The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

Francesco Ciampi

International Journal of Business and Management, 2017, vol. 12, issue 12, 251

Abstract: The existing literature has proved the effectiveness of financial ratios for company default prediction modelling. However, such researches rarely focus on small enterprises (SEs) as specific units of analysis. The aim of this paper is to demonstrate that SE default prediction should be modelled separately from that of large and medium-sized firms. In fact, a multivariate discriminant analysis was applied to a sample of 2,200 small manufacturing firms located in Central Italy and a SE default prediction model was developed based on a selected group of financial ratios and specifically constructed to capture the specificities of SEs’ risk profiles. Subsequently, the prediction accuracy rates obtained by this model were compared with those obtained from a second model based on a sample of 3,200 manufacturing firms situated in Central Italy which belong to all dimensional classes. The findings are the following- 1) evaluating the probability of default of SEs separately from that of larger firms improves prediction performance; 2) the predictive power of the discriminant function improves if it takes into account the different profiles of firms operating in different industry sectors; 3) this improvement is much greater for SEs compared to larger firms.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.ccsenet.org/journal/index.php/ijbm/article/download/71849/39414 (application/pdf)
http://www.ccsenet.org/journal/index.php/ijbm/article/view/71849 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:ijbmjn:v:12:y:2017:i:12:p:251

Access Statistics for this article

More articles in International Journal of Business and Management from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().