Sariev, E;
Germano, G;
(2019)
Bayesian regularized artificial neural networks for the estimation of the probability of default.
Quantitative Finance
10.1080/14697688.2019.1633014.
Preview |
Text
26_11_2019_Bayesian r.pdf - Published Version Download (2MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Bayesian regularized artificial neural networks for the estimation of the probability of default |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/14697688.2019.1633014 |
| Publisher version: | https://doi.org/10.1080/14697688.2019.1633014 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Social Sciences, Science & Technology, Physical Sciences, Business, Finance, Economics, Mathematics, Interdisciplinary Applications, Social Sciences, Mathematical Methods, Business & Economics, Mathematics, Mathematical Methods In Social Sciences, Artificial neural networks, Bayesian regularization, Credit risk, Probability of default, RISK, SELECTION |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10086623 |
Archive Staff Only
 |
View Item |
