A bankruptcy probability model for assessing credit risk on corporate loans with automated variable selection

Ida Nervik Hjelseth, Arvid Raknerud and Bjørn H. Vatne

No 2022/7, Working Paper from Norges Bank

Abstract: We propose an econometric model for predicting the share of bank debt held by bankrupt firms by combining a novel set of firm-level financial variables and macroeconomic indicators. Our firm-level data include payment remarks in the form of debt collections from private agencies and attachments from private and public agencies and cover all Norwegian limited liability companies for the period 2010–2021. We use logistic Lasso regressions to select bankruptcy predictors from a large set of potential predictors, comparing a highly sparse variable selection criterion (“the one standard error rule”) with the minimum cross validation error (CVE) criterion. Moreover, we examine the implications of using debt shares as weights in the estimation and find that weighting has a large impact on variable selection and predictions and, generally, leads to lower out-of-sample prediction errors than alternative approaches. Debt weighting combined with sparse variable selection gives the best predictions of the risk of bankruptcy in firms holding high shares of the bank debt.

Keywords: Bankruptcy prediction; credit risk; corporate bank debt; Lasso; weighted logistic regression (search for similar items in EconPapers)
JEL-codes: C25 C33 C53 D22 G33 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2022-06-20
New Economics Papers: this item is included in nep-bec, nep-big, nep-cfn, nep-cmp, nep-for and nep-rmg
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https://hdl.handle.net/11250/3011180

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Persistent link: https://EconPapers.repec.org/RePEc:bno:worpap:2022_7

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