Multiclass financial distress prediction based on one‐versus‐one decomposition integrated with improved decision‐directed acyclic graph

Although binary financial distress prediction (FDP) is widely explored, multiclass FDP (MFDP) lacks enough investigation. This paper designs a new fusion method of improved decision‐directed acyclic graph (IDDAG) based on the principle of maximizing generalization ability and integrates it with the one‐versus‐one (OVO) decomposition method (OVO‐IDDAG) to build MFDP models. Based on data of Chinese listed companies, we categorize corporate financial status into four states (financial soundness, financial pseudosoundness, moderate financial distress, and serious financial distress) and compare the MFDP performance of OVO‐IDDAG with two benchmark methods, that is, the OVO decomposition method integrated with the max‐win fusion method (OVO‐ max‐win) and the OVO decomposition method integrated with the original DDAG fusion method (OVO‐ODDAG). The experimental results show that the OVO‐IDDAG method overall outperformances the benchmark methods. Among the six base classifiers of multivariate discriminant analysis, Logit, naive bayes, support vector machine, decision tree, and random forest and the three imbalance processing mechanisms of random oversampling, random undersampling, and synthetic minority oversampling technique, the combination of the random forest classifier with the imbalance processing mechanism of random undersampling or the support vector machine classifier with the imbalance processing mechanism of random oversampling or synthetic minority oversampling technique is more suitable for MFDP based on OVO‐IDDAG. This study not only improves artificial intelligence methodology for multiclass classification but also enriches applications of intelligent systems to MFDP.