The investment landscape of China's capital markets once saw quantitative investing as the exclusive domain of private funds. Characterized by high-frequency trading, programmatic orders, and mysterious "black box" strategies, it appeared both alluring and distant to the general public. However, a quiet transformation has been unfolding in the public fund industry in recent years. Public quantitative products have shifted from a supporting role to center stage, with fundraising hitting record highs, popular funds selling out in a single day, and numerous products forced to halt subscriptions.
This arena, now exceeding 570 billion yuan in scale with over 800 products, is witnessing a contest of strategies, talent, and structure, catalyzed by AI technology. Can public fund quant carve out a distinct path from its private counterpart? How will deep AI integration rewrite the rules?
**From "Private Exclusive" to "Mass Alternative": Public Quant Enters the Fast Lane**
Wind data shows that in 2025, 205 new public quantitative funds were established, raising a total of 109.24 billion yuan, both figures setting historical records. This represents not linear growth but an explosive surge, with the number and scale increasing by over 141% and 205.02% respectively compared to 2024.
The momentum continued into 2026. As of May 22, the entire market comprised 861 quantitative funds with a total scale of 575.964 billion yuan, an increase of over 13% since the start of the year. Fundraising activity was even hotter, with two funds selling out on their launch day within a five-day period this month, triggering a pro-rata allocation mechanism. Simultaneously, star products like E Fund Kexin Quantitative Stock Selection and Guojin Quantitative Multi-Factor frequently issued purchase restriction notices, with the minimum subscription limit dropping as low as 50 yuan.
"This phenomenon of simultaneous 'pro-rata allocation and purchase restrictions' is unprecedented in the public quant space," explained a quantitative investment department fund manager in Shanghai. Historically, quant was the "exclusive territory" of private funds, with public quant long on the sidelines. However, since 2026, public quant products have delivered an average annualized return exceeding 13%, with 95% of products in positive territory, attracting a flood of capital due to this赚钱效应. The logic at the distribution channel has also shifted. After the hot sales of private quant products, bank wealth managers accumulated knowledge and sales experience in quant investing, subsequently promoting public quant as a "mass alternative" to a broader client base.
"Compared to private quant, public quant, while sacrificing some strategic flexibility and high-frequency alpha potential, has established more robust risk control systems, resulting in more controllable overall drawdowns. Against the backdrop of recovering investor confidence in active equity funds, this combination of 'stability + alpha'恰好契合当前市场的风险偏好," the source added.
**A "Differentiated Competition" Ecosystem Takes Shape in Public Quant: Giants' Full-Scale Layout vs. Specialists' Focused Breakthroughs**
At present, the competitive landscape of public quant is clear. Overall, a "differentiated competition" ecosystem has formed where "comprehensive giants deploy across the board, while specialized institutions achieve focused breakthroughs." For instance, institutions like Guojin and Bodo have built brand advantages in the active quant track through strategy iteration. Giants like E Fund, ChinaAMC, and China Merchants Funds occupy significant market share relying on their index-enhanced product matrices.
Furthermore, CITIC Research data indicates the market share of the top 5 public quant managers has declined from 50% in 2018 to 31% by the end of Q1 2026, while the top 10 share has fallen to around 50%. Small and medium-sized institutions are breaking through凭借细分策略与渠道下沉. Currently, the combined market share of the top 30 managers is 81.5%.
For small and medium-sized companies, quantitative investing offers the possibility of a "corner overtaking."
Industry insiders point out that traditional investing heavily relies on deep research resources and extensive information networks, which are the advantage moats of large institutions. However, the core of quant investing lies in models, data, and computing power. This means that as long as a strategy is effective, a smaller company can bypass barriers related to personnel scale and information asymmetry, competing directly on the quant track with a relatively limited team and cost. For resource-constrained but technologically astute small and medium-sized firms, quant is not just a tool but a strategic path to truly "achieve big with small." Additionally, neither index-enhanced funds nor active quant stock selection funds have yet formed a "winner-takes-all"格局 dominated by giants, leaving significant room for development for smaller fund companies.
**Headhunting at the Top, Breakthroughs from Below: The Quant Talent "War" Enters a New Phase**
As public quant enters a development "blue ocean," a war for quant talent is intensifying.
Many public fund institutions have begun recruiting quant talent from the private sector. For instance, to address gaps in areas like price-volume factors, China Europe Asset Management recruited Song Ting, an investment manager from a top private fund. Later, to tackle challenges in short-cycle prediction, the firm brought in Yang Liu, who has deep experience in deep learning end-to-end models.
Why are private quant talents choosing public funds? Qu Jing, Director of Quantitative Investment at China Europe Asset Management, cited several reasons: First, hardware and computing power support. Public quant firms are continuously increasing investment in computing resources and IT system construction. Second, a collaborative atmosphere with high-caliber peers. Top researchers do not want to become "cogs in a machine"; at China Europe, they gain open, equal opportunities for交流与成长. Third, the "blue ocean" opportunity in public quant. The scale of public quant remains relatively small compared to private quant, and no absolute龙头 has emerged akin to the private sector, offering vast development space for talent seeking professional fulfillment.
"Talent siphoning" among public institutions has also drawn market attention. Around the Spring Festival this year, Hu Jie, the former "ETF queen" managing products worth hundreds of billions at HuaBao Fund, swiftly joined Tianhong Fund. Subsequently, another quant talent, Qi Zhen, also changed his registered institution from HuaBao to Tianhong. Dong Xuheng, former head of the quantitative and investment trading group at Founder Securities, was previously recruited by Tianhong as well.
While top institutions vie for顶尖人才, a different narrative is playing out at small and medium-sized fund companies.
The quant team at Western Lead Fund tells a story of "differentiated development." Team founder Sheng Fengyan began building the quant business after joining the company in 2016. The team has now established a core approach of "70% quantitative, 30% subjective." Under this system, the team has储备60余套经实战检验的中低频策略 to adapt to various market styles.
Regarding team collaboration mechanisms, quant teams increasingly emphasize "complementary backgrounds." "When hiring, we try to ensure diverse member backgrounds. However, once in the team, research directions are not强制限定. It's more about结合个人兴趣和擅长领域 for自主开展研究. Regardless of the research direction, every因子和策略 developed ultimately enters a unified backtesting and evaluation system and is incorporated into performance assessment," stated Western Lead Fund.
The quant team at Xinyuan Fund provides a "grassroots逆袭" example. When the team was formed in 2022, Yu Li, Deputy General Manager of the Quantitative Investment Department, designed an架构 of "one veteran leading several新人." This team's characteristic is "speed" – if they spot a valuable idea in a sell-side research report, the process from data extraction and programming to完成回测 can be as fast as three days. In less than four years, the team's AUM for public quant products exceeded 40 billion yuan, and they launched their first ETF.
**Tool Iteration Drives Evolution: Public Quant's "New AI Battlefield"**
The evolution of public quant is essentially a history of tool iteration. A head of an index and quantitative investment department recalled that when he entered the industry in 2009, the primary quant approach involved finding several dozen multi-factors, aggregating scores, and then feeding them into an optimizer to form a portfolio. From 2010 to 2014, this method consistently delivered solid returns.
However, as computing costs fell and data accessibility improved, the失效速度 of quant strategies accelerated dramatically. "The era of a strategy lasting a long time is gone. During the brief, sharp market move in September 2024, many quant strategies didn't have time to adapt before the行情就结束了," he noted.
The "weapon" to应对这种变化 is AI.
Currently, AI applications渗透至全链条. According to Wang Xingxing, a fund manager at China Universal Asset Management, current AI applications in quant investing can be divided into two main dimensions. First, effectively broadening overall Alpha sources. Traditional quant research often relies on linear thinking, making it difficult to identify隐性错误定价 in the market. Various AI models, by introducing nonlinear analytical logic, can uncover market patterns难以发现 by humans and efficiently process vast amounts of unstructured data like text, further enriching超额收益来源. Second,大幅提升整体投研运转效率. Empowered by large models, routine tasks like strategy coding, code optimization, and basic research see有效精简重复工作量, freeing up研究员投研精力.
"The level of application varies across the industry. Some firms source over 70% of their alpha factors from AI learning. Our reliance on AI for因子挖掘 is not that high. We prefer factors to maintain strong interpretability, while applying AI more for end-to-end generation of交易信号和策略," commented a Shanghai-based quant fund manager.
In his view, AI is not a one-time investment solution. Building a solid AI quant framework is just the foundation;持续迭代才是关键. The current breakthrough points in AI quant are not in单一模型升级 but集中在三个核心方向: First,落地应用 academic新算法结合金融市场的特性, rather than simply套用; Second,针对市场变化迭代现有模型的细节 to make models更贴合市场实际; Third,挖掘高质量增量数据 from更细分的维度, including financial footnotes, patents, research meeting notes, etc., to find差异化机会 from the data source.
Several quant fund managers also emphasized that teams do not blindly迷信"大模型." "The financial领域并不一定需要特别大的模型. Sometimes, larger models are反而越容易过拟合," they noted. They believe the more important task for a quant fund manager is integrating their understanding of financial markets into the model framework, adapting and modifying traditional models to make them更加适应市场.
**"The Entire Industry Will Become Increasingly 'Inward-Rolling'"**
**Public Quant Must "Learn from Both Sides" in the Future**
Public quant is currently experiencing a "highlight moment" of capital inflows and performance. However, beneath the繁华, the path ahead is not without challenges. It faces严峻考验 from strategy effectiveness, regulatory compliance, AI technology application, and industry competition.
A large Beijing-based fund company highlighted several challenges. First, strategy capacity is approaching its天花板. Public quant faces an "impossible triangle" regarding performance and规模 –规模增长会摊薄超额收益, which is the industry's most核心的硬约束. Simply put, when asset规模太大, many effective trading strategies fail due to excessive冲击成本.
Second, strategy同质化 and model失效加速. When everyone uses similar multi-factor models, trading becomes拥挤, leading to超额收益被快速"填平." Especially with AI赋能, the失效的速度 of a factor after discovery is accelerating, making it harder to维持有效性.
Third, the考验 of adapting to market style shifts. Quant models容易"掉队" in structurally分化 markets (e.g., where a few heavyweight stocks surge sharply). The "black box" nature of models also加大难度 for risk溯源和调整.
Wang Xingxing also坦言 that the industry currently faces significant pressure. As the number of quant products across the market grows and overall AUM膨胀,行业同质化严重, and存量阿尔法持续衰减. Simultaneously, information flow in the A-share market is fast, and sector style轮动愈发频繁. For sustained long-term development, continuous打磨因子体系 and持续迭代更新策略 are necessary to维系稳定超额表现.
"In the future, the entire industry will become increasingly 'inward-rolling'," stated a Shanghai-based quant fund manager. Public quant can neither delve into company fundamentals as深入细致 as subjective investing nor achieve the高换手率策略 of private quant, which确实 is its局限性. But its advantage lies in the potential for相互学习和借鉴 between the two approaches.
"Learning from private quant means public quant needs to use similar data, like高频数据. The难点 lies in如何尽可能提升超额收益 while maintaining a较低换手率, to bring收益水平更接近私募. Learning from subjective investing involves借鉴其对赔率的判断逻辑. The strongest ability of subjective investing is capturing短期爆发力较强的牛股, most of which have基本面支撑. Public quant can better捕捉高赔率股票的主升浪机会 through analyst data, diversified models, and other means, thereby achieving有效结合 with subjective investing," he explained.
Despite the challenges, the future development opportunities for public quant are十分清晰. Wang Xingxing believes that on one hand, public quant普遍持仓分散 with整体换手偏低, giving it显著策略容量优势. On the other hand, quant products overall have均衡风格 and行业分散, leading to更强的行情适配性, possessing天然优势 within the entire equity asset management赛道 with充足行业发展潜力.
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