Artificial Neural Networks in the prediction of insolvency. A paradigm shift to traditional business practices recipes

Authors

  • Marcia M. Lastre Valdes Universidad de Holguín
  • Arlys M. Lastre Aleaga Universidad Tecnológica Equinoccial
  • Gelmar García Vidal Universidad de Holguín

DOI:

https://doi.org/10.29019/enfoqueute.v5n2.39

Keywords:

neural networks, petri nets, insolvency, bankruptcy

Abstract

(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.

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Published

2014-06-30

How to Cite

Lastre Valdes, M. M., Lastre Aleaga, A. M., & García Vidal, G. (2014). Artificial Neural Networks in the prediction of insolvency. A paradigm shift to traditional business practices recipes. Enfoque UTE, 5(2), pp. 38 – 58. https://doi.org/10.29019/enfoqueute.v5n2.39

Issue

Vol. 5 No. 2 (2014)

Section

Miscellaneous

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