CatBoost model and artificial intelligence techniques for corporate failure prediction

Sami Ben Jabeur (), Cheima Gharib, Salma Mefteh-Wali and Wissal Ben Arfi
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Sami Ben Jabeur: UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University)

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Abstract: Financial distress prediction provides an effective warning system for banks and investors to correctly guide decisions on granting credit. Ensemble methods have demonstrated their performance in corporate failure prediction. Among the ensemble methods, gradient boosting has been successfully used in bankruptcy prediction. In this paper, we propose a novel approach to classify categorical data using gradient boosting decision trees, namely, CatBoost. First, we investigate the importance of the features identified by the CatBoost model. Second, we compare our approach with eight reference machine learning models at one, two and three years before failure. Our model demonstrates an effective improvement in the power of classification performance compared with other advanced approaches.

Keywords: Financial development; Geopolitical risk; Resource Richness; Green Innovation; Sustainable development (search for similar items in EconPapers)
Date: 2021-05
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Published in Technological Forecasting and Social Change, 2021, 166, pp.120658. ⟨10.1016/j.techfore.2021.120658⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05238300

DOI: 10.1016/j.techfore.2021.120658

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