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

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

Abstract

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.

Suggested Citation

  • Gutiérrez, P.A. & Segovia-Vargas, M.J. & Salcedo-Sanz, S. & Hervás-Martínez, C. & Sanchis, A. & Portilla-Figueras, J.A. & Fernández-Navarro, F., 2010. "Hybridizing logistic regression with product unit and RBF networks for accurate detection and prediction of banking crises," Omega, Elsevier, vol. 38(5), pages 333-344, October.
    • Handle: RePEc:eee:jomega:v:38:y:2010:i:5:p:333-344
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    References listed on IDEAS

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    1. Domac, Ilker & Martinez Peria, Maria Soledad, 2003. "Banking crises and exchange rate regimes: is there a link?," Journal of International Economics, Elsevier, vol. 61(1), pages 41-72, October.
    2. Eichengreen, Barry & Arteta, Carlos, 2000. "Banking Crises in Emerging Markets: Presumptions and Evidence," Center for International and Development Economics Research, Working Paper Series qt3pk9t1h2, Center for International and Development Economics Research, Institute for Business and Economic Research, UC Berkeley.
    3. Fiordelisi, Franco & Molyneux, Phil, 2010. "Total factor productivity and shareholder returns in banking," Omega, Elsevier, vol. 38(5), pages 241-253, October.
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    Cited by:

    1. You Zhu & Chi Xie & Bo Sun & Gang-Jin Wang & Xin-Guo Yan, 2016. "Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models," Sustainability, MDPI, vol. 8(5), pages 1-17, May.
    2. Bangzhu Zhu & Shunxin Ye & Ping Wang & Julien Chevallier & Yi‐Ming Wei, 2022. "Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 100-117, January.
    3. D. Fernández-Arias & M. López-Martín & T. Montero-Romero & F. Martínez-Estudillo & F. Fernández-Navarro, 2018. "Financial Soundness Prediction Using a Multi-classification Model: Evidence from Current Financial Crisis in OECD Banks," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 275-297, June.
    4. Weiwei Liu & Zhile Yang & Kexin Bi, 2017. "Forecasting the Acquisition of University Spin-Outs: An RBF Neural Network Approach," Complexity, Hindawi, vol. 2017, pages 1-8, October.
    5. Ioannidis, Christos & Pasiouras, Fotios & Zopounidis, Constantin, 2010. "Assessing bank soundness with classification techniques," Omega, Elsevier, vol. 38(5), pages 345-357, October.

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