Title Generalized Logistic Regression Models Using Neural Network Basis Functions Applied to the Detection of Banking Crises
Authors Gutierrez, P. A. , Salcedo-Sanz, S. , Segovia-Vargas, M. J. , Sanchis, A. , Portilla-Figueras, J. A. , FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Hervas-Martinez, C.
External publication Si
Means Lect. Notes Comput. Sci.
Scope Proceedings Paper
Nature Científica
JCR Quartile 4
SJR Quartile 2
SJR Impact 0.32200
Publication date 01/01/2010
ISI 000281604700001
Abstract The financial system plays a crucial role in economic development. Financial crises are recurrent phenomena in modern financial systems. The literature offers several definitions of financial instability, but it is well known that a financial crisis with a banking crisis is 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 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 to 1999. The proposed techniques are shown to perform better than other existing statistical and artificial intelligence methods for this problem.
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