Unravelling Financial Fragility of Global Markets Using Machine Learning

ABSTRACT

The study investigates systemic financial risk in global markets, attributing it to geopolitical instability, climate risks, and economic uncertainties. Utilising 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 10 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.