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Multi-class financial distress prediction using the textual information of earnings communication conferences based on ensemble machine learning models

Author

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  • Sun, Jie
  • Xie, Minghui
  • Li, Jie

Abstract

Financial distress prediction (FDP) is of substantial concern to stakeholders. We categorize enterprises’ financial status into three states: financial distress, financial sub-health, and financial health. As a bridge between management and investors, earnings communication conferences (ECCs) enhance corporate transparency. We construct ECC textual features capturing interaction degree, management and investor sentiment, corporate situation, and question and answer (Q&A) quality, and incorporate them into multi-class FDP models. The SHapley Additive exPlanations is utilized to interpret the models and assess feature importance. Empirical results indicate thatECC textual features can significantly improve FDP performance beyond financial, governance, and annual report features. Feature importance analysis further shows that ECC textual features rank second only to financial features. Among the ECC textual features, Q&A quality contributes the most to FDP.

Suggested Citation

  • Sun, Jie & Xie, Minghui & Li, Jie, 2026. "Multi-class financial distress prediction using the textual information of earnings communication conferences based on ensemble machine learning models," Journal of Business Research, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:jbrese:v:206:y:2026:i:c:s014829632500788x
    DOI: 10.1016/j.jbusres.2025.115965
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