报告导读： 本文主要考察根据因子历史长短期收益、波动率等数据，采用 PLS 模型预期 因子收益在因子加权中的应用，包括单因子多策略、以及多因子单策略两个维度。 在 20 个单因子 top100 组合配置中，对波动率最大的 5 个因子组合采用 PLS 模型预期收益确定权重，相比于收益均值加权方式，年化收益提升约 4.0% （ 2018.01-2025.11 ，下同）；相较于等权方式，年化收益提升 6.6% 。 风格组合配置维度，我们构建了 6 个基础组合： 1 个红利优选、 1 个成长期优选、两个小市值组合、以及两个风格相对均衡的组合 --PB 盈利和 GARP 组 合。对波动较大组合采用 PLS 模型预期超额收益加权，相比于超额收益均值加权方式，年化收益提升 3.3% ；相较于等权方式，年化收益提升 3.9% 。 量化固收 + 策略中的应用，股票端： PLS 预期收益加权复合风格组合；债券端：中证短债指数；构建 10% 股票仓位的固收 + 量化策略， 2018 年以来年 化收益 5.6% ，年化波动率 2.6% ，信息比 2.17 ；权益比例为 20% 时，策略年化收益 8.4% ，年化波动率 5.1% ，信 ...

今年以来，A股市场结构性行情明显，私募股票量化多头策略凭借系统化优势持续获得超额收益。私募 排排网数据显示，截至11月底，全市场833只股票量化多头产品年内平均超额收益率超17%，其中762只 产品实现超额收益，占比高达91.48%，体现出该策略整体有效且收益稳定。 融智投资FOF（基金中的基金）基金经理李春瑜向《证券日报》记者表示，今年以来，A股市场呈现震 荡上行态势，AI算力等科技板块与周期板块轮动频繁。市场日均成交额持续保持高位，为量化交易提 供了良好的流动性环境。在此背景下，量化多头策略通过快速交易能够及时把握板块轮动节奏，动态调 仓能力得到充分发挥。此外，人工智能的深入应用可以帮助策略高效处理海量信息，多因子模型在分散 风险的同时也增强了收益潜力，从而更好地匹配了年内的市场风格。 从管理规模来看，大中型私募机构旗下股票量化多头产品展现出更强的超额收益能力。数据显示，截至 11月底，管理规模在20亿元至50亿元区间的私募机构旗下产品表现最为突出，年内平均超额收益率达 20.12%，在各管理规模梯队中位居第一，且93%的产品实现超额收益；百亿元级私募机构紧随其后，旗 下产品年内平均超额收益率为19.98 ...

青侨阳光月度思考 -- 聚焦医药，深简投资 本文为青侨阳光基金 11 月报投资思考部分节选 ~ 在股票投资的宇宙里，市场每个阶段的经历对于投资人来说都像是在体验开盲盒。 以青侨基金为例，今年最重要的一次调仓是2025Q2-Q3拿了一半多的港股创新药企换美股生科标的，理由是这 两类资产的成长动力都很强劲、长期前景都很有吸引力，但自2025年以来一个大涨一个大跌，估值吸引力发生 了大幅逆转。 然而： 但，要是再往前展望几个月，谁又知道会怎样呢？两者继续分化或者两者强弱互换也都不是不可能…… 过去几年里，类似的经历比比皆是，给人的感受就是"往后回溯处处留遗憾，往前展望事事皆未知"。这提出了 我们不得不不直面的一个挑战： 在波诡云谲的市场里，什么是可以信赖的规律？什么是靠不住的表象？如何 才能不断提高我们高效安全地穿越未知丛林的能力？ 1 一条可行的路径： 长视角下的强劲内生价值增长 首先，可以确定的是，虽然短期的市场定价高度未知、难以琢磨，但长视角下，好业务好公司的未来可见度是 非常高的，在此背景下的回报确定性也是非常好的。 因为 估值是个扰动性而非叠加性变量 ，主流市场里正常公司估值拉升或估值压抑一般也就1倍为限 ...

- The report tracks the performance of various style factors, including market capitalization, non-linear market capitalization, profitability, momentum, volatility, and beta factors[2] - The construction process involves creating long-short portfolios at the end of each month, going long on the top 10% of stocks with the highest factor values and shorting the bottom 10% with the lowest factor values, with equal weighting[16] - The recent performance shows strong long positions in market capitalization, non-linear market capitalization, and profitability factors, while momentum, volatility, and beta factors had strong short positions[16] Factor Performance Tracking - The fundamental factors showed mixed long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[3][4][5] - Technical factors had negative long-short returns, with momentum factors showing more significant negative returns, favoring low momentum and low volatility stocks[3][4][5] - GRU factors had weak long-short performance, with the barra1d model showing some pullback, while other models had insignificant returns[3][4][5] CSI 300 Component Stocks Factor Performance - Fundamental factors showed mixed long-short returns, with growth and surprise growth factors performing negatively, while static financial factors performed positively[4] - Technical factors had negative long-short returns, with momentum factors showing more significant negative returns, favoring low momentum and low volatility stocks[4] - GRU factors had mixed long-short performance, with the barra1d model showing significant pullback, while the barra5d and close1d models performed strongly[4] CSI 500 Component Stocks Factor Performance - Fundamental factors showed mixed long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[5] - Technical factors had negative long-short returns, with short-term factors showing more significant performance, favoring low volatility and low momentum stocks[5] - GRU factors had good long-short performance, with the open1d and barra1d models showing slight pullback, while the close1d and barra5d models performed strongly[5] CSI 1000 Component Stocks Factor Performance - Fundamental factors showed similar long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[6] - Technical factors had negative long-short returns, favoring low volatility and low momentum stocks[6] - GRU factors had strong long-short performance, with the barra1d model showing some pullback, while the close1d and open1d models performed strongly[6] Long-Only Portfolio Performance - The GRU long-only portfolio showed weak performance, with various models underperforming the CSI 1000 index by 0.54% to 1.12%[7] - The barra5d model performed strongly year-to-date, outperforming the CSI 1000 index by 8.55%[7] - The multi-factor portfolio showed weak performance, underperforming the CSI 1000 index by 0.47%[7] Factor Performance Metrics - Momentum factor: -1.93% (one week), -8.36% (one month), -24.78% (six months), 19.89% (year-to-date), 17.64% (three-year annualized), 17.58% (five-year annualized)[17] - Volatility factor: 1.82% (one week), -2.33% (one month), 16.17% (six months), 6.56% (year-to-date), 7.58% (three-year annualized), -11.09% (five-year annualized)[17] - Beta factor: -1.54% (one week), 5.68% (one month), 0.60% (six months), 19.29% (year-to-date), 7.50% (three-year annualized), 8.99% (five-year annualized)[17] - Liquidity factor: 0.91% (one week), 42.89% (one month), 9.98% (six months), 12.24% (year-to-date), -20.32% (three-year annualized), -24.87% (five-year annualized)[17] - Valuation factor: 0.82% (one week), 0.46% (one month), 0.14% (six months), 3.77% (year-to-date), 14.92% (three-year annualized), 5.46% (five-year annualized)[17] - Growth factor: 0.71% (one week), 2.28% (one month), 2.34% (six months), 3.16% (year-to-date), 49.33% (three-year annualized), -4.78% (five-year annualized)[17] - Leverage factor: 0.35% (one week), 2.37% (one month), 3.68% (six months), 15.17% (year-to-date), 6.40% (three-year annualized), 1.98% (five-year annualized)[17] - Profitability factor: 0.49% (one week), -0.64% (one month), 7.01% (six months), 14.10% (year-to-date), 3.12% (three-year annualized), 0.51% (five-year annualized)[17] - Non-linear market capitalization factor: 4.22% (one week), 0.44% (one month), 3.16% (six months), -32.83% (year-to-date), -38.38% (three-year annualized), -30.29% (five-year annualized)[17] - Market capitalization factor: 5.39% (one week), 0.59% (one month), 2.18% (six months), -37.92% (year-to-date), -40.48% (three-year annualized), -34.25% (five-year annualized)[17]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods 1. Model Name: Broker Gold Stock Performance Enhancement Portfolio - **Model Construction Idea**: The model aims to optimize the selection from the broker gold stock pool to outperform the benchmark index of equity-biased hybrid funds[12][39] - **Model Construction Process**: - The model uses the broker gold stock pool as the stock selection space and constraint benchmark - It employs portfolio optimization to control deviations in individual stocks and styles from the broker gold stock pool - The industry allocation is based on the industry distribution of all public funds - The portfolio is adjusted at the closing price on the first day of each month[12][39][42] - **Model Evaluation**: The model has shown stable performance historically, consistently outperforming the equity-biased hybrid fund index annually from 2018 to 2022[12][39][42] Model Backtest Results Broker Gold Stock Performance Enhancement Portfolio - **Absolute Return (Monthly)**: -0.77% (20251009-20251031)[41] - **Excess Return Relative to Equity-biased Hybrid Fund Index (Monthly)**: 1.37% (20251009-20251031)[41] - **Absolute Return (Year-to-date)**: 35.08% (20250102-20251031)[41] - **Excess Return Relative to Equity-biased Hybrid Fund Index (Year-to-date)**: 2.61% (20250102-20251031)[41] - **Ranking in Active Equity Funds (Year-to-date)**: 40.13% percentile (412/3469)[41] Quantitative Factors and Construction Methods 1. Factor Name: Total Market Value - **Factor Construction Idea**: This factor measures the total market capitalization of a stock, which is often used to capture the size effect in stock returns[3][28] - **Factor Construction Process**: - The total market value is calculated as the product of the stock's current price and the total number of outstanding shares[3][28] - **Factor Evaluation**: The total market value factor has shown good performance in the recent month and year-to-date periods[3][28] 2. Factor Name: Single Quarter Revenue Growth Rate - **Factor Construction Idea**: This factor measures the growth rate of a company's revenue in a single quarter, indicating its short-term growth potential[3][28] - **Factor Construction Process**: - The single quarter revenue growth rate is calculated as the percentage change in revenue from the previous quarter to the current quarter[3][28] - **Factor Evaluation**: The single quarter revenue growth rate factor has shown good performance year-to-date[3][28] 3. Factor Name: Analyst Net Upward Revision - **Factor Construction Idea**: This factor measures the net number of upward revisions by analysts, reflecting positive changes in analyst sentiment[3][28] - **Factor Construction Process**: - The analyst net upward revision is calculated as the difference between the number of upward revisions and the number of downward revisions over a specific period[3][28] - **Factor Evaluation**: The analyst net upward revision factor has shown good performance year-to-date[3][28] Factor Backtest Results Total Market Value Factor - **Recent Month Performance**: Good[3][28] - **Year-to-date Performance**: Good[3][28] Single Quarter Revenue Growth Rate Factor - **Recent Month Performance**: Not specified - **Year-to-date Performance**: Good[3][28] Analyst Net Upward Revision Factor - **Recent Month Performance**: Not specified - **Year-to-date Performance**: Good[3][28]

Core Viewpoint - The article emphasizes the rapid development of AI and quantitative technology in the investment sector, highlighting the importance of continuous strategy evolution for the long-term success of quantitative private equity firms like Tianyan Capital, which was founded in 2014 and has a strong focus on innovation and adaptation [2]. Company Overview - Tianyan Capital was co-founded by Xie Xiaoyang and Zhang Sen, both of whom have over ten years of industry experience. The company’s name reflects its commitment to change and deep insights into the essence of investment [2]. - The firm has received multiple industry awards, including the "Golden Changjiang Award" and "Yinghua Award," and ranks among the top ten quantitative private equity firms in terms of performance [3][4]. Performance Metrics - As of September 2025, Tianyan Capital's products have achieved impressive returns, with an average return of ***% over the past three years, placing it first in the industry [3][4]. - The firm manages approximately 2.1 billion yuan across 11 products that meet ranking criteria, showcasing its strong long-term performance [3]. Investment Strategy - The core strategy of Tianyan Capital is centered around a multi-factor model for stock selection, which allows for higher alpha returns at a lower cost [8]. - The flagship product, "Tianyan Saineng," has been operational since May 2016 and has demonstrated significant returns, with a focus on maintaining model autonomy and stability in risk control [10][11]. Team and Culture - The investment research team at Tianyan Capital consists of over half PhD holders from prestigious institutions, fostering a culture of free exploration and innovation [12]. - The company emphasizes long-termism in its operations, avoiding arbitrary changes to risk parameters and maintaining a stable risk control model [10][11]. Market Position and Future Outlook - Tianyan Capital has strategically positioned itself to balance scale and performance, understanding that growth in assets under management should align with long-term performance and research capabilities [14]. - The firm has also obtained a Hong Kong license to enhance its global asset allocation capabilities, focusing on capturing unique alpha opportunities in the Chinese market while catering to international investors [16].

Report Industry Investment Rating - Not provided in the given content Core Viewpoints - Based on the Huatai Commodity Multi-Factor Model, this week it is recommended to overweight copper, silver, soybean oil, gold, and fresh apples, and underweight glass, alumina, soda ash, eggs, and styrene [4][51] Summary by Relevant Catalogs 1. Plate Liquidity - This week, the trading volume of the basic metals sector was 1784.354 billion yuan, a change of 104.21% from last week, with a margin of 50.724 billion yuan, a change of -3.33 billion yuan from last week [1] - The energy and chemical sector had a trading volume of 1641.153 billion yuan, a change of 148.50% from last week, and a margin of 36.5 billion yuan, a change of 0.198 billion yuan from last week [1] - The agricultural products sector had a trading volume of 1222.184 billion yuan, a change of 88.30% from last week, and a margin of 41.853 billion yuan, a change of 1.864 billion yuan from last week [1] - The precious metals sector had a trading volume of 5172.317 billion yuan, a change of 271.03% from last week, and a margin of 76.338 billion yuan, a change of 4.96 billion yuan from last week [1] - The black building materials sector had a trading volume of 1013.342 billion yuan, a change of 161.66% from last week, and a margin of 33.353 billion yuan, a change of 1.948 billion yuan from last week [1] - The stock index futures sector had a trading volume of 3921.85 billion yuan, a change of 133.22% from last week, and a margin of 154.917 billion yuan, a change of -10.672 billion yuan from last week [1] - The treasury bond futures sector had a trading volume of 1592.895 billion yuan, a change of 132.22% from last week, and a margin of 16.084 billion yuan, a change of 1.145 billion yuan from last week [1] 2. Market and Plate Style - Since the beginning of this year, the Wande Commodity Index has a change of 33.76%, the Non-ferrous Index has a change of 2.25%, the Energy Index has a change of -22.63%, the Chemical Index has a change of -17.92%, the Oilseeds Index has a change of 4.47%, the Precious Metals Index has a change of 48.17%, and the Coking Coal and Steel Ore Index has a change of 0.64% [2] - The Huatai Commodity Long-term Momentum Index has a change of 18.76%, the Short-term Momentum Index has a change of 0.20%, the Skewness Index has a change of 12.23%, and the Term Structure Index has a change of 3.39% [2] - The latest VIX indicators of stock index options are as follows: SSE 50 Index Option is 19.26%, CSI 300 Index Option is 20.98%, and CSI 1000 Index Option is 26.67% [2] 3. Plate Premium and Discount Structure - The latest basis of stock index futures: IH is 7.47 points, IF is -17.27 points, IC is -143.47 points, and IM is -159.17 points; the annualized basis rate: IH is 1.46%, IF is -2.22%, IC is -11.85%, and IM is -12.83% [3] - The latest basis of treasury bond futures: TS is -0.02 yuan, TF is -0.05 yuan, T is 0.10 yuan, and TL is -0.29 yuan; the latest net basis: TS is -0.01 yuan, TF is -0.04 yuan, T is -0.08 yuan, and TL is -0.51 yuan [3] 4. Strategy - According to the Huatai Commodity Multi-Factor Model, this week it is recommended to overweight copper, silver, soybean oil, gold, and fresh apples, and underweight glass, alumina, soda ash, eggs, and styrene [4][51]

- **Barra style factors**: The report tracks various style factors including Beta, Market Cap, Momentum, Volatility, Non-linear Market Cap, Valuation, Liquidity, Profitability, Growth, and Leverage. Each factor is constructed using specific financial metrics and formulas. For example, the Profitability factor combines analyst forecast earnings price ratio, inverse price-to-cash flow ratio, and inverse price-to-earnings ratio (TTM), among others. The Growth factor incorporates earnings growth rate and revenue growth rate. These factors are used to evaluate stocks based on their historical and financial characteristics [13][14][15]. - **GRU factors**: GRU factors are derived from different training objectives, such as predicting future one-day close-to-close or open-to-open returns. Examples include `close1d`, `open1d`, `barra1d`, and `barra5d`. These factors are constructed using GRU models trained on historical data to forecast short-term stock movements. GRU factors showed strong performance, with most models achieving positive multi-period returns, except for `barra1d`, which experienced some drawdowns [20][28][32]. - **Factor testing methodology**: Factors are tested using a long-short portfolio approach. At the end of each month, stocks are ranked based on the latest factor values, with the top 10% being long positions and the bottom 10% being short positions. The portfolios are equally weighted, and factors are industry-neutralized before testing. This methodology ensures robust evaluation of factor performance across different market conditions [15][16][31]. - **Factor performance results**: - **Style factors**: Valuation, Profitability, and Leverage factors showed strong long performance, while Beta, Liquidity, and Momentum factors performed well on the short side [15][16]. - **Technical factors**: Across various time windows, low momentum and low volatility stocks generally outperformed, while high volatility and high momentum stocks underperformed. For example, the 60-day momentum factor showed a negative return of -3.11% in the last month but a positive return of 2.12% over the last six months [19][26][30]. - **GRU factors**: GRU models like `barra1d` achieved a year-to-date excess return of 5.22%, while `barra5d` and `open1d` also delivered strong multi-period returns. However, `barra1d` experienced a weekly drawdown of -1.65% [20][32][33]. - **Multi-factor portfolio performance**: The multi-factor portfolio outperformed the benchmark (CSI 1000 Index) by 1.35% over the past week. GRU-based models also showed strong excess returns, ranging from 0.68% to 1.60% over the same period. Year-to-date, the `barra1d` model achieved an excess return of 5.22% [32][33][34].

Core Viewpoint - The article discusses the development and capabilities of the GF Quantitative Alpha Factor Database, which supports various investment strategies through a comprehensive factor library built on extensive research and data accumulation by the GF Quantitative team [1]. Group 1: Database Overview - The GF Quantitative Alpha Factor Database is established on MySQL 8.0 and encompasses over a decade of research experience, integrating fundamental factors, Level-1 and Level-2 high-frequency factors, machine learning factors, and alternative data factors [1]. - The database supports strategies such as long-short strategies, index enhancement, ETF rotation, asset allocation, and derivatives [1]. - The GF Quantitative team possesses a data storage capacity of over 100TB and high-performance CPU/GPU computing servers, collaborating with reliable data providers like Wind, Tianruan, and Tonglian for efficient factor development and dynamic updates [1]. Group 2: Factor Types and Performance - The article lists various factors categorized by type, including deep learning factors, trading volume factors, and market order ratios, each with specific definitions and performance metrics [3]. - For instance, the "agr_dailyquote" factor has a historical average of 14.22% and a historical win rate of 91.97% [3]. - The "bigbuy" factor shows a historical average of 7.85% with a win rate of 66.74% [3]. Group 3: Research Reports - A series of research reports are available for download, covering topics such as style factor-driven quantitative stock selection, industry selection, and macroeconomic observations related to Alpha factor trends [4][5]. - The reports include analyses on the application of factors in the CSI 300 index and various strategies for capturing industry alpha drivers [4].

Quantitative Models and Construction Methods Model Name: Broker Gold Stock Performance Enhancement Portfolio - **Model Construction Idea**: The model aims to optimize the selection of stocks from the broker gold stock pool to outperform the median of active equity funds[39][43]. - **Model Construction Process**: - The model uses the broker gold stock pool as the stock selection space and constraint benchmark. - It employs portfolio optimization to control deviations in individual stocks and styles from the broker gold stock pool. - The industry distribution of all public funds is used as the industry allocation benchmark. - The model's detailed construction method can be found in the report "Broker Gold Stock Full Analysis - Data, Modeling, and Practice" published on February 18, 2022[39][43]. - **Model Evaluation**: The model has shown stable performance historically, consistently outperforming the active equity fund index from 2018 to 2022, ranking in the top 30% of active equity funds each year[12][39]. Model Backtesting Results Broker Gold Stock Performance Enhancement Portfolio - **Absolute Return (Monthly)**: 15.49%[5][42] - **Excess Return Relative to Active Equity Fund Index (Monthly)**: 3.59%[5][42] - **Absolute Return (Year-to-Date)**: 34.01%[5][42] - **Excess Return Relative to Active Equity Fund Index (Year-to-Date)**: 5.72%[5][42] - **Ranking in Active Equity Funds (Year-to-Date)**: 30.38% percentile (1054/3469)[5][42] Quantitative Factors and Construction Methods Factor Name: Single Quarter Net Profit Growth Rate - **Factor Construction Idea**: This factor measures the growth rate of net profit in a single quarter[3][28]. - **Factor Construction Process**: - Calculate the net profit for the current quarter. - Compare it to the net profit of the same quarter in the previous year. - The formula is: $ \text{Net Profit Growth Rate} = \frac{\text{Current Quarter Net Profit} - \text{Previous Year Same Quarter Net Profit}}{\text{Previous Year Same Quarter Net Profit}} \times 100\% $ - **Factor Evaluation**: This factor has performed well recently[3][28]. Factor Name: Single Quarter ROE - **Factor Construction Idea**: This factor measures the return on equity for a single quarter[3][28]. - **Factor Construction Process**: - Calculate the net income for the quarter. - Divide it by the average shareholders' equity for the quarter. - The formula is: $ \text{ROE} = \frac{\text{Net Income}}{\text{Average Shareholders' Equity}} \times 100\% $ - **Factor Evaluation**: This factor has performed well recently[3][28]. Factor Name: Single Quarter Revenue Growth Rate - **Factor Construction Idea**: This factor measures the growth rate of revenue in a single quarter[3][28]. - **Factor Construction Process**: - Calculate the revenue for the current quarter. - Compare it to the revenue of the same quarter in the previous year. - The formula is: $ \text{Revenue Growth Rate} = \frac{\text{Current Quarter Revenue} - \text{Previous Year Same Quarter Revenue}}{\text{Previous Year Same Quarter Revenue}} \times 100\% $ - **Factor Evaluation**: This factor has performed well recently[3][28]. Factor Backtesting Results Single Quarter Net Profit Growth Rate - **Performance**: This factor has shown good performance recently[3][28]. Single Quarter ROE - **Performance**: This factor has shown good performance recently[3][28]. Single Quarter Revenue Growth Rate - **Performance**: This factor has shown good performance recently[3][28].