- The report introduces the **RSI (Relative Strength Index)** as a quantitative factor, constructed to measure market conditions by comparing average gains and losses over a 12-day period. The formula is: $ RSI = 100 - 100 / (1 + RS) $ where RS represents the ratio of average gains to average losses over the specified period. RSI values above 70 indicate overbought conditions, while values below 30 suggest oversold conditions [2] - Another quantitative factor mentioned is **Net Purchase Amount (NETBUY)**, which calculates the net inflow or outflow of funds for ETFs. The formula is: $ NETBUY(T) = NAV(T) - NAV(T-1) * (1 + R(T)) $ where NAV(T) is the net asset value of the ETF on day T, NAV(T-1) is the net asset value on the previous day, and R(T) is the return on day T [2] - The report also tracks **Institutional Holdings Ratio**, which estimates the proportion of ETF shares held by institutions based on the latest annual or semi-annual reports. This excludes holdings by linked funds and may involve approximations due to data limitations [3] - The report provides **daily intra-day price trends** for ETFs using 5-minute interval data, highlighting the highest and lowest prices of the day with red markers. However, some data gaps may exist due to limitations in the source [2] - The report categorizes ETFs into "Broad-based" and "Thematic" groups based on the indices they track, such as major indices like CSI 300, CSI 500, and industry-specific indices like Non-bank Financials and Red Chips [2] - The report includes a detailed table of ETF performance metrics, such as RSI values, net purchase amounts, trading volumes, management fees, institutional holdings ratios, and T+0 trading availability. For example, the RSI for CSI 300 ETFs ranges from 68.14 to 75.77, while institutional holdings ratios vary significantly across ETFs [4] - The report highlights thematic ETFs such as **Consumption Electronics**, **Non-bank Financials**, **Renewable Energy**, **Semiconductors**, and **Artificial Intelligence**, providing metrics like RSI, net purchase amounts, and institutional holdings ratios. For instance, the RSI for Consumption Electronics ETFs ranges from 59.68 to 60.40, while institutional holdings ratios range from 23.10% to 58.47% [6] - The report also includes performance metrics for international ETFs tracking indices like the **Hang Seng**, **Nikkei 225**, **Nasdaq 100**, and **S&P 500**, with RSI values ranging from 46.50 to 72.83 and institutional holdings ratios varying widely [4][6]

- The report uses Mahalanobis distance to measure industry crowding based on weekly price and transaction volume changes[3][17][18] - The construction process involves identifying industries where price and transaction volume deviate significantly, with industries outside the ellipse in quadrant 1 indicating short-term significant crowding[17] - Last week, the building materials industry showed significant trading crowding[18]

Group 1: Report Overview - The report focuses on the performance comparison benchmarks of US active funds, aiming to provide insights for China's public fund market after the release of the "Action Plan for Promoting the High - quality Development of Public Funds" [2] Group 2: Investment Rating - Not provided in the report Group 3: Core Views - The US has established requirements for performance comparison benchmarks with broad - based indices as the main and narrow - based indices as supplementary. The CFA Institute also offers benchmark - setting guidelines [4][9] - US active funds mainly use single - index benchmarks. Stock - type funds use S&P 500 as a single - benchmark index; multi - benchmark funds prefer broad - based and narrow - based index combinations. Hybrid funds often use composite benchmarks, and bond - type funds have concentrated single - benchmarks and diverse multi - benchmarks [4][22] - US stock - type funds with S&P 500 as the benchmark have higher correlation, lower tracking error, and a lower proportion of significantly underperforming the benchmark compared to Chinese ordinary stock - type funds with CSI 300 as the main benchmark [5][53] - Capital Group and Fidelity, two leading active equity fund companies, have different benchmark - setting characteristics. Capital Group mainly uses single - benchmarks, while Fidelity has a more balanced distribution of single - and multi - benchmarks [61] Group 4: Summary by Directory 1. US Active Fund Performance Comparison Benchmark Overview - **Performance Comparison Benchmark Policy**: Since 1993, the SEC has required funds to compare their total returns with the total returns of appropriate broad - based indices, and also encourages the use of narrow - based indices. In 2022, the definition of broad - based indices was revised. The CFA Institute also provides benchmark - setting guidelines [9][10][13] - **US Active Fund Classification**: According to SEC naming rules, 80% of a fund's assets should be invested in line with its name. The ICI classifies mutual funds into major asset categories. As of April 2025, the US mutual fund market was worth $27.97 trillion, with stock - type funds being the largest in scale [15][16] - **US Active Fund Performance Comparison Benchmark Type Distribution**: Among 4938 US active mutual funds, 56.3% are stock - type funds and 32.4% are bond - type funds as of March 17, 2025. 63.4% of funds use single - benchmarks, 31.6% use multi - benchmarks, and 5.0% use composite benchmarks [19][22] 2. Stock - Type Fund Benchmark Analysis - **Single Benchmark**: Single - benchmark stock - type funds have high index concentration and diverse index selection, mainly using S&P 500. Among 1848 single - benchmark stock - type funds, S&P 500 is used 320 times [26] - **Multi - Benchmark**: Multi - benchmark stock - type funds often use broad - based and narrow - based index combinations. 846 out of 913 multi - benchmark stock - type funds use 2 indices as benchmarks. Large - scale multi - benchmark stock - type funds mainly use broad - based and style indices [30][35] 3. Hybrid Fund Benchmark Analysis - Among 239 hybrid funds, 122 use composite benchmarks, mostly composed of 2 indices. The equity index weight in composite benchmarks ranges from 5% to 85%. The most commonly used combination is S&P 500*60% + Bloomberg US Aggregate*40% [37][40] 4. Bond - Type Fund Benchmark Analysis - **Single Benchmark**: Bloomberg US Aggregate and Bloomberg Municipal are the most commonly used single - benchmarks for bond - type funds, with high benchmark concentration [46] - **Multi - Benchmark**: Multi - benchmark bond - type funds have diverse benchmark combinations, reflecting investment characteristics in regions, bond types, durations, and credit ratings. Large - scale multi - benchmark bond funds use diverse benchmark combinations [48][50] 5. US Active Fund Return vs Benchmark Comparison - **Correlation and Tracking Error Analysis**: The average correlation coefficient between US stock - type funds with S&P 500 as the benchmark and S&P 500 in the past three years is 0.91, higher than that of Chinese ordinary stock - type funds with CSI 300 as the main benchmark. The tracking error of US funds is also lower [53][54] - **Excess Return Analysis**: Less than 10% of US single - benchmark stock - type funds with S&P 500 as the benchmark significantly underperformed the benchmark in the past three years, a lower proportion compared to Chinese stock - type funds with CSI 300 as the main benchmark [59] 6. Benchmark Setting of Leading Active Equity Fund Companies - **Capital Group**: As of October 3, 2024, it had 94 products with a total management scale of $2.4 trillion. Stock - type funds accounted for 67% of the scale. The company mainly uses S&P 500 or MSCI ACWI as single - benchmarks [64][68] - **Fidelity**: As of October 4, 2024, its management scale was $2.95 trillion, with similar active and passive product scales. Stock - type funds accounted for 79% of the scale. Single - and multi - benchmark funds are evenly distributed, with single - benchmark funds mainly using S&P 500 and multi - benchmark funds using broad - based and industry/style index combinations [73][76] 7. Summary - The report introduces US active fund performance comparison benchmark policies and industry guidelines, and analyzes current benchmark - selection characteristics. US active funds mainly use single - index benchmarks, and different types of funds have different benchmark - selection preferences [84][85] - US stock - type funds with S&P 500 as the benchmark have better performance in terms of correlation, tracking error, and excess return compared to Chinese stock - type funds with CSI 300 as the main benchmark [86] - Capital Group and Fidelity have different benchmark - setting characteristics, and both show certain abilities to obtain excess returns [87]

Quantitative Factors and Construction Methods 1. Factor Name: RSI (Relative Strength Index) - **Factor Construction Idea**: RSI measures the relative strength of price movements over a specific period to identify overbought or oversold market conditions[2] - **Factor Construction Process**: - The formula for RSI is: $ RSI = 100 - \frac{100}{1 + RS} $ where $ RS $ is the ratio of the average gain to the average loss over a 12-day period[2] - RSI > 70 indicates an overbought market, while RSI < 30 indicates an oversold market[2] - **Factor Evaluation**: RSI is a widely used technical indicator for short-term market sentiment analysis[2] 2. Factor Name: Net Subscription (NETBUY) - **Factor Construction Idea**: This factor calculates the net subscription amount of ETFs to gauge investor demand[2] - **Factor Construction Process**: - The formula for NETBUY is: $ NETBUY(T) = NAV(T) - NAV(T-1) \times (1 + R(T)) $ where $ NAV(T) $ is the net asset value on day T, $ NAV(T-1) $ is the net asset value on the previous day, and $ R(T) $ is the return on day T[2] - **Factor Evaluation**: This factor provides insights into the capital flow dynamics of ETFs, reflecting investor sentiment and market positioning[2] --- Factor Backtesting Results 1. RSI Factor - **HS300 ETFs**: RSI values range from 60.24 to 70.30, with most ETFs hovering around the overbought threshold of 70[4] - **CSI500 ETFs**: RSI values range from 66.04 to 66.37, indicating moderate strength[4] - **CSI800 ETFs**: RSI values range from 69.54 to 71.77, with some ETFs entering overbought territory[4] - **CSI1000 ETFs**: RSI values range from 63.76 to 64.97, suggesting neutral to slightly strong market conditions[4] - **Thematic ETFs**: RSI values vary significantly, with some themes like "New Energy" reaching as high as 74.71, while others like "Semiconductors" remain around 50[6] 2. Net Subscription Factor - **HS300 ETFs**: Net subscription values range from -6.69 billion to 1.51 billion, indicating mixed investor sentiment[4] - **CSI500 ETFs**: Net subscription values range from -2.65 billion to 0.54 billion, reflecting weak demand[4] - **CSI800 ETFs**: Net subscription values range from 0.00 billion to 0.45 billion, showing stable capital flows[4] - **CSI1000 ETFs**: Net subscription values range from -2.69 billion to 0.55 billion, indicating varied investor interest[4] - **Thematic ETFs**: Net subscription values vary widely, with some themes like "Semiconductors" showing strong inflows (up to 6.28 billion), while others like "Consumer Electronics" exhibit moderate inflows (up to 1.15 billion)[6]

Group 1: Equity Market Performance - The domestic equity market showed strong performance last week, with major indices rising over 3% and daily trading volume increasing to around 1.5 trillion yuan [1] - The Shanghai Composite Index reached a new high for the year, surpassing the 3400-point mark, which is a significant resistance level [1] - Financial, computer, and military industries performed well, while the oil and petrochemical sectors saw declines due to falling oil prices [1] Group 2: Macroeconomic Observations - Despite significant tariff impacts and a slowdown in the domestic real estate market since April, the overall macroeconomic environment remains stable, supported by a strong industrial chain and manufacturing capabilities [1] - The policy of replacing old consumer goods has also contributed positively to the macroeconomic performance [1] Group 3: Bond Market Analysis - The bond market exhibited a fluctuating pattern last week, influenced by seasonal factors and the stock-bond relationship [2] - The strong performance of the stock market initially suppressed bond market performance, but increased liquidity from the central bank and insurance demand supported the bond market [2] - By the end of the week, the yields on 10-year government bonds and 30-year government bonds rose by 1 basis point, reaching 1.65% and 1.85% respectively [2]

证券研究报告 麦高证券 2025 年 6 月 25 日 麦高金工团队 (2025-06-24) 数据说明： 1、本表针对ETF各类日频数据进行每日跟踪，不构成投资建议。 2、本表根据ETF追踪指数类别进一步分为"宽基"/"主题"两个子表。其中"宽基" ETF跟踪指数为沪深300、中证500、中证A500等主流宽基指数； "主题" ETF 跟踪指数为非银、红利、中概互联等某行业/风格指数。 3、基金池构建：在每个类型中选取规模较大的一只或几只ETF基金进行分析。 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 | 行业主题ETF | 华夏国证消费电子主题ETF | 否 | 159732.SZ | 17.92 | 1.54 | 59.25 | 0.22 | 0.89 | 0.50 | 58.47 | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 消费电 ...

Core Viewpoint - The article introduces a comprehensive index map that includes various commonly used stock indices, their codes, selection rules, industry distribution, and average and median market capitalizations of constituent stocks, which will be regularly updated for easy reference [1][2]. Group 1: Types of Indices - The index map includes several categories of stock indices: broad-based indices, strategy indices, industry indices, thematic indices, and overseas indices [4][5]. Group 2: Specific Indices Overview - The Shanghai Stock Exchange 50 Index (000016.SH) consists of 50 large-cap, liquid stocks from the Shanghai A-share market, reflecting the performance of influential leading companies [5]. - The Shenzhen Component Index (399001.SZ) includes 500 large-cap, liquid companies from the Shenzhen Stock Exchange, representing the overall market performance [5]. - The ChiNext Index (399006.SZ) is composed of 100 representative companies from the ChiNext board, reflecting the market tier of innovative and growth-oriented enterprises [5]. - The MSCI A50 Index (746059.MI) selects the largest 50 stocks from the Shanghai and Shenzhen markets that are included in the Stock Connect programs [5]. - The Hang Seng Index (HSI.HI) measures the performance of the largest and most liquid companies listed in Hong Kong [5]. Group 3: Dividend Indices - The China Securities Dividend Index (000922.CSI) reflects the overall performance of high dividend yield companies in the Shanghai and Shenzhen markets [5]. - The Shanghai Dividend Index (000015.SH) aims to represent high dividend yield companies in the Shanghai market [5]. - The Hong Kong Stock Connect High Dividend Index (950090.CSI) tracks high dividend ordinary shares listed in mainland China [5]. Group 4: Performance Metrics - The article provides various performance metrics for different indices, including market capitalization and liquidity measures, which are essential for investors to assess the indices' effectiveness [5][6].

Core Viewpoint - The article emphasizes that asset allocation is a personal expression of one's understanding of the world, risk perception, and life goals, rather than merely a numerical game [2][4][5]. Group 1: Investment Tools and Strategies - Investors can utilize three main investment tools: asset allocation, stock selection, and market timing, with asset allocation accounting for over 90% of the volatility in institutional portfolio returns [2]. - Different investment strategies reflect individual preferences and risk tolerance, leading to diverse asset allocation choices among investors [3][4]. Group 2: Personalization of Investment Portfolios - Investment portfolios serve as a reflection of an individual's values and future aspirations, with choices influenced by personal experiences and environmental factors [3][5]. - The article highlights that there is no absolute right or wrong in investment choices, as they are based on different cognitive frameworks and risk perceptions [4]. Group 3: The Role of External Opinions - Investors often face pressure from external opinions, which can lead to unnecessary adjustments in their portfolios; successful investing relies on deep insights and steadfast beliefs rather than frequent trading [3]. - The article suggests that maintaining a clear understanding of one's investment philosophy can reduce the impact of external criticism [5]. Group 4: The Snowball Three-Factor Method - The Snowball Three-Factor Method promotes long-term investment through diversification across assets, markets, and timing, aiming for diversified sources of returns and risk mitigation [6].

Group 1 - The S&P 500 ETF (159612) led the gains with an increase of 2.46% [1] - The Communication ETF (515880) rose by 1.58% [1] - The Energy ETF (159930) saw an increase of 1.44% [1] Group 2 - The Education ETF (513360) was the biggest loser, declining by 1.98% [1] - The Innovation ETF (159538) fell by 1.47% [1] - The Agriculture and Animal Husbandry ETF (159616) decreased by 1.36% [1] Group 3 - The market is experiencing adjustments, suggesting that broad-based indices may be a good option for bottom-fishing [1]

市场调整！抄底就选宽基指数>> ETF午间收盘涨跌不一，标普500ETF(159612)领涨3.16%，游戏ETF(516010)涨2.59%，游戏ETF华泰柏 瑞(516770)涨2.57%，港股通汽车ETF(159323)领跌4.08%，港股汽车ETF(520600)跌4.07%，碳中和ETF 泰康(560560)跌3.09%。 ...