Financial distress prediction: The case of French small and medium-sized firms

Nada Mselmi, Amine Lahiani and Taher Hamza

International Review of Financial Analysis, 2017, vol. 50, issue C, 67-80

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, Support Vector Machine techniques, Partial Least Squares, and a hybrid model integrating Support Vector Machine with Partial Least Squares. Empirical results indicate that for one year prior to financial distress, Support Vector Machine is the best classifier with an overall accuracy of 88.57%. Meanwhile, in the case of two years prior to financial distress, the hybrid model outperforms Support Vector Machine, Logit model, Partial Least Squares, andArtificial Neural Networks with an overall accuracy of 94.28%. 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. With respect to managers, our findings provide them with early warnings signals of performance deterioration in order to take corrective actions and reduce the financial distress risk. For investors, understanding the main factors leading to financial distress allows them to avoid investing in risky firms. Creditors should correctly evaluate the firm financial situation and be vigilant to signs of impending financial distress to avoid capital loss and costs related to counterpart risk.

Keywords: Financial distress prediction; Logit model; Artificial neural networks; Support vector machine; Partial least squares; Hybrid model (search for similar items in EconPapers)
JEL-codes: C45 G32 G33 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1057521917300236
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:50:y:2017:i:c:p:67-80

DOI: 10.1016/j.irfa.2017.02.004

Access Statistics for this article

International Review of Financial Analysis is currently edited by B.M. Lucey

More articles in International Review of Financial Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().