Predicting bank loan recovery rates with neural networks
AbstractThis 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.
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Bibliographic InfoPaper provided by Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon in its series CEMAPRE Working Papers with number 1003.
Length: 13 pages
Date of creation: Jul 2010
Date of revision:
Loss given default; Recovery rate; Forecasting; Bank loan; Fractional regression; Neural network;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-07-31 (All new papers)
- NEP-BAN-2010-07-31 (Banking)
- NEP-CMP-2010-07-31 (Computational Economics)
- NEP-FOR-2010-07-31 (Forecasting)
- NEP-RMG-2010-07-31 (Risk Management)
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