changes in share prices as a predictor of accounting ... 2/changes in share pri… · earning...

International Journal of Business and Public Management (ISSN: 2223-6244) Vol. 2(2): 1-11

AbstractThis study examines the predictability of accounting earnings using changes in share prices of com-panies listed at the Nairobi Stock Exchange in the finance and investment sector. The study covered the period between the year 2001 and 2005. The data was obtained from the Nairobi Stock Exchange, where the information selected were Earnings per share, Dividend yield, Price to earnings ratio and the share price. These information was standardized using logarithm and analyzed using the SPSS program. The OLS was used to come up with an equation. Eleven companies were analyzed and all of them had positive change towards the accounting earnings in relation to the share price. Additionally, the relationship between accounting variables and the Nairobi Stock Exchange information indicated mixed results, with some companies showing a strong positive correlation and others weak correlation.

Danson Musyoki1

1Catholic University of Eastern Africa, P.O Box 00200-62157 Nairobi

Corresponding Author: Danson Musyoki

Recieved: September 9, 2011 Accepted: October 8, 2011

Keywords: Share price, accounting earnings JEL Classification: C20, C12

Changes in share prices as a predictor of accounting earnings for financial firms listed in Nairobi Securities

Exchange

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Available online at: http//:www.journals.mku.ac.ke © MKU Journals, April 2012

Full Length Research Paper

INTRODUCTION

Share price is the value of the firm divided by the number of

shares outstanding, (Weston 1989). It can also be defined as the

price that buyers and sellers establish when they trade in the

shares, (Nairobi Stock Exchange Hand Book 2005).

Additionally another definition is the par value that is merely a

stated figure in the corporate charter and has little economic

significance. Accounting earnings are the gains in wealth from

business that is the amounts which can be spend without

encroaching upon the initial wealth of the firm (Elgers 1998). It

is also the summary of revenues expenses and net income or

loss of a firm for a period of time. Finally another definition is

the monetary measure of a firms performance for a period and

to the extent feasible excluding items that are extraneous to the

period, (Weston 1989). The prediction of earnings has

preoccupied accountants and market analysts for a long time.

Its accurate prediction cannot be certain due to the profound

effects on share prices and the subsequent allocation of

financial resources. According to the conventional theory of

share pricing any condition or situation that indicates a change

in earnings of a particular company or of a specific industry, or

of many companies or of the entire economy will affect share

prices, which will move in advance of actual changes in

earnings and dividends. While the confidence theory states that

the basic factors in the movement of share prices is the rise and

fall of trader and investor confidence in the future of stock

prices, earnings and dividends (Farwell & Leffler 1963).

Shares prices are highly affected by the business fundamentals,

which are either economic or political. These are factors that

affect the share prices but are outside the share market itself.

The many traders and investors in the market are at all times

seeking to know the trend of the share prices, and this trend is

mainly based on the fundamental conditions, (Farwell &

Leffler 1963). Investors are mainly interested in the returns

they get from their investment; therefore they will always select

their investment well so as to fulfill their expections.

Investments are about sacrifice of current gains for future gains

and this involves time (waiting) and risk. Whereas the current

gains are certain the future gains are uncertain and also the

investors have different preferences thereby presenting various

kinds of risks (Nairobi Stock Exchange Hand Book 2005). A

common finding in the literature is that systematic post-

announcement change in share prices is associated with the

sign or magnitude of accounting earnings (Forester 1984). The

argument has been that fundamental factors that affect change

in security prices also affect accounting earnings. Therefore we

need better evidence than has been available about the

usefulness of share prices in predicting accounting earnings.

The situation is whether share prices can be affected by

anything else other than the business fundamentals. The


business fundaments include earnings, interest rates, stock slits,

and economic and political factors. There is also the confidence

theory, where changes in the stock prices occur due to the faith

investors have in a company. There is also the issue of

accountants reporting incorrectly and investors relying on the

reports. The government regulations can also have effects on

the share prices and hence the earnings made by a company.

Also insider trading can affect the share prices either adversely

or positively depending on the information being released to

the public. The different polices of accounting for thinks like

depreciation and valuation of inventory makes different

earnings to be reported for the same company. The study

covered companies listed in the Nairobi Stock Exchange (NSE)

under the financial and investment sector. The period covered

was between 2005 and 2010 being the time when the exchange

market has seen a lot of activities, with listed companies raising

additional capital and / or simply restructuring their

shareholding structures as a means of becoming more efficient

and effective.

Earlier study, (Asiemwa, 1992) used time series in predicting

accounting earning. This lead to establish how effective

changes in share prices can be used to predict future changes in

earnings. It is a fundamental assumption in this study that

investors choose only those stocks that promote more improved

earnings. Therefore we set to determine whether by examining,

changes over time in stock prices, insight can be gained about

changes in the future profitability. The study also seek to

determine whether there is evidence on the association between

the stock price changes and the accounting earnings in the

period up to and including the earnings on the announcement

date. The concern is whether changes in accounting earnings

are correlated with the information used in capital markets

while revising security prices. This is because any meaningful

share price is built on expectation of company’s future

performance, (Reiley 1994). The objectives of the research

were: to establish the relationship between the stock price and

the accounting earnings, and; to establish the relationship if

any, between the accounting earnings.

LITERATURE REVIEW

Accounting is the art of measuring, describing and interpreting

economic activity. When one is preparing a household budget,

balancing your cheque book, preparing your income. Tax

returns or running general motors, he or she will be working

with accounting information. It is often called the” language of

business”. Accounting earnings are calculated using different

methods of treatment using various items in the profit and loss.

Some of those items are the inventory and the depreciation that

have various ways of calculating them or valuation. This makes

the same organization to have different accountancy earning

(Meigs and Meigs 1989). The earnings that are distributed to

the shareholders are called dividends and are the agreed amount

passed by the board of directors of the organization. There are

two types of dividends namely the; cash dividend and the stock

dividend or the bonus dividend. Stock dividend is the term used

to describe distribution of additional shares to a company’s

shareholder is proportion to their present holdings. This

increases the number of shares held by the shareholders thereby

reducing the earning per share and hence the market price of

the shares (Mieges and Meigs 1989).

Earning forecasting has traditionally been approached in two

ways. Firstly the accounting or technical approach that is

largely time series based. These models assume that future

earnings are a function of current earnings. Secondly, there is

earnings prediction based on analysis by financial analyst.

These do not use any specific well defined models. Empirical

evidence does not seem to show the superiority of one model

over the other (Brown 1993). Accounting earnings can be

measured in terms of improved cash flow. Ultimately any

investor must generate a positive cash flow to be worthwhile.

Cash flows help to explain the changes in accounting cash

which is a way can be deemed to be what the organization had

earned during the period. Cash flow is basically the change in

cash movement from prior year. One ratio that is very helpful

in financial analysis is the sustainable growth ratio. This

financial analysis is the maximum rate of growth a firm can

maintain without increasing its financial leverage and using

internal equity only.

Ball et. al (1998) present evidence that earnings growth may be

modeled in terms of price changes, such a relationship is

important because it tells us how efficient a stock market is. In

the last three years there has been an increase in the business in

the Nairobi stock exchange and this has made prices of various

stocks to increase substantially. The assumption this that

earnings will also increase as investors rely on share prices to

buy or sell shares and therefore the prices must be right.

Welcox (1984), Rapport (1986), Downs (1991) attribute current

share price changes to anticipated changes in earnings. Event

based studies established direct relation between share prices

change and earning (Ball and Brown 1968), (Baskin 1989). The

assumption in this studies is that changes in share prices is as a

result of changes in fundamental variables such as anticipated

earnings, dividends and capital structure through stock splits

(Arif and Khaw 2000).

Beaver et al, (1980) found that earnings growth is useful in

explaining share prices and price to earnings based forecast are

better predictors that a random walk model. Elgers and Murray

(1992) used a regression of future earnings growth modeled by

current abnormal returns and price to earnings ratio, both

variables are controlled for size and found that their forecast

model out performed a random walk in predicting accounting

earnings.Benstorn (1966) and Ball and Brown (1968) explored

the relationship between security prices changes and earnings

changes. Ball and Brown found a significant association

between the sign of the price changes and the sign of the

earnings changes. For the years in which a firm experiences

positive residual earnings change there tends to be positive

residual price change and conversely, for the years in which

there is negative residual change. Berver, Clarks and Wright

(1979) subsequently extended the Ball and Brown study by

incorporating the magnitude of the earnings change as well as

its sign. There is a significant, positive correlation between the

residual percentage change in earnings and the residual

percentage in price.

Moreover, not only is the relationship positive and significant

but the magnitude of the difference in security changes is

sizeable. The magnitude of the difference in price changes

indicated that not only is the relationship statistically

significant but it is also large enough to be economically

important. The implication of these findings is that a

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correlation exists between the events that affect accounting

earnings changes and changes in security prices. The evidence

is also consistent with the contention that prices behave as if

investors perceive that correct earnings and statistically

dependent with future earnings and the future dividend paying

ability of the firm. Hence, prices act as if current earnings

changes posses a permanent component (Foster 1984) in other

words a portion of the change in earnings is associated with a

permanent alteration in the level of expected future earnings in

a manner that implies altered expected dividend pay ability. In

this context, the evidence is also consistent with the contention

that prices behave as if investors perceive that earnings convey

information (i.e. altering their beliefs) about future earnings

and future dividend paying ability. What is not known is

whether the same results can be replicated in the developing

markets like the Nairobi Stock Exchange.

Prices at any point in time can be viewed as if they are a

function of future expected earnings. Prices reflect investor’s

expectations regarding future earnings. The potential richness

of price with respect to expectations is described in (Muth

1961) seminal essay on rational expectations. If the prices are

based upon an information system with many signals other than

earnings that is not reflected in current and past. For example

prices may respond before earnings to certain events or

information. If prices are viewed as “reflecting” other

information, then prices can be used as a surrogate or proxy for

that information. Recent work by Beaver, Lambert and Morse

(1980) indicates that price – based forecasting models of

earning can predict future earnings “better” (i.e. with a lower

mean error) than forecasting model based upon a statistical

extrapolation of past and current earnings. In particular,

previous evidence by Ball (1972), Albercht, Lookabill and

Mckeown (1977) and indicates that the “best” statistical model

for forecasting earnings using current and past earnings data is

called the random walk with a drift model. Under this model,

next year’s earnings are forecasted to be equal to this years

earnings plus a drift term equal to average change in earnings

over some past period. This model has been extremely robust

against challenges since its se by Ball and Brown (1968).

Beaver Lumbert and Morse (1980) used a price – based

forecasting model which resulted in lower error in 55% of the

cases. In higher price earnings portfolios the margin of

superiority tends to be pronounced. This superiority is possible

because the information upon which earnings forecast are

based in expanded to indicate price in addition to past

earnings. Price is used as a surrogate for other data that convey

information about future earnings. An example of the use of

hind – sight information is the classification of firms into

portfolios based on information not available at the time of

trading strategy is implemented. For example, a trading

strategy based on the rank of each firms earnings change gas to

wait until the last firm in the sample has announced its

earnings. A related problem is when observations are placed

into portfolios each quarter and the mean aggregated results

based on the individual quarter’s (mean) results; this implicitly

“assume that the trader knows the distribution of standardized

forecast error at the time of the first earning announcement in

each calendar quarter” (Holthausen, Jones and Latane (1982)

suffer from this experimental defect. He reports that use of a

ranking scheme based on publicity released information results

in “the association between post earnings announcements,

abnormal performance and the size of forecast error being

much weaker than those reported by Rendlemen, Jones and

Latane (1982).

In conclusion “the larger and the more visible company, the

more “perfect” its market is likely to be “perfect” meaning that

most of the likely factors affecting the price of its securities are

presumably known to market. Conversely the smaller a

company is the less visible it is to the investor public and the

more. “Imperfect” the market price for its shares is likely to

be”. Mwangi (1997) did a study to analyze the price

movement for selected stocks in Nairobi Stock Exchange. He

developed a model using a PC (version) software package and

using this model, he computed and compared the prices from

the month of Jan, 1992 to April, 1997 with the actual ones. He

did t–test to determine whether the two prices were

significantly different from one another. He concluded that it is

not always possible to develop models that are only as good as

being proxy for the investor’s decision process and are limited

by the inaccuracies in estimating future earnings of the

company. At best they are only a framework for analyses which

is useful for structuring the way an investor can conceptualize

share valuation.

Asiemwa (1992) did an empirical study to identify the

relationship between investments ratios and share performance

of companies quoted on the NSE. She did multiple regression

analysis of establish the relationship between investment ratios

and share price and concluded that earnings per share, dividend

per share, price earnings and dividend yield have a significant

effect on share prices. She concluded that a significant

association between share prices and investment ratios exists.

Kiweu (1991) did a study to determine the behaviour of share

prices in the Nairobi Stock Exchange. He did examine the

behavior of ordinary share price of ten selected “blue chip”

companies in the Nairobi Stock Exchange. He investigated the

behaviour of bid price change over five years from Jan, 1986 to

Dec; 1990. He concluded that weekly returns of shares traded

in the Nairobi Stock Exchange are serially independent

(random). The evidence presented suggested that no important

dependencies could be identified in the stock market. Asiemwa

(1992) in his empirical study to investigate the behaviour of

annual corporate earnings among Kenyan publicly quoted

companies selected a sample of – thirty four companies quoted

in the Nairobi Stock Exchange. He found that successive

changes in reported annual corporate earnings for Kenyan

publicly quoted companies are essentially independent and can

be well approximated by a random walk.

Gathoni (2002) did a study on forecasting ability of valuation

ratios (Nairobi Stock Exchange). She did predictive regression

model on a small sample of fourteen organizations with a

financial year end of 31st Dec, over a period of five years (1996

to 2000). The ratios were then lagged for one quarter in order to

see what impact this had on the predictive ability of the

valuation model. She concluded that price earnings ratio

explains future stock returns. She also concluded that price

earnings ratio have predictive ability in majority of samples

observed and are again determinant of future stock returns.

All the above studies were done in the period between 1991

and 2000, which does not include the period of our study. This

gives us as better chance to establish if there are any changes

that would have arisen after their studies. Also our study is

intended to move further and try to see whether share prices

have forecasting ability on accounting earnings.

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Another study done by Ball and Brown in 1968 analyzed share

prices changes against earnings changes the study was done in

USA. This study was done many years ago and as such there is

bound to be a lot of changes that might have happened. This is

one of the reasons why another study needs to be done.

Secondly the study was done in USA and in our case it is being

done in Kenya, which is a developing country while USA is a

developed country. This brings in the second reason for the

study to be done. At the same time the stock markets were not

known in Kenya by the Africans as it was a dormant for

Europeans. As for today it is every ones interest to deal with

shares either for capital gains or to own a part of the company

and earn dividends. Also miller did a study on how different

investors behavior towards investing in various types of

companies. This study was done between leveraged and non-

leveraged companies and also the investors were from

different tax brackets’. He found out that the investors in low

tax bracket will seek stocks from leveraged companies while

those in high tax brackets will buy in low or no leveraged

companies. The study was done many years ago since it is a

theory and to proof this theory a study should be done. This

why we have decided to carry out this study.

Models used in stock valuation and returns estimation

While the same principal applies to the valuation of common

stocks as to bonds or preferred stocks, two features make their

analysis more difficult. First is the degree of certainty with

which receipts can be forecast. In common stocks, forecasting

future earnings, dividends and stock prices can be difficult the

second complicating feature is that, unlike interest and

preferred dividends commons stock earnings and dividends are

generally expected to grow, not remain constant. Hence while

standard annuity formula can be applied, more difficult

conceptual schemes must also be used. While estimating the

value of a single period it depends on the returns investors

except to receive if they buy the stock and the riskness of these

expected cash flow. These expected returns consist of two

element namely the dividend expected in each year and the

price investors expect to receive when they sale stocks at the

end of the year (n). The price includes the return of the original

investment plus a capital gain or loss. If the investors expect to

hold the stock for one year and if the stock price is expected to

grow certain rate then the valuation equation is:

Po=d1/ks-g

Where; Po is the market price

D1 is divided after one year

Ks is expected return in the market

g is the rate of growth

Another model is the Capital Asset Pricing Model. Any

practitioner who wishes to employ on the CAPM for

managerial decision making naturally wants to know whether

or not the CAPM theory is empirically valid, but the evidence

on CAMP is mixed. It fits the data fairly well, but there are

some anomalies that is, phenomena which are not by the

CAPM. The empirical analog of the CAPM is:

Rjt = aj+bmt + Ejt

Where, â, b = the estimated intercept and slope terms

Ejt

= the random error term around the regression

line

mt = market return

Rjt

= expected return.

The other model is arbitrage pricing model formulated by Ross

(1976) which is more general approach to asset pricing because

it allows for the possibility that many factors may be used to

explain assets returns. Also it makes fewer than the CAPM.

The Arbitrage Pricing Theory (APT) begins by assuming that

the rate of return o any security is a linear function of K factors

as shown below:

Ri=E(R

i)+b

ifi+..............b

ik+E

i

Where;

Ri = the stochastic rate of return on the ith assets

E(R) = the expected rate of return on the ith asset

Bik = the sensitivity of the ith assets returns all

assets under consideration

Fk = the mean zero kth

factors common to the all

assets under consideration

Ei = a random zero mean noise term for the ith

assets

The APT is derived under the usual assumptions of perfectly

competitive and frictionless capital markets. Individuals are

assumed to have homogeneous belief that the random returns

for the assets being considered are governed by the linear K-

factor Model. The theory requires that the number of assets

under consideration be much larger than the number of the

factors and the noise under consideration be the unsystematic

risk components for the particular asset. It must be independent

of all factors and all error terms for other assets. The important

feature of the APT is reasonable and straight forward. Where Ith

– Kth can be economic growth rate, inflation rate, interest rate

or exchange rate.

Accounting earnings as a performance measure

Earnings per share: This the monetary value of profit after tax

on each ordinary share held .it is given by diving net profit after

tax by the total ordinary shares outstanding. Dividend yield:

This is the return on every shilling invested in securities

expressed as a percentage. It is given by dividing dividend per

share with the market price per share, then multiply by a

hundred. Price-to-earning ratio: This is the number of times it

takes a shareholder to recoup his investment in a share. It is

given by dividing the market price by the earnings per share.

Share price: This is the ruling price of shares on the trading

floor of the exchange at a given time. It is normally an indicator

of the level of demand of that security.

METHODOLOGY

The population of interest consisted of companies quoted in the

Nairobi Stock Exchange under the category finance and

investment sectors. This is the sector that deals each other

sector and therefore can portray well the behaviors of share

prices against earnings at a given time. All the twelve (12),

publicity quoted companies a the Nairobi stock exchange

(NSE). Under the finance and investments sector was selected.

Yearly data as pertains to share prices and annual data

regarding accounting earnings as well as the ratio used from

2005 to 2010 December for all individuals companies was

obtained and analyzed.

The research relied upon secondary data obtained from Nairobi

Stock Exchange or other financial intermediaries where data

was not available from Nairobi Stock Exchange we referred to

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financial statements published by companies being studied.

Such data included movement in share prices, accounting

earnings and ratio used in the Nairobi Stock Exchange. We got

yearly data from 2005 to 2010 for individual companies .then

from the data we picked out the share price, earnings per share

earning ratio and the dividends yield. The share price earnings

per share, price earning ratio, and the dividend yield were

changed into logarithm so as standardise this data. The data

was then analysed using the SPSS program, specifically OLS.

The result was interpreted so as to make a conclusion. The

following were the variable used in the analysis table:

Y = Share price

XI = Earnings per share.

X2 = Price earnings ratio

X3 = Dividend Yield.

FINDINGS

This study analyzed all the eleven companies for a period of

five years. on each company a regression equation will be

formed. The equation was be subjected to a one percent change

on each and every independent variable. The percentage

changes for earnings per share, price to earning ratio and

dividend yield was averaged and the results shown in the form

of tables. This result were used to conclude whether changes in

share prices can be used to predict accounting earnings.

The insurance company results on Table 1 & 2 showed a

positive correlation between the accounting variables. It is

having a constant of 0.028 that is the minimum positive change

that can happen, all the other factors held constant. this will

mean that if there is a1% change in earnings the price will

have positive change of 72.7%. it also follows that a 1%

change in price to earning ratio share will result to a 46.7%

positive change and the dividend yield will make the share

price to have a negative change of 10% while the coefficient of

determination is 68.5% meaning that the three accounting

variables (earnings per share, price earning ratio and dividend

yield), while other variables not in the model determine share

price upto an extend of 31.5%. All the independent variable

were tested and found significant at 1%, 5% and even 10%

confidence level including the constant. All the independent

variables combined, as per the F-Statistics indicated they were

significant.

In table 3, for KCB Bank, the bank had a positive correlation

between the variables used the bank had a constant of 1.229

that is the minimum positive change that can happen, all the

other factors held constant. The interpretation for the

coefficient indicate that a 1% change in earnings will result to

11.59% positive change in share price. While price to earnings

will result to 18.9% negative change to the share price and

3.1% negative change will be as a result of dividend yield. The

coefficient of determination (R2 –adjusted) is 98.5% meaning

that there is 1.5% of other factors that were not considered in

the model and which can also cause a change to the share price.

Price earning ratio and constant were found to be significant

tested at 1%, 5% and even 10% confidence level. The price

earning ratio was tested to be significance at 10% but failed at

1 and 5%. The dividend yield was insignificant at all levels.

The bank (NBK) had a positive correlation between the

accounting variables used. it had a constant of 0.002 that is the

minimum positive change that can happen (the regulatory

environment provided by NSE) all the other factors held

constant. This can be interpreted mean that a 1% change in

earnings will result to a 40% positive change in share price.

The price to earnings ratio will cause 68.3% positive change in

share price. The bank did not have dividend yield. While the

coefficient of determination was 80.7% meaning that the

factors used in the study combined can influence the share

price to that extend, while other factors not investigated can

influence share price upto 19.3% as per the model. The two

combined independent variables were found to be significant as

per the F-test.

The bank (NICB) had a positive correlation among the

independent variables used. It had a constant of 0.001 that is

the minimum positive change that can happen (the regulatory

environment provided by NSE), all the other factors held

constant. The coefficient can be interpreted to mean that a 1%

change in earnings per share can cause a 12.2% positive change

to the share, a 1% change in price earning ratio causes 95%

change in share price and 1% change dividend yield causes a

1% negative change in share price. The coefficient of

determination was 71.2% meaning that all the factors combined

could influence share price upto that extend. Other factors not

investigated and accounted for could influence share price upto

28.8%. Earnings per share was tested to be significant while

price earning ratio and dividend yield were not significant at

5% including the constant

The insurance company (Pan African Insurance) has a negative

perfect correlation with respect to the variables used. It has a

constant of 1.108 that is the minimum positive change that can

happen, all the other factors held constant. The constant was

interpreted to mean the condusive regulatory and operational

environment provided by the Nairobi Stock Exchange, for the

companies to trade in the market. This can be interpreted to

mean that a 1% change in earnings will result to a 41.29%

positive change to the share price, while 1% change in price

running ratio will result to a negative 7.06% change in share

price. A 1% change in dividend yield will result to 50.02%

positive change in share price. While the coefficient of

determination (R2-adjusted) indicated a 57% meaning that three

independent variable could influence share price to that extend

where else other factors not in the model could influence share

price upto the tune of 42.5%. other than the constant variable,

all the independent variables were tested and found to be

insignificance. All the variables combine were found to be

significant as per the F-test.

The company (HFCK) had a positive correlation with the

variables used. It had a constant of 0.721 that is the minimum

positive change that can happen (the regulatory environment

provided by NSE), all the other factors held constant. This

coefficient could be interpreted to mean that a 1% earnings

could result to a 23.0% positive change in the share price.

While the same change subjected to price to earning will result

to a 28.9% positive change. Dividend yield was not computed

for lack of data. The coefficient of determination was 80.8%

meaning that there are other factors to the tune of 19.2% that

affect the share price and which were not investigated in the

model. All the variables including the constant were tested and

found significance at all levels 1%, 5% and 10%. All the

variables combined were tested to be significant.


The bank (DTB) had a positive correlation with the variables

used. It had a constant of 0.034 that is the minimum positive

change that can happen (the regulatory environment provided

by NSE), all the other factors held constant. The coefficient

could be interpreted that a1% change in earning will result to

92.4% change in share price, while a 1% change of earnings

per share will result to 39.7% change in share price. The

dividend yield resulted to a negative change of 1.6% to the

share price. While the coefficient of determination was 81.0%

meaning that the combined factors used in the study could

influence share price that extent. Other factors not investigated

by the model account for 19% of change in share price for the

bank. All the variables including the constant were tested to be

significant at all levels, 1%, 5% and 10%. All the combined

variables were tested and found to be significant for the F-

statistics.

The bank (CFC) has a weak positive correlation among the

variable used. It had a constant of 0.081 that is the minimum

negative change that can happen (the regulatory environment

provided by NSE), all the other factors held constant. The

coefficient were interpreted to mean a 1% change in earnings

could result 44.8% positive change to the share price. In

relation to the price to earning ratio a 1% change could result to

61.1% in share price while a 1% change in dividend yield could

result to a 4% change in share price. While the coefficient of

determination was 79.7% meaning that the factors used in the

study could influence share price to that extent while other

factors not in the model accounted for share price change upto

21.3%. The constant and the dividend yield were tested to be

significant at all levels. While earnings and price were

insignificant at 5%. All the variables combined were tested to

be significant.

The bank (BBK) had a weak positive correlation with the

variables used. It had a constant of 0.019 that is the minimum

positive change that can happen (the regulatory environment


coefficient could be interpreted that a 1% change to the

earnings could result to 44.1% positive change to the share

price. The price to earning ratio could result to 84% positive

change, while dividend yield result to - .07% negative change

to the share price. The coefficient of determination was 89.7%

meaning that the combined factors could influence the share

price to that extent. Other factors not accounted could

influence share price upto 21.3%. price earning was tested to be

significant at all level. While the constant, earnings and

dividend yield were tested to be significant. The combined

variables were found to be significant under the F-test. The

bank (ICDC) had a positive correlation with the variables used.

It had a constant of 0.004 is the minimum positive change that

can happen (the regulatory environment provided by NSE), all

the other factors held constant. This could be interpreted to

mean that a 1% change in the earnings could result to 47.7%.

While the price to earning could result 11% and a 2% change

as a result of the dividend yield. The coefficient of

determination was 77.8% meaning that the factors used in the

study could influence the share price upto that extent. Other

factors not in the model could influence share price upto

22.2%. The earnings and price earnings were tested to be

significant at all levels, 1%, 5% and 10%. The constant and the

dividend yield were insignificant. The combined variables

were significant.

The bank (SCB) had a poor positive correlation with the

variables used. It has a constant of - 0.211 that is the minimum

negative change that can happen (the regulatory environment


coefficient could be interpreted that a 1% change in earnings

will result to 32.7% positive change to the share price. While

the price to earning ratio will have 10.76% and positive change

and the dividend yield will have 9.1% positive change. The

coefficient of determination was 72.3% meaning that the

combined factors used in the study could influence share price

upto that extent. Other factors not in the model could influence

share price upto 27.7%. All the variables including the

constant were tested to be significant at all levels 1%, 5% and

10%. Equally, all the combined variables were tested to be

significant under the F-test

RECOMMENDATIONS AND CONCLUSION

The main research objective of the study was to establish the

extent to which changes in share prices can predict accounting

earning. The study analyzed results from eleven companies for

a period of five years between 2005 and 2010. All he eleven

companies had earnings depending on the share price since

they all had positive changes. This is a good indication that as

the earnings of each company represented change there is an

expected increase in the share price. It can also be supported by

the fact that when a company reflects good earnings in its

financial statement investors tend to be interested to buy their

shares. This intends to follows the expectation theory. Some

companies indicated a strong positive relationship, while others

indicate a weak relationship. Either way this is an indication

that there is a relationship between the accounting earnings and

the information used in the stock exchange. This so because the

information used in the stock exchange were the variables used

in the study.

The study was using Earnings per Share, Dividend Yield and

the price Earning Ratio as comparing to the share price. In

essence these are not the only variables that affect the share

price. There are other factors like the interest rates, inflation

rate, government regulation and the investor’s behaviours that

could have been considered. The study recommends further

research on these factors to see how sector yet affect the share

price. Also the study did only cover the finance and investment

sector yet there are other sectors that are listed in the Nairobi

Stock Exchange. These sectors include the Agricultural,

Commercial and Industrial sector. The study also recommends

a study to be conducted on these areas to see the results that

could come out. The findings showed that there is a

relationship between share price and accounting earnings. Then

it means that whenever there is a change in accounting earnings

then it would be expected that the share price will also change

in the same direction. This is so because share price is the

present value of the expected cash flows. The findings also

showed that there is a relationship between the accounting

earnings and the information used the stock exchange. This is

so because the information used is past trend that are used to

predict the future. Therefore it is true to conclude that the

investors follow the trend of the earnings in order for them to

make a decision on what stock to invest in. Also the factors that

affect the share price also affect the earnings and they tend to

follow the same direction.

6


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APPENDICES

Jubilee Insurance Company

Table 1 : Model summary (b)

model

R square adjusted R

square

std. Error of

the Estimate

change statistics

R Square

change

F change df1 df2 sig. f

change

1 0.734 0.681 0.157944456 0.700 61.8197 3 1 0.052

a Predictors: ( constant),x3,x1,x2

b Dependent Variable: Y

Table 2 : Coefficients (a)

Model Unstandardized

coeffients

Standardized

coeffients

t sig. Colinearity statistics

B std. Error Beta Torelance vif

1 (constant)

x1

x2

x3

.028

.994

.986

-.015

.005

.001

.003

.003

.727

.467

-.010

5.423

8.19100

3.72444

-5.609

.116

.001

.002

.112

.069

.034

.018

14.595

29.11

55.923

a Dependent Variable: Y

7


Kenya Commercial Bank


model R square adjusted R

square

std. Error of

the Estimate

change statistics

R Square

change

F change df1 df

2

sig. f

change

1 .996 .985 .0450727469 0.974 90.074 3 1 .077




model unstandardized

coeffients

standardized

coeffients

t sig. colinearity statistics

B std. Error Beta Torelance vif

1 (constant)

x1

x2

x3

1.229

1.257

-.131

-.044

.045

.328

.070

.374

1.159

-.189

-.031

27.281

3.836

-1.878

-.117

.023

.162

.311

.926

.040

.365

.053

24.754

2.741

18.890

Dependent Variable; Y

National Bank of Kenya


model

R square adjusted R

square

std. Error of

the Estimate

change statistics

R Square

change


change

1 0.88 0.807 0.5459 0.86 13.094 2 2 .000

a predictors: ( constant),x2, x1




coeffients

standardized

coeffients


B std. Error Beta zero -

order

Torelance vif

1(constant)

x1

x2

.002

.996

.999

.001

.002

.001

.400

.683

2.99

4.71

8.07

.096

.000

.000

.533

.533

.533

.533

1.877

1.877


National Industrial Credit Bank

Table 7: Model summary (b)

model

R square adjusted R

square

std. Error of

the Estimate

change statistics

R Square

change


change

1 0.783 0.712 0.36727 0.75 62.904 3 1 .001



Table 8 :Coefficients (a)


coeffients

standardized

coeffients


8


B std.

Error

Beta Torelance VIF

1 (constant)

x1

x2

x3

.001

1.001

1.000

-.002

.016

.010

.006

.008

.122

.950

-.001

.052

9.76

1.81

-0.198

.927

.007

.004

.875

.338

.019

.017

2.954

51.857

60.182


Pan African Insurance Company


model

R square adjusted R

square

std. Error of

the Estimate

change statistics

R Square

change


change

1 0.700 0.575 0.3719 0.65 45.100 3 1 .767



Table 10 :Coefficients (a)


coeffients

standardized

coeffients


B std.

Error

Beta Tolerance VIF

1 (constant)

x1

x2

x3

1.108

4.283

-3.889

4.402

.263

6.324

6.115

6.306

0.4129

0.07060

0.5002

4.213

.677

-.636

.698

.148

.621

.639

.612

.011

.003

.008

87.465

289.938

120.787


Housing Finance Company of Kenya


model

R R square adjusted R

square

std. Error of

the Estimate

change statistics

R Square

change


change

1 .951(a) .904 .808 .08121 .904 9.437 2 2 .096

a Predictors: ( constant),x2,x1


Table 12 : Coefficients(a)


coeffients

standardized

coeffients


B std.

Error

Beta Tolerance VIF

1 (constant)

x1

x2

.721

3.053

.902

.080

1.016

.239

0.2303

0.2894

9.476

3.005

3.776

.011

.095

.064

.082

.082

12.262

12.262


Diamond Trust Bank


model

R square adjusted R

square

std. Error of

the Estimate

change statistics

9


R Square

change


change

1 0.810 0.760 0.229 0.79 19.89209 3 1 .001



Table 14 : Coefficient (a)


coeffients

standardized

coeffients


B std.

Error

Beta Tolerance VIF

1 (constant)

x1

x2

x3

.034

.981

.983

-.022

.005

.002

.003

.003

.924

.397

-.016

7.079

5.840

3.419

-8.408

.089

.001

.002

.075

.067

.124

.048

14.928

8.053

20.870


CFC Bank


model

R square adjusted R

square

std. Error of

the Estimate

change statistics

R Square

change


change

1 0.797 0.756 0.19293 0.77 68.0229 3 1 .000

a Predictors: ( constant), x3,x2,x1




coeffients

standardized

coeffients


B std.

Error

Beta Tolerance VIF

1 (constant)

x1

x2

x3

-.081

1.038

1.043

.045

.014

.006

.008

.008

.448

.611

.040

-5.870

1.757

1.372

5.913

.107

.004

.005

.107

.008

.002

.001

132.588

404.266

937.609


Barclays Bank

Table 17 : Model summary

model

R square adjusted R

square

std. Error of

the Estimate

change statistics

R Square

change


change

1 0.897 0.810 0.7251 0.86 17.3110 3 1 .002




coeffients

standardized

coeffients


B std.

Error

Beta Tolerance VIF

1 (constant)

x1

x2

x3

.019

.994

.995

-.007

.052

.007

.024

.023

.441

.840

-.007

.364

1.4717

4.1850

-.317

.778

.004

.015

.805

.214

.005

.005

4.666

209.440

219.536

10



ICDC Bank


model

R square adjusted R

square

std. Error of

the Estimate

change statistics

R Square

change


change

1 0.870 0.778 0.2354 0.85 12.1132 3 1 .001


b Dependent variable: Y



coeffients

standardized

coeffients


B std.

Error

Beta Tolerance VIF

1 (constant)

x1

x2

x3

.004

.996

.999

-.001

.007

.001

.003

.004

.477

.110

.020

.539

6.86212

3.40308

-.128

.685

.001

.002

.919

.568

.026

.029

1.760

38.228

34.770


Standard Chartered Bank


model

R square adjusted R

square

std. Error of

the Estimate

change statistics

R Square

change


change

1 0.797 0.732 0.2401459 0.76 148.7692 3 1 .001


b Dependent variable: Y



coeffients

standardized

coeffients


B std.

Error

Beta Toleranc

e

VIF

1 (constant)

x1

x2

x3

-.211

1.042

1.091

.085

.053

.011

.023

.021

.327

.107

.091

-.3.990

9.82

4.75

4.03

.156

.006

.013

.155

.020

.000

.000

49.290

22.896

22.638


11

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