determinants of equity prices in the stock markets

International Research Journal of Finance and Economics
ISSN 1450-2887 Issue 30 (2009)
© EuroJournals Publishing, Inc. 2009


Determinants of Equity Prices in the Stock Markets


Somoye, Russell Olukayode Christopher
Dept. of Banking & Finance, Faculty of Management Science
Olabisi Onabanjo University, Ago Iwoye, Nigeria
P.O. Box 1104 Ijebu-Ode, Ijebu-Ode, Ogun State, Nigeria
E-mail:

Akintoye, Ishola Rufus
Dept. of Accounting, Faculty of Management Science
Olabisi Onabanjo University, Ago Iwoye, Nigeria
E-mail:

Oseni, Jimoh Ezekiel
Dept. of Banking and Finance, Faculty of Management Science
Olabisi Onabanjo University, Ago Iwoye, Nigeria
E-mail:


Abstract

Brav & Heaton (2003) alleges market indeterminacy (a situation where it is
impossible to determine whether an asset is efficiently or inefficiently priced) in the stock
market. Kang (2008) argue that empirical tests of linear asset pricing models show presence
of mispricing in asset pricing. Asset pricing is considered efficient if the asset price reflects

all available market information to the extent no informed trader can outperform the market
and / or the uninformed trader.
This study examined the extent to which some "information factors" or market
indices affect the stock price. A model defined by Al-Tamimi (2007) was used to regress
the variables (stock prices, earnings per share, gross domestic product, lending interest rate
and foreign exchange rate) after testing for multicollinarity among the independent
variables. The multicollinarity test revealed very strong correlation between gross domestic
product and crude oil price, gross domestic product and foreign exchange rate, lending
interest rate and inflation rate.
All the variables have positive correlation to stock prices with the exception of
lending interest rate and foreign exchange rate. The outcomes of the study agree with
earlier studies by Udegbunam and Eriki (2001); Ibrahim (2003) and Chaudhuri and Smiles
(2004).
This study has enriched the existing literature while it would help policy makers
who are interested in deploying instruments of monetary policy and other economic indices
for the growth of the capital market.


Keywords: Stock prices, CAPM, models, coefficient, efficient, stock market.

International Research Journal of Finance and Economics - Issue 30 (2009) 178
1.0. Introduction
The price of a commodity, the economist makes us to believe is determined by the forces of demand
and supply in a free economy. Even if we accept the economists’ view, what factors influence demand
and supply behavior? Price? Yes, but not all the time, at least there are some other factors. In the
securities market, whether the primary or the secondary market, the price of equity is significantly
influenced by a number of factors which include book value of the firm, dividend per share, earnings
per share, price earning ratio and dividend cover (Gompers, Ishii & Metrick, 2003). The most basic
factors that influence price of equity share are demand and supply factors. If most people start buying
then prices move up and if people start selling prices go down. Government policies, firm’s and

industry’s performance and potentials have effects on demand behaviour of investors, both in the
primary and secondary markets. The factors affecting the price of an equity share can be viewed from
the macro and micro economic perspectives. Macro economic factors include politics, general
economic conditions - i.e. how the economy is performing, government regulations, etc. Then there
may be other factors like demand and supply conditions which can be influenced by the performance
of the company and, of course, the performance of the company vis-a-vis the industry and the other
players in the industry.
In a study of the impact of dividend and earnings on stock prices, Hartone (2004) argues that a
significantly positive impact is made on equity prices if positive earnings information occurs after
negative dividend information. Also, a significantly negative impact occurs in equity pricing if positive
dividend information is followed by negative earning information. Docking and Koch (2005) discovers
that there is a direct relationship between dividend announcement and equity price behavior. Al-Qenae,
Li & Wearing (2002) in their study of the effects of earning (micro-economic factor), inflation and
interest rate (macro-economic factors) on the stock prices on the Kuwait Stock Exchange, discovered
that the macro-economic factors significantly impact stock prices negatively. A previous study by
Udegbunam and Eriki (2001) of the Nigerian capital market also shows that inflation is inversely
correlated to stock market price behaviour.
A number of models developed for asset pricing are two variable models. For instance the
Capital asset pricing model (CAPM) developed by Sharpe (1964) considers the risk-free return and
volatility of the risk-free return to market return as the determinants of asset price. Asset price as
described by CAPM is linearly related to the two independent variables. Many studies have concluded
that over the years assets were being underpriced (Smith, 1977; Loderer, Sheehan & Kadlec, 1991) and
this raises the question of the adequacy of the various asset pricing models to ensure efficient asset
pricing. Brav & Heaton (2003) alleges market indeterminacy, a situation where it is impossible to
determine whether an asset is efficiently or inefficiently priced. Kang (2008) found that empirical tests
of linear asset pricing models show presence of mispricing in asset pricing. Asset pricing is considered
efficient if the asset price reflects all available market information to the extent no informed trader can
outperform the market and / or the uninformed trader. This study aims at examining the extent to which
some “information factors” or market indices affect the stock price.
The rest of the paper is designed as follows: Section 2 reviews literature on factors influencing

asset prices, effects of inefficient asset pricing and some of the existing asset pricing techniques.
Section 3 states the data and the sources, the data restructuring and the model used for data analysis
while Section 4 discussed and interpret the results of the data analysis. Lastly, section 4 is the
conclusion.


2.0. Conceptual Framework and Literature Review
2.1. Conceptual Framework
Several attempts have been made to identify or study the factors that affect asset prices. Some
researchers have also tried to determine the correlation between selected factors (internal and external,
179 International Research Journal of Finance and Economics - Issue 30 (2009)
market and non-market factors, economic and non-economic factors) and asset prices. The outcomes of
the studies vary depending on the scope of the study, the assets and factors examined.
Zhang (2004) designed a multi-index model to determine the effect of industry, country and
international factors on asset pricing. Byers and Groth (2000) defined the asset pricing process as a
function utility (economic factors) and non-economic (psychic) factors. Clerc and Pfister (2001) posit
that monetary policy is capable of influencing asset prices in the long run. Any change in interest rates
especially unanticipated change affects growth expectations and the rates for discounting investment
future cash flows. Ross’ (1977) APT model which could be taken as a protest of one factor model of
CAPM which assumes that asset price depends only on market factor believe that the asset price is
influenced by both the market and non-market factors such as foreign exchange, inflation and
unemployment rates. One of the defects of APT in spite of its advancement of asset pricing model is
that the factors to be included in asset pricing are unspecified.
Al – Tamimi (2007) identified company fundamental factors (performance of the company, a
change in board of directors, appointment of new management, and the creation of new assets,
dividends, earnings), and external factors ( government rules and regulations, inflation, and other
economic conditions, investor behavior, market conditions, money supply, competition, uncontrolled
natural or environmental circumstances) as influencers of asset prices. He developed a simple
regression model to measure the coefficients of correlation between the independent and dependent
variables.

SP = f (EPS, DPS, OL, GDP, CPI, INT, MS)
Where, SP: Stock price; EPS: Earnings per share; DPS: Dividend per share; OL: Oil price;
GDP: Gross domestic product; CPI: Consumer price index; INT: Interest rate and MS: Money supply.
He discovered that the firm’s fundamental factors exercise the most significant impact on stock
prices. The EPS was found to be the most influencing factor over the market.
Studying the effects of the Iraq war on US financial markets, Rigobon and Sack (2004)
discovered that increases in war risk caused declines in Treasury yields and equity prices, a widening
of lower-grade corporate spreads, a fall in the dollar, and a rise in oil prices. A positive correlation
exists between the price of oil and war. They argue that war has a significant impact on the oil price.
Tymoigne (2002) argue that in the financial market, banking convention and financial convention work
together to fix the assets’ market prices. According to him the financial convention creates a
speculative sentiment of whether capitalists are more prone to sell, or to buy assets while the banking
convention determines the state of credit as evidenced by the confidence of the banking sector and
ability of investors accessing credit leverage for asset acquisition purpose. He concluded that
“conventions do not determine asset-price, it is the “law of supply and demand” that does so,
conventions“only” influence the behaviors of financial actors” Inflation as an external factor exerts a
very significant negative influence on the stock prices in Nigeria (Zhao,1999 & Udegbunam and Eriki,
2001).
Factors affecting asset prices are numerous and inexhaustible. The factors can be categorized
into firm, industry, country and international or market and non-market factors, and economic and non-
economic factors. All the factors can be summarized into two classes - micro and macro factors.
Factors in each class of the classification are inexhaustible. For instance, the firm factors include,
ownership structure, management quality, labour force quality, earnings ratios, dividend payments, net
book value, etc. have impact on the investor’s pricing decision. Molodovsky (1995) believes that
dividends are the hard core of stock value. The value of any asset equals the present value of all cash
flows of the asset.

2.2. Effects Of Inefficient Asset Pricing
Inefficient asset pricing could be a catalyst to inefficient resource allocation among competing
productive investment opportunities. Underpricing can serve as positive signal to the market

(Giammariano & Lewis, 1989) to compensate the uninformed and get them to participate in the new
International Research Journal of Finance and Economics - Issue 30 (2009) 180
offer (Rock, 1986; Allen & Faulhaber, 1989; Grinblatt & Hwang, 1989; Welch, 1989). The market is
information-sensitive. Prices tend to take a declining trend few days to the release of a firm’s new offer
and the price recovery starts few days after the completion of the offer, especially if the offer is fully
subscribed (Barclay and Litzenberger, 1988). Easley, Hridkjaer and O’Hara (2001) agree that market is
information sensitive at least to the extent that private (insider) information affect asset returns and
advised that it should not be ignored for efficient asset pricing.
The firm’s beta ratios, its market value to book value, its current price to earnings ratio and the
historical growth rate in earning per share are identified by Moore & Beltz (2002) as possessing strong
influence on the equity price of the firm. They also argue that the identified factors have varying
effects on the price and the effects vary from time to time, sector to sector and even from firm to firm
within the same industry. For instance, they argue that equity prices of individual firm in heavy
industries (chemical, petroleum, metal and manufacturing) are exclusively influenced by the firm’s
beta and market to book value while firms in the technology sector are influenced by the historical
growth rate in earning per share as well as beta and market to book value ratio. The equity price in
transportation industry is affected by beta and price to earning ratio. Though, Moore & Beltz (2002)
constructed a tree relating the impact of each identified factors in each of the selected model but did
not construct a model that could be used in assessing direct impact of the identified factors on the
equity price.
Asset pricing could be a challenge. Hordahl & Packer (2006) argue that a clear understanding
of the asset’s stochastic discount factor and future payoffs is necessary to understand the factors that
determine the price of an asset. Unfortunately, only Government instruments provide their stochastic
discount factor in advance while the future payoffs are not observable directly but could be derived
from some other data.
Corwin (2003 identifies uncertainty and asymmetric information as a strong influence on the
firm’s equity pricing and as a matter of fact lead to underpriced instrument. In the light of the
preceding literature review, many factors both micro and macro-economics, have impact on equity
pricing in the stock market, the impact differs from firm to firm, industry to industry, economy to
economy and from time to time, but one comforting conclusion is that most of the factors appear to

have the same behaviour regardless of time, industry or firm constraints. For instance, increased
inflation and interest rates, declining dividends, earnings, poor management leave negative impact on
equity pricing and vice-versa

2.3. Asset Pricing Techniques
There are several asset pricing models aside from CAPM and APT which are both linear model. A few
of the available (non-linear) asset pricing techniques are reviewed in this section.

2.3.1. Residual Income Valuation
This is one of the oldest valuation model with a trace to the work of Preinreich (1938). The valuation
model discounts the future expected dividends and potential value of shareholders’ funds to the present
value, giving effect to a proposition that the price of equity can be derived from the present value of all
future dividends. Lo and Lys (2000) reviewed the Olhson Model (OM) developed in by Ohlson (1995)
and which has been acknowledged with wide acceptance (Joos & Zhdanov, 2007; Chen & Zhao,
2008). The OM provides a platform for the empirical test of the residual income valuation (RIV). Lo
and Lys (2000) defined RIV as:
RIV = P
t
= ∑R
-r
E
t
(d
t+r
)
Where P
t
is defined as the equity market price at time t, d
t
represents dividends at the end of

time t, R is the unity plus the discount rate (r) and E
t
is the expectation factor at time t. The RIV from
the present value of expected dividend is based on the assumptions that (i) the accounting system meets
the “clean surplus relation” i.e.
181 International Research Journal of Finance and Economics - Issue 30 (2009)
To derive RIV from PVED, two additional assumptions are made. First, an “accounting
system” that satisfies a clean surplus relation (CSR) is assumed:
b
t
= b
t-1
+ x
t
- d
t
,
bt represents the book value of equity at time t, xt represents the earnings at time t, and (ii) it is
assumed that the book value of equity would grow at a rate less than R, that is
R
-r
E
t
(b
t+r
) ) 0
The assumptions form the basis to argue that the present value of expected dividend is a
function of both the book value and discounted expected abnormal earnings. In that case RIV
signifying the price of the asset can be stated thus:
P

t
= bt +∑
t=1
R
-r
E
t
(x
a
t+r
)
Where x
a
t
= x
t
– rb
t-1
.
Testing RIV empirically could be a contention on the premises that it has only one sided
hypothesis: asset price is a function present value of future dividends. A rejection of the hypothesis
when tested empirically may arouse dissenting voices from researchers who had believed in the
efficacy of the model. In fact, Lee (2006) expressed the view that residual income valuation model
provides a better valuation than the dividend model. John and Williams (1985), and Miller and Rock
(1985), argue that dividend is a communication tool for the firm to pass information to the market in
the event of information asymmetry which implies that there is a positive correlation between
information asymmetry and a firm’s dividend policy.

2.3.2. Economic Valuation Model
This model traced to Tully (2000) is developed to recognize economic profits as against the use of

book profit in the valuation of asset. The model builds on the premises of profit maximization by
owners of the firm and the profit is not to be restricted to book value, rather it covers the opportunity
cost of not investing in profitable projects. Economical profit is differentiated from the book profit as
the difference from revenues and economical costs (i.e. book costs plus opportunity cost of failure to
invest in profitable project. The book profit can be defined as revenue less costs while economic profit
is defined as total revenue from investment less cost of capital. Economic profit is higher than normal
book profit because of the opportunity cost considered in the former.
There are two approaches to the estimation of economic value added (Koller, Goedhart &
Wessels, 2005; Jennergren, 2008). The first is NOPLAT less capital charge (i.e. WACC multiplied by
initial capital outlay). The value of the operating assets is therefore the initial capital outlay plus the
present value of cash flows derived from economic value added. To obtain the equity value, the value
of debt is deducted from the value of the operating assets. The second approach involves EBIT less
taxes (i.e. PAT). PAT less capital charge after recognizing deferred taxes as part of the invested capital.
The operating assets remain as the initial capital outlay (having considered the effect of deferred taxes)
plus the present value of all income derived from the economic value added.
Economic Valuation of Asset (EVA) Model as defined by Kislingerová (2000) is stated as:
EVA
t
= P
t
= NOPAT
t
– C
t
x WACC
t

where NOPAT
t
is Net Operating Profit After Tax or the profit after tax (PAT), C

t
is long-term capital
(Ct is the sum of equity and invested capital or alternatively, it is the total of fixed assets and net
working capital), WACC is Weighted Average Cost of Capital. Whenever EVA > O, the shareholders’
wealth is maximized, if EVA =0 then there is a break-even point and at EVA < 0 the shareholders’
wealth is in decline.EVA model serves as a tool in measuring both the performance of the firms as well
its value. WACC serves a dual purpose. It is used in the calculation of EVA and its serves as the rate
for discounting the present value of future earnings to the present time t. The value of the firm is
therefore the addition of the book value of capital and the present value of future EVA. To derive the
value of equity the value of debt would be deducted from the value of the firm.

International Research Journal of Finance and Economics - Issue 30 (2009) 182
2.3.3. Discounted Cash Flow Model
The model uses accounting data as input and the objective of the model is to derive equity value of a
going concern. The value of equity is derived by deducting the value of debt (excluding deferred taxes
and trade credits) from the total assets. Deferred taxes are regarded as part of equity (Brealey, Myers &
Allen, 2006). There are several variations to the adoption of the model (Jennergren, 2008). The
discounted cash flow (DCF) is more adaptable to the valuation of a firm with high level of assets in
place and low level of uncertainty about future cash flows (Joos & Zhdanov, 2007). Cash flows
available for discounting include dividends, free cash flow to equity and free cash to the firm (debt and
equity). A firm can experience three types of growth ranging from stable growth, high growth to stable
growth and high growth through transition to a stable growth. The discount rate could be either cost of
equity, cost of debt or the weighted cost of capital (WACC). The choice of discount rate should depend
on the type of cash flow (equity or firm) to be discounted. At least two models can be derived from the
cash flow model. The Dividend Discount (DD) Model is suitable for a firm that pays dividends close to
the free cash flow or where it is difficult to estimate the free cash flow to equity. The second model,
Free Cash Flow Model is suitable where there is a significant margin between dividends and free cash
flow to equity or if dividends are not available.
The value of firm witnessing stable growth is given as:
C:\Users\joseni\Desktop\Desktop\DISCOUNTED CASHFLOW MODELS WHAT THEY ARE AND HOW TO CHOOSE THE RIGHT

ONE__files\ Image8.g if

or a firm that experiences two stages of growth (i.e. high growth to stable growth), the value of the firm
is:
C:\Users\joseni\Desktop\Desktop\DISCOUNTED CASHFLOW MODELS W HAT THEY ARE AND HO W TO CHO OSE THE RIGHT ONE__files\Image9.gif

The value of a firm experiencing three levels of growth (i.e. high growth through transition to
stable growth) is given as:
C:\Users\joseni\Desktop\Desktop\DISCOUNTED CASHFLOW MODELS WHAT THEY ARE AND HOW TO CHO OSE THE RIGHT ONE__files\Image10.gif

Where V
0
represents equity value or firm value depending on which is discounted, CF
t

represents cash flow at time t, r represents cost of equity (for dividends or free cash flow to equity) or
cost of capital ( for free cash flow to firm), g represents expected growth rate, g
a
represents initial
expected growth (high growth period) and g
n
represents growth in a stable period; n and n
1
are defined
as the period in a two stage growth and high growth in a three stage growth models respectively while
n
2
-n
1
represents the transition period in the three stage growth model.

2.3.4. Dividend Valuation Model
This is one of the commonest and simplest models for valuation of equity in the secondary market. The
equity value is taken as the summation of discounted dividends receivable each year till the year of
maturity and the price the equity is expected to be sold at maturity. The value of an investment is taken
to be the discounted value of the cash flows. There are different variations to the model ranging from:
One period valuation
P
o
= D
1
/(1 + k
e
) + P
1
/(1 + k
e
) - one Period to multi-periods
P
o
= D
1
/(1 + k
e
)1 + D
2
/(1+k
e
)
2

+…+ D
n
/(1+k
e
)
n
+ P
n
/(1+k
e
)
n
– multi- period
and to indeterminate length of time
P
o
= D/(1+k
e
) Infinity and, growth
(including Gordon growth) variations.

D
0
(1+g)
1
+ D
0
(1+g)
2
+… + D

0
(1+g)
∞
P
o
=
(1+k
e
)
1
(1+k
e
)
2
(1+k
e
)
∞


or
183 International Research Journal of Finance and Economics - Issue 30 (2009)
D
0

P
o
=
(k
e -

g)

Where:
D = dividend paid / expected
g = dividend’s growth rate
k
e
= cost of equity or equity rate of return
1 - - n = period variation
One of the motives behind the use of this valuation model is to identify over and underpriced
shares.
Moving away from the simplest form of this model Go and Olhson (1990) introduced a more
tasking process for generating dividends and returns on equity investment which they adopted in some
more specific valuation models. The process is based on some assumptions such that equity holders
would receive net dividends and there exists a linear relationship between variables. John and Williams
(1985), and Miller and Rock (1985) argue that dividend is a communication tool for the firm to pass
information to the market in the event of information asymmetry which implies that there is a positive
correlation between information asymmetry and a firm’s dividend policy.


3.0. Research Methodology
We define the research hypotheses, sampling and data collection techniques as well as the statistical
techniques used to test the data.

3.1. Research Methodology
We test the following hypotheses:
H
o1
: The earning per share significantly affects the stock price
H

o2
: The national gross domestic products significantly affect the stock price
H
o3
: The lending interest rate significantly affect the stock price
H
o4
: The foreign exchange rate significantly affect the stock price

3.2. Model
From the hypotheses, the stock price is a function of the impact of earning per share, dividend per
share, gross domestic, interest rate and oil price. We restricted the influencing factors to five as
representatives of the firm’s fundamental factors and external (country) factors.
A simple linear regression model derived from Al-Tamimi (2007) is adopted for the study.
Unlike Al-Tamimi (2007) who included consumer price index (CPI) and money supply (MS) as
independent variables, those variables were replaced with inflation rate (INFL) and foreign exchange
rate (FX) in view of the significant impact they have on the economies of developing countries.
SP = f (EPS, DPS, GDP, INT, OIL, INFL, FX)
Where, SP is the stock price; EPS is the earnings per share; DPS is the dividend per share; GDP
is the gross domestic product, INT is the lending interest rate, OIL is the oil price; INFL is inflation
and FX is the foreign exchange rate.
SP is the dependent variable and it is used to regress the other independent variables (EPS,
DPS, GDP, INT, OIL, INFL, FX) in the stock market. The outcome of the regression would be the
variance on the dependent variable as resulting from the impact of the independent variables.
To explain the effects of multicollinearity normally associated with multi-variables in
regression analysis, multicollinearity test is conducted to explain the extent of correlation between the
independent variables A multiple regression software (WASSA) was used to test the multicollinearity
among the independent variables before proceeding to conduct the regression analysis.
International Research Journal of Finance and Economics - Issue 30 (2009) 184
3.3. Data Sampling

There are over 130 companies whose shares are being traded in the Nigerian capital market. The
Banking sector in the last five years has dominated the market in terms of trading volumes and market
performance. The earning per share (EPS) and dividend per share (DPS) of twelve companies listed on
the Nigerian Stock Exchange (NSE) and (average) annual GDP, crude oil price (OIL), lending interest
rate (INT), inflation rate (INFL) and foreign exchange rate (FX) are used are analysed for effect on the
stock price. The period covered by the data is year 2001 to 2007. The choice of the companies and
period used for the data gathering depend on availability of data.

3.4. Data Restructuring
Weights are attached to EPS and DPS for each of the companies sampled for each of the year. The
weight is derived as a ratio of the company’s EPS or DPS to the total EPS or DPS of all the companies
for each of the years.
The weight is thereafter multiplied with the respective company EPS or DPS to derive
“weighted stock price (SP), EPS or DPS and thereafter all the companies weighted SP, EPS or DPS are
summed together for each of the year (APPENDIX I).


4.0. Findings and Interpretation
In a linear expression where more than two variables are deployed, multicollinearity between variables
may not be ruled out. A multicollinearity test is therefore conducted for all the independent variables.
Using the Pearson coefficient of correlation, we consider any correlation between two variables >
+

0.75 as strong.
For instance, from Table 1 below there is no significant correlation between earnings per share
and dividend per share. Our explanations for it are into parts. First, all the companies in the sample
reported earnings per share for each of the years covered by the study though in some instances the
EPS are negative but not all the companies declared and /or paid dividends throughout all the periods.
Secondly, EPS movement unlike DPS is largely outside the control of the Management.
There is a strong correlation between crude oil price and GDP. The justification for the

correlation between crude oil price and GDP can be found in the fact that the Nigerian economy
predominantly depends on oil revenue.

Table I: Outcomes of the Multicollinarity Test (Pearson Coefficient of Correlation

DPS EPS GPD OIL INT INFL FX
DPS
1
EPS
-0.302 1
GDP
0.609 -0.523 1
OIL
-0.395 -0.596
0.959
1
INT
-0.498 0.366 -0.702 -0.706 1
INF
-0.521
0.778
-0.492 -0.434
0.988
1
FX
0.724 -0.037
0.795
0.614 -0.424 -0.313 1

A strong correlation also exist between INFL and INT which might be the result of

manufacturers and service providers passing increased lending interest rate to consumers. A strong
correlation exists between FX and GDP. Unexpectedly, there is a strong correlation between INF and
EPS, we do not have any explanation for this relationship. For our regression analysis, OIL and INFL
were dropped from the model. Though there is a strong correlation between FX and GDP, both
variables are used in the regression. FX and GDP variables are significant to the economy of
developing nations like Nigeria, therefore their exclusion from the regression would result in a very
high constant (β).
185 International Research Journal of Finance and Economics - Issue 30 (2009)
A regression analysis was run on the independent variables DPS, EPS, GDP and INT after
dropping OIL, INFL and FX. Table I shows the result of the regression analysis.

Table II: Summary of the Regression Analysis

R R
2
Adjusted R
2
Standard Error of Estimates F – Test
0.99998 0.99996 0.99978 0.4752 5385.033

Β T – Test
Constant - 67.2385 - 9.597
DPS 0.3835 36.259
EPS 0.0869 33.369
GDP 0.3805 21.809
INT - 0.8236 - 7.375
FX - 1.9741 - 11.214

The stock price (P) is highly sensitive to variation as indicated by R
2

of 0.99996. In other words
there is 99.99% and as a matter of fact 100% in stock variation caused by the independent variables.
The variability as measured by coefficient of variation (β) is expectedly positive for DPS, EPS
and GDP and expectedly negative for lending interest (INT) though quite significantly. The β for DPS
and EPS though positive were not significant. Many of the companies resorted to bonus issues instead
of dividends and the Nigerian investors are more interested in incomes rather than capital appreciation
especially where the stock market performance is poor. The failure to declare and pay dividend leaves
two negative impacts on stock prices. The existing investors are denied additional funds to invest and
the potential investors seeking investment incomes are discouraged. The hypothesis that EPS affect
stock price significantly is accepted.
The positive GDP’s coefficient in relation to the stock price is in agreement with some other
studies (Udegbunam and Eriki,2001; Ibrahim 2003; Mukherjee and Naka 1995; Chaudhuri and Smiles,
2004). The β is insignificant at 0.3805 and this might not be unconnected with the increasing foreign
reserve maintained by CBN from the proceeds of crude oil sales. The proceeds of the crude oil sales
are not released to the economy for investment in various productive sectors of the economy but rather
held in foreign economies as part of the CBN’s monetary policies. The domestic economy is denied of
the investments that would have occurred if the funds in the foreign reserve are released for spending
in the domestic economy. The hypothesis that the GDP affects stock price significantly is accepted.
The coefficient of interest which is negative is expected and found to be significant. The
negative coefficient of the lending interest rate is in agreement with the findings of Al-Qenae, Li &
Wearing (2002), and Mukherjee and Naka (1995). Lending interest rate is a strong tool in the hands of
CBN to influence the economy and where the interest is high as it is Nigeria where lending interest
rates hovers between 22% and 25%, the accessibility of the investors to access funds is curtailed and
the impact on the stock price would be negative as shown. The hypothesis that lending interest rate
affects the stock price significantly is accepted
The foreign exchange rate’s coefficient is significantly negative at significant level of 10%.
This is not unexpected. Local and foreign investors tend to invest in an economy that has a very high
currency exchange rate to foreign currencies. The local investors are discouraged from taking their
funds out of the economy for fear of reduced purchasing while foreign investors are encouraged
otherwise for increased purchasing power. The hypothesis that foreign exchange rate affects the stock

price significantly is accepted.
Lastly, the constant (β) is 67.2385 (negative). This suggests that the minimum stock price in the
market is < 0. We had initially excluded FX from the regression for the reason of its collinearity with
GDP but the constant was negative and excessively high. The inclusion of FX has reduced the
negativity which is an indication that there are other important variable(s) that significantly affect the
stock prices but not considered in this study. The stock price cannot be < 0 except the company is in
liquidation.
International Research Journal of Finance and Economics - Issue 30 (2009) 186
This raises an important question of what factor(s) could have accounted for the extra ordinary
stock market performance in Nigeria between 2005 and 2007 where some stocks return over 1000%
per annum. The nation House of Representative’s Committee on Capital Markets expressed disgust at
the hike in the stock prices of companies in the banking and oil sectors (Thisday Newspapers, 2008).
The “hike” which may not be a non-economic factor (such as political, unhealthy competition,
profiteering by issuers who are at the same time market investors) may be the omitted important
variable accounting for the high β.


5.0. Conclusions and Recommendations
The forces of demand and supply have direct effect on the stock price while the other indeterminate
number of firm, industry and country factors influences the demand and supply factors. The effect,
positive or negative the other factors apart from the demand and supply leave on stock price are not
static rather changes. For instance, lending interest rate effect could be positive or negative depending
on the aim of the CBN in deploying it as one of the tools for implementing monetary policy.
The study has contributed to existing literatures in confirming or raising new issues with
respect to other factors influencing stock prices. Interest researchers may want to identify and examine
the non-economic factor that account for the high constant (β) which may not be unconnected with the
current meltdown in the Nigerian stock market.
Lastly, policy makers who are concerned about the growth of the capital market are better
informed on how to deploy the monetary policies instruments as well other economic indices to
achieve the desired market growth.

Bibliography
[1] Allen, F. and G.R. Faulhaber, 1989. “Signaling by Underpricing in The IPO Market”, Journal
of Financial Economics, 23
[2] Al – Tamimi, Hussein (2007), “Factors Affecting Stock Prices in The UAE Financial Markets”,
Singapore Economic Review Conference,
[3] Al-Qenae, Rashid; Li, Carmen Wearing, Bob (2002) “The Information Content of Earnings on
Stock Prices: The Kuwait Stock Exchange,” Multinational Finance Journal, 6
[4] Barclay, M., and R. Litzenberger. (1988). “Announcement Effects of New Equity Issues and
The Use of Intraday Price Data”. Journal of Financial Economics 21
[5] Brav, Alon and J.B. Heaton (2003) “Market Indeterminacy” Journal of Corporation Law, Vol.
28
[6] Brealey, Richard A., Stewart C. Myers, and Franklin Allen, (2006). “Corporate Finance”, 8th
Ed., Mcgraw-Hill/Irwin, New York
[7] Byers, S.S. and John C. Groth (2000) “Non-Economic Factors and Asset Valuation”
Conference Papers on Alternative Perspective on Finance and Accounting,

[8] Chaudhuri, K. and Smiles, S. (2004), “Stock Market and Aggregate Economic Activity:
Evidence from Australia,” Applied Financial Economics, (14)
[9] Chen, J., (2005), “The Physical Foundations of Economics”, World Scientific Publishing Co.,
London.
[10] Clerc, Laurent and Christian Pfister (2001) “The Role of Financial Factors in the Transmission
of Monetary policy” Bank for International Settlements, BIS Papers No 19,

[11] Corwin, Shane, A (2003) “The Determinants of Underpricing for Seasoned Equity Offers”
Journal of Finance 58(5)
[12] Docking, Diane S.; Koch, Paul D. (2005), “Sensitivity of Investor Reaction to Market Direction
And Volatility: Dividend Change Announcements,” Journal of Financial Research
187 International Research Journal of Finance and Economics - Issue 30 (2009)

[13] Easley, David, Soeren Hvidkjaer, and Maureen O’Hara, 2002, Is Information Risk a
Determinant of Asset Returns? Journal of Finance, 57
[14] Giammarino, Ronald M., and Tracy Lewis, (1989), “A Theory of Negotiated Equity
Financing”, Review of Financial Studies, 1
[15] Gompers, Paul A., Joy L. Ishii, and Andrew Metrick (2003) “Corporate Governance and Equity
Prices”
[16] Grinblatt, M. and C.Y. Hwang, (1989).” Signalling and The Pricing Of New Issues”, Journal of
Finance, 44
[17] Hartono, Jogiyanto. (2004), “the Recency Effect of Accounting information,” Gadjah Mada
International Journal of Business, Vol. 6 No. 1
[18] Hordahl, Peter and Frank Packer, (2007), “Understanding asset prices: an overview” 2006
Autumn Meeting of Central Bank Economists, Bank for International Settlements, BIS Papers,
No. 34
[19] Ibrahim, Mansor H. (2003), “Macroeconomic Forces and Capital Market Integration: A VAR
Analysis for Malaysia,” Journal of the Asia Pacific Economy, 8 (1)
[20] Jennergren, L.P. (2008) “A Tutorial on The Discounted Cash Flow Model For Valuation of
Companies”, Working Paper Series in Business Administration, Stockholm School of
Economics
[21] John, K. and J. Williams, (1985), “Dividends, Dilution, and Taxes: A Signaling Equilibrium,”
Journal of Finance, 40
[22] Joos, Philip and Alexei Zhdanov (2007) “Earnings and Equity Valuation in the Biotech
Industry: Theory and Evidence”
[23] Kang, H.G. (2004) “Disclosure-Risk Premium: Asset Pricing Under Asymmetric Information,
Manipulation and Ambiguity”, Working Paper Series, Social Science Research Network,

[24] Kislingerova, Eva, (2000) “Using of The Economic Value Added Model for Valuation of a
Company”, Národná Banka Slovenska, BIATEC, Rocnik, 8, 11
[25] Koller, Tim, Marc Goedhart, and David Wessels, (2005), “Valuation: Measuring and Managing
The Value of Companies”, Wiley, Hoboken, New Jersey, 4th Ed.,
[26] Lee, Bong-Soo (2006) “An Empirical Evaluation of Behavioral Models Based on

Decompositions of Stock Prices” Journal of Business, Vol. 79, issue 1
[27] Lo, K. and T. Lys, (2000) “The Ohlson Model: Contribution to Valuation Theory, Limitations,
And Empirical Applications.” Journal of Accounting, Auditing and Finance, 15 (3)
[28] Loderer C. F., Sheehan D. P., and Kadlec G. B., 1991, "The pricing of equity offerings",
Journal of Financial Economics, 29
[29] Miller, M. and K. Rock, (1985), “Dividend Policy Under Asymmetric Information,” Journal of
Finance 40
[30] Molodovsky, Nicholas (1995),“A Theory of Price-Earnings Ratios”, Financial Analysts
Journal, 51( 1)
[31] Moore, J. S & Beltz, J.C (2002) “Share Price Performance and Observable Factors: A
Perspective from Rule Induction”, Small Business Advancement National Centre,
wdsi/2002/pdffiles/toc.pdf
[32] Mukherjee, Tarun K.; Naka, Atsuyuki (1995) “Dynamic Relations Between Macroeconomic
Variables and The Japanese Stock Market: An Application of Vector Error Correction Model,”
The Journal Of Financial Research, XVIII. (2)
[33] O'hara, M., (1995), “Market Microstructure Theory”, Blackwell Publishers Ltd, Malden, Ma
[34] O'hara, M., (2003), “Liquidity and Price Discovery”, Journal of Finance, 58, 4
[35] Preinreich, G.A.D. (1938), “Annual Survey of Economic Theory: The Theory of Depreciation”
Econometrica, 6
[36] Rigobon, Roberto and Brian P. Sack (2003) ’’Spillovers Across U.S. Financial Markets,
National Bureau of Economic Research (NBER), Working Paper W9640
International Research Journal of Finance and Economics - Issue 30 (2009) 188
[37] Rock, Kevin, (1986), “Why New Issues Are Underpriced”, Journal of Financial Economics, 15
[38] Ross, Stephen A. (1976) “An Arbitrage Theory of Capital Asset Pricing”. Journal of Economic
Theory, 13
[39] Sharpe, W.F., (1964), “Capital Asset Prices: A Theory of Market Equilibrium Under
Conditions of Risk”, Journal of Finance, Vol. 19
[40] Smith, Clifford W., (1977), “Alternative Methods For Raising Capital: Rights Versus
Underwritten Offerings”, Journal of Financial Economics, 5
[41] Thisday Newspapers (2008) “Nigeria: SEC - Capital Market Loses N3 Trillion in Six Months”,

[42] Tully, Shwan (2008) “The Real Key to Creating Wealth”: Fortune Magazine cited in
Kislingeriva, Eva “Using of the Economic Value Added Model for Valuation of a Company”
Narodna Banka Slovenska, BIATEC, Rocnik 8, 11
[43] Tymoigne, Erick (2002) “Financial Inflation and Financial Convention” Oeconomicus, Volume
V, Winter 2002,
[44] Udegbunam, R.I. and P. O. Eriki (2001), “Inflation and Stock Price Behavior: Evidence from
Nigerian Stock Market,” Journal of Financial Management & Analysis, XX (14)
[45] Welch, I., (1989) “Seasoned Offerings, Imitation Costs, And The Underpricing Of Initial Public
Offerings”, Journal of Finance 44
[46] Wessa, P. (2008), Free Statistics Software, Office for Research Development and Education,
version 1.1.23-r3
[47] Zhang, X. Frank (2004) “Information Uncertainty and Stock Returns” An Article Submitted to
The Journal of Finance Manuscript 1149
www.afajof.org/afa/forthcoming/zhang_information.pdf
[48] Zhao, Xing-Qiu (1999), “Stock prices, inflation and output: evidence from China,” Applied
Economics Letters, 6


Appendix

Appendix I: Selected Market Indices (2001 - 2007)

YEAR PRICE* DPS* EPS* GDP** INT** OIL** INFLE** FX **
2001
42.53 430.00 393.29 431,783.10 21.34 24.50 18.90 111.94
2002
43.70 432.72 412.52 451,785.60 29.70 25.40 12.90 120.97
2003
109.21 577.63 459.83 495,007.10 22.47 29.10 14.00 129.36

2004
116.76 552.48 600.59 527,576.00 20.62 38.70 15.00 133.50
2005
110.56 466.97 708.90 561,931.40 19.47 57.60 17.90 132.15
2006
102.33 553.87 1,666.03 595,821.61 18.43 66.50 8.20 128.65
2007
95.87 549.93 894.96 561,776.34 19.51 54.27 13.70 131.43
Source: Central Bank of Nigeria Statistical Bulletin**
: Cashcraft Asset Management Limited / APT Securities and Fund Limited *
189 International Research Journal of Finance and Economics - Issue 30 (2009)
Appendix II: Regression Analysis Of Selected Market Indices (2001 – 2007)

Multiple Linear Regression - Estimated Regression Equation
SP[t] = +0.38353330161483 DPS[t] +0.086971432931437 EPS[t] +0.38049146437789 GDP[t] -0.82357353121514
INT[t] -1.9740597666311 FX[t] -67.238476376193 + e[t]

Multiple Linear Regression - Ordinary Least Squares
T-STAT
Variable Parameter S.E.
H0: parameter = 0
2-tail p-value 1-tail p-value
DPS[t] 0.383533 0.010577 36.259468 0.017553 0.008776
EPS[t] 0.086971 0.002606 33.368601 0.019073 0.009536
GDP[t] 0.380491 0.017447 21.808584 0.029171 0.014585
INT[t] -0.823574 0.111666 -7.375331 0.085794 0.042897
FX[t] -1.97406 0.17603 -11.214366 0.056618 0.028309
Constant -67.238476 7.006084 -9.597156 0.066096 0.033048
T-STAT
Variable Elasticity S.E.*

H0: |elast| = 1
2-tail p-value 1-tail p-value
%DPS[t] 2.201042 0.060703 19.785697 0.032148 0.016074
%EPS[t] 0.359282 0.010767 -59.507274 0.010697 0.005349
%GDP[t] 2.221624 0.101869 11.992081 0.052964 0.026482
%INT[t] -0.200986 0.027251 -29.320395 0.021704 0.010852
%FX[t] -2.822992 0.25173 7.241855 0.087356 0.043678
%Constant -0.75797 0.078979 -3.064493 0.200805 0.100402
T-STAT
Variable Stand. Coeff. S.E.*
H0: coeff = 0
2-tail p-value 1-tail p-value
S-DPS[t] 0.763848 0.021066 36.259468 0.017553 0.008776
S-EPS[t] 0.69251 0.020753 33.368601 0.019073 0.009536
S-GDP[t] 0.729372 0.033444 21.808584 0.029171 0.014585
S-INT[t] -0.09814 0.013307 -7.375331 0.085794 0.042897
S-FX[t] -0.48017 0.042817 -11.214366 0.056618 0.028309
S-Constant 0 0 0 1 0.5
*Note
Computed against deterministic endogenous series
Multiple Linear Regression - Regression Statistics
Multiple R 0.999981
R-squared 0.999963
Adjusted R-squared 0.999777
F-TEST 5385.033289
Observations 7
Degrees of Freedom 1
Multiple Linear Regression - Residual Statistics
Standard Error 0.475177
Sum Squared Errors 0.225793

Log Likelihood 2.086595
Durbin-Watson 3.380955
Von Neumann Ratio 3.944448
# e[t] > 0 3
# e[t] < 0 4
# Runs 6
Runs Statistic 1.333946
NB: Regression analysis was done using a software developed by Wessa (2008)