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Hybridizing logistic regression with product unit and RBF networks for accurate detection and prediction of banking crises

By P.A. Gutiérrez, M.J. Segovia-Vargas, S. Salcedo-Sanz, C. Hervás-Martínez, A. Sanchis, J.A. Portilla-Figueras and F. Fernández-Navarro


As the current crisis has painfully proved, the financial system plays a crucial role in economic development. Although the current crisis is being of an exceptional magnitude, financial crises are recurrent phenomena in modern financial systems. The literature offers several definitions of financial instability, but for our purposes we identity financial crisis with banking crisis as the most common example of financial instability. In this paper we introduce a novel model for detection and prediction of crises, based on the hybridization of a standard logistic regression with product unit (PU) neural networks and radial basis function (RBF) networks. These hybrid approaches are fully described in the paper, and applied to the detection and prediction of banking crises by using a large database of countries in the period 1981-1999. The proposed techniques are shown to perform better than other existing statistical and artificial intelligence methods in this problem.Banking crises prediction Product unit neural networks Radial basis function neural networks Logistic regression Hybrid methods

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