Core Viewpoint - The article highlights the rapid growth and performance of Zhengying Asset, a private equity firm that has successfully surpassed 10 billion in management scale by leveraging a combination of subjective and quantitative investment strategies, particularly in the T0 trading space [2][4][5]. Group 1: Company Growth and Performance - Zhengying Asset's management scale increased from 20-50 billion at the end of 2024 to over 100 billion by September 2025 [2]. - The product "Zhengying Qiji Index Enhanced No. 1" has achieved significant returns, ranking among the top ten in excess return rates for quantitative private equity firms [2]. - The firm has seen its stock T0 strategy grow from zero to 8 billion in just four years, with current product lines in stock neutral T0 and index enhancement T0 strategies each around 4 billion [5][7]. Group 2: Investment Strategy and Technology - Zhengying Asset employs a combination of subjective and quantitative strategies, focusing on market insights and risk management [4]. - The firm has invested heavily in technology, particularly in financial technology and artificial intelligence, to enhance its trading capabilities [4][5]. - The high-frequency trading team consists of members from prestigious universities and has extensive experience in quantitative trading, contributing to the firm's rapid rise in the stock high-frequency domain [5]. Group 3: Risk Management - The company prioritizes risk control, implementing a comprehensive risk management framework that includes preemptive measures, real-time monitoring, and post-analysis of strategies [13][14][15][16]. - The risk management process involves thorough research and analysis of market conditions, industry trends, and company fundamentals to ensure the feasibility of investment strategies [14]. - The firm maintains a robust monitoring system for real-time tracking of positions and performance, ensuring timely responses to market changes [15]. Group 4: Unique Product Offerings - Zhengying Asset's index enhancement products have shown strong performance, with the "Zhengying Qiji Index Enhanced No. 17" achieving notable excess returns since its inception [17][19]. - The firm's strategy focuses on full replication of index constituents for daily trading to achieve excess returns, demonstrating a stable historical performance compared to peers [20]. - The trading frequency of the index enhancement products is high, with an average annual turnover rate of 200-300 times, aiming for short-term gains through intra-day trading [23].

Summary of the Conference Call Industry Overview - The discussion revolves around the **quantitative investment** landscape and its performance relative to **active investment** strategies, particularly in the context of market conditions from 2017 to 2026 [1][3][6]. Key Insights and Arguments - **Market Conditions**: The performance of quantitative versus active investment is closely tied to market patterns. Divergent markets (frequent sector rotations) favor quantitative strategies, while consensus markets (high sector concentration) favor active strategies [1][3]. - **Historical Performance**: From 2013 to 2017, quantitative investment significantly outperformed active investment, driven by the strong performance of small-cap factors. However, from 2017 to 2021, quantitative investment underperformed due to market phenomena like the "beautiful 50" and the concentration in sectors like renewable energy and semiconductors. Since 2022, quantitative strategies have regained an edge [3][6]. - **Capital Concentration**: The concentration of capital is a key indicator for determining market patterns. High concentration indicates a consensus market, where cognitive alpha (industry trend predictions, in-depth stock analysis) is advantageous. Low concentration indicates a divergent market, where trading alpha (capturing behavioral biases, price-volume relationships) is more beneficial [4][6]. - **Future Outlook for 2026**: A structural market is anticipated in 2026, with a high probability that quantitative investment will outperform active investment. The recent rise in capital concentration, driven by sectors like AI and technology, may face challenges as valuations become high, potentially weakening the "herding" effect [6][8]. - **Institutional Preferences**: There are notable differences in asset allocation among institutions. Public funds favor technology sectors, while foreign and insurance companies lean towards dividend and value sectors. This suggests a potential shift in market focus between technology growth and traditional industry recovery [6][8]. Additional Important Points - **Short-term Market Sentiment**: The sentiment towards the stock market is optimistic, with a shift in investment style from growth to value since September. The current market shows a balanced approach between large-cap and small-cap stocks, with a slight preference for small-cap value [7][8]. - **Performance Metrics**: Historical data indicates that the narrow win rate for recommended styles is approximately 40%, while the broad win rate is around 80% [7]. - **Investment Recommendations**: There is a recommendation for a small-cap value style in the short term, alongside a suggestion to monitor the performance of models and strategies over the long term [2][7]. This summary encapsulates the key points discussed in the conference call, providing insights into the quantitative investment landscape and its future trajectory.

Group 1 - The company is recruiting interns for positions in Shenzhen, Shanghai, and Beijing, requiring in-person internships with a minimum commitment of three days per week for at least three months [1] - The application deadline for submitting resumes is December 31, 2025 [1] - Interns with outstanding performance may have the opportunity for full-time employment after the internship [1] Group 2 - Responsibilities include data processing, analysis, and assisting researchers with quantitative investment projects [2] - Interns will also assist in the development and tracking of financial engineering strategy models [2] - Additional tasks may be assigned by the team [2] Group 3 - Basic requirements include being a master's or doctoral student in STEM fields or financial engineering, with a strong preference for exceptional fourth-year students [3] - Proficiency in programming languages such as Python and familiarity with SQL databases are essential [3] - Candidates should possess strong self-motivation, analytical skills, and effective communication abilities [3] Group 4 - Preferred qualifications include a solid foundation in financial markets, familiarity with key concepts in stocks, bonds, futures, indices, and funds [4] - A strong mathematical background, research project experience, and published academic papers in SCI or EI are advantageous [4] - Familiarity with financial terminals like Wind, Bloomberg, and Tianruan, as well as knowledge of machine learning and deep learning, is a plus [4] Group 5 - Interested candidates should submit their resumes in PDF format to the specified email address, following a specific naming convention for the email subject [5] - Resumes not adhering to the naming format will be treated as spam [5] - Qualified candidates will be contacted for written tests and interviews after the resume collection deadline [5]

Group 1 - DeepSeek has gained global attention due to its technological breakthroughs and its unique stance of not seeking external financing, which highlights the management's financial strength [1] - The company is backed by a well-established cash flow generator, which has recently come to light, altering the perception of DeepSeek's operational model [1] Group 2 - Huanfang Quantitative, founded by Liang Wenfeng, has seen a significant performance resurgence, with its stock long/short strategy yielding approximately 50% returns in the first eleven months of 2025, outperforming the CSI 1000 and CSI 500 indices by about 30 percentage points [2][5] - This resurgence is attributed to the revival of classic factors in the market, particularly in the crowded areas of the CSI 1000 and CSI 500 indices, indicating a return to effective strategies after a period of underperformance [5][6] Group 3 - Huanfang's asset management scale reportedly shrank to around 20 billion yuan, leading to decreased market attention as focus shifted to larger firms with over 50 billion yuan in assets [7][8] - Despite the reduction in external management scale, Huanfang's internal self-managed portfolio has remained a critical support for its business, although specific performance figures for this internal portfolio are not publicly disclosed [10][15] Group 4 - The performance of Huanfang's external asset management products, which have maintained around 50% returns, suggests that the internal self-managed portfolio likely mirrors this success, if not exceeding it [16] - The resurgence of Huanfang's performance may indicate potential shifts in the private equity landscape and could signal an increased investment in large model initiatives within China [17]

Group 1 - The report indicates that various quantitative funds outperformed their benchmarks in November 2025, with active quantitative funds tracking the CSI 300 and CSI 500 indices achieving average excess returns of 0.5% and 2.1% respectively [2][26] - Among industry-themed funds, those tracking the digital economy, Hang Seng A-share specialized new enterprises index, and the emerging index ranked highest in excess returns [2][37] - Smart beta funds tracking the CSI Hong Kong-Shenzhen high dividend index achieved the highest excess returns for the month [2][38] Group 2 - The report highlights that broad-based active quantitative funds tracking 27 indices had average excess returns of 0.6% and 0.3% for the CSI 500 and CSI 300 indices respectively in November 2025 [3][42] - Industry-themed funds focusing on the pharmaceutical sector, chip industry, and Chinese semiconductor chip index ranked highest in excess returns among index-enhanced funds [3][54] - The report notes that hedge quantitative funds had an average absolute return of 0.31% in November 2025, with lower net asset value volatility compared to the year-to-date average [4][27] Group 3 - The report categorizes quantitative funds into three types: active quantitative funds, index-enhanced quantitative funds, and hedge quantitative funds, each serving different trading needs [9][12] - Active quantitative funds are further divided into those tracking the CSI 300, CSI 500, other broad indices, industry themes, and smart beta strategies [12][18] - The report provides detailed performance metrics for various funds, including absolute and excess returns, volatility, and maximum drawdown for November 2025 [17][49]

私募排排网数据显示，截至2025年11月底，近1年有业绩展示的量化多头产品有785只，产品规模合计约为603.93亿元， 近 1年平均收益达 38.48% ， 领先主观多头 35.16%的平均收益，并在私募二级策略中位居榜首 。 本文首发于公众号"私募排排网"。 （点击↑↑ 上图查看详情 ） 自去年"9.24行情"以来，A股市场持续震荡上扬，截至2025年11月底，近1年上证成指的涨幅为16.90%，深证成指和创业板指的涨幅分别约为 22.36%、37.26%，市场整体赚钱效应良好。在这样的市场环境下，与股市高度关联的股票多头私募基金今年来收益表现亮眼，其中，量化投资 依赖数据、模型和算法，规避因投资者情绪导致的非理性决策等优势，热度逐步攀升，且取得了较为不错的成绩。 | 排序 | 私募二级策略 | 近1年有业绩展示的产品 产品规模合计(亿元) | | 近1年收益均值 | | --- | --- | --- | --- | --- | | 1 | 曹샌옹杀 | 785 | 603.93 | 38.48% | | 2 | 主观多头 | 2094 | 1794.58 | 35.16% | | 3 | 其他衍生品策 ...

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].

本文首发于公众号"私募排排网"。 （点击↑↑ 上图查看详情 ） 2025年11月，A股三大指数集体回调，其中上证指数曾创十年新高，但随后震荡回落，全月累计下跌1.67%，终结月线六连阳；创业板指、深成 指也分别下跌4.23%、2.95%。板块上，热点持续轮动，海南自贸港、新能源、商业航天等板块出现局部行情。 | 二级策略 | 今年来待合排名 | 交生来源: | रो से के ग्रे | 近1月平均收益 | 近1月平均超额 | | --- | --- | --- | --- | --- | --- | | | 规则的产品数 | 收益 | 超额 | | | | 喜你会杀 | 923 | 39.70% | 16.73% | 0.16% | 2.80% | | 主观念杀 | 2258 | 34.04% | 16.43% | -1.85% | 1.1 1 % | | 其他衍生品策略 | 17 | 27.67% | 25.08% | -0.39% | -0.65% | | 复合策略 | 425 | 24.85% | 8.39% | -0.17% | 2.83% | | 主观CTA | 194 | 24.47% | ...

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。 守正用奇专注于后者。在何荣天看来，量化投资机构的核心竞争壁垒不在于模型工具，而在于对市场风 格、经济周期、资金行为等多维度的专业理解。 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 构，选择了一条差异化的投资路径：以风格择时为核心策略，通过"风格估值-动量-有效资金流"的三维 框架捕捉因子贝塔，强调市场内在规律的识别，而非模型堆叠带来的短期超额。 "量化行业未来真正的竞争壁垒不在工具，而在专 ...

招 聘 启 事 团队介绍： 国信金工团队组建于2020年6月，团队目前共有7名成员，研究内容涵盖量化选股、FOF 投资、基金产品研究、行业轮动、资产配置、港股投资、CTA策略等多个方向，研究成 果受到机构投资者的广泛好评。 团队荣誉： 2024年证券时报 • 新财富杂志最佳分析师金融工程第4名 2021年第十九届新财富最佳分析师金融工程第5名 2021年第十五届卖方分析师水晶球奖金融工程 总榜单 第3名（公募榜单第1名） 金融工程分析师 职位介绍： 职位要求： 1、国内外院校数学、计算机、统计、金融工程、金融经济等相关专业硕士及硕士以上 学历的2026年应届毕业生or从事买方/卖方量化研究工作不超过5年的非应届生。 2024 年第十八届卖方分析师水晶球奖金融工程总榜单第3名（公募榜单第2名） 2023年第二十一届新财富最佳分析师金融工程第2名 2023年第十七届卖方分析师水晶球奖金融工程总榜单第2名（公募榜单第2名） 2022年第二十届新财富最佳分析师金融工程第1名 2022年第十六届卖方分析师水晶球奖金融工程总榜单第1名（公募榜单第1名） 2、具备扎实的数理统计知识和建模能力，熟练使用Python、Matl ...