CHAPTER 1

INTRODUCTION

Broad Problem Area

Over the course of the past year the Pakistani economy has

taken such drastic turns that it has baffled even seasoned

economists and researchers, one such change has been the

unprecedented success of the Karachi Stock Exchange,

represented mostly by the KSE-100 index. Just to take an

example, in April 2003 the KSE-100 index stood a hundred

points shy of the 3000 mark, a coveted position at that

time, and now little over an year later it stands well past

the 5000 point level. Such a radical change has naturally

forced a lot of people to uncover the fundamental reasons

behind the change. This research paper is an effort by the

researcher to find out which are the fundamental

determinants of the KSE index and what is the extent of

their influence on it.

Background Of KSE

The KSE is a relatively young (it was established soon

after independence in 1947) and small market. In 2002, it

had 758 stocks listed with a total market capitalization of

about $10 billion or 16% of GDP. The KSE captures 74% of

the overall trading volume in Pakistan. There are two

smaller stock exchanges covering the remaining 26%: The

1

Lahore stock exchange (22%), and the Islamabad stock

exchange (4%). The KSE-100 index, which is a weighted price

index of the top 100 companies listed on the stock market,

is usually taken as a benchmark index in Pakistan.

Rationale Of The Study

There is a consensus among macroeconomists and finance

theorists that stock market prices are driven by

macroeconomic variables, the so- called “fundamentals” in

the economy. Moreover, it is also agreed that the linkage

is two-way; that is, feedback exists between the stock

market and real activity.

There has been a great deal of research into the phenomenon

described above in the developed economies such as the US,

the UK, Germany, Japan etc, where researchers have come up

with some very informative and insightful results. These

results have helped them explain to some degree the

behavior of their stock exchanges and in turn have helped

them make better predictions about its current and future

performance. It is only logical that such studies be

conducted for the Pakistani stock market so that we too can

benefit from the predictive power of economic variables for

our stock exchanges.

2

Research Questions

Trying to investigate relations between the variables, the

aim is to make it easier to try to answer the following

hypothesis:-

1. Does industrial production affect the KSE index?

2. Do interest rates affect the KSE index?

3. Does inflation affect industrial production?

4. Does inflation affect interest rates?

5. Does inflation affect the KSE index?

6. Do interest rates affect industrial production?

In analysis of this paper ten years’ monthly data for the

period 1994 until 2004 is taken for all variables.

3

Theoretical Framework

To examine the relationship for the hypothesis listed, the

following multivariate model is specified:

U = (KSE, IPI, INF, STI)

Where,

KSE= KSE-100 Index

IPI= Industrial Production Index of Pakistan

INF= Inflation rate of Pakistan

STI= Short Run Interest Rate of Pakistan, in percentage

The Karachi Stock Exchange’s 100 index (KSE), being an

equally weighted price index, is calculated by taking the

average of the prices of a set of 100 biggest companies

listed on the KSE. These companies are sufficiently

representative of the Pakistani Stock Market, because of

the weight of these companies; the KSE-100 index accounts

for majority of the total trading volume.

The Industrial Production Index, (IPI), is included as a

proxy for real economic activity in the Pakistani market.

Inflation (INF) is taken on a monthly basis from the

Consumer Price Index.

The Short Run Interest Rates (STI), corresponds to the

Weighted average rate of return on 3 month or less fixed or

4

term deposits (interest bearing and PLS) offered by All

Scheduled Banks in Pakistan in percent per annum.

Objectives Of The Study

The objective of this paper is to investigate the relations

among key economic variables such as:

Inflation Interest rates Industrial production

and the stock market index in the small Pakistani economy,

where stock exchanges are less mature as compared to those

in e.g. US, Japan and the UK.

Definition Of The Terms

The following terms have been used extensively in the

report and therefore it is appropriate to adequately define

them for the reader.

Liner Regression: Linear Regression estimates the

coefficients of the linear equation, involving one or more

independent variables that best predict the value of the

dependent variable.

Confidence intervals: depicts the model’s ‘confidence’ in

the result i.e. whether estimations have been made at 90%

5

or 95% etc, confidence intervals for each regression

coefficient

R squared change: The change in the R2 statistic that is

produced by adding or deleting an independent variable. If

the R2 change associated with a variable is large, that

means that the variable is a good predictor of the

dependent variable.

Descriptives: Provides the number of valid cases, the mean,

and the standard deviation for each variable in the

analysis.

Part and partial correlations: Convey the zero-order, part,

and partial correlations. Values of a correlation

coefficient range from –1 to 1. The sign of the coefficient

indicates the direction of the relationship, and its

absolute value indicates the strength, with larger absolute

values indicating stronger relationships.

Residuals: Depicts the Durbin-Watson test result for serial

correlation of the residuals and casewise diagnostics for

the cases meeting the selection criterion.

Predicted Values: Values that the regression model predicts

for each case.

Unstandardized: The value the model predicts for the

dependent variable. The unstandardized coefficients are the

coefficients of the estimated regression model

Standardized: A transformation of each predicted value into

its standardized form. That is, the mean predicted value is

6

subtracted from the predicted value, and the difference is

divided by the standard deviation of the predicted values.

Often the independent variables are measures in different

units. The standardized coefficients or betas are an

attempt to make the regression coefficients more

comparable.

Adjusted: The predicted value for a case when that case is

excluded from the calculation of the regression

coefficients.

S.E. of mean predictions: Standard errors of the predicted

values. An estimate of the standard deviation of the

average value of the dependent variable.

Prediction Intervals: The upper and lower bounds for both

mean and individual prediction intervals.

Mean: Lower and upper bounds for the prediction interval of

the mean predicted result.

Individual: Lower and upper bounds for the prediction

interval of the dependent variable.

Residuals: The actual value of the dependent variable minus

the value predicted by the regression equation.

Bivariate Correlations: The Bivariate Correlations

procedure computes Pearson's correlation coefficient, with

its significance levels. Correlations measure how variables

are related. Pearson's correlation coefficient is a measure

of linear association.

7

Correlation Coefficients: Correlation coefficients range in

value from –1 (a perfect negative relationship) and +1 (a

perfect positive relationship). A value of 0 indicates no

linear relationship.

Test of Significance: Dependent on either two-tailed or

one-tailed probabilities. If the direction of association

is known in advance, One-tailed is taken. If the direction

of association is not known then Two-tailed test of

significance is taken.

8

CHAPTER 2

LITERATURE REVIEW

The relationships among real, monetary and financial

variables have been active topics of economic research for

most of this century. An increasing amount of empirical

evidence noticed by several researchers leads to the

conclusion that a range of financial and macroeconomic

variables can affect stock market activity (e.g. Campbell,

1987, French, Schwert and Stambaugh, 1987, Fama and French,

1989, Balvers, Cosimano and McDonald, 1990, Been, Glosten

and Jaganathan, 1990, Cochrane, 1991, Campbell and Hamao,

1992, Ferson and Harvey, 1993, Glosten, Jaganathan and

Runkie, 1993 and Pesaran and Timmerman, 1995, 2000).

The relationship between stock market activity and

fundamental economic variables in the U.S. is well

documented (Fama 1970, 1990 and 1991). In recent years,

numerous studies (Fama 1981, Chen, Roll and Ross 1986, Chen

1991) modeled the relation between stock market activity

and real economic activities in terms of production rates,

productivity, GNP growth rate, unemployment, yield spread,

interest rates, inflation, dividend yields, etc. These

relationships among stock market activity, real economic

activity and monetary variables in the U.S. also have been

studied by (Geske & Roll 1983), (Mallaris & Urrutia, 1991),

(Darrat & Brocato, 1994), (Darrat & Dickens, 1999), while

9

Known, (Shin & Bacon, 1997) studied these relationships in

Korea.

(Mallaris et al., 1991) studied the linkage among

industrial production, interest rates and the S&P 500

Index; the results seem to suggest that the

interrelationships among the three variables are not

statistically significant, contrary to what the economic

and financial literature assumes. Since this finding

challenged economic conventional wisdom, it is worth

determining whether the economic role of the stock markets

in relatively less developed countries, such as Pakistan,

is or is not clearly significant. Specifically, it is

interesting to examine how the Pakistani market responds,

in terms of stock market activity, to changes in its

fundamental economic variables. This question, as of yet,

remains unanswered.

(Harbeler, 1937) has summarized a wealth of economic

theories attempting to explain the nature and causes of

stock market activity and particularly fluctuations. The

great depression of 1930’s and the impact of Keynes’

General Theory interrupted the research of the Pre-

Keynesian economists, and during the 1950’s to the late

1960’s the Keynesian doctrine of aggregate demand and

activist fiscal policy distracted attention from the

monetary and financial areas. (Friedman & Schwartz 1963,

1982), among other economists, have redirected the

attention of researchers to the role of interest rates,

while financial economists such as (Sharp, 1964) focused on

financial assets.

10

More recently, numerous studies have focused on specialized

issues. (Rozeff, 1974) has studied the relationship between

interest rates and stock prices, and (Barro, 1977) has

analyzed the potential relationships between monetary

factors and real industrial output. (Fama, 1981)

investigates the relationships among stock returns, real

economic activity, inflation and money. (Plosser, 1989)

reviews an extensive literature on real industrial activity

and emphasizes the significant role of technological shocks

on the production function and the economy’s real output.

(Mankiw, 1989) criticizes Plosser’s research and cites the

significant role of tight monetary policies. (Kydland &

Prescott, 1990) developed an in-depth methodological

procedure to measure fluctuations for various variables.

They conclude that credit considerations could play an

important role in current and future industrial activity.

(Malliaris et al., 1991) observed that the performance of

the stock market might be used as a leading indicator for

real economic activities in the United States. For the

United Kingdom, (Thornton, 1993) also found that stock

returns tend to lead real economic activity. In related

work, (Chang & Pinegar, 1989) and (Chen et al., 1986) also

concluded that there is a close relationship between stock

market and the domestic economic activity.

(Neftci, 1984) presented evidence to support his hypothesis

that recessions in economic activity tend to be steeper and

more short-lived that recovery in economic activity. (Falk,

1986) extended the study of Neftci to other economic series

typically associated with the U.S. industrial activity:

11

real GNP, output per worker-hour, and gross domestic

private investment. In addition, he studied the behavior of

industrial production in Canada, Italy, West Germany, the

United Kingdom, and France. Furthermore, during the past

decade a significant number of papers have investigated the

excessive volatility in stock markets and questioned the

validity of the efficient financial market hypothesis.

(Schiller, 1989) summarizes these studies and argues that

volatilities in stock market indices are excessive relative

to the volatilities in real or monetary variables.

This evidence increases the challenge to industrial

activity theorists who must now explain not only potential

relations among changes in levels of real, monetary,

economic and financial variables, but also relations among

their volatilities. Actually, this is not a new idea;

(Friedman et al., 1963) had shown that changes in the

volatility of interest rates generated changes in the

volatility of industrial output. In their seminal paper,

(Chen, Roll and Ross, 1986) find that the following macro

variables were significant in explaining expected stock

market activity: industrial production, changes in the risk

premium, twists in the yield curve and, more weakly,

measures of unanticipated inflation and changes in expected

inflation during periods when these variables were highly

volatile.

Studies on non-US markets have mostly been based on the

(Chen et al., 1986) approach. (Hamao, 1988) tested the

Japanese market and found strong relations, except for the

case of Japanese monthly production.

12

(Martinez & Rubio, 1989) used Spanish data and found no

significant relationship between stock market activity and

macroeconomic variables. (Poon & Taylor, 1991) are also

unable to explain activity in the UK by factors used by

Chen et al. More recently, (Kaneko & Lee, 1995) have re-

examined the US and the Japanese markets. They found that

both the term and risk premiums, as well as the growth rate

of industrial production, are significantly related in the

US. In Japan, however, international factors have become

increasingly more important. As opposed to the findings of

(Hamao, 1988), changes in oil prices, terms of trade and

exchange rates were significant in Japanese stock market

activity. (Jones & Kaul, 1996) investigated the response in

the stock market of oil prices in the US, Canada, the UK,

and Japan. They concluded that the US and Canadian stock

markets are rational, in the sense that the response to oil

shocks could be completely accounted for by their impact on

current and future cash flows. In the UK and Japan,

however, stock markets have overreacted to new information

about oil prices.

Standard stock valuation models predict that stock prices

are affected by the discounted value of expected cash

flows. (Chen et al., 1986) and (Fama, 1990) have shown real

economic activity, interest rate and stock returns to be

correlated. However, most of these earlier studies focus

upon the short-run relationship between stock market and

financial and macro-economic variables, which may remove

important information contained in the permanent component

of economic activity concerning the evolution of short-run

13

movements. In comparison to the above, long-run

relationship between stock market and the economic

variables has received little attention of researchers

except in (Mukherjee, Naka, 1995), (Chung & Ng, 1998),

(Maysami & Koh, 2000) and (Nasseh & Strauss, 2000). By

using the concept of correlation, the empirical long run

relationships between stock market indices and measures of

economic activity and financial variables can be

investigated. Correlation between stock prices and economic

activity can be seen to be consistent with both internal &

theoretical consumption and production-based models. These

models suggest that stock prices are related to expected

future production through effect on the discounted value of

changes in cash flows and dividends, (Cochrane, 1991).

More recently, empirical models without any specific

theoretical structure have been applied in a more pragmatic

fashion to the two-way relationship between stock market

indices and real economic variables. The regression model

has been particularly popular in this area given that it

can be used as a framework for formal examination of inter-

relationships within a given data. A relatively early

application of the regression model to the analysis of the

relationship between the stock indices and the macro

economy is by (Lee, 1992) and more recent ones can be found

in (Cheung et al., 1998). Recently several researchers like

(Baestaens et al. 1995); (Kaastra Ibeling & others 1996),

(Katsurelis, 1998), (Kamath, 1999 and 2002) recommend the

use of Artificial Neural Network (ANN) for investigating

the correlation relationship as well as forecasting in

14

capital markets, which has tremendous promise in terms of

methodology.

Moving towards market in Pakistan’s immediate vicinity,

there have been several studies regarding the relationship

between the stock exchange activity and the key economic

variables. Taking the example of India, (Sharma Kennedy,

1977) and (Sharma, 1983) tested the weak-form efficiency of

the Bombay Stock Exchange (BSE). Both of these studies with

the former covering the 1963-1973 period and the later

encompassing the 1973- 1971 period, conclude that Indian

stocks generally conformed to random-walk behavior in that

successive period changes were independent. (Poterba &

Summers, 1988), however, find evidence of mean reversion in

Indian stock prices, suggesting a deviation from random-

walk behavior. Technical analysis of the stock market can

thus be conducted based on this result.

(Darat & Mukherjee, 1987) apply a regression model along

with Akaike’s final prediction on the Indian data over

1948- 1984 and find that a significant causal relationship

exists between stock market activity and selected macro-

economic variables. (Naka, Mukherjee and Tufte, 1996) have

analyzed relationship among selected macroeconomic

variables and the Indian stock market. By employing a

regression model, they find that domestic inflation and

domestic output are the two most prominent factors

influencing stock market activity.

In a recent study under NSE Research Initiative (Kamath,

2002, paper no. 10) uses Artificial Neural Network (ANN) to

15

examine the relationship of macro-economic factors to stock

market activity. More recent studies like (Bhattacharya &

Mukherjee, 2002), (Rao & Rajeswari, 2000), (Pethe & Karnik,

2000) use advanced methods in econometrics to study the

same relationship.

(Bhattacharya & Mukherjee, 2002) test the causal

relationships between the BSE Sensex and five macroeconomic

variables. Their major findings are that there is no

linkage between the stock prices and money supply, national

income and interest rate while the index of industrial

production leads the stock price and there exists a

significant correlation between stock market index and rate

of inflation.

(Rao & Rajeswari, 2000) try to explore the role being

played by a good number of macro economic variables in

influencing the stock market when reduced into a manageable

number of economic factors. (Pethe & Karik, 2000) use

regression and correlation models to test relationship

between stock market behavior and some macro-economic

variables.

(Fama, 1981) asserts that there is a strong relationship

between stock returns with other macroeconomic variables,

notably, inflation and national output as well as

industrial production. The inflation rate is an important

element in determining stock returns due to the fact that

during the times of high inflation, people recognize that

the market is in a state of economic difficulty. People are

laid off work, which could cause production to decrease.

16

When people are laid off, they tend to buy only the

essential items. Thus production is cut even further. This

eats into corporate profits, which in turn makes dividends

diminish. When dividends decrease, the expected return of

stocks decrease, causing stocks to depreciate in value.

(Fama, 1981), (Geske et al., 1983), (James et al. 1985),

and (Stulz, 1986) all attempt to explain the negative

association between stock returns and inflation.

Most past empirical literature shows that stock market

activity is negatively correlated with inflation (Fama &

Schwert, 1977; Gultekin, 1983; and recently Barnes et al.,

1999 among others). (Fama, 1981) explains the negative

short-run correlation between stock returns and inflation

by the negative short-run correlation between inflation and

real activity.

17

Chapter 3

METHOD

Data

In analysis of this paper ten years’ monthly data for the

period January 1994 until January 2004 is taken for all

variables. (N=120, for each variable).

As already mentioned in the theoretical framework portion,

the data used in the study has four portions:

1. The first portion of the data is the information

regarding the Inflation rate of Pakistan, which is

fairly represented by the Consumer Price Index of

Pakistan or the CPI.

2. The second portion of the data is the information

regarding the industrial production level of

Pakistan, this is represented by the Quantum Index

of Manufacturing. This index is taken as a proxy for

real economic activity in Pakistan.

3. The third portion of the data is the Short-term

interest rates offered on very short term fixed or

term deposits. A weighted average of the rate of

return on 3 month or less fixed or term deposits

offered by all the scheduled banks in Pakistan is

taken.

18

4. Finally data for the Karachi Stock Exchange’s 100

index is taken. The reasons for taking the KSE-100

index and not an aggregate index representing all

the stock and companies listed in the KSE is that

the KSE-100 index is sufficiently representative of

the Pakistani Stock Market, since it accounts for

majority of the total trading volume.

Sources Of Data

Monthly data from January 1994 to January 2004 has been

used in this study. Data for the Industrial Production

Index, Consumer Price Index, and Interest Rates were

obtained from the State Bank of Pakistan’s Monthly

Statistical Bulletin, SBP’s Annual Reports and the economic

survey of Pakistan for the relevant years. Data for the

Karachi Stock Exchange Index were obtained from Yahoo

Financial Services and CBS MarketWatch in addition to the

statistical documents mentioned above.

Procedure

To test the predicting power of the key economic variables

over the KSE index a linear regression model is used and to

determine the relationships between all the variables

selected including the independent and all the dependent

variables, a bivariate correlation model is used.

To find out the regression relationship between the

dependent and independent variables the compiled data will

19

be entered into the famous statistical package SPSS. Once

the data has been entered the required tests will be

conducted and the results will be used to analyze the

relationship between the KSE-100 index and the key economic

variables taken as estimators of the index.

Furthermore to find out how the four variables taken are

interrelated, Pearson’s correlation test will also be

applied. The correlation test will indicate the level of

interrelatedness of the four variables i.e. KSE-100 index,

Industrial Production index, Short-term interest rates and

the level of inflation in the Pakistani economy over the

course of the time period taken.

20

CHAPTER 4

RESULTS AND DISCUSSION

The results that were obtained after running the data

through SPSS are as follows.

The results for linear regression will be discussed first

followed by those for correlation between the four

variables i.e. KSE-100 index, Industrial production index

(IPI), Short-term interest rates of Pakistan and the

inflation level of Pakistan (INF).

RESULTS OF REGRESSION ANALYSIS

Model Summary

Table 4.1

Model R

R

Square

Adjusted

R Square

Std. Error

of the

Estimate

1 .867 .751 .745 361.86019

Explanation: The above table gives the summary of the model

which has been applied to the data. This table displays R,

R squared, adjusted R squared, and the standard error.

21

R, the multiple correlation coefficient, is the correlation

between the observed and predicted values of the dependent

variable. The values of R for models produced by the

regression procedure range from 0 to 1.

Analysis: It is important to point out here that larger

values of R indicate stronger relationships. It can be seen

that the value of R obtained for our data results is .867

which is a very high value given the fact that the maximum

value which R can obtain is 1.

Moving on to the values for R Square also called the

coefficient of determination, R squared is the proportion

of variation in the dependent variable explained by the

regression model. Once again the values of R squared range

from 0 to 1. Small values indicate that the model does not

fit the data well whereas larger values of R squared

indicate the model fits the data well. Since the R squared

value for our data set is .751 which is a fairly large

value considering the fact that R squared value can at most

be equal to 1 it can be said that the model fits the data

very well.

Adjusted R squared attempts to correct R squared to more

closely reflect the goodness of fit of the model in the

population. It can be seen that even the adjusted R

squared, which presents a somewhat reduced value of R

squared, is giving a value of .745 which is a significantly

high value. The interpretation that can be obtained from

the adjusted R squared value is that 74.5% of the variation

in the KSE index is explained by the variables selected in

22

the model, i.e. 74.5% of the variation in the Karachi Stock

Exchange index is due to that Inflation level in the

economy, the level of short-term interest rates and the

level of industrial production in the country.

The following are the results for Analysis of Variance

(ANOVA) Model.

ANOVA

Table 4.2

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regression 4586524

4.1663

15288414.

722116.76 .000

Residual 1518936

4.688116 130942.80

Total 6105460

8.853119

Explanation: The above table summarizes the results of an

analysis of variance. The sum of squares, degrees of

freedom, and mean square are displayed for two sources of

variation i.e. regression and residual. The output for

Regression displays information about the variation

accounted for the model, whereas the output for Residual

displays information about the variation that is not

accounted for by the model and the output for Total is the

sum of the information for Regression and Residual. The

mean square is the sum of squares divided by the degrees of

freedom (df). The F statistic is the regression mean square

23

(MSR) divided by the residual mean square (MSE). The

regression degrees of freedom is the numerator df and the

residual degrees of freedom is the denominator df for the F

statistic. The total number of degrees of freedom is the

number of cases minus 1. If the significance value of the F

statistic is small (smaller than say 0.05) then the

independent variables do a good job explaining the

variation in the dependent variable.

Analysis: A model with a large regression sum of squares in

comparison to the residual sum of squares indicates that

the model accounts for most of variation in the dependent

variable. It can be clearly seen from the table that the

value for regression sum of square is three times larger

than the value for the residual sum of square. Since the

value of the regression is larger than that of the residual

it can be said that the independent variables account for

most of the variation in the dependent variable. In other

words, the independent variables chosen i.e. Pakistan’s

Inflation level, Short-term interest rates and level of

industrial production account for most of the variation in

the dependent variable i.e. the Karachi Stock Exchange

index.

Furthermore the significance value of the F statistic is

very small (.000) which means that the independent

variables i.e. CPI, IPI and STI do a very good job

explaining the variation in the dependent variable, i.e.

KSE.

24

The regression coefficients will now be explained and

analyzed.

Coefficients

Table 4.3

Model

Unstandardized

Coefficients

Standardized

Coefficients T Sig.

B

Std.

Error Beta

1 KSE 4352.300 284.287 15.310 .000

INF 3.198 .736 .266 4.343 .000

IPI -1.180 .643 -.097 -1.835 .069

STI -450.183 26.938 -1.049 -16.712 .000

Explanation: The unstandardized coefficients are the

coefficients of the estimated regression model. Often the

independent variables are measures in different units. The

standardized coefficients or betas are an attempt to make

the regression coefficients more comparable. The t

statistics can help to determine the relative importance of

each variable in the model. Once again the values are

significant if they are less than .05, any value greater

than .05 is not significant.

Analysis: It was necessary to present this table since the

independent variables were measured in different units,

i.e. CPI and IPI were measured in absolute units whereas

STI was measured in percentage per annum. The results

suggest that there is a significant relationship between

25

the KSE index and all the independent variables except for

the industrial production index. There is no significant

regression association between the KSE index and the index

of industrial production index. As expected the Pakistani

benchmark stock exchange does not reflect and is not

affected by actual economic activity but is affected much

more by variation in the monetary variables such as the

interest rates and the level of inflation restricting or

relaxing the level of money available for investment into

the stock exchange. These results will be discussed in

greater detail once the data correlation results are

discussed and as, subsequently, the research questions are

answered one by one.

26

RESULTS OF CORRELATION ANALYSIS

This section discusses the results of the bivariate

correlation test applied to the sample data.

As in the previous section the result table will first be

explained and the results will then be analyzed.

Correlations

Table 4.4

INF IPI STI KSE

INF Pearson

Correlation1 -.410 .641 -.367

Sig. .000 .000 .000

N 120 120 120 120

IPI Pearson

Correlation-.410 1 -.455 .272

Sig. .000 .000 .003

N 120 120 120 120

STI Pearson

Correlation.641 -.455 1 -.834

Sig. .000 .000 .000

N 120 120 120 120

KSE Pearson

Correlation-.367 .272 -.834 1

Sig. .000 .003 .000

N 120 120 120 120

27

Explanation: As a measure of correlation, Pearson’s

correlation is employed. The correlations table displays

Pearson correlation coefficients, significance values, and

the number of cases with non-missing values. The Pearson

correlation coefficient is a measure of linear association

between two variables. The values of the correlation

coefficient range from -1 to 1. The sign of the correlation

coefficient indicates the direction of the relationship

(positive or negative).

The absolute value of the correlation coefficient indicates

the strength, with larger absolute values indicating

stronger relationships. The correlation coefficients on the

main diagonal are always 1.0, because each variable has a

perfect positive linear relationship with itself. The

significance of each correlation coefficient is also

displayed in the correlation table. The significance level

(or p-value) is the probability of obtaining results as

extreme as the one observed. If the significance level is

very small (less than 0.01) then the correlation is

significant and the two variables are linearly related.

Analysis: Looking first of all at the level of

significance, all the variables except IPI reflect a very

small level of significance, smaller than the threshold .01

level, signifying a high level of significance. The only

variable which has resulted in an insignificant level of

significance is the index of industrial production’s

correlation with the Karachi Stock Exchange index. This

result further validates the result obtained from the

regression analysis which indicates that the industrial

28

production index does not do a good job explaining

variation in the KSE index.

There is a no significant relationship between KSE and IPI,

i.e. the KSE index is not significantly affected by any

sort of change in the level of industrial production in the

Pakistani economy, this result has important implications

which shall be looked into more deeply later on.

There is a very strong negative relationship between KSE

and STI, i.e. the KSE index is greatly affected in a

negative sense by an increase in the Short-term interest

rates prevailing in Pakistan and is affected positively by

a decrease in the short-term interest rates.

Further detail of the correlative relationships between the

dependent and the independent variables is looked into in

more detail along with support from economic theory in the

next section.

29

Research questions answered

1. Does industrial production affect the KSE index?

The KSE index does not seem to significantly affect the

index of industrial production. There does seem to exist a

very weak positive relationship between KSE and IPI i.e. if

one increases the other should increase and if one

decreases the other should decrease, but this relationship

does not seem to be a significant one since the level of

significance is greater than the threshold significance

level of .01 and thus the relationship cannot be called

significant. This result indicates that there is no

significant relationship between the aforementioned

variables at the 5% significant level. There would however

be significance at the 10% level, but as a norm these sort

of time series analysis are always taken ‘at most’ at the

5% significance level therefore it can be concluded that

there is no significant relationship between industrial

production and the KSE index.

This result has important implications for all who are

involved in forecasting stock markets in Pakistan, since

the KSE is thought to significantly affect the other two

INDUSTRIAL PRODUCTION

KSE INDEX

30

stock exchanges in the country i.e. the Lahore (LSE) and

Islamabad (ISE) stock exchanges, and also for policy

writers. Since the index of industrial production is taken

as a proxy for real economic activity in Pakistan, saying

that it has no significant relationship with Pakistan’s

premier benchmark stock market index means that the Karachi

Stock exchange is not significantly affected by level of

economic activity in Pakistan. A country’s stock exchange

is theoretically supposed to reflect the level of economic

activity prevalent, why KSE is not seems like an economic

anomaly. Further light on possible causes for such behavior

will be shed in the subsequent section.

2. Do interest rates affect the KSE index?

The short-term interest rates seem to a have a very strong

relationship, in fact the strongest relationship of any

variable in this study, with the Karachi Stock Exchange

index. The correlation results suggest that there is an

83.4% negative relationship between the short-term interest

rates and the KSE index. This is a very strong relationship

indeed and falls exactly in place with previous research

conducted into the investment function and how the stock

exchange is a substitute for other means of savings in the

economy e.g. depositing money into commercial banks in

short-term fixed and term deposits.

INTEREST RATES

KSE INDEX

31

As interest rates offered on deposits decrease people find

it more worthwhile to invest their money into other avenues

such as the stock exchanges and real estate etc. A similar

but opposite behavior is witnessed in the case of an

increase in interest rates. When interest rates increase

people find it more profitable to keep their money in the

bank than to invest it in avenues such as the stock

exchange. In this case in addition to gaining more return

by keeping their money in the bank the investor also avoids

facing the considerable amount of risk inherent in all

stock market investments.

This sort of behavior is exactly in accordance with proven

economic theory regarding the inverse relationship between

interest rates and investment. When interest rates decrease

investment increases and when interest rates increase

investment decreases. It is simply a matter of opportunity

cost. The opportunity cost of investing in say the stock

market is the interest that would have been received if the

money had been kept with a bank, and since the money is not

being kept in the bank but is rather invested in the stock

market the opportunity cost of the stock market investment

is the amount of interest forgone. If the opportunity cost

(interest) is large enough then the person ends up not

investing in the stock market but rather preferring to keep

the money lying in the bank.

32

3. Does inflation affect industrial production index?

There is a negative correlation albeit a weak one between

the level of inflation in the Pakistani economy and the

index of industrial production. The Pearson’s correlation

result indicate that there is a 41% negative relationship

between the consumer price index and the index of

industrial production.

This result falls in line with previous economic and

operations research done which suggest that the demand for

a company’s product is the heaviest entity in its

production function. The greater the demand for a company’s

product the greater the level of production the company

will commit itself to in order to satisfy that demand. It

is also a well know fact that demand for a product is

dependent on the level of disposable income available with

the people. Furthering this chain of relations, the level

of disposable income available with the public is directly

related to the prevalent inflation level in the economy,

the more expensive things are, ceteris paribus, the more

money it will take to buy them thereby reducing the amount

of money left to buy other things. This leads to a direct

decrease in the level of disposable income thereby reducing

demand leading to a decrease in the production function of

all industries across the board. The level of decrease in

the production function however depends on a multitude of

INFLATION INDUSTRIAL PRODUCTION

33

other factors such as the elasticity of demand of the

product and whether or not the product is a necessity of

life and so on.

34

4. Does inflation affect interest rates?

There is a strong positive correlation between interest

rates and inflation, following the Pearson’s correlation

result it can be said that there is a 64.1% positive

relationship between inflation and interest rates i.e. if

the level of inflation increases so do the interest rates.

Once again this behavior of the variables can be explained

through monetary economic theory. One of the lead causes of

inflation is said to be “too much money chasing too few

goods”. It is a well known economic fact that when the

level of money supply increases in an economy the general

price level of goods also increases. People simply have too

much money and there are not that many goods to satisfy the

increase in demand that results from increase in money with

the public. This results in an increase in commodity prices

across the board otherwise known as inflation.

The primary way to control inflation is to simply increase

the interest rates, and this is the practice that has, as

expected, been prevalent in the Pakistani market in the 10

year period from January 1994 to January 2004. The 64.1%

strong positive relationship between inflation and interest

rates can be attributed to prudent monetary policy

manipulation by the State Bank of Pakistan. This

relationship is depicted in figure 4.3 on page 50.

5. Does inflation affect KSE index?

35

INFLATION INTEREST RATES

There is a very weak negative correlation between the

inflation level prevalent in the Pakistani economy and the

KSE index. Pearson’s correlation suggests a 36.7% negative

correlation between the above mentioned two variables.

Being a weak correlation it does not deserve too much

attention nevertheless since there is a slight correlation

it is worth mentioning. One possible explanation of this

very weak correlation is that in case of an inflationary

trend the purchasing power of the public’s disposable

income, ceteris paribus, decreases. Inflation also

decreases the amount of investable funds since a greater

amount of the public’s disposable income goes towards the

transactionary use of money rather than towards the

speculative use of money.

This decrease in the amount of money available for

investment use affects all investable avenues e.g. real

estate etc. Investment in the stock exchanges is simple

another use of investable money and it too therefore is

affected by inflationary trends.

6. Do interest rates affect industrial production?

36

INFLATION KSE INDEX

Pearson’s correlation results suggest a negative

correlation between short-term interest rates and

industrial production. The results indicate a 45.5%

negative correlation between the above mentioned two

variables. Once again the negative correlation can be

attributed to interest rates eating away at the demand for

the products of a company. An increase in interest rates

leads to people putting their money in banks and other

financial institutions rather than spending it on

purchasing goods and services. This decreases the amount of

money available to be spent on goods and services and hence

demand for goods suffers across the board. Same is the case

with businesses, they get a greater return keeping their

money in bank deposits rather than investing it in their

businesses or expanding their output capacity etc.

All these factors combine to negatively affect the level of

industrial production in the economy.

INTEREST RATES

INDUSTRIAL PRODUCTION

37

CHAPTER 5

CONCLUSION AND RECOMMENDATIONS

Judging from the results obtained from the regression and

correlation analysis, the following conclusion and

recommendations can be made:

CONCLUSION

The relationship between the KSE index and the various

variables can be summed up as follows:

A highly negative and significant relationship between the

KSE index and the short-term interest rates has been

observed over the course of the past ten years, this

finding is consistent with the findings of (Rozeff,1974) in

which he too observed a strongly negative correlation

between interest rates and the stock market index.

Additionally studies conducted by (Geske & Roll, 1983) also

look into the relationship between interest rates and stock

market activity and find a significant negative

relationship between the two.

Perhaps the greatest amount of research into the

relationship between interest rates and stock market

activity has been undertaken by (Friedman & Schwartz, 1963,

1982). They redirected the attention of the researchers

38

towards interest rates for predicting and understanding the

behavior of stock exchanges. They have come up with the

most convincing evidence, as of yet, that interest rates

most significantly affect stock market activity.

The relationship between industrial production and the

Karachi Stock exchange has been discovered to be

insignificant, this finding is in direct contradiction to

most previous research conducted in advanced economies such

as the US and the UK. It is contrary to (Thornton, 1993)’s

study into the UK market in which he found that stock

returns tend to lead real economic activity. It is also

contrary to (Chang and Pinegar, 1989) and (Chen et al.,

1986) who also concluded that there is a close relationship

between stock market and domestic economic activity.

This result seems to indicate that the Karachi stock

exchange is not efficient in the sense that it does not

reflect the country’s true economic activity but is highly

affected by changes in monetary variables, suggesting

speculative intentions at work.

This result is however consistent with the finding of

(Mallaris and Urrutia, 1991) who studied the linkage among

industrial production, interest rates and the United

States’ S&P 500 Index; their results seem to suggest that

the interrelationships among the three variables are not

statistically significant. The bulk of the studies

conducted however report a significant relationship between

industrial production and stock market activity.

39

The relationship between inflation and the Karachi Stock

Exchange is found to be negative. This result conforms to

previous research conducted by (Fama, 1981) who

investigated the relationships among stock returns, real

economic activity, inflation and money. The results also

conform to studies conducted by (Darat and Mukherjee,

1987), (Naka, Mukherjee & Tufte, 1996) and (Bhattacharya

and Mukherjee, 2002). These studies employed various

regression models and concluded that domestic inflation is

one of the most prominent factors influencing stock market

activity. They also note that an increase in inflation

rates eats into corporate profits, which in turn makes

dividends diminish. When dividends decrease, the expected

return of stocks decrease, causing stocks to depreciate in

value further eroding the stock index.

The relationship between inflation and interest rates is

well documented not only through empirical research but

also by virtue of deep rooted economic theory. The result

obtained from this study also validates these theories.

There seems to be a strong positive correlation between

these two variables for the most obvious reason that money

supply has a direct impact on inflation; and the interest

rate is the single most powerful determinant of money

supply. Furthermore the results obtained are in conformity

with previous research conducted by (Chen et al., 1986) and

(Fama, 1990)

The relationship between inflation and industrial

production appears to be negative. Once again this result

is in unison with previous research findings by (Plosser,

40

1989) which suggest that there is a significant negative

relationship between industrial production and inflation.

The inflation rate is an important element in determining

industrial production due to the fact that during the times

of high inflation, people recognize that the market is in a

state of economic difficulty. People are laid off work,

which causes production to decrease. When people are laid

off, they tend to buy only the essential items. Thus

production is cut even further.

Finally, the relationship between interest rates and

industrial production gives a significant negative outlook.

This result is further reinforced by a previous research

study conducted by (Barro, 1977) which suggest a negative

correlation between the aforementioned variables.

Another convincing study conducted by (Kydland & Prescott,

1990) looks into this very relationship and concludes that

credit considerations, which are directly affected by

interest rates, could play an important role in current and

future industrial activity.

41

RECOMMENDATIONS

Based on the results obtained from the empirical analysis

of key economic variables over the past ten years, the

following recommendations can be suggested:

To recall, the most striking discovery of this

research has been the insignificant relationship

between the Karachi Stock Exchange and the level of

industrial production in the economy. This result

seems to indicate that the Karachi stock exchange is

not efficient in the sense that it does not reflect

the country’s true economic activity but is highly

affected by changes in monetary variables.

The above mentioned fact suggests that the KSE is

moved largely by speculative motives rather than

‘real’ production and performance oriented motives,

policy decisions must be made to prevent this

behavior.

The ratio of blocked to floating shares in Pakistan

must be altered. In Pakistan this ratio is an alarming

80 to 20, i.e. 80% blocked shares in relation to 20%

floating. In comparison the US has on average the

exact opposite ratio of blocked to floating shares,

i.e. 20:80 or 20% blocked in relation to 80% floating.

This can be one of the reasons why the KSE index does

not reflect real economic activity. The staggering

amount of shares blocked reflect concentration of

ownership of KSE shares in the hands of a few,

including large institutional investors and foreign

42

owners etc, whereas the meager amount of floating

shares reflect the share ownership by the general

public. This unhealthy ratio encourages stock market

manipulations by a selected group of individuals and

does not let the index reflect a true picture of the

economy. This tendency must be looked into and policy

decisions be made to change the ratio.

The recent moves made by the Securities and Exchange

Commission of Pakistan (SECP) regarding

demutualization of the KSE are a welcome change and

should be expedited as soon as possible.

The KSE should be converted from a guarantee into a

regular company with share capital and its shares

should be made to float the market just like any other

company. This change along with making the KSE board

accountable to the investor public would also

encourage broader share ownership and prevent

accumulation of majority of shares in the hands of a

select few.

Another way of ensuring that the KSE index reflect the

true performance of the Pakistani economy is to adopt

a share index which is a ‘composite’ of all the shares

listed in the stock exchange rather than an index of

just a few selected shares which are most widely

traded. This move will discourage manipulation of the

index and result in a more realistic appraisal of the

stock market in relation to other markets in the

region and beyond.

43

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Hamao, Y. (1988). “An empirical examination of the arbitrage pricing theory: Using Japanese data”, Japan and the World Economy 1, pp 45-61

Kaneko , T., and Lee, B-S. (1995) “ Relative importance of economic factors in the US and Japanese stock markets”, Journal of the Japanese and International economies 9, pp 290-307.

Kaul, G. (1987) Stock Returns and Inflation: The Role of the Monetary Sector, Journal of Financial economics, 18, 253-276

Kwon, C., Shin, T and Bacon, (1997), “ The Effect of Macroeconomic Variables on Stock Market Returns in Developing Markets”, Multinational Business Review, Fall 97, pp 63-70

Mukherjee, T. and Naka A. (1995), “ Dynamic Relations Between Macroeconomic Variables and the Japanese Stock Market, Journal of Financial Researech. XVIII(2), PP 223-237

Nasseh, A and Strauss, J. (2000), “Stock Prices and Domestic and International Macroeconomic Activity”, The Quarterly Review of Economics and Finance, 40, pp 229-245

Poon, S and Taylor, S.J. (1991), “Macroeconomic factors and the UK stock market”, Journal of Business and Accounting 18, pp 619-636

Andres, J., Mestre, R., Valles, J. 1997. .A Structural Model for the Analysis of the Impact of Monetary Policy on Output and Inflation., in Monetary Policy and the Inflation Process, BIS Conference Papers Vol.4.

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Hendry, D.F. 1999. Does Money Determine UK Inflation over the Long Run?, Nuffield College, Oxford, UK.

Government of Pakistan, “Economic Survey”, (Various Issues), Islamabad, Ministry of Finance.

Government of Pakistan, “Statistical Year Book of Pakistan”, (Various Issues), Islamabad, Federal Bureau of Statistics.

Barro, Robert J., 1977, Unanticipated money,output and the price level in the United States, Journal of Political Economy, 86, 549-580.

Chen, Nai, 1991, Financial investment opportunities and the macroeconomy, Journal of Finance, 46, 529-554.

Fama, Eugene, 1970, Efficient capital markets: A review of theory and empirical work, Journal of Finance, 383-417.

Fama, Eugene,1981, Stock returns, real activity, inflation, and money, American Economic Review, 71, 545-565.

Fama, Eugene, 1990, Stock returns, expected returns, and real activity, Journal of Finance, 45, 1089-1108.

Fama, Eugene, 1991, Efficient capital markets: II, Journal of Finance, 46, 1575-1617.

Friedman, Milton and Anna Schwart, 1963, Money and business cycles, Review of Economics and Statistics, 45, 32-64.

Friedman, Milton and Anna Schwart, 1982, Monetary trends in the United States and the United Kingdom (University of Chicago Press, Chicago IL).

Geske, R., and Roll R, 1983, The fiscal and monetary linkage between stock returns and inflation, Journal of finance, 38, 1-33

Kydland, Finn E., and Edward C. Prescott, 1990, Business cycles: real facts and a monetray myth, Quarterly Review of the Federal Reserve Bank of Minneapolis, 14, 3-18.

45

Mallaris, A.G. and Urrutia, J.L., 1991, An empirical investigation among real, monetary, and financial variable, Economic Letters, 37, 151-158.

Rozeff, Michael, 1974, Money and stock prices: Market efficiency and the lag in effect of monetary policy, Journal of Financial Economics, 2, 245-302.

Sharpe, William, 1964, Capital asset prices: A theory of capital market equilibrium under conditions of risk, Journal of Finance, 19, 425-442.

Darrat, A.F. and T.K. Mukherjee, 1987, The Behavior of the Stock Market in a Developing Economy, Economics Letters 22, 273-278.

Fama, E.F. and G.W. Schwert, 1977, Asset Returns and Inflation, Journal of Financial Economics 5, 115-146.

Lee, B.S, 1992, Causal Relationships Among Stock Returns, Interest Rates, Real Activity, and Inflation, Journal of Finance, 47, 1591-1603.

Sharma, J.L. and R.E. Kennedy, 1977, A Comparative Analysis of Stock Price Behavior on the Bombay, London, and New York Stock Exchanges, Journal of Financial and Quantitative Analysis 17, 391-413.

Sharma, J.L., 1983, Efficient Capital Markets and Random Character of Stock Price Behavior in a Developing Economy Indian Economic Journal 31, no.2, 53-57.

Friedman, B and K Kuttner, (1992), “Money, Income, Prices and Interest Rates”, American Economic Review, 82, 472-492.

Taylor, J B, (1999), “Monetary Policy Rules”, NBER Conference Report series, Chicago and London: University of Chicago Press, pp ix, 447.

Campbell, John and John Cochrane (1995), .By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior,. NBER Working Paper no. 4995 (January).

Bhattacharya, B., and J. Mukherjee, (2002) The Nature of the Causal Relationship between Stock Market and Macroeconomic Aggregates in India: An Empirical Analysis,

46

Paper Presented in the 4th Annual Conference on Money and Finance, Mumbai.

Campbell, J. Y. and Hamao, Y. (1992), Predictable Stock Returns in the United States and Japan; A Study of Long-term Capital Integration, Journal of Finance, 47, 43-67.

Cheung, Y. W. and Ng, L. K. (1998), international Evidence on the Stock Market and the Aggregate Economic Activity, Journal of Empirical Finance, 5, 281-296.

Cochrane, J. H. (1991), Production-based Asset Pricing and the Link between Stock Return and Economic Fluctuations, Journal of Finance, 46, 209-238.

Naka, A, Mukherjee, T. and Tufte, D. (1999), Macroeconomic Variables and the Performance of the Indian Stock Markets, Financial Management Association meeting, Orlando.

Pethe, A., and Ajit Karnik, (2000), Do Indian Stock Markets Matter?- Stock Market Indices and Macro-economic Variables, Economic and Political Weekly, 35 (5), 349-356

Rao, K. C. and A. Rajeswari, (2000), Macro Economic Factors and Stock Prices in India: A Study, Paper presented in the Capital Markets Conference 2000, Mumbai

47

Correlations, Standardized Multiple Regression Coefficients, Standard errors in

Parenthesis, t values in Brackets, F-statistics and p-values in Italic

Table 4.5

Intercept Interest

rates

Industrial

Production

Inflation R

Square

F

Statistic

KSE-100

Index

1.000

-

(284.287)

[15.310]

.000

-.834

-1.049

(26.938)

[-16.712]

.000

.272

-.097

(.643)

[-1.835]

.069

-.367

.266

(.736)

[4.343]

.000

.751 116.76

.000

The table shows the results in a summarized form

48

Figure 4.1

KSE-100 Index

0500100015002000250030003500400045005000

1994JA

N

OCT

JUL

APL

1997JA

N

OCT

JUL

APL

2000JA

N

OCT

JUL

APL

2003JA

N

OCT

KSE-100

Mean

Figure 4.2

Short term interest rates

0123456789

1994JA

N

SEP

MAY

1996JA

N

SEP

MAY

1998JA

N

SEP

MAY

2000JA

N

SEP

MAY

2002JA

N

SEP

MAY

STI

Mean

49

Figure 4.3

Inflation

0

50

100

150

200

250

300

350

1994JA

N

SEP

MAY

1996JA

N

SEP

MAY

1998JA

N

SEP

MAY

2000JA

N

SEP

MAY

2002JA

N

SEP

MAY

CPI

mean

Figure 4.4

Index of Industrial Production

050100150200250300350400450500

1994JA

N

SEP

MAY

1996JA

N

SEP

MAY

1998JA

N

SEP

MAY

2000JA

N

SEP

MAY

2002JA

N

SEP

MAY

IPI

mean

50

APPENDIX: DATA/OBSERVATIONS

Month INF IPI STI KSE1994JAN 265.58 295.7 6.67 2178.11FEB 268.84 274.3 6.67 2291.18MAR 269.97 276 6.67 2528.16APL 276.72 249.1 6.67 2448.71MAY 275.8 203 6.67 2381.72JUN 278.46 200.1 6.67 2244.04JUL 282.95 195.9 6.73 2319.77AUG 285.59 194.2 6.79 2281.36SEP 289.74 189.8 6.85 2196.65OCT 294.6 198.5 6.91 2324.67NOV 298.74 238 6.97 2157.97DEC 301.29 294.2 7 2143.31995JAN 306.17 317.3 6.95 2078.2FEB 305.77 284.7 6.9 1812.56MAR 308.46 234.5 6.85 1864.19APL 308.72 270 6.8 1711.71MAY 310.08 214 6.75 1532.71JUN 312.28 221.2 6.69 1513.49JUL 160.98 200.9 6.79 1605.89AUG 164.23 204.9 6.83 1801.71SEP 165.7 205.2 6.9 1754.53OCT 165.88 226.6 6.97 1663.87NOV 167.66 267.7 7.04 1547.14DEC 168.78 313 7.08 1416.91996JAN 169.41 298.8 7.11 1464.29FEB 170.6 275.7 7.14 1631.94MAR 172.9 289.9 7.17 1727.98APL 174.3 231.3 7.2 1571MAY 174.95 223.3 7.23 1715.64JUN 175.14 226.6 7.28 1749.66JUL 177.59 213.4 7.3 1653.92AUG 179.9 207.9 7.32 1502.54SEP 181.99 205.9 7.34 1353.67OCT 184.17 221.5 7.36 1397.49NOV 186.4 238.6 7.38 1500.47DEC 188.03 307.2 7.39 1474.211997JAN 192.11 290.5 7.48 1371.29FEB 194.2 264.4 7.59 1588.48MAR 193.33 300.8 7.66 1640.91APL 197.96 227 7.75 1602.68MAY 197.57 206.4 7.84 1541.98JUN 196.95 214.6 7.93 1504.54JUL 198.17 212.4 7.87 1989.51AUG 199.46 210.2 7.81 1762.29SEP 200.72 203.7 7.75 1849.7OCT 201.53 217.7 7.69 1875.01

51

NOV 203.03 241.5 7.63 1772.24DEC 203.26 336.7 7.59 1753.821998JAN 203.15 336.6 7.49 1609.16FEB 203.88 323.1 7.39 1681.83MAR 207.49 334.1 7.29 1553.06APL 208.42 263.1 7.19 1562.22MAY 208.73 220.7 7.09 1040.19JUN 209.71 219.6 7.02 879.61JUL 211.52 216.3 7.17 920.48AUG 213.37 219.3 7.32 970.78SEP 213.61 223 7.47 1111.46OCT 214.66 219.6 7.62 841.7NOV 215.68 243.3 7.77 1050.97DEC 216.19 346.4 7.93 945.241999JAN 215.8 331.8 7.82 900.58FEB 216.61 340 7.71 926.21MAR 217.36 362.2 7.6 1056.75APL 217.94 278 7.49 1107.02MAY 217.78 233 7.38 1222JUN 217.43 236.3 7.28 1054.67JUL 218.77 237.4 7.22 1251.79AUG 220.11 238.5 7.16 1206.51SEP 221.45 239.6 7.1 1199.29OCT 222.8 240.7 7.04 1189.32NOV 222.99 287.4 6.98 1247.4DEC 222.75 367.9 6.95 1408.912000JAN 223.2 325.6 6.89 1772.84FEB 223.16 315.4 6.83 1930.61MAR 225.12 272.2 6.77 1999.69APL 226.39 230.1 6.71 1901.07MAY 226.15 259.9 6.65 1536.65JUN 228.52 246.2 6.62 1520.73JUL 229.81 237.2 6.68 1554.9AUG 229.68 250.18 6.74 1518.27SEP 231.92 263.16 6.8 1564.78OCT 233.24 276.14 6.86 1489.32NOV 235.05 289.1 6.92 1276.05DEC 234 314.7 6.96 1507.592001JAN 233.62 344.3 6.98 1461.6FEB 233.43 382.5 7 1423.18MAR 234.54 361.7 7.02 1324.41APL 235.33 265.9 7.04 1367.05MAY 234.27 284.9 7.06 1377.61JUN 234.29 277 7.06 1366.43JUL 235.51 253.9 6.81 1228.89AUG 237.54 263.3 6.56 1258.43SEP 238.57 265.83 6.31 1133.43OCT 239.22 268.36 6.06 1406.05NOV 103.43 273.4 5.81 1358.16DEC 102.95 344.9 5.56 1273.06

52

2002JAN 103.06 415.1 5.45 1620.18FEB 103.39 349 5.34 1765.95MAR 104.74 380.2 5.23 1868.11APL 105.1 332.4 5.12 1898.95MAY 104.4 294.3 5.01 1663.34JUN 104.9 276.5 4.92 1770.11JUL 106.04 267.6 4.78 1787.59AUG 106.37 274.6 4.64 1974.58SEP 106.57 251.8 4.5 2018.75OCT 106.57 273.8 4.36 2278.54NOV 106.65 316.6 4.22 2285.87DEC 106.39 394.9 4.07 2701.412003JAN 106.56 417.4 3.7 2545.07FEB 107.06 394.3 3.33 2399.14MAR 107.09 454.3 2.96 2715.71APL 107.45 368.5 2.59 2902.41MAY 107.14 289.9 2.22 3099.04JUN 106.92 296.5 1.84 3402.47JUL 107.53 287.6 1.7 3933.37AUG 108.24 301.9 1.56 4461.47SEP 108.89 303.6 1.42 4027.34OCT 110.49 317.4 1.28 3781.03NOV 111.15 289.4 1.14 4068.29DEC 112.2 476 0.99 4471.6

53