Impact Of Macroeconomic Variables

Published Date: 02 Nov 2017

Abstract

This study investigates the impact of macroeconomic variables on stock returns of KSE 100 index to see what sort of relation exists between them. The macroeconomic variables used in this research are interest rate, Exchange rate and Inflation rate. In order to find out, 6 years data from July 2005 to Dec 2012 on monthly basis was used. A multiple regression was used to test the affect of macroeconomic variables on stock returns. Diagnostic tests such as autocorrelation (Breusch-Godfrey Serial Correlation LM Test), hetrosedasticidity (Autoregressive Conditional Heteroscedasticity (ARCH) model) and multicollinearity (correlation matrix) have been performed which indicate the data has no econometric problems. For Data analysis, E-views was used as statistical tool.

Regression results indicate 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. The government should make polices for long term instead of short term. The main reason behind this fact is that short term polices cannot improve the country’s current economy. Long- term planning should be done.

Table of Contents

Chapter 1

Chapter 1

Introduction

1.1 Introduction:

The performance of stock exchange is an arguable issue in any country because it plays an important role in global economics and financial markets due to its impact on corporate finance and economic activity. An efficient stock market circulates and diversifies the domestic funds and activates the productive investment projects, which prospers the economy of the country, but this is only possible if the stock market have significant relationship with the macroeconomic variables.

The main aim of the investors is to get better and higher return on their investment. Investment in stock market is one of many investment opportunities available in a country. The return, when any investor invests in stock market. If investors are fully aware of the relationship among the macroeconomic variables, then they will make more learned investment decisions thus reducing their exposure to risk and giving them more return on investment.

The public can take protective measures if they were somewhat aware of what might happen in the economy or the financial market in the near future i.e. the information is available which macro variable can help in reducing the shock factor.

For the above like situations, economists, researchers, policy makers and financial investors are constantly trying to find the relationship between macroeconomic variables and stock prices which are very important to study for many reasons.

1.2 Literature Review:

Sohail & Hussain (2011) conducted a study on impact of macroeconomic variables on KSE 100 index. The macroeconomic variables included consumer price index (CPI), industrial production (IP), real effective exchange rate (REER), money supply (M2) and three months treasury bills rate (TMTBR). Their study revealed that in the long run, there was a positive impact of inflation, GDP growth, and exchange rate on KSE100 index while money supply and three months treasury bills rate had negative impact on the stock returns.

Bilal et al. (2012) argued that examined the relationship between Terrorism and Macroeconomic factors (Interest Rate and Inflation) on Karachi Stock Exchange (KSE-100 Index). Pakistan as a front line state in War on Terror, Pakistan is facing economic instability and alarming consequences like emergence of religious, sectarian and politically induced terrorisms. These problems have done damages to public and private assets and unavoidably affected global equity markets. They found that KSE-100 Index have got negative relationship with Terrorism and causal relationship with interest rate while no relationship is found with inflation.

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 Objective of the Study

The primary objectives of the study are the following,

Impact of exchange rate on stock prices of KSE-100 index

Impact of interest rate on stock prices of KSE-100 index

Impact of inflation rate on stock prices of KSE-100 index

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, diagnostic tests such as autocorrelation (Breusch-Godfrey Serial Correlation LM Test), hetrosedasticidity (Autoregressive Conditional Heteroscedasticity (ARCH) model) and multicollinearity (correlation matrix) will be done.

1.6 Data Collection:

As the research is secondary in nature so the data about is gathered from different websites about KSE 100 index data and macroeconomic variables i.e. interest rates, exchange rates and inflation rate. The state bank of Pakistan, Karachi stock website and trading economics website is used.

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

1.7 Research Hypothesis

H0: Macroeconomic indicators cause to increase stock prices.

H1: Macroeconomic indicators do not cause to increase stock prices.

1.8 Variables of the Study:

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

Interest rate, exchange rate and inflation rate as Independent Variable.

KSE 100 index as Dependant Variable.

1.9 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) argue 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 & 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.

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

Stock Exchange and Karachi Stock Exchange

3.1 Stock Exchange and Its Importance

Stock exchange is a place where investors invest in different securities. In others words a place, which offers trading facilities to investors, traders and stockbrokers. It also called a secondary market. Every countries of the world have their own stock exchange.

For investors and traders, stock exchange is the appropriate palace for business activities and therefore it is essential for both investors and traders to list in stock exchange. it is an important source for companies to generate money.

In old days, doing business is very tough job because of lack of facilities. No electronic media, no legislative facility, no access facility etc. Nowadays the world becomes a global village and doing business has become very easier because of electronic media like internet, telephone and online buying and selling. Due to the globalization phenomena most businesses are done vie electronic networks, because it provide a lot of benefits to investors and brokers like quick transaction, getting more information and more than one transaction at a time in lower cost.

3.1.2 Factors Affecting Stock Market

The major factors affecting the stock market are as follows:

Inflation

Market Trends

Govt. Policies

Global Market

Oil Prices

War in the country

3.1.3 Roles of the Stock Exchange:

As discussed, the stock exchange plays a vital role in the economy of a country. The following are the different roles of the stock exchange, which are, explained below.

Indicators of the Economy

In stock exchange, shares prices are varying depending on the market value. The share prices depend on market value of each entity. The economic activities are boom, the shares prices of the corporations increase. When the economic activities are poor i.e. Recession the company’s shares prices decrease and lead to the stock market crush and vice versa

Creating Opportunity for Low Level Investors

In the beginning of the new business huge amount of capital is required. A low level investor can easily invest his capital in the stock exchange to do business.

Government Projects

Sometimes, government needs capital for different development project, they issues special securities to raise funds. Stock exchange investors are attracted to buy these securities which in turn help the government to raise capital for its projects.

3.2 Overview on Karachi Stock exchange

Now move to discuss the Karachi stock exchange, this is the best emerging stock market in the region.

After two months of Independence, Karachi stock exchange established was in 18-September 1947. It is not too much old; it is relative young and small market as compared to the stock exchanges of the developed world i.e. U.S and Europe. In case of Pakistani market Karachi stock exchange is oldest and biggest market. In 2002, the Karachi stock exchange had been selected as the best performing stock exchange around the globe. The listed companies on Karachi stock exchange were 599 in September 2012. The total market capitalization of Karachi stock exchange is Rs. 3923.979 Billion in September 2012. Over all trading volume in Pakistan KSE, capture 74% market .In Pakistan two small stock exchanges are also working i.e. Lahore stock exchange and Islamabad stock exchange ware established in 1970 and 1997 respectively and capture the remaining market of capital market.

3.2.1 KSE-100 Index

In November 1, 1991 the KSE-100 was began. The KSE-100 index.measures a weighted price index of the top 100 organization listed on the stock market, which is usually taken a benchmark index in Pakistan. Karachi stock exchange began with a 50 shares index. With the passage of time market expended, a representative index was required. From the 34 sectors the Karachi stock exchange consist 100 companies with the weighted index of 85% of capitalization of market. In the KSE-100 index, the interest of the foreign investors was active in 2006 and state bank of Pakistan estimate the foreign investment in capital market was U.S $523 million. KSE-100 index was record of 15767.4 peak points in history of KSE in 20-April 2008. International magazine business week in 2002, acknowledged that Karachi stock. exchange was the best performing stock market around the world. But due to the financial crisis in 2009 and country internal political instability, law and order situation, terrorism attacks and natural disaster i.e. earth quick -8 October 2005 and floods 28-august 2010 crashed the KSE up to the low."

Recently, The KSE 100 index crashed 570 index points in just 10 minutes when the Apex court i.e. the Supreme Court of Pakistan ordered the arrest of Prime Minister Raja Pervaiz Ashraf in Rental Power Case. As the news about it became clearer, the stock index recovered well from it .On 1st Feb, it was all time highest level again i.e. 17,266.23.

3.3.2 KSE-30 Index

Karachi stock exchange was also established KSE-30 index. KSE-30 index is based on the market capitalization of listed 30 largest organizations on Karachi stock exchange. KSE-30 increase the effectiveness and credibility of the stock market because it includes only the shares of those organizations that can float free in the market. If the shares of the companies are not free floating in the market so those companies, shares are not including in the KSE-30 index.

3.3.3 KMI-30 Index

In Karachi stock exchange there is no such facility for those investors who wants to invest their capital under the Islamic Shariah, therefore Meezan investment management in partnership with KSE to design the KMI-30 (Karachi Mezaan index) was began in September 2008. KMI-30 index for those who wanted to invest under Islamic Shariah in stocks of companies involved in pure Islamic finance. KMI-30 has calculated for 30 companies approved by the Shariah board.

Chapter 4

Methodology

This chapter consists of model of the variables. To study the factors, which affect the stock exchange, these are exchange rate, interest rate and inflation rate The research model will clarified with the help of above variables but there are several other variables, which affect the stock prices index, but these variables will consider stable. The conclusion of the study will merely base on the above variables.

4.1 Variables of the Study:

The variables that are used in the study are explained below.

Interest rate, Exchange rate and Inflation Rate as Independent Variable.

KSE 100 index as Dependant Variable.

4.1.1. Interest Rates:

Economy largely affected by interest rates. The fluctuations in the interest rate mainly affect the money supply in the country. If interest rate is increased, the money supply to the market will be decreased. If government announced monetary policy that rate of interest will be increased for coming quarter, it means that the deposits of the people will generate more return then the previous time. Eventually, people will keep their surplus money in banks rather than investing in securities. This relationship is explained in economic theory which confirms that there is negative relationship between interest rate and investment. When interest rate decreases in the economy the investment increases and vice versa. Interest rate is like an. opportunity cost. For example if a person have surplus money, he has to make decision about to invest or keep deposit in the bank. For this purpose cost benefits analysis has to done to make decision. If the opportunity cost (interest) is small then the investing returns so the surplus money should be invested rather than to keep it in the bank. The variations in interest rate also tend to make borrowing expensive fluctuate. If interest increases the borrowing cost will be increased and vice versa. Therefore, those corporations, which depend on debt their profit, will lower down. At the end when interest rate increases it tends stock market a little attractive place.

4.1.2 Exchange Rate:

It can be defined as "The rate at which one currency can be exchanged into another currency".

e.g. 1 US dollar to 96 Pakistani rupees. It means that 96 Pakistani Rupees is equal to only 1 US dollar. There are two types of exchange rates.

Spot Exchange rate: it is the current exchange rate.

Forward exchange rate: it is the rate which is quoted today but it is used for the payment or the delivery on specific date in future.

The variation of currency takes place due to the market forces i.e. demand and supply of currencies. A currency will become more valuable or strong if the demand increases and vice versa.

The investors are hesitant to invest in more fluctuated (weak) currency due to involvement of high risk factors. An unstable exchange rate of currency will trim down trade among the countries. In investment decisions of Investors, the uncertainty in currencies and exchange rate fluctuation cannot be neglected or ignored. In this context, the relationship between exchange rate and stock prices is very important because it influence the fiscal and monetary policies of decision makers. The Policy maker’s main desire is to increase the exports; it tends to depreciate the local currency. But at the same time they should be aware of the fact that such policy affects the stock prices as well. In Pakistan flexible exchange rate is followed, which gives Investors and Policy makers an idea about the performance and share in international export trade.

4.1.3 Inflation Rate:

Inflation is increase in the general prices of all goods and services. due to which people will buy smaller amount of goods with the same amount of money. Inflation rate is the main measure of the inflation, which called CPI (consumer prices index).

Inflation can calculated with the help of following formula.

"P0-P1/P1*100%"

Where,

P0 is the present consumer index.

P1 is the consumer price index of the last year.

When the price of the goods and services increases, each unit of the currency will buy less units of the same good and services. So inflation is the main economic indicator which affects the country economy both in positive and negative manner. The negative affect will be the real value of the money is decreased which discourage a person from savings or investment and vice versa. Normally, Inflation negatively affects stock returns because profits of the firms’ decreases with increase in price of goods due to increased costs.

4.2 Statistical Tool Used in Research

4.2.1 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.

4.2.2 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.

IFR= Inflation Rate.

ER= Exchange Rate.

4.2.3 Components of the Multiple Regression Model:

4.2.3.1 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.

4.2.3.2 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.

4.2.3.3 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).

4.2.3.4 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.

4.2.3.5 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.

4.3 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.

4.3.1 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.

4.3.1.1 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.

4.3.1.2 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.

Breusch-Godfrey Serial Correlation LM Test

The Breusch–Godfrey serial correlation LM test is a test for autocorrelation in the errors in a regression model. It makes use of the residuals from the model which is being considered in a regression analysis, and a test statistic is derived from these. The null hypothesis states there is no serial correlation of any order up to p. This test is more general than D.W. Test.

4.3.2 Heteroscedasticity:

"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.

4.3.2.1 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.

4.3.2.2 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.

4.3.2.3 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.

Autoregressive Conditional Heteroskedasticity (ARCH):

ARCH models are observed in time series data. It models financial time series with time varying volatility. It assumes that the variance of current error and previous period are related to each other which result in clustering of the volatility. This phenomenon is generally used in capital market.

4.3.3 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.

3.3.3.1 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.

4.4 Research Hypothesis

H0: Macroeconomic indicators cause to increase stock prices.

H1: Macroeconomic indicators do not cause to increase stock prices.

4.5 Sources of data:

As the research is secondary in nature so the data about is gathered from different websites about KSE 100 index data and macroeconomic variables i.e. interest rates, exchange rates and inflation rate. The state bank of Pakistan, Karachi stock website and trading economics website were used.

4.6 Data Period:

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

4.7 Sample size:

The total number of observation in the study is 85

4.8 Theoretical framework

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

KSE 100 Index

Exchange Rates

Inflation Rates

Interest Rates

Chapter 5

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.

5.1 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 5.1:

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

LINT

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.

5.1.3 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.

5.1.4 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.

Multicollinearity.

5.2.1 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 5.2.1

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

LINT

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.

5.2.2 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.

5.2.3 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 5.2.3:

LER

LER(-1)

LIR

LINT

LIR(-1)

LINT(-1)

LER

1

LER(-1)

-0.51

1

LIR

-0.012

0.01

1

LINT

-0.015

0.009

0.0005

1

LIR(-1)

0.0034

-0.003

-0.006

-0.001

1

LINT(-1)

-0.003

0.0036

-0.00070

-0.0008

0.001

1

Analysis:

From the above table it is evident that Correlation Matrix indicates all the values of macroeconomic variables are less than 0.2 therefore the strength of relationship among independent variables is negligible. Therefore no multicollinearity exists among independent variables. The relationship among independent variables is illustrated below.

There is a positive relationship between exchange rate (LER) and one month lag value of inflation rate (LIR(-1)). Whereas the relationship of exchange rate (LER) and others variables is negative.

One month lag value of exchange rate (LER (-1)) is positively related to other independent variables and their strength of there is negligible. However, it is negatively related to one month lag value of inflation rate (LIR (-1)).

Inflation rate (LIR) is negative related to one month lag value of inflation rate (LIR) and one month lag value of interest rate (LINT(-1)) and positive related to interest rate (LINT).

Interest rate (LINT) is negative related to inflation rate and one month lag value of interest rate and their strength is negligible.

One month previous value of inflation rate (LIR (-1)) is positive related to one month lag value of interest rate (LINT (-1)) with negligible strength.

5.2.4 Heteroscedasticity:

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 Results

Table 5.2.3

Heteroscedasticity Test: ARCH

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

Presample 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

LINT

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

RESID(-1)

0.183239

0.132904

1.378736

0.1721

RESID(-2)

-0.207441

0.126055

-1.645646

0.1041

RESID(-3)

0.009492

0.127057

0.074706

0.9407

RESID(-4)

0.007084

0.120361

0.058855

0.9532

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

From the table above it is cleared that the dependent variable is RESID and for lags of data have been taken. 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

Chapter 6

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 relationship between exchange rate and stock return significant which also proved by Sohail & Hussain (2011).

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.

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