Predicting financial distress: a case study using self-organizing maps

Authors:
A. M. Mora;J. L. J. Laredo;P. A. Castillo;J. J. Merelo
Affiliations:
Departamento de Arquitectura y Tecnología de Computadores, University of Granada, Spain;Departamento de Arquitectura y Tecnología de Computadores, University of Granada, Spain;Departamento de Arquitectura y Tecnología de Computadores, University of Granada, Spain;Departamento de Arquitectura y Tecnología de Computadores, University of Granada, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 4
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Abstract

In this paper we use Kohonen's Self-Organizing Map (SOM) for surveying the financial status of Spanish companies. From it, we infer which are the most relevant variables, so that a fast diagnostic on their status can be reached and, besides, explained via a few rules of thumb extracted from the behavior of those variables. This map can be used as part of a decision making process, or as a first stage in an automatic classification tool. Results show that variables, identified in an easy and visual way (using SOM and U-Matrix graph), are in agreement with those obtained using parametric and non-parametric tests, which are more complex and difficult to apply.