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Firms Default Prediction with Machine Learning

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Mining Data for Financial Applications (MIDAS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11985))

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Abstract

Academics and practitioners have studied over the years models for predicting firms bankruptcy, using statistical and machine-learning approaches. An earlier sign that a company has financial difficulties and may eventually bankrupt is going in default, which, loosely speaking means that the company has been having difficulties in repaying its loans towards the banking system. Firms default status is not technically a failure but is very relevant for bank lending policies and often anticipates the failure of the company. Our study uses, for the first time according to our knowledge, a very large database of granular credit data from the Italian Central Credit Register of Bank of Italy that contain information on all Italian companies’ past behavior towards the entire Italian banking system to predict their default using machine-learning techniques. Furthermore, we combine these data with other information regarding companies’ public balance sheet data. We find that ensemble techniques and random forest provide the best results, corroborating the findings of Barboza et al. (Expert Syst. Appl., 2017).

A. Anagnostopoulos—Partially supported by ERC Advanced Grant 788893 AMDROMA “Algorithmic and Mechanism Design Research in Online Markets.”.

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Notes

  1. 1.

    Fabio Panetta, Chamber of Deputies, Rome, May 10, 2018.

  2. 2.

    The views expressed in the article are those of the authors and do not involve the responsibility of the Bank of Italy.

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Authors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Tesi Aliaj, Aris Anagnostopoulos & Stefano Piersanti

  2. Bank of Italy - Statistical Data Collection and Processing Directorate, Rome, Italy

    Stefano Piersanti

Authors
  1. Tesi Aliaj
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  2. Aris Anagnostopoulos
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  3. Stefano Piersanti
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Corresponding author

Correspondence to Stefano Piersanti .

Editor information

Editors and Affiliations

  1. UniCredit, Milan, Italy

    Valerio Bitetta

  2. UniCredit, Rome, Italy

    Ilaria Bordino

  3. UniCredit, Milan, Italy

    Andrea Ferretti

  4. UniCredit, Rome, Italy

    Francesco Gullo

  5. UniCredit, Milan, Italy

    Stefano Pascolutti

  6. ENEA Portici Research Center, Portici, Italy

    Giovanni Ponti

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Aliaj, T., Anagnostopoulos, A., Piersanti, S. (2020). Firms Default Prediction with Machine Learning. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Pascolutti, S., Ponti, G. (eds) Mining Data for Financial Applications. MIDAS 2019. Lecture Notes in Computer Science(), vol 11985. Springer, Cham. https://doi.org/10.1007/978-3-030-37720-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-37720-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37719-9

  • Online ISBN: 978-3-030-37720-5

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