An investigation of bankruptcy prediction in imbalanced datasets

• Published in: Decision Support Systems 2018-08, Vol.112, p.111-124
• Main Authors: Veganzones, David, Séverin, Eric
• Format: Article
• Language: English
• Subjects: Bankruptcy; Bankruptcy prediction; Data analysis; Datasets; Finance; Identification methods; Imbalanced dataset; Medical treatment; Performance prediction; Sampling methods; Sampling techniques; Support vector machines
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2018.06.011

Previous studies of bankruptcy prediction in imbalanced datasets analyze either the loss of prediction due to data imbalance issues or treatment methods for dealing with this issue. The current article presents a combined investigation of the degree of imbalance, loss of performance, and treatment methods. It determines which imbalanced class distributions jeopardize the performance of bankruptcy prediction methods and identifies the recovery capacities of treatment methods. The results show that an imbalanced distribution, in which the minority class represents 20%, significantly disturbs prediction performance. Furthermore, the support vector machine method is less sensitive than other prediction methods to imbalanced distributions, and sampling methods can recover a satisfactory portion of performance losses. Accordingly, this study provides a better understanding of the data imbalance issue in the field of corporate failure and serves as a methodological guide for designing bankruptcy prediction methods in imbalanced datasets. •An investigation of bankruptcy prediction in imbalanced datasets is proposed.•The prediction losses increase as the imbalanced proportion grows more severe.•Support Vector Machine method is less affected by imbalanced datasets than other prediction method.•SMOTE outperforms other sampling techniques for all type of prediction models and different training set sizes.