Predicting Credit Default in an Agricultural Bank: Methods and Issues

This study examines the performance of logistic regression, artificial neural networks and adaptive neuro-fuzzy inference system in predicting credit default using data from Farm Credit System. Empirical findings show that credit default predictions vary with empirical model used.

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Year of publication:	2006
Authors:	Odeh, Oluwarotimi O. ; Featherstone, Allen M. ; Sanjoy, Das
Institutions:	Southern Agricultural Economics Association - SAEA
Subject:	Agricultural Finance

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Extent:	application/pdf
Series:	2006 Annual Meeting, February 5-8, 2006, Orlando, Florida.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series 2006 Annual Meeting, February 5-8, 2006, Orlando, Florida Number 35359
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005806522