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Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry

Author

Listed:
  • Jie Sun

    (Tianjin University of Finance and Economics)

  • Mengjie Zhou

    (Zhejiang Normal University)

  • Wenguo Ai

    (Harbin Institute of Technology)

  • Hui Li

    (Nankai University)

Abstract

Early studies on financial distress prediction (FDP) seldom consider the problem of industry’s relative financial distress concept drift and neglects how to dynamically predict industry’s relative financial distress. This paper proposes a novel model for dynamic prediction of relative financial distress based on imbalanced data stream of certain industry, and the whole model is divided into the three submodules: the financial feature selection module based on plus-L-minus-R approach, the financial condition evaluation module based on principal component analysis, and the FDP modeling module based on SMOTEBoost-SVM/DT/KNN/Logistic. After feature selection, the results of industry financial condition evaluation are used as class labels for industry’s relative FDP modeling, and the model keeps updating with time window sliding on. The empirical experiment is carried out based on the financial ratio data of Chinese iron and steel companies listed in Shanghai and Shenzhen Stock Exchange, and the results indicate the effectiveness of the dynamic model for industry’s relative FDP.

Suggested Citation

  • Jie Sun & Mengjie Zhou & Wenguo Ai & Hui Li, 2019. "Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry," Risk Management, Palgrave Macmillan, vol. 21(4), pages 215-242, December.
    • Handle: RePEc:pal:risman:v:21:y:2019:i:4:d:10.1057_s41283-018-0047-y
    DOI: 10.1057/s41283-018-0047-y
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    2. Ming-Fu Hsu & Chingho Chang & Jhih‐Hong Zeng, 2022. "Automated text mining process for corporate risk analysis and management," Risk Management, Palgrave Macmillan, vol. 24(4), pages 386-419, December.

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