A study on the effect of macroeconomic factors on stock market performance in Malaysia

. This study examines the effect of macroeconomic variables on stock market performance in Malaysia from January 2015 to December 2021. The macroeconomic variables included in this study are inflation rate, real effective exchange rate, m2 money supply and short-term interest rate. Johansen Cointegration Test has been utilized if the variables have long-term impact on Malaysian stock market performance; whereas regression analysis will quantify the impact. The results show that the real effective exchange rate has a moderate positive effect on KLCI index. Secondly, the inflation rate and overnight-policy rate have long-term positive effect on the KLCI index. M2 money supply has a long-term negative effect on the KLCI index. This study extends previous studies by examining the effect of macroeconomic variables on stock market performance in emerging market.


Introduction
From corporates' perspective, the pandemic has either posted challenges or opportunities, unaffected is not an option. That's why we can see that some of the industries thrived, while some others suffered, and so are their stock prices. For instance, the stock price of Top Glove, a healthcare company skyrocketed by approximately 380% at the beginning of the pandemic. Such movements in the stock market will give out signals of profitable opportunities and attract more investors.
With the stock market being the most followed financial market nowadays and with the roles that it is playing in the economy, stock market performance becomes vital. Stock market performance refers to the well-being of the stock market as a whole or the well-being of a specific stock in the stock market (John and Ezeabasili, 2020). The indicator of stock market performance is the stock market index, which refers to an investment portfolio that represents a segment of the financial market. It has been widely used in previous studies as the proxy for stock market performance (Mohammad,  According to , the stock market performance can portray the well-being of an economy. At the same time, the economic function can be demonstrated in stock market performance. Due to the nature of interdependency, stock market performance is not always stable and consistent, especially in emerging economies. The studies of emerging stock market performance can date back to the 1990s. Harvey (1994) showed that the return, risk and growth of the emerging stock market were higher than the developed market. This is not necessarily a good sign as the emerging stock market might fail to support industrialization through investment fund allocation (Ahmed M.F., 2000). Emerging stock markets are more sensitive to economic factors, social factors or political factors than the developed market.
Hence, stocks are often considered the riskiest asset to trade as the prices are unpredictable. Since demand and supply are the drivers of the market, there is no way that the prices can be accurately predicted since investors' demand and supply levels are diverse. However, the stock market behaviour can be predicted by analysing macroeconomic variables (Talla, 2013).
That's why this study will focus on macroeconomic factors, such as inflation, interest rate, exchange rate and money supply. Supposedly, inflation will affect the purchasing and investing power in the market; Second, interest rate movements will affect investors' required rate of return, so are their investment preferences and choices; Thirdly, the exchange rate risk posed by exchange rate movements will affect the profits of foreign investors; Lastly, money supply in an economy will directly affect the demand of trading and investing. Overall, the variables included are supposed to affect investors' involvement in stock market in either way they move, thus the stock market performance.
The theoretical motivation for conducting the study of the effect of macroeconomic variables on stock market performance in Malaysia is that it is proven by other studies that these variables will somehow affect stock market performance. However, researchers have been arguing about the relationship between macroeconomic variables and stock market performance back in the 1970s until now. Over the years, the interactions between the variables and the stock market combined with the arguments raised by researchers have brought about an interesting case study.
Besides, it is also proven in other studies that emerging stock markets are more sensitive to economic factors than the developed market. Malaysia, as an emerging country, can be a suitable candidate to be studied. Malaysia has the largest stock market among the ASEAN 5 (Malaysia, Singapore, Indonesia, Philippines and Thailand) in terms of the number of listed companies (Ho, 2019). According to World Development Indicators (WDI), Malaysia had 919 listed companies until 2019, while Thailand, in the second place, with 725 listed companies. In terms of the market capitalization ratio, Malaysia is also the second strongest economy, following Singapore. Given the significant role and potential of the Malaysian stock market among the ASEAN 5, its stock market performance is vital for investors.
The This study attempts to identify if macroeconomic variables will affect Malaysian stock market performance and if they do so significantly. The major stock market index will be used as a proxy for stock market performance and four macroeconomic variables will be included as independent variables (exchange rate, inflation rate, money supply and interest rate).

Literature Review
The research of Tripathi & Seth (2014) explored the short and long-run causal relation between six macroeconomic variables and stock market performance. The macroeconomic variables included were exchange rate, Index of Industrial Production (IIP), interest rate, money supply, oil prices and WPI, while BSE India Sensex, BSE India market capitalization and BSE India market turnover were employed as a stock market performance indicator. Their findings indicated that there was a weak relationship between those variables and stock market performance in the long run, except for exchange rate and interest rate. The stock market performance was a Granger cause of the real economic variables. The reason for the weak relationship might be that the Indian stock market reflected the current movement as well as the expected movement of these variables.
A study investigating the time-series relationship between stock market index prices and the macroeconomic variables of exchange rate and the oil price of Brazil, Russia, India and China (BRIC) was conducted by Gay (2016). The time frame of his study ranged from 1993 to 2006. The relationship between crude oil price and stock prices were expected to be negative in this study. An increase in oil price will lead to an increase in production costs and a decrease in the company's profit. Eventually, investors' confidence levels will drop. As a result, the findings suggested that the relationship between crude oil price and stock prices was inconsistent and weak throughout the BRIC countries. Oil prices might not have a noticeable effect as expected since inflation was not considered, but the oil price movement might produce better results. Anyhow, the exponential growth of oil prices didn't happen until 2004, which was already near the end of the time frame. So, it won't make much difference. In terms of the exchange rate, the findings indicated that there was a weak positive relationship between exchange rate and stock market performance in Brazil, India and China, except Russia. The reason might be the decreasing trend in the RBL/USD exchange rate in late 2003.
The research of Yong & Hassan (2019) attempted to understand the impact of economic variables (inflation, money supply and exchange rate) on the Malaysian service sector's stock market. FTSE KLCI was used as the proxy for the service sector's stock market performance. The findings proved that inflation had a weak positive relationship with the Malaysian service sector's stock market, which meant that a lower inflation rate would boost the stock performance insignificantly. That might be due to the continuous growth of confidence and purchasing power among Malaysians. The fact that the inflation rate could not impact their confidence and purchasing power indicated that the Malaysian stock market was expected to generate stable and high growth. In terms of money supply, there was a strong positive relationship. The strong relationship was caused by the overwhelming market reactions. Investors assumed that stock prices would be affected by the money supply movement, so they traded their shares actively and caused the high volatility. Besides, the findings also showed that the exchange rate had a strong negative relationship. When the exchange rate rises (The ringgit appreciates), foreign investors will withdraw their investments to prevent further losses. In contrast, it is cheaper for foreign investors to invest in Malaysia when the exchange rate falls (The ringgit depreciates). High selling will weaken the stock prices, while high buying will strengthen the stock prices.
Chkili & Nguyen (2013) investigated the dynamic linkages between the exchange rates and stock markets for the BRICS countries (Brazil, Russia, India, China and South Africa). They expected an asymmetric response of different stock markets to exchange rate movements. As a result, the findings showed that the impact of exchange rate on stock market return was insignificant throughout BRICS countries, regardless of regimes. These findings were consistent with the findings of Gokmenoglu et al. (2021). His study focused on even more countries (Brazil, China, India, Malaysia, Mexico, South Africa, Thailand and Turkey) to reconsider the relationship between exchange rate and emerging stock market returns. Corresponded stock market indices were used in both studies to measure each country's stock market performance. This research also confirmed that the impact was minimal. However, the findings also suggested that the exchange rate will shock the stock market returns only in some specific circumstances, like when the market is bearish. Bearish market occurs when most of the investors in the market believe that the market as a whole is due to go down. Only then the impact of the exchange rate will kick in.
According to Ramzan (2016), inflation refers to the consistent growth of prices of goods and services in an economy over a period of time. His research attempted to examine the impact of inflation on stock market performance in Pakistan and determine the casual relationship. The impact could be positive or negative. KSE-100 index was employed as the proxy for stock performance in Pakistan. The findings indicated that there was a negative connection between inflation and stock performance in Pakistan. This finding was in line with the findings of another research conducted in Sri Lanka by Silva (2016), which tested the relationship between the inflation rate of a country and the stock prices over ten years. All Share Price Index data provided by Colombo Stock Exchange was employed in Silva's research. As a result, his findings suggested that the inflation growth rate might affect stock returns positively.
Sahu & Pandey (2018) constructed a study to contribute some insights into the relationship between the money supply and stock prices in India. According to them, the money supply is closely related to the price level of goods and services in an economy in the sense that an increase in money supply translates directly to an increase in purchasing power. The positive change in the money supply should raise stock prices. S&P BSE Sensex and S&P CNX Nifty were used to measure Indian market price movement, while M3 was used as the representative of the money supply. M3 money supply includes all the content of M2 plus long-term deposits. As a result, the findings confirmed a significant positive relationship between money supply and stock prices in India in the long run, but not in the short run.
According to Teitey (2019), the relationship between interest rates and stock market returns is usually negative, which means higher interest rates will lower stock returns. His study was to assess the relationship between interest rates and stock prices on Ghana Stock Exchange. The stock price data were collected from Ghana Stock Exchange and other literature. As a result, the research showed that there was a significant negative relationship between interest rate and stock prices in Ghana. These findings were supported by the research of Uddin & Alam (2010). Their research was to examine the efficiency of the Dhaka Stock Exchange market and also the relationship between (interest rate and growth of interest rate) and (share prices and growth of share prices). It was found that interest rate had a significant negative relationship with share prices and the growth of interest rate had a significant negative with the growth of share prices as well.
Based on the literature review, contradicting results were found as most of the studies focused on different economies. On top of that, there were also differences in variables included, including selected industries, time frame, data sources and methodology. Despite the numerous studies around the world, there have only been a few locally conducted studies. Even if there were studies about Malaysia, they were usually mixed with the studies of other countries. This implies that the relevant study was not established in-depth. Those contradicting results provide a context that not only the differences in settings will affect the findings of the research, but the differences in proxy theory also will.

Methodology
This paper uses 84 observations of each dependent and independent variable from Jan 2015 to Dec 2021, at monthly intervals. The dependent variable is the Malaysian stock market performance, which can be obtained from Trading Economics websites. As for independent variables, exchange rate data can be obtained from Fred Economic Data; while inflation, money supply and interest rate can be obtained from the online databases of Bank Negara Malaysia.
To have a comprehensive understanding of the effect of macroeconomic variables on stock market performance in Malaysia, this paper studies the effects in between and attempts to test the strength of the effects. The conceptual framework will be shown in 3.1 Research Framework.
The equation below, it shows how the effect of IVs on DV is measured. On this subject, the DV (KLCI Index) will be defined by the 4 IVs included in this study. The IVs in the equation are inflation rate (CPI), M2 money supply, real effective exchange rate and overnight policy rate respectively. IF is the inflation rate (CPI). CPI is the proxy for inflation rate; it is a weighted average of price changes with a combination of selected categories. It refers to the general increase in the prices of goods and services over time.
The inflation rate can be calculated as follow, LogM2 is the M2 money supply, which is the proxy for money supply. Malaysia Money Supply M2 includes M1 (includes coins and notes in circulation and other assets that are easily convertible into cash) plus short-term time deposits in banks (John & Ezeabasili, 2020). A log transformation is applied because of its positive skewness.
c. REER is the real effective exchange rate, which is the proxy for to exchange rate. It is calculated as weighted averages of bilateral exchange rates adjusted by relative consumer prices. The weights are determined by comparing the relative trade balance of a country's currency against that of each country in the index (Darvas, 2012). d. OPR is the overnight policy rate, typically known as the short-term interest rate, which is the proxy for interest rate. It refers to the interest rate for interbank lending. Depending on the economic environment, the rate will be determined by Bank Negara Malaysia to ensure a stable and liquid financial system. This study, focuses on the economic variables of the Malaysian economy and the Malaysian stock market index will be employed as a proxy for Malaysian stock market performance; real effective exchange rate (REER) will be used as a proxy for exchange rate; inflation rate will be used as a proxy for inflation; M2 money supply will be used as a proxy for money supply; short-term interest rate (OPR) will be used as a proxy for interest rate.

Research Framework
Firstly, the characteristics of these variables will be described through descriptive statistics, in terms of their central tendency and variability. Then, correlation coefficient will be used to determine if the effect of DV on each IV is positive or negative and to measure the strength of the effects; whereas regression analysis will quantify the effects in a form of percentage. By doing so, researchers and investors will be able to make predictions on DV.
Unit Root test will then test the reliability of the variables for Johansen Cointegration test. It will test stationarity in a time series. There's an assumption in statistical modelling that the future will be somehow similar to the past. That takes various forms, but in time series, it's completely captured by the notion of (weak) stationarity. If a study tends to estimate the properties of a series, it has to work with a stationary series, or it's not even clear what the estimates actually represent.
Besides, for a time series equation to be estimated, it is necessary to decide on the appropriate lag length. The easiest way is to decide using Var Lag Selection Criterion. Once the series is proven to be stationary in first or second difference and the appropriate lag length has been selected, then it indicates that the series is applicable for cointegration test. Cointegration test is used to identify if there is correlation between the variables in a long-run. It allows the long-run parameter to be estimated with unit root variables  Table 1 shows the mean, median, standard deviation and Kurtosis stat of all variables of the 84 observations for the entire period from Jan 2015 to Dec 2021. All variables except inflation skewed to the left (negatively skewed), as their median is higher than their mean. It indicates that those 4 variables consist of a very large group of middle-range figures, with barely any high figures and some very low figures. This condition can also be seen through the Kurtosis stat, where all variables except REER (3.11) are lower than 3. It indicates that they are platykurtic, in which most of the values of those variables are lower than the mean.

Results and Findings
Additionally, KLCI Index has a very high std dev (109.02), which indicates high variability. This is because the index points are purely based on market demand and supply which is everchanging. Based on the combined analysis, it is safe to say that the KLCI index tends to move downwards most of the time, with strong movements sometimes, which is demonstrated by the bad market performance.  Table 2 shows the correlation between each variable in the study. One of the results shows that M2 money supply and Overnight Policy Rate are highly correlated, with a strong negative relationship (-0.788). Such correlation between the two variables (M2 and OPR) is expected. According to the liquidity effect, an increase in the money supply will create excess money at existing income, interest rate, and price indices. Since the interest rate is the opportunity cost of holding cash, then money demand is a decreasing function of nominal interest rates. An increase in money supply must cause interest rates to drop to keep the money market in equilibrium (Alatiqi & Fazel, 2008). Despite the existence of a multicollinearity issue between M2 money supply and OPR, M2 money supply is an insignificant variable in this model.
The degree of relationship between the dependent variable (KLCI Index) and independent variables is more important for this study, given that it can provide linear regression model utility to predict the dependent variable with the independent variables. On this subject, the results show that KLCI Index and IF have a moderate positive relationship (0.358). Both variables tend to move in the opposite direction. A higher inflation rate will result in higher KLCI Index points, and vice versa. For instance, when the inflation rate rises, investors tend to keep their money on the sidelines. Stock prices will then plummet and pose buying opportunities. The influx of funds will push the stock index back up.
KLCI Index and M2 money supply have a moderate negative relationship (-0.615). Both variables tend to move in the opposite direction. A higher M2 money supply will result in lower KLCI Index points and vice versa. This result is consistent with the study by Cornell (1983). He argues that an increase in money supply indicates higher money demand, which suggests an increase in risk. If so, investors demand a higher risk premium for holding stocks, which lowers the stock prices, hence the stock index.
KLCI Index and real effective exchange rate have a moderate positive relationship (0.582). Both variables tend to move in the same direction. Higher REER will result in higher KLCI Index points, and vice versa. For instance, when the REER increases, it indicates expensive exports and cheaper imports, which will lead to good company fundamentals with higher earnings. With the good fundamentals combined with globalization as well as the open market economic policies of Malaysia, Malaysia will become the destination of foreign direct investment. With FDI, the stock market will be fuelled (T, 2021).
KLCI Index and OPR have a moderate positive relationship (0.667). Both variables tend to move in the same direction. Higher OPR will result in higher KLCI Index points, and vice versa. One possible explanation may be that Malaysia's long-term bond rate serves as a better approximation than the short-term interest rate as the discount rate in the basic stock valuation model.  Table 3 shows the results of the regression analysis. According to the results, it shows that inflation rate, real effective exchange rate and overnight policy rate have a Prob lower than the significance level of 0.05 (0.0023, .0.0009 and 0.0175 respectively). It indicates that they are significant variables in the model to explain the movement of the dependent variable (KLCI Index), except for the M2 money supply.
In terms of coefficient, it shows a similar direction as Table 2 Correlation discussed above, where IF, REER and OPR have positive relationships with KLCI Index; while LogM2 has a negative relationship with KLCI Index. On top of that, the coefficient will quantify the strength to which the dependent variable is affected by each independent variable. For instance, the coefficient of 16.26 between IF and the KLCI Index indicates that if the inflation rate increases by 1%, the KLCI Index will increase by 16.26 points and vice versa. The coefficient of -87.13 between LogM2 and the KLCI Index indicates that the KLCI Index will drop by 87.13 points if the M2 money supply increases by RM1 million, and vice versa. The coefficient of 12 between REER and KLCI Index indicates that if REER increases by 1 point, KLCI Index will increase by 12 points and vice versa. The coefficient of 59.93 between OPR and the KLCI Index indicates that KLCI Index will increase by 59.93 points if OPR increases by 1%.
The R-Squared figure of 0.5625 indicates that 56.25% of the variability of the KLCI Index can be explained by the independent variables included, while the rest (43.75%) can be explained by other unincluded factors.  Tables 4 and 5 show the results of the unit root test in terms of probability and stationarity respectively. The results show that all variables except REER (0.0463) are non-stationary at level, but stationary in either first or second difference in all 3 model specifications by having a probability lower than the significance level of 0.05 in the respective specifications. So, the null hypothesis of the existence of unit root is rejected at 5% significance levels and accepts that the series are stationary in the first and second difference. It indicates that the trend and seasonality of the 4 variables are eliminated through differencing, and they are applicable for the Johansen Cointegration test to test the long-run association between variables. REER will be excluded because stationarity at the level implies no long-run association.
In terms of the lag length criteria, this test uses Akaike Info Criterion (AIC) to automatically decide the lag length from the maximum of 12 lags, since this study uses monthly data.  Table 6 shows the results of the VAR Lag Order Selection Criteria. There are 5 criteria, including LR, FPR, AIC, SC and HQ. The selected lag order is indicated by an asterisk sign (*) which is distributed between lag 1 and 12. The decision rule is to select the model with the lowest value in the AIC criterion, which lags 12 at -0.068095. The lower the value, the better the model. The maximum lag length allowed for this model is 12 due to monthly data intervals.  Table 4 and 5, and the appropriate lag length of 12 has been selected in Table 6. So, the requirements for the Johansen Cointegration test have been fulfilled. Tables  7 and 8 show the results of the Trace Test and Maximum Eigenvalue Test respectively. This test uses the lag interval of (1 12) since it is suggested by the VAR Lag Selection. Both tests show that the Trace Statistic and Max-Eigen Statistic of no cointegration (None) are higher than the 0.05 critical value (83.63720 over 47.85613, and 51.75133 over 27.58434 respectively). So, the null hypothesis of the no cointegrating equation is rejected at the 5% level. Both tests concluded that a long-run relationship exists among the 4 variables (REER is excluded due to its stationarity at level). Once the reliability and validity are proven, then the data can move on to data analysis. Before the data is tested with the long-run association through the Johansen Cointegration test, they need to go through the unit root test to test their stationarity. The results of the unit root test show that IF, M2 and OPR are stationary in the first difference, while REER is stationary at level, which implies no long-run association and no need to run it through the Johansen Cointegration test.

Summary of Findings
Based on the results of the Correlation Coefficient, Regression Analysis and Johansen Cointegration test, a summary of findings can be derived in Table 9 to show the relationship between each IV and DV (KLCI Index). REER has a moderate positive relationship with the KLCI index, but there is no long-run association due to REER's stationarity at the level. IF and OPR have moderate positive relationships with the KLCI index in the long-run. M2 has a moderate negative relationship with the KLCI index in the long-run.

Conclusion
As a conclusion, the macroeconomic variables included are proven to have significant effect on stock market performance in Malaysia. The fact that those impacts exist proves that one can predict stock market performance through analyzing these variables. Therefore, it is important to understand the rationale and condition of the market.
Based on the findings, this study shows that REER has a moderate positive effect on the KLCI index, but there is no long-run association due to REER's stationarity at the level. When the REER increases, it indicates expensive exports and cheaper imports, which will lead to good company fundamentals with higher earnings. With the good fundamentals combined with globalization as well as the open market economic policies of Malaysia, Malaysia will become the destination of foreign direct investment. With FDI, the stock market will be fuelled.
The results of IF having a positive effect on the KLCI index in the long-run are consistent with the study of Taofik and Omosola (2013) in the Nigerian stock market; Nadeem and Zakir (2012) and Arif et al (2014) in Karachi stock market. A higher inflation rate will result in higher KLCI Index points, and vice versa. For instance, when the inflation rate rises, investors tend to keep their money on the sidelines. Stock prices will then plummet and pose buying opportunities. The influx of funds will push the stock index back up.
M2 has a moderate negative effect on the KLCI index in the long run. A higher M2 money supply will result in lower KLCI Index points and vice versa. This result is consistent with the study by Cornell (1983). He argues that an increase in money supply indicates higher money demand, which suggests an increase in risk. If so, investors demand a higher risk premium for holding stocks, which lowers the stock prices, hence the stock index.
OPR has a moderate positive effect on the KLCI index in the long-run. One possible explanation may be that Malaysia's long-term bond rate serves as a better approximation than the short-term interest rate as the discount rate in the basic stock valuation model.
This study provides insights on the effect of various macroeconomic variables on Malaysian stock market performance and if they do so significantly. This study will benefit researchers and investors in the way of providing context on Malaysian stock market environment. Researchers will be able to observe the development of Malaysian stock market. From investors' perspective, they will have the interest to have a better understanding on the macro determinants of stock market performance. Forecast can even be done to predict the stock market behaviour towards certain economic movements. Then, appropriate trading and investing strategies can be constructed based on their risk preference. However, which macroeconomic variable should stock investors pay attention to and which variables impact the stock market performance the most are the million dollars question. Up until now, all the other research has been utilizing different proxies to answer both questions. That's why this study attempts to contribute by testing out variables that are rarely included in other studies.
For future research on a similar topic, this research recommends comprehensively investigating the rationale behind the relationship found between each macroeconomic variable and stock market performance, so the findings will be justifiable and reliable. Besides, other macroeconomic variables can be used to explain stock index movement, so the role of the macroeconomic environment can be seen in a bigger picture.