Co-integration and Causality: Evidence from Islamic and Conventional Sectoral Indices at Bursa Malaysia
Mohamad Azwan Md Isa1*, Norashikin Ismail1, Ruziah A Latif1, Zaibedah Zaharum1, Nor Hadaliza Abd Rahman1, Basaruddin Shah Basri1
1 Faculty of Business and Management, Universiti Teknologi MARA, Johor, Malaysia
*Corresponding Author: [email protected]
Accepted: 1 June 2020 | Published: 15 June 2020
________________________________________________________________________________________
Abstract: This study aims to examine the co-integration and causality among the FBM Hijrah Shariah Index (HSI), Mid70 Index (Mid70), and seven (7) sectoral indices, namely Construction, Consumer Product, Finance, Industrial Product, Plantation, Property and Technology. Using the daily closing prices from January 1, 2009 to December 31, 2018, the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests show that the series are stationary or integrated at the same order. Further, the Trace and Max-Eigen statistics of Johansen Co-integration test reveal the existence of co-movements and long run (LR) equilibrium among the HSI, Mid70 and the sectoral indices. The Vector Error Correction Model (VECM) proves that the Technology Index poses the most rapid speed of adjustment by 46% if there is deviation from the LR equilibrium whilst the HSI and Mid70 indicate 21%
speed of adjustment, respectively. The Wald test evidences that in short run, the HSI is caused positively by Mid70, Property and Technology Indices whilst Construction, Consumer Product and Industrial Product Indices cause HSI negatively. Whereas, the Finance and Plantation Indices are found not to cause HSI in short run. These findings are meaningful and beneficial particularly to the stock market investors, portfolio fund managers, market operator and policy makers for economic and investment decision making.
Keywords: co-integration, causality, Hijrah Shariah Index, sectoral indices, Malaysia _________________________________________________________________________
1. Introduction
In the era of globalization and liberalization, stock market indices are becoming more interdependent and integrated. Even though the stock market is different, they still have similar degree of risk and return. It is also said that the pairwise correlations between the returns of these stock market indices are important and show a high degree of synchronization between these markets. In effect of investors objective is to maximize profits, Islamic finance sector must be efficient and competitive to the conventional sector. A country, where Islamic and conventional systems are adopted, the coordination between the two systems is vital.
Islamic finance is experiencing annual rapid growth and plays important role in some markets. The industry is also undergoing a highly geographic expansion, from the Middle East to South East Asia and the Europe. Bahrain, Dubai, Kuala Lumpur and London have become the regional and global hubs of Islamic finance. Moreover, for some countries like Germany, Belgium, France, Netherlands, and Switzerland, these countries started to apply Islamic finance in their countries by developing Islamic windows. The interest of
Islamic finance has shifted from bank-base focus to capital market-based instruments. The capital market system is a platform for capital lenders and seekers to exchange transaction for investment. In Malaysia, the exchange bourse is called Bursa Malaysia (BM).
Bursa Malaysia (BM) is considered as one of the largest bourses and ranked second in number of listings in Asian region recently. As of December 2019, there are a total of 801 companies listed on BM with the total market capitalization equivalent to USD397.39 billion.
Apparently, 77% of listed companies on BM are classified as Shariah-compliant stocks.
Malaysia has continuously shown long term growth and offered persuasive investment opportunities. Nevertheless, the securities market performance is challenging as trading revenue decreased by 12.9% from RM265.8million in 2018 to RM232.8million in 2019.
However, positive performance was found in the energy, construction and technological sectoral indices with growth rates of 51%, 34% and 29%, respectively. Similarly, positive trend was shown in small and mid-capital counters as opportunities in the stock market.
Malaysian capital market is unique due to implementation of dual systems as BM trades Shariah-compliant stocks and indices in parallel to the conventional stocks and indices. Since the two markets operate in the same economic system and framework, the performance of one market might pose impacts on another market.
Figure 1 shows that, generally, the Islamic stock index (proxy by HSI) had fluctuated in similar way or trend as the conventional stock index (proxy by Mid70 Index) over the 10- year period post the 2007/2008 global financial crisis, namely from 2009 to 2018. The two sectoral indices, namely construction and plantation, had indicated similar pattern of volatility and rising trend from early 2009 to end of 2014 before going for downtrend in the wake of the Asian currency crisis. However, the indices had stabilized and shown positive momentum commencing in mid of 2016 prior to witnessing slight fall in mid of 2018.
Looking at the trends, it shows that the Islamic stock market (HSI) is more resilient after the 2007/2008 global financial crisis compared to the conventional stock market (Mid70). This trend is consistent with Saiti, Bacha and Masih (2014) and Ng, Chin and Chong (2017), who found the same pattern in their respective studies.
Figure 1: The fluctuation of daily closing prices or indices
Sahabuddin, Muhamad, Yahya, Mohammed and Mizanur (2018), who investigated the co- movement of Emas Shariah Index with the FBM KLCI and ten sectoral indices in Malaysia, revealed that Islamic stock index (proxy by Emas Shariah Index) co-integrates with the conventional stock and sectoral indices. They recommended a further study to include HSI, small-cap and mid-cap indices. In fact, very few studies found or conducted on co-movement of Shariah index and sectoral stock indices in Malaysia.
Therefore, this study aims to examine the co-movement or long run association-ship and causal relationships among the stock indices, namely HSI with Mid70 Index and the seven (7) sectoral indices. The results of this study would be beneficial to investors and fund managers for investment decision making and portfolio management and diversification. For the policy makers, the study tends to provide better understanding on the behaviour of Islamic index in relation to other indices. The study will also narrow the gap particularly related to Islamic index as well as enhancing the existing literature. The remainder of this paper is organized as follows; Section 2 reviews the relevant and latest literature related to the study; Section 3 elaborates the data and methodology of study; and Section 4 presents the results and discussion of findings. Finally, Section 5 concludes this study and highlights further recommendation.
2. Literature Review
Co-integration and Causality among World Stock Markets
Previous numerous studies were focusing on the co-integration and causality between a country’s national stock indices with the national stock indices of other countries. Among the studies are Aamir et al. (2018), who study Pakistani stock market with 18 world’s emerging stock markets, Majid (2018) focuses on Indonesian Islamic stock market with Islamic stock markets of Japan, the UK and the US, Saiti and Noordin (2018) test Malaysian stock market with conventional and Islamic stock markets of other Southeast Asian region and world’s top ten largest stock indices, namely China, Japan, Hong Kong, India, the UK, the USA, Canada, France, Germany and Switzerland, Samadder and Bhunia (2018) analyze integrations between Indian stock market with the global developed stock markets, namely Australia, Canada, France, Germany, the UK and the USA, Nurrachmi (2018) reviews the movement of Islamic stock indices of selected countries in the Organization of Islamic Cooperation (OIC), namely Indonesia, Malaysia, Turkey, Qatar, Bahrain, and Oman, Abbes and Trichilli (2015) investigate the Islamic stock markets integration among the developed and emerging markets consisting of countries from the European, Asian, Latin American and MENA regions.
Aamir et al. (2018) examine the co-movements of Pakistani stock market with 18 emerging stock markets from South American, African, European and Asian countries. The 14-year period of study from 2001 to 2014 concludes that there is no co-integration in long run between the stock markets of Pakistan with Argentina, Republic Czech, Hungary, the Philippines and Peru. The result suggests that investors in those countries could benefit from cross-market diversifications to mitigate the unsystematic risk inherent in their investment portfolios. Meanwhile, investors from Turkey, Brazil, Egypt, China, India, Korea, Indonesia, Thailand including Malaysia could not adopt cross-market portfolio diversification with Pakistan because there is existence of long run co-integration between the countries’ stock markets, respectively. Majid (2018) reveals that Indonesian Islamic stock market co- integrates with Islamic stock markets of Japan, the UK and the US. The study that samples from the year 2000 to 2016 further proves that co-integration between the Indonesian with Japanese market is ascendant compared to with the other two developed markets, namely the
UK and the US. This result suggests that both Indonesian and Japanese Islamic stock markets are more interdependent and the Japanese market renders superior impact on the Indonesian market than the UK and the US. The author concludes that investment diversification opportunities between the Indonesian Islamic stock market with the three developed markets are shrinking due to greater markets’ integration.
Abidin and Banchit (2019) study the co-integration and causal relationship among stock indices of Shanghai, Shenzhen, Hong Kong, Thailand, the Philippines, Malaysia, Korea and Indonesia from 2002 to 2018. They suggest that only Hong Kong and Shanghai and Shenzhen stock indices have relationship. This is most probably driven by similar macro- economic variables of China compared to other Asian countries. Further, they claim unidirectional causality running from Hong Kong stock index to the stock indices of Thailand and Korea. Meanwhile, Malaysian stock index causes Hong Kong and Indonesian stock indices, respectively. In addition, Shanghai stock index causes Indonesian and Shenzhen stock indices. Shanghai stock index causes movements in Shenzhen and surprisingly Shenzhen stock index does not cause movements in Shanghai stock index. The Philippines stock index is also found to have significant impact on those Asian stock indices. Their findings conclude that Hong Kong stock index has greater impact on these Asian triangle stock indices and motivate emerging markets’ performance.
Goyal and Bansal (2019) prove that there is no existence of long-run equilibrium relationship between Indian and the US stock market. The results have implications for investors and fund managers, who could take advantages for portfolio diversification between these two stock markets for both short and long-run periods. On the other hand, Mohammed et al. (2020) analyze the historical trend between Saudi’s stock market (Tadawul All Share Index) and the US stock markets (DJIA and S&P500). By using Johansen approach, the study suggests that there is an existence of a long-run relationship between these two markets. Therefore, the empirical findings of this study indicate that there are strong economic linkages between the two countries and hence diminish the cross-market diversifications benefit between these markets.
Co-integration and Causality among Sectoral Indices
In China, Wong and Zhang (2011) conclude the non-existence of no long run association among Shanghai Stock Exchange (ShSE), Shenzhen Stock Exchange (SzSE) and Hong Kong Exchanges (HKE). Surprisingly, the two mainland stock markets, namely ShSE and SzSE are relatively not moving together in long run. Based on these findings, the Chinese and foreign investors could take advantage by diversifying their investments among the three exchanges to reap higher return. Nevertheless, the authors claim that the HKE is Granger-caused by both mainland stock markets in short run, more prominently by the ShSE. Ahmed et al. (2018) test 13 sectoral indices at Colombo Stock Exchange from December 2003 to June 2016. The Pairwise Johansen’s co-integration test reveals that Information Technology, Land Property, Motors, Oil Palms, Services, Telecommunication and Trading sectors offer exceptional cross- sector diversification benefits due to non-existence of co-integration between the sectors, respectively. Manufacturing, Store Supplies, Hotel Travels, Land Property and Trading are the main sectors to Granger-cause other sectors while Plantations, Bank Finance Insurance, Beverage Food Tobacco, and Chemicals Pharmaceuticals are the least Granger-caused sectors.
Another research by Ahmed et al. (2017) sample top ten sectors listed on the Karachi Stock Exchange (KSE) from 2001 to 2014. The pair-wise Johansen co-integration test indicates that
most of the sectors on the KSE do not move together in the long run except the Automobile and Cement sectors. This implies that investors in Pakistan could improve investment return and reduce non-systematic risk by diversifying their investments across sectors domestically.
Moreover, the results suggest that Banking, Chemicals, Oil and Gas, and Textiles are the least integrated sectors, which render splendid diversification benefits for Pakistani investors.
Meanwhile, the Granger causality test proves unidirectional causality from Banking, Chemicals and Cement sectors to other sectors, respectively, whereas the movement of indices in Oil and Gas, Biotechnology and Pharmaceuticals, Textiles, and Electricity sectors are greatly caused by other sectors. Maysami et al. (2004) conclude that the US and Singapore Electronic sectoral indices are co-integrating in long run. This restricts the investors to gain diversification benefits by investing in the two indices at the same time.
Surya and Natasha (2018) investigate short and medium-run co-integration relationship among nine sectoral indices in Indonesia stock market from 2012 to 2016 using weekly data.
The Johansen co-integration test results indicate there is no co-integration in the short-run among the sectors. However, there is a medium-run co-integration among the sectors. Their Eagle-Granger Causality and Vector Error Correction Model tests support the findings. The results imply that sectoral diversification strategy in portfolio construction is applicable to investors if applied in the short-run to minimize their portfolio risk.
Sahabuddin et al. (2018) conduct a study among EMAS Shariah index, FBM KLCI and ten sectoral indices from 2007 to 2017. They discover that all the selected indices are having long run association or co-integrating. However, the Granger causality test shows mixed results, where there are bi-directional causalities between EMAS Shariah index with Finance and Industries sectoral indices, respectively. Meanwhile, there are only unidirectional causalities running from EMAS Shariah index to Plantation, Properties, Tin and Mining, Trade and Services, and Construction sectoral indices, respectively. Whereas, there is no causal relationship between EMAS Shariah index with Industries, Technology and Consumer sectoral indices. Shaari and Mohd Hussin (2019) examine and focus on three main indices at Bursa Malaysia, namely FBMKLCI, EMAS Shariah and ACE indices using monthly data from 2009 to 2015. They reveal that there is a positive significant long-term relationship between the Islamic and the conventional stock indices. Meanwhile, for short-term causal relationships, FBMKLCI has impact on EMAS Shariah index but not on the ACE index.
They also note that EMAS Shariah has caused the changes in ACE index.
3. Methodology
This study employs 10-year daily closing prices for the period from January 1, 2009 to December 31, 2018 of the following FTSE BM indices; Hijrah Shariah Index, Mid70 Index, and seven (7) sectoral indices, namely Construction, Consumer Product, Finance, Industrial Product, Plantations, Properties and Technology. Hijrah Shariah Index (HSI) is a tradeable Shariah-compliant index and was launched in February 2007. We exclude the period from 2007 to 2008 to avoid the abnormal volatility in indices due to the global financial crisis that occurred during the two years. HSI will act as the dependent variable whilst Mid70 and seven sectoral indices represent the independent variables. All indices raw data are obtained from the DataStream (Thomson Reuters). The closing prices data is then used to calculate the daily return for each index.
The return of the index is calculated as follows:
Returns = Index ᵼ - Index ᵼ - 1 x 100 Index ᵼ - 1
Where: Index ᵼ = Closing index;
Index ᵼ -1 = Opening index
In order to conduct the co-integration test, the time series data must be stationary or integrated at the same order (Engle and Granger, 1987). This study will ascertain the data stationarity by unit root tests of Augmented Dickey Fuller (ADF) (Dickey & Fuller, 1981) along with Phillips-Perron (PP) (Phillips & Perron, 1988) with the trend, intercept and trend, and none are included in the equation, respectively. This test aims to endorse and account for heteroscedasticity and autocorrelation in the error terms. Then, prior to conducting the co- integration and causality tests, a suitable lag length is selected using the Vector Auto- Regression (VAR) model by looking at the following criteria; Final prediction error (FPE), Akaike information criterion (AIC), Schwarz Bayesian information criterion (SBIC) or Hannan-Quinn information criterion (HQIC). The criterion that suggests similar maximum lag length will be selected.
Once the above two preconditions are met, the Johansen’s multivariate co-integration test (Johansen and Juselius, 1990) is performed to determine the number of co-integrating vectors or existence of co-integration in long run among various indices. The results are analysed by looking at the Maximum Eigenvalue and Trace Statistics. The decision is made on the basis of respective t-statistics values in comparison to critical value at the 5% significance level. If the t-statistics value is greater than the critical value, then we conclude that there is co- integration or long run co-movement among the indices. Otherwise, there is no co-integration exists. Further, if there is co-integration among the indices, we will perform the Vector Error Correction Model (VECM) to examine the long run relationships, namely positive or negative between HSI and the independent variables. From the VECM test, we will also check on the speed of adjustment of each index for any deviation from the long run equilibrium.
Next, the Wald test is conducted to determine the short run causal relationship between HSI and respective independent variables. As robustness, we also perform the pairwise Granger causality test (Engle & Granger, 1987) to discover unidirectional, bidirectional or no causal relationships among indices. The results will be based on the p-values at the 5% significance level. In addition to all the above tests, we also conduct the Pearson correlation test and the descriptive statistics analysis to see the central tendency, data dispersion and normality of series. All the tests are run using the EViews 10 software.
4. Results and Discussion
Descriptive Analysis
Table 1: Descriptive statistics
There are 2607 observations for each variable from January 1, 2009 to December 31, 2018.
Technology sector records the highest average daily return throughout the sample period, at 0.0384 percent. Technology also reports the highest daily return, at 9.2047 percent.
Meanwhile, Construction sector shows the lowest daily return, at -12.93 percent. Technology indicates the highest volatility at 1.3575 percent followed by Construction at 1.0175 percent.
So, it is consistent with the investment theory that states an investment that promises a high return, comes with a high risk.
HSI, Plantation, Property and Technology data series are skewed to the right, where Plantation is the most skewed, whereas Mid70, Construction, Consumer Product, Finance and Industrial Product are skewed to the left, where Construction is the most skewed.
Construction indicates the highest peak curve, followed by Plantation whilst Finance shows the least peak or flatted curve, followed by Mid70 and HSI. All the data series are not normally distributed and the probability values of 0.0000 imply that each variable is significant.
Correlation Analysis
Table 2: Pearson’s correlation test
Based on the Pearson Correlation test results, Mid70 shows strong correlation with HSI, followed by Plantation, Industrial Product and Finance. Meanwhile, Technology shows weak correlation with HSI and Mid70, respectively, whereas Property and Construction indicate strong correlation with Mid70. Among the seven sectoral indices, there is strong correlation
Variable HIJRA MID70 CONST CONSU FINAN INDUS PLANT PROPE TECHN
Mean 0.0280 0.0361 0.0032 0.0338 0.0381 0.0374 0.0223 0.0240 0.0384
Median 0.0000 0.0142 0.0000 0.0171 0.0060 0.0165 0.0000 0.0000 0.0000
Maximum 4.2803 4.4013 6.8984 4.8846 3.9815 2.9137 7.9636 6.4066 9.2047
Minimum -3.5359 -3.9410 -12.9300 -3.0395 -3.8932 -5.7595 -4.5181 -4.8403 -8.7474
Std. Dev. 0.6105 0.6975 1.0175 0.5152 0.6615 0.6973 0.7391 0.8620 1.3575
Skewness 0.0871 -0.3149 -1.6693 -0.1364 -0.1109 -0.5732 0.8230 0.0576 0.1435
Kurtosis 7.5008 7.4766 25.5806 8.7717 7.0280 7.8520 15.0126 9.0002 8.2599
Jarque-Bera 2203.7170 2219.8920 56596.8400 3626.6210 1767.7440 2699.9760 15969.1300 3912.1840 3014.2060
Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Sum 72.8906 94.0175 8.4156 88.0346 99.1995 97.5714 58.1530 62.6930 100.2282
Sum Sq. Dev. 971.1871 1267.7670 2697.9650 691.8136 1140.2640 1267.2550 1423.5670 1936.3380 4802.3640
Observations 2607 2607 2607 2607 2607 2607 2607 2607 2607
Variable HIJRA MID70 CONST CONSU FINAN INDUS PLANT PROPE TECHN HIJRA 1.0000
MID70 0.7986 1.0000
CONST 0.6721 0.7888 1.0000
CONSU 0.6083 0.6274 0.4241 1.0000
FINAN 0.6898 0.7345 0.5756 0.5790 1.0000
INDUS 0.6972 0.7642 0.5762 0.5548 0.6401 1.0000
PLANT 0.7222 0.6280 0.4656 0.4855 0.5265 0.5147 1.0000
PROPE 0.6489 0.8143 0.6472 0.5366 0.6300 0.6612 0.5047 1.0000
TECHN 0.4219 0.5586 0.4237 0.4347 0.4324 0.5391 0.3257 0.5182 1.0000
between Property with Construction, Finance and Industrial Product, respectively. Finance also shows strong correlation with Industrial product, whereas Technology indicates weak correlations with Construction, Consumer Product, Finance and Plantation, respectively.
Consumer Product has weak correlation with Construction as well.
Unit Root Analysis
Table 3: Unit root test
We perform two types of unit root test, namely Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) to tackle autocorrelation problem and to determine the stationarity of data series. The ADF uses automatic selection by Schwarz Info Criterion (SIC) whilst the PP uses automatic selection by Newey-West Bandwith. Based on the two test results at Level order, which include all three equations, namely intercept, trend and intercept, and none, respectively, we found that there is no unit root problem in the data series, which is evidenced by the t-statistic values of each data series that are greater than the critical values at 5%
significance level, respectively. Hence, we could conclude that the data series are stationary or integrated same order. The results fulfil the precondition to perform our next test, the Johansen co-integration.
Lag Length Selection
Table 4: VAR lag order selection criteria
Based on the VAR lag order selection criteria, we have selected lag 1 as the maximum lag following the recommendation by Final prediction error (FPE) and Akaike information criterion (AIC). So, all the subsequent tests are using lag 1.
Johansen Co-Integration Analysis
The following tables summarize the results of Johansen co-integration test.
Test type Test equation CV at 5% HIJRA MID70 CONST CONSU FINANCE INDUS PLANT PROPE TECHN
At Level Intercept -2.8624 -46.6925 -44.7043 -48.5031 -32.8585 -44.4540 -46.9201 -47.0482 -43.8763 -46.1302 Trend & intercept -3.4115 -46.8138 -44.8637 -48.6668 -47.4494 -44.5607 -46.9630 -47.1778 -44.0671 -46.1213 None -1.9409 -46.6195 -44.6138 -48.5123 -32.6889 -44.3396 -46.8091 -47.0275 -43.8574 -46.1065
At Level Intercept -2.8624 -46.6713 -44.9557 -48.6711 -47.6513 -44.4837 -47.0838 -46.9665 -44.4166 -46.4397 Trend & intercept -3.4115 -46.7510 -44.9473 -48.7535 -47.6992 -44.5607 -47.1029 -47.0931 -44.3740 -46.4313 None -1.9409 -46.6264 -44.9183 -48.6799 -47.6035 -44.4300 -47.0417 -46.9460 -44.4145 -46.4241
ADF
PP
Lag LogL LR FPE AIC SC HQ
0 -17945.01 NA 8.09E-06 13.81609 13.83640* 13.82345*
1 -17812.41 264.1886 7.78e-06* 13.77638* 13.97941 13.84994
Table 5: Unrestricted cointegration rank test (Trace)
Table 6: Unrestricted cointegration rank test (Maximum Eigenvalue)
Tests of Trace and Max-eigenvalue indicate 9 cointegrating equations at the 0.05 level.
* denotes rejection of the hypothesis at the 0.05 level.
**MacKinnon-Haug-Michelis (1999) p-values.
Both Trace and Max-Eigenvalue tests reveal that there are co-integrations or co-movements between the HSI with the Mid70 and seven sectoral indices, respectively. These findings are evidenced by the Trace statistic and Max-Eigenvalue statistic values, which are greater than the critical values, respectively. From the results we could claim that the HSI is moving towards the same equilibrium with the Mid70 and seven sectoral indices in the long run.
The results further indicate that there are stable long run relationships among the variables.
Co-integration among variables implies that there is some adjustment process in short run, thus preventing the errors in the long run relationship from becoming larger and larger. In other words, this implies that that the considered variables are co-integrated among them, namely the series cannot move far from each other or they cannot move independently of each other. These findings allow us to conduct the next test, namely the VECM test.
VECM Analysis
VECM test is conducted to find out the long run relationships, namely positive or negative, among the variables besides looking into the speed of adjustment (in percent) of each variable should there be any deviation from the long run equilibrium. The summary of results is as follows:
Table 7: Normalized co-integrating coefficients
Hypothesized No. of CE(s) Eigenvalue Trace Statistic 0.05 Critical Value Prob.**
None * 0.4042 9623.1440 197.3709 1.0000
At most 1 * 0.3864 8274.3000 159.5297 1.0000
At most 2 * 0.3693 7002.1930 125.6154 1.0000
At most 3 * 0.3541 5801.4680 95.7537 1.0000
At most 4 * 0.3256 4662.6920 69.8189 1.0000
At most 5 * 0.3141 3636.5170 47.8561 1.0000
At most 6 * 0.3066 2654.3670 29.7971 1.0000
At most 7 * 0.2873 1700.3680 15.4947 1.0000
At most 8 * 0.2695 817.9892 3.8415 0.0000
Hypothesized No. of CE(s) Eigenvalue Max-Eigen Statistic 0.05 Critical Value Prob.**
None * 0.4042 1348.8440 58.4335 1.0000
At most 1 * 0.3864 1272.1060 52.3626 1.0000
At most 2 * 0.3693 1200.7260 46.2314 0.0000
At most 3 * 0.3541 1138.7760 40.0776 1.0000
At most 4 * 0.3256 1026.1740 33.8769 0.0001
At most 5 * 0.3141 982.1502 27.5843 0.0001
At most 6 * 0.3066 953.9989 21.1316 0.0001
At most 7 * 0.2873 882.3789 14.2646 0.0001
At most 8 * 0.2695 817.9892 3.8415 0.0000
HIJRA MID70 CONST CONSU FINAN INDUS PLANT PROPE TECHN
1 1.286412 -0.649533 -0.579421 -0.26844 -1.379787 0.062781 0.217536 0.232136 -0.07012 -0.02913 -0.0471 -0.03956 -0.04228 -0.03094 -0.03461 -0.01573
Based on test results in Table 7, we have come up with the following co-integrating equation or long run model.
ECT = 1.0000HIJRA - 1.2864MID70 + 0.6495CONST + 0.5794CONSU + 0.2684FINAN + 1.3798INDUS - 0.0628PLANT - 0.2175PROPE - 0.2321TECHN + 0.0055
The HSI acts as the dependent variable whilst the other acts as the independent variables. The signs of coefficients are reversed in the long run. The results show that all independent variables are significant at 5 percent significance level towards the HSI. In the long run, on average, Mid70, Plantation, Property and Technology sectoral indices pose negative impacts on HSI. Meanwhile, Construction, Consumer Product, Finance and Industrial Product sectoral indices have positive impacts on HSI.
Table 8: Speed of adjustment
Table 8 shows that the previous period deviation from long run equilibrium is corrected in the current period at each variable respective adjustment speed. The fastest speed of adjustment is recorded by Technology, at 46% whilst Plantation and Property have shown 27% speed of adjustment, respectively. Both HSI and Mid70 have indicated approximately 21% speed of adjustment, respectively. Industrial Product Index shows the least speed of adjustment followed by Construction, Consumer Product and Finance Indices. From the results, we could interpret that Technology Index shows 46% adjustment in response to some shock and discrepancy, which disturb long run equilibrium. This, in other words, could be explained that Technology Index is the fastest (by 46%) that adjusts to restore equilibrium compared to the other indices.
Wald Analysis
Further, we perform the Wald test to examine any short run causality between the HSI with Mid70 and the seven sectoral indices and the results are as follows:
Table 9: Wald test
Based on the results at the 5% significance level, we found that in short run, Mid70, Property and Technology are significant and causing the HSI positively. Meanwhile, Construction, Consumer Product and Industrial Product are significant and causing the HSI negatively.
Error Correction: D(HIJRA) D(MID70) D(CONST) D(CONSU) D(FINAN) D(INDUS) D(PLANT) D(PROPE) D(TECHN)
CointEq1 -0.2128 -0.2071 0.1732 0.0312 0.0102 0.2909 -0.2744 -0.2729 -0.4599
-0.0269 -0.0305 -0.0457 -0.0230 -0.0295 -0.0307 -0.0324 -0.0375 -0.0604 [-7.90996] [-6.79337] [ 3.79512] [ 1.35394] [ 0.34571] [ 9.46785] [-8.45942] [-7.28608] [-7.60964]
Variable Test Statistic Prob.
MID70 3.5572 0.0004
CONST -4.8153 0.0000
CONSU -3.5682 0.0004
FINAN 0.5448 0.5859
INDUS -4.2561 0.0000
PLANT -0.5344 0.5931
PROPE 3.2522 0.0012
TECHN 2.4538 0.0142
Whereas, Finance and Plantation are found not significant despite both cause the HSI positively and negatively, respectively in short run.
Granger Causality Analysis
Lastly, we perform the pairwise Granger causality test. The significant results are summarized in Table 10.
Table 10: Granger causality test
Based on the test p-value results, we could conclude that there is unidirectional causality running from HSI to Construction, Consumer Product and Plantation Indices, respectively.
However, HSI is found not to Granger-cause Mid70, Finance, Industrial Product and Property Indices. Meanwhile, HSI is Granger-caused by Mid70, Finance, Industrial Product and Property Indices, respectively. The result also reveals that there is no causal relationship running from or to between HSI and Technology Index.
5. Conclusion
This current study focuses on the co-integration and causality between Hijrah Shariah Index (HSI) with Mid70 and seven sectoral indices traded at Bursa Malaysia. Unit root test reveals that the data series of all variables are stationary at the same order. Then, the Johansen’s co- integration test has indicated the existence of co-integration in the long run among the tested variables. Further, from the test result of normalized co-integrating coefficients, we could see that some indices pose positive impacts on HSI in the long run whilst some other cause negative impacts on the Shariah Index. In term of the speed of adjustment for deviation from lung run equilibrium, Technology Index shows the fastest adjustment and followed by the Plantation, Property, Mid70 and HSI. The Wald test reveals that in short run, Mid70, Property and Technology Indices cause the HSI positively whilst Construction, Consumer Product and Industrial Product cause the HSI negatively. These findings are giving meaningful ideas to the stock market investors and portfolio fund managers as these could help them in forecasting of future direction of the indices and ultimately assisting them in planning and making the best investment decisions. In addition, the findings will also benefit the policy makers in strategizing and enhancing the stock market operation.
For the future study, we would like to recommend to test on the other Malaysian stock market indices such as the small-cap, ACE Market and other sectoral indices. This will give more comprehensive picture on the co-integration and causality among the indices. In addition to the current analysis, future researchers could go in depth by investigating the opportunity to perform investment diversification across the sectors. Further study could also do comparison
Null Hypothesis: F-Statistic Prob. Conclusion
MID70 does not Granger Cause HIJRA 8.53505 0.0035 Mid 70 cause Hijrah HIJRA does not Granger Cause CONST 6.76776 0.0093 Hijrah cause Construction HIJRA does not Granger Cause CONSU 30.9426 3.00E-08 Hijrah cause Consumer Product FINAN does not Granger Cause HIJRA 8.05184 0.0046 Finance cause Hijrah INDUS does not Granger Cause HIJRA 10.321 0.0013 Industrial Product cause Hijrah HIJRA does not Granger Cause PLANT 11.5021 0.0007 Hijrah cause Plantation
PROPE does not Granger Cause HIJRA 9.40935 0.0022 Property cause Hijrah TECHN does not Granger Cause HIJRA 2.44038 0.1184 Technology does not cause Hijrah
between indices of different countries. Other types and diagnostic tests could also be performed as robustness in the future.
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Acknowledgement
Authors would like to express utmost appreciation and gratitude to Bahagian Penyelidikan, Jaringan Industri & Alumni (BPJIA) of UiTM Johor Branch. Because of their support through the Geran Bestari 1/2019, finally authors are able to complete this research paper.
