Title: A new non-parametric classifier to predict small-business failures in Italy via performance ratios
Authors: Francesca Di Donato; Luciano Nieddu
Addresses: Università degli Studi Internazionali di Roma - UNINT, Via C. Colombo, 200, 00145, Rome, Italy ' Università degli Studi Internazionali di Roma - UNINT, Via C. Colombo, 200, 00145, Rome, Italy
Abstract: We considered the case of small-medium enterprises (SMEs) in Italy introducing a new classifier to predict bankruptcy up to eight years prior to failure. We considered a stratified random sample of 100 non-listed Italian SMEs, 50 of which filed for bankruptcy during the years 2000 to 2011. Results suggest that the proposed method more than holds its own when compared with standard non-parametric classification techniques. The performance of the proposed method based on recognition rate, sensitivity and specificity shows that the proposed technique is effective in predicting the failure of a firm up to eight years prior to the event. The high specificity makes the proposed technique very effective as a warning signal to determine if a firm is in distress with a sufficient enough time to take proper actions. The performance assessment has been achieved via cross-validation to get unbiased estimates of the performances.
Keywords: constrained k-means; bankruptcy prediction; discriminant analysis; performance ratios; small-medium enterprises; SMEs; Italy.
International Journal of Information and Decision Sciences, 2018 Vol.10 No.1, pp.57 - 76
Available online: 19 Mar 2018 *Full-text access for editors Access for subscribers Free access Comment on this article