TPS 与 SPS 选股因子绩效月报 20250430 2025 年 05 月 07 日 证券研究报告·金融工程·金工定期报告 金工定期报告 20250507 证券分析师 凌志杰 执业证书：S0600525040007 lingzhj@dwzq.com.cn 相关研究 《成交价改进换手率因子》 2022-08-16 《 TPS 与 SPS 选股因子绩效月报 20250331》 2025-04-03 东吴证券研究所 1 / 9 请务必阅读正文之后的免责声明部分 [Table_Tag] 证券分析师 高子剑 执业证书：S0600518010001 021-60199793 gaozj@dwzq.com.cn [Table_Summary] ◼ TPS 因子多空对冲绩效（全市场）： 2006 年 1 月至 2025 年 4 月，TPS 因子在全体 A 股中，10 分组多空对冲的年化收益率为 39.50%，年化波 动为 15.79%，信息比率为 2.50，月度胜率为 77.39%，月度最大回撤为 18.19%。 ◼ SPS 因子多空对冲绩效（全市场）： 2006 年 1 月至 2025 年 4 月，SPS 因子在全体 ...

Group 1 - The core viewpoint of the news is the Chinese government's initiative to support the financial market through a series of policies aimed at stabilizing expectations and increasing capital supply in the insurance sector [1][2][4] - The National Financial Regulatory Administration plans to enhance the capital replenishment mechanism for large insurance groups, indicating that capital replenishment has become a priority [2][3] - The government aims to expand the long-term investment pilot program for insurance funds, with an additional 60 billion yuan planned to inject more capital into the market [4][5] Group 2 - The core tier capital adequacy ratios of major banks are expected to improve significantly due to the capital replenishment efforts, with specific increases noted for China Bank, China Construction Bank, Postal Savings Bank, and Bank of Communications [3][4] - The adjustment of risk factors for stock investments by insurance companies will be reduced by 10%, encouraging greater market participation [6][7] - The insurance industry is projected to have a total fund utilization balance of 33.26 trillion yuan in 2024, with potential for an additional 1.66 trillion yuan in market funds if the upper limit for equity asset allocation is fully utilized [6][7]

农产品期权 2025-05-07 | 期权品种 | 标的合约 | 最新价 | 涨跌 | 涨跌幅 | 成交量 | 量变化 | 持仓量 | 仓变化 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | | | | （%） | (万手) | | (万手) | | | 豆一 | A2507 | 4,226 | 51 | 1.22 | 14.91 | -0.50 | 15.86 | 0.51 | | 豆二 | B2506 | 3,368 | -4 | -0.12 | 2.23 | -0.72 | 8.74 | -0.28 | | 豆粕 | M2507 | 2,766 | -4 | -0.14 | 9.47 | -11.78 | 55.09 | 0.64 | | 菜籽粕 | RM2507 | 2,478 | 0 | 0.00 | 3.11 | -6.49 | 15.05 | -0.23 | | 棕榈油 | P2506 | 8,226 | -54 | -0.65 | 0.66 | 0.15 | 0.65 | 0.00 | | 豆油 | Y2507 | 7 ...

| 离任基金经理姓名 | 田汉卿 | | --- | --- | | 离任原因 | 退休 | | 离任日期 | 2025年04月28日 | | 转任本公司其他工作岗位 | | | 的说明 | | | 是否已按规定在中国基金 | 否 | | 业协会办理变更手续 | | | 是否已按规定在中国基金 | 是 | | 业协会办理注销手续 | | 履历资料显示，田汉卿曾在美国巴克莱全球投资管理有限公司担任投资经理，2012年8月加入华泰柏瑞基金。 | 华泰柏瑞量化创享混合A(010137) | | | | --- | --- | --- | | 单位净值(2025-04-29) | 累计净值 | | | 0.7504 -0.35% | 0.7504 | | | 近1月: - 6.35% | 近3月: -3.27% | 近6月: - 1.30% | | 近1年:6.71% | 近3年: - 7.78% | 成立来:-24.96% | | | | | | 类型:混合型-偏股 中高风险 | 规模:1.69亿元(2025-03-31) | 基金经理:凌若冰 | | 成 立 日 : 2020-12-30 | 管 理 人:华泰柏瑞 ...

中泰证券：调降险资股票投资风险因子 或将带动增量资金规模逾1300亿元 智通财经5月7日电，中泰证券非银金融首席分析师葛玉翔表示，自2023年9月金融监管总局发布《关于 优化保险公司偿付能力监管标准的通知》调整股票因子以来，在持续低利率环境倒逼下，保险资金持续 加大股票配置比例。"假设本次针对险资持有的沪深300股票风险因子下调10%，在50%沪深300成分股 配置假设下，考虑风险分散效应前静态释放最低资本为364亿元，如果全部增配沪深300，对应股市资金 达1349亿元。如果不增配股票，则改善行业偿付能力充足率幅度约1.4个百分点。"葛玉翔说。 (上证报) ...

Core Viewpoint - The recent policy changes by the National Financial Regulatory Administration aim to further promote insurance capital's entry into the stock market by reducing the risk factors associated with equity investments, which is expected to unlock significant additional capital for the insurance sector [1][2] Group 1: Policy Changes - On May 7, the head of the National Financial Regulatory Administration announced a 10% reduction in the risk factor for stock investments within the solvency rules, which is a significant consideration for insurance capital when making equity investments [1] - This policy is projected to lead to an increase in insurance capital of over 130 billion yuan, with estimates suggesting that if the risk factor for the CSI 300 stocks is lowered by 10%, the corresponding market funds could reach approximately 134.9 billion yuan [1] Group 2: Historical Context - The last adjustment to the risk factors for insurance capital equity investments occurred in September 2023, where the risk factor for investments in CSI 300 index stocks was reduced from 0.35 to 0.3, and for stocks listed on the Sci-Tech Innovation Board from 0.45 to 0.4 [2] - Since the September adjustment, insurance funds have been increasing their stock allocation due to a persistently low interest rate environment, with the proportion of stock and securities investment in life insurance companies reaching 12.5% by the end of Q4 2024, a slight increase from the previous year [2] Group 3: Industry Trends - Analysts believe that many insurance companies, particularly large listed firms, still have room to increase their equity investment ratios, especially after the recent regulatory increase in the upper limit for equity asset allocation from 10%-45% to 10%-50% [2]

证券研究报告·金融工程·金工定期报告 金工定期报告 20250507 估值异常因子绩效月报 20250430 2025 年 05 月 07 日 [Table_Tag] [Table_Summary] 报告要点 ◼ 估值偏离 EPD 因子多空对冲绩效（全市场）：2010 年 2 月至 2025 年 4 月，估值偏离 EPD 因子在全体 A 股（剔除北交所股票）中，5 分组多 空对冲的年化收益为 17.65%，年化波动为 10.02%，信息比率为 1.76， 月度胜率为 71.04%，月度最大回撤为 8.93%。 ◼ 缓慢偏离 EPDS 因子多空对冲绩效（全市场）：2010 年 2 月至 2025 年 4 月，缓慢偏离 EPDS 因子在全体 A 股（剔除北交所股票）中，5 分组 多空对冲的年化收益为 16.31%，年化波动为 5.73%，信息比率为 2.85， 月度胜率为 79.23%，月度最大回撤为 3.10%。 ◼ 估值异常 EPA 因子多空对冲绩效（全市场）：2010 年 2 月至 2025 年 4 月，估值异常 EPA 因子在全体 A 股（剔除北交所股票）中，5 分组多空 对冲的年化收益为 17.30%， ...

上证报记者 史丽 摄 保险资金长期投资试点近期拟再批复600亿元 险资股票投资风险因子进一步调降10% 金融监管总局局长李云泽5月7日在国务院新闻办公室举行的新闻发布会上表示，进一步扩大保险资金长 期投资的试点范围，近期拟再批复600亿元，为市场注入更多增量资金。 他还提到，将调整偿付能力的监管规则，将股票投资的风险因子进一步调降10%，鼓励保险公司加大入 市力度；推动完善长周期考核机制，调动机构的积极性，促进"长钱长投"。 李云泽表示，进一步扩大保险资金长期投资试点范围，为市场引入更多的增量资金；调整优化监管规 则，进一步调降保险公司股票投资风险因子，支持稳定和活跃资本市场。 前4个月银行业保险业通过贷款、债券等方式为实体经济新增融资约17万亿元 李云泽表示，前4个月，银行业保险业通过贷款、债券等多种方式为实体经济新增融资约17万亿元，无 还本续贷政策自去年9月以来累计为中小微企业续贷4.4万亿元，更好满足了企业接续融资的需求。 今年1~4月保险业赔付约1万亿元 李云泽表示，前4个月，短期出口信用险承保金额同比增长15.3%，为稳外贸提供了有力的基础；今年 1~4月，保险业赔付约1万亿元，新能源车险承保车辆 ...

Core Viewpoint - The article emphasizes the increasing importance of factor development and iteration in multi-factor models due to the declining returns from traditional factors and the challenges posed by factor crowding [1][3][62]. Factor Construction - The "Index Enhanced ETF Factor" is constructed using daily subscription and redemption data from index-enhanced ETFs, comparing the actual allocation weights of fund managers to the benchmark index weights to derive relative allocation (also known as "underweight") ratios [1][8]. - This process allows for the creation of signals based on fund managers' actual stock preferences, enhancing active management strategies [1][8]. Empirical Analysis - The constructed "Index Enhanced ETF Factor" shows a significant monotonic increase in returns across various indices (CSI 300, CSI 500, CSI 1000, and CSI 2000) during weekly backtesting, with notable excess returns for the top groups compared to the bottom groups [2][22]. - The factor's Information Coefficient (IC) performance is robust, with IC win rates of 62.42% for CSI 300, 64.33% for CSI 500, 72.32% for CSI 1000, and 60.00% for CSI 2000, indicating strong predictive power [2][40][43]. High-Frequency vs. Low-Frequency Data - High-frequency data offers advantages in factor development due to its larger volume and the ability to create diverse features through advanced techniques like machine learning, despite the challenges of noise and complexity [4][5][6]. - Low-frequency data, while more traditional, has limited incremental information, making it harder to extract significant alpha, thus necessitating innovative approaches to factor construction [6][62]. Strategy Explanation - The strategy involves tracking fund managers' preferences through the ETF's daily disclosure of holdings, allowing for the identification of stocks with higher expected returns based on their relative underweight status [8][62]. - The performance of index-enhanced ETFs has shown consistent outperformance against their benchmarks, validating the strategy's rationale [9][62]. Backtesting Results - The backtesting results indicate that the "Index Enhanced ETF Factor" has demonstrated significant cumulative returns across the four major indices, with a clear upward trend in group returns from low (G1) to high (G5) [22][62]. - The factor's IC values have shown a steady increase over time, particularly in the CSI 500 and CSI 1000 indices, highlighting its effectiveness in capturing excess returns [62][63]. Conclusion - The "Index Enhanced ETF Factor" effectively tracks fund managers' actual stock preferences, showing significant empirical validity in its ability to generate excess returns across various indices [62][63]. - The strategy is particularly well-suited for capturing structural opportunities in a rapidly changing market environment, outperforming traditional passive strategies [63].

点击小程序查看报告原文 本篇报告，我们参考海外文献的研究思路及方法，并结合自身实践经验， 从Alpha的纯粹度、置信度与异质性维度出发，探寻其底层逻辑原理，并依此提 出及检验选股能力Alpha的多种改进思路，力图提升其延续性及收益预测能力。 开具体讨论与分析。简单总结来看，在不同因子模型中纳入不同数目的潜在因子，绝大多数情形下能够提升截面回归的预测效果；同时，多头组合的相对 风险指标及风险调整后收益也得到同步改善，以FF3为例，纳入kl=1/2/3个潜在因子，信息比率将由截面回归的0.84增加至1.02/1.00/1.24。 图表：时序回归 vs 截面回归 vs 潜在因子Alpha的计算原理 图表：选股能力Alpha的提纯与改进思路框架 资料来源：中金公司研究部 传统时序回归Alpha有何特质？ 1） 历史数据来看，不同因子模型得到具备Alpha获取能力的权益基金数量占比并无明显差异，基本于40%-80%区间浮动， 若进而要求p值显著，比例将大幅降低； 2） 相较累积收益指标而言，具备更佳的延续性； 3） 长期来看，以其构建多头组合能够获取超于市场平均水平 的回报，但因其中杂糅其他并不能够真正刻画基金能力的成 ...