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Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach

Author

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  • Zeng, Qing
  • Lu, Xinjie
  • Xu, Jin
  • Lin, Yu

Abstract

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.

Suggested Citation

  • Zeng, Qing & Lu, Xinjie & Xu, Jin & Lin, Yu, 2024. "Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    • Handle: RePEc:eee:finana:v:96:y:2024:i:pb:s1057521924006434
    DOI: 10.1016/j.irfa.2024.103711
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