Core Insights - Morgan Fund announced the establishment of the Morgan CSI A500 Enhanced Strategy ETF (stock code: 563550) on May 8, with a total scale of 1.016 billion yuan and 7,765 effective subscription accounts, marking the largest initial fundraising scale for an enhanced index ETF in the past two years [1][2] - The fund aims to provide investors with a new option for allocating to core Chinese assets, reflecting the company's strong confidence in the product [1][2] - The Morgan CSI A500 Enhanced Strategy ETF is the first of its kind in the market, completing its fundraising in just 8 trading days and being the first to establish among its peers [2] Fund Characteristics - The ETF is based on the CSI A500 index, which represents a new generation of A-share benchmark indices and has a long-term allocation value [2] - As of April 30, 2025, the CSI A500 index has achieved a cumulative return of 343.21% since its base date of December 31, 2004, with an annualized return of 7.83% [2] - The index's broad sample domain, balanced industry distribution, and inclusion of leading companies in emerging industries make it an ideal candidate for enhanced strategies [2] Product Line Expansion - The Morgan CSI A500 Enhanced Strategy ETF is the first enhanced strategy ETF under Morgan Fund, following the Morgan CSI A50 ETF and Morgan CSI A500 ETF, further enriching the "A series" product line [3] - Morgan Asset Management's recent achievements include the Morgan S&P Hong Kong Stock Connect Low Volatility Dividend ETF surpassing 10 billion yuan in scale, becoming the first cross-border strategy ETF to exceed this threshold [3] - The company aims to provide distinctive ETF products and differentiated investment experiences, leveraging international perspectives and local practices [3]

从微观出发的五维行业轮动月度跟踪 202505 2025 年 05 月 06 日 证券研究报告·金融工程·金工定期报告 金工定期报告 20250506 [Table_Tag] [Table_Summary] 报告要点 证券分析师 高子剑 执业证书：S0600518010001 021-60199793 gaozj@dwzq.com.cn 证券分析师 凌志杰 执业证书：S0600525040007 lingzhj@dwzq.com.cn 相关研究 《从微观出发的五维行业轮动 月度跟踪 202504》 2025-03-31 东吴证券研究所 1 / 9 金工定期报告 内容目录 | 1. 五维行业轮动模型简介 | | --- | | 2. 五维行业轮动模型绩效跟踪 | | 2.1. 五维行业轮动模型评分 | | 2.2. 五维行业轮动模型回测绩效表现 | | 3. 五维行业轮动模型持仓跟踪 | | 4. 五维行业轮动模型的指数增强策略 . | | 5. 风险提示 . | 2 / 9 东吴证券研究所 请务必阅读正文之后的免责声明部分 金工定期报告 图表目录 请务必阅读正文之后的免责声明部分 ◼ 模型多空对冲绩效：以 2 ...

2025年4月23日，A股三大指数红盘震荡，中证A500指数午后持续走强，成分股卫星化学涨停，新易 盛、杰瑞股份、巨星科技、派能科技等个股涨幅居前。 摩根中证A500增强策略ETF正式发行，是完善摩根"A系列"产品版图的重要一环，摩根资产管理中国指 数及量化投资团队将围绕中证"A系列"指数，打造从被动跟踪到量化增强的产品链条，为投资者提供多 样化的选择，塑造更强的摩根"A系列"。 公告显示，摩根资产管理旗下摩根中证A500增强策略ETF（认购代码：563553）于4月21日起正式发 行，募集期为2025年4月21日至4月30日，投资者可选择网上现金认购、网下现金认购2种方式。 中信证券认为，我国的新一轮内需刺激或打开宏观向上的弹性空间，这种宏观环境更有利于核心资产。 从相对盈利优势来看，核心资产的ROE、营收增速和净利润率都已经提前于全A非金融板块出现拐点， 展现出极强的经营韧性。从长线资金定价看，今年以来不少A股的蓝筹公司赴港上市，全球机构投资者 对这些优质公司的追捧可能会给国内投资者以示范，有利于重塑优质核心资产的估值体系。 招商证券认为,当前市场在"平准"力量的作用下行风险可控，在市场明显缩量震荡且业绩 ...

登录新浪财经APP 搜索【信披】查看更多考评等级 重要提示 1、景顺长城沪深300增强策略交易型开放式指数证券投资基金（以下简称"本基金"）的募集已获中国证 监会证监许可[2025]92号文准予募集注册。 2、本基金是交易型开放式指数证券投资基金。 3、本基金的基金管理人为景顺长城基金管理有限公司（以下简称"本公司"），基金托管人为兴业银行 股份有限公司，登记机构为中国证券登记结算有限责任公司。 4、本基金的募集对象为符合法律法规规定的可投资于证券投资基金的个人投资者、机构投资者、合格 境外投资者以及法律法规或中国证监会允许购买证券投资基金的其他投资人。 5、本基金自2025年5月12日至2025年5月23日进行发售。投资者可选择网上现金和网下现金2种方式认购 本基金。网上现金认购的日期为2025年5月12日至2025年5月23日；网下现金认购的日期为2025年5月12 日至2025年5月23日。如深圳证券交易所对网上现金认购时间做出调整，基金管理人将做出相应调整并 及时公告。 基金管理人可根据基金销售情况在募集期限内适当延长或缩短基金发售时间（包括一种或多种发售方式 的发售时间），并及时公告。投资人认购应提 ...

Market Review - The A-share market showed a mixed performance last week, with the Shanghai Composite Index and CSI 300 Index gaining 1.19% and 0.59% respectively, while the ChiNext Index fell by 0.31% [6][14] - The banking, real estate, and coal sectors performed well, with returns of 4.23%, 3.78%, and 2.62% respectively, while the defense, agriculture, and computer sectors lagged behind with returns of -2.73%, -2.03%, and -0.98% [21][22] - The central bank's net reverse repurchase was 333.8 billion yuan, with a total market liquidity injection of 808 billion yuan [23] Fund Performance - Last week, the performance of active equity, flexible allocation, and balanced mixed funds was -0.04%, 0.00%, and 0.00% respectively [33] - Alternative funds showed the best performance this year, with a median return of 7.15%, while active equity and flexible allocation funds had median returns of -1.17% and -1.10% [36][39] Fund Issuance - A total of 25 new funds were established last week, with a total issuance scale of 20.476 billion yuan, an increase from the previous week [44] - The majority of new funds were passive index funds (12 funds) and medium to long-term pure bond funds (5 funds), with issuance scales of 4.572 billion yuan and 7.221 billion yuan respectively [45] ETF Market - The total scale of ETFs in the market has surpassed 4 trillion yuan, with 1,135 products available, indicating strong growth and increasing investor interest [8] Personal Pension Funds - As of March 31, 2025, there are 288 personal pension funds and 52 sales institutions, with a slight increase in the number of funds compared to the previous year [11]

- The report discusses the application of stock pair trading ideas in factor strategies, specifically focusing on reversal factors which have historically shown strong performance[1] - Traditional reversal factors include "N-month price reversal," "highest price length," and "volume ratio," which capture the trend that stocks with low past returns tend to perform better in the future and vice versa[1][2] - The report introduces a pair reversal factor that captures reversal opportunities between individual stocks within the same industry, differing from traditional pair trading by using periodic closing instead of stop-loss conditions[2][3] - The pair reversal factor is tested using a hedging strategy with a monthly rebalancing frequency, using the CSI 800 index constituents as the stock pool, and achieving an annualized excess return of 8% from 2007 to 2016[3][4] - The pair reversal factor is also applied to enhance multi-factor portfolios with weekly rebalancing, showing improved returns even after considering transaction costs, with a benchmark multi-factor portfolio return of 424.40% and a pair rebalancing portfolio return of 501.59% during the sample period from 2007 to 2016[4][5] Quantitative Models and Construction Methods 1. **Model Name**: Pair Reversal Factor - **Construction Idea**: Capture reversal opportunities between individual stocks within the same industry, similar to pair trading but with periodic closing instead of stop-loss conditions[2][3] - **Construction Process**: 1. Perform cointegration regression on the log prices of two assets to check for cointegration relationship[43][44] 2. Calculate the spread and standard deviation of the spread during the learning period[45][46] 3. Use the spread and standard deviation to determine the opening threshold and execute trades accordingly[46][49] 4. Rebalance the portfolio monthly by closing all positions and reopening new ones based on the updated spread and standard deviation[51][53] - **Evaluation**: The pair reversal factor effectively captures stock price reversals and mean reversion of price spreads, providing significant excess returns at the individual stock level[69] Model Backtest Results 1. **Pair Reversal Factor**: - **Annualized Return**: 31.17% (2007), 50.85% (2008), 51.19% (2009), 21.39% (2010), 14.26% (2011), 14.75% (2012), 25.75% (2013), 9.10% (2014), 59.01% (2015), 17.05% (2016), 1246.06% (full sample)[63] - **Maximum Drawdown**: 4.44% (2007), 4.62% (2008), 4.61% (2009), 2.97% (2010), 2.64% (2011), 2.23% (2012), 2.57% (2013), 4.99% (2014), 5.48% (2015), 4.07% (2016), 5.48% (full sample)[63] - **Win Rate**: 58.38% (2007), 60.57% (2008), 59.02% (2009), 58.26% (2010), 58.20% (2011), 59.66% (2012), 59.66% (2013), 51.02% (2014), 59.84% (2015), 59.43% (2016), 58.27% (full sample)[63] Quantitative Factors and Construction Methods 1. **Factor Name**: N-month Price Reversal - **Construction Idea**: Measure the price change over a fixed time window to capture the reversal effect[30][33] - **Construction Process**: 1. Calculate the price change over the past N months: $(\text{Current Price} - \text{Price N months ago}) / \text{Price N months ago}$[33] - **Evaluation**: Reversal factors have shown strong performance in historical studies, with high IC values and good performance in various metrics such as LS return, LS win rate, LS IR, IC IR, and IC P[33][35] Factor Backtest Results 1. **N-month Price Reversal**: - **IC**: -5.72% (1-month), -4.75% (3-month), -4.10% (6-month), -3.55% (12-month)[35] - **LS Return**: 21.84% (1-month), 20.33% (3-month), 18.13% (6-month), 17.66% (12-month)[35] - **LS Win Rate**: 64.41% (1-month), 59.32% (3-month), 56.78% (6-month), 61.02% (12-month)[35] - **LS IR**: 0.99 (1-month), 0.81 (3-month), 0.77 (6-month), 0.83 (12-month)[35] - **IC IR**: 0.72 (1-month), 0.92 (3-month), 0.78 (6-month), 0.83 (12-month)[35] - **IC P**: 0.0% (1-month), 0.2% (3-month), 0.5% (6-month), 1.1% (12-month)[35]