stock exchange
Leila Farvizi; Sakineh Sojoodi; Hossein Asgharpour; Jafar Haghighat
Abstract
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. ...
Read More
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. IntroductionUnderstanding the drivers of systematic risk is crucial for investors seeking to optimize their portfolios and for companies aiming to develop robust risk management strategies. While many studies have explored the relationship between systematic risk and various accounting and financial variables, the majority have used classical regression methods, which tend to focus on a limited number of factors. This limitation often overlooks the complex interplay among variables that could better explain systematic risk. Given the growing need for more accurate models in the face of financial market volatility, this study adopts the Bayesian Model Averaging (BMA) approach to assess the impact of a wider range of accounting and financial variables on systematic risk. The research seeks to answer the following questions:Research Question(s)- Which accounting and financial variables most significantly influence the systematic risk of companies listed on the Tehran Stock Exchange?-Do the selected variables have a positive or negative impact on systematic risk, and how do these effects vary across different industries and financial contexts?2- Literature ReviewSystematic risk, commonly measured by the beta coefficient, represents the portion of a company’s risk that cannot be diversified away. Previous studies have highlighted several accounting and financial factors, including company size, financial leverage, operational efficiency, and asset turnover, as important determinants of systematic risk (Figure 1). However, the results across studies are mixed, and traditional models often fail to account for the complex interactions among variables. Additionally, several studies have noted that the method of variable selection and estimation can significantly influence the conclusions drawn about risk determinants. The literature suggests that large firms tend to have higher systematic risk due to greater exposure to market and economic cycles, while smaller firms may experience lower risk due to reduced exposure to such fluctuations. Other studies have explored the roles of profitability, debt ratios, liquidity, and asset management in determining market risk, but there is no consensus on which variables are most influential. Figure1- Fundamental Factors Affecting Systematic RiskSource: Brimble & Hodgson (2007) 3- MethodologyThis study employs the BMA technique to assess the impact of 58 potential accounting and financial variables on systematic risk. The BMA approach is particularly well-suited to this context because it enables the simultaneous consideration of multiple models, allowing for a more comprehensive understanding of the relationships between variables and risk. The study uses data from 55 companies listed on the Tehran Stock Exchange, covering the period from 2010 to 2023. The sample includes companies from a range of sectors, ensuring that the findings are not limited to any one industry. Data were collected from financial statements and reports available on the official website of the Tehran Stock Exchange (TSETMC), and the BMA method was implemented using Stata 18 software. The estimation process includes backward sampling, in which weak models are sequentially excluded and the best models are selected based on their posterior probability of explaining the data.4- ResultsThe results of the BMA analysis indicate that several variables have a significant impact on systematic riskCompany Size: Company size has the strongest effect on systematic risk, with a positive coefficient, indicating that larger companies generally face higher systematic risk.Asset Turnover: The asset turnover ratio, which measures how efficiently a company uses its assets to generate revenue, also has a positive effect on systematic risk.Operational Efficiency: Companies with higher operational efficiency exhibit lower systematic risk, as indicated by the negative coefficient for operational efficiency.Long-Term Debt-to-Equity Ratio: A positive relationship is found between the long-term debt-to-equity ratio and systematic risk, suggesting that companies with higher leverage tend to experience greater exposure to market risk.Market Value to Book Value Ratio: This ratio has a negative effect on systematic risk, indicating that companies with higher market valuations relative to their book values are less sensitive to market fluctuations.These variables were identified as the most significant based on their posterior inclusion probabilities (PIP), with company size having the highest PIP of 0.8143, indicating it is the most important determinant of systematic risk.5- DiscussionThe findings suggest that company size plays a pivotal role in determining systematic risk. Larger companies tend to be more exposed to broader economic fluctuations and market cycles, which can lead to higher systematic risk. Asset turnover, though generally considered a measure of operational efficiency, also contributes positively to risk, potentially due to the increased exposure of firms with higher asset turnover to volatile markets. Operational efficiency, on the other hand, shows a negative relationship with systematic risk, supporting the notion that companies with better control over their operations are more resilient to market shocks. This finding is consistent with the literature suggesting that operational efficiency can mitigate the impact of external risks. Similarly, the positive relationship between the long-term debt-to-equity ratio and systematic risk aligns with prior studies that highlight the role of financial leverage in amplifying market risk. Finally, the negative relationship with the market value to book value ratio indicates that investors view companies with higher market valuations as more stable, potentially because these companies are perceived as less vulnerable to market downturns.6- ConclusionThis study contributes to the understanding of the determinants of systematic risk by employing the BMA approach, which overcomes limitations inherent in traditional regression models. The results highlight that company size, asset turnover, operational efficiency, the long-term debt-to-equity ratio, and the market value to book value ratio are the key factors influencing systematic risk. These findings have practical implications for investors and corporate managers seeking to mitigate exposure to market risk. Companies, especially larger ones, can benefit from enhancing operational efficiency and optimizing their financial structures to reduce systematic risk. Future research could explore the interaction between these variables across different sectors and market conditions, and further refine models by incorporating additional macroeconomic factors.
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 ...
Read More
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.
