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Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines

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  • Henri Arno
  • Klaas Mulier
  • Joke Baeck
  • Thomas Demeester

Abstract

Models for bankruptcy prediction are useful in several real-world scenarios, and multiple research contributions have been devoted to the task, based on structured (numerical) as well as unstructured (textual) data. However, the lack of a common benchmark dataset and evaluation strategy impedes the objective comparison between models. This paper introduces such a benchmark for the unstructured data scenario, based on novel and established datasets, in order to stimulate further research into the task. We describe and evaluate several classical and neural baseline models, and discuss benefits and flaws of different strategies. In particular, we find that a lightweight bag-of-words model based on static in-domain word representations obtains surprisingly good results, especially when taking textual data from several years into account. These results are critically assessed, and discussed in light of particular aspects of the data and the task. All code to replicate the data and experimental results will be released.

Suggested Citation

  • Henri Arno & Klaas Mulier & Joke Baeck & Thomas Demeester, 2022. "Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines," Papers 2208.11334, arXiv.org.
    • Handle: RePEc:arx:papers:2208.11334
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    References listed on IDEAS

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    1. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    2. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    3. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    4. Bernanke, Ben S, 1981. "Bankruptcy, Liquidity, and Recession," American Economic Review, American Economic Association, vol. 71(2), pages 155-159, May.
    5. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    6. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    7. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    8. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 123-127.
    9. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 71-111.
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    Cited by:

    1. 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).
    2. Henri Arno & Klaas Mulier & Joke Baeck & Thomas Demeester, 2025. "Business Failure Prediction From Textual and Tabular Data With Sentence-Level Interpretations," Annals of Operations Research, Springer, vol. 353(2), pages 667-692, October.

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