报告导读： 2026Q1 风格轮动模型发出小盘、成长信号。 1 月，单因子策略、复合因子 策略推荐配置的多头行业均涵盖非银行金融、煤炭及钢铁。 重要提醒 行业轮动 1 月观点。 单 因子多策略推荐配置的多头行业为银行、非银行金融、煤炭及钢铁。复合因子策略推荐的多头行业为煤炭、钢铁、非银行金融、有色 金属、交通运输。 风险提示： 模型失效风险、因子失效风险、海外市场波动风险 。 报告来源 以上内容节选自国泰海通证券已发布的证券研究报告。 报告名称： 风格及行业观点月报（2026.01）；报告日期：2026.01.08 报告作者： 郑雅斌（分析师），登记编号：S0880525040105 卓洢萱（分析师），登记编号：S0880525040128 风格轮动模型方面， 2026 Q1 风格轮动模型发出小盘、成长信号。 2025Q4 ，沪深 300 、中证 1000 收益率分别为 -0.23% 及 0.27% ，小盘占优，相 对大盘风格的超额为 0.50% ，大小盘轮动模型 Q4 预测正确。此外， 2025 年全年价值成长轮动模型收益为 37.06% ，相对季度调仓的等权组合的超额为 7.01% 。 行业轮动模型方面 ...

2025 年 12 月 16 日 申万金工 ETF 组合 202512 相关研究 证券分析师 沈思逸 A0230521070001 shensy@swsresearch.com 白皓天 A0230525070001 baiht@swsresearch.com 邓虎 A0230520070003 denghu@swsresearch.com 联系人 沈思逸 A0230521070001 shensy@swsresearch.com 权 益 量 化 研 究 证 券 研 究 报 告 请务必仔细阅读正文之后的各项信息披露与声明 本研究报告仅通过邮件提供给 博时基金 博时基金管理有限公司(researchreport@bosera.com) 使用。1 量 化 策 略 - ⚫ 宏观行业组合：针对所有标记为"行业主题"的 ETF，选择成立时间 1 年以上、当期规模 2 亿以上的产品跟踪的行业主题指数，每个月根据历史数据计算经济、流动性、信用的敏 感性得分，然后根据最新的经济、流动性、信用判断指标调整得分方向后进行加总，最终 得到排名前 6 的行业主题指数，然后取对应规模最大的 ETF 进行等权配置。目前经济前 瞻指标继续回 ...

Group 1: Style Rotation Insights - The Q4 style rotation model indicates signals for small-cap and growth stocks [2][3] - The dual-driven rotation strategy for Q4 has a composite score of -1, predicting a focus on small-cap stocks [3] - The value-growth style rotation model shows a composite score of -3, suggesting a preference for growth stocks [4] Group 2: Industry Rotation Analysis - In November, the composite factor strategy yielded an excess return of -0.58%, while the single-factor long strategy had an excess return of -0.83% [4] - For December, the recommended long industries based on single-factor strategies include banking, construction, non-bank financials, and electric equipment & new energy [4] - The composite factor strategy recommends long positions in telecommunications, comprehensive finance, computers, electric equipment & new energy, and utilities [4] Group 3: Pharmaceutical Sector Performance - In November 2025, the pharmaceutical sector underperformed the broader market, with the SW pharmaceutical and biological index declining by 3.6% compared to a 1.7% drop in the Shanghai Composite Index [7] - The relative premium level of the pharmaceutical sector is currently at 72.6%, indicating a normal valuation level compared to all A-shares [7] - In the Hong Kong market, the pharmaceutical sector performed similarly to the market, with the Hang Seng Medical Care index at -0.1% and the biotechnology sector at +0.4% [7] Group 4: U.S. Pharmaceutical Market Trends - In November 2025, the U.S. pharmaceutical sector outperformed the broader market, with the S&P Healthcare Select Sector Index rising by 9.1% compared to a 0.1% increase in the S&P 500 [8] - Notable gainers in the S&P 500 healthcare component included Eli Lilly (+25%) and Solventum (+23%) [8]

Quantitative Models and Construction Methods - **Model Name**: Style Rotation Model **Model Construction Idea**: The model is built from basic style factors such as valuation, market capitalization, volatility, and momentum. It incorporates a style timing and scoring system, leveraging micro-level features and machine learning techniques to optimize style selection and rotation[4][9]. **Model Construction Process**: 1. Start with 80 fundamental micro factors as raw features, categorized based on the proprietary multi-factor system of Dongwu Securities[9]. 2. Construct 640 micro-level features from these factors[4][9]. 3. Replace the absolute proportion division of style factors with commonly used indices as style stock pools, creating new style returns as labels[4][9]. 4. Use a rolling training process with a Random Forest model to avoid overfitting, select optimal features, and generate style recommendations[4][9]. 5. Combine style timing results and scoring outcomes to build a monthly frequency style rotation framework[4][9]. **Model Evaluation**: The model effectively integrates micro-level features and machine learning to enhance style rotation performance, mitigating overfitting risks[4][9]. Model Backtesting Results - **Style Rotation Model**: - Annualized Return: 16.52% - Annualized Volatility: 20.46% - IR: 0.81 - Monthly Win Rate: 57.01% - Maximum Drawdown: 25.68% - Excess Annualized Return (Hedged against Benchmark): 11.04% - Excess Annualized Volatility (Hedged against Benchmark): 11.08% - Excess IR (Hedged against Benchmark): 1.00 - Excess Monthly Win Rate (Hedged against Benchmark): 55.14% - Maximum Drawdown (Hedged against Benchmark): 9.00%[4][10][11]

Core Insights - The report suggests increasing allocation to large-cap value stocks while focusing on TMT (Technology, Media, and Telecommunications) and cyclical sectors [3][30] - The industry rotation model has been optimized to adapt to market conditions, incorporating high-frequency factors and enhancing the strategy's effectiveness [4][26] - The latest industry rotation model identifies non-bank financials, steel, media, non-ferrous metals, environmental protection, and telecommunications as preferred sectors [30][33] Market Review and Fund Flow Tracking - As of October 31, 2025, the total monthly trading volume of A-shares reached 36.78 trillion yuan, with a slight decrease in daily average trading volume by 10.49% compared to the previous month [12][18] - The average stock return dispersion for the past month was 2.41%, indicating a slight decline but remaining above the median level for the past six months [12][18] - The industry rotation speed has continued to expand, significantly exceeding the average level since 2015 [12][18] Industry Rotation Model and ETF Fund Configuration - The report emphasizes the importance of focusing on large-cap value and cyclical sectors, particularly in the context of the current unclear market leadership [3][30] - The recommended ETF portfolio includes six funds: E Fund CSI 300 Non-Bank ETF, Guotai Junan CSI Steel ETF, GF CSI Media ETF, Southern CSI Non-Ferrous Metals ETF, Southern Yangtze River Protection Theme ETF, and Guotai Junan CSI All-Share Communication Equipment ETF [3][34] - The model's historical performance has shown consistent positive excess returns, outperforming major benchmark indices [5][42] Historical Performance and Model Effectiveness - The industry rotation model has maintained a strong performance over the years, achieving excess returns compared to industry averages, with a notable performance in 2025 [5][42] - The model's win rates over the past 1, 3, and 5 years are 83.33%, 69.44%, and 71.67% respectively, indicating its robustness [43][44] - The report highlights the significance of emotional and price-volume factors in capturing market dynamics, especially in weak market conditions [42][43]

Core Viewpoint - The Q4 style rotation model indicates signals for small-cap and growth stocks, with recommended sectors including communication, electric equipment, and renewable energy for November [1][2]. Group 1: Style Rotation Model - The Q4 style rotation model has issued signals favoring small-cap stocks, with a comprehensive score of -1 as of September 30, 2025 [3]. - The value-growth style rotation model also shows a preference for growth stocks, with a comprehensive score of -3 for Q4 2025 [4]. Group 2: Industry Rotation Insights - For October, the composite factor strategy yielded an excess return of -0.69%, while the single-factor multi-strategy had an excess return of -0.93% [4]. - In November, the single-factor multi-strategy recommends bullish sectors including media, communication, electronics, non-bank financials, electric equipment, and renewable energy [4]. - The composite factor strategy suggests bullish sectors such as communication, computer, electric and utility services, media, electric equipment, and renewable energy [4].

Quantitative Models and Construction Methods 1. Model Name: Style Rotation Model - **Model Construction Idea**: The model is built from basic style factors such as valuation, market capitalization, volatility, and momentum, gradually constructing a style timing and scoring system[4][9] - **Model Construction Process**: 1. Construct 640 micro features based on 80 basic micro indicators[9] 2. Use common indices as style stock pools to replace the absolute proportion division of style factors, constructing new style returns as labels[4][9] 3. Use a random forest model for style timing and obtain the current score for each style[4][9] 4. Integrate the timing results and scoring results to construct a monthly frequency style rotation model[4][9] - **Model Evaluation**: The model effectively avoids overfitting risks through rolling training of the random forest model and constructs a comprehensive framework from style timing to style scoring and from style scoring to actual investment[9] Model Backtesting Results 1. **Style Rotation Model**: - Annualized Return: 16.18%[10][11] - Volatility: 20.28%[10][11] - Information Ratio (IR): 0.80[10][11] - Win Rate: 59.43%[10][11] - Maximum Drawdown: 25.20%[11] 2. **Market Benchmark (Hedged)**: - Annualized Return: 10.36%[10][11] - Volatility: 10.85%[10][11] - Information Ratio (IR): 0.95[10][11] - Win Rate: 54.72%[10][11] - Maximum Drawdown: 8.53%[11]

- The style rotation model is constructed based on the Dongwu quantitative multi-factor system, starting from micro-level stock factors. It selects 80 underlying factors as original features, including valuation, market capitalization, volatility, and momentum, and further constructs 640 micro features. The model replaces the absolute proportion division of style factors with common indices as style stock pools, creating new style returns as labels. A random forest model is trained in a rolling manner to avoid overfitting risks, optimizing features and obtaining style recommendations. The framework integrates style timing, scoring, and actual investment[9][4] - The performance of the style rotation model during the backtesting period (2017/01/01-2025/09/30) shows an annualized return of 16.41%, annualized volatility of 20.43%, IR of 0.80, monthly win rate of 58.49%, and a maximum drawdown of 25.54%. When hedging against the market benchmark, the annualized return is 10.54%, annualized volatility is 10.85%, IR is 0.97, monthly win rate is 55.66%, and the maximum drawdown is 8.79%[10][11] - The style rotation model's latest timing directions for October 2025 are value, large market capitalization, momentum, and low volatility[2][19] - The latest holdings of the style rotation model for October 2025 include indices such as CSI Central Enterprise Dividend (ETF code: 561580.SH), CSI Bank (ETF code: 512700.SH), CSI Film and Television (ETF code: 159855.SZ), CS Battery (ETF code: 159796.SZ), and CSI All Real Estate (ETF code: 512200.SH)[3][19]

Group 1: Report Information - Report Date: October 10, 2025 [1] - Report Title: Shenwan Hongyuan Gold ETF Portfolio 202510 [1] - Analysts: Shen Siyi, Deng Hu [3] - Research Support: Bai Haotian [3] - Contact: Shen Enyi [3] Group 2: Investment Ratings - No industry investment ratings are provided in the report. Group 3: Core Views - The report constructs four ETF portfolios, including the macro industry portfolio, macro + momentum industry portfolio, core - satellite portfolio, and trinity style rotation ETF portfolio, based on macro - sensitivity and momentum analysis, aiming to capture investment opportunities in different market environments [5][8]. - The current economic leading indicators are rising, liquidity indicators are slightly tight, and credit indicators remain positive. The portfolios are shifting towards a more balanced allocation, with an increased proportion of consumer sectors [5]. - The trinity style rotation model combines macro - liquidity, fundamental, and market sentiment factors to construct a medium - to long - term style rotation model, providing insights into market style preferences [5][9]. Group 4: ETF Portfolio Construction Methods 4.1 Based on Macro - Method - Calculate macro - sensitivity for broad - based, industry - theme, and Smart Beta ETFs based on economic, liquidity, and credit variables. Traditional cyclical industries are sensitive to the economy, TMT to liquidity, and consumption to credit [8]. - Construct three ETF portfolios (macro industry, macro + momentum industry, and core - satellite) using macro - sensitivity and momentum, and rebalance monthly [8]. 4.2 Trinity Style Rotation ETF Portfolio - Build a medium - to long - term style rotation model centered on macro - liquidity, comparing with the CSI 300 index. Screen macro, fundamental, and market sentiment factors to construct three types of models (growth/value, market - cap, and quality) [9]. Group 5: Portfolio Details 5.1 Macro Industry Portfolio - Select the top 6 industry - theme indices based on macro - sensitivity scores, and equally weight the largest - scale corresponding ETFs. Currently, the portfolio is more balanced with an increased consumer proportion [5][10]. - October 2025 holdings include ETFs related to tourism, home appliances, chemicals, etc. [14]. - In 2025, the portfolio had varying monthly excess returns, with positive excess returns in September [15]. 5.2 Macro + Momentum Industry Portfolio - Combine macro and momentum methods. The pharmaceutical sector's weight is further reduced, and rare earth and battery sectors are selected on the momentum side [5][16]. - October 2025 holdings include multiple industry - themed ETFs [18]. - The portfolio performed well in 2025, with positive excess returns in September after a drawdown in August [19]. 5.3 Core - Satellite Portfolio - Use the CSI 300 as the core and combine broad - based, industry, and Smart Beta portfolios. Weight them at 50%, 30%, and 20% respectively [20][21]. - October 2025 holdings include a mix of broad - based and industry - themed ETFs [24][25]. - The portfolio performed steadily in 2025, outperforming the index almost every month [25]. 5.4 Trinity Style Rotation ETF Portfolio - The model currently favors small - cap growth and high - quality styles. The portfolio's factor exposure and historical performance are presented [26][27]. - October 2025 holdings include ETFs related to small - cap indices and high - growth sectors [31]. - The portfolio has shown certain performance since 2021, with positive excess returns in September 2025 [30].

Core Insights - The style rotation model accurately predicted trends in Q3 2025, with signals favoring small-cap and growth stocks for Q4 2025 [1] - The industry rotation model showed positive excess returns in September, with a monthly return of 3.33% and an excess return of 2.43% relative to the benchmark [1] Style Rotation Model - For Q4 2025, the dual-driven rotation strategy indicates a comprehensive score of -1, predicting a preference for small-cap stocks [2] - The growth style is favored in Q4 2025, with a comprehensive score of -3 from the dual-driven rotation strategy [3] Industry Rotation Insights - In September, the composite factor strategy achieved an excess return of 2.43%, while the single-factor multi-strategy had an excess return of -1.02% [3] - For October, the recommended long positions in single-factor multi-strategy include the computer, communication, electronic, non-bank financial, and banking sectors [3] - The composite factor strategy recommends long positions in home appliances, non-ferrous metals, electronics, communication, and computers [3]