Download (1892Kb) - UTAR Institutional Repository

The relationship between the stock market and macroeconomic variables is well documented for the United States and other major economies. Additionally, this paper investigates short-run and long-run dynamic relationships using Johansen's cointegration test and Granger causality test, respectively.

INTRODUCTION

Introduction
Research Background
Problem Statement
Research Objective

General Objective
Specific Objective

Research Questions
Significance of the Study
Chapter Layout
Conclusion

Therefore, macroeconomic variables are used to identify and examine the relationship between stock market returns in Southeast Asian emerging countries. The main objective of this study is to investigate the effect of macroeconomic variables on the stock market returns of Southeast Asian emerging countries (Malaysia, Thailand, Indonesia and the Philippines).

LITERATURE REVIEW

Introduction

Review of Stock Markets

FTSE Bursa Malaysia (KLSE)
The Stock Exchange of Thailand (SET)
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
The Philippine Stock Exchange (PSE)

The index is denominated in Indonesian rupiah ("IDR") and is published during IDX trading hours ("Capital Market", 2010). The one-price-one-market exchange was achieved through the merger of two existing trading platforms on March 25, 1994 (“Philippine Stock Exchange,” 2012).

Review of Theoretical Models

Stock Market Returns

Efficient Market Hypothesis Theory
Random Walk Theory
Modern Portfolio Model
Capital Asset Pricing Model (CAPM)

Consumer Price Index (CPI)

Fisher Effect Theory

Exchange Rate (ER)

The Scapegoat Theory
Flow-oriented Models
Stock-oriented Models

Gross Domestic Product (GDP)

Supply-Side Models
The Solow Growth Model

Interest Rate (IR)

Taylor’s Theory
Arbitrage Pricing Theory (APT)

Money Supply (M1)

Tobin’s Q Theory
Monetary Portfolio Model

Stock-oriented models place much emphasis on the role of the financial (formerly capital) account in exchange rate determination. The equilibrium economists seek in the case of the Solow growth model is balanced-growth equilibrium.

Review of the Literature

Stock Market
Money Supply (M1)

There is a research done by Anokye and Tweneboah (2008) on Ghana stock market, they analyzed the long and short term relationship between Consumer Price Index (CPI) and stock market return. In the study done by Carstrom (2002), he concluded that Gross Domestic Product (GDP) and stock market returns are related.

Proposed Theoretical Framework

They observed both short- and long-term relationships between money supply and the selected stock markets. In their research, they observed that money supply and all the selected stock market returns are positively related in the long run. Results presented by Chena (2005) also suggested that stock market returns can be explained by money supply.

Also, Theophano and Sunil (2006) applied dual VAR models and concluded that stock market returns and money supply are negatively related.

Conclusion

Introduction

Research Design

As mentioned above, 60 observations are drawn from the data stream to construct a data set for each of the dependent and independent variables. In other words, there is a software called E-views 8 software that will be used in this paper to study the relationship between the independent variables and the dependent variable.

Data Collection Method

Secondary Data

Sampling Design

Target Population
Sampling Element – Formula

Sampling Technique
Sampling Size

All of these indices are free float-adjusted, with the exception of the All Shares Index ("The Philippines Stock." Previously, Phisix and the PSE Composite Index were the different labels that designated the main index of the stock exchange ("The Philippines Stock Exchange", 2012 The six indices tracked by PSE are the Financials Index, Services Index, Property Index, Industrial Index, Mining & Oil Index and Holding Firms Index ("The Philippines Stock Exchange", 2012).

It is an application used to calculate indices in the New Trading System (NTS) of the PSE ("The Philippines Stock Exchange", 2012). Finally, the final result of the trade will be published in the PSE Daily Quotation Report, as well as in major newspapers ("The Philippines Stock Exchange", 2012).

Data Processing

Multiple Regression Model

Hypotheses of the Study

Money Supply (M1)

H0: There is no relationship between the equity returns of emerging countries in Southeast Asia and the exchange rate (ER). H1 : There is a relationship between the equity returns of emerging countries in Southeast Asia and the exchange rate (ER). H1 : There is a relationship between the equity returns of emerging countries in Southeast Asia and the interest rate (IR).

H0: There is no relationship between Southeast Asian emerging market stock returns and money supply (M1). H1 : There is a relationship between Southeast Asian emerging market stock returns and money supply (M1).

Data Analysis

Ordinary least square (OLS)
Unit Root Test
Johansen Cointegration
Granger Causality
Variance Decomposition
Impulse Response Function

To identify economic problems such as autocorrelation, model specification error, and heteroscedasticity, some techniques will be used in this study. If these variables are I(1), then cointegration techniques can be used to model these long-run relationships. In addition, the relationship between the dependent variable and the independent variable in the short or long run can be explained by Johansens cointegration test (Ali et al., 2010).

Data from a linear combination of two variables may be stationary and will be defined as cointegration. In order to investigate the short-term dynamic relationships between the NZSE40 and the macroeconomic variable over the entire testing period, an impulse response function can be used (Gan et al., 2006).

Conclusion

FINDINGS AND ANALYSIS

Introduction

Descriptive Statistics

Stock market returns, Consumer Price Index (CPI) and money supply (M1) are positively skewed showing that they are asymmetric. Consumer Price Index (CPI) and Exchange Rate (ER) are positively skewed showing that they are asymmetric. Exchange Rate (ER), Gross Domestic Product (GDP) and Interest Rate (IR) are positively skewed showing that they are asymmetric.

Stock market returns, exchange rate (ER), gross domestic product (GDP) and money supply (M1) are positively skewed, indicating that they are asymmetric. The kurtosis values of all variables indicate that the data are not normally distributed, as the kurtosis values deviate from 3.

Table 2 is showing the descriptive statistics of the independent and dependent variables that being analyzed for FTSE Bursa Malaysia (KLSE)

Ordinary Least Square (OLS)

Diagnostic Checking

Autocorrelation

Indonesia Stock Exchange (Bursa Efek Indonesia, IDX) Table 12: Breusch-Godfrey Serial Correlation LM Test (IDX)

Heteroscedasticity

Model Specification Test

Normality Test

F-stats

We do not reject H0 if the P-value of the F-statistic > 0.01, which means that there is no heteroscedasticity problem. We do not reject H0 if the P-value of the F statistic > 0.01, which means that the model is correctly specified. We reject H0 if the P-value of the F statistic<0.01, which means that the model is not specified correctly.

We do not reject H0 if the P value for the JB statistic is > 0.01, which means that the error term is normally distributed. We reject H0 if the P value for the JB statistic is < 0.01, which means that the error term is not normally distributed.

Table 13: Breusch-Godfrey Serial Correlation LM Test (PSE)

Unit Root Test

Referring to table 22, it shows that all variables are not significant at 1%, therefore it is believed that the variables in ADF and PP test are not stationary and have unit root. Therefore, H0 is not rejected. This shows that all the variables are stationary and do not contain unit root, which is supported by Gan et al. This shows that all the variables are stationary and do not contain unit root, which is supported by Gan et al.

Referring to Table 25, the results show that all variables are not significant at 1%, so the variables in the ADF and PP test are assumed to be non-stationary and have root of unity. This shows that all variables are stationary and do not contain a root of unity, which is supported by Gan et al.

Table 22: Unit Root and Stationary Test Result (KLSE)

Johansen Cointegration Test

The result of the cointegration test is shown in Table 26 and it shows that at least one (r=0) cointegration is significant at 5% and therefore rejects H0. The result of the co-integration test is shown in Table 27 and it shows that there is at least one. r=0) co-integration is significant at 5% and therefore rejects H0. The result of the cointegration test is shown in Table 28 and it shows that at least one (r=0) cointegration is significant at 5% and therefore rejects H0.

From the result, Trace and Maximum Eigenvalue indicate that no cointegration is significant at 5% and therefore H0 is not rejected. Therefore, it is believed that there is no long-term correlation between variables for PSE.

Table 28: Johansen-Juselius Cointegration Tests (IDX) Test statistic

Granger Causality Test

He asserted that variables can be used to predict each other if there is a causal relationship between them (Ali et al., 2010).

Value Result Independent Variable

Variance Decomposition

Impulse Response Function (IRF)

Conclusion

H0: There is no correlation between stock market returns on the Indonesia Stock Exchange (Bursa Efek Indonesia, IDX) and consumer price index (CPI). H0: There is no relationship between stock market returns on the Indonesia Stock Exchange (Bursa Efek Indonesia, IDX) and exchange rate (ER). H0: There is no relationship between stock market returns on the Indonesia Stock Exchange (Bursa Efek Indonesia, IDX) and gross domestic product (GDP).

H0: There is no relationship between the stock market return of the Indonesia Stock Exchange (Bursa Efek Indonesia, IDX) and the interest rate (IR). H0: There is no relationship between the stock market performance of the Indonesian stock exchange (Bursa Efek Indonesia, IDX) and the money supply (M1).

Figure 7: The relationship between each variables for Granger Causality Tests (KLSE)

DISCUSSION, CONCLUSION AND IMPLICATIONS

Introduction

Summary of Statistical Analysis

Summary of Econometric Problems
Summary of Major Findings

Summary of Long-run Relationship

In terms of short-term relationships, consumer price index (CPI) and money supply (M1) have short-term relationships with KLSE at a significance level of 1% and 5%, respectively. In terms of short-term relationships, all variables have short-term relationships with The Stock Exchange of Thailand (SET) except exchange rate (ER). In terms of short-term relationships, all variables do not have short-term relationships with IDX except the gross domestic product (GDP).

In terms of the short-run relationship, all variables have no short-run relationship with the Philippine Stock Exchange (PSE). The trail test is cointegrated at r=0 and this indicates that there is a long-run relationship in this model (Refer to Table 26).

Table 46: Summary of Econometric Problems Econometric

Discussion of Major Findings

Finally, the result shows that the money supply (M1) has a short-term relationship with FTSE Bursa Malaysia (KLSE) and The Stock Exchange of Thailand (SET). The result shows that long-term relationships exist between the independent variables and FTSE Bursa Malaysia. The result shows that long-term relationships exist between the independent variables and The Stock Exchange of Thailand (SET).

Results show that long-term relationships exist between the independent variables and Indonesia Stock Exchange (Bursa Efek Indonesia, IDX). However, there is no long-term relationship between the independent variables and The Philippines Stock Exchange (PSE) and this is supported by the studies done by Chowdhury (2004), who observed the long-term relationship between macroeconomic variables and The Philippines Stock Exchange has (PSE) from year 1990 to 2003.

Table 56: Summary of Granger Causality Test

Implications of the Study

Some of the variables, such as the Consumer Price Index (CPI) are negatively related to stock market returns. Similar to the Granger causality test used to study the short-run relationship between stock market returns and a set of selected macroeconomic variables, different variables have different short-run relationships in different countries. As for the variance decomposition test, it shows the relationship between all the variables and how these variables affect each other in the short and long term.

The result of the Impulse Response Function indicates the positive or negative impact on stock market returns. Stock market participants can use this result to predict the trend of particular stock market returns with the information of the macroeconomic variables that are analyzed in this paper.

Limitations of the Study

In addition, due to the different country status and environment, culture, background, political factors and other possible reasons, the results of this document may not be applicable to investors from different countries such as the United Kingdom, Japan, China, Korea, the United States and Europe. Therefore, the findings and results of this document can only be the reference for different countries. This article focuses only on studying the relationship between a group of selected macroeconomic variables and selected stock markets in both the short and long term.

There are several macroeconomic variables that may have the potential to affect these four countries are not measured and discussed in this paper. The result of this paper may not be fully useful to the concerned parties in their decision making process and therefore they are encouraged not to refer only to the result of this paper.

Recommendations for Future Research

Conclusion

An econometric analysis of the impact of macroeconomic variables on stock market movements in Nigeria. The Kuala Lumpur Stock Exchange and Economic Factors: A Test of General-to-Specific Error Correction Modeling. Analyzing the long-run relationship between macroeconomic variables and the Chinese stock market using heteroskedastic cointegration, Managerial Finance.

The Relationship between Macroeconomic Variables and Stock Market Indices: Cointegration Evidence from the Stock Exchange of Singapore's All-S Sector Indices. Retrieved April 13, 2014, from http://www.academia.edu/5436816/Relationship_between_Macroeconomic_V ariables_and_Stock_Market_Indices_Cointegration_Evidence_from_Stock_E xchange_of_Singapore_Sector_Singapore_All-Indices.