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

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

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

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

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

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


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

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 -

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

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

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
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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.*

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)