金融市场，一个由数字、情绪与叙事交织的复杂系统，对多数人而言意味着波动与风险，但对于友山基 金首席投资官金焰而言，这更像是一个需要运用精密数学逻辑与深刻人性洞察去求解的课题。拥有数学 博士与商学院博士双重学术背景，辗转于华尔街顶级投行与买方机构的核心战场，最终将国际视野与实 战经验深耕于中国市场的独特土壤，金焰的职业生涯本身，就是一部关于理性选择、持续进化与边界认 知的生动注脚。 [从象牙塔到交易台] "如果要用'夏普比率'来衡量职业生涯，我认为大学教授的'夏普比率'无疑是最高的——职业路径稳定， 波动率极低。而做投资则截然不同。"访谈伊始，金焰用他最熟悉的金融术语，轻松道出了当年人生关 键岔路口的选择逻辑。 出身于中国科学院家庭，最初的理想是在大学讲堂传承知识、专攻学术研究。然而，上世纪90年代末， 金融工程与量化投资浪潮的涌动，与商学院更具吸引力的职业前景，促使这位数学博士在哥伦比亚大学 攻读了第二个博士学位。毕业时，面对高盛的橄榄枝与稳定的教职机会，他做出了一个考量"机会成 本"的理性决策："当时我想，教书未来还有机会，但去业界闯一闯的机会不多。"这份对未知领域的好 奇与挑战欲，驱使他投身于充满不确定性 ...

展望未来，袁宇认为，过去量化私募主要依靠算法生成股票预测信号，而如今AI技术已被嵌入数据清 理、特征提取、组合优化、交易执行等整个投研流程。强化学习、深度学习等前沿技术也助力量化私募 在风险控制与资产配置等环节实现动态优化。"总体来看，行业的核心竞争力正从资金规模转向模型与 算法的迭代速度，越来越多的量化私募将更像科技公司。" 谈及全球竞争格局，袁宇表示，海外不少量化模型长期在全球市场中占据压倒性优势，但过去一年，中 国本土量化投资策略的实战表现已超越海外头部机构。他认为，这一突破的根本原因在于：其一，中国 拥有活跃且持续成长的资本市场，为模型持续优化提供了充足的数据支持；其二，本土人才优势日益凸 显，无论是AI科研人才还是金融数据分析师，都为量化私募的快速发展奠定了坚实基础。 12月16日，在2025南通投资大会暨上证多层次资本市场高质量发展大会上，鸣石基金总经理袁宇在发表 主题演讲时表示，国内量化私募行业的发展本身就是科技驱动的。如今，量化私募与人工智能的深度融 合，正推动本土资产管理行业迈向新的竞争格局。 袁宇介绍，中国量化基金的起步与2010年沪深300股指期货的推出密切相关，并在2019年进入高速发 ...

Core Viewpoint - The report explores whether the advantages of quantitative investment strategies can be sustained in the A-share market environment of 2026, highlighting the cyclical switching between "consensus" and "divergence" market conditions as a key determinant of strategy effectiveness [2][3][5]. Market Environment and Strategy Effectiveness - The A-share market is expected to enter a "central uplift platform period" after returning from historical lows, driven by the long-term trend of market institutionalization and the recovery of incremental funds, particularly from ETFs [3][38]. - The report identifies "institutional holding concentration" as a core indicator linking macro market patterns with micro Alpha sources, suggesting that increased concentration indicates a shift to "consensus" markets, while decreased concentration favors "divergence" markets [2][26][30]. Market Outlook for 2026 - The overall market environment for 2026 is assessed as optimistic, with a focus on structural opportunities due to attractive risk premiums and the absence of extreme overheating [4][44]. - The report anticipates a shift in investment strategies from capturing short-term opportunities in "divergence" markets to identifying core trends in "consensus" markets, particularly with the emergence of AI as a new investment theme [11][41]. Alpha Sources and Market Modes - The evolution of Alpha sources is linked to market modes, with "trading Alpha" being more effective in "divergence" markets and "cognitive Alpha" in "consensus" markets [17][25]. - "Trading Alpha" focuses on capturing short-term pricing inefficiencies, while "cognitive Alpha" emphasizes accurate predictions of future fundamentals [18][19]. Market Concentration Dynamics - High market concentration reflects a consensus-driven environment that rewards depth in research, while low concentration indicates a divergence-driven environment that favors breadth in strategy [27][28]. - The report constructs a market concentration index based on the top holdings of public funds, indicating stronger consensus when the index is high and greater divergence when it is low [30][31]. Investment Strategy Recommendations - In the anticipated "central uplift platform period," strategies that effectively combine depth (through alternative data and machine learning) with breadth (systematic capture of rotation opportunities) are expected to perform better [42][41]. - The report suggests that quantitative strategies may continue to outperform average active equity funds due to their ability to adapt to complex market conditions [42][43].

Core Insights - The article emphasizes the importance of AI and emerging technologies in driving the transformation and high-quality development of the public fund industry in China [1][4]. Group 1: AI Integration in Fund Management - The public fund industry is at a historical turning point driven by technology, with AI becoming a core engine for transformation [1]. - Zhongyin Fund has independently developed a comprehensive investment strategy model, including a factor library and various models for alpha, risk, optimization, and attribution [1][3]. Group 2: AI Applications in Research - Zhongyin Fund established a quantitative research team as early as 2009, focusing on integrating unstructured and multi-source data into their factor framework using AI [2]. - The use of lightweight neural networks like BERT for sentiment analysis of company announcements marked the beginning of quantitative analysis of textual data [2]. Group 3: Factor Production and Efficiency - A significant transformation in factor production has occurred, with Zhongyin Fund developing an algorithmic mining system based on large models to automate factor generation [3]. - This new system has demonstrated the ability to produce a significantly larger number of effective factors while maintaining quality and diversity, allowing researchers to focus on more complex designs [3]. Group 4: Broader Impact of AI on Quantitative Investment - AI's influence extends beyond data processing and factor mining, impacting areas such as return prediction, alternative research, and portfolio optimization [4]. - Deep neural networks and tree models enhance traditional prediction models by capturing complex market patterns and providing excellent feature combination capabilities [4]. Group 5: Future Prospects of AI in Investment - The continuous advancement of technology and the rapid development of reinforcement learning offer further optimization opportunities for factor mining and portfolio optimization in quantitative investment [5].

Core Viewpoint - The report explores whether the advantages of quantitative investment strategies can be sustained in the A-share market environment of 2026, emphasizing the importance of market mode shifts between "consensus" and "divergence" markets in determining the effectiveness of different strategies [2]. Market Outlook - The company maintains a mid-term optimistic outlook for the A-share market in 2026, supported by various quantitative timing systems and technical indicators pointing to a healthy market environment [3][20]. - The style preference has shifted towards large-cap stocks, indicating a systemic change in the indicators affecting style returns [3][20]. Market Mode Shifts - The A-share market has shown distinct cyclical characteristics, alternating between "consensus" and "divergence" markets, which is crucial for assessing future strategy effectiveness [6]. - In the "consensus" phase (2017 and 2019-2021), investment strategies relied on deep research to identify core sectors and leading companies, favoring active management strategies [6]. - The "divergence" phase (2022 to mid-2025) saw a lack of consensus, leading to high-frequency switching among sectors, where quantitative strategies with systematic and diversified characteristics thrived [7]. Alpha Sources Transition - The evolution of market modes is accompanied by a shift in sources of excess returns (Alpha). In divergence markets, "trading Alpha" is predominant, focusing on capturing short-term pricing inefficiencies [11][12]. - In consensus markets, "cognitive Alpha" becomes more significant, emphasizing accurate predictions of future fundamentals and deep understanding of industry trends [12]. Market Concentration as an Indicator - Market concentration is identified as a key indicator for measuring market mode evolution and Alpha source transitions. Low concentration corresponds to divergence markets, rewarding breadth, while high concentration aligns with consensus markets, rewarding depth [13][14]. - The report predicts a return to a "central uplift platform period" for market concentration in 2026, following a recovery from historical lows [17][18]. Future Market Dynamics - The first phase of market concentration evolution is expected to see a return to historical median levels as AI themes gain acceptance, benefiting expert-driven active funds [17]. - The second phase is anticipated to enter a "weak equilibrium" platform oscillation in 2026, characterized by a dual-driven growth pattern from technology and traditional industries, which may limit rapid increases in institutional concentration [18]. Quantitative Strategy Advantages - In the anticipated "central uplift platform period," the complexity of the market may favor advanced quantitative strategies that can integrate depth (understanding main lines) and breadth (capturing rotations) [19].

Core Insights - The core philosophy of the company is to explore objective laws in financial markets through a technology-driven approach, emphasizing stability and long-termism in investment strategies [1][2][5] Group 1: Company Philosophy and Approach - The founder of the company, with a background in theoretical chemistry, emphasizes the importance of curiosity about objective laws and a strong statistical foundation in quantitative investment [2][3] - The company has established a leading low-latency trading system and invests tens of millions annually in IT to maintain technological superiority [2][5] - The investment philosophy is centered around a multi-factor stock selection model, utilizing over 200 global data sources, including alternative data, to uncover investment opportunities [5][6] Group 2: Risk Management and Strategic Discipline - The company faced challenges during a downturn in the quantitative industry, which tested its strategic discipline and risk management practices [4][5] - A significant lesson learned was the importance of adhering to fundamental principles, as deviating from them led to missed opportunities and losses [5] - The company maintains a cautious approach to asset management, prioritizing strategy and talent development over rapid growth in assets under management [5][6] Group 3: Talent and Internal Governance - The company believes in proactive talent and technology investment, ensuring that team size and capabilities grow ahead of asset scale [6] - An attractive incentive system is in place to motivate research and investment teams, including profit-sharing and partnership opportunities [6] Group 4: Social Responsibility and Future Vision - The company is committed to social value, engaging in scientific donations and encouraging industry peers to participate in philanthropic efforts [7][8] - The founder expresses a vision for the company to become one of the top quantitative institutions in China, focusing on excellence rather than size [9]

Core Insights - Inno Asset has been awarded the "Golden Bull Private Fund Management Company (Three-Year Managed Futures Strategy)" for the fifth time, showcasing its robust capabilities in quantitative investment [1] - The founder and CEO Xu Shunan emphasized that AI is an extension of quantitative methodologies rather than a revolutionary force, enhancing the ability to identify and represent trading patterns in complex data environments [1][3] Group 1: AI and Quantitative Investment - AI is viewed as a powerful statistical tool that enhances the capabilities of quantitative investment, which is fundamentally based on mathematics and statistics [3] - Inno Asset has systematically applied AI across various strategies, including Alpha, CTA, and algorithmic trading, leading to improved model recognition, response speed, and iterability [3] - The application of AI is expected to expand further as data becomes richer and foundational engineering is solidified, providing more "methodological dividends" for strategy evolution [3] Group 2: Efficiency and Creativity - AI is seen as an efficiency amplifier, taking over repetitive tasks such as data cleaning and feature construction, allowing teams to focus on more creative aspects like problem definition and risk control [4] - The integration of AI throughout the data-model-engine-trading chain aims to standardize processes and enhance execution paths while ensuring compliance with regulations [4][9] - The company believes that while AI enhances efficiency and precision, it cannot replace the human element in defining problems and constructing logic [7] Group 3: Methodology and Results - The source of good strategies is not solely dependent on the use of AI but rather on clear problem definitions, reliable data, and robust testing [8] - Inno Asset maintains a principle of methodological neutrality and results orientation, using AI to optimize strategy performance when appropriate, but also valuing traditional methods [8] - AI signals and traditional factors are developed in parallel, calibrated, and combined to create low-correlation multi-source Alpha, evaluated against stability, transaction costs, and capacity constraints [8] Group 4: Future Directions - Inno Asset aims to embed AI into multi-strategy and full-chain processes, focusing on building a solid foundation in local markets while seeking low-correlation opportunities across multiple assets and markets [9] - The company emphasizes the importance of maintaining a balance between innovation and compliance within a risk management framework, ensuring that creativity flourishes within defined boundaries [9] - The direction and pace of AI integration will continue to be guided by human judgment, reinforcing the commitment to delivering verifiable long-term performance to clients and the market [9]

Quantitative Models and Construction Methods 1. Model Name: Stock Index Futures Timing Model - **Model Construction Idea**: The model uses the basis of stock index futures to reflect market sentiment changes and constructs daily frequency timing signals based on this correlation[7] - **Model Construction Process**: - The model groups and tests the correlation trend between the basis of stock index futures and the index itself - Constructs daily frequency timing signals based on this correlation - As of October 17, 2025, the timing signal based on the basis of the CSI 500 stock index futures remained at 1[31] - **Model Evaluation**: The model effectively captures market sentiment changes and provides timely signals for trading decisions[7] 2. Model Name: Multi-Dimensional Timing Model - **Model Construction Idea**: The model integrates macro, micro, meso, and derivative signals to form a four-dimensional non-linear timing model[33] - **Model Construction Process**: - The A-share market is divided into nine states based on macro, micro, and meso signals, each corresponding to long and short signals to form a three-dimensional large cycle timing signal - On this basis, the derivative signal generated by the basis of stock index futures is superimposed to form a four-dimensional non-linear timing model - The latest composite multi-dimensional timing signal is long (1)[34] - **Model Evaluation**: The model provides a comprehensive view of market conditions by integrating multiple dimensions, enhancing the accuracy of timing signals[33] 3. Model Name: Style Enhancement Model - **Model Construction Idea**: The model enhances returns by adding enhancement factors to the multi-style strategy, suppressing single-style fluctuations, and achieving stable excess returns in different cycles[41] - **Model Construction Process**: - The model is based on the multi-style strategy and adds enhancement factors - It dynamically adjusts the weights of different styles to achieve stable excess returns - As of October 17, 2025, the low volatility enhancement strategy achieved an excess return of 6.05%[42] - **Model Evaluation**: The model effectively enhances returns while controlling risks, providing stable performance across different market cycles[41] Model Backtesting Results Stock Index Futures Timing Model - **Absolute Return**: Not specified - **Excess Return**: 4.33%[9] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Multi-Dimensional Timing Model - **Absolute Return**: Not specified - **Excess Return**: 4.33%[9] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Style Enhancement Model - **Absolute Return**: Not specified - **Excess Return**: 6.05%[8] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Quantitative Factors and Construction Methods 1. Factor Name: High-Frequency Factor - **Factor Construction Idea**: The factor captures market valuation and sentiment risks using high-frequency data[11] - **Factor Construction Process**: - The factor uses high-frequency data to measure market depth, spread, and price elasticity - Constructs indicators such as average depth, spread, and price elasticity to reflect market liquidity and sentiment - For example, the average depth is calculated as: $$ avg_{depth} = \frac{av1 + bv1}{2} $$ where av1 and bv1 are the sell and buy volumes at the first level of the order book[98] - **Factor Evaluation**: The factor effectively captures market liquidity and sentiment changes, providing valuable insights for trading decisions[11] Factor Backtesting Results High-Frequency Factor - **Absolute Return**: Not specified - **Excess Return**: Not specified - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Industry and ETF Rotation Strategy 1. Strategy Name: Industry Rotation Strategy - **Strategy Construction Idea**: The strategy uses quantitative fundamental drivers, quality low volatility style drivers, and distressed reversal industry discovery methods to construct an industry rotation strategy[76] - **Strategy Construction Process**: - Combines industry fundamental rotation, quality low volatility, and distressed reversal three-dimensional industry rotation strategies into an equal-weight portfolio - Selects industries from different dimensions to achieve factor and style complementarity, reducing the risk of a single strategy - As of October 17, 2025, the annualized excess return of the industry rotation strategy based on three-strategy integration was 10.59%, with a Sharpe ratio of 0.74[80] - **Strategy Evaluation**: The strategy effectively combines multiple dimensions to enhance returns while controlling risks, providing stable performance across different market cycles[76] Strategy Backtesting Results Industry Rotation Strategy - **Absolute Return**: Not specified - **Excess Return**: 14.75%[10] - **Annualized Return**: 10.59%[80] - **Sharpe Ratio**: 0.74[80]

Group 1 - The total number of A-share investors in China has surpassed 240 million as of June 2025, indicating that one in six Chinese individuals is now a stock market participant [2] - By the end of 2024, individual investors accounted for over 99.76% of the total investor base, with 99.63% of new accounts in the first half of 2025 being individual investors [2] - The influx of personal investors reflects strong confidence in the A-share market and a growing demand for wealth management amid economic transitions and structural adjustments in China [2] Group 2 - The family trust market in China is projected to exceed 900 billion yuan by the end of 2024, with expectations to enter the "trillion era" in 2025 [2] - An estimated 20 trillion yuan of wealth is expected to be passed down to the next generation over the next decade, highlighting the urgency of addressing family wealth inheritance issues [3][18] Group 3 - The investment landscape is shifting as individuals seek effective asset allocation strategies beyond traditional real estate investments, particularly in the context of a changing economic cycle [3] - Young investors exhibit diverse attitudes towards wealth management, with some being overly conservative and others seeking high-risk, high-reward opportunities [7][9] Group 4 - Quantitative investment strategies are gaining traction among retail investors, offering a systematic approach to decision-making that can mitigate emotional biases in trading [11][12] - Basic quantitative methods focus on fundamental analysis, allowing investors to make informed decisions based on company performance rather than market trends [12] Group 5 - Effective wealth management requires a clear understanding of individual financial goals and risk tolerance, which can significantly influence investment strategies [13][14] - A layered approach to wealth management, separating funds for daily living expenses from those intended for long-term growth, can alleviate anxiety related to investment losses [17] Group 6 - The concept of wealth management should evolve from viewing oneself as the "owner" of wealth to acting as a "steward," emphasizing responsible management and long-term value creation [20][21] - Wealth should be viewed through a moral lens, ensuring that its use benefits society and enhances overall well-being rather than merely serving personal interests [22]

Core Insights - The conference highlighted the transformative impact of AI on quantitative investment, with discussions on how AI technologies are reshaping the investment landscape and strategies [1][2][3] Market Recovery and Quantitative Rise - Regulatory changes have positively influenced the quantitative investment sector, leading to a more robust market environment [1] - The A-share market has shown resilience and a strong recovery since September 24, 2024, driven by supportive policies, a shift in macro narratives, and fundamental validations [1][2] - The current market rally is characterized by greater stability compared to previous cycles, as indicated by financing data [2] AI Empowerment and Capability Enhancement - AI's application in quantitative investment allows for deeper analysis of vast financial data, surpassing traditional methods [2][3] - The emergence of large models like DeepSeek is expected to significantly enhance the understanding of market dynamics [2][3] - AI is viewed as a powerful statistical tool that complements quantitative investment, although human judgment remains crucial in strategy formulation [3] Addressing Challenges and Ecological Evolution - The quantitative investment industry faces challenges such as strategy homogenization and rapid market style shifts, prompting firms to seek diversity and alternative data sources [4][5] - Emphasizing diversity and effective portfolio management is essential for navigating market cycles and achieving long-term stability [4] - The use of alternative data is seen as a promising area for future growth, with firms exploring innovative solutions for data processing and validation [5] Industry Development Landscape - The rise of AI may lead to a concentration of resources within the quantitative investment sector, increasing barriers to entry due to the need for substantial investments in data, computing power, and talent [5] - The dual forces of regulatory frameworks and technological innovation are fostering a healthier and more diverse ecosystem within the quantitative investment industry [5]