A machine learning approach to rank the determinants of banking crises over time and across countries

Elizabeth Jane Casabianca, Michele Catalano, Lorenzo Forni, Elena Giarda () and Simone Passeri

Journal of International Money and Finance, 2022, vol. 129, issue C

Abstract: We use a machine learning approach, namely AdaBoost, to rank the determinants of banking crises over time and across countries. We cover a total of 100 countries, advanced and emerging, over the years from 1970 to 2017. The paper first shows that AdaBoost has a better predictive performance than the logit model, both in-sample and out-of-sample; then, it employs AdaBoost to classify the major macroeconomic factors leading to banking crises. The baseline analysis reveals that the US 10 yr Treasury interest rate and world growth play a key role in anticipating a crisis, and that these two variables explain a growing share of the results over time, for both country groups. Other variables, which have been highlighted as important in the literature on crises - such as inflation, current account, public and external debt and credit - are relevant in the lead up to banking crises, but their role has been decreasing over time compared to the aforementioned variables. We also present extensions of the model, which confirm and add to the main results of the baseline model.

Keywords: Banking crises; Predictive models; Machine learning (search for similar items in EconPapers)
JEL-codes: C25 C53 E44 G01 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: An Early Warning System for banking crises: From regression-based analysis to machine learning techniques (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jimfin:v:129:y:2022:i:c:s0261560622001425

DOI: 10.1016/j.jimonfin.2022.102739

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