Mercredi | 2014-12-03
salle B103, 12h-14h

Nada MSELMI

Abstract Financial distress prediction is a central issue in empirical finance that has drawn a lot of research interests in the literature. This paper aims to predict the financial distress of French small and medium firms using Logit model, Artificial Neural Networks and Support Vector Machine techniques. Empirical results indicate that one year before financial distress, Support Vector Machine is the best classifier with an overall accuracy of 88.57%. Two years before financial distress, Support Vector Machine and Logit model outperform Artificial Neural Networks with an overall accuracy of 92.86%. Distressed firms are found to be smaller, more leveraged and with lower repayment capacity. Moreover, they have lower liquidity, profitability, and solvency ratios. Besides the academic research contribution, our findings can be useful for managers, investors, and creditors. Keywords: Financial distress prediction, financial ratios, Logit model, Artificial Neural Networks, Support Vector Machine