We provide a predictive model specifically designed for the Italian economy that classifies solvent and insolvent firms one year in advance using the AIDA Bureau van Dijk data set for the period 2007–15. 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 mark-up and a greater market share diminish bankruptcy probability.

Bragoli, D., Ferretti, C., Ganugi, P., Marseguerra, G., Mezzogori, D., Zammori, F., Machine-learning models for bankruptcy prediction: do industrial variables matter?, <<SPATIAL ECONOMIC ANALYSIS>>, 2021; October 2021 (2021): 1-22. [doi:10.1080/17421772.2021.1977377] [http://hdl.handle.net/10807/189731]

Machine-learning models for bankruptcy prediction: do industrial variables matter?

Bragoli, Daniela;Marseguerra, Giovanni;
2021

Abstract

We provide a predictive model specifically designed for the Italian economy that classifies solvent and insolvent firms one year in advance using the AIDA Bureau van Dijk data set for the period 2007–15. 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 mark-up and a greater market share diminish bankruptcy probability.
2021
Inglese
Bragoli, D., Ferretti, C., Ganugi, P., Marseguerra, G., Mezzogori, D., Zammori, F., Machine-learning models for bankruptcy prediction: do industrial variables matter?, <<SPATIAL ECONOMIC ANALYSIS>>, 2021; October 2021 (2021): 1-22. [doi:10.1080/17421772.2021.1977377] [http://hdl.handle.net/10807/189731]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/189731
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