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Contents

- 1.1 BACKGROUND AND HISTORY
- 1.2 PROBLEM STATEMENT
- DEFINITION OF TERMS
- Stock return
- Inflation
- Interest rate
- Money supply
- 1.8 SUMMARY
- CHAPTER 2
- CHAPTER 3
- 3.1 RESEARCH DESIGN
- 3.2 DATA SOURCE
- 3.3 RESEARCH FRAMEWORK
- Stock Return
- Figure 3.1: Research Framework
- 3.4 DATA ANALYSIS AND TREATMENT
- Ri = bi0 + bi1F1i + bi2Fi2 + bi3Fi3 + A¢”šA¬
- Correlation(r) = NAŽA£XY - (AŽA£X) (AŽA£Y) / Sqrt([NAŽA£X2 - (AŽA£X)2][NAŽA£Y2 - (AŽA£Y)2])
- rp = AŽA±p + AŽA²p * rindex
- 3.5 HYPOTHESIS STATEMENT
- 3.6 SUMMARY
- CHAPTER 4
- FINDINGS AND ANALYSIS
- 4.1 MULTIPLE LINEAR REGRESSION MODEL FOR THE YEAR 2000 - 2009
- Ri = bi0 + bi1F1i + bi2Fi2 + bi3Fi3 + A¢”šA¬
- 4.2 THE ANALYSIS
- 4.2.1 Descriptive Statistics
- Table 4.1: Descriptive statistics
- 4.2.2 Coefficients
- Table 4.2: Coefficients
- Ri = 6.185 + 1.993bi1 - 0.21 bi2 - 1.064bi3
- Interest Rate
- Inflation
- Money Supply
- Model Summary
- Table 4.3: Model Summary
- 4.2.3.1 Coefficient of Correlation (R)
- 4.2.3.2 Coefficient of Determination (RA²)
- 4.2.4 F-Test
- 4.3 SUMMARY
- CHAPTER 5
- CONCLUSIONS AND RECOMMENDATIONS

The impact of globalization has caused the transfers of funds and investment activities to be no longer limited in one country only. Those activities are now expanded to all over the world. Based on the stock valuation model, macroeconomic forces may have systematic influences on stock prices via their influences on expected discounted future cash flows. Study by Nil Gunsel and Sadik Cukur et al, (2007) the relations between them may be motivated using the arbitrage pricing theory (APT) model developed by Ross (1986). Various empirical studies focus on industrialized economies; some recent studies have extended the analysis to the cases of developing economies. According to Mansor H. Ibrahim and Hassanuddeen Aziz et al, (2003), these studies identify such factors as industrial production, risk premiums, exchange rate, inflation, interest rate, money supply and so forth as being important in explaining stock returns. The purpose of the present paper is to contribute further to the literature on stock market - macroeconomic factors on the stock return specifically for the case of Malaysia.

Macroeconomics is about understanding the behavior and performance of the economy as a whole and the forces that subsequently shape the business environment. It focuses on employment, inflation, prices, consumption and the output of the whole economy. The significance of economic fundamentals using the arbitrage pricing according text extracted from the Husam Rjoub, Turgut TuA¨rsoy and Nil GuA¨nsel (2009) the arbitrage pricing theory (APT) was propounded by Ross (1976) as a means of relating changes in returns on investments to unanticipated changes in a range of key value drivers for these investments (Kettell, 2001). Therefore, under the APT framework, all investment have "expected returns" and affected by macroeconomic forces/factors (the range of these factors are not specified in the initial theory). APT starts with the assumption that security returns are related to an unknown number of unknown factors (Alexander et al., 2001). However, Roll and Ross (1980) stated four major factors; these are the unanticipated change in the inflation, risk premiums, the terms structure of interest rates and industrial production. Chen, Roll and Roll (1986) (CR&R) examined the validity of the APT in the US securities market. CR&R (1986) analysis used the US macroeconomic variables as proxies for the underlying risk factors that determine the stock returns. They found several of these macroeconomic variables to be significant in explaining expected stock. Studies by Nil Günsel, Sadõk Çukur (2007) extract from the journal had been writing that Chen, Roll & Ross [CRR] (1986) hypothesized and tested a set of macroeconomic data series to explain US stock returns. They investigate the sensitivity of macroeconomic variables to stock returns. They employed 7 macro series; term structure, industrial production, risk premium, inflation, market return, consumption and oil prices. CRR assume that the underlying variables are serially uncorrelated and all innovations are unexpected. In their research, they found a strong relationship between the macroeconomic variables and the expected stock returns. They note that industrial production, changes in risk premium, twist in the yield curve, and measure unanticipated inflation and changes in expected inflation during period when these variables are highly volatile, are significant in explaining expected returns. Their evidence suggests that consumption, oil prices and market index are not priced by the financial market. They conclude that stock returns are exposed to systematic news that is priced by the market. In the context of the Malaysian economy, the Malaysian Central Bank has been highly active in achieving multiple objectives of stable price level, stable exchange rate, sustainable output growth and low unemployment. According to the studies by Mansor H. Ibrahim and Hassanuddeen Aziz (2003), the Central Bank has to shift policy stance. For example, Central Bank shifted to stabilizing the exchange rate in 1986 and allowed the interest rate to increase despite its expansionary stance in 1985 to cope with the recession. This active participation may have intended effects in the short run but generates risk premiums and uncertainty in the long term, prompting a negative relation between money supply and stock prices.

There are several macroeconomic variables will effect stock return in Malaysia. Studies by Nil Gunsel and Sadik Cukur et al, (2007) they examined seven macroeconomic variables which include: term structure of interest rate, unanticipated inflation, unanticipated sectoral industrial production, risk premium, real exchange rate, money supply (M0) and sectoral unanticipated dividend yield. Nowadays, stock return volatility in several manufacturing company presents strong impact in Malaysia manufacturing industry due to the environment of economic fluctuations. Therefore, the researcher interested to study on macroeconomic effects towards Malaysian manufacturing industry stock return. Based on the result on this research, it hopes that can be a guideline and reference to others in order to improve and maintaining a good knowledge especially for the investment and stock return.

1.3.1 General objective To analyze the impact of macroeconomic factor and stock return in Malaysia manufacturing industry focusing in interest rate, inflation and money supply. Specific objective To identify whether there is any effect between stock return due to the uncertainty in inflation. To identify whether there is any effect between stock return due to the uncertainty in interest rate. To identify whether there is any effect between stock return due to the uncertainty in money supply. To find out what type of macroeconomic variable that will mostly affect stock return in Malaysia.

The main purpose of this study, as mention earlier is to analyze the impact of macroeconomic factor and stock return in Malaysia manufacturing industry. Since it would be almost impossible to incorporate every potential aspect, the researcher limits this study to select macroeconomic variables such as inflation, money supply and interest rate all of which are standard variables in the literature. Data selection takes into consideration the availability of data and their consistency. The time horizon of all the required monthly data ranged from 2000 to 2009 stock index from KLCI.

The study conducted is to obtain as much as information as possible to understand the impact of macroeconomic factor and stock return in Malaysia manufacturing industry to identify the set of macroeconomic variables, which correspond most closely with the stock market factors. In this research, researcher predicts a strong relationship between the macroeconomic variables and the expected stock returns. This research is important to the researcher, investor as well to the equity market. It is important for the investor to use the result of the research as a guideline and reference in order to improve and maintaining a good knowledge especially for the investment and stock return.

In conducting this study, the researcher has to make some tradeoffs between time and cost efficiencies with accuracy efficiencies because of some limitations as listed below. A financial constraint due to it is hard to get the data because almost of data need to paid. Since most of the data were used in this study is obtain from the secondary sources, it accuracy and reliability were fully depends on the published materials. An error occurs in the published sources will provide wrong data for this research. The data available on the Data- Stream is also hard to obtain due to the failure of connection and limited subscribes.

In finance, rate of return (ROR), also known as return on investment (ROI), rate of profit or sometimes just return, is the ratio of money gained or lost (whether realized or unrealized) on an investment relative to the amount of money invested. The amount of money gained or lost may be referred to as interest, profit/loss, gain/loss, or net income/loss. The money invested may be referred to as the asset, capital, principal, or the cost basis of the investment. ROI is usually expressed as a percentage rather than a fraction.

In economics, inflation is a rise in the general level of prices of goods and services in an economy over a period of time. In practice, the term monetary inflation is used to specifically refer to an increase in the money supply. When the price level rises, each unit of currency buys fewer goods and services consequently inflation is also erosion in the purchasing power of money - a loss of real value in the internal medium of exchange and unit of account in the economy. A chief measure of price inflation is the inflation rate, the annualized percentage change in a general price index (normally the Consumer Price Index) over time.

An interest is the price a borrower pays for the use of money he does not own, and the return a lender receives for deferring his consumption, by lending to the borrower. It is also rate that is charged or paid for the use of money. An interest rate often expressed as an annual percentage of the principal. It is calculated by dividing the amount of interest by the amount principal.

Money supply is another name for money. In Malaysia, as well as in many other countries, the Central Bank defines money into three categories which is M1, M2, and M3. Money supply can be defined as the amount of financial instruments within a specific economy available for purchasing goods or services. The money supply is usually measured as three escalating categories M1, M2 and M3. The categories grow in size with M1 being currency (coins and bills) and checking account deposits. M2 is currency, checking account deposits and savings account deposits, and M3 is M2 plus time deposits. M1 includes only the most liquid financial instruments, and M3 relatively illiquid instruments. Another measure of money, M0, is also used, although unlike the other measures, it does not represent actual purchasing power by firms and households in the economy. M0 is base money, or the amount of money actually issued by the central bank of a country. It is measured as currency plus deposits of banks and other institutions at the central bank. M0 is also the only money that can satisfy the reserve requirements of commercial banks.

This research motivation is to determine the effects of macroeconomic variables and stock return in Malaysia manufacturing industry and to answer the problem statement that been stated. To get more understanding about what are the aims of this study the next chapter will be explain in more depth in.

Studies by Nil Gunsel and Sadik Cukur et al, (2007) extract from that journal Chen, Roll & Ross [CRR] (1986) hypothesis and tested a set of macroeconomic data series to explain US stock returns. They examine the sensitivity of macroeconomic variables to stock return and employed seven macro series; term structure, industrial production, risk premium, inflation, market return, consumption and oil prices. CRR assume that the underlying variables are serially uncorrelated and all innovations are unexpected. In their research, they found a strong relationship between the macroeconomic variables and the expected stock returns. They explained that industrial production, changes in risk premium, twist in the yield curve, and measure unanticipated inflation and changes in expected inflation during period when these variables are highly volatile, are significant in explaining expected returns. Their evidence suggests that consumption, oil prices and market index are not priced by the financial market. They conclude that stock returns are exposed to systematic news that is priced by the market. Beside that study from Mansor H. Ibrahim and Hassanuddeen Aziz et al, (2003). There are relative few empirical investigations namely, focus on industrialized economies, some recent studies have extended the analysis to the cases of developing economies. An illustrative list of studies for developed economies includes Fama 1981, 1990; Chen et al. 1986; Hamao 1988; Asprem 1989; Chen 1991; Thornton 1993; Kaneko and Lee 1995; Cheung and Ng 1998; Darrat and Dickens 1999. These studies identify such factors as industrial production, risk premiums, slope of the yield curve, inflation, interest rate, money supply and so forth as being important in explaining stock returns. The few notable studies for developing economies include Mookerjee and Yu 1997 and Maysami and Koh 2000 for Singapore, Kwon et al. 1997 and Kwon and Shin 1999 for South Korea, and Habibullah and Baharumshah 1996 and Ibrahim 1999 for Malaysia. On the other hand, Beenstock and Chan [BC] (1988) identified four risk factors - namely, interests' rates, money supply (M3), fuel and material cost, and the retail price index, suggested by the data. They conclude that unanticipated increase in interest rate and fuel and material costs depress security returns. However, unanticipated increase in the money supply and the retail price index raise security returns. They also considered export volume and relative export prices as risk factors, but these were not significant. Clare & Thomas [CT] (1994) conclude that a number of factors have been priced in the UK stock market and are; oil prices, default risk, and the retail price index. UK private sector bank lending, the current account balance and the redemption yield on an index of UK corporate debentures and loans. Priestley (1996) prespecified the factors that may carry a risk premium in the UK 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. Beside that study from Nathan Lael Joseph, Panayiotis Vezos (2006) extract from that journal. A few empirical studies have also examined the sensitivity of FIs' stock returns to foreign exchange (FX) rate changes while others have jointly estimated the impact of FX rate and interest rate changes. Take first the empirical work that focus only on FX rate sensitivity. Here, Chamberlain et al. (1997) report weak evidence of FX rate sensitivity for US banks. Their cross-section regression results show that accounting measures can in fact explain the degree of FX rate sensitivity. Japanese banks do not appear to be exposed to FX rate changes and the degree of sensitivity also appears to vary over time (see also, Harris, et al., 1991). Other empirical studies that jointly estimated interest rate and FX rate sensitivity provide mixed results. Choi et al. (1992) report much stronger evidence of interest rate sensitivity than FX rate sensitivity although the degree of sensitivity varies by bank groups. In contrast, Choi and Elyasiani (1997) report much stronger evidence of FX rate sensitivity than interest rate sensitivity for US banks.Most of the banks in their study exhibited FX rate sensitivity. Wetmore and Brick(1994) found similar results for US banks. They also report that the extent of FX rate sensitivity has increased over time while interest rate sensitivity has decreased. Sill (1995) documents that the industrial production output, T-bill rate and inflation is statistically significant in explaining the US stock market excess returns. In addition, the conditional variance-covariances of the three macroeconomic factors are important drivers of the conditional stock return volatility. Other recent studies include Liljeblom and Stenius (1997), Errunza and Hogan (1998), Kearney and Daly (1998),Cheung and Ng (1998), Aylward and Glen (2000), Hondroyiannis and Papapetrou (2001), Bislon et al. (2001), Patro et al. (2002) and Fifield et al. (2002). In the real estate literature, Kling and McCue (1987) consider the influences that macroeconomic factors have on the USA office construction using vector autoregressive (VAR) models that include monthly office construction, money supply, nominal interest rates and output (GNP).

Interest rate is one of the influential macroeconomic variables according to Nil Gunsel and Sadik Cukur et al, (2007) the value of a stock is directly influenced by the discount rate. It is common accepted that the interest rate risk factor must be included in asset pricing models. However, it may cause problems since interest rates are highly correlated with many other macroeconomic variables. Therefore, term structure of interest rate can be used instead of interest rate. The term structure is measured by the difference between long-term and short-term government interest rates. In other words, the influence of term structure of interest rates can be captured by the return difference between long-term Government Bond and Treasury Bills. The yield spread represents the intertemporal change in the shape of interest rate term structure. Study by Nathan Lael Joseph and Panayiotis Vezos (2006) interest rate risks are important financial and economic factors affecting the value of stock return. There are important reasons why the stock returns of banks can be responsive to interest rate. Firstly, the volatility transfer hypothesis suggests that random shocks can induce higher volatility in financial markets and because of contagion effects which are highest in more volatile markets (see King and Wadhwani, 1990), investors as well as banks may look abroad to invest in alternative financial assets. If international portfolio diversification also results in an increase in the volatility of those returns (see, Eun and Resnick, 1988), then greater exposure to interest rate risks is like to affect the stock returns of banks if indeed such information is impounded into their stock prices.

There is substantial empirical evidence that found an influence of money supply on stock returns for instance, Fama (1981) and Jensen, Mercer and Johnson (1996). Increased nominal money supply leads to a portfolio rebalancing toward other real assets. This upward reallocation results in upward pressure on stock prices. Therefore, stock returns respond to unanticipated changes in nominal money supply. On the other hand, purely nominal increases in money supply may lead to great inflation uncertainty, and could have an adverse consequence on the stock market. Hence, money growth could be regarded as a leading indicator of future inflation, which in turn affects stock returns. Furthermore, increase in money supply leads to a falling in real interest rates. Moreover, firms are faced with lower discount rates against future cash flows, and also respond to increasing income by adjusting their investments so as to generate greater sales and profits resulting in higher future cash flows and higher stock prices. The above economic rationale supporting the linkage between stock returns and money supply is sufficient to include money supply as a relevant economic force that can impact stock returns. In the analysis M0 is used as the monetary aggregate (deflated by the retail price index) not out of any strong belief in a particular form of transmission mechanism but because the M0 series is the longest lasting, reasonably consistent, and most timely reported money supply series. Studies by Husam Rjoub, Turgut TuA¨rsoy and Nil GuA¨nsel (2009) finds that the importance of money supply on stock returns has been found by Fama (1981) and Jensen et al. (1996). The nominal increases in money supply may lead to great uncertainty in inflation and may have an adverse consequence on the stock market. Increased nominal money supply leads to portfolio rebalancing towards other real assets. Stock returns respond to unanticipated changes in nominal money supply. Increase in money supply leads to a drop in real interest rates. So companies face low-discount rate for their future cash flow and also respond to increasing income by adjusting their investments so as to generate more sales and profits, resulting in higher future cash flows and higher stock prices.

Beside interest rate and money supply inflation is one of the influential macroeconomic variables, which has negative impact on economic activity. Several studies provide a negative relationship between real stock returns and inflation. Fama (1981) argues that stock returns are negatively related to inflation because stock returns are positively related to real activity. According to Roohi Ahmed and Khalid Mustafa (2003), the negative relationship between real returns and unexpected components of inflation is more clearly explained in terms of relationship between real returns and inflationary trend. Studies indicate that unexpected output growth has negative and significant effects in real stock. However, anticipated inflation has positive and insignificant impact on stock return. A study by Floros.C (2004) applies various economic methods to examine the relationship between stock return and inflation in Greece. The researcher concludes that there is no correlation between the current value and the past values, and therefore the stock returns and inflation are characterized as independent factors in Greece.

In conclusion, this chapter outlined selected literature studying that very useful for this research. It can guide on what variable that can be used to study the effect of macroeconomic variables on the stock return in Malaysia. With the resources of the previous study it will help researcher to explain more detail in the next chapter.

The purpose of this chapter is to explain about the aspects in the process and design of this study. This involves the data collection method and sample data used in this study as well as the development of the theoretical framework and the hypotheses to be tested during the subsequent data analysis stage, the research design issues relevant in completing this study and also the model used to test the relationship between the dependent and the independent variables.

This research is designed to explore the relationship between dependent and independent variables. This study engages in hypothesis testing that clarify the correlations and relations between macroeconomic variables and the stock return. 3.1.1 Purpose of the study The purpose of this study is to analyze the effects of macroeconomic factors on the stock return in Malaysia. 3.1.2 Types of Investigation This study involved the correlation and regression types of investigation in order to find out the effect of macroeconomic factors on the stock return in Malaysia. 3.1.3 Unit of Analysis In this study, stock return, interest rate, inflation and money supply (M3) are used as unit of analysis based on stock index KLCI. 3.1.4 Time Horizon This study used monthly basis data from year 2000 until end of 2009.

For this research, researcher used secondary data as the source of data. 3.2.1 Secondary Data Secondary data refers to the statistical material which is not originated by the investigator himself but obtained from some one else's records, or when Primary data is utilized for any other purpose at some subsequent enquiry it is termed as Secondary data. This type of data is generally taken from newspapers, magazines, bulletins, reports, journals etc. e.g. if the data published by RBI on currency, National Income, Exports or Imports, is used in some other statistical enquiry, it will be termed as Secondary data. For this study, researcher gathered the secondary data from DataStream, journals (www.emerald.com), internet and articles from World Wide Web (www) and published data sources. Secondary data is the data collected for some purpose other than the problem at hand. Secondary data helped researcher better defined problem, formulate research design and interpret more insightfully.

Research framework is developed for this study to better understand about the effects of macroeconomic factors on the Malaysia stock return.

Independent variable

Dependent variable

Interest Rate Inflation

Money Supply

Figure 3.1 shows the research framework for this study. The independent variables for this study are interest rate, inflation and money supply (M3). The dependent variable of this research is stock return.

3.4.1 Multiple Linear Regression Model The statistical tools use in the study is Multiple Linear Regression Model. This model of analysis is designed to examine the simultaneous effects of three macroeconomic variables on a stock return. In the regression models, stock return is used as dependent variable, while the macroeconomic variables are used as independent variable. In other used this model can explain the correlation between the dependent variable and independent variables.

Where, Ri is the average return and bi: is the reaction coefficient measuring the change in average return for a change in risk factor and Fi is the macroeconomic factor. In the study following factors are employed; F1: Interest rate F2: Inflation F3: Money supply (M3) 3.4.2 Coefficient of Correlation (R) By simple definition, coefficient of correlation (R) is to measure the linear relationship between dependent variable (Y) and independent variable(X). The value of R is always lying between -1 and +1 no matter what the units of X and Y. Its sign (negative or positive) indicates the direction of relationship between variables directly or inversely. The formula as stated below:

3.4.3 Coefficient of Determination (RA²) It is the test of goodness of fit. It is used to determine how well the regression line fits the data. RA² measures the proportion of total variation in the dependent variables. The higher the value RA², the higher explanatory power of the estimated equation and it is more accurate for forecasting purposes. It determines how well that all the regression line fits the data. It is a number ranging from 0 to 1 (1> RA²>0) and it represents the proportion of total variation in the dependent variable that is explained by regression equation. If RA² show the value of 1, it indicates that all the changes in dependent variable used. it shows that there is a strong correlation between dependent and independent variables, but if the RA² show the value of 0, it indicates that the changes of the variation in dependent variable do not explained by the independent variables .

3.4.4 F-Test It is also the test of overall explanatory power of regression. It analyzes he variance; this uses the F-statistics or F-ratio. The F-statistics is used to test various statistical hypotheses about the mean of distribution from which a sample or a set of sample has been drawn. If the calculated F-value is higher, it shows there is significant effect between the independent and dependent variables.

A hypothesis is a proposition that is stated in testable form and tries to forecast a relationship between two or more variables. Some statement created in the hypothesis can be either supported or rejected through research. Hypothesis 1: H0: There is a no relationship between the level of interest rate and stock return. H1: There is a positive relationship between the level of interest rate and stock return. Hypothesis 2: H0: There is a no relationship between the level of inflation and stock return. H1: There is a positive relationship between the level of inflation and stock return. Hypothesis 3: H0: There is a no relationship between the level of money supply and stock return. H1: There is a positive relationship between the level of money supply and stock return.

This chapter presents the research design that will be used in this study. This study aims to determine the relationship between the independent variables and dependent variable. This research will be done in accordance to the objective where there is any significant correlation between independent and dependent variables. This information was perhaps can be useful by the investors, industry and other financial institutions during the investment decisions to be making. Since study focuses on the data from 2000 until 2009, it would give a better picture on the decision result.

As mentioned earlier, the objectives of this study is to analyze the effects of macroeconomics factors and stock return in Malaysia manufacturing industry. This chapter summarizes the empirical findings as well as the interpretation of the result. The review of the result obtained from the empirical methods used for the study. The findings and analysis of the research been analyzed by using the Microsoft Excel and Statistical Package for Social Sciences (SPSS).

The multiple regression analysis has been adopted for the investigation of this study. It is a statistical method for studying and evaluates the relationship between a dependent variable and two or more independent variables. The SPSS output for the multiple linear regression is the result of coefficient value of interest rate, inflation and money supply (M3) towards stock return in Malaysia. The regression model is expressed as a log linear equation as follows: Model Equation:

The above equation is an equation for multiple regression models and can be explained as the following: bi: The reaction coefficient measuring the change in portfolio returns for a change in risk factor. F1: Interest rate F2: Inflation F3: Money supply (M3) Fi is the macroeconomic factor. The coefficient of a bi0 is a called the y-intercept: it is the value of y (according to the regression line) when x is equal to zero, while the coefficient of F1, F2, and F3 are the slope of the regression line. Their numerical values give the changes in dependent variable, y (either positive or negative).

Average Stock Return Interest Inflation Money supply (M3) Mean 1.5648 0.8013 2.1971 5.8012 Variance 0.0560 0.0020 3.2410 0.0130 Minimum 1.0368 0.6839 -2.4000 5.6382 Maximum 2.2194 0.8910 8.5000 6.0075 Std. Dev. 0.2365 0.0463 1.8002 0.1155 Table 4.1 provides a summary of the descriptive statistics of the dependent and independent variables. The table indicates the average values of the stock return from the ten years financial statement. The average stock return measured by mean reported as 1.5648 with standard deviation 0.2365. From the table shows that mean for interest rate is 0.8013 with standard deviation 0.0463, furthermore the mean for the inflation is 2.1971 with standard deviation 1.8002 and for the money supply (M3) reported that mean for M3 as 5.8012 with standard deviation 0.1155. From the result researcher indicate that inflation rate get the highest standard deviation meanwhile money supply show the highest mean.

Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 6.185 1.120 5.523 .000 INT 1.993 .360 .390 5.537 .000 INF -0.21 .006 -.162 -3.365 .001 M3 -1.064 .152 -.519 -7.010 .000 From the above table, this study concludes that the result can be explained by the following equation:

Based on the above equation, in general we can see that only the interest rate has positive correlation with the independent variable. Meanwhile, the others independent variable money supply (M3) and inflation show a negative correlation with independent variable. Below is the interpretation about the relationship for each dependent variable with independent variable:

H0: There is a no relationship between the level of interest rate and stock return. H1: There is a positive relationship between the level of interest rate and stock return. The coefficient value of interest rate is +1.993. This value of coefficient indicates that every one percent increase in interest rate, the dependent variable that is stock return expected to increase by 1.993 percent assuming that other variables remain constant. Due to the positive value, it indicates that there is appositive relationship between interest rate and stock return. Thus, any increase in the interest rate will directly increase the stock return and vice versa. With p-value at 0.001, which is less than 0.005, this study rejects the null hypothesis and accepts the alternate hypothesis which explained that, there is a significant relationship between stock return and interest rate.

H0: There is a no relationship between the level of inflation and stock return. H1: There is a positive relationship between the level of inflation and stock return. The result shows that the coefficient value for inflation is negative. It means that there are negative relationship between inflation and stock return in Malaysia market. The value of this coefficient is -0.2. This value indicates that, for every one percent increase in inflation, stock return will decrease by 0.21 percent assuming that other variables are constant. Since the p - value is 0.000, which mean less than 0.005 (5 percent level of significance), this study rejects the null hypothesis and accepts the alternate hypothesis which explained that there is positive relationship between stock return and inflation rate.

H0: There is a no relationship between the level of money supply and stock return. H1: There is a positive relationship between the level of money supply and stock return. For the money supply, the result shows that the value for this independent variable is negative. The coefficient value for this variable is negative. The coefficient value for this variable is -1.064. It means that there are negative relationship between money supply and stock return in Malaysia market. For every one percent increase in money supply, stock return will decrease 1.064 percent assuming that other variables are constant. The p-value for money supply is same with p-value for inflation that is 0.000. With p-value less than 0.05 (5 percent level of significance), this study shows that there is significant relationship between stock return and money supply.

R R Squared Adjusted R Squared Std. Error of the Estimate F Change Sig. F Change 0.897 0.805 0.800 0.106 158.053 0.000 According to the table 4.3, the result is explained as follow:

The function of coefficient relation is to measure the linear relationship between dependent variable (Y) and independent variable (X). The value of R is always lying between -1 and +1 no matter what the units of X and Y. From the output result, the correlation coefficient (R) is 0.897, indicating a very strong correlation exists between the stock return with macroeconomics variable. Therefore, if any changes happen on macroeconomic variables, it will give strongly effect towards stock return in Malaysia.

This is the test of goodness of fit. It is used to determine how well the regression line fits the data. RA² measures the proportion of total variation in the dependent variables. The higher the value RA², the higher explanatory power of the estimated equation and it is more accurate for forecasting purposes. From the table, it shows that RA² is 0.805. This value means that about 80.50% of the variation in dependent variable is explained by the independent variable namely interest, inflation and money supply (M3). Another 19.50% of the variation could be determined by the other factors.

Model Sum of Squares df Mean Square F Sig Regression 5.313 3 1.771 158.053 0.000 Residual 1.289 115 0.110 Total 6.602 118 Anova is a collection of statistical models. The test statistics for Anova is well explained by the F test which analyzes the variance. F-statistics is used to test various statistical hypotheses about the mean of distributions from which a sample or a set of sample has been drawn. F-test measures how well a linear model fits a set of data to know the significant of the whole model. If the calculated F-value is higher, it shows there is significant effect between the independent and dependent variables. The null hypothesis (H0) and alternate hypothesis (H1) for the F-test are as follow: H0 : There is no relationship between stock return and macroeconomic variables. H1 : There is a relationship between stock return and macroeconomic variables. From the result, it shown that the F-value is 158.053 and it is significant at 0.000 where it is less than 0.05 or 5 percent level of significance therefore null hypothesis is rejected at 5 percent significance level. By accepting the alternate hypothesis, it shows that all the macroeconomic variables have significant relationship with the stock return.

The findings of this study counter all questions in problem statement that have been developed earlier. Based on the result, it shows that all the selected macroeconomic variables that are used as independent variable in this study have a significant effect or significant relationship with the dependent variable. This is supported by the significance value or p-value for each independent variable which is less than 0.05 (5 percent level of significance). Besides that, result from SPSS output about coefficient of correlation proves a strong correlation between independent variable and dependent variable. In this study, the finding shows that only the interest rate has positive relationship with stock return in Malaysia. This result is consistent with the studies by Kim Hiang Liow, Muhammad Faishal Ibrahim and Qiam Huang (2005) which found that higher interest rate will increase the income to investors in money market funds and then in turn to stimulate the economy and stock market. Moreover, according to the result, inflation and money supply have negative relationship with the stock return. This result is consistent with Fama(1981), Roohi Ahmed and Khalid Mustafa (2003) and Floros.C (2003), the reason for this circumstance is because inflation decrease the value of money, which ultimately effect on investment activities. Besides that according to the study by Kim Hiang Liow, Muhammad Faishal Ibrahim and Qiam Huang (2005) there have economic rationale to include money supply as a relevant macroeconomic factor. First changes in money supply will change the equilibrium position of money, thereby altering the composition and price assets in an investors' portfolio. Second changes in money supply may impact on real economic variables and having a lagged influence on stock returns. Both of these mechanisms suggest a positive relationship between changes in money supply an excess returns on stocks. However, increase in money supply may also give to greater inflation uncertainty and thus can have an adverse on real market. Finally, among the selection of macroeconomic variables that are tested in this study, the result was revealed that interest rate give higher impact on the stock return in Malaysia. This have been proved by the result that are showed in the SPSS output. The result shows that every one percent increase in interest rate, the dependent variable that is stock return expected to be increased by 1.993 percent, assuming that other variables remain constant. In summary, the result from this chapter can give clear pictures for researcher to make conclusions and recommendations at the next chapter.

The main objective of this research is to study the effect of macroeconomic variables and stock return in Malaysia manufacturing industry. From the research, the researcher needs to find out the significant correlation and relationship between independent variable (interest rate, inflation and money supply) with the dependent variable (average stock return). Since the study is taken by referring the previous studies, therefore all the indicators or variables selected are strongly believed that the independent variables have a relationship between stock return. This study assists the researcher to improve general knowledge on the issue related to changes in economic variable. The function of regression analysis is to measure the data. Since the data are empirical data these methods are suitable to test and analyze the significant relationship between the variables. The raw data collected is processed using the Statistical Package of Social Science (SPSS) program. According to the analysis and findings in chapter four, it is proved that all the variables have strong relationship among them. Moreover, all selected macroeconomic variables have a significant effect and significant relationship with the dependent variable (average stock return). From the results only interest rate has positive correlation or relationship with stock return. Meanwhile, the others independent variable (inflation and money supply) shows a negative relationship with the stock return in Malaysia. This study also found that interest rate give higher impact on the stock return in Malaysia. The more uncertainty in interest rate will cause higher volatility on stock return in Malaysia market. To prove the result obtained, according to Husam Rjoub, Turgut TuA¨rsoy and Nil GuA¨nsel (2009) there is a significant pricing relationship between the stock return and the tested macroeconomic variables; namely, unanticipated inflation, term structure of interest rate, risk premium and money supply have a significant effect in explaining the stock market returns in various portfolios. However these results shown a weak explanatory power based on the findings because through their research it has other macroeconomic factors affecting stock market returns in Istanbul Stock Exchange other than the tested ones.

In order to get substantial result, there are few recommendations that can be considered for the next research. Below are the recommendations: Extent the time frame - It is recommended to the other potential future researchers to construct a detailed study by adding more years such as a 15 years or 20 years time period. This may color up and provide attractiveness of potential future researcher's findings of study. Add more macroeconomic variables- The potential future researchers may include the other types of macroeconomic variables as the independent variable such as Growth Domestic Profit (GDP), risk premium, foreign exchange and many others. By investigating more variables, future researchers may find out other new impact on this study. Extent the study to the other Asian countries - By implementing a comprehensive study in other Asian countries such as Singapore and China, the research will be more attractive and valuable because the results will be more accurate and approximately shows the real economic pictures. Use various methods - It is suggested to the future researcher to use other methods on this study such as time series analysis such as exponential smoothing, fast Fourier transformations and seasonal decomposition. By using that analysis, the researcher may see the trend of the previous, current even future economic circumstances.

Understand the impact of macroeconomic factor. (2017, Jun 26).
Retrieved November 12, 2024 , from

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