Abstract

http://ssrn.com/abstract=2188768
 
 

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


Francesco Ciampi


University of Florence - Department of Business Administration

January 2013

Journal of Small Business Management, Vol. 51, Issue 1, pp. 23-45, 2013

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 Central Italy.

Number of Pages in PDF File: 23

Accepted Paper Series


Date posted: December 13, 2012  

Suggested Citation

Ciampi, Francesco, Small Enterprise Default Prediction Modeling Through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises (January 2013). Journal of Small Business Management, Vol. 51, Issue 1, pp. 23-45, 2013. Available at SSRN: http://ssrn.com/abstract=2188768 or http://dx.doi.org/10.1111/j.1540-627X.2012.00376.x

Contact Information

Francesco Ciampi (Contact Author)
University of Florence - Department of Business Administration ( email )
via delle Pandette, 9
Florence, Florence 50127
Italy
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