Evaluation of Deep learning and Markowitz Models Ability in Optimal Stock Portfolio Formation

Stock selection and optimal stock portfolio formation depends on various factors which lead to complicate making decision. Investors can maximize investment return and minimize their risk by choosing the optimal stock portfolio. Therefore always they are used to advanced financial algorithms to form the optimal stock portfolio. This study tries to find machine learning and Markowitz models ability in optimal stock portfolio formation and their efficiency comparison. The statistical sample of the present study includes 156 companies listed on the Tehran Stock Exchange during 2010 to 2019. After collecting the data, the deep learning and the Markowitz models were tested using Anaconda software and Python programming language, and then the ability of each model were determined in formation of optimal stock portfolio by portfolio return evaluation criteria, trenors and jensens index.In the ten-share portfolio of the deep learning model; Return of portfolio 0.697, trenors index of 4.541 and Jensens index of 0.480 and in ten-share portfolio of Markowitz model; Portfolio returns of 0.058, trenors index of -1.648 and Jensens index of -0.158 have been calculated. According to the results of the portfolio evaluation, it was concluded that the deep learning model has a higher ability than the Markowitz model in the formation of optimal stock portfolio.