| By: |
Ida Nervik Hjelseth;
Arvid Raknerud;
Bjørn H. Vatne |
| 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 |
| JEL: |
C25 C33 C53 G33 D22 |
| Date: |
2022–06–20 |
| URL: |
http://d.repec.org/n?u=RePEc:bno:worpap:2022_7&r=for |