Factors Affecting Share Prices Free Essays

string(89) " International Research Journal of Finance and Economics – Issue 30 \(2009\) 78 1\." International Research Journal of Finance and Economics ISSN 1450-2887 Issue 30 (2009)  © EuroJournals Publishing, Inc. 2009 http://www. eurojournals. We will write a custom essay sample on Factors Affecting Share Prices or any similar topic only for you Order Now com/finance. htm 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: kayodesomoye@yahoo. com Akintoye, Ishola Rufus Dept. of Accounting, Faculty of Management Science Olabisi Onabanjo University, Ago Iwoye, Nigeria E-mail: irakintoye@yahoo. com Oseni, Jimoh Ezekiel Dept. f Banking and Finance, Faculty of Management Science Olabisi Onabanjo University, Ago Iwoye, Nigeria E-mail: zikoseni@yahoo. com 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 i nformation to the extent no informed trader can outperform the market and / or the uninformed trader. This study examined the extent to which some â€Å"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) 78 1. You read "Factors Affecting Share Prices" in category "Papers" 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 â€Å"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 â€Å"conventions do not determine asset-price, it is the â€Å"law of supply and demand† that does so, conventionsâ€Å"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 noneconomic 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 he 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 â€Å"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 â€Å"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 (xat+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 Kislingerova (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. 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:UsersjoseniD esk top D esk to pDISCOUNTED CA SHFLOW MODELS WHA T THEY A RE A ND HOW TO CHOOSE THE RIGHT ON E__filesImage8. if or a firm that experiences two stages of growth (i. e. high growth to stable growth), the value of the firm is: C:UsersjoseniDesk topDesk topDISCOUNTED CA SHFLOW MODELS WHA T THEY A RE A ND HOW TO CHOOSE THE RIGHT ONE__filesImage9. gif The value of a firm experiencing three levels of growth (i. e. high growth through transition to stable growth) is given as: C:UsersjoseniDesk topDesk topDISCOUNTED CA SHFLOW MODELS WHA T THEY A RE A ND HOW TO CHOOSE THE RIGHT ONE__filesI mage10. gif 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 n1 are 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. . 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 one Period to multi-periods Po = D1/(1 + ke) + P1/(1 + ke) 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 Infinity and, growth Po = D/(1+ke) (including Gordon growth) variations. D0(1+g)1 + D0(1+g)2 +†¦.. + D0(1+g)? Po = (1+ke)1 (1+ke)2 (1+ke)? or 183 Po = International Research Journal of Finance and Economics – Issue 30 (2009) D0– ke – g) Where: D = dividend paid / expected g = dividend’s growth rate = cost of equity or equity rate of return ke 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 mo dels. 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. . 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. International Research Journal of Finance and Economics – Issue 30 (2009) 3. 3. Data Sampling 184 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. . 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 â€Å"weighted stock price (SP), EPS or DPS and thereafter all the companies weighte d 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: DPS EPS GDP OIL INT INF FX Outcomes of the Multicollinarity Test (Pearson Coefficient of Correlation DPS 1 -0. 302 0. 609 -0. 395 -0. 498 -0. 521 0. 724 EPS 1 -0. 523 -0. 596 0. 366 0. 778 -0. 037 GPD 1 0. 959 -0. 702 -0. 492 0. 795 OIL INT INFL FX 1 -0. 706 -0. 434 0. 614 1 0. 988 -0. 424 1 -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 (? ). 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 R2 0. 99996 ? – 67. 2385 0. 3835 0. 0869 0. 3805 – 0. 8236 – 1. 9741 Adjusted R2 0. 99978 T – Test – 9. 597 36. 259 33. 369 21. 809 – 7. 375 – 11. 214 Standard Error of Estimates 0. 4752 F – Test 5385. 033 R 0. 99998 Constant DPS EPS GDP INT FX The stock price (P) is highly sensitive to variation as indicated by R2 of 0. 99996. In other words there is 99. 9% and as a matter of fact 100% in stock variation caused by the independent variables. The variability as measured by coefficient of variation (? ) is expectedly positive for DPS, EPS and GDP and expectedly negative for lending interest (INT) though quite significantly. The ? 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 ? is insignificant at 0. 805 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 spendin g 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 (? ) 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 â€Å"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 ?. . 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 i nstance, 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. 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Frank (2004) â€Å"Information Uncertainty and Stock Returns† An Article Submitted to The Journal of Finance Manuscript 1149 www. afajof. rg/afa/forthcoming/zhang_information. pdf Zhao, Xing-Qiu (1999), â€Å"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** 42. 53 430. 00 393. 29 431,783. 10 21. 34 2001 43. 70 432. 72 412. 52 451,785. 60 29. 70 2002 109. 21 577. 63 459. 83 495,007. 10 22. 47 2003 116. 76 552. 48 60 0. 59 527,576. 00 20. 62 2004 110. 56 466. 97 708. 90 561,931. 40 19. 47 2005 102. 33 553. 87 1,666. 03 595,821. 61 18. 43 2006 95. 87 549. 93 894. 96 561,776. 34 19. 1 2007 Source: Central Bank of Nigeria Statistical Bulletin** : Cashcraft Asset Management Limited / APT Securities and Fund Limited * OIL** 24. 50 25. 40 29. 10 38. 70 57. 60 66. 50 54. 27 INFLE** 18. 90 12. 90 14. 00 15. 00 17. 90 8. 20 13. 70 FX ** 111. 94 120. 97 129. 36 133. 50 132. 15 128. 65 131. 43 189 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. 740597666311 FX[t] -67. 238476376193 + e[t] Multiple Linear Regression – Ordinary Least Squares Variable DPS[t] EPS[t] GDP[t] INT[t] FX[t] Constant Variable %DPS[t] % EPS[t] %GDP[t] %INT[t] %FX[t] %Constant Variable Parameter 0. 383533 0. 086971 0. 380491 -0. 823574 -1. 97406 -67. 238476 Elasticity 2. 201042 0. 359282 2. 221624 -0. 200986 -2. 822992 -0. 75797 Stand. Coeff. S. E. 0. 010577 0. 002606 0. 017447 0. 111666 0. 17603 7. 006084 S. E. * 0. 060703 0. 010767 0. 101869 0. 027251 0. 25173 0. 078979 S. E. * T-STAT H0: parameter = 0 36. 259468 33. 368601 21. 808584 -7. 375331 -11. 214366 -9. 597156 T-STAT H0: |elast| = 1 19. 785697 -59. 07274 11. 992081 -29. 320395 7. 241855 -3. 064493 T-STAT H0: coeff = 0 2-tail p-value 0. 017553 0. 019073 0. 029171 0. 085794 0. 056618 0. 066096 2-tail p-value 0. 032148 0. 010697 0. 052964 0. 021704 0. 087356 0. 200805 2-tail p-value 1-tail p-value 0. 008776 0. 009536 0. 014585 0. 042897 0. 028309 0. 033048 1-tail p-value 0. 016074 0. 005349 0. 026482 0. 010852 0. 043678 0. 100402 1-tail p-value 0. 008776 0. 009536 0. 014585 0. 042897 0. 028309 0. 5 S-DPS[t] 0. 763848 0. 021066 36. 259468 0. 017553 S-EPS[t] 0. 69251 0. 020753 33. 368601 0. 019073 S-GDP[t] 0. 729372 0. 033444 21. 808584 0. 029171 S-INT[t] -0. 09814 0. 013307 -7. 75331 0. 085794 S-FX[t] -0. 48017 0. 042817 -11. 214366 0. 056618 S-Constant 0 0 0 1 Computed against deterministic endogenous series *Note 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) How to cite Factors Affecting Share Prices, Papers