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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

    (KU Leuven - Catholic University of Leuven = Katholieke Universiteit 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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    Citations

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    Cited by:

    1. 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.
    2. Sobrie, Léon & Verschelde, Marijn & Roets, Bart, 2024. "Explainable real-time predictive analytics on employee workload in digital railway control rooms," European Journal of Operational Research, Elsevier, vol. 317(2), pages 437-448.
    3. Kumar, Ajay & Taylor, James W., 2024. "Feature importance in the age of explainable AI: Case study of detecting fake news & misinformation via a multi-modal framework," European Journal of Operational Research, Elsevier, vol. 317(2), pages 401-413.
    4. Kraus, Mathias & Tschernutter, Daniel & Weinzierl, Sven & Zschech, Patrick, 2024. "Interpretable generalized additive neural networks," European Journal of Operational Research, Elsevier, vol. 317(2), pages 303-316.
    5. Das, Ronnie & Ahmed, Wasim & Sharma, Kshitij & Hardey, Mariann & Dwivedi, Yogesh K. & Zhang, Ziqi & Apostolidis, Chrysostomos & Filieri, Raffaele, 2024. "Towards the development of an explainable e-commerce fake review index: An attribute analytics approach," European Journal of Operational Research, Elsevier, vol. 317(2), pages 382-400.
    6. Chi, Guotai & Dong, Bingjie & Zhou, Ying & Jin, Peng, 2024. "Long-horizon predictions of credit default with inconsistent customers," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    7. 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.
    8. 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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