1 Journal of Modern Accounting and Auditing, ISSN November 2013, Vol. 9, No. 11, D DAVID PUBLISHING A Panel Data Analysis of Corporate Attributes and Stock Prices for Indian Manufacturing Sector Mehul Raithatha Institute for Financial Management and Research, Chennai, India Varadraj Bapat Indian Institute of Technology Bombay, Mumbai, India Stock prices have always been considered as unpredictable phenomena due to their dynamic patterns. Identifying the forces that contribute to variations of stock prices is probably one of the most researched areas in finance. This study relates stock prices to the stock volatility (measured by beta) and to corporate attributes, i.e., size, liquidity, profits, leverage, and returns. The study is based on manufacturing sector in India, and it is based on a sample of 3,027 manufacturing companies during the periods from to collected from the Centre for Monitoring Indian Economy (CMIE) database. The regressions were performed with the dummies for time effect and firm effect separately and then for both effects together. Panel data models have been used to estimate the stock prices equation. The model finds out fixed and random effects between independent and explanatory variables and analyzes them through Hausman test. The paper also studies multicollineairity that may exist amongst the selected variables. The study shows that volatility (represented by Beta), profit (represented by earnings per share (EPS)), and size (represented by market capitalization (MCAP)) significantly influence the stock prices (at the level of 5%). Panel data analysis using Hausman test supports the fixed effect model. Keywords: panel data, stock prices, correlation, Hausman test Introduction Share price of a company in the secondary market is determined by the demand and supply for the same. Expectations of the buyers and sellers work in an opposite direction, and the market mechanism leads to discovery of the stock price. Finance theory considers that market is supreme and it discounts everything (socio-economic, political, technological, and other related factors). It presumes that the value of the asset is based on future expectations. Hence, with new information, the expectation of the market is liable to change and the share prices consequently. As the new information is erratic in nature, it influences the price in a random way. Stock prices are set by a combination of factors that no analyst can consistently understand or predict. Objectives of Study The paper aims at studying behavior of stock prices with respect to corporate attributes through the Mehul Raithatha, assistant professor, Finance and Accounting Area, Institute for Financial Management and Research. Varadraj Bapat, assistant professor, SJM School of Management, Indian Institute of Technology Bombay.
2 1520 A PANEL DATA ANALYSIS OF CORPORATE ATTRIBUTES AND STOCK PRICES following objectives: (1) To analyze the dependence of stock prices on stock volatility and corporate attributes; (2) To estimate fixed and random effects using panel data analysis. Literature Review Early empirical research on the determinants of expected stock returns focused on the association between average returns on beta-sorted portfolios and their betas, as predicted by the capital asset pricing model (CAPM). These include studies of Black, Jenson, and Scholes (1972), Blume and Friend (1973), and Fama and MacBeth (1973). However, other researchers highlighted the danger of focusing exclusively on mean-beta space, as the return generation process also depends on other variables, such as size, book-to-market (B/M) ratio, and price-earnings (P/E) ratio. Studies that document the relationship between stock returns and company attributes include the earnings yield of Basu (1977; 1983) and the size effect of Banz (1981). Reinganum (1981) found that size and P/E ratios could explain variations in returns. Fama and French (1992) suggested that stock risks can be proxied by size and B/M equity. Davis (1994) and Davis, Fama, and French (2000) confirmed the influence of B/M ratio and size over various time periods from 1929 to Fama and French (1993) found that three factors, the market portfolio and the differences in portfolios as indices; the difference in returns on portfolios of small stocks and large stocks; and the difference in returns on portfolios containing stocks with high B/M ratios and stocks with low B/M ratios, can explain the cross-section of returns. These studies were conducted in the American stock market. Studies in other countries also confirm the finding that factors other than beta can explain returns. Japanese studies of Kato and Schallheim (1985) reported a size effect, and Chan, Hamao, and Lakonishok (1991) revealed a significant relationship among returns and size, earnings yield, cash flow yield, dividend yield, and B/M ratio. Pandey and Chee (2002) used yearly panel data from 1993 to 2000 and found that size, beta, P/E ratio, dividend yield, and B/M ratio play significant roles in predicting the expected stock returns in Malaysia. Capaul, Rowley, and Sharpe (1993) also found a B/M effect on European and Japanese stock markets. Various explanations have been offered for these anomalies. Ball s (1978) explanation was that earnings variables proxy for omitted variables or other misspecification effects in the two-parameter model. Fama and French (1988) felt that yield surrogates, such as dividend and earnings yield, are correlated with returns because they proxy for underlying risks not accounted for by beta. As for the possible reasons for size effect, Roll (1981) suggested that this may be due to errors in risk management, in particular, less frequent trading of small firms. In India, Vipul (1999) found that size and industry classification do not affect stock returns, while shares with poorer liquidity offer a premium in returns. Mohanty (2002) found that size, market leverage, P/E ratio, and price to book value ratio were related to returns, and the size effect was most prominent. Connor and Sehgal (2001) studied the Fama and French s three-factor model for the period of They found that the proxies for market, size, and value factors could explain the cross-sectional dispersion of their mean returns. However, they did not find any relationship between earnings growth rates and equity return factors. Dhankar and Singh (2005) found that multiple factors were needed to explain the variation in returns based on principal component analysis; however, these are statistically extracted factors that need to be identified as macro or micro economic variables.
3 A PANEL DATA ANALYSIS OF CORPORATE ATTRIBUTES AND STOCK PRICES 1521 Research Design Explanatory Variables and Hypothesis Researchers have addressed the impact of various corporate characteristics on the stock prices. This paper studies stock prices as dependent variable and stock volatility represented by beta and corporate attributes, such as size represented by market capitalization (MCAP), leverage represented by debt-to-equity (D/E) ratio, liquidity represented by current ratio (CR), profit represented by earnings per share (EPS), and returns represented by returns on capital employed (ROCE) as independent variables. These variables are briefly explained as follows: Beta. Beta indicates performance of securities in relation to general movement of the market. It measures stock volatility. Size. Size of the company usually affects the stock prices. If a company is of large size, the stock prices are expected to be more predictable. Size is represented by MCAP in the study as follows: MCAP = Market Price per share No. of Equity Shares Liquidity. Liquidity refers to the ability of a firm to meet its current obligations. It enables smooth functioning of business. Liquidity is represented by CR which is calculated as follows: CR = Current Assets / Current Liabilities Profits. Profit earned by a company is one of the most important factors that affect the share prices. In the current study, profits are represented by EPS calculated as follows: EPS = PAT Available to Equity Holders / No. of Equity Shares where PAT indicates profit after tax. Leverage. Companies having higher levels of debts are perceived to be more risky. Leverage represented by D/E ratio may also affect the changes in prices and its pattern: D / E ratio = Total Debt / Owners Funds Returns. Returns are represented by ROCE which indicates efficiency with which assets are used: ROCE = PBIT / Total Assets where PBIT indicates profit before interest and tax. Data and Methodology The data are collected using prowess database from the Centre for Monitoring Indian Economy (CMIE) of about 3,027 manufacturing companies during the periods from to The explanatory variables were calculated on the basis of financial information. In order to determine the effect of company characteristics and beta on stock prices, primarily regression technique is used. To identify the variables affecting stock prices, Ordinary Least Square (OLS) is used. Regression model. Regression model is used to study the relationship between independent and explanatory variables. OLS model with variables is as under: Stock Prices = α + β Beta + β EPS + β MCAP + β CR + β D / E + β ROCE Panel data analysis. To analyze thoroughly the relations between stock prices and corporate attributes, panel data models are employed. Panel data provide a set of rich information that can be used to model the
4 1522 A PANEL DATA ANALYSIS OF CORPORATE ATTRIBUTES AND STOCK PRICES changes both in time and in cross-sectional dimension. Moreover, the dynamic or unobserved factors influencing the explanatory variables can be identified. The construction of panel data models follows several stages. The starting point is the estimation of the fixed and random effects models. The essential assumption of the model, on top of the assumptions for the fixed effects model, is the lack of correlation between random effects and explanatory variables. In this approach, the importance is not attached to the value of an individual effect for a certain unit (company), but the parameters of the distribution of the random effects are estimated. Hence, the conclusions drawn upon this model do not refer to single units from a sample but to the population in general. Since fixed and random effects models involve different assumptions, the estimation of both models varies. The OLS method is applied in case of fixed effects model, while generalized least squares (GLS) estimator is used for random effects model. In both cases, the significance test of individual effects can be carried out. The study applies both approaches in order to compare the results. In the course of estimation, the significance of both individual effects as well as explanatory variables is checked, and the insignificant variables are eliminated from the model. Finally, Hausman test allowing for the choice between fixed and random effects model is performed (Greene, 2004). The tests were carried out to confirm the validity of such approach to estimation. The entire analysis was performed with Stata 8.0. Correlation matrix. Correlation indicates the strength and direction of a linear relationship between two random variables. It mentions about inter-relationship between the variables. Descriptive Statistics Descriptive statistics of the dependent as well as all independent variables are presented in Table 1. Table 1 Descriptive Statistics Variable Mean Std. dev. Min. Max. Stock price , Beta EPS , ,426 MCAP , ,796.4 CR D/E The above analysis provides mean, std. dev., and range of all the variables that are used in the study. Regression Results Regression results with stock returns as dependent and other explanatory variables are given in Table 2. Table 2 Regression Output Coef. Std. err. t P > t 95% conf. interval Beta EPS MCAP
5 A PANEL DATA ANALYSIS OF CORPORATE ATTRIBUTES AND STOCK PRICES 1523 (Table 2 continued) Coef. Std. err. t P > t 95% conf. interval CR D/E ROCE _cons Notes. Prob. > F = ; R-squared = ; and Adj. R-squared = Table 2 shows that at the level of 5%, Beta, EPS, and MCAP are turning out to be highly significant, indicating acceptance of its significance on market price. Panel Data Results The panel data are used with fixed and random effects, and it showed following outcomes. Fixed effects model output. Fixed effects regression is used when controlling for omitted variables that differ between cases but are constant over time. It uses the changes in the variables over time to estimate the effects of the independent variables on dependent variable (see Table 3). Fixed effects (within): regression R-squared: within = ; corr. (u_i, Xb) = ; between = ; Prob. > F = ; and overall = Table 3 Results of Fixed Effects Model Coef. Std. err. t P > t 95% conf. interval Beta EPS MCAP CR D/E ROCE _cons Random effects model output. If some omitted variables are constant over time but vary between cases and others may be fixed between cases but vary over time, then random effects can be used (see Table 4). Random effects GLS regression: R-squared: within = ; corr. (u_i, Xb) = between = ; Prob. > chi2 = ; and overall = Table 4 Results of Random Effects Model Coef. Std. err. z P > z 95% conf. interval Beta EPS MCAP CR D/E ROCE _cons Choosing between fixed and random effects. The generally accepted way of choosing between fixed and random effects is running a Hausman test.
6 1524 A PANEL DATA ANALYSIS OF CORPORATE ATTRIBUTES AND STOCK PRICES Statistically, fixed effects are always a reasonable thing to do with panel data (they always give consistent results), but they may not be the most efficient model to run. Random effects may give better P-values, as they are a more efficient estimator, so random effects may also be used. The Hausman test checks a more efficient model against a less efficient but consistent model to make sure that the more efficient model also gives consistent results. Hausman test is run by comparing fixed with random effects. Table 5 presents the outcome of Hausman test. Test hypotheses are as follows: Ho: Difference in coefficients of random and fixed effects model is not systematic. Ha: Difference in coefficients of random and fixed effects model is systematic. Table 5 Results of Hausman Test (b) Fixed (B) Random (b-b) Difference Sqrt. (diag. (V_b-V_B)) S.E. Beta EPS MCAP CR D/E ROCE Notes. b = consistent under Ho and Ha; obtained from xtreg; B = inconsistent under Ha, efficient under Ho; obtained from xtreg; chi2 (6) = (b-b)'[(v_b-v_b)^(-1)](b-b) = 47.04; Prob. > chi2 = The above output reveals that fixed effect is to be used, since Prob. > chi2 = is not larger than Correlation matrix. It mentions as to whether there is any internal correlation between the variables that are used in the study. Table 6 is the output obtained through Stata. Table 6 Correlation Matrix of Variables Stock price Beta EPS MCAP CR D/E ROCE Stock price Beta EPS MCAP CR D/E ROCE Table 6 shows that there is no high correlation between any variable. Between stock price and EPS, 55% correlation is seen, and 48% correlation is seen between stock price and MCAP. Conclusions The study aimed at identifying factors that could affect stock prices. The analysis relates stock prices to the underlying behavior of beta and corporate attributes, i.e., size, profit, liquidity, and leverage. It has been observed that Beta, EPS, and MCAP are turning out to be highly significant (at the level of 5%). Corporate attributes are not statistically significant to predict stock prices. Panel data analysis using Hausman test
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