Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches

• Published in: Knowledge-based systems Vol. 57; pp. 41 - 56
• Main Authors: Sun, Jie, Li, Hui, Huang, Qing-Hua, He, Kai-Yu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2014
• Subjects: Case-based reasoning; Categories; Chaos theory; Classifiers; Corporate failure prediction; Decision making; Decision tree; Definition of financial distress; Ensemble; Failure; Featuring methods; Financial distress prediction; Group decision-making; Hybrid modeling; Logistic regression; Mathematical models; Multiple discriminant analysis; Neural network; Review; Sampling; Sampling methods; State of the art; Support vector machine; Corporate failure prediction; Financial distress prediction; Case-based reasoning; Multiple discriminant analysis; Hybrid modeling; Review; Support vector machine; Neural network; Decision tree; Definition of financial distress; Logistic regression; Ensemble; Featuring methods; Sampling methods; Group decision-making
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2013.12.006

As a hot topic, financial distress prediction (FDP), or called as corporate failure prediction, bankruptcy prediction, acts as an important role in decision-making of various areas, including: accounting, finance, business, and engineering. Since academic research on FDP has gone on for nearly eighty years, there are abundant literatures on this topic, which may appear chaotic to the researchers of the field and make them feel confused. This paper contributes to the current review researches by making a full summary, analysis and evaluation on the current literatures of FDP. The current literatures of FDP are reviewed from the following four unique aspects: definition of financial distress in the new century, FDP modeling, sampling approaches for FDP, and featuring approaches for FDP. By considering the new state-of-the-art techniques in this area, FDP modeling are classified and reviewed by the following groups: namely, modeling with pure single classifier, modeling with hybrid single classifier, modeling by ensemble techniques, dynamic FDP modeling, and modeling with group decision-making techniques. Sampling methods for FDP are classified and reviewed by the following paired groups, namely: training sampling and testing sampling, single industry sampling and cross-industry sampling, balanced sampling and imbalanced sampling. Featuring methods for FDP are categorized and reviewed by qualitative selection and combination of qualitative and quantitative selection. We comment on the current researches from the view of each category and propose further research topics. The review paper is valuable to guide research and application of the area.