Authors : Suttor Yann; Bogdanovic Gruja; Oji Chimezie Alexander

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/bdutsym

Scribd : https://tinyurl.com/4khpd8v4

DOI : https://doi.org/10.38124/ijisrt/26jul1016

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study investigates the relationship between monthly unemployment rate announcements by the Bureau of Labor Statistics (BLS) and the performance of the S&P 500 during the early 21st century (2000–2020). We employ Ordinary Least Squares (OLS) regression to examine the correlation between the change in the unemployment rate (total, anticipated, and unanticipated components) and both the S&P 500 daily return on the announcement day and the returns between consecutive reports. The primary empirical analysis reveals no statistically significant linear correlation between these variables, which challenges some findings in existing literature that rely on earlier time periods. To validate this noncorrelation result, we use a Monte Carlo simulation framework as an integrated robustness test. The simulation confirms that the OLS estimators for our primary regression model are unbiased, thereby validating the statistical reliability of our finding. Our combined results suggest that, in the contemporary market, the immediate impact of unemployment announcements on S&P 500 returns is negligible, possibly due to increased market efficiency.

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