Optimal stock selection using bat and random forest algorithm

The purpose of this study is to optimally select stocks using the bat and random forest algorithm. In this study, based on the analysis of 6 variables: stock price to earnings per share ratio, annual earnings growth rate, annual sales growth rate, return on assets, return on equity and free float shares extracted from 181 companies listed on the Tehran Stock Exchange, It has been used during the period of 1394 to 1398. Six scenarios are considered to estimate the accuracy of the two algorithms, so that for scenarios 1 to 6, the algorithms are asked to participate 5, 10, 15, 20, 25 and 30, respectively. The results show that the nature of the random forest algorithm requires training and selection of features, which makes the algorithm faster and increases the convergence time. One of the main reasons for the higher accuracy of the random forest algorithm in scenarios 1 to 3 could be this. In scenarios 4 to 6, due to the increasing complexity of the problem, the accuracy of the random forest algorithm decreases, but due to the random nature of the bat algorithm, its accuracy does not differ much and it can maintain stability in its selection.