This study comprehensively investigates stock market volatility based on over one hundred monthly macroeconomic variables, applying machine learning models. Methodological contribution integrating the random forest (RF) with the least absolute shrinkage and selection operator methods (LASSO). Importantly, the RF-LASSO model can robustly achieve the best forecasting performance under different circumstances. In addition, we focus on model explanation from different perspectives based on permutation importance and shapley additive explanation (SHAP) methods. This study illuminates novel insights into the realm of stock market volatility, harnessing the transformative potential of machine learning methodologies.

中文翻译：

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宏观驱动的股市波动预测：来自新型混合机器学习方法的见解


本研究应用机器学习模型，根据 100 多个月度宏观经济变量全面调查股市波动性。将随机森林 （RF） 与最小绝对收缩和选择运算符方法 （LASSO） 相结合的方法论贡献。重要的是，RF-LASSO 模型可以在不同情况下稳健地实现最佳预测性能。此外，我们关注基于排列重要性和 shapley 加法解释 （SHAP） 方法的不同角度的模型解释。这项研究利用机器学习方法的变革潜力，为股市波动领域提供了新的见解。