Variable reduction Manuscript_October06.pdf (220 kB)
Variable reduction, sample selection bias and bank retail credit scoring
journal contribution
posted on 2014-07-09, 10:24 authored by Andrew Marshall, Leilei Tang, Alistair MilneAlistair MilneThis paper investigates the effect of including the customer loan approval process to the estimation of loan performance and explores the influence of sample selection bias in predicting the probability of default. The bootstrap variable reduction technique is applied to reduce the variable dimension for a large data-set drawn from a major UK retail bank. The results show a statistically significant correlation between the loan approval and performance processes. We further demonstrate an economically significant improvement in forecasting performance when taking into account sample selection bias. We conclude that financial institutions can obtain benefits by correcting for sample selection bias in their credit scoring models.
History
School
- Business and Economics
Department
- Business
Published in
JOURNAL OF EMPIRICAL FINANCEVolume
17Issue
3Pages
501 - 512 (12)Citation
MARSHALL, A., TANG, L. and MILNE, A., 2010. Variable reduction, sample selection bias and bank retail credit scoring. Journal of Empirical Finance, 17 (3), pp. 501 - 512.Publisher
© Elsevier B.V.Version
- SMUR (Submitted Manuscript Under Review)
Publication date
2010Notes
This article was submitted for publication in the Journal of Empirical Finance [© Elsevier B.V.] and the definitive version is available at: http://dx.doi.org/10.1016/j.jempfin.2009.12.003ISSN
0927-5398Publisher version
Language
- en