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

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

International Review of Financial Analysis, 2023, vol. 90, issue C

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.

Keywords: Financial distress prediction; Time-varying feature selection; Extreme gradient boosting; Genetic programming; COVID-19 crisis (search for similar items in EconPapers)
JEL-codes: G33 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:90:y:2023:i:c:s1057521923003678

DOI: 10.1016/j.irfa.2023.102851

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