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]

证券研究报告 建议择机入场 2025 年 11 月 23 日│中国内地 量化投资周报 本周观点：建议择机入场 上周，受全球流动性压力、美联储降息预期反复以及 AI 叙事松动多重因素 影响，全球风险偏好下降——VIX 指数攀升至近三个月高位，各类风险资产 均承压，其中比特币、微盘股等对流动性和风偏更敏感的资产领跌。我们的 模型认为 A 股经过上周的调整，整体上消化了过高的估值，观点由防御转 为看平。叠加周五美联储释放了略积极的降息信号，Nowcasting 模型预测 11 月 CPI 或将继续上行至 3.7%-3.8%，但核心 CPI 预计保持平稳，或有利 于市场风偏的恢复。建议择机入场，优选低位防御板块，本周行业轮动模型 加大了对低位消费板块的押注，风格上仍看好红利。 A 股大盘择时模型：上周回调消化了高估值压力，可择机入场 我们以万得全 A 指数作为 A 股大盘代理，从估值、情绪、资金、技术四个 维度对 A 股大盘进行整体方向性判断。今年以来，模型多空择时的扣费后 收益 43.84%，同期 A 股大盘涨跌幅为 20.09%，超额收益为 23.76%；上周 模型超额收益为 10.41%。上周，受全球流动性压力 ...

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]

Group 1 - The report emphasizes the importance of analyst consensus expectations in predicting future industry performance, particularly in the context of the current A-share market, which is characterized by valuation recovery and liquidity-driven trends [9][11][12] - The report constructs a composite expectation factor to capture marginal changes in industry prosperity, focusing on the strength and magnitude of upward revisions in analyst forecasts [11][12][49] - The analysis categorizes expected indicators into three groups: profitability, asset quality, and cost metrics, which are essential for assessing market expectations regarding industry fundamentals [16][23] Group 2 - The upward strength signal reflects the breadth of upward revisions within an industry, indicating improvements in industry prosperity [30][32] - The upward magnitude signal measures the month-on-month improvement in overall industry forecasts, highlighting the concentration and intensity of industry recovery [40][44] - The report identifies that profitability-related indicators, such as expected net profit and ROE, significantly outperform cash flow and cost indicators in terms of predictive power and return potential [35][44] Group 3 - The composite expectation score combines upward strength and upward magnitude to provide a comprehensive view of industry prosperity, with higher scores correlating with better future performance [53][65] - The backtesting results show that the top-performing industries based on the composite score yield substantial excess returns compared to the benchmark, demonstrating the model's effectiveness in identifying profitable sectors [70][73] - The report highlights that the top five industry strategy achieved an annualized excess return of 12.40%, indicating strong predictive capabilities of the model [70][74]

Core Insights - The article highlights the investment strategy of Chen Yunzong, a fund manager at GF Fund, focusing on identifying growth stocks and their growth stages through a dual-track approach of "traditional growth" and "emerging growth" [1][2]. Investment Strategy - Chen emphasizes a systematic approach to understanding industry attributes, industry cycle stages, and long-term trends before selecting quality growth stocks [1][2]. - The investment framework is centered around capturing excess returns from diverse growth directions, including technology and manufacturing sectors [2][3]. Performance Metrics - As of October 31, the GF Growth Initiation A fund managed by Chen achieved a one-year return of 88.81%, ranking in the top 3 out of 1,876 similar funds [1]. Fund Launch - A new fund, GF Innovation Growth, is set to launch on November 17, which will dynamically adjust the allocation between traditional and emerging growth to capture excess returns while maintaining industry balance [1][6]. Growth Categories - Growth stocks are categorized into "traditional growth" (e.g., new energy, semiconductors, military industry) and "emerging growth" (e.g., robotics, embodied intelligence, satellite internet) [2][5]. - Traditional growth strategies focus on cyclical growth, while emerging growth serves as an offensive tool for capturing future trends [2][3]. Dynamic Allocation - The allocation between traditional and emerging growth is adjusted based on market liquidity and risk appetite, enhancing both offensive and defensive capabilities of the portfolio [3][4]. Industry Rotation - Chen's investment approach involves a systematic method of industry rotation based on industry cycles, focusing on "industry position" and "valuation margins" rather than merely chasing market trends [4][5]. Future Focus Areas - Key sectors of interest include computing power, storage, edge innovation, brand globalization, robotics, satellite internet, and solid-state batteries [6][7]. - The computing power sector is particularly emphasized, with expectations of significant capital expenditure increases from domestic cloud service providers in the upcoming quarters [6][7]. Specific Sector Insights - The military industry is highlighted as a high-value sector, while the robotics sector is seen as a major application terminal for AI [7]. - Solid-state batteries and low-altitude economy are also critical areas of focus, with expectations of early breakthroughs in these technologies [7].

Core Insights - The article highlights the investment strategy of Chen Yunzong, a fund manager at GF Fund, focusing on identifying growth stocks and their respective growth stages through a dual-track approach of "traditional growth" and "emerging growth" [1][2] Investment Strategy - Chen Yunzong emphasizes a systematic approach to understanding industry attributes, clarifying industry cycle stages and medium to long-term trends before selecting quality growth stocks [1][2] - The investment framework is centered around capturing excess returns from diverse growth directions, including technology and manufacturing sectors, while also expanding research beyond TMT (Technology, Media, Telecommunications) to include military and energy sectors [2] Growth Categories - Growth stocks are categorized into "traditional growth" and "emerging growth," with differentiated strategies for each. Traditional growth includes sectors like new energy, semiconductors, and military, where a cyclical growth mindset is applied [2] - Emerging growth serves as an "offensive lever" in the portfolio, focusing on sectors like robotics, embodied intelligence, satellite internet, quantum computing, and solid-state batteries, which are expected to represent future trends [2][3] Dynamic Allocation - The allocation between traditional and emerging growth is dynamically adjusted based on market liquidity and risk appetite, enhancing the portfolio's offensive capabilities in bull markets and defensive strength in volatile markets [2][3] Industry Rotation - Chen Yunzong's investment approach involves industry rotation based on a systematic method rather than merely chasing market trends, focusing on the balance between "industry position" and "valuation margins" [3] - A significant portion of research efforts is dedicated to tracking emerging growth directions, involving visits to industry leaders and studying cutting-edge trends globally [3] Future Growth Areas - The new fund, GF Innovation Growth, will adopt a balanced growth-oriented strategy, targeting sectors such as computing power, storage, edge innovation, brand globalization, robotics, satellite internet, and solid-state batteries [4] - The computing power sector is highlighted as a key focus, with expectations of significant capital expenditure increases from domestic cloud service providers in the upcoming quarters [5] Market Outlook - The storage sector is anticipated to enter an upward cycle, with NAND flash memory prices beginning to rise since September, expected to maintain favorable industry conditions for one to two more quarters [5] - The military sector is viewed as having high cost-effectiveness, while the robotics sector is seen as a major application terminal for AI, with the domestic robotics supply chain not yet fully priced [5]

1. Report Industry Investment Rating There is no information provided in the report regarding the industry investment rating. 2. Core Views of the Report - On November 14, the convertible bond market contracted in volume and declined, with compressed valuations. The CSI Convertible Bond Index decreased by 0.58% compared to the previous day, and the trading sentiment in the convertible bond market weakened. The total trading volume of the convertible bond market was 71.351 billion yuan, a 9.71% decrease from the previous day [1]. - The convertible bond price center declined, and the proportion of high - priced bonds decreased. The overall weighted average closing price of convertible bonds was 135.02 yuan, a 0.64% decrease from the previous day. The valuation was compressed, with the 100 - yuan par - value fitted conversion premium rate at 31.82%, a 0.82 - percentage - point decrease from the previous day [2]. - In the stock market, more than half of the underlying stock industry indices declined. Among A - share markets, the top three industries with the largest declines were electronics (-3.09%), communication (-2.46%), and media (-2.16%); the top three industries with the largest increases were real estate (+0.39%), banking (+0.26%), and pharmaceutical biology (+0.17%). In the convertible bond market, 23 industries declined, with the top three industries with the largest declines being communication (-2.52%), national defense and military industry (-1.85%), and automobile (-1.66%); the top three industries with the largest increases were steel (+2.31%), environmental protection (+0.82%), and public utilities (+0.27%) [3]. 3. Summary by Relevant Catalogs Market Overview - **Index Performance**: The CSI Convertible Bond Index decreased by 0.58% compared to the previous day, the Shanghai Composite Index decreased by 0.97%, the Shenzhen Component Index decreased by 1.93%, the ChiNext Index decreased by 2.82%, the SSE 50 Index decreased by 1.15%, and the CSI 1000 Index decreased by 1.16% [1]. - **Market Style**: Large - cap value stocks were relatively dominant. Large - cap growth stocks decreased by 2.20%, large - cap value stocks decreased by 0.55%, mid - cap growth stocks decreased by 1.48%, mid - cap value stocks decreased by 1.19%, small - cap growth stocks decreased by 1.45%, and small - cap value stocks decreased by 0.85% [1]. - **Fund Performance**: The trading sentiment in the convertible bond market weakened. The trading volume of the convertible bond market was 71.351 billion yuan, a 9.71% decrease from the previous day; the total trading volume of the Wind All - A Index was 1980.382 billion yuan, a 4.13% decrease from the previous day; the net outflow of the main funds in the Shanghai and Shenzhen stock markets was 62.011 billion yuan, and the yield of the 10 - year treasury bond increased by 0.14 bp to 1.81% [1]. Convertible Bond Price and Valuation - **Convertible Bond Price**: The overall weighted average closing price of convertible bonds was 135.02 yuan, a 0.64% decrease from the previous day. The closing price of equity - biased convertible bonds was 178.79 yuan, a 1.27% decrease; the closing price of bond - biased convertible bonds was 121.53 yuan, a 0.10% decrease; the closing price of balanced convertible bonds was 130.91 yuan, a 0.31% decrease. The proportion of high - priced bonds above 130 yuan was 62.34%, a 0.75 - percentage - point decrease from the previous day. The price median was 133.72 yuan, a 0.93% decrease from the previous day [2]. - **Convertible Bond Valuation**: The valuation was compressed. The 100 - yuan par - value fitted conversion premium rate was 31.82%, a 0.82 - percentage - point decrease from the previous day; the overall weighted par value was 104.59 yuan, a 0.52% decrease from the previous day. The premium rate of equity - biased convertible bonds was 10.60%, a 1.34 - percentage - point decrease; the premium rate of bond - biased convertible bonds was 84.51%, a 0.54 - percentage - point decrease; the premium rate of balanced convertible bonds was 22.78%, a 0.24 - percentage - point decrease [2]. Industry Performance - **Underlying Stock Industry**: Among A - share markets, the top three industries with the largest declines were electronics (-3.09%), communication (-2.46%), and media (-2.16%); the top three industries with the largest increases were real estate (+0.39%), banking (+0.26%), and pharmaceutical biology (+0.17%) [3]. - **Convertible Bond Industry**: In the convertible bond market, 23 industries declined, with the top three industries with the largest declines being communication (-2.52%), national defense and military industry (-1.85%), and automobile (-1.66%); the top three industries with the largest increases were steel (+2.31%), environmental protection (+0.82%), and public utilities (+0.27%) [3]. - **Key Indicators by Sector**: - Closing price: The large - cycle sector decreased by 0.15%, the manufacturing sector decreased by 1.11%, the technology sector decreased by 1.59%, the large - consumption sector decreased by 0.64%, and the large - finance sector decreased by 0.66% [3]. - Conversion premium rate: The large - cycle sector decreased by 0.57 percentage points, the manufacturing sector decreased by 0.37 percentage points, the technology sector increased by 0.3 percentage points, the large - consumption sector decreased by 0.29 percentage points, and the large - finance sector increased by 0.051 percentage points [3]. - Conversion value: The large - cycle sector increased by 0.51%, the manufacturing sector decreased by 0.87%, the technology sector decreased by 1.74%, the large - consumption sector decreased by 0.64%, and the large - finance sector decreased by 1.01% [3]. - Pure bond premium rate: The large - cycle sector decreased by 0.23 percentage points, the manufacturing sector decreased by 1.7 percentage points, the technology sector decreased by 2.3 percentage points, the large - consumption sector decreased by 0.82 percentage points, and the large - finance sector decreased by 0.79 percentage points [4].