EconPapers    
Economics at your fingertips  
 

A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction

Kurt M. Fanning and Kenneth O. Cogger

Intelligent Systems in Accounting, Finance and Management, 1994, vol. 3, issue 4, 241-252

Abstract: This paper examines the efficiency of a generalized adaptive neural network algorithm (GANNA) processor in comparison to earlier model‐based methods, a back‐propagation artificial neural network, and logistic regression approaches to data classification. The research uses the binary classification problem of discriminating between failing and non‐failing firms to compare the methods. The results indicate the potential in time savings and the successful classification results available from a GANNA processor.

Date: 1994
References: Add references at CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://doi.org/10.1002/j.1099-1174.1994.tb00068.x

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:wly:isacfm:v:3:y:1994:i:4:p:241-252

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1099-1174
subscrip@blackwellpub.com

Access Statistics for this article

More articles in Intelligent Systems in Accounting, Finance and Management from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery (contentdelivery@wiley.com).

 
Page updated 2024-12-29
Handle: RePEc:wly:isacfm:v:3:y:1994:i:4:p:241-252