Using Artificial Neural Networks Analysis for Small Enterprises Default Prediction Modeling: Statistical Evidence from Italian Firms

A large number of empirical studies have used univariate and multivariate statistical methods when examining the effectiveness of appropriately selected corporation data in constructing company default prediction models. Having accurate evaluation methods has become increasingly important since the New Basel Capital Accord linked the banks’ capital requirements to the banks’ models for company default prediction. Solutions are now urgently needed in view of the current global financial crisis which is having serious effects on the overall word economic system and is making it extremely difficult for banks to grant credit, and for firms to obtain it. The empirical studies mentioned mostly rely on Multivariate Discriminant Analysis (MDA) and Logistic Regression Analysis (LRA); and they mainly focus on large and medium-sized enterprises. Our study applies Artificial Neural Network Analysis (ANNA) to a sample of over 6,000 small Italian firms, with a view to developing and testing default prediction models based on an appropriately selected set of financial-economic ratios. Our results show that: i) when compared to traditional statistical methods (MDA and LRA), ANNA can make a better contribution to decision support systems for Small Enterprise (SE) credit-risk evaluation; and ii) when the decisional function is separately calculated according to size, geographical area and business sector, ANNA prediction accuracy is markedly higher for the smallest-sized firms and for firms operating in Central Italy.