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

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.