《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 金融工程 | 证券研究报告 — 周报 2025 年 12 月 5 日 中银量化多策略行业轮动 周报 – 20251204 当前（2025 年 12 月 4 日）中银多策略行业配置系统仓位：非银行金融 （11.9%）、银行（9.7%）、交通运输（9.3%）、通信（9.2%）、食品饮 料（7.8%）、有色金属（7.6%）、钢铁（6.9%）、石油石化（4.8%）、 基础化工（4.7%）、家电（4.5%）、农林牧渔（3.5%）、综合金融 （ 3.5% ） 、 综 合 （ 3.5% ） 、 建 材 （ 3.4% ） 、 电 力 设 备 及 新 能 源 （3.4% ）、机械（ 1.9% ）、轻工制造（ 1.9% ）、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（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 ...

Core Insights - The report suggests a shift towards a balanced strategy in response to external disturbances and internal market conditions, emphasizing the importance of equilibrium in investment approaches [2][4][29] - Four industries identified as having "low position + marginal improvement" potential, which are expected to benefit from low valuations and improving economic conditions [2][4] Industry Recommendations - **Low Position Recovery (Dynamic)**: The recommended sectors include Beauty Care, Transportation, Other Power Equipment, Semiconductors, Automotive, Bioproducts, Traditional Chinese Medicine, and Home Appliances [4][6] - **Low Position + Marginal Improvement (Dynamic)**: The sectors highlighted are Beauty Care, Transportation, Other Power Equipment, and Semiconductors [4][6] Market Analysis - The A-share market experienced significant adjustments in November due to three main factors: 1. Federal Reserve liquidity expectations were disrupted by the U.S. government shutdown, leading to market uncertainty regarding the December interest rate meeting [4] 2. The intensifying debate over AI narratives and technical corrections in high-performing stocks, particularly in the AI sector, caused a decline in market sentiment [4] 3. A rebalancing of market styles, with funds shifting from high-volatility growth stocks to low-valuation, high-dividend sectors, resulting in a rise in bank and oil sectors [4] Sector-Specific Insights - **Beauty Care**: The sector is expected to see a revival in new consumption themes towards the end of the year, with a 17.95% year-on-year increase in retail sales for cosmetics [6][10] - **Transportation**: Valuations are low, and improved U.S.-China relations are anticipated to boost export demand, leading to a recovery in shipping [6][10] - **Other Power Equipment**: Increased investments in power grid and supply equipment due to AI-related electricity shortages are expected [4][6] - **Semiconductors**: Continuous demand for AI computing power and a chip shortage in the automotive sector are driving a super cycle in storage [4][6] Economic Indicators - The report provides detailed indicators for the identified sectors, showing positive trends in various metrics such as retail sales, average prices, and investment levels [6][10][22]

Strategy Logic - The report introduces the "Shouzheng Chuq" investment sentiment indicator, which aims to identify potential investment opportunities in the A-share market by analyzing industry rotation phenomena [1][5] - The strategy combines three main dimensions: investment sentiment, volume-price indicators, and analyst expectations, resulting in 12 detailed industry rotation indicators [1][5] - The investment sentiment indicator utilizes market data and alternative data to create positive and negative screening factors, capturing market momentum and sentiment [5][6] Strategy Performance - In November, the overall industry benchmark return was -0.95%, while the "Shouzheng Chuq" sentiment indicator long portfolio returned -1.24% [2][6] - The combined "Expectation Resonance" model long portfolio achieved a return of 0.98%, resulting in an excess return of 1.93% [2][6] - Year-to-date, the "Shouzheng Chuq" sentiment indicator long portfolio has shown robust performance with a return of 30.29% and an excess return of 8.05% [2][12] Latest Recommendations - The top recommended industries based on the latest data include non-bank financials, automotive, food and beverage, home appliances, transportation, and banking according to the "Shouzheng Chuq" model [3][21] - The "Expectation Resonance" model ranks non-bank financials, banking, home appliances, transportation, automotive, and electronics as the leading industries [3][21] - Detailed scores for recommended industries and corresponding ETFs are provided, indicating strong performance in non-bank financials and home appliances [21][22]

Core Viewpoint - Industry profit forecasting has a positive effect on stock price performance, with long-term returns decomposed into dividend yield, profit growth rate, and price-earnings ratio changes [3][9] Group 1: Industry Profit Forecasting - Profit growth rate is the core driver of stock value growth and is more predictable than price-earnings ratio changes [3][9] - Forecasting profit growth can enhance the performance of industry portfolios, with a hypothetical high-growth portfolio outperforming equal-weighted and low-growth portfolios [3][9] - The industry prosperity model reflects future industry profit status through demand and supply indicators, providing support for stock price return predictions [3][11] Group 2: LLM Empowerment in Model Construction - LLM improves the efficiency of constructing industry prosperity models by enhancing qualitative analysis and indicator selection [4][5] - A structured process using LLM for qualitative screening and quantitative testing results in a reliable industry prosperity index that predicts profit growth [5][17] - The constructed prosperity index shows predictive capability for future profit growth, with average and median rank correlation coefficients around 0.25 across different industry levels [5][26] Group 3: Application of Industry Prosperity Index - The industry prosperity index has various applications, including industry timing, rotation, stock selection, and risk warning [6][38] - High-prosperity industry portfolios demonstrate superior long-term performance compared to equal-weighted portfolios, with annualized excess returns of 2.4%, 2.9%, and 2.8% for different industry levels [6][7] - Sensitivity tests indicate that high-prosperity portfolios maintain stable excess returns over time, outperforming low-prosperity and equal-weighted portfolios [50][43]

Group 1 - The article recommends several high-quality original public accounts in the fund investment field, emphasizing their valuable content and the investment methods and opportunities learned from them [1][2] - The accounts mentioned include "复来指数投资," which focuses on index fund investment and offers practical strategies such as intelligent investment strategies and industry rotation strategies [3] - "阡陌说" is highlighted for its data analysis and ability to discover lesser-known but excellent fund managers, providing clear and detailed articles [4] Group 2 - "投资闲记" is noted for its deep insights into value investing and index investment, sharing unique market perspectives and historical patterns [4] - "小鱼量化" presents a diversified investment system suitable for ordinary investors, focusing on low-cost, high-dividend, and convertible bond investments [5] - "懒人养基" promotes a long-term, steady, and value investment approach, encouraging research before buying and minimal management afterward [6]

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]

Investment Rating - The report indicates a marginal increase in investment in the TMT (Technology, Media, and Telecommunications) sector, with specific recommendations for non-bank financials, communications, real estate, building materials, media, and banks [1]. Core Insights - The industry rotation model is driven by three main dimensions: fundamentals, price-volume, and sentiment, aiming to capture market microstructure and industry opportunities. The model has been backtested bi-weekly and expanded to include factors such as momentum, trends, capital flow, sentiment, market structure, and volatility [1]. - The sentiment score for the real estate sector has significantly improved, increasing by 0.98, while the media sector's price-volume factors have seen a notable increase of 3.24 [1]. Summary by Sections Industry Recommendations - The recommended ETFs include: - E Fund CSI 300 Non-Bank ETF - Guotai CSI All-Index Communication Equipment ETF - Southern CSI All-Index Real Estate ETF - Guotai CSI All-Index Building Materials ETF - GF CSI Media ETF - Huabao CSI Bank ETF [3]. Performance Metrics - The industry rotation strategy has increased by 0.25% over the past two weeks, with an excess return of 0.64% compared to an equal-weighted industry benchmark. Year-to-date, the strategy has risen by 34.89%, with a Sharpe ratio of 1.77 and a Calmar ratio of 2.88 [4][6].

Report Summary 1. Report Industry Investment Rating - Not mentioned in the provided content 2. Core View of the Report - As of October 26, 2025, the model recommends non-bank finance, communication, real estate, building materials, media, and banking, with marginal increases in media and real estate investments [1] - The non-bank finance, communication, and real estate sectors are mainly driven by fundamentals, building materials and media are mainly influenced by sentiment, and banking is driven by both quantitative and fundamental factors [1] - The industry rotation model analyzes the market from three dimensions: fundamentals, volume-price, and sentiment, aiming to capture industry opportunities [1] 3. Summary by Relevant Catalogs Industry Rotation Model - The model backtests original factors on a bi-weekly basis and expands volume-price factors from dimensions such as momentum and trend, capital flow and sentiment, and market structure and volatility [1] - Six relatively effective factors are selected to construct the industry rotation strategy [1] Industry ETF Portfolio - The current industry ETF portfolio includes six ETFs: E Fund CSI 300 Non-Bank Finance ETF, Guotai CSI All-Index Communication Equipment ETF, Southern CSI All-Index Real Estate ETF, Guotai CSI All-Index Building Materials ETF, GF CSI Media ETF, and Huabao CSI Bank ETF [3] Performance of the Industry Rotation Strategy - In the past two weeks, the strategy rose 0.25%, with an excess return of 0.64% compared to the industry equal-weighted index [4][6] - Since the beginning of the year, the strategy has risen 34.89%, with a Sharpe ratio of 1.77 and a Calmar ratio of 2.88 in the past year [4] Strategy/Composite Factor Backtesting Results - Different factors have different IC means, IC standard deviations, ICIRs, frequencies of IC>0, and p-Values. For example, the成交均价因子 has an IC mean of 6.19%, an IC standard deviation of 27.11%, and an ICIR of 22.83% [10]

Group 1: Macro Timing Strategy - The macro timing strategy has an annualized return of 7.67% as of October 31, 2025, with a Sharpe ratio of 1.45 and a Calmar ratio of 1.67, indicating a maximum drawdown of -4.60% [2][4][5] - The latest portfolio allocation includes 7.01% in CSI 300 ETF, 7.99% in CSI 500 ETF, 55.94% in government bond ETF, 11.63% in soybean meal ETF, 5.02% in non-ferrous ETF, 7.40% in gold ETF, and 5.00% in currency ETF, with no allocation to S&P 500 ETF and corporate bond ETF [7][8] Group 2: Momentum Strategy - The momentum strategy has an annualized return of 18.25% since January 2020, with a Sharpe ratio of 0.88 and a Calmar ratio of 0.64, experiencing a maximum drawdown of -28.72% [9][10] - The latest portfolio allocation includes 27.01% in Huatai-PB CSI Telecom Theme ETF, 24.92% in Fuguo CSI Tourism Theme ETF, 21.52% in Xinhua CSI Cloud Computing 50 ETF, 16.38% in Huatai-PB CSI Smart Car ETF, and 8.17% in Huaxia CSI Artificial Intelligence ETF [13][14] Group 3: Sector Rotation Strategy - The sector rotation strategy has an annualized return of 10.00% since 2020, with an excess return of 7.27% relative to CSI 300, and a maximum drawdown of -42.98% [15] - The latest portfolio includes home appliance ETF, green power ETF, steel ETF, new energy vehicle ETF, financial ETF, and agricultural ETF, while excluding non-ferrous metals ETF and transportation ETF [18][19] Group 4: Copula-Based Second-Order Stochastic Dominance Strategy - The Copula-based second-order stochastic dominance strategy has an annualized return of 14.41% since January 2020, with a Sharpe ratio of 0.68 and a maximum drawdown of -42.62% [20][24] - The latest portfolio allocation includes 5.00% in Huaxia CSI Petrochemical Industry ETF, 85.00% in Fuguo CSI 800 Bank ETF, 5.00% in Fuguo CSI All-Index Securities Company ETF, and 5.00% in Bosera CSI Oil and Gas Resources ETF [23][25] Group 5: Quantile Random Forest Technology ETF Allocation Strategy - The quantile random forest technology ETF allocation strategy has an annualized return of 13.54% since 2020, with a Sharpe ratio of 0.76 and a maximum drawdown of -29.89% [26] - The latest portfolio allocation consists of 95.63% in technology ETFs, including 4.78% in Jiahua National Communication ETF, 4.78% in Tianhong CSI Photovoltaic Industry ETF, 4.78% in Huabao CSI Military Industry ETF, 76.51% in Ping An CSI Consumer Electronics Theme ETF, and 4.78% in Fuguo CSI Technology 50 Strategy ETF [29][30]

Strategy Logic - The report introduces the "Shouzheng Chuq" market investment prosperity indicator, which aims to identify investment opportunities in industries that can become market investment main lines, based on the phenomenon of industry rotation in the A-share market [1][5] - The strategy combines three major dimensions: investment prosperity, volume-price indicators, and analyst expectations, resulting in 12 detailed industry rotation indicators [1][5] - The investment prosperity indicator utilizes market data and alternative data to construct positive and negative screening factors, capturing the marginal upward beta factor and the super-expected report factor while preventing trading overheating [5][6] Strategy Performance - In October, the "Shouzheng Chuq" investment prosperity long portfolio achieved a return of 0.40%, while the analyst expectation indicator long portfolio returned 1.19%, closely matching the benchmark return of 1.06% [2][11] - The volume-price indicator performed exceptionally well, with a long portfolio return of 3.29%, resulting in an excess return of 2.23% [2][11] - The comprehensive "Expectation Resonance" model long portfolio yielded a return of 2.56%, with an excess return of 1.50% [2][11] Latest Recommendations - Based on the latest data, the top recommended industries for November according to the "Shouzheng Chuq" model include computer, petroleum and petrochemicals, light industry manufacturing, non-bank financials, commercial retail, and pharmaceuticals [3][19] - The "Expectation Resonance" model ranks non-bank financials, commercial retail, banking, petroleum and petrochemicals, light industry manufacturing, and home appliances as the leading industries [3][19] Industry Scores and ETF Recommendations - The report provides detailed scores for recommended industries, with non-bank financials scoring 1.00, commercial retail 0.97, and banking 0.93 under the "Expectation Resonance" composite indicator [19] - Corresponding ETFs for the recommended industries include various options for computer, petroleum, light industry manufacturing, non-bank financials, commercial retail, and pharmaceuticals [20]