Predicting bank loan recovery rates with neural networks

João Bastos

No 1003, CEMAPRE Working Papers from Centre for Applied Mathematics and Economics (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.

Keywords: Loss given default; Recovery rate; Forecasting; Bank loan; Fractional regression; Neural network (search for similar items in EconPapers)
JEL-codes: C45 G17 G21 G33 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2010-07
New Economics Papers: this item is included in nep-ban, nep-cmp, nep-for and nep-rmg
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://cemapre.iseg.utl.pt/archive/preprints/419.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 500 Can't connect to cemapre.iseg.utl.pt:80 (No such host is known. )

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cma:wpaper:1003

Access Statistics for this paper

More papers in CEMAPRE Working Papers from Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon Contact information at EDIRC.
Bibliographic data for series maintained by Helena Lima ( this e-mail address is bad, please contact ).