Predicting Corporate Financial Distress: A Neural Networks Approach
Year: 2008
Type: preprint
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.
Source: SSRN Electronic Journal
Authors: Fathi Abid, Anis Zouari
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2336832622