Modeling and predicting stock returns has always been one of the challenges for researchers and investors. Hence, different methods and models have been proposed, most of which have been based on assumptions such as the distribution of returns. The kernel distribution a
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Modeling and predicting stock returns has always been one of the challenges for researchers and investors. Hence, different methods and models have been proposed, most of which have been based on assumptions such as the distribution of returns. The kernel distribution and mixture of normal distributions were examined to predict stock return in the present study. To this end, kernel functions and mixtures of normal distributions and related parameters have been estimated using maximization of likelihood function and quartiles 99%, 95% and 90% were computed for each of distributions and for 30 superior enterprises listed in Tehran Security and Exchange (TSE) at first quarter in 2019 as predictor values of stock return. In order to determine precision of prediction methods, MSE and PRED error criteria were employed and the findings showed that mixture of normal distributions and kernel approximation might propose favorable predictions for 5-day stock returns in quartiles 90% of return distribution. Comparison of precision between two methods indicated that kernel approximation, as a non parametric method for prediction of returns, leads to higher precision than mixture of normal distributions.
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