Abstract:
In recent years, the scale of quantitative investment in China's A-share market has grown rapidly, urgently requiring clarification of its profound impacts on market microstructure and market manipulation. Using non-financial A-share listed companies on the Shanghai and Shenzhen stock exchanges as samples and employing high-frequency tick-by-tick order and transaction data, this study finds that stocks heavily held by quantitative funds exhibit a higher probability of market manipulation. This effect predominantly manifests during bear market phases characterized by lower investor risk appetite and information transmission efficiency, as well as in small-cap stocks with insufficient research coverage that are susceptible to market sentiment. The mechanisms are threefold: First, quantitative investment excels at capturing short-term, high-frequency non-fundamental signals and executing rapid trades, crowding out slower fundamental traders and impeding the incorporation of firm-specific information into stock prices. Second, certain quantitative investments employ identical strategies, leading to homogeneous interpretations of price signals and convergent trading behaviors, thereby reducing price sensitivity to firm-specific information. Third, quantitative investment relies heavily on unstructured factors such as sentiment data and trading volume data to generate trading decisions; when quantitative signals diverge from traditional fundamental signals, it exacerbates heterogeneous beliefs among investors to some extent. The increased noise in the information environment and intensified investor heterogeneous beliefs create potential space for stock market manipulation. Further analysis demonstrates that the registration-based IPO reform and relevant institutional arrangements regarding information disclosure and market manipulation regulation have mitigated market information asymmetry, increased manipulation costs, and reduced the probability of market manipulation for stocks heavily held by quantitative funds. This study recommends continuously optimizing regulatory systems such as quantitative algorithm registration and black-box examination systems, constraining short-term arbitrage behaviors of high-frequency quantitative funds, and fully leveraging advanced technologies to innovate the regulatory model for stock market manipulation.
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