Neuro-fuzzy modeling in bankruptcy prediction

Authors

  • D. Vlachos Department of Mechanical Engineering Aristotle University of Thessaloniki, Thessaloniki, Greece
  • Y.A. Tolias Telecommunications Laboratory Department of Electrical and Computer Engineering Aristotle University of Thessaloniki, Thessaloniki, Greece

DOI:

https://doi.org/10.2298/YJOR0302165V

Keywords:

neuro-fuzzy, bankruptcy

Abstract

For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm), combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

References

Altman, E.I., "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy", Journal of Finance, (1968) 589-609.

Coats, P.K., and Fant, F., "Recognizing financial distress patterns using a neural network approach", Financial Management, 22(3) (1992) 142.

Dimitras, A.I., Zanakis, S.H., and Zopounidis, C., "A survey of business failures with an emphasis on prediction methods and industrial applications", European Journal of Operational Research, 90 (1996) 487-513.

Flecher, D., and Goss, E., "Forecasting with neural networks-an application using bankruptcy data", Information and Management, 24 (1993) 159-167.

Messier, W., and Hansen, J., "Inducing rules for expert system development: an example using default and bankruptcy data", Management Science, 34 (1988) 12.

Rahimian, E., Singh, S., Thammachote, T., and Virmani, R., "Bankruptcy prediction by neural network", in: R. Trippi, and E. Turban (eds.), Neural Networks in Finance and Investing, Irwin, 1996.

Roger Jang, J.S., ANFIS: "Adaptive network-based fuzzy inference system", IEEE Transactions on Systems, Man and Cybernetics, 23 (3) (1993) 665-685.

Salchenberger, L., Mine Cinar, E., and Lash, N., "Neural networks: a new tool for predicting thrift failures", Decision Sciences, 23 (1992) 899-916.

Takagi T., and Sugeno, M., "Fuzzy identification of systems and its application to modeling and control", IEEE Transactions on Systems, Man and Cybernetics, 15 (1985) 116-132.

Wilson, R., and Sharda, R., "Bankruptcy prediction using neural networks", Decision Support Systems, 11 (1994) 545-557.

Yoon, Y., Guimaraes, T., and Swales, G., "Integrating neural networks with rule-based expert systems", Decision Support Systems, 11 (1994) 497-507.

Zadeh, L.A., "Fuzzy sets", Information Control, 8 (1965) 338-353.

Zadeh, L.A., "Outline of a new approach to the analysis of complex systems and decision processes", IEEE Transactions on Systems, Man and Cybernetics, 3 (1) (1973) 28-44.

Downloads

Published

2003-09-01

Issue

Vol. 13 No. 2 (2003)

Section

Research Articles

License

Copyright (c) 2003 YUJOR

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.