Effect Of Key Economic Variables On Kse

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

naturally

5000

point

forced

a

level. lot

Such

of

a

radical

people

to

change uncover

has 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 Lahore

stock

exchange

(22%),

and

the

Islamabad

stock

1

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

theorists

consensus that

among

stock

macroeconomists

market

prices

and

are

finance

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

4

or 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

report

and

terms

therefore

have

been

it

is

used

extensively

appropriate

to

in

the

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

cases,

the

dependent variable. Descriptives:

Provides

the

number

of

valid

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: serial

Depicts

correlation

the

Durbin-Watson

of

the

test

residuals

result

and

for

casewise

diagnostics for the cases meeting the selection criterion. Predicted

Values:

Values

that

the

regression

model

predicts for each case. Unstandardized: dependent

The

variable.

value The

the

model

predicts

unstandardized

for

the

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

6

value

is

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

conclusion

that

by a

several

range

of

researchers financial

leads

and

to

the

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

fundamental

between

economic

stock

variables

market in

the

activity U.S.

and

is

well

documented (Fama 1970, 1990 and 1991). In recent years, numerous studies (Fama 1981, Chen, Roll and Ross 1986, Chen

1991)

activity

modeled

and

production

rates,

unemployment, dividend market

real

the

relation

economic spread,

stock

activities

productivity,

yield

between GNP

interest

yields,

etc.

These

activity,

real

economic

in

relationships activity

terms

growth

rates,

market of

rate,

inflation, among

and

stock

monetary

variables in the U.S. also have been studied by (Geske & Roll 1983), (Mallaris & Urrutia, 1991), (Darrat & Brocato,

9

1994),

(Darrat

&

Dickens,

1999),

while

Known,

(Shin

&

Bacon, 1997) studied these relationships in Korea. (Mallaris

et

industrial

production,

Index;

al.,

the

1991)

interest

results

interrelationships

studied

seem

among

the

rates to

the

linkage

and

the

suggest

three

among

S&P

500

that

variables

the

are

not

statistically significant, contrary to what the economic and

financial

challenged

literature

economic

assumes.

conventional

Since wisdom,

this it

finding 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

General

Theory

of

1930’s

and

the

impact

interrupted

the

research

of

of

Keynes’

the

Pre-

Keynesian economists, and during the 1950’s to the late 1960’s

the

activist

Keynesian

fiscal

doctrine

policy

of

distracted

aggregate attention

demand

and

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,

specialized

numerous

issues.

studies

(Rozeff,

have

1974)

focused

has

studied

on the

relationship between interest rates and stock prices, and (Barro,

1977)

between

monetary

(Fama,

1981)

returns,

has

analyzed factors

the

and

potential real

relationships

industrial

output.

investigates the relationships among stock

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 various

procedure

variables.

to

They

measure

fluctuations

conclude

that

for

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

11

other economic series typically associated with the U.S. industrial activity: 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.

(Friedman

et

volatility

al.,

of

Actually,

this

is

not

a

new

idea;

1963) had shown that changes in the

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 market

were

significant in explaining expected stock

activity:

industrial

production,

changes

in

the

risk premium, twists in the yield curve and, more weakly, measures expected

of

unanticipated

inflation

during

inflation periods

when

and

changes

these

in

variables

were highly volatile. Studies on non-US markets have mostly been based on the (Chen et al., 1986) approach. (Hamao, 1988) tested the

12

Japanese market and found strong relations, except for the case of Japanese monthly production. (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 reexamined 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

13

remove

important

information contained in the permanent

component of economic activity concerning the evolution of short-run movements. In comparison to the above, long-run relationship variables

between

has

stock

received

market

little

and

the

attention

of

economic

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

investigated.

and

financial

Correlation

between

variables stock

can

prices

be 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,

theoretical pragmatic stock

structure fashion

market

regression

empirical

models

have

been

the

indices

and

real

been

particularly

has

any

applied

to

model

two-way

without

in

relationship

economic

specific a

between

variables. popular

more

in

The 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

14

and 2002) recommend the use of Artificial Neural Network (ANN)

for

investigating the correlation relationship

well

as

forecasting

in

capital

markets,

which

as 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 macroeconomic variables. (Naka, Mukherjee and Tufte, 1996) have analyzed variables

relationship and

the

among

Indian

stock

selected market.

macroeconomic By

employing

a

regression model, they find that domestic inflation and domestic

output

are

the

two

most

prominent

factors

influencing stock market activity.

15

In a recent study under NSE Research Initiative (Kamath, 2002, paper no. 10) uses Artificial Neural Network (ANN) to 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,

relationships

between

macroeconomic

variables.

2002)

the Their

test

BSE

the

Sensex

major

causal

and

findings

five

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

manageable

number

2000)

use

stock of

market

when

reduced

factors.

(Pethe

correlation

models

economic

regression

and

into &

a

Karik,

to

test

relationship between stock market behavior and some macroeconomic 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

16

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

laid

off

work,

which

could

cause

production

to

decrease. 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

obtained

from

Statistical

Price the

Index,

State

Bulletin,

and

Bank

SBP’s

Interest

of

Annual

Rates

Pakistan’s Reports

were

Monthly 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

conducted

and

the

relationship

entered

the

required

tests

will

be

results will be used to analyze the

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 Std. R Model 1

R .867

The

above

model

has

been

displays

R,

R

of

Square R Square .751 .745

Explanation: which

Adjusted

table applied

squared,

adjusted

the

Estimate 361.86019

gives to

Error

the

the R

summary

data.

of

This

squared,

and

the

table 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. squared, squared,

It

which is

can

be

seen

presents giving

a

a

that

somewhat

value

of

even

the

reduced .745

adjusted value

which

of is

R R 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

22

variables

selected

in

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 shortterm 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 Sum of Model 1

Squares Regression 4586524 Residual

4.166 1518936

Total

4.688 6105460 8.853

Mean df

Square 15288414.

3

722

116

F

Sig.

116.76

.000

130942.80

119

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 Regression

i.e.

regression

displays

and

residual.

information

The

about

the

output

for

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

23

square (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

variables

(.000)

i.e.

CPI,

which IPI

means and

STI

that do

the a

independent

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

1

KSE INF IPI STI

Unstandardized

Standardized

Coefficients Std.

Coefficients

B 4352.300 3.198 -1.180 -450.183

Beta

Explanation:

The

Error 284.287 .736 .643 26.938

.266 -.097 -1.049

unstandardized

T

Sig.

15.310 4.343 -1.835 -16.712

.000 .000 .069 .000

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 the KSE index and all the independent variables except for the industrial production index. There is no significant

25

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 INF

IPI

STI

KSE

Pearson

1 Correlation Sig. N 120 Pearson -.410 Correlation Sig. .000 N 120 Pearson .641 Correlation Sig. .000 N 120 Pearson -.367 Correlation Sig. .000 N 120

Explanation:

As

a

IPI

STI

KSE

-.410

.641

-.367

.000 120

.000 120

.000 120

1

-.455

.272

120

.000 120

.003 120

-.455

1

-.834

.000 120

120

.000 120

.272

-.834

1

.003 120

.000 120

120

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

27

correlation coefficient is a measure of linear association between

two

coefficient correlation

variables. range

The

from

coefficient

values

-1

to

of

1.

indicates

the

The

the

correlation

sign

direction

of

the

of

the

relationship (positive or negative). The

absolute

indicates indicating

the

value

of

the

strength,

with

stronger

correlation larger

coefficient

absolute

relationships.

The

values

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

regression

analysis

production

index

the

result

obtained

from

the

which indicates that the industrial

does

not

do

a

good

job

explaining

variation in the KSE index.

28

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?

INDUSTRIAL PRODUCTION

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

significant

relationship

indicates between

that the

there

is

no

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

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? INTEREST RATES

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.

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

increase

in

people

interest find

it

rates. more

When

interest

profitable

to

keep

rates 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 decrease

rates

and

investment

investment. increases

and

When when

interest

rates

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?

INDUSTRIAL PRODUCTION

INFLATION

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

production

product

is

function.

the

The

heaviest

greater

entity

the

in

demand

its

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 decrease

income

in

the

thereby

reducing

production

demand

function

of

leading all

to

a

industries

across the board. The level of decrease in the production

33

function however depends on a multitude of 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? INFLATION

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 interest

positive rates

relationship can

be

between

attributed

to

inflation prudent

and

monetary

35

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?

INFLATION

There

is

a

KSE INDEX

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 Being

a

between

weak

the

above

mentioned

two

variables.

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

income,

purchasing

ceteris

power

paribus,

of

the

public’s

decreases.

disposable

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

available

for

speculative use of money. This

decrease

in

the

amount

of

money

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.

36

6. Do interest rates affect industrial production?

INTEREST RATES

Pearson’s

INDUSTRIAL PRODUCTION

correlation

results

correlation

between

industrial

production.

negative variables.

correlation Once

suggest

short-term The

again

the

interest

results

between

a

the

rates

indicate

above

negative

negative and

a

45.5%

mentioned

correlation

can

two 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.

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

observed

and

over

the the

short-term course

of

interest the

past

rates ten

has

been

years,

this

finding is consistent with the findings of (Rozeff,1974) in which he too observed a strongly negative correlation between

interest

Additionally

rates

studies

and

the

stock

by

(Geske

conducted

market &

index.

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

relationship activity 1963,

has

1982).

greatest between been They

amount interest

undertaken redirected

of rates by

research and

(Friedman the

into

stock &

attention

the

market

Schwartz, of

the

38

researchers

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

Karachi

Stock

between

industrial

exchange

has

production

been

and

the

to

be

discovered

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

relationship

also

concluded

that

there

is

a

close

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

39

conducted

however

report

a

significant

relationship

between industrial production and stock market activity. 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)

40

The

relationship

between

inflation

and

industrial

production appears to be negative. Once again this result is in unison with previous research findings by (Plosser, 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

industrial

relationship

production

between

gives

a

interest

rates

significant

and

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,

research

the

has

most

been

striking

the

discovery

insignificant

of

this

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.

alarming

80

to

In 20,

Pakistan i.e.

this

80%

ratio

blocked

is

an

shares

in

relation to 20% floating. In comparison the US has on average

the

floating

shares,

relation

to

reasons

why

economic blocked shares

exact

opposite i.e.

ratio

20:80

or

of

blocked

to

20%

blocked

in

80% floating. This can be one of the the

KSE

index

does

not

reflect

real

activity. The staggering amount of shares reflect in

the

concentration hands

of

a

of few,

ownership including

of

KSE

large

42

institutional

investors

and

foreign

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 should

company be

with

share

capital

and

its

shares

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

REFERENCES

Chen N.F., Roll R. and Ross S. (1986) “Economic Forces and the Stock Market”. Journal of Business, Vol 59, No 3, pp 383- 395 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 223237 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.

44

Hendry, D.F. 1999. Does Money Determine UK Inflation over the Long Run?, Nuffield College, Oxford, UK. Government of Pakistan, “Economic Survey”, Issues), Islamabad, Ministry of Finance.

(Various

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, 545565. 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 investigation among real, monetary, and variable, Economic Letters, 37, 151-158.

empirical financial

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 Inflation, Journal of Financial Economics 5, 115-146.

and

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 Longterm 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), 349356 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

Industrial

Inflation

R

F

KSE-100

1.000

rates -.834

Production .272

-.367

Square .751

Statistic 116.76

Index

-

-1.049

-.097

.266

(284.287)

(26.938)

(.643)

(.736)

[15.310]

[-16.712]

[-1.835]

[4.343]

.000

.000

.069

.000

.000

The table shows the results in a summarized form

48

Figure 4.1 KSE-100 Index 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0

KSE-100

OCT

APL

2003JAN

JUL

OCT

2000JAN

APL

JUL

OCT

1997JAN

APL

JUL

OCT

1994JAN

Mean

Figure 4.2

Short term interest rates 9 8 7 6 5 4 3 2 1 0

STI

MAY

SEP

2002JAN

MAY

SEP

2000JAN

MAY

SEP

1998JAN

MAY

SEP

1996JAN

MAY

SEP

1994JAN

Mean

49

MAY

SEP

2002JAN

MAY

SEP

2000JAN

MAY

SEP

1998JAN

MAY

SEP

1996JAN

MAY

SEP

1994JAN

MAY

SEP

2002JAN

MAY

SEP

2000JAN

MAY

SEP

1998JAN

MAY

SEP

1996JAN

MAY

SEP

1994JAN

Figure 4.3

Inflation

350

300

250

200 CPI

150 mean

100

50

0

Figure 4.4

Index of Industrial Production

500 450 400 350 300 250 200 150 100 50 0 IPI

mean

50

APPENDIX: DATA/OBSERVATIONS Month 1994JAN FEB MAR APL MAY JUN JUL AUG SEP OCT NOV DEC 1995JAN FEB MAR APL MAY JUN JUL AUG SEP OCT NOV DEC 1996JAN FEB MAR APL MAY JUN JUL AUG SEP OCT NOV DEC 1997JAN FEB MAR APL MAY JUN JUL AUG SEP OCT

INF 265.58 268.84 269.97 276.72 275.8 278.46 282.95 285.59 289.74 294.6 298.74 301.29 306.17 305.77 308.46 308.72 310.08 312.28 160.98 164.23 165.7 165.88 167.66 168.78 169.41 170.6 172.9 174.3 174.95 175.14 177.59 179.9 181.99 184.17 186.4 188.03 192.11 194.2 193.33 197.96 197.57 196.95 198.17 199.46 200.72 201.53

IPI 295.7 274.3 276 249.1 203 200.1 195.9 194.2 189.8 198.5 238 294.2 317.3 284.7 234.5 270 214 221.2 200.9 204.9 205.2 226.6 267.7 313 298.8 275.7 289.9 231.3 223.3 226.6 213.4 207.9 205.9 221.5 238.6 307.2 290.5 264.4 300.8 227 206.4 214.6 212.4 210.2 203.7 217.7

STI 6.67 6.67 6.67 6.67 6.67 6.67 6.73 6.79 6.85 6.91 6.97 7 6.95 6.9 6.85 6.8 6.75 6.69 6.79 6.83 6.9 6.97 7.04 7.08 7.11 7.14 7.17 7.2 7.23 7.28 7.3 7.32 7.34 7.36 7.38 7.39 7.48 7.59 7.66 7.75 7.84 7.93 7.87 7.81 7.75 7.69

KSE 2178.11 2291.18 2528.16 2448.71 2381.72 2244.04 2319.77 2281.36 2196.65 2324.67 2157.97 2143.3 2078.2 1812.56 1864.19 1711.71 1532.71 1513.49 1605.89 1801.71 1754.53 1663.87 1547.14 1416.9 1464.29 1631.94 1727.98 1571 1715.64 1749.66 1653.92 1502.54 1353.67 1397.49 1500.47 1474.21 1371.29 1588.48 1640.91 1602.68 1541.98 1504.54 1989.51 1762.29 1849.7 1875.01

51

NOV DEC 1998JAN FEB MAR APL MAY JUN JUL AUG SEP OCT NOV DEC 1999JAN FEB MAR APL MAY JUN JUL AUG SEP OCT NOV DEC 2000JAN FEB MAR APL MAY JUN JUL AUG SEP OCT NOV DEC 2001JAN FEB MAR APL MAY JUN JUL AUG SEP OCT NOV DEC

203.03 203.26 203.15 203.88 207.49 208.42 208.73 209.71 211.52 213.37 213.61 214.66 215.68 216.19 215.8 216.61 217.36 217.94 217.78 217.43 218.77 220.11 221.45 222.8 222.99 222.75 223.2 223.16 225.12 226.39 226.15 228.52 229.81 229.68 231.92 233.24 235.05 234 233.62 233.43 234.54 235.33 234.27 234.29 235.51 237.54 238.57 239.22 103.43 102.95

241.5 336.7 336.6 323.1 334.1 263.1 220.7 219.6 216.3 219.3 223 219.6 243.3 346.4 331.8 340 362.2 278 233 236.3 237.4 238.5 239.6 240.7 287.4 367.9 325.6 315.4 272.2 230.1 259.9 246.2 237.2 250.18 263.16 276.14 289.1 314.7 344.3 382.5 361.7 265.9 284.9 277 253.9 263.3 265.83 268.36 273.4 344.9

7.63 7.59 7.49 7.39 7.29 7.19 7.09 7.02 7.17 7.32 7.47 7.62 7.77 7.93 7.82 7.71 7.6 7.49 7.38 7.28 7.22 7.16 7.1 7.04 6.98 6.95 6.89 6.83 6.77 6.71 6.65 6.62 6.68 6.74 6.8 6.86 6.92 6.96 6.98 7 7.02 7.04 7.06 7.06 6.81 6.56 6.31 6.06 5.81 5.56

1772.24 1753.82 1609.16 1681.83 1553.06 1562.22 1040.19 879.61 920.48 970.78 1111.46 841.7 1050.97 945.24 900.58 926.21 1056.75 1107.02 1222 1054.67 1251.79 1206.51 1199.29 1189.32 1247.4 1408.91 1772.84 1930.61 1999.69 1901.07 1536.65 1520.73 1554.9 1518.27 1564.78 1489.32 1276.05 1507.59 1461.6 1423.18 1324.41 1367.05 1377.61 1366.43 1228.89 1258.43 1133.43 1406.05 1358.16 1273.06

52

2002JAN FEB MAR APL MAY JUN JUL AUG SEP OCT NOV DEC 2003JAN FEB MAR APL MAY JUN JUL AUG SEP OCT NOV DEC

103.06 103.39 104.74 105.1 104.4 104.9 106.04 106.37 106.57 106.57 106.65 106.39 106.56 107.06 107.09 107.45 107.14 106.92 107.53 108.24 108.89 110.49 111.15 112.2

415.1 349 380.2 332.4 294.3 276.5 267.6 274.6 251.8 273.8 316.6 394.9 417.4 394.3 454.3 368.5 289.9 296.5 287.6 301.9 303.6 317.4 289.4 476

5.45 5.34 5.23 5.12 5.01 4.92 4.78 4.64 4.5 4.36 4.22 4.07 3.7 3.33 2.96 2.59 2.22 1.84 1.7 1.56 1.42 1.28 1.14 0.99

1620.18 1765.95 1868.11 1898.95 1663.34 1770.11 1787.59 1974.58 2018.75 2278.54 2285.87 2701.41 2545.07 2399.14 2715.71 2902.41 3099.04 3402.47 3933.37 4461.47 4027.34 3781.03 4068.29 4471.6

53