Document Type : Review article
Authors
1
islamic azad university science and research branch،tehran،iran
2
PhD Professor (Full) at Allameh Tabataba'i University Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran
3
Assistant Professor at Islamic Azad University, Science and Research Branch, Tehran, Iran.
10.22067/ijaaf.2026.98245.1636
Abstract
This study traces the evolution of herding behavior research in financial markets from 1990 to 2025, offering a comprehensive analysis of its scholarly development. The findings reveal a clear upward trend in annual scientific output, with significant acceleration after 2003, particularly following the global financial crisis. The most substantial surge in research occurred post-2017, reflecting herding behavior’s consolidation as a key theme driven by advances in data availability and methodological sophistication in data science and computational finance. Using tools such as VOSviewer and Bibliometrix, this study rigorously employs a hybrid methodology combining bibliometric analysis with the SPAR-4-SLR protocol to examine research hotspots, trends, and collaborations. The thematic evolution, illustrated through keyword clustering and conceptual mapping, clearly shows a shift from foundational market-based theories to interdisciplinary approaches incorporating behavioral economics, social learning, and digital information channels. Specifically, this study reveals four distinct periods: 1990–2000 framed herding through information cascades and reputational concerns; 2000–2010 incorporated psychological factors including loss aversion and overconfidence along with agent-based modeling, though systemic risk remained prospective; following the 2008 crisis, articles from 2010–2015 identified market crashes and systemic risks as predominant consequences, with asset price bubbles as antecedents rather than independent outcomes; in the most recent decade 2015 to 2025, systemic risks are studied in algorithmic trading, high-frequency data, and machine learning, while asset price bubbles re-emerged strongly due to cryptocurrency, NFT, and meme stock markets driven by social media and algorithmic herding. Overall, this study highlights the dynamic interplay between market microstructure and behavioral finance.
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