2026 年 02 月 11 日 申万金工成长组合 2.0: 非线性倾斜 加权提升组合收益弹性 申万金工因子观察第 3 期 20260210 相关研究 证券分析师 邓虎 A0230520070003 denghu@swsresearch.com 联系人 邓虎 A0230520070003 denghu@swsresearch.com 请务必仔细阅读正文之后的各项信息披露与声明 申万金工成长组合是依托于对上市公司未来高业绩增速的预测来实现的:通过分析师的一 ○ 致预期数据，首先，对全市场有分析师覆盖的股票里，取当前一年盈利预测增速前一半的 股票作为股票池;第二,在股票池内,10 月底时剔除前三季度累计盈利增速为负的样本; 第三，在股票池里使用分析师一致预期变化因子最终筛选出 50 只股票。 考虑到投资者对成长组合的需求往往是希望在成长风格来临的时候有更强的收益弹性。本 文考虑结合行业轮动模型对成长组合进行一次升级。以申万金工行业轮动模型的框架为 例,其主要的使用因子包括了基本面、资金面和技术面,其中技术面都偏价格动量,基本 面和资金面偏向业绩动量,在因子分域的思路下, 申万金工行业轮动模型还根据动量占优 和不占 ...

Core Viewpoint - The collective failure of traditional price and volume factors since 2026 has led to the emergence of a momentum-based industry rotation model, which provides a potential solution for enhancing portfolio stability and excess returns [1][4][54]. Group 1: Industry Rotation Model Characteristics - The industry rotation factor has shown strong characteristics, with a monthly Information Coefficient (IC) of 5.3% and an Information Coefficient Information Ratio (ICIR) of 4.0, indicating its robust performance [26][54]. - The industry rotation model has been effective in improving performance within traditional multi-factor frameworks, significantly enhancing excess returns and halting the decline in excess performance seen in recent years [2][54]. Group 2: Challenges and Conflicts - The industry rotation factor faces conflicts with the industry deviation constraints commonly used in index-enhanced frameworks, which can negatively impact its effectiveness [2][54]. - When applying the standard industry deviation constraint of 2% and individual stock deviation of 0.5%, the performance of the portfolio has declined, with excess returns turning negative in 2025 [2][54]. Group 3: Optimal Usage Strategy - The best approach for utilizing the industry rotation factor is to maintain the individual stock deviation constraint at 0.5% while relaxing the industry deviation constraint from 2% to 5%, which has shown to improve overall excess returns and reduce maximum drawdowns [3][54]. - Increasing the industry deviation to 4% or 5% has resulted in better overall performance, with maximum drawdowns decreasing, indicating a balanced approach to enhancing excess returns while controlling risk [3][54].

Report Industry Investment Rating No information provided in the content. Core Viewpoints of the Report - The collective failure of traditional quantitative and price factors in 2026 is related to their reverse logic, providing a scenario for the factorization of the industry rotation model with momentum characteristics [2]. - The industry rotation model has long lacked practical use scenarios, but its stability in excess returns meets the requirements of stock - selection factors, laying a foundation for its transformation into a stock - selection factor [2]. - The industry rotation factor has good factor characteristics, with a monthly IC of 5.3% and an ICIR of 0.40, and it can enhance the performance of the traditional multi - factor model [2][30]. - The industry rotation factor conflicts with the industry deviation constraints in the index - enhancement framework, but it still contributes to stock - selection and cannot be replaced by simple industry over - under - weighting or portfolio strategies [2][61]. - Keeping the individual stock deviation constraint at 0.5% while relaxing the industry deviation constraint is currently the best way to use the industry rotation factor [2][62]. Summary by Relevant Catalogs 1. Finding a Usage Scenario for the Industry Rotation Model: Starting from the Failure of Quantitative and Price Factors - Since 2026, index - enhancement funds tracking the CSI 500 index and active quantitative funds' quasi - index products have mostly underperformed the CSI 500 index. As of the end of January, all CSI 500 index - enhancement products underperformed the index, with an average underperformance of 3.46%, and active quantitative products underperformed by 1.96% on average [5]. - The main failed factors are quantitative and price factors such as liquidity, reversal, low - volatility, and market value, whose logic is mostly reverse - oriented. In the context of a rapid rise in the index and continuous driving of some popular sectors and themes in January, these factors not only failed but also reversed [7]. - The industry rotation model is a strongly momentum - driven model. The Shenwan Hongyuan Industry Rotation Model emphasizes momentum in its technical, fundamental, and capital aspects, and can complement traditional quantitative and price factors with reverse logic [10]. - The industry rotation model has long lacked practical use scenarios. Its long - only portfolio performance is not outstanding, and its stable excess return relative to the average of all industries has no practical significance for most investors [13][16]. 2. Factorization of the Industry Rotation Model - Transforming the industry model into a stock - selection model is relatively easy. By splicing the scores of each stock's industry in the industry model, a stock - based score can be obtained. However, due to the large number of stocks belonging to the same industry, the factor shows a segmented score characteristic, and orthogonal processing is required [22]. - The monthly IC of the original industry rotation factor has a correlation of over 0.4 with the growth factor. After orthogonalizing the original industry rotation factor against the growth factor, its performance shows good monotonicity, and its cumulative IC and long - short performance are excellent [23][25]. - From 2017 to January 2026, the monthly average IC of the industry rotation factor reached 5.3%, stronger than other traditional factors, and the ICIR was 0.40, ranking third, indicating excellent factor characteristics [30]. 3. Usage and Comparative Analysis of the Industry Rotation Factor - **Comparison of Four - Factor and Five - Factor Models**: Adding the industry rotation factor to the four - factor equal - weighted model to form a five - factor model can significantly improve the model's performance, especially in recent years, enhancing the model's offensive ability in a bull market and the stability of excess returns [35][38]. - **Factor Equal - Weighting vs. ICIR Weighting**: Changing the factor weighting method from simple equal - weighting to ICIR weighting does not show better results. The five - factor equal - weighted combination with the industry rotation factor performs best in each year and is the only combination with positive excess returns in all years [39]. - **Moving towards the Index - Enhancement Framework: Adding Industry Neutrality and Individual Stock Deviation Constraints**: Adding industry deviation and individual stock constraints to the model makes the industry rotation factor conflict with the industry deviation constraint. Although it can control the maximum drawdown in some years, it also reduces the performance of the five - factor model in terms of returns in some cases. In 2025, the annual excess return becomes negative after adding constraints [41][42]. - **Method of Constraining Industry Deviation Ranking through Industry Scoring**: Using industry scoring to control industry deviation ranking without using the industry rotation factor for stock - selection results in weaker performance compared to the five - factor model with industry and individual stock constraints. This method is not the best option [44]. - **Multi - Strategy Portfolio: Using Industry Rotation as a Satellite Portfolio "Platter"**: Using the industry rotation factor as a separate strategy to form a satellite portfolio and combining it with a four - factor portfolio does not show obvious advantages. The performance of the "platter portfolio" is difficult to outperform, and only the 3:7 ratio combination has a slight competitive edge, but it also shows negative excess returns in January 2026 [50]. - **Current Best Solution: Relaxing Industry Deviation while Maintaining Individual Stock Deviation Constraints**: Keeping the individual stock deviation constraint at 0.5% and relaxing the industry deviation constraint to 4% or 5% can improve the overall excess return of the portfolio, reduce the maximum drawdown of excess returns, and have a negligible impact on tracking error. This is currently the best way to use the industry rotation factor [53][57]. 4. Summary - The industry rotation model has long lacked practical use scenarios, but its stability characteristics provide a basis for its transformation into a stock - selection factor. - The industry rotation factor has good characteristics and can enhance the performance of the traditional multi - factor model, but it conflicts with the industry deviation constraint in the index - enhancement framework. - The industry rotation factor contributes to stock - selection and cannot be replaced by simple strategies. Relaxing the industry deviation constraint while maintaining the individual stock deviation constraint is the best solution [60][61][62].

Core Insights - The article discusses the evolution of "fixed income +" funds, highlighting the integration of quantitative models for flexible asset allocation and risk management [1] Group 1: Quantitative Multi-Asset Strategy - The quantitative multi-asset strategy emphasizes systematic and disciplined management of investment portfolios, using quantitative models as key decision-making tools for asset allocation, portfolio adjustment, and risk management [2] - An example is provided with the BlackRock Fund, which employs an industry rotation model for its equity portion, scoring based on multiple signal dimensions and optimizing industry allocation [2] - The bond portion utilizes duration and credit rotation strategies, dynamically adjusting the ratio of interest rate bonds to high-grade credit bonds based on various signals [2] Group 2: Risk Control Mechanisms - The strategy includes a dedicated down-risk control module with hard stop-loss lines and volatility management for preemptive alerts, allowing for proactive adjustments before market anomalies occur [2] - Adjustments are primarily driven by risk control needs rather than timing the market for profit, ensuring that the portfolio operates within a safe boundary [3] Group 3: Investment Strategy and Market Outlook - The core of the strategy's returns comes from trading strategies rather than static bond yields, allowing for significant scalability without impacting performance [5] - The firm anticipates investment opportunities in the domestic bond market, focusing on short-term high-grade credit bonds and interest rate bonds, with a preference for quantitative models to assess multi-dimensional judgments [5] - In the equity market, the firm remains optimistic about the A-share market in 2026, predicting favorable performance for the CSI 300 index, which currently has an attractive valuation based on projected earnings [6]

Core Viewpoint - The "Fixed Income +" fund is evolving by leveraging quantitative models to flexibly adjust the equity-debt ratio, catering to the growing demand for diversified and stable wealth management among residents [1] Group 1: Quantitative Multi-Asset Strategy - The quantitative multi-asset strategy emphasizes systematic and disciplined management of investment portfolios, using quantitative models as key decision-making tools for asset allocation, portfolio adjustment, and risk management [2] - The BlackRock Fund's mixed securities investment fund employs an industry rotation model for its equity portion, scoring based on multiple signal dimensions such as value, growth, and price momentum, while the bond portion uses duration and credit rotation strategies [2] Group 2: Risk Control Mechanisms - The strategy includes a dedicated downside risk control module with hard stop-loss lines and volatility management for preemptive alerts, allowing for adjustments before market fluctuations occur [3] - Fund managers verify model recommendations daily and strictly adhere to risk control directives to ensure the portfolio operates within safe boundaries, aiming to provide investors with peace of mind [3] Group 3: Revenue Sources and Market Capacity - The core of revenue generation comes from trading strategies rather than static bond yields, allowing for a potential scale of over 10 billion yuan for the "Fixed Income +" products without significant impact on returns [5] - The strategy focuses on large and mid-cap stocks and interest rate bonds, with a monthly rebalancing frequency, ensuring that market pricing transparency mitigates concerns about resource scarcity affecting returns [5] Group 4: Investment Outlook for 2026 - The investment opportunities in the domestic bond market are expected to concentrate on short-term high-grade credit bonds and interest rate bond curve trading, with quantitative models aiding in multi-dimensional assessments [5] - The A-share market is anticipated to perform well in the next 12 to 18 months, with the CSI 300 index currently showing an attractive valuation based on projected earnings [5] - Structural market trends are expected to continue, with AI applications expanding beyond hardware infrastructure, and the consumer sector potentially recovering due to positive factors [6]

Group 1: Style Rotation Model - The style rotation model for Q1 2026 indicates a preference for small-cap and growth stocks [1][2] - In Q4 2025, the returns for CSI 300 and CSI 1000 were -0.23% and 0.27% respectively, with small-cap stocks outperforming large-cap stocks by 0.50% [1] - The value-growth rotation model achieved a return of 37.06% for the entire year of 2025, with an excess return of 7.01% compared to an equal-weighted portfolio [1] Group 2: Industry Rotation Model - In January, the recommended long positions for single-factor and composite-factor strategies include non-bank financials, coal, and steel [1][3] - The absolute return for the industry composite factor strategy in 2025 was 38.10%, with an excess return of 11.70% relative to the benchmark [1] - The absolute return for the industry single-factor multi-strategy was 36.00%, with an excess return of 10.37% compared to the benchmark [1] Group 3: January Industry Insights - The single-factor multi-strategy recommends long positions in banking, non-bank financials, coal, and steel [3] - The composite-factor strategy recommends long positions in coal, steel, non-bank financials, non-ferrous metals, and transportation [3] - In December, the composite factor strategy achieved an excess return of 1.18%, while the single-factor multi-strategy achieved an excess return of 0.81% [3]

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]

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].

- The A-share sector rotation model is constructed using six factors: capital, valuation, sentiment, momentum, overbought/oversold, and profitability. The scoring system is based on these factors to evaluate the comprehensive scores of industries[4][19] - The capital factor uses the net inflow rate of industry funds as the main data source, while the valuation factor is based on the valuation percentile of the industry over the past year. Sentiment is derived from the proportion of rising constituent stocks, momentum is calculated using the MACD indicator, overbought/oversold is measured by the RSI indicator, and profitability is based on the consensus forecast EPS percentile of the industry over the past year[19] - The scoring results of the sector rotation model show that industries such as media, social services, and food & beverage have high comprehensive scores, while industries like real estate, building materials, and environmental protection have low scores[4][20][21] - The consensus stock selection model identifies high-growth industries at the secondary level of Shenwan classification over the past 30 days. It calculates momentum factors, valuation factors, and upward frequency using monthly stock data. Additionally, it incorporates high-frequency minute-level fund flow data to compute the similarity between fund flow changes and stock price trends. Stocks with the highest similarity in the top three secondary industries are selected[22] - The selected high-growth secondary industries for this period are industrial metals, home appliance components II, and energy metals. Stocks chosen include Chang Aluminum Co., Jintian Co., and Libba Co. among others[23] - The A-share sector rotation model scoring results indicate that the media industry achieved a total score of 8, social services scored 8, and food & beverage scored 7. Conversely, industries such as real estate and building materials scored -5, and environmental protection scored -4[21] - The consensus stock selection model outputs stocks such as Chang Aluminum Co. and Jintian Co. from the industrial metals sector, Tianyin Electromechanical and Samsung New Materials from the home appliance components II sector, and Shengxin Lithium Energy and Rongjie Co. from the energy metals sector[23]

Quantitative Models and Construction Methods 1. Model Name: Industry Relative Strength (RSI) Model - **Model Construction Idea**: This model identifies leading industries by calculating their relative strength (RS) based on historical price performance over different time windows [10] - **Model Construction Process**: 1. Use 29 first-level industry indices as the configuration targets [10] 2. Calculate the price change rates for the past 20, 40, and 60 trading days for each industry index [10] 3. Rank the industries based on their price change rates for each time window and normalize the rankings to obtain RS_20, RS_40, and RS_60 [10] 4. Calculate the average of the three rankings to derive the final RS value: $ RS = \frac{RS_{20} + RS_{40} + RS_{60}}{3} $ [10] 5. Industries with RS > 90% by the end of April are identified as potential leading industries for the year [10] - **Model Evaluation**: The model successfully identified key annual industry trends, such as high dividend, resource products, exports, and AI, which were validated by market performance throughout the year [10][12] 2. Model Name: Industry Sentiment-Trend-Crowding Framework - **Model Construction Idea**: This framework provides two industry rotation strategies based on market conditions: 1. High sentiment + strong trend, avoiding high crowding (aggressive strategy) 2. Strong trend + low crowding, avoiding low sentiment (conservative strategy) [6][14] - **Model Construction Process**: 1. Evaluate industries based on three dimensions: sentiment, trend, and crowding [6][14] 2. Use sentiment as the core metric for the aggressive strategy, with crowding as a risk control factor [14] 3. Use trend as the core metric for the conservative strategy, avoiding low-sentiment industries [14] 4. Allocate weights to industries based on their scores in the three dimensions [6][14] - **Model Evaluation**: The framework is effective in adapting to different market conditions and has shown strong performance in historical backtests [6][14] 3. Model Name: Left-Side Inventory Reversal Model - **Model Construction Idea**: This model identifies industries with potential for recovery by analyzing sectors in distress or those with low inventory pressure and high analyst optimism [24] - **Model Construction Process**: 1. Identify industries currently in distress or recovering from past distress [24] 2. Focus on sectors with low inventory pressure and potential for restocking [24] 3. Incorporate analyst long-term positive outlooks for these industries [24] - **Model Evaluation**: The model effectively captures recovery opportunities in industries undergoing inventory restocking cycles, providing significant absolute and relative returns [24] --- Model Backtesting Results 1. Industry Relative Strength (RSI) Model - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned - **Performance Highlights**: - Industries with RS > 90% by April 2024 included coal, utilities, home appliances, banking, petrochemicals, communication, non-ferrous metals, agriculture, and automotive [10] - These industries showed strong performance, with key themes being high dividends, resource products, exports, and AI [10][12] 2. Industry Sentiment-Trend-Crowding Framework - **Annualized Return**: 22.1% (long-only portfolio) [14] - **Excess Return**: 13.8% (annualized) [14] - **Information Ratio (IR)**: 1.51 [14] - **Maximum Drawdown**: -8.0% [14] - **Monthly Win Rate**: 68% [14] - **Performance Highlights**: - 2023 excess return: 7.3% [14] - 2024 excess return: 5.7% [14] - 2025 YTD excess return: 2.8% [14] 3. Left-Side Inventory Reversal Model - **Annualized Return**: Not explicitly mentioned - **Excess Return**: - 2023: 17.0% (relative to equal-weighted industry benchmark) [24] - 2024: 15.4% (relative to equal-weighted industry benchmark) [24] - 2025 YTD: 7.8% (relative to equal-weighted industry benchmark) [24] - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned - **Performance Highlights**: - Absolute return: - 2023: 13.4% [24] - 2024: 26.5% [24] - 2025 YTD: 26.4% [24] --- Quantitative Factors and Construction Methods 1. Factor Name: Sentiment Factor - **Factor Construction Idea**: Measures the overall sentiment of an industry to identify high-growth opportunities [14] - **Factor Construction Process**: 1. Evaluate the sentiment of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by sentiment scores [14] - **Factor Evaluation**: Sentiment is a core metric in the aggressive strategy of the Industry Sentiment-Trend-Crowding Framework, providing strong signals for high-growth opportunities [14] 2. Factor Name: Trend Factor - **Factor Construction Idea**: Measures the strength of market trends to identify industries with strong momentum [14] - **Factor Construction Process**: 1. Evaluate the trend of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by trend scores [14] - **Factor Evaluation**: Trend is a core metric in the conservative strategy of the Industry Sentiment-Trend-Crowding Framework, offering a simple and replicable approach to industry allocation [14] 3. Factor Name: Crowding Factor - **Factor Construction Idea**: Measures the level of crowding in an industry to identify overbought or underbought sectors [14] - **Factor Construction Process**: 1. Evaluate the crowding level of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by crowding scores [14] - **Factor Evaluation**: Crowding is used as a risk control factor in both aggressive and conservative strategies of the Industry Sentiment-Trend-Crowding Framework [14] --- Factor Backtesting Results 1. Sentiment Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned 2. Trend Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned 3. Crowding Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned