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The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China

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  • Ding, Shusheng
  • Cui, Tianxiang
  • Bellotti, Anthony Graham
  • Abedin, Mohammad Zoynul
  • Lucey, Brian

Abstract

The prediction of firm financial distress during the COVID-19 crisis episode attracted massive academic attention since economic uncertainty was exacerbated. In this paper, we propose a firm financial distress prediction model based on the Extreme Gradient Boosting-Genetic Programming (XGB-GP) framework by investigating subsamples of pre-COVID and post-COVID periods. The key contribution of our paper is that we explore time-varying prediction features for pre-COVID and post-COVID periods. We illuminate that the earning financial indicator is the dominant feature for financial distress prediction during the pre-COVID period, whereas total financial leverage is the most important factor during the post-COVID period. On this basis, our XGB-GP financial distress prediction model exhibits higher prediction accuracy than the traditional models. As a result, managers can modify the financial leverage level to improve the financial situation of the firm by reducing the debt burden and increasing profitability during the post-COVID period.

Suggested Citation

  • Ding, Shusheng & Cui, Tianxiang & Bellotti, Anthony Graham & Abedin, Mohammad Zoynul & Lucey, Brian, 2023. "The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:finana:v:90:y:2023:i:c:s1057521923003678
    DOI: 10.1016/j.irfa.2023.102851
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    Keywords

    Financial distress prediction; Time-varying feature selection; Extreme gradient boosting; Genetic programming; COVID-19 crisis;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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