Can Geopolitical Risk Improve the Forecasting of Thai Stock-Market Returns? Evidence from Econometrics and Machine-Learning Models

Geopolitical uncertainty may affect financial markets, but its incremental value for forecasting emerging-market stock returns remains unclear. Using monthly data from January 1990 to July 2026, this study compares ARIMA-GARCH and ARIMAX-GARCH benchmarks with Random Forest, XGBoost, LightGBM, and a zero-return benchmark across 1-, 3-, 6-, and 12-month horizons. Forecasts are generated with a target- and predictor-leakage-safe expanding-window design: training targets never exceed the forecast origin, and no realized future predictor values are used. Econometric forecasts are conditional on fixed ARIMA and ARIMAX orders selected during full-sample diagnostics. Among the estimated models, XGBoost achieves the lowest RMSE and MAE at three months, Random Forest has the lowest RMSE at one and six months, and ARIMA-GARCH performs best at twelve months. Nevertheless, the zero-return benchmark records the lowest RMSE at every horizon, while Diebold–Mariano tests generally do not reject equal predictive accuracy, and the Model Confidence Set retains multiple competitive models. Rolling SHAP analysis ranks geopolitical risk first among 16 predictors in the three-month XGBoost model, accounting for 14.38% of aggregate mean absolute attribution. Thus, geopolitical risk provides model-specific short-horizon conditioning information, but its standalone accuracy gain is modest, statistically insignificant, and absent at twelve months.