International Journal of Business and Economy eISSN: 2682-8359 [Vol. 1 No. 2 September 2019]
http://myjms.mohe.gov.my/index.php/ijbec
THE EFFECT OF THE RETURNS OF GLOBAL STOCK INDEXES AND GLOBAL COMMODITIES TOWARD IHSG
RETURNS PERIOD 2009-2018
Rahmanto Tyas Raharja1*and Asep Darmansyah2
1 2 School of Business and Management, Institut Teknologi Bandung, Bandung, INDONESIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 3 May 2019 Revised date : 10 May 2019 Accepted date : 15 September 2019 Published date : 30 September 2019
To cite this document:
Raharja, R., & Darmansyah, A. (2019).
THE EFFECT OF THE RETURNS OF GLOBAL STOCK INDEXES AND GLOBAL COMMODITIES TOWARD IHSG RETURNS PERIOD 2009-2018.
International Journal Of Business And Economy, 1(2), 31-41.
Abstract: The Government of Indonesia through Indonesia Stock Exchange’s “Yuk Nabung Saham” national campaign has the purpose of increasing the number of new investors, especially youth, in Indonesia. The campaign persuades people to invest in stock market because of the potential return offered by investing in stock market, supported by the strong economic indicators of Indonesia. Meanwhile, in first semester 2018, the global stock markets, including Indonesia Stock Market, experienced high volatility with the tendency of weakening that affects the return that investors get. A lot of investors shocked and there are a lot of speculations and news spread among investor about the reasoning and impact of the global stock indices. The results of similar studies show that there is linkage between global markets, either stock market or commodities market, where movement in one market in the global can affect other countries’ stock market movement. A need therefore arises to investigate the relationship between the global markets toward Indonesia Stock Market (IHSG), where the researcher chose to research the relationship between four global stock indices those are Dow Jones Industrial Average / DJIA (United States of America), NIKKEI 225 (Japan), Hang Seng (Hongkong), and STI (Singapore) and
1. Introduction
Indonesia is one country that is highly dependent on international trade in commodities and energies.
Indonesia’s exports and imports are mostly dominated by trade in the oil and gas sector, mining commodities, and energy. This is also in line with global energy demand, where fuel for supplying and producing global energy is currently dominated by crude oil, coal and gas due to available infrastructure, adequate technology, and low production costs.
The numerous and importance of this sector is also reflected in the number of companies engaged in the sector, including those listed on the stock exchange. Currently, a number of listed companies with the largest capitalization in Indonesia are also operating in this sector, including PT United Tractors Tbk (UNTR), PT Indo Tambangraya Megah (ITMG), PT Adaro Energy (ADRO), and others. As well as in the years where global commodity, oil and gas and energy prices experienced a peak period, many large companies in this field had once triumphed in their time and had large capitalization, but experienced a severe decline in stock prices due to a fall in commodity prices in 2009, even to experience bankruptcy and delisting, such as PT Bumi Resources Tbk (BUMI), PT Berlian Laju Tanker Tbk (BLTA), and others. At that time, numerous investors in the Indonesia stock market suffered heavy losses, even bankruptcy caused by the falling prices of commodity stocks engaged in mining, oil and gas. After the crash, investors in Indonesia often discuss and link the movement of global commodity prices, especially coal and oil with the movement of the stock index.
Meanwhile, in first semester 2018, the global stock markets, including Indonesia Stock Market, experienced high volatility with the tendency of weakening that affects the return that investors get.
A lot of investors shocked and there are a lot of speculations and news spread among investor about the reasoning and impact of the global stock indexes and commodities. A need therefore arises to investigate the relationship between the global markets toward IHSG, where the researcher chose to research the relationship between four global stock indices those are Dow Jones Industrial Average / DJIA (United States of America), NIKKEI 225 (Japan), Hang Seng (Hongkong), and STI (Singapore) and three commodities market (crude oil, coal, and gold) toward the movement of IHSG.
2. Literature Review
An empirical study on dynamic relationship between crude oil price and Indian Stock Market indicates the existence of long-term relationship between crude oil price and Indian stock market.
The research revealed that a positive shock in oil price has a small but persistence and growing positive impact on Indian stock markets in short run (Sahu et al., 2014).
the returns of global stock indexes and global commodities as consideration to predict the return and to invest in IHSG. Keywords: IHSG returns, global stock indexes, global commodities returns, capital market, multiple linear regression.
Ono (2011) has conducted a research about the crude oil price shocks and stock market in BRICs (Brazil, Russia, India, and China). The results suggest that whereas real stock returns positively respond to some of the oil price indicators with statistical significance for China, India and Russia, those of Brazil do not show any significant responses.
Adam, et al. (2015) has conducted a research about the modelling of the dynamics relationship between world crude oil prices and the stock market in Indonesia. The study found that there was a significant dynamical relationship between world crude oil prices and Indonesian composite index, both in the long-term and in the short-term.
3. Methodology
Conceptual Framework
Figure 1: Conceptual Framework
Hypotheses
H1= Dow Jones Industrial Average Index has significant effect to IHSG.
H2 = Nikkei has significant effect to IHSG.
H3 = Hang Seng has significant effect to IHSG.
H4 = Strait Times Index has significant effect to IHSG.
H5 = Oil prices has significant effect to IHSG.
H6 = Coal prices has significant effect to IHSG.
H7 = Gold prices has significant effect to IHSG.
H8 = DJIA, Nikkei, Hang Seng, STI, coal prices, gold prices, and oil prices simultaneously have significant effect to IHSG.
DJIA NIKKEI HANGSENG
IHSG
STI COAL GOLD OIL
H1
H2
H3
H4
H5
H6
H7
Data and Variables
Data used in this research are secondary data, with monthly time frame. The data are related with the price of each variable, listed below:
1. Closing price of IHSG from 2009 to 2018 2. Closing price of DJIA from 2009 to 2018 3. Closing price of NIKKEI from 2009 to 2018 4. Closing price of Hang Seng from 2009 to 2018 5. Closing price of STI from 2009 to 2018
6. Closing price of Coal Newcastle from 2009 to 2018 7. Closing price of Gold from 2009 to 2018
8. Closing price of Crude Oil WTI from 2009 to 2018
All the secondary data above are obtained from Investing.
However, to convert the closing price data into returns, author uses this formula:
𝑅𝑒𝑡𝑢𝑟𝑛 𝑋𝑡 = 𝐶𝑙𝑜𝑠𝑖𝑛𝑔 𝑃𝑟𝑖𝑐𝑒 𝑋𝑡 − 𝐶𝑙𝑜𝑠𝑖𝑛𝑔 𝑃𝑟𝑖𝑐𝑒 𝑋𝑡 − 1 𝐶𝑙𝑜𝑠𝑖𝑛𝑔 𝑃𝑟𝑖𝑐𝑒 𝑋𝑡
\The independent variables represent the inputs or causes, or are tested to see if they are the cause.
Independent variables used in this research are mentioned below:
1. The returns of DJIA, symbolized as “X1”
2. The returns of NIKKEI, symbolized as “X2”
3. The returns of Hang Seng, symbolized as “X3”
4. The returns of STI, symbolized as “X4”
5. The returns of Crude oil prices, symbolized as “X5”
6. The returns of Coal prices, symbolized as “X6”
7. The returns of Gold prices, symbolized as “X7”
The dependent variable represents the output or the effect, or is tested to see if it is the effect.
Dependent variable used in this research is:
1. IHSG price, symbolized as “Y”
4. Data Analysis Linearity Test
Figure 2: Linearity Test
Normal P-P plot is used to check if there is linearity exists in the model. The objective of linearity test is to assure the appropriateness of the model specification used where we assume that the relationship between variables is linear, or they perform better if the relationships are linear. The result shows a diagonal straight line without a tendency of horizontal which explains that the relationship between the dependent and independent variables is linear. From the table of P-P plot above, it can be concluded that the data pass the linearity test.
Normality Test
Table 1: Kolmogorov Smirnov Test
One Sample K-S Test Unstandardized Residual
Kolmogorov-Smirnov Z 1.013
Asymp. Sig. (2-tailed) .257
Kolmogorov Smirnov test is used to determine the data used are normally distributed. If the test is equal or less than 5% then the data are not normally distributed. It can be seen from the table below that our data are normally distributed as the Asymp. Sig. is 0.257 or greater than 5%. By looking at Table 1, it can be concluded that all variables used in this study have levels significance above 0.05.
This means that the data used in this study has a normal distribution and shows that the regression model is feasible because it meets the assumptions of normality.
Multicollinearity Test
Table 2: Multicollinearity Test
Model Collinearity Statistics
Tolerance VIF
DJIA .440 2.271
NIKKEI 225 .438 2.284
Hang Seng .298 3.356
STI .294 3.402
Crude Oil .745 1.342
Coal Newcastle .806 1.241
Gold .653 1.532
Multicollinearity is tested using the Variance Inflation Factor (VIF) and tolerance score of each variable. As seen in Table 2, all variables do not have multicollinearity problem as all VIF is less than 10 with tolerance above 0.10, as it is the maximum threshold allowed for multiple regression.
This means that the regression model in this study is feasible because it meets the assumptions of no multicollinearity exists in the independent variables.
Heteroscedasticity Test
We use graphical method to see if our model does not violate the heteroscedasticity test, and based on figure 3, it can be seen that the existing points do not form a certain pattern and spread above and below the zero so that it can be concluded that in this study the regression model used did not experience heteroscedasticity, hence, our data are homoscedastic.
Figure 3: Heteroscedasticity Test
Autocorrelation Test
Durbin Watson Test is employed to test for autocorrelation.
Forn = 119
K = 7
dL = 1.5786
dU = 1.826The value of Durbin Watson stat is 1.944. If the data have dl<dw<4-du, then the data pass autocorrelation test. With 5% significance level, our model pass the autocorrelation test, 1.5786<1.944< 2.1731 and indicates that there is no autocorrelation between the data.
Multiple Linear Regression
The multiple linear regression analysis aims to identify the relationship of the independent variables toward the dependent variable, IHSG return. The regression model is stated as follows:
𝑌 = 𝛼 + 𝛽1𝑋1 + 𝛽2𝑋2 + 𝛽3𝑋3 + 𝛽4𝑋4 + 𝛽5𝑋5 + 𝛽6𝑋6 + 𝛽7𝑋7 +ϵ Where,
Y = Dependent Variable Xn = Independent Variable α = Constant
βn = Regression coefficients ϵ = Error term
Table 3: Multiple Linear Regression
From the table above, we are able to get the value of the regression coefficients for each independent variable along with the constant coefficients. The value of each regression coefficient is then substituted to the regression model. Hence, the regression model resulted for this research is:
𝐼𝐻𝑆𝐺 = 0.837 + 0.261 𝐷𝐽𝐼𝐴 – 0.034 𝑁𝐼𝐾𝐾𝐸𝐼 225 – 0.042 𝐻𝑎𝑛𝑔 𝑆𝑒𝑛𝑔 + 0.630 𝑆𝑇𝐼 – 0.092 𝐶𝑟𝑢𝑑𝑒 𝑂𝑖𝑙 + 0.061 𝐶𝑜𝑎𝑙 + 0.066 𝐺𝑜𝑙𝑑 + 𝜖
Regression coefficients on the independent variables indicate how much the change in the dependent variable (Y) if the independent variable (Xn) increases by 1 unit and other variables assumed to be constant, depending on the plus or minus the coefficient of the independent variables. Regression coefficient is plus, it means that the independent variable (Xn) has positive correlation to dependent variable (Y). If regression coefficient is negative, it means that independent variable (Xn) has negative correlation to dependent variable (Y).
Model Unstandardized Coefficients Standardized
Coefficients
B Std. Error Beta
(Constant) .837 .329
DJIA .261 .128 .209
NIKKEI 225 -.034 .092 -.038
Hang Seng -.042 .106 -.049
STI .630 .129 .614
Crude Oil -.092 .043 -.169
Coal Newcastle .061 .057 .081
Gold .066 .081 .069
Below are the detailed explanations of the equation stated above:
a. The value of constant coefficient is 0.837. It means that the value of IHSG Return will be 0.837 if the value of all the independent variables (DJIA, NIKKEI 225, Hang Seng, STI, Crude Oil, Coal, Gold) are zero.
b. The regression coefficient of the first independent variable DJIA is 0.261. It means that for every 1 point increase in DJIA, IHSG will increase by 0.261, with the assumption of other independent variables are fixed.
c. The regression coefficient of the second independent variable NIKKEI 225 is -0.034. It means that for every 1 point increase in NIKKEI 225, IHSG will decrease by 0.034, with the assumption of other independent variables are fixed.
d. The regression coefficient of the third independent variable Hang Seng is -0.042. It means that for every 1 point increase in Hang Seng, IHSG will decrease by 0.042, with the assumption of other independent variables are fixed.
e. The regression coefficient of the fourth independent variable STI is 0.630. It means that for every 1 point increase in STI, IHSG will increase by 0.630, with the assumption of other independent variables are fixed.
f. The regression coefficient of the fifth independent variable Crude oil price is -0.092. It means that for every 1 point increase in Crude oil price, IHSG will decrease by 0.092, with the assumption of other independent variables are fixed.
g. The regression coefficient of the sixth independent variable coal price is 0.061. It means that for every 1 point increase in STI, IHSG will increase by 0.061, with the assumption of other independent variables are fixed.
h. The regression coefficient of the seventh independent variable gold price is 0.066. It means that for every 1 point increase in STI, IHSG will increase by 0.066, with the assumption of other independent variables are fixed.
We can also see sort the most dominant factor to the least dominant factor influencing the return of IHSG by seeing the coefficient of regression, where the order is as follow:
1. Return of STI 2. Return of DJIA 3. Return of Crude Oil 4. Return of Gold 5. Return of Coal 6. Return of Hang Seng 7. Return of NIKKEI
Simultaneously Significance Testing (F-Test)
In order to test whether or not all the independent variables significantly affect the dependent variable in a simultaneous way, we apply F-test. The F-test shows that it has F value of 15,149 with significance of 0,000. The value of F table for this research with df 1 = 7 and df 2 = 111 and probability of 0,05 is 2.09. The test shows that Fvalue > Ftable. Therefore, it can be concluded that the return of DJIA, NIKKEI 225, Hang Seng, STI, crude oil prices, coal prices, and gold prices affect the return of IHSG simultaneously.
Partially Significance Testing (t-Test)
This test is pursued to test our hypotesis in regards to the significancy of the relationship between each independent and dependent variable. This table below shows the result:
Table 4: t-Test
Model t Sig.
(Constant) 2.542 .012
DJIA 2.045 .043
NIKKEI 225 -.373 .710
Hang Seng -.396 .693
STI 4.900 .000
Crude Oil -2.155 .033
Coal Newcastle 1.070 .287
Gold .823 .412
The interpretation is as follow:
1. With significance level of 5% and two-tailed method, with the p-value of 0.043, we can conclude that the return of DJIA does significantly affect the return of IHSG.
2. With significance level of 5% and two-tailed method, with the p-value of 0.710, we can conclude that the return of NIKKEI 225 does not significantly affect the return of IHSG.
3. With significance level of 5% and two-tailed method, with the p-value of 0.693, we can conclude that the return of Hang Seng does not significantly affect the return of IHSG.
4. With significance level of 5% and two-tailed method, with the p-value of 0.000, we can conclude that the return of STI does significantly affect the return of IHSG.
5. With significance level of 5% and two-tailed method, with the p-value of 0.033, we can conclude that the return of crude oil prices does significantly affect the return of IHSG.
6. With significance level of 5% and two-tailed method, with the p-value of 0.287, we can conclude that the return of coal prices does not significantly affect the return of IHSG.
7. With significance level of 5% and two-tailed method, with the p-value of 0.412, we can conclude that the return of gold prices does not significantly affect the return of IHSG.
Goodness of Fit Test
Goodness of Fit Test is pursued to measure how fit is the model in aiming the purpose of the research.
It can be inferred based on the proportion of the dependent variable variance that can be explained by the independent variables. Coefficient of determination, showed by R2 and adjusted R2, is the indicator of this test. The higher value of R2 and adjusted R2, the better the model resulted from the regression. In this statistical calculation, the value to show the goodness of fit that is used is adjusted R square. Adjusted R square is an indicator used to determine the effect of adding an independent variable into a regression equation compared to R2 which does not account for the number of independent variables effect. The adjusted R2 value has been freed from the influence of the degree of freedom, which means that the value actually shows how the independent variables influence the dependent variable.
Table 5: Goodness Fit Test
R R Square Adjusted R Square Std. Error of the Estimate
.699 .489 .456 3.43395%
The regression model that was used in this study has the adjusted R2 of 0.456. Therefore, it can be taken into conclusion that 45.6% of the variability of IHSG returns as the dependent variable can be explained by the return of DJIA, NIKKEI 225, Hang Seng, STI, crude oil prices, coal prices, and gold prices. Whilst the rest 54.4% variability of IHSG return is explained by another variable that does not included in this regression model of the study.
5. Conclusion and Recommendation
Based on the discussion and analysis result in the previous chapters, it can be concluded that:
1. Simultaneously: Return of DJIA, NIKKEI, Hang Seng, STI, Crude Oil, Coal, and gold have significant effect to the IHSG return within period of 2009-2018, with the regression model:
IHSG = 0.837 + 0.261 DJIA – 0.034 NIKKEI 225 – 0.042 Hang Seng + 0.630 STI – 0.092 Crude Oil + 0.061 Coal + 0.066 Gold + ϵ
2. The model has adjusted R square of 0.456, therefore it can explain 45.6% of the variation occurs in the IHSG returns while the other 54.4% variation might be explained another variable that not be mentioned in this research.
3. With confidence level of 95%, partially: return of DJIA, STI, and Crude Oil significantly affect returns of IHSG within period of 2009-2018. Meanwhile the return of NIKKEI, Hand Seng, Coal, and Gold has no significant effect partially to the IHSG return.
4. The returns of NIKKEI, Hang Seng, and Crude Oil have negative effect to the IHSG returns while DJIA, STI, Coal, and Gold have positive correlation to the IHSG returns within period of 2009-2018.
5. The return of STI is the most dominant factor (variable) influences the IHSG returns in the period of 2009-2018 since it has the highest regression coefficient among other variables (most influencing among others), while NIKKEI is to least dominant factor.
Therefore, the implication of this study is that we can take into account the global stock indices return and global commodities return as consideration to predict the return and to invest in IHSG because they have impact to the return of IHSG.
References
Adam, P., Rianse, U., Cahyono, E., & Rahim, M. (2015). Modelling of the Dynamics Relationship between World Crude oil prices and the Stock Market in Indonesia. International Journal of Energy Economics and Policy, Vol 5, No 2, 550-557.
Ono, S. (2011). Oil Price Shocks and Stock Markets in BRICs. European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 8(1), 29-45.
Sahu, T. N., Bandopadhyay, K., & Modal, D. (2014). An empirical study on the dynamic relationship between oil prices and Indian stock market. Managerial Finance, Vol. 40 Issue: 2, 200-215.
