近日，杭州弘耀资产管理有限公司等机构注销私募基金管理人登记。截至12月14日，年内已有1155家私 募基金管理人完成注销登记。 业内受访人士认为，在监管部门"扶优限劣"的指引下，私募基金行业大量不合规或经营不善的机构将逐 步退出市场。 注销速度放缓 规模创历史新高 私募基金管理人注销数量下滑与行业规模增长、行业分红增多的"背离"，是私募基金行业实现更高质量 发展的具体体现。 中基协数据显示，截至2025年10月末，存续私募基金规模达22.05万亿元，环比增加1.31万亿元，创下历 史新高。值得关注的是，此次私募基金规模创新高的动力更多源于存量基金净值回升的内生驱动：私募 证券投资基金存续规模突破7万亿元，环比增长1.04万亿元，而今年10月份全部新备案私募基金规模仅 为670.10亿元。 行业规范发展与规模增长的最终目标，是为投资者创造更稳定的回报。今年以来，私募基金分红力度显 著提升。私募排排网统计数据显示，截至2025年11月末，有业绩展示的私募产品年内累计实施分红1658 次，合计分红金额达173.38亿元，突破170亿元大关；与去年同期的51.51亿元相比，同比大增 236.59%。 "2025年以来 ...

- The report does not contain any specific quantitative models or factors for analysis[1][2][3] - The report primarily focuses on market trends, style performance, valuation metrics, and fund flows without detailing quantitative models or factor construction[1][2][3] - Key metrics such as PE_TTM, ERP, and style indices are discussed, but no explicit quantitative model or factor development process is provided[1][2][3]

Quantitative Models and Construction Methods 1. Model Name: Three-Strategy Fusion ETF Rotation Strategy - **Model Construction Idea**: The strategy integrates three dimensions: fundamental-driven rotation, quality low-volatility style rotation, and distressed reversal industry discovery. It aims to achieve factor and style complementarity while reducing the risk of single-strategy exposure[35][36] - **Model Construction Process**: 1. **Fundamental Rotation Strategy**: Selects industries based on factors such as exceeding expected prosperity, industry leadership effects, momentum, crowding, and inflation beta[36] 2. **Quality Low-Volatility Style Strategy**: Focuses on individual stock quality, momentum, and low volatility to enhance defensiveness[36] 3. **Distressed Reversal Strategy**: Utilizes PB z-score, long-term analyst expectations, and short-term chip exchange to capture valuation recovery and performance reversal opportunities[36] 4. Combines the three strategies equally to form a composite ETF rotation strategy, achieving multi-dimensional industry screening and reducing single-strategy risks[35][36] - **Model Evaluation**: The strategy effectively balances factor complementarity and style adaptation, providing robust performance across different market conditions[35][36] 2. Model Name: Hotspot Trend ETF Strategy - **Model Construction Idea**: This strategy identifies ETFs with strong upward trends and high market attention, constructing a risk-parity portfolio based on support-resistance factors and turnover ratios[30] - **Model Construction Process**: 1. Select ETFs where both the highest and lowest prices exhibit an upward trend[30] 2. Calculate the relative steepness of the regression coefficients for the highest and lowest prices over the past 20 days to construct support-resistance factors[30] 3. Choose the top 10 ETFs with the highest 5-day turnover ratio/20-day turnover ratio from the long group of the support-resistance factor, indicating increased short-term market attention[30] 4. Construct a risk-parity portfolio using these ETFs[30] - **Model Evaluation**: The strategy demonstrates strong performance, achieving significant excess returns compared to the benchmark[30] 3. Model Name: Capital Flow Resonance Strategy - **Model Construction Idea**: This strategy identifies industries with resonant capital flows by combining financing margin and active large-order capital flow factors, aiming to enhance stability and reduce drawdowns[42][44][45] - **Model Construction Process**: 1. Define the financing margin factor as the market-neutralized financing net buy-in minus securities lending net sell-out, calculated as the two-week change in the 50-day moving average[45] 2. Define the active large-order capital flow factor as the market-neutralized net inflow ranking of industry trading volume over the past year, using the 10-day moving average[45] 3. Exclude extreme industries from the active large-order factor and apply a negative exclusion for the financing margin factor to improve strategy stability[45] 4. Perform weekly rebalancing to select industries with resonant capital flows for long positions[45] - **Model Evaluation**: The strategy achieves stable positive excess returns with reduced drawdowns compared to other capital flow strategies[45] --- Model Backtesting Results 1. Three-Strategy Fusion ETF Rotation Strategy - **2025 YTD Performance**: Portfolio return 25.60%, benchmark return 21.83%, excess return 3.77%, Sharpe ratio 0.24, maximum drawdown -7.18%[39][40] - **Overall Performance (2017-2025)**: Annualized excess return 10.28%, Sharpe ratio 1.09, maximum drawdown -24.55%[40] 2. Hotspot Trend ETF Strategy - **2025 YTD Performance**: Portfolio return 34.49%, benchmark (CSI 300) excess return 19.58%[30] 3. Capital Flow Resonance Strategy - **2018-Present Performance**: Annualized excess return 14.3%, IR 1.4, reduced drawdowns compared to Northbound-Large Order Resonance Strategy[45] - **Last Week Performance**: Absolute return -0.27%, excess return 0.37% (relative to industry equal weight)[45] --- Quantitative Factors and Construction Methods 1. Factor Name: Momentum Factor - **Factor Construction Idea**: Captures the continuation of stock price trends over a specific period[53] - **Factor Construction Process**: 1. Calculate the 1-year momentum as the return over the past 12 months, excluding the most recent month[53] 2. Rank stocks based on momentum and form quintile portfolios[53] - **Factor Evaluation**: Demonstrates strong performance, with the 1-year momentum factor achieving a weekly excess return of 1.13%[53] 2. Factor Name: R&D to Total Assets Ratio - **Factor Construction Idea**: Measures the proportion of R&D investment relative to total assets, reflecting innovation capability[56] - **Factor Construction Process**: 1. Calculate the ratio of total R&D expenses to total assets for each stock[56] 2. Rank stocks based on this ratio and form quintile portfolios[56] - **Factor Evaluation**: Performs well in small-cap indices, with an excess return of 20.25% in the CSI 500 index[56] 3. Factor Name: Single-Quarter ROA YoY Change - **Factor Construction Idea**: Tracks the year-over-year change in return on assets (ROA) for a single quarter, reflecting profitability trends[56] - **Factor Construction Process**: 1. Calculate the year-over-year change in ROA for the most recent quarter, considering preliminary and forecasted data[56] 2. Rank stocks based on this change and form quintile portfolios[56] - **Factor Evaluation**: Excels in large-cap indices, with an excess return of 25.52% in the CSI 300 index[56] --- Factor Backtesting Results 1. Momentum Factor - **Weekly Excess Return**: 1.13%[53] 2. R&D to Total Assets Ratio - **Excess Return in CSI 500**: 20.25%[56] 3. Single-Quarter ROA YoY Change - **Excess Return in CSI 300**: 25.52%[56] - **Excess Return in CSI 500**: 10.16%[56] - **Excess Return in CSI 1000**: 21.98%[56]

证券研究报告 | 金融工程 gszqdatemark 2025 12 14 年 月 日 量化周报 市场的震荡调整态势不改 市场的震荡调整态势不改。本周（ 12.8-12.12），大盘震荡下行，上证指数 全周收跌 0.34%。在此背景下，煤炭、钢铁、农林牧渔确认日线级别下跌， 军工迎来日线级别上涨。市场的本轮上涨自 4 月 7 日以来，日线级别反弹 已经持续了 7 个多月，反弹幅度也基本在 30%左右，各大指数和板块的 上涨基本都轮动了一遍，超半数的行业日线级别上涨处于超涨状态，几乎 所有的规模指数及一半以上的行业更是走出了复杂的 9-17浪的上涨结构， 科创 50、中小 100 更是在所有宽基里面率先形成了日线级别下跌，地产、 食品饮料、医药、商贸零售、汽车、电子、计算机、非银、机械、煤炭、 钢铁、农林牧渔也相继形成了日线级别下跌，中证 500、中证 1000、创业 板指、沪深 300、传媒、建筑、建材也有较大概率将确认日线级别下跌。 因此我们认为本轮日线级别上涨大概率已经结束。未来市场大概率会是震 荡调整的态势，当下的反弹大概率只是一波 30 分钟级别反弹，不改市场 的震荡调整态势。中期来看，上证指数、上证 ...

金融工程 | 证券研究报告 — 周报 2025 年 12 月 14 日 中银量化多策略行业轮动 周报 – 20251211 当前（2025 年 12 月 11 日）中银多策略行业配置系统仓位：通信 （9.6%）、银行（9.5%）、交通运输（9.1%）、非银行金融（8.0%）、 食品饮料（7.7%）、电力设备及新能源（7.2%）、钢铁（6.7%）、机械 （6.2%）、基础化工（4.7%）、石油石化（4.7%）、家电（4.4%）、综 合 （3.5% ）、农林牧渔（ 3.5% ）、综合金融（ 3.5% ）、有色金属 （3.5%）、建材（3.4%）、电子（2.4%）、电力及公用事业（1.2%）、 建筑（1.2%）。 相关研究报告 《中银证券量化行业轮动系列（七）：如何把 握市场"未证伪情绪"构建行业动量策略》 20220917 《中银证券量化行业轮动系列（八）："估值泡 沫保护"的高景气行业轮动策略》20221018 《中银证券宏观基本面行业轮动新框架：对传 统自上而下资产配置困境的破局》20230518 《中银证券量化行业轮动系列（九）：长期反 转-中期动量-低拥挤"行业轮动策略》20240914 《中银证券量化行 ...

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].

2025 年 12 月 13 日 总量研究 大市值风格占优，私募调研跟踪策略超额收益显著 ——量化组合跟踪周报 20251213 要点 量化市场跟踪 大类因子表现：本周（2025.12.08-2025.12.12，下同），规模因子、beta 因子、 非线性市值因子、获得正收益（1.18%、0.91%和 0.82%），BP 因子和流动性 因子获得负收益（-0.55%和-0.38%），市场大市值风格占优。 单因子表现：沪深 300 股票池中，本周表现较好的因子有总资产增长率(2.05%)、 单季度 ROA(1.71%)、换手率相对波动率(1.59%),表现较差的因子有对数市值因 子(-1.00%)、下行波动率占比(-1.10%)、大单净流入(-1.14%)。 中证 500 股票池中，本周表现较好的因子有单季度 EPS(1.61%)、总资产增长率 (1.39%)、动量弹簧因子(1.22%),表现较差的因子有市销率 TTM 倒数(-2.49%)、 下行波动率占比(-2.55%)、市净率因子(-3.06%)。 流动性 1500 股票池中，本周表现较好的因子有总资产增长率(2.25%)、单季度 营业收入同比增长率(2.0 ...

- 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 ...

彭博新一代MAC3代表了目前最先进的一类多资产类别风险因子模型，每日基于超过3000个因子 进行计算，为不同投资组合、投资范围和投资风格提供卓越的预测准确性。该模型不仅用于识别更 广泛的市场信号，还能解析不同市场状态下的风险动态。MAC3融合了多项行业首创的创新方法， 确立了其在风险模型领域的领先地位。MAC3风险因子模型文件可通过彭博灵活开放的API 基础架 构访问，以便于客户在各类工作流程中高效调用。 彭博近日宣布， 全球领先的流动性另类资产管理公司 Marshall Wace已采用 彭博多资产风险因子 模型 （Multi-Asset Class Factor Risk Model - MAC3 ）， 支持其量化研究和系统化投资策略。 Marshall Wace的资产管理规模超过700亿美元。 通过采用彭博MAC3因子模型，Marshall Wace能够获取先进的建模技术，实现更优的模型设定、 精准的风险预测和强大的投资组合分析能力，从而对多资产投资组合风险进行全面衡量与监控。 彭博投资组合分析研究主管Jose Menchero 表示： " Marshall Wace采用MAC3，反映出机构投资者对高精度 ...