Selected Macroeconomic Variables and Stock Market Movements: Empirical Evidence from Thailand

This paper investigates and analyzes the long-run equilibrium relationship between the Thai stock Exchange Index (SETI) and selected macroeconomic variables using monthly time series data that cover a 20-year period from January 1990 to December 2009. The following macroeconomic variables are included in our analysis: money supply (MS), the consumer price index (CPI), interest rate (IR) and the industrial production index (IP) (as a proxy for GDP). Our findings prove that the SET Index and the selected macroeconomic variables are cointegrated at I (1) and have a significant equilibrium relationship over the long run. Money supply demonstrates a strong positive relationship with the SET Index over the long run, whereas the industrial production index and consumer price index show negative long-run relationships with the SET Index. Furthermore, in non-equilibrium situations, the error correction mechanism suggests that the consumer price index, industrial production index and money supply each contribute in some way to restore equilibrium. In addition, using Toda and Yamamoto’s augmented Granger causality test, we identify a bi-causal relationship between industrial production and money supply and unilateral causal relationships between CPI and IR, IP and CPI, MS and CPI, and IP and SETI, indicating that all of these variables are sensitive to Thai stock market movements. The policy implications of these findings are also discussed.


Introduction
The relationships between macroeconomic variables and stock market movements have been the focus of financial and economic literature for many years. In general, stock markets facilitate economic growth by enhancing liquidity and providing funds for industrialization and economic development. They also act as interesting investment centers. Stock price movements are by their nature essentially random, and prices adjust rapidly in response to economic news, such as news regarding domestic and international shocks.
Daily gains and losses by stock market investors demonstrate the extent to which individual stock returns fluctuate in response to a variety of unanticipated events. Essentially, stock prices are determined by sup-fidence in a particular stock will cause an outflow of capital as investors sell the stock; the low demand for that particular stock will be reflected in a lower price.
In sum, the stock market moves up or down based on many factors, and there is no method that can accurately predict the exact movements of stock market.
The factors that influence the stock market can be divided into two broad categories: systematic risk and unsystematic risk. As stated above, the relationships between macroeconomic variables and stock market movements have been studied extensively, especially in advanced and emerging economies. Ross (1976) introduced the Arbitrage Pricing Theory (APT), which states that multiple risk factors can be used to explain the returns on a financial asset. Although the APT was initially accepted, it was criticized for failing to specify the exact factors that should be used to explain financial returns. Later, Chen, Roll and Ross (1986) further analyzed the APT and linked a linear function of various macroeconomic factors to the returns on financial assets. The seminal contributions of Chen et al., (1986) to the APT literature provided a framework for further analysis of the relationship between stock market movements and macroeconomic variables. As a result, many empirical studies are based on the APT model.
Because most macroeconomic variables are nonstationary, the use of regression analysis with the OLS technique tends to produce misleading results. To address this issue, Granger (1986) and Johansen (1991) developed tools based on the concept of cointegration that could be used to analyze long-run equilibrium relationships. Cointegration tests have since been widely used to analyze the relationship between macroeconomic variables and stock market movements. The significance of the cointegration approach is premised on the fact that it allows non-stationary data to be examined in the context of a long-term relationship and permits an assessment of whether the variables are cointegrated of the same order (for example, at I(1)).
In addition, with adjustments for error-correction, this method allows an examination of the short-term adjustments that occur as variables move toward their long-run equilibriums.
The concepts described above have been applied in numerous empirical studies that document the relationship between stock market movements and macroeconomic variables. However, most research in this area has focused on stock markets in the United States (Chen et al., 1986;Flannery, 2002;Narayan & Narayan, 2012) and other developed countries, such as Japan (Mukherjee & Naka, 1995), Poland (Okon, 2012), China (Zhao, 1999) and Singapore (Maysami & Koh, 2000). Although some studies have analyzed the relationships between macroeconomic variables and stock market movements in developing countries, including Pakistan (Nishat & Shaheen, 2004), the United Arab Emirate (Al-Tamimi, Alwan, & Abdel Rahman, 2011), Turkey (Çagli, Halac, Taskin, 2010;Eraslan, 2013) and Malaysia (Rahman, Mohd Sidek & Tafri, 2009), little research has been conducted in Southeast Asia and in Thailand in particular (See Kwanchanok, 2000;Liangnakthongdee, 1991;Seehalak, 2004;Tri, 2005). The motivation for the present paper arose after a review of the existing literature regarding the relationship between selected macroeconomic variables and stock market movement. Although the model relationship has been well established in other countries, only limited research has been conducted in Thailand, and only a few of the Thai studies have appeared in English. As in most countries, macroeconomic indicators play a significant role in driving the stock market in Thailand; therefore, further research regarding policies that may affect macroeconomic variables in Thailand is warranted.
The main purpose of this paper is to identify and explain the relationship between selected macroeconomic variables and the movement of the Thai stock market (SET Index). It is our desire to contribute to the existing literature by supplementing and updating previously published evidence relating to the relationship between macroeconomic data and the Thai stock market. The study period comprises 240 consecutive months from January 1990 to December 2009. Because we are dealing with a time series analysis, we use a cointegration approach to examine the relationship between the selected macroeconomic variables and the movement of the SET Index toward the long-run equilibrium. Liangnakthongdee (1991)  itively affected by the interest rate, foreign exchange rate, price-earnings ratio, and market capitalization over the long run, whereas bond prices and the consumer price index (CPI) produced negative long-run effects. In addition, Sardar et al., (2004)  Fama investigated the relationship between economic activities and stock returns and concluded that there are positive relationships between stock returns and GNP, money supply, capital expenditure, industrial production, and the interest rate but a negative relationship between stock returns and the inflation rate (Fama, 1981). Later, Chen et al., (1986) used the APT model to link stock market returns in the United States (US) to a linear function of various macro-economic factors. They contended that economic forces affect the discount rate, firms' respective cash flows, and future dividend payouts. They found strong correlations between the selected macroeconomic variables and US stock market returns and concluded that industrial production, changes in the risk premium, and twists in the yield curve were the most significant factors in explaining US stock returns. Mukherjee used a vector error correction approach to examine the longrun cointegration between the Japanese stock market and selected macroeconomic variables (Mukherjee & Naka, 1995). Zhao (1999) studied the relationships between stock prices in the Chinese financial market by considering inflation and the industrial production index from 1993 to 1998. The results indicate that both inflation and expected growth in industrial output have negative relationships with the stock market. Maysami, Howe and Hamzah (2004) determined that although the long-term equilibrium relationships between the Singapore stock index and selected macroeconomic variables are not cointegrated, the Singapore stock index was sensitive to interest and exchange rates. Moreover, Maysami et al., (2004) concluded that the Singapore stock market is significantly and positively co-integrated with the stock market indices of Japan (Nikkei) and the United States (Dow Jones). Flannery and Protopapadakis (2002) studied the relationships between US stock prices and economic announcements on a daily basis from 1980 to 1996 using the Consumer Price Index, Producer Price Index (PPI), money supply, the unemployment rate and the interest rate as economic factors. The findings demonstrated that economic announcements significantly increase stock market volatility, which affects stock returns. Moreover, because the CPI and PPI are mea-sures of inflation, announcements of increases in these variables tend to depress the stock market. Similarly, because increases in the money supply lead to inflation and thus cause interest rates to increase, announcements of increases in the money supply also tend to decrease stock prices.
Nishat and Shaheen studied the long-run equilibrium relationship between selected macroeconomic variables and the Pakistani (Karachi) Stock Exchange Index and found two long-term equilibrium relationships among these variables. Specifically, their results indicated that industrial production is the largest positive determinant of stock prices in Pakistan and that inflation is the largest negative determinant (Nishat & Shaheen, 2004). However, reverse causality was Market risk factor, size risk factor and book-tomarket ratio risk factor were used as the explanatory variables. He concluded that although the Fama and French three-factor model has some power to explain variations in excess portfolio return on the Istanbul Stock Exchange, this power is not strong throughout the test period. Narayan and Narayan (2012) investigated whether U.S. macroeconomic conditions (specifically, the exchange rate and the short-term interest rate) have effects on seven selected Asian stock markets-namely, China, India, the Philippines, Malaysia, Singapore, Thailand, and South Korea-using daily data for the period 2000-2010. They divided the sample into a pre-crisis period (pre-August 2007) and a crisis period (post-August 2007). They found that in the short run, the interest rate has a statistically insignificant effect on returns in all countries, except for the Philippines during the crisis period, and that depreciation has a statistically significant and negative effect on returns in all countries except China (regardless of the crisis). With respect to long-term relationships among the variables, although the authors found cointegration in the pre-crisis period for five of the seven countries (India, Malaysia, the Philippines, Singapore, and Thailand), they found no such relationship during the crisis period, implying that the financial crisis has actually weakened the link between stock prices and economic fundamentals. Okon (2012) investigated investors' reactions to mandatory offers of shares on the Warsaw stock market using the capital asset pricing model. That study sought to offer guidance to potential investors and to lay the groundwork for further research.

The Economy of Thailand in Perspective
Thailand is located in the heart of Southeast Asia and has a total population of approximately 67 million people. Bangkok is the capital city and the "Thai Baht"

Stock Exchange of Thailand (SET)
The

Research Hypotheses
Industrial Production Index (IP): GDP is normally used to represent the overall aggregate output of an economy; however, due to the infrequency of available G D P data (Thai GDP data are usually available on a quarterly and yearly basis), we decided to use the Industrial Production Index, which is available monthly, as a proxy for GDP in this paper. Moreover, a significant relationship appears to exist between GDP and Industrial Production Index (Chen et al., 1986;Fama, 1981;Humpe & Macmillan, 2009;Nishat & Shaheen, 2004). Therefore, we hypothesize that an increase in t h e industrial production index is likely to lead to economic expansion in Thailand, which in turn will increase the expected future cash flows of firms listed on the Stock Exchange of Thailand (SET).
Accordingly, we hypothesize that increases in CPI will negatively influence Thai stock market movements.

Money Supply (MS):
The narrow money supply (M1) is discussed in this paper. The relationship between money supply and stock market movement has yielded mixed results in previous studies. For example, Fama (1981); Flannery and Protopapadakis (2002) each observed that an increase in money supply ultimately increases inflation, which in turn depresses the stock market. However, Maysami and Koh (2000); Mukherjee and Naka (1995) and certain studies of the Thai stock market Kwanchanok (2000); Liangnakthongdee (1991) have identified positive relationships between money supply and stock market movement. Mukherjee and Naka (1995) argued that an increase in the monetary supply acts as an economic stimulus, re-sulting in increased cash flows (the corporate earnings effect) and higher stock prices. We investigate whether this theory holds for the Thai stock market index and hypothesize that there is positive relationship between money supply and SETI movements.
Interest Rate (IR): Also called the cost of capital, IR can be classified into two types: the savings interest rate and the borrowing interest rate. Most studies that have evaluated the interaction between interest rates and stock market movements (Kwanchanok, 2000;Liangnakthongdee, 1991;Mukherjee & Naka, 1995;Narayan & Narayan, 2012) have found negative relationships between interest rates and stock prices.
One proffered explanation is that an increase in the interest rate affects the discount rate, which ultimately decreases the value of a stock. A related explanation is that an increase in the interest rate makes alternative investment opportunities more attractive. Specifically, as the interest rate rises, investors tend to invest less in stock and more in other investment assets, causing stock prices to fall.

Data Description
Because of the limited availability of data, the sample

Model Specification
Graphical analysis is only a preliminary step in time series modeling. When conducting a time series analysis, the concepts of stationarity and unit root are extremely important. If these concepts are ignored in the analysis of non-stationary time series, spurious regression problems will occur. Accordingly, using the E-Views program and following Johansen's (1991) methodology, we test for cointegration and apply the error correction model (ECM); the cointegration approach Page | 21 Selected Macroeconomic Variables and Stock Market Movements: Empirical evidence from Thailand allows us to assess changes in the long-run equilibrium relationships between selected Thai macroeconomic variables and SET movements. To test for cointegration, we first must determine whether each variable is integrated of the same order. To do this, we use the augmented Dickey-Fuller test (Dickey, 1988) to test for unit root. The ADF test is estimated in three different forms, each of which is based on a different hypothesis (Gujarati, 2003). Specifically: Y t is a Random Walk and assumes the following form: (1) Y t is a Random Walk with an intercept: (2) Y t is a Random Walk with an intercept and time trend: (3) The Phillips-Perron (PP) test is another method used to detect the unit root (Phillips & Perron, 1988). The intuitions behind the PP test are the same as for the ADF test; however, the PP test uses a non-parametric statistical method to handle serial correlation in the error term and does not add the lagged difference into the model. The PP model is described as follows: Y t is a Random Walk and assumes the following form: (4) Y t is a Random Walk with an intercept: Y t is a Random Walk with an intercept and time trend: In each case, the null hypothesis is that δ= 0; that is, there is a unit root, and the time series is non-stationary. The alternative hypothesis is that δ<0; that is, the time series is stationary. If the null hypothesis is rejected, it means that Yt is a stationary time series at I (0). If not, we have to take the difference until the null hypothesis is rejected. In this paper, the unit root test will be detected in levels and first differences, and we assume that each variable will have a unit root at level; this requires us to take first difference order to have stationary data. If each variable is integrated of the same order of first difference at I (1), it implies that we can test for long-run equilibrium relationships using Johansen's (1991) cointegration method and that there will be at least one cointegration among these variables. The cointegration method is linked to the vector error correction model (VECM). The VECM is similar to the VAR model, but unlike the VAR model, the VECM can be used when all endogenous variables are nonstationary and cointegrated. In addition, the VECM permits us to account for short-term adjustments that occur on the path toward the long-run equilibrium. Specifically, assuming that a given variable Yt is out of equilibrium and that its value is above (below) its equilibrium value, it will start falling (rising) to correct the equilibrium error in the next period. The vector error correction model (VECM) is described below: Where • ∆ denotes the first difference order, for example, • The term Y t represents the selected macroeconomic variables (SETI, MS, IR CPI, and IP) that will be tested in this model, and each variable is a p x 1 vector integrated of the same order. • µ is a p x 1 vector of constant.
• The mechanism comprises the vector autoregressive components where the p x p matrix denotes the coefficients of variables' shortrun adjustments toward long-term equilibrium.
• The equation αβ'Y t-k describes the long-term equilibrium relationship (stationary linear combination of β'Y) where α stands for p x r speed of adjustment coefficient, β' denotes the cointegration vector with Y t integrated of the same order, and k denotes the lag structure.
• t ε is the vector white-noise error term.  (Gujarati, 2003). In addition, time series data are often non-stationary, which could exacerbate the spurious regression problem. In addition, when the variables are integrated, the F-test procedure is not valid because the test statistics do not have a standard distribution.
To overcome these shortcomings, an alternative test was developed by Toda and Yamamoto (1995).
This test may be used irrespective of whether Y t and X t are I (0), I (1) or I (2) and whether they are noncointegrated or cointegrated of an arbitrary order. This method is widely known as the Toda and Yamamoto (1995) augmented Granger causality test and is based on the following equations; where d is the maximal order of integration order of the variables in the system, h and k are the optimal lag length of Y t and X t , and are error terms that are assumed to be white noise with zero mean, constant variance and no autocorrelation. All that we are required to do is to determine the maximal order of integration d, which we expect to occur in the model, and to construct a VAR in their levels with a total of lags.

Empirical Results and Analysis
We begin by modifying the VECM models. For example, by identifying the data generating process (DGP), we can identify each variable's characteristic.
To proceed further, we first need to determine whether our model will include the components of an intercept and time trend. Later, the set of selected time series macroeconomic variables (SETI, MS, IR CPI, and IP) is used to detect unit root. Only sets of variables that are integrated of the same order will be subject to further analysis on cointegration. Next, we must choose the statistical tool to select the lag length order. Commonly used tools to select the lag length order are the Akaike information criterion (AIC) and Schwarz information criterion (SIC). For the sake of simplicity, we will use these two approaches in our study. The number of order cointegration relationships will be determined using the trace statistic and maximum eigenvalue statistic. Once the number of cointegration relationships has been established, the next step is to compute the long-term equilibrium relationship and error correction by regressing ∆Y t against the lag difference of ∆Y t and Y t-k where Y t represents the selected macroeconomic variables (SETI, MS, IR CPI, and IP).

Unit Root Tests
When time series data become stationary, we call this integrated degree 0 or I (0) but when we take first, second, or third differences to make a time series stationary, we call this I (1), I (2), or I (3), respectively. The    Null Hypothesis: δ = 0 (each variable has a unit root).
Alternative Hypothesis: δ < 0 (each variable does not have a unit root). Table 3 represent the t-statistics and p-values at first difference. The results of the ADF and PP tests suggest that at the first difference, we are able to reject the unit root null hypothesis at the 5 % significance level; therefore, we conclude that all five selected macroeconomic variables have a unit root at level. This requires us to take the first difference order to achieve stationary data for an intercept and an intercept with time trend. Additionally, the identification of the data generating process (DGP) suggests that an intercept without time trend must be included in the tested equation. With all five macroeconomic variables now integrated of the same order at I (1), we proceed to consider whether they have long-run equilibrium relationships.

Appropriate Lag Length Selection
There are many methods of selecting the lag length in statistics. The most commonly used methods are the Akaike information criterion (AIC) and Schwarz information criterion (SIC). We use these methods to select the appropriate lag length for our model. Table   4 indicates that LR, FPE, and AIC show significant results at 12 lag length periods. This is considered very long, and it would be extremely complicated to apply this many lag length periods to the model equation.
Due to time and space constraints, we opted to keep our model simple and straightforward. Therefore, the lag length for these five macroeconomic variables will be selected based on the SIC (Schwarz information criterion), which indicates a lag length of 1 period.

Relationships
Because the empirical analysis in this paper is subject to a linear relationship, it does not matter whether the variables have a time trend. This is because there is only one cointegration relationship among the variables (see more details below).

Cointegration Results
The cointegration test was conducted for Thai stock market movements and the four selected macroeconomic variables. Only partial results are shown in this section. Table 6 presents the significant outcomes of the long term cointegration relationships with Thai stock market movements.
The cointegration test was normalized based on SETI.
The estimates of the long term cointegration vectors and corresponding t-statistics are displayed in Table 6. The results show that there are three significant long-term relationships among this group of variables. Specifically, we can reject the null hypothesis and accept the alternative hypothesis for money supply, inflation rate, and the industrial production index (IP), which implies that these three variables form a cointegration relationship with Thai stock market movements. However, the result for the interest rate is insignificant, which indicates that the interest rate (IR) does not have a cointegration relationship with the Thai stock market index and that the null hypothesis cannot be rejected.
The estimated coefficient of money supply is positive and statistically significant at a 99% level of confidence.
The value and sign of the money supply coefficient implies that if there is an increase of 1 million Thai baht in the money supply, the Thai stock index (SETI) will increase by 0.0204 index points in the long run. The estimated coefficient of CPI is negative, as expected, and statistically significant at a 90 % level of confidence.     (Al-Sharkas, 2004;Kwanchanok 2000;Liangnakthongdee, 1991;Maysami & Koh, 2000;Mukherjee & Naka, 1995). An increase in the supply of money in the Thai economy might explain this positive finding.
The inflation rate (denoted as CPI) shows a negative influence on the movements of the Thai stock market. This finding is consistent with previous studies (Al-Sharkas, 2004;Fama, 1981;Liangnakthongdee, 1991;Mukherjee & Naka, 1995;Nishat & Shaheen, 2004;Zhao, 1999) and confirms the negative long-run relationship between the inflation rate and the SET Index. The reason for the negative relationship is that an increase in the inflation rate will increase the discount rate; this will reduce the expected future cash flows of listed companies and ultimately cause lower stock prices.
The level of real economic output in Thailand, which is measured in this study by IP (Industrial Pro-duction Index), is found to be significant in the long run. Although most studies have found a positive relationship between economic output and stock market prices, our results indicate a negative long-term relationship between IP and the Thai stock market. It is difficult to explain the inconsistent result, but one possible reason is that the Thai industrial production index is already adjusted for higher price levels caused by inflation. Accordingly, IP may not be a good indicator of aggregate economic activities in Thailand.
According to the Granger representation theorem, when variables are cointegrated, there must be an error correction (EC) that describes the short-run adjustments of co-integrated variables as they move toward their long-term equilibrium values. Table 7    The adjusted coefficient of money supply is negative.
This means that when the movement of money supply deviates from its long-term equilibrium value in the short run, i.e., money supply is too high to be in equi-librium, it will begin falling in the following month by 4.156834 index points to restore its equilibrium. Conversely, the respective adjustment coefficients of the industrial production index and consumer price index are positive. This means that, when they are too low to be in equilibrium, they will start increasing in the following month to correct the equilibrium error.

Policy Suggestions
This paper establishes that, in the long run, the stock market and certain macroeconomic variables (namely, money supply, consumer price index and industrial production index) work in the same cointegration system. Difficulties may arise if we are missing one of these variables. In fact, macroeconomic variables and the stock market support each other; hence, they are cointegrated.
A good and resilient economy depends on mechanisms that drive the growth of the stock market because the stock market determines people's wealth.
The profits generated by the stock market encourage people to consume more; as people consume more, economic outputs and the money in circulation in the economy increase; these factors have the propensity to expand the economy. However, the fear of inflation arises when an economy develops too fast. The expectation of increased inflation is one of the factors that depress the stock market. Moreover, excess money supply is not the only cause of higher inflation rates; anything that increases the cost of production leads to higher price levels. Accordingly, policy makers must pay more attention to increases in international oil prices, especially in the case of Thailand. Thailand is an importer of oil, and every time the price of oil increases, it trickles down to higher production costs. This subsequently causes commodity prices to increase, which translates into higher inflation rates.
Higher inflation rates depress both the Thai economy and the Thai stock market.
To overcome the challenges of inflation, the Bank of Thailand should develop a monetary policy that seeks to increase the interest rate and thereby reduce the quantity of money in circulation. However, policy targets that produce changes in macroeconomic variables may unintentionally cause the economy to slow down.
For example, controlling the money supply through the interest rate channel may depress the stock market.
Policy decision-makers must have a good understanding of the potential consequences of any policies that are formulated. The

Conclusion and Recommendations
In conclusion, this paper makes a significant contribution to the existing financial and economic literature, and we are optimistic that later generations will benefit from reading this study and using it as an investment guide, either in Thailand or elsewhere. This paper examined the long-term equilibrium between the SET index and selected macroeconomic variables during the past 20 years using monthly data for the narrow money supply (M1), industrial production index, in- respectively. Interestingly, our results for the industrial production index do not support the postulates presented by Chen et al., 1986;Eraslan, 2013;Fama, 1981;Humpe & Macmillan, 2009;Nishat & Shaheen, 2004. This might be because the industrial production index in Thailand is adjusted, which may not be the case elsewhere. However, previous findings on CPI were confirmed by the existence of a negative relationship between CPI and Thai stock market movements (See Al-Sharkas, 2004;Fama, 1981;Mukherjee & Naka, 1995;Nishat & Shaheen, 2004;Zhao, 1999). We also found a positive relationship between money supply and the Thai stock market, which confirms earlier postulates (See Eraslan, 2013;Kwanchanok, 2000;Liangnakthongdee, 1991;Maysami & Koh, 2000;Mukherjee & Naka, 1995).
The directions of causality were also established in this paper using Toda and Yamamoto's (1995)  This is an area that warrants further research.
Macroeconomic variables that were not included in this study that would be interesting to examine include foreign direct investment (FDI), the major global stock indices (such as the Dow Jones index), foreign inflows/ outflows, export/import volumes and political stability. In addition, many policies have been launched to stimulate and reinforce SET liquidity. Future research should evaluate the effects of the structural changes that result from such policies.