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Multi-class Financial Distress Prediction Based on Feature Selection and Deep Forest Algorithm

Author

Listed:
  • Xiaofang Chen

    (Harbin Institute of Technology)

  • Zengli Mao

    (Harbin Institute of Technology)

  • Chong Wu

    (Harbin Institute of Technology)

Abstract

The aim of this study is to develop an effective financial distress prediction (FDP) model that enhances companies’ understanding of their financial states. We propose a novel definition of multi-class financial status and construct a multi-class FDP model accordingly. The multi-class FDP model is constructed based on feature selection and a deep forest algorithm. We compare 11 different forms of feature selection and select the optimal approach for input into the model, with the deep forest algorithm as the classifier. We enrich the indicator set by incorporating financial network indicators to enhance the model’s informational output. The analysis centers on Chinese listed companies from 2007 to 2020 and yields four main results. (1) The proposed multi-class FDP model exhibits excellent prediction performance, particularly in identifying financial distress, light financial soundness, and moderate financial soundness. (2) XGBoost provides optimal results among the eleven forms of feature selection, with an accuracy of 92.02%. Feature extraction and hybrid feature selection also show promising results. (3) The deep forest model demonstrates better prediction performance compared to other benchmark models. (4) The inclusion of financial network indicators in the indicator set improves the prediction performance of the model. This paper introduces a novel perspective on defining multiple states of corporate finance and explores the impact of various forms of feature selection on a multi-class FDP model. Moreover, we apply the deep forest algorithm to a multi-class FDP model for the first time, broadening its application in enterprise financial risk management.

Suggested Citation

  • Xiaofang Chen & Zengli Mao & Chong Wu, 2025. "Multi-class Financial Distress Prediction Based on Feature Selection and Deep Forest Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 66(4), pages 2715-2754, October.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:4:d:10.1007_s10614-024-10761-8
    DOI: 10.1007/s10614-024-10761-8
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    References listed on IDEAS

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    1. Jiaming Liu & Chong Wu & Yongli Li, 2019. "Improving Financial Distress Prediction Using Financial Network-Based Information and GA-Based Gradient Boosting Method," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 851-872, February.
    2. Tsai, Chih-Fong & Sue, Kuen-Liang & Hu, Ya-Han & Chiu, Andy, 2021. "Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction," Journal of Business Research, Elsevier, vol. 130(C), pages 200-209.
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    6. Amal Al Ali & Ahmed M. Khedr & Magdi El Bannany & Sakeena Kanakkayil, 2023. "GALSTM-FDP: A Time-Series Modeling Approach Using Hybrid GA and LSTM for Financial Distress Prediction," IJFS, MDPI, vol. 11(1), pages 1-15, February.
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    9. Diyuan Li & Zida Liu & Danial Jahed Armaghani & Peng Xiao & Jian Zhou, 2022. "Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest," Mathematics, MDPI, vol. 10(5), pages 1-23, March.
    10. Climent, Francisco & Momparler, Alexandre & Carmona, Pedro, 2019. "Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach," Journal of Business Research, Elsevier, vol. 101(C), pages 885-896.
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    1. Deng, Shangkun & Li, Yongqi & Zhu, Yingke & Wang, Bingsen & Ning, Hong & Yi, Siyu & Shimada, Tatsuro, 2025. "Financial distress warning and risk path analysis for Chinese listed companies: An interpretable machine learning approach," Economic Modelling, Elsevier, vol. 152(C).

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