IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v18y2025i3p109-d1596167.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/18/3/109/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/18/3/109/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Junbai Pan & Kun Lv & Shurong Yu & Dian Fu, 2022. "What Mechanisms Do Financial Marketization and China’s Fiscal Decentralization Have on Regional Energy Intensity? Evidence Based on Spatial Spillover and Panel Threshold Effects Perspectives," IJERPH, MDPI, vol. 19(9), pages 1-27, May.
    2. Marina Azzimonti, 2021. "Partisan Conflict, News, and Investors' Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(5), pages 971-1003, August.
    3. Gala, Vito D. & Pagliardi, Giovanni & Zenios, Stavros A., 2023. "Global political risk and international stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 78-102.
    4. Gavriilidis, Konstantinos & Kallinterakis, Vasileios & Montone, Maurizio, 2024. "Political uncertainty and institutional herding," Journal of Corporate Finance, Elsevier, vol. 88(C).
    5. Hohlwegler, Olivia, 2021. "The Value of CSR in Times of Increased Policy Uncertainty: Evidence from the Brexit Referendum," Junior Management Science (JUMS), Junior Management Science e. V., vol. 6(1), pages 1-24.
    6. Anderson, Warwick & Białkowski, Jędrzej & Wagner, Moritz, 2023. "Midterm elections and stock returns," Finance Research Letters, Elsevier, vol. 55(PA).
    7. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    8. Pástor, Ľuboš & Veronesi, Pietro, 2013. "Political uncertainty and risk premia," Journal of Financial Economics, Elsevier, vol. 110(3), pages 520-545.
    9. Dane M. Christensen & Hengda Jin & Suhas A. Sridharan & Laura A. Wellman, 2022. "Hedging on the Hill: Does Political Hedging Reduce Firm Risk?," Management Science, INFORMS, vol. 68(6), pages 4356-4379, June.
    10. Melissa B. Frye & Duong T. Pham & Rongrong Zhang, 2022. "Board monitoring and advising trade‐offs amidst economic policy uncertainty," The Financial Review, Eastern Finance Association, vol. 57(1), pages 5-26, February.
    11. Kaviani, Mahsa S. & Kryzanowski, Lawrence & Maleki, Hosein & Savor, Pavel, 2020. "Policy uncertainty and corporate credit spreads," Journal of Financial Economics, Elsevier, vol. 138(3), pages 838-865.
    12. Tarek A Hassan & Stephan Hollander & Laurence van Lent & Ahmed Tahoun, 2019. "Firm-Level Political Risk: Measurement and Effects," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(4), pages 2135-2202.
    13. Baek, Seungjun, 2022. "Information acquisition and asset price volatility," Finance Research Letters, Elsevier, vol. 46(PA).
    14. Bree J. Lang & Pratish Patel, 2023. "Funding infrastructure under uncertainty: evidence from tax credit prices," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(3), pages 635-677, June.
    15. Eichler, Stefan & Plaga, Timo, 2020. "The economic record of the government and sovereign bond and stock returns around national elections," Journal of Banking & Finance, Elsevier, vol. 118(C).
    16. Lin, Chih-Yung & Ho, Po-Hsin & Shen, Chung-Hua & Wang, Yu-Chun, 2016. "Political connection, government policy, and investor trading: Evidence from an emerging market," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 153-166.
    17. Adrian, Christofer & Garg, Mukesh & Viet Pham, Anh & Phang, Soon-Yeow & Truong, Cameron, 2022. "Policy and oversight of corporate political activities and the cost of equity capital," Journal of Contemporary Accounting and Economics, Elsevier, vol. 18(2).
    18. Sakariyahu, Rilwan & Lawal, Rodiat & Adigun, Rasheed & Paterson, Audrey & Johan, Sofia, 2024. "One crash, too many: Global uncertainty, sentiment factors and cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 94(C).
    19. Liu, Yang, 2023. "Government debt and risk premia," Journal of Monetary Economics, Elsevier, vol. 136(C), pages 18-34.
    20. Ľuboš Pástor & Pietro Veronesi, 2020. "Political Cycles and Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 128(11), pages 4011-4045.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:18:y:2025:i:3:p:109-:d:1596167. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.