EWT‐SMOTE to improve default prediction performance in imbalanced data: Analysis of Chinese data

Abstract

This study aims to solve the imbalanced sample problem in default prediction. We calculate the classification contribution score of each default customer by the entropy weight technique (EWT) for order of preference by similarity to the ideal solution and construct a default prediction model according to several models. Our proposed EWT‐synthetic minority oversampling technique (SMOTE) method significantly improves the prediction accuracy of several typical default prediction models and reduces type II error. We find that the indicators “net cash flow from operating activities,” “Engel coefficient,” “basic earnings per share,” and “total social retail sales” significantly influence default prediction of Chinese listed companies.