Artificial Neural Networks in the prediction of insolvency. A paradigm shift to traditional business practices recipes
DOI:
https://doi.org/10.29019/enfoqueute.v5n2.39Keywords:
neural networks, petri nets, insolvency, bankruptcyAbstract
(Received: 2014/05/14 - Accepted: 2014/06/27)
In this paper a review and analysis of the major theories and models that address the prediction of corporate bankruptcy and insolvency is made. Neural networks are a tool of most recent appearance, although in recent years have received considerable attention from the academic and professional world, and have started to be implemented in different models testing organizations insolvency based on neural computation. The purpose of this paper is to yield evidence of the usefulness of Artificial Neural Networks in the problem of bankruptcy prediction insolence or so compare its predictive ability with the methods commonly used in that context. The findings suggest that high predictive capabilities can be achieved using artificial neural networks, with qualitative and quantitative variables.
Downloads
Downloads
Published
Issue
Section
License
The authors retain all copyrights ©.
- The authors retain their trademark and patent rights, as well as rights to any process or procedure described in the article.
- The authors retain the right to share, copy, distribute, perform, and publicly communicate the article published in Enfoque UTE (for example, post it in an institutional repository or publish it in a book), provided that acknowledgment of its initial publication in Enfoque UTE is given.
- The authors retain the right to publish their work at a later date, to use the article or any part of it (for example, a compilation of their work, lecture notes, a thesis, or for a book), provided that they indicate the source of publication (authors of the work, journal, volume, issue, and date).
