Constructing early warning indicators for banks using machine learning models

Tarkocin C., DONDURAN M.

North American Journal of Economics and Finance, cilt.69, 2024 (SSCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 69
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.najef.2023.102018
  • Dergi Adı: North American Journal of Economics and Finance
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, ABI/INFORM, Business Source Elite, Business Source Premier, EconLit
  • Anahtar Kelimeler: COVID-19 crisis, Crisis management, Early warning indicators, Ensemble model, Financial stress, Liquidity risk, Machine learning
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

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.