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

Philipp Borchert, Kristof Coussement (), Arno de Caigny () and Jochen de Weerdt
Additional contact information
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

Post-Print from HAL

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.

Keywords: Analytics; Business failure prediction; Text mining; NLP; Deep learning (search for similar items in EconPapers)
Date: 2023-04
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Published in European Journal of Operational Research, 2023, 306 (1), pp.348-357. ⟨10.1016/j.ejor.2022.06.060⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03976762

DOI: 10.1016/j.ejor.2022.06.060

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().