23 Pages Posted: 13 Dec 2012
Date Written: January 2013
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.
Suggested Citation: 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: https://ssrn.com/abstract=2188768 or http://dx.doi.org/10.1111/j.1540-627X.2012.00376.x
This is a Wiley-Blackwell Publishing paper. Wiley-Blackwell Publishing charges $38.00 .
File name: jsbm376.
If you wish to purchase the right to make copies of this paper for distribution to others, please select the quantity.