The role of associated risk in predicting financial distress: A case study of listed agricultural companies in China

Wanjuan Zhang and Jing Wang

Finance Research Letters, 2025, vol. 77, issue C

Abstract: This study investigates the predictive capacity of associated risk for financial distress among listed agricultural companies in China. Seven models, including statistical, machine learning, and ensemble methods, are used to evaluate the contribution of associated risk information. Our findings show that incorporating associated risk significantly enhances model performance, reducing misclassification rates by 0.1 %-3.1 % for healthy companies and 10.8 %-40.6 % for distressed companies, with Random Forest achieving the highest accuracy (0.9523). By incorporating associated risk, the ability of models to identify financially distressed companies is improved. Effective risk identification reduces the accumulation and outbreak of systemic financial risks, providing valuable insights for banking regulatory agencies.

Keywords: Associated risk; Financial distress; Listed companies; Prediction models (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:77:y:2025:i:c:s1544612325003885

DOI: 10.1016/j.frl.2025.107125

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