Predicting bank loan recovery rates with neural networks
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.
|Date of creation:||Jul 2010|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://cemapre.iseg.utl.pt/Email:
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:cma:wpaper:1003. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Helena Lima)
If references are entirely missing, you can add them using this form.