Machine Learning models for bankruptcy prediction in Italy:do industrial variables count?

Daniela Bragoli, Camilla Ferretti, Piero Ganugi, Giovanni Marseguerra (), Davide Mezzogori and Francesco Zammori
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Giovanni Marseguerra: Università Cattolica del Sacro Cuore

No dime19_03, DiMSEFA - Quaderni del Dipartimento di Matematica per le Scienze Economiche, Finanziarie ed Attuariali from Università Cattolica del Sacro Cuore, Dipartimento di Matematica per le Scienze Economiche, Finanziarie ed Attuariali (DiMSEFA)

Abstract: We aim to provide a predictive model, specifically designed for the Italian economy, which classifies solvent and insolvent firms one year in advance, using AIDA Bureau van Dijk dataset from 2007 to 2015. We apply a full battery of bankruptcy forecasting models, including both traditional and more sophisticated machine learning techniques, and add to the financial ratios used in the literature a set of industrial/regional variables. We find that XGBoost is the best performer and that industrial/regional variables are important. Moreover, belonging to a district,having a high markup and a greater market share diminish bankruptcy probability.

Keywords: Firm distress analysis; machine learning; logistic regression; industrial variables. (search for similar items in EconPapers)
JEL-codes: C45 C52 G33 L23 R11 (search for similar items in EconPapers)
Pages: 41
Date: 2019-03
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