1. Report Industry Investment Rating - No relevant content provided 2. Core Viewpoints - The report analyzes the secondary - market performance of A - shares, overseas major broad - based indices, gold indices, and Nanhua Commodity Index from July 14 to July 18, 2025. It also examines the performance, fund flow, trading volume, margin trading, and new issuance of ETFs during the same period [1][2] 3. Summary by Directory 3.1 Secondary Market Overview - In the sample period, the GEM Index, Hang Seng Index, and CSI 2000 ranked high in weekly returns, at 3.17%, 2.84%, and 1.74% respectively. The PE valuation quantile of CSI 500 was the highest at 100.00%, while that of the Nikkei 225 was the lowest at 64.08% [9] - Among Shenwan primary industries, Communication, Medicine and Biology, and Automobile ranked high in returns, at 7.56%, 4.00%, and 3.28% respectively. Media, Real Estate, and Utilities ranked low, at - 2.24%, - 2.17%, and - 1.37% respectively. The industries with the highest valuation quantiles were Medicine and Biology, National Defense and Military Industry, and Steel, all at 100.00%. The industries with lower valuation quantiles were Agriculture, Forestry, Animal Husbandry and Fishery, Non - Banking Finance, and Household Appliances, at 15.70%, 34.71%, and 38.43% respectively [13] 3.2 ETF Product Overview 3.2.1 ETF Market Performance - QDII ETFs had the best average performance with a weighted average return of 3.52%, while Money Market ETFs had the worst with 0.01% [18] - ETFs related to Hong Kong stocks and ChiNext Innovation 50 performed well, with weighted average returns of 5.01% and 4.47% respectively. ETFs related to Japanese stocks and MSCI China A - share concept performed poorly, with - 0.02% and 1.02% respectively [18] - Among industry - sector ETFs, Biomedical ETFs had the best performance with a weighted average return of 4.94%, while Financial Real Estate ETFs had the worst at - 0.86% [21] - Among theme - based ETFs, Innovative Drug and Artificial Intelligence ETFs performed well, with weighted average returns of 9.98% and 6.51% respectively. Non - Banking and Bank ETFs ranked low, at - 0.80% and - 0.76% respectively [21] 3.2.2 ETF Fund Inflow and Outflow - From the perspective of different categories of ETFs, Industry - Theme ETFs had the largest net inflow of 17.28 billion yuan, while Broad - Based ETFs had the smallest at - 29.709 billion yuan [25] - From the perspective of ETF - tracking indices and their constituent stocks' listing boards, Hong Kong Stock ETFs had the largest net inflow of 10.064 billion yuan, while CSI 300 ETFs had the smallest at - 8.841 billion yuan [25] - From the industry - sector perspective, Financial Real Estate sector ETFs had the largest net inflow of 9.919 billion yuan, while Biomedical sector ETFs had the smallest at - 2.607 billion yuan [26] - From the theme perspective, Non - Banking and Chip Semiconductor ETFs had the largest net inflows of 8.229 billion and 4.319 billion yuan respectively. Artificial Intelligence and Innovative Drug ETFs had the smallest at - 3.102 billion and - 0.797 billion yuan respectively [26] 3.2.3 ETF Trading Volume - From the perspective of different categories of ETFs, QDII ETFs had the largest increase in the average daily trading volume change rate at 18.95%, while Commodity ETFs had the largest decrease at - 21.54% [31] - From the perspective of ETF - tracking indices and their constituent stocks' listing boards, ChiNext Innovation 50 ETFs had the largest increase in the average daily trading volume change rate at 26.92%, while CSI 300 had the largest decrease at - 18.76% [34] - From the industry - sector perspective, the Technology sector had the largest increase in the average daily trading volume change rate at 17.38%, while the Financial Real Estate sector had the largest decrease at - 10.68% [35] - From the theme perspective, Non - Banking and Innovative Drug ETFs had the largest average daily trading volumes in the past 5 days, at 18.604 billion and 9.440 billion yuan respectively. Artificial Intelligence and Robot ETFs had the largest increases in the average daily trading volume change rate, at 41.53% and 34.15% respectively. Non - Banking and Low - Carbon Environmental Protection ETFs had the largest decreases at - 11.87% and - 7.82% respectively [39] 3.2.4 ETF Margin Trading - In the sample period, the net margin purchase of all equity ETFs was - 927 million yuan, and the net short - selling was - 207 million yuan. Bosera Science and Technology Innovation Board Artificial Intelligence ETF had the largest net margin purchase, and Fullgoal CSI A500 ETF had the largest net short - selling [43] 3.2.5 ETF New Issuance and Listing - In the sample period, 10 funds were established and 12 funds were listed [45]

- The report tracks various types of ETFs on a daily basis, focusing on "broad-based" and "thematic" ETFs, with the former tracking major indices like CSI 300 and CSI 500, and the latter tracking industry/style indices like non-bank financials and dividends[2] - The RSI (Relative Strength Index) is calculated using the formula: $ RSI = 100 - 100 / (1 + RS) $, where RS is the average gain of up periods during the specified time frame divided by the average loss of down periods during the specified time frame[2] - The net subscription amount is calculated using the formula: $ NETBUY(T) = NAV(T) - NAV(T-1) * (1 + R(T)) $, where NETBUY(T) is the net subscription amount, NAV(T-1) is the net asset value of the ETF on the previous trading day, and R(T) is the return on the current day[2] - The report includes various metrics for ETFs such as tracking index, ETF name, price change percentage, T+0 trading support, market cap, RSI, net subscription, trading volume, intraday trend, management fee rate, and institutional holding percentage[4] - The report provides detailed data for a wide range of ETFs, including broad-based ETFs like CSI 300, CSI 500, and thematic ETFs like non-bank financials, dividends, and various industry-specific ETFs[4][6] Quantitative Models and Construction Methods 1. **Model Name: RSI (Relative Strength Index)** - **Construction Idea**: RSI measures the speed and change of price movements to identify overbought or oversold conditions in the market[2] - **Construction Process**: - Calculate the average gain and average loss over a specified period (e.g., 12 days) - Compute the RS (Relative Strength) as the ratio of average gain to average loss - Apply the formula: $ RSI = 100 - 100 / (1 + RS) $ - **Evaluation**: RSI is a widely used momentum indicator that helps in identifying potential reversal points in the market[2] 2. **Model Name: Net Subscription Amount** - **Construction Idea**: This model calculates the net inflow or outflow of funds in an ETF to gauge investor sentiment and fund activity[2] - **Construction Process**: - Calculate the net asset value (NAV) of the ETF for the current and previous trading days - Compute the return for the current day - Apply the formula: $ NETBUY(T) = NAV(T) - NAV(T-1) * (1 + R(T)) $ - **Evaluation**: This model provides insights into the demand and supply dynamics of the ETF, reflecting investor behavior[2] Model Backtesting Results 1. **RSI Model** - **RSI Values**: - Huatai-PineBridge CSI 300 ETF: 74.32[4] - E Fund CSI 300 ETF: 78.26[4] - Huaxia CSI 300 ETF: 65.15[4] - Other ETFs have varying RSI values indicating different market conditions[4] 2. **Net Subscription Amount Model** - **Net Subscription Values**: - Huatai-PineBridge CSI 300 ETF: -1.93 billion yuan[4] - E Fund CSI 300 ETF: -4.66 billion yuan[4] - Huaxia CSI 300 ETF: -0.42 billion yuan[4] - Other ETFs show different net subscription amounts reflecting investor activity[4] Quantitative Factors and Construction Methods 1. **Factor Name: Institutional Holding Percentage** - **Construction Idea**: This factor measures the proportion of ETF holdings owned by institutional investors, indicating the level of professional investment interest[3] - **Construction Process**: - Extract data from the latest annual or semi-annual reports of the ETF - Exclude holdings by linked funds - Calculate the percentage of institutional holdings - **Evaluation**: High institutional holding percentage often suggests confidence from professional investors and potential stability in the ETF[3] Factor Backtesting Results 1. **Institutional Holding Percentage** - **Values**: - Huatai-PineBridge CSI 300 ETF: 83.06%[4] - E Fund CSI 300 ETF: 88.03%[4] - Huaxia CSI 300 ETF: 91.03%[4] - Other ETFs have varying institutional holding percentages indicating different levels of professional interest[4]

Economic Performance - China's GDP grew by 5.3% year-on-year in the first half of 2025, exceeding the annual target of around 5%[1] - In Q2 2025, GDP growth was 5.2%, a slight decrease of 0.2 percentage points from Q1[1] - The contribution of final consumption expenditure to GDP in Q2 was 52.3%, up from 51.7% in Q1[10] Industrial Growth - In June 2025, the industrial added value for large enterprises increased by 6.8% year-on-year, with a 1.0 percentage point rebound[15] - Manufacturing remains the core driver of industrial growth, with significant increases in high-tech sectors such as electrical machinery (11.4%) and new energy vehicles (18.8%)[16] - The industrial sales rate in June was 94.3%, reflecting ongoing pressure on enterprise sales[16] Consumer Trends - Retail sales of consumer goods in June grew by 4.8% year-on-year, a decline of 1.6 percentage points from May[18] - Service consumption showed a steady recovery, with a 5.3% increase in retail sales for services in the first half of 2025[18] - Online retail sales of physical goods in June rose by 6.0%, accounting for 24.9% of total retail sales[19] Investment Insights - Fixed asset investment (excluding rural households) grew by 2.8% in the first half of 2025, down from 3.7% in the first five months[3] - Non-real estate investment surged by 6.6%, significantly higher than total investment growth[3] - Manufacturing investment increased by 7.5%, while real estate development investment fell by 11.2%[24] Risks - Economic recovery may fall short of expectations, and growth stabilization policies may not meet anticipated outcomes[4]

Quantitative Models and Construction Methods 1. Model Name: RSI (Relative Strength Index) - **Model Construction Idea**: RSI is used to measure the relative strength of price movements over a specific period, identifying overbought or oversold market conditions[2] - **Model Construction Process**: The RSI is calculated using the formula: $ RSI = 100 - \frac{100}{1 + RS} $ Where: - $ RS = \frac{\text{Average Gain over N periods}}{\text{Average Loss over N periods}} $ In this report, the RSI is calculated over a 12-day period. - RSI > 70 indicates an overbought market - RSI < 30 indicates an oversold market[2] - **Model Evaluation**: RSI is a widely used technical indicator for short-term market trend analysis, providing actionable insights for traders[2] 2. Model Name: Net Purchase (Net Subscription) - **Model Construction Idea**: This model calculates the net subscription amount of ETFs to track fund inflows and outflows[2] - **Model Construction Process**: The formula for Net Purchase is: $ NETBUY(T) = NAV(T) - NAV(T-1) \times (1 + R(T)) $ Where: - $ NETBUY(T) $ is the net subscription amount on day T - $ NAV(T) $ is the net asset value on day T - $ R(T) $ is the return on day T[2] - **Model Evaluation**: This metric is useful for understanding investor sentiment and fund flow dynamics in the ETF market[2] --- Model Backtesting Results RSI Model - RSI values for various ETFs: - Huatai-PineBridge CSI 300 ETF: 67.83[4] - E Fund CSI 300 ETF: 73.55[4] - ChinaAMC CSI 300 ETF: 57.08[4] - Bosera CSI 500 ETF: 69.93[4] - E Fund CSI 500 ETF: 71.67[4] - E Fund SSE STAR 50 ETF: 55.07[4] Net Purchase Model - Net Purchase values for various ETFs: - Huatai-PineBridge CSI 300 ETF: -7.83 billion[4] - E Fund CSI 300 ETF: -0.03 billion[4] - ChinaAMC CSI 300 ETF: -26.34 billion[4] - Bosera CSI 500 ETF: -1.45 billion[4] - E Fund CSI 500 ETF: -0.45 billion[4] - E Fund SSE STAR 50 ETF: 1.64 billion[4] --- Quantitative Factors and Construction Methods 1. Factor Name: Institutional Holding Ratio - **Factor Construction Idea**: This factor measures the proportion of ETF holdings owned by institutional investors, reflecting institutional confidence and participation[3] - **Factor Construction Process**: The institutional holding ratio is derived from the latest annual or semi-annual reports of ETFs, excluding holdings by linked funds. The data is estimated and may have deviations due to reporting delays[3] - **Factor Evaluation**: This factor provides insights into institutional investor behavior, which is often considered a signal of market confidence[3] --- Factor Backtesting Results Institutional Holding Ratio - Institutional holding ratios for various ETFs: - Huatai-PineBridge CSI 300 ETF: 83.06%[4] - E Fund CSI 300 ETF: 88.03%[4] - ChinaAMC CSI 300 ETF: 91.03%[4] - Bosera CSI 500 ETF: 77.51%[4] - E Fund CSI 500 ETF: 78.89%[4] - E Fund SSE STAR 50 ETF: 43.66%[4]

- 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]

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]

Investment Rating - The report assigns a "Buy" rating to the company, with a target price of 10.92 CNY based on a closing price of 9.10 CNY [5]. Core Views - The company is a leading player in the porcelain insulator industry, with strong overseas order performance and a significant increase in revenue and profit in 2024 [1][2]. - The company has established a solid international marketing network and is expanding its production capacity, particularly in Jiangxi, which is expected to enhance its revenue growth [2][3]. Summary by Sections 1. Leading Company in Porcelain Insulators - Dalian Electric Porcelain Group was founded in 1915 and listed in 2011, with a comprehensive product matrix that meets various national industry standards [14]. - The company has a strong historical presence in the international market, having supplied products for the first domestic and world high-voltage lines [14]. 2. Accelerated Construction of UHV Projects - The demand for porcelain insulators is driven by the increasing domestic electricity consumption and investment in the power grid, with a notable rise in UHV construction [37][40]. - In 2024, the total electricity consumption in China reached 98,521 billion kWh, with a 15.3% increase in grid investment [37]. 3. Stable Leadership and Growth Potential - The management team has extensive experience and a long-term strategic vision, with significant improvements in company performance since 2019 [3][67]. - The company has a leading position in the domestic bidding for UHV porcelain insulators, with a market share of 28% in 2024 and 46% in 2025 for UHV porcelain insulators [2][67]. 4. Financial Performance and Forecast - In 2024, the company achieved a revenue of 1.496 billion CNY, a year-on-year increase of 78.21%, and a net profit of 211 million CNY, up 303.3% [22][4]. - The forecast for 2025-2027 anticipates revenues of 1.723 billion CNY, 1.950 billion CNY, and 2.168 billion CNY, with corresponding net profits of 230 million CNY, 280 million CNY, and 330 million CNY [3][4].

Group 1 - The report indicates that in the last month, equities, commodities, and bonds all experienced increases, with equities and commodities rising by 2.50% and 4.03% respectively, while gold decreased by 0.57% [2][10] - The performance of ETFs used in the allocation strategy showed that the CSI 300 ETF, non-ferrous ETF, and energy chemical ETF increased by 2.85%, 3.08%, and 4.37% respectively, while the gold ETF saw a significant decline of 0.75% [2][13] Group 2 - The backtested strategy from January 1, 2014, to the end of last month achieved an annualized return of 7.71%, with an annualized volatility of 3.53% and a maximum drawdown of 3.17%. The Sharpe ratio and Calmar ratio were 2.19 and 2.44 respectively, outperforming risk parity and equal-weighted strategies [3][25] - The strategy without currency assets yielded a return of 0.48% last month, which was lower than both the risk parity strategy and the equal-weighted strategy [3][28] Group 3 - The latest allocation recommendations suggest increasing exposure to equities and commodities, while maintaining a neutral position on bonds and gold. The final weights for equities, government bonds, commodities, and gold are set at 7.01%, 75.01%, 10.90%, and 7.08% respectively [4][32]

1. Report Industry Investment Rating - Not provided in the content 2. Core Viewpoints of the Report - This week, the A-share market rebounded strongly, with the Shanghai Composite Index breaking through the year's high. The average daily trading volume increased by 22.36% week-on-week. The market risk appetite increased due to the easing geopolitical situation and the introduction of domestic growth-stabilizing policies [1][10]. - The financial technology sector led the rise this week, with both financial and growth styles performing well. The growth style index rose 5.21% this week, and its trading volume accounted for 54.20% of the total, reaching a four-week high [14]. - Looking ahead, the market is expected to maintain a steady upward trend. In July, the market is expected to see an orderly rotation of hot sectors. However, investors should remain cautious before the uncertainties of Sino-US tariff negotiations and the Fed's interest rate decision are eliminated [15]. 3. Summary According to the Directory 3.1 This Week's Market Review 3.1.1 Industry Index - The comprehensive finance, computer, comprehensive, national defense and military industry, and non-bank finance sectors led the gains this week. The trading volume of non-bank finance and bank sectors increased significantly compared to last week, while the trading activity of media, petroleum and petrochemical, medicine, food and beverage, and agriculture, forestry, animal husbandry, and fishery sectors decreased significantly [10]. - COMEX gold fell 2.94%, and the Chinese bond market maintained a narrow range of fluctuations. The basis of stock index futures contracts increased overall, and the net value of stock hedging strategies continued to decline. The average and median returns of neutral hedging funds this week were -0.10% and -0.03% respectively [1][10]. 3.1.2 Market Style - The financial technology sector led the rise this week, driving the market index higher. The growth style index rose 5.21% this week, and its trading volume accounted for 54.20% of the total, reaching a four-week high. The consumer style index rose 1.46%, and its trading volume accounted for 10.93% of the total, reaching a four-week low [14]. - The financial style index rose 3.41%, and its trading volume accounted for 10.07% of the total, reaching a four-week high. The stable style index rose only 0.78%, and its trading volume accounted for 3.45% of the total, reaching a four-week low [14]. - The cyclical style index rose 3.02%, and its trading volume accounted for 21.35% of the total, reaching a four-week low. The CSI 2000 index rose 5.55% this week, but its trading volume accounted for 28.89% of the total, reaching a four-week low [14]. 3.2 Active Equity Funds 3.2.1 Funds with Excellent Performance in Different Theme Tracks This Week - In the single-track fund category, the top five funds in terms of performance this week were Dongcai Value Qihang A, Taixin Development Theme, Chang'an Yusheng A, Huashang Upstream Industry A, and Huitianfu Consumption Upgrade A [20]. - In the double-track fund category, the top five funds in terms of performance this week were China Merchants Securities Technology Theme 6-Month Holding A, Yin Hua Multi-Power, Yongying High-End Equipment Smart Selection A, Huashang Computer Industry Quantitative A, and Hongtu Innovation Selection LOF [20]. 3.2.2 Funds with Excellent Performance in Different Strategy Categories - In the deep undervaluation strategy, the top three funds were Orient Internet Jia, Qianhai Kaiyuan Event-Driven A, and GF Shanghai-Hong Kong-Shenzhen Value Growth A [2][22]. - In the high-growth strategy, the top three funds were China Europe Prosperity Outlook One-Year Holding A, Yuanxin Yongfeng High-End Manufacturing, and Huafu Guotai Min'an A [2][22]. - In the high-quality strategy, the top three funds were Furong Fujin A, Great Wall Jiuxin A, and E Fund New Normal [2][22]. - In the quality undervaluation strategy, the top three funds were Tongtai Financial Selection A, Qianhai Kaiyuan Shengxin A, and Wells Fargo Financial Real Estate Industry A [2][22]. - In the quality growth strategy, the top three funds were AVIC New Takeoff A, SDIC UBS New Energy A, and E Fund National Defense and Military Industry A [2][22]. - In the GARP strategy, the top three funds were Guoshou Anbao Target Strategy A, Guotai Dazhizao Two-Year Holding, and China AMC Panyi One-Year Fixed Open [2][22]. - In the balanced cost-performance strategy, the top three funds were Hongtu Innovation Selection LOF, Chang Sheng State-Owned Enterprise Reform Theme, and Taixin Development Theme [2][22]. 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 0.06% and 0.10% respectively [25]. - The average and median excess returns of CSI 500 index enhanced funds were -0.35% and -0.37% respectively [25]. - The average and median excess returns of CSI 1000 index enhanced funds were -0.20% and -0.22% respectively [25]. - The average and median excess returns of CSI 2000 index enhanced funds were -0.04% and -0.06% respectively [25]. - The average and median excess returns of CSI A500 index enhanced funds were 0.11% and 0.13% respectively [25]. - The average and median excess returns of ChiNext index enhanced funds were -0.20% and -0.17% respectively [25]. - The average and median excess returns of STAR Market and ChiNext 50 index enhanced funds were -0.11% and -0.14% respectively [26]. 3.4 This Issue's Bond Fund Selections - The report screened out the medium- and long-term bond fund pool and the short-term bond fund pool based on indicators such as fund size, performance risk indicators, the latest fund size, Wind Fund secondary classification, three-year rolling returns, and three-year maximum drawdowns [42]. 3.5 This Week's High-Frequency Fund Position Detection - Active equity funds significantly increased their positions in the petroleum and petrochemical (0.18%), coal (0.09%), and comprehensive (0.08%) industries this week; they significantly reduced their positions in the machinery (0.19%), automobile (0.13%), and commercial and retail (0.08%) industries [3]. - From a one-month perspective, the position of the pharmaceutical industry increased significantly by 0.71%, while the positions of the machinery and automobile industries decreased significantly by 0.64% and 0.65% respectively [3]. 3.6 This Week's Weekly Tracking of US Dollar Bond Funds - Not provided in the content

Quantitative Models and Construction Methods Model 1: RSI (Relative Strength Index) - **Model Name**: RSI (Relative Strength Index) - **Model Construction Idea**: The RSI is used to measure the speed and change of price movements. It is primarily used to identify overbought or oversold conditions in a trading instrument. - **Model Construction Process**: - The RSI is calculated using the following formula: $$ RSI = 100 - \frac{100}{1 + RS} $$ where RS (Relative Strength) is the average of 'n' days' up closes divided by the average of 'n' days' down closes. Typically, a 14-day period is used. - The RSI value ranges from 0 to 100. An RSI above 70 indicates that the market is overbought, while an RSI below 30 indicates that the market is oversold.[2] Model 2: Net Purchase (NETBUY) - **Model Name**: Net Purchase (NETBUY) - **Model Construction Idea**: This model calculates the net purchase amount of an ETF to understand the inflow and outflow of funds. - **Model Construction Process**: - The net purchase amount is calculated using the following formula: $$ NETBUY(T) = NAV(T) - NAV(T-1) \times (1 + R(T)) $$ where NETBUY(T) is the net purchase amount on day T, NAV(T) is the net asset value on day T, NAV(T-1) is the net asset value on the previous trading day, and R(T) is the return on day T.[2] Model Backtesting Results - **RSI Model**: - RSI values for various ETFs range from 37.69 to 80.86, indicating different levels of market conditions from oversold to overbought.[4][6] - **Net Purchase Model**: - Net purchase values for various ETFs range from -6.38 billion to 99.72 billion, indicating significant variations in fund inflows and outflows.[4][6] Quantitative Factors and Construction Methods Factor 1: Institutional Holdings - **Factor Name**: Institutional Holdings - **Factor Construction Idea**: This factor measures the percentage of an ETF's holdings that are owned by institutional investors. - **Factor Construction Process**: - The percentage of institutional holdings is derived from the latest annual or semi-annual reports of the ETF, excluding the holdings of corresponding linked funds. The data is an estimate and may have some deviations.[3] Factor Backtesting Results - **Institutional Holdings Factor**: - Institutional holdings percentages for various ETFs range from 2.79% to 96.29%, indicating varying levels of institutional interest and confidence in these ETFs.[4][6]