When the market experiences a significant downturn, accusations against quantitative trading quickly surface. Since July of this year, the technology sector has undergone a sharp decline, drawing criticism directed at quantitative trading from retail investors, company board secretaries, and even prominent economists.
In China's capital markets, particularly the A-share market, quantitative trading is a relatively new but influential force. Despite growing controversy, there remains a lack of clear understanding about its true nature. This shrouds quantitative trading in mystery, leaving many questions unanswered: What is the actual scale of assets under management and trading volume? Why do quantitative firms insist they are not driving market declines while investors remain unconvinced? How are their trading models designed? Are the losses incurred by retail investors truly being pocketed by quantitative funds?
To explore these questions, interviews were conducted with leading quantitative institutions, fund managers, researchers, and academics, aiming to present the controversies and diverse perspectives surrounding quantitative trading at the center of the storm.
Debates Over Scale and Trading Volume
The impact of quantitative trading on the stock market largely depends on its assets under management and its share of total market turnover. However, to this day, there is no consensus on the total size, active trading volume, or proportion of A-share trading attributed to quantitative strategies, making it difficult to see the full picture.
Quantitative trading broadly refers to automated trading methods based on historical data and mathematical models. In the domestic market, quantitative private funds, leveraging advantages in strategy scope, product design, and technological iteration, have developed far more rapidly than public funds and are the primary force in quantitative products.
Due to the lack of unified, public disclosure standards and channels, as well as authoritative and accurate statistics, the overall scale of domestic quantitative trading remains difficult to calculate precisely, with only rough estimates available from various data sources. What is certain is that quantitative private funds are on a fast track of expansion.
According to statistics, by the end of the second quarter, the total assets under management for quantitative private funds exceeding 50 billion yuan was approximately 2.3 trillion yuan, an increase of about 0.85 trillion yuan from 1.45 trillion yuan at the end of the fourth quarter last year.
Within the industry, the number of quantitative private funds managing over 100 billion yuan is expanding rapidly. By the end of the second quarter, there were 71 such firms, with 14 managing assets above 500 billion yuan. At the end of last year's fourth quarter, these figures were 52 and 6, respectively. This means the number of top-tier quantitative private funds managing over 500 billion yuan more than doubled in half a year.
Research data indicates that by the end of 2025, the total scale of quantitative private funds had surpassed 3 trillion yuan.
With advancements in public fund quantitative research and trading models, the scale of public quantitative funds also grew in the first half of this year. According to a research report, by the end of the first quarter, the total scale of public quantitative funds reached 469.45 billion yuan.
Given that these figures are not precise statistics, could such massive scale data diverge from reality? What proportion of total A-share trading does quantitative trading constitute? Interviews revealed no industry consensus on this due to data accessibility issues. A leading quantitative private fund estimated this proportion to be around 30%.
The trading volume contributed by quantitative strategies is related not only to scale and holdings but also significantly to turnover rates. A quantitative private fund manager stated that the industry is currently trending towards lower frequency (reducing turnover rates), with medium-to-low frequency strategies dominating the market.
Despite the lack of precise data, the market influence of quantitative trading is undeniable. An industry insider noted, "Retail investors still account for a high proportion of A-share trading, leading to significant market sentiment swings and frequent thematic speculation. Quantitative funds, with their high share of turnover, directly impact short-term stock movements, deeply linking them to the retail investment experience. This makes them a focal point for public attention and a core market variable."
Controversy Over Quantitative "Sell-Offs"
A central controversy surrounding quantitative trading is whether these programmatic trades cause market "sell-offs."
In 2024, stock exchanges issued rare penalties to a quantitative private fund. The penalty notice revealed that within 42 seconds after the market opened, the fund sold 1.372 billion yuan worth of Shenzhen-listed stocks and within one minute sold 1.195 billion yuan worth of Shanghai-listed stocks. The exchanges stated that the fund's "short-term, concentrated, large-volume orders accounted for a high proportion of market turnover during the period, triggering rapid declines in the Shenzhen Component Index and the Shanghai Composite Index, severely disrupting trading order." Ultimately, the fund was suspended from trading and publicly censured by both exchanges.
These penalties highlighted the characteristics of high-frequency and large-volume short-term trading by quantitative funds, which are key to the "sell-off" controversy.
Interviews indicated that the direct trigger for quantitative "sell-offs" is often a sharp stock price drop causing portfolio volatility spikes and net value drawdowns, breaching preset risk control thresholds and triggering market sell orders from the risk control system.
"It's like everyone holds chips and will sell stocks when the price falls below a psychological level. A person might hesitate, but quantitative systems execute instructions ruthlessly, which to some extent amplifies short-term stock price volatility," explained a senior trader.
The trader added that when market liquidity is insufficient, stock prices can be instantly driven down or even hit the daily limit-down, potentially triggering other quantitative models to cancel orders and automatically execute sell orders, leading to a cascade of selling. This partly explains the phenomenon of a sudden surge in sell orders at the limit-down price after a stock is driven down.
Several quantitative private funds stated in interviews that quantitative trading can indeed amplify trading volatility during the initial stages of a rally or decline but does not reverse market trends.
Industry analysis suggests that large, top quantitative institutions manage massive funds and employ a fully diversified allocation model across the market, holding positions in a vast number of stocks and sectors, making them completely incapable of moving the overall market or sector trends. "Only some short-term quantitative strategies used by small and medium-sized institutions, which concentrate on small-cap stocks with poor liquidity, can briefly impact intraday sentiment and price movements of individual stocks."
An investment strategist believes the influence of quantitative trading depends on sector crowding. For instance, when the technology sector previously accumulated significant profits and had crowded positioning, simultaneous position reductions by quantitative models significantly amplified the adjustment magnitude.
"It's like in a narrow alley; if a group of people turn around simultaneously, it's inevitably more crowded than in an open square," he said.
A quantitative fund manager stated that the core objective of quantitative long-only strategies is the continuous accumulation of alpha, not market timing. Therefore, these strategies typically remain fully invested, adjusting structure while maintaining stable positions through buying and selling batches of stocks. Additionally, some market-neutral products use stock index futures to hedge systemic market risk.
"Quantitative strategies mostly follow the market; they amplify volatility but do not change the direction," the fund manager said.
A finance professor offered a different view, suggesting quantitative institutions might be downplaying their impact. "Direction is determined by fundamentals, but the rate of change is determined by the trading structure," he stated. He argued that when homogeneous algorithms form a one-sided resonance at a critical point, instantly draining liquidity, this amplification of volatility intensity becomes an independent variable. Maintaining buy orders might be a strategic discipline, but when sell orders cascade like a waterfall, the so-called continuous buying is merely a gesture to hedge against regulatory scrutiny, unable to conceal the essence of quantitative trading as a volatility "accelerator."
"In reality, the 'full position, no responsibility' argument of quantitative institutions holds at the micro level but fails at the macro level. A single fund running at full capacity is a rational choice, but thousands of homogeneous strategies adjusting positions simultaneously constitute a fallacy of composition," the professor added. He cited the micro-cap stock crisis in February 2024, where collective unwinding of certain strategies caused a single-day plunge of 9% in an index, rendering defenses of "not changing the direction" meaningless as the direction itself was destroyed by the volatility.
Academic research analyzing high-frequency order data concluded that the impact of quantitative trading on market tail risk contagion exhibits an "asymmetric" characteristic. Specifically, quantitative trading's ability to provide liquidity and accelerate price discovery allows it to mitigate the impact of individual stock tail risks on the market. Simultaneously, in scenarios dominated by speculative attributes, quantitative trading can amplify the contagion of individual stock tail risks to the market.
The Factor Debate
Investment factors are a core concept for understanding quantitative strategies and the source of their alpha.
According to a quantitative private fund, quantitative investment factors can be understood as measurable indicators that can influence asset prices and potentially generate alpha for investors.
The fund stated that quantitative investment factors are currently divided into two major categories. The first is classified by investment style, such as value, growth, and quality factors. The second is classified by data source, such as fundamental, market-based, and alternative factors. Different investment factors are assigned varying weights according to specific rules, which directly affects the performance of the quantitative investment portfolio.
Recently, some views suggest that market rumors and unverified reports have become an important category of factors for some quantitative models, even acting as "keywords" triggering sell signals. In July, a listed company's board secretary posted on social media, criticizing how unsubstantiated online posts could influence stock prices.
"These can become investment factors, but only a very small part," a representative from a quantitative firm stated, noting that quantitative strategies rely on a wide variety of factors and do not depend solely on any single type, with different factors being effective under different market conditions.
An investment strategist also disagreed with the notion that sentiment-based factors dominate. He stated that even if some strategies incorporate such data, it undergoes strict semantic processing and source filtering rather than reacting to single posts. "Quantitative models pursue probabilistic advantages under the law of large numbers, not precise predictions of single events."
Compared to factor types, the homogenization of quantitative factors is of greater concern within the industry.
A fund researcher told that factor homogenization in the A-share quantitative industry is now a common consensus, leading to three consequences: first, the rapid decay of alpha as massive funds chase similar factors, quickly erasing pricing inefficiencies; second, increased strategy crowding, with excessive concentration in the same direction; third, resonance risk during extreme market conditions, where numerous models simultaneously trigger sell or position reduction orders, forming a self-reinforcing downward spiral.
A fund of funds manager also believes that with continued industry expansion and most institutions using similar price-volume factors and frameworks, homogenization and crowding have diluted alpha with massive capital. He further stated that after the extreme trading structure of the first half eased, the advantage of quantitative strategies in capturing pricing inefficiencies would re-emerge. Alpha may not continue to narrow indefinitely but is unlikely to return to the high levels of earlier years, instead showing a mild improvement trending towards mean reversion.
The "Harvesting Retail Investors" Controversy
"The money lost by retail investors is all being earned by quantitative funds." This is another core controversy surrounding quantitative trading in the eyes of many investors and even seasoned market observers.
Looking at the performance of the products themselves, quantitative strategies are not infallible, and their alpha has shown a narrowing trend in recent years.
Taking first-half data as an example, performance figures for over 1,200 index enhancement products showed an average return, with the average alpha significantly lower compared to the same period last year. This indicates that the returns of these products in the first half were primarily driven by beta gains from the index rise, while the difficulty of obtaining alpha increased significantly.
During the sharp volatility in the technology sector in mid-July, net values across the quantitative private fund industry came under pressure, with some products experiencing single-week drawdowns exceeding 20%, wiping out previously accumulated alpha.
Regarding whether quantitative trading and retail investors are in a zero-sum game, a finance professor argued that theoretically it is not, but the harsh reality is that "quantitative trading, with its intraday trading capability and algorithmic speed, creates a dimensional advantage over retail investors bound by next-day settlement and manual order placement." He believes quantitative funds earn a premium based on trading rules and information processing speed, which is essentially a form of regulatory arbitrage.
The professor further stated that in a market characterized by存量博弈, this asymmetric advantage creates an ecological imbalance, where retail investors' capital is silently eroded by algorithms, raising issues of distributive justice that need to be addressed.
The fund researcher, however, believes the root cause of retail investor losses is not quantitative trading itself but "asymmetric competition." Quantitative strategies possess millisecond-level order placement speed and computational power to scan the entire market, while retail investors rely on manual monitoring. Quantitative funds earn "liquidity premiums" and "mispricing correction profits," and many of these mispricings are created by retail investors' emotional chasing of rallies and panic selling during declines.
"A more accurate description is that quantitative funds earn the efficiency gains lost by retail investors due to emotional and rule-based disadvantages, but it is not a zero-sum掠夺," the researcher said.
How Should Quantitative Trading Be Viewed?
"The biggest misconception among ordinary investors about quantitative trading is lumping all quantitative strategies together, simplistically equating them with short-term speculation and tools for harvesting retail investors," an industry insider stated. Quantitative trading is merely an investment tool relying on data, mathematical models, and systematic rules, encompassing a wide variety of strategies with different attributes.
The insider further added that medium-to-low frequency quantitative strategies, which constitute the highest proportion within the industry, are deeply integrated with fundamental research, earning steady returns from market valuation mean reversion and corporate value repair, possessing long-term allocation value. Only a minority of niche high-frequency, short-term quantitative strategies possess short-term博弈 attributes and engage in bid-ask spread arbitrage; the entire quantitative industry should not be negated based on this subset.
Academic research concludes that quantitative trading is neither purely a "monster" nor a natural "stabilizer." Its impact on financial market stability is significantly conditional and context-dependent. Regulators need to achieve a dynamic balance between preventing contagion effects and maintaining liquidity supply.
"Quantitative trading is the market's 'vaccine,' not a 'virus.' Its value lies in compressing the speed of incorporating information into prices from hours to milliseconds, providing about 40% of liquidity under normal conditions. However, the pro-cyclical nature of homogeneous strategies causes liquidity to evaporate first during stress tests, and the algorithmic discrimination built by speed monopolies places retail investors at a structural disadvantage," a professor said.
He believes regulation should establish the principle of technological neutrality but rule-based fairness. The core lies in limiting the microstructural advantages of high-frequency trading, imposing a tax on order placement and cancellation flows, and mandating disclosure of core algorithm risk parameters. The direction of reform is not to restrict technology or simply shut it down, but to reconstruct the rules of the game. Examples include introducing random delay mechanisms to eliminate speed arbitrage, freely providing enhanced market data to achieve data普惠, imposing a Tobin tax on high-frequency trading, and increasing margin requirements for homogeneous strategies. The ultimate goal is to build a layered ecosystem with high-frequency market making, medium-frequency index enhancement, and low-frequency investment advisory, allowing technological innovation and公平保护 to move towards a positive-sum outcome.
Industry insiders believe that the frequent controversies surrounding quantitative trading in the market are essentially growing pains during the industry's rapid expansion—short-term issues arising from lagging public understanding and the野蛮生长 phase of the industry—not inherent flaws in the quantitative investment model. They believe such controversies will gradually dissipate with industry standardization and成熟 investor认知.
Amid recent increased A-share volatility, calls from various parties to strengthen quantitative regulation have directly reached the regulatory level.
On the morning of July 20, the chairman of the securities regulatory commission conducted research at a securities branch and held an investor symposium in Beijing, engaging in face-to-face communication with eight investor representatives. Suggestions put forward by investor representatives included "standardizing the development of quantitative trading and AI applications."
On July 21, the commission held a series of symposiums with listed companies, industry institutions, and experts. Participants offered opinions and suggestions on various aspects, including further strengthening capital market infrastructure, implementing long-term assessment mechanisms for medium- to long-term funds, continuously improving listed company governance, further standardizing quantitative trading behavior, increasing penalties for market violations, strengthening market expectation guidance, and further institutionalizing market stabilization mechanisms.