“Macroeconomic factors and LQ45 stock price index: evidence from Indonesia”

This paper provides the relationship between macroeconomic variables, including ex- change rate, BI rate and inflation, and stocks performance, particulary bluechip stocks listed in LQ45 index in Indonesia Stock Exchange. The study particularly gives insights on bluechip stocks listed in LQ45 stock price index in Indonesia Stock Exchange between 2015 and 2017. The data were obtained from various sources during the period, including the Indonesia Stock Exchange (IDX), the Central Bank of Indonesia (BI), and the Ministry of Trade. This study followed a Vector Error Correction Model (VECM) attempting to estimate the relationship between variables both in the short term and in the long term. The findings of the study showed that in the long run, exchange rate, BI rate and inflation have a negative impact on stock market performance, particularly on LQ45 index in Indonesia Stock Exchange. It implies that an increase in macroeco- nomic variables results in the decline of stock market performance. Meanwhile, in the short run, two variables, namely the exchange rate and inflation, positively affect stock market performance in Indonesia. On the contrary, the relationship between BI rate and stock market performance showed a negative correlation. These findings have sig- nificant implication for the understanding of how macroeconomic variables affect the stock market performance, particularly LQ45 price index in Indonesia Stock Exchange.


INTRODUCTION
The presence of financial sectors plays an important role in the economy by providing demand and supply for money, as well as lenders and borrowers. Stock, for instance, is one of the most considerable instruments for investors. From the company sides, it helps company to enlarge their firms receiving greater funds directly from investors rather than collecting money from intermediary institutione such as banks and other financial institutions. In contrast, from investors' perspective, it provides an opportunity to obtain higher return, instead, they have bigger risks.
The valuation of stocks becomes the challenge and the opportunity for investors. Since asymmetric information exists, it is difficult for individuals to determine a good company with valued stock prices and a bad company with undervalued stock prices. In Indonesia, the presence of stock listed market by several categories helps the investor in reducing asymmetric information problems. Bluechip stock is the most considerable for the investor to put their money, which is listed on the LQ45 stock index. The bluechip stocks are of high market capitalization, considered very suitable for the novice investor (Budiarti, Ratnaningsih, & Penangsang, 2017). This is reasonable, because stocks with high market capitalization are not easily affected by its share price. From the LQ45 stock price index is important for the novice investors, because LQ45 stock price index that includes 45 issuers with very high liquidity stocks.
In LQ45 stock price index, investors can determine how much movement of shares from 45 companies was assessed from market capitalization. Because of that, the stock price not always will continue to rise, but could also decrease in value, which is no exception to 45 issuers in LQ45. Given the ups and downs of stock prices here, in 6 months, for 45 issuers, LQ45 stock price index changes according to the assessment criteria of its shares (Hermuningsih, 2012).
Macroeconomic factors such as exchange rate, BI rate, and inflation have a relationship with the performance of the company and stock prices. Noticeable studies on macroeconomics and its impact on stock prices have been examined in several countries such as Malaysia, Pakistan, Kenya, Iran, Indonesia, India and so forth (Rad, Jawaid and Haq (2012) remarked that there is a long-term relationship between the exchange rate and interest rates on stock prices in Pakistan. Similarly, several macroeconomic variables, including money supply, exchange rates, and inflation, affect the stocks market return in Kenya (Mumo, 2017). Indeed, Khalid and Khan (2017) showed that the exchange rate and inflation positively affect stock market volatility in the long run. In contrast, Rad (2011) mentioned that macroeconomic variables such as consumer price index, free-market exchange rates do not play an important role in fluctuating stock prices in Iran.
In Indian context, Tripathi and Seth (2014) revealed a correlation between stock market indicators and macroeconomic factors. Meanwhile, Sutrisno (2017) mentioned that inflation and exchange rate have negative impact on all industries in Indonesia. Based on previous studies mentioned, however, there is still uncertainty whether macroeconomic variables impact on stock market performance. Surprisingly, to date, there are few studies that have investigated the association between macroeconomic factors and stock market performance in Indonesia. Therefore, this study addressed the relationship between macroeconomic factors and stock market performance, particularly on LQ45 stock index in Indonesia. This paper has been divided into five sections. The Introduction section deals with the current existing macroeconomic problems in Indonesia and their impact on stock market performance. Section one begins by laying out the theoretical dimension of the research and looks at the relationship between macroeconomic variables and stock price in several countries. In addition, section two is concerned with the methodology used for this study. Section three presents the results and the discussion. Final section presents the findings of the research and focuses on the three key macroeconomic factors, which are exchange rate, BI rate and inflation. . A high level of inflation may cause a negative impact on the particular country. It will also cause a decrease in purchasing power of money. In addition, high inflation can also reduce the level of real income that investors obtained from their investments. Conversely, when the inflation level experiences a decreasing trend, it will be a positive signal for investors, as the risk of purchasing power of money and the risk of falling real income decrease. Uwubanmwen and Eghosa (2015) mentioned that high inflation causes a company's profits to decline, which causes securities to become less competitive. Indeed, Daferighe and Charlie (2012) have conducted a study on the relationship between inflation and stock market performance in Nigeria during 1991-2000. The findings showed that stock market investment is regarded as a good hedge againts inflation. However, on the other hand, Antonakakis et al. (2017) remarked that correlations between the inflation and stock prices in the United States evolve heterogeneously over time. An increasing inflation can reduce capital gains, which results in declined profits received by investors. From the company perspective, an upward trend inflation where the increase cannot be borne by consumers can reduce the level of corporate income. It means that the risks faced by the company will be greater to keep investing in shares. Consequently, the demand for shares will also decrease and this issue can reduce a company's profits that make securities in the capital market become unattractive commodities.

Exchange rate and stock market performance
The exchange rate is one of the most important prices in an open economy given the enormous influence on the current account balance and oth-er macroeconomic variables. There are two approaches that are used to determine currency exchange rates, namely the monetary approach and the market approach. In the monetary approach, currency exchange rates are defined as prices where foreign currencies are traded against the domestic currency and those prices are related to money supply and demand. When a country's exchange rate weakens against the US Dollar, companies that have US Dollar debt will experience a decline in profits or even losses.
Studies on the relationship between exchange rate and stock market performance have been conducted in many countries. Olugbenga

Stationarity data test
This study uses the Augmented Dickey-Fuller (ADF) to test the stationarity of data. If the value of absolute t-statistic is smaller than the critical value in the MacKinnon table at various levels of confidence (1%, 5%, and 10%), it indicates that the data are not stationary. Besides that, it can also be seen in the value of prob greater than 0.05, which also indicates that the data are not stationary. Conversely, if the ADF value is greater than the critical value of various confidence levels (1%, 5%, and 10%), then there is no unit root or stationary data. The ADF was formed in order to obtain the autoregressive equation as follows:

Optimal lag test
VAR estimation is very sensitive to lag length used. If the lag in judicial stationarity is too small, then the residuals of the regression are unable to show the process of white noise so that the models cannot accurately estimate the actual error. As a result of mistakes, standards are not estimated. However, if the lag is incorporated too much, it will reduce the ability to reject H0 as additional parameters that too much will reduce the degree of freedom. The testing lag also has the benefit of eliminating the autocorrelation problem, therefore, the use of optimal autocorrelation lag problem is no longer expected to arise.

Cointegration test
The existence of non-stationary variables can cause the most likely long-term relationship between the variables. Hence, cointegration test is required. Cointegration test is a test aimed at finding long-term and short-term relationship between variables. In this study, Johansen cointegration tests are used in the test for the existence of cointegration between the variables. The test developed by Johansen can be used to determine the cointegration number of variables (vectors). Johansen test can be seen with the autoregressive models as follows:

Granger causality test
Granger causality test aims to determine the causal relationships between variables used in the study. The effect of causation is addressed to see the effect of each variable against another one by one instead of per group.

VECM estimation
VECM models can be applied if the studied variables proved to have a cointegration relationship. If the cointegration test results indicate a long-term equilibrium relationship, the results of the relevant dynamic regression estimation are VECM models. It can be used as a method to determine the effect of the caused by a variable in the long term and in the short term. VECM can be formulated as follows: Based on the previous test for its stationarity using Augmented Dickey-Fuller (ADF), it was found that all the variables contain unit root or are nonstationary, therefore, research continues to the next step. Table 1 describes the results of the first stationarity test at different degrees in the group for all the data, which indicates the absence of a stationary unit root. This shows that all the data are integrated on the degree of integration of one (1).

Optimal lag test
Testing optimal lag length on VECM and Var used method aims to eliminate autocorrelation in the data. Parameters that can describe the optimal lag length criteria Akaike Information Criterion (AIC), Schwarz Information Criterion (SC) and Hannan Quinnon (HQ). Table 2 illustrates the optimal lag test results of the study. In this test, it was found that the number of optimal lag is one. This suggests that the independent variable can affect the dependent variable. Determination of the optimal amount of lag can be seen from the number of asterisks (*) contained in the lag of one (1). Table 3 summarizes the result of Granger causality test between variables. From the calculation in the table, it can be seen that the variables, which have a causal relationship shown by variables that have a probability level of not more than 0.05, then H0 will be rejected, which means the influence of the variable to another variable. Conversely, when the probability is higher than 0.05, then H0 is accepted, it implies that there is no relationship between variables. From Table  3, there is a reciprocal relationship between the variables, both of variable inflation, the exchange rate, BI rate, and LQ45. H0 rejection is evident from the level of probability < α (0:05)  in all the variables. DEXR is not significantly affecting DLQ45 and vice versa, it has been proven from the value of the probability that both variables are above 0.05, which is about 0.08 and 0.49 at lag 1, respectively. The inflation variable does not significantly affect DLQ45 and vice versa, this can be seen from the probability values, which are about 0.35 and 0.83, respectively, at lag 1. Furthermore, the BI rate variable does not significantly affect the LQ45 variable and vice versa. It can be seen from the probability values, which are 0.46 and 0.09 at lag 1.

Cointegration test
In VECM estimation, cointegration relationship has a very important role for each item, namely where there is no cointegration relationship between the estimated VECM variables that cannot be used or canceled and replace it with the VAR method. Cointegration testing in this study is performed using Johansen with the critical value of 0.05.  Table 4 informs about the result of the cointegration test. From the table, it can be explained that at a test level of 5 percent, there is one cointegration of rank-related variables. This can be evidenced from the trace statistic value greater than the value of critical value, it means the variables used have a longterm relationship with one another. Therefore, the estimated VECM can continue for further analysis.

VECM estimation test
In the VECM estimation, if the t-statistic is greater than the value of the t-table, then it can be stated that there is long-term or short-term relationship between the variables. In more detail, short-term or long-term relationship can be interpreted as the causal relationship between the variables, both positive and negative.  Table 5 explains the relationship between the variables in the long run. In general, the exchange rate, BI rate, and inflation had a significant negative effect on the stock market performance in Indonesia.
In more detail, the test results showed that all of the t-statistic exceeds the t-table of 1.69389. The inflation rate is about 6.07715, -2.65392 is for the BI rate, and -2.01178 is for inflation. In the long term, the exchange rate has exhibited a significantly negative effect on stock prices. The increase in the rate of excessiveness and persistence will make investors feel inconvenient and opt out of the capital markets. This is reasonable, because the investor wants to invest in the most profitable returns. According to Samsul (2006), a sharp rise in the exchange rate of the US Dollar against the Rupiah will negatively affect the emitent who has debts of Dollars, while product issuers are sold locally. Indeed, Nordin et al. (2014) showed the negative relationship between exchange rate and stock market performance. However, the findings of this study are in contrast with Mgammal (2010) who suggested a positive relationship between the stock price index and the exchange rate.
Another result showed that BI rate has a negative impact on stock price index in the long run. This is understandable, because the BI rate has increased continually, which showed that the country's economy is getting worse. Kewal (2012) argues that the interest rate has a great influence on stock prices. It implies that higher interest rate can worsen the economy, raise interest costs, thereby lower the corporate profits and cause the investors to sell stocks and transfer funds into the bond market.
Inflation, a variable which plays an important role for a country, shows a significant negative effect on stock market performance in the long term. The excessive inflation rate will reduce net income produced by the company, this is due to inflation, which increased continuously, that the production cost will increase. Samsul (2006) stated that infla-tion may harm the overall economy in the sense that many companies will face bankruptcy. This means that high and prolonged inflation will drop stock price in the market. This study had similar results with Mgammal (2010), stating that there is the relationship between stock prices and inflation in the long term. However, in contrast, the findings of the study refused research by Gursida (2018), which states that inflation does not significantly affect the stock price index.  The results of this study indicated the same findings with Jawaid and Haq (2012) who state that the exchange rate has a positive effect on stock prices. In the short term, the exchange rate can have a significant positive effect on the LQ45 stock price index, this is because when the exchange rate depreciates, it can increase exports, which will increase investment. In other words, the increase in investment that occurs is a response to an increase in the utilization of capacity driven by increased exports (Bank Indonesia, 2010).
The BI rate in the short term has a significant negative effect on the LQ45 stock price index, indicating that when the BI rate increases, it will have consequences in declining stock market performance. This occurs, because investment also depends on loan interest rate. It implies that higher interest rate will promote higher level of costs generated, so that investors will rethink to invest the money. The results of this study are similar to Nordin and Ismail (2014) who state that interest rate has a negative influence on the stock price index.
Lastly, inflation in the short term has a significant positive effect on the LQ45 stock price index. It shows that the increase in inflation will increase stock prices, this is because if inflation increases, the costs incurred by companies will be higher, therefore the company will increase the number of shares in circulation. The results of this study rejects the results of Gursida (2018) who states that inflation does not significantly affect the LQ45 stock price index.

CONCLUSION
This study attempted to examine the relationship between macroeconomic factors and stock market performance in Indonesia, especially on LQ45 price index listed in Indonesia Stock Ecxhange. The findings of the study remarked that in the long run, exchange rate, BI rate, inflation have a negative impact on stock market performance. It implies that an increase in macroeconomic variables leads to decline of stock market performance. However, in the short run, two variables, namely the exchange rate and inflation, positively affect stock market performance in Indonesia. In contrast, the relationship between BI rate and stock market performance showed a negative correlation. This negative influence is related to the interest rate of the loan. This is understandable, because the level of costs generated will be higher when the loan of interest is higher, resulting in a decreasing number of projects being implemented. These findings have significant implication for the understanding of how macroeconomic variables affect the stock market performance in Indonesia. Considerably more work will need to be done to determine factors affecting stock market performance in Indonesia.