Bankruptcy trajectory analysis on french companies using self-organizing map

Authors:
Ning Chen;Bernardete Ribeiro;Armando S. Vieira
Affiliations:
GECAD, Instituto Superior de Engenharia do Porto, Portugal;CISUC, Department of Informatics Engineering, University of Coimbra, Portugal;GECAD, Instituto Superior de Engenharia do Porto, Portugal
Venue:
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Year:
2011

Citing 8
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Abstract

As one of the major business problems, corporate bankruptcy has been extensively studied using a large variety of statistical and machine learning approaches. However, the trajectory of bankruptcy behavior is seldom explored in the literature. In this paper, we use self-organizing map neural networks to analyze the changes of financial situation of companies in several consecutive years through a two-step clustering process. Firstly, the bankruptcy risk is characterized by a feature map, and therefore the temporal sequence is converted to the trajectory vector projected on the map. Afterwards, the trajectory map clusters the trajectory vectors to a number of evolution patterns. The approach is applied to a large database of French companies which contains the financial ratios spawning over a period of four years. Typical behaviors such as the deterioration and amelioration associated with the bankruptcy risk, as well as the influence of financial ratios can be revealed by means of visual interpretation.