Unraveling Financial Fragility of Global Markets Using Machine Learning

Vasilios Plakandaras (), Rangan Gupta () and Qiang Ji ()
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Vasilios Plakandaras: Department of Economics, Democritus University of Thrace, Komotini, Greece
Rangan Gupta: Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
Qiang Ji: Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, 100049, China

No 202511, Working Papers from University of Pretoria, Department of Economics

Abstract: The study investigates systemic financial risk in global markets, attributing it to geopolitical instability, climate risks, and economic uncertainties. Utilizing a state-of-the-art machine learning heterogeneous panel regression framework capable of capturing cross-sectional dependencies and nonlinear patterns, we examine financial stress across multiple economies, including China, the U.S., the U.K., and ten EU nations. Through extensive out-of-sample rolling window analysis, we show that while geopolitical uncertainty enhances short-term predictions, long-term risk forecasting is better achieved using financial and economic data. The study underscores the limitations of conventional regression models in capturing financial risk dynamics and suggests that machine learning-based panel regressions provide a more nuanced and accurate forecasting tool. The findings bear significant policy implications, highlighting the necessity for regulatory bodies to reassess risk frameworks and the role of climate-related disclosures in financial markets.

Keywords: Systemic financial risk; machine learning; forecasting; climate risk; geopolitical risk (search for similar items in EconPapers)
JEL-codes: C45 C58 G17 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2025-03
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