Improved Model of Capital Assets Pricing on Basis of Disorder Principals

Abstract

This study examines and develops an enhanced Capital Asset Pricing Model (CAPM) based on anomaly factors. The proposed model aims to analyze the relationship between financial risk and the expected rate of return on assets in the capital market. The research is applied and quantitative in nature, adopting a correlational and ex post facto approach.The statistical population consists of all companies listed on the Tehran Stock Exchange. Using a systematic elimination method, 144 firms were selected as the statistical sample over the period 2012–2022 (1391–1401 in the Iranian calendar). The data are panel (pooled) data, and the F-Limer (Chow) test and Hausman test were employed to determine the appropriate estimation method. The models were estimated using the Ordinary Least Squares (OLS) method. The results indicate that the traditional CAPM and the Fama–French model have limited explanatory power in explaining variations in excess stock returns. In contrast, the current ratio and cash flow were identified as influential factors that enhance the predictive capability of the model. Portfolio formation results show that a strategy of buying winner portfolios and selling loser portfolios based on cash flow and momentum criteria led to negative excess returns, whereas portfolios formed on the basis of the current ratio and cash balance generated positive and statistically significant returns. These portfolios also increased the adjusted coefficient of determination and improved the explanatory power of the models. The findings suggest that the enhanced CAPM can be employed without losing key information relative to the Fama–French model, while providing improved explanatory and predictive performance.