Bankruptcy prediction using fuzzy convolutional neural networks

Sami Ben Jabeur () and Vanessa Serret ()
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Sami Ben Jabeur: ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University), UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University)
Vanessa Serret: CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine

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Abstract: We propose a combined method for bankruptcy prediction based on fuzzy set qualitative comparative analysis (fsQCA) and convolutional neural networks (CNN). Currently, CNNs are being applied to various fields, and in some areas are providing higher performance than traditional models. In our proposed method, a CNN uses calibrated variables from fuzzy sets to improve performance accuracy. In addition, there are no published studies on the effect of feature selection at the input level of convolutional neural networks. Therefore, this study compares four well-known feature selection methods used in financial distress prediction, (t-test, stepdisc discriminant analysis, stepwise logistic regression and partial least square discriminant analysis) to investigate their effect on classification performance. The results show that fuzzy convolutional neural networks (FCNN) lead to better performance than when using traditional methods.

Date: 2022-12
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Published in Research in International Business and Finance, 2022, pp.101844. ⟨10.1016/j.ribaf.2022.101844⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03886338

DOI: 10.1016/j.ribaf.2022.101844

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