Can Credit Scoring Models Effectively Predict Small Enterprise Default? Statistical Evidence from Italian Firms

This paper applies mainstream statistical techniques (linear discriminant analysis and logistic regression) to a sample of over 6,000 Italian firms in the attempt to develop two distress prediction models, specifically constructed for SEs and taking into account diversity of size, geographical location and business sector. For both models, prediction accuracy increases progressively with larger firms, and is higher in the North and in manufacturing firms

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Year of publication:	2013
Authors:	Ciampi, Francesco
Other Persons:	Vallini, Carlo (contributor) ; Gordini, Niccolo (contributor) ; Benvenuti, Michele (contributor)
Publisher:	[2013]: [S.l.] : SSRN
Subject:	Italien \| Italy \| Kreditwürdigkeit \| Credit rating \| KMU \| SME \| Kreditrisiko \| Credit risk \| Prognose \| Forecast \| Schätzung \| Estimation
Description of contents:	Abstract [papers.ssrn.com]

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Extent:	1 Online-Ressource
Type of publication:	Book / Working Paper
Language:	English
Notes:	In: Proceedings of the 8th Global Conference on Business & Economics. Florence, Italy, 18-19 October 2008, Vol. 1, pp. 1-23, Association for Business & Economics Research (ABER), ISBN: 9780974211459 Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments 2008 erstellt Volltext nicht verfügbar
Source:	ECONIS - Online Catalogue of the ZBW

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