Macroeconomics, Investor Sentiment, and Islamic Stock Price Index in Malaysia
M Kabir Hassan
University of New Orleans, United States
Mamunur Rashid
Ng Yuen Yein
Nottingham University Business School
University of Nottingham Malaysia Campus
Corresponding Address
Mamunur Rashid, PhD
Assistant Professor of Finance
Nottingham University Business School
The University of Nottingham, Malaysia Campus
Phone: +603 8725 3565
Fax: +603 8924 8019
Email: mamunur.rashid@nottingham.edu.my
Abstract
Asset prices are strongly connected macroeconomic variables even in the presence of investor
sentiment. This study examines the level of exposure of conventional and Islamic stock price
indices in Malaysia to the relative change in investor sentiment index and macroeconomic
factors. This study reports that interest rates, currency index and FTSE Bursa Malaysia Composite
Index pose greater influence on Islamic price index (LSI) when compared to industrial
production, consumer price index, money supply and investor sentiment indices. The results
continue an important battery of debates on the requirement of shari’ah compliant capital
market for Muslims. For instance, if investor sentiment is a significant determinant in Islamic
stock market, it will raise critiques on the need of Shariah compliant markets. Consequently,
newer studies should investigate the need of separate capital markets for Muslims. The study
unlocks room for new studies on influence of sentiment in Islamic financial industry.
Keywords: Shari’ah Price Index, Investor Sentiment, Islamic Capital Price, Malaysia
Macroeconomics, Investor Sentiment, and Islamic Stock Price Index in Malaysia
1. Determinants of Islamic Stock Prices
Scientists in behavioural finance established significant role of investor sentiment in deciding
asset prices(Baker & Wurgler 2006).Early studies studied the influence of investor sentiment on
asset prices for different sizes of companies (small versus large capitalisation firms), various
countries (developed versus emerging countries), and for various sectors (finance, industrial and
properties)(Brown & Cliff 2004; Fauzias, Izani & Rashid 2013). These studiesreported strong
positive connection between investor sentiment and stock prices.Investor sentiment is widely
determined by investigating the overly optimistic or pessimistic behaviour of the investors.
Investor sentiment is strong when investors rely on noise and invest in stocks those are difficult
to arbitrage(Qiu & Welch 2006).
Islamic financial industry is growing at a double digit rate every year (Hassan, Rashid,
Imran & Shahid 2010). The industry is operated on the Islamic shari’ah. Islamic shari’ah prohibits
interest (riba), and production and use of alcoholic goods, investment in pornography and
anything that oppresses basic human rights (Hassan & Rashid 2010). Islamic finance prohibits
investment that involves excessive uncertainty (gharah) and gambling (mysir). Islam also
prohibits trading on goods that do not have physical existence (Chapra 1985). Islam advocates
the value of entrepreneurship, production through integrity and hard work, and prefers equity
than debt (Farooq 2007). Consequently, partnership, mutual solidarity and brotherhood are the
symbols of Islamic economic system. It is prohibited by Quran and Hadith for Muslims to act on
rumours. Hence, investors of Islamic financial system cannot work on overly optimistic or
pessimistic expectations.
Investor sentiment is defined as the propensity to speculate (Barberis, Shleifer & Vishny
1998). Any kind of speculation, either intentional or partaking, is considered haram (not
permissible) in Islam. Another common concept of investor sentiment is that sentimental
investors value rumours more than fundamentals (Baker, Wurgler & Yuan 2010). Long-run
significance of investor sentiment may also indicate that irrational investors outperform rational
investors (Black 1986). Theoretically, Islamic finance should not be connected to investor
sentiment. Islamic finance should rely on fundamentals, not on rumours. However, if investor
sentiment has significant influence on Islamic shari’ah stock index, it will probably close the
gates for both neoclassical economics and Islamic capital markets.
Recently, the emergence of Islamic Finance attracts both Muslim and non-Muslim investors. In
forecasting stock return, investors have to determine the factors which would affect the stock
market return. The 30 constituents in FTSE Bursa Malaysia Hijrah Index have gone through
different screening processes, which are different from FTSE Bursa Malaysia Composite Index by
FTSE Bursa Malaysia, Securities Commission’s Shari’ah Advisory Committee (SAC) and Yasaar Ltd.
Therefore factors which affecting the Malaysia Islamic stock performance would be different.
Islamic Finance is newly emerging and studies on performance of Malaysian Islamic stock
market are very rare especially from the scope of the factors and impacts. Hence, the main
research objective of this study is to identify which factors would bring impacts on the
performance of Malaysian Islamic stock market and how these indicators performed during and
after 2008 Global Financial Crisis period. Performance can be observed through FTSE Bursa
Malaysia Hijrah Index
Previous studies investigated the relationship between macroeconomic indicators and stock
returns. Scientists in behavioural finance established significant role of investor sentiment in
deciding asset prices (Baker & Wurgler 2006). Early studies studied the influence of investor
sentiment on asset prices for different sizes of companies (small versus large capitalisation
firms), various countries (developed versus emerging countries), and for various sectors (finance,
industrial and properties) (Brown & Cliff 2004; Fauzias, Izani & Rashid 2013). These studies
reported strong positive connection between investor sentiment and stock prices. Investor
sentiment is widely determined by investigating the overly optimistic or pessimistic behaviour of
the investors. Investor sentiment is strong when investors rely on noise and invest in stocks
those are difficult to arbitrage (Qiu & Welch 2006).
Islamic financial industry is growing at a double digit rate every year (Hassan, Rashid, Imran &
Shahid 2010). The industry is operated on the Islamic shari’ah. Islamic shari’ah prohibits interest
(riba), and production and use of alcoholic goods, investment in pornography and anything that
oppresses basic human rights (Hassan & Rashid 2010). Islamic finance prohibits investment that
involves excessive uncertainty (gharah) and gambling (mysir). Islam also prohibits trading on
goods that do not have physical existence (Chapra 1985). Islam advocates the value of
entrepreneurship, production through integrity and hard work, and prefers equity than debt
(Farooq 2007). Consequently, partnership, mutual solidarity and brotherhood are the symbols of
Islamic economic system. It is prohibited by Quran and Hadith for Muslims to act on rumours.
Hence, investors of Islamic financial system cannot work on overly optimistic or pessimistic
expectations.
Investor sentiment is defined as the propensity to speculate (Barberis, Shleifer & Vishny 1998).
Any kind of speculation, either intentional or partaking, is considered haram (not permissible) in
Islam. Another common concept of investor sentiment is that sentimental investors value
rumours more than fundamentals (Baker, Wurgler & Yuan 2010). Long-run significance of
investor sentiment may also indicate that irrational investors outperform rational investors (Black
1986). Theoretically, Islamic finance should not be connected to investor sentiment. Islamic
finance should rely on fundamentals, not on rumours. However, if investor sentiment has
significant influence on Islamic shari’ah stock index, it will probably close the gates for both
neoclassical economics and Islamic capital markets.
Chen, Roll and Ross (1986) suggested that industrial production, inflation, and long term
government bonds can capture the whole US macroeconomy while Three Factors Model
invented by Fama and French (1993) stated that small stocks would outperform large stock
return and high book-to-market value portfolio outperform low book-to-market value portfolio.
For this, Maio and Philip (2013) argued that the three factor model perform better than the
multifactor model. More arguments can be seen through discussions on each variables shown
below.
Maysami and Koh (2003) proposed that positive relationship between GDP and stock price
existed in Singapore. When GDP is high, company cash flow would be increased and stock price
would thus increase. For inflation, Fama and Schwert (1977), Chen et al (1986) and DeFina (1991)
argued that there is an inverse relationship between stock price and inflation. The high
production cost would cause decrease in future cash flow and profit. Geske and Roll (1983) and
Mukherjee and Naka (1995) supported the argument by stating that economic policy become
stricter when inflation rate increase. Increase in free risk nominal rate increase will lead rate of
discount to decrease. Thus, stock price increase. In contrast, Khil and Lee (2000), Ibrahim and
Azizi (2003), Shabri et al.(2001) and Ibrahim (2003) disagreed with the argument above and
argued that equities will be reserved so existence of inverse relationship did exist in both stock
returns and inflation in Indonesia.
In terms of money supply, Dhakal et al. (1993) suggested that in US, under economy
encouragement feature, stock price increase when company profit increase due to increase
money supply. However, Fama (1981) viewed that negative relationship did exist when money
supply and inflation are in positive relationship. The higher the money supply, the higher the
discount rate and cause stock market price become lower. The relationship between interest rate
and stock price are negative. In Japan, increase in interest rates would lead to increase in free
risk nominal rate, and discount rate also increases, so the stock price will decrease (Mukherjee
and Naka’ 1995; Abdullah and Hayworth, 1993). Some companies highly depend on loan in their
business. When interest rate is low, decrease in borrowing cost would lead the companies tend
to expand more. Another point is some investors might purchase stocks through bank
borrowing. Increase in interest rates would lead the demand of stock decreased so stock price
drop (Hussin, et al., 2012). Baker and Wurgler (2006) provided strong evidence showing that
investor sentiment would affect the cross-section of stock returns. When sentiments are high,
new, small and unprofitable stocks would show low returns.
Dynamic relationship between Kuala Lumpur Shari’ah Index (KLSI) and Kuala Lumpur Composite
Index (KLCI) also being examines in these recent years. According to Albaity and Ahmad (2008),
Kuala Lumpur Shari’ah Index and Kuala Lumpur Composite Index would tend to move in same
direction. Yusof and Majid (2007) provided strong evidence saying that interest rate would not
affect the Islamic stock market volatility. This is in line with Islamic principle which interest rate
(riba) is not significant in explaining stock market volatility. Albaity (2011) determined that KLSI
and DJIMI variance is influenced by monetary policy of M2, M3, and inflation rate. Isa, Hassan
and Azrul (2012) indicated that inflation, money growth, interest rates, industrial production,
reserves, exchange rates and international monetary policy have positive relationship with
Malaysia Shari’ah Indices.
2. Empirical Design
The 2008 Global Financial Period began from year 2008 and end in year 2013. Hence, all the
monthly or quarterly data of those macroeconomic variables are obtained from June 2007 to
December 2013. Many tests would be involved and OLS is the method in running the models.
According Hanif et al (2013), they also used the same method in determining the impact of
financial sector variables on Sha’riah Compliant cross-section stock return in Pakistan. Moreover,
Gupta et al (2013) also used OLS in determining the returns in Dow Jones Islamic stock market.
BCI and CSI are only available in quarterly frequency of data. Therefore, two models would be
formed, monthly and quarterly models. The monthly model would not include both BCI and CSI.
Each variable would have their own measurement, thus the all the variables would be
transformed into the form of ‘natural logarithms’ when running OLS regression. Table 1 and 2
showed the descriptive statistics of monthly and quarterly data of one dependent variable (LSI)
and six determinants (LSSI, LIPI, LCPI, LMNS and LINT) respectively. In table 1, LSSI and LSI are
positively highly correlated which indicated that there is a strong relationship between the two.
The relationship between LINT and LMNS is weak and both of them are negatively correlated. In
Table 2, majority of the variables showed negative relationship with other variables. The
relationship between LBCI and LIPI is very small which is - 0.023.
<Insert Table 1 about here>
<Insert Table 2 about here>
Based on Rule of Thumb and Central Limit Theorem, if the number of observations is more than
30, the distributions consider normal. Normality distribution cannot be ensured in quarterly
model because its number of observation is only 27. For monthly model, the number of
observations is 79.
Augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test are being applied when the
data need to be examined whether it is stationary data or non-stationary data. It is the first step
being done before other tests are going to be applied since ensuring the data is stationary is
very important. Data would become stationary from non-stationary by applying first differencing
method.
BG Test is being used when testing the presence of autocorrelation. If the autocorrelation is
being ignored, the OLS can still be run but the results would be inefficient. The R2 would be
overestimated as the residuals of the variables are positively correlated. In this study, OLS is
applied to evaluate the relationship between macroeconomic variables and Malaysia Shari’ah
Hijrah Index (SSI).In quarterly model, ∆LSI would be the dependent variable and ∆SSI, ∆IPI, ∆CPI,
∆INT, ∆BCI and ∆CSI would be the independent variables. For monthly model, ∆SI would be the
dependent variable and ∆SSI, ∆IPI, ∆CPI and ∆INT would be the independent variables. The
coefficient obtained from the result would determine the significance of positive or negative
relationship between dependent or independent variables and the magnitudes.
Since the two original models need to be modified with no autocorrelation, two lags of both
independent and dependent variables would be included in running the OLS regression. BG test
would then be run after OLS regression is run. When the results showed there is an
autocorrelation, OLS would be run again but exclude the variables which give the highest
insignificant value in OLS results. Several times would be taken out until all variables show
significant value. Liu and Shrestha (2008) also applied same methods which combining OLS with
LM test in their analysis of cointegration between Chinese stock return and macroeconomic
variables.
3.0 Discussion
The results finding would be discussed separately on quarterly model and monthly model.
<Insert Table 3 about here>
The results above showed that the quarterly macroeconomic variables are non-stationary data
and unit roots did present across all macroeconomic variables. This is because both ADF and PP
test-statistics is less negative than critical value. In order to ensure all variables are stationary (no
unit root), all variables must be applied first differencing. After applying first differencing, all
data change from non-stationary become stationary except ∆𝐿𝑀𝑁𝑆. This implied that LMNS has
at least one unit root. Since this study is using OLS regression method in analysing data, all data
must have the same integration of order, for example I(1) in this study. Therefore, ∆𝐿𝑀𝑁𝑆 is
excluded.
<Insert Table 4 about here>
The results above showed that the monthly macroeconomic variables are non-stationary data
and unit roots did present across all macroeconomic variables. Again, both ADF and PP teststatistics is less negative than critical value. In order to ensure all variables are stationary (no unit
root), all variables must be applied first differencing. After applying first differencing, all data
change from non-stationary become stationary except∆𝐿𝑀𝑁𝑆. This implied that LMNS has at
least one unit root. Since this study is using OLS regression method in analysing data, all data
must have the same integration of order, for example I(1) in this study. Therefore, ∆𝐿𝑀𝑁𝑆 is
excluded.
At this stage, both quarterly and monthly models would be formed. Since BCI and CSI are only
available in quarterly frequency of data, therefore the quarterly model would include BCI and CSI
while monthly model does not. At first all the two-lagged terms of dependent and independent
variables are being inserted into the regression and heteroscedasticity is being removed based
on White Test. In this stage, variables which will affect the LSSI would be selected based on the
Breusch-Godfrey Serial Correlation LM Test. Then OLS would be run again until all
autocorrelation are being removed. Since two lags are being used, it is suggested that at least
past two values of quarters or months data would affect the current data.
<Insert Table 5 about here>
<Insert Table 6 about here>
Both Table 5 and Table 6 showed the results of Breusch-Godfrey Serial Correlation LM test. In
Table 5, since Chi-Square value of 0.5234 is lower than Obs*R-squared value of 1.2948, the
hypothesis cannot be rejected. Hence, autocorrelation do not exist in the residuals of the
variables. Results in Table 6 showed the correlation among the variables in the model. Table 6
showed that the Chi-Square value of 0.7412 is higher than the Obs*R-square, Therefore, the null
hypothesis of no autocorrelation has to be rejected. In another words, autocorrelation do exist
in the residuals. The OLS still can be run but inefficient.
<Insert Table 7 about here>
The results above showed that there is a positive relationship between ∆𝐿𝑆𝑆𝐼(−2) and ∆𝐿𝑆𝑆𝐼
with probability of 0 which is less than 0.05. ∆LSI brings large impact on ∆𝐿𝑆𝑆𝐼 as the coefficient
of 1.2923 is the highest among other variables. The relationship between ∆𝐿𝐼𝑁𝑇(−2) and ∆𝐿𝑆𝑆𝐼
is significantly positive and the coefficient is 0.1822. The probability of 0.031 indicates the
significance which is less than 0.05. ∆𝐿𝑆𝐼(−1) is negatively correlated with ∆𝐿𝑆𝑆𝐼 and the
coefficient is -0.8605 with probability of 0.0005 which is less than 0.05. Both ∆𝐿𝐼𝑁𝑇(−1) and
∆𝐶𝑈𝑅(−1) also showed negative relationship with ∆𝐿𝑆𝑆𝐼, given coefficient of -0.1921 and 0.4898 respectively. Their coefficients are significant since their probabilities of 0.035 and 0.033
are less than 0.05. Equation of the monthly model is shown as below (Equation 1).
∆𝐿𝑆𝑆𝐼 = ∆𝐿𝑆𝑆𝐼(−2) + ∆𝐿𝑆𝐼 + ∆𝐿𝑆𝐼(−1) + ∆𝐿𝐼𝑁𝑇(−1) + ∆𝐿𝐼𝑁𝑇(−2) + ∆𝐿𝐶𝑈𝑅(−1)(1)
<Insert Table 8 about here>
For this quarterly model, all variables are also significant as their probability is less than 0.05.
Positive relationship does exist between ∆𝐿𝑆𝑆𝐼(−1)and∆𝐿𝑆𝑆𝐼. ∆𝐿𝐶𝑈𝑅 has high impact on ∆LSSI
and it offers the coefficient of 1.487 which is the highest among other variables. The positive
relationship between ∆𝐿𝑆𝐼 and ∆ LSSI with coefficient of 1.2506 indicated the correlation
between these two variables is quite high. ∆𝐶𝑈𝑅(−1) is negatively correlated with ∆LSSI and the
coefficient is -1.3991 with probability of 0.0082 which is less than 0.05. ∆𝐿𝑆𝐼(−1)also showed
negative relationship with ∆𝐿𝑆𝑆𝐼 where the coefficient is -1.1395 which is also significant.
Equation of the quarterly model is shown as below (Equation 2).
∆𝐿𝑆𝑆𝐼 = ∆𝐿𝑆𝑆𝐼(−1) + ∆𝐿𝐶𝑈𝑅 + ∆𝐿𝐶𝑈𝑅(−1) + ∆𝐿𝑆𝐼 + ∆𝐿𝑆𝐼(−1)
(2)
Models with macroeconomic variables which will significantly affect the performance of
Malaysian Islamic stock market have been formed. After several tests being taken into account,
these models are formed with normality, homoskedasticity, stationary and no
autocorrelation.∆𝐿𝑆𝐼, ∆𝐿𝑆𝐼(−1) and ∆𝐿𝐶𝑈𝑅(−1)would affect the Malaysian Shari’ah Hijrah Index
regardless in monthly or quarterly. The phenomenon is in line with Albaity and Ahmad (2008) as
they also found out that both Malaysian Shari’ah Hijrah Index and FTSE Bursa Malaysia
Composite Index would move in the same trend. In addition, all these variables showed the
same relationships with the Malaysian Shari’ah Hijrah Index. The interest would only affect the
Malaysian Shari’ah Hijrah Index in quarterly basis. Another point is business conditions index,
consumer sentiments index, GDP and money supply would not affect the Malaysian Shari’ah
Hijrah Index. This is contradicted with Wurgler and Baker (2006).
4. Concluding Discussions
There are only a few factors which would affect the Malaysia Shari’ah Index. By lagging
Malaysian Shari’ah Hijrah Index by two months, the performance would become significant.
From the monthly data point of view, the past two months of Malaysian Shari’ah Hijrah Index,
current and last month FTSE Bursa Malaysia Composite Index, past two and last month’s interest
rates and currency would affect the Malaysian Shari’ah Hijrah Index. While in the quarterly
model, the last quarter Malaysian Shari’ah Hijrah Index, past six months currency and FTSE Bursa
Malaysia Composite Index would impact Malaysian Shari’ah Hijrah Index. Hopefully in future,
more research can be done on other factors which would be accounted for and produce a more
complicated model. Therefore investors would focus on those factors which might affect their
investment in Malaysia Islamic capital market. The limitation of this study is the efficiency of
Islamic stock market is not being considered. Also, since the LBCI and LCSI only available in
quarterly data, therefore the overview of monthly model cannot be seen. If the data is available,
then a clearer overview can be seen through both version of monthly and quarterly data
movement. Future research should consider these issues.
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Appendices
Table 1: Descriptive statistic for monthly data
LSI
LSSI
LIPI
LCPI
Mean
7.2255
9.223
4.6878
4.6749
Median
7.2543
9.238
4.6904
4.6751
Maximum
7.5056
9.553
4.8065
4.7407
Minimum
6.7434
8.79
4.5229
4.6193
Std. Dev.
0.1888
0.185
0.0577
0.027
Correlation Coefficient
LSI
1
0.960326
0.788797
0.606677
LSSI
1
0.789569
0.529371
LIPI
1
0.445609
LCPI
1
LMNS
13.8869
13.8572
14.1956
13.578
0.1893
LINT
1.1144
1.1663
1.3083
0.7467
0.1734
0.70941
0.733462
0.528533
0.574253
0.406395
0.348699
0.548323
0.354605
LMNS
1
-0.0055
LINT
1
FTSE Bursa Malaysia Composite Index (LSI) is used as the proxy for stock market. Malaysia
Shari’ah Hijrah Index (LSSI) is used as the proxy for Islamic stock market. Industrial Production
Index (LIPI)is used as the proxy for Gross Domestic Product. Consumer Price Index (LCPI) is used
as the proxy for the inflation rate in kadar Malaysia. Money Supply (LMNS) is used as the proxy
for the money supply. Interest Rates (LINT ) is used as the proxy for the short term interest rate.
Table 2: Descriptive statistic for quarterly data
LSI
LSSI
LIPI
LCPI
LMNS
LINT
LBCI
LCSI
Mean
7.0552
9.0777 12.0915
4.6956 13.6858
1.4752
4.5709
4.5667
Median
7.1279
9.1461 12.1129
4.7029 13.7092
1.6094
4.6587
4.6616
Maximu
m
7.2759
9.3639 12.2327
4.7431 13.7651
1.9021
4.8048
4.8211
Minimu
m
6.7762
8.8009 11.9197
4.6548
13.578
0.5306
3.9853
4.2556
Std. Dev.
0.1757
0.1807
0.0836
0.0304
0.0699
0.3685
0.2865
0.208
Correlation Coefficient
0.92956 0.26238
- 0.62444 0.67186 0.47367
LSI
1
7
7 0.64395 0.73461
3
7
5
0.52531
0.65153 0.61663 0.30160
LSSI
1
4 0.36586 -0.4431
1
1
8
0.53498 0.30664 0.50314
LIPI
1
7
7
2 0.02298 0.45046
0.88402
LCPI
1
2 0.10263 0.59942 -0.7145
LMNS
1 0.36042 0.60651 0.65577
0.49038 0.29725
LINT
1
3
1
0.68037
LBCI
1
6
LCSI
1
FTSE Bursa Malaysia Composite Index (LSI) is used as the proxy for stock market. Malaysia
Shari’ah Hijrah Index (LSSI) is used as the proxy for Islamic stock market. Industrial Production
Index (LIPI)is used as the proxy for Gross Domestic Product. Consumer Price Index (LCPI) is used
as the proxy for the inflation rate in kadar Malaysia. Money Supply (LMNS) is used as the proxy
for the money supply. Interest Rates (LINT ) is used as the proxy for the short term interest rate.
Business Conditions Index (LBCI) is used as the proxy for Business Conditions Index in Malaysia.
Consumer Sentiments Index (LCSI) is used as the proxy for Consumer Sentiments Index in
Malaysia
Table 3: Unit Root Test on Quarterly Data
1st Differencing
Remarks
ADF
PP
KPSS
ADF
PP
KPSS
LBCI
-1.711 -1.8377 0.1174 -4.742**
0.0807
I(1)
I(1)
I(0)
4.8675
LCSI
-1.633 -1.7649 0.1446 -5.538**
0.142
I(1)
I(1)
I(0)
4.8235
LSSI
-1.818 -1.7765 0.3173 -4.801**
0.1837
I(1)
I(1)
I(1)
4.7423
LSI
-2.997 -3.2369 0.4818 -5.025**
0.169
I(1)
I(1)
I(1)
5.5577
LMNS -1.127 -1.8093
0.5
-1.98
0.1486
I(1)^
I(1)
I(1)
6.6351
LINT
-2.429 -2.5526 0.0499 -5.076**
0.1188
I(1)
I(1)
I(1)
4.8009
LGDP
-1.655 -1.6047 0.4313 -4.823**
0.1646
I(1)
I(1)
I(0)
5.0247
LCUR
-1.261 -1.2613 0.1683 -4.867**
0.1156
I(1)
I(1)
I(1)
5.0767
Three tests (ADF, PP and KPSS) were being used in testing the presence of unit root in the data.
The null hypothesis for Augmented Dickey Fuller (ADF) test and Phillip Perron Test are same. The
null hypothesis (H0) would be there is a unit root and the H1 stated that there is no unit root. The
results above indicated that the null hypothesis cannot be rejected. The confidence level applied
here is 95% confidence level. ADF Critical value at level = -3.4671, at 1st differencing= -2.899**,
** means the test-statistic is significant at 5% level. The null hypothesis for KPSS test would be
there is a unit root and the H1 stated that there is no unit root. KPSS (Kwiatkowski-PhillipsSchmidt-Shin) at level =0.146, at 1st differencing= 0.463, test-statistic is significant at 5% level.
Items
ADF
Level
PP
KPSS
Table 4: Unit Root Test on Monthly Data
Level
Items
ADF
PP
KPSS
LSSI
-1.595 -2.0095 0.1557
LSI
-2.03
-1.8145 0.1416
LMNS -2.369 -2.5489 0.1404
LINT
-1.919 -1.6747 0.1902
1st Differencing
Remarks
ADF
PP
KPSS
ADF
PP
KPSS
-8.091** -8.1049 0.0787
I(1)
I(1)
I(1)
-6.265** -6.2362 0.096
I(1)
I(1)
I(0)
-5.060** -8.0462 0.0515
I(1)
I(1)
I(0)
-8.046** -5.0841 0.1475
I(1)
I(1)
I(1)
LGDP
-2.332 -4.6916 0.1914
0.0308
I(1)
I(1)
I(1)
16.500** 17.6429
LCUR
-2.569
2.8731
0.086
-8.480** -8.5749 0.1019
I(1)
I(1)
I(0)
Three tests (ADF, PP and KPSS) were being used in testing the presence of unit root in the data.
The null hypothesis for Augmented Dickey Fuller (ADF) test and Phillip Perron Test are same. The
null hypothesis (H0) would be there is a unit root and the H1 stated that there is no unit root. The
results above indicated that the null hypothesis cannot be rejected. The confidence level applied
here is 95% confidence level. ADF Critical value at level = -3.4671, at 1st differencing= -2.899**,
** means the test-statistic is significant at 5% level. The null hypothesis for KPSS test would be
there is a unit root and the H1 stated that there is no unit root. KPSS (Kwiatkowski-PhillipsSchmidt-Shin) at level =0.146, at 1st differencing= 0.463, test-statistic is significant at 5% level.
Table 5: Results of LM test based on monthly data
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
0.5832 Prob. F(2,17)
0.5608
Obs*R-squared
1.2948 Prob. Chi-Square(2)
0.5234
In Table 5, the null hypothesis for this test is there is no autocorrelation while H1 is indicated
autocorrelation does exist in data. The Obs*R-squared shows the probability of Breusch-Godfrey
Serial Correlation LM Test. If theObs*R-squared is larger than Chi-Square, the null hypothesis
would be rejected.
Table 6: Results of LM test based on quarterly data
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
0.2086 Prob. F(2,17)
0.8137
Obs*R-squared
0.5989 Prob. Chi-Square(2)
0.7412
The null hypothesis for this test is there is no autocorrelation while H1 is indicated
autocorrelation does exist in data. The Obs*R-squared shows the probability of Breusch-Godfrey
Serial Correlation LM Test. If theObs*R-squared is larger than Chi-Square, the null hypothesis
would be rejected.
Table 7: Results of OLS model based on monthly data
Dependent Variable: DLSSI
Method: Least Squares
Sample:2007M06 2013M12
Variable
Coefficient
∆LSSI(-2)
0.6111
∆LSI
1.2923
∆LSI(-1)
-0.8605
∆LINT(-1)
-0.1921
∆LINT(-2)
0.1822
∆LCUR(-1)
-0.4898
C
2.7713
R-squared
Adj R-squared
F-statistic
Prob (F-statistic)
Wald F-statistic
Prob (Wald F-stat)
Durbin-Watson stat
0.9566
0.9529
264.245
0.0000
274.843
0.0000
1.87
Probability
0.000
0.000
0.0005
0.035
0.031
0.033
0.014
The null hypothesis for this model is there is no relationship between the dependent variable
and the independent variable while the H1 indicated that there is a relationship between
dependent and independent variables. In this study, 95% confidence level is being used.
Therefore, the coefficient would only be significant if the probability shows the value of less than
0.005. R2 shows the goodness of fit of the model.
Table 8: Results of OLS model based on quarterly data
Dependent Variable: DLSSI
Method: Least Squares
Sample:2007Q42013Q4
Variables
Coefficients
Probability
∆LSSI(-1)
0.8892
0.0000
∆LCUR(-1)
1.4870
0.0024
∆LCUR(-1)
-1.3991
0.0082
∆LSI
1.2506
0.0000
∆LSI(-1)
-1.1396
0.0000
C
-0.0185
0.8518
R-squared
0.9867
Adj R-squared
0.9832
F-statistic
282.559
Prob (F-statistic)
0.0000
Wald F-statistic
690.472
Prob (Wald F-stat)
0.0000
Durbin-Watson stat
2.0894
The null hypothesis for this model is there is no relationship between the dependent variable
and the independent variable while the H1 indicated that there is a relationship between
dependent and independent variables. In this study, 95% confidence level is being used.
Therefore, the coefficient would only be significant if the probability shows the value of less than
0.005. R2 shows the goodness of fit of the model.