Effect of Exchange Rate and Global Index in Five Countries Against CSPI

: This study aimed to determine the correlation between Exchange Rate and Global Index on Composite Stock Price Index in Indonesia Stock Exchange in 2014-2019 partially and simultaneously. Exchange Rate and Global Index is proxied by five countries namely Hong Kong, Japan, USA, Singapore, and China. The data used is secondary data obtained through the Official Website of Bank Indonesia for variable Exchange Rate, Official Website Yahoo Finance and Official Website Investing.com for variable Global Index. The methods of data analysis used is multiple linear regression. The results showed that the HKD, JPY, USD, SGD, CNY, and STI partially insignificant on CSPI, as well as HSI and DJIA partially positive effect significant on CSPI, while N225 and SSEC partially negative effect significant on CSPI. Simultaneously, all the variables have a significant effect by influencing by 93.9% while the remaining 6.1% is explained by other variables not examined in this study.


INTRODUCTION
The present war is not a war to take up arms, but the war in the economic sector. Who else if it is not the world's economic superpower, namely the United States and China. Indonesia as a developing country with a large population would have been very dependent on countries with very good economy, including the two countries that have been mentioned above. Remember that the Indonesian exporter of raw commodities, then the dispute will affect both the breakdown of exports to these countries.
This trade war arise because of the presence of symptoms and the development of the world economy and the problem of the structure of competition in the country's political world. Where each country is trying to be superior compared to other countries, including in the framework of the welfare of its people. Trade war is a manifestation of tension economic conflict is feared because it can affect a variety of other dimensions or ends on a real economic war.
This trade war ultimately affect not only for Indonesia to the two countries but also a variety of Indonesian economy in it and also Indonesia's relationship with the global financial markets. Where Indonesia should be careful in controlling the Interest Rate, Inflation, Debt According to the official website of Bank Indonesia (www.bi.go.id) Since 1997, Indonesia began to implement a floating exchange rate system or a floating exchange rate. The system is the determination of the exchange rate of foreign currencies, especially the USD exchange rate which is fully determined by market forces or supply and demand in the foreign exchange market. Bank Indonesia as the monetary authority can not intervene to control these movements.
Results of research conducted by Murtini & Septivanie (2016) which showed that Japannese Yen positive and significant impact on CSPI, research Bella & Ari (2018), R. Safiroh Febrina, et al. (2018), Fatus All Anati, et al. (2018) Medyawati (2016), which shows that the United States Dollar positive and significant impact on CSPI, as well as research conducted by Murtini & Septivanie (2016) which showed that Chinnese Yuan significant negative effect on CSPI.

Graph 2. Global Stock Price Index and Composite Stock Price Index per month during the year 2015-2019
Contagion Effect Theory embraced the theory which states that a country's economic conditions will affect the economy of other countries. This is supported by research conducted by Budi Priyono (2019) which stated that the Shanghai Stock Exchange Composite positive and significant impact on CSPI, research Bella & Ari (2018) which states Straits Times Index a significant negative effect on CSPI, R. Safiroh research Febrina et al. (2018) which states Straits Times Index and the Dow Jones Industrial Average positive and significant impact on CSPI, and the Nikkei 225 Tokyo and Shanghai Stock Exchange Composite significant negative effect on CSPI, and various other studies that support the occurrence of influence between the index of one country to other countries.

LITERATURE REVIEW Exchange Rates
The exchange rate is influenced by several factors such as the level of domestic interest rates, inflation, and central bank intervention on the currency market if necessary. The exchange rate is better known by the exchange rate, have an important role in the framework of monetary stability and in supporting economic activity. A stable exchange rate is needed to achieve a business climate conducive to the improvement of the business world. To maintain exchange rate stability, the central bank at certain times to intervene in foreign exchange markets, especially in times of turmoil excessive. Economists distinguish between the exchange rate into two: the nominal exchange rate and the real exchange rate. The nominal exchange rate (the nominal exchange rate) is the relative price of currencies of two countries.

Global Financial Markets
The global market is broad-based financial market of the world. The market opportunity is always open to all business activities and investments, usually some very strong economies can dominate a large market capitalization stocks both at the regional, multilateral and global.

Composite Stock Price Index
Composite Stock Price Index is a stock price index figure has been calculated and are constructed so as to generate a trend, where the index number is a number that is processed in such a way that it can be used to compare the incidence of changes in stock prices over time. The index is in the capital markets is affecting the portfolio investment activities that will be undertaken by the investor. Profit increased at CSPI will increase the investment portfolio will be made by investors to increase investment in companies listed on the stock exchange through the information that has been received by the investors of the securities in the stock exchange through rate profit expected by investors from year to year.

RESEARCH METHODOLOGY Type and Design Research
The method used in this research is descriptive and causality. Descriptive research method is useful to obtain information or a description of the condition of a study, while the causality research method is a method that focuses on the impact of changes in a case against a fact which has been there before. In other words, the study of causality is useful to improve the system, or the fact that there has been for the better.
The design for this research generally uses quantitative methods. Sampling was conducted using a sampling technique Non-Probability Sampling. The sampling technique used is saturated sampling. Time Horizon used is a time series in which the retrieval of data or information collected in a time series on a monthly basis starting from January 1st, 2015 until December 31, 2019. The data used is secondary data obtained through the Official Website of Bank Indonesia (www.bi.go.id) for variable Rupiah Exchange Rate (against HKD, JPY, USD, SGD and CNY), Official Website Yahoo Finance (www.finance.yahoo.com) for variable Global Index (HSI, N225, DJIA) and CSPI, Official Website Investing.com (www.investing.com) for the variable other Global Index (STI and SSEC), as well as the literature obtained through books, financial statements, or other Internet resources. The number of variables as much as 11 consists of 10 independent variables and 1 dependent variable and the amount of data that is used as much as 660 data.

Mechanical Analysis
Data analysis techniques used in this research is multiple linear regression using SPSS Ver. 22. According to Wati (2018: 141) a regression model will be used for forecasting, a good model is a model with a minimum forecasting errors. To that end, a model must meet some assumptions before use, the assumption is known as the classical assumption, which consists of a test of normality, heteroscedasticity, multicollinearity, autocorrelation and the assumption of linearity.

RESULTS AND DISCUSSION
Here is the result of calculations using SPSS 22: Normality Test

Multicollinearity Test
From the table above it can be seen that the VIF for two variables (CNY and SSEC) has a value of less than 10, while 8 other variables have a VIF greater than 10. This shows that there is multicollinearity in regression models. This is also confirmed by the Tolerance value of two variables (CNY and SSEC) has a value greater than 0.1, while eight other variable has a value of Tolerance is less than 0.1. It can be concluded that the regression model, there are symptoms multicollinearity. However, it will not be a problem given that the correlation between the independent variables that could have a powerful and influence each other.

Heteroscedasticity Test Graph 3. Heteroscedasticity
From the scatterplot graph above shows that the dots randomly spread and spread both above and below the number 0 on the Y axis It can be concluded that there are no symptoms heterokedastisitas on this regression model. From the table above it can be seen that the Durbin-Watson value calculation result is 1,668. Where scores fall between -2 to +2 so that we can conclude there is no autocorrelation.

Assumption of Linearity Graph 4. Assumption of Linearity
From the ScatterPlot graph above it is known that the analyzed variables follow a straight line so that certainty of increasing or decreasing the quantity of one unit in one of the variables will be followed linearly by the increase or decrease Other variables. From the coefficient of the determination shown in the table above, the Adjusted R Square value of 0939. It shows that of 93.9% of dependent variables can be described by independent variables while the remaining 6.1% is described by other variables that are not examined in this study. The t table calculation for this study is calculated at a significance of 5% (two tailed) with df = n -K -1, where n is the number of samples and K represents the number of free variables. Then df = 60 -10 -1 = 49 So it obtained t value of table by 2.0095.

Multiple Linear Regression Analysis
It can be formulated regression equations that formed are:

^ = Not Significant
The value constants of 5267.657 which means if HKD, JPY, USD, SGD, CNY, HSI, N225, DJIA, STI, and SSEC are equal to zero then the rate is positive by 5267.657. The resulting of value independent variable indicates an increase or decrease on the SCPI value if the other independent variable are held constant. The regression equation shows direction of each independent variable to dependent variabel. HKD, HSI, DJIA, and STI has a positive influence direction to the SCPI. Meanwhile, JPY, USD, SGD, CNY, N225, and SSEC has a negative influence to the SCPI.

Influence of Hong Kong Dollar on the Composite Stock Price Index
The Hong Kong Dollar variable has a calculated t value = 0537 where T counts < t table = 2.0095 and the value of sig. = 0594 > 0.05, it can be concluded to accept H0 and reject Ha1 which means Hong Kong Dollar variable is insignificant effect on the CSPI.

Influence of Japannese Yen on the Composite Stock Price Index
A variable Japannese Yen has a value of t count =-1,236 where T counts < t table = 2.0095 and the value of sig. = 0222 > 0.05, it can be deduced to accept H0 and reject Ha2 which means a Japannese Yen variable is insignificant effect on the CSPI. The results of this study are contrary to the research conducted by Murtini & Septivanie (2016) which shows that Japannese Yen has a significant positive effect on the CSPI.

Influence of United States Dollar on the Composite Stock Price Index
The variable United States Dollar has a value of t count =-0047 where T counts < t table = 2.0095 and the value of sig. = 0962 > 0.05, it can be deduced to accept H0 and reject

Influence of Singapore Dollar on the Composite Stock Price Index
The Singapore Dollar variable has a value of t count =-0314 where T counts < t table = 2.0095 and the value of sig. = 0755 > 0.05, it can be deduced to accept H0 and reject Ha4 which means Singapore Dollar variable is insignificant effect on the CSPI variable.

Influence of Chinnese Yuan on the Composite Stock Price Index
The Chinnese Yuan variable has a value of t count =-1,361 where T counts < t  (2019), which shows that Chinnese Yuan is insignificant to the CSPI, but the research has been positively influential while in this research the results obtained are negative.

Influence of the Hang Seng Index on the Composite Stock Price Index
The Hang Seng Index variable has a calculated t value = 4,567 where T counts > T table = 2.0095 and the value of sig. = 0.000 < 0.05, it can be concluded to reject H0 and accept Ha6 which means the Hang Seng Index variable has a significant effect on the CSPI variable. The results of this research in line with the research conducted H. Medyawati & M. Yunanto (2016), Armelia & Yudhinanto (2018) showed that Hang Seng Index was positive and significant to the CSPI. As well as opposed the research done by Dyah & Nadia (2017) indicating that Hang Seng Index has no effect on the CSPI.
Influence of the Nikkei 225 Tokyo on the Composite Stock Price Index The Nikkei 225 Tokyo variable has a value of t count =-3,948 where T counts < t table = 2.0095 and the value of sig. = 0.000 < 0.05, it can be deduced to reject H0 and accept Ha7 which means the variable Nikkei 225 Tokyo has a significant effect on the CSPI. The results of this research in line with the research conducted by Riskin Hidayat (2016)

Influence of the Dow Jones Industrial Average on the Composite Stock Price Index
The Dow Jones Industrial Average variable has a calculated t value = 4,694 where T counts > T table = 2.0095 and the value of sig. = 0.000 < 0.05, it can be concluded to reject H0 and accept Ha8 which means the Dow Jones Industrial Average variable has a significant effect on the CSPI. This research is in line with the research conducted by Sulaeman & Erwin (2017), R. Safiroh Febrina, et al. (2018), Fatus All Anati, et al. (2018), Armelia & Yudhinanto (2018) which shows that the Dow Jones Industrial Average has a positive and significant impact on the CSPI.
As well as conflicting research showing that the Dow Jones Industrial Average is not significant to the CSPI by Riskin Hidayat (2016), Luh Gede Sri Artini, et al. (2017) for negative influences and by Dyah & Nadia (2017) for a positive influence.

Influence of the Straits Time Index on the Composite Stock Price Index
The Straits Time Index variable has a value of t count = 0750 where T counts < t table = 2.0095 and the value of sig. = 0457 > 0.05, it can be concluded to accept H0 and reject Ha9 which means that the Straits Time Index variable is insignificant effect on the CSPI variable.
The results of this study contradict the research conducted by Bella & Ari (2018), R. Safiroh Febrina, et al. (2018), Luh Gede Sri Artini, et al. (2017), Sulaeman & Erwin (2017 who showed that Straits Times Index was positively influential and significant to the CSPI. As well as supporting the results of research conducted by Budi Priyono (2019) shows that Straits Times Index has no significant effect on the CSPI.

Influence of the Shanghai Stock Exchange Composite on the Composite Stock Price Index
The variable Shanghai Stock Exchange Composite has a value of t count =-2,543 where T counts < t table = 2.0095 and the value of sig. = 0.014 < 0.05, then it can be deduced to reject H0 and accept Ha10 which means the variable Shanghai Stock Exchange Composite has a significant effect on the CSPI. The results of this study are in line with the research conducted by R. Safiroh Febrina, et al. (2018) which shows that the Shanghai Stock Exchange Composite has a significant negative impact on the CSPI.
Opposed to the research conducted by Budi Priyono (2019) which shows that the Shanghai Stock Exchange Composite has positive and significant effect on the CSPI. As well as research conducted by Riskin Hidayat (2016), Luh Gede Sri Artini, et al. (2017), Sulaeman & Erwin (2017 who showed that the Shanghai Stock Exchange Composite had a negative and insignificant effect on the CSPI. The F table for this study is calculated at a significance of 5% with df1 = k -1 and df2 = nk, where n is the number of samples and k represents the total number of variables. Then df1 = 11 -1 = 10 and df2 = 60 -11 = 49 so the obtained F

CONCLUSIONS AND RECOMMENDATIONS Conclusions
Based on analysis data and discussion results, it can be concluded as follows: 1. Hong Kong Dollar (X1), Japannese Yen (X2)

Recommendations
Based on the above conclusion, the author provides the following advice: For Investors 1. For investors who will conduct investment transactions in the Indonesia Stock Exchange will be better if looking at the movements of each change. Either micro or macro. Both economic and non-economic. As a matter of fact, a small change will have an impact on other changes. For Government 2. Further increasing cooperation in investment with other countries, it aims to encourage capital inflows to Indonesia. But it still needs to be supervised to avoid being ruled by foreigners. 3. Strive to become a self-reliant country that does not rely on other countries by realizing the various potential owned by the Indonesian state and change it to be better to compete with other countries. 4. To improve the welfare of the lower class community such as socialization and training, working capital loans, tax reduction, school development, etc to avoid social inequality. The author believes that Indonesia is able to become a developed country when its people also have quality human resources. For Further Research 5. For further research can use the daily data of the stock price index and extend the period of study to get more accurate research results. 6. For the use of the rupiah exchange rate can use the middle value to see from the different side. 7. This research is conducted by selecting a random country which is considered to have more proximity to Indonesia than other countries. Therefore, future research is expected in order to focus on one of the regions such as Southeast Asia's best indices, Asia Pacific's best indices, or the best indices in the world in order to know what strategies can be prepared to compete with others.