proposal pn.suzaida - copy

8/2/2019 Proposal Pn.suzaida - Copy

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CHAPTER ONE

INTRODUCTION

1.1 INTRODUCTION

The relationship between interest rate and stock market has become the focus of

interest among recent researchers in identifying the long-run implications between

these two variables. This study has become essential in stock market after the 1997

stock market crash in Asean countries. The interest rate are said to be linked with

the deposit rates, lending rates and discount rates .Interest rates are also

fundamental to a capitalist society with variables like investment, inflation and

unemployment. As for stock market, it normally serves as a channel to direct funds

from individuals to investors by mobilizing individual owned resources. This

implies that stock market must have a significant relationship with real and

financial sectors and the entire economy. The main stock prices in ASEAN-5

countries are namely Malaysia (KLCI), Indonesia (IDDOW), Singapore (SGDOW),

Philippines (PSEi) and Thailand (SET).


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1.1.1 Stock Market

A stock market is a place where stocks, shares and other financial instruments are

traded through exchanges and over the counter.1A stock market normally serves

as a channel to direct the funds from individuals to investors by mobilizing

individual owned resources.2 Therefore, through these function of stock market,

changes in interest rate can give a major effect to the performance of the financial

sectors of the economy. It is a public market for the trading of company stock and

derivatives at an agreed price; these are securities listed on a stock exchange as well

as those only traded privately.

__________________________1 Wikipedia2 Euro Journals


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1.1.2 Interest Rate

An interest rate is the rate at which interest is paid by a borrower for the use of

money that borrows from a lender. 3Interest rates are fundamental to a capitalist

society. Interest rates are normally expressed as a percentage rate over the period of

one year. Interest rates are also a vital tool of monetary policy and are taken into

account when dealing with variables like investment, inflation and unemployment.

The proxies of interest rates are deposit rate, lending rate and discount rate.

________________________3 Wikipedia


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1.1.3 ASEAN -5

The term ASEAN-5 countries namelyMalaysia, Indonesia, Singapore, Philippines

and Thailand was founded in August 1967 and known as a region of promising

potential. Association for South East Asian Nations (ASEAN) is a political and

economic organization of 10 countries in South East Asia formed originally by

Indonesia, Malaysia, Philippines, Singapore, and Thailand.4 The ASEAN-5 has now

undergone transformations in economic development, in which each country has

experienced economic growth due to the adoption of export-oriented trade policy,

the rapid flow of foreign direct investment (FDI), and sound macroeconomic

policies.5

________________________


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4Asia Econ5E.U-Tokyo

1.2 PROBLEM STATEMENT

A study on interest rate and the stock market has been a major issue in economics.

This study has been undertaken to examine the impacts of interest rate on stock

market in selected ASEAN- 5 countries. If, the stock market has a relationship with

interest rate, hence, the stock market may no longer efficient. As well, statistical

methods of measurement have been employed in order to comprehend the

correlation between interest rate and stock market return in a particular country.

The stock market reaction towards interest rate is observed to identify the

movement of this co integration in short term causality and long term linkages.


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1.3 RESEARCH OBJECTIVES

General objective

To analyze the relationship between interest rate and stock market return.

Specific objective

To observe the long run cointegration relationship between interest rate and

stock market return.

To examine the causality between interest rate and stock market return.

1.4 SIGNIFICANCE OF THE STUDY

This study has central idea for understanding how interest rate can influence the

stock market, since interest rate is one of the macroeconomic indicator that plays an

important role of emerging stock market as well as stock market is considered as

the primary indicator of a countrys economic strength and development.


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1.5 SCOPE OF THE STUDY

In this study, the research is conducted in term to study the correlation among

interest rate and stock market in Asian five countries which consists of Malaysia,

Indonesia, Singapore, Philippines and Thailand. These founding members are

developed countries in term of their financial instruments and one of the leading

countries. It begins with resource and report of data, which comprises year of the

study from January 2000 until December 2009, as monthly in these countries.

Subsequently is the clear explanation to the model specification, which, the sign of

coefficient is affirmed based to earlier theoretical frameworks. As a final point, the

causality test is used to examine on the efficiency of the stock prices, where the

hypothesis stated that if there is a causal relationship between stock prices and

interest rate, hence, the stock market is consider no longer efficient.


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1.6 FORMAT OF PROJECT PAPER

This paper has been organized into a few chapters in order to make it convenient to

be used as a reference in future studies.

Chapter Two reviews the relevant literature done in previous studies. In this

chapter, the literature reviews summarize related studied and analysis to provide a

better understanding as well as increase breadth of knowledge in undertaking the

study.

Chapter Three provides a brief description on the data and methodology employed

in the analysis of this study. This chapter further presents the empirical results and

findings with illustration of tables and graph.


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CHAPTER TWO

LITERATURE REVIEW

2.1 PREVIOUS STUDIES

Gupta and Sayekt (1997) examined the relationship between the interest rate, exchange rate

and stock price in the Jakarta Stock exchange from 1993 to 1997.This study was

undertaken by applying Granger-Causality tests and auto regressive integrated moving

average (ARIMA) model testing as the nature of time series data is necessary to test

stationary of each variable. The empirical evidence suggests that in most of the instances

there were no strong causal relationship between stock price and the interest rate or

exchange rate.Also,in this research, the results of Granger Causality indicate that only in

sub period one and three causality relationship between the variables under study have

been observed. The findings also indicate that the interest rates trend to have more

causality relationship with the stock price rather than exchange rate.Finally,causality

evidence fails to establish that historical information generally offers sufficient significant

a significant short-term predictive content for stock price.


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Khrawish et al. (2010) studied on the effect of interest rates on stock market

capitalization rate in Amman Stock Exchange (ASE) from 1999 to 2008.This study

was undertaken by applying Multiple Regression Model, Simple Regression Model

and Ordinary Least-Square (OLS) regression method. In this study, they found that

there is significant and positive relationship between government prevailing interest

rate and stock market capitalization rate. They also found that government stock

rate exerts negative influence on stock market capitalization rate where it shows

significant and negative relationship between government prevailing interest rate

and government development stock rate.Hence, they conclude that policy directions

might encourage the supply of investment funds through significant favoring

control of interest rate in order to stimulate the growth of stock market.

Ciffer et al. (2007) conducted a study to investigate the impacts of changes in

interest rates on stock returns by applying Wavelet analysis, Granger-Causality test,

Unit root test and Cointegration test. In this study, they found that interest rates

have a crucial role in determining stock returns. They also found that by using daily

closing values of the ISE 100 Index and compounded interest rates, it is proven that

starting with 9 days time-scale effect, interest rate is granger cause of ISE 100

Index and the effects of interest rates on stock return increases with higher time-

scales.Finally, the evidence of bond market has significant long-term effect on


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stock market for Turkey and traders should consider long-term money market

changes as well as short-term changes.

Alam and Uddin (2009) revised on market efficiency of fifteen countries and also

effect of interest rate on share price and changes of interest rate on changes of share

price. They analyzed the market efficiency by utilizing on country-wise Time

Series Analysis, Panel Data Analysis and market efficiency test. In this study, they

applied Random Walk Model. The randomness of stock return is the basic

assumption of Efficient Market Hypothesis that is violated for all countries where

these markets are not efficient in weak form. From this study, they found that for all

countries interest rate has significant negative relationship with changes of share

price.Further, if the interest rate is controlled for these countries, it will benefit in

share market.

Ologunde et al. (2006) investigated the relationships between stock market

capitalization rate and interest rate. They analyzed the relationships from 1981 to

2000 by utilizing on Time Series data and Stock Market Efficiency obtained from

Central Bank of Nigeria (CBN) and Nigeria Stock Exchange (NSE).In this study,

they used Ordinary Least-Square (OLS) regression method to investigate the

relationships between stock market and interest rate. They found that interest rate

and government development stock rate of the stock market is controlled as well as

the great benefit of the stock market will be enhanced. They drawn a conclusion

that in all stages of economic growth in Nigeria, great reliance has been placed on


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the stock market as the medium interaction of long term funds within the units to

achieve optimal financial growth.

Ahmad et al. (2010) had investigated the relationship between stock return, interest

rate and exchange rate in Pakistani economy over the period of 1998 to 2009.They

estimated data of short term interest rate and stock market returns (KSE-100) by

using Multiple Regression Model, to test the significance of change in interest rate

and exchange on stock returns. Their findings show that both the change in interest

rate and change in exchange rate has a significant impact on stock returns over the

sample period.Moreover,they discovered that changes in interest rate has a negative

impact while change in exchange rate has positive impact. They concluded that the

increase interest rate increases the cost of business and lowers the returns while a

decline in interest rate gives a positive sign to the a stock market and stock returns

increases eventually.


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Banerjee and Adhikary (2006) studied the dynamic effects of interest rate on

Bangladesh Stock Market (Dhaka Stock Exchange) returns. The data used for this

study consists of monthly data from January 1983 to December 2006.They used

Cointegration method and Vector Error Correction model (VECM) to see the causal

relationship between dependent variable and two independent variable. Their

findings shows that the long-run equilibrium relationship among the variables from

interest rate and stock market returns in Bangladesh does exist.Further, they seem

to have no significant influences on the stock market in the short run of interest rate

in Bangladesh.

Kazi (2009) conducted a study to identify the influential risk factors for the

Australian Stock Market. The data used in the analysis consists of quarterly data

from 1983 to 2002.They applied Cointegration technique, Philip-Perron (PP),

Augmented Dickey-Fuller (ADF) and Unit root test to check for stationary for the

interest rate and stock market index series. From this study, they drawn a

conclusion that the bank interest rate, dividend yield, and global market influence

are significant for the Australian Stock Market returns in the long-run whereas the

stock prices are adjusted each quarter by its own market performance where the

interest rate and global stock market movements of previous quarter.


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Hamrita et al. (2008) had examined the multi-scale relationship between the interest

rate, exchange rate and stock price for the period of 1990:1-2008:12.This study was

undertaken by applying Wavelet Cross-Correlation transform and Granger causality

test. Their finding shows that the relationship between interest rate is not

significantly different from zero at all scales.Hence, the relationship between

interest rate returns and stock index returns is significantly different zero only at the

highest scales.

Chiarella et al. (2002) investigated well known model of the stock market, interest

rate and output interaction. This study was undertaken by applying Blanchard

model, RBC models and VAR models. Their finding shows that the model captures

a number of features of the data. They illustrates that the changes in stock market is

dependent on the state of the economy. They concluded that the nonlinear models

perform reasonably well on most of the measures which is the interest rate, stock

price and output interaction.


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2.1.1 Literature Review Summary Table

SOURCE/ YEAR PURPOSE OF

STUDY

METHODS OF

ANALYSIS

FINDINGS

Gupta and Sayekt

(1997)

To examined the

relationship

between interest

rate,

exchange rate

and stock price in

Jakarta

Stock Exchange

from

1993 to 1997.

Granger-

Causality test

Auto

Regressive

Integrated

Moving

Average

(ARIMA)

The analysis shows

that interest rates

trend to have more

causality

relationship with

the stock price

rather than

exchange rate.Khrawish et al.(2010) To study on the

effects of interest

rates on stock market

capitalization rate in

Amman Stock

Exchange (ASE) from

1999 to 2008.

Multiple

Regression Model

Simple

Regression Model

Ordinary Least-

Square (OLS)

regression method

Government stock rate

exerts negative

influence on stock

market capitalization

rate where it shows

significant and

negative relationship

between interest rate

and stock rate.

Cifter et al. (2007) To investigate the

impacts of changes in

interest rates on stock

Granger-Causality

test

Unit Root Test

The interest rate is

granger cause of

ISE 100 Index and


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returns by applying

Wavelet Analysis.

Co Integration

test

the effects of

interest rates on

stock returns

increases with

higher time-scales.

Alam and Uddin

(2009)

To investigate the

market efficiency of

fifteen countries and

also effect of interest

rate on share price

and changes of interest rate on

changes of share

price.

Random Walk

(model)

They found that for all

countries interest rates

has significant negative

relationship with

changes of share price.

Ologunde et al.

(1998)

To study on the

relationships between

stock market

capitalization rate

and interest rate from

1981 to 2000.

Ordinary Least-

Square (OLS)

Regression

method

Interest rate and

government

development stock rate

of the stock market is

controlled as well as the

great benefit of the

stock market will be

enhanced.

Ahmad et al.(2010) To investigate the

relationships between

stock return, interest

Multiple

Regression(model)

The increase interest

rate increases the cost

of business and lowers


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rate and exchange rate

in Pakistani economy

over the period of

1998 to 2009.

the returns while a

decline in interest rate

gives a positive sign to

the stock market and

stock returns increases

eventually.

Banerjee and

Adhikary (2006)

To study the

dynamic effects

of interest rate

on Bangladesh

stock market

(Dhaka StockExchange)

returns consists

of monthly data

from January

1983 to

December

2006.

Co Integration

method

Vector Error

Correction

Model (VECM)

Long run

equilibrium

relationship among

the variables from

interest rate and

stock market

returns in

Bangladesh does

exist.

Kazi (2009) To identify the

influential risk factors

for Australian Stock

Market.

Philip- Perron

(PP)

Augmented

Dickey-Fuller

(ADF)

Unit Root Test

Co Integration

method

The interest rate

are significant for

the Australian

stock

Market returns in

the long-run.

Hamrita et al. (2008) To examine the multi-

scale relationship


and stock price for the

period of 1990:1 to

Wavelet Cross-

Correlation

transform

Granger-Causality

The relationship


returns and stock index

returns is significantly


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2008:12. test different zero only at

the highest scales.

Chiarella et al.(2002) To investigate well-

known model of stock

market, interest rate

and output

interaction.

Blanchard model

RBC model

VAR model

Nonlinear models

performs

reasonably well on

most of the

measures which is

the interest rate,

stock price and

output interaction.


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CHAPTER THREE

DATA AND METHODOLOGY

3.1 INTRODUCTION

This chapter provides the facts on the method that will be applied in this study,

where the procedures is clearly stated and defined. It starts with source and

description of data, which includes year of the study from January 2000 until

December 2009 in ASEAN-5 countries, as well as background of the data.

Subsequently, is the explanation to the model specification, where the sign of

coefficient is stated based to earlier theoretical frameworks. Finally, efficiency of

the stock market is tested by using co integration test, whereby the hypothesis

stated that if there is a causal relationship between stock market return and interest

rate, hence, the stock market is consider no longer efficient.


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3.2 SOURCE OF DATA

The data employed in this study are consisting of monthly observations of changes

in stock price; Malaysia (INDEX: KLCI), Indonesia (INDEX: IDDOW), Singapore

(INDEX: SGDOW), Philippines (INDEX: PSEi) and Thailand (INDEX: SET) and

interest rate, from 2000: 1 to 2009: 12. The data of market index for each country

are obtained from the International Financial Statistic, IMF.

3.2.1 DEFINITION OF THE VARIABLES

Independent variable

Independent variable defined as variable whose quantitative value is independently

controlled by the researcher. Independent variable for this study will be the changes

in stock price which set on monthly basis for Malaysia, Indonesia, Singapore,

Philippines and Thailand.

Dependent variable


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Dependent variable defined as variable whose quantitative value is expected to

depend on the effects of the independent variable. In this study, there is main

selected dependent variable, namely interest rate.

3.2.2 MODEL SPECIFICATION

The objective of this study is to test the co integration between interest rate ( fi )

and stock market ( SP ).The investigation on the long run relationship between

interest rate fluctuations and stock market is based on simple general model where

the stock market ( SP ) is dependent variables, and is a function of interest rate

( fi ).Changes in interest rate may affect stock prices through changes in portfolio

substitution or inflationary expectation. The model has been illustrated as below:

SPt = f ( fit) (3.1)


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Regression Model

Where SPrepresents changes in stock prices andfi represents the interest rate; tis

the time trend. As for interest rate, in general it has been indicated to have negative

or positive effect on stock market return. A study done by Banerjee and Adhikary

(2006) has proved that interest rate is confirmed to have a long run equilibrium

relationship with stock price. Prior to the analysis, all data are transformed into

natural linear regressions, as such the equation becomes:

Y= 0 + 1 I1 + (3.2)

In this case, 0, 1 are the parameters to be estimated; is the missing or error

values, y represents dependant variable (stock market return) and I represents

independent variables (I

= interest rate).


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3.3 FRAMEWORK OF ANALYSIS

3.3.1 Stationary and Unit Root Test

Whether a variable is stationary or non- stationary is very crucial. This is the case

where if variables in a model are non- stationary, then the usual standard

econometrics methods do not apply. Hence, it is very important to check the time

series properties (integration and co integration) not only to see whether the

variables establish a long- run equilibrium but even more important is to avoid

spurious and misleading inference. Therefore, stationarity of a time series is

important implications upon modeling of time series analysis. The shock that

occurred in stationary time series is impermanent in nature and would dispel

(Enders, 1995). Then it will revert to its long- run mean level, in which the long-

term forecasts of this stationary time series would converge to its unconditional

mean. In one hand, a stationary time series has a finite variance that is time-

invariant and theoretical correlogram that will diminish as lag length increases, and

mean reversion fluctuates around a constant long-run mean. On the other hand, a


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non- stationary series posses a permanent component that did not possess a long run

mean to which the series returns, and its theoretical autocorrelation may not

decompose, but in finite samples, the correlogram samples will eliminate slowly. In

addition, it has time dependant variance that goes to infinity as time approaches

infinity. Simple approach defining stationary as below;

The variableytis stationary if it possesses the following three properties;

a. E(Yt) = , the mean valueytis constant and independent of time,

b. Var(Yt) = 2 , the variance ofyt, is constant across time,

c. Cov(Yt , Yt-s) = s , the covariance is dependant only on the distance s between

the observation and independent of time t

It should be noted that the variable is non- stationary if one or more than one of the

above mentioned conditions are not fulfilled. If the variables in an econometric

model are stationary then the standard distribution of the test statistics are valid.

However, this is not the case if the variables are non- stationary. That is the

application of conventional econometric techniques to non- stationary (integrated)

time series can give rise to misleading inference. It is therefore essentially

important to test for non-stationary. One way to make a non-stationary time series

to a stationary time series is to take differences of the variables. However, this will

lead to lots of long-run information.


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In order to test the stationary of a time series, unit root test is applied. To illustrate

the concept of unit root, let consider that the real interest rate series sp followed an

autoregressive one process:

(spt- ) = (spt-1 - ) + t (3.3)

wherespt is stock prices in logarithm form at time t, is the constant term and t

is the error term with properties of zero mean, no serial correlation and has constant

variance. Interest rate rin considered to be stationary if 0


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Augmented Dickey Fuller (ADF)

Initially, the unit root test was tested using Dickey Fuller (DF), which developed by

Dicker and Fuller in 1979. However, due to it less superiority as compared to ADF

(Dickey and Fuller, 1981) which was taken place in testing the unit root test.

Therefore, for a unit root test in each series is conducted with and without a

deterministic trend (t) by using ADF methodology. The general form of ADF test is

estimated by the following regression is used to determine whether the variables are

trend stationary using Equation (3.4) and whether the variables are stationary

without trend using Equation 3.5;

Xt= 0 + xt-1 + iXt-i + t (3.4)

Xt= 0+ t + xt-1 + ni=1 iXt-i + t (3.5)

where t

is white noise, andn

is the number of lagged changes inXt

. If the

variables are non-stationary, then it will be tested for the presence of unit root by

differencing at first difference using equation below;


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2Xt= 0 + Xt-1 + ni=1 i2Xt-i + t (3.6)

2Xt= 0 + t + Xt-1 + ni=1 i2Xt-i + t (3.7)

where t is white noise, and n is the number of lagged changed inXt. Equation (3.6)

denotes the regression in ADF without a trend and Equation (3.7) denotes the

regression without a trend.

The null hypothesis (H0) is tested against the alternative hypothesis (H1) where,

H0:Xtis nonI(0)

H1:XtisI(0)

The null hypothesis (H0) is rejected and the series are shown to be stationary or

integrated of order zero when the ADF statistics are more than their critical values

for Equation (3.6), however, when null hypothesis is rejected, the series are shown

to be stationary or integrated of first orderI(1) when the tabulated ADF statistics

are more than their critical values for Equation (3.7).

Additionally, enough lagged differences are included to include ensuring that the

error term becomes white noise. Besides, if the autoregressive representation ofYt

contains a unit root, the t ratio for 1 should be consistent with the hypothesis,

which is

1 =0. Otherwise, if the coefficient is significantly different from zero then

the hypothesis that Ytcontains a unit root is rejected and thus it accepted as Yt is

stationary rather than integrated. In the ADF test, the variables are tested whether


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unit roots are present with and without a deterministic time trend. Stock price (SP),

M1 and M2 series are tested in their levels and in their first difference.

Phillips- Perron (PP)

As mentioned above, the unit roots tests, which have developed by Phillips (1987),

and Phillips and Peron (1988), is taken into consideration, on the levels and first

differences of real interest rates. The Phillips- Perron 1988 (PP) test, is able to

identify serial correlation and time dependant heteroscedasticity and if there is a

structure break within a series. This gives an added advantage over the ADF test,

therefore in this study; PP test is also used to test on the data series to further verify

the presence of unit root in the series. The PP test is a test of the hypothesis =1 in

the equation;

Xt= 1 +Xt-1 +1t (3.8)

Xt= 2 + t + X t-1 +2t (3.9)

whereXt represents the first difference ofXt-1, tdenotes the trend of the variable

andt is the white noise.

The purpose of the PP test is the same as the ADF test, which is to test on the

stationarity of the variable. Thus, the hypothesis for the test is as such;


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H0: Xtis non- stationary

H1:Xt is stationary

The tabulated t-statistics results for the PP test should be negative and significantly

different from zero forXt to be stationary in which the null hypothesis (H0) is

rejected. This is generalization to DF procedure, however it is a powerful test to

moderate and small sample size. As compared to ADF test, PP test has no lagged

difference terms. Instead the equation is estimated by ordinary least squares and the

t- statistic of the coefficient is corrected for serial correlation in t. In Eviews, it uses

the Newey- West (1987) procedure to adjust the standard error.


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3.3.2 Co integration Test

This study is attempted to use of the technique Johansen and Juselius (1990) who

devised a method to estimate whether two or more variables are co integrated via

multivariate maximum- likelihood procedure that overcomes many of the

limitations of the bivariate test of Engle and Granger (1987). These limitations

require that one of the two variables is considered exogenous, while these test do

not have well- defined limiting distribution and, therefore, their critical values are

sensitive to sample sizes (Hall, 1989).

Johansen maximum likelihood procedure begins by expressing a process, ifY1is a

vector ofn variables, all of which areI(I) processes; there exist klag VECM with

Gaussian errors of the following form:

Y1= + k-1i-1 1Yt-1 +kYt-k+ (3.10)

where is vector of drift, the matrixs are matrices of parameters, andt is a white

noise vector.


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The confined point of conducting Johansens co integration test is to determine the

rank (r) of matrixk. There are three possible outcomes, which it can be of full rank

(r-n), which imply that the interest rate series is a stationary process that would

contradict the earlier finding of non- stationary. Besides that, the rank ofk can be

zero (r=0), which case it indicates that there is no long run relationship among the

national nominal interest rates. In particular case, when k is of either full rank or

zero rank, it will be appropriate to estimate the model in either level of first

differences, respectively. Finally, in the intermediate case when there are most rco

integrating vectors 0 r n (i.e. reduced rank) it suggests that there are (n-r)

common stochastic trends.

The co integration procedure yields two likelihood ratio test statistics, referred to as

the Trace test and the Maximum eigenvalue ( max) test, which will help determine

which of the three possibilities is supported by the data. The Johansen trace test

statistic of null hypothesis that are at most rco integrating vectors 0 r n , and

therefore (n-r) common stochastic trends is

trace = -T ni=r+1 ln (1- i), (3.11)

whereis are the n-r smallest square canonical correlations of Yt-k with respect to

Y1 series corrected for lagged differences of the Yt and T is the sample size

actually used for estimation. The max statistic is given by:


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max = Tln (1- r+ 1) (3.12)

Since the asymptotic distribution of the Trace and and max test statistic follow 2

distribution, a simulation procedure is needed to identify proper critical values for

each test.11

3.3.3 TECM Granger Causality Test

Granger causality test is a technique for examining the causal relations among

variables that are non-stationary and co integrated. Otherwise a VAR model is used

in the case of no co integration found among the variables (Granger, 1969). The

standard Granger Causality test examines if there is exist feedback (bi directional)

or one- way causality between variables. Considering two series, Xt and Yt , the

Granger Causality test is in the form as follows:

Xt = 1 + n1

i=1 11 (i) Xt-i+ m1j=1 12 (j) Yt-j + Xt (3.13)

Yt = 2 + 21 (i) Xt-i+ m2j=1 22 (j) Yt-j + Yt (3.14)

Where Xt and Yt are stationary random processes intended to capture other

pertinent information not contained in lagged values of Xtand Yt. The lag lengths,

n and m, are decided by AIC in this study. Therefore, the series Yt fails to Granger

cause Xt if12 (j) = 0(j=1, 2,3,m1) ; and these series Xtfails to cause Ytif21(i) =

0(i=1, 2,3,n1).


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3.4 HYPOTHESIS

Hypothesis can be divided into two types:

a. Null hypothesis (H0)

Null hypothesis states there is no difference between the parameters.

b. Alternative hypothesis (H1, H2, H3, Hx)

Alternative hypothesis states there exists a difference between the parameters.

3.4.1 Unit Root Test

H0: The data series are non stationary.

H1: The data series are stationary.

3.4.2 Co integration Test

H0: There is no existence of co integration between interest rate and stock market

return.

H1: There is an existence of co integration between interest rate and stock market

return.


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Hypothesis 1

H0: Interest rate do not Granger Cause stock market return.

H1: Interest rate do Granger Cause stock market return.

Hypothesis 2

H0: Stock market return does not Granger Cause interest rate.

H1: Stock market return does Granger Cause interest rate.


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TABLE OF CONTENT

CONTENT PAGES

CHAPTER 1: INTRODUCTION

1.1 Introduction

1.1.1 Stock Market1.1.2 Interest Rate1.1.3 ASEAN-5

1.2 Problem Statement

1.3 Research Objectives

1.3.1 General Objectives

1.3.2 Specific Objectives1.4 Significance of the Study

1.5 Scope of the Study

1.6 Format of Project Paper

1-9

CHAPTER 2: LITERATURE REVIEW

2.1 Previous Studies

2.1.1 Summary of Literature Review

10-22

CHAPTER 3: DATA AND METHODOLOGY

3.1 Introduction

3.2 Source of Data3.3 Model Specification

3.4 Methodology

3.4.1 Stationarity and Unit Root Test3.4.2 Co Integration Test


3.5 Hypothesis

23-38

GANTT CHART

39


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UNIVERSITI TENAGA NASIONAL

COLLEGE OF BUSINESS MANAGEMENT AND ACCOUNTING

DEPARTMENT OF FINANCE AND ECONOMICS

BACHELOR OF FINANCE (Hons.)

SEMESTER 1, 2010/2011

PROJECT PAPER IN FINANCE

(FICB 344)

PROJECT PROPOSAL

A STUDY ON THE RELATIONSHIP BETWEEN INTEREST RATE AND STOCK

MARKET RETURN: EVIDENCE FROM ASEAN- 5 COUNTRIES

SUPERVISED BY:

PN SUZAIDA BTE BAKAR

PREPARED BY,SHALINI A/P RAGOO (BF 081216)

proposal pn.suzaida - copy

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