Geopolitics, Geoeconomics and Risk:A Machine Learning Approach
This work addresses the challenge of improving sovereign risk forecasting for financial analysts and policymakers by integrating news-based sentiment data, though it is incremental as it builds on existing methods with new data.
The authors tackled the problem of forecasting sovereign risk using a novel high-frequency dataset of news-based indicators for 42 countries, finding that incorporating these indicators, especially with non-linear machine learning methods like Random Forests, significantly enhances predictive accuracy and reveals amplified effects through non-linear interactions with global financial conditions.
We introduce a novel high-frequency daily panel dataset of both markets and news-based indicators -- including Geopolitical Risk, Economic Policy Uncertainty, Trade Policy Uncertainty, and Political Sentiment -- for 42 countries across both emerging and developed markets. Using this dataset, we study how sentiment dynamics shape sovereign risk, measured by Credit Default Swap (CDS) spreads, and evaluate their forecasting value relative to traditional drivers such as global monetary policy and market volatility. Our horse-race analysis of forecasting models demonstrates that incorporating news-based indicators significantly enhances predictive accuracy and enriches the analysis, with non-linear machine learning methods -- particularly Random Forests -- delivering the largest gains. Our analysis reveals that while global financial variables remain the dominant drivers of sovereign risk, geopolitical risk and economic policy uncertainty also play a meaningful role. Crucially, their effects are amplified through non-linear interactions with global financial conditions. Finally, we document pronounced regional heterogeneity, as certain asset classes and emerging markets exhibit heightened sensitivity to shocks in policy rates, global financial volatility, and geopolitical risk.