Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time

This study attempts to show how a Kohonen map can be used to improve the temporal stability of the accuracy of a financial failure model. Most models lose a significant part of their ability to generalize when data used for estimation and prediction purposes are collected over different time periods. As their lifespan is fairly short, it becomes a real problem if a model is still in use when re-estimation appears to be necessary. To overcome this drawback, we introduce a new way of using a Kohonen map as a prediction model. The results of our experiments show that the generalization error achieved with a map remains more stable over time than that achieved with conventional methods used to design failure models (discriminant analysis, logistic regression, Cox’s method, and neural networks). They also show that type-I error, the economically costliest error, is the greatest beneficiary of this gain in stability.