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Unlocking bankruptcy clues: A novel sentence-based machine learning approach

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  • Hesse, Matthies
  • Loy, Thomas

Abstract

Our study examines the predictive power of Management Discussion and Analysis (MD&A) sections in the context of bankruptcy prediction. Leveraging contextual sentence embeddings from a pre-trained Transformer model (BERT), we introduce a novel prediction model designed to identify MD&A sentences associated with bankruptcies. Our sentence-level model is competitive with various document-level approaches, particularly when excluding boilerplate content. Furthermore, our sentence-level approach enhances model interpretability by uncovering high-risk topics such as performance deterioration, financial losses, and cost reductions. Lastly, we provide insights into critical high-risk disclosure patterns through structural and syntactical analysis.

Suggested Citation

  • Hesse, Matthies & Loy, Thomas, 2025. "Unlocking bankruptcy clues: A novel sentence-based machine learning approach," International Journal of Accounting Information Systems, Elsevier, vol. 56(C).
    • Handle: RePEc:eee:ijoais:v:56:y:2025:i:c:s1467089525000272
    DOI: 10.1016/j.accinf.2025.100751
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    Cited by:

    1. Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2025. "Artificial intelligence and accounting research: a framework and agenda," International Journal of Accounting Information Systems, Elsevier, vol. 56(C).

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