What do bankrupcty prediction models tell us about banking regulation? Evidence from statistical and learning approaches

Pierre Durand and Gaëtan Le Quang

No 2021-2, EconomiX Working Papers from University of Paris Nanterre, EconomiX

Abstract: Prudential regulation is supposed to strengthen financial stability and banks' resilience to new economic shocks. We tackle this issue by evaluating the impact of leverage, capital, and liquidity ratios on banks default probability. To this aim, we use logistic regression, random forest classification, and artificial neural networks applied on the United-States and European samples over the 2000-2018 period. Our results are based on 4707 banks in the US and 3529 banks in Europe, among which 454 and 205 defaults respectively. We show that, in the US sample, capital and equity ratios have strong negative impact on default probability. Liquidity ratio has a positive effect which can be justified by the low returns associated with liquid assets. Overall, our investigation suggests that fewer prudential rules and higher leverage ratio should reinforce the banking system's resilience. Because of the lack of official failed banks list in Europe, our findings on this sample are more delicate to interpret.

Keywords: Banking regulation; Capital requirements; Basel III; Logistic; Statistical learning classification; Bankruptcy prediction models. (search for similar items in EconPapers)
JEL-codes: C44 G21 G28 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2021
New Economics Papers: this item is included in nep-acc, nep-ban, nep-cba, nep-cfn, nep-cmp and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:drm:wpaper:2021-2

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