Causal Relationship Between AI R&D Investment and Stock Market Performance Using VAR and Granger Causality Models - Abdel Badeeh M. Salem; Musbah J. Aqel (Journal of Digital Market and Digital Currency, 2026)
This study investigates the causal relationship between Artificial Intelligence (AI) R&D investment and stock market performance using a time-series econometric framework. Drawing on data from AI-driven firms between 2015 and 2024, the research applies Vector Autoregression (VAR) and Granger Causality models to explore whether innovation spending influences short-term financial outcomes. The analysis employs monthly aggregated data on AI R&D Spending and Stock Market Impact, supported by correlation analysis, impulse response estimation, and forecast error variance decomposition. The results indicate that AI R&D investment and market performance exhibit no statistically significant short-term causal linkage, as confirmed by non-significant Granger p-values (p > 0.05) and weak correlation (r = 0.13). The Impulse Response Function (IRF) shows a transient positive effect of R&D shocks on stock performance, peaking at approximately +0.12% before dissipating after the fourth period. Meanwhile, the Forecast Error Variance Decomposition (FEVD) reveals that more than 99% of the variance in R&D spending is explained by its own historical dynamics, suggesting minimal feedback from market reactions. These findings collectively imply that AI R&D investments operate on a long-term strategic horizon, while financial markets react within short-term informational cycles, creating a temporal disconnect between innovation effort and market recognition. The study contributes to the literature on innovation-finance dynamics by providing empirical evidence that technological progress and financial valuation evolve asynchronously, reflecting their inherently different timeframes and behavioral logics.