables
that capture the domestic economic conditions.
Co-movements with foreign stock
markets could largely explain the behavior of emerging market returns. Subsequently,
we add three prominent developed market return indices to the model including the U.S.
S&P 500, Japanese Nikkei, and U.K. FTSE 100. Macroeconomic fundamentals have a
direct impact on economic growth prospects and therefore on stock returns. To capture
this effect, we add several variables that reflect the domestic economic conditions
including the interest rate, inflation, and real exchange rate (RER).4 Finally, we add a
dummy variable that captures the effect of the Asian currency crisis since both countries
were in fact greatly affected by the crisis. A key focus will be the A matrix coefficient
estimates with significant estimates indicating strong interrelationships among the
sectors.
3
Note that we construct a VAR that contains only one-period lagged returns. As we discuss later on, our
choice of one period is based on the Schwarz Information Criterion (BIC).
4
We construct the real exchange rate (RER) so that an increase in the RER indicates a depreciation of the
real exchange rate and, therefore, an increase in the external competitiveness of the economy.
June 24-26, 2007
Oxford University, UK
9
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
The VAR representation for a given sector in the two countries is shown in
equation (3). Given that there are two countries, we now have a two country VAR model
as follows:
r1,t  c1  A11 ( L).r1,t 1  A12 ( L).r2,t 1  A13 ( L).r3,t 1  A14 ( L).r4,t 1  B1 xt   1,t
r2,t  c2  A21 ( L).r1,t 1  A22 ( L).r2,t 1  A23 ( L).r3,t 1  A24 ( L).r4,t 1  B2 xt   2,t
(3)
We estimate the above model for the consumer discretionary sector first and then for the
three remaining sectors. Here, rj,t indicates the aggregate stock return for country j at
time t. The two countries that we study, Malaysia and Thailand, are set equal to j = 1 and
2 respectively. As before, the c and xt are vectors of constant terms and exogenous
variables respectively. The only exogenous variables in these estimations are the three
foreign stock return indices and the Asian currency crisis dummy variable. The domestic
macroeconomic variables are no longer included because there is no clear rationale for
why one country’s fundamentals should affect another country’s stock return behavior
albeit in the same sector. In this case, too, a key focus will be the A matrix coefficient
estimates with significant estimates indicating strong interrelationships in the selected
sector between the two countries.
For all estimations, we will conduct diagnostic tests to evaluate how appropriate
the VAR model is.
For instance, we will test stationarity of the VAR model by
examining whether the inverse roots of the characteristic polynomial lie inside the unit
circle or not. We will also examine the multivariate extensions of several residual test
statistics
including
the
serial
correlation
Lagrange
Multiplier
(LM),
White
heteroskedasticity, and Jarque-Bera normality statistics to examine whether the
underlying error assumptions of the model are in fact met.
June 24-26, 2007
Oxford University, UK
10
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Impulse response functions
The impulse response functions trace the effects of a shock or an impulse to one
endogenous variable on the other endogenous variables in the VAR system of equations.
Suppose a shock originates in the consumer discretionary sector. Its effect is felt directly
in that sector and is also transmitted to the other sectors through the dynamic lag structure
of the VAR. In what follows, we will generate an impulse as a one standard deviation
innovation to the VAR residuals.5 We then observe the corresponding response on the
variables of interest over a period of six months.6 The impulse response function analysis
will therefore help us determine whether shocks are transmitted among the different
sectors in Malaysia and Thailand and even between the two countries. In addition, we
will determine how long a shock from one sector to another typically lasts in the two
countries.
4. Data and preliminary statistics
We use monthly data from December 1990 to November 2004 in our analysis.
Individual stock data for the Malaysian and Thai companies are all obtained from the
S&P/IFC’s Emerging Markets Data Base (EMDB). For each individual stock, we gather
its sector information and data for market capitalization and closing share prices
denominated in local currency values. We construct MCAP weighted closing prices for
each sector as discussed earlier and, for comparison purposes, convert the local currency
denominated prices to U.S. dollar prices using the relevant exchange rate data also
5
We use Cholesky one standard deviation innovations that use the inverse of the Cholesky factor of the
residual covariance matrix to orthogonalize the impulses. When deriving the Cholesky factor, we correct
for the number of degrees of freedom by taking into account the number of parameters per equation in the
VAR model.
6
The responses should eventually die to zero provided that we have a stationary VAR model.
June 24-26, 2007
Oxford University, UK
11
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
obtained from the EMDB. Subsequently, we compute stock returns as the logarithmic
differences of the stock price series we construct. Data for all exogenous variables are
from the International Monetary Fund’s (IMF’s) International Financial Statistics (IFS)
database.
Graph 1 plots the closing stock prices for the four sectors in Malaysia and
Thailand.7 A visual inspection of the two graphs indicates a close correlation in sector
prices for both countries. Furthermore, stock prices appear to be structurally different
following the Asian financial crisis. For Thailand especially, we observe strikingly low
stock prices in the post- compared to the pre-crisis period. This observation can be
attributed to the fact that Thailand was in fact one of the most adversely affected
countries following the financial crisis. We also note that, in recent years, the consumer
staples sector has had the highest closing price on average for Malaysia. For Thailand,
on the other hand, it has been the consumer discretionary sector. For both countries, the
financials sector has on average provided relatively the lowest closing stock prices.
Graph 2 presents closing stock prices by sector for the two countries.
Interestingly, we observe similar trends in the stock price series especially for the
consumer discretionary and industrials sectors. The two financials sectors also appear to
be closely related except at the beginning of the sample period. The consumer staples
sectors seem to be the least correlated particularly in the last five years or so. We also
note that, for all sectors, closing prices are generally higher for Thailand prior to the
currency crisis. However, in the more recent years, closing prices are on average higher
for Malaysia for all the sectors. In summary, the sector closing prices that we construct
7
The vertical line in all the graphs coincides with the date July 1997, which indicates the start of the Asian
Financial crisis.
June 24-26, 2007
Oxford University, UK
12
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
present some preliminary evidence that stock return behavior may be closely related
among sectors of a given country or even between countries. The VAR estimations that
follow will provide us formal evidence of such relations if they do exist.
Table 1 documents summary statistics for stock returns by sector for Malaysia and
Thailand.8 The averages and standard deviations of returns are generally lower for
Malaysia than for Thailand. For Malaysia, average returns are similar across the sectors
except for the financials sector that reports a relatively higher average return of 1.2
percent. For Thailand, the consumer discretionary and financials sectors are similar
based on average returns but not based on median returns. The substantial differences
between mean and median returns for Thailand can be attributed to the more extreme
behavior of closing stock prices observed for that country. For example, closing prices
appear to be at their highest in the first half of the 1990s but have drastically decreased
and remained low in the later years especially following the financial crisis.
For
Malaysia, on the other hand, closing prices have been relatively high in the pre-crisis
period but the post-crisis stock prices have not remained low.
Instead, they have
gradually increased and appear to be on a path that could eventually reach their earlier
levels.
Based on Table 1, it is difficult to observe any obvious similarities between
countries of the same sector. The close resemblance of the two consumer staples sectors
based on average returns is misleading when we take into account the respective median
returns and standard deviations. We also note that returns are skewed and leptokurtic and
the Jarque-Bera test statistic, which tests the hypothesis of a normal distribution, is
8
An Augmented Dickey Fuller (ADF) test was carried out to test for the existence of a unit root for each
returns series. We rejected the null hypothesis of a unit root at the 5% significance level for all series and
therefore found no apparent sources of non-stationarity in the data.
June 24-26, 2007
Oxford University, UK
13
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
consistently rejected at a high significance level. Therefore, one thing that we clearly do
observe is that the returns series are not normally distributed, which often is the case with
stock return data.
5. Estimation results
We use the Schwarz Information Criterion (BIC) to determine the number of lags
for the endogenous variables and find that a one period lag provides the best fit in all
estimations.
The U.S. S&P 500 and U.K. FTSE indices are both insignificant as
explanatory variables in all our regressions and we, therefore, omit them from the
estimations.
VAR four-sector model results
Table 2 presents the VAR results for Malaysia. We observe that all sector returns
are significantly affected by lagged stock return behavior in the consumer discretionary
and financials sectors. The consumer discretionary sector has a negative effect on all
sectors while the financials sector has a positive effect. For example, if the financials
sector returns increase by 1 percent this month we will observe an increase of anywhere
between 0.14 to 0.30 percent in the next month for the four sectors. Also note that the
industrial sector has no substantial impact on the future behavior of any of the sectors.
The behavior of the Japanese Nikkei is felt only in the financials sector. For example, if
the Japanese aggregate index returns increase by 1 percent there is a concurrent increase
in the financials sector returns of 0.30 percent. Among the domestic macroeconomic
variables, the real exchange rate has a positive significant impact on all but the consumer
June 24-26, 2007
Oxford University, UK
14
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
staples sector implying that a real depreciation is perceived as growth prospects and
therefore higher returns in most sectors. The domestic interest rate and inflation are not
consistently significant determinants. However, higher interest rates result in lower stock
returns in the industrials sector and higher inflation translates into higher returns in the
financials sector. Increased interest rates imply higher costs of borrowing, which may
stifle investment and growth in some sectors. Also, higher returns in response to greater
inflation could indicate a mere adjustment to higher domestic prices. Lastly, we observe
that the Asian currency crisis has had no significant effect on stock returns themselves
even though, visually, we observed earlier that closing prices are noticeably different in
the pre- compared to the post- crisis period for the four sectors.
Table 3 presents VAR estimation results for Thailand. Unlike Malaysia, none of
the four sectors in Thailand is significantly related to the past return behavior of their
own or the other sectors. Therefore, there are no apparent relationships among the four
sectors in Thailand. In addition, how the Japanese index performs is of no relevance for
sector returns in Thailand. Note that the domestic macroeconomic variables, too, are not
good determinants of sector returns in general. Similar to Malaysia, the Asian currency
crisis has had no significant impact on sector returns in the pre- versus the post-crisis
period.
We acknowledge the especially weak explanatory power of the exogenous
variables in our model for Thailand but are unable to improve the model’s fit due to
restrictions in data availability. Nevertheless, we are able to conclude that these four
sectors in Thailand do not affect each other. It is possible that some sectors are in fact
interrelated but our evidence clearly suggests that the four sectors examined here behave
independently of one another.
June 24-26, 2007
Oxford University, UK
15
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
A stability check on the two VAR estimations indicates that the characteristic
roots of the polynomials all lie inside the unit circle. Both VAR models are therefore
stationary as desired. Using the VAR residuals, we conduct serial correlation LM tests at
lags 12 and 24 and find that the null hypothesis of no serial correlation cannot be rejected
at the conventional significance levels for both Malaysia and Thailand. Also, the VAR
heteroskedasticity tests (with and without cross terms) indicate no heteroskedasticity. In
addition, the skewness and kurtosis statistics of the individual component residual series
all improved compared to the statistics presented in Table 1 and one or more of the
component Jarque-Bera statistics in fact indicated normality. However, the joint residual
normality test based on the Jarque-Bera statistic indicates that the VAR residuals are not
multivariate normal for both country estimations.
We plot the impulse response functions for Malaysia and Thailand in Graph 3 and
Graph 4. For both countries, a shock originating in one sector is clearly felt in that same
sector for the first month. The effect of such a shock dies after the first month in
Thailand whereas the aftermath of such a shock is somewhat felt in the second month as
well in Malaysia. Also note that the magnitude of the response of the shocks is relatively
greater in Thailand than in Malaysia. For both countries, a shock originating in one
sector is not always felt in the other sectors.
However, for Malaysia an impulse
originating in either the financials or the industrials sectors has a non-zero impact on all
remaining sectors for a period of about two months. For Thailand, an impulse originating
in the financials sector appears to have the most influence on the other sectors. At the
receiving end, the consumer discretionary sector appears to be relatively the most
susceptible and the industrials sector the least susceptible to exogenous shocks in
June 24-26, 2007
Oxford University, UK
16
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Malaysia. For Thailand, it is difficult to identify sectors that are consistently more
vulnerable to outside shocks. We do, however, observe that the financials sector by far is
the least susceptible to shocks originating in other sectors.
VAR two-country model results
Table 4 through Table 7 present VAR estimation results for the consumer
discretionary, consumer staples, financials, and industrials sectors respectively. Evidence
suggests that, for a given sector, lagged stock returns in one country generally do not
affect the return behavior in the other country. The only exception is the financials sector
for which we observe a positive significant effect of lagged returns from Malaysia to
Thailand and vice versa. For example, we find that a 1 percent increase this month in the
Malaysian financials sector returns results in a 0.87 percent increase in Thai financials
sector returns in the next month. Similarly, a 1 percent increase this month in Thai stock
returns in the financials sector results in a 0.05 percent increase in Malaysian returns in
the same sector in the next month. The finding that the two financials sectors are interlinked could be explained by investor behavior that affects the demand for stocks in this
sector. Among the four sectors studied, the financials sector is likely to attract more
foreign investor interest because of the potential to achieve higher returns albeit higher
risks.
If foreign investors seeking diversification benefits consider investment
opportunities in the Asian region, there is a good possibility that they will invest in a
subset of countries in the region. Provided that Malaysia and Thailand will ultimately
have a mutual group of foreign investors, we will observe a linkage in stock return
behavior that is driven by a common set of investor tastes and preferences.
June 24-26, 2007
Oxford University, UK
17
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
We also note that the exogenous variables in the model including the behavior of
Japanese index returns and the Asian currency crisis have had no significant impact on
stock returns at the sector level. In all cases, we confirm that the VAR models are in fact
stationary. Based on diagnostic test statistics on the VAR residuals, we also find that
there is no evidence of serial correlation or heteroskedasticity in the models. However,
the VAR residuals are not jointly normal even though some individual component
residual series are normal and the skewness and kurtosis statistics indicate a considerable
improvement from before.
We plot the impulse response functions for the financials sectors of Malaysia and
Thailand in Graph 5.9 We observe that a shock originating in this sector of one country is
felt in that same country for about three months before its impact dies down completely.
The magnitude of the effect of the shock is far greater for Thailand especially in the first
month immediately after the shock. We also observe that a shock originating in the
Malaysian financials sector is concurrently felt in the Thai financials sector. This effect
is especially prominent in the first two months following the shock after which it
gradually dissipates and does not last beyond the fourth month. A shock originating in
the Thai financials sector is also felt in Malaysia although the effect is not as substantial.
Nevertheless our finding corresponds to empirical evidence of the Asian financial crisis
where the effects of the crisis were transmitted from Thailand to many of its neighboring
countries.
9
The impulse response functions for the other three sectors are available upon request. A closer look at
these graphs indicated that a shock originating in one sector of Malaysia (Thailand) is not felt to any
considerable extent in that same sector in Thailand (Malaysia).
June 24-26, 2007
Oxford University, UK
18
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
6. Conclusion
The main objective of our paper was to identify interlinkages in stock return
behavior among four sectors in two emerging market economies – Malaysia and Thailand
– from December 1990 to November 2004. We first constructed market capitalizationweighted closing prices for the four sectors - consumer discretionary, consumer staples,
financials, and industrials - and informally observed correlations among the sector prices.
We then estimated a Vector Autoregressive model to formally examine the relations, if
any, among the sectors and between the two countries.
We found that the lagged
behavior of the consumer discretionary and financials sectors in Malaysia significantly
affect all four sectors in Malaysia. However, the four sectors are quite independent of
each other in Thailand. The same sectors in the two countries are also not related for all
but the financials sector. For the financials sector, we found that the lagged return
behavior of the two countries affect each other with the Malaysian financials sector
having a relatively bigger impact on the Thai financials sector.
In addition, key
macroeconomic variables such as interest rates and inflation did not appear to have a
significant impact on stock returns at the sector level.
The impulse response function analysis indicated that, for both countries, a shock
originating in one sector typically lasts for not more than two months in that same sector
and that a shock originating in one sector is not always felt in the other sectors.
However, an impulse originating in the financials sector indicated a non-zero impact on
all remaining sectors for a period of about two months. We were also able to identify
sectors that were the most and least vulnerable to exogenous shocks originating in other
sectors. Between countries, an impulse originating in one sector was hardly felt in that
June 24-26, 2007
Oxford University, UK
19
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
same sector in the other country. However, we did find evidence that shocks originating
in the two financials sectors affect each other simultaneously. Provided that foreign
investors are generally more attracted to the financials sector relative to the other three
sectors that we study, interlinkages in the financials sector returns could be explained by
interlinkages in investor behavior among a mutual group of foreign investors themselves.
June 24-26, 2007
Oxford University, UK
20
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
References
Ahmed, S. M., Mohammad, G., Mohammadi, H., 2003. The Asian currency crisis: a
study of contagion. International Journal of Applied Economics and Econometrics 11(2),
195-215.
Baharumshah, A. Z., 2004. Stock prices and long-run demand for money: evidence from
Malaysia. International Economic Journal 18(3), 389-407.
Bailey, W., Jagtiani, J., 1994. Foreign ownership restrictions and stock prices in the Thai
capital market. Journal of Financial Economics 36(1), 57-87.
Chong, C., Goh, K., 2005. Inter-temporal linkages of economic activity, stock prices and
monetary policy in Malaysia. Asia Pacific Journal of Economics and Business 9(1), 4861.
Chong, L. L., Tan, H. B., Baharumshah, A. Z., 2001. The stock market, macroeconomic
fundamentals and economic growth in Malaysia. Asia Pacific Journal of Economics and
Business 5(2), 44-55.
Habibullah, M., Baharumshah, A. Z., 1996. Money, output and stock prices in Malaysia:
an application of the cointegration tests. International Economic Journal 10(2), 121-130.
Hatemi-J, A., Roca, E., 2005. Exchange rates and stock prices interaction during good
and bad times: evidence from the ASEAN4 countries. Applied Financial Economics
15(8), 539-546.
Ibrahim, M. H., 2001. Financial factors and the empirical behavior of money demand: a
case study of Malaysia. International Economic Journal 15(3), 55-72.
Ibrahim, M. H., 1999.
Macroeconomic variables and stock prices in Malaysia: an
empirical analysis. Asian Economic Journal 13(2), 219-231.
June 24-26, 2007
Oxford University, UK
21
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Ibrahim, M. H., Aziz H., 2003. Macroeconomic variables and the Malaysian equity
market: a view through rolling subsamples. Journal of Economic Studies 30(1), 6-27.
Khianarong, W., Vos, E., 2004. Market segmentation and stock prices: evidence from
the Thai market. Asia Pacific Journal of Economics and Business 8(1), 24-43.
Phylaktis, K., Ravazzolo, F., 2005. Stock prices and exchange rate dynamics. Journal of
International Money and Finance 24 (7), 1031-1053.
Ramasamy, B., Yeung, M. C. H., 2002. The relationship between exchange rates and
stock prices: implications for capital controls. Asia Pacific Journal of Economics and
Business 6(2), 46-60.
Rim, H., Mohidin, R., 2005. On the dynamic relationship between exchange rates and
industry stock prices: some empirical evidence from Malaysia.
Journal of Applied
Business Research 21(4), 49-60.
Sadique, S., Silvpulle, P., 2001.
Long-term memory in stock market returns:
international evidence. International Journal of Finance and Economics 6(1), 59-67.
Wongbangpo, P., Sharma, S. C., 2002. Stock market and macroeconomic fundamental
dynamic interactions: ASEAN-5 countries. Journal of Asian Economics 13(1), 27-51.
Wu, L., Sarkar, A., 1998.
Price transmission and market openness: a comparative
analysis of Asian stock markets. Review of Pacific Basin Financial Markets and Policies
1(2), 215-232.
June 24-26, 2007
Oxford University, UK
22
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Graph 1. Closing stock prices (in US dollars) by country
Malaysia
7
6
5
4
Consumer Staples
3
Consumer Discrtionary
2
Industrials
1
Financials
0
91 92 93 94 95 96 97 98 99 00 01 02 03 04
Thailand
20
Consumer Discretionary
16
Financials
12
8
Industrials
4
Consumer Staples
0
91 92 93 94 95 96 97 98 99 00 01 02 03 04
June 24-26, 2007
Oxford University, UK
23
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Graph 2. Closing stock prices (in US dollars) by sector
Consumer Staples
Consumer Discretionary
7
20
6
16
5
12
Malaysia
Thailand
4
3
8
2
4
Thailand
Malaysia
1
0
0
91 92 93 94 95 96 97 98 99 00 01 02 03 04
91 92 93 94 95 96 97 98 99 00 01 02 03 04
Financials
16
Industrials
14
12
12
10
Thailand
8
8
Thailand
6
4
4
Malaysia
0
2
Malaysia
0
91 92 93 94 95 96 97 98 99 00 01 02 03 04
June 24-26, 2007
Oxford University, UK
91 92 93 94 95 96 97 98 99 00 01 02 03 04
24
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Table 1. Summary statistics for monthly stock returns by sector (December 1990 – November 2004)
Country/Sector
Mean
Median
Maximum
Minimum
Standard
Deviation
Skewness1
Malaysia
Consumer Discretionary
Consumer Staples
Financials
Industrials
0.007
0.007
0.012
0.008
0.008
0.008
0.015
0.006
0.410
0.206
0.715
0.331
-0.331
-0.365
-0.398
-0.390
0.105
0.075
0.135
0.094
0.352
-0.662
0.759
-0.645
5.305
7.009
8.240
6.792
0.000
0.000
0.000
0.000
168
168
168
168
Thailand
Consumer Discretionary
Consumer Staples
Financials
Industrials
0.024
0.007
0.024
-0.005
-0.012
-0.009
0.005
-0.020
5.392
0.627
5.345
1.323
-0.721
-0.679
-0.772
-0.842
0.453
0.153
0.445
0.196
10.030
0.515
10.232
1.567
119.312
8.366
123.457
16.477
0.000
0.000
0.000
0.000
168
168
168
168
Kurtosis2 Jarque-Bera3
Obs
Notes:
1. Skewness measures the asymmetry of the distribution of the series around its mean. The skewness of a normal distribution is zero.
2. Kurtosis measures the peakedness or flatness of the distribution of the series. The kurtosis of a normal distribution is 3. If the kurtosis exceeds 3,
the distribution is leptokurtic and if less than 3 platykurtic relative to the normal distribution.
3. The Jarque-Bera statistic summarizes the skewness and kurtosis measures, and tests whether the series is normally distributed. The numbers in the
table are p-values.
June 24-26, 2007
Oxford University, UK
25
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Table 2. VAR estimation results for Malaysia
Consumer Discretionary
Consumer Staples
Financials
Industrials
Constant
0.0561
(0.0331)
[ 1.69604]
0.0280
(0.0249)
[ 1.12454]
0.0508
(0.0398)
[ 1.27502]
0.0673
(0.0279)
[ 2.41728]
Consumer Discretionary
-0.2445
(0.1052)
[-2.32406]
-0.1640
(0.0793)
[-2.06760]
-0.2561
(0.1267)
[-2.02123]
-0.1923
(0.0887)
[-2.16895]
Consumer Staples
-0.0959
(0.1156)
[-0.82958]
-0.1633
(0.0872)
[-1.87298]
0.0655
(0.1393)
[ 0.47010]
-0.1940
(0.0975)
[-1.98989]
Financials
0.2254
(0.0948)
[ 2.37819]
0.1392
(0.0715)
[ 1.94745]
0.2753
(0.1142)
[ 2.41156]
0.2988
(0.0799)
[ 3.74128]
Industrials
0.0020
(0.1349)
[ 0.01485]
-0.0271
(0.1017)
[-0.26661]
-0.1210
(0.1625)
[-0.74495]
-0.0870
(0.1137)
[-0.76512]
Nikkei
0.1948
(0.1256)
[ 1.55030]
0.0237
(0.0947)
[ 0.24991]
0.3017
(0.1513)
[ 1.99383]
0.1903
(0.1059)
[ 1.79763]
Interest Rate
-0.8812
(0.5104)
[-1.72667]
-0.4194
(0.3848)
[-1.08992]
-0.8098
(0.6147)
[-1.31731]
-1.0812
(0.4302)
[-2.51334]
Inflation
4.2167
(2.5742)
[ 1.63808]
3.0593
(1.9410)
[ 1.57615]
5.1757
(3.1005)
[ 1.66930]
3.4216
(2.1697)
[ 1.57700]
Real Exchange Rate
1.4302
(0.3617)
[ 3.95451]
0.3968
(0.2727)
[ 1.45522]
2.8320
(0.4356)
[ 6.50134]
1.4880
(0.3048)
[ 4.88146]
Asian Crisis Dummy
-0.0277
(0.0200)
[-1.38301]
-0.0127
(0.0151)
[-0.84149]
-0.0187
(0.0241)
[-0.77552]
-0.0247
(0.0169)
[-1.46639]
0.1938
-1.5822
168
0.1023
-2.1469
168
0.2907
-1.2101
168
0.2787
-1.9241
168
R-squared
BIC
Observations
Note: Standard errors of the coefficient estimates are given in parentheses and the relevant t-statistics are given in
squared brackets. The t-critical values for the 1, 5, and 10 percent significance levels given the relevant degrees of
freedom are 2.607, 1.975, and 1.654 respectively.
June 24-26, 2007
Oxford University, UK
26
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Table 3. VAR estimation results for Thailand
Consumer Discretionary
Consumer Staples
Financials
Industrials
Constant
-0.1256
(0.0832)
[-1.50930]
0.0239
(0.0292)
[ 0.81965]
0.0208
(0.0797)
[ 0.26101]
0.0006
(0.0368)
[ 0.01684]
Consumer Discretionary
-0.0504
(0.0806)
[-0.62499]
0.0092
(0.0283)
[ 0.32492]
0.1466
(0.0772)
[ 1.89839]
0.0170
(0.0357)
[ 0.47764]
Consumer Staples
-0.1500
(0.2463)
[-0.60914]
-0.0142
(0.0865)
[-0.16367]
0.3844
(0.2360)
[ 1.62915]
-0.0030
(0.1090)
[-0.02742]
Financials
0.0037
(0.0846)
[ 0.04395]
-0.0031
(0.0297)
[-0.10352]
0.0404
(0.0810)
[ 0.49853]
0.0057
(0.0374)
[ 0.15345]
Industrials
-0.3039
(0.1882)
[-1.61484]
-0.0098
(0.0661)
[-0.14774]
0.0921
(0.1803)
[ 0.51060]
0.0299
(0.0833)
[ 0.35870]
Nikkei
-0.6818
(0.5733)
[-1.18942]
0.0338
(0.2013)
[ 0.16814]
-1.0418
(0.5492)
[-1.89691]
0.1620
(0.2538)
[ 0.63833]
Interest Rate
1.6479
(0.7844)
[ 2.10087]
-0.1359
(0.2754)
[-0.49340]
0.0283
(0.7515)
[ 0.03768]
-0.2421
(0.3473)
[-0.69724]
Inflation
-6.6194
(8.4429)
[-0.78402]
-1.8190
(2.9642)
[-0.61367]
-10.1957
(8.0889)
[-1.26045]
-0.1685
(3.7378)
[-0.04509]
Real Exchange Rate
-1.6177
(1.0044)
[-1.61056]
-0.2482
(0.3527)
[-0.70388]
-2.8060
(0.9623)
[-2.91585]
-0.6419
(0.4447)
[-1.44358]
Asian Crisis Dummy
0.1180
(0.0782)
[ 1.50817]
-0.0045
(0.0275)
[-0.16530]
0.0548
(0.0750)
[ 0.73137]
0.0220
(0.0346)
[ 0.63425]
0.0773
1.4712
168
0.0108
-0.6222
168
0.1231
1.3856
168
0.0323
-0.1584
168
R-squared
BIC
Observations
Note: Standard errors of the coefficient estimates are given in parentheses and the relevant t-statistics are given in
squared brackets. The t-critical values for the 1, 5, and 10 percent significance levels given the relevant degrees of
freedom are 2.607, 1.975, and 1.654 respectively.
June 24-26, 2007
Oxford University, UK
27
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Graph 3. Impulse response functions for Malaysia
Response to Cholesky One S.D. Innovations ± 2 S.E. in Malaysia
Response of CD to CD
Response of CD to CS
Response of CD to FI
Response of CD to IN
.12
.12
.12
.12
.08
.08
.08
.08
.04
.04
.04
.04
.00
.00
.00
.00
-.04
-.04
1
2
3
4
5
6
-.04
1
Response of CS to CD
2
3
4
5
6
-.04
1
Response of CS to CS
2
3
4
5
6
1
Response of CS to FI
.12
.12
.12
.08
.08
.08
.08
.04
.04
.04
.04
.00
.00
.00
.00
-.04
1
2
3
4
5
6
-.04
1
Response of FI to CD
2
3
4
5
6
2
Response of FI to CS
3
4
5
6
1
.12
.08
.08
.08
.08
.04
.04
.04
.04
.00
.00
.00
.00
-.04
3
4
5
6
-.04
1
Response of IN to CD
2
3
4
5
6
2
Response of IN to CS
3
4
5
6
1
Response of IN to FI
.12
.12
.08
.08
.08
.08
.04
.04
.04
.04
.00
.00
.00
.00
-.04
2
3
4
June 24-26, 2007
Oxford University, UK
5
6
-.04
1
2
3
4
5
6
3
4
5
6
2
3
4
5
6
Response of IN to IN
.12
1
6
-.04
1
.12
-.04
5
Response of FI to IN
.12
2
2
Response of FI to FI
.12
1
4
-.04
1
.12
-.04
3
Response of CS to IN
.12
-.04
2
-.04
1
2
3
28
4
5
6
1
2
3
4
5
6
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Graph 4. Impulse response functions for Thailand
Response to Cholesky One S.D. Innovations ± 2 S.E. in Thailand
Response of CD to CD
Response of CD to CS
Response of CD to FI
Response of CD to IN
.6
.6
.6
.6
.5
.5
.5
.5
.4
.4
.4
.4
.3
.3
.3
.3
.2
.2
.2
.2
.1
.1
.1
.1
.0
.0
.0
.0
-.1
-.1
-.1
-.1
-.2
-.2
1
2
3
4
5
6
-.2
1
Response of CS to CD
2
3
4
5
6
-.2
1
Response of CS to CS
2
3
4
5
6
1
Response of CS to FI
.6
.6
.6
.5
.5
.5
.5
.4
.4
.4
.4
.3
.3
.3
.3
.2
.2
.2
.2
.1
.1
.1
.1
.0
.0
.0
.0
-.1
-.1
-.1
-.1
-.2
1
2
3
4
5
6
-.2
1
Response of FI to CD
2
3
4
5
6
2
Response of FI to CS
3
4
5
6
1
.6
.5
.5
.5
.5
.4
.4
.4
.4
.3
.3
.3
.3
.2
.2
.2
.2
.1
.1
.1
.1
.0
.0
.0
.0
-.1
-.1
-.1
-.1
-.2
3
4
5
6
-.2
1
Response of IN to CD
2
3
4
5
6
2
Response of IN to CS
3
4
5
6
1
Response of IN to FI
.6
.6
.5
.5
.5
.5
.4
.4
.4
.4
.3
.3
.3
.3
.2
.2
.2
.2
.1
.1
.1
.1
.0
.0
.0
.0
-.1
-.1
-.1
-.1
-.2
2
3
4
June 24-26, 2007
Oxford University, UK
5
6
-.2
1
2
3
4
5
6
3
4
5
6
2
3
4
5
6
Response of IN to IN
.6
1
6
-.2
1
.6
-.2
5
Response of FI to IN
.6
2
2
Response of FI to FI
.6
1
4
-.2
1
.6
-.2
3
Response of CS to IN
.6
-.2
2
-.2
1
2
3
29
4
5
6
1
2
3
4
5
6
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Table 4. VAR estimation results for Consumer Discretionary
Malaysia
Thailand
Constant
0.0177
(0.0118)
[ 1.50016]
0.0133
(0.0509)
[ 0.26099]
Malaysia
-0.0233
(0.0747)
[-0.31166]
-0.4595
(0.3223)
[-1.42575]
Thailand
-0.0276
(0.0180)
[-1.52909]
-0.0802
(0.0778)
[-1.03041]
Nikkei
0.2128
(0.1325)
[ 1.60641]
-0.6745
(0.5714)
[-1.18046]
Asian Crisis Dummy
-0.0172
(0.0162)
[-1.06232]
0.0245
(0.0699)
[ 0.35096]
0.0365
-1.5565
168
0.0314
1.3673
168
R-squared
BIC
Observations
Note: Standard errors of the coefficient estimates are given in parentheses and the relevant
t-statistics are given in squared brackets. The t-critical values for the 1, 5, and 10 percent
significance levels given the relevant degrees of freedom are 2.607, 1.975, and 1.654
respectively.
June 24-26, 2007
Oxford University, UK
30
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Table 5. VAR estimation results for Consumer Staples
Malaysia
Thailand
Constant
0.0128
(0.0084)
[ 1.51211]
0.0083
(0.0174)
[ 0.47344]
Malaysia
-0.1827
(0.0781)
[-2.33858]
-0.2439
(0.1614)
[-1.51092]
Thailand
-0.0185
(0.0399)
[-0.46365]
0.0121
(0.0825)
[ 0.14700]
Nikkei
0.0180
(0.0946)
[ 0.19035]
0.0774
(0.1955)
[ 0.39604]
Asian Crisis Dummy
-0.0080
(0.0116)
[-0.68902]
0.0014
(0.0239)
[ 0.05896]
0.0380
-2.2302
168
0.0143
-0.7782
168
R-squared
BIC
Observations
Note: Standard errors of the coefficient estimates are given in parentheses and the relevant
t-statistics are given in squared brackets. The t-critical values for the 1, 5, and 10 percent
significance levels given the relevant degrees of freedom are 2.607, 1.975, and 1.654
respectively.
June 24-26, 2007
Oxford University, UK
31
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Table 6. VAR estimation results for Financials
Malaysia
Thailand
Constant
0.0214
(0.0149)
[ 1.43802]
-0.0242
(0.0485)
[-0.49994]
Malaysia
0.0837
(0.0775)
[ 1.08008]
0.8693
(0.2526)
[ 3.44109]
Thailand
0.0531
(0.0239)
[ 2.22635]
0.0536
(0.0777)
[ 0.69001]
Nikkei
0.3087
(0.1670)
[ 1.84881]
-0.7713
(0.5441)
[-1.41752]
Asian Crisis Dummy
-0.0206
(0.0205)
[-1.00749]
0.0673
(0.0667)
[ 1.00957]
0.0762
-1.0984
168
0.0954
1.2642
168
R-squared
BIC
Observations
Note: Standard errors of the coefficient estimates are given in parentheses and the relevant
t-statistics are given in squared brackets. The t-critical values for the 1, 5, and 10 percent
significance levels given the relevant degrees of freedom are 2.607, 1.975, and 1.654
respectively.
June 24-26, 2007
Oxford University, UK
32
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Table 7. VAR estimation results for Industrials
Malaysia
Thailand
Constant
0.0132
(0.0106)
[ 1.24662]
-0.0212
(0.0223)
[-0.95231]
Malaysia
0.1048
(0.0774)
[ 1.35428]
0.1209
(0.1631)
[ 0.74142]
Thailand
0.0272
(0.0377)
[ 0.72113]
0.0377
(0.0794)
[ 0.47443]
Nikkei
0.1690
(0.1179)
[ 1.43398]
0.2089
(0.2484)
[ 0.84098]
Asian Crisis Dummy
-0.0095
(0.0145)
[-0.65416]
0.0301
(0.0305)
[ 0.98404]
0.0346
-1.7851
168
0.0162
-0.2945
168
R-squared
BIC
Observations
Note: Standard errors of the coefficient estimates are given in parentheses and the relevant
t-statistics are given in squared brackets. The t-critical values for the 1, 5, and 10 percent
significance levels given the relevant degrees of freedom are 2.607, 1.975, and 1.654
respectively.
June 24-26, 2007
Oxford University, UK
33
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Graph 5. Impulse response functions for Financials
Response to Cholesky One S.D. Innovations ± 2 S.E. in Financials
Response of Malaysia to Thailand
Response of Malaysia to Malaysia
.5
.5
.4
.4
.3
.3
.2
.2
.1
.1
.0
.0
-.1
-.1
1
2
3
4
5
1
6
2
3
4
5
6
Response of Thailand to Thailand
Response of Thailand to Malaysia
.5
.5
.4
.4
.3
.3
.2
.2
.1
.1
.0
.0
-.1
-.1
1
2
June 24-26, 2007
Oxford University, UK
3
4
5
1
6
34
2
3
4
5
6