Summary:
Algorithmic trading refers to trading technology that uses computer algorithms to automate trading decisions, submit orders, and manage order execution. It includes portfolio selection, trading strategies, execution strategies, and other elements, and has become a significant trading method in the securities market. Currently, program trading in the securities market often adopts algorithmic trading approaches. In April 2024, the State Council issued the “Guidelines on Strengthening Regulation, Forestalling Risks and Promoting the High-Quality Development of the Capital Market”, which explicitly called for the formulation of regulations on program trading supervision and strengthened oversight of high-frequency algorithmic trading. In May, the China Securities Regulatory Commission (CSRC) introduced the “Provisions on the Administration of Program Trading in the Securities Market (Trial)” to provide institutional foundation for algorithmic trading regulation. In line with the political and people-centered nature of financial work and the philosophy of serving the people through finance, further efforts are needed to protect the legitimate rights and interests of the vast number of small and medium-sized retail investors in China's stock market. Improving the regulatory framework for algorithmic trading requires comprehensive theoretical and empirical research on its market impact and mechanisms, tailored to the realities of China's capital market. This paper uses high-frequency trading data and order book data from China's A-share market between 2015 and 2023 to develop an algorithmic trading intensity indicator. It empirically examines the dual impact of algorithmic trading on stock market stability and information efficiency. The main findings are as follows: First, algorithmic trading reduces bid-ask spreads in the stock market while also decreasing price ranges and realized volatility, thereby enhancing market stability by improving market liquidity and suppressing market volatility. Second, algorithmic trading reduces the intensity of informed trading and short selling, thereby lowering market information efficiency by crowding out traditional informed traders and short sellers. Third, during periods of market turbulence, algorithmic trading enhances market stability through stronger liquidity support and suppression of price volatility; however, it simultaneously reduces the information content and speed of information reaction in the market, leading to a more significant decline in information efficiency. Fourth, algorithmic trading does not significantly impact the commonality risk of individual stock liquidity, indicating that it does not increase liquidity commonality risk. Fifth, algorithmic trading exhibits an asymmetric suppression effect on “good volatility” and “bad volatility” in the stock market. Specifically, compared to “good volatility” triggered by positive information, algorithmic trading shows a weaker stabilizing effect in response to “bad volatility” caused by negative information. Additionally, this paper analyzes the heterogeneous impact of algorithmic trading on market stability and information efficiency from perspectives such as company's ownership, size, and quality of information disclosure. The study finds that for non-state-owned listed companies, small and medium-sized listed companies, and listed companies with lower-quality information disclosure, the effect of algorithmic trading in enhancing market stability is accompanied by a more pronounced reduction in information efficiency. The possible contributions of this paper are mainly in the following two aspects: (1) Previous studies have shown that algorithmic trading enhances market information efficiency in mature capital markets but exacerbates market volatility and, in extreme cases, leads to rapid depletion of market liquidity, thereby threatening market stability. This paper, however, reveals the dual impact of algorithmic trading on China's capital market, that is, enhancing market liquidity, reducing volatility, and thereby improving market stability, while simultaneously reducing market information efficiency. Thus, in terms of theoretical implications, the findings provide new empirical evidence on the market impact of algorithmic trading. In terms of practical implications, the empirical results offer a quantitative basis for improving algorithmic trading regulatory rules. (2) This paper enriches the academic discussion on the impact of securities trading models on capital markets. While existing literature has largely focused on the effects of listed company characteristics, institutional mechanisms, and market systems on capital markets, this study reveals the dual impact of algorithmic trading, an artificial intelligence-driven securities trading method, on both market stability and informational efficiency. These findings provide a new perspective for understanding the role of artificial intelligence technology in capital markets. Based on the findings of this paper, the following policy recommendations are proposed to fully leverage the positive role of algorithmic trading in supporting market stability while optimizing the market information environment to mitigate its negative impact on information efficiency: First, regulatory authorities should strengthen the management of algorithmic trading transparency to enhance market comprehensibility and predictability of algorithmic trading behaviors. Second, given the significant differences in the impact of algorithmic trading on stability under different market conditions, policymakers need to establish a market stability regulatory framework specifically tailored to algorithmic trading. Third, since algorithmic trading may delay price convergence to intrinsic value, regulatory agencies should strengthen the protection and monitoring of market information efficiency.
岳崴, 屈建文, 李子潇, 刘悦. 算法交易的市场影响:稳定与信息效率的双重视角[J]. 金融研究, 2025, 546(12): 133-150.
YUE Wei, QU Jianwen, LI Zixiao, LIU Yue. The Market Impact of Algorithmic Trading: Dual Perspectives of Stability and Information Efficiency. Journal of Financial Research, 2025, 546(12): 133-150.
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