Predicting Corporate Financial Distress: A Neural Networks Approach

Finance India, Vol. 16, No. 2, pp. 601-612, June 2002

Posted: 13 Nov 2008 Last revised: 1 Jan 2014

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: November 12, 2008

Abstract

This paper examines and models the financial distress prediction using neural network approach. Nine different neural network models, considering various predicting time horizons and information structures, are considered. in order to test models' predictive capability we used a set of 15 financial ratios. Based on financial statements (balance-sheets, result accounts and cash flow statements) for 87 Tunisian firms from 1993 to 1996, results prove that more the predictability horizon is short and the input information structure recent, more and better is the predictive capability of the neural model. Short debt, capital structure and sales growth and liability ratios contribute meaningfully in discriminating and predicting the firm financial distress. the best model is based on the information structure giving the best predictive capability.

Keywords: financial distress, neural networks, financial ratios

Suggested Citation

Abid, Fathi and Zouari, Anis, Predicting Corporate Financial Distress: A Neural Networks Approach (November 12, 2008). Finance India, Vol. 16, No. 2, pp. 601-612, June 2002. Available at SSRN: https://ssrn.com/abstract=1300290

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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