Core Viewpoint - The implementation of the new quantitative trading regulations on July 7 is not expected to significantly impact the market, as institutions have already prepared for these changes and conducted self-assessments prior to the regulations' release [4][7]. Group 1: Market Reactions and Rumors - There are rumors suggesting that the market drop on July 4 was due to the upcoming quantitative regulations, but institutions argue that the market had already anticipated these changes [2][4]. - A claim by a well-known economist that high-frequency trading frequency would drop from 299 times per second to 30 times is dismissed by multiple institutions as unfounded [3]. - The industry has noted a trend of blaming quantitative strategies for market issues, with calls to stop stigmatizing quantitative trading as synonymous with high-frequency trading [3]. Group 2: Details of the New Regulations - The "Procedural Trading Management Implementation Rules," or "quantitative regulations," will officially take effect on July 7, with prior public consultation having occurred in June 2024 [4]. - The regulations define high-frequency trading and allow exchanges to impose differentiated management requirements on investors engaging in such trading [6][9]. - Many leading institutions have already adjusted their trading frequencies to fall below the new high-frequency trading definitions [6]. Group 3: Impact on Quantitative Strategies - The extent of the impact from the new regulations will depend on the scale of individual products, with larger quantitative institutions potentially facing manageable effects [7]. - Most quantitative strategies are not expected to be significantly affected, as many products do not reach the thresholds set by the new regulations [7]. - The trend towards lower-frequency trading strategies is seen as a response to regulatory guidance and the limited capacity of high-frequency strategies to meet the demands of larger institutions [10][11]. Group 4: Future of Quantitative Trading - The shift towards lower-frequency strategies is viewed as a key trend for the future of quantitative trading in the A-share market, driven by the need for larger capacity and diverse strategies [10][11]. - Quantitative investment is recognized as a neutral tool that extends beyond high-frequency trading, with applications in risk management and value investment strategies [11].

Core Viewpoint - The integration of AI-driven investment transformation, long-term policy orientation, and the responsibility of the domestic quantitative investment industry presents both challenges and opportunities for institutional managers committed to a long-term perspective [1][16]. Group 1: Era Background - The current era is characterized by a convergence of technological evolution and institutional transformation, with generative AI fundamentally altering various industries and providing new tools for long-term value investment [1]. - The development of AI is progressing from enhancing multi-step reasoning capabilities (L2) to achieving "perception-planning-execution" closed-loop capabilities (L3), marking 2025 as the "Year of AI Agents" [2]. Group 2: Policy and Market Dynamics - National policies are reinforcing a long-term orientation, with new regulations encouraging long-term capital market entry, advocating value investment, and standardizing algorithmic trading [4]. - The A-share market is undergoing positive structural changes, with improved information disclosure, regulatory enforcement, and investor composition, creating a foundation for sustainable long-term investment [5]. Group 3: Role of Quantitative Trading - Quantitative trading plays a crucial role in enhancing resource allocation efficiency and market stability, acting as both a "lubricant" and "stabilizer" in financial markets [6]. - Research indicates that quantitative trading can provide liquidity and price discovery, thereby improving overall market efficiency [6]. Group 4: Smart Beta Strategy - The Smart Beta strategy aims to serve long-term institutional capital by providing a reliable long-term allocation tool that combines long-termism with a tool-oriented approach [10]. - This strategy emphasizes a systematic modeling of fundamental factors, focusing on long-term value characterization while adhering to the principles of objectivity and discipline in quantitative investment [10][11]. Group 5: AI Exploration and Future Opportunities - The industry is increasingly embracing AI, with research categorized into interest-driven academic AI studies and more challenging industrial-grade AI development [12]. - Opportunities in the AI era can be divided into application-oriented real opportunities and foundational capability exploration, with the latter focusing on the potential of intelligent systems [13]. Group 6: Conclusion and Call to Action - The current environment presents a unique opportunity for active participation in shaping the future, emphasizing the importance of long-term commitment and practice over short-term certainty [16][17].

Core Viewpoint - The integration of AI-driven investment transformation, long-term policy orientation, and the responsibility of the domestic quantitative investment industry presents both challenges and opportunities for institutional managers who adhere to a long-term perspective [2][4]. Group 1: Era Background - The current era is characterized by a convergence of technological evolution and institutional transformation, with generative AI significantly altering various industries and providing new tools for long-term value investment [4]. - AI is evolving from enhancing multi-step reasoning capabilities (L2) to developing AI Agents (L3) that possess "perception-planning-execution" closed-loop capabilities, marking 2025 as the "Year of AI Agents" [4]. Group 2: Market Dynamics - In overseas markets, AI-assisted research has become mainstream, with hedge fund managers leading the adoption of large models to optimize investment research processes [5]. - National policies are reinforcing a long-term orientation, with new guidelines encouraging long-term capital entry into the market and advocating for value investment [5]. - The A-share market is undergoing positive structural changes, with continuous optimization in information disclosure, regulatory enforcement, and investor structure, creating a foundation for sustained long-term investment [5][6]. Group 3: Quantitative Investment Strategies - The Smart Beta strategy is positioned to meet the needs of long-term institutional capital, aiming to provide a reliable long-term allocation tool that combines long-termism with a tool-oriented approach [12][13]. - Smart Beta strategies emphasize a tool-oriented approach, focusing on systematic modeling of fundamental factors to create understandable, replicable, and assessable allocation tools [13]. - The design principles of Smart Beta strategies include high capacity, low turnover, and reasonable fees, supporting institutional investors in achieving long-term allocations [13]. Group 4: AI Development and Research - The industry is embracing AI, with research categorized into interest-driven academic AI studies and more challenging industrial-grade AI development, which requires significant investment and long-term planning [17][18]. - The establishment of the Wizard Intelligence Learning Lab (WILL) reflects the commitment to exploring the future of intelligence, emphasizing the importance of AI's social value [19]. Group 5: Conclusion and Call to Action - The current environment presents both uncertainty and structural challenges, but also opens up opportunities for innovation and development [22]. - The emphasis is on participation and construction rather than observation, highlighting the belief that worthwhile endeavors are often based on long-term faith and practice [22][23].

Core Viewpoint - The integration of AI-driven investment strategies, particularly the Smart Beta approach, is seen as a pivotal development in the investment landscape, aiming to balance long-term value investment with quantitative methods [1][5][15] Group 1: Technological and Policy Context - The current era is marked by a convergence of technological evolution and institutional transformation, with generative AI significantly impacting various industries and facilitating the implementation of long-term investment philosophies [3][8] - AI is evolving from enhancing multi-step reasoning capabilities to developing AI Agents capable of executing complex tasks autonomously, marking 2025 as the "Year of AI Agents" [8][10] - National policies are increasingly promoting long-term investment, with new regulations encouraging the entry of long-term capital into the market and advocating for value investing [10][11] Group 2: Role of Quantitative Trading - Quantitative trading serves as both a "lubricant" and "stabilizer" in the market, enhancing resource allocation efficiency and providing liquidity and price discovery mechanisms [4][12] - The evolution of the Chinese market structure, including improved information disclosure and regulatory enforcement, supports a fundamental-driven market mechanism conducive to long-term investment [10][11] Group 3: Smart Beta Strategy - The Smart Beta strategy is positioned as a reliable tool for long-term institutional investors, focusing on systematic modeling of fundamental factors to create transparent and replicable investment tools [15][16] - This strategy emphasizes low turnover, reasonable fees, and high capacity, aligning with the goal of achieving "universal access" for long-term investors [16][15] Group 4: AI Exploration and Future Opportunities - The industry is witnessing a surge in AI research, categorized into academic-driven studies and industrial-level AI development, which involves significant investment and long-term planning [17][18] - Opportunities arising from AI can be divided into application-oriented chances and foundational capability explorations, both of which are crucial for enhancing industry efficiency and addressing fundamental questions about AI's potential [18][19] Group 5: Conclusion and Vision - The current environment presents both uncertainties and structural challenges, yet it also opens up new avenues for development through technological breakthroughs and collaborative efforts [20] - The establishment of AI laboratories, such as WILL, reflects a commitment to exploring the societal value of AI and fostering a culture of responsible innovation within the investment sector [19][20]

Group 1: Overseas Quantitative Dynamics - The impact of tariff events has led to significant drawdowns for quantitative hedge funds, with Renaissance Institutional Equities Fund experiencing an approximately 8% decline in early April despite a 22.7% increase in 2024 [1][2] - Man Group's trend-following strategy also faced over a 10% drawdown, prompting a return to in-office work for some researchers to enhance strategy intervention [1] - Systematica Investments, founded by Leda Braga, saw a 20% drawdown in early April, highlighting the vulnerability of trend-following strategies during such events [1] Group 2: Adoption of AI in Quantitative Strategies - AQR has begun to embrace AI in investment decisions, acknowledging its potential for higher returns despite challenges in explanation during drawdowns [3] - In contrast, domestic private quantitative firms in China are utilizing AI more extensively, with teams like Baiont Quant employing fully self-developed AI algorithms for minute-to-hour level return predictions [3] Group 3: Market Uncertainty and Quantitative Strategies - BlackRock emphasizes the importance of adjusting models to cope with increasing global uncertainty, identifying three main uncertainties in tariff policies: target, scale, and timeline [6] - The evolution of BlackRock's quantitative investment system has led to a more granular approach to risk exposure, now incorporating over a thousand risk factors [7] - BlackRock's strategy focuses on maintaining neutrality in risk exposure while seeking short-term reversal opportunities in a high uncertainty environment [8] Group 4: Macro Hedge Fund Perspectives - Bridgewater highlights the impact of "modern mercantilism" on investment portfolios, noting the challenges posed by chaotic implementation processes and the unique risks facing U.S. assets [10] - Despite recent market volatility, Bridgewater believes that asset prices have not undergone substantial adjustments, indicating potential future opportunities [10] - The interaction between AI development and modern mercantilism is seen as a new dynamic, with AI potentially offsetting some negative impacts on productivity [11] Group 5: AQR's Investment Focus - AQR suggests that high volatility factors, while challenging to maintain, can yield significant long-term Sharpe ratios, advocating for the acceptance of these factors [12][16] - The firm recommends focusing on small-cap stocks, particularly in emerging markets, due to their lower valuations and potential for higher returns compared to U.S. large-cap stocks [19] Group 6: Performance Tracking of Quantitative Products - Factor rotation products from BlackRock and Invesco have outperformed their respective indices over the past five years, with BlackRock's adaptive factor selection demonstrating resilience [21][24] - The performance of machine learning-based ETFs has varied, with QRFT showing strong results in certain months while AIEQ continues to experience significant drawdowns [39] - Bridgewater's All Weather ETF faced notable drawdowns due to tariff events but has since recovered, indicating resilience in its strategy [40]

Core Viewpoint - The article emphasizes the investment value of the technology innovation sector, driven by policy support and the growth potential of technology companies, particularly in the context of China's strategic focus on innovation and economic development [2][11]. Group 1: Investment Value of Technology Innovation - The investment value in the technology innovation sector is primarily driven by two factors: high growth potential due to policy support and the inherent growth potential of technology companies [2]. - The Chinese government has consistently emphasized the importance of technology innovation through various policies, making it a key area for long-term investment [2]. - The technology innovation sector is seen as a high-potential growth area, with continuous breakthroughs in fields such as deep learning, robotics, military technology, and innovative pharmaceuticals [2]. Group 2: Performance of Investment Products - The "Longqi Technology Innovation Selected No. 1" product launched by Longqi Technology has achieved impressive returns, outperforming other products in the same category [4][11]. - As of June 20, 2025, the "Longqi Technology Innovation Selected No. 1" product reported a return of ***%, surpassing other products like "Longqi Quantitative Multi-Long No. 1" and "Longqi CSI 2000 Index Enhanced No. 1" [4]. - The article highlights that the performance of the technology innovation products is particularly attractive to investors looking for high-risk, high-return opportunities in the Chinese technology sector [14]. Group 3: Quantitative Investment Strategies - The technology innovation sector, particularly the Sci-Tech Innovation Board and the Growth Enterprise Market, is well-suited for quantitative investment strategies due to their active trading environment [5]. - The flexibility in trading rules and higher turnover rates in these markets create opportunities for achieving excess returns through quantitative stock selection [5]. - Longqi Technology's approach combines fundamental analysis with alternative factors, such as patent and R&D investment metrics, to enhance investment performance in the technology innovation sector [7][11].

- AQR has started to embrace AI in its investment decisions, using more AI algorithms to potentially provide higher returns despite occasional difficulties in explaining drawdowns[11] - BlackRock's quantitative system aims to identify more granular risk factors and maintain neutrality to most risks, while seeking short-term reversal opportunities in dense market trading to outperform the market[1][15][16] - Bridgewater is focusing on the impact of "modern mercantilism" on asset prices, noting that U.S. assets still face significant uncertainty and highlighting the strong allocation value of gold[21][22] Quantitative Models and Construction Methods 1. **Model Name: BlackRock's Safety Engineering System** - **Construction Idea**: To handle high uncertainty by identifying more granular risk factors and maintaining neutrality to most risks - **Construction Process**: - The system has evolved to control risk exposure not only to conventional factors like market cap, momentum, and growth value but also to thousands of more granular risk factors such as Japan export factor and domestic demand stock factor - The system adjusts these factors based on macroeconomic changes and increases the frequency of monitoring event-related factors to hourly or minute levels - **Evaluation**: The system's performance during the pandemic demonstrated that broader data dimensions and more precise risk control are more important than complex models[15][16][17] 2. **Model Name: AQR's High Volatility Factor Model** - **Construction Idea**: To embrace high volatility factors for their long-term Sharpe ratio despite short-term drawdowns - **Construction Process**: - AQR uses the variance ratio to measure the volatility level of factors: $ \text{Variance Ratio} = \frac{\text{Annual Factor Return Variance}}{\text{Monthly Factor Return Variance} \times 12} $ - Factors with higher variance ratios are considered high volatility factors - AQR analyzed 13 major categories and 153 sub-factors for their variance ratios and Sharpe ratios - **Evaluation**: Long-term high volatility factors show a significant positive correlation with Sharpe ratios, suggesting that quantitative managers should embrace these factors and use diversification to reduce short-term volatility[23][24][25] Model Backtesting Results 1. **BlackRock's Safety Engineering System** - **Information Ratio (IR)**: - Economic regime: 1.02 - Valuation: 0.77 - Sentiment: 0.43 - Growth timing: 1.06 - Aggregate signal: 1.83 - **Max Drawdown**: - Economic regime: -2.5% - Valuation: -3.4% - Sentiment: -4.2% - Growth timing: -2.7% - Aggregate signal: -1.9%[40] 2. **AQR's High Volatility Factor Model** - **Variance Ratio**: - Debt Issuance: 1.8 - Accruals: 1.6 - Profitability: 1.5 - Low Leverage: 1.4 - Investment: 1.4 - Profit Growth: 1.4 - Value: 1.4 - Core Stream Size: 1.2 - Quality: 1.2 - Seasonality: 1.1 - Low Risk: 1.0 - Momentum: 1.0 - Short-Term Reversal: 0.9 - **Sharpe Ratio**: - Debt Issuance: 0.7 - Accruals: 0.6 - Profitability: 0.3 - Low Leverage: 0.0 - Investment: 0.4 - Profit Growth: 0.4 - Value: 0.4 - Core Stream Size: 0.0 - Quality: 0.4 - Seasonality: 0.2 - Low Risk: 0.1 - Momentum: 0.3 - Short-Term Reversal: 0.1[24][25][27]

Group 1: Investment Opinions on Innovation Drugs - Rongtong Fund's Wan Minyuan expressed skepticism about the innovation drug sector, claiming that most data pertains to 3-5 years in the future and that many companies are still in early clinical stages or preclinical, suggesting a significant bubble compared to previous CXO bubbles [1] - In contrast, a well-known private equity figure, "Chen Yingzhang," argued that the current wave of innovation drugs represents a historic reversal, with potential for leading companies to create world-class drugs and generate substantial wealth [1] Group 2: Mid-Year Strategy Meetings by Securities Firms - Major securities firms, including Huatai Securities and Guotai Junan, held mid-year strategy meetings, indicating a positive outlook for the A-share market in the second half of the year, with a consensus on the technology sector being favored [2][3] - Analysts from Huatai Securities noted that the valuation repair of Chinese assets is ongoing, with expectations that the A-share market will outperform overseas markets [2] Group 3: Investment Strategies and Opportunities - Guotai Junan's strategy chief highlighted a clearer "transformation bull" market in China, driven by policies aimed at debt resolution, demand stimulation, and asset price stabilization [3] - Investment opportunities identified include financial and high-dividend stocks, emerging technology sectors, and cyclical consumer goods, with a focus on companies with strong dividends and monopolistic advantages [4] Group 4: Company Developments and Financing - Shanghai Jiaqi, a quantitative private equity firm, underwent a change in actual control, with the new controller increasing their stake from 20% to 56%, indicating a strategic shift within the company [5] - Guoao Technology announced the completion of several million yuan in Series A financing, aimed at expanding production capacity and accelerating product development in high-end semiconductor and robotics sectors [5][6] - Shengwei Technology, a virtual machine developer, secured nearly 100 million yuan in funding to enhance its technology and market presence, contributing to the development of the domestic operating system ecosystem [7] Group 5: Strategic Partnerships and Initiatives - Renhe Pharmaceutical established a comprehensive strategic partnership with Western Securities, focusing on capital and industry collaboration to explore high-quality development paths [9] - China Merchants Securities launched the first ESG public financial laboratory and a public investment advisory fund, committing over 50% of advisory fees to charitable causes [10]

当全球资管行业还在对大模型等新兴算法工具应用于投资策略分析的可行性进行探索时，来自中国的头 部量化私募机构正密集进行技术储备，并有望借助新技术实现"超车"。 日前，又一"国产"量化基金秀出了自身的算法"肌肉"：上海念空数据科技中心(以下简称"念空科技")通 过与上海交通大学计算机学院的合作，提出了一种全新的大模型后训练方法。相关研究论文已投向人工 智能领域的顶级会议神经信息处理系统大会(NIPS)并于5月20日发表。 该论文发表后引起行业高度重视。有业内人士认为，自幻方量化推出大模型DeepSeek并开源后，如何 将大模型应用于量化交易备受市场关注，但截至目前，即便是幻方量化自身也尚未公布成熟的应用案 例。念空科技或许抢到了大模型应用于量化投资的"头啖汤"。 5月20日，念空科技董事长、首席投资官王啸在接受《证券日报》记者采访时进一步透露，念空科技已 开始将大模型应用于量化决策中，在企业内部的测试中，其大模型已展现出接近传统AI策略的预测能 力。 与中国量化对于大模型的积极拥抱态度有所不同，当前，全球头部量化机构对大模型仍持谨慎态度，推 进大模型应用的节奏相对缓慢。纵观全球资管市场，海外量化巨头虽储备大量算力 ...

编者按： 十载春华秋实，鉴往知来；十年星河璀璨，与光同行。自破茧初啼至引领风潮，"金长江"评选始终以专业为炬、以公正为尺，丈量中国私募基金行业的奔腾浪 潮。值此华章再启之际，证券时报·券商中国倾情推出"金长江风华录·十年十人"，特邀十位穿越牛熊周期的行业翘楚，以躬身力行的灼见为经纬，以栉风沐雨的 征程为注脚，共同镌刻一部激荡人心的奋进诗篇。此间星霜，既见群峰竞秀，亦显大江奔流。 本期是"寻访金长江之十年十人"第二期。券商中国记者走进百亿量化私募茂源量化，茂源量化创始人郭学文接受了记者的专访。 他14岁考入清华，博士后从事气候变化大模型研究，还曾先后创立两家科技企业，均被上市公司收购，其个人经历相当丰富和传奇。2013年，郭学文创办茂源量 化，编写了国内最早的高频交易策略，2018年发行第一只股票产品，2020年启动资管业务，2021年突破百亿规模。 在茂源量化的办公室，挂着一幅"量化投资之父"詹姆斯·西蒙斯与丘成桐教授讨论数学问题的手稿，时间是2020年9月14日。郭学文告诉记者，这份手稿是由丘成桐 教授赠送，当时已经82岁高龄的西蒙斯，在听丘先生讲座时与其讨论数学问题，依然认真地手写下了密密麻麻的问题，这种 ...