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Dynamic financial distress prediction based on class-imbalanced data batches

Author

Listed:
  • Jie Sun

    (Business School, Tianjin University of Finance and Economics, Tianjin 300222, P. R. China)

  • Xin Liu

    (#x2020;School of Economics and Management, Zhejiang Normal University, Jinhua Zhejiang Province 321004, P. R. China)

  • Wenguo Ai

    (#x2021;School of Management, Harbin Institute of Technology, Harbin, Heilongjiang Province 150001, P. R. China)

  • Qianyuan Tian

    (#xA7;Finance Office, Institute of Exploration Techniques, China Geological Survey, Tianjin 300300, P. R. China)

Abstract

This study proposes two approaches for dynamic financial distress prediction (FDP) based on class-imbalanced data batches by considering both concept drift and class imbalance. One is based on sliding time window and synthetic minority over-sampling technique (SMOTE) and the other is based on sliding time window and majority class partition. Support vector machine, multiple discriminant analysis (MDA) and logistic regression are used as base classifiers in the experiments on a real-world dataset. The results indicate that the two approaches perform better than the pure dynamic FDP (DFDP) models without class imbalance processing and the static FDP models either with or without class imbalance processing.

Suggested Citation

  • Jie Sun & Xin Liu & Wenguo Ai & Qianyuan Tian, 2021. "Dynamic financial distress prediction based on class-imbalanced data batches," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 1-35, September.
    • Handle: RePEc:wsi:ijfexx:v:08:y:2021:i:03:n:s2424786321500262
    DOI: 10.1142/S2424786321500262
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