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

Author

Listed:
  • Daniela Bragoli
  • Camilla Ferretti
  • Piero Ganugi
  • Giovanni Marseguerra
  • Davide Mezzogori
  • Francesco Zammori

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.

Suggested Citation

  • Daniela Bragoli & Camilla Ferretti & Piero Ganugi & Giovanni Marseguerra & Davide Mezzogori & Francesco Zammori, 2022. "Machine-learning models for bankruptcy prediction: do industrial variables matter?," Spatial Economic Analysis, Taylor & Francis Journals, vol. 17(2), pages 156-177, April.
    • Handle: RePEc:taf:specan:v:17:y:2022:i:2:p:156-177
    DOI: 10.1080/17421772.2021.1977377
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    Cited by:

    1. Dimitrios Billios & Dimitra Seretidou & Antonios Stavropoulos, 2024. "The Power of Numerical Indicators in Predicting Bankruptcy: A Systematic Review," JRFM, MDPI, vol. 17(10), pages 1-12, September.
    2. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.

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