为响应国家"人工智能+"行动计划，把握全球金融科技融合浪潮，北京基金小镇、北京中关村学院与中 关村人工智能研究院联合共建的"AI+金融"创新实验室已正式挂牌。 项目开创性地采用"完全公益、全程免费"的培养模式，不向学员收取任何费用，并提供必要的学习与生 活保障。同时为优秀团队提供百万元级实盘资金支持，助力学员在真实市场中锤炼能力、验证策略、创 造价值。 项目由清华大学交叉信息研究院长聘教授、博士生导师李建担任总指导，汇聚清北、北航、港科大等学 者，以及头部券商专家、百亿私募创始人、知名量化基金经理等业界实战导师，打造理论深度与实战经 验并重的教学体系。优秀团队将获得百万元级实盘资金支持；设立多项荣誉与现金激励，并提供进 入"AI+金融"创新实验室人才库机会、头部机构专属实习推荐及就业内推，以及创业项目股权投资、公 司落地、人才落户、住房保障等一站式孵化支持。 项目设置"量化策略实盘"与"金融科技项目研发"双路径培养模式，学员可结合自身兴趣与专长选择方 向。培养周期为期7个月，包括2个月集中授课与项目开发、5个月实盘验证或项目深化。课程涵盖量化 投资全景图谱，从金融数据的结构化处理与清洗，到经典多因子模型的构建 ...

二、分论坛：解码中国量化策略的下一增长曲线 在全球宏观格局深度演变、AI重构投研逻辑、新质生产力重塑产业格局的背景下，中国资管行业正站在"逻辑重塑"的十字路口。2026年1月15 日，由财视中国主办的第十九届HED中国峰会·深圳暨"第十九届介甫荣耀之夜"将于深圳盛大启幕，超过400位海内外金融机构掌舵人、顶尖投 资人与行业专家将齐聚一堂，共探2026年市场破局之道。 中国量化行业正站在一个鲜明的分水岭上，呈现出"量质齐升"与"竞争加剧"并行的新格局。规模上，百亿级量化私募数量已历史性地超越主观 多头同行；质量上，其超额收益能力日益稳健，2025年跑赢业绩基准的比例显著高于市场均值。但与此同时，赛道拥挤、AI技术竞赛加剧等挑 战也随之而来，出海寻求第二增长曲线已成为不少头部机构的共同选择——中资量化机构如何构建可持续的全球竞争力，成为行业亟待解答的 新课题。 针对这一背景，峰会特设分论坛"中国量化策略的下一阶段"，集结量化领域"梦之队"：千朔投资总经理丁志强、广金美好总经理罗山、大岩资 本总经理黄铂、优美利投资总经理贺金龙等重磅嘉宾，将围绕"量化股票策略的创新与前沿趋势""中资海外基金的生态构建"等核心议题深度 ...

Core Viewpoint - The article highlights the growing importance of quantitative fund managers in the financial market, emphasizing their reliance on mathematical models, algorithms, and big data analysis to create long-term value for investors. The demand for high-educated talent in this field has intensified due to advancements in AI, leading to a talent war among quantitative institutions [2]. Summary by Sections Education and Talent - Quantitative private equity funds favor highly educated professionals, with 69.11% of fund managers holding master's or doctoral degrees compared to 56.42% in subjective private equity [2]. Performance Overview - As of the end of November, there are 1,637 quantitative products with a total scale of approximately 135.11 billion, achieving an average return of 27.29% from January to November, significantly outperforming the market. Among these, 99 managers of billion-yuan private equity quantitative funds managed 386 products with an average return of 34.42%, yielding an excess return of 14.04% [3][4]. Performance by Fund Size - **100 Billion and Above**: 386 products with a total scale of 53.81 billion, average return of 34.42%, and 14.04% excess return [3]. - **50-100 Billion**: 165 products with a total scale of 17.49 billion, average return of 25.23%, and 10.31% excess return [8]. - **20-50 Billion**: 220 products with a total scale of 22.56 billion, average return of 26.62%, and 12.21% excess return [11]. - **10-20 Billion**: 176 products with a total scale of 12.60 billion, average return of 25.37%, and 10.10% excess return [14]. - **5-10 Billion**: 224 products with a total scale of 12.15 billion, average return of 25.75%, and 10.84% excess return [17]. - **0-5 Billion**: 466 products with a total scale of 16.52 billion, average return of 23.88%, and 11.19% excess return [19]. Top Performers - **100 Billion and Above**: Notable managers include Xu Jin and Lu Zhengzhe from Ningbo Huansheng, both achieving significant returns [4][5]. - **50-100 Billion**: Top managers include Shi En from Yunqi Quantitative and Huang Bo from Dayan Capital [8][10]. - **20-50 Billion**: Mo Bo from Luxiu Investment leads the performance [11][12]. - **10-20 Billion**: Wu Yintong from Longyin Tiger Roar is a top performer [14][15]. - **5-10 Billion**: Yan Xuejie from Huacheng Private Equity leads [17][18]. - **0-5 Billion**: Xie Libo from Jingying Zhito is at the forefront [19][20].

Core Viewpoint - The article discusses the pitfalls of the "Buy the Dip" strategy in investing, emphasizing that it often underperforms compared to a passive buy-and-hold approach and trend-following strategies [3][6]. Group 1: The Reality of Buying the Dip - The article highlights that over the past five years, investors have adopted a linear thinking approach: buying more as prices drop, believing that the market will eventually recover [3][4]. - AQR Capital Management's report analyzed 60 years of S&P 500 data and found that various dip-buying strategies underperformed compared to simply holding investments [10][11]. - The average Sharpe ratio for dip-buying strategies was lower than that of a buy-and-hold strategy, indicating a 16% reduction in risk-adjusted returns [11][12]. Group 2: Lack of Alpha in Dip-Buying - The report indicates that the average annualized alpha for dip-buying strategies was only 0.5%, with less than 8% of strategies showing statistically significant alpha [15]. - Holding investments for longer periods often leads to returns that reflect overall market performance rather than the effectiveness of the dip-buying strategy [19][20]. Group 3: The Flaws in Timing the Market - The article explains that dip-buying is essentially a value trade executed during a momentum phase, which often leads to poor timing and losses [21][26]. - Data shows a negative correlation between dip-buying strategies and trend-following strategies, suggesting that dip-buying often goes against market momentum [28][30]. Group 4: The Superiority of Trend Following - The article advocates for trend-following strategies, which have shown higher average annualized alpha compared to dip-buying strategies [31]. - During market downturns, trend-following strategies have historically provided better protection and even positive returns, contrasting sharply with the losses incurred by dip-buying strategies [35][36]. Group 5: The Ultimate Strategy: Portable Alpha - AQR proposes a "Portable Alpha" strategy that combines a long position in equities with a trend-following strategy, resulting in higher annualized excess returns and better risk-adjusted performance [41][42]. - This approach allows investors to benefit from market growth while also having a protective mechanism during downturns, effectively hedging risks [44][45]. Group 6: Practical Advice for Investors - The article concludes with three key recommendations for investors: avoid the temptation to time the market with dip-buying, respect market trends by incorporating trend-following strategies, and adopt a long-term investment perspective [49][54].

来源：一年打卡100场路演 诚奇量化总结：截至25年12月，规模470亿；两位管理人分别曾在千禧年和世坤工作；23年以后逐步转 向机器学习为主的非线性建模框架 贝小塔(VX:BetaRicher)整理, 仅供合格投资者审阅。 此举出于三方面考虑: 一是上海金融人才储备更丰富,便于招募高质量研究员; 二是员工更倾向在上海落户与发展,尤其在应届生招聘中更具吸引力; 三是上海作为国家金融中心,在政策支持与监管沟通上具备相对优势, 此次变更为非新设主体,统一社会信用代码及中基协登记编号均保 业务与投资运作不受任何影响。 截至25年12月初:基协显示该公司员工数为36人,其中 高度数量为3人。 正在运作产品为313个,延期清算为0,提前清算为104个,正常清算为6个。 公司曾于2022年底达到500亿规模高点,当前体置并未触及策略容量上限,在 现有约2万亿日均成交环境下运作舒适。内部设定若未来规模通近 700 亿将启动 封盘或参数调整机制,但目前多头策略额度充足,无主动控规模计划。 股权占比:股权渗透后分析得出,何文奇 占比 50.5089%,张万成 占比 49.4911%,两人合计绝对控股。为激励年轻化的核心投研队伍 ...

Core Insights - The conference highlighted the transformative role of AI in quantitative investment, emphasizing its potential to reshape research paradigms and enhance efficiency in the industry [1][2]. Group 1: AI's Impact on Quantitative Investment - AI's breakthrough, marked by Google's AlphaGo in 2016, has led to increased interest in applying AI technologies in investment, resulting in significant advancements [2]. - The reliance on experienced professionals in traditional quantitative investment has created high entry barriers, but AI and machine learning are reducing this dependency, thus redefining research paradigms [2]. - Despite the advantages, the application of AI is not infallible and requires human expertise for effective implementation [2]. Group 2: Practical Applications and Innovations - AI is becoming a focal point in quantitative trading, with companies like Square and Harmony utilizing deep learning models across various stages, from factor discovery to trade execution [3]. - The emphasis is on "incremental innovation" rather than "substitutive innovation," integrating AI into existing robust strategies while maintaining strict risk control [3]. - A closed-loop system combining model development, backtesting, risk control, and trade execution is essential for translating technological advancements into stable alpha [3]. Group 3: Challenges in AI Implementation - The quant market faces challenges such as strategy homogeneity, weak interpretability of AI models, and insufficient adaptability during extreme market conditions [4]. - The core issue lies in aligning the technical potential of AI with the fundamental nature of investment, which requires a balance between efficiency and risk control [4]. - The noise in financial data complicates predictions, indicating that neither AI nor human strategies are superior alone; instead, a collaborative approach is deemed the optimal resource allocation strategy [5].

"量化行业未来真正的竞争壁垒不在工具，而在专业。"近日，何荣天在接受中国证券报记者专访时如是 说。 量化行业竞争格局分化 随着算力成本下降、编程工具普及以及数据获取更加便利，量化行业的准入门槛正在降低，策略间的同 质化问题日渐凸显。在何荣天看来："模型可以复制，数据可以购买，但对市场内在逻辑和金融专业的 认知无法快速复制。" 何荣天表示，目前市场上的量化策略大致分为两类。一类是以多因子模型为代表的大众化的量化策略， 目前规模大、参与者众多，已经形成了明显的"红海"格局。在这一领域，模型和因子高度拥挤，策略显 著趋同，导致边际收益正在下降。另一类则是以专业金融认知为底座，通过独立逻辑寻找市场规律和风 格趋势，虽然小众却更具独特性和穿越周期的能力。这二者的差异使得量化行业逐步形成"工具驱 动"与"认知驱动"这两条不同的发展路径。 ● 本报记者 王雪青 在量化行业竞争加剧的当下，如何在"红海"中保持长期竞争力？广州守正用奇私募基金董事长何荣天给 出的答案是：回到金融本质，以长期有效的专业认知，把握真正可持续的AI量化策略。 相比于市面上同质化程度不断加深的多因子模型，这家成立10年、始终在规模发展上保持克制的量化机 ...

Core Insights - The "2025 Quantitative Industry High-Quality Development Conference and Financial Technology·Quantitative Institution Golden Bull Award Ceremony" was held in Shanghai, recognizing Guangzhou Shouzheng Yongqi for its outstanding performance in the macro quantitative strategy category [1] - The Golden Bull Award is one of the most authoritative awards in China's capital market, aiming to select professional asset managers that can provide long-term stable returns to investors [1] - The Financial Technology Golden Bull Award focuses on recognizing institutions excelling in technology research and development, strategy iteration, risk control, and social responsibility within the financial technology and quantitative field [1] Group 1 - Dr. He Rongtian emphasized that large models do not inherently possess causal logic, stating that "correlation cannot predict the future; causality is the core of investment" [2] - He outlined a future direction for "AI + Quantitative" development, advocating for steady returns and innovative exploration rather than blindly pursuing technological singularities [2] - The investment philosophy in the AI era should focus on enhancing decision-making quality with AI technology while adhering to value investment principles [2] Group 2 - Dr. He expressed optimism about the A-share market, indicating that the current liquidity environment is the best in years and that there is still significant room for market development [2] - He highlighted the importance of relative valuation indicators and advised investors to avoid high-valuation stocks while considering long-term value investments [2] - In the technology sector, he noted that sub-sectors such as AI, new energy, and energy storage are experiencing rotation, with substantial growth potential in the long term [2]

"人工智能必定会取代传统的量化投资基金经理。而那些不采用人工智能技术的量化机构，在未来3到5 年可能会被淘汰。"冯霁对未来的判断毫不含糊。 冯霁说："就像数十年前个人计算机和服务器推出，华尔街是最早采用计算机服务器来进行量化投资 的。今天的人工智能也是如此，AI作为一个强大的建模工具，我们没有理由不去拥抱它。" "AI量化投资与AI围棋有相似之处。"冯霁举例道，"在AlphaGo推出之前，人类下围棋的开局定式几十 年都没有变化，但当有了AI之后，开局方式突然多了很多可能性。量化投资也是如此。"在他看来，随 着算力、数据与模型能力持续提升，机器在市场学习与模式识别方面的优势将愈发明显。 那么，AI投资时代更需要怎样的新型人才？冯霁认为，在AI投资时代，人将变成背后给机器不断升级 的工程师。未来需要的新型基金经理是复合型人才，既懂投研任务，又能把它转化为人工智能问题，并 用AI解决。 当人工智能席卷全球，一个问题变得前所未有地尖锐：基金经理的角色是否会被AI取代？ "我坚信人工智能必定会取代传统的量化投资基金经理。"倍漾量化创始人冯霁给出了坚定的判断。这位 新锐量化投资人于11月28日在中国证券报主办的"2025 ...

Core Viewpoint - The role of traditional quantitative fund managers is expected to be replaced by artificial intelligence (AI) in the near future, as AI is seen as a powerful modeling tool that can enhance investment strategies [1][2]. Group 1: AI's Impact on Fund Management - AI is predicted to lead to a structural transformation in quantitative investment, with firms that do not adopt AI technology potentially facing obsolescence within 3 to 5 years [1]. - The introduction of AI in quantitative investment is likened to the impact of personal computers on Wall Street, suggesting a significant shift in investment methodologies [1]. - AI's ability to explore a wider range of possibilities in investment strategies is compared to the advancements seen in the game of Go with the introduction of AlphaGo [1]. Group 2: New Talent Requirements - The future of fund management will require hybrid talents who understand both investment research and AI technology, shifting focus from traditional research tasks to maintaining and developing advanced AI systems [2]. - The demand for personnel may decrease, but the required understanding of AI technology will be significantly higher [2]. Group 3: AI-Driven Quantitative Approach - The company adopts a unique approach by treating the traditional components of quantitative research—factors, signals, models, and strategies—as a unified process centered around machine learning [2]. - The firm does not employ individuals with financial backgrounds, instead opting for a team composed primarily of engineers and computer scientists [2]. Group 4: Competitive Advantages - The company claims competitive advantages in three areas: high talent density in AI, superior computational power compared to domestic universities, and a proprietary AI experimental platform designed for real-time modeling and trading tasks [3]. - The vision is to evolve from a traditional quantitative private equity firm to an AI-native "computational company" [3]. Group 5: Long-term Goals - The mid-term goal is to become a global quantitative manager, while the long-term aspiration is to establish itself as a significant computational company [3]. - The company envisions that Chinese fund managers will be able to compete on a global scale within the next decade, leveraging AI and new computational methods [3].