Capital market is a financial market which is for long term investment funds with the maturities greater than one year. In USA, capital market was controlled by security exchange and it was established in 1934. While in Malaysia, Bank Negara Malaysia (BNM) capital market has been developed since 1980s. It is a market where securities such as common stock, preferred stock and bonds are issued or traded. Companies, government and other organizations use capital market in order to raise funds for their operations. In other words, capital market helps organizations or institutions whether in public and private sector to gain capital. Besides that, capital market also traded an investment funds like debt, equity and mortgage loan. The central bank, Bank Negara Malaysia (BNM) also played a very important role in develop and care of this market. Kuala Lumpur Stock Exchange or more popular known as KLSE is the only one stock market in this country. All the listed securities in Malaysia are done by KLSE since the KLSE is a self-regulatory. Based on this study, KLSE Composite Index are represented by the stock market, money supply represented by M3, consumer price index represented the inflation rates and exchange rates represented by the Malaysian Ringgit (RM) against the US Dollar. In KLSE it has their own rules where buyers and sellers trade their transaction with each other. Since KLSE was established, there were ups and down movement of KLSE causes by many variables. To measure the performance of stock market, stock index is used. Besides, it can be used by all investors as a benchmark for them to evaluate the performance of holding shares. KLSE Composite Index is comprised of 100 companies listed on the exchange. The movement of stock prices can be triggered by the movement in financial sector in particular, that is the money supply. From this situation, there might be a relationship between KLSE Composite Index with money supply. A negative relationship between stock market and inflation in India and by previous study that also comes out with the same result might support the relationship between KLSE Composite Index with inflation rates. The motivation of this study is to find out whether there is a relationship between KLSE Composite Index with the level of money supply, inflation rates and exchange rates. Thus, if the Malaysian economic are facing with inflation, the stock price will be low and vice versa.

Contents

- 1 1.2 BACKGROUND OF THE STUDY
- 2 1.3 PROBLEM STATEMENT
- 3 1.4 OBJECTIVES OF THE STUDY
- 4 1.4.1 GENERAL OBJECTIVES
- 5 1.4.2 SPECIFIC OBJECTIVES
- 6 1.4.3 NULL HYPOTHESES (H0)
- 7 1.5 SCOPE OF THE STUDY
- 8 1.6 LIMITATION OF THE STUDY
- 9 1.7 SIGNIFICANCE OF THE STUDY
- 10 CHAPTER 2
- 11 2.1 LITERATURE REVIEW
- 12 CHAPTER THREE
- 13 RESEARCH METHODOLOGY
- 14 3.2 DATA SOURCES
- 15 3.3 THE DATA
- 16 KLSE COMPOSITE INDEX
- 17 MONEY SUPPLY
- 18 INFLATION RATES
- 19 EXCHANGE RATES
- 20 THEORETICAL FRAMEWORK
- 21 Dependent Variable
- 22 Independent Variable
- 23 HYPOTHESES
- 24 Hypotheses 1
- 25 Hypotheses 2
- 26 Hypotheses 3
- 27 MULTIPLE REGRESSION ANALYSIS
- 28 COEFFICIENT RELATIONSHIP
- 29 COEFFICIENT OF DETERMINATION (R2)
- 30 T – STATISTICS ( T-STAT)
- 31 F – STATISTICS (F-STAT)
- 32 DURBIN WATSON STATISTIC
- 33 PEARSON CORRELATION ANALYSIS
- 34 CHAPTER FOUR
- 35 ANALYSIS AND FINDINGS
- 36 INTERPRETATION OF TREND ANALYSIS
- 37 DV = KLSE COMPOPSITE INDEX
- 38 M3 = MONEY SUPPLY
- 39 INF = INFLATION RATES
- 40 EXC = EXCHANGE RATES
- 41 TREND ANALYSIS ON KLSE COMPOSITE INDEX (DEPENDENT VARIABLE)
- 42 MULTIPLE REGRESSION ANALYSIS
- 43 Coefficientsa
- 44 B
- 45 16.082
- 46 -.372
- 47 -.029
- 48 -3.126
- 49 REGRESSION MODEL
- 50 EXPLANATION OF REGRESSION COEFFICIENT MODEL
- 51 MONEY SUPPLY (M3)
- 52 INFLATION RATES
- 53 EXCHANGE RATES
- 54 COEFFICIENT OF DEERMINATION (R2)
- 55 Model Summaryb
- 56 R Square
- 57 .858
- 58 ANOVA
- 59 ANOVAb
- 60 T – STATISTICS
- 61 Coefficientsa
- 62 t
- 63 11.955
- 64 -4.406
- 65 -7.577
- 66 -14.610
- 67 ANALYSIS USING SIGNIFICANCE LEVEL
- 68 ANALYSIS USING T – STATISTICS
- 69 MONEY SUPPLY (M3)
- 70 INFLATION RATES
- 71 EXCHANGE RATES
- 72 DURBIN WATSON STATISTIC
- 73 Model Summaryb
- 74 R Square
- 75 .858
- 76 2
- 77 DURBIN WATSON RESULT
- 78 PEARSON CORRELATION
- 79 PEARSON CORRELATION RESULT
- 80 Correlations
- 81 PEARSON CORRELATION EXPLANATION
- 82 Money Supply (M3)
- 83 Inflation Rates
- 84 Exchange Rates
- 85 5.1 CONCLUSION
- 86 RECOMMENDATIONS

Kuala Lumpur Stock Exchange (KLSE) is a formal stock market and it is widely constructed such as the composite index, EMAS index, and the various sector indices of tin, plantation, hotel, services, automobile, industrial and properties. KLSE is a self-regulatory organization and has emerged as one of the top performing bourses in developing countries in 1992. Based on the previous study, stock index is used to measure the performance of all stock market. KLSE calculate the index for each main sector traded however, mostly it will use the KLSE Composite Index because it will comprise the stocks traded on the KLSE. Since it is the only stock market in Malaysia, the monitoring and supervising do by Minister of Finance (MOF) and by Securities Commission (SC). KLSE Composite Index has been introduced on 1986 where one stock index was needed which can act to stock market performance and Malaysia economy. All the data that has been calculated electronically by KLSE can be taken by brokers companies and other customers at any time since the index is base on minute to minute. KLSE are really a well known stock market in the world. In 1970’s and 1980’s, KLSE had major development until it had become one of the largest market capitalization bourses in South-East Asia. However, when Singapore out from Malaysia in 1965, the Stock Exchange of Malaysia then, known as the Stock Exchange of Malaysia and Singapore. In spite of, in 1973 when the currency exchange between Malaysia and Singapore drop, again it changes the name and become Kuala Lumpur Stock Exchange and Stock Exchange of Singapore. In 2004, KLSE has changed it name and now it is known as Bursa Malaysia Berhad. This Bursa Malaysia focused to improve the products and services that they conduct. While in year 2005, Bursa Malaysia was listed on the Main Board of Bursa Malaysia Security Berhad. While in KLSE Composite Index, it has been accepted as a local stock market barometer when it was introduced in 1986. From the investor side, the major factors that determine the stock market are the climate of economic. This study investigates the impact of inflation rates, exchange rates and money supply towards stock market. Based on the previous study, there are several researches that have been handled to investigate this dependent variable and independent variables.

This study is to analyze whether there are significant relationship between KLSE Composite Index as a dependent variables with the money supply (M3), inflation rate and exchange rate as an independent variables. Malaysia stock market performance nowadays has staged at an encouraging recovery and gain in selected blue chips and this can be proved when in 2007, Malaysia’s economy placed the third largest economy in Southeast Asia. Malaysian stock market is able to provide profitability investment since strong domestic spending give benefit sector trading in Kuala Lumpur Stock Exchange (KLSE). The movement of KLSE Composite Index Inflation depends on many economic factors. For this study, researcher tries to figure out whether the economic factors could affect the performance of KLSE Composite Index. The economic factors for this study refer to money supply, inflation rates and exchange rates. Researcher also tries to figure out, whether the economic factors could be major elements of stock investments. Inflation is happen when a country has printed too much money which will increase the rate of consumer price and also will affect the cost of living. Good news for inflation is, the last report of inflation rate in Malaysia is about only two percent which is in November 2010. There was a negative relationship between inflation rate and stock price. This is because during inflation, cost of living and cost of production will increase and investor will not invest as before inflation happen. Exchange rate refers as a payment or change for person that want to do exchange in currency from one country to currency of other country. While for the study in relationship with exchange rate, it also showed a negative relationship. When there is an increment in level of currency, the charges for each exchange also will be affected. This means they have to change the currency in a large amount and it might affect their money. Therefore, the rational of doing this research is to find out, whether KLSE Composite Index are linked to economic condition in level of money supply, inflation rate and exchange rate?

This study is to figure out the relationship, movement and performance of dependent variable and independent variables. It has divided into two types of objectives. The objectives of this study are:

The major objective of this study is to identify the relationship between dependent variable (KLSE-CI) and independent variables which are money supply, inflation rates and exchange rates in order to know whether there is any positive or negative relationship.

To determine the relationship between inflation rates and KLSE Composite Index To determine the relationship between exchange rates and KLSE Composite Index To identify whether changes in variables are significant in affecting the movement of KLSE Composite Index

There is no significant relationship between KLSE Composite Index (dependent variables) with money supply, inflation rates and exchange rates (independent variables).

This research paper is to examine the relationship between Kuala Lumpur Stock Exchange Composite Index with level of money supply (M3), inflation rates (CPI) and exchange rates (Ringgit against US Dollar). The data for this study are gathered a period for 60 months (5 years) from 2006 to 2010. As been stated, the multiple regression analysis is used to measure the relationship between dependent variable and independent variables with monthly basis issued by Bank Negara Malaysia (Central Bank of Malaysia).

There are some limitations in conducting this research. The limitations that have been highlighted are as follow: Limited variables chosen make it difficult to interpret the relationship of dependent variable and independent variables and it been conclude as not really efficient. The data collected are mostly from internet, journals, newspapers and economic reports. Unreliable collected data will lead to unreliable results. The data for this study is gathered for monthly collective data which taken from Bank Negara Malaysia. Only three independent variables (money supply, inflation rates and exchange rates) have been chosen since there are too many internal factors that can classify the relationship and can affect KLSE Composite Index. This study cover period for 60 months (5 years) 2005 to 2010, are considered quite a short period compare to other research. The finding might not be perfectly accurate. For this research, only one country is focused which is Malaysia in order to limit the scope of research. The limitation for this research can be more reliable if the data taken based on weekly basis. Since best research comes with accurate data from weekly or daily basis data.

This study provides some useful information about the relationship between KLSE Composite Index with levels of inflation rates, exchange rates and money supply. The significant of this study is to build better understanding for readers and useful information to investors in making good investment decision. In addition, this study provides two important aspects in Malaysia economy (exchange rates and inflation rates) which can help companies in Malaysia to make decisions to issue their shares during the period of good economic and during the economic when it face with high inflation. Studies examining the relationship between money supply, inflation rates and exchange rates under Malaysian experiences are very limited, and it is hope from the available findings from this study, it can be use as a direction or reference for further research.

According to Ooi Beng Hooi (2011), entitled The Relationship between KLCI and Ringgit Malaysia against US Dollar, he would like to explain the relationship between stock price and currency exchange rate. Researcher had done his research in four years starting from July 2005 until July 2009. Last, he comes out with the conclusion that state that a significance and strong relationship are explained in both KLCI and Ringgit Malaysia against US Dollar. The results of this research are really useful and in can be us as references for future study. According to Noor Azlinna Azizan and Hasyaliny Sulong (2011), entitled The Preparation Towards Asean Exchange Link between Malaysia Stock Market and Asia Countries Macroeconomics Variables Interdependency, they investigated the interaction between stock prices and macroeconomic variables in Asia countries include Malaysia, Indonesia, Singapore, Thailand, India, China, South Korea, Japan and Taiwan to view the interdependency of our stock market to other Asia countries macroeconomics variables. As a result, the researcher found out that only stock price and exchange rates have the most impact to our stock market. According to Oguzhan Aydamer (2009), with the topic of The Relationship Between Stock Prices and Exchange Rates Evidence From Turkey, he disclose the relationship between macroeconomics variables such as money supply, inflation rates, exchange rates, interest rates and stock price. This research is done for 8 years from February 2001 to January 2008 which focuses in one country that is Turkey. After all the research has been made, he then concludes that there is a negative relationship between exchange rates and all stock market indices. Besides, he also stated that other variables are also having negative relationship. According to Aisyah Abdul Rahman, Noor Zahirah Mohd Sidek and Fauziah Hanim Tafri (2009), a research on Macroeconomic Determinants of Malaysian Stock Market, are investigates the relations between selected macroeconomic variables and stock prices for the case of Malaysia. In addition, this research highlights that Malaysian stock market has weak interaction with money supply, exchange rates and interest rates as compared to the industrial production index. Sara Alataqi and Shokoofeh Fazel (2008), with topic Can Money Supply Predict Stock Price said that, when they refer to other previous researcher, most of them come with the same result which a positive casual relationship between money supply and stock prices is frequently hypothesized by some financial analysis. While for both of these researchers, Sara Alataqi and Shokoofeh Fazel theier belive are against with that statement. From the research they have made, the results that they get are totally different with the previous study. They have proved the reason and all the calculated data in their research. As a result, they strongly explained that there is a negative relationship from money supply to stock price and also a negative relationship from interest rate to stock price. Paritosh Kumar (2008), Is Indian Stock Market Related with Exchange Rate and Inflation, said that short-term foreign assets are fully exposed to exchange risk and exchange rates movement might affect the domestic companies. He also strongly believes that, a relationship between exchange rates and stock prices do exists but it just does not rule out any relationship between them. The end result for this research is he admit that there is a significance relationship even though it shows a negative sign which means to a negative relationship. According to Shamail Arzu (2008), Relationship Between Exchange Rates and Stock Prices comes to the conclusion which changes immediately in currency can absolutely affect ups and down in the stock index. Besides, he found that fluctuation in currency rates and movement in stock exchange is negatively will affect imports and exports in a country as well. Koffie Nassar (2005), Money Demand and Inflation said that by doing an analyzing data on the relationship between money supply, prices, inflation and income in Madagascar, it comes to the result which state that a negative correlation do exist and inflation expectations are largely determined by every past events. By controlling inflation in the short run, most of the broad money growth can be effective. It concludes that the variables are not strongly significance with the dependent variable. Ramin Cooper Maysami, Lee Chuin Howe and MohamadAtkin Hamzah (2004), Co-integration Evidence From Stock Exchange of Singapore’s All-S Stock Indices said that the objective of their research is to examine the relationship between selected macroeconomic variables with Singapore Exchange Stock Indices. The result highlighted that the majority of the macroeconomic variables includes broad money, exchange rates and other factors are much more seriously have strong casual relationship with Singapore Exchange Stock Indices. According to Chandran V.G.R and Norazman Shah Abd Rahman (2004), entitled Causality between Money Supply and Stock Prices: A Preliminary Investigation on Malaysian Stock Market, help the researchers in order to observed the relationship between money supply and stock prices. However, based on this study, researchers are using a simple bivariate Granger causality to test the Malaysian stock market. It shows that by predicting the changes in money supply, thus it may afford for better understanding in stock prices. Ming-Yu Cheng and Hui-Boon Tan (2002), entitled Inflation in Malaysia, sait that the objective of this study is to identify either it contribute to the significance relationship or not. Both researchers come to the same conclusion where based on the variables that they have been discussed, it still significance but it cannot be calssified as strong significance. According to Professor J.P.Gupta, Professor Alian Chevalier and Fran Sayekt (2000), entitled The Casuality between Interest Rate, Exchange Rate and Stock Price in Emerging Markets: The Case of the Jakarta Stock Exchange highlighted that stock market are very complex and it can be very sensitive to exchange rates and interest rates. Any movement in stock market will totally affect the economy. When interest rate and exchange rates are fluctuating, it will cause a bad effect. Other than that, they agreed that interest rate and stock prices are independent series for most of the time and it a same result found in exchange rates which have strong relationship with stock price. Both variables are significance relationship towards stock price.

To find the result on this research, there are certain methods that can be used in order to determine the information data of relationship between the given variables. In this study, to determine the relationship between dependent variables (KLSE Composite Index) with independent variables (Inflation Rates, Money Supply and Exchange Rates), an analysis named the Multiple Regression Analysis and Statistical Package of Social Science (SPSS) is applied in order to analyze data and enhance better understanding for the result. This study covers the period from 2005 to 2010. These methods are the most applicable because it will evaluate the relationship between the variables. SPSS is used to interpret a result in research while Multiple Regression Analysis is used to measure the linear association between dependent variable and independent variables

Most of the data for this study are come from the secondary data. The closing prices of KLSE Composite Index at the end of each period were gathered and the data were achieved from Quarterly closing prices KLSE Composite Index over the period 2005 to 2010. Data for the independent variables, which are money supply (M3), inflation rate (CPI) and exchange rate were achieved from Monthly Statistical Bulletin issued by Central Bank of Malaysia (Bank Negara Malaysia) from 2005 until 2010.

Based on this study, all the relevant data are the secondary data. There are:

Kuala Lumpur Stock Exchange (KLSE), which now known as Bursa Malaysia Berhad is a place for traders to do trading. It contains many counters where each of the counters is for different companies. Besides, it is a self regulatory organization that administers the conduct of its members and also members of stock broking companies. The data for KLSE were gathered from KLSE Yahoo Finance.

Money supply is a total supply of money circulation use in economy. There are several types of measurement in money supply which known as M1, M2 and M3. In this study, researcher focuses more on M3. M3 which refer to broad money are consists of foreign currency deposits, saving deposits, fixed deposits, negotiable certificate deposit (NIDS) and repurchase agreement (Repos). The foreign currency deposits refer to deposit of foreign currencies hold by commercial banks, merchant bank and non-bank Malaysian residents. In this research, the data were taken from Monthly Statistical Bulletin of Bank Negara Malaysia.

In economics, the inflation rate is a measure of inflation. In this study, the data were obtained from Monthly Statistical Bulletin of Bank Negara Malaysia.

Exchange rate or also known as foreign exchange rate shows the relationship of currency between one country with others. In this research, researcher focuses on Malaysian (MYR) currency with US currency (USD). Increase in Malaysia ringgit means a decrease in the cost of exchange of Malaysian currency with other currency. The data for exchange rate were taken from Monthly Statistical Bulletin of Bank Negara Malaysia.

In this study, KLSE Composite Index is chosen as dependent variable and money supply, inflation and exchange rates are classified as the independent variables. This means that the changes in KLSE Composite Index actually depend on the changes in money supply, inflation rates and exchange rates. The diagram of the relationship between both dependent variable and independent variables are being showed below: Money Supply Inflation Rates Kuala Lumpur Stock Exchange Composite Index (KLSE-CI) Exchange Rates

This study consists of Null Hypotheses (H0) and Alternative Hypotheses (H1). The hypotheses are as showed below:

H0: There is no relationship between money supply and KLSE Composite Index. H1: There is a relationship between money supply and KLSE Composite Index.

H0: There is no relationship between inflation rate and KLSE Composite Index. H1: There is a relationship between inflation rate and KLSE Composite Index.

H0: There is no relationship between exchange rate and KLSE Composite Index. H1: There is a between exchange rate and KLSE Composite Index.

Data for this study were being analyzed by using the Statistical Package of Social Science (SPSS) Software. Hypotheses are used to determine the relationship between dependent variables (KLSE Composite Index) and independent variables (money supply-M3, inflation rate, exchange rate). In order to determine the influential of money supply, interest rate and exchange rate towards KLSE Composite Index, a Multiple Regression Analysis is applied. This multiple regression analysis used the independent variables to predict the dependent variables. The Estimated Regression Model as follows: Y = c + Î²M + Î²F + Î²X + Îµ Where: Y = Dependent Variable (KLSE Composite Index) c = Constant Term Î² = Regression Coefficient (Beta Measurement) M = Independent Variable (Money Supply-M3) F = Independent Variable (Inflation Rate) X = Independent Variable (Exchange Rate) Îµ = Error Term

Researcher used R2, T-Statistic and F-Statistic to determine the relationship between money supply, inflation rate and exchange rate towards KLSE Composite Index.

Coefficient of Determination or known as R2 is the most usually used in linear regression. R2 present how well the regression model describes changes in the value of dependent variable (Y) that can be explained by the independent variables. It shows how the line fits the data. The value of R2 is range from zero to one. The range indicates whether the correlation is strong or not. If R2 is zero, the equation explains that there is no relationship between the dependent variable and independent variables. While if the R2 is 1, the equation explains the relationship between dependent variable and independent variables are do exist. The higher the value of R2, the better the regression equation will be. When value of R2 is higher, the exploratory power will increase and be more accurate for forecasting purposes. An equation of R2: Total VariationR2 = Total Explain Variation This equation are used when researcher decide to calculate by manual. However, in this study, researcher has chosen Statistical Package of Social Science (SPSS) in order to calculate all the data that are gathered from Bank Negara Malaysia for the 60 months periods. The result of this R2 will be shows and explains in analysis and findings. It also will conclude whether all independent variables will explain the dependent variable or it will not.

T- Statistic is used to decide whether to accept or reject the null hypotheses and also to analyze the significant relationship between dependent variable and independent variables. The value in t-table will be compared with the calculated t-value. T is critical value at certain significant T = (n – k – 1) n = number of observations / years k = number of independent variables If the computed t-statistic is greater than t-critical value at certain significant levels, thus reject H0. If the computed t-statistic is lower than t-critical value at certain significant levels, thus accept H0. T-Computed > T Critical Value, accept H1 and reject H0 T-Computed < T Critical Value, accept H0 and reject H1

Researcher is also using F-Statistic in order to know the consistency of overall regression equation. F-Statistic will evaluate the significant of each individual component of entire regression model. Equation of F-Statistic is as follows: F = Explained Variation / (k-1) Unexplained Variation / (n-k) Where: F = critical value k = number of independent variable n = number of observation If the computed F-Statistic is greater than F-Statistic value at certain significant level, then reject H0. It is a vice versa when the computed F-Statistic is lower than F-Statistic value at certain significant level, then accept H0. F-Computed > F-Critical Value, accept H1 F-Computed < F-Critical Value, reject H1

Durbin Watson is used to test the presence of auto correlation. It is appears when time series data are used. Auto correlation gives a downward bias to the standard error of the estimated coefficient (t-value are exaggerated) and hence the estimated coefficient is concluded to be significant when in reality they are not. There are three possibilities where the auto correlation problem might arise: When the independent variables are duplicated When some of the independent variables are miss specified When some important variables are found missing in the model When successive residuals are positively auto correlated, the value of D will be approach zero. If the residual are not correlated, the value of D will be closed to zero. If there is a negative auto correlated, the value of D will be greater than two and could even approach its minimum value of four. Equation of Durbin Watson Statistic (D) is defining as: D =

Pearson correlation analysis is a statistical analysis to see the direction and to describe the strength and significance of the relationships between the dependent variables and the independent variables. According to Pearson correlation analysis, the result can be ranked as follows: Less than 0.30 = Week Relationship 0.30 to 0.49 = Moderate Relationship 0.50 to 0.69 = Strong Relationship 0.70 to 0.99 = Very Strong Relationship 1.0 = Perfect Relationship

This chapter provides the findings which are obtained from the Statistical Package for Social Science (SPSS). Through SPSS, the relationship between Money Supply (M3), Inflation Rate and Exchange Rate with KLSE Composite Index can be identified. The researcher used regression in order to measure the linear relationship between dependent variable and independent variables. Coefficient of determinations (R2), T- statistic and F- statistic are the methodologies that being used by researcher to interpret the multiple regressions. All the data were calculated on monthly basis for 60 months period (5-year), which are from January 2006 to December 2010. Table1: Data gathered from monthly statistical bulletin BNM YEAR KLSE M3 INFLATION EXCHANGE Jan-06 914.0100 679276.3000 3.2500 3.7510 Feb-06 928.9400 686040.7000 3.2400 3.7135 Mar-06 926.6300 690830.2000 4.7600 3.6860 Apr-06 949.2300 697329.4000 4.5500 3.6255 May-06 927.7800 699037.4000 3.9100 3.6290 Jun-06 914.6900 700537.8000 3.9000 3.6750 Jul-06 935.8500 705585.5000 4.1100 3.6535 Aug-06 958.1200 717140.9000 3.2800 3.6770 Sep-06 967.5500 716265.6000 3.2700 3.6845 Oct-06 988.3000 725351.2000 3.0700 3.6480 Nov-06 1080.6600 737229.6000 2.9600 3.6180 Dec-06 1096.2400 760301.6000 3.0500 3.5315 Jan-07 1189.3500 776100.8000 3.2400 3.5015 Feb-07 1196.4500 789147.0000 3.1400 3.5060 Mar-07 1246.8700 789222.5000 1.5500 3.4560 Apr-07 1322.2500 796487.8000 1.5500 3.4230 May-07 1346.8900 799238.9000 1.4500 3.4045 Jun-07 1354.3800 788610.8000 1.4400 3.4545 Jul-07 1373.7100 799902.2000 1.6300 3.4540 Aug-07 1273.9300 801630.3000 1.9200 3.5035 Sep-07 1336.3000 804248.7000 1.8300 3.4170 Oct-07 1413.6500 807425.8000 1.9200 3.3418 Nov-07 1396.9800 808446.5000 2.3000 3.3585 Dec-07 1445.0300 832737.8000 2.3900 3.3065 Jan-08 1393.2500 867682.2000 2.2800 3.2360 Feb-08 1357.4000 876225.7000 2.6600 3.1890 Mar-08 1247.5200 884372.9000 2.7600 3.1875 Apr-08 1279.8600 893619.3000 3.0500 3.1580 May-08 1276.1000 898652.6000 3.8100 3.2435 Jun-08 1186.5700 899120.0000 7.6900 3.2665 Jul-08 1163.0900 912693.3000 8.5100 3.2630 Aug-08 1100.5000 904562.2000 8.5000 3.3895 Sep-08 1018.6800 912780.0000 8.2100 3.4575 Oct-08 863.6100 900442.6000 7.6300 3.5625 Nov-08 866.1400 909230.6000 5.7100 3.6175 Dec-08 876.7500 931864.7000 4.3900 3.4640 YEAR KLSE M3 INFLATION EXCHANGE Jan-09 884.4500 946005.1000 3.9100 3.6085 Feb-09 890.6700 944320.5000 3.7100 3.6925 Mar-09 872.5500 949445.1000 3.5200 3.6470 Apr-09 990.7400 948276.2000 3.0500 3.5610 May-09 1044.1100 943193.7000 2.3800 3.5075 Jun-09 1075.2400 950848.9000 -1.4100 3.5225 Jul-09 1174.9000 961049.5000 -2.4400 3.5200 Aug-09 1174.2700 973080.6000 -2.4400 3.5260 Sep-09 1202.0800 975786.8000 -2.0000 3.4745 Oct-09 1243.2300 983314.9000 -1.4900 3.4075 Nov-09 1259.1100 1000513.5000 -0.0900 3.3875 Dec-09 1272.7800 1017303.2000 1.0700 3.4245 Jan-10 1259.1600 1021076.7000 1.3400 3.4130 Feb-10 1270.7800 1021628.5000 1.1600 3.4090 Mar-10 1320.5700 1031923.0000 1.3400 3.2730 Apr-10 1346.3800 1025310.0000 1.5200 3.1905 May-10 1285.0100 1030891.5000 1.5500 3.2530 Jun-10 1314.0200 1034522.0000 1.5600 3.2575 Jul-10 1360.9200 1039009.2000 1.7700 3.1875 Aug-10 1422.4900 1052520.2000 1.9000 3.1375 Sep-10 1463.5000 1058471.4000 1.7200 3.0875 Oct-10 1505.6600 1065712.1000 1.8400 3.1095 Nov-10 1485.2300 1082174.4000 1.7700 3.1575 Dec-10 1518.9100 1088969.3000 1.9900 3.0835 The table above shows the figure of each variable, money supply (M3), inflation rates, exchange rates and composite index as the dependent variable and independent variables in a monthly basis for 60 months periods (5-year) from January 2006 to December 2010.

Based on this study, this trend analysis used to observe the movement of KLSE Composite Index and it is based on 60 months period (5-years) from January 2006 to December 2010. Data above shows the interpretation of words that are use in this study.

Figure 1: Trend of KLSE Composite Index from January 2006 to December 2010 The figure above shows the movement of KLSE Composite Index calculated in monthly basis for 60 months which are from January 2006 to December 2010. This data gathered from Monthly Statistical Bulletin Bank Negara Malaysia. From the graph, it shows that the movements of KLSE Composite Index for 60 months from January 2006 to December 2010 are fluctuated. It means the upwards and downwards momentum was affected by economy of Malaysia. The KLSE Composite Index starts to decline in year 2009 when at this year, Malaysia faced with economic recession. This shows a positive relationship between KLSE Composite Index and economic of Malaysia, where KLSE Composite Index will decrease whenever Malaysia faced an economic recession. Besides, it strongly affected investors. Their level of confidence to do invest will decrease according to recession. They prefer not to invest in order to avoid high risk and they not have extra money to spend on stock investment. However, at the end of 2009 the movement of KLSE Composite Index starts to recover as it showed an increasing movement in the graph. At this stage it can be conclude that, recovery from recession has increase the investor’s confident to spend on investment and next, it increase in the stock index performance in the KLSE.

Table 2: Regression result

Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics

Std. Error Beta Tolerance VIF 1 (Constant)

1.345 11.955 .000 logM3

.085 -.304 -4.406 .000 .531 INFLATION

.004 -.398 -7.577 .000 .916 1.092 logEXCHANGE

.214 -.978 -14.610 .000 .566 1.768 a. Dependent Variable: logKLSE R – Squared = 0.858 F – Statistics = 112.949 Durbin Watson = 0.633 The table above shows the result of regression which is gathered from Statistical Package for Social Science (SPSS) that includes all the dependent variable and independent variables.

The specific form of equation: Y = c + Î²M + Î²F + Î²X + Îµ The specific form of equation based on SPSS output: Y = 16.082 – 0.372M – 0.029F – 3.126X (1.345) (0.085) (0.004) (0.214) All the dependent variable and independent variables’ data were calculated using SPSS. The figure above are obtained from SPSS were put in the equation of regression. Note: Figures are in parentheses shows standard error of coefficient. Where: KLSE = Kuala Lumpur Stock Exchange M = Money Supply (M3) F = Inflation Rates X = Exchange

From the table 2, the result shows that there is a negative relationship between money supply and the KLSE Composite Index. It means that when the money supply decrease by 1 unit, the changes in KLSE Composite Index also decrease by 0.372 units.

From the table 2, it indicates that there is a negative relationship between inflation rates and KLSE Composite Index. This shows that when inflation rates decrease by 1 unit, the changes in KLSE Composite Index also decrease by 0.029 units.

From the table 2, the result shows that there is a negative relationship between exchange rates and KLSE Composite Index. This state that when exchange rates decrease by 1 unit, the changes in KLSE Composite Index also will decrease by 3.126 units.

Table 3: Mode summary of coefficient of determination

Model R

Adjusted R Square Std. Error of the Estimate 1 .926a

.851 .06650 a. Predictors: (Constant), logEXCHANGE, INFLATION, logM3 b. Dependent Variable: logKLSE Model R – Square 1 0.858 Based on the table above, the result shows that R – Squared (R2) is 0.858. It can be measured as a high explanatory power of estimated equation. This indicate that 0.858 stand as 85.8 % of changes in dependent variable. This dependent variable was explained by those independent variables that are listed in the equation like money supply (M3), inflation rates and exchange rates. Another 14.2 % of changes in dependent variable can be explained by other factors such as gross domestic product, interest rate and other macroeconomic variables which are not included in this model. The regression above is still can be accepted.

Table 4.1: Anova

Model Sum of Squares df Mean Square F 1 Regression 1.498 3 .499 112.949 Residual .248 56 .004 Total 1.746 59 a. Predictors: (Constant), logEXCHANGE, INFLATION, logM3 b. Dependent Variable: logKLSE Sig. F Model .000a 112.949 1 Regression Residual Total The above table shows the result of ANOVAs. It is a test of significance for the overall regression model and was measured from the significance of F-Value in above tables. Table 4.2: F-Statistic Result Critical F-Statistic Calculated F-Statistic Model 112.949 3.15 Regression Where: The numerator is: = k – 1 = 3 – 1 = 2 The denominator is: = n – k = 60 – 3 = 57 The calculated value of the F-statistic is 112.949. Based on the above data, it also shows the degree of freedom for the numerator (k-1) and denominator (n-k). The result gathered from that model, the numerator value is 2 and the denominator value is 57. By using both numerator and denominator, critical F-value is 3.15. Since the calculated F-statistic is greater than F-distribution table, (112.949 > 3.15) it explains there is a significant relationship between KLSE Composite Index as dependent variable with money supply, inflation rates and exchange rates as independent variables. Therefore, the overall model appears as a useful model in predicting KLSE Composite Index.

Another statistical test that been using in this study is the concept of T – Statistic. This T-Statistics is used to determine if there is a significant relationship between the dependent variable (KLSE Composite Index) with each of the independent variables (money supply, inflation rates and exchange rates). Researcher has set up two hypotheses before. T – Statistic will help researcher to choose which hypotheses can be true. The two possibilities are: H0 = null hypotheses; there is no significance relationship H1 = alternate hypotheses; there is a significance relationship Table 5: Coefficients highlighting T-Statistics and significance level

Model Unstandardized Coefficients Standardized Coefficients

Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 16.082 1.345

.000 logM3 -.372 .085 -.304

.000 .531 INFLATION -.029 .004 -.398

.000 .916 1.092 logEXCHANGE -3.126 .214 -.978

.000 .566 1.768 a. Dependent Variable: logKLSE

If the t –statistic value is greater than the significance level, failed to reject H0. If the t- statistic value is lower that the significance level, reject H0 and conclude H1 Based on this study, the sample size is 60, and to get the number of degree of freedom, there is an equation that has to follow. The degree of freedom is equal of sample size minus number of independent variables minus one. The regression is: Degree of Freedom = n – k – 1 From this study: Degree of freedom = 60 – 3 – 1 = 56 Since the degree of freedom is 56, thus the t – distribution table is 2.660 Table 6: T – Statistical result of dependent variable and independent variables Remark Critical T-Statistic Observe T – Statistic Sig. Variables 0.0000.000 0.000 Significance 11.955 -4.406 Constant logM3 Significance ? 2.660 ? 2.660 0.000 0.000 Inflation -7.577 Significance ? 2.660 0.000 -14.610 logExchange

From the above table, the calculated T-statistics is -4.406. Since the degree of freedom is 56, the calculated T-value is 2.660. Therefore at 95% confident interval, the calculated T-value is greater than critical T-value (4.406 > 2.660). This shows a negative relationship between M3 with KLSE Composite Index movement and the M3 is statistically significance in explaining the KLSE Composite Index. As a decision, H0 will be rejected and H1 will be accepted. It means that money supply does influence the KLSE Composite Index.

Based on the table 6, the calculated T-statistics is -7.577. It shows that the calculated T-value is more than the critical T-value at the 95% confidence interval (7.577 > 2.660). It explains a negative relationship and significance relationship between inflation rates with KLSE Composite Index. As a decision, H0 will be rejected and H1 will be accepted. It means that inflation rates do influence the KLSE Composite Index.

From the table, the calculated T-statistic is 14.610. Since the critical T-value is 2.660, it results in less value compared to the calculated T-statistics at the 95% confidence interval (14.610 > 2.660). This show a negative relationship and the exchange rates is statistically significance in explaining the KLSE Composite Index. As a decision, H0 will be rejected and H1 will be accepted. It means the exchange rates do influence the KLSE Composite Index.

Table 7: Result of Durbin-Watson

Model R

Adjusted R Square Std. Error of the Estimate 1 .926a

.851 .06650 a. Predictors: (Constant), logEXCHANGE, INFLATION, logM3 b. Dependent Variable: logKLSE NO DECISION NO DECISION -VE CORRELATION NO AUTOCORRELATION +VE CORRELATION 4-dL 4-dU dU

dL 2.52 2.31 1.69 1.48 0.633 Figure 2: Durbin-Watson result Based on the table above, the computed Durbin Watson is 0.633. While the number of observation is 60 and the independent variables are 3.

Model Durbin-Watson 1 0.633 By looking at the Durbin-Watson table, the Durbin-Watson statistic is 0.633. Since the sample size is 60 and the independent variables are 3, result gathered from Durbin-Watson table for dL is 1.48, while for dU is 1.69. This result explained that there are auto correlation problem exist in this study. All the independent variables chosen for this study, which are money supply, inflation rates and exchange rates, are not duplicated. From what have been interpreted above, the calculated Durbin-Watson 0.633 does not rely between 1.69 and 2.31. It comes to the conclusion that there is an auto correlations exist among these variables.

The correlation is used to access the strength of the relationship between two variables. It is a statistical analysis to see the direction and to describe the significance of the relationship between dependent variable and independent variables. The sign of positive (+ve) explains that the independent variables have positive correlation with the dependent variable. While the sign of negative (-ve) explains that the independent variables have negative correlation with the dependent variable.

Table 8: Result of Pearson Correlation

logKLSE logM3 INFLATION LogKLSE Pearson Correlation 1 .451** -.451** Sig. (2-tailed) .000 .000 N 60 60 60 logM3 Pearson Correlation .451** 1 -.284* Sig. (2-tailed) .000 .028 N 60 60 60 INFLATION Pearson Correlation -.451** -.284* 1 Sig. (2-tailed) .000 .028 N 60 60 60 logEXCHANGE Pearson Correlation -.834** -.658** .143 Sig. (2-tailed) .000 .000 .277 N 60 60 60 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). The table above shows the result of the dependent variable and independent variables that describe all of these correlations. It explained which variable are significant and at what level do it state.

From the result, there is 45.1% of the variation in money supply that can explain the variation in KLSE Composite Index. This positively relationship can be classified as a strong relationship. It also shows that money supply and KLSE Composite Index have a positive sign which refer to the positive correlation. As a conclusion, it shows a significance result for money supply at the level of 0.01 or at 1%.

Based on the result table, there is negative relationship between inflation rates and KLSE Composite Index. There is 45.1% of the variation in inflation rates that can explain the variation in KLSE Composite Index. As a conclusion, inflation rates also shows a significance result at 1% which refer to 2-tailed.

Based on the result, it explains the relationship between exchange rates and KLSE Composite Index is the strongest compare to other relationship. There is 83.4% of the variations in KLSE Composite Index that explained by the exchange rates. It can be interpreted as a strong relationship. The result shows a negative correlation which means the changes in one variable is affected by the changes in other variables. As a conclusion, this variable also shows a significance result which state at level 0.01.

In the beginning of this research, researcher would like to determine whether the macroeconomics variables which are Money Supply (M3), Inflation Rates and Exchange Rates as the independent variables, that has a relationship with Kuala Lumpur Stock Exchange Composite Index in Malaysia economy. In realistic, there are many variables that can affect the movement and performance on stock index in the stock market. As a result, all the independent variables which are money supply, inflation rates and exchange rates do influence the effect of this KLSE performance. From the problem statement in this research that have been issued before, the problem statement want to discover the movement of KLSE Composite Index on many economic factors, whether the economic factors could affect the performance of KLSE Composite Index . The economic factors refer to money supply, inflation rates and exchange rates as independent variables and KLSE Composite Index as the dependent variables. Researcher tries to figure out the whether relationship between dependent variable and independent variables are suitable between each other or not, since there are other independent variables that are more suitable to used in this research. From the analysis and findings, the result shows a significant relationship between money supply and KLSE Composite Index. However, it result an inconsistent with the previous study that shows a positive relationship which might due to the different value of money, rules and regulation made by different countries. While, the result for inflation rates and exchange rates also shows a significance relationship with KLSE Composite Index. The result shows a consistent with the previous studies that are also state that there are a negative relationship and it shows a significance relationship between inflation rates, exchange rates with KLSE Composite Index. This situation might be attributing to the unique structure of the Malaysian and it may also derive from different perception among the investors. In conclusion, from the result that were obtained from the SPSS it shows all the independent variables which are the money supply, inflation rates and exchange rates have very strong significance relationship with the Kuala Lumpur Stock Exchange Composite Index. In other words, the performance of dependent variable which is, KLSE Composite Index is influenced by these three variables.

Based on this study, researcher has find out several recommendations which can be use for further research. It is recommended, in order to get an exact effect between money supply (M3), inflation rates and exchange rates with the KLSE composite index, the length of observation period should be more than 60 months (5 years) or a daily data can be taken. The more length of observation being analyze, the more accurate the result will be. Researcher would like to recommend, more than three independent variables such as gross domestic product, interest rate, balance of payment and others to be used in future research. Hence, the result could be more significant and accurate. It is also recommended to use various types of index such as Emas index, industrial index and others that could result more accurate movement in KLSE composite index.

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