[Table_Title] 金 金融工程点评 [Table_Message]2025-12-04 金工 ETF 点评：跨境 ETF 单日净流入 18.45 亿元；石油石化、有色拥挤变幅较大 [Table_Author] 证券分析师：刘晓锋 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 证券分析师：孙弋轩 电话：18910596766 E-MAIL：sunyixuan@tpyzq.com 执业资格证书编码：S1190525080001 一、资金流向 ETF [Table_Summary] 数据截止日：2025/12/3 二、行业拥挤度监测 ◼ 通过构建行业拥挤度监测模型，对申万一级行业指数的拥挤度进行每日监测， 前一交易日通信、农牧、军工靠前，相比较而言，汽车、美护、非银的拥挤度 水平较低，建议关注。此外，石化、有色拥挤度变动较大。从主力资金流动 来看，前一交易日主力资金流入煤炭；流出电子、计算机。近三个交易日主 力资金减配电力设备、计算机；增配煤炭。 三、ETF 产品关注信号 ◼ 根据溢价率 Z-score 模型搭建相关 ETF 产 ...

医药行业 证券研究报告 |深度报告 2025/12/01 水稻胚乳里的生物密码： 禾元生物重组蛋白技术的产业化革命 | 证券分析师: | 程晓东 | | --- | --- | | 分析师登记编号: | S1190511050002 | | 证券分析师: | 李忠华 | | 分析师登记编号: | S1190524090001 | P2 报告摘要 核心结论：禾元生物是国内植物源重组蛋白药物领军者，以独创水稻胚乳细胞生物反应器技术打破血浆依赖，核心产品 HY1001（重组人白蛋白）国内先发上市、美国推进III期，成本远低于血浆来源，120吨产能+全球化布局支撑2030年营 收超30亿，技术壁垒与商业化确定性突出，具备高投资价值。 一、公司与核心技术：植物源蛋白产业化闭环，指标全球领先。公司拥有二大核心技术平台：1、重组蛋白高效表达平 台：历经三代迭代，重组蛋白表达量达20-30g/kg，核心依赖胚乳特异性启动子改造等专利技术；2、蛋白纯化平台：纯 度达99.9999%，内毒素符合中美药典，纯化成本较微生物体系降30%+。 二、产品管线：HY1001领跑，多梯队支撑增长。（1）核心产品 HY1001：2025年7月 ...

金 金融工程点评 [Table_Title] [Table_Message]2025-12-01 金工 ETF 点评：宽基 ETF 单日净流出 14.37 亿元；建装、交运、家电拥挤变幅较大 [Table_Author] 证券分析师：刘晓锋 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 证券分析师：孙弋轩 电话：18910596766 E-MAIL：sunyixuan@tpyzq.com 执业资格证书编码：S1190525080001 一、资金流向 二、行业拥挤度监测 ◼ 通过构建行业拥挤度监测模型，对申万一级行业指数的拥挤度进行每日监测， 前一交易日通信、房地产靠前，相比较而言，家电、汽车、非银的拥挤度水 平较低，建议关注。此外，建筑装饰、交运、家电拥挤度变动较大。从主力资 金流动来看，前一交易日主力资金流入电子、有色、汽车；流出医药、传媒。 近三个交易日主力资金减配传媒、计算机；增配电子、汽车。 三、ETF 产品关注信号 ◼ 根据溢价率 Z-score 模型搭建相关 ETF 产品筛选信号模型，通过滚动测算提 供存在潜在套利机会的 ...

[Table_Message]2025-11-30 金融工程周报 大类资产与基金周报（20251124-20251128）—— 权益与黄金回升，权益基金涨幅达 3.01% [Table_Author] 证券分析师：刘晓锋 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 证券分析师：孙弋轩 电话：18910596766 E-MAIL：sunyixuan@tpyzq.com 执业资格证书编码：S1190525080001 内容摘要 太 平 洋 证 券 股 份 有 限 公 司 证 券 研 究 报 告 请务必阅读正文之后的免责条款部分 守正 出奇 宁静 致远 [Table_Title] [Table_Summary] . 金 融 工 程 周 报 ◼ 大类资产市场概况：1）权益：本周 A 股市场中上证指数收盘 3888.60，涨跌幅 1.40%， 深证成指、中小板指数、创业板指、上证 50、沪深 300、中证 500、中证 1000、中证 2000、 北证 50 涨跌幅分别为 3.56%、3.74%、4.54%、0.47%、1.64%、3.14 ...

- The report introduces an **industry crowding monitoring model** to track the crowding levels of Shenwan primary industry indices on a daily basis. The model identifies industries with high crowding levels (e.g., communication and electronics) and low crowding levels (e.g., automotive and non-bank financials). It also highlights significant changes in crowding levels for industries like construction decoration and military industries[3] - A **Z-score premium model** is constructed to screen ETF products for potential arbitrage opportunities. The model uses rolling calculations to identify ETFs with significant deviations in premium rates, which may indicate arbitrage opportunities or potential risks of price corrections[4] - The report provides detailed data on **ETF fund flows**, categorizing them into broad-based ETFs, industry-themed ETFs, style-strategy ETFs, and cross-border ETFs. For example, the top three net inflows for broad-based ETFs include the SSE 50 ETF (+6.60 billion yuan), A500 ETF (+5.84 billion yuan), and ChiNext 50 ETF (+2.75 billion yuan), while the top three net outflows include the ChiNext ETF (-7.26 billion yuan), CSI 500 ETF (-5.56 billion yuan), and STAR 50 ETF (-5.10 billion yuan)[5]

金融工程 证券研究报告 |深度研究报告 2025/11/28 金融工程指数量化系列—— 高值偏离修复模型（浮动迫损版） 证券分析师: 刘晓锋 执业资格证书编码: S1190522090001 证券分析师: 孙弋轩 执业资格证书编码: S1190525080001 P2 目录 请务必阅读正文之后的免责条款部分 守正 出奇 宁静 致远 1、分档止损偏离修复模型回顾 2、浮动迫损策略 3、后续展望 4、风险提示 1、分档止损高值偏离修复模型回顾 基础分档止损策略： 1、计算单个行业指数相对沪深300收盘价cl，以及cl对应的回撤曲线W。 2、使用迭代法计算cl的有效回撤V，若无法得到V，则直接判定该行业不适合此策略。 3、选取V的最大值的80%作为阈值T（T为正数），当W值大于T时，信号值s为1（看多），当W值 为0时，信号值s为0（平仓），当W为其他值时，信号值s等于前值。 4、将每次买入点b0与前高h0之间的空间化为X等分，则每一等分的空间为s0。 5、买入后，当收盘价cl首次高于b0+2*s0时，止损被激活，止损点st0初始化为b0+s0。 6、止损被激活后，若cl不小于前高则平仓；若cl低于止损位置则触发止 ...

2025 年 11 月 27 日 行业日报 中性/维持 房地产 房地产 太平洋房地产日报（20251127）： 杭州 2 宗宅地出让 | 和运营 | | | --- | --- | | 房 地 产 开 发 房地产服务 | 无评级 无评级 | 推荐公司及评级 相关研究报告 <<太平洋房地产日报（20251126）：苏 州成功出让一宗高新区宅地>>-- 2025-11-26 <<太平洋房地产日报（20251125）：武 汉 12 宗地块总成交价约 39.7 亿>>-- 2025-11-25 <<太平洋房地产日报（20251124）：上 海九批次土拍收金 173.33 亿元>>-- 2025-11-24 2025 年 11 月 27 日，今日权益市场各板块多数下跌，上证综指上 涨 0.29%，深证综指下跌 0.11%，沪深 300 和中证 500 分别下跌 0.05% 和 0.20%。申万房地产指数下跌 0.63%。 个股表现： 房地产板块个股涨幅较大的前五名为万通发展、信达发展、天保 基 建 、 市 北 高 新 、 市 北 B 股 ， 涨 幅分别为 10.02%/9.89%/5.94%/4.01%/2.87%； ...

Group 1: Communication Sector - The report highlights the strong fundamentals of the digital virtual goods operator, Bee Assistant (301382.SZ), with a stable business base and rapid growth in IoT and cloud terminal services [4] - The company is expected to benefit from AI trends due to its strategic investments in AI-related areas [4] Group 2: Medical Sector - United Imaging Healthcare (688271.SH) is identified as a leading domestic medical imaging equipment manufacturer with a comprehensive product line including CT, MR, MI, XR, RT, and ultrasound [4] - The company has made significant breakthroughs in core technologies and successfully launched high-end products like ultra-high field MR and digital PET-CT, which are at the forefront of global standards [4] - Anticipated revenue recognition from delayed orders in 2024 is expected to boost performance in the second half of 2025, supported by new funding for equipment upgrades [4] Group 3: Consumer Goods Sector - Gu Ming (1364.HK) is noted as a highly certain and scalable player in the tea beverage sector, with strong same-store sales and rapid franchisee payback periods [4] - The company is expected to emerge as a stable growth and expansion leader during the industry reshuffle in 2026 [4] Group 4: Home Appliances Sector - Midea Group (000333.SZ) reported a 13% year-on-year revenue increase in the ToC segment for Q1-Q3 2025, driven by high-end brands and an optimized product structure [5] - The ToB segment saw an 18% revenue increase, with significant growth in new energy and industrial technology sectors [5] - The company's focus on robotics is expected to enhance its product offerings and support long-term revenue growth [7] Group 5: Chemical Sector - Excellent New Energy (688196.SH) is positioned well in the biofuel industry, with a robust capacity layout for biodiesel and bio-based materials [7] - The company is accelerating its biodiesel project with a projected post-tax internal rate of return of 28.94%, enhancing its market competitiveness [7] Group 6: Financial Sector - Industrial and Commercial Bank of China (601398.SH) is characterized by its stability and high dividend yield, making it a preferred choice for investors seeking certainty [7] - The bank's net profit showed a slight year-on-year increase of 0.33% for the first three quarters of 2025, with non-interest income growing by 11.3% [7] Group 7: Transportation Sector - Jinjiang Shipping (601083.SH) reported a remarkable 64% year-on-year increase in net profit for Q3, outperforming peers [7] Group 8: Retail Sector - China Duty Free Group (601888.SH) is experiencing a recovery in duty-free sales, benefiting from increased domestic tourism and expectations of policy support [7] Group 9: Agriculture Sector - Tian Kang Biological (002100.SZ) is positioned to benefit from rising pig prices as the industry undergoes capacity reduction, potentially enhancing profitability [8] Group 10: Electronics Sector - Huadian Co., Ltd. (002463.SZ) is experiencing high growth in server switch business driven by AI demand, with ongoing capacity expansion and improved profitability [8]

- The industry crowding monitoring model was constructed to monitor the crowding level of Shenwan primary industry indices daily. The model identifies industries with high crowding levels, such as military, agriculture, and media, while industries like automotive and non-bank financials exhibit lower crowding levels. The model also tracks significant changes in crowding levels for industries like petrochemicals and coal[3] - The Z-score premium model was developed to screen ETF products for potential arbitrage opportunities. The model uses rolling calculations to identify ETFs with significant deviations from their intrinsic value, providing signals for potential trades while cautioning against risks of price corrections[4] - Industry crowding monitoring model evaluation: The model effectively identifies industries with varying crowding levels, offering insights into market dynamics and potential investment opportunities[3] - Z-score premium model evaluation: The model provides actionable signals for ETF arbitrage opportunities, but users are advised to remain cautious about potential risks associated with price corrections[4] - Industry crowding monitoring model testing results: The model highlights industries with high crowding levels, such as military, agriculture, and media, and industries with low crowding levels, such as automotive and non-bank financials. It also identifies significant crowding changes in petrochemicals and coal[3] - Z-score premium model testing results: The model identifies ETFs with potential arbitrage opportunities based on their Z-score premium deviations, but specific numerical results are not provided in the report[4]

Quantitative Models and Construction Methods 1. **Model Name**: Industry Crowding Monitoring Model **Model Construction Idea**: This model is designed to monitor the crowding levels of industries on a daily basis, focusing on the crowding degree of Shenwan Level-1 industry indices. It identifies industries with high or low crowding levels and tracks changes in crowding over time[3] **Model Construction Process**: The model calculates the crowding degree of each industry index based on specific metrics, such as main fund inflows and outflows. It then ranks industries by their crowding levels and highlights significant changes in crowding over recent trading days[3] **Model Evaluation**: The model provides a useful tool for identifying industry trends and potential investment opportunities by analyzing crowding dynamics[3] 2. **Model Name**: Premium Rate Z-Score Model **Model Construction Idea**: This model is used to screen ETF products for potential arbitrage opportunities by calculating the Z-score of their premium rates over a rolling window[4] **Model Construction Process**: - The premium rate of an ETF is calculated as the difference between its market price and its net asset value (NAV), divided by the NAV - The Z-score is then computed as: $ Z = \frac{(Premium\ Rate - \mu)}{\sigma} $ where $ \mu $ is the mean premium rate and $ \sigma $ is the standard deviation of the premium rate over a rolling window - ETFs with extreme Z-scores are flagged as potential arbitrage opportunities[4] **Model Evaluation**: The model effectively identifies ETFs with significant deviations from their historical premium rates, which may indicate arbitrage opportunities or risks of price corrections[4] --- Model Backtesting Results 1. **Industry Crowding Monitoring Model**: - Crowding levels for industries such as military, agriculture, and media were high, while automotive and non-bank financials showed low crowding levels[3] - Significant changes in crowding were observed in industries like computing and media over recent trading days[3] 2. **Premium Rate Z-Score Model**: - Specific ETFs with extreme Z-scores were identified, such as the Sci-Tech Innovation Board ETFs, which were flagged for potential arbitrage opportunities[4] --- Quantitative Factors and Construction Methods No specific quantitative factors were explicitly mentioned in the provided content --- Factor Backtesting Results No specific factor backtesting results were explicitly mentioned in the provided content