Structural Dynamics of Economic Factors and Price Discovery: A Quantitative Analysis of the U.S. Stock Market (S&P 500), 2025–2026

Abstract

This article examines the macroeconomic drivers of price discovery in the U.S. S&P 500 from January 2025 through March 2026. Employing Hasbrouck's (1995) Information Share (IS) methodology, extended with the IV/CU-GMM correction established by Fruet Dias, Fernandes, and Scherrer (2026), we decompose the contributions of 18 macroeconomic factors across four Bai-Perron-identified market regimes. The empirical analysis yields five principal findings. First, under normal market conditions, EPS growth dominates price discovery with an Information Share of 0.311. Second, the unprecedented April 2025 tariff shock radically redirected information shares toward the trade balance (reaching an event-day IS of 0.389). Notably, absorbing this shock took over 15 trading days to reach 90% completion, approximately 5 times slower than standard Federal Open Market Committee (FOMC) decisions (3.1 days). Third, factor contributions proved strongly regime-dependent; most strikingly, the Federal Funds Rate correlation experienced a structural swing from r = -0.71 to r = +0.68, alongside a six-fold increase in the AI/Tech Capex information share. Fourth, our IV/GMM correction uncovers substantial OLS biases ranging from -20.2% (overstating the Fed Funds Rate) to +29.3% (understating AI/Tech Capex). Fifth, we establish a novel taxonomy classifying the 18 factors into four behavioral types based on synchronicity and duration. These findings have critical implications for the development of regime-adaptive risk models, central bank communication strategies, and the need for standardized AI capital-expenditure disclosures.