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Predicting U.S. Bank Failures with MIDAS Logit Models

Published online by Cambridge University Press:  08 October 2018

Francesco AudrinoOpen the ORCID record for Francesco Audrino[Opens in a new window],
Alexander KostrovOpen the ORCID record for Alexander Kostrov[Opens in a new window] and
Juan-Pablo OrtegaOpen the ORCID record for Juan-Pablo Ortega[Opens in a new window]
Rights & Permissions [Opens in a new window]

Abstract

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We propose a new approach based on a generalization of the logit model to improve prediction accuracy in U.S. bank failures. Mixed-data sampling (MIDAS) is introduced in the context of a logistic regression. We also mitigate the class-imbalance problem in data and adjust the classification accuracy evaluation. In applying the suggested model to the period from 2004 to 2016, we show that it correctly classifies significantly more bank failure cases than the classic logit model, in particular for long-term forecasting horizons. Some of the largest recent bank failures in the United States that had been previously misclassified are now correctly predicted.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 54 , Issue 6 , December 2019, pp. 2575 - 2603
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2018 

Footnotes

1

We are deeply grateful to Eric Ghysels (the referee), Lyudmila Grigoryeva, Jarrad Harford (the editor), and to the participants at the 2017 CFE-CMStatistics Conference in London, the Econometrics Colloquium (University of Konstanz), and the Economic Risk Seminar (Humboldt University of Berlin) for their helpful comments and suggestions.

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