The concentrated trading of public mutual funds may affect stock prices, liquidity and market stability. Identifying changes in herding behavior in extreme markets and their asymmetry helps to explain the relationship between institutional transactions and market volatility. The existing conclusions are influenced by the definition of behavior, measurement methods and market-state classification, which motivates this analytical literature review. This study compares what the LSV, Wermers and Sias methods identify and what data they require, examines formation mechanisms in relation to information learning, career concerns, relative performance and fund flows, and compares international evidence and research on Chinese public mutual funds along three dimensions: market conditions, trading direction and market pressure. The analysis shows that contemporaneous concentration, directional differences and intertemporal following belong to different measurement dimensions, and the results cannot be directly compared. Changes in herding behavior under extreme market conditions depend on the information environment, transaction constraints and fund characteristics, and common transactions cannot directly prove intentional imitation. Future research should match the market conditions and data frequency, compare different indicators under a unified sample, and identify the source of behavior and market consequences in combination with fund flows, institutional changes and price performance over multiple horizons.
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Jiayu Zhou (2026) studied this question.
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