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]

证券研究报告：金融工程报告 发布时间：2025-11-25 研究所 分析师:黄子崟 SAC 登记编号:S1340523090002 Email:huangziyin@cnpsec.com 研究助理:李子凯 SAC 登记编号:S1340124100014 Email:lizikai@cnpsec.com 近期研究报告 《微盘股继续领涨市场，扩散指数已达 较高区间 — — 微盘股指数周报 20251114》 - 2025.11.18 《连板高度打开情绪持续发酵，GRU 行 业轮动调入房地产——行业轮动周报 20251116》 - 2025.11.17 《连板情绪持续发酵，GRU 行业轮动调 入基础化工 — — 行业轮动周报 20251109》– 2025.11.10 《上证周中突破 4000 点，扩散指数行业 轮动调入电力设备及新能源——行业 轮动周报 20251102》 – 2025.11.03 《贵金属回调风偏修复，GRU 行业轮动 调入非银行金融——行业轮动周报 20251028》 – 2025.10.27 《上证强于双创调整空间不大，ETF 资 金持续配置金融地产与 TMT 方向——行 业轮动周 2025 ...

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].

广发证券资深金工分析师 周飞鹏 SAC: S0260521120003 zhoufeipeng@gf.com.cn 广发证券首席金工分析师 安宁宁 SAC: S0260512020003 anningning@gf.com.cn 广发金工安宁宁陈原文团队 摘要 主要结论： 在前期报告《龙头扩散效应行业轮动之二-优选行业组合构建》中我们构建了月度优选行业轮动组合。而由于部分一级行业暂无 对应ETF作为投资工具，部分时期可能存在无法直接持有特定行业标的的情况。为最大限度获取轮动收益，本报告尝试从选股的角度探讨以 持股来复制或增厚轮动策略收益的方式。 行业个股双驱优选组合月度调仓，2013年以来年化收益33.6%，相对中证500指数年化超额收益 28.3%，IR2.07，相对最大回撤27.8%。 板块行情的底层驱动机制： 受活跃资金挖掘概念主题手法的启发，我们思考板块行情的启动和发展过程或许在微观层面上源自板块内个股上涨的 蔓延与扩散，从最初的行情龙头到概念相关的更多个股，正是行情覆盖范围的逐渐延伸催生了一轮行业上行趋势。我们将此过程称为"龙头扩散效 应"。 收益复制角度： 行业全复制组合收益复制效果最理想，但由于 ...

证 券 研 究 报 告 改革深化 融合加速 ——2026年北交所投资策略 证券分析师： 刘靖 A0230512070005 王雨晴 A0230522010003 郑菁华 A0230525060001 汪秉涵 A0230525090003 研究支持： 吕靖华 A0230124070002 2025.11.19 主要内容 www.swsresearch.com 证券研究报告 2 ◼ 北证四周年，市场近万亿，流动性明显改善，市场功能逐步完善。1）2025年11月15日北证迎来四周年，经过四年发展，北证已逐步融入A股；2）规模：截止 11月14日，北证共有282家上市公司，总市值9008亿，分别相比首日增长248%和212%。北证A股的专精特新"小巨人"数量占比和市值占比为56.7%和57%， 均为全市场最高，北证已聚集一批设备类、材料类、新能源产业链、新能源汽车产业链为代表的优质创新型中小企业；3）流动性：25年日均换手率5.4%，为 全A最高，开户数950万，相比首日增长约1.4倍，吸引了一批公募、社保、保险等机构投资者参与；4）市场功能逐步完善。做市商制度、融资融券制度、并购 重组、再融资等市场功能逐步完善。 ...

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]