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Predicting bank loan recovery rates with neural networks

Author

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  • Joao A. Bastos

    (CEMAPRE, School of Economics and Management (ISEG), Technical University of Lisbon)

Abstract

This study evaluates the performance of feed-forward neural networks to model and forecast recovery rates of defaulted bank loans. In order to guarantee that the predictions are mapped into the unit interval, the neural networks are implemented with a logistic activation function in the output neuron. The statistical relevance of explanatory variables is assessed using the bootstrap technique. The results indicate that the variables which the neural network models use to derive their output coincide to a great extent with those that are significant in parametric regression models. Out-of-sample estimates of prediction errors suggest that neural networks may have better predictive ability than parametric regression models, provided the number of observations is sufficiently large.

Suggested Citation

  • Joao A. Bastos, 2010. "Predicting bank loan recovery rates with neural networks," CEMAPRE Working Papers 1003, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
  • Handle: RePEc:cma:wpaper:1003
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    File URL: http://cemapre.iseg.utl.pt/archive/preprints/419.pdf
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    More about this item

    Keywords

    Loss given default; Recovery rate; Forecasting; Bank loan; Fractional regression; Neural network;

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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