Does machine learning help us predict banking crises?

Johannes Beutel, Sophia List and Gregor von Schweinitz

Journal of Financial Stability, 2019, vol. 45, issue C

Abstract: This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.

Keywords: Early warning system; Logit; Machine learning; Systemic banking crises (search for similar items in EconPapers)
JEL-codes: C35 C53 G01 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (44)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1572308918305801
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finsta:v:45:y:2019:i:c:s1572308918305801

DOI: 10.1016/j.jfs.2019.100693

Access Statistics for this article

Journal of Financial Stability is currently edited by I. Hasan, W. C. Hunter and G. G. Kaufman

More articles in Journal of Financial Stability from Elsevier
Bibliographic data for series maintained by Catherine Liu ().