《微盘股继续领涨市场，扩散指数已 达较高区间——微盘股指数周报 20251114》 - 2025.11.18 《连板情绪持续发酵，GRU 行业轮动调 入 基 础 化 工 — — 行 业 轮 动 周 报 20251109》 - 2025.11.11 《微盘股高位盘整，增长逻辑未改变— — 微 盘 股 指 数 周 报 20251031 》 - 2025.11.03 《上证周中突破 4000 点，扩散指数行 业轮动调入电力设备及新能源——行 业轮动周报 20251102》 - 2025.11.02 《微盘股触发看多信号，看好微盘 10 月 后 续 表 现 — — 微 盘 股 指 数 周 报 20251017》 - 2025.10.22 证券研究报告：金融工程报告 研究所 分析师:黄子崟 SAC 登记编号:S1340523090002 Email:huangziyin@cnpsec.com 研究助理:金晓杰 SAC 登记编号:S1340124100010 Email:jinxiaojie@cnpsec.com 近期研究报告 《上证强于双创调整空间不大，ETF 资 金持续配置金融地产与 TMT 方向——行 业轮动周报 ...

2025 年 11 月 22 日 总量研究 ——量化组合跟踪周报 20251122 因子表现分化，市场大市值风格显著 要点 量化市场跟踪 大类因子表现：本周全市场股票池中，市值因子获取正收益 0.99%，市场大市 值风格显著；杠杆因子、流动性因子、残差波动率因子、估值因子分别获取负收 益-0.41%、-0.43%、-0.50%、-0.68%；其余风格因子表现一般。 单因子表现：沪深 300 股票池中，本周表现较好的因子有日内波动率与成交金 额的相关性(1.23%)、ROE 稳定性 (1.14%)、下行波动率占比 (1.13%)。表现较 差的因子有早盘收益因子 (-2.46%)、动量弹簧因子 (-2.21%)、净利润断层 (-1.72%)。 中证 500 股票池中，本周表现较好的因子有单季度总资产毛利率(1.82%)、动量 调整大单(1.66%)、总资产毛利率 TTM (1.63%)。表现较差的因子有单季度 ROA 同比(-0.66%)、单季度 ROE 同比(-0.55%)、ROIC 增强因子(-0.53%)。 流动性 1500 股票池中，本周表现较好的因子有净利润率 TTM (1.82%)、营业利 润率 TT ...

[Table_Title] 证券研究报告 /金融工程研究报告 "量化+主观"灵活资产配置方案 --量化资产配置系列之四 报告摘要： 本文为东北金工量化资产配置系列第四篇，主要介绍按照哈佛捐赠基金 的基于因子的灵活非决定性资产配置(FIFAA)思路对国内可投资产进行 组合配置的过程和结果。 哈佛捐赠基金提出 FIFAA 是希望将量化方案的学术性/规范性与主观方 案的前瞻性/灵活性进行组合，使用基于历史数据得到的量化结果(ex post)与主观观点转换成资产-因子暴露的预期结果(ex ante)进行叠加，得 到兼具量化和主观优点的组合结果。同时，与本系列此前介绍的方案不 同，FIFAA 要求在构建宏观因子时需要具有可投资性/简洁性，以降低量 化计算中的误差和主观判断的不确定性。 由于在复制方法论的过程中使用投资者主观观点进行 beta 调整和不同阶 段敞口选择在回溯时较难实践，本文进行了一定简化，具体操作过程如 下： 综合结果显示，无论是使用历史风险载荷还是调整风险载荷，两种优化 结果相对于多资产等权均提供了更可观的收益和风险回报。在实操中， 如果持有单资产对多因子的联合关系观点，叠加预期收益的判断，或可 得到更 ...

【导读】富时罗素：未来6到12个月，欧洲主权财富基金和养老基金或增加中国配置 全球知名指数供应商富时罗素CEO Fiona Bassett日前接受本报采访时表示，今年以来，全球投资者从防 御性现金和短久期债券转向了风险资产，包括发达市场和新兴市场股票以及新兴市场债券。此外，被动 资金流向中国和大中华区资产。她预计大型主权财富基金和养老基金或在未来6到12个月增加对中国的 配置。欧洲资产管理公司越来越将中国视为独立资产类别，而不仅仅是新兴市场的一部分。 今年10月，富时罗素宣布自2026年9月21日起将越南股市从前沿市场类别升级至次级新兴市场类别。富 时罗素亚太区指数政策总监杜婉明表示，此番升级后，全球投资者可更便利地触及越南市场。而升级对 其他新兴市场的影响微乎其微。 资料显示，富时罗素为全球超过19万亿美元的资产提供基准服务，覆盖全球98%的可投资市场，涵盖发 达国家和新兴市场。其指数产品被广泛应用于被动投资、主动投资的业绩评估、资产配置以及风险管理 等领域。 全球投资者转向风险资产 中国基金报：如何看待今年以来的全球资本流动？投资者目前将资金转向何处？ Fiona Bassett：投资者从年初的防御性现金 ...

- The report constructs an ETF hotspot trend strategy based on the highest and lowest price trends of ETFs, selecting those with both highest and lowest prices in an upward trend. Further, it constructs a support-resistance factor based on the relative steepness of the regression coefficients of the highest and lowest prices over the past 20 days, and selects the top 10 ETFs with the highest turnover rate in the past 5 days/20 days to construct a risk parity portfolio[27][30] - The report tracks the performance of various style factors, noting that the value factor recorded a positive return of 2.36%, the leverage factor recorded a positive return of 1.08%, and the volatility factor slightly rebounded with a return of 0.19%[41][42] - The report evaluates the performance of different alpha factors, highlighting that the quick ratio factor had the best performance with a weekly excess return of 1.32%, followed by the debt-asset ratio factor with a weekly excess return of 1.21%, and the earnings variability over 5 years factor with a weekly excess return of 1.04%[44][46][47] - The ETF hotspot trend strategy recorded a cumulative excess return over the CSI 300 index since the beginning of the year[28][29] - The value factor achieved a weekly return of 2.36%, the leverage factor achieved a weekly return of 1.08%, and the volatility factor achieved a weekly return of 0.19%[41][42] - The quick ratio factor achieved a weekly excess return of 1.32%, the debt-asset ratio factor achieved a weekly excess return of 1.21%, and the earnings variability over 5 years factor achieved a weekly excess return of 1.04%[44][46][47]

点击注册小程序 报告摘要 量化市场跟踪 大类因子表现： 本周（2025.11.10-2025.11.14，下同），残差波动率因子和杠杆因子获得正收益（0.50%和0.36%），beta 因子、规模因子和动量因子获得负收益（-1.10%、-0.92%和-0.70%），市场小市值风格占优、反转效应显 著。 单因子表现： 查看完整报告 特别申明： 本订阅号中所涉及的证券研究信息由光大证券研究所编写，仅面向光大证券专业投资者客户，用作新媒体形势下研究 信息和研究观点的沟通交流。非光大证券专业投资者客户，请勿订阅、接收或使用本订阅号中的任何信息。本订阅号 难以设置访问权限，若给您造成不便，敬请谅解。光大证券研究所不会因关注、收到或阅读本订阅号推送内容而视相 关人员为光大证券的客户。 机构调研组合跟踪： 本周公募调研选股策略和私募调研跟踪策略获取正超额收益。公募调研选股策略相对中证800获得超额收 益1.82%，私募调研跟踪策略相对中证800获得超额收益1.06%。 大宗交易组合跟踪： 沪深300股票池中，本周表现较好的因子有大单净流入(1.63%)、市盈率因子(1.50%)、5日成交量的标准差 (1.40%),表现较差 ...

Quantitative Models and Construction 1. Model Name: Barra Style Factors - **Model Construction Idea**: The Barra style factors are designed to capture various market characteristics such as valuation, momentum, volatility, and growth, among others, to explain stock returns[14][15] - **Model Construction Process**: - The factors are calculated based on specific financial and market metrics. For example: - **Beta**: Historical beta - **Size**: Natural logarithm of total market capitalization - **Momentum**: Weighted average of historical excess return series - **Volatility**: Weighted average of historical residual return volatility - **Valuation**: Inverse of price-to-book ratio - **Liquidity**: Weighted average of turnover ratios (monthly, quarterly, yearly) - **Profitability**: Weighted average of various profitability metrics such as analyst forecasted earnings-to-price ratio, inverse of price-to-cash flow ratio, and inverse of trailing twelve-month price-to-earnings ratio - **Growth**: Weighted average of earnings growth rate and revenue growth rate - **Leverage**: Weighted average of market leverage, book leverage, and debt-to-asset ratio[15] - **Model Evaluation**: The model is widely used in the industry to capture systematic risk factors and explain stock returns. It is considered robust and comprehensive in its approach to factor construction[14][15] 2. Model Name: GRU (Generalized Risk Utility) Model - **Model Construction Idea**: GRU models are used to capture complex relationships in stock returns by leveraging advanced statistical and machine learning techniques. They are designed to identify patterns in historical data and predict future performance[4][6][8] - **Model Construction Process**: - GRU models are trained on historical data to identify patterns in stock returns - The models are applied to different stock pools (e.g., CSI 300, CSI 500, CSI 1000) to evaluate their performance - Specific GRU models include `barra1d`, `barra5d`, `open1d`, and `close1d`, which differ in their time horizons and data inputs[4][6][8] - **Model Evaluation**: GRU models show mixed performance, with some models like `barra5d` and `close1d` performing strongly, while others like `barra1d` exhibit significant drawdowns in certain periods[4][6][8] --- Model Backtesting Results 1. Barra Style Factors - **Momentum**: Weekly return 3.49%, monthly return -6.50%, YTD return -14.88%[17] - **Beta**: Weekly return 2.21%, monthly return -7.75%, YTD return 28.44%[17] - **Volatility**: Weekly return 1.90%, monthly return -3.76%, YTD return 6.09%[17] - **Liquidity**: Weekly return 1.67%, monthly return 46.39%, YTD return 8.77%[17] - **Size**: Weekly return 0.45%, monthly return -6.89%, YTD return -39.47%[17] - **Non-linear Size**: Weekly return 0.28%, monthly return -6.47%, YTD return -34.37%[17] - **Growth**: Weekly return 0.22%, monthly return 2.03%, YTD return 0.89%[17] - **Profitability**: Weekly return 1.43%, monthly return 3.55%, YTD return 14.39%[17] - **Leverage**: Weekly return 2.13%, monthly return 4.08%, YTD return 16.59%[17] - **Valuation**: Weekly return 3.52%, monthly return 6.78%, YTD return 4.37%[17] 2. GRU Models - **barra1d**: Weekly return -0.34%, monthly return -0.65%, YTD return 4.71%[33][34] - **barra5d**: Weekly return 1.44%, monthly return 5.42%, YTD return 7.34%[33][34] - **open1d**: Weekly return 0.32%, monthly return 1.81%, YTD return 6.02%[33][34] - **close1d**: Weekly return 1.41%, monthly return 4.17%, YTD return 4.33%[33][34] - **Multi-factor Combination**: Weekly return 0.57%, monthly return 2.54%, YTD return 0.89%[33][34] --- Quantitative Factors and Construction 1. Factor Name: Fundamental Factors - **Factor Construction Idea**: Fundamental factors are derived from financial metrics to capture the underlying financial health and performance of companies[4][6][7] - **Factor Construction Process**: - Metrics such as return on assets (ROA), return on equity (ROE), and revenue growth are calculated using trailing twelve-month (TTM) data - Factors are industry-neutralized before testing[19] - **Factor Evaluation**: Fundamental factors show mixed performance, with some factors like "growth" and "profitability" performing well, while others like "static financial factors" exhibit negative returns in certain periods[4][6][7] 2. Factor Name: Technical Factors - **Factor Construction Idea**: Technical factors are based on price and volume data to capture market trends and investor behavior[4][6][7] - **Factor Construction Process**: - Metrics such as momentum, volatility, and turnover are calculated over different time horizons (e.g., 20-day, 60-day, 120-day) - Factors are industry-neutralized before testing[19] - **Factor Evaluation**: Technical factors generally show positive returns for momentum-based factors, while volatility-based factors often exhibit negative returns[4][6][7] --- Factor Backtesting Results 1. Fundamental Factors (CSI 300) - **ROA Growth**: Weekly return 0.38%, monthly return 2.38%, YTD return 26.31%[23] - **Net Profit Surprise Growth**: Weekly return 1.10%, monthly return 2.62%, YTD return 42.59%[23] - **ROC Surprise Growth**: Weekly return 2.23%, monthly return 2.23%, YTD return 35.35%[23] 2. Technical Factors (CSI 500) - **20-day Momentum**: Weekly return 5.99%, monthly return 1.74%, YTD return 3.65%[26] - **120-day Momentum**: Weekly return 1.76%, monthly return 4.01%, YTD return 3.55%[26] - **20-day Volatility**: Weekly return -1.15%, monthly return -4.31%, YTD return 25.86%[26]

Core Viewpoint - The article provides a comprehensive analysis of market performance, highlighting the varying returns of different factors and strategies within the stock market, indicating a mixed sentiment among investors and the potential for selective investment opportunities [4][5][6][7][8][9][10]. Factor Performance - In the overall market, the valuation factor achieved a positive return of 0.40%, while the market capitalization factor and non-linear market capitalization factor recorded negative returns of -0.72% and -0.40% respectively, suggesting a small-cap style market performance [4]. - In the CSI 300 stock pool, the best-performing factors included the inverse TTM price-to-earnings ratio (3.05%), price-to-earnings ratio (2.30%), and price-to-book ratio (2.06%), while the worst performers were TTM gross profit margin (-2.11%), total asset growth rate (-1.80%), and quarterly gross profit margin (-1.58%) [5]. - In the CSI 500 stock pool, the top factors were the inverse TTM price-to-earnings ratio (2.71%), price-to-book ratio (2.07%), and price-to-earnings ratio (1.74%), with the lowest performers being TTM gross profit margin (-2.13%), quarterly gross profit margin (-2.02%), and quarterly ROA year-on-year (-1.50%) [5]. - In the liquidity 1500 stock pool, the leading factors were the inverse TTM price-to-earnings ratio (1.74%), price-to-earnings ratio (1.68%), and price-to-book ratio (1.34%), while the worst were post-opening returns (-3.00%), TTM gross profit margin (-2.64%), and quarterly gross profit margin (-2.50%) [5]. Industry Factor Performance - The fundamental factors showed varied performance across industries, with net asset growth rate, net profit growth rate, earnings per share, and TTM operating profit factors yielding positive returns in the oil and petrochemical industry [6]. - Among valuation factors, the BP factor performed well, achieving positive returns across most industries, while residual volatility and liquidity factors showed significant positive returns in the comprehensive industry [6]. - The market exhibited a notable small-cap style across most industries during the week [6]. Strategy Performance - The PB-ROE-50 combination achieved positive excess returns in the CSI 500 and CSI 800 stock pools, with excess returns of 1.00% and 0.48% respectively, while the overall market stock pool recorded an excess return of -2.00% [7]. - The private equity research tracking strategy yielded negative excess returns, while the public equity research selection strategy achieved an excess return of 0.00% relative to the CSI 800, and the private equity tracking strategy had an excess return of -1.96% [8]. - The block trading combination achieved positive excess returns relative to the CSI All Share Index, with an excess return of 1.08% [9]. - The targeted issuance combination also recorded positive excess returns relative to the CSI All Share Index, with an excess return of 1.93% [10].

- **Quantitative factors tracked** - Single factor performance: In the CSI 300 stock pool, the best-performing factors this week include PE TTM inverse (3.05%), PE factor (2.30%), and PB factor (2.06%) [12][13] - In the CSI 500 stock pool, the best-performing factors include PE TTM inverse (2.71%), PB factor (2.07%), and PE factor (1.74%) [14][15] - In the liquidity 1500 stock pool, the best-performing factors include PE TTM inverse (1.74%), PE factor (1.68%), and PB factor (1.34%) [16][17] - **Sector-specific factor performance** - Fundamental factors such as net asset growth rate, net profit growth rate, per-share net asset factor, and per-share operating profit TTM factor achieved positive returns in the oil and petrochemical sector [21][22] - Valuation factors like BP factor performed well across most industries [21][22] - Residual volatility factor and liquidity factor showed significant positive returns in the comprehensive industry [21][22] - **Factor classification and market trends** - Broad market factor performance: Valuation factors achieved positive returns of 0.40%, while market capitalization factors and non-linear market capitalization factors recorded negative returns of -0.72% and -0.40%, respectively, indicating a small-cap style market trend [18][20] - Momentum factor and Beta factor recorded negative returns of -0.79% and -0.43%, respectively, reflecting a reversal effect in the market [18][20] - **Quantitative portfolio tracking** - PB-ROE-50 portfolio: This week, the portfolio achieved excess returns of 1.00% in the CSI 500 stock pool, 0.48% in the CSI 800 stock pool, and -2.00% in the broad market stock pool [23][24] - Institutional research portfolio: The public fund research stock selection strategy achieved excess returns of 0.00% relative to the CSI 800, while the private fund research tracking strategy recorded excess returns of -1.96% relative to the CSI 800 [25][26] - Block trading portfolio: Constructed based on the principle of "high transaction volume, low volatility," this portfolio achieved excess returns of 1.08% relative to the CSI All Share Index this week [29][30] - Private placement portfolio: Built around the event-driven strategy of targeted placements, this portfolio achieved excess returns of 1.93% relative to the CSI All Share Index this week [35][36] - **Performance metrics of quantitative portfolios** - PB-ROE-50 portfolio: Weekly excess return of 1.00% in CSI 500, 0.48% in CSI 800, and -2.00% in the broad market [24] - Institutional research portfolio: Weekly excess return of 0.00% for public fund research stock selection and -1.96% for private fund research tracking [26] - Block trading portfolio: Weekly excess return of 1.08% [30] - Private placement portfolio: Weekly excess return of 1.93% [36]

Quantitative Models and Construction - **Model Name**: Three-dimensional Timing Framework **Construction Idea**: The model integrates liquidity, divergence, and prosperity indicators to assess market timing and trends[7][12][14] **Construction Process**: 1. Liquidity indicator measures market liquidity trends[17] 2. Divergence indicator tracks market disagreement levels[16] 3. Prosperity indicator evaluates market sentiment and economic activity[19] 4. Combine these three dimensions into a unified framework to predict market movements[12][14] **Evaluation**: The model shows historical effectiveness in identifying market support levels and timing trends[7][14] - **Model Name**: ETF Hotspot Trend Strategy **Construction Idea**: Select ETFs based on price movement patterns and market attention to construct a risk-parity portfolio[25][26] **Construction Process**: 1. Identify ETFs with simultaneous upward trends in highest and lowest prices[25] 2. Calculate regression coefficients of price movements over the past 20 days to construct support-resistance factors[25] 3. Select top 10 ETFs with the highest turnover ratio (5-day/20-day) for portfolio construction[25] **Evaluation**: The strategy demonstrates cumulative excess returns over the CSI 300 index[26] - **Model Name**: Capital Flow Resonance Strategy **Construction Idea**: Combine financing and large-order capital flows to identify industries with strong capital resonance[29][33] **Construction Process**: 1. Define financing factor as the net financing buy minus net financing sell, neutralized by Barra market capitalization[33] 2. Define large-order factor as net inflow sorted by industry and neutralized by one-year trading volume[33] 3. Combine the two factors, excluding extreme industries and large financial sectors, to enhance strategy stability[33][36] **Evaluation**: The strategy achieves annualized excess returns of 13.5% since 2018, with an IR of 1.7[33] Model Backtesting Results - **Three-dimensional Timing Framework**: Historical performance indicates effective identification of market support levels and timing trends[14] - **ETF Hotspot Trend Strategy**: Cumulative excess return over CSI 300 index observed since the beginning of the year[26] - **Capital Flow Resonance Strategy**: - Annualized excess return: 13.5% since 2018 - IR: 1.7 - Weekly absolute return: 2.86% - Weekly excess return: 0.19%[33] Quantitative Factors and Construction - **Factor Name**: Beta **Construction Idea**: Measure stock sensitivity to market movements[39] **Construction Process**: Calculate stock beta using historical price data and market index movements[39] **Evaluation**: High-beta stocks outperform low-beta stocks, achieving 3.05% weekly return[39] - **Factor Name**: Momentum **Construction Idea**: Capture the continuation of stock price trends[39] **Construction Process**: Calculate momentum based on past price performance over a defined period[39] **Evaluation**: Momentum factor records 1.28% weekly return, indicating strong performance of previously high-performing stocks[39] - **Factor Name**: Liquidity **Construction Idea**: Assess market preference for high-liquidity stocks[39] **Construction Process**: Measure liquidity using trading volume and turnover ratios[39] **Evaluation**: Liquidity factor achieves 2.06% weekly return, reflecting market favorability for liquid stocks[39] - **Factor Name**: Illiquidity (Illia) **Construction Idea**: Evaluate stock price impact driven by large trading volumes[44][45] **Construction Process**: Measure daily price changes driven by trading volumes exceeding one billion[45] **Evaluation**: Illiquidity factor achieves 1.48% weekly excess return and 2.11% monthly excess return[45] - **Factor Name**: Volume Mean and Standard Deviation **Construction Idea**: Analyze trading volume trends over different time windows[44][45] **Construction Process**: 1. Calculate mean and standard deviation of trading volumes over 1-month, 3-month, 6-month, and 12-month windows[45] 2. Normalize and rank stocks based on these metrics[45] **Evaluation**: Volume-related factors show consistent positive excess returns across different time windows, with weekly returns ranging from 0.64% to 0.99%[45] - **Factor Name**: R&D Intensity **Construction Idea**: Measure the proportion of R&D expenditure relative to sales revenue[45] **Construction Process**: Calculate R&D expenses divided by total sales revenue[45] **Evaluation**: R&D intensity factor records 0.59% weekly excess return and 0.67% monthly excess return[45] Factor Backtesting Results - **Beta Factor**: Weekly return: 3.05%[39] - **Momentum Factor**: Weekly return: 1.28%[39] - **Liquidity Factor**: Weekly return: 2.06%[39] - **Illiquidity Factor**: Weekly excess return: 1.48%, Monthly excess return: 2.11%[45] - **Volume Mean and Standard Deviation Factors**: Weekly returns range from 0.64% to 0.99%, Monthly returns range from 1.49% to 2.29%[45] - **R&D Intensity Factor**: Weekly excess return: 0.59%, Monthly excess return: 0.67%[45]