Core Insights - The report indicates that the current allocation of the Bank of China multi-strategy industry configuration system is as follows: Communication (9.6%), Banking (9.5%), Transportation (9.1%), Non-Bank Financials (8.0%), Food and Beverage (7.7%), Power Equipment and New Energy (7.2%), Steel (6.7%), Machinery (6.2%), Basic Chemicals (4.7%), Oil and Petrochemicals (4.7%), Home Appliances (4.4%), Comprehensive (3.5%), Agriculture, Forestry, Animal Husbandry, and Fishery (3.5%), Comprehensive Finance (3.5%), Nonferrous Metals (3.5%), Building Materials (3.4%), Electronics (2.4%), Power and Utilities (1.2%), and Construction (1.2%) [1] Market Performance Review - The average weekly return of the CITIC primary industries is 0.0%, with a one-month average return of -4.1%. The top three performing industries this week are Communication (6.4%), Defense and Military (4.6%), and Non-Bank Financials (3.3%). The worst-performing industries are Coal (-3.6%), Oil and Petrochemicals (-2.7%), and Steel (-2.4%) [3][10] - The composite industry rotation strategy achieved a cumulative return of 0.3% this week, with an excess return of 5.2% year-to-date compared to the CITIC primary industry equal-weight benchmark [3][10] Industry Valuation Risk Warning - The report employs a valuation warning system based on the PB ratio over the past six years, excluding extreme values. Industries with a PB ratio above the 95th percentile are flagged for high valuation risk. Currently, the industries under warning include Computer, Retail, Media, Nonferrous Metals, Oil and Petrochemicals, and Defense and Military [12][13] Single Strategy Performance - The top three industries based on the S1 high prosperity industry rotation strategy are Machinery, Communication, and Power Equipment and New Energy [15][16] - The S2 implied sentiment momentum strategy ranks the top three industries as Communication, Machinery, and Electronics [20] - The S3 macro style rotation strategy identifies the top six industries as Banking, Home Appliances, Power and Utilities, Oil and Petrochemicals, Transportation, and Construction [23] Strategy Adjustments - The composite strategy has increased positions in TMT, midstream cyclical, and midstream non-cyclical sectors while reducing positions in consumer, financial, and upstream cyclical sectors [3][10]

Core Viewpoint - Quantitative investment has gained significant traction in 2023 due to breakthroughs in AI technologies and favorable market conditions, with quantitative long strategies showing strong performance in the A-share market [2]. Group 1: Quantitative Long Strategy Performance - As of November 2025, there are 715 quantitative long products with a total scale of approximately 609.92 billion, achieving an average return of 39.07% over the past year, outperforming other secondary strategies [2][3]. - The average returns for various secondary strategies are as follows: - Quantitative Long: 39.07% - Subjective Long: 35.20% - Other Derivative Strategies: 29.36% - Macro Strategies: 27.06% - Composite Strategies: 26.48% - Quantitative CTA: 18.55% - FOF: 17.88% - Stock Long-Short: 15.59% [3]. Group 2: Top Performers in Quantitative Long Strategies - Among the top-performing private equity firms with over 100 billion in assets, the average return for their quantitative long products is 43.46%, with 29 firms having at least three qualifying products [5]. - The top three firms in this category are: - Lingjun Investment - Pingfang Investment - Ningbo Huansheng Quantitative [5][8]. Group 3: Performance by Asset Size - For firms with 20-100 billion in assets, the average return is 41.79%, with the top three being: - Luxiu Investment - Yunqi Quantitative - Guangzhou Shouzheng Yongqi [9][10]. - In the 5-20 billion category, the average return is 35.88%, with the top three being: - Longyin Huxiao - Zhongmin Huijin - Yangshi Asset [12][13]. - For firms with 0-5 billion in assets, the average return is 33.26%, with the top three being: - Hangzhou Saipasi - Guangzhou Tianzheng Han - Hongtong Investment [15][16].

Group 1: Factor Performance Tracking - The large-cap style dominates the market, with significant positive returns from size, beta, and non-linear market capitalization factors, yielding 1.18%, 0.91%, and 0.82% respectively, while BP and liquidity factors posted negative returns of -0.55% and -0.38% [20][21] - In the CSI 300 stock pool, the best-performing factors include total asset growth rate (2.05%), quarterly ROA (1.71%), and turnover rate relative volatility (1.59%), while the worst-performing factors are logarithmic market cap (-1.00%), downside volatility ratio (-1.10%), and large order net inflow (-1.14%) [12][13] - In the CSI 500 stock pool, the top factors are quarterly EPS (1.61%), total asset growth rate (1.39%), and momentum spring factor (1.22%), with the poorest performers being the inverse of price-to-sales ratio (-2.49%), downside volatility ratio (-2.55%), and price-to-book ratio (-3.06%) [14][15] Group 2: Industry Factor Performance - The net asset growth rate factor performed well in the telecommunications, comprehensive, and coal industries, while the net profit growth rate factor excelled in the telecommunications sector [22] - The price-to-earnings (EP) factor showed strong performance in the telecommunications industry, while the BP factor underperformed across most sectors [22] - The logarithmic market cap factor performed well in the comprehensive, telecommunications, agriculture, forestry, animal husbandry, and electronics sectors, while the residual volatility factor excelled in telecommunications and commercial trade [22] Group 3: Combination Tracking - The PB-ROE-50 combination achieved significant excess returns across various stock pools, with excess returns of 0.30% in the CSI 500 stock pool, 1.60% in the CSI 800 stock pool, and 1.59% in the overall market stock pool [24] - The public fund research stock selection strategy and private equity research tracking strategy both generated positive excess returns, with the public fund strategy yielding 1.79% and the private equity strategy yielding 2.77% relative to the CSI 800 [3] - The block trading combination experienced a relative excess return drawdown of -0.95% compared to the CSI All Share Index, while the targeted issuance combination also faced a drawdown of -1.50% [3]

- The report constructs 10 style factors based on the BARRA model, including valuation factor, growth factor, profitability factor, size factor, Beta factor, momentum factor, liquidity factor, volatility factor, non-linear size factor, and leverage factor[16][17][19] - The construction process for style factors involves detailed formulas, such as the valuation factor (BP = Book to Price = Shareholder equity/Market capitalization), growth factor (SGRO = Sales growth rate derived from regression of past five fiscal years' revenue), profitability factor (ETOP = Earnings-to-price ratio = Net profit TTM/Market capitalization), and others[16][17] - The style factors are tested using weekly rebalancing on the CSI All Share Index (000985.SH) with no transaction fees considered[16][17] - Beta factor, momentum factor, and volatility factor showed strong performance recently, with weekly long-short returns of 4.54%, 4.34%, and 3.81%, respectively[19] - The report tracks 53 stock selection factors across valuation, growth, quality, size, reversal, momentum, liquidity, volatility, dividend, corporate governance, and technical categories[21][22] - Examples of stock selection factors include BP (Book to Price = Shareholder equity/Market capitalization), single-quarter EP (Net profit/Market capitalization), and 240-day momentum (cumulative return excluding the last 20 days)[22] - The construction of single-factor portfolios uses a neutral constraint method to maximize factor exposure while maintaining neutrality in industry and style exposures[62][64][65] - Single-quarter ROE, single-quarter ROA, and single-quarter net profit margin factors performed well across multiple stock pools, such as CSI 300, CSI 500, CSI 800, and CSI 1000[24][28][33][38] - The report evaluates index-enhanced portfolios for CSI 300, CSI 500, CSI 800, CSI 1000, and CSI 300 ESG stock pools using composite factors constructed via rolling 1-year Rank ICIR weighting[56][59][61] - CSI 300 enhanced portfolio achieved weekly excess returns of 0.33%, monthly excess returns of 1.05%, and annual excess returns of 13.02%[59][60] - CSI 1000 enhanced portfolio showed the highest annual excess returns of 15.68% among all portfolios[60] - The ESG-enhanced portfolio under CSI 300 stock pool achieved weekly excess returns of 0.59%, monthly excess returns of 1.09%, and annual excess returns of 7.35%[60] - The optimization model for portfolio construction maximizes exposure to target factors while maintaining neutrality in industry and style exposures, with constraints on stock weights, short selling, and full investment[62][64][65] - The model uses the following formula: $Max$$w^{\prime}$$X_{target}$ $s.t.$$(w-w_{b})^{\prime}X_{ind}=0$ $(w-w_{b})^{\prime}$$X_{Beta}=0$ $|w-w_{b}|\leq1\%$ $w\geq0$ $w^{\prime}B=1$ $w^{\prime}1=1$[62][63][64]

● 本报记者 刘英杰 日前，"中国银河证券·中国证券报私募行业星耀领航计划"调研团队走进国内知名量化私募机构超量子 基金，与其创始人张晓泉展开深度对话。围绕量化投资的科研创新驱动、私募机构的行业责任以及科技 金融实践等议题，共同探讨在激烈竞争的量化赛道中，如何通过底层科学探索构建长期核心竞争力，并 推动市场健康发展与价值发现。 "星耀领航计划"致力于打造国内最具影响力的科创类私募赋能平台，聚焦挖掘并培育兼具专业投资能力 与合规治理水平的私募管理机构。本次调研旨在推动多元投资理念的行业共享，助力构建科技、资本与 实体经济良性循环的生态体系。 发明量化投资"显微镜" 超量子基金的创立与发展，植根于张晓泉深厚的学术背景与对金融市场底层逻辑的长期思考。从二十多 年前在华夏证券实习时初次尝试用数据方法分析市场，到后来在金融数学与机器学习交叉领域持续探 索，张晓泉坚信，量化投资的未来不仅在于工程优化和算力堆砌，更在于基础科学研究带来的范式革 命。 "与其他机构不同的是，我们投入大量精力进行底层科学研究，致力于发明'显微镜'，而非仅优化'望闻 问切'。"张晓泉在接受中国证券报记者采访时表示。超量子基金将金融数学的严谨逻辑与 ...

Core Insights - Bloomberg's Multi-Asset Class Factor Risk Model (MAC3) has been adopted by Marshall Wace, a leading global alternative asset management firm with over $70 billion in assets under management, to enhance its quantitative research and systematic investment strategies [1][3] - The MAC3 model provides advanced modeling techniques, precise risk forecasting, and robust portfolio analysis capabilities, enabling comprehensive measurement and monitoring of multi-asset portfolio risks [1] - The MAC3 model is recognized as a state-of-the-art risk factor model, utilizing over 3,000 factors for daily calculations, offering superior predictive accuracy for various investment portfolios, ranges, and styles [1][2] Group 1 - Marshall Wace's adoption of MAC3 reflects the growing demand among institutional investors for high-precision risk models that help in understanding the factors driving portfolio risks [1] - The modular design and strong factor structure of MAC3 support quantitative and systematic managers in enhancing portfolio construction and risk prediction capabilities [1] - Bloomberg's PORT Enterprise, powered by the MAC3 model, serves over 800 clients with advanced portfolio risk analysis and attribution, featuring enhanced customization and batch reporting capabilities [2] Group 2 - Established in 1997, Marshall Wace is a leading alternative investment management company specializing in long/short equity and systematic trading strategies [3] - The company operates globally with offices in major financial hubs including London, New York, Hong Kong, Singapore, Abu Dhabi, and Shanghai, employing over 750 staff [3] - Marshall Wace's commitment to innovation and technology upgrades is a key driver of its success, supported by a robust and scalable global infrastructure [3]

Core Viewpoint - The article discusses the evolution of quantitative long equity strategies in the private equity sector, highlighting a shift towards more diversified and composite strategies that enhance returns and manage risks more effectively [5][21]. Group 1: Strategy Evolution - Quantitative long equity strategies are transitioning from single stock selection to multi-strategy integration, indicating a broader industry trend [7][5]. - The integration of T0 trading strategies enhances returns by maintaining full stock selection while allowing for short-term trading based on price signals [8][10]. - Position management is evolving with the cautious use of timing strategies, where only a small portion of the portfolio is allocated for timing to ensure higher certainty in returns [12][15]. Group 2: Multi-Asset Approach - The shift from pure quantitative long equity to multi-asset trading models is evident, with managers incorporating convertible bonds and tactical allocations to other assets [21][22]. - Convertible bonds provide both equity-like upside and bond-like downside protection, enhancing overall portfolio resilience [22][23]. - The strategy also includes periodic allocations to gold and government bonds, which offer low correlation returns without increasing overall risk [23]. Group 3: Composite Strategies - The trend towards multi-asset and multi-strategy composite models is becoming common, allowing for the capture of diverse alpha and beta returns [24][25]. - A representative strategy combines quantitative long equity with CTA strategies, leveraging the strengths of both to enhance returns and hedge risks [26][29]. - The composite strategy allows for efficient capital utilization through the inherent leverage of CTA strategies, improving overall portfolio performance [30]. Group 4: Market Dynamics - The influx of capital into quantitative long equity since 2018-2019 has led to increased competition, making traditional sources of excess returns harder to achieve [31][34]. - As more participants adopt similar methods, the need for more diverse and sustainable sources of returns becomes paramount for quantitative managers [35].

Core Viewpoint - The article discusses the latest developments and strategies of JQAI in the context of the AI era, emphasizing the importance of attracting top AI talent and the implementation of AI-driven investment research strategies [2][3]. Group 1: AI Talent Acquisition and Engagement - JQAI recently participated as a sponsor in NeurIPS 2025 to connect with top global AI talent, leveraging the event to engage with researchers who have achieved significant results in AI [2]. - The company aims to continuously attract and unite top AI talent globally as part of its investment research focus [3]. Group 2: AI-Driven Investment Research - JQAI is committed to exploring AI-driven investment research, focusing on building a fully controllable investment research system and investing in high-performance computing resources [3]. - The company has developed a new technology engine with over 400,000 CPU cores and over 200 petabytes of GPU resources, creating a cloud-native distributed investment research platform [3]. - The proportion of factors derived from AI methodologies in JQAI's factor mining has increased from approximately 20% at the beginning of 2024 to over 60% currently, indicating a significant shift towards AI applications in investment processes [3]. Group 3: Quantitative Stock Selection Strategy - JQAI's quantitative stock selection strategy differs from traditional index-enhanced strategies by not setting specific style constraints against benchmark indices, allowing for greater flexibility in utilizing predictive models [4]. - The quantitative stock selection strategy is designed to dynamically adapt to market conditions, addressing challenges in index-enhanced investment strategies [5]. Group 4: Market Adaptability - The article uses an analogy comparing the A-share market to a lake, where the quantitative stock selection strategy is likened to a sonar-equipped fishing boat that can navigate to areas with higher excess returns, unlike index-enhanced strategies that are limited to specific regions [5].

Group 1: Bond Underwriting Market - CITIC Securities leads the bond underwriting market with a scale of 1.28 trillion yuan, capturing a market share of 6.28% [1] - China International Capital Corporation (CICC) ranks second with an underwriting scale of 1.09 trillion yuan and a market share of 5.37% [1] - The "Guotai Haitong" combination has entered the top three with an underwriting scale exceeding 1 trillion yuan, indicating an increase in industry concentration [1] Group 2: Investment Fund Establishment - Western Securities, in collaboration with Shaanxi State-owned Assets, has established a 2 billion yuan merger and acquisition investment fund focusing on strategic emerging industries [2] - This initiative aims to enhance Western Securities' investment banking capabilities and support regional economic revitalization [2] - The fund is expected to catalyze resource integration in high-end manufacturing and new materials sectors in Shaanxi [2] Group 3: Quantitative Private Equity Trends - Leading quantitative private equity firms are aggressively entering niche markets, particularly in the domestic GPU and innovation sectors [3] - There is a notable trend of launching products focused on technology innovation and AI, reflecting a pursuit of excess returns in volatile markets [3] - Some firms are also diversifying into dividend strategies, indicating a shift in risk preferences among quantitative investors [3] Group 4: Growth of Dividend-themed Funds - The issuance of dividend-themed funds has accelerated in the second half of the year, with the number of new products doubling compared to the first half [4] - A total of 37 new dividend-themed funds have been issued, raising a cumulative scale of 20.44 billion yuan, significantly higher than the previous period [4] - This trend suggests a growing market preference for stable returns, particularly in sectors with consistent dividend payouts [5]

在量化投资领域，凭借出色的技术实力与清晰的策略逻辑立足的机构不在少数，"凡德投资"便是其中的佼佼者。这 家2012年成立、2014年完成中国证券投资基金业协会备案的全量化私募基金公司，如今已构建起完善的投研、风控 与运营体系，管理资产规模约4亿元。从自主研发三大核心系统，到深耕股指期货跨期套利策略，"凡德投资"始终 以"科技驱动、风险可控"为核心，在复杂的市场环境中为投资者创造稳定收益。今年3月，"凡德投资"参加了第三 届"期货寻星"赛，经过8个月的比拼，最终在量化组排名居前。"凡德投资"相关负责人接受期货日报记者采访时表 示，优异的比赛成绩充分验证了其核心交易理念。 十年积淀筑根基 "凡德投资"的稳健发展离不开经验丰富、分工明确的核心团队（4人负责策略研究、2人负责风控、2人负责市场分 析、1人负责交易、1人负责运营）。其中，多位核心成员为业内资深专家，为"凡德投资"的发展提供了坚实支撑。 "凡德投资"创始人兼总经理陈尊德是团队的"领航者"。他本科毕业于上海交通大学,后在清华大学与香港中文大学深 造，不仅有注册国际投资分析师（CIIA）、国际金融理财师（CFP）等权威资质，还担任多所高校的金融硕士导 师，兼 ...