Bankruptcy trajectory analysis on french companies using self-organizing map
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Letters: Clustering of the Self-Organizing Time Map
Neurocomputing
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Throughout the 1990s, four global waves of financial turmoil occurred. The beginning of the 21st century has also suffered from several crisis episodes, including the global financial crisis of 2008–2009. However, to date, the forecasting results are still disappointing. This paper examines whether new insights can be gained from the application of the self-organizing map (SOM) – a non-parametric neural-network-based visualization tool. We develop a SOM-based model for prediction of currency crises. We evaluate the predictive power of the model and compare it with that of a classical probit model. The results indicate that the SOM-based model is a feasible tool for predicting currency crises. Moreover, its visual capabilities facilitate the understanding of the factors and conditions that contribute to the emergence of currency crises. Copyright © 2011 John Wiley & Sons, Ltd.