Bankruptcy prediction is a topic, which affect the economic well being of all countries. Having an accurate company default prediction model, which can pick up on time the signs of financial distress, is vital for all firms, especially for small and medium-sized enterprises (SMEs). These firms represent the backbone of the economy of every country. Therefore, they need a prediction model easily adaptable to their characteristics. For this purpose, this study explores and compares the potential of genetic algorithms (GAs) with those of logistic regression (LR) and support vector machine (SVM). GAs are applied to a large sample of 3.100 Italian manufacturing SMEs, three, two and one year prior to bankruptcy. The results indicate that GAs are a very effective and promising instrument in assessing the likelihood of SMEs bankruptcy compared with LR and SVM, especially in reducing Type II misclassification rate. Of particular interest, results show that GAs prediction accuracy rate increases when the model is applied according to size and geographical area, with a marked improvement in the smallest sized firms and in the firms operating in north Italy. © 2014 Elsevier Ltd. All rights reserved.
GORDINI, N. (2014). A genetic algorithm approach for SMEs bankruptcy prediction: Empirical evidence from Italy. EXPERT SYSTEMS WITH APPLICATIONS, 41(14), 6433-6445.
|Citazione:||GORDINI, N. (2014). A genetic algorithm approach for SMEs bankruptcy prediction: Empirical evidence from Italy. EXPERT SYSTEMS WITH APPLICATIONS, 41(14), 6433-6445.|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||No|
|Titolo:||A genetic algorithm approach for SMEs bankruptcy prediction: Empirical evidence from Italy|
GORDINI, NICCOLO' (Primo)
|Data di pubblicazione:||2014|
|Rivista:||EXPERT SYSTEMS WITH APPLICATIONS|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.eswa.2014.04.026|
|Appare nelle tipologie:||01 - Articolo su rivista|