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Forecasting the insolvency of U.S. banks using Support Vector Machines (SVM) based on Local Learning Feature Selection

Author

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  • Gogas, Periklis

    (Democritus University of Thrace, Department of International Economic Relations and Development)

  • Papadimitriou, Theophilos

    (Democritus University of Thrace, Department of International Economic Relations and Development)

  • Plakandaras, Vasilios

    (Democritus University of Thrace, Department of International Economic Relations and Development)

Abstract

We propose a Support Vector Machine (SVM) based structural model in order to forecast the collapse of banking institutions in the U.S. using publicly disclosed information from their financial statements on a four-year rolling window. In our approach, the optimum input variable set is defined from a large dataset using an iterative relevance-based selection procedure. We train an SVM model to classify banks as solvent and insolvent. The resulting model exhibits significant ability in bank default forecasting.

Suggested Citation

  • Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios, 2013. "Forecasting the insolvency of U.S. banks using Support Vector Machines (SVM) based on Local Learning Feature Selection," DUTH Research Papers in Economics 2-2013, Democritus University of Thrace, Department of Economics.
    • Handle: RePEc:ris:duthrp:2013_002
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    Cited by:

    1. Kolari, James W. & López-Iturriaga, Félix J. & Sanz, Ivan Pastor, 2019. "Predicting European bank stress tests: Survival of the fittest," Global Finance Journal, Elsevier, vol. 39(C), pages 44-57.
    2. Theophilos Papadimitriou & Periklis Gogas & Anna Agrapetidou, 2022. "The resilience of the U.S. banking system," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2819-2835, July.
    3. Santosh Kumar Shrivastav & P. Janaki Ramudu, 2020. "Bankruptcy Prediction and Stress Quantification Using Support Vector Machine: Evidence from Indian Banks," Risks, MDPI, vol. 8(2), pages 1-22, May.
    4. Gogas, Periklis & Papadimitriou, Theophilos & Agrapetidou, Anna, 2018. "Forecasting bank failures and stress testing: A machine learning approach," International Journal of Forecasting, Elsevier, vol. 34(3), pages 440-455.

    More about this item

    Keywords

    Bank insolvency; SVM; local learning; feature selection;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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