Public offering quantitative "arena"
[Introduction] The "Arena" of Public Offering Quantitative Investment: From "Supporting Role" to "C - Position", Advance and Challenges under Talent Poaching, Strategy Involution, and AI Iteration
In the investment landscape of China's capital market, quantitative investment was once the "exclusive territory" of private equity funds for a long - term. High - frequency trading, programmatic order placement, and mysterious "black - box" strategies - these labels make quantitative investment both alluring and a bit distant in the public eye. However, in recent years, a silent transformation has been taking place in the public offering fund industry: public offering quantitative products have moved from "supporting roles" to "C - positions". The issuance scale has reached a record high, hot - selling funds are sold out in one day, and many products have to "close the door to visitors".
This "arena" with a scale of over 570 billion yuan and more than 800 products is staging a competition about strategies, talents, and patterns under the catalysis of AI technology. Can public offering quantitative investment find a different path from private equity? How will the in - depth involvement of AI rewrite the rules of the game?
From "Exclusive to Private Equity" to "National Alternative", Public Offering Quantitative Investment "Enters" the Fast Lane
Wind data shows that in 2025, 205 new public offering quantitative funds were established, and the total fundraising scale exceeded 100 billion yuan, reaching 109.246 billion yuan. Both figures hit record highs. Compared with 2024, the number and scale increased by more than 141% and 205.02% respectively - this is not a linear growth but an explosive leap.
In 2026, the enthusiasm continues. As of May 22, there are a total of 861 quantitative funds in the whole market, with a total scale of 575.964 billion yuan, an increase of more than 13% compared with the beginning of the year. The issuance side is even more booming. This month, there were two "sunlight funds" within five days, and the end - day ratio allocation mechanism was triggered. At the same time, star products such as E Fund Kexin Quantitative Stock Selection and Guojin Quantitative Multi - Factor have intensively issued purchase limit announcements, and the minimum subscription limit has been reduced to 50 yuan.
"This scene of 'allotment and purchase limit coexisting' is the first in the field of public offering quantitative investment." A fund manager of a quantitative investment department in Shanghai explained to reporters. In the past, quantitative investment was the "exclusive territory" of private equity, and public offering quantitative investment was in a marginal position for a long time. Since 2026, the average performance of public offering quantitative products this year has exceeded 13%, and the proportion of products with positive returns has reached 95%. The profit - making effect has attracted a rush of funds. The logic on the channel side has also changed. After the private equity quantitative products were hot - selling, bank wealth management managers have accumulated knowledge and sales experience in quantitative investment, and then promoted public offering quantitative investment as an "alternative" to a wider customer base.
"Compared with private equity quantitative investment, although public offering quantitative investment gives up some strategic flexibility and high - frequency excess return space, it has established a more complete risk control system, and the overall drawdown performance is more controllable. Against the background of the continuous restoration of investors' confidence in active equity funds, this combination feature of 'stability + excess return' happens to fit the current market risk preference." The above - mentioned person said.
The "Differentiated Competition" Ecosystem in the Public Offering Quantitative Investment Arena Takes Shape: Giants Make Comprehensive Layouts, and Specialized Institutions Make Single - Point Breakthroughs
At the current point in time, the competition pattern in the public offering quantitative investment "arena" is already clear. From an overall perspective, the current leading public offering quantitative investment has formed a differentiated competition ecosystem of "comprehensive giants making comprehensive layouts and specialized institutions making single - point breakthroughs". For example, institutions such as Guojin and Bodao have formed brand advantages in the active quantitative track through strategy iteration; giants such as E Fund, Huaxia, and China Merchants rely on the index enhancement matrix to occupy a large market share.
In addition, data from CITIC Research shows that the scale proportion of the top 5 public offering quantitative managers has dropped from 50% in 2018 to 31% at the end of the first quarter of 2026, and the proportion of the top 10 has dropped to around 50%. Small and medium - sized institutions have achieved breakthroughs through segmented strategies and channel sinking. Currently, the total scale proportion of the top 30 managers is 81.5%.
For small and medium - sized companies, quantitative investment provides the possibility of "overtaking on a curve".
Industry insiders point out that traditional investment highly depends on profound research resources and extensive information networks, which are the advantage barriers of large institutions. However, the core of quantitative investment is models, data, and computing power. This means that as long as the strategy is effective, small and medium - sized companies can bypass the barriers of personnel scale and information gap, and directly compete with the leading players on the quantitative track with a relatively limited team and cost. For small and medium - sized companies with limited resources but technological sensitivity, quantitative investment is not only a tool but also a strategic path that can truly achieve "winning big with small resources". In addition, the index enhancement funds and active quantitative stock - selection funds have not yet formed a pattern where the leading players "dominate all", so there is still a large development space for small and medium - sized fund companies.
Top - Level Poaching and Breakthroughs by Small and Medium - Sized Firms, the "Battle for Quantitative Talents" Enters a New Stage
As public offering quantitative investment ushers in a "blue ocean" of development, the "battle for quantitative talents" is being waged.
Many public offering institutions have begun to introduce quantitative talents from the private equity field. For example, to make up for the shortcomings in aspects such as price - volume factors, E Fund introduced Song Ting, an investment manager from a leading private equity institution; later, to overcome the problem of short - cycle prediction, E Fund also introduced Yang Liu, who has rich experience in end - to - end deep - learning models.
Why do private equity quantitative talents choose public offering? Qu Jing, the director of the quantitative investment department of E Fund, told reporters that, first, there is hardware computing power support. Public offering quantitative investment has continuously increased investment in computing power resources and IT system construction. Second, there is a collaborative atmosphere among high - level colleagues. Top researchers don't want to be "screws" on the assembly line. They can get an open and equal communication and growth space at E Fund. Third, there are "blue ocean" opportunities in public offering quantitative investment. Currently, the scale of public offering quantitative investment is still relatively small compared with private equity, and there is no absolute leading player like in the private equity field, which provides great development space for talents who pursue career achievements.
The "talent siphon" among public offering institutions has also attracted market attention. Around the Spring Festival this year, Hu Jie, the former "queen" of the 100 - billion - level ETF at Huabao Fund, quickly joined Tianhong Fund; later, another quantitative talent, Qi Zhen, also completed the change of employment institution from Huabao to Tianhong. Dong Xuheng, the former head of the quantitative and investment trading group at Founder Securities, was also recruited by Tianhong before.
While leading institutions are competing for top - level talents, another kind of story is being staged in small and medium - sized fund companies.
The quantitative team of Western Lead Fund tells a story of "differentiated development". After Sheng Fengyan, the founder of the team, joined the company in 2016, he began to build the quantitative business. Currently, the team has established a core approach of "70% quantitative and 30% subjective". Under this system, the team has reserved more than 60 sets of medium - and low - frequency strategies that have been tested in actual combat to deal with various market styles.
In the team cooperation mechanism, the quantitative team increasingly emphasizes "complementary backgrounds". "When recruiting, we will try our best to ensure the differentiation of members' backgrounds, but after joining the team, the research direction will not be forcibly restricted. It is more about combining personal interests and expertise to carry out research independently. However, no matter what the research direction is, the factors and strategies developed by each person will eventually enter a unified back - testing and evaluation system and be included in the performance appraisal." Western Lead Fund said.
The quantitative team of Xinyuan Fund provides a sample of "grassroots counter - attack". In 2022, when the team was established, Yu Li, the deputy general manager of the quantitative investment department, designed the team structure as "one senior leading several juniors". The characteristic of this team is "fast" - if they see a valuable idea in a sell - side research report, the team can complete the data extraction, program writing, and back - testing in no more than 3 days at the fastest. In less than 4 years, the scale of the quantitative public offering products managed by the team has exceeded 4 billion yuan, and the first ETF has been issued.
Tool Iteration Drives Evolution, the "New AI Battlefield" of Public Offering Quantitative Investment
The evolution history of public offering quantitative investment is essentially a history of tool iteration. A person in charge of an index and quantitative investment department recalled that when he entered the industry in 2009, the main idea of quantitative investment was to find dozens of multi - factors, summarize the scores, and then put them into an optimizer to form a position. From 2010 to 2014, this method continuously brought good returns.
However, with the decline in computing power costs and the reduction in the threshold for data acquisition, the invalidation speed of quantitative strategies has accelerated significantly. "The era when a strategy could maintain its effectiveness for a long time is gone. In the short and rapid market in September 2024, many quantitative strategies didn't have time to adapt to the index before the market ended." He said.
The "weapon" to deal with this change is AI.
Currently, AI applications have penetrated the entire chain. According to Wang Xingxing, a fund manager at Huatai - PineBridge Fund, the current applications of AI in quantitative investment can be mainly divided into two dimensions. First, it can effectively expand the overall source of Alpha. Traditional quantitative research mostly relies on linear thinking and is difficult to identify implicit mispricing in the market. Various AI models can dig out market laws that are difficult to be discovered by humans through the introduction of non - linear analysis logic, and at the same time, efficiently process a large amount of unstructured data such as texts, further enriching the source of excess returns. Second, it can significantly improve the overall efficiency of investment research operations. Relying on the empowerment of large models, in routine work such as strategy writing, code optimization, and basic research, the repetitive workload can be effectively streamlined, and the research energy of researchers can be released.
"Currently, the application degree in the industry is not consistent. In some companies, more than 70% of the excess factors come from AI learning. Our proportion of AI in factor mining is not high. We prefer to keep the factors highly interpretable and use AI more for end - to - end generation of trading signals and strategies." A quantitative fund manager in Shanghai said.
In his view, AI is not an all - powerful investment tool. Building an AI quantitative framework is only the foundation, and continuous iteration is the key. The current breakthrough points of AI quantitative investment are not in the single - model upgrade but concentrated in three core directions: first, combine new academic algorithms with the characteristics of the financial market for practical application, rather than simply applying them; second, iterate the details of the existing models according to market changes to make the models more in line with the actual market; third, dig high - quality incremental data from more segmented dimensions, including financial footnotes, patents, research notes, etc., and look for differentiated opportunities from the data source.
Many quantitative fund managers also emphasized that the team does not blindly believe in "large models". "The financial field does not necessarily need particularly large models. Sometimes, the larger the model, the more likely it is to over - fit." They believe that the more important work of quantitative fund managers is to integrate their understanding of the financial market into the model framework, adapt and transform traditional models to make them more adaptable to the market.
"The Whole Industry Will Become More 'Involutionary'", Public Offering Quantitative Investment Needs to "Learn from Both Sides" in the Future
Currently, public offering quantitative investment is experiencing a "glorious moment" in terms of funds and performance. However, beneath the prosperity, the future path is not smooth. It faces severe challenges from multiple aspects such as strategy effectiveness, regulatory compliance, AI technology application, and industry competition.
A large fund company in Beijing said that, first, the strategy capacity is approaching the ceiling. There is an "impossible triangle" between performance and scale in public offering quantitative investment - scale growth will dilute excess returns, which is the most core hard constraint in the industry currently. Simply put, when the capital volume is too large, many effective trading strategies will become invalid due to high impact costs.
Second, strategy homogenization and model invalidation are accelerating. When everyone uses similar multi - factor models, trading becomes crowded, resulting in the rapid "leveling" of excess returns. Especially with the empowerment of AI, the invalidation speed of a factor after being discovered is getting faster and faster, and it is more difficult to maintain its effectiveness.
Third, there is a test of adaptability to market style switching. Quantitative models are prone to "fall behind" in a structurally differentiated market (such as the sharp rise of a few heavy - weight stocks), and the "black - box" characteristic of the models also increases the difficulty of risk tracing and adjustment.
Wang Xingxing also admitted that the industry is facing considerable pressure at this stage. As the number of quantitative products in the whole market continues to increase and the overall management scale expands, the industry is highly homogeneous, and the existing Alpha continues to decline; at the same time, the information flow in the A - share market changes rapidly, and the sector style rotation is becoming more and more frequent. To achieve long - term development in the future, it is necessary to continuously polish the factor system and continuously iterate and update strategies to maintain stable excess performance.
"In the future, the whole industry will become more 'involutionary'." A quantitative fund manager in Shanghai said that public offering quantitative investment can neither understand the company's fundamentals as deeply and meticulously as subjective investment nor implement high - turnover strategies like private equity quantitative investment. This is indeed its limitation. However, its advantage lies in that it can learn from and draw on each other.
"Learning from private equity means that public offering quantitative investment needs to use data similar to that of private equity, such as high - frequency data. However, the difficulty lies in how to improve excess returns as much as possible while maintaining a low turnover rate to make the return level closer to that of private equity. Learning from subjective investment means borrowing its logic of judging odds. The strongest ability of subjective investment is to capture short - term high - explosive bull stocks, and most of these stocks have fundamental support. Public offering quantitative investment can use means such as analyst data and diversified models to better capture the main uptrend opportunities of high - odds stocks, thus achieving an effective combination with subjective investment." He said.
Although facing challenges, for public offering quantitative investment, the future development opportunities of the industry are very clear. Wang Xingxing believes that, on the one hand, public offering quantitative investment generally has a diversified portfolio and a relatively low turnover rate, with significant advantages in strategy capacity; on the other hand, quantitative products have an overall balanced style and diversified industries, with stronger market adaptability and natural advantages in the entire equity asset management track, and the industry has sufficient development potential.
This article is from the WeChat official account "China Fund News" (ID: chinafundnews), author: Cao Wenjing, published by 36Kr with authorization.