A Comparative Study of Stock Selection Models Based on Decision Tree Algorithms

In this paper, the decision tree model in data mining is applied to select stock characteristics that can be effectively used for stock selection by using the C4.5 algorithm and the CART algorithm, respectively, in combination with the strategies of fundamental analysis and technical analysis. The paper concludes that the decision tree models constructed by the C4.5 and CART algorithms both have better classification ability for stock selection and portfolio construction, but the decision tree model constructed by the C4.5 algorithm is simpler. The stock portfolios determined by the decision tree model are able to achieve an excess return of 13.4% relative to the CSI 300 index, thus proving that the decision tree model is effective in stock selection and stock portfolio construction.