THE EFFECTS OF MACROECONOMIC FORCES ON INDIVIDUAL STOCK INDEX RETURNS: EVIDENCE FROM THAILAND

BY

MR. RATTANAN JIRAYUPAT

AN INDEPENDENT STUDY SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

FINANCIAL MANAGEMENT FACULTY OF COMMERCE AND ACCOUNTANCY

THAMMASAT UNIVERSITY ACADEMIC YEAR 2014

COPYRIGHT OF THAMMASAT UNIVERSITY

THE EFFECTS OF MACROECONOMIC FORCES ON INDIVIDUAL STOCK INDEX RETURNS: EVIDENCE FROM THAILAND

BY

MR. RATTANAN JIRAYUPAT

AN INDEPENDENT STUDY SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

FINANCIAL MANAGEMENT FACULTY OF COMMERCE AND ACCOUNTANCY

THAMMASAT UNIVERSITY ACADEMIC YEAR 2014

COPYRIGHT OF THAMMASAT UNIVERSITY

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ABSTRACT

This paper investigates how macroeconomic variables affect each of stock market subsector index returns compared to its major sector index of the Stock Exchange of Thailand by using GARCH in MEAN technique. The data are collected from 2004 to 2014. The results suggest that foreign exchange rate has the most impact in all cases. Consumer price index is the second best factor. Unemployment rate and interest rate also have impact on stock market returns. However, the magnitude is very small. Finally, return volatility is time varying and has positive impact on stock market returns.

Keywords: Macroeconomic, Stock Market Returns, Return Volatility, GARCH in MEAN

Independent Study Title THE EFFECTS OF MACROECONOMIC FORCES ON INDIVIDUAL STOCK INDEX RETURNS: EVIDENCE FROM THAILAND

Author MR. RATTANAN JIRAYUPAT Degree Master of Science Department/Faculty/University Financial Management

Commerce and Accountancy Thammasat University

Independent Study Advisor Assistant Professor Somchai Supattarakul, Ph.D. Academic Years 2014

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ACKNOWLEDGEMENTS

Foremost, I would like to thank my research advisor, Asst.Prof.Dr.Somchai Supattarakul, for his thoughtful guidance and advices during my independent study period. And also, I would like to thank my research committee, Assoc. Prof. Dr. Monvika Phadoongsitthi, for her encouragement and useful comments.

Besides, I would like to thank my companions who are always willing to help and give their advices to my study.

Lastly, I would like to thank my family, especially my mother, for supporting me through my entire life.

Mr. Rattanan Jirayupat

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TABLE OF CONTENTS Page

ABSTRACT (1)

ACKNOWLEDGEMENTS (2)

LIST OF TABLES (5) CHAPTER 1 INTRODUCTION 1

CHAPTER 2 REVIEW OF LITERATURE 3

2.1 Review of literature 3 2.2 Theoretical framework 5

CHAPTER 3 RESEARCH METHODOLOGY 9

3.1 Data 9 3.2 Methodology 9 3.3 Hypotheses 14

CHAPTER 4 EMPERICAL RESULTS 15

4.1 Descriptive statistics 15 4.2 Unit root test 15 4.3 GARCH-M(1,1) model 16

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS 23

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REFERENCES 32 APPENDICES

Appendix A 35 Appendix B 36 Appendix C 37

BIOGRAPHY 38

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LIST OF TABLES Tables Page

3.2 Hypotheses 14 3.1 Sample and data specification 25

4.1 Descriptive statistics of return series variables 27 4.2 Unit root test 28 4.3 GARCH-M(1,1) model – conditional mean equation 29 4.4 GARCH-M(1,1) model – conditional variance equation 30

4.5 The p>|z| statistic value for various hypotheses test 31

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CHAPTER 1 INTRODUCTION

Capital market is a financial market for buying and selling equity- and

debt-backed securities. The purpose of this market is to match the demand and supply for funds. Where buyers are individuals or institutions who can put funds to long-term productive use, and sellers are savers who have surplus funds and seek a return from investing activity. Hence, capital market plays an important role in fueling country’s economy by allocating capital into economic system which causes job creation and business activities.

In Thailand, there are two choices for the sellers of surplus funds (in other words, investors) to raise funds to meet their needs. First choice is investing directly in physical business activities which involve buying commercial buildings, hiring employees, trading goods or services et al. Second choice is investing indirectly in capital market such as bonds, and common stocks. Investing in physical business requires a great amount of capital and experience. On the other hand, investing in capital market has higher liquidity, lower transaction costs and lower capital requirements. Generally, investing in capital market is supposed to be more desirable.

Since the early 1980s post-world war II adoption of economic liberalization (De Brouwer et al. 1999) and deregulation, Thailand’s economic structure has changed. The Asian miracle 1980-1996 gives rise to many changes. From the beginning, Thailand gross domestic product is mainly agriculture; by the end, manufacture. We export goods and products; and import machinery and equipment. Present-day, the open economy of Thailand is driven by international trade and investment activities.

According to the fact that Thailand economy relies on global market, macroeconomic factors increasingly become an important role in Thailand’s economy. Subsequently, many scholars try investigating the relationship between macroeconomic factors and the return of stock index. In prior studies, economic-

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wide phenomena such as changes in economic conditions, changes in monetary policy and changes in foreign exchange rate are observed to determine the correlation with stock returns. (see, for example, Bilson, Brailsford and Hooper (2001), Wongbangpo and Sharma (2002)).

In Thailand, many studies try to examine the macroeconomic variables such as interest rate, exchange rate risk on major seven industry sector indexes of SET (see, for example, Tangjitprom (2012a), Wisudtitham (2013), Ato Forson and Janrattanagul (2014)). However, the nature of business of industry subsectors indexes is totally different. That is to say; for example, the 6 subsectors of industrials indexes (INDUS)—which are AUTO, IMM, PAPER, PETRO, PKG and STEEL— have completely different nature of business.

For this reason, it leads to the research question that what macroeconomic factors affect stock market returns? How macroeconomic factors affect the return indexes of each subsector index in the Stock Exchange of Thailand? As in subsector of 8 major industry (AGRO, CONSUMP, FINCIAL, INDUS, PROPCON, RESOURCE, SERVICE, TECH), are those factors give the same impact to each subsector? Is there any function that can explain the correlation of macroeconomic factors and subsector index returns?

Thus, the main purpose of this paper is to determine how macroeconomic factors affect the return indexes of all 28 industry subsectors in the Stock Exchange of Thailand by using the GARCH in mean framework (Hamilton 1994). The scope of the study covers 2004-2014, including U.S. subprime credit crisis 2008 (Hamburger) and Euro credit crisis 2011. Under financial crisis and stock market strain, results of the study would explain the actual effect of these factors to dependent variable.

Understanding the effects of these variables would give contribution to investors using macroeconomic announcement to get a big picture and direction of the economy. Ultimately, finding out characteristic of these variables definitely make a contribution to the growth of the economy as well increase in competitive advantages in Thailand’s capital market.

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CHAPTER 2 REVIEW OF LITERATURE

2.1 Review of literature

Index is usually defined as a statistical measure of change in financial markets. Representing a portion of financial markets, value of index is aggregated by combining several stocks or other investment vehicle. Each index has its own calculation methodology for instance by expressing its total value against a base value from a beginning date. The simplicity and usefulness of index value make it become a widespread tool used by investors and financial manager to describe the nature and the direction of financial markets, to track changes in index value over time or to juxtapose against their own portfolio returns. Thus, A number of studies have proposed the concept of index value composition and what independent variables that affect the value of index.

Capital Asset Pricing Model (CAPM); ( ) ( ( )

Since the presentation of CAPM theory which is used to determine the price and yield of the securities based on the co-relation between securities to market risk premium. The model has been widely criticized why using only one factor to determine the security price and has been developed to forecast return of securities more precisely.

One of the most famous theories being widely discussed is the Arbitrage Pricing Theory offered by Ross (1976). The idea is that securities should not be determined from only one factor. On the contrary, the yield of the securities should be estimated by a linear function of multivariate macroeconomic variables so that each of the variable specific betas can be used to determine the sensitivity of securities. However, the Arbitrage Pricing Theory did not specify that what variables should be used in model.

Finding the proper variables for estimating stock market return is a subject that comes to the study and development to the present. At the start, Chen,

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Roll and Ross (1986) continue to develop Arbitrage Pricing Theory using 1953-1983 monthly data and find that the macroeconomic variables such as interest rates, inflation, industrial production level have correlation with stock market returns.

Thereafter, many papers assume that the returns of stock market today are affected by prior returns of stock market. Dadgostar (1994) find that there are Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Conditional Heteroscedasticity (GARCH) problems which mean the volatility of past square error term affects the present day of stock market returns by using daily U.S. stock return data.

For more supporting papers, Kim Hiang, Muhammad Faishal and Qiong (2006) findings – The expected risk premia and the conditional volatilities of the risk premium on property stocks are time-varying and dynamically linked to the conditional volatilities of the macroeconomic risk factors by using three steps GARCH (1,1) and find that GDP growth, INDP growth, unexpected inflation, money supply, interest rate and exchange rate are related to stock excess returns of four major markets, namely, Singapore, Hong Kong and Japan.

Further, there are some studies find the different results. Follow the research of Sanderson (1972), “The differential impact of macroeconomic conditions on industry profits” find that in different industry, the influence from macroeconomic factors are different. Liu, Loudon and Milunovich (2012) find that some of macroeconomic variables such as inflation and unemployment rate have no significance impact on real estate industry sector index. Addae-Dapaah and Loh (2005) study how currency rate affects real estate investment and find that the contradictions between developed and emerging economies do exist. Wisudtitham (2013) find interest rate play no significant role in stock market.

According to above-mentioned, it shows that concerning about autoregressive of time series data problem, GARCH model is more useful than OLS model in explaining the variables. Further, there are, unexpectedly, some macroeconomic factors that do not affect the stock market returns in many subsector industries. However, the results are different due to its size and region of capital market. Consequently, there are still some arguments on this field of study,

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and there is no evidence base that the correlation between each minor subsectors index (which its nature of business is different from each other) and macroeconomic factors is the same as its major index.

2.2 Theoretical framework

Macroeconomics is widely used in measuring the impact of changes in

economic conditions on financial market. Tangjitprom (2012b) reviews the relationship of macroeconomic variables and stock returns which can be classified into four groups in aspects of general economic conditions, monetary policy, price level and international activities. For more clarification in this paper, the macroeconomic factors expected to affect each of industry subsector indexes are as follows:

2.2.1 Unemployment rate

Unemployment rate indicates economic situations. Increase in rate of unemployment means there are many willing to work but unemployed labors which imply an economic recession. A falling unemployment rate implies growing economy which is mostly accompanied by inflation and causes increasing interest rate. Boyd, Hu and Jagannathan (2005) study stock market's reaction to unemployment rate announcement and find that unemployment rate contains information relevant for valuing stock.

2.2.2 Interest rate The interest theory by Fisher (1913), Interest rate, sometimes called

the price of money, is the reward of exchanging between present goods and future goods. The reward or premium is come from the exchange because of time preference or human impatience. In other words, an individual certainly has his own time preference and different purpose in money management. This leads to the intention of borrowing – that is at each interest rate level, there is a moneylender and at the same time a borrower.

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*∑

( ) +

Following basic formula, where is stock price, is expectations operator based on information, is future expected cash flow, and is discount rate. In field related to stock market pricing, interest rate is widely used in forecasting stock price. Most of stock price valuation models involve using interest rate as a discount rate of future cash flow. It influences the price directly. Rising interest rate leads to increasing discount rate used in stock valuation. Thus, shares price may drop. Also in APT model that uses many macroeconomic variables, Chen et al. (1986) suggest adding interest rate in the equation makes model a much better fit to the data. Joseph and Vezos (2006) find that there are correlations of foreign exchange and interest rate changes on US financial sector stock returns by using EGARCH and OLS model. Flannery and James (1984) find common stocks are correlated with interest rate changes and co-movement positively related to the size of firm's assets and liabilities.

2.2.3 Consumer Price Index Consumer price index (CPI) is a key indicator of changes in

consumer goods and services by weighted averaging. The weight of each goods and services is owing to its significance. CPI can be used in assessing changes associated with the cost of living and very often used for identifying inflation or deflation. Fama and Schwert (1977), Chen et al. (1986) in theirs research claim that rising inflation causes lower consumer purchasing power, increasing industrial cost of production and finally has negative effects on stock market. On the other hand, Wongbangpo and Sharma (2002) find that inflation and CPI have positive effects on stock market in aspects of protecting their portfolio from high inflation rate by using ASEAN-5 countries sets of data including Thailand. Danis (2009) find that the monetary policy of Federal Reserve in aspects of inflation rate affects stock market returns.

2.2.4 Exchange Rate Economic globalization directly and indirectly affects Thai industry

sector. As for stock market, Thai baht depreciation makes foreign investors avoid holding assets in Thailand. The divestment would cause dropping in share prices. On physical economic activities, many companies would be greatly affected as importer

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of raw materials for instance metal steel, equipment, machinery and oil. That is to say movement in exchange rate affects upstream of business as in raw material-costs of production and downstream as firm’s market capitalization in the stock market. Besides, there are two sets of theoretical frameworks that explain the relationship of stock price and foreign exchange rate:

2.2.4.1 Flow-oriented approach (Dornbush and Fisher, 1980) This framework suggests that the changes in foreign exchange rate cause changes in stock price as the currency value affects trade position and competitiveness of countries. In macroeconomic-scale, overall output of a country is affected and causes the changes in earnings of firms. Given the fact that stock price reflects discounted future cash flows to firm, changes in future cash flow would then, consequently, be reflected in stock price.

2.2.4.2 Stock-oriented approach (Branson and Frankel, 1983) Oppositely, stock-oriented framework views currency as goods or products. Under the law of supply and demand, value of each currency is derived from the supply and demand in offshore foreign exchange market. Changes in stock price induce foreign direct investment activities (buying or selling Thai stock in foreign currency) which mean moving funds from offshore into domestic economic system.

 Further, there are many studies support the correlation between exchange rate and real estate industry stock market such as Bilson et al. (2001), Wongbangpo and Sharma (2002), Wisudtitham (2013).

2.2.5 Arbitrage pricing theory Four macroeconomic variables are examined in this study. Multi-

factor assumption model assumption based on APT model is used to test economic conditions.

Where

is return on each of SET subsector indexes s at time t. is the unemployment rate at time t. is the interest rate at time t.

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is the consumer price index at time t. is the exchange rate at time t. APT model is based on law of one price and has flexible

assumption requirements. According to APT, “security returns are described by a factor model and well-functioning financial market do not allow for the persistence of arbitrage opportunities.” (Ross 1976)

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CHAPTER 3 RESEARCH METHODOLOGY

3.1 Data

According to table 3.1, the ending data of all variables are collected

monthly for period beginning JAN 2004 and ending on SEP 2014 from SETSMART, BOT and THAIBMA.

All variables are observed at fixed intervals of time to produce a time series of continuously compounded monthly return. The purpose of converting the data into the continuously compounded holding period return is to eliminate the bias due to the error in data such as the outlier. Moreover, at high sampling frequencies (i.e. daily) the difference between discrete of continuously compounded return is small; at low frequencies (i.e. monthly) the continuously compounded return is recognized as providing a better indication of changes in economic conditions.

3.2 Methodology

3.2.1 Descriptive Statistics The purpose of descriptive statistics is to describe the characteristic

of the data in the study. The results of descriptive statistics are not used in estimation. Descriptive statistics simplify large amounts of data in sensible way and provide simple characteristic summaries of sample such as mean, max, min, standard deviation et al.

3.2.2 Times Series Analysis Times series analysis is a method used in analyze a sequence of

numeric data collected over a period of time. The data changes over time period and might have a pattern, such as autocorrelation, trend or seasonal variation, or

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might not. If time series data shows a pattern in the past, we can use a pattern to foresee the future.

3.2.3 Unit Root test (Dickey and Fuller 1979) Unit Root is to test stationary of each variable. Considering non-

stationary data can cause spurious regression which leads to error statistical interpretation. Especially, in this study uses many economic variables which are time series and usually non-stationary data. Taking into account of feasibility and accuracy, the test of stationary of data is required before using multiple-regression model.

Time series economic variables are mostly non-stationary or in 3 random walk form as follows:

Pure random walk : Random walk with drift : Random walk with drift and deterministic trend

: Where

is dependent variable is beta coefficient is time index is independent and identical distribution which its means equal

zero and variance is constant or for ( ). After that, put the coefficient in front of where the value of

is . For pure random walk : When data is considered random walk which means data is

non-stationary, the test of whether data is stationary or non-stationary cannot be done with ordinary t-test which will cause an error result in case that data has unit root problem. Thus, converse the equation by subtracting both side of equation.

     ( )     ; ( )

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When , ; which means data is non-stationary. As for random walk with drift and random walk with drift and deterministic trend, the conversion would be the same. For testing of whether data is stationary or non-stationary, we would use Augmented Dickey-Fuller test.

3.2.4 Augmented Dickey-Fuller Before implement univariate process, stationary test must be

checked. If variables have unit root problem, the use of t-statistic might be spurious. By adding lagged change in the right side of equation, we have new 3 equations as follows:

Pure random walk : ∑

Random walk with drift : ∑

Random walk with drift and deterministic trend : ∑

ADF unit root test hypothesis: : (data is non-stationary) : (data is stationary)

For hypothesis test, we compare t-statistic with Mackinnon critical value. If is not rejected, data is non-stationary and has a unit root problem. Otherwise, if is rejected, data is stationary.

3.2.5 GARCH-M (1,1) The purpose of this study is to identify the sensitivity of 28

subsector indexes to 4 macroeconomic factors. There are three main reason why using GARCH(p,q)-M to analyze APT model which exhibit in this study.

First, at a theoretical level, asset risk in the well-known relationship between asset risk and returns of APT model is measured by the conditional covariance of returns with the market or the conditional variance of returns. Prior studies, while ignoring characteristic of data, employ Ordinary Least Square (OLS) which is inadequate to explain time-varying series data and leads to biased and inconsistent estimation. Considering high frequency or time series data has its own varying conditional characteristic, Engle (1982) develops Autoregressive Conditional

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Heteroskedasticity (ARCH) models to examined conditional variances by allowing risk to vary over time for more accurate estimation of returns than OLS model.

Second, GARCH(p,q)-M model can estimate both long and short-term memory returns. According to ARCH model which is short-term memory model allows for a limited number of lags in deriving the conditional variance, later modified by Bollerslev (1986) known as Generalized Autoregressive Conditional Heteroskedasticity (GARCH), a long-term memory model which allows all lags to exert an influence.

Third, the interesting characteristic of asset returns in the tendency for volatility clustering is volatility shocks in the present would affect the forecast of volatility periods in the future and GARCH(p,q)-M can measure the level of continuity or persistence in volatility.

Therefore, The GARCH-M model is employed in financial functions which the expected asset returns have correlation with the expected asset risk.

The GARCH(p,q)-M model is described as follows:

3.2.6 Model Specification  

Conditional mean equation

Conditional variance equation

( ) Distribution of error term Where

is return on SET 28 subsector index s at time t is previous return series of return on index s at time t is the unemployment rate at time t is the interest rate at time t is the consumer price index at time t is the exchange rate at time t is conditional variance of is error term which is normally distributed with zero mean is ARCH term is GARCH term

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In conditional mean equation, the subsector index return series are described as the function of unemployment rate, interest rate, consumer price index and foreign exchange rate. Moreover, the subsector index return series are also determined as autoregressive process ( ) and affected by its own volatility ( ).

For conditional variance equation, present volatility ( ) is affected by the past behavior of square error ( ) and the past behavior of conditional variance ( ). Further, GARCH model suggests that the coefficient of previous shock and previous surprise ( ) should have positive correlation with return volatility to secure non-negativity condition.

For more supporting articles using GARCH-M in estimation, Baillie and Bollerslev (1992) employed ARMA model in estimation where the conditional mean and variance are time-varying and extended auto-correlated disturbances from ARMA process in GARCH-M(1,1) process innovations.

Najand (2002) in his study using Linear vs. Nonlinear Models to forecast stock index, finds that “nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics.” In other words, his study object is to find the most suitable models in estimation. The non-linear models used in this experiment are GARCH-M(1-1), EGARCH(1,1) and ESTAR model.

Engle, Lilien and Robins (1987) extend GARCH model to GARCH-in-MEAN or GARCH-M by using condition mean as a function of conditional variance and after that find the relationship between time-varying expected return and independent variables.

Ryan (2004) uses GARCH in MEAN with lag orders (1,1) to identify the parameter effects of market volatility, interest rate and exchanges rate on Australia banking industry. Moreover, Bollerslev (1987) in his econometric field study: A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return, suggests that with lag order (1,1), GARCH(1,1) suits most economic time series data.

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3.3 Hypotheses Table 3.2 Hypotheses

Hypotheses

H1: Prior subsector index return has no impact on subsector index return

H2: Unemployment rate has no impact on subsector index return

H3: Interest rate has no impact on subsector index return

H4: Consumer price index has no impact on subsector index return

H5: Exchange rate has no impact on subsector index return

H6: Return volatility has no impact on subsector index return

H7: ARCH term has no relation on return volatility

H8: GARCH term has no relation on return volatility

Hypotheses are formed to test the context of the model. If the null hypothesis is rejected, it will show that factor has relationship with subsector index return, and accept the alternative hypothesis.

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CHAPTER 4 EMPRICAL RESULTS

4.1 Descriptive statistics

Table 4.1 shows the characteristics for continuously compounded monthly return (log difference) of 8 major indexes, 28 subsector indexes and 4 macroeconomic variables which are unemployment rate, interest rate, consumer price index and exchange rate. The 2 highest of standard deviation are unemployment rate and MINE subsector index which mean that it is more volatile compared to other variables. Due to positive and negative skewness values, the distributions of all variables are non-symmetry, and the sets of data are skewed to both the left and the right of the average. The kurtosis of all variables is higher than normal value of 3 which portrays a chart with fat tails and a low.

4.2 Unit root test

Before implement GARCH-M process, stationary test must be checked whether the data has unit root problem to avoid obtaining spurious results. Since time series data such as stock price and macroeconomic variables are most likely to have unit root problem which means two variables indicate relationship where one does not exist, the difference-stationary process is used to remove the trend of data by subtracting Yt-1 from Yt.

Prior to first difference process, I find out that all of data series have unit root problem that is to say data is non-stationary. However, after first difference process, unit root problem is eliminated. As shown in Table 4.2, Augmented Dickey-Fuller (ADF) hypothesis testing is rejected at 0.01 significance level which means there is sufficient evidence at the alpha level of significance to reject the claim that data have unit root problem. Hence, alternative hypothesis is accepted and data can be used in estimation model.

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4.3 GARCH-M(1,1) model

As shown in Table 4.3 and table 4.4, this paper employs maximum

likelihood estimator method to identify the parameter of conditional mean equation and conditional variance equation in GARCH-M(1,1) model. Likelihood itself has the same meaning to probability but in statistical terms, likelihood is used when describing the roles of function outcome or the robustness of the model. Later, the natural logarithm of the likelihood function, called the log-likelihood, is developed and widely used instead of ordinary likelihood parameter in many statistical techniques including maximum likelihood due to its typically increasing function of logarithm.

According to the ordinary linear regression model used in estimation of time series data is most likely to have inconsistent variance problem which lead to estimation errors and hypothesis testing errors, maximum likelihood technique is used to indicate the consistency of the function. After employing the GARCH-M(1,1) model, all variables are estimated properly, except for STEEL, PR&REIT and CONS variables which are dropped because of flat log likelihood encountered, the number of observation is not enough to find uphill direction. Thus, there are 33 equations tested in this paper.

4.3.1 GARCH in MEAN (1,1) - Conditional Mean Equation

As shown in Table 4.3, the estimated results show that all of the autoregressive lag t-1 coefficient are statistically significant, in 33 cases out of 33. Further, the robustness is also supported by statistic value in Table 4.5 by the rejection null hypothesis of H1 “Prior subsector index return has no impact on subsector index return.” As expected, time series data is found to have autoregressive relationship. However, another of major concern is the direction of coefficient, the rational reason to support the negative relationship between the return on the previous period and present period is Dow Theory (Hamilton and Dow 1960). Dow Theory explains the pattern of price movement.

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First; stock market discounts all news, stock price is affected by news and information instantly as it is released and become available on public. Stock price changes to correspond with new information according to Efficient Market Hypothesis (Fama 1970).

Second; stock price moves in waves, the unique characteristic of price movement is discovered in wave patterns. Investors respond to the new information which leads to the trend of price movement. However; action creates reaction, every price movement up or down is followed by the counter reaction or opposite movement called “the correction of price movement.”

In conclusion, the directions of all significant estimated coefficients of autoregressive variables are negative due to the counter reaction of investors to the stock price. The higher price in the past period will create the higher chance that price may drop in the next period. Therefore, this estimated coefficient is rational and the return on previous period of index subsector is a good control variable in this experiment.

Regarding to the main purpose of this study, this paper will investigate the impact of 4 macroeconomic factor; unemployment rate, interest rate, consumer price index and exchange rate on the return of each subsector index of the Stock Exchange of Thailand. The impacts of these variables are showed by the statistical value of estimated coefficient in table 4.3, and the hypothesis tests are showed by statistical values reported in table 4.4.

In the table 4.3 results, the results rejects null hypothesis H2 “Unemployment rate has no impact on subsector index return,” and accepts the alternative hypothesis that unemployment rate affects subsector index return. These results are also reconfirmed by statistic value in table 4.5. However, unemployment rate coefficients are statistically significant, in 6 cases out of 33 which are 1 major sector index INDUS and 5 subsector index PERSON, PAPER, COMM, PROF and TRANS.

Unemployment rate indicates economic situations and is studied by many researchers in developed country such as United States. However, when we examine the results shown in table 4.3 in aspects of magnitude and direction, we

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find that unemployment rate of Thailand might not be a good indicator. In other words, even though the coefficient of 6 cases are statistically significant, the magnitude is barely small-scale compared to the other subsector significant coefficients. Besides, the direction of the correlations is inconsistent. Three reasons to explain these occurrences are as follows:

First, unlike the other countries, 35% of Thailand labor force are in agriculture industry and make merely 8% of GDP. On the contrary, 16% of the labor force in other industries are accounted for 37% of GDP. Hence, 35% of unemployment rate only indicate unemployment situation in agriculture industry not the whole economic system. (Data collected in 2014 from BOT, see appendices)

Second, unemployment rate is calculated based on the number of people who are unemployed, available to work and searching for work in prior 4 weeks. Subsequently, the number of unemployed people is divided by the total number of employed people in the labor force. Once people stop looking for a job, they are out of labor force and not counted as unemployed labor. On the other hands, sometimes high unemployment rate can be a hopeful sign that the economic cycle is changing. There are more jobs, and people start looking for work again. This event also called “Cyclical Unemployment.”

Finally, the descriptive statistic of unemployment rate of Thailand is different from the United States which represents the world biggest economy, and unemployment rate is found to be useful in many papers. In the past 10 years, U.S. unemployment rate drastically varies form 4.4% up to 10% (data collected from U.S. Department of Labor). But, as in Thailand, unemployment rate varies merely from 0.39% to 3.69% and mean is 1.25% which is usually in very good economic condition. Therefore, the increase of unemployment rate can indicate both good and bad economic conditions due to cyclical unemployment reason. However, the impacts are barely small-scale due to the characteristic of the unemployment rate of Thailand.

Next In the table 4.3, the results rejects null hypothesis H3 “Interest rate has no impact on stock index return,” and accepts the alternative hypothesis that interest rate affects subsector index return. These results are also

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reconfirmed by statistic value in table 4.5. However, interest rate coefficients are statistically significant, in merely 3 cases out of 33 which are FASHION, IMM and MINE.

Undeniably interest rate is an important variable used in many stock valuation models. Thus, it should be the determinant of the stock market return. But, in this paper the only 3 out of 33 cases are not concrete enough to give an inductive reason that stock market is very affected by interest rate exposure. Three reasons to explain these events are as follows:

First, interest rate is classified as lagging macroeconomic indicator which means it will change subsequent to market movement. We can see that when the economy is in the expansion stage, Bank of Thailand will increase interest rate to slow down money supply. On the contrary, in the recession stage, interest rate will be decreased to boost the economic system.

Second, the data used in this study are collected from Thailand listed public limited company. It means that as for the large firms, the exposure to interest rate risk might be diversified by equity-side fundraising and alternative borrowing. Therefore, larger firms are seemed to be less sensitive to the fluctuation of interest rate than the smaller that has higher cost of money.

Last, the impact of Interest rate to subsector index returns might be consolidated within consumer price index which represents inflation rate. In other words, interest rate is sometimes manipulated to slow down the inflation. Therefore, the impact is diluted by CPI macroeconomic variable. Further, the paper related to interest rate sensitivity of Park and Choi (2011) also reconfirms that interest rate varies over time. The correlation of interest rate and the returns of stock market can be positive, negative, or even neutral depends on time period of collected data.

Following table 4.3 results, the results rejects null hypothesis H4 “Consumer price index has no impact on subsector index return,” and accepts the alternative hypothesis that consumer price index affects subsector index return. Moreover, these results are also reconfirmed by statistic value in table 4.5. The estimated coefficient of consumer price index is statistically significant in 9 cases out of 33 which are 3 major index CONSUMP, RESOURC and SERVICE; and 6 sub sector index FOOD, HOME, IMM, PAPER, MINE and MEDIA subsector index.

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Consumer price index, sometimes called “Headline Inflation,” indicates price level and is used to indicate price changes related to cost of living. Therefore, the industries related to daily consumption should be very affected. Given the fact that oil move with inflation, as expected, RESOURC and MINE have positive correlation with CPI. In addition, FOOD subsector index which is abbreviated from food & beverage also has positive correlation with CPI.

In aspects of direction, surprisingly, the major index CONSUMP has negative correlation with CPI, but HOME has the positive. This leads to answer one of research questions that “as in subsector of 8 major industry (AGRO, CONSUMP, FINCIAL, INDUS, PROPCON, RESOURCE, SERVICE, TECH), are those factors give the same impact to each subsector?” The explanation is that when the price level is high, discretionary goods which are more sensitive to economic conditions would be affected than the necessities. This implies that CONSUMP major index industry contains businesses related to both necessities and discretionary goods. Therefore, the differences in their nature of businesses are reflected by the different direction of the CONSUMP group industry subsector’s coefficient that related to consumer price index variable.

As we can see from the table 4.3, exchange rate coefficients are statistically significant, in 33 cases out of 33 and are also supported by statistic value in Table 4.5 by the rejection the null hypothesis of H5 “Exchange rate has no impact on subsector index return” and thus accepts the alternative hypothesis that exchange rate affects subsector index return.

Nowadays, Thailand economy is heavily export-dependent. Thailand has the largest automotive industry in Southeast Asia and is ranked as the top global Hard Disk Drives (HDD), electronic circuits and semi-conductor manufacturing base. Export and import are accounted for more than 70% of Thailand GDP. The fluctuation of unfavorable exchange rate of foreign currency will leads to transaction gains or losses based on each firm’s foreign currency exposure and then, eventually, affect the bottom line.

About the direction, all of significant coefficients are negative. The possible explanation for the negative relationship between exchange rate and

21

return on subsector index is that exchange rate variables are obtained as the closing price of exchange rate of Thai currency against U.S. dollar in form of Baht per Dollar. Therefore, the increase of Baht per U.S. dollar indicates Thai Baht depreciation. This implies that Thai Baht depreciation makes foreign investors avoid holding assets in Thailand. The divestment would cause dropping in share prices. Further, on physical economic activities, importer would be greatly affected as a higher cost of raw materials and equipment.

Lastly, as in table 4.3 results, the results rejects null hypothesis H6 “Return volatility has no impact on subsector index return,” and accepts the alternative hypothesis that return volatility affects subsector index return. Moreover, these results are also reconfirmed by statistic value in table 4.5. The estimated coefficient of return volatility is statistically significant in 4 cases out of 33 which are AGRO, PAPER, CONMAT and COMM.

As expected, the statistically coefficient shows that return volatility, or risk and return trade off, has positive relation to the stock index return. This suggests that as in the Stock Exchange of Thailand, investors are risk averse. In other words, investor will avoid risk unless there is a risk reward because all of significant coefficients have positive correlation with the stock market returns.

4.3.2 GARCH in MEAN (1,1) – Conditional Variance Equation As shown in Table 4.4, the estimated results show that ARCH and

GARCH term coefficients ( , ) are statistically significant, in 23 cases out of 33 and 16 cases out of 33, respectively. The robustness is also supported by statistic value in Table 4.5 by the rejection null hypothesis of H7 and H8 “ARCH and GARCH term has no relation on return volatility,” accepts the alternative hypothesis that ARCH and GARCH term affects return volatility. This suggests that it is inappropriate to employ ordinal least square model to investigate this experiment because “the return volatility is time varying.”

Furthermore, as expected, present return volatility ( ) is affected by ARCH ( ) and a GARCH ( ). Further, both statistically significant coefficients ( , ) have positive relationship to the present return volatility ( ). In other

22

words, the previous shock (ARCH) and the previous surprise (GARCH) have a positive correlation with present return volatility.

23

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS

This paper investigates how macroeconomic variables affect each of

stock market subsector index returns compared to its major sector index of the Stock Exchange of Thailand by using GARCH in MEAN technique. The data are collected from 2004 to 2014. The sample data are 8 major sector indexes, 28 subsector indexes and 4 macroeconomic factors. Unemployment rate, 1-month Thailand Treasury bill yield, consumer price index and THB/USD foreign exchange rate are used as a proxy of general economic conditions, monetary policy, price level and international activities, respectively. The main findings are as follows:

Firstly, consumer price index and foreign exchange rate play an important role in the experiment. In addition, To prove that macroeconomic factors affect subsector index returns differently compared to its major sector index; I find that consumer price index has positive effect on HOME subsector index ,but has negative effect on CONSUMP which is HOME’s major index.

Secondly, unemployment rate and interest rate also have impacts on subsector index returns. But, the magnitude is very small, and the direction is still inconsistent due to the characteristic of economic variables that move subsequent to market movements.

Finally, the conditional variance is investigated, and results show that the return volatility is time varying and has positive relation to the stock market returns. Therefore, employing GARCH in MEAN model is more appropriate than using the ordinary least square model.

The contributions of this paper is that, as for investors, the understanding of how macroeconomic factors affect the stock market will help investors to get a big picture and understand the direction of stock market. Further, in aspects of academic field; the results of this study can be used by economists and practitioners in the development of economic activities that help increase Thailand economic

24

growth and, as well, help increase competitive advantages of Thailand’s capital market.

The limitation of this paper is that there are a number of important economic factors that will be more suitable to employ in the experiment such as gross domestic product and inflation rate. However, those data are only available in quarterly or yearly period. Thus the number of observation will be not enough to investigate those variables. Furthermore, a number of interesting independent variables such as SET index are opened to be added in further investigation.

25

Table 3.1 Sample and data specification

Data Label Sample period Data collected monthly for period beginning JAN 2004 and ending on SEP 2014. 1. Subsector index return specification The closing price of each subsector index return is collected from www.setsmart.com. The sample is composed of 28 subsector indexes and their major indexes including:

Agro & Food Industry Construction Materials Agribusiness Food & Beverage

AGRO AGRI FOOD

Consumer Products (CONSUMP) Fashion Home & Office Products Personal Products & Pharmaceuticals

CONSUMP FASHION HOME PERSON

Financials (FINCIAL) Banking Finance & Securities Insurance

FINCIAL BANK FIN INSUR

Industrials (INDUS) Automotive Industrial Materials & Machinery Paper & Printing Materials Petrochemicals & Chemicals Packaging Steel

INDUS AUTO IMM PAPER PETRO PKG STEEL

Property & Construction (PROPCON) Construction Materials Property Development Property Fund & REITs Construction Services

PROPCON CONMAT PROP PF&REIT CONS

26

Table 3.1 Sample and data specification (continue)

Data Label Resources (RESOURC)

Energy & Utilities Mining

RESOURC ENERG MINE

Services (SERVICE) Commerce Health Care Services Media & Publishing Professional Services Tourism & Leisure Transportation & Logistics

SERVICE COMM HELTH MEDIA PROF TOURISM TRANS

Technology (TECH) Electronic Components Information & Communication Technology

TECH ETRON ICT

are obtained as = (

)

2. Unemployment rate Announced unemployment rate collected from www.bot.or.th UR is obtained as = (

)

UR

3. Interest rate 1-month Thailand treasury bill yield collected from www.thaibma.com INR is obtained as = (

)

INR

4. Consumer Price Index Announced CPI collected from www.bot.or.th CPI is obtained as = (

)

CPI

5. Exchange Rate The closing price of Exchange rate of Thai currency against U.S. Dollar in form of Baht per Dollar collected from www.bot.or.th XR is obtained as = (

)

XR

Where log( ) is natural logarithm function

27

Table 4.1 Descriptive statistics of return series variables Min Max Mean SD Skewness Kurtosis N AGRO -0.2630 0.1258 0.0116 0.0565 -1.1449 6.8482 129 AGRI -0.2770 0.2017 0.0113 0.0699 -0.4337 5.5092 129 FOOD -0.2875 0.1444 0.0105 0.0586 -1.2516 7.4349 129 CONSUMP -0.1344 0.1017 0.0040 0.0341 -0.4337 4.8779 129 FASHION -0.1134 0.1484 0.0023 0.0342 0.4387 5.7350 129 HOME -0.2766 0.1892 0.0060 0.0631 -0.4946 5.9248 129 PERSON -0.1904 0.3167 0.0102 0.0624 0.5060 7.1982 129 FINCIAL -0.3363 0.1552 0.0058 0.0684 -1.1850 6.7892 129 BANK -0.3379 0.1596 0.0062 0.0713 -1.0882 6.1894 129 FIN -0.4300 0.1732 0.0003 0.0805 -1.4515 8.4036 129 INSUR -0.2301 0.1560 0.0136 0.0523 -0.8186 6.8765 129 INDUS -0.4913 0.2249 0.0010 0.0878 -1.5260 10.2851 129 AUTO -0.2773 0.1400 -0.0005 0.0664 -0.7069 4.6482 129 IMM -0.6871 0.2237 -0.0026 0.1092 -2.5481 17.3137 99 PAPER -0.2070 0.4314 0.0095 0.0817 1.3790 8.8368 129 PETRO -0.5216 0.2913 0.0026 0.1057 -1.0570 7.8645 129 PKG -0.3105 0.3489 0.0029 0.0832 -0.0435 6.3289 129 STEEL -0.2115 0.1057 -0.0078 0.0675 -0.9810 4.1856 45 PROPCON -0.3743 0.1735 0.0028 0.0740 -1.0631 7.1119 129 CONMAT -0.3134 0.1869 0.0030 0.0733 -0.6981 5.4403 129 PROP -0.4226 0.2064 0.0037 0.0893 -0.7432 5.8467 129 PR&REIT -0.0573 0.0585 0.0071 0.0207 -0.8093 4.9183 67 CONS -0.0190 0.1163 0.0450 0.0470 0.2347 1.9968 9 RESOURC -0.3989 0.1743 0.0050 0.0768 -1.1908 8.4076 129 ENERG -0.3980 0.1749 0.0051 0.0768 -1.1800 8.3531 129 MINE -0.7108 0.3022 -0.0010 0.1174 -1.5579 12.2927 129 SERVICE -0.3821 0.1210 0.0101 0.0614 -2.1677 14.4423 129 COMM -0.2806 0.1881 0.0188 0.0616 -0.7958 6.5830 129 HELTH -0.3260 0.1625 0.0202 0.0673 -1.0399 7.5571 129 MEDIA -0.4665 0.2336 0.0010 0.0771 -1.6412 12.6358 129 PROF -0.5298 0.3377 -0.0073 0.1063 -0.4585 7.6221 129 TOURISM -0.2544 0.2107 0.0081 0.0599 -0.2669 7.1367 129 TRANS

-0.5326 0.1790 0.0040 0.0871 -2.0082 13.0098 129

TECH -0.2498 0.1377 0.0063 0.0632 -0.4690 4.2210 129 ETRON -0.3990 0.2610 0.0045 0.0792 -0.7860 8.1024 129 ICT -0.2338 0.1481 0.0068 0.0665 -0.3345 3.6038 129 UR -0.6455 0.9136 -0.0048 0.2673 0.7738 3.9963 129 IR -0.4139 0.2785 0.0062 0.0881 -0.6600 7.8960 129 CPI -0.0306 0.0215 0.0025 0.0058 -1.4044 12.2189 129 XR -0.0491 0.0371 -0.0016 0.0166 0.1135 3.0591 129 Note: MIN, MAX, SD, N, stand for minimum, maximum, standard deviation, number of observation, respectively.

28

Table 4.2 Unit root test

Augmented Dickey-Fuller Test Statistic 1% Critical Value Mackinnon P-value AGRO -16.225 -3.501 0.000*** AGRI -16.551 -3.501 0.000*** FOOD -16.068 -3.501 0.000*** CONSUMP -17.617 -3.501 0.000*** FASHION -19.723 -3.501 0.000*** HOME -18.997 -3.501 0.000*** PERSON -17.538 -3.501 0.000*** FINCIAL -16.032 -3.501 0.000*** BANK -16.350 -3.501 0.000*** FIN -14.251 -3.511 0.000*** INSUR -16.647 -3.501 0.000*** INDUS -16.155 -3.501 0.000*** AUTO -16.312 -3.501 0.000*** IMM -9.619 -3.614 0.000*** PAPER -22.503 -3.501 0.000*** PETRO -17.396 -3.501 0.000*** PKG -18.376 -3.501 0.000*** STEEL -12.946 -3.556 0.000*** PROPCON -4.989 -3.750 0.000*** CONMAT -17.041 -3.501 0.000*** PROP -14.124 -3.501 0.000*** PR&REIT -18.019 -3.501 0.000*** CONS -21.123 -3.501 0.000*** RESOURC -18.004 -3.501 0.000*** ENERG -18.005 -3.501 0.000*** MINE -18.983 -3.501 0.000*** SERVICE -16.214 -3.501 0.000*** COMM -17.036 -3.501 0.000*** HELTH -15.982 -3.501 0.000*** MEDIA -19.400 -3.501 0.000*** PROF -19.460 -3.501 0.000*** TOURISM -16.131 -3.501 0.000*** TRANS -16.012 -3.501 0.000*** TECH -17.736 -3.501 0.000*** ETRON -15.939 -3.501 0.000*** ICT -18.002 -3.501 0.000*** UR -22.678 -3.501 0.000*** IR -18.514 -3.501 0.000*** CPI -16.581 -3.501 0.000*** XR -17.043 -3.501 0.000*** Note: *,**,*** represent level of significance at 0.10, 0.05 and 0.01 respectively

29

Table 4.3 GARCH-M(1,1) model – conditional mean equation

Conditional Mean Equation

AGRO -0.042** -0.254***

-0.001 -0.034 0.504 -1.225***

16.308** AGRI -0.009 -

0.314*** -0.012 -0.104 0.757 -

1.269*** 1.964

FOOD -0.012 -0.260***

-0.002 0.041 1.444** -1.230***

4.162 CONSUMP -0.003 -

0.275*** -0.016 -0.057 -0.545* -

0.654*** 3.508

FASHION 0.004 -0.337***

-0.013 -0.082** -0.163 -0.561***

-2.086 HOME 0.003 -

0.478*** -0.013 0.030 1.163* -

0.997*** -0.284

PERSON 0.001 -0.393***

0.025* -0.067 -0.955 -0.454* 0.716 FINCIAL -0.010 -

0.252*** -0.018 -0.012 -1.067 -

2.146*** 3.065

BANK -0.009 -0.277***

-0.024 -0.017 -1.212 -2.324***

2.215 FIN -0.026 -

0.373*** 0.005 -0.075 0.183 -

1.742*** 5.023

INSUR 0.002 -0.211** -0.011 -0.024 0.869 -0.369* 0.054 INDUS -0.003 -

0.293*** -0.024* 0.061 0.886 -

1.970*** 1.159

AUTO -0.007 -0.403***

-0.015 0.104 0.896 -0.713***

1.729 IMM 0.001 -

0.427*** 0.004 0.292** 2.479** -

1.999*** -0.317

PAPER 0.024*** -0.420***

-0.036** 0.023 3.123*** -0.681** 3.644*** PETRO -0.001 -

0.314*** -0.023 0.046 -0.252 -

1.858*** 0.532

PKG -0.004 -0.421***

0.007 0.024 -0.968 -1.701***

1.967 PROPCON -0.003 -

0.185*** -0.017 -0.020 -0.688 -

2.321*** 0.282

CONMAT -0.027* -0.411***

-0.018 -0.073 -0.234 -1.717***

6.875* PROP -0.005 -0.213** -0.016 0.007 -0.575 -

2.926*** 1.550

RESOURC -0.009 -0.393***

-0.026 -0.062 1.827* -1.892***

2.413 ENERG -0.009 -

0.392*** -0.026 -0.062 1.818 -

1.892*** 2.400

MINE -0.015 -0.381***

0.019 -0.291***

3.808*** -1.893***

1.232 SERVICE -0.007 -

0.330*** -0.020 -0.021 1.302** -

1.682*** 3.316

COMM -0.021* -0.338***

-0.029* -0.083 0.767 -1.609***

6.632** HELTH 0.009 -

0.309*** -0.021 0.000 0.814 -

0.862*** -1.632

MEDIA -0.005 -0.410***

-0.009 -0.074 1.909*** -1.475***

1.499 PROF 0.005 -

0.442*** 0.047* -0.086 1.289 -

1.874*** -0.205

TOURISM 0.006 -0.345***

-0.014 0.056 1.078 -0.886***

-0.615 TRANS -0.012 -

0.330*** -0.034* 0.002 0.755 -

1.813*** 2.390

TECH 0.004 -0.447***

-0.010 0.006 0.921 -1.076***

-0.633 ETRON -0.002 -

0.363*** -0.026 0.051 0.808 -

0.783*** -0.054

ICT 0.010 -0.453***

-0.006 0.002 0.802 -1.057***

-1.568 No. of Sig 4/33 33/33 6/33 3/33 9/33 33/33 4/33

Note: *,**,*** represent level of significance at 0.10, 0.05 and 0.01 respectively

30

Table 4.4 GARCH-M(1,1) model – conditional variance equation

Conditional Variance Equation Model Testing

AGRO 0.001 0.195 0.396 35.04*** AGRI 0.001 0.230 0.562** 45.31*** FOOD 0.002 0.348 0.139 47.18*** CONSUMP 0.000 0.109 0.876*** 34.47*** FASHION 0.001*** 0.525*** -0.104 32.00*** HOME 0.001* 0.448* 0.402* 42.24*** PERSON 0.002** 0.603*** 0.153 39.18*** FINCIAL 0.001 0.243 0.525* 119.01*** BANK 0.001 0.274 0.456 117.53*** FIN 0.003* 0.219* 0.376 47.03*** INSUR 0.000 0.507** 0.543*** 12.01* INDUS 0.000 0.334** 0.614*** 75.97*** AUTO 0.002 0.382** 0.283 35.16*** IMM 0.002* 0.914** 0.125 73.75*** PAPER 0.004*** 0.771*** -0.099 69.57*** PETRO 0.000 0.297** 0.708*** 95.40*** PKG 0.002* 0.456** 0.343* 68.99*** PROPCON 0.003*** 0.509** -0.153 152.56*** CONMAT 0.002** 0.316* 0.377* 65.62*** PROP 0.001 0.433** 0.432*** 101.45*** RESOURC 0.001** 0.372** 0.427** 93.90*** ENERG 0.001** 0.372** 0.429** 93.71*** MINE 0.006*** 0.505** 0.078 111.41*** SERVICE 0.001 0.455** 0.337 72.49*** COMM 0.002 0.322* 0.228 66.52*** HELTH 0.001 0.174 0.597* 23.66*** MEDIA 0.002 0.360** 0.412 63.24*** PROF 0.002 0.342 0.494 118.55*** TOURISM 0.001 0.231* 0.627*** 50.85*** TRANS 0.002** 0.325** 0.464*** 63.08*** TECH 0.003 0.170 0.357 37.78*** ETRON 0.001* 0.339** 0.548*** 34.60*** ICT 0.002 0.134 0.473 37.93*** No. of Sig 14/33 23/33 16/33 33/33 Note: *,**,*** represent level of significance at 0.10, 0.05 and 0.01 respectively

31

Table 4.5 The statistic value for various hypotheses test

statistic value

AGRO -3.05***

-0.10 -0.67 1.02 -4.74*** 2.20** 1.55 1.16 AGRI -

3.30*** -0.68 -1.57 1.04 -3.92*** 0.54 1.58 2.11**

FOOD -3.51***

-0.17 0.76 2.41** -4.77*** 0.99 1.41 0.31 CONSUMP -

3.43*** -1.57 -1.58 -1.70* -3.89*** 0.46 1.59 12.94***

FASHION -3.69***

-1.40 -2.16** -0.36 -3.86*** -0.39 2.80*** -0.46 HOME -

4.32*** -0.75 0.41 1.82* -4.02*** -0.11 1.95* 1.84*

PERSON -4.31***

1.91* -1.25 -1.18 -1.64* 0.47 3.03*** 0.91 FINCIAL -

3.38*** -1.22 -0.20 -1.43 -8.68*** 1.02 1.40 1.81*

BANK -3.69***

-1.54 -0.28 -1.51 -8.60*** 0.77 1.48 1.45 FIN -

3.13*** 0.23 -0.87 0.23 -4.89*** 1.43 1.77* 1.18

INSUR -2.22** -0.98 -0.61 1.31 -1.81* 0.03 2.52** 4.18*** INDUS -

4.43*** -1.82* 0.99 0.74 -6.51*** 0.77 2.46** 5.59***

AUTO -4.42***

-1.18 1.59 1.24 -3.01*** 0.50 2.00** 0.90 IMM -

4.19*** 0.18 2.47** 2.23** -5.21*** -0.38 2.55** 0.89

PAPER -5.16***

-2.43**

0.35 4.51*** -2.36** -3.47***

3.70*** -0.68 PETRO -

4.90*** -1.42 0.68 -0.17 -5.01*** 0.48 2.00** 6.82***

PKG -5.37***

0.40 0.37 -1.17 -4.29*** 1.19 2.05** 1.91* PROPCON -

2.66*** -1.24 -0.29 -0.71 -

10.31*** 0.19 2.52** -1.07

CONMAT -4.84***

-1.02 -1.06 -0.24 -5.61*** 1.66* 1.66* 1.66* PROP -2.55** -0.99 0.11 -0.71 -9.35*** 0.69 2.30** 2.80*** RESOURC -

4.89*** -1.53 -0.91 1.65* -7.01*** 1.35 1.97** 2.29**

ENERG -4.87***

-1.53 -0.91 1.64 -7.00*** 1.34 1.97** 2.31** MINE -

4.83*** 0.77 -

3.03*** 2.58*** -4.71*** 1.02 2.53** 0.58

SERVICE -3.62***

-1.55 -0.38 2.12** -7.14*** 1.40 2.17** 1.42 COMM -

3.93*** -1.83* -1.28 1.27 -5.85*** 2.08** 1.85* 0.70

HELTH -3.12***

-1.24 -0.01 0.90 -2.67*** -0.38 1.45 1.92* MEDIA -

3.97*** -0.47 -0.95 2.66*** -3.45*** 0.61 2.02** 1.43

PROF -5.95***

1.91* -0.84 1.14 -4.92*** -0.13 1.47 1.54 TOURISM -

3.76*** -1.01 1.01 1.36 -3.51*** -0.18 1.93* 4.79***

TRANS -3.39***

-1.91* 0.02 0.72 -5.97*** 1.37 2.00** 2.84*** TECH -

4.92*** -0.56 0.07 0.94 -3.47*** -0.11 0.91 0.42

ETRON -3.52***

-1.60 0.91 0.86 -3.14*** -0.03 2.05** 3.93*** ICT -

5.05*** -0.31 0.03 0.73 -3.10*** -0.23 0.75 0.53

No. of Sig 33/33 6/33 3/33 9/33 33/33 4/33 23/33 16/33 Note: *,**,*** represent level of significance at 0.10, 0.05 and 0.01 respectively

32

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APPENDICES

35

Appendix A Thailand GNP and GDP by industrial origin 2014 (www.bot.or.th)

Industrial Origin 2557 (2014)

Total Percentage Agriculture 422,453 8.32%

Agriculture, hunting and forestry 360,639 7.11%

Fishing 61,814 1.22% Non - agriculture 4,653,165 91.68%

Mining and quarrying 108,946 2.15%

Manufacturing 1,898,273 37.40%

Electricity, gas and water supply 187,211 3.69%

Construction 101,530 2.00%

Wholesale and retail trade, repair of motor vehicles, 679,847 13.39%

motorcycles and personal and household goods

Hotels and restaurants 228,638 4.50%

Transport, storage and communications 539,437 10.63%

Financial intermediation 253,486 4.99%

Real estate, renting and business activities 199,642 3.93%

Public administration and defense, 139,847 2.76%

compulsory social security

Education 130,332 2.57%

Health and social work 63,347 1.25%

Other community, social and personal service activities 118,683 2.34%

Private household with employed persons 3,946 0.08% Gross Domestic Products, (GDP) 5,075,618 100.00%

Plus: Net factor income payment from the rest of the world -236,549

Gross National Product, (GNP) 4,839,069

36

Appendix B

Thailand unemployment rate by sector (www.bot.or.th) Unemployment Rate by Sector - Bank of Thailand SEP 2014 Percentage 1.Population 15 years and over 54,929.02 100.00% 2.Labour Force (2.1 + 2.2 + 2.3) 38,850.45 100.00% 2.1.Employment 1/ 38,451.55 98.97% of which underemployment 2/ 240.21 0.62% 2.1.1.Agriculture 13,501.91 34.75% 1) Agriculture, forestry and fishing 13,501.91 34.75% 2.1.2.Non-Agriculture 24,949.64 64.22% 1) Mining and quarrying 66.64 0.17% 2) Manufacturing 6,283.28 16.17% 3) Electricity, gas, steam and air conditioning supply 101.72 0.26% 4) Water supply 156.53 0.40% 5) Construction 2,201.51 5.67% 6) Wholesale and retail trade 6,002.26 15.45% 7) Transportation and storage 1,090.09 2.81% 8) Accommodation and food service activities 2,577.66 6.63% 9) Information and communication 217.78 0.56% 10) Financial and insurance activities 473.29 1.22% 11) Real estate activities 147.39 0.38% 12) Professional, scientific and technical activities 359.29 0.92% 13) Administrative and support service activities 509.81 1.31% 14) Public administration and defence 1,558.80 4.01% 15) Education 1,132.78 2.92% 16) Human health and social work activities 634.16 1.63% 17) Arts, entertainment and recreation 258.46 0.67% 18) Other service activities 832.53 2.14% 19) Activities of households as employers 226.16 0.58% 20) Activities of extraterritorial organizations and bodies 3.42 0.01% 21) Unknown 116.08 0.30% 2.2.Unemployed Persons 310.51 0.80% (rate of unemployment) 0.80 0.00% of which new entry 155.31 0.40% 2.3.Seasonal Inactive Labour Force 88.39 0.23% (share of total labour force) 0.23 0.00% 3. Persons not in labour force, age 15 years or over 16,078.56 100.00% 1) Household work 4,899.68 30.47% 2) Studies 4,283.81 26.64% 3) Too young / old / incapable of work 5,333.81 33.17% 4) Others 1,561.25 9.71%

37

Appendix C

US unemployment rate (www.bls.gov)

US Unemployment Rate Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2004 5.7 5.6 5.8 5.6 5.6 5.6 5.5 5.4 5.4 5.5 5.4 5.4 2005 5.3 5.4 5.2 5.2 5.1 5.0 5.0 4.9 5.0 5.0 5.0 4.9 2006 4.7 4.8 4.7 4.7 4.6 4.6 4.7 4.7 4.5 4.4 4.5 4.4 2007 4.6 4.5 4.4 4.5 4.4 4.6 4.7 4.6 4.7 4.7 4.7 5.0 2008 5.0 4.9 5.1 5.0 5.4 5.6 5.8 6.1 6.1 6.5 6.8 7.3 2009 7.8 8.3 8.7 9.0 9.4 9.5 9.5 9.6 9.8 10.0 9.9 9.9 2010 9.8 9.8 9.9 9.9 9.6 9.4 9.4 9.5 9.5 9.4 9.8 9.3 2011 9.2 9.0 9.0 9.1 9.0 9.1 9.0 9.0 9.0 8.8 8.6 8.5 2012 8.3 8.3 8.2 8.2 8.2 8.2 8.2 8.0 7.8 7.8 7.7 7.9 2013 8.0 7.7 7.5 7.6 7.5 7.5 7.3 7.2 7.2 7.2 7.0 6.7 2014 6.6 6.7 6.6 6.2 6.3 6.1 6.2 6.1 5.9 5.7 5.8 5.6

38

BIOGRAPHY

Name Mr. Rattanan Jirayupat Date of Birth December 23, 1991 Educational Attainment Academic Year 2013: Bachelor of Accounting

(Integrative Business Accounting), Thammasat University, Thailand