stock exchange volatility

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An Assessment of Factors Influencing Stock Price

Volatility of Shares Trading At DSE

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CHAPTER ONE

1.0 Introduction

This section explains the general picture of the study. It includes the background tothe study, the statement of the problem studied, the objectives and research questions,

its scope and the importance of the study.

1.1 Background of study

In recent years there has been increased attention, by both the economics profession

and the popular press, on the topic of stock price volatility. Interest peaked after the

New Economy period when many high-tech stocks that were considered overvalued

experienced a large drop in their share price. But still now there persists the idea that

the knowledge economy (less unfashionable a term than the New Economy), has

resulted in greater volatility, especially of small innovative firms which tend to go

public earlier in their life-cycle than in previous times.

Yet, in reality, there has been no trend increase of aggregate stock price volatility

(Schwert 1989; 2002) . Particular periods have been characterized by high volatility,

such as the 1970s and the 1990s, but the increase has not persisted. Firm specific

volatility has, on the other hand, experienced a trend increase over the last 40 years

(Campbell et al. 2001) .

Various works have highlighted technological change as one of the key factors

responsible for this increase in firm specific risk, as well as the periodic increases of

aggregate stock price volatility. For example, Shillers work (2000) has shown that

excess volatility, i.e. the degree to which stock prices are more volatile than

underlying fundamentals, is highest in periods of technological revolutions when

uncertainty is greatest. Campbell et al. (2001) find that firm level idiosyncratic risk,

i.e. firm specific volatility (as opposed to industry specific or market level), has risen

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since the 1960s and claim that this might be due to the effect of new technologies,

especially those related to the IT revolution, as well as the fact that small firms tend

now to go public earlier in their life-cycle when their future prospects are more

uncertain. And Pastor and Veronesi (2004) claim that the reason that high tech firms

have prices that appear unjustifiably high (at the beginning of a bubble) is not due to

irrationality, but due to the effect that new technology has on the uncertainty about a

firms average future profits. The basic idea behind all these works (reviewed further

below) is that innovation, especially when radical, leads to high uncertainty hence

more volatility.

One of the difficulties in predicting the stocks rate of return is the uncertainty and

unpredictability of investors when making decisions about investing. The concept of

behavioural finance becomes more popular and is taken into account in recent papers

studying about the behaviour of stock prices. The conceptual theory of behavioural

finance is on the grounds that human beings are not always rational; investors may

make irrational decisions when it comes to investing. If investors are not rational, but

instead they are inclined to behavioural biases, then we may need new models that

incorporate this finding, Jirawattanakitja .A (2004) .

1.2 Statement of the problem.

Stock prices are characterized by volatility. When significant changes occur, investors

tend to panic.

Different factors influence the movement in stock prices. For example, when the

events in Asia of 1998 occurred, the prices of stocks got really dynamic, which was

reflected in a negative way even on investors that held high expertise. During the

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following months the S&P 500 experienced one of its highest drops of 20% observed

in recent times. This fall was later followed by a huge increase of 30%, which in itself

represented a record climb. The media concentrated its attention to the Dow and

spoke of stocks as if they were deprived of any volatility. What happened actually

was that different companies experienced the events in different ways since they were

affected in varying degrees 1.

Going back to the 1998 crisis, investors generally bought stocks of companies that

have proven their consistency and were part of the Dow. They preferred them because

they represented a higher degree of stability. On the other hand, negatively influenced

were companies of a smaller size.

Hull (2002) argued that the volatility of stock price measured how uncertain were

about future stock price movents.As volatility increase the chance of the stock

performed well or very poor also increase. For the owner of stock these two outcomes

tend to offset each other

Stock market performance was measured by percentage change in the stock price or

index value that was the return over a set period of time. One commonly used

measure of volatility was standard deviation of returns which measure the dispersion

of return from an average, Kisarika (2007).

The study of behaviour of stock prices has retained its interest to many researchers for

a number of years and continues being a popular topic owing to its unclear and

puzzled characteristics. Despite numerous endeavours and efforts of researchers and

investment managers to discover the behaviour and characteristic of stock prices, no1 http://www.stock-market-investors.com/stock-investing-basics/stock-price-volatility.html

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apparent and well-matched results have ever been obtained. In like manner, various

types of asset pricing models and statistical methodologies have been brought into the

studies conducted by many researchers but still none of them could successfully

reveal this information , Jirawattanakitja A (2004) .

There are many attitudes toward the movement in the price of a stock. For

example some claim that if the price of the stock starts to fall it will continue to do so,

whereas if the price of a stock starts to rise it will continue to do so as well.

On the other hand, others hold the more optimistic view that every fall of a stock's

price will be followed by a rise.

Generally investors try to forecast the movement of stocks in a particular direction.

Those who believe in the first claim will immediately try to purchase the stock when

they see that it has experienced a significant increase in its price, thinking that it will

continue to rise for the years to come.

However, a failure to make a preliminary research may result in losses since the price

of the stock may have been pushed well above its intrinsic value. As a result the

investors who were the first to purchase the stock may sell it and enjoy their profits,

leaving you with painful losses 2.

Several questions remain to be answered. The examples of the cause of being

unsuccessful in this effort are the nature of the stock market that is unpredictable,

uncontrollable and unstable, the unknown factors that affect the rate of return of stock

and the degree of investor attitude. This is what prompted the researcher of this study

to assess the factors influencing stock price volatility at DSE in order to fulfil the gap

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of what other researchers have left, in accordance to the literature have been

reviewed.

1.3 History of Dar es Salaam Stock Exchange.

The Dar es Salaam Stock Exchange (DSE) was incorporated in September 1996 as a

private company limited by guarantee and not having a share capital under the

Companies Ordinance (Cap. 212). The DSE is therefore a non-profit making body

created to facilitate the Government implementation of the economic reforms and in

future to encourage the wider share ownership of privatized and all the companies in

Tanzania and facilitate raising of medium and log-term capital.

The formation of the DSE followed the enactment of the Capital Markets and

Securities Act, 1994 and the establishment of the Capital Markets and Securities

Authority (CMSA), the industry regulatory body charged with the mandate of

promoting conditions for the development of capital markets in Tanzania and

regulating the industry. The governing organ of the DSE is the Council of the

Exchange, which consists of 10 members representing various interest groups in the

society.

Trading activities at the DSE commenced on 15th April 1998 after two years of

background preparatory work under the stewardship of the Government through the

Capital Markets and Securities Authority. The opening of the Trading Floor coincided

with the listing of TOL Limited (formerly Tanzania Oxygen Limited), as the first

company on the new Exchange. Till now there are 11 companies that have been listed

and trading shares at DSE.Such companies are; TBL, TOL, TATEPA, TCC, SIMBA,

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SWISSPORT, TWIGA, KA, EABL, and JHL.The first seven companies are domestic

and the later three are cross listed companies 3.

1.4 DSE Organisation Structure.

The DSE is a body corporate incorporated in 1996 under the Companies Act, 2002

(Cap.212) as a company limited by guarantee without a share capital. The organ gram

of the DSE is spelt out under the Articles of Association of the DSE. The DSE

governance structure is built on three pillars. The apex pillar is the General Meeting

of the members of the company. This is a forum of all subscribers to the

Memorandum and Articles of Association of the DSE. This forum is the final organ

in the governance ladder within the DSE.

The second pillar (below the General Meeting) is the Governing Council, which is

duly appointed in accordance with the Articles of Association of the DSE. All the

governing functions of the DSE are vested into the Council. The Council is

accountable to the General Meeting 4.

1.5 Trading Operation.

1.5.1 Official Trading Hours.

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Trading takes place throughout the week from Mondays to Fridays (except public

holidays) starting from 10.00 a.m. to 12.00 noon. However due to the low level of

activity, trading sessions ends before 12.00 noon 5.

1.5.2 Trading System

Trading is conducted at the DSE Trading Floor through an Automated Trading

System (ATS). This is an electronic system, which matches bids and offers using an

electronic matching engine. LDMs converge at the trading room and post their orders

in the ATS. Matched orders are displayed on the computer terminal in the trading

room and projected in the public gallery. Currently, the ATS operates on a local area

network (LAN). Future plans include operation in a wide area network (WAN), which

can be accessed by brokers even out of Dar es Salaam. This system will enable the

DSE to meet the potential growth expected to take place in the Tanzania securities

industry (More details are found in the DSE Blue Print) 6.

1.5.3 Market Surveillance.

Both the Capital Markets and Securities Authority (CMSA) and DSE monitor the

market trading activities to detect possible market malpractices such as false trading,

market manipulation, insider dealing, short-selling, etc. DSE is responsible for on-

line/on-site surveillance and the CMSA for on-line/off-site surveillance. The CEO of

the DSE has the authority to suspend anytime offers and bids that are deemed to be

suspicious 7.


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1.6 Research objectives.

The general objective of this research was assessing the factors affecting stock price

volatility of shares trading at DSE.To attain general objective, research specific

objectives were formulated and have included the following;

(i) To assess whether the rate of changes in dividend payments per share to

shareholders by companies trading shares at DSE have an effect on

stock price volatility at DSE.

(ii) To assess whether the transformation of information relating companies

trading shares at DSE have an effect on stock price volatility.

(iii) To assess whether changes in earnings of companies trading shares at

DSE have an effect on stock price volatility at DSE.

(iv) To assess whether changes in demand or supply of shares traded at

DSE, have an effect on stock price volatility.

(v) To assess whether changes in price for products or services offered as

business by companies trading shares at DSE have an effect on stock

price volatility.

1.7 Research questions.

In order to accomplish the research target, some questions are asked. This research

has the following questions;

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Ha : There is relationship between changes in dividend payments per

share to shareholder and stock price volatility at DSE.

(ii) For testing whether there is relationship between transformations of

information relating companies trading shares and stock price volatility

at DSE.

Ho: There is no relationship between transformations of information

relating companies trading shares and stock price volatility

at DSE.

Ha: There is relationship between transformations of information

relating about companies trading shares and stock price volatility

at DSE.

(iii) For testing whether there is relationship between changes in earnings of

companies trading shares and stock price volatility at DSE.

Ho: There is no relationship between changes in earnings of companies

trading shares at DSE and their stock price volatility.

Ha: There is relationship between changes in earnings of companies

trading shares at DSE and their stock price volatility.

.

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(iv) For testing whether there is relationship between changes in demand or

supply of share traded and stock price volatility at DSE.

Ho: There is no relationship between changes in demand or supply of

shares traded and stock price volatility at DSE.

Ha: There is relationship between changes in demand or supply of

shares traded and stock price volatility at DSE.

(v) For testing whether there is relationship between changes in price for

products or services offered as business by company trading shares and


Ho: There is no relationship between changes in price for products or

services offered as business by companies trading share and stock

price volatility at DSE.

Ha: There is relationship between changes in price for product or

services offered as business by companies trading share and stock


1.9 Significance of the study.

As stock price volatility has proved to be the obstacle under which most investors

have been victimised, example the crisis of TOL Company due to decrease of its

share price from 500 during IPO to its current 330 Tanzania shillings as DSE quarter

report shows, investors lost their capital. Therefore assessment on factors influencing

stock price volatility is necessary in order to provide knowledge that will enable

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various interested parties to be aware of what causes the stock price volatility, hence

minimizing risks of their invested capital not to be subjected to unexpected losses.

The study is also relevant in sense that, one of the major ways to build portfolios is to

invest in shares of stocks. As the share price has proven to fluctuate therefore it is

necessary to identify what are the factors affecting share price. This study is aiming at

assessing factors affecting share price, therefore it will provide a significant

knowledge to those interesting in engaging ,or dealing with stock investment about

what affecting share price which is the vital tool required since in order to succeed in

stock investment it is important to know factors affecting their prices.

Also the findings of this research contribute to the partial fulfilment of requirements

for the award of Bachelor of Business Administration (BBA) at TUDARCO.

Lastly, the research report is relevant as library material that will be used as reference

in further studies relating stock price movements

1.10 Scope of the study.

The part explain the range at which research will be possible to be conducted .Under

this area researchers limitation of the study and delimitation are explained.

1.10.1 Limitations of the study.

As time and financial factors are considered, this research study was conducted at

Dar e s Stock Exchange only .The licensed Dealing Members, DSE staffs, and

Investment Advisors form DSE were used as source of data collected where .The

study could not make possible collect data from different financial analysts in the

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country as finding them requires time and lots of fund on which this study could not

afford to have had. Findings from research depended on reliability and validity of

data based on the limited information from the sample and not the whole population.

Also the sampling methodology falls under the non-probability methods and thus the

extent to which the sample represents the population cannot be claimed with

confidence.

1.10.2 Delimitations of the study.

Selection of DSE as the case study was based on the fact that, it is the sole market

that officially allows trading shares of stock for all the listed that are trading shares .

Since the study was concerning with the factors influencing stock price volatility of

shares trading at stock exchanges then at DSE,required information are easily found ,

as it is the sole stock exchanges that has been officially authorised to deal with capital

marketing activities which also include stock exchanging in Tanzania.

1.11 Conclusion.

This chapter included the back ground of the problem, where it has discussed how

different people were then came to deal with the issue of stock price volatility, then

statement of the problem followed on which different events of stock volatility were

shown, and how stock exchanges have been victimized with the issue of stock price

volatility with the vivid example of different crisis faced large stock exchanges in the

world. Then the chapter showed general objective which was to assess factors

influencing the stock price volatility of shares trading at DSE. But, in order to

accomplish the general objective, the specific goal of research was outlined .The

research questions were then formulated and to answer those research question

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tentative statement of truth were then established which were then tested to answer

the research questions. After that importance of this study has been explained and

what would make the study successful and those things that were assumed to act as

factors that cause the study not successfully was the shown at end of this chapter one.

CHAPTER TWO

RELATED LITERATURE REVIEW

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2.0 Introduction.

In this chapter different works that relating the problem under study is reviewed from

books, findings from other researches, and other material.

2.1 Theoretical Literature Review

2.1.1 Introduction to Stocks

Stock represents a piece of ownership of a particular company. When you

purchase a stock of a company you immediately become one of its owners. As a result

you have right over the profits the company makes and some voting rights depending

on the type of the stock. So, if you consider the stock profitable and beneficial you

should strive to purchase as much shares of it as possible.

The price of the stock is set following certain rules. Generally, stocks are traded on

the stock market, which tends to determine the value of the company on daily basis.

The major factor that determines the value of a stock is its earnings. They are mostly

in the focus of attention. Every company makes a report of the profits it has made

every quarter. These numbers are of great interest to most investors, since they tend to

base their investment decisions on them. Investors use earnings per share as an

indicator of the current state of the company and its future position.

2.1.2 Bid and Ask Prices

The stock exchanges are the places where the actual setting of the stock prices

happens. They are the places where bid and ask prices cross their ways and the

exchange serves as the intermediary between the two. So, as an educated investor you

should be acquainted with the meaning of bid and ask prices.

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Bid price is the price announced by the buyer at which s/he is willing to purchase a

stock. While, ask price, is the price announced by the seller at which s/he is willing to

sell a stock.

The major role of the stock exchange is to coordinate the bid and ask prices of buyers

and sellers. This service, of course, is not for free.

Bid and ask prices are never the same. In fact, the price announced by the seller (the

ask price) is always higher than the bid price. As a result you are required to pay the

ask price in case you have decided to purchase a stock and pay a higher price. On the

other hand, if you decide to sell a stock you will have to receive the bid price, which

is of a lower amount than the ask price 8.

2.1.2.1 Bid/Ask Spread

The difference between ask and bid prices is referred to as the spread. The spread

goes directly to the pockets of the broker or specialist who was responsible for the

stock transaction. However, the spread is also used for the paying of other fees, not

only the commission of the broker.

Unless you use specific market orders, it will be almost impossible to determine the

price you will get as both a buyer and seller. This is especially true for the actively

traded stocks, which are characterized by their extremely dynamic nature.

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Even though the bid/ask spread eats up part of your profit its avoidance is not

recommended since it has proven its benefits as a working system throughout the

years 9.

2.1.3Factors that may affect the stock price volatility

In accordance with different sources, the stock prices volatility may be affected by

variety of factors depending on the particular characteristics of stock exchange. In this

research ,several factors that might contribute to stock price volatility at DSE are

discussed in following paragraphs.

2.1.3.1 Company Market Capitalization or Company Size

When you decide on the investment in a particular stock you should consider

the size of the company that issues it. Additionally, you should decide on the amount

of the money you would allocate. This is required since companies of different sizes

react in a different way to market conditions and changes.

Company size can be classified in one of the two ways: by revenue, and by market

capitalization (also known as market cap)

The first classification, namely by revenue, is rarely used. This is so since the

differences observed from one industry to another usually distort the size of the

company.

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On the other hand, the most commonly used measure is the second one - market

capitalization. Market Capitalization calculation use the following formula to estimate

market cap: Market cap = (number of outstanding shares) x (current stock price)

Example: Company X possesses 200,000,000 shares of common stock outstanding.

The current market price for one share is $40. So, company X's market cap is $8.0

billion (200,000,000 x $40 = $8.0 billion). By applying this formula to any other real

company you will be able to measure its market cap 10

2.1.3.2 Dividend

Dividends are payments made by a company to its shareholders . When a company

earns a profit , that money can be put to two uses: it can either be re-invested in the

business (called retained earnings ), or it can be paid to the shareholders of the

company as a dividend. Paying dividends is not an expense ; rather, it is the division of

an asset among shareholders. Many companies retain a portion of their earnings and

pay the remainder as a dividend. Publicly-traded companies usually pay dividends on

a fixed schedule, but may declare a dividend at any time, sometimes called a special

dividend to distinguish it from a regular one.

Dividends are usually settled on a cash basis, as a payment from the company to the

shareholder. They can take the form of shares in the company (either newly-created

shares or existing shares bought in the market), and many companies offer dividend

reinvestment plans , which automatically use the cash dividend to purchase additional

shares for the shareholder 11.

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2.1.3.2.1 Dividend Yield Explanation

Different investors should use different fundamental analysis for the different

stocks they target. For instance, it will be hard for an investor, who wants to invest in

high growth technology stocks to find information on them into the various stock

screens. This is true especially when the criteria selected are dividend paying

indicators.

On the other hand, dividend investors, searching for a stock that will return them

stable current income, should use Dividend Yield in their comparison of the different

stocks available on the market, which fall under the investor's consideration.

Dividend yield represents the percentage return by the company that goes to the

shareholders in the form of dividends.

Dividend Yield = Annual Dividend per Share / Stock's Price per Share

Companies that are relatively young tend to pay less in dividends to their shareholders

since their focus is on growth and thus they need funds to finance the growth. On the

other hand, older companies tend to pay more dividends to their shareholders since

they have reached their growth capacity 12.

2.1.3.3 Fundamental analysis theory

This theory is based on assumption that, a stocks intrinsic or real value is

determined by the companys future earnings. The theory is explaining that, for any

companys stock price to increase or decrease in value, it depends on the companys

future earnings. If the company is expecting higher earnings than its presents

earnings, the companys stock should increase in value .Also if the company is

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expecting fewer earnings than its present earnings, the companys stock should

decrease in value, Dlabay (2004).

The mathematical model below is useful to support this fundamental theory. Jordan

(2000) wrote this mathematical model which could be applied when determining the

price of stock at different periods.

The mathematical is as follows;

Po = D + P . Let be equation 1

1 + R

P = D + P . Let be equation 2

1 + R

If P

in equation 1 is substituted by D+

P . of equation 21 + R

D + D + P .

Then Po = 1 + R .

( 1 + R )

Po = D . + D + P .

( 1 + R ) ( 1 + R )

Po = D . + D . + P .

( 1 + R ) ( 1 + R ) ( 1 + R )

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Since we to find the price in two periods, then we add,

P = D + P .

1 + R

D + P .

Po = D + D + 1 + R .

( 1 + R ) ( 1 + R ) ( 1 + R )

Po = D . + D . + D . + P .+

.

( 1 + R ) ( 1 + R ) ( 1 + R ) ( 1

+ R )

According to this mathematical model ,the current price of stock is equal to present

value of all future dividends .But since there are infinite future dividends ,then Jordan

(2000) made three assumption that enable the determination of current price of stock.

These assumptions are such as;

( a )Dividend has zero growth rate.

( b )Dividend grows at constant.

( c )Dividends grow at constant rate after some length of time.

( a ) The case of dividend has zero growth rate

When dividend on a share has zero growth, it means paid do not increase over time

and is therefore constant through out the life of dividend to be paid. When the

dividend has zero growth rates, then a stock is termed or treated as preferred stock.

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Back to mathematical model

Po = D + D + D +

(1 + R ) (1 + R ) (1 + R )

For dividend grows at zero rate, then

D = D = D = D

Therefore the value of Po can then be written as:

Po = D + D + D +

(1 + R ) (1 + R ) (1 + R )

Because the dividend is always the same, the stock price Po can be viewed as an

ordinary perpetuity with a cash flow equal to D every period. Then the value or price

of stock is thus given by:

Po = D .

R

Where ,

Po Stock price

D Dividend

R Required rate of return or discount rate

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If assumption is made that R is constant, then Po = D . Then the mathematical

Constant

model became the relationship model which can be written as Po D in other

words it means ,stock price is directly proportional to dividend to be paid.

(b ) The case of dividend grow at constant rate

If dividend grows at steady rate, it means the dividend paid increases at constant

rate .In this case if we take Do to be the dividend just paid and g, to be constant

growth rate, then the value of future dividend Di can be written as

Di = Do ( 1 + g )

By taking the value of stock at present value Po as an ordinary perpetuity with a cash

flow equal to D every period

Po = D .

R

But, since the dividend is growing at constant growth then by considering the constant

growth rate g, value of stock Po is treated as growing perpetuity and the formula is

written as:

Po = Di .

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( R g )

Di = Do ( 1 + g )

Where ,

Di Future or expected dividend

Do Dividend paid or current dividend

g Dividend growth rate

R Discount rate or required rate of return

From above formula of Po when dividend is growing at constant growth, if

assumption is made that discount rate R and growth rate are constant, then R g also is

assumed to be constant as well. Therefore, value of stock Po is then written as:

Po = Di .

Constant

Mathematically it also can be written as Po Di, which means that price of stock,

is directly proportional to future or expected dividend to be paid.

( c ) The case of grows at constant rate after some length of time

When the dividend grows after some length of time, then stock price at any time can

be written as;

Pt = Dt ( 1 + g ) .

( R g )

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Then Pt = D (t +) .

( R g )

Since D (t +) = Dt ( 1 + g )

Where,

D (t+) Future Dividend after some time t when last dividend Dt was

paid

Dt Dividend paid at time given time t.

R Required rate of return or discount rate

g Dividend growth rate

If assumption is made that discount rate or required rate of return R, and growth rate g

are constant, then, R g is also assumed to be constant.

Therefore Po = D (t +) .

Constant

Mathematically Po D (t+) which means stock price is directly proportional

future dividend after some time t .Time t can be hours , days ,weeks, months or

years.

In all three cases, the model has shown that when required rate of return or discount

rate R and dividend growth rate g remain constant, then stock price is directly

proportional to future or expected dividend to be paid.

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Hence, the fundamental analysis theory is verified by these mathematical models

since if future earnings are expected to be higher than present earnings, then future

dividend will also expected to increase as the results the stock price will also tend

increase, due to the fact from the model that stock price is directly proportional to the

dividend to be paid in the future.

2.1.3.4 Technical analysis theory

This theory is based on assumption that, a stock market value is by force of supply

and demand in the market as whole. Unlike the fundamental analysis theory which

based on expected earnings or the intrinsic value of an individual companys stock,

technical analysis theory is based on factors found in the market as whole.

The technical factors described by this theory are such as; total number of share

traded, number of buy orders, and number of sell orders over a period of time.

Technical analysis or sometimes called chartists, construct charts that plot past price

movements and other market averages. These charts allow to observe trends and for

the market as whole that enable to predict whether a specific stocks market will

increase or decrease in value, Dlabay (2004).

This technique is concerned with such relationships as between the price of the stock

and the number of shares traded during a trading day (volume). Technical analysts

tend to use different mathematical techniques in order to predict future trends in the

prices of a target stock.

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Charts are drawn that picture the direction of the price of the stock and its future

changes. The terminology applied in technical analysis is somehow strange, but once

you get used to it, it will make more sense to you. Making sound interpretations of the

resulting form the technical analysis charts and graphs will result in more reliable

decisions regarding the future rises and falls of the stock's price.

Charts also include additional information which is accompanied by a price history.

You will often find a moving average as part of charts.

Technical analysis includes the daily calculations of the stocks price including a time

period that may range from 90 to 200 days 13.

2.1.3.5 Efficient Market Hypothesis

Pandey (2004) defined capital market efficiency as ability of securities to reflect and

incorporate all information, almost instantaneously, in their prices.

Levy H (1998 ) wrote about the efficient market theory. The argument was market is

efficient, and in an efficient market no abnormal profit is available which means you

can not beat the market. From Levy H (1998) the efficient market hypothesis (EMF)

distinguishes among three level of efficiency; weak , semi strong ,and strong form of

EMF.

According to Levy H (1998) the weak form of EMF claims that, the stock market

behave similarly to the tossing of a coin, and that it has no memory of past

outcomes. The semi strong form of EMF states that a stocks current market price

reflect all public available information, including the firms EPS ,its financial

statement, and stocks past prices. The strong form of EMF states that the current

stock price reflects all public and privately held information.

13

http://www.stock-market-investors.com/stock-investing-basics/technical-analysis- basics.html

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According to Dlabay (2004 ) this theory is sometimes called random walk theory and

is based on assumption that , stock price movements are purely random. Advocates

of efficient market theory assumes the stock market is completely efficient ,Therefore

buyers and sellers have considered all of available information about an individual

stock in such a manner that ,any news whether affecting individual stocks in market

or over all markets stocks. This information is quickly absorbed by all investors

seeking profit thus the current stock price changes.

Gitman J L ( 2004 ) explained efficient market theory as it describe the behaviour of

an assumed perfect market in which (a) security are typically in equilibrium which

means that they are fairly priced and that their expected returns equal their required

returns ,(b) security prices fully reflect all public information available and react

swiftly to new information, therefore those market participants who have non public

inside information may have an unfair advantage that enables them to earn an

excess return , and ( c ) because stocks are fairly priced ,investors need ot waste time

looking for mispriced ( undervalued or overvalued ) securities.

2.1.3.6 Supply and demand theories

The law of supply states that quantity supplied is related to price. It is often depicted

as directly proportional to price: the higher the price of the product, the more the

producer will supply, ceteris paribus. The law of demand is normally depicted as an

inverse relation of quantity demanded and price: the higher the price of the product,

the less the consumer will demand. Everything else that could affect supply or

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equilibrium of price and quantity. The model incorporates other factors changing such

equilibrium as reflected in a shift of demand or supply.

The laws of supply and demand state that the equilibrium market price and quantity of

a commodity is at the intersection of consumer demand and producer supply. Here,

quantity supplied equals quantity demanded (as in the enlargeable Figure), that is,

equilibrium. Equilibrium implies that price and quantity will remain there if it begins

there. If the price for a good is below equilibrium, consumers demand more of the

good than producers are prepared to supply. This defines a shortage of the good. A

shortage results in the price being bid up. Producers will increase the price until it

reaches equilibrium. If the price for a good is above equilibrium, there is a surplus of

the good. Producers are motivated to eliminate the surplus by lowering the price. The

price falls until it reaches equilibrium 15.

The intersection of supply and demand curves determines equilibrium price (P0) and

quantity (Q0). Source:http://en.wikipedia.org/wiki/Supply_and_demand

15 http://en.wikipedia.org/wiki/Supply_and_demand

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2.1.3.7 Dividend Irrelevance: The miller Modigliani (MM) Theorem

The Modigliani-Miller theorem (of Franco Modigliani , Merton Miller ) forms the basis

for modern thinking on capital structure . The basic theorem states that, in the absence

of taxes , bankruptcy costs, and asymmetric information , and in an efficient market ,

the value of a firm is unaffected by how that firm is financed. [1] It does not matter if

the firm's capital is raised by issuing stock or selling debt. It does not matter what the

firm's dividend policy is. Therefore, the Modigliani-Miller theorem is also often

called the capital structure irrelevance principle 16.

2.2 Empirical Literature Review

Murphy and Sabov (1992), were probably the first to study the efficiency of any

transition capital markets. They analyzed the Hungarian market. This particular

capital market has a longer history than others (among the transition economies), so

they also had more data that enabled them to do a more detailed quantitative analysis.

They studied the efficiency of the equity market, the bond market and the derivatives

market. One of their interesting findings is that there is hardly any statistically

significant relationship between the prices of these securities and so called

fundamentals. The credit risk doesnt affect the prices of bonds, the net income and

the dividend yields dont have a statistically significant impact on the prices of shares,

and there seems to be no relation between the stock price volatility and the prices of

the options. In spite of all this empirical evidence, they found no support for the

16

http://en.wikipedia.org/wiki/Modigliani-Miller_theorem

32
http://en.wikipedia.org/wiki/Franco_Modiglianihttp://en.wikipedia.org/wiki/Merton_Millerhttp://en.wikipedia.org/wiki/Capital_structurehttp://en.wikipedia.org/wiki/Taxhttp://en.wikipedia.org/wiki/Bankruptcyhttp://en.wikipedia.org/wiki/Asymmetric_informationhttp://en.wikipedia.org/wiki/Efficient_markethttp://en.wikipedia.org/wiki/Modigliani-Miller_theorem#cite_note-0%23cite_note-0http://en.wikipedia.org/wiki/Stockhttp://en.wikipedia.org/wiki/Dividendhttp://en.wikipedia.org/wiki/Franco_Modiglianihttp://en.wikipedia.org/wiki/Merton_Millerhttp://en.wikipedia.org/wiki/Capital_structurehttp://en.wikipedia.org/wiki/Taxhttp://en.wikipedia.org/wiki/Bankruptcyhttp://en.wikipedia.org/wiki/Asymmetric_informationhttp://en.wikipedia.org/wiki/Efficient_markethttp://en.wikipedia.org/wiki/Modigliani-Miller_theorem#cite_note-0%23cite_note-0http://en.wikipedia.org/wiki/Stockhttp://en.wikipedia.org/wiki/Dividend


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hypothesis that the investors could use these inefficiencies for designing some

profitable trading rules. Trading on inside information also didnt result in above

average returns. This indicates that the lack of experience and inefficient securities

valuation does not necessarily imply above average rates of return on securities

trading. Even though prices of securities behave as sub martingales and therefore

dont contradict the weak-form efficiency hypothesis, that doesnt mean that they

reflect the fundamentals, Murphy and Sabov (1992).

Gordon and Rittenberg (1995), studied the efficiency of the Polish capital market.

Due to the lack of information, necessary for the standard tests of efficient market

hypothesis, they decided to do a more qualitative analysis. They tried to find a model

that would best describe current and past movements of stock prices on the Warsaw

stock exchange. They claim that the investors psychology plays a much more

important role on this market then is usually acknowledged by the supporters of the

efficient market hypothesis. They showed that there is a very strong relation between

the investors behaviour (their confidence in the market, the public opinion and the

fads) and the prices determined by the market. Further on, Gordon and Rittenberg

claim that many measures, which were aimed at increasing the efficiency of the equity

market on the Warsaw Stock Exchange, delivered the adverse results. Limited size of

the orders and the daily price limits, for example, only precluded the market prices

from reflecting all the information, according to them. Using the 10% rule (the market

price of stock cannot change by more than 10% during a trading day) as a sort of

trading rule, they showed that investors could realize abnormal returns just by using

the information on historical prices. This contradicts the notion of the weak form

efficiency of the Polish capital market.

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Filer and Hanousek (1996), compared the informational efficiency of the Czech,

Hungarian, Polish and Slovak capital markets, using the variance ratio and runs tests

on the local stock exchange indices. Based on the test results for weekly and monthly

returns they found evidence that equity markets in Central Europe are close to being

weak form efficient. Further on, they tested the semi-strong form efficiency of the

Czech market and had to reject the hypothesis. They concluded that, to the extent that

it is possible to test conventional types of efficiency with the limited data available to

date, the markets in these countries dont seem to be less efficient than the far more

developed equity market.

Ameer et al (1996), suggest that investments in environmental management and

improved performance can be justified, in many cases, on purely financial grounds,

and that the net financial impact of prospective environmental investments can now

be evaluated more fully than before. Our results show that firms will increase

shareholder value if they make environmental investments that go beyond strict

regulatory compliance. How much further they should go will vary by company,

though this question also may be addressed empirically.

Our work suggests that environmental improvements such as those we have evaluated

might lead to a substantial reduction in the perceived risk of a firm, with an

accompanying increase in a public companys stock price, of perhaps five percent.

Investments in environmental management and performance may be costly.

Nonetheless, when appropriately evaluated, many of these investments may be shown

to provide substantial, positive returns and lasting value to the firm.

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Campbell et al. (2001) study the idiosyncratic versus systematic nature of volatility by

decomposing the return of a typical stock into three components: the market wide

return, the industry specific residual and a firm specific residual. They use variance

decomposition analysis to study the volatility of these components over time. The

firm specific residual is the idiosyncratic component of risk, while the market wide

return captures the systematic component of risk. They find that while aggregate

market and industry variances have been stable (updating and confirming Schwerts

1989 finding that market volatility did not increase in the period 1926-1997), firm

level variance displays a large and significant positive trend, actually doubling

between 1962 and1997. They claim that this increase is related to the impact of the IT

revolution on various factors including the speed of information flows.

Jirawattanakitja A (2004), on her study specifically to explores the degree of

industry effect towards the stocks rate of return of Thai stocks. The based conceptual

theories of this study were the Capital Asset Pricing Model (CAPM), the Arbitrage

Pricing Theory (APT), and the Three Factor Model. The ability of the CAPM in

explaining the stock price behaviour has been questioned by many researchers - King

(1966); Meyers (1973) and Livingston (1977). The findings of these authors were that

the market factor alone could not explain the return on stock and in some periods, the

relationship between the rate of return of stock and beta seemed to disappear. In

proceeding with effort to identify these factors that affect stock price

Jirawattanaktja (2004) found the industry effect shows no significant effect towards

the stocks rate of return in the Thai stock market during the period of January 1998

through December 2002. The revealed that, there was no strong evidence to show that

the industry effect plays a significant role in a Thai stocks rate of return. In the light

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of all these findings, the results definitely suggest that other factors, as representative

for industry effect must be included in the model. It could be said that the industry

index could not be solely representative for the industry factor but other industrial

factors i.e. industry growth rate, any circumstances affecting a particular industry

should be taken into account and added in the model.

Based on the fact that the period chosen in the study falls in a highly volatile period

where the SET index reached its highest and lowest point within the period of the

study, this may turn away the result from what it should be if it is conducted under

normal circumstances.

It is not necessary that firms in the same industry have the same market effect. Hence,

it is entirely possible that a common industry influence could be overpowered by each

stocks unique market influence if the price movements are not adjusted for the

differential market influence prior to testing for the intra-industry effect.

The work of Pastor and Veronesi (2005) provides interesting insights on the

relationship between innovation, uncertainty and both the level and volatility of stock

prices. They claim that if one includes the effect of uncertainty about a firms average

future profitability into market valuation models, then bubbles can be understood as

emerging from rational, not irrational, behavior about future expected growth.

Building on the result in Pastor and Veronesi (2004) that uncertainty about average

productivity increases market value (because market value is convex in average

productivity), they extend the model to explain why technological revolutions cause

the stock prices ofinnovative firms to be more volatile and experience bubble like

patterns. The basic idea is that when a firm introduces a new technology, its stock

price rises due to the expectations regarding the positive impact of the new

technology on its productivity. Volatility also rises because risk is idiosyncratic when

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technology is used on a smallscale. But if/once the new technology gets adopted

throughout the economy, then risk becomes systematic causing the stock price to fall

and volatility to decrease. This bubble like behavior is strongest for those

technologies that are the most uncertain (and the most radical).

Hale G et al (2006); found an empirical regularity that stronger creditor protection

reduces the volatility of stock market prices. They analyze two distinct mechanisms

that characterize equity price volatility: government guarantees and creditor

protection. Using a Tobin q model, we demonstrate that weak creditor protection that

gives rise to government guarantees and tightens credit constraints, increases stock

price volatility. Empirically, accounting for the probability of financial crises, we find

that government guarantees and weak institutions that tighten credit constraints

increase aggregated stock price volatility.

2.3 Research Gap

According to literature reviewed, most researchers findings did not reveal what was

the reason for stock price volatility. Most researchers findings showed there was no

statistically significance relationships between the price volatility of securities studied

including stock, and the variables tasted to verify their relationship. For example

Murphy and Sabov (1992), one of their interesting findings was that, the credit risk,

the net income and the dividend yields dont have a statistically significant impact on

the prices of shares, and there seems to be no relation between the stock price

volatility and the prices of the options. In spite of all this empirical evidence, they

found no support for the hypothesis that the investors could use these inefficiencies

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for designing some profitable trading rules as the result these findings still leave a gap

for other researcher to continue on searching foe factors influencing stock price

volatility.

Gordon and Rittenberg (1995), studied the efficiency of the Polish capital market.

they showed that investors could realize abnormal returns just by using the

information on historical prices. This contradicts the notion of the weak form

efficiency of the Polish capital market hence it provide a gap for this study to test

market efficiency on DSE to see how it support the market efficiency theory.

Filer and Hanousek (1996), compared the informational efficiency of the Czech,

Hungarian, Polish and Slovak capital markets, using the variance ratio and runs tests

on the local stock exchange indices. Due to their findings they showed that efficiency

of capital Markey can be influenced by development level of stock exchange as they

concluded that, to the extent that it is possible to test conventional types of efficiency

with the limited data available to date, the markets in these countries dont seem to be

less efficient than the far more developed equity market. which also give the gap for

this research also to test the market efficiency theory as development level of DSE

differ form other stock exchanges that already studied.

Also Kisarika (2007) when assessing impact of holiday seasons on stock price, her

findings revealed that there was no significant stock price volatility in holiday seasons

compared to normal season, hence she recommended for further research on what are

the factors influencing stock price volatility. Her recommendation has provided the

gap that initialises this research to be conducted.

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2.4 Conclusion

This chapter was used to discuss on some literature that relate to the topic under

study. Various theories have been used to support the study, then findings from

different researchers have also been discussed and then the gap was revealed that was

then expected to be filled by the findings from this research study. Different sources

of data such as books, internet, and journal have been used to provide all the

information to this chapter.

CHAPTER THREE

RESEARCH METHODOLOGIES

3.0 Introduction

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Under this chapter the arrangement and procedure on how the research was carried

out is reviewed and presented .It explain where, how, and by which methods the

research was conducted .It also shows how data was analysed and then presented.

3.1 Area of study

Here the area under which the study was conducted, and the population that was

involved for sampling purposes is explained.

3.1.1 Location

This research was conducted at Dar e s Salaam Stock Exchange, as it is the sole

authority given the responsibility of listing the business firms that are engaging in

security market especially those companies issuing stocks to the public.

3.1.2 Unit of Inquiry / Population

The population that was used as source of data collected on this study included three

parties which were; DSE staffs, Licensed Dealing Members, and Investment Advisors

registered with DSE. The population was chosen on the advantage that they are direct

involving with stock exchanges ,therefore they are more familiar on the stock price

movements and they are experienced with what influencing these price movements

hence this population was best to assess the factors that could lead to volatility of

stock price at DSE.

3.2 Research design

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Selltez, C and others (1962) 17 defined research designs as the arrangement of

conditions for collection and analysis of data in a manner that aims to combine

relevance to the research purpose with economy in procedure. Therefore in

arrangement of conditions for collection and analysis of data, the researcher is

expecting to use both case and descriptive research designs with the aim of

identifying the various characteristics of the problem under study, assessing why the

problem exist and what can be done to it.

By using both case and descriptive research design a researcher worked efficiently

and effectively under constraints of time and cost as the research concentrating only

on a single unit (i.e. Dar e s Salaam Stock Exchange) and studies the various factors

that may influence stock price volatility.

3.3 Sampling Design

According to Kothari (2004), sample is the selection of some part of an aggregate or

totality on the basis of which a judgement of inference about the aggregate or totality

is made. In other words, sampling is the process of obtaining information about an

entire population by examining the part of it.

3.3.1 Sampling Frame

Among the population used on this study, the sample was selected based on some

expertise. From DSE only staff working on finance department was used as part of

sample, from Licensed Dealing Members only Authorised Dealer Representatives

(ADR) were used as source of sample selected, and Licensed Investment Advisors

17 See in Kothari, CR, (2003): Research Methodology; methods and techniques;2 nd Edition; New Age International Publishers; New Delhi.

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registered . At DSE share are bought and sold by these licensed dealing members on

behalf of all investors who are dealing with sock investments, and they are also bid

for shares issued by the company during Initial Public Offering, therefore they are

experiencing price movements for these shares at DSE.

3.3.2 Sampling Technique

Krishnaswami and Ranganatham (2006), point out that, sampling techniques or

methods may be classified into two generic types which are, probability or random

sampling and non probability or non random sampling.

This research applied non probability sampling. Since using probability sampling

might have led in selecting the sample that was not willingly to provide the

information required on time hence due to time constrain, non probability sampling

application was favoured in the advantage that sample selection was based on easily

availability of information required with less time consumption.

3.3.3 Sample Size

Studying the whole population is very difficult as the financial and time limit hinders

the process. Therefore selecting some of units to represent the characteristics of the

whole population is more feasible than inclusion of the whole population. The

research had a sample of 20 respondents, among these 20 respondents, 12 were

ADRs, 7 were Licensed Investment Advisors, and 1 respondent who is Finance

Assistance at DSE. The sample size include % of ADR, % of total Licensed

Investment Advisors.

3.4 Data types and sources.

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3.4.1. Primary Data.

These data were generated through questionnaire which was sent to DSE, ADRs when

they were trading shares at DSE and at their offices, and DSE Licensed Investment

Advisors. Also personal interview was then conducted regarding research findings in

order to obtain data from financial expertise that was then used in discussion or

conclusion part of the research.

3.4.2. Secondary Data.

These are data that were employed in the research after being acquired from some

other researches and data gatherers. They consist of some of the literature such as

journals and publications and other documented sources relating to the case study.

Also a DSE article was used as source of such data.

3.5 Data collection methods

In collecting relevant data, basically the following methods were employed. These are

questionnaire, interview, and documentation.

3.5.1. Questionnaire

The study used structured questionnaire which comprised of closed questions, one

open question, and rating scale questions that enabled the collection additional

information on the subject concerned.

According to Kothari (2004),questionnaire are less cost even when the sample is large

and widely spread geographically, it also give enough time to provide well thought

out answers which were advantageous to this research. Like any other data collection

technique, the questionnaire also have some demerits as, possibility of late reply, and

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mostly applicable when the respondents are educated and coorperating.To overcome

the demerits, questionnaire sent to respondent were made sure that are filled on the

same day they were sent and picked up by data collector, and to ensure no uneducated

respondents will be included, the respondents were selected on the basis of their

knowledge about the capital markets and stock price fluctuation since they are

engaging in capital market and others are finance consultants.

According to Spalding (2005) , questionnaire are shorter, and therefore easier to

create .Because of this, they are inexpensive and can give quick, focused results. The

participants know that their answer are completely anonymous, so they a more honest

set of answer. On the other hand, because questionnaires are generally brief, some

participants may not answer all of the questions or may misinterpret the questions.

But to overcome this demerit, the questionnaire was constructed using the language

that participants understood, and keep question clear and brief, and respondents were

provided with the anonymous of some complex vocabulary found by them in

questionnaire to ease them on answering the questionnaire.

3.5.2 Documentation

Here information was collected through the past records of listed companies at

DSE.Quarterly reports, and other documents such as price index provide helpful

information that used during documenting.

3.5.3 Interviewing

Interviewing involves presentation oral-verbal stimuli and reply in terms of oral-

verbal responses (Kothari, CR, 2003).The interview carried were based on the

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findings on which the respondents interviewed were asked to give their opinion

regarding the findings .Interview involved DSE Licensed Investment Advisors.

3.6 Data Analysis and presentation Methods

Data collected was analysed quantitatively, reliability test was done to provide with

how the questionnaire was consistency. The study involved multivariate analyses,

where correlation and regression methods were applied to show the relationship

between variables under study. And hypothesis test was applied for verifying

existence of the relationship between variable, Chi Square test was used in hypothesis

testing. The analysis and presentation of data collected was carried on by the help of

SPSS as one of data analysis software.

3.7 Data Presentation

The findings from data analysis are presented on tables and graphs.

3.8 Conclusion

This chapter described how the research was carried out.It explain the place where the

research was conducted,thereafter,explaining the population on which the respondents

were chosen, then the number of respondents that were used as sample for data

collection purposes. The type of design used under the research also has been

described, with the technique used for sample selection. Then the data collection

instruments applied on this research following with how the analyses were carried out

and lastly how the research findings were presented. Chapter follow is concerning

with how data were presented and analysed.

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CAPTER FOUR

DATA PRESENTATION AND ANALYSIS

4.1 Introduction

This chapter presents, analyse, and discuss the data collected. Data presentation and

analysis tasks were done with the help of SPSS package. The chapter is organised in

three parts, the first part describe reliability of instrument used for data collection, the

second part describe respondents characteristics, the third part describe presentation

and analyses of data collected here there are testing of research hypothesis that are

used to answer the research questions in order to attain research objectives, and also

there is analysis of correlation and regression used to show the nature of relationship

existed between variables.

4.2 Measure of Reliability for research instruments used.

To measure whether scale scores are relative reliable for respondents in the study,

Cronbachs alpha was used. This measure the internal consistency of the responses.

According to Nunnallys (1978),it was recommended that the minimal internal

consistency should be 0.70,if Cronbachs alpha exceed 0.70 then the scale scores are

relative reliable for respondents in the study.

The research questionnaire used for data collection was tested its reliability, and the

results are presented below.SPSS was used as the tool for reliability test.

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R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)

Fluctuation Reason has zero variance

N of Cases = 20.0

Reliability Coefficients 11 items

Alpha = .7362

According to the results it is shown that the questionnaire used for data collection was

reliable since Cronbachs Alpha calculated is 0.7362 has exceeded the minimal alpha

of 0.70 that was recommended by Nunnalys (1978). Therefore, it is concluded that

the questionnaire used was reliable. .

4.3 Respondents characteristics

This study comprised of 20 respondents who are people that have knowledge on stock

price movements .Their selection was based on job they are current doing and

experience they have on finance field. Professionally these 20 respondents

included;12 respondents who are Authorised representative Dealers from DSE

Licensed Dealing Members ,and 7 respondents who are Licensed Investment

Advisors registered by DSE ,and 1 respondents who is Finance Assistant at DSE.

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4.3.1 Respondents duration of experience on their profession

To get assurance of data collected from sample selected is based on expertise and not

anticipations, the respondents were asked to tell the number of years they have

experiencing with their current job that fit to provide data required in this research.

Table 4.3.1: Respondents years of working experience.

Frequency Percent Cumulative PercentOne year or less 0 0 0One up to three

years

3 15 15Three up to five

years

4 20 35Above five years 13 65 100Total 20 100

From Table 1,results indicates that,65% of all respondents have experience in their

current job for three up to five years, following 20% of respondents who have

working in current job for one up to three years ,and 15% or respondents showing to

have experience of more than five years in the job. According to the results, majority

of respondents used in the research have enough experience in their field as they have

three up to five years of working experience which is enough for them to gain familiar

with stock price movements and what are the factors that affect stock price volatility.

4.4 Presentation and analysis of data collected.

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This part involve with presentation, analyses, and discussion of research questions

that were set in order to full the research objectives. This part included two sections,

one was the analysis of correlation and regression among variable studied, and last

was the hypothesis test which was used to verify statements used in answering

research question that lead in attaining of specific and general objective of the

research.

4.4.1 Correlation and Regression analyses

The question number 3 fro part A was used to provide required data regarding the

dependent variable under study, and all questions from part B of the questionnaire

were used to provide data needed for analyses of independent variables under study.

The independent variables that were correlated and regressed with dependent variable

are; the changes in dividend payments per share to shareholders by companies trading

shares at DSE, the transformation of information relating to companies trading shares

at DSE, the changes in earnings of companies trading shares at DSE, the changes in

demand or supply of shares trading at DSE, and the changes in price for products or

services offered as business by companies trading share at DSE.

The dependent variable that was used in correlation and regression analysis is

frequency of stock price volatility.

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The correlation and regression between the variables under study were analysed by

the help of SPSS on which the results were then presented in Tables and graphs, with

formulation of some equations that were established after the analyses as follows.

4.4.1.1 Correlation Analysis

This analysis is considering to measure association between two variables, and

determining the extent to which the variables are linearly related. And, whether such

relationship exists or not. Correlation is used to provide a measure of the relative

strength of the relationship between two variables. The Spearman rank correlation

coefficient ,is used to measure the relationship between variables under this research

because data analysed are in rank order(ordinal),and Spearman has been developed

for the purpose of analysis ordinal data, Anderson et al, (2003). Correlation analysis

has been used to show the extent and nature of relationship between independent

variable and dependent variable. Correlation coefficient and level significance is

calculated to verify whether the relationship exists is of significant or not. According

to Anderson et al, (2003) the variables are significant related only if their calculated

level of significance is not more than 5% or 0.05, therefore the critical value of

significance level is 5% or 0.05 calculated significance levels above the critical value

means there is no significant relationship between independent variable and

dependent variable.

Each independent variable was separately correlated with dependent variable, and the

results of the analysis are presented below as follows;

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4.4.1.1.1 Correlation Analysis 1:

The independent variable analysed is, the changes in dividend payments per share to

shareholder by companies trading shares, and dependent variable is the frequency of


Question 5 from the questionnaire was used to provide data required for analysis of

independent variable and question 4 from the same questionnaire was used to

providing data required for analysing the dependent variable.

The results from correlation analysis are presented on the table 4.4.1.1.1 below.

Table 4.4.1.1.1: Results of correlation analysis between the changes in dividend

payments and frequency of stock price volatility.

Spearmans

Correlation

Coefficient

Value

Significance

level

Number of

Tails

N = Sample

size

0.471 0.036** 2 Tails 20

** Correlation is significant at the .05 level (2-tailed).

Results from the table 4.4.1.1.1 above is shows that, the calculated significance level

is 0.036, which is less than the critical value of 0.05.Therefore, the calculated results

indicates there is significance relationship between the changes in dividend

payments per share to shareholder by companies trading shares , and the frequency

stock price volatility at DSE. Also the table shows that the variables analysed gave the

correlation coefficient with positive value of 0.471. This positive sign means that,

increase of dividend payments per share to shareholders by companies trading shares

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at DSE goes with increase in the stock price volatility, and decrease of dividend

payments per share to shareholders by companies trading shares at DSE goes with

decrease in the stock price volatility also increase as well. And, its corresponding

value of 0.471 explains the strength of relationship existing between these two

variables; changes in dividend payments per share and stock price volatility at DSE.


The independent variable analysed is, the transformation corporate information

relating to companies trading shares, and dependent variable is the frequency of stock





The results for correlation analysis are presented on the table 4.4.1.1.2 below.

Table 4.4.1.1.2 : Results of correlation analysis between the transformation of

information, and frequency of stock price volatility.

Spearmans

Correlation

Coefficient Value

Significance level Number of Tails N = Sample size

0.579 0.007* 2 Tails 20

* Correlation is significant at the .01 level (2-tailed).

It is observed from the table 4.4.1.1.2 above that, the significance level calculated is

0.007, which is less than 0.01 and critical value 0.05.Therefore,for such results; the

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analysis then indicates that, there is significance relationship between the

transformation information relating to companies trading shares at DSE, and the

frequency of stock price volatility . Moreover the table shows that the variables

analysed gave the correlation coefficient with positive of valued 0.579.The positivity

of such value conclude that, increase in rate of transformation of information relating

to companies trading shares at DSE lead to more frequencies of stock price volatility,

and decrease in rate of transformation of information relating to companies trading

shares at DSE lead to fewer frequencies of stock price of shares traded at

DSE.According to the analysis, the value of 0.579 portrays the strength of such

relationship relating independent, and dependent variable respectively.


The independent variable analysed is, the changes in earnings of companies trading

shares at DSE, and dependent variable is frequency of stock price volatility.





Table 4.4.1.1.3: Results of correlation analysis between the changes in earnings, and


Spearmans Significance Number of Tails N = Sample size

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Correlation

Coefficient Value

level

0.500 0.025** 2 Tails 20

** Correlation is significant at the .05 level (2-tailed).

Findings from the table 4.4.1.1.3 above shows that, the calculated significance level

is 0.025, which is less than the critical value of 0.05.Thus the calculated results

indicates that there is significance relationship between the changes in earnings of

companies trading shares at DSE , and the frequency of stock price volatility. In

addition the table shows that the variables analysed gave the correlation coefficient

with positive value of 0.500.Considering the positive sign of calculated spearman

correlation coefficient, these two variables are said to have direct proportional

relationship with each other. The sign means that, increase in earnings of companies

trading shares at DSE cause these companies stock prices to increase and decrease inearnings of these companies trading shares at DSE lead to decrease in their stock price

of shares trading at DSE as well. When the changes in earnings are increasing, the

stock price volatility increases also, and when the changes in earnings reduced then

the volatility are reduced unless other factor are constant. The strength of the

relationship between these two variables is given by spearman correlation coefficient

value, and the strength is 0.500.


The independent variable analysed is, the changes in demand or supply of shares

traded at DSE, and dependent variable is frequency of stock price volatility at DSE.

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Table 4.4.1.1.4: Results of correlation analysis between the changes in demand or

supply of shares traded, and frequency of stock price volatility at DSE.

Spearmans

Correlation

Coefficient Value

Significance level Number of Tails N = Sample size

0.660 0.002* 2 Tails 20

* Correlation is significant at the .01 level (2-tailed).

It can be observed from the table 4.4.1.1.4 above that, the Spearman correlationcoefficient value calculated is 0.660 and the association between these variables is

significance at 0.002 level, which is less than 0.01 the critical value of 0.05.Therefore

the calculated results indicates that, there is significance relationship between the

changes in demand or supply of shares traded at DSE, and the frequency of stock

price volatility. The positive value of correlation coefficients displayed in the table

4.4.1.1.4 indicates that, when changes of demand or supply of shares at DSE increase,

the frequencies of stock price increase. And when the changes of demand or supply of

shares at DSE decreases, the frequency of stock price volatility decreases also. The

spearman correlation shows that the changes in demand or supply of shares at DSE

have strong positive relationship with frequency of stock price volatility, and such

strength is valued 0.660.

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The independent variable analysed is, the changes in price for products or services

offered as business by companies trading shares at DSE, and dependent variable is





The results for the analysis are presented on table 4.4.1.1.5 below.

Table 4.4.1.1.5: Results of correlation analysis between the changes in price for

products or services offered as business by companies trading shares, and frequency

of stock price volatility

Spearmans

Correlation

Coefficient Value

Significance

level

Number of Tails N = Sample size

0.312 0.181*** 2 Tails 20

*** Correlation is significant at the level greater than 0.05 (2-taied).

The summary of results from the table 4.4.1.1.5 above shows that ,calculated

Spearman correlation coefficient value is 0.312, and the significance level is 0.181,

which is greater than the critical value of 0.05.Therefore, the calculated results

indicates that the relationship exist between the changes in price for products or

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services offered as business by companies trading shares , and the frequency of stock

price volatility at DSE, is not significance. This means that changes of prices for

products or services offered as business by companies trading shares at DSE does not

imply that, there are also changes to these companies stock prices as well. Although

it can be observed from the table 4.4.1.1.5 analysed variables have correlation

coefficient with positive value of 0.312, such strength has also been found by the

analysis not to cause significance relationship between these two variables. Therefore,

the results then conclude that, when there are changes in prices for products or

services offered as business by the companies trading shares at DSE, the changes have

no influential power to cause these companies stock prices of their share to change as

well.

4.4.1.2 Regression Analysis

This is statistical procedure that can be used to develop a mathematical equation

showing how variables are related. In regression terminology, the variable that is

being predicted by the mathematical equation is called the dependent variable. The

variable or variables being used to predict the value of the dependent variable are

called the independent variables, Anderson et al (2003).

In this analysis the mathematical relationships between independent variables and

dependent variable are established, and then extent in terms of percentage to show

how the independent variable can affect the dependent variable and afterwards to

draw charts showing the mathematical relationship exists between the variable.

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The analyses involved in formulate such relationship between one independent

variable and one dependent variable that approximated by straight line, is called

simple linear regression analysis. And Least square method has been used to establish

the mathematical relationship. The goodness of fit of the estimated regression

equation to the data or the percentage of extent to which the independent variable

affect the dependent variable has been measured by Coefficient of determination, R.

According to Anderson et al (2003), by the least square method the mathematical

relationship exist between independent and dependent variable is expressed as

follows,

Y = b1 X + b o

Where; b o = y- intercept of the line

b1 = slope of the line

Y = estimated value of

stock exchange volatility

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