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MACROECONOMIC DETERMINANTS OF MALAYSIAN STOCK

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haciyl aisyah

on 11 June 2015

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Transcript of MACROECONOMIC DETERMINANTS OF MALAYSIAN STOCK

INTRODUCTION
Purpose – aims to explore the macroeconomics determinants of Malaysian Stock Market

Design/Methodology/Approach – utilized secondary data fully by Datastream for the period of 8 years quarterly starting from 2005 to 2013.

Findings – It is found that Malaysian stock market which is Kuala Lumpur Composite index (KLCI) is sensitive to changes in the macroeconomic variables. This research shows that Malaysian stock market is sensitive to changes in the macroeconomic variables.

Research Limitations/ Implications – The data gathered is only the secondary data based on the DataStream for a period of quarterly 8years from the period 2005 to 2013
LITERATURE REVIEW
SAMPLE & DATA
POPULATION
: Malaysia, 36 observations ( quarterly, 2005-2014)
DATA COLLECTION
: Datastream, used secondary data
PURPOSE OF STUDY
: causal & hyphothesis testing
TYPES OF INVESTIGATION
: causal
RESEARCH INTERFERENCE
: minimal
STUDY SETTING
: non-contrieved
Time Horizon :
longitudinal studies
MULTIPLE LINEAR REGRESSION TEST
DATA FINDINGS
FIRST MODEL
MACROECONOMIC DETERMINANTS OF MALAYSIAN STOCK
PROBLEM STATEMENT
This research is conducted in order to know the market volatility for the stable of stock market.
RESEARCH QUESTION/ RESEARCH OBJECTIVE
-
MAIN RESEARCH QUESTION & OBJECTIVE
To investigate whether there is a relationship between selected macroeconomics determinants and Kuala Lumpur Composite Index (KLCI)
-
SPECIFIC RESEARCH QUESTION/OBJECTIVE
To investigate whether there is a relationship between money supply,reserve, interest rate, industrial production & exchange rate towards Kuala Lumpur Composite Index (KLCI) in Malaysia.

INTEREST RATE
Bernanke and Kuttner (2005) applied Campbell and Ammer model to which used VAR which gives result 0.25 percent reduction on interest rate increases stock indices by 1 percent.
. The common interest rate proxies are the treasury bills rates and the interbank rates as being employed by Mukherjee and Naka (1995) which is inversely related to stock market return
INDUSTRIAL PRODUCTION
Tainer (2006) is of the view that the industrial production index is procyclical which is its rises during economic expansion and falls during a recession. A rise in industrial production would signal economic growth.
Fama (1981) hypothesized a similar positive relationship through the effects of industrial production on expected future cash flows.
EXCHANGE RATE
Mukherjee and Naka (1995), This rise in demand will push up the stock market level, suggesting that stock market returns will be positively correlated to the changes in the exchange rates.
(Ibrahim (2000)) analyzed the interaction which used two exchange rates measures, real effective exchange rate and the nominal effective exchange rate. The findings from bivariate models indicated no long run relationship between the stock market index and any of the exchange rates. However, in multivariate model, the results indicated the following unidirectional causality from the stock market to the exchange rate.
RESERVE
Fama (1983) and Slovin, Sushka, and Bendeck (1990), an announcement of a reduction in the SRR will lead to positive and an announcement of an increase in the SRR will create a negative reaction.
The openness ratio is included in order to measure the impact of the financial liberalization since the early 1990s on the aggregated growth of the economy Choong, Yusop, Law, and Liew (2005)
MONEY SUPPLY
Fama (1981), an increase in money supply leads to an increase in discount rates which in turn, lowers the price of stock, thus conferring a negative effect.
Keynesian economists debate that money supply has a negative influence on stock prices due to the expectation of future contraction monetary policy .
KUALA LUMPUR COMPOSITE INDEX (KLCI)
Based from the real economic activities, changes in stock prices lead to an increase in the demand for real money and the interest rate and, subsequently, the value of domestic currency (Solnik (1973)
Ahmad and Ibrahim (2002) compared the performance of KLSI (Kuala Lumpur Syariah Index) with that of KLCI over the period from 1999 to 2002. They concluded that for the overall and the declining periods, the return was low for KLSI, while for the growing period the KLSI slightly outperformed the KLCI. In terms of risk, the KLCI was riskier than the KLSI over 1999–2002.
RESEARCH FRAMEWORK
RESEARCH METHODOLOGY
TEST OF STATIONANRY:
UNIT ROOT TEST
TEST OF ASSUMPTION:
1) NORMALITY TEST
2) AUTOCORRELATION TEST : SERIAL CORRELATION TEST
3) HETEROSCEDATICITY TEST : VARIANCE OF ERROR TERM
4) FUNCTIONAL FORM TEST
Equation used
: Yi = α + LOG X2i + LOG X3i + LOG X4i + LOG X5i + LOG X6i + Ɛ
Represent
: Money Supply (X2),Reserve (X3), Interest Rate (X4), Industrial prduction (X5) & Exchange rate (x6).

Yi = (-3.488066) + 0.054587 X2i + 0.061514 X3i + (-0.043194 X4i) + 1.566239 X5i+ 1.719963 X6i + Ɛ
Money supply (X2) = accept H0, no relationship
Reserve (X3) = accept H0, no reltaionship
Interest rate (X4) = accept H0, no relationship
Industrial production (X5) = reject H0, there is a relationship
Exchange rate (X6) = reject H0, there is a relationship
NEW REGRESSION MODEL (SECOND MODEL)
Equation used : Yi = α + LOG X5i + LOG X6i + Ɛ
Represent : Industrial prduction (X5) & Exchange rate (x6).

coefficient : Yi = (-3.857360) + 1.112222 X5i+ 2.390445 X6i + Ɛ
Industrial production (X5) = reject the H0, there is a relationship
Exchange rate (X6) = reject H0, there is a relationship
1) NORMALITY TEST
2) AUTOCORRELATION TEST
3) HETEROSCEDATICITY TEST
4) MULTICOLLINEARITY TEST
5) TEST ON FUNCTIONAL FORM
CHAPTER 5 : CONCLUSION
RECOMMENDATION
Use different data structure
Using the other best method for testing the data
Selection of Independent Variables

F-test
: reject the H0 and can conclude that at least two of independent variables affect the dependent variable.
Coefficient of Determinants (R2)
: 88.09% of KLCI is explained by independent variables. The remainders of 11.91% determined by either factor.
Adjusted R2
: 86.11% of variation KLCI is explained by the chosen financial data
Durbin Watson Test
: consistent with no serial correlation
HYPHOTHESIS STATEMENT
*
Main
:
The p-value of the F-statistic is 0.000000. Reject the H0 and can conclude that at least two of independent variables affect the dependent variable.
*
Specific
:
Money supply (X2) = accept H0, no relationship
Reserve (X3) = accept H0, no relationship
Interest rate ( X4) = accept H0, no relationship
Industrial production (X5) = reject H0, there is a relationship
Exchange rate (X6) = rejct H0, there is a relationship
F-test
: reject H0,at least two of independent variables affect the dependent variable
Coefficient of Determinants (R2)
: 85.1% of KLCI is explained by dependent variables. The remainders of 14.9% of the variations are determined by either factor.
Adjusted R2
: This indicates that 84.2% of variation KLCI is explained by the chosen financial data
Durbin Watson Test
: non-consistent with serial correlation
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