Journal of Contemporary Issues in Business Research
Volume 3, Issue No. 2, 2014
© Journal of Contemporary Issues in Business Research
ISSN 2305-8277 (Online), 2014, Vol. 3, No. 2, 88-99.
Copyright of the Academic Journals JCIBR
All rights reserved.
CAUSAL RELATIONSHIP BETWEEN MACROECONOMIC
VARIABLES: EVIDENCE FROM DEVELOPING ECONOMY *
ABDUL RAFAY, FARAH NAZ & SAMAN RUBAB†
School Of Business and Economics, University of Management and
Technology
ABSTRACT
Importance of stock market in the economic development of a country cannot
be denied, and macroeconomic variables are important indicators that affect
stock market of a country. Present study provides a great contribution to
understand the association of these variables with stock market. This paper
deals with the causal relationship among KSE 100 index and interest rate,
exchange rate, consumer price index, imports and exports. For this purpose
data of nineteen years has been collected from 1992 to 2010. Techniques of
Augmented Dickey-Fuller test, regression analysis and Granger Causality test
have been applied to examine the causal relationship of selected
macroeconomic variables with KSE 100 index. Results of regression analysis
indicate the presence of strong positive relation between IMP and KSEI.
Furthermore, interest rate, exchange rate, consumer price index and exports
have no relationship with KSE 100 index. Results of Granger Causality test
demonstrate that bi-directional relationship exists between interest rate and
KSE 100 index. Exchange rate and imports have uni-directional relationship
with KSE 100 index and no causal relationship exists between consumer price
index, exports and KSE 100 index. Present study provides valuable
contribution in knowledge. It is important and attractive not only for investors
but also for policy makers.
Keywords: Inflation; KSE 100 index; Interest Rate; Exchange Rate;
Consumer Price Index; Imports; Exports.
INTRODUCTION
Efficient market is characterized as that market in which security prices give very
quick response to new information. In this way current prices of security provide complete
information about security. In efficient market every investor has complete knowledge of
market. He is well aware of all the new information arriving in the market. No investor can
earn extra profit unless he or she has inside information. According to economic theory stock
prices should give an idea about future performance of a corporation. Corporate profits
normally represent the stage of economic activity. If the stock prices cover all the basic
essentials then stock prices can be considered as important sign of upcoming economic
*
The views or opinions expressed in this manuscript are those of the author(s) and do not necessarily reflect the
position, views or opinions of the editor(s), the editorial board or the publisher.
†
Corresponding author
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Journal of Contemporary Issues in Business Research
Volume 3, Issue No. 2, 2014
activity (Chong & Goh, 2003; Maysami, Howe, & Hamzah, 2004). Present study is based on
the association of macroeconomic variables with sock market with reference to Pakistan.
Importance of stock exchanges is greatly increasing day by day as an indicator of
economic growth. It is an important debatable issue in economics that whether economic
growth is result of financial development or whether it is an outcome of improved economic
activity. Deep study of all these factors indicates that they are all interrelated in a sense that
technological improvement is the main cause of long-term economic growth and these
technological improvements are due to easy availability of credit to entrepreneurs. It is an
important area of research to study the relationship between all these factors. In order to
design macroeconomic policies, role of association of macroeconomic indicators with stock
market is very important. Present study can be very helpful for policy makers keeping in view
influence of macroeconomic changes on stock market in a developing economy. Domestic
and foreign investors can also get benefit from present literature in investment decisions.
Many reforms have been introduced in the financial sector of Pakistan. These reforms
have resulted in improvement in efficiency of financial sector of Pakistan. Capital market of
Pakistan is composed of three stock exchanges. First and the largest is Karachi Stock
Exchange (KSE) 100-index established in 1949. Second largest and important stock exchange
of Pakistan is Lahore Stock Exchange (LSE) 25-index. Third important stock exchange is
Islamabad Stock Exchange (ISE) 10-index. Role of KSE is very important in capital market
growth. It has become the most important indicator of capital market condition. There are
four categories of companies in KSE which represent all sectors of Pakistani economy. In
order to create a balanced growth between capital markets and financial sector many reforms
have been introduced in Pakistan. These reforms are helpful to control the crisis of bankdominated financial markets.
There are many macroeconomic factors which influence stability and growth of stock
market. Present study is based on those important macroeconomic factors which play
important role in the development of stock market in an economy. Macro economic variables
discussed in present study include Interest Rate (IR), Exchange Rate (EXR), Consumer Price
index (CPI) as proxy of inflation rate, Imports (IMP) and Exports (EXPT).
Objective of Study
Purpose of this literature is to determine the casual association between selected
macroeconomic variables and KSE 100 index using important tests and techniques.
1. To determine causal relationship between macroeconomic variables and KSE 100
index
2. To assess the strength of association between macroeconomic variables and KSE 100
index
3. To find out the dependence of KSE 100 index on macroeconomic variables
Present study is ordered as follows. Section I represents the introduction of topic
under discussion. Section II represents the study that has already been done by different
economists, analysts and etc. Section III describes the theoretical framework. Section IV
discusses research methodology, data and variables, data description, sources of data and
hypothesis. Section V includes results of study, findings. Last section includes conclusion
and recommendations.
LITERATURE REVIEW
Relationship between macroeconomic variables and stock market is an important area
of research addressed by many researchers nationally and internationally. Previous studies
reveal that affiliation between the both is strong in developed countries as compared to
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Journal of Contemporary Issues in Business Research
Volume 3, Issue No. 2, 2014
undeveloped and under developing countries. This section has designed to provide valuable
information regarding this relationship on the basis of previous studies.
Study of literature reveals that a lot of research has been done to study the effect of
macroeconomic variable and stock market with reference to Indian Stock market.(Tripathy,
2011) examined the causal relationship between interest rate, international market, exchange
rate, inflation rate and stock market. He took weekly data of five years from 2005 to 2011. He
applied the techniques of Granger causality test, Ljung-Box Q statistics and Unit Root test.
He found that autocorrelation exists between all these selected variables and Indian Stock
market. All these selected variables are helpful to predict future changes in stock prices.
(Oseni & Nwosa, 2011) found the effects of changes in real GDP, inflation rate and
interest rates on stock market volatility. They implemented AR (k)-EGARCH (p, q) and LAVAR Granger Causality test. These researchers used secondary data. Their studies
demonstrated bi-causal relationship of changes in stock market with changes in real GDP and
no causal relationship with changes in inflation rate.
(Samadi, Bayani, & Ghalandari, 2012) studied the relationship between
macroeconomic variables like inflation rate, gold price, liquidity, foreign exchange rate and
oil prices on stock return with reference to Tehran Stock Exchange. They took monthly data
from 2001 to 2010 and applied GARCH approach. Results of study revealed that stock
returns have relationship with rate of inflation, foreign exchange rate and prices of gold and
no relationship with oil prices and liquidity.
(Pilinkus, 2009) conducted an important research to check the influence of 40
macroeconomic indicators on stock market index with reference to Lithuania. He used
monthly data from 1999 to 2008 and implemented Granger causality test to check the
relationship. Results of study confirm that stock market returns and macroeconomic
indicators are interrelated and influence each other.
(Gay, 2008) focused on emerging economies like China, Russia, India and Brazil to
examine the connection of macroeconomic indicators with stock market index prices. He
found that macroeconomic indicators like rate of foreign exchange and oil prices have no
significant relationship with stock price with respect to all these countries. He said that there
are many national and international factors which may affect this association of
macroeconomic variables with stock prices.
(Herve, Chanmalai, & Shen, 2011) examined the relationship of five important
macroeconomic indicators with stock prices. Results of their study indicated that stock prices
are influenced by mainly domestic rate of interest and consumer price index. (Dr., 2011) done
very detailed study to observe the causal association of exchange rate with stock indices. In
his study stock indices included financial, national, technological, service and industrial
indices. Findings of his study revealed that exchange rate has bi-directional causal
relationship with stock market indices. He concluded that industrial, national, financial and
service indices have negative relationship and technology indices have positive relationship
with exchange rate whereas exchange rate has negative relationship with all stock indices.
(M. B. Ali, 2011) studied the effect of fluctuation in micro and macroeconomic
indicators on stock returns. His study was focused on Dhaka Stock Exchange. They
implemented model of multivariate regression to study the association. They found that
foreign remittance and inflation rate have negative relationship with stock prices whereas
stock prices are positively related with rate of capitalization, market price per earnings and
industrial production index.
(Maysami et al., 2004) focused on sector indices instead of composite indices of stock
market. Their study was based on three sector indices of Singapore Stock Exchange like
finance, hotel, and property indices along with overall stock index of Singapore Stock
Exchange. Results of study determined that Singapore Stock Market index and Property
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Journal of Contemporary Issue
ssues in Business Research
Volume 3, Is
Issue No. 2, 2014
index have bi-directional signi
gnificant relationship with all identified variabl
ables as compared to
finance index and hotel index.
ex. Finance index has no relationship with mon
oney supply and real
economic activity whereas ho
hotel index has no relationship with interes
rest rate and money
supply.
(GENÇTÜRK, ÇELİK
LİK, & BİNİCİ, 2012) examined the effect
eff
of important
macroeconomic variables onn stock prices with respect to Istanbul Stock
ock Exchange. They
found that stock prices have uuni-directional relationship with industrial production.
pro
(Hussain,
Aamir, Rasool, Fayyaz, & Mumtaz,
Mu
2012) analyzed affiliation of macroe
roeconomic variables
and Karachi Stock Exchange
nge. Results of their study demonstrated th
that money supply,
wholesale price index, foreign
gn exchange reserves, interest rate, and imports
rts are positively and
considerably related with stock
st
prices, exports and exchange rate are
ar negatively and
insignificantly related with stock
st
prices and industrial production index
dex has considerable
negative association with stock
ock prices.
(Singh, 2010) observed
ved the underlying connection of Bombay Stock
Sto Exchange with
important macroeconomic vvariables. He found that stock index has
ha strong bilateral
relationship with index off iindustrial production, significant unilateral
ral relationship with
wholesale index but no relatio
ationship with exchange rate. (I. Ali, Rehman,
an, Yilmaz, Khan, &
Afzal, 2010) provided their
ir great contribution by examining the cau
ausal association of
important macroeconomic vari
ariables with stock prices on the basis of Karac
rachi Stock Exchange
in Pakistan. Results of theirr studies
s
demonstrated bi-causal association of stock prices with
index of industrial productio
tion. They found no causal association off stock prices with
macroeconomic variables in Pa
Pakistan.
Above literature provi
vide a sound base for importance of present
nt study. In order to
provide great contribution in knowledge present study combines thosee important
im
variables
which have studied by differen
rent researchers in different economies. Importa
ortance of developing
economies is increasing day
ay by day for investors as compared to deve
eveloped economies.
Developing economies provid
vide attractive opportunities of investment not
ot only for domestic
investors but also for foreignn investors.
in
T
THEORETICAL
FRAMEWORK
FIGURE 1
Possible “System
tematic Diagram” based on Conceptual Framew
ework
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Journal of Contemporary Issues in Business Research
Volume 3, Issue No. 2, 2014
RESEARCH METHODOLOGY
This study examines the causal relationship of CPI, exchange rate, interest rate of
three months treasury bills, imports and exports with KSE 100 index. This relationship has
been determined using different techniques. Augmented Dickey-Fuller test has been applied
to test stationarity of variables. Regression Analysis has been done to determine the
relationship between dependent and independent variables. Granger Causality test is applied
to test the causality of relationship among the variables.
Data Collection
Annual data has been used for the period of 1992 to 2010. Data has been collected
from different sources. Data related to CPI, imports and exports has been gathered from
Economic Survey of Pakistan. Exchange rate data has been collected from State Bank of
Pakistan’s website. Interest rate of three months treasury bills has been collected from
www.federalreserve.gov. Data of KSE 100 index has been collected from website of KSE.
Sample. Sample of nineteen year data for KSE 100 index and selected
macroeconomic variables has taken. Annual data of nineteen years from 1992 to 2010 has
taken for KSE 100 index and all selected macroeconomic variables which include CPI, IR,
EXR, IMP and EXPT.
Description of Variable
This literature considers five independent variables and evaluates their relationship
with one dependent variable. KSE 100 index has been considered as dependent variable, and
remaining variables as independent such as consumer price index, exchange rate, interest
rate, imports and exports.
Where;
• CPI refers to Consumer price index
• IR refers to Interest rate of treasury bills
• EXR refers to Exchange rate
• IMP refers to Imports
• EXPT refers to Exports
Present study considers the CPI as proxy of inflation rate. Present study considers the
IR of three months treasury bills to observe the association of macroeconomic variables with
KSEI. Imports include those goods and services which are purchased by domestic country
from foreign country. Exchange rate can be defined as that rate which is used to change
currency of one country into currency of another country. Exports include all those goods
which are produced in one country and then sold to another country. Exports are important
source to increase foreign exchange reserves of a country. Every country focuses to increase
its exports in order to increase its foreign reserves which help a country to make economic
progress.
Hypothesis
The main hypothesis of present study is as follow:
H0: There exists no causal relationship between CPI, IR, EXR, IMP, EXPT and KSE
100 index
H1: There exists causal relationship between CPI, IR, EXR, IMP, EXPT and KSE 100
index
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RESULTS AND DISCUSSION
Descriptive Statistics
Descriptive statistics of all the selected macroeconomic variables and KSE 100 index
can be explained with the help of following table. Following table provides information about
mean, median, standard deviation, minimum value, maximum value, skewness and kurtosis
of selected macroeconomic variables and KSE 100 index.
Mean
Median
Minimum
Maximum
Std. Dev.
Skewness
Kurtosis
TABLE 1
Descriptive Statistics of Variables under Consideration
KSEI
CPI
IR
EXR
IMP
4711.7605 7.7432
3.3037
50.8827
1.0091E6
2164.0000 7.5000
3.4300
57.5745
627000.0000
945.24
2.00
.14
24.84
229889.00
14077.16
17.60
5.82
83.80
2910975.00
1.83821
16.91193 8.87459E5
4254.56299 3.94065
.999
.603
-.471
.070
1.212
-.370
.532
-1.140
-.613
.096
EXPT
640003.3684
539070.0000
171728.00
1617458.00
4.29467E5
.916
-.057
Results of Table 1 indicate that mean and median for CPI and IR are equal so these
both macroeconomic variables have symmetrical data distribution. Mean and median for
KSEI, EXR, IMP and EXPT are not equal which an indication is of asymmetrical data. For
regression analysis symmetrical data is required. Asymmetrical data has converted into
symmetrical by taking logarithm of KSEI, EXR, IMP and EXPT. Following table represents
the results of symmetrical data.
Mean
Median
Minimum
Maximum
Std. Dev.
Skewness
Kurtosis
TABLE 2
Descriptive Statistics of transformed Variables under Consideration
logKSEI
CPI
IR
logEXR
log IMP
8.0618
7.7432
3.3037
3.8714
13.4870
7.6797
7.5000
3.4300
4.0531
13.3487
6.85
2.00
.14
3.21
12.35
9.55
17.60
5.82
4.43
14.88
.91509
3.94065
1.83821
.36163
.83063
.381
.603
-0.471
-0.500
.432
-1.528
.532
-1.140
-.808
-1.060
logEXPT
13.1488
13.1976
12.05
14.30
.69729
-0.017
-1.094
On the analysis of above Table 2 it can be concluded that values of mean and median
are equal for KSEI, CPI, IR, EXR, IMP and EXPT so data of all these selected variables and
KSE 100 index has become symmetrical. Values of skewness for all selected variables and
KSE 100 index are between +1 and -1 which is another indication of symmetrical data. Now
this symmetrical data can be used for regression analysis.
Regression Analysis
Regression analysis technique has been used to check the dependence of KSEI on
selected macroeconomic variables. In order to observe the dependence of KSEI on CPI, IR,
EXR, IMP and EXPT following regression model has been developed. This model is helpful
to examine the relationship of selected variables with KSE 100 index.
KSEI = a + b1CPI + b2IR + b3EXR + b4IMP + b5EXPT + e
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Journal of Contemporary Issues in Business Research
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Here,
a = Constant
e = Error term
As logarithm of KSEI, EXR, IMP and EXPT have taken in order to make them symmetrical
so this model can be revised as:
LogKSEI = a + b1CPI + b2IR + b3logEXR + b4logIMP + b5logEXPT + e
Above model is consistent with model developed by (M. B. Ali, 2011) who also first
transformed data into symmetrical and then applied regression analysis technique. Results of
regression can be explained with the help of following tables:
Model
1
R
.911a
R Square
.829
TABLE 3
Model Summary
Adjusted R Square
.763
Std. Error of the Estimate
.44518
In case of multiple regression our major concern is with the value of Adjusted R
Square instead of value of R and R square. On the basis of Model summary results of
Adjusted R Square indicate that 76.3% variation in KSE 100 index is explained through IR,
EXR, CPI, IMP and EXPT and the rest is not explained through this model of regression.
Statistical significance is checked through ANOVA. Table 4, ANOVA helps to
examine the overall validity of the model with respect to statistical procedures. According to
the following table value of significance (0.000) which shows that model is statistically
significant and variation explained through this model is real not due to chance variation.
Model
Regression
Residual
Total
Model
Constant
CPI
IR
Log EXR
Log IMP
Log EXPT
Sum of Squares
12.497
2.576
15.073
TABLE 4
ANOVA
Mean Square
2.499
.198
TABLE 5
Coefficients
Unstandardized Coefficients
B
Std. Error
t
-13.308
7.467
-1.782
-.029
.038
-.762
-.083
.071
-1.166
-3.508
1.784
-1.966
.054
1.405
.038
2.641
2.386
1.107
F
12.611
Sig.
.098
.459
.265
.071
.970
.288
Sig.
.000a
Collinearity Statistics
Tolerance VIF
.482
.648
.026
.008
.004
2.073
1.543
37.817
123.643
251.424
On the basis of above Table 5 it can be concluded that those variables whose value of
significance is not less than 0.1 are not contributing in this model of regression. As value of
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Journal of Contemporary Issues in Business Research
Volume 3, Issue No. 2, 2014
Tolerance is near to zero for most of the variables and value of VIF is greater than 2 for most
of the variables so there exists the issue of multichollinearity.
TABLE6
Collinearity Diagnostics
Variance Proportions
Eigen value Condition Index Constant CPI IR
LogEXR LogIMP LogEXPT
5.607
1.000
.00
.00
.00
.00
.00
.00
.235
4.884
.00
.10
.46
.00
.00
.00
.155
6.008
.00
.37
.11
.00
.00
.00
.002
49.666
.05
.05
.42
.03
.00
.00
.000
131.377
.07
.27
.00
.22
.04
.00
7.375E-6
871.954
.88
.22
.00
.75
.96
1.00
Table 6 of Collinearity Diagnostics shows that as Eigen value is near to zero and
value of condition index is greater than 15 for most of the variables so there exists issue of
multichollinearity. On the basis of above results of multiple regression models it can be
concluded that when I observe the relationship of CPI, IR, EXR, IMP and EXPT with KSEI
there exist issue of multichollinearity. In order to resolve the issue of multichollinearity,
present study rerun the regression through stepwise method of regression.
Model
1
R
.864a
TABLE 7
Model Summary in case of Stepwise Method of Regression
R Square
Adjusted R Square
Std. Error of the Estimate
.747
.732
.47394
According to the above Table 7 value of Adjusted R Square indicates that 73.2%
variation is explained through this model when regression is rerun. Table of ANOVA shows
that value of significance is less than 0.05 so model is statistically significant and variation
explained though this model is real not due to chance variation.
Model
Regression
Residual
Total
Model
Constant
CPI
Sum of Squares
11.255
3.819
15.073
TABLE 8
ANOVA
Mean Square
11.255
.225
F
50.105
Sig.
.000a
TABLE 9
Coefficients
Unstandardized Coefficients
Collinearity Statistics
B
Std. Error
t
Sig. Tolerance VIF
8.062
.109
74.145 .000
.791
.112
7.078 .000 1.000
1.000
Results of above Table 9 indicate that Imports have positive relationship with KSEI.
8.062 is the value of constant, 0.791 is the value of beta and value of significance is .000 ˂
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Journal of Contemporary Issues in Business Research
Volume 3, Issue No. 2, 2014
0.5. So there is strong positive relationship of IMP with KSEI. This result is consistent with
(Hussain et al., 2012). It can be explained as:
KSEI = 8.062 + 0.791LogIMP Following Table 10 represents those variables which have
excluded and have no relationship with KSEI. These variables include CPI, IR, EXR and
EXPT. These results are consistent with the results obtained by (GENÇTÜRK et al., 2012;
Ali et al., 2010).
TABLE 10
Coefficients
Model
Beta
In
Zscore: CPI
-.011a
Zscore: IR
-.108a
Zscore:Log IMP -.363a
Zscore:Log EXPT -.447a
t
-.085
-.719
-1.376
-.694
Sig.
.933
.483
.188
.498
Partial
Correlation
-.021
-.177
-.325
-.171
Collinearity Statistics
Minimum
Tolerance VIF
Tolerance
.985
1.015 .985
.681
1.468 .681
.203
4.930 .203
.037
26.995 .037
a. Predictors in the Model: (Constant), Zscore(LogIMP)
b. Dependent Variable: LogKSEI
Augmented Dickey-Fuller Test
Augmented Dickey-Fuller test is very useful and important to check stationary of
data. Before applying Granger Causality test it is necessary to observe stationary of data. If
data is not stationary it can be converted into stationary using different test and techniques
and Dickey-Fuller test is one of those tests. Following table represents the results of DickeyFuller test.
On the basis of results of following Table 11 it can be concluded that all dependent
and independent variables are stationary at first difference. It can be observed from the
following table that value of ADF test statistics is less than 0.5 for all variables and value of
probability is also less than 0.5 for all variables so data is stationary at first difference.
TABLE 11
Investigation of Unit Root within series at level & 1st difference
Variables At Level
At First Difference
ADF test Critical Values
Prob. ADF test Critical Values
KSEI
At 1% -3.857
At 1% -3.8867
-0.6732
At 5% -3.040 0.829 -4.8848
At 5% -3.0521
At 10% -2.660
At 10% -2.6665
CPI
At 1% -3.857
At 1% -3.8867
-1.4804
At 5% -3.040 0.520 -3.7464
At 5% -3.0521
At 10% -2.660
At 10% -2.6665
IR
At 1% -3.886
At 1% -3.9203
-2.3556
At 5% -3.052 0.167 -3.4919
At 5% -3.0655
At 10% -2.666
At 10% -2.6734
EXR
At 1% -3.857
At 1% -3.8861
-1.0880
At 5% -3.040 0.696 -3.1673
At 5% -3.0521
At 10% -2.660
At 10% -2.6665
IMP
At 1% -3.857
At 1% -3.8867
0.6479
At 5% -3.040 0.986 -3.3032
At 5% -3.0521
96
Prob.
0.0014
0.0132
0.0227
0.0403
0.0312
Journal of Contemporary Issues in Business Research
Variables At Level
ADF test
EXPT
0.2395
Critical Values
At 10% -2.660
At 1% -3.857
At 5% -3.040
At 10% -2.660
Prob.
0.967
Volume 3, Issue No. 2, 2014
At First Difference
ADF test Critical Values
At 10% -2.6665
At 1% -3.8867
-5.5727
At 5% -3.0521
At 10% -2.6665
Prob.
0.0004
Granger Causality Test
Granger Causality test is very useful and important technique to observe the causal
relation between variables. Results are examined on the basis of probability at 1%, 5% and
10% level of significance. If value of probability is observed less than level of significance
then we can reject null hypothesis which means variables cause and affect each other.
Variables
CPI
IR
EXP
IMP
EXPT
TABLE 12
Results of Granger Causality Test
Alternate Hypothesis
Probability
Results
CPI KSEI
0.1220
≠
CPI KSEI
0.2848
IR
KSEI
0.0252**
↕
IR
KSEI
0.0768***
EXR KSEI
0.0763***
↑
EXR KSEI
0.1172
IMP
KSEI
0.2407
↑
IMP
KSEI
0.0962***
EXPT KSEI
0.1057
≠
EXPT KSEI
0.4231
Independent
Bi-directional
Uni-directional
Uni-directional
Independent
1% Significance Level =*Significant
5% Significance Level =**Significant
10% Significance Level = ***Significant
Results of above Table 12 indicate that there exists no causal relationship between
CPI, EXPT and KSEI this result is supported by the results observed by (Oseni & Nwosa,
2011),(I. Ali et al., 2010) and (Khalid, Altaf, Mehmood, Bagram, & hussain). Bi-directional
relationship exists between IR and KSEI this result is consistent with study done by (Herve et
al., 2011). EXR and IMP have uni-directional relationship with KSEI this result is supported
by the results obtained by (Hussain et al., 2012) and (Tripathy, 2011). This result is not
supported by the results examined by Gay (2008) and Ali et al. (2010).
CONCLUSION AND RECOMMENDATIONS
Present literature can be summarized in a way that it has been designed particularly to
study the causal association of CPI, IR, EXR, IMP and EXPT with KSE 100 index. Annual
data of nineteen years has been taken from 1992 to 2010. Different tests and techniques have
been applied. Results of multiple regression models indicate that only IMP has significant
positive relationship with KSEI.IR, CPI, EXR and EXPT have no relationship KSEI. Results
of Granger Causality test represent that there exists bi-directional causal relationship between
IR and KSEI. Uni-directional causality exists between EXR, IMP and KSEI. No causal
relationship exists between CPI, EXPT and KSEI.
On the basis of this study it can be recommended that further research should be done
in this area of study because stock market plays very important role in the economic progress
of a country and it is affected by various macroeconomic variables. This study is limited to
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Journal of Contemporary Issues in Business Research
Volume 3, Issue No. 2, 2014
five variables so there is scope for further research to study their relationship with stock
market.
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Volume 3, Issue No. 2, 2014
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