Improving the performance of Fama-French models in predicting expected returns by offering new definitions of Risk Factors in the Tehran Stock Exchange

In this research, the possibility of improving the Fama - French models using new definitions of risk factors in the Tehran Stock Exchange, has been investigated. The goal is to determine the risk factors with the highest predictive power and identify the optimal model.

Using the monthly returns of the sample firms during the 2006 to 2021 period, the abnormal stock returns were investigated. Then, using the GRS test and the Fama - Macbeth approach, we calculated the mispricing error of the models and the risk premia of each variable. The data used in the study were collected from Rahavard Novin and Codal databases.

The results reveal that including new definitions of risk factors instead of the original ones in the models can reduce the mispricing error in the Fama - French five-factor model. Moreover, Fama - Macbeth regressions showed no profitability and investment effect in the Tehran Stock Exchange, although they strengthen the risk premia of the market, size and value factors.

Originality / Value: 

The results of the present research can be used to reduce the error of the asset pricing process. Also, the optimal model introduced in this research can be utilized to predict stock returns and design investment strategies based on those returns.