CatBoost model and artificial intelligence techniques for corporate failure prediction

Sami Ben Jabeur, Cheima Gharib, Salma Mefteh-Wali and Wissal Ben Arfi

Technological Forecasting and Social Change, 2021, vol. 166, issue C

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: Bankruptcy prediction; CatBoost; XGBoost; Machine learning (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:166:y:2021:i:c:s0040162521000901

DOI: 10.1016/j.techfore.2021.120658

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