Bayesian regularized artificial neural networks for the estimation of the probability of default

Eduard Sariev and Guido Germano

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Artificial neural networks (ANNs) have been extensively used for classification problems in many areas such as gene, text and image recognition. Although ANNs are popular also to estimate the probability of default in credit risk, they have drawbacks; a major one is their tendency to overfit the data. Here we propose an improved Bayesian regularization approach to train ANNs and compare it to the classical regularization that relies on the back-propagation algorithm for training feed-forward networks. We investigate different network architectures and test the classification accuracy on three data sets. Profitability, leverage and liquidity emerge as important financial default driver categories.

Keywords: Artificial neural networks; Bayesian regularization; Credit risk; Probability of default (search for similar items in EconPapers)
JEL-codes: C11 C13 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2020-02-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Published in Quantitative Finance, 1, February, 2020, 20(2), pp. 311-328. ISSN: 1469-7688

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Journal Article: Bayesian regularized artificial neural networks for the estimation of the probability of default (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:101029

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