量化择时和拥挤度预警周报（20251221） [Table_Authors] 郑雅斌(分析师) 市场短期震荡格局较难被打破 本报告导读： 从技术面来看，情绪模型信号依旧处于弱势震荡状态；与此同时，均线强弱指数同 样处于震荡区间。因此，我们认为，市场短期震荡格局较难被打破。 投资要点： 金 融 工 程 周 报 证 券 研 究 报 告 | [Table_Summary] 下周（20251222-20251226，后文同）市场观点：市场短期震荡格局 | | --- | | 较难被打破。从量化指标上看，基于沪深 300 指数的流动性冲击指 | | 标周五为 0.41，低于前一周（0.51），意味着当前市场的流动性高于 | | 过去一年平均水平 倍标准差。上证 期权成交量的 0.41 50ETF PUT | | CALL 比率震荡下降，周五为 0.83，低于前一周（1.08），投资者对 | | 上证 50ETF 短期走势谨慎程度下降。上证综指和 Wind 全 A 五日平 | | 均换手率分别为 1.05%和 1.60%，处于 年以来的 68.85%和 2005 | | 73.75%分位点，交易活跃度有所下降。从宏观因子 ...

- 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., agriculture, military, building materials) and low crowding levels (e.g., computers, home appliances, media) based on the previous trading day's data. It also highlights significant changes in crowding levels for industries such as automobiles, petrochemicals, and social services[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, broad-based ETFs saw a net inflow of 60.55 billion yuan in a single day, with the top inflows being the CSI A500 ETF (+10.42 billion yuan), CSI A500 ETF South (+10.23 billion yuan), and STAR 50 ETF (+8.62 billion yuan)[5] - The report highlights **industry crowding levels** over the past 30 trading days, presenting a heatmap that shows the relative crowding levels of various industries. For instance, industries like public utilities, agriculture, and military defense exhibit high crowding levels, while industries like computers and media show relatively low levels[9] - The report identifies **key ETF trading signals**, recommending attention to specific ETFs such as the CSI 1000 Enhanced ETF, Chuangzhongpan 88 ETF, and Medical Device ETF based on their potential for investment opportunities[11]

- The report constructs an industry congestion monitoring model to monitor the congestion levels of Shenwan first-level industry indices on a daily basis[3] - The ETF product screening signal model is built based on the premium rate Z-score model, which provides potential arbitrage opportunities through rolling calculations[4] - The industry congestion monitoring model indicates that the congestion levels of communication, military, and building materials were high on the previous trading day, while the congestion levels of computers and automobiles were relatively low[3] - The ETF product screening signal model suggests caution regarding the potential pullback risk of the identified targets[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 Shenwan First-Level Industry Indices on a daily basis, identifying industries with high or low crowding levels to provide actionable insights for investors[3] - **Model Construction Process**: The model calculates the crowding levels of various industries based on daily data. It ranks industries by their crowding levels, highlighting those with significant changes. For example, the previous trading day showed high crowding in communication, military, and electronics, while computer, automotive, and media had lower crowding levels. The model also tracks main fund flows to identify industries with increased or decreased allocations[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 identifies potential arbitrage opportunities in ETF products by calculating the Z-score of premium rates over a rolling window[4] - **Model Construction Process**: The Z-score is calculated as follows: $ Z = \frac{(P - \mu)}{\sigma} $ Where: - $ P $ is the current premium rate - $ \mu $ is the mean premium rate over the rolling window - $ \sigma $ is the standard deviation of the premium rate over the rolling window The model flags ETFs with significant deviations from their historical premium rates, indicating potential arbitrage opportunities. It also warns of potential risks of price corrections for these ETFs[4] - **Model Evaluation**: The model is effective in identifying ETFs with significant pricing anomalies, providing actionable signals for arbitrage strategies[4] --- Model Backtesting Results 1. Industry Crowding Monitoring Model - No specific numerical backtesting results were provided for this model[3] 2. Premium Rate Z-Score Model - No specific numerical backtesting results were provided for this model[4] --- Quantitative Factors and Construction Methods No specific quantitative factors were mentioned in the report. --- Factor Backtesting Results No specific quantitative factor backtesting results were mentioned in the report.

Group 1 - Market sentiment score continued to decline, reaching 1.35 as of December 12, down from 2.4 the previous week, indicating a bearish outlook from a sentiment perspective [2][8] - The overall trading volume in the market increased significantly, with total trading volume for the week rising by 15.14% compared to the previous week, averaging 19,530.44 billion yuan per day, with a peak of 21,190.10 billion yuan on December 12 [14][16] - The industry score model indicates that sectors such as non-bank financials, communication, defense, and automotive are showing upward trends in short-term scores, with communication having the highest short-term score of 77.97 [40][41] Group 2 - The correlation between industry congestion and weekly price changes is strong, with a coefficient of 0.33, indicating that sectors with high congestion like communication and defense are leading in gains, while sectors with low congestion like steel and environmental protection are lagging [45][46] - The current model suggests a preference for large-cap and growth styles, with signals indicating that growth style may strengthen further in the future [40][51] - The financing balance ratio continues to rise, reaching a new high for the phase, indicating an increase in leveraged funds and a structural recovery in risk appetite [26][28]

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 Shenwan First-Level Industry Indices on a daily basis, identifying industries with high or low crowding levels and significant changes in crowding dynamics[3] - **Model Construction Process**: The model calculates the crowding levels of various industries based on daily data. It identifies industries with the highest and lowest crowding levels and highlights industries with significant changes in crowding dynamics. Specific calculation methods or formulas are not provided in the report[3] - **Model Evaluation**: The model provides actionable insights into industry crowding trends, helping investors identify potential opportunities or risks in crowded or undercrowded sectors[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 premium rates on a rolling basis[4] - **Model Construction Process**: The Z-score of the premium rate is calculated for each ETF product over a rolling window. The Z-score helps identify ETFs with significant deviations from their historical premium rates, signaling potential arbitrage opportunities. Specific formulas or parameters are not detailed in the report[4] - **Model Evaluation**: The model effectively identifies ETFs with potential arbitrage opportunities, but it also warns of potential risks of price corrections in the identified ETFs[4] --- Model Backtesting Results 1. Industry Crowding Monitoring Model - No specific backtesting results or quantitative metrics are provided for this model in the report[3] 2. Premium Rate Z-Score Model - No specific backtesting results or quantitative metrics are provided for this model in the report[4] --- Quantitative Factors and Construction Methods No specific quantitative factors are mentioned or constructed in the report --- Factor Backtesting Results No specific backtesting results for factors are mentioned in the report

2025 年 12 月 07 日 市场情绪得分继续回落，多项指标 维持震荡 ——量化择时周报 20251207 权 益 量 化 研 究 证 券 研 究 报 告 相关研究 - 证券分析师 沈思逸 A0230521070001 shensy@swsresearch.com 邓虎 A0230520070003 denghu@swsresearch.com 联系人 沈思逸 A0230521070001 shensy@swsresearch.com 请务必仔细阅读正文之后的各项信息披露与声明 本研究报告仅通过邮件提供给 博时基金 博时基金管理有限公司(researchreport@bosera.com) 使用。1 量 化 策 略 ⚫ 市场情绪得分周内继续回落：截至 12 月 5 日，市场情绪指标数值为 2.4，较上周五的 3.15 大幅降低，情绪得分保持快速下降，从情绪角度来看观点偏空。从所有分项指标分数之和 的变化来看，本周情绪指数综合得分周内波动下降。 ⚫ 情绪指标维持震荡分化：本周价量一致性再度回落，市场价量匹配程度下降，资金关注度 与标的涨幅相关性同步走弱，情绪端出现一定降温；科创 50 相对全 A 成交占比继 ...

- The report constructs an industry crowding monitoring model to monitor the crowding levels of Shenwan First-Level Industry Indexes on a daily basis[3] - The ETF product screening signal model is built using the premium rate Z-score model, which provides potential arbitrage opportunities through rolling calculations[4] - The industry crowding monitoring model indicates that the crowding levels of the communication and military industries were high on the previous trading day, while the crowding levels of the computer and non-bank industries were relatively low[3] - The premium rate Z-score model is used to identify potential arbitrage opportunities in ETF products, but it also warns of potential pullback risks[4] Model Backtesting Results - The industry crowding monitoring model shows significant changes in the crowding levels of home appliances, machinery, and media industries[3] - The premium rate Z-score model identifies ETF products with potential arbitrage opportunities, such as the top three ETFs with the highest net inflows and outflows on a single day[5]

[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 ...

权 益 量 化 研 究 2025 年 11 月 30 日 价量匹配改善，情绪指标维持震荡 ——量化择时周报 20251130 相关研究 证券分析师 沈思逸 A0230521070001 shensy@swsresearch.com 邓虎 A0230520070003 denghu@swsresearch.com 联系人 沈思逸 A0230521070001 shensy@swsresearch.com | 1．情绪模型观点：市场情绪得分周内继续回落 4 | | --- | | 1.1 从分项指标出发：价量匹配改善、主力资金回流，情绪指标维持震 | | 荡、分化 5 | | 2．其他择时模型观点：美容护理短期得分快速提升，价值风 | | 格与小盘风格占优 10 | | 2.1 美容护理行业短期得分快速提升，价值风格与小盘风格占优 10 | | 3．风险提示 14 | 请务必仔细阅读正文之后的各项信息披露与声明 第2页 共15页 简单金融 成就梦想 证 券 研 究 报 告 请务必仔细阅读正文之后的各项信息披露与声明 本研究报告仅通过邮件提供给 中庚基金 使用。1 量 化 策 略 - ⚫ 市场情绪得分周内继续回落： ...