This study investigates the dynamic and quantile effect of Artificial Intelligence (AI), Environmental, Social, and Governance (ESG) drivers, Economic Growth (GDP), and Interest Rate (IR) sensitivity on U.S. Stock Market Prices (SMP), with a particular focus on short, medium, and long run nexus and investors’ behavior. This study employs the advanced Wavelet Quantile on Quantile Regression (WQQR) approach to investigate the effects of AI, ESG, GDP, and IR on SMP and the Wavelet Quantile on Quantile Granger Causality (WQQGC) approach to explore directional causal relationships among these variables. In the short run, AI and stock prices exhibit a U-shape relationship across quantiles, while it has a stable positive effect in the long run. ESG initially shows a heterogeneous effect, followed by complex interaction in the medium term and a stable positive effect in the long term. Moreover, lower GDP growth triggers sharp stock price decline, while higher growth yields moderate gains, stabilizing in the long run. Additionally, in the short run, IR sensitivity causes erratic stock market fluctuations, whereas in the medium and long run quantiles, the effect is heterogeneous, suggesting investor volatility and shifting monetary policy expectations. The findings of this study offer valuable policy implications for investors, policymakers, government authorities, and managers seeking to manage volatility, addresses structural shifts, and develop robust investment strategies.
Mumtaz et al. (Fri,) studied this question.