The University of Southampton
University of Southampton Institutional Repository

Beyond Z-analysis: self organizing neural networks for financial diagnosis

Record type: Monograph (Discussion Paper)

In this paper we propose a complete method for financial diagnosis based on Self Organizing Feature Maps (SOFM), a neural network model which, on the basis of the information contained in a multidimensional space--in our case, financial ratios--generates a space of lesser dimensions. In this way, similar input patterns are represented close to one another on a map. The methodology has been complemented and compared with multivariate statistical models such as Linear Discriminant Analysis (LDA), as well as with neutral models such as the Multilayer Perception (MLP). As the principal advantage which distinguishes the proposed methodology from other statistical techniques that have been developed to analyze accounting information, mention should be made of its robustness in not demanding that the input variables follow any distribution function, thus providing a complete analysis which goes beyond that of the traditional models based on Z score, without renouncing simplicity for the final decision maker

Full text not available from this repository.

Citation

Serrano Cinca, C. (1994) Beyond Z-analysis: self organizing neural networks for financial diagnosis , Southampton, UK University of Southampton 15pp. (Discussion Papers in Accounting and Management Science, 94-92).

More information

Published date: December 1994

Identifiers

Local EPrints ID: 36124
URI: http://eprints.soton.ac.uk/id/eprint/36124
PURE UUID: d5e9e832-4aa5-43d6-a050-8c29a468d794

Catalogue record

Date deposited: 03 May 2007
Last modified: 17 Jul 2017 15:45

Export record

Contributors

Author: C. Serrano Cinca

University divisions


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×