Open AccessBank Failure: A New Approach to Prediction and Supervision
Author(s) -
Calvin W. H. Cheong,
Sockalingam R. Ramasamy
Publication year - 2019
Publication title -
asian journal of finance and accounting
Language(s) - English
Resource type - Journals
ISSN - 1946-052X
DOI - 10.5296/ajfa.v11i1.14455
Subject(s) - bank failure , logistic regression , construct (python library) , sample (material) , index (typography) , actuarial science , business , economics , computer science , econometrics , finance , machine learning , chemistry , chromatography , world wide web , programming language
Bank failures are costly to customers and the wider market. Prevention is always better than cure but in light of recent economic downturns, it has become increasingly difficult for regulators to allocate more resources towards in-depth monitoring of banking practices. In this paper, we construct a tool that is able to predict bank failures ahead of time with reasonable accuracy. Through a logistic regression on a matched sample of 536 failed and non-failed US banks, we determine the financial indicators that most accurately predicts bank failure. From the regression, we construct a Bank Health Index that assesses a bank’s propensity to failure. In-sample and out-of-sample tests show that our model is about 90% accurate two years prior to failure, and 95% accurate the year before failure. The accuracy and efficiency of the model and index provides a more efficient and effective tool for assessing a bank’s propensity to failure besides requiring far less resources. With these methods, regulators will be able to take preventive measures at least one year before failure, saving the economy millions if not billions in the process.