Core Insights - The "2025 Zurich-Shenzhen Quantitative Finance Risk Summer Camp" was successfully inaugurated, organized by several institutions including the Southern University of Science and Technology and Zurich University [1] - The event featured notable speakers including Didier Sornette, who emphasized the critical role of risk management in finance [4] - The summer camp aims to enhance students' critical thinking and professional skills through interactions with experienced experts [4] Group 1: Event Overview - The summer camp is a two-week program taking place in Zurich and Shenzhen, focusing on trends in quantitative finance, financial market risks, and the application of machine learning [11] - The theme of this year's camp is "Focus on Data, Seize Opportunities," with a strong emphasis on practical aspects of quantitative investment [11] - The camp attracted over 20 outstanding students and professionals from various universities and financial institutions [12] Group 2: Key Speakers and Their Contributions - Didier Sornette highlighted the importance of risk management and encouraged students to engage with experts [4] - Lin Jun, CEO of Shenzhen Kangjie Wendo Technology Co., introduced the company's business logic emphasizing the significance of data in industry development [5] - Dr. Shao Hao delivered a lecture on the current state and frontiers of quantitative investment, discussing the challenges faced by traditional investment methods and the advantages of quantitative approaches [9] Group 3: Industry Relevance - Quantitative finance is increasingly important for financial risk management, utilizing interdisciplinary methods from mathematics, statistics, and computer science [12] - The development and application of quantitative finance provide comprehensive risk management tools and decision support for financial institutions and investors [12]

Core Insights - The fourth China Quantitative Investment White Paper Seminar was held, featuring a keynote speech by Feng Ji, founder of Beiyang Quantitative, on "Quantitative Investment in the Era of Large Models" [1] Group 1: Machine Learning in Finance - Beiyang Quantitative emphasizes high turnover and has adopted an "AI-native" approach to asset management from its inception, akin to building a tech company [3] - The core of machine learning is generalization, which allows models trained on historical data to perform well on unseen data, as formalized by Valiant's PAC learning framework [3] - The financial market is not efficient, meaning there is exploitable information beyond current prices, and high-frequency data is particularly suitable for machine learning due to its slower drift [3] Group 2: AI and Quantitative Research - The arrival of large models has rewritten the rules of the game, with a streamlined process for natural language processing (NLP) now consisting of pre-training, supervised fine-tuning, and reinforcement learning [4] - Beiyang has divided its team into two groups: a machine learning group focused on accuracy and a high-performance computing group focused on speed, eliminating traditional factor roles [4] - Shorter trading cycles are more susceptible to AI due to their inefficiencies and stable distributions, while longer cycles present exponentially greater challenges [4] Group 3: Future of AI in Investment - AI-driven research systems have the advantage of planned upgrades, contrasting with traditional research that relies on inspiration; Beiyang has a three-month development schedule for internal capabilities [4]

Core Viewpoint - The new regulations on algorithmic trading, effective from July 7, 2023, aim to impose precise supervision on high-frequency trading and strict constraints on abnormal trading, reshaping the market ecology and promoting the standardized development of the quantitative industry [1][4][19]. Summary by Relevant Sections High-Frequency Trading Regulation - The new regulations define high-frequency trading as any account making 300 or more orders per second or exceeding 20,000 orders per day, which will be subject to differentiated fees and additional reporting requirements [3][5][14]. - The regulations aim to suppress short-term speculation by limiting behaviors such as frequent order cancellations and manipulative trading, thereby reducing false liquidity and irrational market fluctuations [5][6]. Cost Implications - High-frequency trading strategies may see a decline in profitability by 30% to 50%, with smaller private equity firms facing elimination due to increased compliance costs [6][11]. - The introduction of a 1 yuan order fee and a 5 yuan cancellation fee will raise the breakeven point for high-frequency strategies by 30% to 50% [6][19]. Market Structure Changes - The regulations are expected to consolidate the advantages of leading quantitative firms that primarily use medium to low-frequency strategies, while smaller firms relying on high-frequency strategies may need to transition or exit the market [6][11]. - The overall market concentration is likely to increase, as smaller firms with insufficient technical reserves face pressure to adapt or exit [6][19]. Market Ecology Optimization - The regulations are anticipated to improve liquidity quality by reducing deceptive trading practices, allowing genuine supply and demand to be more accurately reflected in prices [6][11]. - The fairness of the market is expected to enhance as the technical advantages of high-frequency trading diminish, thereby protecting the interests of smaller investors [6][11]. Impact on Trading Volume - On the first day of the new regulations, the trading volume in the two markets decreased by over 200 billion yuan, indicating a potential impact on quantitative trading activities [8][9]. - Despite the drop, some market participants believe that the trading volume remained high, suggesting that the market's response to the new regulations may be within normal parameters [10][11]. Long-Term Industry Outlook - The new regulations are seen as a step towards a more transparent and fair market, promoting the sustainable development of the quantitative industry [16][19]. - The focus of competition in the quantitative sector is shifting from speed to the effectiveness of strategies, with an increasing emphasis on fundamental factors [19].

部分量化机构已经提前布局降频 量化交易新规7日正式实施。今年4月，沪深北交易所发布《程序化交易管理实施细则》（下称《实施细 则》），对程序化交易报告管理、交易行为管理、信息系统管理、高频交易管理等作出细化规定。 其中，新规重点加强了对高频交易的监管，明确了高频交易认定情形，在报告内容、交易收费、交易监 管等方面提出差异化管理要求等，还对程序化交易可能出现的瞬时申报速率异常、频繁瞬时撤单、频繁 拉抬打压以及短时间大额成交等四类异常交易行为作了进一步细化。 重点加强高频交易监管 程序化交易（俗称"量化交易"）是信息技术进步与资本市场融合发展的产物，在我国市场起步较晚但发 展较快，已成为证券市场重要的交易方式，有助于为市场提供流动性，促进价格发现。 但程序化交易特别是高频交易相对中小投资者存在明显的技术、信息和速度优势，一些时点也存在策略 趋同、交易共振等问题，加大市场波动。 近年来，为促进行业规范发展，监管部门加强了对程序化交易的监管。2024年5月，证监会发布《证券 市场程序化交易管理规定 (试行)》，对程序化交易监管作出总体性、框架性制度安排，并授权交易所细 化业务规则和具体举措。 今年4月，沪深北交易所同步 ...

Group 1 - The implementation of detailed regulations for algorithmic trading by the Shanghai and Shenzhen Stock Exchanges aims to promote a more standardized and efficient quantitative trading industry [1] - The new rules specify four types of abnormal trading behaviors, including rapid order submission, frequent cancellations, and large transactions within a short time frame, with high-frequency trading defined by specific thresholds [1] - Quantitative private equity firms are making significant adjustments to their trading models and risk management practices to comply with the new regulations, including real-time monitoring and strict control of trading volumes [1] Group 2 - The regulatory scrutiny on high-frequency trading has intensified, leading many large and medium-sized quantitative private equity firms to implement corrective measures, including dedicated personnel for compliance management [2] - As of mid-last year, the number of high-frequency trading accounts in the market decreased by over 20%, and behaviors triggering abnormal trading monitoring standards dropped by nearly 60% in the past three months [2] - The new regulations are seen as a means to foster healthy industry development, encouraging capable firms to thrive while reducing irrational trading behaviors and risks [2]

Core Viewpoint - The implementation of the "Procedural Trading Management Implementation Rules" marks a shift in quantitative trading from a focus on speed to a focus on depth, promoting a transition towards medium and low-frequency strategies in the industry [1][4][6]. Regulatory Changes - The new rules, effective from July 7, 2023, specifically target irregular high-frequency trading behaviors rather than the core logic of quantitative investment [1][3]. - Clear standards for high-frequency trading have been established, including thresholds of 300 orders per second and 20,000 orders per day for a single account [3][4]. - Many institutions have already adapted to these requirements, indicating that the overall impact on the industry will be limited [3][4]. Industry Trends - The new regulations are expected to drive a trend towards medium and low-frequency trading strategies, with a focus on factors such as trend, momentum, volatility, and volume analysis [5][8]. - The average trading frequency across the industry is anticipated to decrease, while mid-frequency strategies will remain a core source of alpha [5][6]. Competitive Landscape - The implementation of the new rules is likely to accelerate the process of industry consolidation, favoring institutions that prioritize risk control and sustainable returns [7][8]. - Smaller firms may need to innovate in niche areas to survive, while larger firms will benefit from their technological and compliance advantages [8][10]. - The market is expected to evolve into a more diverse ecosystem where both large and small institutions coexist, catering to varying investor needs [10][11]. Future Outlook - The quantitative investment landscape is projected to grow, but the excess returns may decline as the scale of management increases [10][11]. - The diversity of strategies in quantitative investment is expected to expand, addressing the current issue of strategy homogeneity [10][11].

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].

近日，在2025年中金财富主办的"新科技、新金融、新生态"财富管理发展论坛上，宽德投资联合创始人冯鑫发表了《AI时代的指数化投资：量化投资与长期价值 投资的融合》主题演讲，他围绕人工智能驱动下的投资变革、政策导向的长期化趋势，以及本土量化投资行业的责任与定位，分享了对时代背景与行业走向的深 度思考。 冯鑫拥有20多年量化投资实战经验，曾在SAC、BNP Paribas等国际一流投资机构任基金经理，2013年回国创建宽德投资。宽德投资过往业绩优异，如今已跻身国内 量化私募第一梯队。 精彩观点： 我们正站在一个技术演进与制度转型交汇的关键时点。以生成式AI为代表的新一轮技术浪潮正在深刻改变各行各业，也为长期投资理念的落地提供了新的工 具。 国内外多项研究已经表明，量化交易作为专业的交易者之一，其常驻性与广谱性特征，使其在市场中能够承担着"润滑剂"与"稳定器"的双重角色。 长期资金的崛起、理性投资的回归、AI能力的进步，重构了投资逻辑与市场生态。对于坚持长期视角的机构管理人而言，这既是巨大的挑战，更是难得的时代 机遇。 真正具备生命力的量化投资产品，既要能服务当下，也要具备面向未来的适应能力。智慧选股（Smart ...