Ranking stocks of listed companies on Tehran stock exchange using a hybrid model of decision tree and logistic regression

Much research has introduced linear or nonlinear models using statistical models and machine learning tools in artificial intelligence to estimate Iranchr('39')s rate of return. The primary purpose of these methods is simultaneously use different independent variables to improve stock return rateschr('39') modeling. However, in predicting the rate of return, in addition to the modeling method, the degree of correlation of the independent variables with each other and, consequently, the biased increase of the model estimators is of particular importance. Hence, in this paper, we perform a concurrent model of decision tree and logistic regression with affective variables simultaneously and then make a nonlinear model of return rate. To evaluate the proposed model, information of 100 companies admitted to the stock exchange during the period 2011 to 2018 is considered. The results of our study show that the proposed hybrid algorithm performs better than competing models.