BoE Paper on Predicting Bank Distress in UK Using Machine Learning
BoE published a working paper on predicting bank distress in the UK using machine learning techniques. In the analysis, the main input variables come from confidential regulatory returns while the measure of distress is derived from supervisory assessments of bank riskiness from 2006 through to 2012. Overall, the paper demonstrates practical benefits of machine learning and ensembling methods for providing regulators with advance warning of firm distress. Supervisors can apply these findings to aid in anticipating problems before they occur, thus helping them in their mission to keep financial institutions safe and sound.
The authors compare a number of machine learning and classical statistical techniques, implementing a rigorous, double-block randomized cross-validation procedure to evaluate out-of-sample performance. The random forest algorithm was found to be superior in terms of ranking test observations, while also having relatively better calibrated probabilities than the other techniques. The performance results indicate that the random forest should be used to build an early warning system. To improve the transparency of the algorithm, the study examined the drivers of the predicted probabilities of the model, utilizing an aggregation of Shapley values per test set observation and Shapley regression framework. The Shapley regression reveals the importance of macroeconomic variables and a firm’s sensitivity to market risk, capital buffer, and net interest margin. Finally, the authors also performed simple ensembling techniques to combine all the model outputs, demonstrating substantive and statistically significant improvements relative to the random forest on its own.
Future research might extend this analysis in a number of ways. First, scholars might seek to incorporate additional data beyond financial ratios and macroeconomic variables. Second, future work might delve into more complex configurations of diverse underlying models to reap substantive improvements. Third, the analysis relies on data from a highly unusual period in economic history. Future research might seek to establish whether the documented relationship between input variables and measures of distress persist in relatively benign economic environments. It is likely that in such periods macroeconomic variables are less important in predicting firm distress and, therefore, an early warning system might be better if it were based on data that encompasses more or all of an economic cycle.
Related Links
Keywords: Europe, UK, Banking, Machine Learning, Statistical Techniques, Ensembling Techniques, Research, Technology, Bank Distress, BoE
Previous Article
BDE Updates Technical Instructions for Reporting by BanksNext Article
SRB Publishes Annual Report for 2018Related Articles
EBA Examines Supervisory Practices, Issues Deposits Reporting Template
The European Banking Authority (EBA) published its annual report on convergence of supervisory practices for 2021. Additionally, following a request from the European Commission (EC),
EC Mandates ESAs to Propose Amendments to SFDR Technical Standards
The European Commission (EC) has issued two letters mandating the European Supervisory Authorities (ESAs) to jointly propose amendments to the regulatory technical standards under Sustainable Finance Disclosure Regulation or SFDR.
EC Consults on PSD2 and Open Finance; EU Reaches Agreement on DORA
The European Commission (EC) published a public consultation on the review of revised payment services directive (PSD2) and open finance.
US Agency Publications Address Basel, Reporting, and CECL Developments
The Farm Credit Administration published, in the Federal Register, the final rule on implementation of the Current Expected Credit Losses (CECL) methodology for allowances
SEC Extends Comment Period on Climate Risk Disclosures
The U.S. Securities and Exchange Commission (SEC) looks set to intensify focus on crypto-assets and cyber risk and extended the comment period on the proposed rules to enhance and standardize climate-related disclosures for investors.
APRA Reduces Committed Liquidity Facility, Issues Other Updates
The Australian Prudential Regulation Authority (APRA) announced reduction in the aggregate Committed Liquidity Facility and issued an update on the operational preparedness for zero and negative market interest rates.
EIOPA Responds to Stakeholder Views on Blockchain in Insurance
The European Insurance and Occupational Pensions Authority (EIOPA) published a feedback statement on the responses received to the consultation on blockchain and smart contracts in insurance.
HKMA Announces Decisions on CCyB and Loan Guarantee Scheme
The Hong Kong Monetary Authority (HKMA) announced that the applicable jurisdictional countercyclical capital buffer (CCyB) ratio for Hong Kong remains unchanged at 1.0%
CMF Consults on Basel Rules, Presents Roadmap to Address Climate Risks
The Commission for the Financial Market (CMF) in Chile published capital adequacy ratios (as of February 2022, January 2022, and December 2021) for 17 banks and for the banking system.
PRA Issues Statement on NPEs and Policy on Trading Activity Wind-Down
The Prudential Regulation Authority (PRA) issued a statement on the European Banking Authority (EBA) guidelines on management of non-performing exposures (NPEs) and forborne exposures.