Constructing early warning indicators for banks using machine learning models

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.

Automated summary

This research utilizes supervised machine learning models to provide banks with early warnings of liquidity stress using market-based indicators, contributing to bank liquidity risk management. By analyzing publicly available data from 2007 to 2021, including the periods of the global financial crisis and the COVID-19 crisis, and conducting backtesting using COVID-19 crisis data, the study shows that the ensemble model with the RUSBoost algorithm can predict risky days more accurately, thus greatly contributing to bank risk management.