资金流未见明显结构切换， 建议适当控制仓位 请阅读最后一页免责声明及信息披露 http://www.cindasc.com 1 [Table_ReportTime] 2025 年 4 月 27 日 —— 量化市场追踪周报（2025W49） 证券研究报告 金工研究 [Table_ReportType] 金工定期报告 [Table_Author] 于明明 金融工程与金融产品 首席分析师 执业编号：S1500521070001 联系电话：+86 18616021459 邮 箱：yumingming@cindasc.com 吴彦锦 金融工程与金融产品 分析师 执业编号：S1500523090002 联系电话：+86 18616819227 邮 箱：wuyanjin@cindasc.com 周君睿 金融工程与金融产品 分析师 执业编号：S1500523110005 联系电话：+86 19821223545 邮 箱：zhoujunrui@cindasc.com [Table_Title] 量化市场追踪周报(2025W49)：资金流未见明显结 构切换，建议适当控制仓位 [Table_ReportDate] 2025 年 1 ...

市场震荡下行，私募规模创三年新高 ――公募基金 12 月月报 核心观点： 证券分析师 宋旸 songyang@bhzq.com 022-28451131 张笑晨 SAC NO：S1150525070001 zhangxc@bhzq.com 022-23839033 相关研究报告 ETF 简称迎统一规范，宽基 指数获得资金净流入—公募 基金周报 2025.11.24 跨境 ETF 持续净流入，"南向 ETF 通"名单扩容—公募基 金周报 2025.11.17 权 益 市场 主要 指数 多数 上 涨，债券类 ETF 规模迎来新 高—公募基金周报 2025.11.10 分析师：宋旸 SAC NO：S1150517100002 2025 年 12 月 3 日 通过对主动权益基金行业仓位的测算，11 月主动权益基金加仓幅度靠前的行业为家用 电器、有色金属和食品饮料；减仓幅度靠前的行业为国防军工、计算机和电子。同 时，我们对主动权益基金的整体仓位进行了测算，2025/11/28 的仓位是 81.96%，较上月 上升了 2.12pct.。 ⚫ 上月市场回顾： 11 月沪深市场主要指数震荡下跌，其中，科创 50 跌幅最大，下 ...

- The report does not contain any quantitative models or factors for analysis

Core Insights - Three stock ETFs have recently announced their listing, with varying stock positions: the Yinhua ChiNext ETF at 23.74%, the Fuguo SSE Sci-Tech 200 ETF at 24.83%, and the E Fund CSI A500 Enhanced Strategy ETF at 33.31% [1] - A total of 23 stock ETFs have announced listings since September, with an average position of 23.75%. The highest position is held by the E Fund SSE Sci-Tech Comprehensive Enhanced Strategy ETF at 69.33% [1] - ETF listings must meet the position requirements specified in the fund contract, and the time between the announcement and the official listing is typically a few trading days [1] Fund Statistics - The average number of shares raised for the newly announced ETFs is 580 million, with the largest being the Fuguo National Robot Industry ETF at 2.344 billion shares [2] - Institutional investors hold an average of 10.54% of the shares in these ETFs, with the highest being the Guolian An CSI A500 Dividend Low Volatility ETF at 98.93% [2] - The newly established stock ETFs have varying positions during their construction period, with the E Fund CSI A500 Enhanced Strategy ETF at 33.31% and the Fuguo SSE Sci-Tech 200 ETF at 24.83% [2][3]

Quantitative Models and Construction Methods 1. Model Name: Volume Model - **Construction Idea**: The model uses trading volume data to predict market trends. - **Construction Process**: The model analyzes the trading volume of various broad-based indices to determine market sentiment. It categorizes the indices as neutral based on the volume data. - **Evaluation**: The model is considered neutral for all broad-based indices in the short term.[2][11] 2. Model Name: Low Volatility Model - **Construction Idea**: This model uses the volatility of stock prices to predict market trends. - **Construction Process**: The model evaluates the volatility of stock prices and categorizes the indices as neutral. - **Evaluation**: The model is considered neutral in the short term.[2][11] 3. Model Name: Institutional Feature Model - **Construction Idea**: This model uses institutional trading data from the "Dragon and Tiger List" to predict market trends. - **Construction Process**: The model analyzes the trading behavior of institutions listed on the "Dragon and Tiger List" and categorizes the indices as bullish. - **Evaluation**: The model is considered bullish in the short term.[2][11] 4. Model Name: Feature Volume Model - **Construction Idea**: This model uses specific volume features to predict market trends. - **Construction Process**: The model analyzes specific volume features and categorizes the indices as bearish. - **Evaluation**: The model is considered bearish in the short term.[2][11] 5. Model Name: Smart Algorithm Model (CSI 300) - **Construction Idea**: This model uses smart algorithms to predict market trends for the CSI 300 index. - **Construction Process**: The model applies smart algorithms to the CSI 300 index and categorizes it as neutral. - **Evaluation**: The model is considered neutral in the short term.[2][11] 6. Model Name: Smart Algorithm Model (CSI 500) - **Construction Idea**: This model uses smart algorithms to predict market trends for the CSI 500 index. - **Construction Process**: The model applies smart algorithms to the CSI 500 index and categorizes it as bearish. - **Evaluation**: The model is considered bearish in the short term.[2][11] 7. Model Name: Limit Up/Down Model - **Construction Idea**: This model uses the occurrence of limit up and limit down events to predict market trends. - **Construction Process**: The model analyzes the frequency of limit up and limit down events and categorizes the indices as neutral. - **Evaluation**: The model is considered neutral in the medium term.[2][12] 8. Model Name: Calendar Effect Model - **Construction Idea**: This model uses calendar effects to predict market trends. - **Construction Process**: The model analyzes historical calendar effects and categorizes the indices as neutral. - **Evaluation**: The model is considered neutral in the medium term.[2][12] 9. Model Name: Long-term Momentum Model - **Construction Idea**: This model uses long-term momentum to predict market trends. - **Construction Process**: The model analyzes long-term momentum indicators and categorizes the indices as bullish. - **Evaluation**: The model is considered bullish in the long term.[2][13] 10. Model Name: Comprehensive Weapon V3 Model - **Construction Idea**: This model combines multiple factors to predict market trends. - **Construction Process**: The model integrates various factors and categorizes the indices as bearish. - **Evaluation**: The model is considered bearish in the long term.[2][14] 11. Model Name: Comprehensive National Certificate 2000 Model - **Construction Idea**: This model combines multiple factors to predict market trends for the National Certificate 2000 index. - **Construction Process**: The model integrates various factors and categorizes the indices as bearish. - **Evaluation**: The model is considered bearish in the long term.[2][14] 12. Model Name: Turnover Inverse Amplitude Model - **Construction Idea**: This model uses the inverse amplitude of turnover to predict market trends. - **Construction Process**: The model analyzes the inverse amplitude of turnover and categorizes the indices as bullish. - **Evaluation**: The model is considered bullish in the medium term.[2][15] Model Backtest Results - **Volume Model**: Neutral for all broad-based indices in the short term.[2][11] - **Low Volatility Model**: Neutral in the short term.[2][11] - **Institutional Feature Model**: Bullish in the short term.[2][11] - **Feature Volume Model**: Bearish in the short term.[2][11] - **Smart Algorithm Model (CSI 300)**: Neutral in the short term.[2][11] - **Smart Algorithm Model (CSI 500)**: Bearish in the short term.[2][11] - **Limit Up/Down Model**: Neutral in the medium term.[2][12] - **Calendar Effect Model**: Neutral in the medium term.[2][12] - **Long-term Momentum Model**: Bullish in the long term.[2][13] - **Comprehensive Weapon V3 Model**: Bearish in the long term.[2][14] - **Comprehensive National Certificate 2000 Model**: Bearish in the long term.[2][14] - **Turnover Inverse Amplitude Model**: Bullish in the medium term.[2][15]

- The report does not include any quantitative models or factors related to construction, testing, or evaluation[2][3][4]

Core Viewpoint - The AI Fund Dachen State-Owned Enterprise Reform Flexible Allocation Mixed A (002258) reported a profit of 102 million yuan for the first half of 2025, with a weighted average profit per fund share of 0.2977 yuan and a net value growth rate of 9.75% [2] Fund Performance - As of September 3, the fund's scale was 1 billion yuan, with a unit net value of 3.995 yuan [2][33] - The fund's one-year cumulative net value growth rate was 33.26%, ranking 30 out of 80 comparable funds [5] - The fund's three-month and six-month cumulative net value growth rates were 21.65% and 21.06%, ranking 34 out of 82 and 33 out of 82 respectively [5] Valuation Metrics - As of June 30, 2025, the fund's weighted average price-to-earnings (P/E) ratio was approximately 15.4 times, higher than the comparable average of -1056.23 times [11] - The weighted average price-to-book (P/B) ratio was about 2.08 times, compared to the comparable average of 1.55 times [11] - The weighted average price-to-sales (P/S) ratio was approximately 1.36 times, exceeding the comparable average of 1.15 times [11] Growth Metrics - For the first half of 2025, the fund's weighted average revenue growth rate was 0.07%, and the weighted average net profit growth rate was 0.23% [19] - The weighted annualized return on equity was 0.14% [19] Risk and Return Metrics - The fund's three-year Sharpe ratio was 0.3762, ranking 17 out of 57 comparable funds [26] - The maximum drawdown over the past three years was 28.35%, with the highest quarterly drawdown occurring in Q1 2022 at 21.18% [28] Fund Composition - As of June 30, 2025, the fund had a total of 66,500 holders, with individual investors holding 97.67% of the shares [36] - The fund's turnover rate for the last six months was approximately 99.57%, consistently below the comparable average for three years [39] - The fund's top ten holdings included companies such as Shandong Gold, Sailun Tire, and Zijin Mining, with a concentration exceeding 60% for the past two years [42]

Core Viewpoint - The AI Fund Zhongjia Reform Dividend Mixed Fund (001537) reported a profit of 571,500 yuan for the first half of 2025, with a weighted average profit per fund share of 0.0134 yuan. The fund's net value growth rate was 1.45%, and the fund size reached 39.39 million yuan by the end of the first half of the year [3]. Fund Performance - As of September 2, the fund's net value growth rates were 24.82% over the past three months, 22.22% over the past six months, 41.75% over the past year, and -10.83% over the past three years, ranking 279/880, 286/880, 399/880, and 696/872 among comparable funds respectively [6]. - The fund's recent six-month turnover rate was approximately 1706.22%, consistently exceeding the average of comparable funds for five years [38]. Valuation Metrics - As of June 30, 2025, the fund's weighted average price-to-earnings (P/E) ratio was approximately 40.28 times, compared to the industry average of 15.75 times. The weighted average price-to-book (P/B) ratio was about 2.59 times, slightly above the industry average of 2.52 times. The weighted average price-to-sales (P/S) ratio was around 2.23 times, compared to the industry average of 2.16 times, indicating higher valuations than peers [11]. Growth Metrics - For the first half of 2025, the fund's weighted average revenue growth rate was 0.05%, and the weighted average net profit growth rate was 0.06%, with a weighted annualized return on equity of 0.06% [18]. Fund Composition - As of June 30, 2025, the fund held a total of 3,387 investors, with a total of 42.38 million shares held. Institutional investors accounted for 80.39% of the holdings, while individual investors made up 19.61% [35]. - The top ten holdings of the fund included companies such as Zhongji Xuchuang, Youyou Food, Huayou Cobalt, and others [40].

Group 1 - The active equity fund positions have surpassed 90%, reaching the highest level since March 2021 [1] - The average position of ordinary stock funds is approximately 91.94%, an increase of 1.16 percentage points from the previous week [1] - The average position of equity-mixed funds is around 90.39%, rising by 1.53 percentage points [1] Group 2 - Funds have increased their positions in sectors such as telecommunications, non-ferrous metals, real estate, electronics, and food and beverage [1] - Conversely, funds have reduced their positions in the automotive, computer, and basic chemicals sectors [1]

1. Report Industry Investment Rating - Not provided in the content 2. Core Viewpoints of the Report - The A-share market showed a continuous upward trend this week, with the Shanghai Composite Index stable above 3,600 points. Although the weekly average daily trading volume decreased by 6.26% compared to last week, the margin trading balance exceeded 2 trillion and continued to rise, indicating that investors' risk appetite remained relatively high in the short term [1][10]. - Most industry sectors' trading volume proportions reached new lows in the past four weeks, suggesting that the market trading focus was concentrating on a small number of sectors. Investors should pay attention to the congestion risk of industry sectors and focus on capital flows in the market with rapid rotation of industry themes [10]. - In terms of market style, small-cap stocks had significant excess returns. The cyclical style led the gains among the five major CITIC style indices, while the consumer style had the smallest increase [12]. - It is recommended to focus on three main investment lines: the domestic computing power industry chain, the AI application end, and the consumption recovery sector. These sectors have relatively reasonable valuations and strong potential for supplementary growth under the background of loose liquidity [13]. 3. Summary According to Relevant Catalogs 3.1 This Week's Market Review 3.1.1 Industry Index - This week, sectors such as non-ferrous metals, machinery, and national defense and military industry led the gains. The pharmaceutical sector, which had performed well last week, corrected significantly, while the coal and non-ferrous metals sectors, which had large declines last week, rebounded sharply [10]. - The trading volume proportions of most industry sectors reached new lows in the past four weeks, and the trading activity of the comprehensive finance and non-bank finance sectors decreased significantly [10]. 3.1.2 Market Style - All five major CITIC style indices rose this week, with the cyclical style leading the gains at 3.49%. The growth style rose 1.87%, and its trading volume proportion reached a four-week high. The consumer style had the smallest increase at 0.77%, and its trading volume proportion decreased slightly [12]. - Small-cap stocks had significant excess returns. The CSI 1000 and CSI 2000 rose 2.51% and 3.54% respectively, and their trading volume proportions reached four-week highs [12]. 3.2 Active Equity Funds 3.2.1 Funds with Excellent Performance This Week in Different Theme Tracks - The report selected single-track and double-track funds based on six sectors: TMT, finance and real estate, consumption, medicine, manufacturing, and cyclical sectors, and listed the top five funds in each sector [17][18]. 3.2.2 Funds with Excellent Performance in Different Strategy Categories - The report classified funds into different types such as deep undervaluation, high growth, high quality, quality growth, quality undervaluation, GARP, and balanced cost-effectiveness, and listed the top-ranked funds in each type [19][20] 3.3 Index Enhanced Funds 3.3.1 This Week's Excess Return Distribution of Index Enhanced Funds - The average and median excess returns of CSI 300 index enhanced funds were 0.22% and 0.20% respectively; those of CSI 500 index enhanced funds were 0.05% and 0.07% respectively; those of CSI 1000 index enhanced funds were -0.15% and -0.14% respectively; those of CSI 2000 index enhanced funds were -0.09% and 0.04% respectively; those of CSI A500 index enhanced funds were 0.24% and 0.26% respectively; those of ChiNext index enhanced funds were 0.45% and 0.39% respectively; and those of STAR Market and ChiNext 50 index enhanced funds were 0.18% and 0.21% respectively [23][24]. - The average and median absolute returns of neutral hedge funds were 0.29% and 0.27% respectively; those of quantitative long funds were 1.75% and 1.83% respectively [24]. 3.4 This Issue's Bond Fund Selection - The report comprehensively screened the fund pools of medium- and long-term bond funds and short-term bond funds based on indicators such as fund scale, return-risk indicators, the latest fund scale, Wind fund secondary classification, rolling returns in the past three years, and maximum drawdowns in the past three years [38] 3.5 This Week's High-Frequency Position Detection of Funds - Active equity funds significantly increased their positions in the machinery and computer industries this week and significantly reduced their positions in the electronics, banking, and automobile industries [3]. - From a one-month perspective, the positions in the communication, banking, and non-bank finance industries increased significantly, while the position in the food and beverage industry decreased significantly [3] 3.6 This Week's Weekly Tracking of US Dollar Bond Funds - Not provided in the content