"Retail investors' 'presence' fading, industry insiders: quantitative trading has dominated market's 'micro-pricing power'"

How AI and Quantitative Trading Are Changing the Trading Ecosystem in the A-Share Market

By: Wang Haimin | Edited by: Zhao Yun

Recently, as the activity of retail funds in the A-share market has declined, the phrase “the era of retail funds is over” has sparked widespread discussion. As of the end of February 2026, the number of domestic private equity firms with assets over 10 billion yuan reached a new high, with the number of quantitative private equity firms surpassing that of subjective long-only funds for the first time.

Some private fund industry insiders believe that quantitative trading, with its speed advantage, discipline, and full-market coverage, has dominated in high-frequency trading, micro-fluctuations, and the battle of limit-up and limit-down, greatly shrinking the space for traditional strategies like chasing stocks, relay trading, and emotion-driven trading. Additionally, relying on dedicated channels, millisecond-level order placement, and algorithms that can accurately capture market sentiment, quantitative trading is like “driving a sports car in a pedestrian street,” which puts ordinary retail investors and subjective funds at a disadvantage in the “micro-price setting” game.

The Rise of Quantitative Private Funds and the Diminished Presence of Retail Funds

By the end of February 2026, there were 126 private equity firms with assets over 10 billion yuan in China, most of which were newly established quantitative funds, marking the first time that the number of such funds exceeded subjective long-only funds.

Meanwhile, the “Daily Economic News” has observed a growing circulation of opinions such as “the end of the retail fund era” and “quantitative trading changing retail rules.”

Data from the Dragon and Tiger lists this year show that the presence of retail funds has indeed decreased. According to data from Tonghuashun, in January, the average number of stocks traded daily by retail funds on the list was 72; in February (with fewer trading days), it dropped to 58; and by March (up to March 19), it further declined to 57. Notably, on January 12, the number of stocks traded by retail funds on the list once reached as high as 106.

Furthermore, the decrease in the number of “continuous limit-up” days also reflects reduced retail fund activity. Choice data shows that this year, only 15 stocks in the A-share market have experienced more than five consecutive limit-up days; in comparison, in the third and fourth quarters of last year, there were as many as 20 and 35 stocks respectively with more than five consecutive limit-ups.

Regarding this phenomenon, Shu Qiquan, General Manager of Qianbo Asset in Shanghai, told reporters: “As a subjective trader, I believe that the idea that ‘the market is fully controlled by quant algorithms and human traders have surrendered’ is an exaggeration, but the short-term trading environment has indeed undergone an irreversible change. The recent decline in retail activity and the weakening of the limit-up effect are the results of quantitative compression, stricter regulation, and changes in market structure. With its speed advantage, discipline, and market-wide coverage, quantitative trading has dominated high-frequency, micro-fluctuations, and limit-up/limit-down battles, significantly shrinking the space for traditional strategies based on chasing stocks, relay trading, and emotion-driven trading. This is an objective fact.”

He also believes that quantitative trading is currently impacting the market structure.

“First, the daily average trading share of quantitative trading in the A-share market has reached 30%-40%, which is widely recognized in the industry. This scale is enough to change micro-trading structures, such as making the order book thinner, increasing volatility, triggering stop-loss orders more easily, and amplifying short-term sentiment. From this perspective, quantitative trading indeed increases the risks of market crashes and herd behavior, especially in stocks with moderate liquidity. Second, the idea that ‘quantitative trading diverges from value investing’ is fundamentally true. Most high-frequency quantitative strategies focus on statistical arbitrage, trend following, and volatility arbitrage, without researching company fundamentals, industry logic, or long-term value. They only aim to profit from trading counterparts. When such funds dominate the market, it becomes more prone to zero-sum games, with increased short-term speculation and weakened long-term price discovery, which adversely affects the market ecosystem and the funds dedicated to value investing and industry research.”

“Quantitative trading has precisely broken human trading rhythms”

Not only has retail activity declined, but recent feedback from investors indicates that as the proportion of quantitative trading increases, they sometimes feel at a loss in their daily trading. “For example, when I see an opportunity in the market, I think I can buy in, but as soon as I do, the price drops sharply. Yet, when I want to cut losses, it’s often at the point where the quant algorithms buy.”

This week, veteran investor “Stock Enthusiast Sands River” published widely circulated online articles such as “Humans Yield to Quantitative Trading,” admitting that he cannot compete with quant algorithms and has reluctantly surrendered.

Yesterday, he posted again, lamenting: “The chemical stocks that soared yesterday crashed today, and the tech stocks that fell yesterday rebounded today.”

Some believe that ordinary retail investors cannot compete with millisecond-level order placement and algorithms that precisely capture market sentiment, which has seriously impacted market fairness.

In the view of Li Chao (pseudonym), a fund manager at a private firm in Shanghai, quant algorithms will seize every profitable opportunity. Anything retail investors can think of, quant algorithms will anticipate and act on first, forcing retail investors to buy at higher prices. Moreover, quant trading can make declines faster and more violent because computers are very decisive when executing stop-loss orders, leaving retail investors no chance to cut losses.

Even some professionals interviewed have felt the impact of quant algorithms firsthand. Shu Qiquan told reporters that many investors now find trading difficult, which is directly related to the surge in quant trading. However, the problem is not that “quantitative trading causes valuation failures,” but that it has precisely broken human trading rhythms.

“Now, whether it’s chemical stocks, gold, or geopolitical situations, even when logic is clear and valuations are reasonable, the moment you enter the market, you get ‘hammered down’; when you can’t bear the losses and want to cut, the stock suddenly V-reverses. This isn’t due to misjudgment but because quant algorithms are precisely harvesting ‘human stop-loss orders.’ Quantitative trading doesn’t care about fundamentals; it only captures micro-order book structures. When the market forms a consensus and retail stop-loss orders pile up below, quant algorithms will instantly break key support levels, trigger panic selling across the network, and then buy at low prices and quickly cover. The very second you try to cut losses, it’s the best moment for quant to build positions. So, it’s not that valuation judgment is failing, but that your long-term logic is ignored in the face of short-term quant cycles. Quantitative trading dominates the micro-price setting, turning the original T+1 ecosystem into a millisecond-level game.”

Regarding the impact of quant trading on market “pricing power,” Li Chao also shared his observations: “Quant algorithms will detect other institutions’ buying actions. For example, as soon as I place an order to buy a stock, quant algorithms will buy in large quantities, forcing my purchase price higher; otherwise, I won’t be able to buy. This is especially obvious in small-cap stocks.”

Some industry insiders believe that the fundamental logic of quant trading is to profit from market volatility, or in other words, the money made by quant funds is essentially the money lost by others.

“It’s like there’s limited gold in a pit; the more the quant algorithms take, the less is left for retail investors and retail funds.”

In fact, the high volatility and unique liquidity advantages of the A-share market are precisely what some quant firms value. In late October last year, a founder of a domestic private equity quant firm with over 10 billion yuan admitted at an asset allocation forum: “The history of European and American financial markets is much longer than ours; looking at the US or Hong Kong markets, you see that many small stocks have almost no trading volume. The only special thing about the Chinese A-share market is that all 5,000 stocks have trading volume, which gives us a uniquely advantageous environment for quantitative trading.”

Subjective funds “surrender” is useless; proactive evolution is necessary

Regarding the recent popular “vectorization surrender theory,” Li Chao believes that the continuous expansion of quant trading will naturally influence other strategies, including subjective long-only funds. He said: “Currently, the market share of quant trading is already significant, and it will only grow. Because a profitable product will attract more investors. Ultimately, the one who defeats quant isn’t other subjective long-only funds but quant itself. Currently, the total scale of quant funds is about 3 trillion yuan; if it grows to 10 trillion in the future, the situation will be different.”

He also predicts that subjective long-only funds are likely to continue shrinking because the overall market share is limited, and the growth of quant strategies will lead to the contraction of other strategies.

However, Shu Qiquan believes that surrender is futile; the only way forward is to adapt strategies. His suggestions include:

1. Avoid crowded zones: Don’t chase highs or bottom-fish during emotionally intense “hot moments,” leaving room for quant strategies to operate profitably.

2. Change trading habits: Reduce trading frequency, pay less attention to intraday fluctuations, and use medium- to long-term logic to counter short-term harvesting by quant algorithms.

3. Recognize quant traces: Learn to observe order book anomalies and understand the quant features behind “hammering down and V-reversals,” which can be opportunities rather than reasons to cut losses.

“The market hasn’t changed; only the opponents have. In the era of quant, subjective trading is no longer about speed but about patience, logic, and understanding human nature.” He added, “Quant advantages lie in speed, discipline, and breadth, but their blind spots are in logical depth, industry understanding, expectation judgment, and extreme emotion control. True subjective traders won’t write surrender letters; they will proactively evolve: abandon high-frequency speed contests, shift to swing and logic-driven strategies; use counter-quant approaches instead of direct confrontation; focus on niche areas like emerging stocks, restructuring, and small sectors where quant coverage is weak; and seize opportunities driven by policy inflection points and industry trends rooted in human nature and logic.”

It’s worth noting that even the swift and precise nature of quant trading is not invulnerable; in some extreme cases, it can expose significant risks. The liquidity crisis in small-cap stocks triggered by concentrated buy-ins of Snowball products in early 2024 remains a vivid memory. Recently, market figures like Dan Bin, Chairman of Orient Harbor, have issued warnings about the risks of quant funds.

However, some industry insiders remain cautious about the view that quant trading has only negative effects. Shu Qiquan stated: “I don’t think quant trading should be completely negated. It provides continuous liquidity and often absorbs sell-offs; it replaces emotion with discipline, objectively reducing some irrational speculation. The real issues are scale, homogeneity of strategies, and lagging regulation and risk control. When models become similar and behaviors converge, especially during extreme market conditions, it can lead to synchronized withdrawals and sell-offs, intensifying systemic volatility. From the perspective of subjective traders, my conclusion is clear: quant isn’t the enemy of the market, but its current scale and mode do distort price discovery and harm the long-term investment ecosystem. For healthy market development, it’s not about eliminating quant but about constraining high-frequency overcompetition, encouraging long-term holdings, and strengthening transparent regulation to restore a balance between value and trading.”

He also added that retail funds have not disappeared but have shifted from pure emotion-driven speculation to logic + thematic + leader-based grouping, resonating with quant strategies rather than opposing them. “In summary, the market isn’t a solo act of quant; it’s a coexistence of humans and machines, an upgraded ecosystem. The core of subjective trading isn’t about fighting quant but about leveraging strengths and avoiding weaknesses—doing what machines cannot do. Maintaining deep research, emotional perception, and logical pricing still provides subjective traders with irreplaceable space and profit advantages.”

Daily Economic News