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

Avi Thaker, Daniel Sonner and Leo H. Chan ()
Additional contact information
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

JRFM, 2025, vol. 18, issue 3, 1-16

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.

Keywords: machine learning; explainable AI; US presidential election; stock market; SHAP feature importance (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/18/3/109/pdf (application/pdf)
https://www.mdpi.com/1911-8074/18/3/109/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:18:y:2025:i:3:p:109-:d:1596167

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().