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Bankruptcy Prediction Model Based on Business Risk Reports : Use of Natural Language Processing Techniques

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  • Rasolomanana, Onjaniaina Mianin'Harizo

Abstract

The purpose of this study is to assess how useful risk information is in bankruptcy prediction, by performing a sentiment analysis of the texts. The proposed method involves the use of Natural Language Processing (NLP) and machine learning techniques. The results show that neural networks performed better than other classifiers, with a classification accuracy of 96.15% for this particular text classification problem. This work demonstrates that business risks reports carry information that helps predict the likelihood of bankruptcy.

Suggested Citation

  • Rasolomanana, Onjaniaina Mianin'Harizo, 2021. "Bankruptcy Prediction Model Based on Business Risk Reports : Use of Natural Language Processing Techniques," Discussion paper series. A 358, Graduate School of Economics and Business Administration, Hokkaido University.
    • Handle: RePEc:hok:dpaper:358
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    File URL: http://hdl.handle.net/2115/81088
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    File URL: https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/81088/1/DPA358.pdf
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    References listed on IDEAS

    as
    1. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    2. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Bankruptcy prediction; Business risk; Natural language processing; NLP; Sentiment analysis; Neural Networks;
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