Can Transition Risk Drive European Stock Market Predictions? An XGBoost Approach

This study investigates the predictive power of transition risk for the European stock market by incorporating a text-based Transition Risk Index into an XGBoost forecasting model for the European Index STOXX600. Utilizing daily data from January 2005 to December 2023, we integrate standard macroeconomic factors—such as exchange rates, gold prices, and interest rates—with an NLP-derived Transition Risk Index that captures shifts in climate policies, technological innovations, and investor sentiment. Through an extensive feature engineering process and rigorous hyperparameter tuning (via cross-validation), we assess the relative contribution of each predictor using both feature importance rankings and SHAP (SHapley Additive exPlanations) analysis. Our findings reveal that, while conventional macro-financial variables remain the dominant drivers of STOXX600 price dynamics, transition risk exerts only a modest influence on short-term market forecasts. This suggests that near-term valuation processes may not yet fully integrate sustainability considerations. However, as climate policies evolve and investor awareness grows, the transition risk’s role may become more pronounced over longer horizons. The results underscore both the potential and challenges in quantifying transition risk, offering a robust perspective on incorporating sustainability-driven metrics into real-time market analytics and predictive models. Furthermore, this research highlights the importance of refining transition risk measurement methodologies, as existing models may not fully capture the speed and complexity of regulatory shifts, technological advancements, and investor sentiment changes.