Recently, proposals to halt quantitative trading in China's stock market have been gaining traction. Many investors have asked: If quantitative trading has such negative impacts and is widely criticized, why haven’t regulators responded or banned it?

I believe that loud voices do not equate to fairness or correctness. Quantitative trading, as a widely accepted trading method, lacks sufficient justification for a blanket ban in China’s stock market. The right approach is to analyze its compatibility with the market and find ways to adapt it for sustainable development.

First, the dissatisfaction among retail investors stems from quantitative trading’s reliance on computer programs, which execute trades far faster than manual trading, giving it an absolute advantage in short-term transactions—akin to a harvester versus a sickle. At first glance, banning quantitative trading might seem fair for short-term trading. However, the notion that short-term trading should be a "retail investor privilege" is fundamentally flawed.

Second, while quantitative trading thrives on high volatility—partly contributed by retail investors—it is not the sole beneficiary. Other players, such as private funds, speculative capital, and short-term traders, also capitalize on volatility. Banning quantitative trading would not automatically improve retail investors’ short-term gains but merely shift speculative opportunities to other entities.

Third, the real culprits behind high volatility are market manipulators ("zhuangjia"). These actors employ tactics like price pumping, dumping, and spreading misinformation, breaking down speculative capital to manipulate small-cap stocks and lure retail investors into traps. They are the true adversaries of retail investors. If the "hidden hands" behind small-cap stock volatility are severed, would quantitative traders still dominate? Retail investors’ welfare lies in a stable, regulated trading environment, not in the chaos created by manipulators.

Fourth, the broader impact of quantitative trading must be assessed. At its peak in 2023, quantitative trading accounted for one-third of total market turnover, distorting trading signals and misleading macroeconomic judgments. This is a serious issue. If policymakers had recognized the prevalence of machine-driven trading, retail sentiment would have been clearer, allowing timely supply-demand adjustments.

China’s stock market is dominated by retail investors. An excessive share of machine-driven trading signals distrust in the market—a problem that must be addressed from a political perspective.

Fifth, a blanket ban on quantitative trading would harm market perception. Developed markets widely use quantitative trading, as do many emerging markets. It is an essential tool for institutional investors. A complete ban in China would reject a universal trading method, undermining the image of a socialist market economy.

Instead, quantitative trading should be adapted to China’s market realities. Possible measures include: 1. Requiring quantitative firms to disclose the logic behind their top 20% trading strategies quarterly. 2. Imposing a 0.1-second delay on quantitative order execution. 3. Prohibiting order cancellations by quantitative traders. 4. Freezing accounts for 15 minutes if they trigger five price anomalies within a minute. 5. Enforcing a 5-minute cooling period for stocks with 3% price swings within 30 seconds, allowing only limit orders (buy below or sell above the last price).

Additionally, the scope of eligible stocks should be restricted—for instance, permitting quantitative trading only for stocks with market caps above a threshold (e.g., RMB 10 billion or 50 billion, based on annual averages). This balances retail participation in volatile stocks and institutional liquidity provision for large-caps.

Sixth, strict legal action must be taken against artificial IPO price manipulation and secondary market volatility schemes. This is fundamental to protecting retail investors and ensuring fairness, as well as keeping quantitative traders in check.

Regulators must crack down on institutions that create false trading signals while using quantitative strategies to exploit retail investors. Collusion between quantitative traders and listed company insiders for unfair gains must be prohibited. Fraudulent practices under the guise of machine trading, including manual orders disguised as algorithmic trades, must be eradicated. Further legal refinements are needed in this area.

[Note] Quantitative trading, at its core, uses quantitative methods to select investment portfolios. Common strategies include multi-factor stock selection, sector rotation, and trend following—all based on market trends. While it avoids emotional biases, its drawbacks include inflexibility during major market shifts, leading to significant losses.