◎记者 马嘉悦 "中东、新加坡、欧洲……近一年我频繁飞国外，因为那里有大量配置中国资产的需求。"一位百亿级量 化私募创始人的经历，在私募业颇具代表性。 据私募排排网统计，截至2月9日，当前持有香港9号牌照的内地私募证券投资基金管理人已超130家，较 去年同期增加逾40家。与此同时，多家私募创始人在开年以来与外资的交流中，感受到全球资金增配中 国的意愿逐步增强。 用业内人士的话来说，私募出海从来不仅是扩圈，更是全球资本版图重构下的顺势而为。在这场浪潮 中，中国私募将进一步积聚站在全球舞台的力量。 私募出海趋势已成 135家，是持有香港9号牌照的内地私募证券投资基金管理人最新数字，也是迄今为止的最高数字。 私募排排网最新数据显示，截至2026年2月9日，当前持有香港9号牌照的内地私募证券投资基金管理 人，较去年同期增加了41家。其中，规模在50亿元以上的私募数量达62家，是私募出海的主力军。 "这些年去香港拿9号牌的私募越来越多，有的已经耕耘海外业务多年初见规模，有的则是为了公司未来 的全球规划做准备。"沪上某业内人士感慨称。 灵均投资透露，公司已在香港和新加坡分别设立办公室，作为出海的"桥头堡"，面向境外合格投 ...

智通财经记者 孙铭蔚 百亿私募创始合伙人逝世。 2月2日，百亿级私募启林投资发布讣告称，公司创始合伙人沈显兵于2026年2月2日10时36分与世长辞， 享年40岁。 对于沈显兵的辞世，启林投资表示，公司管理层及全体员工表示最沉痛的哀悼和最深切的怀念，对沈显 兵多年来所作出的重要贡献致以最诚挚的感谢，并向沈显兵的家人表示最深切的慰问。 中国证券投资基金业协会信息显示，启林投资成立于2015年5月28日，目前公司管理规模超过100亿元。 沈显兵毕业于中国科学技术大学物理学专业，曾任职于安徽科大讯飞信息科技股份有限公司、上海易炬 信息科技有限公司以及上海顶间通信科技有限公司，自2015年起参与启林投资的创建及后续运营管理工 作，担任创始合伙人、副总经理。 波动中的alpha机会。 公开信息显示，启林投资法定代表人、总经理王鸿勇为中国科学技术大学物理学学士，与中科大昔日同 窗沈显兵、董成一起在2015年成立了启林投资。启林投资构建了完整的策略开发流水线，将量化投资的 研究过程标准化、模块化，主要包括五大环节：1.数据获取：多维度数据采集与预处理因子研究；2.挖 掘有效预测因子模型开发；3.构建预测模型组合优化；4.构 ...

百亿量化私募巨头启林投资创始合伙人沈显兵去世！ 据启林投资官微消息，百亿私募启林投资发布讣告称，公司创始合伙人沈显兵，于2026年2月2日去世，享年40 岁。对于沈显兵的不幸辞世，公司管理层及全体员工表示最沉痛的哀悼和最深切的怀念，对沈显兵多年来所做 出的重要贡献致以最诚挚的感谢，并向其家人表示最深切的慰问。 一次同学聚会，王鸿勇重逢了技术创业的沈显兵和深耕投行市场多年的董成，昔日中国科大的同窗，毕业后职 业轨迹完全不同，三人的专业互为补充，为一家技术驱动的量化投资公司拼出了"IT、市场和投研"最初版图。 三人一拍即合，并在2015年成立了启林投资，走上量化对冲投资的道路。启林名称源于《左传》"筚路蓝缕， 以启山林"，寓意创业艰辛与开拓精神。 管理规模达150亿元 截至2025年12月，公司管理规模达150亿人民币，是国内管理规模领先的量化私募之一。 据私募排排网数据显示，在2025年百强量化私募业绩中，启林投资排在第47名，与幻方等37家百亿量化同时上 榜。 据中信建投财富管理官微显示，启林投资构建了完整的策略开发流水线，将量化投资的研究过程标准化、模块 化，主要包括五大环节：数据获取：多维度数据采集与预处 ...

Group 1 - The core viewpoint of the article emphasizes that quantitative investment is becoming an essential choice for investors facing unprecedented challenges in a volatile market environment [2][29] - Shanghai Qilin Investment Management Co., Ltd. (Qilin Investment) is a leading player in the domestic quantitative investment field, established on May 28, 2015, with an asset management scale reaching 15 billion RMB by October 2025 [2][29] - Qilin Investment has received numerous industry awards, such as the Yinghua Award and Huayao Award, highlighting its professional strength and industry recognition in quantitative investment [2][29] Group 2 - The founder, Dr. Wang Hongyong, has a strong academic background with degrees from prestigious institutions and has published multiple authoritative papers, providing a solid scientific research foundation for Qilin Investment [4][31][35] - The core management team at Qilin Investment has over seven years of traceable historical performance and extensive experience in strategy development, with a total of 55 employees and a clear organizational structure [8][12][39] Group 3 - Qilin Investment follows two guiding principles in its quantitative investment practice, focusing on the efficient and continuous development of new effective strategies through an industrialized approach [10][37] - The company has established a complete strategy development pipeline that standardizes and modularizes the research process, consisting of five key stages: data acquisition, factor research, model development, portfolio optimization, and trade execution [11][38] Group 4 - Qilin Investment has developed a multi-strategy system from three dimensions to adapt to different market environments and achieve stable returns, thereby reducing risks associated with single strategy failures [10][38] - The company invested approximately 100 million RMB in 2022 to establish a supercomputing cluster, enhancing its computational capabilities for strategy development [12][39] Group 5 - Qilin Investment's quantitative stock selection strategy is not benchmarked against any market index, allowing for greater flexibility in capturing market styles and maximizing alpha returns [15][42] - The strategy focuses on the CSI 2000 index, primarily investing in small-cap stocks, which have lower pricing efficiency and higher trading activity, providing a natural advantage for short-term signal prediction [16][44] Group 6 - The design of Qilin Investment's strategies aims for a high Sharpe ratio, ensuring high certainty of excess returns through strict risk control and refined portfolio management [22][50] - The company emphasizes minimizing alpha loss during risk control, optimizing risk models and portfolio algorithms to maintain alpha returns while controlling tracking errors and style exposures [23][51] Group 7 - Qilin Investment maintains a rigorous risk control system, strictly managing stock composition, industry/style exposure, and market capitalization to prevent style drift and mitigate tail risks [24][52] - The company is positioned to leverage the evolving landscape of quantitative investment, with opportunities arising from market efficiency improvements, investor structure optimization, and technological advancements [25][53]

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].

Core Insights - The article discusses the performance of quantitative CTA strategies during recent market fluctuations, highlighting their ability to generate positive returns amid broader asset declines [1][2]. Group 1: Quantitative CTA Performance - Quantitative CTA strategies exhibited "crisis alpha" characteristics, with an average return of 0.43% during a recent downturn, making them one of the few strategies to show positive returns [1]. - In October, domestic A-shares and bond markets experienced volatility, while quantitative CTA strategies achieved an average return of 2.01%, leading among various strategies [1]. - Year-to-date, quantitative stock selection strategies have outperformed quantitative CTA strategies, with average returns of 42.71% compared to approximately 11% for CTA strategies [2]. Group 2: Market Conditions and Future Outlook - The article notes that the market is currently focused on potential interest rate cuts by the Federal Reserve in December and the upcoming Central Economic Work Conference in China [1]. - The current economic environment, characterized by rising inflation, may benefit commodity performance, with October CPI rising to 0.2% and core CPI increasing to 1.2% [6]. - The article suggests that the current market conditions may favor the allocation of quantitative CTA strategies, particularly in a high liquidity environment [7]. Group 3: Strategy Characteristics and Selection - The performance of CTA strategies is heavily influenced by volatility and trend-following characteristics, with a preference for annualized volatility above 15% [4]. - Investors are advised to consider a diversified approach by selecting multiple strategies or managers to mitigate risks associated with individual performance variations [8]. - The article emphasizes the importance of timing in investing in quantitative CTA strategies, recommending purchases during periods of lower volatility [7].

Core Insights - The report emphasizes the construction of an AI-driven low-frequency index enhancement strategy using deep learning frameworks to build alpha and risk models [3] Market Index Performance - As of October 17, 2025, the Shanghai Composite Index fell by 1.47%, the Shenzhen Component Index dropped by 4.99%, and the CSI 300 decreased by 2.22%, indicating a general market decline with dividends performing counter to the trend [5][8] - The performance of index enhancement funds as of October 17, 2025, shows that the CSI 300 index enhancement fund had a minimum excess return of -2.97%, a median of 0.12%, and a maximum of 1.04% [12] - Year-to-date, the CSI 300 index has risen by 14.7%, while the CSI 300 index enhancement portfolio has increased by 23.7%, resulting in an excess return of 9.0% [19] Index Enhancement Fund Performance - The CSI 500 index enhancement fund recorded a minimum excess return of -0.22%, a median of 0.74%, and a maximum of 3.46% as of October 17, 2025 [12] - The CSI 1000 index enhancement fund had a minimum excess return of -0.59%, a median of 0.60%, and a maximum of 1.81% [12] - For the year, the CSI 500 index enhancement fund has achieved an excess return of 6.8%, while the CSI 1000 index enhancement fund has seen an excess return of 13.4% [24][30] Tracking Portfolio Performance - The report outlines the construction of index enhancement portfolios for the CSI 300, CSI 500, and CSI 1000 using deep learning frameworks, with weekly rebalancing and a constraint on weekly turnover rate of 10% [15] - The alpha signals are derived from a multi-source feature set and stacked multi-model strategies, while risk signals are identified using neural networks [15] CSI 300 Index Enhancement - As of October 17, 2025, the CSI 300 index has increased by 14.7%, while the CSI 300 index enhancement portfolio has risen by 23.7%, yielding an excess return of 9.0% [19] - The performance statistics for the CSI 300 index enhancement portfolio show a maximum excess return of 10.72% for the year [20] CSI 500 Index Enhancement - The CSI 500 index has risen by 22.5% year-to-date, with the CSI 500 index enhancement portfolio increasing by 29.3%, resulting in an excess return of 6.8% [24] - The performance statistics for the CSI 500 index enhancement portfolio indicate a maximum excess return of 12.39% for the year [25] CSI 1000 Index Enhancement - The CSI 1000 index has increased by 20.6% year-to-date, while the CSI 1000 index enhancement portfolio has risen by 34.0%, leading to an excess return of 13.4% [30] - The performance statistics for the CSI 1000 index enhancement portfolio show a maximum excess return of 18.12% for the year [31]

Core Viewpoint - The article discusses the recent performance of quantitative strategies in the investment market, highlighting a shift in market style and the impact on various quantitative strategies [5][6][11]. Group 1: Market Performance - The benchmark index for the CSI 500 had a weekly return of 3.87%, while the CSI 1000 had a return of 3.45% during the week of August 18-22 [4]. - The absolute returns of the CSI 300 and CSI 500 indices have improved significantly, while the previously leading quantitative stock selection strategies have fallen behind [10][11]. Group 2: Quantitative Strategy Analysis - The article notes a significant shift in market style, with funds moving from small-cap stocks to mid and large-cap stocks, which has affected the performance of quantitative models [11]. - The article emphasizes that the recent volatility in both excess and absolute returns indicates a challenging environment for quantitative strategies [10][11]. Group 3: Specific Quantitative Strategies - Strict risk-controlled quantitative index strategies have performed relatively well during the recent style switch due to their lower exposure to micro-cap stocks and a focus on fundamental support [16]. - Extreme volume-price driven quantitative stock selection strategies have shown high sensitivity to market changes, allowing for quicker adjustments in response to style shifts [19]. - Balanced factor quantitative stock selection strategies have demonstrated resilience against the recent market style changes, maintaining stability in returns [22][23]. Group 4: Broader Index Strategies - Full index strategies that track the CSI Full Index have shown balanced performance across large, mid, and small-cap stocks, providing a reliable option regardless of market style [26][31]. - The CSI Full Index has outperformed most mainstream indices recently, with a year-to-date return of 18.3%, indicating strong performance across various market conditions [30][31].

Group 1 - The report indicates that the backtest of the predictive growth factor shows no significant excess returns before 2022, with a notable differentiation occurring in 2022, where the revenue and net profit growth group (0-15%) performed the best since then, attributed to a market style shift towards value investing due to macroeconomic pressures and declining market risk appetite [1][14]. - The report highlights the introduction of the PEG factor to optimize the investment portfolio, which measures the relationship between valuation and growth potential, suggesting that high-growth companies should have a higher PEG valuation level compared to slower-growing companies [2][21]. - The PEG (1-3) factor was found to be most effective in the revenue and net profit growth group (50%+), with the cumulative return for the revenue growth (50%+) PEG (1-3) portfolio reaching 275.45% and the net profit growth (50%+) PEG (1-3) portfolio achieving 296.87% over the period from July 1, 2014, to July 25, 2025 [3][50]. Group 2 - The report discusses the historical performance of growth and value styles in the A-share market, noting a cyclical rotation approximately every four years, with growth style underperforming since 2022 due to economic pressures and liquidity tightening [7]. - The report provides a detailed analysis of the backtest results based on revenue growth, categorizing companies into four groups based on their predicted revenue growth rates, with the 0-15% growth group showing the best performance since 2022 [9][14]. - The report also analyzes net profit growth, indicating that the net profit growth (0-15%) group similarly outperformed in the same period, reflecting a consistent trend across both revenue and net profit growth metrics [15][19]. Group 3 - The report emphasizes the importance of adjusting PEG valuation levels based on historical context and market conditions, with a recommendation that a PEG below 1.0 is considered a reasonable valuation standard [20][21]. - The backtest results for different revenue growth groups show that the 0-15% revenue growth group performed best with a PEG (0-1) range, achieving a cumulative return of 249.25% [24][27]. - The report concludes that the PEG (1-3) factor is particularly effective for high-growth companies, with significant excess returns observed in both revenue and net profit growth groups exceeding 50% [35][46].

Core Insights - Quantitative private equity has shown significant performance differentiation in the market this year, with small and mid-cap strategies outperforming large-cap strategies, reflecting structural changes in the market that deeply impact different investment strategies [1] Group 1: Performance of Quantitative Strategies - As of July 11, the Quantitative 1000 index enhancement strategy has performed the best, with Lingjun Investment leading at a 36.79% year-to-date return, while other institutions like Xinhong Tianhe, Longqi, and Qilin also surpassed the 30% mark [3] - The Quantitative 500 index enhancement strategy also performed well, with Xinhong Tianhe and Abama's related products achieving over 30% year-to-date returns [3] - In contrast, the Quantitative 300 index enhancement strategy lagged, with the highest year-to-date return at only 19.13% [3] - The Quantitative stock selection strategy demonstrated the strongest profitability, with Xiaoyong's strategy leading the market at 46.26% year-to-date return, and other institutions like Ruishengming and Ziwuyou also exceeding 40% [3] Group 2: Market Trends and Structural Changes - The market this year has clearly favored small and mid-cap stocks, providing abundant sources of excess returns for related quantitative strategies [4] - The CSI 1000 index, primarily composed of small and mid-cap stocks, has significantly outperformed the CSI 300 index, benefiting from policies favoring specialized and innovative enterprises [4] - The lower research coverage of small and mid-cap stocks leads to more pricing discrepancies, creating opportunities for quantitative strategies to capture excess returns [4] - Increased market volatility has also created a favorable environment for quantitative strategies, as small and mid-cap stocks typically exhibit higher volatility, allowing strategies to profit from capturing liquidity premiums [4] Group 3: Scale Effects and Strategy Differentiation - Billion-yuan private equity firms exhibit clear scale advantages in index enhancement strategies, dominating the top 20 in both the Quantitative 1000 and 500 index enhancement strategies [5] - Large institutions, with assets under management exceeding 5 billion, achieved an average return of 18.30% in their index enhancement products, with a staggering 99.25% of products generating positive excess returns [5] - Medium-sized private equity firms had an average return of 17.30%, while small firms saw their average return drop to 16.41% [5] - The performance differentiation among quantitative private equity firms is increasingly evident, with over a 15 percentage point difference between the highest and the 20th return in the Quantitative 1000 index enhancement strategy [5]