Herding Among Crypto Investors: Mapping Research Trends Through Bibliometric Analysis

Abstract

Herding behavior among cryptocurrency investors has gained significant attention in behavioral finance due to its impact on market volatility and price formation. This study conducts a bibliometric analysis to examine research trends, intellectual structure, and thematic evolution related to herding bias in crypto markets. Using the Scopus database, 43 articles published between 2019 and 2023 were extracted for analysis. Bibliometric techniques were applied, utilizing VOSviewer for network visualization and Biblioshiny (R Studio) for performance analysis, keyword co-occurrence mapping, and thematic trend identification. The study uncovers key research patterns, influential publications, and emerging topics, highlighting a growing academic focus on the role of social media influence, FOMO (Fear of Missing Out), and algorithmic trading in driving herd behavior among crypto investors. These findings contribute to a deeper understanding of behavioral trends in digital asset markets and provide a foundation for future studies on investor psychology and market efficiency in the cryptocurrency domain.