Financial Distress Prediction Using Neural Networks

Proceedings of the MS’ 2000 International Conference on Modeling and Simulation, Spain, pp. 399-406, 2000

9 Pages Posted: 17 May 2003 Last revised: 24 Apr 2011

See all articles by Fathi Abid

Fathi Abid

University of Sfax, Faculty of Economic and Management Sciences, Probability & Statistics Laboratory

Anis Zouari

University of Sfax - Institute of the High Business Studies of Sfax (IHEC)

Date Written: September 1, 2000

Abstract

This exploratory research examines and models the financial distress prediction using neural network approach. The study is based on financial ratios. Nine different neural network models are constructed to test the predictive capability of the models by considering: (1) the impact of time varying information structure prior the distressed situation using first, independent annual financial ratios (four models)and second, different panel data sets (three models) and, (2) the influence of time varying probability estimates of financial distress in panel data sets (two models). Results support that it is not necessary to have complex architecture in neural models to predict firm's financial distress. Besides more the predictability horizon is shorter and the input information structure is most recent, more the predictive capability of the neural model is better.

Keywords: financial distress, neural network, risk management

Suggested Citation

Abid, Fathi and Zouari, Anis, Financial Distress Prediction Using Neural Networks (September 1, 2000). Proceedings of the MS’ 2000 International Conference on Modeling and Simulation, Spain, pp. 399-406, 2000. Available at SSRN: https://ssrn.com/abstract=355980 or http://dx.doi.org/10.2139/ssrn.355980

Fathi Abid (Contact Author)

University of Sfax, Faculty of Economic and Management Sciences, Probability & Statistics Laboratory ( email )

Road of Airport, Km 4
Sfax, sfax 3018
Tunisia
+216 7427 9154 (Phone)

Anis Zouari

University of Sfax - Institute of the High Business Studies of Sfax (IHEC) ( email )

Sfax
Tunisia

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