Application of logit model and self-organizing maps (SOMs) for the prediction of financial crisis periods in US economy

Purpose – The purpose of this paper is to examine two different approaches in the prediction of the economic recession periods in the US economy. Design/methodology/approach – A logit regression was applied and the prediction performance in two out-of-sample periods, 2007-2009 and 2010 was examined. On the other hand, feed-forwards neural networks with Levenberg-Marquardt error backpropagation algorithm were applied and then neural networks self-organizing map (SOM) on the training outputs was estimated. Findings – The paper presents the cluster results from SOM training in order to find the patterns of economic recessions and expansions. It is concluded that logit model forecasts the current financial crisis period at 75 percent accuracy, but logit model is useful as it provides a warning signal three quarters before the current financial crisis started officially. Also, it is estimated that the financial crisis, even if it reached its peak in 2009, the economic recession will be continued in 2010 too. Furthermore, the patterns generated by SOM neural networks show various possible versions with one common characteristic, that financial crisis is not over in 2009 and the economic recession will be continued in the USA even up to 2011-2012, if government does not apply direct drastic measures. Originality/value – Both logistic regression (logit) and SOMs procedures are useful. The first one is useful to examine the significance and the magnitude of each variable, while the second one is useful for clustering and identifying patterns in economic recessions and expansions.