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Using Machine Learning to Understand the Dynamics Between the Stock Market and US Presidential Election Outcomes

Author

Listed:
  • Avi Thaker

    (Tauroi Technologies, Pacifica, CA 94044, USA)

  • Daniel Sonner

    (Tauroi Technologies, Pacifica, CA 94044, USA)

  • Leo H. Chan

    (Woodbury School of Business, Utah Valley University, Orem, UT 84058, USA)

Abstract

In this paper, we applied an explainable AI model (SHAP feature importance measures) to study the dynamic relationship between stock market returns and the US presidential election outcomes. More specifically, we wanted to study how the market would react the day after the election. AI models have been criticized as black-box models and lack the clarity needed for decision-making by different stakeholders. The explainable AI model we utilized in this model provides more clarity for the outcomes of the model. Using features commonly used by previous studies related to this topic, we find that the previous market direction leading up to the election and the incumbency information combined with the political affiliation are larger drivers for a 1-day post-election market return than sentiment and which party wins the election.

Suggested Citation

  • Avi Thaker & Daniel Sonner & Leo H. Chan, 2025. "Using Machine Learning to Understand the Dynamics Between the Stock Market and US Presidential Election Outcomes," JRFM, MDPI, vol. 18(3), pages 1-16, February.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:3:p:109-:d:1596167
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    References listed on IDEAS

    as
    1. Belo, Frederico & Gala, Vito D. & Li, Jun, 2013. "Government spending, political cycles, and the cross section of stock returns," Journal of Financial Economics, Elsevier, vol. 107(2), pages 305-324.
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