The Impact Of Macroeconomic Variables

Published Date: 02 Nov 2017

Abstract

This study investigates the impact of macroeconomic variables on stock returns of non financial firms of KSE 100 index. The macroeconomic variables used in this research are interest rate, Exchange rate and Inflation rate. The data period start from July 2005 to Dec 2012. A multiple regression is used to test the affect of macroeconomic variables on stock returns. Three tests i.e. Autocorrelation, Hetrosedasticidity test and Multicollinearity test were conducted to check that the model applied was perfect or not.

The research identified that inflation and interest rate negatively affect stock returns while exchange rate affects on stock returns are positive. The relationship of inflation and exchange rate are significant in nature while interest rate it is insignificant.

Table of Contents

Chapter 1

INTRODUCTION

1.1 Background of the study

The stock market is a mirror of an economy. The Karachi stock exchange (KSE) was established in 1947. The KSE100 Index was introduced in November, 1991. The KSE-100 Index consists of 100 companies. These companies are selected on the basis of market capitalization and sector representation. These companies cover nearly 80 percent of the total market capitalization at Karachi Stock Exchange. The Karachi stock market remained very impulsive for the last sixty months.

In a period from 2004 to 2012, three financial disasters occurred. First, KSE100 index dropped nearly fourteen hundred points in the first quarter of the year 2005. Secondly, stock market was crashed in June 2006 when KSE100 index lost fifteen hundred points abruptly. In the last nine months of the year 2008, highly intensive crash was also occurred. In this period, KSE100 index lost ten thousand points. The Board of Directors of Karachi stock exchange determined to place a floor in August 2008 which was removed in December, 2008. The major basis of this volatility was political uncertainty and instability for this disaster in the stock market. Hold of speculators and bad governance in the stock market played vital role in first two crises.

During Last two years, the performance of KSE 100 index is remarkable. It was declared best emerging market for the financial year 2011-12. At the end of year 2012, it reached to historic highest level 16,573 points.

Therefore, it is important to study the impact of macroeconomic indicators on stock returns.

1.2 Literature Review:

Normally, macroeconomic indicators like interest rate, exchange rate and inflation rate tends to shape the stock market. Their impact may be positive or negative. All the studies found contrasting and unexpected results about macroeconomic indicators affecting the stock returns. Mainly, it is due to different nature of the country’s economy under study. Most researchers agreed that interest rate has got negative impact on the stock returns e.g. in Malaysia it has negative relation but in Japan’s economy it has got positive effect. The exchange rate is other way around but in Nigeria it has got negative relationship. Lastly, Inflation is in direct relation with the economy growth which occurs because of increase in demands of public for the goods which is also called derived demand which helps recession to move up.

Very few studies such as Farooq & Keung (2004), Nishat & Shaheen (2004) were conducted in Pakistan. It is therefore, seemed significant to begin such a study keeping in view of the volatility of KSE. The aim of the paper was to see the impact macroeconomic indicators on KSE 100 index.

1.3 Purpose of the study:

The purpose behind this research is to inspect the affects of interest rate, Exchange rate and Inflation Rate on stock returns of the firms enlisted on Karachi Stock Exchange (KSE) 100 index.

1.4 Research hypothesis:

H0: Macroeconomic indicators affect Stock Returns

H1: Macroeconomic indicators affect do not Stock Returns.

1.5 Research Methodology:

To explore the affects of macroeconomic factors on from KSE 100 Index. A Multiple Regression Model is used in this study.

To check whether the model is perfect or not, it is followed by three tests which are Autocorrelation, Hetrosedasticidity test and Multicollinearity test.

1.6 Data Collection:

This study is purely based on secondary data. To get the data Internet Sources are used. For KSE 100 Index, website Karachi Stock Exchange and Berecorder are used. On the other hand, Macroeconomic indicators State Bank of Pakistan and economic survey of Pakistan are used.

As Karachi Stock Exchange (KSE) 100 index as our area of consideration, as top hundred companies of Pakistan are listed in KSE 100 index.

As seven years monthly data is taken, the sample size of 85 observations is made.

1.7 Variables of the Study:

From the topic under study, we will find the effect of Macroeconomic indicators on return on stocks, so:

Interest rate (KIBOR), Exchange rate and Inflation Rate as Independent Variable.

KSE 100 index as Dependant Variable.

1.8 Scope of study:

As KSE 100 index is the barometer of the economy and it is dynamic in nature, seven years monthly data from July 2005 to August 2012 will be examined to see the affects of macro-economic factors i-e inflation, Interest rate and Exchange rate on stock returns of firms.

Chapter 2

Literature Review

It has been observed and understood that macroeconomic variables like interest rates and money supply contributes a lot in the movement of stock prices and it has been widely accepted phenomenon but pragmatic studies for the verification of these theories originally started in 1980’s.

The reports of different literature also connote that there should be wide range of data to examine the effects of interest rates on stock returns because the higher the frequency of data to be examined the more valid the research becomes.

A study conducted on Jakarta stock exchange by Gupta, et al. (2000) examined the relationship between interest rate, exchange rate and stock prices this study was conducted for a time span of five years (1993-1997) which was further divided in to three sub periods from the periods under consideration the results came out to be random as there was irregular relationship between stock prices and interest rate and weak unidirectional relationship between exchange rate and stock price the overall evidence failed to establish any consistent relationship between any of the economic variables under study.

Chaudhuri & Koo (2001) examined that which factor have more significant affect on stock returns in selected country’s stock markets and they found that stock return volatility is affected by independent variables. In Thailand, Malaysia and South Korea stock returns are highly affected by government expenditure therefore investors have to consider about government expenditures. The finding of their research also shows that Asian stock markets depend commonly on each other.

In a research paper Novak (2001) explains that if beta is combined with interest rate then there is a strong effect on stock returns. Earlier researches have shown that there is a strong effect of interest rate and exchange rate on stock returns which play a vital role in determining the share prices in financial firms.

Apergis & Eleftheriou (2002) argues that there are many studies which shows that there is negative effect of interest rate and stock prices i.e. when interest rate increase the stock listed in stock exchanges decreases and vice versa but there are also many scholars which addresses this issue other way round, that the interest rate have a positive impact of stocks.

In theory, the stock prices and interest rates are very much interrelated to each other which is the primarily choice for the investors to make their portfolio. There is a negative relation between interest rates and stock prices which means that investor predicates that if there is increase in interest rate, stock price will decrease and vice versa. This relation is already proven by Paul & Mallik (2003),Nasseh & Strauss(2004),McMillan(2005)and Jayaraman & Puah( 2007).

Chong & Koh (2003) explains that the effects of interest rates can also be demarcated upon stock prices and its return which is supported by the efficient market hypothesis which suggests that there are always profit maximizing investors in market, and for competing with each other they tries to ensure that all the relevant information related to changes in macro economic variables are reflected in current stock prices. This is important for investors because it restrict them from earning abnormal profit by the help of calculated future stock market movements

Nishat & Shaheen (2004) in their research tries to explain that by testing the relation among KSE index and interest rate, and GDP by using quarterly data from 1973 – 2004 and applied the VECM (Vector error correction model). In this study they observed that five factors are connected and two interactions in the long run is present between the selected forces. They also analyzed that GDP has positive effect on stock prices but the affects of inflation on value of stock is negative in Pakistan.

In a research, Maysami et al (2004) observed that the mostly industrialized countries macroeconomic variables like industrial production, interest rate and exchange rate have immense influence of on stock market Having this as a basis many researchers are now turning their attention to study the similar relationships in developing countries especially countries known as growth engine of Asia. Furthermore an investigation shows that policy makers must be really careful when trying to stabilize the economy by editing in macroeconomic variables because by trying to correct the inflation or unemployment they might not knowingly depress stock market, and curtail capital formation which itself would contribute to slow down of the economy. In this study VECHM was used to estimate the co integration vectors. The study concluded that due to co integrating relationship between macroeconomic variables and stock prices, the overall behavior of stock market may be predicted and the policy makers may redesign economic policy if their desire of affecting the stock prices has not been achieved. They also suggested that some indices are affected by all macroeconomic variables while others are affected by selected macroeconomic variables.

Wong, Khan and Du (2005) conducted a study on long run as well as short run relationships between the major stock indices of Singapore and United States and also considered some macro economic variables such as money supply, and interest rates by means of time series analysis. The overall economy affect interest rate and prices and ultimately influence stock prices. They found that before 1997 Asian financial crises, the Singapore stock market where co integrated with interest rate and money supply and this trend was vanished after the crises, they also use the granger causality test to study the systematic casual relationships.

According to Erdem et al (2005), they examined the effect of economic variables on Istanbul stock exchange. The method they used was exponential generalized E-GARCH in modern terms. They used the model to find unvaried volatility spillovers for economic variables. They concluded their research by finding a strong unidirectional volatility spillover from interest rate, inflation to all stock price indexes. They further elaborate that there is a negative volatility spillover between inflation and stock exchanges, but they found a positive spillover between interest rate and stock exchanges indexes. They also pointed out that there exists a volatility spillover in emerging markets.

According to Cifter et al. (2008), they performed a study to examine the impact of changes in interest rates on stock returns in turkey by using wavelet analysis with granger casualty test the result of the study showed that the effect of interest rate on stock price is negative and using this empirical result the Turkish stock returns can be forecasted.

Léon ( 2008) argues about the effect of interest rate volatility on stock returns and volatility. He used Two GRACH models in their study, one comprising of without interest rate and second includes interest rates in conditional means and variables. The results of this study show that interest rates have a significant impact and predictive power on stock returns in Korea, and a weak predictive power for volatility. This study also stress on policy implication for investors. It suggested that Investors may adjust their investment through taking monetary policies as a mean and paying attention on it.

Recent research (Vardar, et al. 2008) has presented a study on Istanbul stock exchange and investigated the impact of interest rate and exchange rate on different sectors. The study shows that the arrivals of interest and exchange rate information significantly affect indices in Istanbul stock exchange which ultimately affect the stock prices. The investor is always concerned about the expected profit over the time horizon. This expectation can be calculated by interest rates existing in the current market. The expected returns have significant effects on real returns so once an investor has expected the return he should also take into account the volatilities in the interest market and then decide in their capital market. Further the study also elaborate that the returns in stock investment should be related to interest variations because the higher the holdings are in capital market, the higher the effects on it. This study was tested by using wavelet analysis with Granger causality tests for the complexity of capital market and nonlinearities in stock returns

In a research Alam & Salahuddin (2009) state that in 15 developed and underdeveloped countries they examined the impact of fluctuation in interest rate on share prices and monthly data was used from Jan 1988 to Mar 2003. They identified that in both developed and underdeveloped countries the resulting affects on share prices by the interest rate is mixed. In Japan the relation between stocks prices and interest rate is positive while in Malaysia share prices and the interest rate is not associated and stock prices of South Africa, Bangladesh, Colombia and Italy are inversely related with interest rate. They founded that except Philippine in all selected countries the relation between share prices and fluctuation in interest rate is inverse

Hussainey & Le Khanh Ngoc, (2009) conducted a study in Vietnam to analyze the impact of macroeconomic variable on Vietnam stock exchange elaborates with the help of methodology introduced by Nasseh and Strauss (2000) found that macroeconomic indicators(foreign and domestic) has an impact on stock prices of Vietnam. The findings of this study included that the industrial production has a positive effect on Vietnamese stock prices, as long as interest rate is concern long and short term interest rate not affecting the stock prices in the same direction.

According to Alam & Ghazi (2009), the result varies from country to country; to find the inefficiency of market they used time series and panel regressions. After this testing they found that interest rate and share prices for many countries were negatively correlated more over fluctuation in interest rate and share prices is also negatively correlated for six countries. By these findings one can say that interest rate is the key variable for many countries and if the interest rate is controlled in these countries, stock exchange can be benefited as people will start investing in share market, and companies will start extensional investments.

Summary

All the studies found contrasting and unexpected results about macroeconomic indicators affecting the stock returns. Mainly, it is due to different nature of the country’s economy under study. Normally, macroeconomic indicators like interest rate, exchange rate and inflation rate tends to shape the stock market. Their impact may be positive or negative. Most researchers agreed that interest rate has got negative impact on the stock returns e.g. in Malaysia it has negative relation but in Japan’s economy it has got positive effect. The exchange rate is other way around but in Nigeria it has got negative relationship. Lastly, the inflation rate has got negative effect on stock returns

Very few studies such as Farooq & Keung (2004), Nishat & Shaheen (2004) were conducted in Pakistan. It is therefore, seemed important to begin such a study keeping in view of the volatility of KSE. The aim of the paper was to see the impact macroeconomic indicators on KSE 100 index.

Chapter 3

Methodology

3.1 Sources of data:

As the research is secondary in nature so the data is gathered from different websites.KSE 100 index data is taken from Karachi Stock Exchange website and KIBOR rates is taken from State Bank of Pakistan website.

3.2 Data Period:

This research includes data from KSE100 index, Interest rate (KIBOR rates), Exchange rate and Inflation Rate of 7 years on monthly basis. The period ranges from June, 2005 to August, 2012.

3.3 Variables:

Four variables are used in the research, we will find the effect of Interest rates, Exchange rate and Inflation Rate on return on stocks (KSE 100 Index), so:

3.3.1. Interest rate (KIBOR rates), Exchange rate and Inflation Rate as Independent Variable.

3.3.2 KSE 100 index as Dependant Variable.

3.3.1. Interest Rates (KIBOR):

KIBOR means Karachi interbank offer rates which are defined as lending/borrowing rates quoted by the banks. The banks under this arrangement quote these rates at specified time i.e. 11.30 AM at Reuters. Currently 20 banks are member of KIBOR club and by excluding 4 upper and 4 lower extremes, rates are averaged out that are quoted for both ends ie offer as well bid. The quote rates available in KIBOR ranges from one week to 3 years. KIBOR is used as a benchmark for corporate lending rates.

Interbank Rate in Pakistan reduced to 9.19 % in Jan of 2013 from 9.28 % in Dec of 2012. Interbank Amount in Pakistan is reported by the State Bank of Pakistan. Traditionally, from 1991 until 2013, Pakistan Interbank Rate averaged 10.50 Percent achieving an all time high of 17.42 Percent in May of 1997 and a record low of 1.21 Percent in July, 2003.In Pakistan, the interbank rate is the rate of interest charged on short-term loans made between banks. This figure below shows historical data for Pakistan Interbank Rate.

pakistan-interbank-rate.jpg

Source: Pakistan Bureau Of statistics

3.3.2 KSE 100 Index:

Karachi Stock exchange 100 index is stock index acting as a standard to compare prices on Karachi Stock exchange over a period of time. The company with highest market capitalization is selected. In this index high market capitalization from each sector is also included. The index was launched in Nov.1991 with base point of 100 points. By 2001, it had grown to 1770 points and reached 12,285 in Feb 2007.A day before ie 26 December 2007,former Prime Minister Benazir Bhutto was assassinated, it was on record high ever 14,814 points. During Global Crisis 2008 it decreased to 9,187 points. It reached to record highest level on November 7, 2012 which is 16,218 points which is now taken as emerging market in Asia. This figure below shows historical data for Pakistan Karachi Stock Exchange.

pakistan-stock-market.png

Source: Karachi stock Exchange

3.3.3 Exchange Rate:

Exchange rate is the value of one currency in terms of another currency. Exchange rate may positively or negatively affect stock return depending on the economy of the country.

Historically, from 1988 until 2012, the USDPKR averaged 58.91 reaching an all time high of 98.11 in December of 2012 and a record low of 4 in May of 2010. It is calculated on daily basis. This figure below shows historical data for Pakistan Exchange rate.

pakistan-currency.png

Source: State Bank of Pakistan

3.3.4 Inflation Rate:

Inflation is increase in prices of goods and services due to which people will buy smaller amount of goods with the same amount of money. Inflation negatively affects stock returns because profits of the firms’ decreases with increase in price of goods due to increased costs.

In Pakistan, Inflation Rate was recorded at 7.9 percent at the end of the year 2012. It is reported by Pakistan Bureau of Statistics. This figure below shows historical data for Pakistan Inflation Rate.

pakistan-inflation-cpi.png

3.4 Statistical Tool Used in Research

3.4.1 Multiple Regression Model:

Multiple regression analysis is a statistical tool for understanding the relationship and their impact between two or more variables. It involves two variables the dependent variable which is to be explained and independent variable which is the additional explanatory variables that are thought to produce or be associated with changes in the dependent variable. Usually, it has two or more independent variables.

3.4.2 Model of the Research:

To check the effects of macroeconomics variables on Stock Returns we use a "Multiple Regression Model".

Where,

R= KSE 100 Index.

ITR= Interest Rate (KIBOR).

IFR= Inflation Rate.

ER= Exchange Rate.

3.4.3 Components of the Multiple Regression Model:

3.4.3.1 Coefficient of Determination or R-square (R2):

It is statistic value which shows the prediction of future outcomes on basis of other given information. Normally, its value ranges from 0 to 1.0. R2 value if it is closer to 1 it means that data is fits the regression line well and vice versa. It measures the percentage of the variation in the dependent variable produced by independent variables. R-square is the most commonly used measure of goodness-of-fit of a regression model.

3.4.3.2 Standard Error of the Coefficient; Standard Error (se):

It is the measure of the variation of a parameter estimate or coefficient about the true parameter. The standard error is a standard deviation that is calculated from the probability distribution of estimated parameters.

3.4.3.3 Statistical Significance:

A test used to calculate the degree of association between a dependent variable and one or more independent variables. If the calculated p-value is smaller than 5%, the result is said to be statistically significant (at the 5% level). If p is greater than 5%, the result is statistically insignificant (at the 5% level).

3.4.3.4 T-statistic:

A test statistic that describes how far an estimate of a parameter (it is a characteristic of population) is from its hypothesized value (i.e., given a null hypothesis).If a t-statistic is sufficiently large (in absolute magnitude), an expert can reject the null hypothesis.

3.4.3.5 P-value:

It is also known as calculated probability. P-value is the estimated probability of rejecting the null hypothesis. The larger the p-value, the more likely the null hypothesis is true.

3.5 Problems in Regression Model

The following problems may arise in regression model. These problems should be removed in order to make the regression model more perfect:

Autocorrelation.

Hetrosedasticidity.

Multicollinearity.

3.5.1 Autocorrelation

Autocorrelation is also sometimes called "lagged correlation" or "serial correlation", which refers to the correlation between members of a series of numbers arranged in time. Positive autocorrelation might be considered a specific form of "persistence", a tendency for a system to remain in the same state from one observation to the next.

Autocorrelation is usually found in time-series data. Time-series data are usually homoscedastic in nature.

It can be found in both cross sectional data and time series data. In cross sectional data modeling, data drawn from one region may reflect the characteristics of the neighboring regions, it is known as spatial auto correlation.

Autocorrelation in economic time series is a reflection of the culture and institutional traditions of the populations which produced during the series, In other words, what people did in the past would affect their present and future. In short, Autocorrelation would occur if the model is not correctly specified.

3.5.1.1 Effect of Presence of Autocorrelation in Data

The presence of autocorrelation does not cause bias in the estimation of the model coefficients, but it reduces the efficiency of a model for forecasting because it increases the variance of the residuals as well as the variance of the estimated co efficient. As they both are inversely related to each other so increase in variance will reduce the efficiency of the model.

3.5.1.2 Test for the Detection of Autocorrelation:

There are several statistical tests available for detecting the autocorrelation in a model. Most often, the following two tests are used:

The Visual Test (Residual Plot):

Residual is normally defined as the difference between the actual and predicated values of dependent variables. The standard error of the estimate is the standard deviation of the residuals

A residual plot is a graph that depicts the residual values on vertical axis and the independent variable on horizontal axis. If the points in plot of residuals are randomly dispersed around horizontal axis or it does not exhibit any systematic order or any pattern then there is autocorrelation in the model.

The Durban Watson Test:

Most computing software like E-Views, Gretel and SPSS calculate the value of Durban Watson Test automatically but Microsoft Excel does not. The d-test is more powerful for models based on large samples than small samples. Some authors suggest that as long as D- test value is less than 2.5 and greater than 1.5 null hypothesis should accepted. In other words, the model used is free from Autocorrelation. Normally, the number of observations should more than 30.

3.5.2 Heteroscedasticity Test:

"Hetero" means unequal and "scedasticity" means spread (variance) so the word Heteroskedasticity is the unequal distribution of residuals. As from the plot of the residuals it is not in the systematic manner, it is plotted in unsystematic manner. The test shows that there is no heteroscedasticity in the data. The opposite of heteroscedasticity is homoskedasticity.

Heteroscedasticity arises in volatile high-frequency time-series data such as daily observations in financial markets and in cross-section data where the scale of the dependent variable and the explanatory power of the model tend to vary across observations. Microeconomic data such as expenditure surveys are typical. The disturbances are still assumed to be uncorrelated across observations.

In forecasting modeling, an inefficient model would have larger forecast errors than an efficient model, therefore it is not good to have heteroscedasticity in the model. It can be easily seen form the distribution of residuals.

3.5.2.1 Causes of Heteroscedasticity:

There are many causes of heteroscedasticity. The following are probably most common:

Where database of containing large value and the other containing small value i.e. the range between the smallest and the largest value is very large.

Where the degree of growth rates between the dependent variable and independent variable vary significantly. It is mostly common in time series data modeling.

It also occurs where data is heterogeneous. E.g. income level data vary significantly from people to people, &at response to certain product, high income views will different from that of low income level.

3.5.2.2 Effect of Heteroscedasticity

The main impacts of heteroscedasticity are

It does presence does not make the coefficient estimates biased but it causes the variances to increase of OLS estimates to increase. It means that in repeated samplings, the estimated co efficient will fluctuate more widely than they normally do.

Its presence causes the underestimation of the variances of the coefficients. This could invalidate the t and f test which will mislead the modelers to reject the null hypothesis, when it should be accepted.

As the variances increases, the efficiency of the models decreases. So the forecast of the model would not be accurate.

In any regression model it is good to have unbiased estimates but also efficient estimates. Therefore, it is not good to have heteroscedasticity in a model.

3.5.2.3 Test for the Detection of Heteroscedasticity:

The Visual Test (Residual Plot):

A residual plot is a graph that depicts the residual values on vertical axis and the independent variable on horizontal axis. If the points in plot of residuals are randomly dispersed around horizontal axis or it does not exhibit any systematic order or any pattern then there is Heteroscedasticity in the model.

3.5.3 Multicollinearity

Multicollinearity occurs when two or more predictors in the model are correlated. In other words, when there is an exact or nearly exact linear relation among the independent variables.

Multicollinearity is not a problem if the goal is simply predict Y from a set of variables. The overall result will still be accurate and R2 quantifies how well the model predicts the Y value. It becomes a big problem if the goal is to find impact of set of independent variables (X) on dependent variable (Y). Then two problems would arise:

Firstly, p-values would be misleading. It means that a p-value will high even if the variable is important.

Secondly, the confidence intervals on the regression coefficients will be very wide. The confidence intervals may even include zero, which means one can’t even be confident whether an increase in the X value is associated with an increase, or a decrease, in Y. Because the confidence intervals are so wide, excluding a subject (or adding a new one) can change the coefficients dramatically and may even change their signs.

3.5.3.1 Sources of Multicollinearity

There are four primary sources of Multicollinearity:

The data collection method employed

Constraints on the model or in the population.

Model specification.

An over defined model.

3.6 Research hypothesis:

H0: Macroeconomic indicators affect Stock Returns

H1: Macroeconomic indicators affect do not Stock Returns.

3.7 Theoretical framework

The theoretical framework of the study consist of dependent variable and independent variable

KSE 100 Index

Exchange Rates

Inflation Rates

Interest Rates (KIBOR Rates)

Chapter 4

Data Analysis

This research report is based on three independent and one dependent variable. Stocks returns are taken as a dependent variable while inflation and interest and exchange rate are used as an independent variable. Ten years data is used in this research from June 2005 to Dec 2012. Stock Returns are affected by other factors as well but they remain constant. A Multiple Cross-sectional Regression Model is employed to examine the variables.

4.1 Model Summary:

After running and clearing from the above tests, the regression model is carried out on E-views, the following results came:

The following table shows the regression results are:

Regression Results

Table 4.1:

Dependent Variable: LKSE

Method: Least Squares

Sample (adjusted): 2005M08 2012M08

Included observations: 85 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

C

1.247371

0.432255

2.885727

0.0050

LER

1.738803

0.731932

2.375634

0.0200

LER(-1)

-1.568439

0.709125

-2.211796

0.0299

LIR

0.021933

0.081411

0.269413

0.7883

LKIBOR

0.217603

0.101450

0.214495

0.8307

LIR(-1)

-0.195639

0.087456

-2.237002

0.0281

LKSE(-1)

0.826168

0.041390

19.96051

0.0000

R-squared

0.901538

    Mean dependent var

9.275775

Adjusted R-squared

0.893965

    S.D. dependent var

0.222917

S.E. of regression

0.072589

    Akaike info criterion

-2.329257

Sum squared resid

0.410989

    Schwarz criterion

-2.128097

Log likelihood

105.9934

    Hannan-Quinn criter.

-2.248345

F-statistic

119.0313

    Durbin-Watson stat

1.700883

Prob(F-statistic)

0.000000

4.1.1 Explanation:

The above analysis signifies the affects of macroeconomic variables on KSE 100 index:

Stock Returns = α + b1 (INT) + b2 (EXCHANGE) + b3 (INF).

Stock Returns = 1.24 + 0.21(INT) + 1.73 (EXCHANGE) – 0.19 (INF).

4.1.2 R Square:

To find out the co-efficient of determination R2 is used, it is explanatory power of multiple regression model. R2 is 0.90 (90%) which shows that model is good fitted. F test is too high and significant which means model is overall well fitted.

4.1.3 Durbin Watson

This test is used to check the existence of successive association. DW value calculated in this research is 1.79, which means that model is free from auto correlation.

4.1.4 Beta:

It shows that how much the dependent variable is influenced by independent. The Bold lines are area of our concern, L before each variable means that it is log. The followings results came:

LER means log of exchange rate. It has positive impact on log of KSE (lkse), result is significant at 5% level of significance i.e. one percent change in exchange rate change KSE by 1.73 percent. Whereas one month lag or previous value of LER has negative effects which are significant as well It means that one percent increase in last month exchange rate would decrease KSE by 1.56 percent

One month lag effect of log of inflation rate is negative and significant. It means that is once percent increase in inflation rate decrease KSE by 0.19 percent. Whereas immediate rise in inflation does not have any significant effect.

One month lag value of KSE effect current index positively and significantly. If today KSE index increases by one percent it will increase future index by 0.82 percent.

One month lag value of log of Interest Rates is negative and insignificant. It means that one percent increase in interest rate will bring about 21.1% changes in the dependant variable.

4.2 Problems in Multiple Regression Model:

Whether the Regression Model applied is perfect or not, the following test was taken to ensure that these problems were not found in the data of the research.

Autocorrelation.

Heteroscedasticidity test.

Multicollinearity test.

4.2.1 Test of Autocorrelation:

It is the mathematical representation of the degree of similarity between a given time series and a lagged version of itself over successive time intervals..

Autocorrelation Results:

Table 4.2.1

Breusch-Godfrey Serial Correlation LM Test:

F-statistic

1.259628

    Prob. F(4,74)

0.2936

Obs*R-squared

5.418542

    Prob. Chi-Square(4)

0.2470

Test Equation:

Dependent Variable: RESID

Method: Least Squares

Sample: 2005M08 2012M08

Included observations: 85

Pre sample missing value lagged residuals set to zero.

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

C

0.043741

0.475204

0.092047

0.9269

LER

0.057643

0.773643

0.074508

0.9408

LER(-1)

-0.064097

0.745860

-0.085938

0.9317

LIR

0.009888

0.084737

0.116692

0.9074

LKIBOR

0.014074

0.103883

0.135479

0.8926

LIR(-1)

-0.014079

0.092377

-0.152412

0.8793

LKSE(-1)

-0.004329

0.049119

-0.088135

0.9300

R-squared

0.063748

    Mean dependent var

1.54E-16

Adjusted R-squared

-0.062773

    S.D. dependent var

0.069948

S.E. of regression

0.072110

    Akaike info criterion

-2.301009

Sum squared resid

0.384790

    Schwarz criterion

-1.984901

Log likelihood

108.7929

    Hannan-Quinn criter.

-2.173862

F-statistic

0.503851

    Durbin-Watson stat

1.989299

Prob(F-statistic)

0.882115

Conclusion:

As from the table 4.2.1 the Prob. Chi-Square value is 0.2470(24.70%) which is greater than 0.05(5%). It means that the model is free from autocorrelation.

Also, the Durbin-Watson stat value is 1.98 which is closer to 2 which also mean that the model is free from Autocorrelation.

The P-values are used to find out whether any apparent patterns are statistically significant

All the p- values of the variables are greater than 0.05 which can be interpreted as the relation is insignificant.

4.2.2 Plot of Residuals

A Residual plot is diagnostic tools which allow identifying the patterns of data. It can good fit if the data are randomly plotted or poorly fitted if the data form some systematic order.

In the diagram below, the Standardized residual of the model (ZRESID) is plotted on vertical axis and Standardized predicated dependent variable (ZPRED) in horizontal axis. In our case, ZPRED is log of KSE (lkse). As the residual plots are scattered and centered on the zero, it means that the model is free from autocorrelations and Heteroscedasticity.

4.2.3 Heteroscedasticity Test:

As we know that Heteroscedasticity is the unequal distribution of residuals. As from the plot of the residuals it can be seen that it is plotted in unsystematic manner. It means that there is no heteroscedasticity in the data. The opposite of heteroscedasticity is homoskedasticity.

Heteroscedasticity Test Results

Table 4.2.3

Heteroscedasticity Test: ARCH

F-statistic

0.045712

Prob. F(1,82)

0.8312

Obs*R-squared

0.046800

Prob. Chi-Square(1)

0.8287

Test Equation:

Dependent Variable: RESID^2

Method: Least Squares

Sample (adjusted): 2005M09 2012M08

Included observations: 84 after adjustments

Variable

Coefficient

Std. Error

T-statistic

Prob.  

C

0.005006

0.001299

3.853385

0.0002

RESID^2(-1)

-0.023577

0.110275

-0.213803

0.0312

R-squared

0.000557

Mean dependent var

0.004891

Adjusted R-squared

-0.011631

S.D. dependent var

0.010790

S.E. of regression

0.010853

Akaike info criterion

-6.185271

Sum squared resid

0.009658

Schwarz criterion

-6.127394

Log likelihood

261.7814

Hannan-Quinn criter.

-6.162005

F-statistic

0.045712

Durbin-Watson stat

2.002707

Prob(F-statistic)

0.831232

Conclusion

From the table above it is cleared that the Prob. F- value is 0.8312 which is greater than 0.05 suggests that there is not strong evidence that the model is suffering from Heteroscedasticity. Also, from the residual plot as it is randomly plotted the same result can be depicted

The D.W. Statistic value (2.00) also shows that the model is free autocorrelation. From the residual plot as it is randomly plotted

4.2.4 Multicollinearity Test:

Multicollinearity means that whether there is any relationship between the regressors or not. If it exists then result of the model cannot be achieved.

Table 4.2.3:

LER

LER(-1)

LIR

LKIBOR

LIR(-1)

LKIBOR(-1)

LER

0.53

-0.51

-0.012

-0.015

0.003

-0.003

LER(-1)

-0.51

0.50

0.01

0.009

-0.003

0.003

LIR

-0.012

0.01

0.006

0.0005

-0.006

-0.0007

LKIBOR

-0.015

0.009

0.0005

0.01

-0.001

-0.0008

LIR(-1)

0.0034

-0.003

-0.006

-0.001

0.0076

0.0013

LKIBOR(-1)

-0.003

0.0036

-0.00070

-0.0008

0.001

0.001

Analysis:

From the above table, it is evident that Multicollinearity does not exist and the independent variables are not interlinked with each other. For example the values can be interpret as if there is 100% increase in log of exchange it will bring about 1.5% decrease in log of interest rate. In the same pattern other values can be interpreted.

Chapter 5

Conclusion and Recommendations

This research determines the affect of macroeconomic variables on stocks returns (KSE 100 index) and data covers a time period 7 years on monthly basis. The period ranges from June, 2005 to August, 2012.

The data tool Multiple Regression Model is used to analyze the data. After using the tool, it is concluded that macroeconomic variables like exchange rates and inflation rates affect the stock return significantly while interest rates do not affect stocks returns insignificantly. It may be due to involvement of other different macro variables. These results concluded that stock returns and rate of inflation is negatively related while stock returns are positively affected by Exchange Rate and Interest Rate. The relation between exchange rate and stock return significant.

In light of this study the national policy makers should carefully be aware of the results of the policies that they are implementing and while designing the policies they should take into consideration the response of stock market. As Pakistan is in list of developing countries so its stock market is also not very much developed and the stock market is something that is greatly influenced by the state of the economy. Investors frequently think about macroeconomic variables when deciding where to put their money.

Common perception is that countries with strong long-term economic growth prospects are more likely to provide attractive stock market returns than countries with slower growth expectations. The study reveals that the exchange rate has significant effect on stock returns and it is positive in nature. Therefore, Government should take some strong steps to maintain or increase it. The rate of inflation has also positive effect on stock return but it is insignificant in nature. The stock returns are negatively affected by Inflation Rate and it is significant in nature. Therefore Government should take some strong steps to maintain or decrease it.