Artificial intelligence and big data tokens: Where cognition unites, herding patterns take flight

Artificial intelligence (AI) and big data tokens have emerged as unique investment options, garnering interest due to their connectedness with other assets and financial markets. Utilizing Chang et al. (2000)'s cross-sectional absolute deviation (CSAD) model, we investigate static and time-varying herding in the AI and big data token markets. This research contributes to the growing discourse on AI and big data token investment through the lens of behavioral finance, with a particular focus on examining investor herding. The study's findings confirm market-wide herding of AI and big data tokens. The results suggest that investors exhibit herding in up markets, low volatility, and low volume days. Conversely, anti-herding is more prevalent in down markets, high volatility, and high volume days. Our analysis shows that herding is time-varying and emerges during a crisis period. The finding carries robust regulatory and policy implications to mitigate systemic risk and safeguard investor interests, ensuring market stability and resilience. The provided insights offer a valuable understanding of investors’ behavior across various market scenarios.