近期，A股市场行情震荡波动，多空双方均有反复纠结的迹象，行业轮动明显加快。 注：数据来源wind，截止2025年10月31日。 这种行情下，对于普通投资者来说，想抓住机会是非常难的。很可能前脚刚上车科技，后脚就开始回调，别的行业又轮动起来了，总是错过一步。 因此，在这种背景下，要在当前的A股市场中获得理想的投资回报，提前确定投资方向成为一个重要的战略问题。 如果你既希望拥有大盘指数的相对稳定，又要在一定程度上兼具中小盘股高成长的红利，那么能够做到二者兼容的中证A500ETF（159338）或许是一个不 错的选择。 中证A500：行业均衡 龙头荟萃 攻守兼备 中证A500指数行业分布全面且均衡，实现100%行业覆盖，且做了行业中性处理，更能代表A股市场。并且汇聚行业龙头，基本包含中证三级行业龙头企 业，覆盖度为94%。 注：数据来源wind，截止2025年10月31日。中证二级行业共35个，中证三级行业共93个，中证A500指数含有中证三级行业91个。行业占比动态变化，仅供 参考。中证三级行业中，将截止2025年10月31日市值排名前两位的公司定义为"行业龙头"。 从成分股来看，中证A500指数成分股包括约50 ...

金融工程 | 证券研究报告 — 周报 2025 年 11 月 30 日 中银量化多策略行业轮动 周报 – 20251127 当前（2025 年 11 月 27 日）中银多策略行业配置系统仓位：非银行金融 （11.5%）、交通运输（9.9%）、通信（8.8%）、基础化工（8.1%）、食 品饮料（7.9%）、有色金属（6.9%）、银行（6.4%）、家电（4.4%）、 纺织服装（4.2%）、综合（4.0%）、钢铁（4.0%）、煤炭（3.9%）、农 林牧渔（3.3%）、国防军工（3.2%）、医药（3.2%）、电力设备及新能 源（3.1%）、机械（1.8%）、电子（1.8%）、石油石化（1.2%）、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》20 ...

被批为"太平间基金经理"的刘彦春 在这条帖子中，刘彦春被提及最多——"一波牛市下来动都不带动的，到现在还亏50%"，从而被贴上"太平间基金经理"的标签。夸张的表达背后，对应的是 景顺长城新兴成长（260108）在2021年高估值阶段后的长周期回撤。 Choice数据显示，截至11月25日，景顺长城新兴成长年内收益刚刚转正，为0.4%；景顺长城绩优成长混合A（007412）、景顺长城内需增长贰号混合A （260109）年内回报也仅在1%左右。若从2021年初高点算起，净值仍远低于当时水平，彼时买入的投资者可能仍承担逾五成的账面亏损。 近日，一条小红书帖子引发共鸣。发帖人回看自己在2021年买入的一批热门主动权益基金，感叹其中一只"动都不带动的"，并给基金经理刘彦春贴上了"太 平间基金经理"的标签。同样被提到的，还有朱少醒、蔡嵩松等当年最受追捧的明星基金经理。 情绪化的表达背后，是一批"山顶买入"的持有人在四年周期中的真实体验：同样是在2021年高位布局，如今这些明星基金和基金经理已经走向截然不同的路 径——有人靠医药、科技反弹修复，有人仍深陷旧核心资产的回撤区间，也有人在监管和更迭中离开公募体系。 如果把时间拉 ...

Core Viewpoint - The equity market's confidence has gradually improved throughout the year, characterized by distinct structural trends in different phases, including AI-led trends, innovative drug sectors, and the recent strength in low-volatility dividend assets [1] Market Trends - The market has experienced significant sector rotation, with notable phases including AI dominance at the beginning of the year, innovative pharmaceuticals after April, and technology growth led by semiconductors and AI in August and September [1] - Following October, low-volatility dividend assets have shown a phase of strength, indicating a shift in investor focus [1] Investment Insights - With the decline in risk-free rates, the cost of capital has decreased, enhancing the attractiveness of equity assets and increasing investor risk appetite [1] - The effectiveness of market pricing is improving, yet small-cap stocks remain under-researched, presenting more opportunities for value discovery [1] - Current market liquidity favors small and micro-cap stocks, providing numerous trading opportunities [1] - The valuation structure indicates that small and micro-cap stocks, primarily assessed by price-to-book (PB) ratios, still offer a good margin of safety compared to large-cap stocks, making them appealing from a defensive standpoint [1]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industries and sectors[22][23] - **Model Construction Process**: The diffusion index is calculated for each industry based on its price momentum. The model ranks industries by their diffusion index values and selects the top-performing industries for portfolio allocation. The model has been tracking out-of-sample performance since 2021, with adjustments made monthly or weekly based on updated diffusion index rankings[22][23] - **Model Evaluation**: The model has shown strong performance in capturing industry trends during momentum-driven markets but struggles during market reversals[22][36] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages minute-level price and volume data processed through a GRU (Gated Recurrent Unit) deep learning network to generate industry factors for rotation strategies[37] - **Model Construction Process**: The GRU model uses historical price and volume data as input to train a deep learning network. The network identifies patterns and generates factors that are used to rank industries. The top-ranked industries are selected for portfolio allocation. The model is updated weekly to reflect changes in the rankings[30][31][37] - **Model Evaluation**: The GRU model performs well in short-term trading environments but has shown limited effectiveness in long-term scenarios. It is also sensitive to extreme market conditions[37] --- Backtesting Results of Models 1. Diffusion Index Model - **Weekly Average Return**: -5.50% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: -0.42% - **November-to-Date Excess Return**: -1.13% - **Year-to-Date Excess Return**: 1.22%[26][22][23] 2. GRU Factor Model - **Weekly Average Return**: -4.71% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: 0.35% - **November-to-Date Excess Return**: 2.92% - **Year-to-Date Excess Return**: -2.74%[35][30][31] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: The diffusion index measures the momentum of industries by analyzing price trends and ranks industries based on their momentum[22][23] - **Factor Construction Process**: The diffusion index is calculated for each industry using price momentum data. Industries are ranked based on their diffusion index values, and the top-ranked industries are selected for portfolio allocation. The index is updated weekly or monthly to reflect changes in industry momentum[22][23] - **Factor Evaluation**: The factor effectively captures upward trends in industries but may underperform during market reversals[22][36] 2. Factor Name: GRU Industry Factor - **Factor Construction Idea**: The GRU industry factor is derived from minute-level price and volume data processed through a GRU deep learning network to identify patterns and rank industries[37] - **Factor Construction Process**: The GRU model processes historical price and volume data through a deep learning network. The network generates factors that are used to rank industries. The top-ranked industries are selected for portfolio allocation, with updates made weekly[30][31][37] - **Factor Evaluation**: The factor is effective in short-term trading environments but less so in long-term scenarios. It is also sensitive to extreme market conditions[37] --- Backtesting Results of Factors 1. Diffusion Index Factor - **Weekly Average Return**: -5.50% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: -0.42% - **November-to-Date Excess Return**: -1.13% - **Year-to-Date Excess Return**: 1.22%[26][22][23] 2. GRU Industry Factor - **Weekly Average Return**: -4.71% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: 0.35% - **November-to-Date Excess Return**: 2.92% - **Year-to-Date Excess Return**: -2.74%[35][30][31]

Quantitative Models and Construction A-Share Market Timing Model - **Model Name**: A-Share Multi-Dimensional Timing Model [10] - **Construction Idea**: The model integrates valuation, sentiment, capital, and technical dimensions to assess the directional outlook of the A-share market [10][12][16] - **Construction Process**: - Signals are generated daily for each dimension, with values of 0, ±1 representing neutral, bullish, and bearish views respectively [10] - **Valuation Dimension**: Uses equity risk premium (ERP) to capture mean-reversion characteristics [12][16] - **Sentiment Dimension**: Includes option put-call ratio, implied volatility, and futures member position ratio to reflect market sentiment [12][16] - **Capital Dimension**: Tracks financing purchase amounts to identify market trends [12][16] - **Technical Dimension**: Employs Bollinger Bands and individual stock turnover ratio differences to capture trend continuation [12][16] - The final market view is determined by the sum of scores across all dimensions [10] - **Evaluation**: The model effectively combines mean-reversion and trend-following strategies, balancing risk avoidance and opportunity capture [10] Style Timing Model - **Model Name**: Dividend Style Timing Model [18] - **Construction Idea**: Targets the relative performance of the CSI Dividend Index against the CSI All Index using trend-based indicators [18][22] - **Construction Process**: - Three indicators are used to generate daily signals (0, ±1 for neutral, bullish, bearish views) [18] - **Relative Momentum**: Positive indicator for dividend style [22] - **10Y-1Y Term Spread**: Negative indicator for dividend style, as wider spreads favor growth assets [22] - **Interbank Repo Volume**: Positive indicator for dividend style, reflecting asset scarcity [22] - Signals are aggregated to determine the overall view on dividend style [18] - **Evaluation**: The model captures dividend style trends effectively, leveraging macroeconomic and liquidity factors [18] - **Model Name**: Large-Cap vs Small-Cap Style Timing Model [23] - **Construction Idea**: Differentiates between macro-driven trends in low congestion and fund-driven reversals in high congestion [23][25] - **Construction Process**: - **Momentum Difference**: Calculates the difference in momentum between the Wind Micro-Cap Index and CSI 300 Index across multiple windows, averaging the top/bottom results for small/large-cap scores [27] - **Turnover Ratio**: Similar calculation for turnover ratio differences across windows, averaged for small/large-cap scores [27] - **Congestion Score**: Combines momentum and turnover scores to determine congestion levels (high congestion >90% for small-cap, <10% for large-cap) [27] - **Trend Model**: Uses small/large parameter double moving average models based on congestion levels [25] - **Evaluation**: The model adapts to market conditions, balancing long-term trends and short-term reversals [23][25] Sector Rotation Model - **Model Name**: Genetic Programming Sector Rotation Model [30] - **Construction Idea**: Directly mines factors from sector index data using genetic programming without relying on predefined scoring rules [30][33] - **Construction Process**: - **Factor Mining**: Utilizes NSGA-II algorithm to optimize for monotonicity and top-group performance simultaneously [33][34] - **Factor Combination**: Combines factors with weak collinearity using greedy strategy and variance inflation coefficient [34] - **Weekly Rebalancing**: Selects top five sectors based on multi-factor scores for equal-weight allocation [30] - **Example Factor**: Calculates covariance between standardized weekly low prices and monthly open prices over 25 days, adjusted by standardized weekly high prices over 15 days [38] - **Evaluation**: The model enhances factor diversity and reduces overfitting risks, achieving robust sector rotation performance [33][34] All-Weather Enhanced Portfolio - **Model Name**: China All-Weather Enhanced Portfolio [39] - **Construction Idea**: Implements macro factor risk parity to diversify risks across underlying macro drivers rather than assets [39][42] - **Construction Process**: - **Macro Quadrant Division**: Divides growth and inflation dimensions into four quadrants based on whether they exceed or fall short of expectations [42] - **Quadrant Portfolio Construction**: Constructs sub-portfolios within each quadrant, focusing on downside risk [42] - **Risk Budgeting**: Adjusts quadrant weights monthly based on macro momentum indicators combining buy-side and sell-side expectations [42] - **Evaluation**: The strategy demonstrates strong defensive attributes during market downturns while maintaining consistent returns [40][43] --- Backtesting Results A-Share Market Timing Model - **Annualized Return**: 24.94% [15] - **Maximum Drawdown**: -28.46% [15] - **Sharpe Ratio**: 1.16 [15] - **Calmar Ratio**: 0.88 [15] - **YTD Return**: 43.84% [15] - **Weekly Return**: 5.28% [15] Dividend Style Timing Model - **Annualized Return**: 15.67% [21] - **Maximum Drawdown**: -25.52% [21] - **Sharpe Ratio**: -0.26 [21] - **Calmar Ratio**: 0.85 [21] - **YTD Return**: 20.86% [21] - **Weekly Return**: -3.63% [21] Large-Cap vs Small-Cap Style Timing Model - **Annualized Return**: 27.04% [28] - **Maximum Drawdown**: -32.05% [28] - **Sharpe Ratio**: 1.13 [28] - **Calmar Ratio**: 0.84 [28] - **YTD Return**: 71.14% [28] - **Weekly Return**: -7.80% [28] Sector Rotation Model - **Annualized Return**: 30.83% [33] - **Annualized Volatility**: 17.74% [33] - **Sharpe Ratio**: 1.74 [33] - **Maximum Drawdown**: -19.63% [33] - **Calmar Ratio**: 1.57 [33] - **YTD Return**: 35.44% [33] - **Weekly Return**: -4.39% [33] All-Weather Enhanced Portfolio - **Annualized Return**: 11.51% [43] - **Annualized Volatility**: 6.18% [43] - **Sharpe Ratio**: 1.86 [43] - **Maximum Drawdown**: -6.30% [43] - **Calmar Ratio**: 1.83 [43] - **YTD Return**: 10.75% [43] - **Weekly Return**: -1.53% [43]

Core Viewpoint - The article discusses several noteworthy fund managers and their performance, highlighting their unique investment styles and the potential for future tracking by investors [4]. Group 1: Yang Shijin - Xingquan Multi-Dimensional Value - Yang Shijin has been managing Xingquan Multi-Dimensional Value since July 16, 2021, demonstrating strong investment capabilities with an 18.02% increase in 2021 despite market downturns [5][6]. - The fund has shown resilience during bear markets in 2022 and 2023, maintaining a single-year decline of around 10% [6]. - Yang's investment strategy includes a concentrated position in the electronics sector, with long-term holdings in stocks like Haiguang Information and Tencent Holdings [10][11]. Group 2: Wu Yuanyi - GF Growth Navigator - Wu Yuanyi is recognized for his balanced industry allocation and impressive performance, with the GF Growth Navigator fund achieving a 143.14% increase year-to-date as of November 17 [12][14]. - The fund maintains a maximum industry allocation of 20%, showcasing a diversified approach that has led to strong returns without heavy reliance on specific sectors [14]. - Wu's ability to rotate stocks effectively has contributed to the fund's success, even amidst a challenging market environment [15]. Group 3: Shen Cheng - Huafu New Energy - Shen Cheng has managed Huafu New Energy since December 29, 2021, achieving consistent excess returns relative to its benchmark despite the sector's overall struggles [18][20]. - The fund's annual returns from 2022 to 2025 have outperformed its benchmark, with a notable 76.76% increase in the latest year [20]. - Shen's investment strategy includes holding industry leaders like Ningde Times while also actively trading to capitalize on short-term opportunities [21][22].

Core Viewpoint - The article discusses the "Leading Stock Diffusion Effect" as a mechanism driving sector trends, emphasizing the importance of stock selection to enhance returns from industry rotation strategies. The report presents various stock selection strategies and their performance metrics, highlighting the effectiveness of the "Alpha Dual-Drive Preferred Combination" strategy, which has achieved an annualized return of 33.6% since 2013, outperforming the CSI 500 index by 28.3% [1][68]. Group 1: Research Background - The demand for industry-level beta timing has increased with the development of flexible allocation funds and FOF products, making industry rotation a core asset allocation need [3]. - The article notes that the return dispersion between industries is often greater than that among individual stocks within the same industry, indicating that selecting the right industry is more beneficial than selecting individual stocks [3]. - Challenges in extracting industry rotation factors include limited sample sizes and the heterogeneous nature of industries, which complicates the universality of factor logic [3][4]. Group 2: Mechanism of the Leading Stock Diffusion Effect - The diffusion effect is described as the process where stock price increases in leading stocks spread to related stocks, leading to a broader industry uptrend [12]. - The process includes several stages: policy triggers leading to the activation of leading stocks, active capital inflow driving sector resonance, and cognitive dissemination leading to widespread price increases across related stocks [12][13]. - The article outlines different migration methods of capital during the diffusion process, including vertical and horizontal diffusion, market capitalization descent, and valuation arbitrage [15]. Group 3: Stock Selection Strategies - The report evaluates various stock selection strategies to replicate or enhance industry rotation returns, including full replication, half-weighted combinations, and top 10 equal-weighted combinations [30][31]. - The full replication strategy achieved an annualized return of 24.9% since 2013, while the half-weighted and top 10 equal-weighted strategies yielded returns of 24.5% and 23.5%, respectively, with reduced trading complexity [34][46]. - The "Alpha Dual-Drive Preferred Combination" strategy, which selects stocks based on both industry and individual stock factors, has shown superior performance with an annualized return of 33.6% [52][59]. Group 4: Performance Metrics - The "Alpha Dual-Drive Preferred Combination" strategy has an information ratio (IR) of 2.07 and a maximum drawdown of 27.8%, indicating strong risk-adjusted performance [68]. - The article provides detailed annual performance data for the preferred industry combination, showing significant absolute and excess returns across various years [29][66]. - The report emphasizes that the improved SUE and active large order factors contribute to the strong performance of the preferred industry combination, achieving annualized excess returns of 8.3% and 10.1%, respectively [18][23].

Group 1 - The North Exchange has reached a market capitalization of 900.8 billion, with a significant improvement in liquidity and market functions over its four years of development [2][5][7] - As of November 14, 2025, the North Exchange has 282 listed companies, representing a growth of 248% compared to its inception, with a total market value increase of 212% [5][7][12] - The average daily turnover rate for the North Exchange in 2025 was 5.4%, the highest among all A-shares, with 9.5 million new accounts opened, reflecting a 1.4 times increase since its launch [2][5][7] Group 2 - The North Exchange experienced three major market rallies in 2023 and 2024, driven by different catalysts: policy-driven in the first two rounds and industry-driven in the last [2][19][20] - The North Exchange 50 Index saw increases of 55.8%, 132%, and 47.4% during these rallies, indicating varying market characteristics and participant dynamics [19][20][21] - The market's focus has shifted towards "style rotation" and "industry rotation," with significant impacts from the distribution of industries and the quality of companies within those sectors [25][26][33] Group 3 - The outlook for 2026 includes accelerated reforms, with expectations for the launch of the North Exchange 50 ETF and new stock issuance reforms, which are anticipated to enhance liquidity and stabilize volatility [2][4][12] - The expected number of new stock issuances in 2026 is around 40, with projected subscription yields of 3.75%, 3.13%, and 2.34% for different investment amounts [2][4][12] - Investment strategies for 2026 suggest focusing on technology and "anti-involution" in the first half, and consumer and manufacturing sectors in the second half, with an overall emphasis on new and recently listed stocks [2][4][12]

Quantitative Models and Construction Methods - **Model Name**: Diffusion Index Model **Model Construction Idea**: Based on price momentum principles, the model identifies upward trends in industries to optimize allocation decisions[23][24][27] **Model Construction Process**: 1. Calculate the diffusion index for each industry based on price momentum 2. Rank industries by their diffusion index values 3. Allocate to industries with the highest diffusion index values **Evaluation**: The model performs well in capturing upward trends but struggles during market reversals or when trends shift to oversold rebounds[23][27] - **Model Name**: GRU Factor Model **Model Construction Idea**: Utilizes GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level volume and price data for industry rotation[31][32][36] **Model Construction Process**: 1. Input minute-level volume and price data into the GRU network 2. Train the model on historical data to identify industry rotation signals 3. Rank industries based on GRU factor scores and allocate accordingly **Evaluation**: The model adapts well to short-term market dynamics but faces challenges in long-term performance and extreme market conditions[31][38] Model Backtesting Results - **Diffusion Index Model**: - Weekly average return: -1.26% - Excess return over equal-weighted industry index: -1.99% - November excess return: -0.74% - Year-to-date excess return: 1.84%[22][27] - **GRU Factor Model**: - Weekly average return: 1.72% - Excess return over equal-weighted industry index: 1.00% - November excess return: 2.69% - Year-to-date excess return: -3.34%[31][36] Quantitative Factors and Construction Methods - **Factor Name**: Diffusion Index **Factor Construction Idea**: Measures industry momentum by tracking price trends and ranking industries accordingly[24][25][26] **Factor Construction Process**: 1. Calculate the diffusion index for each industry using price trend data 2. Rank industries based on diffusion index values 3. Identify industries with the highest and lowest diffusion index values for allocation decisions **Evaluation**: Effective in identifying upward trends but sensitive to market reversals[23][24] - **Factor Name**: GRU Factor **Factor Construction Idea**: Derived from GRU deep learning networks, the factor captures industry rotation signals based on volume and price dynamics[31][32][36] **Factor Construction Process**: 1. Train GRU networks on historical minute-level data 2. Generate GRU factor scores for industries 3. Rank industries by GRU factor scores for allocation decisions **Evaluation**: Strong adaptability to short-term market changes but limited robustness in long-term scenarios[31][38] Factor Backtesting Results - **Diffusion Index Factor**: - Top industries by diffusion index: Nonferrous metals (0.991), Banking (0.968), Steel (0.949), Communication (0.918), Electric equipment & new energy (0.914), Comprehensive (0.885)[24][25][26] - Weekly average return: -1.26% - Excess return over equal-weighted industry index: -1.99% - November excess return: -0.74% - Year-to-date excess return: 1.84%[22][27] - **GRU Factor**: - Top industries by GRU factor: Comprehensive (3.41), Real estate (2.63), Petroleum & petrochemical (2.13), Light manufacturing (1.67), Steel (0.53), Comprehensive finance (0.52)[32][35][36] - Weekly average return: 1.72% - Excess return over equal-weighted industry index: 1.00% - November excess return: 2.69% - Year-to-date excess return: -3.34%[31][36]