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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/15156

Title: Variable reduction, sample selection bias and bank retail credit scoring
Authors: Marshall, Andrew
Tang, Leilei
Milne, Alistair
Keywords: Bootstrap variable selection
Credit scoring
Loan performance forecasting
Sample selection bias
Issue Date: 2010
Publisher: © Elsevier B.V.
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.
Abstract: This 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.
Description: 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.2009.12.003
Version: Submitted for publication
DOI: 10.1016/j.jempfin.2009.12.003
URI: https://dspace.lboro.ac.uk/2134/15156
Publisher Link: http://dx.doi.org/10.1016/j.jempfin.2009.12.003
ISSN: 0927-5398
Appears in Collections:Published Articles (Business)

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