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Extending business failure prediction models with textual website content using deep learning

Author

Listed:
  • Philipp Borchert
  • Kristof Coussement

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Arno de Caigny

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Jochen de Weerdt

    (Research Centre for Information Systems Engineering, Faculty of Economics and Business, KU Leuven)

Abstract

Business failure prediction (BFP) is an important instrument in assessing the risk of corporate failure. While a large body of research has focused on BFP, recent research in operations research and analytics acknowledges the beneficial effect of incorporating textual data for predictive modelling. However, extant BFP research that incorporates textual company information is very scarce. Based on a dataset containing 13,571 European companies provided by the largest European data aggregator, this study investigates the added value of extending traditional BFP models with textual website content. We further benchmark various feature extraction techniques in natural language processing (i.e. the vector-space approach, neural networks-based approaches and transformers) and assess the best way of representing and integrating textual website features for BFP modelling. The results confirm that including textual website data improves BFP predictive performance, and that textual features extracted by transformers add the most value to the BFP models in this benchmark setting.

Suggested Citation

  • Philipp Borchert & Kristof Coussement & Arno de Caigny & Jochen de Weerdt, 2023. "Extending business failure prediction models with textual website content using deep learning," Post-Print hal-03976762, HAL.
    • Handle: RePEc:hal:journl:hal-03976762
    DOI: 10.1016/j.ejor.2022.06.060
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    Cited by:

    1. Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2024. "Machine learning in bank merger prediction: A text-based approach," European Journal of Operational Research, Elsevier, vol. 312(2), pages 783-797.
    2. Yen, Benjamin P.-C. & Luo, Yu, 2023. "Navigational guidance – A deep learning approach," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1179-1191.
    3. Vairetti, Carla & Aránguiz, Ignacio & Maldonado, Sebastián & Karmy, Juan Pablo & Leal, Alonso, 2024. "Analytics-driven complaint prioritisation via deep learning and multicriteria decision-making," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1108-1118.

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