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INTERNATIONAL ECONOMIC JOURNAL 121
Volume 10, Number 2, Summer 1996
MONEY, OUTPUT AND STOCK PRICES IN MALAYSIA: AN
APPLICATION OF THE COINTEGRATION TESTS
MUZAFAR SHAH HABIBULLAH*
Universiti Pertanian Malaysia
AHMAD ZUBAIDI BAHARUMSHAH
Universiti Pertanian Malaysia
The purpose of this paper is to determine whether macroeconomic variables, in
particular money supply and output are important in predicting stock prices in
Malaysia. Monthly data on stock price indices, money supply and output were
employed in this study. The stock price indexes used in this study are Composite,
Industrial, Finance, Property, Plantation and Tin. For money supply we used both M1
and M2, and output is measured by real Gross Domestic Product (GDP). Our results
suggest that Malaysia’s stock market is informationally efficient with respect to
money supply as well as output. [E17]
1. INTRODUCTION
The finance literature contains a large volume of empirical investigation on
financial market integration and interdependencies among national stock markets.
Recently the focus has been directed towards the investigation on the efficiency of
stock markets in the Asian-Pacific region. Among some of these studies include those
by Cheung and Mak (1992), Cha and Cheung (1993) and Cheung and Ho (1989).
Cheung and Mak (1992) and Cha and Cheung (1993) found that some markets in the
region tend to move closely together with those of the U.S. and Japan. Cha and
Cheung (1993), employing the vector autoregression (VAR) approach showed that
both the U.S. and Japan stock markets have strong influence on Hong Kong and
Singapore markets. A similar conclusion is reached by Cheung and Ho (1989) where
they reported that U.S. stock market Granger cause the national markets of Australia,
Singapore and Malaysia. All these studies seem to indicate that the financial markets
are integrated.
More recently, studies on the relationship between macroeconomic variables and
national stock market has been given greater emphasis in the literature. Sprinkel
(1964) who pioneered the study on the relationship between money supply and stock
market concluded that there is strong relationship between the stock market and
*The authors would like to thank an anonymous referee for helpful comments and
suggestions on the earlier drafts of this paper. All remaining errors are sole responsibility of the
authors.
money supply in the United States. Since then the money supply-stock market nexus
has been widely tested for various economies because of the belief that money supply
changes have important direct effects through portfolio changes, and indirect effects
through their effects on real economic activity, which in turn postulated to be
fundamental determinants of stock prices. Among the more recent studies on money
supply-stock market relationships are those by Malliaris and Urrutia (1991) on United
S t a t es; Mookerjee (1987) on stock markets of France, United States, Japan, Italy,
Canada, Germany, United Kingdom, the Netherlands, Belgium and Switzerland;
and the study by Jeng, et al. (1990) that investigated the stock markets of Belgium,
Britain, Canada, Czechoslovakia, France, Hungary, Japan, Poland, Sweden and
United States.
While most of these studies have examined the effects of money supply on stock
prices, very few have examined the relationship between national output and stock
prices. For example, Fama (1981) showed that there is strong relationship between
stock prices and industrial production as well as gross national product. Malliaris and
Urrutia (1991) found that money supply leads the stock market and the performance
of stock market may be used as a leading indicator for measuring real economic
activities in United States. In related work, Chang and Pinegar (1989) and Chen et al.
(1986) also concluded that there is a close relationship between the stock market and
the domestic economic activity. Thornton (1993) also reached at the same conclusion
for the United Kingdom.
For the developing economies, Fung and Lie (1990) showed that Taiwan’s stock
market is closely related with both the money supply and national output. Their
conclusion is further supported by Lin (1993) who found that the growth in money
supply can be used to predict the Taiwanese stock market. Lin’s (1993) work also
showed that both the Korean and Singaporean markets are closely related with money
supply. In the former, money supply leads the stock market, but for the later, stock
market leads money supply. Ho (1983) employing Hsiao’s (1981) method to test for
market efficiency in the the Asian-Pacific countries found that the information on
money supply is useful in predicting stock prices in Hong Kong, Japan, the
Philippines, Australia and Thailand. However, the efficient markets hypothesis
cannot be rejected for the Singaporean case. In another study, Mak and Cheung
(1991) investigated the relationship between United States money supply and the
Asian-Pacific stock markets, namely; Australia, Hong Kong, Japan, Korea, Malaysia,
New Zealand, the Philippines, Singapore, Taiwan and Thailand. They obtained
results that offer support for the efficient markets hypothesis.
The empirical evidence provided by the studies mentioned above showed that
macroeconomic variables, in particular money supply and national output have strong
effects on the stock market. In other words, national stock market are said to be
informationally inefficient with respect to both money supply and output. The
question whether stock price leads money supply and output or otherwise is an
important issue and the answer can only be determined through empirical research. If
market is inefficient with respect to information (for example, money supply and
M. S. HABIBULLAH AND A. Z. BAHARUMSHAH122
output) then it has important implications both at micro and macro levels. At the
micro level, this implies that individual investor can earn consistently higher than
normal rate of returns from the stock market. At the macro level, it raises serious
doubts on the ability of the market to perform its fundamental role of channelling
funds to the most productive sectors of the economy.
The primary purpose of this study is to determine whether macroeconomic
variables, in particular money supply and national output can be used to predict the
stock prices in Malaysia. In other words, we intend to test for the informational
efficient markets (IEM) hypothesis as proposed by Fama (1970). In this study, we
employed the cointegration approach to test the hypothesis. If Malaysia’s stock
market is informationally inefficient with respect to both money supply and output,
abnormal profit may be obtained consistently by using information on the changes in
these variables. Rejecting the hypothesis will suggest that certain policies need to be
formulated and implemented to improve the performance of the market. The paper is
organised as follows. In the next section, we discuss the testing for integration and
cointegration. Next, the empirical results are presented and discussed. The final
section contains our conclusion.
2. METHODOLOGY
Nelson and Plosser (1982) have pointed out that many macroeconomic variables
are non-stationary in levels and contain in themselves a unit root (stochastic trend).
Phillips and Perron (1988) provide a non-parametric method of detecting whether a
time series contain a unit root. This test is robust to a wide variety of serial
correlation and time dependent heteroskedasticity. The tests involved estimating the
following equations for a variable, say Yt,
where ∆Ytdenotes the first-difference of Ytand tis a deterministic time trend. In
Equation (1), for Ytto be stationary, the adjusted t-statistic Z(tα) should be negative
and significantly different from zero.1For Ytto be stationary around a linear trend in
Equation (2), the adjusted t-statistic Z(tβ) should be negative and significantly
MONEY, OUTPUT AND STOCK PRICES 123
∆Yt = µ
1
+ αYt-1 + ε1t
∆Yt = µ
2
+ θt + βYt-1 + ε2t
1See Phillips and Perron (1988) for the details on the estimation procedure and the
distribution of the test statistics.
(1)
(2)
different from zero. The critical values of Z(tα) and Z(tβ) are tabulated in MacKinnon
(1991).
The unit root test above will be able to determine the order of integration of the
variables involved. This is important because cointegration tests require that all
variables involved be integrated of the same order (Granger, 1986; Engle and
Granger, 1987).2To test for cointegration for a 3-variable case, the following
cointegrating regression is estimated,
Following Engle and Granger (1987), a test for a unit root in the estimated
residuals ηt,determines the absence (or presence) of cointegration. Using the
Phillips-Perron non-parametric approach, the unit root test is conducted on the
residual ηtas follows
If the adjusted t-statistic Z(tα) is negative and significantly different from zero, the null
hypothesis of no cointegration can be rejected. Apart form using Z(tα), we also report
Phillips and Ouliaris (1990) Z(α) test statistic. Phillips and Ouliaris (1990) have
demonstrated that Z(α) test has superior power properties in small samples and
therefore should be used to test for cointegration. The critical values for Z(α) and
Z(tα) are tabulated in Phillips and Ouliaris (1990).
Data Used in the Study
In this study, we used 177 monthly data on stock price indices, money supply and
national output that spans from January 1978 to September 1992. The stock price
indexes used in this study are Composite, Industrial, Finance, Property, Plantation and
2Consider two non-stationary series Xtand Yt.They are called integrated of order one,
denoted by I(1), if they become stationary after first-difference. It is generally true that a linear
combination, Xt-AYt, where Ais a constant, is also non-stationary. However, if there exists a
constant Asuch that this linear combination is I(0) rather than I(1), then Xtand Ytare said to be
cointegrated and Ais called the cointegrating vector. According to Granger (1986), ‘an I(0)
series has a mean and there is tendency for the series to return to the mean, so that it tends to
fluctuate around the mean, crossing the value frequently and with rare extensive excursions.’
M. S. HABIBULLAH AND A. Z. BAHARUMSHAH124
Xt = γ0
+ γ
1
Y
t
+ γ2
Q
t
+ η
t
(3)
∆ηt = αηt-1
+ v
t
(4)
Tin. For money supply, we used both definition of money supply, that is, narrow
money (M1) and broad money (M2). M1 includes currency in circulation and demand
deposits held by non-bank private sector and M2 consists of M1 plus saving and fixed
deposits, negotiable certificate of deposits and repos held at the commercial banks.
Real output (Y) is measured by gross national product (GDP) deflated by the
consumer price index. Since data on GDP is available only in annual basis, we
extrapolated the annual GDP into monthly GDP using a method proposed by
Gandolfo (1981). All data used in this study were compiled from various issues of
Quarterly Bulletin published by Bank Negara Malaysia. All the series were
transformed into natural logarithm.
3. THE EMPIRICAL RESULTS
We carried out the integration tests outline in the previous section on all the
variables, first on levels and then on first-differences. After determining the order of
integration, we then conduct the cointegration tests to examine the relationship
between stock prices and macroeconomic variables.
MONEY, OUTPUT AND STOCK PRICES 125
Table 1. Tests for the Order of Integration for Series in Levels
Variables Test Truncation Lag Parameters
Statistics l4= 4 l12= 13
Composite Z(tα) –2.30 –2.29
Z(tβ) –2.70 –2.71
Industrial Z(tα) –2.08 –2.04
Z(tβ) –2.95 –2.94
Finance Z(tα) –2.53 –2.55
Z(tβ) –2.74 –2.73
Property Z(tα) –2.65 –2.66
Z(tβ) –2.20 –2.28
Plantation Z(tα) –3.48* –3.51*
Z(tβ) –3.32 –3.24
Tin Z(tα) –2.25 –2.31
Z(tβ) –2.53 –2.58
M1 Z(tα) –0.70 –0.70
Z(tβ) –2.08 –2.12
M2 Z(tα) –1.77 –1.75
Z(tβ) –2.38 –2.38
YZ(tα) –1.41 –1.30
Z(tβ) –2.00 –2.43
Notes: Critical values are form MacKinnon (1991). For 177 observations, the critical values
for Z(tβ) and Z(tα) at 5 percent level are –3.43 and –2.87 respectively. Star (*) indicates
statistically significant at 5 percent level.
Table 1 presents the results of the unit root tests on the level of the series.
Following Schwert’s (1987) formula, with T=177, the truncation lag parameters are l4
= 4 and l12 = 13.3Therefore, we will use the truncation lags of 4and 13 throughout
the analysis. The results from estimating Equation (2) shows that none of the series is
able to reject the null hypothesis of unit root. In all cases, the test statistics Z(tβ) are
larger than the critical value of –3.43 tabulated in MacKinnon (1991) at five percent
level of significance. This suggests that all the series has unit root with non-linear
trend. The non-rejection of the null hypothesis in Equation (2) suggest that Equation
(1) is more appropriate to represent the data.
Results of the test statistics Z(tα) for all series using Equation (1) clearly show that,
except for Plantation stock index, the null hypothesis of unit root cannot be rejected.
As for the Plantation stock index, the test statistics Z(tα) are significantly different
from zero at five percent level for both lag parameters 4 and 13, indicating strongly
that the series is stationary in level. These results indicate that all the series under
study except for Plantation stock index are non-stationary in their level form.
In Table 2 we show the results of unit root tests on the first-difference of the series
by estimating Equation (1). As shown in Table 2, none of the series is able to reject a
unit root in first-difference. The Z(tα) statistics for all series are significantly different
from zero at five percent level. Therefore, we conclude that all series except
Plantation stock index are I(1) processes. We next proceed to test whether the linear
combination between the stock price indexes and both money supply and output are
M. S. HABIBULLAH AND A. Z. BAHARUMSHAH126
3Schwert’s (1987) criteria sets the lag length equal to the integer portion of two values of
l, that is, l4= int{4(T / 1 0 0)1 / 4 } and l1 2 = int{1 2(T / 1 0 0)1 / 4}, where T is the number of
o b s e r v a t i o n s .
Table 2. Tests for the Order of Integration for Series in First-Difference
Variables Test Truncation Lag Parameters
Statistics l4= 4 l12= 13
Composite Z(tα) –11.68* –11.61*
Industrial Z(tα) –12.32* –12.28*
Finance Z(tα) –13.22* –13.21*
Property Z(tα) –11.67* –11.86*
Tin Z(tα) –12.62* –12.57*
M1 Z(tα) –14.57* –15.14*
M2 Z(tα) –12.71* –12.81*
YZ(tα) –6.59* –7.78*
Notes: Critical values are as per Table 1.
stationary in level. In other words, we test for cointegration.
The results of the trivariate cointegration tests are presented in Table 3 for
Composite, Industrial, Finance, Property and Tin sectors. Further, we also estimate
the cointegrating regression with the inclusion of time trend. Campbell and Perron
(1991) has indicated that the inclusion of time trend in a cointegrating regression are
necessary and possess several advantages over the one without. Among others it
allows for the presence of I(0) series with non-zero trends, it avoids the dependence
MONEY, OUTPUT AND STOCK PRICES 127
Table 3. Results of Cointegration Tests
Cointegrating Regressions Test M1 M2
Statistics l4= 4 l12= 13 l4= 4 l12= 13
1. Cointegrating Regressions without Time Trend
Composite = f(Mi, Y) i = 1,2 Z(α) –16.17 –15.57 –16.21 –15.67
Z(tα) –2.99 –2.94 –2.98 –2.93
Industrial = f(Mi, Y) i = 1,2 Z(α) –23.55 –21.62 –24.27 –22.26
Z(tα) –3.52 –3.38 –3.57 –3.43
Finance = f(Mi, Y) i = 1,2 Z(α) –16.01 –16.06 –14.77 –15.21
Z(tα) –3.11 –3.11 –2.90 –2.94
Property = f(Mi, Y) i = 1,2 Z(α) –8.77 –9.82 –7.89 –9.15
Z(tα) –2.19 –2.31 –2.05 –2.20
Tin = f(Mi, Y) i = 1,2 Z(α) –13.62 –14.16 –16.10 –16.26
Z(tα) –2.63 –2.68 –2.87 –2.88
2. Cointegrating Regressions with Time Trend
Composite = f(Mi, Y, Time) i = 1,2 Z(α) –31.37 –27.35 –23.23 –20.63
Z(tα) –4.14 –3.89 –3.47 –3.28
Industrial = f(Mi, Y, Time) i = 1,2 Z(α) –28.98 –25.40 –25.04 –22.43
Z(tα) –3.90 –3.66 –3.60 –3.42
Finance = f(Mi, Y, Time) i = 1,2 Z(α) –19.04 –18.86 –24.65 –23.47
Z(tα) –3.32 –3.30 –3.53 –3.45
Property = f(Mi, Y, Time) i = 1,2 Z(α) –15.24 –14.87 –11.83 –11.81
Z(tα) –2.75 –2.72 –2.27 –2.27
Tin = f(Mi, Y, Time) i = 1,2 Z(α) –17.22 –16.79 –17.30 –17.23
Z(tα) –2.93 –2.89 –2.94 –2.94
Notes: Critical values for cointegrating regression without time trend; for Z(α) and Z(tα) are
–25.34 and –3.79 respectively (see Tables Ib and IIb of Phillips and Ouliaris, 1990). Critical
values for cointegrating regression with time trend; for Z(α) and Z(tα) are –32.22 and –4.16
respectively (see Tables1 and 2 of Hansen, 1992).
of the limiting distribution of the statistics on the trend characteristics of the data
and it provides one to test the hypothesis of stochastic cointegration.4F u r t h e r m o r e ,
Hansen (1992) has pointed that many macroeconomic series are appropriately
described by I(1) with drift, which is the sum of an I(1) process with zero-mean
increments and a linear trend. Therefore, excluding time trend from the
cointegrating regression will increase the likelihood of rejection of the null when
the data is cointegrated.
Results of our study show that in all cases, the computed Z(α) and Z(tα) statistics
are larger than the critical values tabulated in Phillips and Ouliaris (1990) and Hansen
(1992) respectively. The results of the study tend to suggest that the KLSE stock
market are efficient with respect to both money supply and output variables. It
therefore, indicates that the current stock prices already incorporated all past and
current information of the macroeconomic variables under study.
4. CONCLUSION
The efficient markets hypothesis (EMH) was formalized by Fama (1970). The
hypothesis suggests that changes in the money supply or national output cannot be
used as a trading rule by investors to earn consistently abnormal profits in the stock
market. In an efficient market, current as well as past information on the growth of
these important macro-variables are fully reflected in asset prices so that investors
are unable to formulate some profitable trading rule using the available
i n f o r m a t i o n .
The purpose of our study is to examine the informational efficiency of the stock
prices in the Kuala Lumpur Stock Exchange (KLSE). An informationally inefficient
market will suggest that the growth of money supply and/or national output can be
used as a trading rule to predict stock prices and market participants can earn
abnormal profit consistently. To test for effciency markets hypothesis, we adopted the
two-step trivariate cointegration approach suggested by Engle and Granger (1987) in
our analysis.
The following results were obtained from our study. First, the results indicate that
except for Plantation stock index which is I(0), all other stock price indexes are non-
stationary in level, that is, they are I(1) processes. And secondly, the trivariate
cointegration analysis suggest that stock price indexes and macroeconomic variables,
in particular money supply and national output are not cointegrated. This suggests
that stock price indexes in the Kuala Lumpur Stock Exchange has already
incorporated all past information on both money supply (M1 and M2) and output.
M. S. HABIBULLAH AND A. Z. BAHARUMSHAH128
4For further discussion on the concept of stochastic cointegration and deterministic
cointegration, see Campbell and Perron (1991).
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M. S. HABIBULLAH AND A. Z. BAHARUMSHAH130
Mailing Address: Professor Muzafar Shah Habibullah, Department of Economics,
Faculty of Economics and Management, Universiti Pertanian Malaysia, 43400 UPM
Serdang, Selangor, MALAYSIA
Mailing Address: Professor Ahmad Zubaidi Baharumshah, Department of
Economics, Faculty of Economics and Management, Universiti Pertanian Malaysia,
43400 UPM Serdang, Selangor, MALAYSIA
