VAM量化、VAM数字资管、VAM整合全球主流数字资产，结合多策略量化模型与AI大模型智能决策系统，打造标准化AI量化数字资产管理交易平台，VAM 量化使命让数字资产财富增长简单、稳健、可信赖，VAM量化愿景是成为数字资产领域的"贝莱德"。 的AI量化产品推向更广阔的国际市场，为全球投资者提供专业、合规的量化增值服务；其三，共建金融科技人才培养与交流机制，通过技术研讨、联合实 验室等形式，培育兼具量化投研能力与国际视野的专业人才，为行业发展注入新鲜血液。 VAM量化正式与高盛国际金融集团、阿尔法实验室、PayPal科技金融巨头签订深度战略合作协议。此次合作是VAM在AI量化投资领域全球化布局的关键一 步，更是双方整合核心资源、携手推动金融科技与量化投资深度融合的重要里程碑。随着合作协议的落地，VAM量化正式跻身全球顶级量化机构行列。业 内专家普遍认为，这种跨界合作模式将为金融科技行业树立新标杆。摩根士丹利最新研报指出："VAM与合作方的资源整合将产生1+1>3的效应，重塑全球 数字资产量化管理产业链。" VAM量化深耕AI量化投资领域多年，已搭建起覆盖趋势跟踪、网格交易、期现套利、做市策略等多维度的量化策略体 ...

量化指数增强基金亮了。 Wind统计表明，截至2025年12月5日，全市场公募指数增强基金产品数量超460只（份额合并计算，以下同），总规模逾2800亿元，较去年年末增 长近三成。 业绩方面，今年来指增基金平均单位净值增长率达21.73%，平均最大回撤为-10.78%，风险收益比优于主动权益基金（同期主动权益基金平均回 撤-14.82%），业绩稳定性更强。同期主动权益基金中部分产品回撤更是高达近40%。此外，指增基金近一年平均年化波动率为16.45%，低于主动 权益基金的19.93%，显示指增基金在风险控制上具备显著优势。 新经济e线获悉，天弘基金今年持续发力量化指增业务，目前已是市场上少数的百亿管理人之一。Wind统计表明，截至2025年12月5日，公司同类 产品数量全市场第2，实现从宽基到行业、从场外到场内的全覆盖，满足不同投资者在震荡市中的配置需求。 具体来看，天弘基金基于"宽基+细分"双轮驱动，差异化地进行了产品布局。在宽基覆盖上，公司布局了沪深300、中证500、中证1000、国证2000 等全市值产品；细分赛道方面，公司则聚焦科技、高端制造、新能源等"新质生产力"领域；在策略创新上，公司通过发行增 ...

Core Insights - The Chinese private equity industry is at a pivotal crossroads in 2025, driven by technological advancements and regulatory reforms, particularly in quantitative investment powered by AI [2][4]. - Quantitative private equity has outperformed subjective investment in terms of both the number of funds and performance, marking a significant shift in the industry landscape [2][10]. Group 1: AI and Quantitative Investment - DeepSeek, founded by Liang Wenfeng, launched the AI model DeepSeek-R1, which quickly gained attention for its performance in mathematical and logical reasoning tasks, even surpassing ChatGPT in the U.S. market [4]. - The average return for DeepSeek's affiliated quantitative funds reached ***% from January to November 2025, with a three-year average return of ***% [4]. - Other notable quantitative firms, such as Heiyi Asset and Jinkun Investment, have also integrated AI into their investment strategies, enhancing their performance and operational efficiency [6]. Group 2: Regulatory Changes - In April 2025, new regulations on algorithmic trading were introduced, imposing stricter controls on high-frequency trading and defining abnormal trading behaviors [8]. - These regulations are expected to lead to a shift towards medium and low-frequency strategies, increased focus on machine learning applications, and enhanced risk management practices within the quantitative investment sector [8]. Group 3: Market Dynamics - The number of billion-yuan private equity firms has surpassed 100, with quantitative firms leading this growth, indicating a return to the "double hundred" era in the industry [10]. - By November 2025, the total assets under management for private equity funds reached 22.09 trillion yuan, with private securities investment funds contributing significantly to this growth [15]. Group 4: Performance Metrics - As of November 2025, quantitative long-only products reported an average return of 40.34%, outperforming subjective long-only products, which averaged 34.25% [30]. - Among the billion-yuan private equity firms, those focused on quantitative strategies achieved an average return of 34.40%, significantly higher than the 28.49% average for subjective strategies [30]. Group 5: Talent Movement - The trend of public fund managers transitioning to private equity has intensified, with 304 managers leaving public funds in 2025, many moving to private equity firms [38]. - By December 2025, 859 former public fund managers were working in private equity, with a notable average return of 27.70% for those managing at least three qualifying products [39].

两位创始人陈泽浩和邹倚天均为国内第一批华尔街归国量化投资经理，均拥有18年海内外量化投资实战经验。目前，黑翼资产已实现全流程AI量化投 资，并构建了多元化的策略矩阵，覆盖量化选股、指数增强、市场中性、多策略、量化CTA等产品线。黑翼资产的两位创始人既是基金经理，也是核心量 化策略的控制人，公司管理结构非常稳定。 黑翼资产；历史不代表未来，市场有风险，投资需谨慎 （来源：中信建投财富管理） 在A股市场风格频繁切换、波动常态化的背景下，兼具"市场贝塔收益+超额阿尔法收益"的指数增强策略，有望成为投资者穿越市场周期的重要配置工 具。其中，聚焦中小盘成长风格的中证1000指数增强策略，凭借高弹性、高成长等特点，吸引了众多投资者的目光。 在这片蓝海中，黑翼资产凭借多年海内外量化积淀，以AI赋能为核心，力争打造出具有市场竞争力的1000指增策略产品，为投资者提供配置该策略的有 力工具。 创始人及投研团队 黑翼资产成立于2014年，是国内首批成立的量化投资机构之一，始终贯彻科学理性、策略多元和长期稳健的投资理念。黑翼资产专注于数量模型研究，公 司注重回撤控制和长期业绩表现，以投资者利益为核心，力争在控制回撤的基础上获取可观 ...

Core Insights - The article highlights the rapid rise of Fan Ruizhe, founder of Wenzhou Ruiyi Technology Co., Ltd, who transformed an initial investment of 60,000 RMB into personal assets exceeding 160 million RMB, while managing an investment scale of 80 million USD through RY Capital [2][4][8] Group 1: Company Development - Ruiyi Technology focuses on innovation and research in the fintech sector, achieving breakthroughs in AI quantitative trading and digital currency investment analysis [2][4] - The company developed a proprietary "Digital Currency Quantitative Investment AI Technology Application Software," which has received multiple technical certifications and is widely used in investment decision-making [2][4] Group 2: Investment Strategy - RY Capital, established in 2023, specializes in quantitative investment in the secondary market for crypto assets, growing its management scale from 2 million USD to 80 million USD within three years, with investment returns exceeding 40 times and an annualized Sharpe ratio stable above 2.3 [4][6] - A partnership with AC Capital aims to launch a fund focused on the secondary market for crypto assets, with a planned investment size of 50 million USD, combining RY Capital's AI trading technology with AC Capital's market expertise [4][6] Group 3: Cross-Industry Integration - Fan Ruizhe is actively promoting industrial synergy by integrating technology, capital, and various sectors such as commercial real estate, supply chain management, and content media, creating a decentralized industrial network [6][7] - The emphasis on a systematic approach to investment decisions, driven by data rather than subjective judgment, reflects a broader trend in the industry towards algorithmic and logical clarity in the crypto market [5][6] Group 4: Future Outlook - The practices of Fan Ruizhe and RY Capital are seen as a new paradigm in the crypto asset management field, marking the beginning of a new era driven by the combination of technology, capital, and cross-industry perspectives [7][8] - The development of a "technology-investment-industry" closed-loop system represents a strategic response to the evolving business logic for the next decade, redefining the boundaries of "certainty" in a chaotic market environment [8]

Core Viewpoint - The article emphasizes that AI-driven quantitative investment is becoming essential for public funds, with Minsheng Jianyin Fund leading the way in this transformation through a comprehensive AI quantitative strategy development over the past four years [1][6][14]. Group 1: AI Quantitative Investment Strategy - Minsheng Jianyin Fund's quantitative investment director, He Jiang, initiated a focus on AI quantitative investment strategies in 2021, creating a "data-feature-strategy-portfolio" closed-loop system that is evolving into a unique competitive advantage [1][10]. - The core barrier of Minsheng Jianyin's AI quantitative strategy lies in effectively converting subjective insights into machine-learnable optimization mechanisms, continuously refining investment rules in high-dimensional space [10][11]. - The shift from traditional linear models to AI models allows for the capture of complex non-linear market relationships, significantly enhancing predictive capabilities and investment returns [9][11]. Group 2: Motivations for Focusing on AI - Traditional quantitative strategies face limitations, with the average excess return of the CSI 500 index-enhanced public funds dropping below 3% in 2022, indicating a need for innovation [6][14]. - The explosion of AI technology, driven by improved computing power and algorithm advancements, enables models to uncover complex market relationships that are difficult for the human brain to analyze [6][11]. - Minsheng Jianyin possesses unique internal research data, which has been integrated to create a proprietary fundamental feature database, enhancing their AI model's effectiveness [7][11]. Group 3: Performance and Future Outlook - The Minsheng Jianyin CSI 2000 index-enhanced fund, managed by He Jiang, has shown impressive returns, with a six-month return of 17.18% and a one-year return of 49.66%, significantly outperforming the benchmark [13]. - The CSI 2000 index is viewed as a valuable asset for long-term investment due to its structural opportunities in technology upgrades, including AI, semiconductor growth, and high-end manufacturing [13][14]. - The future of public funds is expected to evolve into an "AI-led quantitative + tool-based index product" ecosystem, with technology finance becoming a fundamental aspect of the industry [14].

Core Viewpoint - The rise of AI-driven quantitative strategies is transforming investment approaches, allowing for the identification of complex relationships in data that traditional methods cannot capture [1][4]. Group 1: AI Quantitative Strategies - AI quantitative models can analyze hundreds of high-frequency signals, uncovering non-linear relationships in data, which enhances predictive accuracy compared to traditional models that rely on a limited number of factors [1][7]. - The AI quantitative strategy developed by Swiss Bank Asset Management focuses on around 400 high-frequency signals, contrasting with the typical 20 signals used in traditional quantitative strategies [7]. - The AI model's ability to learn complex relationships allows it to improve the prediction of stock price movements based on analyst ratings and other signals [6][8]. Group 2: Market Expansion and Interest - Global capital interest in the Chinese market is on the rise, with plans to include A-shares in AI quantitative strategies as they expand into emerging markets [4][5]. - The firm is currently exploring the potential of AI-driven strategies for domestic investors in China, contingent on obtaining additional QDLP quotas [5][6]. Group 3: Investment Strategy and Risk Management - The investment horizon for Swiss Bank Asset Management's AI strategies is approximately 20 days, differing from many competitors that focus on ultra-short holding periods [8]. - To mitigate overfitting risks, the firm employs methods such as using economically sound signals, integrating numerous simple models, and utilizing extensive historical data for training [8]. Group 4: Role of Fund Managers - The role of fund managers is evolving with the integration of AI, shifting from model building to training AI models and validating their outputs while still conducting factor research [8].

Core Insights - AI-driven quantitative investment strategies are evolving, moving from traditional models that rely on a limited number of factors to more advanced models that can identify hundreds of high-frequency signals and non-linear relationships in data [1][5]. Group 1: Company Overview - Swiss Bank Asset Management, part of the Swiss Bank Group with a 220-year history, has an asset management scale of 711 billion Swiss Francs as of June 30, 2025 [2]. - The quantitative investment team led by David Wright manages $25 billion, with plans to expand AI quantitative strategies into emerging markets, including A-shares in China [2][3]. Group 2: Market Interest and Strategy - Global capital interest in China is recovering, with plans to include A-shares in AI quantitative strategies as the team develops a version for emerging markets [2][3]. - The current AI quantitative strategy products are primarily focused on developed markets, tracking the MSCI World Index, but there is a push to include A-shares in the future [2][3]. Group 3: AI Model Adaptability - AI models can adapt to the Chinese market, with backtesting showing that identified signal relationships are transferable to emerging markets, including China [3]. - The potential for excess returns in emerging markets is higher than in developed markets, although trading costs are also higher [3]. Group 4: AI Application in Stock Ratings - AI models can enhance the predictive power of stock ratings by incorporating various signals, such as the timing of earnings reports, to improve decision-making [4][5]. - Traditional quantitative methods typically use around 20 company-level signals, while Swiss Bank's AI strategy utilizes approximately 400 high-frequency signals [5]. Group 5: Differentiation in AI Strategies - Swiss Bank's AI quantitative strategy focuses on a holding period of about 20 days, contrasting with many competitors that prefer shorter holding periods [5][6]. - The strategy emphasizes factor neutrality, maintaining balanced exposure across various investment styles without overexposing to any single factor [5][6]. Group 6: Mitigating Overfitting Risks - The company employs several methods to mitigate overfitting risks in AI models, including using economically sound signals, integrating numerous simple models, and applying cross-validation techniques [6]. - The role of fund managers is evolving, shifting from model building to training AI models and conducting factor research, while still maintaining oversight of model outputs and portfolio construction [6][7].

Core Insights - The article discusses the advancements in AI-driven quantitative investment strategies led by David Wright at Swiss Bank Asset Management, highlighting the transition to a 2.0 era of quantitative investing through enhanced computational power and open-source tools [1][2]. Group 1: AI Quantitative Strategies - AI quantitative models can identify hundreds of high-frequency signals and uncover non-linear relationships in data, surpassing traditional quantitative methods that rely on a limited number of factors [1][5]. - The AI quantitative strategy team at Swiss Bank Asset Management manages $25 billion, with plans to expand into emerging markets, including A-shares in China [2][3]. - The interest of global capital in the Chinese market is on the rise, with potential AI quantitative strategies targeting A-shares expected to launch next year [2][3]. Group 2: Market Adaptation and Performance - AI models have shown that the signal relationships identified can be generalized across countries, indicating that these models can be adapted to the Chinese market [3][4]. - Emerging markets may offer higher potential excess returns compared to developed markets, although trading costs are also higher, leading to similar overall excess returns relative to benchmarks [3][4]. - The integration of fundamental signals alongside emotional and price signals in emerging markets has been found to enhance model performance [3][4]. Group 3: Differentiation and Risk Management - Swiss Bank Asset Management's AI quantitative strategy focuses on a holding period of approximately 20 days, contrasting with many competitors that prefer shorter holding periods [5][6]. - The firm emphasizes the use of traditional data for model training, covering longer historical periods, and maintaining factor neutrality across various investment styles [5][6]. - To mitigate overfitting risks, the company employs economically sound signals, integrates numerous simple models for training, and utilizes a cross-validation method with 15 years of data [6]. Group 4: Evolving Role of Fund Managers - The role of fund managers is evolving with the rise of AI in quantitative investing, shifting from model building to training AI models and validating outputs [6]. - Fund managers will continue to conduct factor research and oversee investment portfolio construction, maintaining the same number of personnel despite changes in responsibilities [6].

Group 1 - The report highlights the significant role of quantitative stock selection funds in the public fund market, with a total of 277 funds managing a combined scale of 90.32 billion yuan as of the end of Q2 2025, offering broader investment scope and higher style exposure flexibility compared to standard index-enhanced funds [3][12][13] - The report focuses on five fund managers with distinctive investment frameworks in quantitative stock selection, including Feng Xixiang from Xinda Australia Fund, Gao Chongnan from Guotai Fund, Lin Jingyi from Xinda Australia Fund, Shi Yunchao from Penghua Fund, and Zhai Zijian from Western Li De Fund, providing insights into their strategies and product positioning [3][12][13] Group 2 - Feng Xixiang employs a unified framework emphasizing the effectiveness of factors and the universality of alpha models, integrating static multi-factor linear models with machine learning dynamic weighting models since 2023, achieving balanced allocation in his representative products [4][16][23] - Gao Chongnan focuses on the Calmar ratio, selecting high dividend, quality, and growth styles to enhance the stability of risk-return profiles, with a product positioning aimed at low volatility value style [4][35][36] - Lin Jingyi implements a "HI+AI" approach using an integrated research platform, employing a three-step method to replicate successful peer consensus and enhance index tracking through multiple alpha models [5][22] - Shi Yunchao's strategy combines multi-factor linear models with a higher proportion of non-linear models, focusing on short prediction cycles and higher turnover rates, while maintaining a diversified portfolio to mitigate risks [6][24] - Zhai Zijian adopts an AI quantitative investment strategy with a "core + satellite" multi-strategy balanced configuration, utilizing machine learning for long-term predictions and high-frequency data analysis [6][24] Group 3 - The report indicates that as of the end of Q2 2025, Feng Xixiang manages a total of 4.54 billion yuan across seven quantitative stock selection products, with representative products achieving cumulative returns of 40.66% and 74.91% since inception, significantly outperforming their benchmark indices [17][21] - Gao Chongnan's strategy iteration has led to improved performance, with the National Strategy Yield Fund achieving an annualized return of 28.72% in 2024, reflecting a notable enhancement in risk-adjusted returns [36][37] - The quantitative team at Xinda Australia Fund consists of experienced professionals, with a comprehensive product line that includes 11 quantitative stock selection products and 2 quantitative fixed income + strategy products, aiming to reduce volatility while seeking absolute returns [32][33]