Articlegreen2012

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

145 citations0/0 files storedJournal of Small Business Management

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Financial Distress and Bankruptcy Prediction Forecasting Techniques and Applications Working Capital and Financial Performance

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.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 2006A more complete conceptual framework for SME finance 2002Small Business Credit Availability and Relationship Lending: The Importance of Bank Organisational Structure 1972A Discriminant Analysis of Predictors of Business Failure 1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)1977Early warning of bank failure

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