Models for Default Risk Analysis: Focus on Artificial Neural Networks, Model Comparisons, Hybrid Frameworks

Greta Falavigna

CERIS Working Paper from CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY

Abstract: During the last three decades various models have been proposed by the literature to predict the risk of bankruptcy and of firm insolvency. In this work there is a survey on the methodologies used by the author for the analysis of default risk, taking into account several approaches suggested by the literature. The focus is to analyse the Artificial Neural Networks as a tool for the study of this problem and to verify the ability of classification of these models. Finally, an analysis of variables introduced in the Artificial Neural Network models and some considerations about these.

Keywords: Artificial Neural Networks; Hybrid neural network models Expert Systems; Default; Bankruptcy; Rating Systems; Credit scoring models (search for similar items in EconPapers)
JEL-codes: B41 C14 C45 C53 C63 G10 G30 G33 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2006-12
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:csc:cerisp:200610

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