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Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali

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  • Enrico Supino
  • Nicola Piras

Abstract

The study addresses the problem related to the performance of the tools for forecasting corporate crises in periods characterized by strong macroeconomic instability (financial crises, pandemics, wars, etc.). The results obtained show how the performances of the models decrease over time and how, in a period characterized by strong macroeconomic instability, more evident drops in performance are observed. Particularly, with reference to the hotel sector in Italy, in correspondence with and immediately after the financial crisis of 2008, it emerges that artificial neural networks produce more precise and less volatile predictions than the classical models used in the literature (linear discriminant analysis and logistic regression).

Suggested Citation

  • Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    • Handle: RePEc:fan:macoma:v:html10.3280/maco2022-002003
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    References listed on IDEAS

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    Cited by:

    1. Simone Poli & Marco Gatti, 2024. "The relevance of cash flow information in predicting corporate bankruptcy in Italian private companies," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2024(1), pages 179-202.
    2. Diego Valentinetti & Michele A. Reaa, 2023. "Intelligenza artificiale e accounting: le possibili relazioni," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2023(2), pages 93-116.

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