finance thesis
TRANSCRIPT
DETERMINANTS OF STOCK PRICES IN THE CAPITAL MARKET
BY
Jimoh Ezekiel Oseni Ph.D Student in Business and Applied Economics,
(Finance Option) Department Of Banking And Finance,
Faculty Of Management Sciences Olabisi Onabanjo University, Ago-Iwoye, Nigeria
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.
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, 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 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 = Pt = ∑R-r Et (dt+r)
Where Pt is defined as the equity market price at time t, dt represents dividends at the
end of time t, R is the unity plus the discount rate (r) and Et 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.
To derive RIV from PVED, two additional assumptions are made. First, an “accounting
system” that satisfies a clean surplus relation (CSR) is assumed:
bt = bt-1 + xt - dt,
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 Et (bt+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:
Pt = bt +∑t=1 R-r Et (x
at+r)
Where xat = xt – rbt-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:
EVAt = Pt = NOPATt – Ct x WACCt
where NOPATt is Net Operating Profit After Tax or the profit after tax (PAT), Ct 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.
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:
or a firm that experiences two stages of growth (i.e. high growth to stable growth), the
value of the firm is:
The value of a firm experiencing three levels of growth (i.e. high growth through
transition to stable growth) is given as:
Where V0 represents equity value or firm value depending on which is discounted, CFt
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, ga represents initial expected growth (high growth period) and gn represents
growth in a stable period; n and n1are defined as the period in a two stage growth and
high growth in a three stage growth models respectively while n2-n1 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
Po = D1/(1 + ke) + P1/(1 + ke) - one Period to multi-periods
Po = D1/(1+ke)1 + D2/(1+ke)
2 +…+ Dn/(1+ke)n + Pn/(1+ke)
n – multi- period
and to
indeterminate
length of time
Po = D/(1+ke) Infinity and, growth
(including Gordon growth) variations.
Po = D0(1+g)1 + D0(1+g)2 +…..+ D0(1+g)∞
(1+ke)1 (1+ke)
2 (1+ke)∞
or
Po = D0--
(ke - g)
Where:
D = dividend paid / expected
g = dividend‟s growth rate
ke = 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:
Ho1 : The earning per share significantly affects the stock price
Ho2 : The national gross domestic products significantly affect the stock price
Ho3 : The lending interest rate significantly affect the stock price
Ho4 :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.
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).
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 R2 Adjusted R2 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 R2 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.
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.
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 *
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
Variable Parameter S.E. T-STAT
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
Variable Elasticity S.E.* T-STAT
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
Variable Stand. Coeff. S.E.* T-STAT
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)
BIBLIOGRAPHY
Allen, F. and G.R. Faulhaber, 1989. “Signaling by Underpricing in The IPO Market”, Journal of Financial Economics, 23
Al – Tamimi, Hussein (2007), “Factors Affecting Stock Prices in The UAE Financial
Markets”, Singapore Economic Review Conference, https://editorialexpress.com/conference/SERC2007
Al-Qenae, Rashid; Li, Carmen Wearing, Bob (2002) “The Information Content of
Earnings on Stock Prices: The Kuwait Stock Exchange,” Multinational Finance Journal, 6
Barclay, M., and R. Litzenberger. (1988). “Announcement Effects of New Equity Issues and The Use of Intraday Price Data”. Journal of Financial Economics 21
Brav, Alon and J.B. Heaton (2003) “Market Indeterminacy” Journal of Corporation Law, Vol. 28
Brealey, Richard A., Stewart C. Myers, and Franklin Allen, (2006). “Corporate
Finance”, 8th Ed., Mcgraw-Hill/Irwin, New York Byers, S.S. and John C. Groth (2000) “Non-Economic Factors and Asset Valuation”
Conference Papers on Alternative Perspective on Finance and Accounting, http://www.departments.bucknell.edu/management/apfa/dundee%20papers.htm
Chaudhuri, K. and Smiles, S. (2004), “Stock Market and Aggregate Economic Activity:
Evidence from Australia,” Applied Financial Economics, (14) Chen, J., (2005), “The Physical Foundations of Economics”, World Scientific
Publishing Co., London. 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, http://www.bis.org/publ/bppdf/bispap19h.pdf
Corwin, Shane, A (2003) “The Determinants of Underpricing for Seasoned Equity
Offers” Journal of Finance 58(5) Docking, Diane S.; Koch, Paul D. (2005), “Sensitivity of Investor Reaction to
Market Direction And Volatility: Dividend Change Announcements,” Journal of Financial Research
Easley, David, Soeren Hvidkjaer, and Maureen O‟Hara, 2002, Is Information Risk a
Determinant of Asset Returns? Journal of Finance, 57 Giammarino, Ronald M., and Tracy Lewis, (1989), “A Theory of Negotiated Equity
Financing”, Review of Financial Studies, 1 Gompers, Paul A., Joy L. Ishii, and Andrew Metrick (2003) “Corporate Governance
and Equity Prices”
Grinblatt, M. and C.Y. Hwang, (1989).” Signalling and The Pricing Of New Issues”, Journal of Finance, 44
Hartono, Jogiyanto. (2004), “the Recency Effect of Accounting information,” Gadjah
Mada International Journal of Business, Vol. 6 No. 1 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
Ibrahim, Mansor H. (2003), “Macroeconomic Forces and Capital Market Integration: A
VAR Analysis for Malaysia,” Journal of the Asia Pacific Economy, 8 (1) 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
John, K. and J. Williams, (1985), “Dividends, Dilution, and Taxes: A Signaling
Equilibrium,” Journal of Finance, 40 Joos, Philip and Alexei Zhdanov (2007) “Earnings and Equity Valuation in the Biotech
Industry: Theory and Evidence” http://papers.ssrn.com/sol3/papers.cfm?abstract Kang, H.G. (2004) “Disclosure-Risk Premium: Asset Pricing Under Asymmetric
Information, Manipulation and Ambiguity”, Working Paper Series, Social Science Research Network, http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=333508
Kislingerova, Eva, (2000) “Using of The Economic Value Added Model for
Valuation of a Company”, Národná Banka Slovenska, BIATEC, Rocnik, 8, 11 Koller, Tim, Marc Goedhart, and David Wessels, (2005), “Valuation: Measuring
and Managing The Value of Companies”, Wiley, Hoboken, New Jersey, 4th Ed., Lee, Bong-Soo (2006) “An Empirical Evaluation of Behavioral Models Based on
Decompositions of Stock Prices” Journal of Business, Vol. 79, issue 1 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)
Loderer C. F., Sheehan D. P., and Kadlec G. B., 1991, "The pricing of equity offerings", Journal of Financial Economics, 29
Miller, M. and K. Rock, (1985), “Dividend Policy Under Asymmetric Information,”
Journal of Finance 40 Molodovsky, Nicholas (1995),“A Theory of Price-Earnings Ratios”, Financial Analysts
Journal, 51( 1) Moore, J. S & Beltz, J.C (2002) “Share Price Performance and Observable
Factors: A Perspective from Rule Induction”, Small Business Advancement National Centre, http://www.sbaer.uca.edu/research/TemporarilyDisabled---- wdsi/2002/pdffiles/toc.pdf
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)
O'hara, M., (1995), “Market Microstructure Theory”, Blackwell Publishers Ltd,
Malden, Ma O'hara, M., (2003), “Liquidity and Price Discovery”, Journal of Finance, 58, 4 Preinreich, G.A.D. (1938), “Annual Survey of Economic Theory: The Theory of
Depreciation” Econometrica, 6 Rigobon, Roberto and Brian P. Sack (2003) ‟‟Spillovers Across U.S. Financial Markets ,
National Bureau of Economic Research (NBER), Working Paper W9640
Rock, Kevin, (1986), “Why New Issues Are Underpriced”, Journal of Financial Economics, 15
Ross, Stephen A. (1976) “An Arbitrage Theory of Capital Asset Pricing”. Journal of
Economic Theory, 13 Sharpe, W.F., (1964), “Capital Asset Prices: A Theory of Market Equilibrium Under
Conditions of Risk”, Journal of Finance, Vol. 19 Smith, Clifford W., (1977), “Alternative Methods For Raising Capital: Rights Versus
Underwritten Offerings”, Journal of Financial Economics, 5 Thisday Newspapers (2008) “Nigeria: SEC - Capital Market Loses N3 Trillion in Six
Months”, http://allafrica.com/stories/200810210014.html 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
Tymoigne, Erick (2002) “Financial Inflation and Financial Convention”
Oeconomicus, Volume V, Winter 2002, 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)
Welch, I., (1989) “Seasoned Offerings, Imitation Costs, And The Underpricing Of
Initial Public Offerings”, Journal of Finance 44 Wessa, P. (2008), Free Statistics Software, Office for Research Development and
Education, version 1.1.23-r3 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
Zhao, Xing-Qiu (1999), “Stock prices, inflation and output: evidence from China,”
Applied Economics Letters, 6