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

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@Article{DING:2023:irfa,

• author = "Shusheng Ding and Tianxiang Cui and Anthony Graham Bellotti and Mohammad Zoynul Abedin and Brian Lucey",
• title = "The role of feature importance in predicting corporate financial distress in pre and post {COVID} periods: Evidence from China",
• journal = "International Review of Financial Analysis",
• volume = "90",
• pages = "102851",
• year = "2023",
• ISSN = "1057-5219",
• DOI = "doi:10.1016/j.irfa.2023.102851",
• URL = "https://www.sciencedirect.com/science/article/pii/S1057521923003678",
• keywords = "genetic algorithms, genetic programming, Financial distress prediction, Time-varying feature selection, Extreme gradient boosting, COVID-19 crisis",
• 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",

}

Genetic Programming entries for Shusheng Ding Tianxiang Cui Anthony Graham Bellotti Mohammad Zoynul Abedin Brian Lucey

Citations