An Application of Decision Tree and Genetic Algorithms for Financial Ratios´ Dynamic Selection and Financial Distress Prediction

Aiming at improving the predictive ability of corporate financial distress, a method integrating decision tree and genetic algorithms is put forward to realize dynamic selection of financial ratios in the process of modeling. It uses genetic algorithms to optimize financial ratio set, so the ultimate decision tree model for financial distress prediction has a good balance between accuracy and generalization. Empirical study shows that this model´s prediction accuracy for training samples and validation samples are respectively 94.67% and 93.75%. This indicates that the proposed method for financial distress prediction can dynamically optimize the financial ratio set and effectively avoid the over-fitting problem of decision tree to improve the generalization ability