An Interpretable Multi-Class Financial Crisis Early Warning Model Based on D-S Evidence Fusion
Release time:2026-05-11
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- Affiliation of Author(s):
- College of Shipbuilding Engineering, Harbin Engineering University
- Journal:
- Journal of Systems & Management
- Place of Publication:
- Shanghai, China
- Funded by:
- 国家自然科学基金资助项目(71503108,62077029)； 江苏师范大学研究生科研与实践创新计划项目(2024XKT2647)
- Key Words:
- multiple-class classification; financial crisis early warning; information fusion; SHapley Additive exPlanations (SHAP); decision suppor
- Abstract:
- To address the limitation of traditional binary financial distress prediction models in providing fine-grained, tiered early warnings, this paper constructs an interpretable multi-class financial crisis early warning model based on the fusion of financial and non-financial information. First, management discussion and analysis (MD&A) tone information is incorporated to enrich data sources for small and medium-sized enterprises (SMEs). Subsequently, random forest (RF), light gradient boosting machine (LightGBM), and support vector machine (SVM) models are utilized to predict the financial performance of SMEs, which are then further integrated using an improved Dempster-Shafer (D-S) evidence theory. Finally, the SHapley Additive exPlanations (SHAP) is introduced to facilitate interpretable analysis. The results show that the information-fusion model exhibits a 1.3% improvement in the F1 score compared with the best-performing base classifier, effectively avoiding prediction “disaster points.” The model also identifies key early warning indicators such as the debt-to-asset ratio, undistributed earnings per share, and return on equity. Overall, the proposed model achieves more accurate financial crisis classification and more stable predictive performance, thereby providing a novel perspective for financial crisis early warning research on SMEs.
- Co-author:
- Feng Gao,Jiawei Li
- First Author:
- Mei Song
- Indexed by:
- Journal paper
- Correspondence Author:
- Wei-Chiang Hong
- Discipline:
- Management Science
- Document Type:
- J
- Volume:
- 35
- Issue:
- 02
- Page Number:
- 452-461
- ISSN No.:
- 2097-4558
- Translation or Not:
- no
- CN No.:
- 31-1977/N
- Date of Publication:
- 2026
- Links to published journals:
- https://link.cnki.net/urlid/31.1977.N.20250319.1119.002
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