The Integration of MultiFactor Model of Capital Asset Pricing and Penalty Function for Stock Return Evaluation

One of the main concerns of investors is the evaluation of the return on investment, which is conducted using various models such as the CAPM (singlefactor model), FamaFrench three/fivefactor models, and Roy and Shijin’s sixfactor model and other models known as multifactor models. Despite the widespread use of these models, their major drawbacks include sensitivity to unexpected changes, sudden shocks, high turbulence of price bubble, and so on. To eliminate such negatives, the multifactor model using the penalty function method is used, in which, instead of averaging, the optimization and avoidance of the effects of abnormal changes and other factors affecting the capital market are considered. In order to evaluate stock returns, it is possible to select effective factors, to simulate and develop a model appropriate to the conditions governing the capital market in Iran. In the present study, by forming portfolios of investments and identifying and refining effective factors, the classification and estimation of the hybrid model of penalty and multifactor (P PCA) functions were performed based on the functional data during 20072017. The results of this study indicated that the extensive use of the simulation algorithm for the penalty function in the form of P PCA estimation method improves the efficiency of multifactor methods in stock return evaluation, and that the use of the hybrid algorithm of penalty and multifactor functions, compared to the exclusive use of multifactor models, brings a higher accuracy in estimating stock returns.