Application of SVM, Decision Tree and Logistic Regression Algorithm in Stock Classification and Prediction

Abstract

This paper uses three data mining algorithms, support vector machine, decision tree and logical regression, to establish the stock classification prediction model. The paper compares and analyzes the prediction effect of the three models, and summarizes the relationship between the financial indicators of listed companies and their stock intrinsic investment value.The results show that: (1) among the three prediction models, logistic regression model has the best performance, followed by support vector machine model, and decision tree model has the worst performance.(2) The significant influencing factors of stock intrinsic investment value include the actual operation ability, profitability and the continuity and stability of operation. The conclusion of this paper can provide a basis for stock investors to make investment decisions.