Visual Predictions of Currency Crises Using Self-Organizing Maps

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 severe sub prime crisis. 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 in various parts of the world.