Investigating the Impact of Accounting and Financial Variables on Stock Systematic Risk: A Bayesian Model Averaging Approach

Numerous studies have investigated the relationship between systematic risk and a wide range of accounting and financial variables. However, most empirical studies have adopted the classical regression method, which entails limitations such as a restricted number of variables to preserve degrees of freedom. To overcome this constraint, the present study employs the Bayesian Model Averaging (BMA) method. Using data from 55 companies listed on the Tehran Stock Exchange between 2010 and 2023, this study examines the influence of 58 different financial and accounting variables on the systematic risk of these companies. The research aims to identify the key variables that significantly contribute to systematic risk. The findings reveal that among the examined variables, company size has the strongest impact on systematic risk, with a positive coefficient. In second and third place, asset turnover and operational efficiency demonstrate significant effects, with the former exhibiting a positive coefficient and the latter a negative coefficient. The fourth influential variable is the ratio of long-term debt-to-equity, showing a positive coefficient. Lastly, the ratio of a company's market value to the book value of its total assets is identified as the fifth influential variable, exerting a negative impact on systematic risk.