BoE Paper on Predicting Bank Distress in UK Using Machine Learning

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.

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Keywords: Europe, UK, Banking, Machine Learning, Statistical Techniques, Ensembling Techniques, Research, Technology, Bank Distress, BoE