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Genetic Algorithms for Small Enterprises Default Prediction: Empirical Evidence from Italy
Abstract
Company default prediction is a widely studied topic as it has a significant impact on banks and firms. Moreover, nowadays, due to the global financial crisis, there is a need to use even more advanced methods (such as soft computing techniques) which can pick up the signs of financial distress on time to evaluate firms (especially small firms). Thus, the author proposes a Genetic Algorithms (GA) approach (a soft computing technique) and shows how GAs can contribute to small enterprise default prediction modeling. The author applied GAs to a sample of 6,200 Italian small enterprises three years and also one year prior to bankruptcy. Subsequently, a multiple discriminant analysis and a logistic regression (the two main traditional techniques in default prediction modeling) were used to benchmarking GAs. The author's results show that the best prediction results were obtained when using GAs.
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