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

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.