Constructing early warning indicators for banks using machine learning models
Coskun Tarkocin and
Murat Donduran
The North American Journal of Economics and Finance, 2024, vol. 69, issue PB
Abstract:
This research contributes to bank liquidity risk management by employing supervised machine learning models to provide banks with early warnings of liquidity stress using market-based indicators. Identifying increasing levels of stress as early as possible provides management with a crucial window of time in which to assess and develop a potential response. This study uses publicly available data from 2007 to 2021, covering two severe stress periods: the 2007–2008 global financial crisis and the COVID-19 crisis. The current version of the developed model then applies backtesting using the data from the COVID-19 crisis. The findings of this study show that the ensemble model with the RUSBoost algorithm predicts “red” and “amber” days with a success rate 21% greater than the average of other machine learning models; thus, it can greatly contribute to bank risk management.
Keywords: Early warning indicators; Financial stress; Machine learning; Ensemble model; Liquidity risk; Crisis management; COVID-19 crisis (search for similar items in EconPapers)
JEL-codes: C51 C88 G21 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:69:y:2024:i:pb:s1062940823001419
DOI: 10.1016/j.najef.2023.102018
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