Measuring financial soundness around the world: A machine learning approach

Alessandro Bitetto, Paola Cerchiello and Charilaos Mertzanis

International Review of Financial Analysis, 2023, vol. 85, issue C

Abstract: We use a fully data-driven approach and information provided by the IMF’s financial soundness indicators to measure the condition of a country’s financial system around the world. Given the nature of the measurement problem, we apply different versions of principal component analysis (PCA) to deal with the presence of strong cross-sectional and time dependence in the data due to unobserved common factors. Using this comprehensive sample and various statistical methods, we produce an alternative data-driven measure of financial soundness that provides policy makers and financial institutions with a monitoring and policy tool that is easy to implement and update. We validate our index by using alternative macroeconomic factors, confirming its predictive power. Our index captures important aspects of financial intermediation around the world.

Keywords: Financial soundness; Data-driven; Cross-country; Policy framework; Principal Component Analysis; Random Forest (search for similar items in EconPapers)
JEL-codes: E02 E32 E42 E61 F02 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:85:y:2023:i:c:s105752192200401x

DOI: 10.1016/j.irfa.2022.102451

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