Abstract : In the rapidly evolving landscape of artificial intelligence (AI) and big data, understanding the financial dynamics between tokens and stocks within these sectors has become increasingly essential for investors and policymakers. This study investigates the connectivity between these asset classes using Quantile-based Vector Autoregression (QVAR) models with "Extended Joint" connectivity and frequency-domain analysis. Covering the period from December 17, 2020 to February 11, 2025, our research reveals significant interdependencies, particularly during periods of heightened market volatility. The findings indicate that major technology stocks, such as those of Microsoft (MSFT) and Amazon (AMZN), act as dominant shock transmitters, exerting substantial influence over other assets. Conversely, AI-focused tokens, including SingularityNET (AGIX) and Numeraire (NMR), primarily serve as shock receivers, responding to, rather than driving, market fluctuations. From a portfolio optimization perspective, AGIX stands out as the most effective hedging asset, followed by NMR and CTXC. These results provide critical insights for investors aiming to enhance diversification strategies and manage risk in AI and big data-related markets. Furthermore, this study contributes to a broader understanding of asset interactions within the digital economy, shedding light on the growing financialization of AI-driven innovations. The findings offer practical implications for institutional and retail investors seeking to navigate this rapidly evolving sector.