TY - JOUR
T1 - Predicting Bank Failures
T2 - A Synthesis of Literature and Directions for Future Research
AU - Liu, Li Xian
AU - Liu, Shuangzhe
AU - Sathye, Milind
N1 - Publisher Copyright:
© 2021 by the authors.
PY - 2021/10
Y1 - 2021/10
N2 - Risk management has been a topic of great interest to Michael McAleer. Even as recent as 2020, his paper on risk management for COVID-19 was published. In his memory, this article is focused on bankruptcy risk in financial firms. For financial institutions in particular, banks are considered special, given that they perform risk management functions that are unique. Risks in banking arise from both internal and external factors. The GFC underlined the need for comprehensive risk management, and researchers since then have been working towards fulfilling that need. Similarly, the central banks across the world have begun periodic stress-testing of banks’ ability to withstand shocks. This paper investigates the machine-learning and statistical techniques used in the literature on bank failure prediction. The study finds that though considerable progress has been made using advanced statistical and computational techniques, given the complex nature of banking risk, the ability of statistical techniques to predict bank failures is limited. Machine-learning-based models are increasingly becoming popular due to their significant predictive ability. The paper also suggests the directions for future research
AB - Risk management has been a topic of great interest to Michael McAleer. Even as recent as 2020, his paper on risk management for COVID-19 was published. In his memory, this article is focused on bankruptcy risk in financial firms. For financial institutions in particular, banks are considered special, given that they perform risk management functions that are unique. Risks in banking arise from both internal and external factors. The GFC underlined the need for comprehensive risk management, and researchers since then have been working towards fulfilling that need. Similarly, the central banks across the world have begun periodic stress-testing of banks’ ability to withstand shocks. This paper investigates the machine-learning and statistical techniques used in the literature on bank failure prediction. The study finds that though considerable progress has been made using advanced statistical and computational techniques, given the complex nature of banking risk, the ability of statistical techniques to predict bank failures is limited. Machine-learning-based models are increasingly becoming popular due to their significant predictive ability. The paper also suggests the directions for future research
KW - risk management
KW - bank failure prediction
KW - machine learning
KW - statistical methods
UR - http://www.scopus.com/inward/record.url?scp=85161835201&partnerID=8YFLogxK
U2 - 10.3390/jrfm14100474
DO - 10.3390/jrfm14100474
M3 - Article
SN - 1911-8066
VL - 14
SP - 1
EP - 24
JO - Journal of Risk and Financial Management
JF - Journal of Risk and Financial Management
IS - 10
M1 - 474
ER -