Purpose The purpose of this paper is to explore market efficiency in the Metaverse economy by analyzing the behavior of major Metaverse tokens. This study examines how information diffusion, price discovery and systemic dynamics shape efficiency and speculative activity in this emerging digital ecosystem. Design/methodology/approach The methodology used in this paper, applied to five Metaverse tokens, is structured in three sequential phases. Firstly, the authors test the efficient market hypothesis in its weak form and the Random Walk Theory to assess the integration of historical information into prices. Secondly, the authors carry out Bubble Timestamping using explosive unit root tests (GSADF) and price/fundamental gap analysis. Thirdly, the authors quantify the magnitude of market inefficiency through the Adjusted Market Inefficiency Magnitude (AMIM), capturing its dynamic variations. Findings The results of this study reveal persistent inefficiencies and recurring speculative bubbles across all tokens. Explosive price trends often coincide with major announcements, amplifying volatility and distorting valuations. These patterns confirm the adaptive market hypothesis, showing that Metaverse markets evolve through adaptive cycles periods of speculation followed by phases of improved efficiency. Short-term price movements frequently overshadow fundamentals, creating systemic vulnerabilities and raising concerns about market integrity and investor protection. The findings of this study highlight the need for adaptive investment strategies and regulatory frameworks to manage systemic risks in rapidly evolving digital ecosystems. Originality/value This study offers a detailed analysis of the efficiency of the Metaverse market, a field that remains largely unexplored. This study goes beyond traditional approaches by examining speculative dynamics, responsiveness to global shocks and adaptability through the adaptive market hypothesis. By incorporating AMIM, a tool designed for emerging markets, into efficiency tests, this paper provides a dynamic interpretation of bubbles and inefficiency. AMIM also serves as a practical monitoring tool for regulators and helps investors anticipate risks in volatile virtual environments.
Basty et al. (Fri,) studied this question.