Relationship between stock market development and economic growth

When the change in stock market occurs, there is an important implication on a country’s economy as this serves as a leading indicator of the economy. The turn over of this is also true that changes in the economy do have an effect on the stock market.

A relationship exist between stock market development and growth of the economy and stock prices are generally believed to be determined by some fundamental macroeconomic variables such as interest rate, inflation, and money supply. Empirical evidences have shown that changes in stock prices are linked with macroeconomic behaviour in advanced countries (Muradoglu et al., 2000; Diacogiannis et al., 2001; Wongbampo and Sharma, 2002; Mukhopadhyay and Sarkar, 2003; Gan et al., 2006; Robert, 2008) inter alia.

The relation between the stock market and macroeconomic forces has been widely analyzed in finance and macroeconomic literature. The linkages between equity prices and macroeconomic variables such as real economic activity, money supply, inflation rates, interest rate and exchange rates are of crucial importance in analyzing equity returns in relation to portfolio investment. Many researchers have concur that macroeconomic variables have a significant contribution in determining stock performance.

An illustrative list of studies includes Fama (1981); Friedman (1988); Chen (1991); Mukherjee and Naka, (1995); Nasseh and Strauss (2000); Tatom (2002), Hope and Kang (2005). They discover the significant effects on the stock prices by changes in macroeconomic conditions.

The results from previous studies also indicate that asset prices sensitively react to macroeconomic news. Researchers believe that various patterns of stock price movements are due to different expectations among investors towards future cash flows as well as different levels of discount rate for their investment. They conclude that macroeconomic variation is considered as a significant factor in explaining stock price movements.

Changes in macroeconomic fundamentals that could have different effects on sector specific index have not been discussed in most of the previous studies. Study such as Geske and Roll (1983); Chen, Roll and Ross (1986); Keraney and Daly (1998); Fifield, Power and Sinclair (2000); Panetta (2002); Masayami and Sim (2002); Christopher, Minsoo, Hua and Jun (2006) are more worried with the aggregate stock market index as the measurement for the overall performance of the stock market instead of individual sector-specific indices in their analyses. Beside that, the study on the movements of sector-specific indices is still missing. It is expected that the changes in macroeconomic variables would generate different effects on stock market.

Although there have been a number of accepted evidences examine the relationship between macroeconomic factors and stock return in developed market, little effort appears to have been made to document whether a similar relation is also true in less institutionally advanced countries like Malaysia.

In the Malaysian context, Ibrahim (2000), Ibrahim and Aziz (2003) and Janor et al. (2005) investigate the self-motivated interactions between stock return and economic activities by conjecture that the stock market leads the movement of macroeconomic variables. In contrast, this study aims to examine the determinants of the stock market behavior in Malaysia instead of the predictive role of the stock market itself. It is hoped that the finding of this study would provide some meaningful insights to the body of knowledge, policy makers as well as the practitioners.

For the academic field, the results from this study should build up the theoretical framework of the determinants of stock market movement from the perspective of developing economies like Malaysia.

Last but not least, by knowing which macroeconomic variables affect the stock market the most, both the personal and corporate investors would be able to proactively strategize their investments according to the change of the monetary policy.

Apart from using the latest data, we employ different macroeconomic variables that are considered as most relevant in the Malaysian context.

Therefore, there is a need for such study is conducted in Malaysia in order to examine the relationship between macroeconomic factors and stock returns.

There is the definition of several macroeconomic variables terms:

Industrial Production

A measure of changes in output for the industrial sector of the economy. The industrial sector includes manufacturing, mining, and utilities. Although these sectors contribute only a small portion of GDP (Gross Domestic Product), they are highly sensitive to interest rates and consumer demand. This makes Industrial Production an important tool for forecasting future GDP and economic performance. Industrial Production figures are also used by central banks to measure inflation, as high levels of industrial production can lead to uncontrolled levels of consumption and rapid inflation.

Money Supply

Total amount of money available in an economy at a particular point in time. Money supply data are recorded and published, usually by the government or the central bank of the country. Public and private sector analysts have long monitored changes in money supply because of its possible effects on the price level, inflation and the business cycle.

There is strong empirical evidence of a direct relation between long-term price inflation and money-supply growth, at least for rapid increases in the amount of money in the economy.

In addition to some economists’ seeing the central bank’s control over the money supply as weak, many would also say that there are two weak links between the growth of the money supply and the inflation rate:

First, an increase in the money supply, unless trapped in the financial system as excess reserves, can cause a sustained increase in real production instead of inflation in the consequences of a recession, when many resources are underutilized.

Second, if the velocity of money, i.e., the ratio between nominal GDP and money supply, changes, an increase in the money supply could have no effect, or an unpredictable effect on the growth of nominal GDP.

Inflation

In economics, inflation is a rise in the general level of prices of goods and services in an economy over a period of time. When the general price level rises, each unit of currency buys fewer goods and services. Inflation’s effects on an economy are many and can be at the same time positive and negative.

Negative effects of inflation include a decrease in the real value of money and other monetary items over time, uncertainty over future inflation may discourage investment and savings, and high inflation may lead to shortages of goods if consumers begin notice out of concern that prices will increase in the future. Positive effects include ensure central banks can adjust nominal interest rates and encourage investment in non-monetary capital projects.

Stock Return

It is the capital gain or loss in a particular period. The return consists of the income and the capital gains relative on an investment. It is usually quoted as a percentage.

1.2 BACKGROUND

The Kuala Lumpur Composite Index (KLCI) is a stock market index generally accepted as the local stock market barometer. Introduced in 1986 to answer the need for a stock market index that would serve as an accurate performance indicator of the Malaysian stock market as well as the economy.

It is used to be the main Malaysia stock index, and is now one of the three primary indices for the Malaysian stock market, which the other two are FMB30 and FMBEMAS, Bursa Malaysia. It contains 100 companies from the Main Board with approximately 500 to 650 listed companies in the Main Board which comprise of multi-sectors companies across the year 2000 to 2006 and is a capitalization-weighted index.

Bursa Malaysia is committed towards extending the Malaysian capital market’s global reach by offering competitive services and infrastructure through adoption of internationally accepted standards which are globally relevant.

As part of Bursa Malaysia’s strategic initiative, the Kuala Lumpur Composite Index (KLCI) was enhanced to ensure that it remains robust in measuring the national economy with growing linkage to the global economy. Bursa Malaysia together with FTSE, its index partner, have integrated the KLCI with internationally accepted index calculation methodology to provide a more investable, tradable and transparently managed index.

The enhanced KLCI, whilst remaining representative of the Malaysian stock market, provides a platform for a wider range of investable and appealing opportunities.

1.3 PROBLEM STATEMENT

This research was brought in order to discover how far macroeconomic factors such as gross domestic product, inflation, interest rate, exchange rate and import and export may affect stock return. From previous research, it is found that the macroeconomic variables play an integral part in influencing the stock return. It tries to grab those variables volatility impact on investor’s stock return in a given economic environment and horizon.

The relationship between macroeconomic variables and stock market returns is, by now well documented in the literature. However, due to the changing environment of the world economy, past researches cannot be deemed as suitable for current application. There is needed to revise the finding from the previous researches so it is consistent with current environment and economic situation. This study will base the Malaysian market as the background of research by where Bursa Malaysia as the indicator of the stock market performance and as the background of research. The horizon of the research will cover from the beginning of 2001 to the ending of 2010 where the world economy is starting to move into depression and based on the monthly data.

1.4 OBJECTIVE OF THE STUDY

The objective of this study is to determine the relationship between macroeconomic variables and the stock market performance and how this information can help investor in making right decision in their investment timing.

To identify the set of macroeconomic variables, which correspond more closely with the stock market.

Investigate the impact of inflation on stock market based on monthly data.

To investigate the sensitivity of stock market towards the macroeconomic variables.

Focus on the determinants of the stock market return from the perspective of macroeconomic activities.

1.5 CONTRIBUTIOAN AND SIGNIFICANT OF THE STUDY

These study are conducted to look at the relationship between stock market and macroeconomic variables.

For the academic field, the results from this study should support the theoretical framework of the determinants of stock market movement from the perspective of developing economy.

For the policy implication, it is hoped that my findings would help the regulatory bodies to better understand the stock market behavior towards achieving the desired monetary goals.

Last but not least, by knowing which macroeconomic variables affect the stock market the most, both the personal and corporate investors would be able to proactively strategize their investments according to the change of the monetary policy.

CHAPTER 2

LITERATURE REVIEW

It is an uneasy task to select correct macroeconomic variables that could be most valuable in tracing the relationship between macroeconomic variables and stock market prices. The current issue has been investigated for a long time (Miller, Modigliani, 1961). According to Chen, Roll and Ross (1986), to select the relevant and proper macroeconomic factors requires much efforts and it would be useful to consider theoretical and empirical literature in this field of study before undertaking such a decision (Humpe, Macmillan, 2007).

Dritsaki (2005) observe that the most important thing in selecting macroeconomic variables is to protect that those variables would objectively reflect not only general situation in the country’s economy but also financial status of the country. As well as this researchers, Fama, 1981; Chen, Roll, Ross, 1986; Cheung, Ng, 1998; Binswanger, 2000; Lakstutiene, 2008 believe that financial resources are closely related to economic output of the country which is measured by industrial production.

DeFina (1991) was a point out that is inflation negatively influences companies due to speedily increasing costs. Looking for the relationship between stock market and macroeconomic variables inflation is most often measured by consumer price index (Atmadja, 2005; Dritsaki, 2005; Laopodis, 2007), though some scientists also include other inflation reflecting indices, for example, producer price index (Teresienė, Aarma, Dubauskas, 2008).

Another popular macroeconomic variable is Money supply stands for another macroeconomic indicator that many scientists accept when they seek for the relationship between stock market prices and macroeconomic forces (Urich, Wachtel, 1981; Chaudhuri, Smiles, 2004). Tan and Baharumshah (1999) argue that it is more expedient to analyze the narrow money M1 while others operate with broadly defined money supply M2 (Tursoy, Gunsel, Rjoub, 2008). There is another group of scientists who avoid this scientific discussion and enrol both concepts of money supply in their empirical investigations.

Researchers Ibrahim and Aziz (2003), Booth and Booth (1997), Wongbangpo and Sharma (2002), Chen (2003), Chen et al. (2005), Maysami and Koh (2000), and Mukherjee and Naka (1995), said that the Rate of inflation, money supply, interest rates, industrial production, and ex- change rates are the most popular significant factors in explaining the stock market movement.

How the money supply affects the stock market returns is also a matter of practical proof. According to Fama (1981), an increase in money supply leads to an increase in discount rates and make the price of stock lowers, thus give a negative effect. However, if an increase in money supply leads to economic expansion via increased cash flows, then economic growth lead by such expansionary monetary policy will give benefit to stock price and its fight by Mukherjee and Naka (1995). In the case of Japan, the study shows that money supply is positively related to stock market. Maysami and Koh (2000) agreed with Mukherjee and Naka (1995) for both long run and short run active interaction between money supply and stock returns.

Other than interest rate and money supply, inflation can also affect the movement of stock prices. According to Asprem (1989), he said “inflation should be positively related to stock return if stocks provide a hedge against inflation”. However, stock market has negatively effect the inflation and this is conclude by Barrows and Naka (1994), Chen et al. (1986) and Chen et al. (2005). When expected inflation rate tends to rise, inflation rate lead to warning monetary policies, which would have a negative effect upon stock prices.

On the other hand, as price stability is one of the macroeconomic policy objectives by the Malaysian government and also an expected target of the Malaysian citizens, we believe that the relationship between inflation and stock price is insignificant.

Study done by Geske and Roll (1983), Fama (1990), Koutoulas and Kryzanowski (1996), and Kearney and Daly (1998) exhibit a positive relationship between industrial production and stock prices. On the other hand, Sadorsky (2003) fail to tell a significant effect of industrial production on stock prices.

Tan, Loh and Zainudin (2006) look at the dynamic between macroeconomic variables and KLCI during the period of 1996-2005. They found that the inflation rate, industrial production, crude oil price and Treasury Bills’ rate have long-run relation with Malaysian stock market.

According to Hashemzadeh and Taylor (1998), there investigate the direction of causality between the money supply, stock prices, and inflation in the US. The relationship between money supply and stock prices is reflected by a feedback system, with money supply explaining some of the observed variation in stock price levels, and vice versa.

Soenen and Johnson (2001) investigate that effects of changes in the consumer price index on industrial production and stock market returns for China, and from the investigation that done by them, the result are positive and significant association between stock returns and real output.

From research done by researchers, they reach results showing that short run and long run equilibrium relationship exists between inflation, money supply and trading volume and the stock prices in the Athens stock exchange.

Muradoglu et al. (2000) found that the relationship between stock returns and macroeconomic variables were mainly due to the relative size of the respective stock market and their integration with world markets. According to Wongbampo and Sharma (2002), relationship was found between stock prices and interest rate for the Philippines, Singapore and Thailand are negative but positive for Indonesia and Malaysia.

Chen, Roll and Ross (1986) was the first study to select macroeconomic variables to estimate U.S. stock returns and apply the APT models. They employed seven macroeconomic variables, namely: term structure, industrial production, risk premium, inflation, market return, consumption and oil prices in the period of Jan 1953-Nov 1984. In their research, they found a strong relationship between the macroeconomic variables and the expected stock returns during the tested period.

They note that industrial production, changes in risk premium, twists in the yield curve, measure of unanticipated inflation of changes in expected inflation during periods when these variables are highly volatile, are significant explaining expected returns. They found that consumption; the financial market does not price oil prices and market index. They conclude asset prices react sensitively to economic news, especially to unanticipated news.

On the other hand, Clare and Thomas (1994) investigate the effect of 18 macroeconomic factors on stock returns in the U.K. They find oil prices, retail price index, bank lending and corporate default risk to be important risk factors for the U.K. stock returns. Priestley (1996) prespecified the factors that may carry a risk premium in the U.K. stock market. Seven macroeconomic and financial factors; namely default risk, industrial production, exchange rate, retail sales, money supply unexpected inflation, change in expected inflation, terms structure of interest rates, commodity prices and market portfolio. For the APT model, with the factor generating from the rate of change approach all factors are significant.

For Japanese stock market, Hamao (1988) replicated the Chen, Roll and Ross (1986) study in the multi-factor APT framework. He put on view that the stock returns are significantly influenced by the changes in expected inflation and the unexpected changes in both the risk premium and the slope of the

term structure of interest rates. Through the APT, Brown and Otsuki (1990) explore the effects of the money supply, a production index, crude oil price, exchange rates, call money rates, and a residual market error on the Japanese stock market. They observe that these factors are associated with significant risk premium in Japanese equities.

Tan, Loh and Zainudin (2006) look at the dynamic between macroeconomic variables and the Malaysian stock indices (Kuala Lumpur Composite Index) during the period of 1996-2005. They found that the inflation rate, industrial production, crude oil price and Treasury Bills’ rate have long-run relation with Malaysian stock market. Results indicate that consumer price index, industrial production index, crude oil price and treasury bills are significantly and negatively related to the Kuala Lumpur Composite Index in the long run, except industrial production index coupled with a positive coefficient.

A multiple regression model is designed to test the relationships between the ISE-100 index returns and seven macroeconomic factors. In the regression models, the ISE-100 index returns are used as dependent variables, while the macroeconomic variables are used as independent variables.

The results of the paper indicate that interest rate, industrial production index, oil price, foreign exchange rate have a negative effect on ISE-100 Index returns, while money supply positively influence ISE-100 Index returns. On the other hand, inflation rate and gold price do not appear to have any significant effect on ISE-100 Index returns.

CHAPTER 3

RESEARCH METHEDOLOGY

3.1 Introduction

This chapter will discuss the methodology used in conducting the study in this study. In this study has used secondary data. Secondary data was obtained from Datastream and the reference to written material, whether from the journals and web pages using relevant information. In the analysis section, I use the method of descriptive analysis to explain the significance of each of the data. This method made use of the application of SPSS (Statistical Package for Social Sciences)

3.2 Data Collection

The data collected will be put in the database management software and in this case SPSS software used for these purposes. Researchers from the system to transfer data from Datastream to the SPSS are coded to facilitate analysis. SPSS was also used for descriptive analysis.

To connect the independent variables and independent variables is bivariate analysis, namely by measuring the relevance and the relationship between these variables.

3.3 DATA ANALYSIS

Coefficient of Variation (CV)

A measure of relative variability that indicates risk per unit of return. It is equal to: standard deviation divided by the mean value. When used in investments, it is equal to: standard deviation of returns divided by the expected rate of return.

CV = Standard Deviation of Returns

Expected Rate of Return

Coefficient of Determination (R²)

Coefficient of determination or test of goodness of fit will tell about how good the line best fit. It also measure percentage of change in the dependent variable which will be explained by the changes in the independent variables. The value of R² is range from 0.1 and it normally being valued as the higher the value of R², the higher is the explanatory power of the estimated equation and is more accurate for forecasting purpose.

T-Statistics

In order to get the statistics, we first must perform the T-test. This test being done in order to identify whether there is a significant relationship between the dependent variable and each of the independent variables.

The formula for the T-Stats is as follows:

T-Stats = Value of Coefficient (b)

Std Error of Coefficient (se)

Analysis of Variance (ANOVA)

A term used to describe a statistical technique used to test whether there is a difference between means of several populations. To describe the ANOVA procedure, consider a problem with K populations. For this reason, ANOVAs are useful in comparing three or more means. One way that the ANOVA model can be written is

yij = µi + eij

where yij is the jth observation from population i

µi is the population means for population i

eij is a random disturbance for the jth observation from population i

F-Statistics

This is use to test the hypothesis that the variation in the independent variables explained a significant portion of the variation in the dependent variable (to test the significant of the overall model).

The formula for the F-Stats is as follows:

F-Stats = Explained variation / (k-1)

Unexplained variation / (n-k)

Value of Coefficient

The value is used in interpreting the independent variables in order to see the effect of it on the dependent variable.

3.4 CONCEPTUAL FRAMEWORK

Literature review in Chapter 2 is my attempt to study the relationship between stock market (KLCI) and macro economic variables. As mentioned in the Statement of Hypothesis, three variables have been chosen to measure the shares price. A conceptual framework will be introduced to investigate the relationship between these factors and KLCI. The independent variables adopted will be Inflation rate, Industrial Production and Money Supply; meanwhile the dependent variables will be the KLCI (shares price), measured in terms of relationship.

I suggest the following framework to investigate the relationship among the variables and effect of these factors on KLCI.

Theoretical framework

INDEPENDENT VARIABLES DEPENDENT VARIABLE

Inflation

Stock Market

Industrial Production

Money Supply

3.5 STATEMENT OF HYPOTHESIS

I have chosen three variables affecting KLCI (Kuala Lumpur Composite Index). The variables are Inflation Rate, Money Supply and Industrial Production.

Thus, the hypothesis are define as below:

HYPOTHESIS 1

HO: There is negative relationship between KLCI and inflation

H1: There is positive relationship between KLCI and inflation.

HYPOTHESIS 2

HO: There is negative relationship between KLCI and the Industrial Production.

H1: There is positive relationship between KLCI and the Industrial Production.

HYPOTHESIS 3

HO: There is negative relationship between KLCI and Money Supply rate.

H1: There is positive relationship between KLCI and Money Supply rate.

CHAPTER 4

RESEARCH RESULT

4.1 EMPIRICAL RESULTS

This chapter presents the findings of the study. The secondary data collected and collated for the research were discussed and analyzed. Also to discusses on the results from running the SPSS software from the gathered data. These results are compared with the proposed hypotheses of the research and new propositions are presented according to the optimum research findings.

4.2 CORRELATION COEFFICIENT

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

Pearson Correlation – These numbers measure the strength and direction of the linear relationship between the two variables. The correlation coefficient can range from -1 to +1, with -1 indicating a perfect negative correlation, +1 indicating a perfect positive correlation, and 0 indicating no correlation at all. (A variable correlated with it will always have a correlation coefficient of 1.)

From the table, indicate that the strength of association between the variables is very high (r = 0.847), and that the correlation coefficient is very highly significantly different from zero (P < 0.001). I can say that the variation in industrial production is explained by the KLCI.

In conclusion, the strength of association between the variables is very high (r = 0.891), and that the correlation coefficient is very highly significantly different from zero (P < 0.001). Also, I can say that the variation in money supply is explained by KLCI.

Money supply has been recognized as a significant for most of the sectoral indices in positive direction in the stock market. This finding is even with before studies that indicate positive fundamental effect by money supply towards stock return (Mukherjee and Naka, 1995; Naka, Mukherjee and Tufte, 1990; Ghazali and Ramlee, 2001; Ghazali and Soo, 2002; Gilchrist and Leahy, 2002). According to (Kwon and Bacon, 1997; Masayami and Koh, 2000; Chong and Goh, 2005; Muradoglu, Metin and Argae, 2001; Ibrahim,

2001), the finding of the present study related to effect of money supply on the positive direction of stock returns concurs.

The positive relation between money supply and stock return could be observed in terms of investment preferences among the investors. The changes in money supply contribute certain effects particularly in constructing portfolio investment strategies among investors. It reflects the different preferences among the investors in determining the portion of investment instruments including stock in their portfolio investment. The stock price will increase in response to a higher demand for stock investment and because of that, when stock price increase, that will lead money supply to increase.

Industrial production and stock prices: the industrial production and stock prices are positively related because increase in industrial production cause to increase in production of industrial sector, which cause to increase the profit of industries and corporations. As dividends increase resulting share prices raise therefore it is found positive association between IPI and share price according to economic theory.

4.3 MULTIPLE REGRESSIONS

Multiple Regressions is an extension of bivariate correlation. The result of regression is an equation that represents the best prediction of a dependent variable from several independent variables. Regression analysis is used when independent variables are correlated with one another and with the dependent variable. Beside that, multiple regression analysis is a method for explanation of phenomena and forecast of future events. In multiple regression analysis, a set of predictor variables is used to explain variability of the criterion variable.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.927(a)

.859

.855

.10064

a Predictors: (Constant), LogMoneySupply, Inflation, LogIndProd

ANOVA(b)

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

7.131

3

2.377

234.696

.000(a)

Residual

1.175

116

.010

Total

8.306

119

a Predictors: (Constant), LogMoneySupply, Inflation, LogIndProd

b Dependent Variable: LogKLCI

Coefficients(a)

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

-2.552

.358

-7.135

.000

Inflation

-.026

.005

-.177

-4.821

.000

LogIndProd

.851

.135

.406

6.306

.000

LogMoneySupply

.418

.044

.594

9.431

.000

a Dependent Variable: LogKLCI

The goal of linear regression is to find the line that best predicts Y from X. Linear regression does this by finding the line that minimizes the sum of the squares of the vertical distances of the points from the line. Linear regression does not test whether my data are linear (except via the runs test). It assumes that the data are linear, and finds the slope and intercept that make a straight line best fit our data.

The result from the multiple regressions will be an equation that shows the relationship between independent variable and the factors affecting it:

STOCK Market = C + βIndProd + βMoneySupply + β + ε

STOCK Market = -343.299 – 28.738INF + 10.263IR + 0.01EXR + ε

In order to run the regression test, factors related to each variable are grouped together and analyzed versus independent variable namely Kuala Lumpur Composite Index (KLCI).

R is the multiple correlation coefficients between predictors and performance. R= 0. 927, which is acceptable and favorable. R square shows how much variability in loyalty is accounted by the independent variables. R square= 0.859 shows that 85.9% of the Kuala Lumpur Composite Index (KLCI) is explained by the independent variables i.e. Industrial Production, Inflation and Money Supply.

It tell that 85.9% of total variation in the stock return (KLCI) can be explained by independent variables such as inflation, Money Supply and Industrial Production. The left behind 14.10% cannot be explained by this regression analysis. The factor might due to other important independent variable whichever not being considered in this study.

This result shows that the relationship between stock price and the independent variables has high explanatory power since it is more than 50%.

According to the table of the coefficients of regression model, the sig. column shows that Sig= 0.000, there greater than 99.9% certainty that the difference did not occur by chance . The validity of the data; the data is valid if the sig. amount is less than 0.005, therefore Inflation, Money Supply and Industrial variables are accepted as a predictor.

According to the above findings, the regression model can be presented as follows:

STOCK Market = -2.552 – 0.26INF + 0.851IndProd + 0.418MS + ε

The result indicates high negative correlation between Stock market and independent variables such as Inflation. The negative signs of the Inflation, suggesting that an increase in stock market brings about a decrease in inflation.

INFLATION

Table show that when inflation increases by 1%, the stocks market (KLCI) will decrease by 0.26. That means when the inflation increase, the stock return will decrease. Therefore, there is negative relationship between inflation and stock market.

MONEY SUPPLY

Table show that when money supply increases by 1%, the stocks market (KLCI) will increase by 0.851. That means when the money supply increase, the stock return will increase too. Therefore, there is positive relationship between money supply and stock market.

INDUSTRIAL PRODUCTION

Table shows that when industrial production increases by 1%, the stocks market (KLCI) will increase by 0.418. That means when the industrial production increases, the stock return will also increase. Therefore, there is positive relationship between industrial production and stock market.

T-STATISTICS

T – Statistics is used to determine whether there is a significant relationship between independent variables with dependent variable.

Number of observation = 120

Degree of freedom = n – k – 1

= Number of observation – number of independent variables – 1

= 120 – 3 – 1

= 116

Since the degree of freedom is 116, the critical t-statistics from the t-distribution table is 1.645

Decision rule: –

When observe t – Statistics > critical t – Statistic, reject Ho

When observe t – Statistics < critical t – Statistics, reject H1

INFLATION

Based on the table above, the calculated t-statistics for inflation is 4.821. At 95% confident interval level, the calculated t-statistics more than the t- distribution table, which are 1.645. Therefore, there is significant relationship between inflation and stock market. As a result, H0 will be rejected. The coefficient is said to be statistically significant.

INDUSTRIAL PRODUCTION

Based on the table, the calculated t-statistics for interest rate is 7.522. At 95% confident interval level, the calculated t-statistics more than the t- distribution table, which is 1.645. Therefore, there is significant relationship between industrial production and stock market. As a result, H1 will be accepted. The coefficient is said to be statistically significant.

MONEY SUPPLY

Based on the table, the calculated t-statistics for money supply is 8.860. At 95% confident interval level, the calculated t-statistics more than the t- distribution table, which are 1.645. Therefore, there is significant relationship between money supply and stock return. As a result, H0 will be rejected and H1 will be accepted. The coefficient is said to be statistically significant.

F-STATISTICS

The degree of freedom for numerator ( k – 1 )

= 3- 1

= 2

The degree of freedom for denominator ( n – k )

= 120- 3

= 117

From F-Distribution table, the critical F-Statistics is 3.09.

Decision rule: –

When computed F – Statistics > F-statistics value (f-critical) reject H0

When computed F – Statistics < F-Statistics value (f-critical) reject H1

Since the computed F-statistics (234.696) is more than the critical value of F-distribution table (3.09). Meaning that, the variables measuring performance; Inflation, Industrial Production and Money Supply are significantly affect the stock market. As a result, H0 is rejected and H1 will be accepted.

By consider the results of the data analysis in support of or against the Accepted Hypotheses: research hypotheses,

H1: There is positive relationship between KLCI and Inflation

H1: There is positive relationship between KLCI and Industrial Production

H1: There is positive relationship between KLCI and Money Supply

CHAPTER 5

5.1 CONCLUSION AND RECOMMENDATIONS

Chapter Five draw conclusion and presents recommendations resulting from the study. Through this chapter, I will look at the hypothesis regarding the outcome of the data analysis. Furthermore, critical issues of the study will be discussed as well in order to conduct further researches. Through this chapter, I will summarize the study and point out the conclusion from this research paper.

The findings show that market stocks have positive relationship with macroeconomic variables. Changes in the inflation rate would influence nominal expected cash flows as well as the nominal rate of interest. To the amount that pricing is done in real terms, surprising price-level changes will have a systematic effect, and to the extent that relative prices change along with general inflation, there can also be a change in asset valuation associated with changes in the average inflation rate. Based on the result in my study, I can fight that stock prices probably could react negatively significant to inflation rate.

The findings also show that market stock there is a positive relationship between money supply and stock market, as the coefficient for the actual change in money supply is positive. These results support the real activity theorists’ argument that an increase in money supply increases stock prices and vice versa.

The findings show that an Industrial production has a positively significant relationship between stock market show. Industrial production has stronger dynamic interaction to face the other monetary policy variables. The relationships of macroeconomic variables and share price movements have been studied properly in developed countries.

This has no doubt confirm the existence of the relationship between stock market and macroeconomic variables in Malaysia. However, further studies can be conducted to examine the relationship between macroeconomic variables and various sectors in the Malaysia stock market.