Stock Return Forecasting Using the Bayesian Model Approach in Tehran Securities Exchange

In the present research, stock returns were predicted using the Bayesian model approach in Tehran Securities Exchange. Therefore, the research hypothesis that based on Bayesian method has higher accuracy in predicting returns than autoregressive models was developed and tested. In order to examine the hypothesis, information related to the index of 30 selected industries in the Tehran Stock Exchange during the period from 2017/03/25 to 2020/08/24 was used. The index return was predicted based on two methods for 30 out-of-sample data. First autoregressive models were fitted on returns of each index and then the next 30 days of returns were predicted based on these models. Then after identifying the optimal model lags through the Bayesian Model Averaging method, autoregressive models were fitted with the optimal lags and the next 30 days predictions were obtained under this method.In order to compare the accuracy of the methods in predicting the return, RMSE and MAE criteria were used, and the values of these error criteria were compared using Wilcoxon Nonparametric Pairwise comparison tests.The results showed that Bayesian method leads to increase the accuracy of model prediction in out of sample data.