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Small Enterprise Default Prediction Modeling through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises

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  • Francesco Ciampi
  • Niccolò Gordini

Abstract

Having accurate company default prediction models is vital for both banks and enterprises, especially small enterprises (SEs). These firms represent a vital part in the economy of every country but are also typically more informationally opaque than large corporations. Therefore, these models should be precise but also easily adaptable to suit SE characteristics. Our study applies artificial neural networks (ANNs) to a sample of over 7,000 Italian SEs. Results show that (1) when compared with traditional methods, ANNs can make a better contribution to SE credit‐risk evaluation; and (2) when the model is separately calculated according to size, geographical area, and business sector, ANNs prediction accuracy is markedly higher for the smallest sized firms and for firms operating in entral taly.

Suggested Citation

  • Francesco Ciampi & Niccolò Gordini, 2013. "Small Enterprise Default Prediction Modeling through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises," Journal of Small Business Management, Taylor & Francis Journals, vol. 51(1), pages 23-45, January.
    • Handle: RePEc:taf:ujbmxx:v:51:y:2013:i:1:p:23-45
    DOI: 10.1111/j.1540-627X.2012.00376.x
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    Cited by:

    1. Carmen Gallucci & Rosalia Santullli & Michele Modina & Vincenzo Formisano, 2023. "Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(3), pages 873-892, September.
    2. Oleh Kolodiziev & Andrii Gukaliuk & Valeriia Shcherbak & Tetiana Riabovolyk & Ilona Androshchuk & Yaryna Pas, 2024. "The Impact of Refugee Startups on Host Country Economies: Business Models and Economic Adaptation," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 175-201.
    3. Flavio Bazzana & Marco Bee & Ahmed Almustfa Hussin Adam Khatir, 2024. "Machine learning techniques for default prediction: an application to small Italian companies," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-23, February.
    4. He-Boong Kwon & Jooh Lee & Laee Choi, 2023. "Dynamic interplay of environmental sustainability and corporate reputation: a combined parametric and nonparametric approach," Annals of Operations Research, Springer, vol. 324(1), pages 687-719, May.

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