Leila Farvizi; Sakineh Sojoodi; Hossein Asgharpour; Jafar Haghighat
Abstract
Previous research have been conducted to establish connections between systematic risk and various accounting and financial variables of companies. Many empirical investigations have employed the classical regression method, which is not without its limitations, notably the need to focus on a limited ...
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Previous research have been conducted to establish connections between systematic risk and various accounting and financial variables of companies. Many empirical investigations have employed the classical regression method, which is not without its limitations, notably the need to focus on a limited number of variables in order to maintain an acceptable level of regression degrees of freedom. In order to address this limitation, the present study employs the Bayesian model averaging method. By analyzing data from 55 companies listed on the Tehran Stock Exchange between 2010 and 2023, this study examines the impact of 58 different financial and accounting variables on the systematic risk of these companies, ultimately identifying the key determinants of systematic risk. The estimation results reveal that, among the investigated variables, five variables exert the greatest influence on systematic risk, with company size ranking first. The average coefficient for this variable is positive. Following closely are asset turnover and operational efficiency, which hold the second and third positions, respectively. The average operating efficiency ratio displays a negative coefficient, while the average asset turnover ratio exhibits a positive coefficient. The fourth determinant is the ratio of long-term debt to equity, which has a positive coefficient. Finally, the fifth explanatory variable is the ratio of the company's market value to the book value of its total assets, exerting a negative effect on systematic risk.
Ali Rahmani; Elnaz Tajvidi
Volume 3, Issue 11 , October 2005, , Pages 227-246
Abstract
In view of the expanding capital market, it is of great significance to recognize the variables affecting stock return and its price. There exist different methods for the prediction of stock return such as the Capital Assets Pricing Model, the so-called CAPM, Market Model, Arbitrage Pricing Theory and ...
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In view of the expanding capital market, it is of great significance to recognize the variables affecting stock return and its price. There exist different methods for the prediction of stock return such as the Capital Assets Pricing Model, the so-called CAPM, Market Model, Arbitrage Pricing Theory and Factorial Model. According to CAPM, β (Beta) is the only variable capable of predicting the return. The studies and researches carried out with respect to predictability potential of CAPM model and application of other variables; demonstrate that there exist other variables which outperform stock return predictability potential of the β (Beta).
Included among such variables are the size, debt to equity, Book to Market, earnings to price and sale to price ratios. The present research was aimed at testing the above-mentioned variables and the β (Beta) for the prediction of stock return in order to recognize the variables which are better capable of predicting the stock return in Tehran Stock Exchange (TSE).
Independent variable were tested against the dependent variable (return) on an annual basis for the years 1 376- 1382 (1997- 2003). Further, multivariable models were tested, both annually and pooled cross-sectionally. The pooled cross-sectional test results demonstrated that the model was statistically significant. However when the model was compared with single variable models, the increase in pred1ctabiltty potential was accepted. In single variable tests, no significant relationship was observed between debt to equity ratio and the stock return. Furthermore, no significant relation was observed between Beta and the Stock return, as predicted in CA PM model , and the results were dispersed and scattered. No significant relation was observed between magnitude of the total assets (logarithm) as size variable and the stock return in 4 consecutive years; however, when the size was defined in terms of stock market value, a significant relation was observed between the size so defined and the stock return in 4 consecutive years. There existed a significant rela1ion between the sale to price and the earnings to price ratios with the stock return in 4 consecutive years. However the Book to Markel ratio demonstrated great dispersion in results, indicating that there was no significant and stable relation. Considering the potential effect of the statistical models on the research findings, complementary tests were carried out on the basis of formation of portfolio based on Beta (β) and Book to Market ratio variables. Three portfolios were formed, taking into consideration the magnitude of each and every variable. The findings of such test substantiated that, during the years 1379-1380, portfolios with high beta (β) proved to have higher return compared to the ones with low Beta (β). With respect to the portfolios formed on the basis of Book to Market ratio, the findings proved compatible with the regression models.
